diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 84d16492a..f6443726e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -25,12 +25,12 @@ repos: - id: mixed-line-ending - id: trailing-whitespace - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.13.0 + rev: v0.13.2 hooks: - id: ruff-check args: [--fix, --exit-non-zero-on-fix] - repo: https://github.com/psf/black-pre-commit-mirror - rev: 25.1.0 + rev: 25.9.0 hooks: - id: black - repo: https://github.com/rbubley/mirrors-prettier @@ -48,7 +48,7 @@ repos: - id: codespell additional_dependencies: [".[toml]"] - repo: https://github.com/crate-ci/typos - rev: v1.32.0 + rev: v1.36.3 hooks: - id: typos - repo: https://github.com/jumanjihouse/pre-commit-hooks @@ -56,11 +56,11 @@ repos: hooks: - id: check-mailmap - repo: https://github.com/henryiii/validate-pyproject-schema-store - rev: 2025.09.11 + rev: 2025.09.26 hooks: - id: validate-pyproject - repo: https://github.com/astral-sh/uv-pre-commit - rev: 0.8.17 + rev: 0.8.22 hooks: - id: uv-lock - repo: https://github.com/adamchainz/blacken-docs diff --git a/src/paperqa/clients/__init__.py b/src/paperqa/clients/__init__.py index 4fba8a8bf..f8d27f776 100644 --- a/src/paperqa/clients/__init__.py +++ b/src/paperqa/clients/__init__.py @@ -175,10 +175,8 @@ async def query(self, **kwargs) -> DocDetails | None: doc_details = ( sum( p - for p in ( - await gather_with_concurrency( - len(task.providers), task.provider_queries(query_args) - ) + for p in await gather_with_concurrency( + len(task.providers), task.provider_queries(query_args) ) if p ) diff --git a/src/paperqa/clients/openalex.py b/src/paperqa/clients/openalex.py index 91d703d27..087682442 100644 --- a/src/paperqa/clients/openalex.py +++ b/src/paperqa/clients/openalex.py @@ -136,7 +136,7 @@ def parse_openalex_to_doc_details(message: dict[str, Any]) -> DocDetails: """ raw_author_names = [ authorship.get("raw_author_name", "") - for authorship in (message.get("authorships") or []) # Handle None authorships + for authorship in message.get("authorships") or [] # Handle None authorships if authorship ] sanitized_authors = [ diff --git a/src/paperqa/clients/unpaywall.py b/src/paperqa/clients/unpaywall.py index 8080755d8..fc372df60 100644 --- a/src/paperqa/clients/unpaywall.py +++ b/src/paperqa/clients/unpaywall.py @@ -165,7 +165,7 @@ def _create_doc_details(self, data: UnpaywallResponse) -> DocDetails: license = data.best_oa_location.license # noqa: A001 return DocDetails( authors=[ - f"{author.given} {author.family}" for author in (data.z_authors or []) + f"{author.given} {author.family}" for author in data.z_authors or [] ], publication_date=( None diff --git a/src/paperqa/types.py b/src/paperqa/types.py index fcf032731..146098241 100644 --- a/src/paperqa/types.py +++ b/src/paperqa/types.py @@ -1108,8 +1108,8 @@ def __add__(self, other: DocDetails | int) -> DocDetails: # noqa: PLR0912 best_authors = ( self.authors if ( - sum(len(a) for a in (self.authors or [])) - >= sum(len(a) for a in (other.authors or [])) + sum(len(a) for a in self.authors or []) + >= sum(len(a) for a in other.authors or []) ) else other.authors ) diff --git a/tests/cassettes/test_get_reasoning[deepseek-reasoner].yaml b/tests/cassettes/test_get_reasoning[deepseek-reasoner].yaml index 6e3478d01..fcac7071b 100644 --- a/tests/cassettes/test_get_reasoning[deepseek-reasoner].yaml +++ b/tests/cassettes/test_get_reasoning[deepseek-reasoner].yaml @@ -45,20 +45,20 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//jJLBbtswDIbvfgqB53iI3TRpcxsGDMgwoMAO24A6sBWJjtXJkibRybog - 7z7ITuJ07YBddODHn+JP8pAwBkrCkoFoOInW6fTDp59f8eFB2lW+mt9/lvtdvq8FR24+/n6GSVTY - zRMKOqveCds6jaSsGbDwyAlj1Wxxe3c3m2fZrAetlaijbOsondk0n+azNMvSfHoSNlYJDLBkjwlj - jB36N7ZoJP6CJZtOzpEWQ+BbhOUliTHwVscI8BBUIG4IJiMU1hCavuuqqp6CNYU5FIaxAkiRxgKW - rIDv71fsC+4U7guYDJR31FgfIn8s4BtqzfecCBkS47qA9SlPWhVzTKd1YY6Fqarq+n+PdRe4PmVc - AW6MJR7H1ztfn8jx4lXbrfN2E/6SQq2MCk3pkQdroq9A1kFPjwlj636m3YsxgfO2dVSS/YH9d4t8 - KAfjEkd4c4Zkiesxnk1nkzfKlRKJKx2ulgKCiwblKB03yDup7BVIrky/7uat2oNxZbb/U34EQqAj - lKXzKJV46XhM8xhv/F9plyH3DUNAv1MCS1Lo4yIk1rzTw/lBeA6EbVkrs0XvvBpusHalvK9xfru4 - wQ0kx+QPAAAA//8DANDmEcyMAwAA + H4sIAAAAAAAAAwAAAP//jJJNb9swDIbv/hUCz8ngePlocysGDNi6U4Fhw+rAViQmVieLgkS3aYP8 + 98F2EqdbB/SiAx++FF+S+0QIMBqWAlQlWdXejj/dzp/JxM9Ufw9fv73c2eylWv/apWbnb69g1Cpo + /YCKT6oPimpvkQ25HquAkrGtOlnMrq7T62k270BNGm0r23oeT2mcpdl0PJmMs/QorMgojLAU94kQ + Quy7t23RadzBUqSjU6TGGOUWYXlOEgIC2TYCMkYTWTqG0QAVOUbXdV2W5UMkl7t97oTIgQ1bzGEp + cvh580Xc4aPBpxxGPZUNVxRiy+9z+IHWyifJjAJZSJvD6pinybQ5rrE2d4fclWV5+X/ATROlPWZc + AOkcsWzH1zlfHcnh7NXS1gdax7+ksDHOxKoIKCO51ldk8tDRQyLEqptp82pM4APVngum39h9t8j6 + cjAscYAfT5CJpR3ik3Q6eqNcoZGlsfFiKaCkqlAP0mGDstGGLkByYfrfbt6q3Rs3bvue8gNQCj2j + LnxAbdRrx0NawPbG/5d2HnLXMEQMj0ZhwQZDuwiNG9nY/vwgPkfGutgYt8Xgg+lvcOOL2XyyzmaL + hV5Dckj+AAAA//8DAGv/g2aMAwAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9a974dabeb1e-SJC + - 9854a4489b7c1666-SJC Connection: - keep-alive Content-Encoding: @@ -66,7 +66,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:21:55 GMT + - Fri, 26 Sep 2025 17:57:06 GMT Server: - cloudflare Strict-Transport-Security: @@ -82,13 +82,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "484" + - "493" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "512" + - "561" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -104,7 +104,73 @@ interactions: x-ratelimit-reset-tokens: - 0s x-request-id: - - req_b82b602e0da54e4f93bd941d5fc9d104 + - req_96660437996545ed88fd4355775ac9d5 + status: + code: 200 + message: OK + - request: + body: null + headers: + accept: + - "*/*" + accept-encoding: + - gzip, deflate + connection: + - keep-alive + host: + - api.crossref.org + user-agent: + - python-httpx/0.28.1 + method: GET + uri: https://api.crossref.org/works?mailto=example@papercrow.ai&query.title=XAI+Review&rows=1&query.author=Wellawatte+et+al + response: + body: + string: !!binary | + H4sIAAAAAAAA/6VVS3ObMBD+K4zOyJYAG5tbmnQ6SdM2ddJLbR82IBwlAlFJuPF4/N+7AidxXp3O + 9Ca00mq/xy5bYh241pKM6DsSkkpYCytB3aYRuPdbmzuqpHUHobUwVuoao3zABuwpQrItKSEXDrNt + dyFx2oGiRthW+a0oHvMkJNKJCr/mWyLrQtyLwl8rwAnagPHn5vOIRUk4CdPlMuwjTla+Gr9P2YSy + 9IqNs2SUcfYTn/dRRFE1JONpFDMec87G4zGWYEQpjKhzQXPd1o5kLCRNe42IboTBjFcGamudUApM + cKZbU4MKLvyBHByCtAHURTATVoDJb4Jj3EEwUOeb4MLINdYWnMsKIRVYh7S29WWOcZ3r2ona0UJX + IOsO4n41R1C50dZWgNwiO87I3HWElqCswKrtjTaO+hR4QxiE7xQmnpPTs6PLGVk+YShoY6TH9ZpC + jvRhrpNvp14pNoiSKEkXQ3kL1hQi9weixLPXK33bg6eYQOb4mi9SgHtbHp6GnIfT1/rwlHJO2fSK + 8YxPsjh6qc+IMz5lMY8YY1hd09mG8DSmfOKtZLGM3G8de4pQvo5X+qhjQa83B1o+MvNBr5Gq4AtY + 3JI2DD4pfY1afm9RQDCbYLFYkCNUci3Fb7/uWMS08v6AHnxsrVXbgfEf0DqUojPrSq6F9/xnqEAV + QjSBcAGoQYjHSqik2rwTtOJX6yvHcClN10lQllJJ6EWfo0pbUkP36NXZxez4+SUoCulPgnrj5tI3 + X3XdeZmzOHmw3kvfoBlR3T7No891GRyt0Hzo6Nbg92Uu/ZvPLO9pUlCv2l4opAAlF4228n+sESVZ + Mn3bGuPJtLeGzbXBm1EymE7YOI19Mz+4Y4vSSeyfjV/+mJ3jAzfONdliuBi628bkA21Wi+G+Sxpo + cGQthhEdMeqfidgk5oxi6q6hTj4e990waIqS7Hb7Tn4HXtdVj360B2Z8aKH9HNg+Gwj/2rGYHNRe + rbWgsvD6HaESdBqlIy/HE17bAS607OH+pclPLy+/+kRRhBQwNkp9Iiyv3g96NPh+DvTVYR+A6mp/ + urE7nDvv17/cj3iKpNO+v3lI0M69Wqg2zrZu9Hek9TajiNj/FepWqd1u9wfxCUyYlgYAAA== + headers: + Access-Control-Allow-Headers: + - X-Requested-With, Accept, Accept-Encoding, Accept-Charset, Accept-Language, + Accept-Ranges, Cache-Control + Access-Control-Allow-Origin: + - "*" + Access-Control-Expose-Headers: + - Link + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Length: + - "850" + Content-Type: + - application/json + Date: + - Fri, 26 Sep 2025 17:57:07 GMT + Server: + - Jetty(9.4.40.v20210413) + Vary: + - Accept-Encoding + permissions-policy: + - interest-cohort=() + x-api-pool: + - plus + x-rate-limit-interval: + - 1s + x-rate-limit-limit: + - "150" status: code: 200 message: OK @@ -143,7 +209,7 @@ interactions: Review and Dataset for System Health Indicator Construction},\n year = {2024}\n}\n"}, "authors": [{"authorId": "2167438752", "name": "D. Nguyen"}, {"authorId": "2184162840", "name": "Khanh T. P. Nguyen"}, {"authorId": "2267984723", "name": - "Kamal Medjaher"}], "matchScore": 58.470894}]} + "Kamal Medjaher"}], "matchScore": 57.77719}]} ' headers: @@ -152,97 +218,31 @@ interactions: Connection: - keep-alive Content-Length: - - "1440" + - "1439" Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:21:55 GMT + - Fri, 26 Sep 2025 17:57:07 GMT Via: - - 1.1 05704fecd1992557082ae04fd0558b5e.cloudfront.net (CloudFront) + - 1.1 29474fd3a24c6552dfdc04ee03c03aa2.cloudfront.net (CloudFront) X-Amz-Cf-Id: - - fmUVumxE9zFLiKAj96alWtV3fX52J4SPqV15YdMAhbrp1mhlg6s7rw== + - 0vdGxRsAQ-2yT-tBvjdp5vfeLQ1R-GH35XanvHNqOlhBHPQSxRYeZA== X-Amz-Cf-Pop: - SFO53-P7 X-Cache: - Miss from cloudfront x-amz-apigw-id: - - Re9pjFwivHcEQhg= + - RhYOAHi9vHcEZmQ= x-amzn-Remapped-Connection: - keep-alive x-amzn-Remapped-Content-Length: - - "1440" + - "1439" x-amzn-Remapped-Date: - - Fri, 26 Sep 2025 00:21:55 GMT + - Fri, 26 Sep 2025 17:57:07 GMT x-amzn-Remapped-Server: - gunicorn x-amzn-RequestId: - - 65207180-cebe-4415-af52-a9c4666ddead - status: - code: 200 - message: OK - - request: - body: null - headers: - accept: - - "*/*" - accept-encoding: - - gzip, deflate - connection: - - keep-alive - host: - - api.crossref.org - user-agent: - - python-httpx/0.28.1 - method: GET - uri: https://api.crossref.org/works?mailto=example@papercrow.ai&query.title=XAI+Review&rows=1&query.author=Wellawatte+et+al - response: - body: - string: !!binary | - H4sIAAAAAAAA/7VVTW/bOBT8KwLPok1RtmX5ViRdIEHbNZL0srYPjPRsM6VILUm5NQz/9z5Sauwu - ksVe9ibx43HmzQx5Is4L3zmyIOYbSUkDzokdUH9sAce+G/uNKun81dQBrJNG42w2YiN2mSGLE9mK - CjxWO51T4o0XilpwnQpDPJ8xnhLpocG/1YlIXcMPqMO2WnigrbBh3WrFGZ+k87TYbNJ+xssmoAnj - lM0pK57YbDEpFln5Fx4fZpFF05JFVvCcZTnPy9m0QAgWtmBBV0Ar02lPFiwlbfeMjPZgseKTFdo5 - D0oJm9ybzmqhkmVYUAmPJF0idJ08gANhq31ygyNIRujqmCytPCC25JNskFKNOKRzXYCZ43dltAft - aW0aIXWkOHytkFRljXONwN5id7yVlY8N3QrlAFG7vbGehhK4AyzS9woLr8jd/eflx4dbsrmwqGlr - ZWD2RhPZZoPVbv+8C1qxEZ/wSbEey5emBVu/dDosKbMAd5D7pe8AxRqywiMDUhD+HY0YasTfEokF - kTiKxBZ5/oZI03I+mc/zsmSMIcA2eoeU02JKy+mc42qHQKoweBM6hSrG9tJXOWv6fLyS9LVBH1Cr - g4TvyVIgx8To5A9j6uQRqs5Kf4yNwyLyx1VHsPTBqC6Cz4KdRedRgGjRnTxAcPoH1L1Bq4gG/QE+ - EWqU4sqtaKQ6vj/v4O8uwMUVW2ljisR2K5UUveAr1OdEtIhnP90vH25+3yTqWoaVQr2xcxOC1zxH - H2csn8wG2/3TM2hEFLUv8+pxs00+Q7UXGo2uosmX1tRdNGLyUe+wBqCvdhfvhzW3cABl2gadHVqp - hN51vXjYJbQBtMbJ/9surjIWd/LJqCxnJech5b/8ckJ5JQbrGD6/PnzCA/bet4v1eD32L62tRsbu - 1uMhPG0wiVuPOZ0VNBwzZbwoOcVMDEm7//plCMmorbfkfB5S/g7DmLdXk7orh/5K1nBHnH67LP5r - lrG4UIOaB6CyjpZHcWiWFwULklw4u0i6NrKn/O/5v3t8/BKKcT4p6WxexloIUQ8PAUZhuCJ6hBgZ - oSL+y47z9a30PofN8ARQBEH76GcpQcv3oqHoePfFpyE2rncfRdbh1dCdUufz+Sf6r3t5tgYAAA== - headers: - Access-Control-Allow-Headers: - - X-Requested-With, Accept, Accept-Encoding, Accept-Charset, Accept-Language, - Accept-Ranges, Cache-Control - Access-Control-Allow-Origin: - - "*" - Access-Control-Expose-Headers: - - Link - Connection: - - keep-alive - Content-Encoding: - - gzip - Content-Length: - - "853" - Content-Type: - - application/json - Date: - - Fri, 26 Sep 2025 00:21:55 GMT - Server: - - Jetty(9.4.40.v20210413) - Vary: - - Accept-Encoding - permissions-policy: - - interest-cohort=() - x-api-pool: - - plus - x-rate-limit-interval: - - 1s - x-rate-limit-limit: - - "150" + - 06e71f01-6f93-41ba-8190-c4c567b4aadf status: code: 200 message: OK @@ -1335,1695 +1335,1697 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA5x6y86CSrTm/DzFzp5yErlX1ZkhIHKTQlDEHgECAipyK6BO+t07+Cfd6aRHPTFR - UaDWWt+t+O//+Oeff9u0zrPx3//6599XNYz//uf22SMZk3//65//8R///PPPP//9e/2/jszfaf54 - VJ/yd/jvy+rzyJd//+sf9n9/8n8O+q9//jWOherNHPsCq6kpPPjKwRMfhtQM+bpGFaQieybF83vS - liwEOYy9UcLe9+xri4ysCiF6yUnOkTVdd4oawFfF7cmep1dtfbzsBlrqCWHDcR1N6It9DGE3pvjY - s2+tPVB3gFxPo4nbkX3PR06mw8+OXqfZADUlcHh6oGxkFrtWe3GWV+Ix0FwzmyS0dMEqvtMBau5D - 9uA8fdOxt4YKVn2UYRwzt34OncmDTnoBxFQnSVuYBRvw0PoYX28t7anDlDLKBjmcGhfx2iqXq4wm - UOjYGB6Vsxw+rg+g6Qek+OR9OqwkzGH7XV+elFRTP3vyWUXb+k1vT1VTjvozD9zPcsHRidk582dm - V0Rf14kcLHNHZ9HreFifA4uome5q7HHsZciamo7NL2JCOkCfQXQeHXK3fIOO5d1UEIuYmVhnLewH - v6lUCL5dgXF/FjWagbVCAiteSWZ8XTBFTmbIJtVsbMaV0XOQ/8jIHVKB7N995NBX1eUA8r2Ija7V - auEzFAosyusVH/F+qWlgWj6MRdvCJ/mZOzyXUxsF2nzB57DBYGlOpwam9XDGKnUCZz0dlAa+L6M9 - vb3dja5zgRN4GfQD8fklTbnxmfqweSkpKdhPnQrfY9KhpdcJtp07A+idPUNUntucpHZeO91OjXzY - JCdmQgbL13MhVB1S1UHEmcqXDlcXnxWyyZDgg22J9XqWPB2gz25PDCN+9ax9CCbE9W5ELls9x08Q - mPCsPhySPCOYruVQm6iI85S4zElPV/W682FU7G0SlrKTEvt5v0D+rkBs5OE5pedTpSNhbFKSPGrc - T+8+MuSnfvgQ44U8jRzv4oo4t6nxbQmfoYCyVIG743cmpnV7aPMJvhSETvFCTima0vkVrSpq7MzB - d3w4OKzUrwly/SIl2BBth1/ss4eCKaqnNXj5YGX39Vu6J0pAlMm2tBEI7QCab8uQu5Oa6Uw+q4zY - dycQHfACXYLStkFfDjbxzbkKf/OIbp/2RB7XneKMcjaa4GC1GTYl8whmSzz7iLD6ET8SyNVzI3UJ - DMm9IPvdJ6n56tKagMUAYVt01H4O72WMxtrA2GGLLFxe9vL+O78hS0HNGc3sI1U7i0TZZT5gzdSa - kKInkSdvv2d37fmCxEWasN1odc+7x+wNtanxibIoSsj2YQyhv1tYfLnkKliOzz6DdsLucHxIlZQ7 - KTKPnpPOTxI+HDT2cEU29D8ywPaGB3P5UjLUyetj4oeH6rCCz/PIHqsrtuKmTIX4Zifg07QGdr/6 - UM/p1ahgZrsI79ubXvORpRjovTvf8cO3n3Sll8MMPel9n1aRPlL+3Uc6MLPqgXUXRZqAVTjB2+Ep - YMcN9/0aiQsPHyjG5MoeXs5csg8e9uVkE61tWo3KzZFHn7vwJfi4T5ymMQUWHo+QmeooEcL1fO9K - 8DL9BesNWcA6qWKHLoNxwNfDlToz937xsOMneVoGe0m5AswJEkTSe3y/uiF/TasAnVg8Yj31o3oQ - mewNbqQ2cEgiAOYXf31DUVZYnImx4XC2FHjISMyY2DsGO3znez6wvPhNYt4q63F/YSKYBUfBE89S - 1VO3P4lwh5QMX7rGd5b9S69gLyoaTkRHrddp7WKw4Sc5RLkNRsuBmby3Dxw5UWI76zNRIFLPUk+O - Pu9rq+6pOdIwfOBIq6Z0Tbs6QeoYHomSairlz1YWgfxjBUTfjR8we7eyQyCI3uTHTysUnx2yv9lK - wjE+1Wv1US/A7rQV24e2ppPsxROE2bH1urxpw3mX3Ew4Fp/ek62g7KlCWBW9zXUk9thP9Wxzeg7d - E9bx8TKWDieehRWadG+Tmx6f6Bw6bxfu7SOHHeBqKX0/rBaK6X2PcyqHVAjsawDshN9Nu8it+9WQ - SQINQ1Sx7TxUjWPOdYXqMoe/+wPEx4wuPaU8JOZZquof3sPp1Ob4HuhuOjvqSQQhwxbT89U90tnf - Sy2kkQmwfgildND5cyXPT3EkIT31YD3fqwrhbF9N0kut+rUHvoJyfbKIsW8UjX+nWIT7lrQe3fWT - Q1+qZ8Affxw/iNdmNHsm8HsmJ3jnf5yN30sIzSAgni5WDrvuvh38Vky7zZOS8otsQ9ic14O3k7Nn - PUtOpKDrc3nh04U5ptyt2suIzJ/G44C49MMuKUxgHtwG58VXBzPg3vA3XxtezHRR1YuJ2Eq5kNuc - ivVPj6Cx1rGHtP0XzAEzzHDDF1xk3Kdm8ZtX0B8fdn6RLkt07qC2sB+S62KlTRefFdGNPA2clfeb - w++NrwyrFGQk8TDvzOG9jdGmJ/C1WZyeRYwSgbF49SRdRjMU3rtVRdlMOmKSr9vzho1a8OKqG8HW - tE959SoEf/13txNcz7tYs9EPz9IIvsKtHyPk73d401Nt2Jb6oENr9mfsm9KarqZm8j+9hC+iMIPZ - byoFAWxgjCk81bOUDq4sZQ/kVbUQ14tb1x66B8MdG4LxAqthow5wSnskbkaS9I+/pwsTk8c6LeFa - 63cGLevu+9dfCx+fY6SFLkuUV0HrtWEWHfk0iIl9H4p62b/cCn7aIpsEmhQ1VR53Fu5u+jyxswvC - 7jt3Hdz4wINedq/7rd8RLBoHp9C6OxRZIg9b7F3xySxfWosKAUr6bQ6Jts0n/4mBCKcLjPFxTsWe - uk51Qa/Olon1UquaPtHqI+vxunvvNVDCSeSfIhz5y4scXSfp1+JMGmg1lxCrtzufzvf2W/7hr9ZV - fDih404Hl+vNnV5nyoVzOKhvGC3Tc9rFe1UTGKlrIDWgiS2IpnSBBmMDs2ZvJOI95NAnkn2Y1tN5 - 66ddOBhlVSIvijRykmPPYQ/h00SjkkjEpeOczudi70KTrBTrSqQBcsVfFjqm8cLm0B16+n7sW3CU - hCPRnqBxVh7aAzwWvoDPg72Ei+zmDKw+vjTBKzJSoT5OMsTy94oNIz70HDfiNwi09TKtPDeB+eiZ - ERyJ2hNvnXttia5mBc7iTpiE9WFrQ1qbg9S3H42Ynx0B00+fjcpOIt7FjNNZ6uUERqhFREsDReO8 - 3E7g9S73E9oLT2ddj3oCV6E2PaFhiDOcHvtc6jKzIz7dfUJqnx0bJo6c4oOkJvV8m6NILqE3Eqvl - cDg7wUX/0w+Rz0Ypa92gLAbSpExLOR4ptedpgD8+cD8x4/SesS/R1LrTBGjP96M5NSyMDV8hV8de - 0i9z7kt4vi/6NEuftabhy+d/fEMsPdun1KKggvXZt7y1jw/pIs6mDa957eFjZQqANOvcQQE/WxwV - l86ZD1oCf/6G7Kmy8UF7jtDufa5xmBkHSrhgnQCAIkcuy+my8feVhb/r/63/1xcPJnT3fovtZBTD - lezq+Yd/xGNf374rdocIJuFyJdpDS0O6030VDl/RxY+9sHeEZx/rkPGK0Vvryawnq/RjiAC+Efsq - BYA+e9+AXmfdiVMnx1qoTniAm172oG+22no+mhH4Xa+TVwwdJnd3Ae5eaInlnWC6aDscAKE7igSP - RtTTRPvEsBG5BeP3Re1XdJV8+F2a9E8PrFDmWrjhmydhj9Nm78620N/HmETL+VNTVzINOD/lkZye - mVXz6SAkcLLeYBKuHddTZSxVRNSV8Wbw1R0uC0EGZ8l7YyutP+li5VELjgUneCgMRNB/37wr/ebH - OF6Bsxa7UwQ/7F3y1qfUpd0gOh6U4u5DMpVXHG7/cktwU6TLhBhHpuTlKwEa0/XqURuNYI1rlZVH - 5pTjeMNnbvOjEE7zn55Ku60+iDeub7LpAbBIhQThYfc28entE22ptLsKD5m4krTT+noBQjuhh3QZ - iKm+vhpNlpMN14wRiQnXpp5VIEcwJd3oiV/EpEOLGFtOV0nH5vwIUiHn7wPUbv6NaPd73s9P1Tdk - cepvv/lIlxvbGGC53HhytFwazoyzn+HsxMpWj5SOQ1PqcFwGFW/81y8eejHwHkx3Ysy+RxduPL7/ - 9GU0PCqNLIw/wK1fsRn3fD+vJM1BIfQeNjb/OZ304wwFTXGJLTpVvXyq9gIfUR8RNchKSgknRPDB - hdYk8Q85XZ+WvMJdkJ2wPXcc/RhDwIBD4w0es98H2npXpwwWpy+DTd4q+8W2yhxQm+ZT2V8WOmR4 - nIBc5g9yrBWlX2bLD1AxhAtx8nflkPytVQgdwp7sd3nXz9bxPUApKxA5OPY5HZNvlUEAZQ6fmqAE - S3TLKjQkhfvj31o4tYsI78/IxafTpeuHwAOqzL5bAV9Wz6X0gLUE3aXTwRPFI3GW2Yp9aEQ6t/mH - MlzG5ZtD+SCfiCM7dr+QQWllpZNUbCYlC+YQFAz88cHph4fne1VC/RLOxDBPjdPe5ugCxSJvsMOV - V20RqxMDl4IEHqyzZzrf5vwClFs7kLNxYOmw8T2wPbnC9nVWwfr4RG9Z8GCL73r2TFcUH3x0f17c - CQkVT+kecglqX7ZKDvvqHf7uB7bPb0HwN/yCNe36GG5+dNMPQTr/8gDpEyRYP9jfdJtHBf3mT3kO - bzq/+McbGt+bQ+yv+ATLwiEXTjGdiPEBMFwVqczRm/+E+PhdLz1/fTAB0F6iTVSe8+jiah4P+YdR - YJUVlJp1VjGHy5p8f/2lzXxUdBAJ8R4rHPuiq3vMGrjxL7H4hxzOhcR0gHh2gn94OK2fdoaPSqEb - PoJwPo61iGhwiDGWX0s9p7Uywd1iKMTFjzWk+5PTgrhkfVysgKXr9cH7YAVNj4OSPGsq2octz+Ew - UR3oAh61aQYXPneJyV7UVPAxb4CDDyNcnDpG+/OHjJij33rSxTE/LeQfeoGdCB7SxWSAAhelKrFS - sowzFxLfwq3/8Em6celyEJIMCMnlMPV7iOq1KJ4zNLhzOzW3hwW4X760MNcDsUj/TqceDYr8GHfY - Q5v/nvSy6f7m5yDogzZ/pnYFu+9seIHAf+rpO3ctxC5L8HHj3xUwdgZi+xBhUyNnOlf5xMMhLwk+ - KM9TvfHBBfzyh21+wnkJNBHlN6aYSBgN4dy9gAkvrVKRu1EX9ebXG0TmV4N/+mGstLP65xdP63QO - uWO9y4CHUuIx50hPZ1mk65/fc6bp+bueHBwX6GEna+ae5Fcugtr09nFGGyWd8yTVYUracZoShoSb - v9Bhvlbkx1egfRk1C4u7luIjG7T9ipm3iBj2nkz8mvrhjNnZhRKBJU5CoKfrfdZdyJSNTmyy+cUS - LhewzYe3/vpxqQJDhrfiQPan974XaPscQJNgZgqH2wXMqqu8IfOQR3ziGrPmdQQ9aDVR6L03/Fw/ - MZWRJzX309KWbL3uja+IQpIWWHMYox84nlUhq+2e+Mj7EVh1z85//m8SSF4B7usuEQz7xsLF+SID - WpbVBDt+kD22fT/p6n/nAanPqCeKOavhEqzDBbDpOfGE77ep15bXMuj3MCfaiR605csIBtj0HTZ3 - ctq3N8Yv0ebH8EnbW2DFhRPBXXcziBbMSb2CkfXRxdoRrCkX2+HvftkhO3Izom58OpbyroGi63lY - ExSxXg99okLmDM844+tjzbq+qUNVncSpec1dvcCbe4H5DRYTpxYAkM9wU+Dwvn/JXbhqDtU4pfnL - yzb8refuRW3U70SIbyF300ZfPJnw2wnPSd7qxT25fIVBFR9JAnettumPAORmXXgrU/T961OVETL9 - Zo/zUYd0lAqJgVueSpSr1/T03mgz8gKhJHYI9JBqzRqjMgr3084QO23SEeshywhLj+e9hzPx0J7A - 5t+JHmVnZxkpyqDxzAJsZOcd2PIyEd5W+sCKI0rp8vP3zF2wiePjPKUXdnKBxvXol985M80k/8/v - gHip6XKsdzl8OJVB9oVUOesrzwZZJ71LDN5D2iT7w/DT01gxpSCctzwSaph5ePOp+dAlNWRdSrPd - zUPZctGGzuzfYOC0DKvXtHXouVtaeB61hKjr3gqFjPEq2I+nN3FoH/VTcw4vUEOehk9eTlNaUFf8 - 9Tc5wEEIhw1voU4Ml6hSbzhCfUM60PnYwA4O2prrj34Fb43TEc1dh5D0HnBlnRF2WDcVpecN+ZPA - zV+SR7aw2nS+dxWMPVPCia+z9XTS8QwZ480S4xHEPbf5UXA9nq5k/xT8ngb21Yfn/P4h1lwM2lqc - P2/w0yvxeX+nQ50lPOhH/MZYzp79Yg0xC3/nd6q7B355h5QC1fa+caOE/CKrEO4vYkB0ZiDalrcr - 8Oc3DgHrOXNzNxSo19M8zeH9rfUv3wzkXz5pbfkgZzJABb/+4JYYgaUuywY6RZwTZ+B2/fjLpzc8 - HOUQNOly/XQTuIplPsFRDDSBGKcGOoxyxtbW//zdb1uoGnNKEo2+wuU3X2sGRWIyyHBofI9i+PiC - gdhwZ2pzi3gbfvFwJpqgxPWiHCGEm373lkxdQwqegIdu1urY3/hzsWPEy+bBa6a3YByAEJj74OfH - iXtF73TspSiAzJk5e3B7T5MQveGuuxpYZQqnXx05NWFXZw0uNn6bT0POi7986HAr6p74pR5Aq5FC - nLjnI6XjvXzDwQnuHqiTY7+w206Kf2lf+ISLQSP7k9ahJZx5cvnWQtr356P72//Ah4CdHAJ3Tixv - +h17n3BfTyg++XB2EgVb2tL3tLLBBK8vwSRbXt/3oQBUwBu3N1FFitLMTPcT2vAYu9IV9FueVcIt - HyK4/oCQHM5FAu15HrAH0rLe8q8OdIf7gg9j9nQIVtoBbnxEbh8UOZR9XcQ/PY2boglnWOsr7E6V - QNTNby3b/gx8daZM9MeB0wYJHFcYoQ5hdVc26WzatESf68n47SfUk2N/Y7Dh81Q65JsuX2ZngCt8 - 74khYA4su4nqUFEA9SY3T3sqCRULHNc0cRovGl3HD6eiGfkWPp60Oh0uksbAyykLiDZ4OuAGXRvQ - RK8MVgX+06+vzJxgyI93bF2nr7PeIisD8S5TsYEFpea2/oB3tqlwcZQqQJm750GtcHx8CoOY0omf - bVAcvAPxrsre4dBTmsBv/2Lz+84sLKkLLGvWsK/6U/jL/+UTjOiEBinUlvfBn9GWP5Cj8no6c5yW - JmxH1iQJ9j26smdphpFyj8lx479f3gfWs5J5NNcvzlwcRVH+GGqI1cF1tjxCmcB2P5NQmTfQe9dI - /M3b9DxAHXBUZbrf/REl50k/QoMxwV3CB2+ph4kufvxsQO5+VGxc25TOiFEu8Ldem5+t1/H1VYCQ - Wyk+/fCqB7ECfvOmY4icWQLH+cff064/i85yEIIM3WJ8mmR1HepZ0T7eT7/hh9G/61kCeIVJf/Cw - LV7WfrhqSwIf6VnC9jQc0wm7XxvqQP1O3NbPS9IzOQx9dyS4bNlw1t/aBVoQu1u/tXX/HAcdfu7B - 1xNK2+rXlncyeGnViti+fqmHKn/z8PR6yeRwv6gO23puDOvdd8IWzUOHiN03gxsf/fifCkX4jGC3 - SxVv1Pk2HLhbnEPLOJeeNH5ZSpmPkiA1Ny4Eu+U17aqPHcFv2k1/+nxOuEpEYliZePNnNfsOXy6a - Jfc9cXC4hVv+HqMB2RVWmlymvfleTVhepz3ee3OXzm+t+Jtf755UU036pW9+/IQTK53rNR128c8f - k31yCunCTkqMNv1LdIvxQJscFhMK3UH0Pk+nCqc+oe1ffqf2Ty5ccaFd5C3vxodt/4S6/UGGNi4q - cqjgqPHrfXpDq2X4aXd6TtryWBwF+vFkEtvOrJBueA0KMl9w/g5aMP32U7SuGom6+15SesVPFr20 - 1sO37fzDNQrfsPuUR+wZoxJOH+M+weCmdMQFt9EZz9fLjKIpt//y/vWriCJMmGuDDRHo4WLifQDV - +ZkT9bT0IRkl3YYK7xfYzG864JKeyQAuqgNWt3yKVwCb/87nPbtrQNsH0jJ0PosOvt4fq7M25zBC - +8F1SNFaT2c5h+YMj4pxwGp8yjTql7oPg9ur89AdwC0/nxi4t0oO/+Zx2E8iIxvJGGONU+2Uax7h - jHBRHohtJgIl/fORgBiggjjh4ZpSI3Nn+O/vqYD/+Z//H08UcP/vJwrwkmnY7CSN8tcEBhAzWo6N - e6FrbH7DFTB8nBNtGCFYFlKx8MaHh+mZFBVdBGR1aM3mC/EvtpIKIy7fSDqfLsQ6oFPNq8nKQnYP - uKnajXuNg8yXB+LSecQx73o/SykLkRscR2ID9gY4C3g+PH3fvBdJPK1Hdfxk8MYuPi5wN/XLfdp7 - 0qe3BnI87XtAn3Lnwl0p8kQf2MWhbx/IoHqskGhBk/bjB1wa+GpdiYRs5QF6q+YETS3+YD0p94DW - RitDrRDP5PLMO0rET5LAr5JZOPOmjzP53JyDScvtae2x5gxHA6zQ7aaWGLV9dNbOLwLYpvBLonuh - O+xMxwsQjX2Lb/EtcIQyNioonuyJKOGqUv76fScwUIYLue9ffsjvqqiFTq4d8Wkddxo9p7EMv8Nl - IEX1+PYLOR5ZZIn5SPa3pE/Jwb3LMD2yJn60XU6XJ+UMZBx4Qo4F++5JH2QGvCXxkxzbw7Xn4nYv - oyrYXYjZEE6j1752oaUhD1ufWgu5nDxjyLYXzZOHfAFf9lia6FQVM/Ye6QDm8RtEyGbIDitqcKLz - 57mySI6nh8ez9QhW77uwMFdm3+Nf80qbd0ortIuqIzGw/k65/n52UZM+Y3Ke/TfgpaozfvX2WqWm - PQXLGMPBin1PWpi9xs9yksGud2Wsh+84XJ7qJ0Dfco9x9Iy5fqmy2Ib30+x5u1NWhlTu3zpMH2pB - nFKoet4MSw89BWMmNnF6sNhHHcJjUPnkqu2+ztyZlQ8VyxSIIryP2tTtRxNOWmaTNNCP9W+9ofVc - n9hL528/87enj0AoW8RyQdSPrn7OpM9rdyWm/Pmk82JGPLop4YJPlnejvIv3OmIqpSV3tRTpmKjr - gGDjuwSfReSM9tFloKm9HPxYknvKrpmS/NaX6Db/AOwoujl8WGeDZPs3SVe0f7//5tFJfYbOEzm3 - 8EPnluR9PjpTXWQ8FBO4J0VADI3Ph2mF2flwIG5oNOFXa9gOYeVrkHBsXnSVqk6H552XEw1rn7Qb - q6uLnLX6YOXlfOvFwsIKd6MbkrxdQo2f7NmHj5txx452Hvolf+5MaHV9gvEgHkPerIII1WeuIodD - gDX+4zoNJHe1IY50LXuW2Yk8Kh7jtNXDAcLSvgZ4bJQDjnot7tmzBU0gxF+MbbHfa8Lt6pnAQRHG - lzTt0uV9WxSYTuUFB/zLSvntftGrKW84shalZsOj2cEEjTe894KwZ8PrukLh0L+w/uJNR9gXtY0E - m4OTGBtfh7PXNIOAPjDZM30TLu93b8Nkz7LYKo2JrntFNZAQZiLePw5TvbzftQk/lU/xaasXf7lA - KO+KVPGQeKkoLy4XD+n5XEx87COwkDWPID7OLrncvyJY68lj5HwRHGJYS1nTLvVk+PyGj+n+KDWN - KrziwuJ96aYNr+r1YF8NyZGknuj+Y9/TBd1sNJZZiS+qWtO5M7sAnsfhi72t/qwo3iP4UVGPtfWi - hJz1qFQUNtfdBP39yVnJWcjhdK5THM9QDzlanAw4B1OK92t+dVZMFhs5+7HzKkUKwDY/LmpT5kus - 7CtrYyXBBHLlwSEWgzElW30g5Nb9ROvnISXWnubg+KIiNq/7c93nB3OAp+oxk5PmCOErUeUJqtmx - mwAJFIe/2XkJ0bl9YWubj7mOPxMSBHjz+ldYpHPLNwFCD6iS4no9U36yRR+mGLvkcNSVft59wxXl - er4S9zrM2tIkTYWS294hqQ4xHXbJs4U7EDZYXQKtn3PfvMDkUEee7B+rcKsHhJ1eTdNWP0BvV8OG - Z3vaEbPWz2BpDsAA9AYVfImj0OHOl3qFgepa5Mz0eij8+KabpAx7nGTTed9VM5p1ycQX8nS0rd9Z - SG+MMiElf6Qrz+IJStxAsTYJTk1vX+GNBNBK5C4JQc8/NE2E5bOMyAk87j25VXOMdMnxsEo+Siok - 8R3CZ/A6k+MJXlLiX++NfBJiCx8l9AF0iMMY6QM3k6R4tCnNsrEDyU1zsCuVl3R56ziD7ZjlJJRP - Cxjuj8BHn1k38PVy6JyFKc4QiEvr4eh7OvarqHgx8Jq5IurJUB3uXuYQ7pjBxJ4W9D1HxGuHAn73 - JsdK5bRpNe42WKS1w8fncaipLMUXZBtPBltHP0z54/H5RndGsLzSH51+Ox8Dl4do/NbL4XezKcJb - L0eTPPNY457+vUT98ZiToyZROoviOYJ+nwrYzQIAlsv7OaAdODc4urEl5bxzAeFzGPY4aqO8X1Y7 - 7MD4ERpyKJg0pU+5ctES6jkOD7GectJNzqEpvQuy4W84+feLjg59rmDf6ByHiyKNAc6edOSYWFrN - 34+9CiNwvBMckLdDT4WoQCZjfZLLZ9NZvpk+oRt/PpD7TdqFXcLMNlINEJKTXJ2dFTV2AG/KeZnm - wVrrJnKyC0S1meBofDVgDvZWBLvyHuD4Njl9Z6yzjl4n3ib7CSl0boa7D1exE7GNmDddBIfyQK6P - 7iQ8j0NPMU4C8Jsv4eDeAWXxw4Tfm30lWWjoIbv9PzrMbUzuT7EMKXIjF0maqZFwV95q2mtfCMsY - JAR3U5wuRqvk6Lk/Zt47zOp62ce8gnLuOWKcH9xw0b3iDTY8x56/lnTguEOF2AYU2Ku/qbNA5smD - ljV7oii71qHKveYhDOYb2b7X+Jcn6PApLwE2ysfJWc/+00eZMUTk/Hz0dMHSClEschw+hWfRWYfs - KkPY9z6xwLdx1vJqsfCZi5PXYdIAGkITQiA3V6y0/lOjWC5niB6M6m39rVFGqxM4nIwV71V4CmeL - MUVIhdqY5lEHznxguRVd4ckhV/kI+9V9AA8Qeg+xJRXXdDRv1YB+ehVXnAHY0ssG6OpKRvTeOoE1 - VLQSLG3AeQwn2WB+n6wYyqPMerdXYdGF7ksWvqJRJ04T83TyW8+FwjNw8HGr13LLtADN3/hDTFBh - wH8SNMHme+mxAbMinRas/fU3UR9uBWbhUb7R1r8eMkMSToD7yPDVepK3LA5JpytzY+EeVs0ETTfV - 1rq4sHAvsio5n269Mz79cwU9kE1eOzRHjc30/QSt6Qq990OJUzpY3wAyWEVTH95O6cydv1CO4gMh - 18rswtl0qYGyC/1MdOufRbrJGfg8jNRrnScMqV7zvEzEV0einSWla/kFPqzWIJu41eud/lcf9e2l - 0y5ejbqtnK8LX+urxV41rf0UF0cIz2zCT2LGa/3KhkULam0+T+jS5NoCxK4C4PbuiaY/QdhlRRmg - OmZa7CrhtycSvasQcvMeR7stx0+b+A2dnk1w4N2PYNMnb/hYISKK7K50ndnUhupXRPg+ekY9P5Mw - gmUsJSRmH3E/6xOTQVlVz0RvdjunNwGrQ8ubeazeiaexVrDGyN6egPj1L62akyn1gphiZxEdOlXc - IkLzgSRs3qa+X6kbZ/AMqDyBlbJhc2N3Log5KySem9T9ovmaB5+CPpNDVR5rzvm2LXS7ocX6SKR+ - CvtEAR8cmtg2dl1IWtMyIAn5E9FF+5aOm/6QvEOREDfRT+lqJSSGvZZAbN7vHqD6dXuC+54w2Gxd - Cwhjngzyj689+Wxqmx4w0f2pCcRs3S9dxHIM4OXdLcSqHeOnz3OgCoP/p+d+/gKE+ioR7fheexq2 - txh2d3LAds0d+hnS1YPZZfmQ0+MS9xNl3jY0xLQg3ncA/azQxxvaKpd5lK0msKbCwYNK/XyTq/Cc - wbJ/tz4cfONB9DYLAXVRFkChWK/Elt7ffvOfLWSTcsaFpWsO97jMHdr0vofA416TM8Nm6ByXAQnW - 06At+VMwwbDrK6wITBOu5+lUwSHrRXLIuKZfzgepQ7524ifuClJtppdHDltjDYj1XRZKrFehQuM5 - hlgVygVQSdU8wBimjsOPpPa8jAL2z08exa5yaOOWLrSZbD9B9jTVnT3FEML1+vAWdd/QP38ShDuI - 96IVp2s+vFf5G5LFk+7mi34LPZCh0o5oklgX13T4nCqIr8KD7BeHhFPwCgZ43KspPtpF0c/lPCfw - 93uUO/uefe3VGVq1H3m7l3ZNJ+90zcDG/+TReKrGyyWTw41vsDI/Mdj6M4ffQ1fiTQ/R1WPqAJZe - 1WM9Mpt6GOpQQZve8xjg+j096m8GXv2oJafcefZzIfUesAfoEiU/Kf0aF0cGjrogE+dG7Jqy/VKh - ne1+Scr7lrNev1MMz9pHIqf8k6erPcUMOJ5ePD6p/gQWFdwnlN4/YKqWQKuF/GBO0MoaC18Wd0pp - j6oG1PX1hlPvE4DJcrgA7Yq74rGb/p7rc1misOwf2PQ0s5+WvoyQQbFJ9klRgWXTN+DtTrFHcdr0 - 8/hNLsCRQE+0qsvSJSSXSs65evReV5A6s9uIkcz3AY91Z3zTP/2oesp94nt536/G68SCusse5J5G - rEOfw44B3ipQ7G54TJ1wSeDceP6fnpo/hyhDWew5+OcPuNvuzaO0HgZPyJ8lXUOP66BQzFfsXu52 - uhRcOcDNf+L9hEo6+VDo4KhzMlZ349OZNzyB2XfHeNxRL/vlzT0CqLLngZyM1tWm+fVVYDaJN+KH - a0Xpte89eDjffHzY5m19nnIR/PIPXRaWsGuiToVbvTyWLy7aOoiyD3BgA2ymThnOJy8MUOWGR6y6 - +K5R4Fn8bx6ICeSTxv/qHx9nGZ/OzBpOpPgOcJAfDnFfSVhTNZovKDk8I+85GcL/AgAA//+kXEnX - srwS/EEsZJKEJfMsQVDEnSgqIINAAuTXfwefd3l3d8lRj5DuruqqTqAb3hYAMWrx69+S2Z3TDn72 - GCNNZ746PUXSHY43yURht1B91nb3EM55eyKHPfJzlgcGC1x29oljhekwm5djCVQjrVB2cF76fKuu - NTi/+UdI7Wat6EXoWckwBhLuJoroSns4wnsW+Cjd4snaCrxDydt7SN38i0XUVQX6DjsjLY33CXXT - WoTJVY6I4mtOLnzajIH2oeXRL3+6X3+3Xi8znk1SDjPwnyEYPxGLrnthHcZHsRbyN2IDsk3Akll2 - qhEC+kTILt4KmB8Z9aB7fuooMIYGLKH24WW/nk6h+Pk+KvIMkAQKxuuQec0QpZ2UzrKSMh7ybl5Z - bfn7gk0RHEgxNx995aPakbfnCVt1MKvVPnQSvLA0QocrNRKBHFgGqMjxsXisISBZkKf7w+n2RMFp - aYcFUfr99S/hfdMX82bkgqvVO+jn163ZE0F4KWwdsxmek42vX8AJQisUpycafn4O2PQdSjY/cY5e - 10BeHHkiWnhtQXcTdeXXvyEXqu/qj+8upBGJbTF+MgXG9Q6YqKRILeelIouvFhBYFo+j/dHLuXu4 - pNBhMwfZX1TntLiuHjznXoT8HQh0YSfNJSw6eAx3e5f3B4vIBTSK9bnpKzYhWzxlPpIcFCytBoSv - MMQ/vgrl9pIBunQT/uE13vIFcFkSFbJ3uhzRppf91TE5CMebaCLlklz0utt2iNsX3IZTbnn+XI/H - CP743p3Cppo/a1TL6WFSkH45pf/02C+/3YJ2dO1iTZTdKk7DF1PP/noSsi88wPpFjht/4EYxoKx3 - i49uN68c/vyFIQxrLG/6e7QeESuvVfEm5lOP6d961MIVoh8es+h2FOEqdiJyrJCvprWZFZg8/TZs - EZJ8zD+LCNqrmZJCfUn+aK9e9Mdf7BLgZOZdQfvhLbqPGayWl3cL4JC1CeaZMKL8tslfPjZtRRQt - nij5BqcZGEZP8CqISF9ZYyohvnkpcpWvTUcv22OIZUf5+bFgFs9yB6MUvcL35u9QstNmmROxRRx8 - y/zVBF/pr7/S7uc9IDHvdtLGl6GoVMmwLOTLAsZlG2T57TLMZ/OswUK6W+h4juBAHgdQQr8mJ2IL - eT6smuVmECt6Q6ytH1493btLe6I1yOtVWd/4IYXRI21I0Jyhv15AnQLtNDZEEVOVcgeI7yBW8AkF - XtrTVa0+X6h/jH7jw1e1uCCMYex+DliT5GaYyvAjwV99qfbYgrnZP2cw10FEEu4hJAvoxRvUx+dE - /EUcQB8+tRDuGOyEUPwMYB6rXIH3flTIb/2JS9UYFofLgLtNT/JZoRjwc2A9ZOCmrDjufq3/9Jlf - ZyldXV+OgS2NR/SY9qbPq4pnSUINJ/JgJJxMasZoMLgGFOUe4ip8ODsZfKLuQeLO8wF3xWoAT/al - D2OrPgOy4Q88CDcXf6LunNDPFY/QfrVv8us/22iVeWC9SYLsqHgN9MSmCszOzWG7P62aM+fF/9Uz - fV/4imrRooFPopVhxc+yThl2CaCwO+fE2jaxEjMPFOi5sMRsPBA6IWfyAJJOEP30wco/0xhO1zVB - m/8O6MeDNYh8fiF2L9h6h2T5Doh/OOLd+XwEi9jUDRwvz3coplTXudxZFZgz5g1tfgRYDrC5g6d+ - VkgitI2++q47w9zI6z89+d1b5gg2/w8dui9D//S/KvIa+vXzc3ezxT8893qx0QknzJ78uV0bpKa2 - WQnf4LTKd7jrwsudoJwP73YDn/pFIYfXMuUE21wHuTY4onzLB6wMUSPvdU9H7o9PfO3eAUWHL5R8 - 7UWn/TJA0Pp1Q4Ixuw9LO5YF3PgVxYXtJ8vu0mo/P3zDw1TvY19k4O//uRl88ukjOyHkXrZP0Id5 - 0Nl91y+gX5IUeek0gf7jrzcQF0uNwksW5Fu/A+Gshg3yeJcF68//eR6dhEQxuILNL1uBR7sr2fhi - GAH8aLLswQw9E1vP6TOwRfB7XrVP5WSN4O4Lv8YLI7XWLxX95U9j0104mxczn2uXq+EsiXdy2fB+ - eX88CPfDyfj5hRV7eZkrVJcRkuI0xMnU2cmmR6M7UXYCRyeRK7Ck9ZL8m7/436s4s3LJCMLm72p0 - Qc7kAO/QG5ixiJJzayuIMEH1myitWA2ku68lFG+MisXcVxLqfp7Kn59WEP6b4PJ6LmU+Eh0UtaJe - LdrUFnAfKBAvqpdv+skVYRI3AfrN12Y6Lzws04FH7ltUci4wrgVMnbIPxUUIKP3xlV+oNoo3P4C+ - B9GC+vtmI1VKbP03H4NKwF2xvOULXfGkgGiPPGJsfjrd/EN472UGBZdCrYRdcffgrrEPSO1ylq73 - ZxfBbT6DJct4A7rN88TIZxfy7F6ev/WLAeiL95VoW/0v9Glakr++2k2fXoeNPzSYekqBdH5++Et+ - GT2AOsEOeXd5DSM/xqM0q0FDfA9xw9LZeQz7Gy+E3/uMkwlLaiyXTv0J+f7QVrNkZAo02VQKmcPk - JtjTteLnv4biO1QAVQzpBi6LhUO4lkMyW6ip4U5oNaLYHzqsKfdRoM+wBkKuUflz/p4gbFrbxmxg - 1vmy07haeoWvAR1VD9D5zH95cJ+zA7KQYeXCxieQHjiEvMgu883fuUE2mB6Ybn7qVj8KxLY3Eq26 - Lf6bPzXdn///XKtvMm3xBVnUsxg0pTmQbhg7uPmLGAzckEzjsYlhDj8f4oaT47OFlhrQ55GLKVuG - dGxy+pKz71oju7zFiXDlmBB4T28frmR4AtJbjwI6x1uBFKFp9TnSWR5ehbuJ3boY/bFNHAyVFHoo - fk2CTtkCRaC50T4UNv92PVTrpo/6B753Qpcs0W6NZDXa7zBgLSmf110FoeFYSbjLFRYs4xHHYJvv - 4UQPzwm5D88ZXs6Bh47vhw8ouRMDttYNEae9Ix0XI57hNh/54wvy2KmpFMg4/tVP1YuvTywKSSGi - g3pukjlzOh6+PNZFmq6rwzxH7gp/+OEdb53O6alWwhYdnXCXg0eyDrXDQof3AbGhnyZbPkVw66fR - Jb/3+fSdGQ9eBjElmTaVOW1TGshfB0UoTBSi92rGa/vzZRoxvRszHd7SN4TlzboRFGmB3wCuFeWF - NxyUfJ9hMl5eh3X/w9tfv8xu5yhBq+0GcsjUlX6fyPvz15C5+YlLlmQFYKfyhqzNXxc2/x/elNMO - +V+ppHSaAvZvHnLWd67PjRrriNt8hvzmWXONoPjjT+SzlpSs2bqUUHw5V0wXqPsrPyklpOWJkMvN - rYYNPyXQy+wb/ea/gkvVSN7my+FkTtWw+QUYCgJzwXKaomr96bH/Y0cB/793FLj2tSV+27fJmgRs - BLPRI6FwhGI1jzYsoPy+TESpwjYfG8dg4JvLQuIWzlmn6kS+MIGRhbkarGC+FKiAoZTX5BA4YcW+ - r84KcuaQE726RjoPptGCr5ezI5b1mvQFP7Va5pt9jXT8cQD7wdcVZhdpH3L9YxlIndIvmNzogC7W - vAN0aMsSeDKJiDeH0jAez5EHm32L8Xq58z4Om4GH51fJh3Ph9WD0itMNorC/kewhljle9cKDuuKy - yD2cCVjes6DJ3Ls9kaAVXcor5/0NNm22hrAVXUCKeQiBqLUNMXaNkgjBnr1Jr/UCwrGWBbD07tLI - 92zWiGkdvYHj3bqDMvcq0S0Lx3zOvyOGFqQWOTzuZTLfyoMGmY6KRH2IMl2prhkAafsjukh+QGc9 - NQPIxJFJHuajzpdK/WaywFwIsfusHboBq5G8ONYdhUVI6RrThwe1QC6I6x9Ow8y+dU3Wu+FFQlJd - gICszyovXpJhoc/ail7ClwjdvL6jQ6VjSuXDFcL2+7GJ+2j4nHZVJMq78/1G7EzXB4Ebqq88U7ZB - Vz8wEuIhVZQn3liIV1yEga7i9QbAcuWQlyFY0dnMY3itBhguc4RyTn19FWg6rwvJytvbZ3e8X8rH - ii1Rsl/ew4zo8STPlG/CTkk6vc32eQavJNLRIcsMyj3yIyNP7jlEhlRqgL1drxE88npBVEcFOgbT - aIARdx9SsM0nF5ZiTeXt89CEWexjMEWKrHf1kySXr5OQQFMM8IxjlRyOJPcprpzsLx7eQdUT4XEx - HPjgBwXZnE3yRfeVl4z6QCV3ZaI51l39C13Cx5iV1FPOoX1nyShmI5QpnEXZ9nuZYdLZR6L0+2O1 - SskM5SaCCnHVVsxX+cWkkHROjY74uwJ2+Xw8uYyDlVzq09fnhmFO5dT3HuRsEh/QukMWxCBVUGKy - Z8p9byIDsou4J9cavXwyxQ8LWv44kmsdRcmaN4UEJI9xkUVZpC9QPGHogfJEXO7DJlROzVF+DaxN - 0OMq0jVsKlbe6gdZ2WXWqctohWzogU2igE91Vrh3InSouxC37PVhibnGgDzwb+Fa9DudxvPsyT3n - tCST8l212ueXKKM+VInF3fxBsLw5lsdVvIdLPLBgMT72CONWE5Benspc6E2BBa9LfcTv2DGpAM3w - BLUqQmiLZzK425mIMrIEpNO+yUlkXbGMwUlBRw8edHZv5wVU7eaAVL/wgfDcbxPQ8/2GblLKDcvV - Uhj5KuxX4vSt53O38NXBS1z3KPQTmixLpXgyEyE/XNg11OlegRjGPaKYeYycTvzuBeWwsaawwWEM - 1sf1bEEPvE4h5aJE59oU3KRBAV8SzONzoC5TlFD/0pz4Ka/59M4uETjfLB35V0UGo+X6GdC/442E - WScMWFxWB9r6YY9ZKnaABppiwS1+WDC/KuCRojHQ3u1yFB7zcBj3dlLIjL87YaEH6cBSZnaAK7Uw - ZCSsJNyHhQ0wVq/Bu/Le+Fgtjpbs2mmDooDndTrFqibrp1OPOV9oANEYooCyKbpwrmPT5/f6oQNg - F4gkr5yM0g1/5W/jKXjZ6o3a9ZBJPoN7fPP2nb7iaa/BdtElpHICAKt9Zpk//Dd2xnsgVNl7MJh6 - h3gZug9U8U8s1I7OiZz7u5rP7zdbygnz1EI+sPqqj7mbtu3JDkJ+VzYVB1p1e4fI40QUyW0p2SmR - J+dznP7qgwpLiy34Ka2SHPx2B9YryGL40vM7MizN8Vf2cxd/+UF8yzjowyt/zGA3WxqylMsbrGdM - NdmHDSYoOw16vz/wMTSLnYvLS/4BAtOalrwvq4L4PaoAnWJXgZPtxH94yO7t9iX/8LQgSkcpfyjv - cHeucizuihHgOhNL6TUAByn4pvjjFCmW7CVmT/Lab/L1s+5GkAqSQgyF+VYre1Lv8qVjbkTPAjtf - NTEIoGLc98htJ9UfaSZk0pGDj1CU8t3w+z30meVLYix+fMoUx1SmMZaRurvbdCGqmsknOsakqOJj - Mq87I5C5R3wP35egymdO+v7lG7rPjpLzgfEx5KMh6iQImD2gH1HKoH2OaFj6uBi4+caW8utKD2H/ - +DQbf3s1zHupRoZVN3RRM+0kfx9dgtJsjwHrNy9Nni+2jjlpMXO+/voQbviLnlBVBv6zVp384/dD - 73s66xp9JHc7NiXJThrpeLrKPKw170vCx3j26U4Zgx/+oksP0oqnPsfK7Er4rb4eYB4+71rG4KqS - vA7SYWELyYC7tj6jDA/2wA7Y3d6h5Fak8LlYX7+Rc5Ivg58i1yc0p2pNRdl82yYxzFefCMZFMqCe - FjUJy4M+sI8HD+EnxzNB8+m6rddVk6F6YknUX1awyugrwTZWbuS+E0V/ne0xAEXyqEOws5pq4ch+ - O9AQ2ej4EGWwFu8Xli/KHaM7O5Bk1Uj9kmlvYOLUVVCxbTZHv+u/fF7enR/DTrdY4vziv+GDzKp1 - jrQ6HHJM+GyEzecskjh7VtVav7PwV18kwGVFOwlR54fHJFfVHV35CBXg8FofIZDqFmDI66sch2+P - WEHnJPj2GBx5MpULOj8CX18PReDARubqkOlzCAgx21Rmz+wRRVf7PiyWJRVwtLw9MbK75E9du65Q - yUZCrH486WuOy5cs0mZHjH4+gB9+ycPwPhJ7t+6TubFnUY7W2UGPPUPyVexvd9hbgUOuqtHnlL3V - MfT2zxve9T3xZ+GkhqBSrYg4nqxSjswuhJl6t4kDFR/8/g9K7TqFjHQ40tkkUQC29ScnYC5gDpQ8 - lD+P/UiOuC8Hrlc/DGy7zCdIvUlgtEvmBbvr/UMKCb/yhXejGt4zQSdqjkt/bq/BCcjcpwqX7Ljk - v/gD43zSUOx1yjC3VyMFSSLkeMfIJZij4gJhfycA2fXJ8+mOZFgO8vcB+by+z6mLHgy0xAKFy3Y/ - Wzxm+Bp4G+84iRtmKB0bWDy9MeTxQRuW6Zl+oah9GoJ666XTU87MMIVHkehWylbrbEfe33ps+Vv9 - /R7JpUgM7slVdGmxIVXn3EYBbd75upeuPCjFpiLm7ACwxfMOq/PVJsfYTfVFvrXNXzwDYsZgFRfJ - AW7ePzb+21XU7zoIv272IOfsG+Rrhx81dAzviKWv9UxGk2SB1Hh1SPxUWPPFnzRWlq1axw/pXeTT - MV8Z8AxNnyiY2SXd8Rw58pRnDl4uB1rhy3sfwl9+mif3QYdHWBeyaT0fIVVmMZ8pI3rS1i8jV22z - nEAzTCEvwgJdjld34JGwi8Etrm7hLg4IpcPshfB9m5u//ooarw8LdMWixOSCazIO2I32bRpHxNr4 - CF+iPQt7zmuRdtXWinJwUOCVxDpxe34cxtP1PctT/nTxHkuePoNlLwKfod9wvWrrMKeGsw0BzT1R - t/5vFfv4Ds57uyIqJ+RgQYtzg1b3joh5fOgJa9+0GZ6WPvvjt64HvgE7MyDI7fmgwjHoVvjhTxmJ - qtDOOSg6X/gw64bkOHnnOHK+HdzWD9ncl0nwdMIKPOzPHgqr+JhzGXeuoeU2C/FaqFds0Wj1X/7n - ahkmy8OLG3nW5oCcpfVcCUhe4O/7eKU3HfA8Qxn403e+/+3z+eW4mdRbRxezOB4rmu3JXbpaQkbM - 4phRuoqfAt6NV0BOfmfk3NVyGGCHexU5+Wz7dPMxYPq8JSQE8XM74y47v/iRG5LNYTbOiycHU2Pj - 3Tdg/J+ekQ/eY0Rxz10A9+QOFrzmyoFoXL2r6JziEa5+tJCcShOdE6e8wS1f0a//pd5F/kIvEify - vKyjThStt/74XFNeZrUiQYikq7A5aiVjADqnDYb2Trohl947QOXDkYFMnSno3D+OwzhFJw+OKq9h - QDPij22UNfAmAh+pUvbN18oxA/j87gkJFM9IViHLDfD8AoJc8tjrtOgujbStJ1JjxwRU8Z0VJuPZ - JqYyhMmSan4GnREzIQ9NjmLXAQ64WlyGtFw2BtY13jF8diNFynGv59xQ1SOI5WePAn/vg0mTXAuM - q3QnmtVHlDeGooBe0PGhtPVzi88xDXh+efzjfzq7h7MCXi9vh0zO7/UhXoxAnqeCDedUVgAfPmYD - Fp4shLs56f1lehbfHz8Q65FPAz415AaWexoTu5esfDp3WQiN1WnI8ULiZITStQHkGz8JMkGfb3g4 - wuB6OqJs93z48xnARn4UVowOwSBXs3B6xJATRAMlHCrBEFnHEWz5j8zq1Phj/o2+cuoqJfFZxh/m - lY/u8vE9tugmnbcz6EskyRmOv8SUkDUsx/r2hbn+YUPmUvXDqhiSBmWuLIn+iAzATWkpya4ZDkg7 - cu7AMvyzgWdHdZFnZdeKOtPFg2deakNIjLLa9MkIhja8EYtyz2SNqWrB7vpuNn5Hydx+nytQsmXG - MnePBiEc11o+lGKGTvUYJTw9nUv5JvIeOtS9n4zMx/BgbUQfhLJznsx3kGk/fkCHx25J1lX6nsB2 - TcxfP20UGQuYQgmR48lvgKV79oXQ7Acs1CdPp8G+O4FP6VfEyeJGHzW6phAHRYlcfM2HORDAHczK - 94isTNrp47GpGvg9yn4oPvbrgAlfr1AYPYpQXb+TIfBtSQan9oh8dgfoKiOGgZyrNOhwEVKdfTwY - BiJlPCMdgVdFJ7dPATSHgagSuCYsgqbx6yfxjG8vfS5B+/3p8bBM961PK/MVgmEoImR3vEOFJp5q - WNanCSlYJvp8pidJDq7XmAS1fAHsb33LOKGY/SYQTLPbWz89glzra1aC9zqn4GB5HJbKfZqMlQ5S - EN3O91DoAT9UU6wqMgOZCekXpx/WJOgCiMLminzl8RrGl6P+q78JWl4lxPtBhLLVG1iqi3JYHt6t - gezNskPQL+WwXOV3CFX+NYZ8tb1jaz5dR5ix5IFQrWnD4mr1TXY0LQ3FR1pV5LO37pCzdiNSZ8rk - 06+e+HjAyHuw3TBnQlzI3v52RejgrMm06XspSrUP0or+6a/SvR7lSpZZ5PeLVnEH9BjhQ1wBCS3E - 0VljrUz22su2oyuHdNMzGlCW4I6sPfQSoRxVA9bW8x3S/j3kVD4vHaxHIT4Mu87Mf3j/67fDPitF - ugxVjeHRkPRwPmQsJRx7yX73i1nokmo8XTn2h0dEkbzMH0r2NYtNwRvIqi0BzAgeDLg+y/anz/xl - GObTTx/+xXuV/GsEM/Z7R34d8AO1th3mpyH4EDcrM0CFoueh36Uisi9K6nPUTDRYup2Bzmoo5+tZ - uGFo5AKH3Men8ecC8yfgtXGBlHLshi97l0NoRXodSq1MwZhTI5Pn/CohY761YEXCEMJiP58IgtoM - 1lHHnbTFh2iP6u3z3zfh4XxRtDCTbiSfEnQI4fFRzNtWrGO+Pg27g8qkNv/wGGm5CLjceZMwSF+U - qnfFg5uftuXnZ5in6O5Jj7uebX5XSTESdhHc/DHiU1D45J27GOqKQcnxYIoDeaSiAYWvIqJkl4X+ - bDZzCUVuAUSXklcy7+3kLvPRfSVxKgl593LOBXQ0JUUFiLf39/hXBXascdj0bJ1P8w2+pPdtbZDm - da9h/OUrSKeAXOZPNAw6im/gzT0QCQK5rxb5SHm5kYUamcRgNj84r+G7Op9Q4Et3nSWS1MBqmEXy - ANoIpihJIklAJ4QCbn749OcPnc19iFDJ1AAzr2/8yzfiH6MPWE8NyeBiMm+k+3AGszN4zp9+Muln - HFamVxlYU0cnh5w3wUIPK4abX4QZn4v9NaDHGso3USIbXuVkW2+5PaEUBbUs0O8T5Dz86XvTo27+ - 8w+AeV8PxK/9Jhn3DMDQYC8lstqqqxb79akleyfnxIiTcOC5CTZwCPGEk4vjVgtU2DuU3+dpw2uQ - zFIW8+Aj3hPiUqatyN493WQnjQP804PD1cMSNLHooFNl6hUJ9l0KhdGh5LH75HQO6XSDXjEvP/89 - +ekdSIfki7SyC8Hi9IcI+kfRImn59H2sKQPc2zvxRhJPqPzlm5sK3PwiZB4fVcLnuCzB8uo94qvq - E6z4M92ka26HSKHPCcxsz68wMP07QeXd0jlnqRnoZJmNHnNGdTysbQgjy2yIEXh6PjP3KIORZTeh - EJxHvz9dMhbG9U7G3MS89OW8rzq48Rlxgk8BfnqFu/8HAAD//6RdydayMBZ8IBYCIkmWzCJTEBBx - J4gIiMwB8vR9+P5e9q73HsxE3aq6NxfisNiUkxvdNjU34SvXAm/VBTzMlZ/wcEx81gOGjsItNmcP - ZsZkYjm5riG1WySBlQUScfYew9PvTSzxe+n4v/NQkWMcO6C9Rz2x5t9EiWRLG3giW8ZOC3/Vstib - 9OdPE+cgCOrQx0cPDjfjTaxAL+j83loDvMrUIp7NbSp9WI0I6CF/z1C8DgNlr0IJA8sJiX8vyI6f - soT8YvKIS7kKzE3ElGATTYxlOkkq+z26JWTql+yxu3+9Fp5vopq6Gjar4q2u31HiURWGjgfK74Ue - uWHo4ePlXvB5Ua+AtcmPB0y7Cru+cIb1864auOsT7EhjpY6H81uBe7yfqURae+sOGfwXb2PzfKLD - hYkL6LCKuPvRa9VRz1jg9hUeOA2iKtzKwzeDDxOF82f372beP+fgyjGvGdonm67n4lHCbI0X8jfe - ZedXsBezI36LaW1Pols6sPrl8gyulAWbA1YfAemW4otDV3W6p30OVQb6JCeSSRdmGngw/wyLWNeq - tGk5Xgz4qit99/trOnWW28I78nxyjg9fuuSN1cDGGS84vo46Xb1CluBCumU+dZFLaf4eWBA9jH4+ - Jmo1bPP3m6C/+PUx0CtctJ/TwHfcnYjvhD+1PZdMCeKLUmKpwseqO1b2DAo9i2c6Nyd16PQDC2/+ - 7+gJ+/mk1/vDgKfcazzGzRiwpJ8FQqapIdFyJQ7JhbEyiKU19o5sXYSLnUsR3AQp+ucPdry0iGi5 - 6yqRSwjC0bgerH/6D999N+UX/w0BUF7x7FmLlnJEfu0lrq2Ns5LLhnX03gEIQy7FXgftsIMSzGHY - uw7W6ssnXdbvZEHb7bEnWMymLuussWDijXXe/Wl7jRU1gc4qHEjC0hJMwnDIwB2FV09wDTdkaXJ4 - iq8U3jFmuR/9Gz+aKob3jlJTp3v+YBQ9wWlJdtfrYS30JQPit93+5TNmbYhzCPqDsuOzD1ZucUsx - qAWIXbvB1casaBTLS69hlZ1qdQTdHIi3sl89+rpy9mqFwwb+/P5db6frftcARCRL5+OcfVVasu0C - I2U8zRQ88nAyzkb5j88e/vzDRq00mI56gU0jo+HOPyE03kxJXPZegs3RrhKAixfgs/Rjhy0+iAk8 - 3PInsXTOqrZQy5o/fwQ/dj6zcVNbiGcVn7B93f7mK2SwDK4UeyVRUg6QloFNzmre6mpmunG85CAr - VFrvIDLh7o+QGhJ5o0Q3ApEOwSKYf37JDO5JASZzHSFc1nkgaleCalm/DwPm4bve7wrrgO1fFwE8 - g8+T2FK+0O1ayiPa128+G05RLQdeLf78HyIbCKVzdviIsG+w/McfqpWXNeaPf2LZeM7hlHCXEqhx - VmP/tzQqcWws/Ok9rLqSV/3xPZSl8oHonH2xVwHy7F/+i9immwz0b/wFm3D/3d+HB2cYMk8ZW8b9 - Ei5WniXwfH8TrFUGBf3LRwlk73tFOWeIVfuQDP8vf0LkhebpIkyt/88f3fUCWKvgswAbL5YnwEqk - U/ReSqS6zBHbttkP+/powLMT7S+fVLHPxzVAMQwFco5fP3WT868JDx29EmWOjZQmp18G9/zFvp/J - P38TEqa8kT89MmaayqK1qC/4KrGbTUZZbsV65AKsOzACY9KkBkSvMz93ux88CaytwRmk8iy+gguY - /vJfhPnFMzHyJNxeptxD9s44u947hLs+WuCxVwRv1W/FsCriywR/8fBnX+d0zWRRhIY9jx5T39/h - 5jVHAcZVa2Fddl2b/27HGYa3Q0xcQ6K7P8+Y8IhTHyuV5VZHyc5YaFJtIXL5cMJVDWEAt2tbkThe - aTUu0bRB8/lT57lLM7C2S5CD7f1tiTkP52pqkOShPV6QQK8rlfK6soD/o6Lg+L8rCrJpcDymBKK6 - Vfc4gUvg8R4ruD1dQ5XZu/akA8FsT+xpcVcDhd79NnP6T6HU9ZUSPcwJE9m0F3tb1NqD652NyU28 - NOr6OwUM7Nh59RjlfKl49iDxUIwO4bweZJeu31mFqObnaaaw98HxCf0A3t/xhLWeBsOinEsThi3m - sfbAk72d9xpdvnn5xF6aZqAX4bnAKg8z4mjV3iW4PVoQMOoDKwH7Szvs+hJcvZNB3vljCtf7M7LQ - IYg+HjglXUrj7xage/zY411uVlzUwg0+GCXCCqlWe2UOegMmeuyJIa2AbteB4cVbaGZE5R1zoLf9 - jWarsiPmWaFgLUXZQfd3Z2KNYy8p3x5PM0xmdiYpFyoVe3sECjTbfCLWy/hWmxKjCPix3uFHePiC - JYXJDJUUnMj5oR4G0rzFDJlvVyHPjU2GZRBYH2WNeJyH6nyuWC1SeOSqkk3ST6qkvHJKGnj5riyx - Q1ilx2SVA3SQ7J/HLP5QdX/zk2fgYtcU+nC9spuE/Bv1sOKkhs27J32Bye2rYe3dzimVv8YTab5h - YUXtVJubXlWCVrE1iEpnKeUO51MAIGwu85qZR5U+H5wCxVd9Jglm44HVT3KGRt4ziSSAr33UThcF - PUt9xZeLytu0YPQEHYIeEl0MabUuw22BIW1T7/gOwvB4txYGfYaIw/mh4NPNKn8LCGQFE8eIHsPc - mGgTpY9Yzrzd1oC//dIS1nwSzKKUKQP7cLMF/m7PiGRGdKrmKYwL2JVuRrSAt6vpd5slyAvMl5wv - 80S3u8c1J5E5UiytB87exOrhw9CAFgm9uxwuBD9j8BMcjcT+2xuOvpR46GN6PZbzQFO5Mjm0UNqq - hKje/RNuvP2pkXHJBuwaT8Um2ddR4L5eOH0rEgCx+lbggTOv5Pahuc3qYmghCzUeSeCVpmv0GwxY - hwLCae/qKus/zQByviOSRHc+6qKf5Bw5y/FK7EjuwyVpc02E4tRjrLnBsK1tyUPFYlqiNPxN3eZM - MVHeHXNv4uE3pBRzEfqwdYHjDzOFK6RMA3EZteRWoUE9pmXbQi/gfxjnOWtv3PfhwOF4oFg5zxfK - daUxIyZLviQzNjnkMw9H8K0HKrGt9TAsn6Kz4NCMZ3ym+JnSa2cLsCs/R49fw7fND/poAj1WdHJ+ - BzRcgRPV0ODYD37O/WCv0gJLmGQDi88jf1KnYf49kamjBHuPw4XySbKJSK0Vgs3eOtgbeYUsKs+J - j9OOoHRNN5uHk69188EMHik7CNAHxrcwPDRbSsV94iyAXzo9sEbij7rCm+RBOjXTfNSNpNoaC2eQ - 8nxCdEe37Y3vjABKLQqwNcZLtX7CRYQXZejmVYeZvdSBVKLyrfPkovTssAQKK8Hq1TdYu1lGOjBz - MsKb5Toe63l8Op+DQEJdz/pEYWYuJP2xtyBNU+oJ7YPaazG1JUjvWorDtabh+CCLgrjOuhDZFxyb - jTxVgQAllbeJnVex2nLjAfeu1Zk/AXbfXxTDoBcJfijFWd3OhJkhi7QZex+pqDr2227wY3IFkW7P - BBwlhlGA/ONfBE+dEE7AiRqUrtc7PvfJAubYeTogXcM7xlctU5ezQUXISs8ca174slnl3FswjTeJ - SF/THrYLfCeg2cSD+ztuJl1dRZbgGT6CvQJKtamVVxZqDG/GstzK9hqkyhNVr7bB+Gd0IS8eRl+8 - G7pKXOxotKfJUiJUSAvJs8QOjw869jB1ixt5gg87UPGhJRDS+I3l8iOkcyi8CxhjfsQaLbyKutPP - E0X74WLLfT0GWhzeBvR/jEmkhsnTefk9LPiScoRVvf+mPNrvPHJdXhPMx2w4cixMoGK1Ln5CTQ2P - qxq1yLisBs6H3KxocbgbUMBVTLzn5Z5SltdbQJSRw3f7SdUNkRMDtWKSiD9xv6pttVMDbuP4IaFp - usPGZlMNo2U4e6Mtc0OLKssS33qTe+OvudLlK/gshL4TYZxHzbD9lvcCPambyDM/rgNtr1ce7fiL - A49IFeXlbITx47kRPcjHcG2uC0Tr55nNtXwfwXp+WwEUteiFg+Dk2ZxZpBA+5P49n+OesRfWqzUU - 0lgi8q34pLw0yw1yj/qPeCVPw/YrJLyoMA8PJ5f0lm7xXoEVLdGIrw79ppwC1RLd+ODnPTdWGFZZ - PLFQSKiML/iT26ssriz6wzdFfMsqd5L1Gom360wc1++q+k3kDZW9JxFDlciwpT8GwiMjFvjFbknI - mn3LouLSidiflgKsCtMIcHy+TPIOvc5ewSIXaIGPFT/wh7HXFjDRcX49CIl3/KGaGIyoiDYN65Wg - pdylf/jwdhFWEnNrQ1kOcwsMnnVEgoCJAMt8vwU8jK5M/taHNtpqIY9r7//+jz6SIYYtk+0VBflm - U6mCM5xK8eExQxuri9sWAvJ/0MRRqh/AympvDx64bMQBLz9CSp6tgMLW5YlWvwWVTn3oIN/5VOTy - nU5qW1ZaCWPJNme2vlnDkpUfA00+eePL27LUTSvEFuY4PpPHVTwM308cBWiPZ+Rsv090Zu6RhNbP - fCTnNZIoKxeSBg2O/xDvG4Gqw95nRgUDDzPLsd3OD0QJ/eFxprVRuJ24GYru9PnMDX2Ndo0ONwup - z7zDbuJm9pYkoggXuZGI9bKdlAJ4imGraF9iVmUXLt7g+H98cj5sjgH4N5NB2LswxefzzUnX71DU - qNn8C/bV8TtsDDAh/JjHYi52PGElGGqwVoOzBzvuC0h5yTJYfoKVmPPGhusw/xK48ztstNmt2sLM - KWA9mb9ZuMRetWBObmCZqAZRCz0a1jlHBrJq6ULul+5n88bVjGCfBzxxh/VjL+JaLkichJLciWTY - vMMl4x++kvjDuCltfcmEz88aY085X4bt+U1rgHFxxPlH/6UzUM4zDGmfYrf7sfZy8U8O9C7+B0cP - 7NpjcedK9IWRi/NydQYuyQ4l3HJjxNp+PpbxJULIvRuVuNfetfm/503QVOexPHG0ezFjCXM/fMxo - j+dbQP0F/fE/TVTR0GtVrIAcSgk++w5vk9Y3TXCZn+0en98VrW/IAsJ2QMSo3U+62KdRAFl0PhPd - n/avDL2nGXijdiXJ3Bp03fhxg229fyUlNb+AGqZrgj3/QZy7cFAXdHiZ0BZPN/J4W5bN14UlwRiX - 1/k88g975f2DAHa9QbC5yDb79/sjh67E3vkQ7W6GCFuOa4j9Y9VqSaxTAmGlaPPWSUq4luApwiN3 - uM4IOzX4lqLs/cOXQ6JvYKW5x8MOniF22U1I9569BuwStOJXtljDkUsKBtrXmz/XN6v5L99J84uN - 009ahlv46CygL1VG7NFkwCLILwtelVX0hNfFtEd97ypaW/ERK2I3V8vJMhfIPn1mPz9ndb3zeykL - yH9EVvOQrm/zI4A5lmqCReYyHN/xkMBhCdxZxL9m2JrzqInbTMp5j9/Ddj76PQw4ffA+whSGx4hn - fAjCUiUXQIx0bcWxBSAs1HkV83e4XPzVg+hUcyQ8InZYjNy0oCUfMTH7qrApM/sjotFNxfbOH0c7 - M73T98ELGFtNCNY//vuHR4ZgmSE3RocNsFc12vGhAPuFRQNZaRoRJdeCkIt+gwb7lby8rdMv9h/+ - Avt69+cUR569dqofoZ3veCwUG0oTfJzBGT8b4mw/s1oXrGzgcmRmYjC5Ao5/eLkt4gu7+Yevtqv3 - tKDBv+7kwZg/QMtJTtBpLab9fZtSuqRiAx3RAER/vCt7m7h1QR9WAPiJDhngrXZR0Idz45nd93eZ - f4uPgtp8kjx7bep2KZwERs3SeKjSvur6uVkN1O7rQqIYbOGIPkKO9vNBnOUFhqkJrR66atSQ4Czc - wyEtix4+6hx7X9X7pJuNpQS4h0TC+VCRdLo+lhk2nZJ7pz3ekKB/NXDHR2JxrmgvB9Kzp5p/BsSV - bC8lIRsIsGlnFWv0ewtX8bI6cMFTgTVbTIfNHr8G2PGOqFRw6aqNVwcG16jHRn2f6fQX7/JEcPEl - Ts7pUbxv4yl7KTG2vz0/bHIo+bD7nGSsH7khJN3NE07olwKiOm4U0tCfGBg5oo4NbjUoWV6Of4pe - XkEMhVdU7nq8PUGOozPOFaUEZNOaCEWvAnjH1+eXbs9vWMPrcamxwvpTughNuEE3E4eZbzNuGOXC - 1IBV9weii55Dx1N7sGC2mCX50z8rv10ycNe3ljgfswMUXZ4akLUxwPHZetmEcbGFOCN6YOWvP09f - TDnY9Q82aKKHK4wfoqjHcYG9JNQqTr5rInLVuCFSw/qAA8ul+Ns/nCWpS+c//vKFbUvUuq3sdXs+ - Nthyx8ZbuvsBrH/zdbSVn+3hZKW8Vf42+DwLHFE6qUzXfnQUmB1YY+cfQrgxsRX/ne+ZAVfF3vHC - hIxqAWIe66NKwWdRgE5qC/tZbNI/viFur+3657fYNPCUGoX34Isx22OVQ7D0UHLzO+yiOrUnm2t9 - 4K+lN9NdT5LTxiqgd5kUO9iA4fYsFwceBqHx2HfrhWttdBAY/PvugUyglMqWa+xdvqMJnEoezDaW - nvBbaRW5v91xmGhusOJXCugf/7LZ/PdjwbMfv17gFCMdXUPvYSkWKfYEdK62z+RDWLYD9LbSO9DZ - 4fwZGdb5g3WimhV93B89XIa8mKeltSj7pF8DRq8SYHepapUyC9TESjM+RHEKh/Kv28dHyqxRLOvb - XK3o6TGgfqJpXm9f02av77MEr8wxwrby0iqOQUCAu9+Bk+Ozq6ZAkg3kxHAhGu2hvRRxxf/zV2TN - 6NLtM/c9+MNrd7bK/U7OwQE+uYfkzNhWSv74ySO43v70akX0D1wgOjUcltR1TOkA3g6sR96Zl3v9 - pcTpRBM2uhhiPb1xdD1VLQv3+XttlDn2Wp9BD3GnIWLFOEvXmLvlsBRZ3mNpn9kr9v4DAAD//6Rd - ybayvBZ8IAfSSZIh0ncSBEScASICotIFyNPfhecb/rM7PMvmBJNdu6p2svMd4MONqXftkpl+zzdh - gjzbcdhg64vT86/tTJxtGjg/+Va/8FfJhnw2X7DNX98OfcR1igAadtNqnxOwdFsPm0w7QWzevQl8 - pi78oCZsq6l/lV9l8cp9ApXeeBD35MiUXW+miqxCPRFbbX2FkrQUkc6ZcHrdmy+dJZipP34yDXMS - gTloP1D8JjHA3jk4AU6gSos+u5nFcrt4IcdKaQvKVyLia3fn6znQrgl0DDMinrcLlWHJqPTzg7Cl - E5BN2/pBZe5gbCWD7LCN5vto4y8Efx82GD5fwQUKc7H/xQ/ObuWP/3kwbFSlMwg3QPFtnojRenU4 - wJ1TwOuTU/DJpErPP0Q/gs3x9sGubA+//LCi45u546ijaz8+w1kQD9fOxV4xvABBVqrDK0E1lhmn - rQdf4D6oPYUFNuTPnI23luEAe8osj9n8Rzoa+x04viv5b345X2srWPNshT1vR52PIE8cPCysOXHN - dQK0Dkz17/tVyJbOakRsAi6jDIkhj7yy4A5OAHY3TCz1YPbL51HHsP0MCtbajIZzlvQzhMZWOP24 - tTMpgO4g+xCL6cdHNn+khJpSzvi6reeN/6bQqxeBHJn6RVesKCZynn1P9M8TZl1rDDqQxznB52WA - 9fx+utuOwqLH+HpHIWmEJAF8tl6w7h31jF2YY4Lim3olweb39JKeyKD4sgWRdGk7YWUUIhQnF+F0 - eqkhPbdliTY/1qNHI6Z8NVopTM9mSRLcfZX+h+8bH/bQSb4C4h+fInrh3dUTXsAGyx1aNmzsPiDS - 7FkOQa9RhXamHrAN2iwjjrxVZI+3j7fjGt3hOwFwMItnCdtW6DrDpkcARcD0WGKSjBK2c+FP70Ze - iJyu1xEHz046Ya1GjjOnsZnCXJkHkqv12NO5uOzAtt6JedopdM5RlUJwlCesLdd6a0WaSL/4IBfF - O2Y8znvv9zpx1wGHf3zwnvaCBzd8np8Q2gBSdCFm9Azo2qlpAe+QW6cDNRZl2H5/8eFGFIcPTnKY - 8pLawENXREzNPSrbeo7RQ/MVsuXXkKyfLIDyLvOwx30HSnB2Lv/8NlcBibP55f/4qyvdtu8Tk/mX - b7EilHXI4UdsQrv57CeBT789SdF3Bx1jsXG6+aVsOZYVnA42IadXNYbzNh4YwxLh2/QAdDg+bysU - HXX1crVowcI6BxNO2mBh6XJZnVZq3x+oHWx/QiW7hH98K+z0CzHfC+pHBCv3cD9qFONvc66p8/RK - yF2YFOPjgQcr74IAvpUnO12M+6XmyGPvgd//3/hQvYBT68OCZg3xmMcDLAJKcvB4mDUx2A+gP78H - fHu7x6Ys6Nl37lQd1MU5xxZgTsoaMFUOXtVskodq9nSZTwdVTFosks0vrWnENo0gvx57om31DbYz - ryo4rFZNrFlA2cpkoQjfrWL/6XE6F3cIEyc1p/l7fYAxTyL3h7fTSTqWdH4Qa4W8dZaw1p5fYJFW - z4Wrysse77uxQqxo5ODzvM5Y8XJT4cuynFBuP49E3fQ996rGVARpQ4nRJT6Ymymr4PtuiN4+DDGY - P+rSosSZ6mnm3nZGN/4G51vYYiutIrr49BMBdGfZn3+y5TdmhhxJHtgMToIy+ueTDvYsNaYhNkJl - uq5dBe3sFnlwvmf1pidX9BnOJfEO8pSNxX7nwuOlr7EdLpFC9xdxht+q5j024k/gL19uz+MJUT4o - q69NFbyu+YEkG56T3/uP5PTx2sVaehLdbi00EzvAUnjks7E9zzvgnohGNvxW5sZuW2iplYllsXnT - TXtzYPOjvb0ZHLLvNv+wjfmGHEVeypjN/4Yin3+IPWR8SKnXd/Cnv87j/ROuVQQk4Jwl9q/+QYxR - WWHPfxN87D0mWzVDkZCUxDnW/cdU083fh3dOv3vi52yB+W4uObrGWUHMzZ//6Wn488MM/wzpLz/D - p+l22C/LOFydPkyRdztcvdU2+nDcPUsRYezmxI5ttl9ew7r1oDx/JhBCJePiz+jB3t4b2N1/9j2B - tTWA8Oq/pv5L7iG9ZHAHy2hWcTSeRUA99mij3/zNqTwp65GEvvg15ZJoy9MHszpfmD+8iq1615PN - fwdWjidvTwRX2fzLDhZUs7C25J0y09ccIbMQ3gQTYXDWBk0DMHS6EuvUNj1xdc2F56i7EZeVOoeu - 32EHNnzH1vC1/vwNcbC4wOOTQ6Cskh11cG/Ombdu+oBWpjxDahcVPnLRAmb6tmVgXeydh8Ls5kyU - u3gQNObhpz/pYqomhGMl3CYxuln9HHmKBBnj7k3PImgcGrizCNtHnkwjdhs631xHhaqv2iTI+hms - vc5yoH0UCfZetuf8+U+jXtbkmCt+SIlwasEWXzjUule4Mul2wmCr5+Dxm2TjmRFlYB+9m8caiQdW - M8Y6VL1TN31kQQ8LX5vKX33C4zF61hs/gJDyJiWhP8ugrPWlhG+hfBAHDDWYj1X9r54XuFmr/PmD - jKQqJN7V336rt5rgzOcW0bzbqx715QHhbl/N2Eznth4MrfKAl+J1esdd4cxk4CXxpMgOlkcc1/RX - P5gzU8MGkXSFOiqoQOj1M9bvseBMauVGsLzoD4yZlxmyWvBs0FmmItGi9e1QkBYqXKpjgE8cfGXz - VWJUqM34+Fdf6fzuZQPJW2NsbflruSyfBJ4dNcRbPaxe0XqPYEVX/a9e3FtCMP/xO3lH/+ojJdq9 - YI9VkhlgTj6xCvvIWze/jq2pPIoyJLkv4mN17QBNTlEEBs41PVbxniH/06s77r35lfakrHuYfiD/ - ijNiUCyGg15IJgzSNsKOyunKsulxtNVriJTRVOn30VNFv/g/Hz9PZ1nYTIf7+rxiG4mxQ7UnXOGp - teOpCZYGkNNIXLjVc7AHzpWz3vaK9/f7qF+57YeNH0MBtMtfvavhkm8CkXqeJwhuTrYOPSzA5l+R - v3r2r7578NInVprxSwefy3xoN1Hz58evj3IOtoZnAok2PUN/eoCXyx4boq7UzLX5Sr94IPoBMFl3 - j9cdSitj2fLXWq+aGU1gIkD1+J1/duYbmSXY2BFPjn0InMFPpQAhNZynmavqmqoHS/p/ehQI/72j - QGdBTRz5otNFW14tVOzXxxN2sUDH3k09uM6yTJxlCLPPfn1yCCzciO07lbK5V1sOpRLRiLSbJIdt - 3oMJ7cUZifa8TpRCpAmQnp5HDzrJteZ2UqmLd6Nzp4bd60pvcIuPprugElM5qoB9iM8Bwjg8T8xj - ONFZx5ccOolS4mMsP5xlL4wtJNFgkIKcc4cEpsBAe+ivHn0VtTNk19YDAFd77FiD2M8LiG1gPjSF - GM000PVkGzK6HZYSa+UMnE/pNR1Y0BAR03iKCr0OXgCpJQ1E0p65s77Gtwh5NeCIFnaesnyKpUPP - gHlhbyhLhUB0EmGXdS8PpX4NZnln+cCHdxtnnlg5/MP0RSgrtCVSdC/rBRyCFbXvWPImHH/DWUO1 - CeNSYPHxje2Q9tV9gkBVLkQOPZ2uV1tzUeaVOYnlp5ax+6iDsFrfT+99oPeQfxtaCw9cOBFNu6Ce - 4PHWwiWIXO8QyfeMaWnaoSD/SFMr2ds9qx88iH2Qf3BoRm7NfDQXwovXCRN/FrcuGlwxg5nsRqw2 - wavn273pIb43vziEtMgWdNcTdHoaD4z368NZj7JTQXWZlIl9629K273pgu/Insj9LOcZf5QsCIX9 - oye6JdwcdnAdGyFSbR1dLsee5eVTBEWkzcTsxL3ztT+DBM0lrzx4RQFlihLJ8Kr4IfbzXM24wzj4 - cIcCa1oM1lKWayzPsOgPdxKYiQpY4U5blFX7I1YZXgJMhs8caOZTS+49UrP1MTUm4Ow9JRgEXzqP - 2hyhHmRPYlchW099cIdQbi0V5zUQs+X4iWcojEVM1MM1dphQnz0okSoi+g1WYJUFo0ACSl449VtW - WT2Lh/BK7YUoc/QK5+ZZJohBH53kz54AGu7gCg3sx/hyRGPPlOs7gEvlZ+S+s9OMqbXVRnb9GUma - vARAb8ZYitPIbfdcXrY9amrtwZCmAck+Lzucw05kANhn92mvNUy44tfkwomAG7agHCszuVYt4vVJ - I3pJn2G2Y7pC/Ga33cTY0rcn9u5dohOtJmwyhl1zXdPGqHurD2K1u2e9rV8BCafS8MCobWe+6qOK - WqvgiTwciLKKVtCi+nuPyEPNscJ3u1BCoVsV06xbrsPbzZkBWVH03nRT24z2g/2BGpJSjP2U69f0 - nsgwnEFAPI54IRt/MhOqbWDhy77TAUc5KMCJHG44cswJDAFSPGSE64BljBWF76vLhAj6SkQ/xKpD - uZ1sIqYdqu0MkQy4a65X6ND4N6w+YdLzn4Tn4AA60zvc/IYui3JsEbMn7rSqBy4kQSrav/jzej3q - auqd6wgZOIgJvhztjL+e5xblFZ0mXt7t6tWNHAm4x6jG0id1AN8NtwnaxL4SfRHOYJFLOwHqJyy8 - Vf/29TrtuQIe1NgmyqtQHBoMIIfb80xLcjNDYjWlCF3jsNtuz/B6zj9GFRJOlYEfbO0DgtlxB/+e - Nyjmns/TOUb2x5KxXEYhZd7fuIPHNr5i88DV/XCtzBgqpNDxBfd2Rhv07qDyKRYsxTqfLWw7qaJ+ - 2UfYcnurZ4Kb1KCkhBbJaTJnZA/JCtvxkHnsiNpsuT0+H8hgZsLHytAUmvS8B6PU17CVfM8OIyWt - Dm/GnXqHdnfs10L42HCX3Hqic49XP/Ol6sLqpT69sZgPzlJ+bjESUPrC6mM4gakUbjp83+mByPYH - hxxvCRO8a5ZLTodOU4a92nuwZg4eSapMV3hipMxfPMvFS6wHdPI6Mb6KNrZLxVFWIxJcWO0MZ1qs - pgrHu6K5SHjuBmzOZgv6o3TcIVE/IaLAwKWcEc2emEjfBVsa0zuceG53f/iF39s9nvhYN7/x4ZNZ - 1+FSyJyJDmfWxsZHz+nMQr/ddrCMxHAazpmXa1ZtZ/Y6fC/mg0IHX4vR33p/1vtsFa20Ac3k+sSJ - Dy/65gaJE7f5mdpaceiSKnUEPmhHiNPv4mzmPVuAnW8C7EivpZ93HyhAmzaQZEaOwZziUkSWEDRY - e+3knju/HuZvPJOYN7Bedyk7o1uRXac1QpLDXuZ3CUZUA2+93dlsbB/SBAE9UOKc+iLjr4PnQzcJ - jElMXgngCvtbwc+tcojF57tsfR0TAZYMyvFjbE4KfzqvCRqZ+oyldOydJSzYAOmftCbquz/31Lvt - B/DQ/DN+aE0Ufs9LNKDdUThi88J6NXe3hQYFBdGn+VTuaTdaYgWFQL5PFDv29v+fJSI3L8eZfGkp - zzJ3F863o4XvbvV2Rv1xcVHPcDo5ZcnXoXEjQVQ4yMPZZZF6JknUCDYzbkkwBFk2XBnrgywz0fGj - 3C9gfUhjCXB38v7yNcOXqgcSxZCIctw9em4v3WT4nmyZGG1RObOdNAF6GklPHpO2q8fz5ZrCX/yE - uvqkaxXOMZqaG8Hb+uwpQS8GRKIb4TM9ncEvPx924SHGV3m36+eVWjNqZMYk5+ZK6GgVexUexRVM - y2MRlCZDfgFSvhWwNNxwPZ+1awfbeM9PQpW1znBYuBwdGO9Dor1OlCbvTR2lU3Mi9/IRhCvPGQNi - vzImJmN09fzsAgFJn3JPdIH4GZ3YTwnOV6BiO4jtbF6eZo6qbBynA58X2Zq8aA5Odfvwtttss2FQ - ryt6vbWbR4l+7+ccWSl8Gz7GP3ybxceGB9OlJdblfFQ4M0clnJmHgp2vDgCNfKLCbT0QnJo0pONN - 9CFHmDO+VdeRTkehKOFzXRmMw4MKmDMOP3CM3rK3rIOUzUcmiuDj4pXEPehjuDTKt4SvlxSS43LP - a9qbkgyfRbw5toNGuSp3J7A6p4jY86cNv6/lpiNZWdq//MvdMj6H7oURJn7Lt0vi6jMqC+NDpG91 - dnhmd3NBT9CbqKf9mBHwvHaiiIwZW65y6z+PbvMQF2fEmqPGfT+xZYmaqz9gPGm7/svJUQKhOZ// - 1i95sWGAJN16Yqm1jw7TXu8xrAobeuvx2W6O6LkA9SGridyvL0DprgwQe28vxEvdvt7+9sUovzjY - WPNvTX/xmn8XhaiYVP36CgMP2vb5MO3NLACLDtJS9G3nTjSxetazqa8yrIxTgq1A5LczGKENcRh2 - E/OLV/B8fMD2N/FCrqjXT8Om25YkAbsPyPUTKwUc9Fgum7jo9KX0ObgFcHfLQtLXqjqr/pYa8FXF - iMjHpx5ycX5voE+Lcpq+x3PPv9jMh1r3cibquIZDXvUZggC2eNp55NL/Po/Sl/Wdns/y/FsfAeTO - h8QTWgCyoaHfCUYe40yv6Fw5C5XFCpbWK8RGtBz7IUncGBzb6Irv908I2NfhlYO3EeBpXr+pMsX5 - vYX3Wrrg4gHUeg6Q42571EOiwXXImuUbceD5nS1ig/0NLJUiNL/8jh1fftfky0wq3PQQkdavqMxc - mfmw54BLDNXVw/UjiatYFSYknrx4Du+aWg7MZxUStQyfYEGRKMGnkfbEtUgJZpt5NrCCskZwWZQh - qzxABDe+PS1SvO3hF+sSnv0DmXrNfWXrYRwCeHroFjHOQeisXTPFADyFgoQukDKul/IPjIo5xSkp - Jbp6pPKAUz9SjHeWmTF86bogzpMHVm3Jqpn0WXmCkCkGOaV+TecyObeoPSGXHCmj1UyyNUMaShcT - T58XMFedwol5wD+w9mGfzpDTPIfusObYuBxasNLpU4LwukuIcr/dlZWRniKwBqYjxU3VM9ZU5wLh - 8Nxh7fG51H/8+GrxEzluvcfH4WMWYD+ab3JVRx68xPetgIE25X98YE6gDuGJfTH4kc5KxkbxR4SO - XZVTXb2beo3sPoKz2rxIMH2bcNo11QDNZxniy4jacLm7ov3jy+S4DmU4X7p+RczpOk35aWrq5fZm - ZQiOEJP4oX6UeXlKBbDOyTKtTXjt+42fA6hfZO+wxf8yHtYBfk/ibnrfzhzY8s8OHs68PfG7dHHI - ddADsMX/1D+NxFlRIBTbvfMcPp7qVqFxY8I/PmBHmtLz6f6sgq75+hMxs4DO52e+g5Z47SaecSo6 - BDezhUs7z/jEKueMevvvDmix+can8rFmdOn1BHoX0Z1qK1/oApzcBhKFB6wMl2dNQn9nQ0W4SV7t - uG9nRl9lJ258duLMmQLC+U4Fsn7fYzt5CXSK8uaD1JATvaUwn9kaSlwCPbdOPD6xlp7ieviAWW1f - xMkl7IzppenEOE8fxJmbyZnORNV/+ZZklmMpw+drtICp3BPJOezRWa4UCFOq1tg6su/s+764E/rh - q3x6nCjrirEP5ZTY2HAUtV7A/VJAVtgVHvvayTX7Ws46itJg8yN03+EUQlXY3C4rLja9vY78wYbK - sNc9YePHtE+tCGzxib1DO4Ahgox5eDP9SKyFUQC3HCUZdl/n5R2Op2tGK0Ocf37E9nml5jQniGFi - 65eJbR+RQ2JO9uH+PEzkVj+oQp+pYkJ6f/hYhR7Tf/1Hlv75E0cBTIBu70fX0K2wG+6IsviXqYBB - MerE/H3/U7BlII1KRHTla/fMx1sEaIvNGT9OdaKsGV98ANoXC8bbRVG0T48R0veRQYzNHyCfCnDQ - PxvyL1+Fq7CHH2jRGZP8oI9ZB17bGUnrqeHjdWWyeQajCxx+TzzhYynK+FLSAopto+Nz73nO58oc - P3DmhAC7BXMOV2eWYrRUQYa9aq0d0qV+ClEvnSa4Y/WQ9RaRA+eZ33k72dopi1Teclhr0tYu/6rX - nGftISwCkXrcE7T1cnosFXzPVkKOvecpy+G486EByQufeOBQvq/uA2R6l8c3lr7rTT8V8MPt0+3E - x+zMGfJzxJj8jWihkobz/amq0D3GNf7zo5pz8oExe7kRC9kgnD7TkkLxhANvbbcTdM9Yguhb+neM - SSRnPOpQCa8lqbCy8Rt6YllZrC524DXA6MM58lAJz+x8wvENyoAfe9ACQRA3U/B6db4T4SZowfmA - 3ehVO6Pxtc2//KnNjEqZvbtv//wi+7hgZT2FAELjeL1jVTpI/cyqowRv88pt+caoabIvbVRr8uw9 - xyIBCxMNM3wdqs90eOJP/TXjZUYD+JhEeRs3OkPNiuBKtltd9jJH1765+sg+hphoV7lQiLqvV/TN - st3EUPNdr4GwSEjuvjfy89vofTh8RHk0b/gqneR+DSckw5f1sbF99jRnjfKhA3NWWTjU7p6y6Z0S - gJOynxjoRfXirP4OPmUhnpZtvOtylCT4KaQS3wk0Hab29BJ1X+tFjCp5gMm/nxlYWUOCt+vqw3l+ - EAEOzdRg8x5KlJ72i4SeDSv+xcOUvoYUslsPG/Vtlw6LlZcttofLBcs+fweLpkw7uP1e+FjPGe22 - 9S/yQv+d5tvrRSnO/AA5pnbGad7kNSV1YqLwUlvYDbgvWG7LdwccXXLIMe6Is2z+BNj49QRFrupZ - 9VsG4Ojua6werpyzIFmY4DpLMsaWUtEVjp4JBfMlbHyG7X/zKzYqf8en4X6j4/nySMHXjz2sdQVP - 6XTrXfAbr37g2Xot7TSHoo4RUVepyqZyuuhwOHgHYk/nu7P85vtxcUscXIqlX7Z8CodovpAHFp/K - moSriczwfZv2stuEc1m+dnBuoYKzeh/UbHPfu5AZQ4Tl+bxXpnu+iBCdX19v9/0e+5lbo+aHN6SY - rBjMT+/Swp2extNh0Gy6FDOzoo/zOuOgaopwBpqfoP3jNGJZsZGy5d/4h7fkhNNjPQqtAQ+dM8VY - X8475zteZh1pSE6x6+FYIT8/YdOPBKOj4vA/v0gajxHRzunmgJ9H88f3vJn7IDA7TLDdorNYHvzh - 2Y2UKdj8YOIGnAV+/gAsXrHskc+9ceac5gUc2qXDrpNc+03/iLC4iSZxo5fiLM1S2ujtzA1xxhHX - 38OR86Fb8BXW9p1OacBiDuxSsSd4yN9g4NTLDn5vsUCkYHpnXTV5MpBH+4ZdXajC9acXN/zwBFpy - 9SLehwpuh3D++ZdcdB+AwwpfnFy8FFCXU1Vx10Yvcj8pQr3upI8K7sbHndBZhuH4vqgTLKudgZXa - 5JQpe92jw+bfEinarQqJG3MHf/nCOItbj4JoamAQ4nzazY+acq1VuGDjG0Q3cRWOsnrSgSi8M/x7 - frbZjs5yHw7+8MGZ4KjbkNzcHGemooAZrbMIt/EQz3r72c8/hWz8lrDzSAqwvi3DFDc/Hcs5VOj8 - 3LYrPrTg7HWwHcEwqI8Vok99whrRnL4B30zmf/6mCjm1nx09E8C7xBY58i5br7W1nRwFnYldNB6V - n74DwdmMSYom6lD/HPkwd4A4sVy+z8bE1VdI9XrA+jwPNbW5zIRUfw5Y6aexLjHuBiAIgk8y5T44 - 4xA9SjhE62XKk0YH02skgliUGSW2yEc19/PDf/qDNbQVrIajBHDjI8RbpEu44QuEsaIMxDnYWj8f - q0yCy4fl/vQu/2UmHeq16BGTPFew6ZEIHvZDgLNxJPXkyscJsVyNsTOxDN30VgeNbvK93e4k9bzw - RCvUEgt6hXPV+jU/Oh+wC0Hsscag92QxnBVqe60kbuAd6PirDyxOsjUGh0G4LqEVwed3tYiVfyK6 - uKXUQuluaJ6IHz1djp6lii/fYb2DNMrbrcuWB8+ecJvQxq/X9NWkSNmnl82/WfrOJ5kpknbMMY5u - adY5bZbAV6FP2Lydin4Ok0MJcT5407OetyboBk7hd+RPBF/PEV0G0VpFK2Qkkmzv//kzh83P3uKf - c8gW78B9Buvmr8uUUZ9QhN1BY7A6+6MyZPjMHCrPG7Hm1S+HyJngQv8jt8TWrL0zjfzBhLWfd1jv - ZVLTEe8CWF7LE8nf6WfD19qFOnuosXLIppB4+yeEwaK/vAYbQf/Hxzb+ME1wl1P683OrQ8tN5Sft - wXw4AQam1+67XYHgZyNb3Aaw5TdsObWqzOB6HmDpVwI+UuZVb/WSFJXvPCcqV/L1mLje+vMPJ8RR - vWeDhxvBl9XZWCrhoV/MD9VhPScHHJXhEbzU7ycAEqfsvap4pfVinFzulw+we0UrmLZ6GjqZ5otI - 5NwpY3PnPQhwuff4d2tkzAW+VGgpD55o2/qdPSQkkJT2EcvJZf75o95GuNuJKqc6nCv96CKm8k7T - IZJRNnfwm0Px8XmSkHfqfjFOKgd2y/ogVpBW/SpkogRkoQmJWcBr3+/9ZIW0vQNvb0RWTw99NUA/ - LM9YqYkFqMEdfPjDf5c1vv2SP+wKim2rE+96OSkE9m4DNn98Eve7e73M5P351c+mZbnn/d/4Nz8L - m5a/7bjb/AqQwoO3Plwv+3aiz8Cfv6XKFZ/Rcpcz26EUF19qQ3W4UNol8DGdbOJhPqXUTb0VHsnl - iC9hNyn0OagFvPo0xPZ0Rs7US1EHL526/uWrmQKsg1/+OBrs11kYm2fgJbS8idn8tuF2RT784Xdo - 5We6xGKU//k53VYvnUQraH71FmLpl7yeffqK0Z2vOvLzzxf11EHwmLA9IZuOdF347cR1sF5//nzN - qLeyghv+YXuW/uoVJrwWg0u0huzouOkl+NpJiEjnVlaGTc+CBhAfH5c77EfW9ZOfvsY4DBaF0o6P - kJteT1t+VfolFvMcRu9Fm7gfn/7h5Wd9aN5+bE4Otwu+CeCF7xcbS6Rm1OZCE8rpaGNTNSeH2IFf - oqOLaqIqVq+Ml/jMoPv3NZNUsx7OuvDO9nwnGxt+/8iGbNsRZOQ0xo4e2fVqR4Eqfq30S2RUdNks - fA8m8Lzde9rTsFU4W/ZXJAK52/TMKfwbHzq/v9NYEQiIZMbRX/3uZNZKuMb5pYVIhDm5lHPmjL96 - YW4xDE4u7NS/SZ3Y4Bef+lYfnZLjzAGE25AYbPhReiV7mmgc34O3P778+vd7wY0/eocXuYOZfGsR - nvPsRqzbEoeEvwk5TI0hnfgDRdmiLLADxAkrD0ws86sHpdCkz48nPGmsUHVfzz89tu3I1ejyLC4x - IvdhIZtezRbzA1S44wLm56crc03nGW71i0mQLDsbrrCQYe0XHdEYavbjeBAnqLa+5T19EDus/ri7 - 8Le+pN1UOvOGpwCKI0tkZ2b6QX0ywv+zo+Dw3zsKeEGVid/5fk0ElyRwaCLWg6pOHMrYuIIoQV98 - nPR3OC87PoYZ1mWsWda7pkyscshZM3dCl88R8KngcOByBCYxH9e1nnkGpfBmJBdi96KhMJe7KEOm - vfhYyVyFzo4zSrCj6oHYdN+C9SnqJdjRaMRRfNJqgj5cBMnt4uK4g32/nD5nG12ETiD2+sjDtREX - iJwFaji8vN79PMJHDkgePfH9Zd0BLaYphTrgDuRk50AhMr5UKOdEAeO3RZQpbpJOfMZjSmRO5Oja - rU0Bk7tremv1ffUzuFuD6IB9ShS1OSrrjdVXhE+TO4ECibRzA6eD34t9JjqhQc9WxYmBzuv5xdf1 - wDgrT4oJ8NdVmdYEfWs6YL9Dl5ebe2JvL3TWDOSB3e31xjjGYriu/OBB9Eo1gg+dFLLJlTPRnsKe - HL0vyWbenVXESnrsIdm3amZBsigOfGxP+9udOmMrlDHaLHhyOtWVw7Wz2qLcd0yife9tvaiogZAN - 9wesNIKkcN3a5MgcMZp83O178qyjEpmiUWInoFnG3x+mh1KIWWxi/RkOzv79Qd7MdVM0DrhnmOS4 - g6dhxcQ5uXtlte73EgpJeiTnVvB6TjDOCYyuAyCPGKcZ37PfCol15eAj5xgOjdrOB6rYIiJdsO30 - EBv+5qdOBBsvLeSf1lhAg7wj7F5vRsb2pijB4km2rsMvTtn6DJvwLCcvco1rRSHt6RIgujcTrBGH - 6eljOgpgmw+SP14CGMIirqATaCdy0w92uMarncCdf7yREJQVmFIPCfDEdXfs7g88mFscJKgdzYY8 - ktZ1uMMkupDlGJ3o72kFMxReHqrui4Xd48ep+fZ092Fi4IKc0jatl1prB9jH6YnoIm6URRR3HHQD - X8U3b9c5LPuZV+jm4Y14bn/pWSV2bUhmFZGEf5QON7z3KqSXwMGe9FCVFcyyjazbLiG36h4567dZ - J8hdTc3bP1QcruyQRuCzcAjLuSNn9CO/RdRx9YWc5kvTzwfMFLAkZUVUz9TooA7nD4p2RY9VWzwp - v/EgcugdYt+LC2V3KB6gdmoDrMPR6BeOq0p0qK8RMdjrtV6GOeXA/RR/yQmd+3BVljJCB9wK2L6u - t55hloN88BMvwUffdwAVmkpCk+kb+OQq157l1DyGs1UqROlaT+EHzZjB5aDtcZSpbs/tkhUiq+Za - j0vXwZkCi7PRa49kLBfNC/QXT7HRkr5N75CZ356endqHlp/YxOKLtKfpoFeozHUd22iNAD8b7w7c - lrYi8lP+hnNGshYy3+iFT9YrUtbToSngyN5EYiFFc1ilOubowdI70Y+DBhh3OSeQP8aBNx+NvOaD - vZXANvSvxHCNBSxJbE2/9T+9pmis1xMvyPBZN8iD8DuG1AmFGT7kozytTvjIaAnABxZRciPHQjnU - Y29PBYzSwSfaLXw7C1cQEZ7iqMDX7JnRAQ5vEb7V4524I64d9nPYiiDLTvNYM5Bq/uDaInwzXoSP - CtacdbkjG1p+auNwXR/9Z03aAvLXWcHnDU+2+ZChx/sZVtU66/nD7qyiDY9I4sWDMhTrdbfVrHOs - 2vYVELbmGniIpQ676VtSRvz96lBxl/u0lkHk0LRLdZiubjnBzswV+pFogTY8Jfb4VPo5v0gS+q45 - 2O5J9x2mvOUxWndl6r3l7NKv4zttoJFMOdGr7Uza7CspKm9PwSPp6ROus6rrcK9HMkltclRYcTzp - IJGWkaQgOmcr8WiONjzGkhvW4XpsjRLCIrqQe5B+ndGmtYTcfTNi98bbYLXVXQr9vmO8aztW9coM - Owa8u1jf5vsF2CQ+DrB55TyxuXuX0VcidwDbE4/dQxbVHDNwDCIyyDA+n481m7dZDMdndfX4u3Dq - l7skF9B732qccKQDo9E8YxQ9XxaRhvfWRavsA+A8DJ7I73KoZ9iBHKaXGHnwLLOUFmI9QV3YugiL - WHVYj5MFFON63nZwRYAX7O1OcJzvSBSSylkOvd+hQDp62M/vMmConbjQeW2Hz0di0TX/MgK6p55L - 1F2rZnxtoxQMI1CwLsYXOjuW0MCbwefktDuWPSuKOwYOp7c67bzgDDgR1juEzLIi8p44gEtoGkG7 - kFbsqxc2XL2HL/zF2/nCmw6vqU4HhSP0SfqGB2cZmnsJPSbUsRqNmbJabGKiZn9VsU6PTsi0Z51B - l2Nx9OC7bsEoEWMF0u67wxoBXzAXLlZhKHkafiyvsmafzFz+4X1GeVKPxYlxkf0G0sTfzSSjwdtb - wcf4zNhlmrEf/NMn4QV26ol/sruMXDzHRMY+d73tHJnDHVtcATasVnI0wTPkxuzuQ44HDra6KgRc - 9RC3e6E7xhO39TDX7bNFZazlxBfTWZmxjFt4nwyy5WscbuuxQJgXb9Muvp57WsfiAAc+sr2dfgqc - VZoZUVzeeY9D91vSsbgdSiRk3H1a3OBOZ+kOc/A5VzsiNV8m7OO36Il6lVF8vLtBT/llO7fOXQeP - unPQzziVXfTUg5DkPlCV+ZwFNiLclyM38fWsKRMlCcpD806ugBUpoVMNUQlZF0v1elKoNcAE6tWN - YlO435z5uTcHtO72z4lb7SZsTw+UQD452eTUlyQblepYoIFfzh5ttltNJrX3gSbgdppxfaJrkl0n - EOe3kPh5w9QbP6jAb/zOWO6c2WdfA5LU+7rhQ13Ten7O6G3cDziAbags02vMYRkbuXcYLD9bdiie - xHx+GNOBL8R+PiqlgKaDciIG67/oep4NCabr942ViF3DxWi+ESTIeBM7AEHNZu2eAwpeV2JrOO43 - Cj8h5rsMJNGeV+ePv2384/f8ynqeNgVobZyymM6AP4FzA1XGcbFsQBmw6vLuQLo1CVUFvNKJhsYM - O6ofsAOPOJuHEkC4VxbGQyI1KEXSQQIEdiVW1nXfj/tasZG/9YRW1Rr0MyGog1E6+ds92V5Ghrud - bl3NBO9huUfAtkMvw9vu7RIpWNp63mtiKXaHssPWVflSKmcpA6YjE3vQB43yzXZrAS/PrWdMICQh - peLXh+DdvIjmzQplXvx1gOasO1gx1Dhb/aivYM9b4UR/47NO3AcICW8SeXFNSlva2vBoahqRyWvI - iHEoPCGGJJjW93sX/q3XVue3u0nZqqdEsBL4py8ezF5ZSSrMkFYjwuZ9jjMqxLMPhYy5/+HnSLO6 - Asc1DCauMuKaBtRVoTYKZyLnThXOX1WqoHS+XLb3P2uyHG4xrMr1QbxSZMA0voMG8F4MvbfLv53R - 5UgKrvVDwaYjfHoKM7WBYvz5kB/+0xO4tfDjqU8ch/dnvbD4OEHuNOyImRxwOIfquwCSvzmC70vZ - z0itWrj2tfbHj1/O2/LEXspqouelGM6RoDVQYxabBF7ahuO5WyQYv5iUnBTD7+dHY21WVQsn7sy9 - skl59RKEybUncvXV+vV7lXbw9F1FchLFW82g4tzBWK71iQNcma230zGH93e/m6DJqRmN2X4ncte3 - O0H4PWXLj08+S/NJcteZwCyd9yb8FM8PttZ32I+RJuiQ5PETe9o9B1x+3zcgEHFKVPNp1Vy3DgW4 - QsEioSM34XyPYQEVbt95n1MbUzrg5APB02YmJiSyw0jfwwzvk0bwxtezFVqLD7/OdjuaEzVgkc0g - gUhTdXIajZ7OGUhECMFnT8wDUnpWrl47WExknLLvbQinV3ypkA2uOZHkSQDrd+/5cJCalAQ3Zq7n - o/IR4Q/fMDRrMHjrM0Lb9+FiPo3ZbDeuDTc9RLTlmyjrB90YuCiDT3LRscCqexkDOSYn+HGLn+Gy - u5zTv/g+xxNR5u+Bb1FGzy+sPRMdLGAoW1iDoCOWMS8hVbhdDA9y4xHvlNmUvhK7gwXrPLw5OC7h - NMu3DxS8nejtSnvfU4W1RDgr9c0D8hfQb6B+TJjo63VihP3HGYghxPCVe298ikw+XFZ2r4JPUX88 - 4UsbZQqewgqLKWgnkH38cMWj3MEcDxJ5bHx/0uW4Aqkm77DRjnLP+Mdeh3EG3Yl8yxLQOWx2232T - Gf6Nl1njVQb2WTaJ5vGXepGnOoB4khxSTK1P58cCCvC9vBKi+dcymz3XL2AYaR+Mn9lTocpXbdD7 - fuiIvFJNmR9ImMXsA32sqvmX9idwbtHGj4gejG5GNe7G/eGPmqlD/Z1erwLtw89AQloxdOV0K4fH - iWOn/ab3yC6PShhKroaDSJf7ii3vMjIOUoalpqpqPhUUBurbjlAF7dqMWMJhAolERw/AIwnXtJ0r - xI7pniiO3GTLZUxEYBfyit1OWMKZdwUdZpaWELW9ag7nFiaE6Vl8evQYm9nf65INaiI1ldzzv/ne - 8BvrQ/1xVtF8RrDjdG/qCq9XVjs/yPAwHCpiuLuELhN9uPCXr3UjlJX5Lc8tbLT3Hh/JqPTjZxR3 - 8GXODn6s+btfRJFjkDI/DxiX670nMVvvoO65PnY2f2GJLoUv+k7MEnd/uAK64Z24IDHH2/qn03rO - YrBEMvbYcIL90gYUopK7ZZ5QlH1N7yXTwaCOK6Jbi1Yvd8kuwOkhDCT54e9BOjUgWW49dje8+OEH - 73uRjW33TsKpTxLxj+8c1vJIZ3ZfrSA4rCE+RcLQT6UQ6AgYQYuPJjiG7MZ30UE5XrzL8yHU6z4l - KdSnCv/yXU3b0z0ATyQp07gz13C5wqBBr21Hr9TzA93wUkZhWanEDaoy7I5NpoLheg/w43anyvwp - IhW9bdnAXjLV4fLjH29VuRM1Y56Anq9lgx6vKN70fkLnrv/GojLXB2/HZxplZXiXINwbGbGjCiuf - gyfJUJT63XTZXyJlOPJ1iYDhtxMZNE75ou0WMn3ax1iJprKfA2+OwKQFq1fTKgLT2bQGWBslxljX - RDBrurnCaqeZRMmVXhnA/TiAPkwFD8En55BOdhKw8T2vrNfRScyYEwHa+wEuDodvSJhKtoHBKTrG - 70Pzb37ehrRinX1MdJlblMLXV7VxvuqdM0vfZYazXk1kex2Mp+7UwHo36hOz+WcsOekRfIfChcjd - pIVM48k5YvUeb/nmVQ/grKYoH+8L/uHlEhumBCNwj4l29t7ZzEvrDB8X+ep144fN6MGVRfTpoIo3 - /tGvW/6AaXFw8L10NIVufBJ6J9XzitLe11RoOkm0VfNF7GpXZvR8/bQwQaWBj1mhZhzaKww6DKAi - 3vb+YTc4qRiKfILxmhv98tYDEz3IpceOQLn+dS7ogKAKMNniA4x93K/wjDNMTh+/AcyBO8U//w7H - zOe95Q89gHWWrVgHp5zeUU06cDQNjbgtXeq1OlQyerziGBsvdMo2/CjRnVFT7EMh7MmghDFc3kU/ - iahXHH7jD9stESdymbJHxr3rbgVGfVx+/o5D50cVwO/Dar12jwJlWJZTA9UxCDd9zDirGygdoK8x - IFo45fWWLwPw0+cG5+1qsvkJwBKDK1GOsRku52CXQLsTLkTv3Dik3DVfQfbNrsR9umy2yg6A0Mm+ - Gc7ZqAIzOLspZCm3bDtmYjAcla3nmfJ5TvsHo9bcsF8mOKgyhw05u9T8j5+JKElwxD3Vfs4teYDP - 8v3BbkvP9WxOhxLCY07JleNlZwlHlMIvW/hYw++Okl2eV3CCUoWzje+SKw5VJKaaSuyjOCjLxo9h - JDEKuW38Yz314gTs4n7wItvmwXgj84qcrM885NKBTp8lL+Gyu9TEbgavn4/n4IMOlXjypoMsh3zj - niLIX04ssW0uAJOe3mw4NpD+8Y/NL46hyO3eRMrGU8ZkIBHgjMjLE25ftV5P2pf7i1d3w4clW8wJ - 5uNjIRhith5NBldQmy4Jud9I2C/zug4/vj6JSH7UVHJvNtzyDcGM2DsbfrXwbT88Ij2YhzLWPd/C - Vk8YfIkhG9LsLgRgYBZITEUJ6/nkSBV6feszMfVkp0yz6ulg09PY6rLKmSQoeuLml5GTkKohb1eG - DL9OoGBDXd9Zp31IAmrgd1jJFceZ8jvf/NYb2fAiXIbqq4NEn6+kmKOHsyxp2Pz4AlZ3bRNy7+sr - B6ODR3IUvl1NHdH6AF1I7vgKpq/zy//Qs/XDhETx1tMjXXbQpYqNf/H1tW5gB97vePHARZSzuXLF - Clo6UjwR9fX2e6Tt3/hM4X5QhoBDHejLICP6kV7rKXZjGXSc6uG79WKcTT/mMDjMITmx2w5pJC0S - qIubQYo+SRS6l80d2PDM2+1LrV/r8GbCpNGOJAa641D57Zuwl271T99kU/H1U3ROmNhjQlIpP/4F - Xq3KestJps5YKh4D9yl+Ef3mYEq8as1/enpqs+oYLkKPbMiG5UqC97sI//ze6u352BiDK1gtXm0B - fGGPWN7OVuZpstxffidOv2I6XdZWgJX4GbBSPadwze98CzVPuRGJGWSFf5SJKX4FccCmz+Caevae - g3jJPxO6j8eaS7LHBLpD1Xn7Q1dm/H07Meg7CySnkT86089PH66PAHtqe6H9GkoFykP7jj1HvTkj - x3Ul3O0FH/+PtCvZWhVGwg/EQiZJsmRQZA4CIu4AFUERmQLJ0/fhv73sXS89B1FCquobklRqa2G8 - Pr5NqMBHeiFa15aMvchNBOwh3oK16S9sDURHhtdQ/wa0Xa7bjoGqBYIomsR9iD+XbPoT8hdvJKf+ - 8DS2+M9g244MO1N3KbbxssBFKzXs2dNQbPpyCZuvGGB1/NbxypGRg9PcaORPj/xXH7QEdiQRu7Rg - sQcCuBivG9aa5u3+wwuey0d/+hubGgkqMO/YDQeXL4ynVu5SIL26My7UMIufqfZKobaLITGciBjT - rWElSvFrwVh5nWJp+L0DGLoMktPdymLac78FZMaqY/+zjzcH/pXATR/Banmv2WdtQxlxOyWcucX3 - Y/E98z0MptwPwM7pXPptagrli8QCsvkZS6NmI5ToMSOO8eqKnj2PB2hX0hnr4+XFFhwcFDRWyu0P - nzWDnI0zHHib+4vvQno1ZQWmwvwEYn98D7RdvBaC6zThR+U8m8V2LiaMG1hh/87HMetU8PjzP/Dm - R7mLepacf/kk8IuezfuaV+D02j0C6a6Zg7jeBQvE1c4lh0Npb/7WWIJg2s586upPzFLtl4L35yER - 443LZiltZ4Q3avxmYf3JRnUdwQjCzMtIkkzAXf0y5v/p0VrXQkCNtUtgH1FlhuUliHt9J7bwME15 - wK6ca5BMBDLIvmZKdD5/NZt/tijXn8LPXKUkYOKFvP3TM4LdOWPxmMj+G9wON3Vepg8wxt9eev/5 - geR0U3YN+X69BDxlSrHhRNhY/vycPHgcAkF5TC57JjkP1STpyWEIPFeQj+8QXpUrwFu+dZf8RBfE - b2dm4/Z2A2Tjz7BLUI5Te/00C/8BI/yYGAX7+s67f3oyTF5fe/O/tEI6HeM35GynCpiy+IPkJPcI - KGnfBa8ppS6FTZfAW+cWxHFby53dw00Gmx4fnMvyXtAyPPJo4zvEuHKDwaLbXYexlEMSDJnszn98 - yL7BjBjJrDZs40NIJyXb9OqeUem5VKBLdvnMSoW6U927FlQaFAU7O46bdeEWHdlKeMV+3irDv/cr - 9u0L/+mHxLwrI4xuZYJx1nqG9Of3bc87Uz5/DVv+fsOv/2j+6SOL+DU6IISvOzZtLSzE5I2cvTI1 - VXC2gFawe6zLyO0qDlvYj12aeqmKNr05OPt7xSWGyCXwYMMdKdc0YaOhEBlcAkCDTnpWxjJWDEID - LxR73esJJnUf9pDRViAbvi/mlOo5FMwfxtqaVIZQUMGBf3qU1X6GmEqXbweOIADEAJXOVs4uTfBp - jtWfntb8q8cnHF+In+y44uf4uaU8M6Bg/6N77mzhgcLakGEgnOrB7U4IU+CX4BowvB9i6spJhc4k - OmHP5+8GMQRNhsH9dfnjL8V84d6Bgj6S9y//rLbPdUrRcSH+84PpTo15GE/1HOza2x7804dj/ydi - T4mKodo9f2+w6cc4yQbbmA5gqdGAYUHO2cUd2NsJ5X9+TViD1vjzM4A2ZDds/tWrjb+jRy46xOfL - NN7wUo82/4wY5+oL+A0PgrTgPOLGzTIsK3Jk5c/f8TlNHaj/FLL/Z0WB8r9XFFSRNRCnq34NNRe1 - QjfnesO+d20GJj4HBWhMYMRfmp2x+GSJUL26fjCbyropelKA3HDck0hVW/Az60sHDu1okWRvn5mU - pXSBsm6NeKMZTLyftBEN87HGWlTgYlFzj4fCkcF5fwlqd1x8f4SXE9wFMmz9hl3iKQeXl3bCN+Tx - Ljt/dAq7Vt/PvNK8hkG95wm0uh7jU3g1CkYu83aKzQdhLXt8ARUbO4dTlxrkOH/f8XpIfg6a6HTA - 0e0QFHxj7jfHp/wF++ezYdPNdnVIuHHBx9UkgGbPRoSr9Q7mnX/5uvS+TDNMhUnGQRy4YF1MX4RA - cY5EPXpVIWpWP0LHK3Ns1tQFUkx3EYQDr5Fw/znHs/gKA3Q6hldSOP6+oZpVz2h6jgF+oBQY7Err - CF3vKiO3c39x1+miqEiUeECeDqoKZgsutxdgcCYnws6xUD+NB/L0XCD49IWAhDvbBBeriILluvsA - /jMCE33mvCbWzrsPizP2Cky48o2PmT0Zq9jbIdzGI/gpUzdQ+diNcO+VJb7eLrkrctwlR+ocbX09 - C1LQ3V7jUZRPP+ymr5jR8/WqQDfSKNFQVxkzeTx5MHa3mJT5PgQULpWMSoZvxCR+D7bxmREdsxt2 - 3oIElt1khOjhmyI5PrqtLwlNZRhK13UmpxuLV9cGNTwCwcPX6yFzefX+DaBmyiE5kdFrKMnRAwxx - hIN1MAwgpIfDA7Glk+c95PqByWquQ+u5dWFYDj+w7n6fBe4JeBO8s05MGl/LG/1aEZOChb5L3ESV - kZnoM7bN3wfQ+gYXaMaWRdKh4Ivl8wu396ueAll3Du6ymhaHhGSf4hIav1g8+q8QJf4QkKC2xGJy - bVahXVNmxOmiypgeNNKR+Pzd8Fm/PAZ+KtQZ3gvJJ97jXQ9iZp8UGHOnF3GwzIPt92akM9oQ49Ys - 7jwF1IFdWT3INUVxMd0FmMOnIhBiz9GvmYqhDiHL7uk8HhKeTU6ziqiU+YykpiPFiye3ASh/0TB3 - EzuBmZ1aGQnHFeL09P4a1JjfKTod+AMpxcvTED2ddeiuyRUJ+HZi/eNXQaT160BsdCTxlAVpi1o9 - 5sjpIyFGI09egH997WehF3wgvVnPwWH6YXzbrmfFva7QpFo1cUt6AZLOqTmCQlKS8ptgxuuKIsPB - 6XP8rLTOZW3qhHD/gRKObz3PiFMce/Qtv3OgXPiuWf/mf/3UUbADbwWsZ1JZ6EJmP/jLJ8vjGbcI - 3i0FY8r7g3iUuRK8382RmPV+adYg8XuYJz8HW/dJMpavK+uKFoocxv2hiinH3TMozmlFrPGWudJX - yWeAQcQH8y37Geyz7XSP6N4lejqeBkmQVh3JWmRiPUO/YQXcp4a+kOazDJ8do/hEA/R3v20+uOvn - 7ahQ/nz5eV9MU7HsXwkHswPdEK+vDWxykx4xLLzwbZFvYJXO0IEPv7HnFYm/Qdgvex0+d28La/rl - 0azR7igiKdTv2BL7PSOWPKegUugTZ0rQghXlqoki+pAxvv+0Yl1yuQXaXn8SI1ROg/QrLB6Gc/wj - ujDWoCtT2iMumxNs9dpasFUseeXtlghra+EMS6VRBfpG98DxaI0xpU2aKzblVOK/tSJezuVrBPxL - ec0gJfdiCsVRhp5VnrCr3ceCjp38QLL3GrH2OoXF71LeWvgSoEjOWz7haX/O/71PedocOafwe+Uv - np9/+eFv/ttaeCC3+VC5zClFHi7W8NhOsQyKubdBCxdSmeTA/IfBPrd3h9IoXIh/OS1gufZagJD4 - PhLnwluNUFd8p1hMC3HJUFbwz1zV4dHeHIWlD+PlErQjKKQ3wQ/cc2wJP88OTtq9wHlr6YzpgylC - Kij8nJ/wWLAyVy2Y3U4LxuCtsB8q1gidRT4gYZUfXf7OTaXyEqWSWPenP8yRt1DkX5ttT1N+bMSZ - 6W94EmE8C8k1HdiOMQvestYnrpD3xb96rfOBSJzVpi55jSxA5nFVsK6e3EFSnLeFzsf2M8N8OMUi - y7sSBT4M8I2/6K6EzrEC2tOjx/Z8UF32+E08MAp4IGo+5kAImVPB38sPCa7nEggH7fsGknZJsU4K - FQi/aOxgGMs/XIQhNWgs+xCWYS9hC7b+wJ9fQoSyVjljDUODsStflPuUFyIc+9ZqrPU9CtEte/tE - jy2lWZRSy5DycoPtFMMqXn8heEBd3/nBwmoZEFsUHIgOio2j9FMz6WemBxR7kowPd76LZ2GQEmi/ - oz2xEx2Abf4n8GdrMz4r7hVIr8HR4fwuVBIe44872gf5gEJoOtgBMXalsJsceB3fFbkPo8fEbQk+ - 5NRDRW6vn+bylvmK4AdBTIr0FQNJLLUa6WdPI8/yDozpUp5bkFztH3G9B2jGO+QjNF/OLdaG81RQ - llcPqCaLhh9qog3SvvNE+LmkBb71XwnQx5Bb6O/+l9XEjEq2e4D38lwF9HkJjUU5pT1ktwvEjqBP - BbNZTmFW23KgtPg0sPPHWWCsq5/t/49gqI5WjkoNLSTckbmYlfjiIa7+6SQt462vsX/kkThTNlM1 - WOK5ta45YF/njo2mteK1DOw3Oh+diFgu3g3kyMgC3FPwCLiC2xuUyrYDy0KTsCfl1cBiUioQCLeR - BHL2jruU7hx4o4eOHKeCM+hy+ckwn7/SPBnwXfwW88hD9/x5zsLjFTfk9xg86N8dGetqsBTjq3UT - qBcfDWtvqruCop8gPE/UnPf7HA0z2VZ4wTjwA15ptH/fB0q6+Ph+UEC8ti6U4SCeD8Sxb3Yj1jdI - oaWqBYmbaXJpsxNzNDhdTk7qE7qrU1sdZEsvY0ynjpFDncnoNcozOUf+EUjS8fP+e55ZMYZdQdX6 - pypdMCHsutV7WFM9DmEWjAT70UttZmOwIVx1hLF5ep/cZR6vb7jn3gPOL8Av2I37tIBw84Ld3TC6 - y4ZHlUfZN4GS5TJg5dZFbHt+coDAKgTx8qghvjkfErwrZLCfhSMI7ISQoNbOzfwSxmgPxyghTou/ - A/mZqamEsL4TJ15Dgxlv3QTiJPskKy5hLOW1aMLfkvYkwP0DrOF6ThXarvksgtfHpfBOUsUI7Iac - uFYyFqdfZ2Sp5YwP98loSP0LRSia34jY/KU2WEVNChq4iiSNPmlBP9VYgfvn2hH/oBQxswUDorgP - zFkyFwTop3pXcP85nGdxu55RqpSKc4ECUR/XsaGVsuMhXpPTPLTfdzN/7o4Dt3oW0FgTwHzubw/w - WxZEvHDrK3g/ZjWUtdDEj+7guOL1ddGhtY5XHAF0GOhVWjhUoF4jaljsXfJFyISc86yw0d2VZvmb - r2Gs/Ij+mlJAZ54tcNZyN3jtUVCwLL9YypbPg/2lWeIlO8MDKGZOIbjEa0O/41eECfd4E/V3vhrL - 55eN4HrId9iWbDCsN4cP4DHG2Xx/a0WxzopTAgzIiaiGbrji7fip/ps/doNniJb2LP/hg12ZpjGB - Q+TB9RwYM1LOtjGTmxfCZ8olxB3XF2Ms/yTAkZsr0ZWVK9iWH0EZem8c0JMez49fxcEjyPfYuj+n - hiVd5sDD1SnwKfx4bCUvlEBRjgPibXh28YkcwT/8+jx9IVs8wRXhxjcwFuK9u+yO2QMe2tki+FTJ - LmuT2wE2DSHBpP9uxZJSyQL3nPvO+wKkgNiCwYE7c+zg9HU6sKZ6EUFJBUawoNAwFtKhAL7dfUie - gscXk1lfevnjRTFx8xwbiwyK6F/87+RFi4ejYyzw9PxdcXxrQpf3xK3rmrUbAyDkfTyvKBmR1rOB - bM9fkB0DjsLHw4SxLT/Yht8h6Izy+lePDeaLnYPM6SDgY5A8XObcygesDCAR3Ss+jLZFxoH6WDWB - JJCPMe+3PRQbnpw5vp3ActTqETa1YJPDKEjD/IyPOsTOfiUGB1Y2ct7dA5ereQuyLZ+O6N0nUP64 - r1mG7dRQt9xzQPpVEjaFa+cy0lUpRI+sIXmqRwaNvGVRJuZeCW4jm82/Wh2hKIkgoI7TADagzgRC - oi9/41MI25paOMRz/ccHGb2+pfyvfuHT/bQ0DCVcBD+n0zeQlZPujkejUOBWD8nf+513UtbBo1LH - 2InLF3sLVEngNt/x4Q58l17fuwz6hudi15fdYl1H/FDg3VEI1qxzM64nxflXj831c25Ym5xNdIy3 - U7jnnVbQIBAh3Pgvyf3oDcY/PaC4PCp8fL9Hg2z5DQVfNSXWNBoGL6yaBSKVQmzpZ4uxVPR68Mdn - S034xXSb3yjSfnWAIjEAoqc5JXxWex3HzdNo/tVrKXwswTQ8jII/1dkC+1pvsev4t4GF/SMFp+7s - zfL31w8rAIaDbC06BH/5aSnjLASlLG5d1zLb4INbksItn2A3947DKMVVCYejVpBE7M14rZ9uCT97 - X8TWYUniqcTKohzrhOKjcfwUS+PEPMqd0CZaoihsjXa+CEv+tCP+0/kZkz/mCxiORkG8a7Bz2Rca - MlRl38bJdcVsETNe/4vnP/4Qj193UZGl7jVsQ85pptux6lEnODE5HofLQPWjmqCdIzbEshItXhX9 - xEH78qXYHhQd8Ajv3vBoiddZnHeveHUxNv/4PXZDeypmtTVyeB3bKgC592kouR0iNHv48Y+P0RQU - qRLg+DTD4HFm1NKuJaCB+wn4h75u7+vQQfSVOYJ3eGyW+/4mI8VCIz5gTW+Y4l1SOOdlRJ6adR6o - dLrJAFhyufG/hPFTYY2wsmsRuym5x/SrRT2kRhKRR7etoLqScoH2O9zP64Y3u1lzcjDlrxpbixs0 - kqvhBGonKhKvUIVm9lC3nYL8fc7UfEbGb6fay1/9JoFH1EHMSrsEkbpAfGA+Z7SHZNaVsnfPsxSY - WixAqVug1XUYnyV2dqUsvzuQLrFA1FKUjVk5j7pCjeWD9a3+LKQTPLgOzoLdDS//w3Oq2fh402ca - djlDD7pn18Nut3MLdouzDMretuc2I/HAlqDXoX35ULzhq2JpI7iAVTg9Zn5vr+B3erAQ/dVbQ22n - eDEq2ALttIjkvjRPg37uuoX+8iPv3oVmq781Co9+HbTxzjSErLETSNJdTZzy6Az9fqgT+KeHaK/g - 4tLD2ejRxqewAS8akE4/q1duuD7iu2lqTb/pX4rN/fb49Ct6tj5opKIBJikxO+FdsKD8iXD7f3/1 - dqC9efaQndMHwbt0jacZsA72tnTAzoPuivGTTBBO3ZLixE1DtuX/EDiXuibGNZqN7TjeAN5qFJB0 - wyuMXW6L0n3eLY7v5VAsRRkEMNh1DvaSuGJr1tgp/G27JownEJslPb04hF9Ljs1JeTLWKN8RlSer - xDkxb8PGR3KUeOkF2xvepG5hUdiVpjtX77gqRPvzVcCmX+CnzDmMyFxF0WvUrQ0Pqw1bYvT+px+5 - V9AME5YfB2CKnxBj4UgaWt/4BT53rUW06ccbZCddajgexRWrj/DLqN/PJYxdFM088zn33/0z/L0E - NdfwbDZZLSO91BIccRe34H0W9eh1CBVib3reL3kyiLhc87FaERb/01N/tjHjo/RIY169Ew/eWVti - K/g0BjPn0oPiz+bJYal+MQEne4Hw+wIk2nZE0UvGm9AouMNMveLIxBfVFDS/pRhrnmEx4ouVhUTT - 2s3V9ns0JJMO0hHfSRBfwoaRS0uBAL0zxhpOYyK+LibiG5xj4yc58dbHnQdN97CIbj6pSzltx8OE - 08+zsJqEUS5ZM7RY2A0koVMLRh94hlv+xN6efmPydAoOBPh8IofzzS5EfblXcOOfM4q6Y8EUvJv/ - 8C8+vI9O0atf2MKZu04EU34a1vI8cPANx5Yc5/nnsl5zKuhwTjrvulUoSJ1eI9B92pYcl/U1zEmZ - PsCXyT3JClU3Zn075Xrj/1gfJMaW66Dk8G/+mLu8illkxxE0PjLFB8gbw/BF6AB70Kj4lHcflxZt - 8QZB2xrEfKqXWBpf8hs8F70kJqeGDVOc0fmnn6j5R3Q3fbT6039IcFX1mJaBWcKD8m1mWQYFWGNZ - O/zpxzM97V9MlGzjAGPtHZEneOdg5IOXsnet0wG7Wvodlk8ycZDdrpDonzZyiQCKDOTTdMaXLDyD - f5+vgnchWwfVYkXPogPw8WTYVtoGsMc7D+HGx4Mf5PrmZ0pWDv7484OrX8UMaWTCruRWfERdPIyP - GkHI1wn/xzeLqfTCHMZvh59FYy/Ea219Kzg3cJqL3PsMgyW3CTx9xf0/PZnpu/1bIaebEBRudRjY - e10prKKt60GvrfGSnn7wX7119/FxWIDuHv7xV+F494zVafY8VParPtNNT5i6uXTA9n6CnaZ5jD+9 - bQj+5sPh6MWAh8PXhBu/JBEpVLbQUX//6cEk2fSl1bg/D8rXeH2J9V1wLHytvIbk0v2ItelX67Vt - ZviZeQFrD2I37M5lb7DpDTi4nQ8xv+mV4FK9eaJv8SxaqRPA9qRfiQoPJVuy66kHSUrSgAV1Babv - YXGQ0V6PgVjTAWxlfNMrKcM4BpKx8ZENT5UVcX15iMfgViYgtZYLjiJldTe9XvyHx2eF4wtqac8H - uFTOi7ibP7E4P6tGf/MBd/3SEM6zErjpv9iS+aux+Q0HKL9JgE1AgmHShbcH80uzzIB8ipjRx2mG - 2zEn5H5QioJOCk7+4nHTF3UwNP4vhShj73mKiOPS8/UpA41JjJgSko1xq99QsV43HBCxill1VHOw - 8QNsqdydUZl4I9R180u8/VAPK3cKKrj5TzjHu1tD/+Lb8fQT8fnTaKzZW6mAXB5vszzeMoOg+GjC - +xtcsPYqH8Oi3r8e4tdHQP7qAbXlX/mv3mpWdnXn4LVUUHq+bWIf77thIV9PV2SWXYgeJi+X1pnR - wugnGNi9qc+Y7qSwR2Vvn7Eh96Gx1fcAKi87+Mc/V7//vaHtHa+bXj4BSm5eBC1q9Tg8nDi2+WEz - xDHaEy8Us2adElWEm56PnzepByy5YvrPL7BuPGCLZUUPeBbFYOObxUCLl6UDvQQKsS8XExBwDN+I - CmqA8xZ/m1XstRBK9L0jJ6h+DZqiQ4V++Khu+CRyqf0hMvjTPx7P2nTpfeYrgJ37g/iROINl8Y8z - DI+4nnfnQC8WvLvA/YeQYt5pWCx+d97wQKrI73mLJ0A9TX+AI8j25FTuT8Z8vj4VUIuLhx2pu8fL - 09r1gN6jHXHl0y5mUp6lyoOjdOMP6zAlTwCh4ZMQ+xt+Z/EamMrmDwTill8pB0cPXIN1N4vpYx2W - pDn/nRFwxZaLn8N6sw0VfXVvntFKPjFrzu4MNFZUWFfVlm34PwHgzL2DX/+9/uWzCO5L3ca468Nm - 8tizhsfx/AyE2jsW0rw+Ivirj+L8Qp1qCH/60V/8Xukhjmn3aUwgJrw57+foN8xvmKho8yuJHmsC - IzP5mFBApkS0R3dga74IPHymMMEnj6UFEaS9Cu08dfFhuR0a8YDzB6xvBRdwf/7j33j88ef+hMd4 - 7fqVorEr4rk5ZAYYlqDWYVfCFete3TfLH9/s7Xz9wzPNWsWqhf78xCP+2sYi7+sD6vhZ9RV7lgDd - +CXY9DnsllQAy7n8jVBm+YU4tDeb+Q1LFfLw/MIF+YCYmXPioaSQdfK4tLQZ//giEIpxVny3Mibs - qzKKWRRg4x682PqSwhaKx3nEf/WO1b+MBxjaOfFfxdaViI9TqGt9MlMzm2KJUvr404+IY39jQ1Cy - QYbiz+VndnhaDZ3l1IKfRLbwTV/6ph+75QE1Uh9IQNDH+A2CIaO8Sc//8Pim9/UwKRQ9YETRh1Xf - By1Mh1QiutmKbMKBcvjn9xo+Flidd4cFbPoaNvjdY+uq7MM/vYH4KznGUupVHXznzgEfztFhkHKp - 44FyDaogVtupoMW7zmFxUf2AfThs0IEgD2x6dsB54if+09OQeBxHEvERM+aNr0HtlDZYe4hzMbV+ - e4Crcav/5nuxynyco66sH5tersbrPr+bUGZ8h48FMAoJxf7h7zPRSVGBKlapB5Qq7Db/SXXnkEz/ - V9cD8L9XFHSPqzNLgvAFJL+1PKofzR5r5/VerAQlOnjYwoDVVe7i1Y5sB2r0G89j9EqNdc+tNUo9 - XiJXVy2NLlOkGQ7eIBPvpfHGeuztDjbwygerQX+MFhfDQuvlIgZcJNuA+XnPKzcjO2C9fHLub77t - Q9Az5mHNvpwM0SHJAXIe+GBvpLBYYnIpYQArRlzCIXdxvz8L8jtyDMRQ1FwhTpiJxGzdEEN9cOm7 - Bx7AP7ztITI5Nt2HXQuNIX0Hsn7m3HUujiO87IIOYwNUzSp/HzO86/gThEGvFVRoqhxd2p+BD59Z - Bbw8RibKxAPE564J3MUFbIZokg4Bq9euEMO7N4IunCHW9vQ10GiwHvCV7lkg9Pt3zD5hKKJr3lPi - eo5i0BY8RdiWk4dNlzuzle5nE6pBWJIy6UlDrUuWofASHkleTLtiG38Liuv1MAvHr8DYtWUimiT4 - xSfJjIzmWXcBfB/2OdbD39uVaGDlyAiLgJz09wf8hMHr4fi5OQEhH8Ro4FUmut71hnj0citYaIgL - lMuHGci7493lcSiY6PYNTthlMXHZrBQlDOLuhNPAu7tT/PN7oHqYkuCencBij02F3tHrR24/xWKr - RF4JOvH0QvS6+Qx8PNs62o/cPhDmbA8oXp8hNFOYb9d3jNm0SaBvrDU2dqLAVkKrGiov2cLxE1VA - xOs1gsM1smZJrARGXt0them++c78PGiDmOXHGjnVFBE9MU5A+n0iB867PU/838tuaKKEHRQuskJu - VLIbPlN2I6zjc0nSUHy5CxUfPJCmccRxvVqFVD6MCgWvMCXlqQCAjZlvwRsi+kyHj2ysXZ5yiKtp - vJ15cWokhKAFD/ntSE49vyvmsJBDmHqihA1DT8Fo9FUKBeni4eQaJ4Mgt3oIrwv/Jc9cSoDgCI0C - vcA7khBmbbH42ltB8Uc7EudxNQr+/Fh4+EoBI4ENbINw/OUAz7n0Cp5edIyFtlxKCJvaJPZiJQbN - cKSji+pYxNTTxljKV73A5/XXYttxrwV13t0b6VjJcaL3KF7ffhchbTxcSdJpd8afHzKP5l++D5rr - EBiElC8e/fzDm7j3nc2orUcROkaGR/BoWg1/slUKf823IkcmHl0BrWMNj4KA8XkvxfHyq+YFuube - xCrk6kFUPk0LV9D0RB+qNl57FgRArW9fnIMyZNNlqhO0M9sU3+84ZQvo3g46fNJy3q3zNV5vnk7R - 33hhijXAx7OmIjX4NtisHbPg0XyPYCiOV3LtiT/w23iCe2ZGxNbsN5tXqolQOgk7HJivT0zxeg2h - /9R2OM6fQjNLr0CGcm5eiHdKHZev4SWA7XkViXPJ62Hx1ayEDg535AA/RiG8A35GVMuzeYXt1sf9 - WvHKcidkRhymDVtUr4fP+o2Iy+0+wwoCbUHtmB2JlmyHEEzHkw5fO+c8w/EUFGxW4gc6XGc/WBcl - GtamwRE83/1oFkK9dvn51zkg844HbEyQj1mdfnu05Q+cvjkf0Ccbavi+lzscuSsPWNuXNeQfIcPH - 5RUXo3OMRFhH3KaYHHlAv8s7QZmdeSTjcmIsaL5EKLWOF3LabV1F3gE/7jvDnLDPDu2woiU04ZRK - JT5mO51RsCMhfMjfIzlKYccWYU1K8Mq4+yxNjyamdTGo0LylLg6aloJVmbAIpdlgWFuECqzzee4h - /bYeiT7oF/PD3NfwrMcwGPyzyiT8Yzp0+0wnKuT0Rpzuao20WNoTc6AXd35wVx2e2e2E77+2KNjl - MNXwutozUa9f3VgJ7SrEPyI270zSGcN0xDpUr649cz/ixdKieh143sYQRw2Pgfi5IhNu8YNVq92D - ycj3D0i16YnvoHANqkRTCc3bL8JJuVbGdPIyHekZO80KJ/IDQyFNITYMglVF+BnEe2sKir8EkUN9 - C4BUu7wDcVsPRF+tS7HurbAHQy1MWG04s5FiK36A+fGsiVN0frOUmTtCWu7gX7wU1DRVDy0fGJCr - lk3FGF3OFEHVcLBmtudCsCPNQpLcGdjNypGxh3qT0c6OA+xzjgqEm38NoNjeJnzS7k93Nh7WDB8i - ToJ9bVeDaJqWB/lbc54Zf/QBr+qVAitJivDp3SYFn1a8CpYPF+Bon3fG8ruAEJ6+londxwEMLNT7 - FHZfLM20fD4MplrVCMaHEuDnBxFjaDSrg48eigFRVdowILsqrCN4IR4Nv80YIk6BRVEj7FEgDHNz - Ch+ISC+An+RzZ3zaxwe0e9517I7PE/udvFBFenrAJIYlLUjtQgv09zDE2VbPRLD7Rkia5pE8LRQX - i7q3F7QbqUMMnNiDUIt+DpXAPJB08DI2a0BJgA4jg1hj5Q/8/LpUcLXp+V99ER9Ib9GXfBZSsJgY - y8VcUjTIYz7TLZ/R7t4GEK7WhZRbPFMl+jzgUZAw9unDKdZpdkXQr7cCh3OTFr3I+yriuKtEgjZX - YtrZRQctSOpAHE5fYz4xXUfgHG+nXPuqyz8dcYaCs2twQM0FrCcv1MET1THZnO3mL/7Rcp8IUY+U - FsvmekFcfBKSy53dSKrVjWh6UTbH4l4wVvmbjkoZ2wds7jBjtDmFJeq7ev6HJ5jUaCM8LyPGxi2+ - G8t85R7Qp9W2woGv3fWkzCEQYk8nDzvnGrIOig45Tl+IZkigYYevdkDb+GHz9QNsGea6+ss/M9fm - eUxc72uCkY4RztV1KOjugUfg7L8cxuRzBySpn5YSpbJB4mCeir/3rTSZ1wdKxrcF3d7HPnhFKTaO - IhevKZZHiI9FFSiXXG8k1817aISHE3lUEio+5tWt0fE8Hon5zs6DBGRDRfd8dTC2ZK0Zq0F+o0Cx - dXKXte9AP2nXwU89XYir7mZ3QutYwdOP57BuBlewSkrJAwgzOr+adjL6B/dUYZTU0bzmxmKQAggW - VIyngH1+vAy0/ugmLPkQ4ydNWDNhzQzgnfv4M9AOT7D2657C9ts1wV7OTSBEaxjB/mSWxGhdzxCC - hyRCv9TVWUrNtFjMzuRhuC8wdr63C6NKvpggUeKc4FziwfIKCQ+HRLmSdArVRpoMpYXnD5XJ6VZ0 - xbKDNxWC+Pea94dOKfoVdAr47Ut/5vK3Ggt/9TZy4Yi9X5Y1q1wOASBzeiJqdPi4U04+NcjI/kSS - jy8WU5yeanDssErMm/8ZpkcavtHlcbrP2fO8B4sxwAXuk4M/R5AfAAXns47c+0EhunSehzWi7xHB - 7E0CdF+2M7Jo2UOhEs/4FDb7DR+NMij5T0o8j9u7KxcrCuTV3Yh10zZd4avGIVhlkWK1u7gF+z5/ - OrQSFxGNRE1Dorfcw6NJZ+w+XT0W3kCsoXZ2NXJway8mv3Zu/+pBMNLLLaaide1BRloVB56dxL/i - TVXg7EOJbHgjpvXHMQFuq4G44/PLpppfIJRO0g47Xh0P0ji4EczNLwngQ84HJrM2VarEwGRbY1SI - TJ8rUIalt9XjhAloCQ/wWRZWsFst4M79yGUwFy42dm04s+nkjyVMnbtJHF2/DCTU6+QvXxJ9VnqX - 8Z13UJBjaPigf9yBNdKv/sPPGBu5aCy3mKjAuYoffPp4S7OOmq4iEX5eATknV0atylXgH/59RAg0 - 8x+feV6HNqDStodhq0f7Ony/Mf5FmisBrbdgkYlHot7x6i53espgU9GQBC14A2pcuAMA81vCejhf - wPAQFQp3h2YM2F+83nkngKtzygKPG++AvdYlB/XvGRINDjkjlok4OJrJj/jFuB/IhrcBE6YAPyrq - DmLr2y30SH8Nvn6vurQNUhV9X13x7/tjJb4P0Ho9j9i8RVLMsugVQTUVdXLP/Hc8b/kaFUWFcNBt - XW3aHPVwfx5LYjTX90BP8GWC4Rpa+DhnNza7hVbBX6V0GEvSMrA/fMIFHMbGxlfYwNkZ/Jd/y7dh - zFU15WDLF/MOr8owcTFVoAWnmqh58WvWe99YUGzNFrvqLnD5apBbyB/zE1GTKIzX+Lt60DjVL6Im - sjf8CIMQ8mr/wXidrwV9C+2GDxDEB7H8gU3kSuAH9cOs2LNnDHjAD9BSoG2nANuGJN3qHvLH7ITd - FjoNNR7qjHDxTbb7j8N4uscJnDLZxOUX79yV5/QFBrBmwXzn3u6ikXpEzj6SAs5sr+5kl7BXFJWi - mUPS3p0k8ktheZQg0ZD3iZlqIwdW45sn2c2YwXj1kQxX8a6RPE5zxhh8dPA3SwhjPFqsf3aLzkli - GBFdgrBZmvU1/+kP2DrxhcFcj5jwjusRe8XtaEi/tm1BOkGdeElPhkVhzw4kqRXiXNanmMbHQwD5 - 96PDQfPSXFFDJgWqsvPmQTvswKYHzIDE2RrAPz4s5bsN/1nWNh909w8fg30fajj6XH2D3Yaw/lcv - zNJ8sLXkhBH2h3Egd0WYGdF29hvyP8/BZo0zd0zKZwLKXyLjaDWMQdiuR1s+I/pqNM2q9iSFWzyR - ktMIG//wxBYfxFdx4a7KADKw1T98tuxgoI/zJYAmsoKAbnh1sKGTAVVBHlZVp2mYDBYOHQ85mvfe - jmNTipcRlSGLAxp8YDzRWWmhzkGA/UdBisVcJxnUVBXJ+Z7VG3/M9b/5E/wHAAD//6RdSZuyMBL+ - QR5kkxRHdtkkCqh4A0QUVDYTIL9+Hvqb49zm2IfuhiRV71KhStB4aWC53I5QWC+e+s/boWaB6OgK - 4qaRcIdJrBe5CCr5/cpCjOfuXU+tYmygGawQWxL7GJTeN0eQtNcUvk34Mupgp1FK99XgP/77G33+ - BX1bEbru18rf3xWs/k0oR8PIZvtqVJAmfY89Zo3x9ELOgkrhkKy95rxhWhy1UQSzTag99U3MxotE - AD7hBgdKzmr2nKUU6em8p8nxcchX/Arg+t4dQ+nCG/WcN7IKp6zKcOhmm4GJR6ORBmT72C25sP7L - n+iduZQgdRsawvgFXRZyH2NT3F8RL+6cDLBOWhpyshSP3GEminy7vHCQlxe2rPwOPK++r88fxHP1 - UULoZzvBKnWjer4PYgOvOC7w/lef/cVInv16kD7YNxPV57GQtQhq/oA1Mu98hnaPEn1vNyCwGe9s - 1o2yhVfnuDQit239yz9hCbBZampmezCWna5yyrR2GldQPhisd7gNfHo1oSHH2zU/XYwEeOe70IM6 - veulutoJCFiew2X32KFJ0eJSMaJlG6Ln7TAsTP+8oK49DReb7+i3wqcCWPkADrOmisfn61b9rS+1 - d1/LF5vcvABP5g++x0fjn18GqWADDZEx1xPpKkf540OGUWvxhBY/+MNLvH/dZdQtO2KC2rCUeiPE - Bp1rmsnVIx6p9uakeGm2h0X+HrgrjgWuzuc6zScU+rcWh7i0jclcp4oKelTTQ7+3GDvsRwKHrsUr - 385yJjQXRyHl/YXV9+c80AoGGZh7fv/zKxdTFzdwrYMUq1nuDoIrIQ959d7AxnyRjMbaSS/w77aM - /WAS4rkd1RbQrgppKN/P+SI4az46ZYTwJHb80f2JG1kRfjPVT7jKu8PUVSgu7g9syLHE5q+aHwFb - 9o0e6k0QT3h/0eF0x1EopzvJ7wqn4EC/3C549dvQ6j958BKtB9UtfZv/lndcKaufSv/0EacVY4Ou - ZrHB+wM9Ma4SRvOPD+PTyoeW97Nw4DNmFtU+Zx39y//Su5Npsi0hZspRvqA/v8WQw69B+OMrUniv - L3AQFWa8+k2q8pffvZ7tVv1RV+s3xwm9XrSmXn6JIqHqcRrxwYv1YRE4S1fSvZ5j9SM+2HJv8gv6 - fPua2v6u8kdH0BtFmr4ngtTbiQmScT7CHz7bv2M1LB90FYAn5SNUgueP/c5kMyHfrM/YabirL+7H - JYA/fu/GTmrQXK4IPLfOiTCBq+MRLX4I9LLv6N47h75IJi4DPS1U7AqnQ86zrFX/9DFpH+LFmIWQ - FujP3z50z7Vr94nJcIykhKYNfcdf7/ELIBE/L6xGpmWwx03zlP4eHXGwh8/q/6YCvCv1jsOzVser - f17BH99LRP3jzwdZbZSmTS8rvwuZoFw9Bx6aN1D38kJsoPH6RV13fNJD8DwgMTrJL/j65QcHKz63 - 6WQGqH/0GhEvYzSM9bJxgMnFRHF1rmJ+6rQCLHsitNAORzTJ7NqjJywVGVc+SskEqXR7FW/qq7cZ - tbfP20EvqzvgMxw61hPRDGF6LLfwy8u3msBT6aGfzQTH04TQbLF2hNuUIyKykcS/MxEW5E/yhhrC - 7uzPebPosOsjLRSu5uhPQemZaD0vWL/e/XzZSR2RQ5GdqCbfab1o9k+CKVdFsl351bhpNgF8nI++ - +nG9vzQ8IfI9Kwy85+XbQOdXBuiq7w84jKSOLZpiT8iR9T7khMJFU3S+LXAZdinWxK41WGgfj8rq - BxL+ET2Gd/zdhfAFWQrfzbR+Q5zfmj8+R4NukyBOP8323/vTvf+RYjZwrwSt+EtPHG7qefQ6G/En - NyXrvf6aP8z8BiD9UMIf9zEaILtlwJIIwuXVivHcmG4A9Kmp1DeTypjVZQdIsU4PInrIy8XHqwoV - RD7iP/606jkT+IN+CM9Ds8RTcw5ekOwgpqt+ybl+V6ryT05S6pp1W09NPNmQEeFA8Yl/xfN0vS4o - G8sC43Y0kbji8W6agzHkre8Zzfd+cNCjMDV6rIzVP2ilf/FJ9cTZ1c9T2qn/8FdzXFL/lJKkaPUT - MM7vJ3+dWpagiaEmnFf9S7brjYJ6k5WEZVmNZr+fUoVsEUedx+mGlm7T2qA/O47uwWD+jIWoR3/v - k5XXOmb0o0oQJeItlKt3WM8n7AIoVvwg22ypjdZRN55MDGzRgBWqL9rC7Qirn4rj0DkZ01m5hXDF - NCec+OOM5WO8KsjsY4v3bAxjdo0VAn/+UInrK6KO9awUPXckGpSsZSu+pEhoiyvWHNqj2aCBiuaz - Sv/4zrDyIxUdylGittmQfBK3aQj14RRTN+70mpP42VO83Vo9/In6MA8l6uG6Kfiw83wxX0JDUxVh - 0/vhTKN6YPMr28DqN+D126p6OW1zHXZJnlBj1/ZsvFuHD+IWU8Q34fLwV3+EAB/XLnW9mdVTLLY2 - qmtHo7jmzvX4p++C3/dHPemHcrbW96B/tCt/VdyYW/MrvK+FSG1BovFscbyjSO8Lo3/5+Pd0jQ84 - 1IrpLf3U9TJbjg7u3M7UqAshn8BbKjiiWcXOWP3qv/eB5/aeUm8zvxhTXd4DNZcG6ufHxF/w7inL - P+lEQqZfW0afs5TBvdpsw2n1Z8WVT4NNeZuau+Mczw8jK2BHLz11V3xb/vJhMRoTds02y6dP7UR/ - 9ZwQykNQCzvpSdC2pxURU/mO2PYqylCNHw4fqqZFbB5kFZqo7rB1SDfGpPwcAYjKJ/jsW09jeiTn - CeqzsCO7bpsN7FFzvfIOGw/jKluMsavIAr/EabDzzOKcn/qz/FcPwHqRBTG3PQ49qsTKp1f/adfT - 6JAI+ZO0wX/xV12DZ/G33/Tg/j75TI5ZiDhDv9Dwqwr1P7/veP4GRLLUmP1W/a8MZ1xiM9sXBr/s - PjZseWWDfU4Yc2qENw64j3Cj9rPpjdVfs5U02JtEPPSVT29CoP/5mdjpr5zB8uViQuAWNV75RszM - 9l6BxZzrn58WE7SJOKWweMCGfm3R1D2y5M9vDueJV5GIyABg3U/dqleMmgyHKVEKoVXx/a++4ftZ - C2PxulP7xp6M+X7UKt3hNqz1XxdNzvbZgpO4Cj3MypZNQTLof/Ur7F/CZ73+rIJ35d7U7p89+udn - wHbfE2nVe9ynViNla6M7WRLnVvPF3klhCDqJ7NY2KMujndS/eF/fN/HH1NDT3cqX6D6VFdb2QRL8 - 8zO1pGI+O1+HHuQtfye7bRj686UxIsiom1DrsHMGUtrPERbmz6Ecw9Vnh5M/Qa5v0xAp0gaNSdMB - rH4steO+jdnx3Bboz79AZS+if/m2w6wIhReW/EUyzhHseavAuLtGjK167V99WPgdq7pf9Tds+/RG - 03W/au6eNtAObYQvzy5nM5j9+IcX2Dn6W/ZzI837hw+qmFv59EzlEiJkHEIu24M/nS4kQLLhELof - zi6bbu8jgf0YUurGJMn/8ev/40YB/O8bBfH3uMF6Gv5qRm3hBftjIVNsoS5mnwrZ8BVHlUbBndbT - /fHpQai5B95XocCmZnJLhdYFo0mb7wehpV8C4auYaLbYUsx87ZlAez3V+NC4L8agmD/gOKaGbe6y - Q1Nl1pPy6T5Hsp2ZnYsxTJm86aYBu2VrG0s0aBLgJezxfnkN/mLdqkVpg09JtQEOiAW7koPv5/0M - L/LjMPCePcvwaU7n8BXc8TBhLYjQgC8fGjyeL+PnNo2sODfiY/fsXBCb4Z2g5yUNqOk6v3pBvm8D - XOw8ZF9mM1ZqxwuM1Kyoi6ugHnNr9OCcxUH4xofr8JMXJkMg1QURl5fvc8uyreA2ziF+9LqTC2rW - ebDzmBEiK5zZdLucZWgijKmddErMPla4oLcTBtR4bddRpcPyUS7Gq6Rn4YSH+ezfJkW9qjEt9N0u - n6tvoAP1LyoOLP0XT7iYPopYJRY9vygbxnPzqKT8lH9D7vt95YJ+THXl6TUtvfXMrlnCvTeQNwMK - N8BDzX4saRTiPh9Umz2+HrNiU4Ey1Rk+RNI5F5d3tFGKhotwstzXOZHrnJ14vfBlO2WBmD/eGzSb - nEj9oTQHMuZWCV02hjSamnDgj9NvAsviG5qUKPWFTu4jJTNiEaul/UZMQCPAOd9dqMoePzb3qa7C - mKA9kSibDDYJJlGKOHSwkbfr3NWTr6JimYf1jutgTAf/nMJ5ijxqvcdg4N+PWleKrJeo2Y+Cv9we - UwpeeKnpOfKmgUIxN8pU8jta5DiL1zu0BUjTu6d5rkHOlncEaPv7adiKdxriZu3VKu+72dOYjtth - Vk7HDNprXFM/FHI0OqQnynresb3zNmyWw2r9Vq5H1HpHvfH7WZ9CkWv7QPEm7fKpuIU6XG/mBj+E - Q4u4vaV4kJSSRZNPFA8c/1MvypAZAw387cefTBgv8JakkMz6dx8P9bVPZPo2FXp6ys94qYY5gtyy - U1JD7dV/+wWpfjnScDe1aA6+Z13Z921Mjwp7xbS76w3cF9CpXXF3tjylpVL0y3nCKWVHQ7wLZ6KE - oQV0z1vPmttUBxWWT4Cwdxnqgfa9wSlkdr70UFPVEHebuw0LJhFVR7jWfKYcTeXv71tfJfeFWDI8 - 5bJ8LuF7ox8GPtI1VbnWUYqDvf/0Oc/5RZBU1RF7a76YtEILgCp9gLPqd8wXHNsmBO+Tg2+7TspZ - YTmCsuYDcjyhd7wELr92iO/dUHH7AxKtqhgh6ucUG8/jceAiJSbK+8to2BTHr8/BJCxQLcaNulqp - GvMcTC+ldrMM73/kY7B72hO03LsBG82hq3+iPznKvb7/qFraFuJLUVlk9jSv2GavpBaq5SbJOB81 - 7HeU5VO7lQVY7sNAD5Kl5YJSNgA9fBUiv9F6af0028o8bjt6kL6bYbnc1aMyD9dHuCk+L8bEJvtA - J3VfIiTdPZ8VvtGVSwUBvkFAEIukSwn08ODD7Wn85lzTVYLyzeUiFDXBNYSTnlyU85JvQ7ktw5y5 - wodAtB0EHKd65y9saCPwpn4KpRB/B2bGcamszxc+Uy4ZeL54SkocZRX1Hb9HI2yfgmKlnwMR7ufv - QC5aDmCrPxX7Ymf5ixl+Qhm90xvOnPmE5tMtTqHMbY56KLD88bhvZDR27E2YEb1zMnRSBbErHvEh - 6+L456q3SPnDB5ceOn/aGVkGPxGdqbe7x4y78guB42bE9HYRW4Oh9NGDjyqf6sPlwxjtjFLh9SKn - 5yRuhl/0SVo0ypKJPXnRh2XqBQ5ab5PQQ3f1Yu47rmPMv12DA/ZG+WztsAdidbFokPEiG6+ZncoT - hRSf1DIexLWvJozUrqhGLI0tu9mp5EvwTnH08D/xwtdnTuHe6g/fDjOKl/NP1gEe7vkvH6DRTFMC - Sff1sCVxH+O3J4Ku+GAI1P3sIZ84D2XwF0+Jfyv8xX/iEQacfPBV/g1D7+6tELS3Z1B3c34N7KhW - oHxs8YvxO6zzubl5Kfyst0tTGW/z5fdxdDi1jwCrJ2TlYkEVE+5PsSZ87V6MxSlSSTnFH4LDM/7m - U9k/Q5Dw7osD+ZXENH8hAV0e4kiDsdqjf3g2uduc6t33PIiF/yyVQrI/oagcdZ9f4wNOs1rgSHx+ - 40khkqms+IgNs/r5zNKfRBHv75xa+ZLV5GUhAYneeY+vT6X1p3E4NootP4AMkhgw3s/iSYmutUl9 - Xo4Zs3/nDYzJbo/xfKvRRIk6KpIV1vh6qh6Ma5K7A2rA7XAaG+PAzM1+VCaShPQyn2x/WarfCHXR - E6zO7RMJH1H2IOpZSvPL94aW6vJulL/9jq6Zk4tNUzfgwnij2at41kL/vnxk3HUZdfzx5Iv+eG5A - ezvGPzxmxmyXypqfaLSRXmwKNomtdDnekU3f4nhZv3dXVj1FusgwfF6zPAeqm/TBkc1buSA+3on8 - BInDR3T0GXWFz6jsr7ue7MrQqMVJCEa4l9+QcIMt1cvyDS8A1/GNcSDpOX8RQhU5pOmw8Sklf1rC - WlIAvga9dJVjLNq3zxTVDyg9vZIazRBfTSVXsEcPE3mzacOnI4zVONBHTmL2qwPNg5NsXTDepG7O - SnfbAgt+dbhFfMR+lT5lyP7ilmyaB6ppDFKmHOaJhA0+PfM/PEfOLiYU7xozXsJaFIC49YMaqe4a - YvkeAFxkYnxU0N0g/b5ulJl/7aidB6ye+J+aKNz+s8VurbfD0v469S9+qL0/nv3ZSBsVDJFyZBGf - +3xJE54DZvQd9ld+tbh7K5Bvx1IN2ZR6huADqPLrNMdUF9cuiL6jAShv4Uz3QSTl4zikH+WP35Vn - vM+FBvUtgFmeSKGGTryYfVQpuVGGNCw+OhLc166Cu21sCTGXh8/+/t7N7Iu1S3GSz3dVPUL1OfDk - Lz+PydpjpLHzgepb9V7/pEemwqRXA062Sh23TqvbShL4BVY70UDcY9YE5fPIBGx5jPdHmdg9iDNg - avU29Zd0enmwiy8x9WOnrics1x9gjzLA+1BzhykQWYmYFTHqGufAn8O7IcliUj3Jx2XDMKehK8tX - i+vpxbq5hrjmU6QlRhPy73EcxkEfdPlfvAiFjZZ7rJTo+Oiv1H7V2ODEoXzBgVS3EFVPn01Vldpo - +TkqAaugbL6clgzOv2dFz/gr+7MctkfU3IYt2Y7bZ7xEc19Bmbg05PNU8n/uTmpgnvAm5CEu8unz - 3epojY9QSPaq/1PPSIJmTNcKzeuZ08JSBXQ5Oy22XjQexi2OI3ArScD+wqu+sD9dLiAmrye23/Z7 - IFMvCPCHn/E1NJBQHQNA2rsjVK+3frwE72ev7KZJw4V0stj0Sg8tKgUppefk9/U7da6CP35IHpu0 - i1+Hi/BBf/pB25YdW25P0QQ66jrF5amPR79fwv/uVyXta8ZHYgYm8h44UB6/fNYsz5P/+KAeBhVa - gBcTYE/7+hePiPHe0QarjiOySOegFmryTJXMaRbs+rZnLEMycojnli/W3I3OFjnnBMQz706Dlc/+ - mG8TEEhd/+XbYfazfALZah84EJwTm0lER4STBbDnXGm+aMRXgZzPBpF+r2e9WAqkkJZDufL5pp7A - XudcXqOOBt6RYxPL3x6YX8mnXihbgxi2Pxt+qXgPpd9LG/jXngbIJt4vlG+XBK36TQZjSN90zb9s - +nxFHU7apqGGm0T+5KJhA+mcOmQiQ4v+4WHOTxktmwcaVv6jwjCpHS2pcGEzl+INOHlyJoxtvvnS - nmCd02IFuFjxfIJps8DNPn5CYL7EBgs0VRmag0Wj7hD7M4m+I1i3vRRu/PM5nhDCMphecafnby3V - o0IkW5k3toM9z67qafxdI3Af0UCNlV/PMbvLaF8KFDvpcEEs2mxtedUT4e0GCE0bPVJl0+FiwuGh - RqtenWSzvQlUc5sqpvp2fsH5FETUpKmaL9sMSsRfL7twquarQd+SVcDvMb2wM9x+wyK/ziV83fUq - 98FP4mVzQOEfnpG5Y7Lxx4/lc44uIXJ8D037Z1dClx922GAG8qf+pqTo4S9CyCfx6ngvZgX8mVAi - 92c5nsShrOSrUI1U3fYkZ9fgKYB5J3M4Pp4vn176RAAt23TYeo9jTV5BsvanerUk94pbPnMMImnb - 9Hts7uU3YpMQEFC57UjX8xWPxIo94NutQS30EI3h61ZE6Xn3QE/Sc/bHf/otRxeqyb3JFt9ICHoJ - jo1P5NHkU+zddWXNpxTHyplxyfVyhHBzXLvu23ef6JepRfx5pPiI05BNEp1KpXqPHrXHu+aLxzIH - EN5Fh8tBP7GTct3pIHPkStVblg8TE08lyjRPD5/ku/WZs5k9dDl77T/+KiSVNMFG2Mt038pPnxVd - UgAXViZe+zcbooYeH7j0hh1+gskcpgH0BHUZCekh37yM+bG5ZCjp3h7eF7Sqp4KTL6A0oY+N8pf5 - s3MyVKWt9/sQsasSz7vjZ6OgPNiG4sRujA23zIZfrYpkdz31w7w3tQnUQNhhh8/lnO4yt0S5cvCw - Tic3Z0ziOMXBr/kf36Tni7yB3TMeqf53/g8/pYWtGifUX/jKYBa4KtJulwf2bpOYT/cH6ZE3LKdw - 0V3kv/0NzdDjYXjYylPJmGN2lpBZ2F+8rl+9ZF5DlO+0IPLUDyZb/vLvqmex/w2ATd8x3YCdyTds - Yy02Fh2ZRxDG3Y36nPFlU/22JeU1iX4o2WVXT/NbiP7p07PHzusUOr+CP38qMeyKTXTapxD6sMen - RMU1U6a8BP1b4JWvaQPnzxkHnOpget3uiT9u9EiHcHj2+FG8dv/8JOV+PNyoynZTzAbRMiG5JAIZ - ruAM05YRCaTYh1DenCS0lIKewro/2PoqyCfK+9CgZp1qsmuOU7ws1XsEbt9sCaSNmwsiuvWwV+Qv - /cv3v4tg63DII4s6Uef507l5vNCjFwi23pFnsPfhFoIzBRzWVj3P9F0qwW0yamxLrTYs7Ec2Mqd6 - GGOPhvkU3VRbaTbqglXjQPJ/eLHGMw5W/J2q12VSjEi8UEudnWHeNPIRldLrRd1Vn8xH0U6VSHlp - oZSp7/pfPj1m1wKbu+xg8N3pmaG+iiiRE4hyeixjkEOemhiPWy3nynXqnjhvMNYG+CEaZXWJ1HPm - 4vjZM2Oy3HcLr/13F76FE67Z9weXnYZ9i7TP3SZnwy2y4fLgR+xLYoCEovkGimabPj34zxtjIT2M - kLClwQG5xqh/SnIFlZgk4a6/NcZ07HbcrrFvA1bf92fM9tLxBQV9XqiqMx/xy21egNBXgVVratA8 - YLGR6XXA4S61MKsP+SCgP/6/d79f/9fcvAyy6vf+44+IkCOXKFSge6wqSPFntFU8EKLwHI7kytDa - Y9bcvazSpIH+WPsJvy8fWPUnfhjpgph5A1s2P5EWsmtosFneFRWcaHZa+djoTwatbXjnZRUKzVgb - M66bCrqbaeI/ffFD9pmDaNsJK79/xX/xrnTx7FL9ynlojW8BjqfoRTV+I9fUsU6c8nVdhk+vxEBi - rZc9WEo5UKztm5is/ESRa/NAFFU3mHid+BJsOom4BL6o//lzI8op/udvdm1VwOdkaCGoYZuv/K8A - Kvz29KQfTDRFN8ferfyNbNprxRajnmWlC6UeHx/Kia1+5gTb5VuRnTfuaz7nrR7GnAto/No+2S/e - bV5IyGOMXQWp/iQlpqfwcLhioxBCf7HEeAHrAgYtgpuDBIbXHgseYdSZtStbzJAEcKEXjcAlP+es - Lr0NJHxKsIuWwKDdXpBReEMVkeXlNTDfcQEqaVNic6vU+QS18kKOtBh43yVGPRbRL1t7Hu3XbzQz - f6I+WhBV2oA+vo8ZzW4zSshfYOVL88zm6jf2cFRMi67n3V9q8szgL972g0/rZbKHEL0GGLHh+6Xx - u0mCByufJPLqb/3hAezQ3aJOiL/1qo+43dBgCx+O3w9i1kdJ0E4b16lThhGLGT5toHvnMTXNxKmn - cqQS2i2pj4P7MqF//iYb5pyat99U/ygH/Z8eDj/7XhyYO4Xmn18ZivpxqEVL78jukB8teq3CC6IH - ZVpAmJmEDerpOX9L+go115dHHT7P8mmL80gOjmmPkx8eayJMD1tetMudbLW9mQvXPXzQ57qxMb7I - IVowWVK4B4pJULZ71mzF37XnRES1Znv227/6wR++3/3nDf3FM0r15IhVU2M1ra+vBO6gAjVRrTJh - VLIU/vTmjsc8YzwZRigWNlDb3H6G6eDfUyiKk45dlHb175F5i+wk+RHb43bzL352+WV84cA7JmgW - hSaFCeIvNY+Pzl8ifF97iGyT8L3uP5sNe+Wn0FDDtL2asyXGAUfpTHb9zfQFS4wnKLfbiJrF8fvn - F45wpm4fHr2mQ/RSQwAociwc+G+jFht8leH4e0/UvqSkHum3MoF8lMO/87TyeQGKT1lg9VU5MX8M - Mu/f79tVpA48yp8vuW6LiHqGux/Y+JBHtNZnsMl9HGOqXuUCr70ZYhXMk896Swqg5egJa9Jz/vPP - R/juEo/iu/hFbC+lFfrbb3uYL/WyC9kHvrlUYPWAkT+dBGZDYtgvIt7d2p/c21VXmpu33tATjZrt - NncTxntxwdkfv//T5+v6h9s1/42rPwP562WHfGG+6mVYHgL8al2kuq5V+S/3bil66+hEQ+BhID5w - uiI/SwOH1+qMJkHtI7Tq7z++Zfzjc6v/vvp5r5oux/0RhuNmxk6lR/H4/J0uiI6qTm+TFuYrPw5g - mbIzdiPDMPjX/huif/nkq0rxyhcKmDotXv1dt17Up7YAe5En9l6FVlPZv6TKitcEyCzkv0c1ZCh6 - jiXVN9ILTSNuIiR22ZZ6KHkgdhrOAVr9P7IlwiEn0VkIdn9+Nr5UY8y0Y31RVr4eclRW6o5EXwLj - pT6F28h2kfgiMMJsCiL2+rOcs2vslSCnEsN5SJ4GWao3gVVvhkv9btkikGOmdGrF4+RPP5SCnilZ - NAXUfJqBMT5ywQPkfnhC/DOfz+NBkmQp22t0L8edP1/fyRHOuNnjBJ+0WLTZ5oN69jTwnz+3EKYd - laDuN3R/bCqDivd5A8MmLf/V836Tc2nQgbxuREifr2G+ul+Al+q74WZ3+cStffnIMKnyM7zsvJKN - lTlMskkvKHwbxW6Y60Bz4Jr9HGy2fO9P1vu2ASX6aBjnJEa/8SDJsCuNKxE/V68WuEWVIJs5c/W3 - UoOdZoVDf/zWD96/mJxKp0XdeEnxgX/17E8PSn/1JHz2lYHFxs2EP722nod/9SCF+7QiduR9U3dZ - G2SAvmq9rmcVz0Y66jCI9xv+8+957pe3MtaqFruHrhrmUXp7SDVPPdaq9/LHryKU3R/HsKHyvZ5/ - FilkHOCROj9oDMZCtZE3rfOk+/lQ5H3N66qC9puBcA7xDXp4maUysfBHpOdcD396AFSu0Ij82Pn+ - 6h+kSriZN3/1jFq0PsoF1edfh+3x/jSmQa915IMmrPVVnfGPTF9g9H4/6p9uUU71XSr/yy/mev6W - c3QYwW/ZCx9eKmHzvj2nSnP8WjR8qu+cKmFYgZJQ4U+P5ORhGCMEY3DARn1r8sm9PXT5T19PeY3z - 6XFeewCVQ4mtAFxj+nimqvR+eqTB3tf8lZ+ayh/enTqWGczGPUB/17OQO1qfeh6T0wvutrbFvjKc - ECE8BvmfPm40y//zq+AVVhvC1noM/9YmXXkndU6+YR4Yy/K1E1jrl2EjiEJNwnoroJdVmPix4tPK - /z6ACt6l59MU+kvyPaXKITmr+FFxKpv9LF7QTwmP4Zc4Tj3+5l2w+8s/3vvJx2P9DiXg4qz+qyfU - rOiKApV2cMFF2rjx/NYmFXS/H6j567Axaefqg5rogLFONMmfnqefLuNk7XG21k85rcQytKaGsU0I - rX965KfQ1OINq7eoQvMtNzz48OhB9U1Z5aseWvMdzH/4xWZfcEwYGWyxQ8wNI47syyAfLEzdYzIz - svIPUH4nCzsl5w2cy/vJX72YBuTK2PinP1DkWdQZbodhSr+4/X9uFCj/+0bBMK5dX+3pVc/7YDOC - x04GAf/QxePtfLaBHdhIjUk/1d2nV0oIy00ebuvhY0yFt+uV+m4fqTddAn/ZrV3qHOz8OXakZudU - dUDq41PIklZjvERvGWyDoMCae/v5hOa3FALe2RzEwvV9zhigRVqX6aSsT6o/mlxWgYlqhlWIaT7H - SH0pqqx41HF/+4EfurhRWiFrqfX+vnxaVcYLyJaXQ46f9XjOfvcXFO4mx9b7qxucq9uLcgW1w5qe - CMPf84P74UgoStYSz+bxuQGv2gtks+y7YWkpfBDPWyE9TqrJWNV2GSLupBKFXE/50tjdWs/kB7L7 - 7g0k6I+Wgw5XHL5/owyJ94jpinHeRvQQeiRvW+fUQJ5UPHV+g1KPiXEDONHUDZfr2Bjz/r6UIB9s - ld6z7RT39W9fKd/tLqJRKkv5wh30Ec4f5Um6cxgxzs/bTNGP9Ujt52Oqf6SUPBllHxO7V/pC4n6W - SiWQLj7VLo+ejU9LFZRuERRsF6rKhNc5byD/DRrGnvbyFxQEFaBdd8T59eoOvJ5bGYx72VrXH+dL - n44JPHXTxfYz5NBsBrGu3MczpYaQJPUsZiGBTmtjGpP6OXDC/V2Amo4zfSzLkHNvQbaV1nQwjszE - yMXtRj9CElclPbMbxPOSrBn/2fpUT+U0FsSLGSmWEFk4kZJnzrVX2kNmopg0dO1KxJ3bBpZLbxA5 - 5Eqfu2mSDdnrWNAyDVxEDtxNBTu8vGgRjLecWZBLsJybhKa/QRl+HnZMxemSnib3T+jT/SwV0NGB - 4vAdfQe2y/0Upni80fxq/xATO2UCflEmqnb3NyP3PMiUyG0sXJrauRY6eKbKuwSfRo/71ZgP38cI - 6r7TCZcb2J9EZRshs2wsfKuat8+5erhAcWdADWeSfFayQAB+64TUNA8WmpdEytBrrGUixJ9HvFiz - c0S7dHRpZBw5fybl5ClolSay1AkD6dQpU+7mKcIGiSw0LYGtKx53WahlfgdEhE16RIf2YeOAFchg - VoIlGPTigcuCaDFXrXfGk6ik9MDyKhbTpzsCTM4HJ/gH6NdBIYGSp0ciH7OGDZO9keG1yGdqbVUv - nu+GDzB6nUjtj+b4HBsfAnia3uNQf72HWR6IA62rPbDqoyvi5oHzFOoZAnW4EGq676IGru5yxMeY - aYPIdZGJpOP+gv36lubzM10uSry7vMgUlhfG5sL9gGrVDdbepjTM6cEYFThIAXWzTZnPHRN15SX8 - hFDkGfV5zTgV6JPiOOTTpxnPHxpkIBJ5CE9K1daL/qgExU/uP1zqJ5fN6exMsNxuPD2Euefz/oaC - rDuPEOvR9RYLVttnoFrPBmuC4g68vz/IULxDm55HQRlYP40TCjJFpx75qLEodvyiJGPbhxt83dfL - 1485ICeupAfqmTX3Fe8LeHgjY/1zmtn409tFwdGxwX/PR8+p40HuZy0NqvPFF/a/uw75UNfU2UV+ - LVZNVCqT9kHYf2dbNmdzU0J0yD/4NA32MJnSs1ScoXBwPj6EnA1a9VH83ZKuXSdPgyAZHsCG8DE9 - jVmPGIwvQZHiY42dzZznDDm6Cet5xObrpsXzKDgZONgjGN+cfT7JV2gB1eREzfpyG+g5ngMwNz+V - Btt1rvIzXRJlY/321EP5Kx7nybfBNT9njB+Zlwucw5Z/8Z+E4cHgSFYQ6CKi07//T3GghP/iib9u - g5rbzPNG2X7LPS3f2oBoFjop7JsNw/4brjGzi9+EqqB9hUfgP2yp2kIC+1ho9OBLgz/95ZPl/Emo - l8uIzaLdEXQEU8OF6sQ+35uyAPMW78jsOnM8XdtLhupCfeJMuMZ/50tVDtsHh81uJ9VL+72MO3ND - VRqW5YjYEHxHeLdmijU8HI2xa+tMsVIZaBAovD/5LLmg+/ms0WtPpGFsTb0Ftkl0fDP4Pp4u208L - Ub1oVC3sIJ/b+BeA+1624XwJsTGbhuLAdlPwNOXCYpjJcvaU63HaEniwTb5wDpsA7TMRh55esMW8 - flrl5x567IvxhJgudhFkNw9j/2R96t43jBK2roapcU42f/GWQBK/SjKv51uYHtVFsSxKsbnmAxaX - SQsR4kqc6c+4XmyHOyov/AnCiR5OObtIp41SK9jHluXW+bC5dRUMRhXix1ZyDRq2SapEMyv/rS/T - qZEq5dfUaPF03JzeI6SDletZGBOt9xfh8EmhWfoFn1UricVXZ5lKi04SNmx6HFgghhPMXSRTbBz2 - OafaU6DsimePr/b0GsTCtgNgGHn0FvTjMPubLyh/8RxKtxEt0O4zuOs+R/273Nas5VIdtkpuEyjf - Xs7fDQNAeKYtdYKjPQj8L3kpl18pUJ+eXj69afkHoLWdcAdVEC8TO1yARXqHreH6q1tsMk5JaTLj - 6/Gl+MvZFmVwG3rEOLO2wzR2e1MRHrFL5E7dsWXUChMmDhmET49O/XsMqIf80sfYPrxTn1yurIc7 - eRywB3KUT5UUAwz5Z6CXg8AYoVevVKgdnqn5cqyaY8/jqFC5CWhYHk7G8jDA+eNH1NypChuzTyqA - cjofscHbVT0/n8cA9i/xTLH18Oo+PMYATzl+4vBFnZoVwmQrdJP/sP64X31m+PYF9e2xJgrvBz4P - yV0G53Q8EL58+bWAx5KDXDu32Nyc22Hx5Hf1hyf0Qt4knvJNMEE1UILD9G7V8zuUG0g2i4OxP/+G - xRCLCczTWaJBMO5iovbBEbqBC//Ww5huSmfKiV+o2FrXS9zcpxLwkj8IXGdaT/bdi9D+tzEprtpy - mPfn5KNsg7Cg6oM/5nyeFQGMnU2xlmt6LIxaYioNG5/0eK4PuUDz/wAAAP//pF3JlrIwFn4gFjIn - WTKJCEgQFHEHiAiKyJAAefo+1N/L3vW6TqmQ5JtucnNOUfwK99SSwtVjqskcOBkBDIRSu5iLUDsy - aM3ExG6u5mzkpkaDum8opwVVTjPckfUGp3OR4j89yRcZJqDGbx9f+ccwLHOCOPhj8ohPujUPbHwM - JbjuZ4mga6UNoqjqCThUVKKH+GM18z4FCdj0D/X5yo1XKbFC0ID9RPPHbwSj03o89NqPR71UVIZl - iszL3+cFIO/2saDv3BJGP6pRUw8rsD5BslXENRefLtMpX05UaKEvRQU9xicvlqpxTlBXJi72zkkd - s3jQCyh4vE21VrUZL5KXD91UT+mmJ3LBx80Kz4ap41NYLM3EukcKoXj4UBMcDmApvRePjjL0sW8f - 1Xj6giWA39VYCJd4dc6Wm9pB6uoiDjZ9svzYzoDve/XBplc3gAH/rMFDNkuBeAFuviiGWILwK+2x - ozTffLFR10KZq074ccXWtoNzNcCP53Jq3CevYRcDFYBE6glH0D+z2eKjCtJXodGHMDXDqniVCJ2T - HmJ9hka+6asUsiP0AjDOq8kMIPvKnx48IAGxgVJDhhv/EuiP93j6GPt1qxDssLN7k4Fpdj5CFtsp - tlVZZkTxKh7G1GlpRq7WwDubY+/cQ0BQkLumtDPOIdDua4X1GHpgrn+/Fmzvk2pofZpLdRDfMChE - RA/O4nrz7aJkYJjIkf5bL9MH8rA7HxB2D5eWTbrKKuBxvw5Hy/ftzalg+tAMuCZgpHk183CyfBBI - mkKjB3eIhekEM/Bj6kiDT3RoxI0fVGHdzWQ5/PRcTAq3hWblBTgQf7onvGYSQoX8HIzb4OMxqzob - MBa9ayD0QuLN4mMqgC8GU6CKtjSsRn5WkR1ctnu0A3mgLC0TqCeBQMvHcB9mYb9UsL48HtSljdzM - 05kT4VlM5Y3fpXylN7eAt+Mc4jN46p7wJtUIAtnqqJ9dyND93pcCcRVc6XVX62yxlXsHN/0dcKn/ - Y6ywg+Dv/+nz9FLidficXaQJhoexymfm0AiLirrcRXR/IyFb/YG5QKsYxt4jXbz1i+v1z69hHWd8 - zrCSOlDloj4QrK8HxKfJu+r9/PWoXr6Ugbjqp4bBQx2p9vXqZjkHuoy29/vn19hSo9iGrmHdsZ0N - AljepCLQCewd1cPTgbFl2w1Cfiqgf3jHrg2O1OVqcsFb71uwZsc3ByexZVhHVTcslzxK//wF6cp3 - GrNk+DigjIT9P7xfib5bAf/qPXrA5uTN32Ze4WsP+kBR0Cle4aVOwXsf//CeKh9zud/nCmk6MTe8 - lT0mEZ+oI/BP2PaEMV9uXS3DON/d6enLr/mm913469cnxafXth7cpkdZc/FonB3Ow2qfIwdQuSDU - 7gzavMNpqEA+/XSME9Hy5sTxW5RMhRjQZrBNSfqhVbXerz3e+HmYJ+68AhSlO5wmbB8LgbZkaKg/ - Gd7yh2GG2juFdnvXcJIduXiU7GsA96kM8ZFlQc7vU3ABvNjkWJena85WmKeq7DpJoMbfJV/HwFHh - UH8zah3ROR8OUHr/0x9O9dmx9XCfDSQJIMMYHvfeHMCHDED2tmiI1p25xG7SA6wdbWyzw4et0+uj - wqMkP3DypNafv0uRvpQVGWTbiqVWHet/+KlfUZwz2T108LGOLtWEJz/Ms1dfIPnJIEBCcQDdxudg - 9NkVB5IkeGRAJwumUo6xP420GerqG8Cew3UAI93ymF6Xb6gYN0qPl/zZLF1h2mh/hwO+6fkNCJfb - dkvDABQib/nEuli6i1JyNrBLm7RZDSLKMPjsXHzgbtd4fladAWsypIEaZhZbleeeh/HblMiu6sqG - fHG/wleNrQ3/z1unyGmG9Jm8iALx3FBl3V2ga7oOPgA+iRe5frewQ/eE6o25eBPd6VBN7ljFh46b - zKWoVwuSZXfHRqKEjOnB8oaf2RUJ2PzhfDHPEP3p+yi6KfkqLE0Jvz8Y4xJ3Tby+OTNE5CyW9CC2 - dTxy02Co6tUpsZV/2mG2P50D+6eT4MNyvg1zjew3NO/uc8MD1ZsNTuCgVbZ7al+cm/mXl4FJeFn4 - cNyVntBrgwqjsOWxMfIzW9pbWMIzSbIALPzLY3/+7c+f7C35MEzlMxAh+rz9P38br2yUeYCO4brp - n8/ANvz/tx4/eDRM8tPkDFbXmWKz0wzGJiREysH53QPp93c659w5aPMPW/6iD8vucSvgIPIJDVu1 - ZX94gJ5pOODjNYgA+Vx3EbyvixCUzbky51xBPLAvcR0AyRC8jW81NVS4BnupeB/mX3WqYO6lHT51 - 8qth/m7s1INkn/H9WV3ZetOGDMRvXcL6wzgyIQZOBc65d8LYLE1vudy8EvTfgMenw5du/L526NK/ - fLqXQMzWqD+HsP6tDtZlbjGpsL9q8E2f72B3seR4tqCcgnldPkQ6FDtzautbC43wNdJNPzfUVVUN - PG7lEx/e0zn/wze4+LsKB1a0xMuy+/SQtreQiK3umPT7HQK46WG85Z/D6GtHHx2b34vacfvMtx5K - /Z9+w3/5zFx/sh6m1uFA1p4dc77iTwa8XN4nfBEWI+dNrKxAPATjP36fnt9x66UgQ3q8z30+/uk1 - FfngTy+bsxwAG+wp6APp7d3BcEy251/VK9ZOcBf/3MGAaBVXDuu9822WmhcCCKrGIkC+aaYg1JqM - wnZJsJshc/jnh+PKT+ijS1xvOb9aGYX794j1tFq3e+LdEsifVMexM6em0LyADN3pYuLL5l+XI+eH - sDkqW8fDpfXmcFGLf/ybm/41356HAMu9HQnyZM+U9peDivTR47EF1l+z1IrvwPvKBFKRQWFrsTuW - cC9uFWz68hhZ4lsAZS/YB7PtjTm5wUuGim0H8wHK+4b84C+Df37FvtxXMKOK8+E35A80PZdqsxBi - +xDzFaL4cuW9jT9V1MmVjR0zt3I29WkI8uv7TU8Reg1bXlyhVuy9YOcbx2b+vYsC0OJ4CTj6doeV - 5vcMLE+Lo+fm4rP1J2clOKjKSo1EmcEYZbUPHVa3VJ9hHa+veV2hlpKFusgj3iy5ows8uwjpc33h - Zna3Ci1tryG2jqMwdCJ5BfAslV/CmU+09XB89rAVOw+fAHHZn56Df3ra8cO2GXKS+aBQDwkN3Pcl - FtpYr9EXz7d/fmPVgWb85Sv4pF/9XJraYwISbHKki84nQBdcrFA8Xtwtf+u8vzwMTmY40H3JvrHY - cgSqZUi/1FBU0gzhQ+Zg7pc3rOUG8mbfSh3Qh75N09E/ASYnfAnt8bbDtnD/mSPPCyOIgFhSY38G - w0p0aQaHCVpU751DMz5T14H0gc0Ake5kihdqysrpm9/o9vnmX54EAnNc8D0a4i1PFWaw6SHslx83 - XhRWJdBzzwthWTrFcyXnHIAXFQTc+cBiulZhBZPhlm7+ux3WS9tqsL51PXYDvjT/8llwWr4xNjsE - TJaTKIBBI4zBXHQNWAP/pMGoXyk93sqvx/pGgnB/5waKvx7K6e8gWFBINIMe+dOLieXoONBMrRkb - bX0aJOf4LqAVPxJ6qq91M7u3ewG+g7Zddc5FA6PUlQFbvTfV9B/PWP072bDIDZdw0vXRLOco5aF5 - AQ02RVNmTDP8Fe5r84jtC+jj9SbvC7Br05Ta3nse1oTSFn6Dsdv4VogJt5cNeLBfAOvvg5YL3HEt - kdx5jPCPa5Svu1bn0T3IEfaO9BKL6ncYwZbHbHhReUuwdxJYlbOGy5uTNQt6uQT6qfD+449h1mw5 - gFgPBGoM14otV/AO0CzAleZNz5u9fnMhLJGpYV3BI5uhNqaQXdwDxs3QeuRt/3yI8iyk9hcXJrlK - vxUSbaY0tk+KOZbe9QLF7+1LUEGL7ST3zvirLwS7D/rl4+oHBqRK/aDW2TgwZkivCMngpgW/bbzo - 03I6aJ1v8uaHBvb5yycSUn+oNmtvMPejelH/8nUnqY+NxF2zFnLJKpLzI943krTGHeTLEpFl81NU - WuOtZ4B7wPp2Dd2q2wcfsfX4pnogF2wWXK34ez9Uqw9sqLrbt4ODdtWpr77OA+Mec6E80ioIXpZ0 - HUZ7phE4SNYZZ7rRAPLmzAj96I+S0xVv+sirRFRdV7rlMZ+B8RX0oc09HwHf57O3Kl4nwm294D98 - m8Sl5BT9sr+RZXXFmCW8aUCyk1R8zCIdiI6qi0jtdruA4zLenIt6ev/lG1hHi5ULMB1FoF8ON2q8 - Sm7oub2s/elB7PUnZs6Taogyx6ytfic3zdoXIPzLX4jigy5fJxIRuOlvmj6+lbmcg6MMC0ceMVZa - 2xxzI57hctBem/79eIvl5wZU5/SINYd48b/5tuVRWz7Cm+N1PHZquG/HQHpPS/4PD6xjnAW7Bups - ub93PTjtHjw1hAOIF3i5bvyUmDS4OWrzmb/LiFYyVvg49TVb1Fj1ocQWiN35+2XMqu4aMNTpS4P3 - HJoLr5wdtBeCJBBZqg+LFf4gpHJJsNMXhsm3eaQifSkqfN1pfczSYl3/vi9YNr+/AN7XQFvFC3Z/ - 2h3MWX1UoX29nYMWZHPOHnmqqpu+2PKtoVm236e6QvaiuA325nqVXjPkDwXe6hvFQLxsl/7l8dTO - hiubcyNfoSYxRJ3PQcoJ0XczKL5IJtw5pZtL2o/QDYCHXeQFpnA/Zz3kKm7FDgVnsG75I9xdIpXE - 4oVvFv58cuEkkzNZZA9t72cN0Of4q/DeVQhbn7vJgePOJPiYcVw8tzcTovmyOtRJm8WkcjRV//K/ - 7X0Cdsw6X/nTD3951Pznn/7qTTxB32GiKtDgoKczvhqSZkr+dpnljisF6ig6ZuyK+xAqRdMTqUb7 - YVn9rIKsP9jB8rnyMWmETwpdDFWcu/WhWW5dr4LyDRvs3Czd49uAWHCr9/zT26J+czkoTSKkmvPA - DU+HtIDZy5+IUsW/mDmRkcC9emixvYNf8GLdI4Oy5+/pXQ1+Hnvepg4KES/gPBEib9m/bQjA682o - 0dZTs+bJfYX3YzXRP38xlzHz0W6IO9LhR+zN+GjMYONjnNkrjifzq/rw7rYF9QLWxHNpSAXUT3ZH - vsG65Gy8nlp109/4BHb2wKdK5oDAWiusXaNlGNlDnxFysIn3a2Ft9YAigX/4H5/4rzfrQwjRoQEW - Nrf6uWiCYYTi4zjQTf8DaZuvqES6Rv/86Dtc1BJu/hVrQZkAsdgdC7it72Dc8kEii4kN2/vZoMaD - ++bz9auO/8+OAoH/31sKALllQXsUsmZUOr0EVH+cgnk/B7HIHZ4BlLrvmQbjZ2bsvE854EjvDjv1 - 6DW8M8gu6raLF+2zag1T8uY64H2rIzb4iWdMe19TKM+1iXEMkbmKsV2oWUq2i8aV2WPq6Vyhp35v - qNsIr3y5cLIFG3LHOHguZ0Dpl5+hkMxnnEYv2WNU7VyoefcDLXLvAWafQBneHi6jp8tLBIvahTag - 5u+Cg3AZPVqGfID4SefI0jQ3c2Q1JbC59zk2erbmbPgcVNRJ+YXMLgTxqp4MFZlauqf2bv8D6yiN - PWhZdqLxak7NHIduBm1fv+LTA/tet2uqCCnqqBD+/rS81VJEGXowRLjMPhYQW0tM0Dd9fekhU0gz - H14oUDNR/VDfq8dmkT6Nj7BtUOyuYGbLXlVngHjhTKPbZIBZe0MNtfJRp2EBDoAfdSOD7iMPsVaA - A2PKtxbRY3ir9PE132Dkm9UH+zl/k7s45AMfAtjC+Jb2NC2tHaAn+RxAd8BHrL890+TzdfXh5LBt - 03Bfm7OadG/47keM74ohbpvmmIFeTePhfV525tINFY/A/d3iA1a2yIqbbXRK5gK7v14BTaCAFh3A - uaMufyg9/m98oHtd6cm2okFslKONHtMWBVCvA+vXdkTgHU4v6sRfMLBS0ApURc8bUcZYb8SjcCVI - LNMMl+CTDKKoeQEsOV6gj4ObmFKj0DfMjeqG7eEWDLxIThr4dVZNw15wTTFtaQf5kdPog0ffnCjl - MMP7vvSoc5J9wJeZGaLLFVEaHsUbWB92tgKPYYWwr3kY+KlVNBh8uiM9dXg1p192E1Xp3CXYbWeU - z4EHXPQ3v/eq/4v512d7f0/rQW/TrjQXP6YjbNWbE0jr98em4jZtbaDhEV/y5TKwXgxVdE81mV47 - wQHCJHc1pGL1o8/T29zaFMoWhA17BKOtvOL59elaiDxdo1Hu6uZqxHKG6EtiRFRUGPe12xswVcQ7 - 1W4HwgjjPyLCsmoR/nSZB+J/nr6qd/aEjdx9mUwn6QX+1AFj+8nbTDCvvgP8XSXT4pucY16SnyI0 - bzcNB4Z0HFbZ6ypo334HrGfqAJYHijtYhXFIFPi8DIuucSmM8EOmxzorBwEvc4bKrctWaUi/YflU - ZoWMef/Adqr2g8g3PgfTo+dRTYhIPH/Tlofnzr3huziAYS3gc4bPTDjhgxRePZ53ygx+wGwG80Tt - hq+kYwn79XzHxxtSc1ZFyRs9u5+NT9kVD6KbWxmao0bHOtHyXAh/Tx5+d4tLtZpWDd3mG9w7LMMa - 0Lc23sa4QrSV8OMIO2DVoo8MvaueEaAJcc4rSp8AlHQr9l7nhv17vn/zM6pURmSvq+GT1iaNuUD3 - +LBbEtRZpU7gkUcx45t4a3T5tYLfD7Zg/r6eoarvaw1bu4PeiHPlBKDoLjtsKbHDxt9dSdB38h84 - OjKtWS+hVEHA9UYAr7sZ8NpRSxCQ2D4A8y2OpQxYBhqMVsc6x6smyZ46gWd6iPD5/LuwufkaHMy/ - w4KdDQ/YyStqmHDwQs9vz/SkUWtm5OXyjd6fz3pgEW5WdHsfKMa3O8gnuG991IsQ45u+a/MpsksL - 3A9dtD1/Bta3b6VwpNc3xQ685ezUypd//KFPj/vWNvzmox2f+jR/e1q8WOYQwbwiIpFvRxPwX8hs - 9E5PFT3vbjmTvmw0YLruCrJff/ogVNPVRX/r6+Dv/PhvfSpndbLoNdVdb1EtT1RfIVdi7aUv8RL+ - niLs8WVP49R8MbbcuBDCvsuwPt9Eb97tYg7e48gi3+Vlx+Jy+/ZQ9MCA9zGocpEuLxuKzRHTp9hz - 8TuV0QoyVz3hvVxm3uhOUQXBCfJ4W79MWvtfJ7361KFxdl1z9mx2BXCT8krx4dTFRD+iEtZrJlP9 - 2Hj5/F6uM1A7kNINTxuGUATRd9lF+OSFz2aRvbOMmvB7o3pF3oCFycIjxwZZIF74l7e81YsI7/vC - o/EtvwLpwRAHL0+DkTeCF0/ypGCEV0DuRF+7rzmnMprh1ZEssru8j96M0NIidswfATPdlM0HdNbU - 7PnTqb+wa9OdLkOrOitY6WG1HE/8nNoVqevVxPuT/vbm/u07YN8iJRjN3Zv97Nuiod0zoQFQyQlI - 9OkncH+3DXzy81PMhqkroSHCDEeIG8CkiU2F9lL0Ivz4iRsJ9fQNGSkUGjn7JV5G91hCh7hbm6zq - 561elEOoMAHiWxKaMY/qxwwfgvHB+5dQg3XSpRqkCbJo7tp93AsO69CPjRecKtLHE4S3w8PT73ak - sTvqHq9N7wLl1SjikJbXXHBqU4TPWI5o1p7yRmquXgn8QOoDLpWEYV4HLVL5c4KDaamOnsgGIYPy - Wz7TpNjauOUB3rYQCA49fL8mW8+/toR//ImJHnvrUbiOKLjeXtjwCRzY2+xbeNeWKw5l3wUrNzQF - fAytig+quzQbXrxBBNKI7rWYMLbLzsEff2G/D09A1PWTAcxqHTC+z6L3fZv9G9ZUdOkNvvxYPMBm - RUQ2a/rH78vHoiPUg8GgGomceEkK2weHTDGow4+3pvZlt1Wq++WCk/KnxEvU1xdYhecwqARhNslq - 2zPscbLHGn5/zWGMvwbsc13A6X5t8rnxqANOPASBuvvZniBKvg2Hew2pYZzlmAliKaqAXDOyoP2Q - L6EFRnhm85He8YV6DDLNQYGaoqAbUD2wT/Lh4TE7XmlwDKx80zNvmMPLFx+e9Z1Rn5QhRI6oYOOb - 8OaUSLUGo/Ua4L0WB2zC5jlBR9PPsBHvJXPdbSWskhMFql3vz2EuXvcEkZBt19ac9jkfvYYQbPyJ - rfQiNounvQuoVcWbXvPS8RiwXhbid/VExtp952ztXx2SpvBAFpOuMTHouLWlTNpg0aJLTgtIUvCn - Hx437gJW9eTK4POUftj/WBrj3ezrQrFCDnYvIvCoWqoZpA+LCyLjnMZLl6IS5qQi1LA65s3G48OB - oEw/9NaFNF4qR9Gg9D6Z9DhDHxBX7AIQe7NM7c9vFy85UEKQasaR7JBtestxTHq47NMEH39bm/C2 - 2i7+Nd0PduN+AstEZRsWMv0E34h9vNXy+wTuT0VI73rmM8kvUAafmXTC9qu5suX0qDu4NG+B2nu6 - mGvUzxCVzcmiz/3z7v3NP1jrtMfHw3LYDmmJGtzP9zc1aIEGdhlfEEVnriP8Com3Jsd7C7ffS13L - gDlbLGZAm1N78jef61TJNfVPDyEsbwHs80hgsXvXhD1fz5ztn9AHQ/iUg+uGT4vRfgz4PI8ejm5T - DRbzAVvAy8NM7a865+MDzyVsK6XcTnsL8SR9Bh9y2tOkATzVA0O2V/zxLXZJ9GkG85LIcJfEA1Hb - +RHPCClvaMWdRtOP2cbLyt98WL4MRB0ROY20sMwG0bu5Yt21T94ceMyBkLf21Gu0aGD3eQn+4V08 - StWwXrO1RPs8OlJjTTKTVdOaqU9amdhN3m4+4+QEwcGtCvo3XpNMRQPGH7+hafzNm17SpzcIf0NI - jWkKAOszZMFdD184KW4Oo3/PQxPHxOemuXnCybC3NphkIDvyvrD5b/5bATrSjd9jVtZXG5AAnwO1 - SM1YWFYoQqC5MJCNEHiL/burcPiBGrvOwc4l920WyBVFgI2Qtc34cq0VFufUwgH3ZB7d9ARgd7In - wlLy3tpWkQP4XTXRTf8M85c3+j+9sc0fxVumSxHC0PryRBbwPV65mhnIWZUVayTqckKXl4WuUUjx - xWpBPt9VJ4KvLtCp01XvYV37WEP//M8Fb7LnedIgfhb3YLlodCCZOakw8a3d1vFiZfOfXjPa34oN - 5Vo1y/5ZcnDGXEwD+/JlS3w/lvBTuRFZ/McXbPNLA4HWTsFaTw5YykNbwVqfesKZmZcLfvwl8IqJ - TqRHeIzX2q0NSDj5SS8rDMz5uXXFtbIWkN8NqTFRryCBk1I8sGbEs7fIlDPA/XwEdL8DqcmSYxXA - 7zN90xK3ZbwEvr6C3W66Bvz72XvsvA+5f+8jro5Dvnw6BmE8i1eSBjlpZsWfOlgU9ET3r+hlLvHo - E6DV/UDWuEtZ91btQBm8yQyWilhs+SKdwA+CLn4s1dHkPyAdYUyuH+zljx7MsudFUA5POREo/Zi/ - nUB7EN7shp48rHni8H5l6G9+6p9b6M1F8OHAsqSExlgmHgstuwDpioqgSOp1WMLT6AJ/hF7AWaYI - piKALcIZayj+gWPDe8cgBJv+wfvmvQwrTU7hn7/DRblXY5L/LjVK80nGgRLbnrTxJ3Sf3xv2uZcL - 5hc5G+jV+Tq29CPyVnn2O1jEbw8/Hyc+ntJLFUGlc3yMv/rJW15EquGVlwqKt/XBIjzMsMLyD2uW - 8TQXPjup0G/3AVHDEwZr2n472FagpHu3/+XTH976jXLAJz0/5P/w7TM8c7w/9zjfPr9Cu2xQSLH5 - u6UL9waSaBngg15Yw/K+zgSM6i4JBE2cG7o7dzN8Z/dvgAK38Da9ncEv3kNsO7YH5qcgQqhubVnD - Tf+S587q4WloQnrqq8YcIxsXarFr6z98axZFkTmIKuuBb1hxBpF8WxfIQiTRfVsGJlMTnQN/ek0v - fhlj1/xoq8fbLsAeJQc2N97XhWVZYrrp80Yow5IDp8o/0Dt88s3MhVCFiJfOwXyKE6/b9D003o+B - 1JM7mf/4fvOz2DG5Pt7mZw8/l7oK+HJYzD9/rAaHV4evUjSy+XnOeHhv+h575e0bz/mqBvDHyCXY - OffrID2GfYW2PIMom79iwtGI1O+Con/4vmq7/g3V9WbSg3eGrL9mawHF6y3Buv0LWF9gz4cr3/8C - 9LleB6ouQvfHr9gTag0In2VMla/MY6on56pZAz9MN2L6BtxtcHKJqpWLpstdx9bpEjZ/3w+zk3Wh - zoaX1DREAzIYrdgO/WtMNFF1//kTf8uzRlPRV5B/fwvVirT1un7kW/Wn/nAgSJEPmKq2NvzKIg5g - H06M7AQuBGnUigTSQgVk0qUKxVx/ogfGEjbvf14I//SfmckXc33FUYFGXsvxfluvYzA/I4iSft3G - V2XrHhwzuPkbgr6tPfAwdSH406PmLzNyXrmWFty2d2JDJ2Izv8hdA337++LH/Vg3y4Z34C0mOvYM - rWKM3bUZycqwDywzC0z6UVcL/M03c6kGwFLPeKNk71rkZ44ob2myD2H/flbYhywf6NW5jrAXOYyx - JAxgaAwngE/bvge7Mn2x9WRcS/ixTjK1woWwdTie3upgzQ51e+GQL4mW1fDhBRccvD4uk84vv4c2 - J/fUj/d8/CoCvkXrJTTxkRhzTBeLaej7zN7Y2Pz2PBxX8c//Yf3BOzk/g6SHjrJSam7bj1a/IOtf - 3oKddr2aZMM7SMJRp6dbzjx6bZYKdVah09tHuQzdpTl1atnbesCer11MIzZrUObuChGcexozFoaX - Pz+IbXGnxb9AKS/w/IqzrekCiVeUVgFcTZQT5REsDYF7nkcEjimObMXypCQKe/TuCaanaD2A5ce9 - LXT0rwWRlYsZS6J2FpHfHoJtfDS25ZMXuJvcDh/bZ9fMqin4MPnU/b/1s6oL1WCdpBXNdr/Wo30z - Var45ff0bl2OMUuIlaAtb8SOmLVs+pycEP6y6EePbNn98RsH16RwaSweb+ZwvMv9H14Eh+Vl5wJ/ - fpRwd64lfLCUGtBzoGdQU9SAYhC5njBt12LIym9Pne9VZNl5714gK+2Y8DfnmLOaXlakXz8cPj6f - xsCUby8CQ3FfRFakvbdyNdDg398N38vAvP+ZIThfhw6bn2saM0vd1zA6w46mqRyxZSSfVrXc4Eo9 - T1BMsl9D+C8fOXFLZTIA5hb55ySj5vO1y9dmTntFpeuVWgN0TLYHqQFiWNj40gcpW18S16r0Ge/J - BZ4+8XoVW/cfH+9U2sVMfqTGdkgxwvpHPQP2sF/p1hz/TO2522/+gLrQsZVs04+TORSVSoCLFhHr - jSZs/LGr/vKcf3i25XsB/MtPjLiTwXIvgS1v4xdws0FNfoiaCAxXl9CNL9jysb4EcGocY+NjNcM8 - 0V8J4k/QUFtrLG+ZHj6B23zCpz++yavRAtXNOdFiVRSPPZjAqdd1uJLl27081jyYDD+eJVA94Y1Y - +FiUqEGm3eh1KS/manpPHkgZS/F+BlE+vdw8he/WvOPDubs1a1xrFdjWf/C7ZXKz3L9DDS96AMhP - Eo9mHzyUrURVZzQY4Cf/y7vhn3/d8ghzNhV9VpvDccTBlsdNIsl7+MdHwWxQb020lwP37U6h/leJ - vXXIZQi2fPC/+dKQ94kKxcgnnGUmTPjLbzf9EcC9t3rLn17e8hCi/O4fc+WGoYDvfcdolCYkZ7W8 - D8C48BfsIBQMhKr3GkrnPiGSFF7NCf7UAqiPuMX2qorxDFOX+5dfBvL8jpfgogbqq2gxAbHaeH/1 - CDWR9zcya+9XvtyI8gaj67s4yX3gbfmQikr26akzHTGYWU1HcIbAw1qme7GYfE4RcHX2JLtrPw6z - +e1UcFWqCIebn1+TouDB5yn8iPouRzDfVS2Ef/r7D29Jor1c5MsD+MtLh4GaaQe37wtEcVf95Xch - 5O7ytOm/vbmcoefDYyl6gcwfr4ANb9mAX3NmNK9IvW2BXSo4Rb5Kt/qDN3+sY/CX5+JE0p5s+csL - HkrGAo7Elbn84YMUi49A3OYjuX9X908/48umD5brGaTwtU9G6n++xsB/LD1QTI8GgSoIUzzrGl0B - 3Lcc4Z9BxFZL4VRozIcH/cufx/KgGWDLs4I20YhJ7n55gc+22AXK5nf5wHQJFCRHIuoX9Gzd8lko - 2cabGiel99ZeNCJ4vRQi9q1uzpcYTW9wBeMdP5Q2aiaO0wL0lwcgDj9A13jUVSKnRkQKCPDIHz5t - eIAPZeKDcQZlDzmOO/5bj6wUnEJucrHGntHUzUyfPQfb40/9p2clfocjuOUReJ/qvfl7qxceRnSt - iLiqYv5vPW35E3U+ph0zhDIOZDa+4RO46GxFyErgMfOuwRye1uZv/NGfnlI3/lyta7SisNRL+rTi - 0VtaFsxQV4eMoFF+xKtBR/svD8NGuUfeLET3BHCHi4YLZ08auuV5AHZlTETQDQPZ8jDgPu4hNf/8 - rnB+W0CoUxObL1n1yLId8dr8InUqDsbL9ehC0N0MQgNnqBsGb+8ELNnRxdj4wKaLeVL95RvYEu9j - vOan7QjAAEV6KJMRLF7hhEp84naBuuUr63GcQniX7yU+XCe94XkhttEPCgl+8Nm+Eb77NUBN+Llh - N5yXgebBIVFUp17/jS+T4ocNDy+JC+TnSr3li47kzw+QjqvCmL2Kew1m4q70mF2uJrNv5gyJIOWk - PrJqWIOrw8OtXkj9VJniFaxKDU/cAW163Bj4FzlryPkGxn/zgt9duUBZKjh8fvBOLP7lHfpHbrGu - egH4xx83sS0DYf29mvUPb666fNzqb5zXbPkCurlvG3tvf8fWmgYOfMxXip1wPcai/asqtNXLyMq9 - pqYDp9lBO3PksPOV5YEGxtuCfiD02FTMeFi06BbBS1eW+NgF0jB2Q+Sji3k3cNxWe/YJLTaiY/PR - qK4/Xmy9jPkFTPkVYv82VeY8eY8AdFVxxyfrarJ1tYNZveb3BZ9ghb35fa1tuFfKDrv4/c7nTS/B - HZ/5f3ornqm/XVsvNE/qwbplf/rzX/6dbHpJaG+xDb3PdiQPX7C30JMdwEvDOhrUzjgsltmESDPC - PQ7TGJq/ylkM+KPmI+B3h1cz45LL4E8Td0QsbamZ5dKuUGh9+EDY+IVxfF1By/Wv+KLEHWP2KhWQ - GKpDPsG8svWZVZWyt3Ifn9iqDJN5tRz4HYwEu+49AX95MTiaQYatY3Dw1hPNHDhJYYKLye3zmW/U - AK4/TaKPDq/enOS7N8yGkFEHIdIseJlT9M0oIVB2w5w5w7lGsle0FEdz6C3EYClAeOEDSqmRS11t - OohrLin1CluMF/CU6r/n/cfPLOZcEfw/WwqE/72lQL/v9nR/V6qGKQ+DgPsiulSj1t7kZesXQdOL - I+rUaWoyKfQ42ErmJVh73owl5W636BzPhFojVbzOGFoHGAc5DrLG15m0TN4Mcqz2GJ/wzRQKPb1A - UBUONmBJ8lGG3gyzM3oQWDSTN14+rwr+0swm3IpBTJ1T8Yb34mlvpzynYf7m3QrTLVI9Z+o8rMJT - neGrEk2qP/0rWDwSqODiTS0+FikdZu107+HllrrUJfmbsTF62VDAFcXPxZzYxC1Oiz7AcgMS6q25 - vgp+RYMRldjUZjYswYetkPM/Rxr3k8/m/NqVcBdjIYB18fQWWScqPKyXGHsh2A1rOisceE3TDz8N - 7eLxpBBKdCQXnRbSuYmZ8z2ssG1lHWvKEsUMo7iAfpeesfG8UraONbIV6eNz9CE0lcce6+AjYz9D - WoyWkLPn7gXBozBHbN9DPp6FcNtIH6iEOlAsvdY6fH0Qyw814DXlNEjQSQw0nWKXejv4zhdP7UrI - Jecf9h97gwm/AxLVop+tAKEPD0h/5S7gpSsM4/3JyMUyVl0oXs2tRNtY8XpzNQ29vGJH2EmMwXze - JOx+7C1q+vhrzqcqJUjPuAN1wrIAYq7AbYuCHNHDRUoH8cIPK6q1XRJA8nBypj0eHfw8o5gIhwfO - +bIcLFi1e0i4LjnG0tzVJXI+NMI+Gr1c+KTHAAKjUmlWOkm8DmU+w6MDuWCxrwXgE/J24WU+dNTl - aMkWqckuUHnur/S+zQdylDQZ6jeBUGf31AdxrJEF+Sna06Ru9+ZS3e4jTNf5i/Xw6QBBm0cNhr6n - nZRrvgASHAMf3M5FRnXzYHqS8LtzyAPFHl/5W9tI7zMN4fOoPWjuzo7JOyis0DaegWSgh7dcqEOg - 34YqTte7novmL+uh/xgpvbSiDXj2GDmg7dyQBiWJTYKzow15V7vjQ0QEtkygrWF5EBqaXmsnX1y8 - ZijABiSCr2+U3Iwt3JVySubxWMTLTnipaCe/ZXrtnzvwPi1tAcP8yGEjN/uBlo/zBV0Wq8dP7Zgw - HkEWQWDUKj3pXNhI78wnELQywHpiX4ffKO19eP2+DVwmbtd8F1sy/n4fDZVljWdw/M6wqeOGqHgr - EZMClZAW9Qdb0pg0bFfnFXQbMSXE+FW5IPCurHrJYZP7FxKvomEWAJ/2D5wu39xb+OOFh9khBti6 - lnoueXu9RMLpnmKj1pxYeqxDAMf188P6w2T5rLSLjHr98CbT6WF5IuUfLhzOcRGgbT1Jc9eX6jYf - gvMpyoY2PiICP4WnYtuTSTNP1dKiQ7HDWD/Wg0nvFz+F3UslZHld00E6OCyCzqVF2DatuBGsUQ2A - ZQCNLPvwA9YQ3gnMT0tMzxw55HzzzFoonPsDWTWpN9fSuxBkyrgkPx41+SKcRAdkU6dRR+Z/8Qq9 - NAPDAFGw5p8qn9UhD5CX+necVee7x4zw5UPj1bJACop5kJprkyH8DHjs0DKNeStDLaT8gVDb60pz - RauRwHzru3/Gw2nos9vAQZ0NH3zaESlfNvyDr+J7od71IeRssXfaP3y9pCPwZisTWvQ8t12w+6JT - Psk6kSHH7BpbvjM3y61eClD5eYGNDrXeYtpaCJ3mC8i83WG9FVoruPuWDVHWyAXiclxSxGTVorfz - 4WjS8Pl2kTzyAzXjXZIvtjJzyKPLixZy92RiouwttYKlToS9WA/LR5hrNB5WngaSKcZLoYeJ/LQV - mxb+eomXLPBXuI0/Dmywb0S9iFV1l1wC6oqP+1BneWcD5zNFwYi9bz7vRoMgWdcEeq0uTiM0xmSA - TysV2HTtdy6lqVajd69G9PmKBJPei5cI2bVPsa75aj7Bt84jVA4Anz7h0qyf7ebCJIcmzZS0HuZP - UBvwmB3iYJmS1z/+QZYcmTT4ocRjE78QcHKaN91HsGZL9t1BeEUmCy63PTGXH3Hf0FSCim7ve1is - UfUh//rU9PBxD96SyyoPPicH0vvKa4N0m6IM5ZFhEP55i3N+IdcKUkxHIvmCyUTJHme48R3dl94H - LLvJL2GYexxh4WvMp3Try7+NL+kVZ/XYiywpvA5KGXzF2h+EbOBtdNeXD97Pez2f9194gdFLwtSK - zrHXgSOdYaYJTgCuj2vOh7hyEeRRQdYun+OFi+8R7Dmd4jPJUzZi7uOj+n0ysFNhoVl+n4+DSH4d - 6I1JF5Ott62vptrP1LcVwxOi3RKiaffi8TUao1iS1+INDyd4xvcixcMstN83rO7+lUay//M2PTMi - eD9+cLHxtbRM5tb18PahG14Pf3iFukoZ8b1c6mFpjI8G3Lsj0nLFeSySxOnhmZSY/M1/ASJBA5Zf - +NQJPu4gHLldCcGbvak7RkZM9LeSwPAjVETM7wkbHwxyf+sf3yr5BegOBiPUHX7Ge59VOXsRJYPf - cOLx/WZww1JzZoeIixOydLXNeLzd7Fu7dR1w5Dmw1fyUNTyOfk/aC4c9Sbw4Nny/bjY+mq8kn50Y - dzB4ZxqNRV+LRdbtVbTxCVGz1AZ9x5wtAmkwxc1xjZlXwk45qPKDbvrMW3/Obrs5LyzxHb1ObD5f - 9AJ9dnkbNIfcA8uXYw7k9QLjcJASMOytOwfNK+RxPOB6YLfDqYfod33gTT96PHp6HPxmsUagVVFz - ha9DAcXWy6kzSd4wv2bPgXs73lHHcg0gNp9dCs81GwMF2B+wdkwr0JNJEVmMz5SvT9GY4c2eZaxp - QtuMBx5YcBaiDrv5jg1kkAMI7EAm2I9Phbl0v1eE/vhnT5shXpyqhuBcLyP1qlHOJ43vXMTdwoFe - lob3WLiXOWU9MhgwzwPxGu2LFH7WSqP3+yR5ix2/fbR+vowwAVKwPrnKR2bVJ9hNbL1Zt98Pbjyx - sSe28TDWSJdhI1ZneuD4Baze9VGAC9lF2NuPsGEOeyXIHaIT1rtIzBlvdAYUlfuBGhv+T/K+Mf6t - zxmvljcjWXIhf9dvNKWkY3OpsRn21FioY9qSNxVNBZXkQK/0IA80HvvqG8INv0jh/s7DXAc3Ah/k - aOJyJy3x3E+aiP7wzHvKD295RnIGG3aXqc0NN7B0jwOBP/l1pw/0ubAVn2gNlbSv8fHd82ys0VFW - 9vVBpC7qD2yd96GFvl5k0OiE3FwqCbXA/pil+Ji/LzHd+AtJXl1SR8cKWBFeRjSdzi42jsPek3TH - m0EXrnf6h7/LQeD5P71D2v0piRd/p6/gsg8Z9rKXxmZ+J7RwUBEI5ivKByZ+HiLE5WmlRq11+Xx8 - uzXMG+eGL4f3Hgicy0Q4mZSjB1FL2Fr/QA2d5gPw/SneGyaJ4QhXX77SO9E+w5rHXx4qt18VSBNX - e+yGGwtJOooC9WyIJuufj0p9xM+aep+5Ydv386BhuUzg6eKa/GlpSyAjpGHdltZhG18NFgozqPby - uYHYZuiD43DqaO7/snzt72oJ+8/zs/X9TJp1FyYqKPVsDk6nx9tc9IgnsG/LL3Xer4z9wLOq//wH - jRY8xDPkryIQV+5CbVdf8knr7Dfk5p2HNZDuzKec/lJIh6bBARdag3A9XUOI1+JFRHfbgqcrxwLc - H4VDc7zdPN8+xggI9VrQQ5X45p/eha5jvAj3tt7ebGyn0E9iseLTZz3kwtSHM/KefYCPcCebY7/d - VFk/5gA7hq556939BNCzuIkGR6vL18PuosGxeQ8BN9opYKl6dlGNwIsWvBzmS5/KI2x+NKWetaeA - /vHbNt5/4w/WcnkVaAGrhq8vcM2lLolUWMFC3051T95yOZ9T1LfFl56VHrDPM5JT2IHPCWPhyszl - Gc0ZAlXp0HNiC8282wsc+lsfzifWTZEr5hJm9zyiWhu6HpmfXgQSLphIs/ElQ9fLDJOm8XFAdX0Q - lSkZoUOjkuzuoZbzPyNbQZbXvwBpN5Mtvf90IXOkJ9UlHAOqHmQeCsuS/dPXo4sQgR1sMfamNR1m - R7mkcIzKhG5/b5YBHBMYsIyjwaa/1zDxOSh9kyvWzsN2syn/ECH1shCfuIM7MOUevOE1vl4oxl1v - rolystReekPqzFoQr/KS8epnd2/pYS+3Jj1QTYM+FW1qVV7w7/NBLqYmfhTcGC+t+BhhB74n7N7F - xVsvUewCR+E0Ih/U1fs4VQ/h/YgzejKqNl95RVvRqZgjejNPp4bsdrqNll/3o8mQaZ7wEeQKRtqo - kl0zON7a+YzAhU88asvac2AuHDjk+21GDwZC5vxgPIRT3QX44qe6tz0vD9j7l+DC/S3NL/1GK/pV - 3Jmgv/l4PmMLbs/751eGcbnUCeSbUQm4Szzk81ikoTpHBsQBKst4EXhXhce4r4i8HkPwH9LOZGlZ - WAnDF8RC5oQlk8hkovAhuANxAFTGBMjVn8L/LM/uLKmySs3wdr9Phw65Tbr/4wFU7/VrTUgUVuAt - aQs2yndnsepsOr/1iuZZS4slvu/5H9+gKbCrYhWUiQNKfbHJ8rh9ivklPz+QGpK96SFfzH9XDGFG - nyqin4tei9xfbP7iPz6eqVes0f4v/30eveWDaE22M92A3JxeWK/f56EjOpZVh0UzeW1HxOni7ExZ - LL+nTW+igZ6b4x3almzScqRva0FvMIP8vCupFQRFPHPfV65t+SfGl/cB8LP1vUFqig5FwbFnc5Ur - Edzyb2woPQBdYkviz99hK9r6TP/WY5HdBaIpMa03HvEEXF+l1AyihTEf1hxomWFhM3Qmq9VkyYWf - j2qgpmqMmndTa9a8wk7wvdNEaw5xKULP5ThEtW8Wr+9Xm0PdOFr4/Lg5sfBcQhk+umDGGOpBzc8d - DOVWKUWyVrpbiLZ4zrQyLiskXt4FGH0nRlCk4xdfz0MVryQKnzASbECPeRyDMTBTGzr9+EbbfLOF - a7463PSTwD6og+1w6B9M2WRjfYsvMz6YI9TUcqWosW1LPICHDEHcYbIEXgqEtepFaCUcT93OOtWk - OlQqlHbqg4i30hw+pSlFIHiuDQ705BnP4+0U/b4PUYfHVtt39gix0/bUd5IWDDqwITjmPcCGejO3 - e4SmHtpT88JIIYTR7i7/wXtU78kOt37Awv2n1TIV3Yg8JQlbHs6Fh+vJNWmxio3F/GqetW8WdOgi - V0K82geKYPlw93QfUL5eSWRv97JgDQ3YPBb85W/mwNTtDZyjVBuYds1kqPHmDqNzG4HVDSNefXTe - jG8cMqw5Lm0bCgvLsYf1jrGN/8HI9SnilFCxpvFayoD3zStGr+swsHKtQy0huoCaRxrHa1CqMkRC - leAQGBFb/RPv/n4fivmkthbRMAhkpXPc4lfz81u+2q3OZ9OP1uo1vBDlvVY6tYq7B+YqXyLt53/x - /Mos4d76ETyLRkatT5AGLE7PpratbwR5eY6XMj1kWnKTn9i8hlOx9Q7h4KV+hfgwH2wgrDarIFqv - cNNLJ+Cz5O6Dk/URsfPiOTBB4fEBfyw08Bbf4hk/BVlT03FPk71oDvPl44zwtTdD0jf1PZ4b3yVq - v/geeV8PasyepfiEPEoFbOxPe0Z586lru1kn2Bs9t/jHd0CJCrIofQGWB+JPPz3Fl769Ar6EUa6t - n49AQyH/BNPd0P7xVCRdPY3N6y7L4ePctDhdT4eYf+xLG278FxuP2yeetUcAQfeEZ4y9x3dYumCB - kFhzTUuqcUNfdewJIvStqD4Oq7V8X5aoGaN9w39foDFidXkLU2eVyRrwh5q/rM9I65J4omboHK3Z - fXQhkLOvhjZ9AfPt3FwgHIsz3h9KGi/Gdq+GZUnllr+5Ba/Vuq3tnfPuH39eA1CFv3hLuHQRCrbF - e22LX/jwlA3WT0NUgYtxqqhjlB8wK57fwPDPq5F3ndJgHWTEgS2+ovfjBAEpTekEL91uR60Huhbr - bdJdePHEke739i1eb5PrQjl7a9TLHD9edjjxf3pIDWTrQHQ/SAdCotU0PNvKQIc5y6Dc0z9sAijH - VEv+VqhDQ0J0Enf1Qq8tggbffhGYy7PFCqsQwZZfYCuxqqA/3fscLKE0/9uPS9t1ERya2qXBLkf1 - fJ5gK7UPdsEBNqeCjX7QwjBscvqIVVws9PpEmkjJl+qjBwsaev0MPLMKkBJH1BLtCUCQ8qNDz0Qj - 9bTFK6W07w22rsmhEOjZnIFT+QXdd9u9atVF82FppkdswIQvxiPnmurP72LhbQ1Czl8r9VGNGjUD - /jvMdl3o8M5dAJFso2UroUAETJwnWvRmZ5GXSCFYJepjT54iwKbUz+DRtBd85bU6nvataAPZMAXU - H6BQz75TIOjvFZ+Gntmw6XvIWniJcon64tQHy+S6Ljw4zxs1Ai9la1CuMnTvvvcbT2uVOBz94i92 - u7dTzKdlnqH6yt/Uns4EMBRNGciv14gG6CkNyygdQ1Du4ows63S3BAG2f/CcH19oElspWLd8HX54 - oP3XP3PSO4PNKVxoSi0lWHj6auC2H5CYpqbFdtAh8Ou5A7bTPQrWHw9fVsTjYJ6MWsxQd4F/DBnY - rPe7eKma0x1at+JLvlDjhxV5DgLSxFwENn7P1N67AWWQET4XvhHwdl2YUJfGgmbNKwcj//EzeOl3 - Bj407RKvG/+CZfOV0PnIzwWZjgOCr4v7wul0G+Ntfl049Y5FTeq0w0+PwGe68BRl77mexeQsamZr - feix7pr6W56iBm75LPYP+Q5M0FBCqFpliv08c9ibHU9Qm6yJI5XCBPDzIzD+Jm96fYhKzdqjdlN+ - erhvPkfAlNIn0GSjj4ukTGISgArB5HzUqHGM8nq+32sHktZtsPmJjjVr9GsFHSCqGN/uqrXxNFtz - ufi21R8OgfSbzzbFIeLs1bJE8+CbIO/rL/WGZLJWOkgE1u7XorpoVIz6PKrA5ofQRyJ5sM5wbbRw - pBPiaW9s/GU1YR7BB4Lj3xt8ehg44LVwGXWtdgQzzKsetLDB+Dg/+IHU5MaD5+cAsU1kUHfEfV+0 - 9LiN+kqP9bzuTplC8TTSg+8Hxbg9a+EnUvG/8cB1G/3mA1uv9Bb8/IW68XXCZZbK5mMKIhhx7QFH - D2k/iPCF7xA1mY7/ItYydjIgLw16cCDKO+w3v/nU4fB++jRe+r4eNt4EJA6U2N+d+ZrtKy+C2//F - OO3vbC5hnkFDjz5EbJfbxhtXBLPOOdDH3+1dfDLU/cGzdwipuYqZtS7fdgScdOWQoAVbV+FbjGBD - McFeKB3i9iYpNsDPxMc6F7cBW5rPBdxOTk1/vGGespsPHvo9RlBuH2CRkX7XLvBwIdpfbjC+VvYr - 4JRXQv2kGdgaz/5Nfe0EHfuzeQbjtv4Bud9t7O22rhs7oVPhxeNHsnLSqxhPwajDzbyiJVtQIFjF - 7g/mjc1jS6rh9lb/Gmn54QzwQyKOJWw8CKZVQ/HPb1M3WU34doiH3blqguVa33u46SP294z99MeH - RRUk6OVIUb2GuvWBRvyM6LkfunjAdXuCg0vPhLi8BKhLpxDyVyslYNv/Usd/898z9Yi+H/h15fjf - /kJK9YqsMS5DG5a+kuIHuCc1L12BD2UhEzGSD2Yw18pxBUGGrqRNqjYmr90+h2povJAyzYJFSnMX - QW7v/WEfvN4Wm9AIITp50TYfrrXVJ2/aPq8memDJPZ5/vDGw4US+ERHAkt21P7DVF7F3a/VgxtyE - 4Hy9L9g2n3m85bey9qG3AP/pyrEeu6bOtIHYfzTb8t/1xxt//Gqr5xbEtbQccOHXQ2odvsDUi60J - cm5O8CU9nOvZgp0OzPm93WuCk2E1GuUPvm+eSvch+3V53vkwLAmlwXnas238MkhN3qH3s6NZ6yyL - N0C/4QHnA7rUY6NRW938Kg3ipg7YRxdHbV/bMk7FaqzHrT4Ld/FRQHz/ucRrZZH1V1+hdvGt2Qyj - fajd+tVGirU4bO7MfAYn1UY4/SyMzadgNBVr18U/Pm1t/EGGH+7hYfuKTuCn/wAoeoS9XRYEkvCE - T3B7IRnNknMFLFe5P3j3OBXb5fwZ1vvS3WFIBHvjt89i/RvR369eQcRffer6Oevax+oTelzl7zA/ - vxOBm3+l3rbflx/fitC7wiEvz//ineZcmxNZhOxSs84wTAhcbaJW+Q3rbquXq//PkQLxfx8peDKk - UuOoOkxk+9RVv1C3cZC+zVogqUtgckI1RdbFqOlt0Xx4I4lKHWHdxewMDQSP9++b7KToj62S52dQ - pKZFKmdPLfbgwAne2yXHplyKFrsUYwseyoio7fThsMhi3WjOoY3pAeMqmCuQPWHZ2h11zqIdjOa6 - Xb0iZRm+ba+18V9pjNTEEDNqvhy7kJx94sJ4PGF68T8lYy2nRnAWlR77Xp0XXaFGF+hpKKChp3X1 - 8nc4V5qz+Du0jBEGa9G97tqo3Bm2H/Iz7pP4xGlfcHBxYNRbI8u3jECR5jqNBMeM10PIXJDNio36 - OgiKadmJHJiS6kTkuSkGdq98CAXhnOAsbB6DJAzFBXKdstDUCfN6bnriKuC9+xC1mEU2e6/7CZa7 - iGAHdEbBdg0wYW2F/X8AAAD//6Rdy5ayMBJ+IBZykxRL7nKToCDiDlBRFBEwAfL0c+h/lrObZZ8+ - bQupqu9SSYWWh0UvRcvsJnXcVoyWZWCXAt4dNWDjvSfseX8mk42Sp2r48okm5u0XjJ6uFFB5Y0vP - u025Xi2HFjh+fy22ihINY7k7P0FnJMbuJZfNxVa9HLoWf6PNPvWSruWCF6JCrWGruZmBFHOwgJbp - DhkENRjm0/VZq+lL2mCj/G5KBmdegfFU59iNSr2Zr7+EqA8WLrTo/GPJb5UrD9cgTbBvhO0gysbg - qMnJ8aO/z1twttfgLrN1vA7EJUuo/4Ir3XxwNDmnRhB8vVUL5fLBB/dhIDHNrCdY5YujJe/7yeQ9 - shiS7e+2ft8m4CN9VoCTEyCbAL2DHx/XssrtvZKeY5YOFOmNvFoMQLZL6DbMFZ6yOs6nhV6Kc84m - QclSIExOcNpd/UDSPT1Xn3qrks+dD5oFUdtB8r5wqPETIVhe3+6m3jZxiw/c9ttIFfVekNVhSeOT - EjLBm/VKvUwyYKvr8mReVLMAfNFtnB3EVyC8xV8L2YGP6cHd2QP/1jYRCuwZ0Ti6yyXdyycOttXd - wK6ia4mQKYUIhKQGPXNlMxBqjA7UWfXCHt782Fh+WAsZ93qTZ2MxNpzucaF6KrII+8htwnA3TCCQ - HSaredfQsPx1kAnTgJOnbZtSCDpRlddpS+/BmJmCWcgcGHpwJ/BIy3K2Zo9TNttwwp57Ccp1T86i - Cu3WpwlvHtk8tUsOlVE/aLaE1cD6GfWgKIKNywTdzd/r+tBU1fjx2DS/ZcmrrzBDt++Y0ZwWTiJu - hEFBcb03cdZsNmi5CFRG38Nrj7PpUpYT5YwWTrzLYRwroSnG+E1UrmzaaEOse7Bo9eGlCi3ysbf1 - NgFDeqOot5DTyKZwH0gotO0I2indU9zKl2baRbOheub9ivflZLCFNq4PG0f28cFMRjaVu3sNkWfu - o3OD64AfeTlEnfa9YO+dTyY7lD8XyFkUyU8lZFhSsW7V0znvaB6+NsP8FX8OWHWVYWeDLVOKqOnA - Yh9+2GDrdcWU7HJ59yoeUbfMiskG6jrAFk7CYTY/gyUbT0/V4vgaJ+a3TGbN3zn/3ldVuCck9huZ - g1ulW4Smp7qRBDv2VTltQ4pzZ27mj3POwDv4FBcsk5olud8LOE4Rwzv2VoZpb70roHH/pd4hX4L1 - +V0411FCrbdxGiaH5yP1txxbjJNdP0ycck0hPRcT1m/p05wPF+kI8z4XsTWLUSAialtoGzkStZwY - Jz+kGrnSh+OP7pT2gMbbbAMUI5dTXzkT9BDaWVH7jxCtOxR3A390Y6IarSbSy3v/YJIJ/igNG9hR - /55dhvmmbSPoDy+bVhr3ZEKyl0UYr5eMZg0/D+w98z76LZs71tb841vObFFepgzj3zZGywOcEHit - 83AFNYfYId+2ULi2/g//pGvXHyEcXg7O3Gsd8IdoMtTYClpq259rMNdnP4L6dn5i82KozU8xH5Oq - dvUOFyzwkn7p2gV9KZzpoToekOg6CQ/b5uvikxF/k0V+RhxQqjF6WeNzTma3gmvHnjT0XQnNZ4Zq - 2I2HO3XOhZgsUTWmUODEoSH3pOY8bzgAA2qDuq3+TL4X8xeD29IddV0O0NQWjFOHadDxjuhdOXd1 - asHltJTUkXjaTKU1AByjjx2hb7Puuj2rBXzAsKK5uMcl70ufGGjefLDW2pdAwi4egWXhg0Zv4g+i - Vr1F1RQNQib+KKCXpys5Gkpr9VoFB01aOblqensJ2FSrDAl35SCqUuG/sCUcnsOvjx8KAkM3cXmb - XXNuNHlU6+z2IpeUS5mAqjZV1/q74hdioxHNT/XiXFOivk7hILCzG8Ilak84ptaGTcR6gDrV/QN7 - SWGUEg5QBR75qNTfTTJa+KpO1TlRTHwu9L6UPGq2EN0+V1px5xrNsdnG6jvZJjg7YzfgVzxVGNZK - evSGAxMqxrR/9dG1cyVYrCbs4ZPhme4kDQfimBkG/G6ZT/2aGxGLoo+B4i6zqMESDf0U55oh99yZ - OK4fDluEnH+pT++KsP26vgKWR0ST0+VxWK/mMoeJvexUtQrZpuF9cpnwt97HaTtST+NENqtJcoO5 - 7i7Y0rqqFDfLlKmbyMHYsY1gmPdSHf/lE017QWTLDrdEPT7Ejgbm22qWYFF5kMvpQN2j/vqrJxZ4 - HnzJ1UQsWB5VnyHj3A047Xu3ET85Z8GDRQvea75R9lk4d2DvowH72sVOZt2kC8TBecChxj3Romxj - Xz1bEo02iiwNc6iUIkRumuL9SX0Mv+wchEDJTiJCgONgyd5iCFm3u5DtRtODBR2rCUpN4bEWkU/5 - b33v1U+IFiNWSybYsQt6RiXCpusUjK1y58F6eDhSjNZEP+H7u6ElffHYvBzC5tfUVQub9Hz5i++E - 7ThhgWPfP+ku7ZyE/MbSUNf3T7XNrQzE8vuoUHGaEhpJ6GPO2Ldf6h8ep5mQoxXPOhifmw/FaR2h - Jca/ESzzqhJlN+Wo7zcygEefW2xwtmcuTlLcgF3zljzl8R10zWxXcKtMax0DtbA5OwcR9N3k41LN - 62CezX4BL3jdqOMIIer3TpIhHk1fjFd8m7ffFwe6F3c01xcpmb1ZvynPtkrpXz4Q00lTNcrInRqO - TYNl35gdbJquxqFGGZrVyZz+4VElZV7DHx/XI1AYbVr8mqykGq4JqOGtp/a8R4glQyZC2H99mnbX - 3pze1pNTFa28RTIt2uS3xufW9WCOpIDfD5TO65aTm32m2XRB5RJORgyD3GzI/TWggLwbz4Vwds/0 - UrQ7JPnDnYc1XyJ5U7zKCf3uETjN9UfdV/QI2EWnOQwbbkc21sloprUeABimid1fngVkc0yP8O6L - e6TqajNMSSFwUCvFBmv1UUITs743GLEe4Gisj0jifemI/t5P5FKbzbh/dLAMjoYPs/JkzKahhewq - odTb5lEwH5Qkg8t1uBCEHlc0fT+KhaK31ePddUHrOXIM4Nd4oKECYzKiPUvVbz4dqbPTkDn99iCi - k87n1HlX1h/erIO0jzmpT4OKmDwr7ha5zzONnJ9QLjy3WUBBi06t7uqbgvGIY8i3mr2OZJqb8fyo - FpQ/6y098ddnsOSwiyBW5ZQeDrXX/OUjUg/2erX3KR3YzV4tfssVcX7J5WCUP1ML0hd3ePfYYzQ5 - eSD/4xvG/pA2y2OzBfRXf4t2Juasm58FIjdLqX9ZdslffUbyUy+jPgutQSydCw+biLnrmNBxWBqK - X+DlGR8Jnemwa3nhYjSXlkvXemf+MpE6SiyOmOZM+wbL5lgd0eEhThEqIEuWP/11kHdbmp0qh/XL - WNSQP59b6qh7hpjjaYr6pdw5WpT4XP7mJur+6b2YP56QkJ3NEDkn+0n/8I89jvGk3ix+Q1c+h4Zt - 9KxBjC8ijvT7ppmMRfdVg/ATTfyt3kzC2SHq437MiNIEQ0Kvw0kE9AsGGr69fTB78btAvnk/YUMe - 7UB6yWWFxkZucLau77QE7qLwivTD2CPtMN/qq4OIfu7JvB8kNL72qois+pZR+yT2AfVo8IKVL2Dz - GbNkGO2Dj8zg7VDT3+rDFO8lEb3MbUkjt87YfNPmCO4NNqhH2jdb4uRyU9z7Q8f25vspZ8VftyBr - vRcJ3n6DprU+Ikl6M+qt+DpWjzQG/p1coy68zMnyNn4O/E51TIA7a2jIu35S6i1xqP0VO5Pl1LdQ - 6RGCsTxcgq97DkeoXzwlCvhTOf36nwuptn3R6Jv+ylmb/Qre+86j4aPpy7mp0/affo4wCoJZZpPx - px+p8/mapXgPpw5tTvGJWqM5omXKVUdZ8T3a9DdIiBSonPrqliXiliQ1efkjt6Ds3g22ywJKqr9n - 8ldf8OoHJAKIjaOeQIdIFZ9eIOVRq0F3hztO3sMOUcN4Ov/0627VxyN52Tykb8fAwXJ2EQs5NVbi - eAvYfItiMMqvUAQ+2x6jrdN6iAKyaiRPZYudSF+Sf3g3HU4F3q2N6kmXuwpNYjjT24GMDXXecwj1 - S6TYKuJfSa3TLkOCninYDbR9IHa4CdV8CoNoKb+bhInctKjuuTfJPFgXNAnFcgPxkC7YVfP0v/m4 - 6pVo8Ofe/Ks3kG8Nm+4aXJvLVHU8OOPhQ2jh6mzmQ8EHZ3iRCP08Zi4rPvzzVy5CU5V9ZbSOuqQt - j/f37D2wgWqWevhSQg3jPiYMPfCCvmZkRKwadZPqnpfD5ou+WEdJi/7Fz2wnAjauh6UhhzEz0MW5 - pwTlnMNE8HQFNrNNcRCeqoZc840F6/NSH/w4EfdOmcIOPzz8pxfmW31ygLgNjVTNf5ZTpU853ELQ - cPbdn5i4BXOEP/1Znt/PZh5bRUFIKzOq/Zy4FA9jpqnTpSpptE2DcpE6cgSP8Bq+yqe+oUmwcGj1 - o4jqed9gnA+1g9D5UxLufS+HBbrfhNZ6QKb60aJZbCIX/v3sOWm5bIRBhpXvEt4QFDaH6i4H/m4k - //SsWEzugsLmalNNVyJG5LdC4PuRMiJoZo/m7S8mYNu7HhulHCWLNB4NOMj2FmuXmg4DeuAJge9b - 0UaufTQc8rmF/dtZ8P5ClmEKh1r88zP++S80tctOWfn9P75FDPwJ1W88HMik1kOznCYvRgg1Ig3s - vDWn27wH4Oz4Sl378zUZOxmZisnnRI327ibLNZcc1JiooY4gaoGQxK8cvsFOw/Zs7AdxjRe4DOmA - s7/4/9M7cdEt+K9+CnflwkMYfX6Em06vhnnHJIar5P2ofrNZQHBma//44HGuODSh2QBVoFpAM2HZ - lONHGmPlgeyJXMxASJa98XmBM7Qk4q9ibJJ9E3Sw6nnqCUtQsq9ccMCaYYoaag1sYfbdR2E/+Fj7 - w9eE+i2seIxvb9I3TOTkBYZy/mI9H2lAuQ9pwTu4FOM1XybK+S+1QFlDfs/nEPyi/TYDz+O+hJlf - VLK90BcQ7EOTGl5tDoJ0QUcQ1CLDdtilCZX5H0ErX6HmgItgTs3f9Je/1FQP3bAc3yL5W19qSfho - TsUcVUhbt/TuHnvKJvo4xmBPtwibsZkxdrrnObw2IBGh7paEGRx3VCyR3qm59GQYt5z+Uh+3c4Fd - xOuDNATyDQ5F19MLad/o17y0VI0vhkpD6SEx1lLtBdLRbLAnvcOGj/cbHtSw6rFxTwzG02tbQ3zg - 5ZWv64NUnzcyjNV4pbmht8nv45sW6K3B0b26BcaUz8dRzcvm8Bf/yWI6VQrh0DrU/y12Ith+1MJE - 6Yvq+0Fiw/zin+izJBHp4/XU5m68G7A+Dzbfw47N/O8QwSsJn/R43O4GSbDuAL24v5A+5Nbz33MA - 6CVJIhlOzXqxjnPK1NVPjiB/NevFl3IK9/m9p17+Og+rPzWpQ8m+JA9Z1MzJLyVoMlSMnbHzE2Z3 - 46Q84Xf989Oacf9RHSRy9i5C1csbFvfbVMh6BJgo92zbjB9eHmGwPi8iPHtjEP7wWD1qiPwujZWw - wbVGUDK5x5HtNebyuF8VWOM5qqeRmSx9bZ5/eg7r1XFmVD7cHLTyYXwsf3Uwp7vLE0nmkFPsbVMm - GvgTgbzPHWp10q4c3FxpwfTlPd6vfhN7UM/6x6dST1yCUUmLHlQ6hjSLYr2czrqsoD+/0Pfy0RRW - vwHZzwlW/wmb44EkCtxqucKXylfKxfwOAAZf6lT/rlsoTpoSwef1gdUP2AZTKpEjKGOlULvulnKq - X7sMwq15irbbnARsGkoenF2f0P3V25niMzM1ZB6CMzXi0TP/9A560jjC5YoP/ZCrrRJ8uUsEM8mG - uasrBzWHViA/8fk1p+FKDVh6QV3rWdDUPio1pBqUpyFxE8Tqk5WClRCNRvr93pCozi3IU5fg4+ua - m51Wyi5a3x+Oj6kfUHWee/jtXJvmn82BjQvJcxBbZJDN/fFGrHvqAH/9ib1KokbaceoEnhoG5J2a - LuLtQrqhKshK6n2KOpl2h9cLkqts0Uw17GF+/JIQ8Rk6RkfJmlEdzDsO4PNOsM5tvWHRqh8P3Vm4 - 4ejk6eac+2ELn7s+YNN2tESqjocj+uNfe5qZg/Snx0xf2VN37S8s8nsh6LLLY3xuBQPNx+TOwVbx - Uxpx8nX4Pne5BvtlsbCtrFMci8OzBSsZNeoOvNmwezn08nEvhzgprT2b0UYAGHfuHv/V+y9R4gxU - zjqv+rhNiC72LQhxr//xweD3q8sXQF5jfI1PpPnXrxkdfIjGlKsRJSeDV/vSkqnpSodyPrLVrdp5 - V5qXRZcs8lsZ4Uv1Emu3bTLMw6+IlHbUQnxf/diFbuQbXIvyTOTNe2lmfz+Lf/oPOw/xaf7kdarL - 6pfh4OKdzH/+TkYPNxrUfljyFRe+QHmdt1G9kHUqCzctQPh3SP/8c1Fctinq0LylwWeKEPnze1c8 - iebHICBmGE9LrfHxFvVeoySrfurB9XSBalvFZePaHwHDV07U/HylkvUnM4NzmSL6V0/Jiv9//jwu - BXUY2A85hhobj5ka5fee/POvx52/j9QB0YB2tyyEcbA6Av3vXs7P7+f212/DenLbBoycDBHm7nGm - ZnR/JXT126G9XjjsEMFF4inJOUiW5oL3tZKbrNFJgWZJvGFLN7bDoLhmCMMzFunta3PluOYTuknB - G5vTNTYlwTUJJEpX4Pz8FhvWPI1K5Y50JLyzTrVY9Qew8drT+8ovfuqySRH/al80oIFZjjoPo1KS - 1o7EsevLie2uBgqfU4ir8bQ0ZO3XoIdrHbB9Ev1AMu5FDrZt92ShcpgQJ3OMv3ofzat+XeZlFtUU - jrt/eDVFtz5E5K4hGqz6eyFvNqJTex1p4M99QN2TwyNVUDMyJ7et+edvbZNZrKm28repfGuasvr9 - eB+BX0qSfKmAvGs3+tN3k0FOhbr6R/jy3uuMX/W1MmIziP76k4zxJNxa5l3FXjNVaLrwS6TeEuxj - 6zE80ByqOAdROOzJ76TqjaiLzxc0H8+m964O2YR+5xDZ26NLFOUcIabhelQ/bGvSyD25jVTnk6Ny - 0sekUU4btPiqK//rF+J9+k1Yf7i/wDxaCf77PMke8yfEAktwdEvEtbpoFmyi2Y0UWz03Sxgq/Z++ - I3wnfZK/ePjjR1jfOm6yJPdzDhUv7LC18inx4EU5+MVQUGejCcGv/RXjH99a/QN+WLLvF6BR+wPG - xvMRsGcw3/71Y31xDs3lO5Y1rHwbn9Z6s+pd65/fs+3n1JzXeIEaxzeyCI5RTtYjm1B/PHL4z29b - 9VEEu19Y4ph/WIyt/RB0Pj50jNPDBq39ihzkJ3emhnzNTNGYgiNE2Xinh5VPLPS5A5jLWacXeCUN - 07t3DKu+xkZm/8zlEC7+FuuSRrXNrw9WP4tXdzc+xud9VaPpMHQZXHZFHD3TY8Dmv37Ess8J2az9 - lOdHaHNV5ZwzdqrLNZmG60eDLDibf+vPerqRq3WLpU33mUvZNBUKgCzXn2j+JB0b1MPpBYvc4Ejx - 8jAQ8r7rlK+ROOuRqzta60cK+ZdwEar9sSRCphtqO9gxzc2NgNh1uPLQS/6JqHWtsEkolAqWZ+zQ - S+UX5XxMY18VzaGm5vm7LUlDdy1C6CFi1+UqtJwN8oI85DoyjJyF5ih+KZCic0D/Pc/ePUTb/2dL - gfS/txTsM0mhhizzDYOjWyh50kkkfS1esmD3FgGRrjvqweWXsCmzjyo3MSFC3lZo5styFyE0cpM6 - D/+KFvPVd+DK8VpCN04iPpgYo2DD+zhm9bkRHs91l93dT6i5ecZoXrLJUg9lOGJPGoNg4VhVwUCe - UbT9vJpgoVZewfh5Z7jqezlgKPtlkP+6HS23XyMQFspr6rV0dBo8fIxI/XJFmC55jAu/3wTksnUX - 8N9ZQYoTOyTM+jSG2j9GEVv7BzWHSdWP0G57IIDsNlkQenBqmSsdGbcSKZeCLjF85lagx9r0G7I5 - +AY02ciwWXyf6DeHdQuS/XgSMA8CmvTXjoc9ORzXu0GSQbruDkTdFLNNcyLUjAROGkFWhjcckBoz - tl56AeVMa+zX5+/wu+/7G5xZ/qGnzl5LzCy4qvx8B7TsNISmWt5riBxPZ+zUjw2bKtE01InUGi0q - aAYqpUOG5DELSN1H5cBL+1ekqkPwWu/i4YNZqx9HECDysRNyB8Y/+8sLvq/tFTteM6Bf2kYp6ttX - hv2rdCpFx9340EzEwq73HZtZ76dJja55gjNL2SP+mMRPdSpmSvKzcktoxydElVOZ0eqS5oFYvzRe - 9ZmxRJtQugfCRJChOhsxJ4o9fEv282ECZ6/FdFeETsmTR+PCup54f1biRNjkFx/cStbx9TQ7A+83 - RIbLXj7ToyynzXJqugne+o2n+lE4IT4MBg6et0nBWsqfB2IRxQXveDRo9cghIMEvjxRIC5tGc/wz - F+xmoRr5fEmPj4+STOHdAwhu5QOv8VuKPu4JwvoHUW/6qAEtTnsXKq09UMsZzEDsbKVQizOmWHvB - vRGSA03hitIDPasPq+G7NO7U2172Ir7PLuaijZMPdnA64wvtPoGoWPIThPug051w1QYhi4ICdlO6 - pcl2UNCsn4sJTif+hrXN8cMmJQcCF1G903MROskitx4HN/19wDtTvK7xtDPgq3EO9htvvcvl8ziq - QnffEleDvpnq9RTGcP9eiWAbl5J9q3hST67r4CJpzUS8fl6Lejdjnx6lNk+EGwUFAu47Y2e97fBX - OqIMxEdcxGGiIfb0UwKn6GLTMuFxM03P0QUyph/qSJURCM3vAkr7xC/sNubUzBaiIaDjLyTvJn8M - vECEHkhtUmpqrcamdkxecPceHs4/YzTw6imeICjGAsc4bIblUAkRWHg8Y/+sf5N5fH6fcL7FIvWm - g51M8EtGVbGeKXm1ml4KaSmKqoGubfTKWBAIp/NFVt5bv6I7SZ+SmbI7B/fvfMFBKSfNcu9+y7ol - 6oiDvbpdBztyNTj1pqM7d++W0gF6B5xbSbHvp7tEqIwLwC52vth2r5tyYSdHASNhHPV/VjCIW+96 - A83jHGwKBrAF0jf85RP1y36X0Nt550IdbT1Cy1Iyx4xphlodNhMO9+EULBHn9Oq+FgJcRKpdzsrb - fKFzJtyoXTjvQXoFVwKDrjr0L5+laZeLsGyfh7V+mqbwk9waAtd/4uNidM3rst8bAJuJx1kkfNBy - zacU6CjtaJSXTSC17Diq8pgGNG/MaZhFO3VUcx38GOx2+4EdTq0F0/2LcVFUNJnk4UIUu49cbGqQ - D0LaOhl63OICh6+rX5IFdwBv3yypk9zeaCF0zlXpVSQ0xGYbTDk931RH3OURaHcSsLTMeyhzuaPH - XVQnYnq5HGEUb7t/+cbG54Zs7zE8aXy4HBqJz3EsXW+zS8+h6JRz/Ct4MBdDw3ulTxvhYygGFJWN - Iyk+jMnUk+sLNKZHkZC0czlhVzPU4zq4WGm8QyJ8stiA8LcsOBi/FyTcH271tz40XeOJWEYe/cU3 - LnByNedc1HJ1jzY99nUjSKZNdlggO9xvFF+kXSnStleQjJmBzX1iMCbfFlG132lHTymmw19+Qnmd - XHqGZGzo/diH//BvA5+hYYr6TeHkbH7UfP4cNGv0OoJy71n03KjP5kt+igajJLV09+GVgN2Pz0jd - R6YRKe3rXErcuF8HRfY6kSAJG/7jey7sM0GhDn/mA9aG2Yieu9OT+tv17m29+XawfR0tHISXL2IO - +BrU7WuhGnnoSNLp9FQjtW+wtubj/DWRBmFslfigiWc2G4IWq3tWmtjeliYSXDk4wlE+nnDUjimb - Juy4UG/dHt+aA2GLh4JOPbm+Q7gDipioaBEAt56aOjqoCJbAqSLYGcWBBqXMhlmW6pdaseKC42LY - N6J7lkN46xWPq6/TJf/wd8VXeg+lTbD0J8qDszdinPvSa5Aq1XfBihqOXsy2RZOp5rlap5qCU16Z - k8Er5Bf6q78XV25MkXOWGqQEK9EW2zESV34AgpcbVNPMeZh42fKBPCKXht7cB33XhSJcx3tNLbNN - 2VrvIth7wRObhj8Ns2oqLsTmkY9kcWsh/oM/Fiy53eHciZxBMoSsUqff+UPdYjM2i1WmrnowWUrD - h2El/b5AEQQzXgf/qjrib1JogKZhB+/cfVfO9iWd1KzOME00yJtJfH989Wpzh/VURZMs3SXO1R8S - d0TaRlyyCMVcqZtrpkbDyueIVaa+IkqCj2Mn3iSkul5jWCz/QDoN+mGOxguPIl8s/9Xf0foqBcSi - BdjYRXW5KFrEgaX2m2gSznvEU4vrQXtnImGh3TNyXwe5ZSZzIlmlE3opG3kEq91/aNjqRcCyx+YF - aUuv2KDPNGlvMx+rd7n38R6LYcn35PSC7Su2sOUMjcm26ymSQKM+kYmgob/6gNzceEQf9WENP1pU - BjJ30x1HF0s3+QerHYijMqG+PXjJnOOwULv+C9FtMk4BL/F1CL9XQSP5M5KG5anlqoX1lWj20RPE - MHpYakS5gMzpGAbLZatN6u7ZPfB+vDwZadxrpih0a2KPP3nJcDoa/naNz0jJk/ewmAerA8Wq03/x - P8XcfFMJmzQcQ10H0/r91OnjdGu8TYhc8yn7i2eiumU7LHXci/BwBIPmtkaaiaXyCC451NRMl5GN - OT1XsKGKQPcvI29Yn/hHxDCLybTywanyvhYkanfCN0w0xs+XUw9itBjUdqd38z2GeqEG2s+nFjPL - YKLD0sM2fJW0+FlDw/ZK7ah/+RwWzziYjG7O0T43kojdLJex+dClQPwtRy/eQw/4xzraUfJLI+KW - mx3Q84gtUI7RSHdrvRbj7WNUx0bGVEfVFMwjNp+KWN0K8scX5z4yash1ZxNlz/yEfpnyywC9Ewv7 - 32xT/sL2E0PNaT55GcY7mMh0blG/9SMcFGmDZvUc+2BG5g27lWgg/tkfXvDg53XLhLVtxta+t6gg - 3x2utDsxZ35SNbi/0Jb+4TnpLnkB9VAdI1G265Jh//r645PYDKqw+UkXZMG7Twa64l3C8y/igvM7 - 1lR7Y5uJfXEo4Bb+JuoM/YzI7xjIyprfZPzTM0U3uGDsa45W8BXLWQL7CcOluFGdqo4pGW2vwV10 - juR+qFVzDPTqhtJGy2ixoMfAjKp4goD0mTqhqZkT5h8AYlUVhBddrxQ2140FntrNOC0rb71r822A - sLc9XO3DOFiuuZwC6t0XDixj07AsMV305mOg5estsIl/tT4MT5wRWWy5ZhaQ9YTpUsTYaGO5nLt2 - X6HLxZBwYMfSMJn3XoYbEud/vydWWbmw5k8kVVyULPHxTeA8bKK15TcHjC7bBawWf9Z8i4bvc4w5 - 9Xw7ivRgHVREgss62PZT6/TKs1szr/is9ls3IryU6clM4rCG60bP8VEcnEHIn32Bhr39prgspWDU - g0CGHbrm9HBLN4j8uB8PUHEj3m+dF1tOt030x59pUmTQTFH44tT7STQwRtwvmZ1mPIJb0o5q1oib - 5QbBhB7375ZsTwjQVJzTNT6tDb48fj+T2Bx9KSv/wsZGNQap+o4+HNyOj6YX3Ich96CHaW+fqff4 - /YJZ2/7CP30cfWxXMSnpnkfoN5c91WbYIal08lFZ+Ru1eOWQTIezegM1/4w0GriYMYy+DvzF78pX - h7mV+xto5+6G3bPClfMzXzhlWxwZ1Q+H1pwKt3PQs1sCGo4PkrDfPC1g9q8PtaTskczH3VlEJ4WL - /uIpmOpv3MI2fjBsyFgcBsajCVw/8yLO1XjEflI2gqJzVaRytjLM0oVZULH8QrXv+zosxBUKeIq3 - hnpm27LXr597dcV/evS/TjlL3ecJ0+/0ofvXkA3s6MydsvoHOLwHxKQr31Z5Pj6u/C1MxOw3xcAR - DJF6N5/D5IqmgnSNtthp2j3jE7Hh1IFnEeG7PS6FcBInMDuN4rvJv01ib70eDvtniu1jvi2XWfvI - sPIlbFq6w2h5Tzuott+c2mEwDMu2Yrd/+izYp+Uw9TrqlN3+OWFtxcfl9/4qMOkgU3Myd8G0O8oV - 8uyNRwNz/DTkQeIUHbkmjV4JqdAsoPC5BbtJ1+/7Q8t0TXOVtq0bCbn8a5hzigzwL/ojetcwN1R4 - 3tZbPuKJqJdwHGgcZC+IdLvExi3ky5+htCHaI7Vf43VfLg2/iaFKmw7v1nwbvsM+go2URTQ6Ly82 - zdqFoLeTpvTgKiWaz/lRURcRLjhWyxlNhcnV4FG4UEf9xub08XX/Dy+xNYq/pANtngD8go9Y6kX/ - 8Basgy1he+9cy7kM6grVuVzQcxOAyd7wiED3q5ZIwrVulpFMueoHJ4/iodmYbBdsahgbBeMoud+a - JXPgCFUtPsiy8r9/fkQVjBOOk3jXiAhrmvqnh9PfvDFXPHZg29gDxcWyDZYf9+bV6T5gIi7sZU5+ - 0yqgILPBpnAyzYUomoxWPfnnZ5W/VR9Cyl8EajzWLa9//HVj1Q61hd95mNpcfcGq/yNOea5boHob - AJUH9lfPGYnkelSt6MFhm19sJNVeSaARNybd/9Rjs5SKt6BO3fNYO1zmZpxE30dZJ4sYr/x+vfvY - QPq9zXAk8sC+v+ZZA0fzE941N6OcbS3m1RvxLziQloB9Rxw8keF+b9iMSYdmVAoWeGw7U2/F2ynV - WK0u31ingVz2AQ3c8gWrnvx7vmYOrZT/5981a7wvcqtzKmGL9t/68qfHb2X7oMbhfiwFcz6PgNOA - rVvIN2iyvkqO7Pru4lXPsaWOnyKY9LbD93U9p5v/tOCXjWdsCFfSTGXkaDC5WCKLcYRgBOc+Ik74 - nrCTxXskJWpjqfOiyvRPT084g+ofn7d3Ny+ZRLMa4U9PeHcxY/M1/FZ/+E/9Pf0lv2/FccCnhhF9 - +pQk8808chCWkYKjwH7++WUGMME74HvjmQnzCrkF+7hM5PreRg0rF6UH37l/8d70/GTd4qyp99d2 - i72V778NQTv+w/tdNmuleIqNCTRmRtR54TCQbpzpg2Y7Ld5765S955hz6HEfttFmxeuX+tqHUBj2 - huornk1in49wikqbyHlTNd3BOlfwla6nCL3Zj7F764ZAt66GrfSMTFoPrQvg5zx1gkJnIlv4HIxk - 5miw2/2G+aDPsvr9LuqfX1aKK19UxTh40uiyWVDXaUun+nWlUGerLeY08q4P6XqkJb7RQ8P7UihD - 942v+Dw8nYQP7zqA4BVGxCf5O5guXFyr+e7MUS+LzGB2TxMPLLQ8WjFITaYPDqd68rxuiea0ZNl6 - 1woaJDb033pJv4Ojrn4pvaHpEaz5KCNbC2XqrHxqiduA/NMzlu94Jg2rpYWuH4DucBcF/FZ4hOrq - HxA+1c1kPp0PMirdPie873iBwClNq+KPYpFntyPNKPb5Wt+ylBqWt5Rzt20q9bkx/YgZfjz8bC0X - 4eevW2JvxbGZJtFw4SRahJ5RTxJab0L/n38irf7ab5hPgIz9kyM/0+sTVoDuK1bzpXT1b9E61ayH - xBf3VNMP36Y/Rtryp++wEwkfxi5rC5uE1pnA+n6EIS8MWP1vIqz4U7O7G/3jp+bNctGa7yK4u+lA - q3sQmbxgJORffmHt2iTLFwYRal//YpOEE6LmtK1hIk+NGsaxMsf9Cd3gPVQWvR6zeph/tqAB8P2J - 3ITtYPLHXKxAWpyBGvl6KnXktZXf7TN8j2wpmNPWSSFDN0K20+dqUr8nBnCGsMfRcnsHP2+9CM0p - xo4w9fIJpm/g+6BeFhsb9yIsRdNRXcjz+kxX/pFMu4EvUP7rd3Q38c+GXXeHEQk69Ph0un4G0vCb - I3rzR4jUZqwb9louBBXnPaVOFv/YfH+4N4i7/IWjy+bIxtX/Rd9HmP3TF0xeYh5yhnY08iSfrXgs - oz9/zsxQ1izMKSrUIf6EvfouBMT4bX1EhWFD94ffc1j+/Km/vzfcJS7ZR+3Dv+enTl9dmIj8WwWs - 1h/Y9aV1C+2GEhSfxJFsPoueMH2IOOVqw4Fmq38+Jddag9d3Dsiy+jPLFfkTkt7zQr3t9hJMXMGN - f34NDnOuTpbgskTqWY53f5/f8BIVO6g+u5oo0/0wLBp6tX9+VCQMyzVhG29R0N/g/3N+mkqWp6EP - uc3d6e72vP35Zx18X+iKvcPbNqV760bI0WhOzfkjMpoJ1xZ2QnTBmG8+zfTnr+r2x4mY27/Y2l9J - Fcofa/qnZ8e3p/nq6STeqJY/GzRHOeKBPkiHjcgakwUbU6zSHT1H6pzLDX2ffiH4x+VJ1GLHlaSK - OhG04SNQLNy2ybL2A+AYaTyZ1s9b/t5n64g8xsLtkvzxEWhvx5CGQVebUxSOHHzZEpE//2XeksJB - 4b5+4vgrN2yuz1uA1W+kZj/7SODPs4Wsa06xBrVm8m+hd/7+nuJbbSeil88hyEJgkq2RR4hGycvZ - rv4iDpwbHpatUoOqJ4cb3s+GxsTTcoogRFefbPuNZ7IcaS68pGtDrfViMfLa1iIUcL5H24SQ1d+Y - UyTsdx4O16Ezf/Gv7r/lQtQVXyb1tY/+/G+qEekyzHuhTqH62DXGpPuV7LbOt/vn7+BgHmh841t4 - kW6g1ijuk/d9/7zBH98N05NRil70IKrUcsHaP6ibeciPBhhC+MH75HAsp0CvKuTcW5fqvO6X81Os - qz99iE+T92HT+Y05NAJ1sIYFveEP1r0Cx/WnaJiRgGZFfrSQhPuR5tdQGeYRBzVwI3+ljqX8GBGY - kENjuYj6OFEDpnMjh1JjcfCqr4exvz98+ChBFU2b4wfNz1wBEOdXEG2D3kczU4In6Bu+JUtt9sNL - qPUIlL2D6H42asa4xCvQBYkb6tLpONB+8wq3HW2qf/E6ZTrU2/X7R9XM+mS6JnILqC5fRNItMSDD - q5bhWIvq2l8cgmnFN3Vdn7/1ZxKX6IXq3C4Un9zrJmFsLy2wbXbDv/5GP9w3GYzVTcTR6g9N5zA0 - ICujG94fM23g9ydW/fP/tbvqDXWkThXciHuhhbJ5N4syFxUSzlZBdW9vBPP+aaTqc7weyap3G5Fa - 8U0txfc65c2Ty99B38rAG9I3ugW2wRgBLVb9c2fg6iBEJv/MWKvWw+34x6+G2ZWDGF6kH2io/1pz - 6dk1g6X/1pGoC/dmrLyHo+LhE+DdTbASVsVdiFa9jcMfdRLJ5yxN/SF+R+/1L0kW8a2B+mnkKzVf - Co/m8KzwsPqzeHdp3FJ87KpUaco2wX74OCW/weI1kD+3Ae/h4aPfUW5SNfG+PFHvXFouX2h4sJUq - p1oSfwYSMclFqz+CNX+rJn2+E2IomrrDqx5hNOn3Fiq+7IMNd5lKJiR9CgdzTqn1PJ7+1bM/PKXX - dSTOn9+C1vdDdUKJ+Y9vtwP3wjrYUcI+vRXB+U0wddHzifp9wUL10G0tnB9+UzPe020Lk7NOzT4g - wtg/f5uu+fThC3ORp0Olrn4a4UyYmql4jBoMA2T4GO6zZAkg7NCfP79b+ZPY0EMKYD9S7Pv9PZh9 - KkxKnvQS9vPEHqSlf8XK6g/jIL3u0Xzf9xX4XHDA+2J3S1jtJUTNxyuhpfhiiPzxKYvC59/6Mqcb - NUBXaSCrn9D0f/ixjRtG1/dZ/oLqvIB23V6pt9Y3AVrrBTsjP1Bd/d6C6c/vMzpEaJiML3PuWvv2 - f118IP/vLQUevU/Um9rBZParBlU+TzG22D1JljNOOADQtuShHDUmmB57qvkPb/AuvQeMDVVbqG7U - 76h9ah/lT8AvUBtV/GHr7RkJv8+9Gtry0GCPg8oUtoxVcMinG9UV2y2ljiFAtWIdcCgtXTCXFScj - j2QLWY7FKyDOXi3Q+ZaKOPYjOkz66dpDdsgj6h+TD5rl3z1cB0XvKN5cf8mSn6ZI3VyuFJukewxz - WduGaldigHfIzxuxS91e/ZhhRA9THSGCukOmVhnX4v13LyVPc+Q5VTwqagTZsUH8IE4WiKmOaJK9 - heS99G4P35J7YAdNHFvuy8kHPSqNKB7QWDL918XAHkqD9fz9Cvj7xDkwebNGq7fhBEupywu009xF - 6BGhpPfEz4QEeGd4N/K7ZpL3gwIOuWb01MBSMvvVgbrtjld6k3xlmOFyntBv1n50L4U6E0nxzlT9 - NX7pCS1OICZNUkBcP3MyuNv7IFrIjNQPYhN1NXosx1f7MNSdGWyxlo/PZDnG3xjuF1TjvXQ7lEJ9 - tgnspw7wPpbRwA7ds4VOepfU7EsvICWqKpAdIDg65S2avFHjVBa8gEZ3Q0pYA+gIpfeQ6Ek+RKX0 - 2rFKVeXOwWdk6wPffzctnPqDjR3pIKLphIwR+Mr26GEv6GjJdg9QWbNSuPzdmhIR+AW+ZGxwPCf7 - QSqiLSA9rJ703kVNMqlh9gLZCcIIShOVyyzpIiS3hRL1x9fsdTzmKbh4tumx5zM0D5f5BRRvBarN - yb5ZtseLpVbnxaUFl53Y79P3ClK1XMMH63lG/C5zR5DtUx+JjiuV9e354aHlpB1R+BnK5U0VRx3k - t4D16+HVSOnhqsE3cDf0+vt0CbOyYoSbEHE4ApOxaXQqF5nWJcVX6ZAjKfKnFITz7kjt7yM0F8QO - lUqyeqa51wUJW97BDXE336fG9v4Mfhe7UwALNabF+v/ZZmNnoAjYweabdsNozYuP/vI3uQ8tYs32 - 58Pj+OWJ0MBxbXV/U7SxW0pe4fbakC5eahUt6gGvn5ewu+cuqnC2jzQljyYRW+N1hHw43LHm0c/w - O/PhDe0+zw2RcGoGwkufnqoo3h7U6+kDsY4hDp0M/kE1X5cCNjZ+p9iPmuDyPjcm+3SMV4uoIeTV - 746leHlXEQzb8kx3m9kwl/e8E1GF1lPdp1YvhUacUmjMTYO163XLSBjVN9jqnESWs2M1YmwkhooW - j2KrfsroVxcJr14PQCMOp+9gfJ6sXk0n507da5MFYjQeCepiV6Kh+j2UtENvTv3S2sY3/aQ0kzLc - fIjMnMPX0zFsGJPUF9DmXtBdfExKYTplPrrd0HmNT8fkrXlx4dUqGY7gaiHxe9cWYI35oPZrvft6 - VtMFDZ9KonreI5OlUaSAb6kCtpfiHSzH8lH8xSu1sgPXkMUwDJUflQH7xwWjZXs8OOrB0SqcGtOL - kSZrRqg/w0C9oXyWfLtNfHXkHYv+1TceYHmqPij3aMsBmJPSKjG8H8od35PtruTTLm/hLx+T8haV - jLqEg/MTS9TYzodBGB/DC75VolL/bG1KFr/Tmzp8bhKOnF85/MWv0g/fEBdv2g/LJN9baJCyx3q0 - m0v266GHy5sL6T6IfMT221KGMeQQ4cT13MnL62sw3OOe6tPvU86HT/CE5I5+OHoV8rC0xngES4s2 - 1ODiSyMJYef/+/442X7KeaPxN3gu/DoYusgaPq8/GuLd0KMln0cDiZWag8gK66hWbpkpRbtnpB4g - M7AWla45w+U+QbEvJrKd2iFYWk7L1OJpJ5GEL4XJj6ZjwOzke3y+XEskDvMib52dttDi6OBy2t9R - jVhIFWzJ7/XuuCXoIHsnEbZ3bjS8vtcHgd3XNwmEkVH+4Yfaus0Du8PJNafu+G3hY0YR3dnP2SRm - d3BAPMoqzXR0Y/+e76oZOPoPaV/S66yv9Lm/n+LV3UZXYQo2744pQBhsphAitVpACAkkIUwGLPV3 - b5HzVy9avev1c855EttVv6HKZarymsZyycmFoWh4RPOyAdDLuFTwOdkqOVIH9oNzAj648QUl8tHa - OXNPHzuJvK4igtlOz7n6emBgPOcAiVriaRw7qZVk63sWCc8CO+s8KBOcy/sT1Yz8BGvPlas43AcJ - jbyrAFZ6+7604RVxkn7pV7gnCdSSrbEia2j+jVHlSpWifrFe3R508IoxhWXFC1P75iqHOSolgpEr - jzje+MQcvewCpDtWwwEyl2hmFzOWDOs0o71vyBqnvWwIY1Pltngo++VqcB0c0PEwUe0U9mtxeq6/ - fIjPztnUmFyXGgjGu4uVkyiAMfMfKnQZPiCosU4O09MHlD6gjHFOvBdgk6Yqf/hNEkOrcrbzTVda - 0njE1/0p7ufR3Qa/7/Y9cZ2nUrOsuULpFs4BNpQvzml3YkJ434VvJGXOsef6RRQgb6vH6RAO57o/ - eVEKGrx4xBs/VkT34qOUkrcxY3c7Dzyy9hC6T3HBR1gaYHkF8fNwC9cA39RzAJgtviQT4gfRhXSo - 112gzVJJhgPBviE7X/sB33BV6xi71Pj067XtYrCjLcaXUZGdxeioIPnlcSHBLdD7pVh5W4qJD4lR - J642w8OQwod3ImitJA8s87m0oBTdSvR8W0m0mDANgbB3GXwLjrHWPv1qluJZUCZuzDwwJLuUgSWZ - DlglFR8tQ7MMQNv55ZavH/liJ7CE8NJTbHdt3a9Nc9jBx/19JppZa/WIPNYWuYa9bfcdlZzKz9YG - cT6mxP7lE8dyXXiVaYOVU3uKJu9aNRLX8LcJFFoI2EghA3QiZBADzCWl1blsYZ7vbti2pimnl/Hw - hHq4VzZ+YtCZPOcOfB17T3QTD/SHbxDqzZkEp/abk6/xtiV1nAM0iz3q//D+SBoPK6lDtUXc0UZ6 - +UFO7jZlwdicnhXUel6Y+GU/O3Q3RC783o53olvWW1u9bBcDR/mo+Hi+Wj/8tUDT2DG2meGVL5Gl - QXHZddH2litfLxufBjzjDjiitHOo0GUZPDL+jK3+WW0TheSnVBY7HoFgVHpeenG7v/yXd28YLfJr - gGL2FFiS+O5VW07YLiBK4JNk5Aqc5VgrIrwbakXs1A4czhZaC+qK+sAIXKi22mymQmb5IGK1Sgvm - oyyXEvyG/La+O0COj4yD4vOmE2+NLDozjzr54Tc2R6VyWshaFTycq5zcY/5ST917nSX69PYk1DoF - sPj8saEZzxI6nNSjtoxkYaQ0fdxIothmzs4n2Ye7lRmJHRokn+s0W8HG14m3jRek5VFs4fUjDpt+ - gFrvP1ECvzQ3MDpC3ZkPaSND9xGNE1SBsLXQPyrIWPULq+FAe7qUHwSX3iBYFl2j58SXg0DmW5Rs - fD//mHykw0ZgOHwtJEznNtrHsAC9RWTUyj3744Oe+YnQtOHXINu3Cp6T7DhRXXXouvEXsKTJSBxP - UbRFmMcY3mQZT3N6P1HeIA/9oBkXBvWFtoLpLYouLF3nPK3dp8zX0b3rgIRxQYKL9wSz9SzfcDHG - L8HYM+i2PiEwxiTF8lH7aBPbMRl8PXSJJHUyaM1bOSI4KQwmZzhz0RpVxxDMK/clssyhnlfGypeu - kWtj/0Q+NRnXdwdPFjdh77wbtcH1TilUT1cHo7MzRbN8PLQAFDHa9pOvV629GoeBySviJIKtMYkK - V1gBD+OyLqycDSVtBaciC7FrcIrDso6iS99pqjHedy/w9/dbrrjh4rc/NTcnsI+OC9HVesipuzxE - mA5HhVi3eaop0mND+vGHdKhm0N4ih4EPzyHk2ORZTbtgCmF1Js40uPsDHYOepPC9Y018aS3TYT/P - WYBOfNaJtb85Of/dQR0ae18g/psVKZG70JKmb0ix0n/Y+i/eJDQDklzvmsa8loct9bIaoeXiqZTs - hhzBDZ/+9MRyGbIC6mL+Qqwye/16iXgGsKBMsbfxeXpZbynYzg/69sqSL6fQf0vCdZiJcT6Gzrhb - /OxPz8l2dXfoOKmztOk1tMjne7ROyWnj19V+ovS99Osl7AyAhc9I7K0mupR6/obzLJyIyV0f2sqt - UybGs6hgd1vfjZ9tg5nlBZ9uBQ/W0yTN4Oh4JlGLrQTI6yoDW3yJJ7ZpI40qbBYCMeifRCX7fTRs - /EHUlYtErGeBNRoZhQDF513HCmEmjV59c4CfjzBjU+bmfubFZZBK1t1Nosayziq+NFf6xZcJLovz - 0wPwyBohOTZJ3U82G6pSkK4lUcKHmvMG40KxHvB3Ivie5OtpbFXoPoIRe05eR923HjppVwneJJI3 - radDeDXAi2kfSGSWIv/jo/frocIFdXY19av5LVEPyahm7jZgsgObQpUMC77Y49ZCJO1VWBzPTxy8 - MdPTpDEteJWXBluy3+WrETgV3NYTsSMFPdGc8P3jv8S88QNdMWkZKMcPYcOHSGPbcVEl4pMUMdlV - dsa88mSY7yuFFFlDozm6+QX45XP9zglOP072CqZPHGH1UbBg/GpLLAFzb6Eu71tK3ZXv4IXYKTkd - zcKh72xsoJrLGHtZN9bTaaxU2OJzjKRNn3yD/pOKlv9uJ+oNRTT3+z0DFf9cY3Pjs3Rcpw7UafxF - Q5W8+l7Ud42IzpeSGB82dtb7uITSdp5wnAS7fonkK4STDkaCVndrQWdcBNNup2EtODCUvh8nBga7 - zw7j+GVptCWpAe/ZqCLRmlDUHpUEwSOrh+T2zQiY2rR5wjrJTpu/Y2nd52RVUHoZT3Q4mlBbDY9N - pRezl7BNTKce1iNiDrcXTyZxp7/zRW6pAerLw8TnhnVqJjBTFwKDsXE5RktN+WebiflSPIm9O93y - tT0nqfTLx+56wNosHA8iTATNn0RKB21xaQrhbLUqkZHoO230YGKBoJWfpG19X52PXbCdb2KKwtdZ - f/wZs0+Mj+VyAlRZXQtcqHIl1hvw2tKKSIA3jAaseV6cM4HpI4mgmSdW3Ena+HKF7k/vyTfeBtPu - /OkOwlCuWH3gW7S0ayGKu5Ubf3wnJ1t8ibpwOxLlqRbOOt1AAbMShRP/2QbRjsx5AgnvTfh4FmYw - MnCdIYLle5qftZPPAbZdidm3I/njs9azbA566NwJyvRPtMjDyYVs13LYMLQqWpJHlsCfn2LF6Vi3 - lziw4eHdX7eSrO388fH7Pt/4WD6Dlddt7vBy0wyn+1uft9mqZTBUOBMH72LU6HbjE9I7VLG5VK02 - d+2XgZHLYdSosw5W9ZZC2EmvATvdG+aLX0spDPpnRRw6eHSExSgA6bDoWG/IEs3Nh5thnAEOay9m - X1NvP9tw6cWAeM64UoIsfgfNNnkg8aHZ/+gHObEgTm/Bu1/FW4AkjK4GLnH80lY3CUvpfSw0vOF7 - zUqfSYVJFE/45x/+/CGp6ypENGG30uWccQYsdNSglYwELCb0Q/DMSUFcd38AizurzJ/+t646cnpi - XhHc0X2P5ci0eqrKYSExc+lP/LLy/Spb7xJexXYilg47Ov/80Xya7pseNiP2hNUCwqRise0Nu2h6 - 7+RYEoyTi+W+0CMen+cCOpFrYEuH9s9/tKSfnpJvXZizu0kNpf31Toh9Ked8CSa2koJu2qEvygyN - e5HVAG095PjC9X7NTzCQpZ8+O701JWIZ2dqmbFkxMUaPzamehIP0cJmIZNvnIZbthaKc2HCCguTT - OWbepVRcZguf7/4CFv5G3b/8tem3mrIeK0IdXwti1uo5Wq5qtkLpjSu07DUhX5rT8wnZi8BsfjON - ph8f9cnEIqFVLMAkO5+RvsWskcsyyvWKZq4RDVNdkXS+v5zfekoPVs+mR9rl2uqitoDBzn8SV7K/ - +dzUoSB9wDIjbvTOEbl7TQfkVthNE34zdduGjwb4zDUl+eukR/cdc9z2r8XoqfkzHe7zTof57ZgT - Y43elHqwk4F2dDni0J0UdTovGWBVxpUo7NUEVFj1FDxvPUQ98Y50YfGwgxufRqueX2tqF3wK3sdS - wzK7R9qcFo4lYiEMsDM9jv/47SVjyAQPYx/NmKUrfNT3BImUutq3Q4cSWpgeEdAwAhS5hg7Xc5SQ - zY8H02liV7i92kzsTqTafEgHGaapq5Drjn9p/ASvMrQ1jieny2pq40gWDhap2eCjczYdTn1ABto7 - Zk/cDS9m1ztl4FQFEVYP4aleeZiuYL+T6OSVvOHMp3lgxN/5+fG55XgHMzx/LXPiNj7FG0O4gzhw - TkTZ+BS9Oj0j/vA4rm4KWPf3TAVnrdOwk3OiNpRPwsHSOVNicPtPtLpJVsKvlUlofRRnMM/ds4VT - 8lw2fkIckt8sF8qfVZ64zBfrYV6LFuoBo223elcwKKFZQfSdGoxv0skZrl2QwR+fQiVb0jVevi3c - 0Q6T0624gKUGsQv4YjWJSkUrYikrz1ISPly02/wv/iC0HXBLhDY9XmnjsVYEcFmE7UoavuVzz80G - 5Mvpg6gw1NE0z+Mbthw9TNKU3sG8CLMA/apK8e18f2lbftEhfZgKAtbhXtPx7c/wly/mq9Plczd6 - E6RuWk4g6YN60xeJSIKniE3OtXoGdFb285vIadcNdae1Vx1SdqkRvfcG5Ww5RMDhxRYx37bLe8qQ - AvgFpFv9pgfDEa6+6MQXfZJecgmW2RfDv/hPQEkiOtRqJ0X1LZyWpwqdiddtBj7zVMaF83zUy7G5 - tNJZ5R5I2Pj78r7MM9z8ZWK23k5b9gc1A1o3iCR9f82a3w25C0H+HDD6fCtADwcuPWAvtBA46ETb - 4iODpSNfSKDDlq6yNZWwTsYXQbwt1mTzr+FMWweXxghqKn90GZ4sZpoatXbzv3qBE9VPxNt5py2i - dq/E5xhX0/42+/USycEOcviSEt2ZGEAvbF2JXKwBvOG5MwvHRZAc3D0mqW6lehVMf/fDH2KM2UjX - FV0EUf98azS1zKlnHONlHza+McEpM52vJTMIvu5WgW9TugeTNmuGpNxRQ05v7RFRDVUDuLjjuvET - 4rTaW5QPFiciJG1+6UzRAcH3OJ2w8eQbhwQjKkH1iTFRLbar6aYnf98PW3Hq9T8/S9rvusuP/0R0 - 6waDOye1cKYlnrO+yKrDONseDpMbRZvbiI/F97794niKbTp3330DbtEBT88tvrno/agkpBQl/vnL - 2/52MB9F6a9eR4Y7rP7iZVs/OpTXSoci6xnE/T7QdsXAXOGWr/HGrzTa+/7zp/cmsvknq4s5FzIW - OqF5yOJ6cD0lg7dXFU2b/1LPP/81Qa1DFFkj9MfXgOJfaoLteATTvpYEUFZVgbXQ8CKSPA+pqBln - Bp03fTird1mFPz1tbPp4nsd5lvTXqCEQut+ewutDl9RT7qCnfrI1/sYPyU/f4Ts8IY1/Xe6lmGB6 - Qnsso4gO2xW3Tf9i1J4f/ZDsfE5ywrTBRcOO/fqwzgWoe6PHKtnf8+/nKYjQvqQ5CbSl0la4Cxq4 - 8X1iIRqBIUwYVyqdCyXKVk/lQ1BMcN6uaJLRpdHSjgcZfotVQzRZR9CiWulgIfkuLmgkaxxTq5kU - yp6GU+5yAKvWBrr0WieFuK/4XI/ELlWYmgQgDl6semVSOwT5qeax+53e9SLuQAP7cD1j88a7lJe+ - R12SxUol6VYfmbX+JkDPKM/bw/YcWF6u0MITsWN80pOmpx58ytDLLR6rjJD20xYPkNXqCVvooUf8 - dFAFKSf8bYvXrF6Mdyr/6sdIwqKT//llsvhUsekfMzr25bkAnvmKcPLkdWdtY7mVLqcunpjKP+V8 - HJxl6W6c4fasjBOx8HpfYSIwK/nVa5Zi3VugRSgh6uOLIo56qv2Hp7i0H/VyoKAAhRS6v3qMRkSt - YiRS3zIcBQcGjFkaqfAAT7d/+HAtIAN6o3n5859faye30nroiokDJc6XKnwwf37Rr34x7qXJB798 - lW75b+ZN3ML0Gu8Rv+XnxaX+DuLo+J1EYvb98hw9CM86VbArXRSw3q7G/ON75Fjf7Gi9d7UtxSaT - bvXmc/79+WPxdM9QF66ErsfdNYbZ/ewTlxpmPxbvRyht8T1xW75Y1xOjQqHv/WkN78CZlfO5hX7h - BNOc7gKw7GH6FH/7HczVBEj+xvD/6+GDw/+7peBRriHRv7tXRNf8nErfZbslO/Y9nS+VvEKyphAj - Daz1AAW/gqT07tObS2SNNRF9S6luH4mtVGM05bsAHa53m58AfoQat0f7BoYf4YwDYR9QNnWPUCoE - Sce67Tx6HiS1DM34AtEK766zfMLRgHsmGtC8Nx81zeW2hLuvf8D3l2M7rH6tW2Dt9odp4PmPQz9c - sIIBzg7JK9rnC/mmTwjUSUE8U+/7eViWDLprpWG1BXbEnHbaKgkhvBPr+dz19PMtKmh/XzxWzKTu - 59KuOumljCqxRJHN13ywbDgcnjxBF/voLGJEdjAo3TsuRe1dzw6sCuk5qlfEfh5bSZ+pZUiMq4gN - 6j9zfr4MIiw3c7zz7CJfxVvbgOZ55Key4kW64PQ7w+6BDLx5h7RWLKsA1k46kJzEZ7AKd02Qcl5n - SGp/x56qC5cC7bUvp6d8aOpFd9+Z5LhlQozTO6z7V8dz8HLftVir3GfPlpd7CemcvYhTUSen7bmy - pQUnR2xxL0NbWh4j2L6hRDxYHQFjjqUKr/6dxQ52n2D94Krcyl3HiUi1TNnFWwcYFmyOlXOY5yte - bxxE/KtCl4k70VmWugT0qoVIZgu0pxe/iSE3WyY23Ivi8LdTs0qv+5WduN1LdZg7UWfohoJA4r5I - 6WqWsix1nvrF2t4JNE5D0hsecVjiY3TCOcNMXQw7T/6SK/6AiA63kw0vTSVifAsuOddflBIWEKXE - MaSKjjlUZRgG+YgWM6nrCYFnIyEurMnRmdSI0+llkLjJI8TUeRswkb9LIMV8gD0Fjz2tp3mWVm4g - 2Fzgp1/3iG9++0WOEA3RYjesLT3PLoPzL/eMeK+vJvi2nJocoxOJhmu9hPB6t3hs5WbQz5bpN3A7 - X/h8NizAnhNOhb6oaCTAYgyWXYvcw/Z9yU36dtpaveQ35Lzog3aWHYPVSftZqG3iEZS2KViUJYXw - BCMByxp3irgxO+zEvVecsSpnJR0ETe6kPVrCafd9qfV6esxPcH3vbtjU+Y4OPS0GqIhyjcu5lyN2 - +GiJ9MsHFyu8UL6ehPnvPOM71XoafeFOnKTgghX2uHemOiQGTPZyRQK7UKLVu1glzNdnTeyeAT0p - W2BAfLjN+AYuSsRPEUwhH2yDus3v1WHCwzWD5uXjT3/x/uitEI5P/4ULtbZy1nh+dqAqRBXf13qO - KLpZFujEuCWml0NtksL5KcG9JJDT1+L6VX4/BclXogRr3nJ02O9u7iAbCDIJ2EPssL56nyFzrc2J - i81btHY8MEBqaxFG9+tH61uPmyBvv9744rtdvTJNZUn23b4QIzZvOStx7xVy0epv6/msGz8JG+km - zx0+anc5Z4rDysGg/e7/2c8X7EvYFG6LtUT+aPxbFzNoxKmGTdE51UufIR3ifIXEWd67eny6n1R8 - sJ8vmmdg5Gx6YDjpe3n6iJ0PQz8xl6cg5Zfbl5h8lju89PFXiXzDKzl6LqdRjjVLqKCtJadWmmhl - 7FcHPy99xFt8AVZrXQRBztQ4UyovYvSjlklp+WnQAQ5pTi+73IUzmldix37mtGx1WiUhW3W0q6ck - X7f9Bp9BCHGBvEu+kvJZSl/HuuKLJyqAD4RzJY13xSehj/R8PpyvE1jHxp2GV3Giy/M6tLAqBBW7 - vj3Tzkn7FX5sheIrDl2H+3yOCXzYSCFRPRY1D/IdcxjVb0aMD1Brrv9mFaxKd4/lqEojXgvcFVrX - e0WUsQudVafbEx29dcJuP4URM9UpJ9mzXhFlH4j5gj5qLN2OMcAn31ByfqoOLTikhka0sxtqy3jj - GkjiwcI3/1D0DP81DGlxjQWba8DR0VMrJMWTcMI3I13oUEp1DBlpqCcxAna/1nKpwnoSvojl1BnM - xnmxpJobMC4d9pV3g9WKcOx0m+Qo9QAPW2gAhNiVyMEtAH/n25f8y4a3Yc1eztkATw/uSvSnvm6D - 5LkBSnStiDnTOhqNJ4FQvrgOCarQAeyblhN0u+GG7/bD7nl58gR4+R4dbO81pLHl5VJIcd+xWLXi - pZ/xIqRAGPPd1Hx3x5y7QFeG+yckSJK+tsMehcNOPJ/Hiij04+Tc5F4zkAWahfWEhHQwxciHCY8A - VpTpUM/557uC7ppWJIdgl1Pf2NmQLdse536X1BxzvHIHExUjTi6rB9aPuZchbeQJ+yQqtJWUXQFv - 6RsT6zI09eTvcwOeRAiwueH5us8ZDp4+4wfHzH3WZsOiIoxWYGGdufvO+DDzBjo0GolRFwVgAu/I - SPA2UFycI9bhXnXBwclcd+Syjz3AGl2QSuvTLif2HuoauWgfQ9r4A7mJtZtTZ3Rl2HJvC2MAkp7l - DGDD6i2/yGm5pD2d3l4Bj5X7IaUgqhEfhr4uWUoP0P40svXMB18EvYe1xwiecb4clFIA7vwapgM6 - fcDCGdSS0pv/wZrdf/KVaVpLQuckJwZzNSgXcEa83YK3sac0BZiVsxlKPXQFrEU47fnGOE8QH+4z - tmu/y+fmvFpSslcrcg8+Vr5cztkkjUdhIic+XsCSGqIFOQd2+HQI+uhtsY0g7YbKw5rbypQ9eakP - tIIBuFxkXLMxT1r4xVO34fHiLBfmoULt665YeRkVpWJ3z4D6nAMcm8ca0O9ne5jkmlVY/awvsLQL - yIB+y9Af35rUVI2lNkwHgt73hq4DqN9wNHGP5vsO5quyM2fI6JD744cz1pVK+lLBJHK1Bv265b9f - /iCFb/t0XQ48Aw02cbAThnLPd/vEgD0SD+SP34RTHcNGccjUnQVd6/NweUobnyVxk+QRJbKlSg6L - dOwxQQ3Yjxf4kl6ODjHid61RZxtk1jL98pd/ZmICGbZ9jQgOIN/PlRIJ8CUNGtbKJs2Xk+eHUrVm - xW9/6fqeQ0Oc+aoiqhUH9bo8sx3Y8jU2GeRHq82c3sDODxq+uY0XscOhyGCJ89dU6xNyKHUhB08P - 5oqkQX0AKjwiQ9zyL4kcOanZd37gQMsfjtiKvHI7H9iHx+XREPWul876qmMGrvUtR/nDynpWPFkJ - cO/fehusO9LZun47iDi/JgaXyA5LitwHyv6wEC0UT/m85X/4lLt6WlFgU67ig07KBl3BF/5QUfoo - NRmeDyIkWvUIwPiMUwZs+YOUFZ/RNT2dEpgowxMlGx9eHN3Z3vblPWJYq9RT9iCnUi1oLqr25qOn - RJZVSdTu/cR8PA6sj6SSJYwMC6uGaoF1XEsOfr7dDpv3R+fM4VBYP/6BwJdT86W53WxgsLGDE2u9 - 9TNqqC3RS8ejJROPOeVVfQVuYrnkLJ/22hQb0AIBABF2PT3Slr0ZN1DO54JE7RyBNUU3W7xG5ITR - NBlguXzqVWwNlUXCjmk1ujt8OaCoHzo9MSnzH3+FNJUYjMSU3QaLggrUwK8weiRyz9vWdosKeFdy - Ym5cvpjwWUL+FH6Jqw1uz3F5lB42/oA9/exErHiSY2j79zNa6tMnotba+tATYEOuj/HUz937UsBe - tdG0nKS8niuDDHB/5dyJi8EaEclLfEgzwcGJfpuiadedMzHdIYIdK49rPr09GqkgzAMXqsDX9Biz - IQDN3sB6AaZoFUKjBaB1MxL5qaQt55qG0rFvz1hpHx5YvsmtATsXqFgJey3nzkJUSf7TK5EoiScw - vM1rAxfOKSZmfSb9GnAoBiTRjpPkH2BPoi/zx++IIfYkH4blkMFFHEdyhZeTNmznD7gxZonL4ZtG - zY7jpPNYHzEO56Gm5JgzYOceVJzgugf0K9o+ZANRxpj9IoexSraQ+DB3iQHuTT5AIa3gu+wcgt7y - XltYplNhFAUSORqGEM1T7XM/vEWzlYlg+CzmEwjDUZmWcwjyldSc/6ePVBTYgPvEjL+1eEXY/KI7 - HaPKXqF7uZ+wkQl9P9+uigufnAUnUX872nwvDhN8SZNGnJJbAA3D1IChFdkEHXBQjzv/kcAIkZ4Y - p0DL+TU5vSFoUUZ0iU+iH18EGx8jVv9i6xU+rgbc9CQ+iRmuaQQN+bBEwgVHZSPkpI45X/KLGJM7 - d6s16knLDD2nfWG/rCzKb+NfIVKPJpo/AtHo2axkkRi5SLCOVWdBhzwFXrxY2AmoCNrWUXXQSPaM - dgjUzrJntUI63UITWyoRwfpNh3l7C9ZG3OvK5UvVegiOuzYnJzEjNT1d7BKavJuTUl1RPktEmyF7 - rE6kvNgvbQ22MdQvsSRo90iqHz82pO38kJO8KPUCXYGD3tCMv/Wtp9/P//RJLC5pzb4UPhZvXn3G - xnYe1hvrFnC7g4bdqo0BrXawBZ3SAByEfZ1TZtBsiInqb3h1BNTdnw3YdijA2u5SaAPEUgnvUsiT - 07pX6z6gygA7dHnjTd/U4ylsDEkUxfyPf0zy9dUCtYhaojN2RWlXNgW4HZ4XfNKkIZrTNWBE9r5v - sasNQ09BXfnS03bEienN7SmqrHlKUhkaU7UvZjB1totA0L0TxGT2ni6eZ3JQlkmNpAbqzgLGrgTu - +tTQkL+kerbcJgPyaF4nQQk+0egE1goMxSJETW9GtOqTJkBjrF7YrW3FoZZ+yKRf/ITBcAHzsQkG - aZqrgVxE51vTRlFECYbR8adX+qXI/VR6eleEj/zedtbrHj5B2z8QOW14N7DePEH9/VwmId/eRlWZ - 21sc689z6rPH8x+8ZIJ4RDBilWh5IaaRMtZtcDpMmUZ++sXuK0T8zubBHG1vt1JNf+OCSnG0XsV2 - gNv3Q0Q56RE/Kn0i/vJ5aXh59Pf5M+7xINv+OZ1rOwiGvjFjVT6Seg1qyQfL2YuJnStWNN/Kswqq - o3FD0k0NIrpPZ1f64R+Wahkw3Jox8MwmPTocAidfyIPoUNErmzjGqadUXXapuOEPRoaX5+ym94Bu - gDc53fOKLq64myBjuTbi1IqhyzNOOSF96R7226GJxi2fwmW6cNhWv0G91plfSUEmOcRzmzEnHIvL - Q42jEivx6eIwljuk0NKaECvjWQWMoFkd3Pw0xB6gVvM8FATp7DYS2fCvHrvZNyQj3cVEnsE72sY2 - uQBXF4RdT7C0NSgfJeAmTIj741PXsS/Bjy/e+joH67kvfdhI1ozPELnRagb2+sf30rWPcvKqfCR1 - 2XjDV01yI+YIuDcg3N5D++ZydpbFEyd4v/XDdPhCp9/4owy45a5jS7+8nJn1tilLqV2TX34hkD3E - 8MGEA6Is7ej60yPrdDhh5ZXP2jw0jA207kGmtRqsfPM/dnBqR2PKtNu75u/3KYbIq1jsI7Wh5NNY - 08+Pwijp79HSqw8k7fmHPL3rAtL51ochlOu3ilWQmH0HzKSR8nEXTIt+b52xuZ1t6SD7Jdb99doz - 23kEcXp9EP3Kzv2icMMKAzocsJKJr2jcs04J+XNbbfgzaV2zsCn8+UN69n31f/HbKW+A5cBSa+rV - ex/cNdcjccMqGgNmokNfrzp8C1I1Wrir6wKmaRTEnnNBW+qdsULY559p87N6wt2ejXSOsxqbZXoE - /FQtnbTlN3JJ86Gn9/s7lvx2MogZnCe6Xr4rBK39MrB25MPoz0+z7beBFfrpo3WLF6BqJwPtvOjg - bH6MD8Q88gkS0zOg+RGG4PMyRuT78VjPa7dw0gBXByvKtcu7Ms4YwE9UJbp766JFM8MGbP4o0Rym - 71vbj0Uo7ssBK2Yf0rV8+IbUNOr20EMqOe18CDOpeZr8dKiaY84T+9PC17O3pzW9vSM62IIBPCaN - sDsxvsNVt7sNaeFZxLX80hklbdChe1rP2Asf756im2xJXlQ62Fu/13pRn3MqoaRPp3bHWBp9sh9B - 5JxdR/TxuuaLxE0zaPRcwxjvd9pYHwUfar7uE6Uuk5oOmtHCQ8wQcg3wsV8f8FVCeASIqA/l0q9k - pk8g9VDc/B7TWQGWVUmf4m0YrW9o/A9/zPgMJ5msCqCvKNuJ2un1wUpmzPlKZvCEnBd80KqgiP79 - /E8/u+8XcgaOnzooqtaEXVXge3pVtRX+zqeJ8KINbuxlcMNnorXzNjTs8TZEU5lUYmrTO+IXTajg - dTZlclzGRz+kegeBqjkGItl+6ulwvg8wrrD55xfPun1B4hYPiCvYl7auV4R+/BaxhpHm06p8kz89 - bqvfpV9/fIj9bFOCxOIbLYl26sDGL4m14VHrMHtVfPL3A1aNy/YWO9OoUG6EPRIibyvxX48ufLpG - gJY5r+r5UlkzxPX7ObFCfK1p8VIbqFl+hxXH2INlDXr3UGblHRt9gvv2sVJfOhxkB+fJB9Mf3sLP - 8f7EmnBo+yUE1ftPH235k86MDAr4eiQeMX236+dVUkWQ3Scby1+F1svOf8TS85D50252s55L0XnT - /+prEkwk1ywYuwJEUSQR3UJWvna6kIKrf9um3rBuT9SFy8DKTQSbl9By2ITTE6iIao04mySU+enJ - zQ+e6Ny969ky0+bPb9j0eb4WZG7h59vuyIkcCe37WXqCzc9FB4Cafg4PQQafh9QnRZRsz9Uw2hsE - JboTBXBjtLqSJ0IjvA3EdYlGp+SyTVVC64rV4GL1iz8bLTxeDH2iHCTa4zZ+C/jze8peSnM6N09b - Eo1hj9XLd3EI0rwW/Pz/EN5djRKqDlJyrcxp43vR4qmVCzrQv4gluTvn67KdCLbOUaJS89WPqbFa - f/6bxxI+H6ErMDCMv8rUbPyWqkP3hkMD3yTe8sd8TaAMNr1JFPWJ6qVs4gFqvuH/fj/607ubXkfd - 7/zJ17GDP76Fd8NLoxYPdRhHvD/lz2BH/9arVp+UoNJXN77arDBozAmfyEEHS1eHjPRlsT6tPmqi - mTFwCOu63CZoQqxRyC4xPEuWT0zR+faUda4VvO22qYIBfvV056w7Mb9m8sTus4tGYnS3IbA+mHhx - KWs/PgQ2PxlBnje19WTuZIgh0YlmisJfPoBacPXwcaQTXQV35aBVTAzBAunBuOojBNW9htg+ZrLD - bX4CTFeqTLu0TSk1FFkQiZUEWN3yx199KRfyhXjZbNXTHr4L8NorMr67pa5xj6RVxY2/Izo2LV0H - jxmkr2Nficq8DlFbYqaUSFAyRJb3I11+fnrrcRlRsvxek1e35w5bvE6b3qPLVt+Dl6/pYPVea3SZ - +IMBi+HjIUbbGw7fz2wFtnw50bIRIvpwDjv48asCY6neWraZrwrltLa2ettLW3Glz8DCskxOXZCB - NbvbIoS3ieKfP7N0dcaIX6FISVbsFMqrw/MNf3yOT04aXY/hW4f30zbVqxrafI2VKv7tF9a/931N - 4zboJJ5rWpzYmVlzynEJYZkVdySEdRUNbednUqpbx83fftD5eTmX0s8/u+fxDWyfV4bsZ0H4+E4f - W/3rmcCg7fdEI2oRTfkg23DzB5AEqxcdkkejS7djAojhXh7OMjDWG9ov5U28Ne4iyjpBJWUtMxLP - dN+ACu9JgEtatnjDN40XX84bMl8rxJFavHKy1Qtha38Mot4Fqq0/PNzOD9HNjGoTUO31z09zWOep - LSZ5ubDGQbnVh3aUvl6DDDBITZJ3+3dPU7K20o6TRmwuVtjPhtbq8OcHam5bAaKzbxH6u1eMZUZp - HOrNsILb72P/GEvaQsuqkKSQS4ksOyn95QNpJwg6ViLG7Ld6mA9zMbGmw5bPqAwcGx6UV47T57Os - +4s/xD9/Y3qVtMznM45kCdfNk/iM0mi/erS04QtxkcdHqxQKTxHuWkAytjcpnz0yA06f7IRP8PLV - KGyhDnu++xB7Eg/5VL5rQXo+mh5nEej6pfKrBN7b4b3pBajNi9M/4V6cY3IfSZ6P5+DpSvI5YtAs - Rm5P8iP04eGgOti7fpp8MgN7/vk5U8RZmbaQIvJh0DUJsW5C5Uyx0ibgfjJz9Go7u2edQJ5/fAU7 - +/VK13x3daF0f2nEs8Nmq5cOCdxP2Y0YuHbokkmvGIZRouPN/6///C5g+iGxpF6j1I9ABussSaf5 - TWzABvYJga97S7B6BVdAiy7JoP398Kjd8JXes6yCH916kWS4M87gyYEM/2kp+Nd//df/2BoE/v1u - b+VrawwYy2X8z/9pFfgP/5/hnb1ev8aCf09DVpX//u9/WhD+/e3b93f8n2PblJ9h6zWQRIH9azf4 - 99iO2ev/+qd/bf/h//rX/wYAAP//AwArVeQJugUCAA== + H4sIAAAAAAAAA6R7Sa+rTLfe/P6KV9/UkUxfVd+M3vSFwcY4I7AxBowxXQF1lf8e2TtKFOmOksmR + 9jbHQNVaT7dq/+d//PPPv7q8Lm7Tv/79z79e1Tj96799f3fPpuxf//7nv//HP//8889//v79v64s + 2ry436t3+bv892H1vhfrv/79D/O/f/N/Lvr3P/8yDw81WFjmBTZLkznwkaInNsbcirm6RhWkAnMk + j+fH19ZbDAqYBpOIg88x1FYJ2RVC9FSQgiVbvu1lNYKvilWIwtGztt1fTgNt1UfYdD1X44eHkkLY + Tzk+DEyrdQb1RsgONJnZPVEGLnFvOnzv6XleTFBTAsdnAMpGYrBndyd3fWXBDlrbzSEZLT2wCW0+ + Qs27SwFc5k8+DfZYwWpIbhinu8uwxO4cQDc/AWKps6ituxWb0OhCjM+Xjg7U3ZUSuo1SPDce4rRN + KjcJzeChY3O8V+5qvL0QQCuMyONdDPm4kbiA3Wd7BWJWzcMSSEcVfddvbgNVzVkaLhzw3usJJ/5u + 7y7vhdkQfZ1nYtjWni5C0HOwPkY2UW+6pzGHaZAgY2k6tj5oF9MRhjtEl8klVzs06VReLRkxaLcQ + +6jFwxg2lQrBp39gPBwFjd7AViGeEc7kZn48MCfuzZQsqjnYSitzYCH3lpA35jxR2iFx6avqCwC5 + QcBm32k1/x4fMnyU5zM+YGWtaWTZIUwFx8a+9Cxcji2ogyJtOeFj3GCwNr7fwLwej1ilbuRuviE3 + sD1NztwG+wvdlgfO4GnUDRJya56z0zMPYfOSc/Jg3nXOfw5Zj9ZBJ9hxrztAr8wRovLYFSR3itrt + 92oSwibzdzMyGa5eHnzVI1UdBXxTudJl68d7g0w2ZthwbKHejmKgA/TeK8Q009fAOEY0I3bwEnL6 + 7uf0jiILHtW7S7JnAvOtHGsLPdIiJ97O1/NNPe9DmDwUh8Sl5ObEeV5PkLvKEJtFfMzp0a90xE9N + TrJ7jYe5HRJTeurGm5gvFGjkcBU2xHpNjS9r/Ix5dMtluD98FmLZl7u2+PAlI+SnK/FzNOfLK9lU + 1Dg3F1+xYbiMOGwZ8sJHTrApOC63OscARXNSz1v0CsHGKHUrXjM5IvLs2NoE+G4EzafbkaubW/lC + 3puEmLbniQ44nq5R6ThgKEeHhNZSxb9+RJd355P7eS+7k3SbLGDY3Q1bonUAiy0cQ0QY/YDvGWTr + pRH7DMbk+iDK/p3VXHXqLMBggLAjuOqwxNcyRVNtYuwyj1u8vpy1/bu/KYlRzZrNEiJVOwpE3t9C + wFi5PSNZz5JA+v5/Zt8dT0hYxRk7jVYPnHe4tVCbm5DIqyzHzBCnEIb7lcGnU6GC9fAcbtDJmD1O + jVzOWV+WOPScdW4WsWFojHFGDgzfEsDOFw+W8iXfUC9t95kb76rL8CHHIWeqzthOmzLn04uTgXfT + mdj76GO95GezgjfHQ1jpLnrNJbZsonZ/vOJ76DzpRk/GAgOxvc6bQO851w6JDqxbdce6hxKNxyqc + 4cV48tj1YmXYEmHl4B2lmJwZ4+UuJXPn4FDODtG6ptOo1Bw49L7yH4IPSuY2jcUz8HCAu7lOMj7e + jte+BC8rXLHekBVssyr06DSaBj4bZ+oubPviYM/N0ryOzpqzD7BkiBfIEHDD5sXcOa8i5DN4wnoe + JvUo7G4tuJDaxDFJAFhe3LmFgiQz+Cakpss6YhQgM7NS4ux32OX6MAiBHaQtSTm7rCfltEvgLTrw + gXAUq4F6gy/APZJv+NQ3obsqL72CgyBrOBNctd7mrU/BFz+JkRQOmGwX3iTFMVjiU+K42zOTIVKP + 4kAOIRdqmx6oBdIwvONEq+Z8y/s6Q+oUH4icayrljvYtAcXbjoi+n95gCS5lj0CUtOTHTxsUnj1y + PreNxFPq11v1Vk/A6bUNO0ZX01kK0hnC26EL+qLp4mWfXSw4Pd5DINlROVCZMCpqrW0izjTM9eKw + egE9H+v4cJpKlxWO/AYtqjjkoqc+XWK39aDiHFjsAk/LaXu3OyjkVwUXVIopHznnCDgZt5/3iVcP + mymRDJqmoGLHvasauzvWFarLAv7eD5AQ73TxKRYxsY5iVf/wHs5+V+BrpHv54qq+AOId85ifr/6e + L6EidpAmFsC6EYv5qHPHSlqewkRi6g9gO16rCuGbUs3iS62GbQChjAp9tompNLLGtTkWoNKRLqD7 + YXbpSw1M+OOPwxtx2oKWwALhsCsI3odv98vvJYRWFJFAFyqX2fafHn6qXfftJznnVsmBsDluRrCX + bs96Ed1ERufn+sL+aXfI2UulSIgs7yZggbAO4z57WMAyvAYXj48OFsC28NdfX7xY6KqqJwsxlXwi + lyUX6p8eQVOt4wBpygcs0W5c4Bdf8OPGvmsGt5yM/viwDx/5uibHHmor8yaFLlTafAoZAV3I08S3 + 8npxOcX8SLDKwY1kAebcJb52KfrqCXxuVndg0E5OwPR4DSRfJyvm2/2mottCemKRjzdwpoM68GKr + C8H2rOSceuajv/q7Ohmul32qOeiHZ3kCX/G3HhMUKnv81VNd3JX6qEN7CRccWuKWb5ZmcT+9hE8C + v4AlbCoZAWxijCn060XMR08Sb3cUVDWf1qtX1wG6RuMVm7z5ApvpoB6wcncg3o1k+R9/z6ddSu7b + vMZbrV93aN32n7/6Wrn0mCIt9hgivx603prdqqOQRilxruOjXpWXV8F397jNPM0eNZXvVwbuL/oy + M4sH4v6z9D388kEAg9u1Hr71juCjcXEO7atLkS1wsMPBGftW+dI69OChqF+WmGjf/uTeKRDgfIIp + Piy5MFDPrU7o1TsSsV9qVdMn2kJk31/XoN0iOZ4F7inAiTu9yMFzs2F7HEkD7eYUY/Vy5fLl2n3K + P/zV+oqLZ3TY6+B0vnjz60jZeIlHtYXJOj/nfaqoGr8T+wZSE1rYhmjOV2juHGDVzIUkXIBc+kRS + CPN6Pn7raR+PZlmVKEgSjfhSGriMET8tNMmZSDw6LflyfCgetMhGsS4nGiBn/GGga5kvbI29MdD2 + rnTgIPIHoj1B424cdEZ4eIQ8Po7OGq+SV+xg9Q7FGZ6RmfP1YZYglj5nbJqpMbDshFsQadtp3jh2 + BsshsBI4EXUgwbYM2pqcrQochT0/89vd0ca8tkZx6N4asd57AuafPpvkvUiCk5XmizhIGUxQh4iW + R7LGBoWTwfNVGmak8E932w56Bje+tgK+2RF39O9KIfY3qych3b9j6hxdB2aulGNDVLN6uSxJIpUw + mIjdsThe3Oik/+mHJGSSnLEvUBIicZbntZwOlDrLPMIfH3jvdOcOgamUaO68eQZ04IbJmhsGpmYo + k7PrrPlndxxKeLyu+ryI762m8SvkfnxDbP2m5NSmoIL1MbSDbUiNfBUWy4Hnog7wobJ4QJpt6SGP + nx1OHqfeXQwtgz9/QxQqf/mgOyZo3x5rHN9MgxI22mYAoMCS0+qfvvx9ZuDv+X/r/wkFw4KeEnbY + ySYh3si+Xn74RwLm9Rn6x95IYBavZ6LdtTymez1U4fgRPHxXeMXln0Oqw13wmIKtnq16tsswhQjg + C3HOYgTocwhNGPT2lbh1dqj5yscj/OrlAIZWp23Hg5WA3/O6RbWj4+ztT8BT+I7YgQ/zVdvjCPD9 + QSB4MpOBZto7hY3Arhi3J3XY0FkM4Wdt8j89sEGJ7eAX3wIRB6y2BFemg6GSYpKsx3dNPdEy4fKU + JuI/b3bN5SOfwdluwcyfe3ag8lSqiKjbLljAR3fZWwxucBGDFtt5/c5Xu0g6cHiwfIDiSADDp+U8 + 8dc/5uEM3O2x9xP4Zq5isD3FPu9HwQ2gmPZvclM52WWVl1eCiyyeZrRzJUpeoRyhKd/OAXXQBLa0 + Vhlp2vkFTr/4zH79KITz8qen8v67P4gzzy356gGwig8RQmPfWthvQ6KtlXZVoXETNpL32lCvgO9m + dBdPI7HU10ej2eo7cLvtBGLBrakXFUgJzEk/BcIH7fKxQztHyjdRx9Zyj3K+4K4j1C7hhWjXazEs + TzU0JWEeLr/+yNcL05hgPV04crA9Gi87V1ng4qbydz9yOo1NqcNpHVX85b9hDdBrB6/RfCXmEgZ0 + ZadD+6cvk/FeaWTdhSP81iu20oEblo3kBXjwQ4DNr/+cff2wQF6TPeIIblWv76o7wXsyJESNbiWl + hOUTeGdjexa5u5RvT1va4D66+dhZepa+zTHaAaMJxmCnKJG2XdX5Bh/+Z4ctzi6H1bHLAlCHFnM5 + nFY63vA0A6ks7uRQy/KwLnYYoccYr8Qt2solRatVCBnxQJR90Q+LfWhHKN4eiBiuc8yn7FPdIIAS + i/0mKsGaXG4VGrOH9+Pfmve7VYDXZ+Jh3z/1wxgFQJWYtuPxaQs8Sg2sZegq+kYgCAfiroudhtBM + dPbrH8p4ndZPASVD8okruc6wklHuJLkXVWxlJQOWGDx28McH/g8Pj9eqhPopXohp+Y3bXZbkBIVH + 0WCXLc/aKlT+Dq4PEgWwvj3z5bIUJyBfupEcTYOh45fvgRNIFXbOiwq2+ztpJT6AHb7qt2e+odQI + 0fV58mbEVxylCmQz1L0clRhK1ca/94Hd8/Mg+BN/wJb3Qwq/fvSrH6J8+eUB4jvKsG44n/zbjzL6 + 9Z/8HFu6vLh7C83PxSXOR3iCdWWRB+eUzsR8AxhvslgWqOXeMT58ttPAne+7CGgvwSEqxwZ09bSA + g9zdfGCV4eWacTehgOuWfX71pS1c8ugh4lMFyyzzopt3uDXwy7/E5u5SvDzEXQ9I4GT4h4fz9u4W + eK9k+sVHEC+HqRYQjYwUY+m11kteyzPcr6ZMPHzfYqr4bgfSkgnxYwMM3c53LgQbaAYcleRZU8Ex + vnkOi4nqQg9wqMtvcOUKj1jMSc35EHMmMEKY4Iff77Q/f7gTCvRbT7q61ruD3F1/YDeBRr5aOyDD + Va5KLJfMzl0eItfBb/1hX7yw+Wrw2Q3w2cmYBwWiens8ngs02WM3N5e7DdhfvrTuzgaxydDm84BG + WbpPexygr/+e9bLp//rH4PVRW95zt4H9ZzGDiOfe9fxZ+g5ijyH48OXfDeycG0gdI8GWRo50qYqZ + g2NREmzIT7/+8sEJ/PKHb//EyxppAiouu8dM4mSMl/4FLHjq5IpczfpRf/16g8jyavBPP0yVdlT/ + /KK/zceYPdT7GwhQToLdMdHzRRLo9uf33Hl+/p6nAIcVBti9NctAijObQG1uQ3yjjZwvRZbrMCfd + NM/ZjsRff6HDYqvIj69A9zJrBj6uWo4PTNQNG961Atox12zmtjyMF8wsHhQJLHEWAz3frovuwV3Z + 6MQhX79YwvUEvv0RbL96XKvIlODlYRDFb5WBp91zBE2Gd3M8Xk5gUT25hbu7NGGfbaya0xEMoN0k + cdB+8XN7p1RCgdhc/bUrmXpTzI+AYpI/sObuzGFkOUaFjLZ/4gMXJmDTA6f4+b+ZJ0UF2I+3JjAe + Ghs/jicJ0LKsZthzoxQwXfukW/hZRqQ+k4HI1qLGa7SNJ8DkxyzgP5+m3jpOu8FwgAXRfGpo62fH + m+Cr77C1l/Khu+zCEn39GPY1xQYbfrgJ3PcXk2jRktUbmJgQnew9wZp8clzuGpY9chLvRtQvn06l + tG+g4AUB1nhZqDdjyFS4O8IjvnH1oWa80NKhqs7C3LyWvl7hxTvB4gIfM6s+ACDv8SLDsb1+yJU/ + ay7VWLn5y8u++Fsv/Ys6aNgLEF9i9qJNoeBb8NPzz1n67hf7ZIsNRlV6IBncd9pXf0SgsOpHsO0e + w/B6V2WCrLBRcDHpkE7iQ9zBb55K5HPQDPTaaAsKIr4kTgz0mGrNlqIyiZV5bwq9NuuICZBtxmXA + ccHdnTnozODr34me3I7uOlF0g+bzFmHzdtyDb14mwMtG71h2BTFff/5+d+Ud4oa4yOmJmT2gsQP6 + 5XfuQm9i+Od3QLrWdD3U+wLe3cokykOs3O1V3EZJJ4NHTC5A2iyF4/jT01i2xChevnkk1PDuHix+ + 86Zrbkq6mN/2lwDd1pM29tbQgpHVblg9551Lj/3aweOkZUTdFDvmb7uggsPkt8SlQzLMzTE+QQ0F + GvaDgub0QT3hV9/EgCMfj1+8hToxPaKKg+ny9QXpQOdSE7s46mp2OIQVvDRuTzRvG2MyBMCT9B2/ + x7olywNnSu8Mfv0lud9WRpuP176CaWCJOAt1pp59HS9wZ7YMMe9ROrBfPwrOB/9MlCcfDjRyziE8 + Ftc3sZfHqG2P47sFP72SHpUrHetbxoFhwi3G0u05rPaYMvB3f7e6BuCXd4g5UJ3gkzZyzK2SCqFy + EiKi70aiffN2Gf78hhExgbs0V1OGej0v8xJfW214hVYk/fJJ+5sPstYOqOBXH+yaIrDWZdlA95EW + xB3Z/TD98ukvHk5SDJp8Pb/7GZyFspjhJEQaT0y/ge5OPmL7W//cNew6qJpLTjKNvuL111/bDQrE + 2iHTpek1SeH9A0biwL2lLR3iHPjB45FovJzWq3yAEH71e7De1C2m4Ak46N06HYdf/lydFHGSZQTN + 3PKmAfjIUqKfHyfeGbX5NIhJBHfH3TGA359pFqMW7vuzidXdwx02V8ot2Ne3Bj++/Lb4Y8EJv3zI + uDzqgYSlHkG7EWOceccDpdO1bOHoRtcA1NlhWJnvJCU8dS/s48eoEcXXerTGC0dOn5rPh+F48H7z + D2xEzOwSuHdT6avfcfCOlXpGqR/Cxc1kbGvrMNDKATM8v3iLfPP6YYh5oALOvLREFSjKb1auzOiL + x9gTz2D45lkl/OZDBNdvEBPj+MigsywjDkBe1t/8qwe9cV2xMd2eLsFyN8IvH5HLGyUuZV4n4U9P + 4+bRxAus9Q32fsUT9eu31u98Br56SyL63WC1UQSHDSaoR1jdl02+WA4t0fvsm795Qj27zicFX3ye + S5d88vWz25vgDFuFmDxmwbqfqQ5lGdBg9op8oCJfMcD1LAvn6arRbXqzKlpQaOODr9X5eBK1HTz5 + t4hoY6ADdtS1Ec30vMMqz72H7XWzZhhz0xXb5/njbpfEvoF0f1OxiXm5Zr/1Aa9MU+HHQawA3V2D + AGoPN8R+HKWUztzigIcRGCQ4y4rLoqc4g9/84uv33YVfcw/Y9qLhUA3n+Jf/Sz5M6IxGMdbW1ggX + 9M0fyEF+Pd0lzUsLdhNjkQyHAd2Yo7jARL6m5PDlv1/eB7ajfAtooZ/c5XEQBOltqjFWR8/95hHy + DL7vM/OVdQFDcE6EX7/NTwPqgKXqrv+9H5ELjgwTNHcWuIrYCNZ6nOkaps8GFN5bxea5y+mCdvIJ + /tbr62frbXp9ZMAXdo79H14NIJXBr990DJG7iOCw/Ph73g9HwV0NPrqhS4r9WVK3sV5k7R389Bu+ + m0NbLyLAG8wGI8COcNqG8aytGbznRxE783jIZ+x9HKgD9TOz33pes2FXwDj0JoLLjokXvdVO0IbY + +9ZbVw/PadTh+xp9Ar507GHrOPcGT51aESfUT/VYFS0H/ddLIsb1pLpMF3gprPefGdu0iF0i9J8b + /PLRj/8p/4ifCez3uRxMOtfFI3tJC2ibxzIQpw9D6e4tZ0gtzBPBXnnO++rtJPCT9/OfPl8ythKQ + EFcW/vqzmmnjl4cW0WtnFo6X+Ju/p2hEToXlppDoYLWbBcvzrGAlWPp8abXHX/8G16yaazKsQ/Pj + J5zZ+VJv+bhPf/6YKJkf05WZ5RR99S/R7V0AusxYLcj3hhC8n24Vz0NGu7/8Th2ebLzhh3aSvnk3 + Nr7zE+oNhgQd/KiIUcFJ47br3EK723Hz3n/O2npfXRmG6WwRx7nZMf3iNXiQ5YSLNurA/JunaH01 + EXX/OeX0jJ8MemldgC/f+4/nJG5h/y4PODAnOZ7f5nWG0UXuiQcukzsdz6cFJXPh/OX920cWBJjt + zg02BaDHq4WVCKrLsyCqvw4xmUTdgTIXPrBVXHTAZsPuBvCjMrD6zac4GTDF737Bsz9HtLsj7YaO + R8HF5+t9c7fmGCdIGT2XPDr76a7H2FrgQTYNrKb+TaNhqYcwurz6AF0B/Obn8w4qdsniXz+Oyizs + JDObUqyxqpOzzT1eEH6UBnGsjKdkeN4zkAL0IG5snHNq3rwF/ut3KuB//Lf/hxMF7H99oqC3RhXL + laBRlhIvgmduX2DrLuoav0mkAA/vfSeO0EOwPHbbAsMt281NIFZ0BdpnRvycnkjWWXLOG3e5Rc/h + fiLOHfg1/4YbA7H4pnOVh4rGEP7KgeGlBkSjhp6v79MJokkBX8bbLoC7jnMIDW6OgtjWaUw26X2D + LvMJcR6t87AdiqMqcpfnSHQjH8AmnaUQUv/EEm3ZVndNGMCBziogwZ/85s5Hq+lgJ+giiQ5mAKid + lxmaV/eND5KkABr3HQeFQj6STBYHbRTeTgZ9ObXx+cS/c+JISwSOcebMrD1qw0x9d4MdDT5EXuRD + To/KPoKPqfmQwr/rLtOdpxTY273DZ+YcuexyaSu4UwNCgsdJpXyZmxmcy+ZEInYfxnyOkg7O+H3A + B1DttW0+WRJkR28k10j+DOtOeC8onKqJOPtxGIgSiRKcTqGFb21SUBpJhomqj0OIKvctIMd2NGEv + hU+CXfecM065Sije+BORHxxDFyhqHgz5Y4C9A9Ji3iZKCgPSqYE0aAsYAjHUURbwBLtMNYJ16bYE + ecNhj9UU+XRbs4hBahcUwZaiCdD2c2Tg4Hs44O5HStvoFFco0tQD8XK1zflHunro8/mkJPo+H3Oo + MhMdO34K3hOg+ep56AbbKg0D8fhUNEZ+ZTe4L0oJH+5RWtOjwkfIu90DHD0Tdlj0m+VAsz3hgK58 + GVNFaXV4DfoH0U9cNTBdHAZoUbaFHB6vAfzqC7ZVFpLUDz859RU1hDkf8sSRikM9cYpvwWOcOiT7 + /ryEpwsD3SAosXohn2H1CiVE6yWySfDJLvm8RuUoxoN2Jn4svPONObQcYsPDig8f70K54fzUkf+E + HckFT9RmrEYjKk6eR5Ruj9yxMMcdTI6Tiy/H9Jrzv3osdvWeOC29A/Zu3Cr4gS+T3NMjybdTwLVQ + 8vkCOyHca1swrx3kaNeRiz1PLrlcIAefmaCQczubGlONwQYVdTK+/dXEXd2ceuTIT5Ocx+FFt+Hj + 6PCNzYLIhd3lH/bJeiivsje2I/lT0+nGb/B2t2Jy4YWIsq5ThtA3oitWZ2YcaPzEFuSkR4aNE3+I + efe5JejtrE9i3EOsMZEOGji/ioaYyqkceGWfcmiUjJn42HYBb+1eHcyXm4HDt5IO3EEcLfC+Txhj + xVA0Xq4KCwx+gPFVNfv82+8yDG7jCR+jvZ0z5Qty6GNaF3zi3nLNGn7XQxu9Llj3mXjgwSHaoLMd + XljbhZbLma3mIH97gnl7fzrACT24wcPzg4ldfZp4GSbXgbdWYLBj6TOl4b5SEahPAjYnaa6XYdIs + GH90itXxec35lPdGSdQfSgBjvqoZs9cDJERCNUPBR2C7+EUGf/td+K0AaFiRREqOxCXuKJT18vR2 + EpzgcJ1zzde0dX3KHkwOsJ8xSfN6O5VqJkbyZyQm5ygD5dNLgFQ5LfHlatd03Swn+l2P5fg+uVzs + fTJoq+yAtZWXY2bfqCoqds/9LJxy36X0ymcwV/IchynQY84e7yZk906GAzM/u4tBng66G89X0Nzn + CPBeJnvIz5wPOSiFpJFF9gq4r68uMVMN04k/zRYUbEeZIcfpYCqOWgZK9i3gwEtPtPNdYYRZwBLi + hgWI3z3IejgrdT8v+SS7zNvelfD1YBp8uJwFuskXfkbRQbgEb1A93OXC6RF6TYJK7mx8pIwKBQ8e + HweP+PAsg6180g0N2FmJsuJFo3PJVOj3fI/niun8zJQOng6kwX7Qa/l6S7oTdCZ8CngGVfG22liG + 72GbZq6MK7Dyp9aB1azuiVuLR7CymWsC0+5kHOmn2GUCM95gvessElNDj5mPlHlwH97v2Liprrb5 + tbqgT7Na+DI8XI19makATbuX5/2lubtrrjw2SCKLYs+5uvWm8ocWAbcUyQWx0cCKoyZA+RaeycHN + r8M0VWWKJGsfYCuZ5ZzlXiKES/4MiX98nVxiEzuVfvxkxv0bLGqqpSgU/YU84rzLt+jm92B67B1s + Xk4ndzNYkkKETgU5S9E6TI/bFiJ/kU18ensfsPTXygLLg/HxkVkOw/L5BCkw3aYixqtUXVbXA/jH + N/JwHAZ2v7A9EkzaEocLmHry0dUBXZ712H4ZY03nzTqhTrf32HwHcc4e5GOLpJD6QZux7rDZR2UH + 9/vS/K2Xy+WdIEDebJMZeAzWeAuLJdLquiC6y1O6Ors1gX2OeayHIQBrMikjanO7wdkteGr8p3hA + SOeTguP2VeQLn8cV+O4/sfZ6nm86jTx0iOXiV/85x1+yApogeJBrR1I6Tg9GR+ndlHFsdq7LbKje + QL47fIj15rSalw+uCnVpfyVuErXuyjw6GZpFGZLrh7Hc9ZA3M5r3vk7OrbWPh4cRfk/o4JjYZXJ0 + F62WIsiGxjrDaKPxq9TgCSaanuGoyhuwiJsYwatxj/B5r/juH/+uuHCI2wKZbqr8OcHmZQrYOxot + papIpd/+zkKWjsPW9X0EovTRz2CMrmDbB8iCxsE8k+JQ6jGnyp8EWVuYkvjHl/W19RC/njRy3bWX + epW1K4R+RzMSXOo0p9m7LJAGLrdgjrO6XpbWlNH+fJ7w4dx5MfUaXIEdes5YPi5lPQrquUVFQx9Y + dZbc/e0vYBl5IIaOOnf5rie0Mz0hJnnnGn+WLzokdIqwRhsPLFp4DBFvNglJ+2igKy9GEJmezWJP + N4WcdvxZggebD4mvLo27nn2RgcNumYOX8Hq5S3ZNIbSfwhnrY//UqFqUC7TOrR1cLa3XVvdFM3gP + 5g3rzMmPaeSmEL6O9DDv95EENoYxNuR7hkvugQ4HSgc3AJiwEZYxPA/kEqojWh6cTzDBJuB3/jhC + 79zdiFsNPthe+/oGXs3MBtR7OWDh408KH04PgvMq2nRbaLhA2is60RuRq8fxPXvwu79YPjpXsG55 + Hf3pBWsoMWCz7D7D9eoNOGDpwyXtqkForIxKjGtbgbVv5BaJUfUIBD77ntJQ+R0EbiUGK/DIMFeE + ZyBfR69ZNJRc2wJHZ2BqhCqJzW5wiYXXCk6m1wdzWR00vn+vPXzMNgzKbpfmyxl8Asheo9080rOf + U569CtIBXAmJk6qP6THWTGRvj25m7+RSbyLXn0A+VUnQXhcY//hKitJ7T8LLQcw38MlDeB6ifAbG + Z8g/71e5QEl1bvNq8Kb2KbuPBX96WUt320A+xQXCSCy4GR6Qlm8qjxvw1ecz3MJCW9iubwHLqAPB + JAV1Jz7CCOVZ22FjFT9gNAPRhJBCBSdLkgBi3K0Wsnsrw8eZHgC/v+YtlHIBElt9bZRiChyYWTLC + j9Uz6y2JaAL9bs1IJOTpQAuwu8GYd47E2NTd8DledAtG4o3D/mwFGte6UYooZ8l/9bswy20UdxDm + +PB4uXSK56cAg+ws4p9+3xrDukFDJ9IMLvutbjK8D4GZ+TGRG7b+1p8WQBE3y58+5ZOP1UH7YHXY + 3bCYj/21t8AWvW3spWFfz0gWTRjGm0/cs5+A8c3dC/Gj8BlRC8nPKb3uU6g9W4jNAQVgq07jCItc + 3WGvc2zALI4kSGN0XIgy0wP96gELGcyFJ17nfCh9tiiCzGlbifHTl82tr4AjnsKvvnYBm0zKDDrB + FImiNduwwDefQvjmDay4jDEs4FUFME6N9x++jOO7DeAi4Mdv/4YlHr6JaHi/B0xazoCGGRtAwVxb + cto/FkDvrRVCTVbvxNqGeNgAhBGMyHwmX7060Av8dLCF1oIvL01z+c8p7NGTb8OAAbtrPJ745oZu + DIzI2XFHbVWMtwXaal9jT6BNvcXgXkHjVgvk4ONm2Khx7VHKX5m//vn6kwIquRQRWV83bdKavQrb + 2YiwEhcr2L76EkD9pOPj/qYO7PkZMfD5WI5EkT+Vu0i70oK6cUBzdTBn2lmzBWEX+fdAOO4bukr7 + RoAfjUIs//pPpUklff3E3+ef8zPioLS/onk/zDje2naqoCe6d2JWMomni1wtsFikHMv702NYcz7M + 4PWtrcF2jJWB/Qzb+MPfYPfVe/PnZZzAl/9JsviqxiVzUMAD2hwsjw8M6BgJBUSvqsTmXj3SRbrS + BGrjPGCdZ5t6iioqo6ByhUBs43CgyaPdwaYMOqIe4+ewfHwQgauYekS53eVhBdJhBz/5WyLGc+/U + 63V4VsjPrA+Ju6udrxu3S+GF7AXi3Lgi/+LzDtCS5fFh62dAARVnpKF4N/diqNVspwszHOHNxg/0 + mnP6xWcQa/4FPwQuAvOgnyOE9kc52LZCABt7lEskvYY7tu+SBeZXEybocrxYBPdKBVat2cvgYzqX + QHpfm4EWvnMC04cMxNm9b/n2OTaJ9PXDwfQScnclipBI0HU4bO+HltKXwJmwS8psZspFARQpiAEV + 1O8kE1wmX7bxsQOIO1DsZnlGF2VVEiiwXPinpxY9am/o3GbuH5/y6Y7jkLedhgC0oKR0Ohv9Tx9i + 5WA47uK7ywj7a3zG2gs9tfk1X3q4rb6ELYd5ussXT+DXrwf7g1oOCyr9CO6RPRBtX3gaCUdbhty8 + XEhKl4r++A6KTzfEctYt4OtPBPDTl8ojXbV+sjL15zeCZVhO2nIi0glIdAZYfTtlvHx8GiHDBwfs + Ct5VW4rhuoHgwujEqQVfY7bxsoOTq0vYK8atnh+SOELOX91fP9eLci9PaPKVW/D+eDxd3vauApLP + FsSvQR4vjZuMsEicGRsD7LX1VmQ3GPWcgb2UUI3S3RjA5/A4EbPc3Jx9sYYM2D50iFdLybDuVaUC + cRPUuLBwqW3PzO6+uds9WLVmq9evvpbq04MEjPnBdD3k4wjfIuPiM7krA7/I3g3qwdHB/mNrhz8/ + eurGBcuXgxhvm3ISIGueQ4Lj3Mq5kRF2kJY8j3/1MxTi56v/DuuMsqYaNid+OIC+LAYf7eeWz69R + LdCu012i3QOjXq807uCVPWBs54U8LCiNHegsrvbTf9/6Yjk0pGIUSC290wldHxLYP4sPNo4Jpusg + cgti+8jBwetY1Qs/MyVkpM4nZ+H10rYx1C3kNUc/mGFi1Ivtp9Lf/uPI12NmXRkJaBF0Z7q7QjAp + fb4TEUoKrDDLe1iYG50hCvswKJLzKV9nu7+BqH1aOLcMWn/rHUJy0bSZkccl/vJ1A6ZTZAVcqmFA + WfEQgW/ehB/3JwRL14oemhRxInp4eLsfNtN0+JokFcsG94ynRvpAMFWS+OMbSm7ZNQUkcih2HvZa + E5soGbymuTxfVdPJ+aVZI2ih9PB9nyZfkL45kHHVEFvl2YuZZxK2MHWsKNjJBTt04nMq4P1TPIjV + rkw8JdZJQEmiWtg1HBWwjyWPfvnLF09SsNWdP8PtmoN5/1wx4MdoKdBpexyxentL7irFBoQXozOw + dWNO9I3TToaXQ/sOxsp33F+eBO/pPf6ecGvrJRjkBh10JGO95xKXKtEqAPmSGV+86ugqppWAaJ7F + waj0i7uCxeqhG3gliSeroeQsNAKShJeLU3Gqho12hQRpGDXz7pPx8ezcFgYxWCpJMNsRpQy3mRBc + fIgtaDuUu8GjADmhFHDAnrh6RlMow6++C6rv+0xhsQthsPgJOY26lJNVyEL48F73YI9e89dPH9Qf + 3uL048N6+9iSB5m5Ps7SoIWUaW5VhQQur4kShBMdyxfDgOtbWWeUHnG8iUdUwdJKEqzL/oHOW2vP + ML3rMrmstx7Q6ex3f+vfffmfakbFoC2RTBLcy9RdHCaTIDRZEattIw7kp1eT9ImCfQfjYW2nTACB + vbRYp691oK3+UqHep+bveYf5tR8KiPT6RPQwzAElxieFL3ppyY+PqaP2N8kudi3WdldI16WTElje + g5boQQzd7b2dMiA/rZbgvaDUrF8WN3CpktNPj9Kl4dke7p+3D9G5d1nTSioiSDlHnjV8aYfpkrMS + dFv1geXS7Nx1J5AF4MYKSRxEfLw0JyGDHCYTMUNhdPtLUQUw+6h2IC77AaxRBWT4zSvIb/1HLTz+ + L7zp96M7MPC+6H94pjl2VbPX4VMC61AORC9gQlfs3iPQOs0Rn1bBcPnt0gdSplkTOVX3OZ4Lp1D/ + 8q80lNh6bE5CCl3E3En2+/6uXT3IXeoxuMhh4pJrYS7wy2dzq9zP8TpdgxGqNXgSm66H4d0nEwfa + +RBh5XYvB/pOTRm+I9XH+olT6+1uyRyUW5Wbma9f2jL7qQLBNeuAXBqkrSvz9CDw7zk5hOICJt6D + Mnw2XfXtR0LH3PIdcMw8iDF7BvHKskkAv/2DPVvS3IWfYQkUVKxEO57MuFMfqAQLPeOZ26tHQCel + 6X/PG0Bb0jTGUioZ5so1x988AtACcDfArHeZFOOtjWlnfRb41ZN/fnLIP2wH2Ojm4QAsu/rnz6Fy + UFXsGe6hpgN6M9A8XzRsrnmrjdKudNBtmlqsJk+j5s1vBGGf6TW4jBPOGfFzaeFiDjIx9srkzs3h + 3MGeTY/4ju0BTBdhaRG/Jhp2YrzlkyXBBpxipsRRb6zaQvYuBOXda4muJ7dhtalaQO94fuBwDdyY + SgmvwualCwF4OEncTXq3g9/8I4Cj+MqnghUC+Msvlbtxp9T9NDdAL4cEe+98dgdyVTPwy2MMOfAG + +stj5nv/wofkwAA6HJQWwacck2smXMH61bOg0coruV8qOowONFSUZEKKb1+/R833QQAHfS8T6x6j + mM4Q97Ba4IxlQbjU29NCHEhksg/W42rk9GYbDVxgefvmc02+OU0P4WcRdPLlz5pFx9cG10IH5Bo8 + Io0Epzr88+/YCFg6mXXBSWNeIGyU7h4Myj5k0POecwFXxup3vuLr4Lm7qjNdeTlnU5WHEPnjkxyu + dg3GL7795aGccpLrtQMFBNdjE+AHl/X13LavCq17/YAjJtLo9hn5Akp0BLPAHnLAze+PAA+vnYd/ + 87VVrZ4c7GuN++W5OZs7YgG/eWwgfViPbiq5F3DGrwPODacCmzF0Jvwo0eEvj98k9RjBy05JZoY5 + TTlVZl8GD/7ikAO49vnCnO0Gaui4wz+84tk7dGDBE/+b3zJ0ER9pCL/zmZmXpCfYHp9J4lMwrqTg + 3zZYoOh6QJbtjFh95H7/go/hpK3s3thut6s7AQnvwPARCuxs092l4eEWgBy6h4AOUjmM9lmTpR9e + qt95Di33IIDRsReCt/Ce6/k8rBECxe0doFh4x1//AGFXRiDY7qMdE0etCpgE6+37ffKwnUonA+pk + zsE3X4hXVzEbmC5vlRyGHR3Wrx6E2zzqWBEftbt4M4KwbPeHeUlBky+Cem6k82KN+BSGgFKxczhw + DhoPe7lq5vzPX15ZA+MDg6p8dXZiAjPxdZsFm6zg2z8ylA7q+Jsn5JVgmh1c3p1HirXv4++8TgBY + fNGZGYCRj6HodTBa4s8sSnCIp7DgIkgs9CJBMFgu/5tnRQGxZxCtQT3yeVyi3umbb39HMbfTggjs + uUQMhHQo3bkzUAFZXN2xK+fvmH40nYNHRSGzKs0DmNeom2HdMg6OT5jX1vpOQvC9f0A5mdXWqY/K + X348X05ZFy+SGZ1QNyA0M1++X3mPkeFctqcASDsGbJm7CwAbFd58DMdEI9Ntv/x9/6OsXbDcg4cO + q0cWkK+fpASdCwYSKDzI2bNcMFmPZyH9/PQh9hrtg91zJHweEo/9F2ljKigWB7V+sbGbK8qwrMln + g0ouRMT+MJ3GT+JWQMMXD4HULfd6FdaOgaWxB8SU4uTrR0AIPXnxcJZOn2GKdoHzlz+eH3WVL2ZS + e6iwDiEO2h3ROll+9eKxY6dZPN1W2us0C2A9zBlRRzUEr2+/oySRLXwR3kE9f+cnIjUrDTtTosfM + d34EbJUfiDXOjDbkQV/98knsf/PEdTd3BfBQkWHz/D4M/JJfWngtvT0OHqeKLr40Mn/zkOsF2S4f + joosZImZE/9SxcO2KTcBLmH+xHYIpHgLA6WF2SRcZ/6bd2zVa6ngW4OEnNR9PazoepFAi29PrD6T + 88AX4jNEaUe1YLKmGozUjmb4zetm6L5xvfiP6/j/c6KA+69PFOxm+03kfn3HKz3rHqy7lgTiWRLq + 1aq9Ai6pOxG1vb3dsQyaHUxep4BYWXXWNjLhHsrd6TDvx/sGtgGTAp6luCG4qYKa7UC6AXA75wSr + INT4I+OZMKblnlj+e9KWQ7E1CHFrg21zbwEusz8c3EWOFIBrvg6jfa97UF09Hz+O3B5sn33VA/m0 + D4nPbdIw3nelAxVrWGaBxFw+3RmXg2yYcQHKlN6dElvPYB48M3LE6ROMTLhzoMi+KMYoJmBtlouK + /AGciHvJbcpUo53AmJQk4C68DSbFBAFosfYiwSLLNVcctJ10uwMYfHSdB8vLfrboUesaCSrsDMxw + 0TsY816FI+iP+QZrb4Z3OJhEj4+VtigxUmF8cwVizhOiC6GRDspqPeKbK3iUMrezBxsNGiTjSJMv + mpKlSNhhQuyufecfuV1DxOXFDeMDoN/r7wH0bmxB7FA6gbWIqIq8ri6J4lYXwAb784zm5+E6i/T5 + rte+lwWYRcwNO0Sc6eLlNoSRtR7IYZa5/Pc5mtg0I3qjagPbD3GPrtrS4ohGujaBYBVQZ99W4qtn + fthW+5oANZ9YLF8NWC8RnwdwTPYw2OgFu8yk9zJkrfRCkt316TLWDCrE78onvrrSc6DRtp4Q62Xt + 94TBEL8S0U0hGEYN2/CiUw4+1x3ai36A1eKpAn6dPiEcg/2daKCX6vE53XQgL6eWJIbyyrm4qBJ0 + FA80UB/tcZjcRyij3PEe5ChKVjx7W6iDg7ZTiI+m3N2iXZdCYA0CceujpvGVxViwsWIZa0tPcqrB + 5YZc21JIItY0J6JV9zCmQTzv7PqUM1BMTaSpS4ijx9OseZy8R9i890eiZPGx3ty4hCjiGZko0V3I + aVoWCdQ/TIOTrdsAS1+Gg3TV2khy4T+AW8UwQepg3smR37sDfb2JCXsQyTh5bWfK2nInAcfURZKP + hzKfVdc34bwsE0kMM4wXle4kcFYjGwergeNlNJgNymF/JoFdM/EyndkRWdtoEfdUCnT1HzGDysdh + wraqL9r6QluBTm1zIGmTJhon1ZYAn8l9/auHjV4THV7sSxas5nuvbWgpHaTc5De543Bfb30QCsi1 + HYXgNXUHjjhlhHYv5h6AS8kA+lsf4xXwWEniKmeOH34B1kU25ykyDcr+T9KuZFlZGAs/kAuZJMkS + mecgIOJOFBEQmQPk6bu4fy9710vr3rIgnPNNJ0QhnGNor0+M7/BW00F3+hKgx8Zh/xE32Wzq1ox6 + EEg49X6ewuiSk8M8lj3sA8kBfCUcINzrF79eZ3ZYBm05IGueNiLfG9thrvVSw0/HtNgDE40Wr1ts + 1NOf49P3yaObU8MePpQ3M3M5z0Zz1gYQlVrZ+x+qhmCtgqsNl1tx9RGzRQpjvYZeNOKoJ47Zv4cV + LH4J+ZuTEekiydlKo/UJqjZUsD5sKBvjxonBKtYP4h8kPhv3/oex8oHzQa9aQN2k0OHdyK2Ztboz + YF0cihA/hgz7s+2DKTkpD7QewHVmt2syMJ+nlAL18YY+2iYp4toF5uBlHpoZ3ubGIahedbR/xpfG + Z+nyfK873tXtjBr+50yKfpTAp+NaH0mB5nCQei3Y5PpEsrhI6bLjL3ps/nlGaALKWpwHUwzNPJtj + UWiVZWQ6GYL6JmLZYwFYlio+wAmRmhguLh3iX+82zNOrSezYe+4TmpiBhbzEJJeqc7aePnGJGIEo + /tHgWtr5sy1DUr89n39rTcWv0yeAr6VLiLXj196/OnILPyE5kBzK0XuuQwdyH4L/+CHAQgKHKnpi + 47Ca2fa+uQK0r5tHNJm6UYdnjwGV25yxtePPSn6VjG53eSa+XrV04DM9hPM5M+cqPHwBv01fHT01 + kBNv3CqwjulJghUMQpwJsloxo2EUiMueT5yex5au105+Quf6zuaTL07O9CoFW+QyamKNc6RhfOJF + Rz/93pFbbDfZGvh4AQ2nS8QrYF9RxV2fyDroD2L+IsNZVW0M4OylAvad/DzMz3tTi2Ftvnzevx8H + ihMyQjx3Pbmfj19nKcdzgnhsQywzoanQ0vik6P1TQ/LOxku01VEcIDTnd79toirb3lfRhlb+sHA2 + GlLGyY6moqoNFOK18ARovPYp1CXh5DewygH797yOJ+z7RSI1O3+LNSzK/ItxWTaUVrcyRiaOI5x2 + 0Qx40EgyGuebPCPH0TL+GAMId/zFd+Yp/fFLix4+42LjbtsKU6C7i9LjkpDM/o6UvJ0XBze/6Qi+ + rDHYttPThOA4hzhe5aTi386VQVv547A6Gi9Am++5RmZ2PZPHekqGdb716l+/4mjujYG9gC6EKNNK + 8ugOYbTXS4waR0n+8CCj+RAJ6JM4GvG9vIuYPO9V+JzCmji9qwy8ejAk6CzcSpx3dnfYp2rJaCEF + JcG6boCO54cIrx/4IFd3FjIaldAFE5pqn3ttTbWpbfeEO/7u73QhsPzaYEY/WZ1xtPQkoneiFn+f + iaP0bsQd30GAstydscJ+TWehBQhhweXMjjfHYZ2ETUR6XD+xvTLDMPLIHGGleQJ516+qWg6t6aMn + GynE/9lVNEg4MtF8UZ8kcs0jXZ3zMQeHL/fyD5zUOuQJqw35IrKJqyCzIhLOzL/ngeN+30FT1qMJ + d73mC9ULOaTTjASBWLrg4K49B1rrjxz6WXgiznQRHSL+wg06V5MQR+tjhTasXCC+5w5ET3kPsBNf + mAgO9wvRvu9TRU9GIPzx0996ZOu1s/+tF7l7YpdtTKuGcDB+j3kZV+Isl9slAf0xxMSxi/OuF04Q + Bp/UIAbDOWDTf2EBlcc2+0zpXuhW1EUMngs0ybV6r2C7HIGNWm8dyfvxKwfuctYO0DsGzq4PgDOD + OC9gcEq/5LZyRbbFU1DAv/XUgrrMNi6BKYDR9Pb5GK/Z4g22CH6XRcYxI0gD/TZxAt4RyWaUwxJs + ttJKsHZ/4K/enc3gzBnh7erj8zM6ZXT10QG+nUfgw1dXAuL+6AKRv5nzqVzYYa/vBub8PPnMw5UH + +hqbHiZX1BBr+hTKImk+A5VIOxHXE5hqexmS/W89rF+jRGtirD2cvYdAjKfNVtvjgQqRXQwDn9XX + Z6DDehdBoW8VcU0EAGUz+wm//NkgwcAlyuK5twZWbU3IeefDbel7EwDh/Pqrx4oOnxTCpItf5Hm8 + us4KCq+GIJYvMxvObzp+nmYq6mPgE6nntow+x5JB9nXx5vyk5cOEvPIADivrEMXyjtGA4sVEbKDa + 8yKMNJren86HPf06xLLVd9QbRpwjpaFP/+QfBWfF71svBitZMMZc6pA/vZXTMcfPC7UGtockASXC + d389ioTSTyv68GXC5p++WvxMW4CQ+xvxMj2lc/o4jSdq9QE5535TkV7uGHi56j+s9Y8toksOVPib + coV4sB2z6VScF9S72Ji3e2Mrf/oa4Hnofb6TtmHBaRv8ez76rv9W4Ry24NUe/9Y7A9u5Ex5wydiA + yN+nErEZGy6wvHxS4kTfI+gXcVDhHx6Y+uhW0+6v4DmVUpINrpHxXCf0cNfrO79+MtJWYgsvnmpi + GdwO0bTpuQTDxXKwml0uGZOF1xq2Vr4SYwBKxZtiWMOXEDvkUvh+RHlra9BpgS65l8u1YtA9lGD9 + PmwzMyMFMO9DdYBGcMmIhrsu29jmVIjzpFnzprFjtRLzOIp+FKXEeOCUbo8HW8KDbrokAD8147I2 + PYAiX89YJ62xT9Q5EZo4iYgVHN9go9bLhKUg5OQV6hpYcv9jo13fzofNPjjr4xoIKLCvI75q9Aa4 + ipl0KP9Un5y98litee2PUHLhSuKXOytLnMgPqMqvHJ/vqUC3MX/1UGeCiUSGOVazJ991lKfSE5vO + T4uWpf6NYqBLKwkVVgX0kOgz3Pl596stWNzsfIANp0r4kZqXYQ4xY0MwzMrM8gHJiDa0DbxpNwc7 + edJnW11dXWjyV0Lk3U/91Q/Y/d1f/0dLlP5E8ZdrGZbjRs+2my1s0DrcdeIWnR/R63lI4U2Yj75Y + KCwlmze4gHuvN+wfgTowS3gOoToxFO/1kzHuMx7B7XXrsClaDvjTY2C+6E8ie0tAGfHl51CzCtY/ + cc9WWV9s3oAs92di1NqJ0lDVVIDn5IDNXd8MNV+7yItEzj9C/+ywg7mo0G40zl/tV+esKD/0UFu9 + LznfowlMf/1Gv0lIvPtZz0j+MP1/9XnnPpEy8eu9B/TVvwn21y6jP0VYYHQrLjgp+Jez9uDZIKfl + wj/9HC3fBwqhwpkqvhRWlQ0a+BTgi+Mb1q5140x6JfWoWoKSWMvHGf75t8tV/eHQXrQ/fyuiQ273 + xCxSfc8T+h5u+onx4YV2A305Dxmm7qMkNupVwD3NUESPhBuw+4cf0wM3UC++Nnaa5F6tzduw4enc + /PzTc6iUUfCsEWiC+CCGf39X62tbdTjf783OZzhaLt1xA51mLTNjZcHA8d+yRpezmuI//8bt/YG+ + P9ne+dyppnCobSiwwher8jdTlicWdCgKCd77d422KO9jwFEbE0mxz9kqHgQB7PiKFe9QOnPyNHt4 + 687DvOcZdA2GNgbX/FcSL9S/dGKq7fF3v9hX22xYGw88AZ9zAdYk4aiMqVI10H5opo8auDlk+sQb + TEedYlcVPlUXQp5DRUkveOcLulSv/PAPT7XhkSi7HztAVy1i7ArXgm4fpgvB8VsNxCvP94iNwFdF + EV/5MzOhQvnDb/gQed6vfuTnUF8LfLDzIfbOgUmZLHzVsHKFCZ83lSjre2NElDJaSLAKboC/aG8O + WDPZZuFZQjC/bpb+l0dhze+0irN0rQTwqDPzqRsSZWIoSAB/szJ/nS3RKZ7vVULY7ydsRmU30Pxh + urAr/Ae2WVIMZM8roKBXkj8V0K74fMgEyAiTMqP4XA6Ut8QGSm1i+Kf7Ug5LgS7+H177y03qwGp+ + TiPE3O2FnUsmZ+ueT6FW4BL/9GuqaMr0poBvmo1/eJ2R1zj3MEiUGauItsPGcluOLuN2x1Y9bNFM + BkYUa36f4Bj521n2fke2fWewdV/kinmdvRGKQg+IVrxZutxpkyIYkTdR8wukNHPbA/jTj06e2BE7 + Gx8TshIp/XVSh4wa/aWF50LifW/X78wSxTK6u8bif2+dUP3LF5RHLvswpqwyWjMfw7Z7z/PKHEk1 + HdF3gWMfU+Lqcpa17yM3865UahirgAdrBCYV3t/5j/h+7jnLxQhS+PTqCBvS3aRUIncX6nHzxP5o + c//lz/hQfwlO1nTY+vzOwQuzCdjuD4nD7vkXNECs4kejoIw+kDjDPsUs1hO+cRbu2LigFMQca+rc + OZ16nXw4hUrlc7rOOBO/1SmibAewsxk/8C/P+vOrkmMvw4YUvxWN43EgEio/Dr/NRw72FPC+Rzfi + TH/fVzHh6h+S+yVbZpavIVetP+x9eSVjPOAwgNTmh/zrhz0/gTu/YNv3vgMVOpiLMslSbORaSWeT + fwfwrmx7IKvlA2mj0wzrN9xI5n2FjLhJq8LrCQo4KIHvLPRdlNC1LEDkLSyitfHo81++dL+KwtB2 + wjWHwkdI8F9e88/PbnLg4Svv1NkYGU9XtOy5+etXQG5Hq4U46Vxy+fih01c0TIByePnkfEm7aoF2 + xaHd72OVlw/K0qZZDdfIi7EcLM+IMxO7/7s+8rDCEcz+FgliCBiM7bf5BAtOi/Cf/jWUew2msniE + 8HmKwn2H3xdsdoNTmH3FD8bBujr0LdrqP/+ETW4EK0KfA8xegkzsi6k7610v57/8dz7VQ5ht/PXT + QvmWiuR8YD/OPGWLjUQrSvCeX1atBxwOqvI7Jzb2rIwurzYBhwvnEyO2m2geDtkMUWaUf/1X0Vtw + TcWIsBmRvpw/MPY0NrB/i/38inSrWvf6gbufwJIUg2jBdyqDIyNERFuDHyUcUBN00G135kj7i4aP + 7YuwkwoL57Om0Hk9pQlMR5WS1B8zuprUe8BUSjei7/9Pj9HHh3zm9NiaGx/Q5DMFMA1UneSd7Tgz + 07rByTqoD3ItH5Xzx9/w0zvxXn9VxEZimIA/f+dE3zdYeXl6iIN49LH8aCawHVduhsdP9SROkukK + 99FY+S/f/8srKTGzX/jPb5wLS8m28FCkEHG09jc2HMBwfwkM3POo+TBphUKHshrh6RraRN/zg80L + HxLfSQyL9V1v01g+7H5+DH0KvngYu5vJwUVhNn9pAIooO+Y23PSviY0Yr9H6+UwS2PNZIh1/dJgN + k/hiuLx4rPzpN57jAsDt2a3yPk105rRiBmZ2O2Nv2n7VXi8QxkoFd77gaffH3+/34000pH+U2VZa + GdgstYn5SzdlLUKOA87FLmbAG8NA6yYt4eIwEUmOOaHrUz1LCDBnn2h7/+x+cd9xX2AskVFS2Ac3 + lZBA8+gL73Q/o8iQTIQUTcWORt7KZtULh24Xxdv5waL8yRz6f/rCeNuXgdvsnwj+/LynB+6w/PHx + 7k+w/TlUyp7fyfDtpMG8cscf2D4akmCcCzG+RbEQDU/EFX/zFOIZwRJ1R69ZIFurd5w2URWtp/X6 + hIx2xXPhYDDMbsyXYB6313wUIldZtMIq4VDuZyTvfmvRD0sP2VHlcbJWtTMWXujCt/E4z2zSMWC9 + gE+AiLs+sBMWq0LYzM4hnzMBee38ve7zGYD6fD/jRC+dbRpOOmTjTNv1a12NAKAa7vrjnz7Z+G/f + wNeztXCCoVbt+aUEz2a3zGxWe9UysmABGhS7WdjzrNWkWopG7vbx5zp+ReujGRtIO3Qi1/76i9pd + 7wLJPJRYc2ch6kTJmYG6FreZJ5KotFv0ZqDdGNyur690+9MPq+E3PkSPA1jFwyJAakG4z0uSaNQW + +wlLjU38dTkW0T/+qg0Y/+nNaDgZgYhc6CnEV1tQTc0b2/+uR44GL+MjBgtgkRw82xxRM66VUA7v + leng5A6fw9J2JASKoz2wfshdZbhJzxzykeViRTI/GY03ZP/pQR9+uk1ZD1PNgJ1/5j2fdjbNrFL4 + +yxHEr+MyhlHAxdg3G6hv4apFzEY4F68Z88U/+ETXfYdnn/zM8p862yhC+OKe15M0qNaD9u5W1Lw + vNbbv3nGdDonOZTGQd7x+eIsr7M3izOzQOyJE662wwctogESFWtLUCuTEM6JGDci9fkrZp3tlWYc + EIUYY+Nj3zP68MAMDraZzUed+yqbwKQL3PPomZunFx2zMinh+zb182GRpIEhSqRCt/0U2BUfNNr1 + J/yn3893vwSL/QwEQAo7xDYOmWxThT6Fq9g8iPJh7Gq727CBwiTo+PqnZ/SD0IpKZJywcVrrYZW1 + 9gll6cxg+dbIGT980gPc8yN/EX5mthG7CJD28Vv/tPuZZWaPNXSf5UaUhYhR63WCCaExrjPg5WKY + FmOEUI4eA5EcDKrFP5106BFQkbObaoC1g44Be/8Q1xUWupqfdUR/+Qlmo6JaW1gVUD7qI/mbJ8yj + dz7ANM7OWMLat6KSVB/g2MsGdprLXI2bdHoAKW5rnCR8o0whPDKwexYctuuDX3HTsKpo8q9Hottn + y6HnomFgoRgSMaI4zdaJb01Yg5bB2qW7ONvgP2fI29wZny+pVf3lX7AQKcEK21NnuAWvFF6Yp0uM + v37a9Rba2MNEdLbNs+2471hB8/NOTH8EYHWPFwFYour4J+Ej0llJixJ9EMdjtTL6YZ2NzgTZqVb/ + 5kkVF7zXEP3NT/d5tLIo8teEbl1diP+I9WxzY76AD2wK+PaXN/zNo4s5vxLlc7pWI7QrBl3lwMJJ + sW3OuOcZ4sJ/QuxFTjzMxRfo8EEcdp72PHiUKNjzY/78l+c4E8PqKgxdmsxT4KTV3/wXJlbvEsPI + j8r8auPlb77lH9moGBZFnkyw531z1TBztrr4IcKwtUf/aIN3tIUfg4HrQ7Cw5Yuewyz1b4b7fIfs + 9ZytVzE34bKftSalkkfZl+oykAjmQtRz4e55/jOEphJXJIJfWpGD7m3/rrfMhudAT0LZA134tsQY + gVGRo1KEKCejT4KSq5TtFIcj+D92FPD/e0fB+UQtn0OWqGy/lUthtDSbj77Pni6bfLCh75CBuB9E + ALG8s44a7h3te/Jkuok4LNE7/gREy+9kf2dd1SGVloTkLW2UZRXKAzwk/uozn86q+ONWbDA9//B8 + MEZfod9Jgah9ieO8SU4AeFEtQoj4ecZGt4SAEmFT4fescNg1XiNYo9LsocxdAuICpnHW9mEzkFyz + J1GS3qu2+c7bUL1ED+wu7ncYTmZhwq+LdJJ+31O0wLC2EfNcXv7RcDpn1fMwRGpxeRJpycyKv5yf + Ijybc4wt/F6d9Tdfe1DdlJ4ocwbotlyOD3E8MU/ivCZz2LhXqMOZJh2R84qCpdg+LrpVmou9xLUy + xgpPG9QcdyZX35QrpgtLH6pgnoisi99o8a6eCa6S1ePgVTXZksntBvPhdyJS5R2GcbT7JxrKk0wu + bzUFK9erAYLA/83lfTMq7sOWHDpOrU2i+13O2L/1Ed0rQ5R6rQbe0fYJE3P7+iDxftUwRu4Gg8Pg + YWdk+2hF501Ff5+t0NcAEyONgUN5V7D9nedhr4cHWpPNxiZoFIf7faonWoRFJ/poSAMjzYII9r/P + gM05uurL14blIpgkyU/JwPfuWqD4IRtEWc5fh5H7k4ye63fGTndgAU3INUV98gBEgx9a0cfnysAj + bF2fn4ooYul3OaAFMizOppnLNvbMC+DZHXzi3uaHM4vvKRfhy69m/k6++/UNOazm1J3p6yIPjPVx + F/hqDjF5s+OJEvXYpJBrry+iKJsTTX25J6x60xA3ZGeFFsjJTwP7ZrCOO9bZwq4LoP5pbZKA25ku + niyG4LC0Gnkmk5+xeDNttD9/7F+pqnDUOrbwR993Yt+vn2q7vj81av10wIr+kZzpVj9lWGHRwgnl + JQCa01uGxmwG5B5LucOZL2qjCj18kq8udaiEMhn6NxPh/BVpCi+qbQh7Iz2Rdxt+lOUoXHJ0eTkB + MR2jV+jp56ui70wDVlU5BMvKVTJU/flHJNaN6aqVoYk29qb6FVi/Ed3OWowuVl3gS65MysJvhwaq + /vgj4VEcFDaqzRr+AvGHPQfR4d961JvCYIvxLcofI31GF179kvconyvee5MUPo4HmTixcRzWNLRs + KDijgc9NfQcrZRwI6+3M+Ojsvx3mYI8pKC+iTqzyRqMtXpknZMTlgx9GPjjblcASvuYfxW6WixXp + p9sDsSKbYj1JLcossORQC8QFyxlzdLZhT72fp31HQhCgYcMaECFqx3FGSLtn/O/nmkAPTcsXC1au + OGKMIZy50x3b19cn2gxc2H/9OaPTNaWLDt4pFMv91NnP4jjLJnM2vOynUtvCvFSL4AccPH5pN/O3 + 0zNb2DQoEW1OApHYmBmoc4tdyKbbF0uvSs/aTG1HeB4uun/EDDuMjr1JyEDthejRh42mAxJtuJ3A + 5m8PSjN64Noe5CV84uzm02h+LPgA45NsEfOBHcD8Pd+JY0qf5rZfcXu/gn5h8LysTyaiV/oKoWbP + BIeJrdNt+B422I7CjH0ZfOhwZNoZPtpvQVw1T/f+9ziQb/mL2LefUI1Gp5boPmp3rDDi6pBrZcdg + /oEbPp/qjG79bX8H+de/sLv5L4fj7w8fHhRbIipzd52lsvATuEKt+OprtaJt0AIBvuk5In94Ql+W + YqN2fBCsU3J2aPKRH+h3SRtsMUlXcYsypqIpeSpxr6NctXe7KBE5MmSvT0fhNjA2cHSZhAT7eq+S + WtdwSpM31teTMBA/IwWMFH3CWnT2q4UVuUScnncfn0vmnlHnR2zYMZxBVHd4Z+QXn0JoKDPCxv33 + zbjWC3rE3sOaWN2RUaYsfaZQcl0PXy+CorCSXLdof574bUQm/cc/LLjdiN7cb8OmCdoCDozL4vCm + bJSGky3DWnpJJAzDpupestUD5TyW5OrcPbCBbKphY709/yPVnDNss/gQsdREfpccQ2UR7QDCVwNj + 7B7rZlhs8l5gf3qNJP/CdaABe95QNj0NHPmWRFdJHltYGvlGzNdxjNYklSB6sfltJl00gqX3+wSC + YHzh6PHwHb4mjgDlasxn2a0RWJlFVdHlPB2JJi+fjNumS4Pi1Wr/+I52mW6VYpMiHwcCvP7Vf4HU + cRxwxJ++Dq9OUYm6u8D4abkIw8Z87wwcouyMrR3v6CStAlpPMcT2dad5WdJq5J8mQpTbcI/qJv9w + KHqVEnFLjQz0i3IBgsO8n4rKpwojLQKDZJcV8Y6HziLhhIEmmkyS+EvnbNfPp0AGe1lx8H4dnM0c + fZt/Sd+FBJWXDxsywgV1ye54XoGa8Y5mxfCQuCu5tXxD+fvv2sIT68bkPv1iwEwxW8OtucpE2r+P + 2s7ZRlQKbuQ+Np2zXsQsgbLmZuS6XjZnaWa4wW/6SH2mt5No3X6LgI7YNPC72CSHTReiw/YljDja + 73eRZmGfqF94oveWoCx0qVwUh1ZDNDcUlX491jm8xYY9cy/BBrR5rDoiFLyxvJS2sr1RX8Mjtg0S + TgM7NOGJ8dGOnwTv/DfKQIGoE0SO4PohUf4lLCa0r3ZJlLcjRsOgrzOS0HqemdfaRavaPCTUVMIH + 57COI9qu70DUT5du7v3D6PxWTfORc+k77Dbz01niURThjbMQMT+8O1D3bOVw72eCHaWLqHN7ulAI + b918WCLDYVHoSfD7Sx/4nFSuQz/9UqNeYSycf5TvsN4SS4KfScnnqVOdgauHyoSCMxs+27bfYcqS + 5xMuOFn+8LZadHBLYfqSCmyqwZUuRucW8N4W3XwUMr9aM/1cQvFGdaKrl9hZ3Juno2PemuT+an4O + OwIhgNmn5IlOycehXLktaOLiitwTXXdYsgo10JhWJe8FeMNmXAoVhraVYOd7tbLlnoICeA+Tx+F9 + +w1zAo0GBs/mgX1YMxlt0pMJp1/8wa8ROmCy6m+J/Ej18dP4ugM/dO8ShrQZMd7rgzucHxAePrZK + zO/Pc/jxe+vhWL2FOboNp6jVBzeHrXx8zIeP84223ggY9Jq/lJgvXcqm6ZHsKjpIMb49uYyE+CeA + 8Nq0RM6Yt/LHDyDYCCKaLHwcOmNXADV6a0RvsZQx15N3ALUjXPb1MRQ6XVwOnk33SrQxbRy6Kl4A + 6vf7TLyzf1RWfZ7cP34k8fduOyw6iyq0gguY/+lX0L9HwAS8R84v++xw2R25EGyXC1HsizSsibCf + AphPX6K0H1VZDvAe/+mJGd5rmW6H8+MA0+B3meErrrK62D4+2vWbf1jjDSyn/sDB/X6wt7I8INbr + KUNsTiuOO8seOMwWIjwqIzeX/qOJ/umdr/hycHS/lxF1NCsBD/v3/LsfhxLL0//q2Qf+y3DIy3Bc + 2AkPHmvYmqOFD9MRSu/gQJ7HWq92PjH/6S0Vl1FF8encgrstNUQPC2vglTVL4cmY8cwyUgPWX26P + 4q7P53VVsLNkajHDwXzN/hd2UcQ17CGAGc4V4o+ZDla+cVOgK60+89v9Ha1pcPah55gsCRuHGZa1 + SXUIKuoTRb8X2eaBYkRY7WQshY6mEDd6Jiev64Vdz0XD+qd/vaQ2sMEYZsRK4bEBSkwj4vFVMWx/ + ehQGNCbWgw8jXgLZfsYIePvg87IcZtcz4H4vf/Mz+frDcguKGP2Oj87f748uBn8rwRxzDZG/oVkt + nrr5gKMbIfgYyoA/7xObG5GfWC9Xjq5/+udXoZRkifYDK39dn+hjP0dyNbopWy6c2MCr0wCCj3bl + bPR3WZDOjAA/0O3lMGsbyEipL/G83IdG2b67DPzehDuJYLlS6uHnE6ZDUflL6HyVDXiPBg6310KC + DtJo/kVpjtKLFpAzimE2znI//8OvjE1uSreQpYcHuT/7ox1/snVEiws+THrG0bcnw8iHwQyb+FH4 + p11/jhfN6yGy/Y6YmyQ624hH6TS8kwvRHh88TK4dClD72so+ob4q1DHOAXSLqcRnltnP2IFaD7LX + QSd4oR5db/FZherL7bHvPOdqDgJDgv2H8bDPKMbAqaI3CufbnGD55HDDZnqFC68nTcL6txiq8Xko + GFEeeEjkMouV1UsnEepKr++Jnx5N95G4p51fiS9TWeE0QdvAH18FdlVns9XqAVqdWPAPjPPLKDJp + vf8qTYXtJJ+cf3qQu27DDMqcAUQaTBPU0lv644NqRPnRh+mz+JDLPZTAcuTuBUD23BJ56TuwdHFv + gl0P4+tpy8E83482up2lO9a/qUOZ4YkSUClPEUt+pNJN+4qiyAzbG/v3Sq24e8WI6G7LDfHzOABM + uZ8ayrZBgy+T49OxavZTN3T1t9dfCSjmrRlmzK/1uXN4BIsXaT6Ah/XmbUxoD2z+/M0wzmt2X4/S + Wf/82+5/8SWLhWjLrD4Equ+381psZ7A9XkiFOjMDYqs3PqLemzzECqf2P324LtMyildWvvhiyYvO + EghbgS6/psYm88MKewTbzqdth//0ODl3pguM2Q52P1tW49OnHPg2YoaN2wlG/54vst3O/+O3pU16 + FRSXKvG3XZ+tGjPJ0MXmNOuZxTnTzC45bDahIo/9ec161EoiH4YbxqN9zlhV5yF4OtD2PeY5UsKy + Wg+DZ/3A1lc3lSUXCwhF8Sb4XH2TlCWTiw0tMvj8ww+KT1YPL3f9M9fd2aZ8BK4yfL5lgLVXWit0 + 91/ixWoK4hpft2K5bA1QmTyZv7yhovtryqBWz8vMhHfTYRn5JsHf9X3FbjuqFTdEw/6ra6aDU+PW + VROtVhm1Y0qIsjkwo/WqcHDPk4iXxJ2zzFK/gXhqeGzIx7Jahe39BLMFIuIwL8eZo2/3gDtekN2f + K+vbfjJQE3wGn5NszNa9H6CvNN7MKJcmIlFnm9BAdogxHvcd2oeWgffRuPuVUTqAfk5OAyW8IGKd + paezyZLW/OlJn32HGVhfkjVCLOmxf4HZEvXusW1g+c05bOXvyOmTV9f+w+PgSK1hoWXgwwHFV+z4 + 3wZQhUYJOmXBcRZvv3T4DwAAAP//pF1Jt6q8Ev1BDKSTJEMERPogIMIMUBEUQZoA+fVv4bnDb/aG + Z91zj0iq2XtXpWp1du0L+vUIsP5s+7wVqrpFvqQ/psabO3UR+EcEhdP1QazXqFBBtFrtx4/JKTR8 + lb7KWUI36XCc+u7YUVrYtvaH/4kWRYDeRNGQdosH8OHdu4Bvb3WDqnfBY+/08kJewlkFxHGWcCj3 + Qr3OZyGBNw7GRLn3YU0KV5Vhx0YGxsiRwLDZz0+vwEcrUWzhxs8OCto+JcrbMgBRn0YBTGe0/vxn + XUhawqz9lN5yV121u8Bm+OlDP/+lk71tYQ1hpWDn5qg9b57KBOrZ+YM1aA7/+JD5aW/Y71jaDxFy + 2b0+fx3sDMXbHmxD0mGkmi/sDXlDSX2MB7ThTWyx/JyT4aJNQOKo5YlPMgBaSLO+VeiUzd6KnOV5 + /g7n877GZnhaQP863HnoH0xzYuxi6hePNbS/f9df19KeN30NLPsYEr3qBHU23s4XgHrxfvmxX3wm + 9GB3YY/4tCS0Xt3tXihj0Qexhkvdj+ys6RD4020S5usJrCsaSsjow4yTgFiAuo9PBn/6g62673pe + 2VpDywi+5LhfGdCCaNDBkLMJ9n0XUuq5RQlR6vVYzkdUk6oVW2Dk3wtWvEzPudw4FMj4tlcSZ4YV + 9vj6+QJHGe9EA74cCgaZJPhlRQZnAqeFc2qVJdr0AY8dpZjyUd3dYeKWD+Kzdqf258UUgZl8c49F + StKTUV4kNBTx1Vt3km2vI0p1CM9hSOx5tPph00fhbnEAdh5ebo8eK2uoyg4vj2l7Pd/igQQJmGXs + ipFrD3Z6VEDMfU6eCK+kX3ZAcuDh412nwGBR3mfPGw9DpZmw2zl2vox7MYY/fPGY6Qjmyecm8MMX + zlNT6WLGQfX3+1Z7q8OVRwKEDwKfP72rZ+nce5CU/EROdolDqqfGAK8fG3izc4PhrC1OALjhciFm + RoJ6ebVWBpnLtE6we881IbYrS7kqszje8Dafnr8KeJgfRHAbHFS+ufgZynGhEhUrAyU/vLTp5798 + qw478Vz86W3aPthmNniLBILWzoiRerLNRXdjhqzkV3/PL0i17sD9d5Gnn/49pWHHwPvnaeGEMkrO + f85+BbNLRYgLnTHc8rEFF5FF2JcRCCf6SVf4rWLH2/SLnpquqcGf3rDxS/Ay6LWFW36bqPhd6lnv + nQLiRxMRM5+ZfMC2W4ibPo1t2TvXCwm2jm1XS//w+mqmIIAs4bzJh8OF/vgx2OLpDw+Fi+80PuzG + x4soj9ejp8fcaEGzJi9irSdQj7z0cMCPP3lWrfTfPqwl0J5RgQ9326HLqQi2mWrFiYTqt6/pYxUj + aVgfErHxPaCz9XB4oRnUHTkGskL5KRUMwKTLk3j1EdnL5RvycKuXEKMp9+paHRCEu7tlTvPjdrcH + jWoOVOk5HnmyK+v1977qGe7wjy/NLMNsW/Lw0Vu5MA7Jzx6du0WwfvINVWD48vvj30TPZKtnuwpZ + UnUqVqLY9JzPfNNX8C59gMfdz7697M9LgxKgvCY+M6x8DQXoQz0LP9hcHheV3lnRB7FkckRWIQmH + ZmBZeF/gHauGLdbkrBY82DsBmsqMPdfj+2VVv3y42W8RLks/r0iIL0/yqwdMZuv5MFLt18Zno3At + 998B3p2P4ElPxrPX0MA+DAOdePRU9XROO+8O87nckzg/8CFJL9YM7YubemUMl5x8rX0DX3kWYHvv + CfkgGWcFSEZ4JO7MDird9GVYzZKx4dtPTfyQrKC83RtP/Pr7vuWSpPyzB5f2cs4N97sBP7ukI+73 + KGxb58AEEyC/SPAO2no1bsAAv3qCEZAvnfpLzUN1Xa74p9curh7K6MdH1Ls11cum70Np9TJvP983 + E5bOCWrLx4PIjUbtYWkTGX7tW0u8xwXWC9HUCtpl2+OzoV3V9VPXGSKXfe6totKrw97xJfTmk4IY + WsL1y+urRLBQ399JGi5qz4uf0YO0vhr4578r7dIBXE6lMw2rd1PnTwsZuOkP+LHl03UCZwsNJDem + 5V1P6ozhmkgPnynJ8b33ARX7I4SAcZ7kp38R/ZMPYNOrPTQPTsjWInuHtOJMbLfoq9LHOkcIWUP7 + e//26iXeDPboulXIzRcgF+7iwPdVSgn2aAdWbhkkwBb+Hdu3twEoUNpIWsPg7K1hGISz8da+8MAN + mbddiLd/ehtMBqXCZrxbbToxewnIj4Dxlk2PHfqCi2HFFuKPf9azZCUQ4scrmna2auZURqEMY+Oo + TNOFvuy5YEoevpzEnyrVetGtXiL/1WvulbPYi9m872DT7/HhXXv2/GSgDzlzrok1M9sWJRNNoMsS + iu+bfvKr78C0Vb5Y/5qJPYLTXgKGZCUegyMPzOrp8YcXpoFFZ/oMtan81Se9pZSe9fzZYxFWOqTk + BsKnXerFUsAmEh/k5OGXvf7qZ4Y8quT6xM1f/ofZDiokZeOu/8UH8KvneW3fhBOPdhA+BWbZ8nET + jlu9ElwTez99avMGFqnQBkn8eha2lyaul2q2LPjjaz8+t4yGnYE1AzO2jJMAhiNxIsgWwf0v3rB1 + 83whxREkosRWA371I5iQd4CPL/G98Z1oe9+fA7ZcJP+dDzg0U4K9yllUCrcbuYbThjh7T1691NLN + h8xs6eQYdyRvg3mdUVmqKfHyd2GvtlqWKDvJAz7uylNPZbvR4HyPV6zdv1w425akwDooIFZE6wsW + btIMMKez5omv+7PmfnyVSemTqHE1UpperQFefSUnzsMD6vS6+Qa8NfcNv1S6SvVUntCv3uDu7GfY + N9mioXmMWFzorxLM8QtYcD9dZmyKu9iezaezwh/fmm75CwzH3cOH9LrnsQ6uT7AMnurB5nM7YwcM + TT5h2kI4e/xC5MpZwTuzzATiZuSmXaba+aa/f8FPv/vVs2fRT1bISkGFtU1fncwriH718qnd8OVM + y23m5hYvi2aC9nJY0xaidPhiL1DVmqP1XoZSNaTExs3af6ulYpC8CAvRZGutl0q+WADX9tGbQ+1s + z0vry7ATRZ788Pdk2GWA9vJ1nGad1nT1l07+f2YUiP/dUSDXQkV0wdZr6ofvBtY613gzu9+rk2xb + Hpxj5kCMpQnzryieeQTUbMBHM9mB4aTwPHrMWCd6rcs2W14dH75deyAeN0501rkjC63HUfY4hK41 + 53/KTBJr7zwNsb+jr2BafHSlmkbkFWmA1etlhlZV+5P0vnjhsnRcAdv9tcSmbz7sRX2iBp4M+USi + Bmb5FJxEFp6f4OLxO/CyRzfiY3D1dzJW4Cj1M130GBTzTSPG8hhVqsVXBZnx8Y6NZdz33aREHgjd + IiLyt5LU5SNOAWTgqyMuaHNAI0PgIT01HFGH3Kvp4fb8Im1n1FgHWhmORzBKUBqYxlsOUQ2WM5dG + oNm9LezXpLL54DTz0IHhm8iKVdaLLior0pjJ8j5W2oVLxdUajFiHxUZ1tsKl1cYJQsG+ELt+nkL6 + ub0dhKWhIKFpHHOuzyQRGjq+ec9uuIXCS+Du8P7C29YIwvRjRfcVPDGF4y3dcMuF01f6oowfpuk9 + 32BPxzvDSsG1aPEt8pyaV9mbDKEfCNMOpao9l800g8duHbDju+9eGBjDQ9+D+MU+SW9gbXZ6hB5l + /8Aevz7sxer7O9yTrzLRJG7VleGNCJzWxSOpDIqcryMTwmWn9kR/VKnNGWdgocORaTwAkkPOuvIY + wZc6LgSfb0o/mo9Bg7HcNt4uqAIq8MpNh6wkhziRDC3nw7fjQ36QrAno1KzXtFpnePeXO8mdUAMc + 8cIGQTeU8fFFZcCl3nMFHVne5HIZtJx+uRqCMqspcQ9yR5cdKX10u14rIqOOryfRHCHc10jDOWWl + fClHnYXyWl2JKbOxzUWub8HoFl8IfucVmG3+ekeHI2xwOFQspfmulSFdlIVo9PpWF/9TJugoGjoJ + eYWAxd06Olw2ifEVOGPPffRrACM65+SWqVnOfsrVQk0yDKQQ7yJYdjJMJFv7RtgS7mVNG0WNYf+M + AxKLilWvpAQyoPbjtm1LYtXlcGEc+LBwiq1evVDKr2uFJverEZMx7fy1PqRAutyRNC2X9JsPc3p6 + IeHrTdixRitkN39BisjeyFHZIrhWlCIS1Mj3xJTf1XMeHTT0uTQCMbZdmEt3UBo0cM+IPNweq0L3 + oTI6LVIxCenHsYX08/TBsW0eXkf7xp65bY91AV85Pq01n1OuThR47B5nYm3+LpBPrkHwZkzsK2cd + CL1aQAjQmOJMlYlNSqUOkHGMe3zYfVSVf9rvCT3MRSZY1dV+eX5WA91C+YnxO1cA+3LiCjGdkWHZ + QEkvjMNnhT//A6/zpijfDw16P4g57XcnLhxcZFkwRcT2hnv/ralzDyN0a5QrcWdo5cJ0nRtEcNhN + u+XI1FQ7WhBwblFhx49swO77dIL+y0qIWw3nfmbuUgLSE3l49Ob2lO66OIOtbVnkWKpqvqJ9X8Ar + zwx/9jwQ0WegsKAdNkPN61n+olVovQUnHCVXbBPB9xTIs65CHJ2deyFJ5xh506hib3BDyk1nfoI4 + Cq74KB7qftyRNoC7h6Xj8yW18rX+nBp4yiWCj++dkC+Sz7DSFj+xfH6Y/fb9XkjxoEHyTJztSXpj + Hj7K7uHBI9/0y+fdtvDWFDNWyU0LaRZdA5gG8IiPb/Fsc9wttmCcpLy3e7CHnDqZ6EGCzx2xIX7n + iyxF/p9/v7hwn8/JlMYoKtc31tRt5xrV9h6E0Uck+lrhUHhQY4J7H3mbPx1VIj9yD+L+7ZHIVHSV + TytpBg19UOIddxIlu+6eSaF/t/EREVulpzwxINq696X36UmJIV0M9BK3O2cgbfMvEhYJtfKFIdZp + dmpB3dvV/gP3E9bysrdZ9XbS/+KrrUiYrtGitnCnXSVsGo86nBUcG2hqUxubg1DQ1TH8FX7E10iO + RcLba30BFWybyxc/vG6vzq78jlFV7M5EEdxdTm3/uw3vLs/E0NuCfiJWbqRqZZipU3mbLn22iuAy + fgnRjSjul1/879+OhJU0W/KZmQoRNoMBSDp3fr5et57oZ/V9Yf0eKz2r3rAG6xZ+p4VVYbi6n8uM + 9OZaTLvYlW0+dwUH5AKQPG573qkcyi8ci3Ql6uF1z/lM9nw4o0yfoHhPAF9I+wo+K88mnsUz/Xo9 + JSIUtWeBMzq4Ksvc1wRd8lOAleXc54tsHANE2OxFrMYJ7PnkkxmItXPG1+wdq+1I2AF530TBmpZ6 + lFMU8YW04qNPexfLdMCxVEFR9vKJ3eyDj8G5QIeWKXDCJQ1lTwfXhzJNTZxu8YW4e85BgaQcib7m + nT37Fxmiu3vxcPht5J5lQWRAPbw2f/hhHNd9+4vPOO7WBVBajBHY7fcusR5lGfL8pClAGrMdwc30 + 6NlL1Cm/fEfkLb9Td9ACtKdlT5ItPk4fR4jhO08Mcn8+K3U+P+UYRcFxxhu+sGlzfrPg+XAiXDh5 + YAt26Uh7wp1jHCOG6efddz8jSdcMkgyE1MPv/EDMg0noVBC+qkdZge8l3mNz7vyQGqYwweN43U/g + IjU2sZ98gbhaakm4xZvPIRd1tNLBJQ9WCNT5yV4H9EgkjzhbfJuvsyKiwW53xF55P18vY1sC3/to + f/44F6VRoC0/T7D83MFc1PULKCJ/8yRlb4DhyV4n1JjvuwdX5tbTrjHvMNlBD8uXRM2XfN9Y8M2/ + W+Ka3UHl+O5WwsupV7ELWgCW4vQw4PdQnIkupFRdyylz4J5qAb5M7VSPJvFesPdWFuuHVgMs0GkL + bWZ38FhRkPPlfdMSKDbfkpjmbQyXgk0L6LtDQI7zrajXSiwVOH8zn6icdqRcEmy4+pJeiMNXr7oF + QqojPbw0f/mXR5bwgupjABMN2jJcI7aZ0ePWt0TO3bPNvdPOB794Zr0vUz6mjuhJJx8vWCbBtf+a + 8xnC6SJM2Ih2OeiKpiwRoxoTVuSrDMbf817i+Yyvx+pdj5RVAyTXXIXlVT/Y/P4yxpCwPPIWv2p6 + ejksDYiZvCJHen4DuqIyQH/4g517umrzIkphtDj4yBodpZk+RfCHd/U1qsAqBdvtwfdzN4HYCPrl + oOeDNCn5gzg4ftbrFv+g9Xim2JSuAliUINRh5ebfaccKgb3Q4yMB9ip8ic3293DdX94xfDyP4l/+ + nqherdA8r7eJ6XFXz6o7NEC6nxfyyDUVzO+j7wOtnyKiSIseCn44vuA7559TSe1zzsqN7UO1Nu0J + 1M9tTyFZZXDDzXma3TG2Zw/4L2Sb6fT7/XAZGimAVYHOntRb0B7kVzrB3hKd6RN1lb3CXqrgTd2H + +HBXDv2QXQcPSGl5xVchDAE71FwEhsULp50cZOF4O44N3PA/LqqXFq5PaBuQ3h8hUfeg698iF0mA + c2WTnOx72s/Voy3hx5sSrAakqSd88AzIW/FIzFsnqfPv+7T4YRNPVnU6b3hCctpk69gYPJuf50sJ + VJsPiZrgJ1jNOYVwy0dkw3f9PA3nF+T1+5EYz7AMuTsGCWT7631CmSqFpCrCF0wZ8zURsXv3tMEw + gElamUR/5AFYkpC5g1RN7iR5hnLOc5XT/vAlvmJFruk+qQLARTTb8p+Rc9F9cEANYIntDX/wH2cX + i9+EGkR7zzXdnqdB/NN1/vmPSbxG2vAg0ch76ZfUD2PpfAd37Ln1E0zMqSjgFh+wlxw+9mpNbQLo + 3CTE4LedfrN+YMCWj0j0lPWc5dX5jhJ27LCZrHE4U1KsMLHDkRxPhd2PsyXeQTInLcnfMZe/Fb+r + 4IYviZanGl2/60eGyDtQnBY3tRfmpZWg0cbPadj8m5ZK78Ml8t8kbbQX3ex1hsPihDjczm/hHEuH + 6PbJiPrz93K2V6RRwEyRmr/qFW0VUWtyPJIwu1add53sAfJMpglkom+3qLqVANaj7rFcmOaLLFYD + 9EVVngYd8IDG7sxAPQDutPeixR6TkL+D5ePnU9PtE5uanHGHsitw+LTcG5UWbioDYpWYqHtWzfn5 + cSjAaz/eptG/BHTRRshAeLC7SYi6Sh2qR1vBq1vOWD3GZ7BiIingNIlvrKbRaq9OHic/vjB9A2+h + P3sB30u03/zjWY/5frLgs9aQ9/mcPvbSfAJPSlJYTdDu2Xx4GyADXvUZMGYZMSRTp7UIA4vz0PPz + zH98EMpuXnj7JljyJRaLEugP5UVke+flU4nYSRLi9bH9POUDAayOhDjSSao1Zk0c8snAkM0uSZIF + h3MNKYTPqn1tU3VbuzuFw4SW42fGttC4lHW92IfgG1pYWayjutru+w6XT5B7qPeUWjC6g46q/qsT + zZN9m89hrUHKXVZcXLQuXL3K1OGGL7x9cav7+TbuExAeIoRxkA89kbE47Lf4TdxEUAH3lHYrePOf + 1lu699WmYSfNULupD0/gPbXm+zGI4d78xhOVmygfh2b1YWcUE8lyjqpUakIHBv3Vx6fXccn7/tBn + ACfWDm/6CKAPdvUR5P0aK6tB1JmZvTs0jotGTtvf576uyQBwDSOi9i+rFwRmEaHOyWecs69EXav0 + 3gLOl1Zs0bdO5z89xWwNogfKXSX7LOfhxoeJHMdjuOZM0UKuLjCJX2Cy+36VedQZ+xN2tvOm58cA + QRScZk/qmGM9Cm8pg5320rcOSLtvI+PZQrnQznjjN/XK8HKMei/Osd0dqn7Y/BnuHMOfpFOnh9wj + l1agLqyMZZIiuuGDAp5ykRA1IHrNDfudCPXgznrSqWvCHx+DTsvFxPgYnrrKxd2HO+naYM/bGvwA + ubUw+IoCjr6fT015ua9gYFwzojvunNM6mQsEEEmJ7tpZSP0zK8NR+1b41HITncd728KH5aZEWWNQ + E009Z1DfXwNP2MHe7mN3hgg+2BtWnlDJ//DKN8VPfGz0W7/CIlEk5jLZ3li9+3BW81sJN/vFhbxT + APus7S+QC/1MvPBxAW3ZNCuE/ACx6ooVIBEnGZAE4x5raq7VfP3BDTizexvLD+TVi/DpIRyq+o7N + p3uwF8RCCDd/wwfdPtVL6sweujX32ftcuKSnbDsM8DBk/cTOXqd2qfec0fTSLKICOaVLn5rRPz5V + Bjzd+JqPrq8cE7c739QhZMIVgdyGkxiqn3rB+VNGGx4gP32HMlaaSCj1U5xlswLW+jrqMPQL+w+P + ra96uAN14k18u3EuXfKv3QLP+ewmGr2ieoVfmYHmaw4nirU03/CYDH/5pvjkhs0vjF6gTW8keLLL + nATFgYX+y0hwsJztcB7pg4XOh3lhOTNkSitn0dCq3AA5WamZj/a+yODkaTdi2VVp81ylfaVmeEd4 + s3973V/vEpRSZsam+Qrr9mE7orThmQkc4JvSIPMDlEXmxkeGIly3O3moF3sTuwnztddhn05g+77E + 2vQHmqm2A9x71m17mKueC2LMSOXp9NryF7/ttd/46rbVwcrmii75fjJ+eGnaPWduu2HhiFIxgxuW + 2X2qDrLwiAH+BC5271SgtD/0CZBXZsb61HH1bF2zAm7nQ9Q3X9vDbL51eAvvEsFhWYD5cXrO6Pc+ + 4wu79JvelMGM9y8kB8kzXIBdGWjv7q4TrF7blscrx8B7Ias4qA9Bzba7nQFb+cpg+abs1PHhHXjI + zPveowk+gLWrtRL++GlcBjH404d3VRNP3I9fy09tRSFwzzg8vO4hvaV+ghL7PGKNt5C6DsAO4FM+ + nQh+sIeQiMUx2X8/UoxxWELQ57tSQXkxZRjvpwsdf3oYKj9nYsxP1eaiQmrhdp7E1oc6XC7DzYBY + 8lVvvbQILOZtrVAx3lyPK62u/254H3wHfCV2tpiAe+cZC6fujLzmg2owXxd4/30/LOfRNaeiU0mQ + IssgJytQbdpsU+i/IHqTA8/YtN34PRyNXY1dcz6FM0jwBDZ/I/gBPv24V48SPHaKSA7rqbX723BX + wO/7ePtHFS4475Q/vZY3Ir6mt+9whx5/ANN8fpg1zYNxAKEQdfjaqXm+Ku2llQTkvIhvILFeIjcx + wPzd7qBv/J3gI/uFPRsb+MjGfDhx/vySZiYQCS72CyWxKzJQ9e0L+cWb2WqnEioxSac9m9eU716M + BqgNeKIZdhWOl+FmgTknOf5732cujcEUMXDj9/d+Ymveg693Uvzhq+Uu+xKcVWiT01r4Nk3SOYJK + HMr41EkPe0lCvpC6RL5gJXyr4fLOA1ZSgizxBvyabGKYuwkure1i50jKvEnCxBfOYxNir1i0nrY5 + aIH7Dg1iDDxH//AElnkDa3F1UKml7AuwyGxM4ke+gnU0WB96vAomhl939iBZ8Qovdt1jWeyHej0l + uQP7+DhgB12KusgvUgvcG/SJL/ZDT64TKeHGT6biyOv9yPdYk64HYSWypkeU6wcPgnZ/KSdJFVaw + npLQg+7enon7qS/hHBwYCA2zH8iBF479XBNbhnV25sgWv1VuvjE6PJ8sj5hXsOZDEw/RLx7iAFNC + SSkvExoeJw87dcfWiwV3FTS92PUErMg9N2ruCt332fAup8sxX6PFbkFiCdHGD/V+2odg26p5LonW + QCkk91YS4dhrH3z32kCl6afz//jBMV6imm76EvQmonp7le/pIr4MVlLQ1hGAjoq6pk/Tg6BJqkmK + 5WM497GW/fFN52KTvsWxVEoLeBf40KZl3x7ffQQrUk3Y2fSBFdzNFwRvaE5v7QnVaeMT0MuJS6x9 + FlF6zTtJ2vIDMV7uvafjzfXFDZ/+xc/RvEklCLT7utmvQllmKiTIv48s1u14CKdXkCT7j9iMWDWN + dz5IXWv86i9EOZ0ZMEXPzoDl3mmxNUFSb3q4B/HHd8njXXXqHFmhA9Xw/cJHVx3V8eseIDxems6r + +F0A5vWx+mirh00kLAs6//Rcvlzp9Dl0PaCpYM+/3yfek3j9xj8HIHTzBet4p9ZzZhwG2HJ3EVsd + 8643/J2h3otyclS/okqeD2b65eNp9w30njvuYQSnl25hK4f7fMOLHpwZX8Q3bviA56YvAE7ByCOD + l21T+Z0VPj85g63GWe3hos/6X7ySz4+unnJX8OD5fjt6uxM65UJ1PxoQ7x488aRtKx2vigV0reqA + TW0kdNn+/k8Pm+AA6nBNnwcHCXC1J6mgKJ9NsyvhfRUrEvR13S8sfnvAOVQPorGnCszGNskLBDAk + ByWN7P6IkxUez9PBE7b8POtLNcC4aYNffSun948Z/fIHdkal6xfzJlXQLZkTOcC3G04NKmYwq4w9 + 0Z1wq+eYu77++M7sskX+06PgtdQ87GQKDf/e1xINjMdNTyP/BqeZhWgIMJHzSMjndi5asNXD8N2g + Ws7SxYuglKUmOcT3rF6mMzPBTB4VfDeXKdz0pAz27z7Eht5CMOVM9IUp8ld8Es+MTcUbwwB4MDt8 + wGsLFun+meGWfyZhw5fE3SMHeif2TbLAO1OKXLb8xUevrIliD5c4eKHOuE/EvupFvTinY4bE8Psl + h+6+dYA4XxFs/jKx32mkPz6EhtN6/enzNWuAuYJFbMR405fV8fmRDOhdE2fTa5lwkp7DBKsVMps+ + qajEEPsGbPVU7H1eMJ/wJBdQK7eRcMluUWdZuEZImgVMfnrdlp8KyFuLPgFFImDupG8B5jxUPHF5 + uDa34UkQa+iDLSPV7JUrQw32Ymfio7gbe9JuN8oki6uIA/0hHOz6zCJ3b87k5tePnA64iMCm92B8 + NO/2RM5JCdMvuWDlcLHost9Vg/TzL8MLv/b6Ls0IrKzVTCg6NVs9QJ4QA5uOqMzqhjTG3xLUc1hN + vUEQILsuTqD+3qt4y+fq6rvHCt6LoiDJMqb9+OPny2dg8TUt3+Cz1TfBaRELcsoGmI/BwV//9Blz + rr5q+wyfBhKmmni7a+CHCz1eE8hs6+HnrR66OMeQ/+XX7SJJUo/xxyh+5zPRECB7vTCwAfNqPz1p + n7FgqwdlcCddGk/Y9O+1nNX5x8ewD196uDRfLkZqNawbP+76uZ16DXaTRLHtZL06MzeZhVv9a5Im + zsyH+XpXoKCuHVGT2LAHZ7FWaOHd6hWdH9vC5zY6MBTibqvPl/b8sgYGdNTliLJiLh8etib+Px0F + +//uKAgFUSFZI5/VESAcQe4MeY+P9sSmbPyooIHfHbb12yech/AaQ6f7Kvj4WT7hetFZHol3wZo4 + cTwAfuABD4RnbZDTuVrrRRhuGaxoeyGaH5xC/lp/FehSdMauL2jqIqmuDM+JvCf48v3Y8yLzJfBr + bcT+x9foqKpxAlnPdXBSh32/fMuzhSqNF4nuykW9Kv0CUX0ejvjulp9+YdZHCSKoPfENSbetZ9fL + YNIEe+KVRxCSvc5VKGxjETvNjoTDsDcYqeYvGTmcfJ7Omvy6Q1UZXI+9je9+qVA3S50W5pvCc1Dn + mjYrwpPkTmtWA9qlV7D18Hs+8U5j0HPcZWThO3l3ONn1rL2stzsPQkFSJgFFXb085fKLtBcsPC7o + Froo+s0Dju+2+NgfpHB+aEUA3Zg5Elsmcsj2cWwg/eT0xHxAYq9hMGsoYu+JJ6DIrPksWT0JnTJz + ovplBVPFlDHiWWYlh7GsbOELXw2aw9Agaik2dHbLCEL6UffYfrGyyvJLVKDQoNMUNf2uH+1OK1Et + 90+M6SvPudM98RBMQg4ry/dZDysSWvS6VeNUKBLuWee0KjAYKkzc43GnLsaMShgb3oGEC/B6/qGf + E3jstopXbGa5IG9dlyK/2tioHzpYtNpKwF7yEDGPd9Nuh/AawflJJ+L2z2PIKsbtDrtuF232d8rZ + ryzJkPIflWi1yqtrvtwMOK/amwThQ1OHwjkGCHZRit3MZXu6vs8vsJ0HuTH63p4O36aCA0EO8YPI + qufP10qgSsaE3LpPbZPdcmOhuUo3bGMqgFU6KQnSofgifss4Nnvaep7H8aUTt25XMEvHY4AmBZnY + O+7smiedu+1JFm7E6tqsXrUHP0Dwylxi38KXuo74zkP2Xqr4Ue2/NtupMw8bvU+JPDSXXkjywYKY + kxEJcrm02ah5aDDClY0P5VGl88grFmL234RctVOUr7VYTRBmmu7tJgXXs7VYCbiqAcIHM1dyKsmC + hBx/dyHWxXz19HOKtqlJ8tZBIekhsftDi1rG6vHhM7oqr0zzipz0ahPnllwovzvzA0QPJcCqY5zA + fPlUJRLjbW/lcr7Ws59/V8COSkc0Y+nDdSeUESq1ao9Ni0179iXslb1+YhJsZYLd0/OlkpHOywY2 + Xty1Z2PViSGRXyrRpKOncsx6LYEBzR1OrNrpBe5aQQRcpvH4eDf0Y2PEFqL8W/3Fo7yNddVCRSKc + vPXedv1aX9UIPpjEJPiFM7Bu5438qDlh+XmOgFB01wAE/VoRx3l24fz52A10LPaNLYojdeaX6A4d + cpPIKc2PNvt8Hgp0u+Q3oujaEbBqeyjgEHiRx2/xh2cZM4FW+4qJYjdLP+cXc4L1eTpOXXcd63W2 + RR1mjHvw+Coaw1WZxBk6h1GZAASPnupS30Jm3ybEtNh9TaqYuUPThT5RZfqxqSTvJPg1yzuOD31O + SaR/GKg4Y0HsVa1tvt/NARIK6+jN6lUO2TywJHh7TxE+SORoz5M3WtCsvxYuar0AvfvQM3gWXiq+ + zCzJl9N+YeCugTnWVifvBYTOGur27oek/NpTctUNBZ4P1g0fTn4MiB/FL3jmtQ4b+p2hXdx2OnzV + 3W3aKUUI1j7PdNiF7HuiX6VQ59dCMzT3jkFkp1H7hcllGUWXFmCLmX1byH0Yo+/OuXoTd7j0lIzW + C2Zkyslp59mUFzyaoYs/Ct53HNuQFkOsw+qVKMT3hoPKIfFmgT7kRuKzp3O/sHldIOmcnbF7rOpw + fQ2fAvKTdiFhs3b2mO5DGXV9MWFX+lpgYbh7DOvS671wSCu65s62llz/6tt5vwH3ZZYB9jQRiMyn + 33wV5mAFr4slYEWUolqAe51FwkxzbO/uh5CzBzuGq3u/evtj4IL581UyeJ8PNfaD6NsPa/2MUcsd + DKK3JyXnr2XvAXT8iETphaGmTyYvYLnw0IO3hKOLcQlXKIjpixylj2ZzrrJCNBzseZKYVwTY2Vwh + ymRxRyK2rnLaF/4XLWjv4nBIFcBDJXEgezx1+JRik9KcRCJK3btD1IrRcsG/3jJQm1TDx5Nxoeub + ituUd1wQDTFlLyjbZY80x8eJC7oz4BAIGbTlV6IUlWOzwZpFMM3himN65VRqXH0RcTav4zuUDJs7 + lnYD4XnwSSBO+3xlIlTCt5Dr2HWaXJ0vY2sgZ6dq+BSydsitt5hFg8IcPHTXP/YUP64rYEqXwcYw + fe3Vsh4a/O4YDcdcVdYCus8lml0jwY9JITUZw8hBs/1lptnKE3vVHswEhlSbsbqLBzDuTaUV9JPX + k8TovvkQ67aBqkG2PehcPFsQbDKB47NaydEMn6HwTJEPM06wMd7sm78NXwYaD4t6IBy++Txet61H + l7Eg17Cd1UWVHw2UUE+IRXMcbvZ4R7N0zycRLWcwJ4U0QNEsDG+HhcCmWskFkvsuepwm5VMdw/O+ + ROH5XkxSsN4otbyiADW5I3JQi1Vtn3frKw3MY8WHlQ/69Ye3XLf+evvO3qZ8nlcHPXglJA8t11Qq + h4qFDvGZJ+mHe9bzkLUJMlfxRh4vLNHBT2qILPPgYMst3HCu9kMCB+a24kOHU3vxGWNAFvepJnGQ + 3uFn3Kaamflokg0/5YPaHu7o5i6htxeNol+9R+6Dbo8/E1Wenrrk43UFl90zJPFLYes1ycYG3A87 + ivE5ZeylexwHtH0escZ9Xa8Fec6Ifg57/MidgK7Oyy0gVz4Kjys0P18uinCXoGLrEw0TqZ+fSimi + k0hc4mXgTeluusrw1Z8bbHb2Gi7jpYtg7pAP2d5XzXIU84DE07rtrov7n32ga5oOJD4IV5ufTyML + Nzz0+/4qDVjlhRwhAeSy2b8QbRVf5UsdrAvF5l/pZwKf3CDYYb21Hmz1M0MeB9sdcA3ba8bmEJpf + l3js43OiM5ZNGRgPqcRevd+BCWXUQiAS1A1vgXwWptsXNjHjY2dHvHwI1iyG12/he5cNX7NQzvXf + 5xPlnDb1asRZJH2Z4os9nHV0rcXvAEbIph4fXmu1m4Kqgls+JcejkoT0KHcRvHTJi2i9oVJO5z4D + /FqBjeUrH+eL98wruPauP9Grs8tHXWxKYA+qQcyuNiidX7q1dbQdyVEth3zwGFQJZngNJ64umfDP + XomeA2LrXdWvv/yYx4ZCAnXcqfPFMWboFgvCrrPG+UoPsv+X3yyyO6jk01IecK0QTExxjuvF20EN + zhUbEOuKK3XOE7mCfGxesLnZM8nENIaMyjyI2kac/fNv4AgZ8Mq39cmn10DuYMOL2DLbtl/Kkn1B + bBQfoqZXC9BoSBv44wvR0XzS9T0vE7SjZPfPfwE6xcDwvx5Rrazc8pPSwMDZHbFXFU/QFGvbSONQ + V0QeMilcZPJ+wdcrtUgGjKYmtvPU4OETZQSngd8v7nG/Qnabi7T5Uz5YuJdhkD/6H97Nl5D6EsSn + SSIOB9Oao/Jh+ou/Uvkt+9VTlxKqE2YmusXveRfZvHSjD3eiu9gB88WRV3g4tE+SvfQJ0KnfGZCc + zBafNrxIPE3UoY6bJ5YtXAAuEfEL6LdrRkxWNGuurZwK/PDTQ+Be4Tpcijsss771Bq2N6fKU2xau + UcxO4qNXbG7HdzP0WnPGv+ef7/3iw5OdW9hctBegV1lJ4OOgncjBVXu67mKDgVBgd+SkLGrPiRav + QEHohSkNxkElhctVyMx2BXG+HxEsHZp8+JCjjNye20yop9JK8KMF4fb/635Cu3OEmlHb4auejjbl + o+EP7xDZyhN1vlupCDm18EmR8ybY3hcLHaUgOLyQZzh/q3OGfvwkRD4J55t++iK72r/xwcQney3W + soHGtfoSrcZLOBvnewbd+8sjJ+1h0XmLxxAN6t3j+GmpxwNO2x/f9AAEu37xgSnBe5unHtsIEm1l + TtRgXTLJxGx8dXJ1MYbfg9Vir2+2PZc+NsAWrzwx9mtKslRc4dlYm4kntR8ue1P5Qvp0DuR8qDAd + GS6OgdBJDPbGRem5dJ8r8LOHzlSuSwlWVYsY4KlVjo8OsKhwvlQKuASVQTzucKmXpQkDuOU/kgZP + n1LZt2OwV5crkT9FaS9Q8+9QfI8f7F1PT3U+cewLue7z+8PH6jpxRrE10vjY2iud2sHD+Yu8LBMI + LqnTr8sn5eG17Xbbz0P9dV7HOwrqZCBhG3HqsnfMAjJzxU1Aq48qObhaCb87qOEzm9z78vhwFSQa + Q46VRKpqfuApC398xTy9mn5731/AETJ5QpuQkKL7XCEo8DuCBe6Vz5yUSIBRtynBB7CEczMbCmTO + Y0KOb+Fo88eXASGzbJMD32cjpz0SFXg5bHcgE0npOVW9Z3B1UordY9zaM3gt/i9eTs/D1Kvzxm9+ + +I7Ilzmh1FiIA3t3QsRTroo6Ezw38Nr2O6yHV9UePkavQJGfbZxVyaenP/z4y3cKrO722Ko1A1dq + nLEMiERXwLiipHkVR05ln9hrKqWN9EDfArssrOkYjnkAUmnFHiMR2FP6CCES1Fvi7fZ1X8/viW2g + ECoVOb3mI6WYkTLwwK+BBOSxdWwxqATbeWJdZCYwr2jX8rdJs/CmN6jEuCYSfF4rzmOq4kDnMapW + cDarEP/i/+RVgY4eXNDgQ2UfQmEQ1gzddNPy4uQp1qvP7jI4cp6PZVDv6qXc3SzAN4UzlY/rGq4s + UF7oeXIZctLTUR1htSjoHq4awbp2C7sd/ULAtVyAYzehKn0WrPbH5374fmaywx0NB3AjJ8epbMre + yhdKj1pM/N95nAUzk+zVFr3ZK49UsAYk//DEtmXJq797V1bg6uR0uvh1FBKOV0s0Qm2YGtcRaFeE + hwLJ1emKjU4v++VZyTLQAC96Q6Be7Om8N1sohDPGxnknASoE25aSDR9pLd+r42wtJTCdO++hWeDt + 4bWABJTVq/MaFUV9pT34CdjgFeCzbXbhwAuBDq4Z0LEnn1/9qlaoBK1RrBhX40TX9X7L4JC9LBzv + ra89B+EyQ/HKTMTJv1M/Ss/xBTHs9Gle38ea++lnsjNEf3oKHzlrgQLts23JsN/qxMWvDB3N94I3 + +1Lnev5AeJovVyJv+XP282qG1+/d916AcjZlklVChVocsdf4TU+9Ar8gGhb7T69bxTGEMB5Onade + nV1IVSOLpJ8+49lrmdPra9sCU7EnfCCZlnOLRkV0n9WaGGe6q4e5zSvJQrsEOyk89XPCBBoy4H7A + 2nBn+4/Yh8Pf81ssAvng1vkKMRv6xG7bF2Btc8x++Bpf1GHTA7k4huC7Wzf9oa/vn+fuC8r94/jj + 6/VSLJWC0qMeY72K3HzDFyXa8g2+ZW7UE7Dd4Nj0jQl1H9XmUMq+wKYPkvDqPHKu1b8r+JTPdeNn + L3s9X6sAbvnDe9+YoB5lMr5+fHwC14iC+TDQL9COaUBU7lrQZbw8Y/Dj5/q6Y+qxCM0CyJJyJfZ5 + NdRFVe/Jn3664bNwDodiAgtXXwlOdS6f/aSHEDJjvvG9atunOGTbDIxl08die1DNVkSzNlfT7jpr + 9cZnJ/i4ejx2D7fLP/3ix5/i70HrqbmuLUR132KvuJ0pvY1dCQN3WEmy5fsZSbd//vyz70nPnQpm + llzhM3P1evK6qBoKHHQkWqQP6vp5Ct8/fSdOxhCsnixN4Ch0Ty/f9LXxxc0r2vCHx9b5UI/2NlPs + cri9iOl/vH4pfKVFW7z1nmauhPy9GSM49gv3p7cSxU4tWH6dFZswPfbT/hPHEBv3D1EEx825eUhE + eGFo4/Hfg1YvPJfykHAlxs73k9C17YwJHs3PQsz4wtUjsz4qmH5vCUnmOOxn/rIOYL3oz4lt+kdN + f5/Xh8JIDoept7fn+0IyfjyiXp1HOBjethXsw7L49q64cHaGxAPvDwcJbp5hvVwsuULPKT8TUw1R + PYoWowA8GSV2JvEJxvc5a6RCmRIi50BThd10VaBq6yo+OXID2szfFcC4ll+sl5ltE684vX5855fv + QopQqgPclFdyOx4f9ioH6gtySynjY1a9QrZwjj4oPteR2OHwDana7QdAW+2Gk2fa5Utc9Xdo7tf9 + tLCvFGz6OgOpDyxsa/Xb7i9NzoBKqhYPNJOSL1PwreBioMOPb9ibftOAunR6LF9mkU44vX3B5bnm + RHedK52eUqyD9ch6OMkbNp96uBTwo/nhdiOkr398DuzwciL+ht9WIdpvfUzvk8fs5GO/GmlqwN37 + diBpf7PtFSq+A8fhWRF3XZKcLK2fIZFJIk989JVKRbxnQJG+WA8M253ymk4z9GehIYo54HDy5HWQ + OtsWplfnHepl05Mgz8KVbPijZl+s5EB1Vny8PT9YRZttQH3IvW1vtqXOn9F0fvmdKJeDr06niy7C + 6xKN2DgYU7io8rWBayVk5Bi1isquTWtIsN1mKNxrHK7fK+ZhpCftNDP6oeYGsvsCAILO27t2mXPS + 891ANrpAcqxUOZ9+evpkfQJ8gMcLbevUv6OfXr3hDzCVavaCBzL4+LG/+uHsatSR+Em/kFP5KOj8 + sTsemMcm83avPFbn2ZQg/IRV44mbHvurT/zpX5587PIJ7cMJPR9iTxR1fKhLRm8JVM8Oxe67uuSb + vmj8j6VzW1JVh6LoB/EgSEMWj3JRbAIJCo36BoiKoNwD5OtPwT7fkFWZa45RqaBC7HRqnLw2mdSi + uEKKD4Ra4jsP+Ph1BXB1ZjDnme2TyfgNd5AUVcX8pW+OG6kl8DnUN2rZZoGnHaYxBLel7z2zMhiE + baqCj9GN6ucbBEMuVBFa96PbZFk8w+QVQYQuwHZbj5n9yv/uYT2uviAQq31B4Ehk+Nd/55TVI6qb + XqfOtTzz3k9fIcD1JFEvO3zNj1f6y6/PxB823dELxLQvGpCDmRKO8grzOT/PsPgQ0uunOJmn5lpB + FWlXpjNS49bppB2k/uNE7fH04nMtFFtNwocbmWpT49VUpgOc415gbjdby4tL94muX+NDfl5l0fIG + ug+k37+eLvkSzFSVDPh3PzyyIODNpo3h6WKDLj4KT2/fdjS5xqeljzR8ELahCrbT3okEr0Mrlt6f + i6bdFzNC418+Te80RQGjKx8uzYWPRIjrjfy/T2sVtQJm42qY8J/1LkuSjGjl07dXhvCYJ6YI03fM + qCldAPH7WIWguKo6IOVxCtrSPTTreRKZK9js+lMLyHSiiFk1eb2neIwKdd335coN8XAxnAYUF3tE + EZ/c7PJtXyBFdbVBWHzdsPBbpG+Lnq374/DMIES3RJipqfk0mCH95DATYhF56X/TYx+LMKddw+zH + r4vFhxX6YIwIqI1+BTxVh3zUNpfhyEzLjHFnX1ID2rCMaXjvy/cYFW23zgv5Wf1dlL9TOKvBkTpY + 1RO5F3kBqRO9iOi0Xiv3/j1G+f5Tkk9+nPG8kasQHn8sZYYtHPGwzltbqCdynrx7wr1dKWqVNEvM + 5kprjvnVM+C8XxyIk/zgNf/XPF54w+49Vbn11JS5ECkdSMPHzPM7tPjI4T8AAAD//6RdS5eyPJD+ + QSxEQJIsuYncg4CIO0FEQEUuCZBfP4d+v+XMapZ9PG13QqWeS4WqHYMLoHkMLHgP7SiQvHPMFuE2 + m8jKwivWN39sXaxDBPxJeG31DV6f4kAe4RJaF2wkpqcLTWJUaDs/RHblV84i3mmh7QcN/fPHV327 + RTeqhwdWZxjmgoK99bDVi4Nmi785jzQJmWnBYTvJ43zWPoKCQr1pgtLOD2C0zmUGiZvsaPg6J1v9 + gkLQ+lgI6lyp9H988Y/fbvnFHYEZEijW0Z5qhtMMdNMTMDLeGNtLWOn7andx4N1qLKpO1tCwOhY7 + QMsUUKduNbYMEjSB8psqfBRSoJM//kFEcKF20HF593V/vPyTnjI2mtwbiH7KV9gpBgzAfhiHXz49 + VyDqzyw4nLIhZgzxFWqxdsL+Nyxi0n1UCUaenxGKxfWfXpMrnXkUax3VNz9xlPd6GeLjg4uGeZfo + PPzmDg3kMj6A2QqeH9j6voCd5TW5TTLeWrDVc3DSQ6shbzbXKD4XBb2dMjf/89Ph7cjr+CEIH50t + r1MCPmV7w8qGF7PAxxFineBQrKRpPO+uWo/2S61Q/5d/wf49whRUA/E2/2keFs2783LuTV+qpoIy + LHd2yf4/Nwrk//1GAY+MH3WW8tfMpFcqVBbNDatp0AzLR8hX4LWXPT3BYaevoThHKBCYFXyHwxJP + CRADtKrKgd5j7+v+ds2+BeciPNHLC58Zf3W0GT6jasLqcx80oo/PI3Lv6IX1+oUHZolwhu/2CQh/ + NV6AJsE0Qpufd8H6s/x4HqXJAdfz7YTL2uXduXtGBGq2LBChO7Ru/33LGVT1D8bHMNfdubTLFb6e + NsBKYn6H5THcUuhMnE6NErfxPBcHB/XbHKPCt4Ocl5VDCV0jmwN++5z8skGD3WGcsZsiOrDSbQR4 + UUePLHP9zVkSbD0Ehm1uU5O4YCbDY4Wtzxk0uFtVzpOzM8Lsmt2x24su2OsfGsFDV6n0zuRIH3tx + DhAts5Tmu98hXuEU9ei1zBifXyXQWXWNIrRWEk/PVzMBi/a+K0jtFUAfVK7yJSZ5cFgccqancD03 + +4Orl2iIyJ7aZwhzol4yDvRPEAWyon9c3v66JtIKrqb4cHwA5ukOBw912GLvfJr0tQh+Hhzwzwp+ + x0M3MGJ2I7SBUuBrf7m7e1U5lkjwyB2b+Y2681F9ScjJ/QGb9y7Rl6K6CjA77RnVh+uzmciezsCU + 9jGNxsvZXU+nrcu2crpTu3MHd9sfgvgqzHEQnEWwMtiEyFhXgTqDm8XMDz8QGrDJyA8R1jDLcD/w + /Fw8XJQkc/eBc4rgtR9DGjwXr1niy+MOPiczCERFMVwx99oSdTaUCeSdftj2T4MzUU70fgp/w1oF + ex7Kx6GlhtRYuvD0qxbtviamd37wcxL1lYSWNqVYD+EbLM4MZ4h3/IkmnMfnM9rNM3QmqAcSa/Rh + PZ0sDp3hI8UXd/zp+91lCZH1dre5Wm9hmK5VU6HBNq5UFfknI7sx0tBDOd7xvSzKYT/6M4FjgX16 + OnH1sCf77wo/17ymmprwYKn6kKDyLTTUn+d5IA91dWDh8SUtYy0Co/7zUjgPZ0oDNnSM2mG03Rme + juTn1jwb03QRUF0WGb0zW9SXrtj34OKWhPx88ZTT3k4lVBz2EN988RsvMpekyLQ6g16Dx1MXFqS3 + CBdSRa0AEvbbLzNE5vEwUlWZZ336heYHRSvjtvhCbLGbbP7LF0SwwsDd286dg0h9h/gyuTRmQrlW + iJCuoq5UXID4nOc7Go9dQSPziJn4yO4STGXujotx17nswuQQPsxExAnm+JgOl3ePzCMYg+X86JqF + v8UlioGAAnZRZbCYc2WhIeOiALrVCywu0muk+C3AVir7g+DKpACVpx9p4IpzzIbL1EP0erjYNUNR + X59+95FTniBsvsdqeyfzUcCVbV2kkipzhSffE2B/uTkg235v612B3L1dao7mCexvB1VDSiofsZ7Y + v2HWiv0HWlx5Jbu90rFVsbUA+XP5+osHwM5qr0D3BPZk8cZpmF2/5WDy1jRsOl91WI066ZHNvV74 + 9pBuYLnFhQM9+ekTubz+hv2s2BqMpPGEnX1TNrOn7GVklZ9im9N5aIhskTsQ7eiJkyj+ADbGs4nE + dZWwrT9Vd0166wP+1mdZ9WkQgCnxMA3ojwZm0Lrdga97tDsFCVbew5LPxxxCufhsXYkcwQGrBtYV + 7phU4nuQjzpbzX0ql6OpUOWo5fHipK8RTFrwImJ5KwbKmw8FUrW1sLXsxpxZP6lESmJP2Iz6Sz6A + x+0D9U8i0NAfK120H+odvZYVB8gwLZ2pB06TfXProXDSWbz8ft4d/najTq9XuXJZe/1IsKjwExuU + D1wiEvCBh6wzKXbEB1udxOjQlt+pf/vNwxys5wCpLDlSa12thld6A8ocUTG+D26W8w3ZcWA1gn6L + 91CfSzudAb6NFGfkwLG10OkIL8shx+lt1hi78aYMdw92JFkpjfm61qEHx+Nuxr5nA/Y7tGqE2r4L + 6BVdj+6eVx6dfC6+OdXMIwZTYs4rokNzwN6bPzb74qm10DgoMZHGR5rPOGMGnC6RT/Gk9e4/vP76 + 9z3VuuuaT1mlB4hX9gcceJ07iOk9sdCb12oiCfMpFmM/K5AOiwDHO6QC4SY0HKBu2mMstIq7fB4P + CN59ZlBVeucur2p9BS9H/0zNm1EAPneuGVgCP8WOqClAcBKvg+Ie/nDxfSyMRThQ4E7hRKx+mZ/z + dnCM0L7Szth2er1Zf7vJOoS7wxnfq2XRl5+mhYgso0+Neyo3i5uqGdqGI1ITsiqere9wh4rT4ABl + x0NO6HJx4OkY2DjVfjXjJZwaaG3zA/YNs9Pp17lWkGbOgXoOD8AW/xkUHirBD+BcgehiWYOGrm9v + lL/bYdpznYKUk+Zg04hwziMfOfAvPyXi6jFBvWQQXoLuSc+1o7ri+7Kk8FEUmIYXGgPhjtUaPZ6d + QjO1kPWxOSwlSOpzT7UPk9lo3/kUreLjjbVUnXImiHMKd/dQwVcM1VwYdp4AF/Ge49QkIphtJlsI + Eg/Tm/zCjHF6bsFA37+C3e4e6suWb+FznSB2Xu2Ur9bUrzCuX3wwJ+ZpmAsgjzC8FW8aN8kAhtm0 + 7miqbzO9gj0B48fce0jUVY1e7LBq5ua055EgBIyITJx1+sq+NWjF8oGd+mLFs6naLTpL3JlaN6y4 + yx++X8X6GSCDO8RzP9gO1B5nARsnsFU8zYKD+kMdqS8ea/ZbeOrA9jR+aVD8OJ111g/CcR1k8rnN + NejIcJlhp08PIs35RafrE1jwXAsHrOYiBSSM3AxK9KFi42qogJf3ogR5lp4IE2PkjjxWC2id1yCQ + ukyNyYHkIfC+ho+fdwbimUgeDwf60qlrve1mH9bjClnM5xveT/laq58SFePWRYxNADBeyjp4kRwJ + e2f5F4+XppMQP3mERmh/BMK07AsYIOVN9ie2y5lT/Az5riCEtS2elqzSPXiMugUHC1Zi+rfe5vXG + WK+OJ5cZT7GFpwBuN0iwPzAwXGTwnaIZa7fd6K7dajsyCEgTzCEngcXiDAdyZ1+luhhY7p6q3Aee + 3/KbGsEd6cvnjAMY+CGlR8c6s/HWoPrw8D4JtYT5m0+K8l7lqnZKau29UF9NeXXAdAl9WtZcGIve + I9XgOdX6jd8/3RUPr1p+jEtDxNh+u0un7wS5KV811b4fUZ8l9byi9CsRrM+q0VBFqlboi3pEDeK+ + 2PrUvibQD5NAb3qbunN/hC1o89OXKmK4zQGnDUT1qz6SnSMisK5zUsHbyQr/5Tcmnx1e3v9mRvHL + HJuljjAP3WtRkPr6bpsxePcOjOugCuRCEVxyHuwAbHhENX1GOYFmVsNiNx9xaDiOK179twmnBV5x + dH0bA1ulmUOHrlZp4MQHlyrm5EC+YZsCXeV41eydA4Vb/aNBitNN/8QzLG9aEoxVGOTLpm/kqc7n + YOd7c8xqBA3wPpuAYrFY4pk3Thy8XvuWavJ01dl1143g8+h32HsAAGZ4bwM4uTudhGKYu3N8lQsw + L7lFjY+vu+J0OSbgnqg/qpmSp4vc+VnA7CQyIounCxt1TfPgRxAMIm/nkzT3MYRIXhOq79CLre7p + 0gL6YlfqKz6XM/chJKDSwgYr6qw1G7+V4aAJMlbqcmoWc+4c6F7LAvtT4zH2bR4J/Mu/3lv9xXNW + dxE0j21GUxVBxk7GIMNx5/D4b//Yc5buMIjvFnUvNylf4tPNgM+VwmDo6tuwDJroge6hfcju+rkO + NOp0AaRqoQXXNO3AuqvcCEr0qQb7MNf1ubr4ARSf+5BezjGfj3wu1dJzXyb0mBo4XhkcInguohOB + ew2w74/qMxyKxxVney9095V00aA6f6cAfbI+pu3cjsgVh4EG71gDUx3KH3n7HDvupWzm7XyDLT7o + 3VIN9rcfqJYMAQfFr3SZfhxrqCqiSJ3cebP5I0kCyB9GF2znQ6fD6ZeA+z02yW7GE1jv2jrDLl1s + qpZnMZ/EXcLBOjgsNNAPS0P2jW8BFZnfIJtTxV25UU4gO12rP/7XLM/S5kD+k8Q/PHPnrK4i+Lhl + Dc1rK9JXy6w6mT+dUuqkN4fRplFGOAFyCKTjpQHzr/pyYFcJKzncP6or3DV5hhv/Clb8fTO2TmIJ + wXkOsSHv5oZ1KYlgUsd9IIGXlk+XlytArq8dinEnAaInUgff3yDGeiU18du89gn8TLWM3c/Bd+fc + eWYw1wsX4zNy84VnpJZ/n7tMlfdwjsfkencgm0qJmmg4NywMziYKvV1PzQdRh/UWigbUKgXT2yy3 + YDruvBny4b3CVimNm7+wC9D1zl+ogU1dF9jlZYFSkyE2d3i74ZR4BCRkbvF98y8W0FQc4uPDO1gs + EgDhxTsZ3H/eGr5t/IPP0nONzJ05B3Xe6LkY+dIMA6S9sSMnt+FvP4CE3ybhL1U/sJfTBH/5Ktjl + bZpv8Z6BRpYzHGStre95r73DfRxo2B/sY05jPyxgdFBzms6G2SxnAiq45UOsVbe4IfLW0lCKOobN + Crxzlt0Yj47SbFN9tGS2JsNDgKENOKr9wK8hT74fwWUBOT0Fj53LTkYjQfuo2vj8x0dfr5iDe1uW + yJqqUzza3qygv/WqYLKbUSmUHn31Pqa4qS/u7O+UEElHoaXW+azG8wOdOPhM9RUfh1oDAnNoB42f + kxGB37+aNXli8y9+sQeuxCX1N06hFZBXAN/8u5nfcRuhat+UweF46Jq5j9xUHvBgES5Xzs1iZ9cQ + vGrcBXs+XRjr62SEI844GgjSVt+Rb//5CafnUWuWvrik0OnniGb30zmfX/JtBps+xLfDnDDxl0gj + 1D+pgA2pe+hLHa4fSM58RG9DRoZ/el37vBWyN57Hpg9LuQSf663G7okPYh7xuxD+8WUVpYJOc64z + wCXSK7J6z2vcA9keYZ9aMz3aoTKI9/WWgM1PwEauKc2vHvxe7tgzJvORqPG/+CkUJcTn+HB2xT88 + ieuGp97pK+nEjz1N3vgYdv3UGNasv3hw04/Y89JVXyLb7GGQAh9v/gxbLM6zoBDHHlbI3d2uVEsJ + NH7PEdufbzyw4m1z8D5PDHvR0ObzIR95oDasJPsXXsBgnFmItv2mLl4nfT7lRQ80WxJoRg4lW6+h + ZqHt+wL+edo3M8iNGh0ydA+679fUxaq1E9iUTU2tUvLyLr6uBdz8AqxuftT6uTU9Yql0wr63V4Hw + 3mW1DNXaxHlXH5r3wMu8TJ7HA9YORs9WWd7eINjww8+UNp8L6yfA7f/DR6uVh7VWzh6CQCupdT8t + McFRM8JOGAxs2tYun1D7kGDodSnOkR+ydXJAAsw6qqkflURf/vjlL74EtNTCd76ey7sl/w7FB1/N + +5Czw8AFsHhKDvbircfjfb2lMDs76M+faVhnvbg//of//Jn1CL8j+hdv5nID7E9/3nVyweqlRgMz + +2yFCAkK+bzHKhdzrjPBVBsFjj3XYSPabfpN/Vh/+ogtuwa1cHpbFj16jzYnc0sMEBWXMzYGgcZL + WLcz/Hw4m5r6i9fHP79Au60LPsb9l60PzBV//JEc5oADf3wLmZIYB7/DzDMa8au0Td5O8HPLv6L2 + 1Hp03CUyDS7Xd963dx2iqfv52LMyFrM08O8ADXjCp1BNm/3njD0437kcG9hs9FV+jd6fnqAa0n4N + EazbDFtLBfRcfX5seQu8CUUYmISF8MhEIp9lNMBrtPkddkO44+yhb2/sSHuFTJ/Pu0IGxjg8qJck + IfvTLyAtwxArvJrGU5LsTfSUdjfsrnunIQd4bcG2f9TdKjT/+J/f9BGRO0rZP3148Fw34FdbGdYj + pARar87G6lx/4zH0PA0w+XyirszsXCiKqYK9V0dE/IRHd2UO7YF1ngOMX6Y3dG3ifaBcXwnd+NCw + zmiQ/+mn417+ufM//cvdL2S5mjwgEKQcGI7yh2Ilew3kgYUSJIH0o7l0Vdj4G14FPK+Ex8ooMDZn + dl/C5MylONDVKl5ODosgyKsV25rCgY+cIAtyZ6xixQJvdzHdvAX+lpl8urvEotxbHdj8HGqSOozZ + 9CrMPz8Dqx8suOwNQQH//BA76rR4vZZmAV/rqdn4UQ6WU7MY6Da1kLCNH4qcHltw3GcRLR4gB8Q9 + nINDr58MjCH3HRg9IQ4Wh20q0q6M3Im/5QWY1V+EH+N6Bv9+Lm9KQi3rWwxsHw0z2PIdNjc//p9f + 57bfXfAqukH/ffPuDvgwqzb9/HKJN9YOrJ7mggNsJO4krZMEwzTksRs2Vk7hr0pha9QC2YNmH68c + ObVQU6yEpFuPzL5jaQZn/nOg9i7qwSK1v1budpwfnIPQAKtmvWQ4EbnHStAum//0G+XqaSz4KEzH + fPWewACPX1QGcpB7+uqPNg/jAWnk8KN1PAUUOoDkLR/sDyePiTv8k4Cxh19qOW4MBM0WHbjpSxqF + maovf/7Mn79cnHdlvOkxT27z45caIcXNnn2cGl5P84/6uX9is9npBL7yTsA6De14XWqpAsN7fuGj + 9zDiPYdiAng/YfRk3t1cRKETQFreU2o1pIjnzySmwD6LWbAOzwpMpViZ6Ii/RiC/69GdY3fTy1HP + sH7mRX3+08v7kX9Sf/8amtG+wwRItnLBN6FZ3FHe72RQSjIOpkLg83l80hTgQq6ol86HeCa9VaMy + jeEWf3M8nvwsgX947S37TKdGfzDg5ndj59xhMFYvPvzjE2R3Z3m8pmeRQOWkOPSf3rlkNIPJIpzp + kVBz+P3VZ4K5GQidNMddroTyYNM71CpeEqNG2adQD4837CesahaurlKwZ7fzP7xkTw6O8DN/vvQk + ePWwKteg+jsP+G6Zt83v8/o/vUA9lRvY33kGm19H+NjN9OlPL9y/p8ufHhiW6nn10LepA3os5Wc8 + N4dDAZ+puuLj8ZoCav3mAlZ7z6HG5b0b1s1flJ/7IqHa9jxmXtA/cPNXNvx6xsuyq3rEp+i8+Rn4 + D9//w6vgdT+4jB4OLcz65U4EuZzA2tzHCK6G12/1Aa6ZzhdE4HJCB2oHNGuY2YcrNH5WhvOc9MNy + uOEVWtGpppu+ZHM7aXfY9n2w6c0crGudBeB3YDI1fncTjLVZtehzMgKcPX/feNaCvxvg0o7qwPuw + deTaChXmW8UbX//7/hksbUJpqCAzn7fnCzh5X1LroxOwPA6X/k8/EzDfNJflXGUc/FNcEIjfAvi1 + euOBN6/URNQKb5hpWN/B+AwP//xW4pIn+efXKFz+aNb0vCPAPQc7alZ4F89rkQnytS3ZVu9aBnJ6 + FQqULRpiZXfSh39+MOIrMdiDsoqXtwANkDx9RIRPuwyreFoK+Bv7K9Y+2hOse6hBJK0VIXthesdz + U4EepNzziY3f/cNW8g6yf/qMvLMrWLJx64mo9g72zTps6CujNTz+/DrgLtdjLk5WGcGARTqhQqvo + 4p9/RCWFp5fbOdb//HHQxaFBGA1/OdFoq6CtXknN37KPJ9wcTbj58VR9Xw02P9IjD7f8ipVKTvPJ + v/wMeFGJh932YTTCxmdhMD25gFm+584FHSBEy9UMqk8xxmvL1BX98e/pC9VhOJaaBjkjW7Fxqfrm + X35W/XT54zMNu3iz9c/f886yHa8Nq40/P9nntvrc7CgKBPUZldiLm/3A1OEwwuwbXf7qHYyy2DNg + P04v/JxkEM+LaHgoeSsafdJybSb1LSdw099k+at//ZaKR67T+xt/fDFGzIrAinAj9vr+0rBX81PA + xkepfTZcd551lkLjoMVEdh5TLBggKtFNkE3q+1Os7x008DBKTzxhR8WK55e19RR6WxYuh6nXeyGp + 6n/1WWPK3k3fuwz+rRfjcjy6q55IPczkVA0E7aeBJZO5D7z7pUhdvhT1cQkd41+91/utRvyqpIsC + /vjgXz11yg8IQv2hjzQYy2PMu7uqhQYMjD9/bBD2F4sHmx8X3J3HlK8vXrvDhhh2wMV7/Od3eGDL + T8HeeL6bbX8E1Hz5kV49fmXEMZUEVlrU4OADqTs57UeB2f3wDPbVtRpm69vcEXcty80vV2L22D00 + CN2ww9ra67l4zpEBZcJ31Hwr97yy2WoB/VZ96R//GB67x/9r6gH4P24UXL4GmWHzBTSTPzP6LSLc + XpV45OtF0D9ghe8fPkpexxbPuAWwxTuZ9MJ0adZod66RZ3oiTdd7w36ydP1AKxNlerT2fLMYgd3B + YZfzAa9c+nh5l7qFPMFfAr6LbLCM2mOUkzg0sIImPh9Kz04AL4o29m/Xky4uUqtAPdQ77MUzdNmn + PLbQdD1GtfdnuyP1/Vmwee6UgCk3NecnzBxk+bcyQEJpDMz83TugGOJWUffRprApgUFDikDAOufO + p/exgrtJ7rGbXF/6snsTAQreNQ4uTFdd5i9ziYT9TcOK+1CGPf/RTOR1BcRXvvOHJe4aAsWZOQFb + nl0u8h+vAu9PD/HpML8GRqTuDs/ewgW8qbUNM67KitYLWambDrI+TwMVYG9ePHyqjud4ec8kgmak + POhlp9Nm+/0MKQI80nCNd8O6dfSBoT5ohAFf0NnNawTkfawGq7ZVDJ9fbQXwZ9g59kevdcU6ke5o + wrFHMZqHvD+fIYH9nS+C6ke5eM7uoYkeUd/Sk3q+5XNomzxstMALdsZYAKGvLyZSFdPCKgM0Z1d9 + qOD1kpzwNb3lgK6fxwpCK+apJ46Wu/A07tDbVn/0vm9sfR5rNUMXWYupV7fvQZDhTUP3SJYDlJsH + sEwzzqB0DO80I0LXzHKmJ/D3uVXYQ3dBZ0Ea9tC7jyd8MfiXK2jk68DLdQ0Iq02pmbSjncLzXpyI + NOy2uWb4XSPgIkydcXcCvP5WZRhaZ56e4NdqFgeEPHy4vEzv/sdueKWgIxxNv6QXpr/c+WhzEIxq + O+I7O1v5niNNhb64S2l008CwcCIyIJeTA0HyU9Lnoy1wKFu5BJ8888T2r0thwEtxU6gF8m2g+SyF + kNM+Ij7e0NWllTub/57381slg0BQlMFOyD40qaSLux+ShoP2VtF/5tXHZY7RygjON5Oe7lc938ep + 0sLf8ytQ60ZMfTSKowef0+4cPJzXURcuTVXAPYyO1D4Pic7Ua6QhrPcW9XZDo6/0tbbwfnqNGBun + dFji1GrROtY5LrIENcvZtyK0170rPZ/HBxPvZcejXEznoK4d3BCrffGoctuW6uJisXnN6gD15tWj + SvixYj6aFAGWA6uoV0RHV/iERQmleKvQPuMoXquxnOHttnexd7jVg/jS2B1Od9pRjd/mGt+tkgAq + vz44Dd0oJhKsQ3TzzBTnv+qqr3QwHDTF64MswT5l7DGvBOFidmhgx+og5rqqoODcvDHOK9MV58ej + hDfPSGl+efnDPnjyPTgcSEj96/ET00arHRiPb4g1KrRsOV1THsriY4fz1GSMgu2daJTIKfWS2HEF + r32X8NLbe2ppW1dpiWUjXMafToPM1geBg0mPfn6ACTxwHWB3/WsdYMQY2ZWQxWs4FTXEfbWjNpPf + w7I+1BmpP8+h7v33GtZqTFeYxk5KUF0Fw4wjvUbPr2YGPJ6jfHl9dwE8LUgnhzR4AeFxkzRweb90 + bDUFYyzyrx+0nW98dW5+vjpR/oHe0u7wWd/x4N/fv12rFR9DHg+jJWo1dK06xcb9w+dzNSYJ6njD + p7cnpM2cN+8IOc42Wkz9GkDkxL6TPk/S4+OUfsCSy5QDh3koNgdEY/P+tcsg9cQjNT7KT1/yzuDB + F/dbl8RDoy/+6kiQeIKLrbu0Dsv7sBNgNQwrPo5LNaz9QHrIHVKPZov1i4Ugd3oo/6gTfMVSYXwT + NhG8ep5B3QJoMf8dqg/SPrsDdctjAEaWiSbMq9cJZ5/4Dpan7JfwvN9PNMBvTV+sNauQdZYpOewu + E/slT6rBYgAB2Q8/L+YFBrdJ6FWI74GLgfDkJg3+5T/zlhzAKLxuEXy57xKfa+Zt71BPBTTpcsbZ + M69isj0PpGiNTVDB+Hx17I3Rrd8Fn5qqZ/S6OTC6jTkacFwAxGNgmFAsgoHq6y8BC5PCFGz/L9a8 + h8l49R07YL1fW2qfTZ/N6wvM8FY+EQ1+548709dsoZ1WYHoj/pCT33BekTVdHWy31dnlE0G1kFQY + OsbZcWzmGf8kZOMvxuoXKGDvVFcHvoczwcb6foDpWWYrtBfxEgi5Ug3ia848ODS7G2F09cH+8lRk + yJV6hE/gmeQiU9sM7LQS46xrO31uwiGEV1LYWEm+ACz9Rw4g3OsSka+HR8PqZLtJf+ECHMLmy7ou + tGb4EH0t+KSPlS1AdQ1Iz0lKA1v6srE4PzUIjxqHnWsi5uTSVCXKuTPAWVw+mHC2GgtpN9/Aut8U + cecJlYJ808A0EacF0O4BE6C5BcYP5fxwBYq+EbKsdaRZ8onc5bj+ZuQ1gkMdsLeHfX94pHB27gbN + K3hhNFp6Hnjq50gV6+vngm8fO3ip5DNVguQ0CPS1flDjPmaaVDFhTPlVEVrj7EZYdX2z9XP7aDCN + rZSep18A1qo5BnBWryG2v8AZ1vQ+rCC7/u74YjYx6HRxUtDLB+I/frA+93kBhWBrvgjDXzPt7NVB + f/F01Gol389JmsLj41thf0rngZmVaoI/vL0U+MpY/c1rtGB1pmborvlsivkMsfOOaWa7NuMRszpk + 7WqVPL8V38xha1VyqVyO+DivvL5ES5X9nReMrb7Nl8A/V/ARJi52dsJDX8umLKFzXlQc4Hftztc7 + 54EXGlWaOB0XU/V61yA6BZTadx4083N/VpDB38M/PqezsKurv/NOONbedfI9XVewdMoZX57HwZ2j + CFtgPRsKTrd4HF/Ns5O3eKbR5UPyn0iv4UGprCI4uOjjzr/w0h+0iST4NLy4eN7W94dHAbOQ1uzF + uP/88ReaXJ2T+zOrvEaBXB0p5i7ngXfbRkHWdHFwsA9VfVxx1qEtPulVvnzB/B26FibNJaJuzog7 + XiavgKCeDxh7wxUsRjXOYMsPpMofVP8N20DfQ/UJyJLyCyOuckzgA4g89oB1yWeGtBTmdofxTUxX + Nma7NIXB03fJKpBnvnwONwIPUPkFUqGYgNfEberS5fOgvp95usAFogCLltMJc8ZLvuHHDJPmGmHz + KKfNeoA7R/4Zbk7VAfL5fG3xDLd8TB/ZqjDxOzsfaG4Vv6M7fMGqE3u78fWQyOE4HtwOBl0NNjwg + B79XGj7ZRQTJR2vEzlPKmtVsgQkep8CivoHbga6fywrE7+9E70a5H4g3iRrQlJNGj+36Hih9KC0q + 169Dbtv3zwSNPIwyrSUGdkd3EZ3zxg8lmZ4iRAYmOO2IYj0bA+4i1myUsuIDm7Y8Y9c5SWDt64cB + 9sw/Ux8uEmDR0guwc0/jP/znIyHKQPzuF+wfLu7Act3W4Kty0YYHtT5u64OuI1D8hyfieDDrP35N + T/31zMb2RD5A1bJX0CaHu77A2DKB/ll1bO/kRO8MuzaAUhd7qn9+Zry2nFMD8sp6ejzevjFdPooE + /+K36KJ4EL0nKOHzkE/BIQrvOXvqRi9v+YAex0UZeF8JKgCVzMMbHjDhYWEJbHgSQGrLOU0XUkAN + PGx8Qh5h5BMWBfy1xxNVHhSDif9oBjSovKPaV/z94X0lk/tXwWqK3WF9NPYHbs8LW3wrNPOCuRkc + avmLvdyZ2XoZVwUZGMXBb/llDZsTV4Z/fPCyXOWYZpeuht9O/AY8aZ18Fl7nUBZO4wvrf/pr4R0F + KgJ3pL7YLe5yW74djEQupPr9+Ha3fFEAdCh4fEp62+2NXF7h8+p2AZDA5iBlfQCR3ViBifXSXeJU + +QDg7jBVy/Cp04f+kGHuwo6a0iS59Bh4BlDaJcDxvXdz4VvfSnhuSjMgk7gDJN/vIfpJ7ZUa3iVj + U33gvT9+gl21ERt2Ml4RdKVSo9GvbePRFU0HDTiTsfltX2DV3o8PTC7tgzqXdzusH7RE/54PHn9F + PG76Bp52/Q+7HZjBcpdjE/rP4Iw97fNymfWzM/iHD5hcdJ24ip+CW9XtyaK0IJ/oI9bga9o31H5e + f/G85R/4GNYPVpAVuHwxZgRyV/NEtbYIY/b9LAqswb2mzlOShvd3fiiQfeVmw8drvkTpZ/zHZ4OM + /MCPl94edBYyETAsht4145MDlyJXthuots7beU3gbW8dMcYvV1/2akiQ17MLdV7KCKb1EydwjqsT + ft74Xc7KSyTBDe+Ddj63YLku0YzioibB3tonw6QyL5IPb6wQeXcWwEQTu/zzC+ipBm0zgyMyYQtC + nmY0J2D8hY8ONk+k0PQl5frG/0e48SNsh69Ts+G5xoWZFf3pfTZnzav/xz+MxyOPWQBKGf5eK8H6 + /XjUBe74icD+rijUiz50WKD6hECY5xDnqNymHv4aGZIs+mJv3qv5H/8B+/4UkOly2w1zPF0IiOTw + F0gPehvY4uIAXsXWw0qoau5yldsEnFVPxc/q57O1OoU1UvKfi935WupsflwKuJuknl63N2AmWNw6 + WHDGEdvFK3UpFz8z8Bw6CZ/3uj7sXfAe0VLKAbWEfdOwr7tL4ZB6Lc3sedZJslMi5MW7kR7t+x2w + 95zz4EtbDYfBwQd//PyPXwQCKYr8J8Z9C241CrDq8E38L58mV1kgiF8Rm/ZqOCIqYT/goAWaMb7L + H5jIFsDmsHV1z0s0AtsPBXrxszpen+PWYyPQhmAV7IO7DJrFQ9uPBKoEB58thiGZCLawJ4I0SVvP + Ka+QmzwNML4E74aFC2/C8/mMscNzNRuJyRVQ1e6vgHC3rqH0YbV//gN2ejvJJ0F9f6B1lii9rukI + 1tfpnUH3Rb+BuKYjm/N7PMKP+emw5muTvgbngwkegR3T5C8ffYeqRXxvJNT+pm286TMCHwmHsLvp + uXUwuwy8optFL0fOH5Z36VpQ0bZez+dC11mj9RaU30aGvUriwDIl91G6ktLGZrvNiQ38Ww30UO0I + UxufiYNbCLKCQIhPPcxc/t5kd7jpI2rrdymeYHHukeGmr62nzbWZN34Hs4o9cDBavs7UH3Lgj5YJ + Vpss1hlLThUURJrjkx8m+bx/iXd5P+ZffDKR4u6bi8MDcV4c7ErpwV15TDnQYiQTWEklY+5attBc + oE0fKN3pZNcH93/5Ql2OUF+E98yjJ78Cwr2iQf/zF2D4Ci/UawUzFsqQJX96jJqx+m7WXW/+y28B + 4LrDsFoTuyMAZBCszuzlLF0+JdT9XsGP3Xd0fwpU4D+9pEXOS6eCZFewfz4C6prmMRdulhFBE+/f + +O6res57UTFDryshtbX9qs/ysfKQwWchVntfbRYMXA++h5hg/Hwdhh+IiQfv4jOj1l2Kmsmpno58 + /VJCDUeX9PV7eq7y6yNlOBmmJl9nPCbgebU7bFydk86O10cPo0B+0eCzO8brnx92OIwh1cLyni+U + ChZ6CeiDzdCNcvqHfy5+vAI0Lk6+8q/MhPsiuWFznOxhf5dzE4wP/YRdVPp6u+Ksh+9HfcD+BAS2 + lIe5hafEC6hjlJeBRf6zBS+TG8nf942XZpXl4WFTGpR56/aPxm7Bez1m2MofElvLECQwuH9u1CmJ + p7MdeXMwlXV/6xkiDj2PvBnieErx7Xa0wL4+wAAG1q2m/pbPJuawCrlp7NIj606xYLy9BPzxdZus + 50YI2sKDU9+aODXchi1p6nmwz2qd/unD0bN1D5bkeKCPLoANo5+7Azy1PVK1rb4xlR0tQDeS5tg0 + XJ0xoq4KaiNrpXqbHWKmHfUZ+l2S0GQ+t4ytn8cMGNkTvOGhuz65t4asRcixssbPZg6mIQUbH6R/ + +E/PvjaieDn5BLnDueHhdiNncTIO6x+jcmdP6DTIKVEZcM6D6NMxKCtwJ9cLVpFxdffmVYVQensm + dbnu1kx2OhO46g0gw7C0+tiEQwRBEHfUuKuBy0tZ8oHoG6OA/So/3xvsJEG+uRzI+3y7sD+/Evz5 + 216k/vKZ63UOXmQlpre2Ouuvx+JbsHtEDVam6cjW9+3soNzuMbZu5KOvytsi8GFXr22/GrbcF7WC + f3wiPM6ffM5Pc4uOu/mKLZoHjM/PjgX5OBqoz/ti07ecVsPZGx/ULAsf8I+7U0KYC1+s1wMHOi9K + ZvCNVI7MphyB6e5xIdyJykQDhVaxsLxeCRzvI6GZp4VgRpnJAUS0jLwLfAXTplekmcCaWvJ5Br9R + u4yASr6PzwzQuA/aJIDHtL8EH6yXOslG1MMGtfHmBwOwrW+EORcDAvkzaaa322mgMQWOntBycZfv + Y5H/+GEg3eMxZ+r1rgB9yPZ//MOdg/a2ypwBwr+evM2cnJG0vZEoEP7kLS61f2UIwUUzqD8ufc5Y + gmvZlQoNe6V6BaQ4AQW06OvjY/r6sflgmgnQKo38qzcwSbVleDm+r1g/hZ2+7o9ziMJXdCEsMnv3 + /VffqNZnEVAb983Gl1voPJyAnuI6AfwjnGUov82MasdOihf3p7VgKaWAJlsDYwZtiQP6YwrI7D6U + RpizvQyXPu0J61IFdGNt3+Fz6KVArHqxWd9bz4WLHnI0IFwVs9fhNwOanV9EWHeOywe5EqDaLQV6 + LKuW/enxbcrcKQiVbo0XHXof+KNFQo9+puX8iDhDds/djf5bX4cqDcJbHVDHpHWziPQaga3+gf2t + PsJv/pGsr94zWObqAlaOuQaQgaTSaHpfGbt0lobcc3+j21zJuL7tfwaskPDDJ76bGtLVJAHX9+2C + g9aO3NUVwAiS9zAEcJgmnT4v3xXOHnmQuRYbsAp9mKFN/1IDCreBiXHm/PNTj12wgnWPVQFkK0z+ + 28/wGUIYHnd5sBeeQcyu8k2Cfuo+yLzjXvGv8CZN3upXVP8Y2xTPux1CzocGzjsQskUiBwfyAbsT + ETz5eEm9tYMbv8Mnbwj02bInAiMRhjhy5QwQZqoVilQP/PMvqQGxAkYcRtjCjz5ftcJTQFBIK/nL + v2vcHEbAtTyk6vQjYJ7NLIV/fqmlJRrbq9XLQcrJBvh0Zpr758dAWz38gunIifkcn18K+jsP4P1s + hnXpZRmG+k/DN8H7xCsKeg6qJz2mXmMPbBI/PgGc1oobH3/m62LSHqZXZtE/fjPTl+QAa1epVHfG + Szx+QiKBx+1LqaMtwP3nD7RCp+EChnazJ9LKw+QqCdQJXMqY7789RMd1oZpAnjEZUdPDa3GOaHwo + m5hd0qv8L39g4yQM//ZTFeAO4+o3sdmOzhYcGnSj5mg18aoo+xL6fTdRBU1JzppDWMuPwysPdkT+ + xaQ6ZTVMZAcEixZq+p4rlPuff0utKFni+RT1GYQt11OLCNawipljQU/AC/7D61nPpRTic4YC4SJ5 + Df/jKxlcvmJBeLBNSX8fRAF+22CPcdd1YL2M8n/1RGN9I7b8+Z1P8Zjg+8eq2CJ+jiOM/gcAAP// + pF252oI4FH0gChGQhJJ9lyAoYgeICIgoSwJ5+vnwn3K6KS1UIDdnuyHJEmnetUbukXp//kpM5btI + ez7XeAQo+MJLpr+RtYLY4/Nyv52KcdhW5H78mB9ULwJDLHj4YtysmBzMIAGbv0E/PH0/yLOQtv4a + dji/yxe2c01QttwFqy+Fa9ZRGnoYl7w1C00R0ynsK116eu8SGSem0HgWJyb81ZO94ecI6E2ALeZS + bN2Db0yZ+WRKHfDMGURGlY9WDhgovAJz09dsvDJGF0JyYSukh3G1jY9UQOksJPiH73hXqKyUFDcR + bfXvLTb46r+8ecuD5WE/HnwdBnT5YmWvac1UEBJKm99C8ZuudBWNbw/NSL3jANTPZg1ytf/ltcFe + 8G1A/eQpQM05Mhjd2N2/9Z2DXY2cJHhS+jiA7dRwucPH9vjxZnmOQugoYDtlask1lphhIrUJbWcW + creGvy72GU7aYTczMyPHPzyHu7q5I7T11+YtbxBMTE8Y3Y9yg9+Hsy19RJEg7U1XsNg6mH/9v5kX + xcBbC4uqELyMCAdWZ3lj2jz7nx4OhOhwHf7y05pol4Bs/dixHQ4sdFpvxk5Ce41YTsoC7vJIAlgq + PFi3vBlEDMgD8nEFj2z9SXg8KhnS4SmOyekkhtA4KDAQtv7BsPWb4e3i33BuS2PcBEnaQ+NYRejk + 2EWz4qfYQlg6MrIWX44XYV5c6VZ995vf0fP18HRLuPmRYH9ioEfjfrYB/zrPWEOsE9OsrDjI7muC + tfF6zpdOHc7/Z0UB/O8VBadCZ5BZ6VOzfm9dDc9GKGIk5594YV/AhOOrkHE0W7hZT0XyhTkHH8hD + L64h8f5WSuaiU1wefGvYN851hfTEEpzVVyGmhnAKYdVfGmTQV92sk3/qYHLXFaTdnwdAdJGO0vfJ + hbPILma+Z19hJn5iMiD95pgauXy2XSZ484uMdzx4RLuSVTKyqMTKmh8BibiShebZHoM8WY8Df9Gf + IsTm6Rg0dYuG5YBhAPC8dli7OFuC+GRFiYych6zjdi71zXz1QGp1H9tSPjUko54Ky8rNg13fWtry + 9kkEV62vsA1OfjyPYDRhDnI/6C1wHbCgNyK8O+9ipg70PL7GjwpeRiNACcPaOXdyHRfOF00PyF1f + tk2ADBE+Tg3CppFI8ToeSwbIe8bHKOIVyq+fqJOEiCnx+aqggYqOQyRXOMc4Re4hJ4JYqJDLRBlp + STjFax9XnURPo4HLXUrzETsFEU4++gSQedU59wp6VaoB2+NLNJjNyuZ7Bq5FIwbiXoQxeRK2laRQ + qbBRh/t44uFcwCYFGTKT6ZJzx0ZlpNmCEbrnTQzwsG1xNQ2yh1TuXgxkfUwFkDudx87B0IdxfbxK + qPt6gM8+HwzckZUI5A2lxdFzn3q8u7iRlH4wv41v561NDiE0z6crtm7drFE7qGVYVrE5r9QnGoWj + PkthktnbOYNmzmXfDAL9cxqQ8lIHbX2hVwrFI+PgoCz8gWfvmipNuBSwx0ictypjlcJUWhtcLjsy + 4OpzaqWUUQ44tG7XuO89u4JXvP/gR9/Dgd6kiAXP211BMlgVwKa7qJUezPmLcz3dgeVdyhms+muD + XUpyD6/id5ZkxybIf9tMs9VLKImDCrCMq288hmpXSK0jHrFyp598CWigQqvrGRQrfQ/4Jd12pWcK + A8erFQ8sq8iZpH/iAbvyucuX6QATyF7DcF5eqqd92tu3Er9cKOGCzZ+/+RVBZmJuc2PzbsNRYWXg + RL8hVtK5B6RpX6p0nkiMs2dQx/NiqC0cD0TFKnzf6erhqJK8y5OgE4NDjc3oa5YeFwliXWCeDXu9 + 32X4fUKADJFtc6y/G1aCifzGQe7J2l663k3IT1mE1YJcm/2NDXWp0c8qdlwu93h2jl3Ju0RZ8FmE + 48DxwVOWlKG+IvehPz0+ze8RfIdyiKxzI8TE8J4+PE6Bj05uFHqrGpo6fHI3G13PjZAvhdtzEiq+ + rzm80FdMPOEVSff71w6WDQ84LI8jvGHnio6OEQ78d9Rm6VUM+2AWrLfHRVO3Queb3/Cxm2WN9J5c + SydBzJDVSZ1G00zs/urLs9CnmW9pZUtL9Jkw6jIDsPt2VsVTGCZIH+czZa3wQ0TzLCvomJU0p/Is + ctCFYMBe/FQ8jjQ6hAf9Ic3Q9HlKvqFiStv8xBbJmGE91lUo8TKuA3Zn1w3xBLeDi6v08wrqu0ce + A6tKGz6hm/aah1XXuBr+7kd4tu+c+3AyJw3DfA/E4+RorLljI2nde7sA+ucgp4+byUGj5Dl0ZZOP + tx63dw5vTYKDJWnfA+VCWkrpeoXBVPTngbWfiyBhhauwe4ZfgDc8lWQxCma2St9gFD6eAN892aEj + zI2coMp0RVDYNxR/hRMg3D4uIAcjFhsiq+ezczwzQOqHdl7c8JXPqiRUcBcPIbIU+RxPEe8EUnae + GiR/kk++uAcxgX7zvmDnFsWUv43q/KsPXDKnXluTC/rC7833cGB9O7r9XymVlZ3jbKrbYU60Mws2 + PET6zlaH7f5ZOPvRGftT4MacvvQQUnpskeLdQL489UcAp8DUsS5afDN/eNMX51a4oVNK4mFfHEAC + owv3xMqIVG0t7v0oYrBPUcmrXUyPN4OVCsJO6C61IKb13VXhoZASpKkLA+bi3s+wFID7V1+TOnCq + 5K0PHqMNb5Ze8TKIZ9Lh21EuciohNP4+o3svjl6/SIYLZX3WsPpc6oF8GSJIbhu/kfmymny9vrIU + Mtzk4PJk7fJ1d7fVHz+hgAhGvr8qkw4pA54zb6NEo9yzFyQzLWdkb/WzlMwpgG57eiPj7p3pPNw8 + DrRDPmLjvLfAH58dXu8cO9XzMvCPVCmlm2p2AWt0qscf95UNmTosUNHs3zHZvQRdulypj9xnOG17 + 1CyzdHi9cqy454yOwxOI4CRdLFQ6bO9RtZZb6Tc/WqP3KctfKZGY2dJx4MtnjQQBp0KFPVlI4ctm + WLlRHiWjFhtUTNGDsu77bkOrlA8otK7jsEhXnkg//klUzfTIW5RGqCXfGR0n6Qn45O268NOjFEez + k+XkVl9aqd0VKUrfs53zn4y2UNmxN5ya67Ph5Om1iiNzyTAaniePOzavHmpJpeHLnh7y5T6bpVSy + LxvfidVs54a25m98Z26IkbagVf5KoAXO3CKieVzjujYcC9ihEzMZOb9TL4UoBjqLCvbua+NAu1H6 + drd+XvhSa3jcwhEKRAtnpuq3XYQPTAJ3cfVCzrio+b5XZh3A7/mDZAEIHjnFsSC1u7eGS4e1tbUd + v5nUfVuM09hswDqVb12al52HXTq+GqpuK6Ia2R7wqRTPzTykz20N/SFBGu86OcE52t6pd9pgOT1P + FOePsARnB7/nPWOBZs6BnUlHfaRBZYbPnJBsFEBZWPNv/sfU1a4c/BreA1vawaZc2XjwD+9vV/ce + zx+uqSRWMw/YRCVtKFtUZ+ndrzskt6cerHzwkeElfrg4SNyLR4z7WYaG27Cz8GytfD2yewJ3o/tB + qsEslHSfVyvezA8TUAG5Gve1wCj6j1OEXRFY8R7nC4SWyF2wW1+FHKu13UlqQSp8ixQr51JLHOHb + yrz5/Lrb8brxpcSqTIBtG6pgX1WHCvqPXJrbYSjB8vu9AGQFMsPmnC/0LIdw1Q7czAnfdz4zftTC + H5/7FXNvMG4zGXYKHND1Mbfxx7lGprTpR2TssQZ4WzpxEi93PLLEmgW4tLgZZj1B2Bxi7BFHjwLI + KGqMdX/fUMKZzReGO9VHls44w6LBpgTHbUW88bn7+RozZ190HPk1t1o5DCsD3ExMbOGLb7fQ0fbQ + xF9w75omEMBpzEc5zkTRh+aM3D1nApqOUwaCaE2xfoJI4/U+qKFhwCI4fD8eJbTsTbDxzdxfIkzX + 6yvKYDwuFS6iSvToDQo2wHwoY3/TK8umX2DLKkuwPveCh/c7u4VrxUubHi1yEltYBZ9H0Qdwinag + //FNd+x7rAzuMx8/U7WCTc8ik+Hi7ftNBMkIObTxuceHpZnBy/v7RAozvXJsuB0HebnwURqbGuCl + YJKBpxxn7AWlFy9W8/xKP/4OM8Gg6+0r9SCISLr5mffQd33lQ/99uM4bvzQNb5gd+N4CDyml8KG/ + +QSJ16lYs/lvMyV4Df7076/eFju9lrB2uwfS3WnKifTMAjE7l0eM/KkCy/PyPsM57K7YT7sXILSs + TBjN73AWC89vuAddUol29oqQ+3Jj0sSFAA568Ea+d1Tp0vCtCPL6e8eoK/MYC0kywwm/G6zHWpYv + +sMjMH3ZD6R9hRNdpOuOAD2sIfqrp4x6m94+mTN3F54NtcYxhc2zeWA/KtqGtPushIe0/uCtHim9 + uoYLN/2P7exiDNzC3k1Ik/gecHdBGdjp/bDBeBCngDeNM1hsU2F+eIc3/KWL2b5V+DhlLVYN5gSo + IQAGik5vzZQzerDs7Tz7w9+IxGAgI9VliBv9g8979arRtkEMVCIhnkURvHNqI3+EObj56PTjc+ER + rHCt2new0Pqg9bq4rWBaFgM/VhIBktz5Ee7QQwiofbjE62IiBvY34Y7z/CDQyZ9s86cnkRcUVUPO + XyuC5tkdNz3vxuuRlVbANV+C3NxOhp9/E4dBokFG9gCsC11TkWX968yXnwYs7CtMxcfxwmNb7qtm + mnqlhotDImz5njzQJ/AzcJwSPhCUJWkmfzYKqJ70Bh2JMwHiO/vyD+8O5fUck1zPA8g9kmmWQCZq + tOS6ShxfOAl2QHVzeug+JfS4ZTt1jAPectvfCyBGGR8c9L5tltnTK0iP9TSzsSbGVEjKUTwU7ICN + /TIP6/P95OBVXYWgNbraGxNfF//w1btMkzZaJ72QzoaL5rN/uOWr7MruQXS+1p+fo5ekmGFp0RE7 + +hfHOEgbF6b2VcNON3DNZ2+RWdr8OU5Ug4D5598w3yRYOfQ6JbOgdyB5jSa6bH6DEuuuSpGy7LG1 + 6Rc2XJIQWl0V//nP6SNXI6hMiNHFvCGNshMpJY0WLjb6RPE4YRkgXFnhg+KZe3vl17qpcPMf+Ci1 + eU6s3akDt1lUgk9523lUfysueHzVHh89xvDYoksJHG0gbr/39P6eH0GCjkKwKvSHn7A3sBnMmOrD + 0tpRCnTfDLAzLnW86I+uBP18cZGZz1VMyJAl8H5iPOTsncyj7wuVJabOrUAUdKlZx2PCSKl90QLm + u96axblmJvzeztzM717bngnic4RWqR4QKs5iPh48pwa1O7kIsYKT02ZmWamZzAWhLnvR6ciIIjyF + zYh/eElleRrhutfOm3+rNMqvBxmUwvpAx6/C54tOmA7wNzMM2GFgwBuMOAOrRj1kVqYQL/nHEMDu + zb2RSuK8WevD+Su5mDvM4/VpaET7mhGscttErriDdP09b1aob0hb1FgjYsGeofO93bCGwbtZDiQR + pP3l7QVitH4a8tlxEfz5x0sdXuKVk0D155cvffLUqI3e2ymFtoVOxhE1iwaHEp4NG+EUIGXg5Vlk + YUjPCBf+ec6xHdQq/IrTFxV+cvjLk6RNv2OXziSmrrq3/57vM51tsBxIKcDHZQeDw7oTwMoANYWG + aVnIP39Bjm/SdkrE8ysG3HInMZFfxgiX0t/Ny506+V6UPzM0QP3GR6xP3qyGpgnN8Wtgy9Rcb6m7 + XQnCozgjbdMnVNCdACaiTZFshs94uecChNL5WiMt1JXNvwScuM03hDI7yEl3DU1Jw+GKFE2b8+XG + hiZs0hUje+PfRXiYRKpJnOBjVtgDhVC0wXplarxdf0x+eUSl1mogbvrvD/8qLy82vX7UuIN6KoE5 + rmRmmDgC06mLibh0vI58jVE89ootDrp4DpDu8/MwcbemBh01HRQvKtUWWTDaP3/63MZnxfI4H15F + 4861NDI5qZXahC48DEitZh/wFnP1pRhWLlY/+o2SjE7jjx+Qx8/n/LsYbgvfQn8Odg+v1YgcCf5h + KS9fFNwvz3jdZ6SD42tKsPswPcCJ7TLDjQ+RVtctINP7aouvl4eCH342g5RzYBS1FOuP+O1NUiJm + 8Ho9vpBfrsswVsH5LNlXYCEl6iVvabNj8NOLwcB9KJg95ZgeKpPTMTpyCWCHOungc4H57/69dcsH + RXQolWC/x1uHPR5beGfEE9b1ZszXt0pdiE23DnY3pdHWZ69XsNjLOlL84pTPGTQIZIDDIdvja+03 + 3yXVfjnYX2MXUCidOdivdY3R8ybSaXafrLTxD7ookQb22/yFcP4OOPjOrYatydKl1hGO8zoArdnv + PKOGbl7x6HRqtlMb3YmFTIkwsoXkqG36voCnMFcCush9Tm6nYwEN07BweJ51sJTMLTgsnYTnnT9V + lNjPRZQ2/4ASYYy05RHOBFat9pxXT7Ga/c18faHltD7OCudJZ11jagAVPkT+epI9Gp3OrvRYlxRZ + ty7w1uGmcX/+Kjp6NtgLxxhK9/tKsS9+rnT5RIwPdyRRZn73vuRLd8wYeHjaM0In1tfw4d1xIB5p + NdMkqIdtx24B2qNZoqOMm2HtKqkG4XHVkJfvtXjelccMati1MCrOWU7uTr6C4+T7eBMHYJFMHwIr + OkMUsM5C18vqf6E59gY+vx7UWx7omcFiHC5Yvyq4oWo9RCD20hGheb0345aPwOfUp/NhEoWY8GlS + wt6YTIymz7sh6Cj0B32hBrJ6tQPrC00p6Kju4OydavH+p59uZhNjJ/XsZr3yWADX4Owic+NPorTy + GdKTlGM/7Ukz14fiC7UMkqAXUh5QlS11+PZ2TbBm7dCwL/P7PfyuPz4dr8MUaiEHX+FOQD7zUnOW + 0cQKxLB2sfrzxyjIE9Fvii+6fO5jPDUC6sRZqu/zIjR6vgdRUYMP6Szkn27Bv3i5dHt9Fjb/vG5+ + D/zyQWvTg59yl3QwbgQT3X78Z0s3FvA3PUTe0tIYX4UohG3US1gLdjLlXTUroLTEn3l56XtKeZpv + eHv9YsXPumF5Dfcz3PgSbflqPLulm4kGfw2Rkq5MvIDTwh7WK6yRUorngWz6BWbT9Y3d4/TxaGcc + W7hD12OAf+OvWJ0KU4k0WGkkN+ZLTmOhYaJ1lriT7nHlibIQPa0Yy3f1rVG/FccfvgfpLH292eR9 + /08fqL/5xnwsEW75KnZWadbwTgl1qJ6OwZ9fXR3or9AwmAKZ7tWO//KJ3/d/+pDL/bAT74xw2vjS + GpZ1dXsgi+kBBczJ/s23FUp9GiCr24WAynNqw9fhcUKunCwawc6ZQNfSXWw60RsseW9X4HG88kin + UhIT7dNsp3qRAhmSCjz6p9c+5XNm2rbxfnpdegvfM3a3elwlMOlwCEiCrtt40u/wkeEpvCnBch1W + MKMkHOGkZlZA7KxuqFQ9OLjpT7zpn3wak0MKLiQ/YVM/QTAZSmtKcOU0ZObrBdCtPwD0MDths6BP + jSj7sJRu2fhF/u5bUwzyawiPcragQF+ieBqMZwSI16r4Li9BvvQY+L+8D8nHh6bxB4FjgPO1NVzs + ZiEmn9kr4M18xsgodKchYHquMAfREzl4VJr5ayepdHcSc2a1isunzY+AYRjv2PeONVgimzXBlk9g + BMwHWBl/v+2JSMb5lz/jurTSQxv4CFnRdYwJCuJE2vKnAFaM1PTJnZ9/4xPsDpID+J9/0XiRR4qB + xJwuqVvCMKkouvfCMx63PB5KrekHvHbo6eYnMumXF13eJzmnrhqVEhlZD//4e/zMnQu3/GB+1eE+ + /+U74hAABQeI+Xg0rtvwTz9Fj7cS88ah/AJrd1LRUXhkdB0+SyhZ3ZfBykuu4hnLqgojpXrgO/GE + ZhKOXA9Sv85mAvl6oMzyhvBlIzsgWx4/bPj747vgSi93Oj1CXxdPhckE9Uc/gFV4LDbsM8VGxtP5 + egtoHAZqaqAgZctb8T22RXjD3nXmksFtWO0jC3/86ExiGlPlJLFgUusVOSOdtOlXz2XOpchf4y9d + RPkzCtS5JNi5ulJOdPEmw59f2+rB2/o3qfTjPxOf2ng4U5hBlJ4bdPW9qqHCBaqQHj8p8tlcyfef + KK9E/2G/f35wWEhomOCnP4wzvw5rk2cRMNHuHOAN7wmfloU4IG3ER+7Uagt3k1kx/RRP7D8OBRjg + UMtSI7vDTKWz24zPR1tKsZeNM/SjZvj5AdidWHnm/eO26z16p9KWZ6GjVIcN5ynHDJz84wfpWlHR + 5V02JvjpBev8UCnbXdcV8txpwiqoonz8MoII7etaIOU6Sc1KD/ceWg5foyMXz3TdlUYqbfyDDXBo + AS65uYJbfy5YrpM0YD2JR7jpB2SY1zZfHk9GFDc/HxwOFsqXDk4FvB9BiZwtT6JD0MrShufY+b4V + j9xCWZfy8m7gDe+1Vc5FCBeg5gF5SF1MzFkp4Xh67tAxPEc59ttQBl/OjrDhFTqgT+8gQv7mg5kL + n5+G82VZlbb+wNyEph8v3JCcIZmMZ9ClPNfgDW/Ar14eD0/3CM6tDnqz4uDk5QTe+stTGOMuo3Df + y3TRH9oKrDo4BW1X23R6ajf2cLK+J4zM914bf/1lm52bv+dLJ7UowPNzTlD4SZx49WVZhtQJBmxs + /nP9tGQGvvkJkDKlgkcY/R6I2eRv2Zl/9vbMB4lwHW8IGVaJmw1fUsg9HinSgl0F/vC3o9cH1o5i + lVPkcyF0mJZseeCZLh3odfgV2x1CLGToVDJAhAJRQhxY49KMv3zgpR1MZHm2O7Cq5Z3hwV52+Jg3 + dLPluxIc9omBna0fR50V9f9nRYH03ysK0veYYU8BdUOMYzBCRia7WYykTzOaO52BzZEfsfZeT01/ + GY4lFMSuCPh47LSllz6d1DFriN0n7wFqhh8I1NnG2Kiyma4TCXVoFkMUkD5QKE/xLYPmqy+Q1T1G + MGX9IYUH97BOzth63l5exx5M7sWZL1Yke3g/fStolPHmoA7YI+2FdFLfnDysBY017L81bSWnXHvs + ds1zGA+XpoPRdAQBUwxqswz2sYPgwOQIdYWq8Y5qrtJsjx+koh03kOX0+cIeExzsbbDGS3k9MZAU + dD+LvfAZiP6GM3jsnQDnX6I3S2V9MnB/6urMz+kpX+L14MI1fI6zxEUa4PePnoXHXcuiWxNngI/9 + WJWm/S76PQ/wfdmnFqJryGJVmKR4zmtHgJeUOAEXwjZeZi0q4eyVMo6Qhelnmq6VFGEpwhkxhJwc + 5nqEJ2+Z527WI7oX8j6T1l9i4CBCRy/ia7G+MzqSc1IDdr/apXQ3VA/LtfGlc2zInKSjDiLt3ckN + VwWgheikqcgx+dpb0W2soGwbIYr51Bm43XGfwcnpDCSHGhoWN4Vn+Cx0B+nDjh2IK9SM5B0VgtWr + fI4Jl5UztJQxxtk2HuxlMApIRnnB+dAPORuSzJQM3UfogQ+atz8vagjVoL3j1NFhQx9nkkqHdXSx + hcNU4xjURpJlMQbK+/Mz59kr/kJo0mSu07TSlktjtzDJvvrMcfvS4z/AVqEYhQXOesEZcPR2ZJh7 + ao3Tqrl5VNA8CMNLf8a39Sh5k3cRdGlwzl8cozzI8c4QKrhyV4K0tnwPdN6DFGpyccNXDCdAYHRk + 4cpdCA4Mv9Pw9QkzKc5DA13A8RKzLVxSScGyh68vetVW3t+NMOqOxrzcWOQtQ4Ii8JhbA2XV7uXt + 3+95hlrWQOw9AA9WgxYcdIbwiG2cGgO1b0INzBsAMzHXR7x8TcEHcNUdXLxD1iO7NHQl+z4swbrV + 85hsKxIs9hIhI+wNQHvfVKVkmhds+8IwjMVDOIPPyzMR6iWgUW7eCXBp7QcqJ0eJ97W4V6WvVWJ8 + 5LkqZrPkQ+C3qToUvmcIpsMeCtspEGgmwG7oUJ9LEeZKdMH2PXFj8gkHCLnnRcB2UNgeV512HByf + 6hcZ3+Y1ELWcbfiEtwcystMV8GhgXQlWFocNG8EGf4jawmwoQ5T5vQK4nlt1gOkjQZr4SvMliKJE + 8oSkmYXLJ6H0cvl0cL40LTJ3mZBTctRGyc96H2uqW+ZkZC1VUssDDZYbiz02qp4VyCk4B6Lx1rW1 + 2sMMXvokD6720DdUj0JOAu19QrFVO5QCJiWQFMseG2vheiypcSuO9iNAR/F6i/ezlpUwZU41siLb + GdjlPImQ3wcmLixfyqnC+ASkraRtz1OO+Zy/rJJqn4eAtqXVLIrXsJB6bIm9DV+47/e4wuX6FRHS + wlWbH2q/Sm7Yt+h8X/oYp6XgwkttfrD79BKPXaZJhcsZNDhIaq/hbstaSonyBUjdtmimXnsu4VGm + b5QcFjMn6e5USm1e2KjgXC5fMoV00vezpnhbWTHs+fALIapeZ3y/oy8gT1pz0urABgUHK8+XI1Bl + OFEEkBfdlHhZoZBBfi0xcunB8tbZHXv44wPUVDeAH2DxIQ70HXZFsQX0na5nKYvuFkbwWmtYKDzz + Vz/IUDs3Z5O7tkrhMno4O92OGivGcIZDYSrYCs7ZgO/vKYA2LHbzwhG/Yd/DiZH2BmfhCzgM3pgo + dgpNLaHIEddrvBwKiQAhl93gFpCuWex+FKBZ28qmoAZvuayP71/9Oj2B2hKqnxU8bKKg+HqOAM/p + Igd9ezjMIj1uu1prZgm+x/GJzsI9Hrb6kqXd48EiV0ZCsw6yPR7GAMtYccURLKJqEaj3dorQ5xPS + membTJrkDmJNcPYe2e/1Geivm4JjFwvDeC7XHgZlqqLUyr/x8n5349/9a0nhe+uY3X1Yi+sukF42 + ileJm3TYMtUeX2xUDOS2vlwp7g15Pngsk9NhjkcoMCaP5PdcUHJyu176+K8vQvFMAI2ZWwQ/jIuQ + egk7+lXutIR5aSCMpDOTLw6tz/DedvdZmiZn4NguTKSu0cj2exAsUcC2v/pFZ/sdU/Jy9FDyVOYY + AMc+5VReFkZSnJ2HbMWsh+EZOxWcDT9ASR05dMJ9m0rvwbvPzFFaYvKqtVQSeF3BUYAcb653LgNF + JomCy1p8PfptknA7a3dFj944x5zEvXRpwxsk0zzMyVAwBHqJKuJjSa2cy8+y/3e/pUnqgTfizofF + Y+fiyB/GgX6XK5QMjg+Ql7vjQJojn8GgRiyWP2bfkMzuTfgKLGsWrNzN2WahELpc0WNZPpnDPp3a + WrIIx2NTVWsPX0VQw6QqnYAmha+tu6OUQYs3P8hZlVH79jplJYjPC4r7RvKWq/Nm4JFaIUIZ2A2U + fCxdqnjNmQ837kDJlS1kmLY7bYZsZtMZrcMX8gwTI+XVpd6obomAVTZHZB9JlK/eoEEY5N/hxw/a + yF+zUlJU5oJ91zYa7vUMRymdoI+N2+2kra1U6D99hJGtSA3Wo5SDYX1HSBbGqlmfK7Ghd2suWJ3e + rtb/7r+XdzXSJmA3i8HLpiS8rBkd8f3qUSPuAqD7+nMmp7fvsbJ1F+E2n2d2tb2GW6xAgEygvNGx + DfuBuNOrgvJkhxv/zjH58COBuvCYkSmXRkMeRbbtKrnayH6s00CFr09gnl8ELKProcFzAUMoEBb9 + nodGCvPgi4ZbKMiyB7vhA6Eq4bTDj5nTVtysBpNFYNNH2PHYMt/qt5OMI1NgN96HOS9mhQ+LosTI + yzk1Zq/sWZaGV/rEoeIcc/4QKKl0XqGBVd1cczqVjb3tAgsDjn/GdEnElAXdXtWQHsG8GfdvTYZ8 + JNwD324d7XsWzgUYlSJFJX7pDXt74xls44FOSzwMVIN3EWqVPCD5VpFhVe6gBOKF8DOvJvKwV5Jn + BKZ7zuHg8tUp4Zk8AjLNNGykTzemPaOHwOL3Ey734jjMdeex8KeXXWc8bL9HC7DGJy1Ydx8jZuVh + 6xgfsIy141ABsg6cC62JOEjO9aNHsHTp4PiOCozM1IvZ4kESiaOJhwKuq+ONTwrI3SsTq9rFpGzo + nELIpUqKLz5Wco5V4xVueIhc7bo0U7SXUjh03gv7mmWBBSwKkdolPCKFrcR4nrf9tG+TSebtSOKc + nEuxh2du4ZCS6kqzBttxKEJZvZC2kxqwEP8pw2gs+ADUnptT68uVoDw0BnJvzDsnJW93EBA/QCeS + 62BfzXUApNM3/+PLlQ3vBdjwCt1L67TVW9RCc4d32DDmZliKm8xBprqcNn2setNxks9wu95AkLpV + o4+zkB66ZtO/Pi81X1FUWbjp65nX+VszXtXLCnHjyghleB6W1BxGKJzXFKnlQfjpcRYqdfX++YuB + 924PVXzsvWCGkeJqbP162qA6rBU6dqMHiNk4HYjkXMHHZX1oS3BJWshCTsKyOjlgJcahBpd0dbAn + 1tOwnDufhfniSci6Oe943LFxBUDw6VHy5VuP3EfNhjUftYEkxc/mr36CS3rAMXOzYjYI/Ays+27E + sjNbDXc5xV9xw/+ZuQZKzn+2PS7iJx8gX0WKt/eZIIRzrThI/n5f3vq8LSoczHccHJZLktNbdayA + MdVzcHAhPxDaL6KUe3KNN/4cRkTLBMp6t8dXnb8N9BkrFeSkfYE1FwvNKukzB6trwSNtsfl86dtv + AYPUDjd/oOQs74QE/PBZJy32enpnix/e4vhqK3QpZGeEsAj1gN7vH0qNeA7+8OcuXg/x6pSLK2nm + 7CGLnW7xp+UUUQpAIGEn0EO6UEeLQHl/BCg40MUjqlqv8Hx3ZWRzLzankWz7sHeYIVjy1Bv2+yqu + RfU5eFiX1cMwF5dLB3e2OOLAJHVDDtZzK6RoRFrxajSSHuMA7i/pdurguAfrIMszvKB1h53ztmsu + Ts4rrM8MwEFFUm2JtJ0rshaVgpl5dGBRHZaB8Zys2ytEfb5+AzWFcu5J85cYabyO2cUHT+GgY/3J + SZQcv2gF+py52BQ/k0eUe7jCRGzGAIb3Y7P50QI8hOGDNPx4adS+kUoKykxFFg4Fj94O/ldkdq2P + kLcf87W/rxDuht0Nu8J3/el9F15f3APbXznTsCfFnXQ3ZA8nTH0altVdbRDb1YwthiNaN/p5BQzJ + kJHsRbq3irbfST9/PuGTqfHrev+K4H3U//hjHZnTCtqo2KFowzOeGZdE2g3SDSGZncHipuwZHg6T + jKLkzDRjHO8DePq2EPl4H+T73hvO4KLFOfLH6ZKTh5YXYnvxk4Cf0yWnjZYyf/dj3cVoGNjru4eb + n0L2M99Rim9ElcgtzpDlIsMjt9ssgyTrdXxf1p22+nyygvLwNJC1q1+UyNZFhFoX3tF9h3Xww48/ + PzOSpx7zhPXrP/wM1jXOyetl9dA0dRd79wc7rAYThfB1PosB+Pi293FeVQ+aykuQu3xYMP/0IMda + CFktIPGHj9/Bzx8FdCdpgFZS0MKTR2csb36SCoVmStkMBxRdiivgLDiksMXxYV43PCO9fnKl9/uj + oqOhp82yh4kA+f3ORUgZLw39+e1gxmkg+LL+uz8BnjnKzQfwLbVxWtwVPgWgI/dDwniy0USg2mb1 + zLoeibFC0Rm6k2sjJO6SmH4v5w7amZNgWRCWYdrqQ5QMICLLbyeNFNMq//4PeQJz0tbH59nCq1Zz + M4/D1KMQPgWpqgrlN59zcmubEj76c4yKk9PEFOn0LLFxXWL7qtTxPMOvKLJxte3q+uqG9X3qbYhl + 9or83fuarxhz7a9+kf2VRe/np+GWR2Br8/eLemV64EQnHWmrXHr7QfQ4WKwRi5xmRxrialUJ/XOW + BasnPj36829CGlGELGiB8fEoOXhcz/7mX9N4PXbp9kbDeUWoFV7D2qMlhEygvYP6dFXjMazTDHIZ + wcgEF5WuZdm6B293uQU7o9Kb3/VLfTGq+OefaafyBXQTIcFpKXZ0YWs13U6FHBASmtibtjwCVjEs + gosVVdpajhMLUiauA7r5kw1fbDFpmBZZGx6Ty3As4KXWP0gvDs+GXpYRiq+reUL5SblQ6nReAiLr + xiNf4h3KGSRtwVR5AVIJ1vLFXUAJ9qnIIhP3OF9XXe2l5Hk8YnnhY0p++mHTz0g9t4s2+ncWwkfE + vwKyJ0L8my8gOU/9zMFoF2Nk8R3EJ2fCPnww8Xw/ZDZ4hdy2i3FzymlR4BWWklYhW4+XeHm4ry9M + ZxzOOwtadNIACGCXQh+pm94YN/0oRZ+pwmq+PIbFPBVfeFNUEdkPkweLMbpfGHXImJf65uQs799V + uOjtEZVcp+ZscHM4sOE72vC+mYu5GKHJQIBRv3w9PO9BBrbPSB7ja0xV4pngh7eH8JaC4XZXQvg+ + dxdkl6qsja5QQ+n1dRnkzPa7WTvwCiBugD4zg7GjyymvBKmpnATZZqMNPH9fGMlj2gve8gtA4icn + SDcrHDd/sMY0z7MMjA1RULnxB888gQB3ea+hu6ELDbl54xmaxSea9YDvvD9+RfrzjlPfvwBsFXAF + 5wY78x4cPI07n6+iRAuPRfLafBoiHwobbnnUPJbDgf70CHyaoYFP1d6n0w+PSlM0g4Ppjfl4gG0m + bfMdbXlXMxvXWwl3Q3DBx3e7Dhs/+7BfKwtfg7PYUK7ifMidKwk7QsJ6RM2folSagonQI9fzpXTS + EGC3bfHxnT6H9TSRbcUy4weccHTosreKFKjG6xxQuHfBkvWHDHiAZXA2nH26CrXYAfv+WbAnMIs3 + X7PahxzHddgeYR0v7V1dYWpkC7YSZfY2/2SCPilCnNwm1ND7ranh+3kL0bGTOe/boyWCmeO+Z3bj + 2y0PnGE+nT3kTzdvw69PBzf+RZYWvONPNX99YGdeglHknuO9eFtq6QCL63YM6+qRzK7Mv3zMMT0/ + 3//y6PPdlucFHo8AMzZc4ZZ3YMN5994vD/v5ic0Pv2PeUQMivt75GxvuY6b9bpcycPq4V+TzquTR + u9TrYFgEE5+66AjoSdPLnx5G+sH5xLORXUbgi0GJA+cEBsLwVwKafa9jt8ntGMevzIbgjfSAEz9H + jQ3eWnU4WPkVO+tR0kjCqSJIjXRB4cLHYDkUewKkZzAh9Tq62mIFYQZPzxedYZdOMX3avgm+SiAF + 8E6pNmUKaWG2arfNf3fDAlhThqdc/iKT25faun9PIxjnOPrLN9cTWAO4+aFg+TLNsLxvkwzDm4vx + kZ3f+WonPITZzAzYUa9SPh8/Fx1u701gy6PPhnuugg2DpSBITezjsP84bAExvSd/+m3l2k8BtrwG + //KiVRRdFtQqarHMn1m6JNLdhJWeuTOTevd/88Qtr0P+JRDor78At/mEUMJ9Nbqulx7EInvFsvYi + A/3l3c1d7ze+3TfzobUYeCleIlL6Ws5Z/hqVUtXG6wz6KsrX7vFkpU3vIf94Ocf76A1GcDWj47xg + 8ACkeAgJ3OoFnZ1H1qzVW5xhjJ5tsFYnfSA0EwJIl2CPneulopQFbSBtfITPkc7S7wy/wp+fC0x9 + pOtIizN8Z5z10zdgFkfHhvLkhtic0ruGm+9thfxaYJwd6kM8Cs0r/OWR80rDwsOwwyqcn5ERLHb5 + GcbbbVYhOnR3bMap1dCYOUWSdaZy8MqMDszyqe8h97wKyG3yPm5pRnwYFeIL277cApIgVxf5vW/+ + 8tiGsy7fP709P1rBaPblK+7hVk8zsxf9YUrNZv67/qMUK8PaV7wvzZdni3952dq4cgF/n9Xn/uk1 + P/1XxpOCXe16GlZzh3shBEUavMDxkmNJ2kXgppETuspr6408E0fSZe6Os5JVOv3Llzf/s+Uxr2E9 + qkUI1QXdA1ZUiEd/+cs2X5CsKqw3yfKcHYxsuc47ccfFS8fGKhTNBiA5ihWwJ/TJSdv8DA51zmrb + WuEWbP0QhPaJnrPXvBDBpFtXrK0y430Zv5fhxu/IVAOq/fJdodDsEAdQaxoi3b0Q7l8Yz3zd9fna + mfUMLwPL4bzmK43Y4o2FXhGOyIpHUxuTu0Zg6aZPpMivl0djP1chyFIH+XnpaaRmtjfYrGgMoOSx + 2ugrh1a8l8l3yweXfPFrVQTP5J0Hh2JVtV+9g93jzuLgYIGYosOehc3+q2PruoCmrfanUXpE+hO5 + iVFvezJkPiyrJ0Qq278pOVQfGbinW4+tFoQxHQ4nW8Iydw2kd6oMv/4k3J4/MlVV1djivIoSfJAH + Su/JN6awqVe4+ZtAGKGaL+edB8E1vC7IvCg3QNT8I8ION+egojnJie4dEpHXhufMc9LQrI+wLsRH + ZD7x5gc02nxPBG79Lqxu9T42x10KZenRYdv3L5S+0LDCzb9tepL3cJ+hEZD1Jc77rV8479+vEdIS + eEg/+YHGnkvxC7GerEgtlcgjatiWMLbreS7rM9vQWZNcqOpc+C/fXLM6+OkZpGA80wUcjz781DlG + muoyW7+mgdKLujY2tHLR8ObP//jLOR/fYCGv3j78xltjQzIs+tMIIOZ2SyAw0RtgRgTyr3+K8pqX + Nc7nEyL5V3GPjQ+PKFUdN4QzuPYzJ5fGQEU/q376KTjYFRtjkhspJAiK6K4LVkOKsyiCF9c3yOi+ + iseNSqnDgJ9rrBTOVeP6u8jAMBIl7JA7arj7kBZw0zczu/ETaZo1+j1f9Os/Vc9+yuChEIwtT/54 + y8OdekgXf48uNRd5tD4nAuBIy2I/vE8N/TjOCh22nTCK55DS51PzpQHsPnN/PcbbG3gRASZiryir + YhRPG1/DKmYKjC5ME//pbex27fwM6JIv0+POiYoKL5u/MIf9OLo2gI/1gZz4uYDJaxYiuYedhswv + r3sL6Pzk1y/A5xm9PbLbhVBqwreOXL9XKP9CA/n1m7Hu5yrgLt9olf7wCJZK0y/Ct4QQsAnykjIB + e8f/VDA4CWXQW7kbj8wnMeHxcFGxxtze+Yb/4/9ZUbBn/3tJQa6AKHgfp1ybdtUyA+NqHAP+tgYx + L1UPF75hfMJagklDLp4gguktv5Gh9x7dH6TUleBcu/gYfI0cg0c5AjhXLrJ4haXEuhgp3A6GQm6T + Sho9569KrPediexXSjySTqdKiplTgx0CngN9hKkNJXOPkN7dzt5kGywLk6Y9odMMBY8GV8GFiXez + cAnqOyCCNAqQ42eKldeBA0t3QavY67cLstA4DPhx0gMJcCcwMwm6NBjf0AzjI5cjawRrTvHhLUoH + H5znQ76CmA5mLUr6OlrYu5EPWHdP/wuG1Tzis+NOzdrSLIEss5yRmUYo/7qdHElADHczOQe6t1Cv + EyBf6QyKnmcdcK3GJRKn3XvsDre5oefhnolMXL6wAjdKNvU4lApm27bxQhaN+NaXAJI+TziM/G1R + 1cuXJfn8VPDloFiAzXdqBAPvESI/OloNKUnESTMYRXzbKa03oXRNwU0Wufms2vnAvtiig1YMvzjy + HdlbRl5xYaPwDnIPlUr5w732YXRvAoTsdx0Trkh7GN1ShKJvxuULO8SqlD0HD7mB22uk00NBEo3z + Cykj6MHyPlemtNhsiZScaOClUa+TXq7UY38sS4+XlVGAN0PZY1eso4Fv1I8rdTusBMv2fdKd7RV4 + zqHC2+rgfGWIXEh7E6Qz3D2VmKOesUpj3eboujOSgUv8wYUvEO5xEfiJtufvjx6Svr0if70Hw+// + wKSyNQ7nwtW4W/Do4Vf6yrgsot6bb7NHYPLIPOx/bz7gfs9/tu4YF0l4BdTeu18QEnCYD9bbGnjW + PcjQSaGLfdgsMX6EqStKNbwi8x/SrmRLWZ5bXxAD6ROG9CIgQUHEGSAiICJNAuTq/0W93/DMzpBV + tRTDztMlZF9CJd8kZbQVcud15EbnX8Q/SdpCm1RPktyvpbGa+zHDsB7dgAL1Z0zirOgwMWIXve4k + HjciVrICBlUk4e/hAGE7iR0shvZHCtUwou1k9SYMom8cNDF+R/R9STtY394quXe8Zqy/T5op73fE + YXoaFTqwuatD91GmJIA1MUjXcrzSnDYPi/1jGecvR6Cctt2MfLF6G9t8TFPYJEaADAJsyt0L6ICD + 6Ijk1nCXiBOVAw8Jkg9IJflpXNOnU8HvkbOQqdkjWOOxaeFi3n3Mz8/YW2axjKFQWBIx0FyO3G/f + VGkRFaFnxf7AEj6iSkFIKpAuvodR0FbIQCs3PKKpHW6WUuVZyNn4jp49AICW4YGFUF7PKIiCm8d/ + 2CCD5M7qAZ1ku2HT+VFD9/d7IK9g5XHBKd8qJs/Z6FhzaGTbiM0Usx41pIlGngt/n2flmke08/CO + 5mNbVXBhhQyZrv+MNpLCDc6l2KCYqU/exo4fCPXz44FpwUW5YLdZAo6svyKzSxrK6tewVF6in6PH + bZGbOX06NYTB1SDZWmie0DZaomRaYGB5x799vDoFyNdD8NM+Hdh6HoUy4F0Vma+L1nA951zBWWcP + KKhjhxIu/yXKvVefqJBearMdP98KqmGmB4BXF8DZppooXwMcA+4rRYbwqlhb+fs8L9ukZn6SFUOl + PFxR3Oc3Yzn3OgNTCFbkdNkBbFgsaig5y41cH6HhsXSgk7LPJ5JuRQ0WVYv2Ni1fgo7BJI+Y821f + Sb4VQoV7+gB8HRgTLHF6Ra9ZycBWvOIYerrWEj8M7t7GHMQYKjq4EO9BM2/liruvXLXQJ/EDqXTr + uTyA77fN4nXHDz7xG1eJulNF7hHJKX9KfB3+JAfj+0/QRvY+W66iX9KSmObRj4TwF1eSKa8meQ2S + 6y3Vml9lm9RP5EnaGm3wtdcvgRa5huc3pVJZhvD+Eh9IawQOrOKz1SF1NYDncLIjTnjeBxgHzYA0 + r69y/iysOmSHDyIZyVTjdxbPG7CuTIAM7Vt4OB71Ajp+zKIiDx3KcmBJpeyWOiQD781b9QbFQHK2 + GwnquKfTj3+WUMhlkbj5xfNW8riJQLoZKXHsY9/QjLlCZXLzC/LWwytaTtZbVF4tuhOzT1uwkvTN + Kq37ygKh498erbeWh4R1PHL9Pm6Ab+iZgcw9a3BTC7HHdyazQLaurjjVzG+0UfBkIWUiG68TcMCK + Ra1TAirkweEjPQz61qpJdpZVI8effKE/cc5lea8XcjRax2OJlGzKeJKMHe8bsCbTpIJDam3Bj8I3 + HbX3RVU+X3sJpDd3BsIqwSscT1hHx5icjVUOnBpG2pKhNL3MHvEEo1Kqtm6xyFVR8w/PHw9VIsWO + t9sq/RI4cJghzsn5eX/fB2vtDdF9BUYkCM8nC+eM+SBP0Wuw/ZJvDS6fk0me5dbT8fmivdJ0Zowe + ivHx+BKJLDyP6PRvfnJHziyU9+xw6Nqmt5ETfIrhXs+knJ45ZdV15MF2fo3B1qbcuMhYKaWk6S5B + p5snj/3xt/JPn5BYrPqIMOohg9njfSSe3pjGdivsASZR4yHttkY5FWZrUrgGvZFpunDc4jzroPY7 + x6gcUnekxSFK4ZMGABnxujaUWaIUTHZ4Jeb1jOkW9JoLWf3qIfdzPwOBzQsGGBY/oJM/S/k3VbIK + qhC7JN7nB+9wzaYweVMT/R7UBiWP/RCM1DD2enOaP7wEHVgNcvpJqClV7cFIS1XEKAKGFK3VWw/h + eTyfgpE/LNFEpGSBMEssFGTh1/ixgGdg2K8cSrlnk2+2hWLgOCkfbN3d9niFQBsOvwES/3YRI1qg + Qybb90+OxfA85qvggwnCujiRYi2Jt9790Fe+heYGny9fA9q1HPs3PsR4c2bO8hNsoZeoPbIt50Hn + NStjOFxqCan5ld2rvlbh6ygFSHddFE2Ssl6VF3QeyBQqwVgD8eqAjRs4YqX0NdI/fKVX70DM8WHt + x9gBHwR0yJBrn/hmY9S2gBxwGvI4/ZxxPd1WU/mMOsbNm2tzGoNLr1jB4mDGcrZmurqTCWP92gci + +449zPmBD4honsgf368X14WAfzU/5Ix3tWFX+R7A8aodke16wMO6nmUwkp02iDgtbVYmVEp4QMtM + fM2i3jI59hWsP/ND8hkSY0mgA+E+H0kgvYMRz8c0A52RSsS8hgdj6W8nH1DZPeJD8DO8lY72AC3q + 35AjvS1DqNdlU+zD1iKnjOZdz/U2PPrGN+i258ejO9/BLzRD8pyBT4Vdv8LTB52R3qa3ZuP3QxgO + lcoRdPyuxpqVKlTOd80k16XaDzx5GRs8pscf0kbxGG1szpv/+OGYU2Vcxm6Fysfmf3izjtijsXfq + oBVsDlHlBnqbeI50eCM2g+8/eaXPLwtEuXLyIhDvwTjOTyJhuPpLgcF6eOX0dfF9cKgQHxS21Xrb + IsUM/N5FDyVjWoP1wRQbAGewbwHwl5ysj6qEE8M9UYyfHJ21zXMg1HOD2P29Hjc5yAvYgU5Hwe38 + oeN4t1m4PZoZgx94NsuQnHoo9KpKnuHURTQndwdeTFkh1sFwGsFm3QA0fHRD5wWe87VpDAfeDo5J + 7D67AsqNlwDeXFEil51vNr7QSwXW5Yn4z1tm7P6olrNzaCLL+Lg5/VqlCbiAzcipZOccX+UPA206 + NaT8pI9m/MTnCiRfEhIPg2BcEqioEEXiG8Xg7TTzh+6NdxPWQJdevucczZMFhk4y48OdxM1mm3kI + YP1zSQKqLlofgYlBQuk1kMSDEbG3ouD/8Cng7B/wNjpIMrQFoUYoX2yP/91oodRaApDn/Do6P5h4 + g2Wemsg/Hikg5dIwQO1rC8P9mu7+A/BNhYkzP7SRDp0+KPj4IuRu9yJY72c/hEc23zDLfB7RZjVU + VwbrsSL19+jB9G1XU/H4cI80eJDT9Zcm8MG5GrGnvM23pIhUxavNgYRt8h1XiBT1T58H0k8iI651 + RYaqUxxI9GQ3SvVNS5QUSityt7xqNvHJMPB1Z2Ki4/xLt2p9lLB5MiFmudcX0KZmIfiUzBT88Sn9 + 2UkFt/NzxMyUeSM/CEcM67nUMa9Zp2avdwa60vQi991fryxgIKSdZ2DcPmSDMKqXwErd+9eX8eJt + V3lmwJ+ethQzNVZJCV3oncWWRGgumwUE7wFM5ScJBK//AaquqqykVU/QBWhjvo0J/ee/8JM74GZ7 + 188eitN4JnYTv42tDCcZbKdkwuwrvtBhv1+pO8xawNPCbNY//ajCyUVZ6J8M7qH0E6yW9Yts2xnA + n96CXG498VrQT/S7Ta8BcMBtiLdB1eOKl5YpaFJbcurl8E9PM0Bee0xejI69TTjbPdjrPYgyuuVr + b01XMF5YOzi8KJ/jwwLrf/xlyNqp4aoV++AeHnOkned1XO7PZwiJt6koUiw5ws8DWyvD7Scia1Fs + j/vzJ7ufRMbBdMG26znlwTnaXx7h0W3fAsvqoYfCW0jp/De+2Vx5yMmv53wJf0IJVxUURCvZuKHE + GRf4uYojOj65slkgUWSYjL8AS/vvW2/TvYft9VsSNEiDN7/1pINfQzoiky+P+T98415G/i8/WD/s + t1LO1PFx5TrvaHupnK40l31H8c53SxerG+jN/Baw2WVpZsdyFihfPn0gZTQH649jE1iip4LOveiN + 69UVVFi1VYueirlQEj7aAWacEBJXqmo636ZXL6MXU5MTr9TROiY9AyemL/7wYeT5qcuAgHieeKMc + GOv5vjKgteMcqWuV0UU6/wI5GcdgzwMcY7Parw1PiouI3d20hj2LeD+EyrTJ42CyEb3IPg9Fll4C + WbNv+ejyxr7E8ixxdznPxuZyi/OXnyB1fQ4NNaLnANuuqwOZe60RfanVVd7zE/ScwUTXy0NmIZ9n + A1Lb5NtQ6TkE8Dp3cbBW/G3k35tVKfv3Yeh7HVhcu07k4P6+opgUXLQcVLmHJiY6cT8ZY4w/qy6g + beQJOt2JZwx3CHw4N/YvODzW2zg5H26CtDsZyCpWFXBP9TlJYl8FBKlN1Sz6NSzgPHpdIO36gM9x + 6Cq7P0f2+xY26/qor7BXw4S4aQQ83FCLgZ/G3ZAxyrcIs17mQpjFFkGi8RynDF8YAM7SQs6X8QOG + LGh1WZx+50BML2eP3hXbhdZoXwJGrrCBozzwQbxtPKbQlgHB4rdQXkd8Jp4/3Q1aIC+GnnWoyXE5 + xAY9xnWhSBPM0EkxPgbueXSFx2Xf4jArMl2O5q+E34Y546WO7ZEtqwGCYe5k5MovPefZnDH/8Bgd + DxbfUBacTPCHB3ci1REljyMGpXHVEDLaii63vFqU08VwAsP5BQbmxi0GCvVLpPLVCLZPtLVK8q0R + HjSby1u75UIYdV6Fjre1AGTXy3/1hdzmNoG+qcUA3nMmD6Tdb2zvzaoh/s78v/xgu2TnRf7Tk5as + HvP1rQ01bDo7Rsfz4FIe99MAj6E6EUv9ZtH7+Ik75UZNA7n0uUTkZFFV4YTgg44Vq0aLf6w3uI8/ + 0mbZydmD1mEYmhkhmmLDcTMjZoORtmVIf99vxiRjLYNqmOrkfOxpPsnlpVJKI9RIwb7Q+HuG51Y+ + u5sWcCk9NHjkKxWa7lPAK4apsWWMqgL3+b3/G9+vHOAU9uk3Q2eewca2543wvtxqzHfp+sevi8Lp + ywMVtmV6vM8vnSKyXfjPr22uHTtKe/2U+I+/+VrXeAXK9IzM3X+zxYGPIY+SHv35zy3oOR+mRfn7 + N3/oJBAVco1akVe+dB4RoNLKf/VaZJdTtB5NNlP0D213ffeNSOA44d/8IcdGOERbMjHMv/zsT88O + Pi92kGW6KPDPrp1zf/oWN52AHJrX+Tzf3xl8GRsi2sq4Hk9SuEDKXGxyfLHEa/pbFsMvG8SYed9O + +QpJvCkP5XHY/Z8OFiwOGMz2Vu0b9S1v+8ZAheYwM8i5oNyj0z3yQWznX+RdSBotDvfpYKyHPUlp + eW32/CKR85ucEA1D0cBLv6qQZdqIBEpdGdvf+L6g+yDeejjki1w+ammZ6hs556vzl9fZYM+P0fPF + ps2WwL1D+jFX8euGPxHtuc6F7jNL8KHm+mhRL6kO9/wdnRbmAlZGvVQgW/GFBLezRXmJeblQOZwf + CG3KSAdj3/JwqM78rr+5cTt+SPWX5xAVfhRjjsZXANeb9Sbqr5A8+iRjxUkfsww274Qpp22GCw5o + m0lAco8uWyaU4FUcI4TOQzOu81GqgU1xQ4yaNcAiPH38b/75fVbkm2sXDsh+7XnX99KuTz6MvPMT + 5pv47W3gZSxw99M7v+gRb5mBLL8qMyUp+44N6vf7kplIU2SWt2s+rZmXQjtvHuhk/u7N5n/VCUjv + Yxa8Z0Vs9vuv4ecQUNwj90RHXZNCqAA7I8YgfkYcuXUIM+1hEt+2WoMa13crv01lb8sWg4g8X2CA + iefbRFUt4tGrqznwIN4lcpK1aH/Fq4dgz8ewVVNtFIYk22QrkX3MLq+ECn9+ueVpE+zzDVCIOha+ + H96Kmchr6WrooIJG7VAS+x72FrNhebDnacj0vcDD7/evgza6JphJL0kzzV85BTtfoPNl3+UunnP9 + 3/MNfLfdt+x5ibykSYjF3U/veUAm73//N77LkZUqYLmsi9ILAt6eD8n/8nYjnBFYaooW0DwOPjJ1 + y4u4/PAMgE1IidmjM42UDun2lyegRy8vxp53LmDnV8yszwksbaTG8Djtb6fv+EyuruYql5MBiX2/ + ll7/tz7AkPkbcI+liqjb5T7k78WE+e1pGf+uz4fgHBzSLfH+1fff+GRC+aaUh1oFu1chk5N4MPJ1 + +j6u0BEnAxVs/aLbJBQ2fF4ZNpBGsTL++d/V4cuA7noIc6AO/vAI/eVDy1/9yJ9uIuYz0kcBHnhZ + XNQ8CKSSnaM9f9/Anh/hJQqudNn1CezbsSBO4A35bEdLAJ62uAUTKbAxV28mhmbXHgJmffqU70wX + /90/Fs7DQNe7nwaw3YaWqLgYvI0btwTG28Ij8x4u3vbezhWYuuqBrtn1SrGCqqsyf/VvsJ6Hdz4m + PmNL9MIweGHfIJ+vcq5Ciwa3vU2OD+ZQDgZYHroTClynGTfw8hbhvr/ksudblEKSMXCYV/D3Chbg + 2hglkG/2JfLjcYgGcjJFWCRZi5WQ8CNFYhgDzQceMefc3vNflwHLRBJkvVndoOtgZnC/DmhGt2i9 + wypUdjwLlBs5jhQE26ZI3OVF8oqbvJ3vFmjfvzmGuHga286n8rfSEfJxoXjrfP9lwFlaFZXSGzf4 + l5AK7PyLYX0bvfkyJBsIvGf4p5/G1aBGC/749+iNsjcLz9cALy00iI0CGC3lkrEAH694f751s0R5 + nIHmLLl/eowSQIL2zz8gxDFTRC9SuMHhVgjk9KIT2Fgvc6Tb6KkBbO4yoLo+x1C/fF7IZZ9aw0tK + YyvLNCcohbXVcIF4dRXlayXIE/UVzJPABdL7pm/ozBhLvvtpHe7zIZA6kXj7ITgYpmHa4ybCobHc + fSkBh4ihBCXLzVj2PB0W9fjFI6dU43a+p+w/v+/P2Rzt87mGJXopxHis+si/louqQD3Qkemezh7V + t1MMu2/LoOtaOBEfPh4Y7vkPOhY0ANu4t/3Rn+Ur2P7y7T9/8c3ZE/FYRvO+kXwJlPJb2MidywNd + 8lfpwO3xnlHwvp0ilp/UStn9JKbBiOn4CkNHuUWqgvStFD2cM6wJEXv7IfWKonyNxnsAXWV4Iptp + BY/cp9pX6vdHR0+oa0Y38FGv3MaTSjR/fNMF/bwUmNcPQNaOF+vcnxNge3GKjnvXU2qb2Jffj9OK + jPOE8q25bi5Erdv/4eG4DN3cwqt29cmelzarYZAO7nhGULl2zZoFD/PPv5HbY1EjdjlFNhxPk47u + LxZ51Gb5K/y4h57YgT+NK+fRUDH53kKh3ECj/7IVA1f/8AyEn/Bu1s3FJawfJYMBmoVoi0e+Unb/ + HUgHrTXW8KRXcJmqG3p9+J6usScUf3ksJmjc6PZtB1H89kcfWepXzvGuv6BSM3ekfaRkb0Bg1eBP + L5r0af+rT3g/LgkqimjIaf9xA/iWfIG8Tr/No4f3q4WDFm7/1o/XIxumSpyMGMv0Ge7641IrqDc7 + 4nzS0FveF1r9racGn4rTx53vHaVP0wexxZqP/vge2s34JLuej/7hwf9nSwH3f28puHJHi7geX0Xb + jPQBHMTMJT6KLIN9bY8AlubrSuyxSI0txp4M0+51C5ihMyLBye1OebkOJuhQSeNoSJYIqGakAcoO + GuW2kycCU91GhH7p3WC93gmhzZon5AYMznEgjAvk/UuOebmYAAnfawW/eXnCQsyAiIjO1ELwITby + 6nIeFzvdO5+Q8ENSz1rGNT67C9yPCCaeliT5dgiRDkJt/aKj05JxvclSB8UndMmZObd0SdS3DcPW + XNDtjuYIP2exUx5BLgdP/dgZS/tqN+WRbiVyepmO67mnGyx/nxN5auPZoLErljC+NVywDdbLW0WX + kWEg+lcUwOYw0h+QGDAUtx+6uF7sCcPMlYr6bDVSDl0TbUQ+8vC7tBpCVX2N1mKICsi0xRWdm4rQ + 5XRXSomtUobkd/0FNswBX3EXCMnVmbh8PbFvCM4qmtHxkrHReuurUvk2GSHnwErBJ8iEEHj3kxIo + T+s8Cr9HoiuvNHfJaf60I73bTg03e/0h+5TolCO20slpPmmBrHOcN0khjoHGPTaEtoeeCxdncCHr + RTYy6sSMKH94QfgKZhXzOIrGxQ0PAYzO2CQaJ3yNtb71WPnM/JH47bEAfOEVPeRl9Upc0UxHPhDG + TbnIQhJAkXPyhcNzDwMxuGLp6KFc6K7AhIMqQayQ38ngm+e1VJLMuKJzd/dyQTmfAhgqi0QuqpRE + 67kHC9wMCAJ2ZgrANcc2gI+H0BP7FZV0UTM5hWv0SMglJQTMbh2y8JBKhJzQoo1CjM4mjGBtkfIk + W8YGftIEVXvqEMoaB7CFXpiwbdrHPAJtG+d1xg54CjAjZlkYHut8JEYBTGyh233sIi7IDiFMH/BJ + SqFwDFaW1UqJT6oSrL77HBfj6WC43hwZZcyg5QJi3QFWDiQkx18bCO6zkMHHwSFR8Ro18x09bMh1 + ZobMIePo+vkmNXy654Zk29vxtoO3ZYpVMwfM5rzs4cIrhr//x6K3Fcb6aTRZOQBfJE9h0/O+rO0U + jsxbQRqSBoCt9yVWZLH/oef9njQc/hoB7IEsEcezwoYrBThACbUS0vZ6HjPr48OHW+ko29690R3C + ow5v5fQg9+GxReuXFSZIM/LGkl0agPuFzxJ+H/iDjEROmqVMQAXbpnvgzgZVLjgITHKfUJ0EkYSj + rRtoDwRJeqLITHJvs6OYhdKHAqTKZy3n/ftaKvL4vCNknJxI2Bf7oLSef8gvHZovbXwRlaeLGvz2 + b6bHgfM5gIIEngFd5MrjcCzbcsMuaZDYQM27+HseoCMTGalZgJslnrROiZQvQufNHBuMHT+FimVP + mGogHdkANwEs6iuDguUUUSGXsytYNKRjZtY+YMnqH4YH9xeR0FWOOTuuQwd93bbx8rAGg1a3GCs+ + 3zS4+gRNvs5bYoK58A57vf+M9TqnGfirl+X4qPIlwGOgWJ76QDkWH/mauW8HFmnHBoz7XUY+0ZtM + 8SnmkC2c0ogLn+cO4iTH5Nz1T0rfy5bBff6jl3c4ez/OzBn4lIwW6UYm5PQJ+w6KLyMm+o4vi/pC + Kvw+pg+5nAjwtuX26ZSUDt9ANi6BN4suI8JY6mpkyM8l2m6ndw86IS+Qrgydt3yTyofC6S5iJolD + MGXSsYCjKO/1oLuA/9qXVNm6q0mubOpEs1a3rkIVdSCaKiX5ElkXXTHw801efvii3OnAsrL0lg3M + HF/1SDMt7JQYuyyxYc5HmyCxFR81kk3u1hwbW63BDX6aV4vU8Wc17JxGgdyMZkC8E+ibFyOLOiC2 + EgWVq3zz9XLZsPI8phx5GF8nYq+1ov/7Paen2ObC3VZr5eXJV3KNH1xDtOrCQ7DJKXL6Uh73a1Z5 + KAggU2TWZt3xAeqeb5CLiOtxRaNuQ6FCccAl9Ztu91KzFd0LDKITegPr+Kh1wAleS/TYaYyVfR8g + 1FtEgxdDsbGM69DC0bUrompoGen94PoQWKc3MdrVBhQ/5R7Qd6uQ8G6rI1/VeqY8pMTAS2NGuYB/ + VgXN7jBhHroG5WsNLv/4UF+VD1j4oqihz9ID5qPb5BE6Dw48FjrGnzDdvMWVLyl0D+d7MJDBH7kD + b9pKd7189r4SWr79zQ/aeYigbYjB0BtkgTIvWYEc1LecbfLQVWg2vzHFy2JQVfoFEE0/gvLjmjYE + Dh9fuccXDamPjWvoji/KKCkjeYYwjhZreMuKIw0LOX1d3eNbT4sVylgsugzCNeLkrmhhUvd7pykd + jfRVfVv405cbeWVND7ZLVU8KBu8Puu58LTiITrD8Ch+y43W+DO/7ohjiaURXM6/BKl44B3yqnif5 + Q8oj/nAVB1iQEmGQrOecP5/Y/WyG2Cea6ruAzxNS/9UbORepbpD+dsrgg1FeeAmrO50RXzDQX+Qj + eqHre5xWCy/w1BcrOitmlW/+XSqhEWksyuI3A/7w748fMW1Xm7KDK6uQU5kmEPefR60XU8Mf7/S4 + H9cAcG8s2v/0kZZGCaBNfughMgaVZB6vRpyef2QFh9sVr7Qwx9+t7wvYXF+IoAltzbqPn1RRvyRe + v58y8KpIBy84LtH9F54pPY2XVJmfoAkq0/Y92l2pCY2tQujV2o/815cPBrL2xKIXY9fjuunnATKf + d4HUEFm5kIoeA58X+4BZviHGGl/uKXySe0YsALyRTqfchwoRDkQ3/3bdViiFCojmgOfFD9gSISyU + gHyvWLBPs7ckUr3AV8OKyMPfjk6O6Jnwx7s9suuO5tOOX+DbpAT5/lxESzO/r8pxdTvkjcsYbTLU + WRB73Ei0YyyOczs4rgKTcCRPC7HearZyKQ0qgAF9LSDaUFmkEBm9Sp7bKnir+WR9RePyDdPGIGA9 + gcpXrkaZIMMPNLql+asAO96j4/sYj3NfXiC0S3gh5+C9AjrMSgFKGkVIfSawof1NyxQ9ls/IP+x9 + mrjly0DY7p0EtbrKZz2MdAhX44CVu2169KQJARy+6508X7Cni58ZC9x+9kbcUBEAljm/FVXxfiPW + WSHNvOtBWHZbjhPhc8mXb3fE8HJ8GqiU8rXZftnCK2YRpMiUxqe3haqYQRtfBBJ8Pqm31efjsL9I + 9iD3iY0pfQyohNMX18jVOc4gw69npZt554maDMdmOZwWU9GKTCcp7t1cMLyXCVinTJFjBrcIK8zF + UU6WXRLHuktgkfzLpCD69pBpPS2P9TNvAaxwfRDtS9p83fEfTuI64a/jJM36/q48GOeCIqf9aQa9 + cdwABfEkBYeSzfOtTBUe6u2ZkpPx7fPtmw41lFnzjmLxYnssPhgbLB4jQ071KaFUf+Qd/F0tgGK1 + eFD6FpYengb/Rsp3/BnpdP6ysE7PVSBMTZ1vH9MwFev+vgQy9+MNylyerLynHcTSmobS13dmwR// + 8enXNXhO6AJQIvGALPDaxv35Qvind3SdYfLZUkMf/OZ3T7I8zbzFSNwSMmH+IadrkTTrFicbUKK7 + ExjJrzWWQ2BiKHn6lwSPoqTjzk/w+3Ei8ngXY7R+AceA8MbHxBWrdZxOg91CZBIXea/v1Xg7ZymF + yuXbIou9m0AoVS6Ekqd+sbj5a7Quv0cFmGxxyP3Cz9Ha6fAKGMbNiX3sPcr/5hMD5ZJ5YeBvrUc3 + LwigHFUb0q32mPPdq1oUb2ICZN5ygc7TMZWhyzhndOQ41aNw+ATwttkz0dm0z5dCj00Yx8UYKLt/ + ocpZCxRgeW9yP6RhvhqjOMF8JQ9i6SHJya5nFDn+QKQ/MxVsGK6FQnJdRXGk3nI+v17/OoPr5KyN + s7f96Q2Vq77kNbxB0y5OmsIhsM5IvzDUWC5llSncWjvkWtkcpZ/SYpR3fjKQ/2s1Q6Bslf3nf9vY + zfFDARnQI/eHhzic8k0t2AmKrOejf35i4bsJbmxdYUUx1Vy4kgwDDwRDwPCR0VDbJi58//IX0cTD + zZt/vsNC633O0ZGiuzdngbJBmOoI/fmlJYRmCrMJJ8Qs51uzfoVfAj9ezfzTM+sfv6yTfvuHH1RV + FB5alXtBjnxzx+U0BO0/Pao3j8FY+9/EyqMTQ+Ial8BYTAhUeffL5Lz731l5hiq0kvJI9JALmi20 + zjp4N4uBirKdolUS5wnq4Bgg892u3uLRyAUitXUs7/X2fRUZhNzzlZHzme3ypVfCTXl54pW8GBoY + JJ1WW7l27I9c+Ej12DxMW8g8Wx4zQuF4268zMDyFtU8C2LzGVTyMjMLHck4cplOMZb7EEBrGFKAQ + V5q3FOC5gN3foRxoW9NzzXVTjuftipddf/OWcDBhlPkQ3eO5B1N+uibwyft8sObd6FFGFlV5dBKI + zoxeRstzcWXYKtcaM+433PlBdZXwUD+Ju21ZgwUJ1qAipwVplfgzVhxvtrL7neAgWfecvr4fFnJr + 5ZAHL9T50s1nBtTXzsQgOnf5ypjqAB8VMZGX3Nh84xYC4a5fg/6M1EYwZ6orddAk6Dgd975DHzb7 + 48+gWzBvEN88V2AUxTdyxeoyjsQ5iDLirgTXLPuOpttjKaWzdrgg83uI8nny5hqK1NRJbD0/xrbn + LeDboSc5VmPebKfinSl/fsD8Xo+AT/N7AQGTWOSI8oHSuvxdocPzOjKvLcx/eLzzsMjzGvnxaTCm + 4+GUQObZ8ZgefyTaTLmawEiCOzFcc6WreGgYsCyagWy5mOjv2N4deJ9kNSDoqjXcdG0WZcdbFPNH + 3lghVTbokQ0GLazThl5Qn0EHn3X0ih27Yb2pECFhjWXncy/iKzavxItkbXhRVicX/vTKHZ674MAH + OZj2+oTvQvyifC7/Xpkoqr+8gCDUxN580O4q7AaxDcT6dTE2HX5VaJfMBbNl0XiUSGkMV8wZyPt0 + +biEhj7BLXxS4vmbaXBrR0Toft4BFrPkDvj07vKwNXSW6HkY0iljNx4WsV5jKnQx6CxBsEGpDS2y + bVBF9KcvV1g+7UtQd8rFGJuwXWAtVz9yGvDPGyc9hqAmNkDee9W97c+vtkpYI1W5kWjGuRhD6REZ + GNLGzemg2r3yFJgMi+R2o1SYbRb2JNXI/d62xnbklkU5L/QalMeFM+hJOwSQ3kyLnMacbTaWacs/ + PxCMu94VrFO4gT99frtYykjnVhT/6hHp+uUKlgA3vvw3H8rW04z1wJsm/Jxo9q8+tumdL3DxBxIc + WluKcP6cF2B5+gOd5tM4bpijvjJNIRvgtogaClxZhKchuCGNb6/NWvmxA/1BHYPkZjTGmvLafqoc + HyD0O7YNrYKylN+F/MVcP7TNL7uxunR8BCpxz9wJbHqnXRV7fJ2Q+XZSg00D9wrH5JOS87bevTWK + NV3Z/z84cO/dz1bHVKFN/EbHCz/nGMY8Ayte8//xJVsmtIbAeCjEO1a2x58qRgccm/DI3fX79GRf + NVgerIZUZe0jWtecqOQdtEimt/pIg0s3QXy0HfwR/TLa5sSR5b/rKgGysbRpV0FTEzgUvGOrIX95 + W5nHM9JOpTNO2Qw7yHV2hsFfPR7dOPyX34Re8ADCHz/Ouc2Ro/7pPOxxzwI2l8wKlK5X6HJ7iCVc + w7ZHV6s9RhybzCaUeWAha8+vVs7MIbAq54L8C/6O22/WILTiqSEJUQ/eUEhGAeLq0BAteq50DVuD + V0S9eqKrzjDRfE6GHrYrz2E2/xwbbqyqq7LreWLLhU/XpyX5gB6ENDi7ygAoTtgM3kZ0QX96df3D + F9Qfn8SWIifn3ngxFbhqB6J379SgfL/58N0KZyxGgMsX+Nhk5S/vtW+GYYyWfq1BEas1Of6mDtCT + MlRQEEwSoB2f93xDB4rNh8FEWQhm7/X14dN5HfYtoI+RdsfKh1w2TEQ9/3b/kKfOv+etNw/XWGTm + 5sLlwWvELm8qEFyCVWD5VkO8xpM8vH3EFB6bQ4ycdRGjqWpjDB1PkoJGex0aenyKAdxeaR/AfA7p + thQjD5hcuO5++p33aiZnoCLeQo7vI9sszfy7ws8NOcTMT0Gz8ZEBxd0fIuuO5nzrBtDDGfQZuTst + +rv/YD+190scbwfS2zAsQOtsPxBBSQwubD0WtKpvk6I6YDrhg7FIf3j0p99YsaxZwKAkIybHNw2V + yNOFUn04I+39YcfprgiZfP7cW6IuwBi5T3OSZVCokASp/gW0vo8q1O8YYAnFfUNvEMhAoeJM7pba + N/Oeb4A/Perb+Aq2gyenUEP+gq4N1xjTUvIOyM0BBvjlcs02yqMLD2R1yRlXbbO/79pCvnEF4n/5 + wVustXfg8V4URM/YO6WPz8bCff0CGS/wphv6kesf/yIUO/a4nKKKhd0gt8QcePyfH2Ny7krMExbG + RZFmH8DH8YFX4VIaHNqcGOqHXxV8vzUPFmdafDgyjUIssm3RNrysAv4u6kouJZa89SavLXykSxlQ + 4aZHf/odfhI47esdQb7t/gxGh4Sio1VoDfen575LpyFjr48VOEsHDykgeC5+7EjvRXcFMxqPgbTn + 98vxcIqBN8H/9I9Q30cd2n2fk0KyMzBrvyGFwjhqSO2TNVq3kyHDhxQbwU86LPns614AI3l5o2t2 + nKJVvCgO3PMickJxP26m3GNgZiX7lzc1y/uh8YoQ04543u3VtD1/raALsx9CfngA8z6f//Ih5EVn + O/qE6QKVqJtZjAeBA5uif3hYfrkPSa27RLdTxajSX/2d9/WJlfIyhoSdXBTv+nZO3nUAh8MMidr1 + WbQ4omFDe4s/yEj7c7Oh6FTDx12XkemfZIMKydtUjoFQEGMpjx7fNUoK0oJ6wRY7hrHnJzoQt7wn + hg/2PtHGd4OaQQ2i7/U22wquQXeNPkGb85m3nI9brzwjMO95nUa5Xd/BykyfAeSYCbSO6NnA44d0 + x7MJrPymb2CiTohQHrL5PLZQBG5/h8g+SkzU156VKP7lMQXSVzg3FA6zL87fz0wcHXkAR1BNlb/f + Y/S3M9hGkl7/ngfyFLXwtsPdCuWrUSSYEWa52ULfu8Jb5hxROP6skU/zVwlf91hFj9f/AAAA//+k + XcnWsjyzvSAG0igJQ3rpJAiIOANEBB5EmgTI1Z+F7zf8Z2fosktTqdp7F6lSerrES+kJbZef8cK+ + hnHbuNcR7OtLkgMemu9ubyBV7iVCncQ2q3J/hPBy0Gaklmm568ViCnur7jD05iKnuRju/+ecyS1g + R7drXTv+8R3iTPc0Wv5UawGveyL7ks4o47/8gS8JGMlCYtBvFNsyYBbJQTLe7yB/dL4DEGQ1UeTn + M6clnkxwv5aRL5jZa6Q735LC73jHwtVWqDDFnQmW9u+288+RrkdzCMQ9/4RcoITjVI0ZA9woNJAu + BFmznS2bgcgsMKYv8AY4aycZOtm58tdT7rt82B5iCFaLRfJXg/vv1aH04//Xs2ZqQnfuffjTj1SA + mma22TcDy823kR26rbs5ZzzA7urnyAxP1F1Vw3Lgni/w5z8QRqsMaP3TI8m9yr7a1+OPAbR5csF9 + lghgLpqnB/f8KN7Xq+Hu3qeEtXS/E+tvM0ZWgD4LtSsz+JL/vtL5efV0WEu3Owor80b5/pCbUMIL + h+z8qQCqnS7/8kV4mq1ew8PLKOGgfit/ff+xDfEk5ENUPiJkOemftnrAO0Ju+wuRs70tjbXvXCGV + eTITx2LLZh0fgwx7If7g6u/F7Ve0LynY9WDk8ovs7v7Rh6sYrkjP+UzDu54mvf3eRS9qXig+fppU + +umvhScHdEuEtPinX+32O061e0mAfwO2z3Wodn/6G/jh/4DVrg0N+RT+41fOrh+vRviNf/oO0eBH + jniLIgdaDk+Ix2cG5X56YgQrgxSUhXQ5BV0Mfufjl8+aVIoLMXPyC/GqsMl/+Ev6+IGAgqaaosm+ + cyXkxpn3gZIk0RoNeIORXFOCTLmha9HcPImNRNNfB8eky7EcWAAeno8ecsxq655vOX2Kd0RQN6ka + m7EbC/k7sZEcwABsV8bE4KdPK3nnugJ3KSpQ/qmCzzvLA2wA+zE8MZmI7DbqwDpju4QZeesk3fW/ + 3/tgGt4B5t9PTLe4XmVpx8fEe+YfQNP1guHOD4iF+yGnyxjt/OxRI3fplpxaesVKMScHWHKHJKJs + flXhRb7MRDc7uxn4suTF/88jBfz/fqRAu24iOa+dSYWsu6fiKagM5Gur2rDcu8fwWW0NsbZK1WZ/ + fDqwilZALuHlENF0vIZQ1N0OH163mC5lKKaw6QcTj1lPtA0mIIbwo2TIPr54bRuEqQXeUPhE8RUP + bMc3baUKyhGx6Kl2F8E4ttDkly9RJVEfJ+wqHuQsOUUh7oyRy3IvFLlCvJNLT/Wc8xlOh2/MIpJG + 6pMur5MYQp+8v8hivGz8spyawMuFcYnXKN9mPVyvtXSfN8YHIYNGenteS0k/mhT5TVFp/ZoEjCTn + uYVQd9gfxrb7BKAuk0lU3NVo/R6oDmTPsP13y7ouqSAvAi93EKbFI8/XsXUgDIXbDd2P+mtkr38g + gXWqrCRlvKyhvVjGp3b8dJhNA0FbmKUMoHHYMPJDUcmXW+qqcI2mgaTSQcl52+wXiTyOlKTnxMi5 + k7i37g3QiI94rKPF16NO4kB1I1fHm92phEMCN0/vSHR/54Cr1nGD6PnskK9RME7Yv9fw3WYBsp/C + UVvGwymF/vfw9WESO81w4vIKiKknI+vjaS4XngseJpfZxO+yccd1C7ZKeiXNAZ2P+mFcr1UsQu4e + 35H24ZVo3VNHkqGwG8mmKcw5nT5ZyDtWhNT3qRvZmz6a0nFWz34nRG5OxUSSIXOmEeZWN8iXfM1a + ePpreqRPn1vDMbHSSSfx8UFPyVEBP5VtDe/zwpC7WTjaFvd8AK/qo0QaGhqXAy+Fh877JeGjvf25 + 0xZVR+k6PnMSr8PNxewjOkLtuohYeGZWs05CfZRqzd5I+RVTujDUjCH/kkN0tzXH5RV3TaX0GkI8 + 6Q+Xbq1wM4Gf1SbRwgi6y3E6ltL5xXao2O2LY+RvD1kMM3L1FY+yw3QtJJVaEPn8lmrrnxBlcBxt + A0VK27r8U7h0kN+qgISUNXKhYF8+4D5PQMq1PeY43Vt7TY+ziuxLJEfczIo8RJdYJZHxaADe3MKE + 5+OxRbL0wtrkZ3trLDBVeAh1NupPg5xJ8uluYOGods3i1mCB3+KFcOf/4Qg/L88erno6otTeDE24 + Mm8sVfXjROKGJBq7JikDu+RcYsZBeb40WFTF7DQtyASW69JAufLSQbAdkg1B2CzlV02h1bFv8jzN + xbjWkzv8m2/4sV8RNj+rLHl/M0XWYuYjj6cpAY6YJiT8nM2ItftcBMny1lDZDrLLfrcXC+owvqCo + kXOXGv42wPvWM8iTW0/ji8DAUttpnS/F35e7fPN3K91udwep37+Du7GaJkpoZGRMa/oGnKQ9Jlif + jxdie+dHs5nXtyp1f/kTWaOi0gW+LAde7pODckGY6IL9VwU/vHD3I/FQuXyw9B4I9fcDGZy8aPRU + PC2YiskJVxyHR/o+VZ1UtF5P7vv52FphL3wWwgRpqqRr3BBFJmyM6/zbL7fPnGw6Pke/9r8aErVF + KlITPqJMQNZW1e5Wsrdast7BG6VrnEf0dRNUwE1eQh7SfANCQY8MnJ6Sgr+WUTV8qASOdNFqjxif + YI1ofrkn//xL7K0CXQj3ymAmDxRZhz9xXB3vr4DFK+mJbDw3l3661ILu7IdkP3/jYvC6L51b3CFl + DIaR8qIUw+XML0grmVqjRSWE8PFkeWQi6rt8E9w88NDNI5G/FmrINV4X0dTgTIwmv444zQ0InXxL + iZ3nx7xBy1uUDvrNRxc5OI+8oy1YwnzKk/QI31Roczk9NnFhEf/FPsZN174+9NvAIOm3rylv2ykP + pQeXkFuTr+O2CqwJQAteaI8vlGsirQOZ6VEkp10AloPGB/CyFjZ65gYzLt/828G7PivkYlsmFRzF + SWCDPXO3v8rlo1BWf/ZFLpv2dKnbOz4M5KhB1q2UGnJl3pPkG/0ZJVLr0LFueB44VZ+SwD1dR9ZF + EQtH1bBQ8ff8RrT6lgwkr4KS8Gefl7+0gCJsaqL7jrBLdG4FO+/2Isi68836eMIYjtf8TNxfPHXO + SIZt0mrE+qN7pUvu6f17X7NzCDZaaIw01YKCzk/U52vx1XXItnVO3JAhzVoK7hHKomb63Oou+Val + cwb9L2/43NYGOW+M5xi2CfkgvzMerpCsrx7SSH8T5Ss4I2c8DV7a7QkzMzmO7avLYrCowYLU19ME + 9BPIlqQ/Cg7Z+JEA9g+uvPS8qy3y1bQeZ5G78kCpHxq6P5EV0QqmvWS76gvH3CumLFPwseTWS0Se + 0h1q5GavtdQz7xCvwdMb+WuWetBF4Q1Fx4usceJROUolyirknzs150XTLeCrihiiMfwRLOIniCWk + ihoqMnvIeaFtBgiY8UkiUlVgi8MukHrWiFDoA8vlxa0pxZcLcxLY4ZVyY9DIMF+mEWk4Ft1taL0B + CstnJaZmInePxwysK8YlitdPYHOzjgHtOdSJrbCKS46viw/2/UJPPzGb9dazrVT5J4Csh9m6G06K + +MhU3BWl1aKNlG//YqkRoEE8ctqrXhxLCH3zORH9UfJ0E02t3AvZPtAF2kXOI15OpPc3REh5rW6+ + iOISSF/+4pOXAXhKW8vEUoGHD7FCTm+2h3o5wrfshcRKhjbaHPqRYXquJHyznjSnziCGwOytEd0z + ajV8wjM6NBR+Q1rlqe6Xkd89NJ94RJd6NrRNBgRDP7+PSIvnPbZw+y09fiT+qROFcXEA4KH7kGOk + Dc93PkVV7kH3+BIwsOYgpwPLezB0zg/MxJaSU8aFC8yUhEUXpfzkS3t4HyU+ngX/mExSvoX3yoJv + RRMw8OrFnfb4AKODEvjHh6m7hB0uJXjAgkUuFL0Gj++ig5C/P3xwfgURXd5/G6R2UhO1g6ZGZG5U + pa91vRLffeaukOjXFFR8EZEzcT7aVjp/rfTDu+UYpWCt1XyCp0PUEfep+2ArgssEOYuT8MnHj3ww + r18ZpgfxhJTDx9aoZQ4ltLS4xbOgN+PXqbkCJsVdR+gkbXRjrrn/L95Ep65yqc44GFJolbs9eWAA + WpSBFVtfdNnjGwXynwrPpt6T+MYK0TKo71rkjSAmd64/7PWs9Viin/D18xfuWk9aD2mdVghRgY4b + P2gLrJ7pDV0FyW4E4S2F8KR4JomfUTJiJ5MxNGd1INY8AEB3fwZvzskh18UatOV+UEUJ0ObpCzzb + NSRNX+1pKSbigz69AMy01IKS876TqNhb23NbGMDKBwCX7hO45PF3siB3Xe7k3oVnIMTNi4Wtx+v+ + MdDake74B17Wv5n4uVSBzbiTFB6F8oyP81NtfvOFk460nU8kLiZRG8Iy6l6+xI3NuKW5wUDV2Q7I + B5ww7vg2g4da8pARnEMg7PEQ0GkvxK5GBqXR37uHYurLKO1RTbdtb+B3Kc+EuH/Qd9fOoRkM/UOK + pT/3CejFy3RQGO2ArDUG0cJogSydvNdAzKGboumKmlhyhT7c7QloW/+dNvBi45RYga8DGnvfFjrV + kOI+UiVAC3qEJ4er78RRFy5fBuPAQ9ESFeIVf47Gim0VwEaqDOLw0toQpvY2gMPjiZSOWrvLqt1D + 6JZtTK5iazfL8HhtgB0uB8wCEOfUPA4t1PWWR4/ie8wnvpC7f/ha/ohoXF/OeISPJ88jXZzjZmkn + G4Kf/03CDGv01/qTN8KYnGd4jvjrahbg81wffh9y+sh2wYOFN3K2/DV4TuOm8qiFD15kfdjx36i8 + IcYDDWotcpn6VpvY68sUvVuLyP1uf92NRFMIspNKfPZtJdHy41+8eT6RNPB12hd4qCAOxRNB+EvB + ZnmyKL3YJPVPA7nn5M8oe9gc1wHF4+M2sqFHAxAYdk1Ue+7zzS6WRVJv3oFcr/w0fnf+Annmyf34 + S7NgdnWk5ZIuJAWN0iynu4mlF7Pd8dpdRo0Q8Y+HDdFGYj7xxaWc9JcBqfnckMoKhsuiO6jAs4sb + lP7293WwsHjsDzO6eFI3bs1ldsCPr7EWPLpkEaQNVM/sRs5nbnDn5pu3kAuLdLfPjQ4Go6hAZp8m + 0bpeGZfBEHjw0ZWc2PSTNJs+Xx14R4JC1GH9o5tafENRec8qUu/0k9PMO5nwOGPLl5zmAFZGQUcg + XAxKdKaOo/28BPB6PpR+FcA1WhG8qNBT0ivemLME/vDmLOKP33inQ68tdZ7pYNklpR0vj9849SZo + HBaMlyJY8n/4F/Hvlig6M+f0TJ0C1uJkE2Wehnz7Bi3+x5+V6uDmC0MWVXJU4UCs5q7lLF/ILeCW + 9PZf/OzyiRFd3bn5x06GEcHSU5SyIVv9wzePNd4djh0UP98GaVcD5tPnrey3KA8HpClEj/jDMTIl + 5/2UfDiMtsupyJRhWU4vFNqzlZOLF5q//0cX6V64hLQ3FrZ/oopUVbPAvl+W+IvP6M5zI5FayMNo + 4EJ/Pbn2OMlm24N2eXXIwcYW0Q/cH019rg+kglIHtHbSCpBjtZJrpE8R1h6rB5e7Q5D5Os8uyU9C + ArTCP+2vLy4bOZon7f7f3z72IVqoWG2STzsdn876A6ybr5aQfL0NGTIT/3ce4eVQ+J8zN2gYJjSG + J9M0iMamVbRZe+PFk/ccMHYDha7+zXDgcmYX/yBaVFsf5zaGhVN8SXQ1iny4s6Yq6f3Gokuu/Y30 + c1p0qQYNJu7hPUWb3KMNtFdV85mslek0Ro9/egE6b4e99Tc7q9A7nzkky9nWzCoyVdDvVUOlH54O + dEWER3TB6CKCIsJ2hXR4eQYOMYQt0HgojzEcmqeDTPmFoq19Gyb84Qda3Ot8870gheJlkdHrim6U + exva8psPKrW1bqiqDSKQMLkRY9cPOPUvkSV20HOiNJ3rbnHPhLA99DK6y/LQTMktZMCn/j6wEN+/ + LvlWiwrgM8oxYyj5uBrZcwG7foEF9tKBLWlKC5I2plgMxjhfbGY8woxpCaYdJ9LNuH9S6M5ihLSX + m+cs26YbkE83g5j23W+mrBowlL7uDcOrOYDNWQIMG4oGZPp/frQepVqF3HoRkVwZizve+MMC9v33 + N32wwBjVawezW70h43bbxs3iFl66vpUCnaXwkOPTy61EW+Jz/9RGApi2RPCkr0quGNqfsaF35RuD + HS+RvUWWRuevBKH96J/E+4Zfjdr3MJF+63cuFKtZtkTwgVifG+J3muzyWGVTqKyagsx0vIyc0psm + XPfGCbek2SJaXzoe/OyzRIbSCMF8YmEj5xjv8bxZP3kTwGMvzcR5X6g7oz9OhrzDxijVIQOosm1Q + ulygS573/ODimRSpuOsPOCY+F23a81zB/D7MPsc/Ag0L7djD9jD850+osokMPMZn7Hc4GOlCpYMJ + bg5w0OXPlcAe/zrIP4oQXadyaBYq9htsLeOLfviI/KVM9/NnyBKwQpf8PPSS9TRrPHnamE9T8E1g + EYtfTF8uyJfy62TQZ2SNXLhRG7lPPoawSusEOdEzjsjiPjdwrDuLaGTO3NXULwskjL6S8zPrx+U0 + Jxj4X0cnzjSF0RZQpgKsWzyJceQIXapLGEDvkFyQvcoJ3fy6T6G2pEcsTtMWLUzBJGJn0teOPzEg + 0UlpJRi6OfKRoYy8j6wSmrM8kGdc/AHSpkEszbdBIj4rHzVaoaWHgSE0yAWT13CHmLDQ/BQDujCN + SoUuMSuocfCI/Ov0u3JzOMJyjp/k6btdNA9RpMPZTBhilQncU4RnU/pa0RX5hpI3Pz4DG+ybRN1y + IxL+VNxBMXr9Efs9CfR769kOxDDyfnrFSF/qy4TBcXyhc3s6U5r2bx8+ub4mYfh3HtnefUF49bgn + 7owZuZgWLgRvtmHxME1bvnpnI5HejOX7x2ppxkX8pDGM39yF6NHlni+dqCzSHo9x3lR+tMapjkEc + XRA6G76zv/YmETDfJ3lyY9PMPiOZYEyk8z9/v4lbU4BvBxCmy3Ki5HWwJijWtMWMmqoj/4vHqRif + cH+76tEe7yZorscBXaRbo20P/GuE+Vx8ciRUo+l4qGET/01ItsOVzkrvm2DHw6hsisqlN/otQRgJ + KdF3Piw8z4L/0yOImvJnt89zp4Peabkg077jcSHWwxKFc4rILUs3d16SbIBCVVz+6cfLFvUiaK+y + Rjy5nTShOfcFUI8TRG4Z+g1mSSPCl1IV6KEh0V2s0YVw6nOFKEvkRCuvOT50ujtE9tU5uQtj4gRq + agGIka+buyzFPYESo8X+sutZi/M37nqvE/3Gq7HJWEMgvfJk9ze2tvbq6AOvkhEK7/QcDSX7rMXX + 0D380+YnewrUU4FxdVY8DeNXo6/bQYUvzpYw5ZhH8x47IP/2f8eDEVi0vziGYjjIRN31RdI3vQ6X + rsAo7pJEGz+now6OMDbRdX07LgnBOsAfv77dbmEznQYr/fEJvM3pH1jaSYFQKAL/n//ms8Nz+YeP + Pqln7ff5zzV4uUxO/NenihZ5iVvo40knqU6Mce3VxgOf+Zz62f1MQcVoqQqj/B0hRZDscTWeFxYC + 9lIibTop2pqEUwePkj0iv+LkiFde1+SfvnwRdG0UKhgMUMi7C9EeUj5SA4QD8MwgQOm3V8HSuy8G + 8g4fEy0lOeg/ggDhHj+Q9Wa0SDA9tYMtW8lEs2SNLhLHZ0eP6T0UQNPX6CybMvRY74LU44lxu11P + +OFRcgm2LpqXr9j987ceuHzA3ERuC3d7QwlicLSXza/gfv79r/GtwJSijZUedn8kDpSv+WLNUIXX + +/VJ7oWyN5bdC23D+5wj59lE41JVoi/+xhMJ90dDa9Yq4WeN7vhwcLdmBdt7g7s/Rb6j1tokmbIO + A4fUyLG9m7ah1xJLgNxKYo6s53K3g9fCKh1Fv//Nv1QWHsb87BG/9vf15b4xqOb3ibgPhNzpp/fu + +rB/CHsOLLWh6pIFcOXjWyNG6yI7A3xnK0dUK7YifPTzHnKgvhH/MAj52pVRArVLC4hy5b0G29Ta + 4OlgqSjZjHFcVdCp0i//87PPdUnCAdZhcvFBrJAc3zrTg+sW93jr7698IW+h/OXbkB3Bk7ukncrD + sJ/vxLoWbTP/1oM9GAw6J4wFhOKcMvCTogeyqzLVaB/gDNhOUiI7bE7uIH0aDz7qiSep1TE5dihR + gWhrf7t+HWhcrUYYsqc0Qw/64ZvlOW6FdBQ+IwZiNI3rIf7wsGFuAym5x9HFxohisP05LdEQp+Xk + sMFe9F+Z4cN0GX75HBXU195D2fjdmvl+0Uuwnx+k7OdPsDMnhafg+8Wr13v0X35j9/c+/OvtZt31 + QcmXN4v4/Eeg1GUyD6TXABKVOT/BJly1CfieMv3jT6RueBbs+i9mWuEU/fDbidZZRdRi7MAWLJUn + Cp18QV5eOzm7Z9thY1i2v77WMaLc55ZJB/T0UXkRFMrFuMDi7t/9PT6O//J1ez4JXei1ANRlQl8a + mpeDtHf2BpuuowIeHpcLxtdJaQTzWLcwuT8Nct3nR59nYT84qo15o/bBwrnBJCW8oRFvgVbDR/Fi + Ssr9oO3nswG0OKcQCuO8EkWSvxHd9RW483GUVI5Hhbrqa9h/xwjZ4Mq7P7wOb8zj7DOSdm/Wn//f + +QGWZOMTLZJdZvBiowV5QmppdM/vQvnPPiPXBoEm7PODcPpkRKca5+54dYL505+x6PHsuJ7aL4Tv + bLgiW7u83U323+W/fKz5vXjRP736ZaY5evInbcfvFx1y2oHBLHOPtWWxDim0nbjE9LGo+eZDcwL3 + bWCQb/F5jrVF8aEtsTl6fha9+Y0PTDWnoLPIH0aada8UVr6ZEBQoicZ9VhBC+gle5JfPWKXsDKEC + bYVkhRM1VDgaAXRYPkEuF8za8uA3fLrj8rDHsyFfhI1nJV7qA/TY/dWOFxJYX8qL/35Bt6G/fEQe + 6jOm97humo9kpj89AF3I5/nLv8hw3y9k8bc37Y/TsQDJUzOIXE+ELsktg7/8q7/pr775WqHRwu9I + fX+9VZ7LO0s6iYn6MdHFaV5g/VhaDHd85q+RPuX4l4+KTs+API4v3t2yUWKha+MbZoJKpMvztjd6 + fKUGiYZXlu96uyOBQKuIBrxTjpX6MwBXuvFIs/MC0D8Vt3Dnz7jf9Yntk7IivLkfl1wm7hktfXkd + Tv+fRwqE//1IwTJFJ3IJRzbahCWtxbP1V+Ace3azfO/Yh5F+sYiGzFnbausvlEQIFl8wH1yzZafD + Bo3PUSPOaSrd1S6dHo7HdCXGpJgNr/7xHnhHlYvK8XBv+OvRLsAAcETMmb2663VadEmLvQlpEXXH + 9QyKAiqh4/uHs924602wCrie1wTdyOforqkohXC8xtZehl11eZ9hZYm5dwrRJBzkuEYWD92LFaAs + jA45frTWBo3J2fCzMK7NVr01VaoPkEWeP6/a9/1WQvipB4i5bOyixYRXRho4fsRTesb5VtZqAE9a + zZGyPlh0MuGDgehscUitv/U4lYegg6nGvTGVfd5dlfGzwKPIhSir/6KR+8orlsr7wyCPLK60uSha + H9Yc+0TuhFGz2eLe+oK774Uuv99xehZDCYtb/CF3kzZgQ9ufJbGW7ZKw0AGg6yh5wObtO5Ifp0Oz + XiWqSh3Ty+RWGc2IzSEPwVZuCH+Saz7y44F1pM+J/GHBNVl34x5rCB2nc5Ctt1fKeYdTBdk/44l8 + GY0AvzomBs/LckOyyN5y4WATB5pSqCEXFVO0ladqkW5SFaFAly6An42gllwqmfg3Pxy/Giyd+SMl + L+6eugJ4BqxE0nrzTzJ4ucKJ5KqkncMMH6b7N1/axzTBE5GvBHGrmbPJ0lgwzn0FOUYUNPw9PTnw + VrUqClbWHAX9jI+wLeU7SQ9O3NCpOC7Q/agsuVyYG2DrbGSgz8YAyfw5AVh+Oxa8VZ1KInXZhe1T + v4l8Wxv/2fNun9IZLjm5ioaoUXi1IVxqt0a7/Y58NIodcP9yQDy0QkAe8tOCvC8GBKUvLefXo5NI + LyoQ5LCHVyNcPq8A+nNwJXEo643gmEEveUfL8A+v+BEtHls5kL9c7ygE/sflMq+vYXgdVWLML3kU + hNbN4FoVJxLHnAjW7uUs8HY/vpBrXXqNZu60waD5e5FXm5vRPl4GbkQKkD3FT7qI97MK9/khj3us + +XYi11Da1xf7pB8iypUfHfIH7olPX+ORr/JtWaQgDUx0C4kWCa+3vklnwtrkai5pxGWmx0D/b12Q + nRYCmGs/YaF4j6DPx64MlvbRYshzs0nuQ4Po1g2FBZ/Z9CHucldz1r2KslizqEWyd1roUjh7cxhq + +7jNs/coHN7GAO0lIsRJEjlamj/aQs1926j0XB8IKF96qBd9hm5c3ox0Sm4+FN9tipzN+UardzjV + UI5Ygbi3j6GtckcnafcfuH/+KTkPio6XsGDPfkdC1+Vury8Ua45/EseIlob+WYEq/bF/D+SWn4iu + TD7z8ON2IXLe51NDjZCpoHH5fIinIyvntmowoedqBNmxfW5YpTtBeIy2LzJi8zBS/dyJcIwODFG/ + Z3cUotdcQjEeTCQ3EWy25GxAaFuqThytNrX5Mp91OItPhKf6T9AIoYEqXeVmRepLJmA1LgmWisPF + QU97NMYNOdoETtlfSc6i9Tfyp17CsD6/TeLH5yvlf/6uTsyrz/aaFnH9xdoLv5Y1ih/w03TCFzLw + 5/8CM/+A1fXkAh7ds0m0SWlcfpm2STrCwiVZ9l3G5ZC3pjS+Zh8ZwLsA6tHOgrpvIJS9EqLR+CE6 + ovzqzsh7HtORC7QuA4e7/kAXoXPcyaf9ERLyyYmNvc5dCLOmUh8wEUGR0blUYu6ldFei3Adrjt3l + 8T120BGCnrwEVEWsFZ52f59Y5NLmKlhWoexO2zi9Sfo+XhveBIknHPKbRRLlbo7LuxNZmBmnA9Ky + a9zs9qdC9TgjX+yXKVov+tzDSTjZ/tGr13F5sC8GnpKE+ALwrpFwKgMTLrO4Idelj5GfjbT62SPJ + kkTO52+a+tBejz26H4NntCpvOZWu0X1Eymq40YLpe4O1MpZEo8E5584hUEF/Qyrax09X4qi89Jtf + nqck3xb9GkJVjC3yjPlZw47neJC2TuBv93WMljfziCEW3Jl4tDfBeidzD3PEdD4Wq3czKpqow2Qc + O2KyoeguoN986Tt8VP+I9Hsu2OycQrnOVMzhxouEDto6fJUXkSiBwLrLKiQtyMq/N3HCeR436n97 + SPVMRwoyv+PyCDMd/vy5oZoK4LdsqaXmmDVIBn4zbtdqgHBplhyV0u1Oly9XBVI85er+vjYKr+MY + QuvV3ZBfaDHdqGXqEGrHAUWzgenCA9BL06lWMbMJPuW4tjzCVtFNkrBh5lKx8fZCz05AXPtKAVXy + oJUOd/OBsnC+NOzVsTzoXHsWFfDaN1T5EyC8fpaSxCY+uOtNIcff99FjO7Wj8CCZBWeGMOReGR+X + UqlPJf4GRRTCw9p8G6GvQF4cj6RcaaOxz6Iu/vm7pUYB4IV0U2FksApRx++aL1bD+nBwmTNRzk43 + DlkPeXi4vSpiB1VMqQZfPszKz/vn/8AK3cGCsBl4/8CUOhAO9seC+dXu0fXsmCM3610l5Ve3J2b3 + NzWUu7SWFNFDTKx7ZjVf6ZD78MgeBuTUBwUIqTepsCmAiZSI9i59vfVFyg0ekeJ5TBt6sD+OdLmL + VyJrXhMtklulUoFVG69GyEQ0N9ZCKl4l43/uhGvmFsadaI5fBxXm/UBxbc3Bz3/h/vQYcpqJ3wXo + xd4YYPe/mL8MGZQ6DyIkoCpfPorPwGfMH3yRe1yA0ETMAKeBEbDQK4M2hdIpgPHjY/mHe8rnn3VL + F0hs6UMU0cjcJTuQFuY8Lf7Fs/bexoH0wyvyknijwPFGC/Ww0pCu3Bttuz2OKazI3cWL1cnj+q0P + HWCu+OH/Ldhwsf0tVJDMwQspJJYpV82BA0P8iYisYDtaBrFIpMuA3R2m31wOz4sH862e/a2ZcUTF + RrckSecEci+KON8i7WpJ/mJ6+HD9eO72aOVFysPljTT7VtPJvs+JaJwMHcnN7NA+LSvm9DdXxJfs + 8W9cKWR7qOpTjMr3G9PtelRKCf1NMkre5QtsA5BNqfnrvr5IDstIWklOII/7AnOT0o3USjMeRndF + JSGn4Wbhh+MEE+ZZEa9gJ0rY6F7AUAh54ln7LU+kZw5QjwRh3jrl+TKxXxlar/aG7t5F0QTR/Rvg + 8h00gs52o/V/0ZpJD2TYxKgvubst04Yh9aucPJ/N2Pwb3+YyN+Sfy8Clf8taAJzzsc/s+JheP1YA + 76PNkDCUFZd3DDyA6K6pvvRSjBF34KXDNMwmYhg3Mxc8UZmk+23yiXVFi7s8DY0Rs8DPsRQXJaCR + X1fw701fflCtCcCAvSQwEyIdIeod8olUQgyTxrLw2El/LtXg3QejX1+QuT4bsP72WySHEl1Oqwr4 + 6viu4J9nD8R16akhtzPCgDEkC+UOmekSXyQZYqU5Efc1HkYc6GkGH3Mf+6IiV/kWQamCh4JRkPG4 + eNFcMbkOPwkYiXV6OBH3FUsd3uuhIlbFGlQIszX74Sdi5GRziTYWgTjorw4PX/a4108FFixeBUPK + aeJz+nT+ahhKTknQju94IRVlGN7rARf8U9KwZk0ZKCMrIaVYvcdlfDo1/GJu/Y23mZtKlaHkxSnm + 6o+dc7qKdJiidkW369WO6EO+ORBfrjaK+jTIaStZMWTW4u+HXxp6uzQBAG4ByNVzuIbeys6HDEdi + LAgN06z4Ew+/+ITMMDru450rsJ9vZJ9NYVz+YMbCw9VfkZKax5Fwl8mC9vPJ+vz76GtrtxgYfvWD + T2ThbwGrlJw2SOzDxxfHMHS/67Qw0sHPeLLP18UPrw7ggBeFFOtSNnR4LqqUyLGPJaZUoq2ypwqK + 2/r45z/ZPhQdMOjPjngCEFzM8y4L6/iZkvIryO6mrRcWGgwzIUdHLV3fGvIh5W5XkqsP2CzmJWak + hGNUpM/s3GzMDYaQHl49cZYJRevzA1gQWU8BC9dWclftjy3gFskHFO18YsdvMvDn8IosrVZHfrkV + /o+P+PSUV27vYa+Dr9N8J5dDPbu0GZ8W6H0r9etqFaNZZkIf3grbJ7KZnwHrlNZRdDOzJqouXaP1 + 0EklTCxhIu7pftXo8rEd2KsPCxmDeBqp7jsllJhjiTxXZ/LVO6y1mBO8Eb38dtr299eboPIdhyjy + AWuLTGX+h2eI/+e8m6UdPgNIR9Mnhrgdxrm/yB288cqGLk3MgW8MwQL37/vHg8OOlP/yE4xvycOn + 2BZHSvRIhgHnpcTXuGe+ROpfBo981xAtEISmO7bKIIX3aiC3/fwv5++nhsbl70OQqiY5VbRNF28t + u+54c6ZTI2WmVCf61efOgqcJ3rUKoLbdD76wefW48bzGg1gG3Q+fUiESNEZK3g3CixygnEPHZIJt + ahH0OFd/dP4bHwPUSRkj/VGf8i07CQu8rVRDysybdP5e2R6+kXHf8fs4LgGiNdzxFFG4NB+X5g+0 + 4tCo6873Ds3Wpw8GfriCJ+fKOLtUiKwCjHVjE4cXPpTAvirAKjS1P6G1AJtlzuJJdt0YyeQ7A3pX + 4lT6rSeblnOzDU9f/e2/P+36BInXYBBfL5bijXmO4yyQroKDaGTovG5sTs6laYH6/RiQrdwuOf3x + zzUmPbIACLThSS8+VD+1T1z5+qet1e00gD3+kFKvC3cZrpso/fB67N9XsMR8WUBlPWbkUm2Btr21 + 1Yd97cZIcR6j1h/LNwtLp6Q+zYk/LvhGMGyTN4+sGj5zuvN9gG05I49VgdF65xUfnpKY4PXBVJRS + qUolEn4dos/ZIdr+FlTARVcRsjJU0vW9eT7cRvzGO1/O/+kRvRUs6OWk54a/okqWdnyHHjI4aOth + vqrwyEoDse/k5NL8cGMlibo+FlK71ZZoNHnoLWOLdr4e7Z+XARsdC6Kn5TzufJaH7vHC/eKfO0s4 + CCTuZenEPpv3cSs+UgttZxT9k2h93e3v3MlwORiUGLOxRTj2q0WamZlB2vVqAN5hxwGmRaMRZRDD + ZkksG4NsfFNkvY9rM8dxloBB9gR0sS5js5RHSQXuASfIyWcYfQ+3rYJnx7shRQOqu9VIZqVkMTN0 + 8URHG5173oGL9tyraC49WHvzT4ZX+b2nLBLFXftSq6RDARWizuzgEmXKK+j/0YWcN69u9r6+rKRe + 0Oa/d3tfb683I7WRKBPz4Fy1rQu5Ab5UsSbuhQlzPvCFBaYBpegXDzf+MqSAMQ4W8pX5j/7sB96e + 2xk9xs9BW8rPpkN+Le5I3fHJmrSJDBUxP2I2sGCOs9NhAc9SSpDrkAvg2oha0titR6IC/6zRaipS + uNKAIi842nQ9hsUEmZ6qSD2/7hqt8kcBtfKuEbtX5mY+oJKB3MvR/Q8NcLSQTyhCYUzEPd7XgJrD + U/35d3SrjL1KzHLsoKlsHxxymt/seuUAvafwRT7pnWh5sHcoLdP1hNxkDMDn9akS+IsHzjjJORfa + 6gQH5+4THXEuYNWsMSE+bh36+a+Fe31NsM/XZ62TQFtwkiyobbcDsadYApvT9BN8TdTAwKWP5qvy + Qgq/7Zz5C/nOdElevQfb6CgjtdABJT++/R5alux4n3K/ePP7PR+0c06/QGGl+4ORkE2lIBeyUE6k + M3Broh5D6o6jqLaSdSpOBL1fm7Z4bO/A17QaKBX8a8MqAjzCk50+0S0bzYif5xXCjhlkH+x4aoPG + 0krK58P807t+5xuaWWGT0DvFEU2fHSO9zvYVRadc1lbxuldFOGYN8eq/u0br7m1K1OopCY/TO98/ + D4FZTkfif4XKXeUO4H98Rj4tdkNGUe1gkAFIlM/ou1zhvgMJZyjHbDJrGjUfoQ5uA37gk2jZLgdZ + rZPQH5Zxs/NJgvLj8MOjxLnBzd39dyUhJbr40J+v7k//gwZFhS/wr5Aucx9asDrKmBTpGUfz/ez5 + UIjD637r9qzNF/2vB9rbYTCRmaFZ43HhxR+eU18yAnT/v59eTbTHpaf9fFgGyEUZi4wx/tCVjiuG + flbcMbs+5YiHOFPhYx7i/Zbq2e2kq+X/w6cq/7ZGut/Zgnu828fjRwKaaPfjl8gpw+aHP3hYmd/v + bp8LIOz6qGCvqwq5vPmcEngaM8g+Fp0kKleNVIk5GTJ4GHDygKPGKlWSwm9Vjj+9fscDlQlNU0lQ + MiWCu22QTyEPOoy5lJYa/sWj0+XrIfWG/kYCDDGBc5N2eNv1y6X8iCZEoWju+rCXs5svWb/zT86e + wDTb30uvwe4fyK6XN/Tcviewx3/0498kCV8J6HsV+qDBVbNct+8Ang8DEzmLZrq99GMJA9Q2yPw7 + h7/zDsHOX8jzvinu+kyWI0Tt+UyMp+jQn14B6vJDkJnLCV3TrxgDg2VvSLMAO87i/aSCzAAH4vWP + eqRyYEHol8QkKOaDka6ZGPzmT9zt9aA8/DAFVJbrG9nbSR/5Pd8BdvyIWVNXmiUafV58y9OVPEx1 + BMtJCWT4bmcPH7W6btZzCGSQBisl/hU+3BUdywn+8IqjyFVES33zpYNjndF+4Bvh0fMtZORzhTdd + uubrMYwxvLX86gu73vTzt2DMixeKJLzkq21ODjzo4YuY2qt0l35Ie1hemydy5auhcX/q0QesAO4E + 9Zhv5os+D/Cnn+h699n182WB1eaavrjjYbrr26Ju89W//cI/vher9Yt4ityA7YenPW/okamdpoie + oexJVkXvvhiXR202WsmD6VGt8HZKGPAvH1O7lCPoFZ+ibRJlFvrKwmFAhmakP/7lwYxFsqk8Grq3 + coN/2uCR3/osbg0ZKL2ZC4aVds3/8dn3s6/RVQiaPX59ISRlmhGDBg5gZSqz4MpOC7J1RtY46A4O + 3Pkg8dib0XB3XvHgIgo6Po6hPxJWjesTPGQmUtgYAWoDGUrNWSmR+feWKf8pjRCi1jjjgzHa2vqn + Lh4UtkdL5GYewCzflg0mzKvyxV0Pna+3awsmAdjIrLJYW9i/OJO+WFjxySdOtBqt5EPhePWIeeke + YMGuHEN8fryRnYxzvmy39D9/+NOXJlXTO5iG6URk1zKjv+mwZXDnj8isWDVnE0vB0juqXSI/YdWs + TaWqcNWqDzq/7RBsOesVIJVEi1it5uRr8K4K+MjYDD0Y7tMsTLL8px8hxCsNnxuogH9nEfj1s+bA + Rk5KB6fiMZFn14j52pdu9YsXxIirmc5rd0vhrp8RldGkfGsjKIIaYAMZsn8aiRgqPrxor9LnxvgD + 6IBFCC3Oc/ylmR2wxH95DfENTnjJohl8fp9vthoQZceLS9GfEkD77EDMaA3zKbub1unnvw9M2dIl + OBbw5DLnp/8ojKFZ4jjtIKtGDWZ2Pj3H48JCWVGlH351d7wDpd1f4NOV8Sl38t6Z9Mtv7Hqhthzl + zwbVQJt9btfz25N7yH56CtLh4ebSn94mMWKJfvkaLtiaFPK22iOtUg/u8ybIBZSn6kFSVP41ax2L + KfjZJ9pqBSzGqsbScrsEmPFcnwpCJJcSt9xMvDj46OJgOrHQ/KO1/whETfvZh6QIi4qu8sHXBOce + ddIvP3EJbDxu5pAH//QUJz520RKpcwbt6Pb86RXRxL3epuTk1EUXTtejFc9HD0wEdWjXX6J/+dPB + hWdStksULU9WhpJ/7p/E+rLsSEMtO8ITCg3kPf6snBeUvBIPJzFCLpUiOn0WDf7iCdr1tBxncpRK + 9MGxmPHneNyA2xzhVWxT8tMnSSXdY0DcrkJm5zLRMNG/AL64okf3a/vUpvIoyf/Gp93VZa9C48S/ + +EQc6t809odf9nhK4k+iNyyccQB+eqz5PmJtpeMJg09ft0g5KX60LbfYh9ok+kSlYeuO54vmSeTv + qaMnby0aOZmnDu75aPz9xJhuwhLU0s//eDc30+hgXitpz//iXY9rFm6ZZOgepgRFtzbRVgphD3SR + zsQ5tGbE/kVrCtXb6YZ0E7/czTG6XhxvnYDU88nIhT0ei7fvudr53wUs9eYUsPKa687Xy4iWczRI + 59sbk+D8oiPxnUsBTWX5EP0SddoWxYUOn48zxswf+9IGx1N9acfn5PKwpR1fCxssr+8nsa+MD7j8 + Grew/IMBUYywzFf98dfBVQGYaHLfRttqG8n/q/HB8X8/UoD7aCEmQwe6eUiGkil6V+R6cRRtGFEG + Zlnwh2d/kSkbVlEtJUslo3A+uXRj2y6TPiU+E2Rmb3falBZKR5WZkW4FaiRM6qOCiXRtkGY7hca6 + tyiFD9fKiVyLlssKf2ACL6cPkWUcepfazEsG0+gTzJVxA2bozQnghoVHt23eGxkc5w76Q+8Tu5I+ + YEXGIYYXXTsTXZPmaCtVOZROi4KRnR12COv8qdKflVyQF7Qp5Sqm7yTesS4kLr6+O5f9mkjnSPwg + 5Ty9mjYPDVXy3VDwjwtsAC/vhbCs+wmQZK99NbTGcYDx4jfIqY8MXVbx5kDez7D/fNnTuGpuGkP5 + bNbI6bvWZV+y78CbeZVJ/nyb7mKejxhW/rfzl8iHzZCl5wJE4+OOnAM4/x9pV7KtLKysH4iBdJJk + SCciYEIn4gwQEbCjC5CnP4v93zs7szNk7bUVQ1V9TUhVtD7WXoEg2yf08krWfu1OPxV97exOb5mr + 5MuoXlPgSC2lZyfSGC8fLgk6POIv3Rq75mImGiEMQddO3/fy6CVVZyHy2n6mp2Ef94PnPi10TXZ7 + QnZrbbAj3PvwSo4PcvixoBe/90MHq0lFRAsqABbnE9bwq48FdbKLDcbnB1YwLtqO6PrlDbZ44ZDL + y4iS/ic1s+nkIezuF4lm1YxzcS6jAhX2bJH4d9OA9Ooeb6hxh61ElSJYlZ3Ow3A6nGi6FzWwpiSA + 6MS/KnIMzLfBM5ufYFHyDQl659yLzv72BbUz1LREVWMwQxMLiNvdGcNvAvIVFZoCbV4cJ/53e7JW + Ne347/toetISsMAmSKG53iTqmOaZLX64N9Ed1DYtLtqVjWOZY/AX37EAr0A8J+kAi+vljbnywbuv + s3SdYdozc0LPCebrJHUWWs8LT4z3uW3E2LirUL3bO+rXytdgN1UZoHB0OHJaEtbMD+qpAGuXC8mE + 983lj0QtYNeBkB551zOWJHkWaLrDhT4eo8PYEOVv8NNKh2rSvc7p5yAr8LL3Cb3/fX+OLw6UM8n6 + lz/jwC1vsGpqQJJwfYO160cHPo43cUJ2HjazXJxSENhKPQ1Z/TDGXRBWKB9/PikfDYxYpcgTuv0O + Ib1o3yaSXMMMYeXfG3K0na87JJchA2TAaGJnznD59lTVqHammh6h9gSzxvcciM5FRV35IuWLZbiz + snu2E8k7uTGWMzZkpLlHcZqaW+jy6lhg+I2blLr0ohtLrts6eLV3m/jbsRFxfPg+5PefhpxESWFU + 6vwO3gK8n+boZTb8IWh01IQXSrRSksHAzYxH2PUlPA/BK6dUiycUZFNJT250AYKdMh1cfrJMdXnx + 8+ETJjoy5PhAyuquNGypuBBGUcWRUjl6ERva+xdeSvdGnfARuWLICyWQmk9CdGs9MNHuVht+C/1C + DnfJBLzSVQo88Z+KasuLucunbxVgfk2ZqqmtsIViToF/8eI92MudHaTV8MsUSi1eQw3t2MIhxIk9 + sY+Gny+a8NSRO38L8hDl1hhb1MxwUo4DPb93dS5GveEgVdMPlHy8tBHxVNeIXnCNlYFAYy4TxYOm + ih/k+ouOvaCb9hv+5eOVBmfA0pxacD/kAtWvbtCLtwlU8HpwEXWLcecu8jUu0dcPJYILNe/Zqchk + xUE/j4RB1+VzqD8mGAYcIYS7LlsLfq+DJ/XtUVUcnJ7FRS/DFH3U/4+XrGu3t9jPFOPmkzP+1Zdw + PxsTwb+r3DPX8EJYTTqi5qjfmGCGXwuWL1wSV5g//Zxc2gwSM27p4VonkVAtbx7EaXyiETBIPj5m + zYJuOuv4+cWJIV5tHaPFmAxixDe7WRRCZGik3DQB4ndgHvI5Q+8HumGe7DOD1w7vbZa4eiZBUOWA + V13f3D+NYqXXc09ydmnAF3RuA4j91Q9s1Xd5C+fv50xOa5aDJyi0Dp70zJ7m/VfPhT2sVFT83JrY + dW83s4NONbwi8UxP93YxRuHx1KHkDpCGB65kMzbiGtrcSrCU5YYhXO97D2q+Q6jtS0O/hu6zhW66 + 6vR8D1A+mE7uA1/7rtRQfc7d6ieH9sNNwFyumDnvq3sIPUIAFuP5HPEXFFbI7huGFVRgsJyRtkLE + jmfc9Me6nzNjrJV7fSa4WyrdlaJB9dAOjV9K1HoB7L3QBD5sMcFy54j5F6q+h+R4/RHrhypjcn8o + hid/952a66VyBe/KYdjJxUgCv4iaGa9ZCtZ5MUniwMVgApZSpFXPHs8nadesDekg3IeWRJ1aLgG7 + ddYbGoqjTWLjhv1iB00ItCfnkky3jpHo1GMLvZW4xBSgBMYtX+B+v/P/4ZXUsQUi333HJLTWFxDv + ylzCUxc+abGaVS56+tHbZvmOJK4/cb7yXZrBE3F7qhZQY3yh6f/Hh7wrIvkqRG0CD4/ki5WjesiF + 6dVBGKfJaVq2ePtGn8YHwxKcqVZQO5qfw7NEGz8g6hYPfACIDG+tuPzFD2B58uP2T4J94svn0BXy + 1rPRokcNJRIcouVSRTOyH6lMT7Ktud97NdRw/3VjcvCrD1jvrtIC/tASEu2/O5fGRSOj/LlfqR+9 + zH5BreSg8347VSKbHlt+ipfCrV5ifuM/S9xhE6LPZcL120iMFcJvCEIz5cntsOLmew5nHi2dC6ev + i3BO0++Xh+98ksmxlyVjvd2XAlx09UGK8fPMt8/PYEddRlTBbfIl3hrRLTscU5skB2M81q9EKemz + oOe+0HK2l2UHSMGSbfWi62d7N5hQrkBLDl96iugOVhWyKpBOaziFPW+8yBf+otDa6tPDWF8q/sKt + vhOnmqecXcL9G5Is14jFgBXNre6voPaZSvVfMbDlc/qu8PrX2P70++WT0IsOetnxGfP7C+556ygP + 0IiHM7EzYd1OeUZfRNYgp/Eu4sGEp7qC+Takh79SCtZfFMVQcG8P+sdXWLaUMZCHXifnAdmA8eJY + gQplMbFJ8nKXmpq+8nftiY4UrY8+dsCjrgaS1McuX49Nl8H3Lp6Je7WqfmbvuUYg6QQMwp/WS2n+ + seCnnV802+r7uvEN5RapAg2y7MYWo8hSGNhyTe/1dqrOVxcOyt/sSb3BC1zBvX89GJ3L6t/vW7WH + osODxjB1Y+fnLjvVL5H5tWRqbPE0NFEmwvNFMKgZYrthY9Yk6NEbEzFbWIPeCb8tHFu/oMlaJcZ4 + qvQZ2dZh94/P8fV0xNA07yoW88FkK+UWHpnZ807v/nTM//HrrT7Q47mn+cwk5Q0eU+hQ8xQ7/Tr8 + nBZuficxkufe+EU1l8BwBw5EJ4MB5mvB2zB/gnVa7yfZZZbwrCA905bg3Zf1a/64YrgXJkqMe2T1 + 0nXNHQCSr0Cd+L7vW69obLhdk+sJ+xGzJeLDldsdqXq/qUCqXEUE2zX+XDjTHdz3fXtlY2v553Eu + W4UMWgBX00BV1deiBXpjDG9V60+C+z41wrF+xXuz+gA8bfcz/OGxQI3rxCW/Ml8e3cMEf+tXVnzd + z/dlekP+s3yo0xCLCX6mikCnYUq8ZP8xhqMcJ/BGUkgfaRxHb7MUMGyMmdByDcRmbtpYAf4r7Kj6 + lXEvwm02JlJah1yP45eNO5Z0f3yDOK/zGFHw+FXQW88uMXbVFG3504I/fRVYXyla6lLH+/5LKnp2 + r44h3QK4QuN8JuTainbPd1cDAxBmMSFWprmiCRcVHV9TQ47r+dUP2fMXwwzJdxLHTM7ZL/YzuD5e + Mz1vemn9DE8OwggZ1DE+U7OmVayjxLVcegna2f1yM+Dhnz5VUztjy55OIYyJQabhy5RoylJSwJVD + R5I1xAKiMqkz3PCFOtPRzaXOhCa8Bd6exm4BjDFSVw9d+oknzjsQmuWZKCUMqhluesIw+Ch5YrTV + YyyUscFGCHoM1+dtnGQuqtgS7rsUVv6jwewTeGBNTx8IhHi6bfwzBcvdGGcw0IePh8hYcraDVY22 + fKZOAkN3AHs1Q2MbFlt+Pdz5IuozuhXcFfPS/WHMP26fgJi/m9N6dZd+ka0sA/HlOFJylS4Gwymo + obcf7A0PnsYS5tNbYXaoEQ2qcs+iS+HA0PJmojWxBGaZO1fAB5cjta7kxdbCrHkIMpBM+7iLDLaL + lQyki1TTc3zdRcOPsFIpG8ZRT7CIMcdHT4Yx/zD/6ms0c9lxhpEMV2LO+dyvYqDNaNM7E9TI1mXj + angov7aMqo/nks9i2XPw/Qsj6p5o00+PLLRQG1h36vhXveffLvYVF+W76TNcknx9nFIdFodn98en + WL/xSXRbU2/ab/qJvrmfBU7QvGP42Rfu6gR9pdDHsyLX8cY1zAmr9z8+8ZyfDtj0cQyNo72SIMv2 + jP7x3V/zq8lt2PP9XE9HDy560BBnbbt8fqK+gr/9uMPLpn/pM9LfKD+sElWXdWiW01GGf/qWqNIc + Msk2NQsdyC7Ae/hS3dH+IRPGAGo092IWLXOjpoA/8TNRbSbl377MOHCb5oiQKBPdyRCePvr8DAu/ + T/KXzcbr2MFrmt3o+TkV+d/zg6D1fGI+rdGYrpJqweCl3TF/869R1xuSqvBj+Z3A63M32OWw42EQ + nZ/EOjZZxL7HsgPb/+OGts/+u7gPX3lj905dV4rdRUiDED1r/kR8HHP9osR7+Z++ME9x16zwBTGE + t84kZv3hI7ZaJxmqnwdHzl5gR/M7tXVI7NcBz7ALjM5/WBju6Tegf34Nne22/ONHxJyUk/G7sW8B + n7Lyxiu5ALa87ocUCcEDEeuQn9hI8/mrmDxh0/L6vfO5NlkCzqZ2JL5I3IYfG9uGj8l3SH7BS7SU + zTFRXqvaUMMc7+7mR6VooutInSIiEWu+exE+jzSaJN4dDPZgXwiJahpUJ5ccbPH1lSPJESdJS5Wm + rSfiAVmHIyVG+cuZkt7mf/pGzVTHXQ4AQHApTzeqP9+SwcR7KUNnn/XkKJA4l/by7KCv70sUJz8u + Gu5S+v7T38S1Eg8M3GO/7veNwojXbLOIF3EMFT7vBkJQdOqp9f3MCt9sr4g/miJnSe+m0L9n/rR3 + IwGsD+Otg42vkcP5uLjjJ6t5+OCT7yRfYrf/03voD0/vKIn7uTre5b0iRSU9mcEnYoScPPjnp53g + qzKY+VBCCB/Dk2rKYzH6N/e0ICabHo/G06aXgQprg7zIYfLnfh3nMNkHQ5WRvId93pcoCuFjto4k + HZvxj4/oEOaFSryk+BrzZ9662EhlhOsPOOT/8merp+S8+ReMLOcY6m5XUUN8+9F4We48kIefTv7u + Z27he4A1R0Wi3yrV2PyAEBq6EtA/P2U6DVcFnqB1x3OmOoYk8g6El9SE5DrO736u2YLRqRgtEsNt + ln1m6yUqz7G58Su1keJx5OCfP4Hj58jYJVxatPFpqkrzytjBv3BQfr9fGM6/2Z03vwPszaagRHsr + 7vK3/nlQJ8T4kSjvy+GGoSc+BoLfqw3WV6kX6HAsybRyodTPx1NSwvsIR+oE22ARS/hVsC7qkp79 + 6RhJobBWcPJsgbhPhzOmY1jF6BX+PKJNjRkJz8OcwlUsDuTQhk60+T82MtafSY7cKcylCOkhOlgG + pdqdzf0yfA8VUh65iYc8sQxe0dcVqN2wbSmNfsN77jZ4Hqk9se+OFokesSFs2+FC3WMmuOtlX3+R + PcCIJj+kGkzLz5Yybl0Zd+bOZ+wJkxJlpmdv+mV1l7GMPNjWyo06n8udLX0lrNB6HHKKxenSzP2r + m+CGN3j5w6tZqhOI7yYjh2rPmjGPHOvP38GCZ9hAXPNZRodBNmh2C9Rm+75CuRfOsvHJFvzzn8ci + 1afhm+QRk5dvCkf/21ItDX/54ve6jE7k1GMu0y/GdHibHDC4tJ7eWcA33RdoBbCqfUrTu/PMH1fj + EkL7RiUc3NncDNeR8+DIvTLq8fqbMfmk+GB7PvRUvXes+7X3DOy580KPtmO7K961LdjwE3fNcGCM + Kp4CKadIGLD8ZrAjlSqgCW+DGFqPo6Xoc1lBCRcSfL0fInbYRSJM5lql6q3pjcUyjBVufhNWNj7Y + heE+ga/2YWNl81dXO7ZMWAngSlX/d3aH6nhRoKM7gKrJh0Xsj/+UD1ulsb5/GdLk71WozqtM//QR + PavPCZY5aImx3x1dSfIK/p9fja/3V75mrxMGeS7EhBjlKZqL2RbB+8mSycZ7y2XvavSV2Jkncnik + gzsHN3eGQFS9SRkNw+Abz7CgSx42NV1mGAvg80IB2+C/LOa1bVBepwNx99aIuTxkNgSAKDBa7sKW + D5+IuZ2SwWx1dpi7vBJ3HX56C7t3t1ByKigYjknqwbHI9Gne/LVR8r0BVpq8dUFUVzBi7lPBynJa + gk+/U04HPaihaNOR4tB7RP/qe/5yMCVOlbrr9Wb6YKtn9NiKdiMeTJVH+e7uYUmZ817c/H+w+TMb + X3pEo5AGPtjqIU1pf+/XP3w2uKzG6CDWbJh71MHNL5q4s165M3tUM7ybfvqvHi0HTzTh+SIZWFjC + R8Skzv9ChyV42lVO566Vg9Y//rzxwaBZUlwoit7UCjn+8e87903gFk/03Bod623zZMJfILwxWiSL + 8XgNM5DU3AuLStKDTg5pBZzXzMjmL+R//EP5KJI+wax+uKugdyGMVvlCY16mzRKsYYc2v2/iHg3M + h4u4TWHe6vtV957NHOrXAWWD9dzicYoYTTf+fbUm6pU7zlgbUnPAyniF+hY+NpKqAx92cjkScxQr + MBe3eN4fBsXY/KiJzdHpU0Lp5l/pfUBftiz3MoPzfP9QUoZKM4XhPoZ4KBzyuAmgmf2AV+Hnp1lT + 7c1ezvdiu8LosGsw3907Y55NUin8Yj6nNf/6zeIJqw4HV0qpuoo8WJpXAJVNn/35Ge6ixIuMSu39 + nqRFRA3zE5+DKKIiPYDP2Gz+tKrsYi3CdMNr4ehE9R561m+CEJrgty9bDK9WeyfhrVJdNj2Zhfhd + 2FJ9oc9mORF1AJq3X6m1PY/fsD+2+8MtdDG/6d1539ycf3zpNC0NoHLPZSAMIKH44HXRuukr+Gv6 + etNv51xwEr9Em/6gOLfu0SqeFP1PH5DQjs75fHVXE9qnJKfYfmkGC1u5UHrH/5H4FDsNO9JdBaZW + I9P73Z4iadPPqA3MO8F/enT3vNaQ6xJEnXI9ggHkXgsv+5BQDFy+Gd6p+m/9qbn5SbN+lxSo6rxG + 9L/1rrjq/bd/MFXTTQAsl98mHOTVwQxpFzYe90EGz4KXTct7xM2y9oICO97z/vKbLX5m88D9XGt6 + OqxTP30uSAbSsci3euMZ0/69V5Vtfw7nE5cZs+r6FrTM+U6t23mIGCf4M9r8C7xPjR9gb+5pomah + Fv4Y/omJxIIZ9HM0k0tWYkPsjZ2unBzgYNlFOFpZY3dwiY8PomXLEwzFrIrobz8qM80RLKfF5MGj + rgdyXLu279Z825+J+JxmSlYZM/dYZsjYHG3xFgO6EN5DjxtZ6aHvVSZaYzHBt57v8OCcWDSv6U2F + ysWy8HJNe/f7lZ5v+AzUM4muF9WQdvGaoT9+FMZEcZfHrJkoMDuNYo1FzdjfJh2SdwMxGpDN/vwk + MJm9TByYvpt/fvUfX8Ep5zHRWw4m2vxyWiifJJ9pO8rQeUwXLPBYctf2IH8hY2tE3EPY9pufBGF+ + mCXyt58y6HdJhqmXj8Rx72Ykxmsoo5/wKKm91zO26TkVbv4qnkXi9uxNGg9WmmKQP39/0k4HCPzD + PiJR1xk9+57VL7onVjzt994pFx63i4pyHOwpVqkbCUVMRSjAdv3bD422/QsT4KOe0D885Vs4TXtB + TgJibvt/a1D0X3D/TR41vGZlow7nGZnD5Uay00twh/UXWVC/oAexn58qWhSR06G+/1ypufl9r02P + IPisy0nqrgSw1dJkGEm2SP72n4eXwVWgw/OVlst5itabvxv+/Ay8qz4n91/9GE+n98bP+34p4B1C + Lm00cixl3V3vozjAP//OIA8nWkjDHMRJckr9a5/332PiezDQ8hfuRZ4yxse/FL5Uwd8Go57cYQm0 + EL0vXT7xx6R1F+XFOPg55Hji3QK4zLq+Bjjc+3jisRAAFty/k/J7mTpNhpqC6TQ85P9p8MH+v79S + 4IxWSMlYv6K1Ky4pUrmGErViPVuVoFphBSAk2uu2Mop/cwU9+5lNn2usbls2xhv1fHigJIcDm0pm + iHvPEuVp3b1Cgw980kI5VS/kdtYDJviHF0RX/Xkgp/r87EVuZCo8wCPE61n1XEYztDUKf0xYUvpn + s3CdnMHbYd6TMsCOKxqUzaCfJThNq/5xl0saKECbeJdGH6/P2adOa9gNWMNc/Nn1rOmWDPbbIGLD + rpxI8Dm2Ij0fSor1N9fPFxW28H4TJKJf/AYsXVh1CAZ7ndr7VsiZ/LIdKP8miZJWOrhzdthxUOfS + B8kv33ezqkJVoP1s3fDysSxXur4jFZriqJCzEda5eA88BU6HA5l+nFPks/T8foFu7oMphKrCZtm5 + zdA7dwei7/WRPblOTkH5OO9pnMaJOx8qQ0bt0edpJg5jz4RGTEHAdrepa0HbLNr5naEBlwk96ic/ + 6l8nSYGR6vyIm891Ln7TRwklKLZUvd5ddw7us4PSvXIg1m2wjLWRCYbZXHHUCrQDEPcI61CAO56o + +VgDtvPVEmWXy3lqb2+VifpRH+DeXwqivbbZtVqPRFgMqMUJ3p8Ye3JOAohaYJoXb9YvRtDGcMer + R+I9gApE1TBXROSzMEHlo7tCUK0zTBtTpmlnpYzpZaUidip/hFz4wBCe9v0N7Z9YkhPwSC4pYhfD + 9+j9aE56EC3GsHfgUPMK8faXa84vj6CEt/eaUl00K0bVrDZh9CYTRu9fw8aPsX6RaZUNPfFYb/jL + 6zMglXtSatxEB0jshhMopJ+AeBke+yWm1YzwrpiIYx4//WJqxxYedvWBHio6RGx5vRzUXVKeRJe8 + jqTIUVco1KyhBwNQYxrbIISH7yATfSJBz36j2kLRWHRymc824K23qMPZvhj0er5e3OWCzurWWeBB + i3HojFWE6hs6Sv/F8oBjsOyiXyUrU3+m7vBIe3brUgjvsJGJI/Onhnf3P045mPGFOKtURtPdqDqU + jzd/WpRWb9bbpJbAPjp3YlVKx+h5GAZoWkVDwnZSIzH8sASZJRfSi5pcGW9bNg+DGinEFQujX7d4 + VqS3diVaknBgkIWdDg3NfNIMmVrE3tdvCZ343VDb/IB+2sm9BT8XNJPIyLRIkM9FCotHhsnRJTdX + vGq3Ej6/EpnE6+nZzFMvh3Aph5ZcI9POeWynOljuuk7CqJkjdn/KJmCn4kdV8gVsyKKqRoEa7Olx + nsR+PX5CGaHnIyHejR1cqZfmrQuCp9L8B2OXv7zoAN28P06MM+7RkmiuDojYRMSOyJv1x8jqoOQI + b3Lroy5ij6GykXWartTx3XsutU9rhfd4DSYlFe/N512GLdrikXj4peZ8pdQi3AunHfXXNgbMnvIS + erX6I3jVP4a05Qd0U98gloVPzRLn2IRMTCA9PmzOoNvvU4KUffH+l1u51CmtiHRRIViey6Gfno9V + Rr2BftSb6twVYlqtKOqTGz05isBYfrqWEEznJ1F3ZhstcDuFOhnpSMKIS4GU3SCGi6c25L4/nCOp + KowMJaXUYvDhU5eJO9eDnT4wepqCa94P1W9FpZCYeB+9Enfp3zsd+MALSWrRa77S61qiqI9vpFig + BsR4f6n+4o3Gjm3mrLvsJxDc5WhqDOHEGBfCLwzeqUGcsplZT275CoUuYCSLLp7Lf4ZLCKW01Gg0 + jAXjDQXDfcovGXWrXGdi2jkVtFN+R3Trm0bCxxlWeJSkimIzCl1W848J8mf7RCwSh5FYN7KICIor + ejrflXweP2uMWPQFxGvfWi51l/0A3DQ0qFcsoTGPQtJCq5Vtcg/2Rc8/O8tClHMWYgWSyKbQUDH6 + +/yiWFaDHlIWw9fBfk9c3Tr90tucDrduinhN8QzWH15spIsyIZed2oJ+E77w2PkOLZ38DITrb9uS + WgVGdVEJwCLtZBM8eDklx90zbASSOANkAXejhumv/dICcYC04Sqq7UjNJr6hEMY/z6URldxeaAE3 + wYlr7yQKXKcXxc8ow0gTXIKJhQ0pP10LVLqiQJx+v+RbvhZgL7i7abLUQy46GTShjb8j5ja8FQxH + wUq2nqttS8fthfrwy4AcEJuQvRs2o8kaH46HGhB1v+6b+RHsV1BLQ0Vj3+Ty1U44B/KY78l1npJG + bLwft8fFPJJIG7E7b88X8rw3kai5FgaDWlZA0jiEakfaNvS2uBY8lzIgWjPs8rnoeRH64uFDMrue + jSU8NQrs251NztGbgGlW+xZGRjPSo1rcXelzFnhkIMjI9XwVXFE/QxFqqc7R21XYTtF3S4pI1j2m + eWeaEQ2jj4WcUNFolqpev/6tRxQ4NvH6XdKLb9V1IDd+X9TonDRfZPFcwJ0af2h0yfWI9yPVRKZI + FSyV4zYL8vbDEJx4jhjFjeRLC7gB7N63YZK06QNW79jYaI3NDzl44uePb9jo6XE5NXLOaiT1a8Uo + TiqHGMNYgBklxxCtky8TYxXSXnJ2Qgfx+TETqyBdz/L7aqMTxRUttM7OF5I4E5re5kTNrFjA/D12 + NlTTrdFpLn6athRbGV2m9kyObacy4VnIPshcG5ByvZNGqn70C8dS78h5Chd3ESLNgmLhL8S7+lXD + bhxNQFPLAcl3xwYsj/mEIUFJRTzKv8A69m4GtnpPzWfGgbFK1xgdezjQY123bHk/WQflEHRYZBbM + Wbs7zpC72CIx6efZr6v5rNCN+kd6mL9BPldKzcPlrur0/vv4bHGHIw+fl9IlpI7VXgRH0YEP8a1Q + 7WNZBvvLTy0gy/TcEZ11q/mskTXPkCZxlkfzhi8ovWKTHKq4AVJ1CXx0W38exbzTGLPtKRDyLVjo + +Yj0Zna2WaHYZZhq80fq14PS8LAUYpNYAk7zBVz8EGl7/T6tkf1hS9TXovKHjxv+N+vxkyngti9v + xDmf/Wi+Lb8aNMXBII/ic44kSxtKWB9oPbUvgt3ldylEeKZyhvltPWavjixlq7+0JM+k4Y/tSQTL + eTwQB54f7owS4kPxcG83PCld1iktD4dAyHG277JeTNJvAi7w1BDLtEbGSPjbLC+vpt5JUV3BZL0P + NLZfqZlpp63L0W+Fs9U9p2WNHSb4stahbvA0cvOlpzE710aFFeAg1XQ9yIcn952BbniURmafG3Mc + 3BJ4ScwS3zc+vEATVLBqHmd6PK+on1fNL9D4oB7+bXpg1VbKwfX66KfdbhIBy6+qisYisclZrG0w + q+0kQh5M3Mbnu3w+u54HHU2uMffm9XyZ1bsDJm1wSZaK937+vBoHlVMtYliXh3xeTLMDT9n2aJAk + HJu4ZLBBYvYRwfUuMpa6iyv4Pc8FjWoY9YtQjYkiN9upEzpaYH42EacsCRYw212+xjJaJxF88+s6 + fT6szLf7h0D/BTzxLpzAGMeDFiR5URHTB2ov7ulvBq9Ay+gpeonuPKOwhDth+tFzT71eOEWs2iND + UcnG1w2RBmoM6U6KMKeMn2ix29SH5s9uaVEkp379ptcCYrR6k3g/5M2iHOkAtUl0J87m1mhwj5YP + 82PskiQopmgwWCwqdVFTop/zuBHwGLQomewnuW3WNbtJAgbXF7GI17eTMV/DZAYfO81oRirIFqlt + QtT384WYRXsGDLzPLSA7Qyf2ezRycVabFkXTeMf8rDv5oO5PFfQQKKZ97Cf96g2lD+5Vf5j+8n26 + B6YCzGupU7ecaE5hc8ugdQ1GmtXMbsa/9f2pVPir58bKFFFEy5keiGeeB7b4HJgB2Wk6yZyuB0zu + FR92p1olxoYvkuy/CvSegEu9wG7BpKtpC4HfefS4nHbGchu3V2rgAVJLwHI025Yqwi0/Md/USk/v + 8bEGm16d2CkB+bL8LA9u90P+9ILAS7wHi5BFBN/DBxukb7bC4difyCG69f0if5/eH35NyBgdtkrt + foJczxlUi9IFrIG8Dbp67BxqXpagGeIgSOAN73p6vkRGLmrl7Q0/dpZR7QKSaK1JaQO340xqRj+h + WfLsZ8HOOkjE/dxIM0/XS7t/hN6VpMUs59PunviIwoLQiBbNFq8aD03Av0gRqTYT9k6F4cE/nzAr + bjRa1mAX7zf8olbOa4AdUhCDlyXY5NSUwO3cclWB/lJmLJd6465UYAX6w8dTxhSwfJxhhgriHMw5 + opizdb5jGFdDQc8Y0GalV6WE497LaRIUOF98js0wYvyJXvvzy5hX23nDnMsoXg5S1axHD1vo1rkF + 9X6B1rDpnIp//IRYq99HI7d09T+9Ha1C2giWK32Vq2NciP2xe8CeJqyg11CXHM0hBivjvQH88b9S + Fpp80b8MQ3EM/Q2vDoBd6osFDRgGxJrywqCyP5bw9UikTU+qrA/uwQCb+fEh2vQAzYRc3kL8NcmJ + HjV+Pl3Mwxds/J06bKoaJn34GGz6gTipOjTsFummIs3kR5yqH8A89bOPyicAE18Pv2adIr5GffA2 + p0/0mMF44wcHbPoec/dwx9YFH0V4uT1qvAtF052vplIDuaoPuHs+EZvDPs5Av3Nvk2IUH4OO+ncF + dhBTqhqtZcyPtJH/+AtxkKnly8bnUHI/6STp5Wu/Su0yoE/YDvTRZ79oBUogoqveHAi2mxNYl3xO + kfZ4YnIEluOymgxvsF1TY6uHw37wJyjs3/OkzG3A2LZ+ymd6PKaJPuqeBa3BQ8XxO8z5TGsYtM0W + BYe0Jf5Q3IxRF68lpLcZ07vVS2Dd7e4YlpL6JumniaP5weQBLldwxV95MiOJ48Fb+Vq+R29NmRv/ + 7n96BBXdnp/bfR2Aoc3rMzGeJm3WbzDGwD+dY6rK0G6WWb04IGbZHfNnK4jm8al6CFenlZq3twqE + oXV4OIDkh5GduvkyLNSESu6f6Pky9WzxDyNUNr5LVD3Mc2nTe0BypDcl3a5iC+LLCbKusPHO1Xi2 + iEBxZOHincml3bUGLawmhJ11lMhf/s9arlboPe1dqu4eA6CfmHL7CvUlwbF3dYUyhik0oB8Q+3LV + gShC+w3F/cnEO60yGiE92BA9yy+iPujuzdTNs4W4qIypRpd39Cux4wNebTA5V0/bWK9aUAJeJzNV + +cFnDDzyEvzxRZ98c7C+Ws6Hv3M8k5Qyz2A+yCZwj+eAJJ0a9eMjVDEaj+c72fhyI/YHqwYqlM6Y + 16OL+8c///j4JODJzRdmBypA18gkOoMtWB84HaCUOQ3F55sCRsidYpgZXY/FzZ9gM4QqXC/LidhP + dTbmt9FicOMv07TmzHbX3SJz8O1U3+liVO9m829ieEWDQMI+aJthP6QTgJaKiWvKj4iVzhOjUZHR + 9FELZCySVYcwMCb9bz3c3rKTFkmEC6adaXzdwSkvDrJ/fElMU7z1gtqiENjZ80mPDj/3DEvF+s/f + wZb6iiYqgBKKulkRc31NTbf5qfC+t3lyKg4v8C9/VcIpxPo5erPMR+ID+/H16HW8aIbwF1/Wu+2I + j696tH6+0APW87HD76GRjWW3s1a41ZcJbHyb7pK1Rf46NYQo8gGIxfPZof0yg02/DD3LqneMHt1k + UTdIJ7YsZmQCsxMs4mpKGPERzWJ4K94WUVO7b+bvnGPQt8jG6Hndu2uSfmMAYsOnxya9gGUb9gyO + p7DHj1c4NmzqAxHlx8QlRzH/gU4+dzwQ3w+DWte2M5azG36BWnEZJc57yPvNz4AfIRwJQWLI2P05 + W0hvMSZHtUDul651hgx3J0xLcDjkfJRJX7jxrwnyj/ef/2OBxawion4S3+UVlTp/+EW1g1rmE3Kh + CeVUv5DTar37uU98G/3pA3W/3jY/eU5RPYNoqnNmG+wxfG3lbE4dPWXpup0SnnhghMwgKrxwxhSf + ZR/+pphQvPHtf/4AEFRKQxQe+jWcXiXMvgamJuiv+XKKWA0eYqsQO1eOLuNJpaM/PHbt0DKk3X0Y + wFN2vOkPXxdGXEfpluBLjvppztdZ7d+wPGhfDJ5BxPhbUgzgTz8bVorzEfymDv7pZzcrpX7BvDFB + gKqSnEW0RJPfoAzWOzzTE6KMrfh2SJT7jtP+5T+fRd8KgqHeUe0hP/Phecgg8Pn8gBtiTP3mnwyQ + 83qLnv/q3R5Kb+W36AFe7OxlzJyMMRQfPwHLxZzmw3TaJ7CZ75/N31z6GV94H+bO1hVNa34ROx/3 + b/CTqye1q9en6ePy4SjSh+6JhlBqrBt+Qh4MHObcoGTr2xJ82EVTiAX5vuF/9Z3/6Q9BMG6MbX7b + H34Q++jsABOSzN5v+n37/UH+2/wItPkhJLTOhLF4Xfg/vCTnUfr2zOerN4TNq/2n55bjyS2grtVn + agRu17NLXnPgL17soGbNPJ2WGP35fbuvn/Vis1wseG3qdlLYpDbS5leAbf+C6ls9Wv78ewEinp4/ + ppePmz8P/vHt7e+CfI4zuPnPmFksYTyyexX+lLMxzTZ5N3NRpi1Uk9UmJxT3+WzO1ffPP6BWvU7s + dyToDYTD3sc8Zm2/IHspoX/gfXqbecDoGTcTEHWrovp8HZul484KvGHUU3dZjGi8pDcForO1kg3/ + +vVSW1/4xzek6DGzhptOBdR+Y0UzG6U901+hgy4Hc0c0XV82fnP+Aut8yMlDA57Bfmwd0LD/Hqfl + AsRo21+wgRE/XvQs+Jz7myZF2QZB8tSyk1c/fI+1jaJRBcS2qJTT/wAAAP//XJzJrqM4GIX39RSl + u41KIVwCpnbMQyB2biADUqsFhBCmMBtsqd+9BbfUi15jBPLiPz7nO/J4vDMA9Rd5HA/s1aNuyVer + 3mPPSluPWCdXEk6zJGNTbGBOKKP1IJfgaX3f+/a7qTS78HUY5o6Kk9iCbZfdkKF2pToVHNCAZjn6 + 6L/CDZ3toxyCVhkphvipdPPxrBEQMLcRuV+WJpDnRWHEx3Grjnx8KrzpUKIzCOqEh0AskUqt98sH + 09M6YS3rm252syZd/Rsk4rns5nw8J3yRGPI42fot79f5BJMnwnK93Po4m49ReJ7hDRKZmN58PiUS + aGNBw1If750lb9iAai6PyB7YkS7/w4LH/sBgqUl6p49tkRN6LxfRcYglhwX5YQPweSuP03u+5xOQ + J46/su0Xgv3Fo7RPmwm8uW7GOsfZ6sDa11hQHoGEbu5LU9lFj3ij4C04y++azuWWqcXjAwbYUALe + a/Z3LRE/5ZbBK58i+mUagQ+VEK96OmL1SfYnAZ7HfSDalHb5BQDj8naQszVVSndcYICFV8BNLBvR + zkS7QjCHDo4bIec8cvDtDRAsK17yp5RSZmgUoJ/QcsvXV6nSqtR6QVQ5CStACoXZZ0MeqOKGoqNb + 5+r8lfIT71n1HT9FSaYMsZQKjJvqAXepr9LJnCsNPMsyQsgd64jaduoDN1MaBP33NidqI7fiXuNq + dD48zJzptzIEOAcpJEOQ5sOmnUKxY076km+/KE3ueiLS3mrR7fp4CIv/0gAjvSCyK+5FqalnIRgs + YYuPNoy9fuFtYGe+CdySY5kPU+Zr4pKXYkOnL4eemLoCD9SUGIlR65HAlVNxyY/xwiMEstNHAAhL + 6kWPEpWZK6cCt8A/ozhLyggv+e33vDbKC/XIa3geQJo/lnlWUXVkFZ6Ar2exQ3IDM5WS6eICyskJ + Onxmm3zGt14Sqig18XJ+6mjdKLW4+O81P4+mxqk1sPol23ylEa7Zigfs1FyQNamF881P0+PdRN7u + LqqEVmksPhn+vvKpnJxVyf3eP6kSzA4b8XwCaWrYo+hVmNJQEA5gGx9D5Ff6U62bwPVBLmrZ2JLP + JJqvsiqJ4qXIcBiVOSVmmhLx7Xtw4ROf3sqH+cVf45s8mvRTClsDSPvERjrHNeq0mWMN3IXqjV3H + 2Xf9ylO9Xdqha8q3As0U6brm94tfAOqcuVEGCh74OKjtqOtPAXFFdMkZyC98YpANcPrOG5WsKqJe + 7dte2OxfwRhoJFSpCtUT6GF8xUoxphHOFOsq0PL2gAUzHLqFD07g208v62f9s3HX+Y919lJ0M6v0 + VyC07AMb28qh8y4tfbDmk3auknwOmN5az/8YHRyVUiPvQuDy2X2cIDkI7JI3C9PueEVKsw2EqX2y + CdCDSIDjqq8rPymyuFx4GOOMrjRL4E+l4MfPn38tBYGPqn4k5VIMGJJ5+PVfVeDX56++CstyLRZ8 + jH2YJh+//1QQPpqurprh76Eukne/dA1Entt91w0+hnoIy/89+rF88J8f/wIAAP//AwDC3vsbugUC + AA== headers: Access-Control-Allow-Origin: - "*" Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9aaa08019459-SJC + - 9854a460dbf6cf13-SJC Connection: - keep-alive Content-Encoding: @@ -3031,13 +3033,13 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:21:57 GMT + - Fri, 26 Sep 2025 17:57:10 GMT Server: - cloudflare Transfer-Encoding: - chunked Via: - - envoy-router-867c855bff-4vd4w + - envoy-router-5b786845c4-t5v8q X-Content-Type-Options: - nosniff alt-svc: @@ -3049,7 +3051,7 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "218" + - "343" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: @@ -3057,7 +3059,7 @@ interactions: strict-transport-security: - max-age=31536000; includeSubDomains; preload x-envoy-upstream-service-time: - - "257" + - "401" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3065,15 +3067,15 @@ interactions: x-ratelimit-limit-tokens: - "200000000" x-ratelimit-remaining-requests: - - "199999" + - "199998" x-ratelimit-remaining-tokens: - - "199979820" + - "199979816" x-ratelimit-reset-requests: - 0s x-ratelimit-reset-tokens: - 6ms x-request-id: - - req_178a3245796d46979adf007f9d81c6cf + - req_a851e05c4aab4ce3bd96f53efe70eace status: code: 200 message: OK @@ -3123,122 +3125,122 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//VJpZD6pMu6bPv1+x8p7aOzIoVXxnzDKXAqJ2Oh1BRFBkrIKqnf3fO7g6 - PZyYiAThqWe474v6z3/9+fNPm9VFPv3z7z//fKpx+ue/rcce9+n+z7///Pd//fnz589//j7/vzOL - Jisej+pb/k7//Vh9H8Xyz7//cP/nyP896d9//nGV6EFcmXuxxY2tt/ye1EcI5V6p55zmuURrqiNt - KzRszmlSwOGz3EhkzIdaUGRbkndHUSJOOlagB8cgh/rm4iB0CJxYOIGJg80uZESPhyPjR4WN8qJR - g6Rn9w0Yhs9CKkUa4k0cJp74xLSS9Zq+yLNwHgPpdpcSoooNyLhmx0zsds8SlqqLwvnW2gM/KmCE - +7D5EpSZcj1NgxVC/PAxSc7U8ngaq5Zcc68CBc+vG4uX8YLlZchHkm4aIxNc67OBwIENigXYxLO0 - 4VxIenFLHNRqgAuRa8BrnkKiGecTmK1XZcvfI8iRdnUBG/K4VWQcZBfixK4GhOzbtjDqtQvyYuU1 - CFU6YfgyQpEYG0bYctPZTvaebEI22wfxWBndDp65/TfkNoyAZdpnLfzwrkd8Jxgy/mOGCWA2SMLt - 2HX6KL6TEIzm9UCO0TdilG7tu+wB3iAFrhggb/ZR4LVQYnLbTv7A9VSl8un8VJEt2+JAPEXx/8YT - XW9OzMdbJsniy1ZIsmEIdIyELvzKToZPl3ses/fn2UCCwwnTKQUDY+97Ai3LWdDtq4ke/WwzA/Tv - ux1WoAAxLWAwgsvlLZPkKR89AVymQrLJ8YHy0tkO1Dzakvygt4qYw4Ky2ew0W0beViPGPBx17iAT - Hzic9iBr/OqlL1QXzvF5i4pHynnsGNsu/LQ7j8REk1hnhrkF39XRR7fyNnnMZU8B8toX4uXd5DG2 - qcLJRVHswv1trsAypJ0rWZf2iRylF2t2pfe3rNKtR4JQ4djwu/7bUnwU7z/HgZwM2wVR81CwEL4N - Ni63hpP3zmWPQQd5MO1ZaEF3N+fo2blxLO7j5x1q2cCQNd3f9VKgTIDGdXJICL/dsAiNqsHtPqzR - I2k4MC9fB8PX3nmSZ/fp9OWUZhAOe/tALu/y7eHOm3pwVvuUOFFyicci43KIB9fETHuePM7o4Aju - Hw6g6NpmrN/pylVe6xPdoF3Vs8MnPfxGDSNuctVi3r1xmqipBy9kA5hrKtzKK1RwxiEFbexh8W5q - IbueiklR3hNdXJ8fJp97ipzbOfDwVEwN5Oe0R3aFpoHKYZnKG/sWkEdH3EzYT5Ekz1xrEMPi7vUS - al0Oj0jt0UVwa7Zcb68U3JI+DmkIloFmd3OGX6Jn6/1VMfXFjwQ1ba5Q0ptHT4xvFyrtNuyIrBm+ - 65k+X6PcTbNNDmmGYwpTKsnVlQZr/sweU7LPVeYvrYr8SH7Wy9WLJVl37i/iC01Zz/6nt+CZA19k - oi2p2WeICjku4JZoYQkAhY8yhZVfBcj7xt4wO1AzZPqSdLyxP67HvXxHgp9PwqHHKHD1WAnJFW5e - BwEZI5ayv9ev97NPTu3my9br55KGkItFOw5rvivYCKwK2SSYRkUXpVsUwYrXAnLZMawv+z0r5RjP - KYrp9jjwpzTeyLnFScTjr69s+eovHyz790gefeBmf+t7c/Xf6DC8ayb24riB3ZNbkHsx0vX/wAjp - JhmIsvZL4fCeNpALRIj8y9nTRT1qj3Bd7xBUOx6QfSkdIXt9ZKKbNoiX8qBG8tqfiRlbN/C+8M4G - RO1ZCXcd1mO+qq0USqRpCJJS5k3n3srl8nENyfWkB2zNpxx2YGej4xC1MWGpsYPG/V5i+XDn2IIa - q4K3ao7R8xA4mYAiU4LLIxlJOlo3XTwXrQGx97pgdmmtesF93MrSBFC4GZSYiRdmuhITeo2o+oFm - iy2qOeSEMwz3/WtkQkaUXg72B52Etstly2jHWPKsXY40lH7j2StgC92+zfH4KPmY7qdoJwsN7FCB - TkUmCLf2Liuax2MBVO8Yy6mdwuk9EpRdo9Rb4kO8kfZ37os5N5UGzL+UK4w1ooRNHr4BCZPPEd4P - 4I0OD8nwxNs7sKFyuNbksYtKb1lM7MMgc0aiofQQU2ksUxDkoo70t3hk4jxd1nq3TXLr36K32HOu - gBM8deTyNnGG96BMZHHYn4jVaTePhcknksVT0qGTE8GBtQnNIe9UKroeUn2YQI4NqJjER+a9MjNx - w6Rwfwm0hITUiIFYc883FCX19FuvTDTIRYLhcr2Ts5N+BnoJWAQma7liehBPNVXbUIOZKkVI22aL - vpDFMaRO0TOiDfbB49Z5BVE231BepQpY/NMrl6X+skMGo3dvNjpuhOHro6OnuZcB5X2zBLetFhFL - 6nA9Bgj2cE7xFNJqBgPb330O5i9fQrF+bRgz+fsMiTSkSNUr2+MYwDN0khsJp+fXzXjwDg1wcpQr - WvMRcKdF0+S1PsjtuRE9JlaXq6ycqzpUVKFluD4zV56b0iBZw3u1cBfKFtR3rwi5xTKB0IjAgjbq - CPHNwcvWfn+FRWkfyTHsZZ2Sd5vDNf6hxMYXE7oosyApDYwO0T7MuLNALMnWgwMx3/1nza/yKnOa - pCGPk9SMLw/O8dc/0Znfo5j3DauR3O8JYxC6qbfohdXLgJgiusONkNFtftnA9uZTvAjDAkhwvnAQ - pa+BPE5GAviWe7myJxODeNfJAyRpFAtcm/0HefKyZczEn0SOo+qCjEMk1bjQmC+ju6mhB3fu4wWN - DMqdfnWQaUXmwO6Z9JbW+kLKqj/E9Tvka0lF5qv5ZGzVVxAF/Q05w70a2rrEG1iIzmud9ySjCS1H - yJ9nFRXrPMTndxwBITwq6Bq8ArDob2jB1Bg36BZSMSbbyzOBGoheyJsUHsxBWhlyChKZpHNhDLwt - Ojmsg32B2SdqBrxd7gLYlcOJOPxhBOwoPmcox8WHeKA5160UOD2s6lQgQTO4cb/T7btcCoZIrE95 - 80ROueR/9SpqxzheMtAd4S+fXUJGr6Uu5uAR91cUppMDxLg0Q3i4TwfM74WPvpzFLoGZPkd4EsXe - Y/u7wUn65uwg5Tu38dJdwwiSFzVxZUeHepa/rzcEkbhDxvT+sOXiWwU8HS5qWH29UyZsDkoqP4J8 - H9bvHOj9nqMSOCqTRk6jGem8mXcV7Ehrk4i7nDKqNeXuF991/TlASZopMLlEKfKH4JmR6+OJ4fq8 - eD7hh06TAUQwtwSJeEqZxnMmEQUe+rIgZ8yTjJlQCcHzLSGiKJUcz5qSU1iM7x0J1/7IDE857tU3 - 7xG/4Eo28yOmkLWlRp5sjnWG4SWHBY4QMvP5BuhrKN/yA9lHcn4jMszPJM+hHm/dkIoQZKPy6EP4 - cTccOdyvb491WwPKg3g8EF14zfXoFbCHZdeG6BLTt06UjnJQaIWOHEr7WlMuCyr4yyf9u7mzEcwY - wwdn7MOdBQIgYngpYF5JJ+Jty5e+uK9cgdyU9+hOkjQWTTylkjxeBKJrRhazJn02ACQvFLLdKIHl - qVr3vaC4KpZ4Kc3wOk8Bf+lVLK/rPSJFs+DX1lyij6lVi8kz6qHvtRlypvbiMe4cJfI3tV10uvDU - W1DRVD+9Qcz0gwZ2D59YUqSBC/emVuvsspfeMOvfEnK84yXrLVWRoPk82ySX7LKmfPyp4DLZOrEO - JtLJ2Zl8kHhaiqxKDMEyItWGYn6QQqk9psNIrw6G+Smr0SFvrrUo3e5HaHwc+us/8WIFr1L+6ddf - vs6SlfuQx11Enr4sMxpL9gh/+k4xnrG3cLDTYLHXXyjka2dY8MJaORaHNvxQ+tGZYUTc/j3f/XAS - 1c1A1nyRlbivkSlfTR2nuYLl3HRqEg5FDKb8AwTY7eo7ceFGiGdj7l2gug+P2PptAiwLPq7MU03F - deZatTCNTAPrPEXRaXuqaSMyQ952mU8UzNmZ+NKfLTzi9kpcLRUY83etDeTX+4OyOuUZcw9lCC98 - maBiKBhbqAFaSI+3M2bUkhieWJlDdh0H5FrxOIz7gPPl+dpMyBcaZRCzYLJ/+Y7s2tZjmpdKLx/y - axG2T28alq0MCvAawRPvf3o8VGALDiybiUMVbRB/9eGUA0EeqL1sbtxwA09m1xP0aVk8zYEVwv5q - 26SI5kSfifwaocq1FXFMTdf5jGMCrJ93H7P4CHQcyO0Mz7vjGUubSAVtqzoCTGq8xeQosYGN92cl - ufwNI+36OQ30QNldfp6NXThwBs+WR5Fx0BVNkyhe89Anfegq4J2VHItAuuqLvemKv/Ucxl0HmIPo - LCXnZktUZ6LZIlyxDZNKbZEJjf3AXHYRwBAYM1IphjoD+siB1T8TzbudBhZPrQW3wZknBRo5fUIj - 2MAQOgNBwk2O2csNLHn1oyFYhFdMtzslgrJyEEOx7ncxsY+fdN/oRw89rnbCBOlp5tAI7RI9rNfT - m3lJbWXVfXrosPqp6RapKfTOWo4OJsIDXay2AFte5HF28wYwua1T/NUrx26TM67oVUk+nUCPkMbL - gP34A08V9ccLsnGJQuE3/1G2CK/sb33g73IhtlXuVr+eh+A21W9kKflHX9L2lUKsUAtZikqyWe+/ - R3mXZCfkAZuv6Y7/FHvHKx/oF0+83bZHaD8fFvIO79SbOUfFssFMBzMrbxjRNs8dME9KSZwa+Ezg - MrOE+jFjSPXTfJivjQphAa8IhdrD1blHThTYp5FNwl+8k62TyjzmAfrxGNqfylI28qgMN9qj90Zp - Z2HIAxMgA/mNTg/7kvv5L6Qsj2igm4OdwK6/3ZFJ6ccjP73amUcnXPSq9aioSxu4vwvftZ+CeO4o - l8BphgTDgisBnYrPG05pQ9DPzzHt1R5hwsqUOMNdG0Si+1DqJH9BN6W/1KJ6lzjo3jyMmVA0MVVL - rMCJtta6Pg8wb7xwhA/kHjH09tuadCYtoHOZHwgdVZnN3f41w+B4zcLPAm81Q7E0gm40FnQ2bwgs - PK8q8qOrjsR6zsZAuba9QqsKbKSl929GX4U5wmwsJCwruKzZbWmV37xH6NPGGZ+PXQSeboWIUrOo - 5la/CiPuo4RLdf0ydnY+ITzfX190u80Vo7HaUnCTU4DMR4fqpYFgBImnpMgkMYnJS7/0UD+3L3RJ - BcPjApX4cGD7W8jJ0l3vZem5kVY9h8HN81gH+tU/lv2FqCff1sUdoRGsO8gTNehbfRkQwFK/gTO6 - V6Yfs/j2pPBdgAcJoc/AfHO+BtTr+UXSU/sGi3AK7R9vQtEhz9ic8lwJZ9810HFXtNnYP0IJWlR5 - omdwZfWiD68Kso+XIDP9kJrOttlIJZeVyJbrKZvhC2NQCz6P8vfL0yntg/Ln/0N+n5EYSx8qgVEf - Z2RtGiPmntVrJ+dpe8KCpxlMKBmzYS2EPJaOQpVR8lAKmcabEW83ccuI975wsAHURQ6G2jBP/otC - 83mx8eKnsF79RyF3+t0hxomf4wX29gbGqt4hV2C+1758dSdnol5j4Tq+AXv4tia7rMbEf5R8NjPd - 4uChkQ/Ix6dtNpcbvIGisVNQDLtjTZr08oatc8uI/omsYX70XA6909NETr8d2XLuMgmKU9OGrVxP - MfH2d7z/6e8DHrDOsu2zl/7q7VVPsqN4maGaEg+5VanpvKJxNhyycCauFfv1+nyzLAGhCWXJLIZF - vDga+PEe+9nomWA/Argzh4+NfvOCEx7NDA93ckDu6+vHK3/NpTXeRIseS03drORgbFwvyOSV/TDX - /IuDvFOqJIw7B8zbE3Thqr9DqoaHGvumLUBR9ztk//T788AE+LS5HVFfVR7jSIsT6FgxIStvGWZ3 - 4lo4dOkcMr69g37UygpSUxjxZ5+heN66uQu92M0xfSiHjNN3rgvNDizEOMJ7zZ0NTQHvSX+E6zwd - llGyQpk/U5XYTLkMbDNGvqyf+xcK8lKJW2/SDHhayuuvn8bLuQ9z2IDZRSZ679k4mVwCX4P6JIY+ - SwPJffoGuNiZJEBJmTHBuqcA8q+QXJNDAKbFz1qYxPsW78GcAubvShuS7c4g+ZoPpBDulaT6bhXu - zyzQ2U9vP+PbixhY+zD6uJa9nNzcFGnzPRtmxSYp+M3fX79l2zcN5Yq5QXj7ahedD7kYQi7gIdIe - yjejycAiIBP8Rm4maZ7AvaQNRK1yI+mhfINxn7x6yBFbJce7ZQ/YtFsJ7oyvRBzbmWLBuJQSPML5 - Sozue/RYUFEMSnQMwxJzdjzh3cuAySb+kiD4bPXf/AS//uu/X54n/Pr5w2v2eFu945jyL/sOnc/u - QQ6Ix9ksWYkP/UbJSHSG35jNnL+TLKd8ksPjGcaUv0cb8Np7T+Q9uTNosalSOMbVAamWF2WTokEb - EphOeKE411c9IoA8tiRiX426nrIGX2G6kRLkvy0+W+akCcH29uXXfoNWnnfP90fjeEOHm4zjqX02 - R7iplgB5dXoGLOhaG67riyyrv8VYGsvkr150M3XMKHy0CVx5a0h1CTAWVrYNV76z+sU+mzktj+D4 - 3BCcTM5RX1aeB6NgvpDE4qSaBgJI4Oo/MQHIZiMwXy3UJvtFvKhSBxxxJgfvgRthqksZo/O5zWFI - Jg2p9wLGs/DAM9xtliM5THSXkYt6b8GrPIbkqm12HpG5kcJl34zIwJoJxJXHA+hdRGKN1l6fyh2Y - f/wO+a/H4H22ZlYBWe9CEnwoV5Mfz79tlQjpj2vmLaAHJVTMyQ9laZPE08p/YfPgGHHFUAWLRLMK - VI0UEP+2Ocd0nKa3xJYsWfVuHY8z7kZAYziu/FEH08cDFBCpS0lcnI7gp//hbLOUGPPxHi+e17zl - Y8GrmIL9oveDa5dAcEGEdKGwYu57dARIdAETf8fxOl39wf58Tzfh/D3TGoevYAdb0XXWeRPXy7mL - d5Ac7ibejiHL1n46wiZ3cmQk+znrQqDe9z//YH0AZlRO7QQeLs5MkOI4A81vWQhwlmLk084FzNTL - o6zDgYbzyisnu2saKO8+AjI6X4pHRL825ORIxduV19NSKhsYtRcl/D5UnC2R0pTQlKMnWuM9LO4N - anC/iPtwvzeqgSh1cAU5WCrkOvw4THKmQNmWrw/y8C7uwBtvpwRZ3tUkkMxNPeVP6/r3/v3LrICZ - XrkjPPf2GSUrP1t05lTQ8J9OuPeOYrYo0OqBjPUAKTRtY3q1sgg0oZ5jrs5N0ARyS4Go1hEyvL7R - l51muXKtRsHKN42Yg8LrLQtbXsKC+yXewgTswnNobUKi8TJbQuBcIRNajTynQ+eNgfoN4eereOS+ - iV6M3gnwf+8vUFKrx7+8cr++30EoEZ2Y9N2zhIrUcT9exeYlsgTw0x+utozD+OMH23FTo8A4KjX/ - 8dgsb/d+vfKZvYd3/FSAgwtmZBXSzWOn9jIDScYNUdQs1unxWVIIu1NIlLCXvb98fp1nJMj6ZOCu - 168Ft+LL/+l59r5Mr42MBPuGrOy691p+bGb4fR9dXOWHpWYXNeplh1MeKIahBMZ8IBpMDbwJebRF - 9cq7RlAxO1idyMTG5atiOBbxNcxX/Xn55lHx478YxmzQf/0JrHoiFER3xxjkLwk8drcey9G1jGft - oPmyKR+f5BQgGs9DniRwfb+H1PV9nPDza6seQulFuP369wgxp1+Ic75e4pV3UXgelXPY3c6Bzqby - fod+UGVYks7+Gq8nBQt32YT1J2pq2sNPA9KkOhNv9T/dL96rPgrx91V4vEO6fHf3ChA2VX7zWNzQ - Hr4x5rBcakom/p2Pe+sTStlzzMiPz/KJ1eOPFZk17byphR+/91a///HGfQBDsOoD5D/MbT2HyLUg - vHxEVJCsqzG4TDmcKoGs/PjrMT6gidzTyg53q5+iR3eSIGS8SNyP48dC5NgUOoetRdZ6jydVU9+g - /SaUnJ7JgdEKBRgAmjdIM84LWwJXzeVoZ19Q8VgcsPLVN1h598qzNvHf+1n5AwbysgUk+k4+xLfh - Ei5W0+rYcOICjtgiSP1IByDGjdTD74ajyPeJ93tfcpQ/X81DyNw//urnv/OkUXLTY4cmojA823d0 - ZsePTrJMUeCpvl9R0LelPq56VQ6+2YjsQ1rX9EvKCvLlK0batJzisdt3M2j5ghKjfs46EzZSBK/h - 7YOs0EGe8OMVFA4J0sYdGJbXtgulUrBEcgjhbmC1KpXQlnRCvOGoZvSzm1r4z29XwH/968+f//Hb - YdC0j+KzbgyYimX6j/+zVeA/xP8Ym/vn83cbAh7vZfHPv//3DoR/uqFtuul/Tu27+I7//PsP2P3d - a/DP1E73z/97/F/rX/3Xv/4XAAAA//8DAF7yhDXgIAAA + H4sIAAAAAAAAA1R6WQ+zurbk+/kVW/uVPgpTsNlvBAhhdhjC0Gq1gBACCUkYbMBX97+3ku/qdvcL + EmAx2GtV1arl//jXX3/9/S67upr//uevv5/tNP/9P77XrsVc/P3PX//zX3/99ddf//E7/n8j676s + r9f21fyG/262r2u9/v3PX+x/X/m/g/756+9zbtREsYJW2ya7f8iv8pr6Ow0p3fp0KlaitqEhV+16 + uvL8o4CpxuXkoginTmh3mSTTLZUIWkA/jkUkZ5BPiY0UtFkRL+28BfYdZol/MQKNY7buLY/PRCcX + 0XqMS2feJEkIMMKw82OHz/KtlSVRasgFwsaZHPJqYCA6IzLY67lkLw1pYNPVgb9PXHNk5dhZ4KDw + b3I69nKHby3vw9stwyR6xYbDn4zAkH3mc0OGsdoRb24pltejOZOEJ3rJJ/TJQ8hOPar5S98trcDa + 8OClDDEYXgWCmdk6zBMVEFOtg3G7dKopy/uoRCazydHnhkxFHk4kJYZy1hzeV94TLKQ2Rdr7dAcC + vM4Ygq4QiW+NpKOvKBLlzrsR5Bi1H803IWfhAx4+Pgj2BGxRPL5hnQ4OcYNwLAX7ihsQgDT3t6z5 + RJN903gQFqxB6uYedot0EGtZNAKd3IaWAvxpnjrU6kdEborsjrymrrwcyacDUm1FdLDMLC507t2E + EIusjmfriJFDcFZIKcoIjO6ttuFSXxKcXlGl0W689VAotwnLJgUjpaGUwYjKKyoVUSjpySldcJz7 + kz/5Hui2dy1XAKiQIUkDziWfcddaykt4RcHzuhsXBE1e3tfXOzmeMh9sRdmacoh3B2Jl81njunWn + AGQzNTkZ8j1ahWo1INTXHbraIevQhYo2vFqNQ6oklrT3a6lsmGyii4poN4H1UN54+I1HDD98oU35 + rLDynuUZXwizFqwg+tSSep1uSC+wQNf7VDzk5XByiBl4vPaOdm8DBlLmovi0i8opT1MJ4Mu0w/LD + M+iEqLHI2v7FYAakXIlFubbhfIUlSt4oirg1JwVcspJF+nB4dEu3HzfIWU+bKGj7lNs3HmEG2g7F + PWQBRWGOYdLlN3Lp2DelbQFEqN+qEykuz4dD1mnGgOvsCzlxj7Sbe0+vIHQZFctFEziC9J4gSPYL + QNHRbqLPxC+xfOHmHJWEbaNNclgMSdJT4j0VNeJUVu7FcndyfWocl269eucMcg1g0cmqTbAp7qGW + 6eWIyXVNYo31T6YN1/d2QWYtOeMc1tcepi7zQVoYzeN2kZZQDqHnkWTm7JIPbq0krwBq5BSwRbfe + 91YF82o/oBKgjtLYXjH45rsvYLiOtNWOC1RXoST+q2+15frqVdj3YovyCZ0dngd9K/UzDZBJwUPb + pCWY5Plu2sRiE9yt4lFlZKfbPOLxFhnpueUyWY3eKjoy+5tG24JKslgNDXEE0FCaHgYDaujUI0UV + SUSNJKzlz9XdEdOMoLPyryWBptB6SM8V19lusWrKfKxaGGwXC3BGtZfg8pgoqnWR1bDyemTwXgMO + 2VYvlRvL2waMzMYlIWu96IKF8C2d5pOPRX/yO4E7UROkJ+1EtGxSIg6arQ/t4+CRm1DNlB6t7iHX + rJii0LmdR55BESNj3twT47a/lxurrgoQymUi6U62Hb66Bpv8my/XXzvK5gBKMFbOC1LDU9JxrjxO + MFIfA7G+eMl9BE/64Rvy7drR+Isknv/gDxsVLJiWvX2GnvSUyWG/AY0O+B7KIuF9goaoGV+fu1QA + sHx0f41CLRJuu76G3I3pifFGtCT03lfyXoY+CZnV67YuvWcQTI2Jcs4atPmwY1l4x0WLBcdlu23V + jB565BGiwM6sUtCX4wbZXTORfPhklHNmU4EKf7jghbRGtPmV9pY36ZX4jC7GmuDeElvi+kQjyHlu + 5cqXhwqGIFR84D8nyq+uMsjKHmkEaTUFW/9hsfQY9QrpOX111OaqN7zocYjvpsFF2wm3onxh2A+6 + vj51ye6krJAzeFswJeWjw7YlJj9+QZdrmZQ0OSe2pFv6iEVQS+XcHpcMMo2x+fd6foBpiZ9nWHPC + A1nFTnf4QzWb8KaaHQntR1Nu2K1dKM7PmRx311NEuelsA+1y09CByueOZemrgmkUG+TKDoKzJU6l + gO96kcIRcTl/81dmP9aZmLtnBhbOO4Zy6sIPCuwXHNeKbg9o+OoBlfqijlPb+ApMWeIh1xiOJReq + 0iCC/RYTBY4REHLp9oBDZgWolKp3yX7xD6qMmZM0n55g9XdRAobTnOItkUJteWVYhWnTh8i/GKu2 + oD4VJTt5FcSeypPDVrvYhobY5KgUgPLF33slm3opIuQbOaCKHk/QsT0VhW+bcdaoObrAI31I3LeJ + I8xFFYY6v60+iF5gXJ0FsvBNmj3KV+6lUajaC2RNkiAf+6bDHlWfhUrLvfy+iOySXS2sALsVc3Te + NA8IS6WqshzAjRRGKJRr/35lct5Fp/mpCK+OjAm15YlWOsm2yulY93huwH3ral9UliMQAlja8MrK + C3E02ym5/k0yuAAdkWq1ZW1J+KyC33jw15Nwp2xUOgY8TOyI3Lj3S+FwJ6F0Efcn4mXbs+RezDmW + 92esoRM+HEqBOX/O8pC6OgoCBUV8BLla4po9i7lFTsp1f+kHGX0sAWViwDu0Wk8M3IvKhOlypeNc + JG8F3rr7QC73Ngbc8bXa8sxFR/LNd2faSecCML18R35h7iiVb5dYbjQ1/eFXhMU7dWXtctVQ9joM + 2jbMEZQXL7OQdzwfy/XdD6501Y8Q2dgKKbfbFQ3Ud9IBeVP8LFfmkG5QcvwCHS9i4wzMwEgwGPY3 + Yg8cASvbKxN04/cBJcY6lAR7XQuC2VRQHWy+s9oAGpCdYgbFmitQfAQkhl89iGzuygF631pFVl+L + /NWP+siZ9aeBWSDnWOzml4NHyeYBDruAoPg2jetwuy3Q9cMH8bWwoqOQ5APM+Zoj3pWNtU97FAsZ + ro1IjE3IHfbAvBrIg2QjR9mJIvrkrfMffkKdNIP3L748ZGfIPQoWYL/5BotG9jAzSE9tE/afGFJD + vOBXvB/KpWZjUZLh1ULuvXlHm4KYEN4uSYDv4e1El6a4P+AvH06z/6Q09vsW3iqk+J+HGYz8bVxC + GZsV9bvzYd99ZLmVgNdfVRJ374ByXmq1P3wlV4MJnEUJGxEavnIgiTCw46qkowLfnJQg93KvR9wf + bxgSQZkx852P7TiBED44WyJag5JundudAmOtqslZFUm5WuM3XvjaJ9aTl6OVZeEGG2mRiGV7NVje + m7bswdmziVvvGrrkD3+D0IUq+emhtRJeGaRpgpD6lvORHrHykO/rIyDnDyTlWlVuBeOBnnx4J2Cc + 55vtw8YzWGJW+aOkJn0q8k8PHapyoSTuqwHCwvVRUM8PilVeFX96hHhTl3Vrmsg99NrlgFx1K7TZ + zX0M30jf+Tuh8oAgeGkPC5cPiOMKd20LQqhAF8ABxVuaRKxRTr0UHkuBWEVZ0uXSkBYkhhX5W8MD + h5pKxOyLs6pgtsApmK4vrALtvqmY8towYpZXDfhgQpsomWJ0nJqHA/zDNzWXlMuotLE86qKNbrfT + Vi4DSgr40y/KB6JyOWlMLbk3tPps3XfagoXiDYuHCZAyXRMwhpeFgbpmWeT8kJpu++ID/OIFUaUB + adjXrw2ghnRBehUjZwH8av70sC8GXuJgackxxCht0W++uA7bLlR9b0N2GBy09b4/VPJPD9qAf0eL + 9oAuTB9BRJKilbv19Hkv8IeP9ukYgq185ypkL7sWHa6SVa57SXvLxyCyfvxHv/+X7WcFn31ScUyJ + qbec5XQuOmT1xbGbmVTBcsdcOqLtxBiQV1RK0JhQQY7Xko+2W2zb4LkcXeIxZ+xshXA05G/9h3FL + jY6duE4Fd5y1KJP1oKOSEplyeyQucXrJLHnpdXtD/3rOiHLMebp+8RFoZfZEafLitbVIGhUyiRKj + W6pQummj84ZNaoWYZ0Kp++JlBn1bH9DBm6ZyqnaxKU/uMCOkIWXkL8ysw8OxUZH9OenaFu2VQf6u + t9+m8zzSz8VpAdPv7lha/bZbnnhqwLf+I86Xj4W5Sh+Q7RBG7oN1yo3dMxIs99ZAjsmJRliUExsq + T90kVefHf/QvbGHTkmPuahpXvjQMg5wJ8Z7XpWhWV3OBr9eS4E29HsCHNHse3qZBwo/TjpYUWbdB + +vIXcqwtKJeB0kI+uNnqD9yVo3RygAi54aATf7uW0RyiTwtu7eOKWXxO6ZqaVgv3MuMjdwIfsByk + bZKErGfIQdm2chPl2oR8sL6Rdj7sx62KUwzCzVzQ8aHBaC2KigVb4VTkQPjQWX71RZ/mHDkHL0qx + W1QqhKk8EyTWkG6MOhvySboUPk/Le0Qb++zD1AO8vwBR1Ijjb/W+mM8OShozphz34RrILdkd3Squ + LmnHBm/5uZxcpCe3x0jI6xDCZsFXZO4XPK6gyRKg8NoFXzE3jXOlfFpoGu8c3cS6opxzCST50nYj + OtJaHheWkuoPf10B6UfS8jUPe0k/o+Srz6nCvR+QKF5KrG0Rosm7QB/ErPBEfpo9ta2+HQp4/TBH + dHi0pKQN+3LlL18h5O+56Mef+2aZrsh5kw3gY5DF8J4djD/4QXMtwPL3HO/r6KXNj1OjQLY934kd + 7VzKz+mlgvpw25B2olW51NkK4ePKnpHFWbbG5cpOh1JdmAQNLaX4fbYK+fHmAAqt9QGoXDaNbGTG + zWet5zDODTAGeGsuEKkXt48WBBVWZszFRL67C8dNycQYHrq5RAYaH+XUBK8G5rvH0Yec+i7XTzkw + cKyZF0GzBiI62PoZsjd9w7KhNWB711wFm7nFX32EI3oiogvfnJgQqzPUke0mKEqnSFlRtthpx0vd + wEKTiya8/b4nI74Ca1E/IvVt185aqniCFz0J8d72GEoen7CAxaBfkSaFMqV2vk6/+fEDnObdyvnF + A3z1IMqz+exsgRAo8n3tA3JwHB0sOynLoLHLTWQku1dJr/ZzgVMu7TGrLY22XmpTgUlYhciI56gU + BvwJwbj1Z+Ltr2HHL7tggtRbdX/zji+6eNYxgcJu//rq8bajn/27BefOhshmUkQ3vnEe4Ok0Cfr5 + RTjsUvxbX1R8TrojsAlxYd4cc5/rL+do/OXzs7p2WBB4pxtjqjXy1IQJUaL9iQqFGBawZiBL0Jt9 + a6tiQ1sKHsqCKstx6VcfbfBR0ytxAoMt12Mt6HD2mjs5N6ensyrUg8BK4QmFFam6hdweDXxJjI4u + IHkDvN9qCbp4qtEZjpSuZn1voRdFMfrW493arYIqnV16Q8fuMJdLXeENVOHEoctttqNNj64NnIPx + 6u8ntGjzT98kU0OQwRM9+uafKPvyFOK1nnUq7ESqw6E2KAaEbculeZ1r+UTrEYNeekez46bszz9D + 5qNRwWaCYIN+7Jh4tV+w+9aHtbx4hUXU22HpqHfNGDid6QeZZW+On7O3ivK+vt0xLfePcb0hU5WF + 220mh9ubc5b8YSzQKj8m0o/zrtxUfafC5PlWUMJez9FsXl4VlPdBSXz+YozL5ag/IFsCHZnDZdY2 + BpUMdMt28Z98N0cktGxj35IwQX6wYe33POmL98hnqqe2Drd0gesN2cg/62rE5c9Yh9p7WL/61u22 + +2Vd5Gk2Xr6UXWrw+x/Azf5GDlGolYL5WKD41WfIGGOVsorVszCqxhPy59LttvxKYukdnjE5rstK + F1FRWOisSo5UeNuP6z5YWUhtXSPeeLfGX/zAisiKTyXe0OYkMjeYlfoHqYrjgYV22gbnt7L/+imV + Nvu7KIazf8PE+fqbSyk/GnipC+Lvn/y1HOnQtPC18izG1oi6P89vYdtiUUAnh3v3gw+/eogYqV90 + LBxX8ccfPtCXdtzQPvHlU2UfiKb3mbPON9X9+T1IhzsYvSpJVaAVLznynmDrloBlsp9/+K0/9xS/ + x0cMzfehIephvx/nUN3eIBZ0gyhi1YxLldoJCPDRI+GIvBF/8xNCMX9h2qBkXF73Rof9xOokXwMG + zOO16KXv9/vSF4+3Kr5hqAb5nfz0/TZFzSD/1ts+3EpA5+rWA89yeV+UusFZa671ZSvecr+KlFQT + wjUS4eV2gch0nVdJkdS14HTcnr94KDnxaDM/viKxKj9GLC0B/ukRkn+wOU5K+JYg/ggSOWXeHHFP + 2GAI8rggtrYisGRRZ4A4ebt+w8gnilVv1aFUOS9yZPY7bdvjVge5uRCknEYb8Cc8SNCeGB7zuzXS + 1okXExhycU3M3sfjEvqxC7e+Kkkqfl7RxgazLgUvtybubvC1hX3QAlSSViPPpOX4YeyAh4GLDWSd + +2Ccpjc04SOVMKYnWkWLHDsbaDNJIj8/dXJhnUFpHGL0y8dN5ngb/Pw3fyGoW9t7Ge8vm5Ihq2Fx + R9xbYkITeD6yussF0BFk5o+fkEaGlM5ZvGTy1UMuUWkyOd//ieGxKHJ/P8uA/hm/t+OenA7pUFKi + TiE8GDeEs/Vz1hb7prEwq88pSVJf6tZIADGUbukFN6Vn0+kZHR7wq0+JslxUZ5bzowi//IKZr776 + +vcV/NZzSMMCjJbu4i+QNJ+AmG9t78xqXrwBw1c+CVooOvOgQR5uUTij46IaDuvNQww8FwjEtfdi + RwBXsjAFT4icrz//slWnBz9/9vT128iXL+DwqM4I5feypDF1GjiIT8dn3F3Y4RgfCpi9Y47okXMY + aT6OLTh9Co8cqH3RNsuRG0lvygv61mcRQWE+ga/f8T3Xy7m5jRjsD15Cotk6j9TyVBZO3S4lFq8X + 0Xa9Gw95WeEO78eKjcYjNh/g/oxC9KdeYvOchz1rY6L3kKVr+XKwaMGT6oPDuNEZk1mEVNgsFHJC + 1C0K1ljIs7WGd4xOAfn5QWKzVsg4hisYv/6I+PI9h+jmRrQ1i8UMZvC6EH//scYNL8AHRSIRpL6A + PVJVOLvyuKTEZ/afTzezV2OA9fbh0aHg9t3crYICze3IYPbpf7SN15sB7uNS93GwYYfmJ6OBR4u5 + odN2ZsdVXqAK623k//Qf8Hf+wBdvkKkFs4OJHShyQqYrCc+5Dfij/qlAlT8f5Ks3O/yJeHcP8cUi + Xv5UAGUm3f35C6iw+qJcBufTw+zinHyxegvjGgQ9Bh4l/p/6bbUMUIDPK7ri3a5zy0f9fPOgpWWI + 0DXvO7oMvCGDqTUxK3d6xx5O97fsuXsBr6JOShqkjA07NjL87quftvfZyuBOiFXy8wenlyPYsKGi + Q86//iH9lC40LfaEUkE7Oxs32uz+u37ILyZTmw4MaWCV5DxmBXulVJn6FtB3VBFFsqdyent4g2W6 + deiAGCXiEtxNctQpD6IAUXTmXSG3IKK7FZ1ykP/6QSYgVvIi5r2MtDWJlA0m/dMjtsDIDp4fegzz + KUvJqcbxyCEkGL/7SMtMUXvoTMDIxyLL0emKoDPyWcLC7KC4eGjxGq2hug1yKrNXlLmFNM7d+uX7 + PD34i1yjaP3yPThPZ5cgRZ46PBrrBr9+qh9KA9FqawlbGNi3J5a7YtRWyZAeQMXj5Iu7p0jp4f46 + Q/3q9XgRq6Zbodm6spHpNxLE8RZRFuguXIL78afnI7ZRaf3zT9DF8/JykZvtDYmCUuIoYhr96X99 + 9bPfyanXfft1BWx9vsLsoXadlT3fNvD1Z3z5PPZ0ETVuADNTXIg1q+P4OR21Rf7Wk35zkmqHfT9j + Ufz2L/32sM/H7/sGKHF4wzSblJL7+jVAK4unDxc0gfnbL4bVU+rwa/aPdPkU1zfMDqqLfn4nMZgp + BF+/DSnRZdfRSrJVmHp7HpU1/VAcQflPPUBOwfNVLmndxvKQ+rq/XVtRW+Xck+A3Polqhm7EYmTy + cCWjTqyjs2jkeA8mIHHTRm6X0NS2hM48YFS2R+anXbvNlg+ZfO2XFJXffo6gdYkIHod7SHxuZSJ6 + 3wYVnmeUY3q0lXIZK+8MX+Ut9Xdf/Tw9QVTAWAkXpHrHE2DlRhrgUOsUmU7mdItmBWf56/8g/Z5c + AS1NxoS7hx37A9sex8UZNwx32lSg+C71GlkiBAEaiwxZDLlrmBNWLCtqOSOfHrruW4+1sJ/X4Fev + R6QfbQV855+Y9nPRfvrkv+pt30UOx7+WGFpvECOXC8BIy0D0pTDA/Lc/IAJqJEUFv/0MovfzYVwM + X57g379dAf/5r7/++l+/HQb9+1o/vxsD5nqd//3fWwX+Lfx76ovn8882BDwVTf33P/+1A+Hvz/ju + P/P/nt+P+jX9/c9fQPyz1+Dv+T0Xz//3+r++r/rPf/0fAAAA//8DADS3AQXgIAAA headers: Access-Control-Allow-Origin: - "*" Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9aad0b7c9459-SJC + - 9854a46478d4cf13-SJC Connection: - keep-alive Content-Encoding: @@ -3246,13 +3248,13 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:21:58 GMT + - Fri, 26 Sep 2025 17:57:10 GMT Server: - cloudflare Transfer-Encoding: - chunked Via: - - envoy-router-5ddb44fcc-9cl2p + - envoy-router-7cbf67d78d-m5wcw X-Content-Type-Options: - nosniff alt-svc: @@ -3264,7 +3266,7 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "73" + - "290" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: @@ -3272,7 +3274,7 @@ interactions: strict-transport-security: - max-age=31536000; includeSubDomains; preload x-envoy-upstream-service-time: - - "110" + - "353" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3282,13 +3284,13 @@ interactions: x-ratelimit-remaining-requests: - "199999" x-ratelimit-remaining-tokens: - - "199999933" + - "199999930" x-ratelimit-reset-requests: - 0s x-ratelimit-reset-tokens: - 0s x-request-id: - - req_4067d5cedc2b429bbca59a801843500b + - req_3659b54c406140549232e0171623336a status: code: 200 message: OK @@ -3334,122 +3336,122 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//VJpZD7pKu+Xv30+xs2/pNyIgVbXvmETmYhY7nQ4gDigyF1An57t39H9y - uvvGRMSINaxnrd9T//Gvv/76uy3qqpz+/uevv9/Pcfr7f3yvXfMp//ufv/7nv/7666+//uP3+v/d - WTVFdb0+P/ff7b8Pn59rtf79z1/sf1/5vzf989ffXjSU8/40+kXPy+YdVg/T8J6mtYDWNIsQ5lJt - z/v7aa+OjNfHEEx0JceGlHQd5rOC8mgvk1hUkmINdNVC3PtyxJampvbSelUKdl1OyGmUHsVkesiD - UyoAfJ1tD+yvnLhAwZg1fFT4EOzZfGKgDoE1Nx55RJuWoxy23tPDiuCSYfPTlwZDRbKwUm89WD/A - 14BrTgfvMMbTsD1h4KNMiy/k5iVStOckdYFv9lPP9fCs6GrvLgwkxDt4E9h3gF4j/wlT/S4Sg9Gd - iNtNfo5g7RfYPyU9GKnql0gpDIvEQTLRTQ6WEnbJKOMMVK9oc5XWAKVCa6JrF6vgXupRhFv54PFV - sdRiKnZdDs6rUs+M73v1SrSgResSY3Ll4qVYhZs+ogvUAhJJaaAunXqO4eMTlCQ+DeGwPy9yiw6y - JJKjrLXDOlWAg58xKXD6TIyCC2paIavMAK64DwPo/lAq8KLyAtbvRgj4xTqkUHqsb1LKJI/Ya88p - sJ7EgNjd/FA3k3/d/3wfMyujzm/atxByrwBH07UGVOAlD4XlvcO3upQoG1yLBWYn3SRGWe7UWZci - D+htFczz8NIGvtBlC0XeKpBwC9SB38CHheHz5ZMgulk2W77kCvWipRIPneWIf22+gmxT98jxNC4R - Dz7cAvG1YYhWs01B+61JEWv0Llbsc1MsrHDJkXAILaLv7JvKZa6oAOKfEdZOwzYsFr16QHyXkMTd - EkaUUa4veOD7hkRHvh+2eZ0MqIfZgBPot3R5hqMDDhfWJ9kG+IHWudyjttgqYuf1GG1BDSrwcvY9 - PsmbpK7UEDOw3M86sVFs2Jy6gAzs6AETyeo8Ol3lAwszz9+I/A51wC7+4EP3vdeJQT5RTbngWSH1 - Tnz8+/+rxEo6ajtpJrFz96OtPTU92vMNIeZUFHTJi9QH246fZmRFL8B26zADzpHbGdyyDmw35q1D - veFf8+7DRyrvBycFCvH5Q06kScDyEJMNqQZzJ1q5RcXEZ7khrinccDxVcs0bQyggfBf2uBgeQ7Tf - ax8INoudsTS4MV1CV3gB4V6x+DceVJHjDOG6fROvOxN1OvCJDp1z2eOkYGixaQ/fQac0/HgjYB2b - q9cwRLoeVLg4dY+CN5FeooXLRqweeWugqjds4HIBFba9sRhG9GpTAE+mh8uPcQVbctQ50LFDjo/m - QVf3+CNZSKidFz7rbhjNn3DqoXViFGLK+VLw8Sa9YABgjf1FCQc+ZX0G1X6vzCCZZ7C6XJ3C8yrV - c+udg6EzPeTAEyOn3jI/BNAURzuH7tyWOBGel4E/5J9MTDt2wXjHH4uND/caZDHHYbNtqoLQiAmh - 37k9LveOVlD/eefQgZMPJLSCnU2PBTVQ095uxDOoaW8e3+nQDypAHLexbe6gBiJg5tTEphvI6sTN - mQG3IWexZD6lYZ9/IgmUvkqJOjwZumJ1W5DHus+Z/Rh9sWKBjsDy+f3cg80fyPIZRORUXYQ9wH2G - FQHEQdE8E4yHtxLtP0uggzgjBbHwsbO/69+DfkUsYsPWsdmjJt5h90hexJnUJ6VHYZoh/9Qzj494 - NxrR5RHDyM8bLCl3t1hufsT+9B0X3HhX+R1aU6gt84VorN7VSxmSFlaLcvSYIpTr5dozEowWOOL4 - 8fHp/j0bs7CfdDzTLFWLLZAaHSEjyEnBHm/RYjGlBy+TG2O/k27FJD3nEOSPY0BsHb2L+bv+kc0c - XVISVBS/eoCIDVRsA+oVe5w0Kfz+XyzjVaTL3Dx8pLKyTkyGNnYHa1JBeLI9bDCbUXz1Z4Md55+w - /XJPA3/f9Rx80duZuFakUf4qMyN4PpwdxpCJAP/CVgtHLt7jOHrWEWWMJYeTUioeHc9wmE5E9OD+ - LHUk3rs82IQTp8Pjyr7JFexNwPFXqIn3KTmQ+FLhguafs4CCIr9i6YHe9jaGvQeXSOe8oVOO6nIZ - BQ0+1bzD0uvt2tSaBh+yiXglSds/ABtIDwcNIMRYRUSnZIcOMayYyxkHO1Giy054+ch4DSp2GhnX - Xa8l+sF57CxseOe13j4Xo4K8tzywNAPN5hFoIdQv73hGDxcDHup3BV6N0JlfxfAC+1JPFXh0YYtj - lF4ianr3GMG4qLAC9Ic6i0LOikshOETVNKlY6httkFqGKT5pUglYK+1b2PBpSbzLcVT78CQJ6Fwv - EVFC1qZ7S2UceAzCG9YE8LGnLFdbmErX2CvkcqDrUNYlSo7ZTOT5mdZbcx17mBRlTLLkogFWgKIB - g3mMcHkbsoKzWzlGSGUfJMZsrP4ZL+Z2c7xVOd3AQoSSg5ekensj7DpKjz60wO0ta+TEEz6ix530 - hOEd1cQ5fNKBqkOaopY+PHJ+po26XMznjLaoPeKcC3iV2s8xBDC+VN7hq09UiU4QjlVzxFfhoavL - vLYh+gAn84S7NUf0dWccoDClhC8bTKL1XBEOZEHTEanCe0qji+gB0/YJOQ+2OlAxtR24+WKCzSRX - avZ5enuoPnMDOcY9qSmNOB9ek3tJ8tVq6bY6KkS/9Wy9d7G9JtMaos58sSQE+sNeMA02lKfNSrzy - sqjLObzECDO2RmRwrKNtFf0UYFW74bLwdXu9YK2HXSn2WJKm2qavO+eg0m4OxM3fHzqqSm/BEROD - GDbphq0vfA518SnG2guwtHO5OkZ16jLklLuvgZJkiRGpB4qVd6TVW3iSRNE9GBD/9Kq9Ma0D81t2 - IpKYmcO+MpoMWWH5xBd+3ShtgkiCO3AUsHRKLLqdw9QBZ7+6fuunBrjuhiSoKYI672XNGHhs5RVM - n+2JVF78HJZsjX2o3iR9XqzOA/ODmAL07UtMHEylgcv6sQF2mlZYzohoz6SZDdCkW+rBnbHYsyiE - LMRplmBz8y/RuH9GOeBLZSWG+WTttShjBQoJjHFVNe9hi2I/RNe7BLFXy2dA23cFIX9ENk5tebO3 - ewqe8BTdeaJ/OBms94NtwftecrH91cc/35erip/Hj4HA/FJdEQqn45kc0+uB0qcMQrGvFI6osjLZ - 76/eAT2xinlBUjDMwonRoKD0FcZKRerZPkAB1japsP5MjGiD4uDA87nT8ZGEZs2S5PTHbxK7kVZK - z/I9Q/wlkL1DHBcRu2uEEaZs4s/E7bSC3q1OhAmUco9lSmmgQ20bUHnJiLgz31MiO0UG6fb64JOc - sXQ0bkEIry0XerRt9YKA+dKIMfdYyXF45RHdXHlESMEsMS/7GGwv8z7DB0oUchSeh2Fm7qYCXXZU - sTtpUr0EBKdA3b0c7FedrP78F4SmwJDSOK7D4kIpQ+hsnedduGz1Kry7JyysaiJKne2G1eDmBfzG - 41tPIi5J2x4+yg3Obn+91TN6TR4w6F0il55GgD9O8yiGH9kk2Edt1F8KM4Ps59N44svpvutJFtB0 - mwPi+B5VqV96iqhjjSXOKkgRhXqrgOHDXIipgw6sXMmlsHIMA0eCaBZcmMESPlrBxAHDXuqFl6sM - jjAriLVyGuBHcLmDDlULOR31l01/43tFke3dL8apHj/hfUF8Ka04uYq3ghS6bCAUaCuxHIHWC/8a - FghKvSXY+gTqIofHGN7cMCbmg+GKTVU+CmiNqp0XozwU9FsfwTdPYHl339ejADcDJW+GxXr1uajL - YaUWEuLk4yUFE0UrszAsvC/dQjRrlw4d2fgN4nhtidulQbQMLdLhi0hXEnuyCVYVuRI8XDgf26Kk - qUu1+hyyW/VDDJoogDskbiV6l0nB52YVCqKtq4Ue4jskLkrVgaV3VUIq5Mp58XeNuj70/AXt6+Pk - gW6WbRbKIYOY1q8xPvSEkq1CITSDNCVWMAX2bOYHBp665krk1WLqaad1OtgyncHWyr3ANhxzDna9 - IZLjtC42jVXWQYbvWV4LtIu9LM+UhaUXd+T2qnExDYdKgvsl00lShOfoTz2t81cx74N0GH7+HzjT - 08cWkwv1ON/1CuaPU4CtCxfWaxEkCoz2xdsjDzxEk727QPDNS9iUoqc6t55cwqmED3we7Hr4o4ed - 2bDe1uQb/ek5jORHR9Qz5OlyPoUpmm948WZT5ItN8HwRfv0kVlB8pasfWJZAQKwTK5ETdcNjr/+p - J9fIUCLylIEPxp1lY1Uxi2Id9XeFtvfZm1kSDGDpn9IT7Zk1w+FU6vZqILOB88uKyekWh4ATp4KF - dB+f521SRLBZ1IghlmKCzflRq0Q8ZA2SxUYj1r3K6+lRHw0Yv7gWW6Qj6mLRxIELXnY4o8mTLr/5 - bNTyPv/0Y51x1sK3vrpENe152Jx9NkNvz2HiQihEQ/2WWuQWaYOtpJAK7p0KGnTnvsQ2s3/UnK8t - d+TIF83b3x9SwZ/B8Y6q4vTApws9RGvo8DMIqO2Srx4XdP9pS6S8VITlUmXU6eS8ONihcsFfP0JZ - 3SY6rG9qTPTkbBac+oYZVMhHxcrVqtWBMZYM1MB4eB8YvqIV7s0Qvhy+n3nI3Au61woNvs07IrbJ - M/YaRuETXoqnPe9Okasuv/r49Z/4bCuEzhcFKKJ1GWxvb9CKLj0DdGCcc4qVx06q2VsRsjBVNBnn - xlUu2Og2WjB3aT7zcbgD255Zdagt4wXHkZGpe4XzK8Rejzq2z7iNlvHKaJDreIbgUEtrnuHTJ/zV - d/vQCOBdsM8cnUzl7DF00u21HoQUZiJzxFqdA5WMmWPAt5LG33x9t2d02lr4HJcjviQSH1FppzLg - q5ceU8s8IPSdabAegxdOvryIsqPTQrCvjjM6tGxBLeHpQ3iIZVKqkADasPccyCrMcJSldbSZ8c2A - Irg/SZjIck0PBmXBoKqcx3z5xV6a3j78zu8sfv0GVV7dApJjPs+CY0F7qZqAQ3RBosdWu5ZOH5Bp - wGLN4Tv/u2gs2qkHYF8ecdWotsp3aEqh+K6gx1zFrv6Tx7JPj7GVP+dhnbHf/+FPckg9lT70WwUK - OMGZLQYN7PVnz0F6wZa31AsppvtBNcTmWRyIdqlw9PUTEtSqxxubsFm+9SDN4M048ER6oKPKs6PW - /+op/vo9lYy6/0Kz2ng4Vv17QX/52B9Qjm9Jltc0GqYNJuTgfv3OvV7YRJN+PMYTdM9Xt68/AAxo - LvNvPoYK5SW0yLxi+S6A+psHOVh3NevNZMfR9YKdHnzzlSe8hxPYmBU2wtcv4LDeLEDnPL7/8gjB - m2tFS89QDR7fU4a9IpSHDYaHFnbzm87TC6vqyruRcTirnos1qbvVC6zcJzwqlyf++mNAu/u2IADb - GWtMeR9GY9VY9F3PP75IaZJ8Nii6o+PxDyiCV87UPcKqfiN2lnJg+eXbI9skc9WdiU0dZw7/7N+z - +rYicsO6DvfqTLHcn7SCK0QthSxmOZxWn4O9DWc9gxfDSbCERnfYOlWpkH9+Pua1vHYR/eVFzbju - 8BHvVzrHZhCCOZNPWB3PsJ6uy5kRPwEHPVbqdgM9ILgcWhi/cAyxbbO2PbTwfvxYRBEO9rB8XnGM - ksm+Yid/WfYvfyJ+n3vYDEZabG+maxGcAcLHhkDQOycnBQILJqzqdVMvHxLFf3iG8+UtVD8iA07s - FHjsj0ctZwvCWXi7RC2Ph2Frrq8ebhY3e6JxfUT7gu0z2ObmSkwdu5SuwSSAL2/xmG+eoyzONfDN - D95QMVW9wvpTwXWSMpIxWxstwmn1kL0PeI+mpjvQjOtSOJntgI3BeRQ0fB0cyJzmq7dbH3O0YF/L - 0Dc/EQXFiM65+WkOh8nQfzw0mllzsdArgzz2/V1j0/xzEwDf1AE+fa5ssX3zKMxOmklwr7WAyqTd - 4G/+fzxu8+JoRHA+IPLNU8OafNISAtjPWOJ6MaLeZd//Wa/fPFRsnWDnsLk8daKTXR3RNLBfAL2y - D3a9+ayu9vmeQwNeti+vOEaNZF97kLeBSOTDDtpfPyjCbx6daRUSe12DToJhEks4Mm2v5l9Y6dHK - 3wVipeytWIMnmkGcxwa5fPnT13+xoAz6+4wIc1GplcQi/NZLfMpdbeDanlVAPUYvckqyj8qHZPHg - kM/mn/lfE6sWobskDZaC4WlT9wVbWIHhQ7TqjeqhBIoOSb5IxJz2H3URLbkBuZk+iONNl2LjHOMO - 045biHusPtGqRLb+x4/qjLYAYhjuHZZx7hMjGJJ6+/kpC+5kbw3pbA/2cR2RpHAOPu69/UDaJYG/ - 8Z65IkrU+dRfDTjdxgA7hd+oVD7nOawnIcBXa3cHW5EyEIKnhbGpfo70y0d0qOtRNS/88hjGg/de - wMF5SeTqAwdwYnH3oRLGLCm//ns6decMGv3Y4firF8tZ4p8wvaU+wbj0ag4/Bg1+/SyRXLTR1RvG - FDw46+ItX/61grkpIbc+Q4/NzrJNjCEXwM7gOu+SzfpA8OYo4CV7xrc/4db7Ccw5DPWbOsPh2alL - vRIDPoxU9bZEftSLU4sSHAKBYnVSs4Feo+wOO32vEb3XmHp4Olt4iA/Rg3x5qL0YUOVgsWo5Dqai - LVZ7FzBQzYfuW1+8+o+fEbfJ+er3Yq87Rncg2zkRTj6mof7080BAqnto0qSBn9yjBavi+CB2tjzt - dYogB7/1DWsEnej2KkofOtUQzQeu14bptb0EEIgJR5TUIGCydwEE7YM7YnmDD7BVt6eFjtnVmGm5 - JgNFbvkCjGOn3/7BzabM+8nAZg05rNPoVKzc7Fso74TPDKbr+cs/PhxEzEHHyqLN6vabv/qtA4zL - 3AWz219meJlwTJSpbFQK9bsE4jw1iJesrbpNp8EBv3z80w9ac6wH9y3ZvGXnftQxuxwF+Dp2MlG2 - oB428Iw5oDuJhh2CimjbZA3CuHbBfGx7GdBzlUOYZWv4zbP3euNl8wnuRbsQGU12sX75JHQcaOHT - KMnFuPh1iMyja+Lw1J3Vj7nhCgZLcf2vfkDQXbPf/sexN73BWmHZQ/gw29gwuMzefn4IH0Ybh6mB - 6SZOEYd+/v6rX7T/5bFvvwyb/LqBUbiMC+fYyZVcx0wbWMDad1j6MsWy8hjsyXEeGZI/vkds9+1G - bOulKXAPFiTeKcTR6NSbhOZG0Mjtge2Ijz3giSl/kmbh+7w8sDUP3gzA41M82t/nE3vw8/cOU7Lq - GBAci+ginLw1jj+AuIT2wFSul/kd1Q918J93Fr6DYCL2t38x3HdPDqDk0WO5ofdoYvigRzfr/iLG - 7sDV9F3mivit119+4aqLdGBEcOX05yzIl62maRcw0M/DYF7IR1LZ+gYa+OPtLi85BacuNIM8XR/Y - /vJpgtVdD5r2esOnb17cPP6hIZPtHY+uAqeSmoMe/L4n+mAdwT7QVQOVLJNi2U7bYXu0RAddKfQk - 2yMLbEr2VhA9PeWZOd7e6rjQpf3xtVnIJoMSa6rDX/+IGO3OpnNzrHzAvO+UZKwl1V99lg7f/EQU - PgsAyztJBg3LqomZKdZA0ydvCLuJb7G1LNdhfSdbDP3kwBLLb55g/fnrndxLHlyrvbocvGmDHlla - rEVGZs963TbiI9o18wU2fr2lbez94XWGY0F16reu+uVbD1RMNazefYr/6M+RUSN1/e5v+NV77xSH - N/qHN8Crx87iNy/tz4vZQjOIU1y5Hii27bq7Q7OWbzMUZfkPj0O//oK+fYbo57/APOsT0Ut2plvM - hA6YemfFbtsnxdb2UAKczzi//VeM8tVJwbf/6B3ufKuytWpKgOXKGtuCCOnPX/zpb/z8N5263Qw/ - wMs84bt/OArvM7q0W0HUbx5kDw00fvz6uz7GaIoFIRTBemznfV0IYElTdAf+a1vxKeKnaFnFyYGT - 2Q/Y/lBoL5/wvqHDZOlYE+e1mH987Ld/tep9rTc1NjgIMfOa0d5V7B+fg8RPENYgHuzt+bJE+Osn - eg05qZvgZQL88v8vD40Bn3GPGB0YHhO3v+6GxX/wzE9/PPic7Joz47MFv3kOq7tnYK8BcHRR2QQR - +45lDMsoq0+08k8Ba3RkbDpYcIHehSgzqBim3lK762HqknpmjscDWOFe9tHfv1MB//mvv/76X78T - Bk17rd7fgwFTtU7//u+jAv/m/z02+fv95xjCPOb36u9//usEwt/d0Dbd9L+n9lV9xr//+evw56jB - 31M75e//5/K/vj/0n//6PwAAAP//AwCh+t4M3iAAAA== + H4sIAAAAAAAAA1SaWQ+6Srvl799PsbNv6TciIFW175hE5mIWO50OIA4oMhdQJ+e7d/R/crr7xkTE + iDWsZ63fU//xr7/++rst6qqc/v7nr7/fz3H6+398r13zKf/7n7/+57/++uuvv/7j9/r/3Vk1RXW9 + Pj/33+2/D5+fa7X+/c9f7H9f+b83/fPX3140lPP+NPpFz8vmHVYP0/CeprWA1jSLEOZSbc/7+2mv + jozXxxBMdCXHhpR0HeazgvJoL5NYVJJiDXTVQtz7csSWpqb20npVCnZdTshplB7FZHrIg1MqAHyd + bQ/sr5y4QMGYNXxU+BDs2XxioA6BNTceeUSblqMctt7Tw4rgkmHz05cGQ0WysFJvPVg/wNeAa04H + 7zDG07A9YeCjTIsv5OYlUrTnJHWBb/ZTz/XwrOhq7y4MJMQ7eBPYd4BeI/8JU/0uEoPRnYjbTX6O + YO0X2D8lPRip6pdIKQyLxEEy0U0OlhJ2ySjjDFSvaHOV1gClQmuiaxer4F7qUYRb+eDxVbHUYip2 + XQ7Oq1LPjO979Uq0oEXrEmNy5eKlWIWbPqIL1AISSWmgLp16juHjE5QkPg3hsD8vcosOsiSSo6y1 + wzpVgIOfMSlw+kyMggtqWiGrzACuuA8D6P5QKvCi8gLW70YI+MU6pFB6rG9SyiSP2GvPKbCexIDY + 3fxQN5N/3f98HzMro85v2rcQcq8AR9O1BlTgJQ+F5b3Dt7qUKBtciwVmJ90kRlnu1FmXIg/obRXM + 8/DSBr7QZQtF3iqQcAvUgd/Ah4Xh8+WTILpZNlu+5Ar1oqUSD53liH9tvoJsU/fI8TQuEQ8+3ALx + tWGIVrNNQfutSRFr9C5W7HNTLKxwyZFwCC2i7+ybymWuqADinxHWTsM2LBa9ekB8l5DE3RJGlFGu + L3jg+4ZER74ftnmdDKiH2YAT6Ld0eYajAw4X1ifZBviB1rnco7bYKmLn9RhtQQ0q8HL2PT7Jm6Su + 1BAzsNzPOrFRbNicuoAM7OgBE8nqPDpd5QMLM8/fiPwOdcAu/uBD973XiUE+UU254Fkh9U58/Pv/ + q8RKOmo7aSaxc/ejrT01PdrzDSHmVBR0yYvUB9uOn2ZkRS/AduswA86R2xncsg5sN+atQ73hX/Pu + w0cq7wcnBQrx+UNOpEnA8hCTDakGcydauUXFxGe5Ia4p3HA8VXLNG0MoIHwX9rgYHkO032sfCDaL + nbE0uDFdQld4AeFesfg3HlSR4wzhun0TrzsTdTrwiQ6dc9njpGBosWkP30GnNPx4I2Adm6vXMES6 + HlS4OHWPgjeRXqKFy0asHnlroKo3bOByARW2vbEYRvRqUwBPpofLj3EFW3LUOdCxQ46P5kFX9/gj + WUionRc+624YzZ9w6qF1YhRiyvlS8PEmvWAAYI39RQkHPmV9BtV+r8wgmWewulydwvMq1XPrnYOh + Mz3kwBMjp94yPwTQFEc7h+7cljgRnpeBP+SfTEw7dsF4xx+LjQ/3GmQxx2GzbaqC0IgJod+5PS73 + jlZQ/3nn0IGTDyS0gp1NjwU1UNPebsQzqGlvHt/p0A8qQBy3sW3uoAYiYObUxKYbyOrEzZkBtyFn + sWQ+pWGffyIJlL5KiTo8GbpidVuQx7rPmf0YfbFigY7A8vn93IPNH8jyGUTkVF2EPcB9hhUBxEHR + PBOMh7cS7T9LoIM4IwWx8LGzv+vfg35FLGLD1rHZoybeYfdIXsSZ1CelR2GaIf/UM4+PeDca0eUR + w8jPGywpd7dYbn7E/vQdF9x4V/kdWlOoLfOFaKze1UsZkhZWi3L0mCKU6+XaMxKMFjji+PHx6f49 + G7Own3Q80yxViy2QGh0hI8hJwR5v0WIxpQcvkxtjv5NuxSQ95xDkj2NAbB29i/m7/pHNHF1SElQU + v3qAiA1UbAPqFXucNCn8/l8s41Wky9w8fKSysk5MhjZ2B2tSQXiyPWwwm1F89WeDHeefsP1yTwN/ + 3/UcfNHbmbhWpFH+KjMjeD6cHcaQiQD/wlYLRy7e4zh61hFljCWHk1IqHh3PcJhORPTg/ix1JN67 + PNiEE6fD48q+yRXsTcDxV6iJ9yk5kPhS4YLmn7OAgiK/YumB3vY2hr0Hl0jnvKFTjupyGQUNPtW8 + w9Lr7drUmgYfsol4JUnbPwAbSA8HDSDEWEVEp2SHDjGsmMsZBztRostOePnIeA0qdhoZ112vJfrB + eewsbHjntd4+F6OCvLc8sDQDzeYRaCHUL+94Rg8XAx7qdwVejdCZX8XwAvtSTxV4dGGLY5ReImp6 + 9xjBuKiwAvSHOotCzopLIThE1TSpWOobbZBahik+aVIJWCvtW9jwaUm8y3FU+/AkCehcLxFRQtam + e0tlHHgMwhvWBPCxpyxXW5hK19gr5HKg61DWJUqO2Uzk+ZnWW3Mde5gUZUyy5KIBVoCiAYN5jHB5 + G7KCs1s5RkhlHyTGbKz+GS/mdnO8VTndwEKEkoOXpHp7I+w6So8+tMDtLWvkxBM+osed9IThHdXE + OXzSgapDmqKWPjxyfqaNulzM54y2qD3inAt4ldrPMQQwvlTe4atPVIlOEI5Vc8RX4aGry7y2IfoA + J/OEuzVH9HVnHKAwpYQvG0yi9VwRDmRB0xGpwntKo4voAdP2CTkPtjpQMbUduPligs0kV2r2eXp7 + qD5zAznGPakpjTgfXpN7SfLVaum2OipEv/VsvXexvSbTGqLOfLEkBPrDXjANNpSnzUq88rKoyzm8 + xAgztkZkcKyjbRX9FGBVu+Gy8HV7vWCth10p9liSptqmrzvnoNJuDsTN3x86qkpvwRETgxg26Yat + L3wOdfEpxtoLsLRzuTpGdeoy5JS7r4GSZIkRqQeKlXek1Vt4kkTRPRgQ//SqvTGtA/NbdiKSmJnD + vjKaDFlh+cQXft0obYJIgjtwFLB0Siy6ncPUAWe/un7rpwa47oYkqCmCOu9lzRh4bOUVTJ/tiVRe + /ByWbI19qN4kfV6szgPzg5gC9O1LTBxMpYHL+rEBdppWWM6IaM+kmQ3QpFvqwZ2x2LMohCzEaZZg + c/Mv0bh/RjngS2Ulhvlk7bUoYwUKCYxxVTXvYYtiP0TXuwSxV8tnQNt3BSF/RDZObXmzt3sKnvAU + 3XmifzgZrPeDbcH7XnKx/dXHP9+Xq4qfx4+BwPxSXREKp+OZHNPrgdKnDEKxrxSOqLIy2e+v3gE9 + sYp5QVIwzMKJ0aCg9BXGSkXq2T5AAdY2qbD+TIxog+LgwPO50/GRhGbNkuT0x28Su5FWSs/yPUP8 + JZC9QxwXEbtrhBGmbOLPxO20gt6tToQJlHKPZUppoENtG1B5yYi4M99TIjtFBun2+uCTnLF0NG5B + CK8tF3q0bfWCgPnSiDH3WMlxeOUR3Vx5REjBLDEv+xhsL/M+wwdKFHIUnodhZu6mAl12VLE7aVK9 + BASnQN29HOxXnaz+/BeEpsCQ0jiuw+JCKUPobJ3nXbhs9Sq8uycsrGoiSp3thtXg5gX8xuNbTyIu + SdsePsoNzm5/vdUzek0eMOhdIpeeRoA/TvMohh/ZJNhHbdRfCjOD7OfTeOLL6b7rSRbQdJsD4vge + ValfeoqoY40lzipIEYV6q4Dhw1yIqYMOrFzJpbByDANHgmgWXJjBEj5awcQBw17qhZerDI4wK4i1 + chrgR3C5gw5VCzkd9ZdNf+N7RZHt3S/GqR4/4X1BfCmtOLmKt4IUumwgFGgrsRyB1gv/GhYISr0l + 2PoE6iKHxxje3DAm5oPhik1VPgpojaqdF6M8FPRbH8E3T2B5d9/XowA3AyVvhsV69bmoy2GlFhLi + 5OMlBRNFK7MwLLwv3UI0a5cOHdn4DeJ4bYnbpUG0DC3S4YtIVxJ7sglWFbkSPFw4H9uipKlLtfoc + slv1QwyaKIA7JG4lepdJwedmFQqirauFHuI7JC5K1YGld1VCKuTKefF3jbo+9PwF7evj5IFulm0W + yiGDmNavMT70hJKtQiE0gzQlVjAF9mzmBwaeuuZK5NVi6mmndTrYMp3B1sq9wDYccw52vSGS47Qu + No1V1kGG71leC7SLvSzPlIWlF3fk9qpxMQ2HSoL7JdNJUoTn6E89rfNXMe+DdBh+/h8409PHFpML + 9Tjf9Qrmj1OArQsX1msRJAqM9sXbIw88RJO9u0DwzUvYlKKnOreeXMKphA98Hux6+KOHndmw3tbk + G/3pOYzkR0fUM+Tpcj6FKZpvePFmU+SLTfB8EX79JFZQfKWrH1iWQECsEyuRE3XDY6//qSfXyFAi + 8pSBD8adZWNVMYtiHfV3hbb32ZtZEgxg6Z/SE+2ZNcPhVOr2aiCzgfPLisnpFoeAE6eChXQfn+dt + UkSwWdSIIZZigs35UatEPGQNksVGI9a9yuvpUR8NGL+4FlukI+pi0cSBC152OKPJky6/+WzU8j7/ + 9GOdcdbCt766RDXtedicfTZDb89h4kIoREP9llrkFmmDraSQCu6dChp0577ENrN/1JyvLXfkyBfN + 298fUsGfwfGOquL0wKcLPURr6PAzCKjtkq8eF3T/aUukvFSE5VJl1OnkvDjYoXLBXz9CWd0mOqxv + akz05GwWnPqGGVTIR8XK1arVgTGWDNTAeHgfGL6iFe7NEL4cvp95yNwLutcKDb7NOyK2yTP2Gkbh + E16Kpz3vTpGrLr/6+PWf+GwrhM4XBSiidRlsb2/Qii49A3RgnHOKlcdOqtlbEbIwVTQZ58ZVLtjo + Nlowd2k+83G4A9ueWXWoLeMFx5GRqXuF8yvEXo86ts+4jZbxymiQ63iG4FBLa57h0yf81Xf70Ajg + XbDPHJ1M5ewxdNLttR6EFGYic8RanQOVjJljwLeSxt98fbdndNpa+ByXI74kEh9Raacy4KuXHlPL + PCD0nWmwHoMXTr68iLKj00Kwr44zOrRsQS3h6UN4iGVSqpAA2rD3HMgqzHCUpXW0mfHNgCK4P0mY + yHJNDwZlwaCqnMd8+cVemt4+/M7vLH79BlVe3QKSYz7PgmNBe6magEN0QaLHVruWTh+QacBizeE7 + /7toLNqpB2BfHnHVqLbKd2hKofiuoMdcxa7+k8eyT4+xlT/nYZ2x3//hT3JIPZU+9FsFCjjBmS0G + Dez1Z89BesGWt9QLKab7QTXE5lkciHapcPT1ExLUqscbm7BZvvUgzeDNOPBEeqCjyrOj1v/qKf76 + PZWMuv9Cs9p4OFb9e0F/+dgfUI5vSZbXNBqmDSbk4H79zr1e2ESTfjzGE3TPV7evPwAMaC7zbz6G + CuUltMi8YvkugPqbBzlYdzXrzWTH0fWCnR5885UnvIcT2JgVNsLXL+Cw3ixA5zy+//IIwZtrRUvP + UA0e31OGvSKUhw2GhxZ285vO0wur6sq7kXE4q56LNam71Qus3Cc8Kpcn/vpjQLv7tiAA2xlrTHkf + RmPVWPRdzz++SGmSfDYouqPj8Q8oglfO1D3Cqn4jdpZyYPnl2yPbJHPVnYlNHWcO/+zfs/q2InLD + ug736kyx3J+0gitELYUsZjmcVp+DvQ1nPYMXw0mwhEZ32DpVqZB/fj7mtbx2Ef3lRc247vAR71c6 + x2YQgjmTT1gdz7CersuZET8BBz1W6nYDPSC4HFoYv3AMsW2ztj208H78WEQRDvawfF5xjJLJvmIn + f1n2L38ifp972AxGWmxvpmsRnAHCx4ZA0DsnJwUCCyas6nVTLx8SxX94hvPlLVQ/IgNO7BR47I9H + LWcLwll4u0Qtj4dha66vHm4WN3uicX1E+4LtM9jm5kpMHbuUrsEkgC9v8ZhvnqMszjXwzQ/eUDFV + vcL6U8F1kjKSMVsbLcJp9ZC9D3iPpqY70IzrUjiZ7YCNwXkUNHwdHMic5qu3Wx9ztGBfy9A3PxEF + xYjOuflpDofJ0H88NJpZc7HQK4M89v1dY9P8cxMA39QBPn2ubLF98yjMTppJcK+1gMqk3eBv/n88 + bvPiaERwPiDyzVPDmnzSEgLYz1jiejGi3mXf/1mv3zxUbJ1g57C5PHWik10d0TSwXwC9sg92vfms + rvb5nkMDXrYvrzhGjWRfe5C3gUjkww7aXz8owm8enWkVEntdg06CYRJLODJtr+ZfWOnRyt8FYqXs + rViDJ5pBnMcGuXz509d/saAM+vuMCHNRqZXEIvzWS3zKXW3g2p5VQD1GL3JKso/Kh2Tx4JDP5p/5 + XxOrFqG7JA2WguFpU/cFW1iB4UO06o3qoQSKDkm+SMSc9h91ES25AbmZPojjTZdi4xzjDtOOW4h7 + rD7RqkS2/seP6oy2AGIY7h2Wce4TIxiSevv5KQvuZG8N6WwP9nEdkaRwDj7uvf1A2iWBv/GeuSJK + 1PnUXw043cYAO4XfqFQ+5zmsJyHAV2t3B1uRMhCCp4WxqX6O9MtHdKjrUTUv/PIYxoP3XsDBeUnk + 6gMHcGJx96ESxiwpv/57OnXnDBr92OH4qxfLWeKfML2lPsG49GoOPwYNfv0skVy00dUbxhQ8OOvi + LV/+tYK5KSG3PkOPzc6yTYwhF8DO4Drvks36QPDmKOAle8a3P+HW+wnMOQz1mzrD4dmpS70SAz6M + VPW2RH7Ui1OLEhwCgWJ1UrOBXqPsDjt9rxG915h6eDpbeIgP0YN8eai9GFDlYLFqOQ6moi1Wexcw + UM2H7ltfvPqPnxG3yfnq92KvO0Z3INs5EU4+pqH+9PNAQKp7aNKkgZ/cowWr4vggdrY87XWKIAe/ + 9Q1rBJ3o9ipKHzrVEM0HrteG6bW9BBCICUeU1CBgsncBBO2DO2J5gw+wVbenhY7Z1ZhpuSYDRW75 + Aoxjp9/+wc2mzPvJwGYNOazT6FSs3OxbKO+Ezwym6/nLPz4cRMxBx8qizer2m7/6rQOMy9wFs9tf + ZniZcEyUqWxUCvW7BOI8NYiXrK26TafBAb98/NMPWnOsB/ct2bxl537UMbscBfg6djJRtqAeNvCM + OaA7iYYdgopo22QNwrh2wXxsexnQc5VDmGVr+M2z93rjZfMJ7kW7EBlNdrF++SR0HGjh0yjJxbj4 + dYjMo2vi8NSd1Y+54QoGS3H9r35A0F2z3/7HsTe9wVph2UP4MNvYMLjM3n5+CB9GG4epgekmThGH + fv7+q1+0/+Wxb78Mm/y6gVG4jAvn2MmVXMdMG1jA2ndY+jLFsvIY7MlxHhmSP75HbPftRmzrpSlw + DxYk3inE0ejUm4TmRtDI7YHtiI894Ikpf5Jm4fu8PLA1D94MwONTPNrf5xN78PP3DlOy6hgQHIvo + Ipy8NY4/gLiE9sBUrpf5HdUPdfCfdxa+g2Ai9rd/Mdx3Tw6g5NFjuaH3aGL4oEc36/4ixu7A1fRd + 5or4rddffuGqi3RgRHDl9OcsyJetpmkXMNDPw2BeyEdS2foGGvjj7S4vOQWnLjSDPF0f2P7yaYLV + XQ+a9nrDp29e3Dz+oSGT7R2PrgKnkpqDHvy+J/pgHcE+0FUDlSyTYtlO22F7tEQHXSn0JNsjC2xK + 9lYQPT3lmTne3uq40KX98bVZyCaDEmuqw1//iBjtzqZzc6x8wLzvlGSsJdVffZYO3/xEFD4LAMs7 + SQYNy6qJmSnWQNMnbwi7iW+xtSzXYX0nWwz95MASy2+eYP35653cSx5cq726HLxpgx5ZWqxFRmbP + et024iPaNfMFNn69pW3s/eF1hmNBdeq3rvrlWw9UTDWs3n2K/+jPkVEjdf3ub/jVe+8Uhzf6hzfA + q8fO4jcv7c+L2UIziFNcuR4otu26u0Ozlm8zFGX5D49Dv/6Cvn2G6Oe/wDzrE9FLdqZbzIQOmHpn + xW7bJ8XW9lACnM84v/1XjPLVScG3/+gd7nyrsrVqSoDlyhrbggjpz1/86W/8/Dedut0MP8DLPOG7 + fzgK7zO6tFtB1G8eZA8NNH78+rs+xmiKBSEUwXps531dCGBJU3QH/mtb8Snip2hZxcmBk9kP2P5Q + aC+f8L6hw2TpWBPntZh/fOy3f7Xqfa03NTY4CDHzmtHeVewfn4PETxDWIB7s7fmyRPjrJ3oNOamb + 4GUC/PL/Lw+NAZ9xjxgdGB4Tt7/uhsV/8MxPfzz4nOyaM+OzBb95Dqu7Z2CvAXB0UdkEEfuOZQzL + KKtPtPJPAWt0ZGw6WHCB3oUoM6gYpt5Su+th6pJ6Zo7HA1jhXvbR379TAf/5r7/++l+/EwZNe63e + 34MBU7VO//7vowL/5v89Nvn7/ecYwjzm9+rvf/7rBMLf3dA23fS/p/ZVfca///nr8Oeowd9TO+Xv + /+fyv74/9J//+j8AAAD//wMAofreDN4gAAA= headers: Access-Control-Allow-Origin: - "*" Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9aaeaa6ffab2-SJC + - 9854a4677a7fd045-SJC Connection: - keep-alive Content-Encoding: @@ -3457,19 +3459,19 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:21:58 GMT + - Fri, 26 Sep 2025 17:57:11 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=vh5fhSMPN0_o0c9UUhYHW7sGQ4diDYhi9mUewk5.UnQ-1758846118-1.0.1.1-NZ5AaNpA28p9xrolLLr60TLsYLtSTzVv7WuC117a4H8RsjtoJ0b5hxf7_BswxFzZrGow26YmMhp9Ew69aczmNlePRk2OiDPWnWDWSK5LoLw; - path=/; expires=Fri, 26-Sep-25 00:51:58 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=kCL6__qOwwf56E2ZkNfDjmzdp3oeDjbxSMV30IZjLJU-1758909431-1.0.1.1-67aLeAmpK5P05DzziZEs.0Cu9Tc89ohD6x8owercBxxhyLgopKfOi7pi964M6remzVkJzLh4nxTy0ob9a26hjQeLrypjqBMTfSt7vP9bvyI; + path=/; expires=Fri, 26-Sep-25 18:27:11 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=yzIcYwNnVMgdKYAYHCwbt2UdM8PKmLylBotJ0muz6rw-1758846118303-0.0.1.1-604800000; + - _cfuvid=tkvcrGOX8Y5zj77.D9.AYgB46kH4pWOHrlIHvy3L844-1758909431198-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Transfer-Encoding: - chunked Via: - - envoy-router-5b55659bc-dls7k + - envoy-router-55b84f9469-86z8g X-Content-Type-Options: - nosniff alt-svc: @@ -3481,7 +3483,7 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "51" + - "94" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: @@ -3489,7 +3491,7 @@ interactions: strict-transport-security: - max-age=31536000; includeSubDomains; preload x-envoy-upstream-service-time: - - "83" + - "155" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3499,13 +3501,13 @@ interactions: x-ratelimit-remaining-requests: - "199999" x-ratelimit-remaining-tokens: - - "199999996" + - "200000000" x-ratelimit-reset-requests: - 0s x-ratelimit-reset-tokens: - 0s x-request-id: - - req_46ec3b8ad9e3444ea4ae68babb682bf0 + - req_d57ae26d5f374c57800edd452b165242 status: code: 200 message: OK @@ -3515,15 +3517,13 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 1-3: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n - A Perspective on Explanations of Molecular\\n\\n Prediction Models\\n\\n\\nGeemi + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 1-3: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n A Perspective + on Explanations of Molecular\\n\\n Prediction Models\\n\\n\\nGeemi P. Wellawatte,\u2020 Heta A. Gandhi,\u2021 Aditi Seshadri,\u2021 and Andrew\\n\\n \ D. White\u2217,\u2021\\n\\n\\n \u2020Department of Chemistry, University of Rochester, Rochester, NY, 14627\\n\\n\u2021Department @@ -3598,7 +3598,7 @@ interactions: connection: - keep-alive content-length: - - "6215" + - "6045" content-type: - application/json host: @@ -3630,26 +3630,26 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//dFRNb9s4EL37Vwx4aQvYhu3ESda3oBug2fa4WLRdF8KIHFnTUCTLoZIY - Qf57QcqJlTa96MA38/TmzcfDBECxURtQusWku2Bn7//58R9fibF7d3IWVvRlH9oPf6Wr9+HDv1/V - NGf4+jvp9JQ1174LlhJ7N8A6EibKrMvz9cXF6dlyeVGAzhuyOW0X0uzUz1aL1elsuZytFofE1rMm - URv4fwIA8FC+WaIzdK82sJg+vXQkgjtSm+cgABW9zS8KRVgSuqSmR1B7l8gV1Q9bB7BV0ncdxv1W - bWCrru6DRXZYW4LLmLhhzWjh2iWylnfkNMHbz5fX74AFEBoma6Dxuhcy4B2E6G/ZsNsBu0QxREqY - LRHwDRiiAJYwuhzw9u9P76B4ASGSYV3ipoDGRBLJIakleFNb1Dez2t+/AYepj5SpUktCQ7bM4fPl - NSB3AskDuRazyhR7SYDOQC9Ys+W0h3oPvmkoZm7KlbqDuMbHlyLuWtYtaHTQkg3QC0WB3hmK2VFT - lMWSjJagppaHx24OH2kPiWIngCJecx4CuOPUFpnstO0NjewZtE3hey/Fb3zywZlBZG5HiZmXPoyz - IFKTlSVfFBnaRSr+tH2HbiT4Kd43gINtpUZLv/wWMBL86NElzo27JegoRdYCqcUE0ofgYyo/Kwbf - +ZhadiSlwSML53D1QjugznR2D9pi5IZJCkvxwaEFQ5qFvZt1eJP7E6LXJDIdjVSZ3fuhqRp7od8a - N9+q6TDVkSzd5kGoRPtIebqXiwOWh7XiDnck+b1BK7R1j+M1idT0gnlLXW/tCEDn/GGk84J+OyCP - zytp/S5EX8svqaphx9JWkVC8y+snyQdV0McJwLey+v2LbVYh+i6kKvkbKr9brk5OBkJ1vDYj+HR9 - QJNPaEfAyfnF9BXKylBCtjK6H0qjbsmMcpfr1XMR2Bv2R2wxGdX+u6TX6If62e1GLH+kPwJaU0hk - qmOvXwuLlC/yn8KevS6ClVC8ZU1VYoq5H4Ya7O1wLJXsJVFXNex2eeF4uJhNqNZny3q1Pj83tZo8 - Tn4CAAD//wMAxPegtToGAAA= + H4sIAAAAAAAAAwAAAP//dFRNb9s4EL37Vwx4qQ3Ige3Y6dq3oC0WQfa0h90u1oVBkSNpGopkOWQa + Nch/X1BSYnWTXgSIbz7evPl4nAEI0uIAQjUyqtab5Yfbqy7+WN9+K6/s/u9d88/vl5/+/Ljf/DB/ + VR9EkT1c+RVVfPa6UK71BiM5O8AqoIyYo67f737br/bby3UPtE6jyW61j8utW25Wm+1yvV5uVqNj + 40ghiwP8OwMAeOy/maLV+CAOsCqeX1pkljWKw4sRgAjO5BchmYmjtFEUZ1A5G9H2rB+PFuAoOLWt + DN1RHOAoPj14I8nK0iBch0gVKZIGbmxEY6hGqxDmn69vFkAMEipCo6FyKjFqcBZ8cPekydZANmLw + AaPMkjBIqwFzdDs+VC6ARvRgUAabXeYf/1hArw74gJpUb1iA1DogczaJDcK70kh1tyzdwzuwMqaA + 4KqMMA7efAGfr29AUssQHaBtZOYdQ+LY80gsSzIUOyg7cFWFYWDMVDeRM3UH35sO5MimlXfIwB5V + FmRK7gJusQPlrELfe/aZySqTNE40GNPNM3+NdcCec5NaaSFZjSE3Sj+bueo59aKAr4n7Poyyzb8l + aSNlWe8RWoyBFENsZBzkJfsTdW5cMhrKsXzUi+J1K+YaWQXyw5+r+ghvlQsyILRS42KQmBi8DJFU + MjKYDqj1LuSRy0r0w8Fg6A5BNdgSx9AV8L3BgJOis/Svmg5KWqgTaYSm865v7jhDlnPHh0b3fKyL + 5xlinwK5xKBcCGiGAi+OohiGPaDB+zwNJ1YuYB769epon6YrErBKLPOG2mTMBJDWunGc83J+GZGn + l3U0rvbBlfw/V1GRJW5OASU7m1ePo/OiR59mAF/6tU8/bbLwwbU+nqK7wz7der2/GgKK86WZwNvd + iEYXpZkAl9t18UbIk8YoyfDkdgglVYN6mnO3eSlCJk3ujK1mk9pfU3or/FA/2XoS5Zfhz4DKu4X6 + dB6Pt8wC5mv8K7MXrXvCgjHck8JTJAy5HxormcxwKAV3HLE9VWTrvL00XMvKn/S+wqvd+0ssxexp + 9h8AAAD//wMAVzXaqDYGAAA= headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9aafcb82b976-SJC + - 9854a4695fc9fa32-SJC Connection: - keep-alive Content-Encoding: @@ -3657,14 +3657,14 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:21:59 GMT + - Fri, 26 Sep 2025 17:57:13 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=waOgHSy8BZbDlrCkQjCHyUlGFcdX6YdTE2ybkug96zU-1758846119-1.0.1.1-ZR2Xh1r4aHLgx414Ei4Xsl9CijXpwQrd40nIGnZqYFY3f9pMeRwFhvtPdjRJAj3tblcWApeHdpfA4B7RUkDOA2srZ6x.a5Sc8kQlKb2NdNU; - path=/; expires=Fri, 26-Sep-25 00:51:59 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=NyRHcqMQ3SZjerDwjfVil5go4ndnghLGrPSyU7Xqn1o-1758909433-1.0.1.1-49ZYQLMXK.bih_3iObUPTRt4YqBTtmcfspHh.ShCBuB9fVhpZVjlMz0sNsFqFN2w8CzFNbLxSv8ppchCUsox9bL0h5OLzVz4ZLqb94.0qik; + path=/; expires=Fri, 26-Sep-25 18:27:13 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=mkbU4bg69L.iHO1oNYdTZ83xIYK2oCQz2xPlsC5UNRo-1758846119887-0.0.1.1-604800000; + - _cfuvid=xo2a4PlB0qWYXLHoLbA2CotojGuV.2xhyQ8CWW7d4dk-1758909433250-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Strict-Transport-Security: - max-age=31536000; includeSubDomains; preload @@ -3679,13 +3679,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "1423" + - "1919" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "1435" + - "1936" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3695,13 +3695,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998514" + - "29998556" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_754d199b95aa42539907d44ab193ca20 + - req_54f2ff7120834661ac558673837f4a2c status: code: 200 message: OK @@ -3711,20 +3711,18 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 20-22: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nnal - molecule. The counterfactual indicates\\nstructural changes to ethyl benzoate - that would result in the model predicting the molecule\\nto not contain the - \u2018fruity\u2019 scent. The Tanimoto96 similarity between the counterfactual - and\\n2,4 decadienal is also provided. Republished with permission from authors.31\\n\\n\\n - \ The molecule 2,4-decadienal, which is known to have a \u2018fatty\u2019 scent, - is analyzed in Fig-\\n\\nure 5.142,143 The resulting counterfactual, which has + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 20-22: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nnal molecule. + \ The counterfactual indicates\\nstructural changes to ethyl benzoate that would + result in the model predicting the molecule\\nto not contain the \u2018fruity\u2019 + scent. The Tanimoto96 similarity between the counterfactual and\\n2,4 decadienal + is also provided. Republished with permission from authors.31\\n\\n\\n The + molecule 2,4-decadienal, which is known to have a \u2018fatty\u2019 scent, is + analyzed in Fig-\\n\\nure 5.142,143 The resulting counterfactual, which has a shorter carbon chain and no carbonyl\\n\\ngroups, highlights the influence of these structural features on the \u2018fatty\u2019 scent of 2,4 deca-\\n\\ndienal. To generalize to other molecules, Seshadri et al. 31 applied the descriptor @@ -3792,7 +3790,7 @@ interactions: connection: - keep-alive content-length: - - "6209" + - "6039" content-type: - application/json host: @@ -3824,25 +3822,25 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//fFTBbuNGDL37K4g520acTTZZ35JFW6RAb8U2Qb0wqBElsR7NaIccb4wg - /16MJNvKdtOLAM0bPr5HDvkyAzBcmjUY26DatnOLz79/+8JP4Uv6a4dPv97/1n5/vG8Ofnd/F/94 - MvMcEYp/yOoxamlD2zlSDn6AbSRUyqyrm+vb26uPq9VtD7ShJJfD6k4XV2FxeXF5tVitFpcXY2AT - 2JKYNfw9AwB46b9Zoi/p2azhYn48aUkEazLr0yUAE4PLJwZFWBS9mvkZtMEr+V71y8YDbIyktsV4 - 2Jg1bMzj3cMcQoRfnjuH7LFwBHdRuWLL6ODBKznHNXlLc4hUURTQAC1pE0oB9CUo2cbzt0QCSajs - YdwRaEPQRSrZ5hoJhAruHqAvhgB7pdhF0j5jpkm+pJjll/2RBmhSi16W8OB7rt7Js2aeNjiyyWGE - LoaOoh4mmebwmPOMCh3vaHLfhpQzV2g1oQPKtj0OArOKksRG7jTEH7BIJ3c01AoKh3a3KMLzaGoJ - n/+Hnf0+uD1By55bdCAak9UU0YFt0Nc0FHZQOvygbZj2g/OIouzro2EmmcP3hh29KzkJgaQYQ41K - x7pryAx7Lgk8Dtkd+jphPXTBNtSyRTdhXRSYjbMXrhuVJfzZkNCpwOQb9JZAYxKdA1pLIlywYz3M - h85q/zM+APZQhhbZj73pM4rGAxSHUVv2iX0vT69jCFlEcrRHr2+cLjdmPjztEba0FRsi5Sf+aYRy - 87bcYk2Sjyt0Qhv/Oh2VSFUSzJPqk3MTAL0POuTKQ/p1RF5PY+lC3cVQyA+hpmLP0mwjoQSfR1A0 - dKZHX2cAX/vxT28m2nQxtJ1uNeyoT7e6/HQ9EJrzxpnAH46oBkU3Aa4+jHvjLeW2JEV2MtkhxqJt - qJzmvD3vHEwlhzN2MZt4/6+kn9EP/tnXE5Z36c+AtdQpldvzaP/sWqS8ld+7dqp1L9gIxT1b2ipT - zP0oqcLkhoVp5CBK7bZiX+fdxMPWrLrt9cdVcXl9c1MWZvY6+xcAAP//AwAkk+ixPgYAAA== + H4sIAAAAAAAAAwAAAP//dFRNbxs5DL37VxA6jwPb+Wjtm5G2gNHdPfUQYF0YtMSZ0UYjaUVOaiPI + f19IE9uTbnIZQPPEp0fykc8TAGWNWoHSLYruopvef787its8fFngw7c/Olnzl2/3f80O7dfv92tV + 5Yiw/4e0nKKudOiiI7HBD7BOhEKZdf7p9vNytry5nhegC4ZcDmuiTG/CdDFb3Ezn8+li9hrYBquJ + 1Qr+ngAAPJdvlugNHdQKZtXpT0fM2JBanS8BqBRc/qOQ2bKgF1VdQB28kC+qn7ceYKu47zpMx61a + wVY9rDcVhARfD9Gh9bh3BOsktrbaooONF3LONuQ1VZCopsQgATqSNhgG9AaEdOvtvz0x9EwmwzSw + gbQEMZGxOpeJIdSwd6gfp/twgFIWriBiEqt7h8kd4TWoqD5IDuiCo4JCTCFSkuOI8gp+tAR00JSi + gLGse2ZikF8BHtabk87ViEWH3gulGrX06AapHgd9OR1DrJONEtIb7Ar+/ICCwfqn4J4IOutthw5Y + Uq+lT+hAt+gbKjXDkwgqJyeUwAqfsiFzStASV/CrtY4+EpMLDdynFBoUeq1kZo0pPFlD4HF43qFv + emyoZKZb6qxGN2Kd7jG37G2iP1piurTYdqXj+Eiw3rxpZxcSgc2liImkeCe/gwUuxzokMKHLXqBD + zo0rIN+i19Y3IKlnKSE94946K8ehocNE5O5fmgiGInnDEAaLYG9stuXgwWy0PsXAJUqKKc45XW1V + NXg/kaMn9Jp2rEOiPAPLrX8ZD0yiumfM8+p750YAeh9kqFEe1Z+vyMt5OF1oYgp7/i1U1dZbbneJ + kIPPg8gSoiroywTgZ1kC/Zu5VjGFLspOwiOV5+aL288DobrsnRF8fUIlCLoxsLyr3qHcGRK0jkeb + RGnULZlR7Px2cU4ilztcsNlklPv/Jb1HP+RvfTNi+ZD+AmhNUcjsLsZ771qivJs/unaudRGsmNKT + 1bQTSyn3w1CNvRvWpuIjC3W72vom+9oOu7OOO7Os6e720zXt1eRl8h8AAAD//wMAl975E0QGAAA= headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9aafce01eb31-SJC + - 9854a4696f80172e-SJC Connection: - keep-alive Content-Encoding: @@ -3850,14 +3848,14 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:22:00 GMT + - Fri, 26 Sep 2025 17:57:13 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=agq7G9BIZM4iv5.z7Lokyoa5SslUq6TRzrlwqPvV0JM-1758846120-1.0.1.1-k41oB64hSK57GYQTtNzCGp4dlM716r852DAhuxtcuJG.A_MqptaQYD7X9QggcIbXWMdm5QbnasD8VxtwwuHmVH.XIZNUEGlGcA59LsHM474; - path=/; expires=Fri, 26-Sep-25 00:52:00 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=KI1us9bcHgZyekUwZRFYpJeJ.3J80PAVLexry552usk-1758909433-1.0.1.1-jHfBBcp8dmwCSYhj0niU3toVnmAax4QGA_BDcz1fFKVn0.6C9bYnwsbGtO31IFmKlJoyBCTUi8ucrbFSztGNa62AViO8oCwjh8UEYygIiK8; + path=/; expires=Fri, 26-Sep-25 18:27:13 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=msbLxKnyg76M6DG6r2.0bX4o5QNJvsOFHjTrpLtHB2I-1758846120034-0.0.1.1-604800000; + - _cfuvid=nLKsgnhaE8imEGT8X0oeyx_WAFT7AnbVoF4YhqX8XiA-1758909433295-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Strict-Transport-Security: - max-age=31536000; includeSubDomains; preload @@ -3872,13 +3870,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "1585" + - "1949" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "1604" + - "1966" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3888,13 +3886,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998513" + - "29998555" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_e67ff8a5088649a28596fe86b78ac8cd + - req_04ebec073eac49a1849df2b96b182332 status: code: 200 message: OK @@ -3904,16 +3902,14 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 3-5: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n - a passive characteristic of a model, whereas explainability\\n\\nis an active - characteristic which is used to clarify the internal decision-making process.\\n\\nNamely, + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 3-5: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n a passive + characteristic of a model, whereas explainability\\n\\nis an active characteristic + which is used to clarify the internal decision-making process.\\n\\nNamely, an explanation is extra information that gives the context and a cause for one or\\n\\nmore predictions.29 We adopt the same nomenclature in this perspective.\\n\\n \ Accuracy and interpretability are two attractive characteristics of DL models. @@ -3985,7 +3981,7 @@ interactions: connection: - keep-alive content-length: - - "6190" + - "6020" content-type: - application/json host: @@ -4017,27 +4013,26 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//dFRNbyM3DL37VxA6JYAd2N581begDZoUvRRdLFLUC4OWOB5uNJIictx4 - g/z3QjO2M2mzF8PDx0fykSJfRgCGnVmAsTWqbZKf/Pzb0xe+++n+ry+zP2/vvk3/8L/ecCOf59+r - hzszLoy4/kZWD6wzG5vkSTmGHraZUKlEnV1dXF+fX85m1x3QREe+0DZJJ+dxMp/Ozyez2WQ+3RPr - yJbELODvEQDAS/dbSgyOns0CpuODpSER3JBZHJ0ATI6+WAyKsCgGNeM30MagFLqqX5YBYGmkbRrM - u6VZwNLcPiePHHDtCW6ycsWW0cN9UPKeNxQswcnDzf0pZKooC2iEhrSOTgCDAyVbB35qSUBrVGjw - kUBrAkeWhWOYNPjIYQMpR0siJBAruLmHrikyhoRZ2bYes9+BI0rgCXMolJNffj89+nFQyimTdqWW - 1G1wlIteV0xn8HBzDxy20W9JAEH/iRNRSofMC6g4i47B0ZZ8TCUDBkBr24xKsG4V2vA+TZd83Aut - KQA6V2hUmhawjL5rCKtAyuTYdqYzuB06pBy37Ai6STxrF81iW1pRxTwkjiFWFeWSgoPwplYpumPX - 0E6u3xWwIVtjYGmkV30YiMUAayqUXPgWToR8NTmUG/Nu385TiBno+eiWouikjva9MkzJMznASimD - ZuQyltMzuN2ib1FLKe+brpp53SoJeH4kwE4Wrtmz7sawXxgKJFK+ciar/YeLDXLoM9ojoWJH/b8c - 163sfUv/pLWWQ88+g881CQ2z1+QTYHlt0vXuqcUSp3822kUvz/CdWg6wxcyxlUMZ/TCXZtzvTSZP - WwyWVmJjprI/s+kea4XcihvckBR7hV5oGV6Hi5ipagXLHQit9wMAQ4jaJysn4OseeT0uvY+blONa - /kM1FQeWepUJJYay4KIxmQ59HQF87Y5L++5emJRjk3Sl8ZG6dLP57LIPaN7u2QC+vNqjGhX9APh0 - /Wn8QciVI0X2MrhQxqKtyQ24s4v5UQS2juMbNh0NtP+/pI/C9/o5bAZRfhj+DbCWkpJbve3fR26Z - ys3/kdux113BRihv2dJKmXKZh6MKW9+fYyM7UWpWFYdNuTDc3+QqrS4uZ+v5xdWVW5vR6+hfAAAA - //8DAKTglcGcBgAA + H4sIAAAAAAAAAwAAAP//dFRNbxs5DL37VxA6JcDYsB27aXxzP4ANtruHPRVYFwZH4sxwo5GmEseN + G+S/F9I49nSbXgxjHh/5+ETyaQKg2KgNKN2g6Laz0/d/vjnKwx/x6/LD3/3Rr8zCfX//z1s6vPv2 + 6S9VJIYv/yMtL6yZ9m1nSdi7AdaBUChlXdyu397N71Y3iwy03pBNtLqT6cpPl/PlarpYTJfzE7Hx + rCmqDfw7AQB4yr9JojP0qDYwL16+tBQj1qQ25yAAFbxNXxTGyFHQiSouoPZOyGXVTzsHsFOxb1sM + x53awE59fOwsssPSEmyDcMWa0cK9E7KWa3Ka4Orz9v4aAlUUIoiHlqTxJgI6A0K6cfy1pwjSoEAX + /IENgbYYWI7gHUhDwE4oOLRgSHNk76YtPrCrU7ymGCmCr2B7D9mrWECHQVj3FoM9giHqwBIGlyhX + Hz5dn+Jm8Hl7D8ht1lWixaQ3FZSAhqa+qqAk+UbkALXuA+pjlp31dIEES7ZJZ3mEikMUMHQg67tU + aGAIQdkLWIpxTLN00jDY0KQKxiQaJUsdpsHIsqQhDtAFMqzzxxl8HIewy11odFASaBSqfeDvZAAj + 1NaXaMEHsF6jLXI1zEoCu8garpJVyb3UdpZ0ncLp8RLgo0wbr69nsIXaezOW+FI+Nr63BtgQWntM + SjCrTZ0WcJr1/C8E0lKA8S2yA+w6y3qIqpClqXpbQPBlH2VQG3ut2WkZHovjz2/bR6p6m1RUTNZE + sPxAoBtqWaNN89FRkOPIv+Tpy5ilDutGsh8eooReSx9oeqYFsoPNDXdxtlPFsAOBLB3SsOyj9oHS + LizmO/c83pxAVR8xLa7rrR0B6JyXIWva2S8n5Pm8pdbXXfBl/B9VVew4NvtAGL1LGxnFdyqjzxOA + L/ka9D8tuOqCbzvZi3+gXG6xuL0bEqrLARrB6/UJFS9oR8DNzap4JeXekCDbODopSqNuyIxrrpfn + JrA37C/YfDLq/VdJr6Uf+mdXj7L8Nv0F0Jo6IbO/TMJrYYHSkf5d2NnrLFhFCgfWtBemkN7DUIW9 + He6nisco1O4rdnVaeh6OaNXtzV1Fb9a3N1SqyfPkBwAAAP//AwBTK8o3TQYAAA== headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9aafc9c516a6-SJC + - 9854a4697d4fce48-SJC Connection: - keep-alive Content-Encoding: @@ -4045,14 +4040,14 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:22:00 GMT + - Fri, 26 Sep 2025 17:57:14 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=ee_HqOMzhzdpz3ho6In_9.WXe3479y6WgO9ZKBbiHK8-1758846120-1.0.1.1-0fjLOki5bmP2tYdgnApIpZemFs0jtI1CeZ0plQ74o7emwaz8aC.lSlsaw4bqvy3512vRFiIqOOEWTzRRhY8uJjWntyx.BIZefm9w0OrpBs8; - path=/; expires=Fri, 26-Sep-25 00:52:00 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=tnnLwKcMA8gxwZP2mc5MsMGoZj.iO.j5sDJSblwOAa4-1758909434-1.0.1.1-j3WRpjUktVGCsxSa56.RRZqMmS9CoeHMI.X.xaGZDBhQfnbuOT5SaNx8I5l2HKttIgYgmg5.g1zuGwD_HF.AIRvNt7RWFooFaaFdUcIG5mc; + path=/; expires=Fri, 26-Sep-25 18:27:14 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=0N4wivU7Jsk4XfoySwrSZh10x6u2E7JwADXMgLJ_9xc-1758846120100-0.0.1.1-604800000; + - _cfuvid=mXqo0NWwjpyyY56_53C_3B1NvjBDIrdDKGLNajvA.eA-1758909434001-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Strict-Transport-Security: - max-age=31536000; includeSubDomains; preload @@ -4067,13 +4062,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "1656" + - "2632" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "1671" + - "2660" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -4083,13 +4078,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998521" + - "29998562" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_c2323e8cc4f64289b2822489167c30f8 + - req_bef5cd3999d0450ebcdcdbd00e29f012 status: code: 200 message: OK @@ -4099,14 +4094,12 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 25-28: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n2021, + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 25-28: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n2021, 25, 1315\u20131360.\\n\\n\\n (9) Wellawatte, G. P.; Seshadri, A.; White, A. D. Model agnostic generation of counter-\\n\\n factual explanations for molecules. Chemical Science 2022, 13, 3697\u20133705.\\n\\n\\n(10) Gandhi, H. @@ -4182,7 +4175,7 @@ interactions: connection: - keep-alive content-length: - - "6231" + - "6061" content-type: - application/json host: @@ -4214,26 +4207,26 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//dFRNbyM3DL37VxC6pAUcw06dxM3NbffgFgX6sVssUC8MWuLMMNFIsyKV - xAjy3wvNeO1J270Yxjzy8T2K5MsEwLAzd2Bsg2rbzl/++PPnv3ixfO42Tz9Y/XDP6/m8+v0p/+o6 - 96eZloy4vyerX7JmNradJ+UYBtgmQqXCuri9Xq2WN4vFqgfa6MiXtLrTy2W8vJpfLS8Xi8ur+TGx - iWxJzB38PQEAeOl/i8Tg6NncwXz65UtLIliTuTsFAZgUffliUIRFMaiZnkEbg1LoVb9sA8DWSG5b - TIetuYOted8Q0LOl1Cl4FhV4xMQxCySqKFGwVP764gw0wrvnziMH3HuCdVKu2DJ62AQl77ku8fDN - x/Xm2ylwsD47DjVUMQeHpVPo4SmmB5mC5PRIB5kCBgfYdZ5tHyHAARxXfXEFF1vkIDP4uN5AFW0W - EogBWnwozOsNyEGUWoE2JgIOSqlLpL3AQq0Jg3RYyKaAziUSKZm2Qe8p1CTg+YFgzyhT0JRFj5qs - jTko7tmzHmbwCx3GPRnc0WCnKOqjKVVoNaMHKo0Kg6XebYo1KkE/DEfbiersUWM6QJWwpYGrl6MN - wbsPFwIXietGS+tHjLML2CiglwgthaFtP63/+G19IX2juhTrhG1f5NjoIrFASrYJ/Dn3FqBi8u5Y - kgKl+nDqZ8lt0TYcCDxhChzqGbxvSGjch4brxg8SGwJuu5gUyxTECnJwlMpE9lNQCO+zKFeH49M5 - siy9+GIvSE6FWroYhM/vl0WfYtLmMHrt2dZMh2lO5OmxFNyJjYnKVH9/hLKQ23GLNUn5XKEX2obX - 8XYkqrJgWc6QvR8BGELU4fXKXn46Iq+nTfSx7lLcy79STcWBpdklQomhbJ1o7EyPvk4APvUbn98s - selSbDvdaXygvtziZrUaCM35yIzg5dUR1ajoR8Dqu+OpeEu5c6TIXkZnw1i0DblxzZvlyQRmx/GM - zScj7/+V9H/0g38O9Yjlq/RnwFrqlNyuS+TYvrV9DktUDvHXwk697gUbofTIlnbKlMp7OKow++FG - mmGcdhWHutwNHg5l1e2ubxb7q+vbW7c3k9fJPwAAAP//AwDvYN/MMQYAAA== + H4sIAAAAAAAAAwAAAP//dFTbbiM3DH33VxB6aQuMDTt2bn5zuwXWKFB0LwUWqBeGLHFmuNFIgshx + Mgjy74U0Sey9vXgMHfKQ54jU4wRAkVVrUKbVYrropn/8dTXIdvXvh/vBtm+6d+/cqv777fuL7dF9 + +F1VOSMcvqCRl6yZCV10KBT8CJuEWjCzLq4vb27nt6vlogBdsOhyWhNlugrTi/nFarpYTC/mz4lt + IIOs1vDfBADgsfzmFr3FB7WGefVy0iGzblCtX4MAVAounyjNTCzai6pOoAle0JeuH3ceYKe47zqd + hp1aw059bBHwwWCKAo5YGI46UegZEtaY0BvMf11WBhLgz4foNHl9cAibJFSTIe1g6wWdoybHw6+f + NtvfKiBvXG/JN1CH3ludndIO7kO64wq4T0ccuALtLegYHZkSwUAeLNWluIANnSbPM9gKdOjHiDsc + IOqIiUt2lpjo0BcwM5sWNMObzft/Nr8wfNpsIabQJN29loXgAbMUX4oWmi89Fz3jCXnotGnJIzjU + yZNvxl7rpDssIqAOaWR5MWQ7g48tMp6b11LTOmpaAWkRqIshic42hbq0Rj53d6TilCTtOeqcOmQH + BVNMKPpAjmQYG5DUs+S0zRZ4YMEuu8hwj87lLwl/Z2hN6CyDozsE02JHLGmoAD2mZjhj8RYSNr3T + EtIAZcIpNzuDt+Eej5iqouJlYmxABh8ELCU04gawWGfLsrBDL8/KkEGXW8IHKablFll6O3x7+7Od + qsYpTejwmEvv2YSEeVpvdv7pfLQT1j3rvFm+d+4M0N4HGcXnpfr8jDy9rpELTUzhwN+kqpo8cbtP + qDn4vDIsIaqCPk0APpd17b/aQBVT6KLsJdxhKbe4ulyMhOr0QpzBy+UzKkG0OwOub1bVDyj3FkWT + 47OdV0abFu1Z7uXy6lWE7i2FEzafnGn/vqUf0Y/6yTdnLD+lPwHGYBS0+5jQkvla9iksYX5Ffxb2 + 6nVpWDGmIxncC2HK92Gx1r0bHzg1ju2+Jt/kNaHxlavj3t7WeHV5vcSDmjxN/gcAAP//AwBDdj2z + 7gUAAA== headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9aafcaf417d2-SJC + - 9854a4696ceb67fb-SJC Connection: - keep-alive Content-Encoding: @@ -4241,14 +4234,14 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:22:00 GMT + - Fri, 26 Sep 2025 17:57:14 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=daP72L4VaURDeUfw_pkYuSfZ3mpHDuhWnTGz6FaLkn4-1758846120-1.0.1.1-9tQFjQ7x9p2Z_yE67SorHQJ9Zjnj0tFYiEkml7t.Pkx4Ep4B3F0ruM28NtjXQo0TJ0YHDrYIh8d7nKxf4ZPJ.LU68t1xYxiVx.HSru1Wi5I; - path=/; expires=Fri, 26-Sep-25 00:52:00 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=OgA_PaGcnjFXaZZb7LNYj0YEPVQXfD3t8t42oUr.t4o-1758909434-1.0.1.1-pvvwy5Ey8MSNWkDavAjUpmUq_kDDs3N5d6E5HWFJsbIUXAw3bh4beIMNgLSAM34z0E.UH4bMXQ3f1RJYRvqRPV6zsVovFLQ5DMa2qxhmLcw; + path=/; expires=Fri, 26-Sep-25 18:27:14 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=FFJsjqG1I9TEtZ6iQnHsBjEMaZ7p_qSX_7VqjRGlKnA-1758846120228-0.0.1.1-604800000; + - _cfuvid=OfY8fjrAvu7xrnuNxrvJZIKxZ058HNPHZn.0XUdrGOE-1758909434982-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Strict-Transport-Security: - max-age=31536000; includeSubDomains; preload @@ -4263,203 +4256,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "1746" - openai-project: - - proj_RpeV6PrPclPHBb5GlExPXSBj - openai-version: - - "2020-10-01" - x-envoy-upstream-service-time: - - "1762" - x-openai-proxy-wasm: - - v0.1 - x-ratelimit-limit-requests: - - "10000" - x-ratelimit-limit-tokens: - - "30000000" - x-ratelimit-remaining-requests: - - "9999" - x-ratelimit-remaining-tokens: - - "29998517" - x-ratelimit-reset-requests: - - 6ms - x-ratelimit-reset-tokens: - - 2ms - x-request-id: - - req_daab631b2f784a3bb549055fa04d627f - status: - code: 200 - message: OK - - request: - body: - "{\"messages\":[{\"role\":\"system\",\"content\":\"Provide a summary of - the relevant information that could help answer the question based on the excerpt. - Your summary, combined with many others, will be given to the model to generate - an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 8-9: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nrepresented - with equation 2.\\n\\n \u2206\u02C6f(\u20D7x) - \u2248\u2202\u02C6f(\u20D7x) (2)\\n \u2206xi - \ \u2202xi\\n\\n\\n\\n 7 \u2206\u02C6f(\u20D7x) - \ where \u02C6f(x) is the black-box model and are used as our attributions. - The left- \u2206xi\\n\\nhand - side of equation 2 says that we attribute each input feature xi by how much - one unit\\n\\nchange in it would affect the output of \u02C6f(x). If \u02C6f(x) - is a linear surrogate model, then this\\n\\nmethod reconciles with LIME.35 In - DL models, \u2207xf(x), suffers from the shattered gradients\\n\\nproblem.62 - This means directly computing the quantity leads to numeric problems. The\\n\\ndifferent - gradient based approaches are mostly distinguishable based on how the gradient - is\\n\\napproximated.\\n\\n Gradient based explanations have been widely used - to interpret chemistry predictions.60,66\u201370\\n\\nMcCloskey et al. 60 used - graph convolutional networks (GCNs) to predict protein-ligand\\n\\nbinding and - explained the binding logic for these predictions using integrated gradients.\\n\\nPope - et al. 66 and Jim\xB4enez-Luna et al. 67 show application of gradCAM and integrated - gradi-\\n\\nents to explain molecular property predictions from trained graph - neural networks (GNNs).\\n\\nSanchez-Lengeling et al. 68 present comprehensive, - open-source XAI benchmarks to explain\\n\\nGNNs and other graph based models. - They compare the performance of class activation\\n\\nmaps (CAM),63 gradCAM,64 - smoothGrad,,65 integrated gradients62 and attention mecha-\\n\\nnisms for explaining - outcomes of classification as well as regression tasks. They concluded\\n\\nthat - CAM and integrated gradients perform well for graph based models. Another attempt\\n\\nat - creating XAI benchmarks for graph models was made by Rao et al. 70. They compared\\n\\nthese - gradient based methods to find subgraph importance when predicting activity - cliffs\\n\\nand concluded that gradCAM and integrated gradients provided the - most interpretability\\n\\nfor GNNs. The GNNExplainer69 is an approach for - generating explanations (local and\\n\\nglobal) for graph based models. This - method focuses on identifying which sub-graphs con-\\n\\ntribute most to the - prediction by maximizing mutual information between the prediction\\n\\nand - distribution of all possible sub-graphs. Ying et al. 69 show that GNNExplainer - can be\\n\\nused to obtain model-agnostic explanations. SubgraphX is a similar - method that explains\\n\\nGNN predictions by identifying important subgraphs.71\\n\\n - \ Another set of approaches like DeepLIFT72 and Layerwise Relevance backPropagation73\\n\\n\\n\\n - \ 8(LRP) are based on backpropagation of - the prediction scores through each layer of the neu-\\n\\nral network. The specific - backpropagation logic across various activation functions differs\\n\\nin these - approaches, which means each layer must have its own implementation. Baldas-\\n\\nsarre - and Azizpour 74 showed application of LRP to explain aqueous solubility prediction - for\\n\\nmolecules.\\n\\n SHAP is a model-agnostic feature attribution method - that is inspired from the game\\n\\ntheory concept of Shapley values.44,46 SHAP - has been popularly used in explaining molecular\\n\\nprediction models.75\u201378 - It\u2019s an additive feature contribution approach, which assumes that\\n\\nan - explanation model is a linear combination of binary variables z. If the Shapley - value\\nfor the ith feature is \u03D5i, then the explanation is \u02C6f(\u20D7x) - = Pi \u03D5i(\u20D7x)zi(\u20D7x). Shapley values for\\n\\nfeatures are computed - using Equation 3.79,80\\n\\n\\n\\n M\\n 1\\n - \ \u03D5i(\u20D7x) = X \u02C6f (\u20D7z+i) - \u2212\u02C6f (\u20D7z\u2212i) (3)\\n M\\n\\n - \ Here \u20D7z is a fabricated example created from the original \u20D7x and - a random perturbation \u20D7x\u2032.\\n\\n\u20D7z+i has the feature i from \u20D7x - and \u20D7z\u2212i has the ith feature from \u20D7x\u2032. Some care should - be taken\\n\\nin constructing \u20D7z when working with molecular descriptors - to ensure that an impossible \u20D7z is\\n\\nnot sampled (e.g., high count of - acid groups but no hydrogen bond donors). M is the sample\\n\\nsize of perturbations - around \u20D7x. Shapley value computation is expensive, hence M is chosen\\n\\naccordingly. - Equation 3 is an approximation and gives contributions with an expectation\\nterm - as \u03D50 + Pi=1 \u03D5i(\u20D7x) = \u02C6f(\u20D7x).\\n\\n Visualization - based feature attribution has also been used for molecular data. In com-\\n\\nputer - science, saliency maps are a way to measure spatial feature contribution.81 - Simply put,\\n\\nsaliency maps draw a connection between the model\u2019s neural - fingerprint components (trained\\n\\nweights) and input features. Weber et al. - 82 used saliency maps to build an explainable GCN\\n\\narchitecture that gives - subgraph importance for small molecule activity prediction. On the\\n\\nother - hand, similarity maps compare model predictions for two or more molecules based - on\\n\\ntheir chemical fingerprints.83 Similarity maps provide atomic weights - or predicte\\n\\n------------\\n\\nQuestion: What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate - connection: - - keep-alive - content-length: - - "6230" - content-type: - - application/json - host: - - api.openai.com - user-agent: - - AsyncOpenAI/Python 1.109.0 - x-stainless-arch: - - arm64 - x-stainless-async: - - async:asyncio - x-stainless-lang: - - python - x-stainless-os: - - MacOS - x-stainless-package-version: - - 1.109.0 - x-stainless-raw-response: - - "true" - x-stainless-read-timeout: - - "60.0" - x-stainless-retry-count: - - "0" - x-stainless-runtime: - - CPython - x-stainless-runtime-version: - - 3.13.5 - method: POST - uri: https://api.openai.com/v1/chat/completions - response: - body: - string: !!binary | - H4sIAAAAAAAAA4xU224jNwx991cQemoB20ic6+YtGxRtCmywRYJF0Hph0BJnho1G0oqc1G6Qfy8k - O7GzTYG+GGMdXg4PL08jAMPOXICxHartk59c/frty4P+bmdnv328v/K3d/cHpzeXB6ef8ePZrRkX - j7j8k6y+eE1t7JMn5Rg2sM2ESiXq4dnJ+fnx6eHsoAJ9dOSLW5t0chwns4PZ8eTwcDI72Dp2kS2J - uYA/RgAAT/W3UAyOVuYCapj60pMItmQuXo0ATI6+vBgUYVEMasY70MagFCrrp3kAmBsZ+h7zem4u - YG7uOgJaWcpJwbHYQYQEfloljxxw6Qkus3LDltHDdVDynlsKluCH+8vrH6En7aKTMSTMynbwmP0a - OIB2BDX3SiE2wEEpp0zKoYWUybEtygk0OfbQo+04EHjCHIpFlUzGwMH6wZUXR5R2OAYHbcbUTZYo - 5Lb2U7hW6LjtPLedSrFwTEG3RphSjmg7EvD8QJVTm0vPXi1lXD+vLj+Na46fb262WlAew18d2w4w - EwwlnkagDfimouUa2FFQbtaFKvcp5tIVaAh1yCQQM8iwrPxlCp8K9wm2IYqyfVF0Q/H2l8vPRQVJ - nMmV0LcdJk9reEQ/0Fa+Fnsqgse8Hld66CWCI7GZl+SgifklOaBq5uVQqE7hC8uAnv/G8heUbBf4 - WwkrQylUQNAzBbuuYgj37DGzrqHHJDVTXwqNYZtkCI5yGUG3aaKnOhHgUHFfoyncdST0WityX9Ts - 8aEMTVmr1bal0MdMu+mpE7lc76r4bpo0giSyZWCBQxreil5Cx1DaPJ2b8WYbMnl6xGBpITZmKlvx - YQuVJi+4x5akPDfohebheX+7MjWDYFnuMHi/B2AIUausda+/bpHn1032sU05LuU7V9NwYOkWmVBi - KFsrGpOp6PMI4Gu9GMObI2BSjn3ShcYHqukOj45ONgHN7kjtwSezLapR0e8Bx+dn43dCLhwpspe9 - s2NsWSS3893dKBwcxz1gtFf4v/m8F3tTPIf2/4TfAdZSUnKL3UC8Z5apXPH/MnsVuhI2QvmRLS2U - KZdmOGpw8JsDa2QtSv2i4dCW6eTNlW3Swn1o6PTk7IiWZvQ8+gcAAP//AwDC9QDVbgYAAA== - headers: - Access-Control-Expose-Headers: - - X-Request-ID - CF-RAY: - - 984e9abb9d3417d2-SJC - Connection: - - keep-alive - Content-Encoding: - - gzip - Content-Type: - - application/json - Date: - - Fri, 26 Sep 2025 00:22:02 GMT - Server: - - cloudflare - Strict-Transport-Security: - - max-age=31536000; includeSubDomains; preload - Transfer-Encoding: - - chunked - X-Content-Type-Options: - - nosniff - alt-svc: - - h3=":443"; ma=86400 - cf-cache-status: - - DYNAMIC - openai-organization: - - future-house-xr4tdh - openai-processing-ms: - - "2167" + - "3546" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2179" + - "3635" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -4469,13 +4272,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998507" + - "29998558" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_9564f94c05ae45fb879a00abbd734d03 + - req_60c310d5bd8f46feb7c82a8337e24d7f status: code: 200 message: OK @@ -4485,47 +4288,44 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 22-25: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nut - to models informs the XAI method.\\n\\n\\nConclusion and outlook\\n\\n\\nWe - should seek to explain molecular property prediction models because users are - more\\n\\nlikely to trust explained predictions, and explanations can help assess - if the model is learning\\n\\nthe correct underlying chemical principles. We - also showed that black-box modeling first,\\n\\nfollowed by XAI, is a path to - structure-property relationships without needing to trade\\n\\nbetween accuracy - and interpretability. However, XAI in chemistry has some major open\\n\\nquestions, - that are also related to the black-box nature of the deep learning. Some are\\n\\n\\n\\n - \ 22highlighted below:\\n\\n\\n \u2022 - Explanation representation: How is an explanation presented \u2013 text, a molecule, - attri-\\n\\n butions, a concept, etc?\\n\\n\\n \u2022 Molecular distance: - \ in XAI approaches such as counterfactual generation, the \u201Cdis-\\n\\n - \ tance\u201D between two molecules is minimized. Molecular distance is subjective. - Possibil-\\n\\n ities are distance based on molecular properties, synthesis - routes, and direct structure\\n\\n comparisons.\\n\\n\\n \u2022 Regulations: - As black-box models move from research to industry, healthcare, and\\n\\n environmental - settings, we expect XAI to become more important to explain decisions\\n\\n - \ to chemists or non-experts and possibly be legally required. Explanations - may need\\n\\n to be tuned for be for doctors instead of chemists or to - satisfy a legal requirement.\\n\\n\\n \u2022 Chemical space: Chemical space - is the set of molecules that are realizable; \u201Crealiz-\\n\\n able\u201D - can be defined from purchasable to synthesizable to satisfied valences. What - is\\n\\n most useful? Can an explanation consider nearby impossible molecules? - How can we\\n\\n generate local chemical spaces centered around a specific - molecule for finding counter-\\n\\n factuals or other instance explanations? - \ Similarly, can \u201Cactivity cliffs\u201D be connected\\n\\n to explanations - and the local chemical space.149\\n\\n\\n \u2022 Evaluating XAI : there is - a lack of a systematic framework (quantitative or qualitative)\\n\\n to - evaluate correctness and applicability of an explanation. Can there be a universal\\n\\n - \ framework, or should explanations be chosen and evaluated based on the - audience and\\n\\n domain? For example, work by Rasmussen et al. 58 attempts - to focus on comparing\\n\\n feature attribution XAI methods via Crippen\u2019s - logP scores.\\n\\n\\n\\n\\n\\n 23Acknowledgements\\n\\n\\nResearch + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 22-25: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nut to + models informs the XAI method.\\n\\n\\nConclusion and outlook\\n\\n\\nWe should + seek to explain molecular property prediction models because users are more\\n\\nlikely + to trust explained predictions, and explanations can help assess if the model + is learning\\n\\nthe correct underlying chemical principles. We also showed + that black-box modeling first,\\n\\nfollowed by XAI, is a path to structure-property + relationships without needing to trade\\n\\nbetween accuracy and interpretability. + However, XAI in chemistry has some major open\\n\\nquestions, that are also + related to the black-box nature of the deep learning. Some are\\n\\n\\n\\n 22highlighted + below:\\n\\n\\n \u2022 Explanation representation: How is an explanation presented + \u2013 text, a molecule, attri-\\n\\n butions, a concept, etc?\\n\\n\\n + \ \u2022 Molecular distance: in XAI approaches such as counterfactual generation, + the \u201Cdis-\\n\\n tance\u201D between two molecules is minimized. Molecular + distance is subjective. Possibil-\\n\\n ities are distance based on molecular + properties, synthesis routes, and direct structure\\n\\n comparisons.\\n\\n\\n + \ \u2022 Regulations: As black-box models move from research to industry, healthcare, + and\\n\\n environmental settings, we expect XAI to become more important + to explain decisions\\n\\n to chemists or non-experts and possibly be legally + required. Explanations may need\\n\\n to be tuned for be for doctors instead + of chemists or to satisfy a legal requirement.\\n\\n\\n \u2022 Chemical space: + Chemical space is the set of molecules that are realizable; \u201Crealiz-\\n\\n + \ able\u201D can be defined from purchasable to synthesizable to satisfied + valences. What is\\n\\n most useful? Can an explanation consider nearby + impossible molecules? How can we\\n\\n generate local chemical spaces centered + around a specific molecule for finding counter-\\n\\n factuals or other + instance explanations? Similarly, can \u201Cactivity cliffs\u201D be connected\\n\\n + \ to explanations and the local chemical space.149\\n\\n\\n \u2022 Evaluating + XAI : there is a lack of a systematic framework (quantitative or qualitative)\\n\\n + \ to evaluate correctness and applicability of an explanation. Can there + be a universal\\n\\n framework, or should explanations be chosen and evaluated + based on the audience and\\n\\n domain? For example, work by Rasmussen et + al. 58 attempts to focus on comparing\\n\\n feature attribution XAI methods + via Crippen\u2019s logP scores.\\n\\n\\n\\n\\n\\n 23Acknowledgements\\n\\n\\nResearch reported in this work was supported by the National Institute of General Medical\\n\\nSciences of the National Institutes of Health under award number R35GM137966. This work\\n\\nwas supported by the NSF under awards 1751471 and 1764415. We thank the Center for\\n\\nIntegrated @@ -4568,7 +4368,7 @@ interactions: connection: - keep-alive content-length: - - "6218" + - "6048" content-type: - application/json host: @@ -4600,26 +4400,25 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//jFRNbxs3EL3rVwx4lgRLtWJHN6MNULuXHoIgQBUII3J2dyouSQ9nZQuG - /3tBrmwpTQr0ssDy8T3OvPl4mQAYdmYNxnaotk9+9uvD45f9gZ/C6jddDQ/S//7pefXlz89Xjw94 - a6aFEXd/k9U31tzGPnlSjmGErRAqFdXFzer29vrDYnlVgT468oXWJp1dx9nyank9Wyxmy6sTsYts - KZs1/DUBAHip3xJicPRs1lBl6klPOWNLZv1+CcBI9OXEYM6cFYOa6Rm0MSiFGvXLJgBsTB76HuW4 - MWvYmK9391OIAp+ek0cOuPMEd6LcsGX0cB+UvOeWgqUpcAaEnrSLDoZMDjQCjURIQo5tcSNDj45g - d4Q+erKDR4EkMZHo8eIaVFvyHO4VkPtcxDgUEzMVdQGVIStgcEAhD0KgHeqJBigEnlAChxZsFCGr - YDvq2aKHJBwsJ095Dl/v7sFigI58gp2wa4sSQYsJdqRPRAF2Hu1+tovP7/LBAQclSUJaXeGQue00 - wxNrFweFUn+JPecSAVo7CNrjHP6gI9gOvafQUgYONQAO1g+OQCgJZQqK1YPYjAaG+pun4KjhmtLZ - O1eLWvxHh0kLeMmBJpY7TUNCQQEHx6VauVTVU4sehB4HFuopaJ5WbpTq2ptbOWGVDw4cHcjHVOB8 - zEo9KltoBHt6irIfX6MD+gF/iGQOnzvKBJgTWR1LZGWojYS52tDHQ/VEIyRBq/V5TMmzPWXDATi4 - IaswZfC8J+gIvXa2yI3NcGCJoaRTYrc13fnGTMf2FvJ0KH5ts41Cpc0XVyesNO2We2wpl/MGfaZN - eL2cF6FmyFjGNQzeXwAYQhyrVif12wl5fZ9NH9skcZf/RTWloLnblsaOocxh1phMRV8nAN/qDhi+ - G2tTGivpVuOe6nOL69VqFDTntXMB//KGalT0F8Dq42l5fC+5daTIPl8sEmPRduTO3PPWKT0VL4DJ - ReI/xvMz7TF5Du3/kT8D1lJSctvz1vjZNaGyl//r2rvRNWCTSQ5saatMUorhqMHBjyvTjC2/bTi0 - ZfJ53JtN2q4+LHbL1c2N25nJ6+QfAAAA//8DABQB9a9ABgAA + H4sIAAAAAAAAAwAAAP//dFRNbxs5DL37VxA620H8kTTxLVh0UbfAAou9FFgXBi1xPKo1kpbkODGC + /PeFNE7stullDvPIp8fHj+cRgPHOLMHYFtV2OUz++HJ7PLSz/s+/5ze3Hz/l9uGvVfi8yjT//s+1 + GZeMtP1OVl+zrmzqciD1KQ6wZUKlwjr9cHN3f32/mM8r0CVHoaTtsk4WaTK7ni0m0+lkduK1bfKW + xCzh3xEAwHP9FonR0ZNZwvX49U9HIrgjs3wLAjCcQvljUMSLYlQzPoM2RaVYVT+vI8DaSN91yMe1 + WcLafH1YjSExfHzKAX3EbSB4YPWNtx4DrKJSCH5H0dIYvIDzYnsRcuAjaEtQ+Z8UUgNdCmT7gAyZ + UybWI2Qm523xCKoLcgUrBfSdgCbocE8XIQJdYgIflTgzaRWD0YFyL/qYWNsjbAtpOnjn4w6oqI44 + JDeJqyJH1stAh45Kwjag3U+26elNxNeHFXSkbXICFiO0FDKgCInAY0vaEp9iAZkgEHIsD9rETFbB + ttR5iwEy+2h9DiRVaQnOyOqrD+EITIEOGBVQijihV1pljOKrMz5qgsZTcALB7wlawqCtLWSFlOLB + c4odRcUAYn3pxhV8oSPYFkOguCMp/ShV+WhD7wiYMpOUlPpGan4wawyOGl9LOnfN1ekpjWba9QE1 + cSngv94zlcdlXDkSVydeHZCMJaUIdXSgkHKB5ShKHaq30DB29Jh4P7SIDhh61J/bd7U242E+T5ZZ + 2ohNTGVO79fx5XKomZpesOxU7EO4ADDGNFRc1+nbCXl5W6CQdpnTVn5KNcUMaTdMKCmWZRFN2VT0 + ZQTwrS5q/8Pumcypy7rRtKf63HQxvRsIzfk2XMDT2xOqSTFcADfzxfgdyo0jRR/kYtuNRduSu8id + 3Z2vA/bOpzN2Pbqo/VdJ79EP9fu4u2D5Lf0ZsJayktucd/m9MKZyP38X9uZ1FWyE+OAtbdQTl344 + arAPw2kzw3RtGh935VT44b41eePuG7q9+TCnrRm9jP4HAAD//wMAkoAL/+gFAAA= headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9ab9afc4b976-SJC + - 9854a475f9c5fa32-SJC Connection: - keep-alive Content-Encoding: @@ -4627,7 +4426,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:22:02 GMT + - Fri, 26 Sep 2025 17:57:15 GMT Server: - cloudflare Strict-Transport-Security: @@ -4643,13 +4442,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2672" + - "1819" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2693" + - "1842" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -4659,13 +4458,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998519" + - "29998560" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_73ca3fed71514b159b52dc39f7812c18 + - req_e85c8b47bf8e40c7b4c16e827cf86e5b status: code: 200 message: OK @@ -4675,14 +4474,12 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 12-14: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nnterfactual + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 12-14: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nnterfactual approach, contrastive approach employ a dual\\n\\noptimization method, which works by generating a similar and a dissimilar (counterfactuals)\\n\\nexample. Contrastive explanations can interpret the model by identifying contribution @@ -4755,7 +4552,7 @@ interactions: connection: - keep-alive content-length: - - "6175" + - "6005" content-type: - application/json host: @@ -4787,27 +4584,26 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//dFRNcxs3DL3rV2B46WXlsRR/RTeP66ROxpkeOmk6VUZDkdglKi65IUA5 - qsf/vcNdWdrUzmUPfMDDe1gAjxMARVYtQBmnxbSdn958+PZ5w3/tfv2gze1FrNvf3cfZn7+9ceuH - dx9VVTLi+h808px1YmLbeRSKYYBNQi1YWGeX51dXZxez+WkPtNGiL2lNJ9OzOJ2fzs+ms9l0frpP - dJEMslrA3xMAgMf+WyQGi9/VAnqa/qVFZt2gWhyCAFSKvrwozUwsOoiqjqCJQTD0qh+XAWCpOLet - TrulWsBSfbm+qyAmuP3eeU1Brz3CdRKqyZD2cBcEvacGg8EKEtaYGCRCi+KiZdDBgqBxgb5lZBCn - BVq9QRCH0CW0ZEqDGGIN13fQd4KBgmDqEkpfrnDkYDEV7bZ/kggutzrwCdzEXKJrbSRrD1h0Bj2Q - 6oSgYYM7kBg9UIDeTpfiliyFBnRfvVBWQKHwG5x63GIJZmqcMKx3QBaDUL0rKcbp0GDRCBS6LFCj - lpyezT3E7C1oL5h6j72jX3jk9QT+cMj4UmmDAVOZEBCXYm4cxE6opX/7mFEbK+BsHGiGlkIJKLr2 - MsDSYAMeHHkEDJxTb3WvvAgfi7kL0EaPJnudwDhsiSXtKjA/9JXBoe8gBxO3mIC7nChmhoR+cOCo - G/4256ZBlmK8DMneXxmJQxWWlM2+ZxG0cYRbBItMCS3ELCa2yCfwWQ9F9sNUgacNwv399c1tBTfv - pu8/fdqPJaaqL35/+74Cp7cIa8QAtvzJ2JWOxkN7XzirYwJLdY0Jg4DsOuzHcT+LhdZq0SdLVQ37 - kdDjtrR4xSYmLHvydg9lRruiVjfI5bnWnnEZnsb7lrDOrMu6h+z9CNAhRBnaVTb96x55Ouy2j02X - 4pr/l6pqCsRulVBzDGWPWWKnevRpAvC1vyH5h7OguhTbTlYSN9iXm83ns4FQHc/WCL54RiWK9iPg - zdW8eoVyZVE0eR4dImW0cWhHubPz+cGEzpbiETudjLy/lPQa/eCfQjNi+Sn9ETAGO0G7Ou7Ea2EJ - y2n/Wdih171gxZi2ZHAlhKn8D4u1zn64uop3LNiuagpNuXE0nN66W51fzNbz88tLu1aTp8l/AAAA - //8DAEhH6F6DBgAA + H4sIAAAAAAAAAwAAAP//jFRNbxs3EL3rVwx46WVlSLaURLo5hhu4hYMUCIoCVSBQ5Ozu1Fxyyxmu + rRr+7wW5iqU4DpDLHvjm4817O/M4AVBk1RqUabWYrnfTq9/f7O//oLn77dN7vzqffXq4WS0/z8Lt + 8D6iqnJG2P2DRr5mnZnQ9Q6Fgh9hE1EL5qrzt8t3q9lqcbEoQBcsupzW9DJdhOn57Hwxnc+n57ND + YhvIIKs1/D0BAHgs30zRW3xQa5hVX186ZNYNqvVzEICKweUXpZmJRXtR1RE0wQv6wvpx4wE2ilPX + 6bjfqDVs1F+XNxWECNcPvdPk9c4hXEahmgxpBzde0Dlq0BusgPwQ3IAMHUobLIMEIC8Y+4gC2ltI + 3mLMHCxc3kAZHPqIlkzWic/gKqScUGsjSTvA3NbrAoKOCBrucA8SggPyUNj1MQxkyTe5VyKhASvQ + pWChS56paYVhtwey6IXqfY42rfYNcq5Dvk8CNWpJERmk1QL3ITkL2glGkBZHsr/wCd0z+Nwi4/ck + G/QYs9cgbQypaSH0Qh39V2JA0LSe/k3IFXAyLWiGjnwOyLxyM1uMMgg7lHtEDzqTPLxl9UgYzLda + 3bfkENBzirmMBkt1jRG9nHCGkMSEDl8qPRLvdRQyyeno9pAY61RkrgmdZXB0h2Ba7Igl7qEOEZI3 + YcDSj/sUKSSGiG7UoqWeC1lOTYMshdXRl4P+JxoEe7CmCw4LDWCJyRRXzuBPPTY4/F3VSOj29vLq + uoKrX6cfPn48/KYYq9L49vpDBa0eso7oweKALvTZl/Bs0gsZucx1lM5q0SD7HsdRyl/AZxtVjcsS + 0eGQXdmyCRHz0qw2/ul0wyLWiXVecJ+cOwG090FGqfJufzkgT8/b7ELTx7DjF6mqJk/cbiNqDj5v + LkvoVUGfJgBfytVI3xwC1cfQ9bKVcIel3Xz+bjEWVMdDdQIvlwdUgmh3AlxcrKpXSm4tiibHJ6dH + GW1atMfc453SyVI4ASYng3/P57Xa4/Dkm58pfwSMwV7Qbo8b8VpYxHzJfxT2LHQhrBjjQAa3Qhiz + GRZrndx4ZBXvWbDb1uSbfAZpvLR1v7WrGt8s317gTk2eJv8DAAD//wMAzsZDG3IGAAA= headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9abadcd916a6-SJC + - 9854a47aad58ce48-SJC Connection: - keep-alive Content-Encoding: @@ -4815,7 +4611,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:22:02 GMT + - Fri, 26 Sep 2025 17:57:16 GMT Server: - cloudflare Strict-Transport-Security: @@ -4831,13 +4627,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2483" + - "1965" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2553" + - "2008" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -4847,13 +4643,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998523" + - "29998564" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_6e69c433a3af4791a7383694588a7cdf + - req_41064b44e5c242d599fb9b24ce4394c1 status: code: 200 message: OK @@ -4863,13 +4659,199 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 8-9: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nrepresented + with equation 2.\\n\\n \u2206\u02C6f(\u20D7x) + \u2248\u2202\u02C6f(\u20D7x) (2)\\n \u2206xi + \ \u2202xi\\n\\n\\n\\n 7 \u2206\u02C6f(\u20D7x) + \ where \u02C6f(x) is the black-box model and are used as our attributions. + The left- \u2206xi\\n\\nhand + side of equation 2 says that we attribute each input feature xi by how much + one unit\\n\\nchange in it would affect the output of \u02C6f(x). If \u02C6f(x) + is a linear surrogate model, then this\\n\\nmethod reconciles with LIME.35 In + DL models, \u2207xf(x), suffers from the shattered gradients\\n\\nproblem.62 + This means directly computing the quantity leads to numeric problems. The\\n\\ndifferent + gradient based approaches are mostly distinguishable based on how the gradient + is\\n\\napproximated.\\n\\n Gradient based explanations have been widely used + to interpret chemistry predictions.60,66\u201370\\n\\nMcCloskey et al. 60 used + graph convolutional networks (GCNs) to predict protein-ligand\\n\\nbinding and + explained the binding logic for these predictions using integrated gradients.\\n\\nPope + et al. 66 and Jim\xB4enez-Luna et al. 67 show application of gradCAM and integrated + gradi-\\n\\nents to explain molecular property predictions from trained graph + neural networks (GNNs).\\n\\nSanchez-Lengeling et al. 68 present comprehensive, + open-source XAI benchmarks to explain\\n\\nGNNs and other graph based models. + They compare the performance of class activation\\n\\nmaps (CAM),63 gradCAM,64 + smoothGrad,,65 integrated gradients62 and attention mecha-\\n\\nnisms for explaining + outcomes of classification as well as regression tasks. They concluded\\n\\nthat + CAM and integrated gradients perform well for graph based models. Another attempt\\n\\nat + creating XAI benchmarks for graph models was made by Rao et al. 70. They compared\\n\\nthese + gradient based methods to find subgraph importance when predicting activity + cliffs\\n\\nand concluded that gradCAM and integrated gradients provided the + most interpretability\\n\\nfor GNNs. The GNNExplainer69 is an approach for + generating explanations (local and\\n\\nglobal) for graph based models. This + method focuses on identifying which sub-graphs con-\\n\\ntribute most to the + prediction by maximizing mutual information between the prediction\\n\\nand + distribution of all possible sub-graphs. Ying et al. 69 show that GNNExplainer + can be\\n\\nused to obtain model-agnostic explanations. SubgraphX is a similar + method that explains\\n\\nGNN predictions by identifying important subgraphs.71\\n\\n + \ Another set of approaches like DeepLIFT72 and Layerwise Relevance backPropagation73\\n\\n\\n\\n + \ 8(LRP) are based on backpropagation of + the prediction scores through each layer of the neu-\\n\\nral network. The specific + backpropagation logic across various activation functions differs\\n\\nin these + approaches, which means each layer must have its own implementation. Baldas-\\n\\nsarre + and Azizpour 74 showed application of LRP to explain aqueous solubility prediction + for\\n\\nmolecules.\\n\\n SHAP is a model-agnostic feature attribution method + that is inspired from the game\\n\\ntheory concept of Shapley values.44,46 SHAP + has been popularly used in explaining molecular\\n\\nprediction models.75\u201378 + It\u2019s an additive feature contribution approach, which assumes that\\n\\nan + explanation model is a linear combination of binary variables z. If the Shapley + value\\nfor the ith feature is \u03D5i, then the explanation is \u02C6f(\u20D7x) + = Pi \u03D5i(\u20D7x)zi(\u20D7x). Shapley values for\\n\\nfeatures are computed + using Equation 3.79,80\\n\\n\\n\\n M\\n 1\\n + \ \u03D5i(\u20D7x) = X \u02C6f (\u20D7z+i) + \u2212\u02C6f (\u20D7z\u2212i) (3)\\n M\\n\\n + \ Here \u20D7z is a fabricated example created from the original \u20D7x and + a random perturbation \u20D7x\u2032.\\n\\n\u20D7z+i has the feature i from \u20D7x + and \u20D7z\u2212i has the ith feature from \u20D7x\u2032. Some care should + be taken\\n\\nin constructing \u20D7z when working with molecular descriptors + to ensure that an impossible \u20D7z is\\n\\nnot sampled (e.g., high count of + acid groups but no hydrogen bond donors). M is the sample\\n\\nsize of perturbations + around \u20D7x. Shapley value computation is expensive, hence M is chosen\\n\\naccordingly. + Equation 3 is an approximation and gives contributions with an expectation\\nterm + as \u03D50 + Pi=1 \u03D5i(\u20D7x) = \u02C6f(\u20D7x).\\n\\n Visualization + based feature attribution has also been used for molecular data. In com-\\n\\nputer + science, saliency maps are a way to measure spatial feature contribution.81 + Simply put,\\n\\nsaliency maps draw a connection between the model\u2019s neural + fingerprint components (trained\\n\\nweights) and input features. Weber et al. + 82 used saliency maps to build an explainable GCN\\n\\narchitecture that gives + subgraph importance for small molecule activity prediction. On the\\n\\nother + hand, similarity maps compare model predictions for two or more molecules based + on\\n\\ntheir chemical fingerprints.83 Similarity maps provide atomic weights + or predicte\\n\\n------------\\n\\nQuestion: What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - "6060" + content-type: + - application/json + host: + - api.openai.com + user-agent: + - AsyncOpenAI/Python 1.109.0 + x-stainless-arch: + - arm64 + x-stainless-async: + - async:asyncio + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.109.0 + x-stainless-raw-response: + - "true" + x-stainless-read-timeout: + - "60.0" + x-stainless-retry-count: + - "0" + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.5 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: !!binary | + H4sIAAAAAAAAAwAAAP//jFTbbhs3EH3XVwz41AIrwXZsx/KbEfTiBkmDJkhbVIEwImd3J+aSLGfW + kWL43wvu6ubUBfoiaHnmcuZy5mECYNiZazC2RbVd8tNXry836/r2x9d/fgmX7z7etr+s49XJ/Pdf + u6+/fTZV8Yirz2R15zWzsUuelGMYYZsJlUrU05cXV/OT+fmLiwHooiNf3Jqk0/M4PTs5O5+enk7P + TraObWRLYq7hrwkAwMPwWygGR2tzDSfV7qUjEWzIXO+NAEyOvrwYFGFRDGqqA2hjUAoD64dFAFgY + 6bsO82ZhrmFhPrQEtLaUk4Jjsb0ICfywTh454MoT3GTlmi2jh9ug5D03FCzBd3/c3H4PHWkbnUAv + 5EAjcFDKKZNCyuTYlvYIdOgIVhvo0LYcCDxhDhwaGFojFSTMyrb3mP0GOIC2BAPztUKsoYueBhQw + OGgypna6wpLSoeIMbhVablrPTatScMcUdGuCKeWItiUBz3c0UGxyGdXeUqrh76ubN9WQ4ae3b7ct + oFzBl5ZtCzQ+PKlrtQF2FJTrTamGuxRzGQDUhNpnEogZpF8NjGUGb0q5U2xCFGW7b95A6/3PN+8q + GCnHAO9bTJ42cI++J6kAMwF6ieBIbOYVOahj3iUCVM286gutGXxk6dHzVyyfoGTbwH/3JCC9bQEF + BD1TsGUgSYaKhTv2mFl3b5mgK5XFsM3UB0e5rJcrpUpCLSuxy1+GtSMwRizywDwOeTe9o97N4ENL + QvseIHdlfzq8IxiVtd5uB3Qx02GxhqVcbQ4Vh+bJTDSCJLJlZ4FD6r8ZhubeDl+zhalGQWTydI/B + 0lJszFSEMV+Ex2MVZap7wSLi0Ht/BGAIUYc+D/r9tEUe94r1sUk5ruQbV1NzYGmXmVBiKOoUjckM + 6OME4NNwGfonYjcpxy7pUuMdDelOz+ZXY0BzOEZH8Pn5FtWo6J8AZ9UzIZeOFNnL0XkxtijHHXwP + twh7x/EImBwV/m8+z8Uei+fQ/J/wB8BaSkpueZj6c2aZyrX+L7N9owfCRijfs6WlMuUyDEc19n48 + pEY2otQtaw5NWUEer2mdlm5e0+XFyxe0MpPHyT8AAAD//wMACaV/+1YGAAA= + headers: + Access-Control-Expose-Headers: + - X-Request-ID + CF-RAY: + - 9854a480cdbb67fb-SJC + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Fri, 26 Sep 2025 17:57:16 GMT + Server: + - cloudflare + Strict-Transport-Security: + - max-age=31536000; includeSubDomains; preload + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + alt-svc: + - h3=":443"; ma=86400 + cf-cache-status: + - DYNAMIC + openai-organization: + - future-house-xr4tdh + openai-processing-ms: + - "1609" + openai-project: + - proj_RpeV6PrPclPHBb5GlExPXSBj + openai-version: + - "2020-10-01" + x-envoy-upstream-service-time: + - "1630" + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-requests: + - "10000" + x-ratelimit-limit-tokens: + - "30000000" + x-ratelimit-remaining-requests: + - "9999" + x-ratelimit-remaining-tokens: + - "29998547" + x-ratelimit-reset-requests: + - 6ms + x-ratelimit-reset-tokens: + - 2ms + x-request-id: + - req_7e807fb1cdc642c08d06ac682a13ad31 + status: + code: 200 + message: OK + - request: + body: + "{\"messages\":[{\"role\":\"system\",\"content\":\"Provide a summary of + the relevant information that could help answer the question based on the excerpt. + Your summary, combined with many others, will be given to the model to generate + an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt from Wellawatte2023 pages 14-16: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nsame optimization problem.100 Grabocka\\n\\net al. 111 have developed a method named Adversarial Training on EXplanations (ATEX)\\n\\nwhich improves model robustness @@ -4944,7 +4926,7 @@ interactions: connection: - keep-alive content-length: - - "50934" + - "50764" content-type: - application/json host: @@ -4976,26 +4958,26 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA3RUy27rNhDd+ysGXLWAbcSJ84B3QXDRpoXRLIL2FvWFTZFjaRKKZIbD1EaQfy8o - ObZ6HxsteGZG55x5vI0AFFm1AGUaLaaNbnL328ufz3//+suMHpZPjw9m9+Qu/n356495zg+/q3HJ - CNUTGvnImprQRodCwfewYdSCpers+vLmZn41Oz/rgDZYdCWtjjKZh8n52fl8MptNzs8OiU0gg0kt - 4J8RAMBb9y0UvcWdWkBXpntpMSVdo1ocgwAUB1delE6JkmgvanwCTfCCvmO92WyeUvAr/7byACuV - cttq3q/UAlbq8+39GALDp110mryuHMItC23JkHZw7wWdoxq9wTEwbpETSIAWpQk2gfYWBE3j6SVj - gpzQFpi8IEdG6QKyt8iFoQVpECwaShR8glZbhGoPrTYNeQSHmj35Gjrr0hiiZiGTnWa3B4sYvw6Z - wr3vinZ6dwJhC6bBlpLwfgyfb++BEugYHfXMsjfhFRmScDaSGSeRQ0SWPTA6XdqaGoppDCmbBnSC - yGjJSPln5UKwk4o1eag0MyFDRG6xyys2puByRY5kX5i0waHJDtMUHk8mOXpGWPaQZlgWIXBb+5CE - DNyFXMzbaiNZu74vvucFPy2Xt3effu5MtZgMU5TAgMMYzXhsQ+TwShZBm4J1vSWfqG5koC/lusYk - vacfpNpgywgcapaOtqUYwsEt+tDRy/+QPDRgCo8NJvyW3at2ueOyDXyalN5fbZ4nVdgdutsJrTPZ - AuIuIlOLXrQr6qn205Ua9zPN6PBVe4PrZAJjme3Z2QErbqyp1TWm8i6cceXfV36z2Qw3hnGbky4L - 67NzA0B7H6TnX3b1ywF5P26nC3XkUKWvUtWWPKVmzahT8GUTk4SoOvR9BPCluwL5f4utIoc2ylrC - M3a/m13dXPcF1enwDOD51QGVINoNgJuLi/F3Sq4tiiaXBqdEGW0atIPcy4urowidLYUTdjYaaP+W - 0vfK9/rJ14MqPyx/AozBKGjXH/s3lH0KYyzH+UdhR687wiohv5LBtRBy6YfFrc6uv5sq7ZNgu96S - r8tEUn88t3F9eTWrzi+vr22lRu+j/wAAAP//AwCjjuu6RQYAAA== + H4sIAAAAAAAAA3RUTW8bNxC961cMeEoAyZBsy6p8E5wUMFJfigYwUAUCl5zdnZpLMsOhatXwfw+4 + u7LktrnsgW/m7Xvz9TIBUGTVLSjTajFddLO7LzeH/e93X/6hR1x9/fT1rlqv44OmXzff2yc1LRmh + +guNHLMuTOiiQ6HgB9gwasHCulgtf1nP19dXVz3QBYuupDVRZtdhdjm/vJ4tFrPL+ZjYBjKY1C38 + OQEAeOm/RaK3+KxuYT49vnSYkm5Q3b4FASgOrrwonRIl0V7U9ASa4AV9r/pl6wG2KuWu03zYqlvY + qsfN/RQCw+fn6DR5XTmEDQvVZEg7uPeCzlGD3uAUGGvkBBKgQ2mDTaC9BUHTevqeMUFOaAtMXpAj + o/QBOHCDtAgWDSUKPkGnLUJ1gE6bljyCQ82efAN9udIUomYhk51mdwCLGE8hHz799nGMu4D7gbk3 + +iwQajAtdpSED1N43NwDJdAxOhq0ZW/CHhmScDaSGWeRQ0SWAzA6XfqZWoppCimbFnSCyGjJSPlx + 5UKws4qLnUozEzJE5A77vFLIFFyuyJEcipIuODTZYbqAP05lcvSE8DBAmuGhGIFN40MSMnAXcilf + rY1k7YbO+EEXfHh42Nx9/tiX1WIyTFECDxU+xmjGt0ZEDnuyCNoUrO8u+URNK2f+Um4aTDLU/iiq + C7YMwcgpAUqbcF86mIjRwlg16r21mPC9ihZdhIrJNti3p9ERKpS/ET1UTpunWRWexx72dtrcaQ/Z + W+QyxJZ8M4VOPxVZhWAM7QLjab56RyU7J6yzgzowRC5ujXZD10cLF1s1Heaf0eFee4O7ZAJj2YPF + fOtfz7eGsc5Jl6X12bkzQHsfZGAs+/ptRF7fNtSFJnKo0r9SVU2eUrtj1Cn4so1JQlQ9+joB+NZf + gvxuuVXk0EXZSXjC/neLm+V4CtTp+JzB18sRlSDanQGr9RF5R7mzKJpcOjsnymjToj3LXV7dvJnQ + 2VI4YfPJmff/Svo/+sE/+eaM5af0J8AYjIJ2d1zFc9unMMZyoH8W9lbrXrBKyHsyuBNCLv2wWOvs + htup0iEJdruafFMGjYYDWsfd8mZRXS5XK1upyevkBwAAAP//AwD9KhzVSQYAAA== headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9aba6d16eb31-SJC + - 9854a4764f46172e-SJC Connection: - keep-alive Content-Encoding: @@ -5003,7 +4985,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:22:04 GMT + - Fri, 26 Sep 2025 17:57:16 GMT Server: - cloudflare Strict-Transport-Security: @@ -5019,13 +5001,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "4082" + - "3534" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "4129" + - "3561" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-input-images: @@ -5039,7 +5021,7 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29997760" + - "29997802" x-ratelimit-reset-input-images: - 0s x-ratelimit-reset-requests: @@ -5047,7 +5029,7 @@ interactions: x-ratelimit-reset-tokens: - 4ms x-request-id: - - req_fae36727d7b34f088bf5c97d00996f3c + - req_bd76b62350fe4cd8af9411dce2f9997a status: code: 200 message: OK @@ -5057,18 +5039,16 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 28-30: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n - M. T.; Singh, S.; Guestrin, C. \u201D Why should i trust you?\u201D Explaining - the\\n\\n predictions of any classifier. Proceedings of the 22nd ACM SIGKDD - international\\n\\n\\n 27 conference - on knowledge discovery and data mining. San Diego, CA, USA, 2016; pp\\n\\n 1135\u20131144.\\n\\n\\n(36) + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 28-30: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n M. T.; + Singh, S.; Guestrin, C. \u201D Why should i trust you?\u201D Explaining the\\n\\n + \ predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international\\n\\n\\n + \ 27 conference on knowledge discovery + and data mining. San Diego, CA, USA, 2016; pp\\n\\n 1135\u20131144.\\n\\n\\n(36) Dhurandhar, A.; Chen, P.-Y.; Luss, R.; Tu, C.-C.; Ting, P.; Shanmugam, K.; Das, P.\\n\\n Explanations based on the missing: Towards contrastive explanations with pertinent\\n\\n negatives. Advances in neural information processing @@ -5141,7 +5121,7 @@ interactions: connection: - keep-alive content-length: - - "6207" + - "6037" content-type: - application/json host: @@ -5173,26 +5153,25 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//jFRNbxs3EL3rVwx4SoGVYAnyR3Uzgh6USx00CNxUgUSRb3cn4pJrDlex - Y/i/F9yVLCVNgF600Lz5eo8z8zwiUmzVgpSpdTJN68Zv3z18bO7469y8vZOH3fvd3cf9+7//fPf0 - 6ZtbqiJHhO0XmHSMmpjQtA6Jgx9gE6ETctbp9eXNzfxqOpv1QBMsXA6r2jSeh/HsYjYfT6fj2cUh - sA5sIGpB/4yIiJ7739yit3hUC7oojpYGIrqCWrw6EakYXLYoLcKStE+qOIEm+ATfd73ZbL5I8Cv/ - vPJEKyVd0+j4tFILWqkPNQiPBrFN1MawZwuhiBIR3kAoBdrryKET+hriTkh7S5I6y72fy9Sz0x+P - rdPs9daBbmPikg1rR0uf4BxXORm9ub9d/jahDzUExN64zoIapDpYoTJEwpCEfUVthGWTVRYKJRmX - WZaMKAVlblFL4j2GEK97x4Kw167r/+Sg+9slsacmZ9KOuNEV+6rIJSOboeT97bLoOZVRNxgoZruF - cNV3ksFjXl/1SeVJEhqZ0DKRdhKogR9aTTWoZkkh9iUt9nChzXBBptbOwVeQ4qCiThiHcpxqjHVM - uVf2CbGNSL2QjTY1e5CDjrmXCf3VwmRtKcHUnh86CDnegSwMS+adIkD96EkxfAmPKepey6FwRNU5 - HfnboFSme1aXHacn0hEDNctiOhHY48OdDYcJzsHkd3BPVHNVO67q1ItQBtMJBU+N3mXZTqpREyJ+ - IJq76rxFzHNse1MK1AmiTFaqGOY2wmGvvcFaTIjI83tzgDqBXef3hWRzqZ1g5V9WfrPZnG9FRNmJ - zkvpO+fOAO19SMMU5X38fEBeXjfQhaqNYSs/hKqSPUu9jtASfN42SaFVPfoyIvrcb3r33fKqNoam - TesUdujLTa8u50NCdTouZ/D8cAhUCkm7M+D692PcdynXFkmzk7NzoYw2Newp9nRbdGc5nAGjM+L/ - 7ednuQfy7Kv/k/4EGIM2wa5Pq/4zt4h8fX/l9ip037ASxD0brBMj5sewKHXnhsOohvlbl+yrPHo8 - XMeyXV9eTbezy+tru1Wjl9G/AAAA//8DAFw91hUmBgAA + H4sIAAAAAAAAA4xUTW/jRgy9+1cQc2oBOYiTON74FhQ9BHvsAg1QL2x6REnczIc6pFwHQf57MSMn + VrdZYC8CNI98fI9DzssMwHBt1mBsh2p97+a/fb59Pn5eLMKel38s94/Nn9fd8mj/bpNf3ZkqZ8T9 + N7L6lnVho+8dKccwwjYRKmXWxWr56e7y7uZ6WQAfa3I5re11fhPnV5dXN/PFYn51eUrsIlsSs4a/ + ZgAAL+WbJYaajmYNl9XbiScRbMms34MATIounxgUYVEMaqozaGNQCkX1brf7JjFswssmAGyMDN5j + et6YNWzMl46AjpZSr+BYVOCAieMgkKihRMGSAIYaRIeaKR+7bBc0wu/H3iEH3DuC+6TcsGV08BCU + nOM258Ivj/cPv17Al46EgIN1Q03wT0xPAjEAjQwcWugT1WxzWwViA9ZlWw1TkgqymYSifKAxJWAJ + rIAO6Ibyk5Me7x+AA/jMhA7YY8uhrcCTJrYCTUw5piqGmoSeRiX5vCbhtijJ4BtvaAupPIuSlwt4 + UEAnETyFUSoHpdQn0tIFj7bjQOAIUyil+dQMLnCeCBnLe9Iu1mNt9n2Kh1xsQseO9Tm72ju0T/N9 + PJ7ySzen19Nx2zluOwXtKJPFpJibHxvw+JR5zx7Ax0TfyS6OJ5eJNkURqLkpNRTq6JFzv2WwHaCA + R6XE6ATEclZRge3Is2h6Hv3VaWhPTb3YmGocvUSODlnaVmxMlEfw0ya8bsJut5tOb6JmEMzLEwbn + JgCGEHW8/Lw3X0/I6/umuNj2Ke7lu1TTcGDptolQYshbIRp7U9DXGcDXspHDf5bM9Cn6Xrcan6iU + W9wuViOhOT8CE/jq9oRqVHQTYHVzXX1Aua1JkZ1M1tpYtB3V59zzG4BDzXECzCbG/6/nI+7RPIf2 + Z+jPgLXUK9Xb84Z+FJYov5I/CntvdBFshNKBLW2VKeXLqKnBwY0PmBkHddtwaPOM8viKNf22vmvo + drm6pr2Zvc7+BQAA//8DAMVbdjnOBQAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9aca0bce17d2-SJC + - 9854a4820b0cfa32-SJC Connection: - keep-alive Content-Encoding: @@ -5200,7 +5179,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:22:04 GMT + - Fri, 26 Sep 2025 17:57:17 GMT Server: - cloudflare Strict-Transport-Security: @@ -5216,13 +5195,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2273" + - "2102" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2293" + - "2321" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -5232,13 +5211,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998520" + - "29998561" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_75c177bf633d46e4857e04bf6e1d95cf + - req_3ced14c6e18b48a281270bb133188ae9 status: code: 200 message: OK @@ -5248,13 +5227,11 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt from Wellawatte2023 pages 16-20: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nssion challenge and is\\n\\nimportant for chemical process design, drug design and crystallization.133\u2013136 In our previous\\n\\nworks,9,10 we implemented @@ -5330,7 +5307,7 @@ interactions: connection: - keep-alive content-length: - - "188462" + - "188292" content-type: - application/json host: @@ -5362,27 +5339,26 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA3RU224bNxB911cM+NQCkuBVfIn1ZhgOILcFCiQxAlSBRJGzuxNxSYYzK9sx/O8F - Kdlat8nLAsszc+bM9WkEoMiqOSjTajFddJPr2+93/sefi0/V5vPtdudubu8+3v24vElN/COqcfYI - m29o5MVrakIXHQoFv4dNQi2YWauLs/fvT8+r2bsCdMGiy25NlMlpmMxOZqeTqprMTg6ObSCDrObw - zwgA4Kl8s0Rv8UHN4WT88tIhs25QzV+NAFQKLr8ozUws2osaH0ETvKAvqtfr9TcOfumflh5gqbjv - Op0el2oOS/XlajGGkODmITpNXm8cwlUSqsmQdrDwgs5Rg97gGBLWmBgkQIfSBsugvQVB03r63iOD - tFqg01sEaREsGmIKftLpLfkGYgoGmZEh1HC1gFIgBknac9QJvRQ+8oIpJpQsZgoLX8hKQg+SXfNv - TGFHFnP0BxnDl6sFEIOO0VF+DGBa7MhoVyi74ND0TieICS2Z3D0eA/emBc3AwfUbciSPxZoNepmw - pN5InxASOl08Woo8hYVAn5OQEByDo20W12fRtTbSa8djsMgmUZSQAHNlvT6EfKumJt/kXMkLw284 - baZjuLn+8Hcx++vq+vrj7zkXsuiF6kdowz1wRJPbM6DhfvOqloF87frcsGGyU/hQtOg8vPt6Ge0h - 4Q61K7za2twkbciSgSaFPmauPN4520GNQioOBpNo8lD3vsTQ7sWrFAyz8lexpaY8hU8tMgJ5pqYV - Bu2o8XBP0sLWh3t/bFsuiqHocD9kh36D1aInNtEO/ZvKQh3SfqDeZL1U4/3QJ3S4097gik1ImIe/ - OjlgPaNdUacb5Pwuqcelf1769Xo9XKmEdc86b7TvnRsA2vsgexl5mb8ekOfX9XWhiSls+D+uqiZP - 3K5yhYPPq8oSoiro8wjgazkT/ZvNVzGFLspKwhZLuFl1erknVMfLdISr8+qAShDtBn7vqsN9eUu5 - siiaHA9ujTLatGgHpJez423SvaVwxE5Gg9z/L+ln9Pv8yTcDll/SHwFjMAra1bHfPzNLmK/3r8xe - a10EK8a0I4MrIUy5HxZr3bv9YVX8yILdarCz2aSOq7PzajM7u7iwGzV6Hv0LAAD//wMA97hbFWYG - AAA= + H4sIAAAAAAAAA3RUTW/jNhC9+1cMeGoB2bDdOGlyM4JdbFAELdAegtYLgyZH0jQUyXKGjt0g/72g + lERqm70IAmfmzXvz9TwDUGTVDSjTajFddPPbny7P59PFl4d4ut/+bC8S4/Z8ubT69+v7L6oqEeHw + Jxp5i1qY0EWHQsEPZpNQCxbU1dXmx+vl9cUPl72hCxZdCWuizC/CfL1cX8xXq/l6+RrYBjLI6gb+ + mAEAPPffQtFbPKkbWFZvLx0y6wbVzbsTgErBlRelmYlFe1HVaDTBC/qe9fPOA+wU567T6bxTN7BT + D9u7CkKCT6foNHl9cAjbJFSTIe3gzgs6Rw16gxUkrDExSIAOpQ2WQXsLgqb19FdGBmm1QKcfEaRF + iAktmVKgwdGiIabg551+JN9ATMEgMzKEGrZ30NeJgbxgigmlJ1MCs7eYijLbP0mANnfa8wLufJ+p + F3mSgjMkDkeyWKidpIKH7R0Qg47RUXkMYFrsyGjXo3fBoclOpynhCjibFjQDB5cP5EjOvTcb9DJn + SdlITggJne4jWoqFkEAuiiQEx+DosZDLRVGtjWTtuJoktMgmUZSQGL7DRbOo4NPt51/6RPfb29tf + v6/6/yNx1o7+HjIVBTj0C9rwBBzRlIZNgDkf3imWitYulxZOFS7gc0iAJ12muAJtbemJNmTJlIk4 + aCYDTQo5FoQy3UXYWI6BmsEkhUidfY+r3VuMTgiaORgqawFPJO1ItS8jL+C3FhmBPFPTCoN21PjB + 9dGHJz92KibyhqLDYZYKuMVER7RQp9CB1aIhc9FgESM41Mn3irztJ2Ac08VOVcMqJHR41N7gnk1I + WFZitdz5l+kCJawz67K/Pjs3MWjvgwwdKav79dXy8r6sLjQxhQP/J1TV5InbfSlo8GUxWUJUvfVl + BvC1Pwr5X3uuYgpdlL2ER+zTrVer9QCoxjs0mlebzatVgmg3iVtfXlUfQO4tiibHk8uijDYt2gno + 9Xq8RDpbCqNtOZto/z+lj+AH/eSbCco34UeDMRgF7X4c5Y/cEpZb/S2391r3hBVjOpLBvRCm0g+L + tc5uOKOKzyzY7WvyTTlLNNzSOu43l6vDenN1ZQ9q9jL7BwAA//8DANFsL+FUBgAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9acb2d80b976-SJC + - 9854a487e834ce48-SJC Connection: - keep-alive Content-Encoding: @@ -5390,7 +5366,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:22:07 GMT + - Fri, 26 Sep 2025 17:57:22 GMT Server: - cloudflare Strict-Transport-Security: @@ -5406,13 +5382,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "4794" + - "5753" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "4822" + - "5813" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-input-images: @@ -5426,15 +5402,15 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29996986" + - "29997027" x-ratelimit-reset-input-images: - 0s x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - - 6ms + - 5ms x-request-id: - - req_c4fadef57872437282e9a76c788c9fb2 + - req_aa3cfc740afb477eb13d445ad20d9039 status: code: 200 message: OK @@ -5443,57 +5419,58 @@ interactions: '{"model": "deepseek-reasoner", "messages": [{"role": "system", "content": "Answer in a direct and concise tone. Your audience is an expert, so be highly specific. If there are ambiguous terms or acronyms, first define them."}, {"role": - "user", "content": "Answer the question below with the context.\n\nContext:\n\npqac-910fccbc: + "user", "content": "Answer the question below with the context.\n\nContext:\n\npqac-b9baa037: Explainable Artificial Intelligence (XAI) is a field focused on providing interpretations - of deep learning (DL) model predictions, addressing the ''black-box'' nature - of these models. XAI aims to enhance trust and usability by offering explanations - for predictions, which can help users understand the rationale behind them. - Key terms associated with XAI include interpretability, justifications, and - explainability. Interpretability refers to the degree of human understandability - of a model, while justifications are quantitative metrics that support the trustworthiness - of predictions. Explainability actively clarifies the internal decision-making - process, providing context and causes for predictions.\nFrom Wellawatte et al, - XAI Review, 2023\n\npqac-23138741: XAI, or Explainable Artificial Intelligence, - refers to methods and techniques used to interpret and understand the decisions - made by machine learning models, particularly deep learning models. In the context - of chemistry, XAI is applied to uncover structure-property relationships, such - as predicting blood-brain barrier permeation or solubility of molecules. Techniques - like Molecular Model Agnostic Counterfactual Explanations (MMACE) and descriptor - explanations are used to provide actionable insights, such as suggesting molecular - modifications to improve properties like permeability or solubility. These explanations - are valuable for interpreting black-box models and guiding experimental design.\nFrom - Wellawatte et al, XAI Review, 2023\n\npqac-becaf49a: XAI, or Explainable Artificial - Intelligence, refers to methods and techniques that make the decision-making - processes of AI models transparent and interpretable. In the context of the - provided text, XAI is applied to chemical and molecular predictions, such as - solubility and scent-structure relationships. It uses tools like counterfactuals, - descriptor explanations, and molecular fingerprints (e.g., ECFP and MACCS) to - identify how specific molecular substructures influence predictions. For example, - XAI can reveal how adding acidic groups increases solubility or how certain - functional groups relate to specific scents. These insights align with known - chemical principles and provide data-driven explanations for model predictions.\nFrom - Wellawatte et al, XAI Review, 2023\n\npqac-18a877c4: XAI, or Explainable Artificial - Intelligence, is a method used to explain predictions made by molecular property - prediction models. It aims to increase user trust and ensure that models are - learning correct chemical principles. XAI can help bridge the gap between black-box - models and interpretable insights without compromising accuracy. Key challenges - in XAI include representation of explanations, defining molecular distance, - adapting explanations for different audiences or legal requirements, exploring - chemical space, and developing systematic frameworks for evaluating explanations. - These aspects are crucial as XAI moves into practical applications in industries - like healthcare and environmental science.\nFrom Wellawatte et al, XAI Review, - 2023\n\npqac-3dab76be: Explainable Artificial Intelligence (XAI) refers to methods - and techniques that make the decision-making processes of AI models, particularly - deep learning (DL) models, interpretable and understandable. XAI involves a - two-step process: first, developing an accurate but uninterpretable model, and - then adding explanations to its predictions. Explanations provide context and - causes for predictions, offering insights into the underlying mechanisms. XAI - methods can be intrinsic (self-explanatory models) or extrinsic (post-hoc explanations - applied after training). Evaluating XAI involves attributes like actionability, - completeness, correctness, domain applicability, fidelity, robustness, and succinctness. - These attributes help assess the quality and utility of explanations in various - applications.\nFrom Wellawatte et al, XAI Review, 2023\n\nValid Keys: pqac-910fccbc, - pqac-23138741, pqac-becaf49a, pqac-18a877c4, pqac-3dab76be\n\n------------\n\nQuestion: + and explanations for deep learning (DL) model predictions, addressing the ''black-box'' + nature of these models. XAI aims to enhance trust and usability by offering + insights into why a model makes specific predictions. Key concepts in XAI include + interpretability (the degree of human understandability of a model), justifications + (quantitative metrics that explain why a model should be trusted), and explanations + (descriptions of why specific predictions are made). XAI is particularly important + in fields like chemistry, where understanding model predictions can guide hypotheses + and ensure models are not learning spurious correlations.\nFrom Wellawatte et + al, XAI Review, 2023\n\npqac-022374a5: XAI, or Explainable Artificial Intelligence, + refers to methods and techniques used to interpret and explain the decisions + made by machine learning models, particularly deep learning (DL) models. In + the context of chemistry, XAI is applied to uncover structure-property relationships, + such as predicting blood-brain barrier permeation or solubility of molecules. + Techniques like Molecular Model Agnostic Counterfactual Explanations (MMACE) + and descriptor explanations are used to provide actionable insights, such as + suggesting molecular modifications to achieve desired properties. These explanations + help bridge the gap between black-box models and human understanding, making + the models more interpretable and useful for practical applications.\nFrom Wellawatte + et al, XAI Review, 2023\n\npqac-11c9a26e: XAI, or Explainable Artificial Intelligence, + refers to methods and techniques that make the predictions and decision-making + processes of AI models interpretable and understandable to humans. In the context + of the provided text, XAI is applied to chemical and molecular predictions, + such as solubility and scent-structure relationships. It uses tools like counterfactuals, + molecular descriptors (e.g., ECFP and MACCS), and visualizations to explain + how specific molecular substructures influence predictions. For example, adding + acidic or basic groups increases solubility, and certain functional groups are + associated with specific scents. These insights align with known chemical principles + and are derived from data using deep learning and XAI techniques.\nFrom Wellawatte + et al, XAI Review, 2023\n\npqac-3ee8d98d: Explainable Artificial Intelligence + (XAI) refers to methods and techniques that provide clarity on the internal + decision-making processes of AI models, particularly deep learning (DL) models. + XAI aims to balance the trade-off between accuracy and interpretability by first + developing accurate but less interpretable models and then adding explanations + to their predictions. Explanations in XAI can be categorized as global or local, + and as intrinsic (part of the model) or extrinsic (post-hoc). A good explanation + in XAI should ideally be actionable, complete, correct, domain applicable, faithful, + robust, and succinct. XAI is particularly useful in fields like chemical property + prediction to provide insights into structure-property relationships.\nFrom + Wellawatte et al, XAI Review, 2023\n\npqac-0b695ce0: XAI, or Explainable Artificial + Intelligence, involves methods to interpret and understand AI model predictions. + Counterfactual explanations are a key tool in XAI, providing intuitive, actionable + insights by identifying changes in input features that would alter the model''s + prediction. These explanations are generated through optimization techniques, + such as minimizing the distance between an instance and its counterfactual while + ensuring a different prediction outcome. Counterfactuals are particularly useful + in fields like chemistry for uncovering spurious relationships and suggesting + actionable changes, such as modifying molecular structures. Various methods, + like MMACE, CF-GNNExplainer, and MEG, have been developed to generate counterfactuals + for different data types and models.\nFrom Wellawatte et al, XAI Review, 2023\n\nValid + Keys: pqac-b9baa037, pqac-022374a5, pqac-11c9a26e, pqac-3ee8d98d, pqac-0b695ce0\n\n------------\n\nQuestion: What is XAI?\n\nWrite an answer based on the context. If the context provides insufficient information reply \"I cannot answer.\" For each part of your answer, indicate which sources most support it via citation keys at the end of sentences, @@ -5515,7 +5492,7 @@ interactions: connection: - keep-alive content-length: - - "5417" + - "5532" content-type: - application/json host: @@ -5527,60 +5504,55 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//4gIAAAD//7RZbW/jNhL+KwN92Swgq3ZeNlnfh0O6TYGgzd3htsAWuBwC - ihxZU1OklqSSdYv974ch9WYnadLi+i2OJZLDeeZ5nhn/lpHK1tnp+TGeLOXFopTvcXEqj88W5fL0 - 3aI6UdXyRC3L8nSV5Zktf0EZsnUmaxEKaZtWYyBrsjyTDkVAla1X52cXF6fvVsfnedZYhTpbZwqx - 9YjbhUPhrUHHL9SWJPps/Z/fMjIKv2TrZZ416L3YYLb+LXNWY7bOhPfkgzCB37EmoOEDXH1ptSAj - So1w6QJVJElouDYBtaYNGolw9PPl9VsgDwIqQq1AoSLJp4RggUxA1zoMZDYgjAKphaNqxx9DjdA6 - fpqj82ArENMmNN8khuhzaPkB2Wnh9A44XtAonOHV/M4HbHzOuwqlHHrPO5ADMjU6NAFus1ILuV2U - 9sttBrIWTsiAjnwg6eGo/Szk4v1qWUlZyhzixxMlyvN3Jb4t4OfLa0BTCyPRQ3CdDzGgzouSNIUd - lDtonb0nxcdBvjojUmShFgEc3qPQMWoX/y80Qok1GZXiA4WSPL+Q81MOyx00YjtcVbxKI6bHFv2X - rbMSvY+nEsa3IgYbz2YUOk6riil8MsLVhbg4P5enbwv4AXcgrZHYBg9kpO4UThnsw8zhoSZZg8MK - nefb5sMp3DhETmHdNcIAo9ZhjYYPGjObYszjuXDA1d6KQga6R70D1J0kJQLGDIIUHQcX0cPQ/BKg - sm4POvuRvS1uza25NiDaVhOOr3nwHe/jQdbYkA9ul8e0BpS1oc8deuiMtPfowAfXydA5XLTOtujC - DhzqlM+aWp/HM+AXwdWZA5nxPGYDjdUYUQr9y4QeNG0RvNVdDxfroNTWqkXpBBkohXOEDlp0DcZ9 - +qiOT1YnF+enqz5fJUpRnb4Xbwu4wVBb1a98M+55E8F0uTGWgQ0fbMcprIQMndBwNQfm0c3N5Yer - t/FuFXrpqA0xrNkzCdMY02MTF5DxtKmDZ8iTQhP6iiZT6Y4/Cj27A9+V4236HISmTazYBwo1bI19 - MCkfUmhoHRlJrUb/YvScONG2zgpZowcpDJQRro6PJzkn91bfR25AXS2GoKzbjXwSYx1fYLjEOFrr - w6K2cv8iRBXQcY1RPP7RIUFc3QvdpczZipHrcX8BaY0nrkgQITgqO0b4AMnhevuSqEhh+otz42zZ - +WAip1lA4zvH3Mnv8K11IUHq0ZE+1EJrNBuc6hmrCodCc9g69JywA8bKQSjRhp56moh1RffoPILo - FDEp+7yHzT1q204ULBh0lRMNPli39fFdwfTkG+alowPWyfIs6RWZzd0kPd+T8yHyIHBdxlslv4bb - 7BOTKXnO/99vM7gGg0lrhPEP6KAUHhUXGkdoTS8zEcMjExRwXSVu6QlFWfTmTYBa3COgsd2mZjRb - 18QLyeEafG07rcCLHdxm14w3Y0O/aXGbwbddgMrZZr5uT+QgHELT6cDIBm87FzWEA1FYkUEOJgdv - p21KhFhqY1yR036aHTmiyYeomxXxsb9IdG3wOaCQdSovAZJCwuQWd2t4SgGeKbEDfTgQxAKuoWEN - lBSwB/sWdz6VZOtIBL5/MtDs5hHcDJ9mgVJKkg87jUkrvKTIKiSjJZAa11GZyCMwXnv8Sdsreyuc - 2DjR1r6A67B3h7YLcLxcwoN1yudQdmGgCm3NBl0xQ1CJoMihDIPY8IY5VyefbwB+NDuG6wVdiEF9 - HOgtPRfjW/MXC/gYhAtDLmKyaWCIny+vi/TQDQfLAA8e2s61lsvMKL5QEL5FGXz/5HVfxVHZZM8r - 1s0VjCpWKrwXJuR9MVgzAj8tc5UYJOKEPRWH5Lu2tY59W7kbUZP2/f4A1n1se2Baw3cRyj4RM9e9 - 7PrNJ1s0uYnR8333Y29/ZoKeDxZuMD9vRuf2Bozgq052JaBr/PoJk/JL5yN+Bj7btxzFLIAB/ut4 - 8HElDzc/PvZlg6MgM3mIyHAv+YX5jkOFreEjNaSFyxNAgrW6V3O5J9o+f06d58sOlZoC6QP2j83I - 7uCmqfHJqnN34TF525z98aF2jHrRkyKOijc/x0AR6RyN2KIH/iNq7jxVyTixSMc9eh3+ZlRkaJLB - mW80k87k8sbi9TvDNES/chVSXNAy/w0c0ZMQfCSu4QNheaQrM8aPQB6URFqH8zrmMg01Rjrgv71m - Z5SUpy/lfIqDpXBWuTGCf3ZBk8FYU6uCmytnVRezM9RUvMjSWaF0gu5xAf9Ki6/hU70DCm88UMP1 - K1hj+xSOvUkOGGSRfPHJvtFfx3Zuv3gO/Tm/zK+eFnA5O/4arpicSOjE9DNXLTwb2T3WORsN6xq+ - dYSV3kHTE99EX/HRd3vOhSE/5n/Nyu1bIRGE1vYhvfADt4IUBsou4JKSaRHOdkZNAhCf/mSdShW2 - npE/Y6ERAQv4Efk6ucgm0jMD0zNbKicqNkc9D0bxtg5e0S7n8045GoDemvhH1Dg2SoN95Hie6Jkb - IWvGyNgJv6JZHh551CvbVnzucKTYo49JFJKxOXQwOWCxKXL43ca5160w8kzfQz/XQs863mca3Xmb - W7zQ1KbdR6V4sasl/2Q7O2+kabLlL/SxadZBQ25N7G/6Xv3w4MM9mX1pT2Kw362Shyiswf6BbrVL - UjrZhLEV3m8P9zuevbZwkcxET2bFCw3aWBrDC486tMPuKzEpzat9kodZxxTvZK9diu8OHVNx2Aal - k3x4Sc4424+in9ogC4qqqpeSoQUqDtsZ3usf9iEferTRR8UWQFrH7lJHy9CIEFCxr20a8U2iNI6g - IfZgCd41xgZvrLSjF0vtOwuxLVEq3q0waDs/b2UK+BhIbhkyFI/ST3DmVP3JsalnRn/CYMddmAAP - ze4fosFpfDTAI+Z/wmckx7FM5+X2ZwaHOAnVs0zYUKCNSN0MuYNpYWLEF4eEP8WjUSPcDjZW6N7n - R155kfommzwOCBPr/wXjwT84C+y98e+Tp0Jjh6HdM7z5ylHgVPb7XDrOjZ+YCfaj1FeMBSMWuLdU - tokW+ZmxIPnR73PyhqnkK2eDZDhwxvqfmAymKWA5H+s8Pwn7aaoabFptd7FBSbn6q8aCQ6Ysc+L/ - aTb4qpHfgZrwLw0b6+hXVJzB2fjvIU5e+q6D2fdwCng4/pvmhc/M/xRPkNIUMFXky7PABNfDQeBT - qvbMHLD/+Qd59pfv6dxsEpgI63VTwP53DF5XJNv3gg7mfbuzh15FCdz9mUaZfGRYH00N+dI1boQG - h587csg9gH80FhyFVNYot9G8J6vCHEs+WXCu0NbZUpR6N1j91dlyMdl9iIM6EIrnAXHRf6eOis/W - dwt8tfGrq6c02xpeO4kdGsW3M5tAcfCjLU7zr6jzk6R7ZO0MqNIkhZii9ydgrNdx7igMM7rbKh6J - 99gUwOT2tzjPmAnuXKYD2C4U2dc803bD1+Gztem0zjNOm6/v0nw1W2c+2Db7+t8864af/1pnmzbc - BbtF47P1arU65x8Ah58cpy/OVhd5FmwQevzf8buTs3x/hTuFQZD2vLTkubwan15+fWLh+fPTFHg6 - zXt+q98hrndX03Ta5cF3DXm/F8rXPEsz6buKeNbHvy7wcLlq76rjs5NKrt6r1V3rrFpeHC/vqvbi - bnsf18q+/g8AAP//AwCynW15tR0AAA== + H4sIAAAAAAAAAwAAAP//nFhdb9s4Fv0rF3pqANljO+k0Vh8WQdrBGjstZrddtMBmEVDklXUnFKmS + lFNP0f++uKQky47bzuyTYUkk7+c55/JLRiorsrVYiQWu1zNxvRCzq9VyMbuultezaqWwXC/V+oWo + sjyz5e8oQ1ZkshZhLm3TagxkTZZn0qEIqLJi+eL59Xqxvrpa5VljFeqsyBRi6xEfZg6FtwYdL6gt + SfRZ8Z8vGRmFn7NikWcNei+2mBVfMmc1ZkUmvCcfhAm8xpqAhg14/bnVgowoNcKNC1SRJKFhYwJq + TVs0EuHZx5vNBZAHARWhVqBQkWQrIVhond2RIrMFMgFd6zAI9sWDMAqQ9zf9g8o6CDVC63iD9MxW + wF6BRuEM7xJ99TkIpRx6z49CjeSATI0OTYC7rNRCPsxK+/kuAyNC55AtQVMLtje4zod4eudFSZrC + Hp61n4ScletSiMXli4s5fLzZQIOhtsqDoIbXN+IB4WYDCiV5smbWiAc+vnVWovfoJx5yvOIJRqHj + sKr4KFiou0YYn0MrXCDZaeH0Hso92KpCl8LkaVuHuJuFx3oPvkXJkT8KjHAIjVDYm75YrS5fXInn + OcS/y6Vci9XPeDGHf+AepDUS27in1J3CqaUxADk81iRrcFih82wnJ0Lh1iFyDqLVR96kuNkKRErJ + S/i987FAUjaHHdnOT50wgTjvO+SoOpI+pZ5iTtnJfhvwte20grLPE6qXTwpl2Fqhl474yxrBxXdC + I5RYk1FARtGOVCf0UdxOM31n7gwnO6CsDX3q0EMpdCwUIWXnhNxHA55GzFYBDadO4Q61bdmTtCRg + X6ZQketrLdRouGhjxVgfZrWVx+WfLLtEvFbra3Uxh/c1ejz+RgrDoeHu2lpHf6AC4WGrbSk0WAfa + SqHjeSIWkONqkvwGP/d/cnikUAMpjJGxLbpA6MF3nC0PJSZHYjhLjTlI6xzKkMd9lW0EmZloW00y + FvUTu29t01gzts9Qc9J2HMNKyMBJOZdRUmgCVXtoyFAjNJBpuwCyFmaLsSyFDuimCU1WBWu1B00P + HHmNsa/6+miDdR6e4Xw7z+H17S+/5fDm5vb23UXEmx35Tmj64ygHi/Ln9XOJi6fNtDEga2zIB7fP + I0gw7nEsEtp1RtodOvDBdZKBZ9ZHeA8OdTqkptbnY7gHV8wWvNXd0FcOSm2tmpVOkIFSOEfsN7oG + 4y6D2+C77RZ9mLjdWHVoQwi1s922ntZ3DNObNze3r8+Dx+D+xTzLs0QlZLb3B1b4has6j23HG/JB + QL6Au+xDLQKH5OPN5m93GWzAYAqMMP4RHZTCo2JHUe/Bmh7umSFQQTzgc4iFP/zhXyamyAQed+hi + 6Uh0DGeVsw18QK3FowgBAQMIPU+J+RfuCB9zWC1WlzmgkHUqfAFdjANISkwED7ifwwYapgVr9B46 + zwhZWdekD+IxIXajt52TmMhLUojY0wwdovcRTtj8ndCkjo6IkF3AEfwMAT8P3v3fobNOsjOfRPdg + CaWYJtsH5kBVgLRNI37yrZCM65oaYoJuBVNmjYGk0B5EiKvRqBRuTrhEn6eSOYFOLtJn33PnYg5v + LdhQo+sNSrQltLaPqGKs3uyH0jgAf++DD3uNiWG8pAgMJCHSpsYichr5CE+J+Ht8Is5EqiHRKYoS + hZvUMOKgCzl4C5vJcTVtaz1h2YieO0sKRFPStqOwn8ZaYUWG2X3fv7edh4Cu8TmUXUjWE0M1G9dD + RFqkBtf64u5NaZjvwdhwegaFVHpeOhFk/ZK1QUCh8oP9fs/p8/QH9oxdkaFeTvVVO9TsWJtPAi5K + 2wVYLRbwaJ3q/eipRluzRTcNABofFRW3emwrljKcp2bMJTGdtK11XGPlfuyDiVFjs2+q6X9eSsZ3 + FSvNmNTRVYet3sNdtmHTOFrpsPwui/bGnR0K1UvCQ5ApgEdsPDgaQGDa3dZwlmJ0fsUADUauFDJw + 10JryQxIE70dDPWG2hZDwetmx21dDGn/E/I5h8rKjlGRQfTPSORXv/bq4ljbFWckyqkkm+41h03D + GYpShya8Np94NLRy79FwwMQyMh7e9BaN0piPiv0ktN4fbR49+BE7zuH9ObbKJ4x+7MrE4gE8k8Ws + 2T2L9qkEPJLp+ZS+o5nyWDHO4T2Li+JEv/j8vNLITyTF1LYByZNtPe95kFq4SPrmzHiRD3LUf1+P + zuG2F4Xoi14R/hTlYH4Qgj+NKnAO/2Qjo/KzFWytVd8M6cA4QxHsrN6hH/VdTNCfEHcM/WRCR3EM + iCg7EZlbNOji0LgjAbYN1PQhnMO/PVadflqkB0w6hsFI3yw6Rm6YACO7y/0eGWIiX6B0VqgzIubd + hC6SuqPgJxvm0HautYz2D5N+zCeSK1FTr5n71vas5o6gkF16N7RGfJMQroeYd4FxttcwKVz6jF/p + 4x55oqm9ecXJzAzjmNwPyXk/Isf3T4fkfuc3zMRJ0vz/2JO2ekVedt5PAnWmy77R8zlgkP0+f2cG + jyQ6DfE3Mahf9dYm0TTOeyWGR+Qp7ftzHz/99OPmeZ1oMnLGoKS+TYzzgw7oS3rrbNeCp4YYYXhc + m6jOptOBWn3Qo49c53CYyqIJH6xTKaAF3FATsVc42xl1IPs59KRnu6DJYCy2JU86wVnVxQ5lcz7e + bAp4dag2NqUvrDk8uz1n1AVvtTq5gYhjy5jvYalDjTthwtHSyzncHLUMp2PkrGHpGAFuDPws+LIs + rb/iUZSHTx/bw8XxO90SgMIgaBgZ5YicnM4nmfzg+ByuoCQ5p8J4BJhWOLF1oq3Tmtsa5UMK+LFI + LE4kYdSAxo5pd9iLv85o9P4g4Y/HovSRBzrRszy6hNr6IykYLfqFDHdCPug3Y5POMci2TQTRS/CB + 5AOzNPFMM9wGTWHqlRMVw8NR1czgtyEIsPxrGmi4OoyiciTGUznE4Tx3GxgHzIRt/WXgX4W2Hwwy + p96tCtiEAxNOoXAc7H+MiE/vtZJR6J9M69+h17jorBT5llffuN04dfKyOL3gIJPSNBg1irq/dPMR + G3UQD7EYOSvHLjGWjeIgelgJCnXV6fn5O4vx+om9eGsfc3h0/VDcs+g8+5pn2m5bZ0ufFabTOs+4 + RXx9ny45siLzwbbZ1//mWTdcj7fONm24D/YBjc+K5fLFki/Ihyv5w4vVi8s8CzYIPT5bXV1d5cc7 + 3PfAw1tLIWtU49eLr2c2nn5/uIoZlqyvrr6OB8Tt7ms6GLs4edeQ90eefM0zv/cBm/uKeMxrHcUL + nqq9r1bPLyu5XKvlfeusWlyvFvdVe33/sIt7ZV//BwAA//8DAAZle5LTGAAA headers: Access-Control-Allow-Credentials: - "true" CF-RAY: - - 984e9aeafcf0cf13-SJC + - 9854a4ae083fface-SJC Connection: - keep-alive Content-Encoding: @@ -5588,12 +5560,12 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:22:08 GMT + - Fri, 26 Sep 2025 17:57:22 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=dfhIPEmVPHtHpMS5uxZUEkYhpS74WoylF9AgeEPk83Q-1758846128-1.0.1.1-JoQuVCs0PT0P045O.5catH7poML6jLXug3Ud61uY.qbfjU.Jhc6ezJt5A4okhK2vZJdpN4WfvxgeFsKNxUg1ec9TU2_PnJus6kHsqFDtf1s; - path=/; expires=Fri, 26-Sep-25 00:52:08 GMT; domain=.deepseek.com; HttpOnly; + - __cf_bm=TMtq.ptILJ1trguPMJ5l4XV_GJr8kok0Qlb2iB1cBY4-1758909442-1.0.1.1-dUBuxCzyEyfJZFAMfXo20ikSg1u2FHRtlJwPbVTADm4Gb6Jol2b.tP9kAeEfK4m2BVkXALwgKxULwvRc3ua5e0o7GSpN4DlneeTR9aQnU60; + path=/; expires=Fri, 26-Sep-25 18:27:22 GMT; domain=.deepseek.com; HttpOnly; Secure; SameSite=None Strict-Transport-Security: - max-age=31536000; includeSubDomains; preload @@ -5606,7 +5578,7 @@ interactions: cf-cache-status: - DYNAMIC x-ds-trace-id: - - e817396126f2f7b39c255215367a884a + - 5a922a6211566111ce0769b1a2b82d1b status: code: 200 message: OK diff --git a/tests/cassettes/test_get_reasoning[openrouter-deepseek].yaml b/tests/cassettes/test_get_reasoning[openrouter-deepseek].yaml index 5b899a157..4c7cecb13 100644 --- a/tests/cassettes/test_get_reasoning[openrouter-deepseek].yaml +++ b/tests/cassettes/test_get_reasoning[openrouter-deepseek].yaml @@ -45,20 +45,20 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//jJLBbtswDIbvfgqB52SI06RpfBuGHLLbhg0tVge2ItGJMllSJbpZG+Td - B9lJnG4dsIsO/PhT/EkeEsZAScgYiC0nUTs9/PT56WH2ut99W6QuXbz+eLpfiu/L+Ze5xcULDKLC - rnco6Kz6IGztNJKypsPCIyeMVdPZ9O5ucjseTVtQW4k6yjaOhhM7HI/Gk2GaDsejk3BrlcAAGXtM - GGPs0L6xRSPxF2RsNDhHagyBbxCySxJj4K2OEeAhqEDcEAx6KKwhNG3XZVnugjW5OeSGsRxIkcYc - MpbDw8cl+4rPCvc5DDrKG9paHyJ/zOEeteZ7ToQMiXGdw+qUJ62KOabROjfH3JRlef2/x6oJXJ8y - rgA3xhKP42udr07kePGq7cZ5uw5/SKFSRoVt4ZEHa6KvQNZBS48JY6t2ps2bMYHztnZUkP2J7Xez - cVcO+iX28OYMyRLXfTwdTQbvlCskElc6XC0FBBdblL203yBvpLJXILky/Xc379XujCuz+Z/yPRAC - HaEsnEepxFvHfZrHeOP/SrsMuW0YAvpnJbAghT4uQmLFG92dH4SXQFgXlTIb9M6r7gYrV8h5hbfT - 2Q2uITkmvwEAAP//AwCgJZxKjAMAAA== + H4sIAAAAAAAAAwAAAP//jJLBbtswDIbvfgqB52Rw0nRJfCu2y7DDsB62AnVgKxJtq5MlTaLTDUHe + fZCdxG7XAbvowI8/xZ/kMWEMlISMgWg4idbp+YfP643/Fr6mLZrmY4V39z/xoTGKxObwBWZRYfdP + KOiieids6zSSsmbAwiMnjFUX69vNdpGmq20PWitRR1ntaL6y82W6XM0Xi/kyPQsbqwQGyNhjwhhj + x/6NLRqJvyBj6ewSaTEEXiNk1yTGwFsdI8BDUIG4IZiNUFhDaPquy7J8Ctbk5pgbxnIgRRpzyFgO + D3ef2D0eFD7nMBso76ixPkT+mMN31Jo/cyJkSIzrHHbnPGlVzDGd1rk55aYsy+n/HqsucH3OmABu + jCUex9c7353J6epV29p5uw+vpFApo0JTeOTBmugrkHXQ01PC2K6fafdiTOC8bR0VZH9g/916OZSD + cYkjvLlAssT1GF+kq9kb5QqJxJUOk6WA4KJBOUrHDfJOKjsBycT03928VXswrkz9P+VHIAQ6Qlk4 + j1KJl47HNI/xxv+Vdh1y3zAE9AclsCCFPi5CYsU7PZwfhN+BsC0qZWr0zqvhBitXyG2F72/XN7iH + 5JT8AQAA//8DAGQk1+iMAwAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9cd0ea5717d2-SJC + - 9854b3824cd36800-SJC Connection: - keep-alive Content-Encoding: @@ -66,7 +66,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:26 GMT + - Fri, 26 Sep 2025 18:07:30 GMT Server: - cloudflare Strict-Transport-Security: @@ -82,13 +82,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "485" + - "714" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "499" + - "744" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -104,7 +104,73 @@ interactions: x-ratelimit-reset-tokens: - 0s x-request-id: - - req_ffe2e26f958745499c79f3b013c9f795 + - req_93ffecc1efb34a16bba2fd1202cbdfbe + status: + code: 200 + message: OK + - request: + body: null + headers: + accept: + - "*/*" + accept-encoding: + - gzip, deflate + connection: + - keep-alive + host: + - api.crossref.org + user-agent: + - python-httpx/0.28.1 + method: GET + uri: https://api.crossref.org/works?mailto=example@papercrow.ai&query.title=XAI+Review&rows=1&query.author=Wellawatte+et+al + response: + body: + string: !!binary | + H4sIAAAAAAAA/6VVS3ObMBD+K4zOyJYAG5tbmnQ6SdM2ddJLbR82IBwlAlFJuPF4/N+7AidxXp3O + 9Ca00mq/xy5bYh241pKM6DsSkkpYCytB3aYRuPdbmzuqpHUHobUwVuoao3zABuwpQrItKSEXDrNt + dyFx2oGiRthW+a0oHvMkJNKJCr/mWyLrQtyLwl8rwAnagPHn5vOIRUk4CdPlMuwjTla+Gr9P2YSy + 9IqNs2SUcfYTn/dRRFE1JONpFDMec87G4zGWYEQpjKhzQXPd1o5kLCRNe42IboTBjFcGamudUApM + cKZbU4MKLvyBHByCtAHURTATVoDJb4Jj3EEwUOeb4MLINdYWnMsKIRVYh7S29WWOcZ3r2ona0UJX + IOsO4n41R1C50dZWgNwiO87I3HWElqCswKrtjTaO+hR4QxiE7xQmnpPTs6PLGVk+YShoY6TH9ZpC + jvRhrpNvp14pNoiSKEkXQ3kL1hQi9weixLPXK33bg6eYQOb4mi9SgHtbHp6GnIfT1/rwlHJO2fSK + 8YxPsjh6qc+IMz5lMY8YY1hd09mG8DSmfOKtZLGM3G8de4pQvo5X+qhjQa83B1o+MvNBr5Gq4AtY + 3JI2DD4pfY1afm9RQDCbYLFYkCNUci3Fb7/uWMS08v6AHnxsrVXbgfEf0DqUojPrSq6F9/xnqEAV + QjSBcAGoQYjHSqik2rwTtOJX6yvHcClN10lQllJJ6EWfo0pbUkP36NXZxez4+SUoCulPgnrj5tI3 + X3XdeZmzOHmw3kvfoBlR3T7No891GRyt0Hzo6Nbg92Uu/ZvPLO9pUlCv2l4opAAlF4228n+sESVZ + Mn3bGuPJtLeGzbXBm1EymE7YOI19Mz+4Y4vSSeyfjV/+mJ3jAzfONdliuBi628bkA21Wi+G+Sxpo + cGQthhEdMeqfidgk5oxi6q6hTj4e990waIqS7Hb7Tn4HXtdVj360B2Z8aKH9HNg+Gwj/2rGYHNRe + rbWgsvD6HaESdBqlIy/HE17bAS607OH+pclPLy+/+kRRhBQwNkp9Iiyv3g96NPh+DvTVYR+A6mp/ + urE7nDvv17/cj3iKpNO+v3lI0M69Wqg2zrZu9Hek9TajiNj/FepWqd1u9wfxCUyYlgYAAA== + headers: + Access-Control-Allow-Headers: + - X-Requested-With, Accept, Accept-Encoding, Accept-Charset, Accept-Language, + Accept-Ranges, Cache-Control + Access-Control-Allow-Origin: + - "*" + Access-Control-Expose-Headers: + - Link + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Length: + - "850" + Content-Type: + - application/json + Date: + - Fri, 26 Sep 2025 18:07:30 GMT + Server: + - Jetty(9.4.40.v20210413) + Vary: + - Accept-Encoding + permissions-policy: + - interest-cohort=() + x-api-pool: + - plus + x-rate-limit-interval: + - 1s + x-rate-limit-limit: + - "150" status: code: 200 message: OK @@ -143,7 +209,7 @@ interactions: Review and Dataset for System Health Indicator Construction},\n year = {2024}\n}\n"}, "authors": [{"authorId": "2167438752", "name": "D. Nguyen"}, {"authorId": "2184162840", "name": "Khanh T. P. Nguyen"}, {"authorId": "2267984723", "name": - "Kamal Medjaher"}], "matchScore": 58.360786}]} + "Kamal Medjaher"}], "matchScore": 58.36999}]} ' headers: @@ -152,97 +218,31 @@ interactions: Connection: - keep-alive Content-Length: - - "1440" + - "1439" Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:26 GMT + - Fri, 26 Sep 2025 18:07:31 GMT Via: - - 1.1 cb0f9f6369baeebf7c66aebe4cb453ac.cloudfront.net (CloudFront) + - 1.1 ad0f81beb11a40b80a59b548e3794d00.cloudfront.net (CloudFront) X-Amz-Cf-Id: - - aojqjkExtCFGtihvACdIRLteoL3LimJF9c_duT-rR6prJrqcGHVcIA== + - mek5s1-3ekyGmCyWEVm69rJoAfaSDmPczrqx5M0SYQ27YRQWqFz_PA== X-Amz-Cf-Pop: - SFO53-P7 X-Cache: - Miss from cloudfront x-amz-apigw-id: - - Re93yF8TvHcElXA= + - RhZvdHm-PHcEM7Q= x-amzn-Remapped-Connection: - keep-alive x-amzn-Remapped-Content-Length: - - "1440" + - "1439" x-amzn-Remapped-Date: - - Fri, 26 Sep 2025 00:23:26 GMT + - Fri, 26 Sep 2025 18:07:31 GMT x-amzn-Remapped-Server: - gunicorn x-amzn-RequestId: - - a9d7d7c9-4b5d-44da-b3df-7309419a6f6b - status: - code: 200 - message: OK - - request: - body: null - headers: - accept: - - "*/*" - accept-encoding: - - gzip, deflate - connection: - - keep-alive - host: - - api.crossref.org - user-agent: - - python-httpx/0.28.1 - method: GET - uri: https://api.crossref.org/works?mailto=example@papercrow.ai&query.title=XAI+Review&rows=1&query.author=Wellawatte+et+al - response: - body: - string: !!binary | - H4sIAAAAAAAA/6VVS3ObMBD+K4zOyJYAG5tbmnQ6SdM2ddJLbR82IBwlAlFJuPF4/N+7AidxXp3O - 9Ca00mq/xy5bYh241pKM6DsSkkpYCytB3aYRuPdbmzuqpHUHobUwVuoao3zABuwpQrItKSEXDrNt - dyFx2oGiRthW+a0oHrM4JNKJCr/mWyLrQtyLwl8rwAnagPHn5vOIRUk4CdPlMuwjTla+Gr9P2YSy - 9IqNs2SUcfYTn/dRRFE1JONpFDMec87G4zGWYEQpjKhzQXPd1o5kLCRNe42IboTBjFcGamudUApM - cKZbU4MKLvyBHByCtAHURTATVoDJb4Jj3EEwUOeb4MLINdYWnMsKIRVYh7S29WWOcZ3r2ona0UJX - IOsO4n41R1C50dZWgNwiO87I3HWElqCswKrtjTaO+hR4QxiE7xQmnpPTs6PLGVk+YShoY6TH9ZpC - jvRhrpNvp14pNoiSKEkXQ3kL1hQi9weixLPXK33bg6eYQOb4mi9SgHtbHp6GnIfT1/rwlHJO2fSK - 8YxPsjh6qc+IMz5lMY8YY1hd09mG8DSmfOKtZLGM3G8de4pQvo5X+qhjQa83B1o+MvNBr5Gq4AtY - 3JI2DD4pfY1afm9RQDCbYLFYkCNUci3Fb7/uWMS08v6AHnxsrVXbgfEf0DqUojPrSq6F9/xnqEAV - QjSBcAGoQYjHSqik2rwTtOJX6yvHcClN10lQllJJ6EWfo0pbUkP36NXZxez4+SUoCulPgnrj5tI3 - X3XdeZmzOHmw3kvfoBlR3T7No891GRyt0Hzo6Nbg92Uu/ZvPLO9pUlCv2l4opAAlF4228n+sESVZ - Mn3bGuPJtLeGzbXBm1EymI75lCe+mR/csUXpJPbPxi9/zM7xgRvnmmwxXAzdbWPygTarxXDfJQ00 - OLIWw4iOGPXPRGwSc0YxdddQJx+P+24YNEVJdrt9J78Dr+uqRz/aAzM+tNB+DmyfDYR/7VhMDmqv - 1lpQWXj9jlAJOo3SkZfjCa/tABda9nD/0uSnl5dffaIoQgoYG6U+EZZX7wc9Gnw/B/rqsA9AdbU/ - 3dgdzp3361/uRzxF0mnf3zwkaOdeLVQbZ1s3+jvSeptRROz/CnWr1G63+wMwlnzylgYAAA== - headers: - Access-Control-Allow-Headers: - - X-Requested-With, Accept, Accept-Encoding, Accept-Charset, Accept-Language, - Accept-Ranges, Cache-Control - Access-Control-Allow-Origin: - - "*" - Access-Control-Expose-Headers: - - Link - Connection: - - keep-alive - Content-Encoding: - - gzip - Content-Length: - - "850" - Content-Type: - - application/json - Date: - - Fri, 26 Sep 2025 00:23:26 GMT - Server: - - Jetty(9.4.40.v20210413) - Vary: - - Accept-Encoding - permissions-policy: - - interest-cohort=() - x-api-pool: - - plus - x-rate-limit-interval: - - 1s - x-rate-limit-limit: - - "150" + - 0c17ec7e-312f-4e3c-bb8c-1e1abb42885b status: code: 200 message: OK @@ -1335,1695 +1335,1695 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA5x6y86CSrTm/DzFzp5yErlX1ZkhIHKTQlDEHgECAipyK6BO+t07+Cfd6aRHPTFR - UaDWWt+t+O//+Oeff9u0zrPx3//6599XNYz//uf22SMZk3//65//8R///PPPP//9e/2/jszfaf54 - VJ/yd/jvy+rzyJd//+sf9n9/8n8O+q9//jWOherNHPsCq6kpPPjKwRMfhtQM+bpGFaQieybF83vS - liwEOYy9UcLe9+xri4ysCiF6yUnOkTVdd4oawFfF7cmep1dtfbzsBlrqCWHDcR1N6It9DGE3pvjY - s2+tPVB3gFxPo4nbkX3PR06mw8+OXqfZADUlcHh6oGxkFrtWe3GWV+Ix0FwzmyS0dMEqvtMBau5D - 9uA8fdOxt4YKVn2UYRwzt34OncmDTnoBxFQnSVuYBRvw0PoYX28t7anDlDLKBjmcGhfx2iqXq4wm - UOjYGB6Vsxw+rg+g6Qek+OR9OqwkzGH7XV+elFRTP3vyWUXb+k1vT1VTjvozD9zPcsHRidk582dm - V0Rf14kcLHNHZ9HreFifA4uome5q7HHsZciamo7NL2JCOkCfQXQeHXK3fIOO5d1UEIuYmVhnLewH - v6lUCL5dgXF/FjWagbVCAiteSWZ8XTBFTmbIJtVsbMaV0XOQ/8jIHVKB7N995NBX1eUA8r2Ija7V - auEzFAosyusVH/F+qWlgWj6MRdvCJ/mZOzyXUxsF2nzB57DBYGlOpwam9XDGKnUCZz0dlAa+L6M9 - vb3dja5zgRN4GfQD8fklTbnxmfqweSkpKdhPnQrfY9KhpdcJtp07A+idPUNUntucpHZeO91OjXzY - JCdmQgbL13MhVB1S1UHEmcqXDlcXnxWyyZDgg22J9XqWPB2gz25PDCN+9ax9CCbE9W5ELls9x08Q - mPCsPhySPCOYruVQm6iI85S4zElPV/W682FU7G0SlrKTEvt5v0D+rkBs5OE5pedTpSNhbFKSPGrc - T+8+MuSnfvgQ44U8jRzv4oo4t6nxbQmfoYCyVIG743cmpnV7aPMJvhSETvFCTima0vkVrSpq7MzB - d3w4OKzUrwly/SIl2BBth1/ss4eCKaqnNXj5YGX39Vu6J0pAlMm2tBEI7QCab8uQu5Oa6Uw+q4zY - dycQHfACXYLStkFfDjbxzbkKf/OIbp/2RB7XneKMcjaa4GC1GTYl8whmSzz7iLD6ET8SyNVzI3UJ - DMm9IPvdJ6n56tKagMUAYVt01H4O72WMxtrA2GGLLFxe9vL+O78hS0HNGc3sI1U7i0TZZT5gzdSa - kKInkSdvv2d37fmCxEWasN1odc+7x+wNtanxibIoSsj2YQyhv1tYfLnkKliOzz6DdsLucHxIlZQ7 - KTKPnpPOTxI+HDT2cEU29D8ywPaGB3P5UjLUyetj4oeH6rCCz/PIHqsrtuKmTIX4Zifg07QGdr/6 - UM/p1ahgZrsI79ubXvORpRjovTvf8cO3n3Sll8MMPel9n1aRPlL+3Uc6MLPqgXUXRZqAVTjB2+Ep - YMcN9/0aiQsPHyjG5MoeXs5csg8e9uVkE61tWo3KzZFHn7vwJfi4T5ymMQUWHo+QmeooEcL1fO9K - 8DL9BesNWcA6qWKHLoNxwNfDlToz937xsOMneVoGe0m5AswJEkTSe3y/uiF/TasAnVg8Yj31o3oQ - mewNbqQ2cEgiAOYXf31DUVZYnImx4XC2FHjISMyY2DsGO3znez6wvPhNYt4q63F/YSKYBUfBE89S - 1VO3P4lwh5QMX7rGd5b9S69gLyoaTkRHrddp7WKw4Sc5RLkNRsuBmby3Dxw5UWI76zNRIFLPUk+O - Pu9rq+6pOdIwfOBIq6Z0Tbs6QeoYHomSairlz1YWgfxjBUTfjR8we7eyQyCI3uTHTysUnx2yv9lK - wjE+1Wv1US/A7rQV24e2ppPsxROE2bH1urxpw3mX3Ew4Fp/ek62g7KlCWBW9zXUk9thP9Wxzeg7d - E9bx8TKWDieehRWadG+Tmx6f6Bw6bxfu7SOHHeBqKX0/rBaK6X2PcyqHVAjsawDshN9Nu8it+9WQ - SQINQ1Sx7TxUjWPOdYXqMoe/+wPEx4wuPaU8JOZZquof3sPp1Ob4HuhuOjvqSQQhwxbT89U90tnf - Sy2kkQmwfgildND5cyXPT3EkIT31YD3fqwrhbF9N0kut+rUHvoJyfbKIsW8UjX+nWIT7lrQe3fWT - Q1+qZ8Affxw/iNdmNHsm8HsmJ3jnf5yN30sIzSAgni5WDrvuvh38Vky7zZOS8otsQ9ic14O3k7Nn - PUtOpKDrc3nh04U5ptyt2suIzJ/G44C49MMuKUxgHtwG58VXBzPg3vA3XxtezHRR1YuJ2Eq5kNuc - ivVPj6Cx1rGHtP0XzAEzzHDDF1xk3Kdm8ZtX0B8fdn6RLkt07qC2sB+S62KlTRefFdGNPA2clfeb - w++NrwyrFGQk8TDvzOG9jdGmJ/C1WZyeRYwSgbF49SRdRjMU3rtVRdlMOmKSr9vzho1a8OKqG8HW - tE959SoEf/13txNcz7tYs9EPz9IIvsKtHyPk73d401Nt2Jb6oENr9mfsm9KarqZm8j+9hC+iMIPZ - byoFAWxgjCk81bOUDq4sZQ/kVbUQ14tb1x66B8MdG4LxAqthow5wSnskbkaS9I+/pwsTk8c6LeFa - 63cGLevu+9dfCx+fY6SFLkuUV0HrtWEWHfk0iIl9H4p62b/cCn7aIpsEmhQ1VR53Fu5u+jyxswvC - 7jt3Hdz4wINedq/7rd8RLBoHp9C6OxRZIg9b7F3xySxfWosKAUr6bQ6Jts0n/4mBCKcLjPFxTsWe - uk51Qa/Olon1UquaPtHqI+vxunvvNVDCSeSfIhz5y4scXSfp1+JMGmg1lxCrtzufzvf2W/7hr9ZV - fDih404Hl+vNnV5nyoVzOKhvGC3Tc9rFe1UTGKlrIDWgiS2IpnSBBmMDs2ZvJOI95NAnkn2Y1tN5 - 66ddOBhlVSIvijRykmPPYQ/h00SjkkjEpeOczudi70KTrBTrSqQBcsVfFjqm8cLm0B16+n7sW3CU - hCPRnqBxVh7aAzwWvoDPg72Ei+zmDKw+vjTBKzJSoT5OMsTy94oNIz70HDfiNwi09TKtPDeB+eiZ - ERyJ2hNvnXttia5mBc7iTpiE9WFrQ1qbg9S3H42Ynx0B00+fjcpOIt7FjNNZ6uUERqhFREsDReO8 - 3E7g9S73E9oLT2ddj3oCV6E2PaFhiDOcHvtc6jKzIz7dfUJqnx0bJo6c4oOkJvV8m6NILqE3Eqvl - cDg7wUX/0w+Rz0Ypa92gLAbSpExLOR4ptedpgD8+cD8x4/SesS/R1LrTBGjP96M5NSyMDV8hV8de - 0i9z7kt4vi/6NEuftabhy+d/fEMsPdun1KKggvXZt7y1jw/pIs6mDa957eFjZQqANOvcQQE/WxwV - l86ZD1oCf/6G7Kmy8UF7jtDufa5xmBkHSrhgnQCAIkcuy+my8feVhb/r/63/1xcPJnT3fovtZBTD - lezq+Yd/xGNf374rdocIJuFyJdpDS0O6030VDl/RxY+9sHeEZx/rkPGK0Vvryawnq/RjiAC+Efsq - BYA+e9+AXmfdiVMnx1qoTniAm172oG+22no+mhH4Xa+TVwwdJnd3Ae5eaInlnWC6aDscAKE7igSP - RtTTRPvEsBG5BeP3Re1XdJV8+F2a9E8PrFDmWrjhmydhj9Nm78620N/HmETL+VNTVzINOD/lkZye - mVXz6SAkcLLeYBKuHddTZSxVRNSV8Wbw1R0uC0EGZ8l7YyutP+li5VELjgUneCgMRNB/37wr/ebH - OF6Bsxa7UwQ/7F3y1qfUpd0gOh6U4u5DMpVXHG7/cktwU6TLhBhHpuTlKwEa0/XqURuNYI1rlZVH - 5pTjeMNnbvOjEE7zn55Ku60+iDeub7LpAbBIhQThYfc28entE22ptLsKD5m4krTT+noBQjuhh3QZ - iKm+vhpNlpMN14wRiQnXpp5VIEcwJd3oiV/EpEOLGFtOV0nH5vwIUiHn7wPUbv6NaPd73s9P1Tdk - cepvv/lIlxvbGGC53HhytFwazoyzn+HsxMpWj5SOQ1PqcFwGFW/81y8eejHwHkx3Ysy+RxduPL7/ - 9GU0PCqNLIw/wK1fsRn3fD+vJM1BIfQeNjb/OZ304wwFTXGJLTpVvXyq9gIfUR8RNchKSgknRPDB - hdYk8Q85XZ+WvMJdkJ2wPXcc/RhDwIBD4w0es98H2npXpwwWpy+DTd4q+8W2yhxQm+ZT2V8WOmR4 - nIBc5g9yrBWlX2bLD1AxhAtx8nflkPytVQgdwp7sd3nXz9bxPUApKxA5OPY5HZNvlUEAZQ6fmqAE - S3TLKjQkhfvj31o4tYsI78/IxafTpeuHwAOqzL5bAV9Wz6X0gLUE3aXTwRPFI3GW2Yp9aEQ6t/mH - MlzG5ZtD+SCfiCM7dr+QQWllpZNUbCYlC+YQFAz88cHph4fne1VC/RLOxDBPjdPe5ugCxSJvsMOV - V20RqxMDl4IEHqyzZzrf5vwClFs7kLNxYOmw8T2wPbnC9nVWwfr4RG9Z8GCL73r2TFcUH3x0f17c - CQkVT+kecglqX7ZKDvvqHf7uB7bPb0HwN/yCNe36GG5+dNMPQTr/8gDpEyRYP9jfdJtHBf3mT3kO - bzq/+McbGt+bQ+yv+ATLwiEXTjGdiPEBMFwVqczRm/+E+PhdLz1/fTAB0F6iTVSe8+jiah4P+YdR - YJUVlJp1VjGHy5p8f/2lzXxUdBAJ8R4rHPuiq3vMGrjxL7H4hxzOhcR0gHh2gn94OK2fdoaPSqEb - PoJwPo61iGhwiDGWX0s9p7Uywd1iKMTFjzWk+5PTgrhkfVysgKXr9cH7YAVNj4OSPGsq2octz+Ew - UR3oAh61aQYXPneJyV7UVPAxb4CDDyNcnDpG+/OHjJij33rSxTE/LeQfeoGdCB7SxWSAAhelKrFS - sowzFxLfwq3/8Em6celyEJIMCMnlMPV7iOq1KJ4zNLhzOzW3hwW4X760MNcDsUj/TqceDYr8GHfY - Q5v/nvSy6f7m5yDogzZ/pnYFu+9seIHAf+rpO3ctxC5L8HHj3xUwdgZi+xBhUyNnOlf5xMMhLwk+ - KM9TvfHBBfzyh21+wnkJNBHlN6aYSBgN4dy9gAkvrVKRu1EX9ebXG0TmV4N/+mGstLP65xdP63QO - uWO9y4CHUuIx50hPZ1mk65/fc6bp+bueHBwX6GEna+ae5Fcugtr09nFGGyWd8yTVYUracZoShoSb - v9Bhvlbkx1egfRk1C4u7luIjG7T9ipm3iBj2nkz8mvrhjNnZhRKBJU5CoKfrfdZdyJSNTmyy+cUS - LhewzYe3/vpxqQJDhrfiQPan974XaPscQJNgZgqH2wXMqqu8IfOQR3ziGrPmdQQ9aDVR6L03/Fw/ - MZWRJzX309KWbL3uja+IQpIWWHMYox84nlUhq+2e+Mj7EVh1z85//m8SSF4B7usuEQz7xsLF+SID - WpbVBDt+kD22fT/p6n/nAanPqCeKOavhEqzDBbDpOfGE77ep15bXMuj3MCfaiR605csIBtj0HTZ3 - ctq3N8Yv0ebH8EnbW2DFhRPBXXcziBbMSb2CkfXRxdoRrCkX2+HvftkhO3Izom58OpbyroGi63lY - ExSxXg99okLmDM844+tjzbq+qUNVncSpec1dvcCbe4H5DRYTpxYAkM9wU+Dwvn/JXbhqDtU4pfnL - yzb8refuRW3U70SIbyF300ZfPJnw2wnPSd7qxT25fIVBFR9JAnettumPAORmXXgrU/T961OVETL9 - Zo/zUYd0lAqJgVueSpSr1/T03mgz8gKhJHYI9JBqzRqjMgr3084QO23SEeshywhLj+e9hzPx0J7A - 5t+JHmVnZxkpyqDxzAJsZOcd2PIyEd5W+sCKI0rp8vP3zF2wiePjPKUXdnKBxvXol985M80k/8/v - gHip6XKsdzl8OJVB9oVUOesrzwZZJ71LDN5D2iT7w/DT01gxpSCctzwSaph5ePOp+dAlNWRdSrPd - zUPZctGGzuzfYOC0DKvXtHXouVtaeB61hKjr3gqFjPEq2I+nN3FoH/VTcw4vUEOehk9eTlNaUFf8 - 9Tc5wEEIhw1voU4Ml6hSbzhCfUM60PnYwA4O2prrj34Fb43TEc1dh5D0HnBlnRF2WDcVpecN+ZPA - zV+SR7aw2nS+dxWMPVPCia+z9XTS8QwZ480S4xHEPbf5UXA9nq5k/xT8ngb21Yfn/P4h1lwM2lqc - P2/w0yvxeX+nQ50lPOhH/MZYzp79Yg0xC3/nd6q7B355h5QC1fa+caOE/CKrEO4vYkB0ZiDalrcr - 8Oc3DgHrOXNzNxSo19M8zeH9rfUv3wzkXz5pbfkgZzJABb/+4JYYgaUuywY6RZwTZ+B2/fjLpzc8 - HOUQNOly/XQTuIplPsFRDDSBGKcGOoxyxtbW//zdb1uoGnNKEo2+wuU3X2sGRWIyyHBofI9i+PiC - gdhwZ2pzi3gbfvFwJpqgxPWiHCGEm373lkxdQwqegIdu1urY3/hzsWPEy+bBa6a3YByAEJj74OfH - iXtF73TspSiAzJk5e3B7T5MQveGuuxpYZQqnXx05NWFXZw0uNn6bT0POi7986HAr6p74pR5Aq5FC - nLjnI6XjvXzDwQnuHqiTY7+w206Kf2lf+ISLQSP7k9ahJZx5cvnWQtr356P72//Ah4CdHAJ3Tixv - +h17n3BfTyg++XB2EgVb2tL3tLLBBK8vwSRbXt/3oQBUwBu3N1FFitLMTPcT2vAYu9IV9FueVcIt - HyK4/oCQHM5FAu15HrAH0rLe8q8OdIf7gg9j9nQIVtoBbnxEbh8UOZR9XcQ/PY2boglnWOsr7E6V - QNTNby3b/gx8daZM9MeB0wYJHFcYoQ5hdVc26WzatESf68n47SfUk2N/Y7Dh81Q65JsuX2ZngCt8 - 74khYA4su4nqUFEA9SY3T3sqCRULHNc0cRovGl3HD6eiGfkWPp60Oh0uksbAyykLiDZ4OuAGXRvQ - RK8MVgX+06+vzJxgyI93bF2nr7PeIisD8S5TsYEFpea2/oB3tqlwcZQqQJm750GtcHx8CoOY0omf - bVAcvAPxrsre4dBTmsBv/2Lz+84sLKkLLGvWsK/6U/jL/+UTjOiEBinUlvfBn9GWP5Cj8no6c5yW - JmxH1iQJ9j26smdphpFyj8lx479f3gfWs5J5NNcvzlwcRVH+GGqI1cF1tjxCmcB2P5NQmTfQe9dI - /M3b9DxAHXBUZbrf/REl50k/QoMxwV3CB2+ph4kufvxsQO5+VGxc25TOiFEu8Ldem5+t1/H1VYCQ - Wyk+/fCqB7ECfvOmY4icWQLH+cff064/i85yEIIM3WJ8mmR1HepZ0T7eT7/hh9G/61kCeIVJf/Cw - LV7WfrhqSwIf6VnC9jQc0wm7XxvqQP1O3NbPS9IzOQx9dyS4bNlw1t/aBVoQu1u/tXX/HAcdfu7B - 1xNK2+rXlncyeGnViti+fqmHKn/z8PR6yeRwv6gO23puDOvdd8IWzUOHiN03gxsf/fifCkX4jGC3 - SxVv1Pk2HLhbnEPLOJeeNH5ZSpmPkiA1Ny4Eu+U17aqPHcFv2k1/+nxOuEpEYliZePNnNfsOXy6a - Jfc9cXC4hVv+HqMB2RVWmlymvfleTVhepz3ee3OXzm+t+Jtf755UU036pW9+/IQTK53rNR128c8f - k31yCunCTkqMNv1LdIvxQJscFhMK3UH0Pk+nCqc+oe1ffqf2Ty5ccaFd5C3vxodt/4S6/UGGNi4q - cqjgqPHrfXpDq2X4aXd6TtryWBwF+vFkEtvOrJBueA0KMl9w/g5aMP32U7SuGom6+15SesVPFr20 - 1sO37fzDNQrfsPuUR+wZoxJOH+M+weCmdMQFt9EZz9fLjKIpt//y/vWriCJMmGuDDRHo4WLifQDV - +ZkT9bT0IRkl3YYK7xfYzG864JKeyQAuqgNWt3yKVwCb/87nPbtrQNsH0jJ0PosOvt4fq7M25zBC - +8F1SNFaT2c5h+YMj4pxwGp8yjTql7oPg9ur89AdwC0/nxi4t0oO/+Zx2E8iIxvJGGONU+2Uax7h - jHBRHohtJgIl/fORgBiggjjh4ZpSI3Nn+O/vqYD/+Z//H08UcP/vJwrwkmnY7CSN8tcEBhAzWo6N - e6FrbH7DFTB8nBNtGCFYFlKx8MaHh+mZFBVdBGR1aM3mC/EvtpIKIy7fSDqfLsQ6oFPNq8nKQnYP - uKnajXuNg8yXB+LSecQx73o/SykLkRscR2ID9gY4C3g+PH3fvBdJPK1Hdfxk8MYuPi5wN/XLfdp7 - 0qe3BnI87XtAn3Lnwl0p8kQf2MWhbx/IoHqskGhBk/bjB1wa+GpdiYRs5QF6q+YETS3+YD0p94DW - RitDrRDP5PLMO0rET5LAr5JZOPOmjzP53JyDScvtae2x5gxHA6zQ7aaWGLV9dNbOLwLYpvBLonuh - O+xMxwsQjX2Lb/EtcIQyNioonuyJKOGqUv76fScwUIYLue9ffsjvqqiFTq4d8Wkddxo9p7EMv8Nl - IEX1+PYLOR5ZZIn5SPa3pE/Jwb3LMD2yJn60XU6XJ+UMZBx4Qo4F++5JH2QGvCXxkxzbw7Xn4nYv - oyrYXYjZEE6j1752oaUhD1ufWgu5nDxjyLYXzZOHfAFf9lia6FQVM/Ye6QDm8RtEyGbIDitqcKLz - 57mySI6nh8ez9QhW77uwMFdm3+Nf80qbd0ortIuqIzGw/k65/n52UZM+Y3Ke/TfgpaozfvX2WqWm - PQXLGMPBin1PWpi9xs9yksGud2Wsh+84XJ7qJ0Dfco9x9Iy5fqmy2Ib30+x5u1NWhlTu3zpMH2pB - nFKoet4MSw89BWMmNnF6sNhHHcJjUPnkqu2+ztyZlQ8VyxSIIryP2tTtRxNOWmaTNNCP9W+9ofVc - n9hL528/87enj0AoW8RyQdSPrn7OpM9rdyWm/Pmk82JGPLop4YJPlnejvIv3OmIqpSV3tRTpmKjr - gGDjuwSfReSM9tFloKm9HPxYknvKrpmS/NaX6Db/AOwoujl8WGeDZPs3SVe0f7//5tFJfYbOEzm3 - 8EPnluR9PjpTXWQ8FBO4J0VADI3Ph2mF2flwIG5oNOFXa9gOYeVrkHBsXnSVqk6H552XEw1rn7Qb - q6uLnLX6YOXlfOvFwsIKd6MbkrxdQo2f7NmHj5txx452Hvolf+5MaHV9gvEgHkPerIII1WeuIodD - gDX+4zoNJHe1IY50LXuW2Yk8Kh7jtNXDAcLSvgZ4bJQDjnot7tmzBU0gxF+MbbHfa8Lt6pnAQRHG - lzTt0uV9WxSYTuUFB/zLSvntftGrKW84shalZsOj2cEEjTe894KwZ8PrukLh0L+w/uJNR9gXtY0E - m4OTGBtfh7PXNIOAPjDZM30TLu93b8Nkz7LYKo2JrntFNZAQZiLePw5TvbzftQk/lU/xaasXf7lA - KO+KVPGQeKkoLy4XD+n5XEx87COwkDWPID7OLrncvyJY68lj5HwRHGJYS1nTLvVk+PyGj+n+KDWN - KrziwuJ96aYNr+r1YF8NyZGknuj+Y9/TBd1sNJZZiS+qWtO5M7sAnsfhi72t/qwo3iP4UVGPtfWi - hJz1qFQUNtfdBP39yVnJWcjhdK5THM9QDzlanAw4B1OK92t+dVZMFhs5+7HzKkUKwDY/LmpT5kus - 7CtrYyXBBHLlwSEWgzElW30g5Nb9ROvnISXWnubg+KIiNq/7c93nB3OAp+oxk5PmCOErUeUJqtmx - mwAJFIe/2XkJ0bl9YWubj7mOPxMSBHjz+ldYpHPLNwFCD6iS4no9U36yRR+mGLvkcNSVft59wxXl - er4S9zrM2tIkTYWS294hqQ4xHXbJs4U7EDZYXQKtn3PfvMDkUEee7B+rcKsHhJ1eTdNWP0BvV8OG - Z3vaEbPWz2BpDsAA9AYVfImj0OHOl3qFgepa5Mz0eij8+KabpAx7nGTTed9VM5p1ycQX8nS0rd9Z - SG+MMiElf6Qrz+IJStxAsTYJTk1vX+GNBNBK5C4JQc8/NE2E5bOMyAk87j25VXOMdMnxsEo+Siok - 8R3CZ/A6k+MJXlLiX++NfBJiCx8l9AF0iMMY6QM3k6R4tCnNsrEDyU1zsCuVl3R56ziD7ZjlJJRP - Cxjuj8BHn1k38PVy6JyFKc4QiEvr4eh7OvarqHgx8Jq5IurJUB3uXuYQ7pjBxJ4W9D1HxGuHAn73 - JsdK5bRpNe42WKS1w8fncaipLMUXZBtPBltHP0z54/H5RndGsLzSH51+Ox8Dl4do/NbL4XezKcJb - L0eTPPNY457+vUT98ZiToyZROoviOYJ+nwrYzQIAlsv7OaAdODc4urEl5bxzAeFzGPY4aqO8X1Y7 - 7MD4ERpyKJg0pU+5ctES6jkOD7GectJNzqEpvQuy4W84+feLjg59rmDf6ByHiyKNAc6edOSYWFrN - 34+9CiNwvBMckLdDT4WoQCZjfZLLZ9NZvpk+oRt/PpD7TdqFXcLMNlINEJKTXJ2dFTV2AG/KeZnm - wVrrJnKyC0S1meBofDVgDvZWBLvyHuD4Njl9Z6yzjl4n3ib7CSl0boa7D1exE7GNmDddBIfyQK6P - 7iQ8j0NPMU4C8Jsv4eDeAWXxw4Tfm30lWWjoIbv9PzrMbUzuT7EMKXIjF0maqZFwV95q2mtfCMsY - JAR3U5wuRqvk6Lk/Zt47zOp62ce8gnLuOWKcH9xw0b3iDTY8x56/lnTguEOF2AYU2Ku/qbNA5smD - ljV7oii71qHKveYhDOYb2b7X+Jcn6PApLwE2ysfJWc/+00eZMUTk/Hz0dMHSClEschw+hWfRWYfs - KkPY9z6xwLdx1vJqsfCZi5PXYdIAGkITQiA3V6y0/lOjWC5niB6M6m39rVFGqxM4nIwV71V4CmeL - MUVIhdqY5lEHznxguRVd4ckhV/kI+9V9AA8Qeg+xJRXXdDRv1YB+ehVXnAHY0ssG6OpKRvTeOoE1 - VLQSLG3AeQwn2WB+n6wYyqPMerdXYdGF7ksWvqJRJ04T83TyW8+FwjNw8HGr13LLtADN3/hDTFBh - wH8SNMHme+mxAbMinRas/fU3UR9uBWbhUb7R1r8eMkMSToD7yPDVepK3LA5JpytzY+EeVs0ETTfV - 1rq4sHAvsio5n269Mz79cwU9kE1eOzRHjc30/QSt6Qq990OJUzpY3wAyWEVTH95O6cydv1CO4gMh - 18rswtl0qYGyC/1MdOufRbrJGfg8jNRrnScMqV7zvEzEV0einSWla/kFPqzWIJu41eud/lcf9e2l - 0y5ejbqtnK8LX+urxV41rf0UF0cIz2zCT2LGa/3KhkULam0+T+jS5NoCxK4C4PbuiaY/QdhlRRmg - OmZa7CrhtycSvasQcvMeR7stx0+b+A2dnk1w4N2PYNMnb/hYISKK7K50ndnUhupXRPg+ekY9P5Mw - gmUsJSRmH3E/6xOTQVlVz0RvdjunNwGrQ8ubeazeiaexVrDGyN6egPj1L62akyn1gphiZxEdOlXc - IkLzgSRs3qa+X6kbZ/AMqDyBlbJhc2N3Log5KySem9T9ovmaB5+CPpNDVR5rzvm2LXS7ocX6SKR+ - CvtEAR8cmtg2dl1IWtMyIAn5E9FF+5aOm/6QvEOREDfRT+lqJSSGvZZAbN7vHqD6dXuC+54w2Gxd - Cwhjngzyj689+Wxqmx4w0f2pCcRs3S9dxHIM4OXdLcSqHeOnz3OgCoP/p+d+/gKE+ioR7fheexq2 - txh2d3LAds0d+hnS1YPZZfmQ0+MS9xNl3jY0xLQg3ncA/azQxxvaKpd5lK0msKbCwYNK/XyTq/Cc - wbJ/tz4cfONB9DYLAXVRFkChWK/Elt7ffvOfLWSTcsaFpWsO97jMHdr0vofA416TM8Nm6ByXAQnW - 06At+VMwwbDrK6wITBOu5+lUwSHrRXLIuKZfzgepQ7524ifuClJtppdHDltjDYj1XRZKrFehQuM5 - hlgVygVQSdU8wBimjsOPpPa8jAL2z08exa5yaOOWLrSZbD9B9jTVnT3FEML1+vAWdd/QP38ShDuI - 96IVp2s+vFf5G5LFk+7mi34LPZCh0o5oklgX13T4nCqIr8KD7BeHhFPwCgZ43KspPtpF0c/lPCfw - 93uUO/uefe3VGVq1H3m7l3ZNJ+90zcDG/+TReKrGyyWTw41vsDI/Mdj6M4ffQ1fiTQ/R1WPqAJZe - 1WM9Mpt6GOpQQZve8xjg+j096m8GXv2oJafcefZzIfUesAfoEiU/Kf0aF0cGjrogE+dG7Jqy/VKh - ne1+Scr7lrNev1MMz9pHIqf8k6erPcUMOJ5ePD6p/gQWFdwnlN4/YKqWQKuF/GBO0MoaC18Wd0pp - j6oG1PX1hlPvE4DJcrgA7Yq74rGb/p7rc1misOwf2PQ0s5+WvoyQQbFJ9klRgWXTN+DtTrFHcdr0 - 8/hNLsCRQE+0qsvSJSSXSs65evReV5A6s9uIkcz3AY91Z3zTP/2oesp94nt536/G68SCusse5J5G - rEOfw44B3ipQ7G54TJ1wSeDceP6fnpo/hyhDWew5+OcPuNvuzaO0HgZPyJ8lXUOP66BQzFfsXu52 - uhRcOcDNf+L9hEo6+VDo4KhzMlZ349OZNzyB2XfHeNxRL/vlzT0CqLLngZyM1tWm+fVVYDaJN+KH - a0Xpte89eDjffHzY5m19nnIR/PIPXRaWsGuiToVbvTyWLy7aOoiyD3BgA2ymThnOJy8MUOWGR6y6 - +K5R4Fn8bx6ICeSTxv/qHx9nGZ/OzBpOpPgOcJAfDnFfSVhTNZovKDk8I+85GcL/AgAA//+kXcmW - qrwWfiAG0kmSIZ30EARFnAmiAiJIEyBPfxfWGf6zO6xVVRYkO1+3kxTd8LYAmFGKn36LZ3tOWvje - TxNWNear0VMo5XC4SQcctAvVZnWXB3DOPifi77GbsTzQWWCzs0ssI0j6+XA5lkDRkwqnvvXU5lt1 - rcH5xd8DajZrRS9Cx0q63pNgN1JMV9rBAeap5+Jkm0/WlGEOJWfvYGXLLxZRU2ToWuyM1STax9RO - ahHGVxQS2VWtTHh/Ugaa/ofHv/ppf/puvV7maT6Qsp+B+wjA8A5ZfN0Laz/ci7VA35D1yNYBi2dk - VQME9IGxWbxkMN9T6kD7/NCwp/cNWAL1zSO3Hk+B+P7eK/LwsAQKxmnx4ZpiSlspmZGcMA52bk5Z - bfX7hE3h+aSYm7e28mFtoe19go/SH6rV9FsJXlgaYv9K9VggPssABVvuJB5rCEjqZcneP90e2Dst - n37BlH5/+iXIN38xb0EuuBqdhX953Zo+MISXwtQmNp3meOPrJ7C8wAjE8YH7X54DNn+H4y1PnMPn - 1UOLhUaiBtcPaG+iJv/0G7ah8qr++O5CGpGYBuPGo6dfc8CEJcVKOS8VWVylgMAw+CncH52My4Ml - gRabWtj84jqjxXV14DlzQuzugKcJO2kuYdHCY7Db27zbGwQVUC/Wx+av2Jhs84n4ULKwt3xUIHyF - PvrxVYA+lxTQpR2nH15PW70ALo3DAjmnyxFvftldrQMH4XATD1i+xBetbrcd4uZl+gRjZjjuXA/H - EP743h6Dpprfa1ijxB9lrF1OyT8/9qtvu6AtXdtIFZFdRUnwZOrZXU9C+oU+rJ/kuPHH1Mg6RFq7 - uPh2c8r+L1/og6Ce0Oa/B+Mesmitihc5PLSI/o1HLVwh/uExi29HEa5iK2LLCPhqXJtZhvHD/QQf - jCV34h9FCM31kJBCeUruYK5O+Mdf7OJN8czbgvrDW5wPKayWp3PzYJ9+4olngpDy2yZ/dGw+FZHV - aKTk651moOsdmVZBxNrK6mMJp5uTYFv+mnRw0v0EJ2TJvzwWzOIZtTBM8DN4bfkOJTt1Rpw4GcSa - bqm7HsBX+tNXan7eAxLxdittfBmIchX3y0K+LGBstsGG+1n6+Xw4q7CQcgMfzyHsyd0HJXRrciKm - kGX9qhp2CidZa4ix6eHV0Zxc2hO1wU6nIG3jhwSG96QhXnOG7noBdQLU09AQWUwUyvlwykEkTyfs - OUlHV6V6f6H21ruND5/VYoMggpH99idVQk0/lsFbgr/1pZjDB8zN/jGDufZCEnN3IV5AJ96gNjxG - 4i5iD7rgoQZwx0xWAMV3D+ahymSYd4NMfuNPbKpEsPAv/dRufpJPC1mHb591sD41ZcVx+bX+82du - nSZ0tV0UAVMajvg+7g8ur8iOIQk1HMmdkaZ4VFJGhd7VozhzMFdN/tlK4QO3dxK1jgu466R48GRe - uiAy6jMgG/5AX7jZ0ztszzF9X6cBms/Pi/z05ydcEQ+MF4mxGRbPnp7YRIbpufG351OrObWe/N96 - pq8LX1E1XFTwjtUyqPgZaZRhFw8Ku3NGjG0TKzlkngwdG5YTG/WEjtgaHYClE8Q/f7DyjySC43WN - 8Za/A/p2YA1Cl1+I2Qmm1mKEckBc/zjtzucjWMSmbuBwebwCMaGaxmXWKsOMOdzwlkeAxYdNDh7a - WSax8Gm01bXtGWZ6Vv/5ye/eOAxgy/+w334Z+uf/FZFX8U/Pz+3NFP/w3OnERiOcMDvofbs2WEnM - QyV8vdOKcrhrg0tOcMYHudnAh3aRif9cxoxMJtdC7uMdcbbVwyT3YYP2mqNh+8cnrpq3QNbgE8df - c9Fot/QQfNy6Id6Q5v3yGcoCbvyKo8J042V3+ai/PHzDw0TrIldk4O/vczN4Z+MbWQHknqZL8Ju5 - 09l+1U+gXeIEO8k4gu7trjcQFUuNg0vqZZvegXBWggY7vM2C9Zf/PI5WTMIIXMGWl63Aoe2VbHzR - DwC+VYQcmOJHbGoZfXimCH7vq3QJitcQ7r7wqz8nrNTapaK/+mlMugvmw+WQzbXN1XCWxJxcNrxf - Xm8Hwn1/0n95YcVenocVKssASXHqo3hszXjzo2FO5J3A0VHkiklSOwn9+i/u9yrOLCoZQdjyXZUu - 2Bot4PidPjEGkTNu/QgijHH9IvJHrHrS5msJxRujTGLmyjG13w/5L08rCP+Np/J6LhEfihYOP6JW - Ler4KeDek+G0KE62+SdbhHHUePjXX5vpvPCwTHoe2y9RzjhPvxYwscouEBfBo/THV26hmDja8gD6 - 6kUDaq+biRUpNrVffwzKHned0FYvdJ1GGYR77BB9y9Pplh/CvEMM9i6FUgm7InfgrjF9rLQZS9f8 - 0YZw689MkqG/AN36eWLosgt5tE/H3fSiB7ridSXqtv4X+jgYkrs+P5s/vfYbf6gwceQCa/x8d5fs - MjgAt4IZ8Pby7Ad+iAZpVryGuA7m+qU1swh2N14Ivvk8xeMkKREqrfod8J3/qWZJT2V4YBMpYPzR - jidHU4tf/hqIr0AGVNalG7gsxhTAtezj2cBNDXfCRyWy+ab9mnBvGboMq2Ns65U7Z68RwuZjmhPr - Heps2alcLT2DZ4+PigPofOa/PMjn1McG1o1M2PgEUp/D2AnNMtvynRtkvfE+0S1P3daPDCfTGYha - 3Rb3xZ+a9i//f6zVNx63+QVp2LETaMpDT9p+aOGWL06g5/p4HI5NBDP4fhM7GC2XLdREhy6P7Ymy - ZUCHJqNPlH7XGpvlLYqFK8cEwHk4+2Al/QOQzrgX0DreCiwLzUebQ43l4VXID5NdF4M7fGJrgnIC - HRw9R0GjbIFD0NxoFwhbfrv61br5o+4+5a3Qxku4W0OkhPvdBFhDyuZ1V0GoW0Yc7DKZBctwnCKw - 9femWAvOMcn7xwwvZ8/Bx9fdBZTkRIcf44aJ9cmxNhXDNMOtP/LHF+S+UxLJQ1P0Wz9VJz7fkSjE - hYh95dzEc2q1PHw6rI1VTVP6eQ7tFf7wwzneWo3TErWEH3y0gl0G7vHa1xYLLd4FxIRuEm/1FMJN - T+NLlnfZ+J0ZB156MSGpOpYZ/STUQ18LhziIZaJ1Ssqr+/NlHCaa6zPtX9I3gOXNuBEcqp7bAO4j - ooXXLRx/H0E8XJ7+uv/h7U8vs9s5SvBRdz3xU2Wl3wd2/vI1fNjyxCWN0wKwY3nDxpavC1v+D2/y - aYfdr1RSOo4e+9cPOWs72+UGlbXErT9Dfv2sucZQ/PEndllDitd0XUooPq3rRBeouSs/yiWk5YmQ - y82u+g0/JdAh9oV//V/BpkqItv5yMB7Gqt/yggkKAnOZUJLgav35sf9jRwH/3zsKbPP6Ie6n+8Rr - 7LEhTAeHBMIRitU8mLCA6HUZiVwFn2xoLJ2BLy4NiF1YZ40qI/nCGIbGxNVgBfOlwAUMpKwmvmcF - Ffu6WivIGD8jWnUNNR6MgwGfT2tHDOM5asv0UGvEN/saa9PbAux7uq4wvUj7gOvuS0/qhH7BaIc+ - vhjzDtD+U5bAQSQkzhxI/XA8hw5s9p9pWi85705B0/Pw/Cz5YC6cDgxOcbpBHHQ3kt7FMptWrXCg - Jtsstv0zActrFlTEvT4n4n1Em/LyeX+DzSddA/gRbUCKuQ+AqH4aou8aORa8PXuTnusFBEONBLB0 - 9tKgPJ1VcjCOTs/xdt1CxD1LfEuDIZuz7zBBA1KD+Pe8jOdb6auQaalIlLuI6Eo1VQdY3R/xRXI9 - OmvJwYNMFB7I/XCvs6VSvikSmAshZpd++raflBAtlpHjoAgoXSN6d6DqoYLYrn/qZ/alqUhr+ycJ - SHUBAjbeK1qcOJ2ELv1U9BI8RWhndY79SpsoRf4Vws/3bRL73vAZbatQRLtzfiNmqmm9wPXVF82U - bfDV9fSYOFgR0cjrC3GKi9DTVbzeAFiuHHZSDCs6H7IIXqseBssc4oxTnl8ZHqznhaTl7eWyO94t - 0bFiSxzvl1c/Y3o8oZnyTdDKcat90n2WwisJNeynqU65e3Zk0GifA6xLpQrY2/UawiOvFUSxFKBN - YBx0MEztmxRs886EpVgTtH0/OMA0cicwhjLS2vpB4svXiomnyjp4RJFC/CPJXDpVVvo3H46vaLFw - v+gWvPO9jE3OJNmiufIT4c5TSC6PNJs0W/tCm/DRxErKKePwvjUQjtgQpzJnUPbzvcwwbs0jkbv9 - sVqleIaoCaFMbOUjZit6MgkkrVXj4/RdAbu83w4qI28ll/r0dbm+nxOUuM6dnA/EBbRusQEnkMg4 - PrBnyn1vIgPSi7gn1xo/XTJGdwMa7jCQax2G8Zo1hQQkh7GxQVmsLVA8TdAB5YnY3JuNKUoOA3r2 - rEnw/SrSNWgqFm3rBxvpZdaozagF0jXPJKHHJxor5K0ILWovxC47rV8irtEhD9xbsBbdTqPRPDuo - 46wPSaVsV63m+Ski3AUKMbib2wuGM0doWMU8WKKeBYv+NgcYfVQBa+WpzITuILDgeamP0yuyDlSA - h+AE1SrEeJvPuLe3MxFlaAhYo12TkdC4TmgCJxkfHehr7N7MCqiYjY8Vt3CB8NhvHdBzfsM3KeH6 - 5WrIDLoK+5VY3cdxuVvwbOElqjscuDGNl6WSHcSE2A0Wdg00upfhBKMO04m5D5xG3PYJUdAYY9BM - QQTW+/VsQAc8TwHlwljjPgm4Sb0MvsSbh0dPbaYoofalGXETXnVpzi4hON8MDbtXGYHBsN0UaN/h - RoK0FfpJXFYLmpq/n1gqtoB6qmzAbf4m4fBVAI9llYHmbpfh4JgF/bA34wIx7u40CR1IepYyswVs - 6QMDRprkmHuzsAH66jTTrswbd1KKo4FsM2lw6PG8RsdIUZF2OnUT5woNICpDZFA2RRvMdXRw+b3m - twDsPJFklZVSuuEv+jaOPC3beqNm3aeSy0zddHP2rbZO416Fn0WTsMIJAKzmmWX+8F/f6a+eUHnv - QG/sLOKkOO+p7J5YqB6tEzl3uZLNrxdboph5qAHvGV3VRdxN3fZkewG/K5uKAx9lu0PkfiKyZH8o - 2cmhg7I5Sn7rgwrLZzLguzRK4rufHVivII3gU8tyrBuq5a7sOxd/9UFcQ/e1/pndZ7CbDRUb8uUF - 1vNEVeTCZiI4PfVat/f5CB6KnT2Vl+wNBOZzMNC+rAridrgCdIxsGY6mFf3hIbs3P0/0w9OCyC2l - vF/mcHeuskncFQOY6lQspWcPLCxPN9kdxlA2kBMfOpLVbpOt73U3gESQZKLLzLda2ZOSo0vL3IiW - ema2qqLnQVnP99j+jIo70FRIpSMH74EoZbv+9/vQZZYviSbx7VKmOCaIRhPCyi436UIUJUUnOkSk - qKJjPK873UPcPcqD18WrspmTvn/1hvPZkjPe0986OuqiRjyP2QP6FqUUmueQBqU7FT0339gSPa/U - D7r7u9n426lh1kk11o26oYuSqif0vbcxTtL9BFi3eapovpjaxEnLIePrrwvhhr/4ARW5599r1aIf - v/ud62isrXchandsQuKdNNDhdEU8rFXnS4L7cHbpTh68H/7iSweSiqcuxyJ2Jfy2vu5g7t+vGk3g - qpCs9pJ+YQtJh7tPfcbp1Js920/2doeSXZHC5SJt/YbWCV16N8G2S2hGlZqK6PAyD0Q/PLtY0C+S - DrWkqElQ+lrP3u88hO9smgmeT9dtvK4qgsqJJWF3WcGK8FeCn0i+kXwniu46m4MHivheB2BnNNXC - kf12oCE08fEuIrAWr+eELnI+4ZztSbyqpH4i2ukTserKq9hPOoe/r//qeXm1bgRbzWCJ9Zv/DR8Q - q9QZVuugzybCpwNs3meRROmjqtb6lQa/9UW8qaxoK2Fq/fCYZIqyoysf4gL4z/UeAKn+gAny2oqi - 4OUQw2uteLrdewuNB/mCz3fP1Va/8CzYIK4OmC6DgJDDJ0HsmT3i8Grm/WIYUgEHw9kTPc0ld2w/ - 6wrldCDE6IaTtmZT+UQibXZE72Yf/PAL9f3rSMzduo/nxpxFFK6zhe97hmSr2N1y2BmeRa6K3mWU - vdURdPaP27TrOuLOwkkJQKUYIbEcpFCOzDaEqZKbxIKyC35/D0qfdQwYyT/S+UBCD2zjT07gsIDZ - k7MAve/7gRynruy5Tnkz8NOmLsHKTQKDWTJP2F7zNymk6ZktvB3WME8FjSjZVLrz5+qdAOLeVbCk - xyX7zT/QzycVR04r9/PnqicgjoVs2jGoBHNYXCDscgKwWZ8cl+5IOiEve/nY5bV9Rm18Z6AhFjhY - tufZ5mOGz543px0ncf0MpWMDi4czBPzkq/0yPpIvFNV3Q3BnPDV6ypgZJvAoEs1I2GqdzdD5G4+t - fqu/38eoFInOPbiKLp9Jl6pzZmKPNq9s3UtXHpRiU5HDbAGwzWcOq/PVJMfITrQF3T7N33x65BCB - VVwkC9hZd9/4b1dRt20h/NrpnZzTr5et7XSvoaU7x0n6Go94OJDUkxqnDoibCGu2uKPKImTU2nSX - XkU2HrOVAY/g4BJ5YnZxezyHFhqz1JqWi0+r6fLaB/BXn4eTfaf9PagLdDAe94DKs5jNlBEdadPL - 2FY+aUbgIUggL8ICX45Xu+exsIvALapuwS7yCKX97ATwdZubP31F9eebBZpsUHLgvGs89JMd7j9J - FBJj46PpEu5Z2HHOB6tXda0oB3sZXkmkEbvjh344XV8zGrOHPe0nydFmsOxF4DL0G6xXde3nRLe2 - JuBhT5RN/61iF+XgvDcronBCBha8WDdotK+QHI53LWbNmzrD09Klf/zWdsDVYXvwCLY73qumCLQr - fPOnlIRVYGYcFK0vvB/qhmRT/Mqm0Pq2cBs/bHJfJp7G0yRDf392cFBFx4xLuXMNDbtZiPOBWsUW - jVr/1X+mlEG83J2oQbM6e+QsredKwGiBv5+fVnrTAM8zlIE/f+e63y6bn5adSp1xtCd2ioaKpnuS - S1dDSMmhOKaUruK7gLn+9MjJbfWMuxoWA8xgr2Arm02XbjkGTB63mAQgemxn3JH1mz9yw+jQz/p5 - cZA3Nua0+3qM+/MzyHfuA4467gK4B+cb8JrJPlG5elfROZkGuLrhQjIqjXSOrfIGt3rFP/1LnQv6 - QicUR/K4rINGZLUz/vhclZ+HasWCEEpXYUvUSkYHdE6aCZo76YZtmreAIv/IQKZOZXzu7sd+GMOT - AweFVydAU+IOnzBt4E0ELlak9JutlXXw4OO7J8STHT1ehTTTweMLCLbJfa/Ror000jaeWImsA6Cy - a60wHs4mOch9EC+J6qbQGiYm4OGBo5NtAQtcDS7Faob0nrX1VwQf7UCxfNxrGddX9QAi9Oiw5+5d - MKqSbYBhlXKiGl1Ieb0vCuh4LR9Im55bXI5pwOPLTz/+p7Ptn2XwfDo7fODcTuujRffQPBZsMCdI - Bnxwn3VYOEgIdnPcucv4KL4/fiDGPRv76dSQG1jyJCJmJxnZeG7TAOqr1ZDjhUTxAKVrA8g3ehB8 - AF224eEAvevpiNPd4+7OZwAbdC+MCPtej6pZON0jyAmijmMOl6APjeMAtvrHh+rUuEP2Db8oseWS - uCzj9vPKhzk6voYPvknn7Qz6EkoonaIvOUjY6JdjffvCTHuzAXOpun6VdUmFiCtLot1DHXBjUkrI - PgQ9Vo+c3bMM/2jg2VJs7BjptaLWeHHgmZc+ASR6WW3+ZAD9J7gRg3KPeI2oYsD2+mo2fsfx/Pk+ - ViCnyzwhLg97IRjWGvmlmOJTPYQxT0/nEt1E3sF+3bnxwLx1B9Z6+MY4PWfxnINU/fED9u+7JV5X - 6XsC29fk8NPTepGygCnkAFsOeoFJytMvhIeun4T65GjU27cn8C7dilhp1GiDStcETl5RYnu6Zv3s - CSAHs/w9YiOVdtpwbKoGfo/IDcT7fu0nwtcrFAaHYlzXr7j3XFNC4PQ5YpfdAboizDCQs+UG+xch - 0dj7nWEglocz1jB4VnS0uwTAQ98TRQLXmMXwoP/05DRPt6c2l+Dz/fnxoEz2H5dWh2cA+r4Isdny - FhWaaKxhWZ9GLE+IaPOZniTkXa8R8Wp0AexvfMsophP7jSEYZ7szfn4E28b3UAnO85wA33C4SSr3 - STxUGkhAeDvngdABvq/GSJERA5kRaxer69fYaz2Ig+aKXfn+7IenpfxbfyM0nEqI9r0IkdHpk1QX - Zb/cnVsD2ZthBqBbyn65olcAFf45BHy13bE1n64DTFlyx7hW1X6x1fqGLFVNAvGeVBV5740ccsZu - wMpMmWz8rSc+6ifs3Nm2n1MhKpCzv10x9q01Hjd/L4WJ+sZq0T3cVcrrAVUIsdjtFrXifHwf4F1c - AQkMzNFZZY0UOZ/LtqMrg3TzMyqQFy/Hxh46sVAOig5r4/EKaPfqM4rOSwvrQYj8ftcesh/e//R2 - 0KWlSJe+qid41CUtmP2UpYRjL+nveScW2qQaTleO/eERkSUndfuSfc5iU/A6NmpDADOGvg7XR/n5 - +TN36fv59POHf/O9Su41hCn7zbFbe3xPjW2H+an33sROyxRQoeh46LaJiM2LnLgcPcQqLO1Wx2cl - QNl6Fm4T1DOBw/b93bhzMfEn4HyiAsvl0PZfNkcBNEKtDqQPomDIqJ6iObtKWJ9vH7BioQ9gsZ9P - BEN1BuugTa20zQ9R79XL5b8vwsP5IqtBKt1INsbYD+DxXszbVqxjtj50s4XyqDT/8BirmQi4zHqR - wEuelCq57MAtT9vq893PY5g70j3X0i3vKumEhV0It3yMuBQULnll9gQ1Wafk6B/EntwTUYfCVxZx - vEsDdz40cwlFbgFEk+JnPO/NOEd8mK8kSiQha5/WuYCWKie4ANF2f497lWHL6v7mZ+tsnG/wKb1u - a4NVp332w69eQTJ65DK/w77XcHQDL+6OieehrlrQkfKoQUKND0Rntjw4q+GrOp+w50q5xhJJamDV - zyK5A3UAYxjHoSTgE8YeN99d+suHzod9gHHJ1GBint/oV2/EPYZvsJ4aksLlwLyw5sIZzFbvWH/+ - 6UDfQ78yncLAmloa8TP+ABbqrxPc8qKJcbnIXT16rCG6iRLZ8Coj23ijzwkn2KuRQL8PkPHw5+8P - DrWzX34ADvnqE7d2m3jYM2CCOnspsfGp2moxn+9aMncoI3oUBz3PjbCBfTCNU3yx7GqBMptD9DqP - G16DeJbSiAdvMY+JTZlPRfb26YasJPKmnx/sr84kwcMkWvhUHbSKePs2gcJgUXLfvTM6B3S8QaeY - l1/+Hv/8DqR9/MVq2QZgsTo/hO5RNEhSPlx3UuUe7s2deCOxI1Tu8s0OMtzyInw43quYz6ayBMuz - c4irKA+wTu/xJl0zM8AyfYxgZjt+hd7BzQkuc0PjrKVmoJWmJr7PKdWmfv0EMDQODdE9R8tmJg9T - GBpmEwjeeXC70yVlYVTv0MSNzFNbzvuqhRufEct7F+DnV7iceCy2lPRM11UrLHgv9ChYDiLupypM - eTikIRsA44DiNbGmAObGaGElPS4xdVskg4UFMvG2O4bHz4M40tvu+F89VERIEg+0l9OXONNnpER2 - 5RXckKtgr4Wfap7dVf7l08TbiaLWfxMhgP3ZeBAnOjzp9FhbA9zLzCGBy60avTqNBOiueExQOvY9 - ZY9iCSPHi0l4eZINPxUZhc8xID7lKjA1J6YEq2RhrNBR1ti34JeQqe9KwG759fIMQgvV1NexVT0f - 2vIeZB5VcewFoHzbVOD6/guvd9/G5qwdAeuSDw+YdhE3f+H1y+tRNXDzJ9iTh0obduZDhRvfT1Qm - rbt2uxz+8W1imXva20zyhB6rSlsevVQdDYwZrm/xirPoVMVruXvn8GqheHpt+d3Eh2YBjhxzn6C7 - d+liPq8lzJdkJr/nnTd9Bb9SLuCHlNXuKPmlB6tPoUzgSFmwemAJEZDPGbY9umjjJfsWUGNgSAoi - W3Rmxp4H08dwiHOsSpeWg23Ae10dtry/pmPn+C28oCAkZrJ707lonAY23mDj5Dgc6BI8FRnOpJun - fXfyKS0ePQtOV+M7CalW9ev0fqfox18vA93jWf94DXwk3Z6EXvzRWrNkSpDYaonlCgtVJ1TuBJ6H - PJno1Oy1vjvsWHgOP0IgbvVJj5erAfdF0ASMnzNgzl4zhExTQ6IXahITm3FyiOUlCQS2fsazW8gn - uIry6S8f7Hh5ltB8OWhEKSGIB+O4c/78H76EfsbP4QMCoN6TKXBmPeOIct+2uLYuzksu75cheEQg - jrkMBx104w7KsIDx1/ewXtuvbF7eowNd/4sD0WFWbV4mnQUjbyzTlk+7S6JqKfQWcUdSlpZgFPtd - Di4oPgaib/gxS9PdTbpn8IIxy33o7/nRWDF8IMhNnW39g0EKRK8l+eVQ98vzMOdAerfrXz9j0vuk - gOC7Uzd8DsHCzX4pRbUIse82uFqZBQ1SaX91rLFjrQ2gmyLpXH6XgN6PnLs4cb+CX96/+e1s2c4a - gBPJs0mY8rdGS7ad4Ukd9hMF1yIeDdMo//Ts7pcfNlqlw2w4PLFl5DTe9CeExoMpic9eSrB6+lEG - cA4ibMoftl+TnZTC3bm4EefAOdUa63nzy0fwddMzKze2T8nU8B67x/X3vmIOy+hIcVASNeMAaRnY - FKweLL5uZSvHyx5yYrUNdhITb/kIqSFRVkoORiTRPppF65eXTOCSPsFoLQOE8zL1ROtKUM3L+2rA - In7U21nhA2C/d1sEt+h1I65czHQ9lsqAtvGbTMN7VvOO156//IcoBkLZlO9eEvw2WPnph2rhFZ35 - 6U+sGLcpHlPOLoGW5DUOP3OjEc/F4s/vYc2Xg+qn91CeKTty4FzbXUTIs7/+F3EtP+3p7/mfbMr9 - m99rACcYMzcFO8bFjmenyFNoXh4E65VBwfceohSyl21HOWdIVXuVjfDXPyHKTItsFsc2/MtHN78A - lip6zcDFsxOIsJLoeHrMJdJ8RsCua337bXx0ELip/usnVezteoxQAmORmMn9o61K8bbgrqNHok6J - kdF0/8nh1r/Y5jP9yzchYcoz+fmRIdc1Fi3P2sZHmV1dMihKK9UDF+GDB09gSJvMgOhu8lO35cGj - yLo6nECmTNI9ssH4638R5pNMxCjSeL1byheyF8bb/N4u3vzRDIWvKgbL4fzsF1W6W+DHhx/3OGVL - rkgSNNxpCJj68ojXoBFEmFStgw+K77v8exUmGJ93CfENmW75PGNBAWchVivHrwTZzVloUX0mSnn1 - 4kWLYQTXY1uRJFloNcyncYXW7aNNU5flYGnnqADr490Sa+rNamyQHKCNL0h0qCuN8gd1Bv/HjgLh - v3cU5GPvBUwJJG2tLkkK5yjgA1b0v3SJNWa7tSfrCWa/xB1nfzFQHFzOE3f4qJT6oVqiqzViolju - 7K6zVgdwubAJOUt2oy2ffcTAjp2WgFFNu+LZncxD6bSLp2Wn+HR5TxpENT+NE4XfEAg3GEbw8khG - rH9p1M+qWVowbjGP9Sse3dXc9ujyzT0k7tw0PbXF2wyrIs6Jp1fbLcGt4EDAaFesRuwn67AfynAJ - 9gZ5FNcxXi63k4N20ekVgH3aZTR5rxG6JNeN7wqr4k4tXOGVUU9YJdXiLszu0ICRCl9iyAug67Fn - eOkcWznReM/q6Xlb0WxVdsQyVQqWUlI8dHl0FtY51s74VthPMJ3YiWRcrFbs+Rqp0GqLkTh3412t - aoJOIEwOHb7GuzeYM5hOUM3AnphXbdeT5iHlyHr4KrmtbNrPvciGKG8kYeor06xY/aTyyNdkl2Sv - TM14dZ820H4vLHFjWGVCuigR2snuJ2DmsK+63/spE/Cxb4nfeDmyq4zCMw2w6mWGy/v7wwzT81vH - +qOdMqq8jRvSQ8PBqtZpLjfeqxQtUmsQjU5yxu3MfQQgbOxpyS1Bo7crp0LpXpskxWzSs4e9kqOB - Dywii+DtCvreVtGtPCzYtjXepU/mkKJd9IXkIMW0Wub+PMOYtlkgPKI4Fi7OzKBXf+JwsXvy2eqU - nxlEioqJZ5yu/dRYaJXkl1ROvNvWgD9/shLWfBpNkpyrPXv18xl+zrcTyY3TvprGOHnCrvRzoke8 - W42f8yRDXmTexLSnka6XgGv2EiNQLC87zl2l6hrC2IAOiYOLEs8E3xLwET2dJOEj6IVQTgP0soIv - VopI17gy3bVQXquUaMHlFa+8+6qRYec99o2b6pL87alwGy+cPVQZgER7qHDHWUdyftHCZQ9S7CAH - NQFJ4ZFmy+nTG7CORYSzr3/Q2PBmRZALPYmkB++lzYe9UiBvFo7EPSnfeE7bQpegNH4x1v2oX5e2 - 5KHqMC1RG/6srVOuWqjohCIYefiOKcXcCb3Y+omTFzPGC6RMA3F5asm5Qr0mZGXbwiDiPxgXBeuu - 3PvqwV7YUayak025rjQmxOTpm+TGqsR8HuATfBwijbjOsuvn17NzYN8MJjYpvmX02Lki7MqXEPBL - /HD5/jBY4JCoB2I+IhovwDvV0ODYF75N395d5BmWMM17FpsDv9fGfvrckHVAKQ6uO5vyabpKSKtV - gq2vs3NXco9ZVJppiLOOoGzJVpeHY6h3086KrhnbizAExvtpBGhy1Ip7JXkE33S8Yp0kL22BZzmA - dGzGSTgYabU2Ds4h5fmUHLyD6658Z0RQblGEnSGZq+UVzxK01b6blgPM3bmO5BKVjwNPbPXL9nOk - sjKs7t8G62fHyHpmSgd4dnwvYIOAzyYzimTUfdmQqMzExeQrfB1Is4wGYnul7vIc2xJkFz3D8VLT - eLiSWUVc59hECUXPZU+BpkKA0ipYpS6oWH0+84B71NrE7wG7zS9KYPSVCL6qT1NbTcJMkEX6hIOX - /Kw69t2u8GVxTyKfbykQZIZRgfLh7wSPnRiPwDs1KFuOF2x+0xlMiXfzQLbEF4yPeq7NpkElyMq3 - AutBfHdZ1fw6MEtWmchvy+1XGz5S0KzSzv8Iq0UXX1VkaMJrtO2A0lzqFJWDGiOYsKK0irtEmXpD - 1b1tMP4YXcxLuyGULsZBIz72dPql6Vwi9JRnUuSpGwtXOnxh5j/P5AZebE+lq55CSJMHVsqXmE2x - +HjCBPMD1ukzqKg/fgJJcq8+dvz7tafP3cOA4YexiNwwRTbNn6sD73KBsHb4vjMebWceua6oCeYT - Nh44FqZQdVof36CuxcKinVpk2IuBi76wKvrcXQwo4iohwc2+ZJTlDy0g6sDhi3uj2orInoH6c5RJ - OHKfqm31fQPOw/AisWX5/crmYw1Pc28Gg6twfYsqx5Eeh6YIhk9zpPNbDFkIQ++EcXFq+vUzP2YY - yN1IboWw9LQ9Hnm04S+OAiJXlFfyASbX20oOUTHES3OcIVpet3yqlcsAFvPhRFDST3ccRfvA5axn - BuFV+T4mM/ky7swGtY5imshEOT9fGS9PSoN84fAhQcnTuH2LKS+pzDXAqZ2dszXZdmCd5tOAjx59 - Z5wKtRKd+egT3FZW7BdF2rNQTKmCbfwq3EWRFhb98E2VHorG7ZVDjaTzcSKeH3ZV/SDKispvIBND - k0m/Zh8GQoGRnvjOrmnMWt+WRU+7k3A4zk+wqEwjwuF2t8gjDjp3AbPyRDO8LviKX4y7tIA5CdP9 - Skiy4Q/VpWhAz9Oq40Ml6hlnf68hPNviQhJuaSjLYW6G0a0+kShiToBl3u8n3A2+Qn7jQxt9cVDA - tZe/v0evaZ/Alsm3HQXF6lK5ghMcS+kaMH2baLPfPkUUfqCFT9lhBxZWfwRwx+UDjnjlGlNya0UU - tz5P9PohanT8xh4KvVdF7Pe419qy0kuYyK41sfXZ6ee8fBloDMkD2w/H0Vb9KbWwwIlJrkdp179f - ySlCG58R033s6cRcTjJaXpNAzOUkU1Z5yjo0OP5FgvcJVB0OXhN6MnA3sRzbbfpAktEPj3O9PcXr - npug5I+v19TQ++DWaHd2kHYrOuynfu6uaSpJcFYamTh318sogPsEtqr+JlZVdvEc9F7405PTbvUM - wD+YHMKvDzNsmmcvW979s0bNGto41IZ3vzLAgvBlCc/pueEJK8NYh7UWmQHsuDcgpZ3nsHxFC7Gm - lY2XfvqkcNN32Gjzc7XGufeE9Wh9JtFOgmrGnNLAMtUMoj0Pp36ZCmQgp5ZtcrG7j8sbR+sEv0XE - E79fXu4sLeWMpFEsyYXIhst7XDr88JUkL8bPaBvKFry9lgQHqmn36+2d1QDjp4CL1+GTTUA1JxjT - b4b97sO6sx3uPRjY4Qufrth3h+eFK9EbnnxclIvXc2m+K+FaGAPWt/qYh7sEIfdoNOIfv77L/z5v - hJY2DeWeo92dGUpYhPF1QhufrxENZ/TTf7qkof6rV4kKCiin2Aw93iVtaFnAnm7txs+PitZn5ABx - 3SFi1P4rm939IIL8ZJrkEI7bfxl6jBMIBv1I0qk16LLywwrbevsvKZn1BtSwfAts/Q/iXcSdNqPd - 3YKutD+T68NxXL5+OjJMcHmczIG/ugsf7kSw+Q2CrVlx2d/PCxw6EnfTQ7Q7GxJsOa4h7ofVqjl1 - 9imElapPayer8VKCmwQFbnecEPZq8C4lJfjDl116WMFCi4CHHTQh9tlVzLY7ew3YpWjB93x2eoFL - nwx0j+dwqs9O80/vZIXt4uyVlfEaXzsHHOYqJ+5gMWAWlbsDj+oiBeLdttzhsN0qWjuJgFWpm6p5 - 71gzZG8hs9WPqS0XftvKAooPUbQipsvDeolgSuSaYImxe+GR9Cns58ifJPxp+rUxB11aJ1JOG3/3 - qymEXxhxhz54iWMcCyeeCSGIS43YgBjZ0kpDC0D81KZFKh7xbIdLANG+5kgsILafjcJyoKMImFjf - 6ulSZgoHRE9nDbubfhzc3Ar27ysvYuw0MVh++veHR4boWDE3nHYrYI/aacOHJ9gOLBrIybITUQs9 - irnTp9fhdyH3YO0OtvvDX+AeL+GU4VPgLp0WntCmdwIWSg2lKRYmYOJbQ7z1Y1XLjNUV2AIzEYMp - VCD88HKdpTv2ixdfrcfg5kCDv1/IlbE+gJajkqL98hy39TZmdM6kBnqSAcjh+qjcdeSWGb1YEeAb - 2uWAd9pZRS/OTyZ2m995+swhimrrRor8vmqr/fRSeGrmJkCV/taW19lpoH5ZZnJKwBoP6CUWaKsP - 4s130I9N7Hyhr50aEpniJe6z8vmF17rAwVsLXtnqYjkF/i6VcdFXJBuP13mCTacWwX7jGxJ97w3c - 8JE4nC+584582X3N3yLiy26QkZiNRNi0k4Z1+j7Hi2QvHpzx+MS6K2X96g5vA2x4RzQq+nTRh6MH - o+Ppi436MtHxx3dFKvrYTlIzE6TLOuzzu5pg9/3l+1WJ5RB2r72CDwLXx6Q7B+IefTJANM8/xTQO - RwaePOmADW4xKJnvXrg/3YMnMVRe1bijcL6BAp9MXKhqCciqNyd0uj9BINxfn2y9veMaHoW5xiob - jtksNvEK/VzqJ77NuX5QnpYOnPq7Iwcp8Oiwb3cOzGerJD//s/CrnYPLYW2J97I6QJF904GiDxFO - TOfuEsbHDuKM0xWrv/t5vs+xAJv/wQZND/ECk6skHZLkiYM01itOuegS8rWkIXLDhoADs/38zR/O - 08yn00+/vGHbEq1uK3dZb9cVtpzQBHN32YHl976evvCT2++djHfKzwpvpsgRtZPLbPkOngrzHWts - +kOMVyZxkl99Tww4qu6GFxZkNAcQS6gFjYLXrIIDqR0c5olFf3pDWu/r8Ze3uDQK1BrFl+iNMfvF - GodgGaD0HHbYR3Xmji7XhiBcymCim58k+5VVwddnMuxhA8brrZw9uOvFJmAfbRAvtdFBYPCPSwBy - kVKqOL6x3fJ9GsG+5MHkYvkG35VekcvDH/qRFgYrveWI/vSXyxafDwtu3+EdRN5zoINvHL6wlJ4Z - DkRkVutrDCEs2x4Gaxns6ORx4YQMx3zhA9Gsil4v1y+c++I5jXPrUPZG3wY83UuA/bmqNcrMUJcq - 3XgR1Xt6lL+fXyFSJ51i5bBO1YJuAQPqGxqn5fy2XPb4MGV4ZIQTdtW7XnEMAiLc8g6cCreuGiNZ - MZCXwJno9Avd+ZlU/F++ouhGl62v6fsFP7z2J6fczuTsPBCSS0xMxnUy8tMn1+h4/vnVihxecIZo - 33BY1pYhoz14eLAeeG+aL/WbEq+TLNgcpBgfsjNHl33VsnB7/6A95Z671Cb4QtzpiDgJzrMl4c4F - LCWWD1j6zd0FB90AH15Cg8s3nWl3vIoTFLgvj02uOru98N7OxDmWiXM/tPtFuMgOFLL5jB3h8nHp - I6luCKCBmVbnmILlu91hkx18iK17MIF2+sYtquOmnPr3s9OW4LlLodabD+L5rkq59WrpyC50nzh6 - E2qU3J4SMngLTu973dFZhpn+0yfTMKcnMEdNC6UuTQAOjpEPeJFqDWqZmcNqswQxz8m3BjzfqYQv - 37tQzdHhkkLXtE4kCJhYG5aMyr88CNsGAdm01Q965i7GdjqoLlcfwhBt+oXg7uGAoe1ED2js2fm3 - fnB2ff70XwDjWte+JuEHKH0sn5hNUMUDZNwCXl68hn2Lar3wkMITrJVriz3VGX78sCLlw97x6UvX - fnzFsyjtL18PB8XwBgTZNwNeCKqwyrpNNYQi36LGjwtsqu2cjdeG5QHnZ3bAbvkjHc0dA5RPqf7N - Lx8emhJWAlfiIGCo24rqxMP9wlkTX18mQKvI0v8+X4fc013NE5eC86hCYqqjoC34CycAv1dMbH1v - 9Uv7qBLYtIOGD01G4zlL+xlCc2uctl7lThqgDOQeUjH99MiWjzzhQXvO+LLV86Z/bzCoFpEobPWm - K9Y0C7mvvidG+4LZtzEHA6jjnOLjMsBq/ry8bUdh0WN8uaOY1GKaAiFbz9gIFCPjFlZJUXLVLyTa - 8p5eNlIVFB1XENmQtxNWZiFBafIQvk1vPabH5vlEWx4bUMVMqFCO9g3ejtaTpPjbaf0P3zc9HCBf - vQASKi8JvTFzCcQ3cMByh7YDa6ePiDwHtkvQe9Shk+l77IAmy4irbh1Z5doGDF8brvAVAQ+zZJax - Y8eeO2x+BFAErIAjFsko4b4e/PndUxAj99sbiIdH9zbhQ4Vcd74l1g3m2jyQXK/Gns7FmQFbvRPL - ZzQ656i8QaCoEz4sl2q7ijSVf+uDnLVAyQSc98Hv+8RbBxz/6cH7rRcDuOHz/ILQAZCiM7FOr4iu - X/1WwDvk12lPzUUbtvGXHt6J4vjByy77PN8cEKALItbBU7StnhP0OIQa2fg1JmubRVBlsgAHfDdQ - grPj8y9v8zSQulte/k+/evJ1+zwpnX98izXxWcU8fiQWdOp2N4nCrevJDXUMdM3FwbctL+We47OE - 094hxH+XYzxvzwMT+ET4Oj0AHZTXdYWSq69BrhcNWDh3b8HpMNhYPp9Xt5GbTwsPeyec0JNb4j+9 - FX+NM7E+C+pHBEtvf1cOFOOuPlbUfQXP/7FwLjurwlAYfSAHgigtQ+53WxDkx5mgICgqCC3t05+g - Z05CSMO397dWAG6OwhljbScBJoXgAJ/GTZyOzuXYbki1RuB7/2Ufamew7yJ45cWdIKGqwLxV8hJU - ldsSR3wB/uU94D34A3b1rV28aW/aoL3GJfaAsDfYQWhK8GioSyrTHfhM9ztTzjssk4WXtjwV7/et - /qjWxFr8hti7fybYMa8lHt0qBROKRIbPzvB/fZzT6wXCPDi7E33/VWAs8zT85u20V7Wa04p4DEpe - rGKrix9gVhkKITMlHUlRmBnES8cNvMWMYgOVriHVdT0ppX/TiLn0+82jGc8yON85cfo8AvQ+FQ18 - XhwZrZMEA/oy507Jg6md6ObpF3zZ3yA9JR32zk3K54i/UqBcRPHLT5b5JlC4IXmF3cN+a4xRvLfB - WuTO9MmcxJj+WN9AvzilCNJL0S59kimvT1wTtNOnYryuVyHUjkOL/WRODb4+yhS+m1ZCYirtwW9e - Ls+Dtmn5MVhkTQ38Y+WO5Euek+/1Gtm/UDd780DS06mDbu4fsJpoUjF2MV2BcE8ssuS3Qe9+10HP - bFysy/cnX7r3Biw8Gq3dw654L+cPu0y6E02W1EJY+DeUpfJF/E8hJZyjoYff/hWPl1fCmhSoIIhV - 8ec/iDMaDA7SO8fagISCWY6hKmqeldiOqqnlC9+Hl419QfIr9gC9uHOp/GXFlbgLn//2afjlYU4U - Q/6dz/Dmhj2O6jpLWDAkZwWddn+I+c6QjKtbLSsYhyXxM18c5seHLf+gjF8TSKBRbLLXiODgrx0c - rl/rgcDW+4DkL3pMw5tcEn4s4ArWKTVxOsYy4EjUfOV7fvSsTwbTSBLJb1eviTXfIkBNehR+eZV5 - 7WogC38HXokntCbb0Fj4ZQ+v3PKwNZe9QfmDpop73T4JJttPwO7K9AGOzRnx9t19IKFthTBO+xMJ - RbUPOHt/VmDJd+x93t6Pb8gfb3NAUr47GEz10x6uXVogtvQD3rg6hdy/NljbpDOg/OnrwDv6K6Qk - xSmY+OaIILi7u2//5LNruhCOzfY0yenJG2iKDBUKzgVNt+vhHvBDSGXYVWU+jTi8c3oKAxOakemT - QzFQwAZb3ICuuuYYPXwU/PjTaNct0UojSjjZ7juwvF84sfpHwoTz8oXB4nPw+M6LMRZkHfgaOiHR - yRFgboZtaKJ9P730rZ1cI2uqv34CSVi5tct+ACGXXE6SiOqgbu25hs9tXZEAfFpAtab97/MOYdEZ - Pz4oqKZBslX7Hhbf6oJYKj1iodOjHe25gnC1bih2z7RrP47VIIDOmE3PrL8GlHwkVd4beoD1EWct - //oDWrgWdohqGzwwQQMSNFBsX7JtMJlNmML6aFcYCw83Ea3D7a7EOpeJlbJnwMH5asK50Q54v4GP - gv6pggktirWfX+mj/uEDFbEMe8v8mo/zK4dxYCZ48WEtU9glhQ1n9tcX/wMAAP//pF1Ll7IwEv1B - LOSdZImAyDsIiLgDRARElEeA/Po59DfL2c2yT2t3IKlbt25VqpzBEsPlH79TGfovP1Ih5g0HrJP8 - DJa0T3Q4xP6263VcQ9VJViEpAhkf69sP0NSLYzDyrulzmv+KhL94leE/u15pz9p2gFkPhXeSkzPF - cjQapWLCMOti7Oi8oa17PI72fA1RcpppwyF+6ejP/i/H/uWsK5cb8NBcNmwjOXHo6QU36HV2Mrfh - 2gLiTcSFez4H++BSO9v9oPn/3o/+Vbth3PkxFEG3/st3tXz6TSHSL8sMwd3Jt3GAJdj1K/Ivn/2X - 35X87IW1dvrSMeDzANpt3P7T47dntYR7wzORxHs8Q//iAUGtBnyWDa1hb+1X+bMHYkiAzX+PZGNQ - Vp/X3X9tzXYy4xnMBOi+wAQXZ7mTRYGtHQvkOETAGYNMCRHSo2Ve+LppqC5Zyv/To0D83xUFBgca - 4qhXg66n9d1BzX73vsgkIp0GN/PhtqgqcdYxyvvD9uIRWPkJ2w+q5MugdzzKFHIiCjMrDtd+RhPa - qzOR0+s2UwrRSYTUex196KS3hmeUypAf5587t9zB0IYzvwZofog6MbWjDrin/BohTKLLzD5Hjy4G - vhbQSbUKHxP16awHceogicczKcmlcEhoiiy0x+Hm03fZOGN+63wAcH3AjjXKw7KCxAbm86SRczuP - dPPss4ru0lrhU7UAp6/89gdWNMbEPL9kjd5GP4TUUkainF6Fs72njwwFPeTJKfr52tqX6w+9QvaN - /bGqNAKRJ8Nf/nv7KAsasKiMFYAAPmyc+3LtCE8zkKGq0Y4o8aNqViCFG+o+ieLPOPlGywk1Jkwq - kcPHD7YjOtSPGQJduxI18g263eyTi3K/Kkiivk45d4h/ENbb5+V/JPqIhM/51EGJj2ZyOl3RQPB0 - 7+Aaxq4vxeojZzua/VBY9MrcKfY+Z7XHozyERY8jM3Ybtj+5EF79nzgLF3nvosGXC1gIM2G9Dd+D - 0B1MHwmD+cURpGW+ooeRIu91fmJ82J7OdlSdGurrrM3cx/hQ2h1MF3wnziOPi1rkwlGxIBQPz4EY - lnh3uNF1bIRIvXd0uR4HTlC9GMrotBDzJx+cr92PCjTXovbhDYWULSukwpsWRDgoCj3npWkMIINC - a17PnKWtt0RdYDlIDxKaqQ448UE7lNeHI9ZZQQFsji88aBevI48B6fn2nFsT8PaBEgzCL12m0xKj - AeQvYtcR18xD+IBQ7SwdFw2Q8/XYJwsUpzIhunRLHDYyFh8qpI6JcYc12FTxXCIRpW+cBR2nbb4l - QHij9kq0JX5HS/uqUsSi3iDFayCARgzc4BkHCb4e0TSw1fYJ4VoHOXkwdpazzWmzkd30E8nStwjo - /TxV8jzx+5zL616jpjc+jGgWkrx/29ES/WQWgEP+mA+nlo02/J5dOBNwxxZUE20ht7pDgjGfiFHR - V5Qz7K+Uv/mdmVlb+Q7EZj4V8mg9Y5M92w3/a7sE/T76k1gd82r28ysi0avOPphO+52v5qijzioF - oo4S0TbZCjvUfB8xeeoF1oQfEykocutyXgzLdQS7vbAgL8vBn+96l9NhtHt4QkqGcZDxw5Y9UhVG - CwiJzxM/4pI+N6HehRa+Hn4G4CkPRTgT6Y5jx5zBGCLNR+doG7GKsaYJQ32dEUFfhRhSojuUZ1QT - sd1Y73eIVMDfCqNGUhvcsf6C6SD0qcDDEfxMX7oHLV1X7dgh9kDcedMlPiJhJtt/9ucPRvxrqH9p - YnTGYULw9Wjnwu2ydKio6TwLKsM0mxs7CnCPcYOVPnOA8BvvM7SJfSPGKl7AqlZ2CvQ+Kv3N+A7N - Nh/4Ekp6YhPtXWoODUdQwP155jW9mxGx2kqG7lli9ukZ/sAHx7hGolef8ZNrAkAwNzHw3/OG5TII - RbYkyO4tFatVHFH2801+8NglN2xKfDOMt9pMoEZKA1/xYOe0RZ8f1PpyxUpiCPnKdbMuG9dDjC13 - sAY2vCstSitokYKmS04OkGywm6Tc5ybU5ev92feQxeyMj/X5pNF0EHwYZ8EJW+n34rBK2hnwfn5Q - X+qY47CVYm9DJr0PxOCf72ERKt2F9Vt/+VO5SM5a9fcEiSh7Y/05emCuxLsBPw8qEdXuccQLljjD - x8lyiSf9Ttp40AcfNqzkk7TODU0g54z9Z89q+ZabEXn+T05uso3tSnO07RyLLqyZszOvVltH00M7 - uUh8MSM2F7MDw1E5Mkg2PEQ0GLqUP8eLL6fKd8XWiR0cXr50zD/8wp99jic+Nu3f+rBnNk20lipv - IunC2fjcGwVdOBh0ewXLRM5OyzvLesvr/c7eDz/KRdLoGJwS9O+8v5pDvslW1oJ2dgPiJNKbfvhR - 4eV9f+au0Ry6ZloTgx4xhDgDk+SL4Nsi/AUmwI7yXoeF6aEIbdpCkp8LDJYMVzKyxLDFpzejDvzl - /TT/1jPLRQubjcm4Bd3L/DZvMVIc7rp8KjChBvjb/cHlU/dUZgioRInjDWUu3EY/gG4anmc5faeA - L+1vDft77RBLKJh8ex9TEVYsKvBzaj1N8C5biia2uWAlmwZnjUouREafNUT/DJeB+vfDCJ6n4IKf - pzaOvpc1HhFzFI/YvHJ+wz9ssUVhSYx58aoD/U2WXEMxVB8zxY69//9XhcjdL3CuXjsqcOzDhcv9 - aOGHW3+cyXheXTSwvEG8PP06NGkViEoH+Ti/rsrApqkew3bBHQnHMM/HG2v1yDJTAz+rwwq2pzJV - AP88/5+/ZoVK90GqnRWiHZnnwB+Uuwo/s62Sc1fWzmKnbYhe53Qgz/nENNPlesvgn/1Ehv6iWx0t - CZrbO8H7+RwoQW8WxLIb4wv1LuDPP0tMJCX4pjLMsGzUWlCrsia5tDdCJ6s86PAob2Ben6uotTkK - SpAJnYiV8Y6b5XK6/WCXHIRZrPPOGaWVL5DE+j2JDwbR2mIwDZTNrUce1TOMNoE/j4j7qpiY7PnX - LK9fKCKlrw7EEEmQ05nrK3C5AR3bYWLny/oyC1Tn0zRLQlHmW/qmBfCa7unv02zzcdRvG3p/Tnef - EuMxLAWyMvg5Bxj/4dsiP3c8mK8dsa6Xo8abBargwj417HwNAGgcEB3u54HgzKQRne5yAHnCXvC9 - vk10PoplBV/bxmIcSTpgLzjq4RR/VH/dRiVfjmwcw+fVr4grGVO0ttq3gu+3EpHj+igaOpiKCl9l - siu244nydeHOYHO8mNhL30Xf93o3kKqt3T//y99zoYDulRVnYfe3a+oaC6rKc0+Ub31xBJa5u2Ag - 6EN07zDlBLxuP1lG5wVbrnYf+udv1xBXZ8InR0+GYeaqCrW3YMR4PjHDl1fjFEJzufw7v+TNRSFS - DOuFlc4+Omx3eySwLm3ob8dXtyuilxI0Ut4QddjegFKmChH36K7Ez9yh2X8O5Li4Ovi8Fd+G/tlr - 8V01omNSD9s7Cn1o2xdpPph5CFYDZJUc2M6DnOT61SymsamwPnsptkJZ2O9gRDbEUfSb2T97Ba9n - D/afiR/xZbP1LZftJUkidp+QH2ZOCXnoc3w+87H3pfQ1uiVwmXUl2XvTnc34KC346nJM1OPLiPik - eLQwoGU1z9/jZRDeXB7A0+/tzNRxzw55NxcIQtjhmfHJdfj7Psre1nd+varL3/kIIX+RUl/sAMjH - ln5nGPusM7/jS+2sVJVrWFnvCJ/j9TiMaeom4NjFN/x49BHg3tK7AJ9ziOdl+2banBSPDj4a5YrL - J9CbJUSOu9eoR+QEtzFv12/Mg9d3sYgNDnew1prY/vl37ATqpyFfdtbhHg8RZfvK2sJXeQAHHrjk - rLtGtPWKvMl1aULiq6vvCK55KoD5qiOiV9ELrCiWFfg6ZwNxLVKBxWZfLayheiK4KquI054ghjvf - nlcl2Wv45aaCl0Ai83By3/kmTWMIvadhkfMljJzt184JAC+xJJELlJwflKKHcblkOCOVQjef1D5w - mmeGMWOZOStUrguSIn1i3Vashs1etS+KuXYmXhY0dKnSS4c6D7nkSNlTw6Z7M6SxcjHxjWUFS/3T - eLkIhSc+9dzLGQtaFNAdtwKfr1IHNjr3FYhuTEq0x/2hbazykoE1sj9S3nUj50x9KRGOLj98evbX - 5h8/vlnCTI577/Fp7M0SHCbzQ276JIC3/LmXMDzNxT8+sKTQgNDj3ix+ZouWc3HSy9Cx62pu6k/b - bLE9xHDR2zcJ528bzUxbj9B8VRG+TqiL1ocr2398mRy3sYqW62/YEOvd5rnw5rZZ7x9OheAIMUme - eq8t60spgXVJ13lro9sw7PwcQOOq+tJu/+skbSP8ejIzf+4XHuz+h4HSRbBngclWh9xGIwS7/c/D - 65w6GwrFcp87z+Oj13QaTVoT/uMDdnzSBiE7XHTwa7/BTMw8pMvlVTDQkm+/WWCdmo7h3ezg2i0L - 9jjtklP/8GXAKTE/2KueW07XwUihf5XdubGKla7AKWygUChhbby+GhIFjA018a74jeN+nAV9NUbe - +ezMmwsFhA+cGuTDYcB2+hbpHBdtj/SIl/21NF/5Fil8Cn23SX0htdaB4mbswaJ3b+IUCnam7Nr+ - 5KTInsRZ2tmZL0Q3/vwtyS3H0sb+e+4AW7seKXjs00WtNQgzqjfYOnKf/Pu5ujP6w1fVe3qUc+Uk - gGpGbHx2NL1ZweNaQk5kSp97M2rDvdeLgeIs3PUII3B4jVAdtvfrhss93t4mQbKhNh4MX9z5MR0y - Kwa7fWJf6kYwxpA1pQ87TMRaWQ3w61FR4e/rvH3p6N1yWp/l5U+P2L+vNfzJCROY2sZ15rpn7JCE - VwN4uIwzuTdPqtFXppmQPp4B1qHPDt/gmWf/9ImjCGZA98+jW+TW2I0Yoq3BdS5hWE4GMf/+/ku0 - VaBMWkwM7WsPbO+vIrTl9oKfXpNqWy6UPUCHcsV4HxRFh+wYI+MQn8l51wdIXwMeBpez+uevok08 - wB5adMGkkIwp/4H3fkfSep3w8bax+bKAyQWOcCC+2FuaNr21rIRy1xr4Mvi+09/YYw8XXgyxW7KX - aHMWJUFrHebYr7fGIb8syCAaFG+GDGdEnL/KPLgsAuMzqsVoq1LdC9iclL1d/s1oeN86QFiGMvX5 - F+ia1XuuNfwsVkqOg+9rq3RkAniG5I09AThUGOrHCNnBFfCdo59mj59K2POHbL/xsThLjoICsaZw - J6dIy6Ll8dJ16B6TBv/To9pL2sOEu96JhWwQzf28ZlD2cOhv3X6D7pUoEH2r4IExidVcQD9UwVtF - aqzt/IZ6HKfK9dUO/Rach2iJfVTBC7d4OLlDFQjTADogivIuCt5uzncm/AwtuEjYjd+NM52/tvnP - f54WVqfswT10//Qi+7hibfMiAOH5eHtgXZGUYeH0SYH3ZeN3f3NuaHqobNSc1MV/TWUKVjYeF/iW - 6n6WXrhvvmayLmgEvUm0z/lOF3iyYriRfarLQeXpNrS3ANnHCJPTTS01oh+aDX3znJlZan6aLRRX - Bam/75386W30MUq9rE7mHd8UTx22aEYqfFu9je2Lf3K2uBh/YMlrC0enh6/t8U4FgKcdZhb6cbM6 - W8DAlyom87qvd1uPigL7Uqnwg0DTYRvfqNDva73JuU6fYA4eFxbW1pjifVx9tCxPIsKxnVtsPiKF - Uu+wKujVcvI/e5iz95hBbu9ho3/syuGw9rblTrpesRoID7CetJmB+/vCx2bJ6W8//7IgDt95ub/f - lOI8CJFjni44K9qioaRJTRRdGwu7If8F6339MsAxFIcckx9x1l2fADu/nqHM1wOnf6sQHN1Dg3Xp - xjsrUsUZbouiYmxpNd3g5JtQNN/izme44W9/5VYXHtgbH3c6Xa7PDHyDxMenXylQOt8HF/yt15AE - rtkqOyugbGBE9E2p87marwYcJV8i9nx5OOvffj+vboXDa7kO6+5P4RgvV/LE8kvb0mgzkRl97vNB - ddtoqao3A5cOajhvDmHDtY+DC9kpQlhdLgdtfhSrDNHl/fWZ7/c4LPwWt394Q8rZSsDy8q8dZIws - maXxZNO1XNgN9c77gsO6LaMFnIIUHZ7ehFXNRtruf5M/vCUezo7NJHZnKP2cOcHGemGc73RdDHRC - aoZdHyca+dMT9viRYHTUHOFPL1KmY0xOl2xXwC+T+cf3/IXvEVgcNtyn6KyWD//w7E6qDOx6MHFD - 3gJ/+gAs34nqk/7ROktBixKO3frDrpPehj3+kWF5l03ixm/NWdu1stHHWVriTBNuvtKRD6BbCjU+ - HX4GpSGHecBk8kDwWHzAyOtXBn7viUiUcP7kv3r2VaBO9h27hlhH21+8uOOHL9KKb1b5MdZwv4Tz - X/2Sjx8jcDjxi9OrnwHq8rouM138Jg9PE5uNUXodPM69O6OLCqPpc9VnWNXMGWuNyWtz/n7E0q7f - EiVmNo0krcnAP39xvsh7j4J4bmEY4WJmlmdD+c4qXbDzDWKYuI4mVfcMIIufHP89P9fuV2f5nod/ - +ODMcDJsSO5ugXNT08CCtkWG+3qIb32C/E8/hVzyUbDzTEuwfayzKe96OlYLqNHltZcrPk/hxf/B - bgLjqD83iPrGwydycoYWfHNV+NM3dcjrw+IYuQg+FbbIUXC5Zmus/eYo+JnYRdNR+4vvQHgxE5Kh - mTo0uMQBLBwgzxxfHPIpdY0NUqMZsbEsY0NtPjchNV4j1oZ5aiqMfyMQRTEgufYYnWmMnxUc4+06 - F2lrgPk9EVEuq5wSWxbihv/Tw//iD+582sB2drQQ7nyE+KtyjXZ8gTDRtJE4kn0almOdK3DtOf5f - vCt82dmARiP7xCSvDezxSAylwxjifJpIM7vqcUYc32DszBxL93jrB8+/OfAZxlMGQXyhDZ5SC/ql - czsNW3F0esBEIPG582gMZD07GzwdThVxQ1+i019+YHXSvTE4DKNtjawYvr6bRayij+nqVkoHlcf5 - 5Mv4OdD16Fu6/A4czpeUSd2nLls+vPjifUY7v96yd5sh7ZBdd/1mHX4ByU2ZdFOBcXzP8p/T5Sl8 - l8aMzbtXDkuUShXExejPr2bZm6CfcQa/k+ARfLvEdB1la5OtiFVIun/+T5+Rdj17t3/eIbu9A/cV - bru+rlJWf0EZ/qQTi/UlmLQxxxdWqn1/wie/eTtEzUUXBr3aEftkHZx5EiQTNkHxw8agkoZOmAlh - das8UnyyfsfXxoUGJzVYk/I5Iv7hBWG4Gm+/xedw+MfHdv4wz5ApKP3Tc2up4+eqzwawSB5gYXb7 - ffcRCEE+ceV9BLt/w5bT6NoCbpcRVkEt4iNl382eL8lQ9SkKovOV0Eyp629/+uGMeGoMXPh0Y/i2 - fjZWKigNq9lTAzZLKuG4io7grX/7ECi8dvDr8p0169lz+T9/gN0b2sC859OQZ5pvopDLT5vah+BD - gKuDL3y6c85e4VuHlvYUyGk/v4uPxBSSyj5iNb0uf/qovxPubqaa10RLbRxdxNa+N0uxivLlB78F - lJ/9i0SC0wzr2dN5wKzbk1hhVg+bmMsKUMU2ImYJb8NwCNIN0u4B/MM5tgYqDfUIg6i6YK0hFqBn - XgrgH/673Pk7rMXTrqHcdQbxb1dPI3BwW7Dr47N8YB7NupBP/5c/m9f1UQz/1r/rWdi0gr3ibtcr - QAYlf3u6fv79yQEL//QtXa2FnFZMwe6XUlx8bc66w0cKk8Ln7NnEx0JGqZv5GzyS6xFfo9+s0deo - l/AW0Ajb8wU586DEP3j96ds/f7VQgA3w5z+OZ+7rrKwtsPAaWf7M7nrbeL+hAP7hd2QVF7omclz8 - 03N+e750lq2w/cu3EMu4Fs0S0HeCHkL9I3/6+ap7PwieM7ZnZNOJbquw37gOt9ufPt+w+r2q4Y5/ - 2F6Uf/kKE97K0SWnljB02uMl+GYURJRLp2rjHs+CFpAAH9cHHCbODdK/+BrjKFw1Sn9CjNzs5u3+ - VRvWRC4KGH/W08z/8ek/vOy358k/TK3n8Ez4TYEgfr/4vMZ6Tm0+MqGaTTY2dXN2iB0GFTq6qCG6 - Zg3adE0uLHp83wvJTtbT2VbB2Z/Ps/E5GJ75mO8VQeeCJtgxYrvZ7DjU5a+VfYmKyl++iF/JBL7P - fOYDjTqNt9VgQzJQf3s840X/1ocun+881QQCophJ/C9/55mNFm1Jce0gkmFBrtWSO9NfvrCwWBan - V24ePqRJbfBnn8aeH53T48IDhLuInLmo1wYtf5lomj6jfzi+g+bvfcGdP/rSmzzAQr6NDC9FfifW - fU0iItzFAmbnMZsFiaJ81Vb4A8SJah/MHPuXD8qgSV+9L75oolH90Cx/8dhekXui66u8Jog8xpXs - 8Wq+mj3QIcOH7J+eri0NXRa45y9mUbHsfLzBUoVNUP7IiaXmME2SPEO9Cyz/FYDE4Yznw4V/50th - 5spZdjwFUJ44ojoLO4z6ixX/n4oC6X9XFAiirpLgFwQNEV2SwrGNOR/qBnEoa+MaohR98XE2PtGy - MkICc2yo+GRZn4ayic4jZ8vdGV37IxAy0eHB9QhMYj5vW7MILMrg/ZxeiT3IZ429PmQVst01wFru - anRxnEmBP6pLxKaHDmwv2agAQ+MJx4l3agjq+RiS+9XFyQ8Ow+r1FxtdxZ9I7O1ZRFsrrxA5Kzzh - 6Pr+DMsEnwUgRfzCj7f1ALSc5wwagJeIZxdAIyq+1qjgZRHjj0W0OWnTn/xKpoyovMzT7be1JUwf - rulv9fc9LOBhjbIDDhnR9PaobXfO2BD2ZncGJZLpzw2dH/xe7QsxCA0Hri49Fjrv1xffNol1NoGU - MxBumzZvKfo2dMTBD13fbuHLg73S5XRGPmDu7w/GCZajbRNGH6J3diJY+ikRl954Ex0oHMjR/5J8 - EdxFR5xiJD5SA6thV6TK8igk9ny4P6gzdWKVoF2CJ57X1A7fLXqHisAxyen76JpVRy2EXHSQsNaK - isb/trZA5oTRHODfYSCvJq6QKZ8r7IQ0z4XH0/RRBjGHTWy8otE5fHrkL/xvjqcRDyybHhnojRsm - jucetM16PCooptmRXDrRH3jxfElhfBsBeSY4y4WB+9ZIbmoHH3nn7NC4+wVAlztElCu2nQHic7Dr - qTPB5/cpEl7WVMIz+cTYvd3POTeYsgLLF9m7Dr95be8zbMKLmr7JLWk0jXTeNUT0YKb4RBx2oM/5 - KIJ9P0jxfItgjMqkhk548sjdkOxoSzY7hUxwvJMIVDWYMx+J0ON/D+weJAEsHQ5T1E1mS55p5zq8 - NMsu5HjWIMZn3sACxbeP6sdqYffYO43QeY8ApmdcEi/rsmZtTt0IhyTziCHjVltlmeGhGwY6vvvM - z+G4ftmgW0R34rvDdeC0xLUhWXREUuFZOfz4OeiQXkMH+8pT1zawqDay7kxK7vUjdrZvu82Qv5kn - //DUcbRxYxaDfuURVgtHzWmvfmT045sr8ZZrOywSZktYkaomum+e6KiPlx7FTDlg3ZY97W89iEiD - Q+xHeaUcg5IRnrwuxAaczsPK83WFpOYWkzN3uzXruGQ8eHjJl3joMkSbtlYxknAnYvu23QeWXSVV - ClI/xccgcAAV21pBsxmcsedqt4Hj9SKBi1VpRPt1viaMp/MCrtLpgONcdweeSTeIrIbvfD7bRmcO - Ld5G7wNSsVq2bzBcfc1Ga/YxfSk3vwO9OE0ArSC1iSWU2UCz0ahRVRgGttEWA2E5f37gvnY1UV/q - N1pykneQ/cZv7FnvWNs8qS3hxN1lYiHt5HBafSzQk6MPYhzHE2Dd9ZJC4ZiE/nI8F40QHqwUdlFw - I2f3vII1Taz57/zP7zmems0TRBW+mhb5EH6niDqRuMCnelTnzYmeOa0A6GEZp3dyLDWpmQZ7LmGc - jQE53aOPs/IlkaGXxCW+5a+cjnD8yPCjHx/EnXDjcL20J0FW5uRzZqg0guTaMvywfoyPGj452/pA - NrSCzMbRtj2Hfku7Egq3RcOXHU/2/VChLwQ51vUmHwSJuehoxyOS+smojeV2Y/acdYF1274BwjV8 - C6VE+WE3+yjahL9fA2ru+pi3Kowdmv0yA2abW83wZxYa7RVaoh1PiT29tGEproqCvlsB9jnpgcNW - 9yJBG1Nl/kfNr8M2fbIWntO5IEa930lbAi1D1f0l+iTz+mhbdMOAByNWSWaTo8bJk2eAVFknkoH4 - km/EpwXa8RgrbtRE27E7VxCW8ZU8wuzrTDZtFOQe2gm7d8EGm60zGQyGH+vfuqluNnZkWPD5Jca+ - 32/ApclxhO27EIjNP345fafqD2B7FrAr5XHDsyPPIqKCHOPL5dhwRZcncHrVN194iN6wPhS1hP7n - 3uCUJz8wndtXguLX2yLK+Nm7aFVDCJznWSDqpxqbBf5AAbNrgnx4UTlKS7mZoSHuXYRlrDucz6si - SnCz7BVcMRBEe58JjguGxBGpnVUagh8KlaOPg+KhApbaqQud9375fCIW3YovK6JH5rtEZzo9Fxob - ZWCcgIYNObnSxbHEFt7PQkE85lgNnCwzLBy9jz4zfngBvAwbBiGzqol6IA7gU5rF0C6VDQf6lYs2 - /xmI/+ztchVMRzjpzg+KRxiQ7AMlZx3bRwV9NjKwHk+5tllcaqL2cNOxQY9OxHYXg0XXY3n04afp - wKSQ8wYU5svgEwFfsJQu1mGk+Cf8XN9Vw73YpfqH9zkVSDOVHusi+wOUWXiYaU7Dj7+B/twv2GXb - aRgDr08FkZsHEnj2LydX3zHR+VC4/n6PzOGPHa4BF9UbOZrgFfFT/gggLwAHW786Anz9lPe50D/W - l/fzsDTdq0NVcipIIGeLtmAVd/Axn8nur3G0n8cSYUG+z0xyuwy0SeQRjkJs+4zhhc6mLKwsr59i - wJH7rehU3qUKiTn/mFc3fNBFecAC9JeaIUr7ZaMh+ci+bNQ5xceHGw5UWPd76/xt9Km7hMOCM9VF - LyOMSBEAXVsueWgjwn95cpffr4aycZqiIjIf5AY4mRI6NxBVkHOx0myeRq0RptCo7xSb4uPuLK+D - OaKNObxmfrPbqPOeKIVC6tnEGyqST1p9LNEorBeftvtUk1kfAnAScTcvuPHolua3GSTFPSJB0bLN - zg9q8Ld+Z6oYZwm494gU/bHt+NA0tFleC/qcHxIOYRdp6/yeClgl58KXRivIVwYls1wsz/MsCaU8 - LEetEtEsaR45c8GbbpflrMBs+36wFnNbtJ7bbwwJOn+IHYKw4fLuwAMNbxuxTzgZdgo/I/a7jiQ9 - vW7OP/6284+/59e2y7xHgNbOKcv5AgQPXFqos46L1TNUAaevnx/I9iahuog3OtPovMAfNSTswCPO - l7ECEB60lfWRTM+UIkVSAIG/CmvbdhimQ6PZKNh7Qut6A4aFEPSDcTYH+5xsPyfjw872rmai/7Tc - I+C6cVDhnfm4RAnXrlkOJ7mSf1L1w9ZN+1Kq5hkL5iOb+DAArfbNma2E19feMyYU04hS+RtA8Gnf - 5OQvGmXfwm2E5mI4WDvrSb4F8VDDQbCimf6tz/L4HoipYBJ1dU1KO9rZ8GieTkQl7zEnZ6n0xQSS - cN4+Hyb6d147Q9hnk3L1QIlopfBffPFkD9pGMnGBtJ4QNh9LklMxWQIo5uzjH35ONG9qcNyicObr - c9LQkLo6PE3ihaiFU0fLV1dqqFyu1/3zr4as0j2BdbU9iV/JLJinT9gCwU+g/3GFjzO5PMnArXlq - 2HTEfqAw11soJ31P/vCfeuDewd7XXziJHq9m5fBxhrw3MsRMJRwtkf4pgRLsiuDnWg0L0usObkNz - +seP387H8uVByRtiFJUcLbF4auGJXW0S+lkXTZffqsDkzWbE087BsDxba5eqOjjzF/6dz9p7UCBM - bwNR6+9p2L43hYHed5OJJ8v3hkXl5QcTtTFmHvBVvt29YwEfn4GZocnrOU24gZH528edIfx6+frH - J1+V+SKF68xgUS4HE/blq8fW9omGKT6JBiRF8sL+6VEAvngcWhDKOCO6+bIa/reNJbhB0SKRo7bR - 8khgCTX+8PN7r0soHXHaQ/Cy2ZmNiOqwylda4GM+Ebzz9XyD1hrAr7NPR3PiFqyqGaYQnXSDeNN5 - oEsOUhlC0B+IKSFt4NT6zcByJtOcf+9jNL+Ta41scCuIos4i2L4HP4Cj0mYkvLNLsxy1XoZ/+Iah - 2YDR314x2v8eLhdvyhe7dW24x0PktH5TbevRnYWrNgakkB0LbIafs5BnC4Kf9+QVrcz1kv2z70sy - E235SkKHcnp549MrNcAKxqqDDQh/xDova0Q1nkmgpLY+8b3cpvSd2j9Ycs7TX8LjGs2Leu+h6DOy - z1T2YaAaZ8lw0Zq7D9QvoN9Q702YGtttZsVD74zkLCbwXfgf7MWmEK0bd9BBXza9L35pq83hS9xg - OYfdDPI+iDY8qT9Y4FEhz53vz4aa1CA7qQw+d5M6sMFxMGCSQ3cm36oCdIlaZp83meO/9bJbsqnA - vqgmOfnCtVnVuQkhnhWHlHMX0OW5ghJ8r++UnIJblS++G5Qwik89xq/8pVHtq7fo85B+RN3oSVue - SFzkvIcB1vXiSwcPXDq08yNihJOb0xN/5//hj57rY/Od3+8SHaJ+JBGtWbrxhlXA48xz82GP9whT - xBWMFPeEw9hQh5qrHio6S0qOlbauGyETNRYae0WohpguJ5YozSBV6OQDeCTRlnVLjbgpOxDNUdt8 - vU6pDOxS3bD7E9doEVzRgLl1Sone3U4O75YmhNlFfvn0mJj5v98rNmiI0tbqIPzt947f2Bib3tlk - 8xXDH2/486/0B22zC0mF0ijV5OwyKV1n+nThn782zpGqLR916WB7+hzwkUzaMPWTzMC3uTj4uRWf - YZVlnkXa8pIwrrbHQBKuYaDhuwF2dn1hja9lIAdOwhH3IN0A3fFOXpFc4P3803m75AlYYxX7XDTD - Ye1CClHF33NfLKuhoY+K/cGwSWpiWOupWR+KXQLvKY4k/cNfSfFakK73Abs7XvzhhxD4sY1t90Gi - eUhT+R/fkbbqSBfuUG8glLYIe7E4DnMlhgYC57DDRxMcI27nu0jSjlf/+nqKzXbISAaNucZ//q6h - nfcIwQsp2jwx5hatNxi26L1X9CqDMNIdL1UUVbVO3LCuot+xzXUw3h4hft4fVFv6MtbRx1bP2E/n - Jlr/+MdH1x5Ez9kXoJdb1aLnO072eD+ly2/4JrK2NJLPCPmJcip8KBAezjmx4xprveQrKpSVgZmv - h2usjUehqRA4B91MxhOvfdE+hcyYDwnW4rkaltBfYjCfws1vaB2D+WJaI2zOFcbYOMlgORnmBmvm - ZBKt0AZtBI/jCIYoE30EX7xDfqqTgp3v+VWzTU5qJrwM0CEIcSlJ34iwtWqDM68ZGH+k9r/78zkr - Gza450zXpUMZfH91Gxeb8XMW5bsucDHqmey/B5P381rYMJMxs7t+xhHPiOEnEq9E/c2niG19tUCc - MeDd37ybEVz0DBXTY8V/eLkmZ1OBMXgk5HTxP/kiKNsCn1f15v+mnsup5Koy6n9Qxzv/GLbdf8Cs - lBz8qJyTRnc+CX1P9/2ysg8NFdufItu6+SZ2zVQ5vdz6DqaoOuNjXuo5jw4ai6QR1MTfPz8yo5PJ - kSykGG/FeVg/RmiiJ7kO2BEpP7wvJR0R1AEmu32AaUiGDV5wjonXBy1gJd5L/vQ7nLD9Z/cfRgib - PN+wAbyCPlBDfuBonk/E7ejabLVUq+j5ThJ8fiMv3/GjQg9Wz3AAxWggoxYlcP2UwyyjQXOEnT/s - UyI8cp3zZ85/mt8Gzs1x/dN3HLo86xB+n1bndwcUauO6ei3UpzDa42PW2dxQ+wH6nkJyiuai2f1l - CP7i8zPvMw3Z9QRgyeGNaMfEjNZLyKTQ/olXYvzcJKL8rdhA/s1vxH25XL6pDoDQyb85Lri4Bgu4 - uBnkKL/uFTMJGI/a3vNM61/z4cnqDT8e1hmOusrjs5pfG+GPn8koTXHMv/RhKSx1hK/q02O3o5dm - MWepgvBYUHLjBdVZowll8MuVAT7hz48SpihqOEOlxvnOd8kNRzqSs5NO7KM8auvOj2GssBq57/xj - 8wZ5Bnb5kPzYtgUw3cmyIScfch+5dKRzvxYVXJlrQ+x29IfleAl7JNWy58+SqkZC63oxFK4eR2yb - D8FsZHcbTi2k//jHrhcnUOaZD1HyycvZHKQiXBB5++L9qzebd/ry/+zV3fFhzVdzhsX0XAmGmGsm - k8U1PM3XlDzuJBrWZdvGP74+y0h9NlRx7zbc/Q3BrDw4O3518GM/faI82ac2NYPQwc5IWXxNIBfR - /CGGYGRXSExNi5rFc5Qavb/NhZhGymjzovsG2ONpbP3y2pkVKPvyrpcRT8z0SLDrswq/Tqjhs759 - 8t+pJyloQPDDWqE5zlw8hPbvvJEdL6J1rL8GSI3lRsolfjrrmkXtH1/AOtO1Ef+5vQswOXgiR/H7 - a6gjWz0wxPSBb2D+On/+H/q2Ic1Ilu8DPdKVgS7VbPxnX1/rDhjw+SSrD66ymi+1K9fQMpDmy2ho - 9veRdf/WZ4oPSRtDHv3AUIU5MY701syJm6jgx+s+flhv1tnjxwKG0hIRj9srpJGyKqAp72dSDmmq - 0YNqMmDHM585VKdha6K7CdP2dCQJMByHqp/AhINyb/7im3wuv0GGLimb+GxEau2Pf4F3p3P+6qnU - mSrNZ+Ehw29i3B1MiV9vxV88PXd5fYxWcUA25KJqI+HnU0b/9N764wf4PIU3sFmC3gH4xj6xfMbW - lnm23D//Tpxhw3S+bp0Ia7kfsVa/5mgrHkIHT752Jwo7qprwrFJT/oryiM2AxQ317QMP8Vr0M3pM - x4ZP8+cMflL98w/Sr8qFx35jMHBWSLxJODrzn54+3p4h9vXuSoctUkpURPYD+45+dyae/1WQOYgB - TqxjEK3lpwlkWCZXcuy7gtIXufOAlvzdX5vfla4+b4vwFqgff+uW235joOoAx/MGcUr+65Bdf0Le - 4o7k/NOf2m7/Key6kWJ76q/5/r5McD0WR+xa05Dv+nIBmw/vY2X81NHKkJGB09wcyZ8e+c8/HGPY - k5jvk5xGLvDhor3u+Ng0rfOPL7gOG/7pb3RqBCjDrKd37F8/MJo6sU+A8OovOFeCNHomx1cCj4cI - Es0OiTbdG1qgBL8WjOXXORKGb+vDwKGQnB9mGm0/5ruAVFtV7L2laM/Av2K46yNYKR41fa9dICLm - IAczs3hexLcz+4P+lHk+ONi9s32aeoPiVaA+2fMZS6OkIxS2U0ps7dXnP/o86dCqhAtWx+uLLtjX - ZTRW8v2PnzWDmI4zHFiL+bPvXHg1RQWm3Hj7/O/UDlu3uB0Et2nCZWU/m8WyrwaMGlhh78FGEe0V - UP7lP/Cej3IW5SLY//DE9/IfnaWaleH0OpS+8DgaA78+OBNE1cEhul5Ye35rLIA/7T2f+vod0eT4 - TUD7LgWitbholsKyR3jftO/MrV9Rq24jGEGQuimJ4wk4q1dE7D89+th3EGza2sfwF27yDIurH/3U - A99BfZoyn94YRyMpD0SQfoyEqGz2avb82SLfvjI7M5Ucg4nlsu5Pz/APl5RGYyx6Lbjrd2VepjfQ - xq8ktH/5QHK+y4eGfD5uDJ7itmHNDrG2/OVzMr/UfU4uJ4c+44yFShz/iD74rsOJpzaAN/kG8I63 - zpKdtwWxe89s3N3vgOzxM+xjlOHEWt/Nwr7BCN8GRr5UP1jnT0+G8etj7fmvYy6cT1ELGcuufCov - 3iDY8SMEcvLr/deUbM4Gmz6G997Jie10pjM7+l0Eux7vX4rikW9FcGLRHu8Q7cYMGg3vDxVGQgaJ - P6SiM//FQ9YdpkSLZ6WhezyEVFLQXa/+0U14LhXo40M200LenKn+OSaUGxT6ByuKmnVhFhVZcnDD - XtbJw7/95X/dC//ph8R4yCMM70WMcdq5mvCX79ufd97Y7DXs+N3Cj1c2//SRhf9oPeCC1wMb1jHI - +bhFtiRPTeVfTHDM6SNSReT0FYNN7EXOlriJgna92b94kuwQjWdiqFvwQIo1iemoyUQEVx9sfi88 - K20ZKwqhhpcNu/3rCSZFCn6Qbh1Hdn6fz8mmZpAzvhgf17jSuHzjbPinR5nde4g24frpwQn4gGig - UunKWIUB3s2p+tPTmn/++IyjK/HiA5N/bS8z5WcKZOy9VdeZTTxssNZE6HPnenD6M8Ib8Apw8ymW - hmhzxLhCFxKeseuxD41o3FGE/uN1/Ytf8vnKtL6M3oL7D39Wy2N6Oe+ZAP/lg7eDErEwmurZP3R3 - CfzThyPvy2NXDvOhOjy/Ldj1Yxyng6VNOlhqNGCYk0t6dQba2oH4L18T1KDT/vIZ4Dikd2z8+as9 - fkdlxtvEY4sk2vnSD+35M6Jdqg9gdz4IkpxxiRM1y7CsyBblv/yOxxyVYfOeXPr/VBTI/7uioArN - gdh99W02Y1EqdLdvd+y5t2ag/HOQwZFylHhLc9AWjywhqlfH82dDXndFT/CRE4wSCRWlA1+jvvZA - 70aTxJJ1oUKabAsUVXPEe5hB+cf5OKJhPtX4GOY4X5TMZSF3onCWrn7tjIvnjfB6hgdfhJ3X0Gs0 - ZeD6Op7xHbmsQy9vdYN9p0ozKzevYVAeWQzN/ofxObhpOSXXee9i80b4mJYfsPGNlcGpTzRymj9t - tOrx10bTNuk4vOt+zjaGtGd8iq8vPZ8Nne6Wo0LCjAs+rQYBW/pseLiarT8fvOvH2R7LNMOEm0Ts - R74D1sXweAhk+0SUk1vl/NH8jdB2iwwb9eYAIdoOIYQDeySB9L5EM/8KfHQ+BTeS257UbEezntH0 - HH1cogRo9LbVIbo9FErul9/VWaerrCBeYAF52qjKqcU5jMRB/0LOhF4irn5qJXLVjCP4/IGABAfL - AFczD/3ldngD9j0CA73nrCbmwX0Miz3+ZBgzRYtPqTVpK/+zAri/D/8rT/2wiad+hJJbFPh2v2YO - zzDXDClzuM/1zEm+HaQji8Js+mIneUV0u9xuMnTC40aOqK+0mZRPFoz9PSJFJgVgg0slooLiOzGI - 9wP7+5nRNqZ3bLecAJbDpAWo9AyenMp+n0uyJSIMhNs6k/OdRqtjgRqeAOfi201PHVZ5fHx4NMSA - nMnoNhvJUAmGKMT+Omga4BJdLxFdenGWIPMbqKhkKjSf+xSGRf+C9fB9L1AioCX4YJ6pML6WFn07 - HpOcBp5DnFgRkRGrM7aM7xts9R0u0IhMkyRDzubL+xvs+6ucfVG1dWdZDZNBXCwluIDaN+JP3itA - sTf4xK9NPp8ci1bo0BQpsfuw0qZyC1XEP793fFGv5cBOuTLDRy54xC3beuBT6yzDiDm/iI1FFuz/ - b0Yq3Rqi3ZvFmSd/s2FfVCW5JSjKpwcHM/iUOUKsOfw2Uz7UAaTpI5lHPWbpZDcrjwqRTUli2EK0 - uGLng+IbDnM/0TOY6bkTEXdaIU7O7UfbtLlN0FlndVLw16fGuyrt0eMoVsRnu4n+ym8F0fG3DsRC - JxJNqZ90qFMjhpzfAqJb6IoL8G4vaeZ+nAeElv6Y/5B2JdvKwkj4gVjIJAlLJpFJgoCoO0BEQETA - BJKn78P9e9m7XnqOlyuQqm+opApOvy9C9+37rHg0tfrTnIb4Jb0AyeC0XIVCWpLykyLGG4oiw8kb - c/Ss9cFnfeZFcP+GEkruI8+IVxxG9VN+cKhc+KFd/9Z/8zTUcAc6BaxnUjvqheBT+JdPluqZ9Cp8 - OApClD9N4kHmStB17YHYzX5p1zA9jTBPvx5yHj/JXD6+bCh6JHIIjVadUI573KCIs5o48/3mSx8l - xwCBmA/x/fY12Xs76R7TvU+MbD5OkiCthirrsY2Mm/qdVsC9G3gSshzL8Dkwio40VP+ut60Hf313 - ngbl94fH++L3K5b9K+XgzaIb4z3pE/v56agyJLzQfZHvYJXO0IPVqXXxqorfSdgvewM+d52DdONS - tWu8O4iqFBkP5IjjnhFHxhmoFfpENyXswarmmq3GtJIRenz1Yl1yuQf63ngSM1KOk/QtHB5GOPkS - Q5gbMJQZHVXuhlPkjPpasFUseaXzSxXpa+FNS61TBZ7MoULJ7MwJpW2WKy7lNHLq9CJZzuVrBvxL - eWGQkUfxi8RZhoFTHpGvP+aCzoNcqXLwmpH+OkbF91Lee/gSoEjOWz7h6XjO/71P+bdV5LziNCp/ - 8fz8yw9/69/VI4vcsVX7zCtFHi7OVG1dLMMCjy7o4UJqm1jsVJnsfe8GNYujhZwuxwUs11EPVVXs - DsS78E4rNDU/KA7TI1Qy9Vbwz1wz4MHdKgrLGCXLJexnUEgdQRUaObZE7+cAf/qjQHnvGIwZky1C - Kig8zo9oLliZaw683Y8LQqBT2Fct1lg9i3xIojo/+PyD+5XKS5RK4jyepwnHwULV07XdzjTlh1bE - zOjgUYQJFtJrNrEdYw683/oT8YV8LP7htcGHIvFWl/rkNbNQtQ+rggzt6E+S4nWOej70bwzz6ZiI - LB9KNTzBEN35i+FL6jlRQH+sRuRiS/NZ9f3xwCygRbR8zoEQMa+G39cpIqjBJRAs/dMBSb9kyCCF - BoRvPA8wSuQvKqKImjSRTxCW0SghB/aniT+/hFi99coZ6QiajF35otxnvBCj5OSs5to84ki937oT - MRJHaRel1G+q8vLDrYthnazfCFTQMHancGGNDIgrCh5ULcVFcfZumPS1M0tNAklG1oMfEixMUgrd - Lt4TNzUA2NZ/Cr+ujtFZ8a9Aek2eAXFXaCQ6JG9/di3ZUiNoe8gDCfKlaPh58Dp3NXlMc8DEbQs+ - 5DSrJvfXV/d5x37F8K1CRIrslQBJLPVGNc6BTp7lA5i/S3nuQXp1v8QPKtDOD8jHKr6ce6RP519B - WV5XUEsXHVVaqk/SfghE+L5kBbqPHwnQasod9e/6l9VGjEqub8FHea5D+rxE5qIcsxGy+wUiTzB+ - BXNZTuGtceVQ6dFxYue3t8DE0N7b75/BVB+cXC11dSHRjuACK8klULnma5CsTLa5xqcDr4qYMky1 - cElw71xzwD7eA5lt7yRrGbqdej54MXF8tJvIgZEF+MewCrmC25uUyq4Hy0KXUCDl9cQSUioQCPeZ - hPKtS4aM7jx4p9ZADr+CM+ly+cowxx8J/0zYFd/FPvDQP7+fWKheSUu+1RTA08OTkaGFSzG/ej+F - RvHWkd5RwxcU4wjh+UdtvN/n6oTJtsMLJuEp5JVW//f3QMmWE3pYCkjW3ocynMSzRTz37rZic4cU - OppWkKT9/Xza7sRcnbwhJ0ftCf3Va5wBsmWUEaK/gRGrucnqa5YxOcenA5Ckw7v7ux+smNOuoFrz - 1ZQh/KnI9+tuWjMjieAtnAk6xS+txebkQrgaKkL2sTv6C56vHdxz3YTyCzgV7M69e0A4vCB/N83+ - svFRpSrHNlRuuQxYuU0R2+6fWBA4hSBeqgaiu/cmYVerJvs6KIbATQkJG/3c4pcwx3s4xynxevSZ - yNfObCWCzYN4yRqZzOwMG4g/+URuxSVKpLwRbfhdspGEaKzAGq3nTKH9mmMRvN4+hQ+SKWbotuTI - 9ZK5eOOKVUcrMbIeP7MlzTcSoWh/YuLyl8ZkNbUpaOEqkix+ZwV913MNHu/rQE6WUiTMFUyoJmNo - Y8leVEDfdVfD/ds6Y3H7PqNUKRXvAgWiVde5pbWy4yFa0yOe+k/X4vfD8+CGZyFNdAHg83ivwHdZ - VBJE21zBx+HWQFmPbFQNlueL19fFgM46X1EMVGuiV2nh1EIddaJFxd4nH1W1Iec9a2QOD6Vd/tZr - lChfYrx+GaCYZwvEeu6Hr70aFuyWXxxly+fh/tIuyXI7QwsUmFMIKtHa0s/8EWHKVR3Rvueruby/ - txlcrXyHXMkF03r3+BAeEnTDj04vihUrXgkQIEeimYbpi/fDu/5v/thNgSk6+rP8xw92ZZYlBE5x - ANdzaGJVObsmJvcggs+MS4k/ry/GWP5OgSe3V2IoK1ewLT+CMgo6FNKjkeDqW3PwAPI9ch7PX8vS - 4eZB6+oV6Bi9A7aSl5pCUU5CEmx8djkROYZ//PV5/EC2BIIvwk1vICQke3/ZHW4VtHrsEHSsZZ/1 - 6d2CbUtI+DO+92LJqOSAR8598L4AGSCuYHLgwTw3PH68AayZUcRQ0oAZLmpkmgsZ1BB2/j4iTyHg - i5/dXEb5HcQJ8fMcmYsMivhf/O/kRU+mg2cu8Pj8XlFybyOfD8Rt6pqzm0Mg5GOCVzWdVX1kE9nu - vyA7BjyFT6YfQq5csY2/QzCY5fUPj012EgdPtX+WgA5hWvnMu5cVrE0gESMo3oz2xY0DzaFuQ0kg - bxPvtzMUG5/EHN//wHLQmxm2jeASaxakCT+TgwGRt1+JyYGVzVzwCMDlat/D25ZPZ7UbUyi//ReW - Yf9rqV/uOSB9awnZwnXwGRnqDKrVrSV5ZsQmjYNlUX7MvxLUxy7D30aboSiJIKSe1wI2qYMNhNRY - /p5PIWx7auGU4OZPDzJ67aT8D7/Q8XFcWqamXAzfx+MnlJWj4c8Hs1Dghofk7/3inXQb4EFpEuQl - 5Yt1AlVSuK13ZD3AyafXbneDJzPwkX+S/WJdZ1Qp8OEpBOnOuZ3Xo+L9w2N7fZ9b1qdnWz0kWxdu - vNMLGoYihJv+Jfkp7sD85wcUl6pGh66bTbLlNzX8aBlxfrNp8sKqOyDWKESOcXYYy8RgBH96ttSF - b0K39a3G+rcJ1VgMgRjoXgmf9d5ASfs02394LUXVEv6myiz4Y3Nb4NgYPfK9031i0Vhl4DicAyx/ - vuO0AmB6qqvHVviXn5YyuUWglMVt6trNNfnwnmZwyyfIz4PDNEtJXcLpoBckFUc7WZunX8L3/iQi - x1rS5FciZVEOTUrRwTy8i6X1El7NvcgleqoobI13JxGW/HFHTk/va/5Oc76A6WAWJLiGO599oClD - TT65KL2uiC3ijTf+4vlPPyTzx1801dH2OnIh57W/+6Ee1UHwEnI4TJeJGgctVXee2BLHSfVkVYwj - B93LhyJ3UgzAq2jXwYMjXrGId69k9RGy//Q98iP3V2CtN3N4nfs6BHnwbim5W7GKA1T902M0A0Wm - hCg5YhhWZ0Yd/VoCGvrvkK+MdXtf1gDVj8wRtENzuzz2d1lVHHVGFtKNlinBJYM4L2Py1J3zRKXj - XQbAkctN/6WM/xXODGu3EZGfkUdCP3o8QmqmMamGbQfVlZQLdLtoj9eNbw5Y93Lwy18NchY/bCVf - RynUj1QkQaEJLQ7UYeuC/Hliaj9j87vT3OUPv0kYEG0Sb6VbglhbILLYiTN7K8WGUo7+GUuhrScC - lIYFOsOA0FliZ1+65Q8P0iURiFaKsomV82wo1FzeyNjwZyGDEMB18hbkb3z5H5/T7PaENn+mZZcz - DKB/9gPkDzu/YPfkdoNysJ25vZFkYks4GtC9vCna+FWx9DFcwCocK8zv3RV8jxWL1D+8NbX+lyxm - DXugHxeRPJb2adL3w3DUv/zI+w+h3fC3UaPDqQn7ZGebwq11U0iyXUO88uBN435qUvjnh+iv8OJT - 62yO6qankAkvOpCOX2dU7qg5oIdt6+24+V+Ky3336PgtRrZWNNbUCaYZsQehK1hYfkW4/b4/vJ3o - aJ8D1c1pRdAuW5MfBmyAoytZyKvorpjf6Q/C37BkKPWziG35PwLepWmIeY2xubXjDeG9UUOSbXyF - sct9UYZ316PkUU7FUpRhCMPd4KEgTWq23lo3g9/t1IT5BGK7ZMcXp6LXkiP7pzwZa5XPrJZHp0Q5 - se/TpkdyNQ2yC3I3vkn9wqFwKG0f111SF6L7/ihg8y/QU+Y8RmSupuprNpyND2stWxK1++cf+VfQ - Tj8kVxawxXeEkHAgLW3u/AKfu94h+u/Lm2QnXRo4H8QVaVX0YfQ04hImvhpjnp04/9/1b+hzCRuu - 5Rm2WSOrRqmnKOYufsGfWDyqLytSiLv5ed/0yaDK5foJaTVhyT8/9euaGB2kKkt47UEC+GB9iZzw - 3ZrMxmUAxa/LE2upvwkBR3eB8PMCJN5ORNHLjbehWXAWpkFxYOKL6oqKOylBemA6jJzE2lFF29nh - evt/NCI/A2QzepAwuUQtI5eeAgEGZ4R0lCVEfF1slW9Rjsyv5CXbHHcetEPlEMN+Up9y+o6HKWec - sbDahFEuXW/q4iA/lIRBKxitEIZb/kTBnn4S8vQKDoTofCTW+e4WorE8arjpT6zGw6FgCtrhP/6L - rO7gFaP2gT3E3PVHEOV/01qeJw52cO7JAeOvz0bdq6HHeRneDatQkCa7xmB49z05LOtrwmmZVeDD - 5JHcCs0wsbF1ud70PzImibHlOik5/Fs/9i6vExa7SQzNt0yRBXlzmj6qasERtBo65sPbp0VfdCDs - e5PYT+2SSPNL7sBzMUpic1rUMsWbvX/+iZa/RX/zR+s//4eEV81IaBnaJbSUT4tlGRRgTWTd+vOP - MT3uX0yUXNOCid7F5Am6HMx8+FL2vnO0kK9nn2l5pz8OsvsVEuPdxz4RQHED+e93RpdbdAb/Pl+F - 4EK2CarFqj6LAcDqyZCr9C1gVZdHcNPj4RdyY/u1JScHf/q54ppXgSGNbTiU3IoO6pBMc9WoEPJN - yv/pzeJXBlEOk87jsWjuhWRtnE8NcQt/uMiD9zQ5cp/C40fc//OTmbHbdwo53oWw8GtrYt26UljH - 29SDUV+TJTt+4T+89ffJYVqA4Vv/9KtweATm6rV7Hir71cB08xN+Ay49sL2fcKfrAeOPnQvB33qw - DkECeDh9bLjpSxKTQmMLnY3uzw8m6eYvrebjaSkf8/UhzmdBifBx8gaSy/AlzuZfrde+xfCNeQHp - FXFb9uBuHdj8BhTez1bCb34luNQdT4wtnkUn80LYH40r0aBVsuV2PY4gzUgWsrCpwe9jLZ5q9tdD - KDZ0AhuMb34lZQglQDI3PbLxqbIm/kmekjm8lynInOWC4lhZ/c2vF//xcaxwfEEd/VmBS+29iL/V - Jxbv6zTq33pAw7i0hAucFG7+L3Jk/mpu9QYLyh0JkQ1IOP0MoQtgfmkXDMi7SBitjhhubU7Iw1KK - gv4UlP7F4+YvGmBqT98MqjfW4V9MPJ+er08Z6ExixJZU2Zw3/IaK87qjkIh1wuqDloNNHyBH4x6M - yiSYoWHYHxLsp2ZauWNYw63+hHK0u7f0L769wDiSE3+czfXWKTWQy8Mdy/P9ZhI1Odjw0YEL0l9l - NS3a4xOo/FqF5A8PqCt/y394qzu3q4/D11JD6dm5xD08dtNCPoGhyOx2IUaUvnza3Mwexl/BRP5d - eyZ0J0WjWo7uGZnyGJkbvodQebnhP/25nsZvB93gcN388h+g5B7E0KHOiCLryLGtHoYhStQ9CSLx - 1q6/VBPh5uej510aAUuviP6rFzh3HrDFceIKnkUx3PRmMdHi5RjAKIFC3MvFBgQcok6lghaivEef - dhVHPYIS7XbkCLWPSTPVqtUvOmgbP4l96r6JDP78j+rZ2D59YL4GyHtU5BSLGCzL6YBhdEAN3p1D - o1jQ7gL3b0IKvNORWHwfvBmATJE7vMUToIFuVOAAbntyLPdHE5+vTwU04hIgTxoeyfJ0diOgj3hH - fPm4S5iU3zKl4ijd9MM6/dIngNA8kQidNv7OkjW0la0+EIpbfqUcnANwDdcdFrNqnZa0Pf/1CLgi - x0fPab27pqZ+jABjdSXvhLVnHwOdFTUyNK1nG/9PAThzXfgdP9e/fBbDfWm4CA1j1P4C9mzgYT4/ - Q6EJDoWE1yqG3+Yg4pc6aKbw5x/9xe+VWklCh3drAzHlbbzH8XfCHUw1datXEiPRBUYwedtQUG2J - 6NVgsTVfBB4+M5iiY8CyggjSXoNunvnIWu5WK1oor2BzL7iQ+6s//j2PP/08HtGcrMO4UnUeigS3 - 1s0E0xI2BhxKuCIjaMZ2+dObo5uvf3ymXetEc9S/euIBfVxzkfeNpQ481k6KiyVAN30JNn8O+SUV - wHIuvzOUWX4hHh3tFnew1CAPzy9UkDdImI3TQE0L2SDVpaft/KcXgVDMWDn5tflDJ01WExaHyHyE - L7a+pKiH4gHP6A/vWPO98QBBNyenV7FNJeKTDBr6mGJq336JRCmt/vwj4rmfxBSU2yRD8evzmFlP - p6VYzhz4TmUH3Y1lbMd5WCqok8YiIVHf5ncSTFnN2+z8j49vft8I00IxQkYUY1qNfdjDbMokYti9 - yH4oVKx/9V7zhATW5IO1gM1fQya/q7apyif45zeQ00oOiZQF9QC73LOQdY6tScqlgQfKNazDROt/ - BS26JofFRTuF7M0hk05EDcDmZ4dcIL6TPz9NFQ/zTGI+Zibe9BrUj1mL9ErExa8/9RZczXvzt96L - VeaTXB3Kptr8ci1Z9/nDhjLjB3QogFlIanKy/j4TgxQ1qBONBkCpo2GrP2k+jsjv/5p6AP73joKh - unpYEoQPIPm959WmavdIP6+PYiVqaoDKFSakrfKQrG7selCnnwTP8Ssz1z23NmoW8BK5+lppDjdF - wnAKJpkEL50318PoDrCFVz5cTfpltLiYjrpeLmLIxbIL2CkfeeVu3ixklE/O/+L7PgIjYwHS3cvR - FD2SWpALwBsFM4XFkpBLCUNYM+ITTvUX//N1IL8jh1CMRN0XkpTZqnhbN8bQWD7tRhAA9EXbGSKb - Y7/HtOuhOWVdKBtnzl9xcZjhZRcOCJmgblf5U2H4MNA7jMJRL6jQ1rl66b8mst5YA7w8x7Z6Ey2I - zkMb+osPGIbqT7JC1qxDIUaPYAZDhCHS9/Q10XhyKvjK9iwUxn2XsHcUieo1HynxA08xaQ+eIuzL - X4Bsnzuzle6xDbUwKkmZjqSlzuV2U6NLdCB58dsV2/N3oLheLSwcPgJj156J6k+CH3SU7Nhsn80Q - ws7a58iIvp0v0dDJVTMqQnI0ujf4ClMwwvl990JC3iqjYVDb6vVhtCSgl3vBIlNcoFxWdijvDg+f - R5Fgq/dPeEQ+S4jPsFKUMEyGI8rC4OH/ku9pBFqAKAkftyNY3Lmt1S5+fcn9qzhslcgrVY88vRCj - ad8Tn2DXUPcztw8FfNsDitZnBO0M5tv3B8Zc2qbwZK4NMneiwFZC6wYqL9lByVOtgYjWawyna+xg - SawFRl7DPYPZvv1gHk/6JN7yQ6N69S8mRmoegfR9xx7Euz1PTt+X29JUiQYoXGSF3KnktvxN2c2w - Sc4lySLx5S9UrHgg/eYZJc3qFFJZmbUavqKMlMcCADbfTg68q8TAdHrL5jrkGadyDU22nhfHVlJV - 6EArvx/IceR3BY4KOYJZIErINI0MzOZYZ1CQLgFKr0k6CXJvRPC68B/yzKUUCJ7QKjAIgwOJ4K0v - lpPeKWry1g/Eq65mwZ+rhYevDDASusA1CcdfLHjOpVf4DOJDIvTlUkLYNjZxFyc16Q3FhnrRPIfY - RtaaS/lqFvi8fnvkev61oF43dKqBlBylxqgma3caYlWfrStJB/3B+HMl8yr+5vuwvU6hSUj54tXv - yeqI/9i5jLpGHKuH2AwImm2n5Y+uRuG3/dTkwMSDL6jr3MCDICB03ktJsnxrvEDf3ttIg1wzicq7 - 7eEK2pEYU90n68jCEGjN/YNyUEbsd/k1qbqz+ww9HihjCxg6T7XeWYl3K74m6z0wqPr3vBBFOuAT - rGuqFn5aZDeeXfAqfsQwEucruY7kNPHb8wSPmx0TV3c7hleqi1A6CjsU2q93QtF6jeDpqe9Qkj+F - FkuvUIZybl9IcMw8n2/gJYT9eRWJd8mbaTlptxJ6KNoRC77NQuhCHqtUz294hf02x/1a88ryIASr - HKItW7RghM+mU4nP7d7TCkJ9Ufv5diB6ujUh+B2OBnztvDOG8zEsGFaSSrWu+BSuixJPa9uiGJ4f - pxgLkdH4PP4OHrgFBwuZP8gnrMk+o7rlD5R13AnQJ5sa2D3KHYr9lQesH8sG8lXE0GF5JcXsHWIR - NjG3OSYHHtDP0qXqzb0F5MblxFxUfInVzDlcyHG3TRXpQn7eD6b9Qydm9dOqLpENf5lUosNtZzAK - diSClfw5kIMUDWwR1rQErxv3wNKvahPaFJMG7Xvmo7DtKViVHxKhhE2G9EWowYrPeIT00wckfqvf - hJ/w2MCzkcBwOp01JqEvM6A/3gyiQc5oxd9Da1Q9kfbEnujFxxV3NeCZ3Y/o8e2Lgl2sXwOvq4uJ - dv0Y5kroUKt8FTO8s8lgTr8DMqB29V3MfUmQSIsWDOB5nyMUtzwC4vuq2nCLH6Q5/R78zHxfQar/ - nugBCt+kSvwroX3/xigt19r8HYOboRo3dsQKJ/ITUyOaQWSaBGmK8DVJ0OmKmnyISqzmHgKp8XkP - or6ZiLE6l2LdO9EIpkb4Ia3l7FZKnKQCuHo2xCuGU7uUN3+GtNzBv3gpqG1rgbq8YUiu+u1XzPHl - TFWomR7S7f5cCG6sO6okDybyb+XMWKXdZXXnJiE6cZ4GhPvpGkKxv//QUX88fWxWDoaViNJw37j1 - JNq2E0D+3p4x4w8nwGtGrcBakmJ07Pq04LOa18Dy5kIU7/PBXL4XEMHjx7GRX1lgYpExZnD4IAnT - 8lmZTHPqGcyVEqLnWyXm1OrOAKsRiiHRNNoyIPsabGJ4IQGNPu0cqZwCi6JRUUCBMOH2GFUqkV4A - Pcn7wfhsTCx193wYyJ+fR/Y9BpGmGpmFSAJLWpDGhw4YH1GEbhueiWD3iVXph2fydNSkWLS9u6i7 - mXrERKk7CY14yqES2hbJpuDGsA6UFBgwNokz16eJx69LDVeXnv/hi1ipRq9+yHshBUuIuVzsJVMn - ec4x3fIZHR59COHqXEi5xTNV4ncFD4KE0IlWXrH+sC+Ccb0XKMJtVowif9JUjrtKJOxzJaGDWwzQ - gaQJxen4MfGRGYYKzsnW5fqk+fzTEzEUvF2LQmovYD0GkQGeapOQrbLd/sW/ujx+hGgHSotlq3pB - VLxTksuD20qaM8zq70UZTsS9YK7yJ5uVMnEtZO8QY7Q9RqU6Dg3+xyeY1OozPC8zQuY9eZgLvnIV - PNF62+HAN/56VHAEhCQwSOXmXEvWSTEgxxkL0U0JtMz66Ja6PT9kv76ALRNu6r/8g7k+zxPiBx8b - zHSOUa6tU0F3FZqBt/9wCJH3A5C0eTpKnMkmSUL8K/7et9LegjFUbnxf0O197MNXnCHzIHLJmiF5 - huhQ1KFyyY1W8v18hGZkHUlVS2rxtq9+ox7O84HY3e08SUA2NfWRrx5Cjqy3cz3JnRoqrkEesv6Z - 6DsbBvhufhfiazvs/9R1ruHxy3PIsMMrWCWl5AGEN4pfbf8zx4p7ajBOmxivubmYpACCAxXzKaAT - P18m2rwNG5Z8hNCTpqz9Id0O4YN7nzDQrSdYx3VPYf8Z2nAv5zYQ4jWK4Xi0S2L2fmAKYSWJ8FQa - GpYyOysWe7B5GO0LhLzP/cKoki82SJUkJyiXeLC8IsLDKVWuJPtFWiv9TKWH5zeVyfFeDMWyg3cN - guT7wntrUIpxBYMCvvvyhLm80xLhD29jH84o+N5u7SqXUwgIzo5Ei623/8vJuwE3sj+S9H0Si1+S - HRtwGJBG7PvpPf2qLOrUS3V84NvzvAeLOcEF7lPrhGPIT4CC89lQ/YelEEM642mNaTer8NaRUH0s - W48sWo5QqMUzOkbtfuNHswxK/p2RIOD2/soligJ5bTcjw3ZtX/hoSQRWWaRIGy5+wT7PrwGd1FeJ - TuK2JXEnj/BgU4z8p28kQgfEBupnXyeW3wQJ+fa4/8ODcKaXe0JF5zqCG+k1FAZumnyLjmrA20cS - 2fhGQpu3ZwPU1xPx5+eH/Rp+gVA6SjvkBU0ySfPkxzC3PySElZxPTGZ9ptSpici2x6gQmYFrUEZl - sOFxygR1iSz4LAsn3K0O8PE4czeYCxcX+S7E7Hc8zSXMvIdNPMO4TCQymvQvXxIDK6PP+CGwFNUz - dWQZb39irfRt/vgzQmYumss9IRrwruIbHd/B0q6zbmiqCN+vkJzTK6NO7Svwj/9WsQpa/Kdnntep - D6m0nWHY8GjfRF2H0DfWfQnoowOLm3gg2gOt/vKgxxtsaxqRsAcdoOaFswDAnYSMCF/AVIkKhTur - nUP2F68P3gvh6h1vYcDND8Be65KD5vuMiA6nnBHHVjk42+mXnIp5P5GNbwMm/EJU1dSfxP7k9jAg - 4zX8nEbNp32YaernNRT//n6uxc6Czut5QPY9lhJ2i18x1DLRII/bqUvwlq/VoqhVFA7bVJs+V0e4 - P88lMdtrN9EjfNlgukYOOuDbnWG/0Gv4rZUBIUlaJvbHT7iQQ8jc9AqbOPcG/+XfsjNNXNe/HGz5 - Au/Qqkw/LqEKdOCvIVpefNv1MbYOFHu7R762C32+nuQe8of8SLQ0jpI1+awBNI/Ni2ipHExfwiCE - vDa+EVrxtaCd0G/8QIXIEssv2EyuFL7VccKKiwNzQhOqQE+BvnUBdk1Jujcj5A+3I/J76LXUrDSs - ouKTbtefp/n4SFL4u8k2Kj9o5688ZywwhA0L8YPr/EUnzax6+1gKObu/+j+3hKOiaFTFnCrt/Z9E - vhksDxIkuhq8E6a5qgfruePJ7W5iMF9PqgxX8aGTPMlyxhisBvjFkooQmh02PofF4CQxiokhQdgu - 7frCf/4Dco58YTI/IDZ8oGZGQXE/mNK373uQ/aBBgnQk06Kw5wDSzIlQLhu/hCYHK4R8Vw0obF+6 - L+qqTYGm7AI86dYObH4ABiS5rSH808NSvtv4n+Ns68Hw//gx2I+RjuL39WSy+xQ1//DCLu2KrSUn - zHC05ok8FAEzou/cDvLfwEN2g27+nJbPFJTfVEbxapqTsH1f3fIZMVazbVdtJBnc4omUnE7Y/Mcn - tvggJw0V/qpM4AY2/ENnxw0nWp0vIbRVJwzpxlcnF3o3oClqgDTNa1smg4VTD1au4n2w49gvQ8us - lhFLQhq+YfKjWOmhwUGATlVBisVefzJoqCaS8+PWbPoxN/7WTyjqgjyxQhlmWB4agfiv+6llgeQY - KuCXGfOnRWqpUga18m7yEKH1+26XQTU52E2HEB1k1puEPLgIynqzhG8LfhhxkNOpldt06I///mZf - aOA41Jhs72vj7+8abv5NqMTTzFb7atbwlo4j8thhTpYGOBRU4indes1500IdrVNFa0iJvYxdwuZM - xhD2IYcCtWAte63yDRi39UjS6HkqNvwK4PW9j0I5E8x2LTpFg+e8zlHo5tzEpMjs5AnYPnIrPmz/ - 8id45y7BQNuFpjh/oKGIhY+QJR2vQJD2Tg6RgQcS8oqczPxpxapyzxoUFFXG6MbvoOe1j+33B8la - 92oIx9VOkUbcuF0fk9TBJklKdPy1F5+a6WvcFlKPfCvVfAGJ+QBgK5yQjte9z8D+WYHP/Q4x5OYH - Ww2zGmDzdVwS4/uu/RV9WEHI0ZZY+RGadG9ovLpsncZVUEwmGx2eg/2opSTkBbsVlsxMoeB8KDlp - y7ul9dVOoYiUNaT75x4sqp5UqhnTXQhe99NEmdE3sG09HZXcZ/YHsa8h3PgACvOuTuZXc6//ni+x - 95+DL3WFlUEBrz16JJH5zy+DN9GGJATm2i74WzvqHx8yzVZPFkD94A8v0bF5KOBL99iCWsduxJth - YpK1JblSP5OZ6G9eTmi3O1Hlc+KvKBH5tljbW7GA0L8PKESVbS7WNlVUNOKWnMbjgbHTccbw9B3Q - xrfzgold5qi4ejRIe/eXidRwUiBzL+9/fiW1DImD1za4IS0v3El0ZeABrz2ayFwz2ewOe7mB/sNW - kB8sYrIOszZAsK9DEiqPS0FFZ8tH5xxjASeOP7s/iVNU8bcS44zq4ntavjVIyscTmUois/WjFRFE - B/tOTi0XJAs6ZgY8P1AcKre97H9Lp+Shkd0ztPltYPOfPNhIhycxDsau+NF3Uqubn0r+9BGvl3MH - rlbJoeOJnBlfi7P1x4fReeND9P0qHdjP+YHo/cUA//K//P4qJN1VMGFqpGTgz28xlfBjYiFqYlXw - xhIFcWklm9+kqX/53RvZftMfbb2dOU7JNdO7lv5SVQb18zyjk5cYExX5g6HejkaBtF56Mvroigz0 - n7Eltr+v/dkRjU6Vl88ZA+1+ZqJsXiL4h8/2L6on2oOrCAVcPUM1eP3Y74K5BfhWe0FOx1996TjT - AP7xezdxbiYplBrD1845YybybTID6oeQZMcvOXqX0JfwwufQuJUacsXzqRBYPmh/+hgPTykzVzEk - Jfjzt0/f19a1+8wUGMVySm4deScf7/kLYCr1DdJi62Cy51331PERRyg4wn7zf28ifNfaA4UXvU02 - /7yGf3wvlYzeX0+K1qndcMs2fhcyUb16Dnzq3kTcrAFsIsl2ou4bvcgpeJ2AFJ+VBn78qkfBhs/D - bbECMD5HHUvZHE9zSzkHMqVcCKovdSIsX72EB3vBpNRPEVgUdh3BC9IazxsfJXiBN/nelG/ia/cV - DPf+7YDm8D2hCzx92YglK4TLk97Dj6DcWwxf6gjH1UpRsiwArAc2zPC+FABLbMbJ74JFCvxF4Ygp - 7i/+WnTUgPsx1kPxas3+ElSeBbb1gozrwy/oXv5iJZTYmejKg7RUt38yXApNwruNX81cxwWwd3pj - 8+NGn3YCxsojL010FJT7RNYmh+BqHE8ojOUvo7pqL8BRjDHkxdIFS3y5U5hN+xvSpe9gstCOInXz - A7HwjJ/TO/nsQ/iBihy+u2U7Q1zcuz8+R4IvlwLeOK/23/2To9/LCZv4JgUb/pIzj7p2nb2vDYSz - e8Pbvv5WOK0CB+GtJ1iIjgmYYH7PIUtjGNJmkJK1s9wAkpeuEd9Ka3PV6B4C9XB+YskDXiE9mzpU - Ae6lf/xp03MWFE7GKbxMHU2W7hI0MN3DhGz6peDHfaUpPyW9Eddqh3bpksWGORZPBJ2FJlmX65WC - fK5KhIbZAtKGx/tlDeZQOHwuYH2MkwOepaWTqDY3/2CQ/8UnMVJn377Ot6/2D391x8XtT63wDWx+ - AkLF4+xvU8tSsDDQheumf/Fu21HQcnmFWZ63YPXH5abiHeCJ8zzfAf1ygw2N15cnR2gyf0ViPIK/ - +8mra5sw0msyjFPpHir1O2zXM3IhVA/JE+9y2pqDo3Gegk10IAErNV+yxXsENz8VJaFzNpeLeg/h - FZEC89KPN2lvNjXM7WhARzaHCbsmKoZ//lCF2isgzuFVq0bhyCSo2MA2fLkBcSivSHfICFaTBBpY - Lxr54zvTxo80cKpmmdhWh4tF2t1C2J7OCXGTr9HysrB6qrffqoc/yZjWqQIjvHKlEH49XypoaOqa - KnKjH64kbie2NjkHN78BbWerWnreFQbcp0VKzP0wsvlxOPWAp5aE7mL29Dd/BEMhaV3ieitrl0Qa - bNC2jk5Qy1/a+U/fBb/Pj3jyDxRsq+/B8Tls/FV1E37Lr/B9LSViizJJ1gMvOKr8zhj5y8e/l2v2 - 0CGHhNxvfdvS9eAY0F2HlZhtKRYL9GgNI7BqyJnrX/t3P/C1e9yIx60NY5oreFAr5In4RZT6FO1f - ivKTzzhkxnVg5LXKOXzU3C5cNn9W2vg0tIlgE2sfrcn6NPMS7kk2EnfDN/qXD8vZXJBrDXmx9K0T - /9VzQlidglbcyy8MdiOpsXRTHoDtrpIC67nn0anuBsDWSdFgF7dfdDjdOHNRf44IsSak6OIfXuby - TC8LbP8DAAD//6Rdy7qyMBJ8IBdykyRL7nKTICDiDhARULkHyNPPx/lnObtZnoUeTNLVVdWh+8ad - 5lN3TAf6qpkefbzGxrhMN3XsynmDU2Q22HynYcYu/U38qwdgJU/dkDn6Qw9KvnTI3Xkb9TKacwCc - RTjgv/gr7+47/9tvcrGmb7bOfuoBRlVi4v0krv7n9/m3nzsLuhTSadf/aLjhAmvpOVfZ7fQ14JFF - B+ww3JgR1XswkPlyD2K8m17d/TUDJe5Zm/lLXzrkwbnKn5+Jzf7OqDTbYg26Vl7jnW+EVGufJdSp - ef/z08IZHAIG5ToLsarcW7B0rzT685u9dWElwIN5gFB/Xrtdr6j1PFyWCOVcK+HnX33DcdIWjnn1 - JMaDvil1nKBF3eUx7PVfCyzm8d1CM7IQuazoSBc3GpS/+hV2Yu9d739L0L4zH2L07x788zPg8dzP - wq73mG8tBehogOe8ReajZvOzmcDB7YT5tLdB2V7tIv3F+/57I2dMVCU57XyJnBMR0bZ3I/efnylH - JXXo7T70UDyyz/l09DxnjRs1gCmxIqJfTuYwF8Z7hBt1Vk8M4d2hl6uzwEw5Jh5AwgGMUdNBuPux - xAj7NqT+rc3Bn38Bip4H//C2wzT3uAoLziaotwCeWT3HuLsHlO567V99mJv8su53/Q2PffIgyb5f - NfNMGtgObYDjd5fRFWr9+JcvsOk7RzpZgWz/yw8Sn+nZ8k7EAgZAvXhMeobOco1nF4iqOZPzcLPo - 8vj4MzyPHiFWOEfZP379f9wogP/7RkH48w9YSbyppsTgKnj2c5FgHXQh/ZbAgD9+lEjgPkm9PF/f - HnI188Ln0uPo0ixWgUidUxK12XngWvKboVflC0k3QwipI78j2N6vNb40VkUpzNcvNE1NxgYTn8BS - avWCvt3Xn48rNTI+hEsqHrplwFbRGuoWDLIA8eb1+LxVg7Ppj3JDrfstiDzAC6DuqWDg7/t5e7H4 - ugysbawi/DbXm1e5TzwsWHYDMOD4S9zXu1Inq2lEZD5mB1s3MwZ0hZ8IvOPEJZplTvUGHMeAMDYy - j/6oQWkh+zEciVYSC5duPWb6aMNbGrreB1/uwyRuVISuUOczv1WOw2zbsYSPcfXwq1fMjJPSzoYn - m6oe0L2VLo/4JsImwJgYUYdC+tW9DXxMzyVqddxHlQ7bF8VqVZAbd8XDenMeC5LuUkhy5XTK1vLn - KpA4sYRdXZnCBefLF/FlpJNbRegw3ppXKWTX7Ocxv1+VcYqfKOhtNy159NSoacR8DjBrBuAdIAtr - OtGoQbP1fhF5tdl6TPNDCdFSp/gSCLeM3z7BAeUNE+Boe+5zIvc5O+F+4cswixxQZ3w2YNUYnjhD - oQ3zmOkF7NLRI8HSeAPrL9MCdZ1tSFSAxOE6sQ9QqoY8lgrjAygHRghv2SkmEn1NdO0TRYJjBM6z - QOii0oXTZpSHnonVrN3nrl4dCeTbOux3XAd1uTi3BN6WwCb6Z3QH9vOqFZSnvUC0fuSc7fFaEmh7 - cU1ugb0MBOZrg5aCPZE8w2m436HNobB8epJlMszo9gkgOE6TjPXwJANmlasWfZ5aT0IyHocVXf0U - tvewJo7HZWA0535G+3nHxsk+0FX0yv1duR4Q/RP06jTp3xyJtXEh+JB02ZI/PAXeH9oBv7hLC5iz - jmwYFYJOom8QDgw7STEaUnUgrnP8OosGxxh+BMGbV+V3Dof63kci+WiIXN/iO9zKYQ1gphvJXMPa - rv/2CyZK7BPvtLRgdX83BZ37NiQ+olVIuqfSwOcGFWKUzJNub2ErkRLfFpwQ6qv8k7vNyPN0SM6s - /q6ZQ3mR4PZ1AbbjoR5I36sMmlfzRy41kVT+dHgacMNzQKQR3ms2Rb6G/r5f/6HM4UJBtVG8fWPv - c1AuAxsosoTudZBg9+y8HcY2pwBGZelje8eLRc5lFxLUuzgtJz/bcGho0P1cTfw4dUJGc93k0I4H - s38Fn3BzLXbvEN9bHrL6C+D1Mh9h0K8JVt++PzABCmf0+VHiNbn/cxi4cBssN/VBLLmQ1HV1lwrV - Vpri8zR/VfpM+hlsz27AanPp6ol3FhM96+dEpMLQAVvwaBPpW7tjg1ZRzZXbQxBxNsrY6QjNlvYo - cnB7DgO5CLqccahoIOzhD83iB+yX1q+rgdbx2JGL8DsMW/yUfLQO95d3yL8VpXyTfmEndL+Zi7pn - tiK2UVBcQhc/oDsDGghxAcnlxXrH6/jLmKYrOfTLxNzjZc5SuasSxei2ZUdPbAsvoxb3nWFwHDgc - JkrnbHRoA2gv/eIJHv4NVAvDAu3P570TJhpYNn8LKAzSkjim04MRHt8c0pPvZeaet98wx3IGoSFN - Enb4Tnc2zft6IvgkD5ya6xWs10eYwCIzGGIDV3dG/9yIYOzoZ6Zq8MnmoRNKGFq8jy9pF4aTJT0C - 9JcfLHLpnOWkpimceHAj9ukZUubObjP0DyMmj5hvVQqSVw8dUDpEGeIvpaRTC8QqeUZuUdgMU/CN - WjCKgoZtcVOGbek5Brb2ISKX7m6HzG/cx5j/uga79AOyVT9hG/JlrBM3ZXk63lMjERcCE3yVinDg - 976acCRGSeRZl+l2Ws1SjN1PgoOX8w03tr4xiPlIE35cVhBut0lUIHxZtz88AKOWJDOMup+NdYH5 - qtN55hTkQJUj1vcMs4WxQQr/4ilyHrmzOW88wgFHX3wXp2HorbPuQfljq8Q63KqB+lIJ0dfgfxh/ - vDpbm4edwEn/WCQR8THbpq+pwGv7crF0BXrG5wRp8Pnm65mtrVjdzDwR0DX8zti74V+2FP3bgwI+ - /bArVlFIsgpwIH7xI3HH8gz+5bPFOmZE6X63gc+dd4Fywfh6PPIVh93jA15XKccB//6FC5oFDe35 - EataOTlUV94z4p+fjOjZltZzpQMO8PbtjO9v1DrLOPgNMsQXnAeBdynrpOGCgnutEYcVQ0qN6XaA - Y3Q6Y7w+arCQWRqRoHs1vl/LF2Wa6GlCyWVOOAnVcaDa4TyiZY48Eq9Xw9m2chphnfczltb2Dbgv - L9ow6GlCsvj3AFsZfxr0t9/BPTUzvmnqBlpwfJC0yt8113/ir4i7LiWmM14d3hlvDZQ/pvovH1N1 - NQq04xMJDkJFF/cQGajL8Gk+9C0Ot/19d7TrqbkLVNVhZd02YfkQvjgwWD3j+NcnEt9QYLAPfIcS - i/uO6Hw/9fOp8NSaXzh3hM/i583MYAj1tv28GML7+MHYFZSMjTlPAubcdFj9FoKzbF4tIAh/Kom7 - 0lQ3+denSHJcQq5VVIMVhncNZQjb5LLMH7oc2GSEYzkO5JXNIZ1qV7bhVdRjjA+JldHCOraQulPt - HQEb0KlUlhQYP9zOh+YFahJCIUWXdZm9Bl/f2V8+B+YpnAk+NVq4eTXPwdmqX0RNFEvli88AoQU0 - jH0Enurcn+sGrWx1Ikbm0nphJylCzPl7xFattMPWTp30Fz/EOPs3Z1WTRoIqT5h549/nbEsiloFU - 7Tvs7Pxqs866Kz78QvLoktgq50AoidV1DYnC710QHVOGEH24Gzm7gZCN45B80R+/K274nHEN6FsI - teI655JnhpvWByXK1MIjXv5VAGdVpxI+DfU4z9r2cujf9z20Pt+7FEfZ+pQkH5bfCzv/4fMY7T1G - GiMbiHKUnvUkvFIJLko54OiI6rA1W8VAkevkWOp4FTCvVebQ95VyWLcp64zibPSQXyEmem8QZ0uW - yoanMA6JE5p1vWCx/kL6Klx89mRrWFyeFoDqASWWenOd1XuqgshH5Xv+WnQY1sSzRPGuMz2J9Yel - 8jueAjlSG4/9jOMwDsqgiP/ihcsNsD1DVAD/1d+JUdVYZfihqOBlLh8eKN8OXcoyMcA2mdIM9ZzQ - Nb5uKbxN75Lc8E90VtFrfdA8huN8HI/vcAvWvoRFZBGPzRLBmayT0MB1wQePhWGeLd/fUQF7fHhc - dJacSboBATZjsldoqndGcl3iQHwzW6xXJBzGIw4DaJUCh52NlRzufI1jyEfVGxsf4zPMS89x8C9/ - hndPBVzpuxDIn24mSn10ws39vHt0WhYZ58JVp0uVXFpQcEJCbtH0czppLd0/fji/DkkXVpeY+4I/ - /SAfi45ujzevQTIqCsHFtQ9Hp9+8/+5XKZxrygZ8CjVgv7CLXlO2yrpti398UPHcEmyQ5SNI38b9 - Lx4BZW3fgHodBvMm3Nyaq+d3glKz2bDlGLa6DdHIAJbZfli2DgrdxIzhAEvtJ3F3PjtRx5ghN9f1 - H94Oq5NmCxT19oVdzrzSdQ7ICHC0QWybd5Jt8uxIcL7d1FmYqne96QgmMCmGYufzTb1AY59zeQ86 - 4to+QxeafWyo/QSH2J6oD7zXTgacEv7pCVMlD2x1Ji4wZnvyxEccgV2/iVAdkg/Z8Zcu3x+vwKt8 - aIhqRYGzWGA4wGRNzHmZhxb8y4cZu6SkaF5g2PmPBIdF6khBuJiuTIIP0Myi20zp4Zdt7RXuc1p0 - F+d7Pl/gctjgw/C/HqSOQAcdyhIamotOgu4SOusc/EaoP86Cd3But3ABAItQs/Mnuf1qoR7RLBho - PRgmtm2jrJdxugfQegUDUXd+vYb0KYJzwRFsJkMMaHA4GuKuJ7zHAwKwHJRAEjWTCWcGDzXY9eoi - au2DI7LVlCFRjmsFb1c3IBpJpGw7prAA7D0+eUu53lXyEfQcTq+lwubwmIZNrG4F/Fn7Ve6LE4Xb - 4QK8v3w2rx0V1T9+LN4yEHvAdGywnN9dAbvscsIqVYGz9A+UgJezcR4bhbvjvWklZG8zmcX+JoYL - PxSleOfKkUjHfs7o3X1zUHvOqze+3pVD4j7ioJweOqx/xrGeKzfa+1NV7ZzZ+SNbGQoD4dj0Z6yd - xQ+gC+fOUGKOI9nPVzjOemhDtj2qRAcvXh1+VjmjnrUu5Cq8V2f8p98yEBNZ7DW6OWo0g4ozDXyd - X022hPZTQTueEhyiG2Wie+xD7+DvXfeNpzMr8dIC9jYS7OPEo4tAlgKVn9EmxviUHd4vMgi5T97h - YlCu9IruJwWKzHwn0iPNhoXy1wKksq147/l3dKh5WG0Q3+z2H3/lolJY4IE7i+Tcim+H5l2UQ8Yr - Nbz3b1Z5Gby+MO5Vw/u6izYsA1Qi0KWzRy7ZoVLX1yFOQdR9bHzOSVkvOSPGEDWeg9ViSp3VvKoS - auvz2QP0jsL15H8PCGTu0eMX+qB0eKQGnGqJn0/3az+sZ01eoORyJ2yymZiRU2oVIEMXGytksTJK - BYZBJq7Wf3yT3GLxAE/vcCTK3/m/TKiFRymMiLOxpUp1aElAfsQvbD8WPluer7kH9rBdvU2xgPNx - DiQFr5dqYz1LBHUN6U0AWm788L5+9ZbazYx+ywbmt3LR6PaHv7uexc7PhXT5jckBGqn4wAaWQ3VT - gOZDbjw9iMOoP7rUH0NA1cI7nmAUXb2sHy74p09vNr3tU+icEv75U5FqlHQhyzmBngPP+BpJuKZo - yQqo/HK88zV5YJw1ZSAjmZjcj+fZGQ9KoEBvePf4lVenf34SevqXB5HoaQnpwOsajOKIm4c7NIfl - SGcBCqEDPfFwFcBWcEoC9/3B+g8BZ0afSwOafarJqfGXcNvKzwiZc3OcYdJYGceDRw/PSPyRP7yf - Ys5Q4CULdGIGne0st+ZVgVfPzVj/BLZKP5eHB83FZbC863mqnBIBPha1xobQysNGp/kgMpKNMbaJ - ly3BQzJQc5A2LKmXOfuXL/Z4xu6ef5eyihekBnxMdGk1h/XQiD4ohKoi1q5PVp83EhSgSvaEVPrU - //DUT+851k7pRWW76zsFfRmQWYxgkBG/CKHosUTDeDzKGVPsU/f49YCxPMAJkCCtCyDdUguH756q - i259Wlidfyfvw11xTX8TjE8ydvS5fZ8OGR0egQHjFztiR+BdwOXNz0WyoTnk4rwflHrkMsKIbg12 - 53sI+rcglrDko8g79Y9GXfzuxJwa4zFg6fN8h/Qs+BXMyTsmkkIdwG6PdYMzqXIs6UsD1gHzjUju - A/ZOiY5pfckGDvzx/7P1+zlT87BTmJbT548/gnn2mQgRjpyxhAByVnBENuQC7+aN852Cvcesdqr0 - QiOu8tr7CX/iL9z1J36pyQao9oCGqH0D2aN3T6WreMpLeCXpdedjo7OopDbgJytKj2vGWl1x3ZSw - e2ga/tMXEzBuDAyOHbfz+yr8i3fUhatFlDtjgz2+Oehfg4rI7EGsialfGfSzLIqvVaQCvlaKHuqo - GAiWz0047/wEibV2mZGkqJS/L2wBDbLwuIBsXv/z50aQEfzP3+zaMoffqyp7UPLabOd/OSTcdCZX - 5aKBJXiYxmnnb/OhvZd0U+tVRJ0n9Nh/oSvd/cwFHrdfOZ/s8VyzGav3cMwYl4TV8U2n8HSoAJeF - GFsISM4iRJqNWHi5YzXnPGfT+XCDegxVkrsPE3AU7z0W7JkSc5XvdNO82YUxieUZxtkto3VhH2DE - JjO2wOaqpDtzIvAeoJxFcasG6pgWhKVwKLB2RHW2wBpVwBQ2FZ+7SK3HPJjSvefReX9HM3UW4oAN - ENS65PV7rWC1mlEAzgZ3vrSudC2nsYc+0nSyn3dnq+d3Cv/i7Tw4pN4WY/BANcARq45TqNND4Gy4 - 88lZ3P2tv3wAT+CpE9PDv3rXR8xpaLCOL/7vC6j+RRE4yeM+dUpVQz7F1wPsPllINC0y66UYiQBO - W+Jg97kt4J+/SYc1I9pjWuqJMLD/08Pe99zzA7UWT/vzKz1e8Yea15VuPl0yXyf30osBuaBlg9xK - BawSW8nYR9SXoLlXNjHZLM2WI84C0fWTHkcTHuuZW16GuMnxcz7KZy3j7mf4Bd/7wcA4Fj2w4XlL - 4NNF2gzS07ume/7de04ERG6ON6f9qx/85fen836Av3gGiRL5WNJkWpP6XkXwCSVINFBLlBtRmsA/ - vXliMUspOw8jzDc6EEM7fofl4jwTmOdXBVsg6erpldqbaEaZj43xePgXP6csHivs2n4EVp5rErjA - 8Ec0/9U5W4Cfew+RY+R99v2nq2rs/BQ2RNUMu2YMgTKQIWSdT/1DczidDxdYHI8B0XL/9+cXjvBG - rN7z7aYDJK6hC0Fg6th1PmrNN/guQn/6LMSIk7keya/U4PxFl3/naefzHMy/RY6lqjRD1ndT+9/n - jTKQBhZk70qs2zwgtmqdBzq+xBHs9RmsMV9TXcqq2GB11jwsQe3q0F4XXNgy5Ipl4b3++ecj/J0i - m+An/wP0LCQl+NtvY1jjejt59At/mZBj6YKBs1w5asBINaqZf1q1s1iPu4Kah73f0OPVmp4OTw2O - zzzG6R+//9Pn+/p7xx3/xt2fgVlVGR6ba1W9DduLg1Ot8ERR5DKbMvuRgI8CrsSDLBxmBzIKEt+F - ir17eQMLJ/UB2PX3H99S//G53X/f/byqJpt/9uHgH1ZslkoQju/pGgMySgp5LLKX7fzYhduS3rAV - qKrKVuefB/7hyU8Swp0v5HDp5HD3d616k97yBmk1v7Fd5XJNRCdO0J6vZzivXDa9yiEFwXssiHIQ - KrCMuAkA36VHYoPoBeh1uLlg9//m48xdsjm4ce7pz8/GcTmGVPbrGO183WOIiOpuDn4zHOP66h0D - wwJ8NcMRrhrHY7u/iRm9h3YBxUSgOPPmtzpv5WeGu970tvrT0o2b/RR1Usni6E8/FJySojRYXKK9 - NVcdXxlnQ2B92Xl2bmy2jhdBEIX0LJOzGHbOev9EPrzh5owjfJVD3qCHL+jpW8V//tw2U9lHbt0f - yNlvSpXwz/UAh0NS/KvnTYsZN+AyV4+ZS97VsN6tH4SV5Fje4RR/w9aIvyJcJPHtxSe7oGOpDYuo - kRh4HzU/DWvtyia8p5OJtZbtnUX/PA4QBV8Z42wOwTReBBGeCvU+89+7XXPMJgkwXRlt97cSlV5X - xIA/fuu4nymcr4XZgm6ME3xhq57+6UHhr56Ebw4aaKg+NPin1/bz8K8ehJhvy2NTPDd1l7ZuCsFP - qvf1LMNVTUYFDvzzgf/8e5aZslbEctli69KVwzoKHxtI2rXHcvnZ/vhVANLny/caIj7rddLnXMQu - Hok5wUal1JMa8dCab3JeL3nW16wiIXA+DDNjzo5KLpVWoIV60yy813r40wNQYnJ5Fl8nx9n9gwR5 - h/XwV8+oef2LYlDfpg4b4/OtLoNSK8CBMrfXVxXKvlJlg6M9TcS5PoKMKKdE/Icv2n7+tltwGaHT - 0gpfKmmm67m9Jajxfzrx3tInI8jzSogiwv3pkWx+qeoI3dG9YLV+NNliPV6K+Kevl6zG2fK67T2A - iqHAugstdfnamoR6J/GJe3ZkZ+enGvrLd9eOpio1cA9h/1RSj/H1b72O0bWCT0M+YgcNVzDPLIbi - P33cyLrz51fByisPM93rMexHXhT0ieps/nmZq27bz4jgXr/0Go7n6tmrjxyo9FzDrz0/7fzvC0HO - WuR2XTxni37XBF2im4RfJSPR1UnDDUzI873fbJr1OK0n9/SHP/bnzYZj/fEEyIRp/VdPqGne5Tko - DDfGedJY4fqRFwkqTj8Qbeqwusi38gua4IKxMsuCs7yvkyLiaO9xttdPGbnAImw1GWNjnkk9KYGT - wKbmH1h6BCVYH5lqwy8LXkQ5FGW266Ed7+D6l7/o6nCmBkcKj9ictQOdTdERoXjRMbH8aKXzzj8g - mq46NgvGHhiLdaK/ejFx5zul45/+AIGtE3N4XIYl+eH2/7lRgP73jYJh3Lu+GktVr2f3MEKbXtUZ - OpcuHB+3mwHphY5EXZRr3X17VECvOGTesR6+6pLbpx7VT8Mn9hK7znbau9SZ2Pxz7Oaa3hLJhEIf - Xj0atTJlBfJI4dF1cyxbj8mZSfZIoMuahwufW47DqANsgdylylzUV8kZNSYtoQZqiiUYkmwNgVQh - SUQ2Ma3pPLBDFzao5dKW6J9f5ZCyVCs4H1nRY9hVCdd0elYwtw4Z1j8/RWUsxdjQHUodlpWIG/6e - H1pfZvZ4Qd/CVfPfB2iXZ24+bOdu2FoCv4BldY/4i6RRWrZdCmZrkWY036/Z1hjdXs9kh/n0O6uA - U14tAztcMvj5C1LAPwOqIPV2DMjFs+esbc1rA7OoZIk5DageI/UB4ZUklrfdx0Zdz8+tgOLFkMgz - PS5hX0/nEv2Op4AEiShkG3NRRnj7ovfc3byAMk7Wpkjx65EY79dST3Mh2CJIvxq27qQC/HkVCuQK - sUPk+NXT8a1LHOo2DmEjlyTKVbesgdk0yBjbcuVswHVLCE6dj7P73RpYJdNTOJ5FfV9/nG19Mkbw - rWgWNt4eA1bNDRX0HG+EqFwU1SufejPs5DYk4Vy/B4Z7fnIoJeNKXts2ZMyHEw3UaibGgRapGX88 - KD6MwrIgN/qA4bpFO+K/W4coiZiEHB9rAdK5QMeREL0zpr2THqYaCOeG7F2JmFvbwC3u1Vn0mMJh - HrJgwLTyc1IkrgXmC/OQoOHFFcnd8ZFRHWYC3G5NRJJpQMNkY1NDZhf1JHp+PYecVyGHHRkI9j7B - b6CnzEngEo4Pkt2NCVC+QwtkN7QQqXt+6PzM3BQFVqPjQpNvNdfBd4I+BXRI8Hre1fXye41QOnfK - zGQqdhYeHQOgFY2OH2XzcRhL8TaYPykkqrkIDi2oy0H2aHpE0y46WLdISEE11uLMhd9XuOmr6YNT - MlokUH3GWedisRHYpYkodNwwd9KSoqd2DbA6BzpYNtdQkM3EG9G13wBm7pD44NK+DOzSHKhUj7AA - ByV/4SKf5ZAp9zvjUVAQcqFZGfLJ2xohXMwvjvAEwdTBXIAoS/xZ9NOGDotxEGG1iTeiHyU7XJ+q - A+FodzwxvrLpMHR8cdCWlR57SvUZVnGYTdha8gtLDrgDZh0YGxFb5YjJeLAm5y5o4N3afOyHVB54 - pgs0IPjnGDv1I8nWd7LFKDzF1bx4RUzpmltfKOl1g+WPJgxrclFHBC+CS6z0UGRrR3kFVdzEeTxL - icPK6jUH3wSHHpu8tXD9EjeF/CwO3hWVbb0pr5JDTvSccKFcLbomq7nA7fFgycXLbId1DgSKivny - sBLcHyGnt30KJf3dYJlD1sA654sI849nkNvIoYH2y7gAN0UKseevFPJ8x24oGtveO+D7ud5+TsjA - +coU5EJsrWZ+/HODNj6IWPleVzpOSrshHPgN/ns+cktMG2ZO2hK3vMUOd56eCsyGuibmKXBqvmyC - Ai3yF2Dnkx7pmq5NAYNL9sXXZTCGRRPeBTKH3MTZ+OIyOsjlFzmnLdm7Tl4HTlBtCA8zG5LrmPaA - wrHikBD6NTYPa5ZRYCoa3M8j1qqHHK4jZ6bQxPaM8cM8Z4t4hy0E9XwlWh0/BnILVxdqh0ki7nGf - q/xOtggd9OlMbJBV4bgujgEt7XvD+JXaGceYdPsX/5HnXVRmTvMZdsGskL//T7CLvH/xxN6Pbs0c - 1vWAjr/iTIqPPACSemYCz82BYucD7yE18mkBpdtWng/ZL93KNheg4ecyuTjC4Cx/eLLdvhGxMxHQ - lTe6GfhQk3EumaHD9prIwfWIT/NqmWu43Ns4BXUuvXHK3cO/8yWhy/HFYK07CfXW/uLxpB2IRLyi - GAEd3N8IP62WYBkPvjp2bZ0iPREhcV3EOotDoxg8bzeZ3PtZGMZWU1pID5GCHyrbh0t8/LYwqDeZ - SLnhZmsbTi60PtvRW2MPq6umIhMeDzlLEsbLh3Xebja6+8txhi96yDbGpAsE55THnq3kdNPu3xZN - 1qXHDh8ugCp8F8D0YWPsXPVv3TuqWsCjJWOi3qLDX7xFMAqrYl73880trzJGuk4I1nY8oGERtTAA - TIFT5R3Wm2EyPqrw1/UWcrlmNBauB1Qj7GBdt+psODy6Eg5q6eHXUbBU4rVRgoKVFv/WlypETVDx - 02SSv00rI88AKFDPlNQLZ7l3Nu7yTWCz9Ru+SXoU8lWna6gFVwGrBvEH6vLeAtcuEAlWL+eMkYzF - Raf83eO7sVQDnxuGCykGNnm4/TiszuEH0V88e8JjBBtszyl8Kg5DnKfY1rRlEgUeUWbMsPjYGftU - VQi5d9IS0/WNgWOnqELxVHDEIdfKIQ85+0LYGqZ3gqUbbgu9xJAGSof14T7VLdYogxISrfjuV8jZ - bgYvQqshPsapfhyWsTtriHuF1ix20oluo5xrcGGAOrOJb9bTawA9zOI+xMblkzhzfKc9fM6vC7ah - GGRLKYQQDtl3IPGFo3Qmd7tAxPBuRKtMvWbo2x8RERuXeMXlqm4vFZp//IhoJwnRMf0mHETXm49V - 1ijr9f32XXiu+BvB+suue88PIXyL4Rt7FTFrmnOLgcghm7Dyet4dqjpGDPrWr2fEOq7DwugpQvPq - X2a2qJyaw2PBwEy+tVg73Nphs8VP+ZdPSDx/5nDJDu4Cy4HM2Eueer1+PLGB0WEzMXbWadhUPl+g - dr0JxHXHUzhLvevDbmC8v/VQlwfqNDFycgnr+3rxh+dSQLxlrxneV1IvxtMOwHk6aASXbTGs51v0 - RUfXy4n0Yv2MzdLchWNnECxnshJyoxxpqKHjm/i3+pJxJLsmKHz7OtF4f3OoqFITTooHPbaQInVl - K1MAXzVWsZ2JGR0PUy1B2VVOlxWVZj08kNaAyzVP8B+fZPIUz6DCjYtvzHMY1iVGB9hRYcQXWVsG - Oj6HAtz0hZ/RrZQGjhPlGJxLwpNz+NHqRU9ADHb+Q1ymtMONjzUf1ECfSPbsRjCaX4eBzvfjECfh - TsM6BWr0930eyFo9ZOWjXcCgIxJRZb8E2wvEe0VcsvElmi7ZeiHsF7p8kBMrvDghX45LjNoitrFz - jauQhoOcQ9ZhDCJ9RYMy3Px2oZ3ICdn5RMa6uN7gVVFlfPHztZ5o+0wg5M4fooLzGayF82aQJUAX - u4YlhtMPrB78bco6H2Knyuh6F1tIbJnD3s5P1o4eFdg8yg9WnaoGFLhXCZ7Thfe4CNjZelK4Avg/ - Xsfmqf5lq4HaLxQO5QU/b1jbb3BuCuiYQ0aUx+TUNFJQDuZAvOAAule6aExQQvLOJfJkp3rYTk7J - QfMi+1heoJLt/CqB1IKOB8ZlU6kCBPf0xwfPiEV0IEQR4J5/Z+iOj3D6KPq2VwiO2Dw280AlIxsh - DY0EG6Ig0PnklAwMifkl6XzTBsbcFXtrn70ZeZmt8kfl6gPpsZVYDqEDlqrrvmBfTyKh7aWu5Zlr - oJdziJzN1XaWe3RKwTDNFvkXL9MHMrC9nhG2z9GXTrJIS+AcuhYH669xloRVXah6h9qjc/2ul+Gi - ucDjpRMJnodzyE4XmIKOiiPxPsG55vb8ILLbcZnXcydnXJzbX6iWjoc9rpMd9r3MPjzNnYnx1/s4 - VCuvCgw55+axPRs7C/eccuBy3uSJnMEPm5JdRWR40T5H2xMGQpMihnLssaR4Do9hYfW1hFX0fBKb - 1EK9TNcDB69cIuz5nc82crdzeLcWH1/BS3bYZi5H4AlaS9w0moe2a6IcHUq4kduxkulqnB4t3Pm3 - d0jcjtLc8Ly/z5PX5X0Kt+FztZHEKg7GIpOqQ82uImozGxH9Pvt0cwdqA6mkGDvPZHW2H662P72G - ZZwyGcWnxITiIeg9Vvs5gHupjC0+rj+HyMX7NMy2+Kmg9xRHIv2cql6vniygfX3/9BpdKxQa0Fa0 - BzbSgQVrM5czND3jSGT/cqZ03W+DzJ0IyB/e0VuNA3G9qQevkfsv2FKrOcCJ+1Iso7Id1igLkj99 - MbdFk4Q0Hj4mKAJW/4f32ywfN8C8e4ecsTo5y69eNvjWQe+dTugSbjCqEtDoYYd1cvqo6+OxlEiS - Z3XHW8Gh/OzO4gjcCzYcdszWe1sJMMyOD3L5MVu2830bdv32Ivjy3uPBrnuU1pFDwvR8HTbjGpiA - CPlMjFYhdeNPQwmyqZMxjjnNWWLT/aJ4yjmP1IOh8nyHNlFr3jre8/OwTIfrBlCQHHESUz1kPWlN - 0VB9Urz7D8MCpSaBxvch4Ti1DuHIGzcP6okAsUVTL2P0BESA4eoMy8J0y+gGs0QUbDP2xPC3Ztvo - mSIcql9KNAtds+EM+eYf/zDLz5Fu58eiIJ4FKcbQ0p3Fg08BgLTRiI+2o7qGdtwDLFkGNuj5Q7fp - /RGhxQtPHL+I9qfvEiSvRTkPgqGF/Fccq3/4Kd9QmFHBPrfwuY02kdgXMyyLU0Vw7gTgITY/g3bP - 52B06Q17PM8684AuGkz4DGN3Gkk9VOXPg/0BVx4MZM2hclU08KTcCbGi7FWvba4aSH/AAd/l7A7Y - 6L5PaRjAaRZ2f2JbNdlGyXxVsE3qpN6UmROg9zna+Hy438LlVbYKrOYh8UQ/1eh2eukMDBuVn49l - W9TzD/cbfFdY2/H/uneKnBZIXvF7PkG81OS0HSNoq7aJz4CJw1Womi9s0SMmcq2uzkSOMhTjBxbx - uT1M6ppXmwbn9fjASnzyKZW9tYGfxeZmsOvDJVKvEP3x+yC4n7KNXesC/joY4gK3dbg1B9VH85Ur - yJn7VuF4mAZFFG9mgbXs8x0W49OasH+ZMT6v1/uwVMhooPqwXzseiM6iHNgD1IqvTozIvKt/fhmY - 2LeGz9axcNheGkQY+F8GKyOz0PV79wt4nePUAyvzduiffvvTJ7omnIepeHkcRJ/G/dO34UZHgQHI - 8red/3wGuuP/v3j84FFR504SUljeFoLVVlIonRAbnM5m9/D47u/tnGtrol0/7P6LPKzH5z2HA8fE - xP+KX/qHB+iV+AO2bl4A5s/tGMDHtrJeUV9LdclOiAFGFFYe4BXW2fOtJPqnQ42dhHsMS1deSpg5 - SYsvrfCuqXscW/HMG1f8eJU3ut2lIQVhI/NYfioWZUNgluCaOReM1UJ11ujuFKD/eQy+nH9kz+9b - i6L+7RKdByHdgv7qw6rbTCwLh1UlrH6TYENejXeMNCFcNCgkYNnWz8yf86M6fav7Fyr+eyQ7f66J - LYoSeN6LFz430zX7wze4uscSe1qwhut6/PSQfO/+zH1lUyW/3+DBnQ/j3f8cRleyXGTV3ZsY4feV - 7T2U+j/+hv/8maX6pD1MtPN53npqZUzJXBQYRc0FR+yqZIyKTxvgzt74L79Pr9+491IQILEeS5+N - f3xNRC7448vqInjAADoBvcc3zgMMVrz//k28YekCj2FnDwpEG7cdsNybv3qtGNaDoKy1GQh3SWXZ - ShKQ/11jbKdIHf7p4bB0Y/JsY9tZr++vgHy9GbGclNs+J94ugPBJZByaS6Ky9RsI0J4iFUe7fl2t - g+vD2jrtHQ/X738AAAD//6Rdy7aqMBL9IAbyTjLkJSIgURDEGSAiKCKPBMjX9+LcHvash3edu+SR - yt67doUqbz4vavGPf3PTj/PteQiw3NuRIE/2TGl/PahIHz0eW2D9NUut+A68r0wgFRkUtha7Ywn3 - 4lbBpi+PkSW6BVD2gn0w296Ykxu8ZqjYTjAfoLxvyA/+MviXr9jX+wpmVHE+/J75A00vpdoshNg+ - xHyFKL7GvLfxp4o6ubKxY+ZWzqY+PYM8fr/pKUSvYfOLK9SKvRfsfOPYzL93UQBaHK8BR9/usNL8 - noHlaXH00lx9tv7krAQHVVmpkSgzGMOs9qHD6pbqM6yj9TWvK9RSslAXecSbJXd0gWcXZ/pcX7iZ - 3a1CS9v4jK3jKAydSF4BvEjll3DmE209HJ89bMXOwydAXPan5+Cfnnb8c9sMOcl8UKiHhAbu+xoJ - baTX6Ivn2798Y9WBZvz5K/ikx34uTe0xAQk2OdKFlxOgCy5WKB6v7ua/dd6fHwYn8zzQfcm+kdhy - BKrlmX6poaikGc4PmYO5X96wlhvIm30rdUB/9m2ajv4JMDnhS2iPtx22hfvPHHleGEEIxJIa+wsY - VqJLMzhM0KJ67xya8Zm6DqQPbAaIdCdTvFJTVk7f/Ea33zf//CQQmOOC7+EQbX6qMINND2G//LjR - orAqgZ57WQjL0imaKznnALyqIOAuBxbRtTpXMBlu6ZZ/t8N6bVsN1reux27Al+afPwtOyzfCZoeA - yXISBjBohDGYi64Ba+CfNBj2K6XHW/n1WN9IEO7v3EDx10M5/R0ECwqJZtAjf3oxsRwdB5qpNWOj - rU+D5BzfBbSiR0JPdVw3s3u7F+A7aNuocy4cGKWuDNjqvamm/3jG6t/JhkVuuIST4kezXMKUh+YV - NNgUTZkxzfBXuK/NI7avoI/Wm7wvwK5NU2p773lYE0pb+A3GbuNbISLcXjbgwX4BrL8PWi5wx7VE - cucxwj/iMF93rc6je5Aj7B3pNRLV7zCCzY/Z8KLylmDvJLAqZw2XNydrFvRyCfRT4f3HH8Os2XIA - sR4I1Bjiii0xeAdoFuBK86bnzV6/uRCWyNSwruCRzVAbU8iu7gHjZmg98rZ/PkR5dqb2FxcmiaXf - Cok2UxrZJ8UcSy++QvF7+xJU0GL7kntn/NUXgt0H/fJx9QMDUqV+UOtiHBgzpFeIZHDTgt+2XvRp - OR20Ljd5y4cG9vnzJxJSf6g2a28w96N6Vf/8dSepj43ExVkLuWQVyeUR7RtJWqMO8mWJyLLlU1Ra - o61ngHvA+jaGbtXtg4/YenxTPZALNguuVvy9H6rVBzZU3e3bwUGLdeqrr8vAuMdcKI+0CoKXJcXD - aM80BAfJuuBMNxpA3pwZoh/9UXKK8aaPvEpEVbzSzY/5DIyvoA9t7vkI+D6fvVXxOhFu+wX/4dsk - LiWn6Nf9jSyrK0Ys4U0Dkp2k4mMW6kB0VF1EarfbBRyX8eZc1NP7z9/AOlqsXIDpKAL9erhR41Vy - Q8/tZe1PD2KvPzFznlRDlDlmbfU7uWnWvgDnP/+FKD7o8nUiIYGb/qbp41uZyyU4yrBw5BFjpbXN - MTeiGS4H7bXp34+3WH5uQHVOj1hziBf9i7fNj9r8Ed4c4/HYqed9OwbSe1ryf3hgHaMs2DVQZ8v9 - vevBaffgqSEcQLTAa7zxU2LS4OaozWf+LiNayVjh49TXbFEj1YcSWyB25++XMau6a8BQpy8N3vPZ - XHjl4qC9ECSByFJ9WKzzD0IqlwQ7fWGYfJuHKtKXosLxTusjlhbr+ne9YNny/QXwvgbaKlqw+9Pu - YM7qowrt+HYJWpDNOXvkqapu+mLzt4Zm2e5PdYXsRXEb7M01ll4z5A8F3uobxUC8bJf++fHUzoaY - zbmRr1CTGKLO5yDlhOi7GRRfJBPuktItS9qP0A2Ah13kBaZwv2Q95CpuxQ4FF7Bu/iPcXUOVROKV - bxb+cnLhJJMLWWQPbe9nDdDn+Kvw3lUIW5+7yYHjziT4mHFcNLc3E6L5ujrUSZvFpHI4Vf/8v+19 - AnbMOl/50w9/ftT8lz/91Zt4gr7DRFWgwUFPZxwbkmZK/jbMcseVAnUUHTMW4/4MlaLpiVSj/bCs - flZB1h/sYPnEfEQa4ZNCF0MV5259aJZb16ugfMMGOzdL9/g2IBbc6j3/9Lao31wOSpMIqeY8cMPT - IS1g9vInolTRL2JOaCRwrx5abO/gF7xY98ig7Pl7eleDn8eet6mDQsgLOE+E0Fv2bxsC8HozarT1 - 1Kx5cl/h/VhN9C+/mMuI+Wg3RB3p8CPyZnw0ZrDxMc7sFUeT+VV9eHfbgnoBa6K5NKQC6ie7I99g - XXI2xqdW3fQ3PoGdPfCpkjkgsNYKa3G4DCN76DNCDjbxfi2srR5QJPAP/6MT//VmfThDdGiAhc2t - fi6aYBih+DgOdNP/QNriFZVI1+hfPvo+L2oJt/wVa0GZALHYHQu47e9g3PxBIouJDdv7xaDGg/vm - c/xVx//nRIHA/+8jBYDcsqA9ClkzKp1eAqo/TsG8n4NI5A7PAErd90KD8TMzdtmnHHCkd4edevQa - 3hlkF3Xb4EX7olrDlLy5Dnjf6ogNfuIZ095xCuW5NjGOIDJXMbILNUvJNmhcmT2mni4Veur3hrqN - 8MqXKydbsCF3jIPncgGUfvkZCsl8wWn4kj1G1c6Fmnc/0CL3HmD2CZTh7eEyerq+RLCo3dkG1Pxd - cXBeRo+WZz5A/KRzZGmamzmymhLY3PscGz1bczZ8DirqpPxKZheCaFVPhopMLd1Te7f/gXWUxh60 - LDvRaDWnZo7ObgZtX4/x6YF9r9s1VYgUdVQIf39a3mopogw9eEa4zD4WEFtLTNA3fX3pIVNIMx9e - KFAzUf1Q36vHZpE+jY+wbVDsrmBmy15VZ4B44ULD22SAWXtDDbXyUafnAhwAP+pGBt1HfsZaAQ6M - Kd9aRI/hrdLH13yDkW9WH+zn/E3u4pAP/BnAFka3tKdpae0APcmXALoDPmL97Zkmn6+rDyeHbYeG - +9qc1aR7w3c/YnxXDHE7NMcM9GoaD+/zsjOXbqh4BO7vFh+wsllW3GyjUzIX2P31CmgCBbToAC4d - dflD6fF/6wPdeKUn2woHsVGONnpMmxVAvQ6sX9sRgXc4vagTfcHASkErUBU+b0QZI70Rj0JMkFim - GS7BJxlEUfMCWHK8QB8HNzGlRqFvmBvVDdvDLRh4kZw08Ousmp57wTXFtKUd5EdOow8efXOilMMM - 7/vSo85J9gFfZuYZXWNE6fko3sD6sLMVeAwrhH3Nw8BPraLB4NMd6anDqzn9spuoSpcuwW47o3wO - POCiv/jeq/4v4l+f7f09rQe9TbvSXPyIjrBVb04grd8fm4rbtLWBhkd8zZfrwHrxrKJ7qsk07gQH - CJPc1ZCK1Y8+T29za1MoWxA27BGMtvKK5tenayHydI2GuaubqxHJGaIviRFRUWHU125vwFQR71S7 - HQgjjP+ICMuqRfjTdR6I/3n6qt7ZEzZy92UynaRX+FMHjO0nbzPBjH0H+LtKpsU3uUS8JD9FaN5u - Gg4M6TisstdV0L79DljP1AEsDxR1sDpHZ6LA53VYdI1LYYgfMj3WWTkIeJkzVG5dtkpD+g3LpzIr - ZMz7B7ZTtR9EvvE5mB49j2pCSKL5m7Y8vHTuDd/FAQxrAZ8zfGbCCR+kc+zxvFNm8ANmM5gnajd8 - JR1L2K+XOz7ekJqzKkze6Nn9bHzKYjyIbm5laA4bHetEy3Ph/Hvy8LtbXKrVtGroFm9w77AMa0Df - 2ngb4wrRVsKPQuyAVQs/MvRiPSNAE6KcV5Q+ASjpVuy9Lg3793z/4jOsVEZkr6vhk9YmjbhA9/hz - tySos0qdwCOPIsY30dbo8msFvx9swfx9Pc+qvq81bO0OeiPOlROAorvusKVEDht/dyVB38l/4PDI - tGa9nqUKAq43AhjvZsBrRy1BQGL7AMy3KJIyYBloMFod6xyvmiR76gRe6CHEl8vvyubma3Aw/w4L - djY8YCevqGHCwSu9vD3Tk0atmZGXyzd6fz7rgYW4WdHtfaAY3+4gn+C+9VEvQoxv+q7Np9AuLXA/ - dOH2/BlY376VwpHGb4odeMvZqZWv//hDnx73rW34zUc7PvVp/va0aLHMIYR5RUQi344m4L+Q2eid - nip62d1yJn3ZaMB03RVkv/70Qaim2EV/++vg7/zob38qF3WyaJzqrreolieqrzNXYu2lL9Fy/j1F - 2OPrnkap+WJsuXFnCPsuw/p8E715t4s4eI9Ci3yXlx2Jy+3bQ9EDA95HoMpFurxsKDZHTJ9iz0Xv - VEYryFz1hPdymXmjO4UVBCfI423/Mmntf5306lOHRlm85uzZ7ArgJmVM8eHURUQ/ohLWayZT/dh4 - +fxe4hmoHUjphqcNQyiE6LvsQnzyzs9mkb2LjJrz90b1irwBOycLjxwbZIF45V/e8lavIrzvC49G - tzwG0oMhDl6fBiNvBK+e5EnBCGNA7kRfu685pzKaYexIFtld30dvRmhpETvmj4CZbsrmA7poavb8 - 6dRfWNx0p+vQqs4KVnpYLccTP6d2Reoam3h/0t/e3L99B+xbpASjuXuzn31bNLR7JjQAKjkBiT79 - BO7vtoFPfn6K2DB1JTREmOEQcQOYNLGp0F4KX4QfP1EjoZ6+ISOFQkNnv0TL6B5L6BB3a5NV/bzV - C3MIFSZAfEvOZsSj+jHDh2B88P4l1GCddKkGaYIsmrt2H/WCwzr0Y+MVp4r08QTh7fDw9LsdaeSO - usdr07tAeTWK+EzLOBec2hThM5JDmrWnvJGa2CuBH0h9wKWSMMzroIUqf0lwMC3V0RPZIGRQfssX - mhRbG7c8wNsRAsGhh+/XZOvl15bwjz8x0SNvPQrxiIL49sKGT+DA3mbfwru2xPgs+y5YuaEp4GNo - VXxQ3aXZ8OINQpCGdK9FhLFddgn++Av7/fkERF0/GcCs1gHj+yx637fZv2FNRZfe4MuPxANsVkRk - s6Z//L58LDpCPRgMqpHQiZaksH1wyBSDOvx4a2pfdlulul+vOCl/SrSEfX2F1flyDipBmE2y2vYM - e5zssYbfX3MYo68B+1wXcLpfm3xuPOqAEw9BoO5+tieIkm/D4V5DahgXOWKCWIoqIHFGFrQf8uVs - gRFe2Hykd3ylHoNMc1CgpijoBlQP7JN8eHjMjjENjoGVb3rmDXN4/eLDs74z6pPyDJEjKtj4Jrw5 - JVKtwXCNA7zXooBN2Lwk6Gj6GTaivWSuu62EVXKiQLX4/hzm4nVPEDmzbWzNaZ/z4Ws4g40/sZVe - xWbxtHcBtap40zgvHY8B62UhfldPZKzdd87W/tUhaTofyGLSNSIGHbe2lEkbLFp4zWkBSQr+9MPj - xl3Bqp5cGXye0g/7H0tjvJt9XShWyMHuVQQeVUs1g/RhcUFoXNJo6VJUwpxUhBpWx7zZeHw4EJTp - h966M42WylE0KL1PJj3O0AfEFbsARN4sU/vz20VLDpQzSDXjSHbINr3lOCY9XPZpgo+/rU14W22D - f033g92on8AyUdmGhUw/wTdkH2+1/D6B+1Nxpnc985nkFyiDz0w6YfvVxGw5PeoOLs1boPaeLuYa - 9jNEZXOy6HP/vHt/8Qdrnfb4eFgO20daogb38/1NDVqggV3HF0ThhesIv0Lircnx3sLtfqlrGTBn - i8UMaHNqT/7iuU6VXFP/9BDC8mbAPo8EFrt3Tdjz9czZ/gl9MJyfchBv+LQY7ceAz8vo4fA21WAx - H7AFvDzM1P6qcz4+8FzCtlLK7WtvIZqkz+BDTnuaNICnemDI9oo/vsUuCT/NYF4TGe6SaCBqOz+i - GSHlDa2o02j6MdtoWfmbD8uXgagjIqeRFpbZIHw3MdZd++TNgcccCHlrT71GCwd2n5fgH95Fo1QN - a5ytJdrn4ZEaa5KZrJrWTH3SysRu8nbzGScnCA5uVdC/9ZpkKhow+vgNTaNv3vSSPr3B+TecqTFN - AWB9hiy46+ELJ8XNYfTveWjimPjSNDdPOBn21gaTDGRH3lc2/8W/FaAj3fg9YmUd24AE+BKoRWpG - wrJCEQLNhYFsnIG32L+7CocfqLHrHOxcct9mgVxRBNg4s7YZX661wuKSWjjgnsyjm54A7E72RFhK - 3lvbKnQAv6smuumfYf7yRv+nN7b4UbxluhZneLa+PJEFfI9WrmYGclZlxRoJu5zQ5WWhODxTfLVa - kM931Qnhqwt06nTVe1jXPtLQv/znijfZ8zxpED+Le7BcNTqQzJxUmPjWbut4sbL5T68Z7W/FhhJX - zbJ/lhycMRfRwL5+2RLdjyX8VG5IFv/xBVt8aSDQ2ilY68kBS3loK1jrU084M/NywY++BMaY6ER6 - nI/RWru1AQknP+l1hYE5P7euuFbWAvK7ITUiagwSOCnFA2tGNHuLTDkD3C9HQPc7kJosOVYB/D7T - Ny1xW0ZL4Osr2O2mOODfz95jl/2Z+/c+ouo45MunYxBGsxiTNMhJMyv+1MGioCe6f4Uvc4lGnwCt - 7geyRl3KurdqB8rgTWawVMRiyxfpBH4QdPFjqY4m/wHpCCMSf7CXP3owy54XQvl8yolA6cf87QTa - g/PNbujJw5onDu9Xhv7iU//czt5cBB8OLEtKaIRl4rGzZRcgXVERFEm9Dsv5NLrAH6EXcJYpgqkI - YItwxhqKf+DY8N4xOINN/+B9816GlSan819+h4tyr0Yk/11rlOaTjAMlsj1p40/oPr837HMvF8wv - cjHQq/N1bOlH5K3y7HewiN4efj5OfDSl1yqESuf4GH/1k7e8iFTDmJcKirf9wUI8zLDC8g9rlvE0 - Fz47qdBv9wFRzycM1rT9drCtQEn3bv/Lpz+89RvlgE96fsj/4dtneOZ4f+lxvv1+hXbZoJBiy++W - 7rw3kETLAB/0whqWdzwTMKq7JBA0cW7o7tLN8J3dvwEK3MLb9HYGv3gPse3YHpifggihurVlPW/6 - lzx3Vg9PQ3Omp75qzDG0caEWu7b+w7dmURSZg6iyHviGFWcQybd1gSyEEt23ZWAyNdE58KfX9OKX - MRbnR1s93nYB9ig5sLnxvi4syxLTTZ83QnkuOXCq/AO9wyffzNwZqhDx0iWYT1HidZu+h8b7MZB6 - cifzH99v+Sx2TK6Ptvjs4edaVwFfDov5lx+rweHV4VgKRzY/LxkP703fY6+8faM5X9UA/hi5Bjvn - Hg/SY9hXaPMziLLlV0w4GqH6XVD4D99Xbde/obreTHrwLpD1cbYWUIxvCdbtX8D6Ans+XPn+F6BP - HA9UXYTuj1+xJ9QaED7LmCpfmcdUTy5Vswb+Od2I6Rtwt8HJJapWLpqudx1bp+u5+bs+zE7WlTob - XlLTEA3IYLhi++zHEdFE1f2Xn/ibnzWair6C/PtbqFakrdf1I9+qP/WHA0EKfcBUtbXhVxZxAPvz - xMhO4M4gDVuRQFqogEy6VKGI60/0wFjC5v3PO8M//Wdm8tVcX1FYoJHXcrzf9usYzM8QoqRft/VV - 2boHxwxu+Q1B39YeeJi6EPzpUfOXGTmvxKUFt+Od2NCJ2MwvctdA3/6++HE/1s2y4R14i4mOPUOr - GGN3bUayMuwDy8wCk37U1QJ/8WYu1QBY6hlvlOxdi/zMEeUtTfZn2L+fFfYhywcaO/EIe5HDGEvC - AIbGcAL4tO17sCvTF1tPRlzCj3WSqXVeCFuH4+mtDtbsULcXDvmSaFkNH15wxcHr4zLp8vJ7aHNy - T/1oz0evIuBbtF7PJj4SY47oYjENfZ/ZGxtbvj0Px1X8y/+w/uCdnJ9B0kNHWSk1t+NHq1+Q9c9v - wU67xibZ8A6S86jT0y1nHo2bpUKdVej09lGuQ3dtTp1a9rYesOdrF9GQzRqUubtCBOeeRoydz9e/ - fBDb4k6LfoFSXuHlFWVb0wUSrSitAriaKCfKI1gaAvc8jwgcUxzaiuVJSXju0bsnmJ7C9QCWH/e2 - 0NGPCyIrVzOSRO0iIr89BNv6aGzzJ69wN7kdPrbPrplVU/Bh8qn7f/tnVReqwTpJK5rtfq1H+2aq - VPHL7+nduh4jlhArQZvfiB0xa9n0OTln+MvCHz2yZffHbxxck8KlkXi8mcPxLvd/eBEclpedC/zl - UcLdpZbwwVJqQC+BnkFNUQOKQeh6wrSNxZCV354631hk2WXvXiEr7YjwN+eYs5peV6THHw4fn09j - YMq3F4GhuC8iK9LeW7kaaPDv74bvZWDe/8wzuMRDh81PnEbMUvc1DC+wo2kqh2wZyadVLTeIqecJ - ikn26xn+80dO3FKZDIC5Rf4lyaj5fO3ytZnTXlHpGlNrgI7J9iA1QAQLG1/7IGXrS+JalT6jPbnC - 0ydaY7F1//HxTqVdxORHamwfKYZY/6gXwB72K92a41+oPXf7LT+gLnRsJdv042QORaUS4KJFxHqj - CRt/7Ko/P+cfnm3+XgD//BMj6mSw3Etgy9v6BdxsUJMfwiYEQ+wSuvEFWz7WlwBOjSJsfKxmmCf6 - K0H0CRpqa43lLdPDJ3CLJ3z645u8Gi1Q3ZwTLVZF8diDCZwar0NMlm/38ljzYDL8eJZA9YQ3IuFj - UaIGmXaj8VJezdX0njyQMpbi/QzCfHq5eQrfrXnHh0t3a9ao1iqw7f/gd8vkZrl/hxpe9QCQnyQe - zT54KFuJqs5oMMBP/ud3w7/8dfMjzNlU9FltDscRB5sfN4kk7+EfHwWzQb010V4O3Lc7hfpfJfLW - IZch2PzB//pLQ94nKhRDn3CWmTDhz7/d9EcA997qLX96efNDiPK7f8yVG4YCvvcdo2GakJzV8j4A - 48JfsYNQMBCq3msoXfqESNI5Nif4UwugPqIW26sqRjNMXe6ffxnI8ztagqsaqK+ixQREauP91SPU - RN7fyKy9X/lyI8objK7v4iT3gbf5Qyoq2aenznTEYGY1HcEFAg9rme5FYvI5hcDV2ZPs4n4cZvPb - qSBWqhCft3x+TYqCB5+n8CPquxzBfFe1M/zT3394SxLt5SJfHsCfXzoM1Ew7uF0vEMVd9effnSF3 - l6dN/+3N5QI9Hx5L0Qtk/hgDNrxlA37NmdG8IvV2BHap4BT6Kt3qD978sY7Bn5+LE0l7suXPL3go - GQs4ElXm8ocPUiQ+AnGLR3L/ru6ffsbXTR8s8QWk8LVPRup/vsbAfyw9UEyPBoEqCFM06xpdAdy3 - HOGfQchWS+FUaMyHB/3zn8fyoBlg87OCNtGISe5+eYXPttgFypbv8oHpEihIjkTUL+jZuvmzULKN - NzVOSu+tvWiEML4WIvatbs6XCE1vEIPxjh9KGzYTx2kB+vMDEIcfoGs86iqhUyMiBQR45A+fNjzA - hzLxwTiDsoccxx3/7UdWCk4hN7lYY89o6mamz56D7fGn/tOzEr/DIdz8CLxP9d78vdUrD0O6VkRc - VTH/t582/4k6H9OOGEIZBzIb3/AJXHW2ImQl8Jh5cTCfT2vzt/7oT0+pG3+uVhyu6FzqJX1a0egt - LQtmqKtDRtAoP6LVoKP954dho9wjbxbCewK4w1XDhbMnDd38PAC7MiIi6IaBbH4YcB/3MzX/8l3h - 8raAUKcmNl+y6pFl+8RryxepU3EwWuKjC0F3MwgNnKFuGLy9E7BkRxdj4wObLuJJ9edvYEu8j9Ga - n7ZPAAYo0kOZjGDxCuesRCduF6ibv7Iex+kM7/K9xId40hueFyIb/aCQ4Aef7Rvhu18D1Jw/N+ye - 52WgeXBIFNWp13/ry6ToYcPDS+IC+blSb/miI/nLB0jHVeeIvYp7DWbirvSYXWOT2TdzhkSQclIf - WTWsQezwcKsXUj9VpmgFq1LDE3dAmx43Bv5FLhpyvoHxX7/gd1euUJYKDl8evBOJf36H/pFbrKte - AP7xx01sy0BYf69m/cObWJePW/2N85rNX0A3921j7+3v2FrTwIGPOabYOa/HSLR/VYW2ehlZudfU - dOA0O2hnjhx2vrI80MB4W9APhB6bihkNixbeQnjtyhIfu0Aaxm4IfXQ17waO2mrPPmeLjejYfDSq - 648XW69jfgVTHkPs36bKnCfvEYCuKu74ZMUmW1c7mNU4vy/4BCvsze+4tuFeKTvs4vc7nze9BHd8 - 5v/prWim/ja2Xmie1IN1y/705z//O9n0ktDeIht6n+2TPHzF3kJPdgCvDetoUDvjsFhmc0aacd7j - cxpB81c5iwF/1HwE/O7wamZcchn8aeKOiKUtNbNc2hU6Wx8+EDZ+YRxfV9By/RhflahjzF6lAhJD - dcgnmFe2PrOqUvZW7uMTW5VhMmPLgd/BSLDr3hPw5xeDoxlk2DoGB2890cyBk3ROcDG5fT7zjRrA - 9adJ9NHh1ZuTfPeG2XBm1EGINAte5hR9M0oIlN1zzpzhUiPZK1qKw/nsLcRgKUB44QNKqZFLXW06 - iGuuKfUKW4wW8JTqv+f9x88s4lwR/D9HCoT/faRAv+/2dH9XqoYpD4OA+yK6VKPW3uRl6xdC04tC - 6tRpajLp7HGwlcxrsPa8GUnK3W7RJZoJtUaqeJ0xtA4wDnIUZI2vM2mZvBnkWO0xPuGbKRR6eoWg - KhxswJLkowy9GWYX9CCwaCZvvH5eFfylmU24FYOIOqfiDe/F096+8pyG+Zt3K0w3S/WSqfOwCk91 - hq9KNKn+9GOweCRQwdWbWnwsUjrM2unew+stdalL8jdjY/iyoYArip+LObGJW5wWfYDlBuSst+b6 - KvgVDUZYYlOb2bAEH7ZCzv8cadRPPpvzuCvhLsJCAOvi6S2yTlR4WK8R9s5gN6zprHDgNU0//DS0 - q8eTQijRkVx1WkiXJmLO97DCtpV1rClLGDGMogL6XXrBxjOmbB1rZCvSx+foQ2gqjz3WwUfGfoa0 - GC0hZ8/dC4JHYY7Yvp/5aBbO20H6QCXUgWLptdbh64NIfqgBrymnQYJOYqDpFLnU28F3vnhqV0Iu - ufyw/9gbTPgdkKgW/WwFCH14QPqYu4KXrjCM9ycjF8tIdaEYm1uJtrGi9eZqGnp5xY6wkxiB+bJJ - 2P3YW9T08decT1VKkJ5xB+qcywKIuQK3IwpySA9XKR3EKz+sqNZ2SQDJw8mZ9nh08PMMIyIcHjjn - y3KwYNXuIeG65BhJc1eXyPnQEPto9HLhkx4DCIxKpVnpJNE6lPkMjw7kgsWOC8An5O3C63zoqMvR - ki1Sk12h8tzH9L7FAzlKmgz1m0Cos3vqgzjWyIL8FO5pUrd7c6lu9xGm6/zF+vnpAEGbRw2efU87 - KXG+ABIcAx/cLkVGdfNgepLwu3PIA8Uex/ytbaT3hZ7h86g9aO7Ojsk76FyhbT0DyUAPb7lSh0C/ - Pas4Xe96Lpq/rIf+Y6T02oo24Nlj5IC2c880KElkEpwdbci72h0fQiKwZQJtDcuD0NA0rp18cfGa - oQAbkAi+vlFyM7ZwV8opmcdjES074aWinfyWadw/d+B9WtoCnvMjh43c7AdaPi5XdF2sHj+1Y8J4 - BFkIgVGr9KRz50Z6Zz6BoJUB1hM7Hn6jtPdh/H0buEzcrvkutmT83R89K8sazeD4nWFTRw1R8VYi - JgUqIS3qD7akMWnYrs4r6DZiSojxq3JB4F1Z9ZLDJvevJFpFwywAPu0fOF2+ubfwxysPs0MEsBWX - ei55e71EwumeYqPWnEh6rEMAx/Xzw/rDZPmstIuMev3wJtPpYXki5R8uHC5REaBtP0lz15fqFg/B - 5RRmQxsdEYGfwlOx7cmkmadqadGh2GGsH+vBpPern8LupRKyvOJ0kA4OC6FzbRG2TStqBGtUA2AZ - QCPL/vwB6xneCcxPS0QvHDnkfPPMWihc+gNZNak319K7EmTKuCQ/HjX5IpxEB2RTp1FH5n/RCr00 - A8MAUbDmnyqf1SEPkJf6d5xVl7vHjPPLh8arZYEUFPMgNXGTIfwMeOzQMo14K0MtpPyBUNvrSnNF - q5HAfOu7f8HDaeiz28BBnQ0ffNoRKV82/IOv4nulXvwQcrbYO+0fvl7TEXizlQktel7aLth90Smf - ZJ3IkGN2jS3fmZvlVi8FqPy8wEaHWm8xbe0MneYLyLzNsN4KrRXcfcuGKGvoAnE5LilismrR2+Vw - NOn5+XaRPPIDNaNdki+2MnPIo8uLFnL3ZGKi7C21gqVOhL1YD8tHmGs0HlaeBpIpRkuhnxP5aSs2 - Lfz1Gi1Z4K9wW38c2GDfiHoRqeouuQbUFR/3oc7yzgbOZwqDEXvffN6NBkGyrgk0rq5OIzTGZIBP - KxXYdO13LqWpVqN3r4b0+QoFk96LlwhZ3KdY13w1n+Bb5xEqB4BPn/PSrJ9tcmGSQ5NmSloP8yeo - DXjMDlGwTMnrH/8gSw5NGvxQ4rGJXwg4Oc2b7kNYsyX77iCMkcmC621PzOVH3Dc0laCi2/seFmtU - fci/PjU9fNyDt+SyyoPPyYH0vvLaIN2mMEN5aBiEf96inF9IXEGK6UgkXzCZKNnjDDe+o/vS+4Bl - N/klPOceR9j5NeZTuvXl39aX9IqzeuxFlhTGg1IGX7H2ByEbeBvd9eWD9/Nez+f9F15h+JIwtcJL - 5HXgSGeYaYITgPgR5/wZVy6CPCrI2uVztHDRPYQ9p1N8IXnKRsx9fFS/TwZ2Kiw0y+/zcRDJ44He - mHQ12Xrb+mqq/Ux9WzE8IdwtZzTtXjyOwzGMJHkt3vBwghd8L1I8zEL7fcPq7sc0lP2ft+mZEcH7 - 8YOLja+lZTK3roe3D93wevjDK9RVyojv5VIPS2N8NODeHZGWK84jkSRODy+kxOQv/gWIBA1YfuFT - J/i4g3DkdiUEb/am7hgaEdHfSgLPH6EiYn5P2PhgkPvb//hWyS9AdzAYoe7wM977rMrZiygZ/J4n - Ht9vBjcsNWd2iLg4IUtX24zH22Tf2q3rgCPPga3mp6zhcfR70l457Eni1bHh+3Wz8dF8JfnsRLiD - wTvTaCT6WiSybq+ijU+ImqU26DvmbBZIgylujmvEvBJ2ykGVH3TTZ976c3bb5Lxzie/odWLz5aoX - 6LPL26A55B5YvhxzIK8XGJ8HKQHD3rpz0Iwhj6MB1wO7HU49RL/4gTf96PHo6XHwm0UagVZFzRW+ - DgUUWy+nziR5w/yaPQfu7WhHHcs1gNh8dim81GwMFGB/wNoxrUBPJoVkMT5Tvj5FY4Y3e5axpglt - Mx54YMFZCDvs5js2kEEOILADmWA/OhXm0v1eIfrjnz1thmhxqhqCS72M1KtGOZ80vnMRdzsP9Lo0 - vMfOe5lT1iODAfM8EK3hvkjhZ600er9PkrfY0dtH6+fLCBMgBeuTq3xkVn2C3cTWm3W7f3DjiY09 - sY2GsUa6DBuxutADxy9g9eJHAa5kF2JvP8KGOeyVIHcIT1jvQjFnvNEZUFTuB2ps+D/J+8b4tz9n - vFrejGTJhfxdv9GUko7NpcZm2FNjoY5pS95UNBVUkgON6UEeaDT21fcMN/wihfu7DHMd3Ah8kKOJ - y520RHM/aSL6wzPvKT+85RnKGWzYXaY2N9zA0j0OBP7k150+0OfKVnyiNVTSvsbHd8+zsUZHWdnX - B5G6qD+wdd6fLfT1QoOGJ+TmUkmoBfbHLMXH/H2N6MZfSPLqkjo6VsCK8DKi6XRxsXEc9p6kO94M - uvN6p3/4uxwEnv/TO6Tdn5Jo8Xf6Cq77M8Ne9tLYzO+EFg4qAsEco3xg4uchQlyeVmrUWpfPx7db - w7xxbvh6eO+BwLlMhJNJOXoQtYSt9Q/U0Gk+AN+f4r1hknge4erLMb0T7TOsefTloXL7VYE0cbXH - brixkKSjMFAvhmiy/vmo1Ef0rKn3mRu2XZ8HDctlAk9X1+RPS1sCGSEN67a0Dtv6arBQmEG1l88N - xDbPPjgOp47m/i/L1/6ulrD/PD9b38+kWXfnRAWlns3B6fR4m4se8gT2bfmlzvuVsR94VvVf/kHD - BQ/RDPlYBOLKXant6ks+aZ39hty887AG0p35lNNfCunQNDjgztYgxKf4DPFavIjobkfwdOVYgPuj - cGiOt8nz7WMMgVCvBT1UiW/+6V3oOsaLcG/r7c3G9hX6SSxWfPqsh1yY+vOMvGcf4CPcyebYb5Mq - 68ccYMfQNW+9u58AehY30eBodfl62F01ODbvIeBGOwUsVS8uqhF40YKXz/nSp/IImx9NqWftKaB/ - /Lat99/6g7VcXgVawKrh+AXiXOqSUIUVLPTtq+7JW66XS4r6tvjSi9ID9nmGcgo78DlhLMTMXJ7h - nCFQlQ69JLbQzLu9wKG//eF8It0UuWIuYXbPQ6q1Z9cj89MLQcIFE2k2vmQovs4waRofB1TXB1GZ - khE6NCzJ7n7Wcv5nZCvI8voXIO1msqX3ny5kjvSkuoQjQNWDzENhWbJ/+np0ESKwgy3G3rSmw+wo - 1xSOYZnQ7e/NMoBjAgOWcTTY9Pd6TnwOSt8kxtpl2Cab8g8RUi874xN3cAem3IM3jKP4SjHuenNN - lJOl9tIbUmfWgmiVl4xXP7t7Sw97uTXpgWoa9KloU6vygn+/D3IxNfGj4MZoacXHCDvwPWH3Li7e - eg0jFzgKpxH5oK7ex6l6CO9HnNGTUbX5yivaik7FHNKbeTo1ZLfTbbT8uh9NhkzzhI8gVzDURpXs - msHx1s5nBC584lFb1p4Dc+HAId9vM3owEDLnB+MhnOouwFc/1b3teXnA3r8EF+5vaX7pN1zRr+Iu - BP3F4+WCLbg971++MozLtU4g34xKwF2jIZ/HIj2rc2hAHKCyjBaBd1V4jPqKyOvxDEgxae6fH0C1 - Xrs3hIR+DT4SWrD++PxMVl8M+y9eg3lGt3yJyj3/52/QG7DqfBWUiQNKk1hkeRZtPr/kqoVUl6wN - D/l8vt4xhCmt1IC2idaI3DUy/vgfny70mK/h/pr9/f/gIx9Ec7LsqQDy+/zCWvO5DD+iYVm1WTiT - 13ZEnC72zpDFx/e84U040Mv7VELLlA36GOnHXIIPmEF22T2o6Xl5NHPfV4Y2/Ylx8jkAfja/BaSG - aNPAO/VsrjMlhJv+xrrSA/CLLUn8y++wGW59pv/iMU9LgSAlos3mR1SA6+sbNbxwYcyFDQc6ppvY - 8O3J7JAsObBtVT1412+94Z2bOaNjbsW4/CHRnH38EOHR4biAom8arZ9Xl0FNP5n48izsSKgWX4bP - nzdjDDWv4ecf9OVOeYhkrTUnFy3xkqJH9KgDMfnkYHTtKIAiHb/4fhnqaCWhX8FQsAA9ZVEERs+4 - WdDux0+wrTdbuPdXgxt+Eth7jbcdDr3CG5ssrG38MuODMUKkPlYavC3LFA/gKUMQ/TBZvOMNCGvd - i9CMOZ46P/PckPpQq1DaqU8iFg9jaB+GFAKvWt/Y0+IqmsfiHP5dL6A2j82u/1kjxHbXU9eOOzBo - wILglPUA62phbHOEph5a0/uFA4UQRn+lfIVl2OzJDneux/x926FUDQoiT3HMlqed8HA9OwbNV/Ft - MreeZ/RNvV+QyLUQrdaBBvDxdPZ071G+WUlobXNZMAoGbJxyPrnOHJh+ex1nwQ0NDN1TGSLe2OHg - 0oVgdfyQV5+/44wLLtDNOXpYFhQWluEj1n6Mbf4fDB2XBpziK+Y03h8y4F3jjoPXfRjYY218FBNN - CN7PWxSt3kOVYSDUMfaBHrLVPfPO3/0FER835iLqOoHsYZ82/nr/5Vuu+lvtdsOPzuwRXojyWWuN - mnl5BHOdLSH6y3/x/EpNoezcEF5EPaVm6908Ft0uBtriO4C8PEfL43ZIUVzIFTbu/pRvvUM4mDQv - Hx/mgwWE1WI1DNY73PDS9vg0Ll1wNlsR2y+eAxMUni24Ml/HG79FM64EGam3cU/jvWgMc9LaI3zt - DZ/076aM5rfrELVf3CP53A9qxKqHWEE+uAlY35/3jPJGpaHdrBF8HI9O/s/fAY8gJ4vS52B5Bvz5 - D09x0nd3wD9gmKG1bQXqC1nrTaWO/vmpgXQ/IjavuzSDz8u7w7f1fIj45/5hwc3/xfqzaKMZPT0I - fhW8YHx8fofl5y0QEnNu6IMibujrH6tAGHxrqo3Dai7flykifbQKfP0CxIj5yzp4s1eZrB5/aPhk - rUL0i6OJGr59Mmfn+fOBnH5RsOELmIvLO4FwzC94f3jQaNG3uRqmKT02/ebkPGo0C+3ty+6f/7x6 - oPb/+JZwt0XI2cb3aOMvfKhknfXTENYg0c81tfVHC2bl6L6hfz02wfE+3bx1kAMObPwafJ5nCMjD - kM4w+e121HwG93wtJs2ByVEc6X5vFdFaTI4D5fSD6DG13WjZ4dj9w0OqB5YGRKcNNCDEqKH+xVIG - OsxpCuWeXrEBoBxRFF9XqEFdCugk7pqF3rsA6nz3DcD8uJgsN3MRbPoCm7FZe/257DOw+NL8bz8u - 3e8XwuHdONTbZUEzXybYSd2TJdjDxpSz0fU66PvvjD4jFecLvVcBEin5Um08wpz6x34GR6P2AiUK - qSlaE4Dgxo82vRBEmmnjK+VhlW9s3uNDLtCLMQO7dnO6/21z1eoEufBh3E5YhzGfjyfOMdS/fBcL - H3MQMv5eq896RNTw+O8wW02uwZJLAJEsvWMroUAETJwnmvfGzyQvkUKwStTFR3kKAZtubgpPhrXg - O4+aaNp3ogVk3RCC/gCFZnbtPIDuXnGpfzTebPoe0g4mYSZRV5x6b5kcx4EHuyqo7h1vbPUeqwyd - 0j3+vU9zlTgc/vEvdn4fO5/PyzxD9ZV9qDVdCGBBOKUgu99D6gWVNCyjdPLBYxelZFmn0hQE2F3h - JTu9gknsJG/d9DpseYD+mz9z0ieF77O/0Bs1FW/h6esNt/0QiLebYbIdtAn8Hp0BW7d94K1/fviy - Bjz25klvxDT4JfDKAh0bzX4XLfX7XEKzyL/kCxE/rMHRDoA0MScAm3/P1P5YAGWQA3zJXd3jrSY3 - oCaNOU3frwyMfOumMOl3Oj68uyVaN/8LPt5fKbic+Dkn02kI4CtxXvg2FWO0ra8Dp942qUHtbvjD - I9BOCU+D9DM3sxhfRGR0ZktPze/dfB/n8A03PYvdQ7YDE9QVH6rm44bdLLXZh53OEE3mxJFaYQL4 - y0dg9I0/9P4UlYZ1J1Qof3i4f7cnwJSHS6DBRhfn8SOOiAfqAMaXE6L6KcyauSwbG5LOeWOjDU8N - e2v3GtpAVDEuStXc/DQLOVxUbPWHgyf9rWd3w37AWatpisbBNUDWN196HOLJXOkgEdg4X5Nqol4z - 6vJBDbZ8KGglknnrDNc38kc6BTzt9c1/WQ2YhfAZwPH6AW0PPRu8Fi6ljtmNYIZZ3YMOvjE+zU9+ - IA0peFC1B4gtIoPmR5xPgm6n7a2v9NTM6+6cKhRPIz24rpeP27+R34Yq/vc+cNOFf+uBzdet8P7y - C3Xz1wmXmiqbTzcQwpDrDjh8SvtBhC9cwuCdavgaso6xsw55adC8A1E+fr/lm5UGh0/l0mjp+2bY - /CYgceCB3d2Fb9i+PoZwe16Mb33J5gfMUqhrYUvE7j+kXcnSskASfCAOskkXR3bZpFEQ8QaoKIjI - 0s3y9BN8/xznNkfDCEPoWjKzqqvmYtMblwDSn3Wgz7j45E0a/GI4OQefaguf6sv8bQfECDcm4GRv - mypcRAHUFBPs+MIhagthbyBcXlysMFHrrXPdJKgIrYr+6Q3TmBYueiqPKACxfaJZDJSHnMAhIXKc - qStb7c0FMfvXhbqXul+XaHIL6bXjFOxO2gkNm/0j8ngY2NltUzd23E+CxGEHsjDCKx9Cb1BgI6/B - nM6Bx+n5LoasNlisCxVst/qXs5wdTgg/BWLp3KYHwfVdU/zHt6l9WTT4WMTB9vSuvflWPTrY4iN2 - zXX9iz8u5G/vErws4VwtvqI3oEblmZ66/hf1uGpD6G16IsRmBURtOvrA3vQrQZv/Cz/2m/19pg5R - zJ5dFob9869g/36d9SG6+wbc3f0VP9HjUrHCDbkgcimPA/GgeVO1Py7IS4MbaS/vNiKvnZmB5Kuv - YD9OnE7u2u4MjOnE2EWvj76OwQAQhM55Ow9b3+qThWxm75Ee1ssjmv70Rs+AkXzPhENz+pBjtNUX - sVO0ijdhZgxguj1mbGhlFm34VpQbWng4VvbHavjVVSr3xIhpuuHf5U9v/NOvtnpuTmxdzhDjf51A - qvwXGju+1VDGTBecXA+natLhpyBt+mx7TfClX9R6H8OncCRq+uvflOedC/6dUOqdRnPd3l8KVGMt - +jhZsr5MIl8g+vUPOOuDpBpqmRrSxlepF9WVtzYKP8hmZYj4yr+Hatjqs7CLjlzAdk0SLW+dLH/1 - FWrk32qd4Gz6ctEtRrDXZ2udflo2oVAyAnxt5nWdQm/Q9vruF/3p0/qmP4jQME8HG7cgRH/xH6G9 - csbOLvU8gSuhRMUrEINJsG5ozSQmhofDSNi4T02/PObfA3zCGZt+W+ZLPATxX72C8H/1qVtzUuRG - 7y70uIjffiq/I4GNv1Jn8/f5T986B5839llx+pfvZOtWh2Tm0qRaf6qqAbLlker3r1/9tnq59P+0 - FPD/u6WgXAOJqkfJWvnVvNrSFxQDe9ePVnHkahO4hEFFAz1RK1rMsgsFuUjU4pZdtJ5ADeD4+H7I - TjjH6yI4bgo81XTytkyqr08GhfBo5wxr4p3X1yQfWvTcDwE1rM7vZ5Gvatk6tBE9YPz2pjdKS7i3 - xo9aJ97wBm3ZVq8IaYqL7Vob+xWGs3RR+ZRqL8vIBcu82BANIaaJ29zXtWWkM0z8vsOuU2X5L5fO - CThy4FHfkX/VHB9Ob9ma3V0wD2eMlvz3esjD/rFi4ymWUXeJQkb+ooONPbXaBll+xADl10yhZ87S - ouXgrzZKp70RdJXn5eO84xk0Xt4hEac679fH2wXguNMFp3797AWuzxNgfvuZXi0/q6a6I/YefXYN - kfKJXyfn9QjhvjsTbKGfmq+7GmlQ6X5H89Oi5ryht5M87IuV5rln5hw+nBVYh2dH1vfzHU0mit6y - 5ooXGumP0RscVcqgcIaGXg+7fFsthxY4/8YGG1mO+iE/XN+griTE9i0V9cWUnRTaBv+C3TF2orZh - vBpRrlSwUT10TwgZWEBJVIv0nOz18+X+LuW4FnZYy3+7fIUrK8FwKVNsB7lazfcxIvJr9Reate45 - Z/fSnYW7F0fY1fym50Wtt+ToYrnB3+8tODkq8BTXbbwOhPkaUbeGO919cTBZl4rjXLWRM+n2xSf7 - pSE+Tow3GHnN0Jx13WhyXkkI0X58bP+38thAnSVgxAjIzkMfb2TDUpSZo5PTa7jGPUVqJW4SA5D9 - 4tvVanNvUR7my0Jv2TVdJ05KYiCrGOG4vbueoDpqKr/VRibfJ+tVC6KmhcRjZlFt5MFb6l/7kB+7 - sMEnZv+rhII6NSSln9PwIvkr58xqId8mEbDRtmk0L7KeAb6pJk5OfO1xH35sIDmxIT3ZB7NnP8ou - QJ45IxoGTzGnR/HCwL54atiWVCXiEinjgZBYo1cmr3pCtcGCMilq7ODduA75d20gYeoPeVfGuvaX - Z5jJjowMsn7FJlpx20/AkQMmm3hXUT8fW0i4qcfR2zR1wQeVyFJ92dOnNyQ6p2ciA5rqPQm84jyf - jdlhpN3en7Bj37x868lZZK7ZuzRi9fM6T82SQqGVL5osftGv3Yw6kCTOxHmEnvpY31+KLGsji3X9 - l+esXPsJevyGhKY0syJ+x/USCsujjpNqt0PLjaMi+p3qI06mW55PlNEauLA2g3Eo+Tof4g+Rmbxq - gh0xnt6ilKda5hrkYmfv7LwVqZUkP3xGIbvMfiEuU/YDKJf4SHEj3qrpEMya7OjPOz7mk7YutLJd - 2Fmii096NKxTfniWEDj6MbhWuPTYgRV91Cq/G3Y+6aSvp3y0gVx5nowyIf0S82UjX65pS1O/3vXz - jx8tMMoiwdYOG7oQUN2CxTyNWFu3dcWUHFLxUGevoF1mSV97aluwLoyA/WR+e0syXN6ywbAljvRf - Hs2Ke7D+va8isy+I73YiA49CNQiNL2UlcGboymLc+BSn1lzNX+uagHNyKc7WRKiW6PnM4DwFKz6s - H6mfjsanABp2P+qc0sXbnt+GaxlE1Phol36yWDaQx+XcYBwdun5ipHsM8TWbsPqI3/p8uglnmI8p - j42ZDzweUdNA+8ASqGGFOBqRrKVS5w8jPUjNCQ2P2QTIBialrnQl6MU1syR3Xy7YOhQPPXu2QyJr - jcLT2+f4WgUd3EHod3Cg7jO59fND2QfQnWqTFgrzXrnoKPIw3G8JTSp27tfPzLpoXHZPrGz+xzaM - 3qA0j1eMx32IlhdYPrBK6+ACSgatp3TfQGab6r/8J9zb7gx+X1s4se+lx56CSZNDw2uoaX7v3lxe - 3QDKx/WN9ZsmV6OkvyZZbssDzlbPibqlbRb0o3Clp+J8QrxtRSzsq5+NL1r4ixbxHTBAqbLS22af - czTbBdzb9U191xbQfF1RCYfh9KTWNeOjJSiGGDIcWdRn3lSf5x0DoEGpUbtR39Hvpo8h2A09UNtm - AE1NtjJyP/UqPhC1zee2jA24XZacWgJLqyk3eoBz8DUD9Ku2rturnMEXNCOYs2eYs67wDYGm1Rcr - jXnzBGzjAdbEf9HgQ9yeV4oPL+u8RsjEnjlUO6qUoj43Nq2Vs9Ck5JMtx4+aw7pcJIh7SideFjK3 - xgZ3evdjF74kBJqq4/wx2/pcKeIgl8mjJreYiVcOFU0sb/F3y19oHbRgfss36x4Tub74PbdebR9u - QXPBITV260SMF8hT2b2wE2VaLmAPFeCQr0zdwySihS3KWJ4jScfXTO1ywaF6A8Hje6cFcy3RHOpN - KH+ifYSTK7Y9dsun0oqVnJ6d/rRyxboq/+KjbaaStxiV38E3wTM9CAr2+CHRNBgfiUvdkhnQGgRf - DYVtYlBtjRQ0StY9Qfa11XFYvqx14VK2lt/OHWGzvtfemgZEEePlddpWc+n9tNZmLBuZaFL/Odkr - 93fe52k/UEdh+HWWo+gBc9nesKG0Rc7vlimRd4GFsWVqXj8fhTL88ycadxy/LgfcEPn84lvq6R+j - WrxFZkHMpxO1z2r9F08McBz4kbuOVm95FV2CtGvb47jr7Ir/powBrzVY8FFxtbxL/LkF8xj02FVu - ZjSrOl0g9K499hXmjRZpH7ry1RBosJNEoZ99KechsOMYHy/yqx+Tq+cDJQeBcB4OvSX58D4k7eFG - 9jtF9RZ0LibIFYnFSkC++b/zfRYjFyxaKOcrZ4Y2qAkVyDrdJ29opCcLxsvBgaQ1Ohq53/hAS1yz - WL+d/GqsyqKBXXy9/dl3tB4YboFz173pIW6tiIxDrsnb+6fK7pF7fP57FSi7TBENBPTVZ+yatfyX - j+OES9GWz1oY3rsvxXEZoCXE4wCGfpeJdJhS1HU7EcCh7z3WGNPRFyvKHrDe04a8xeHjtdVsFvAo - dGMbA7Wsc3L1AujaycW5nJbePOvdAo5XP6hlcT7qjlaUIBZNP4y3/DbvfzUDqhO2NFUXIZqdWX1I - 76aI6Z8/EN2KYzlIyJNqlkm95VjpLeyqtsS+Qlc0y5M+/ctHhZA4FXt+3c9AYTBpNlZJThVcEpD9 - R0fN+YjQGvUJD373c2nc3jt9+hhvRpaU/BGINGuicbPPve3AHAgee+wpnbeWk4d5pcl0Q/niT1oI - vVjtyLPukUc+lWODP9tXesuaAxLc/snC5i+BuMvqfELjMwCruo/UroOXt95UmkK/Yw5kZ1y0atri - AYCm69ge08Qju3N8hk+XPQNZlat+ijKOgVLKdlgpzwKaVuP3gAGrHg6G8owE1hXO6O/9BDY11xl3 - rxaW3lLwaZbe62pS30BmEVHq7NPAm09SlMDt3t8IQq87mn5fyUDBx+jw4b6g7R45BnBL3FNfgiEa - 0HGN5V86nal1UJA+jUfg0UVlU2p9CuMv32yDtM8pKS+9jFZxluw9st9XGlgjly8ss1tAQotKjfbu - 6pz2CkNI94q5jWSaq+H6KhaUvss9vbD3t7ekcAgglMWYnk6lU/35I5JP5rba+xL368PcJH7D5nF6 - S0VvEL9TA8IPt/jwOmI0Wakn/sMb2vEUV8trtwf0F3+zZib6rOrfBQI7ial7Ww7RX3xG4lvNgy7x - jZ7PrRsLu2C1tzGhQ79UFNfgpAkbcK1urff8xoRozg2bbvFOHxOeWlLID5imq/Lzlt25OKPTi58C - lEESLX/86yQe9jS5FNbaLUNWQvp+76klH1e0Wo4iyT/KXINFCq/5OFdB+4/vhez5grjkqvvIuphv - +pf/1tc5nOSHwe7ohudQvw/eJfDhjceB+txVk7aorqwRdqKRu1eribtaRH49zwmRKq+P6L2/8IBG - r6f+xzl6sxN+MuTqzwvWxMH0hFrMCzRUYoWT7XynxbMXiZWEEWOHNP38KO8WIuq1I/OxF9BQH2Ue - GeUjoeaF7zzqUK+GDS9g/R2uUT+YJxfp3seiurtX+yk8Cjyq9X1OA7tM1vmhzAE8K6xRhzSfdQmj - 20Oyny8Vm7vfN58ld2tBVjon4JzjDk1bfESC8Fmps+XXoXjFIbCf6B60/m2Olo82WjBeypAAc1VQ - n7bdJJV7YlHzx7f6mlLXQLlDCMZif/N+9tUfoKxZSiRwp3wau9GGWNnXNPjFYz4rs1vA59g61H9V - XT5XZdz8488BRp43i+uk/fFHan1/es4//alFu0t4ocagD2iZUtmStvwe7LoHRETwZEau22UJmCWK - dVb8ig1Ih0+FzTyDnKqfmfzFF7zpAREHfGXJF1AhkPm34wlp0CjQPuGJo09/QFTT3tY//nrY+PFA - apOF+GNp2FuuNlp9Rg6lMNwD1j887w1i7fPAJvtzsLcaB1FARonEKW+wFahL9C/fTadLhg9boXpS - xbZAE+/P9HEiQ0Wtz+xDWfMUG1k45tS4HBLEqYmEbU85enyLK19OJ98Llvy3i1aemRbZvnY6mXvj - hiYuWx7An+IF23Ia/9cfN74S9O7c6X/xBtK9ZtJDhUt9mYqWBWs4fQnNbHWdWZ9zweprEqDRWfVl - yw//9JUbVxV5V2iNJS9xw+LjM/n0a08VQz79KKGa9hyiFb3wgn56oAVrMag6VR0nhd0P/bCKogb9 - s5/ZjDis3U9LRU5DoqGb9YwJShlr5cFRJdjNJsWefykqck93BmzPS11ww4g/WnkMB/xy8B9fmB/l - xQJiVzSQFfedT4U6pfDwQcHJ73hZ+T3oA/zxz/z6eVfz0EgSQkqeUGW0wpw/DYkiT7cip8E+9vJF - aMkZHMIq+C5euopG3sKgTY8isuP8vGE+lRZC129OmM8z7xdoxwlt8YBM5atBM18FNvz77Fhxvuy4 - XoQN7xJW46R19uVDCuxTi/7xWT6b7AX51d2kiioFKxE/EoHfV0gIp+gdmvdjSMA0Dx3WcjGIFmE4 - a3ASzT1WbiXte/TCEwLXNYKdWLqoP6VzA8ePteDjjSz95Pcl/6dn/NNfaGzmrbTh+394i2j468u/ - sD+RSS77arlMTogQqnjqmWmjT4/5CMCY4Z3a5venr+tFS2RMvheqNU87Wu6pYKFKRxW1OF7xuCis - U/h5BwWbs3bs+c1e4NbHPU7+7P+P74RZu+C/+Mk9pRsLfvAdCTNd6mp1zlEId8EZqfowV4/gxFT+ - 4cHzXDBoQrMGMkcVjybcssuHrzCE0guZE7npHhctR+1bg9U3JGDvfKiTY+W1sPF56nCLl68/MWNg - rfopqKjRr8tqPl3kd72Llb/8GlG3gS0f48eHdNXKM+ICfT7/sJoO1KPMlzTgnGyK8eYvE2XcWs5Q - UpHx/e69MTjuE3Ac5kdW/Yfy9ch1GXhHX6eaU+o9J9zQGTg5S7Dpt3FERXYkaMMrVO9x5s2xPk5/ - /kt1+dT2y/nDk7/zpYaAz/qUzUGBlK2l9/A60nWir3MI5vQIsB7qybpenmkK9Q4EwpXtEq0aw5wl - g6dPqi8d6Yc9o9by63HNsI1YtRd6T3zAKWs7eiPNB41VrcRyeNNk6gsvYV0bqtQgnPUKO8LHr9jw - uGNB9osOa89IW1l6b0oIT6y44XW1F8rrToShGO401dQmGr+uboDaaAw9yntYV+n7tWT9tjv92X+0 - 6FYRg983FnXHxYw40w0amCitqXrshbWfa/aNvksUkC7cbm0ehqcG2/Ng/dMf1pkdTwHUkf+m5/P+ - 0Auc8QTo+OONdD6z3f+ePUC1IPCkv1TbYh3rksibnhxAWlfb4ksxhuf8OVInra/9pk9Ncp+vP5L6 - a1DN0RgTNGkyxtbQutFqtsMkvWG8/+lp1XD8yhbiGfMQoKJ2+sX+VQUyXh4m0jPZV8OXFQfojW9N - uHen9dxfPpbPCiLjrTKitbeNAaRE7HBgOpW+vJ53CTZ7DsppWPU1rnfvPz6H1eI8r1Q8PSy04WF8 - zsfSm+PD7Y0EvU8pdvbxymv4G4B4TC1qtMIh7+1UakB3xSM+bnrT+qKO8Q9PxQ6/eIMUZx3IdPBp - EoRqPl1VUUJ/eqHrpIPObXoDMt8TbPoT1ocTiSR4lGKBb4Ur5Yv+6wE0Nlep+ttaKC6KFMC3/sKm - B+y9KRbIGaShkKhZtks+lfUhAX+vX4L9PiXeOvU5C9ahi+jx7hx0/p3oCtJP3pVq4eDof3wHvWkY - 4HzLD12fyo3k/ZhbADNJ+rktCwtVp4YjI//+6VN/pxosHSdv8cyrShflCpI1ylKf2BFay4sRgxER - hQbq81mRoEwNSGOb4HN9T/VWyUUbbe8Ph+fY9ag8zx2MB9uk6Xd3WoeFpCnwDdLI7vn6oLV9qwB/ - 9YmjTIJKODDyBI7se+QT6zZizUx4oMJLcup8szKaDqe6huguGjSRNbOfX2PkIzZB5+AsGDMqvfnA - AHw/EVaZvdMvSjGy0F65Bw4ujqrPqes38H2qPdZNS4mE4nw6oz/8daSJ3gt/fEx3pSO1t/rCIn4W - gm6HNMTXhtPQfI6eDOwlN6YBI9773/uQKnBcFgOb0jbFMTu9GzCiQaF2z+rV+sz7TjwfRR9HuXFc - Z7TjAIaDfcR/8f5HpDABmTGuGz9uIqLyXQNc2Kl/eNAbxzKvAdIS43t4IdW/es1g4VMwxEyJKLlo - rNzlhkh1Wzjl83nd1KqDc6dpnrXRIn6kAX5UzbHy2Ef93I9ZIDWD4uPnpscudCc+4J7lVyLuPks1 - u8eZ/+N/2Hrxb30Ut6kum16GvZtz0f/pOwk9PahXun7OFoxfg1Rf90G5kG0qCzMtQNiPT//0c55f - 9jFq0byn3ncKEPnTe7d8EsyvnkOrpr0NucTnR9A5lRRt/KkD21E5quwlex22+ghornSh+vcn5Gt3 - 0RO45jGif/GUbPn/T5/HOSf3/ToiS5ND7TVTLf89o3/69XBwj4HcI+rR9pH4MPRGS6Abn/n8/n0f - f/U2rEaPvbeSi8bD3L6uVA+edUQ3vR2a+43BFuFsxF+ilIFoqW74WEqpvlYqydAs8A9sqNq+7yVb - 96F/hzx9/EwmHzZ/Qg/B+2B9uoe6wNk6gUhqM5xeP3y1Vm+tkJkzHQhrbVMtNv4B63Dv6HPDF6O8 - 7GLE1k1NPerp+aCyMEg5acyAH9oun9bDXUP+e/JxMVyWimz1GvSyjRM2L7zrCdozS8E0zY4sVPQj - YiWW9hfvg3njr8u8zLwcw/nwL19NwaPzEXkqiHob/17IZx3QpbkP1HPnzqP2xWKRzMkJmaPHXv/T - t/bRzJdU2fDblH8URdr0fnwMwM0FQbwVQD6lHfzxu0kjl0ze9CN8+xzVld34tTRg3Qv+6pPryhJ/ - b+hPGTvVVKDpxi6B/Iiwi41X/0KzL+MUeO50JONFVite5d81VF/HpM+29NcJjVcfmfuzTSTpGqBV - weUgf9e9TgP7YldCmU6WzAhfnQYprdDiyrb4r16Ij/EvWrvTswb9bET47/cEc0jfEHJrhINHxG/R - RTFgF8x2IJnytVp8X+r++B1hW+Eb/dnDHz7C6t6yoyV6XlMoWO6AjQ1P8ScnSMHN+oxaO4XzxmbM - hj+8tekHbL8kvx9AJXcnjLX3y1vf3vz4V491+dnXl9+Ql7DhbXzZ4s3Gd41/es++m2N93uwFShw+ - yMJZWj4Zr2RC3fnM4D+9beNHARxGP8ch+zLWdauHoOv5pWIcn3Zoq1ekIL6ZK9XEe6Lz2uSdIUiG - Jz1teGKh7wPAnM8qvUEdVavafkLY+DXWEnPUl5O/uHusCgpVdmPnbXoWKx8ebIivx6JE06lvE7gd - sjB4x2dvnf/qEcsxJWS31VPeX65JZZmxrtgqbvdo6u9fBRLvqv+d/9rRnVhsLZYmPSY2XacpkwBE - sfwG8zdq114+XWpYxAoHkpP6Hpd2bSv9tMjarlw90RY/Ykh/hAlQ6Q454RJVk5veDGmq7zi03vs7 - C53gXohcltI6cZlUwPIOLXor3Cyfz3Hoyrzel1S//vY5qeihQQi9eGzbTIGWq0ZqSH2mJf3AGGgO - wlqCGF09+u95jvYp2P8/LQXC/24pOCaCRDVRZKsVznYmpVErkLhenGjB9iMAItwP1IHbGK1TYp5l - Zlq5ADl7rppvy5MHX0t1ar3cO1r0umvBFsMthO6siH+tfIi8HevicC2vFfd6b112Tzei+u4donlJ - JkM+5f6AHWHwvIVZiwJ68g6C/beuvIUaaQHD95PgoutEb0XJmEA6tgea73+axy2UVeR7bqnUe7kY - kbK2eZhuaYgzt9t55La3F3A/SUayy3qKVuNbaXL3GnhsHF9U7ydZPUOz74AAMptoQejFyHkqtWTY - CyRfMrqE8J0bjp5L3a3I7uRqUCXDivXs90bj7JcNCObrTUA/cWhS6wMLR3I6b7tBol64H05E3mWz - SVPClSvxrDiAJPcf2CMlXtdt6QXkMy2xW15//fg8dg+4rumXXlpzCzEzZ8vi++PRvFUQmkrxqCBy - vlyxVb5261TwuiZPpFRoVkDVUyHuEyQOiUfKLsh7VjjWgSz3Xr3t4mG9WSlfZ+AgcLHlM6eVfXe3 - Gn71/o4tp+rRGDdBjLqmTrB7Fy45b9k7F6qJGNh2fkM1q900ycE9jXBiSEfEnqPwLU/ZTEl6lR4R - bdmIyGIsrrS4xanHl7XCyu6qLcHOF54eNxGkydaOT4lk9r98HV2YwDoqIT1kvpWz5FXZsJ0nPl6l - MOJ26c0FuxBVfL/MVs+6FRHhdhSv9CyKcbVcqnaCj/pgqXrmLoj1vZ6B92OSsBKz154YRLLBOZ81 - WrxS8Ig3poEEcWbSYA5HfcF24suBy+b0/PpK0eQ/HQDvkb/wZr857+KOIKx+EXWmr+zR7HK0oVCa - EzWsXvf41pQyObtiipUanhUXnWgMdxSf6FV+GRXbxmErP46iE7BdctMXZZhcML3LFd9o+/V4yRDf - wD17lR64u9JzSeBlcJjiPY32vYRm9ZpNcLmwD6zszt91klIgcOPlJ71mvhUtYuMw8FA/J3zQ+ftm - TwcNfgpjYbdytl0u39dZ5trnntgKdNVUbrcw+ufvTjhTu+Xrrwgn+WLbFs6iRo/4+7de5KceuvQs - NGnEPShI4DG/GVvbtsMxt3gRiIuYgMFEQevbjQlcgptJ84jF1TS9BxvIEH+pJRSax1XjDaTmjWts - V/pUzQaiPqDz6JNPlb56liNcB6TUKdWVRlmnZohqeDovB6ffIehZ+RJO4GVDhkPsV/1yKrgADDxc - sXtVf9E8vH9vuD5CnjrTyYwmGKNBlox3TOpGUXMuznle1tC9Cepk9Tzucr2J0mfvFvQgqFM00/XJ - wPM337CXi1G1PNtx2Vqiztg7yvttsCNTglXuWnqwj3YunKCzwHrkFLtufIi4QrsBHELrh037vsuX - 9WJJoEUrQ93R8Hp+79wfoDiMhXVOg3WB+AN//kTdvDtE9HE92FAGe4fQPBf0IVkVTS5Ouwn7R3/y - loCxOvlYch7OAtnMZ+mj1+iacA9qZtanF2rvTqBXZYv++bMwHVIelv37tMVPXedGwS7Bs903Pi9a - W9W341ED2E0sTgLui5Z7OsVAB+FAgzSvPKFZz4MsDrFH00qf+pk3Y0vWt8GP3uFw7NfTpTFgev4w - zrKCRpPY34hkdoGNdQXSnosbK0GvR5hhv767OVlwC/Bx9Zxa0eODFkLnVBbqLKI+1htvSun1IVv8 - IQ1AeRJvjfO0gzwVW3o+BGXEx7fbGQb+cfjnb+vw3pH9M4Q3DU+3UyWwKQ6F+2O26dXnrXwOx4wF - fdEUfJS6uOK+mqRBVpg4EMLTEE0dudegrGoQcFEz5xO2FU0+b4OLpco5Rdw3CTXwx2XB3vC7Ie75 - sou/86HxZk/E0NLgz75xhqO7Pqe8kspHtOuwq2peNO2S0wLJ6fmg+CYccp42nYREvGpYP0bauoqP - hZfNT9zSS4xp/+efkN8nm14hGir6PHf+v/y3g29frZL8i+Fi7Uaqv0cLzQq9DyA9uzV47+R39SOj - pMAgCA09fFnJW5/ndyAfA10LpKa+5gIzHLdBkZ1KBIj8iv26jg3HhJOoxV5Zb238ZEDvw+VN3f22 - e1utfi3s67OBPf/2Q6sFrgJlUy9UIS8VCSqd3nIgdxVWNn+cfzpSwA+NHJ8U/rrOGqeE8nHNdWzu - cx1xtuid4SyeLzhohnidJmzZUO7tDj+qE1kXB3mtfLFdizAnFKy8pAQAzHZr6myhzFs8qwjgoGUn - 6uXi2s+iUNZysWY3HGb9seLtq+jDRy1YXPysNvqXf7f8Sp++sPOW7kJZsI5aiFNXqHuhkF0bjKBi - 6E1vGjTpcprKZaxIOGalOeqdTKzRX/y92WKl84y1lCBEWAr22AwRv+ED4JxUo4qiz/3EioYL5BXY - 1Hfmzuva1ufhPjxLauhNvG7xLoCj472xrrlTP8u6ZEOon9lA5PcGYr/4a8CSmi1OrcDqBY1LCnka - r19qZ7uhWow8tuWTvsbUf2lG1B0zFIA3423wr6wi9iH4GigKtvDBPrb5bN7iSU7KBNNIgbSa+M/X - le8mc9puVVTR0t7CVB4RfyDCPmCihcvmQt7dEznoNzxHjDx2JV7gXBxa4S4ixf0ewmK4J9Iq0PVz - MNxYFLh8/i/+DsZPyiDkDcDaISjzRVICBgy52wUTdz0ilhpMB8on4cnqm91Kntsgt0RfrUCU6YRq - aScOYDTHL/UbNfPW5LWrIW7oHWv0HUfNY2ZD+Sl2Lj5i3s/Zjlxq2NehgQ2rr/R1v90i8RTqEpFw - CvqLD8hOtVfwlV9GP9Ks0JB+mJ44uBmqzr7W0oIwyCPqmr0TzSn2M7ntfhA8Ju3isQJb+jDWGQ3E - 70CqNY0NW86Mn0CTrxqhFaOXIQeU8cgcD7633PbKJB/e7Qsfh9t7JZV9TySJ7nXssBcn6i9nzd1v - 9hlIafTpF/1ktCAZZfzP/qeQmR8yWScFh1CW3rT9P3n6Wu1mbxMi93RK/uyZyHbe9EsZdjy8LE6j - qamQalpjcQCbnEqqx8uwDim9FrCjEkePtZZWaxe5Z7TiNSTThgenwvkZEMntBT8wUVZ2vl064INF - o6Y9farf2Vcz2VNGlxqrnnsT7ZcO9n6d02w0+mo9SqUl//mzn71Db9LaOUXHVIuC9WHY6zqf2hiI - u2fozXmpHvvaRjsKbq4FzPIwPXodsAHSORjoYYvXfLh/DfJQiZiqqJi8ecD6W+KLR0b+8OLcBVoJ - qWrtguSdXtCYSGMC6BMZ2P0lu3z0m28IJaO4pNa0jzeR6dqgbu8G2MviCs3yNXRBD/QHtgteQ+y7 - O9XwYuetZcLYV0NjPhuUkd8BF8qT6DM7yQo8a7Snf/mctLc0g7IvzgEvmmW+Yvde/+FJrHuFX43C - DRnw6aKebvkuYtma2GCN55IqH2yufJedMnj440StvpsRGc+eKG3+TYY/PpO1vQ3asWRoAT8+nwUw - 39DfsgdVqWzpgtZ0Cjx560yep1LWB08tHiiulIRmC3r1q1Zkb+CQOlPL1xV9wuwLgC+KjLC87eTc - 7r4zwJHbGcd54Wy7Nj8acEfTwcXRD73lnooxoM6usWdou2pNIt1GHzYEmtcfbp3YunGhf+OEiHzD - VDOHjDdMtyzEWhOK+dw2xwLdbpqAPTMU+kl/diI8ED//+54YeWHD5j+BUDBBtITnD4Frvwu2kt/s - rXTZL2A0+Lv5W9D/3kPIyNfHmacn4yQj4t22wbbfUqV3dn1U85af5W5vB4QVEjWaSeiXcN+pKT7z - vdVz6bvLUH80PxTnueANqueJcED3lJ4e8Q6RkRlZgIIZ8HFv1etyeeyCP/xMoyyBagr8mpGfF17D - GDFjNFvVcAY7py1VjAFXywO8Cb2evz3ZXxCgKbvGm30aO3x7jaNOTIbW0oa/sLaTtV4ofoMLJ7tl - g6mGZ9+nDnQwHc0rdV7j6M3KfvT/+HHwNW1Jp6R9n6Hb3Y5UmeGAhNxKB2nDb9RgpVM0na7yA+T0 - O9CgZ8J1xehnwZ/9bni1nxuxe4BybR/YvkpMPr/ThZH22Xml6unU6FNmtxZ6t4tH/eFFonWcpwX0 - rv5SQ0he0Xw+XHl0kZjgz568qfyFDezD14o1EfN9v7JoAttNnICxFRato5AMIKlMEciMKfWzcFsN - KNb0RpXf594vxOYyePOPijp606z12M2dvOV/enZ/Vj4L7fcN03j50mPdJ/16tuZW2vQD7D89otMN - b8ssG543/OZHfDJOITAEQyA/9Xc/2bwuIVWhDbaq5riyEV8xcs+uAWHbI845f+In0FuF4qfOfnRi - 7p0OTsd3jM1zus+XWfmKsOElrBuqtdL8GbdQ7H8pNX2v75d9sT7+8TPvGOf91KmolQ7H94SVLT8u - 4+cnwaSCSPVJP3jT4SwWyDF3DvX04VuRFwljdGaqOKgjUqCZQ/57D2YVb/93RMt0j1OZNo0dcKk4 - Vqt1CTRwb+or+JQwV5R7P7YtH+FE5Js/9DT0khoC1cyx9vDZfNSkxkdHJHebvR7zpWJ3IRRx1eLD - 5m/9rz8GsBOSgAbXpV6nWbkR9LHimJ5sKUfzNT1L8sLDDYdyPqMp05kSHAo3asm/UJ++rur+5Uts - DPwYtaDME4CbscEaO8G/fAvGyRSwebTu+Zx7ZYHKVMzotfJAXz/wCkB1i4YI3L2sloFMqex6F4fi - vtrp68HblTBUEsZB9HxUS2LBGYqSf5Flw3//9IjCGyYcRuGh4hFWFPmPD8fjvNO3fGzBvjJ7irNl - 7y0j82Hl6dljwi9rrU9u1UggIb3COnfR9YVIiog2PvmnZ+Xjxg8hZm8c1V5by+sfft0ZpUVNbrz2 - U5PKNWz8P2Ck99YC1ZkAKD+tf/F8JYFYDrIRvBhssouJhNLJCVT8TqfHUT5XSy45C2rlI4uV022u - hol3XZS0Io/xhu+33ccaUp9NggOehfU3Vu8SGJpe8KF6aPlsKiErP4h7w56weOtvwN4bafbvgfWQ - tGhGOWeAs+5n6mz5doqVtZSXX6hST8w7j3p2XsPGJ/+er5p9I2b/6XfVZu+L2KiMTNZF+W98+ePj - j7x5Ue30POecPl8HwLG3bi3kOzQZPylFZvm08cbn1qUM3zzo9HHAz+08p4f7NmBMhivWuDuppjyw - FJhsLJBFO4M3gPUcEMP9LthKwiMSIrky5HmRRfrHpyecQPEPz5uHhxNNvF4M8McnnCefrPPd/xV/ - +Z+6RzpG469gGGBjTQu+XUyi+aH/BwAA//+kXUnTsjAS/kEeZE9yZJdNgoCIN0BEQUS2APn1U7zf - HOc2R6vQwtDdz9JJEx6gl/sS9l3j/eeXqZCy9gU/a1uLqJ0JLTTCbZkfH9Gvab5JPXTM5w+fNduJ - 9i3OMno2oojtne9/VFYO/+H9KVnlnLsG6gJlqvnEbLDn8uVBc6BsmC0+2/uUvfeYHsDrOYj+ccfr - BjVnD2aqcSTKjmcL16cjvPq5MQtpXdTdRb8V8Mc/rj740InSZ2t5kIiWjPX4BjRSDa0FoZMyxHQz - hXJ0Y1KoRuuBuKfTNKwXZRXQ77ehP78s53a+iLjAfRP/ftxA18lbh5yqkIgpypu2jIzlwHg/0hKU - 5FIzDu8JsPsFD3wb3mbEeE8FQtbOVJ+J0o+73A9BhdLT7UDsxNfc1bouDKSebpOCwlijymAekC2s - +5bogxxtov0oYA24mvx7Xvx0MdHul5ISLC93z0cBGLInEHPnU1vQuvM/PaM7pq0Rr9ha2PUDJCfc - +S4jsi8P7f7BzMSKFq3X20UAudWnM+OYtssepLpF+Cvp87s7zfXI9ele35KYqLq95Wsn1gV6HzXH - p6oTDJMhpxycnH1LbJmF9bJwqgWvnD6TG+jniFRHz/nnn/C7vzYN6xUC9fw+zJNm9xHNoOJIev0j - ZPdvwT7VrIeRw52JrFx+dR/68van77Dps19K73sLe/b02wz39WGHNFPh7n/P7I4/FX1a/j9+qpW6 - BfZ856B1Wi6keLq+xrBqNP/LLyw/6mj7wYGDlaP8sDZ7CyDaIlZwmd8yUdWw0MbzFZTwMxQ6eYRJ - NayTwcoQMv11Lllx0Jgw5QrIb+ZA1HQ/lToy8s7vzgl++gbvrnFrxjAB5TyLy/ehEaefVXhQ2TP2 - t/LjTvb+IjQzG7uZovvXXX6u40B03wysPjMv5zQTWTBNqxvZ+Ue0nAYmA+nUn8hpYd41fZwuI2AV - 2OPr9fEd5po5huDDhNBH9VjVtNnuM8huZ0LMJJjo+nxZJQy6tMH+/RjScfd/we/lJf/0BRW2gIEp - BSfi27xDdzwWwJ8/pyUgqTdqZgXoAHPFdvVk3VmdRAcQdjiS82V6D9ufP/X3fdXagpx+Ue/9/X9i - 9sWdcsApC0gr5YUth9+30B7JDIIrN87H76ZEVBn8g/Qw4IUku3++RI9Khs1vdedt92e2B3AWwH/W - jdiieHeXQ3YY//wa7KWHKtrc++ajmxCc/n6/ZnjCdbD4nqpZWp6XYZNB0/75UT47bI+IHu1NAn+D - /2/pdclpGnsOTI3Dk5zKd/nnn3Xw14AHti8fQ+OfreUDUyYp0dYvR0nCPlp4Yv07xkz9rZc/f1Ux - vqZPrb6he38llggTVuRPz44fW3bQ9cqVRE7fNVj9FDCQvOYOq74+RhtWlwCRE7n5aE2Fmnyukwed - cHvPKDsd8rnwOw7Kw5clmC3FaNv7ATD0ZWZe9t/b/tazNTkGY7a8R398BLZl6BHP7Spt8b3xAH90 - 8+c//2UV58wE3rl64+An1HStbiKEu99ItH51AMvcVh3oj5RgGVayxnzY3vz7PsFlZUScna4eFFhX - m0U19QHxo8YUd38Ru2aJh02UKoiU6FLi86rKlLtuVx964OHMYn+0NZoC2YIN/6iJvr9YbG7EioMZ - vD19MZrn3d9YY8CeTzb29qEzf/GPzr98m9GOLwtqzv6f/03kmb8P65mtYlh8jQrjuZtyWu7z7f75 - O9hdBxKUTAubuRuIPnLn6PM8v0v4x3e9+KrmnO2/ZsS3B3fvH1T1OqShClXW++JzdAnzxVWKApjP - 1iIKozj5+uaq4k8f4utif+ly++ADGCExsYxZpWYu+rOApuUs/rACFqyS8Gph5J1Hkj48aVhH7Fbw - MDIPYurSRGeWsimsdQsQB0fIpcphPIBY3Uy86+th7J8vB34lt/CXY/gF6zuVIOTWxvVFt3fASiX3 - DZUj085bpfVDw1aKD6WzCch5VStKD5GdgTvgjsQiSziQ/th4Ykfq4l+8LokCK3G/f79YaR8tj0ho - IajyZuYVnXPnoakEGFYc2vuLg7vs+Ib25/P3/Cl/iJQMmeWd4Kv1OEaUnvkNivVp+Nff6IfnMYFj - UXLY3/2h5eZ5Kkxyv8TnMJEH5nylxT//X34ie6h8tBSwnK07yaTjp96kNSsAe9Mzothn1V3PbzVG - 7/ERzrverTmiByXKuc8+5c0W8umiiAJkVP7nl66hUjpDOUDOrVNxcWF9jXkntEXVUIZ//GpYLcEN - YDP3A/GUqdW2nj4SuPW/yucU9lmPhf0yER6+Lj6VrB7RIug8sOtt7E3EjHjnoMtoAsyJPKspijbu - I0P0rYUH0RqJAat3kxi4+7P4dK+tnHudiliq8zbCjve6RtOgMzIUvuWAz/DlgCkU6hhF9o+Z0fMQ - 59sP1gw0pCIlchR8h9mnvAV2fwTLjoiiPj2xAczqqsO7HqEk6s86yH70i1VrW3LKRn0ML9oaE/0d - Xv/Vsz88JY99JM6f3wL29SHKTGbtH99uh0ODFWj4Ef32ug9vnxkTC7zfoD9n1EOXTtRxepmWenzG - YgsXc5+afQEzpf/8bbLn05fJtE1YLgXa/bT5oMGlXrLXKMNhgAkOvXMSbS70OvDnz592/sTV5BJD - aLxi7Dj9010dwi5SGvU8dtLIGPitbwJp94exGz/OYH2e+wI6B/eCz9mpjGhlRzNKx8dMcq6hYP7j - UzqB33/Pl5rdKEPw4Id59xPq/g8/xKCmZF/PfHKL2wblh/gg9l7fWNjqDTyp6YUo6Fe6y5/fp3Zg - Jl40NtratUb5f734QPjfWwqOHV6IaQs9XR5Yhgjk8IJVx44imljRATYk6Ob3O5Ipp1baGz0ZS8YJ - H7h0NQYuQ95hPhHZrl7ufFFiiBgSjtiONlVjOfVeQf471fj0sAuNc7cohcW85MSaRcvlMzyMALNW - iD3l1bl00YgMuu+bzMyZ1MM0Co8ELCedw0U5kmEbhUcL4znwiXPmv2ALDBJD/kpPxATPKVp4VQ6R - WykzPp2KV75wzkdFQluesV57ac2zh7RFQw7P5FItfk5+3StBPNt/sTrURdT8Xb/+OMFfGVgDdhxl - D4ruA5D8VKhRJ+4UkpWdGhssOtAltlgfyjez92Nqj8Na34UYVl/1jXFqNS5LjgcTkvYsk9h4mS59 - +MIMf/n961OWBfXv1X4LIE32DZ+K9FQv7jZI8NKLCblVwjZQTrVllGX+g4R+J+Xr3/XdtyIEa/uu - qixiE2SG8Ecu3by/y3SuQzirZTdP1ucJGEalIXow34U4SRvn001XTNRyQMTnCr+j7abbMRye2hOf - 1utl4KTrp4f3PkbY+D0BoKev+oZv9CvIOVAtQIzvWMF3NvbYKeMW7PFyQP1oIaLTH19v4JKHMF+Q - 8G99mVcZFagydBPfGaDslu6zhWJ/NbDjpxygKN4EmBVnm1y7UgFLgS8QBZJSYcxqrcZdrWaGsFxq - XBzb88Be5d8IoqJ6k9Soam19t0kB6TH3/FVPQL6qhSJB4+JMs/SO3vQTwy6AvvYyyRWBBCxmfUkh - OV05YhH7XNMy/OlIy98WCU3nFo3o6joALYWM7wjcAJvI6QhF5/HxRbZYQGvzpwXGONdnMaAwXxa+ - N1Gj3FnslV5T85t2lqHjBkdSHJNOW2NZGuHRCA9YQ5TWC+8UMtjcNcbP9XN3uXGUC6j0ICQ4cDxt - K7BSIBfBleSB42prEeUt8C+lQwzkvPdByIIEJdvDJBxqmP+LPzo/TWwC/AUjbyo9uK7yBZfz2oLF - HCYHpvcfP9MgD+vV6X8p4JTDc26G4VlPcvqu0IY+Ad5/L6LjaM1I6cWQhJlRRwzV9BAqGXpjvSz2 - XaqCl4CXdTjMS5trLn8IqjdSs/lNjK58gSVAowqeelARvUS8u6aey0hznc64uKJa28QzFdBiu9vc - 0Hvost3k+XAKh5T81Yv1y94O4NDcLZyVupKz5jMIIHhqNdbNUKqnUy33EHO+MKNY0Sl3TGsVMduF - YDPjBEBmnjLo+tZ5n9cenx2i4xnFZVISFSxXwPssVcH6YwSiFb/AHVmHU5HU6wbOPzeppvBehnCO - igN+rJ4X0UOBOph50Z3I4TNymYvyKcGZfhNslrGxD5JXLZi27ys2FqgD7ioHB1jqdUVObEPd7aLE - BzBtlUDOD0WiG+vPEvyLF0VLPi612vW9Qx4h7sqieuzH1wFNcj9ila+CnAbsRUWHMC1wkhlNNNkz - XaDcn0binop3zliD5iBe3QzyV99Yn93+PQ//KPtQ255J78H9eeOnGJ4GLobdGyrCYuK86Hx3q3ts - QnvMWSL7wWVg7BlU8MOeEMHeeHTp46aX6A1bDisHN8+3YzrIUn3+ePjSkD5f5ebYQ+4iYWzE7xWs - WuP1sDjMLpGN1RlWuRgEKHcneRaMSnO5KssaeH72Z2K29TdfP5+hhG6lzdj1CmHYpMsYQu/JISLD - 053yM+lMGHzVEntt/x0WwdczSPSgIQ59JRHPONwCnN6ySRpiPEzt62LC7lpp/lc/JxqTWqqPzJek - YZ/KVrQx/lGAx6s/zxsf9IDqeZUhIf3cfQEImcYfq1aFOz7g6++Z/9WrRjhE6UbiFmB3Yc2hA6L7 - BNgCJ6NexTPooNV9z//uv1ALpYdGZZ5m6fxVc+YKKxk9v9Eb67xl1Stmf28IH9uZGBq3an/1GIb5 - CMmVRSXdukkvId/0F5/ZBE3jquTuwU9gYuKm93Ggn9elgarUqkTNTZRPvOMGYHo1GzEhPrjrM3kd - 0J7v/nFh9IEp8B1CuFHoi3F4jrgQqRW650/qS0niA/rKFA4e2Jvvj8B8D+sjPyfSQ5l8v7Er1f3D - P3TQXx3xwHcdtlnGGTQC5+azP8zlfSQsFrrF2w/Ld1Rp5PObYrhIWjdPp7hymXdb+tC2iglH2iGK - aCBmKZibu46LFqzaNsun9A+v/Y1RjjW9WQ6E17fJk/NVLcFW5EkL50umznBwwmGdeZqA9iK5OEzM - U8Qk3tTA9DS42BwiHozG86XCi30MiJpdLcBtQIHocpNinM3rBzBUWkpoJe2LJKVe5TxWeA/dpWDC - 2Y6Pq9unGczxaSBKqiiUyfEbonsXX7Db8HhY8qhJoBkefv7Gy0bOvUsHwrfUOvOxEOK6N79RAPh2 - OhO7m61oG8ZLidhNJdjZ44F1pKMA9WeyYCVWDbBpT+ktXlf1gvP0F7q8VkMLMXJek5MLx4iqlbYg - NR+Fvb4qYJAu4xu+7GOMlQV9wbbnE9itWJyQ7QAIvGoCGi/sRtLzfmxLbr4OOl0XQGwx9+jmbGMK - f/A6+RTV/j5l56BDMb8PfkOyRNtSTUjAzMQMDrISa/0zrBik5Edhnv2Tn88TERio5rOALZLx2pK5 - rwZwCnzi5Px95UuoFCUsNspgNTPqnKYn8QDjox8TlZeNiPC+4Uh35/Eg7k1U8i0SBAcMvnEnVjv0 - w8aaowW9ADT43Mx2NAVsUKHVPqYz4wfhwMrNsYNYC00im++ntonXQwe3rC+xsZ7nfC1DsYVpVyvY - 1R5mtDaPagZwITJRitdIVyoJM0zb6koS4/fLidIkDlLvo+cDNvaHf3ifbMwZm0ey0YU16w591HNO - 7uuDAaM7vytY468wS8eZAOrMdQBZ9fwkuvFqtXUQ5hgswlHFSlVZYGl+qAHU3VtYmH5cas9MIOnn - LMaq6fDRuh5jExzUYsRX1utzqhZOBtsQrth7u9VA7bZ6o3w6sD4z/5SBKfKvCT8v/UOee31fCYCj - xKmQJfeHcqfrIZZSWH3lN7m/ApBvsfw6QD6eX0TzvIvLfx6pB68v84VVEm50hfsW2YdCfKLFzs9d - s1NVoqpThX/1f2xeEgeF20cjcjtY9VJEUYJW5zhjpQjq/PcMuwbiOCjIRTevdDx9wwUliXIkpZEq - gE2Crw9bTVF86ejrdOVNRUBW+nuQa7e/GMjtAwsmIZyIPZ1JTptX34JrZbpET3xnWB+R0/zxOeyy - jFj/irxMYF5qxs6vdXeTi8aC/rXeZhoPgktRsFawkrQG63xHh/Uz8fvpBJ9g2V7MgY9k4ANRClhi - 1jEAX72uLZiaHYsLPg20f3rCPRxPxL+yMuCrLOPA/tlvM0PPx2eJCqg/En0+ZFeXUuEMHbDjNTGi - TdG2yDjH8BPoeF4Dzaas4OudaGiR6LckpIDUD8mDCuDTWRrqMl964aiDv/WLmtt7oNV7bv/0AVEB - Nikfw2oDzdFPsT6LX238wz9D8RAJSpRon+909SF3ETCJvGI/ZfJhDqCss55oceYPbAyrANExdfCl - qjo6YYWbYbP4BDt8MFKCArGCXIRc7IDnHO34VAFtLHyShF8+Wm9B4ot/fEYHN0fjOafYoA2uGD9H - 1ho4cIlCcK7aGHscr7hMBFcZmapa7/j4GcZpsWN4xuMD3+NNyBd4ljN4MsWV6Lte2urHevgXX4al - z3Q1vo2KpuXtkdvPXNxh5gED//Tp+aFkdNM6P4S/LL/M34MmaeR+Oxbwjy+GAJtgX68F7vhCzLJx - czbWoQ5NTRbJRU9ANKaW6iHt8Waw7FzYenmXTgkLXwAkEjVN45vHy0f60Q981JQanSfG9aHcG+PM - CqCi23lzUhh69O1zLfUADe0bBOZbumOfX1KwBso0AljRwG/5as1pqi0t4vVlJcbkhzkJhSVD60kq - yOn3fbqbz24L+sNT4Vs8NarlYga2fNJnppzWYaMHKQGtoU1EP/JXbVMr9w23e2cRrbBef/Wvlx7P - Vtm3YAn5XzxCOhULVjOOB1s7PQrQRuhEfLC/xO8RqQy89CCZj+kv0rZ+7EswrvRNFPl2jMgCIl9y - g9OBuJ6LtU3wPQEeW6zjcxDO0Srd+QXufAO7cbYMC7ooC0oipM3rD7M5fZeah5JaoAQH2TrQ5jMc - oHjKImKVXj2Mryw0UfKWHnuLQR2YyPY9aYW8NE9WnOTrMehUGNztHivrXES9ORQ9OrxjbwaVQOvx - ndkm0Iqq9IXsULhbmQBP0sirwpFeHuqtvi8tipbH4P+KygGc/mWDf/f7+JUiHYucmND/PN74WQjM - X33w4JUxaqy+mz6nsT408I6uR5/d9e9cZWGL2lriicbGY71d5RRC6Xzhd3wIKfunpxGtQ59HquzO - n9+kw77XFXLxYhrt/DIFl9/+oiPZ5ED/Lp0DOFdNjG125PIxYF8Beo+D6X9G1NHVV08zFNPkTvb/ - n28YoQoqNyHAptJP0bjxsglNVsx98YNvUXc+3GRJtLLfzHvjQ9tsHi8wfl1f2FedLFqVZu7Bs/EC - v31HL/BLAuJJwD89iMs2sbsaz1eIOBnaOH+lh4Ee4p8A3xMZiStGfb09bp4PL19Ox/5XY6INnO8C - xKf8gI30YkVUvQkqdA/o5Etv5kKHsuR8OCtFSKITIvusACaDYhfa2A7UEx0WmhZw+aqtz9UxoIva - XFOkK0+ElYY4Eem7IJYukN/mra3bfNuOWgKuoX3CsbG6NW+9BQvqc+DgexCt0Xp4nd7Sl+1q4tO1 - 2I90tCl6vNqZKEGEo/XXiRzk+SGaAXqP2kYlAUJVatS/egI6Ki2FuPPNGez677OvDxDMZiIqVX75 - gi728k/feGXhuJSqUP7HVzxV5bUFPUoBug91wIo4xjkXCYuD9CXgibbH47zyVgu3w4/DapJ4gKzA - 8cWk3gdhfYuHRntmKqVw6Ed8EiN7IPcbX0h/foX2SYthtQY3hTs/nA9DyIJVCE0VrA6a8Sn9rTs+ - bwxkL+ZvPhxLN19znHnII8tEoimJhxXc50XE36gkZ0y/2lpg24O7Psba+Km0RU77EOKP9ybqoK3R - jzm8TFgfnynRTpMNGMFxZXgz+Q/2fLwMy7iEifjuuwxfA60H3ZnTQpi8pBN+ovek0U07q7DOC/nP - L9D29Wdg+c5iv08MI/+XP/rlPmGZF6G7jsIjhsrpUBFL2YJ6SryJAXs9wd6uD+hnake4HQYOn5Aq - a5zdViFsN+5CnK9GI6KNJwnONCn946o72r/7XToZ4hyF7bAR+vLRe/yZOLG7z64/wgzx6mJg/WjK - Ndsz6ACPV2/G3rGd6rUM1wb94ekf/6OxxR7g52V+fFr8Fnd7+EsJFsstyOmgSS4NkbrAg1Im2P75 - Ud59P7YPSy4fsdlvFljepVqg3ynD8/L1+GGNbLOEAAkTUcS8p9RqxeqPvxJbdE/Rrt8r6ECLxefP - E9Gpvi8xeiYXD/vcUY8YeJZT+BMC41++UN43LOQtq459xwpz3p3DECWfI9nXcxmo/mUr1ODc8ptj - YmoMvIQbuHRMhmPiBDWXuxcdSbk14PMyKhF/P3UQnprxSmSSsS71NrVDGdAjctvXf2WdKZTOl16a - 2eYY0GWCSYmCVrZ2/bK5a4ZrD+aeeiemmT0olavPBvOnkhOP7641/Y39DJf76e2DXS9T3lQzaN9T - +s8fmz6v3oTDdBB9tPuNHDzLAjovlU7SyyzXG+u3gnSIss3nu/bj7vmeITlz4ZwOea5teO1SOIyw - JnZ6+eWbUb8FlGNj8BlVvWpk0wwV+GFRzV/twESdN18qUARsSm5Tm2sP1vmEUDpHvB+U01JP7TR7 - 8JWJGTFitaWraEsBQK+GI+quV7uLMpXAv742YgJ8AvRYNB0If53sg6Y1tcXZoASn7S34KN7u0Uo0 - vgK3etawjXo/2vIoZ6RV2EL8p4+W9RhxEC2lTOTyMexbQKL5D6/9g9Gdoq4MxQRK6HTyRdT7OXW4 - Voatvg/OFnMPzJx/leDnbQLi8xWN1j/+YzwtmaTv/KMxxvMn/90PcVFo1mOVvGa4DnyDvcvh5LI3 - r2Dg5S7Ku1775Msez//8SpUqdrT2XceB3Q+b5U003Y0xkSUFN33GevYYXapW7gJx43kz3fkU27yo - Cff4I7Z21bRFLoZOulvFiqOFUYZFtCUV7Poeq85doEQbsQTVx8rs+fD98wsy2FTh0RdUNXE3wr8b - eLfKlSjNkYD5lVgejGCmzgw8SXS6KMUIzZeg4USRNjB7h1sFL6dDg3GR2sO863F4EshE7MflGVH9 - +Vvg1rc+sUqUuiu8xinY6xnBI2vVf3wSvXrW8WFI8oEznukb+O8sIIrkPaNRSS8B0Ms4IwXpH8Py - D59VtfZ5PLzpbAxTD7OjC+bljip3C9hggUGip/jRTZ9ou3r7ER3hqPrr/nuLli/dnz8287HTu5R1 - pg3CM/OYV8u40G3TvE2CZBOxJkbWwItGl0HxlmjkdJp6+rvptg5n/dX43Jk3Kdt+whLEJGz8TenH - fNiFDhC+FcVmeBrz+ftRRimEgzov5vvpLqzahzA+ejHJG57Uq1G/exS6qzev6wzz8ar3zL/6HpTe - q975yoiK4/zyD/5pjha7XRb4SvcXMVPxoC3eFkrg7SwSie3lVDOMCgJoW+WEHSOtAE28ayyOHqf5 - nCHOlLrSN4M92FuGgdrRDT38DMbb5UtUv5Oi+fO6x/BPzxQhATWlKiNDWVTM+dcQL2ctom9w99v8 - ZZZ6bfMOz0r6y3cBfIN6TfTQhJXlpsQHhAH0+1GgxMYuwEa68Tk9xC8BKanUzjQGqP7rb8Ch0v76 - BdO//yu9bBT73x2vmT0/RfLx+5lpc93tHzd9n+oxPvCzOMnuusc/2tefeJf5VS+isXRgse2NeO9h - GIZdj4vWtXV9IEbOQE+17cAb09v4JGqNO+//H/z5/Qbr9dHuB8sQ1d/3zgfPOf9K5BL98UU1Nx/R - tgH7AF83xsIBn57d9fN66/Anhjk5jR9FW0RD6KQM6j986zinXgxyrICKfvH83vObi+BaoRqlBZbP - 5D1QPry9oSW80d5vO4G5zGADcyhhImczU4/mM5Dh07iaBE+G7y6TfJP++C9W0ndAt5WXWyjf7Xbu - M5YFi3TlLLgYpuOLL/lKJ6d/ZTD35Pu8KJtfr550leDO94nBFoRuu98E9udJPGLPAzGGSQA/Mcgx - bjivnqok3cv1xPmXmGbRcoWV+rf+xIjDMfrLN/R4f3SfLaofWFb+oiOWaoZfnyabcuuxSOBrEBec - OrKv8YfqqErbODg+N6h+tJ5qq4eNnD+xufPneSILh/76Udf0NYFN8HXhT/9jz6gatz/VlgRbBeYk - 5fhKW935skA20COC2ebqjonVeGjntwTjXqb8d/JmuADt4L/+9IKS2jKsDNPc58YP+cAcXi1kseXh - uM9kjT3EW4ZC+FNxyK8ioBNZdcQzrUK8bIrqGd5L9c+//8NPSnnTycCziwR8Gtm2ptvRbeCE+ys2 - uoNHuc/vo6PNGlUSht8k35TmIfz1M/xtiDjwFy+wM9oIayhshuW8KQLkPyOPlXRL8hE+vgL8wduE - tdNDjxhvCwU01d+SGCLM6ALsVIa7v+oLie8Oy+dTe/DPj1dEeKdztxr7FL1fhNPS0wYq70dw3/a8 - T6Wd7ZxDd0NG/XwWic16bsTd4icHI6/ZSKk6UrQUOdGB0nEJ+cNT9lgd3mKfORfsG+mLLljcZ5Lc - OI+cKmGrx5suMyjzLnccH3nWnb8/zYQJKz7xGdlVRB1uVmEq1jfiOQEzfE613KHjxylnjl4wWJjD - ykDFghz+6z8TIfQrAEbhRq7reY5WX8Uj1M4f3QerbrtrlQQHqC2P73xko2HYJniG8NffFCxzguqu - 5dMc//wngjFwouWvn/M6CSmJSZ8PXdcsuz491f77zRC6HuNfCsv4ERD/zFtg+tNve37PwiY07vYq - o8Ofnz8vzgu4W2yxI7TEOpqPPzN0NyW1NullCiq5WzoB83la5P9rS4H4v7cUmOUWEvx4fqL1ml9T - hFawYPN6Huh2+wb74HsLYtnqt3qSs6CCZnl+zhO9yBqfnbQWeUtmEI8Tp2jMUy0RdZDx83Z6hRp/ - fZAG8jfmilNmP0WL9StEW7/q2Mu618BKSS3DVq6hLwbQczf2jkw4kefow6x71VsoWhlMXEvEN9F1 - XIaz6wUEj4if+/bxdSlpLxKIisUlwZUMOd2W7g0vRi/7MHgfhzXBrxLOcaxhR5CciD0iuqEOeyU5 - m6/DsLyEooKP+4vHRrcPHhGdoEfz6aISeSvYfAGj5cAL3/PEN0LDpfI+aIjxrSe+cVpbbx0MCkTV - NvMPQ2W6TMjUMmQyQ8LyM3jn7P02ShAoqz2Ts13km3FPO3Dw72iOFl6i9JHeFxgqvoltVevrV2lZ - BchLUSRlgxN3mZ6agJSrx5ASm9OwcTqXguvn9JiHWGjq/XOGfn6YEGtf31+V8xwUiNThs+e+B66/ - 4RJajPMhCrq4+UoesoNGezawIn1MbeO4ow+HQ4rISa4MwBDoq3CschZruH+DVcfVvotqwvMP1TLl - jsV7hL235th8XfKcztuZg8n7XviBytmUXlspAbcq8Mn1U9JhPQVNDAcvNbHs3RSXhVIzI32+cvOy - fVSXvz63BfZvRiBPR0np0peyjFI7/GGvHy8afzydW8i6ZYm9r4VzDsxZDN2z/CNl3oJoUx62A4kZ - S9i4X245d1hfGTz1SUqsRKnovDmbDsU5n3y03euaCOrWIT5OauLzkxrxZ3oaUcNNhJx/sQPYMZgT - 6BH+gs14nYYVzcuCQm4kWD2X32FBR76CKUkM4gXDGNHm+nH+ng++Kek7Ys9UniHsTjXxvhaJpqek - hPDRNgLWz9NlWF5j1UALswp+sIYFWPRpVTjZSCNF9ozBsh0fuvh6mU8S96de269vYSM8O3+9mDGg - hgYqgYP1mXhOmA40XS0If+lTwCYT2TW3OZIpOVtwxTb/KGsSqUuPzsgOZuH6UWu6LdUbQE56YBxw - PZ2gNI5w4bwap8wgR4zyrBP0Vw8KeLlR1kbCAvd43PNDA6tlj5v0peINy+blAMavRkxIZeZFystT - iVYddyX8W19MKRgm7pWbcGF/C77RmxKxjwim8KGYGLtDd3c5R/6VcI5oMItu86rXs2SF8LqkHxxp - ipWzwqlTgVVIKg41tEQbMjsLbMryI9Yhgdpej95IjSaR4N+JG+h32gSUM8cEe4/FcBntsPSwVASZ - pFchdvkzxSNMbsN+Sld9REvIDPsWg2OE8UP7ar/lwPXQkz4tDmnQRwsYZQuxZ/9GTqL6yBn9227Q - d/zLDH/oFX2kx7tBQ939sEwUOece4puDtfU5kmxDMViiaSihQIQOe+fjV+NerJRAH8gqlhPXrunp - ftAhvXOQnK/cIRo79itIiA4/X1wkM+cVseEQ7DPso04Yh5G9vQW08Pcf8XuQu7ybBhs6u86daCXH - 0lVhTyW8IfTC/gCbaImdTw/9yJvwTb7dXb5LoQ8TNNb4MlXniHUjmqFOzj/+wbDTfI0OuQdtuaPE - O3NZPmhPcUOUdXQfTmmSrzlDVOBBIcSPqL3lC1O+S3R2rTt+bKwCGCpcq794I+nd1nN6utozEK5W - PL/61Ka0u8AOvlJdw+r+PIfvE2xQ+iGKw43xXK6TjBDama+Qq9cVlHkXZSA+c/tO/CZVKQ8Fp4JO - Vhyx7ddpxO7xCJ/3vCLO2Q1deqZ4hgqSbazlcxjxp9ri0NlOq/3/7Nsgn2GMrmdGwkrTKDlrE7ED - r9TUyLnYB1EOR66CSTNaOPTEYuCmV2IijXAr1oM7R8mkBj4CJWPj6JCulJznKIZv2DUz4z6cgX5O - swoby+v9dekWsOXXl4XsdsT4In1a96db3T4kRXfIxefOgE0/owri7UWJ8ZEu4F98s2fvhnUKwpo7 - Fv0Ip9d2Jzv+DlsltSOs6q0iGH5qbfbeTwiru+ySHFN34CTqz5BI3QPfVc0ZmMd8FqCpPVys1pqv - 8aV1KlD7TFhs69E6LP3NKsCquoeZPJ57i/wOdRiv8eTzO97y8OiYkngRK2J5ujvw2/uXAXY5WthV - 55DOj60OIDD2fs5rEut/z/tvvYMGHPJlMA8OvB66AV+CPqnZ0RUP4gSrCd/uP9+l/InIMAutGaeH - pNBW/+YU0G5nTFyjbujoH3MTzrYMMK7bY073fIDP2++LswIuGkW1xsGOIRZW2y8GJDu5DaxqPBE3 - zwvALh7LoHwaKQ5/EesyuC446N62A3l+ozPg015JUa4enrNgMHo9frWviepwVkhYP718g2Ghw4fK - WdgAIBl4xhwcqCbyh6hjng4LP00FnA5BS25Kqkbc2Vl0JN9PwN/MH1tvt+ruw37Vj9j+3rFLW3Fm - AHP+jPMq2V9A5WttoeizfDE+z998vTadhYabkxPlBsya71kuRpngOftg1QKsfsyHKOhjAVvATwdW - N68zTJC7YJUGfb7N17eFxPlQkdwarJxmV2dG56swEzXhVkAt37H+4hO71tBF35WNBdSM1Rmfva9M - +d/ZCsDLWwCOHyWu+Uv37KCD5x77ob+6m6RdTKgRZv2bSlAvyUwS0KnjBT9qfT8V/hV9aA5hhR0Z - t+5G5KEEvN36f/UtH+1UjZG1pSOxS6Wh27uiPeRS3PvsE8GcWohf4FYG3N4SeQ1bpa0VQr/iRHCb - X/Jlr3/71CKVBLYZUCqIXwZO19DFRhLKA3PxEwe+HVMi2vtkahtlaQyvR36dvxzQtN8Yrm8UyxUk - 9+WaR1soW+pffcOn4FIDHpyVAD39n0uUZp+rk/wkCCHzXYlWHtV6lZJBhvBx84lhQH5YX4EmwNOz - 0bDDGWlOcVyFKDmWxbyE3y/d4kRtJXMIKqK27qWmZzaTQPV537FTwECjwfH3Bu3AajiGzTniFXks - 4bW+NfN3z49ViwsOTq/l7rNC9QI0Sakv7fWX3OpHUnOu+OPA4T4Z2K6sp7v68X5KVXk05HTSSpey - fcPA7ae4fsBa2cDcbSsBdnOvsR1XE93S+73/w//9zTiyy84c8MAbGis5J7md7/x0g55nvmZhnh3K - EP7SI/gQFBx4t5dGLVmT/4tfB/GSj1dPYEB70QkJXjSjlL+JCUzt4OeX/PTOt97NK+gkxzM5/SY0 - rIJcFejwPXr+uOuBLZRlFf3yYz9LtceBVU8qGWlOYuHzzpcWeSs5qIv9AfvuoXfXAHgWjL5V5Qud - pOZruzxMMF0DF5cu+8g3r6kddMzenI9YZORb1sU9GG/QI8lFP2rT3EEdHFocYS8DkbacjkwFazAW - 5PGk0UAP/tmRZmMf0zOmJliUtN4kmresf8hJp22/730D8VbTuW76clhLLMtQHH8MNsyUpav6yyuw - 3x92PEEemK8nMsBQfxnBsOBy2jJbCctv9iNG7XsD8/tRKDaVKmPreXc13nkGMby9nrEPDv032pTB - CiB3kxsSfbA9bP3tVMCNM72Zu8C8Xtjrc4QfkfNmIYy3aErOSfCHDzgAr1kbt+PVlITZIRjvU77Y - 7vFq0BEvL1x+KV+vJGIzcIuPJj53xRwt59DswDP1MpJaN6Rt/KCFqM+bK/a+zXlY0Tg1IPapim3X - 0nKmW2iFzuKv9Fd3dvKZO9kNJNO3mGGmJQPt2UMM3EQzZoEoMB8/bSMBH6gqwS1Phvmw/jJo9tNE - riS36IxncQO7HiG6GD002ooth3jwNbBR+2O9foycAefqp+Gikkd3c7c++NN/+LTnD2fcjAKNDnCJ - PSXNMB+OXQW9Q+8STZWP2iIzvQo3/IDE4Qwh2qa+4v7w1geHlzTMr/HbgpP60mZxuYJ8XQwugM/G - ZbGTBA7gZI7xYCgPETZq40ln8M02+BcvfskPw7bup6J0sTvM6Hp0tW0qfjM8o1n7V4+XTyeYsO1P - DjFYfKkJf1sT2PnDQPAUaDnbPX4tTCUzI67BJdEWdQcd3GGvE59v2Hod76IJ+bfCY9zyuN7kjtXF - ay3c8N2vhXyEMRcgzgswuZePWlu+3spA9j5+8C1BFuXES+DDJbBPPhdfSbSg47ESlSSXiCVg1V0/ - Xp6Cjpks7Gi9BH5Ot+mg/PSLD4xj7dInjApUVwcLyy8qgSVOxwXGmen4wOC4nJpf5EPEeAVR/IzU - a4f7EjainJN05fx8/RgRA991Z5Pb1/loS7KPod7j0xc+SVVT4BxMBH98QUyXVSL6yjsOfst0wsoE - h3r8u15J7hLO3ktaM7s+k1j0vGLffQ+ArrAooHhyXYx/9a6vzGIEltIAXBpzna8prR0IJjPA5ygy - wNouV/OP32Nr5yOzcTuX8PDweWIIlUKH1/k1wuMharGSJ6CeYRibqBHVHBvvJHAJV3xGcIO4I/Jz - rOr1XjYFUO33DcsAjdH22RRG0i/8D6uBM4L1u1UB+j54aQbP5ldvdci80aHdzPm180cSXkcHBJGZ - +FA5H+mOjxtMqqH26Sro7pa62RvkfKj6k1AhSsWWScDG1vd5YS/fiBztdPtXj/2hMjUqTlSAr2P8 - 2bdcKe6a62KGXq6i4tQYbmCLGmX8h88JE/3qdVUUCaVhZPzplYFKuZwi+/7zsZJrjrucjvANML/6 - xF7KTpuVRp6hPjnLjEhzqdfv7/GWwodbzS/+8R52vsb8l1/uepCOJ71BkLUafPme7jW5h/wbLsO+ - pam0ebDq50cI/Ri2+LnXq4VK3Qhb7pv6Q3fWI/4J81LaON3bh7zl2r/7P2zTa9eHhds5juvD4SUt - 2DRkUm++MsWAd9aYKPk+6D97XU3Q37nCZx7qJdqMKvDQ9ciuxEa1DPhL3zOQf7e9Txvq5pu7PHX4 - ZQqH6NMw0K0bfUva+S7WRZLn7OvmlsCTvi0xTFTRrZLmfXB1bPkwezKU6tZXEg6sfMbRKjTa+DxH - IeTfGv/nD9Tr7q/94x86bKZ8VJrnLMooL/EpsG4uy3tFCqtbEGIPcirgJ5C2MOBY01+VUqs5HwoC - +vNzbkn6oGTlKxMJVycm9iK10U85ZB5YyvyMLQta2rb7JcA5YEIMbQzqP70K/vji9fDNAZ0HP4D6 - ZC34WRNPW+yLs4FX01zwrbpG+bzXD2R17AMHAHkRyzJmCzSBP/vHzL+6f/wTmj2Z5nWC7rBYkyID - 08A61r95Ayg+dyOsxUNNnOQoDROL7jG0tmz0Adp6ujjQk2F8Vmx8HuVFWzi18YHnvuYZ/PHbdLUO - 0Eknc36OaUsZ4TnHcEQFi5MybOpROwg9uKiWj02FPKPlDhQHFfxLnoc8h5TS+B1CcunVnX+f8u5y - Shr0p++R9v4C4jwMB103q8T2st4Hdo9HMKTX175ey7CNXLFBhAUBO634iYihgTecKrnC5+M60d/u - p8I/f8gF5mfYYo/noE9maffn1Jr236MH3DU+k7JhFI37i69Hr/c47SQ12vTRs8DF2FvURiZoG4e4 - DR6WYzcLXdm4c5yoDdKDfWrcOzUAW3eXHil1AcgzyMZhKb9mjAYvM4mc8jNdj+4GgSZ9TGyLXBj9 - 89PW/m1is3kPNU1S4IPk9jv5bMqK7vI4pB6I8ygghplewSZz0AcSrzL+DUZTvR37F4equ+piWeV+ - oBM9iQGSiTXiVUUfUSioFZB96U6cOzMOPxroEvQjf/qnH9f7fTGR06g+dg8GzHteVDM01CduXg3V - yPlLd9unej2dWdzjdzs7ggk2Jo6wGoWBy9T+04FM/bP/8d35T68t4nzFpv9qh93vtBClmbvr03tN - zX6vX19wnX8v24qWsecZqbH8ntgit+U04MoFnNRaw+ePe9DIrofgT5YDoijXhK4sYzbwHjOEhHds - DBS71ze074NPzrsfQplj/QZ//sKZfZ3cnQ+q6A+PHRmbGvOHP8Pi8PMfvm4xdlXpsKAOW29j2b8/ - vGEjPDpf+Jwj+u/6P/187go/n12+7P+Lf5zAD5tOtRkCsSnx+SCu0XTNHxm8/LKFmGigdGnTzybB - h6QQWZvbiJWztIKodI7EzsRXPn2iDP7xPX8flj0sSvMcYR2BEzG8q5uvu58sKefDxT8Y4Uej6ffg - w0v/4XzOZ1J3HGw7+afH5cNvHbY/PvSOeTyjJP9FS8bYLfA8/UWc9vHVend5mlKXuyK2H0Kq0UWL - Tcij+OAv5qWk1PyyHpTIdvEBk1X1tj6FBT7b8jUfYvVOV3N8NzCYmB5bm30E608ZYlEawyf2FyEY - +t2PQH9898oOmP7hLVx+7hvbF6Yb/vmpf/rIvcYqpbbopvCZzx7BldsPW4w1FcjH2cHGp6Q15W9r - jHZ+MDO6lw1MarIOLMp3My/PUa75y9qn4KNhRLQwsdz1jdIYjNWdJbqrePlYfs0E3GOOYD8nlssb - LJP81TsfOnNCuRi7Mrx6hjIfUretN8fvGhiYrYUVdBnydaiW7m99iJa2RBvc5NyCKXlcfIZbmmFd - LKWE+FQF5CaWoCbjVZsB4ztPctrzk/4gkmDn/wbiG7MWjfXtJ0E8bxv2KsEa1uXA7ZOHSn2Ge//m - bbr3Cj7v94pcdDEdtvihOujPvziN5ppPAv/owJ//H2eJp60rVUcUmI01wynldn+2soB/Ah/i/AoE - fkdXOvzVi32s62cgb/NtIWAEAJ+jls8JiC0GVudJm4ed367m2LfwxDEtKfb6saafUQZJUijkfD/5 - NXUafYTvlxRgbSV8tKnGMYAXxjb8d+uuwzZ2jx7ufi0+/fFJiYc6vE+8M/+HqzPpdRSHwui+fkXp - bVEJ8pIHpnZMARIIZsoktVqQEMIUJmODpf7vLeeVetFrFiC4tq/Pd4DoeOTorLpBArSioO/nL9H8 - FS9MyZmg6cmGRLbpIshftmRMUnuoIpKUfAhstZE87s5DfYGrOQZ2d/GxKqNumMfwKwfb0Ws9wZjr - gSioyETGLyc5as4ROvMPE3jiA2JWrxSLgdxLEzbPHn+YrIjxQgUsPTWwPXxtpJndL5CpxwN0Cn+i - 7Ho+wevWC9hITqODhjPavOsNat1GcVZOInJgVWB1EpT2QslVU4iYmlkAbTZ/kGDXEVAAa8Y297Dp - SPvTRdKwqsBLmxn6WjDOnNhD0/LAoWzp7HJC+82TraYW9e6aVZnsBZmA37yAvHl6mHIJdv3iUaLh - RTzRGs1gooG4o5Tle9/9tiMVOqU9vzPBuYSH73zsM02OucT4yfTllZtoZjwQkDW4Qafe5pQUdacB - D0Ab77yx1il3NEZp/8oV7Il+ItEXEkWQoonCN59Z1steEOOmuuCwu6l0lddaA/SjeWdfNdMpfYYn - AwRqnbLx1zqEqnkMeE9r4XZJ+HJ+r5dXs2rhPVxb5foZP0OgHOzcI1jNI2z2SiLzgrGFgSo+6bI9 - bzP57IAesv5KWkT9agDW/8LDsnrSOXLDBJT2i8eeLNxKdKzyPbCaiDLjtaZoagVDnnaahL1KeDo0 - 0jY9QM9Dw3483EeL6QS5LG4IwlvebSQKALcBV7Np2XqU6avLODTg+mWHMGb1hlheCORbamIrP9KI - 4jvcv+sHe9qd6iPr18A1Mj7htnMKncRB7QIrfWYsH+LKmWauIq2d2MJxwzfD4udLK0eNjKBK3HBY - Wv1igPd4NWUhH9C+aUTQe88YWgqonO/8lCDXgvFXJOv0syY3OTWmCz7MwqWkSPNd+QpaAx7CtTVM - 0aD6oCj73cSfC0wJJzl7UA1yAm+sntroMcYgL0k+9eaSpctklYo81JcCJ75S6cv8IIt8E3QPa/vD - OnrnRSLjlTgQdxZdcVfRBMdXsoPa7dzpM+M7wOyKF1ZC8Ssdo6bcyPqzGuDD/+wlct0pp/f+Am9x - CPTFD6UCVCKJ8cWmaYrvQejK+fMheLyfuANyt8D/5o2qpFbpWO56Ijly8JrOTZ3oZBtTHxS5fcb7 - /JCnI1Xbk3RXypuHNt1+WPM7n4B0c7ehZ6BEJ5i7usAtj+wViLBieel4AgWX3Nn+wqGLLB9j0GxE - AzqhvpRzT0cboCgPsbLsdErmNE2AOGmXN5+T1k5w9SRvg07QWolXidZKk4G+pmuvy+2HPu+fYg5s - wa5ZHiY4U6WoCvijFPz4+fMvJgh8NO09q5kYgLIZ/fpPFfi1/jU2SV2/xYKPaUzy7OP3HwXhoxva - pkN/o7bKXiNzDWRxs/rWDT5Qi5L6f4d+sBP+8+NfAAAA//8DAEkU7SC6BQIA + H4sIAAAAAAAAA6R7SY+DTLfe/v6KV9+WSGYyVfXtmMxsCoONcVaAMQZsY6YC6ir/PcIdJYp0V8mm + pfbQQNU5z3Sq//M//vnnX21WF/n4r3//869XNYz/+m/ba/d0TP/173/++3/8888///zn7+f/9cni + nRX3e/Upfx//vVl97sXyr3//w/7vV/7Ph/79z78M86H6M8e+wGppMg++UvjEhyGzIr6uUQWpyJ7I + 4/k9aksegQIm/rjH/vcUaIuE7Aohei5IwZE1W3eyGsJXxSlE4elFW+8vp4G2ekTYcD1XE/qHkkDY + jRk2e/attQfqDZDraTxxO6L0fOzmOvzs6GWaDVBTAoenD8pGYrFnt2d3eaU+A601d0hKSw+s4jsb + oObdJR/O0zcbe3uoYNXHOcYJc+3nyJ186GZnQCx12msLs2ADHtoA48u1pT11mVJC+SBFU+MhXlul + cpXQBB46NoZ75S6HjxcAaAUheXyKPhtWEhWw/a4vf59WUz/70klF2/pNb19VM44GMw+8z3LG8ZHZ + ufNnZldEX5eJHGxrR2fR73hYn0KbqLnuaaw59hJkLU3H1hcxER1gwCA6jy652YFBx/JmyYhFzEzs + kxb1Q9BUKgTf7oFxfxI1moO1QgIrXkhufD0wxW5uSBbVHGwlldFzkP9IyBsygSjvPnbpq+oKAPle + xEbXarXwGR4yfJSXCzaxstQ0tOwAJqJj46P0LFyeK6iDQm0+41PUYLA0x2MDs3o4YZW6obseD3ID + 3+fRmd7+7krX+YFTeB70Awn4Jcu48ZkFsHnJGXmwnzoTvmbaoaXXCXbcGwPojT1BVJ7agmROUbvd + To0D2KRHZkIGy9fzQ6g6pKqDiHOVL12ufnxWyKZDig+OLdbrae/rAH12CjGM5NWzziGcENd7MTlv + +zl+wtCCJ/XukvQZw2wth9pCj6TIiMcc9WxVL7sAxg/FIVEpuRlxnrcz5G8yxEYRnTJ6OlY6EsYm + I+m9xv307mNDeuqHDzFeyNeIeRNXxHlNja9L9IwElGcy3JnfmVj29a7NR/iSETomCzlmaMrmV7yq + qHFyF9/w4eCy+35NkRc8MoIN0XH5xTn5KJzielrDVwBWVqnf+1sqh0SeHFsbgdAOoPm2DLm5mZXN + 5LNKiH13AtEBL9AlLB0H9OXgkMCaq+jXj+j6aY/kftnJ7ijlowUOdptja2+ZYLbFU4AIq5v4nkKu + npt9l8KI3B5E2X3Smq/OrQVYDBB2RFft5+hWJmisDYxd9pFHy8tZ3n/XN6R9WHNGMwdI1U4ikXd5 + AFgrsyck62nsS9v32V17OiNx2U/YabS65z0zf0NtagIiL7IcsX2UQBjsFhafz4UKFvPZ59BJ2R1O + DpmccUdZ4tFz0vlpjw8HjT1ckAODjwSws+HBXL7kHHXSep/44a66rBDwPHLG6oLtpCkzIbk6Kfg0 + rYG9rz7Uc3YxKpg7HsJKe9VrPrZlA713pxu+B86TrvR8mKG/f9+mVaT3jH/3sQ6svLpj3UOxJmAV + TvB6eArY9SKlX2Nx4eEdJZhc2MPLnUv2zsO+nByitU2rUakxefS5CV+CTSV1m8YSWGiakJnqOBWi + 9XTrSvCyggXrDVnAOqlih86DccCXw4W6M/d+8bDjJ2laBmfJuAeYUySIpPf5fvUi/pJVITqyeMR6 + FsT1IDL5G1xJbeCIxADML/7yhqIkszgXE8PlnH3oIyO1EuLsGOzyXeAHwPaTN0l4u6xH5czEMA9N + wRdP+6qnXn8U4Q7JOT53TeAuykuvYC/KGk5FV63Xae0SsOEnOcSFA0bbhbmkOAeOHClx3PWZyhCp + p31PzIAPtFX31QJpGN5xrFVTtmZdnSJ1jEwiZ5pK+ZOdx6D42CHRd+MHzP617BAI4zf58dMKxWeH + nG++kmhMjvVafdQzcDptxc6hrekk+ckEYW62flc0bTTv0qsFx8en9yU7LHsqE1ZFb2sdiTP2Uz07 + nF5A74h1bJ7H0uXEk7BCiyoOuerJkc6R+/ag4pgcdoGnZfR9t1soZjcFF1SKqBA6lxA4Kb+bdrFX + 96shkRQahqhix72rGsec6grVZQF/zwdIgBl9/9wXEbFO+6r+4T2cjm2Bb6HuZbOrHkUQMexjer66 + ezYHyr6FNLYA1g/RPht0/lRJ81McSUSPPVhPt6pCOFeqaf9Sq37tQSCjQp9sYiiNrPHvDItQaUnr + 010/ufSl+gb88Yf5Qbw2o9m3QNAzBcG74ONu/F5CaIUh8XWxctl19+3gt2LarZ/kjF8kB8LmtB78 + nZQ/63nvxjK6PJcXPp4ZM+OulSIhMn8anwPi0g+79GEB6+A1uHh8dTAD7g1//bXhxUwXVT1biK3k + M7nOmVj/9Agaax37SFO+YA6ZYYYbvuBHzn1qFr95Gf3xYRc8smWJTx3UFvZDCl2stOkcsCK6kqeB + 8/J2dXnF+EqwykBOUh/z7hzd2gRtegJfmsXtWcTIMRgfr55ky2hFwnu3qiifSUcs8vV63nBQC15c + dSXYnpSMVy9C+Fd/NyfF9bxLNAf98CyL4Sva6jFGgbLDm55qo7bUBx3aczDjwNqv2WppFv/TS/gs + CjOYg6aSEcAGxpjCYz3vs8GT9vkd+VUtJPXi1bWPbuFww4ZgvMBqOKgDnNyaxMtJmv3x93RmEnJf + pyVaa/3GoGXdff/qa+GTU4K0yGOJ/HrQem2YRUcBDRPi3IZHvSgvr4Kf9pFPAk0fNZXvNxburvo8 + sbMHou47dx3c+MCHfn6r+63eEXw0Ls6gfXMpskUetti/4KNVvrQWPQS4169zRLStP/lPAkQ4nWGC + zTkTe+q51Rm9Okci9kutavpEa4Ds++vmv9dQjiaRf4pw5M8vYnpu2q+PE2mg3ZwjrF5vfDbf2m/5 + h79aV/HRhMydDs6Xqze9TpSL5mhQ3zBepue0SxRVE5h910BqQAvbEE3ZAg3GAVbNXknM+8ilTyQF + MKun01ZPu2gwyqpEfhxr5CglvsseoqeFRjndE4+OczafHooHLbJSrMuxBsgFf1noWsYLW0N36On7 + rrTA3Asm0Z6gcVceOgM0H4GAT4OzRIvkFQysPsF+ghdkZEJtThLE0veCDSM59Bw34jcItfU8rTw3 + gdn0rRiORO2Jv869tsQXqwIncSdMwnp3tCGrrWHftx+NWJ8dAdNPn43ybk/8s5Vk876XUhijFhEt + C2WN8wsnhZeb1E9IEZ7uupp6ClehtnyhYYg7HO9Kse9yqyMB3X0i6pxcB6aulOHDXk3r+TrHsVRC + fyR2y+FodsOz/qcf4oCNM9a+QkkM95M8LeVoUurM0wB/fOB9EsbtfUMp0dR60wRoz/ejNTUsTIxA + JhfXWbIvc+pLeLot+jTvP2tNo1fA//iG2HquZNSmoIL1KbD9tU8O2SLOlgMvRe1js7IEQJp17qCA + ny2OH+fOnQ9aCn/+hihU3vigPcVo9z7VOMqNAyVcuE4AQJEj5+V43vj7wsLf/f/W/xuIBwt6StBi + Jx3FaCW7ev7hH/HZ17fvHrtDDNNouRDtrmUR3emBCoev6OG7Iiiu8OwTHTL+Y/TXerLqyS6DBCKA + r8S57ENAn31gQL+zb8StU7MWqiMe4KaXfRhYrbaeTCsGv/t1i4qhw+TtzsBThJbY/hFmi7bDIRA6 + UyR4NOKeptongY3ILRi/z2q/oss+gN+lyf70wAolroUbvvl77HPa7N/YFgZKgkm8nD419faWAeen + NJLjM7drPhuEFE72G0zCpeN6Ko+lioi6Mv4MvrrL5RHI4bz339jO6k+22EXcAvPBCT6KQhH03zfv + 7X/9Y5gX4K6P3TGGH/a299fnvsu6QXR9uE+6D8lVXnY55eWV4CrvzxNiXImSVyCHaMzWi08dNII1 + qVVWGpljgZMNn7nNj0I4zX96Kuu2/UG8cXmTTQ+AZf/YQ3jYvS18fAdEWyrtpsJDLq4k67S+XoDQ + Tui+Pw/EUl9fjabL0YFrzojEgmtTzyqQYpiRbvTFL2KyoUWMI2XrXsfWfA8zoeBvA9SuwZVot1vR + z081MCRx6q+//siWK9sYYDlfeWLaHo1mxlVmOLuJvO1HRsehKXU4LoOKN/7rFx+9GHgLpxsx5sCn + Czea7z99GQ/3SiMLEwxwq1dsJT3fzyvJCvAQeh8bm/+cjro5Q0GTPeKIblUvn6o9w3vcx0QN85JS + wgkxvHORPe35u5StT1ta4S7Mj9iZO45+jCFkwKHxB59RlFBbb+qUw8fxy2CLt8t+ceyyANShxVT2 + 54UOOR4nIJXFnZi1LPfLbAchegzRQtziXbmkeGsVQoeoJ8qu6PrZNt8D3OcPRA6uc8rG9FvlEECJ + w8cmLMESX/MKDenD+/FvLRzbRYS3Z+zh4/Hc9UPoA1Vi362Az6vvUXrAWopu++PBF0WTuMtsJwE0 + Yp3b/EMZLePyLaB0kI7ElVynX8ggt5Lc7VVspSUL5gg8GPjjg+MPD0+3qoT6OZqJYR0bt73O8RmK + j6LBLldetEWsjgxcHiT0YZ0/s/k6F2cgX9uBnIwDS4eN74HjSxV2LrMK1vsnfkuCD1t80/NntqLk + EKDb8+xNSKh4ShXIpah9OSo5KNU7+j0PbJ/fB8Hf6AvWrOsTuPnRTT+E2fzLA/afMMX6wflmWz/K + 6Nd/8nN40/nF39/Q+F5d4nzFJ1gWDnlwSuhEjA+A0SrvywK9+U+Eze967vnLnQmB9hIdovKcTxdP + 83nI340HVllBrll3FQu4rOn3V1/azMePDiIhUbDMsS+6embewI1/ic3fpWh+7JkOEN9J8Q8Pp/XT + zvBeyXTDRxDN5liLiIaHBGPptdRzVssT3C2GTDx8XyOqHN0WJCUb4McKWLpe7nwAVtD0OCzJs6ai + c9jyHA4T1YUe4FGb5XDhC49Y7FnNhADzBjgEMMaPY8dof/6QEQv0W0+6uNanhfxdf2A3hodssRgg + w0WuSiyXLOPOjz3fwq3+8HF/5bLlIKQ5ENLzYeoViOr18XjO0OBO7dRc7zbgfvnSwlwOxCb9O5t6 + NMjSfdxhH23+e9LLpvvrn4OgD9r8mdoV7L6z4YcC/6mn79y1EHsswebGvytgnBwkziHGlkZOdK6K + iYdDURJ8kJ/HeuODM/jlD1v/RPMSaiIqrsxjIlE8RHP3AhY8t3JFbkb9qDe/3iAyvxr80w9jpZ3U + P794XKdTxJn1Lgc+yojPnGI9myWRrn9+z52m5+9+CmAu0Mdu3sw9KS5cDLXpHeCcNnI2F2mmw4y0 + 4zSlDIk2f6HDYq3Ij69A+zJqFj5uWoZNNmz7FTNvETHsLZ34NQuiGbOzB/cEljiNgJ6tt1n3IFM2 + OnHI5hdLuJzB1h/++qvHpQoNCV4fB6Ic30ov0PY5gCbFzBQN1zOYVU9+Q+YujfjINVbN6wj60G7i + yH9v+Ll+Eiohf9/cjktbsvWqGF8RRSR7YM1ljH7geFaFrLZ7YpMPYrDqvlP8/N8kkKIC3NdbYhj1 + jY0fp7MEaFlWE+z4QfLZ9v2ka/CdB6Q+457I1qxGS7gOZ8Bmp9QXvt+mXltey2HQw4JoR3rQli8j + GGDTd9jaSVnfXpmgRJsfw0dNscGKH24Md93VIFo4p/UKRjZAZ3tHsCafHZe/BWWHnNjLibrx6VhK + uwaKnu9jTZDFej30qQqZEzzhnK/NmvUCS4eqOolT85q7eoFX7wyLK3xMnPoAgHyGqwyH9+1LbsJF + c6nGyc1fXrbhbz13L+qgfidCfI24qzYG4tGC3054TtK2X9yTK1YYVolJUrhrtU1/hKCw6oe/Mo++ + f32qMkZW0Ci4GHVIx/1jz8AtTyXyxW96emu0GfmhUBInAnpEtWZNUBlHyrQzxE6bdMT6yDai0ud5 + /+5OPHQmsPl3osf5yV1GinJoPPMQG/lpB7a8TITXld6x7Ir7bPn5e+YmOMQNcJHRMzt5QON69Mvv + 3Jnm++DP74Bkqeli1rsC3t3KIMpjX7nrq8gHSSe9RwzeR9okBcPw09NYtvZhNG95JNQwc/fnY/Oh + S2ZI+j7Ld1cf5ctZGzqrf4OB03KsXrLWpaduaeFp1FKiroodCTnjV7Afj2/i0j7up+YUnaGGfA0f + /YJm9EE98Vff5AAHIRo2vIU6MTyi7nvDFeor0oHOJwZ2cdjWXG8GFbw2bkc0bx0i0vvAk3RG2GHd + kuWeN6RPCjd/Se75wmrT6dZVMPGtPU4Dna2no45nyBhvlhj3MOm5zY+Ci3m8EOUpBD0NnUsAT8Xt + Q+z5MWjr4/R5g59eSU7KjQ51nvKgH/EbYyl/9os9JCz8Xd+tbj745R37DKiO/00aOeIXSYVQOYsh + 0ZmBaFveLsOf3ziErO/Ozc2QoV5P8zRHt7fWvwIrlH75pL3lg5zFABX86oNbEgSWuiwb6D6SgrgD + t+vHXz694eEoRaDJlsunm8BFLIsJjmKoCcQ4NtBl5BO2t/rnb0HbQtWYM5Jq9BUtv/5acygSi0GG + S5NbnMD7FwzEgTtLm1vEO/CLhxPRBDmpF9mEEG763V9ydY0oeAIeenmr42Djz8VJEC9ZB7+Z3oJx + AEJoKeHPjxPvgt7Z2O/jEDIn5uTD7XeaRugNd93FwCrzcPvVlTILdnXe4MfGb/NxKHjxlw8dro+6 + J0Gph9Bu9hFOvZNJ6Xgr33Bww5sP6tTsF3abpATn9oWP+DFoRDlqHVqimSfnby1kfX8yvd/8Ax9C + dnIJ3LmJtOl37H8ipZ5Qcgzg7KYytrWl72nlgAleXoJFtry+7yMBqIA3rm+iihRluZUpE9rwGHv7 + C+i3PKuEWz5EcP0BETmcHil05nnAPsjKesu/OtAdbgs+jPnTJVhuB7jxEbl+UOxS9nUW//Q0bh5N + NMNaX2F3rASibn5r2eYz8NVZEtHvB04b9sBcYYw6hNVd2WSz5dASfS5H4zdPqCfX+SZgw+epdMk3 + W77MzgAX+FaIIWAOLLuJ6lCWAfUnr8h6uhcqFrieZeEsWTS6jh9ORTMKbGwetTobznuNgedjHhJt + 8HXADbo2oIleGKwK/KdfX7k1wYgfb9i+TF93vcZ2DpJdrmIDC3LNbfUBb2xT4Ye5rwBlbr4PtYcb + 4GMUJpRO/OyAx8E/EP8iKy6HnvsJ/OYXm993Z2HJPGDbs4YDNZiiX/4vHWFMJzTsI215H4IZbfkD + MeXX052TrLRgO7IWSXHg05U97WcYy7eEmBv//fI+sJ7k3KeFfnbnhymK0sdQI6wOnrvlEfIEtueZ + hMq6gt6/xOKv36bnAeqAoyrT/Z6PyAVP+hEajAVue3zwl3qY6BIkzwYU3kfFxqXN6IwY+Qx/67X5 + 2XodX18ZCIWd4eMPr3qQyODXbzqGyJ33wJx//D3t+pPoLgchzNE1wcdJUtehnmXt4//0G74b/bue + 9wCvMO0PPnbE89oPF21J4T077bEzDWY2Ye/rQB2o34nb6nlJe6aAUeCNBJctG836WztDG2Jvq7e2 + 7p/joMPPLfz6QunY/drybg7PrVoRJ9DP9VAVbx4eXy+JHG5n1WVb30tgvftO2KZF5BKx++Zw46Mf + /1PhET1j2O0y2R91vo0G7poU0DZOpb8fvyylzEdOkVoYZ4K98pJ11ceJ4Tfrpj99PqdcJSIxqiy8 + +bOafUcvD8177z1xcLhGW/6eoAE5FZabQqK99V4tWF4mBSv+3GXzW3v89a9/S6upJv3SNz9+wqmd + zfWaDbvk54+Jkh4jurCTnKBN/xLdZnzQpofFgkJ3EP3P062iqU9p+5ffqf2Ti1b80M7SlnfjwzY/ + oV5/kKCDHxU5VHDU+PU2vaHdMvy0Oz4nbbkvrgyDZLKI4+R2RDe8Bg8yn3HxDlsw/eYpWleNRN19 + zxm94CeLXlrr4+t2/eESR2/YfUoT+8YoR9PHuE0wvMod8cB1dMfT5TyjeCqcv7x//cqiCFPm0mBD + BHq0WFgJoTo/C6Ielz4i4153oMwHD2wVVx1wac/kAD+qA1a3fIqXAVv8ruc/u0tI2zvScnQ6iS6+ + 3O6ruzanKEbK4Lnk0dpPdzlF1gxN2ThgNTnmGg1KPYDh9dX56Abglp9PDFTsksO/fhyUSWQkIx0T + rHGqk3HNPZoRfpQH4lipQEn/vKcgAehB3OhwyaiRezP81+9UwP/4b/8PJwq4//pEAV5yDVvdXqP8 + JYUhxIxWYOP20DW2uOIKGAEuiDaMECwLqVh45aPD9EwfFV0EZHdozeczCc6OnAkjLt9ofzqeiX1A + x5pX05WFrAK4qdqNisZB5ssDcel84lo3vZ/3GQuRF5ojcQB7BZwN/AAev2/ej/c8rUd1/OTwyi4B + fuBu6pfbpPj7T28PxDwqPaBPqfPgrhR5og/s4tJ3ACRQ3VdItLDJ+vEDzg18td6eRGzlA3qt5hRN + Lf5gPS0VQGujlaD2EE/k/Cw6SsRPmsKvnNs496ePOwXcXIBJK5xp7bHmDqYBVuh1U0uM2jHdtQse + IWwz+CXx7aG77EzHMxANpcXX5Bq6QpkYFRSPzkTkaFUpf/m+UxjKw5nclFcQ8bsqbqFbaCY+ruNO + o6cskeB3OA/kUd2//UJMk0W2WIxEuaZ9Rg7eTYKZyVr43nYFXZ6UM5Bx4AkxH+y7J32YG/CaJk9i + todLzyWtIqEq3J2J1RBOo5e+9qCtIR/bn1qLuII8E8i2Z82XhmIBX9YsLXSsHjP279kA5vEbxshh + yA7Lanik8+e5skhKprvPs/UIVv+7sLCQ58DnX/NKm3dGK7SLK5MYWH9nXH87eajJngk5zcEb8Puq + M3777bdyTXsKljGBg50E/n5hFI2fpTSHXe9JWI/eSbQ81U+IvqWCcfxMuH6p8sSBt+Ps+7tjXkZU + 6t86zO7qg7ilUPW8FZU+egrGTBzi9mBxTB1CM6wCctF2X3furCqAsm0JRBbepjZ1ymjBScsdkoW6 + Wf/WG9rP9Yn9bP72M399BghEkk1sD8T96OmnfP957S7Ekj6fbF6smEdXOVrw0favlPewoiOmklty + U0uRjqm6Dgg2gUfwSUTu6JgeAy3t5eL7kt4yds3l9Le+RHf4O2BH0Svg3T4ZJFfeJFuR8n7/9aOb + BQydJ3Jq4YfOLSn6YnSn+pHzUEyhQh4hMTS+GKYV5qfDgXiR0URfrWE7hOWvQaKxedF1X3U6PO38 + gmhY+2TdWF085K7VB8sv91svNhZWuBu9iBTtEmn85MwBvF+NG3a109AvxXNnQbvrU4wH0Yx4qwpj + VJ+4ihwOIdb4j+c2kNzUhrj7S9mzzE7k0eM+Ttt+uEBY2tcAzUY+4LjXkp492dACQvLF2BF7RROu + F98CLooxPmdZly3v6yLDbCrPOORfdsZvz4teTXnFsb3INRuZVgdTNF6x4odRz0aXdYXCoX9h/cVb + rqA8agcJDgcnMTG+LuesWQ4BvWOiMH0TLe9378BUYVlsl8ZEV0VWDSREuYiV+2Gql/e7tuCnCig+ + bvvFn88QSrtHJvtIPFeUF5ezj/Rifkx8EiCwkLWIITZnj5xvXxGs9eQzUrEILjHspaxpl/kSfH6j + +3S7l5pGZV724ON97qYNr+r14FyMvbvf90QP7kpPF3R10FjmJT6rak3nzupCeBqHL/a3/WdF8RbD + j4p6rK1nOeLse6WiqLnsJhgoR3clJ6GA06nOcDJDPeLo42jAOZwyrKzFxV0xWRzkKmPnV/I+BFv/ + eKjNmC+x86+kjdUeppArDy6xGYwp2fYHQm5VJlo/DxmxFVoA80VFbF2UU90XB2uAx+o+k6PmCtEr + VaUJqrnZTYCEsstfnaKE6NS+sL31x1wnnwkJArz6/St6ZHPLNyFCd6iSx+VyovzkiAHMMPbIwdTl + ft59oxUVerES7zLM2tKkTYXSq+KSTIeYDrv02cIdiBqsLqHWz0VgnWF6qGNfCswq2vYDwk6vpmnb + P0CvF8OBJ2faEavWT2BpDsAA9AplfE7iyOVO53qFoerZ5MT0eiT8+Kab9jn2ub1DZ6WrZjTrewuf + ydPVtnpnIb0y8oTk4p6tPIsnuOcGirVJcGt6/QpvJIB2T257Iez5u6aJsHyWMTmC+60n12pOkL53 + faySj5wJaXKD8Bm+TsQ8wnNGgsutkY5CYmNzjz6ADkmUIH3gZpI+7m1G83zsQHrVXOzty3O2vHWc + w3bMCxJJxwUMt3sYoM+sG/hyPnTuwjxOEIhL6+P4ezT7VZT9BPjNXBH1aKgudysLCHfMYGFfC/ue + I+KlQyG/exOzUjltWo2bA5b92mHzaQ41lfbJGTnGk8G2GUQZb5rPN7oxgu2Xwej22/UYuNxF47de + Lr+bLRFeeymepJnHGvcMbiXqTbMgprandBbFUwyDPhOwl4cALOf3c0A7cGpwfGVLyvmnB4TPYVBw + 3MZFv6xO1IHxIzTk8GCyjD6lykNLpBc4OiR6xu2vUgGt/ftBNvyNpuB21tGhL2QcGJ3rcnGsMcBV + SEfM1NZq/mb2KoyBeSM4JG+XHh+iDJmcDUghnSx3+eb6hK786UBu1/0u6lJmdpBqgIgcperkrqhx + QniVT8s0D/ZaN7GbnyGqrRTH46sBc6jYMezKW4iT6+T2nbHOOnodeYcoE5Lp3Ay3AK5iJ2IHMW+6 + CC7lgVSb3iQ8zaGnGKch+PWXcPBugLL4bsHv1bmQPDL0iN3+PjrMbUJuT7GMKPJiD+01SyPRrrzW + tNe+EJYJSAnupiRbjFYu0FMxc/8d5XW9KAkvo4J7jhgXBy9adP/xBhueYz9YSzpw3KFCbAMe2K+/ + mbtA5smDlrV6Isu71qXyreYhDOcr2d7X+Jcv6PApLSE2yvvRXU/BM0C5McTk9Lz3dMH7FaJE5Dh8 + jE6iuw75RYKw7wNig2/jruXFZuGzECe/w6QBNIIWhEBqLlhug6dGsVTOEN0Z1d/qW6OMVqdwOBor + VlR4jGabsURIhdqY5lEH7nxguRVd4NElF8mE/erdgQ8IvUXY3j8u2WhdqwH99CquOAOwpZ8P0NPl + nOi9fQRrJGslWNqQ8xlu74D5fbQTKI0S619fD5suVClZ+IpHnbhNwtMpaH0PCs/Qxea2X8s110I0 + f5MPsUCFAf9J0QSb77nHBswf2bRg7a++iXr3KjAL9/KNtvr1kRWRaALcR4Kv1t/7y+KSbLowVxYq + sGomaHmZttaPMwsVkVXJ6Xjt3fEZnCrog3zy26ExNTbXlQna0wX677ucZHSwvyFksIqmProes5k7 + faEUJwdCLpXVRbPlUQPlZ/qZ6FY/y/4q5eBzNzK/dZ8wonrN8xIRXx2Jd/Y+W8svCGC1hvnErX7v + 9r/9Ud9+Nu2S1ajbyv168LW+WuxX09pPycOE8MSm/CTmvNavbPRoQa3Npwmdm0JbgNhVAFzfPdH0 + J4i6/FGGqE6YFnty9O3Jnt5UCLlZwfFuy/GzJnlDt2dTHPo3E2z65A3vK0RElryVrjObOVD9igjf + Rt+o52caxbBM9ilJ2HvSz/rE5FBS1RPRm93O7S3A6tD2Zx6rN+JrrB2uCXK2ExC/+qVVc7T2vSBm + 2F1El04Vt4jQuqM9tq5T36/US3J4AlSawErZqLmyOw8knB0R30vrftECzYdPQZ/JoSrNmnO/bQu9 + bmixPpJ9P0V9KoMPjizsGLsuIq1lG5BE/JHoonPNxk1/7P3DIyVeqh+z1U5JAnsthdi63XxA9ct2 + gvuWMthqPRsIY5EO0o+vfelkaZsesNDtqQnEar0vXcRyDOH53S3Erl3jp88LoApD8Kfnfv4CRPq6 + J5r5XnsatdcEdjdywE7NHfoZ0tWH+Xn5kOP9nPQTZd4ONMTsQfzvAPpZpvc3dFQu9ylbTWDNhIMP + 5fr5JhfhOYNFebcBHALjTvQ2jwD1UB5C4bFeiLN/f/vNf7aQTcsZP2xdc7n7ee7Qpvd9BO63mpwY + NkenpAxJuB4HbSmeggWGXV9hWWCaaD1NxwoOeS+SQ841/XI67DsUaEd+4i4g02Z6vhewNdaQ2N9l + ocR+PVRoPMcIq0K5ALpXNR8whqXj6LNXe15CIfvnJ02xq1zaeKUHHSZXJsgep7pzpgRCuF7u/qIq + Df3zJ2G0g1gR7SRbi+G9St+ILP7+Zr3o96GHEpTbEU171sM1HT7HCuKLcCfK4pJoCl/hAE1FzbDp + PB79XM5zCn/fR4Wr9OxLUWdo10Hs717aJZv84yUHG/+Te+OrGi+VTAE3vsHy/MRgq88Cfg9diTc9 + RFefqUNY+lWP9dhq6mGoIxltes9ngBf01NTfDLwEcUuOhfvs58e+94EzQI/IxVHu1+RhMnDUBYm4 + V+LUlO2XCu0c70syPrDd9fKdEnjSPntyLD5FtjpTwgDz+OLxUQ0msKjgNqHs9gFTtYRaLRQHa4J2 + 3tj4vHhTRntUNaCuL1ec+Z8QTLbLhWj3uMk+u+nvuT6VJYrK/o4tX7P6aenLGBkUW0RJHxVYNn0D + 3t6U+BRnTT+P3/QM3D3oiVZ1ebZE5FxJBVeP/usCMnf2GjGW+D7kse6Ob/qnH1Vfvk18Lyn9aryO + LKi7/E5uWcy69DnsGOCvAsXehsfUjZYUzo0f/Omp+XOIc5Qnvot//oC77t48yuph8IXiWdI18rkO + Co/5gr3zzcmWB1cOcPOfWJlQSacACh0cdU7C6m58uvOGJzD/7hifM/WyX97cPYQqexrI0Wg9bZpf + Xxnmk3glQbRWlF763oeH0zXAh63f1uexEMEv/9AlYYm6Ju5UuO2Xz/KPs7YOohQAHDoAW5lbRvPR + j0JUeZGJVQ/fNAp8m//1A7GAdNT43/4n5izh44lZo4k8vgMcpLtLvFca1VSN5zNKD8/Yf06GQDe8 + LQBmlOKn36LZnuMWvvbThFWN6TR6DqQcDql0wH67UG1Wd7kP5+xzJsc9djOWBzoLbHZ2iWX4cT8f + rqcKKHpc4+Roldqc1rcGXJ783afme63pVfiykq73xN+NFNOVfuEA88RzcbztJ2vKMIeSs3ewsuUX + i6gpMnQtdsZqHO4jaseNCKMbCojsqlYmvD4JA83jh8e/+ml/+m69XedpPpCqn4H78MHwClh82wtr + P9yLtUBdwHpkm4BFM7LqAQL6wNgsnjKY7wl1oH15aNjT+zdYfPXFI7cZz7746u41eXhYAgXjtPhw + SzClrRTPSI4ZBzupU9Vb/ZbwXXhHUszvl7byQWOh7Xn8j9If6tU8thK8sjTAxxvVI4EcWQYo2HIn + 8dRAQBIvi/fHc/rA3nn59AumtPvpFz/f/MW8BbngZnwt/Mvr1uSBIbwWpjaxyTRHG1+XwPJ8wxfH + B+5/eQ7Y/B2OtjxxDsqbhxYLjUT1bx/QpqIm//QbtqHyrP/47kreIjENxo1GT7/lgAkqipVqXmqy + uEoBgWHwU7A/ORmX+0sMLTaxsNnhJqPFbXXgJXMC7O6Apwk7aa5g0cKTv9vbvNsbBBVQL9bH5q/Y + iGz7ifhAsrC3fFQgdEIf/vjKR59rAujSjtMPr6etXgCXREGBnPP1hDe/7K7WgYNwSMUDlq/RVWva + 7YS4eZ0+/pgZjjs3wymAP763R/9dz681aFB8HGWsXc/x//Jjv/q2C9rStQ1VEdl1GPsl08zuehaS + Dh5hU5LTxh/TW9Yh0trFxWnqVP1fvtD7fjOhzX8Pxj1g0VoXT3J4aCH9W49GuEH8w2MWpycRrmIr + Ysvw+Xpc37MMo4f78T8YS+7EP4oAmushJoVSSu5grk7wx1/s4k3RzNuC+sNbnA8JrJfSST3YJ59o + 4hk/oPx2yB+d3p+ayGo4UtJ55xno+pdMqyBibWX1sYJT6sTYljuTDk6yn+CELPmXx4JZvKAWBjEu + /eeW71CyU2fEiZNBrClN3PUAOulPX6n5ZQ9IyNuttPGlL8p11C8L6VjA2OwbG+5n6efL4aLCQsoN + fLoEsCf3I6ig25AzMYUs61fVsBM4ydqbGJseXh3NyaU9Ud/Y+SpI2/ghhsE9fhPvfYHuegVNDNTz + 8CayGCuUO8IpB6E8nbHnxF+6KvWrg9pL/258WNaLDfwQhvbrOKkSevdj5b8k+OsvxRw+YH7vHzOY + Gy8gEXcXogV8xRRqw2Mk7iL24Os/VB/umMnyofjqwTzUmQzz7yCT3/oTmyohLI7Xfmo3P8knhazD + 15F1sD69q5rj8lvz58/cJonparsoBKY0nPB93B9cXpEdQxIaOJI7I03RqCSMCr2bR3HmYK6ejhcr + gQ/c3knYOi7gbpPiwbN5/fqh0VwA2fAHHoXUnl5Be4no6zYN0Cw/T/LTn59gRTwwniTCZlCUPT2z + sQyTy/u43Z9az4lV8n/9TJ9XvqZqsKjgFamVX/Mz0ijDLh4UdpeMGNshVnLIPBk6NqwmNuwJHbE1 + OgBLZ4h//mDlH3EIx9sa4S1/B/TlwAYELr8Q8yuYWosRygFxj6dpd7mcwCK+mzccro+nL8ZU07jM + WmWYMYcUb3kEWI7wnYOHdpFJJHze2ura9gwzPWv+/GS3Nw4D2PI/fGw7hv75f0XkVfzT83ObmuIf + njtf8a0RTpgd9Epvb6zE5qEWOu+8ohzuWv+aE5zxfm6+4UO7yuRYLmNGJpNrIffxTjjb6mGS++CN + 9pqjYfvHJ66at0DWYImjzlw0+l16CD5u8ybekOT98hmqAm78isPCdKNld/2ovzx8w8NY+4auyMDf + 9bkZvLLxhSwfcqXpEvxi7nS2n00JtGsUYyceR/B9uWsKwmJpsH9NvGzTOxDOiv/GDm+zYP3lP4+T + FZEgBDew5WUrcGh7Ixtf9AOALxUhByb4EZlaRh+eKYLf8yrfGEVrAHcd7PRywkqjXWv6q5+3SXf+ + fLgesrmxuQbOkpiT64b3y/PlQLjvz/ovL6zZa3lYobIMkBTnPozG1ow2PxrkRN4JHB1Frpgk9Suh + 3/zF7W7izKKKEYQt31Xpgq3RAs7xq0+MQeSMWz+CCCPcPIn8EeuetPlaQTFllEnMXDmi9ush/+Vp + BeG7aKpulwrxgWjh4CNq9aKOnwLuPRlOi+Jkm3+yRRiFbw//5msznRceVnHPY/spyhnn6bcCxlb1 + 9cVF8Cj98ZVbKCYOtzyAPnvRgNozNbEiRab2m49B2eNuE9rqha7TKINgjx2ib3k63fJDmH8Rg71r + odTCrsgduHubR6y0GUvX/NEGcJvPTJKhPwHd5nli4LILebSl42560QPf4nkj6tb/C30cDMldy8/m + T2/9xh8qjB25wBo/390luw4OwK1g+ry9lP3AD+EgzYr3Jq6DuX5pzSyE35QX/C6fp2icJCVEldW8 + fP57/NSzpCcyPLCx5DPH0Y4mR1OLX/7qi09fBlTWpRRcF2Py4Vr10WzgdwN3wkclsvmi/RpzLxm6 + DKtjbOu1O2fPEcL3xzQn1js02bJTuUYq/bLHJ8UBdL7wHQ/yOTliA+tGJmx8AumRw9gJzCrb8p0U + st54n+iWp279I8PJdAai1uniPvnzu/3L/x9r3UXjtr8gCb7sBN7VoSdtP7Rwyxcn0HN9NA6ndwgz + +HoR2x8tly3UWIcuj+2JspVPh3dGS5R0a4PNKg0j4cYxPnAezt5fSf8A5GvcC2id0gLLwvujzYHG + 8vAm5IfJborBHT6RNUE5hg4Oy1HQKFvgALxT+vWFLb9dj/W6+aPvfcpboY2WYLcGSAn2uwmwhpTN + 666GULeMyN9lMguW4TSFYJvvTZHmXyKS948ZXi+eg0/PuwsoyYkOP0aKifXJsTYVwzTDbT7yxxfk + vlNiyUNT+Ouf+iuWr1AUokLER+XyjubEanlYOqyNVU1T+nkO7BX+8MM5pa3GabFawQ8+Wf4uA/do + 7RuLhRbvAmJCN462egrgpqfxNcu/2djNjAOvvRiTRB2rjH5i6qHOwgH2I5loXyXh1f3lOg4TzfWZ + 9k+p82GVGinBgeq5b8B9RLTwuoWj7uFHw7U8rvsf3v70Mrv9HyX4qLueHBNlpd0DO3/5Gj5seeKS + REkB2LFKsbHl68KW/8NUPu+w20kVpePosX/zkIu2s11uUFlL3OYz5DfPmhsMxR9/Ypc1pGhN1qWC + YmndJrpAzV35Ua4grc6EXFO77jf8lMAXsU/8m/8KNlUCtM2X/fEw1v2WF0xQEJjrhOIY1+vPj/1/ + nCjg/+sTBcxkf4jcLZ9ooRfdg3X7Jv7+Ion1YtVeAefEHYn6zj/uUPoNA+PX2SdWWl20lYy4g3J7 + NqfdcF/B2mNSwIsUNQQ3lV9zLUhWAPJLRrAKAk04sZ4BI1ruiHX8jNpsFmuDEL802DZ2FuBT+8tD + JnQkH9yypR/se92B6uYd8ePE78D63VUdkM+7gBz5VeqHO1M6ULH6eRJJxGfjnXV5yAUp76NU6dwx + tvUUZv4zJSecPMHABowD99yLYowiApZmvqro2IMzca+ZTdlqsGMYkZL4/FWwwagYwAdvrL2IP8ty + zRemxkj5HUD/q+sCmF/2xpi1rhG/wk7P9le9hZHgVTiExyFbYe1N8A57g+jRqdJmJUIqjHJXJMY0 + IjoTGuqgrJYTzl3Ro5TNLx5sNHggKU+abNaUNEEigwmx2/cn+8rvJUB8VuQYm4Bun7/70Mu5gtiB + dAZLEVIVeW1dEsWtroDzd5cJTU/zNu3p81MvXSeLMA3ZHDtkP9HZy2wIQ2sxiTnJfPZ7H41ckhK9 + UbWe6/qoQzdtfuOQhro2An8RUWvnCzmqF6FfF/sWAzUbOSzfDrCeQyHz4RDvoL/SK3bZUe9kyFnJ + lcTM7emy1gQqJDDlE99c6dnTcF3OiPPSt/8ZQR+94r2bQNAPGrbhVac8fC4M2u2PPlaLpwqEZfwG + cPB3d6KBTqqH55jrQJ7PbxIflFfGR0UVo9PepL76eJ/60X0EMsoc70FOe8mKJm8NdGBqjEKOaMzc + NWTaBAKrF4lbnzRNqCzWgo0VyVibO5JRDc45cm1LIfG+phnZW3UHI+pHE2PX54yF+8RAmjoHOHw8 + jVrA8WeAzWd3IkoanerVjUqIQoGViRLexYwmZRFD/cs2OF7bFXD0dXCQrloria/CF/DLPoiR2ht3 + chJ2bk9fH2LADoQyjl/rhXK23ErAMfQ9yQazzCbVPRpwmueRxAcjiGaVMhK4qKGN/eWAo3k4sCuU + g+5CfLtmo3m8cAOy1sEi7rkU6XJ8RCwqH+aIbVWfteWF1gKd341JkiaJNV6qLRE+4/vyVw8rvcU6 + vNrX1F+Mz05b0Vw6SMnlD7njYFevnR+IyLUdheAlcXueOGWImBd798G1ZAH9rc/h5QtYiaMqY09f + YQbWVTamMTQOlBPD6QydJcf4Bq8N7Q23qwBKVx776fmdTZZhT6gDgYyT4+eosYbsFrA4q0fsA9kF + Qi0yEG71i+93hevn/jAzyJ7Glai3t+Oyl2Zu4PPLtvgIRhrNx+/soI5+XP9/knYl3crCWPAHuZBJ + kiyZQaYgIOIOEBEQlSlAfn0f3tfL3vWS43sKyb11q+omgT5PHt2cFvYwU5/MzJU8G835N4Co1uve + f1EtBGsTXG243Kqrj5gtUpnzY+hFM4564lj9c1jB4teQvzk5kS6Skq80WgvQfEMVG8OG8jHunBis + YpsR/yDx+bjnP4zVF5wPRvMF1E0qA97N8jyz558MWBeHIsTZkGN/tn0wJSc1Q+sBXGd2uyYD8yqk + FGjZE/pom6SI+y6wBA/r0M3wNncOQe1qoP0aXzqfpUvxXHe8a78z6viPM6nGUQKvH/f1kRToDgep + 9wWb0p5IHlcpXXb8RdnmyzNCE1DXSh4sMbTKfI5F4asuI/NTIGhvIlY8FoBlaeIDnBBpieni2iH+ + 9W7DMr1axI69YqAliBlYKUtMSqmR8/X0imvECET1jyb3pT9/thVI2qfn80+9a/h1egXwsfwSct7x + a89fA7mVn5ASSA7l6L00oAO5F8F/9SHAQgKHJiqweVitfHveXAHa180jukLd6Ifn3fF1Oxmfd/xZ + yadR0O2uzMQ3mi8d+NwI4Szn1tyEhzfgt+ltoEIHJfHGrQHrmJ4k2MAgxLmgaA0zmmaFuLwocCqP + X7pef0oBneszn0++ODnToxZskcuphXXOkYaxwIuBPsb9R26x3eVr4OMFdJyxK0bYN1R11wKdD0ZG + rE9kOqumjwGcvVTAvlPKw1zcu1YMW+vh8/79OFCckBHi+deTu3x8O0s9ygnisQ2xwoSWSmvzlaLn + RwvJMx8v0dZGcYDQXN79bxc1+fa8ijY8l9kZ56Mp5Zzi6BpqvoFKvC88ARqvfQoNSTj5HWxKwP7N + 1/GEfb9KpG6v32ILq7p8Y1zXHaXNrY6RheMIp79oBjzoJAWN802ZkePoOX+MAYQ7/uI7U0h/9eWL + Mp9xsXm3bZWp0N1F6XFJSG6/R0qezoODm9/9CL6sMdi2U2FBcJxDHK9K0vBP58qgrf5wWBvNB6Dd + W26RlV9lkq2nZFjnW6/95SuO5t4c2Av4hRDlek2y3yGM9niJUeeoyR8e5LQcIgG9Ekcnvlf+IqYs + ew0WU9gSp3fVgdcOpgSdhVuJ88zvDltoZwUtpKIkWNcN0FHORHh9wYxc3VnIaVRDF0xoan3usXXN + pn1/Bdzxd9/ThcDy+QYz+ijajKOlJxG9E636uyaO2rsRd3wGAcpLd8Yq+7achVYghBVXMjveHId1 + EjYRGXFbYHtlhmHkkTXCRvcE8mwfTbMcvpaPCjZSif+xm2iQcGSh+aIVJHKtI10d+ViCw5t7+AdO + +jqkgM2GfBHZxFWR1RAJ59bffOC4Vxx1q9vRgjtf84XmgRzy080EgVi64OCuFwNtjayEfh6eiDNd + RIeIn3CDztUixNH7WKUdq1SI77kDMVLeA+zEVxaCw/1C9Pfz1NCTGQh/9elvPPL1+rP/jRe5e+Iv + 35ivFsLB/GTzMq7EWS63SwL6Y4iJY1fyzhdOEAav1CQmwzlgMz5hBdVsm32mdi90q9oqBsUCLXJt + nivYLkdgo6+3juSZfeqBu8j6AXrHwNn5AXBmEJcVDE7pm9xWrsq3eAoq+DeeetDW+cYlMAUwmp4+ + H+M1X7zBFsHnsig4ZgRpoO8uTsAzIvmMSliDzVa/EmzdD/iLd2czOWtGeLv6WC6iU05XHx3g08kC + Hz5+NSDuhy4Q+Zs1n+qFHfb47mDJz5PPZK4y0MfY9TC5oo6cp1elLpLuM1CN9BNxPYFptocp2f/G + 4/zp1GhNzLWHs5cJxCxsttmyDFUiu5gmlrXHa6DDehdBZWwNcS0EAGVzu4BvXjZJMHCJunjurYPN + tyVE3uvhtvS9BYAgP/7isaHDK4Uw+cUPUhyvrrOCymshiJXLzIbzk46vwkpFYwx8IvXcltNirBlk + XxdvLk96OUzIqw/gsLIOUc/eMRpQvFiIDTR7XoSRRtPz9fNhT98OOdvaM+pNMy6R2tHCP/lHwVnx + 89aLwUoWjDGXOuSPb5V0LHFxoeeB7SFJQI3w3V+PIqH09RV9+LBg949fLX6uL0Ao/Y14uZHSOc1O + 44me+4DIpd81pFd+DLxcjQ/W+2yL6FICDX6mUiUe/I75dKrkBfUuNuft3tnqH78GeB56n/9J27Dg + 9Bv8mx9j53+rIIdf8Pge/8Y7B5u8O8hLzgZEeRdqxOZsuMD68kqJE72PoF/EQYN/eGAZo9tMu76C + ciqlJB9cM+e5n9DDna/v9fWVk28jfuHF0yysgNshmjajlGC4nB2s5ZdLzuThtYXfc7kScwBqw1ti + 2MKHEDvkUvl+RPnz1qHTAl1yr5drw6B7KMH2edhmZkYqYJ6H5gDN4JITHf9++cZ2p0qcJ/08bzo7 + NiuxjqPoR1FKzAyndMsytoYHw3JJAD5azuXf9ACqcpWxQb5mviiUE6GFk4icg+MTbPT8sGAtCCV5 + hIYOltJ/2Wjnt/Nhsw/Oml0DAQX2dcRXnd4A1zCTAZWP5hPZq4/NWrb+CCUXriR+uLO6xImSQU15 + lFi+pwLdxvLRQ4MJJhKZ1tjMnnI3UJlKBbacjx4tS/sZxcCQVhKqrAboITFmuNfnXa9+weLm8gF2 + nCbhLLUuwxxixoZgmNWZ5QOSE334dvCm3xzslEmfb21zdaHFXwlRdj31Fz9g13d/+R8tUfoRxU+p + 51iJOyPfbrawwfPhbhC3+vkRvcpDCm/CfPTFSmUp2bzBBdxzvWH/CLSBWUI5hNrEULzHT864RTyC + 2+P2w5Z4dsAfHwPzxSiI4i0BZcSHX0L9XLH+iSu+6vpgyw7kpT8Ts9VPlIaargE8Jwds7fxmaPnW + RV4kcv4R+rLDDtaiQbvTOX+1Hz9nReWhh/rqvYl8jyYw/eUbfSch8e6ykZMys/x/8XnnXpE68eu9 + B/TRPwn2119OP6qwwOhWXXBS8Q9n7UHRIefLhX/8OVreGQqhylkavlTnJh908KrAG8c3rF/bzpmM + RupRswQ1OS8vZ/in3y5X7YNDe9H/9K2IDqXdE6tKjd1P6Hu4GSfGhxf6G+jDyRSYullNbNRrgCus + UERZwg3Y/cOPKcMdNKq3jZ0uuTdr9zRteJK7j38qhkYdBe88Al0QM2L692ezPrbVgPP93u31DEfL + 5XfcwE8/LzNzzoOB4991iy6yluI//cbt+YHeH8Xe67nTTOHQ2lBghTfWlHeuLgUWDCgKCd7zd422 + qOxjwFEbE0m15XwVD4IAdnzFqneonTkprB7efvIw734GXYPhG4Nr+amJFxpvOjHNlv09L/a1bz6s + nQcKwJdcgHVJOKpjqjYdtDPd8lEHN4dMr3iD6WhQ7GrCq/mFkOdQVdML3usFXZpHefiHp/qQJequ + xw7Q1aoYu8K1otuL+YXg+G4G4tXyPWIj8NZQxDf+zEyoUv/wG2Yiz/vNh3wc6uuBD/Z6iD05sCiT + h48WNq4wYXnTiLo+N0ZEKaOHBGvgBviL/uTAeSbbLBQ1BPPjdjb+/Cis+z+94c6GXgN4NJj59BsS + dWIoSAB/O+f+Op9Fpyqeq4Sw30/YiurfQMvMcuGv8jNss6QayO5XQMFoJH+qoN3w5ZALkBEmdUax + XA+UP4sdlL6J6Z/uSz0sFbr4f3jtLzfpB1brdRoh5m4P7FxyJV93fwp9BS7xT5+uiabc6Cr4pPn4 + h9c5eYxzD4NEnbGG6HfYWG4r0WXc7vjcDvsKjYERxZbv39g0y6ez7PmObPvO4PN9URrmIXsjFIUe + EL16snS50y5FMCJPopUXSGnufg/gjz86ZWJH7Gy+LMhKpPbXSRtyavaXL5Qrife9nb8zSxQr6O6a + i/++/YTmn7+gZqXiw5iy6nie+Rh+f895XpkjaaYjei9w7GNKXEPJ8+/zyM28K9U6xhrgwRqBSYP3 + Z/khvl96znIxgxQWXhthU7pblErk7kIj7grsjzb33/oZH9o3wcmaDltf3jl4YTYB2/0hcdjd/4Im + iDWcdSrKaYbEGfYpZrGR8J2zcMfOBbUglljX5p/z066TD6dQbXzOMBhn4rc2RZT9Aexs5gf887P+ + 9Krk2MuwIdX/iubxOBAJ1S+H3+YjB3sKeN+jG3Gmv+9rmHD1D8n9ki8zy7eQa9YP9t68mjMecBhA + WutF/uXD7p/Avb5g2/feAxV+sBQVkqfYLPWazhb/DOBd3XZDVi8H8o1OM2yfcCO59xZy4iZfDV5P + UMBBDXxnoc+qhu75DIiyhVW0dh4t/vlL96soDN+fcC2h8BIS/OfX/NOzmxJ4+Mo7bT5GZuGKZ3vu + /vIVkNvx/IU4+bnk8vJDp29omAD18PCJfEl/zQLthkO73scarxzU5ZvmLVwjL8ZKsBQRZyV2/3d/ + JDuHI5j9LRLEEDAY20+rAAtOq/Af/zXVewumuspCWJyikJgefoPN7nAK87f4wjhYV4c+RVv7p5+w + xY1gReh1gPlDUIh9sQxnvRv1/Of/zqd2CPONv76+ULmlIpEP7MuZp3yxkXiOErz7l83XAw4HNeVZ + Eht755wuj28CDhfOJ2Zsd9E8HPIZotys//KvobfgmooRYXMivTl/YOxp7GD/FPv5ERnnZt3jB+56 + AktSDKIF36kCjowQEX0NPpRwQEvQwbDdmSPfTzS8bF+EP6k643LWVTqvpzSB6ahRkvpjTleLehlM + pXQjxv739Bi9fMjnTo/Pc+cDmrymAKaBZpDyZzvOzHzd4HQ+aBm51lnj/NVv+OqdeI+/JmIjMUzA + n75zovcTrLwyZeIgHn2sZN0EtuPKzfD4agriJLmhci+dVf78/T+/khIr/4T/9IZcndV8Cw9VChFH + W39jwwEM94fAwN2Pmg+TXql0qJsRnq6hTYzdP9i8MJP4n8Sw2Nj5No2Vw67nx9Cn4I2H8XezOLio + zOYvHUARZcfShpvxtrAZ4zVaX69JArs/S6Tjhw6zaRFfDJcHj9U//sZzXAC43btVn6eJzpxezcDK + bzL2pu3T7PECYaw2cK8XPP391e/nM3sSHRkvdbbVrwJsltrE+qSbulYhxwHnYlcz4M1hoG2X1nBx + mIgkx5LQtdBkCQFG9om+58+uF2twQBXGEhkllc24qYYEWkdfeKb7GUWmZCGk6hp2dPJUt3O7cOh2 + Ub29Ppwpf7KG/h+/MJ/2ZeA2+yOCPz3vGYE7LH/1eNcn2H4dGnX37xT4dNJgXrnjB2wvHUkwLoUY + 36JYiIYCcdVfP4V4ZrBEv6PXLZBttTtOu6iJ1tN6LSCjX/FcORgMsxvzNZjH7TEfhchVF70613Co + 9zOSd721GIelh+yo8ThZm9YZKy904dPM5JlNfgxYL+AVIOKuGXbCalUJm9sl5EsmII+9fq97fwag + vtzPODFqZ5uGkwHZONd3/to2IwCohTv/+MdPNv7dd/BRfM84wVBvdv9SgrL1W2Y2b71mGVmwAB2K + v1nY/azVonqKRu728uc2fkRr1o0dpD90Itf++om+O98FknWose7OQvQTJWcG2lrdZp5IovrdoicD + 7c7kdn59pdsff1hNv/Mhyg5gFQ+LAOkZwr1fkkSjvtgFrHU28dflWEX/6ldrwviPb0bDyQxE5EJP + Jb72Bc3UPbH9736UaPByPmKwABbJwbPNES3nvhIq4b2xHJzcYTEs3x8JgeroGTYOpasON6koIR+d + XaxK1iun8YbsPz7ow9dvU9fD1DJgrz/z7k87m241Kfy8liOJH2bjjKOJKzBut9Bfw9SLGAxwL97z + IsV/+ESXRxWjv/4ZZd5tvtCFccXdLybpUWuHTf4tKSiu7favnzGd5KSE0jgoOz5fnOUhe7M4MwvE + njjhZju80CKaINGwvgStOgnhnIhxJ1Kfv2LW2R5pzgFRiDE2X/Y9p5kHZnCwrXw+Gtxb3QQmXeDu + R8/cPD3omNdJDZ+3qZ8PiyQNDFEjDbrfV4VdMaPRzj/hP/4u3/0aLHYRCIBUdohtHDL5pgl9Clex + y4j6Yuxmu9uwg8IkGPj6x2eMg/AV1cg8YfO0tsOq6N8CKpLMYOXWKTk/vNID3P0jfxE+Vr4RuwqQ + /vK//mnXM8vMHlvoFvVG1IWI0df7CRaE5rjOgFeqYVrMEUIlygYiORg0i386GdAjoCGym+qAtYMf + A/b8Ia4rLHS1XuuI/vwTzEZVs35hU0HlaIzkr58wj558gGmcy1jC+ruhktQe4NgrJna6y9yMm3TK + gBR/W5wkfKdOITwy8FdUHLbbg99w07BqaPKvR2LY8tmhctUxsFJNiZhRnObrxH8tuK9Bw/rld3G2 + wS9myNucjOVLem7+/C9YiZRgle2pM9yCRwovTOES8y+fdr6FNvYwEYP9lvl2fKYBRHNxJ5Y/ArC6 + x4sAzqLm+CfhJdJZTasavRDHY60x+2GdzZ8F8lOr/fWTGi54riH665/u/Wh1UZW3Bd22uRA/i418 + c2O+ghm2BHz78xv++tHVXF6J+jpdmxHaDYOuSnDGSbVtzrj7GeLCv0LsRU48zNUbGDAjDjtPux88 + ShTs/jEv//k5zsSwhgZDlybzFDhp89f/hcm5d4lplkd1fnzj5a+/5R/ZqBoWVZkssPt9c9Mxc766 + OBNh+LVH/2iDZ7SFL5OBayac8dkXPYdZ2s8M9/4O2eM5X69iacFlP2tNSiWPsg/NZSARrIVocuXu + fn4RQkuNGxLBN23IwfC2f/db50Mx0JNQ98AQ3l9ijsBsyFGtQlSS0SdBzTXqdorDEfwfKwr4/72i + QLl/zv5x1UV1Xd2ugCdzW32GzXq6QcY34CdVByJ1/eKMJHoZyBqe0cxyrEIX4G81siEKiNL8CKB9 + 1hqwrb4JKbxvpy4Ht1HgFierD1F3bvjvJm3w6xA8A0n3o42TNQmxSTLNdLQCwGhaEMLU7GZsXqcQ + UHENNahoEYet+jgCGn3THqqnV0Ds4Nfl9HrPGCh3x4J4Z9ej9OSYNkxrmmHr1nf5t0skCzrzyyCP + VzFF6/3C2Ij5wMLnj+efQ6+POkTj+M6JnCMr4hVpFOGkGzG25vfqrG9P74DW8T3xXR2qq7GQUNz/ + n2inwRoWFisGXFvxR2S7ooDO6+oinzwc7KT8OWd+zn2DSrHMJE4MpWEiFNqwmw8TMfPoHdEp9izA + kdMPxyfY5esJWDM8KlgkxigdwLycxQJt26SQh8mnYLkvcYBklHTzuBKzYY+zwiEwVTYJ15uSs/rr + 28PLeGaIBaNm4K7aK0E3ibz9FbPfZrC0cYOxDHwsU9iry10ONfQbiYf10NUBb0GdgZviqVhSmnlY + xNXIkB0fbOxuF9Xh54oWKH8wBjEXXRrY+JOKAEy1PfNTztEV5LoNsyk2SVhzycB93EuFop9oEmk4 + toAv+rOCrPQyY+sBWLB18ztFQuIDYucOjbagejMwdRjbP7JCFLGXd3BAmjvup1YOXL6+obmA9Wv4 + RH6hzJm1AtXi/dc3M6NPb4cFH1DC8fp1Z/DolYHzP+4Cg4TbO5i+qI7iaqRww/KDmAfBicbra5Zg + mXcdkTI6q2t3HcuT/SUMlkXCOuvjdw5gR12bXNyvom6lYodAClqdXI61n7OP9WsjX/V/2A5ETeUr + gFvojHlK7Ef0amjx3k8ZVNMRW9LnmP+qK1Tg87id8S1nJSDw/VGB2lpcSJnDB+BfD9VGhRf65JrO + 1FmeECgwSyDCxcHSVaYKrRCeePdE7tb4UumBf5VoLoeASOq+J/MmHyyxit8DVvljOFA3ihTYlvWH + +D8rpqufbRZ6nW+GX0XkvXcYrjESfmmF04Sf1O2wHjpoLsGX3AE/qJxYfFtoVdsHO0eRDnQgpwDC + lTLYkT5nyslhMiOmE94kXQq5YdrymELxYCgES+px2Er7Z0PeaU1ssb87oNdg7yh2k+CvuvN0OCjB + AjTytq85j2m0DIJWQF4QXjgcoyHfMuLW/35PEVuxmaTxkyFuk1N8Nr9nymVg41D7KBds3JajQ/OY + Mii+xxgHLw0NNA2BCKc6nWf+pdxzbgKuBWApnf2FSZWG4SU3hCFhU2xP+SvamGKx4RPU03zqzyld + pZGksMZ2SrzX5DgLfnQ29NDpilUhWJpFkyQOkvH5mxHPFvn2CpYaSc1FIOY0MsMCFM2FqdW/sXp6 + 63mf2sIIOeZs+NT0WTD3tiIhLpMuZM8HSniS2ZB/3lb/oMs0/8M7YO8rgNLSpc2s6oGCQks8E392 + HcBNamNAI4hfPm+bfsOUTZIB30j9mT8xTERb30tgmdUEx4/RoMvn7W/wfShmfB5p3fz+8GaZ5Scx + rDwFLBsiDpzr5EGcmyTQ8ctpNWJlOcNqIa3OVNV9DA7X4w2f5SqntEzoBilfPrCE4ofDp8/eh5pq + SETKNQdQZ30WoC2yk7dVxTnasLbsp+7LEfEbS3WogSMb/d2fXnKyQ4dPuL/1gHljJY5/DaPWxSjG + 9KHteKY238iWamSRkezx6aisLMMenjKYkNvfeH85rYJXwX9iLW+FYSyzYwX3+oL9y+A3yzAktviF + no81Nb4761d5+vAPn5x6XwfAxPcQRlGN9vl658wvWnoEM64l2KsYddbeRQpPheThaz+pKlvg9ou0 + QTfwXbDO6nK9mzYkh8+NqE/vNtD2zArg/qlYfDlKG13jUlTgRZEl8qhx13xt8NuATpgXCW+bBzY5 + Qy1cg8Hzf2XDDH2vi5147+fMn9lXGNEDWAT49N0IG9nQDet5fi7wfLqM5GKv67BC5rWhA5ZMHB1M + iVLfGr8wZruNmHY6RtsnqCDyYi6d3/JlBBvniQl8elaB75HvOwyeBwGaIfSnk3dDYBVerYbe7elI + 1Bf/yjldfHWoSd4fokslbQajOJfiS1h9HA3d1Vmdd1WhV1EN+B6H75wxRlojTYKhH+/zsT7aEwOD + SyPvp048wFKbFwF5Uguxuh5llbWP1wq1hkzIuc+S5oMfK4cgMSSCQ4UM6x3OAjSC5IVjnklV/r1Y + DHJPZxHveOhsSWEw0PZeFgmr4pdTNZMrVL/lFd+04JAvEUNDQViuC0lGqQRLdFJGNHS+hm1t03LG + RKcAXjlt70CNHeVO3LWFk67F5PZqY8DJGdtCE+kK0W/3g7OV9stGTWXdyF2wfs6mDCCBn1ebk8Tz + N4fqysjBfbx9QR+SaI0/gYC0rbLwRVolh5cJMeCHFCMOzsm9ofVsCUjRLhyRE0NQl4SjLtoOjzcx + te1Eh6vb1nD0sT0zbGaDDWUXA/36zxPrj81WlzuyW5hNiUkuNuacrj9pPtrxk9hddaJzRiOInmLJ + 7W+5kBrGNAIXuk1YE3+vj31pXGZUfO7qfBDfv4jW715CC1u8cP7p4z/+E4hOf+nnl1oMzsfXrj7q + X+UPq+VQOMuZ2iKcz1dE7OfBHTZf+pWww9WbmFT+Rev2GF34Yj+/+TReTIe11UmCIR9k2K8116FJ + H7SoqoIzvlv2e1i260+CVpYXcyefnIFHPbWgwx4s/7R/Ps6im8Id///wttmiK1/Av3pmZ+K1WQ+v + ooK5qP3mw4P60cJGlxpmV2AQh24RWA8Pz/j7PRJf2o/D3qZvALtJ5Iheci+Hpnm4IBIXDXkcnobD + tmvagrZeNBJQ1RvoBgMJxhJ7wyaVz/nyhkMBlHzkcfDsPjmpgNnBarYzrBUGk9OkO1swvrkvfGVX + B4xhw9Zozzd8+TTuwIhfUkPO4EZ8Prn7qe9+D6Gl1RoxOslzWLzdetgkn89cFuWp6R+rW8IqIff5 + FIfvaAuNhUGzeKJEWb+SM57vXQjcNr5j6WByOVkkXgDNrHyJJX2e0XZp0b6z94b++7yKNAqALMQg + EvOTcs4upgO4D8tlHx9Tpah0OThq7ZVgUL7Byt68AIw4komsO0d1i7vdP0GnKyneie1wjphJ0DDZ + 71wy892hGY9HsOMb2fPdYYW758K5/AXEuVBpWPQoEeFfvGPyUemigVMMb0WmzKdqUOjylsQDhHpz + mTfL/YF3Ncg+uuj+2+ev0wa2pPA5+NEBwvKc8MN47kcF/r6PFV/l2h6Y+3k5wCyVmLkPfl1Es+Pc + QUl4Ozhcb3W085sQ/OmJ/XkcGlsPAwbi++CfqrOZE8XLXVieDjw+m8YcbX/13D5qB/KMDKNZrnds + QQlyHyIn36hZu379gjeWOuLPj/PAuAtI4eFQ4vkk3DuwDJrIiOaX72Z0yrGzdJdlhke6Ln7zJPuK + xfscwGQQVeL8gv18tJ0v/VjXmLnb8lQXT159WPWQJUFnMcP6ol8D8s3HJ1pyq/LFVKQFnbizgqUn + r6vjzndO8uALWLarCCx//FdvJBPbRWVFnHc5dqA0omjHh2pYSavaKAlvMTmXTBgxhQg0aJzy0kdX + 7uywEFlfUHtKPwdK4w8bPksxqlH98/fno5vp8TMI4rkjuERWs1Z1nYBz4BPik0ABTMIBHz49u8BK + MnN0e2n2vkL6dCPR5H72PYqXAmneMpK8fUwDfb2yDhoPG+z8YXfMn+uC1nABONj5B2t9FgWd1Nd1 + hrv+3OZ0CRB/Lu4k/j5XSmlRFJC9pa1/mI9vdSmOdg0N/rKQS73QaG7DvxPRz5jgdoTDmAC7/4df + RbPcoq+tSTOEknfwxyx85UsTSy7omEDGwUEkA8FdMMOrOVf+svPPqXa8Hh7z7bfrFdHZ/KFwT/dh + uxCndTEYv8omQItVVCx5wlVdobC6kHjya+8I5vmCX2wH8gdnEC1Y97cu6au2n6E3YJlmc0OO2JRg + +xo9rFgPc+BeR3EUtludYCyb3PB3fzDVHxLWNHGgRKqqQnRSBxLzxMQRPd4mERpNbWA59Y2IXNDB + PeXArojXrYrKGDm7gT8+cXFvbT62nyRAb5Tyez588uVgRS3UfKbBuMwnZ1mliIOvoh5mdpkYMNpW + aoGp2veDCJurzlqAfVj72otc+4MEtta+t+CUcl/iruUPUPUqagB5xQXn4rsE4/WObfRT2vt+/w7l + fsuUgOHJnLBnBxpdH0V2EAvQP7GyGlrDmY0mIvHEdfue2ADwB/5XwDJvO5zxyI+m37h26F8+kqAG + 65H7zdAw+a9/uvv7joXlnQBeEF8ejQd74A6/2ww/ZcoSfPvWznZbCwV6CTRwTgMhWpu2D//wdEZH + WR62rEcaXMMNEJV/8OqOd7P4xIGNc9a06GJbgSV+6j70meMgOou0hhVKT3aLXavFKhOA0EcmXr5Y + CR/3/UzE1AXZGAbzaefr451pZnDhthzv8dn8m199Xvp//P/f+OUMTfwN3ChdTXkyoM9sz0m4UM4h + ApRK6GxLQ0LBnnLirLdC9BV7wyaeZIf/qw/XtyJ66DSOlFwUfYbITTNsf7+WSsFpgbC4qCd/lVZJ + 3a7WsqFMIS+Mec9Sl2B/K5bsieXcWI1NOfmpK/B34MCuj1t1o0dXEo8FVxH507gNK+eXAP1wSnd+ + Pjd/+gTs/G8+2Inl8H/xvUnmFZ+FXmvY7g4EKCLJwdFJ/VEiFRflnx6wMxPmS7g0HPyQciSGH/6c + DXr9Bs5Pm8eGgupmIw0pwO5fEaO72ICcy3MGJf4VEyNKJHX7MXCBUdwxWFKYMae309OFx/ngz2i7 + dRHJX70Fn4c5xKq5r9CWDxYDQXTM/UFKXGe7rEMHHdKi3S8qHEojtoP6NFL/sF9vivD7QrcrTb+I + x5n+jkerg+w15P7p9R4l5/3zwsTlXi92/Ar//DSMjUMHlmCjCdq/f2byNh3oKlgt/A8AAAD//6Rd + ydaqPLO+IAbSScIQAZE+CIgwE1QERNoEyNWfxbu/4T87w3dtt0JSqaepJPWhT4CctB3HfvvQTg5u + 2xMTY+61LcsOMax+zptYO35yBy3VZe8mIuKZINCoUQaSbE4fG4/Kr6fUuuY6FIfijkf0iEc6iqkp + DSSRkHHqPMBxNW1lezF5dDpUfiS49+EF6lGU0Cv4CTWdtUsKfY5Ndj0fRVPFbBA6HDSRebYkQPb4 + kX85CtCeXxzuQUpX1sR3Rjxs7DdblV0BvvZq/1s/64yPJdzjy2ckzaODOPMTPCmKR1wmqyjBJXjB + 15tXkfZptb2LopLCXZ8guzOmcbHBZ5PvmftE0eVNRzI7ODielspFVvn4Ovhn2gYE4bH501P1bPF8 + J3tQeCFdT5cc+z99Azvf9rldL1EsBQZstU3d463I+b4zXjD9zDVSts/m9G8F8/Dd3UxMyyveP5/q + sO2+NTopa+ms2fnMAqa3IVEyImiL0RQDEOqvT8z6Y+ZbI9c+rLfgjP70FZWacfk3f+fwWY84JrHx + h5dYYpcL2HgIS5i0zYLer8kGS/MSHn/+ClEuv2+9FBLV5ezqjASl0cEZ9vwFKmSmKL0qkNLlUZRQ + lrYRqVcg1yTq0g6cfOaGjJYaOVtcPoW8xwO5Qt3Whv79a8GBO76I529KxH4+vgR5fWLQsy31aCH3 + pZQ/Arn7y1dIKBfX1gsWoHv/6SOtv5dHCGAu5b6kZmmOb8pVkpXXK/GPQHCcTYVHA07yGBI38+2c + 7P4ozAUIdv/nMRIsBbo8gtPXl4LeyDnbdSSYeamCTijyHAxhw4Dsm198tlvIuPt1AbSal4MfjC+D + IShnHs7VAyOnwU6+uRfzAXmDnUie3mawPdrvAHKBAcRcHI2u96KqoK1gjCwzr6NNO6cK7E9lRe7a + 95TzJgH+3/cRW3uiaGPTboL8TIHPixmMqFwVIRgK60bc6RFSirLhAfOjuuHNyZYI7/lGIhvLojv0 + lJwj06CCnb8Sy5xOGv+Ll4f8pze0+DHRadXHEPL0gpA3N7M2L8taQNYe1F0vpg4dxw8PtMvhQf70 + A8s8uwUuOqz+PT//qBIXJvFTxcdJ6fPpfc4YKH2/NgrYVB353b+ClEsIUUV7jqgSSTaUxU5GMXKA + NkO23+DxwST+OzLacSFRr//5vUhHMz9+R+UywS6yr5hlf2u9jKJbQAkwMdGGinFmLpRZ0e5mFtko + vkYUXl8l3PkZsh+9AFYvzEP4VL8EP8XuRtkd/8Ge75Bj//Jo4xbDhWqSN0T3+/e4liTtQHo0G3L+ + baCed30Kwq89IdU9aM6YhlQC99NaINfkXLrlihqDqBcvJHv/xnqTX10pGWciEZvmIaX6/G4FOtLD + nl9Uyi6tYIKdjxE9V2VnK4WIh2w1WkTX4qO2CPQpwt0fxyJ3eeV4OOouBLg5YaXISkrD93GDOFgO + yC7W1qF7/oGVJZz9g4eSCP/F42kKyT/85KCsDLI5f0/E2DJ7FJbEM6TDY9qI8X1dx1X8ggrKHwR9 + gGDgLNv12sq/smowLamdU1WH8Z8+RJdbcdOo1XYBmLMjt/NNQomqNCJE3+CFkMuINXa+Lg/CWmUw + GU7XiHzrRwWT8Bbv8VtE1G2CHd/nDzExj8HU/F7B3/gjL/jE0RId7QmOIhB8pge+8w8v9/qNv+T6 + SLe+81+QasuR5L+Mj2Y7HhY4P88Pf0Dxmk/bNWthCsIQnZtYyOdhPamA6S7nnS9O2mrx/PDnZ6AT + vP0oZvo3Brtf7x+94zEfPoFYQssSGmJcfCUXdv8bimnck8vNEaJF1HMM4YNtSHFCXb2I5miCv3oC + ek0DJXlMeUiY7x0pu37alnOtyH965JwFuN7xz4Zuk2Q+r9pWvvnKp/jDB3KxvtQhCe4UGDFzR3Qa + wnq7c/Xrj4+hMMEJpWtFHzKI5Nxn+3DU8BCWknw1goKc3Rs37noohsPj9MMijLRx18M+5CLHRKZ7 + U5ydn05/fBi3zvGpLWHnMnBsXR2lO57+6T8ZJBcLc1yJtTVIq066KHxJ/OsjAFQVz/Af3/7zv+Yo + dCbAE9D7Euu7EX+q4xcEydlC2gQGbY3HMpYVN+7+xt+hvoBZEO2X3aoN0wBM2K8LBeuVkctj7QHN + 3YIBzbt5IYe7mGDXn7oUisnV5xYmrFfxGQ/Q0eLcly54dhZibAvUD0n1Dy/XR3WUQOfxss/ufixp + /HMCpd8kEp98ynrJLBHC7V4m+N/4XzmqwPU667i2ksah30XhIWrjG65S/qvt9RIFatfSJq9zuDrL + 7cu9wD5/yN/jc2sFN4Bh69bESfVAo1fVw4AZXPrnV0bLZNkhzK12QH/5dKoumQSY+ZH60nv2wRqK + RIVqkjW4At21rv/yFwgo76+d+KnXt4hEGJ91Sq7J/Qbqql+Lf/inS17j0L/62V5fI9cmabWl2twB + vt6sSuKz049rZTAuUD+KQ077v5MeHuBf/RShyGhrEuZVAtyjI+GB8Z9gLZybK6UOb6PLsU5q+lc/ + WEzzgsx6M7Q/PxJ4Eln+5dfphosYXtbkjUzSmxrXT2sjR/AnEcOvW7CV7UuHVi6H//jRdl9jBX4d + 4YQuOz5NSnFOgHBkUoR4a9Xoe0lTCKYiQtGU+fWaXJ4xPJSqQVyzIU4XCOoiE/+SkrNTFQ4VoVLI + O94ie+cvy0oSBVbwtSHt/eKipdAeKjSe5q4XLgNYSd4EYOCm87/xFf70KvG0D7HeyUxp/nxM0NGS + nCjrE2iYLRYTmpt0Qx7zNrSNTUss0885Iv79+tF653PVZV12WVTwVQnWKMltmH++K9LZc+IsSlls + UCVMhMf+04wz46IY7n4sQvXyAfSVaz7k1PMVOX3Qgim5HBW466fd76FjG6p9CtvkyWHePzn5RiXY + gqbadHJu+BAsF9Xc4H6dFFLLoo9IendiWMApwe1mTmCB7yWUUaUfSSJmMP+nFypPGdA5fGo159eZ + As+lmBFHq7exM1aVkWPsLOS8MFu9HaWvCmL1cPY5V7g6q/lbFGi9TYH88e+pDJVQto7RhGVmqek6 + rJby/7mjQPzfOwqUWqiIIThGTYPo28La4Fp/YY9HDSuO7cMlYU7EXNsoH0TxystAe0zobKUHMF1U + npffCzKIURuKw5Z3N4Bfz5mIz82YLgZ3ZqH9Pis+J8v3mgt+5UMSa/+KpyQ40CbEayDfqa4TZZN1 + wBr1ukC7qgMsfW9+tK49V8DueC+RFVhvZ9U+cgsvpnIhcQsfOQ4vIguvH3Dz+QNonNmL+QTcg4OC + VDhL40JXIwHFslek1vesUT25q7KVnF/IXOfj2GM19kHkFTFRhkrS1p+IQ8jApice6HJAY1PgIb20 + HNGm3K/p6fkZZP1g1sgAehnNZzBLUJqY1l9PcQ3WK5fFoD3sDK0mlcOHl4WHLoy+RFHtsl4NUd1k + ncG2/7OzPlorrtZhzLosMqurHa2dPmMIBedGnPpziejv+XVlJE0FiSzznHPjQxKhaaCn/+mnZyQ0 + AveCrwbtfZIIM84VPVbwwhSuv/bTMxcugzTID37C+Ls84UjnF8NK4b3o0DP23ZrX2KcCYRAK+CBn + mrOULV7A+7BNyA287yhMjOnLw0kcUECyJ9jagxHL73J8I5/f3s5qj+MLHsmgYpomnbYxvBmDfUMK + yRRQ5HwdWxCuB20kxrvKHM68Als+nZnWByA95aynzDFstHkl6PpUx9l6TzpMlK71D2EVUoFXnwZk + JSVCqWTqOR993QDyk2RjYFCr3rJqW+ArWF8kdyMdcMSPWhl6kYLODVUAl/mfDfRk/ZLbbdJzOnA1 + BOWjpsQ7KT1dD6QM5Of9XhFF7vkai9YM4bGWdZRTVsrXcjZYqGzVnVgKmzhc7AU2jJ/JjaBvXoHF + 4e8v+XSGLYqmiqU0P3QKpKu6Ep3ev9oa/MpUPoumQSJeJWD1IneDHpsm6A7ceeR+xj2EMV1y8nxo + j5z9lZstt+k0kUJ8iWA9KDCVHH2IkS28ypq2qpbA8ZOEJBFVu95ICRRAnfdz75bEauvpxrjwbaMM + 2aN2o5TftkrG3qATi7GcvNneUijdXrKE11s25NOSXRpZGHyMXHu2I3ZfL7Iqsk9yVl+fmtOLUpQF + LQ58MeMP9ZLHJ13+3VqBmHsvzLU/qa08cZ+YvL19F0P/o4p8WaUCC9nPdYTs9wnAuWvffk/H1lm4 + vY91AZscXbaazylXpyo89+8rsff1LpBfrkPwZSwUqFdj39RQQAjkOUMPTSEOKdU6lM1zMqLT4adp + /Mf5YvltrQpBmqGN6+e3mfIzUj4IfXMVsI2bVDLTmw+kmHI6CvP02+Df+gPN9att4HVq5e+bWPh4 + uHDR5Mm2DTOZOP70Goeauq8olp+teifeAu1cwPellQmKenxYz0xN9bMNAecVFXKD2AHsccwwDJp9 + B0I1XceFeUkpyC7k7dOnN1J66JMH7BzbJudS0/JNPo4FvPPM9C+eJyIGDBRW+YCsSPdHlr/plbw9 + wwuK0ztyiBD4KuRZTyWuwS6jkGZLIvt41pA/eRHl8JXHEMXhHZ3FUz3OB9KF8PC291v+Mjvf6t+l + hZdcIuj8PQj5KgUMK+35EynXtzXu79fIqg9Nkj/ExcHSF/HwXfZvH575dlx/366Dz7ZYkEaeekQf + 8T2EWQjP6PwVrw7HPRMbJmnG+4c3e8qp+xB9SNC1Jw5E33xVpDj4t74bLjrmS4qzRI7L7Yt0be+5 + RvWjD2H8E4mxVSgS3tTE8BjI/r6ezhpR3rkP0fj1SWyphsZnlbSAlr4p8c8HiZJD/3pIUfBy0Fkm + jkYveWpCed+9L30vH0pM6WbKjbifOQNZlw+ysEpyp9wYYl8Wtxa0o1Mdf/CIkZ6Xo8Nqz4vxL786 + qoToFq9aBw/6XUKW+a6jRUWJKeMuc5A1CQXdXDPY4E9sZnIuUt7Z6huoYNfeBvT2+6O2eMo3kavi + cCWq4B1y6gRDCuquvBLT6Ar6i1mllaqNYXCv8Q5dx8cmgts8EGKYcTKuf/l//LoSUrPHmi8MLkTY + TiYg2dIH+Xbf90R/qqFBxitRR1Z7Ih3WHRzwymow2rzfbZGN9l7gQ+IpDp97ggtyAUg+tz8vLqdy + gHORbUQ7Na+cfyh+ABf5YWAovlLAF9Kxgp/Kd4hv88y43S+pCEX9U6AHnTyNZV5bKt/yS4jU9Trm + q2KeQ5mwj4bYrRs6yyUgCxBr94ruj2+idTNhJ9kfUhXpeuZTTlXFRtaLn4GPHlLohBKpgqLi55jd + 44NPwLWQTx1ToJRLW8peTl4AFZpZKNvzC/GOnCuHknomxpb3zhLcFCi/vJuPoqFVRpYFsQmN6N7+ + 4w/zvB27v/yMkn5bAaXFHIPD8egR+12WEc9jXQXS/DgQ1OL3yN7iXv3DO6Ls+E69SQ/lIy1Hku75 + Ef9cIYHfPDXJ6/OptOX6URI5Ds8L2vmFQ9vrlwWftxujws1DR3BKVzoS7pqgRGaYcTkMx0WWDN0k + 6URIPf3NH0h4gIVeA1FTvcsKDLfkiKylDyJqWgKG5/l+xOAmtQ5xPnwhc7XUkWjPN79TLhryRieP + vFkh1JYPe5/kdyr5xN3z23JfVFGenO5AnI0P8u02dyUI/J/+bz0uRWkW8o7PGJa/F1iKum6AKvJP + X1KPJpg+7B3LrfV9+XsdeKR9a71geoA+Um6plq/5sbXhl/92xLP6k8bx/bOEt8uoIQ90AKzF5W3C + 4VRciSFkVNtK/HDhkeohuuEO17NF/AaO/sbujrYOWGDQDjrM4eSzoqDk6/epp1Bsh5JY1nOO1oLN + Chh4U0jOy7Oot0osVbgMj4BonH6mXBq6EsC37EZcvmrqDgiZIRvRrf2Hv7xsCw3U3hPANOzKaIvZ + dpHfz7EjSu5dHe6b9QH4y2f294bzOXNFX7oEaEUKCe/jYC1XCPFNwMiMDznoi7YsZUYzMVKVuwLm + v+e9JcsV3c/Vt54pq4WyUnO7wjZODn+8zQkkLC/7a1C1I72d1hYkTF6RM71+Ad3kMpT/8Q92Gemm + L6soRfHqojNr9pQ+DBzDP75rbHEFNincTw9+PwcMEjMc15ORTxJW8zdxUfKptz3/Qfv9yZAl3QWw + qmFkwMrLB3xghdBZ6fmdAmcTBuKw4yvajrdvAt+fs/gPvzE1qg1a1+2JmRH19aJ5Uwuk13Ul71zX + wPI9BwHQdydclVYjEoJobuA35z+4pM41Z5XWCaBWWw4G9WfvU0g2BTxRe8WLNyfO4oOgkR0rw3+f + j9aplUJYFfLVl0YbOpPSZBiOtujiX9xXzgZHqYJP7Rih00s9jdPjPvlAyso7ugtRBNip5mIwrX6E + D0r4iObneW7hzv9RUTV6tH2gY0L6ekdEO4J+/IpcLAHOUyxycV7ZuFTvroQ/H6dIC0lbY3TyTcjb + yUysZy9py9/7dOjtEF/RDLrsfEJyu1QmZ3XyHX5ZbiXQHD4iWoo+YLOWDMIdj8jO78YFT9cG8sbr + TMxPVEbcC4EUsuP9heWHJkWkKqIGZozVYCL235G2CIYwzSqLGO88BGsaMS+QaemLpJ9IyXmucrs/ + fonuSFVqekyrEHAxfez4Z+Zc/JpcUANYImfnH/zPPSTikFKT6N+lpvvztDL/8dz/1o9F/Fba+SDR + yXcd1yyIEun6Ai/ke/UHYOZSFHDPD8hPTz9ns3GXArq0KTH5vaffYpwYsOMRiT+KkbO8trzklJ17 + ZKVbEi2UFBtMnWgm50vhjPNiiy+QLmlH8m/C5V816Cu480ui55lOt2H7KVD2TxRlxVMbhWXtJGh2 + yQdP+/qmpToGcI2DL8lavaF7vC5wWt0IRfv8rZxrG1B+/h5E+1vv5eJssk4Bg2Mtb+pNBjcV2tj1 + ScocOm059IoPyCfFGDzEwOnk6lkCWM+Gz3JRlq+KWE0wEDUFTwbgAU28hYFGCDx89OPVmdOIf4H1 + F+S47Y+pQy3OfEHFEzh0WV+tRgsvUwCxS0S0I6vl/PI+FaA5zk88B7eQrvoMGQhPTo+FuK+0qXp3 + Fbx75YK0c3IFGyKSCi5Y/CItizdnc/Mk/dMLeAj9lf7FCxhu8XFfH596zo/Yhp9al/3f7/Jz1vYX + +lKawQrvvULy6WuCB/Cr34QQy4gRwb3eyQjYnC9/fp/8Tw9CxcsL/9iGa74mYlEC4602RHEOfo5L + mcWSkGzv/W+cTwSwhiwksUEyvbVq4pLfA0yPxSNpuqJoqSGF8FN1DXJ/eef0l937WM+/BTlC61HW + 85MAgiGykbraZ21zvO8Lrr8w9+XRV2vB7E+GXI2DQXRfCRw+h7UOKXfbUHHT+2jzK8uAO7/wj8Wz + HpfnfExBdIplhMJ8GomCxOm452/ipYIGuI902MCX/3X+2n/vDo16aYH6U3v7Au9rNT/OYQKP1pBg + qrRxPk/tFsDeLDB55BzVqNRGLgzHe4AuzXnNx/E0PgBK7QPa/RFA3+wWyJAPaqRuJtEWZvFf0Dyv + Orns388NnsUAcI9ioo2NPQoCs4rQ4JQrytkm1bYqe3WAC6QN2fRr0OWfn2J1JjFC9aWR4yPn4a6H + iZIkc7TlTNFBri4QSRqAnXHcFF7uzeMFuft80+t7giAOL4sv9cy5noWv9IC93hjoXW7O2MXmp4NK + oV/Rrm/qjeGVRB79JEdOf6rGaV/P8OCaAZYuvRFx71zagLayClJIJtOdHxTwkouEaCExam46HkRo + hC/Wly59G/3pMeh2XELMn+lrm1K8AniQ7i3y/c/e8Yk8OxgOooDi4ferKa+MFQzN+4MYrrfktE6X + QgYyyYjhOY+IBldWgbM+VOjScZgu86vr4Nv2MqJuCaiJrl0f0DjeQ184wNEZE2+BMnyzT6R+oJr/ + 4ytDhj7o3BrPcYNFqkrMDTv+XH3HaNHyZwn3+EWFclAB+6mdASiFcSV+9L6BrmzbDUJ+gkjzxAqQ + mJNMSML5iHQt12u+/qEWXNmjg5S37Ner8BshnKr6hayPd3JWmYUQ7usNnQznUq+Zu/jys30t/u/G + pSNlu2mCp+kxYnbxe63P/M8i40a3iQaUjK5jZsX/6aky5Omu1wL53uSIeP31qU0RE20yyB2IxUj7 + 1SvKP4q88wHy5+9Qxs5SSc6CDD0eiwq2+j4bMAoK5x8f25p6egEN8xZ6PjmPrvngdMB3fwdM42bv + GDcoDLSaJcIU6Vm+8zEF/uFN8ctNh18Zo5B3v5Eg7JQ5CYsTC4PGTFG4Xp1omembhe6PaZDyMBVK + K3fV5U19AnKxMyufnWPxgNjXn8R2qtLhuUofpHb6xmiPf2c73l8SlDJmQZbVRHX3dlxR2vkMBif4 + pTR8BKH8iK1dj0xFtO1n8uRRHC3kpczgbNMxw2B/X2Lv/gN9aI4LvNej3/swVyMXJoiRysul2fGL + 3/va73qVdCqyH0tF1/yIzT++hA+fhRvXNHRFqVjAEynsMdMmRXgnAP1CD3kvKlA6nsYUKBuzIAP3 + XL3Y90cB9/kh2pevnWmxvgZ8Ri+JoKgswPK+fBb5bzyTG7uOu9/0gA8+uJEcpJ9oBU5lykfvcMew + appoGe8cA1+FoqGwPoU12x0OJuyUO4OUp3rQ5rd/4iGzHEefpugEtr7WS/inT5MyTMA/f/hQtQnm + /vS18tE3OQLeFUWn5hXRZxakcupcZ6TztqxtE3BC+FEuF4Le7CkiYnFOj8NPShCKSgjG/FCqcl7g + B0JHfKPznx8ml78rMZeP5nBxIXVwn0/iGFMdrbfpaUIkBZq/3ToZrNZzq+Rifno+V9r9OOx8HwwT + uhPnsVqA++YPFuL+KvvtT67Bcl/h6+/9kJLH95yKbiVBKtsmudih5tCWKW15APGXnHjGod2u7+Fs + HmrkWcslWkCKMNjXG0Fv8Bvno3aW4LlXRXLaLp0zPqeXCv7exz++q2hFea/+82t5M+Zr+hymF/T5 + E8DL9W3VNA/nCURC3KN7r+X5pna3ThJktyGBKYv1GnupCZZhP4O+63eCzuwARzYx0ZlN+AhzwdJI + CxOKBBXHlZLEExmoBc6N/OWbxe5wCdW/HcdsXlO+bxgdUAfwRDedKppv09MGS05y9G+8r1yWABwz + cNf3rxGzNe/D5psW//jV+lICCS4adMhlKwKHptkSQzWJFHTppbezphFfSH2q3JAafbVo/eYhK6nh + I/Un1GCHmNYBw7VzPOSeSZm3aZQGwnVuI+QXqz7SLgcd8L6RScyJ5+g/PoEU3kR6Up00aqvHAqwK + m5DknW9gm002gD6vAczw28GZJDvZ4M2pR6SI41RvlzR34ZicJ+TKt6Iu8pvUAe8JAxKI4zSSOyYl + 3PUJLs68Mc78iHTpfhI2ouhGTLlx8iHojrcSS5qwge2SRj70js5CvF99i5bwxEBoWuNETrxwHpea + OAqsH1eO7Plb45YnY8DrxfaJdQdbPrXJFP/lQxQiSigplRXL0/viI7fu2Xq14aGClp94voBUZeRm + 3dug972a/u1yO+dbvDodSG0h3vWhMeJjBDZIwmtJ9BZKe69aSYTzqP/Qy+9CjWa/PvinD87JGtd0 + 95egj4nmHzV+35DWmKykyj/Bl+Szqm3Zx/IhaNMKS4lyjpYx0R//9KZ7c8jYoUQqpRV8C3TqsnLs + zt8xhhWpMHJ3f2ADL6uB4Ast/NU/UMO7noB+TjxiHx8xpfe8l6QdH4jZeK+Rzk8vEHd++i9/ztZT + KkG4dzXVPFGlLIMLCfLfM4sMJ5n2Hb9pevyJ7Yw0y/zmk9R35l/9haiXKwNw/OlNWB7dDtkYknr3 + w32IfoFH3t+q15bYjlyoRd8GnT1t1ubBO0F4vrW9X/GHECzbewvkvR6GSVQWdPnzc/lyo/h36kdA + M8FZ/j5P/A/xx11/TkDolxsy0EGrl4d5mmDHvURk98y33vn3Qx79OCdnbRA18nkz+A+P8WEIjZE7 + H2EMcWPYyM7hMd/5og8XJhDRk5t+4LP7C4BTkeyTyX9QeuzcDX5+OYPs1t2c6WYsxr98pVzffY1z + T/Dh9fU8+4eLfMmF6nU2ITq8eeJLzTYuvCYW0LOrE7L0mdB1//4/PwzDCdTRln1OrizAzcFSQeV8 + say+hK9NrEg41vW4sujrA/dUvYnOXiqwmPtNXiCEETmpWeyMZ5Ru8HzFJ1/Y8Xkx1mqCSduFf/Wt + nL5+VvyHH8id1X5cradUQa9kLuQEv16EW7lYwKIxDqYH4VkvCXdv/umdxWOL/M+PgvdS95H7UGn0 + b7zWeGJ8Dn/MfAgvCwvlKUREyWMhX7ql6MBeD0Mvk+o5S1c/htIjs8gpeT3qFV8ZDB/KrKKXteJo + 95MecPyOETKNDgKcM/EAMznY0EW8Mg4VnwwD4Mnq0QltHVil12+BO/5gYeeXxDvKLvQv7Jc8Qv9K + qeyx5V9+9MuaqM50S8JG7s0XJs7dKOrVvZwfshgNAzn1rwBQ0R1EsK8XzA54pn96SJ4u2/3Pn69Z + EywVLBIzQbu/rM2fn2TCveHI7tcyEZY+E4bVBpndn1Q1YopjC/Z6KvJ/DcwxwkoB9XK/Ei49rNqi + CPdYlhYBkT+/bsenAvL2amCgSgQsvTQUYMkj1RfXt+dwO58EiS7/kG1murNxZaTDUewtdBYP80i6 + NChlyeYq4sJgiianvrKyd7QW8gzqd04nVMRg93sQOlsvB5NrWsJsIDeknm42XY+HapL+1pfpR4Oz + fUsrBhtrt1iOL+1eD1CwzMC2JxqzeRFN0FCCeokqPJpEBuTQJyk0vkcN7XiubYF3ruCrKAqSrnM2 + zn/6fP1NLLpn5Rf89vomuKxiQS6PCeZzeAq2f/6MtVSD1n2ijykLuCb+4R4G0UrP9xQyCxj9Za+H + ru454v/wlZhQT+s5+ZnF3/xgGgHZ2W4MbMGyOR9fOj5YsNeDHvAg3Vpf2P3vrVy05U+PoQA2RrS2 + A5fIWjVtuz7ux6XDow57LFHkuI9RW5inwsK9/oUlzFn5tNxfKhS0rSdampjO5K72Bm102PyiDxJH + +D1nF0ZC0u/1+dJZGntiQE89jqgb4vLp7eji/2dHwfF/7yiIBFElj1a5ajOQUQy5K+R9Pj4Sh7LJ + u4Im+vbIMZ6/aJmiewLdflDR+bf+ou1msLwsvgQbc+J8AvzEAx4In9okl2u11aswPR+wot2N6EF4 + ifh7PajQo/IVeYGga6ukeQq8psqRoNvw28848CUIan1GwS/Q6axpSQpZ33NRWkfjuA7l1ZYrnReJ + 4SlFvanjCuX6Op3Ryyt/48ps7xLEUP+gpyw9AfXe/gOmbXgkfnkGETkaXCVHXSIitz2QaJqOJiPV + /O1BTpeAp4uuNC+oqZPns8/5uzd97Bep16N8d3hO2lLTdpMRljy8PWpA++wOBhgUfkD8yxyOHHeb + WfhNvz1KDyPrrNvzxYNIkFQsyHFfrx+lHGS9gYXPhf1KV9V4+sANvA6dx5MULW+9CKGXMGfiKESJ + 2DFJTNm4uCOx3pA4WxQuuhyzr9QX5Niq+Ue6+ZJ8eViYGrcN4IopE5lnmY2c5rJyhAE2rbxEkUm0 + Umzp4pUxhPSnHZHTsIrG8mtcyJFJMY7b8TDOTq+Xcq2MH4Rok+fc5ZX6MkwjDqnr8KmnTRY6uXlW + My5UCY2se9lUGE4VIt75fNBWc5FLmJj+iUQr8Ef+bVxTeO73ildiPXJBYftKFvnNQWb9NsCq13YK + jpIvE+v8spxuiu4xXD4UE2/8nCNWNZ8v2PeHeI+/S84OiqRAyv80otcar235+jThsulfEkZvXZsK + 9xzKsI8z5D08dqTb99qAfT7IkzGODj4NbQUnIrskCGO7Xn6DnUKNzCl59r/aIYf1yUJrk57IQVQA + m3RRU9mAYkOCjnEd9gIkF85zYxCv7jawSOdzKGNVtpB/Pjg1T3rPhSsSnsTuu0e96W9+gqB5eMR5 + Ro22zejFQ/ZVauhdHQeH7bWFh60xZkSZ2tsopPlkQ8QpMglzpXTYuH3rMEaVg07lWaPLzKu2zByH + lNz1S5xvtVjtezB1wz9gFdWLvdopuGuhjE5WruZUUgRJdoPDjdg3qxnp7xLvtyYp+w4KyYiIM546 + uWPsEZ1+s6fxKl422c3uDnGf6Y3yhys/QfmthkhzzQtYbr+qlMXkHRNjvd7rJciHDbCz2hPdXMdo + OwhlLJd6dUSWzWYj2whH9WhcmBTZD8EZ6fVWKbLBKyYyG+4+sonmJpAojUZ06exrHLPdS2BC64BS + u3ZHgbtXUAYe0/p8cpjGuTUTW6b8V/vLR3mXGPudDqlw8bdX149bfddi+GZSi6AGPcC2z7ccxO0F + KZ9rDISiv4cgHLeKuO6nj5bfz2mha7NfZFMUawu/xi/okqdELll+dtjP51TIz1v+JKqhnwGrdacC + TqEf+/yef3iWsVJod01CVKddxyW/WRjWV3zGfX+f621xRAM+GO/k81U8R5uKxQW6p1nFAIL3SA1p + 7CBz7FJi2eyxJlXCvKDlwYBoCv05VFIOEhys8oWS05hTEhs/BqruXBBn02qHHw9LKAuFffYX7a5E + bB7aEnx+cbzfsXF2FuzPNrTqwUZFbRRg9N7GA16FRkO3hSX5ejmuDDy0MEf65uajIMtXXe6P3o9k + /DZScjdMFV5P9hOdLkECSBAnDbzyeo9M48XQPul6AzZ1/8QHtYjANuYPA/YR+8V0UAttaVb6kJfR + NYnittq4MrmiyPGtA8hmlsAR8gAm8nBw7z7mTreRknk/Y01wTi4H36G84NOHfAtmwR/muYtoMSUG + rJpUJYE/nTROFp82GCNuJgF7uY4rm9eFLF0fV+SdqzramulXQB7rNxK1W+/M2TFS5H4sMPKkwQYr + w70SWJf+6EdTVtEtdxkIHGMw9vn+Am5g1gmONBWIwmdDvglLuIHmZgtIFaW4FuDRYGVhoTlyDq9T + xDmTk8DNe9394zn0wPIb1Ad8LacaBWE8jNNWfxK5404mMbqLmvP3cvSBfP6JRB2FqaYfJi9gufLQ + h8+Uo6t5izYoiFlDztJPdzhP3aA8nZwFS0wTA3axNig/FPFAYraucjoWwSCv8tHb94yqgIdq6kL2 + fOnRJUMWpTmJRTnzXi7RKkbPheD+fIDaojo6X8wb3b5U3G95RwXRZaYcBfXiizDL0RlzYX8FnAwi + Rt7xlahF5TpsuD1imOVwQwm9cxo174Eocw5voBeUTIc7l04L4XUKSCjiY74xsVzCr5AbyHPbXFtu + c2fK7kHT0SVinYjbngkrTypz8uWX8XNw8r5vgCk9BpkTHpzNtt86HA6MjhKuKmtBfi2lvHhmit5Y + JTWZo9iVF2dg8GLnqbPpbwaDKdMXpB2SCcxHS+0E4+KPJDX7IZ8SwzHlalIcH7o33xEEh2Bw/lQb + OVvRJxI+mRzAByc4CO3xzT+ngYHm26Y+iKYhX+b73vXoNhfkHnWLtmrKu4WSPJL9zAOK9nh8yYv0 + yrEor1ewpIU0QdEqTP+AhNChesmFkvctRpSl5Uebo+uxlKPrq8BSuD0ptf2iADV5yeSkFZvWfV72 + IE3Me0OnjQ/H7Y9veV49+Mfe2W/5vG6u/ObViLz1XNeoEqm2fEquPMl+3KdepkeXytYmPsm7QRKd + grSGsm2dXGR7hRct1XFK4cQ8N3TqUeasAWNOss39KixO0jf6zfutZlY+W2TnT/mkdaeX/PTWyD+K + ZjFu/jsPQH9EP0zVj6+t+XzfwO3wiUjSqGy9pY+5Ba/TgSJ0zRhn7d/nSd5/j9jzsa63gnwWmf5O + R/TO3ZBubuMVkCvfhc8VepCvN1V4SVB1DEyjVBqXj1qK8kUkHvEf4EvpAd8V2IzXFlm9s0XrfOtj + mLvkR/bxqlmOIh6QBG/ElL/J+Bcf8j3LJpKchLvDL5eZhTsf+nt/jYas2siukAJy2+NfiPeKrzpQ + FxlCsa+v7IfBLzcJcll/qydH+y2QR6GIPEdHzvZgcwitwSM++/5d6IIUSwHmWyqRXx8PAMsPassg + FrSdb4F8EfBzgG3CBMg9ED+fwu2RwPtQBP5t59csVHLj7/eJes3aejOTRywNTDEgHz16utXiMIEZ + spnPR/da63FYVXDHU3I+q2lEz0ofw1ufNkQfTY1yBveb4GCHDlLufJKv/iev4DZ6AaZ395DPhtiW + wJk0k1h9bVK6NIa972g7k7NWTvnkM3IlWNE9wlxdMtG/eCVGDohj9NW4/eFjnpgqCbX5oC0311yg + V6wy8twtyTd6UoJ/+GaTw0kjv47ygOuEEDPFNalX/wB1uFRsSOw7qrQlT5UK8ol1Q9Yez+QhZglk + NOZNtC7mnL/1DVzhAfzya/9y3EzkBXa+iGyr68a1LNkGIrP4ES2724DGU9bCP70Qn60P3b7LiqET + p4f/1i+QLwkwg8Enmv0od3xSWxi6hzPyq+ID2mLrWmme6ooo00OKVoV8G9g0mU0ewGxr4rgfHZ5+ + 8YOgLAzG1TsfN8hOIfxbT/lko1GBYf4e//huvkY0kCC6YIm4HMxqjion/C//SuVQjpuvrSXUMGIw + 3fP3cogdXnrSt4fpIXHBcnOVDZ5O3Yc8GmM/IzkeTEguVocuO18kvi4a0EDtByk2KgCXiqgBxvP+ + IBYrWjXXVW4F/vjTW+CaaJtuxQuWj7HzJ71L6PpRum4/M8Fi8T2qDnfg+wX6nbWgv+dfXuMawIuT + 28ha9QbQu6Km8H3SL+TkaSPdDonJQCiwB3JR1/1Mjs2rUBBGAWfhPGmk8LhKth6HgrjDTwRrL+MA + vpX4QZ4fvNTLR+0k+NPDaP//9YjlwzWW21k/oLuRzQ7l4+kf3yGKnafa8rIzEXJaEZAi5y2wjxcL + XbUgKLqRT7QM1fUh/+mTSA5ItDyNyyA71fGLTha6OFuxlS0079VA9Bqt0WJeXw/ovRqfXPS3TZc9 + H0N50l4+x+O1nk8o6/70pg8gOIxrACwJvro889lWkGincKIO65JJMbPrVewZYgKHk90hf2wFba0D + ZII9X/liEtSUPDJxg1dzazFP6iBaj5Y6QPpxT+R6qhCdGS5JgNBLDPLnVR257Jir8HeELi63tQSb + pscM8LUqR2cX2FS43ioV3MLKJD53utXr2kYh3PGPZOEnoFQJnAQctfVOlF9ROivUgxcUv/MP+ffL + R1suHNvInvcZ/vixtmHOLPaNNAGyj2qv9fB0HWT/8RAIKqk7busv4+G96w/731M9uM35JYd1OpGo + izltPbpWAZml4jDQ67NGTp5ewuEAdXRl09dYnt+eKovmlCM1laqan3jKwj+9Yl2adtzHewAcIdgX + upREVH4tlQwF/kCQwDX5wkmpBBhtvyX4BNZoaRdThcx1Tsn5K5wd/tyYEDJrWPnwezVzOsqiCm+n + d0PUVFJHTtNeD7i5GUXeOemcBTRr8Jcv8eeER23Z9c0fvyPKbUkpNVfiwtHDMvHVu6otBC0tvHfj + ARnRXXOmnzmqUOQXBz2q9DfSP/74h3cqrF7O3Gk1AzdqXpECiEQ3wHiipPsVRy7lmDpbJmWt9JaH + AnksrOkczXkIMmlDPiMROFL6jqAsaM/UPxzrsV6+mG2hEKkVuTTLmVLESA/wRs1EQvLed2wxcgn2 + +USGyGCwbPKh459Yt9HuN2jEvKcS/Nwrzmeq4kSXOa42cLWqCP3lf+xXoSG/ubBFp8o5RcIkbA/5 + aVi2n6Qfsd4C9vCAM+cHSAH1oV7Lw9MGfFu4uHzft2hjgdrIn4vHkIuRzdoMq1WVX9GmE2Toz6g/ + 0AECruNClHgp1einYPV/eu6P3y/M4/SSpxN4kovrVg5ln2UjZ2c9IcHffFwF6yE5myP6i1+eqWBP + svLHJ/YuS349HD1FhZubU3wL6jgiHK+V8gz1CbeeK9C+iE6FrFSXOzJ7oxzXT6UoQAe86E+hdnPw + 9Wh1UIgWhMzrQQJUCPcuJTs/0jt+1ObFXktguS/elxeBd6ZmBSkoq6b3W02Ox0p/8xg4oAnR1bH6 + aOKF0AD3BzCQr1ybcdMquQSdWWwIVTOm2/Z6PuD0aGyUHO3BWcJoXaB4ZzBx8wGPs/SZG4hgb+Bl + +55r7s8/U9wp/uen8LG7FXKo//YuGc5Xw1zSPOSz9V3RHl/aUi8/CC/L7U6UHT+XIK8WeB9egd8A + yjmUSTdJLrTijPw2aEfqF6iB8rQ6//y6TZwjCJPp0vva3T1EVDMfsfTnz/jOVub03uxdYCr2gk7k + oefcqlNRfi1aTcwrPdTT0uWVZMuHFLkZvIxLyoS6bMLjhPTpxY4/cYymf89vszLIJ6/ON4jYKCBO + 1zWAdaz58cev0U2bdj+QSxIIhsO2+w9j/fp9DgMoj+/zn16v12KtVDk7GwkyqtjLd35RyjveoOfD + i0cC9hMcu7+B5f6nOZycsQ3Y/UES3d13znXGsIFf+dl2fdY42/VehXDHD//7ZMJ6Vsjc/OlxDO4x + BctpogPQz1lINO5e0HW+fRLwp8+N7cDUcxFZBVAk9U6c62Zqq6a90n/+6c7PoiWaCgxWrr4TlBlc + vgTpCCFk5nzXe9XeT3F6QKQY6+6PJc6kWZ0oL/pS4cN90etdz2L4vvs88k7P23/+xZ9+SoaTPlJr + 2zoo12OH/OJ5pfQ59yUMvWkj6Y73iyw9/1vPf/GNjdyt4MNWKnRl7v5Impumy6Ern4keG5O2/T7C + 8M/fSdI5ApuvSBichf7j57u/Njfcssk7//DZOp/q2endEt5Oz4ZYwc8f1yJQO3nPt/7HytWIf7Vz + DOdx5f75rUR1MhuWg7shC2bnER9/SQKR+foRVXC9nFumVIQ3hrY+P5z0euW5jIeEKxFyh19Kt643 + MTxbv5VYyY2rZ2Z7VzAbnilJlyQaF/62TWC7GR/MtuO7pn+/N0bCTE4nPDr78w2QzD+faHf3HU2m + v3cF+7Esen4rLlrcKfXB98dBgtpPVK+3/Y7FD86vxNIiuZ5Fm1EBwmaJXCx+wPy9PlqpUHFKlBzo + mnDAdxVqjqGhi6u0oHsEhwKY93JARvlwHOIXl+ZP7/zhXURlOTMAass7eZ7Pb2dTQq2B3Foq6Pyo + mogt3HMAit99Jk40DRHV+uMEaKc/UfrJ+nxNqvEFreN2xCvbZGD31xlIA2AjR6+/znhrcwZUUrX6 + oMVqvuJwqOBqyqc/veHs/k0L6tIdkXJbRIpR9hzA7bPlxPDcO8UfKTHAdmZ9lOYtm+MRrgX86UG0 + nwgZ6z89Bw5ovZBg52+bEB/3fUzfi88clPO4mVlmwsP3eSLZ+HScDaqBC+fpUxFvW9OcrF3wkEUm + jX3xPVYaFdGRAUXWsD6YdOpMNcULDBahJao1oQj7yjZJveMIuOn9U73ufhLkWbiRnX/UbMNKLtQW + NUD784NNdNgW1KfcJ+aEbW35zZb7h+9EvZ0CDV9uhgjvazwj82TiaNWUewu3SniQc9ypGru1nSnB + rp2Q/6pRtA13xMPYSDu8MMap5iZyGAAAYe8fPafMOenzbSEb3yA5V5qS4z8/Hdu/EJ3g+Ua7Ogte + 8p9fvfMPgEvt0cATmQL0Pt6DaPF06ko8Nm7kUr4Luvyc/yPtSraVhZHwA7GQSZIskUkmCcog7gAV + AZE5QJ6+D/fvZe96fY9eDFX1DZVUOh5YepN6hzqL1WWxJAh/Qdl44u7H/vUn/vlfnqx32YyOwYw+ + b3Egijq91TWlzwSqV5fiy7eMst1fNEHNjiesXC9Dtkp1ncDcMTyssVUZ0OXnMtA9EYXYxUvPVsUK + ZZjVbUv8XW8uB27wYGN0D6yd1dpZZQenMHjseq94fYOZ4XMJ+g544NPtAYO5ZNoY/PGjx6pp9OV4 + nxjG4A6JzF+IOv35f8+wW/76BQHb6rUHTU+A//TvlpNuAV0/nbCdfG908vNPCGFy5fDlZfzU5vL1 + 91ufPX8+jOYlYPOp7qEQbNijoGwdupW3De79EG86XdNsW/ukhW2MEnIiXucM9sjJMPffV3xerh+6 + dUzNI84xHt7aqYi26zef4S2dGOKOm7afuHQLkPyUxhM/33qgPRwbmP+iCe/4EmxY4hT4rz68X0FA + +8OQwsJ1FLz3o5y18s82EjrnuuuRns4MH0rwbA9Pj4MfY2C/l8gFq/xziIdTi65rlecgIPjPH/6q + uz8SA3rqhf/204aj1EJydtp5dSKt+n69bAF//vTj8wLOUmYqC9ff8sIqd4eAPpc2hEdXkmZwfF+D + 4esa/d/79AR6dNRxug4QqHYcE63zPtWaLnEt/fF9oXVDZ74rdg+PrnPxjmxB1bHkpxocJRfNzN6v + m3f/Fpz4eiJ//HEuXjAEj4zZsIp8HGwwb0q4eZ7mCbv+W996ysItH3tyfluuw7610IfKAiA+A4tx + 1tYoF3S4zyZRNTV1xvM9V+AQflMcPqdvtcT1MP7Fiyf+9e/issrhTQpMbDvSKRMmltYwt+OPx9rD + ZRAm/5mCUm++XlOam7MdhDaE74jkRDkzpjP/xdtQS1fvtl6eGb3IXxa13MaRMz0O6lImFwXe9L0H + Ymei84f/f3i8+w1ytbalVqDjVrMYz15Pl9fFH8Hej5wPFK6AZAEwYepbN090rwFd+cdiIDPx71jd + /bFtNY83cJn4z97fYNUp8KQRrr4ZYS00XJWvQq1Ae/7MkiN9Mnpj7RpaF68if/74pu676MbT8YlP + C/QzXsbudtz7xV61x9+S3RQRGXHOYCvMgmxRGl5GvlpV3svKjmA0r68Ezk54IP7nGu79CwJBfcG8 + V2Zyof7ji3/8dq8vzggMf4ZCeeOIotnVQHY9AW/aF2Nr9QuVKw6RDVOzMslpMoeKloHQAvKKAbHL + WqHrIEIDyN1UYJ2PgTr/8Y9ZABGxvJbJ2p/TsVInviWsVZk7zOo522Ara9AD3DAOXTa9NyCo78Q7 + npMhoBSxBaqxcsaXn58Hc9ucRHhzL8lMsLD902tSoVKXYKUl6u4njhKnvnysP5nbsBxClYW/zCae + 9AqOYDG9dwPry4XH9vqZnCocHzXY+zk47KFZzV+6lCi45jl5nBMn+/PT4UNnVfzk+Ual6+ccguZV + 7zNHjVO18GxwQ7TlbYLlOA6Ww13pEbeWMrl02Q9w3xHGoBhmd/eflmFV3JSVMnfaZ2jy8rCmNEr+ + nx0F0v/eURBdtI6op1dX0XO3FIiV1RTb/aUa6LkAPHiFV464Zn9Ql1FYbgiYle1NXb9W8xP8PISv + 8pHkzK0BwyRyBRCgeya55V0p675vC2S2fMImYb2KS/B1RExxKbGSIjxsAwMXyAq7t3pQP2Ak3jRC + eb3I3hYOl2BxDpMNtlN0xuHmsM72eyszfB95fj4WYgXa/islUI9ijL0xVZ31d39tcCk7iOWx/A10 + HKwYrkhSyaX36oAqeWcjPdA1HDwtL+OM8/EF1TBZPbT/fS7fgwJ/Tr7sz0eG1XcqHmZEdGYp+fyy + hXjTDF1yErCDAwcsbn3ZYP+eNWLGZpGxiyWNENIlxerKOIB3mvcNfpLiRB5SfVPJWSg8FHhJTK64 + OwYLD8oeGeuCcWzEQF2/99sNLUrLknfThoDaX1tGrwcE5E2fRbbFcxYfI2m+Ek9erxX3K4IXsm4z + Ry4lC7M58UwGnIJ34G2i2ji81gADVaNUEq11n2A1VZuB3DzWWHO9Sd2W88OFEkC693ke22GxDm0L + xxTmOJ/D1GGfZ/2FHu6c7lNE9nuLpSuLXo41YLf5heq2cPcN9veVkvMQv6v5VWAWHHgUkGu4Xp0t + 8WT4F0/kVDiDw0++NKOHO6ZY3a4CWAZY+agsZ56Y3pwES+o3EIZM8JiL60yrBWlOA8fccnFqXBOH + v1vCDT4T0Sf62Lt04bxLCqbP7HminGuOYOrhC7WrLM1wbPqBajRVIP9eziQ9j92wChHHQqwPNbkk + pamy4qWoUf0zMLni+JLN9VqIyK9jgl0XfMG2LuPy7/OZ4bAZDQ/FAj2D1Typv6jDkp1NBnXjI8bP + bOhU1o9WH1XXyiMa++QH4hZBgXy9uBPzUb8rYn8VBbHyKcWvOXsNAsqWGZ7N4UJM+ikH/m89M8sp + CUaEBVTo5RnJOl8R7dotw0i+NwMy+fIicfO9DRObjjHUzxMh3kfu1Oni30Ioo1Wbf5vL0rmIPzzS + 5zwhfmYJ6sb0XA8yJ53mDyecs1GwYhFN3Qpxekp/Af0yWoympNZIluZvlW2QWqPgXhTETaVZbVX5 + JKMHx43ElMmizhLmG1QphCHqkUGUvitzAdnw4GZUYM/hlGvKwPj09XHO2SRYhddWIP/VFsRWD9Eg + CMKSIhKwOXkYGqbC/Z2K8ChJKQ45sXVoPvY+lBpfwCnR2IDYXtSjGB5G79jlbUX7h/pC001iPOF4 + lACtF99E44kPvL96QmVIS4SGEeDzL74MQvdkciCBw94REZZgPUdTD+/Xp4NNRPe9XqvZSDTsEd7z + OVju0TOHIWUKYrPvxGHrJp3BY2YmbxbPnUpXMZkBZ6wOwa5+BsIorgoydUPHKhm7bH3n3wY6hZfM + y+nZ0kW3FA+RIv0QZYwLh/qxJMNcELhZjOpdKR80CWbxpuAzY5+GtevCHj23U4lfX+EB6Dlwbdik + 2WWWOKkbWFW2FJj0+RljvXxV61f+SmjwyhzLz+xYjeeYScHDur1x/nMasF31wkBsXYpYoe+TQ0+i + WYLgXhZEHsvzIISHdoFtiXti1HKdtX/1qbp7IT6365pRnE2ylCQuxMrtYYNVvCo8VDbzhVOLGVV6 + 8PRS+qU3meihmQWrbF4XMKvMZ4ZGkg8kujzlv3zC5i0bs7XsxBcKWH3CGIqx0ytD18BP7PPkppiF + yt2fpxT9CON5jLGc6fapX4pEY9Ek10ylwXpd3BQu11wjvkiLbBm+vAhZ9/fCejh6ztggp4EHOzGI + WgpPunIu26L2Jy7ENfRl2N7byUP9e9TIydjMiuuyGkoP+4mxrzpJxnXFmwH+b+uJ7Aa+uv7u8QLm + R0twzosMpV8Ot3D8cBlOTsE+lZs1JBgN4DT7hjhmq70ULkTde8Gy2UC1tevTDVlS65Hr6a47nLxO + odS6VUYsQ8OADEaxIeV+OGL9yeoV975vNbzfzevMWFGcbXZSafDG3S7kzCm98w+v6/2SV5PDWzZF + CfWQ7nNHbMitM3BaGppIF5pyXjnhHAhDmOTIfLkejmhxAqzFVwyYn0aP7csgO3SfkQ/+1sNg68xh + ZasvoNN/rsRGag64PLknYDC5eFc4MhC2ALZQOI8dDpp6pRs9ThBCZRawpW6XjOsv3A2lvnLd+YRa + rfLhKR+7N3fFD+uyqutgKT6iVX4hVhpJFU2jNUGHflfsEy6C9fDLUgj0M/ao6h6dOeh1G06RbeH3 + qy0pj7GhId0Xjlh3D606jva9gFMcH8mpUwAYA+kVQut5mnGQ2XfABVhSYKEcZBKd6zobQ6aVkcF5 + Nr64V5zx78tkQ1IkH5Jnq0vZKNrvhfeTgmSxdXKEcTjFUMn3M363XwDYFp9KpONWJvn9Iqlj+byW + 4KevPTHbs0QnNq1jdP49v9i2zlO2XYUlhsaoydgH8JTxgQB5GP7SDAc7Hm4e7U30mV1Mkg1huopq + ZkLrHBXe4bVPadNpXMIN6wBbmzxly3nqN1gev6y37Pm7ZaAf4WolX5LhYAC9Y5gpksvHQu44mcFs + Gl8X0etFIe8kLqoVnb8sAry9zTxIF3WM1HsDms57YjXKzWC7YKtGfCtdiRJh2dkW7r2A9Du/vc1n + jsFy3CwbhjtDNs6vYqCb4TIw8j8Tsay1oj1g3zbUfuOPKEvHqOtidhAen4E0F65Qgtat9QWi8+U5 + i1YQ0ml8AxPO5XbEisqTYdYfTgL5VD9hfY9XjlMFCI9Jf57F+YEconprDkf95nlLwZ2qcYCDD26c + f8HhVwLB4h9cFpoZUoljnayKX5dxg/wmZyQtvCmj3Cl+IcZ3U7Lnp7O5YtJCahoi1gzaBXNfJSIK + JncmQX/VAavLegEvt7CZj852yJb43PlSpz0RdrK6HtYooS6slXrFskjlYOrjI4TP9+phTf6cnQVf + fzVsGHHAjz0/FlhHDDCetwXbFjM6qx51tvS+N5WH2ELcr/RhbXi8oRNxg4vp8BJmGhiExpeY8wOp + W/fGHhyRS4izna90btYpPRavOCSnc/fLSBlFjaRs9ovgPX6WjpbG3/qQZ/PxA/Y1NAq8aV5PnDR/ + O2tff3rp0l3q+SCevhlVbLJJIvMsiYm/gkrV02lDkdXO2BVlrZpisdig3tIb8WL7Qym8/xQQdR1P + XkUeZ4vvwhqIQP39w4PF4iuIrE+pz7QUEFh8l60hc9b8mWuGOKNcYrNScFwowWE3Vlt9PexTpcX3 + 3E9xTefwm9qQZ5q3d4Ai74z58eEBdtUQMSQeZVNsJCVUyaJjX25shxfCrwGvH3jHwc/UhsVaFwZ9 + kvJEtKoVwfw9PA04XA8FtqteCqhrERty4Nbt3bR4oA9eXeChMCKvxnfPoe1ZryX9TIjHW+4S0IAf + NfC58YB482ENVk4VGCj+mpqYzXhX6TcUFyCvWManKwBgW1zNg0qKtTkiOHM2VUkL4Fwzk+hbpzo8 + I35DMFeoI9YmuCrLvEkO+zul8/qMIjqbp82Ff+sHRMMMRpyOPlQ6JSSXH/zQHX8LoHP3OzmFl/2W + FykOgcKMJbYDpAQ7v5VguyoSNqN6qmi9JDachDTHelS5lFr5JYR/9VfjTl2wcWVyg11UJCT97rpG + 1AYJ2gebxeqaHh0qCGIKWRqbRF4fYrZW3kODfQGQVzyzx7AB9m4CQ7N/M0r9+0Aut4oHmLCm55+e + LViEwrlBesWKx0SBqtJDhjwYmJNPsueNzWaYCJuo6a+QuJyNg2Wdsxu8e8p5XpDBBN+cqAus+u6O + 35zjO4Iq6gq83YfJ2/l/RUyhHtH79xt2fq0Mk/CSSilgzxO+wMuLLlOeiGCPDxK4J43+rQeSW43H + O18Ay6SPJbwVVCDOa/rS5bC2PIi7ovPE1/hVyZYfQ0AnaszCL56GLbK2BSrNapHz8y5k00vUGLh+ + 0UbcIlrp/L5MJlCd28+LBHoA0/nVh9CuaDGL7TBVlJ4eDEiyVsAX0v3AxpX+DTJdWJGbYe5nzg5y + LjnCOSbyihyVCL08QseZj97RWiuwStxPAi+Z32ZqNyeHTRRpgd7mff70oLqIk/CC8r4DwJmZpVpg + PN/++J63ba2SkV3vw7ovbeISRwRzkokt7Lk0wJgjJf3+Pa86lRJWX/LFWfPknUCZ5g4+W8jJlos5 + p5LRpBIx3OhajfdXakMfvcT9zV+rtfKuBvp2957IBXcaaO3/NLjrX5Lt9XOcBbj85fcfPwzmJyAe + Ek9iTPSsVFX2UK0mkEoeYhx4Jl3voTuDcCm+ONj9iwVXBYMs9fj1Fsv2AP8bpQSu7KrgNDDUit3x + Gf2c1+INtFQzYc9PSEbmi2UjfAzLQr0biMSnMbMW7Ic1syvvr155LPuMBwq7NgFnW0qwMdaWKux6 + CebUU7Dxs/SMrIacw65FGbknhVFRYx4KeDd0HsstF1Tj+Q20/dZEis/j65ut9oOyaBBH6x9fWIft + ycPCEpgdr7pqnnxpBA4dsr0+H5xF1CoRfsSPhROZ+OritQEDbw9JnI+bPAXjoBcyuhpfBducZFVj + Fi79nx4ilzsTOVvHLD6SNKUmyul6CtYnFPb4PGzYAIwCBMF+t7DJ+2ReAPwEtPaJAdmZW7H6tOZs + zgs1huOv/Hjck/1W2y0Ibyi+B69/emwJuCGW6k9mzWh/35vT3H2wHYPWg3v8r/dQG6Fj+gzxcDhW + Ky89xH9+wmkKlGp1o91dtccbec2Ha7aNkrUA79vm+I1ISAWUiSP8xDceW8Kc0zVtlAZuan0jDwPM + wz+9/iHJYZZirNH29pJeAPyiEisZ6wXcQTmE8I8vn9eIVwmvtyZoH+diXqtfonaGZI3Qis2F4BOW + B47UVgi+oint9YKhv2P+LKW3SYN57e1TwLUXcfmXTz7Or84//vjd3iw5VT9RJSvJFcnP3C/Wxqc2 + 0GOvu7D9SQtW2HhTt9Lke/jjhQve/Rm6UN114fVNHGwGN8fZroIYwuSRjfhM1GCgn9Fi4MBeKNZf + Q51t34vLgn39Z6ndNqdFV+ojbnNaYhfeFKycC3uw1zOS8+KLLk9fMdGDE0ZPygyu2uqU3R1MLvU+ + 2c9Q/34vFJl3SXY8AO1LueWQzdIZY/wJwZI+qh5JnHbGhsidAK+HSSqVa2ngeLuJVXNseigd3qcj + VuVfT9fzeJPRH3786cmlOz14KLx+vz+8zbblfHXR21FexJCqNZgOj2qEKj9o2CqPh4zE9UWEu1+G + b9zFp8vXBiFoFa8kWvOaVXpqsA2VKvLI26LfbF8fKNld3uAX8xiy1RsY71992vlptZLaiiH/LP92 + y/DVupgfBr29/IF1gb4pNeF9RH/x9vKWB1j/9Cej9hHW1w8a1rxPNviGxmkuPmORcYclMYCvaDl+ + nic7+ONDiH9vZ3wpqEzXdz7VMNRNk3hJXmejvDEa6OH1itXiRqpNKMMFug2z79D9ser05xfIWbn+ + 1X9KCZ5zCHQdz+hrMODf9wcm8b1P6rIVuU6liO79N8T3vf5yylvpUZ2Ne/1VvkMbpipEP567YDtk + abCN1TMF+EUnrOM6DnjiH1xIj3aGLz+9UpffZ3Thd3uy//xgop6s3Z9FgDzGb0dpwNcGfI2eMUMX + 6JTjpauEqvl8w7prWgF5MYWJ1F47zMNZoeqfHgAp/D2J6oY+3Xa/CADP9bHRynEw7/wM3QXngTUL + 2hXZ1x/88QnliTdn61XCwtvK3+a1JYSuT2/d5z2prsf0pjxQE75nOK+1hW3a/tRR1aACvPR6JppB + rYzjq6mA7mIE8xJYukMF+90DDJMLttLjJWvtzG2gG4GZyPZpGpYCDdI//aQzcfePn8HjHEcz3wqc + MyrWTwGuLjXEOr8/2XiO+RSwL7Yj0XiX6Tyc1wIuSs9i4ztTumVW+oL3YI7x+XAqAirb1Q1mZ7hh + 5VEyTp2HyITpCZ+w5UpfZ7V+QwGiSFGJSg5RwOI1aYEzbjnRU8YP1vyTG39+Bj6nHu+sGQQ5/PND + 7J+mBLR78jlkH041r1eQAZpWHw0djQTOaOeH3HatTHiNkhu57n8npne1j3pw1rBZML9hvRwnCU4d + hbufcMtI/3ByYH66G4447zqQxQY5UDo5JO7S5MM6adkCfi9A8XmKamd9UMmHVghl7xj+BrVVsjYF + XGkWJBDZjzNex9KGzEFZsaai0PmX3xdRZnf9bjpjEfoxlFuDnwHYd2So87mG14S9zOFGGjAAaiTw + wjbHf37y4ptWLe18xXscTA1sJv5I8DJLPcaGsQY0ih+jxBzkFXvZV8+W0+xoYNdD3jFbXHVL91sY + EcvpM7VIGcyAjDZo65zzuN1f5pXnUQTVMf8Rb70GQDDugg23s3wh+fsl/+FPDe+efCb+9/AKtj2f + JOn+/BEnnHElsF+phBzwO3Ih3ZnSBakzPDg1jx2ArWBhDkkBLtHywV6Sa4FwIioPqFNQ8seneNW3 + PRh4afy33tV2Tc43UHxo4rEBKACBgm+gz3DQvC0qx/3EBUhgvPUUy5svqJv8u4vwPrL7rSnpUO1+ + cQiyoxxhH/WrM0kckYCSv7A37Hx7bcEhBv6rL8ipXI4BPXdiiZZygOSC2v2WDkb0YT5PAz7teEnC + 9ajBkwE8LP9SDGaDsD6kSrbNrEezgAZvYYaNAW0ShWaW/eOLx892Jd5rNoZhy48x3P3c+ccptrMl + 85sFu94hF7YQKdG+fQzT4vTA+ISLatm64gbI9XvF//w8nYEjbLZ475C55UCvMVP85QN+h/qDUlEf + eyhA70zUlRno4k52Dna/bga4udPJWHUFbnc1wt7ux2/d++wil5YescrjO9jWY5f/8Xdsr3EMprJb + cvirEnv38w/DengMs/SRlpC471cBlgapDQzx84T/Pr9Yh6JHXvW4YjPj8R++e//w6k8/rfB4rOF6 + ttKZuT0nsCW6e4PblvT4dQ+Zah6jaYbKHR2J18xJtZa5vMHfUUtw5to92GBy2GAe/UoiSyyg22/c + Uti3vIdP0MrAai+tB3a/hWhZa4AZGPvMv7vm4eu5+wVb4H38P75E3J/T0CVi6gIJ/OeEL11/Hfbv + X4Bfh4QkLDSyJWM0F7Du90Xwpsxg4eRohq6Mi5lLHopD14S0YiXgfN68kQftOgc+ON38aubXgzv8 + PR+wiH/857eSvX6D3f/b8fVZ7X5qD4ySORC79Q4BDQ/tJgkFT//0dUaEj7ufKAX+n584bL/YU6TX + M+G9w+5n0YsGTdAXRzRzZ3kd1ttwTeDF7e/Y8KQ32GZVlZH0NKeZ2cZvsF1n0IORyd74Ej0aupy+ + XgLiruy8Wk/uYO8/3OCvSm1sd51PxyYhJcwsq/QOhaRn7J9+iteXOnd8Lat/9fqvnpHsZQXqnz8O + 3D7RZlCY3UBWEspo71cSTTW4f/gHo9smEoWVNPqvP6XpeYjPaxQ7pIgeGsyI5Pzxx4rd+eyfPvCk + j+46m0syCF/VWffaXS8tMz1tSH8P5lwPigyGTLoyMK+TDbvRu6/+1ee/+rvzmWql7mL+8/c07mQF + S3XaTNRXlnkBiBXAJp9PMhA/xxdWcMUNFNfduN/aE/31O+jUalCDsjt9cOhREGwvnnVRFi8Kidj7 + 9pe/Iaxd0M/czq9mKBYsWvrmsvPHD91uvdzDE0hHrKA1qujWdzLwE5QSK/w5zt4fvP3pz3ltnlPA + GuD2QnIjGUTtpkDlAcpYWB5/7AyKgxmsgmmYf3wLXz9KRzs/LEroXBiN4EL6Bm3qUIjud/uKFXvU + wJKFYg+hxije4dUqYGuluYHClArkr37OIpY06DlP4R//f60lp4HXKkVYPTJPMP/53ZFfTeQ8vvSA + rw9+DQdoa3/+2CB4F5EFJ7cJvcefX7vcy/SvfnhcxmF1U0Lkgt3P8Bj59a3Gw1rwqOLlkTxbutGx + MeQQnm63CusbJM78iw0ZNrAr/uJ9oHAOUlT8Xi/y5zfu/roCma/YYvVgqBm/lkiDspK0xImtj1OG + 35sJyqf8I261MqDb/fj/Z0cB+N87Ctjop80LrH6AJFKzoG4V4H5U4pltEa82YIPfDuui29LV1R4e + rPFBmnt+iqrtdriWyDVcgcRbWtFOEu8NNBNBIrrJsdWqeVYLh0PGeqwc9cH6fakmcvnL6rHtzQLr + qDxHKQx8DctoYrPh5VohYAXBwpfH/awKq1jLUPXVFrvBAh3avPQaGo5LifJt9j1Sv86E1fsge1R+ + nDJ2wtRG5uXx8hD/0gZqdGkLZE0oCT5eUDXKOZmhV825x2OVcZbzVy/gYZJ67IT3j7oevjMPefce + eBFVTw69rMsL8dxDwbLzlAeObRQDuW0O8Z1tL8MatNUMhYXaHl3fbSawjVuAb9NDfD4un4HOYpvC + q7syHmsodUW1u7yhLZo34uy3oy7TQHjYG5GLz4V+DdbvMt+gcZOfJDqopNo/nyCZhzrxt+AwbPtE + H+irgzJTcOFV+nArHrmNWeGTZeZD05WmBzvNyvBldGtHKEMxRRMOXILRMmT99Qpn2Kds7hUdYYIl + SX0DPW99Tc6n6yNbfMtgYaV4rnfQxhzwfRkZ6CQbJj5RQDJ6V3eHJArP+B4/MkC25rkB3wxY4gqj + 6awsCVr0tU4dSbnKUpexPCUokpSAuGX9HXgJPhSU3iTJQ5lxBOu04ASKup+SZObbapESNYRd8yiw + i1JepV7s99BNxzOONPbj8Mr8s2F037yZloZYTYpuxfDKCdMsDofTwFL8LRFwECb2eDgDVv2eJOib + V5ac4c+sVhv4LHw6rETSS2NVrJyTEY7G5UUiqn6cRbcYCMZTPeKUXs2MY+aqQD/cxuT2UMCwMgLS + IJPNxxlJb1FddItnULIxIT67xplynyjXYJQ/ZGKCDGVTvog+ZJRGwPoD3R1SOIvx732/f0U48DO6 + JbDlk4aEhRg53BBWDLTMViPvrGgcamu1hODyMMg5vasZF8RyDbv3jyfmYzbUUct1F76nw9V72h9d + 5aOqyCEHbzqxrkOo0tP9piCs9iZxD0OlbuSz1TA9f0aMtXM8rEFs1mgbywznSYiq9Xoxb4hT3Tu5 + XscnFdJXy6JMiBevLG1czWb9YVHh1DVRhdWky5aUHuqNu0tkvzED9jbJPHwNtCBuftMdvvHzFxSD + E8bpO7gFWzG+Fvh4cA52j49yED4KTeGUkpYobNoES2q+ZkCkT4Nj37kFswhLHz1cI8ZZV9zVjQya + jaZge86rx8WUPpdtRjhfbOJZwWkQMvUkI+9afTHOCsMRlufzBR+uFpMs+lwGznuzPTgeZ59c7noT + kEopbRiMX4gVwtd0Pd9jFkrC84Cz2KCUgP1MNAqlmLhhYDu8W39fMOotjpjKPlVapMkI17FTiZdY + 6sAzMOxRd/HwDI9MC2iq/swjvFE6H16QBps/5SXEfXEgFpW+w7o9Tws6da5NnLT7DFsxxhuMAzue + UVl4w4JvaoneP8XwWLzcsvXzO3jwvCJ1PsbeB/DPh6iA6PtRsVnllNLb5d6gPb/x3X5css2+ZQ10 + 1/qAr+qBBf/+/+NebFj3WTyMpqCU0DHLGGtpw2ZLMYYhalntQh5vSKolq743ZNvoTpTTT9vvpe1b + sXnPPdanuAFrJhEGHJchx3rQK3ThPocEElfQidbInbpmrcaCH+73KYnHSl0vmy3C2eUdbKbiNqzf + 44GHxTBsWB/XYtj6Ye4hc4xdkqxmF/BeZvdQ6ojt/YSXTNnKr27w7roacXKgBOxvKBqkNIcjcV66 + B0aaCAbMis8ZJ02QgvUtXV7wynET8fBXUVdzSwpkXiUyHw/RRLvwTRSYD8CbuaFzA5anMAEFKHyc + eg4G/JuZFPhX/4xHeAQj/3nc4Mf5vvC1pO5+hnrKoUHWK07eWRHM+/tAslJZM8opm222pSiQ2X4r + PldFT8l9d2BUCzPEYxgPCLqnGVDIvYGoWxeClYp+DPbnxYr7NCh7+gY22NJ7TayrcaHL9gELfLze + iHjdtXEW8llMdFByTB7zZcjmbrhuyJzuNrbq4uqwIX8ykZhrKsaJPlbLgjsRWfiH8ekHZMDZxd2G + 3+E6Y237PsH0fiUbtFYh8vhMLgbhsyQuHKrDY6ZkuwAuessSZF7qDZ/BO8wEeqoTcFBeGCdt3apL + 5Q8+vM+5heXwB8DaN5IHIaeKs3Q/PitahvtO+ojxsA+rH21b31zgU7goXhM/N7qCk6NBcg1j4lni + j4759a1AqCsMtu+hkM1RVbxQxlwBToLXk/JXszKR8rhoWL1UedC6fCGji6FhEgrTCkj7hCFQnBzj + p3x9OjxBvxsyzW0kSdjcnFXfugW5FW8TG3DWwPXHZwwXO9VIVsCIktvas8A9NTqRzd8l4y+W3sKo + kK5E9sLzwJPP1qDKeS4kLIKZUrkrbmgLksdMi/uXbs2jUWAcmDG5Tp0HtqLSPbic7j62fsAetjgd + NpDcuxRHRhWAVhUmGX0uQPjHD7Y3l+WQ9/bhi9DvqulgbTb6iyddKeWMW8I4hvrzV+DLFC8DNYqT + Af7wNsrxndLyl5VoxaeFGL6zZYshZAvE9jcgieVYlEXUbJF5KE/z+1ew1eLXZiG95EjH+rKx6npb + i+QvXzA2+zpbvcu1gE8/dLB94J/q9qpeL2hf1xP28Ld0lnvKuOCDxhMJ7ZYJyOmeKhCdPUKslAXV + 8uauMtLY1P/jcyr127L4y/eZoXWqzr/zfQNrK19x9NYHZ7ndsAm2qybjeI/H8VO9W2mPZ3KLmjnr + BHL3j3Jh5t7RQY2zdH7UH5VpDvF5+DDBsv++PzzyqImUihOCvvnjLyS822enM4qsRJ5U6AQz0XVg + nbqSkTlFNvY4/6SOG05atMcnuUvRDyy/oa1hWEU34mR0dsZocnMIyuWIsTvcwaoV4wL2+jAX2ZOo + 3fDDGjwWjTevMbvS2ZH1ED6BwGIXmFG2UKTEMLNajB9CvNExOcQx9N4XZ974+Z2tzfExwyOUO0/M + ZQOwirDfuhQ1T3K5JK7KM57Aw7xm1JnaY5Tt+LHAsLrfsKFLcbUd4cGWOs3JyGmAbLbca7zAvR6T + Z7LJVPgtdgON6SUS3Rl+YFNnS4bk9BTnoz4enRZ6bQl2PJiPl16u2PBwm5GkmyO232JSbUYNDPA8 + eya5aLgeyNZEGxB+3Zmk2osbZncSFKDIZ4Xo9fYdCHnKNXptP3t+7N+/zGhk4S1R6lnDzuisgn3d + +aEokfMNzQPl7XpEgZqMHhMJJR3FJG9gVb+u2LHPItj68qkBjl6u5AJXEdDb2vOwdc7jP/xnb/wt + AcG3X/HlGDkDzVRLgZ/CQTselOq4/z7o2DzBf3gijEej/OPX5Nzfr3Ssz3MDTkry8erwmKorDEwD + qM2mYusghWqrWaUG5DLniNp0RrDVjF2C+ZP0RNcfv4CsjSzCv/jN21swCO4bvOD7mE3e8eanGX2r + Wi/t9YDo4yoP7EX2CgDlxMU7HlD+aWIR7HjiQWJJGYnXOYcKeFr4jNyZzo2f57Cr9TORnwSDiW0U + DWpEOhDlJ3R/eF9Ic/qT8SnGzrA9K6uB+/vCJlvz1bJiZgHHUvphN7MXukXjJiMNo8Dr1i6p6BI6 + Evzjg9F6lwKSRG0Jf63w89i5trOF/1x9iT+PH6z+6a+VtWUo84xOLkK7Outj/bXwJjA+UVP96+z1 + IgfomLP4HPaW02uZtMH33Wk9IIJDsGxJ70FkVaZnYPXlrEEsNwA4B0xOL/+tkqf6lGDmwJYY4iQ6 + RPdcDcj16uEg7Z2M/5WPF7xWL8Obp32fesZxEHVifSeaGyV0Ko+s+8dPsHOqhIqetc8NOuJLIbeu + roPREQwbDTiRsPGrP2BTvs8GhlH9JHb0rYetQevt3/vBY5cH465v4PnQd9hpwQLWVAoMeHl7V+wq + zcehZmcl8A8f8Byp6uzIlxg8ipabV7kG2USegQI/E1cR633vgmWvP/A5bA2Wkek5bD4mM2Tuxpko + de4H9NesMixBWhL7LYrD97c8ZUh/UrXj4z1bb3Ez/uOzXjJ3oGPFrwvtdZ5mMKya2lbjmwFRnsnY + 3vUZa2XlDB+cqe8dQ0dduZM/I7enEbE/8gimrQlCuATFGb8f7CGjr+gmwh3vvXq51mC9r7cFBXk5 + e5zJhcN0ou5NOn6xPEuHKw8mElqvP7+AnEtQVwvQkQFr4LMkIdkMxs5/trB6I5nEHzFTd/4/wp0f + Ycv/nKsdzxXGT8zbn96nS1J9+n/8Q3s+s4B64CXB7rPNWE11XeUZvbkBLpVl4t4aMqzw9IaAXxYf + Z+i133rYVRKck9sPuwt3yv74D+D6szdP0eMwLMEUzeAm+Z0nPsljoKuDPXgXahfL/klx1rtUh+B6 + ck/4XXQXuhVnv0Ry1jnYWe4vlS7PKIeHSezJfT8BM8H80cKc0XRs5Z/YIUzwTsB7aEV85VR14Bzw + HdH6kjxi8lxV0Z9ziOEQuzVJrGVR5/Ag35AbHEaiW2kK6HfJWPAjtYJ973gBf/z8j194/JznWScE + fQ0eJfLwyWar4F89De8SPyN2Q3TiTv6IiIgvHgNNUI1BKjUwlEyAjWGf6p690Aisi8+T6JKUwfYe + 9xkbnjJ4G28dnXVQTBZalxtPZO94oaumiQaCNexnXpzEfeaUm0tVFnsYR963ov7KGvB6vWJss0xJ + x9lgcnhS0o83M4//AAAA//+kXcuWsrwSfSAGIiBJhtzvEARUnIEiCiJyC5CnPwv7G/6zM+zVq21J + Krv23hWq2oqQu1X//AfsdHaSj5z8bqB1FAi5rOcBrE/znUL3ST4Bv54HOudZPMDGaFqs+MqorsHx + YIB7YMck+eHRpy9rxHZaQuzPuY43fTbBe8Ig7G56bu2NNgXP6GqRk874/fIuXAtKytbr+XhTVVop + nQXFt5ZirxQYsIxJNgiXqbCxUU9BtQT+9QXUUG4nKlc+5Xv3xokSAiE2O5i6bFalGdz0EbHVTIhH + eDt2SHPPz62nzaWaN34H05LecTBYvkrlL3LglxQJlqs0VilNzBJyPMmx6YdJPu+ffCbuh/yDTQNJ + 7r46OSzg58XBrnA+uCuLCQNqjMQJlkJBqbsWNTQWaJM7Ou/UadcF2R9eyIsO1YV7zyx6sCuYmGfU + qz9/AYbP8ES8mjNirghp8tNjxIjld7XuOuMP3wLAtId+tcZtLjQQQbA6s5fT89IUUPU7Cd93n8H9 + SlCCf3pJiZynSjjBLmH3uAfENQw9566WFkED798482U1Z73oNkOvLSCxlf2qzqJeekhj0xDLnS9X + CwauB999PGH8eB76L4gnD2b8IyVWJkTV6JQPR7x8yEQ0RxXU9WM+VvHZCClO+rHK1xkPCXhc7BZr + F8dUqX65dzAKxCcJmp0erz8/7HAYQqKERZYvhHAWenKowUboRjn55T8X358BGhYnX9lnasD9Lbli + Yxjtfp+JuQGGu2piFxW+Wq847eD7/jpgfwQcXYrDXEMz8QLiaMWpp5H/qMHTYIbp93nDqVpFsb/b + hARFXrvdvbJr8F71FFv5XaBrEYIEBllzJU4xeSrdTW8GnkXV33qG8H3HIm+GOB7P+HrVLbB/HWAA + A+v6Iv6GZyN1aIncc+wSnbZmzGlvLwE/vm5P67HigvrmwbGrDXzW3Iou57PnwS59qeSnDwfPVj1Y + TPqB3NsAVpQ0mQM8udaJXJefmIiOEqDrdM6xobkqpZO8SqiOrJWodXqIqaKrM/TbJCHJfKwpXZv7 + DOi0n/CWD931wbwVZC1cjqU1flRzMPZnsPFB8sv/5OgrA4oX05+Q2x8rFnYnDy5OymC10Up39rhW + gYwUFQHj3Cd11IOiBNl0OWEZaRd3b1xkCIW3ZxCXaa/VaJ/nCa5qBaa+X2p1qMI+giCIW6JlcuCy + Qpo0EH1iFNBv6ed7jZoCZKvTYXofryf68yvBz9/2Ivmbz0ynMvAkSjG51uVRfd4X34LtPaqwNI46 + Xd/Xo4Nyu8PYuk6Nukpva4J3u3xu61XRJVvkEv74RKjPTT7n5lwjfTdfsEXygLL50bEgG0c98Vmf + r7qaUV5w9oY7MYqbD9h75hQQ5twHq6+eAa0XJTP4RDIzzYYYgTHzmBDueGkkgUTKmFuezwQO2TCR + 1FNCMKPUYACalHR63/AFjJteEeYJvoglHmfwHZTTAIjg+/hIAYm7oE4CqJ+7U9BgtVCndEAdrFAd + b34wANvzDTBnYjBB9jhV49ttFVAZHENMtJzc5XNfxB8/DIQsHnIqXzIJqH26//EPdw7q6yoyGgh/ + PXmrOTkiAT6ikptY01tcYn+LEIKTohF/WLqc0gS/RFe4Kdgr5AuYbiaQQI0+PtbPzy+dD4aRAKVU + pr96AxVkW4Qn/X3Bqhm26rrX5xCFz+g00cjo3PevvlGuj1tAbNxVG1+uoXN3AmLGrwSw93AWofg2 + UqLorRAv7lepwVIIAUm2BsYU2gID1PsYTLN7lypuTvciXLpzN9H2LIF2eNkZfPSdEPBlx1fre+u5 + cFK3G3YTU8b0efjOgKTH58StO8dlg1wK0MstOKIXZU1/enybMmcGodSu8aJCr4FfckuI7qdKzg6I + 0UT32F7J3/O1qFQgvL4C4hjkVS08uURgq39gf6uPsJt/JKqr9wiWuTyBlaGuBkQgyCQa3xdKT62l + IPfYXck2VzJ+XfdfDZaI+2KTbcdqal9TAi7v6wkHtR25q8uBASTvvg9gP44qeZw+K5y96T7NL74C + K9eFKdr0L9Egd+0pH6fOn5+qt8EK1j2WOZCuMPm3nuEjhDDUd3mw5x5BTC/iVYD+2b1P8455xt+b + NyriVr8iaqNtUzwzO4SMDzWctyCkizAdHMgGNJt48GDj5eytLdz4HTa9PlBnyx4nGPEwxJErpmCi + hlyiSPbAn39JNIglMOAwwha+d/mq3DwJBDdhnX74u8bVYQBMzUIij98JzLORnuHPL7WURKF7uXw6 + SDJtgM0jVdyfHwNt+fANRp3h8zk+PiX0Ow/g/aj6delEEYbqV8FXzmviFQUdA2VTjYlX2T0d+caf + AKPU/MbHH/m6GKSD5wu1yI/fzOQpOMDalTJRneEUD004CeB+/RDiKAtw//yBmmsVfIOhXe0nYWVh + chE44gQuodT33x4iw7oQhZse8TSgqoOX2zEi8aGoths6F/EPP7Bmcv3fesoc3GFcfkc629HRgn2F + rsQYrCpeJWlfQL9rRyKhMclpdQhf4v3wzIPdJH7jqTTTF0xEBwSLEirqnrlJ2c+/JVaULPFsRl0K + Yc10xJo4q1/51LGgx+EF//L1rObCGeJjigLuJHgV+2VLEZw+/G1iwTYl/X3gOfipgz3GbduC9TSI + /+qJ2vpGdPn5nQ9eT3DWWCVd+EYfYJSd0bSr9dydX/ukQ0zpOVh9Ptd4ADjo4CnTPthcQezyebHf + pmIcVqxv/jHfK24E+lhwyUm/mvF8MIIz2PQN/uHp5zE/b2irrxGb85p8YRvHAEXNnYjylrlqHVDf + wrjgzUmobjEdw7bU0NPd3pA7MjeVZ8nZgL94sjb8HAC9CrAmXErMe9DFlJmOBmqAa0wg0st8MHPA + QOEdGBu/ZuOV0ZsQzie2xFoYl9v+oBtEiXAmP3wnu5vCovPtKuIt/t3FAp3285s3P1jq98PB02BA + l47Ie1Wtxts8h2jTWzj+0JWuot610IiUOwnA61mtQa60P7822AueBah3fgpQtX2G4Cu7+xffOdi9 + sH0OnpQ+DmCbGi41xK/9rztJUxRCWwbblKklV9nZCM+oPtN6YiF3rfjLYiVwVA+7iZkYKf7hOdy9 + qjvGW31t2vwGwSD0SPDdlyryOSQW+orijNUPXcFiaWD61f8mXhQDd72ZVIHgrUckMBvTHdLq2f74 + cCBEh0v/55++ZvUUzFs9dqj7Awvt2p2IfaatOpt2ygLu9DgHsJB5sG5+M4gYkAfz1xHceatPQt+X + M6zBYxzPx6MYQv0gw0DY6gf9Vm+G15N3JbmFhrgKzmkLdb+M8NG2btVKnmINYWFL2Fw8KV6EaXHQ + tez2m97R8vXwdAq46ZFgf2SgS+N2sgD/TiaiYtaOaVaUHGT3r5mowyXJl0bpk//nRgH87xsFx5vG + YKPUxmrtrs0LJnooEizl33hh38CAw/smkWgySbUeb+cO5hx8YBe/ue1O+rVAxqJRUhw8s99X9mWF + 9MjOJHtdhJjqwjGEZXuqsE7fr2odvWMDz3dNxur9eQCzJtIBdU8unER2MfI9+w4z8RvPPdautqHO + p+/WZYI3Oqx/4t6d1cu8Ij2LCiKvuQ/miCtYaCTWEOTn1e/5k/YUITGOflC9atwvBwIDQKa1IerJ + 3hzEJyuieeBcbPrbXOqr8W4BqjWPWCgfqzmjrgKL0smDXVub6vLx5giualsSCxy9eBrAYMAc5F7Q + muDSE0GrRHi3P7eJ2tB1+Rd5lPA06AE+M6yVc0fHduB0UrVgvmvL1gRIF+HjWGFi6GcUr4NfMEDa + Mx7BES9Tfv1GDRIipiDJRcY9FW17Ro6QxCTFziGfBfGmQC4TJayewzFe27hsED0OOil2Kc0HYt9m + 4ejhbwCZ9yvn3kGroBdgW3KKeqNa2XzPwPVWiYG4F2E8P2e2RiiUS6K/wn088nC6wSoFGTbO4ynn + /Eph0GTCCN/zKgak31pcjb3kYoW73/p5fYw3IDUaT+yDrvXD+ngXUPO0gCQeH/Scz6IZ8rpck+i5 + T13eWZwIpV/Cb/vbuGuVQwiN5Hgh5rWZVGoFLwkWZWxMK/VmlcJBm1B4zqxtzqCRc1mXQaB9jz2W + 30qvrm/8TqHoMzYJipvX8+xdVdBICoG4DOLcVR7KFKZorUix7OaelN9jjVJGPpDQvF7itnWtEl7I + /ksebQt7ekURC57Xu4wlsMqATXdRjR5M0pFcS3dg+RRSBsv2UhGHzrlLVrGbkGRbM/Y+FlNt8RIi + sVcAkUjZxUOoNDdU26JP5Dv95ktAAwWaTcvgWG5bwC/p1pWeuekkXs24Z1lZypD2jXviSEmTL+MB + niF7CcNpeSuu+q2vXSl2XIjIjc2fv/MVQWZkrlNl8U7FUWFl4Ei7kMjp1IK5qt8KSsY5JtkzeMXT + ois1HA6zQhT4udPVJVGJ3NNzxkeGhCqb0feEHicEiSYwz4q93O8S7J4QYF1k65xon4pF8Cx9SJC7 + krpHl7ux3emKiHKbL9X+yoYaqrREIbbD5S7PTrGD3FOUBd9F8HuOD54SkvvXBTsP7enyaX6P4CeU + QmwmlRDPuvv0oD8GHj46UeiuSmho8MldLXxJKiFfbk7LIXzr3lN4ou94doV3hO73zgqWDQ84Ig0D + vBL7gn1bD3u+G9QJvW/9fusp8HG5aGxWaHf5lfjNJKlz60ovdBTEDJsNalSaZmLzF1+uib/VdE1L + Cy3RdyS4yXTA7utJEY9heMbaMCWUNcPvLBqJJGM/K2hOpUnkoANBT9z4KbvcXGkQHrQHmqDh8XTu + QtlA2/kk5pwx/eq/yhDxEnkF7M56VbMrOA1cHLmdVvC6u/OjZxW04RO+qu+pXzWVe8Hf8wjP+pNz + X07iUN9P90D0R1tljR0boXXv7gLoJUFOH1eDg3rBc/jCbnfu/VcbwWt1JsFyrj895UJaoHS9wGC8 + tUnPWs9FQETmSuIksANkw1MkiVEwsWX6AYPwdQX4aecd9mGu5zMuDUcEN+uK4044gpnbxzfIwYgl + ushq+WT7CQNQ29fT4oTvfFKQUMJd3IfYlKUkHiPeDlCWjBWWvudvvjgH8Qy96nMi9jWKKX8dlOkX + H6Rgjq26nk+4g93Vc0lgdg3d/l+BitLKSTa+6n46qwkLNjzE2s5S+u35WTh5UUK8MXBiTltaCCn1 + ayy7V5AvT+0RwDEwNKKJJl9NX97wxKkWrviYznG/vx3AGUYn7knkASvqeru3g0jAPsUFrzQx9a86 + i24zO+I7qkFMX3dHgYcbOmNVWRgw3e7tBAsBOH/xNSo9pyB3ffAEb3iztLKbQTLNDbn60i2nCOPh + 9zO+t+LgtgvSHShpk0qU5/Lq546ZBeTU8Qcbb7PK18s7SyHDjTYpjuYuX3d3S/nlJxzMgp7vL/Ko + QcqA58Rb+KxS7tkKyEiLCVtb/CwFcwygUx8/WL+7CZ36q8uBus8Hoid7E/zls8P7kxO7fJ56/pHK + BboqRhOweqO4vL8vLci8whu+VftPPO/egoZOF+ph5xmO7mqay4QO73dOZCfJ6NA/gQiO6GTiwmZb + lyovqUa/81HrrUdZ/kJnxEymRgJPStQ5CDgFyuzRxDJfVP3KDdKA9JdY4dsYPSjrfO4WNAvpgEPz + MvQLuvAz+uWfs6Ia7vwR0QDVczdhf0RPwJ8/jgO/LU5JNNlZPl9fpxrVu1uK089k5fw3ozWUd+yV + pMb6rDhpfK/iwJwygvvn0eX86t1C9Vyq5LSnh3y5T0aBCvZtkftsVtvc0Nr47e/E9TFWF7xKHQI1 + sKcaz6rLVY5jweEGG3xkRj3nd8rpJoqBxuIbe/fUoafNgLrm2k4LX6gVT2o4QGFWw4kp262L8IE5 + w11cvrE9LEq+b+VJA7BLvlgSgODOxzgWUL37qKSwWUtd66HLUNPVhKSxUYF1LD4ampadSxw6vCuq + bDeiKsnqybEQk2rq06cDv/HhjFXesfOZ5LiGZmDXwXJ8HinJH2EBEpt8pj1jgmrKgZUhXxtoUBrh + M5/nbBBAcTOn3/mPqaNeONjp7oOY6sGiXFG58A/vrxfnHk9frioRqxoHYuCCVpS9lQn6tOsOS/Wx + BSsffCV4ih/O9g78yZ31eyJB3anYSXjWZr767H6Gu8H5YkVnFjo333ctXo0vE1ABOyrXmWAQvccx + Io4IzHhP8gVCU+ROxHldhJwoL6tBym0uyTWSzZxLTXGAHzNzp+R9t+J1y5eIVZiAWBZUwL4sDyX0 + Hjma6r4vwPL7vABkN2yEVZIvNJFCuKoHbuKE7pNPjBfV8JfPvZK5V4TUmQQbGfb48pjq+GtfIgNt + /BHre6IC3kJHDvFSw2NTfLGAFCY3waydMTH6mLizrUUBZGQlJpq3r+jMGVUHw53iYVNj7H5RYVUA + X5so0b93L19jJvFE25beU60Wfb8ywMnEsyV05HoNbXUPDdKBe1NVgQCOQz5IcSaKHjQm7Ow5A9B0 + GDMQRGtKtCPEKq+1wQvqOrwFh+7r0pkWrQG2fDO1p4jQ9fKOMhgPS0luUSm69AoFCxA+lIi38ZVl + 4y+wZuUlWJ97wSX7nVXDteTRxkdv+RybRAHfx60N4BjtQPvLN43ftkTunWc+fMdyBRufxQbDxdvf + VxGcB8jhLZ+7fFgYGTx9uieWmfGdE91pOMhLNw+nsaECHgWjBFzZn4gbFG68mNWzQ7/8HWaCTtdr + h1oQRHO66ZlP3zZt6UHvc7hMW36pKl43GtBdAxfLhfClv/MEZ7dRiGrxXTWeyRr88d9fvC1Weing + y2keWHPGMZ/RMwvELCl8gr2xBMvz9EngFDYX4qXNG8y0KA0YTZ9wEm+uV3EPuqSINtaKsfN24rmK + bwI4aMEHe66v0KXiaxHkr+5OcFPkMRHO5wmO5FMRLVazfNEe7gzTt/XAaicc6YIuuxlo4Qviv3jK + qLvx7aMxcXfhWVFzGFJYPasH8aJbXc31PivgIX19yRaPlF6crUdML7nEyk56zy3s3YD0HN8D7i7I + PTt+HhYYDuIY8IaegMUyZOaHd2TDX7oY9UeBj2NWE0VnjoDqAmCgaLfmRDm9BcveyrM//I3mGPTz + QDUJkkr7kmSvXFRaV5iBciTEkyiCT04t7A0wB1cPH3/5XHgEK1zL+hMs9HVQW03cbjAti04e6xyB + +XznB7jDDyGg1uEUr4uBGdhehTvJ84NAR2+0jB+fxG5wK6s56cwIGokzbHzeiVefRSvgqm7GTm6d + +59+E/se0SCb9wCsC11TkWW9y8QX3wos7DtMxYd/4okltWU1jq38gos9R8T0XKmnT+BlwB/PfCDI + y7kavUm/QeWoVdif7RHMnr0v/vDuUFySeM61PIDc4zxOCGSiSguuKcXhTc7BDihOTg/Nt4Aut2xT + xzjgLtf9/QbEKOODg9bW1TK5Wgmp/xonNlbFmArnYhAPN7Yn+n6Z+vX5eXLwoqxCUOvNyx3Onib+ + 4at7Gkd1MI/aDSW6g6fEO1zzVXIk5yDanfmn5+jpfJtgYdKB2FpHYhKklQNT66ISu+m56rs35wlt + +pycFX0G00+/Eb46E/nQanSeBK0B5/dg4NOmN+hs3hUUycuemBt/YcPlHEKzKeM//Tl+pXIApQEJ + PhlXrFJ2nAuk0ptD9PYsu5yw9BCurPDF8cR93KIzrwrc9AfxUZ3ns7k7NuA6iXLwLa47l2of2QGP + TmmJ7zK6y96adIaDBcTt857u3/rNWNBwCFaZ/vATtjoxgolQrV9qK0qB5hkBsYflFS/aoylAO50c + bORTGc9zn53h/ci42N7bmUs/Jyoh5pWbgShoqFoH/8yg1DqpAdOt12qxL5kBu2vCTfzu3fX0KT4H + aBbKAeNbIubDwbVf4OWMDsasYOe0mlgWVaOxYNxkbzr6jCjCY1gN5IeXVJLGAa57Ndn0W6lSfj1I + oBDWB/Y7mc8XbWYawF+NMGD7ngEfMJAMrCp1sVEaQrzkX10Auw/3wcoc59X6OiQdcgh3mIbLU1dn + tTMiWOaWgR1xB+n6W29WeF2xuiixOos3NoF2d70SlYBPtRzms4D2p48biNH6rebvjovgTz+eXuEp + XjkEyj+9fGrPT5Va+LNNKbRMfNR9XC0q7IvtnSRMUoDlnpcmkYUhTTC5ecmUEyt4KbATxw7fvPPh + z09CG38nDp3mmDrK3vpb32c6WWA5zIUAH6cdDA7rTgArA5QU6oZpYi/pQE6uaJsS8ezEgFvuczxL + b32AS+HtpuW+vRMtSt8J6uD1IT7RRndSQsOAxtDpxDRUx11eza4AoS9OWN34CRU0O4Bn0aJYMsJn + vNxzAUKUXF5YDTV50y8BJ27nDePMCvK5uYQGUkm4YllVp3y5sqEBq3Ql2Nry7yI8jBm95vhM/Oxm + 9RRC0QLrhXmR7fvH88+PKJWXEogb//vDv9LNbxtf91XuoBwLYAzrPDFMHIHx2MSzuDS8hj2VkV32 + QkwOOmQKsObxUz9y1+oFGmrYOF4Uqi6SoNd/+vS57c9KpGE6vG+VM73QwOTzS34Z0IGHHivl5AHe + ZC4eimHpEOWrXemc0XH45Qfs8lOSd4vu1PAjtEmwe7i1OkuR4B2W4tTh4H56xus+mxs4vMczcR6G + CzixXia45UOsvl41mMfPxRLfbxcHP/ysepRzYBDVlGiP+OOO6Cxm8HLx39gr1qUfyiBJkHUBJpaj + FrlLnfnBjy8GPfelYHJlPz2UBqcR7HNnwPavcwOfC8x/z++umz8o4kMhB/s92Srs8VDDO7O9A6hV + Q75+FOpAYjivYHeVK3V9tloJb3tJw7J3O+ZTBvUZMsDmsOXyL/V33pFivW3irbEDKEQJB9v19SL4 + eRXpODlPFm35B5/kSAX77fxCOHU9CbqpVok5mhqqbcGf1h6o1X7n6i/o5CWPj8dqm9rojCxkCkyw + JZx9deP3N3gMczmgi9Tm8/Xo36Bu6CYJk0kDS8Fcg8PSIDLtvLGks/VcRLTpB3wWhkhdHuE0w7JW + n9Pqyma1vxrvDpp27ZHsZj/ppKnMC0CZD7G3HiWXRsfEQY91SbF5bQJ37a8q96evIt+1wF7wY4ju + 95UST/xe6PKNGA/u5rM88bvPKV8aP2Pg4WlNGB9ZTyWHT8OBeKDlRM/Bq986dgvQGowC+xKp+rUp + 0QuE/qpiN9+r8bQr/AyqxDEJviVZPt/tfAX+6HlkIwdgQYYHgRklEAesvdD1tHodNIZWJ8n7Qd3l + gZ8ZvA39iWgXmVRUefURiN10wHha79Ww+SPwObbpdBhFIZ759FzAVh8Ngsfvp5qxL7QHbaE6Nlul + AesbjyloqGaT7JOq8f7Hn65GFRM7da1qvfBEAJcgcbCx5c9ZrqUE0iPKiZe2czW9DrcOqhmcg1ZI + eUAVttDgx91VwZrVfcW+ja47/L5/fPQv/RiqIQff4U7AHvNWcpZRxRLE8OUQ5aePcZCfRa+6dfj0 + vQ/xWAm4ESf0uk+LUGn5HkS3F/jOjYm94zX4h5dLs9cmYdPP66b3wM8fNDc++C125wbGlWDg6y// + WejKAv6qhdhdahqTixCFsI5aRNRgJ1HeUbIbREv8nZa3tqeUp/mGt5eOyF7W9Mu7vydwy5d481fj + ySmcTNT5S4jldGXiBRwX9rBe4AvLhZj088ZfYDZePsTxx69LG92v4Q5f/ID89l82GwWmaK6IXCEn + 5gtOZaFu4HVC3FFzueJIWYifZkyku/JRqVeLww/fg3RCnTsZvOf98QPld96YrynCzV8l9oomlezk + UIPK0Q/+9OpqQ2+Fus7csOFcrPjPn/j9/Y8fcrkXNuKdEY5bvjT7ZV2dFkhiesABc7R+522FqE0D + bDa7EFBpSi34PjyO2JHOizoTO5mhY2oOMezoA5a8tUrw8C881ig6x7P6rbapXvMN60gBLv3ja9/i + OTF1Xbk/vo4+QpcQZ4vHFYFRg30wn/Fl20/a9V8JHsOrHCyXfgUTPocDHJXMDGYre1UUlQ8ObvyT + bPwnH4fzIQWnOT8SQztCMOpybSC4cio28vUE6FYfAFqYHYlxo091lvdhga7Z0GFv170oAfklhL6U + LTjQligee/0ZgdmtFXKXliBfWgK8n9+HJf+hqvxB4Bhgd5ZKbrtJiOfv5N7g1XjGWL9pdjWD8bnC + HERPbJNBrqbOOqfobp+NiVVLLh83PQL6frgTz/VfYIks1gCbP0EwMB5gZbx9CLzPPEw//5m8CjM9 + 1IGHsRldhnjGQXxGm/8UwJJBVXu+89Nvf4LdAdmA/+kXlRd5LOtYzOmSOgUMzyXF91Z4xsPmx0NU + G17Aq4eWbnoiQz+/6PQ5Sjl1lKhA88C65Je/h+/UOHDzD6b3K9znP39H7AMgkwAzX5fGrzr840/R + 4yPHvH4oOmDujgr2hUdG1/67hMhsOobIb6mMJyIpCozk8kHusytUo+BzLUi9VzbNkH/1lFk+EL4t + bAXz5sf3G/7+8l1woac7HR+hp4nHm8EEr692AKvwWCzYZrKF9afduQuobAaqSiBjefNbyT22RHgl + 7mXizr1TsepXEv7yoz2KaUzlI2LBqLxWbA90VMdfPBc5l2JvjTu6iNJ3EKh9OhP74qB81sSrBH96 + bYsHd6vfpOiX/wxyrOM+oTCDOE0qfPHcsqLCCSqQ+t8Ue2wu5/tvlJei97A+Pz3YL3OoG+DHP/SE + X/u1yrMIGHiXBGTD+5lPi5vYY3UgPnes1YW7SqyYfm9P4j0ON9DD/iWhSnL6iaLEqYbnoy5Q7GbD + BL2o6n96ADZHVpp4z9+63uNPijY/C/voFVacK/sZOHr+F2vqraTLp6gM8OMLZvJQKNtc1hXy3HEk + CiijfOgYQYTWZb1h+TKiaqWHewtNm39hn4snuu4KPUVb/iE6ONSAFNxUwq0+FyyXEfVEO8cD3PgD + 1o1LnS+PJyOKm54PDgcT50sDxxu8+6DA9uYn0T6oJbThObG7j+zO11DSUF7cdbLhvbpKuQjhApQ8 + mB+oiWdjkgs4HJ877IdJlBOvDiXQcVZEdPemAfp0DyLkrx6YuPD5rThPkhS01QemKjS8eOH6cwLn + UX8GTcpzFdnwBvzi5fFwNXcmudlAd5Jtcn7bgbv+/BRGv0s43LcSXbSHugLzFRyDunlZdHyqV/Zw + NLsjwcZnrw6/+rLFTtXf+tJRud3A85uccfg92/HqSZIEqR30RN/05/qt5wl4xjfA8pgK7sxo90DM + Rm/zzrzE3TNfLMJ1uGKsmwWpNnxJIfd4pFgNdiX4w9+GXh5E9cUyp9jjQmgz9bz5gQldGtBqsBPr + HcYsZOhYMECEwiyHJDCHpRp+/sBbPRjYdC2nZxXTTeDBWnbEzyu6yfJdAQ77s07srR5H7RW3/8+N + AvTfNwp2ZZIRb1xe1VKfgwH6qQYnceC+dIrZmoFUMkdiHFAYd8PHL+Du1uTBwsNGpYx87VB+LI7E + 93aeu7DDVwCxIxESdMeJrvqttGB538UB5L9yxRaBXcB2D3MszXDMp0tjpxB/2WEyR991eXWCLTie + 7WE6l7td38aCU8LD/KBYtwDJlzgOGzSD0SGu/DJ7ThHVGh26riW2vX/mxNdoAxdFBwEbR0q8KAVq + IGtHOfbWUVHZ1D2vqOHhF+uKyPVUOV07WNwTEsCYX1V6fy8iPKQ7bqJe9O0X7uRxYE78gIQup1XU + 2HiTIJfyJL72R3cmziGAJXvoJvhCKuB6LLDweZ1ZfCzH3GWhHSvo2JCImPY49b1ab+/4Rdqe+PcQ + xeO2XrCCmhNQ+VPHS3lcM2h6jUROUJmrPuHMEqXrKSZH5iTkayqvLDSU/TB9ijCirNO0GapTvidB + 9pyr0d61itg+FA1Lj+YF2JRLC9RfJpf4N9LFw8kOOZRUE8KONUiUdRa3hJfZVbb1ebmz1HklHKwl + xHm32D3v1u8MOldFxw73xvl6DYYE3sKbgw3vzYL593y97S/EX/Wkovcp6OBSJzEJu+zZ73fX/Q0K + F2EheS73OSePmYG60sM4n0TV3UeKkkBwtAqS9xGsFvqYU7R9PrEf+1TlBE+LEBE7Hcel+8xZVJIO + EkiOUxdsXeq6q1DD7Xkn/jsV7p7zBAOeaHgj8dmx+uHl2BKkTvQiN2e9uvMpBhB+WZiQMGdgPlWZ + pSGtCzty1asgH3W1LSECYMby1/r06/LOU/ictJSkXj25805CLPycjjNxLpc3JUk/ZIiIrY6TmJ5i + XqPH7f5C4pEYqpd4vQDcwlw66dP89LC7lBl2gHFMDJzd8dvd1+dggudSRSTwSsH9xRt83ISAqOyk + 9yvy2gaYLQUTHZzH9s54agH7Wdok045svp0vB3G3agkO9M3lZODnMzo2Y4S92Nf7OTw1CmLLYCXq + /tn3U3RPE7B3gIFN5wJUqnQPAfqz9cCPMZNj7qmdDFTcz4Qog1VWvGxcZ4g4rcH3e4jycW94EDZC + iSdODyu1te6TCL8skxBdCR2VOhcgQEs6CkTNDctlh3W3QlQ0X6xw9N0vmVJ4UFjlEuurfAHcFyYO + eq0XjpjSA6rknb9a2N2KEF+1gwz2a/CywHpwz1jl3NRddjg6I29xmmkPDme61s2hgcGCa+ykrNDT + g6oOSGZSl1jNWOSUJx8FNWihwfz0iLsX2ucN9M84CbbzSum2f5AoTRVkoGvjtcczh5jqOuIzY9h0 + Oe7TGcrWdf97PpcN40crOq9PgLVvdI33cpIVUB0PFVaii93vg3AU4aAwBkmHN+rn7DZKIBbuCtFL + JFXsTtpzKN6HXcCwkVnNb7lioVGXBTForm2/RxykfiFijPq1GlhDWBE3eRW+WGJbTfqtdWBHupao + Hjy7e/OLFChkVUUkY3ErVm+jAtm1A7A8Jzs6Pz51AQdCGny5N0ZOI1V+oRSWFk5swOVruAtfyDWL + lBhbVwtekxwIRXiMyXkOOkAr9OLQ7SFUWI+eeb4y10iDi2ICbGFRjpdatgr40hWC1ZIx8/X8HFoo + UedI8Dpl+cSvTw9+AmFHJJer+3W4vRIkFUeD4M/rpU5slBvwvEYn7L1YJ2f3ZbwiglOHXMOLr7JA + 8Cb4DjiZ6K/jtZ8m3Y/g4/GUJuhYXsWD5Mgg0ogmyR+o2+YrtAkcZo5i91ldVGobYwu25wmuSdlU + iyVAAW74TRT46vOlG3cdFGBwIhruAJ273WECQevJOCZlBFhidRz8iliYwINd4ln6cA4o8+SJj9MU + 91t8SSgGJos16gjxsoZCebAEIJFAVgawCOgzQIytC/au5BhPyzvO0KfJINnmooAZnbUXkK9PmaQ3 + RgBD9FVaOHQ3BaeD1lUrMzcDdOaXQvT+4bnr8Tx6cGIiZotnrK67FWmwRsOexB699dRX3gHa1nta + ZJ/pV7lXB+jkHY81Zrmrqxo3LdryFdaBMYP1Ex8iGO+5EPtK0tB+UOIXdIo9JvoiMDm9ftcE7izu + PnGyaff8D1/0/kKwTW4QLJlW17AtbgV+rHxMlyBiQyR3nR9Q+XTMVwUeGfT1Lu6Wb0rQQvi9wVQO + A5wNnL3hZ50ibQC3icqnJZ6zgqboXnkyua211ZMYAwV+y24OLrzeufPdPCdwwzMc9nyi7k+Ht4aW + 75vHquuH2xxkZoC3hhOJ8lHNnMdp6CE+f3b48WpePR9nZwue77lDTk019HSnthJ62jjArucPgL4r + PoOrbLLEW+u2Wn75RGB4c4K/+DzFFMKOtC1RBmD0rNRpL8QPHUecHX2CwVncF0xYzg526sFTFw2i + M8yT7oudSzDE7flFWcSW3oqv2/rNN91k4FF8hDjYFbueXldTQ5JTWRNt8gOlH/4mwTV4yBPqK4uO + euB20HKVGNvf8ZIP7aNq4GkPAqxNlyin108F4Wt49eQxAjYeN3xCsBJPxC5SveLGYh7Qxwt9oh3c + o7rmw6DBE41uxH1mqCLXu8XBp+0H2CiEMp4Px9mD5IRPRAt8O27rtRKgebg8sXp2rGpNQGggjlUn + bA3TxV28tTGAEc7ldMiw5/7h4ZYPp3nDp33SFixUjesHa4ne9qvI7UuoiWxITmc6xfNNHGaomPn0 + y1/VvDRdDanDWVgr5rFfdQpn+BV9gcjdW1THIIIhPAAv2N6xTNUlE+1UVBNBxqYlWtVeV8sXxCkp + p12RkGpm4y4CCso0onJK0c9FWjfo9/y/+OGy++DB2FEI9smgxPtwZiVUq9KTZP7Zz/k4klNUfxKd + eDG/ujSSqAUVbYXBPNOY0uvdYsHumqlYzvY5HcaukmAWTNdAucS22m/xDnasluKYabTqlx+BGEAP + Z3ej72cS+wzc8gNW7Gju5/bRN+A0QX5aJl/qWSZ7RmDjp8SuQ43OHerP4PpZ1Y3fOfHisewN2Nwy + kptjDT35nnIW7l7II1hoDv1iDmoK0p0lBot60GOes7MXnLpKIpg7l2DJlHPwx+98aPg5tTdHGNTi + jbi57MZcdA/PPz6JHZF/xTOEzxtknoJJlH5nUC7dyyGcxjElN+Et56wVqyt8PCoJm19tqcbIG2/Q + PPJvYtuDCWjBLjPSo8THxjEV6VAOcgDVPlqmve+/8vllii08pIjDLtPJMeUJUWCq3RqsWG3t0ua+ + SDAKZjYQe9FxKTlwEVianY4dq/rki8a2zR/e3K66BjjmGhmAn5mcKKBxq5livwQdZXx8+WrHapH0 + qIa7WZDI7XOocmozIQfL/T3E7mGSwTS7cwrFaHADrvNXddaen+Rg4+NMrOGNqu5zfAnQDjM07V/H + azW57nuFrhZJ2GZeU7+iizvALCpS7B6pQCcwlTPsaPohWzz0bI2wIlLBxROTdY66D5tQAK0zlRhr + exfQ8G1PIPGwRBTqPtT5ezBqyI3dljPfjru+94cCiE1hE2mQRrB8uBsLXY9AbF6mj7rpoxoY3anF + Z/ZS57QMKw/KI/cO0L5+Vsstrm+gPnsHcg1aM2ZLbjiDznIGIn8ts+L0jxqJG/5PDH3IOefKTgev + 2zthrt7LLk8OzFbxtW0sGeLbpXtYMjBdL3HA2uCcz4aMSuBO2RSsyOPBwg5PEVEnfJHA2KZQ1CQ4 + w+Bi7EkWwWu+GsKxhHn83HpyfIWKiv7EwXuQcNhgBT5f5CS7wXjPhvgXb7zNhCw4dG1L3P1tAv2a + aTe0o+VKUoeR6ewdrwMsd60esPrxS+mzC4I//Dmy90M8R7ej8xffOL5c4tahRxGVpoKIW85HdeEv + cQAMEQTYLefFXfldNP30GradM5tTvE2E0rqoC2AouWAfGMoqQqw6xIfzAYxk0Bu4z4uBWDv5Va2q + exTQtr7Ya/eVOt8gdWBUaVdsos8eUOFbdlAeox0xQt6kS9rUKxS/BiBu3l/orD3JWQQHTwoQxA1Y + tZoVIQDc+oevtLVeCVy+H34aki6N10nXQzCpska87Iro+nZ2K1h5wyGe7o0ubcG8QjK4fSAcS7+i + fBqlYOeAL/YS+x3PXD2XCLwLZcNbwZ03fiY+ytbH5us65JselGAO1CsxK2/NaZwVDqTP9UE8dMhi + 0tO4Qf1lcMm1Yo79ciIyBOOgjUSL4USb3d69ge61l7HzMTV3ybShQcFF2wdt/jRUdln8SSwEWcOW + sW/z9WTJHUgekMHHDc/2B/55RpZwyvBWBejnkqsTaAkHCZ87nqkGDuoOTKoBYS0Rgp6fyjwE3SvO + cdBtPWOkey6JxV04BfBKlpxeAouBn+ByJe5RikCLyk8LA6ZZsHlldnS5FuF243eXYfXQ6e6MXowE + 7u6gkSzpd+pics0LxCEy8E8v0pNwEiHdl3ccGqLuLkbUpqhu1sfUsBctZqXOe0E9uV6Ja2lxTjXr + MsAfH/ZXne03/ZPAW8OKwex9HdDnQKrBLarO2DIfLBhPh1GD92OFsUcvU9X5n4sDM62qA2H2NXf+ + HoIaEq0nJDAej3hxr6qxTcHqcYS8C+CLtU+hy+LDBDY+sATW0UH711Xd+O0WPwonwB1HHOxN0ama + b2uq/PY7OGz5iJrtm/2LN6EZi3js2W6F2pdo2GnHMJ6EFM1QuRrlxDzuszpoT5L85XeDmc/xxicb + aD/2ZyLrj6UfDD5KRGF5iFh/fkZ1McJIgtNIUmwRENJt/1s4ywo30U0frs/DIqDoIUgbfhxyimNa + wNc3ifExeVcxfchVgk7kdSemrb7U4Zg5q1iubIH93/lJQKrB58JesHZQLj3FxbmG2/5g29NFd1b0 + PfPzQ4j60/fHqhgACmQVO0VVuKypuSK8Mg7FzmeZK+qLYQaHJbsGbGE83XX9Cg782g7F1o61euJJ + BQc3vYqx/Uhj2gJhBib2Vqyt33e+iMYzhLPatwFpC5mO9CFk8KVLBDt7oNBZPmjZYatFB/PPn5pO + goU2/bD5L3I/75hPCrPGO5NzQRo61+81/dtvddM3w+uBI/gt2zk4UrtUF3BHAvjFC7PpE2o0j5u4 + cq8aWxfjunV5RjcYyskX+1u+oEdmGETPKI44uqBTta5sHgFtOHHY2PQzN41CDd4SCLDBYDWn4zHP + AGgNFku3kPQLPiktio7IJwELE3X9xEsIa8xZ2L90i0okeS/9+EKwMKetIlRbCRgsv5sY47GLJ7u/ + NHDzC4mCVCaeHrlogbYoCmzM+jHfrsdPP/6HJatc4iWc3h00Pm44zc3RUqe67gP4wy/pXbzc6arZ + Htr8CRLsikc/Q3OrIEuBiK2G48GCT04Hizo2JnFZ7Zwfsa/AH385+b6Sc6p7FQH+cgP2zeUQD0EH + B7geBkh06HbuyB62CtWkARyc04u6tGbvgA+kQ3B47FO3ladnCF96c8K+cZfi8bCPIEovHYNd+fWp + 5s/9FMA8MdVJDOiOLhs/QLfoecbul1F7btceGbThF3lsfGC27o2A4mc5YPcE13gNJicCP358lM+p + ytZrL0BnmFV8+eyEeFYsL4HVciHjvhkad71N4g3i2b+TxxWfe0JGbwVYps6Ezryr7lvMi+gR5CzW + Te9brV94s+Ar4w9TtemXGZNrAZl9q5PL/e7RcfryAeTPkREcMjy4U7qrM/R5b1P0rrpGyet5KH5+ + APECsPariSfvj1/miipWK3vjPJj2KSTyd2XdxXwcRaR8WwPr4k3L6WgIIXhQ9k0chX32f/lqAp0X + MI/Grn7+JTDwNw5YIXXdefOjga8lDDlOvK/SputeIHm9F2LM+pIPl7fiQcQZDdF9/xUvII5WWNjN + QuR6N7lzvIcO2PgHufcZjtf5HL9gjWz85y/3K36eYaUp7QS3fPvnN/jy4OEgXF11PVl2B8VocrGV + Pxu1CyYnBJ83fybKa0li7nyRX6hwrAuxnuaaz35cGvBhO+GWb718n9v2+bef09qIPhh+fknazg7R + rnLrzk1YDTCXhH6bI/2JOZEllhhcyYf4fUbi74OzGFg4zgUrNkQ/fLLAsW4Ncj51PvjhC2wB3m3+ + 1rciXLxvwZHnCuKPEuhplJozMN6aRpS7a/2dP6iddkbAiLKv8r7y9g4P7nEhGrMgdeneLxFsfvDP + LwALAvsWZMgZsPveO+piRGUGdXVPJ9E/j/HykAYHkNP/SLuSpmVhJPyDOMgmCUd2WZQgm3gDRARF + ZEmA/Pop3m+Oc5ujZZViTHc/S9K9AZ9hWKrPURS8//I/QaLZjcvzkxrwmIoDUmy/iuipn1lwIXsP + o6QBOvVyLYT14z77guC346YnsgJhlBKy42FvdcIbhNGrmYjqmfJI/OvHgKnOasQKjBcVnoJtwzzp + F2SW42Vkrzc2g3u8EdR0TUvb5RgDQV40ktZJ5C1ylotAn0FLXDHm9KWfHha8JJWLV898tNufPzCf + UIt2vEsXf4AYNsbT2Zv6D/oS0s8CZtO+EYtTF7AE0XOAzzLukbuVHJ3555eB4vqQkML5SsGRV1PJ + Z4o2vB6bsKCwvbLyi95kZCWvOGKdxusBfwsvWNb7J1jTNUvhxJwVlGT3vN2EQ77rz5/WF8nV+MOv + PpxmiyXaeK7p8jLfoQwAu5HHZrLtYMcuhOY+peP0eEx0E+Tz3tNGOiFf9bpiOvx+Ngx8N9j1hEeL + jfy+wVVRyF89jmbT4wIoUP2L10AuAfnjc9bHMnwByr9iChKswb/6rDWnE90yTg1l5qJB/xe9O0C8 + MuuhrUTiH7/Q32ejPkM+rz5EZ7r3uPyWIyvdleCE0OPltOzaDR28p4OAHxfbbLmtiHqYjTnEB0G5 + FDhwKIbjwT8hTyHquMTodpaX0XkTzYEPfRHokkHAPN7EXX8E1EF062F8vijE2fHr0py2/ni2yus+ + Ujsp5gqiFCjUvqKqou2It33OOcocCf/lt+XPf1kVjZB/eni1TmdoB0Llb76/eCtpRR6ODr0hTzuz + HmEcnB93PIR5ZuGjP78FhvAE/vFRQRGuvLzHp7+MAquvZLq8wcPDBlLOm1EIagAl4DyFlLjxePB+ + 9ypToPeIGnR2DlTfpGcticdvGRCHGm20FDMI4CUnGG8Pry/Wx3vDUFsUgVRMVOt/6wsrlp0QEk2r + xX/6osNOL3Tq84+3/tJRg+NYOsgioaevn8BOYfWysC85F0rnrjyy0u4H+Me3tBarZIQSON7b0t+y + TPvzSwZwLmaOaI8JRDteFuFVzA3y51d08VGdZFop9Y5/GropVX6Gp34FSC+1L112vwcs+q//51eu + E7/a8uezZX/+40j9kwPhvv7IkUVN57/XRpLHLavRfT4M0dbXzQZ3fuOLlqYV27D72nHzXZHngfu4 + np53CR5X/e5/oLaM9H04+tLncKvxqkxj+6ePS3WRvoj7QKa+rd/XAoOriIhjXXKAxZRk8FUWHdF2 + vEQLc9ygeD/IO54UCmLYzwkcjgnEzHDFYN7zI2Sfo4fs89vXhU8xDDDN/Q3pXBZ6214/obpqDP6L + zy38zBY8At//V282pQp9+XNIauSCF6b/9OO9vqNd341W5rJPRRIam/zho7kuHzW8/ziEzmL5BVvf + HO3jYNsTMv9+b/lKXPjnNwHm9QXTPIwK1A8KQVfqKDq3ffhF7rmcI359R5R6uRvA/h39sJRF5rgd + H3kNuWo0fFpPbDSvrplBJlgAuoXbqV3qn8Tv4dagUwhUj9351D//7k/P3/0J5k9fIUoZoHb3N0s4 + FOKAmbr5RX/+0D+/w8zu+dhUt0cOd3+OFGT+eX/x8ee/onS4hB51m5sC7oxNic+Zc0tx9Nv+/Gjy + xy+WS6Wf5eT1HfBk6tG41L+NBb9HeUP3Y4f0eT0M53/6qZIPbfQPb2eZ/8bTzh//9Dwp3YIEGV/O + Glk7yff6atXIaZl1nG18XeRVIjry74LhLUtSptA7lRkprOr7p59AOXoTA6Ed/wuFOe5T+pyReJTX + Rpb5hpuMh5dC/vSx31OTGpjiOkVKiFLApzenhrffcvVfQelQ3F0sFw5PU9v18W9BT5U7/T8nCjj2 + fx8p+F71q//2uJzOH/eKgfqYz/762/yIN+unCx3sXYnnDUu7MZEtgUxQvkiZXI+yopy58lfJPeKt + gVkQT60m8FUyD2lpy9KtjLkMsnmjI+2Qyfo2VeYkhWZnIWMpF4+eZ7WWwZS0xHyD17hdbqINn/aM + 0GnzIkAa881Ce52uKMdA9Pb3XXi8XE7knnYPQK9yKcLy3lHiriIPtmetaCCv1hhZJjuOM3M1fNl7 + rAAflzjVcewiDAEZCqSWYCvW9fiV5GoCMV4PG4i22GokWQyWE7Fj9geWrzANINesCylTc25XVZJS + eN1eCbrEEwK/e1WHssolGj5oVAfLwlksrBWDQQ85MgCnwzSU79GjJ4Zwx+1qDXMj/Wj1IX57mtpl + MqJAPlUbRpcLWXV6WlwWpNnrSjLy0fexDGdF7uOHSp7H6gQ49h2mkC2KAJnIObX03je8HD3eEgnP + xbuYllALQIbyFZfDqRiF9nPu4MwtA6nOtuItqqX60FUFB5kvRqMcy2pnyD5vPnI4s4noVto9VH4Z + Qvkm8MXibVST1evoIes69TqtDUWUP5/4gy5T2YMlLRVL7kyjQhfuZo3f7wY6WYdcT0ylqjxWDCYR + bvbMEaNrwpEvtJ8r34qD6jOU6cetXMUNdPOnJv7qgXEFi1LK47O44aOgqxFneuYm38N3ga7ITEd2 + jj0XoiLgSPrCqS5ID9TDpIxvyGIe/sjBbWLBXWMbUn5kV2cPh+cbPr1NIcWYfsFc7Ze2lFd4Jifr + fgbs3/oHtwcht1d8A1tXDQNAB+GIWe97GvnJPSpQzESXmKu6RvNws0NJmOMUnRtPLtZaHi35+7Y0 + ZHqXX8RlJHvDITpXJNjSSt/e22GB7Og5PvyCn05oNWvQ1GMXVeY9HtderCW5LkSRlL1kA/6gZR08 + De8fufW6Hq2O2RtQNIW7jx3vFW3YETsYqatC4iZT9aX7ZLmsXyMOH8gmt7934WrQfjEZ0TeR6LP4 + 5ni52UIPH+7bMmIeoF5i+HxGNlu/9HU4ZRn0Dc9HPpAsykrkfAazFYikCsJrK0gy4WGkUgU5WeoA + uo1iDUNONhHqvyOg342+Yfq+nfGWvWKPHnI/hrWYHIlXTNXIdvuhSoohQtkeT1tbt7Vs868SnWtm + GLlhKBm43E8ecS86bmkxWAtsLHxD9+cLjGsdHFj4PP8u6DT6icdurJ/DlNSKz4hHqxUen18Dye9z + R5q6SCP1rtZblp9XE6kdh0ZBi965vCxERU5ZFwX393lfyHn7YN1XhLGzvKHjPnPk/9AjWp0MbnBv + kIMe5OF4C0tNCBvqZFg22KjgRStPAYPrBSlu2lL+ECqVHPBxgZ7SKLVT/LAbODKhTtJfqXp83qqp + vOc/vLCxHO3r1cn7evrvy6cDa0OetvQVNQUp0VVtOYj6EBxr8YAUoNstHotfKuui8kA5bJV2OX++ + NRw3X/O5TVkAm8A6lP0InPxDJEY6H9xjS27vkoIMfzu2U0BWDFv/EKJCThN9i/mNgfrdW5Ey5Aew + umLZwK5fElKpSPeEZGgn+bG3CY6rsgFbo+qbHH++BFnhUQJzYnRn2RBqhG6V8wGTcmQMUBlZiLK7 + nIPVu7ExfBwfb2K1Swq2xyLGMO/GK9Hda+7RfDydZRgZZxJckULXegb7dVSL7llZB+wc664sdGZN + ngIuKAv8SYNENW44zBN15B9s4srC5f0g/ts6R3z27d7HKZ8NUjSF662LBSoJv9IKWWy7RgvzPPCw + OkCTVMb5RTe7qgJ4dYM7cgWeA8vpY2gwvOYq7uvJigTpcRqg4D8HdDl964JlD1cN5sMHkUgGcvt9 + CvIG6Cb5yMmb0psualPDdQpYlKWbTQXk1eJROdo2icfX5lG7OcSg67fd0o37fS7mXMHbYRCJZVHP + W9R7IoLqo2dE1059u+lMCOXQLa7Itk/PaJPMqygbLLoRDZXvcXllL1aGil74tOJf3gq3Nw+rt+2R + XOATwF41X4Ovwm0xdrPYY1eDWWAT1iEOzeYbbUUis5A9nE74INT2uLxEtZNpSwufu0V3fQWHJZbe + VqISPw2Cth8+QJL2/UL0k2V7/PFobXK73XWENNqCbXNLAzAHh/f7PZ5+fb8qMoObxWePtwvgyqBM + 4fPEa8hv7Iu+nqy+gYd1ylHmgAngsNdrWe/DN97uddT+y+eMyx5J2BRrtIqbk8LIxAzRVfvnUX4q + FaitL4iiBOgR7zweLIwn/oOsT90ACuA3BywvGyRLt57+yDfq5WtqxCi56x+PkxSR/cvHJPlIqidw + c1zK3sXmUCHeklEQixb/rRe5BY+CCl1U8GDfD77EcvsgMeaRHpPeDf2X3DmegPCn+sMne5v/XseG + csih+nudiNW0hr6+GauDsv700GXPH5s7m5N8b9ELnd8OHBfSSB2spmOMUm9zR2q+ogy6awrQ5bys + 7VJAPQZ/z3OObExXs1dduOMtZMLjBXCwgQzgQ21Auhccxy7hpBqSaXBJtccHO3DtJjde2xDFDxt9 + 3e5ogu1W6MS87JdepKx7gyJ1dHJplmp8xJrDHyuljFFJtWNECyEMYPpOzv5o/5YI32yLhd/SN5HT + BF99lEHHwN/04FB8f7TFcjFRDG70LfiAhJbHHWpoQXfOITGTqxhR6YEGKUdqgVd2HIslP4MJjk3p + kOQIibeIcmDLvf2z/DaXGrA5j4SFO54jSGWNYsczb4idqUfGheY6wS8c/8UTciXKtrM2aAoMw6uP + HKNBOiHyK5QNJN7R5fAU9O2YamcQ/XKOoJF/FhuMnVT2THAgin43PaFfgQ0OK86Rnjp8u9XNO4Ov + wm5JGsz2SJeHasifCya4Nm/vYrvTtZer3nDwEZ63Fu/xALGDex/qcexNTeGfwf4+uQvXeFw8bYAg + XosBGd+b0vKidPLhc7pbyLpKoJgWLc8hezBPfqB/s2jRskcFf89lJsanpXt9tVLwcIwPifb6Tn0g + Qug+fjpBzewX5JpnKbC+9pGcK3TQ6TM+nsF1HWwMJE/3lrq0Bii8doqT7GOS5iHYZKx2H6Rt0QyW + b9xbcH8+v/EfH28zyx3vzjAgN+t+piyvyhVko+iCPPGWtJshhzWs24Uj9uG76ktUKVD+i79iet49 + FmJ9g0Om723hk1O7NANvwC6Z3+TyxjKgZfeCcmDxPwwTC3urEP46yPrYJqeogt6KYqrBZ9y6OJm0 + lT4F6IlSsoDS34A/juQgOBjiT1Biejw8i+UPv+z5yk+bdh/surIMTE+ihwI7bj16vZx58LiAhfg3 + tHgkcOsKXivugSqt5Ci5HQsbTnjUiBpwzbg+/aKE+RHvR27ZTv9xN4uF8bGdMZXBo6Xnx72HX1FR + SFZPXURheTrD45uXicp87JZjWdcHqTImyA2US7GURWtDZw4M4l+SEGyOdPXhEU5H8gzHuljbUqvk + sakcYl6TXKeyqLmSqirmjpfccRXNygAvhs2JF7bzODXSh4E4qVtyPTlZNEyz/AZQGANiSsAfNwxn + BW7f9wvFzs/ea3tRQsgvOsqX460QpqZbYHYLMRYpidt17YozaJufSyJvb8LDXlgMxhGFPl+/9Ign + 5ZmHzO6pg+8beOtnOEpw+NIGueNieZyf0FKOHp2EnC/q2ikl8QbTW28g83uiAG+5zoA9f2Ba6qy3 + 9Y/mDFaozMR43dVxSzptkDNKCAmdSQQ0KmDwh1fwqnr3aCnHSJPP5n1F3ifrAXm+X4YsxTFBscWD + YsO/LIVqJqlE2eNzYcpIkflQGUjRJ9+R0mpWILgtT58Xn2ScWvchwSe0D+Th0I2u4qamsgS5FZnD + sW6X58QwMHn7Mbls+ZeuhnisoJJUVyxv+RessGEhmFd+8qUXb4PtxPDlfglhwtINegXrWacNho9K + w1RxnJYytxcDN1A/STjHfkRlgCEsQmTiLhYkHUenMYXnIH4i9cMv3tJIMwOy0jwSX9b3I1M4cCEv + s29SFFPV7vlnABT8bv4K+h/4xyeYpiSoGoyxoALURchvvwUnEsDtwjX7mIGcXohWxC99dZyzBLLC + nzBQ5lDvSyzwR0t/aD6b1rtFybwwHKvaRbExODqrmfYCDWX9IpUbBkCt1QvhHZgPfBy9Nx2zCQ0g + Y6SWWCRXPPb6VHP573lNgUFgNccPAz5qj0lxfWOPzlvag7FqXP/hbltB33EZgjSaLH9FJj/Oe5NJ + +U6ihvhYcVoBr9UZ7PgHeea0jjTwL8Efv0PlbEjRdDywjex9fiJSDdnyBHGM3vA6jBnyR90FqyWs + mrzzhz89wtt8d+phswUeepw42k5fY/H/5QtDSi/Flvy+FbyGtCT2Hh9bo3oLDDRxRHu9bBf1/pDg + SE2E1w8feBsPTj20t29FvKYYPPyrre4PDyAkPU7Fv/zWjFGB1C+LPHrSb295/z48SJ9XtAgKp8mX + aEPIE3yjWD68gsHlXSQ+ROelnf7W/0V/nb8ytACLxbEpZMhdRqYW7mNd3K8Cz0b9RrejsdB5ur8H + qJpCQFTEN3R6+M9e2tqhJcazaKLlnfYMTJT6gZ47HmSDkE9BqWKB6AvvR9TDKgP++I3z2C+x5a2T + ShB8faTpb1tfluRrQUG2ENHyRG059cEwQL0YFomeBhutuTpJsGLp1ad3Kyl+yNRdSByuxvXvMuur + wy22XGDlgLxHObSrlcsDPNtV49OfvEbr3V9yaddP0P3ymCgF94GFGXEH5Lyu33Y7pq4P7SMf7/gs + GVluM2u51cAVi8+5G6nzDBtJTD4hKg9HLqKDMvSwD1qdnLxGpoN50Uo4RrcUqdt81nsGgjPMWuvn + CwNKRlx8PhMMj0cdqfGqAOHZX8RjsiiIOEe1brfHSylh4kSdzzo/e+T2+JSPpaygP/y8+C8thEkQ + pMTOGFBMLDUZqInbhjyQJXTmdMmF3/JsEjvQHyM+FasEHpfjQoyg+Yz9j3/7knk7+j57SM5g7WTL + hfAUXv1tjnGEbezbQDI6Hi+ML43zJn5L+XLDF6Jd+pu+RacxhsQEDVHjQ6yvbNyU8jAaOdL8x0ef + LiZK//gXsu+yRDdo3CuYUOaCAY6sUfhdcwj+8vlpULWCMwrGgFvw3ZD5uPDtEvp3Axy2zxeF8a1p + 1+1+wsDXQxW5h3dNN6ZZFtnxi8xXyXKJcGo2JbjnU4XcVB7B2pzDt5xAK8C/PhNA501mABWtqBFa + lxLgHS9D6PkIXR7feRyvg+3Dg7wVPlvmjb5wm9nAU/HgiU4F3G5M93hLf3hSYZRTsVzUoYGXExPv + +cql7K7nwXllJ+IEude27cfo5PBRasggjyWaN5Mq8uvmf5A5iopO7cOGYZuvF3T+He2Cl2weQ97y + CXEfJhzp1OANGki6I6Psk2ja8x1clFgjZzWkBXl8XrXs64FKUvGJxnFRLpMku1j15XI7tPjyrRVY + YkfAEgaZvnzj2gBkbm/InjZe/3weOIPXjuTIthisrxDVIazU5IXpm1kpYRljkbtAvKO0aQ2PO/FL + J3dsFxAFanax/KzYlgVJrTB7f+gR95Fem/ztwQUpO/8WHj8rhp9H1aM//rl8eu4MXyjtsZB5HVhZ + gSjw1So1ycel8ybNnt/SOMQmud8TJ6KcbKRyl5A3UhjlG80Q9cFf/BB128cY/dVP+FVcUnJK1v5O + vNhBoPMv//RwrYKNArmC0acSkD23TUEq7ppDQ2gQOX9k1+OcDC6w8K8WsfHCeU2a5DG8plaMpVJw + imXHF7L2+x2QK/TaSO2DhMGAwidmk5fpLXMLFPiWfgxCx6nwVsRFZ4CH0xfZXyHT11uUDPCgnXuS + rl3Y0l0vkuyPlBI9OgsUu/d9zFD1jvYrbLW+ZiTo5PPk3ol+PBwK6k+OdazOTUK87mZHKyS2Blit + P6EkDjO6oIjZpKN5MnDJbp9oFbjOhXecJxh0tz6iodNrf/o6Uh/SFaxC+HqDC+ID4nwuJuUU5unC + hlzu6OSkY/ujhtSBl3Lhkbvz0eX8ITWUoLASJ37LOslH5MMkMV/E+RRHb4t5CXK7PuvDnT9ytdxa + 4KMOmNjo6NGtW4UUmOdThNx1aHeL9NiA4ZS3RHFTHdCwhBgqvxyhU1SV3vKzShs4zvuy4/ujtw1P + y5VM20vwette3nrp2gVaB4UjZiNrkVC4RJPOi5GR655/lllAE8gtkKHzKQgLYr6KDI7e845U53Rr + Ny9b3uBStpn/63Kx3VQyNhARn+K3gl06ONL9DPHRz4kZG59xet6bAKrr3SCnznzrS3N7ZdL5epmQ + Igagncm3GKDziy1iqwrZm4ioBqzF23HnU5G3cMiGgD09Q6ymVB352BokqWHSM5ZhlVLujy8nV6/1 + mShawRp1FvuX/7D4/b0p/WpjDdf6TEm58xd6vRg8UC7vGLkX3R/nLnc6SFTrhoF0TyMCsyEDAfA6 + ZOQDr286kzMw1+QcnV7Wu/3DU9Ko4QCvvtDu/Jhiaf//8VbErz9+9AZUWlwUKnsbe8m8SrKg3X8E + PRc00lN7WIBzPJyRPxhexN3Dhw+Gw1jhw/CbxvWX2fifXnf98Iu+iHW5/OVvLFj3CSyGocRwOCNM + vPw5AlxoL1dexQMkln14jOOfP9BduK+/pH4d/fGFv3yKubtu6v9e736Jz+ZD6m1L1mtwDg1Kki98 + 0Q2wrxoiVErEql96sbDPY/iPj9xO3bNdHwRacJ1C1udJXuv/+G9nWpV/2Pk/vn00H35Wu0bpru/R + Piwy+Em6iSiXSBsFWJ58cdez/Y2Gc7R4FtqAfNAYfDxcQrr1CyP9w3OmiYZiHm5KCH4ncfXfbIaj + KQiZGO54xBd2vstdDRdD6VxueDueB7ok58yHwT4m2s2Pw86Pt/TPj0B/fHLti7kG966+oxtnh+28 + 52vZg8PXP3JmU/xu7aU5vitfxseyBgX5qaMBB15KkLZcLx6WbAbDC+kcZJZKC+gxbwPxUGkNOv20 + hm4fkjOw7K4AIfxxAftXv8tPjpFpBAP9OY4hwkNuvTFfE36kymuJwRcKHlHUwtLpqc4ZkOdCitQr + q+krs75z2Ain1JfdbYuWjlMC+aq56T726zRunb9t8l1wnuRm3yZv+dMTx1NbYM4qH/o63adBShYN + IdMqZW+5Pp0cSJfbAXnBB7dzcCI1IDYTYVA+Ro8Ug7UB8fcKiAvbelxGM5qAj2INXZZOKsiuD0HV + gDpR+BBGdHi5LNj56f7/Ni19G+8cOPLR/cNjlDASrsE3Th1ku9IUbd4x2GD0KQWiLdfZW99ebh+n + 5qv6rMdJYM20OYaVeX2iEzmo0V8+lNPznKI4aMxW2Pm2PEZJisxJW8Effz16ibahE6Mvxc6nNSgl + CPosnxOPYuaHoV6WP0zyNdCXKz6G4KczdK9XiU53PR2+DuSLe+5Zj2tvZ+w/vm/K4hzRP/+CIYVM + kLNoI/enn+76PkKf4eLt+mcMfeHNoMdc2BG71cd9bI7xQWpPfbDmjK3Bz+rW/j5BYxdmfx1MPdYh + +lm1vG7XF+SdTyFtrQ50G56+DePja0bOXr+FbVxqOZ7YD2ZKF+sDsRdbtp4BRGfuIHrTjh8g7pwf + OqthVGx/epVw6R5Isd6Ct8fjWXavHw09T5sefS581Mt//pQn2y+6uPqYgRmvEtJrto6WuJ5TINyz + bPdPdLoaZ/8s6XdnRefQQcVmwdCC/IfpkUun/crD7fH+59fteKulJ/3ZwT2fEQ9H3T/8+cffyPW+ + KNF+bd2C6biPhatDVKwJy4fQdg89UVc8jf/8VvE9mSixSlkfl2lhoCDplU8fwqtdAt2v4C3iGcxX + qxAt9cjX8in6SD73VN/68qf3N3mfoPzF93TXmzP456/1wrjRNXgPorj7YcjtFKmYPSE2YBfzN2QP + bAo4KTYbANouRxc2O3lrFO5H8G5LilJ7HYrNnAYfWsAQSHb8bd6mv55vWI/9+s8/phuoM/kQChiz + 5BEUFFdqI+tx3BF155/rrr+Acy1//FfAaaPQr3sfoPBdEMVu+IjevW8FR/37+FefN/4rMeD/OVLA + /e8jBYZ5MsmFZHW0iU3TgeSquQTlnKkL7Ob40I+fIXE/YRatBfYkaJ/G2OdPhr7fauY7OW8zQlCi + HoufMiQieAFQ+xZ4qJTLNsACIcAjMn/8Tef0ug9gWfY2sl8i9mb2VSyQ+V1KfJzH2SPt8qrh541d + LE4U6FOplm9oUGChM7rP4wIDcYPjI/uQ8F0tYG0KaYGBk+pEg0VabHyLGcCs8xddfioZ6UlzBpgi + 1iWef3nT9desGizC84KSfpn1KeayTo5snfezE/3Q9ZQZm3zptwoZi0THLfL2W1by1SFX2lx0Ot37 + Cl6HgvVlxnh663bHEpxcJURu0B5GypiSBe7n5IceoRt7/DBzlczLtUqCU9NGayudePgBioJ0aW/s + ba5tCWNPDJF6HAhdLvdzeOyCjCFhc3qC9ciBswwXEZLcWzhvydkXBGP5nJB+z9mIXshSyRoXErK3 + ove+WvA9g/ucML5kmpeR+7idJisGcclZPr3HFQtZBRfp8UNOLWqUnby5kRSz130O2CyY2Y0JQHBe + N3Tx71rBv+zBhVx1sJDCp0a0/hYCYb3dVXxQjQhQLSA+XJ+NQXyZ/+r0XopYLmb+RM6OVgL+oMP+ + b32INRnZyKXCuMmh36a+LC92scWc3MP3swsxaGZUsIQrFGjhu4Sl8efobPjeKtka9BApieQV3Bb/ + fDii95FkS5Dqi3ABLISaAXzWP5aA86a3D5Vn0ROT9Z/6Sho3g8frKyVPmpBx1k6BCOf+SIi7jCrg + U3QxoAMbkxR3ydSXeb1Pf/sL2UlvA76h5b/n8TXvuY340u0U/r7kROVL3WOP1Z2RNz82Ufgcu4g1 + 80MAWbA8SCGUts5eJaWW0UE8+GwXPYrlmPQYtoktobL9qAVXfvIBhiIkJNu+FuDO/iSBPMMBsQc7 + iqYJ3XdLZm9uMAgcXQ5Xq4Fn99KSpL/Y3uZ7Wy7j48BgKhuSNz3h1MHjAO9YmOYyoulwlWTXyUQS + Dpw6DpJoZdCWOBn5ujgAnC9qLHPU+KHoxqUtLzjRvr7dkRitGbRsI8ABel9FQvvzFOMjTs7wkC4a + Cvj0Hb0F9aZBh5nuJJLuW7Q8WGGC7vR94e3e6oAbnpcKFg7+IH3/fPpNxxq+fC3DX8LXBf89gVoa + 3KdGXHrE0cKG7QIOmfpAMRFzsPQ4XmDHUYD86qwW/CG4VrIbmzdk6V874o4c8GH2ePXI/w20oFys + ivIDtB/c15vh8aU5+/CktQ9/zaTaY6/akZceEYz957a5Yxdz8gD5fJSQxYy4pd24drIxfxHSSn9s + Z2qfM1gv/oxFPstGLj/rIWSqRkbKYYpawXm7IVDFUcNAVj9gxYqzwQNOIvLw4akQjmve/e0vLF+N + Qd/aJMZyZAoN/olhWyzDlhrgt+oHzPTvX7QsrdiA9DzJPh1AXdA7Lnz5+rPv6JHn92IJ6tWGDzxs + vtR8l5FVXD2XhZ/PoT2+Wm5J5g6WXYGJsSmPdruetAoGWeGi6wFdip8ceQycg+KDPLgJI+W8bIA9 + 0WNiXxyu2I7VQYG69/6QcsXAo8g3O/mjdL3PI+EyTpvLiND+dQ1S0r3vp31Ve4CTtkSace28tXGD + ALI+2iWSOAAzKm4lPLyqFz4wTxdwwLpmcs+HBnnIix3N9hq7MhbKgagqmxbrSV4YOV+uDcnL4EkF + 12fPUn+qdHyo8wYsV0lp5JPvssS0CB+te73hDXq0yPO8xjo1pRJD+Ine6CL0ZstlG+WlDBg+QWef + 6NXxaltgg5eLPwbJd1wZN8Ty91NzJDcau+UyMFsg/Rblnu/eBU8spZHhA4ckvMdcS7hYlWD23jKk + +a5U4LC+svLpgQAyr8e1pXOFbdifSp0847kZ10PbWFBITpG/8c2LrlOlWvJfvbKXIPW2pHhJYH9e + gqZTq//Lr679PPjBl2B9kZS8hz6f1+T88Jdx7Zb8DN3p8yLKtFpgZdx8Ap/XWyYR3ysjvyxbLj+x + r2FI9KhgicW9Iai9GTPxV6eCKZUT9JPMI66Wf8B2vJQN/Nt/PNtO3tR0uQ2Vnp9w2xxWsFSOWkL4 + ub79uTifR6GTY0tOwusHuW9GHWmoTBk8ZzoiKn+Jwfh8oQWyamL6fNgkBVdGiys3w/zCm3lbdPqH + J2T4ISgbxizCzPA5y+LyUpEfZ1y7Fjxny1tmjuSewfi/+Qscu4Wop17zhEx/xfKLMVl050AYCfKt + fMO2XkJ0f1toXGL99IaW0Sf7zO8ebMxdm2Tu9/ug+9F5egLV2wVyv/FDsuelK9aH/l1kN72OqHg+ + m3GV6o8NFK3mSQiZIhL+4kNarCte9/gR5oQVwem+nAna8wcXpqSB56P+JqjONToVl2MOg+P9iVnt + d2vJ9VsyMDK6E8p44wXIaFYLLFVlt8SMulgqx6ngGsosSqYXA1Yy0F4+XtsUL9NqUV4Cx30SKN/4 + 0ryOe/73G/geghV/0OoDVsKiBctNt5B691NA7wPpodkOCnmQTIk4ZUgk2WuGEEOt04txuvclLCbg + E/WwbdFmjNN0lBOlIt59TMGW1KSDJhNXKEx/F/r3/8tBe/r5w7x6YFtDasBjWCKUgjUbhyw9MrDj + J/bf+tGVXjqYfu8lutS+WQi1BTX4V5/WtCX6dsFCBh+RXhD7+/DGhRuBDfPSO5AdjwDuXKMMTl40 + +9K+P1csBKX8TA8R5tjHXGy6srFQrW0RWVPUURz+PAOamdYjTYroSKSZKEAqIEbe2pfRdmPXUAZX + q0N+EY4RDWCrgPDwGomxPsRx2m8Iy6QLRnK/INZbBkaSjt/3F/qC04F2jasyg2bbK+Q+rIK3Dml8 + lpUvoPhw0AigmrYEskWrFCmTr9IVFc8S5Mo+B/6ex8V0O15FOBbnKzEf3QpWf5lrsP8ehJgAthtM + 1Fx+8viC7IPMe+tv+TKwc80TcYZHXWB8a7W/fIC3T2B4VAu+PtyS443kT9jTJenpAj93ayNaRARA + 5P5giGJDEqKzV0LJ6XSKYbelZ3z3TteClh8BQ6lyNFRci7VdD/nCy4GUZugck4e39oqYQ9a/CPvn + 3fb99B3gu/rdSSENMd2CAVWw/uIG2WrJ6bi65MZxq788ueS/U7uKWmDIKG808vyabsGJAVKAg6QM + IfCJKb4yV1uWE60i+no7gnU01knuucRDavIw9/gcF6Am1Z24PH0Xy/nAsrDs7hh/L3rarlhRN8BY + CkVn21N1yuJkgJ/MOfpAY4tie3xnHr7QlZIT4/cFja9uA591dkOhcbU8gWfoBp3fyJA9Xij1eq/7 + w3foxod73+VxmSCT2wkp1vgzbq/Ll4WEOpUPbLMp/vaPHL+ugS/xb15f0miepPbeNkS7ty3dv58F + xvUgYhp8XZ1bhc4Htf5RkKv227j0LwHClQUaMZMTU2A+WRSw4xcS22teLOFbqqAcnz7EDMu0pVjp + eBCItuEHw++tL7pvYGj8/C9RZvPRjsGoNLD82BFJr+UYUZtJNXBRwpicsssKiP7j3//qP3Liu97A + xMng9Pq+kW7cDMDJBXeG3pvt8Kpe12htIqcGZxvaJDryc7RMGgxBUWslsdj2rP/hXYgx88Rivb29 + jXi+D+XbeUOOq54K9vFVFvnnbz6ybFmgkyr2PFQq+4K8+03xlkeb+FBk0ok44n7L/IPfBqzoMvj8 + nk+W1Vx9ObzpLxKkj6BYpXc2QW8ld6I2ASnwX327BUeIXFVQAK25ayl/gKag9K/eWHPIw5sBNYJ+ + l9lb//BG+Y2/JH/OUvu27SyDrnS/IO123ujmfpZcjmhuk7RCXLvoKcfIrc/pSLmcVJ0d5iD/x39P + 4s8FWPuOOXgRbcYf8zcVy4caE7zy+hmdSaiOglOnE+zYpsbgYyiFUDMuBgfPHXzRyfR2ZSziwtfl + 9CTa5ZsUuDJ6Fn4ujxxd2kMKZvH02ODaawidSZcVS+qyJUQlTol7eyTRli9OCi2BZ4jtgL6lf/Ul + zdLkX/5YGHLh/+oV+nveTS/8NxT1Y0xQkQ36gsBZlC5GD4n/O/kR/RFJlDBWO2JHdafP96w2/uoV + 8VnWb2nczBogq6ijG5KnaL01jwm2t5uPXEldPZpS3QVklXS8npl17MYyh5C7egUx8rArKCMrmwwf + U0iyl+jrBIOXJVsW+yPPKtyPHEh2De1x4TEQStuj16rFEE+bR9ygfY70sIyM/Lf/lGSU9WWtDQjr + T+2j/H5VC1rxDxa82nuKAtXb2qEZmk2eRD7CB6c9FRxjERs+7Bii0uf7cRozLYVBeuZ9yR1Gb8u/ + mSFxVgWRmZyqaHOEXII/hWmxSOcATPKguHIS+RVBop3rJAHwPwAAAP//pJ3LmrK8EoUvyIGASMKQ + k5wlKIo4A0QERY4JJFe/H+xv+M/2sAfdDUmlaq03IZWCgvIL0l6s0+fNhZry6neCZW5v2fw8XEWo + k9EiWUSrjGmWvAFJ1RiYPocmW66G0kNPIcbPv2d/eq/fn/TgsyClFtDENLn3hxi5Q+d4lIu5FJ79 + mxY0fCfo2DeOJUAf/4XMmou8VsKzAUo1HXFl4ZKR++e12V9D64TcOY8GMsCpgEQ3NHK6Pj46u9zB + DIhxW7/Sb7J6yfNXKq/6BanD2QL8T6+/pP5ADD3rGRWK7gy3MdVQMF6BN1T5TYBellVIdaOOjdKu + i+H6aRHeflsSzbe5bIF9MhOiV9uF0c2LbcDZv2pIx86kD9u3ZcN1vQZVz6v1T4/Ja75FoWAJOk3H + B4Xrl+XBSJokYl2RpLAKVX3112bEYzaut+rd5tWfeTWXxX0rLsvC4x9P4GS6JPIU3j/B9ulkYIw1 + PYBwM37ROh8Rk3K/hMMmkQha+QWRpq8Ce/x+BzP/PukUppYBMyE+Yx7c6l89TKCnTAZCUZ4Nswyq + Fq58hATlxdD5hn+KEFwfAd6c2Q3sDNQLMK41jhi3JIywCTUBJsHqf8zulK18JliP+L3RWt+jRfmE + AYwe5ikgfoD04f02xl8+/K3vYe08ZwM4SxAFcqR5y+V77OEMkgrp+Y5E2OrbC9S+Tx3L+eJm9K2Y + rdxzUobh4sX6TF1BhMU2UUkxqu+Iyjic5a65lcHKk/Q/fqNG4YF4WOZquj9eKigEoxLIUnTMOBjO + FCzmQUWJAOSBarEtQnQyt8g4sfPKp5gvbYb7jMI1Xy4HwTCgIVgpQmjo2C/eYPUQcLArxH005u5x + vUVmc0fK6zsMv9+XO93eBZi/RTWzXEmEm9S9IsM0zzVtO86Gk3opgufqj1j3VXsYvoQAqZr+rmfh + WcTSWg8xHPimbhEwpL28dxViObwDqFC8znKi7Vyk3O1E37VH6Qyz+JMQp19uHrX9lyYXSqMG9GjM + 0eKXViL/1pe+F6Zs7X8uwdd+8v/Vy2avF1D8HmQSIM30eCcPTKBo1dp3qd8MJOJQBdbxRvo+aiNa + Vrwonxp4ING90wZqn5oRVl1h4i7WimhOBxFLb8u08ahJks6eSVxC7VDzyBrvhxr32yeE6q2dkBFO + NvjjO5FJMwyCVzYs0+kdythuO5TtjnfAh+I5lS244UmgXz9g8vjH33oOOHmSGT2+7OJvPdxd1Yp2 + gjUZ8JKwA9JWfkVBM4hAmo0T0lP/O/x4IzxpXP2Xr/HrFCUg4oaaqO+E6ssurwV5PJcPdHruN9G4 + rkd4Owk8Xp4fq969v/NZhsV2Itb35rOFXjof7K9gE/z403IpLil87G+nH6+t2ef2leA20R9E30d2 + JqTf0JC7Rd3+9CibP6/Kh9rJO2LuBviMti9Nkr/654sOn0qJ2t1Ba4CK7Gr1xw2Yd2vfjKp9HYMj + GW8ehSkywerng9awIMCquvN/+pocytN9mAsl9GHSbEby5x+gn9gw2bxkYr2QG/3plzh2VWJoVwUI + FsEKyB/7mjhHuvdIVyQJLA/sghQRiWw0nQuFxkkWA3wttvWifJIArnokgFy73nqUDwLAYHde+dYr + 6ye+j8FmyGaifyyuZsJ0P0PzVNvE3AxBvSjMskVV2sXI18cpo2Q/tJCSS0pu3RMNyxMpwY/XkDUR + ebjq+xnsTM0PwE0hOpe8PQ6kim+Sm5tgRph/Vfa/fITWeslp9XkG7lSlxA+iup7T9uhCqRmOyKtD + LiNr/pJ+ftd2En3YhWxPpR9/UJ+f7zAHYabAy9YEmLr3tmaQzyTgaMZELgelrae13oGfHnXXI7Vs + MtIEGmdxQT8+jo+NaQPu1oMAlweesZh5LgyTu0tM+fOuR/a031BZ4t1Pz3isWJK1c/uY/8UHa7iK + gw/p7CIDCpW+OO9t/Ku/yH4ezWz1Oxykd+1NVFfAgJn5IwHN+XMmhtbthnlrP0LADJJgGYWFvrP3 + yQUWc/cIhk+y8/7io9PQ5s8/z27zyX9+njzO096jFju9ob+Hj0AO7lpEPV+g0I7CEWlYD7Jly/tv + KGQNQ8YnV2vhp+eEACu//YN6OdtzA18oYpi8PW6Yp6lxQeNaVrBo+ntg2WZ/ASvPQslZVj0B3wYN + Ruacrfo2HaY/3k1uKtK24qLTu1pLUDLINkhjac7GWPMCaLvzCz1dc4yoyB0NKGau/ldvaBLYGARi + w/14U00XVxVkIXk2JDBpyd67tW9unFYdMr/aFuDT5m5DretuyJlsW3+vbaJkNZ44jAnlwZInvAA/ + H/lDnuN66+LCju/9ysuJorTHga36GB6bt4vCYrrW4+dVBTBBEyR+8E0jeu50E1ac/UHWZjlGTPW6 + CrJWkJAuG5I+j+hkyM1g5cQVgeXx31pOfvEVbEdL13nYphpAXNQSxElTtPIeCmlN9D/ehgsHV2D1 + Q8F79V8LQFr7y1cBFLYq220O1IRgl+TB7nsbwZvOgwnms5SQY7MfAf18qABO2xyhtX4O04IhBDsx + guio1zIbKpeP5aU79T9+Wv/8v5jtPhMxH4434NGdczmTsITM7H0E81sQz7/9LOR9QO7NW/saSoU2 + X7G0+pcl9b0z9NK175P4PgDh58ev34uC8rRu2VyffENY92vwboT9wNrzFoLPe3TJI1/6qMvzLgXW + civQX73/6bv1fZEz5QWj8ywlkLsWX8x2zzybHaat/8+1SMFxeHgn5+7y8zvELG/Jr36NYI3fgPvk + 6iAkzzqAjysiyHR8U2+Pn84AqPs46Ej11psNUWhAIp8rorySR7Y+rwlWvR7QNH0Os6eXK9+3bphb + HJXxZiloYLFPV2Izd2BMn3pbCvGkINtSzwNe4x+s+0/IfU5pvdxsZwPD4zhg6b15DVgJcgWm6vMV + cLkceLzz3l7+eJXvaJAtllmd5ZgeAEqi0tQ5JW4DqPHh2kfqULMR3CsNXqrAQdonfHvsE2x6uOZH + ZBcZ8xYgtyZ8mx4K6nU/j3r3qIEAiyeSDl2nt/pJDH/5Eze2vAPjo374cN3/wnP5UmveL60Uat1w + I4eFHgZhngIONjjoAnDAp2hc5xOe5esNFeXhyrjIBO7PLyFf2Kpg9T8CkNRziLvJbvWxC68FvJ+7 + MlgKxkXk4G3PkPRdhFDKf/QFORDCTTidkUVfts6t+Us+DfFEjk1f1Ay4vQI1M8d4/BT8v/y46jfk + FLPisc3+EUDYpgtyxCbVx69eifJmzn0Ur/GNtW+dyIIiXklafdcjtRsx/+XLtW+rBEZjPsaAzFs3 + WKy28iZRS0zQjMoVnfzNqaaHUwIBMV8L0u6X67D63QvkB35PrNJZeSZDLux3Jln59YFx3umWQAeW + B5JfiKzP8Crk4G99rHxnbB6bVlrHnxzlY5399JfcLVBED52OOmnVawHrbBKCnRrHEfMzTOGhrBjx + U6Vma7335b1IDwG3jCajJnQ5MO/9AGWewBjdx0e6/+33HLVR04XPh3IQdV8Hae8iBKx1TQwM2z+j + AH08b7f6D3DH510gL9wdzDceX/7ymTsFDVgU7BTQ274Mcv1UZUYlMUjAVBxDLB8kzGa3Pykyjosr + cV7ZF9DbcsRwnAJADk3bZz89BgddrpDhDbO3+u9ZfgrjEcv2J67n7qJq8Jd/1NfkRq1LCiz9P0cK + hP8+UlC/GokcHdVkHHqLF4nObxNZu1ardxyfYNgoaU2MUFN0gsIpgHx1BUSz4TZa9OHkwk7Nvni/ + Ky7RwhM3gXRuTNx7W6LP0mO4wNP5miGHRII+py18A4e1R2KZzdFbuDt7y6J7uRCdG6qM7dvkDTeh + 3ZLj9aZnZGsv68W7Y4LOan0YOCuXXYlccUJMyTAy/jN8bBhQJSTXYniwuev6MzzUrx4ZsHhk3eTR + M5SPsUe8z6GrqRwvlZx/mBLI1xoNi5m9CtnHDUV+4j3q4VTNG1nSgY0MArlsvtl2Bba7SiGRftb0 + 5RNFIdhuriggTPc8vPadBAHVQryPaZYtV1+C0Dp8ruj+7p4DJz2GGObNayGnRExreoZFubeP+gdL + m73AmLQEIRwFiaDjo1KHRdcHE55O757EKq9mAonsWd4MJSO3w+EwcPadrq00UIeXzVTVdPywSp5y + 8UoyAU7D1CZSCg+i0ZAIGrknXLuBQlodWnSoBOiNZ/tbwVpxQ2QXtRjR28XJYRhuu0B43/26y6as + BO743QaS7OvrFiyU4CY+mnicS99jKleVK0LZIufdbQdWdG/6Nz+uQlU2FzjCsqnnlITWcvY4232I + sGzaCzp0l2bg1/eTr8/rEHyGrectIfUhpN4txJvOCTMab6Q3FGXUIudoXmuOO58ameSvL7qtp5CF + WX838P1oN+TGO25E8yoOoaZ9CqQOr9oTrqeTBFlXi3gnFZ8BF84syvrzmpFw3l68sTnVIjym7Q7L + ArJrtpOpKFdLR8mjOiTRclPMC7woSYTCe+56O1SqiRyYK+aaS19foup6BkVVmMRyWpjN7iIWcvm6 + fNGpULt6J5tOCw/jmJJnL/qMN7NXLl9OnIxMM0l0uiQshaJ3OKDLa//2uKc59X/xm2+Uw8DT1fK8 + 7wsgp+Nun5HH5bqBlgs0ZAdbJeLZmApwqnydREFUZ5P1yk0o+P4bBdMF15Pz1nuIsHHB+PDlojYH + SiybgWfgrbZtInrthhkGZ8vGzVHDjKi76Q3pFQ4oMu2Dvhvg2vqc6/bkMiyxvjP8dgNlw8swLOMM + sFcsadIHKTPySsnL5kP3orK4c1zySO7nmmqHcw43M/ciyZjmA/1S0P+9b5wdnjpuJlWRLa9jCKEp + y4Qpys/AW0+bP7nErHnSAAzC4aOjp/9UPP5obGdgYsNH93V9zepb6yHxDBnZ1c3X+dPzg+XXvW7X + i2af2aK2r7f8ip4uUip36zFbqyVZP7oKngfrBXhO7Ub4zsUj8bfTPVrs4qTJfB7lyP6oGqOhabsw + YYmLsoRM+nLrtzkMU/ceXAWu9ITPtg1BeT4kCE2YMPa6yytCwRx+NyEeaHYvG7nXlJY8BLrNFm97 + NCETlRihIjZ0bjdFGhyZOiJdL3DW3pujImY4JkEbJ5K++LXoQpJRAR23pPJoUHwqeR1PdGnylM0X + 72aC9e+R7HO6Ap5d7A3knIeCOyUpayE0FVd2WH8kRmcuNZ3iWwyvUUDQLZB3NdVKkkIxTBmyuY8E + 5jy+XqBnFh0xrhL16CNPbKjthIgYknwdFmS8z/LSSQ3yZrMH7O0eE7g+L7KuWaXPmmYFkGSzgBC/ + DzyudK4+2PvFjrhPL6gnLdYuUg6MiQQuRAO+0wOE/CzcicMvyvAxkkWSE8wfkUUjK9ul9ozlMfYF + cpHDFxMeXKWIvQcdYr23qce29j6A3VM0SPJRK8YPs0jhm7vHJEzlZaBteUkBh+oC6V7n17vZ1ivw + tTiGFOycvCVnpg3ReXRQdHxtAKvvXQOnzWVLdFU22e5tuTGMeMVCqS2XHm8opSazze5D9Ov48Ojn + JgXwYqEKoQ+RGU6N0yy3um2hQu49fWAgFoBeiwmJI/E0CCTWOViVRxtdNksXsRcNNnCqIEfSKdiu + X+HaOWyvt4rYC9mBZc2/0ArlF1E9VYho5voJ7G3LJPprIDp9oK0C7xdDJ4ifXqxPjCmExpJZRKUS + BMu9rzdyl+sqOnycNpvD68WGNj1nxNBFUtOHDyDk+PoQCMZuHthSPlI42NgKtmkVZrsnuSWwaqIG + Bfxw93hRIi28p/aLKGboDjvy+Ajyt61GzC0nAhrtkOZgzffICk0T0Ece2rLyTgSkaHkMuJ32kmR9 + e66RoY91hsV66YEbPnT0uGi2zlJbHGVnjwd825cXttM+5kVOszIi10qA+hQtSyFT7xpiMAA/417Y + 9uGUS1d0Hpwto0ReVkkrvJDRG1omyMRLYHP+bojt3URAl+t8kQ+HjY7Oy7fP+F9+jENSkLMpluBX + P2Tvto/QXd3b3s6ZjUZCxyQjJz8+MeECdQWab3tA2usheWymsId5Uy/EiAzk8Y/DaQONz9klSn6e + PGrHsQC6p2QQtOavcdvKJsi7Vke30DTZ/N0ab9muFxnZWlcDpuZhu1/fD0VBpGfMfXwusowVgyhb + zmZcc8EQssIZibvqhbm9RAUUheS+1vPcEzajEsul2yB0tEYvWyJLucjFXg5IuOMFxs5D08vJvmiJ + a7VGvdw1WYQfG56JT7fvaKErQu/zVsMF2TKPRbKUgt/73uSLXfOMPET400cWjHegGY+vFppvd0CB + 9jno9PRFGO7K54D017MCs6WWgfyNtnOwC+TdQOGYCZA35gtyqs/LGzfh4MM9N+zxjGCYMWkxQzjZ + tzuWZ6x6c8zDEaK3y9Z88M0YK1VRXspJCOb+Kmfs0oYG3MTbHRbPKhlGAAgHne3iBKLW6QDvL48C + JLYhII17HKNpfOUNdD3vGrBxDCNaJQcMZ1l4kd/zkw83aPL3sT8RrS2yjJuImoDbAUbkkNpfnX69 + w1sugk4it/smAYvz9lp4mkhDlIsVgFlkxxZmH2eLwcO8DUOP7gpEUSGh3/zRIOsLeMuVBdf3y3sY + nrdDCXGfGcgs9pTR/JkF612xLrqqWenN30bCEIpKQZTP5gzag8owSGk4oGB7KwCjtbGB0+3dksJ+ + 7KL5XYSVtI4vWeulPr8D7iJjXnsSbTwTb4mlqIVmMj/R8ZuyYTEWfYZn1l7RLYZOzUm8HMAsFw/k + HOcJGMXbjOHoCj2xgyMAlPkmhV/8cUlBu47RY0Il2VqGNNh9z40+Sc6cSM5doQF9nY5g3KwX6dub + +4083gBkM5I1H+ZgV+B48wUevtR7Gwany42cs48FdoaCOGhnlR7QAbyz9VaidQuIn4gFWAnm92mb + /PITZtygRT+9Dt3wqSN0PV690asuZ/jStDIAz3s90FPACzBO+/WrV3M3zPvDvYCe+PERmt9nsEt8 + 6wzMhD4J0rZmNNeOOsKhSxX0qyf0M/sG2OyGmZgsPQ5UJnoKr+/hjkVXewzz9iMpYC9zHdIfOxAt + +EIgRAYaiPm+j/U4WdFFztrwREwnADo9HGH/V6+dMzKGmTvfV+QYHPGnGOQVIfXJ/tRIN2I1Fz6b + K4QESI1KJQ6xHMa9sOJDMigHooVgiaY08TFIFXFPrrh7gb/6XkP/QsK2derlaGzpikg3WA6DS8ay + l/uGF1oK6Lau33HmygpmQfZF6uUVevMdZBx8tXSHvDRcv8L+duJf/N+9z8SYoVgCFN34QtR9YkUc + NzUloN/7NSCeawxcETscDFPgBku5G4dZXW8Fi9GZBVJyHUASPTYXwHWJTcxsV9ejs9s0kt2IiJyW + Y+ctvJXHwNQLGkBuF9dzlZUbec0P5G4FetTN0C3hwyz2xBV1zmNECyW5eLhJIN+Mm4fFbfCGD+vY + o+sYX4Fgf5gCwqWryK/+LUVaznJ0MLYk/cYjGFb/At/Sg0O2uttGjNKXK696lpzyUtMX52tiOb/E + MZbIa9DxL55UFY0k4J8+YLp0dUFxj67ISk4HTzjvsxycaFij5zoeiz7cXemTeRNyZ6EZaHeUXbDG + B5aGaZeR41HuwT2gV3LIN703zrvhDc19nqBg1evD+n7g/L6bxJBDFSxHY0eB0KkpQSctZnQKTwFU + tkwlKr41+tJhJ5CU7aIi1y2+A93eOxMmLHWDRbltAXuMhAPku2fEfJErm5DxDuEL767BmG0Xfabn + yYRb6kcYJrLmtY6VctLewyZxdudWZ0KY+uB87DFSD2Pm9UM4jrCEI8XAvs9rK+PJhkO6r9bDK1PG + youbw+vEOcTinT5j3IerAFw2Cgnk3vOWx6RoMrN3W+JYgZ4JYzXn4Be/jvIeAYOOj1dBeAtm24Zs + xMfjRoYVXgIw5RddaInYQNM41sjLlrVH5m3B0FCfW7TygIjzd7UpM5vfBrvedjzhT2+Gdonu7cny + cO9SU84+3nb9/dwjTsrP8HszNaQEmb3emiL7kmZ3EBmSzA/EL3MJqrvjORCCm+uRFr1LsOpBdPAi + qv/Vu9v0yJBu2gdvPmzEN9iK4UIen/2oT4JxCiGBBUG/8cHregUrb0H2lBwzbs2X8iY1nEAW6DZi + zrakMmKphqUwvoN58elf/UBWFl3Wemvhv3rWHy99TbRKD6G+Dw7Ef9llNH+DloOn/YHiMZE1fS6d + awDr2McBYxXTWRQblz++ENIyG9qHLpgy25gc0gT4GahOZkPWXR0TV6rGiMXOE4N1fAJx9Z/TGzgX + GI9WtwKQBvzFT7QAHhkmpvXYdDwFefmMsHh4mmx3fJwE6HwcguwxzeuJpsiAAzf7xJ6DMOImK7vA + MN27yBwQiuZty5tQut5owPRz5c38t0xgWnIqitf45aGsjzCcxQWdfvyDZf0GWIfvlQQrP+AxjBW5 + vMKM/Pzp4nWbM9SPtoLuBhn0yRYqCXTnQ4n52e0GzJlKD9Z6hrerH12U+MGBIwoHLDtqA5acrV91 + mfmCt9tPBOhJGEQYEzhitugSY/tsl0Pc9RFy9+9s4BVgCyBaHIMEkXVk+MD3GK7Ph8Hqn5YDVjA8 + 1HWPkFIG6/o6B/CU3PfosBdI1sN82wLvZBoBTAtnaFuoVjA9NRTZa+ve5XgKJXnNB2jlb9m06hEp + Y/0j2EqhOODN8+vL+xohvG/gUP/4DmhYtCMenzX6Yu8nCE+mXxCTI51Oo6CK5TWeiBopdsS6680F + N59VBMmi4vHz5ZLDxnyqCN3GYya8rrELX49kQOcnpBE7XOMe8C5HUXEGar07Pu4cTCeC8dZz32tr + eT2EyXs/ElM22TA97KsCV97357/odqFQ/sVDaMmbbNVrb+nVaibOO4GP5rP9LSFX0ClY+VA0xVLW + woeQqgSt/G0O+34Dz3c2BN9xM+rzxXuaQNx5Ljqs9ZV9d2nz07so06a+Xn5+MR4PHVr9lIefZ9zA + gokzslNFrZeDmbYyvMUfPFnjkBHpuo+hctd6LAwZGKh2SAvYuBeDOOJXH3ZIzM5Q0t0bUiP/UpO+ + nzYglhqLOLKbZhQa0wzT05sSbU/bYe6tRgAnGBjEpIdzNO9LnADrUz7IoenJelFwFcKt6QbIdcqY + zfy3TaAtXvZ4thaqL1y+6SWvjp5Eu5tkmILN8pY/5TNDDrqrA8d/2xTqxbsnxbFpsql8hBd54CuZ + GHWzY4ssKS0cUlAhR3n7TFAeWw7KetIjIxY1JsyZkMPoM4tIex3Ugfvp6yqYHyQ6OU00hXdmQH+/ + 2RBr4CFb/YMp8wo4/XhOxMwtDGGWSweipMshEs6wqGDN9Dcx81Sq+0K69OC8ZSfc8Us5LMlENNjk + 9RNZT8tifzzZPr0rkrqFNQhU2Ipw1999/M1bNEzbKVdA2XoCLvcuzWat489yCn0/2C5VPTBPSxLY + vPgjMe/v28AUS+V+vBJn3znQZ1Jd6I/fIl8c3GgWEt+XwO7+IHlIa0Zed9kEGXcPAuFVO8PM93UO + YMsQ5rJsXxOOT0Y4ReSz8ldt4AXjdIbqwSC4TzlDX9JkPWJ4evfIlf1aXxh5SPC4e0zBeOMoY0GD + qt/PSGP2wqYL2mhg1cPocX6V2TwRJwXAuyXEl7UL46332rhGse01fuyhHe9SA+lT9BGqJjxQ45Fw + Uum+EYneL+oR7tj3cB0Pcq8+qveLd/C9GRrRunzUeU0WEzBbHEDqd0LRePzoAmxeRo6eyihlDGQD + hCfxphLV5V2ddZLrwuiEIPr56dm0cAzVMAdEsRbqMVPbpfAYkjjg9jn2luIxcPCipBFx2pOl/+ob + gIkVE8NJnIhNNxAAzZsDdOJ0X+/GPS6k+HNOAx7cYrDqbxO4Hzrj13LsdAYKosH8wIt4eQ9K9PVt + TwEFuTFi12I0MHPLhfDH551396xHuksMuHtfMHoSdIvalReAkOQmSsvS9caYLv2fnr4Wz1NN3lhM + INl5GgZ81ni0+r5EOMnvtfHsLqj5tR5A5dgWGH9bexC08puC8rrJiO49yoitegD+/Hoy7A7D/Hvf + Z0ia4OIHfvZ6TLYGK/CJkG1fHUCLz5GDGyg/kPHUVX1WYV7BlYcjsz0qEb+cTilY89+ql/WBPxsh + hfFdCojz21/41S95n4QoRUwDq//YQN6gl5WnlcPg1zcI5x3VkZZLesSNd9rAWxYqv79XL5V4ouIa + H6hY6xPr+w+EW9MO1nqEvWa5zjHs3PeNGAA00XQw0/7njwgaj21GDufsDb0xR+ixBTii6mSXUIyf + p2B82WU2Xh8VJ29vb3H1K6dsBp5vwm93epA4rtpoTsN+hOZ7nyOfqtFAhZdrSr/ned7Ee720SltB + fLVueDu/KVvgexHgUgv2H2/EoanYP16GnPJ81f/4zlrPiRPN/spv/DeUhWgTtCdej7hvUAoQBY5P + rEejRNxSOSEg1lFa9eoxG7fH1oX38QoDiYn8wIB+Nn48KXjvW2m9pa3vYbTseXI4yTYjw9VroTH3 + MXGkyy5jeVDH0GgUQFQiHmvibFsK1TnR0WN/GIY5nBpNjr37TGx194zoPJx7SNzYD5bvRDIS4diH + P73x49d0jS8oFVWF/O20z5jxpgLE18ONqEL1jshPT636FnniyfE4m9jSj5+s+zlJNAf9pgBQ1Aqk + kF4c+vwZ+VAuZv5ffe2FjQTuh+8H+f4m1HfPDWvgXeJSdE8sgc1yreWyvbd6LJ+ncWCP8StAe3/o + SXp5iR6Oum0IsvPmTfzvRvfGyvdnSRsLK2C10mes8eEG3JrWR+mkUTY9PaMBylYJkbLupwgrL4Tb + 5Nph6eD4EUlesQlzMr4C8fly9HkYT4KMi7P1r1592t4AnzSBxH6jIlv3l0bwyZyJ+J9nD0jY7gyw + 45cE76mz1+mX34z778MtiX14NmAJyCPZ30keIL1W3IwLUFfCb5UHATxHQ7QE5JrKFXoFKF33r4TM + 9VNp5S1BtNd8MPtIVval9ZTRj/8uJV8FsrNlzqoXXsMsb54J/PHJ6VGptVBftRZy/OuwHjHya+qZ + XxtsY8HCi1MGgDphOMrr/gbRjLtd7y5taMotinTih7Reb6ESRTiRw0y0pul09j2TN4Rje0aFYfpM + UCe7gvJld0G6shW8eXss7d//C/i3d6tpvTZmqs3tEy/W9qsvsrZJYexlM9I3N1v/06vTozOR5eSh + vkOguEB3h1Ly53di7I6wySjG84Nxw9zs7xC6BJ/Qwc9f2aztT8Xffp+65u/1TvUS+haXoWRdbwzP + RwOOE9ngDVAu0R+/Ea9j+eM32Y83gnTcyOiXz8alUs9w5a3oCWuDrfUkAXLvqOgQl9uBPckz+ekr + crT1WN/lwRD/eBY5xdqunn2uVWCXqyo5GV5U0411vUAmajHyXXvSWeMs0n7lQcQXhz6j17Hh5F88 + xle1BEv5SGK46C4KWsX1Vv7xkuSVf2IxTlK9upE4kTu3uSHb1R41W+MNfhyio3W+2LDWDyB61oH8 + 9oNoc0ghZNW43nJmd/og6p83ZLYVBAun+55QOKIond+ZiTwvew6z9KgvkI+DTSCtfGIEtqrJ5v0V + koetC95yHCcOqoN5xbsTk2q2V/oSWsF8ILcTS4d1/l05EHdPoiTFPsO/+jRcPzxSbDsHi6pv3nD1 + 7/jHa+aeXCS4PCOPOMH3oVOx4fD+/zlSsPvvIwUVuO2Jeda4aHkZSS99A77HF/PhRAwccADN+mGT + I7lN+vLyP2c52EV8ANCBr2f6QgJMe04jwUErMno6ui3cTcZCjG1mRjswmzZIVN9D6XC91cJX73Kg + XLSI+M/9KWNqoBhyzcoRBcjxMmZs83y9uBIFc2PX3lInYgkn9ROjpwBFbymPxxjevdwi8bHWPK7i + OEWeg7NKfFlFa5c1W4BqkIToIZsbgFlnUxjehQ++ecopYmURabJwHAXkF/USternFcCC72W8beqm + pg/62siSEDe4mgjOaMJrIRQqgSfFU3Uj/LBdDfolx5CpRBWYaj1ceyd3JQaexYOZCtYME/F0Rhn/ + jAahPrywTA3+QOLuVtYTR4wAnkXugfzNFdU0jp4K7CvyRM4t6QbS36UCfs7il1zHrAZUJFdbPnRH + j9yLFwDLh58UcEv2N3TE1baeOS0y5bmwFZLK8dsbcZql4KY2AcaClg07F3KufLmACi/ag/OWy/MU + w+lCPWQpyYkJb9K9oTJcc6SnywCmoxIk4LKDMfKu0zXbdRVxoQGpjo67aKwp65RZvo4wQon7PgK+ + TuZKvhqvJ07y0zMaWxpheae9GXnum8QT6Fbh5N0joIH4+jy93ZMBTY6YmWLOsbqMbmM4/+brFy8Z + t+DIh4e8UpF1f4e1MKmOC+cgVNHzczQHITkXIuxAeyOh7FyipTiIHPTNmCNKNVwB9y7ABnLsDZBy + XuIBp5xrw8NJ0kgEEzhgPrKxdJ+LAzmOw6TTjy/4cvsqc/JEgRTR4uFA2NVWhdB3Z2bcvpco2HEZ + IPrEyRkW77IN428QEi946x6vQimWwRkQpBvzs+YgRSGM/fFEUs4wamEnK29Ze4h+sP0ad32+WqUL + x/FxQ6mRfjNBPLUVfAy1SszupgyCHnkp5Nu3SJ7WWQIU1f0MocQ9kbPnvmyOUY7hOt7kqhAzYmy4 + b6C+l08oQP2D0Vsbb+C9bEykCtaS0X5Sz3JViAvWl1Nfs+H1VWB3lGvMSe3dW4p0nmXDmk2UeVCP + uOvnQuVpGzrk6lZJxLdPX4JIWBZ0OLqiRzDfcHAdj2DxgnULaM/10DryB3IOP4jNTy/3oTRwX+LV + VPOEq9fnUnfc1ut8zjX7isSH44tP8DDg18AdBr6BIWCY+FqgRFS51G84S5ODzl8rGAReLlvYmW2K + wlyohzlory7sXkqCArrvoiUs9xXkJygSd7QP+iIP+igrhnvBw6KoGW/cTUGulc8+wJHlebyzv4uS + /Ake5O957huygap3SJH5glFNR0cW4JqfEHrQfc24By6hnA9fgoyDnQkZ600YNjVB6La1It7e7yFE + pGmR7hrbjE1lI0E/0jfE5qA38EVwLKBhURM5SIY1k4mpQONbGcQoZVsnXfW14evS8vjzUQQ2xkup + yZKYzUhDJwJoDuJePtS8h66308GjylYvQT9dC3I4bj8Dfw+PGC7Xg0VU8jzVAhe1FA6f6hTMJadH + /AuLb9h8+wplp6DWGzjBDRTknEOX6vQF86koL3BBO4sc4LX2OKk/j/KU2h45wWkG8+vJmbKxfxyR + va53+hUEAxLvgdA5O5JoCV77XsIOtREa4wQIVRCnYJP6KdL7zgWjGdgQDuM2I2hyGo+2WE3kwdPO + xD1vmowG462Qb+vFmHLOsLdU96SBkgdbEuOhjITh1q09lWOTeHGlgdk6lpLU0LAip5mdas6qbVHM + XotDLuBhDrN4lTh4WHoFmYFxqXlLSjU4K1c/kPdgjGYzfrzhYX9wA/jcL9kyXmZNPs4xCRbBOkX8 + uJ01WDk9RcaU3AEXuG0J5/LgkrhVlWGMHm0AP8f2iyJmP3QqNUoiw3M2IBu8PZ3tNguGirIrCLpH + ViZ4H3cDHtmgIYWkWj3vlbMgS/G7JbfRJAMdNPUMsTPbJHkZYz0ZILXhqGhhwEp9qGmnOwkEUzYR + 1xlNMOujPMKdtWFBW8jPqLPekgLb4tYQZ9tJHr0/tUB+mJkeSCS6ZQKXyQlM3Y2OZXDyIwEPnQFF + p5OIai0MLLZmtqCLjjU5NodpWOezhQZ1DRR03w6wZUyNv3puXjwVCCIsKxkMcY0O870eWMlSCDfo + naKnJN0YhVIZyvT91ZGnXnQgHM5DDC+0iZGCP1d9ObeNAfej36Pzi2HGLiJoZfzULCx7n4DxuhGI + 8F6+TXJpjdRbeDy6cCjdEzFfkA2zX5ZvuYurO3rU2bHmImb78HUXOXSfxDaa550lwu/OLkjkj1tv + 1SccjPMmRCmd34OQOr0P/SPbkOcTNGD5aGIuP/lQQuf7jkRdSZIW/PLJb73s+jvNoegMUrA7wxDs + aHzWfvWIWEa8DAtnvM8Qv88WcQqly/rNCAW4zVhJvDK6MPatyRl2dPtC1iWcAc0c1/7Vq0DC1AC7 + Xz7+Mr5FKVLMgXdVs5Rbx/oS3d2N9SLcDVueGnIhiLEDG/InCOB3vZXX+84q4MVp1KApfk2EDlmb + LX1hzLJaV4g8By1hrKu+rqwweiLKs6wjCoBykdf1hMXZ2UTL2V1ymWFhG5S5KugTWYxCyvzORbkt + KPp8jB4+vO6KM8b5rR+WNd5AO7oZcoawrcmoSynkRAiQerPLjCZyIUF7QkogOsER8LYW9NA7xTu8 + D+deJ1twD+Gy/R4COdzh4fM8rJ81WPeWuNrpDqivPd/wuq9zpMy4qr92ewllTTy7SPc9fxBmyr/h + wW815AXvWp/Dp53A+fr1MICSMlBXf2Kw4Zok6Ef74JEjgBo4R+MTedxG1YXtvnThtNwuJFhqJ5pz + H6by+j5BbNCrx7fxHMI6qHBA2wzXc1lebDmwXjty8kg0UDy8DHnVT5hdH743O7tylk94fCEvEiuG + Z/vRSw9v0ZEtSl7Uodep2Ys+RwLe7z7DWi9b2PPiBd32MWYMwqWQ52RWUBKpTzBfqWLKUty0wbLm + 3xU0xLCpkhpL4aUZWJ1LArTPk0bO6/NRvm1HeHkvL2LfuEkf98xKoPcKBIKIktQ0dfoA4OZ2whsO + Z2C507sCr0d/PRVeqTp3iPkeptdGJ66E31HfJq9UNhPeJs50yDx6qqoeytsxIwk9D/XSK6Erb/Hm + iqzcDzO2AcsFDAcaBdzx4OjzybEv0D8uG5JxG9XjnVvQANYV22DbeWaGr93TgPsTHsnhR7nE7jTK + 48cOiOZFs8fQJ5KkSI9zLDb7AsxbmebwXMRFkBnPOMP8LMeQXoiBjoBuAD4ptwv8SBbB1yv4eOyz + /8Zg/LgBUuKgBks/hC68Pr0CeVdfA8KbvN7wBE79Gm97NvIu6sGSPiz0eD4mRkEiK7BWvntyZMI2 + wyBsU9jP5SVYkrzM6GLJb0hYryG7U3x95G7AgKdGH0jwGtyID7vAhl+fluRgDwcmrOP5009Exc4y + jC8+N6QG3TAm3FnMZjoBGzo+3JAw0YVsyd6fBn5hVRDUeeZ6MeGa3y3tgYtnBRkxuzwFfTjHJOuT + 18BaIlV/f9+3KkVf/QuEmwuX4n2hOBm3zZABV/2H7nnvRPMJXjb/9FWE0DCfivYCm2P4Rva9W/3E + jRngeS0hycI7X9PmJbhwOj2veKOlm5rp10sDB4mGyC0OYkZZ/shB2Z93SNH2u4EekSvC42GzoIP2 + FgfifXMbHr/LPqDjPYgoZjyG6VE/Ev/jLR59ZR391a9gZ8SnoZ/7ciN7w0YgEbNlD9+f2uoHSpUU + Oi6i+ZYrmqxroosBjdWIGX1ewpujJuhX/3m9kc4gCtWGuPN3l5HwDTioSnxCHrjaDqM6ydwv/tDh + sXvX69n2AN7Py4ncTydYL1/X2MirX0DaRp6iuXjlZ/jdfFuiPXikz18LzGB9H8zm9aLP9MiVsAjL + LUqCaqrXeGql+BafVn+ogZ33zV34eoZcIC/uM+tGOvZwXKYbseNi8qh0fxgglUY1IGElRWTSzzE0 + sBwQFEwWEFhnzxJUioqo3HKK1vEu4CmrR6Lpt7CeNdS5MFs6Gxl03A+sM6QKnh3jiWzMb7JFb+hZ + 8puAreutieg2aF2w5i9yzJ5YX4rDLPz0DDFT9oro4XDbgJsfB0R9zIq3vPDcQHRTGVJ2O27owOhx + MDmZbgAqmRtodopnuK7XgCs4aaBuqCvwosE70dvyMSxP8RDD5kZr4npaw5p3+upldDB68uxfZkZ9 + 7db85f9Df4kHet5Vb8mfR4q0ZcL6ZEqSKf/Wnww/fsSdCyWEL9/aBiAtq2F+MyaBKNQbpM23QOf3 + k76R8fAN8W42UCb8nu/4GQl67pZPPR2rO4bXG74g1z3uM2qmNxE+TrqOjJm39PH2vrQ/f0L0Uh8G + lvhRAW3b0ojTPLJsbnMwStW9WtCaHyLGKfcNLN/ijriWYXl0D+0cmAWyifEGXzZV9/AN4oueBTiR + HhnlMjndJw8Wrc+LvWXJjETWz9QJqBhNNe28QoPw2/XBWO8WfVr1qbSjlwWLpB/AtOqDf37lMnIZ + bnaxDdZ8huxzeczmn//8sl2LtHW9tf7mEUBVpAFRVO2j08p0KOiH8LLyhAzMu/gsyZv3eEen3lsA + faKi/PktYjUcYktcnAKoG9sLOkY1rju/Wmb42mo02O3jFZHiJ4ZBpQrIOVwfHhVhWYKozVNy0RWo + 0+H2OsO6UyYs9FNZz+FTSeTstHfJUSi2+iw6qIQqdgOk3y5FzZwzPEMYxi+8Das0I/sMh6DC84zu + W9uqd9qxVORV36EHoBtGcayaf/nckNq9t8TwwMmplgV4beCl0xTGFG5Gr0bKs9Sj2fHLGax6ldhH + fxoIcRsB7os7T5TerMD0VeeLbJzD9SvK42348ws//bfhjl3GNoapwHf/YcR3brTG3EYZZUFyNgjt + lsPAb/qhhw/VM4iafc/133i/r1cOWZ6yRL94BqVkC8jm1i3/jZZLYHspbsi5H6Hebs80h32sXJFp + Ai1bHlnIyZuLkKKfXmn7JWuAUk4PFGxYO8wI8gqUxPtMzFemevN4YKWcloZGgqfaZ+R6zN4wPEUz + +emp+RJeOBlaYAxIvTf/8a7sEChrPj7ps3G7YnjC+EVMnp4zwb7fZijpGUN2pG4BXf08WA7IRgdj + anRW51SAetXbKPKSrc6WmBpwe8lv6Bh7OFou3w/81VO8v+2hNzIPzWAy5RhpTD2CXcFFhix2d5Gg + JzAZO3Qwh75q8MhJRqdmqgJHaNGdjg71GDMmn/Y5tNqbQeyrO0VkFPAGpr2gBdP6/9iLUgm6N1NC + v/mkki6b8G47pz++QUXYVlDcBRNO1HMQzbCTenhW6h7p9OvW1Jhv8McLkP66edn75CgxTEtTW/Wt + kglIrEZIrtuA6BfL9wQL6i78PKoGHWgYe0tmihsgXrx9II3drn4fZdmGr9N1SwJ/I4PlScQRLu+t + iTctzaM+UG851LbTJZDLZWJ0g2wfZggq6xY/0HHjxzb0Q8gR87xTGT+bRg536C4TTdpOA31aqigX + QJORlYphxq3PK6/8aq0XS9av+fTnb4gJU6rPP/9qJY8DytPPqRagBuEfj0zPGzPaccoJwlTCasCH + 309Gd2flLf/8kjLkGlhyc73lc+VD8WW8RD/eJONsOqFTkivR7Gzl5C/fK4fXLVr1sin7jc/I6SC9 + skU9WArw4nFHTNqV3tzSDMPvxeqRvnk5DEd+1cAq+wJi6zDw+JgtvrzyAzxXrh6t9SwEm96841Vv + eoLS6428zg/+bjc4Gp1dS8Ga34naHKlH1Yteyofi6wVUU8MBs01L4XYGzwBq5Mxo6lQ+rB0bk/Nw + wNHE3cfgz39LRmpFo6bxHHD8zQZ3XtFHS2KfXCn01Zlo1z0aZt9re7jyVoKOty/ryjTE0A83HEK2 + 3uq0ylQMN3uYYLjyLd56SxrseemCFytUs3fE7OBPnwYbZg8rj1sbRV1OJDGbQOe6hfUQG6mNvGNY + RwsYPQF+u2uHVJEs/yPtWrZVhZHoBzGQd5IhAiICJgiIOANEBFTkFSBf34tze9izHrrOUQipVO29 + q6hyp7f5a+AcVntqXpospIkIUmh+S5NeQqfsZ/8iaJCQ6TcVl6U3BDe2cqi8Pj01yqYw1j8+pF/P + MbnWgwiWs2YlUF7EcWIj/wintuR0SIXRI867fGd0NNIYEtX8Tuph+rrL5s+g+dAP5AwuXsbjANlw + R7wbxcbEhcucNQUwxuBI3bSs6uVcXQawxX8SDdI3my7aMwaOyCkYqEHJmMzdOwAChVL7hEc2E8cu + ICiH5p8+R6kz8WDjL3TjL9t5myHMc3drRiS7W8mUsr2VLc3EkeuYrZHnlGDHe1eiCWceDIZ6soAr + 17u/++mXPJNlaKGdRb3p4feLcUl9uOlBf3yUSeJxyv/0faI5kdnzfEkmQCLuN4mxsK/XJChU9W+/ + CuPcg01P0iDNrngCN7Gq2euhQrDhJ2oK0T1brAQPf3iX7O9rGTJ6WzFKRM/e8KhZi8U7bmAn7YoJ + FvUlm2MSTXDDG5hL1Ec4g13QgW/ePklg/+Zsfgm5Bb8v9UmNqC36VWztFvZH6UGcsTgY/PXYOuCW + gBvVLg+RjVkzfuCvXxNC4t83/NPzYQbDIxbyumFbfiVSLwouqbX/VWzY9Gr0lZyCHtih7hcQ9zxU + L7jd9nsI59AufTS8pAQLR1kOx+o1en964yT/Xlz/Lx+jFJlAN7wVruuv5KFr8fI0J3INWPmhHRBl + iycmr93r1VN1FQ67zqP7JC/D9U+vHLr0PEmYu7hsGlMMLpivSKZpjbHYzR3C8AEzavQ7Bwibng1k + 3ZuJZ3maIWYnx4F7lmJK1PshFIf9y4P247af1pHibLhcIk7Z9BCiRzXp54OiQXR9ngqy90uNCS/p + gOEsXE8T7y4nYw6Z5kEuVhqKyanPBrsqRdgFUomR+54YRZclB9nNPZG9/ogMZoV8it6H4zopju2E + s9duJfz63qXksd77+XLSIhip+3LDb2O2lGkyQfjtO+J9hRnQP75Vc3a/4f25rrv7WsDQYR/iPmw9 + +5dfGlPHpdaGr9jarzp8j8mbWHwR9MsyDwkwHqlNDaA47qp8yhy2UEvJXS6+bD0cnhxYuptFtF2w + r3mFkQQ+3ynAvZcJgMm/ywThURloVNpqPw9ZVsL2qD2oLvQjG5zdNYJOnqv0ZO5RtnyeAwdMPbUI + SQoFDLr6cmA33grMl6h1ZzFWIdRrzcVccnfA3FvZB/pN/p5kHvb95w8vPtMVUKsxSvan7//pB/R4 + 1C89LU7MVnrpmeLtxeeaGTtYKuXBUfDdsbtwlQL785e/mQT2Ed1pvmoQPrUAEXxZenctHnuIZuF2 + mtYXw//4OfIvl5kkr/fOWO37bYUf5dtj7llB8H0rNIal6ojEsuyrO1fPXIfImQricr7W82PK8n/6 + /97KQlApUMsh98zv9I9vzChUG/CHpzW5091l/w0iZERCOK3fI6554mgFYtzATQIuZXd65j8ZqiNY + 8BVeDWOFvhYhqmg6SQ4+NqTrOfygTY8jR1mbAMsE4ENMuZ4SKnyM5XkeU9jZ5xKL47UMB6N5WX/x + jzjqZIZztLY22PRF4lxbK9yafGvoL5+acUtoLFnuQxRwZk7//OV6VjoeTrxjETO92pmg3Hqoavc0 + JJa5Xo2hKRiEWMD9P7xE72IYIUVW+Ak8fhFY1JshQ24XJZRcrt+eNs03AbDjyi1fhdjvJR08+Da0 + ltxrIasndXpo4M8/HnNvztab00UwQb+Abs/fEH6ajOHuxN+o73ZmLcEDtsGfPmC76sgW96yIwFem + hujUwMY/fTaWLEIP3G4b6XOpPXRsrya5Pao5HH3xXkGume7TiMnEZlxqFbotsbfp6amxfqdXjv70 + S/h5zmzbfw12bhmT7HiKDWZ0sAGfuzRu+QwrFCJ+iWByViJieMPTXd9V7Kllo0rEeVWHXtrH0aB2 + NimJ7apnwLq7msNNnyOb/67nLd6jD7lONM4iBiZmoRyShm/pwbh/DObC3ISXkg0TH8uZ0aWnCqNm + J63/8pMDc48r7PXHg+ovhoEwHc0G9urs/zc/Pz+uH/gxvxPVPNiEq3S4Bv/X4AP5f5cUcLSeqW74 + HZsvRIMobqMLcXdiaKxBEXKQhXw/vdRQYwJfGhV6RFvV90V22do/rBQpuXik+NuVWyOpCCJ974zE + 44ke8kJyL+HVvdfEupxyQ5iuRgKvUp7Ro6rYrni0swGcD3ZAjClv3aUJnhq4IZ1OaA1rMEn0HACc + ziLJqoH2c/hDH0jVBlNveH7Bgg40glW4O1InhGM4j7oWIGveT8R65q+MVcZBR4JYnInx9JJa6rjk + gyw5OtNUiXBPH+0rRvy7+5KT3uZh83beOvIsLGPheq+BUA2aB3n3AejzPel1v30fvnBcEfONOMa0 + /RVDdNQ7HILT0K/5y47g46ZXRIefxpXiBVsQtnuNhteX5bIAyxP8PtEbL88ahr/z55sD8XG6EWtI + jvWiKUCFhTfGNC7ltWe9ftLQzcEP+mA7NZvN6BaB6DBQelDDPRP1wyFGr6ff0nzwrUyEUx3AdW/9 + po6bnz3/0VmA7uZ3psbXjfrRM/cWMtSvsu1PFc5BoETwS4wncffrpRd/13cH64hHxHw8AVjVr15B + ef3ldP+52oAqEyyhNiUdOavLBzB8LDm0/T816E+qWQUyDIXzRaJ5EuCMD4swR17tW+RxT/aAJ93z + A3VeOZCTlYhgkepKhvJTONG/v8+EXCC686+SGNfXxxD2+2iCbQ5rkh8/517aL0oLfK+saPZDtbEe + /DiHz/PXw/Ld32apni8qJFk3TPzsN2FjCYkP26dwoHcricHMda8EdrerSDXfPDMWBj8TXcbYpiHd + GlWHhYuBF0QaSQRwAyI+tgM8Gb8P5o5ozb7w8OXh93w0JtlkMJtrqbPQ83IXiDt5TS1VxlmDVubv + aDjlrTHrStfCR8Jx5Oj4rJ73aNAAoY+IpHf77vLpo8whSqWAuofKM1af7HMk5eZCA8N32Py1+g6o + 9HOi58mssnE8yCqUTh6hyXb9BakHB0J5G0wB0RdQ19x/QLjXLuTxtz9C/3D+1jupyzWo2XBQEtDz + QTNN4utpTP1pLdGE3j65bPbIXoM9IZQKAX08vnXI14YZQFrea7K//NpsBOuQAnANuGmnpoYrzie/ + Qt4VV9Sr61e/PPiMA7npl5Q4T8ld8M2FarpvJvJU09pYuzOTERopP9XsHrhCMnoYquYzoWZ21Y0l + FG4ccD8Xm1xf330m7D6lD83TtybmCFVGlazsoF7rygTbvcnEJal1hIvfROx3IQP6lBiPPr0n4eXk + vrNxsx+k5VVB99J8BUL8YhZ42JpMjbTw3fEbWDr62v6BpI+bWi9LiwPY1TlHcuPmGWzMUQuvMLxT + EhShy/f3awXkhcbkxC0Hxi/RakNpa9R4MKEJREfzOXjn63LrOM/cmea8CoAZyfSMRZWtNZ5UuD8c + BKKF8dtdgmD5QCNUKd0TC4U0fe85lHBiT7zl7GfMFC46AkGSk8SSG2PYmt9A6BgDtfCuyiS1Yw6q + fe5A//ybsBfWCvleVWHEY2isQdx5sNKcJ3megmMvnQ35A32nsUjatNidTxm1oAkzgRJMLr0Epr6E + pXBE9AyHncvON7NAtaFK5BQesmw2UKapz/PbI/fzr8vmodl1sCotQjwhX8DMGq+D3zt2qbuznX7t + zkCGvUi0CfyQ4QokdBqojOuZYvL4Zqzg3QJaszER/XWT+7k2vABu9kXt6/7O+PtdtiB8OwVxxe7b + L8M1SqGjtQ090Vcc8t0jloEo+ycauIhkNAxeFvxdaxXH2jk2eNfWMTL3qkGcNrHrtcQ7+e95TyIZ + O7AqWZmiyduICrymoSDQWIdtD88kV+8Z4O/aU5ZVlqw0wxpx1+nqtoB3n4B4t+ZQs3rnNtBsv2di + D/4hK9X60sFMq+xJeX31jHdgqSGogoqQtbPrxRB+1X/Xr9SL8eeP4f7iIRq9UcHWjxNVcPzoPuYt + 2TD4/H3yoAYLTI/nZehn7G4psTzV/+wlo0vS++BO5oUeZsK5m//k0Mj9RCx+VTOT3KMC4Xa/WE6C + cygekF4iRcx4PKMcg9UQXitsS+bh8tlU/XLOzrHaH64Orh+l7v7FP4TPY0td8F36OVxoDHXfuWHB + SQTQe5rmoUb8/IgpkzKc/OoRwb1gvKeyX0uX9z8Fhoc2H0nsZ2G98rmTgI6/myT7gMVYU+2YoMU8 + 9Hh9ibt6PpMOQiZscR9ukMzJ4g9cUKBNDPtBv46XMAD1S3XJda2Poehl5wYaB8klutfLLlWfFx1C + ++j/i1fCiy0QzW4VkTu3vIFQNHMBvw5+0SLgykyEe8lDceePpMjlqF+0LklhTI891Yb9nvFnUkF0 + EMsLOe+eJJvdsInhLtsGgdweh4zPBhXCq9mdpvUXR/XvktYRwO7iUZuf7JDV7FWgV7kpTpOpZ7yo + 7mTImnUhtns89At2f6JSr+KFpMkvcEW+hjaiZVZTK4RDuLLSmBEHWpk6t7MGfkuZV3AEW2PNzd+v + YZo2wAxnQqI34sCw5LWMvi9hpUnWmP06Nl8Hib0HqNa8PDZLfZ5A6OwHDIoHdlm8YBO+veuIaQo3 + iDzKAUiDkifP4kGM37MteXSJjd3UbXhpyF4JD0dFlIlmXqWQHZR9AiKhfW7+eqti3ucFjLInI/vp + UGczIXcO7s9pRLUwPhi02L0/amWiB7We+T5bUSU7oLveU2omRgfWtzXY8NfUDXEycAqnEyxLtMWH + f/vPD82uhV4dWH/2YbB8xzWQpl1BvCSYsvW6za6M5HpP9CHZJPNmnkCL1R097bOBMXpKVvhpoyvN + 5+mXTbSJHeTxnofXPMX9v3gf6fyZnAt+ZUt3Zi0q13NG46gTXIqmoITF8ytPqjFRwO4/I4IH9/6k + 5GB8DFYet0Zl9k4n2lDaYFbFsQSXHY6I+WRvl9G8adW/z+f+JYVLtYssoFb5QCLkddmyRGoKqxUu + xBXdsl+5T1mhKO4EvBPdfS/uPcmCTJffNFfvgC1+MQzqWkGB5q/izv7OF3zctIrenhlwV1t7cZCP + ptcm8fmALxrZg8/+U5LDjq5sBl9Hh/c9xVQvnZ+7kGNZoIety/R4y3fuEL9UEdbvxaDu52zXMw7D + GA3pbiLa7NfZLwraBj4sM6cZfsbGJDzXGV3i/Y5efuIeSM7ziGHK3zW8rJHJ5i+38MiQfw8aydMx + E0I8e3/+gZ73Hc3Ym1M/YIapQ8lldvrlL15seI4crVIxOlxNMbxNxoFYITbAMueNDan2ZJP6e8vu + rE2XEo6Z1BDbPDCwXEYJw/exooTcqdXz9Oo64PWyBUocjnO/asdsGMWtQDIp8Q0mijsfiuh4pOdR + 0ICoCeoKhqNr4Wb2zWyKx0cORSk2JlRdXbZUu3zbv2KgWnbdG6t+OEeQ+/L+BMn7VIt+dY0Uyz9y + uJZa1k9/8fin98mkZHWRbfHXBJ7p5zSQb1XPztX0gcZx+dKzuliM90xNBK8dTojTnL7GWMhmDGca + QRoYXBh+SfHGcDv/NH3HYr2sjamCjY9QfbNn3hJ8H5VO45AkfLdbr7l4gn/+6I8v0cVXSribHg45 + B8cpZK55aoAGc0yv+CuFi6rGjtJ9biU1jzfHkD5OvkK5XHyStondiwVvYJBVaUSOXLp3Bc/cm0hf + 9Xrzb+9+jNNTBE+74UHyB5Gztef8GOqxslBj40tLGL84GNTIoKZvToytpamjFE8ujZScgv4pAR6q + Mpmpe/PS/+Kz4WcEEw2QaoznD83hr3rbJHCJBQQx0WZ4X98mPXSNmwkbKtskLOXvPBj0qFUe2qGC + J8TyhXq+Fk4Bf/4A6e1e/sXbPUY8310wEzPTmArexTC17sOERqFky251EpgdwwqviXt2VyO68SBq + pjs5PcMErKXxmEG70AC/emPJ1tNWgredZ4qPKMioLc8p8swgp9b9+3RXTVhnFCvBDXMbHmcH5ZSC + 0Ub6xIpt0o1cpykwEjpSA0hXY+FLt4L019pU5+2XMcsiV6nOed2Tc2bK2TLngwOzJprJoQ4lwPaH + cwPYdTnSE7e8GTORPsNzCuIJFXzwt54UWJP7ovb5sgupn9ai2hqUoxaqtzQb9mQIPsQkWjtPIRvb + LcXIkpXgRpr7hU8uM6KPlzbNbyJkqx8zD8XAY/QAyiVbgyLj4PEUhNTib3U/RGlgIf1lPejR5PVe + 8D9FrvbA4KZRP8XZIvitDn/CoyN4MnKj3fAkAmvkTWous3oaDooD3t/8iXe9nLurR/pcfWRCSS58 + wdVLFGxdwqTLB7dt6QBJFa8+7PbNSq76XWHDH951Do+KpF7G9+wOb94/fnMA+y5jltk3kJ1+O8y+ + Hejp7VV9/vAvdVM81POF2PCP327+M2C8DRcdiR654KXZ7/ru+htNqLjynmY7kRkLePol0HbbfuCv + lHVR4XBA4YaQaNEqZoMpvHzktUcTNxC1bGXs1kExie9Ue095tnAIlTBvPJ+4qzoaEz2UDnwPlwdW + I+VWtymMc1WQ098kasPDYPBAebjw6EU80UnDeWqmDvB8e8GdNb9Ay7OnrR7p7kHJToxcJiT7ADEf + nkh6Tbh+FvOTDB+IDtTdh109X99wK77tTLJnLz6cv5zCQ+eYcUTLeNtY5Zusw497OWJWjJe6z6CF + YfpsL/RPr5k4hU+hKAcngtuva/RvluQwv+LPP37CluaQoI1vEGyQE5vMpUxUn3fZhD6HT7aueZiC + S3U6kvC8uDW/7xIbzjBxtnaZS7h6lRSrwjev6fm75GBeezFBp+c6UqMDxFjyVhEhk/pwWtt2MOZy + SCB8C6VBDf6Ygm68nzXZYYU4KfID1m/Dpx7gxWak56n+Zav8/M2QvVRC3C533LUEPQSm+rhTa3xL + xtw9Chl+TnFPvB+OMtGWZweZUiNRjftx4fBs2w/03L1I3BR7/Xh/p4WiBxYjJMsextzfH5X6L74d + wlM/Seo3UZXLz6In+ZH3C1qzBG7+aFJSQQDLxleB871O5FDxM6BhGvDQ+zjtJGz+biEk9ZD8kEca + /OKon69LESnuMSyo1l6+4Xw+Kh7809MM810ai/p1Ati9yoo6yW812rd0seA+dO9UD8cTEJek31KS + dUNINcw9Uya+UvZFlJInsPu+5UsjgPpsHUnctKPBKuOsw91x1shRVVpjLecT/3f/uKvVQ7Y6WgLh + 670fif2nX2TyI4JYWUt6Kt5+OJreyIN5vhjE3fjBksJ4gMt7JxH39dQMYT75wd/vUR0xVo+bXgDD + Sn1gDpmOITx5B0Kk8JA87/TTrzV7YXRWBYtc7+07ZIYSpAhLvEnMm6XVfDSOHNyeF/GOn7He+HiD + LpyP6R8+YrotcPAtp2/8h/f+8BjoWzenlmio7hy/Vh56RhyTQ3C7gN9zuG/xIhvIH/+Yr4Weo88t + JdPu6Ur9qqpx+nc9uj9sg0XgRynhnz6htZdjKNlm0MAfmQXiPjtUj8l9jtCQHDxyUiszFL6SFsHY + yg/EKy9OOHfXt43++NTZsYNMOCA9QBdhR+lxv879yr5CiTY8gxsSWwYfv1YRoA9MybMa/Fok7sVE + kmv35GwO+1DCxxb+w8ekvwvuulv1FonHJKQhl2rGzH+Rrp6WTp34duezNRw/BYK8bW/8ZXXn27v2 + oFGpd2qK6YPNIr2uUMiuGTXi6Vov18KZYK8cK7z0lZwtlVTF8KAkjBziDwuHKE0tSFAlYzl82724 + nDUZ/XLeoE+bafVyXeJGvZN1wdyzbwCjTZwizQiVKbn7WbiaS5vATM8bajeXX7aCbpXRZm+Y6frV + mIB41YF1j+qpfGmMtUJyKUGiCQktzD5zH2/nHcDd6zbiopHmeozGyYObPkAPD+nDluGk+mDbH2qY + b82gS3NOwYE7L3Svrkcwe7doABG67XB9fVkGU9dBhXd/kvH88O8hi4RjDt5CZZDTocMh2/CN+hC5 + gOhH7bB9ZiL0gngb3FH1xvzgw+kvXmOYsaPRBoESQ7XJbMwLZ5yxYyia0PjS28ZvPDC98VWFr9UC + 9O95Lv/wD7U1ehOztyGpz7sG7WKLZ+fAqqlP9tM/vGzszaMrHL2ch7141qh1e7yzOX+fMBjBEpHz + tJVgZ69EBNZADpP9ZJa7pvzoqf6UTMSoHoO7stKdoTLO50na8JTousyCJclsStTUMFYdZaV6bfOF + hDa/7+di+HGgX1KdEDzJ9UQHosJNT6Wng/sN//AJ1Gtuh9fxHbtrJVUNvLbFQr38RcF0i20P0mTS + J87sgTHu9/kAIzYYJImqFdCVfktY/NaGmDf+5I6dfqng622MVOu8Z/jPv6ODhalhlYk7pzMfgXBR + jxTLgl1LJ1jO6H16uBhYNOvFE2wroM6VT/fN/ln/xW+w+UMa6vOjn1tJs+DO6SqMbFCxke/H7k9/ + mTj+WLqM+5Qz7IMkITkc3+GqiB8NPrmnjuXz5RkyYynbv3gzibnTuUt/f6xQRvxjmu3Dha1OOIiq + vFsVst1fL/ZcEsPNnqghqr3x88yTCX/Nq8HLObWYcH8HBdjWj7lj0Wd9HTxLwEslI2dpP2TTNd03 + 6nb9SbrlT3du9S6A1aJdaSFEtN7Of4fQSfEmRW9hNthmx8MS5BrJ3tOrnlX9OCAJ6i+sHLop/Lc+ + T7ImevwqnDH/+ZsinVUa3udjzX904MO4C0ZyHoUSrDUIoDKajomF01amuzvdChgr/o1GGx6aGzyl + cOffv9QTHTUc/vIVf/E/syio59aJtL/8wdQO1MsEKG6DRk7fGoMu74y5wrtErQP5Nc1J5bPlZAYW + RD1L6CEVeLAQEnjq3LiAmP4qZWyNXjIaj/pnUt8C2vCexkG7piL9iw+rMcqaGpj7EPeX+dSLW35M + uQ3Wb1pMaID+UkQYNtHwINnrqblzkNYWov2noUR0X/Xf/oKzJ6yUXHeD222/p2x6KV6bu9Mv+qI4 + f3oSOZ6Mxh3WnkvBn95vIq8LNz1Yg4rxrYimnM+ZFMdagTb+Qe3VfYTsp6f6Hz8gERHO7np2KxPa + A5fRo/beG0tN5Ua1Ov9Hsn3o1GyltASv+nCZXrv9KRQM4VWiEiU5ca+XreTGkCp4sCtEjSm33Qmn + sIG2axHqeBlfT/Jt1mABfxbFywG7a4klFQpRsyfa9rzXj6R9IHf+NdN70wtZvFgm3Pg9XqT5yqha + 71OYsSidWExwvf75rxQPLjWVnLK1ojIPtnhBN36aDcUO8WDLH27264UD+iiaGpMTwL7N0pAdBU3/ + e/7UU7whXA+CP6PX/nzAc278wPLjXiZSLr2Fq3A8MeltDSlU5fNMsuKBDUk7YU6dht7BAtNxuGpd + 0sFnmT2Js+HnqaKziBQ8/+Wjxn69zzz/x/+Jxj8b0MqZrELemDOaP6PSmHfTZYZraYbUirqrOzqE + 91B/d9c/PY9Jt9GbIPBzDcs/zMJVTE4atAF3wH/6dNdIl60rje2RBEuaISzRmiK8jRK4ylh157d0 + MVFTrXvq5HJYT9290OHjwSCeh9Jm//jjTdvJmx72qZd87hsI9kVMtnjBxOvvbSKaDDqNXiTOlrl5 + yPD+EK9Y1c6Suyxnu4Xv6yckROObnrnHZdNnB2njL3E2tI+vDN/ebST67WGGErfqMlKvWUHNEaaM + jXtZg1t8wFKB3X7JBuZBpukGcfvfvd7ilQwqQQjJbfWMflnOWos2PWGC83TKxPF+0NDv+lKpjTw3 + 5J2aiJAMzUqvt4Ma/tNPRpGLN/vAofQOcKV8O+dCzvfkxf7p9dNX9KjuZ2tN//z5FV7u5L7jhGy6 + b4NV5ur8JAfRLMN5abH+L55u+kD/ESK//csfTSiOCVjLec/DZ96IxAzf334sjKkEMpRv9PGnn+g+ + HeDh/jDw7vw6ucs19rk/PWfa7cO+X3N4hv/0yS3f5f7Dt2LqCxTfgBOyv3xOcZQTGn67rP/96WP8 + vX7i30mhbJ7P9wSm0cOnx+ts9yP6LAHazvdfvjP7l6//sRpPO70E7uzc3gPszGc0cT8rcBcxsVf1 + /DZ1Gh5W2o+CX2r/V0mB8r9LClyAA4rv1TtcvfyaoOdhN5Pz3e/Zer5rKyw7ExKHO641ndK5hPfz + r5g+JNIM8abVHzRW6YHuj8NoUJsFgXL4iPK0a/QglCjZNVD52Fdye+gXJnHRFaK4/B2IHZ9fPe9u + bzVAaECMXsBzWZAiCz4QGLDgnl71EqxJCoP3rJDsPDiu9LsaPLh6tTyNqf51Zzd/ieDUmy69Bn6f + sahKKjik8R5D+7vrGe6WFFqtbxDN5JyQ32apIgjagrrih+tZaXsNjBRBIuQx1mCZXa1DwkvRqR5w + QrZ4x8SB62mSqDdeDy4zt0ZDCEdPku7bT72MQpkj3rfueA4tyxW5T6hBmt5V4kpBlYn7i6dCWd+f + pv6A82xuXm0Lis89nx4DUNlyBfcB7kl1IMf3uBqvUWhzcKVnhQZlFLss+IYyut1tnhb7YKtKfsQ+ + KEKaTb8INPUMLCtFY/6JqeegS9jrJ0mF4RS0xCFzlYlW8iygnHIN3XMv153vse+g49gdiPXDlrHu + ZYJh5JccNdH+AKTzhHU45jueEDJUgA2+VqDLUTlO9eWjMeGCqwEGzikn5MWybL3m21vdR6HE0aqc + 2Bx/1Rg4fo5p/KjYhhWbCOIit4h7AxoQGsNc0TE5CxOYvrrLP9tghkFoyjSerIStc1FqKH1aP+IU + 88Xg/dvjA42TWBC38Ekm1WIXwXL0fvTu9luHwffdgbs+UoiDrrdMIP6lgGmjJtQDj5JNXVqZ0I3J + hBX3XbORGGuLuDitqd1ivRZ3/G1A/fM1UeOROEAK7ziGr/x2Id59HvtlT8sZ2bt8Ikd7mxVW7Y/N + 335RbemHkIXvt4PmpeVJcMqqUNIcbYXjymp6EicaTmu+BPDw4WVC0uHSz1U8N5CGi06i5GwDMUWW + DmvnbNDURRFgH+bxykzSgmbnoTPmU+F/4FUwWizKOAIzfve2rAzsTK13lvRrv8gQvmQmE1eLT7Ww + r0+c6pXNlRxCuzAmxyg7pPLCZWJCq9fs4s0p0CXnQYxk7OppHoYBLp1dk0s+aaEYlGGMdlgNaGjG + NybxWxcIe0UqOT4To18aw1vV2drfCHGuHJiWkFoQvbQX9UN5H7Lw1hbQ1aeaYh2Cnt5/vQWfBzST + C0j3oSAcYAJvzxQT4zveXTFTlBSWnx2Z/s77bLAkgG+/eZOrZNqZGH9sHQxI18lT0+Z6TWbZBOph + +NHjwAO2+aMKAYYUal48sV/oN5CRuH/GxD2xgyu+rXmCh9LT6F2AkSvu+OcAbx/jOAn74yNcuU+m + AzuuQ2JXy4f1mvvZGlMJH5K4YRcybyhtpGbVjdqe+8jErv2s8OCkl4kX7JJ9cRE0SAvlH/Gmo5bx + UAxUuBxOO/q47SPAXs+sgNaq/cg51b/GZr8xHJCmk6MQnOqZz7AJ04yDFMMdF47F+oWqAPovlq+Z + lUkfZoqo0LCP1Wc1ZFOVBDLKBeFHna7KXCFttRVFv+JO9TISjdVKbgUsa6Ei+PhqwvUC3x/YGslI + iiG7u6LXQgz7XKtJOtbnULJyI0W2Rt943qPEnf058+Ajahi1zSDJ+rj8rchqAwOr1S92F3jb6eAw + mgGJvudbtia3tUA4i+4k1dM9EAz72qC0f/n0EnzMjF2vygRCPETT7/U8MVYFsP23fnutZ9a3TrbC + 0bkwksaS50o3cAj+/DeN2JLXwvPEycpj2afUiTKdiWnVldC24I5oTpuE/BJ4K7Suu5KeTiRw2fp+ + TlDo/RMxn1EQiqSWRfRnn/a8lWTF3zVCGjZV4iaffcar8qkFYVQZlNS3C5s/o9hAO9RsUkQo3xpb + ihaavWAhpPdFNmmGhhF92CcSsONq0MMljOC6etvgn9Lpl8++0GFm5gOeBzyDpXosNjKvGiHxSWr6 + XvRbFeqq79C8ys6Af30GHYTksP5dH7AmTUwwUv9GLPcV1MIFdwNUOZxQ7/Vbe2a+rBl2BldSjZGK + TZd6a7yteC4NiOT2ggG4CdJheJDL4jq94HJnGe6uL5dg3GJDtJJbjm5AFIhTZ0u29J2cgLqu0TSI + 2iETT2FuwqNqDhgK2HH5XXJX1VUWnvQcvd1eZN9fDJYLsTc8ENRThw0flrcUEN0UlXo+XZQV3E5N + SQu44zLWLoUFJ8z35CpPcS3seiVQdNyO5Ck62F2pvdMgKPOJFF6Qh+y9T3NoXnVCT/dPw6i2uBa8 + Kz4gh5HfZWt6alSof9GXpJo5G7N0qlXYNDubGO8LASOy3QbeKBuoKecPV6qsK49Mz2Tker8JrvA5 + QxHuY4ejsR1iV8LdkqD4oz4nThdNY1rCr4UOq7qn1xZ6/Vq50IR2qNvEYbt401pcBz71/E33k5Nk + Cz+NOVT86EvDGemhxAWziWiaqVheFqFemvaH4euZcwS/I5Ix84VnYOrjMAm/6QuWfVzbKHSbL9Ev + 0Tdjn7dtowxyGdWPnFULk2RF6GQnDsGPNgdzFh8D9J5thexFIenF4Hid4GF8zsR2wy5jRRzYyNjh + kt51285mKXYmVB3htNnXAubLsbPh0Rw6Yj+iPmz2YiOjLV4TK+80xgtN4oPzWQPk4TxILZ7GXQO/ + hd4Rco4Xl5XjS4ez5y/EJX5Zz0VPU1BU8oWk9FiDf+t1HnFJzkb8Bou69inY/P0/vDWekjVCOPEG + ahyLhi3ei3XQeNx+mKcWzFaMjzyUD9tbfBs+XB/mq0TZIzlS02wvGbueAvnPf9Cn2ftsbocjD3+X + ySU4jbReapfYgvf4o9K92lrGonxZBPuVzNMwXg9G9zBfFdL8GdLIT7NwKdYvh0IJm8T0oxpI7vXi + o2ehuPTcOLWxOl0Hoby4C3UB0ustXmhQe/SYEv4j9ctFN2RY1rlJjMuQZKt+ngOkR0U+zcD+snX1 + g0L1+7yke51c6rVKUhVs/poYI/SNWap+FYgfyCAP+XsORf/kFZA+6u/UNAt2lz97BZqc/uHhrcQg + 1FUXeAHNb6+45pvrSQSPy8PaBp083TmLiQ/VZWyobptFxj7M5GHzEjKcPry0l35qG4Dp8GiImVsj + W9Xg18Gm9yrq7tVttjlwfRDA+0L36fOUrag9rbDpcTXNQeQwXpX3Hfp43p4ktvQyZm5vaLDsLEg9 + 0l76AXPtDKBhUxoRmrL5flJiuNkHzjY8vJy8rIT3+nmmh8OK+llR5gR9CPXwT6Gvfo61WUfz+9tP + nDGJ4B8ebIfYJuaGl+bgOomQ/4kcMXy9y9jiQA+Ks1dilPJ6Npfk4YCKNS5JfPHRz/hdO4gTOAlz + 9+qQsSrgO5DLtkeDa8oxun3/X/z3pl1ozKESlbAFck6LWgr7VR3PjrowcCKOE1pgdQrTUZvWEvC8 + Y63x93zA6IRsqjxa9AzamgbHHPHEeXACm5eib8C2/8Swgdbz3PnHg+Z5utNj9hZddg6rCo734kfP + 9OL1In3xrbLTdY1gKXcNXqJaBH96fcUAuN9wznvZh5xiNjTT4lO/KqdbDuNO9yb+fsjqJTnSAUJu + dafdMK71JF9F/y8+kMt9nAzq+wddJWUwEyzIUS0E76VBTqlV5KZCqV6hJGBg2juLuEYzGSu1JQ1k + qZ/S5FhCtjyaOkAs2d6iPbbnfvZvjwaciKGTYzwa2cYHG/S+nR+Y+bqT0V5WSrgaz8fEX7y4X18t + jkCdA3P6O++0RbwKdlKqUyP/0J7iTknhkqKR5lfJroe/eFLOVPjz5wY79KKIujM9EM+lQz3vDXcG + 2ytBJOm6HszRw/Hhel01sr+P2JVS/52jYO09avZaA0aqJQ089Za7DUbYGYs+qjqcnwukuD7I4Urq + WYTHrkrwjqxqNh3rYwVQcjhMyykG2VzIlge3+yFaEDmA93Dkw6vOQuJdgicbLKqu8H3sT8TiHn2/ + NO3Lg2Fvg0mOfq7BJnyfoAI4g56BsIANz1owmY4OdersEk7s+4qhz+166h1CIxMPsfKBV89JKbmr + cTjrHTbB+W6ZVM9+Qr2U4cmCU3CViHatSP2HD5SQN28kvAxy9rf/qIP/AQAA//9cnUvPgs6y7ufr + U+z8p2ZFRKW79wwBkYt2o6BicnICiMhN7g10cr77SfOu7MEeGg1g6K56nl8VRUTo7RNl6ri6fAV4 + rGFBgq1sMCH4yhhObG/iaXxRdwrfRNjz/EUVbVTs+ZaEHshzZJDTJwZ2U5FZBtVupniLlcye7xsW + oTu5ncgpYBKYbKsbYcpKGyPRF0M28KeyFC+KqJ0Cms3+U4phVZ9D6h4lHI7iio3QKDqTeqwt1Ok3 + NSmMopTiVeAliz7W0c2yI2ru6SFj4cUXoRGN/V/+6pffQ72WiBs8/WwT/bY7iTTqnZxORgtYjKII + HjNqE537pyndRRXoWAXIx3lm4VypLoZTf3MILndHMH/qjQ5XEF+JVoaRSpl5ieGHPLbUyDJFbdJ8 + 6mA4fn6ENDHIhtQWdJTaekjkX+aEdFffKwC7T0X/8mP70yLA/QM5VrDLmOoqmpSp65oYsO3AGLWj + g2D9BMP0seps2odein7lSh9SP5xsHp8wkJ/KA+91vGZT+D6J8FJ/UjxPb82ezkMQg/4zqLjNC8SY + 2Gq8UO6+hvUh/6nD5WXMYFPIlGKa6SprP2wHZ5oUBP+agz1djnWA7uCqkIcbPNuZ/z/0veUdjy91 + Ns3mVUJq6R7Jucv44OZw9JEOJkw0kFv22E0wBfwzVX73Su39tzNARVDGYS/nVzaJ+3cqSefnd+iz + T9rO21wVoOdWLV5190PGEkPLkV34OXEn9cVofzylsKlHTF8/cwvmdP3G8HM3SnI/H7yMMWB08D08 + fVwezpq7LQRQStnDOf/l87/rL+k1oQe5De1WAcCCpmKN5LDXaMZG1jvgXrw9etm/jGyMtUIH9yl9 + 490WX9VReCVntOQ/9VrKYOPllgA5j8C7t8dfpDBRDVZr2aT2ZmgZuwxYlqy3l5Dzg4aheN+AGKTx + r/zjD1MkxAPkfgED9hHY/NbqdCcy70JiRcxVrp9vUFr9RKJd3GvGOF9DBTrY9Hw/9iEtC9mSbuss + JpbQPO0//3y0/Csxur0CxAwaJeTxCaMpUbONdjQgCoYKUf/1fbPBH0cdzZuHRy3Oq+oTthzA+QG5 + dKqhTpyXgN3woZRwPTWdDm0Mwr0w0OujC8Gs5ysHZhdvJKH0OqvjpwrmP70XbRs37IRXgtHHPL5J + 8EnPrriWxRKct+SCN6/n3Z6uuBmgxbJ+AL1qt+PVkyEQf65GuP4Ds439DraRktHjUEptfznWPjz9 + ghZPe7Fh7A6hDK/oahJ9I4/qnD9yC7TzZRzGNTNsFqe7FUT9jg13VykzUUoGD177bkOu4TXPuvxb + NWCry5j7h4/LTOuLUSj2h6Fa8tkJKzfoHkSFyP18CpvCeOTI/8W3Ye0WP9Dpj42FotSLidr5r3bz + uKMb4LyLHmNhbFlRRTNc9DhBWuH2EIUxbFSY/OXPZn25+3D7GRkx6a9oub4VIbvOgKgLj6nytQPQ + IbpQ39ge1M2yvmQxb8jiJ2ZEzgboTpWMRf+4U8cW6yKkI6kHwVvnNq0ec46eq1X6F7/E0/fbIHYd + AX06cdfOUlJ6SzyhxlEc2HgOmAaczUUn5lW6uVtrZXl8KoFO7F5tM+7/MdjS+wkjY7+3F70BgjZz + qCFGdzCWOryBlXsT8ScX+oz57VVEj9PDJqre1W3N9wuIXqFCNZA36hgaSg6ALAVUIdu2bRb/9t3c + emIr842xPHB0NG4lTE7TB9rVc04DJLVgM6yRdgxFVP8quMupNex3qHSnYr3TQaclLrk8mWNvC/Kx + 4MoxTc5P4nBY/Fq3l+7EiPWyZet3YiCej4jxq1/Z1Dajj9Ir9Yb0dzdUtn5XhiT5QUN1XZzDSdMH + AYjaTyV/PKa6+w5c8ucRvR5s/rl6BZd46gmPY8vC8J5Ct1IxPfjms505DwcfoZOIhVcnm2UkUVCg + nJ/EFG66ujHXsALRzjoPanVT7Ok+SJIEzelHdNTypzLltoTp91rh/XR12dYqzh2QgNYSwv1yH9ZD + Azc3byC2Im/bURDUAaqmEJOzhSa3nzMUwDqMR2qYlLFpeh0f0uuzOlA5nEpX+J6NBAI+CFp2d1/e + KG1BYO3ZCX9/6tDOCx/bRq1OL0Sy7eka/QLpKjZXvHK2hTrGO4zhqdmLGHgHv+1m5fWA4fj+cQk2 + tSx4aw78HU9kmFeH2h33/asEXF9Szk+zypepJdET3ZND8fHV2XQ9HQq1sMLzPYgZG6rNGRbucMPM + AEk2X17GCNFX+Q4iPL0Y47wN9p3RkCOw1mCS1tZ5b0rNh6hr5IBa61QHPU6eTZzgQhibskmASbZN + yfm+rdoZCUkJhazI6SX7KNk4mHYE0SG+0MNkN+2YG5kClvViViXLxpc5ecjX5uuAKidoN91uoy/x + agDEkTNxdoIIjB8Gqczj0RyHOx8kY7+hR84Puu+lDEDaNJQov7thb6KLFyx+Ck+YPZhgO7YMqfhW + h9WIy4w9eiP/4w1c34Tzau1UMDx4ayqft9StaoJKcMJvgoXynrej+JxiqP06h0afAGTD4Z0NYEz1 + hB7Ozz6bcrGXIOcN1PpOqttxngOzeWDEtPhUjLAuK8h6UR1G2W3UrB7MCO73dUI9tOd8ubhZiJ+f + KF99CvutdqnAqj+EC69T2ZXNHTr11XGYDm/Rnafa0UAVqAU1Xl8E2rsZSOC9vQnUfppF211PqYHK + IgGc129DGl58YeHhQ3cTH+4c9kEJXye5pB6PH7O0byG48ZYYFRc4m58Cf7GIu3KIeWdb98/vXhs+ + 5i7oeAtu/W7gorc0ridHcwc1GHUndVj83l89iP//5f63bJUIM7zmz4HYB0MDUx+lAnq81+qwPzv5 + cvwb1CMs4f2sEnWWxa+3+FmqNbhup2toJrCsmgozRyjaUQ1ujbTXy8MArPbJ6BKfLt2PUJPrtz62 + +wYECn7isZ9P7sicWIY1UDWKu3Bvs4dnraB7vF+IWfsD49cjwufLEig5vjubIrPfLeuNnItItsUs + s1ZQsTN5AFHns7E+jLtlvxK9vLiM87gR3mA40XOQGmyA7cMHm89FJnf41VShEbeSFL6DE14XZcXG + BxY6dKD6ixq/ep/Vsa/FqMlSgR6uSs/mhacbHymgZ+P3yQYYr8v9TFbXYYuQyebXfIews22bHPnx + 2Xf30mHiuZg/2KWHm612rMBLtPEwWtXOnYKwXkGoOxHnTwmbe1grUFs9DXpa0ULl92dc8h+9dCAA + Y4YkCWpnnZGzXGXqaEiNIF12lU/jST4wcVPMJRQt8Y2no6MyVk0PDT43RUguwlCFs2gm3l997Gz8 + 1tnU14cGdeJYEWd+nzLOf25we9t9//xHf5udAPWpfyTvS/xlTPaPMVriT5gXbzC7vIVn300XYhfw + y+bnRQlg7oM1tc19lC36DX7Ac8Z7+iuy/pF6GupYA6hhbL/2jISqhHj6ltR+PBt3atxvgjh/pZbl + lmDuV3i3rC+CBS9WhSluS+j/ohuJRLkIaecYERxXmU616c7cad1/LHjeXi5U6xqm9t1VmuHKdUSC + 922qjsa3OMPOOMTEaNNVNvXOWQZfkJwo108tc2qlQtx/k4Pj3Fq2uu00uOxXbXwnLZV+pQSV8Ojx + +5sv+j+BnM8Rbxchdd5/5Aj9rpZPDQPxKZkgMZAgJ0dyVsVTy+thDjSKwRwQMSibiQUwbAkKyHP9 + Tdw6UDpv0U9DEbI4XPgSWqldSp+JmqsTS5IZlWmIKY8fLuM8S+K8knr6+cREJ7V0CPaBSXQtqP+z + /127/FEdnvaALvVU7j/IXZMawBxFfiz+gmplCFXOp1L4kaBHP7y+2XXWfEbulwl4fXyfbcrunQOj + IrYJoWoe0rYLOsB59HB/O4E6Lzy8XEUPautFEnaxWD3AWFEffzmP376UZFziFbFc8cXG59o8w6g7 + qvTUv/J2rK7dA9JueNPjp7QZm1d3Dy588sTUORtzeDYW/U8vkq0ylnhtsNQHhtUwWWCDviYGNZ86 + RJToBUbrI8aQohPA7foas1lurOSPXz+anWD3O3mS4X9aCv71X//1f3iDwD9l9Y4L3hjQx1P/7/9p + Ffj39t9dGRTF0ljwz9AFSfzPf/+nBeGfuq3Kuv+/fZXHv473GiBpt/lrN/inr/qg+F9f/Yuf8P/9 + 6/8DAAD//wMAIluFlroFAgA= headers: Access-Control-Allow-Origin: - "*" Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9ce45abff99f-SJC + - 9854b3981b15cec1-SJC Connection: - keep-alive Content-Encoding: @@ -3031,13 +3031,13 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:29 GMT + - Fri, 26 Sep 2025 18:07:33 GMT Server: - cloudflare Transfer-Encoding: - chunked Via: - - envoy-router-867c855bff-ckt4l + - envoy-router-5b786845c4-sp67j X-Content-Type-Options: - nosniff alt-svc: @@ -3049,7 +3049,7 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "223" + - "377" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: @@ -3057,7 +3057,7 @@ interactions: strict-transport-security: - max-age=31536000; includeSubDomains; preload x-envoy-upstream-service-time: - - "323" + - "399" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3073,7 +3073,7 @@ interactions: x-ratelimit-reset-tokens: - 6ms x-request-id: - - req_3ee5cd60b73044459407827fbac5ed4e + - req_aa17a6b9dc2d477393b29eb3dba1a4bb status: code: 200 message: OK @@ -3238,7 +3238,7 @@ interactions: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9ce7be24f99f-SJC + - 9854b39bb846cec1-SJC Connection: - keep-alive Content-Encoding: @@ -3246,13 +3246,13 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:29 GMT + - Fri, 26 Sep 2025 18:07:33 GMT Server: - cloudflare Transfer-Encoding: - chunked Via: - - envoy-router-575fcf7dd6-bm7mx + - envoy-router-bc9b5ccf9-tsd7m X-Content-Type-Options: - nosniff alt-svc: @@ -3264,7 +3264,7 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "38" + - "151" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: @@ -3272,7 +3272,7 @@ interactions: strict-transport-security: - max-age=31536000; includeSubDomains; preload x-envoy-upstream-service-time: - - "59" + - "173" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3288,7 +3288,7 @@ interactions: x-ratelimit-reset-tokens: - 0s x-request-id: - - req_e91a667a79fa4d279d5e0df89f0876a1 + - req_b4c57ba96e16434bbb5241ba8f4946e7 status: code: 200 message: OK @@ -3334,122 +3334,122 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA1SaWQ+6Srvl799PsbNv6TciIFW175hE5mIWO50OIA4oMhdQJ+e7d/R/crr7xkTE - iDWsZ63fU//xr7/++rst6qqc/v7nr7/fz3H6+398r13zKf/7n7/+57/++uuvv/7j9/r/3Vk1RXW9 - Pj/33+2/D5+fa7X+/c9f7H9f+b83/fPX3140lPP+NPpFz8vmHVYP0/CeprWA1jSLEOZSbc/7+2mv - jozXxxBMdCXHhpR0HeazgvJoL5NYVJJiDXTVQtz7csSWpqb20npVCnZdTshplB7FZHrIg1MqAHyd - bQ/sr5y4QMGYNXxU+BDs2XxioA6BNTceeUSblqMctt7Tw4rgkmHz05cGQ0WysFJvPVg/wNeAa04H - 7zDG07A9YeCjTIsv5OYlUrTnJHWBb/ZTz/XwrOhq7y4MJMQ7eBPYd4BeI/8JU/0uEoPRnYjbTX6O - YO0X2D8lPRip6pdIKQyLxEEy0U0OlhJ2ySjjDFSvaHOV1gClQmuiaxer4F7qUYRb+eDxVbHUYip2 - XQ7Oq1LPjO979Uq0oEXrEmNy5eKlWIWbPqIL1AISSWmgLp16juHjE5QkPg3hsD8vcosOsiSSo6y1 - wzpVgIOfMSlw+kyMggtqWiGrzACuuA8D6P5QKvCi8gLW70YI+MU6pFB6rG9SyiSP2GvPKbCexIDY - 3fxQN5N/3f98HzMro85v2rcQcq8AR9O1BlTgJQ+F5b3Dt7qUKBtciwVmJ90kRlnu1FmXIg/obRXM - 8/DSBr7QZQtF3iqQcAvUgd/Ah4Xh8+WTILpZNlu+5Ar1oqUSD53liH9tvoJsU/fI8TQuEQ8+3ALx - tWGIVrNNQfutSRFr9C5W7HNTLKxwyZFwCC2i7+ybymWuqADinxHWTsM2LBa9ekB8l5DE3RJGlFGu - L3jg+4ZER74ftnmdDKiH2YAT6Ld0eYajAw4X1ifZBviB1rnco7bYKmLn9RhtQQ0q8HL2PT7Jm6Su - 1BAzsNzPOrFRbNicuoAM7OgBE8nqPDpd5QMLM8/fiPwOdcAu/uBD973XiUE+UU254Fkh9U58/Pv/ - q8RKOmo7aSaxc/ejrT01PdrzDSHmVBR0yYvUB9uOn2ZkRS/AduswA86R2xncsg5sN+atQ73hX/Pu - w0cq7wcnBQrx+UNOpEnA8hCTDakGcydauUXFxGe5Ia4p3HA8VXLNG0MoIHwX9rgYHkO032sfCDaL - nbE0uDFdQld4AeFesfg3HlSR4wzhun0TrzsTdTrwiQ6dc9njpGBosWkP30GnNPx4I2Adm6vXMES6 - HlS4OHWPgjeRXqKFy0asHnlroKo3bOByARW2vbEYRvRqUwBPpofLj3EFW3LUOdCxQ46P5kFX9/gj - WUionRc+624YzZ9w6qF1YhRiyvlS8PEmvWAAYI39RQkHPmV9BtV+r8wgmWewulydwvMq1XPrnYOh - Mz3kwBMjp94yPwTQFEc7h+7cljgRnpeBP+SfTEw7dsF4xx+LjQ/3GmQxx2GzbaqC0IgJod+5PS73 - jlZQ/3nn0IGTDyS0gp1NjwU1UNPebsQzqGlvHt/p0A8qQBy3sW3uoAYiYObUxKYbyOrEzZkBtyFn - sWQ+pWGffyIJlL5KiTo8GbpidVuQx7rPmf0YfbFigY7A8vn93IPNH8jyGUTkVF2EPcB9hhUBxEHR - PBOMh7cS7T9LoIM4IwWx8LGzv+vfg35FLGLD1rHZoybeYfdIXsSZ1CelR2GaIf/UM4+PeDca0eUR - w8jPGywpd7dYbn7E/vQdF9x4V/kdWlOoLfOFaKze1UsZkhZWi3L0mCKU6+XaMxKMFjji+PHx6f49 - G7Own3Q80yxViy2QGh0hI8hJwR5v0WIxpQcvkxtjv5NuxSQ95xDkj2NAbB29i/m7/pHNHF1SElQU - v3qAiA1UbAPqFXucNCn8/l8s41Wky9w8fKSysk5MhjZ2B2tSQXiyPWwwm1F89WeDHeefsP1yTwN/ - 3/UcfNHbmbhWpFH+KjMjeD6cHcaQiQD/wlYLRy7e4zh61hFljCWHk1IqHh3PcJhORPTg/ix1JN67 - PNiEE6fD48q+yRXsTcDxV6iJ9yk5kPhS4YLmn7OAgiK/YumB3vY2hr0Hl0jnvKFTjupyGQUNPtW8 - w9Lr7drUmgYfsol4JUnbPwAbSA8HDSDEWEVEp2SHDjGsmMsZBztRostOePnIeA0qdhoZ112vJfrB - eewsbHjntd4+F6OCvLc8sDQDzeYRaCHUL+94Rg8XAx7qdwVejdCZX8XwAvtSTxV4dGGLY5ReImp6 - 9xjBuKiwAvSHOotCzopLIThE1TSpWOobbZBahik+aVIJWCvtW9jwaUm8y3FU+/AkCehcLxFRQtam - e0tlHHgMwhvWBPCxpyxXW5hK19gr5HKg61DWJUqO2Uzk+ZnWW3Mde5gUZUyy5KIBVoCiAYN5jHB5 - G7KCs1s5RkhlHyTGbKz+GS/mdnO8VTndwEKEkoOXpHp7I+w6So8+tMDtLWvkxBM+osed9IThHdXE - OXzSgapDmqKWPjxyfqaNulzM54y2qD3inAt4ldrPMQQwvlTe4atPVIlOEI5Vc8RX4aGry7y2IfoA - J/OEuzVH9HVnHKAwpYQvG0yi9VwRDmRB0xGpwntKo4voAdP2CTkPtjpQMbUduPligs0kV2r2eXp7 - qD5zAznGPakpjTgfXpN7SfLVaum2OipEv/VsvXexvSbTGqLOfLEkBPrDXjANNpSnzUq88rKoyzm8 - xAgztkZkcKyjbRX9FGBVu+Gy8HV7vWCth10p9liSptqmrzvnoNJuDsTN3x86qkpvwRETgxg26Yat - L3wOdfEpxtoLsLRzuTpGdeoy5JS7r4GSZIkRqQeKlXek1Vt4kkTRPRgQ//SqvTGtA/NbdiKSmJnD - vjKaDFlh+cQXft0obYJIgjtwFLB0Siy6ncPUAWe/un7rpwa47oYkqCmCOu9lzRh4bOUVTJ/tiVRe - /ByWbI19qN4kfV6szgPzg5gC9O1LTBxMpYHL+rEBdppWWM6IaM+kmQ3QpFvqwZ2x2LMohCzEaZZg - c/Mv0bh/RjngS2Ulhvlk7bUoYwUKCYxxVTXvYYtiP0TXuwSxV8tnQNt3BSF/RDZObXmzt3sKnvAU - 3XmifzgZrPeDbcH7XnKx/dXHP9+Xq4qfx4+BwPxSXREKp+OZHNPrgdKnDEKxrxSOqLIy2e+v3gE9 - sYp5QVIwzMKJ0aCg9BXGSkXq2T5AAdY2qbD+TIxog+LgwPO50/GRhGbNkuT0x28Su5FWSs/yPUP8 - JZC9QxwXEbtrhBGmbOLPxO20gt6tToQJlHKPZUppoENtG1B5yYi4M99TIjtFBun2+uCTnLF0NG5B - CK8tF3q0bfWCgPnSiDH3WMlxeOUR3Vx5REjBLDEv+xhsL/M+wwdKFHIUnodhZu6mAl12VLE7aVK9 - BASnQN29HOxXnaz+/BeEpsCQ0jiuw+JCKUPobJ3nXbhs9Sq8uycsrGoiSp3thtXg5gX8xuNbTyIu - SdsePsoNzm5/vdUzek0eMOhdIpeeRoA/TvMohh/ZJNhHbdRfCjOD7OfTeOLL6b7rSRbQdJsD4vge - ValfeoqoY40lzipIEYV6q4Dhw1yIqYMOrFzJpbByDANHgmgWXJjBEj5awcQBw17qhZerDI4wK4i1 - chrgR3C5gw5VCzkd9ZdNf+N7RZHt3S/GqR4/4X1BfCmtOLmKt4IUumwgFGgrsRyB1gv/GhYISr0l - 2PoE6iKHxxje3DAm5oPhik1VPgpojaqdF6M8FPRbH8E3T2B5d9/XowA3AyVvhsV69bmoy2GlFhLi - 5OMlBRNFK7MwLLwv3UI0a5cOHdn4DeJ4bYnbpUG0DC3S4YtIVxJ7sglWFbkSPFw4H9uipKlLtfoc - slv1QwyaKIA7JG4lepdJwedmFQqirauFHuI7JC5K1YGld1VCKuTKefF3jbo+9PwF7evj5IFulm0W - yiGDmNavMT70hJKtQiE0gzQlVjAF9mzmBwaeuuZK5NVi6mmndTrYMp3B1sq9wDYccw52vSGS47Qu - No1V1kGG71leC7SLvSzPlIWlF3fk9qpxMQ2HSoL7JdNJUoTn6E89rfNXMe+DdBh+/h8409PHFpML - 9Tjf9Qrmj1OArQsX1msRJAqM9sXbIw88RJO9u0DwzUvYlKKnOreeXMKphA98Hux6+KOHndmw3tbk - G/3pOYzkR0fUM+Tpcj6FKZpvePFmU+SLTfB8EX79JFZQfKWrH1iWQECsEyuRE3XDY6//qSfXyFAi - 8pSBD8adZWNVMYtiHfV3hbb32ZtZEgxg6Z/SE+2ZNcPhVOr2aiCzgfPLisnpFoeAE6eChXQfn+dt - UkSwWdSIIZZigs35UatEPGQNksVGI9a9yuvpUR8NGL+4FlukI+pi0cSBC152OKPJky6/+WzU8j7/ - 9GOdcdbCt766RDXtedicfTZDb89h4kIoREP9llrkFmmDraSQCu6dChp0577ENrN/1JyvLXfkyBfN - 298fUsGfwfGOquL0wKcLPURr6PAzCKjtkq8eF3T/aUukvFSE5VJl1OnkvDjYoXLBXz9CWd0mOqxv - akz05GwWnPqGGVTIR8XK1arVgTGWDNTAeHgfGL6iFe7NEL4cvp95yNwLutcKDb7NOyK2yTP2Gkbh - E16Kpz3vTpGrLr/6+PWf+GwrhM4XBSiidRlsb2/Qii49A3RgnHOKlcdOqtlbEbIwVTQZ58ZVLtjo - Nlowd2k+83G4A9ueWXWoLeMFx5GRqXuF8yvEXo86ts+4jZbxymiQ63iG4FBLa57h0yf81Xf70Ajg - XbDPHJ1M5ewxdNLttR6EFGYic8RanQOVjJljwLeSxt98fbdndNpa+ByXI74kEh9Raacy4KuXHlPL - PCD0nWmwHoMXTr68iLKj00Kwr44zOrRsQS3h6UN4iGVSqpAA2rD3HMgqzHCUpXW0mfHNgCK4P0mY - yHJNDwZlwaCqnMd8+cVemt4+/M7vLH79BlVe3QKSYz7PgmNBe6magEN0QaLHVruWTh+QacBizeE7 - /7toLNqpB2BfHnHVqLbKd2hKofiuoMdcxa7+k8eyT4+xlT/nYZ2x3//hT3JIPZU+9FsFCjjBmS0G - Dez1Z89BesGWt9QLKab7QTXE5lkciHapcPT1ExLUqscbm7BZvvUgzeDNOPBEeqCjyrOj1v/qKf76 - PZWMuv9Cs9p4OFb9e0F/+dgfUI5vSZbXNBqmDSbk4H79zr1e2ESTfjzGE3TPV7evPwAMaC7zbz6G - CuUltMi8YvkugPqbBzlYdzXrzWTH0fWCnR5885UnvIcT2JgVNsLXL+Cw3ixA5zy+//IIwZtrRUvP - UA0e31OGvSKUhw2GhxZ285vO0wur6sq7kXE4q56LNam71Qus3Cc8Kpcn/vpjQLv7tiAA2xlrTHkf - RmPVWPRdzz++SGmSfDYouqPj8Q8oglfO1D3Cqn4jdpZyYPnl2yPbJHPVnYlNHWcO/+zfs/q2InLD - ug736kyx3J+0gitELYUsZjmcVp+DvQ1nPYMXw0mwhEZ32DpVqZB/fj7mtbx2Ef3lRc247vAR71c6 - x2YQgjmTT1gdz7CersuZET8BBz1W6nYDPSC4HFoYv3AMsW2ztj208H78WEQRDvawfF5xjJLJvmIn - f1n2L38ifp972AxGWmxvpmsRnAHCx4ZA0DsnJwUCCyas6nVTLx8SxX94hvPlLVQ/IgNO7BR47I9H - LWcLwll4u0Qtj4dha66vHm4WN3uicX1E+4LtM9jm5kpMHbuUrsEkgC9v8ZhvnqMszjXwzQ/eUDFV - vcL6U8F1kjKSMVsbLcJp9ZC9D3iPpqY70IzrUjiZ7YCNwXkUNHwdHMic5qu3Wx9ztGBfy9A3PxEF - xYjOuflpDofJ0H88NJpZc7HQK4M89v1dY9P8cxMA39QBPn2ubLF98yjMTppJcK+1gMqk3eBv/n88 - bvPiaERwPiDyzVPDmnzSEgLYz1jiejGi3mXf/1mv3zxUbJ1g57C5PHWik10d0TSwXwC9sg92vfms - rvb5nkMDXrYvrzhGjWRfe5C3gUjkww7aXz8owm8enWkVEntdg06CYRJLODJtr+ZfWOnRyt8FYqXs - rViDJ5pBnMcGuXz509d/saAM+vuMCHNRqZXEIvzWS3zKXW3g2p5VQD1GL3JKso/Kh2Tx4JDP5p/5 - XxOrFqG7JA2WguFpU/cFW1iB4UO06o3qoQSKDkm+SMSc9h91ES25AbmZPojjTZdi4xzjDtOOW4h7 - rD7RqkS2/seP6oy2AGIY7h2Wce4TIxiSevv5KQvuZG8N6WwP9nEdkaRwDj7uvf1A2iWBv/GeuSJK - 1PnUXw043cYAO4XfqFQ+5zmsJyHAV2t3B1uRMhCCp4WxqX6O9MtHdKjrUTUv/PIYxoP3XsDBeUnk - 6gMHcGJx96ESxiwpv/57OnXnDBr92OH4qxfLWeKfML2lPsG49GoOPwYNfv0skVy00dUbxhQ8OOvi - LV/+tYK5KSG3PkOPzc6yTYwhF8DO4Drvks36QPDmKOAle8a3P+HW+wnMOQz1mzrD4dmpS70SAz6M - VPW2RH7Ui1OLEhwCgWJ1UrOBXqPsDjt9rxG915h6eDpbeIgP0YN8eai9GFDlYLFqOQ6moi1Wexcw - UM2H7ltfvPqPnxG3yfnq92KvO0Z3INs5EU4+pqH+9PNAQKp7aNKkgZ/cowWr4vggdrY87XWKIAe/ - 9Q1rBJ3o9ipKHzrVEM0HrteG6bW9BBCICUeU1CBgsncBBO2DO2J5gw+wVbenhY7Z1ZhpuSYDRW75 - Aoxjp9/+wc2mzPvJwGYNOazT6FSs3OxbKO+Ezwym6/nLPz4cRMxBx8qizer2m7/6rQOMy9wFs9tf - ZniZcEyUqWxUCvW7BOI8NYiXrK26TafBAb98/NMPWnOsB/ct2bxl537UMbscBfg6djJRtqAeNvCM - OaA7iYYdgopo22QNwrh2wXxsexnQc5VDmGVr+M2z93rjZfMJ7kW7EBlNdrF++SR0HGjh0yjJxbj4 - dYjMo2vi8NSd1Y+54QoGS3H9r35A0F2z3/7HsTe9wVph2UP4MNvYMLjM3n5+CB9GG4epgekmThGH - fv7+q1+0/+Wxb78Mm/y6gVG4jAvn2MmVXMdMG1jA2ndY+jLFsvIY7MlxHhmSP75HbPftRmzrpSlw - DxYk3inE0ejUm4TmRtDI7YHtiI894Ikpf5Jm4fu8PLA1D94MwONTPNrf5xN78PP3DlOy6hgQHIvo - Ipy8NY4/gLiE9sBUrpf5HdUPdfCfdxa+g2Ai9rd/Mdx3Tw6g5NFjuaH3aGL4oEc36/4ixu7A1fRd - 5or4rddffuGqi3RgRHDl9OcsyJetpmkXMNDPw2BeyEdS2foGGvjj7S4vOQWnLjSDPF0f2P7yaYLV - XQ+a9nrDp29e3Dz+oSGT7R2PrgKnkpqDHvy+J/pgHcE+0FUDlSyTYtlO22F7tEQHXSn0JNsjC2xK - 9lYQPT3lmTne3uq40KX98bVZyCaDEmuqw1//iBjtzqZzc6x8wLzvlGSsJdVffZYO3/xEFD4LAMs7 - SQYNy6qJmSnWQNMnbwi7iW+xtSzXYX0nWwz95MASy2+eYP35653cSx5cq726HLxpgx5ZWqxFRmbP - et024iPaNfMFNn69pW3s/eF1hmNBdeq3rvrlWw9UTDWs3n2K/+jPkVEjdf3ub/jVe+8Uhzf6hzfA - q8fO4jcv7c+L2UIziFNcuR4otu26u0Ozlm8zFGX5D49Dv/6Cvn2G6Oe/wDzrE9FLdqZbzIQOmHpn - xW7bJ8XW9lACnM84v/1XjPLVScG3/+gd7nyrsrVqSoDlyhrbggjpz1/86W/8/Dedut0MP8DLPOG7 - fzgK7zO6tFtB1G8eZA8NNH78+rs+xmiKBSEUwXps531dCGBJU3QH/mtb8Snip2hZxcmBk9kP2P5Q - aC+f8L6hw2TpWBPntZh/fOy3f7Xqfa03NTY4CDHzmtHeVewfn4PETxDWIB7s7fmyRPjrJ3oNOamb - 4GUC/PL/Lw+NAZ9xjxgdGB4Tt7/uhsV/8MxPfzz4nOyaM+OzBb95Dqu7Z2CvAXB0UdkEEfuOZQzL - KKtPtPJPAWt0ZGw6WHCB3oUoM6gYpt5Su+th6pJ6Zo7HA1jhXvbR379TAf/5r7/++l+/EwZNe63e - 34MBU7VO//7vowL/5v89Nvn7/ecYwjzm9+rvf/7rBMLf3dA23fS/p/ZVfca///nr8Oeowd9TO+Xv - /+fyv74/9J//+j8AAAD//wMAofreDN4gAAA= + H4sIAAAAAAAAA1SaWQ+zurKm7/evWFq39FGYgu11xxTCaBMghLRaLUjIACGEyYCPzn9vJd/W6e6b + SBBLIFyuet636j//9ddff7dFVV7Gv//56+/Xcxj//h/fe9d8zP/+56//+a+//vrrr//8/f5/K8um + KK/X5/v+W/778/m+lsvf//zF//ed/7von7/+PmbVZVL8NOq75Xm+QFX92PguSYv30VUvgoxu3Ela + CM8oFpUQnp1qoc58vhrztpd0dBIcjea8eSzYzjRcpAQvk/haewSz/cEpEC8NpYao3MH0KAIM33wN + yOXQYMDPMJ/hvmlM4nJTBCSUBRx0rp4/NYg+4oVmYw5rzsVEN3e0nxPLNKHNqQ5xorkD86HeyErU + byUsddrYL1tdS5BcyWd61JgaC/cPm2FgS8001oebsZrow8GjygE8ZV7nzadIfUJDMQE19IsfS+o1 + zNFplQty0c+9NwJtvqArzlwaJ/HI5pmFGUz2vkZSI6vjlfdbFWAFPKnmCa4nLetOhK/bQyJ5Xhge + DcVPDm7XtJpm38EVa/VHi1TWEnrVP3OxcJdmQFlwP9BIsA/GHJNTAj1xvNBbeImA5EyHFknlsKXY + Stp+XfpChNfgVZAi7e1C8h6sRLfNHZAMnDjAKDfo8HWrJOKDTdSLpXNOYXxwXvTk5Xks4LOoQ0vs + Impi62HMe6G+o8CTAcHM5eIJb90WQls9kNBXKrDaSMWIXOUPyfmDZkhzDGZYpZNDvV7dGPS73+Cu + N4ep35dmL/bmw0VzuZNp5HpGz8/bPQ+91Q9pyU6uJ6WfR4mysTSoHgpaLB7lUEebVMTUVLW5Em52 + OsPLfdrQHU6bYp7mNEVIsQJiBLumZwLa5qhmpft9/s2QpvuZA9qNQrJT2rWfJfmKAW5bSE+XMYrZ + sr/WcLPVG3pQgq5fUn/04YYNPTnuh7ZaPQxtoPRhSDPekcDC3ZYO+Ru9pH4SDtUsmKAEifzqCEGD + aqz8sctAF98s6suz7Um7CWQAG2dC/UOD2SSqDg9ZMK/U/bQWEAPch7CLrxbdr3VcLeFrLVFnS4R4 + 5KoZa9GHFiJZPdGLfwiNFe2sDuV7l1K85S/GDPrGBp52o9PWDWogJAaYgM8e/cR2VuctdBQsKMvF + a5JUP46FTN3rcL3v39RVT6nHdpKwIuG23inp2whQvXFkRVn5lVw0QauE5+cpo3rOBHJkeR+LyqZV + QV+qE3F3Q8JYJGZ3cF5Enhj6ZYiXcpMk6K6YNdXBOlXDztu5MFTajpQXyvoZTXcfiQelx5/27RcS + FtcIPSqhJFGrPXqxG6wLmp16IDao3X7lmr4DaBuXhKTHwhvqIEvBUb5icj7JV7CKUBRBdQQ5Ud3Q + MiTrdXfRqsKaHO0wiik3Xju4fHKd2kc6F4LuhzU8gntFjtUlKqRaUTnkCrk2CX0xgXUDWAq75l5M + n+wTFx8YIx9eo+0JgwCIfXPm+xSKC38lIZrPPT/sRKhI22QmGPa7nm1OgglNkovEIlPZD0+3jKBu + oo7cdNUs1uE8i+gonLc0OvgbbxZzw0ZFUd2o4RY2mI/vjwU96irUGaHn8XttUUDx6FziXncqo9ar + tWExNTxxd1u1F4pHbAOHFiu1wYlj807XZ/SJX89JJKArZp5WAwjmkzLVqhb29PLqFXR/jAnBivYG + S3EMRDi3lBICdD0WLXSwgNTEBdUc9+OxS3jB8A5PLrVkzvd4383vcLjuKqp+iieb3XhcIY2sM+at + HhvjRtASOO7yhqhBGRQr11QtGJSVkqR73w2JuksO5Uo5UzPefGIWlLcWntTJwNKw19iCz5wKE9Me + yHXRQibw27aTvRWHk2iORrEkvmgh0XnkNOWsm8GexiWC2fBJSC63JZj8EWPwjJeQYhy/vOko7Cw0 + HxxMQ3Mq+tnTQhtd+sIgqu/gXmCemMKGFgXxm0FhK7o+QgS6447qwrYp+s19U8L54GHi6NguJOGV + rvDkZXuC9599IWpIEeE2O51oIO1NxjOMB+AgkyOafY6BtC2UFrqnQSCHdVPFi4DnHH74xMBilMFi + WBsFw86qP7SwGwks28SyINzca3rZTw6Qwu5iKsD/bGk2haSYD7Uko3ifXon6XF4ee2cuhvDWrPh5 + 13cGa7ayCWGHP8RzToG3rkcQQuf1vNLLWXsAcZEeNnJ3JSF2e9obUw3PCTxehRPJKl5lzKBmiA6D + pBP/m286Lk/TLbapS3yULBXrT20JRXh/ED86mR7vaB8VTnR3mICwI0CKIOGAhqPddPdeNRBl+OIg + CC4tOfn0HDNuNyfoUUklMV/RI572syIrO+r7dKcxtZifadUgPuhSohfipZfipGshJyoXikN/rD5v + LuTRQfBjagZHj0ki4XzY8uWNeJn6LiaZVTX0fe2ECzz0bH3l1QWdgnak5PZJ46Xs/A56g5zQ5Bqb + QNJCxYfxASYkUy9ZL5zHJUF7dnnQg7IkhnTiFhv1CvDwxtFuYO7fgwjVeLrifnh+2DxGfgRmsJjU + mA2pWkeqPmE3HysaeH5aLLHUpOhNjgEtrsfGWHk1mtDmcdmR8IMlY72+YQSySCuxgKK5WC7sJEMJ + 5jtyNmXLYGeujRAftCkWb/ZkrP6IfTBRWSUFCY/xEgY3EQSL+6G701Vg8zeewKDMlF4t0yjW4Ql8 + aLzFI3F8Sa/4ZXfEqNjinlrzhlbrwqc+tO35Qs9z07IVHWKImpqeqPXuE2+e2CNC4j7kaUGuD2+x + usOKKB8tVN36s7FsdSdB7/PGpNZtW8Wr0oUpaPnLjZy8t+UtiEsa+M33BLdO5S1a2fioOEdb6jH/ + HY8zyV0oF5VNHXr4gD/58cOqhOyylWfdBrAElcuWo1Ze1/1CizlBF8NgxLT2ZsUkdH8qj0sIiS9a + XdGNZhZCGLU2DT7M6YVYar7r+Se59e3K1iRlKrx2V5kEm85l/66felR+66cJeE4d1R+/TZKK7J7v + y66ErR7uadQMz561YhJCpvL2xDUGBtO13MpwQNqRGrdJ7SXbhx0wrrgkZBIVj84XzgZdL6ZYidXZ + G9AzkmFaXI4kEJWzMRinKgW/72nGmPeWW1JbsNnVCUl149UzOZojtFY2JLrDnQDTnxyE5/3BI+Et + Xb0ZSKCESzGIdLeKGli1tndhb10C4oz0Fs9lHkaoijt+eo4j501NNYqw3HxOlJy6LVuKR+EqwQFL + lKTPBtQ34Zb/3n/iqvPBG+huMmFSWyVxnAut6MhBGZ45VpKA2nY8X2Xg//iF6OnkVNLmLpXoy+/U + PMoLmzs1zBCujhqWW7eIpZpkAzxRYTeNJjV79lDOItR38xkv+4Parw0FNvTxAVHzI3Zs5Mciga8w + eROVynxFq3qJfnyKpSeziimsOkvBOZqp+iB5NW/Nw4De2punznRP+uX4CifIbR2detvPthiT0NHh + bssbxJlGNV7b4ZYCoqkBSZtKM2brFJWwfs0cvaBk6Zejcs9++WiCYLNWi79+nvCzRBPdsWDTr64+ + 8X/4xKswjoXJajt4WF1usp/ijVGVXl0wGrZKD9UrBtJTwrJyXQWHalBq45662+zHY3hNp09P9UaT + Ube4EbU+KjNmspSWogqy8MuP8RJeZAt4p+ZMd/rpA9gnaXLYF7NNYpk5hbAr/Qsc5LtDyld0rtbS + whlEuV1Qa5lNwNsnpwZXtVvo7qDWHqsNL4N5bfi4/daDIXTmGZUoW8j59Lh7U28+7N81xRfKqrWX + ixkuoGupj+HBYJz+yqDuWQm1HEEsfnoPWL71mcD3e6/f+ggwmA7E+XgCGw9At9GPF3X7czbmdMcw + Et5HHZcXGldLP5U8NC5nSvVKOHttHe9XGOrHlu63+BCvCrxa0Lj6JQ3nj+ux+DqqcHHKkOiwMo35 + QmcRZfviTV2D6ICHadAo3/0n5+Yhe9MiPVyUWOeIejYzeun2ilX0rS+TOBeNwQZBuUO5eNiYx5bm + SdEmUv7w5A4rlE1vHERwvSsp3T/1g0fts8NB6q9X+tNHv/gCah9xxCu7Giyp0YmQflqF7hpr9lbR + MX00HSMX13x0Lr78ysNsM3xoHHEY0FrkVHiNB4vGs3GqpE2h6tB43vMJzkvfs3aTdiC9rweipQe5 + oiFLS1jN1YFgIYyq1SE7HV7k+I6f7as3vu8HQaM/N8Q/aw82dfSQ/anfqbSr+mUilQqlWheweOBX + xszwk8BC3X6o/nBlYz7vnin68iEepl4qWLlXOXi7VDwhnHFli9uMlrzU5p5anns0ZnXbWVA4mhca + RkSvpi9/AOXe+MQrWFEsxDyWCNwongAeerDu3/MTfaYgI1E5Wt7MDucO0lOe0CD2IiCFm37+1euJ + +bICVqXLEli1kBIdyU82VLLdoFDOTaoNdm7QRnzZ0E/ylrj0QY0l9V8+fNNwQ47p8cmYbD05dHaT + +zQ/Llaxbo92C6/jI6Cq70z9emfyBON3RCgR7hJrzWpu0VcfEn1M1YLvj60Jx7t+Jeo0PyrJN8I7 + EiJtj0HPqZ7YyMIdlUfjQb75Jl6M92kCE18E9JuPi+Uythf006P7/M0ZU+omItQ8fybpMptMNJWb + Dp/4llBXcZ2CzyuYQS+pTKJ9+bEPsrsPOK2l+NX6dfzdrxT6rOqnOdnei4WAQoXoMSCqf/P9+nhH + TxgkkT9txENgLE9B9qEf2TrJY3U2plF2FeX1fAcYCtuyWvdFj0FvT4zsl4ta8SKNeOgKmUZu8kkr + BItdXLgPb/kkfPPfDKbFgjQyz6RQuMwQNX4uUT8He7JXUVsx8zmZ0DwTjnoBSCv+IVhP+KvvZpfz + Rf3qnzkasuaEBZxaxSJgOf/DP6YRAoOCerChq7oJMQR4A0M2RzWUYLYjxzGR4uX8qHVQHsUQI5nI + 3ijUtgnV8VGTeHuKwCw/YQujo7ubNujGF4xxeggbhddoJF4pYP3pXoJCGzJymeTKWMP7zYZabz/p + dWNp8aw8qxk8U0PEMtPMXhy1XQL12/s+CaL5+fLQeQb3WzlO3JaH3rLUDxE55gLx4rgfYwwvsgnW + fOmpf8421RS74wSio70j5xF6hsj4IIWLlQMsw+2nYl6HRCg9VkLU93HqWXkJO/jzd4LLDccz59Ac + NCJC0+y9TCD5fq7At9k7eHZ52g/0wDJlLvcyNS9PUv3xwxK0fRGtr+eY0X1zgRs5kKgniDtD4vfm + BMOsjIg+Okk1XHdqjYYKY3Ix3HuxsDrk0KY/5iT81tdF+VxX6Fwdn+KV3asZx6b6q3eYHT6hwbY1 + UsFXD06rw0mgK/j8AkuUL8RcWvCtH/EKH/g943d8FhkbE78DCwIYw69fMp8KV5G7dtG+/prnrbDh + 7yh23ga1Hd+Nv/6hDXdsPJPdqdP6hbttWwi5UZge6tswVnsrhNssEv/NPwvgxhLeteVJ9skkAKY2 + 0Ywe0J/ILtvc+/Ew8Twq7lLx8xfZigVpgtLbdDFrKPTqCVb/9nuCbS2CLw+HENVrNF3alXrLmHER + jDl+JrksuNV0K1ILEiNihHz1vvgJ+ByaJBNJwkfbn1+VQNxkKdHRPuiXWdFzZG6528Rk9onXKchb + +JmvG2KdnKWi12XBoPacPbGiDMaTPb455XGJIGZGvSlY0A1w23lyTYo+cIGAQ9BCcDRc6u5sr1ir + nE8QUUn55WO3WMj+nv/8NOJkH/aFaadF5QK4r96CfZtXMAdCEo9Ei/mmWvgxTv74GWbHXcESnkcb + 6ptHhOH7GhpMtjoI3e02oH4obPs/vH+8KyNeQ+ERi0ctv0D2DBbqT/uArRa5ykCfUx2LMjcY8+bp + qmBe9ip+PPmyWsSP9ISx32b09Msf0NIwCuaj8vVvg37ZCE4KqynsiBP5j2J+Bh//p2/x6uRTvJpW + naGv/qSG3KBqfHOZuPV93iIxZ9vGsNh3F91NXiJfPeatb20DgXOSDsQ5JfyPV3hYpYND99KxBTM0 + bRHeou5GncjX4vUWxQP68WnwfOnFWm+tDD4gnsjey5V4DU6vDjkSM6jNp1XPOtHL4fbsWtT0LlU8 + G3rRgh/P7rvXyVjzVM1hbe9W6sivXfx6Xq8NYKeXQlXJAl8/TlPg7ZpU03IcqbdsT2cVbsRZJcnW + wJVoW3qH0FuVqWHUt2LdmGgFjy206WX/KL1ZpDkP9qx8TNuHf47nJa8VmOfY/OmnXkSHmAN1a9Q0 + OB7ehvj1c+EgPx3iN9umYnDLRLgYx4YYy+vpffVzC8EC3nS3v0H2mUnkwldpqtTMP29jsTaPEuRv + 60Gdh38u5lNk3+GPN1UTvOPZvQELWoNaUEe8Ld54lK4XqId5SL3ucqzYj6e6lml4KfQRfG7eY0Df + +kXM107op9V5wR9/T5s7d4zHXzxiMBzI3ns3BssvSg6/fjlJvdMdzMOOg3AiHCbOV0+vRZ9ZcFXo + dUIb61HQ8rwbfnqa3vZbv+fLPAzheeF5eu1OzKBJ886g5ZsfEt+me7+49F3Cr59NMZZwJb2ywoTD + q+Sp3RxWxsjRz8FlKjMsRUJQzEqaXuCX5/E8zVo/oGcug85qPjgpuB0YNy3UAYw6G4MNDWL+SHAJ + n4zqk4wfH2OZfeLDD58aWDh1j2o+54oKs6PJiLrZZf/+vvJdML98sYk7rqm6rQFPD/r1Q71FO1Yi + VNQ2//YD2mL9mKsOHcX4EOeBcPWHZ57nnYclrpq9VXqnISz2akwu8mwby+c1ptvg0+yxkG3Unu+j + owvLo/agbjE/wJq6FxF+/Wbi1rFtfP2QEIbS+ziJeW32lC08BOR5Fum+OlEwCd1qgs2j3JHdKj7A + Cq6ri6gz2pOssGMx74XhDiTwTr9+ZglWXK0cpBwnEI/5+2I+8KGLvudnEsbkFLOmO4kQ7XcWcWdx + Mr79CP/Xv/leY298cZ8JWo6R0GBZG2M9yqEKsta1KTkErfHTryC7rdL0yx+zduIx/PqpGH3Pw594 + 22kPjTrkWfUsf/Hrz+8hqjkVFdtLtfzzY6f9sNfAYhxzCLMFhZjdpnu1SsfzE1zVdqH2XvKKZX+Y + ItiEvkf0VtP6KcBVhL56kITP+hy/XEpLqGzYhfq3Mq6+/a0Mfv0ocs2bF/iTD4/n1COBTDKPxcmz + hcdz4pGynkJj9WpDRIdj+CB6ZrRVez69LfDz777xCaZbVAzibjpf6e3sm73ob4s7vBgaIypNeo9S + d8mQ3Q6YmkoWxKI/Wt9+SQepfTKJMWyeuor2TW3SfGd7sXRIwFP5+IY6cRKIK1F0TAw3MpGI961H + LE66Dtwv54xY7543xut1kyjESmws3IM3oN/8Ag7+Npxe3/jupTbkob1HI9W+/ZjuEekieF+Cjuw0 + do/pJDy6P/0FSw5ExpKPkirwmvjU5pXAWPwcK0Dr3eckk91asel+4H76cOLUQo2F8eU1UODJje7S + rV9Iu4llcBuMd4LV4h4P/p40QOCDGzFOT7mYj++HiUy99PHGvIrxdOIh/vE0JdTYAT6MmI0yG6dE + Z6Tt5/OJWuBbn2gRai5Yp/SoI8fu1AkI4ssYPr3aIi24pRM/3O2Kfvtz4NsfoOZm9A369TPB93zT + 2HyqlRQcj+H2q5+oV5ADEJObkEH/M1XUIsAtfvwjDyfWEkL6a7ESHGXQb7c8/eojMP/4mqfaBm+c + VTBmmqP1jx+7f4VZQX0jeyo7fOunQ1+H8bKONYb7VuzI/noAbPIKp4Tv01Ti5cmX/VyeggQ+RJkR + 76jExp/4/9YbHAvwxv74DfWcC189ohbCzfsMEKxDSiJtAwqmybc7rNHxNsn7WGM//YmQxjyKV72P + F/p4r+AUdCN1kDMx9njp9u/8EHLnjsUiCwCC734Qu3WLYvj5rwto2y8/toaQG44J5sSvyF6dIZtv + gWL+6W/8+pGL8b5N0FHEDM/+mHlSp4crYnJaUPWrB6X0erHhCp7+Nz6GmK43GSv49XpPm9nbeuz1 + uF5+/glR626sll9/yNk1PbHjD/SWIgtX1OrRnviNvRTje1+EkHvUDxJE+bVaragVIdw860kub7r3 + 8/egdhsh0dtX/4d/4S/ePZXfG+vqyzK8V0j59duAWMNDgiC8EapZ8aZfvzwIM26rYOQhr+Ib9Hah + 4FOPYOV1KJbXfnAVV88UcqW2XayWFT8Reusy8fcPzlt2Npwht/X0aXnyXLVeeaeBXx6YlLje9j/e + RX//pgL+619//fW/fhMGTXstX9/BgLFcxv/471GB/5D+Y2jy1+vPGMI05Pfy73/+PYHw96dvm8/4 + v8e2Lt/D3//8tf0zavD32I756/+5/a/vg/7rX/8HAAD//wMAcKb2hd4gAAA= headers: Access-Control-Allow-Origin: - "*" Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9ce9191e1679-SJC + - 9854b39edc2beb36-SJC Connection: - keep-alive Content-Encoding: @@ -3457,19 +3457,19 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:29 GMT + - Fri, 26 Sep 2025 18:07:34 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=LyN1hbKDIbQb2Bq4SQ.TUVN89s_OjxFyVy8.FBcmu0o-1758846209-1.0.1.1-BsEQQd3H7GMqSoHZk7pAr3hR1AGIbqiXJYaKixhCxrXS68yUD_.xclsjM90LFLjwQrlOfI734PgV7G5GYZSwwSy5hqOMpY1UC0iwquDf0ks; - path=/; expires=Fri, 26-Sep-25 00:53:29 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=nvwbbN5vM4JlBRTEUwkcQgGIGsbfMyubdQnkY6ybNZk-1758910054-1.0.1.1-0k11MQpWYkBqfckWP3_cSdevtBU3POcac75_M55KEHcEFoXw9YxToK0XXtJIDdhBTfEkWeCoRIPjkWSoom1coIpgr0VKyH5z_HjmFD0A3xI; + path=/; expires=Fri, 26-Sep-25 18:37:34 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=y9tWbtLnr79wzxEWm0DQ_pGYpDungTYpTuNnejQUICw-1758846209673-0.0.1.1-604800000; + - _cfuvid=aO9a33_V8Vje9WP1qBebbfSDRKULQMhYRbOu2D1Y_AY-1758910054393-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Transfer-Encoding: - chunked Via: - - envoy-router-867c855bff-hcbpq + - envoy-router-7464cfc689-rnmbl X-Content-Type-Options: - nosniff alt-svc: @@ -3481,7 +3481,7 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "99" + - "128" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: @@ -3489,7 +3489,7 @@ interactions: strict-transport-security: - max-age=31536000; includeSubDomains; preload x-envoy-upstream-service-time: - - "151" + - "147" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3505,7 +3505,7 @@ interactions: x-ratelimit-reset-tokens: - 0s x-request-id: - - req_dd75b078adda44e89220f4216efc1b48 + - req_0befd9be88944ebfad2801f2630f5abb status: code: 200 message: OK @@ -3515,81 +3515,77 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 25-28: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n2021, - 25, 1315\u20131360.\\n\\n\\n (9) Wellawatte, G. P.; Seshadri, A.; White, A. - D. Model agnostic generation of counter-\\n\\n factual explanations for - molecules. Chemical Science 2022, 13, 3697\u20133705.\\n\\n\\n(10) Gandhi, H. - A.; White, A. D. Explaining structure-activity relationships using locally\\n\\n - \ faithful surrogate models. chemrxiv 2022,\\n\\n\\n(11) Gormley, A. J.; - Webb, M. A. Machine learning in combinatorial polymer chemistry.\\n\\n Nature - Reviews Materials 2021,\\n\\n\\n(12) Gomes, C. P.; Fink, D.; Dover, R. B. V.; - Gregoire, J. M. Computational sustainability\\n\\n meets materials science. - Nature Reviews Materials 2021,\\n\\n\\n(13) On scientific understanding with - artificial intelligence. Nature Reviews Physics 2022\\n\\n 4:12 2022, 4, - 761\u2013769.\\n\\n\\n(14) Arrieta, A. B.; D\xB4\u0131az-Rodr\xB4\u0131guez, - N.; Ser, J. D.; Bennetot, A.; Tabik, S.; Barbado, A.;\\n\\n Garcia, S.; - Gil-Lopez, S.; Molina, D.; Benjamins, R.; Chatila, R.; Herrera, F. Explain-\\n\\n - \ able Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities - and Chal-\\n\\n lenges toward Responsible AI. Information Fusion 2019, 58, - 82\u2013115.\\n\\n\\n(15) Murdoch, W. J.; Singh, C.; Kumbier, K.; Abbasi-Asl, - R.; Yu, B. Interpretable machine\\n\\n learning: definitions, methods, and - applications. ArXiv 2019, abs/1901.04592.\\n\\n\\n 25(16) - Boobier, S.; Osbourn, A.; Mitchell, J. B. Can human experts predict solubility - better\\n\\n than computers? Journal of cheminformatics 2017, 9, 1\u201314.\\n\\n\\n(17) - Lee, J. D.; See, K. A. Trust in automation: Designing for appropriate reliance. - Human\\n\\n Factors 2004, 46, 50\u201380.\\n\\n\\n(18) Bolukbasi, T.; Chang, - K.-W.; Zou, J. Y.; Saligrama, V.; Kalai, A. T. Man is to com-\\n\\n puter - programmer as woman is to homemaker? debiasing word embeddings. Advances\\n\\n - \ in neural information processing systems 2016, 29.\\n\\n\\n(19) Buolamwini, - J.; Gebru, T. Gender Shades: Intersectional Accuracy Disparities in\\n\\n Commercial - Gender Classification. Proceedings of the 1st Conference on Fairness,\\n\\n - \ Accountability and Transparency. 2018; pp 77\u201391.\\n\\n\\n(20) Lapuschkin, - S.; W\xA8aldchen, S.; Binder, A.; Montavon, G.; Samek, W.; M\xA8uller, K.-R.\\n\\n - \ Unmasking Clever Hans predictors and assessing what machines really learn. - Nature\\n\\n communications 2019, 10, 1\u20138.\\n\\n\\n(21) DeGrave, A. - J.; Janizek, J. D.; Lee, S.-I. AI for radiographic COVID-19 detection\\n\\n - \ selects shortcuts over signal. Nature Machine Intelligence 2021, 3, 610\u2013619.\\n\\n\\n(22) - Goodman, B.; Flaxman, S. European Union regulations on algorithmic decision-\\n\\n - \ making and a \u201Cright to explanation\u201D. AI Magazine 2017, 38, 50\u201357.\\n\\n\\n(23) - ACT, A. I. European Commission. On Artificial Intelligence: A European Approach\\n\\n - \ to Excellence and Trust. 2021, COM/2021/206.\\n\\n\\n(24) Blueprint for - an AI Bill of Rights, The White House. 2022; https://www.whitehouse.\\n\\n gov/ostp/ai-bill-of-rights/.\\n\\n\\n(25) - Miller, T. Explanation in artificial intelligence: Insights from the social - sciences. Ar-\\n\\n tificial intelligence 2019, 267, 1\u201338.\\n\\n\\n\\n - \ 26(26) Murdoch, W. J.; Singh, C.; Kumbier, - K.; Abbasi-Asl, R.; Yu, B. Definitions, meth-\\n\\n ods, and applications - in interpretable machine learning. Proceedings of the National\\n\\n Academy - of Sciences of the United States of America 2019, 116, 22071\u201322080.\\n\\n\\n(27) - Gunning, D.; Aha, D. DARPA\u2019s Explainable Artificial Intelligence (XAI) - Program.\\n\\n AI Magazine 2019, 40, 44\u201358.\\n\\n\\n(28) Biran, O.; - Cotton, C. Explanation and justification in machine learning: A survey.\\n\\n - \ IJCAI-17 workshop on explainable AI (XAI). 2017; pp 8\u201313.\\n\\n\\n(29) - Palacio, S.; Lucieri, A.; Munir, M.; Ahmed, S.; Hees, J.; Dengel, A. Xai handbook:\\n\\n - \ Towards a unified framework for explainable ai. Proceedings of the IEEE/CVF - Inter-\\n\\n national Conference on Computer Vision. 2021; pp 3766\u20133775.\\n\\n\\n(30) - Kuhn, D. R.; Kacker, R. N.; Lei, Y.; Simos, D. E. Combinatorial Methods for - Ex-\\n\\n plainable AI. 2020 IEEE International Conference on Software Testing, - Verification\\n\\n and Validation Workshops (ICSTW) 2020, 167\u2013170.\\n\\n\\n(31) - Seshadri, A.; Gandhi, H. A.; Wellawatte, G. P.; White, A. D. Why does that molecule\\n\\n - \ smell? ChemRxiv 2022,\\n\\n\\n(32) Das, A.; Rad, P. Opportunities and challenges - in explainable artificial intelligence\\n\\n (xai): A survey. arXiv preprint - arXiv:2006.11371 2020,\\n\\n\\n(33) Machlev, R.; Heistrene, L.; Perl, M.; Levy, - K. Y.; Belikov, J.; Mannor, S.; Levron, Y.\\n\\n Explainable Artificial - Intelligence (XAI) techniques for energy and power systems:\\n\\n Review, - challenges and opportunities. Energy and AI 2022, 9, 100169.\\n\\n\\n(34) Koh, - P. W.; Liang, P. Understanding black-box predictions via influence functions.\\n\\n - \ International Conference on Machine Learning. 2017; pp 1885\u20131894.\\n\\n\\n(35) - Ribeiro, M. T.; Singh, S.; Guestrin, C. \u201D Why should i trust you?\u201D - Explaining the\\n\\n predictions of any classifier. Proceedings of the 22nd - ACM SIGKDD international\\n\\n\\n 27 conference - on knowledge discovery and data \\n\\n------------\\n\\nQuestion: What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 3-5: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n a passive + characteristic of a model, whereas explainability\\n\\nis an active characteristic + which is used to clarify the internal decision-making process.\\n\\nNamely, + an explanation is extra information that gives the context and a cause for one + or\\n\\nmore predictions.29 We adopt the same nomenclature in this perspective.\\n\\n + \ Accuracy and interpretability are two attractive characteristics of DL models. + However,\\n\\nDL models are often highly accurate and less interpretable.28,30 + XAI provides a way to avoid\\n\\nthat trade-off in chemical property prediction. + XAI can be viewed as a two-step process.\\n\\nFirst, we develop an accurate + but uninterpretable DL model. Next, we add explanations to\\n\\npredictions. + Ideally, if the DL model has correctly learned the input-output relations, then\\n\\nthe + explanations should give insight into the underlying mechanism.\\n\\n In the + remainder of this article, we review recent approaches for XAI of chemical property\\n\\nprediction + while drawing specific examples from our recent XAI work.9,10,31 We show how\\n\\nin + various systems these methods yield explanations that are consistent with known + and\\n\\nmechanisms in structure-property relationships.\\n\\n\\n\\n\\n\\n 3Theory\\n\\n\\nIn + this work, we aim to assemble a common taxonomy for the landscape of XAI while\\n\\nproviding + our perspectives. We utilized the vocabulary proposed by Das and Rad 32 to classify\\n\\nXAI. + According to their classification, interpretations can be categorized as global + or local\\n\\ninterpretations on the basis of \u201Cwhat is being explained?\u201D. + For example, counterfactuals are\\n\\nlocal interpretations, as these can explain + only a given instance. The second classification is\\n\\nbased on the relation + between the model and the interpretation \u2013 is interpretability post-hoc\\n\\n(extrinsic) + or intrinsic to the model?.32,33 An intrinsic XAI method is part of the model\\n\\nand + is self-explanatory32 These are also referred to as white-box models to contrast + them\\n\\nwith non-interpretable black box models.28 An extrinsic method is + one that can be applied\\n\\npost-training to any model.33 Post-hoc methods + found in the literature focus on interpreting\\n\\nmodels through 1) training + data34 and feature attribution,35 2) surrogate models10 and, 3)\\n\\ncounterfactual9 + or contrastive explanations.36\\n\\n Often, what is a \u201Cgood\u201D explanation + and what are the required components of an ex-\\n\\nplanation are debated.32,37,38 + Palacio et al. 29 state that the lack of a standard framework\\n\\nhas caused + the inability to evaluate the interpretability of a model. In physical sciences,\\n\\nwe + may instead consider if the explanations somehow reflect and expand our understanding\\n\\nof + physical phenomena. For example, Oviedo et al. 39 propose that a model explanation\\n\\ncan + be evaluated by considering its agreement with physical observations, which + they term\\n\\n\u201Ccorrectness.\u201D For example, if an explanation suggests + that polarity affects solubility of a\\n\\nmolecule, and the experimental evidence + strengthen the hypothesis, then the explanation\\n\\nis assumed \u201Ccorrect\u201D. + In instances where such mechanistic knowledge is sparse, expert bi-\\n\\nases + and subjectivity can be used to measure the correctness.40 Other similar metrics + of\\n\\ncorrectness such as \u201Cexplanation satisfaction scale\u201D can be + found in the literature.41,42 In a\\n\\nrecent study, Humer et al. 43 introduced + CIME an interactive web-based tool that allows the\\n\\nusers to inspect model + explanations. The aim of this study is to bridge the gap between\\n\\nanalysis + of XAI methods. Based on the above discussion, we identify that an agreed upon\\n\\n\\n + \ 4evaluation metric is necessary in XAI. + We suggest the following attributes can be used to\\n\\nevaluate explanations. + However, the relative importance of each attribute may depend on\\n\\nthe application + - actionability may not be as important as faithfulness when evaluating the\\n\\ninterpretability + of a static physics based model. Therefore, one can select relative importance\\n\\nof + each attribute based on the application.\\n\\n\\n \u2022 Actionable. Is it + clear how we could change the input features to modify the output?\\n\\n\\n + \ \u2022 Complete. Does the explanation completely account for the prediction? + Did features\\n\\n not included in the explanation really contribute zero + effect to the prediction?44\\n\\n\\n \u2022 Correct. Does the explanation + agree with hypothesized or known underlying physical\\n\\n mechanism?39\\n\\n\\n + \ \u2022 Domain Applicable. Does the explanation use language and concepts + of domain ex-\\n\\n perts?\\n\\n\\n \u2022 Fidelity/Faithful. Does the + explanation agree with the black box model?\\n\\n\\n \u2022 Robust. Does the + explanation change significantly with small changes to the model or\\n\\n instance + being explained?\\n\\n\\n \u2022 Sparse/Succinct. Is the explanation succinct?\\n\\n\\n + \ We present an example evaluation of the SHAP explanation method based on the + above\\n\\nattributes.44 Shapley values were proposed as a local explanation + method based on feature\\n\\nattribution, as they offer a complete explanation + - each feature i\\n\\n------------\\n\\nQuestion: What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" headers: accept: - application/json @@ -3598,7 +3594,7 @@ interactions: connection: - keep-alive content-length: - - "6231" + - "6020" content-type: - application/json host: @@ -3630,25 +3626,26 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//dFTBbiM3DL37Kwhd0gLjwDYSx/XNKdLGPXa7aNp6YdASZ4aNRpqInCRu - kH8vNOPETrt78Rh6fI+PFKmXEYBhZ5ZgbI1qm9aPf/zl4e76z9ubn26vd5Pb64dPj/Wen/SPp58/ - PcxNkRlx9zdZfWOd29i0npRjGGCbCJWy6vTqcrG4mM8mP/RAEx35TKtaHV/E8WwyuxhPp+PZ5ECs - I1sSs4S/RgAAL/1vthgcPZslTIq3k4ZEsCKzfA8CMCn6fGJQhEUxqCmOoI1BKfSuXzYBYGOkaxpM - +41Zwsb8VhPQs6XUKngWFXjExLETSFRSomAp//W5MtAIN8+tRw648wSrpFyyZfSwDkrec5Xj4bu7 - 1fr7AjhY3zkOFZSxCw5zp9DDU0z3UoB06ZH2UgAGB9i2nm0fIcABHJd9cgUXG+Qg57BWaCgMETYG - S61KAYrPMcSG6SBka/SeQkW9zN1qXQAKPJH3+Sst2ewYRDvHJBAD0FtB7Fn3mdWgrTkQeMIUOFTZ - q60z38YuKKUSrXboB2oYXPcFpVihEvT3fTDkaMcouQlKtg780JGcw8o5Htrh9wWwfmx21XnUmPZQ - Jmyo7xd4vifQmuDm85nAWeKq1nwfJx7O+oQ55vealeA2dkJnAqs1XLP3EEv4NdOkgKeabQ3UtDUK - /zMIc9PGpJgvMJagCYO0mD3tB93Uieb2rNYge1Fq5HxjimGkEnl6zNSt2Jgoj9biAHVCbssNViT5 - uEQvtAmvpyOaqOwE84aEzvsTAEOIOvQ3L8eXA/L6vg4+Vm2KO/kP1ZQcWOptIpQY8uiLxtb06OsI - 4Eu/dt2HTTJtik2rW4331KebzheLQdAcN/0Enr2hGhX9CbCYzouvSG4dKbKXk901Fm1N7jTn/OK9 - COwcxyM2GZ3U/n9LX5Mf6udQnah8U/4I2Lxc5LZtIsf2Y9nHsET5NfxW2Huve8NGKD2ypa0ypXwf - jkrs/PBQmWGctiWHilKbeHitynZ7OZ/uZpdXV25nRq+jfwEAAP//AwA4T/1ptgUAAA== + H4sIAAAAAAAAAwAAAP//dFTBbiM3DL37KwidEsA2bK+zTnIL2i0atCh6KNpg64VBSxwPG42kihxv + 3CD/XmjGdiZt9jKY0SPf46OGfB4BGHbmFoytUW2T/OS7n1bXX5H2v3z++Ps/v/7QrD5fz3xe6cPy + jx8XZlwy4vYvsnrKmtrYJE/KMfSwzYRKhXW+urq+mc9mV8sOaKIjX9J2SSfLOFnMFsvJfD5ZzI6J + dWRLYm7hzxEAwHP3LCUGR0/mFmbj00lDIrgjc3sOAjA5+nJiUIRFMagZv4I2BqXQVf28DgBrI23T + YD6szS2szaen5JEDbj3BXVau2DJ6uA9K3vOOgiW4eLi7v4RMFWUBjdCQ1tEJYHCQcrQkQgJao4L1 + mLk6gNYEHJRyQA+OLAvHMGnwkcMOYgV399B1RcaQMCvb1mP2B3BECTxhDiXw4vufL89xX2u2NWAm + iJVSALS2zagE21bBk0gvmDJpcTOFh7t74LCPfk8CjvbkYyqsOMjtyDsjWhdO50oElaYELFfbGWYV + SJkc2/NRynHPrpgU3tXaicfOdhscZX8oPA3ZGgNLI301p8ZZDLDtGpRLvoULIV9NTrIxH46uLyFm + oKdzWIqikzratxViSp7JAVZKGTQjl+5dTuHTHn2LWkp50wxUzbxtlQQ8PxJg5wu37FkPYzj+2BRI + pHzlTFb7Dxcb5NAr2nNCxY76txy3rRxjS1eltZZDnz2F32oSGqrX5BM4UsoNB+ra93eLharLbrUT + KD/MG8McYI+ZYyunSrrzcZGrAQVsTQ1b9OWaEmU9DK5vujbjfhAyedpjsLQRGzOVgZjP1uFlOD6Z + qlawTG9ovR8AGELUXrcM7pcj8nIeVR93Kcet/CfVVBxY6k0mlBjKWIrGZDr0ZQTwpVsJ7ZspNynH + JulG4yN1cvP56qYnNK9baAAvr4+oRkU/AD4sVuN3KDeOFNnLYK8Yi7YmN9S8WpxNYOs4vmKz0cD7 + /0t6j773z2E3YPkm/StgLSUlt3m9z/fCMpVN/a2wc6+7go1Q3rOljTLlch+OKmx9v0SNHESp2VQc + dmW3cL9Jq7RxNxV9vFp9oK0ZvYz+BQAA//8DAIWGGxZSBgAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9ceacb38f98b-SJC + - 9854b3a06c604f08-SJC Connection: - keep-alive Content-Encoding: @@ -3656,14 +3653,14 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:32 GMT + - Fri, 26 Sep 2025 18:07:36 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=4Sbdz1TSSAcQIrtdaVZBCj8wYAbeEdzhkoCkduoNrOo-1758846212-1.0.1.1-B5htphEaL_7wJvkGDn6p7D6JRT4zLvCrHope4ZhwjR0pxqrqwIhD5Rxz9l1MJKos5uLClbyv2xgiQbVpK9U2E6CrmODnBuBuZBAA0D.Sx_A; - path=/; expires=Fri, 26-Sep-25 00:53:32 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=6EiViPhqekwy.9FFVYLKL6HTuwOXldN82T6IWQy9Hso-1758910056-1.0.1.1-OepT.AvaSu3uI72XfSJyEkni4PMmGeSbdkyofBPPgCY8NExHQ1Qt97UZ62MlDzp9py38b6PIXhhBTL7lGiL8kMlMzl397MlYWwlfgj9HqLc; + path=/; expires=Fri, 26-Sep-25 18:37:36 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=qW7XmWbpUhLvmLr4MeWgeq1p2pWeSpwnW3CwFRThl98-1758846212064-0.0.1.1-604800000; + - _cfuvid=NE13XNiGnBChyyAOeAbQXK9t9P3Z1eIEgL1E1s9E6Ms-1758910056435-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Strict-Transport-Security: - max-age=31536000; includeSubDomains; preload @@ -3678,13 +3675,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2265" + - "1857" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2284" + - "1879" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3694,13 +3691,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998517" + - "29998562" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_1d53c5dab753458c957f02ae24c23c3c + - req_62a7684dfb224f77859ebf666d168958 status: code: 200 message: OK @@ -3710,79 +3707,79 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 20-22: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nnal - molecule. The counterfactual indicates\\nstructural changes to ethyl benzoate - that would result in the model predicting the molecule\\nto not contain the - \u2018fruity\u2019 scent. The Tanimoto96 similarity between the counterfactual - and\\n2,4 decadienal is also provided. Republished with permission from authors.31\\n\\n\\n - \ The molecule 2,4-decadienal, which is known to have a \u2018fatty\u2019 scent, - is analyzed in Fig-\\n\\nure 5.142,143 The resulting counterfactual, which has - a shorter carbon chain and no carbonyl\\n\\ngroups, highlights the influence - of these structural features on the \u2018fatty\u2019 scent of 2,4 deca-\\n\\ndienal. - To generalize to other molecules, Seshadri et al. 31 applied the descriptor - attribution\\n\\nmethod to obtain global explanations for the scents. The global - explanation for the \u2018fatty\u2019\\n\\nscent was generated by gathering - chemical spaces around many \u2018fatty\u2019 scented molecules.\\n\\nThe resulting - natural language explanation is: \u201CThe molecular property \u201Cfatty scent\u201D - can\\n\\nbe explained by the presence of a heptanyl fragment, two CH2 groups - separated by four\\n\\n\\n 20bonds, and - a C=O double bond, as well as the lack of more than one or two O atoms.\u201D31\\n\\nThe - importance of a heptanyl fragment aligns with that reported in the literature, - as \u2018fatty\u2019\\n\\nmolecules often have a long carbon chain.144 Furthermore, - the importance of a C=O dou-\\n\\nble bond is supported by the findings reported - by Licon et al. 145, where in addition to a\\n\\n\u201Clarger carbon-chain skeleton\u201D, - they found that \u2018fatty\u2019 molecules also had \u201Caldehyde or acid\\n\\nfunctions\u201D.145 - For the \u2018pineapple\u2019 scent, the following natural language explanation - was ob-\\n\\ntained: \u201CThe molecular property \u201Cpineapple scent\u201D - can be explained by the presence of ester,\\n\\nethyl/ether O group, alkene/ether - O group, and C=O double bond, as well as the absence of\\n\\nan Aromatic atom.\u201D31 - Esters, such as ethyl 2-methylbutyrate, are present in many pineap-\\n\\nple - volatile compounds.146,147 The combination of a C=O double bond with an ether - could\\n\\nalso correspond to an ester group. Additionally, aldehydes and ketones, - which contain C=O\\n\\ndouble bonds, are also common in pineapple volatile compounds.146,148\\n\\n\\nDiscussion\\n\\n\\nWe - have shown two post-hoc XAI applications based on molecular counterfactual expla-\\n\\nnations9 - and descriptor explanations.10 These methods can be used to explain black-box\\n\\nmodels - whose input is a molecule. These two methods can be applied for both classification\\n\\nand - regression tasks. Note that the \u201Ccorrectness\u201D of the explanations - strongly depends on\\n\\nthe accuracy of the black-box model.\\n\\n A molecular - counterfactual is one with a minimal distance from a base molecular, but\\n\\nwith - contrasting chemical properties. In the above examples, we used Tanimoto similar-\\n\\nity96 - of ECFP4 fingreprints97 as distance, although this should be explored in the - future.\\n\\nCounterfactual explanations are useful because they are represented - as chemical structures\\n\\n(familiar to domain experts), sparse, and are actionable. - A few other popular examples of\\n\\ncounterfactual on graph methods are GNNExplainer, - MEG and CF-GNNExplainer.69,104,105\\n\\n The descriptor explanation method - developed by Gandhi and White 10 fits a self-explaining\\n\\n\\n\\n 21surrogate - model to explain the black-box model. This is similar to the GraphLIME87 method,\\n\\nalthough - we have the flexibility to use explanation features other than subgraphs. Futher-\\n\\nmore, - we show that natural language combined with chemical descriptor attributions - can\\n\\ncreate explanations useful for chemists, thus enhancing the accessibility - of DL in chemistry.\\n\\nLastly, we examined if XAI can be used beyond interpretation. - Work by Seshadri et al. 31 use\\n\\nMMACE and surrogate model explanations to - analyze the structure-property relationships\\n\\nof scent. They recovered known - structure-property relationships for molecular scent purely\\n\\nfrom explanations, - demonstrating the usefulness of a two step process: fit an accurate model\\n\\nand - then explain it.\\n\\n Choosing among the plethora of XAI methods described - here is still an open question.\\n\\nIt remains to be seen if there will ever - be a consensus benchmark, since this field sits on\\n\\nthe intersection of - human-machine interaction, machine learning, and philosophy (i.e., what\\n\\nconstitutes - an explanation?). Our current advice is to consider first the audience \u2013 - domain\\n\\nexperts or ML experts or non-experts \u2013 and what the explanations - should accomplish. Are\\n\\nthey meant to inform data selection or model building, - how a prediction is used, or how the\\n\\nfeatures can be changed to affect - the outcome. The second consideration is what access you\\n\\nhave to the underlying - model. The ability to have model derivatives or propagate gradients\\n\\nto - the input to models informs the XAI method.\\n\\n\\nConclusion and outlook\\n\\n\\nWe - should seek to explain molecular property prediction models because users are - more\\n\\nlikely to trust explained predictions, and explanations can help assess - if the model is learning\\n\\nt\\n\\n------------\\n\\nQuestion: What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 25-28: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n2021, + 25, 1315\u20131360.\\n\\n\\n (9) Wellawatte, G. P.; Seshadri, A.; White, A. + D. Model agnostic generation of counter-\\n\\n factual explanations for + molecules. Chemical Science 2022, 13, 3697\u20133705.\\n\\n\\n(10) Gandhi, H. + A.; White, A. D. Explaining structure-activity relationships using locally\\n\\n + \ faithful surrogate models. chemrxiv 2022,\\n\\n\\n(11) Gormley, A. J.; + Webb, M. A. Machine learning in combinatorial polymer chemistry.\\n\\n Nature + Reviews Materials 2021,\\n\\n\\n(12) Gomes, C. P.; Fink, D.; Dover, R. B. V.; + Gregoire, J. M. Computational sustainability\\n\\n meets materials science. + Nature Reviews Materials 2021,\\n\\n\\n(13) On scientific understanding with + artificial intelligence. Nature Reviews Physics 2022\\n\\n 4:12 2022, 4, + 761\u2013769.\\n\\n\\n(14) Arrieta, A. B.; D\xB4\u0131az-Rodr\xB4\u0131guez, + N.; Ser, J. D.; Bennetot, A.; Tabik, S.; Barbado, A.;\\n\\n Garcia, S.; + Gil-Lopez, S.; Molina, D.; Benjamins, R.; Chatila, R.; Herrera, F. Explain-\\n\\n + \ able Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities + and Chal-\\n\\n lenges toward Responsible AI. Information Fusion 2019, 58, + 82\u2013115.\\n\\n\\n(15) Murdoch, W. J.; Singh, C.; Kumbier, K.; Abbasi-Asl, + R.; Yu, B. Interpretable machine\\n\\n learning: definitions, methods, and + applications. ArXiv 2019, abs/1901.04592.\\n\\n\\n 25(16) + Boobier, S.; Osbourn, A.; Mitchell, J. B. Can human experts predict solubility + better\\n\\n than computers? Journal of cheminformatics 2017, 9, 1\u201314.\\n\\n\\n(17) + Lee, J. D.; See, K. A. Trust in automation: Designing for appropriate reliance. + Human\\n\\n Factors 2004, 46, 50\u201380.\\n\\n\\n(18) Bolukbasi, T.; Chang, + K.-W.; Zou, J. Y.; Saligrama, V.; Kalai, A. T. Man is to com-\\n\\n puter + programmer as woman is to homemaker? debiasing word embeddings. Advances\\n\\n + \ in neural information processing systems 2016, 29.\\n\\n\\n(19) Buolamwini, + J.; Gebru, T. Gender Shades: Intersectional Accuracy Disparities in\\n\\n Commercial + Gender Classification. Proceedings of the 1st Conference on Fairness,\\n\\n + \ Accountability and Transparency. 2018; pp 77\u201391.\\n\\n\\n(20) Lapuschkin, + S.; W\xA8aldchen, S.; Binder, A.; Montavon, G.; Samek, W.; M\xA8uller, K.-R.\\n\\n + \ Unmasking Clever Hans predictors and assessing what machines really learn. + Nature\\n\\n communications 2019, 10, 1\u20138.\\n\\n\\n(21) DeGrave, A. + J.; Janizek, J. D.; Lee, S.-I. AI for radiographic COVID-19 detection\\n\\n + \ selects shortcuts over signal. Nature Machine Intelligence 2021, 3, 610\u2013619.\\n\\n\\n(22) + Goodman, B.; Flaxman, S. European Union regulations on algorithmic decision-\\n\\n + \ making and a \u201Cright to explanation\u201D. AI Magazine 2017, 38, 50\u201357.\\n\\n\\n(23) + ACT, A. I. European Commission. On Artificial Intelligence: A European Approach\\n\\n + \ to Excellence and Trust. 2021, COM/2021/206.\\n\\n\\n(24) Blueprint for + an AI Bill of Rights, The White House. 2022; https://www.whitehouse.\\n\\n gov/ostp/ai-bill-of-rights/.\\n\\n\\n(25) + Miller, T. Explanation in artificial intelligence: Insights from the social + sciences. Ar-\\n\\n tificial intelligence 2019, 267, 1\u201338.\\n\\n\\n\\n + \ 26(26) Murdoch, W. J.; Singh, C.; Kumbier, + K.; Abbasi-Asl, R.; Yu, B. Definitions, meth-\\n\\n ods, and applications + in interpretable machine learning. Proceedings of the National\\n\\n Academy + of Sciences of the United States of America 2019, 116, 22071\u201322080.\\n\\n\\n(27) + Gunning, D.; Aha, D. DARPA\u2019s Explainable Artificial Intelligence (XAI) + Program.\\n\\n AI Magazine 2019, 40, 44\u201358.\\n\\n\\n(28) Biran, O.; + Cotton, C. Explanation and justification in machine learning: A survey.\\n\\n + \ IJCAI-17 workshop on explainable AI (XAI). 2017; pp 8\u201313.\\n\\n\\n(29) + Palacio, S.; Lucieri, A.; Munir, M.; Ahmed, S.; Hees, J.; Dengel, A. Xai handbook:\\n\\n + \ Towards a unified framework for explainable ai. Proceedings of the IEEE/CVF + Inter-\\n\\n national Conference on Computer Vision. 2021; pp 3766\u20133775.\\n\\n\\n(30) + Kuhn, D. R.; Kacker, R. N.; Lei, Y.; Simos, D. E. Combinatorial Methods for + Ex-\\n\\n plainable AI. 2020 IEEE International Conference on Software Testing, + Verification\\n\\n and Validation Workshops (ICSTW) 2020, 167\u2013170.\\n\\n\\n(31) + Seshadri, A.; Gandhi, H. A.; Wellawatte, G. P.; White, A. D. Why does that molecule\\n\\n + \ smell? ChemRxiv 2022,\\n\\n\\n(32) Das, A.; Rad, P. Opportunities and challenges + in explainable artificial intelligence\\n\\n (xai): A survey. arXiv preprint + arXiv:2006.11371 2020,\\n\\n\\n(33) Machlev, R.; Heistrene, L.; Perl, M.; Levy, + K. Y.; Belikov, J.; Mannor, S.; Levron, Y.\\n\\n Explainable Artificial + Intelligence (XAI) techniques for energy and power systems:\\n\\n Review, + challenges and opportunities. Energy and AI 2022, 9, 100169.\\n\\n\\n(34) Koh, + P. W.; Liang, P. Understanding black-box predictions via influence functions.\\n\\n + \ International Conference on Machine Learning. 2017; pp 1885\u20131894.\\n\\n\\n(35) + Ribeiro, M. T.; Singh, S.; Guestrin, C. \u201D Why should i trust you?\u201D + Explaining the\\n\\n predictions of any classifier. Proceedings of the 22nd + ACM SIGKDD international\\n\\n\\n 27 conference + on knowledge discovery and data \\n\\n------------\\n\\nQuestion: What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" headers: accept: - application/json @@ -3791,7 +3788,7 @@ interactions: connection: - keep-alive content-length: - - "6209" + - "6061" content-type: - application/json host: @@ -3823,25 +3820,25 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//fFRNb9s4EL37Vwx4lg3btZvEt7RYYF1ggT30EKAujBE5kthQpMoZunGD - /PeCkj/UbrIXAeIj5703X88TAGWN2oDSDYpuOzf9+On7w4ftP657WPz97+rTfP5ztdLrI7O5N0dV - 5Beh/EZazq9mOrSdI7HBD7COhEI56uJmfXu7er+c3/VAGwy5/KzuZLoK0+V8uZouFtPl/PSwCVYT - qw18mQAAPPffLNEbelIbmBfnk5aYsSa1uVwCUDG4fKKQ2bKgF1VcQR28kO9VP+88wE5xaluMx53a - wE493G8LCBH+euocWo+lI7iPYiurLTrYeiHnbE1eUwGRKooMEqAlaYJhQG9ASDfefk/EkJhMD+Mj - gTQEXSRjdc4RQ6igdKgfp2V4gj4nDNYLxS6S9MQ5WvKGYnZh8tEMtr4P1Nt4khykDY50chihi6Gj - KMcRTQEP99uLPGcfaXRfh5T5KtSS0AFlzx4HdZnbEOtoOwnxDyzSxRoNiYIm/Dib6N2OnWIZkoBu - qLUa3VmmJZ7Bx/+RYP0huANBa71t0QFLTFpSRAe6QV/TkPrBzvCDTiiOuQv40VhHb1pJTMApxlCj - 0NmAhKzxYA2Bx4HQoa8T1kNNLk6uUacl5oRYz7ZuhGfwuSGmS+LJN+g1gcTEUgBqTcy2tM7KsRjq - LP1PLmgu2LkdwIQWrT+VridmiUcojyeJ1teAvddLy/zeRGO7s50qhqaP5OiQJe1Zh0i5+e9OUK7s - 3rZYE+fjCh3Tzr+MhyhSlRjzDPvk3AhA74MMXHl8v56Ql8vAulB3MZT8x1NVWW+52UdCDj4PJ0vo - VI++TAC+9osh/Tbrqouh7WQv4ZF6usXybj0EVNddNILf3ZxQCYJuBKzeLYtXQu4NCVrHo+2iNOqG - zJjz9rqNMBkbrth8MvL+X0mvhR/8W1+PorwZ/gpoTZ2Q2V8b/7VrkfK+fuvaJde9YMUUD1bTXizF - XA9DFSY3rFLFRxZq95X1de40O+zTqtuv3y/K5frmxpRq8jL5BQAA//8DAFVxNU1YBgAA + H4sIAAAAAAAAAwAAAP//dFRdbyI5EHznV7T8snfSgCAJkPDG7u0HinQ6nXJStMsKGbtnphePbdk9 + JKMo//1kTwKzXy+AXN3lqqLbTyMAQVqsQKhasmq8Gb+7XV4//P3eVx9v727/Mws57RZT9+Hy8Ont + zWdRpA63/4aKX7smyjXeIJOzPawCSsbEOlvOr29m0+n8KgON02hSW+V5fOXGF9OLq/FsNr6YvjTW + jhRGsYIvIwCAp/yZJFqNj2IF0+L1pMEYZYVidSoCEMGZdCJkjBRZWhbFGVTOMtqs+mlrAbYitk0j + Q7cVK9iKuxoBHxUGz2AocoSjDOTaCAFLDGgVpp8mOQN28P7RG0lW7g3COjCVpEga2FhGY6hK9fDH + /XrzZwFklWk12QpK11otU1LSwIMLh1hAbMMRu1iAtBqk94ZUrohAFjSV+XIG7RpJNk5gw9Cg7SsO + 2IGXHkPM3clioH2bwcSsapAR/lr/+8/6TYT79QZ8cFWQzelacBYwWbH50kzzrY3ZT39CFhqparII + BmWwZKteaxlkg9kElC70LK+BbCZwV2PEYXg1VbWhqmbgGoEa7wLLFJMrszSySd2RclIcpI1eptYu + JcgYfECWezLEXS+AQxs5ta03ELvI2KQUIzygMembOP4UaElodARDBwRVY0ORQ1cAWgxVN2CxGgJW + rZHsQgd5wimJncAn94BHDEV28Tox2mEE6/jFAYIETQEVg8aSLOUke5+TrSj6+Qto8JhId1G5gGkO + l1v7PBzagGUbZdoZ2xozAKS1jntbaV2+viDPpwUxrvLB7eMPrSKpifUuoIzOpmWI7LzI6PMI4Gte + xPa73RI+uMbzjt0B83WzxXzWE4rz7g/gi+ULyo6lGQDL5XXxC8qdRpZk4mCbhZKqRj3onV8uTiZk + q8mdselo4P1nSb+i7/2TrQYsv6U/A0qhZ9Q7H1CT+t72uSxgeh9/V3bKOgsWEcORFO6YMKT/Q2Mp + W9M/XaIfyF1JtkoLQP37VfqdvilxMV9e4l6Mnkf/AwAA//8DAOmbmq7IBQAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9ceacc1c251d-SJC + - 9854b3a05b94fa7e-SJC Connection: - keep-alive Content-Encoding: @@ -3849,14 +3846,14 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:32 GMT + - Fri, 26 Sep 2025 18:07:36 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=btjcecByNsb3gfe5DWKv_0vVKQGwdNppOhiu2Izqb1Q-1758846212-1.0.1.1-nEzohRMl7plTDUZHa0_G5rgYvf.oT76CxB2o4AItWhu9wmTjlPUaSeEfOyLBwyFqqu9RK29clif39lhUrWoHN2tiFwm1vLey_sJ1zQsGKTw; - path=/; expires=Fri, 26-Sep-25 00:53:32 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=e84RcFMRr7f0XHCV03mG047wtodhkUB_vLWOY2eEa_Q-1758910056-1.0.1.1-GMUhhIVOcp5_003q7U1n_l78oDkYyWdOw4x0XRCCNfShAJzy1ieVsKTYPnDDuGj2VUytiWGAmu9_EV_.jpA.3Cf6c8.ICGWj8DPUrG7HNxc; + path=/; expires=Fri, 26-Sep-25 18:37:36 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=.Jf9vANf3h7JP1UI6d.RzTLji3qwKXvDajnR4c_21cc-1758846212339-0.0.1.1-604800000; + - _cfuvid=BF4SaVobgxZD2L.SvQcShh7zLstDs4iQmssE6S6dxfc-1758910056710-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Strict-Transport-Security: - max-age=31536000; includeSubDomains; preload @@ -3871,13 +3868,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2522" + - "2104" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2544" + - "2126" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3887,13 +3884,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998512" + - "29998558" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_8492315ecdf54559b5f6845f3f1aa86a + - req_bd152ca4ed1f405187eaf6d6b8d9074c status: code: 200 message: OK @@ -3903,79 +3900,77 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 3-5: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n - a passive characteristic of a model, whereas explainability\\n\\nis an active - characteristic which is used to clarify the internal decision-making process.\\n\\nNamely, - an explanation is extra information that gives the context and a cause for one - or\\n\\nmore predictions.29 We adopt the same nomenclature in this perspective.\\n\\n - \ Accuracy and interpretability are two attractive characteristics of DL models. - However,\\n\\nDL models are often highly accurate and less interpretable.28,30 - XAI provides a way to avoid\\n\\nthat trade-off in chemical property prediction. - XAI can be viewed as a two-step process.\\n\\nFirst, we develop an accurate - but uninterpretable DL model. Next, we add explanations to\\n\\npredictions. - Ideally, if the DL model has correctly learned the input-output relations, then\\n\\nthe - explanations should give insight into the underlying mechanism.\\n\\n In the - remainder of this article, we review recent approaches for XAI of chemical property\\n\\nprediction - while drawing specific examples from our recent XAI work.9,10,31 We show how\\n\\nin - various systems these methods yield explanations that are consistent with known - and\\n\\nmechanisms in structure-property relationships.\\n\\n\\n\\n\\n\\n 3Theory\\n\\n\\nIn - this work, we aim to assemble a common taxonomy for the landscape of XAI while\\n\\nproviding - our perspectives. We utilized the vocabulary proposed by Das and Rad 32 to classify\\n\\nXAI. - According to their classification, interpretations can be categorized as global - or local\\n\\ninterpretations on the basis of \u201Cwhat is being explained?\u201D. - For example, counterfactuals are\\n\\nlocal interpretations, as these can explain - only a given instance. The second classification is\\n\\nbased on the relation - between the model and the interpretation \u2013 is interpretability post-hoc\\n\\n(extrinsic) - or intrinsic to the model?.32,33 An intrinsic XAI method is part of the model\\n\\nand - is self-explanatory32 These are also referred to as white-box models to contrast - them\\n\\nwith non-interpretable black box models.28 An extrinsic method is - one that can be applied\\n\\npost-training to any model.33 Post-hoc methods - found in the literature focus on interpreting\\n\\nmodels through 1) training - data34 and feature attribution,35 2) surrogate models10 and, 3)\\n\\ncounterfactual9 - or contrastive explanations.36\\n\\n Often, what is a \u201Cgood\u201D explanation - and what are the required components of an ex-\\n\\nplanation are debated.32,37,38 - Palacio et al. 29 state that the lack of a standard framework\\n\\nhas caused - the inability to evaluate the interpretability of a model. In physical sciences,\\n\\nwe - may instead consider if the explanations somehow reflect and expand our understanding\\n\\nof - physical phenomena. For example, Oviedo et al. 39 propose that a model explanation\\n\\ncan - be evaluated by considering its agreement with physical observations, which - they term\\n\\n\u201Ccorrectness.\u201D For example, if an explanation suggests - that polarity affects solubility of a\\n\\nmolecule, and the experimental evidence - strengthen the hypothesis, then the explanation\\n\\nis assumed \u201Ccorrect\u201D. - In instances where such mechanistic knowledge is sparse, expert bi-\\n\\nases - and subjectivity can be used to measure the correctness.40 Other similar metrics - of\\n\\ncorrectness such as \u201Cexplanation satisfaction scale\u201D can be - found in the literature.41,42 In a\\n\\nrecent study, Humer et al. 43 introduced - CIME an interactive web-based tool that allows the\\n\\nusers to inspect model - explanations. The aim of this study is to bridge the gap between\\n\\nanalysis - of XAI methods. Based on the above discussion, we identify that an agreed upon\\n\\n\\n - \ 4evaluation metric is necessary in XAI. - We suggest the following attributes can be used to\\n\\nevaluate explanations. - However, the relative importance of each attribute may depend on\\n\\nthe application - - actionability may not be as important as faithfulness when evaluating the\\n\\ninterpretability - of a static physics based model. Therefore, one can select relative importance\\n\\nof - each attribute based on the application.\\n\\n\\n \u2022 Actionable. Is it - clear how we could change the input features to modify the output?\\n\\n\\n - \ \u2022 Complete. Does the explanation completely account for the prediction? - Did features\\n\\n not included in the explanation really contribute zero - effect to the prediction?44\\n\\n\\n \u2022 Correct. Does the explanation - agree with hypothesized or known underlying physical\\n\\n mechanism?39\\n\\n\\n - \ \u2022 Domain Applicable. Does the explanation use language and concepts - of domain ex-\\n\\n perts?\\n\\n\\n \u2022 Fidelity/Faithful. Does the - explanation agree with the black box model?\\n\\n\\n \u2022 Robust. Does the - explanation change significantly with small changes to the model or\\n\\n instance - being explained?\\n\\n\\n \u2022 Sparse/Succinct. Is the explanation succinct?\\n\\n\\n - \ We present an example evaluation of the SHAP explanation method based on the - above\\n\\nattributes.44 Shapley values were proposed as a local explanation - method based on feature\\n\\nattribution, as they offer a complete explanation - - each feature i\\n\\n------------\\n\\nQuestion: What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 20-22: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nnal molecule. + \ The counterfactual indicates\\nstructural changes to ethyl benzoate that would + result in the model predicting the molecule\\nto not contain the \u2018fruity\u2019 + scent. The Tanimoto96 similarity between the counterfactual and\\n2,4 decadienal + is also provided. Republished with permission from authors.31\\n\\n\\n The + molecule 2,4-decadienal, which is known to have a \u2018fatty\u2019 scent, is + analyzed in Fig-\\n\\nure 5.142,143 The resulting counterfactual, which has + a shorter carbon chain and no carbonyl\\n\\ngroups, highlights the influence + of these structural features on the \u2018fatty\u2019 scent of 2,4 deca-\\n\\ndienal. + To generalize to other molecules, Seshadri et al. 31 applied the descriptor + attribution\\n\\nmethod to obtain global explanations for the scents. The global + explanation for the \u2018fatty\u2019\\n\\nscent was generated by gathering + chemical spaces around many \u2018fatty\u2019 scented molecules.\\n\\nThe resulting + natural language explanation is: \u201CThe molecular property \u201Cfatty scent\u201D + can\\n\\nbe explained by the presence of a heptanyl fragment, two CH2 groups + separated by four\\n\\n\\n 20bonds, and + a C=O double bond, as well as the lack of more than one or two O atoms.\u201D31\\n\\nThe + importance of a heptanyl fragment aligns with that reported in the literature, + as \u2018fatty\u2019\\n\\nmolecules often have a long carbon chain.144 Furthermore, + the importance of a C=O dou-\\n\\nble bond is supported by the findings reported + by Licon et al. 145, where in addition to a\\n\\n\u201Clarger carbon-chain skeleton\u201D, + they found that \u2018fatty\u2019 molecules also had \u201Caldehyde or acid\\n\\nfunctions\u201D.145 + For the \u2018pineapple\u2019 scent, the following natural language explanation + was ob-\\n\\ntained: \u201CThe molecular property \u201Cpineapple scent\u201D + can be explained by the presence of ester,\\n\\nethyl/ether O group, alkene/ether + O group, and C=O double bond, as well as the absence of\\n\\nan Aromatic atom.\u201D31 + Esters, such as ethyl 2-methylbutyrate, are present in many pineap-\\n\\nple + volatile compounds.146,147 The combination of a C=O double bond with an ether + could\\n\\nalso correspond to an ester group. Additionally, aldehydes and ketones, + which contain C=O\\n\\ndouble bonds, are also common in pineapple volatile compounds.146,148\\n\\n\\nDiscussion\\n\\n\\nWe + have shown two post-hoc XAI applications based on molecular counterfactual expla-\\n\\nnations9 + and descriptor explanations.10 These methods can be used to explain black-box\\n\\nmodels + whose input is a molecule. These two methods can be applied for both classification\\n\\nand + regression tasks. Note that the \u201Ccorrectness\u201D of the explanations + strongly depends on\\n\\nthe accuracy of the black-box model.\\n\\n A molecular + counterfactual is one with a minimal distance from a base molecular, but\\n\\nwith + contrasting chemical properties. In the above examples, we used Tanimoto similar-\\n\\nity96 + of ECFP4 fingreprints97 as distance, although this should be explored in the + future.\\n\\nCounterfactual explanations are useful because they are represented + as chemical structures\\n\\n(familiar to domain experts), sparse, and are actionable. + A few other popular examples of\\n\\ncounterfactual on graph methods are GNNExplainer, + MEG and CF-GNNExplainer.69,104,105\\n\\n The descriptor explanation method + developed by Gandhi and White 10 fits a self-explaining\\n\\n\\n\\n 21surrogate + model to explain the black-box model. This is similar to the GraphLIME87 method,\\n\\nalthough + we have the flexibility to use explanation features other than subgraphs. Futher-\\n\\nmore, + we show that natural language combined with chemical descriptor attributions + can\\n\\ncreate explanations useful for chemists, thus enhancing the accessibility + of DL in chemistry.\\n\\nLastly, we examined if XAI can be used beyond interpretation. + Work by Seshadri et al. 31 use\\n\\nMMACE and surrogate model explanations to + analyze the structure-property relationships\\n\\nof scent. They recovered known + structure-property relationships for molecular scent purely\\n\\nfrom explanations, + demonstrating the usefulness of a two step process: fit an accurate model\\n\\nand + then explain it.\\n\\n Choosing among the plethora of XAI methods described + here is still an open question.\\n\\nIt remains to be seen if there will ever + be a consensus benchmark, since this field sits on\\n\\nthe intersection of + human-machine interaction, machine learning, and philosophy (i.e., what\\n\\nconstitutes + an explanation?). Our current advice is to consider first the audience \u2013 + domain\\n\\nexperts or ML experts or non-experts \u2013 and what the explanations + should accomplish. Are\\n\\nthey meant to inform data selection or model building, + how a prediction is used, or how the\\n\\nfeatures can be changed to affect + the outcome. The second consideration is what access you\\n\\nhave to the underlying + model. The ability to have model derivatives or propagate gradients\\n\\nto + the input to models informs the XAI method.\\n\\n\\nConclusion and outlook\\n\\n\\nWe + should seek to explain molecular property prediction models because users are + more\\n\\nlikely to trust explained predictions, and explanations can help assess + if the model is learning\\n\\nt\\n\\n------------\\n\\nQuestion: What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" headers: accept: - application/json @@ -3984,7 +3979,7 @@ interactions: connection: - keep-alive content-length: - - "6190" + - "6039" content-type: - application/json host: @@ -4016,27 +4011,26 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA3RUTW8bOQy9+1cQOiWAHdhuPn1L2wCbxQK95BBgXRgciePhRiMpIseJEeS/LzRj - O85uezE8fHwkHynybQRg2JkFGNug2jb5ybc/nx+/Vt0L3ny9fqDnm7uH8x/f72Yv3+o/fjybcWHE - 6h+yumed2dgmT8oxDLDNhEol6uzq4vr6/HI+vemBNjryhbZOOjmPk/l0fj6ZzSbz6Y7YRLYkZgF/ - jwAA3vrfUmJw9GoWMB3vLS2J4JrM4uAEYHL0xWJQhEUxqBl/gDYGpdBX/bYMAEsjXdti3i7NApbm - 7jV55ICVJ7jNyjVbRg/3Qcl7XlOwBCePt/enkKmmLKARWtImOgEMDpRsE/i5IwFtUKHFJwJtCBxZ - Fo5h0uIThzWkHC2JkECs4fYe+qbIGBJmZdt5zH4LjiiBJ8yhUE6+/3V68OOglFMm7UstqbvgKBe9 - rpjO4PH2Hjhsot+QAIK+xIkopX3mBdScRcfgaEM+ppIBA6C1XUYlqDqFLnxO0ycfD0IbCoDOFRqV - pgUso+8bwiqQMjm2vekM7o4dUo4bdgT9JF61j2axK62oYz4mjiHWNeWSgoPwulEpumPf0F6u3xaw - JdtgYGllUL0fiMUAFRVKLnwLJ0K+nuzLjXm7a+cpxAz0enBLUXTSRPtZGabkmRxgrZRBM3IZy+kZ - 3G3Qd6illM9NV81cdUoCnp8IsJeFFXvW7Rh2C0OBRMpXzmR1+HCxRQ5DRnsg1Oxo+Jdj1cnOt/RP - Oms5DOwzeGhI6Dh7Qz4Bltcmfe+eOyxxhmejffTyDD+p5QAbzBw72ZcxDHNpxsPeZPK0wWBpJTZm - Kvszm+6wTsituMU1SbHX6IWW4f14ETPVnWC5A6Hz/gjAEKIOycoJ+LlD3g9L7+M65VjJf6im5sDS - rDKhxFAWXDQm06PvI4Cf/XHpPt0Lk3Jsk640PlGfbjafXQ4Bzcc9O4Ivr3aoRkV/BHy5/jL+RciV - I0X2cnShjEXbkDvizi7mBxHYOY4f2HR0pP3/Jf0q/KCfw/ooym/DfwDWUlJyq4/9+5VbpnLzf+d2 - 6HVfsBHKG7a0UqZc5uGoxs4P59jIVpTaVc1hXS4MDze5TquLy1k1v7i6cpUZvY/+BQAA//8DAISs - Uu+cBgAA + H4sIAAAAAAAAAwAAAP//fFRNbxs3EL3rVwx46UUSJNmqbN3cIEWMHoKgReEgCoQRObvLmktuOUNZ + guH/HnBXWq1TuxcJ2Md58958PY8AlDVqDUpXKLpu3OTDH6ubpz8//T27+k1/Lr98veJV8ynaw+8P + HxdJjXNE2P1DWs5RUx3qxpHY4DtYR0KhzDpfLW9u57PZ8roF6mDI5bCykcl1mCxmi+vJfD5ZzE6B + VbCaWK3h2wgA4Ln9zRK9oYNaw2x8/lITM5ak1v0jABWDy18UMlsW9KLGF1AHL+Rb1c8bD7BRnOoa + 43Gj1rBRD3f3YwgRPh4ah9bjzhHcRbGF1RYd3Hsh52xJXtMYIhUUGSRATVIFw4DegJCuvP03EUNi + Mi2MjwRSETSRjNW5RgyhgJ1D/TjZhQO0NWGwXig2kaRNnNmSNxSzC5M/TeHet0StjYNkkjo40slh + hCaGhqIcB2nG8HB338tz9pEG73VIOV+BWhI6oOzZY6cu5zbEOtpGQvwJi9Rbo65QUIUnYIlJS4ro + oCCUFIkHAinbK1zKtTtLtcTASVeADKzJyxQ+/I8o6/fB7Qlq622NDnSFvqS2A9in+YV7IdQiTiiC + FT6Xhcwg/RieKuvoXa+JCTjFGEoUOrdJQmbYW0PgsTPs0JcJy65puqLaanQD1skOc8WsZ1tWwlP4 + qyKmvjPkK8x1kZhYusYz7qyzcswlzD08TwiYUKP1p262qVjiEXbHPGfWl6+mDNv/fpxeD1gR4okt + m6YoPN2ocbcWkRzts6Yt6xApr8ftxr8MdylSkRjzKvvk3ABA74N0Bcxb/P2EvPR760LZxLDjn0JV + Yb3lahsJOfi8oyyhUS36MgL43t6H9GrlVRND3chWwiO16eaL5U1HqC4naQBfrU6oBEE3BG6X4zco + t4YErePBkVEadUVmEDtfLnoTmIwNF2w2Gnj/r6S36Dv/1pcDlnfpL4DW1AiZ7aX/bz2LlM/2e8/6 + WreCFVPcW01bsRRzPwwVmFx3URUfWajeFtaXeahsd1aLZmtuC/p1ubqinRq9jH4AAAD//wMAWIpn + tV8GAAA= headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9ceacc327af2-SJC + - 9854b3a06e1ffa36-SJC Connection: - keep-alive Content-Encoding: @@ -4044,14 +4038,14 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:32 GMT + - Fri, 26 Sep 2025 18:07:36 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=YZtGK7Pe3.jz4c9_seUkqK.L1X_UdPWXmH2XCaHADEE-1758846212-1.0.1.1-pc9sDfCvHOxIqYZtRg5pkUq8rr18K4CSQ5x4uwUpZmh4Bt6bXjFgOy87H7htV1V8s7JR8VeQ6KTv3A9MtPeXUCwG9.7FvxZDEYkio4P7xcU; - path=/; expires=Fri, 26-Sep-25 00:53:32 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=h.D1xTuRNqXbiBPsxDZ8vLapq4HQEdoAcX_xRLvXwNM-1758910056-1.0.1.1-fSrPck4.NuVrAw_7sRlHWXzWfpPgePj1U4j4giyqjklYhHPrALBMY4pajmX187nSNpCPKlYq._KIwthzIG11FVYcaN7xV9Fd39Go2wLhnTo; + path=/; expires=Fri, 26-Sep-25 18:37:36 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=t_sRF7DXsEoNalRMornpbifaKwgAuAXGbaoBUHG0rYw-1758846212471-0.0.1.1-604800000; + - _cfuvid=YLGUErfCMe.RtlX0MPZM1ToaTKl4EJfSO8SRnnLQbR0-1758910056808-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Strict-Transport-Security: - max-age=31536000; includeSubDomains; preload @@ -4066,13 +4060,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2627" + - "1906" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2645" + - "1926" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -4082,13 +4076,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998521" + - "29998555" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_40aeacf4812e4475aec0803ffe532db2 + - req_010b2ffc272f4538acd769568b285cf1 status: code: 200 message: OK @@ -4098,15 +4092,13 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 1-3: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n - A Perspective on Explanations of Molecular\\n\\n Prediction Models\\n\\n\\nGeemi + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 1-3: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n A Perspective + on Explanations of Molecular\\n\\n Prediction Models\\n\\n\\nGeemi P. Wellawatte,\u2020 Heta A. Gandhi,\u2021 Aditi Seshadri,\u2021 and Andrew\\n\\n \ D. White\u2217,\u2021\\n\\n\\n \u2020Department of Chemistry, University of Rochester, Rochester, NY, 14627\\n\\n\u2021Department @@ -4181,7 +4173,7 @@ interactions: connection: - keep-alive content-length: - - "6215" + - "6045" content-type: - application/json host: @@ -4213,26 +4205,27 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//dFRNb9s4EL37Vwx4qQ3Yge3ETepbtttDuh/nAOtCoMmRNAlF0hwyiRLk - vxeUZFvdthcB4ps38+bzbQIgSIstCFXLqBpvFp+/Hu7/qNLn1eHTa/t6+PLg//GvX/9Vhg/rJOaZ - 4fYPqOKRdaFc4w1GcraHVUAZMXtdXW9ubq4+rpefOqBxGk2mVT4urtxivVxfLVarxXo5EGtHClls - 4b8JAMBb980SrcYXsYXl/PjSILOsUGxPRgAiOJNfhGQmjtJGMT+DytmItlP9trMAO8GpaWRod2IL - O/HlxRtJVu4Nwm2IVJIiaeDORjSGKrQKYXp/ezcDYpBQEhoNpVOJUYOz4IN7Ik22ArIRgw8YZS4J - g7QaMHu3w0PpAmhEDwZlsJky/fPvGXTVAR9Qk+oM5yC1DsicTWKN8GFvpHpc7N3LB7AypoDgyoww - 9my+gPvbO5DUMEQHaGuZdceQOHY6Ess9GYot7FtwZYmhV8xU1ZGzdAfPdQtyUNPIR2RgjyoXZCzu - Av7CFpSzCn3H7CKTVSZpHNVgCDfN+jVWATvNdWqkhWQ1htwofTRz5TH0bA4Pibs+DGWbHpK0kXJZ - nxAajIEUAyfvXYg5jV5yl+yzC7Emi8yz+c8NmGpkFcj3f64cUj5nB8+SoZEaZ31BicHLEEklI4Np - gZocU9qY8+5GgcHQI4KqsSGOoZ3Dc40BRylmhRxDUrlvCx+cxxBbCGh6VTV5BiUtVIk0Qt1613V2 - GCDLud19l0E7sC5mbptnj30K5BKDcuHk72In5v2cBzT4lAehYOUC5nlfLQcsj29BjayQ83spDePO - vo8XJ2CZWOa9tcmYESCtdcOQ55X9NiDvpyU1rvLB7fl/VFGSJa6LgJKdzQvJ0XnRoe8TgG/dMUg/ - 7LfwwTU+FtE9Yhdutb687B2K8/0ZwZvhVojoojQj4PLmyPvBZaExSjI8uihCSVWjHnFXm/UpCZk0 - uTO2nIxy/1nSr9z3+ZOtRl5+6/4MqLxxqIvzuP7KLGC+0b8zO9W6EywYwxMpLCJhyP3QWMpk+vMp - uOWITVGSrfJOU39DS19sPq726831td6LyfvkOwAAAP//AwCQiFNmTAYAAA== + H4sIAAAAAAAAA3SVTW/bOBCG7/4VA17aArZhu3HT+Ba0ATZID7tYoC2wLgyaHElTU6R2hnIsBPnv + C1KOLXfTiw965/OZ4fhpBKDIqhUoU+lo6sZNPj1cf3z89q1b/Pn3/q+vXxcPsfy0L+2B7wT/UOPk + EbY/0cQXr6kJdeMwUvC9bBh1xBR1fr38eDOfzZZXWaiDRZfcyiZOrsJkMVtcTebzyWJ2dKwCGRS1 + gn9GAABP+TeV6C0e1Apm45cvNYroEtXqZASgOLj0RWkRkqh9VOOzaIKP6HPVT2sPsFbS1rXmbq1W + sFZ3h8Zp8nrrEG45UkGGtIN7H9E5KtEbhLffb+/fAQloKAidhSKYVtBC8NBw2JMlXwL5iNwwRp2Q + CIQCLGIDDjX7ZPD285d3kFlAw2jJZLsxaGsZRZJJrBDebJ02u8k2HN6A17FlTKFihYK9t0zh++09 + aKoFYoBa7xA+fzlqUAdGaL1FTihsbkx7C5FbiY+BY9XBtoNQFMgpJSYA/lhzEThlIh5WOIUH7CAi + 1wJaJBhKY4ZHilUuhLxxrcUBgC05it0YfraSieqXTr3t8yXg2WaaSQ+9gLFAzp0lGhZLxkygamvt + Lzrr7clHJi9kkovuMYzhsSKHvxQAmhH+bbWPlIa0R6gxMhmBWOkIFgtMoCocwCKPkmd5AeTuognQ + JkVzHRinmQpCyVEyEK8dWDQkFPyk1rvEvOFgUGQ82J68poeYERndCvbDuMiaYQs0miOZ1ml2HVDd + BE47D+T77RRwtEMwFdYkkbvEAi92oi/hFBiM9lC2ZBGqrgl506SflZe0fsfNSvR8iOeFlqZlCq2A + CczoesjTtRr374zR4V57gxsxgTG9t/ls7Z+Hr5OxaEWn4+Bb5waC9j4cX1K6Cz+OyvPpErhQNhy2 + 8ourKsiTVBtGLcGnVy8xNCqrzyOAH/nitBdHRDUc6iZuYthhTjef33zoA6rzkRvIy5ujGkPUbiC8 + Xy7Hr4TcWIyanAzOljLaVGiHOZeLUxO6tRTO2mw06P3/Jb0Wvu+ffDmI8tvwZ8EYbCLazXk9XjNj + TH8EvzM7sc4FK0Hek8FNJOQ0D4uFbl1/o5V0ErHeFOTLdAWoP9RFs7E3BX5YXr/HrRo9j/4DAAD/ + /wMApTxBC7EGAAA= headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9ceac92dcf25-SJC + - 9854b3a05ddd236e-SJC Connection: - keep-alive Content-Encoding: @@ -4240,14 +4233,14 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:32 GMT + - Fri, 26 Sep 2025 18:07:36 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=SfJS.6LKMow1mAaEcmR7MZ4qW5reI2mQoYOFB7QF5sM-1758846212-1.0.1.1-nrgnJGcyNVSndagYPcgw34HQvuRYstKKfFp1zBLgn9LszMxBkR5Kax4PmMSwBzxcNpNtU7We7G3FvQFMp29P6j0_r006KxkQn4CaIyjuTNE; - path=/; expires=Fri, 26-Sep-25 00:53:32 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=rVg2CBdQO7gJLS5Urfv5VrFmn5Yy4dhsfOD6SL_YUCE-1758910056-1.0.1.1-6TcSYfVUd7SwWluIrNIGZpEwTqd.PQa_682AFRNt.KzFWFyFzeFLaHNZWE4ZwdQojjtgUgBaOMZkkep8dL.Y33K4oOsvrsAPMvnNqDuDYdo; + path=/; expires=Fri, 26-Sep-25 18:37:36 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=ApXYaC5PUtA5EPRdiwGMyJZxowN1plEJBCPNQdzQdH4-1758846212641-0.0.1.1-604800000; + - _cfuvid=4Xw5gZYk4SKxgs0sTAjEQyV4lpd1bA5MvbUSu0zojkQ-1758910056914-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Strict-Transport-Security: - max-age=31536000; includeSubDomains; preload @@ -4262,203 +4255,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2811" - openai-project: - - proj_RpeV6PrPclPHBb5GlExPXSBj - openai-version: - - "2020-10-01" - x-envoy-upstream-service-time: - - "2845" - x-openai-proxy-wasm: - - v0.1 - x-ratelimit-limit-requests: - - "10000" - x-ratelimit-limit-tokens: - - "30000000" - x-ratelimit-remaining-requests: - - "9999" - x-ratelimit-remaining-tokens: - - "29998514" - x-ratelimit-reset-requests: - - 6ms - x-ratelimit-reset-tokens: - - 2ms - x-request-id: - - req_922d6dadd50143d380005cb9b4e49cfd - status: - code: 200 - message: OK - - request: - body: - "{\"messages\":[{\"role\":\"system\",\"content\":\"Provide a summary of - the relevant information that could help answer the question based on the excerpt. - Your summary, combined with many others, will be given to the model to generate - an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 22-25: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nut - to models informs the XAI method.\\n\\n\\nConclusion and outlook\\n\\n\\nWe - should seek to explain molecular property prediction models because users are - more\\n\\nlikely to trust explained predictions, and explanations can help assess - if the model is learning\\n\\nthe correct underlying chemical principles. We - also showed that black-box modeling first,\\n\\nfollowed by XAI, is a path to - structure-property relationships without needing to trade\\n\\nbetween accuracy - and interpretability. However, XAI in chemistry has some major open\\n\\nquestions, - that are also related to the black-box nature of the deep learning. Some are\\n\\n\\n\\n - \ 22highlighted below:\\n\\n\\n \u2022 - Explanation representation: How is an explanation presented \u2013 text, a molecule, - attri-\\n\\n butions, a concept, etc?\\n\\n\\n \u2022 Molecular distance: - \ in XAI approaches such as counterfactual generation, the \u201Cdis-\\n\\n - \ tance\u201D between two molecules is minimized. Molecular distance is subjective. - Possibil-\\n\\n ities are distance based on molecular properties, synthesis - routes, and direct structure\\n\\n comparisons.\\n\\n\\n \u2022 Regulations: - As black-box models move from research to industry, healthcare, and\\n\\n environmental - settings, we expect XAI to become more important to explain decisions\\n\\n - \ to chemists or non-experts and possibly be legally required. Explanations - may need\\n\\n to be tuned for be for doctors instead of chemists or to - satisfy a legal requirement.\\n\\n\\n \u2022 Chemical space: Chemical space - is the set of molecules that are realizable; \u201Crealiz-\\n\\n able\u201D - can be defined from purchasable to synthesizable to satisfied valences. What - is\\n\\n most useful? Can an explanation consider nearby impossible molecules? - How can we\\n\\n generate local chemical spaces centered around a specific - molecule for finding counter-\\n\\n factuals or other instance explanations? - \ Similarly, can \u201Cactivity cliffs\u201D be connected\\n\\n to explanations - and the local chemical space.149\\n\\n\\n \u2022 Evaluating XAI : there is - a lack of a systematic framework (quantitative or qualitative)\\n\\n to - evaluate correctness and applicability of an explanation. Can there be a universal\\n\\n - \ framework, or should explanations be chosen and evaluated based on the - audience and\\n\\n domain? For example, work by Rasmussen et al. 58 attempts - to focus on comparing\\n\\n feature attribution XAI methods via Crippen\u2019s - logP scores.\\n\\n\\n\\n\\n\\n 23Acknowledgements\\n\\n\\nResearch - reported in this work was supported by the National Institute of General Medical\\n\\nSciences - of the National Institutes of Health under award number R35GM137966. This work\\n\\nwas - supported by the NSF under awards 1751471 and 1764415. We thank the Center for\\n\\nIntegrated - Research Computing at the University of Rochester for providing computational\\n\\nresources.\\n\\n\\nReferences\\n\\n\\n - \ (1) Choudhary, K.; DeCost, B.; Chen, C.; Jain, A.; Tavazza, F.; Cohn, R.; - Park, C. W.;\\n\\n Choudhary, A.; Agrawal, A.; Billinge, S. J.; Holm, E.; - Ong, S. P.; Wolverton, C.\\n\\n Recent advances and applications of deep - learning methods in materials science. npj\\n\\n Computational Materials - 2022, 8.\\n\\n\\n (2) Keith, J. A.; Vassilev-Galindo, V.; Cheng, B.; Chmiela, - S.; Gastegger, M.; M\xA8uller, K.-\\n\\n R.; Tkatchenko, A. Combining Machine - Learning and Computational Chemistry for\\n\\n Predictive Insights Into - Chemical Systems. Chemical Reviews 2021, 121, 9816\u20139872,\\n\\n PMID: - 34232033.\\n\\n\\n (3) Goh, G. B.; Hodas, N. O.; Vishnu, A. Deep learning for - computational chemistry.\\n\\n Journal of Computational Chemistry 2017, - 38, 1291\u20131307.\\n\\n\\n (4) Deringer, V. L.; Caro, M. A.; Cs\xB4anyi, - G. Machine Learning Interatomic Potentials as\\n\\n Emerging Tools for Materials - Science. Advanced Materials 2019, 31, 1902765.\\n\\n\\n (5) Faber, F. A.; Hutchison, - L.; Huang, B.; Gilmer, J.; Schoenholz, S. S.; Dahl, G. E.;\\n\\n Vinyals, - O.; Kearnes, S.; Riley, P. F.; von Lilienfeld, O. A. Prediction Errors of Molec-\\n\\n - \ ular Machine Learning Models Lower than Hybrid DFT Error. Journal of Chemical\\n\\n - \ Theory and Computation 2017, 13, 5255\u20135264, PMID: 28926232.\\n\\n\\n\\n - \ 24 (6) Duch, W.; Swaminathan, K.; Meller, - J. Artificial Intelligence Approaches for Rational\\n\\n Drug Design and - Discovery. Current Pharmaceutical Design 2007, 13, 1497\u20131508.\\n\\n\\n - (7) Dara, S.; Dhamercherla, S.; Jadav, S. S.; Babu, C. M.; Ahsan, M. J.; darasuresh, - S. D.;\\n\\n Dara, S. Machine Learning in Drug Discovery: A Review. Artificial - Intelligence Review\\n\\n 123, 55, 1947\u20131999.\\n\\n\\n (8) Gupta, R.; - Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R. K.; Kumar, P. Artifi-\\n\\n - \ cial intelligence to deep learning: machine intelligence approach for - drug discovery.\\n\\n Molecular diversity 2021, 25, 1315\u20131360.\\n\\n\\n - (9) Wellawatte, G. P.; Seshadri, A.; White, A. D. Model agnostic generation - of counter-\\n\\n factual explanations for molecules. Chemical Science 2022, - 13, 3697\u20133705.\\n\\n\\n(10) Gandhi, H. A.; White, A. D. Explaining structure-ac\\n\\n------------\\n\\nQuestion: - What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate - connection: - - keep-alive - content-length: - - "6218" - content-type: - - application/json - host: - - api.openai.com - user-agent: - - AsyncOpenAI/Python 1.109.0 - x-stainless-arch: - - arm64 - x-stainless-async: - - async:asyncio - x-stainless-lang: - - python - x-stainless-os: - - MacOS - x-stainless-package-version: - - 1.109.0 - x-stainless-raw-response: - - "true" - x-stainless-read-timeout: - - "60.0" - x-stainless-retry-count: - - "0" - x-stainless-runtime: - - CPython - x-stainless-runtime-version: - - 3.13.5 - method: POST - uri: https://api.openai.com/v1/chat/completions - response: - body: - string: !!binary | - H4sIAAAAAAAAA3RU224bNxB911cM+NQCK0ESJNvQm9AkqNvnFkKrQKDI2d1JeDOHK1s1/O/F7Cq6 - JM7LAsvDOTxz5vI6AlBk1QqUaXUxPrnxb388bT7+7ZpPs+A23WFTf/iw+HO9Nk+0+OsfVUlE3H9B - U75FTUz0yWGhGAbYZNQFhXV2v3x4WNzNZ/Me8NGik7AmlfEijufT+WI8m43n01NgG8kgqxX8OwIA - eO2/IjFYfFErmFbfTjwy6wbV6nwJQOXo5ERpZuKiQ1HVBTQxFAy96tdtANgq7rzX+bhVK9iqzfqx - gpjh40tymoLeO4R1LlSTIe3gMRR0jhoMBisgBg0eSxstdIwWSgQcAiFltGTEDQavLcL+CH3iXEHS - uZDpnM7uCBTAR4f9L6QcE+ZyvAqfwGMBTZ6FnYK4yggld1zgu3d0sICBu4ynp0BnBIc6BwoNmJgz - mgKmRU9GO0iZgqHkkCewWT+eUmF5xXUWh1yCFnLImDIyhjL8/oKTZlJBwZdSXenXpWTad72cXysw - sQsFc61N6bSDBgPmIf6ZSgueAnn6D+0Vg+1LJu5KNiYGJnsKYoj1RTwnbbA3hxj2aKKXHE8GUWjE - Wp9ilgYQo2pCZxkcfUVoUbvSGnFn8OxAOQYv2TlgQ0N5n1vMNyZIJY8QcKi0RyzgsNFOGsZGrymM - OaGRZoGMTx1lFE6ewO/xGQ+YKzCtdg5DgwwZJaIC7kwLmsFiTX2d3jMDD9p1ugh8I4iPXNDrIpa4 - 42Ba0eRilqulRS9Sz6p0Z/vk+CKZJ1tVDZOQ0eFB3tuxiRllImbTEyb9vSOvG2Q5r7Vj3Ia369HK - WHesZbJD59wVoEOIQ9/0Q/35hLydx9jFJuW45+9ClRjC7U4qGoOMLJeYVI++jQA+9+uiu9kAKuXo - U9mV+BX752aL5XIgVJcNdQOf0BKLdlfA3fS0Z24pdxbFYL7aOcpo06K9Jp0+nJMQz+MFm46ucv9R - 0nv0Q/4UmiuWn9JfAGMwFbS7y4p471pG2eI/u3b2uhesGPOBDO4KYZZ6WKx154YFq4Ze3NUUGsyy - W/otW6fd8m62ny/v7+1ejd5G/wMAAP//AwCFpjX1bgYAAA== - headers: - Access-Control-Expose-Headers: - - X-Request-ID - CF-RAY: - - 984e9cf99ed4f98b-SJC - Connection: - - keep-alive - Content-Encoding: - - gzip - Content-Type: - - application/json - Date: - - Fri, 26 Sep 2025 00:23:34 GMT - Server: - - cloudflare - Strict-Transport-Security: - - max-age=31536000; includeSubDomains; preload - Transfer-Encoding: - - chunked - X-Content-Type-Options: - - nosniff - alt-svc: - - h3=":443"; ma=86400 - cf-cache-status: - - DYNAMIC - openai-organization: - - future-house-xr4tdh - openai-processing-ms: - - "2674" + - "2254" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2696" + - "2380" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -4468,13 +4271,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998519" + - "29998556" x-ratelimit-reset-requests: - - 6ms + - 1m0s x-ratelimit-reset-tokens: - - 2ms + - 1m0s x-request-id: - - req_8f14b88d6cfd46fdacbc7a03a2b251c1 + - req_8e5a98fd9940480cb2ffc75935470ed4 status: code: 200 message: OK @@ -4484,77 +4287,77 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 12-14: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nnterfactual - approach, contrastive approach employ a dual\\n\\noptimization method, which - works by generating a similar and a dissimilar (counterfactuals)\\n\\nexample. - Contrastive explanations can interpret the model by identifying contribution - of\\n\\npresence and absence of subsets of features towards a certain prediction.36,99\\n\\n - \ A counterfactual x\u2032 of an instance x is one with a dissimilar prediction - \u02C6f(x) in classi-\\n\\nfication tasks. As shown in equation 5, counterfactual - generation can be thought of as a\\n\\nconstrained optimization problem which - minimizes the vector distance d(x, x\u2032) between the\\n\\nfeatures.9,100\\n\\n\\n - \ minimize d(x, x\u2032)\\n (5)\\n - \ such that \u02C6f(x) \u0338= \u02C6f(x\u2032)\\n\\n - \ For regression tasks, equation 6 adapted from equation 5 can be used. Here, - a counter-\\n\\nfactual is one with a defined increase or decrease in the prediction.\\n\\n\\n - \ minimize d(x, x\u2032)\\n (6)\\n - \ such that \u02C6f(x) \u2212\u02C6f(x\u2032) \u2265\u2206\\n\\n - \ Counterfactuals explanations have become a useful tool for XAI in chemistry, - as they\\n\\nprovide intuitive understanding of predictions and are able to - uncover spurious relationships\\n\\nin training data.101 Counterfactuals create - local (instance-level), actionable explanations.\\n\\nActionability of an explanation - suggest which features can be altered to change the outcome.\\n\\nFor example, - changing a hydrophobic functional group in a molecule to a hydrophilic group\\n\\nto - increase solubility.\\n\\n Counterfactual generation is a demanding task as - it requires gradient optimization over\\n\\ndiscrete features that represents - a molecule. Recent work by Fu et al. 102 and Shen et al. 103\\n\\npresent two - techniques which allow continuous gradient-based optimization. Although, these\\n\\nmethodologies - are shown to circumvent the issue of discrete molecular optimization, counter-\\n\\nfactual - explanation based model interpretation still remains unexplored compared to - other\\n\\n\\n\\n 12post-hoc methods.\\n\\n - \ CF-GNNExplainer104 is a counterfactual explanation generating method based - on GN-\\n\\nNExplainer69 for graph data. This method generate counterfactuals - by perturbing the input\\n\\ndata (removing edges in the graph), and keeping - account of perturbations which lead to\\n\\nchanges in the output. However, - this method is only applicable to graph-based models\\n\\nand can generate infeasible - molecular structures. Another related work by Numeroso and\\n\\nBacciu 105 focus - on generating counterfactual explanations for deep graph networks. Their\\n\\nmethod - MEG (Molecular counterfactual Explanation Generator) uses a reinforcement learn-\\n\\ning - based generator to create molecular counterfactuals (molecular graphs). While - this\\n\\nmethod is able to generate counterfactuals through a multi-objective - reinforcement learner,\\n\\nthis is not a universal approach and requires training - the generator for each task.\\n\\n Work by Wellawatte et al. 9 present a model - agnostic counterfactual generator MMACE\\n\\n(Molecular Model Agnostic Counterfactual - Explanations) which does not require training\\n\\nor computing gradients. This - method firstly populates a local chemical space through ran-\\n\\ndom string - mutations of SELFIES106 molecular representations using the STONED algo-\\n\\nrithm.107 - Next, the labels (predictions) of the molecules in the local space are generated\\n\\nusing - the model that needs to be explained. Finally, the counterfactuals are identified - and\\n\\nsorted by their similarities \u2013 Tanimoto distance96 between ECFP4 - fingerprints.97 Unlike the\\n\\nCF-GNNExplainer104 and MEG105 methods, the MMACE - algorithm ensures that generated\\n\\nmolecules are valid, owing to the surjective - property of SELFIES. Additionally, the MMACE\\n\\nmethod can be applied to both - regression and classification models. However, like most XAI\\n\\nmethods for - molecular prediction, MMACE does not account for the chemical stability of\\n\\npredicted - counterfactuals. To circumvent this drawback, Wellawatte et al. 9 propose an-\\n\\nother - approach, which identift counterfactuals through a similarity search on the - PubChem\\n\\ndatabase.108\\n\\n\\n\\n\\n\\n 13Similarity - to adjacent fields\\n\\n\\nTangential examples to counterfactual explanations - are adversarial training and matched\\n\\nmolecular pairs. Adversarial perturbations - are used during training to deceive the model\\n\\nto expose the vulnerabilities - of a model109,110 whereas counterfactuals are applied post-hoc.\\n\\nTherefore, - the main difference between adversarial and counterfactual examples are in the\\n\\napplication, - although both are derived from the same optimization problem.100 Grabocka\\n\\net - al. 111 have developed a method named Adversarial Training on EXplanations (ATEX)\\n\\nwhich - improves model robustness via exposure to adversarial examples. While there - are\\n\\nconceptual disparities, we note that\\n\\n------------\\n\\nQuestion: + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 22-25: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nut to + models informs the XAI method.\\n\\n\\nConclusion and outlook\\n\\n\\nWe should + seek to explain molecular property prediction models because users are more\\n\\nlikely + to trust explained predictions, and explanations can help assess if the model + is learning\\n\\nthe correct underlying chemical principles. We also showed + that black-box modeling first,\\n\\nfollowed by XAI, is a path to structure-property + relationships without needing to trade\\n\\nbetween accuracy and interpretability. + However, XAI in chemistry has some major open\\n\\nquestions, that are also + related to the black-box nature of the deep learning. Some are\\n\\n\\n\\n 22highlighted + below:\\n\\n\\n \u2022 Explanation representation: How is an explanation presented + \u2013 text, a molecule, attri-\\n\\n butions, a concept, etc?\\n\\n\\n + \ \u2022 Molecular distance: in XAI approaches such as counterfactual generation, + the \u201Cdis-\\n\\n tance\u201D between two molecules is minimized. Molecular + distance is subjective. Possibil-\\n\\n ities are distance based on molecular + properties, synthesis routes, and direct structure\\n\\n comparisons.\\n\\n\\n + \ \u2022 Regulations: As black-box models move from research to industry, healthcare, + and\\n\\n environmental settings, we expect XAI to become more important + to explain decisions\\n\\n to chemists or non-experts and possibly be legally + required. Explanations may need\\n\\n to be tuned for be for doctors instead + of chemists or to satisfy a legal requirement.\\n\\n\\n \u2022 Chemical space: + Chemical space is the set of molecules that are realizable; \u201Crealiz-\\n\\n + \ able\u201D can be defined from purchasable to synthesizable to satisfied + valences. What is\\n\\n most useful? Can an explanation consider nearby + impossible molecules? How can we\\n\\n generate local chemical spaces centered + around a specific molecule for finding counter-\\n\\n factuals or other + instance explanations? Similarly, can \u201Cactivity cliffs\u201D be connected\\n\\n + \ to explanations and the local chemical space.149\\n\\n\\n \u2022 Evaluating + XAI : there is a lack of a systematic framework (quantitative or qualitative)\\n\\n + \ to evaluate correctness and applicability of an explanation. Can there + be a universal\\n\\n framework, or should explanations be chosen and evaluated + based on the audience and\\n\\n domain? For example, work by Rasmussen et + al. 58 attempts to focus on comparing\\n\\n feature attribution XAI methods + via Crippen\u2019s logP scores.\\n\\n\\n\\n\\n\\n 23Acknowledgements\\n\\n\\nResearch + reported in this work was supported by the National Institute of General Medical\\n\\nSciences + of the National Institutes of Health under award number R35GM137966. This work\\n\\nwas + supported by the NSF under awards 1751471 and 1764415. We thank the Center for\\n\\nIntegrated + Research Computing at the University of Rochester for providing computational\\n\\nresources.\\n\\n\\nReferences\\n\\n\\n + \ (1) Choudhary, K.; DeCost, B.; Chen, C.; Jain, A.; Tavazza, F.; Cohn, R.; + Park, C. W.;\\n\\n Choudhary, A.; Agrawal, A.; Billinge, S. J.; Holm, E.; + Ong, S. P.; Wolverton, C.\\n\\n Recent advances and applications of deep + learning methods in materials science. npj\\n\\n Computational Materials + 2022, 8.\\n\\n\\n (2) Keith, J. A.; Vassilev-Galindo, V.; Cheng, B.; Chmiela, + S.; Gastegger, M.; M\xA8uller, K.-\\n\\n R.; Tkatchenko, A. Combining Machine + Learning and Computational Chemistry for\\n\\n Predictive Insights Into + Chemical Systems. Chemical Reviews 2021, 121, 9816\u20139872,\\n\\n PMID: + 34232033.\\n\\n\\n (3) Goh, G. B.; Hodas, N. O.; Vishnu, A. Deep learning for + computational chemistry.\\n\\n Journal of Computational Chemistry 2017, + 38, 1291\u20131307.\\n\\n\\n (4) Deringer, V. L.; Caro, M. A.; Cs\xB4anyi, + G. Machine Learning Interatomic Potentials as\\n\\n Emerging Tools for Materials + Science. Advanced Materials 2019, 31, 1902765.\\n\\n\\n (5) Faber, F. A.; Hutchison, + L.; Huang, B.; Gilmer, J.; Schoenholz, S. S.; Dahl, G. E.;\\n\\n Vinyals, + O.; Kearnes, S.; Riley, P. F.; von Lilienfeld, O. A. Prediction Errors of Molec-\\n\\n + \ ular Machine Learning Models Lower than Hybrid DFT Error. Journal of Chemical\\n\\n + \ Theory and Computation 2017, 13, 5255\u20135264, PMID: 28926232.\\n\\n\\n\\n + \ 24 (6) Duch, W.; Swaminathan, K.; Meller, + J. Artificial Intelligence Approaches for Rational\\n\\n Drug Design and + Discovery. Current Pharmaceutical Design 2007, 13, 1497\u20131508.\\n\\n\\n + (7) Dara, S.; Dhamercherla, S.; Jadav, S. S.; Babu, C. M.; Ahsan, M. J.; darasuresh, + S. D.;\\n\\n Dara, S. Machine Learning in Drug Discovery: A Review. Artificial + Intelligence Review\\n\\n 123, 55, 1947\u20131999.\\n\\n\\n (8) Gupta, R.; + Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R. K.; Kumar, P. Artifi-\\n\\n + \ cial intelligence to deep learning: machine intelligence approach for + drug discovery.\\n\\n Molecular diversity 2021, 25, 1315\u20131360.\\n\\n\\n + (9) Wellawatte, G. P.; Seshadri, A.; White, A. D. Model agnostic generation + of counter-\\n\\n factual explanations for molecules. Chemical Science 2022, + 13, 3697\u20133705.\\n\\n\\n(10) Gandhi, H. A.; White, A. D. Explaining structure-ac\\n\\n------------\\n\\nQuestion: What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" headers: accept: @@ -4564,7 +4367,7 @@ interactions: connection: - keep-alive content-length: - - "6175" + - "6048" content-type: - application/json host: @@ -4596,27 +4399,25 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//dFTbbiM3DH33VxB6aQuMjdibq9+CIG2zxe626AUp6oVBS5wZbjTSrEg5 - MYL8e6Gx4zht9mWA0SGPDg9FPo4ADDszB2NbVNv1fnz1/uvt9UUf3//9689/xmt3k3/78vsvn+4f - Tk/f3ZuqZMTVF7L6nDWxses9KcewhW0iVCqs07OT8/Pj09l0NgBddORLWtPr+DiOZ0ez4/F0Op4d - 7RLbyJbEzOGfEQDA4/AtEoOjBzOHo+r5pCMRbMjM90EAJkVfTgyKsCgGNdULaGNQCoPqx0UAWBjJ - XYdpszBzWJjrh94jB1x5gsukXLNl9HATlLznhoIl+P728uYHSFRTEtAIHWkbnQAGB0q2Dfw1k4C2 - qNDhHYG2BH0ix7a4sw10ZFmGv1jD5Q0MpghwUEp9Ih0UlMAcHKVShhuONEKbOwwygT9aAnqwlHoF - x2KzCAnYmAtHjVYzeqBSUMDdxQIId7QBjdEDB7i9vKmgx6Rss8fkN+XQttSxaNpM4OoVmUCf4pod - AQ6VFEEVcCjqLI09ramwCjetCqw2wI6Ccr3h0IBtMTRUKoSaUHN6tsjG7B2gV0qAWx++kwO/JvAX - Jo5Z9kbXMUFDgRLqwPxaZAWSbVtq/fDh8up6MPHqx/FPHz/umkupAkwELTetL1rJVXDP2u4SVlRY - ByFjbEIUZTuwYN97ts9tWEVtIVGTSEojhwjry6Or2Q6Gg6LcyaS4DOglQnl8CUVlex26NSXBVF6Y - JuTAoangvmXbQh1tLu2MobQwyl4SrLMvpa/YszIJuJwK+EwACbWlVLwN0EfRcRvtq2cwWZhq+/QT - eVqX3i3FxkRlBC52UBZyS+6wISnHNXqhRXg6HKVEdRYskxyy9wcAhhB1e1cZ4s875Gk/tj42fYor - +U+qqTmwtMtEKDGUERWNvRnQpxHA52E95FcTb/oUu16XGu9ouG46m023hOZlIx3AJ+c7VKOiPwDe - nV1Ub1AuHSmyl4MdYyzaltxB7vRkti8Cs+P4gh2NDmr/v6S36Lf1c2gOWL5J/wJYS72SW76Mzlth - icrW/lbY3utBsBFKa7a0VKZU+uGoxuy3C9XIRpS6Zc2hKTuLt1u17pcnp9PV7OTszK3M6Gn0LwAA - AP//AwBO0WbnXgYAAA== + H4sIAAAAAAAAAwAAAP//dFRNTyM5EL3nV5R8TlACZIDc0Gh3hTiPhLQZRRW7Om3itk1VdSAg/vvK + bkgys8ylD/2qnl+9+ngbARjvzAKMbVFtl8Pk+/3V9f7qZvZ60T7w0+OPH49P09vZ6+X997//eTXj + kpHWj2T1M+vMpi4HUp/iAFsmVCqss6v59c1sOp1/q0CXHIWStsk6uUyT8+n55WQ2m5xPPxLb5C2J + WcC/IwCAt/otEqOjF7OA6fjzT0ciuCGzOAQBGE6h/DEo4kUxqhkfQZuiUqyq35YRYGmk7zrk/dIs + YGkebu/GkBj+eskBfcR1ILhl9Y23HgPcRaUQ/IaipTF4AefF9iLkwEfQlqDyvyikBroUyPYBGTKn + TKx7yEzO2+IRVBfkDO4U0HcCmqDDLZ2ECHSJCXxU4sykVQxGB8q96HNibfewLqRp552PG6CiOuKQ + 3CSuihxZLwMdOioJ64B2O1mnl4OIh9s76Ejb5AQsRmgpZEAREoHnlrQl/ogFZIJAyLE8aBMzWQXb + UuctBsjso/U5kFSlJTgjq68+hD0wBdphVEAp4oQ+aZUxiq/O+KgJGk/BCQS/JWgJg7a2kBVSijvP + KXYUFQOI9aUbZ3BPe7AthkBxQ1L6Uary0YbeETBlJikp9Y3U/GLWGBw1vpZ07Jqr01Majc4xiRSY + adMH1MSllqfeMxUdMq50iaspn2ZIxpodHTjaUUi5wLIXpQ7VW2gYO3pOvB26RTsMPervnTxbmvEw + qh/uWVqJTUxlZG+W8f10vpmaXrCsV+xDOAEwxjQUXzfr5wfyftilkDaZ01p+SzXFF2lXTCgplr0R + TdlU9H0E8LPubP/LGprMqcu60rSl+tzscnY9EJrjmTiBZ1cfqCbFcALML+bjLyhXjhR9kJPFNxZt + S+70zen1oQjsnU9HbDo6qf3/kr6iH+r3cXPC8kf6I2AtZSW3Oq71V2FM5ZT+KezgdRVshHjnLa3U + E5d+OGqwD8OVM8N0rRofN+Vq+OHUNXnlbhr6Nr+6oLUZvY/+AwAA//8DAM+pkZjzBQAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9cfc1ce97af2-SJC + - 9854b3acf97f4f08-SJC Connection: - keep-alive Content-Encoding: @@ -4624,7 +4425,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:35 GMT + - Fri, 26 Sep 2025 18:07:38 GMT Server: - cloudflare Strict-Transport-Security: @@ -4640,13 +4441,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2543" + - "1706" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2558" + - "1740" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -4656,13 +4457,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998522" + - "29998560" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_8c8d105af8d44e50bad86fe2a9e79bc0 + - req_f930aecec6b24c268489437352d3b43d status: code: 200 message: OK @@ -4672,14 +4473,12 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 8-9: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nrepresented + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 8-9: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nrepresented with equation 2.\\n\\n \u2206\u02C6f(\u20D7x) \u2248\u2202\u02C6f(\u20D7x) (2)\\n \u2206xi \ \u2202xi\\n\\n\\n\\n 7 \u2206\u02C6f(\u20D7x) @@ -4755,7 +4554,7 @@ interactions: connection: - keep-alive content-length: - - "6230" + - "6060" content-type: - application/json host: @@ -4787,27 +4586,26 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA4xU224bRwx911cQ85QAkmDJdhz7zUjT1EFjpE3aGq0CgZrl7jKeW4Ycx6rhfy9m - JVtK6gJ9WSx4yOHh4eVuBGC4MWdgbI9qfXKTV2+/XL2WL/4q/naYfm1/8T9c/vHz2x/tnyclfzbj - GhFXn8nqQ9TURp8cKcewgW0mVKqvzk6OX748ejGfzQfAx4ZcDeuSTo7iZH4wP5rMZpP5wTawj2xJ - zBn8NQIAuBu+lWJo6NacwcH4weJJBDsyZ49OACZHVy0GRVgUg5rxDrQxKIWB9d0iACyMFO8xrxfm - DBbmY09At5ZyUmhYbBEhgde3ySEHXDmC86zcsmV0cBGUnOOOgiV4dnV+8Rw8aR8bGUPCrGyLw+zW - wAG0Jxhy3yrEFjgo5ZRJOXSQMjVsq3ICbY4ePNqeA4EjzKF6DJLJGDhYV5pqaYjSDsfQQJcx9RCo - ZHQQSL/GfC3w7M3lpTyfwoVCz13vuOtVqm/DFHSyQqEGMKUc0fYk4PiaBnZdrt179JTx8Pvq/N14 - yPbm8nKrCuUxfO3Z9tBGWwRigJZQSyZA1cyrUisbgqSsNizZp5gVg6UpvKu1TbALUZTtg4IbIh9+ - On9fq5bEmRpYreFDj8nRGm7QFdrK1aGnKnDM6zFgzeskAm3pNdDGXGHOUKQWBz46GnqzJ/1W4yn8 - zlLQ8d84WJVsH/hLzSXF9oACgo4p2PWmJPbsMLOuwWOSIb2nUGO/yVyHsqbesto09YFFg4r7UzCF - jz0JPWqB7EEjeLyuQ1TX7HZLF3zMtJumYUJX653w302XRuCQij50SCDm2hXRXOxgmC7MeLMXmRzd - 1BYtxcZMdT9Ot1ARapbssSOp5had0CLc7+9ZprYI1jUPxbk9AEOIOmg7bPinLXL/uNMudinHlXwX - aloOLP0yE0oMdX9FYzIDej8C+DTcjvLNOTApR590qfGahnSzw8PjzYNmd6724OPTLapR0e0BR6dH - 4yeeXDakyE72DpCxdZGaXezuWmFpOO4Bo73C/83nqbc3xXPo/s/zO8BaSkrNcjcKT7llqvf8v9we - hR4IG6F8w5aWypRrMxpqsbjNqTWyFiW/bDl0dS55c2/btDx+MVvNj09OmpUZ3Y/+AQAA//8DAPZN - wxh4BgAA + H4sIAAAAAAAAAwAAAP//dFRNbxs3EL3rVwx4SgBJkFU7lnwzgiI1irgB4hYNokAYkbO7U3NJhjPr + SDH83wvuSpbSOpfFgm8+38ybxxGAYWeuwNgG1bbJT97+frn4fvsphMX8j+XD7M9lm7b1+d2H+4V7 + 89WMi0fc/ENWD15TG9vkSTmGAbaZUKlEPbu8WCzPZrOLyx5ooyNf3Oqkk/M4mc/m55Ozs8l8tnds + IlsScwWfRwAAj/23lBgcbc0VzMaHl5ZEsCZz9WwEYHL05cWgCItiUDM+gjYGpdBX/bgKACsjXdti + 3q3MFazMXUNAW0s5KTgW24mQwK/b5JEDbjzBdVau2DJ6uAlK3nNNwRK8+vv65jW0pE10MoaEWdl2 + HrPfAQfQhqDPvVWIFXBQyimTcqghZXJsC3MCVY4ttGgbDgSeMIdi0VMmY+BgfefKiyNKRxyDgzpj + aiBQl9FDIP0W873Aq3e3t/J6CjcKDdeN57pRKbaOKehkg0IOMKUc0TYk4Pme+urqXKb3bCnj/vft + 9ftxn+3d7e2eFcpj+NawbaCKthOIASpC7TIBqmbedKWz3km6zVAltylmxWBpCu9LbxOsQxRle2Bw + KOTjb9cfSteSOJODzQ4+Npg87eABfUd7umpsqRAc824MWPJ6ieBIbOYNOahiBhqKHcj01E/mhPg9 + w1P4i6VDz9+xf1WyTeCvJZN0tgEUEPRMwe6gxSRDV9yyx8x6eMsELYUSYJ+8C45yWcV+dJJQy/oc + WCprcaBpiFikhLnY2oZatsWYQ102hoPKFO4aEnqmCrkFjdDiPcGgwu2+H2hjpuOy9QtcEmjGIAkz + BZ2uzHgQQiZPD2Uma7ExUxHEchWeTtWTqeoEi3hD5/0JgCFE7Tnrdftljzw9K9XHOuW4kf+4mooD + S7POhBJDUaVoTKZHn0YAX/qL0P0gcpNybJOuNd5Tn+5svlwMAc3xCJ3A5+d7VKOi/wGYj18IuXak + yF5OzoqxRR7uNOfieIawcxyP2Gx00vv/S3op/NA/h/okyk/DHwFrKSm59XGRXzLLVA71z8yeue4L + NkL5gS2tlSmXeTiqsPPDDTWyE6V2fbKMxaRKa7es6M3F5S+0MaOn0b8AAAD//wMAcKT/IlEGAAA= headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9cfd2b1bcf25-SJC + - 9854b3afe837236e-SJC Connection: - keep-alive Content-Encoding: @@ -4815,7 +4613,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:35 GMT + - Fri, 26 Sep 2025 18:07:38 GMT Server: - cloudflare Strict-Transport-Security: @@ -4831,13 +4629,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2412" + - "1688" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2431" + - "1724" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -4847,13 +4645,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998506" + - "29998548" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_064632507e7c4bb885b26aa5a75509cc + - req_67de750050af4ec894e4b8806944784d status: code: 200 message: OK @@ -4863,13 +4661,11 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAw0AAADsCAIAAAC5c90NAAAACXBIWXMAABcSAAAXEgFnn9JSAACCkUlEQVR4nOydd1gUWbr/nbv33t195u7c3UnOzs7szuzsYiBnUYIgoqggipgwYwBUMIJ5DSAGDIgRE+acRhQDmDCgjmHMKGYUEyIGYERv/77b78/z1FQHOlQ13XA+f/B0F9WnTlW99b7f99QJtRQcDofD4XA4HHXU+r//+7+qrgOHw+FwOByOOcJ1EofD4XA4HI56uE7icDgcDofDUQ/XSRwOh8PhcDjq4TqJw+FwOBwORz1cJ3E4HA6Hw+Goh+skDofD4XA4HPVwncThcDgcDoejHq6TOBwOh8PhcNTDdRLHsrl582Zubq7BPz937tyVK1fo8+vXr48dO/b8+XODS8vLy/vpp58M/jmnqqioqMCtLywsNOznsBn8HPZDX2FRFy5cMLgy5eXlKO3JkycGl8CpKszKHaEy3B1JAtdJHHOntLR09uzZbdu2dXNza9SoUXh4+KxZs5j7+Ne//uXk5GRw4a1aterTpw99vnbtmpWV1cGDBw0ubciQIT4+PvSZQu/9+/cNLs0Y8Fxv3bo1JCTE1dU1LCxs9+7d2vfPz88fMWJEYGCgp6dnx44d09PT3759K9wBFycyMtLDw8PPz2/69OkvX76Us/qycPbs2aioKF9fXxcXF5zp4MGDDx8+TP/C6eDWr1mzxrCSYTP4OS4RfYVF4RoaXM8HDx6gtB9//JG+3rlzx5jQaySPHj0aOXIknjsvL6+xY8c+fvxY057Pnj3rro6FCxfSDitWrFC7Q1U9I4YBdzR37lyLcEeoDHNHwFLcEQxGrZ3AwGgH2OSePXumTZsWEREBl2uC+nOdxDFr4JWCg4NtbGzi4uJWrVo1b9682NhYeCKWJ23cuBH/Mrj8pKSkRYsW0WfjHRMiAXwTfabQywo3MUuXLsXRca3Wr1/ft29ffMaF0rQzAjMuKVxYamoqtAJ+hf0RHdkOSEyxQ9OmTXELUlJS7Ozs4OwgBE1yKtKwa9cunBTiEILc8uXLJ0+ejFDHYhLMLCYmJicnx7DCL126hJ/jMtJXI3USgi5KYxYOVWpM6DWGp0+fQh5BDSxbtiwtLQ3KwN/f/9WrV2p3pmoLwRXGNccFpx1ggaIdYHK2trYWpLnJHVlbWw8bNsz83REqw9wRqHJ3hDsOdzRgwAB8Xrdunaad4UVFduLo6Ijcpry8nHaANkIJ2IIrLxSC8sF1EsesQRaCR2LHjh3CjW/evNHkrI3BeMckpAp1EiIWwg/8C33FM96jRw8HBwf2YkgEKomqHjlyhG2JioqqU6dOSUkJfY2MjMTPHz58SF83b96M/Tdt2iTnSUhM48aNmzVrhjgn3KildcQYjNRJIqpQJ02cOBFmcPnyZfp64cIF3PfZs2fr+HOICfycmY0IWCkEx9ChQ6Wpq0kgd4RgL9xoEe5IUXU6idxRdHQ0iQ1yR8i1dHylCPuBFQkFH9KSO3fuKJQPGtdJHI4iOTkZj7eW5mIkH4ji7Gv37t0zMjIOHDiAQOXt7T1o0KBHjx7ByLEbstsmTZrEx8cLn8/Ro0fPnDmTPosc082bN0eNGtW6dWtPT8+WLVtOmzaN6QaAo+BY2DJp0iRfX9+uXbsqlC3G1A787NmzLl26oDTk39RoDFWxf/9+fGCtDsTu3buxUdrOKDgWDn306FG25dChQ9iiqbkbVwD/ffHiBdsyf/58bKEIhxhQv359YZZcUVHh5uYmvOxmDgwApwNj0LQDQh3uQnZ2Nn3FBcRXXBB6cxEcHEytcbBDqM+AgADYUmZmJvv5+fPnhe+PRDpp5cqVCAxQaTCkiIgIUasV2QYsFiaEq3rixAl6gYUPCqVFwbrq1avH3j5A7OJERGLl/fv3SNNx14y9Ur+GGTYDl6J58+a6/BZ5AmIhzlfTDngkcVPoNC2F1NRU1Dk/P1/TDrq4I2xfs2YNuSNIyaKiIra/dneE/7Zp00Z3d4TKMHeE/1qKOxIxb9487Hzx4kXVf3GdxOH8m7Vr1+IhSUpKEnWXYYg6BGDn8PDwwMDA9PT0BQsWODs7w7PAu4WGhq5atWrGjBlIYfv168f219IhYMmSJXB5aWlpSB/hlRDDQkJCEJDov9QGA/eHPAn7YGeFoH8StAW5VMSJNCUnT56Et8LRU1JShPWHuwwLC1N7auXl5fe08ssvv6j94fjx43FoYesRFA+24CzU7p+bm4v/wqvS17KyMlw0REQ62TNnzuC/uHrCn8ARe3l5qS3NPGnUqFGDBg0QbNT+V9Q/afr06fjauXNnWNe6det69uyJr5A7iEC4htgN5lS3bl3II9pfe/8kPz+/hIQE/Io66GDPXbt2sf/ia/v27RH8YJwICVeuXBH2T4J0g+C2sbFJ+wBuOuqABF2oa48dO4afZGVlqT07hD0tVsS6fYhAOoEyJ0yYINyIC4KNkJVaL/a/oSd3z549mnbAo4fraVkBiE4KWkTTo1epO4JchsYVuqO+ffuy/Y13R1FRUfhM7oj1T4I7wg91dEdA7amZzB0JwQnCSIKCgtT+l+skDuffIIAhecJD5e7uDheAjFmUWKg6poYNG7IWo507d2ILoj6TWfALderUYTtocUzv3r0THgiJrzArIscEFyDcR9iPW+17N+wAecFKvnTpEvbZvHmz2nP/6aefrLTCArOIgQMHOjo6CrfgiNh/1KhRavcHO3bsoB7ckH1IfKEM2OCvffv24besrYWgPkyaSjNDtm7dWq9ePdQZYQB3bfv27cJuMWp1Ert3uHoIb8IMGIEHV5jdfe06SWhI8LeQmDBItgU/RFHCl1Oiftyq793u3r0LG2a6FsTExOD2aeoxhvposSJNPWHxoOG/iKnCjXRlRI0QasF1hjDVFDvpFR50YaXlmBXMHUGmkDtiWpkwwB1hCzUyKfRxR5TbaHdHon7cOrojTR0ZJXdHunTBPn78uJUghRPBdRKH8/8pKytD7oW0DA8bPZBIylmQU3VMwjfZd+7cEXkHxHtsYf0utQ8wgTs7c+bM7g/gv5SoKT44JlH7RKU6iXwNOwSqihRT1GmGgQzsolZwZdT+EGek2qNFi2OC20L8Q3KJ6N6/f/9GjRoFBAQg46T/ImBbqfSToK6UakszW65cuTJy5MimTZtCZKDyuPJMi6jVScIXIoMGDcL+Qm8ZGhrKWgIqHe+GQHjgwAGyosGDB0OxsX9ZKfu3CneuVCcBCFmYLn1GPXHvtHQbwlOgxYo0vdQmWxWNAdRRJ9GjlJSUpGkHWL6WrkvmDJQfrkmVuCM8p+fOnRO6I/amVa07qlQnqbojnJSmXowmc0dCsA9sW1NPJq6TOBwx79+/R8aD7ATP2IgRI2ijqmMSJqkUcrZt28a2UEjTxTEhU/T29sYWX1/fNkqEjoYck8g1VKqTQFBQEPVgePPmDbzSxIkTjbgk6hk6dKiNjY1wS3l5OSozbtw4tfvTK0Iku/QVkQCu387OjsIYdSbYu3ev8CeQU8Jgb1ng1mzYsMHNzQ2nQKFFrU4S/kR4ZwkoIWY5WnQSjBbiDJoA1zM4OJj6lwgLx2ccTliyLjqJGvnOnj2Lz4sXL65bt67kmiMvLw+HgG0IN9KVYe0fmkhISFAN2ww8NTijXr16SVbXqgB39vLly+SOWNc3Wd1R48aNRe6IWY5ad1SpTlKouKOxY8caej00oskdDR8+XPsPX7x4gR+KsgghXCdxOOqBe2rSpImHhwd9VXVMQl8gCjkKnR0TjuLv79+2bdunT5/Sf6mtWKSTRHXTRSetX78e4RmRhkaNIRppOtMLFy74aUVTHJo6dSpKZjUHt27dEmafIqhLqXDL4cOHsf/WrVsVymYY1RMJCwsLDAzUVHOLgLqXpqenK+TUSVu2bLFSDthkL8WojzwrxzCdhNK8vLygwODAmzVrJuzjogp202JF0DRqf4WgC0MVvasdNmwYNmp6m0bgv9CgWkb84WpY6dyN18whd9SgQQP6Krc7YgqV3JFIJ4nqpotOErkjLfOjmswdMVavXm3163G4IrhO4nA0An/h4uJCn2VyTAUFBVa/HvoukguV6iREC+zAJtljvH792tnZGT6iffv22keP379/f5pWNA1LycrKwqG3b9/OtpAmOHbsmNr9kVPCzQm3kE6i8c9v377F1e7evTv7b0lJibW1tTHzxJgDOTk5rOuDfDpp3Lhx3t7ewh+KunZVqpNSU1NxtVUdNbbb29vTi5sDBw5oOVNoNS1WlJGRoemHwcHBEGHs0Pjg6enZqVMnLcdSKMdMWWnudQe6dOkCIaVdbFkQVeiO9NVJ2t1RSEiIltOU3B1VOl1Z69at8eywvuqqcJ3E4fwbuHhkn0KXCi9ct25dNmZNJsdEg+Hj4+PpX6hA165d9dJJwN3dXW2j8cSJE11dXa1U5oWSCtTWy8sLXo+6GhQXFzdt2hRZL3vYcUFQMZb/0VsS0Xs34XsT7FCnTp3jx48L9z916pQclZeJCRMm3Lhxg32FMKKJGyiBlk8nUcevu3fv0tejR4/CrvTSSRRU2CRGjEePHtWrVw+GhIppiSXGsHz5ctasCDZs2CAyWliRahNFr169HB0dNY2Jw6VAIXK8bjYBVeuO2FxTqEBERIS+OqlSd7R27Vo9roXOkDvCqVEvLvyFO/L19WUtrPv37xe6IwIGjyrNmjVLS8lcJ3E4/2bu3LlWyi63yGIRvP39/fEVOe69e/doB5kck+LDEGiojQEDBuBpHDZsmL46iSrfoEEDPz+/xYsXs+35+fnYDt+kqeej8Zw4ccLBwQHZf2RkJPwj0nfhaBTSAewiIOLShWX9uEUeCq6tTZs2iPcIgcHBwZX6LzPE1tYW1W7RogWsCKeJiwORwfqOyKeTYKguLi4wYNwISG3YanR0tF46CWm6h4cHLj5CCwxJOKUhFcUmvJYcRLKBAwdCB6Dm7du3x7Hi4uKEIQNb2EVglYekHjNmjKYyZ8yYgV9dvXpVpjrLCj3RuInkjmgUZJW4IwgLfXWSdneEJ0KO2TIJoTtCBeCOhOMEqfKipehoNgG1gwzwnFqpIOwvLzlcJ3HMHTzGq1atQtID5zt58uSdO3cK87lLly4hHWFfkd4J85LS0lJsEQ7Pefz4MbawARQ5OTns+USwxL/YHM14NPAVj9+kSZMyMjLoKyscH1Q7WODhF40Lu3DhAg1OEcqU169f29vba+oXIhXw3ampqbhocEOijreojPAiKJSdUXbt2oUzxf6zZ8/GVRWVVl5evnnz5nHjxuEWnD59WtaaywGuOW4NTg0niLNYsGDBrVu32H/fvn2LC8Jafej6CH+uemfh+pnlkFGxQU/YLpw+EeY3c+ZMHBeBCp9FliOyDYU6o0XJ2dnZZEjCicRSUlKoc4khV0Q3YPZ79uzBU4AHMCsrSxQvUB9ReCsoKMBGLZ3KcWU0zfNkEcAdrV69mrkjUfOSydzR+/fvhZaj1h2hMrq7I+E6RXKgxR1R5UWD2o4cOXLo0CG1ReE53a2CqsuSEK6TOBxTs3HjRivNo4E4HF2oqKjw8fGJjo6u6opwLBtyR6ovdjkMrpM4HNOB7HP27NlOTk4WtOgHx9woLCxMS0vr169f3bp1r1y5UtXV4Vgq3B3pCNdJHI7pGDlyZJs2beLi4oRzGHI4egFtBCvq2LGjllVBOJxKIXc0fPhw7o60w3USh8PhcDgcjnq4TuJwOBwOh8NRT43TSYWFhcIljuUbCcmRnJcvX544cWL37t3Z2dnFxcVVXR29KSkpSU9PHzFiRExMjC4riZoMVAZVEg1cUqW0tBSPjCVeeRF5eXlZWVkwpAsXLsg085CsnD59OikpafDgwZrWB60qUB8ta7oplEvR5eTk4MqfPHlSOHDPEkHssGh3hPqvXLly5MiRFueOYDk///wzHuHMzEzTPMI1Tie1atVKOOlCvXr1unfvnp+fX9X14mgDj/SYMWOsra3Zjatbt27fvn01LeEpLbAQ4Uy4BhMWFubm5obwhnMxZiw3vDOq9OzZM+OrRNBMLcJpXUQMGjSoefPmuOaqk/1YFgjSNOcNw8PDY9WqVSY4dEZGhnBOc4M5cuQIzXSFCGekWY4ePVp1gmZjGDJkCFudV0RZWVnPnj3JhNiVt9AJAsgd2djYsHOpU6dO7969a7I7kvDctbsjCCNEbeEjDJNjM3rIRE3UScHBwbS+8fnz55Hf29nZeXl5yTfjH8dIXr9+HRISUr9+/Xnz5t27d+/du3fPnz9HJtG2bVvRYuYyIYk4oCnmNmzYYHx94EGsdFizXXcq1Ul+fn6xsbE0+6Ll6qSdO3ciTsNsjh49ilCHrBQnjtNhawXKitqZAA0AUc3d3V2SHBrOUJc123VHi056+fJl69at169fT02Subm5TZo0QeZz+/ZtCStgAjS5o3bt2lmcO5KkPZLckWgOMGPQ7o7u3LmzcuVK7IPLjnuxY8cOWBFiuqxKpibqJNGiWrNnz7b6sPI2UV5e/tNPP1HjMJs7jgH3Ss2thw8fLiwsFP33yZMn2H7gwAFNi91w9IVWydi1a5doO26EMI345Zdfzpw5s2fPnqtXrwqjSEVFBdwZreBB4L/YUlJSQl/htSkZQuzEjTt16hTbmfbE0ceNG0cvakVzM8IS9u3bJ2qPpPdTqA8M6dixY0ianz17Riumbdy4Ef9i2Rv2xOFgS6i52lfA2OH06dO0Ay0EQS/vaPpaqhKOolBO4yZq/IcrFNYW54Irg+uTk5OjOu2kdp3ECrRcnfT48WN7e/sWLVqoZkSiiawePnyIW3bkyBHRxHe4iSJtyixH8WujunLlysGDB4XzWKIoWg2UvfEXmij2xBFhKkIrVSg7CZAbwQ4wM0QI/BC5e0BAgPDWK5Rrb6GE7Oxs4UGFQI7gv9iHVRgfmjVrFhkZSUXRgVB/NrU0gSphC1tigs4aQnPv3r1INUXvzrTopP9TItxC6+vROsQWhCZ3BMEkbFPB44+Yoos7UigfXqE7olsAhwArMsAdXb9+XVi4Ae4IvkW7O8IO5I6wG7kjFFipOxI2gRvvjhhjx47F/qqxWEK4TlIsWLBAKIf3799va2uLdMHFxQXb7ezshAs6njt3ztPTE9sdHR3pNRBb/AgPAK3lhJ/jV/Xq1UtOTq5pl1dy8HjjUrdp00b7brgvjRo1qlu3rrOzM24KMlf22KiuFUCLVLD1BGg9dmSE+EsNuQ0aNCBfQ3uqnR0fTziOiLtMv+rRowfzWbQWwaZNm3x8fOhXiB/CQsgCkX1aKyFLQ80Re4QnhVTJ1dUVJ4Ud8BdhHh6TmiWE0It8VQWDo7Pa5uXlubm5YR8UBduuU6dOYmIiM86aoJOWLVuGymt/0YNHeMSIEVbKNRxsbGxwa4SLTqguXUKWQ5/JVGbNmtWrVy9Va6FFJ4RQAgabobVHyJ/ABoSrp9EqFuy31J6neutpzRmUQGuzREREIJ6xQhCAUQi2w35wXuwOMmsnaLkM1UYvUePlxIkTYTw4EA6H7b6+vjAt4SXSpJNUoZVchQtomD8GuyMmoVTdkeLXy5vQXYZ7EbojSFKFzO5o+/btQneE0xRN8A1pCB+Cu0/uCDYAba26hIgWd8QaL1Xd0fjx4/V1R4z4+HhURtN6gpJQE3USe+8GMjIycMPCw8PZdcjNzYU0pqnoi4qKhg0bBtNhbiIoKCgsLIxyL3jV8+fPs2lMoYqwJwrEduQWpLJN0xJbjaHVEKdNm6ZlH6Qj7u7ubdu2pdt05swZfMWNpiRYF50E/9K8efOff/5ZoZzsHybRpUsXtr/qM4+sC55i8uTJFO3gGnBENlcbOSZIHBgDDIkavUSrNSmUy2iz3qyPHz9GoEIkY94NKTsOMXDgQFq4AF4AforaQtS+d9Ouk27evAlBTzkigiiFQ0hD+m9N0Em4vLie2sdtQOXQM4vnF1dp5MiR+MrEqy46CQkS/ACOgjsFSYEtbAETVQkCn9O5c2fkXRRacIvHjBkDU2QuBTaMAiGkCgoKUCZZAk5EpEUWLlxIGTkMHkaFnwiXxEHIRLRGHMVJ4euNGzfYOu2q790q1UkIt6yBBLlEKyWsvUQvnUQN+XhaddzfHNDFHeER9vDwELmjwMBATe5IoU4n4TIK3VG7du2EO+vijkSLD5I7Ki8v1+SOjhw5cvz4ceaO+vfvr9YdkaXBVmFR1GKk9r2bdp0kcke03LJe7gghGOEb13b+/PmomOpizNJSE3WSSP+GhoZqabLDXYTgxY2kr/A4EyZMUN3txYsXkLSi5yc2NjYgIEDS6tc4qH1Y+3t0Cm/C1mY8hNhCCy3popOsfr0K48yZMxGu2DsF1WceGRjUtnALsjHsRk6EHJPoJ6qOSQRcEqV97BCIoOzFihADdJIq8LxwhcK6VW+dhPuFhFvLDlC0Dg4OwomJEdiaNGnCQpQuOqlTp07sv+Q6UlNT6auqBEFKZvXrNzjwxr6+vmylLTgrlC969a+qk0RMmjTJy8uLPiPy4RDr169Xu6cBOkkEvTtjMVJ3nQT5iKxS2t5RJkAXd7R06VKRbti5c6cWd6RQp5OE6wMa7I6o75dp3JFeOkkVfd0Ra1avU6fO2LFjhe+F5aAm6iQofXqTmpeXB9vFFjhQZGxsHzz8cFWwjDZKIFfZLR88eDB839ChQ3EXhT2QyF9AQq0XEBUVhbto6jOsXujSiQGxDdFFuAX5EH6FhFWhm07CLaZsm6DGZBafRM884h/279atm/Bek1aDG1V8cEzHjh0TVknVMeHR27dvX1xcXIcOHcjSWK2QoMPMNC26bphOunr1akJCAqpNx0IAZi+ga4JOCgkJ0d5f+8aNGzi7devWCTeOGzcOjzA14+mik0QXB/9lt0BVgkBCYcuSJUuEhtSyZcuwsDDaAa4J90tUT1WdBGtHUX379qU727BhQ3YgMktNo9YN0ElQkxs2bEAGiMCGY9HgQdZApaNOunjxoru7O0oQvh+0CHRxR3D7kBTCLSUlJVrckUKdTtLdHcGNQHEa744UyhZug92RvjrJSHeES3r37t0zZ86ghjh9hADej1tKVPsnXb9+HXeFGcG8efPwFY6A+S9bW1t2y1+9epWSksKGFuM204vnHTt24CsUWHcVTHt+1Q16ZoTvEVTBDRV5Z2HQ0rF/kvDn2h0TFQhlpnqvz58/r/jgmOAIVE9E6JigqqG3oLmXLVtGlsZqRY41OTlZ7fkaoJNwXBwLF2r+/Pl0LOGDUBN0EnUDEnXNFnL69GnsgNRfuJGCFlmCLjpJ1P6vXSdRxyNVKxo9ejTtQP2TRPUU6aTbt2+7urr6+/sjNMJucWcjIiLYgegQmk5ZX52E4A2bcXBwgOmuWLECx6LO6cyqddFJMDZUODQ01BLnHDLMHSkEj6eOOkn4X+3uiHyF8e5oypQpmtwR2bZ2d6SXTmLuCNHWYHfEoAa8o0eP6ri/AXCd9G+xbPWhGyN10xO+WauoqIBcVY0Njx49Wrt2rZ2dHbIHxYc8Q9TxjWM8uDtIziBMtRhq//79RQkcOaO5c+cqlIOGrH49IB8O2hidRO1Jal+/EuSYRI5D5JhQOAqhFJOABBfWClaH3E5t+Wp1kuprXw8PDxakw8PDEZmESWrnzp1rlE6iRdGFvaRF3Lp1CzuI5lIaOXIkMmnqrYgQ4u3tLfxvYmKiMTqJ2pOo15FadNFJM2fOFPYjUXwYkEWfKevTdAhVnZSWlob9hV1iN2/ezIzt7NmzVoJ+JApltxW9dNLNmzfd3d1bt26tOo7YItDFHcXExOAchTsUFRVpcUewLmN0ErUnGemO4HxQiDHuSFS+WnfEjE0Sd8SgDEfUEiwtXCf9+005rvLw4cMVH7T5ggUL2H/37NmjJTYgQlOKiR+6uLjgCZGx6jUV8vX4K9r+5MmTc+fOKT44d+HMDkh2WVMzlC5CHbIl9l+6p7rrJKS/ogyya9euCJma3hro4pgKCgpE7nLTpk3CWkVGRsKiXrx4oVo+dZK4cOGCcKO/v7+wbw0djgXpli1bCv/78OFDOL4apZPwhLq5uTVq1Eh1Sj3qOAKvTUM62HbIBezP3nwhigh7giNkUv8h+lqpTlq8eDF2ePr0Kfsv0n1676apzrroJDguYb+rt2/f0rAm+kr9jjX1cg0ODmadQgjqScM6kiuUPQ2YTkIeiM+XLl1i/x01apTuOunOnTsQGYGBgRa96qomdwSpSj2vEbCtft3fkfyJJnd04MABvXSSqjvq1auXGbqj3r17s6/Qx1bKcXb0VRJ3xJg/fz72z83N1XF/A6iJOgm3UPgeF87Rzs6OuQY8xn5+fmfOnCkuLoZfaNy4McyaYgNcJHLKI0eOIELD38G94rdMGyETxd1CAorbDHvKz89PT0/HLayyU60uwPXjkcO1jY+Ph6+5ePEiJNHChQtx8Wk4Ia52kyZNcFuRWOCuZWRk4IYivDHbhqxBzr1v3767d+/injZv3lwvnYRcB6Fo27Zt+C0FCdQBCRMe7OPHj+PoyBFxUGGrcqWOCVEWrg3GBv8CDwsPha/CWuFAdAhoQRzixo0bOGVK9OFWsGe/fv12K6G2BLhORPG1a9fiHOG5kLLj5yxII5rCE2VmZmJnJAYU4HV3TDt27IAYJQ/epUuXNCUWF+3gSWEGkBGIZAhpuIl4hPv27cvu/sqVK3GCM2bMePz4MYI6XDku6cmTJ+m/+An+C2GBRxt3B0+6kxL6b6U6CYHTSjnzDd016pYbGxsL94LE7P79+7jLOMTMmTPZVCO66CRyO7ANeKTr16+jzjRin+0AB4VbD5UGgfjs2TNEZXajR4wY4eDgANujGXEUyq5OMBvk+jAJlAb3SCPbSSfBtOrUqYMK0KQ7c+fOpTHkuugkOE8vLy/sPGnSpDQB7PJaCpW6IzykTZs2FbmjTp06MXeEn2t3R9p1Ejyb3O4IFqjJHSEykjuCRdF8SOSO8ByJ3BFMReSOWJVocQXD3BGsDmaZk5ODs4bx4DMe0pCQEFlXL6lxOgk3w0UAHl34EeG7W7gqMhEA08c9hlSiJlMI9rZt27JJ02EHAwYMEL5lh/Bq0KCB1Qfc3d1NsyRCtQe5PlwqzVzFri0cLnvdgKiGW8PuS3R0tLBhH/+FC6D/BgQEIF7i1rPOmLi5uMXCwyGXwg6s5SAvLy8sLAxHxEa2fBXiCtJxVh94AcQ8+heeYewJVyIsE1+xkfV4VSjDNsqknyN4o0BhrWgH1JYdAlqQpYxwnfDFZMPUqFZSUgIHSnvCCLOysuB9WG3h0RD86L/w2suWLUNtIyIihHUTvk8RgT1dVBCdoEWAW4koxVbPgKm0a9cO0oHtMG/ePJpkiC6jqLsSjBCyBv/C3/Hjx8+ePZtZDqxFdPsA/itc7ywlJcXX15euHllXeXn5lClTEDXZXUaSRt1vFUpnxYyKgS3CFvFffvmFmnyslIv5QBBDaaF8tgMOgaqyNX/wYenSpfQvWAVspmHDhtifHQghll2BgQMH0rPARgRDStIsTQDBCcFMaNWjR48WtdYzUIKqCQFyrZYFuSMWJnRxR8IwAWHRokUL5o6gPETuSHj7FCru6Pbt26ruCE6Ael4b445oBADAqVXqjmDJ7Hx1dEes453QHaGqerkjXA0cWnimKFbCRZzUUuN0ki4gY0DChAxP+AKVAceEf2laEBQ/wWMgnOSUIyHs2qrNHvC04L9quz5g//tKpE076Igo1rBFPcvKyujnmmqFxxMni310bLyhZZ6pP40IHKKgoAD/tbhBRpKDkEMjXkXTIhO4evgXrpXam0IzVqt9AWEwzKUY3ET3+PFj7UsUv3nzRndDpZ2FrwiF4Pni/o0w2B3huTZDd0SWr4s70lGXyOeOYJz0CMs9IwDBdRKHw+FwOByOerhO4nA4HA6Hw1EP10kcDofD4XA46uE6icPhcDgcDkc9Zq2TysrKHjx4IOwy9urVK7VLzIjASb148cI0Pbw45g91b2T28Msvv5SUlOjyw9LSUrVdfTk1EHge7o44xsPdkcVhpjrp5cuXNHmJlWByqvz8fFtb2ytXruhSQseOHbVMUWo8Dx8+RPlhYWG9evXatm1bpcMWUO3hw4e3bdu2f//+R44cEf0XP1+/fn3Xrl3bt28/efJk1ZEmd+/eHTNmTGhoaO/evfms37qDa8UGu7IJrGNiYvr166fLz0+ePOng4IB7LVP1Hj16lJWVNXPmTNxcmqROO/CtK1euDA8P79Chw/Tp01XHN+EZGTlyJMwMj092drbov3jYYas9evSA3SYmJmpZ/pkjBPEJ15MmBDFDd4RAe+HChTVr1vzrX//ScZj99evXR4wYQe5IdTFU7o5kQq07Gjp0aHV1R7dv3yY7weODkkX/FbojPB3m7I7MVCelpqZaW1vD0eNCswSuW7duAwcO1LGE3Nxc+LWbN2/KUT1kAzS3b1JS0qBBg2D0w4YN016Z+vXrBwYGTps2rXv37th/8eLFwh3wqEAUxsXFJSQkoGQvLy/hUgNwao6Ojj4+PlOnTu3bty9+DlOW47yqGW/evKEV4K9du8YSuHPnzlmprHakhS5dusTHx8tRPZpOjSbjsdJh/lk8qvCnsBMooUmTJjk5Ofn7+wt9E4IlIjc2wsz69OljpTLtIayUIj2CH/y1u7s7rU7I0U5aWhoue2Zm5q1bt5g7gl2ZiTuiew1wCF2WodXFHVkplyiAmNbujiCzrCx2inYTo9YdnT9/vk6dOhbtjljYqtQdicIWLT5oEe7ITHUSrX0t3HLp0iVc0zNnzuheCFyGpiWOjQS2bm9vf+fOHfqanJxspbIgMwPJGWzFz8+PJgoj84IKZPkEhDZ+ziZ/gzOFeSHbYyXg2YAZsUm9yLxE86tyVIH3sfr1clQK5b3D9dS9kIyMDIQfOXKdoqKi7du343YjH9DFMdFayxs3bqSvly9fRsXYdM+gdevW0O7MTkh85+fn09cjR47g57NmzaKvMD8oLdGyFRy1IBcKCgoSbqH1QPRyRyhBJncEB3LixImXL1+qXYFVhCTuyMPDg82fRO5Il+aHGk41c0e0iA1b6uTKlSt6uSOIdQtyR2ank/D4wZsgWYEyGKOE5MjEiRM9PT3Z6y0kdviXsCkPj/348eNXrlzJtqSkpEC/q53kyhhKS0thEKNGjWJbSkpK4GjYZKMiaL2CFStWsC20rhOrKrJSZ2dnYT0HDBjAak6LagnXA8IlwhYkc9KeVzVj06ZN8EG4UBERETAVmhj92bNnuHe0vACB+zJhwgQ2161CudoX9meN22VlZYgTCxculK+qtLBApY4JyUODBg2Eb3iRpbEteXl5Vr9edorWVGJbRo4cCSsV9m+gRV4tdEVS04D8WBd3hIe0Une0YMECJFey9i/RRSddvHhRrTtiwgjuCPUUuiNk/JW6I+EWjioid0QNeLq7o9u3b9PX8vJy6AnhCqSSo7s7QtgSTmgZFRXl6OhIDwUek+rkjixGJ3l7e0OQCvfE04vnmTVl4792dnZMrio+LCPMFgFQBfrmhWY09dCkFzewe+HGdu3ahYSEqN2flkUU5luwLRsbG7b8MnI70WT/tIwrPBo+7927F59FfU3go7t27arpvDgKDTqJmmSERoLPsCJmWjAnfBWKYIXyhS/ur6YDIX5osSJdemjq6JiQxLOp/QlakpMeEFrX/dSpU8Id4MjgvOgz0ruWLVsK/7t161b85MSJE5XWsMaiSSf5+vqK3BH+pd0dnTlzRrs7QoQwwB0J0UUnbdiwQeSOENggg9g7xICAgA4dOgh/wt2R8ajVSTq6o8GDBwuL6t27N55lTQfS7o50mUfeYHdE69HSA0KtTUePHhXu4OrqaqHuyOx0EoFEWSgdnjx5gisoWjsJortZs2ZBQUHwIFu2bFHVLtCq2KilYyOEuZVmNC2yvW3bNlV/hwojJqndH5kW9he1lMLzdurUiT7jv4MGDRL+FzaKjYcOHVJ8WJtTuIK3QinLAgMDNZ0Xh6CWPGE3VaT48Dui3eiGwoRgSDAnGBUtN8tITExEoqOpYZJWqdSEaIVdtejimCoqKrCPqM2SPAutYEqa6d69e8IdcC4scOLERX6NjitawoyjisgdPX/+3EplxfjS0tIWLVpocUfYrt0dwScY4I6E6KKTKnVHsBORO4KFVOqORCskclRRdUcJCQmVuqMmTZoIm5cUxrkjq1+vsKsWI90RpWrkjq5fvy7coZUS+gzHKHJHMDCzdUeWoZNope5du3aJdrty5YqNjU10dLSq6CZEb9ZF7N+/f7dmNHW6JEOk4CSssKaISO/vRc2JcEx0grj++K/wta7ig06iJwr+0UqlNxJ+ixI0nReHUHVMeDIhHVT3jIuLgwnBkKytrVVHMNEtYB04RNCi35qAjVVaT10cU0lJiSY7IVOkZcZFlRQ6JivBWC3dj8tRaHBHO3bsEO2GqGBnZ2ewO8LtMMAdCdFFJxngjshOuDsyElV3FBkZWak7unDhgui/aWlpBrsjXQYn6uIWYD/aw1al7sjR0VHkjuj6mKc7sgydRG/QVAcWguXLl1spl1IXiW4C2kV0M4xHjvYk0argqu1Jly5dEu4QGhrK25MqRdUxhYeHqw0kpaWltAa1qM2S0K6TjEf3BE70QpDaLYTtSfCSwh14e5IkiNwR2ZVadwT7MbE7EiJVe5LIHam2J6m6I96eVClq3ZFaN87c0bJly1T/S7fAbN0Rb08yHSLHREMWIVBEu717965r1674FzSK2iGFdevWHT9+vKajQM5310xGRobaX/H+SZaCqmOKiopSm/hiT5qsa9y4car/pWdeODRaCG6NFiuCjVVaT94/ycxR646E3W8JI90RJJQB7kgI759kzqi6I9xxXDq1e8rkjkCl9eT9k1SxDJ30+vVr2A0bQ8hITU2l93GQGm3atBG9skXOpKmFgIBSidGMpjcmNN5NOInFixcv6tevr328mzAzOHv2rJVgvBuOBXFdVlbGdoAxiQaYJCYmis6Lj3erFFXHNHv2bBiSyE6eP3/u5eUFpUvxQLVpeuTIkS4uLpqmEs3JydFiRUwNa0H3ASZubm7CaZ179+4tGu+GE2T/vXHjhpXKABNhv3LUzWwHmJgVIneERxVXctq0aaLdtLujoqIi7e7oX//6lwHuSIju491U3REb74Zj2dnZCd3R4MGDK3VHfLxbpejrjlavXq3WHSHQGOyOQKX11N0dIWwJK4+cUDTeTYs7GjVqlAW5I8vQSQplg41IvUJ4wshoijM8/LjoolyNJgLRfQov3YmNjYUrEc2fdPr0afr65MkT+FD2Yg5XuEWLFr6+vsIJS2xtbVlCQBPbLFmyhL7ShCXCTAInLpo/qU6dOsJREhy1qDom5DeiRhfcDqgN+B1qAEBWjYdf1BgQHBzM0iA50OSYtmzZIgzGoglLaP6khIQEtkP79u2FdjJ06FDswN7EnTx50kplwhK13Wg4IiR0R9QqIxNqdZIc7kh1/iQ53Gw1wwB3NGTIEFV3FBYWJrI9adHujti4S7Jn0fxJursjei1jKe7IYnRSWloaHlc2EOnp06eNGjXCPu/evaMtq1atEqlvKFa4Azmqd+/ePXgKGMGECRPgZUQ92qhZXjhHbW5uro2NTbNmzWBGqLNqo/3w4cPhZCG/xowZ4+rq2rRpU2Sf7L/Xr1/Hk+Pt7T1p0iSaPxdXQ47zqmaoOiZkP25ubsKGSeoUyfqaIL+BzbRt25blSZQuy9S7MCgoyM/Pj5YyaNCggZ8S9l+axJZ9xaMKuQY7gTeBncCtICgK068LFy4g70cJMDN6ASSaZ5lCWv/+/fEBh4NFmfNaAeaDqjtCrq+vO4JsgmlVusCRAaxdu5YsB04G+ow+s1uv1h2h8trdETYiZ4Cd4HnR4o569eoljHYcLRjgjl6/fo3bJHRHkKfwAFXojpjDYe6IhS1Vd+To6Ch0R6KwNXHiREtxR2aqkzIzM0XDSSBLcdG3bt1KX/Go46KznIaAv8ADT2dUVlYGE2SNyZKDQ8P19OjRY8CAARkZGcLLCJ+CuiF9F+4PbwVrgMoZNmyY6itY/Hzz5s2RkZE9e/bEY8M0OAMpBawNP4+JiVHbgZSjCp463AhR1+YpU6YgXFE8g/dZtmyZyOkgM8avWA/E+fPn4xkWvoaQkPT09DQV2H/xFIg8C6oNI+/bty/i09y5c1U7CyP7JzuBlsrJyVE9Ik42OjoadpucnPzkyRM5Tqr6oeqOXrx4YT7uCKm5qhUx/6PWHcFOoNtgJ8jyVe2EuyM5UOuOZs6cqd0d5efnm5U7Er5oq9Qd3bt3j7kjtTOHMXc0bdo0c3ZHZqqT1AKTatGihY4J2cqVK5HhqR11wqnJIPW3t7dXHdStFjgFHx8f+dQ2x3Lh7ohjPM+ePXN2dubuyMyxJJ30+vVrZD86rmsGGbtnzx65q8SxRLZu3Tpjxgxd9jx79uyIESMkX/qGUw3g7ogjCdu2bePuyMyxJJ3E4XA4HA6HY0q4TuJwOBwOh8NRD9dJHA6Hw+FwOOrhOonD4XA4HA5HPVwncTgcDofD4aiH6yQOh8PhcDgc9XCdxOFwOBwOh6MerpM4HA6Hw+Fw1MN1EofD4XA4HI56uE7icDgcDofDUQ/XSRwOh8PhcDjq4TqJw+FwOBwORz1cJ3E4hvDixYtOnTo5OjqGh4cnJydnZWUVFRVVdaU4FkB2dnarVq08PT0HDBiwdOnSM2fO8JVNOfoCK2rZsqWPj09sbGx6evr58+ffvn1b1ZWqtnCdxOHoDQKbv7//jh074JvgoeCn4K3gs2xtbYOCgsaOHbt58+b8/Hz+cHFEXLhwwcnJ6cmTJ8XFxQcPHpw1a1a3bt2cleADvnLBzakUZkUwFRgM8jRka8jZXF1de/bsOWfOnCNHjpSUlFR1NasPXCdxOHrTr1+/uXPnqv3XrVu3tm7dCqkEwWRtbc2bDTgMBDZEshs3bqj+C7YBC4GdwFpgM7AcEtywJViU6avKMVu0WFFZWdnp06fT0tKioqIaNmxoZ2cXGhqakJCQkZFRUFBg+qpWG7hO4nD0Y8qUKZGRkTruLEz4HBwc2rRpAzcna/U45klpaSkEUHZ2ti47wy3n5+dv2rRp1KhRLVq0qF+/PjST3DXkmD9v3ryBFR04cACfy8vLte/8/v37vLy8DRs2xMfHt2rV6uDBg6aoYnWE6yQORw927NgREhLy7t07fM7NzV25cqVeP09NTZ09e7Y8VeOYL3CzEMqrV6+mr0lJSffv39erhEaNGhUWFspQNY7FACvq2LEjWRHSLRcXF91f0V67dq1Tp05y1q46w3USh6Mrp0+fdnNzoxf/N27ccHBw0DfaPX782MPDQ57accyXUaNGjRgxgj7Pnz+/c+fO+jreOXPmpKSkyFA1joxAzfz000/szWlwcPDr168NLi0+Pn7cuHEKPdsmGQ0bNnzz5o3BR6/JcJ3EqYbAqo8fP75x48YjR468f/8eW77//nsjy4Qkcnd3J2H04sUL5PcXLlwwoJymTZvevHnTyMpwZAJ3NiMjY9OmTXSPkLvHxsYaWSYKCQ8PJ0+7d+9ef39/A3qqFRYWwuSMrAnHZOB29+/f/8svvwwNDa1Xr167du0qKio++eQTGJhhBa5cuZLkNcCHJUuW6PhDuCl6T5eUlLR+/XrDjl7D4TqJU90gEePs7BwXF+fh4dGyZUsYea1atYwpE3mYm5sbUkN8hr+jwW56lTBgwIBr167hw7JlyyZPnmxMZTgykZmZ+fnnn3fs2BHa6Ouvv0ZQmT9/vpFvK6DUkfqTMGLDlHT/OYwtMDCQPsPqbt++bUxlOCYjJSXFzs7u1atX+Pz27dtdu3bhg8E6KTs7G/kVWVG8Et1/e+PGjebNm+MDjCc4ONiAo3O4TuJUN2JiYlq3bk2G/f79+ytXruCDMTrp3bt3ISEhTBhFRkYifOpbSEZGBnXFLS4uhoYzuDIcmUAQ+vOf/7xhwwb6WlhY+Pz5cyN1EkJUgwYNSBg9fPjQwcFB7TAl7aACpLCXLFmSlJRkcGU4psTV1RUZkWijYToJNoPSyIpWr17drl07faN2w4YNnz59ig/e3t7wP/pWgMN1Eqe68d133+3evVu00RidFBsbO2PGDPqcnJw8YMAAAwpBGIazo89QXRcvXjS4Phw5OH78+KeffkpvaRnG6CRERAhikjg0TOnw4cMGlAOBzhS2i4uLYZXhmJhvvvnm6NGjoo3QSenp6fHx8ZDjeXl5ImNTC+QRpDbJ6+zsbFiRAX2MZs+evXDhQvqg+ws7DoPrJE5146OPPlJVIdBJqampHTt2nDp16r59+3R/94FIyWYBEA52M4CIiIiTJ0/iw/r160eNGmVYIRyZWLduna2trWgj6aS2bdtGRUWlpaWdPn26rKxMl9Igi5s1a0Y9bWEwwcHBbLCbvqAo1gAZFBR0+fJlw8rhmJLvv/9+7969oo3QSffv3z927Ni8efN69+7t4+Pj6+tLppWbm6sqgGg+W5LXkEqOjo6GzSpSWFjo5+enUDZqokCDTqhGw3USp7rx5ZdfqmZy1J5UUFCQkZGRkJAQGhrq5eUVGBioPbeDomrRogUJo3Pnznl4eBgzYARRMyYmRqFsXbCzszO4HI4cZGVl/fWvfxVtJJ1UUVEB5b1q1aohQ4YEBATAcioV3L169Vq+fDl9jouLGz9+vDF1Q2mksCHmxowZY0xRHNPQvn37wYMHizaqvncj04KGHj58OIS10LQeP37M5pKAmTk5ORk2cIRo2rTpgwcP8KFJkyZ8ggl94TqJU92Ao0FkEm1U+97t1atXwtyucePGffv2XbhwIeV28EqNGjUivyYc7GYw0Fv29vYkyBB9KfJxzISXL19+/PHHP//8s3Cjpvdu2gV3UlIS62lr2CwAIhA1adjd69evucK2CGBI//u//zt79uzr168fP3588eLFCt36JzHTgmyCPqaNZ8+epTFrBrNkyZJZs2bhw6JFi/gUbvrCdRKnugHHVLt27dGjR+/duzc9PZ26vurSPwk65sqVK2vXrqXcztPT8969e/QvRDtjkjlGTEwMvYuBKzR+wDlHWmbMmPG3v/1txYoVmZmZiYmJiEw69k8SCm4PD482bdqQXy0vLx8wYIDx69XAMh0dHZnCPnXqlJEFckzA5cuXIyIimjRp0qpVq6VLl2JLt27ddG+Qxk2XcCaI4uJiODR8ePr0KX3g6A7XSZxqyN27d8eOHdunT5+4uDh6BzdlyhR9C+nfvz8SQWkrhgIpR0TstLa21qUjJ8eU7N+/f+DAgZGRkZDXhYWFyON//PFHvUqgl6qS+1VYIynsHTt2qL7Q4VRLILIl1MSQ7zQrWGBgIJ/CTS+qm04qKChYtGjRwYMH+ehHjpEcO3YMIVPaMvG42dvb08JMvXv3NrItnWOedOnSJTc3V9oyIfe5wq5pIK2SUBNv2rSJZm5bsWJFYmKiVMXWBKqVTqIhlGlpabRau4+PD625vWXLlvz8/Op0phwTAINxc3MzeHSbJuLj47du3apQduvu06ePtIVzzIHMzEzqsC8hsEY7OztS2BBMhw4dkrZ8jhmCm46IJpUmLisra9OmDT6UlJTwCSb0ovropDdv3jRu3FiUxhUXFx88eHD27Nk9e/aEbPLz82Pje0tLS6uqqhxLIS4uLisrS9oyz507FxoaqlDOgWltbf327Vtpy+dUORUVFdA0kjf5DBs2jCnsvn37Sls4xzxBWoUQJnmxISEhknS4rCFUE52Es2jdujU5ES0gJrHxvWwQ5vLly6vHReBIDjQNG3IiIXPmzKEPMTExGRkZkpfPqXJYdyIJuXr16r59+xTKHr5cYdcQ4IIkb3WG/TRv3vzSpUvSFluNqSY6CaKbzeivl/ouKCgIDw+XvLsup9rQoEED48craeLw4cO8YaBakpOTExERIV/5KPzEiRPylc8xHxo2bCitJqZhChIWWO2pDjpJOGOy8LOOQCQZthIFpyYwYcIEfZe81RFkdUFBQWw2Qk51An7VwcFBJoV948aNevXqIceTo3COuQEXJGGrM0Ikz830xdQ6qaio6Keffrp16xZ97dSp0+PHj40pEDHM39+fOtsatqyETN11OdWD69evd+zYUY6SY2Nj9Vr3m2NZ4Obu3LlT8mJfvHhhZ2cn+Xg6jtkCF2TMYsxCKFzK10BeXTGdToIQiYqK+vTTT9u2bYtkqE2bNhUVFd9+++3du3cNLlM4Y/L58+chd169eqX7z8eOHUszuMOj7d+/3+BqcKo3Xl5er1+/lrZMZHXQ9NWgNZejCXgnqcIbAxHOz89vw4YN0hbLMXO8vb2NWTGJEIZLjl6YTictXbq0Tp06z58/VyjHg9CK7sbopPv37zs7O9NSEoYtK5GamkozuMvUXZdTPZgxY8batWslLHDfvn1GLhXHsQjglKS9y3BTvGdJDSQ5OXn9+vXGlIDg6ODgcPv2bYlqVLMwnU7y9/en9WWEGKyTIIrd3Nyo8Zk+Q+voW8jjx48RrugzPtDcJByOiHv37rVu3Vqq0pDVWVtbG7buN8eymDx5spHhTQgUUs+ePaUqjWNBwAWFhISwr9u3bz9z5ozur8/UTprD0R3T6aTvv/9+165doo3QSQsWLOjdu/e8efOOHTum41uzd+/eBQcHU+9afG7RooXBPW2bNm1KM7jL112XUw1o0qSJJDO8Qx7Z2dnxmUtqCPAtwvBmDNu2bfPz8+M9S2osQhe0fPnyqKgoLy8vd3f3zp07T506dd++fdSHRBUKl5VOmsPRgul0EsLDxo0bRRuhk65evQqdu3Dhwr59+zZo0MDe3j4sLAyZU2ZmpqYbHxkZOX36dPY5JSXF4FrB4BISEhTKISQdOnQwuBxO9Wb+/PlLlixhX4uKigwoBFmdh4eH5BNXcswZHx8fFt7ev39vmNqGh3RycuI9S2oycEGIkqKNsKi8vLwNGzbEx8c3a9YM0dPf33/48OEVFRVsH+GkORzDMJ1Oio6OVh2xr/reTbhmO265jY0Nbj/uNEwBBgGzwC1n5Qg/G0ZJSQkcEH2Wo7sup3rw9OlT2CH7OmzYMFtbW4TA2NjY9PT08+fPVzrBCR60jh07wtnJXFOOeZGamkprxSuU8trT0xMZY0hICDVg69Lr4P79+/gJEjmZa8oxa5KTk5s0aeLt7a3deAoLC4WZmAET5XBUMZ1OwnP+6aefzpw58/r16ydPniRprEv/pIKCgoyMjISEhNDQ0Pr167NZAF6+fNm3b1/jx/OzGdwl767LqU60aNFC1MCJsAeXBP8VHh7u6Ojo6uras2fPOXPmHDlyBPpb9HNo/bi4OBPWl2MWPH78uHnz5sItcLn5+fmbNm0aNWpUYGBgvXr1/Pz8hgwZsmrVqosXLwpbAhTKzpewK96zpIYjHM8vdDvI7QMCAjQZT3Z2NrI7Pm+78Zh0/qRr165FRERAFEOaUGKN4KHXKwzYgbW1tcgajAQOi6axkba7LqeakZKSAhkEa9G0pnJZWdnp06fT0tKioqIaNmx4+fJl9q/Vq1cHBwfzObpqJohkSUlJiG2afB30d2ZmJvYJCwtr164d2049S2A8pqopxxyBSnZzc9P01lXodnx9fX18fKi/77p16/gsAFJhefNx9+/ff+/evRIWCDuztbWl6wDNTjMXcDhCnjx54uTktHDhwgkTJkDl29nZeXp6DhgwYOnSpZUOPDl48CB25rMA1Ex27NiB0LV8+fLY2Fh8gKsJCgoaO3bs1q1b2XS7moiOjuY9S2o4NJ5f9ylvWI+lGTNm8BnbpcLydNLRo0e7d+8ubZldunShJd5E3XU5HIWy/zWEjmhZ0+LiYgigWbNmdevWzdnZGb4sPDw8OTlZ1Gxw48YN/OvBgwcmrzWn6lHbEgB5BJEEqQTBZGNjo0lwwxfBokxeZY4ZIZz+hlOFWJ5OQoWRk0k711FmZmb//v0VKt11ORzYW4cOHVasWKF9N0Q4xDlEO8Q8RD5ra2tEwZEjR9rb2/NZAGomOrYECLubYH8nJyco72HDhvH1JWo4uPuwAT5bjTlgeTpJoezVJO1sEBUVFWvWrKHPqt11OTWZeCX6/or11aXZuTg1DYNbAqi7yfbt21WHAnBqFJGRkaozM3OqBIvUSefOnRP2dpSWlJQUCafQ5Vg0ixYt6ty5syU+I5wq5N27d7wloCbTqVOn77//vrS0lL42atQoIyNDrxKMn/KGIyEWqZOAs7Pzy5cvJS+WuuteuXJF8pI5Fkd2draXlxd/98HRF0S45OTkqq4Fp8qATvrmm29GjRpFX/XVSXx4rLlhqTpp4sSJkg+XVdtdl1MzuXDhAhQzX4WNoy+8JYADnZSamlq7du1r164p9NRJubm5fJFsc8NSddKNGzdatGghYYE0XXJ6erqEZXJMTGFh4aJFi1hDI4wkMzPTgHIgjxwdHfkMyDWHnJyc7du3s68wG8Pu/pYtW4KCgnhLQA0HOmnNmjVz58718/NTKHUSDOPBgweVNk7T8FjdZwHgmAZL1UmgYcOGhi2zpRbDuutyzApEu1q1ag0YMIC+wlUZIKZ5s2INJDIy8qOPPjp27Bh9hdmwgR26o30+QE7NgXQS5DLSrU2bNkEnzZ49u2XLlu7u7pBB2NigQYNWrVr16NFj+PDhycnJyM8zMjKgzp2cnPjwWDPEgnXSzJkzFy1aJElRixcv5t11qwHQSXA0P/zww6lTpxQG6SQ+A3LNBDqpQ4cONjY2tMiDATqJtwRwGKSTFErp/Le//Q1OSfTerby8vKCg4Pz58/v27cOeKSkpY8aMCQoKwt8qqjJHGxaskx48eECtmkaSnZ3t6ekp7YRMnCoBOgmp29q1axGxaK4HfXVSfHz8+PHjZaoex2yBTpo/fz4yfpr/Wl+dRLMAIOzJVkGOJcF0EujTp0+tWrVIJxUWFj59+lRTzH348KG0nUk4UmHBOgk0adKEzXQMI9Nx/W0hvLtudYJ0kkK5olZycjLppFmzZk2ZMmXp0qXbt2/HDlevXsXtVmv2iJS8WbFmQjoJ3uOzzz67efMmzGbevHmjR4+G8axatWr37t2nTp26c+fOq1evVH/L5wPkiBg1ahTrGfns2TMPDw94HnyePHly06ZNHT/QsGHD4ODgnj17smYkFxeX9+/fV1m9ORqwbJ20aNEiNhMXvBisEw7O2trax8dn4MCBtBRAWVmZpp/z7rrVDKaTrl+/joA3c+ZM2MPFixfhsxDt8HXkyJHwSkFBQfBQcF6urq74gK/Y2K9fP19fXz4LQM2EdBI+QFIjdMFsli1bduzYMagfuJHExMRBgwaFh4c3a9asQYMGbm5usBxYS4cOHaKjo9u1a0e/5XCI4uLiPn36VLpbaWnpvXv3zp07d+jQIdoCR4SvMteOozeWrZOKioooLqpuz8rKmjFjRrdu3ZycnBwcHDp27Dh16lQYJduHd9etfjCdBMaNG/fNN99U2o4NYUQdBUaMGAEhJX8dOeYI00lv376tV6/en//850rfu7169So/P//EiRPu7u5Pnz41STU5lsHs2bOnTZtmwA/T09PnzJkjeX04RmLZOglap3Hjxt7e3lFRUWlpaSdPnlQ77QR8HwIhTPDOnTu0BWcdEhLCu+tWM4Q6qays7IcffmA6CYIYNnD//n02Sa6I69evyzfJO8fMYToJILmvVasW6SQooYyMjNzcXLiO169fq/3t+PHjt23bZrq6cswbBBc7OzvDpPPNmzdDQ0MlrxLHSCxYJ7179w5aZ8eOHUwGDRkypGnTpg0bNmzfvn1SUlJmZubDhw/V/jY+Pn706NEmrjBHbgoLC1NTU58/f05fz507R70EysvLJ02aFBMT06lTJ39/f3clbm5usJaOHTuylgN7e/sqqzqnSoHCzsrKYl83bdpEr+PPnDkzZsyYfv36wdV4enpStxIXF5fAwMCuXbvSPgcOHBg0aFCVVZ1jZuzbt69Hjx4G/9zBwUHCynAkwYJ1Uv/+/TUtE3j//n1kgQkJCe3atYNsCggIGD58+KpVqy5evFhRUcG761ZjGjVqpHuHs+Li4ry8PNb3PywsDF9lqxrHfMnPz4ej0HFn+BAocjgT6tb95s0bDw8POWvHsSSCg4PPnj1r8M+RuV29elXC+nCMx1J1ErSO7osDwJ0dP3584cKFffv29fLy6t27N++uWy2Be2rVqpXBP09NTV28eLGE9eFYCkOHDjVm9WuoczmWm+RYHEi61HaZ1R2EtrS0NKnqo1DO4RQQEPD999/b2tqOHz+exz4DsEidtGPHDtx4vjgARwQU8J49ewz++c8//9y1a1cJ68OxCEpLS62trY2JH3FxccYYHsfSefDgwYoVKxYtWrR7924jJ9S+ePFily5dpKrYpUuXPvvsM0RMhEtUsnnz5v369ZOq8JqD5ekkWCFfHICjSnFxsYODgzH2/P79e945oAaybNmyCRMmGFPCrl27RowYIVV9OJbFzp07a9euPWTIkISEBGdn59DQUGNyeHgwCb1Q9+7dhetxPXny5OOPPy4sLJSq/BqChemk+/fvw4Zu3bpV1RUxBKSt8+fP79Onz8CBA/fv31/V1aluzJo1y/ghtUFBQfpOVcqxdDw8PDQN+NARpG3e3t5S1YdjQeDW/8///M+JEyfo6y+//OLq6mrki7Pg4GA2NNtI6tatKxqMaW1tbdjq4DUZE+mkmzdvsvHYFRUVt2/fNqCQly9furm55ebmSlkzU/HmzZsGDRp06tQJqeeGDRtsbGz4gDsJoSTM+D4i06dPl2+2iLdv3z5//pz3DzAr4E/wVBpfjru7u5YpbTnVlYyMDCsrK+GWRYsWId0ypszk5OSVK1caV6//zw8//LBz507hFoQexCBJCq85mEgn1apVi71zRb7+ySef6FuCpS9QOm/evMaNG7OvyF9///vfG6YXOars379/wIABxpeDqKnLRLr68v79e8jir776ysnJ6S9/+QsfSWA+dO3alTUGGMPAgQPZrMoSUlRU1K5du9q1a3/77be2trai5VQ5Vc6CBQs8PT2FW9asWWNkV+5Tp07BRRhXLwW9dWnTps3UqVPZRridP/7xj/n5+UYWXtMwnU6yt7cnP2KYToqMjExMTJShaiZCZK8K5RiZ9PT0qqpP9eDcuXPIvaZPnw7TKikpMb7AiooKZ2dn48sRMWvWLBcXl+LiYoXy9WtgYOCwYcMkPwpHRxAtNm3aNGXKFAQ5qWbk37x586RJkyQpSkjDhg2HDBlC/V2g5z7//PMzZ85IfhSOwezYsaN+/frCLUuXLjVgOVuExbZt216+fFlhtBdCTIdtQ73BzjMzM6Gwb968qVBma4MHDxam6xwdMZ1OQsZfr169t2/fGqCTZs+ebekzHnl5ecEpC7fgWeLLQhnDuHHj6tSpM2PGDKgQqPCIiAhJim3atOnjx48lKYrxj3/8Q9gSkJeX94c//IEP2KwSXrx44ejoGBoaiudx5MiRtWvXFr2YMAzYDCzH+HKEnD179k9/+pPQTsaOHStHeyfHYIqKin7/+99fuXKFviJIQYikpqbqXgJiIlJod3f3I0eO0BYaqwT/ZkC3OSRjQUFBgwYNQrEKpTaCbvvmm2/gIf/6179Cij179kzfMjmm00kK5Qxa0Lmkky5evHjq1Cl81rSOBAOC3cfHx9LfU3Tv3h1OWbjFxsZm69atVVUfS+fatWvIrdniAG/evPnqq68kGZs9adKkzZs3G18OA48Y7F/UMfPjjz8WrjbIMRlDhw4VdkiCfv3yyy8lWaTd2dm5oqLC+HIYq1evFs1guWbNGtFbHk6Vs3jxYkiQefPmrV27NiQkpFGjRuXl5devX9flt4cOHYIkmjZtGlnOy5cvIXH8/PwgkXH3Efjat29/+PBhHWvy008/2dnZbdy4kb5CrtEsADDv58+fVxpqOZowqU4qKCj44osv9u3bB50Ek0Ji1Lp164YNGyK9c3BwgKBu1apVjx49aEXSVatWZWZmbtiwwdbW9smTJyaopKzAHX/33Xfs3dDRo0c//fRTms+XYwDwSvAgwi2xsbGSvMyC5xo4cKDx5Qj5j//4D2GfADx0v/vd7x48eCDtUTi6YG1tjdSLfcW9gE4ycs4bAg5N2lEm69atg1cUboFO4gPrzJDTp0+PGzcuLi4OtwyK5927dy1bttS+FO6jR4+6dOnSrl07li+tX78eoRCBTxiUz58/D63ToEGDBQsWaI8XixYtcnFxIX2GPeEeYZB8bIEkmFQngalTp+KWq33vxlZuhzyCrUAqQTA1a9Zs+/btclQpJiZGODxy1KhRmzZtkuNADCSy//jHP5Au9O7d++uvv+aDDowhPj4+KipKuCUpKUn3Kdq1AM8iCk4G8+zZsy1btuCDk5MTFD/bjrSvdu3akhyCoy/ffvstshThFhsbm5ycHONLXr169fTp040vR6HseHfjxo2LFy9+/PHHwlA3ePBgOC5JDsGRFUil/v37R0RE0PsvIe/fv0ea5+zszALQtWvXAgICBgwYQF0YVUGCjZ94eHhgH9VVTd68eRMeHt69e3daBh5mA70lU+dX1PDmzZs1LcM3tU6C0dSpU4fppEr73m7cuHHKlClyVKlFixZsAVTQqVMn+XoLlZaW0nC/vLw8nNGPP/6o6Xng6AhuFlIx4RYIUIhdw0p78OBB586d2cxJSNmNn8j01KlTtra2pPLXrl373XffwX8plHNkIDBrzzU58oGLL5xRhtqTqP+sAezfv79v3770GfZj5IBwYtGiRT4+PtTMEBgY2KtXL4p/u3fv/vTTT3V8ocMxByBuIIDYytwKZQho1KjRuHHj6C0Y7uyIESO8vLx0XBLu4MGDHTp0aNasGRIw6rgG2QTJxRZcgliHSCJXIy2vX78OCwv75ptvmjRpgkemT58+lt4ZRndMpJNCQ0PZm/tDhw5169aNPuOW4x67KmnatCliVWxsbGJi4tKlS6klvLCw0ICxA7pgSp20fPlymvD34cOHot7cHMO4cuXKZ599JuyfhAfYgAHeEO7Tp0+H+bFxT+Xl5W3bth06dCgr3ADmzp3bsGFDNiEqHMqyZcusra3/+Mc//vOf/5w1a5ZFD0qwaIYPHy7qn/TDDz8Y0D8JOgZKvX379uz9KRSwm5sbRJjBPfSRpoeHhws74WILAtJf//rXb7/9tnHjxpJMYcAxJXv37oVVsNfuRUVFbKFuJFFOTk6QOPqaH8JiQkJCgwYN8Bcy69y5cwql44KpwCYlGfmrCvIBxGvSRvC3/v7+NWcKQBPppJUrV86YMUP7PpCrd+7cOX36NL13Y/oaMUySXpYioJNw16d8wN7eXj6dxCb8HTNmjPD9C8cYcDGF493wGMOY9Ro1DW0E65o2bRpLjPbs2QPhjjKR07u7u3fp0kXfyITABrkfGRkJt6VQOq/+/fvLMWKcYxgvX74UjXdDjl5QUKD7Yg6wlqSkJNhJVlYWbSkrK0MiZGtri2weCtvBwQE76Nur8urVq3AUbGwHqoTE/dq1a3oVwjFDLl26BD8jfLcLSd2qVavevXsbk4xBjkNpBQYGIg9HSgbZlJKSIkV91QDXKhzWBw4fPowEQ6bDmRsm0kne3t4Gj7Xu0aPHzz//LG19FEqd1KtXr0UfgB3LpJNyc3OpxzHcKxy06utqjsGw+ZOOHz+uUGoUX19fXQblIgjhpkAos8aAu3fvInZCGAnjJe4dtkAwIeeDjq+0WIQ65I5sNlSU6enpOXPmTN56ZFYI50+CJWAL7AdSW5fe3Pv27cNTDBnEtPXu3buhkBITE9mW0tLSJUuWIG517dr15MmTulQJNtOoUSP2Tm3v3r3wSEgaDTk9jvkBPeTn54f8nyQ1BLFUXf7hl3x8fGBssrY1Is+vVetXauH58+esO021xxQ6CcHMmJUBli1bNnfuXAnrQ5jsvRt8JWUSa9euNXK5TU6lIFZ17969f//+mt59YIepU6ciCB04cIBtQciEGNI0/hY+btq0aQ4ODjExMVry+w0bNqAQ1ssSoQ4/4S9KLAVoFAggiB5NO9BMgK1bt2b92PChjRJNawJCfsGx0IyymkYelZeXR0ZGsqFJ79+/Hz9+PMrkXRirGbjRyM3gE5DISTt3GnIzOV65CCFVRG3kBPLJ//qv/5L1oOaDKXQSXIBogIlewH+JRoBLgml0EkIslD599vb2NnK5TY6OILlv1aqV6qCMrKwsFxeX5ORk1lsuOzsbW2bMmFHpzDfwRLt27UKx/v7+W7ZsEe7/9u3bAQMGdO7cmY6IPceNG9esWTO557O4cePG/v37T58+zaeslISioqLGjRur9iCEkp48ebKdnR0bo0qv3uzt7XWZsuvRo0cJCQnYOS4uTrRU0a1bt7y8vJYvX05fYTCBgYFQ7bK6ZSiwH3/8cePGjXl5efIdhaPKvn37YmNjJS8WQlzaibvU8s033wgzyfXr1zds2FDug5oJsuukFy9eGH81kedJUhkhptFJU6dOpbWjz54926FDB8nL52iC5p65f/8+23LixImOHTsyqYoPuOlhYWH6zmOE2IaAh7A3YcIEFIIo6OPjs2jRIvovlHHz5s0nTpwoa4aHxA7mVL9+/d69e/v6+uIDX7NJEnBhqSe1UHoOHjx4/PjxbJo+aGt4JOGLNl1AJIO8btKkSXBw8N69e+F4d+/eDRNlQ5NycnJcXV3ZpMwygVThiy++6Nq1KwL2d999Fx0dzV8KmwxEHDlW34IrMMFSoampqba2tpcuXXr+/Hlubu7f//53mvSkJiC7TsLFZSHEYNq1a0cr1EjI3bt32bgDhTI1N34ouAhESkRTGtPbo0cP6kPDMRnU6US1ZzciFi24tn//foMLR0BdtWqVl5dX27Ztly1bRhshxZycnKRaMkwLkydP9vf3Z7Ecp4Pjyn3QGgJcImRuUFCQao80yGLEpJCQEE0v2nTh8uXLUVFRuF9jxoyh3lE44syZMwMCAuRugITUo37r9BWuqV69enLPG8dh4DldsmSJ5MWOGDGC9SKQiaKiItjt8uXL3dzcvv/+e/g9Nut3TUB2neTn52f8nFQpKSmSz5oVGRn5+9//nsnwFi1aSL4W986dO5GJKpRtDKL1Bzim4datW87Ozj/++CPbgqwdSTx0hlSTf1D3u5MnTyIlgLVT5JMbRFnhPKUQbf/5n//JpiHgGA9SfzyzrK0R2hpSxsHBQTg5rTGUlJTMnTsXaqy4uLhNmzbQTCZ4ebpv3z47OzvhlqSkJDl6NXDUMnr0aOHcXVKRlpYmh/wSMmnSpDlz5iiUI2Bq4FAkWXQSki1ar3HUqFGqk4cawNmzZ3v27Gl8OUKgkxBsWrVqRV/l0EmBgYHUZDVt2rSlS5dKWzhHR54/f960aVMEucePH3fr1s3IxgC1IKbGKTFBLwHiT3/6k2gquW+//Zb3GZcWSGp7e3vo4CNHjri6uiYkJEg+sR6kGFzl3r17pS1WEwsXLhRNR8cXQjElvXv3PnbsmOTF7t+/3+ApdnUBbq1evXovX77E58aNGxszl4GFIr1Ounz5cu3atZGm4OalpKR8+eWXui/jpwlkWs7OzpJUjwGdBIHMFnuSXCdBIUEnKZRv3yDI+BqEVQgSoC5duiAmybRWDHzf0KFD5ShZEz/88IPoNe4nn3xy6dIlU9ahJpCfnw+zadmypeTamujUqdP58+flKFktmzdv9vLyEm6ZP3++JHOIc3QBSZoc06nfvHnTmBHllbJly5bo6GiFchm7Hj16yHcgs0V6nQRxkJyczL6uXLlSkl7YzZo1e/TokfHlMKCT4COys7P/+te/vnnzRnKd9OzZs40bN2ZlZW3fvt3EQZSjClJ2+SZlePDgATygTIWrpWfPniNGjGBfc3NzP/vss5qzjIAp6datm7Ajo7SMHDlSpvUr1fL48WPoaaEjbd26tUwLQ3FUadSokXANE6lAHujp6Sl5sQxfX19a2Kdr1656TeRbbZBYJ71///53v/udsJ9EcXFxrVq1jG+pS0hIkLa/IekkhTKlg7eCTtq2bZsky/uhkLS0NB8fnwEDBtjb20N+CUddcaqEVatWyTffukL5AkW+wlVBVlq7du2ZM2ciw1u/fv13333HX+zKRPPmzSUf4cFYsmSJfHMoq2X06NEuLi5IG06cODF48OB//vOf8p0dR4S1tbVMJUu1dLcqUEjQSQplL1sENZmOYuZIrJNevnwJVSR68LDF4FZrNrj68OHDAwcONLZ+AphOKiws/Oqrr2xtbefOnevk5BQTE2PwtCIXL16Miory8/OD+6PBMgsXLkxKSpKw2hzDgKTYvHmzfOWzWbJMwMOHDyG+79y5M2zYsHbt2vXt29f4V9scTcg6SUx2djYN9TANkNRPnjzZsGFDly5dwsLCEhMT+WyWpkQ+neTv7y+cBFJCENFoLR1YC7JNOQ5h/kj/3u3zzz8XNs1BIf3mN7+hsfF6gYqlp6dDwJJUKisrk0oyFxQUBAcH9+rVizUwpKamQswh9uBYe/bsCQoKQhK5Y8cOHUegwEBXr17dtGlTlHnq1Cnhv169egUFJvdkqZxKGTlyJBsOLQcwGDla1NUyduxYWiUQEt8E86bUcGRtKbx586bJ3tiWlpbWr1+fXs7m5OTwt7QmpqKiQjTYUF/GjBkjjK3IwHEfsXH69OlsI/J/CfvSlZSU1KtXr0KJi4tLjbUZ6XVS7969+/Tpw77iLiKE6FsItfUNHTqUvQijeZORQBs5kdKBAwcgXI4cObJmzZpDhw7RRughZHXC1Z2QrI8YMcLV1XXq1KlaXhreuHEDVfLy8kpJSdHUfA09LlP3YY7uRERECBdxlJzo6Gi2crOs0CqB5LDwgJiyd0vNRNb2JAo/8pUvZPny5bTAO4Kfvb09T95MDLKaJk2aGFNCo0aNhJ1oaapkbPztb3/Lxrp+++23uixTqCNz5sxJSEhQKLtyk/HUTKTXScXFxcjAmjVrFhcX17p1a4iS+/fv//zzzzq+BUfSEx8f37hxY7b2LcwrPDw8LCzs3r17W7duDQgIgPD68ccf9X3Ocaa45X5+frovDF5eXg5DxE+6d+8uXJMS3g01adWqVadOnZjY0sTFixdbtmypV1U5kgNTlHUSP6R0ppmvb8OGDWPHjlUonxQHBwce7WTlzZs3/v7+sh5CjsUG1AK3TB0lU1NThS0QHNMA+WLkkgyadFJsbKydnR1NSiKhTkLERLHkNqHwEH8lKdYSkWX+JPjuI0eObNy48eDBg3Tz9u/fj6e00vZAGIGTk1NaWhrVCuXMnTvX2dlZNL9IXl4eLAMp0eTJkx8/fqxLlYqKiiBWDJ7h5vz583369PH19V20aNG4ceNgmklJSToeGnh5eck0rpijI7hlskoKiCTTxB4YIU1+uHjxYt71TW5u374t64hr4OPjQzPTyMqZM2fatm2rUAY/+Nhnz57JfUSOiOzsbCO72MKJ9e/ff9EHbGxsSCchbgYHB0+dOlUhqU5CnMXhFMrXO2Q8NRZTrINL4Oa5urr+9NNPav+LRAd3olu3bizpx56wAIgSTetsI9VDqHB3d4cj077O7unTp5F5Gz8R6osXL6Kjo0eNGqVvxIU1jxw50sijc4xB7iUbz549GxUVJeshFMqHqF27dvQZll8DJ3wzMXAdMTExsh6iZ8+eEr4o0URERAQtbYHctXv37nIfjqPKhg0bJk6caEwJCIihoaFxH/j73//OdNKtW7c+++wzyHqpdNLbt283btzo5+fXunXrCRMmyNq50/wxnU5SKKeZ8fLyonkdGWyxLTZmp7i4GDI2ICBAxym5jh8/3qVLF4iwBQsWqA7sh+52c3OTanavR48eGdAO/8svv1hbW5uyE1xhYSGEJl/IgiH3uP3nz58b0A9PX/r27UsOKycnp2vXrnIfjrN79+5JkybJegiUL1xXRw7gUeFgydVDZ4vGmnBMw5w5cxYuXGhMCZreu9HGxMTEsLAw6KS0tLSlS5caPLPx/fv3x4wZY2dnN2LECAgv5AlSrdVjuZhUJymU47+Cg4NppRji2rVrwsW2cOMdHR1pOI9ePHnyZMqUKbi70Fg0KdabN2+gn5A8STsXdvv27Q2Y+Bjyf/369RJWQxPQnT169Pjzn/8Mh1inTh1fX1+5F9c0f5AbmWDmD/mmMCEQ7ZAM0Gc4RL5KiQlIT09H1JH1EKtWrRL6QzlA+ampqQrljBKmnMCCIwT5PAUmg9Guk8rLy+vWrfvb3/720KFD0EwIhUOGDNG9geD9+/d79+4NCQnx9vZeu3Ytm2UAdeYztptaJymUg8uio6NjY2NFo+4hmAICAgYOHFhSUmJw4bjZO3fubNasGeIi9Na4ceOMrq8YWKEBb1hu3rxJs3XJzbRp0zw9PellJW4usoEa/mpZoWxdk+oizJo1S2if8+fPhwzF35ycHPYWTKYJjhHt5s2bp+DRzoTMmDFjy5YtxpeDYCPs5p+fn7969Wr8nTp1akFBQVFRkUL56laOhiU4Achr6gI1fvz45cuXS34IjmnQrpMUyi5QtWrVovduCK/btm1DSEU01D7HzbNnz6ZPn25vbx8ZGan2nV3jxo1reP/aKtBJxMyZM9u0aUOTMZaWlo4ZMwb3W6qR1d27d4fqevToESxJ8h6LuGIuLi4GzNwdGBgox+I+IurXr79//372FRfho48+quFT7p4/fx4uQJKiPvnkE6HLoN4A+Pv1118z/QRXJcmxhAitbuzYscuWLZP8EBxVhg8fDgVsfDmIZ8IFaBHYKLzBVOLj42kj1LYcfcZZXldRUWFjY8MXmrRcHjx4IJyJEOkfHIJo4507d0QdPPLy8pAt29raqg57ys3N7dq1q5OTExIwLc0TsF4EaOnOw/KoMp0Etm7d6u3tjbwK92nhwoUSDkfCvafJcqKjo0+ePClVsYyUlBR9F8FANO3Vqxd7BQbntWTJEskrBn7zm9+ItP8f/vAHE3QUNWeysrJoLL3xaNJJbdu2ZR1+5dBJ0L409oT6uvFoZxp69uyJjMv4cjTpJC8vry+++OLq1asK2XRS+/bt6XXPpk2b+EKTNZbXr18vWrTI1dW1c+fOcCaLFy92c3Pr0KGDLlP5l5eXQ2G/ffvWBPU0T6pSJ4EdO3a0atVK9wH2OjJu3DhqikxOTl63bp20hSt+3VNER1Cfjz76KDw8nL7K5BPB//7v/wo9+7t37/77v//bBO1Y5sz69eulWkULOgm38qcP1K5dm3TSzz///OWXX547d04hj05CwKZot3btWh7tTEZQUJAkgwqhkxo3bnzzA8iRSCdBPCGV9/PzU8jjE54/f96sWTP6jKPIt6Avx1LIyclp06YNkna91pWHzzHN/HDmSRXrpIMHD0ZHR0te7IoVK6jf4tatWxMTEyUvX6GMW3otqgWfCP1ubW2dlZWlkFMnQXcK27pyc3M///zzGj4bIUSSASMD1AKdhAjX5AO//e1vSSfdvXsXwc/d3R2XWnKdhId09+7dLVu2zMzM9PDwyM/Pl7Z8jiakmk4COumzzz5jZmNvb890Em4uzAb2KYdPKC0tTUtL69Onz/nz500wHpNjESCRDg4O1usnyLRJzddMqlgnbd68WY6u1lAwsbGx+ID8PiIiQvLyFcqJVdq3b6/7/tTSvmfPnh9++KGsrEw+nXT8+PEvvvjixx9/RNp64sSJ+vXryz2axvwZO3YsyVPj0fTeDRvxKEHELF++HDrp1KlTkqiZoqKi5ORkJyenqKgoJIII2zW8Q6WJkVAnqX3vRhvhTP7yl79MmTKF5vfXcVlJ7Vy5ciUmJsbT0xOuZsKECUuXLi0oKDC+WE71wN/fX19PAn0vyTtoS6SKddLChQvliOL3799v1aqVQjkzpHwDwhEUHz58qOPO5BkVypb8MWPGyKSTTp48CXl07NgxpAsIrngYTDMZgZkDnWTkiFyGFp2kUHYY//rrr6GTEO0CAgIQBXfv3m1YYx5uZffu3d3d3YWzgsFmhOvncOQmNDRUknK06yTQr1+/r776qmPHjkOHDrW3t09MTNTrtQjjl19+wSMP24OpsB7oT58+NcG8GBwLYuPGjaNGjdLrJ5s2baLWhxpIFeukiRMnSvVCRAgiE1s1CU5H8vKJJUuW6DIHXXl5+bp162bPnk06CQH1iy++iI+Pl0Mnef2/9s48rolr7eP+cW3V3mtba+tSl0u1rQtCWAUEAQFRFBdEtC6gCCKoKFdEQKRataIVEUUrFEEWhSqIgAguLGFTKiBwWUREKKsiGJayGdH30bnNm9IISWYmCzzfT8xnksx58uCcnPM7c855Hi0tIoUT3nWgib51ErBz507OvFtpaen27duhKnp6er548YIf++3t7VCvQIKvWbPm71HmU1NTN2zYQMXfgYiUfnVSY2PjqFGjiDahs7MzMDAQTgDZxP9uu4qKCmdnZ1VVVZ7ZnKA6EYvnEOTNu5ByDAZDoNDHcDKxlWQQImadZG9vT9WESC/k5OSIA2VlZZoCYUOXNmvWrD4Sxj1+/Hj37t0g1Pbt2xccHEzopDfvQhxBd0u5ToL/SSsrqzfvFiVwUsoj1BIVFcW9Cxe6uubmZnjmvNna2gpjNe4ibW1tZ8+eVVFR2bRpUx991cOHD+HnAI2Xh4dHH6FBlZSU+JRciORQVlbGHdQYVHV0dDQ8c78JkigxMZG71G+//bZx40aQPjwzDRC8evUqNjZ28eLFixYtgoP33bwE40TjgCAErq6uAt2kgA5LRkaG09D1CuY0sBGzToL/+ry8PDosz58/n0gXumLFCvp2e23fvv3vaeNAqkdGRhoaGi5YsODq1auEkOLMu715F8gEBBblOklfX59YE+Pj43PixAlqjSPkSU5Ohtqora0dFhbG2WQLBxEREXp6etDVxcXF9TtJBxfX09OTfmcRSYGzRo2TaYCgvr7+0KFDoJudnJz4yVCkpqbGYrHo9BSRJkCmC7Q0GzqsCRMmwMifeIk6SXRA187/Eh+B2Lx5MzFt4ejoSF96Gmi2DAwMOC+h5u3du1deXn7Pnj3l5eXcZzY3N3MvgoM2rqCgwMbGhpI1mwD8sUTQge7ubgUFBSKAJyKB1NTUEJXExcWFOCDyKPFZHCoSg8EQ788WET0goBMSEpYsWQKS+uzZs6ampiC4Q0NDOfkl+gVKURUdAxkYGBsb879wE3QSDNK+/PLL/Pz8N6iTRAl900NHjx4NDg5+8651EDQmpEBAa1VcXEzc+oYmLDw8nP+/CByjKizCokWLiNCaAQEBMMqkxCZCH1BJTp06NX/+fP77OQ6bNm26efMmHV4hkk9lZaWRkZEQyzrb2toUFRVRYSMc4uLitm3bxufJoJMuXrwIfRYR/QR1kuiYMWMGTZahHSEiDty4cYPWuHyXLl2aOXOmg4ODcHsmt2/fTl7G5eTkEBtzoPoqKyvj3XVpQUtLS4iUMnC5ly5dSoc/iFTAZDKtra2FKGhjY8Od1AgZ5EB/wWAw+Jx8IHQSFFFRUTl//jzqJNEBCoMmy1VVVdAiZGRkJCQk0BqNurS01MLCQujir169WrZsGcmZQRMTEyI1XlhYmLOzMxlTiCjx9fX18fERoqC6ujpuaRzMqKqqCqGw8/PzobWhwx9ESjly5Mgvv/zSxwlPnz49fPgw1DczMzPQSW/eJWweP368oqIi6iRR0NraOnv2bJqMZ2VlTZw40djY2NLScurUqaCFqVoJ1Ivnz5/Dt5Cx0NLSAtpc6BRshYWFRLAoIrBvH1ulEEmjra1NTU1NiIIBAQF79+6l3B9EWgB5TaQcEJS5c+fCGJJyfxAp5dmzZzx7YehNkpOTQRtB3xQYGNjR0UHcTyI+3bFjx5AhQ1AniYLHjx8LGj2dT9hstoyMDGcKv729ncFg0JR3FuQXebVXXV0ttMRZs2ZNRkYGHMTGxnJSsSLSgq2tbWpqqqCloNmaNm0ahn4YtMDgStAUkwShoaGosBFuVq9efe/ePc5LFovl7e2trKxsZWVFTFMQuLq6cuY9WltbQXDHxcUJcVNTGhGnTsrKytq4cSNNlkeNGsW9xTo4OFhfX5+O73rz7h44eSNQU3V0dLhj8/ADaE3OhjtOkElEiigoKIB2StBSDx488PDw4NTwtLQ0jCI42Ni8eXNKSoqgpcLCwq5du8Z5GRgY+L7ITMgggclkmpubv3kXr8vS0hIUko+PDz8CKCIiYt68eYNhtCZOnXT9+nVOMAZqiY2NnTVrVq/vkpWVpeO73lCkk4DIyMjvvvuu7yvCZrOfPXtWUlKSnp4eExPz888/u7m5JSYmcoJMIlKHrq6uoLcSjxw5MmTIEM4d0y1btsA7NLiGSC55eXkCpZgkgGbwiy++4Gz16BVfHhmcKCoqqqurw4BN0Hvb0OwIvTy3trMt+unjsNqH91h1bMnO1C5OnQRDmePHj9NhGUTD5MmTud+JioqiSs38HRDgwuXw+jtQ7aDPCwoK8vLyAgFkZ2e3atUqAwMDJSUlBoOhoKCgoqJiaGgIcmrbtm3u7u7e3t7BwcGampo//vgjppGXUkJDQwVVOXD+4sWLJ06cSAz7UCcNTrS1tevr6wUqAjppyZIltra2xEvUScgbchshra2tBW18Xvb02BcmyTFDvko6P+GO30xmkEraxSwWLZEUKUGcOglEEkglOizDaGn48OHcG/U3btzo5OREx3cBoGOeP39OiSkYI4LoOXnyJKif69evZ2ZmlpaWgvG+ddiTJ0/4396JSBpdXV2CSm1omPbs2QMtFCikN6iTBishISGCBksDnZSenj5p0iQioTLqJATYv3//rVu3hCvb3d09b968Xsma+qDn9WuT7Jh/J/qPv+PH/ZiREpTeVCOcD3QjTp3U0dFB39Smp6fn9OnTY2Njs7Ky9u7dC+3C33NDUoWZmZlwwZN6AT0liCThErn4+vpSFbISET2Ojo5xcXH8n0/opMbGxi+++OLu3buokwYnoLAVFBQE2skLOqmgoCAqKkpJSQkKok5CgFOnToWFhQldvLm5WVVVlXsxeB9E1T/+Njmwl0giHqrplyQzDqqY4yfRSkxMjLm5uampqbu7O1X3e3gCAiUzM5O8nXPnzkF/KVxZuI6GhoZCjwkQ8VJWVsZ/dInU1NRDhw6BToLjCxcuQAtlY2ODOmlwsnv37ujoaH7OhHEpNFOETnrz7i746dOnUSchQHh4OEglMhaqqqqmT5/OT11a+FsUT5EEj+kpFx60SGJcm4Gsk0SGm5sb9xYS4Xj27Jm8vDyZubPq6mpoBDEYt5QCMrfvVgYGbdCWKSsrW1paQpUjdBL8fufOnTthwgTUSYOT8vLyhQsX9n1OaWnpzp07GQyGt7c3RyfBm2PGjBk2bBjqJOT27dtE+goy3Lt3733hT0GjQ/eUl5d3586dSd/bfbz9u39ZLPlo2bxhuiofO23g6CSZRP+r9WUk3aAD1EkUcOLEiYCAAJJG1q5dGxsbS9JIUFDQ+vXrSRpBxMLVq1ddXV15fnT//n1ivy7oJKIZIubdiE+Li4uHDh2KOmnQYmRkxHMPB5vNjoyMNDAwgBOgbSEWwHF0EgBVaMiQIaiTkNzc3K1bt5K3Ex0draenB6ZWr14NFU/hTzQ0NIyNjTds2LBr166Jdms+cbT49IDdZ167R/vv/1BpxmeeuwidNCUp4FZDJXk3KAd1EgVcuHCB5Ma9xMREIkEbeZYuXQqNIyWmEFECvZqioiL3ir329nZ/f/85c+ZAo8NkMrlPfvDgQVZWFudlfHw8dIR43QcnMTExvebr6+rqfvjhB3l5eScnpydPnnB/dOXKlaamJuK4ra3N19c3MDAQ464NcqACrFy5krydGzdumJmZZWRkPHz48Pnz5zzVhUtJ2gSuubYx4Uf/MWXi2JhTcDyLGcx6KXBecBGAOokCoIvav39/QEBAbW2tEMWJ7U41NdQs9X/69Om0adPgmRJriChxd3cnQiIVFRVt376dwWB4eHjwuf8ABJahoSEIbpp9RCSOnp4eGLJ3dnZCY56UlGRqaqqlpRUcHAwNCz/FCwoKQIsPksDKCE86Ojr09PRIGoEaqKqq2tjY2PdpT7vaQQ9xL0v6xGXTcEONqUkBu4sFzkwgGlAnkcXFxUVOTu7IkSPOzs4wOBPCAmgskmvoegFDRpoSwiC0AqM6FRUVXV1duHzx8fGCBuWCrg6Kl5aWkvHhdUcH++FD9uPHr9lsMnYQUXL48GFzc3MYbllbWwsRmT0mJgaqHMkMmLWdbfktDfBMxggiLjQ0NEha2Ldv3/nz5/k58+6LOkZqyJdcUulf2iq6J79/9VpCo02iTiLL559/zud+SJ48evQIBnOU5+iF+tr9jvLycu57S7W1tdypUerr6zFrgUQBAzIykyBlZWVqamrCJQp81dDQevhw87ZtLDs71tatcPCHr+9r/u5JIOIlOzvbyMiIzD0hT09PTvBJQbnT8LtGRvgsZvBMZpAsM1gtPezGsyf9F0MkCXV1dTLFoSODAR7/cqKpu9O5JA2qjUraxSX3oyMf5snLy9O6LZ0MqJPIMnv2bGNjY+Fm3ID58+fn5ORQ6xLByZMnx48fr6OjM336dPgNEMsUQJNxJ3mGtpWTAhqRBKCt6ezsJGMhLS1NU1OTzzkXDq9+/73Z3p61YcNfHpaWLS4urwXMOYiInrq6OmhJSBqxsrI6e/asoKW8nuRMS7nQa4P3t8kXjpRl9V8YkRhI3k9asGBBfn4+GQvQMZmYmJCxQB+ok8gCCsnMzGz48OHz5s2rrq6Gly9fvuSz7KVLl3bs2EGHV1FRUVOmTOHkNDh69OiMGTPgWqNOknBWrFhRVVVF0khoaOjatWv5/2m/fvmy2cGht0j6Uyq1eniQ9Aehm+7ubmVlZfJG9PX1uduHfsloqp2REvS+WDiJz3EnndTg5+eXnJwsaBocgvDwcAcHB/I+bNq0KSQkhLwdykGdRA0dHR22trZEyhE1NTU9PT13d/fExMQ+4iGxWCwlJSWapr1WrVrl5eXFeclms0ePHl1YWIg6ScLZsmVLdnY2eTt79+7lBA7oly4mk7V5M2+dtGFD8/btr3BbgMQjLy9P3khDQ4OioiIncEC/GGZFvi9mIDzmZvKbywIRI6CPDQwM4LqbmprCcFrQcVpLS4uKigolHVlra6uCgoIE7r5EnUQZ8fHxMjIyxPGLFy9iYmJ27dqloaEB0sTR0RFecrbjEoCuioqKoskZqG29xoXq6urwDjgzZcoUxT8ZOXIk6iSJws3N7ebNm+TtwO96zZo1oaGh/Jzcdvr0+0TS28fGjZ24jU7igZ8zJXZKSkqgs+RnlyW7p0f2rxuXej3g064eildeIpQD3RAx2yBccXt7+4iICKqcuXv3blFRUU9PD4zqc3JyOKtpQYdx3+uC94Ve6yIEqJNIwWazFy1adPLkybNnz8rKyvIMaQoXOCEhwcXFRUtLS0lJyc7OLiwsDCTL0qVL6XMM9FCvugvN6K1bt+D98PDwF38CwwjUSRIFkQKZElMdHR2amprckQKgKlZWVubm5oIUg0ro4+Pzww8/QDNnxmAYfPnlnLFj4aE5duwKGZleUqmTdLh5hG6UlZXZFG1RvH37NgyruDd8dHV11dTU5OfnJyUlQQNy5syZAwcO2Gy1+1hf/UPlGUO/njR06tvHsDmML0J+5OikWczgFjZdGTwRqkhJSYEBs3Crix48eAA9ILX+3L9/f/LkyXp6ekuWLBk7dqynpye8Cf2UkZER5xxizE/t9/YB6iSyFBQUwIX08PCIjY3t9z8Tui6olNDEyMjIGBsb839/W1D+85//cK98qqur++ijjxobG3HeTcIJDQ09ceIEVdYaGhpUVVWJe4fQj+ro6JiZmdna2u7bt8/b2xsuPSh4GLQ9/Omn2nXr3ns/ydq6m8SOTkQ0LFiwgMJU39CgQW2BOsNgMBQUFNTU1BYvXmxhYQENC3zk7+9/7dq1tLS0r4M9xkR4jrt1jhBGo31cPmB8y3kpywyW2J3eCIeenp6dO3eOGDECxDGfuQI5BbW1tXnGghca6CLHjx/PcaO+vh6kUlZWFuqkQUd5efn8+fPz8vJWrly5fPlykM+UfwXU3dGjRwcGBj5//rywsFBfX3/Xrl1vcL+bxHPz5k1nZ2eqrF25cmXDhg39nsYuLW3euvW965Ps7XHLm+Rjbm5eUlJClbW1a9feuHGj33glFg8Ses21/XPtopE2psSx7t0rVPmD0E1raysM0j799FNoNEAWr1q16ueffy4qKuqjiK+vL4z5qXUDFNL06dO533F1dbWxsUGdNOg4dOgQZ26luLh4/fr1UANSUykORQqN5rp16xQVFefOnevt7U00eW5ubtxhCI4cOQKDQmq/FyFDdna2paUlJaba2tpmzZrFZyyl1qNHWZs28dBJdnYdOOkmDezcuTM9PZ0SU4mJidB08HNmZUdLr9jK4xLODp3x1ef+++F4JjOoqZtUkAtExMC4ncgUWVlZGRQUtHHjRnV1dVNT09OnT//3v//lVgvQsMyePVvQ+CP9cubMmV4TeaDeiPH88OHDZf5kzJgxqJMGOMrKyr12Bzx58sTa2lpfX//WrVvi8gqRBCoqKqhauObg4MB/gPjXnZ2tBw6wtmzpJZLaAwMpcQahm8OHD1+jQtFCzwf9H/+Jj1Iba3qt5gaR9IHsVBBMcGyVjw2apHPv3j0/Pz8YocXExHzyySd/HzlXV1eDWLGysgLNBEIKRt15eXkWFhZ09Fb+/v46Ojrc74BvJiYmoJMMDAw4K2vDw8NRJw1kcnJy1qxZw/MjqI729vba2trR0dF4XQYn7e3tampq5O3k5+dramoKlPnkdU9PV3p66/79zQ4O8Gg9duzlw4fkPUFEA3QnAQEB5O0cOnQIxvQCFanpbNtZmKyeHjYt+X8BJ0duWfnP9YuJ48j6MvJeIfRRVlbm6Ohoamq6bt26frdg19XVhYWFwcmTJ08+duxYa2srtc4UFhaOHDmSew/BihUrvLy8cN5tcAGj/Pj4+D5OePbsmZOTE7ExTdAMX8gAQFZWlqQF+FHPnTtXiDxfiPQSGRl5/PhxkkbKy8tBXpNJo7S9MOnt7Nutcx8oTBt9xpUIOFnX+d4wcog0Arrq0qVLJ06cgMZq3759/ea+FQiQawYGBtnZ2SUlJe7u7lOnTgU1hjppEEFk9uZn+25TU9OBAwfU1NQuXLhA1XZfRCogn5Dy/PnzNMV5RySW9PT0gwcPkjSyePFikttKWC+7lNIugjz6IvTHodNkxt04A8ff5d7AjmbAAL2YnJxcd/fbiA/w7OfnBy9h/N8roBHUBNeH6cppF+WYwarplw48ustnkAiQ6adPn160aJG+vr6TkxMRNiktLY1YOEWQk5Pj5uZG6Z/VF6iTREpiYqJAHRjoaA8PD2NjY/pcQiSKwsJCGKtt3bqVyGQshAUY20Gz1dLSQrlviMQCVcXHx2fLli3Ozs5ZWUImVouIiBA6FS43zMZqYsbtE0eLj0z0iOOg6r52TiFSREpKirW1Nfc7oGwuXryopKRkY2NDJBJ90t4MCmninV84q9bgWCXtUkW7VLZLqJNEioWFBSVZKZABSWlp6Weffebt7X358mUXFxf+EwVyY2Vl9euvv1LuGyLJ7N69W1dXNzw8/MKFC8Jd/ba2NkVFRRaLRYk/e0rSiN5xmIb8Z567iOOZzCC1jLDj5dndGKRbmrG0tOS5Sxq0xLVr1zQ0NNasWcMI+YlniHaNjHC2FC4mQZ0kOjo7O2fPni1uLxDJ5ezZs/r6+mQsZGZmGhoaUuUPIi3IyspevXqVjAUHBwcKU5C2v3oJPSL0i2OuHB/6zeSxMac4PaVM0nm19LDaTlryWiJ009XVxWAw+pYNB6+EDFecPkyDQSxQ4358kxyY0FAhKmcpA3WS6Lhy5crhw4fF7QUiuaSnp48YMeLYsWP878rmhs1mgxB/9OgR5Y4hEo65ubmcnFx0dLRwc7V5eXl6enrU9gW/seqJrvHT77eMWKjZq7+ckxH+UgrvKyAgx11dXfs+x6bgztvg7KecP1SX+1BpxmgfF+5Lv6MwWSSeUgnqJNGxfPnyyspKcXuBSDQglYyNjUEt2dvbt7a2ZmZm8j/75uXlxTPDIDLggUpy4sQJBQWFUaNGXb9+vbCwkP+mhtgdWVxcTK1LYPab5ECiaxxuoDbq0DbuznJKUkBwNcXfiIgAExOTfsO+r3tw4/+Dafm5fyD/LfelBxUlGlcpBHWSiGhqaiI5pYIMHurr68eNGxceHr5z504tLS1DQ8ODBw+mpaX1Ef22pqZGUVGxsxPDHw9qzp07N2nSpLi4uFWrVqmpqa1fv97f37+srK8IRn5+fnv27KHck6LWRk6o7rHXTr6dfbt6gru/NMyKpPxLEVphsVj87MY9VfGAewX3P6ZMHHfzZ85q7p8rhUm4K15QJ4kIX19faI/E7QUiNaiqqkZG/q8jaWlpuX79uqOjo7a2Nqhtd3f3pKSkjo4O7vPNzMwSEhLE4SkiQeTk5IwaNYrz8tGjR6CT1q1bB5pp9erVf8/Y1dDQoKCg0E5D/r7bz3//9s/7SfD47JjDp99v4dZJimmhlH8pQitQl06ePNnvaU+72mcygzgX+gOFaWOuHOes5W/okr5kkaiTRISBgQFVe0mQgcrx48eNjIycnJyWLVv27bff8ox129bWdvPmTRcXF9137N2799atWzExMStXrhS9w4gkAG04qOq1a9fu2LFj4sSJP/30E8/TKioqOBm7TExMTp06lZ+fb2FhIVCKeP7JYtX3ymfS66GVibsypQzoxfhcOun3e8GMlP9JpWHayp8HHCAWcYfXSmWIf9RJoqCqqgq7MaRf4MeYmZl5+fLl69ev87Mgt729PTExcd++fVOmTDE1NaV8iQkiLYCkjo+Ph5oD0oef82tray9evLhq1apJkybRkX0C6Op5JZ8a8j6RNPHOL4cf3aP8SxH6qK6uXrhwIf/nRz99/O9Ef7jWHy3T/cxrNxy4Pcygzz1aQZ0kCqAZgkombi+QgUlRUZGxsTGTyYRWbMWKFbm5ueL2CJEOXF1dg4KCiOwT7u7uTU1N1Nr/4dHdqUkBPHWSXGpIY3dH/yYQiQH0NGhrgYoQSWz+ZW786QFbOPD9vYAm3+gGdRKCSDd79uy5fPkycZyTkwNSycjIiGcgOAThAC0/yCNilVtXV5evr6+cnNyuXbvq6uqo+opXr3tW58Z9zbVKiXjIMoOZjThulDLmzJnzxx+C5enb/+guXO6Pt333iaMFHHg8/o0m3+gGdRKCSDE9PT3Q2/Xa5lZcXGxhYaGnp4cru5H3kZqaCpWE+x02mx0aGqqoqGhra1tRUUHJt0D/4lmeLccMnsUMln33vCDraukfLygxjoiMoqKidevWCVrqdMWDtwG03DaP3GwKB3tKpHXwhjoJQaSYlJQUS0tLnh9VVlba2dlpampGRUX1YEw/5K9YW1snJib+/X2oKlBh1NTUzM3N+42Uwyc9r1+XtzcXtjayXr43sAUiyTQ1NQkhnS/VlhBbHT8ymw8H1vm3aXBNFKBOQhApxsrKKikpqY8Tnj596uTkBN1eSEgIm80WmWOIJNPV1SUrK9u3ek5ISNDR0cEVb4jQxDdUvA01eW7fiAVz4GBlznVxeyQkqJMQRFrhp7cjYLFYBw8eVFVV9fX17SNYJTJIuHr1qqOjIz9npqenL3wHHNDtFTLAyHqXu2ZM+LFhcxhwoH8vQtweCQnqJASRViIiInbv3s3/+X/88YeXlxeoJarmUxApZfny5Xl5efyfn5ubu2LFisWLF9PnEjLwSH5eBfJo3I0zH8h9DQcT7vj9WlsqbqeEAXUSgkgry5YtKygQeKttd3f3q1ev6PAHkQpYLJaSkpIQBSkPHIAMYMr+YM38M9Tk0G8mEwfTki8ce3xf3K4JDOokBJFKoNNSVlYWtxeI9OHr63v06FFxe4EMZHpev56TEc6JBDF0+lec4xkpQYWtjeJ2UDBQJyGIVAK93bFjx8TtBSJ96OjoVFVVidsLZCCTxarnTvE2dMZX3AG0LPKkLF4J6iQEkUq0tLRqamrE7QUiZVRWVurq6orbC2SAE1hV+CWXMBqmpTg29hTn5ZyMcHE7KBiokxBE+qioqJg3b564vUCkjx9//NHf31/cXiADnNCa4kmJvwyYFMiokxBE+jh06FBgYKC4vUCkDwUFhZaWFnF7gQxwStqaZJnB79NJu4qY4nZQMFAnIYj0gb0dIgS5ubmmpqbi9gIZFCy7H/MlL5EE+qmms03c3gkG6iQEkTKys7PNzMzE7QUifTg4OERHR4vbC2RQ8OJlp1p6WK/ZN1lmUNyzJ+J2TWBQJyGIlBEXF8czMxeC9I2zs3N3d7e4vUAGC23s7r0PM5TSLs5iBjNSQ5bdj5G6iAAEqJMQBEEQBEF4gzoJQRAEQRCEN6iTEARBEARBeIM6CUEQBEEQhDeokxAEQRAEQXiDOglBEARBEIQ3qJMQBEEQBEF4gzoJQRAEQRCEN6iTEARBEARBeIM6CUEQBEEQhDeokxAEQRAEQXiDOglBEARBEIQ3qJMQBEEQBEF4gzoJQRAEQRCEN6iTEARBEARBeIM6CUEQBEEQhDdD4N9rBEEQBEEQ5K+ARvo/4tmi0XNhvG0AAAAASUVORK5CYII=\"}},{\"type\":\"text\",\"text\":\"Excerpt from Wellawatte2023 pages 14-16: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nsame optimization problem.100 Grabocka\\n\\net al. 111 have developed a method named Adversarial Training on EXplanations (ATEX)\\n\\nwhich improves model robustness @@ -4944,7 +4740,7 @@ interactions: connection: - keep-alive content-length: - - "50934" + - "50764" content-type: - application/json host: @@ -4976,26 +4772,25 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA3RUTW8bOQy9+1cQOo+D2M3X5hYUWSAFuj2kC2SxLmyNxJlho5FUkpPEG+S/Fxp/ - tttedNAjqcf3RL5OAAx5cw3GdVZdn8P0/YdvD7f/fLxv7t//fX9//kd99unh818X/33SP8OtqUpG - qr+i013WiUt9DqiU4gZ2jFaxVJ1dnl9dnV3MZ/MR6JPHUNLarNOzNJ2fzs+ms9l0frpN7BI5FHMN - /04AAF7Hs1CMHl/MNZxWu5seRWyL5nofBGA4hXJjrAiJ2qimOoAuRcU4sl6tVl8lxUV8XUSAhZGh - 7y2vF+YaFubh5q6CxHD7koOlaOuAcMNKDTmyAe6iYgjUYnRYAWODLKAJetQueQEbPSi6LtK3AQUG - QV9gioqcGXUMGKJHLgw9aIfg0ZFQigK99Qj1GnrrOooIAS1Hii2M0kkF2bKSG4LlsAaPmH8OOYG7 - OBYd+31RSA24DnsS5XUFDzd3QAI250AbZkN06QkZRHlwOjBOM6eMrGtgDLbYKh1lqUAG14EVyIye - nJY365CSn9ZsKUJtmQkZMnKPY16RUVIYagqk68KkTwHdEFBO4PNBpECPhe9QNGqs08EGwCJ/3Dw/ - auZRHFPWxNPaFlV/jGDca505PZFHsK5go4EUhdpOpfiQoEvPOyaWi27F3F2hpkGnsNWARqYdCu4N - 7jBkqJl8i6POrc1Qoz4jRqiDdY/TOr1szRiJ760vTKpjVo8xPQcshZrEO5fkZGGqzb9kDPhko8Ol - uMRY/ufsdIuVZpfU2xal3CsPuIhvi7harY5/PWMziC1DF4cQjgAbY9JNz2XevmyRt/2EhdRmTrX8 - lGoaiiTdktFKimWaRFM2I/o2AfgyTvLww3CazKnPutT0iONzs4ury01Bc1geR/C7sy2qSW04Aq7m - s+oXJZce1VKQo3VgnHUd+qPc83cX+ybs4CkdsNPJUe//p/Sr8pv+KbZHVX5b/gA4h1nRL3czdNz2 - IYyxLNjfhe21HgkbQX4ih0sl5OKHx8YOYbP7jKxFsV82FNvyCWmzAJu8PL+Y1fPzy0tfm8nb5DsA - AAD//wMANOWMuwkGAAA= + H4sIAAAAAAAAA3RUS28bNxC+61cMeIoByZAcy3J8c9MenMehRYAWjQKJS85qJ+aS7MysY8Xwfy+4 + q5fb5MIDv3l8883jaQRgyJsbMK6x6tocJm/fL66/L6r70L17/O5/+fTxj9+//f3uz9vZ2/eX12Zc + PFL1FZ3uvc5danNApRQH2DFaxRJ1tphfv5lNp/NFD7TJYyhum6yTyzS5mF5cTmazycV059gkcijm + Bj6PAACe+rdQjB4fzQ1Mx/ufFkXsBs3NwQjAcArlx1gRErVRzfgIuhQVY896vV5/lRSX8WkZAZZG + ura1vF2aG1iav27v4NVvjzlYirYKCLesVJMjG+AuKoZAG4wOz4CxRhbQBC1qk7yAjR4UXRPpnw4F + OkFfYIqKnBm1N8AhNmiD4NGRUIoCrfUI1RZa6xqKCAEtR4obePXxw1nv5xHzyfevH86gF1TO4W6I + 1pf4qJBqcA22JMrbMZSCSMDmHGjg00WXHpBBlDunHeMkc8rIugXGYEsnpaEsY5DONWAFMqMnpyVx + FVLyk4pLCZVlJmTIyC32fpAYJIWuokC6LUzaFNB1AeUcPh2lCXRf+HZFmdo67WwYhIlD+l3F4piy + Jp5Utmj50oLxoHDm9EAewbqC9W2jKLRpVIr6CZr0bc/EctGttHQfqK7RKew0oJ5pg4Iv0zUY8l5W + gS565DJjvmf6YAN5qzh05CBXijKG1t4X3W7vdu2CNjGCso2SLWMcpuIwI4X8+dKMh9FkDPhgo8OV + uMRYRnQ2XcbnZVyv16fjzVh3Yst2xS6EE8DGmHSooSzWlx3yfFilkDaZUyX/cTU1RZJmxWglxbI2 + oimbHn0eAXzpV7Z7sYUmc2qzrjTdY59udjXf7aw5XokT+PUe1aQ2nACL6z3yIuTKo1oKcrL3xlnX + oD/xnb++OhRhO0/piE1HJ7X/n9KPwg/1U9ycRPlp+CPgHGZFvzqOwo/MGMsl/ZnZQeuesBHkB3K4 + UkIu/fBY2y4MR87IVhTbVU1xU+aIhktX59X8alZdzBcLX5nR8+hfAAAA//8DAKW9H2fyBQAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9cfb5bef251d-SJC + - 9854b3aeae11fa7e-SJC Connection: - keep-alive Content-Encoding: @@ -5003,7 +4798,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:37 GMT + - Fri, 26 Sep 2025 18:07:39 GMT Server: - cloudflare Strict-Transport-Security: @@ -5019,13 +4814,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "4463" + - "3048" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "4504" + - "3090" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-input-images: @@ -5039,7 +4834,7 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29997760" + - "29997802" x-ratelimit-reset-input-images: - 0s x-ratelimit-reset-requests: @@ -5047,7 +4842,192 @@ interactions: x-ratelimit-reset-tokens: - 4ms x-request-id: - - req_8acb72f81692468785d50c5efe4ef580 + - req_14034003e93345acbb46813c6a001f54 + status: + code: 200 + message: OK + - request: + body: + "{\"messages\":[{\"role\":\"system\",\"content\":\"Provide a summary of + the relevant information that could help answer the question based on the excerpt. + Your summary, combined with many others, will be given to the model to generate + an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 12-14: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nnterfactual + approach, contrastive approach employ a dual\\n\\noptimization method, which + works by generating a similar and a dissimilar (counterfactuals)\\n\\nexample. + Contrastive explanations can interpret the model by identifying contribution + of\\n\\npresence and absence of subsets of features towards a certain prediction.36,99\\n\\n + \ A counterfactual x\u2032 of an instance x is one with a dissimilar prediction + \u02C6f(x) in classi-\\n\\nfication tasks. As shown in equation 5, counterfactual + generation can be thought of as a\\n\\nconstrained optimization problem which + minimizes the vector distance d(x, x\u2032) between the\\n\\nfeatures.9,100\\n\\n\\n + \ minimize d(x, x\u2032)\\n (5)\\n + \ such that \u02C6f(x) \u0338= \u02C6f(x\u2032)\\n\\n + \ For regression tasks, equation 6 adapted from equation 5 can be used. Here, + a counter-\\n\\nfactual is one with a defined increase or decrease in the prediction.\\n\\n\\n + \ minimize d(x, x\u2032)\\n (6)\\n + \ such that \u02C6f(x) \u2212\u02C6f(x\u2032) \u2265\u2206\\n\\n + \ Counterfactuals explanations have become a useful tool for XAI in chemistry, + as they\\n\\nprovide intuitive understanding of predictions and are able to + uncover spurious relationships\\n\\nin training data.101 Counterfactuals create + local (instance-level), actionable explanations.\\n\\nActionability of an explanation + suggest which features can be altered to change the outcome.\\n\\nFor example, + changing a hydrophobic functional group in a molecule to a hydrophilic group\\n\\nto + increase solubility.\\n\\n Counterfactual generation is a demanding task as + it requires gradient optimization over\\n\\ndiscrete features that represents + a molecule. Recent work by Fu et al. 102 and Shen et al. 103\\n\\npresent two + techniques which allow continuous gradient-based optimization. Although, these\\n\\nmethodologies + are shown to circumvent the issue of discrete molecular optimization, counter-\\n\\nfactual + explanation based model interpretation still remains unexplored compared to + other\\n\\n\\n\\n 12post-hoc methods.\\n\\n + \ CF-GNNExplainer104 is a counterfactual explanation generating method based + on GN-\\n\\nNExplainer69 for graph data. This method generate counterfactuals + by perturbing the input\\n\\ndata (removing edges in the graph), and keeping + account of perturbations which lead to\\n\\nchanges in the output. However, + this method is only applicable to graph-based models\\n\\nand can generate infeasible + molecular structures. Another related work by Numeroso and\\n\\nBacciu 105 focus + on generating counterfactual explanations for deep graph networks. Their\\n\\nmethod + MEG (Molecular counterfactual Explanation Generator) uses a reinforcement learn-\\n\\ning + based generator to create molecular counterfactuals (molecular graphs). While + this\\n\\nmethod is able to generate counterfactuals through a multi-objective + reinforcement learner,\\n\\nthis is not a universal approach and requires training + the generator for each task.\\n\\n Work by Wellawatte et al. 9 present a model + agnostic counterfactual generator MMACE\\n\\n(Molecular Model Agnostic Counterfactual + Explanations) which does not require training\\n\\nor computing gradients. This + method firstly populates a local chemical space through ran-\\n\\ndom string + mutations of SELFIES106 molecular representations using the STONED algo-\\n\\nrithm.107 + Next, the labels (predictions) of the molecules in the local space are generated\\n\\nusing + the model that needs to be explained. Finally, the counterfactuals are identified + and\\n\\nsorted by their similarities \u2013 Tanimoto distance96 between ECFP4 + fingerprints.97 Unlike the\\n\\nCF-GNNExplainer104 and MEG105 methods, the MMACE + algorithm ensures that generated\\n\\nmolecules are valid, owing to the surjective + property of SELFIES. Additionally, the MMACE\\n\\nmethod can be applied to both + regression and classification models. However, like most XAI\\n\\nmethods for + molecular prediction, MMACE does not account for the chemical stability of\\n\\npredicted + counterfactuals. To circumvent this drawback, Wellawatte et al. 9 propose an-\\n\\nother + approach, which identift counterfactuals through a similarity search on the + PubChem\\n\\ndatabase.108\\n\\n\\n\\n\\n\\n 13Similarity + to adjacent fields\\n\\n\\nTangential examples to counterfactual explanations + are adversarial training and matched\\n\\nmolecular pairs. Adversarial perturbations + are used during training to deceive the model\\n\\nto expose the vulnerabilities + of a model109,110 whereas counterfactuals are applied post-hoc.\\n\\nTherefore, + the main difference between adversarial and counterfactual examples are in the\\n\\napplication, + although both are derived from the same optimization problem.100 Grabocka\\n\\net + al. 111 have developed a method named Adversarial Training on EXplanations (ATEX)\\n\\nwhich + improves model robustness via exposure to adversarial examples. While there + are\\n\\nconceptual disparities, we note that\\n\\n------------\\n\\nQuestion: + What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - "6005" + content-type: + - application/json + host: + - api.openai.com + user-agent: + - AsyncOpenAI/Python 1.109.0 + x-stainless-arch: + - arm64 + x-stainless-async: + - async:asyncio + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.109.0 + x-stainless-raw-response: + - "true" + x-stainless-read-timeout: + - "60.0" + x-stainless-retry-count: + - "0" + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.5 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: !!binary | + H4sIAAAAAAAAAwAAAP//dFRNbxs3EL3rVwx4yWVlSIpd2boZQhKorYMADZCgVSCMyNndqbnkhhzK + UQz/94Jc2d40zoUHzsyb9+brfgKg2KgVKN2i6K630/Ufy8vv57N3+utRLzZ/fbr58Oen35f91Ye7 + 9d8bVeUIv/+XtDxGnWnf9ZaEvRvMOhAKZdT58uLyaj6bXSyLofOGbA5repme++litjifzufTxewU + 2HrWFNUK/pkAANyXN1N0hr6pFcyqx5+OYsSG1OrJCUAFb/OPwhg5CjpR1bNReyfkCuv7rQPYqpi6 + DsNxq1awVZ+vNxX4AG++9RbZ4d4SXAfhmjWjhY0TspYbcpoqCFRTiCAeOpLWmwjoDAjp1vHXRBGk + RYEObwmkJegDGda5QBF8DdcbKJWIwE4o9IGkpMsYyRkKmbspX+KhTR26eAZrn7J3jVoSWqDM0+EA + ioEA4ZaOIN5bYAdFTh/8gQ27BthlTE1TSweyFWBhU1Kwi9y0EmF/BDbkhOtjDtEtuoYyR2DXJ4Ga + UFJ4FHfnkzWAVigUjUXRqzjSegYfW4r0M9OGHIU8ISBt8KlpwffCHX8vPqMyVhCTbgEjdOyyQ+aV + kxke5MCe5I7IlU8fuGGHttRR/1itR/0R7lq2BORiChkNTzqzzDH1z7lJp95aviW4ublevynQ67fT + d+/fn+aEQpGUIpkM0XlLOlkM455X4OuawqkPQ7HZ5eHJNYM9tXhgH0791/4w+MY+BfYpQiA7VK/l + vvRDArLLLgYFz7aqGuY5kKVDVrmL2gfKc321dQ/jJQhUp4h5B12ydmRA57wMWfL6fTlZHp4Wzvqm + D34f/xeqanYc210gjN7l5Yrie1WsDxOAL2Wx0w+7qvrgu1524m+ppJvPL88HQPV8S0bm18uTVbyg + HRsW8+oFyJ0hQbZxdB2URt2SGee8WDyJwGTYP9tmk5H2nym9BD/oZ9eMUH4J/2zQmnohs3uelpfc + AuV7+yu3p1oXwipSOLCmnTCF3A9DNSY7nEIVj1Go29Xsmnx4eLiHdb8zVzX9drF8TXs1eZj8BwAA + //8DAMqF+r8YBgAA + headers: + Access-Control-Expose-Headers: + - X-Request-ID + CF-RAY: + - 9854b3af4d4bfa36-SJC + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Fri, 26 Sep 2025 18:07:40 GMT + Server: + - cloudflare + Strict-Transport-Security: + - max-age=31536000; includeSubDomains; preload + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + alt-svc: + - h3=":443"; ma=86400 + cf-cache-status: + - DYNAMIC + openai-organization: + - future-house-xr4tdh + openai-processing-ms: + - "3250" + openai-project: + - proj_RpeV6PrPclPHBb5GlExPXSBj + openai-version: + - "2020-10-01" + x-envoy-upstream-service-time: + - "3380" + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-requests: + - "10000" + x-ratelimit-limit-tokens: + - "30000000" + x-ratelimit-remaining-requests: + - "9999" + x-ratelimit-remaining-tokens: + - "29998564" + x-ratelimit-reset-requests: + - 6ms + x-ratelimit-reset-tokens: + - 2ms + x-request-id: + - req_d6d22bf0eb844f7cae34fad549445e18 status: code: 200 message: OK @@ -5057,18 +5037,16 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - Wellawatte2023 pages 28-30: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n - M. T.; Singh, S.; Guestrin, C. \u201D Why should i trust you?\u201D Explaining - the\\n\\n predictions of any classifier. Proceedings of the 22nd ACM SIGKDD - international\\n\\n\\n 27 conference - on knowledge discovery and data mining. San Diego, CA, USA, 2016; pp\\n\\n 1135\u20131144.\\n\\n\\n(36) + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from Wellawatte2023 + pages 28-30: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\n M. T.; + Singh, S.; Guestrin, C. \u201D Why should i trust you?\u201D Explaining the\\n\\n + \ predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international\\n\\n\\n + \ 27 conference on knowledge discovery + and data mining. San Diego, CA, USA, 2016; pp\\n\\n 1135\u20131144.\\n\\n\\n(36) Dhurandhar, A.; Chen, P.-Y.; Luss, R.; Tu, C.-C.; Ting, P.; Shanmugam, K.; Das, P.\\n\\n Explanations based on the missing: Towards contrastive explanations with pertinent\\n\\n negatives. Advances in neural information processing @@ -5141,7 +5119,7 @@ interactions: connection: - keep-alive content-length: - - "6207" + - "6037" content-type: - application/json host: @@ -5173,26 +5151,25 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//dFRNb9tGEL3rVwz2lAC0YSmS5eimFEajAgUKxEiNVoG02h2SE+8HuzNU - rRj+78WSksWkzoUA5828fW9mZ59GAIqsWoAytRbjG3fxy2//3P/6eVJ/+/C5/fOvD/Hb73hXvye5 - /bif16rIFXH3FY2cqi5N9I1DoRh62CTUgpl1PJ/d3EyvJ+NpB/ho0eWyqpGLabyYXE2mF+PxxeTq - WFhHMshqAX+PAACeum+WGCw+qgVcFaeIR2ZdoVq8JAGoFF2OKM1MLDqIKs6giUEwdKq32+1XjmEd - ntYBYK249V6nw1otYK3uagR8NJgaAUcsDHudKLYMCUtMGAwy6GCBpbWEOeyyXZAIt4+N0xT0ziEs - k1BJhrSDVRB0jqpcC2/ul6u3l3BXIyNQMK61CP/G9MAQA2DPQKGCJqElk9vKEEswLtsqCRMXkM0k - zUJ77EuC7hILwL12bfeTi+6XK6AAPjNpB+R1RaEqwKMkMgxlTDmn6AyVSXvsleS4RaaqU5LBE2+o - OlI+sKDnS1gJaMcRPIZeqkepo2Vw9IDw6ePyD3jzqdaNwwMsraVO8u1A8tsCKAimJqF0jfPa1BQQ - HOoUOrX5fIuGOLuShAjdTeplnvpiOrquscNJ1VTVjqpaQGoE8k1MovMcYjk8lxzJoTd6mmEfogDL - 1ek8bVJkBktlxy9go9eU286tqUEzeC2YSDsGNpQVFGBq9MSSDkcjqa2Ovb1cq6K/gQkd7rOsDZuY - MN/EmyPUMtpNHhxyDpfaMa7D8zpst9vh/U5YtqzzeoXWuQGgQ4jS9zpv1pcj8vyySy5WTYo7/qFU - lRSI601CzTHkvWGJjerQ5xHAl25n2+/WUDUp+kY2Eh+wO258PZv2hOr8TAzgdzdHVKJoNwDm7yfF - K5Qbi6LJ8WDxldGmRjuonb27fjGhW0vxjF2NBt7/L+k1+t4/hWrA8lP6M2AMNoJ2c17j19IS5qf0 - Z2kvve4EK8a0J4MbIUx5HhZL3br+lVP9Rm5KClW+1NQ/dWWzmV2Pd5PZfG53avQ8+g8AAP//AwDB - 2mJZ8wUAAA== + H4sIAAAAAAAAAwAAAP//dFTLbiM3ELzrKxo8bYCRIclry9JNeRyEIAgC+LBAtBAosmemIz4m7B7Z + guF/D8iRrdms9yJALFZ1VU83XyYAiqxagzKtFuM7N/3l9+Vq9tfPff3sn37t4iqe6z/P/Me//eN8 + Y1WVGfHwDxp5Y92Y6DuHQjEMsEmoBbPqfHn3sJrPZncPBfDRosu0ppPp5zhdzBafp/P5dDG7ENtI + Blmt4e8JAMBL+c0Wg8VntYZZ9XbikVk3qNbvlwBUii6fKM1MLDqIqq6giUEwFNcvuwCwU9x7r9N5 + p9awU48tAj4bTJ2AIxaGk04Ue4aENSYMBhl0sMDSW8J87HJKkAi/PXdOU9AHh7BJQjUZ0g62QdA5 + ajIXPn3ZbH+6gccWGYGCcb1FeIrpyBAD4KBAoYEuoSWTu8kQazAup6kJE1eQMyTNQiccKEGXixXg + Sbu+/MmkL5stUACflbQD8rqh0FTgURIZhjqmfKcqgeqkPQ5O8rlFpqY4yeCbbmiKKJ9Z0PMNbAW0 + 4wgew2CVgmDqEkrpgtempYDgUKdQStOlGVTgPAg8lPcobbRDbfJdiqdcbCRHjuScUx2cNsfpIT5f + +KWb48/TUtM6aloBaTGLxSQ6Nz/W4PUx6262FzL0wWLKU2KL42zl2wzZUM+l708kLeiuc2T0W1yo + CZ1lcHTMeQUTacfAhrKZCkyLnljSeYhpU99cenuzU9UwgQkdnrLDPZuYME/iwy68jsc2Yd2zzlsT + eudGgA4hyuAmL8zXC/L6viIuNl2KB/4fVdUUiNt9Qs0x5HVgiZ0q6OsE4GtZxf6b7VJdir6TvcQj + lnLz+/lyEFTX7R/Bi8UFlSjajYDl7ar6QHJvUTQ5Hu2zMtq0aEfcu9v79xC6txSv2Gwyyv69pY/k + h/wUmpHKD+WvgDHYCdr9dU0/upYwv5A/uvbe62JYMaYTGdwLYcrfw2Ktezc8XmrYuH1NocmzScML + Vnd7u6rx/m55iwc1eZ38BwAA//8DAEdRAU7KBQAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9d0aff65f98b-SJC + - 9854b3b878a54f08-SJC Connection: - keep-alive Content-Encoding: @@ -5200,7 +5177,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:38 GMT + - Fri, 26 Sep 2025 18:07:40 GMT Server: - cloudflare Strict-Transport-Security: @@ -5216,13 +5193,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "3367" + - "2228" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "3386" + - "2241" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -5232,13 +5209,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998520" + - "29998561" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_900a14e7a77c4f08ade688ed7de496e3 + - req_a3ed4c79cff144f082705a8f4a2c76c0 status: code: 200 message: OK @@ -5248,13 +5225,11 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt from Wellawatte2023 pages 16-20: Wellawatte et al, XAI Review, 2023\\n\\n------------\\n\\nssion challenge and is\\n\\nimportant for chemical process design, drug design and crystallization.133\u2013136 In our previous\\n\\nworks,9,10 we implemented @@ -5330,7 +5305,7 @@ interactions: connection: - keep-alive content-length: - - "188462" + - "188292" content-type: - application/json host: @@ -5362,26 +5337,26 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA3RU224bNxB911cM9nllSIrl25sbNKgLBCiKoAhQBdKInN2dmEsynKEvMPzvBSnb - q7bOywLLwzk8Z25PM4CGbXMFjRlQzRjd/OPvP77+9vFub3/58+LzHw/nq/PP67Ph8pP90v+FTVsi - wv47GX2NOjFhjI6Ugz/AJhEqFdbl+fri4vRstVxXYAyWXAnro85Pw3y1WJ3Ol8v5avESOAQ2JM0V - /D0DAHiq3yLRW3pormDRvp6MJII9NVdvlwCaFFw5aVCERdFr006gCV7JV9W73e67BL/xTxsPsGkk - jyOmx01zBZvm6/VNCyHBrw/RIXvcO4LrpNyxYXRw45Wc4568oRYSdZQENMBIOgQrgN6Ckhk8/8gk - oAMqjHhLoAOBJcPCwc9HvGXfQ0zBkAgJhA6ub6AmSIC9UoqJtD5eGLO3lIolW480wJBH9HICN74y - V3cPWnjKb0zhji0VKQ/awtfrG2ABjNFxOQxgBhrZoKvsY3BkssMEMZFlU0opLUg2A6CABJf37Fgf - 620x5HUumrLRnAgSOawRA8ciSCEXRxqCE3B8W8Tl4qhDoxmdtGBJTOKoIQGVNHt8ebLy5/1Ejh7d - o3BNMVvyyt0jDOEeJJIpNTkS3xGWmJK/zuVSoGM/J/CpPoelWVtAa0sF0LBlU+q9R2EDfQo5FobS - xMXGZP6gzlBSZA9d9pUX3WsMFrkiwXDpfrhnHSaVNWlyAl8GEgL2wv2gAui494ertz7c+6kuMbE3 - HB0dWqqQW0p8Rxa6FEawqAhZigdLFMERJl8deVvrPTXhyaZpD42eyNEdekNbMSFRafjl4gXLQnbL - I/Yk5VxTpo1/3vjdbnc8Rom6LFim2GfnjgD0PuihjmWAv70gz28j60IfU9jLf0Kbjj3LsC35Dr6M - p2iITUWfZwDf6mrI/5r2JqYwRt1quKX63Gp5enkgbKZtNMHL9ekLqkHRHcV9WHxo36HcWlJkJ0f7 - pTFoBrJHpJeraR9hthwmbDE78v5/Se/RH/yz749Yfko/AcZQVLLbqdPfu5aobOyfXXvLdRXcCKU7 - NrRVplTqYanD7A7LtJFHURq3Hfu+7Cg+bNQubtdny/1qfX5u983sefYPAAAA//8DAJvtOk9aBgAA + H4sIAAAAAAAAA4xUTW8bNxC961cMeF4JWjWOY92EoAWEopemLVJUgUCRs8uJuSTLGUpWDf/3gruO + paYOkMse9s08vnnz8TgDUGTVGpRxWsyQ/Pz9z7d3bd50wS1/af/54/c/7z4fP+Bx+NW+/9CppmbE + w2c08iVrYeKQPArFMMEmoxasrO3tzbu7drm8uRuBIVr0Na1PMn8T56vl6s28beer5XOii2SQ1Rr+ + mgEAPI7fKjFYfFBrWDZf/gzIrHtU65cgAJWjr3+UZiYWHUQ1F9DEIBhG1Y+7ALBTXIZB5/NOrWGn + Pm62DcQMPz4krynog0fYZKGODGkP2yDoPfUYDDaQscPMIBEGFBctgw4WBI0L9HdBBnFaYND3COIQ + UkZLpho0BVo0xBTDfND3FHpIORpkRobYwWYLo08MFARzyiijmJpYgsVcK7PjL4ngyqADL2AbxpfG + Ih+k8hiHAxntx8QhejTF6zxxU+gb+LjZAjEURluZyGIQ6s5jPE4ujJwZvR61O0oMB5QTYrhiZMnF + SMn47IJDyrWkhFkIuQEuxoFm4OjLgTzJuRrNBoMs4KeYAR90naAGTCy15k4bKdrzV9ItssmUJGYG + nRFwSD6eJ/WOeuepdwIunoATmtq5F23ag3E69Fhd7XypbZysuG7OAn5zxKBTylEbBw59YtCe+gCb + 7dxipiNaoMD1JYYTiQPk2h9ih/bi+X2IJ4+2n9pGQQrVFxp47vilyUPMCJJ14KQzBpk8zIXlFLO4 + 82KnmmlcM3o86mBwzyZmrGPbLnfh6XrIM3aFdd2xULy/AnQIUaY21vX69Iw8vSyUj33K8cBfpaqO + ArHbZ9QcQ10elpjUiD7NAD6Ni1v+s4sq5Tgk2Uu8x/G5VduuJkJ1uRUXuF29fUYlivZXeasf3jWv + UO4tiibPV9uvjDYO7SX3cip0sRSvgNlV4f/X8xr3VDyF/nvoL4AxmATt/jJhr4VlrMf0W2EvRo+C + FWM+ksG9EObaDIudLn66c4rPLDjsOwp9vRs0Hbsu7W/etofVze2tPajZ0+xfAAAA//8DADwpyfD1 + BQAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984e9d0cac647af2-SJC + - 9854b3bc1b2b236e-SJC Connection: - keep-alive Content-Encoding: @@ -5389,7 +5364,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:40 GMT + - Fri, 26 Sep 2025 18:07:42 GMT Server: - cloudflare Strict-Transport-Security: @@ -5405,13 +5380,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "5376" + - "3147" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "5418" + - "3177" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-input-images: @@ -5425,15 +5400,15 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29996985" + - "29997027" x-ratelimit-reset-input-images: - 0s x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - - 6ms + - 5ms x-request-id: - - req_08500a990eb3444580fe601771c4e3df + - req_5bafe3383e0e435e8f9250d51e363514 status: code: 200 message: OK @@ -5442,57 +5417,57 @@ interactions: '{"model": "deepseek/deepseek-r1", "messages": [{"role": "system", "content": "Answer in a direct and concise tone. Your audience is an expert, so be highly specific. If there are ambiguous terms or acronyms, first define them."}, {"role": - "user", "content": "Answer the question below with the context.\n\nContext:\n\npqac-fc7d30d6: + "user", "content": "Answer the question below with the context.\n\nContext:\n\npqac-1026f14b: Explainable Artificial Intelligence (XAI) is a field focused on providing interpretations - and explanations for deep learning (DL) model predictions, addressing the ''black-box'' - nature of these models. XAI aims to enhance trust and usability by offering - insights into why a model makes specific predictions. Key concepts in XAI include - interpretability (the degree of human understandability of a model), justifications - (quantitative metrics supporting model trustworthiness), and explanations (descriptions - of why a prediction was made). XAI is particularly important in fields like - chemistry, where understanding structure-property relationships can guide hypotheses - and ensure models do not rely on spurious correlations.\nFrom Wellawatte et - al, XAI Review, 2023\n\npqac-1dbe3919: XAI, or Explainable Artificial Intelligence, - refers to methods and techniques used to interpret and understand the decisions - made by machine learning models, particularly deep learning models. In the context - of chemistry, XAI is applied to uncover structure-property relationships, such - as predicting blood-brain barrier permeation or solubility of molecules. Techniques - like counterfactual explanations and descriptor-based explanations are used - to provide actionable insights into how molecular modifications affect properties. - These methods help bridge the gap between black-box models and interpretable, - actionable knowledge for chemists.\nFrom Wellawatte et al, XAI Review, 2023\n\npqac-04c41457: + of deep learning (DL) model predictions, addressing the ''black-box'' nature + of these models. XAI aims to make DL models more understandable and trustworthy + by offering explanations for their predictions. Key terms associated with XAI + include interpretability, justifications, and explainability. Interpretability + refers to the degree of human understandability intrinsic to a model, while + justifications are quantitative metrics that defend the trustworthiness of predictions. + Explainability actively clarifies the internal decision-making process, providing + context and causes for predictions. XAI is particularly important in fields + like chemistry, where understanding predictions can guide hypotheses and ensure + models are not learning spurious correlations.\nFrom Wellawatte et al, XAI Review, + 2023\n\npqac-f26b2ee3: XAI (Explainable Artificial Intelligence) refers to methods + and techniques used to interpret and explain the decisions made by machine learning + (ML) and deep learning (DL) models. In the context of chemistry, XAI is applied + to uncover structure-property relationships, such as predicting blood-brain + barrier permeation or solubility of molecules. Techniques like counterfactual + explanations and descriptor-based explanations are used to provide actionable + insights into how molecular modifications affect properties. These explanations + help chemists understand and validate model predictions, making AI models more + transparent and interpretable.\nFrom Wellawatte et al, XAI Review, 2023\n\npqac-5bd670f4: + XAI, or Explainable Artificial Intelligence, refers to methods and techniques + that make the predictions and decision-making processes of AI models interpretable + and understandable to humans. In the context of chemical and molecular modeling, + XAI is used to identify and explain the relationships between molecular structures + and their properties, such as solubility or scent. For example, counterfactuals + and molecular descriptors are employed to highlight how specific structural + changes influence model predictions. This approach helps align AI-derived insights + with established chemical knowledge and intuition, making AI models more transparent + and trustworthy.\nFrom Wellawatte et al, XAI Review, 2023\n\npqac-690b9f30: + Explainable Artificial Intelligence (XAI) refers to methods and processes that + clarify the internal decision-making of AI models, particularly deep learning + (DL) models, which are often accurate but less interpretable. XAI involves developing + an accurate model and then adding explanations to its predictions to provide + insights into the underlying mechanisms. XAI methods can be intrinsic (self-explanatory + models) or extrinsic (post-hoc explanations applied after training). Evaluating + XAI involves attributes like actionability, completeness, correctness, domain + applicability, fidelity, robustness, and succinctness. These attributes help + determine the quality and utility of explanations in various applications, such + as chemical property prediction.\nFrom Wellawatte et al, XAI Review, 2023\n\npqac-0278ab77: XAI, or Explainable Artificial Intelligence, refers to methods and techniques - that make the decision-making processes of AI models interpretable and understandable - to humans. In the context of the provided text, XAI is applied to chemical and - molecular predictions, such as solubility and scent-structure relationships. - It uses tools like counterfactuals, descriptor explanations, and substructure - analysis to identify how specific molecular features influence predictions. - For example, adding acidic or basic groups increases solubility, and certain - functional groups are associated with specific scents. These insights align - with known chemical principles and are derived from data using deep learning - and XAI techniques.\nFrom Wellawatte et al, XAI Review, 2023\n\npqac-8d7c22f9: - XAI, or Explainable Artificial Intelligence, is a method used to explain predictions - made by models, particularly in molecular property prediction. It aims to increase - trust in predictions and ensure models are learning correct chemical principles. - XAI methods include explanation representation (e.g., text, molecular attributions), - counterfactual generation with minimized molecular distance, and considerations - of chemical space. It is becoming increasingly important in fields like healthcare - and environmental science, where explanations may need to meet legal or domain-specific - requirements. However, challenges remain, such as defining molecular distance, - evaluating explanations systematically, and tailoring them to specific audiences - or domains.\nFrom Wellawatte et al, XAI Review, 2023\n\npqac-3dab76be: Explainable - Artificial Intelligence (XAI) refers to methods and techniques that make the - decision-making processes of AI models, particularly deep learning (DL) models, - interpretable and understandable. XAI involves a two-step process: first, developing - an accurate but uninterpretable model, and then adding explanations to its predictions. - Explanations provide context and causes for predictions, offering insights into - the underlying mechanisms. XAI methods can be intrinsic (self-explanatory models) - or extrinsic (post-hoc explanations applied after training). Evaluating XAI - involves attributes like actionability, completeness, correctness, domain applicability, - fidelity, robustness, and succinctness. These attributes help assess the quality - and utility of explanations in various applications.\nFrom Wellawatte et al, - XAI Review, 2023\n\nValid Keys: pqac-fc7d30d6, pqac-1dbe3919, pqac-04c41457, - pqac-8d7c22f9, pqac-3dab76be\n\n------------\n\nQuestion: What is XAI?\n\nWrite + that make the predictions of AI models interpretable and understandable to humans. + Counterfactual explanations are a key tool in XAI, providing instance-level, + actionable insights by identifying changes in input features that would alter + the model''s prediction. These explanations are generated through optimization + techniques, such as minimizing the distance between the original and counterfactual + instances while ensuring a change in prediction. XAI methods like MMACE and + CF-GNNExplainer are used in molecular predictions, offering insights into model + behavior and uncovering spurious relationships in training data.\nFrom Wellawatte + et al, XAI Review, 2023\n\nValid Keys: pqac-1026f14b, pqac-f26b2ee3, pqac-5bd670f4, + pqac-690b9f30, pqac-0278ab77\n\n------------\n\nQuestion: What is XAI?\n\nWrite an answer based on the context. If the context provides insufficient information reply \"I cannot answer.\" For each part of your answer, indicate which sources most support it via citation keys at the end of sentences, like (pqac-0f650d59). @@ -5515,7 +5490,7 @@ interactions: connection: - keep-alive content-length: - - "5429" + - "5397" content-type: - application/json host: @@ -5535,45 +5510,48 @@ interactions: AAAA//9CYgIAAAD//0JiAgAAAP//QmICAAAA//9CYgIAAAD//0JiAgAAAP//QmICAAAA//9CYgIA AAD//0JiAgAAAP//QmICAAAA//9CYgIAAAD//0JiAgAAAP//QmICAAAA//9CYgIAAAD//0JiAgAA AP//QmICAAAA//9CYgIAAAD//0JiAgAAAP//QmICAAAA//9CYgIAAAD//0JiAgAAAP//QmICAAAA - //+EWF1v3LoR/SsDPdmGdmvny8m+FEZvLmAgt8UFUjRFXQQUOZLmLkUqnJHtvUH+ezGUtNJuEvTN - 3qVIzjlnzhnt14JcsSsaDJub29dv37568+LF9ea6urb/Zvz9tnltXvOnm7tPvz8VZdGn+EgOU7Er - PprUxFCURRcd+mJXOMSeEfd/mf/YpJuiLGL1B1opdoVtjWxt7HqPQvlJm9AIumJ3PPimLGwbySIX - u/98LXxs+hQrLnZh8L4sagrE7eeEhmModgVL7IuyCEboET//5FsKDp+L3XVZdMhsGix2X4sUPRa7 - wjATiwmit4lBMOhN3z/33lAwlUe4S0I1WTIe7oOg99RgsAgXn+7uLwGDFmSYkaFDaaNjMMGBoG0D - fRmQwSFTE9CBRKAgmPqEkhdZbxLVB5AWwaElphg2ndlTaKBP0WLeNtbQGdtSQPBoUtBvM+RcQm+S - kB28Sf4ACvuy5OKXD5fABxbsuISnlmwLJiHEWjCAjYGVR3TwUFTe2P2mis8PBbgB9aLSIiWIvfky - 4HjrYDx0aFsTiDuGi/6LsZva3rqX1+5NCfnfl85Ut28qvNzCp7t7MNSxboahNQqZpIGlhIFNRZ7k - UGYUjLVxCDJ9BtUBRpFpEe3QmbAZgsOkLLnMCAWmphXWe8URCugTOrIqKy6BB62VgRwGofqgO+3x - ADUaGZJCmiAZXWw8MlTYUnDAPVplGuIg/SA/KfHGVfjy3c27yy18bEnv8Bj9IzLIU4Q+UWfSAUzf - p2hsi7yDqysKkvTO9uoKLkbmTkRRITD6eoOqumAkpsNlRubqCp9Xj/aRZdNGC/NCrVbP8oQOTC2Y - JjQkGVIRXE41rHiBh/AQ7gPYFjtiSYcyU0UMNpGQNR7qmAD9YMkZUehY0mAVuE2fYo9JDpDQj8e3 - 1K8An1nICvWYhQkc/TBxGxNUPka3qfSCUJmUCBP0mDrM+5VKv6cwtcCRU4Up31jv1ycKlnqlztMe - oR6CHcmEJsWhV3FLomoYH40JeKjmIlTFUaie6Z35nOi9fmVf3bx6fav0Li1MwfrBIVxdZaliqo2V - wfgTJpQi3DbbUkmYVLdCYQYx15INg1cVgjZWgzwT75Btol5i2lSGlV5ZStKTWmpar22gxxiJnQpX - eUMrKQayi9op1H7AYHVlHMTGTs+5OKl3Kv+tu7UvXtTvZqGoNPDR+CGXqLSTtk/IYsFE5ki9GSmY - iL7ok36gbA0ycV+foHVZQk0Ox9XGUxM6DAJPJO0k4gpb80gxXY424WKnmslyt8s5Z/L+W2u8R0Xy - SJrDmsIZGdSReq8c1LQTWc6iPyVXdZ3t0+RC/AEejZ97Yl3KeD8x5GOaMM7+IXG69GaxlgQJm8Hn - JoeEXwZKqIXPclwR8LFFRsC6jkkYMPCQcDJ+MI+R1LGGRHFgsDEdO3L0VAV0BBNZTOWJW3TAlrIj - koV9iE8eXYNnNne5LcoiYT2w8XPqjoFKoSl2xT/25lDCPQQczcsEfsKUI0xbJcvkofhXa0Q95dPd - /V8fChhYcdE10/zgco/is2zhAwp0CCwmiXZ/QuPG1SkOTQtobKvi0aenhwC1v8dOaoy00/lq8X2k - ILxV7f6aYgcnte1mqzNVHGTkcJTGLx++z5FcnXNJG3WMw2NOQsittYV7+XHIZRKOOXes5Rj/xwD8 - Y+DMx1pJa21t4b7rY9IJBWjl2lmv/8+YMwwnLjchMF+D4be58Hn+4BIwyzUr/rsjpaXQTL67MvYV - bD80zrPOGhv63OJOYk1zMZHqU6ltTA8VyhNiOLZAHh1G0/G46Hkpeva2sejO7JHh7n6pdE2Hx3JW - aYx+KvBHt15FCYIJxh84TwELTlt4/2y6HE8NPWKY1LZCK+9jMchm2eonxM1+MNYwKZZXVnYm2Mkm - siFY+WFm6hCYp0R0W/htmllnolYcQMI+IWOQWZzf+WMeYqdJkpfDuDcWT7w4w7lc2uWZ287enE/L - yl+iBp+JZcFhtvhZwNPQNQ3Lc/ueUQpI2RyOA1jWtKbk8+qDLbxfjp1jdqXdk2QrZ2gDsraK2C18 - fIobFuznmX13bKlH9LHPuSatCsG58+zIFf49PmnWBGmR6c/JKhlnM9MkmH2W2zh4N+KGikUJur9e - nYShH1IfGeFi5W2La81vDaNV5XPUrC7LbJ02Bou9JtH3NnWW3Ku3m4vvRLH0tS6d8lqmhlvPnWeM - G7UdLffOcywhRMGFOXjk7ULbyauWPcpsBHMKpqkTcnzoa1IecUly9GQGlflfsxhyt+o7EgYQ47Ok - xqad8dRz1hiVeavTdDkzvrVxHpNo9uG84YlJnahweWDW/fLA7Agn/bU8cPxasbjLY0JCNwRngj1s - 4Tejxq3AyCKq2scnBh+bsSHKMYwVhTxBLE1azoCUMwOnBJcrPstzcuCfnBPcEusLj75rW7Uj1vl/ - PZqwHHy2D7R7EJ0kjPcK+DzgLAbncyp1RpTXfNk9HiY4VgPD9iEU3779tyyG+fW/T7Hr5bPEPQYu - djc3L1/oDwDzbxPHL96+elkWEsX4Ze2729ffvv0PAAD//wMAoZygiF8SAAA= + //9CYgIAAAD//0JiAgAAAP//QmICAAAA//9CYgIAAAD//0JiAgAAAP//QmICAAAA//9CYgIAAAD/ + /0JiAgAAAP//QmICAAAA//9CYgIAAAD//0JiAgAAAP//QmICAAAA//9CYgIAAAD//0JiAgAAAP// + QmICAAAA//9CYgIAAAD//0JiAgAAAP//QmICAAAA//9CYgIAAAD//0JiAgAAAP//QmICAAAA//9C + YgIAAAD//0JiAgAAAP//QmICAAAA//9CYgIAAAD//4RX227cRhL9lQJfohE4tOTYkj1vgi9ZYSVn + gVwQ7HphNJs1w8o0u+mu4oxow0A+Il+YL1lUk5yb5c2TRiS7u+rUqXOqP2dUZYtshX5+ef38xcvL + i4urp/PLH7a/3vzr7qW5+rc8/Lr+ZeNvfZZnbQwbqjBmi+xnE1dBnzWhQpctsgqxZcT1k+nHPF5m + eRbK39FKtshsbaSwoWkdCqWVNqIRrLLF/uA8s3Ugi5wt/vM5c2HVxlBytvCdc3m2JE9cf4hoOPhs + kbGENsszb4Q2+OEbb8lX+JAtLvKsQWazwmzxOYvBYbbIDDOxGC8aTfCCXiN989A6Q96UDuEmCi3J + knFw6wWdoxV6i3D2283tDNBrQoYZGRqUOlQMxlcgaGtPHztkkNoIWGciLXuQGqFCS0zBzxuzJr+C + NgaLaYewhMbYmjyCQxO9vj27v5ulLRXVg8ev72aQkOccTFVFZNbnUiNFIF9jRC/wPiudset5GR7e + Z+CNdBFBAqCvjWYh0XhuTURv+yHw2LFsQxSNghnO2o/Gzi8vnl4tL5+VOaR/l0+vyqeI388K+O3m + FkZSMNRdY/y88xVGBbVKAJIXjG1EMVr1IUmNG9qIFdnhodQxdKsaZBugjdSY2IMiGzx64QWcn++3 + KcmR9OfnOWxrsjVEXDq0wglc8hLJM9kBcun1PDOc+B0DS+xsAiFEcGFFNk9pn5/jVPOT3Y1Vbrke + VugxGkGGRJQHmXOLVrkBMaVmHDIsQzxK7FH8npfV1fXF8tmsgJ9rZIR0uN8BJOiB/Ca4DcLHzngh + SQyH3ztObBy/lAAb46gyggeHQkRHYx4n588KgPf+vb/10EbNzGKeSrjjbkSwRnAVIn3CCgwP0A+Y + np/D2cA5qJBp5bHSGEoERrecT1mE2OfAnYLH4MijiSNVZwq7Yn2wYRtY5nWwhz1j2tYRVpDeSTSk + nM/B0RrBhk6psDRWOuOOoJuN6V69vChfLr+/mBXw6ttf52BgjX3KX0JwOVCFXrRNG/LUGAfk2040 + 9gPQz7BYFTk0waHtnIkHnLK18Svk2dDzxgkekSEHVE3RNjXpydgfTKtaJqpcPL1+Ycrr669b7daD + rbEhFsVXoyYGG0nIGpeI13kbNhj1gF1Q8zaGFqP0SoshhZpa/uuPP6cKcXDdyJYQoaSQKE/S//XH + n2WvBRxl6qBdw0H+FbKN1EqIrBuc1IctehMppOy5S8EZRyvfqD5tSWqoQmPIw9qHrcNqhakjGxJa + GUm5tF2k0GnjxV0SI1xTK41wTehNRFeUcGNclxYpzSNZBvLWdRXCkioc2qQOW9iiO2bIJC2jYJVY + mw2FOMshhrJjUYUc9GNMIfHWjr13kPDRptpjZRCVFtupoqQdDhihpURfzTvGyF9ROsuziMuOjZtc + cTA88qtskf24Nn0ODuU7BjF27RCkJgZtqwSBKUMnyp6kPaBnwNZ4YTBQoVpo+qw0jBUEn0R1FPhq + Ur4C3lJkyeEWPA4a0JrIuJNxNLZW4dXF4xpAVRFM3FkZqTGCcU5JiRvjBcgvQ2wSRIVW7icxMVU/ + UeRIxRZwK8Cm56kJhpyGKHXJqeUooK/vdnY5+q7U2MCJWR1bYF/AP7EHwdgwqKOm2p06UX4iywMj + jv2kSMPD4SqNW+GZnGln2YcRPbp/CmJicoVL9CnpE+d+LIpxDElV+PYgUsBt04YoQ1VgSegqHrV3 + 0p9Uonf4ICcytUglibhU5qpKjL4iYQ8b3N89eX23O51PlY2Uu53ST+v2d0KWQzcMPnv3+DubGMep + SbXmE9eRC/gHupYfc9VhVWPWY8n4YHaSBMeRHi3gJ2pI5VHC2EO4URFL09o4FSpWdTowKSIomyc3 + SLRPqKi677RxSvc4vTG4xyS5gDdNWxumT8jfGPf20U8qM5Rxz5ZUO2/cvmja3buWKuB+rLM1XseB + /RR2djoZpBFgNwDs7X9WwJu9UI/TD4MRiVR2MpV1ksmxM5Ij2FGIUWwBv2gtye+h29FmX8t9wpNf + LOBtsF2awf3/pw6PU8MwMUxDYQUbMhBaoYY+DSIG9zpM6JrdnDh1Q8rk/v7m1ZtUhFdv5z+8ezde + OlCn94NSHjBwaLqwzYF7LzUyfRp1jHFQc+N5ixG4Dp2rgFVCByKZMVJ3oPG5rvQpmf2oDWdfy9ux + iMzyXR5nuzo/2VU0P6XmLJ9scUCDjpo9idS+7jrOYCRTwLvRWJKH4uAoreajQw9JMqNUe9cX8KpG + u05yofOPM+3QEDY0pV6m1FsSDmoTEavOV8bbvoB7bWce70TNUK/hGjEqoMWTeHe13OvNV0nsOVvA + zSZQpS2f5h7fqzqvwIckrQf2WMCN45DrUXatCQ5brbEf7GuXfwF3KNDgwdiZDJ4FWyj79Ld477Mv + X/6bZ9103W1jaFr5IGGNnrPF5eXlM73wTnfx3Yvr68s8kyDG7b998eL5ly//AwAA//8DANtZHwdi + EgAA headers: Access-Control-Allow-Origin: - "*" CF-RAY: - - 984e9d3059752283-LAX + - 9854b3d18fcecf8f-SJC Connection: - keep-alive Content-Encoding: @@ -5581,7 +5559,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:23:41 GMT + - Fri, 26 Sep 2025 18:07:43 GMT Permissions-Policy: - payment=(self "https://checkout.stripe.com" "https://connect-js.stripe.com" "https://js.stripe.com" "https://*.js.stripe.com" "https://hooks.stripe.com") diff --git a/tests/cassettes/test_pdf_reader_match_doc_details.yaml b/tests/cassettes/test_pdf_reader_match_doc_details.yaml index 95a663e94..ea0e95df4 100644 --- a/tests/cassettes/test_pdf_reader_match_doc_details.yaml +++ b/tests/cassettes/test_pdf_reader_match_doc_details.yaml @@ -46,20 +46,20 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//jFNdb9MwFH3Pr7jyc4uarO26viEGD4xJRZOYxlIlrn3TeDi2sW/GUNX/ - jpx0TYEi8ZIHnw+fe4+zSwCYkmwJTNScROP0+N3H75Kv7sz13eLq4Yv68NU2n25uZHrt7uvPbBQV - dvOEgl5Vb4RtnEZS1vSw8MgJo2t6OVsspvP5ZN4BjZWoo2zraDy142ySTcdpOs4mB2FtlcDAlvCY - AADsum+MaCS+sCVMRq8nDYbAt8iWRxIA81bHE8ZDUIG4ITYaQGENoelSl2X5FKzJzS43ADkjRRpz - toScvYUV+uBQkHpGsAbevzjNDY/TBbAV3FqNotXcw8qjVCICcBsHCzkb9X68pdr6EB0fc3aPWvMf - nAgBCbjO2frAk1ZFjmm1zs0+N2VZnib2WLWB6wPjBODGWOojxSvWB2R/3I62W+ftJvwhZZUyKtSF - Rx6siZsIZB3r0H0CsO5aaH9bLHPeNo4Kst+wu25x0duxofYBvLg6gGSJ6+E8zbLRGbtCInGlw0mN - THBRoxykQ+e8lcqeAMnJ0H+nOefdD67M9n/sB0AIdISycMfGz9E8xr/iX7TjkrvALKB/VgILUuhj - ERIr3ur+wbLwMxA2RaXMFr3zqn+1lStm83STzS4v5YYl++QXAAAA//8DAGyqnmS+AwAA + H4sIAAAAAAAAA4xTXWvbMBR996+43OdkJE6zfLyNbqysFEqhrFAHW5GvY3WKJKTrLiPkvw/ZaZx1 + GezFDzofOvceeZ8AoCpxCShrwXLr9PD6dnY9v9k81aOn6tt89HV9+/jZP+zCw+NNHXAQFXb9QpLf + VB+k3TpNrKzpYOlJMEXX8Ww6X4xH6TRtga0tSUfZxvHwyg7TUXo1HI+H6egorK2SFHAJzwkAwL79 + xoimpB0uYTR4O9lSCGJDuDyRANBbHU9QhKACC8M46EFpDZNpUxdF8RKsycw+MwAZsmJNGS4hw09w + Tz44kqxeCayBLzunhRFxugC2gjurSTZaeLj3VCoZAbiLg4UMB52faLi2PkTH5wy/k9bip2AmIAah + M1wdeaVVkWMarTNzyExRFOeJPVVNEPrIOAOEMZa7SPGK1RE5nLaj7cZ5uw7vpFgpo0KdexLBmriJ + wNZhix4SgFXbQvPHYtF5u3Wcs/1B7XXzSWeHfe09OFkcQbYsdH8+TtPBBbu8JBZKh7MaUQpZU9lL + +85FUyp7BiRnQ/+d5pJ3N7gym/+x7wEpyTGVuTs1fonmKf4V/6KdltwGxkD+VUnKWZGPRZRUiUZ3 + DxbDr8C0zStlNuSdV92rrVxeLir6OJ1NaI3JIfkNAAD//wMAt5s/VL4DAAA= headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6982bd0f9ea-SJC + - 9854b8769984aaaf-SJC Connection: - keep-alive Content-Encoding: @@ -67,14 +67,14 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:06 GMT + - Fri, 26 Sep 2025 18:10:53 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=.AbshRRefKLk9WY8Px5wp0DmzmvpHKbeKA5cnca2vn0-1758846606-1.0.1.1-mlnLq85HuMEfkVt7ULW4RHfTQIAAOKQxiLh_VGTAMBx2EGVVyNidPAdnDZbM4ixRli1r6vpy66vZ56Fit9E57NAiz_nJZaM4G1.s_ytPxMI; - path=/; expires=Fri, 26-Sep-25 01:00:06 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=aasQNABEhNt3snSL2Zqg8NCydmInniMDAvP1eDtQyco-1758910253-1.0.1.1-S3KAlkG2Q2O3SyPDPGI5PyMU0PYc0eL8uRi3kB3Mc69yp_Fv3lkbkFXhIcu8Nu8CUaGp8jSeZIHfywQ5LlpRiCEmXxN7xH9nYAl7izSbyu8; + path=/; expires=Fri, 26-Sep-25 18:40:53 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=7gnJkUsfZXsU0yOl5388BvN_FXZfL2duD.ml0ceWl04-1758846606621-0.0.1.1-604800000; + - _cfuvid=j5DTh9nrw_JfE1bMR2fi_.Pokj5G2c7OmuDxsy88OtE-1758910253135-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Strict-Transport-Security: - max-age=31536000; includeSubDomains; preload @@ -89,13 +89,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "406" + - "467" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "429" + - "495" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -111,7 +111,7 @@ interactions: x-ratelimit-reset-tokens: - 0s x-request-id: - - req_f309aabdb3d6487c98ddf467ee0d590b + - req_b0a97d7ec841498fa85c91f91ac5c645 status: code: 200 message: OK @@ -133,14 +133,14 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAA/62TQW/bMAyF/4qhc+S4bpptvgXL0PUwNOgw7ND0wEp0LFSWXIlOYgT+76O8AWl2 - bQ3oYD75eyT4fBKRgPooKuFfxEy0GCPsUNLQIdcOPrxIayK9kfYYovGO1au8yIuzIqqTqEEhMe00 - zgR5AisDxt6mUll8vlnMhCFs+e3xJNb3dwlS5OVycb3YzlWDbTiavSyLspSv9b4oGQ49NT5MH+zM - HpPvLWJrsk3Oag2tsQPXfqO1cAAi5GrE1x6dShPUJkzdQ10ba4CmzpnloE3yL2emeWjIfJ09eO4h - EgYxPo2zs+F3JMhWF3634HRjLr1Aa5MMwL7XcJVAb+1+YmxAhw83vH/4erfmWw1RF6vtfDv3QRmd - +7Dbzgt+JJ9SLpeLT/L6y/Lm30LQkVFAqOV0XVQ12Iizc/9OBzxk68sVNeb/7bxzgCeOU+SE1RgS - UMvnQSrfOxLVFefPkGXAo1hlGwZ0qIhJmXfZt2NnwU1OMUF/eIuqtxCyTUBtVBK4qNFG8dclhVZ2 - GGQ3BZ3pPEMYUuT5BwokjdN4FFWRxoOgGsktppy73tpxHP8AZovVN2gDAAA= + H4sIAAAAAAAA/62TQW/bMAyF/4qhs+W4buZtvgXN0PUwNOgw7ND0wFp0LFSWXIlOYgT+76PcAWl2 + 7QzoYD75eyT4fBKBgIYgKuFeRCo6DAF2KGnskWsH51+k0YHeSXv0QTvL6lWWZ/lZEdVJNFAjMe00 + pYIcgZEew2Biqci/lHkqNGHHb48nsb6/i5A8K8rl9XK7qFvs/FHvZZEXhXxt9nnBcBiodX7+YKf3 + GH1vETudbDJWG+i0Gbn2G42BAxAhVwO+DmjrOEGj/dw9NI02GmjunFkWuij/snqeh8bENcmD4x4C + oRfT05SeDb8jQbK68LsFq1p96QVK6WgA5qOGqwh6b/cTQwvK/3fD+4ebuzXfaon6UG0X24XztVaZ + 87vtIudH8ilkWS4/y+uv5ae/C0FLugZCJefromrABEzP/Vvl8ZCsL1fU6n+388EBnjhOgRPWoI9A + JZ9HWbvBkqiuOH+aDAMexSrZMKDHmpiUOJt8O/YG7OwUIvSHM1gPBnyy8ah0HQUuKjRBvLnE0Moe + veznoDOdZ/BjjDz/QJ6ktgqPosrjeODrVnKLMed2MGaapj/h6A3gaAMAAA== headers: Access-Control-Allow-Headers: - X-Requested-With, Accept, Accept-Encoding, Accept-Charset, Accept-Language, @@ -154,11 +154,11 @@ interactions: Content-Encoding: - gzip Content-Length: - - "443" + - "442" Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:07 GMT + - Fri, 26 Sep 2025 18:10:53 GMT Server: - Jetty(9.4.40.v20210413) Vary: @@ -1263,1697 +1263,1695 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA5x6S6+CSrTm/P6KkzP1JrypqjtDXiKvQkDFHgGCAiLyKqBu+r93dCfd6aRHPdmJ - yg5FrbW+V/Hf//HPP/92WV3k07//9c+/r2qc/v3P73f3dEr//a9//sd//PPPP//89+/v/3Vl0WbF - /V69H7/Lfz9W73ux/vtf/7D/+5v/c9F//fOveShVf+HYF9gsTeHBRw6f2BgzK+LrGlWQiuyJlM+P - p615BAqY+JOE/c8p0FYZHSuEaFyQgiNbtjGKGsJXxe3Jnqdnbbu/7AYeVQ9h03EdTRjKfQJhP2X4 - MLCt1hnUHSE30MvMMWQ/8Bcn1+Gboed5MUFNCRyfPng0MovdYxc76yv1d9Dacpuk9OGCTWyzEWru - XfbhMn+yaTiOFayGS45xsrsOS+TMPnSyGBBLnSVt3a3YhEYXYHy+dnSgzu4ho3yUo7lxEa9t8mOT - 0QxKHZvjvXJW4+0GAFpBSMp3MWTjRqICdp/t5UtpNQ+LL59U9N2/ufVVNeNosPDAfa8xvng7xlne - C7sh+jrPxDhaDF1Ev+dhfQqPRM11V2MP0yBD1tJ0bH3QLqIjDHaILpNDbsfApNPjZimIRbuFHE9a - NIxBU6kQfPoS4+EkajQHW4UEVjyT3Py4YL44uSlbVLOxlVTmwEH+LSN3zASyb4eLQ19VXwDIDyI2 - +06rhfdYKrB8nM/4gPdrTUPrGMBEtI/Yk5+Fw3MFtVGoLTE+RQ0Ga+N5Dczq8YRV6oTO5hlKA9t4 - sufWZ650W0qcwnjUDRLwa5Zx0zMLYPNSMlKy7zoTPoe0R+ugE2w7tx2gN/YE0ePUFSSzi9rpGfUS - wCb1djMyWb5eSqHqkaqOIs5V/uFwdfneIJuOKTbso1hvJ8nXAXoze2KayWtgbSOcETe4FxJ/6zm9 - w9CCJ/XukPR5gdn2GGsLlUmREXfn6dmmnpkAXsq9TaKH7GTEft5iyN8UiM0iOmX05FU6EqYmI+m9 - xsPcDhdTfurGm5gv5GvkcBM3xLlNja9r9IwElGcKZA6fhVjH611bPPhSEPKSlXgZmrPlddlU1Ni5 - g2/YMBxWGrYUuUGZEWyKtsOv9slH4Xyp5y18BWBj93Ur3VIlJMpsH7UJCN0Imk+3Izcns7KFvDcZ - sW0vEB3wAl3Dh22D4THaJLCWKvrNI7q+O4/cz4ziTHI+WcA4djm2JOsAlqN4ChBh9QO+p5Crl0bq - UxiRW0n2zDut+SruLMBigLAtOuqwRLdHgqbaxNhhyzxaX/ba/t3flKWw5sxmCZCqnUSiMHkAWCs7 - zkjR04svf/+fZbpTjMRVmrHdaPXAu4e8hdrcBERZFSVihyiBMGBWFsdxoYL18BxyaKcsgxMjUzLO - U2QePWednyVsGBprnJENg7cMsP3Fg+XxUnLUy9t95se76rBCwPPInqozPibNIxOSq52Cd9OZ2P3o - Y71kZ7OCue0ivO+ues1fjoqJWuZ0w/fAftKNxsYCfam9zZtI7xnfDhcdWHl1x7qLLpqAVTjDq/EU - sONG+2G7iCsP7yjB5MwaL2d5sHceDo/ZJlrXdBqVmwOP3jfhQ/BhnzpNYwksPBzgbq4vqRBtp1v/ - AC8rWLHekBVssyr2KB5NA5+NM3UWrn3xsOdneV5He824EiwpEkQy+PywuRF/zqoQeSyesJ4Fl3oU - d3kLrqQ2cUQuACwv/txCUVZYnIuJ6XC2FPrITK2E2MwOO3wf+AE4+klLEv74qKd9vLvAPDwIvniS - qoG6gydCBik5jvsmcNb9S6/gICoaTkVHrbd56xPwxU9iXAobTEcH5vLeNjjiUWI72zNVIFJP0kAO - AR9om+6rBdIwvOOLVs3ZlvV1itQpOhAl01TKn475BRTvY0h0ZnqDxb8+egTCS0t+/LRB8dkj+5Nv - JJoSr96qtxoDu9c2bBtdTWfZT2YI80Pn90XTRQuTXi04le/Bl4/hY6AKYVXUWttE7GmY68Xm9AK6 - HtbxIZ4eDieehA1adG+Tq554dImc1oV7+8BhB7haRtv7sYNidtvjgsoRFUL7HAI75ZmZubj1sJky - SaFpiiq2nbuqcbtTXaH6UcDf8wES4J0uPaUiItZJquof3sPZ6wp8C3U3WxzVE0G0Y8v5+erv2RLs - pQ7SiwWwbkRSNur8qZKXpziRiHoD2E63qkI431ez9FKrYRtAoKBCn4/E3DeKxrcZFuG+I51PmWF2 - 6Ev1Tfjjj8Mb8dqCFt8CwbArCGaCt/Pl9weEVhgSXxcrh92YTw8/1a77zpOS8atsQ9icNsNn5PxZ - L5JzUdD5ub6wF+8OGXet9jIiy7vxOSCuw8ikpQUsw21wUX50sACuhb/5+uLFQldVjS3EVkpMrksm - 1j89gqZaxz7S9h+whLtxgV98wWXOvWsWt7yC/viwD8psXS+nHmor+yaFLlbaHAesiK7kaeL8cbs6 - /N78yLDKQE5SH/POEt26BH31BD43qzOwaKdcwFS+BpKtkxUJLbOpKF9ITyzycQfetFEHXlx1Jfg4 - 7zNePQvhX//d7BTXC5NoNvrhWXaBr+jbjxcU7Bn81VNd1D30UYfHJVhwYElbtlmaxf/0Eo5FYQFL - 0FQKAtjEGFPo1YuUja4s5XfkV7WQ1Ktb1z66heMNm4L5Aptpox5wSncgbk7S7I+/53iXkPs2r9FW - 67cdWjfm89dfK5+cEqRFLkuUV0nrrdmtOgpomBD7Npb1un+5FXx3ZT4LNC1rqtxvLGSu+jKziwui - /rP0PfzygQ/9/FYP335HsGwcnMHjzaHoKPKww/4Ze9bjpXWoFKCkX5eIaN/55N8JEOEcwwQflkwc - qOtUMXr1tkyOL7Wq6RNtATreXze/3UIlmkX+KcKJj1/k4DrpsJUn0sBjE0dYvd74bLl1n8cf/mp9 - xUczOjA6iM9Xd36dKBct0ai28LLOz5lJ9qom7KS+gdSEFj5CNGcrNHc2sGr2Si68jxz6RHIAs3o+ - ffuJiUbzUT2Qf7loxJMT32GN6GmhSUkl4tJpyZZTuXehRTaKdeWiAXLGHxY6lvnC1tgbA23v+w4c - JOFAtCdonI2H9ggPZSDg02iv0Sq7xQ5W70Ca4RmZmVAfZhli+XPGppkYA8dNuAWhtsXzxnMzWA6+ - dYETUQfib8ugrZezVYGTyAizsN1tbcxqa5SG7q0R680QMP/02aQwEvFjK8kWaZBTeEEdIloWKhrn - F3YKzzd5mNFeeDrbdtBTuAm15QvNjjijd98XUp9bPQko846ofXJsmDpyhg1JTevlulwu8gP6Ezl2 - HI4WJ4z1P/1wCdhLxh6vUBZDaVbm9TEdKLWXeYQ/PnDfyc4ZfHP/QHPnzjOgAz9M1tywMDEDhZwd - e80+u9PwgKfbqs+L9N5qGr0C/sc35Kjn+4weKahgfQqO/jYkRraKi2XDc1H7+FBZAiDNtvRQwM8O - X8q4dxZDS+HP35A9Vb580J0uiGlPNY5y06CEC7cZAChyJF69+MvfZxb+1v/b/08gGhZ090GH7XQS - o40w9fLDP+Kzr8/Ql4xxgWm0nol217KIMnqgwvEjuvi+F/aO8BwSHe78cvK3erbq+fgIEogAvhL7 - LIWAPofAhH5/vBGnTg+1UHl4hF+97MPA6rTtdLAu4Ldep6h2dJxdJgbuXujI0fdgtmoMDoHQH0SC - J/My0FR7J7ARuRXjNlaHDZ2lAH7WJvvTAxuUuQ5+8c2XsM9pi39jOxjsE0wu6+ldU1eyTLg85Yl4 - z/xY89kopHA+tmAWzj03UGV6qIio285fwEd3uDwCOVwkv8XHrH5n67G4dOBQcoKPolAEw6flXek3 - P+bhDJytZLwLfLM3yd+eUp/1o+j4UEr6N8lVXnG4/ct9gKsixTPaOTIlr0AJ0ZRtZ5/aaAJbUqus - PO28AidffOa+fhTCefnTU1n/rQ/izXNLvnoArFIpQWgwrYW9NiDaWmk3FRq5uJGs14Z6BUI3o7sU - j8RSXx+Npqtnwy3ficSCW1MvKpAvMCP95IsftMvGDu1sOdskHVvLPcyEgr+NULsGV6LdbsWwPNXA - lMV5uP7mI1uvbGOCNb7y5HB0abTsnP0CFydRvvXI6DQ2Dx1O66jiL/8Nq49eO3gL5xsxl8CnKzcd - 2j99eRnvlUbWXTDCb79iKxn4YdlIVoBSGHxsfv3n7OmHBQqa4hJbdKp6fVddDO+X4ULUMH9QSjjh - Au9cdJwl/i5n2/Mob5AJcw/bS8/RtzmGO2A0/ujv9vtQ227qnMPS++ywxR8fw2ofHwWgNi3mxxCv - dMzxNAP5UdzJoVaUYV2OQYjKMVqJU7SVQ4pWqxAyooHsmaIfluOhHaGUl4gYjn3KpvRT5RBAmcNe - Ez7AernmFRrT0v3xby143SrC2/PiYs+L+2EMfaDKbNsJON58l1IDaym6SZ7hi+KBOOtyTAJoXnTu - 6x8e0TqtnwLKhuwRR3bsYSWj0slKL6nYSh8sWCJQ7uCPD7wfHp5u1QPqcbQQ0/Iap7sulxiKZdFg - h3uctVWsvB1cSxL6sM6f2XJdihgo124kJ9Ng6fjle2D7coXt86KC7f6+tLLgww7f9PyZbSgxAnR7 - xu6MhIqndA+5FHUvWyXGvmqj3/PA7vkpCf5EH7Bl/ZDArx/96ocwW355gPQOU6wb9if7zqOCfvOn - PMeWLi/+3kLzc3WI/RGfYF055MI5oTMx3wBGmyI9CtTy7wgfPls88Of7LgTaS7SJynM+XV3N5yF/ - N0ussoJSs84mFnDd0s+vv7SFv5Q9REKyxwrHvujmHvIGfvmXHPm7HC2ltOsB8e0U//Bw3t7dAu+V - Qr/4CKLlMNUioqGRYCy/1nrJamWGzGoqxMX3LaJ7z+lA8mADXG6Apdv5zgdgA82Awwd51lS0jW+e - w2GiOtAFPOqyHK584RKLjdVMCDBvAiOAF1x6/U7784c7sUC//aSrY707yN/1EjsXaGSrtQMKXJXq - gZUHu3OWUuI7+O0/7ElXLlsNIc2BkMbGPOwhqreyfC7Q5E7d3FzvR8D98qV1dzbIkQxtNg9oVOT7 - xGAfff33rD+a/m9+DEEfteU9dxtgPovphwL/rufP0ncQuyzBhy//bmBn5yCxjQu2NHKiS1XMPByL - B8GG8vTqLx/E4Jc/fOcnWtZQE1Fx3ZUziS5jtPQvYMG4UypyM+uy/vr1BpHl1eCffpgq7aT++UVv - m08Rd6iZHPgoI/7udNGzRRbp9uf3nHl+/tZTgMMKfezkzTKQ4sxdoDa3Ac5po2RLkWY6zEg3zXO6 - I9HXX+iw2Cry4yvQvcyaheVNy/CBDbthw7tWRDv2ls78lgXRgtnFhRKBD5xGQM+226K7cPdodGKT - r198wDUG3/nwt18/rlVoyvBaGmTvtftBoN1zBE2Kd3M0XmOwqK7Swt1dnrDHNVbN6wj68NhcIr/9 - 4uf2TqiMfKm5eWv3YOttb35EFJGsxJqzM4eR41kVshrzxAc+uIBN9+3i5/9mgRQV4D7ueoHR0Bxx - eYplQB+PaoY9P8o+27VPugWfZUTq8zIQxVrUaA23MQZsdkp94fNp6q3jtRwGAyyI5lFDWz87wQRf - fYctRs6G7roLHujrx7Cn7Y9gw6VzgUx/NYkWLmm9gYkNUHxkCNaU2Hb4W/DokX1xc6J++XR6yEwD - Rdf3sSYoYr0ZQ6rC3QmecM7Xh5p1A0uHqjqLc/Na+nqFVzeGxRWWM6eWAJD3eFXg2N4+5CacNYdq - nNL85WVf/K2X/kVtNDAixNeIu2pTIHoW/PTCc5a/9eKeXLHBsEoOJIVMp331RwgKqy79bVcOw+td - PS7ICpo9LiYd0kkqpR385qlEOfvNQG+NtiA/FB7EjoAeUa3ZEvS4RPuZMcVem3XE+uhoRg+f5/27 - M/PQnsHXvxP9kp+cdaIoh+YzD7GZnxjwzctEeN3oHSuOKGXrz9/vboJNnAAXGY3Z2QUaN6Bffucs - NJeCP78DkrWm66FmCnh3KpPsS6lytleRj7JOBpeYvI+0WQ7G8aensWJJYbR880io4d3dX7zmTdfM - lHUpy5mrj/I11sbeGlowclqO1XPWOfTUrx08TVpK1G1/jIR851dwmLyWOHS4DHNzimKoIV/Dnl/Q - jJbUFX/9TQw4CtH4xVuoE9MlqjSYjlBfkQ50PjGxg8Ou5oZDUMFr4/REc7cxIoMPXFnfCQzWLUUZ - eFN+p/DrL8k9X1ltPt36Cia+JeE00Nl69nS8wJ3ZssS8h8nAff0oOB+8M9k/hWCgoX0O4Km4vclx - KUdtK0/vFvz0SnLa3+hY5ykPhgm3GMv5c1iPY8LC3/2d6uaDX94hZUC1/U/SKBG/yiqE+1gMib4b - ifbN2xX48xtGyPrO0txMBer1vMxLdGu14RVYofzLJ4/ffJCzdkAFv/7g1gSBtX48GuiUSUGckWOG - 6ZdPf/FwkiPQZOv53c/gLD6KGU5iqAnE9Bro7JQTPn77n78FXQdVc8lIqtFXtP7ma8uhSKwdMh2a - 3C4JvH/ASGzIWNrSId6GHzyeiCYoSb0qBwjhV7/7a65uEQVPwEM373QcfPlztRPEy5bhN3MrmAYQ - Qmsf/vw4cc+ozaZBuoRwd9qdfPj9TNMItZDpzyZWd6UzbI6cWbCv8waXX35bvLHgxV8+ZFzLeiDB - Qw/hsZEinLqnA6XT7dHC0QlvPqjTw7Cy35OUIO5e2MPlqJG9p/VojRaexJ9ayIbhdHB/5x/YCNnZ - IZBxEvmr37H/jvb1jBIvgIuTKviorcNAKxvM8PwSLPLN64chEoAKePPaElWkKMutbD+jLx5jVzqD - 4ZtnPeA3HyK4foOIGKcyhfayjNgH2aP+5l896I3bio0pfzoEK90Iv3xErm90cSj7isU/PY2bsokW - WOsb7L1KIOrXb63f8xn46i2Z6HeD00YJHDZ4QT3CKvNossWy6QO9z575O0+oZ8f+JOCLz/PDIZ9s - /ewYE5xhuyemgDmwMjPVoaIA6s9ukQ1UEioWOK5l4SxZNbpNb05FCwqO+OBpdTbGkraDsZeHRBt9 - HXCjro1opucdVgX+PWyv3JphxE83fDzPH2e7Xo45SJhcxSYWlJr79ge8sU2Fy4NUAbq7+T7USifA - XhQmlM78YoPS8A3in5W9w6GnNIPf+cXX7zuLsGYuOB4XDQdqMEe//F/24IXOaJQibW2NYEHf/IEc - lNfTWZLsYcFuYi2S4sCnG3uSFnhRbgk5fPnvl/eB7aTkPi302FnKgyjKb1ONsDq6zjePUGbwfZ5Z - qKwrGPzzRfzN2/w0oA44qu763/MRpeDJMEFzZ4GbhA1/rceZrkHybEDhvlVsnruMLminxPC3X18/ - W2/T66MAoThm2Pvh1QASBfzmTccQOYsEDsuPv2dmOInOaghhjq4J9mZZ3cZ6UbS3/9Nv+G4Obb1I - AG8wHQwf22K8DeNZW1N4z04StufxkM3Y/dhQB+pn5r79vKbDroBR4E4EPzo2WvRWi+ERYvfbb109 - PKdRh+9b+PGFh30cto53chh3akXsQI/rsSpaHnqvl0yMW6w6bOe7CayZz4yPtIgcIvafHH756Mf/ - VCij5wX2TKb4k8530chdkwIezdPDl6YPS+nuraRILcyYYPdxzvrqbV/gJ+vnP32+pFwlIjGqLPz1 - ZzXbRi8XLZLbzhwcr9E3f0/QiOwKK00h08FqNws+zvMe7/2lz5ZWK//m17+l1VyTYR2aHz/h9Jgt - 9ZaNTPLzx2SfehFd2VlJ0Ff/Ev2480GXGqsFhd4Q/ffTqaJ5SGn3l9+pw5OLNlxqsfzNu7HxPT+h - 7mDI0MZlRYwKThq/3eYWHrsdPzPec9bW++ooMEhmi9h2fozoF69BSZYYF23Ygfl3nqL11URU5hNn - 9IyfLHppnY+v3/uP50vUwv79OGDfnJRofpu3GYZXpScuuE7OdDrHC7rMhf2X928fRRRhujs32BSB - Hq0W3odQXZ4FUb11iMgk6TZU+KDEVnHVAZcOuxzgsjKw+s2neAWwxe9+/rM/h7S7Iy1Hp5Po4PPt - vjlbc4ouaD+6Dim749NZT5G1wINiGlhNvFyjwUMPYHh99T66AfjNz+cd3B8fHP7N47ifxZ1splOC - NU61M665RwvC5cMgtpUKlAzPewoSgEriRMY5o2buLvDf31sB//M//z/eKOD+328U9NaoYqUSNcpR - 4obwzDMFtu6SrgmbTApQuu87scUegqXcbQsMtnQ3N75U0RVonxkJcxKTtLOUTDDuSouewz0m9h14 - tfCGGwux9KZzlQV7jSXCjQfDS/WJRg09W99xDNG0B1/G266Av41zAA1+Dv3oqNOIbPI7hw77CXAW - rvOwHYqTKvHX50h0IxvAJp/lAFIv5oi2bKuzXljAg84qIMGfLHfmk9V0sBN1iYQH0wf0mD1SNK/O - Gx9keQ9o1Hc8FAvlRFJFGrRRfNsp9JTkiM+x8M6ILS8hOEWpPXPHURtm6jkb7Kj/IcqiHDJ62jMh - LKfmQwrvrjtsd54ScNzuHT6z59DhlmtbwZ3qE+KXsUqFR2amcH40MQk5JoiEDF06OOP3AR9AxWjb - HFsy5EZ3JLdQ+QzrTnwvKJiqidjMOAxkH0oynOLAwnl7KSgNZcNE1ccmRFX6FpBTO5qwl4MnwY5z - zlj7scoo2oSYKCXP0gVKmgsD4eRj94C0SDiSfQJ90qm+PGgLGHwp0FHqCwQ7bDWCdem2C3KHA4PV - BHl0W9OQRWrnF/6WoAnQ9nNi4eC52OfvJ0rbMI4qFGrqgbiZ2mZCmawu+nw+CQm/62MPVWqiUydM - /nsCNFtdF+WwrZLAl07PvcYqrzSHTPGQ8eEeJjU97YUQufndx+Hzwg2Lnls2NNsY+3QVHhHd71sd - 3vy+JHrMVwPbRYGPlv22kEP5GsCvv2BbpQFJvOCTUW+vBjATAoHYcnGoJ37vWfAUJTZJv5+XIL6y - 0PH9B1av5DOsbrEP0HoNj8T/pNdsXsPHKEWDdiZeJL6zjT20POKCw4oPH/dK+eH81JH3hB3JRFfS - ZqyGIypi1yX7jkHOWJjjDl5Ok4Ovp+SWCb9+LHY1Q+yW3gF3N/IKfuDLJPfkRLIt9vkWyp5QYDuA - jLb589pBnnYduR7nySHXK+ThMxX35NzOpsZWo7/BvToZ3/lqoq5u4h7ZytMk53F40W342Dp8Y7Mg - SnHssg/35FyUVekbH0PlU9MpFzaY362IXAUxpJxjPwLoGeENqzM7DjR6YgvycpliIxYOkeA8twt6 - 2+uTGPcAa2yogwbOr6Ih5j5+DMKeSXg0ysZMPHx0gGDtXh3MltzAwXufDPxBGi3wvk8Y472x1wSl - KiwweD7GN9Xss++8K9DPxxifQuaYsY8X5NHHtK445t9KzRle18Mjel2x7rHRIIBDuEF7O7ywtgss - hzdbzUbe9gTz9v50gBd7kMPD84PJsfo00TJMjg3zVmSxbekzpQFTqQjUsYjNSZ7rZZg0C0YfnWJ1 - fN4yIRHcUZb0cu/DSKhq1ux1H4mhWM1Q9BDYrl6Rwl+9C68VAQ0qcpEvJ+IQZxQf9fJ0dzKc4HCb - M83TtHV9Ki68HGA/Y5Jk9RY/1FQKlc9ITN7eD1RIrj5SleSBr7djTdfNssPf9ViJ7pPDR+4nhUeV - G7C2CkrEMo2qomL3ZGYxzjyH0puQwmyfZThIgB7xx/FuQo6xU+yb2dlZDPK00d14vvzmPodAcFPF - RV5qf8hhX8gaWRS3gEx9c4iZaJhOQjxbUDza+xnyvA6m4qSl4MG9Rey7SUw7zxFHmPocIU5QgOjd - g7SH877u5yWbFId9H3cP+CrZBh+uZ5FuylWYUXgQr/4bVKWzXHk9RK9JVMmdi06UVaHowlN5cIkH - zwrYHk+6oQHbK9mveNHo/GAr9Ftf+VwxnZ/pvoPxgTTY83stW/NLF0N7wrEvsKiKtvWIFfgetmnm - H1EFViFubVjNKkOcWjqBlUsdE5jHTsGhHkcO65vRButdZ5GIGnrEfuTUhUxwv2MjVx1t82p1QZ9m - tfB1KB2Ne5mJCM1jr8zMtbk7a7YvN0hCi2LXvjn1pgqHFgHnIZEr4sKBk0ZNhEoenMnByW7DNFWP - BMkW42PrMisZx78kCJfsGRDv9IodciTHRP7xkxn1b7CoiZagQPIWUkZZl21h7vVgKhkbm9c4djaD - IwlEKC7IWQ7XYSrzLUDeopg4frsfsPS3ygJLyXr4xC6HYfl8/ASYTlMR4/VQHU7XffjHN8pwGgaO - WbgeiSZtic37bD156GaDLkt7fHwZY03nzYpRpx8ZbL79KOMOyqlFckA9v005Z9iOp/0OMszD/O2X - w2edKELBbC8zcFmsCRaWHkir64LojkDpau/WC+wzLGA9CABYL9N+RG12bHCa+09N+BQlhHSO9zhq - X0W2CFlUgW/9icXoWbbpNHTRIVKKX/9nvHBNC2gCvyS3jiR0nEpWR8ndVHBkdo7DbqjeQLY7fIj1 - 5rVaUA6OCnWZuRHnErbOypadAs3iEZDbh7Wc9ZA1M5oZTyfn1mKioTSC7xs6OCLHx+XkLFoth5AL - jHWG4Uaj10ODMbxoeorDKmvAIm1SCG/GPcRnZu85f/y74sImTgsUuqnKJ4bNyxSxezJaSlWJyr/6 - zmKajMPW9X0IwqTsZzCGN7AxPrKgcTDPpDg89IhXlc8FWVuQkOjHl/WtdZGwxhq57dprvSraDUKv - oynxr3WS0fT9KJAGrrk/R2ldL0trKog5nyd8OHduRN0GV2CHnjNWTsujHkX13KKioSVW7SVzfvUF - HKsMxNBR5yzf/YTHVL8Qk7wzTTgrVx0SOoVYo40LFi04BUgwmwtJ+nCgqyCFEJnukcOubooZ7YSz - DA9HISCeujTOevYkFg67ZfZf4uvlLOktgfD4FM9YH/unRtXisUDr3B79m6X12uq8aArv/rxhnY29 - iIZOAuHrRA8zw4Qy2FjW2JDnGg65+zocKB0cH2DChVjB8DyQa6COaCl5j2CCTSDsvHGE7rnLiVMN - HtheTJ2DVzNzPnVfNliE6JPA0u6Bf16lI90WGiyQ9nud6I3E1+P4nl34rS9WTvYNrFtWh396wRoe - GHBpep/henMH7HO0dEi7ahAaK6sS49ZWYO0bpUVSWJW+KKTftzRUYQeBU0n+ClwyzBURWCjU4WuW - jH2mbb6tszAxApVEZjc4xMJrBSfT7f35UR00oX+vPSznI/Qf3S7JljP4+JC7hbt5pGcvowJ3E+UD - uBESXao+oqdIM9FxK7uZu5NrvUl8H4Nsqi5+e1tg9OMrOUzuPQmuBynbwCcL4HkIsxkYnyH7vF+P - Bcqqnc+rIZja59F9LPjTy1qy2wbyKa4QhlLBz/CAtGxTBdyArz6f4RYU2sJ1fQs4Vh0IJgmoO6kM - QpSlbYeNVfqA0fQlE0IK9/iyXC6AGHerhRxjpfg00wMQmFvWQjkTITmqr41STIENU0tBuFxds94u - Ib1Ar1tTEopZMtAC7HIYCfaJGJu6Gz6nq27BUMp57M2Wr/GtEyaI8pby178Lu+SjtIMww4fy5dAp - mp8i9NOzhH/6fWsMK4eGTuQZXJmtblLMBMBMvYgoDVd/+0/zoYSb5U+fCpeP1cHjweqws2EpG/tb - b4EtfB+xmwR9PSNFMmEQbR5xzt4FjG/+XkifvZAStZC9jNIbk0Dt2UJsDsgHWxWPIywydYfdzj4C - drFlUR7D00L2Mz3Qrx6wkMFeBeJ29ofSZ4tCyMbbSoyfvmzyvgK2FAdffe0A7jLtZ9CJpkT2WrMN - C3wLCYRvwcB7hzWGBbwqH0aJ8f7Dl3F8tz5cRFz+6jcs0fBNRIP73WeTxwxokHI+FM21JTFTLoDe - WyuAmqLeibUN0bABCEMYkvlMvnp1oFf46WALrQVfX5rmCJ846NFTaAOfBbtbNMZCk6OchSE5286o - rXvjbYG2YmrsirSptwjcK2jktUgOHm6GjRq3HiXCjf2bn68/KeA+k0Oi6OumTVrDqLCdjRDvo2IF - 21dfAqjHOj4xuTpw52fIwme5nMhe+VTOIu8eFtSNA5qrgznTzpotCLvQu/viiWnoKjONCD8ahVj5 - zZ9KL5X89RN/v3/Oz5CHMnNDMzPMONradqqgKzl3YlYKiaarUi2wWOQMK0xcDmsmBCm8vbXV307R - fuA+wzb+8NffffXe/HkZMfjyP7ksnqrxl9kv4AFtNlbGEgM6hmIB0at6YJNRT3SRb/QCtXEesC5w - TT2FFVWQXzmiL7VRMNBL2e5g8/A7op6i57B8PBCCm5S4ZJ/flWEF8mEHP9lbJsaTsev1Njwr5KXW - h0Td7ZitG79L4JUwIrFzvsi++LwD9MEJ+LD1M6CASjPSULSbeynQaq7TxRmOMD/iEr3mjH7xGUSa - d8WlyIdgHvRziBBzUvxtK0SwcSflgeTXcMfHu2yB+dUEF3Q9XS2C+30FVq1hFPAx7asvv2/NQAvP - jsH0IQOxd+882z6n5iJ//bA/vcTMWclevMjQsXl8ZIaW0pfIm7C7PNKZfSx7QNEesaCC+p2kosNm - yzaWO4D4A8VOmqV02a/7CxQ5PvjTU4setjk6t6nzx6dCsuN55G7x4IMWPCidzkb/04d4fzBsZ/Gc - ZYT9LTpj7YWe2vyarz3cVk/Gls0+neWLJ/Dr133moD6GBT28EDLoOBCNKVyNBONRgfy8XElCl4r+ - +A5KTyfAStot4OtPRPDTl/syWbV+slL15zf8ZVhibYmJHAOZzgCrb/sRLR+PhsjwwAE7onvTlmK4 - bcC/sjqxa9HT2G287uDk6DJ2i3Gr51KWRsh7q/Ob53rZ3x8xmrx97r8/rvC/AAAA//+kXcmWqrwW - fiAH0kmSIdL3QUDUGSAiKCJdgDz9XVhn+M/u0FW1LAh7f90OKTp/rF0FRI8tiFeDLJpfTjLAIrEn - rPWwU5a8SHMYdpyG3SuhCqW7wYfP/hETvVydjH2zmgTYLrCJW4tJv+zlYwWil1/jwsSlsj5Tq91y - t7u/KK+1XjZ9Ldbxg/iM/sV0MbJhgJ8D4+AzuR97fpbcHKr+ycbeY236Pz8at8OMpYtxiNb1GAuQ - 1c8BwVFmZtzACDtIS57Hv/rpi8N303/GMqH0VfWrHT1sQN8mg0/Wc82m9yAXaNeqDlHuvlYvNxq1 - 8MYaGFtZIfUzukY2tGdH+em/rb5YDvXXQ+iLDb3TEd0eItg/iy/WTgmmS3/gZsR2oY3996mqZ35i - SsiIrUfOwvutrEOgmsh9nTx/golWz5Z3Ff+ePw49NWKWhRGBEkJnorsbBOOxy3YHhJICH5n5089M - TieIgi7wi+QcZ8tkdTkIm6eJM1Oj9VbvEJKLokyMNMzRxtcvMMah6XNXBQPKHowQbHkTftyfEMxt - c3DReDyMRA2Mj/NlU0WF71GUsaRxz2h8iV8Ixko8/PiGkjy9XQEJbYrth7XUxCLHFN6umTTdZN3O - +Pm1hNBEV2O7n1c2I3W1IePIATbLsxsxzyRo4NU2Q38nFWzfHp5jAe/f4kHMZmGiMTFjASWJbGJH - s2XAPuYs/OUvG55cwVq33gTXWwam/XPBgB/CuUDx+jhhOf+IziJGGoQXrdWwmTMx/eBrK8GL0Xz8 - ofJs55cnwfv1Hm073Jp69nvphQwVSVjtuMShx3ARgHRJtQ2vWrocrpWAaJZG/nDsZmcBs9lBx3dL - Eo3mi5Kz8BKQKLwdfD2MVb/SthAhDcLXtPumfDTZ+cwgBosl8ScrpJThVh2CiwexCS2bcjk8CZAT - SgH7bMzVExoDCW76zq+2+xmDYhdAf/YSEg+qmJFFSAP4cN93f4/e0+anDfmHt/j69WC9fi3RhcxU - nyaxVwLKvPKqQgKX1eToByMdyjfDgNvnuEzoesLRejihCpZmkmBV8gw6rY01wetdlchlyTtAx7PX - /q1/u/E/VbSKQWsi6sS/l1dntplUhFBnD1huXoee/PRqcn0if9/CqF+aMRWAb80NVul76WmjvmWo - dlf9d7399N73BURqHRM1CDJAifa9wje9NOTHx9SWu1y0il2Dld0N0mVuxQSWd78hqh9BZ/2scQqk - p9kQvBeONeuVRQ4uVRL/9CidXzzbwf0z/xKV+5Q1rcQihJSzpUnBl6YfLxkrQqeRH1gq9dZZdgKZ - AX6ZAYn8kI/mVyykkMNkJHogDE53KSofpl/Z8g/zvgdLWAEJbnkF+a3/oASnf3jT7QenZ+B9Vv/w - TLGtqmZv/bcEplH2RC1gQhfs3EPQ2K8TjhdBc/j10vliqpgjiav7FE2FXch/+dc1ENl6eMXCFTqI - uZP09/1ts7iQu9SDf5GCxCG3Qp/hxmdTc7yfo2W8+QOUa/AkFl2M/tMlIweayQjxMb+XPf1cdQl+ - QtnDaszJ9Xo3JQ5KjcxNzOaX1tR6ykBw9NonlxdSloV5uhB494wYwWEGI+9CCT5fbbX1I6FDZno2 - OKUuxJg9g2hh2cSHW/9g1xIVZ+YnWIIjKhainGI9auUHKsFMz3ji9vIJ0PH46n7X60NLVBTGPFYS - zI63DG95BKAF4HLALHeJFEPeRLQ1vzPc9OSfn+yzL9sCNsxd7IN5V//8OTwasoxdzTFq2qMPA/Xz - RcH6kjXKIO5KG+Xj2GA5eWo1r28RhHWmN/8yjDhjDt9LA2e9l4i2P47O9DLOLezY6wnfsdWD8SLM - DeKXRMF2hNdsNEX4AnHElDjstEWZyd6BoLy7DVHVJO8Xi8oFdE/nBw4W34momPAyfL1VwQcPO4na - UW13cMs/fDgc3tlYsIIPf/nl8a7dKXW+rxzQi5Fg95NNTk9ucgp+eYwm+W5Pf3nMdO/e2EgMBtDe - ODYIPqWI3FLhBpZNz4KXUt7I/VLRfrChJqMkFa443/we1T+GAAx1LxHzHqGIThB3sJrhhCVBuNTr - 00QcSCSy95fTomU0t7QXnGGZb/ncK1vtVwfhdxZUsvFnzaLTe4VLoQJy8x+hQvy4Dv78O9Z8lo56 - XXDikBUIa6WzB/1xHzDoec84nysjeZuveCp47m7yRBdeytirzEOIvOFJjJtVg2HDt788lDvGUr20 - oIDgdnr5+MGlXT01zbtCy141cMiECl2/A19AkQ5gElgjA9z0+QrQeO9c/JuvLXL15GBXK9wvz83Y - zD4UcMtjffHLunSVyb2AE34bONPsCqxa3+rwewyNvzx+FeVTCC+7YzIxTDxm9Dh5EnjwF5sY4NZl - M3O2XlBBpx3+4RXP3qENC554W37L0PnwuAZwm89MvCg+wfr4jiJ/BcNCCv5jgRkeHBdIkpUSswud - 7Q0+hhPXsv1gq1lvzghEvAP9VyiwvY53hwZG7oMMOoZPe7HsB+usSOIPL+VtnkPLPfBheOoE/yN8 - pno690uIQJF/fBQJn2jzDxC2ZQj89T5YEbHlqoCJv+Tb90n9Gpd2CuRRn/wtX4gW56i/4HX+yMTo - d7RfNj0I12lQ8fHwqJ3ZnRCEZbM3pvkKXtksyOeXeJ7NAcdBACg9tDYHzv7LxW4m6xn/85c3VsPY - YFCVLfbukMD08M4nwSIL2PpHgqIhD795QlYJut7C+dO6pFi6LtrmdQLAhzedmB5o2RAc3BaGc/Sd - DiLsozEouBASE72J7/emw//mWaFPrAmEi18PfBaVqLO719bfYcTtFD8Eey45+MK1L52p1VABWVzd - sSNln4h+FZWDp+ORTLI49WBawnaCdcPYOIoxryz1nQRg+/s+5SRWWcYuLH/58XSJ0zaaRT2MUdsj - NDEb3y+8y0hwKpvYB+KOAWvq7HzAhoU7nYIhUciY7+e/73+UtQPmu/9QYfVIfbL5SUrQuWAggcKD - nF3TAaP5eBbiz08bkftSvtg5h8L3IfLYe5MmosLR5KDSzRZ2suOxn5fku8JjJoTE+jKtwo+HtYCa - dzB8sZ3v9SIsLQNLbQ+ILkbJ5kdAAF1pdnF6Hb/9GO58+y9/PD/qKpv1pHZRYRoB9psdUVpJeneH - U8uO0yHOF9qpNPVh3U8pkQc5AO+t31GSSCa+CB+/nrb5yYHqlYLtMVEjZpsfAUvme2IOE6P0md9V - v3wSe1ueuOymtgAuKlKsnz9Gz8/ZpYG30t1j/xFXdPbEgfmbh9wuyHL4YDhKQproGfEuVdSv6zEX - 4BxkT2wFQIzWwD82MB2F28RvecdavecKfhRISCzv635Bt4sIGpw/sfxMzj1fHJ4BurZU8UdzrMFA - rXCCW143QeeD69l73Ib/Z0cB9987CnaT9SFSt3yihZ5VF9ZtQ/zDWRTqxazdAs5XZyRyk3+cofRf - O5i8Y5+YaXVWVjLiDkptbEz74b6CtcekgGcxehH8qvyabcF1BSA/ZwTLIFD4E+PqMKLlnpjeZ1Rm - o1hfCHHLC1v63gRcan05uAtt0Qe3bOkH6153oLq5Hn6cuD1Yv/uqA1K8D4jHrWI/3HelDY9mP08C - ibhsvDMOB9kg5XyUHjtnTCw1hZn/TMkJX59gYIKdDQ/sm2KMIgKW13yRkdeDmDiXzKJMNVgJjEhJ - fO7CW2A86sAHDVbexJ8lqeYKQ9mJ+R1A/6uqPJjf1rNBj1pViF9hu2f6i9rCiHcrHEJvyFZYuxO8 - w14nanSqlPkYIRlGuSMQfRoRnQkNVVBWywnnjuBSyuRnF74UqJGUI69sVo7pFQk7TIjVNp/sKzVL - gLisyDE2AN1+/+5DN2cLYgViDJYipDJy27okR6e6ANbfnyc0PY3bdKDPT710nSTANGRybJPDRGc3 - syAMzcUgxiRx2e/naGSvKVFfstKzXR916KbMDQ5pqCoj8BcBtVa+EE8+8/26WLcEyNnIYummwXoO - +cyHQ7KH/kov2GFGtZMga14vJNndng5jTqBC/K584psjPnsarkuMWDdtth0GffRODs4Vgn5QsAUv - KuXgc9mh/cHzsVw8ZcAv4zeAg7+/EwV0Yj08x1wF0hw3JNGO74yLiipBp4NBffnRnPrReQQSymz3 - QU4H0Ywmdw1UYCi7I/HQmDlruGuvEJi9QJz6pCh8ZTImfJmRhJW5IxlV4JwjxzKPJDnUNCMHs+5g - RP1o2ll1nDHwcNWRIs8BDh9PveZx8hng67M/kWManerViUqIQp6RyDG8Cxm9lkUC1S/zwsnaroCl - b81GqmyuJLnwX8AthyBBcq/fyYnfOz19f4gOOxBKOHmvZ8paUisCW1cPJBuMMptkx9PhNM8jSTQ9 - iGaZ7kRwlkML+4uGo3nQmBVKQXcmvlUz0Tye2QGZ62ASJy4FuniPiEHlwxixJauzsrzRWqC4eRnk - +romCifWpgCfyX35q4eV3hIVXqxL6i/6Z6+saC5tdMylD7njYF+vnR8IyLHsI8HL1ek5Ypch2r2Z - uw8uJQPob320t8/jYxJVGXP68jMwL5I+jaGuUVYIpxjaS47xDV5etNedrgIoXTnsp3GTTaZuTagD - gYSv3sdTGF1yCljEsod9IDmAr4UdhFv94vv9yPZzr807ZE3jSuRbYzvM+TW/4PPLtNgDI41m7zvb - qKMfx6ePg0dX5wU7mCoPZuIKno2mrA0gqrSq859UDcFSB2cbzpfy7CNmjRTGuvedaMRRRxyze/QL - mP0K8hcnI9JJkrOFRksO6jZUsN6vKBvixonBIr5S4u8kPhu2/oex8oTTTq9bQN2k1OHNKKyJtb5H - wLo4FCFO+wz7k+2DMTkoKVp24Dyx6znpmWcuXYGaPqCP1lGKuHaGBbibu2aCl6lxCHotOto+41Pj - s3TOH8uGd692Qg3/cUZF30vg+eVaH0mB5nCQei1Y5deBZHF5pfOGvyhd/eOE0AiUpTz2phiaRTbF - otAq88B8ZQheFxHLHgvAPNfxDo6IvIjh4soh/vlmw+J6Nokde/k2oYkZWMpzTAqpPmbL4RlXiBGI - 4u8NrqVff7JlSF4Pz+cfWlPzy/gM4H3+JsTa8GvrXx25pZ+QAkgO5eit0KEDuSfBP34IsJDAvo5y - bOwWM1sfF1eA9nn1iCZTN/riyWNA7TZHbG34s5BPLaPLTZ6Ir9ct7flMD+F0zMypDndvwK/jW0e5 - BgriDWsNluF6kGANgxBngqzWzGAYJeKyPMfX49DS5fyVc+icH9l08MXRGe+VYItcRk2scY7UDzme - dfTRb19yie0mWwIfz6DhdIl4JexqqrhLjqydnhLzExnOompDACfvKmDfKY79lN+alxi+zLvP+7d9 - T3FCBoinb0dux/3bmavhmCAe2xDLTGgqtDKeV/T4qCF5ZMMpWl9RHCA0FTe/baI6Wx9n0YZWkVo4 - Gwwp42RHU1HdBgrxWngANF66K9Ql4eA3sC4A+3te+wP2/TKRmo2/xRcsq+KNcVU1lNaXKkYmjiN8 - /UYT4EEjyWiYLvKEHEfL+H0MINzwF9+YXPrxS4tSn3GxcbNthSnRzUXX/ZyQzH4PlDycOwdXv/kS - fFpisK6H3IRgP4U4XuSk5h/OmUFr9eGwOhh3QJv38YXM7Hwk6XJI+mW6dOqvX3E0dUbPnsA3hCjT - KpJ+d2G01UuMGkdJfniQ0aKPBPRMHI34XvGNmKLoVJiP4Ys4nav0vLozJOjM3EKcR3Zz2Fy1ZDST - kpJgWVZAh2MqwvMTpuTsTkJGowq6YETjy+fua1OvavvN4Ya/2ztdCMyfNpjQR1YnHM0dieiNqOXv - M3GUzo24/SMIUFa4E1bYt+nMtAQhLLmC2fBm3y+jsIpIj185them7wcemQOsNU8gj9e9rudda/oo - ZyOF+B+7jnoJRyaaTmpOItfc08U57guwe3N3f8dJrUNyWK/IF5FNXAWZNZFwZv6eB467bQdN9RpM - uOk1X6jvyCFfzUgQiKUTDm5a3tOXnhbQz8IDccaT6BDxE67QOZuEOFoXK7Rh5RLxHbcj+pX3ADvy - pYlgfzsR7f041PRgBMKPn37rkS3nr/23XuTmid9sZVo1hL3xSad5WIgzny6nBHT7EBPHLo+bXjhA - GDyvBjEYzgGr/glLqKTr5DOVe6Jr+SpjkM/QJOf6sYD1tAc2ar1lII/0U/Xc6ajtoLcPnE0fAGcC - cVHC4HB9k8vCldkaj0EJf+upBa8qW7kEXgGMxofPx3jJZq+3RfA5zTKOGUHq6buJE/CISDahAlZg - tZVWgi/3A3717qwGZ04Ir2cfH/PokNHFRzv4cNLAh/dvBYj7oTNE/mpOh2pm+62+G1jw0+gzqSv3 - 9D40HUzOqCHW+CyVWdJ8BiqRdiCuJzD1ejck+289rE+jREtiLB2cvFQgRm6z9ZqmqBTZ2TDwUb0/ - e9ovNxGU+loT10QAUDazc/jmjwYJei5RZs+9NLBuX4QcNz5c564zARCO91891rR/XiFMvvGd5Puz - 6yyg9F4QxPJpYsPpQYdnbl5FfQh8InXcmtF8qBhkn2dvKg5a0Y/Iq3Zgt7AOUSxvH/Uonk3EBqo9 - zcJAo/Hx/Pqwo2+HWLb6iDrDiAukNDT3D/5ecBb8uHRisJAZY8xdHfLTWwUdCpyfqNWzHSQJqBC+ - +cteJJQ+W9GHdxM2f/pq9jNtBkLhr8TL9CudrulhOFCrC8ix8JuadPKXgaez/sFal64RnQugws9Y - KMSD7ZCNh/I4o87FxrTeGlv56WuAp77z+a+09jO+tsHf89E3/bcIx7AF93b/W+8MrMevkMI5YwMi - v3MlYjM2nGF1el6JE733oJvFXoU/PDD1wa3HzV/B41W6kqx3jYznvkIHN72+8eszI20ttvDkqSaW - wWUXjateSDCcLQer2emUMVl4fsHWKhZi9ECpeVMMX/AuxA45lb4fUd5aG3SYoUtu1XyuGXQLJfh6 - 7NaJmZACmMeu3kEjOGVEw99vtrLNoRSnUbOmVWOHeiHmfhD9KLoSI8VXuqYpW8GdbrokAB8147L2 - ugNlsRyxTlpjm6hzIjRxEhEr2D/ASq27CStBKMg91DUwF/7TRpu+nXarvXOW9BwIKLDPAz5r9AK4 - mhl1KH9Unxy9al8vxcsfoOTChcR3d1LmOJFTqMr3Ah9vV4GuQ3HvoM4EI4kMc6gnT77pqLhKOTad - jxbN8+sziIEuLSRUWBXQXaJPcOPnza+2YHaz4w42nCrh9Gqe+inEjA1BPykTywckI1rfNvCiXRzs - FEmXra/67EKTPxMib37qVz9g83e//o/m6PoRxU+hZViOGz1bL7awQmt304lbfv2Ino/9FV6Eae+L - pcJSsnq9C7jHcsH+Hqg9M4fHEKojQ/FWPxnj5vEALvfLF5ui5YCfHgPTSc+J7M0BZcS7X0DNKln/ - wOWtstzZogFZ4U/EeGkHSkNVUwGekh02N33Tv/iXi7xI5Pw99I8O25uzCu1G4/zFvn+dBRW7DmqL - 9ybHWzSC8ddv9J2ExLsd9YwUqen/1eeNe0bKyC+3DtB79yDYX74Z/SjCDKNLecJJyd+dpQN5g5yW - C3/6OZrfKQqhwpkqPpVWnfUaeJbgjeML1s6vxhn1WupQPQcVsean0//5t9NZ/eDQnrWfvxXRrrA7 - YpZXfcsTug6u+oHx4Yl+e3p3Uhle3bQiNupUwOVmKKI04Xrs/vBjTHED9fJtY6dJbvXSPAwbHo7N - xz/kfa0MgmcNQBPElBj+7VEv93XR4XS7NRuf4Wg+ffcr+GrWPDFWFvQc/65e6HRUr/jn37itP9D7 - I9sbnzv1GPYvGwqs8Maq/M6UOceCDkUhwVv/LtEaFV0MOGpjIin2MVvEnSCADV+x4u0qZ0pys4OX - 77GftjyDLkHfxuBcfCrihfqbjky9pr/7xb7aZv3SeCAHfMEFWJOEvTJclbqBdqqZPmrg6pDxGa/w - OugUu6rwrL8h5DlUVvSEN76gc30vdn94qvVpomx+bAddtYyxK5xLuj6Zbwj277onXnW8RWwE3iqK - +NqfmBGVyg+/YSryvF9/yMehvhb4YOND7B0DkzJZeH/B2hVGfFxVoiyPlRHRldFCglVwAfxJe3DA - msg6CXkFwXS/WPovj8Ka/9VqztK1CsC9zkyHb58oI0NBAviLlfnLZIlOmT8WCWG/G7EZVd+eFqnp - wm/pp9hmSdmTLa+Agl5L/lhCu+aLPhMgI4zKhOJj1VPeEhsotYnhH25z1c8lOvk/vPbni/QFi/k8 - DBBzlzt2TpmcLVs+hVqBS/zDp6mjMdObEj5oNvzwOiP3YepgkCgTVhFt+5Xl1gKdhvWGrVe/RhPp - GVF88dsExygezrz1O7LtG4Ot2yzXzP3oDVAUOkC08sHS+UabK4IReRC1OEFKM7fdgZ9+dIrEjtjJ - eJqQlUjlL6PaZ9ToTi08lhLve5t+Z+YoltHNNWb/ffkK9V++oKSF7MOYsspgTXwM2+9jmhZmT+px - j94zHLqYEleXs6x97LmJd6VKw1gFPFgiMKrw9ig+xPcLz5lPRnCFufeKsCHdTEolcnOhHjc59geb - +8ef8e71JjhZrv3aFTcOnphVwHa3Sxx2y7+gAWIVp42CMpoicYLdFbNYT/jGmbl944JKEAusqdPX - +arn0YdjqNQ+p+uMM/Lr64oo+wXYWY0P+Muzfn5Vcuy5X5Hit6Kx3/dEQtXT4ddpz8GOAt736Eqc - 8fd9NRMu/i65nbJ5YvkX5Orlg703r2SMBxwGkJf5JH/9sOUncOMXbPveu6fCFxaiTLIrNgqtopPJ - PwJ4U9YtkNWKnrTRYYKvB1xJ5r2FjLhJq8LzAQo4qIDvzPRRVtC1LEDkNSyjpfFo/pcv3c6i0Ldf - 4VxA4Skk+JfX/PnZVQ48fOadVzZERu6Klj01v34F5LK3WoiTr0tOTz90upqGCVB2d58cT9dvPUO7 - 5tDm97HKyztlbq/ZCy6RF2M5mPOIMxO7+10fSa1wAJO/RoIYAgZj+2HmYMbXMvzTv4Zye4GxKtMQ - 5oco3Hb4vcFqN/gKs7f4xDhYFoc+RFv980/Y5AawIPTcwewuyMQ+mbqz3PRq+uW/0+HVh9nKn58t - lC9XkRx37NOZxmy2kWhFCd7yy7r1gMNBVX4UxMaeldH53iZgd+J8YsR2E039Lpsgyozq1381vQTn - qxgRNiPSm/N7xh6HBnYPsZvukW7Vy1Y/cPMTWJJiEM34RmWwZ4SIaEvwoYQDaoJ2uu1OHGk/Uf+0 - fRF+pdLCxaQpdFoO1wReB5WSqz9kdDGpl8KrdF2Jvv0+3UdPH/KZ02FranxAk+cYwGug6qT42o4z - Ma0bHKydmpJzldbOj7/hs3Pirf7qiI3EMAE/f+dE7wdYeHlMxV7c+1hOmxGs+4Wb4P5Z58RJMl3h - nhor//L9X15JiZl9wj+/cSwtJVvDXXmFiKMvf2XDHvS3u8DALY+adqNWKrSv6gEezqFN9C0/WL0w - lfivxLBY3/Q2jeXd5ueH0KfgjfvhezE5OCvM6s8NQBFlh8KGq/42sRHjJVqez1ECWz5LpP2H9pNh - El8M5zuPlZ9+4zkuANyW3SqPw0gnTisnYGaXI/bG9VNv9QJhrNRw4wuefn/8/XikD6Ih/alMttLK - wGapTczPdVWWMuQ44JzscgK80ff01VwrODtMRJJ9QeiSq0cJAeboE23rn80vbjvuS4wlMkgKm3Jj - BQk0977wuG5nFBmSiZCiqdjRyENZrdfMoctJ8TZ+sCh/MPvuT18YD/vUc6v9EcHPz3t64Pbzj483 - f4Lt565WtvxOhg/nGkwLt/+A9akhCcaFEONLFAtRnyOu/M1TiGcEc/Tde80M2Zd6w9cmqqPlsJxz - yGhnPJUOBv3kxnwFpmG9T3shcpVZK60K9tV2RvLmt2Z9N3eQHVQeJ0v9cobSC134MNLjxCZfBiwn - 8AwQcZcUO2G5KITN7ALyBROQ+8bfyzafAagrtjNO9MpZx/6gQzbOtE2/vuoBAPSCm/740ycr/+4a - eM9bCycYavWWX0rwaH7nic1eXj0PLJiBBsXvJGx51mJS7YoG7vL0p1d8j5a0GRpIv+hAzt35E7Wb - 3gWSuauw5k5C9BUlZwLqUl4mnkii0q7Rg4F2Y3Cbvj7T9acfFsNvfIjSHVjE3SxAakG4zUuSaNBm - O4eVxib+Mu/L6I+/XgaMf3oz6g9GICIXegrx1RbUY/PA9t/1yFHvZXzEYAHMkoMnmyNqxrUSKuCt - Nh2c3GDez+2XhEBxtBTru8JV+ouUF5CPLBcrkvnMaLwi+6cHffj8rsqyG18M2Phn2vJpZ9XM+go/ - z3lP4rtRO8Ng4BIM6yX0l/DqRQwGuBNvWX7FP3yi87bD8zc/o8z7lc10Zlxxy4vJda+++vX4na8g - P7/Wv3nGeDgmBZSGXt7w+eTM96M3iRMzQ+yJI67X3RPNogESFWtz8FJGIZwSMW5E6vNnzDrr/Zpx - QBRijI2nfcto6oEJ7Gwzm/Y691ZWgbnOcMujJ24a73TIqqSCj8vYTbtZknqGKJEK3fZZYldMabTp - T/in3483vwKznQcCIKUdYhuHTLaqQneFi9ikRHkydr3ebNhAYRR0fP7pGX0ntKISGQdsHJZXv8ha - m0NZOjJYvjRyxvfP6w5u+ZE/Cx8zW4ldBkh7+q1/2PzMPLH7F3TzaiXKTMSo9b6CCaExLBPg5bIf - Z2OAUI7SnkgOBvXsHw469AioydG9aoC1gy8Dtv4hrivMdDGfy4B++Qlmo7JeWliXUN7rA/nNE6bB - O+7gNc6OWMLau6aS9NrBoZMN7DSnqR5W6ZACKW5fOEn4RhlDuGfgNy85bL92fs2N/aKi0T/viW4f - LYcey4aBpWJIxIjia7aMfGvCF2gZrJ2+J2ft/XyCvM0d8fF0tepf/gVLkRKssB11+ktwv8ITk7vE - +PXTprfQyu5GorNtka37bccKmvIbMf0BgMXdnwRgiarjH4SnSCflWlboiTgeq7XR9ctkfE2QHV7q - b55Uc8FjCdFvfrrNo5VZkd8mdF/1ifhprGerG/MlTLEp4Msvb/jNo8upOBPleTjXA7RrBp3lwMJJ - ua7OsOUZ4sw/Q+xFTtxP5RvoMCUOO41bHjxIFGz5MX/85TnOyLC6CkOXJtMYONf6N/+FidW5xDCK - vTLd23j+zbf8PRuV/azIowm2vG+qG2bKFhenIgxbe/D3NnhEa/g0GLikgoUtX/QcZn59JrjNd8hW - z9lyFgsTzttZa9JV8ih7V10GEsGciXos3S3Pz0NoKnFNIvimNdnp3vp3vVXW5z09CFUHdOHdEmMA - Rk32Shmiggw+CSquVtZDHA7g/9hRwP/3jgLu8DH9eU8P9SL5TQzDQF79gzT3Cr14kw1fbDQQ3ZPm - fjJuRx01dxBP4HyX6frRwwq99bdPtP1KMhrvox3ktDwhsZY10WJw4Q5qN3vxmVGzaq5/SOK2BwdP - u2bvR3PVMhJ6vYtpEqYmAOyclwnMjhXB8tiEYJm8SoVp+uGwHcUDWDOrneDv72GfbRwqKDYDi1OU - E8W8ePXyTngbiqNxw5q8fJze2EkmVApNJ9fuPtDZfjE2wj6sfJ5zv85sd3KIjPiYEmN8mzV/MAYO - yrddjB3TXbKFB+cGLDfSEd8sAaVfggtR3A93cgwtM1vi/SpDQ046YutXCla4O7lImo82NidgZcw9 - OKxwGeKJ5NpbrnnHDRN42TUjUVTtXa9Uu8dgJ717fJ/eTU/zWVhhOioCkbXD3hn2OM0R8dkjifjh - 6qxXdpsI3dd06ibNqJm+WDmEPNcmRfGSM9Yvrx30opEjGn+pM3YplgRdrPrjr9Hjq/SnyZ2gEACM - 1V3U1b/1RVNae1iBjAY4M31DqBdvDet9NPU0e+op8j66iyVzewfo0yg5gtdYJz7DSz17OVkJkErf - nRCwuJrW3lmHbuWaJP1mSc+nj2eJtnNSiEMPL8AevJuMDup9wTi7cc4sTWyMjkcREqNdab3qActA - Lc0jf129KGK1Zd6hvGVYfJdTLlvrgYfgE2/vDLZzlg26660i1arPNDePt8O6UZ/CsoD2xIhHueeG - Ip+3G4rJfVIP9cht27LN66Egxo5a9WA9JgnqXvEmSrCblKWdb92hIQqDtXfLZnPNfgPoqa1NHrp2 - rOed1iVgPUGN5DvTz3hjZ9rIlrkvVi+GQvle2L/gfcffyPGhPSPq358vxI/xgCXMINCamStDyVst - HJfu0TmgdC/DZWwDEmDmDliY1TaK851PklnZEEV2dOiYwfYOHVEpZ5atD+vFFElqH0o639CpQIDy - AfErrovm1rzn4q+ePF0IwTrUlQg9JWmJnXvnejbL0ETS/WP4z4f/jlaqnWN0ZNsSP7TDqFBemKrt - VNyWPEq3j/jP13zB5BJ+8PZ8+r/1CNdoxY77tLZXDpIV7b/wTZJTd6yZI33E0ExTmdi6u8/mcTzo - MNJaE6uSeMuWB+wZKO1PvE+7+eEw9guaoPEqnaiZSqPlU7+u0BmkJ44KpXeWvM8bGMWEwW4bwGiS - BSP99Su285utcMK74lDbcQtWunnvLPpEGXTK8628fARoyPYc3LHtMvFhe8v+/t5OVrEv5pNcM0jK - fYgcK8HKrD4jGkmSDYXQX6Y1V27RfLf3JcQ0vBA3LBxnrdrzDg7L+YxlbM/1LEvz+sPHaXcy8mxe - zblCTLociGNMTL/Q9yuGlrd7YbP7HrNvPpgDtLmb4x8+BesMRlJJ6H1zQ+JqKlsPyWD78KA+Fn++ - +7T/4R24EynDjwe/1lM97HewfhQWkbnKAax4ojJs4VD74tvya2ancj6oF8GdRH/PRHMKxwQ6qU3w - WV4MZQ6DYoICniesyW2tdIrWcvBceU+iCNEVsBF7X0H3FnMibXww4serQjE65xjb8uIMdmcHoJkv - F2zswrymBV+v0P7Yd+wEn3vGVJNtQz9LJaKQk+vQ4PnIQSXuI1+OHpayZlY5QIGeT8S4fGQwn1+1 - jlRuItisZAnMV3Y7E6UUmg0/vxF/n91W3LdfhUjXmVdev/VVuYGQsy46Cv9d3QbCVkjIPR+Zfj45 - zABPo/zAWBuFfuCvpPzhLXZ3ph/NM8fq4rP+eth7328ObTLiQ+6SmER6fx/9sBxvKZxuO4Qxl7wz - /ngLOnTQqzfRzzOrTC7NY8g4g4cvl49MebWKW2QtTwNfONdSlv3rY0M7rm/kiD4XhxbruQW1G7D4 - zktrvcR7UYYsZvbkjJ993WvEEkGI8yfJzic/o3KKSnisldyfPuaatR7npGInJIX/nvQTXQ/dDGF7 - G05YMt5NNhsBmeGLPQ0kC+gCZq54rijRTQOn+/aorF08tFBgOkqOSThE8/CSIBLdyZmGVzQAevHT - EM6HOMdpXvkOPyiZADM2iCZP1hCg8MqoSFxk6YdnGedLxw7dB/QhknFb6HfQ7Z3Yp0cfZ/B+dhY3 - KUt0PMYDjhjvnXFHgVaoYKLJl6a30M9d/mVgwSoStvr57sxd/mTQJ1YBlprkqHDclX2hcD2tRBaZ - iDbF+cmhQl4l4kWIAArP2wvAdfrE52t9jVheaQUUD0dxw9OnM08fjoHofjRJ4p2/DnWjJf/jm6QP - ds5qZKF+CB8sIUGcFT3t+HBAobSqWLqdVIetI2s79R4u5HHJm5o5+doAr8wck9/9cQo6l5CtTjLx - G4jAYoCnjqgKL+SUzi1YJeKkcGyuGbltZ4DN0XngoBskiT+D7lyv/UMSEBRnE1+/cA/o7kF0mO7V - AV8yfKO0EFsB+de3SKT3TlDWF6u4aI7ZhvjCg6+71mYqmI7UnvbZ2+7nuDzqSL6AEv/4bM6ttIWE - iibJNZXtX870CpG7RAzxNv6bJvKSEJc0HNGCWKIcEaUAbuETOVYcjPrse+qQ9h2P096+fOtZXG0J - +UNZ4rjuI0rv3a4U48GFUx3sJuf1Ylgb1XPTYauocmcRTFuE2fOAiLFMbr+uulXAB3AbogjJN1qd - 7+BCHjndBHd3HTDxI4dQWYIMO4zjObPESy9kPQcLF6fHO1sO3m07E+KjTe/918l4S6Uu9NTO9vc7 - 9AaEdvkVsjgkROpypp7vNl/C8Cm8sBJ8zzX10yGHgTD0E9RuvrJehVMKbwLQCR61CMwzh3R0EAOL - RDfx47BVIARQuVccUbd6WgNXntH7Nlck4Efd4aTVLMG7V1Vy+1Cvn6PtHSD68S7YvqxWtqSPLAff - 1RRw+i0+zqDp/PZfOMIMy/bAOLNZWCbUzNcTp6vv9AMbaxXq+NnHmZK7PcNfSQVfijxg26d7QMu9 - LUDtJirEEM+ew/cC38DqazynRzuDqGVPsILeWblO6754UcrtyhlR5U6JLuVSP+2tJgRbPWL7KPLZ - mCif7Z2VriU65Yt6iSEUQaVdEJGa5OnQ83GUAPdxdKLhUXLYOZl0oJnbGV4lr9er7g8cxAFzJnqa - Ng69nb0AEJ8/Evv43tH1MiIT5gd0Jqd+ZwEexKkK2VqOp4cf35xlMggDWsPwiI+Ko8NcV+TC6vo8 - Eae3pIyWu2SF3+XWEH++qQpd6+8VAlZUp9lUFOWvvvpU8adD/Xz3DdydfHRmuLfPlw51qDFPK2yv - FGGdcflsfOq5DH/9H+8iu+dDodzBJTu6U6ucGoU+mKmDTPZ08P3Nbjt+D7cGTGafEQ9HO7DWBOlw - gh7y1/VmAHIJ+gAeDhWPnfNpiih+mwPMQnVPwgvSNv+xN2EuTp9Nz0TRT2+Bx3V+EevTWT3vXbP8 - x98T+un9p96poi33nwkmH+ysH6fsoI++kj8kc6Sw13ByoYMShej1Ue9/9QwIFcyJdaSiXs+vxYaH - i8qS5GQx/Vq+Wh2ehsjb1rPMZi4PZrR9H1ZfZ02Z4MHRD/Vii3/6cJmTSYX592VgpTdNhe0YXIH8 - UkdE1puyX3569PfZkrKwZs+yo8Ij+yj8/XG2HPaxbyEw4nWYkj7y+/UlSDGKkrX14e7e0K0fKwC/ - RUOsJDdrmj7XCixDMhHnfJcBX4e9D+9EzrCaNBydzTK1f/1Ekhl/wB9+b/hIzs9kzNY2sDv4ihpI - TDeps/keLDMq5xbgmHXuDq8zgYzm+JRMy4d906UK5gCNCryRgH8uNf20eQ7nRkh95nV+KzPjpRV8 - i9ZM0uNCI+Kc2gLt9eVEHFOCYAiMdIKKC5tth1qkdNgoV3gTLMYfXo9ntt72cwlUPj/ihIjEIQwz - dzAeq8KfDQ9lIw/uDUwO05ccpVTM6Fqh4CAc9RPRciEAYw/DGbZ9o2D/nMd0eefLFeKnV+Ofv1nL - 4s0BC4c6cdzOo/OzX0x4yc0eS2A/KWPtGRI82ZKH1fBrZFwRo0HYA+6C/f7EZfTnf88n7YjdsOiV - AWvHQcyeABErCWKFxqLHQXIqdKy4g64QcMrhoU/Xkkj0JSus8mAb8A1yE99lUGdjmicxWtIc+lzf - fpyl7ZUSHvTyjbXt+VBBizioHrl+4i86C4ixM01w2tl7Yrq2T8dPSULInsyKROQu9fTLWgOoC/tD - 5Kv+BZSEqQmm0Tzha2AU2fhO9jay4yHD+nx1KCNNKAGGLIhYegpqTQUt48RM2j3+PvOj9hKR4u42 - C3cMALfqVg6bom02f+Mp00+//PhIMpXaWYLhtsJXyHe++MPbpmQTcH7QYtKmzM648naZIKEmR7w3 - Wzk03mc7KANGx2lkCtGyPO3tepJuEjZ9v/GJCmdBFjf9xyvLfHrsxDmJbRxu+neZ8zIWH1c/9qHH - iM7so6pE++/ujY1cxgprG1WIkkvwwdL9cukJubQB6FM/mJY2qmqCK6UBRuzneKtPSs/WHMByVntf - 2Nb3b/22fMYHBFM6QwHJUL+z1JecigWDQ4IUbvkHCaZldEYxFkwRP6oVu6/HMeN71ZTA5vd9+YpH - Sjrm3EB6YDKsRXtTWb/nYIblpHA+i0dJ+fkf9HXrJ/7lJ3/ryU+dP9Xn3K6ZqtdkaL85sPnjms5v - mAfisvNLol2JS1lLOwXIfUgUK7f3VFNBLiZQOm9mYqO96TDX8aPC+p6d/z1fnfYCFN3BwelT6urh - QBYZ/fTa5g//1d/Gn8TY8h3ajl0DSmfbkV47FV1OzaMEkfKJieH3FiBudCjg7XKMiXb193Q6dC4D - WcZm8KaXs/nGP1zIu747gWDfRMSSOxf2Qxfi43zjFBpMAgNzrX/6lZ66zpJ/nQZe9iYiVm3l/ZLV - bAHVOBj9w6aP5sywWpjeoetfQrTQTlrNCrrPgsOWV+K+z763Fv7p901PzL/nlXfmBZuC1PTre0cT - JGbwMNFfHuQh4QWPp1H8+aXsG3yUFk1Dc5ymU/uNVml6BHCrZ2INjUz5AxZU5GT5ibg1GyjLdgwJ - au6HeBqf5bdeJgxUSBY+nk61EwN6u19LsZw7gK1N77ByFjXIfebcn59iQ84SwQ9/I6Lx9SoLxhXq - j/hCNn6hZDjKAkyd0say+ADZqC0zRGyQBdiODdnhqilwUcDvb0Q3J7Ofrpx5BUZ0t4kXtXw0U8cq - IUuzyv/ptzZOdQZe7rlHnKav6cDgvoJCuFOw369Kxl056Qrv9vONbZEdfv6CQ/bHvOPr80nBVITD - cNieJ8blswbDNNs6JOvtheXv+omI1nAt2vgNe09ncciWZ4FMwp7PH5sBzFooyfBtdjJRjDHP2LzR - C5io3xrbGRFAG6c+B4va8ifGGqds42P1D8+9i/jIKC80JVBfHPrDh42PCsAg5P3d/zLfah82Xqlv - p47TaFHfzgDbzngQI/frfrIejP7Tk5M4aUZPWcYt4fUozfgKLbtfvt0lhZ/6cCD6mb7rFR4UFakv - oyd4bCRA4BXqoCnLGw7kBtLVR24Ou6YYsCkfkDJIvPkCt4ufYCOy9Ix5709XdGivF3KS7jZtf3ye - 1d6DyNwkRexTKURoGvMOR81eVbb8o0RpBq7+OpKE8s3zW0AQmiW5jO+27iPxAAEI7dJHk3rrR7U7 - cWjL53zx+3Gc5WndbGh5ONz4wALk4txN+Ksv1XreAIkkyURZsNz9NX/pGatPgIOZHBzxL28gjwOz - gtK96D7/+pJ+sY0ugO7jrUz3zc+2jxta4TFaJ2xZF6dfEl9I4OZXSeZ2I1i+5F2Bka8AkTlejRYv - k6u//EAzcF3PkOUFeK2Eipwe2jFjYdbbcMuvieckOFp++Y6cfHZbv0NlhSDXQaBoZyLFn0iZ9zgt - oAYaOjHDvCik7cZchJcXg08ev+3o31kd+PHzlodQ/hKVKVr2pUwcJR9q8tNLO+aCsXrURzpQ51jC - 0y45EuOYXjOqvE8rUKM6I5ZVSw4TuC0DzxiWeNNLEbvxN5T1RZqYUfv25GB/d9DjDjZO80numReS - UgjCaSaK8Bij9fyxbZjuW4gvD06MyGmxtv+6GXV+Uh+bfn02BxXqT9fG1ulL+0Z3PgM8vvVgom65 - 1GtrwxJufEecLY8bsxrlwuZPsALbU7Ti41T+9Bj+H0tnsqWsEgThB3IhKlLJUkAFAYEGbOgdowii - MtT49Pfgfx+iTkR8UZl5Sls5Z10yhoCiSsep6d3abfImOnqnsPaP7W8e8Y6fj1Da7ZNYza3Ov3kZ - ffnj+XhFLXk/64Wn89m3UrxCr0UPkLktC98wqSsEf/IU7RPJJD/XcRR0/0jeyqtFe3LZ26GgcR3D - 3r/WK3LiVBdSrpkpIkRtyLnSVYeFVHC4Y2GR67Pcf/2TBKYxuJhNx3KcWS65YBCQ8S2I70IYzz8O - eErXvpctVwrXFxxAdKvP3k7L0miejesWqq6i//R1t/jHr18ndnOwx62VFivlOcSc+IodIJpp6AHW - YKw8JOYgZ1nX9OpG9B2WNpPtMMd1A2hp/vZPjRYLJjvWASmX24ZoPxIR830lSZAfoPYdtJajyYwm - BSV2uMdEsQNBJLLM2EIZfPXKEAWlXL3VrCInvcBLnsTL1bS8993XKjbo8ZRRWMyWJ11m73+9xJds - 9uQ/fxRMT6sKEjvYk3LpB+YRMgob8ffwPu2NOXPWfXrQzUfo25El5yTWf2xEDXImtn4ehTj12wHa - q778eEevaI5QoMOZKC9vXT8U580t+Q4X6j+J362XHQJLXurZ8UMOZbJr2SChAdJN3JHqmL0jlt7G - GPW1u/XNJByi+YzF9l9+//JaHtftQf3mETPPccTjx9uG2Vj9ebvFL3B+1goVNetq0TeO8NQvO2hG - 9UPMVQgt1SSjgp0tD/7fsfttWd9EmdrTW+LxZWJyHrX7Sm00KInj3ja5SLwwAKNgHabBYIxbhmYb - WhKZvjcFB4d+wv2EAmN84kDvC0EV4iqQdu7x+x5zRm6ap7660cHybpwFk09MVhb+ufR1wciwuwFo - tlJDUueujrM0OwX68jhoJ9fY5X38gG9f5WX+R4gkuqcqfRUDOZTt5AjF9SQ03Q1Brk3yHKdO2lhA - eyUjRrAZHDbs3AFVWlH5/pIH+clPQYnXWeChhxYKVoxxD7FzLDy11GeH7Vbh9OVNvn0fucMG5cKR - /BCaJ8fFL8LXzfMXMpvKC5+6C16XFkBJZQ8jt7kgrpAQINq7A37woEXCaA8c1JJmeLprnRAWcg7Q - /gcAAP//pF1L13NM0/1BBnGKbkOniEOiBRFmiAgiBN3oX/8u1/0Mv9k3z1qCql1772pVr8Imz71+ - b0/31IGuqlOEmP6aU6aeLn/9hT0fEF2Pp+sGDolB0f1pfMKVPDIPZsXvh07Dmub49UoZEPa/zBOj - FeWrfSgZyDTqjPv3/RA2+tOr/vqTHjDQm+78QJGn67KRbG6l8b317wLeZuVFTohv3ZWd9UI+nq46 - SXurC3d/dtf7rEaiHY835sAYoLpUFlG610efIHcQoZX+FmT5r2+Ix/dNAuYS8vi9481abnSSHMNx - 0cUeY33n/w6M+9ZGBteY+h9egI/kLvuS5f0Eog8jeO5+JfKMzAqFv/6UeiMS8b5RB+ialxYcyjRE - 12T5uOtZbCC04Kihs7qp+Xzx7gGIB2d/nusabjtf+/NHUbHzsz99C08wPpHTIM+gv900Vt77scTV - 08Jdt84v5LmDIzIXdAZrbPEGTJpyQ4aycuE66UfmH58wPPcH/vpBYJ0NxWN7+m64P7zc9QYx7BmH - G59LE/xqTE5UW5Xo9Fj9CCKfv//Ts0vvV1jeXkefGC/chsMZvA1590tQbOtvl9oG8ODlkhJkcNV9 - 3Kay2KD9xjZujNNn3PWqD5WC45CZ3Wp3IVtoQs5PfXRhnh0gk5kqf/1t4hQ1drs/vSnWKsH/+Iw/ - FD/w59/89bOXwus3OMpe/S/fJuYMIuj3Vo6/O7/c4skP5PluHMm+/ilfvXro//nD6vGtN0Ig9hAq - ZZQSZz1S92eGGyPv75do7WELt8BvN8DVoebJb/fmLvNYGXC5tyJBMTiOs6UrgZz4AsaH6t3QNZQH - 5f8zo0D8v08UbNWjJgpezGa9MVwN9eTz9cBxO+pz42YefD40lVwYOxiHbH3zcvZiRnQxnoq7+rrJ - y20VmuT0aBRXEP2LD7OBTMTMvpiuefIR4dm8qt7xxjwaAZfVTyJTjPAbx4ewzXnVl4VbZBA1zAzA - d8W6wK8cIgzDj6cvv+JUwNOIKmTpQQkWMb92kE+UMwn9KMunQrVYyDB55C152ebT9uMD4B6CA1Jc - XsrpJe8y8FbeBlH9ftaXMBY0OebtEiHmcsx7GRomYNo+IobUSPqy/ZgAPm1rIMb9mIOFtR883M4/ - jlxI44WLO6k/ORjEBrmZUzUE2zID2VnrvKMoNoCife8OWFMXFS9Su1y8T3E6EuFDjGtQNQsvBpvs - VBry8P09hEu17+E5aRWLtPPXCelE5h8UP687sVF9DmkUcxfZzJaC+Cf7lLNt5Ijw3buF18nzMxSE - 6F7DSKEzOX3OzDgJrF3DcLMuHi/Pz5znRucnp3FP8I8t4biUALPSRah65CfTpWFTTTYgKUsBQzvT - 9/fpsaBA/ITsyPuM3E9IPDleLj/0LILSpd0hjuSk01/oOoCXuzojKKFTeBqWifSlaxJaEXiyH4+U - Cixy7nxNITSQOxLn0aQuJ6WuI4MT03lCk6q5oG2zD0V9XYl7K7Vxjl/QgOmQfL2FaQO6/z8T8iMb - ontnG7lQToUPO8g4eKGz3Wx2oy3wuH1KkiiRAVjh3HTy4fpQkKqwCuAu2xuDI+E+JHU+Rr6+jQYC - LvtS4n7UgS4CWXz5L/5dp+abqb9dIbzcOQM9IiyNi/CKWfhVtgdBz1vsCsj0HSi53Z1YtlKDteW/ - pTy1lw4FxZmli+ZZBlzf5kpUknz05fJdEpntLZNEtU7AOoTFBjWjiNGDXueRB+Y3gA5lc3LDpyxn - 7bfmyItUTcTvKxFsUgMLaQmkCCGtq5oVs2EA0eF3I8EeL9vn7Srgic5PLB8wq69M7F1gkTQpMr3L - ndKKarVcPLFBLiSw87Z8/WJpOqgShiL4jdhNH+3uOWHkSNgJuW2NY9ly+icxYvBuWKdYRJnjfOQJ - 0eHQ0E+kGjL3zQRiLAnRt6sVdHIfchF5fSakC69eV+Th6xWYX/uLy9/1dwK2uHt7X33q3OV9lFoY - XvocObjlc1r/eg2OT/1GnDn2QqH7jgZ8GoGNbg42AevqEMLzzKXoNu6Oba7RQNa5bETO19V19hKe - sHy43hXivs76uMpEs+R7aLyROxca4L6XrpbZ9JIhV+WTkbvO5w1yJXY8sQg++vp43Do55s4Ohl+L - C6efkXmQXcHZG+Pp11CrbCKZq+sHuRSyk/NzonTy8iQ95j5nplncJROBLyk1cqu7C/iDMmwwOjEJ - OX+427iKtpSATqAvD1T3ka7KwGd/1ycmQ3R3/SG3gotUT3i7Q7shougzMAYqg+zm5I38uYhq+T3H - Z5QedA8Qzi41aPEnjVw1bhm5JFRiOWBWHSkTCinPByaGQWzujvKnySeBiAHEL89E4cA7OT18Hx18 - v0qCLF4R8q3A5ST9dHJH3vqyx/3+WnlloEUiRl3cKf8QHvbfofIWvHTj+p7EHl7aZUFKXhohhddz - DGNjOSH99Ly5/PLsHGhBlfeoJKv5pueWBxWSDuRSTZ+cHlAbwR0/vXdVHHPa4zSWPYX/IMvxHDCb - jO1AbnVFcg4aFPI1TTBU1ZNH9Nv3pGPpNXrwMQ4euaW6qfN24ywANA0l1uUH9Dm94kwKtMBFJ3Vx - 9RXmvQW/JnHxwUJvSmbpY8lVL43IaYre/WWiKsnPUGaIPa6XRhhUkB0fy5sgPX+PLhc+H+Y/fHUx - QHRL1rCHRx9I6PJJmn1HUWfJXP900XlSCrrc1WqDrdXPxPJT3qXPp1tDyUx/KN/r6UrOXCz/xbsF - vEO+bb6UgDPP3sip5dOms1G1Scv4VTCpyEVfy1wTQSBjQmzrHo9rmTsiLPVKQggya74l3CTCL7wA - krU/P18apZLkwShbpNUPbRTO48v4q2eYUSAMqfL9LPJigwJLtae4wv33uAAEGsk7qCEHyF6foQrf - G7HmT5kLiVL68MH+TCwckwQIrW13UK9ql+iMyIzr89yLkA+4AmXNfNW5V6clsm/nAVK5YMwpK508 - OatxSy7mNXDX64wWMOr+DT1fxp0O9mJM8ssUNWREmUdZRbdaOfFyEx+0Ttm/kZdq2J3LDG+HxgHc - AN6FLIhMgeJ72lEWq08fmvRpo/vz2eWElU4XWc3ME1FLZXDp465AubumHnppnTLygt1eYBzmHcne - lyyfT9Tu5ZdcmOjl7RNGOEWOwDlTr8TO5SrkrIoyoK+pQk54eY28Oxw1mMiMRtRfXru0nKJA5sJ2 - JI8dH2eSP2LYPPcTdWld69R9K7FscPOCdn7hbtHrI4I78iOUKUrgsudydo7S/R6j+16/l3S0F3no - LIuk/kKaOXy+DKgjScIrPklhS52qBj8uOyJby/1wS5MHhnH0krCw4+t0reNCXnSmJ8HsjfTDFZYp - t/pyJa/fEOibwn4n+WYxHnF3fFvcJRBlDKYDOScXf6RHPamAMJ4NZH4PDqBpZRVy7t9WfPy8S7As - ll6BtdKennQ72y5W2C+W5eBeeRtSnuOmv9ISzrDwkM7f9HxpR96BKbV7Yg0/VefbYa7gd3jpSKM/ - ALb5TCz4F/8XxFN9db6/CyxvSYDuzICbKX+XLRSrjkNmeTIAy5z1Hk5ao3kSc1Hy9fiMElgbTEUs - uZzDZWH3vXPPJSAOWxbNkq+LBmup9Ikin05UGKtCAlvxvhO9ga3em8JgynGYdv/qL/+RzxU0DgvA - m3uqwvXX8csf3hO18W4uF8m2D/7w7GTO2CXPUxJITEEWpLNMMvZ4eEOong4YOT8uz/usWypZai54 - r9/KOP/93/1+UfCNPs3EzWEgT+e0Rnp5UF2hvssxfDqe7DFT3Y1UntYafJhXTXQ9+IBtlJdAjg/O - nbh/9WS6r4uURdwF/eMzydmLoPFUdaIx9xpszyDw4Hz/HDDc+fsqem4vfc6gJO6Xvpttxz/Yvu8Z - UhhOAMsxaEz4sA8/vPrHwF1jgUTgGYAfUau5DGm9700m/VVE6Ij4nBTXbYP5qSx2PjY0S36FHfBw - upJX89YBfZ58H4g5jsjldzdDgQnlFoaNV+H34XLLOczmPtSlm4vXbjq7BLw3BZx5/ob5nxO7W8T6 - rVxdTt+/34fLo3IC2BjXm7fj2d5xTDEsWcvBpL3W7vadnBp2jRwi+92p4yxrkwcqgX2gOBZDINR3 - LgKVGoSYL7ZMx+5J7qApVxHKFtYItwzmFpS9vaPe33rw3V4sA6y0tYmLXum4ZJ1YwcLbEmQ8Ll1D - HLW0oHrxZnIdfpJO1ZfrQ4Ial2iTbFIK3pImeX0h7/Hiuawv3gvQO2ZIkIXeYO2XAcK9HhETvapx - FaR3C9u4PhF9japQQKWbwOP2LbH0vkghaYumhTBVG1y9z/s3jf4lgEdhswlKotDdssgrgbpOJQnX - SMkFVBf9H79EwVdXGirA2gOllWfIOWRWzv4e8AIYsajQiUnshkuYZycuiWAR9bQ2dLuVt04m6O3+ - lz8qz9TSECY+8bZ5HbcQUlMqmUOJ0P785+o4JXB8mE/kQu3r0qDqE9Avv4R43ucZbmp0Y4D/8H/k - xqlmLmz6Uso/Yx2QNT3icIn7afvLH3I6Tu44d4FVgtAvehIoNju2sX+sIXOTCnL9yQbdzFZQIJd9 - KLqzpT7ywipKEKKgxkPXtA3NNeDDY2x8SKa9WzrFd22BleqHyMdL12wozExoP0lGvK6vwq1b8k1+ - hgcG+0LRNvT4umsQlhePZE3R65ueKx54HqYZc+7v5g6/eq7AaF9P3loVaU75tZ7glr9UvJ9UBMtP - URjYPpsrpqfn6mIq8Bn4ClGO3xlN3M3kkhLeZ8AhNdU7fftcBwUEIovIH36yA6cmYNerGMM4oOsH - Xxh4VcMBQ3190ynrxBpGc7sgS6U38Hc/YJOtDzK4++au6bXb8Ya/4fGHVrqOZRGAIuyP6C//p3Zk - HGi9Ktn7IOvrLo9KC6Sdz2JJ8Nkcxw83A9U2TuisFmI4WUPbyyODOY97X975uq1xBHc+4UlbuOYL - u04VOBCtJa4deSNusmiTMM+/iBIpeJx2vS13ZmKScn3ZzeRcHiVAqXElt1FCOuU4CqHQXVpkXuYe - DHUIsWxwZEGu8L1SzvU6H2pO7qDdL6B0ibjyD3+8I0Faw6X1zZRvOTaJwai+y4eQGtC6Pzf0MIYh - XNM6NeHOLzyOLZtxn6mRgGEyIDL1+zQSDyTt8S/+bFHUAbvzX7Djvycpt4e7+oWzQM19ld4yUL1h - y1cdQ80oYwzTV5RjF2s+/IgVJi82oCHNNerD3M19pA2vBQx3f4xBYHkHpG0uBtTgNF/+Rm2D0Gci - +qYsZQlJfTOIESC94TPRlsDpkUdEaz7OyIP8BuHxrtzQK/ok+qoZ3gTiZ7ki69GZlL7x6svmMFn/ - 8HpSsr3jtgCVOIdxDjeOmXr4bSpEUi6bwW/ZfF7GvXxGXj6x+XpyIAT78/ZAnJ+a+dU5GbQD0UTB - zXfH4Q+f4qS9IS13bs0q8n4s7/mIlMKp93xVSkimCGHh9TPDnZ9vgKGtgpD5lukGu7SA3z4h//wm - TrNeELpjST3h9evCPz0GH9YxJkhUvXD3Y3xITdCha127VJjI3MPgBwWU38i32eQjKCHix4wgel/c - 7ZUqhXws9JTofpSFW+0aCrRWr975JaYb++h76JlrSjyCQDNZtzWD5egGHnOwfuOv9BQo+3pRIu8J - tfwfXxGP4Ru52vk5UmQmjhRuzsWbft0YUiF6VnC6tFcUVe0+Jf+X/0CcdDei3vm7O/JJt8E8iyDS - s7BxMc87FjQ17ogUoTAabuNIB5JWdv/x3w1gAP/wHiGpVt3txV0gfBYMh9wb2bcyXhVP3v0Zrw5I - Mi5ouExw18N4Xe59OOx8QPaDxCEoPKZ0AWkawYAlIrHbkKcbML++fAsIIo7jPvUpY5pNDvMQ4kVM - v3RJ8lWRY/RJicGzL5fqn2MltY82RU86aICaD9mEHYQO0rjBAJtSwBJQzNvomfJXuh2jcQLz/XvA - m2JHzeo2PvOHt1hcozRf7+KiwN91qVDWFJYrsExcyNQ8dkRzT1U+0/uNhaf2kqAbF7jh5lHCwj89 - s/s5dJuuqiHr6QDI2RjsfK83GaQv8bn7j5XLFxmLJYZJI2TMbukuxoOR4KXdFnS6hUE4fPAFSn/8 - RrzKH7rewiqQlfN6Q+HYFeHaCKIlbxaxERryn7vteh7oVeUST38Sl0Z6fgFWWQ6YN2k9cndx0QD5 - ui26+JTPtz+9+n1NGjLrtabb48hYkFttEUvwyI30+Hoq0rAcngiZfkLnWCAxUIT4iq5xItC18N0E - MBqzIDv7cc16sLMC7u+HOO61BnNocyYs9VoiRlMXgB7P70XOoVKhx4dbx/WElwx6fHQnjyZ9h2vh - bpb8Vr8JhlRtw7VOTgzMZlZHkasFDXvekAUV/8Ug86kf9Ono3XjoJKfRO1hIBVT6RdU/fl5uTDz+ - 84d3/onlRXB1+nxHm1yA2w3ddrzZkrRK5DZIMXKnTNZXzOYB1ENyJq7LqSEpPp1yfJ2ZGBlNDcEP - HRZN1oCWITcR7pTcfw8PNga6EatldJdTn04Pa+cYEVe3m3A5l7MFd7/YO5hveZ86vdXy7g94FLrD - 2HP3SgMcDB/E0TYbcI/8x8KAnUWvOTMNWObtUkL/rY5IMY+PfGkjTYLN07P+XW97Mosjn7/ih5xW - 5dr0u76HMNUbZCjCuaFUIxsgzm8k7jx/Acb6XYKnoykSPW57dyw+WANs6mVIM9M6pGU0aLBJ9Kd3 - tO58Q7PzpYZ//huwdbtZD+/rBHZ9jsKnnOf03p2gtF79loQsI+5bSXoLCB/Lx9yVgyHpTeMH8yiz - 0OkMeB1f2aWSVi/e81tb6dyeLQZefXQn7lzUYPV6poKeSVPM+0VDhZ2vAgvqPFGUSx1ils4OgIDm - 6N/zzuEQg+WkQXJZD+VI1tb0YH4qCvRUY32k/r2SIOYWl1jd03e3W6ZE8BQ/FPSHF5tH4lZ6Jsb9 - T/+FOz80JCHIEq9RKuxOnfPC8JzpV2QdDpX7sZO0EE7XX4jObWyMdCzcHoT3h0XcROAofZtTCxif - t9D5nKs6JfMxAR9fjInPtBugom/40K6/ALM8e3Cx7nTbn9+MlGSamk16jRcoPK8T0hoeNc9ckXqw - FhefpIhOOWaqQwXvKIhw9LiY4wwUdJGs+2sjnvCLGnaSMAS/x/uNARU3sFydxoN7fSTn5Hlv1pfm - QSh37rTPdDiN20XOFShKd46cTp6qs8+pNCF7yjyiE2PLJzGGEYTsEqLMmhadbJaKZfblesg0GpZu - HxnV8EN/nrd9dWVkJ/6JYTjYkRe48SlfwoM7gXw93Hd9aI4TrfMN7vqbWJIshfhROyJ0z/0XFeY1 - 0OkbH31opZ1NDC2Oml1PdfByFwxPfAgjXfkxYaXdb/MO7VXT1+879f78OSzE6inc9XUm//nd2lkk - eU9Lp5KYcSjQrk/yXvuACN6uPEZaMpSAOp+0hWlbnfHYrYBi+4ACeAf0Sq63PKLr+Zoy0vcyHvZ+ - RTmuXjv74u5no7Np8fnu91Sg7IJt168aFW5wkqDLXVlkIzqFu5/pH7dfNqNzrH7yaVhEC3Z50BFL - DxhApnCw4ACUHrmBTJrdD/fgdq+u5HlsBn2tuNCCvHJq0eUEZ30+X28QNoE5eO1XCcDOf325/44V - bpu6oEswDhhqaczjua5HsOuDBXrfbCDnSvTGKRiHCex4i0y/0OkmquoEh1MtovN6+DR//Sf5XSY5 - sb6jqE9DVWI4vEIRy2ZojlwqXSKoLj8Hebl8zP/F1+pFIsrCYwWa+CR6YCkp9FrLz+jm8NMGpfOL - +cfviWUupqyW8EO89TU087t4eNCeBtNbivw88qF/t+Dn0PBEqbttpJsuFlDKahUpxy+hi3+1POg7 - rw5zSdWE6/d9u8j9xLtY/ELZpU97qKC1XmqS7v2tjaUfB+jv7kUU0aoBHfZJXrhRQnLqP9E4IKvf - oNLUqrceGfs/PZRYRYD0P/+5x2kE6faLkWvrw7jOH6eGsGLORHEe13AewmIBmWu6+PgSnw396N8e - /vPbr1yRL+E3KeDjJnrIC3QabtKr8SCYW8ajW23lY3NcFrj3F4k+3oV81ZapB2fFv/7p+5zb1jKC - Sva2yWl7Zc3GBx6Gz2rV0MOQ8L94h11zCP/ql4s5pv1BkC7bzicYd893BizPuUdmPw4uzUphgUfr - esXszi8JK10v8K9+ROljn4mpGBWsBubgYZXR3EmN61a2TxomdoCKZuXP90ymDf4RbXj5YJEFiQX1 - PF7wqoUz/dNDciXwD6Tt/QFW0ZXuX//IZTlen/XeseDJTy67X8uEJH5DDI/yJJOTJ2g6aYuxA6H2 - 9ZE+djAnaaUUUIIHjNBbWfU1Fr7RH/4QdTH0nNIfLCC7Hs9YdF4E0L98fPmj7onv6vqf/9V91C+6 - HgPD3aaqMeD9dLXRRRbncZ5vVSVrtdoQJzYmHc/FjZXfw3UhxXl6uUtCJx+8rkcHuZJeuhN1+go2 - Er0jRY4dulix1ksr5Qdi8tHPXdQgjYD92Dos7Scw+Jr6WD7C37DrmWu4x08FpnNe4y83yID8DwAA - //+kXcvaqrCyfCAGAiJJhtxFbkFAxBkgIiAitwB5+vPxr31me7bH/suFoVNdVd3pOFmbQOuD1J1v - q+qG3FsFa8/PSZGhRzb9DtsIK0NicXF7/7LPXt8EZDFzouh3OMz79wN+4EOC3eePDmP4Nv/0p7fg - yA//1gv6b3Xw+DB9gjXUa/6v3kaUgCT1+BeffuvE/9k/ZgVb4Dbq20PXjAXLQRFTWOvXjwdYLlYX - WoXLnx7DaWca4ZI/9RjJ1bIR/05+2VLOQINdwVBsOdmgrvzgs//4/sniL9n0u88KlG77eux8mqDV - 2uB9qYmXsVxs8/LoOlDRq57I5FzuJ9JHEWQ8xxHna3LZqGmR8L90FJz+e0fBUdAU4ve+XxPBIQkc - m4jzoLafK2ctXEGUoB+WZ+MbLitzjGGGDQXrl8u3pmys8cjeMmdGt04Gx1SweXCTgUnM132rlyOL - Uvg4JzdiDeJZZW9PUYFse/OxmjkqXWx7kmBPtROx6KEF21s0SsDQaMJR7Oo1QR0fQfK4OTju4TCs - bne10E3oBWJtrzzcGnGFyF6hjsPb5zssE3zlgOTRGz8/lyegxTyn0AD8ibhWDlSi4FuFcl4UMP5e - iDrHTdKL73hKicKLPN36rSlg8nRMb6t+n2EBz8so2uCQElVrZHV7cMaGsDs7MyiQSHsnsHv4u1lX - YhAaDFxVuCy0P+8fvm8n1t6OpJjB8b6p85agX01H7Pfo9nFyTxyslS76GXmAeXy+GMdYDLftOHoQ - fVKd4FMvhVxy5010oHAgsvcj2XJ0Fg1xkhF7SPEvNbsiRRTHY2zNh8eT2lMrlDGC720jrltXNt8u - Woty3zaJ/nu29aqhBkIuPJyw2giSyvdbkyNzwmj2cX8YyLuOSmSK5xLbAc2y4/NleiiFmMMmNt7h - aB++HfIWvp+jacQDyyYyA91xw8R2nYO6XZ7PEgpJKpNrK3gDL5yvCYzuIyCvGKfZceB+FRLrysYy - b59tGrW9DzSxRUS6YcseID77UNHJTPD5o4fH92Uq4Jl8I+zcH+eMG0xRgsWbqETOPry6Zhsy4VXZ - pxLHtaqS1r0FiB7MBOvEZgf6mmUB7O+D5HstZwyLuIJ2oLvkYZyscIs3K4GMLz9ICMoKzKmHBOjy - /RM7h9MR7Lc9JKidzIa8ktax+dMsOpDjWYMY33kDCxQ+Hqqe6wU7cmfXx9Z9+jA544K4aZvWa623 - Ixzi1CWGiBt1FUWGh07ga/jhMb3Ncd2yQScPH8RzhtvAqbFjQbJoiCTHV2nz4/egQXoLbOxJL03d - wKJY6PJgEvKonpG9/Zpthvzd1L3DS8Phxo1pBLqVR1jJbSWjnfIVUc/XN+Iut2ZYTpgtYEnKimie - qdNRG68diphiwJoluurf8yByGmxiPYsb5RgUj1B32wAbcDoPK89XJTrV94icufu9Xscl5cHTjX/E - Rdch3OeEReiEW2E/s/kYWHY9KSc/8RIs+74NqNBUEppN/4xdR70PHK/lMVwupUrUvvXU46ifF3A7 - 6QccZZoz8EyyQXSp+dbj02205+DCW+hzQApWiuYDhpunWmhNv6Z3yszfQK927cOLn1jkcizSgaaj - UaEyNwxsoS0Cx+X87cFjbSuivJVfuGQkayH7iz7YvXwidXNPTQEn7iGSC1J1m1MrOUcvjj6JIY86 - YJ31msCjHAfeIp/z+hgcLglsQ/9Ozs55BWsSX+a/+J8/czTVm3sUFPiuG+RB+JtCaofCAl+KrMyb - Hb4yWgLQwSJKHkQu1FM9DdZcwCgdfaI/wq+98gURoRtHBb5n74yOcPyK8KvJT+JMuLa57rQEyF4Z - 3ePMQKqPJ8cS4Zf1IiyrWLe39YksePFTC4fb9hq6LWkLeLwvKr7ueLK/DwV6Rz/DmlZnw/HEXDW0 - 4xFJvHhUx2K7M1CAVo41y7oDwtV8A0+x1GMn/UrqhH8/A6rO+py3MohsmvapAdPNKWfYm7lKO4kW - aMdTYk1vdVjymySh35YD7IZX32bLRx6jjSlT76tkt2GbvmkDz8mcE6M62pRffDVF5eMteCR1u3Bb - NMOAByNSSGoRWeXEyTVAIq0TSUF0zTbi0RzteIwlJ6zDTW7PJYRFdCPPIP3Zk0VrCTmHZsLO42iB - zdKYFPpDz3r3dqrqjR0ZFnz72Njf9wdwSSyPsPnkR2Lxzz6jn0TpAbbmI3ZOWVTz7MiziCggw/h6 - lWsub7MYTu/q7h2fgjusT0kpoPd91DjhSQ+mc/OOUfT+XIg0fpWMA+UQAPt1PhLlW471AnuQw/QW - Iw9eFW4/s17P0BC4ZscPzeY8XhFQjOtl5r5GBI6CtUFEcM6QKCSVvZ4Gv0eBJHvYz58KYKmVOND+ - 1D/sTeRCt/zHCuiZeg7RmFbLjrWFUjBOQMWGGN/oYl+EBj7Ox5y4jFwOnCgyLBzdrzYzXnAFvAhr - BiGzrIhyIDbgE5pG0CqkDfvajQs37+UL//bb9XY07aOu2T0UZOiT9AtP9jo2zxJ6bGhgLZoydbtw - iYmaw13DBpXtkG2vBotuciF78Fu3YJLIeQMS82OwTsAPLIWDNRhKno5f66esuTe7lP/wPqNHUk+F - yzrI+gJpPj7NJKPB19tAd+4W7LDNNIy+2yVHgZsH4rtWn5GbZ5vofMgdT2R5z+blFleAC6uNyCZ4 - h/yUPX3IH4GNL30VAr56iQyctJ71xD0elrp9t6iM9Zz4YrqoC1ZwC5/zmez5God7PBYIH8XHzMT3 - 60DrWBzheIwsj9k7LDZpYUVx/eYDDp1fSaficSqRkPHPeXWCJ12kJ8xBd60YIjU/Nhzir+iJRpVR - LD+dYKDH1eaBwt9HjzpLMCw4VRz0NoKQ5D7Q1OWaBRYi/I8nD/HzrikbJQnKQ/NJ7oATKaFzDVEJ - OQdL9eaq9DLup5iqB8Wm8HzYy/tgjmhjDu+Z36wmbN0XSuAxcS3iDiXJJrWSCzQe16tHG5wPdNYG - H+gCbucF1y7dkuw+gzh/hMTPG7be+UEF/p7fnkrGXnzuMyJJe247PtQ1rZf3gr7n5wkHsA3Vdf5M - OSzjc+6dxoufrQyKZzFfXuf5dCzEYZHVUkDzSXXJmfM/dLsuZwmm2++L1YjbwvXc/CJI0PlLrAAE - NZe1Bx6oeNuIpeN4oOu7mBH7W0eS6O+7/Y+/7fzj7/er23UOGuRedk5ZzFdwdMG1gRprO1g5QwVw - 2vrtQWrnBGsC3uhMw/MCe2qcsA1lnC1jCSA8qCvrIZGeKUXSaT8p1pdY3bbDMB1q1UL+Das7/oJh - IQT1MEpnH8sz8jIyPq0UbtdO8F4XRwZcOw4KfDBfh0jB2tbLQRdLsT+VPb7c1R+lSpayYJbZ2IM+ - aNRfxmwFvL03iUiBkISUij8fgm/zIbq3qJT9HO8jNBfDxupZi7PNj4YKDsdLONO/57u4fAeEvcKs - rI5JaUvb/ZYxXScK+YwZOZ+K/ZQXCebt+2XCf/HaGkdAbJerBkqESwL/6YsXe1A3kgoLpNWEsPlc - 4owK8eJDIWOf//BzolldAXkLg5mvznFNA+poUJ+EK1FyuwqXnyZVULrebvvfv2uynh4xrMrtRbxS - ZME8fYMGHL0Yel9nv9XH4UkK7vVLxaYtdAOFmdZAMe468of/1AWPFnae9sZx+HzXK4flGfLuyBAz - OeFwCbVvASRf9Ij5ve33kmtVC7eh1v/x44/9vXjiIGU1MfJSDJdI0Buos6tFAi9tw+narxKMP2xK - XPXsD8urucyQyVs481f+k83qZ5AgTO4DUaqfPmy/u8RA97eJxBXFR82i4trDWKmNmQf7rRsPV87h - 8zswMzR5LaMxNzAif/86M4Q/N1v/+OS7NN8kd+wZLNL1YMKueHf4sn3DYYp0wYAkj9/Y05854PPn - oQGBiFOime9LzffbWIA7FC4ktJUmXJ4xLKDKH3qvc9uY0hEnHQRvi53ZkCg2K/1OC3zOOsE7X882 - eFl9+LOJhWU7asCqmEECka4ZxJ3OA10ykIgQgu5AzBNSB06pPgwsZjLN2e8xhvMnvlXIAvecSMos - 7FPpPR+OUpOS4MEu9SKrnQj/8A1Dswajt70jtH8fLhZ3H2ncOBbc9RDR11+ibh16sHBVR5/kon0B - m+FlLOTZnODXI36HK3O7pv/29zWeibr8TscWZfS6n7lPDLCCsWxhDYKeXM7LGlKVZ2J4UhqPeG5m - UfpJrB4WnP3ylkBew3lRHh0UPEb0mNI6DFTlLiJc1PrhAeUH6C/QOhMmxnafWeHQ2SM5CzH85N4X - u5F5DNeNO2igK+rOE360UefgLWywmIN2BlnnhxuelB7meJTIa+f7s6HEFUh1hcHndlIG1pcHA8YZ - dGbyK0tAl7BhwDPcMvz3vOwWbwqwroq5O063elXmOoB4lmxSzK1Pl9cKCvC7fRKi+/cyWzzHL2AY - 6R3G7+ytUvWnNej7PPVE2aiuLi8kLGLWQR9rWv6jgwuuLdr5ETGCycmozj/4f/ijZdpY/+bPp0CH - sBtJSCuWbvx+pkOeeW4+7HqPMHlUwlBydBxEhjJUXPlU0PkkZVhqqqo+poLKQuPNjFhFTJuRi3Ca - QSLRyQNQJuGWtkuFuCk9ENVWmmy9TYkIrELZsNMLa7gcHcGA2UVPiNbedZt3ChPC9Cq+PSrHZvbv - c8kCNZGaShmOf+97x29sjHVnb6L5jmDPG97cF96gblZ+UuBpPFXk7DAJXWf6cuBfvjbOoaIuX2Vp - YaN/D1gmkzpM3SQy8GMuNn5t+XdYRZFnkbq8TxiX23MgMVcz0PAcH9u7v7BGt8IXfTvmiHM43QHd - 8U5ckZjjPf7pvF2zGKyRgj0unOGwtsHuQPKPzBOKcqjps2R7GNRxRYzLqtfrU7IK4L6EkSR/+HuS - 3AYk62PAzo4Xf/hx9L3IwpbzJOE8JIn4j++ctlKmC3eoNhCcthC70T75pxQCA4Fz0GLZBHLI7XwX - nVT55t3eL6HeDilJoTFX+C/f1bR1nwF4I0mdJ8bcwvUOgwZ9DBcRaTiOdMdLBYVlpREnqMqwl5tM - A+P9GeDX40nVpSsiDX0t5Yy9ZK7D9Y9/fDX1SbSMfQN6vZcNen2ieNf7CV364ReL6lKfPOaY6ZRT - 4FOC8HDOiBVVWO1OnqRAURqY+Xa4ReooH+sSgbPfzmTUefWHIjlHxnyIsRrN5bAE3hKBWQ82r6ZV - BOareRlhfS4xxoYugkU3zA1WjG4SNVcHdQRPeQRDmAoegm/eJr1iJ2Dne15Zb5OdmDEvAnTwA1yc - Tr+QsJVigTOvGhh/T81/3s/3LG3Y4F4zXZcWpfDz0yycb0ZvL9JvXeBiVDPZPweT27sNrJnJmNnd - P+OIa0TwGwo3ovSzHrKNp+SIMwa855tPPYKrlqJ8eq74Dy/X+GxKMALPmOhX75stR2lb4Oum3L1+ - 6riMnhxFRF0PNbzzj2Hb8wdMi5ONn6Wtq3Tnk9BzNc8rSutQU6HpJdHSzA+xKqbM6PXetTBB5RnL - WaFlPDqoLDqNoCLe/vcjM9qpGIrHBOMtPw/r1whM9CK3AdsC5YfPtaB7VxLAZN8fYBriYYNXnGHi - dn4D2BPvxn/+HY7Z7rvnDyOAdZZt2ABuTp+oJj2QzbNOnJau9VadKgW9PnGMzx/kZttfB+iT1VLs - QyEcyKiGMVy/xTCLaFDt484fgLQ6LrnN2Svjv3W/gXMtr3/+jk2XVxXA3+vSeu0BBeq4rm4DtSkI - d33M2psTqD2gnykgejjn9Z4vA/Cnz8+8x9Rk9xPARQzuRJVjM1yvAZNAqxduxOidOKT8Pd9A9svu - xHk7XLYpNoDQzn4ZzrmoAgu4OinkKL+SC7JiMMr7DLdQ7d7z4cVqNT8e1hmOmsLjs5Ld6uMfPxNR - kuCIf2vDkl+UEb7Lb4edll7rxZxPJYRyTsmdPyr2Gk4ohT+u8LGOvz0lTJ5XcIZShbOd75I7DjUk - prpGLFkc1XXnxzCSWJU8dv6xuYM4A6t4nrzIso5gepBlQ3Y2ZB5y6Ejnbs1LuDK3mljN6A2LfA06 - dKpE15tPihIeG8eN4PHmcsSy+ADMRvqw4NRA+o9/7H5xDEWe+RIpm9yMzUAiwAWRjyc8flq9ufqP - /7dfnR0f1mw1Z5hPr5VgiLl6MllcQX2+JeT5IOGwLts2/vH1WUTKq6aS87Dgnm8IZsXB3vGrhV/r - 5RHpxb7UqR6OLWyNhMW3GHIhzZ5CAEZ2hcRU1bBeXFuq0OdXX4lpJIw6L5pngF1P48veoT5LUPTE - 3S8jrpBq4dGqzgr82YGKz9r2zXq9Iwmogd9jNVdte86fx+Yv3siOF+E6Vj8DJMZyJ8USvex1TcPm - jy9gjWmbkP/ePzmYbDwRWfj1NbXFSwcMIXniO5h/9l/+h55lnGYkio+BynRloENVC//tr9/lARjw - /carB26iki2VI1bwYiDVE9FQ7+uRtv+ezxSeJ3UMeNSDoQwyYsj0Xs+xEyug5zUPPy8f1t71Yw6D - 0xISl3OGmiJplUBdPM6kGJJEpQfFZMCOZx5zKPVhq8OHCZNGl0kMDNumytc34SA96j99k83Fz0/R - NWFjjw1Jpf7xL/BpNc5bXYXaU6l6LDyk+EOMh40p8aot/9PTc5tVcrgKA7IgF5YbCb7fIvzn91Zf - z8fnKbiD7XLUWgA/2CMXj7HUZZ4vzl9+J/awYTrftlaAldiNWK3ec7jlz2MLdU99EIkdFfX4KhNT - /AniiE2fxTX1rAMP8Zp3M3pOcs0n2WsG/anqvcOpL7Pj86y30LdXSNzpKNvzn58+3l8B9rT2Roct - lAqUh9YTe7b2sCee70vIHAQfxxfZD9fiW/siLOIbkbs2p/RNHjygBf/w1rq/0dXjLQHefeXrbe1y - z7bkU7aA4/eZwgX/s8nuPyF3cUZy7rWXuu//BLbtSLE1dbdsXy8T3ORcxs5lGrLdX85h/eU9LI3f - KlwZMjJwmmuZ/PmR//KDHMGORHwXZzR0gAcX9f3Acl039j++4Nhs8Oe/0ak+QhGmHX1g7/aF4dQK - XQyO7+6KM8lPwlcsv2MoH0JIVCsg6vSoaY5i/F4wFt/n8Dj8Gg/6NoXk/DSTcOuZ3wL2Tkzsfk6h - OjXPdwR3fwRL+bOin7X1BcQcRH9mFtcN+WZme+hNqeuBg9XZ27euNijcjtQjez1jqaVkhMdNT4il - vruspy9dg5fyeMXKeHvTBXuaiMZSfPzxs3oQknGGA3th/vZ3dnzXeQmmzPh4fK83w9YuTgvBfZpw - UVqverlYNwOGNSyx+2TDkHYSKP7qH3ivR9mLdD1a//DEc7OezqeKFeH0PhTe8SkbA78+OROE5cEm - mpZf9vrWmANvOkbY7qpPSGP5F4PmUxyJ2uB8n3FpjfCxqb+ZW3+CWt5HMAI/cRISRROwVzcP2X9+ - tNy1EGzq2kWwDzZxhvnNC3vlwLdQm6bUo3fGVknCAwEkXyMmCpu+671+toj3n8jO+6xLMLFc2v75 - Gd7hmtBwjAS3AQ/tIc3L9AHq+Dsdm796IDk/xENNvl8nAi9h27BqBVhd/uo5qVdoHicWk01fUcpC - KYp6og2eY3OC3vjwLt4B3vHWXtLztiCW90yC28cDkF0/wy5CKY4v66de2A8Y4cfAyDtVT9b+85Nh - 9P5e9vqXnB3PethA5mKVHhUXdzha0TMAYtx33nuKN3uDdRfBR2dnxLJb055t7SGA3Y/3rnn+zLbc - 11m06x2i3plBpcHjqcDwmELiDYlgz3966PKACVGjWarproeQQnK6+9U93Y6vpQRddEhnmoubPVW9 - bUKxRoF3uIRhvS7MoqCL6N+xm7bi8O/98n37xn/+ITGe4giDRx5hnLSOevyr9+2/d97Y9D3s+N3A - r1vU//yRhf+qHeD89xMbF9nP+KhB1kmc6tK7mkDO6DNUBGR3JYPN/d6ALXZiCe1+s3d1T6JNVJ6J - oHaBB5KvcURHVSQCuHlg87rjq1SXsaQQqnjZsNO9X2CSTn4P6dZyZOf32RxvSgo544exvEalymUb - Z8E/P8psP0O4HW/fDujAA0QFpUJX5pIb4FPr5Z+fVv/Lx2cc3ogbHZjsZ7mpKb4SIGL3ozj2bOJh - g5UqQI87V4PdnRHegJuDu0fxaQg3W4hKtB/fxY7LPlWicrIAvef79qdfsvnGNJ6IPkfnH/6sF5fp - xKxjfPxXD94OUsjCcKpm79A+TuCfPxy6Px47YpAN5eH1a8DuH+MoGS7qpIGlQgOGGbkmN3ugjeUL - /+o1fgVa9a+eAeQheWDjL1/t+h0VKW8Rl83jcOdLPdrrZ0S9ll/A7nwQxBnjEDusl2FZkSWIf/Ud - l5GlYXNfXPK/dBSI/72joM7gj9hy96tpS6US6dh+YOuF62ERzoMI0PrjiPWjB3XxtyVAdLNNr3WY - tR719e4hwJgCyef0A3oW6gnIlsgkt6a6Uo78lAWm72TCeil5lG+t94gWfHtj9XzBw/IEzgLJ0Rbn - Q47eYD5baIS/sTl4J5e4IX0krgf48nbGrydgbXo8KjNksHicF+7VZB15pwn8Dh7Gqrip9npn5hl2 - tyfC5s38DrRgfzFsYa8S87Q7ZKp/shATyBqOP6uXHQNwSiEVI+ItfdKExIxsBfoILti+UjLQc083 - 6AW+O69h/LUXI3BnyMPHEWsU2GA9lc8N1oGiE9Vly4xDYT/CHmsp1o2LDY6wxwEcf5JMIuUWhPND - WgLEzjAmj4o5hYvEBD26KBHGN+UD1NUItgCZhKXkado3e53LXkKsIAASLK9y2G7uEJ/eDH8lnv++ - 1sdDRAv0OfAskV0Ch5H9dDyImzD0YO60NgurwUA3HFfEJcYT0JjpRUje5gfLHJnUlX9dHJj+XN37 - TFY3rJ4pjLA1pRzf4Zra/GrdCtSDKsVaZhJ7ucCVRQnieoyBH9LtfTvyUJV/lFy86VXPk0cWwJ0+ - IXk9Hld7+ygSRM0CUnLOTj3Y12dGA5uk2C3AEWzpqvqolnueODRJ1IU3DAird/idyxXQmq59VsHv - iByci6/EZmX3GMBGaq5EAaVTr1fLDcBQBZ53oqMKjgluCsSM0XEWmrUf1nEWFZjny5mkJP0NKwe5 - Be7HlwjWRlM9NrLfoPOpwOSx2a49959SQLdrQbBzen/AVt3z5d+/L04vNtsEX2JhdTF179R8NHtp - LgmDpuEZ4/DA/1SOXFYfNcrdI1KJeTBtdVii8CzcieUUr3Cay0pBWjWlOOOHYuA8c5nhx8Yu0dW+ - Gvi/9Zz5V0X0mGcBNUd/RlEl1sR0ygVMJa8Y8EWcgoSPUzCQCecxtKFLiJPPP3VSSODD4fyx56l4 - sHS+WiuPFjtPyL0Qj+oa8zoDUo2Z5hpEhj1JriGg/utC/NCGb0hbUYuRdjI1klbjSz3Wh/2674dQ - EtXzSd3nQIIosm4jURZA6MTpcYva0mb2+EJ0VXxzAcyss/MhOXo2d9NFBh6kj48jTSThlqVBidKX - XxLVdG/geJKlArVNnpO09TE9ZnuP7V+8xRvo7FV7Wz48cM4RF98rG86h+unR1bEnj5usrqaoVgvk - 5C3j8fgpDtt38E0k3bfAO1z5N1gHT20RVRuIzcJxB14lTA5E66ATE6pLveCH28PpcbMwrp5HlTJN - l4qkYxC2Lb8MN35n0OJUlET3r4nNSkzag1ckDl7zsH/q+o6FFlinh03wIT4P7BVcFVRdLB2rWfrL - luqrV3BdtmRGutVReg0qD2X2XBLHkUt7pZolQfGu8rNQONOwKQXLQFtmFCzJqTws7Nj0//Ay8Q4P - sFnP3ILd5+jMovj+DeyTNZm/+MSOeSpq2tm6iDSteuLLrJ/CGXdzCp4n8YUfrPe1t+O5NNBTDwQs - 3x6yTTMxaQEMtpKYN/M88FyRsPDBk56c7bkBnbooPYJ+H2GPRWtGKXhK4nY3IT5zngXWXq94KFdL - gfNQGdUlgp9WJF9PItrHz8Llq75HwF+Yal7ueg4IVJ4STG3JxHaSjAPdBqFAwfqcsMx6cdafkr8p - aBJPQnQv1eP9dU3R+alg7xDSM90ix0tF8eGY5BFQGtJ3PKbwUy8aCYdLaa97PEPhBAoshZU7kM86 - tPB1TAziiu6TLnwRdWh1zIWYLFwGStmrhz7nSCN6q5o1f+NujfgCV4yjoEwy/noiIqhCoycXxPrq - mlfGCPZ8gdNqPKgbOOEOys07wzk0FLqgLBZhOtyvc/iCY7YUT9+Bl/61YOU9Q3UYX3KArMzxSHFS - dZt9qlMjlm6WEVvEOCOquWxoHV8n7D6RXvNBGDRwzxfzpt5ie2GiUINeELjkwi69/S9f17PBEd12 - VzB3We2hjbudsHEW7Yy3npH5h/+zgJpzyPFxl6MfhB4u2Ldis+eNtcDb8nps9tcDIDfkQlAzuUb0 - iGY2+9KtEoLL+0qkJc3B8dvfE+A+rzG+EEsC7Ml3OlgLyQ/fJGNT10grJCj3LY+lI+NmnJfcAqTt - 9/IqnKzWa1NOwum0/AJ877T1L9/5yOVYlxjdJtb0cZUTVN+AR9QElOF2ru0UKkPoeScYnrKpTnQL - Jrf5gvP9DBBn2q2GNu5+wmfH71RS3s45zF6MQDSJBcOckDmC3hPN+FmAO+DmsldgVxOJvPC1AaMg - dRriPrOFNUfGGddekAUzeyxJYCOHstW9gxBh50WuXiDbxzqRY/j1Fkx8IwkBp1vXCs1FJJF7nYvq - HML3BtL60hN9M0VK9s5I9F3dD74o3ZSth2eZQm9sJOzfOznj/UsuQrNTMnyb2iNYI5Sa6PyUMEl9 - xVc33rBNyHeXl7c5L19dadi28A+vZcuZ9nvO0g3ueOudtOGc0S3sF+jx5Ydc70Jvd7poxsgyTgt5 - qucZTLnCOUitZIUEv6Ss183WWTQm/TafgusSkso4FqCa0ic2rJcZbsB6NKjKgiv5i4/5fTuwf3jh - iXZ6UikEDwuKd5nHyn2fY6f5IwP/8oN5eTV1h4WXBVu+6Yhr9Yy6MvAkQNbDwjzqeW3/9v8fPsEv - n5nciuqZRpkJgTyfsPJjCRjdwxDBz+ct4/PZkQFvbl8Ii7ky5sMtRNkUsvuU8MFyPMbXlXB8CJkP - Zk1ycWQDEG7RCPcZVk9157eXmi8o3OCv6jLyiPgp2yzdKBCW2AfZ96e93gqz++OXWLPQT537Tyeg - uI9mEq2MDtjqq5dwAuxnPnXKYVjZ6eGI3XpC+Hy8NsPyragDsdmte/6Qwkl8PCA8TDLGl9/5bFP3 - fG4gQsuA/fHoDtR/tApYHWvB2nMa7S1gL4q4bmLj0YwXhk01NQ9+U04ixgZMm+P0ooVzEnwIPhGk - LlZ08GCUNITY4hqE02Mp5lN6aiNilvg7jF/rU4myERfkwk++SldbUcB0b1xyVb9+eHR4Q4HKe7+1 - yyle2ZZe103c883MvOaPTT8CSUVnQ/u87edR3Z7qe0O8UM5YfiuaOnOFz0PhSwNyPjtvusTu1wIn - 8cOTa3qKsw2KYwNKcPj+ywdbu4UQVVyrz4gYCKyayTbQ0Ex/Ph3WfSaBbjWidTMpUS/zWG/1mbBQ - 4KXbPJ/Ujzo9StGCf/G3zQIHJnZ6eMDfOzCx56CMDHFXQanSDHyFkWVz00s3IKuUd+zrUBuW12dh - kJPyMlGYtwCmv/0eCrjEf/izhNnBglrc/4iZDzFY4KCyMIuVyJsPlWdvq3XLRcsAi3cqX0u4ZadR - Are6BTsfX+vtevqK0B/4huBfc1f/+DS4FOkBq9crABQNjfcvf0egyOytIn0OwvpgEpt6qs3/00+j - /CNOqDjqsbzhHCofTGfxYcTqdCeKCZte1GZuz1eEbUYfgvMWEax2b0ovgPOB/T3ciTPdmGyhNu+A - MJVqrOcHpR5bIIkweW/izoemevnsc2mNOMixMj4cuqqvKYLqmXjEFoJfuPibEMDlKyTE/4WQbicp - E6EhViy2Ht3JXtEg7FNyDZM4zVXI9viV4I7H3k//PYYlzI4mMLq0mzl2ugMS1vUMhl5rvZv87cCW - V2AfFooVj21vCv3TexAynE/u4ZW197lVs5BtRUTUbcThEhtDDDNVNGdaKlI4XPL9LueXfseBnPg2 - R3zOgLcQj54Q1H09dpQd0d2sB6K1NwWQ49GaxeCmTtgunwXdyqKDYI8Pcn9AjW7fIbHQO494/E8v - OTGsoPAmR7LrAbr1n04E59XvPCb6fsJpaB8+EG6DMS/vZRroFlYLvDI/k3gDPWbkWHAKPCfTSnRx - Wevp3T5NAM1i9JI/PD2SNIIcIK9ZWNKpXo/fHwOKwjxiHNIvWDxZiiHtnJrkkRGE68lfHdE9kZjI - zmqF0+Xjj3/v09v8Tw02c/sy4Jqmy3yyZtnmeCYd4alp3x5bLx9Ks/VbQGNpfGz+uKXefjYTwLS2 - e49dcwWMcLB5eLtXFvl7vySOhQ6iRxViS2jqugGpFcF7IopYkXjXpr8cJzCsHBsbb2xn2zUtUjGG - hUjON/6qkvTRW//ysS4u13qzi9VAjzTsibTnv6XLvhL8+SUmL4ZthvHPDwB8XGLnDEZ1VA/EQ7Gh - xX/7Qz0eJtkEIRMA7Ny+F3VbZDgDfEgaHPrhr96Kcd/f8bv20O3mAX7u0gTW4arg9IXV+qi83hXy - cm/1Bm9Ts6MhdiN0p/6DL/7yGLamKwKw6+F5wVI/bL0bekguKt07/eo4W0XRTMDyFROsNcFF5T5y - lP7j6/qR6BlZRCnfO64yEsaCEa7szS4hUJ88Pr9fkToeD7YmXhyfYi/6frLlkFMWCbVwIUbciDUd - 54mH9ftwILt+CCfwFkfwjsOMWNV4sLejVwtwub8vOLi3mG6N1CiwKKzjvBz0KSSSV0pIhpOCtUC7 - qNOtkHrE6ExIVLDcsg2fFh9dzaDZ7wGXQ6pKiQKNMVyxFp1V+6hJuIPkpSTzYr7f9fI2XgY8c5cV - 40Gf9nxcx1CUlbfHpemnXgUcBUgk+OmhZerqRROGVFzu9WVGj+OVLvp6d0At2q2HSmal2+xrI3Rv - OUMsCseaHrifgG6hO2I3iJSaKu0nhuUHBiR+OddsyazTAozYz/HzU0b0aBXmCN95zGMJMzndXLK1 - UC67gDy3fh62THFY6JwkZuZwqNfdmxcLMPPPCnv44IW7H+NDhRY8kVmPD+fBE0wQNUM5cxcrUbsC - /kZ45dmFKMtLGrjmeonAnYtE/Bd/vf5wZzE+H8N52dePP3yS5d9+KqB2zY4fH1nw0B5YYguzUE/Z - CnoRB/CDpcTSBhqxHwcGdbBg/Y8vr2++hUZ1d7F3r571ct6gCS3nYGOsnG170UUzglFLR3yRPuHw - hw9Q+bgUmy3bZH/+GbgHx9e8JSa1u2dGffSi545cIn4KN9GEPZD7hifZjnc0fVQmCicwerTIuJo6 - MVuhy/ZLvOG4GOqxYH8R9OxjRTxysu2OvIME3o1txtoQ3uz1Wdc9gj0843P5lgFbPBNPfEuBga95 - KavdMwO+uEqyuOvDntKe2yS0PZ37n/+WLX5w4eH+fNgAkphR8fJ20AGlBfEqsobkflZHiC62hv/i - nXweTwFyWRLveuGq7vgfgaPCV+RsubO6/PHL+nbySJzYn2xrrd8o2kLU4jg1h2xb3p4Hb/f91gUN - lnR9Tb8Y+ouBiDlsfE2RujLIOWiPXf+86Eak+/LnR/zT22uSXFP0KOcb9pYjGrbDJ9mgvinaPNzu - ZcZ2TWIBYxtz/Gpri45AXDaU59v5Tx/R5VG6DfzzQ+UHaGxy0QoNZPf1itXhQ8KtoOwCj8fiQjCe - WHWM51sL5z5d/+mZzeWZBJ4iN5gBaZjh3/c75fHuzbsfQh77jLFgcqP999oZr3Vbj/jZEYm6uG3W - 9U8Voo/tuvjykWm4ShOKgU/IhC/3WxyyVnRw4BfF2R7f9X5r3+hA6XCixCLpr56f6m+B0+cB/vyz - erljzYCvlDFmJHU6PR64t4juyA6wqjuXeqylxUHo89Hm32jScLEmRwQpej2JZCKfrvNitGD377B9 - CG8hGVvdQJ/n/YElsbbC+X07smDP70SbhM3eXg1mYd1Y13nb/aY1teQEnQF2PLGYpGFbNzzDHT+x - iZpvOBZNroBIepjEbrVLxtcvVMLndwtnpud0e0P9oQfOCF389/mgYthCXw1nYv75L8MNMH/8nji3 - 78/eSiTmkJ2ZeOa0jrPHwb0rYDSqlsiq/bZnmTEKUB3KH/EXJIdEu15L2F1bFpsXQOnSIzGFfBTE - WDm0ZbjlFQ3gAJ3tn37+dWDSoOFjGXsn9WOvvzxrQKdXKtGU4y08Rh+zA/qJz4m88/UlGEbjH7+1 - DIG3qbhmOUy/uUl06irhZoZx/udvzvT7yobFamUNrYUPZx7qlcr++QcS6wQknNtsILv/dkKfr4Yl - q9j9cs1lYP/FkDi6EGSz5IIc/On1VxBc7Qn0WQ7AeYmIXKM8W/HJHoF0ABRf6FiDVSGp/5e/vE9d - DPVPMJMC/L9+ftsk+W4WnF1mxSZXhMO0+wHwHEMWOwNngvE8LPEfPs90+XE1tW/3Br6jspiD3GtB - HzlGAh99fyI4ug32KrenUhyB5Xqv90sDmx7IIpSg0mNv4teQKq9fKU7vcsPuR9OHTfwNGqgkpvDg - zq835/tg4V5fmFEQVfW8+2vgEyact30Ch7LK9yeADudfYl5ACNhd38A/v+JhzbK64XfVQOiVZ5Kz - eREujkEcsdK4L1Fvd1zzWZpW8K++cnZ8U13xSZ3hjsdYZcklXEfcleCxsW98kS5ayH5ONQM6RqLE - 2PczF6HUgpG/3Ymc8Hm9XPuzBYYHuXvr8C3BOJ4XC4El1D349kd7MXM7gdbNov/8zF2PCBAN3Yvo - +/sg3QAjIFya25//Ys9W82LA19uwN+/+Nl3WVwzSV1CSS3Y8hWs3mhUawgGSyyNZwvHy7CIY5/qA - XblN1PkMHtqfP4OVYsKAnEzNh3qdbTNbXLJw42/fGXIX3yK3y5jZdHQPCVS39EqsejWGX3O9xHDn - 4/Nv+ln2dntiAex6h9h+IND59LZiaLDPB7YEpayXsCkDkA63K/bk6EmXVnEW2F+9L3GDqBrWg1OU - cPcHcfJ29xk9MuzhjsdEA8pAlzcv5qDu5HSGsZqEhJt0BfpHcMM22ophMeuzgwad98iFXl7hasJL - Dg3Mrdgctngg27DkkM1Zi1xKcBiWWRoYMZ+FkFyafYp6faAt3P2VPX+91NUXyx6JT/36tz50UXDu - /ctXVkNO9sZylwYaifyYj6dlAquA8wDKrNDj7BEzdN75BqTX/RZdP0joBmm5Qf8xJvgObz3YJo9s - EK92RVTLAjvebCm0jr2H1STJwFJLgrdjt0hk2Txn5K/eYR07D/uz+A2pcF8dOFyFA7GMvKXrWWxK - VC+ugt2SuYKtG/ACbteckCzNjIx2AxuBU3wriH7dZrDY3KeHY0PqHV+UfT+u5Wn3M+dDFh/t/m2E - PuCeZTWLB9UZlpeuVODRdyey73c6C+fXDNxL4u759VlTxXv1oOeKw+5HHELqYmEWrbWlxFjTNZtf - T0eC6Eh8bK1PdVi/wVMUSzU/euIeH1vU5BrY/bGZo791WOP2ncBgKO74cm5fA7V1KqG7scwzr6NP - SPN+6AH3Cl/Yutbtn7+cgDpjft60+4U7ngUwLlMLyzn0Q3L3cAXHZqo9xi31jG9TL4CfetPmYRsl - lfPFroOmmrAkSMxQpfOibMAMF3UGJvkNY741EjI/Iibqp+LqqZ9uxp/fRM4fVaPr/P2w8MQI0V9+ - zma7OGnw6hfOH3+s+eadFnCvt3gwvTj7iVIbQr68n72Bm8Z9Jsx7Q+1Su/NbtWV7mL6VAp1W2LD5 - 6Pt6OaDe/4e/O5+pl/G8mGjPr0St3pdwLXlFQ4soNRO3+5Hbly4CSK9ygVXT5cD2/l5GKFzaG7Gu - tUHHO3Y0eLXkN46KCwh3f8NB/d1XSPbMt/qfvo3z8zDT3c+f6ViyyBkZF58f6ZuuqVz2EIzKiNUi - u9XL7reCilxSYjCNbW/3QxjAfBbD+aR0U8i++a1Akt4bRNHFUGW5GxCgFL7YmROuZr3KfKz95UN8 - rQ69OvyapYJqGejE+YYf2o0ehUj5VVeM40QDNDG7Hs5FLHnsy1PAOjJFC5/rLJCzgI8qqcte+1fv - /fOvqwBoLHhowQ3jW/jM/vxyGKyviRiJrYfHOF46qJ0sDZt+qw387h8CPAWJ99j932UiVQp3/81j - 9y7rNXk9HSByvekhEHzCkf2UPMrjcSRhsG316G+LD/f6H9aNYQbE4Q0JPsxL8Rfvw2L0YYrKrC/I - n9+4rspkwLPGdtj+07fzHWnwM40dUVpaDzULVwjM5/gl9jZK9kR8pP0vHQXgv3cUsGamzuxd6ey9 - xrwg23pB7DHgCejhRCvA5HKHpW/V1VS8nTxomhTNDZVv9eYqa4tuen4kV1f61F3Jfyt4fR5FYn5M - tt5keGkggzLeWxOzD+m2hCaSDxfOE+D7AhZauppo+bmGJcbkhm54/nwg3V4XrEX+WT3uHj3cLPuL - NfcEM2rUegOLtGSJJ1wg2D71yYTNIZS807OQM/7JhAYK8/XpCepHGxZx7EuwYbUimMSoHk8LmaGi - KoW3BgcmW9iKKyEfFAOWbs+3uhwHj4dCC32vfuw9OKe2LJCayyq+5PpeQfltBprmBeAkyt1hfV/U - Gc6SbXmHdeoyvsxhA/hTCrBmk/ewKkBI4cylkgeMvgnXzJE2RN8FJWfkiSqtI8LDo39z8MVrr+Hq - cUwAD/aYk/wQkXo9WGaOiFTq5DEwh2x7PC8OnE5AmkGX8Or6foU8qmHZYG16htnXNIUAut41xUaN - asDqU5eiIVLd/R7sye7aeJzh0+lCr1JTRl3dWDKQfksbYjzaR7at51aADzFwPUa45IClpW6gfOFN - 7BJCsvW9gRKeo+iMo0OVgukjTRvgJswSvERnsBm07tBorD9yL4JLvfrFNUF3dQ6JyW+fgUfrQ0GF - bxw9VlhOYOWCVw5J1aQkfTFdvdh9HcEwuJVYb7h9BoEjbfAD8jPOy/pt8yt7tiDTx97MqMkpnPLP - JYbnMeznldNlwIuGXqHckDHRj8J54L+hLMLz9maJdldMdTm+ywWegCaSa0wuNWv+cAcfn6kg/u35 - tjeHviTQG+aIw/ZrZkdlqEu0zWNMgvkKhu0SuCa8eLM4M6ssqNtH4hl0leIbPqfnMz36dq7BZEoO - xBRWZpgOVPChsngCNvsxBuS8lQa8Hd8O9j0QDawYBwk8XGFLorW72ccuoQwUbFMjxWFu7SVtNRGp - 149BjKJRM+5Q+g0kvyO/r6deT0nJOdAXt8CLK6Kr3BeWJcxBoBO7caOQLsyVQdVpM4mUyXVInbjK - oQ+fI5bVd2zT98NskOIWGU64AwrpHj/ItP07ud8uT8o7Ycei9C1+vNEJ/Hr2yJVFegpbIt/ii7qp - N8VD8404BK+TGbLG4vP/R9qVdCnLc9sfxEAEJMmQTqRNELCbgSIKItIlkF//Lep5h3d2h7Wssgzm - nLP3Ph2Mov5Ffcvc+1JpDwUMlgchEfDjiqv3SYQoGT1Cvvydb9MTL+Bg8x81tLCpWDHjDljPR0Nu - x2tcDS5eUpQemjM5fb8Xk90k0VtrPvNJdcDFXMRnPKEz1wKqnSW9F237pSEvlj9kb8a2L73fjwKm - h/pM7+4p7Ld0Y51Bc5yO1HHdrzmMleFBZesiYozSx1wCW2KwHF4bUrS9WFFXpxYcPsuZOoHt+f+2 - jByNUaKOmb0BK5AywFrcmtRLHmYu4kXsED0veIJG2AKmfN12V/r+PElk4Mlc4aGBX4Y0Gl7tT8/K - 24sh7+X4FIvNq+fuyV5glXvnic133LO6rt5ocAUPI2mI8+UzPjG0H6Mx7Qr5BSRjOQjATZFFPOnG - Ob+8Lg3aztaDpFUV/tnrG+bUEsi1eW39mZ7ub5iHAyP7DET+ZOrvN7RE40xC7y7mS/KrUwQSa2VM - Oa2WMd7GyFReZxpmowVEOyKW0mlNR+xGbcB87KgK2oreib8dzGrRb+QKg4Hs6d4jP3O9vwpovkI2 - CeJcJcz7qgqMVNsnoX1c+vkxPyU4PMyFYP1W9nO+KToYKllA14r4RL656gRP6IDxxEONy5swieET - QotarmMkqz00yAl7hZrDOegHOzrYaw/DgVxZnvvzPIdvqLe7gR6C0DBZN11LZGxtPikXc+A/8iEG - tKtNMM2PdQvMnA3BOuMmIunnQMAWbkMDfkBxIM7424FRXH4YPqdfQbLPGHBuB+gOEw3F5OIYZTKB - U2ugtyn7E6hDMWd/96sKZEbM1u84rV5HFe0PB4H6+YCB9DRFA+Jy6qllimnPuil6gwc+jQSjg82l - 3960wa3JPzTs67BaJOAPf/GX+iD6gDnjmoOawMH0Lga9v25HWlABvy6x+l0E5PeiO2hrKibRH8NQ - cR/9RNReQESCstJ9WXUPHlSqmZKw2Bf5UCBlgpGXX/C2D8peuujXAPZUvk6S3IRAjMNIgK50iYkn - L2m+2lMKHvqbkKz4tCaDOYjg4ae4JLy+oL98N50Hf81TmTb0/Kjm7M40cPgYmFxOsDF/ZnsdYESO - Ma42v4Uz2vYa/NXWhTrKuTXHv+/H1NQN2X80GUyXkRUI311AYr59cDkLKgfJ5GcRR9PypAOnUkMh - gpgmijwDGuGgBJV3J+TWZA9fPotyjHa3aaCZlh0Bh9OOIavFHjUUwQWiGIwZVHzPos/H8WxOke8p - gGrrFHI9DXP5D39k+yym/iY/9OL0WRpUBVtGI12fOC+HMkZUce7TVt99Kvbb2h7EuD3Rx3TGgNvS - J4ZzXEXENRMPLHEDVPAZHtmfv+vbTYM0pKimQv3loJrLsfbvEHyeE96GepdQlr49tET5TPAsarlc - SvYZPmvyIpoesJ6/F90Dj6BLaDHrV3PZP/I3Kn13plbXL/6/+4N+KKH3OXP5llCnRbJ8vE+pKYrV - cukdTZ3t0SL2cS+aSy2ue/XSjhLSC7W/NNHx/t/3mycPk6e0yOC4eRgkqE5vn/0KQQO6Fhk0u30F - c9yzzoA7Gk/UrjxQLVqla0h/dRHRbhpMmDq8yz97n6Rbc0uGB24NsP1ER3I/zL3PDpCm4JC0Gklu - 4JUP0YFZAASiSR+zw/yW9cl9t/eiJ57xrfHn+ngydoftdCJr/K34Kb62cFrIE8thY1TyfMkaKNza - Az2LG9z3Rt+/kddoNrV+8bGXl8zUUK7uPHLIgcD79e+R+7zp9HR4fAErvm39d1+p91OmfgT34A6v - 90EhOjtcAD9GEIIV/0zd0FHe/8WPnzG5k/iL52ow9/sUNrdeJH57PfvL97ScoQcjQhJk8Gr4+ecz - jMGNTOguP/t5b7kLfIhRj4GDbSBd9AjD7Ww/qCG8A1NM7xcVKvvGmLbV/ZSzuDsPsH/6R6LPp3O1 - wJfwVkVm5tTEmpiz4bdhf/GFXtxc43I4qA0sTt1uxUetz57dTYPN7SdOsnBXQFeLbQaGrD1O8k7S - qm3vxQviERuIfQ2vfB5CH4MlPDt0bx7qnA7R2QCn4XagcXnZgkG+fD3AZt/4h/+G4lvWyJ5yd0rB - ZeezZq34eEzaa8K3cMhnM50NdMq1HXVyPPWLQ9IBwRJOWBTguxr0E2zg42AfiS7tFDCz6WGB9vGI - qX0NFTAnUJWg3oKBGKy1c+kO3i3ojvFM9oT7OX/nOwP+Xl9EHb59V3TXtAXMHW8if/FEYrndwHHz - NKhnBjEfcYAL4AfwjL8Bz5I5uysGsC5vk3g4vVTtit9AN1y31P1t7IrfePcGGdJ+1A+/32QKfgzC - 50vUSLZmhETW5wX8RYRiQfpkOVsO1lu9WZeIWhbS+q2xL0qwxIpP/E+T8u3qL+Fp3nhYDriaU6ed - 7rA9hC6xvhM16SOFdxh/XJseGo7BuGdvDZKYa9Rwqs7nYhAEqn31NRL2Nx/Mv+utgUmDHeL/Zqli - cVcMoFrsLwmXF6uW7y/W/vgOnpbsWjEkAgnqS3qht1JS+XQZlQIKd7PF8rP3/FmkjKk/NS3Jvv3o - 6x5gVYPn93lPQ67O/vyw5BqWzhLRFV/5Mw7wHcQ42JL1efW/w1Nd/vAfFsJ4kyzmycPwUTU1Nvj2 - AWazjSbgMh6teLqoxuUxSvCyZS3VkqPij1M8WICILib55eznMjBu77//h+mbaP4cumcNgV16pvYt - v1T0LtQRjD++TSx7lqtl1uYYPr+ZQY8Y18mw9xqM4JJCsv8JL8DtKmyg/2gfFPtO3bOn9sJgmzkB - 8c7bOx8U9ZVCY5J+ZH9NGeBNktjQUtQjwcby8pmi/lIYyZ+ZmoVtmgOXHhhcA22eFJYDf3q0hgDb - HfpQd4C/albQG0IYxV+iDSb2RTtzJqho04HiN4yS1T40uH9KL+qPZ83/1ceHBjf3rCJheLvkK35k - 8Pf6IKKfzj+/290+wTpVdJpk2Oq8v4QbFYiGrJO9R1xztY8ODljcE+ux8c35G7MJoU11ogTch3wc - qySF1LYOpAjjTT4ncBGhJRQJ7sWiBlyHMUNfrHZ4W3bpWgpzj//u2yRMa0W1dfkVkIobQHVo1JxL - x9H+x99Ppzftp7cy1jCvfzpd+STnp1wY4IqPiK/87KrdLpEhlDiNqf2CkHP/e5ygLfbJv3jPp5Og - QuMnTGSvlXtTHprGA4IDNRpsUtqz9P5UwLqlgyQzHU1OtqYKM7n4EvLR9Vx8PhoB9ObFnj7yfpMz - c/40gOfBDwvm9ZbzyXhieFZqTLDm62Ap72INwnutkaeAw4odvfKN/vicd/sW5hyw0xVuHlZH7940 - mZPg3Vr4FgObeACd/WkWiQNikykk3w5mL3n9fkBWtU5pfT6rinsTOUPoDTV9GDbjwwNoMVr9GV3P - m7Pg4rfgD88lxhiCmVmfGF6wE+EZtg//Jx+9AZjfLSHGr6+Sud1EAgo8SZ6UfY0q6vrlgIjoY6ym - JuCjrKpvaO6Z+udP/AU4CIJvNkj0IujvZH4IrgprY5mwKqg7n6uJwyAfGpnixxWbLDooNtq52ucf - H+Y/JxjUs9Jg4n7DT7LI39qAV2EOie6Z74qmtLhC52JXuL7mLR835bVGkr7UJPzLMAX2p4E7Gk00 - +4wDYH/x0hs3P7yQcuCLBMwBrludiPn3fU7vVgB33U3pmUZezv7iy6pvUH21Z/ZnP0wyEMF9zPn8 - ba5XELxmhx7vRQgYFYAD7enm4t3rZ5pzN2cOTENn7ZF3BMDup0cnR/XbJe7ugTkbPLcBt+b2mbbH - S8i3Xgff6tnoj8T4gKu//dN7YJ71FGuNkkzZc+7Q5WO/yJ//4cWINCho5EECHwacke1oQ+Vtn8iB - vROT00m+w1m45MT5OGnPV3yu6g/+JebF1fxtAlUR/OET3S52/tLYTwms/HUSL4+iWliOa/gzBpfe - x074e94Z9K/Lm7rfFnDuvpiI7EOxm6Th15tsE9UqvLjRiQY/y05kfDfTPz5GsXD6VDMO7PvfeTCK - 0a6fd1pSoPh2VrH8IkHPjmjd4ofPGnkGH+r/2s0KgC6CQzAPSz41za+G7WUXUX8n73O53acxVMLX - h0S5buZiRiCDde1B6jzrxeSKqjlIoMpKMz29YpXiBzAL+olY5AD8zh8FBx4Iv1Bt1RPGh7Vp1PX1 - f897WUpaqNstvJKUqNXKZ4cUhPTXErKAgznfqrGDj8l4refbV4tD7hMsHRZRXx+ynDvQdtDqj4m2 - v8T5JB9zFSoHVGGobby1Z1GxoS4rNxK+tm4v63EngHqzORDrefWrJkDXDp5yY7f2wEt8gVNZwz8+ - ogXpKZ9/CmFgxRvTol68fDzKR0HdvveM7pf0k/+scVeD5wNdiHvvd+ZyDEEKHU3KqBMdfc4isBeg - +U5CvNRvue+P6sDgys9JEckOkNv9PYbPOnzR8C5v+sHXeInmS+5TQ84OifhJYATM80MnTuEfK0lG - awVLo9kkRVPF5/M+CGDS2yYNk4/mj867CuDeRzuaH9YF7WPleeD8Tvf0cJ2+yeAqbw9leZeT8ALM - atWbIPo+65ka9bhL5rExGfTka0pPDNR8oQbWQKjOI8HibOYshycDAbHIiWEEz4Tjtd9wjf/0kIMC - UCTGLSLC15/YQThWcmHtA2hqyoZYZ/IEK18x4N6LnxhkyZQMRyRcga2SE7H214sv7pIXhHcQ7akz - /m586KxyghaTt9P3ONcJzdaKxqcIWko2X+zLj95qIL77AMMdC3O51BwNVhe0nUrBOVXzw9rUYO9v - dlSrwS+f3SMX/vRUenoeeZUXVhjARsAV8UG053N7ennojx+Hj2dj/vNHhlOXxK2MqmJJcyxhY9cB - KUjS5GxvshoNfXolqz7At7atBnCNr6v/B0k/X+IGXm7wTgPghkC+PrIMGrf4S/Z2t+n7YyRCkBU6 - nHbxnPiDFOMI+mI9UWt7LZNt28///XzphKhfbqotACCo16m60Gs+cvNaKkbhvKlpjCPoVLpNgXr5 - hCQ+kdls0Sv1oCdnKaaLU/BJvT+6v/OTI5LAf3rY+vwm9adM1bRJ5Amczp5ANUhPPmetLsEBS3uM - 2mXoZ0nxLODwWiJho/n5snZHq+roE2p9DpTz5PwQoaG220nJbywfm9uU/nc/v7jzF2Mhgnq4BQbx - 0OvaT9u374CrwEOy99cOyQFKEdi0mGLWpC5gJd2p8HbSL4TMY5uwn84iJHyKZFoM8QO+f/mNdpcU - uA2fXbX6+xqCtiMUd9bJl7SAqdDaNVdqWZ6S8E/0bsFDLwk9V6jiC8odFST2K53ARLVKLvlJhRky - ftMab8EnKm4ZhHDZYdmV5GRuT791C04trPi/rNhQ3gbgxdvPtNkqni8eCw2jjaJKlPwuNWf7sbz+ - e37xVV+SGSz3BlanOqW6Whi5BF9r73foXOl+d2zXGYnMhtvnhGlQT2++NDf5DRw7uxMLD1a/bXbM - UgeoPbHI9VPPmyS3wPtn6f/0A7ZdrgbKzvY61TvGZplUP+vvPIQIeKxGXhYReF5vJ3IY2REsUg1q - UGrmiOfnfjSHHftOED/P90lZvhX4pydc7INI8Yqv5man2DA7zGsF+XbpFx3qDfg7T0ytijNZZgrs - P3mONyTAybKRdgp8Tn0x8cKuzH77vp/VF/etf3hY+qhu9Kefk7Qpo2TRdjcPfoZnNqnyIiZzLxo1 - HI3oRw6fDCcswuMEo20QrQ36135QorlERR4AaljXNpluKoZg24kJwSej62f3CCBIRcYmwQzl/i+/ - BexQA9TcqhNYLpf2DGXoXqgXWwYXD/fZQ4Z3VMmqP/p81Rvgqm/jibfyH9/VkBcVFhasc9Wv+qgK - v9LL+PMHCV/jC0S/TUKdYzckf/YGxqqVSWwEz5yldNPAl5A4VHPforlUjuOBng86DZUo5UNfFwpY - 9UcafFLgM7tqGIwS0SB3s3GT7dN8izDYR1tqg5lWy8oXUB5OjDrOueAD06sOamCMaQpPVcIvoaxC - fk4nqhFbyhfRNep//nh/vIx85UcBPJD5QsPzu/rDwxnc9+1IQ1VOfZb+dEFNLmG+5v9+nKbQKWCw - FyBeup1hbjeeVkD7uj1Q/Jnmai409Qqd79JR7ft2+oXkqgMj+btW3PEsZ1KhnCFHkYD/9FvpU80e - 6NXNY5K+5bqFRVk/79fYEu8ptD2v5U6Db+n7Jb5nIM7kV7tAc0EpOe6Vks/z4TTAUTwLKz/L/WXY - iR16KpZLwm0y86lOiw5m8v1L3NW/boX6tMAlus2EgHuQyJswj4HelD5NvtuDyX5r3x0KHxoJNWXx - G0HU7+gmTj71sqzJl1xRMVjzR/QfH9eifID59uJMQtun1RieNQvNHBRED/O7KaeG5MGq2qtknwij - P/3Zw58/Ojz7zpyjebbRqr9PUv4t8+HzzYU/f0FCVRZNnt7sCB7vTkl07VkmjLzDK3zEwZneiMjM - UfnGIlqkn0rstvit+rXnwMv9gvAsJVq/ffvQgcr119E1fidTVUQR+qhMI8mq3y6tkbV/9r7mY17V - bLvvFv3lP4UVn7BReokwzX4C9R1xY87lTRUgcPs3cQv5xZkn+Bpc85nUuom/nAZanMLGzPtJJIfc - 3GZudEbWDdTTLMa3Sh7d9gr/4p0wi1oybziLoOWu+DrPTv605oeVFZ9Tyzvr5njeWwGSxoYSzWML - WIpLPkERuM8JehP2FyurbPgMj9GaHzr4K35vISi/HM+rvseCMWewTcwTVkC16Sd+24l/+JT6UGyT - BVkKA7eTecFgqGTAJ+OCwTOpMry7rTPP75t9Ab2DfvsP718PXgQVVVcwSj6l2a/5Zqig4EZvt53M - 31fLaaEylDHJ5f6e8PmS/ePLK/7Z8Gm972gPzyI1hofV81n3CmhvDhGewxz6cwanAHT5MFF9zXey - LmMSHGOBrXpgmnN899P/T0UB/L8rCj6NIhBPccaK64vdwMtaQ3RA88/kB9jbcDy1Gr3zN624TOwO - +ofgSfYmk/jifH8FekiM07v6PfRb7MkL3OI7pY/PW0mWUz2nMFx2b+Ie/HfFxdfrDZl814jTDjsw - q6bJkOfgeBLTrZ3LV1fP1P27HojxTfbV/KazCMnn3ZHDRez9OX+yBb1fWUHt6RmCJfhhETY4tzA+ - wrCXf8pRhYaNMG7LH/GXRoNn0CDcUNKCVzKcKktFYJf5JPiG5342um0NNnng0+BjjBXzLr4Bj8r5 - gRkRDyaranaGHMKSWvAU8CH6BjYM5Qng0n9fwTR3pgpr5XCf+L7yfdG70xqOUA9JMilOLk3Pmw0l - cWNihc9zxbTsI0FH/a497G+UsJLiBUTWOaDmZqtz+VkZDfqdjILmdkv65WLsGPrYQ0LTLN/1vD0G - NlShqhHLKceEwzdrEDgqe3pPHjyfNNcadswzv5h1v3cuteRqoLsatfT6te1kHorGgNn5ArGMVZjw - qKtrlB5fBfV/5baiIS3ucLnLGbH0/JRLuz4WUJhpMYmTXdqPu+AYIDcaAmLqxztgQYhqkO2gTD3h - Y4HxJ30K+BAsTPPtFveyhx4MetWnpudZvfpSu3gxEhYuE/cMPmC59wOESadfqD7cpmTxyVuD169v - TfJ8ZybfeemCOiQ5xLcedi/yAWiguX164jjHPuEInq5wDzOXkm0T9DJ1EgPZc7OjfudIPpsl7Q4J - NipavCyWj2U+1yjCtx0tfofc/AVWW0JZczt6BSLM51v9bsE6iJLs23Wqa3V4t+gRsI6eZrb2JEZR - BqVrX1GTSrlPy9GbUH26UuLaqVDN3I4idEEeoEHQ/fiYTvYVLXYRUv/y+fnLSSwMuJvZhkSnoQUy - bB8eTHC7p9GLJv12fX/k+Hyg+PFrfDa6QwazGwumTbojvAurLlJNkSEa4+SVLIfH6wzfXC2nSgNe - JfKBr3tfM0INo2kBHy97A/VqmtDzI3ibE5bfJRRzZlDTez348rONGj1VREnuR5EpfpvThJL3COnh - qL8q0fkgDaZ9Coi2nT/+ILdcRLEotjRUZ83c3o4PG0ZAiqlFwCWRNLN00OxCgxoGzP3tPagwut6K - HFdpHwJRLHQNvV31QkKov3IZLo8YhocyIuHqL1hbzg6sKQ7IrThHPhe/tgX9eueQ2+Wo+OugSAlV - edZM14f0SdjHOZ2RW2AXb1UjBOLrfG/h1L6uZB+NUb+1GnNCnnRocO/uv75MzPMCL8Xltu7Z3vAx - sMo3kr5xRszy2iT8eFQLsN6HdQ9XW01SWTpIrR8D3aPDHmwljs7qQNILMSFPuTjEO1EdW8cgzu7H - c55QVYJq/Rxo6Ne6L54qS/nzdxSfLjKfFeNoIz8/dNQyGyFfflUUobDMS6zg6V0xZ+c1kLPtMHH1 - /Mh5e7RstPonkjlg6pellAo4ykTE/NN+c5nRUkLNaSrx9p65ptRo4hltIN/gja9jnxu2rUKF+BI5 - 3bOfv9zvTgy/X0yx8H19+zm6JQVyKUe4L6O0l3h+VBD+GU+KDbED0zWYVWQfs7VCwvv202bOFfjc - zxpxz8k+n5PajtX3/Xojz2I+5nNRVncIL7ZIneRs+qOvWQLgt8tnYhu98am9V2pouM+IhOfsVE30 - d8Mo3ek10eP5lzP7452hMT1Tag1awiXlFUsQFldCL5rVmsvAaQen2PGpl7Zfc+6/vEDY0nL6hH3d - TzKzRFB+FHuNN0bPkllSoJdNKSW97SXyvGsh3KmPipB3AHp22m8wFKppT8N7LCdT9/xe1XTr3Mjx - HCS9vBn7DLLcK6lHH4a5GJf2qkIyX8lRLhqTX6yPiB7PaCDXpQYJ14fO+PNnxONY6CkalAn6cu4R - 52s3yVSBxkDJg8rUWf0N/3v/BgUNTbb2PWccPoe/n0myziD43Y5rDesLm3SNn/2s55GCVv9OnORc - +fPnol7heNFdmrfJJmep29qwtHhIDr/bPpeMb2hBKCfPSdWlc8XE1FFQcj9PxGnP356F92MM0evY - EicZTgkNlF4Ff/c3WOO5ZCabEk7AzNb4cOq3400v0FhKX6x4tuGLp6QM4C8u7+SZC99q9prWQk57 - CEhQZqPPemee0Ks53ulh98z45KS5CvTn40AefGh9TvOyRqs/mrZsCbgkfBOGfiXZ09B5p+Zq/zbk - insgluBVPaefckBqZVekiOMnlxQrdGAe3HfkqbtDP2NPZuj1VDB9/ES7X17fkUEjiSdCxuoFpL8e - 58W7XGnSNZnPxOpTIxNa13/xWpRSXkO+v2c067avSvR221h15f2NBrvsmMsuP7WQZKlJc6Tscp5L - TYHi68uhcepX5pz6qYdKcaNMwNCIueycckLO6etNP5+bvjg2qgXZ0n5Jbob7XH58xbtq32uRnNbz - T7fmPKDV/0w7Jze5lF+GAca96U3A4gpnkStk8ClbNcFoMnIR9wgCMFo92aOH4s8gNBWEX9SiR2t3 - 4PPy9DKklBGjz0NTgflaHixUGolPg+HwqRj3HAZXLk0v52/K6WWr2zCodieCx5ubLzu2KeGQhBVe - 9re4+vOXAHd5Oy2KDvikiG2GXOEq4spmr3z2tQACvzxQankny1ye0mWBy0t+UpOXDt/aqg/hpgwi - crx9H2vLgln+i9chvPCKnR9lilJ20Qhu5xaw73mnwb/Pa6SvU75cPrUGv+ZXnGbreMi5h04MCkP2 - I4H7mTmzD2Kr1tNRwPPl5nIxt/NaJTxMqO1tDsmW6YYGX3J2oiS+K/ngddcG/b5lSePZP+Ry8lEH - WDWfcIp1wUmWu7CUyK0wpsF9YwD5lrglXO13qshcAC5tXgp8hnZOsLZJV/+qRTDTT8oEwvHbD2/0 - vsPnZ9NT4zwUCdWHToNfI+hJzo2at+XzbaPCet6Jpykm2L7pLKEiX2Tiwmbbj+fAlv7hKQ0FNGfr - 78NsjhN6eL6qajloSQcV1Q7JwYVuzxtudqANMKfO8RXkzA0NRV390/Tp333P3NBT1VsHO3pxU9eU - jh/SAfquXphjPviDa3qL+ucfbFW0wayd0RnQl3elwaQSc3tS8Bt6S5BjOb37fBkPXwFUYiJNpXuh - nKvfdwGL36Ok1zFVc4aeVwvsXH8zIb9+mQwFXgkNw91iaVYVf9QO1xZC54lWPHrPZxcLAri19xYv - qNTyEYS+AmWF/ajXbV/94HVRA0iitcRckrifJsPM4Hr/SGikmi8tZ+kM4/D8Iuv97idRlCYYWemq - 2LomEAUDWUBw5onqj6Nf8eyhd8iXNJ0cF2lfzZ02toC+nCs95Uubt4cgWhWS42vK6glW76ZqGtBr - Z5/gRf9xPnxkDc6uYND9q+vMsQ6XGC72PaSB4R0qtrSXAt61uCRmZI75zHTPUB99E1DD7Uswi8b3 - Ci9icVnxxQcseax5kJT0OKGUBJX4E+crytapvvp6n+crurM/PkOs3ds02eEoLkD1mzs1pDmrRkeV - OmhuNhUl1zTzF1b6DKqFVRCrcWKTNRsighWfkvB7oT5b8TQ8fbf2JK94jz++8A7bIS+oeS7rir1R - V8Du+P7Rw/krcj699h4MljKg+Kbv+3/4z919c7yTrnovD5+NBtb7g3dASAFTa12AYyl+qStdX9X6 - vAx4/8Q1PdjysZ9t1RegIAWHaQFmC3j99DNIeZvRu3MFPYseJoTdsfzR1E/OnNc1FeCKv6Zdydee - OgAH6O92AcmeBcjnc4Al6AvDB4tZvqt+xueoIZZs9zR6HBKfcXgZ/vwHVof8ZP57v5PnPGjc7nbm - gJ5XG33vhkPCza2slju+xDA44+EffuAuf3QALDYjnpqc+xmaQqEKyyzjW/8DYAlub0f16HCd4JtU - 4I8P/L1ODVSWyco/GrjdsIQ6Sq/1bNgMGHiaKuPN8kqracy2JdTPrCKanI79EgenNxRGvkyKd0+T - Wc59DLe4oJOaYTXh3VFiao0PCV6Cwuvn53FXwPOiqwT/DsBfxgyVAA62gsGS1QlL9lYJO+k9TvLu - qXKuVFhR1/hB3aiY+pnKugR/PtHwpz68ehoLtfTPv/rDZ6wmMbbuSNnG4pRFwi1ngXMzdvufeiB/ - fGWRmmD6h39tU6XJSPYJhqj9mjQsVZn3B8Im1Ci3kOZBOef01Hyu8IX5iTo3bPFFaqzpH34706H2 - eeyMBiqfiFMnSs/rDE07gEViJf/4J30X0QB6V6OkyEViMreMChSUmkctamhgm+h37e/+kCz2SV85 - lSLA/BxfaLiN837VFyQwvwoXN5/Pxp/7vY5BebdbajO4z8WPoTBY3xKVHoLby2cgE0s4B3eLpCw2 - TPHTkwaWP37EPTcswI74CMHFKzDVn/Y7WT63pgBxr3skiJ9lxaiqnuHjZ3tE35NstT+uIdZcDpir - Z5TM9eMsIOn90LFQkVuy3M6qASOxlCfl8Ot6tst0Efbbt0pCZqj9dHise6/BySde5bg+t+RURMp7 - Wafk1x8+rP4eNrdvT0NAY8BnNxwg4SShoZGWJhMLVwOH7TrAIS/kfHZUoQOvd3bEUvBRwRf4pABV - evCJRybFZDnfQ8CW7ku8Z5En84WnHfJQrk/NkFr8jw9AoRr2xGU7ZHJB2xkw3Xo3glmQmDPqrQhG - 3XijIbW+FZu0M0TytPGxWrFftbi6fYaNWBr0kpNTwjQZlPBPn7oEt5e5rPgcuv5wIGneEr7YUl7A - +jWQFa/pvQz3nQL/8N9xuE35MDaLDRGfO1IUX4Uvn1RX0B+es64Rq3gSfxyYFxBMv8Z0+iVTCwjX - eIWXdrfzeXEz7hCLxP7HFyYwhC0IP3iHwWZkJv/Dg4f+o09IgW4uRmtwXfExNZ7N6A9kK9nQ2eE9 - dTnwfLa0zwKUfFU0C+jyJc12GK76GdFet1eyxLYC4dW5vMkff1xqMp3V1nwTgvMA93ynMgMteisS - 85ZM+fzZajacMokSXRcp58Q8M9Rb5Ey9onT6+Qw7C1R29qb7KwPJ/Nqer+hh2TbmT/aplvFABdj3 - 9L7i9dD8w/cgCg02gROL8+GppIG66jGEtEDPJdWSJbjeb+LP4bTuca7iP/shaX/l5vzYnWp4XkwV - T/c74fz2nvBu9T/TBGUhZ/ujYUO8uQ1k1dPAtpkvAdrumU91NGQm247jAJ+yXf/TnzosdyUs1PqG - 51qozfk6KtZu5cdEv0mvhH0U1kClf5ypu+M+EB/6a4JrPCSe7dRgUS1ZVOdQDjHfbF+82My5CiDh - V2rblwaMl6jL4M/cfYhb4xlQnaQpGq3qQEz1jPKl2IwY9rEU4gpUvB8LtHN2N7qY1ChuZyDGD6mD - x4+Yk4IF3GePOhdUcpF0jLLB5PP5dy8hdbsjtevvkPO3b3oQLdkLs/745vNmY5X/7MV6lceernwS - dt+dRFxZe5uLC14D2puzR/Xrz/cZm2sVnqj3ooGEdnwo/JeIbsOJk2zFj7K+4AYKgtpT5yJU1cin - r4V2xzqd5OfLrGQETxn8kKtMsnq6V//0uWb6UqJNVlCN30QroSZvdAztpfWZb6I7ZM3psPJrq2eV - eDJ2zfSh0+L2Jefa+6WiFU+SZOxjk3dHgcF2zqsJqt9DJSXnUwfPMgxoSo2Sj8yeGuCgQ0TIJtiA - 8R3XHvrjy5r5XGdArXrqH796vrADxDX+o2kjcWp8fxc+j+3kwL2R6dOGuyd/bjeqCkUAR+IKeZCM - LpYEYHab5wTvmzfg2vunQF1fnsQ8RlXP2SZ8g5VPk/3yNatpiFAGz2/BoYFR3sBsNf4EHn0d0KKD - M2Dv8s5AKwWQkOo28yWM7hO8LZFNY6Hg/iJYrzd8DJsT9WKfVtyN+gyUL2skwVl5VJOQ2vhPL5/Q - nx5+ic8FjG19bWX/fiv263bR7u5sLHLgxwYsz2QsAZAUlxb4ZSbiH34KXnlCDzvXqWa5owowutol - gXZiYNWnU2juUU7xZ2B8gDXs4LB5GbgtArnn4hdb8ImfNeZC21eytP0tu/33uqfXt3j1R2mKFhh0 - B5lo3c/IpZ/t1aC+T94fXwczvASGuo+sjsSZMFRjKG0K9ahKz2kXmlYu2gfYgUqebBJ0Me4ZVZcU - vuHHmgTt9+L8tgUMFImdUGOHY79b8TOs5MEmdwndVv70U0AmsIiYoOIV3T3eKdRiiFZ8rHGphd4V - /vHN7YK2fHG8nME//czNqyaff8OYwsbfGcSlya+iq/6mBgWICIaykMxH7ZXu1MqqyOH8TQGbkrqE - VrppqfGHNwX7UcM//0DvMOsXZ7QNGD/Filrj1qtE48ThP/zFj4rliyQxRSiKfUxtVWz40kXeOgPK - PuMLrjt/POAggt+DZpHDam/Syo/gqq9S4uuTOdCrZsEV/xD/6Mx/eH7556+s6uQkUrDOdFq/H/KH - D7fpOmXHbq4x9U7RoeeTkImAXlKV2P73wP/yB//49MEgR5+rlhLAm7RWlG9ejPMCiAP8y2/sL94X - zM3GCcDKX4m37c8my+2kgdbRehC3ualgOT4rG6564rTzeeWzg/g10MeeEhq8vmay9BBZMNxoZ3L6 - jSmYH3yn/cVPLHZs6aedUw6QDNIeb8fLu+Kn5ClBm9oSdUen7Kmv767gnF6Of/mPnG6vqY0AyQxi - 2eYJcPXbFaD/SkfqWNLLXPX7AsVy2hFL31fmdMeXCCaTxFa+f+TU9+c3+NOz778O+8u4VqAd8Tn9 - x4/Ecp3tF0PHpCe3VarlZYESfukuIVqWugnXrq8FXpnwJsalMpIhcqUrWj//JJdPqadzk2dA19mT - Gqlf+fz3sc5gNy8biuP4Ceb8c0rByucnFV1xP/pJVu62HSPE3Qvr6CeSnNF3N82Yrfme/h19J7jv - kwgLi+4CSXODAW6VQiFehteZPd+u+IcHLmQuk2lP9h20sepj8YtaPn9dLUN/etF1vR/c9N4FWvMD - VPvdA3Mc28aDK5+YvjnZ5gz9roFquXytMIC/fH7f6wgGj/JAUt7pidwdhQXgZ6gT3ZWziuXVK0Lv - vS1QY2+WCb0Dw4CH7fVJL9NGqejKd8HxI+UTqN9vwOv6C+He5B4G5vbDu+BnS3Dlx/iYXu58sH4w - UA83e4N7z9wBftnqFvzT/6wx7Hym1q4AfcnQSZDuU3+8/5R1ay+4rnjXq+SqjUR4n2uLaLZ6TWay - hDXYRsJCtGUYzXGbXUuwk5or0eqgN2c9vypKlrsneog81P/Tj5QyZuSQ7UyfCTxK0V/8c499zVsQ - wewf3/p73n/+DibwdfuHv6SSgUAV0/uXeJ9P2TPnszUAu79+BNN6yefKVD2weR7OePTGh7lgRCxV - v/oD3cukNnkJ6XVX2dc3DZmR9V1TGOvW5/P473yUlHWBxF3xPwAAAP//pJ3LtrIwEoUfiIHcJMmQ - u9wkKBdxBogoiiiQAHn6Xpy/hz3r8VlHJSRVe3+VpAYirJ92mN/vVoXXW6SRveD75R8fQHvX3WEz - j6JWek6oAObn9sPmlk83/6CDlj+JGItMZ1L8XgnUcnek3gOcwYSfvQLZhaswHkfE1k2/QmZLLbY8 - gTB2uKc52vIPPXyv73K8l2EDN34ZCp8IlSQ9tTM8WmqID437KlfPD2tl4y+h4N9wyVoJNXCf3u9Y - 1zuHLbGWqOhSwYga4KhtPLoxEQKWRW+zXrTz7ufJcDyJRYi+fNcuf3747/nsWD/7ZKsfgb94554G - EyxPuhdh344KWXfDNxYLR9WRrbMLGe+ez+b9vcv//ETYzJIYk7t4X0ET5ya+bvmJ0aPUQS6yvM0P - hOUcdqcc/dUvykJX2xXd4h8wdDsK30fFZmRkn37fZ/qJBjcktFMWhDw05a7F1nTR2xkUsAHG95Xh - XBTdlg2sUeEkiAM1biBkqyk0K8DkGP7jsevHg7Ui3iqItYJPfEH6UQV+vD3+p9dJzIYEymiXY0N8 - N2D588t/vNR6Sk0563IXwOJcLTi0cWqsqtE7//yQgReO0WIHFBgE15DaYbG0Y0cHD2oBsrADbQ8I - 8ttPYPfUVepgk2fEiHdPoFudRd1YPAL2jOj4/+woQP97R8Ew9gU92POzXQ4BN0KPnQwC/eM3Hq9p - akN2ZCM1Zv3UfrsfqmFYc2W4a4fOmCtv/0PtzY6oN2eBv+4PVxk42CFUb3ekZWmuOlD+xaeQJb3G - BJleC7gLggpr7nXyCS2vOQwEhztKlev7vDHAHmjfQid1e1L90eSLBpqgZViFMS2XGKhPpCrIo447 - HQZh+MYv1ItFT6335+nTpjGekOwEJeSFRY+XYro9YeVyJbbeH93gXd1e0QWqX6zpiTj8/X7odjwJ - Jdla48WMHhz0moNIuPXwHdaewg4IghXSaFZNxpr+WwDizipB5HIq15f9DeHPFQay/xwMIOr3nodf - 3PD49jkXQLqdmY6MdHemx9AjZd87pxcsk0agzjSgdkyMK4QnmrvhehlfxnK4rTVUjrZKb8Vujn/t - dGjQZ7c/03OuyOXKH/URph16kG8anhnvl32B9Kgdqf24z+1EatlTQNGZ2L3QJ5AOi1yjQM58qmX3 - Hxsfliqi7yoibFeqysRnWr5gOQ0axp729FcQBA0E+2+Ey8vFHQS9tAo4HhRrG39crr98TOBDN11s - P0IeLGYQ6+g2ppQaYpK0i7TVHr9aH9OYtI+BF2/vCqr5uND7ug4l/xYVG/Wmg/HZTIxS2nF6BJO4 - qWnKrjBe1mTOEf/ofarnSh6LUmaekSWeLZzIyaPk+wv9wcIEMXlR5c5WPu1fcM1+BlFCvvb5q7bt - eXxGFa3zwAXkyF9VaG8H+KtgvG48pJThuu2BzKcBDZOHHRM53+RHk1sX+vSwyBX80oHi8H3+DGxf - +jmc4/FKy4s9ASZ90QyFFc1U/d7ejNzKoEBn92Xh2tTSVvzCR47eNfTp+X67GMvxcx+hevjqhC8N - 7M8S2p2BWb8sfG1eb5939XCF1Y3B7b5z2Wc1C0Qo7JyQmubRAsuayAV4jq1CxLi7x6u1OBHY56NL - z0bE+wupt1uvPu0aKvJXHMhXnQt0M09nbJCzBeY1sHXk8dlKLfMzACJyeQSO/d3GAauAwawEy3DQ - qzuuK6LFfMNbOkrONaVHVjaxlD/cEcLZ6XCCJwimL6xkiMo8IkpUvNgw25wCn6uSUmunevFyM3wI - R+8rUbvTHJ9n412EnrbdQqY/38OiDMSBvavdseqDC+CXgfcQ9QyROnwIW3r4nl/w4q4RjmKmDRL/ - PZtAjg4Z9ttrXi6PfM1QvM+eZA7rjLGlcjuoWu0La29THpb8aIwIHuWAugVXl8uXSTp6ipMYSgKj - vqAZpwp0OY5DIX+Y8dLRoIASUYbwhJq+XfV7IyI/uU241k8bwV+cGa7Xq0CPYen5gs9RqOjOPcT6 - +XKNRav/FVC1Hi+sicgdBP9wVGD1Dm2ajiIa2G8eZxAUSKce6dRYkr7CipKx/4Ucvhza9ePHPCQn - vqZH6pkt/5FuK/Qwp2C9Oy1snPR+RfgcvfDf76Np7my3shc9DZo088XDdNNhObQtdfZnv5Wa17lG - s9YB7L+LHVuK5VXD87Hs8Gke7GE25UeNnKFycDnexZINWtMhf7/mW9eG0yDKhgchR4SYnsbiBxgc - nyKS46jFDreUJQOObsJtPmLzedXiZRSdAjrYIxhfnUM5KxfYQ9CSEzXb7DrQNF4CaHLT35mxF9je - X4I4azpQD5TPeFxm34au2aUY3wuvFHmHrf/WfxKGR4MnRUXg90x0+vf9FAco/LeehMsuaHluWTi0 - +9QHWr+1AdAidHJ4eHEM+294iZldTTNogv4ZRlDo2Nr0lQztqNLo0ZcHf/6LJ2vaJdQrFcAWyf4S - EEFTw5XqxL7wMxURLju8J4vrLPF86bMCtJX6wIV4if/ml4qOuzuPze9ebtf+k417k6MqDet6BGwI - PiN892aONTxExvjt2wJZuQJpECDBn32WZOCWphq9/Ig8jL2p95BxiY6vhvCL52zX9fDcrhpVKzso - lz6eAui+1124ZCE2FtNADtxxlUBzPqyGhayphy7RvCPwzrhy5R02Q3AoJBx6esVW89L1aHKPP+xL - 8QyYLn3PsLh6GPsnq2t/vmHUcOdqmBppwv2ttwQm8bMmyza/xfneZMiyKMXmFg9YXCc9PAO+xoX+ - iNvVdvgIPXEXhDM9nkqWyScOtQj72LLcthy467eBg9GE+L6TXYOGfZKj88Lqf+PLdGrkqP6YGq0e - jlvS2xno0Cr1IoyJ9vNX8djl8LX+VpyqVhJLz69loh6cZGzYNBpYIIUzXL5nhWLjeCh51Z4DtK8e - P3yx5+cgVbYdQIaBR6/BbxwWn/tA9LeeQ/k6ghX2hwLedJ+n/k3pW9bzuQ53qLQJrN9eKdwMA0Lx - kffUCSJ7EIUpeaJsqkXq09PTp1et7CDsbSfcwyaI15kdM8jO+hdbw2Vqe2wyHuU0WfAleiJ/TW1J - ge6LRhgX1m6Yx+/BROI9donyVfdsHbXKhDMPDCLkkdNO9wH8YJn9Ymwf37lPsgvbzlzdj9iDyrmc - GzmGcCi7gWZHkTFCL16NqB2m1Hw6VsuzRzQiqrwCGtbHk7HeDej86SNq7lXExqLLRYhOaYQNwW7a - 5fHYKqpPKaXYunvtL4xiCB9K/MDhkzotq8TtBlmunLB+v122TRN2Bn591BIk+IEvwOSmQOcUHYlQ - P/1WxGPNw1JLe2xyaT+snvJu/vIJzcibxHPJBTNsBkpwmN+sdnmHygsm3Opg7C/TsBpSNUPzlMo0 - CMZ9TNRfEMHvwId/42HMV/Q1lcSvVGxt4yVxt7mGeC3vBF4W2s72zTuDw8SZFDd9PSyHNOnQLggr - qt6FqBTKogrg+LUp1kpNj8VRS0z0YuODRml7LEVannIUPyKLmlK0+kwxmAMnPYShUKuJsQhPRwad - kRnYK5WSjdzUqlAL9P1xQY3TDldkvsDxVOX4T0/yVYEJeOJXgFP+NgzLnCEOfpk84qNmzgMbb0MN - UmuWCEobdRBFRcvAoaESPcRvs52tHGRg0z804BsvXqXMjEALrImWt+8IRmfrfOV3b5/6ubgfluls - JH+fF4Kyt2JB23k1PH+pSg0tasB6B1kIpUX18DGZjuVypEIHA+lcUTc++rHUjHOG+jrzsH/KnjGL - B62Cgs/bVO0Um/EieQTQy7WcbnqiFALcrvCkGxo+RtXSTqy/5RCKhzc1wOEAlj8i7sowwIHtKvH0 - AUsIP6u+EC7znyVbLkoPqaeJONz0yfJlOx2+rs0bG/6zBQwEJxUeilkKxQR45bLXxRpEH8nCzr79 - lIuN+g7KXHPEtxSbgD9Yqw6+PFdS/Tr5LUt0VAFyVo74DIMTm03+3ED6qFR6E6Z2WPd+I0LnqEVY - m6Febvoqh8yFfgjGeTWYDuRg/6cHD0hAbKBUl+GWfwkMxms8vXVrhefdd4ed3YsMTLXLEbLYzrGt - yNutnn7Dw5g6HS1Iag68c73rSu8dQoLC0jOknX6KgHpdG6zF0Afz8/vtwDaeVEXr3Viag/iCYSUi - enAWz58vyb4Aw0Rc+m+9TG/Iw/50QNg7JB2bNIU1wOe+PT4vn5c/54IRQCPk2pCR9tHOw9EMQCip - e3q+cYdYmI6wAF+mjDR8nw+tuOUHRVh3M1kOX60Us8rroNH4IQ7Fr+YLj5lEcE++DsZd+PaZ2Zx0 - GIt+Ggo/IfNn8TZVIBDDKVREWxpWvTwpyA6TrY92KA+U5XUGtSwUaH0brsMsWEsDn8ntRj3ayu08 - nTgRnsRc3vK7VK704lXw4s4RPoG75gsv0owglM2eBkVChv77SirENXCl6e6pscXeX3u46e+Qy4Mv - Y5Udhn//T+/Hxz5eh/fJQ6qg+xgrfGEMrbAoqC89RK0LidgaDMwDasMw9m/54q8f/Fz//BrWcMGX - DO9zByrc+RcK5sf/u4PIU66nj0+1+rEfiKe8nzC8KSNVP/6zXU6hJqNtfP/8GlueKLahp5tXbBeD - AJYXaQh0QntHteh4YGzByQrJVwH0L96xtMVnZUkNLnxpvw6shfvi4CR2DGuo6YclKc/5n78gff3K - Y5YNbwfUZ8H6F+9Xou1WwD9+Pj1gY/LnTzuv8GGBX7jfo2O8wuSZg5cVf7FF929juV7nBqkaMbZ4 - K/tMIgFRRhAcse0LY7lc+qcM43J3pccPv5ab3vfg97feKT4+tvXgtT9UtIlP4+JwGlb7dHYAlStC - 7V6n7SuahgaU01fDOBNNf86coEPZVIkhbQfbkKQvWhXz9bDwlp+HeeJOK0DnfIfzjFmxEKpLgYbn - u8AbfxhmqL5yaHdXFWeFy8WjZKchtHIZYpcVYclbOUgAL7Yl1uQpLdkKy1yRPScLlfizlOsYOgoc - np+Cmi46lcMBSq9/+sNp3ju2Hq6zjiQBFBhD1/LnEN5kAIqXSSO07owl9rIfwKprY5sd3mydHm8F - upJ8w9mdmn/+LkfaUjdkkG0zljplfP6Ln1qK4pLJ3qGHt3X0qCrc+WGe/WcCyVcGIRKqA+i3fA7G - gKU4lCTBJwM6mjCXSoyDaaTt8Gw+Ifxx+BnCs2b6THvWL7jXL5S6SXlvl74ybGRd4YAvWnkBQnLx - K/gewJ7IG59YF1PzUE5OOvZom7erTkQZhu+dhw/cJY3ne9Pr8EmGPFSiwmTr/m7xMH4ZEtk1fd2S - D/6t8PHE5hb/Ty3d9Dik9+xB9hDPLd2vuwR6hufgA+CzeJGfrw726JpRrTUWf6I7DSrZFSv40HPT - 1nVsNSFZdlesZ/uIMS1cXvA9eyIBmz+cE+ME0Z++P58v+3IVlraGny+McY37Nl5fnBEhchJrehC7 - Zzxy06ArSurU2Czf3TDb796Bv7uT4cNyugzzE9kvaFy9+xYPFH/WOYGDZt1Z1E6ci/HHy8AkPEx8 - cHe1L/zUQYHnqOOxPvIzW7pLVMMTyYoQLPzDZ3/+7c+fWKZ8GKb6HooQvV/Bn7+NVzbKPEButG76 - 5z2wLf7/W49vPOoG+apyAZt03prcqDpjExLO+4PzvYbStzHbGZ16B23+YeMv2rDsbpcKDiKf0ahT - OvYXD9A9jwbspuEZkHe6O8Prughh3Z4aYy73iAd2Ej9DIOmCv+VbVYn2XIv9XLwO87c5NrD08x4f - e/nRsmA39spBsk/4em9Stl7UoQDxS5OwdtNdJsTAacCp9I8YG7XhL8nFr8HvE/L4ePjQLb+vPUp+ - j4BaEojZev6dIvj8rg7WZG4xqGClKnzR+yvcJaYczyaUczCvy5tIh2pnTN3z0kE9eox0088t9RRF - BbdLfceH13Qq/+IbXIJdg0PzvMTLsnv/IO0uERE7zTHo5zOEcNPDeOOfwxioboDc9vugdtzdy9kY - 4O9Pv+E/PjM/38UP5ubhQNYfc0u+4Y86TJLXESfCope8gfcrEA/h+C+/T/fPOMLYkLdbw+dfOf7p - NQUF4E8vG7McAhtYFPxC6eVfweBm2/OvSorVI9zFX2/QIVrFlcPaz/m0y5MXQgia1iRAvqiGIDxV - GUXdkmGvQMbwzw/HTZDRW595/nJ6dDKKrNeItbxZtz7xXg3kd67h2JlzQ2gfQIbelBg42fzr4nJB - BFt3b0yQXzp/jhal+pd/SyNIy+15CDC9i0uQL/uGZCUHBWmjz2MTrN92ee4DB15XJpCGDHu2Vju3 - hpYYWfRCHz4jS3wJoeyHVjjb/liSC0wKVClWhg9Qtlryhd8C/vkVO7muYEYNF8BPxB9ofqqVdiHE - DiDmG7Tdes/7W/5UUC83NnaM0izZ9MsjUKavFz2e0WPYeHGDOvHnh7tAd9v5+6oqQCs3CTn68oaV - ltcCLHeTo6c2Cdj6lYsaHJT9SvVsP4PxXDwD6LCty8EMn/H6mNcVqjlZqId84s+SN3rAt6uI3tcH - bmcvZB2kXRph0x2FoRfJI4Qnqf4QzrhvFZ3d/Qc7sffxERCP/ek5+KennSDq2qEkRQAq5ZDR0Hsl - sdDF2hN98Hz55zdWDaj6H1/BRy0NSmnq3Axk2OBIfz4dAV1wtULRTbyNv/X+Hw+DkxEN1KrZJxY7 - jkCljuiH6nuFtEN023bYBfUFq6WO/Dkwcwf8osCm+RgcAZMzvob2eNlhW7h+jZHnhRGcgVhT3dru - bCWaNIPDBE2q/ZxDO95zz4H0ho0Qkf5oiAk15P3xU17o9vnGH08CoTEu+Hoe4o2nCjPY9BAO6rcX - L3vWZND3TgthRT7FcyOXHICJAkLudGAxXZuogdlwyTf/3Q1r0nUqfF76H/ZCvjb++Cw4Lp8YGz0C - BivJOYRhK4zhXPUtWMPgqMLzb6XUvdQfn/1aCULryg0Uf3xU0u9BMKGQqTp1+eODifXoONDIzRnr - 3fM4SI77qqAZ3zJ6fKbP7QTitQKfQd1anXPngVHqyYCt/ouq2pdn7Pk92rAqdY9wUnprl9M556GR - gBYboiEzpurBurWjcLGdgF+8XmSrArsuz6ntv+ZhzSjt4Ccc+y3fCjHhLFmHB/sBsPY6qKXAuWuN - 5N5nhL+l53LddRqPrmGJsO/SJBaVzzCCjcds8aLxl9ByMtjUs4rri1O0C3p4BAa58PrLH8Os2nII - sRYKVB/Shi0peIVoFuBKy/bHGz/t4kFYI0PF2h6PbIbqmEOWeAeM26Hzycv+BhCVRUTtD64Mkkrf - FRJ1pjS2j3tjrP00geLn8iGoopVPL/5O/6svhLs3+pbjGoQ6pPvnjZon/cCYLj3OSAYXNfxu74ve - tzO75ukib35oYO8/PpGR55uqs/oC829UEuWPrzvZ020lLt3uJMxWkZxusdVK0hr3kK9rRJbNT1Fp - jX/wwnkHrN1eWrlq9iFAbHVfVAvlis2Cp1Z/40PV54ENTX/59HBQU40GyuM0MO42V/tb3oThw5TS - YbRnegYHyTzhQtNbQF6ccUZf+qXkmOJNH/mNiJp0pRuPeQ+Mb2AAbe5+C/lfOfvr3u9FuK0X/Bff - JnGpub2WWBeyrJ4Ys4w3dEh2koLd4rx1gVQ0ESn9bhdyXMEbc/WcXn98A2toMUsB5qMItORwofqj - 5oYfZ8nqnx7E/u/IjHlSdFHmmLnV7+S2XX8ViP74C9kHoC/XiZwJ3PQ3zW+fxlhOoSvDypFHjPed - bYylHs9wOaiPTf++/cUMSh0qc+5i1SF+/G++bTxq4yO8Maaj2yuR1Y2h9JqW8l88MN24CHct1Nhy - fe1+4Li78VQXDiBeYJJu+SkzaHhxlPY9f5ZxuzO6we70e7JFiZUASmyB2Js/H8bM5qoCXZk+NHzN - kbHw+5ODLCHMQpHl2rCY0RdCKtcEO79KN/iuPCtIW6oGpzv1F7O8Wte/7wuXze8vgA9U0DXxgr2v - egVz8XQVaKeXU9iBYi7ZrcwVZdMXG98a2mX7fYonFA+Ku9Ay1lR6zJA/VHirb1QD8Ytd/sfjqV0M - KZtLvVyhKjFEnfdBKgnRdjOoPkgm3Cmnm0uyRuiFwMce8sPtBGfxg1zDrdih4ATWjT/CXXJWSCwm - fLvwp6MHJ5mcyCL7aBufNURvdzuj5e0JW++7yYHjziDYLTgunruLAdGcrA518nYxqHyemn/8bxtP - wNyiD/Z/+uGPR81//umv3sQT9BkmqgAVDlo+41SXVEMKJHtGO64WqLPXMGMp/kVwX7U/Ij2RNSxr - UDSQ/Q52uLxTPiat8M6hh6GCS+95aJdL/1NA/YItdi6m5vNdSEy41Xv+6W1Ru3gclCYRUtW54Zan - Q17B4hFMZN/E35g5Zz2DlnLosL2DH/Bg/W27czaw6FUJvz67X6YeCmdewGUmnP3FetkQgMeLUb17 - Tu1aZtcVXt1mon/+Yq5jFqDdEPekx7fYn7Grz2DLx7iwVxxPxkcJ4NXrKuqHrI3nWpcqqB3tnnzC - dSnZmB47ZdPf+Ah29sDn+8IBobk2WE3PyzCymzYj5OBth2NlbvWAKoN/8T8+8h9/1oYIokMLTGxs - 9XPRAMMIxZs70E3/A2mbr6hGmkr//OgrWpQabv4Vq2GdAbHauRXc1nc4bnyQyGJmw+560ql+4z7l - nH6U/2tHgcD/7y0FgFyKsHOFoh33vVYDqt2O4WzNYSxyh3sIpf5zouH4nhk7WTkHHOnVY+c5+i3v - DLKHemh71D4p5jBlL64H/qdxsc5PPGPqK82hPD8NjGOIjFWM7UopcmJjPd/PPlOOpwbdtWtLvVZ4 - lEvCySZsyRXj8L6cAKUffoZCNp9wfn7IPqNK70HVvx5oVfo3MAcEyvBy8xg9Jg8RLEof2YAa3wSH - 0TL6tI74EPGTxpGlbbeLrp+UwPb6K7H+Y2vJhvdBQb1UJmT2IIhX5agryFBzi9o76wvWURp/oGPF - kcarMbVzHHkFtAMtxcft5rp+1zZntFfGPeGvd9Nfzb0oQx9GCNfF2wRiZ4oZ+uSPDz0Ue9LOhwcK - lUJU3jTwn2O7SO82QNjWKfZWMLPFUpQZIF440fNl0sGsvqCKOtnVaFSBA+BHTS+gdysjrFbgwNj+ - 8xTRbXgp9PYxXmDk2zUA1ly+yFUcyoGPAOxgfMl/NK/NHaBH+RRCb8Au1l6+YfDlugZwchjGLvo9 - jVnJ+hd8/UaMr3tdLNklZjp6tK2PrbLujaUfGh6B66vDB7zfkBU32+iYzRX2vr89aMM96NABnHrq - 8Yfa5//eD/TSlR5t8zyI7d610W3aUAD1e7B+bEcE/uH4oE78AQOrBbVCzfl+Ifsx1lrRFVKCxDov - cA3e2SCKqh/CmuMFejt4mSG1e/qC5dYo1x4u4cCL5Li1JzOfNPoJniHmHe0hP3IqvfHoU5J9Pczw - atU+dY5yAPi6MCKUpIjSyBUvYL3ZxQp8hveEfYzDwE/dXoXhu3fpscerMX2Li6hIpz7DXjejcg59 - 4KG/+W0pwTfmH+9t/O7mjV6m3XYNZkxH2CkXJ5TWz5dN1WXSIcdBFyflkgzsJ0YKuuaqTNNecIAw - yf0TUrH50vvxZRjrXpdNCFt2C0d7/4jnx7vvIPI1lZ5LTzNWPZYLRB8SI+JegfHv6f10mO/FK1Uv - B8II498iwrJiEv6YzAMJ3vdA0Xp7wnrpPQymkTyBX2XA2L7zNhOMNHBAsGtkWn2yU8xL8l2ExmU7 - NKpL7rDKft9A+/I9YK1QBrDcUNzDJoojsof3ZFg0lcvhGd9k6j6LehDwMheoriuMa136Dsu7MRqk - z9YN27nyG0S+DTiYu75PVeFM4vmTdzw89d4FX8UBDGsF7zO8F8IRH6Qo9XneqQv4BrMRzhO1W76R - 3Br+1tMVuxeklKw5Zy907782PhYpHkSvNAs0n1sNa0QtSyH63nn42S0eVZ+0aek236DlsAKrQLsZ - M9HHFaKthB+fsQNW9fyWoZ9qBQGqEJf8fv/LAMr6FfuPU8v+Pd+/+XluFEZkv3/CO30aNOZCzeej - fslQb9YagS6PYsa3cYdC+DHD7xd2YP487pGiWU8Vm7uD1opz44Sg6pMdNvexw8bvdZ+hzxTc8Nll - arsmkdRAwP30EKa7GfCqq2YISMwKwXyJY6kApo4GvdOwxvGKQYq7RuCJHs74dPombG4/OgfLz7Bg - Z4sH7OhXT5hxMKGnl2/40qi2M/JL+UKv9/tzYGfcrujyOlCML1dQTtDqAvQTIcYXbdeV09muTXA9 - 9Oft+QuwvgIzhyNNXxQ78FKyYycn//KHNt2uw/rOLgHa8XlAy5evxotpDGdYNkQk8mU7RPqBzEav - /NjQ0+5SMunDRh3m664i1vrVBqGZUg/9ra9DsAviv/W5PymTSdNc8/xFMX1ReURcjdWHtsRL9L2L - 8IcTi8a58WBsuXARhL++wNp8Ef15t4s5eI3PJvksDzsWl8vnB0UfDNiKQVOKdHnYUGxdTO/ij4tf - uYxWUHjKEVtyXfijN50bCI6Qx9v6ZdL6+/bS45c7NC7StWT3dlcBL6tTig/HPiaai2r4XAuZam7r - l/NrSWeg9CCnWzxtGUJniD7L7oyPfnRvF9k/yaiNPheqNeQFWJQtPHJsUIRiwj/85aUkIrxalU/j - S5kC6cYQB5O7zsgLwcSXfCkcYQrIlWhr/zHmXEYzTB3JJLvk5fozQkuHmFveQmZ4OZsP6KQqxf2r - 0WBhadsfk6FTnBWs9LCaji++j92KlDU1sHXUXv78ewUOsDq0D0dj92Jf+7KoaHfPaAgUcgQSvQcZ - tK62jo9BeYzZMPU11EVY4DPiBjCpYtsgSzo/CD++41ZCP/qCjFR7enasJV5Gz62hQzxEj0vz9Vf/ - XEK4ZwLElywyYh49bzO8CfobWw/hCdZJk54gz5BJS8/+xT/BYT36sjHB+V56+4Lwcnh4/F5cGnuj - 5vPq9KpQ2YwijmidloLzNER4j+UzLbpj2Upt6tcgCKVfyOWSMMzroJ4V/pThcFoa1xfZIBRQfskn - mlWXntEyxNsWAsGhh8/HYOvp29XwL39iosX+6grpiML08sB6QODAXsavg1d1SXEkBx5YuaGt4G3o - FHxQvKXd4sULnEF+ppYaE8Z2xSn8y184+EVHIGraUQdGsw4YX2fR/7yM3ws+qejRC3wEsXiA7YqI - bDzpX35f3iYdoRYOOlXJ2YmXrLIDcCj2OnX48dI+A9nr9s01SXBWf/fxcv49E9hEpyhsBGE2yGrb - M/zhzMIqfn2MYYw/OvyVmoBza23LufWpA448BKGy+9q+IEqBDYfrE1JdP8kxE8RaVABJC7IgayiX - yAQjPLHZpVecUJ/BrY1HqOQo7Af0HNg7e/PQLdyUhm5olpueecESJh98uD+vjAakjiByxD3WPxlv - TJn0VOF5TUNsqXHIJmycMuQaQYH12JKMdbeVsGpOFKiaXu/DXD2uGSIR21ETHq2SPz+GCGz5E5t5 - IraLr74qqDbVi6Zl7fgMmA8T8bvnRMan9yrZ+nv0SJqiA1kMusZEp6MJhWPWhYt6TkpaQZKDP/1w - u3AJWJWjJ4P3Xfri4G2qjPeKjwfFBjnYS0TgU6VWCkhvJhee9VMeL32OaliShlDd7Jk/67c3B8I6 - f9NLH9F4aZy9CqXX0aDuDANAPLEPQezPMrXf3128lGAfgVzVXbJDtuEv7pj94GLlGXa/V8uQuqZZ - 0cPw3tiLfxNYJirbsJLpO/yc2dtfzeCXQetYRfSqFQGTggoV8F5IR2w/2pQtx9uzh0v7Eqht0cVY - z78Zoro9mvRu3a/+3/yDT43+sHtYDvFy50QVWvP1RXVaoYEl4wOi84nrCb9C4q+Ze+3g9nupZ+qw - ZIvJdGhzyo/8zednvi9V5U8PISxvAPbubsfRXk/C7o97yaw7DMAQ3eUw3eLTondvHd5Po4/Pl+kJ - FuMGO8DLw0ztjzKX4w3PW2PCfY1j2xTiSXoPAeTUu0FDeHwODNlb2xHup2OPnN/tYCSZDHdZPBCl - m2/xjND+Bc24V2n+Nrp4WflLAOuHjqgjIqeVFlbY4PxqU6x59tGfQ585EPKmRf1WPQ/sOi/hv3gX - j1IzrGmx1sgqzy7V16wwWDOthXKnjYG97OWVM86OEBy8pqJ/72uSqajD+B20NI8/ZfuTtOkFou+w - tRmYQsB+BTLh7gcfOKsuDqN/z0Mzx8Cntr34wlG3efi7k4HsyCth89/8N0Pk0i2/x6x+pjYgIT6F - SpUbsbCsUIRA9WAo6xHwF/t7VeDwBU/sOQe7lLyXUSFPFAHWI9a148MzV1idchOH3J35dNMTgF2J - RYSl5v21a84O4HfNRDf9M8wfXv/96Y1t/uz9ZUqqCEbmhyeysB3a5p5MR866X7FKzn1J6PIwUXqO - KE7MDpTzVXHO8NGHGnX65jWs6y9W0T//k+BN9tyPKsT36houiUoHUhiTArPA3NF4GFc2/+k1vfuu - WN+nTbtY95qDM+ZiGtrJhy3x1a3hu/HOZAluH7DNLxWEajeF63NywFIfugY+telHOKPwSyGIPwSm - mGhEukVuvD69pw4JJ99pssLQmO8CB6FZdIB8L0iJiZKCDE776oZVPZ79RaacDq4nF1BrB3KDZW4T - ws89f9Ead3W8hIG2gt1uSkP+df/57GRF3L/xiBt3KJd3zyCMZzEleViSdt4HUw+rih6p9Tg/jCUe - AwLU528ga9znrH8pdrgf/MkIl4aYbPkgjcA3gh6+LY1r8G+QjzAm6Rv75e0HZtn3z1COjiURKH0b - 351AfyC62C09+lj1xeH1KNDf/NTel8ifq/DNgWXJCY2xTHwWmXYF8hVVYZU912GJjqO3Nbr2Q840 - RDBVIewQLlhL8Re4Le+7YQQ2/YOt9rUMK82O0Z+/w1VtKTEpv8kT5eUk43Af27605U/o3T8XHHAP - D8wPctLRow80bGou8ld5DnpYxS8f329HPp7ypDnDfe8EGH+0o788iPSEKS9VFG/rg53xMMMGy1+s - mvrdWPjiqMCgs0KiREcM1rz79LBrQE0t7/ctp794G7T7Az5q5aH8F9/ew73E1umHy+3zG7Qrhj2p - Nn+39JGlI4nWIT5olTksr3QmYFR2WSio4tzS3amf4au4fkIUepW/6e0CfrAFse3YPpjvggihkjgv - HG36l9x35g8ehzaix1/TGuPZxpVS7brnX3xrl/1e5iBqzBu+4L0ziOTTeUAWzhK1ujo0mJJpHPjT - a1r1/WtL4dqKe9mF2KfkwObW347h1DWmmz5vhTqqOXBsggO9wjvfzlwEFYh46RTOxzjz+03fQ/11 - G8hz8ibjX77f/Cx2DO4Xb/PzB9/Jswn5eliMP3+shIdHj1PpPLL5fip4eG1/P+zXl088l6sSwi8j - Sbhzrukg3QarQRvPIPvNXzHB1c/KZ0Hnf/F9VXe/F1TWi0EP/gmyX1qsFRTTS4Y1+xuyX4X9AK78 - 7xuid5oOVFmE/i+/Yl94qkB4L2O+/8g8plp2ato1DKJ8S0yfkLsMTilRpfHQlFw1bB6TqP37flgc - zYQ6W7ykhi7qkMHziu0oSGOiior3z58EG88ajb22gvLzXaha5Z3f/0a+U77KF4eCdA4AU5TOhh9Z - xCH8RRMjO4GLQH7uRAJppQAyaVKDYu53pAfGMjZbXz+Cf/rPKOTEWB/xuUIjr5bY2tbrGM73M0TZ - b93er8JWC7gF3PwNQZ/OHniYexD86VHjW+glv09rE27bO7GuEbGdH+Sqgl/3/eDb1X22yxbvwEvM - NOzrasMYu6ozkveDFZpGERr0rawm+JtvxtIMgOW+/kKZ5Znka4yo7GhmRfD3ujc4gKwcaOqkI/yJ - HMZYEgYwtLoTwrttX8NdnT/YetTTGr7No0zNaCFsHdzjSxnM2dkuNTqUS6YWT3jzwwSHj7fHpNMj - +EGbk380iC0+flQh36E1iQzsEn2O6WIyFX3uxQvrm9+eB3cV//wf1m68U/IzyH7Q2a+UGtv2ozWo - yPrHW7DTralBtngHSbRdO30pmU/TdmlQb1Yavbz3ydAn7bFX6p+thez+2MX0zGYVytx1TwTnmseM - RVHy5wexLe7U+Bvu6wSeHnGBVWsm8YryJoSrgUqyv4VLS6DF84jAMcdne2/6UnaOfuj1I5gez+sB - LF/uZSI3SCsi7xMjlkT1JKKgO4Tb+1HZxicTuJu8HrvdvW9nxRACmL2fv3/rZ1UWqsJnlje02H07 - n/7aqVHED2/Rq7kd8s2ImaGNN2JHLDo2vY9OBL/F+Utdtuz+8hsH16zyaCy6F2Nwr/LvL16Eh+Vh - lwJ/utVwd3pK+GDun4CeQq2A6l4JKQZnzxemWzBCef+1qPNJRVacLC+BrLZjwl8ct2RPmqxIS98c - du93fWD7z08E+t57EHkvWf7KPYEK//6uB34BZutrROCUDj023mkeM1OxnvB8gj3Nc/nMlpG8O8X0 - wpT6vrA3iLVG8B8fOXJLYzAA5g4Fp6ygxv2xK9d2zn97ha4pNQfoGMwCuQ5iWNk4+YU5Wx8S1yn0 - HlskgVsbmFTsvH/5eKfQPmbyLdfhq5XOWHsrJ8Bu9iMHAyxO1J57a/MH1IOOvS82/TgZQ9UoBHho - EbHWqsKWP3bNH8/5F882vhfCP36ix70MlmsNbHl7fyG3XUzKD+f2DIbUI3TLF2x5mx8COCWOsf42 - 22Ge6LcG8Ttsqa22pr9Mt4DAbT7h41++KZvRBM3FOdJq3S5xuTGBU9J1SMny6bdG8jcmw7dvClTL - eD0W3iYlSlioF5oudWKshn/ngVSwHFszOJfTwytz+OqMKz6c+ku7xk+1Adv6D7+XQm6X62d4wkQL - AflKomv8wtt+K1E9CxoO8F3+8W745183HmHMxl6blfbgjjjceNwkkvIH//JROOvUXzP14UCr2+1p - 8NnH/jqUMgQbH/wvXxrKX6ZA8RwQzjQyJvzx201/hNDyV3/508sbDyH77/VtrNwwVPBl9Yye84yU - 7ClbIRgXPsEOQuFAqHJ9Qun0y4gkRakxwa9SAeUWd9heFTGeYe5x//hlKM+veAkTJVQeVYcJiJXW - /6tHKJlsXcisvh7lciH7Fxi9wMNZGQB/40MKqtn7R53JxWBmTzqCEwQ+VgvNj8XsfTwDT2N3skt/ - 4zAbn14B6b4542jz82tWVTx434UvUV71COarokbwT3//xVuSqQ8PBfIA/njpMFAj7+H2faEo7po/ - fhdB7ipPm/6zjOUE/QC6teiHMu+mgA0vWYcfY95uoyTPbQvs0sDpHCh0qz/489t0wz+eizNJvbPl - jxfc9gULORI3xvIXH6RYvIXiNh/J9bN6f/oZJ5s+WNITyOHDykYavD/6wL9NLdwbPg1DRRCmeNZU - ugJodRzh7+GZreZ+a5M1H270jz+P9UHVwcazwi5TiUGuQZ3Ae1ftwv3md/nQ8AgUJEciygf82Lrx - WSjZ+ovqx/3PX3+ifoZpUok4MPu5XGI0vUAKxiu+7btzO3GcGqI/HoA4fAN961Nvf3aeiEghAT75 - i09bPMCHOgvAOIP6BzmOc/+tR1YLTiW3pfjEvt4+25nefxzs3K/yT89K/A6f4cYjsJVrP+P7UhIe - nunaEHFVxPLfetr4E3Xehh0zhAoOFDa+4CNINLYiZGbQLfw0nKPj2v69f/Snp5Qtf65mel5RVGs1 - vZvx6C8dC2eoKUNB0Cjf4lWno/3Hw7BeW8ifhfM1A9whUXHlWKSlG88DsK9jIoJ+GMjGw4B3u0bU - +PO7wullAuGZG9h4yIpPlu2I1+YXqdNwMF5S14Ogv+iEhs7wbBm8vDKwFK6Hsf6GbR/zpPnjG9gU - r2O8lsftCMAARXqosxEsfuVE+/jI7UJl4yurO04RvMrXGh/SSWt5Xoht9IVChm98YbXCx1pD1Ebv - C/aieRloGR6yveI813/vl0nxzYaHh8SF8n2l/vJBLvnzA6Tnmihmj+r6BDPxVuoWSWow+2LMkAhS - SZ4ua4Y1TB0ebvVCGuT7KV7Bun/CI3dAmx7XB/5BTipyPqH+X17wve4TKEsVh0833onFP96hveUO - a4ofgn/54yJ2dSis30e7/sWbVJPdrf7G+e3GF9DFe9nYfwU7tj5p6MDbnFLsRKsbi/a3adBWLyMr - 95jaHhxnB+2MkcPOR5YHGuovEwah8MPG3oiHRT1fzjDp6xq7fSgNYz+cA5QYVx3HXWOxd2SyEbnt - W6WadnuwNRnLBExlCnFwmRpjnvxbCPqmuuKjmRpsXe1wVtLyuuAjbLA/v9KnDa193WMPv17lvOkl - uOOL4E9vxTMNdh00hPZOffjs2J/+/Me/s00vCd0ltqH/3o7k4QT7Cz3aIUxa1tPw6YzDYhpthFQ9 - snCUx9D4Ns6iwy81biG/OzzaGddcAb+quCNibUvtLNd2gyLzzW9tZ18G4/hnA00vSHGyj3vG7FWq - INEVh7zDeWXrvWiavWWWAT6ydT9MRmo68DPoGfa8awb+eDFwjbDAphse/PVICwdOUpThavJ+5cy3 - SgjXryrRW49Xf87K3QsWQ8SogxBpF7zMOfoUlBAoe1HJnOH0RLJfdRSf58hfiM5ygPDCh5RSvZT6 - p+Egrk1y6le2GC/gLj3/nvdffmYx54ng/9lSIPzvLQXadWdR67pvWra/6QRcF9GjKjUtg5fN7xka - fnymzjPPja1VPAc7yUjC9ccbsbS/2h06xTOh5kj3fq8PnQP0gxyHRRtoTFomfwYlVn4YH/HFECot - TyBoKgfrsCblKEN/hsUJ3Qis2skfk/ejgd+8sAm3YhBT51i94LW629spz2mYP2W/wnxDqqdCmYdV - uCszfDSiQbV7kILFJ6ECEn/qsFvldJjV4/UHk0vuUY+UL8bG88OGAm4ovi/GxCZucTr0BqYXkkjr - jPVR8Ssa9HONDXVmwxK+2Qq54O3S+DcFbC7Tvoa7GAshfFZ3f5E1osDDmsTYj8BuWPN5z4HHNH3x - XVcTnyeVUCOXJBqtpFMbM+dzWGHXyRpW98s5ZhjFFQz6/IT1e0rZOj6RvZfeAUdvQtv47LYOAdKt - GdJqNIWS3XcPCG6VMWL7GvHxLETbRvpQIdSBYu135uETgFi+KSGv7o+DBJ1MR9Mx9qi/g69y8ZW+ - hlx2+uLgZulM+B6QqFS/2QwRevOA/FIuAQ9tzzC2jnop1rHiQTE1thJta8brxVNV9PCrHWFHMQbz - aZOw1vgzqRHgjzEfm5wgreAO1InqCojlHm5bFOQzPSRSPogJP6zoqe6yEJKbUzL1duvh+36OiXC4 - 4ZKv68GETWdBwvWZG0tz/6yR86ZnHKDRL4V37oYQ6I1Ci9rJ4nWoyxm6DuTCxU4rwGfk5cFkPvTU - 42jNFqktEri/Wym9bvOBuJIqQ+0iEOrs7togjk9kQn46WzR7dpaxNJfrCPN1/mAtujtAUOdRhVHg - q8d9Wi6AhG4YgMupKqhmHAxfEr5XDvmgsnDKX7pWep1oBO+ueqOlNzsG76DoPyxdydayOhB8IBYy - aTpLJhGZoqCIOwYHBkWGhOHp7+H779KjR5J0p6q6gPQLr/H0JQ3nznRhFgX3c9qReL6rqaj/khbc - vGfs8hFNxC95zyFlY5+Y/6ChTklyNIG3lTs5BFRYpgF9CngchJLF18JKJ5vMCfaJBlRw1ZWSy/4D - m4cc07E/ZuG0Ed47vJErmV3b5wZV3vTJ4JQeOaKletuxR36+4MtktOSpHKOFx7AEgLRixzyVO5VS - lbgU0EdGRI3Ma/frpb0L12+lkUdkN+V3MiXtb3zstJ3mcETH7whlEZZ093fOMM3wA1hW1MSQ+qhc - NkX6ArsUY0q13ysVBN6Wd050WOX+hYazqOkZIt4+J/H0TZ2JP154SA4hIsb1oaaSs1cfWPDuMdEK - xQqlfO586Of6R9RcX9Jx+5lk3KqHig5ebjgi43MbunOY+XjdT9LYtI/dmg/+2QuS7hMeMYU6c9Zz - xGRajsNr+uBDtiFEPRadzu4XN4bmvaN0el/jTjpYSwDW5YOJqRthKRj9zkeGhhQ67U81mk9wp5B6 - U8jOHD2kfPlMPiCc2wOdFanV54dzoViXyYP+eFymk+CJFkqGRmGWzP/CGZw4QV0H2J/T+pWOuy71 - sRO7d5K8zndn0U5vF7T3Z/ElPxs7qbyWCSZPnycWe8QhbyT4A4w/UGY6zUOf8axFkP6+NjmTzuva - 5NZxoC5dTbwNldJpxT94Z98Lc665kC6TuVH+4esl7pEzGonwwc/zp/E3X+ylg6xSGbjFLIjhWmM5 - 3YopQy83zYjW4I8z6aZyAqv8Ijoq4QmtN1pfsPk+SrqdAxuJ03GK8SLvDHY7H446Oz0rG8s93zE9 - 3ETpZG5HDjtserNMbp6LGG33xu4FD5UKe7HoploYC9wfZp75ki6GU6aeIvlpbk2WufMlnBLfnWGN - P/FNtC9FNQt3u0108Zkt5veuSNLGRFY9BH5PnG86bnqNYllVBHZ9XaxSKLVBQ/VHyohum1UqxbFS - 4KrdBez5DgSd3bO3CMu1jYmquLt0gErlMX50iHj1aSrneuYsiFLQWbKNi26s/UKDY3II/WmI3v/4 - BxtyoDP/hyNnGfiJIs8qK7YPoFim5LsBuGJ98S+3PdWnH7Ur0Lf+i63r3U1Gv3OBf9cFO9T2wZlS - ecej2rOA3Wde6aTbECQ4DTSN8s9bmPITvb6AEdZTyRX0RZTMfoSV79j+4dRo2gzuA06pw9Hl9O7T - Id7b1l98abu1Zmd50ymGa7d9+F+xcDsh6XgT39WpJvtxr6bj/gsXCN4SYUZwDp0GHdkIiSJYPrrm - 15Q/kZeNgccZnZt0DCcuvAfQciojZ5rGS0+42sVF5WnEehGhnH51bWGaXjt2W6SLvsy3aYd/u3Zk - rrnVHCHYTCc8bN48uQZ9EErynFVw8OBM7llMulH4fCt43d0rC2T356x6psdwP9YkW/lamgZ9hPB7 - q9mK190fXuHmte3J/TEV3VRqtYLsuyWyx0zSUKSR1cKZPgj9y38BsKAgw81cZvm13QlHbvMAVC0V - s/tAC6labSM41cKLiuk9Wvp8Ae5v/5PbS34jtgG/B9XiR7J3l1e6vOk2ge9p4Mn9pnHdVHB6g6lN - Ijo1hbnwpNopUNhF4XP02S2zXj8KOPZuSz8XjjiSeLFMqN43kxz1d5SOVkga8KtEYaHoKqG4NPsd - XvmE7pLYRG2zWKsFUhJGyuMcLs4Dmu1hJ+ds1WfO/LM2H7DY6UHu+O0t4/miZrjepB+/PKQOmr7c - YgGvZoScOilC3d64c6BfgSdhR4puuR28FvDvmpNVPzo8fjocfJNQoWC8mD7D+5CB+HFSZg2S043v - 0bFgb4YbZhm2hsSy3sRwLpbe3yKzRnOzKBl+LlJAJ60e0vkpaiPczFEmiiJ8yv7AIwNGIWiInW6W - jnayD8j0ZUrc0Mv0qfm9A/zHP3tWduFkvQpA52LqmfPq5XRQ+MbG3O3UsctU8s5y2svcdj4u4C+O - g8I52Gcx1PNLYff7IDmTGVYunuvvQhcBGJqf3MvF+quNiB2Zajmv40c3nprEET/h2vdAlaEUX2d2 - 4PgJzc41z9CFbgLi7HsoF2t5R9juAo+oTSCmC681Gojb+4FpK/4P8r7U/u3PkcyGM2JZsoG/qzcW - M9os40NZRmiZNjFLNyVnyMoXbKMDu7KD3LGwb1/fE6z4RTP7d+7Gwr9RyOlRJ4+NNIVjOygi/sMz - 5ynnzvQM5ATK5S4zk+tuaGryA4Wf/L6zHNeXZSYeK2AbtwU5Vi2/9AU+ytt9cRCZjdvDMo/7k4G/ - TqCxwMN2Kj0oM9D+mMTkmFaXkK38hSWneDBLJVs0YzL1ePDONtGO3d6RVMsZUXOa7+wPf6eDwPN/ - eod+9l4UTu5GndFlf1qIk7yVZeQ3wge6HUb+eMVpt4h1LgJ5eDPTCqVJx2NlF5CW1o1cDtUeCZy9 - iDDojGMHUYmWufihAqyyRuT+FO/lIomnHmZXvrI7VepuTsMvD9vb7+VLA1c4y42UBpZUHPi7sybq - S/vMX7s8fBbMqcdyWa/Po3JJZQrexdZ5b/o8kIyxQlRTmrs1vgpk20VjytvlOmrqJxcdO69hqftL - 0rm97x7Q1s+aHZpdVM6bU7RDDzUZfc/LK31SA55C+3l8mVW9k+WHnq/ir/5gwUS6cAT+KiJx5i7M - tNUpHZTGrIAbNw5RULzRn3L8i4F1ZUl87mR0wtW7noDM2ZuK9voInro9ZuieZxZLiTaE8yfvAyQU - c8YOr8jV//Qu2Jb2plxlVM6orW+he2I2E6+eD6kwtKcRO8/WJ0fYyHrfDpYIRT76xNJUxZnvdu2D - Y3AD849Gk86HzUWBvqw6n+vNGC3x7mzjAqM3y3j5lE5tLPdQ/ljMHGPPEPvjtzXef/FH82N6Z3hC - s0Kub3RNpSYKdvCCTF3f6h6c6XI+x7j9ZF923rZoqZ+BHEODao8Q4bro0zMYE4xeD4udI1Mox81e - 4PDf/rDqUNVFLhsfkNzTgCmfk+3Q8ekEKOL8gZYrXy74ehkhKkuX+ExVO3E7RD1YLHjQzf2kpPxP - S2aUpMXPx8pNX6bWfdqwWNKTqRIJEdsdZB6EaUr+6evexphCAx9CnGGOu9HaXmLog0fE1u/LqUPH - CPwl4Zi/6u/5FLkcSN/oSpRzJ6Yj8LkIzElOxOMOdrds734F1/B6YYQ0rT5HW8/YtVIFzBoVP5zl - KeF39eb+YYe9/NHZgSkKuEw0mfFy/H//j1Ix1kmeree4fsS8hwZ9PWLfxcmZL0FoI2vLKVQ+7Gan - tl4twP1IEuZpr08681tlxl42Buyme15JNxvVxNOv+bGoSxRHqAX5BYHS7+im7CxnbtyFwsRHDjNl - 5dktNnQcdt1Pwg4axvqYLzzAUDQ+ubix6qzz5dFS/SKS2b+p/MXfYMa/F3em+C8fz2diwDrfv3ql - 66dLEQFf9lufu4RdOvZZfNqNgQbEx49HOAm8vYNj2L6oPB9PiGaDYv/5AUxplXtJaeAWqJbwRNS8 - /ulLcdbMv3z1xxHf0il87Pk/f4PdkFGks7AdOLQtI4NOz+yTjm/59QGmSsaKh3w6Xu4EIGavnc8+ - kVKK3CXU/vifeGd2TOdgf0n+fu/X8kHUB8McMiRXpzdRyvrc/ahC5J25BCN9r4+Is8ncaLKYf08r - 3gQdO1feAwxd1ljes1qf/BqNKDlvcqY7ThqO3Ped4FV/EhLVB8SP+jcDpokm8x2vXcYi2Qaw6m+i - bluEftf13Pu1viN6EP/K4S8f0/ghULwNWbn6ES/EtcWNaU4wLYsNJYeaRdWJ5pqD3mBZsuDz2al+ - VVRqyVs3fcTH1LiSxw+L+uiSXISjxXE+w984nOt3k4Ciejo5PzMzFF6TK8Pz54yEgOKU/PgDV262 - uUjnQrFS0RDPMc7DvPDFqE5Rb5uhDyLrv+R+7opwpoH7gkAwEPOSMES9o90MMNu+9td4LxNXfRVY - 8ZNCu55zHAjWBW7LYBBl5ZeRHLQe8C6fmV8Zhi4e0FMGFP4InZzjDQlz0YqgXzmeWT/9VNLiUOxA - 2uyeVMxyrfvkmhQg5zVXxFGur3Dss1Pwdz2fmTzRm/Zn9EDMpmW2eW1QpyADkJe0iKi7THOmnT+0 - YAzVm/hbShf2e8gXeATlnm5IYzuLu/80ON75GZWH63WZnmbEw3yyNJbOYqUvdjGO+Bs7Pz+SCyGc - jQPzIX9ae7Z3GF/ONDAKuJ4J9juieSkfXUYODb+9ShL/hrsF32MZMK9tiH9uAjRbbsDvnr/jSDLO - V/UxzA0DhGlJyJEov2VZ/T8ILJv53Nbd6kN/z2XE29qd+O971y35XLr4ShXBr563MJydfCeDLxRX - 4iI1WGb7xFt/4/ND/lrqk6iqFJbc9Fb+qv7qLXv3m83Pih+N3mIy0W09FwrT08cRjUUyBfiv/iXj - O9aFR2MHcBbVmOkf5+Ys4e2s4TW/feDlMZzy2yHG10x+Ee3uDul6dggHUfl2yWE8GEiYjaUAf77D - ipemw8fXh41O+kck5pvn0ADC84Mui6uSld/CkbwEGe9u/Z5d96LWjdHH7OG911zaVuUjHCvbort2 - so+0vh924fLKxRfw/k0g6v60XxivvRS8GRVKjv3RSv/5Oyj3Uzpt2xRNT58//eEpidrmjvgcggTP - n4/AXCH5OMNDxf/8VF+6H/Eyzps4gee5ashtPh1C/rnPDVj9X6I+s0844qcD6PeCMyHH57ebfs4E - QPWxZDnDXNcWv+WFAv9bMKXvZn36vnURq72RkcsX4YXqv6SBmznLdHb4Q8lH8yvAv2s4MM01PX20 - nj8XyfEX+yu+oDE7VxFAn57J/pCzcFL77w50XcpX/WalPC4VA+/N8+af/zw7qHD/+JZyt0lIl5Xv - 8cpf5PCS1aUduqBAkXoqmKnmHzRuj3YF7uVY+sf7cHPmTvY5tPKrXz9PgGiuSSeIfpsN05/+PZ2z - QbEgOoo92++NLJyzwVo7R9eYHWPTDqcNudp/eMhU31CQaH18BQlXXDL3bGw71o1xDHLLLkRDIIcM - Xy8zKKBKPhvETTmxe+ODyjdfH435WV9SPRXRqi+IftULpz092gRNa+fzv/04Nb9fAF1VWszZJH45 - ngdopOa5RMQh2pAuve004LpVwp7hjqQTu798LDL6ZUp/hJS5x3ZER61w/G0YMF00BgToxvfm2lmL - lsPKV9vceFREv18PqcDO2ojMwk7Z/meV5VhE2IZcu3lEhSuf9h5nabu/epcItd4JCX8vds+ix2uT - 2G83GmWqwIOLEJUMtVlmypCIFnEcWNpqP52+RQZolphNjvIQrH1Z7Rg8zZjIncdlOOwb0UCyqgl+ - ewChHG0z9cHeb23mHrVqGb5rn7QoSCRmi0PrTINlWXAwXxlTneNtmZ18lsF62Me/9dRniSPBH/8S - 61eb6XiaxhF276RmxnCmaPGDIUbJ/R4wx39J3dRLnovyTRjTaR4euiBAc4Fz4r39QWwkZ171Onx4 - hP+vnzmpjqE6uRO7MX3rTDx7V7DuB1+83TR92YBJ4Xu0OmLc9r4z//nh0+zzxBkHtRRj/xfBZfFV - opX7TTgV1ekBepZ+6Rcw383+0fSRNCyWj1b/ftm1xwxtO9kn59RWHd4oUw0UqU9ZXL0T1PMfO4ao - 3ajkUDVTOK/+F+TVV/LPHj+mdPA6H96R9Sa3IevDNb4WDK2pM42ZTfeHR+gzRDzz43osR/F6FrHW - 6B/mlb+q/OanoIJVzxL7kGzQAOrWhZ2e34idxOZSL94J8KAPHC22i4D+6hEIv9ea3Z/itlwaD2fb - PzzcVx8PLdvcpqAtvU3Sa34NqYMKH65nDzPVC5JyfDxKE2hjVUT7BF65VMq9ABOJO0Kyx05f/TQD - W1yYrfcfDo70F8/mRlyfM2ZdF7WDraGkLb/s2F0HfWadRKG0vjpTRLVYmM37BVrrIf8j0bVPHMwV - dns2+Dxr1dV/mTVIAnj60F9q9GnBMdF74mJm6U2PRkiKFjVQEeKNT76jJc149PocgBhURuWPWnWE - b9666jPzynHenOItI0PPDrbtpP36GbufYEf+rQcpm+AvHkR/3zLnr77Yrf465WJ9t4zeDQUQcM2B - BE9p34nwJg/wq1ghl2BpluWkAi91inOg29pt13rzpUBXv2wWTm1bdqvfhCQO5cTenPly2RfHANb5 - EnJrH8uYw38AAAD//6RdybKqQBL9IBYySSVLZJJJCgURd4CKgoiAVUB9fQf39bJ3vbwRhleoHM45 - mZWZZ7DTTi0Ru7lc9cYlhOxr7+kjKd9Fm4XfBI7uPqD6ImbGMn+6EXHSlQsF1V+nCpdxCA3FBLuB - tI+7UtqaCFdnD2tc3PlsbtoUlZFd0z+9YfplpYce2j0OQe4eaJZD7a6msE+JmuQ7xtdba0Hc9nmm - 3rkZ2BJPXqk8N4KGvUk/onG1f0TudxO7m3Xqxkb4KpC6/EgWTnoWY+SPGqzkNZyzOfQFo9gkkDcm - jw2phvVW/3JS8/0R4YdEbENY9SC4vBqK//g2dc6LDm+buNiZXo0/X+t7D2t8xJ7F2F/88aB4+efw - aUunegk0o4VdXJ3osR++8YDrLoLBoUdCHF5C1KG/APircSFo9X/py3/yv7+pSzRr4JeF4//8K9y+ - nidjjG+BCTdve8EPdD/XvHRFHshCJuJQ3uv+VG8PC/Kz8Eq686uLyXNj5aAEu2e4/U2CQW765gSc - 5SbYQ8+3wX7hCBBG7mk9D8dY65OlauWvH92z8z2e/vRG34Qf+ZyIgObsriZorS9it+w0f8LcL4Tp - ep+xqVd5vOJbWW1p6eNE2x7q8dvUmToQM6HZin+XP73xT79a67kFcQw1R1zwcUOlDp7o14udvu69 - OeP0sj/WkwFfDenTe1714POw7JptAu/SVagVsL8pzxsPghuh1D/+LLa+vwyoztv0frRVY5lksUT0 - E+xxPoRpPTYqNZWVr1I/bmqftZo4qlZtyvgivsZ6XOuzsIkPQsj3bRovL4Msf/UVahafmk1wsgK1 - 7Bcz3Bqzzaavnk8oUswQX9qZsSnyR31rbL7xnz5trPqDDC33cLF5DSP0F/8R2mon7G4y35eECipU - PkM5nCT7iliucAncXU7B5m1qh+U+f+8QEMFc9duqWJIxTP7qFUT8q09d26OmtkZ/podF/gxT9fkR - WPkrdVd/n//0rVP4fuGAl6d/+U61r01EZiFLa/bd7XRAjvqjxu0T1N+1Xq78Py0F4v9uKahYqNDd - ukpCZNbFUT6gmdi/vPVaIBeHwDkKaxoa6a6m5ax6UJKzQm1h2cTsCLsQDvfPm2ykU8IWyfUyEKlu - kJdtUYM9OBTBvZtzrMs30WBpMXbosR1Datp9MMyyWDeqve9iusf45U8vlFVw68wvtY+i6Y/6snPg - ImUZLtdrbfxHGk/KeSdmVH/aZiHZ1tmBeIwwTb32xljHKSeYxG2PPbfOi2+hnFJw1dCngat+6znZ - H1+qPXubcB5PGC3F93lXx+2dYfMhV3F/jiNO/aC9g/1dvQ6yfMshKi65Rk+CrcfLPmAOyqatGfa1 - 7xe/eSNy6Hd+RUSemmJg95cHIAjHM86C5jFIwlCkwH23M73YQV5PTU+cLXpvWqIUk8gm93mP4LY5 - EWyj765gmwbpUBtBT4vjsitE0+gmddyWjBaFbxUC3p80YOOjJ+z1eMWTheKXqnvymcbG/eeP7k7J - oXTHll72mwIJyxUtcPr+WmzmBRrGYn95wY6RCDvXTDYWS3Uz6Fr8DTeHxI27lvMbRIVKw2Z9N3wp - 4mABLd3ZZBBUf5jPt1elJo20wXrx3RQMLrwC47nKsBMWu3q+/WKiPlmw0LzzTgW/VW483Pwkxp4e - tIMo64OtxmfbC/++b8HpQYOHzNbxOhCtq+i9Bm5088HhZJ9rQfB2rZor1w8+Ok8diUlqvsAsGo4W - vOfFk/tMI4i3v/v6e2ufD3ezApwcA9n46O3/+KiSVe7gFvQSsWSgaFfLq8QAZLsETs0c4SWr43xe - 6DW/ZGwSlDQBwuQYJ93N86Wdu8vU165VyefB+/WCqGUj+ZDbVP+J4C/Nt7ur903U4iO3/dZSSd0G - 0iooaHRWAia4865Ur5MM2Oy6LJ4X1cgBX3cWTo9i4wtv8ddCeuQjenT21sC/tU2IfGtGNAofckEP - 8pmDbfnQsaPstFhIlVwEQhKdXriiHgjVRxuqtGywizc/NhYf1kLKNW/yqk3GhvMjylVXRSZhH7mN - Ge6GCQSyx2QV72oaFL8OUmEacPyyLEMKYEdUpTlv6cMfU0MwcpkDfec/CDyTopjN2eWUzTaYsOtc - /WLtyVlUod16NOaNE5undsmg1KsnTZegHFg/ox4URbBwEaOH8WtuT01V9R+PDeNbFLzaBCm6f8eU - ZjS3Y3EjDAqKqoOB03qzQctVoDL6HpsDTqdrUUyU01s48w6HcaQEhhjhN1G5om7DDTEf/qJVx0YV - WuRhd+tufIZ2taLeA04jm9x5IiHXtiNo5+RAcStf62kfzrrqGo8bPhSTzhZaOx5sbNnDRyMe2VTs - HxWErnEILzWufH7k5QB12veK3Xc2GexY/BwgF1EkP5WQYUnEqlXPl6yjWdBshvkr/mwwqzLF9gab - hhRSw4bFOv6wzu7UHyjZZ/K+yZ9ht8yKwQbq2MAWTsJBOr/8JR3PL9Xk+ArHxreIZ83b2//eV5k7 - ZyT2G5mDe7kzCU3OVS0JVuSpctIGFGf2XM8f+5KCe/Qozlkq1Uv8eORwmkKG9+ytDNPBfJdAo/5L - 3WO2+OvzO3Cpwpiab/08TDbPh+pvObUYx/t+mDjllkByySe8uycvYz5epRPMh0zE5iyGvoioZaJt - aEvUtCMc/5CqZ0ofjD+6V9ojGu+zBZCPXEY95ULQU2hnRe0/Qrh2KO4H/uRERNVbTaTX9+HJJAO8 - URo2sKfeI70O813bhtAfG4uWGvdiQnyQRRhv15SmNT8P7D3zHvotmwfWVv/jW85oUVYkDOPfNkLL - E+wAeK1zcQkVh9gx27aQO9buX/6Tbl1/gmBobJw6t8rnj+Gkq5Hpt9SyPjd/ri5eCNX98sLGVVfr - n2I8J1Xtqj3Ome/G/dK1C/pSuNBjeToi0bFjHrb118FnPfrGi/wKOaBUY/S62uccz04Jt469aOA5 - EpovDFWwH48Pal9yMV7Cckwgx7FNA+5FjXnecAA6VDp12t0r/l6NXwROS/fUcThAU5szTh2mYYf3 - ZNcVc1clJlzPS0Ftiaf1VJgDwCn8WCH61mvX7UXN4QO6Gc75Iyp4T/pEQLP6g7XWuvoSdvAILA2e - NHwTbxC18i2qhqgTMvEnATXuTsnQUJir1irYaNKKyVGTeyNgQy1TJDyUo6hKuddgUzi+hl8fPRUE - +s7AxX12jLnW5FGt0ntDrgmXMAGVbaKu8XfNX4iNeji/1Kt9S4janINBYBcngGvYnnFEzQ2biPkE - dar6J3bjXC8k7KMSXPJRqbefZLTwZZWoc6wY+JLv+kJyqdFCeP/caMldKjRHRhup73gb4/SCHZ9f - 86nCsFbQkzscmVAypv2Lj46VKf5i1kEPnxTPdC9p2BfHVNfhd0896lXciFgYfnQUdalJdRZr6KfY - txQ5l87AUfW02SJkfKO+3BvCVnNrfJaFRJOT5XnEZdYYw8QaK1HNXLZo8JgcJvyd92najtTVOJHN - ahzfYa66Kza1rizEzTKl6ia0MbYt3R/mg1RFf/5Ek14Q2bLHLVFPT7GjvvE268VfVB7kYjpS57Rr - /uKJCa4LX3IzEPOXZ9mnSL90A0763qnFT8aZ8GThgg+apxd9GswdWIdwwJ52teJ5Z9AFIv8y4EDj - XmhRtpGnXkyJhhtFloY5UAoRQidJ8OGsPodfevEDoGQvEcHHkb+kbzGAtNtfyXaj7fwFncoJCk3h - sRaST/HvfB/lTwgXPVILJliRA7uUSoRNt8kfW+XBg/l0cajorYF+wvd3R0vSrKuGj0H9q6uyhU1y - uf7Zd8z2nLDAqe9fdJ90dkx+Y6Gr6/un2uZe+GLxfZYoP08xDSX0MWbsWY36l4+TVMjQms86GF+b - D8VJFaIlwr8RTOOmEmU/ZajvNzKAS19brHOWayx2nN+B3bKWvOTx7Xf1bJVwLw1zHQO1sDm9+CH0 - 3eThQs0qf56NfgHXb+7UtoUA9Qc7ThGPpi/Ga36bt9+Gg50bdTTbLVI8u/PurrzaMqF//kAMO0nU - MCUPqtsW9ZdDbXSwqbsKBxplaFYnY/qXj0opdWv+9LydgMJo0fxXpwXVcEVADe49teYDQiweUhGC - /uvRpLv1xvQ2X5yqaMU9lGnexr/VPreOC3Mo+fxhoHReW07u1oWm0xUVSzDpEQxyvSGPZkA+edeu - A8HsXOg1b/dI8oYHD6u/hPImb4oJ/R4h2PXtR50mfPrsuqMZDBtuTzbmWa+nNR4A6IaBnV+W+mRz - Sk7w7vNHqO7UepjiXOCgUvIN1qqThCZmfu8w4p2Pw7E6IYn3pBP6ez+hQy024/7ZwTLYGj7Oyosx - iwYmssqYUnebhf58VOIUrrfhShB63tD0/SgmCt9mj/e3Ba33yDGAV+GBBgqM8YgOLFG/2XSi9l5D - xvQ7gIjOOz6j9rs0//LNOkj7lJHqPKiIybPibJHzutDQ/gnFwnObBRS07KjZ3TxD0J9RBNlWs9aR - THM9Xp7lgrJXtaVn/vbylwz2IUSqnNDjsXLrP39E6tHaEMjOycDu1irxm46Is2sm+6P8mVqQvrjD - ++cBo8nOfPkf3tAPx6RenpstoL/4m7czMead8VkgdNKEetdlH//FZyS/dkXYp4E5iIV95WETMmcd - EzoOS01xA26W8qHQGTa7FVcuQnNhOnSNd8YvFamtROKIaca0r79sTuUJHZ/iFKIc0nj5419Heb+l - 6bm0Wb+MeQXZ67WltnpgiNmupqhfyl3CRYkuxW+uw+4f34v40xkJ6cUIkH22XvQv/7HnKZrUu8lv - 6Irn0LANXxWI0VXE4e6xqSd92XmqTviJxt52V0/CxSbq83FKiVL7Q0xvw1kE9PMHGrzdgz+70TtH - nvE4Y10eLV9q5KJEYy3XOF3Pd1p8Z1F4Rfph7JJ2mO/VzUZkd+nJfBgkNDYHVURmdU+pdRZ7n7rU - b2DFC9h4RSweRuvoIcN/29Twtrthig6SiBpjW9DQqVI237U5hEeNdeqS9s2WKL7eFefx3GFr8/0U - s+KtLcha74aCe9igaY2PSJLejLprfh3LZxIB/45vYRdc53h56z8bfucqIsBdNDRkXT8p1ZbY1PqK - ncEy6pmocAnBWB6u/te5BCNUDU+JAt5UTL/+50CibRsafpNfMWuzV8L70Lk0eNZ9MddV0v7jzyFG - vj/LbNL/+CO1P1+jEB/B1KHNOTpTczRGtEyZaitrfg83/R1iIvkqpzbdsoTcEicGL3/kFpT9u8ZW - kUNBd++Z/MUXvOoBsQBibatn2EGoii/Xl7Kw1aB7wAPH72GPqK6/7H/8db/y45E0Fg/J29axv1wc - xAJOjZQo2gI23qLoj3ITiMCn21O4tVsXUUBmheSpaLEd7pb4X76bjucc79dC9bSTuxJNYjDT+5GM - NbXfcwBVI1Js5tGvoOZ5nyJhlyrY8bWDL3a4DtRsCvxwKb6bmInctKjOpTfIPJhXNAn5cgfxmCzY - UbPkv/648pVw8Obe+Is3kG11i+5rXBnLVHY82OPxQ2ju7NjMB4IH9tCQEP1cZixrfvinr1yFuiz6 - Um9tdUlaHh8e6XtgA9VM9filhOr6Y4wZeuIFfY1QD1k57gy6c90MNl/0xTsUt+if/cxWLGD9dlxq - chxTHV3tR0JQxtlMBHenwGa2KPaDc1mTW7YxYX1e6oEXxeLBLhLY46eL//jCfK/ONhCnpqGqea9i - KndTBvcANJx+D2cmbsEY4Y9/Fpf3q57HVlEQ0oqUaj87KsTjmGrqdC0LGm4Tv1ikjpzAJbyGb/K5 - r2nsLxxa9Siiuu7XH+djZSN0+RSEez+KYYHuN6E1HpCperZoFuvQgX9/u3ZSLBthkGHFu4TXBYXN - gbrPgH/o8T8+K+aTs6CgvllU2ykhI/JbIfD9SCkRNKNH8/YXEbCsfY/1Qg7jRRpPOhxla4u1a0WH - AT3xhMDzzHAjVx4ajtncwuFtL/hwJcswBUMl/ukZ//QXmlhFp6z4/h/eIjr+BOo3Go5kUquhXs6T - GyGEapH6VtYa030+AHBWdKOO9fkajJ31VMXkc6Z6+3Di5ZZJNqoNVFNbEDVfiKMmg6+/17A164dB - XO0FrkMy4PTP/v/4TpR3C/6Ln8JDufIQhJ8f4aZzUzP3FEdwk9wf3d0t5hOcWto/PHiaSw5NaNZB - Fajm01RYNsX4kcZIeSJrIlfDF+LloH8asIeWhPxNjAxyqP0OVj5PXWHxC/aVcw5YPUxhTc2BLcx6 - eCjoBw9rf/k1pl4Laz7G9zfpayZy8gJDMX/xLhupT7kPacE9OhTj1V8mynmNmqO0Jr/Xa/B/4WGb - gutyX8KMLyrYQehz8A+BQXW3MgZBuqITCGqeYivokpjK/I+gFa9QY8C5PyfGb/rzX2qox25YTm+R - /J0vNSV8MqZ8DkukrS29++eBsok+TxFY0z3ERmSkjJ0fWQbNBiQiVN0SM53jToop0gc1lp4M45bb - Nerzfsmxg/jdIA2+fIdj3vX0Sto3+tWNlqjRVVdpID0lxlqqNSCdjBq70juo+eiw4UENyh7rj1hn - PL21FURHXl7x+m6QqstGhrEcbzTTd238+3iGCbtW5+hB3QJjyudjq8Z1c/yz/3gx7DKBYGht6v0W - KxYsL2xhorShu8MgsWFu+Bf6LHFI+mi9tbkfHzqsz4ON97BnM/87htDEwYueTtv9IAnmA6AXD1fS - B9x6/3v2ATWSJJLhXK+Ldexzqq56cghZU6+LL+UEHvP7QN2suQyrPjWpQ8G+JAtYWM/xLyFo0lWM - 7bHzYmZ146S84Hf709Pq8fBRbSRy1j5EZeMOi/OtS2Q+fUyUR7qtxw8vjzCYn4YIr14fhL98rJ40 - RH7X2ozZ4JgjKKnc49Bya2N5Pm4KrPYcVtPIDJY0m9cfn8O78jQzKh/vNlrxMD4Vv8qfk/31hSRj - yCh2twkTdfwJQT5kNjU7aV8MTqa0YHjyAR9WvYk9qWv+w1OJKy7+qCR5DyodA5qG0a6YLjtZQX96 - oedmoyGsegOyXhOs+hM2xiOJFbhXcomvpacUi/EdAHS+2NHdd22hOGtKCJ/mA6sesPWnRCInUMZS - oVbVLcVUNfsUgq1xDrfbjPhsGgoe7H0f08PN3RviKzU0ZBz9C9Wj0TX++A560SjExZof+iFTW8X/ - ctcQZpIOc1eVNqqPrUB+4utrTMON6rD0grrGM7+uPFRoSNUpTwPixIhVZzMBMyYaDXePR03CKjMh - SxyCT80tMzqtkB20vj8cnRLPp+o89/DbOxbNPpsjGxeSZSC2SCebx/ONWPfaAfzVJw4qCWtpz6kT - uGrgk3diOIi3cumOSj8tqPvJq3jaH5sG4pts0lTVrWF+/uIA8Sk6hSfJnFHlz3sO4POO8Y7busOi - lT8euotwx+HZ3Rlz5gUtfB67ARuWrcVSeTqe0B/+OtDUGKQ/PmZ4yoE6a31hkd8LQdd9FuFLK+ho - PsUPDraKl9CQk2/D97XPNDgsi4ktZZ3imB9fLZjxqFFn4I2aPYqhl08HOcBxYR7YjDYCwLh3Dvgv - 3n+JEqWgcuZl5cdtTHZi34IQ9bs/POj/flXRAGQVxrfoTOp/9ZrRxsdwTLgKUXLWebUvTJkajnQs - 5hNb1aq9e6NZkXfxIr+VEb50V2Dtvo2HefjlodKOWoAfqx670I18h1teXIi8eS/17B1m8Y//Yfsp - voyfvE51WfUy7F/ds/FP30np8U79ygsKvuSCBpTmsg2rhaxTWbhpAcK/A/qnn4visk1Qh+Yt9T9T - iMif3rvmk3B+DgJiuv4y1Qqf7mHv1kq88qceHHcnUG2rOGxc6yOge8qZGp+vVLD+bKRwKRJE/+Ip - WfP/nz6PC0EdBvZDtq5G+nOmevF9xP/063HvHUJ1QNSn3T0NYBzMjkD/exTz6/u5/9Xb8C6+b31G - zroIc/e8UCN8NDFd9XZob1cO20RwkHiOMw7ipb7iQ6VkBqt3JEezJN6xudO3w6A4RgDDKxLp/Wtx - xbj6E7pL/hsb0y0yJMExCMRKl+Ps8hZrVr/0UuVOdCS8vU61WPkHsPHW08eKL37qskkQ37QN9alv - FOOOh1EpSGuF4tj1xcT2Nx0FrynA5XhearLWa9DTMY/YOoueL+mPPAPLsnqyUDmIiZ3a+l+8D+eV - vy7zMotqAqf9v3w1hfc+QOShIeqv/Hshbzaic3sbqe/NvU+ds80jVVBTMsf3rfGnb23jWayotuK3 - qXhrmrLq/fgQgldIknwtgbwrJ/zjd5NOzrm66kf4+j7sGL/ya2XEhh/+1ScZ40mwNY2Hit16KtF0 - 5ZdQvcfYw+ZzeKI5UHEGonA8kN9Z3dXiTnw1UH9ciz66KmAT+l0CZG1PDlGUS4iYhqtR/bCtQUPn - 7NRSlU22ykkfg4YZrdHiqY78r16ID8k3Zv3x0YBxMmP8932SNWYviAQW4/Aei2t00UzYhLMTKpZ6 - qZcgUPo/fkf4TvrEf/bwh4/wbms78RI/LhmUvLDH5oqnxKMbZuDlQ07tjSb4v/aXj394a9UP+GFJ - v1+AWu2PGOuvp89e/nz/V4/1xDkwlu9YVLDibXxe483Kd81/es+2nxNjXu0FKhzdySLYejGZz3RC - /enE4T+9beVHIex/QYEj/mkyttZD0OX03GGcHDdorVdkIL+4C9XlW2qI+uSfIEzHBz2ueGKhrz3A - XMw7eoUmrtmue0ew8musp9bPWI7B4m3xTtKotvn1/qpn8er+zkf4cigrNB2HLoXrPo/CV3Ly2fxX - j1gOGSGbtZ7y+ghtpqqcfcF2eb3F03D7aJD6F+Pv/FlPN3K5tlha9JA6lE1TrgDIcvUJ50/csUE9 - nhtY5BqHipsFvpD1Xad89dher1w90Bo/Esi+hAtR5Y0FEdKdrraDFdHM2AiI3YYbD73knYlaVQqb - hFwpYXlFNr2WXl7MpyTyVNEYKmpcvtuC1HTfIoSeInYcrkTLRScNZAHXkWHkTDSHUaNAgi4+/fc8 - B+cYbv+flgLpf7cUvIfHlgZnj49ZguRcqb0DJdfl7sbL7nAPgcZPh1qX68+YbgfrpDb9Rgih74Wa - UQ6LwLumTsOfeS9m3847+KJspu6+tGO+T2wHibj08M1TLrXUGNsSyZYYU/uIjwXDYWSq8SUYMf58 - /GL+bKAEyUpxuHBu7S/a1algeR9TfDtxsj/zhZqC8DD3NNs1ui+KgqmpbtnuqCUNuBipJC/wCroI - X1+IQyN9ZQt8XOVNjoZ+jOfjnenqEGgitvPPXHdCdFxvOZ84wpZ3Wy8XZcep8BJb0qsTKZYCXhGc - T5xAkx/1ajo6uQ6bpmTYCZIXIhrR7vA4CQ+y6JGAZkP8TLB3riecCs94kD7WTNSrqVr08suqejzk - ZghvR75h7VththzijQb803hgszn3/s8xlTvAAh96Y5t6mErJctTJPfr07pcITYX4M9HtKFyweblv - 6uWj17bKuGDd/XppfDLmQ46e5elAiG0Wg/BSE0+tDpsXgdM6aPTweKbgd3cf78z8yHjgrg24j/Xa - 4W83+jRbwgTJYpDi/ZGeCxEnGw940hrYwuexZvk3mtTDt4xx5LUHJGpX7aXah2NIrs/PwyDzUhM1 - WUZGT+8g86ViE/Fq8mqXkFOqhy/BG+nq9pnmZDs732IeLuUEr0t2/LOXQkiqOID1PLE2t1Etes7X - Az5MdvhqLvbACyaRYeuPF3rRjkm8WLbDwzkLV4g5npHIr7ci07UzTj/qKSLX1nPgXnk6jd8ECmJO - DlF43rao10U/YxqSNlCVXVPSG4uUeEmbL0Bl4Bc22NYuxFrrRRR1PqLGLKoFteODA6/9aR0k+TZ8 - sVS9VK3xg2AzWR41DwuN4FVOR3oDy6z5UYwa1f/xfriI/dWYUap54NyvF3xvik8hpUf5BYY/7OhB - Q9rA7+IhB02pZJq163J3uVYmiM7NA4c/4cOWDR7Jn73RqzbZf7+XgznfHbGxVW5sCTqRA54TLez3 - zlyw5+94UvfZMyY4E/p6OcSSBkK7rcn6e/zFLLRJ9S68jW9UNWI+5c1FFTaJS099k8V89hgVqPvb - jPeKJfvE+4g8oKRGoeqdd/7cD3wP18fPotGfPcv+GACo44ceCqb7Eh/klSKMcb2e51SzdosDsNXv - kXwgeCK+Vt4tHAZEaABbLZ6v66BSE74uLgwnHPifOHXQdFWOb7949YfgHULYjBk27+gbs/d1+4Kh - lWXq0Mhkkz3Uo3powoR8tvddwQvXVlSV8CmEjeT6vtTPW17BQnqjtveYajZvMAcON+c4CPO4nhGv - LiBr9xgf3vy2ZsU9rOB5xR9qOnunEIxRseEgDhQbsNvX0rhsAdQ4/GLzZG+KJVBFBY6xz1HcqP4g - edrhDoIZ2tjZbKCeuWeqAV9zJg2lg2PQpt47cFZ5kYyyI7KxmSddlbt6wtaGUjQtv7ZXrZPr47vj - W8VkbIwSPQ63O3UU7T0IW/dGwDx+99Sn1bHmaSAvEGztKGSaYMTS5SM30F5fLxybUl031yrg4Hxr - eJzY9INYHEUJXDebdapIWvtCUJ5G9RloPo3404Qm/2raauGfD9iT9AOaN1Jqgn27YnyWBhovuPre - FU+2HaxTyAZRD9MU/Z2XZyUeouU5A2DTUNLw/Gl9VpNjpkrIPtEw37RrF550Vx1SZ6FQCcSfxkpu - 4bPpOnrpxyoWg4d7ggfKbXp4HnTEiu8kKm04PWkx8sdatEtHls1569JYQvbA5FDhgdV3De8WO6nF - TOl1sD7fIBRcdYzZIf01QLmnF6LhPhfz9qzpaqKHNES9c4z59f3DhtoL1tX8iqQzrLuaooNHS9hq - xdje5BBOx6TDV+tyM1jSRpkqneoB4wr58RIfngvwEbpTQ127/vFNUVD23ehYlwq9nsP9IqqqU3Y0 - qhw6sKd+PEFVdA59CNZY/8xf7kBAUhzyyB9iVhvXDORD/aOGcLTRrPC3EXr/tYR0Fz3YMN4UDZ7h - o6VOMSg+o49TqBppbYSqLV8KQShvGTx83SCCHwcxzytXE97tQaFBvDDESt5uUBWfa6pZ4W+Y4Hvt - /uwT44B+0ZIhz4Remxa6h2CHpBSml3os7jV2srwelvfYA2znssAX/ndhU8emSCU6MvBhPhtISJ9F - CvWpT/FO9M7GvHmKJpwg6fGZHAmbi23RqZX/2hOBfUL2L74/qs6mt6nJi/n1AQ9eJ/1IA9dkA7t8 - pkbdtu0V50w71AKMTgAbfrXnw62L2fTdy6BIwZ0mAdn4s/mkPLyCPsI3bm4G8Yp6B8CLOXqvrh9/ - 3hhOqe7OmYLX86iHrMs6tMOyTE/AakNaxFMG72mDwknhIiSNt0UHt2x2dKdl87BsA/MEQpLuqcv4 - bzHobSCC4KOK7p3L2ViO665aL0FPrK3+tFyj3oF+WPiQ05mJpNdrr4EoPTt89e/2wE+7tlIDreio - 95LHegqujaPu4zhZ/cc2Bu5ThPAKNz0+XOYd4n0S6EDkwcbuU+sKtns3kxrULaan3yljrKn3nvr3 - /gzjWccsRlGirv5EuJ/JxUvo70pVPytc+Nq3AqNaZC5K3qseLmVJM9ieW/OdqsekN6Av2M9RTITz - e4HNVOrYb2/m+X/z7cWtiklca02/17ALJzc6IKnkwxaGQBfJ5rTvjVFC2wjMXW2HqLVnvzWtboJn - rHb0MARXtJz1TQPudSixS40Ha/BkRqqfpR72qiAY1nzRAH8vdXzo37UxfVfJNe83HpF+mTYsF2ND - UK2+LmEdbcyBCF6gI2WGBz44m53Bt7cphPL5SKh5fbvxgg5BruK+LcPyuS7W+dyjCL6+TkKh25J6 - fp0SR1WSnUTLgx2jiW9mU30Guk8E8x34E5WmSV3jEdaV/YvRw3hoFWPYGhgvl8AYPvncb5NTMoWs - nt/Fmi87qPdT8s/+J0U93lXW8Rp+7KIHYtE2slX1IHahGD4nNHZulAJayppw2bkd5vCWi/BbZp1G - kUbqxZycER7y7Umt1PgZI8c+GZxrW6RrPK4ZdZUQ2epwJJw5FWhi260G9S0641NZ7wwpvFg9PKze - oEb8beI+rHa5ao5Phx44rfBnv1h6+MNHjz4e6lnRqnXRk5hibQyjYtqejiWS/TAON9rXNVgcZQkc - b1uOrt/v/50/umzbTcioZPu0kbEJ8VFcW2QmuxBjeTeqohCE1C+Sab0FELeKUqYl4ST+jmY3XypQ - juQWXupDWowP+ZeC0Q4mdp+fjT/C8on+4iPJZnj7E9w+KQoyMcSGh2vEoibywN3Ed6zLs47Eo7Rr - 4Mkfeuqk8Zat11d7REN3j3M+I8Z09Q8avCO6pX/5/LfJnBxeqhaHS3Srillybs0fnsTeXQxiChEy - IRD9gbpq5MX88CUORINd0TDCFpPqbM4hTLcz9Yk3D0QSRlORWTyTX5TIxRwLhQN61G1oOZliMb3e - VgsKtHcaJrEdSz+WaxD2dkbi8a4av3dS5kie+JRe1Oo5zN6Uv2Ab7maqx3vNWNLmCX94iWyXu1vw - 24KasLnDjP/4HDNPlg70u3PxmbtiNHXrLdd2OzUrft3UDFCsoYvBAz2CLNSL9ko9KOfPmfB+ytXz - V+N7kHoxwntuLxezsL+VaCg9CR88RVr3KeUyJKf7jB2NyQORqtGBd/TbhkIjhfGs8GcCluofqH04 - zD77FtvlL3+F09xGw3DrJ05d4wW9l6AWPys7BXDvpx2902m91VlGuupniUck2dvVi1qPFThXN/sX - P6XZU1JUyeqb6pdJ8n+3d8ED87YZvY6XDfqhh8qDyoUDPqhFU0+opCHMyfdIMzWGmhnHhlN5g+hY - 28u/eG46OEHg+x31zQob0z4dJvQ+r7sui2Ydmbs3K7gQnsP5lP7YuHFppUjv9rjyQx3xBxp4cM4C - PpQ6vRq6hAU9OMb38uev/jxdVRMx1uzC39Qo8c8mpxMUxS2kbn7aI/7ny52CXumL+t1yjKdqvXUu - ycZI7YMfsYnHWw+uwtn5w6sDY6Jyh/fXeWA3l7iCEbbYytmx2epvbTwrWueham1m0MKOGGv8Wv6L - 18PL8y9/Kii4hyHdvxPNX3SqtbCNnwzvsSH4nTMOPPiHuxNK9oYfJt5oJ1CCsAiZJyrD0v4MDbZt - c6XuobgN00N+p9A2p3p9ng/7rPFQVeukp1fbtotZ4z+vv/xH91WdDox/LpWy6gfYqgRiUMIUW30t - URwqpRfE0ukdRQCWsQnV1/M1TOt+TeRXfos9PgsN4dHGnGrfCkyQ88KF9KjsCb7r7vn4dXvX5PT6 - Eqj3S4IPtbMtljyXZDCOvoGNbbw3xoSZ3R8/o15kDsMyWvUdfrKvU6d/FMVcbtGoJB9xxuFG3sRL - il0OhnSSqIE2e38aPLlC9B471LTph439YyqRa/pFWDPv5s/ooYrbBQ8x1jD80BQVTab6uuiG02pv - LM5DHaTfcQirjTYblMRY3Canciab5TL644oPgA1ujoNrwA/jeLYjFAzXHlu5dShY2+AInvGmw1pp - 4/i7WOoJJK8NaWg93sYSyy5BcpEkNC7T0p/xbVHUkOev66KjGU3TjlSwFcecal0QGdOfPlICTrDF - XSkbvsU8weYrslAgxxDNekUJ4JMrYte63PxFN6vuD//TuNbBYM/LfIKu7wYiEVLV0/cRZWp+fns0 - VKqNwb4Il/Ac8xC7Jb6zKbSCE7Sm/iTME/Nh/PUkQsnNnPCduvtaCg+TpkZWeccrvjPYXZttWJ+f - Hm7T1p9aePPqtDFCwhzUGIsH4gLVfV/jA/kY8USkaERbMHNqZfNvHbEiLrDyQ6pvHrVPT8oUqfy2 - tKh+cy7DP74QnQYl3OzPX39WxTNAKLmM+jIsNb1y0ai2822D13iPeGvwezCesUn9TXeql9Xe0JYc - GbaPeI7JNeo9dJZLEeNGHep5vICIwqS94DDgIR7Y81RCfZHP+PAQ9GKxior/00swTl0v7mnpt+hP - rzEjv0MzJYIGNf1NVL9td+vu9LhS70KlU5MafUGIPTRwmIuJOs7pVbNd1PDqi+Ix/Dq8zebdMHMq - POYN1eTkaDB8eRP4F2+U6FQIf3pWvdks2My9DWLMV0pE49rBYXJsDSaHiwjeQhyc4HxjsJ+zmFC/ - pgvWTj2JWUwtAIWPt4TzHPAp6fCElv6bYuvuHpBw4pmprvGU/vnfAnVQQv90BOwKnlvP/a4c4afW - OtZjkrJpe1pLAllhUiuhv5gEHcfBGq/Dav1/M2YnBXbnXPnDY2iezJsNtnw8/tM3WAryC9L4viW5 - OoXx7M/rlMhq6LG56ntLMUmgRgdXxt4h84u3qmvp3/fjg/HW1pLEMoK11CH1MifwJfUYe/B55C0O - JjMtln3ZcSg6+HIIbSfETSkdHFjtg4ZvTUXs2snjH/4lLOeKuo93UgnLY05WvPxj7IizAFY9Dx9g - RDE9W60D/VPj6SEtdkxoE74EtHmq1GPab2D9/iirl2uqYv+RRgW/4kXVexuv9Txn/+tPS6OOe0eh - Pi8vBjs2cgike1q4IN6x5jMhkP/pkddVrxV2wwxwf+e7kN2sd8Eup6hRjzfEUR3ddDTNh0mGP33o - 2JEk/tOb1FXPweWm1ow/PABIEus1/16MxWU7W131Uno77J7FJFkfDf3FSz3eV/4yLwOBJ497jHHh - MioESwutTxHVYi70eREdA5W3jZKo36MRz5ZxilBzW66EP9euz5t93f75H+mhJDUxC2dBh22bUF8M - F38ykrpSp3Dww0noIvRPH1711nC+hCfGqLsEwH0DQs9DTeJffYLwv/rJqu/QyLB4tPIjQi+PPl48 - 5+kpf/7mHxQ8TD9f7iGI7yENp2dn9EZeETjrIo9N0+oM1hVHAtuCzwhwWIul8abosP4+IrW95n/+ - zuMPn5onwRnYNZ6Wf3poFHahwVuD0YPQ3B18+B7reHLGQQTpsPtiG0sTom9z28A90nfUSE5FTUZr - uIMrNSY9FX41zNQQNOj6fiCJwAaDp2lbQnc+DdSA9m5M7TJ5cNv9UnxaZ5NPRGsz6CbvR7YevcX0 - Lx/lghDg0PHfxc82lBQ+zfQh2zD4+GzNz//0RsuPg4IvgoMDtVpdaEgnLp7fHL9eLLD3VBetV80+ - 07FDp3UX5f1NPz45apsU7bx+Gy6PQ8Umldv2SI53lHornlr5+R2OEd9ge1vHjFZiyKOVv9Cbs9n5 - y36vAZhBbFNcbn2DjU6uoT/9bNV/2XLv+hId+OSMd6zkh5FXrjZa8QM9/OGHuc5kMI/Dntp7FhWz - lSkRfKOcrvzgyniakhL42a2w85XNQcyumwWpWfslEifuavaSQk75O6+zgQfEwjrSYNp+Q6LW0que - I6YAWvETdZzttZhYRkbwFdRi/cyqeLayU6hWduRgIz2btbTqMfA4SQ8ipu9jsWxwQ+CL8jlks3iL - mXRYCELB+MDRGE7FIgmjDcrMPai54+7FYtCug7u0ueHQelgr38085LjGhep4FOs/fgrbQsxwOOw/ - 8URf0QRmWexDeWwaNt0Pv0SJ3qSizqF/sfG9nzw1su536hR2Pcx/ePp0TLv1vMd4+jhTpN4+lyxU - L5oc/5ZcDeBocS+yvdbcQPMkEwGffJE6Id7G0/SaeNAERyZyWdZo3nm0R32b81iX9WvNlGZRwFe5 - gOrRrYpnQkoOAoccCGrtoz9bPyVE7V1+4dh/14wNtysAzsuCeo7m+6JpVRNa+Tbet71miBnybHh+ - VoHPk6yYF5TZgQzvNTKd03AY32At21UPwX7Q4GHZLxqo7uZ4x/vR1JjkcO/wT48mfIbdmJ2UKADf - OjYUt8ehIFZRicAj9gjROoWRYHFXIlOKXbzr9cSY30mSq1uHLUTRWy+ebq9DCPn751OdI9dh5i5V - Ao+XWq347Ves+YGAdGE99l/mhIgaNi2oXDDQVY9jr3v3uv+zn8PN1Yt/9SUxD326X/HVrKeLDnAr - 33gXVadhfidlhla8Ts1h5/mLRrQSmp+c48S6fthSWBsO/elH+hDvap5jNIO045Rw7DQBLafXk4Ak - 78a/+kixkGKo1pai2z//HB+hlcEYVAp1TUMt/s4L/cWDlV8j4rvr/J8I3UNuenb+HDkKgK9CECqr - PrwQe2jh48pvslkbKtvgsTvBWPaI6tmzYkv0dF/ot1821J/EIyKLU/NbNvh5yC5DU8/dGZrtuPeU - 8JG6fcwup6z9q9+QxQTRp+VmkuEScyo2BTb4bLgdQc2th0tW/Mp4+Tjn6mG+Tvj6/GyMJbtKC7yu - dAj/7LdRHZqDlp/EP/3Sn7xs1CHyxDs+6EdtEH+FUcJptDtsGqe7//p7vxg3V5qh9F0z4ZU3CHtJ - Tp21XjnvyZKoa32QIMMJa773ovt6hW5DllqUEDmHXxma6SGFtzc2jD99/g/P4ehwCg3hemCt+hqV - E/YNhwzzWSwiUFk7UoNKrTE9wkMOa30glL5pFY98M9t/+Q87n8CM//AFWusZ2LXtdeoXJJpa+dWe - lvsxNpZ9WYGKKZR0Ny08msNF4eEh6jZ2X6lT8O7Pl5VV78E7nj8bo5cZ8JdPsCk//OKXiixR13hH - 5mlJ0OReahkaQ7tSc3f5DOQ0SAla9ZH1fXFGd5PeAaiG2eG7XxaMmp+fhsSX8cG2MU/+bF/zBMrj - OabBqz0b0nErexBe4UKzsTGZdLaIg+6Vo9OVT7GJ22wXdBzaBnvIDI1/+uxT8jA9rPWjb+yxQDXa - r4mjXzPFI1K3L6gXJSPvPz1uqKKXKj7adUXZkhvL6s+q9tYlsupxbD1/DWTUpPhkn1JjSpygQ3/6 - fJhv7Fg8CLsESjgkqz718GeozrIy7HIJO8o6heJ8N0fl0V0qbNWPA5qdPC9hToYjNs3+Xi/TYvRq - jW+EHj8pQ9Q//0o4pWO36kFtPD8BTDhoaCTspBRGnx/1UF3xOQ3zSPXpT92Tv3ou1a5ZiIROSxp4 - 7eSIuol/L9ghfbfw4imhRqA28bxYwun/Wnwg/++Wgk2HJ2q7cs+mG9ZARQUcse65ccxSJ+agoVFH - Xq9YY6JeGS/1wTsaTqXIZ7M1iLkacGRPNbd6+uS4S0Dl6WnEbrzohiDq1wqkz6/G+5tbGqK/xBmU - ZCqoQ7aOL+V4GBEWnBMOds/OZ5NBNdR9XpTwB1oPv1G+pWjamyIu7yMdllG+tZCQKKTeQfqgJbJo - AtKZ7amNHr94knTtpPrVjuD9vnwWk+i9dVVu7wds1kFWSwKXtepQwIEeqyks6Ld7pqok9B+sD3UZ - N3+fn7+iHM481EgYRy2ArX9DtNiXetxtLbkHQfNqbAkqx6bEEULQLnYfJswdh7m+yglUH/2FceY0 - vkA3nA20PWg0sZ62z26hTOBbXD8hEwRUf5/tp0TKz73gfZnt68lfBgWO/Tall0peBibqrqbmeXij - p7BTivnv892nohQba1dVHgupap/gu0L8dZcpqU9A9HtHfs77gXheZyf1xn8m6qVtUvwu5s5WWxFt - 8aHCr3i5mG4Cw8N44P18Pg6icn73cO0TFVvfB0Js/9Ff8FK/JT1EuoOo9RkreOVjj7178h/SrqRp - WZ5Z/yAXMidZMouABAERd4CIgIhMAfLrv+J+3uXZnaVVakHo7mtIumnBHi8H1I8WIjr98fUGrlkA - swUJ/9aXeRdhjkpDN/GDAQpgqv7VQrG/Gdj2Eg5QFG0CTPPLmdy6QgFLjq8Q+ZJSYsxqrcbdrGaG - sFhqnB/by8De5N8IwrysSGKUtbZWbZxDesxcb9VjkK1qrkjQuNrTLFVhRT8R7HzoaW+T3BCIwWLW - 1wSS040jFjlfaloEPx1pWWWRwLTv4Yhujg3Qksv4gcAdsLGcjFC0nx9PZPMFtGf+tMAIZ/os+hRm - y8L3JmqUB4vdwm1qftMuMrQd/0jyY9xpayRLIzwawQFriNJ64e1cBpuzRvi1fh4ON45yDpUeBAT7 - tqttOVZy5CC4ksy3HW3Nw6wF3rWwiYHsah+ELEhQOruYBEMNs3/xR+eXiU2Av2DkTaUHt1W+4mJe - W7CYw2TD5PHjZ+pnQb3a/S8BnHJ4zc0wvOpJTqoSbejj4/3/QjqO1oyUXgxIkBp1yFBND6CSogrr - Rb6fUhXcGLytw2Fe2kxz+INfVkhN54oYXfEGi49GFbx0vyR6gXhnTVyHkeY6mXF+Q7W2iRcqoOXs - bHNDH4HDdpPrwSkYEvJXL9Yvez+AQ/OwcFroSsaaL9+H4KXVWDcDqZ5OtdxDzHnCjCJFp9wxqVXE - bFeCzZQTAJl5yqBbpfMerz0/2XRVohlFRVwQFSw3wHssVcH6YwSi5T/fGVmbU5HU6wbOPneppvBR - BHAO8wN+rq4b0kOOOpi64YPIwSt0mKvyKcCFfmNsFpGxD5JXLZi01Q0bC9QBd5P9Ayz0uiQntqHO - dlWiA5i2UiCXpyLRjfVmCf7Fi6LFH4da7VrB+2cf5LOyqB778X1Ak9yPWOVLP6M+e1XRIUhyHKdG - E07nmS5Q7k8jcU55lTHWoNmIVzeD/NU31mO3f8/DO8oe1LZX3Ltwf974JQangYtgV0FFWEyc5Z3n - bHWPTXgeM5bInn8dmPMMSvhhT4hgdzw69HnXC1TBlsPKwcmy7ZgMslRfPi6+NqTPVrk59pC7Shgb - UbWCVWvcHuaH2SGysdrDKueDAOXuJM+CUWoOV6ZpAy+v/kLMtv5m6+czFNAptRk7bi4Mm3QdA+i+ - OERkeHpQfiadCf2vWmC37b/DInh6ConuN8Sm7zjkGZtbgN1bZ5IEGA9T+76asLuVmvfVL7HGJJbq - IfMtadijshVujHcU4PHmzfPG+z2gelamSEg+D08AQqrxx7JV4Y4P+PZ7ZX/1qhEOYbKRqAXYWVhz - 6IDovAC2wMmoV/ECOmh138u/68/VXOmhUZqnWbp81Yy5wVJGr29YYZ23rHrF7K+C8LldiKFxq/ZX - j2GQjZDcWFTQrZv0AvJNf/WYTdA0rowfLvz4JiZO8hgH+nlfG6hKrUrUzETZxNuOD6Z3sxET4oOz - vuL3Ae357h0XRh+YHD8ghBuFnhgFl5ALkFqiR/ainhTHHqDvVOHggb173gjMalif2SWWnsrkec25 - VJ0//EMH/d0RF3zXYZtlnELDt+8e+8Nc1ofCYqF7tP2w/EClRj6/KYKLpHXzdIpKh6nawoNnK59w - qB3CkPpimoC5eeg4b8GqbbN8Sv7w2tsY5VjTu2VDeKtMnlxuagG2PItbOF9TdYaDHQzrzNMYtFfJ - wUFsnkImdqcGJqfBweYQ8mA0Xm8VXs9Hn6jpzQLcBhSIrncpwum8fgBDpaWAVty+SVzoZcZjhXfR - Q/InnO74uDp9ksIMnwaiJIpCmQxXED266IqdhsfDkoVNDM3g8PM2XjYyripsCCuptedjLkR1b35D - H/DtdCHnbrbCbRivBWI3lWB7jwfWlo4C1F/xgpVINcCmvaRKvK3qFWfJL3B4rYYWYuSsJicHjiFV - S21BajYKe31VwCBdxwq+z8cIKwv6gm3PJ3AKF4xjsh0AgTdNQOOV3Uhy2du25OZro9NtAeQsZi7d - 7G1M4A/eJo+i2tun7Bx0KGaPwWtIGmtbogkxmJmIwX5aYK1/BSWDlOwozLN38rJ5IgID1WwWsEVS - XltS590AToEvHF++72wJlLyA+UYZrKZGndHkJB5gdPQiovKyERLeM2zpYT+fxLmLSraFgmCDwTMe - xGqHfthYc7Sg64MGX5r5HE4+65doPR+TmfH8YGDl5thBrAUmkc3qpW3i7dDBLe0LbKyXOVuLQGxh - 0tUKdrSnGa7Ns5wBXIhMlPw90pVKwgyTtryR2Pj9MqI0sY3Ux+h6gI284R/exxtzweaRbHRhzbpD - H/WSkcf6ZMDozFUJa/wVZuk4E0DtufYhq15eRDferbYOwhyBRTiqWClLCyzNDzWAOlWEDUw/Dj3P - jC/plzTCqmnz4boeIxMc1HzEN9btM6rmdgrbAK7YrZxyoOe2rFA2HViPmX/KwOTZ14Sft/4hr72+ - rwTAUeJUyJLHU3nQ9RBJCSy/ckUebx9kWyS/D5CP5jfRXPfq8J9n4sLb23xjlQQbXeHVVuFTIR7R - IvvnrOmpLFDZqcK/+j82b4mDwv2jEbkdrHrJwzBGq32csZL7dfZ7BV0DceTn5KqbNzqevsGC4lg5 - ksJIFMDG/teDraYonnT0dLrypiIgK/k9ya3bXwzk9L4F4wBO5DxdSEabd9+CW2k6RI89e1ifod38 - 8TnssIxY//KsiGFWaMbOr3Vnk/PGgt6t3mYaDYJDkb+WsJS0But8R4f1M/F7d4JHsHxezIEPZeAB - UfJZYtYRAF+9ri2YmB2Lcz7xtX96wjkcT8S7sTLgyzTlwP7Za1NDz8ZXgXKoP2N9PqQ3h1LhAm2w - 4zUxwk3RttC4RPDj63hefe1MWcHTO9HQQtFrSUABqZ+SCxXAJ7M01EW29MJRB3/rFzb3aqBlNbd/ - +oCoAJuUj2C5geboJVifxa82/uGfobiI+AWKtc93unmQuwqYhG6+d5l8mAMo6rQnWpR6AxvB0kd0 - TGx8LcuOTljhZtgsHsE274+UIF8sIRciB9vgtW/JsL8SaGPukTj48uF692NP/OMzOrjbGs/Z+QbP - 4Ibxa2StgQPXMACXso2wy/GKw4RwlZGpqvWOj59hnJZzBC94fOJHtAnZAi9yCk+muBJ910tb/VwP - /+LLsPSZrsa3UdG0VC65/8zFGWYeMPBPn16eSko3rfMC+Euz6/w9aJJGHvdjDv/4YgCwCfb1WuCO - L8QsGidjIx3q0NRkkVz1GIRjYqku0p4Vg2X7ytZLVdgFzD0BkFDUNI1vnm8P6UfP91BTaHTeDwND - uTfGmRVASbfLZicwcGnlcS11AQ3OdwjMSnpgj18SsPrKNAJYUt9r+XLNaKItLeL1ZSXG5AUZCYQl - RetJysnp9305m8duC/rDU+GbvzSqZWIKtmzSZ6aY1mGjBykGraFNRD/yN21TS6eC26OziJZb77/6 - 10vPV6tglVRC9hePkE75gtWU48HWTs8ctCE6EQ/sL/F7hioDrz2I52PyC7WtH/sCjCutiCLfjyFZ - QOhJjn86EMd1sLYJnivAY4t1fPGDOVylB7/AnW9gJ0qXYUFXZUFxiLR5/WE2o1WhuSiuBUqwn64D - bT7DAYqnNCRW4dbD+E4DE8WV9CSnhVEHJjx7rrRCXponK4qz9eh3KvQf5x4r65yHvTnkPTpUkTuD - UqD1WKVnE2h5WXhCesidrYiBK2nkXeJQLw71Vj+WFoXLc/B+eWkDTv+y/r/rff4KkY55RkzofZ4V - fuUC81cfXHhjjBqrVdNnNNKHBj7Q7eixu/6dyzRoUVtLPNHYaKy3m5xAKF2u/I4PAWX/9DSideDx - SJWd+fObdNj3ukKubkTDnV8m4PrbX3Qkmxzoq8I+gEvZRPjMjlw2+uzbR9U4mN5nRB1dPfU0QzGJ - H2S//2zDCJVQuQs+NpV+CseNl01osmLmiR98D7vL4S5LopX+Zt4dn9p25vECo/ftjT3VTsNVaeYe - vBrX99oqfINf7BNXAt7pSRy2iZzVeL0DxMnwjLN3chjoIfoJsJrISBwx7OvteXc9eP1yOva+GrMf - wXsIEJ+yAzaSqxVS9S6o0DmgkydVzJUORcF5cFbygIQnRPZZAUwKxS4447Ovnuiw0CSHy1dtPa6O - AF3U5pYgXXkhrDTEDknf+ZF0hfw2b23dZtt21GJwC84nHBmrU/NWJVhQn30bP/xwDdfD+1RJX7ar - iUfXHCxK0ybo+W5novghDtdfJ3KQ54dwBqgatY1KAoSq1Kh/9QR0VFpyceebM9j132dfHyCYzURU - qvyyBV3Pyz994xa57VCqQvkfX3FVldcW9CwE6DzVASviGO1H5BYb6YvPE22Px3nlrRZuhx+H1Th2 - AVmB7YlxvQ/C+uZPjfbMVEjB0I/4JIbngTzufC79+RXaJ8mH1RqcBO78cD4MAQtWITBVsNpoxqfk - t+74vDGQvZq/+XAsnGzNcOoilywTCac4GlbwmBcRf8OCXDD9amuOzy7c9THWxk+pLXLSBxB/3Iqo - g7aGP+bwNmF9fCVEO01nwAi2I8O7yX+w6+FlWMYliMWq71J887UedBdOC2D8lk74hapJo5t2UWGd - 5fKfX6Dt68/Aokojr48NI/uXP/r1MWGZF6GzjsIzgsrpUBJL2fx6it2JAXs9we6uD+hnake4HQYO - n5Aqa9y5LQPYbtyV2F+NhkQbTxKcaVx4x1W3tX/Xu3QyxBkK2mEj9O2havyZOD53n11/BCni1cXA - +tGUa7Zn0AEeb+6M3WM71WsRrA36w9M//kcjiz3Az9v8eDT/Lc729JYCLJaTk9NBkxwaIHWBB6WI - 8fnnhVn3/Zw9WHDZiM1+s8BSFWqOfqcUz8vX5Yc1PJsFBEiYiCJmPaVWK5Z//JWcRecU7vq9hDa0 - WHz5vBCd6scSoVd8dbHHHfWQgRc5gT/BN/7lC+U9w0LusurYs60g4505CFD8OZJ9PZeB6l+2RA3O - LK85xqbGwGuwgWvHpDgitl9zmXPVkZRZA74soxLyj1MH4akZb0QmKetQd1M7lAI9JPd9/VfWngLp - cu2lmW2OPl0mGBfIb2Vr1y+bs6a4dmHmqg9imumTUrn8bDB7KRlx+e5W09/Yz3B5nCoP7HqZ8qaa - wvMjof/8senz7k04TAfRQ7vfyMGLLKDLUuokuc5yvbFeK0iHMN08vms/zp7vKZJTB87JkGXahtcu - gcMIa3JOrr9sM+pKQBk2Bo9R1ZtGNs1QgRfk5fzVDkzYufO1BLnPJuQ+tZn2ZO1PAKVLyHt+MS31 - 1E6zC9+pmBIjUlu6imfJB+jdcETd9Wp3VaYCeLf3RkyAT4Ae86YDwa+TPdC0prbYG5TgtFWCh6Lt - Ea5E40twr2cNn1HvhVsWZoy0CluA//TRsh5DDqKlkIlcPAdtGZlw/sNr72B0p7ArAjGGEjqdPBH1 - XkZtrpVhq++Ds8XMBTPn3ST4qUxAPL6k4frHf4yXJZOkyj4aY7x+8t/1EAcFZj2W8XuG68A32L0e - Tg57d3MGXh+ivOu1T7bs8fzPr1Spcg7Xvus4sPths7yJprMxJrIk/67PWE+fo0PV0lkgblx3pjuf - Yps3NeEef+Ss3TRtkfOhkx5WvuJwYZRhEc+SCnZ9j1X7IVCijViC6nNl9nz4/vkFKWzK4OgJqho7 - G+GrBj6sYiVKcyRgfseWC0OYqjMDTxKdrvsRJPMtaDhWpA3M7uFewuvp0GCcJ+dh3vU4PAlkIufn - 9RVS/fVb4Na3HrEKlDgrvEUJ2OsZwSNr1X98Er171vZgQLKBM15JBbwq9Ykiua9wVJKrD/QiSklO - +uew/MNnVa09Hg8VnY1h6mF6dMC8PFDpbD7rL9CP9QQ/u+kTbje31eGuF711/79Fy5buzx+b+cju - Hcra0wbhhXnOq2Vc6bZp7iZBson7EUhr4EWjS6F4jzVyOk09/d31sw5n/d143IU3Kdt+ggJEJGi8 - TenHbNiFDhC+JcVmcBqz+ftRRimAgzovZvVyFlbtAxgd3YhkDU/q1airHgXO6s7rOsNsvOk986++ - +4X7rne+MqL8OL+9g3eaw+XcLgt8J/uLmKl40BZ3CyRQ2YtEovNyqhlGBT48W8WEbSMpAY3dWySO - Lqd5nCHO+4m1bwp7oN/J01c7uqGnl8Jou36J6nVSOH/ejwj+6Zk8IKCmVGVkKIuKOf8a4masRfQN - 7n6bt8xSr23u4VVKf/kugK9fr7EemLC0nIR4gDCAfj8KlNjIAdhINj6jh+gtICWR2plGANV/+xtw - KLW//YLp3/1K7zOKvO+O18yenyL5eP3MtJnu9M+7vk/1GJ/4lZ9kZ93jH+3rT9zr/K4X0Vg6sJzP - G3GrYRiGXY+L1q11PCCG9kBP9dmGd6Y/45OoNc683z/48/sN1u3D3Q+WIaq/1c4HLxn/juUC/fFF - NTOf4baB8wG+74yFfT65OOvnXenwJwYZOY0fRVtEQ+ikFOo/fO84u14MciyBin7RXO35zYVwLVGN - khzLF1INlA/uFbSECu37bScwFylsYAYlTOR0ZurRfPkyfBk3k+DJ8Jxlku/SH//FSlL5dFt5uYXy - 49zOfcqyYJFunAUXw7Q98S3f6GT37xRmrvyYF2Xz6tWVbhLc+T4x2JzQbfebwP48iUvO80CMYRLA - T/QzjBvOracyTvZyPXHeNaJpuNxgqf6tPzGiYAz/8g09q4/usXn5A8vKX3XEUs3w6tN0ptx6zGP4 - HsQFJ7bsafyhPKrSNg62xw2qF66n2uphI2cvbO78eZ7IwqG//ahb8p7AJni68Kf/sWuUjdOfakuC - rQIzknB8qa3OfF0g6+shwWxzc8bYaly081uCcS9T/ju5M1yAdvDef3pBSc4yLA3T3OfGD9nAHN4t - ZLHl4qhPZY09RFuKAvhTccCvIqATWXXEM61C3HQK6xk+CvXPv//DT0p5007BqwsFfBrZtqbb0Wng - hPsbNrqDS7nP76OjzRpVEgTfONuU5in87Wd42xBy4C9eYGe0IdZQ0AzLZVMEyH9GHivJFmcjfH4F - +IP3CWunpx4y7hYIaKq/BTFEmNIFnBMZ7v6qJ8SeMyyfT+3CPz9eEeGDzt1q7FP0fiFOClcbqBz5 - HarO8z6Vdj5nHHoYMurni0jOrOuE3D16cTB0m40Uqi2FS54RHSgdF5M/PGWP5aES+9S+Ys9I3nTB - 4j6T5M655FQKWz3edZlBqXt94OjIs878/WkmjFnxhS/oXIbU5mYVJmJ9J67tM8PnVMsdOn7sYubo - FYOFOawMVCzI4b/9ZyIEXgnAKNzJbb3M4eqpeITa5aN7YNXPzlrG/gFqy/M7H9lwGLYJXiD89XcF - y5ygOmvxMsc//4lgDOxw+dvPeZ+EhESkz4aua5Zdn55qr6oYQtdj9EtgET194l14C0x/+m3P71nY - hMbZ3kV4+PPz58V+A2eLLHaElliH8/FnBs6mJNYmvU1BJQ9LJ2C+TIv8/zpSIP7fRwrMYgsIfr4+ - 4XrLbglCK1iwebsMdLt//X3wvQWxbPVbPcmpX0KzuLzmiV5ljU9PWovcJTWIy4lTOGaJtvespfy8 - nd6Bxt+epIH8nbnhhDlcKY/1G0Rbv+rYTbv3wEpxLcNWrqEn+tB1NvaBTDiR1+jBtHvXWyBaKYwd - S8R30bEdhjvXC/CfIT/37fPrUNJeJRDmi0P8Gxkyui1dBa9GL3vQr47DGuN3Aeco0rAtSHbIHhHd - UIfdglzM92FY3kJewufjzWOj2wePiLbfo/l0VYm85Wy2gNGy4ZXveeIZgeFQeR80xHjWC985ra23 - Dvo5omqbeoehNB0mYGoZMqkhYfnlVxn7uI8SBMp6nsnlnGeb8Ug6cPAeaA4XXqL0mTwWGCieic+q - 1tfvwrJykBWiSIoGx84yvTQBKTeXIQU2p2HjdC4Bt8/pOQ+R0NT75xT9vCAm1r6+vzLjOSgQqcMX - 16kGrr/jAlqM/SEKujrZSp6yjcbzbGBF+pjaxnFHDw6HBJGTXBqAIdBT4VhmLNZwX4FVx2WBMm7C - 8w/VMuWOeTXC3l0zbL6vWUbn7cLBuHrknq9yZ0pvrRSDe+l75PYp6LCe/CaCg5uYWHbvisNCqZmR - Pt+4edk+qsPfXtsC+4oRyMtWErr0hSyj5Bz8sNuPV40/ni4tZJ2iwO7XwhkH5jSCzkX+kSJrQbgp - z7MNiRlJ2Hhc7xl3WN8pPPVxQqxYKem82ZsOxTmbPLQ96poI6tYhPopr4vGTGvIXehpRw02EXH6R - DdjRn2PoEv6KzWidhhXNy4ICbiRYvRTfYUFHvoQJiQ3i+sMY0ub2sf+eD74rSRWyFyrPEHanmrhf - i4TTS1IC+Gz35rzLdB2W91g20MKsgp+sYQEWfVoVTmekkTx9RWDZjk9dfL/NF4n6U6/t329hI7w6 - b72aEaCGBkqBg/Xe/RQkA01WC8Jf8hKwyYTnmttsyZTszb/hM/8sahKqS48u6OzPwu2j1nRbygpA - Tnpi7HM9naA0jnDh3BonzCCHjPKqY/RXD3J4vVP2jIQF7vG454cGVus8btKXincsm9cDGL8aMSGV - mTcpri8lXHXcFfBvfTGlYJi4d2bChf0t+E7vSsg+Q5jAp2Ji7Azdw+Fs+VfAOaT+LDrNu14vkhXA - 25J8cKgpVsYKp04FVi6pONDQEm7I7CywKcuPWIcYans92ntoJ5Hg34kb6HfaBJQxxxi7z8VwGO2w - 9LBQBJkkNyFy+AvFI4zvw2nmRfUZLgEz7EcMjiHGT+2r/ZYD10NX+rQ4oH4fLmCULcRevDs5ieoz - Y/Rvu0HP9q4z/KF3+JGe1d5t0v2wTBQ5455ixcHa+hxJuqEILOE0FFAgQofdy/GrcW9WiqEHZBXL - sXOu6elx0CF9cJBcbtwhHDv2K0iIDj9PXCQz4xWx4RDsU+yhThiHkb1XAlr4x494Pcgc3kn8DV0c - +0G0gmPpqrCnAt4RemNvgE24RPanh17oTvgu3x8O3yXQgzEaa3ydykvIOiFNUSdnH+9gnJNsDQ+Z - C89yR4l74dJs0F7ihihr6x6ckjhbM4aowIVCgJ9he88WpqgKdHGsB35urAIYKtzKv3gjyeOsZ/R0 - 27uiblY0v/vkTGl3hR18J7qG1f15Dt8X2KD0QxQHG+M6XCcZATynnkJubpdTpsoLX3xl5wfxmkSl - PBTsEtppfsRnr05Cdo9H+HpkJbEvTuDQC8UzVJB8xlo2ByF/qi0OXc5Jud+PlO1d/BG6XRgJK02j - ZOyZiB14J6ZGLvk+iHI4ciWMm9HCgSvmAze9YxNphFux7j84SibV9xAomDMOD8lKyWUOI1jBrpkZ - 52kP9HOaVdhYbu+tS7eALbu9LXRuR4yv0qd1frrVSTBKdZtcPe4C2OQzqiDa3pQYH+kK/sU3e3Hv - WKcgqLlj3o9wem8PsuPvsJVSO8Ky3kqC4afWZrd6QVg+ZIdkmDoDJ1FvhkTqnvihavbAPOeLAE3t - 6WC11jyNL6xTjtpXzOKzHq7D0t+tHKyqc5jJ87VvkT+gDqM1mjx+x1seHm1TEq9iSSxXdwZ+q34p - YJejhR11Duj83GofAmPfz3lPYv3vef+tt9+AQ7YM5sGGt0M34KvfxzU7OuJBnGA54fvj5zmUPxEZ - poE14+QQ59rq3e0cntsZE8eoGzp6x8yE81kGGNftMaN7PsDX/ffFaQ4XjaJa42DHEAur7RcDkp6c - BpY1noiTZTlgF5dlUDaNFAe/kHUYXOccdO7bgby+4QXwSa8kKFMPr1kwGL0ev9rXRHUwKySoX262 - wSDX4VPlLGwAEA88Yw42VGP5Q9QxS4aFn6YcTge/JXclUUPuYi86kh8n4G3mj623e/nwYL/qR3z+ - PrBDW3FmAHP5jPMqnb+AyrfaQuFn+eK9jS5bb01noeFuZ0S5A7Pme5aLUCq49j5YNQerF/EB8vtI - wBbwkoHVzdsMY+QsWKV+n23zrbKQOB9KklmDldH0Zs/ochNmosbcCqjl2dZffGLHGrrwu7KRgJqx - vOCL+5Up/7tYPni7C8DRs8A1f+1eHbTx3GMv8FZnk7SrCTXCrFg9eWW9xDOJQaeOV/ys9Rqs6Ct6 - 0ByCEtsybp2NyEMB+HPr/dW3bDwnaoSsLRnJuVAaulUl7SGX4N5jXwhm1EL8ArfC5/Ytkfewldpa - IvTLTwS32TVb9voHrVxQiX82fUoF8cvA6RY42IgDeWCuXmzDyjYlolUnU9soSyN4O/Lr/OWApv3G - YK1QJJeQPJZbFm6BbKl/9Q2f/GsNeHBRfPTyfg5RGqvWlvgnQQiZ70q04qjWqxQPMoTPu0cMA/LD - +vY1AZ5ejYZtzkgyiqMyQPGxyOcl+H7pFsVqK5mDXxK1da41vbCpBMpP9cB2Dn2N+sdfBdqB1XAE - m0vIK/JYwFt9b+bvnh+rFuUcnN7Lw2OF8g1onFBP2usvudfPuOYc8ceBw2My8Lm0Xs7qRUcf5sqz - IaeTVjiU7RsGbj/F8XzWSgfmcbZicG4eNT5H5US35PHo//B/fzOO7LAzB1xQQWPdu+LO2c5PN+i6 - 5nsW5tmmDOGvPYJPQcG+e39r1JI1+T/8OojXbLy5AgPaq06I/6YppfxdjGFy9n9ewU9VtvVOVkI7 - Pl7I6TehYRXkMkeH79H1xl0PbIEsq+iXHftZql0OrHpcykizYwtfdr60yFvBQV3sD9hzDr2z+sC1 - YPgtS0/oJDVb2+VpgunmO7hw2Ge2uU1to2NacR5ikZFtaRf1YLxDl8RX/ahNcwd1cGhxiN0UhNpy - OjIlrMGYk+eLhgM9eBdbmo36jPGYmGBRknqTaNay3iEjnbb9vo8NRFtN57rpi2EtsCxDcfwx2DAT - lq7qLyvBfn3YdgV5YL6uyABD/aUEw5zLaMtsBSy+6Y8YtecOzO9HodiUqoyt18PRePvlR/D+fkUe - OPTfcFMGy4fcXW5I+MHnYevvpxxunOnO3BVm9cLeXiP8iJw7C0G0hVN8if0/fMA+eM/auB1vpiTM - NsFYEKKa7Z7vBh3x8sbFl/L1SkI2BffoaOJLl8/hcgnMDrwSNyWJdUfaxg9agPqsuWH321yGFY1T - AyKPqvjsWFrGdAst0UX8Fd7qzHY2c6dzA8n0zWeYavFAe/YQASfWjFkgCszGT9tIwAOqSnDLk2E+ - rL8Umv00kRvJLDrjWdzArkeILoZPjbZiyyEefA1s1N5Yrx8jY8Cl/Gk4L+XR2Zyt9//0Hz7t+cMZ - dyNHow0ccp7iZpgPx66E7qHfuxDko7bITK/CDT8hsTlDCLepL7k/vPXA4S0N83v8tuCkvrVZXG4g - WxeD8+GrcVhsx74NOJljXBjIQ4iN2njRGXzTDf7Fi1fww7CtyepCXewOM7odHW2b8t8ML2jW/tXj - 5dMJJmz7k00MFl9rwt/XGHbeMBA8+VrGds9fCxPJTIljcHG4hd1BBw/Y71OuGrZex4doQr5SeIxb - Hteb3LG6eKuFO354tZCNMOJ8xLk+Jo/iWWvL110ZyD7GD77HyKKcePU9uPjnk8dFNxIu6HgsRSXO - JGIJWHXWj5sloGMmC9taL4Gf3W06KD794gHjWDv0BcMc1eXBwvKbSmCJknGBUWraHjA4LqPmF3kQ - MW5OFC8l9drhvoCNKGckWTkvWz9GyMCq7s7k/rU/2hLvY6j3+PSET1zWFNgHE8EfnxPTYZWQvrOO - g98imbAywaEe/76vxA8Jp9WS1MyuzyQWvW7Yc6oB0BXmORRPjoPxr971lZmPwFIagAtjrrM1obUN - wWT6+BKGBljb5Wb+8Xts7XxkNu6XAh6eHk8MoVTo8L68R3g8hC1WshjUMwwiEzWimmGjin2HcPln - BHeIOyK/xrJeH0WTA/Vc3bEM0Bhun01hJP3K/7Dq2yNYv1vpo++Tl2bwan71VgdMhQ7tZs7vnT+S - 4DbawA/N2IPK5Uh3fNxgXA61R1dBd7bESSuQ8YHqTUKJKBVbJgYbWz/mhb1+Q3I8J9u/euwNpalR - caICfB+jz37kSnHWTBdT9HYUFSfGcAdb2CjjP3yOmfBXr6uiSCgJQuNPrwxUyuQEnR8/DyuZZjvL - 6QgrgPnVI+el6LRZaeQZ6pO9zIg013r9/p6VFDydcn7zz2rY+RrzH7/c9SAdT3qDIGs1+Po9PWry - CPgKLsN+pKk482DVL88AehFs8WuvVwuVuhG23Dfxhu6ih/wLZoW0cbpLsozPtH/Xf9im964Pc6ez - bceDw1tasGnIpN48ZYoAb68RUbJ90H/6vpmgf3C5xzzVa7gZpe+i25FdyRnVMuCvfc9Avmp7jzbU - yTZneenwy+Q20adhoFs3epa0812siyTL2PfdKYArfVtimKikWynN++DqyPJg+mIo1a2vJBxY+YLD - VWi08XUJA8hXGv/nD9Tr7q/94x86bKZsVJrXLMooK/DJt+4Oy7t5Asu7H2AXcirgJ5C00OdY01uV - Qqs5DwoC+vNz7nHypGTlSxMJNzsi50Vqw59ySF2wFNkFWxa0tG33S4B9wIQY2ujXf3oV/PHF2+Gb - AToPng/1yVrwqyautpyv9gbeTXPF9/IWZvNeP5DVsU/sA+SGLMuYLdAE/uIdU+/m/PFPaPZkmtcJ - OsNiTYoMTAPrWP9mDaD40o2wFg81seOjNEwsekTQ2tLRA2jr6WJDV4bRRTnjyygv2sKpjQdc5z3P - 4I/fJqt1gHYymfNrTFrKCK85giPKWRwXQVOP2kHowVW1PGwq5BUuD6DYKOff8jxkGaSURlUAybVX - d/59yrrrKW7Qn75HWvUFxH4aNrptVoHPy/oY2D0ewZDc3vt6LcM2cvkGERYEbLfiJySGBio4lXKJ - L8d1or/dT4V//pADzM+wRS7PQY/M0u7PqTXtv0cXOGt0IUXDKBr3F1/PXu9x0klquOmja4GrsW9R - G6mgbRziNnhYjt0sdEXjzFGsNkj3+xrLVWIAtu6uPVLqHJCXn47DUnzNCA1uahI54We6Hp0NAk36 - mPgsckH4z09b+8rEZlMNNY0T4IH4/jt5bMKKzvI8JC6IstAnhpncwCZz0AMSrzLeHYZTvR37N4fK - h+pgWeV+oBNdiQGSiTXilnkfUiioJZA96UHsBzMOP+rrEvRCb/qnH9fHYzGR3agedg4GzHpeVFM0 - 1CduXg3VyPhrd++gpL/sWdzjd7vYggk2JgqxGga+w9Tey4ZM/Tv/47vzn15bxPmGTe/dDrvfaSFK - U2fXp4+amv1ev77gNv/eZytcxp5npMbyenIWuS2jPlcs4KTWGr58nINGdj0Ef7LsE0W5xXRl9y7y - R8QQEjywMVDs3Kp9qppHLrsfQpljXYE/f+HCvk/OzgdV9IfHtoxNjfnDn2Gx+fkPX7cIO6p0WFCH - rcpY9t8PFWyEZ+cJn0tI/33/Tz9futzLZocv+v/wjxP4YdOpNkMgNgW+HMQ1nG7ZM4XXX7oQEw2U - Lm3y2ST4lBQia3MbsnKalBAV9pGcU/GdTZ8whX98z9uHZQ+L0rxGWIfgRAz35mTr7idLyuVw9Q5G - 8NFo8j148Np/OI/zmMQZh/M5/qfH5cNvHbY/PlRFPJ5RnP3CJWXOLXBd/U3s9vnVemd5mVKXOSI+ - P4VEo4sWmZBH0cFbzGtBqfllXSiR7eoBJi3rbX0JC3y1xXs+ROqDruZYNdCfmB5b2/kI1p8yRKI0 - Bi/sLYI/9Lsfgf747o0dMP3DW7j8nAqfr0w3/PNT//SRc4tUSs+ik8BXNrsEl04/bBHWVCAfZxsb - n4LWlL+vEdr5wczobjowicnaMC+qZl5eo1zz17VPwEfDiGhBbDlrhZIIjOWDJbqjuNlYfM0YPCKO - YC8jlsMbLBP/1TsP2nNMuQg7Mry5hjIfEqetN9vrGuibrYUVdB2ydSiX7m99iJa0RBuc+NKCKX5e - PYZbmmFdLKWA+FT65C4WoCbjTZsB49kvctrzk/4gkmDn/QbiGbMWjvX9J0E8bxt2S8Ea1uXAdVBe - C32G+/5NZTqPEr4ej5JcdTEZtuip2ujPvziN5ppNAv/swJ//H6Wxq60rVUfkm401wynhdn+2tIB3 - Ah9i/3IEfkdHOvzVC4Jt+zOQyqwsBAwf4EvY8hkBkcXA8jJp87Dz29Uc+xaeOKYl+V4/1uQzyiCO - c4VcHqd90lOjj7B6Sz7WVsKHm2ocfXj9HwAAAP//XJ3LjqM4GIX3/RSt2qJWSJGCn95BIEACwdxy - k0YjSAjhFgIYA5bm3UdOtWYxaxZG+Bgff+cg+O3GzWt76ua+ubXAeC0y335SFECHCxZ2Q3g4cHRS - HT+GdZ7T9/xLNHtGM6vkDMhwZV0aN8nMy1+WpA9Ssy/DMS4WAVhqLbncbYG0GS2nCKzX2SOqjF/d - 1AdfGWx6t3F5faq6UcF5KjJ+OchhfQrxaXE3wBXviDC9UiL6cisNxDi5i/1ghowXKjC3VCdW97WS - Jva8IFUPe2Tn3kDZ/XzC89ryRI+PvY27E1699YbWr5ViL+1Y5GCZE3XgleZMx8taGcXESH1ksffH - 6G9fI+RgTsTi7hbtaXs8S2uiKujcpLom8PqJE1tkmC7si4ZODsc33zzZrCtRe13SMpVdP+XJmxeM - b54eJFxMHC+/F7h7jq5o9oY/UF/cUsryvW+/bUu5Rmm72BpwKtD+Ox/7TOJDJjF+Mny5xSqcGA+E - UYArsqtNRse8eq3BBWSRrdtXGuUOei/tnplCXNGLJfrEoggJHih685lZmHe8GNXlmQSvq0qXWbWu - QTsYN1fwfI3SR3DUwVerhK2/xh6pmkWwcNcN2szxopje++XFKBt0CwSzEB7RIwBlb2XuSNQsJEar - xPKC1zfIV8UHnTenTSqfbGgR81fSLGoXHZj/Rft5+aBT6AQxFNZzQVyZvxb4UGY7MOuQssZrRfHQ - 8Lo8bNcScUv+YdNwvWoBP/Y1+/FwG86G7WeyuBox2SycWqIA3AouRt2w/SjVlue+q+HyZQUoYnrD - LC8E+ZoYxMwONKTkhnZv/RB3faNaz/waXEL9E21edq6NkV85YCaPlOVDXDHR1FEkwY5MEtWLupu9 - bG7ksJYxUkcn6OZGO+vwXq+GzGcd3tW1CK37iJCpQGl/56cjdkwUfYWyRj+r8Son+nAm+4k/FxSv - PUe+QKOjfSCY3RB2qgd50W6HxSkndOQkewdlJ8foyvTUhPc+gqwYs6E15jSZB7NQ5K465yT2lFKb - p/s4y1dec8l6txfCd14kMl5JfHFr0iV3EQ04POMtWl9PL21ifAeMV/4kSiB+JX1YFytZe5Qdunuf - rTRetsrxfb4gGxKANnuBlEMpjhE5WzRJyM0PHDl73Hl34cVOh50NeN+8UZXUMumLbTtKtuw/h1Nd - xdq4iagHeWadyC7bZ0lP1eYo3ZTi6uLVa9cJi603QrK6WcjVcayNhLs44BQH9glEULK8tD9CzsU3 - dr6w6SzLhwjqlagjO9DmYmppbwEOs4Ao81aj45QkMYjD+vzmc5Jg+xdXclf4iMyleJFopdQptBUV - 3Fdm3bVp9xAzsHirYnkYbw+loirwp1Lw4+fPv1hB4KNubmnFigE4nfCv/6oCv4RffR1X1btY8DH0 - cZZ+/P5TQfh4dU39wn/jpkyfPesayOJq+V03+MANjqv/XfrBBvznx78AAAD//wMAo+o5QboFAgA= + H4sIAAAAAAAAA6R7SY+DTLfe/v6KV9/WkcxkqurbMZnZFAYb46wAYwwYY6YC6ir/PcIdJYp0V8mm + pW67G8M555lO9X/+xz///KtNqzwb//Xvf/71LofxX/9t+9kjGZN//fuf//4f//zzzz//+fv6f70z + b9L88Sg/xe/tvxfLzyNf/vXvf5j//ZP/86Z///Mv3Xgq3swyb7CaqsSBrxi88HFIzZCrKlRCKjBn + 8nx9T+qShSCHsTcesPc9++oiIqtEiF5ykrNkTde9pATwXbIykTl6VdfH266hpZwQ1h3XUfn+KccQ + dmOKjZ5p1PZI3QGyPY0mdk/knoucTIOfPb1Osw4qSuDw8kBRiwx2rfbiLO/E20FzzWyS0MIFq9Ck + A1Tdh+jBefqmY28NJSz7KMM43t36OXQmDzrpBRBTmQ7qsluwDo+tj/H11tKeOrtCRNkghlPtIk5d + xWIV0QSeGtaHR+ksx4/rA2j6AXl+8j4dVhLmsP2ub++QlFM/e+JZQdvzmxpPUVKW+jMH3M9ywdFp + t3fmz8ysiL6vEzla5p7OgtdxsDoHFlEyzVUZY+xFyJiqhs0v2oV0gP4O0Xl0yN3ydToWd1NCDNrN + xDqrYT/4dalA8O2eGPdnQaUZWEvEM8KVZPrXBVPkZLpoUtXGZlzqPQu5j4jcIeWJ3PSRQ99llwPI + 9QLWu1at+M/wlOCzuF6xgeWlooFp+TAWbAufxFfucGxObRSo8wWfwxqDpT6daphWwxkr1Amc9XSU + athcRntqvP2NrvMTJ/AyaEfic0uasuMr9WH9llLyZD5Vyn+NpENLrxFsO/cdoHfmDFFxbnOS2nnl + dHsl8mGdnHYT0hmump982SFFGQScKVzhsNXzs0ImGRJ8tC2hWs8HTwPos5eJrsfvnrGPwYTY3o3I + Zavn+AkCE56Vh0OSVwTTtRgqEz3jPCXu7qSlq3Ld+zB6yjYJC9FJif26XyB3lyDW8/Cc0vOp1BA/ + 1ilJHhXup6aPdPGlHT9EfyNPJcZdWBHr1hW+LeEr5FGWSnBvfGdiWreHOp/gW0LoFC/klKIpnd/R + qqDazhx8x8ejwxz6NUGu/0wJ1gXb4Rb77KFgiqppDd4+WBm5ag73RAqINNmWOgK+HUD9bXfk7qRm + OpPPKiKm6XiiAY6nS1DYNuiLwSa+OZfhbx7R7dOeyOO6l5xRzEYTHK02w+bBNMBsCWcfEUYz8COB + bDXXhy6BIbk/ibz/JBVXXloTMBggbAuO0s/hvYjRWOkYO8wzC5e3vTR/19fFQ1Cxej37SFHPApH2 + mQ8YM7UmJGlJ5Inb7zP79nxBwnKYsF2rVc+5RtZAdap9Ii2SFDJ9GEPo7xcGXy65Ahbj1WfQTpg9 + jo+plLInSeTQa9K46YCPR5U5XpEN/Y8IsL3hwVy8pQx14vqYuOGhOAzvcxyyx/KKrbguUj6+2Qn4 + 1K2O3a82VHN61UuY2S7CcnvTKi6yJB01+/MdP3z7RVd6Oc7QOzT3aRXoI+WaPtKAmZUPrLkoUnms + wAneji8eO24o92skLBx8oBiTK3N8O3PBPDjYF5NN1LZuVSrWBoc+d/5LsCEnTl2bPAMNA+6mKkr4 + cD3fuwK8TX/BWk0WsE6K0KHLoB/x9Xilzsw2bw523CROy2AvKfsEc4J4gfQe169uyF3TMkAnBo9Y + S/2oGoRd1oAbqXQckgiA+c1dGyiIEoMzIdYd1j4EHtITMyb2focdrvM9H1he3JCYs4pqlC+7CGaB + wXvC+VD21O1PAtwjKcOXrvadRX5rJewFScWJ4CjVOq1dDDb8JMcot8FoOTATZfvIkhMltrO+Egki + 5XzoieFzvrpqnpIjFcMHjtRySte0qxKkjKFBpFRVKHe2sgjkHysg2n78gNm7FR0CQdSQHz+tUHh1 + yP5mKwnH+FSt5Ue5ALtTV2wf24pOohdPEGZG63V53YbzPrmZcHx+ek+0gqKnEmEU1JjrSOyxn6rZ + ZrUcuiesYeMyFg4rnPkVmlS2yU2LT3QOncaFsm2w2AGumtLmYbVQSO8yzqkYUj6wrwGwE24/7SO3 + 6lddJAnUdUHBtvNQVHZ3rkpUFTn83R8gPt5ph9chD4l5PpTVD+/hdGpzfA80N50d5SSAcMc8p9e7 + e6SzLx9aSCMTYO0YHtJB486lOL+EkYT01IP1fC9LhDO5nA5vpezXHvgSyrXJIrpcSyrXpFiAckta + j+77yaFvxdPhjz+MD+LUGc2eCfx+lxO89z/Oxu8FhGYQEE8TSodZ998Ofstdu82TlHKLaENYn9ej + txezVzUfnEhC19fyxqfLzkjZWymLiMyf2mOBsPTDPnmawDy6Nc6fXw3MgG3gb742vJjpoigXEzGl + dCG3ORWqnx5BY6VhD6nyF8zBbpjhhi/4mbGfisENJ6E/Puz8Z7os0bmD6sJ8SK4JpTpdfEZAN/LS + cVbcbw4n618RlinISOJhzpnDexujTU/ga704PYN2UgTG57sn6TKaId/sVwVlM+mISb5uz+k2asGb + LW8EW5OccsqVD/76724nuJr3sWqjH56lEXyHWz9GyJf3eNNTbdgW2qBBa/Zn7JuHNV1N1eR+eglf + BH4Gs1+XEgJYxxhTeKrmQzq44iF7IK+s+Lha3Kry0D0Y7ljn9TdYdRt1gJVag7gZSdI//p4uu5g8 + 1mkJ10q779Cy7r9//bVw8TlGaugyRHo/abXWu0VDPg1iYt+HZ7XIb7eEn/aZTTxNnhWVHncG7m/a + PDGzC8LuO3cd3PjAg152r/qt3xF81g5OoXV3KLIEDrbYu+KTWbzVFj15eNBuc0jUbT65TwwEOF1g + jI05FXrqOuUFvTtbJNZbKSv6QquPrMf77jVrIIWTwL0EOHKXNzFcJ+nX55nU0KovIVZudy6d7+23 + +MNftSu5cELGXgOX682d3mfKhnM4KA2Mluk17WNZUfndoash1aGJLYimdIH6zgZmxdxIxHnIoS8k + +jCtpvPWT/tw0IuyQF4UqeQkxp7DHMOXiUYpORCXjnM6n5+yC02yUqxJkQrIFX8Z6Jj6G5tDd+xp + 85BbYBx4g6gvUDsrB+0BGk+fx+fBXsJFdPMdLD/+YYJXpKd8ZUwixOL3inU9PvYsO+IGBOp6mVaO + ncBseGYER6L0xFvnXl2iq1mCs7DnJ3592OqQVuZw6NuPSszPnoDpp89GaX8g3sWM0/nQiwmMUIuI + mgaSynq5ncDrXewnJPMvZ10NLYErX5keX++IM5wecn7oMrMjPt1/QmqfHRsmjpji40FJqvk2R5FY + QG8kVsvicHaCi/anHyKfiVLGukFRCA6TNC3FaFBqz9MAf3zgfuKd03u6XKCpdacJ0J7rR3OqGRjr + vkSujr2k3925L+D5vmjTfPisFQ3fPvfjG2JpmZxSi4ISVmff8tY+PqaLMJs2vOaVh43S5AGp17mD + PH61OHpeOmc+qgn8+RsiU2njg/YcoX1zrnCY6UdK2GCdAIACSy7L6bLx95WBv8//e/5fXzia0JX9 + FtvJKIQr2VfzD/+Ix7y/fffcHyOYhMuVqA81Dele8xU4fAUXP2RedvhXH2tw5z1Hb60ms5qswo8h + AvhG7OshAPTV+zr0OutOnCoxKr484QFuetmDvtmq69kwI/D7vE5e7ugwufsLcGW+JZZ3gumi7nEA + +M4QCB71qKeJ+olhLbALxs1F6Vd0Pfjwu9Tpnx5Yoci2cMM374A9Vp29O9NCX44xiZbzp6LuwdTh + /BJHcnplVsWlA5/AyWrAxF87tqfSWCiIKOvOm8FXc9gsBBmcD16DrbT6pIuVRy0wnizvoTAQQP9t + OPfwmx/duAJnfe5PEfww94O3vg5d2g2C48FD3H1IpnCSw8pvtwA36XCZ0M4RKXn7UoDGdL161EYj + WONKYcRxd8pxvOEzu/lRCKf5T0+l3VYfxOnXhmx6ACyH5wHC474x8anxibqU6l2Bx0xYSdqpfbUA + vp3Q43AZiKm8vypNlpMN12wnEBOudTUrQIxgSrrRE75olw4t2tliuh40bM6PIOVz7j5A9ebfiHq/ + 5/38UnxdFKb+9puPdLkxtQ6Wy40jhuXScN458gxnJ5a2eqR0HOpCg+MyKHjjv37x0HsH78F0J/rs + e3RhR6P505fR8ChVsuz8AW79is245/p5JWkOnnzvYX3zn9NJM2bIq5JLbMEpq+VTthf4iPqIKEFW + UEpYPoIPNrSmA/cQ0/VliSvcB9kJ23PH0o8+BDtwrL3B28lyoK53Zcrg8/TdYZOzin6xrSIH1Kb5 + VPSXhQ4ZHicgFvmDGJUk9cts+QF6DuFCnLwpHZI3aonQMeyJvM+7fraMZoCH7InI0bHP6Zh8ywwC + KLL4VAcFWKJbVqIhebo//q34U7sI8P6KXHw6Xbp+CDygiEzT8viyei6lR6wm6H44HT1BMIizzFbs + Qz3S2M0/FOEyLt8cikfxRBzRsfuFDFIrSt1BwWZSMGAOwXMHf3xw+uHh+V4WULuEM9HNU+20tzm6 + QOGZ19hhi6u6COVpB5cnCTxYZa90vs35BUi3diBn/cjQYeN7YHtiie3rrID18Ykakfdgi+9a9kpX + FB99dH9d3AnxJUepDNkEtW9bIUe5bMLf/cD29X0S/A2/YE27PoabH930Q5DOvzzg8AkSrB3tb7rN + o4R+8ye9hobOb+7RQP17c4j9FV5gWVjkwimmE9E/AIardChy1HCfEBvf9dJz18cuAOpbsInCsR5d + XNXjIPfQn1hheKlinFXI4bIm319/qTMXPTuI+FjGEsu86eoaWQ03/iUW9xDD+XnYdYB4doJ/eDit + n3aGj1KiGz6CcDbGSkA0OMYYi++lmtNKmuB+0SXi4scaUvnktCAuGB8/V8DQ9frgfLCCusdBQV4V + FezjluewmCgOdAGH2jSDC5e7xGQuSsr7mNPB0YcRfp66nfrnD3dCjn7Pky6O+Wkh99Ce2IngMV3M + HZDgIpUFlgpm58zPA9fCrf/w6XBj0+XIJxngk8tx6mWIqvX5fM1QZ8/tVN8eFmB/+dKyux6JRfom + nXo0SOJj3GMPbf570oq6+5ufI68N6vyZ2hXsv7PuBTz3qabv3LUQuwzBxsa/K9jZGYjtY4RNlZzp + XOYTB4e8IPgovU7VxgcX8MsftvkJ5yVQBZTfds+JhNEQzt0bmPDSSiW569Wz2vx6jcj8rvFPP4yl + elb+/OJpnc4ha1T7DHgoJd7uHGnpLAp0/fN7zjS9fp8nB8YCPexk9dyT/MpGUJ0aH2e0ltI5T1IN + pqQdpynZkXDzFxrM15L8+Aq0b71i4POupthggrZf8a4R0I65JxO3pn44Y2Z24YHAAich0NL1Pmsu + 3BW1Rmyy+cUCLhewzYe3/vpxKQNdhLfnkcinRu552r4GUCd4N4XD7QJmxZUauHuIIz6xtVlxGoIe + tOoo9JoNP9dPTEXkHer7aWkLplpl/SugkKRPrDo7vR9YjlEgo+5f2OD8CKyaZ+c//zfxJC8B+3WX + CIZ9beHn+SICWhTlBDtuED2mbV509b/zgJRX1BPJnJVwCdbhApj0nHj891tXa8upGfR7mBP1RI/q + 8t3xOtj0HTb3Ytq3t51foM2P4ZMqW2DFTyeC++6mEzWYk2oFI+Oji7UnWJUutsPd/aJDduRmRNn4 + dCzEfQ0F1/OwyktCtR77RIG7MzzjjKuMinF9U4OKMglT/Z67aoE39wLzG3xOrPIEgHyGmwSH5v4l + d/6qOlRlpfovL9vwt5q7N7VRvxcgvoXsTR194WTCb8e/JnGrF/ti8xUGZWyQBO5bddMfAcjN6umt + u2ffvz9lESHTr2Wcjxqk4+F52MEtTyXS1at7eq/VGXkBXxA7BFpI1XqNURGF8rTXhU6dNMR4yNLD + wuM47+FMHLQnsPl3okXZ2VlGijKov7IA69l5D7a8TIC3lT6w5AiHdPn5+92dt4nj4zylF2Zygcr2 + 6JffOTPNDv6f3wHxUtHFqPY5fDilTuTnoXTWd54NokZ6l+ich9RJ9Ifhp6exZB6CcN7ySKji3cOb + T/WHLqkuaoc02988lC0XdejMvgEDq2ZYuaatQ8/d0sLzqCZEWWUr5LOdV8J+PDXEoX3UT/U5vEAV + eSo+eTlN6ZO6wq+/yREOfDhseAs1ortEOfS6w1c3pAGNi3Xs4KCt2N7wS3irnY6o7jqEpPeAK2o7 + fo81U5J6Thc/Cdz8JXlkC6NO53tXwtgzDzjxNaaaThqe4U5vGKI/grhnNz8KrsbpSuQX7/c0sK8+ + POf3D7Hm56Cuz/OnAT+9Ep/lOx2qLOFAP+IGYzF79Ys1xAz8Xd8p7x745R2HFCi2941rKeQWUYFQ + vggB0XYDUbe8XYI/v3EMGM+Z67suQa2a5mkO743av30zEH/5pLXlg6y5Awr49Qe7xAgsVVHU0HnG + OXEGdt+Pv3x6w8NRDEGdLtdPN4GrUOQTHIVA5Yl+qqGzk87Y2vqfu/ttCxV9Tkmi0ne4/OZrzaBA + zB3SHRrfoxg+vmAgNtyb6twizoZfPJyJyktxtUgGhHDT796SKWtIwQtw0M1aDfsbfy52jDjRPHr1 + 1PD6EfCBKQc/P07cK2rSsT9EAdydd2cPbt/TJEQN3HdXHSu7p9OvjpiasKuyGj83fptPQ84Jv3zo + eHtWPfELLYBWfQhx4p4NSsd70cDBCe4eqBKjX5htk+Jf2jc+4eegEvmkdmgJZ45cvhWf9v3ZcH/7 + D3wMmMkhcO/E4qbfsfcJ5WpC8cmHs5NI2FKXvqelDSZ4ffMm2fL6vg95oABOvzVEEShKMzOVJ7Th + MXYPV9BveVYBt3yI4OoDQnI8PxNoz/OAPZAW1ZZ/daA73hd8HLOXQ7DUDnDjI3L7oMihzPsi/Olp + XD/rcIaVtsLuVPJE2fzWsu1n4LszRaI9jqw6HICxwgh1CCv7ok5n06YF+lxP+m+fUE2O/Y3Bhs9T + 4ZBvunx3ex1cYSMTnccsWPYT1aAkAepNbp729MCXDHBc08RpvKh0HT+sgmbkW9g4qVU6XA7qDl5O + WUDUwdMAO2jqgCZ63WGF5z79+s7MCYbceMfWdfo66y2yMhDvMwXrmJcqdusPeGfqEj+NQwno7u55 + UH06Pj6FQUzpxM02eB69I/Gukuyw6HWYwG9/sfl9Z+aX1AWWNavYV/wp/OX/4glGdELDIVSX5ujP + aMsfiCG9X84cp4UJ25ExSYJ9j67M+TDDSLrHxNj475f3gfUsZR7NtYszPw1BED+6EmJlcJ0tj5Am + sN3PxJfmDfTeNRJ+8za9jlADLFV23e/+iJRzpB+hvjPB/YCP3lINE138+FWD3P0oWL+2KZ3RTrrA + 3/Pa/Gy1ju+vBPjcSvHph1c9iCXwmzcNQ+TMB2DMP/6e9v1ZcJYjH2ToFuPTJCrrUM2S+vF++g0/ + 9L6p5gPAK0z6o4dt4bL2w1VdEvhIzwdsT4ORTtj92lADyndit35ekn6Xw9B3R4KLlglnrVEv0ILY + 3fqtrfrXOGjwcw++Hl/YVr+2nJPBS6uUxPa1SzWUecPB0/stkuP9ojhM67kxrPbfCVs0Dx0idN8M + bnz043/KP8NXBLt9KnmjxrXhwN7iHFr6ufAO45ehdPeREqTk+oVgt7imXfmxI/hNu+lPn88JWwpI + CEsTb/6sYprw7aL54DYTC4dbuOXvMRqQXWKpzkXam81qwuI6yVj25i6dG/X5N7/ePSmnivRLX//4 + CSdWOldrOuzjnz8mcnIK6cJMUow2/Us0a+eBNjkuJuS7o+B9Xk4ZTn1C27/8TulfbLjip3oRt7wb + H7f9CXX7owht/CzJsYSjyq33qYFWu+Om/ek1qctjcSTox5NJbDuzQrrhNXiS+YLzJmjB9NunqF05 + EmX/vaT0il8Mequth2/b9YdrFDaw+xQG9vRRCqePfp9gcJM64oLb6Izn62VG0ZTbf3n/+pUEASa7 + a411AWjhYmI5gMr8yolyWvqQjAfNhhLnP7GZ3zTAJv0uA/hZHrGy5VOcBJj8dz3v1V0D2j6QmqHz + WXDw9f5YnbU+hxGSB9chz9Z6Ocs5NGdoSPoRK/EpU6lfaD4Mbu/OQ3cAt/x82kHZKlj8m8dBnoSd + qCdjjFVWsVO2foQzws/iSGwz4SnpX48ExAA9iRMerynVM3eG//qdCvgf/+3/4UQB+1+fKMBLpmKz + O6iUuyYwgHin5li/PzWVyW+4BLqPc6IOIwTLQkoG3rjwOL2SZ0kXHlkdWrP5QvyLLaX8iIsGHc6n + C7GO6FRxSrIykJEBO5X7UVZZuPtyQFg6jzjmXevnQ8pA5AbGSGzA3ABrAc+Hp2/DedGBo9WojJ8M + 3pjFx0/cTf1yn2Tv8OmtgRgnuQf0JXYu3BcCR7SBWRza+EAE5WOFRA3qtB8/4FLDd+seSMiUHqC3 + ck7Q1OIP1pJCBrTSWxGqT+FMLq+8o0T4JAn8SpmFM2/6OJPPzjmY1Nye1h6rzmDoYIVuN7VEr2zD + WTv/GcA2hV8S3Z+aw8x0vABBl1t8i2+BwxexXkLhZE9ECleFctdvk8BAGi7kLr/9kNuXUQudXDXw + aR33Kj2nsQi/w2Ugz/Lx7RdiGAyyhHwk8i3pU3J07yJMDcbEj7bL6fKirI70I0eI8WSanvRBpsNb + Er+I0R6vPRu3sojKYH8hZk1YlV77yoWWijxsfSo1ZHPyiiHTXlRPHPIFfBmjMNGpfM7Ye6QDmMdv + ECF7R/ZYUoITnT+vlUFiPD08jqlGsHrfhYG5NPse955XWjcpLdE+Kg2iY61J2f5+dlGdvmJynv0G + cIey03/19lqpoj0FyxjDwYp977DsZJWbxSSDXe+KWAubOFxeyidA30LGOHrFbL+UWWzD+2n2vP0p + K0Iq9o0G04fyJE7Blz1nhoWHXrw+E5s4PVhsQ4PQCEqfXNX915k7s/ShZJk8kfjGUKdOHk04qZlN + 0kAzqt/zhtZrfWEvnb/9zN1ePgKhaBHLBVE/uto5O3ze+ysxxc8nnRcz4tBNChd8srwb5Vwsa2hX + Si25K4VAx0RZBwRr3yX4LCBntA13B0317eDHktxTZs2k5Pd8iWZzD8CMgpvDh3XWSSY3JF2R3DR/ + 8+ik/o7OEzm38EPnluR9PjpT9cw4KCRQJs+A6CqXD9MKs/PxSNxQr8OvWjMdwtJXJ+FYv+l6KDsN + nvdeTlSsftJuLK8uctbyg6W3860WC/Mr3I9uSPJ2CVVusmcfPm76HTvqeeiX/LU3odX1CcaDYISc + WQYRqs5sSY7HAKvcx3VqSO5KTZzDteiZ3V7g0PMxTls9HMAv7XuARi0dcdSrcc+cLWgCPv5ibAu9 + rPK3q2cCB0UYX9K0S5fmtkgwnYoLDri3lXLb/aJ3XdxwZC1SxYSG2cEEjTcse0HYM+F1XSF/7N9Y + e3Omw8vPyka8zcJJiPWvw9prmkFAH5jIu74Ol6bpbZjIDIOtQp/oKkuKjvgwE7D8OE7V0jSVCT+l + T/Fpqxd3uUAo7p+p5CHhUlJOWC4e0vL5OXGxj8BC1jyC2Jhdcrl/BbBWk7cT84V3iG4tRUW71BPh + 6xs+pvujUFUqcZILn82lmza8qtajfdUPzuHQE81/yD1d0M1GY5EV+KIoFZ07swvgeRy+2NvqzwjC + PYIfBfVYXS9SyFqPUkFhfd1P0JdPzkrOfA6nc5XieIZayNLnSYdzMKVYXvOrs2Ky2MiRx84rpUMA + tvlxUZvuvsTKvqI6lgeYQLY4OsTaYUzJVh8I2VWeaPU6psSSaQ6MNxWweZXPVZ8fzQGeysdMTqrD + h+9EESeoZEY3ARJIDnez8wKic/vG1jYfcxV/JsTz8Ob17/CZzi1XBwg9oEKe1+uZcpMt+DDF2CVH + Q5P6ef8NV5Rr+Urc6zCrS53UJUpuskNSDWI67JNXC/cgrLGyBGo/5755gcmxijzRN8pwqweEnVZO + 01Y/QG9X3YZne9oTs9LOYKmPQAf0BiV8iaPQYc+XaoWB4lrkvOu1kP/xTTcdMuyxB5vOclfOaNYO + Jr6Ql6Nu/c5AettJE5LyR7pyDJ7ggR0oVifeqejtyzeIB+2B3A980HMPVRVg8SoicgKPe09u5Rwj + 7eB4WCEfKeWT+A7hK3ifiXGCl5T413stnvjYwsYBfQAd4jBG2sDOJHk+2pRm2diB5KY62D0Ul3Rp + NJzBdsxyEoqnBQz3R+Cjz6zp+Ho5ds6ye54hEJbWw9H3ZPSrIHkx8Oq5JMpJVxz2XuQQ7neDiT01 + 6HuWCNcOBdy+IUapsOq06ncbLIe1w8bLGCoqHuILsvXXDluGH6acYbwadN/xllf4o9Nv19vB5SHo + v+flcPvZFOCtF6NJnDmssi//XqDeMHJiqAdKZ0E4R9DvUx67WQDAcmleA9qDc42jG1NQ1js/IXwN + g4yjNsr7ZbXDDowfvibH5y5N6UssXbSEWo7DY6yl7OEm5tA8NE+y4W84+feLho59LmFf7xyHjSJ1 + BxyZdMRILLXi7kavwAgYd4ID0jj09BQkuMsYn+Ti2XSWb6ZN6Madj+R+O+zDLtnNNlJ0EJKTWJ6d + FdV2AG/SeZnmwVqrOnKyC0SVmeBofNdgDmQrgl1xD3B8m5y+09dZQ+8TZxN5QhKd6+Huw1XoBGyj + XUMX3qEcECvDnfiXMfQU4yQAv/nij+4dUAY/TPi92VeShboWMtvfR8e5jcn9JRQhRW7kooNqqiTc + F7eK9uoXwiIGCcHdFKeL3ko5eslG5jVhVlWLHHMSytnXiHF+dMNF854N2PAce/5a0IFljyViavDE + XvVNnQXuXhxoGbMnkrRvHSrdKw7CYL6R7XWVe3u8Bl/iEmC9eJyc9ey/fJTpQ0TOr0dPF3xYIYoF + lsWn8Cw465BdRQj73icW+NbOWlwtBr5yYfI6TGpAQ2hCCMT6iqXWf6kUi8UM0WOneFt/q3SnVgkc + TvqKZQWewtnamQKkfKVP86gBZz4y7Iqu8OSQq2jAfnUfwAOE3kNsHZ7XdDRv5YB+ehWXrA6YwssG + 6GpSRrTeOoE1lNQCLG3Aejv2YIO5OVkxFEeR8W7vp0UXKhcMfEejRpw65ujkt54L+VfgYGOr13LL + 1ADN3/hDTFBiwH0SNMH6e+mxDrNnOi1Y/etvojzcEsz8o2jQ1r8eMkMSToD9iPDdegdvWRySTtfd + jYEyLOsJmm6qrtXzwkBZYBRyPt16Z3z55xJ6IJu8dqgNlck0eYLWdIVe85DilA7WN4A7rKCpD2+n + dGbPXyhG8ZGQa2l24Wy6VEfZhX4muvXPcriJGfg89NRrnRcMqVZxnEiEd0eivXVI1+ILfFiuQTax + q9c7/a8+SuOl0z5e9aotna8L3+u7xV45rf0UPw0Iz0zCTULGqf3KhM8WVOp8ntClztUFCF0JwK3p + iaq9QNhlzyJAVbxrsSuF354c6F2BkJ1lHO23HD+t4wY6PZPgwLsbYNMnDXysEBFJdFe6zkxqQ+Ur + IHwfPb2aX0kYwSI+JCRmHnE/a9Mug6KinIlW7/dObwJGg5Y3c1i5E09lrGCNkb2dgPj1Ly3rk3no + eSHFziI4dCrZRYDmAx2weZv6fqVunMEzoOIEVsqE9Y3ZuyBmrZB4blL1i+qrHnzx2kyOZWFUrPNt + W+h2Q4u1kRz6KewTCXxwaGJb33chaU1LhyTkTkQT7Fs6bvrj4B2fCXET7ZSuVkJi2KsJxOb97gGq + XbcT3Pdkh83WtQA/5skg/vjaE8+muukBE91fKk/M1v3SRSjGAF6abiFW5eg/fZ4DhR/8Pz338xcg + 1NYDUY1m7WnY3mLY3ckR2xV77GdIVw9ml+VDTo9L3E9019hQF9In8b4D6GeJPhpoK2zmUaacwJry + Rw9K1ashV/41g0VuWh8Ovv4gWpuFgLooCyD/XK/EPjTffvOfLWSSYsZPS1Md9nGZO7TpfQ+Bx70i + 5x2ToXNcBCRYT4O65C/eBMO+L7HE7+pwPU+nEg5ZL5Bjxtb9cj4eOuSrJ25iryBVZ3p55LDV14BY + 32WhxHo/Fai/xhArfLEAelBUD+x0U8Ph56D0nIgC5s9PGkJXOrR2Cxfau0yeIHOaqs6eYgjhen14 + iyLX9M+fBOEeYlmw4nTNh2YVvyFZvMPdfNPvUwtEKLUjmg6Miys6fE4lxFf+QeTFIeEUvIMBGrKS + YsN+Pvu5mOcE/n4f5Y7cM29ZmaFV+ZG3f6vXdPJO1wxs/E8etaeonFjscrjxDZbmFwZbf+bwe+wK + vOkhunq7KoCFV/ZYi8y6GoYqlNCm97wdcP2eGlqzg1c/askpd179/Dz0HrAH6BIpP0n9Gj+NHRw1 + XiTOjdgVZfqlRHvb/ZKU8y1nvX6nGJ7Vz4Gc8k+ervYU74BxenP4pPgTWBRwn1B6/4CpXAK14vOj + OUErqy18WdwppT0qa1BV1xtOvU8AJsthA7R/3iWP2fT3XJ2LAoVF/8Cmp5r9tPRFhHSKTSInzxIs + m74BjTvFHsVp3c/jN7kA5wB6opZdli4huZRizlaj976C1JndWohErg84rDljQ//0o+JJ94nrRblf + 9feJAVWXPcg9jRiHvob9DngrT7G74TF1wiWBc+35f3pq/hyjDGWx5+CfP2Bv+4ZDaTUMHp+/CrqG + HttB/jlfsXu52+nyZIsBbv4TyxMq6ORDvoOjxopY2Y8vZ97wBGbf/c5jDa3ol4Z9BFBhzgM56a2r + TvP7K8FsEm7ED9eS0mvfe/B4vvn4uM3b+jrlAvjlH5rIL2FXR50Ct3p5DPe8qOsgiD7AgQ2wmTpF + OJ+8MEClGxpYcfFdpcCzuN88EBOIJ5X71T82ZhGfzrs1nMjzO8BBfDjEfSdhRZVovqDk+Iq816Tz + dMPbHOCdnP/0Wzhbc9TC92GasKLuOpVefDGDQyIesdcuVJ2VfebBOf1cyOmAnZThgMYAi5kdYupe + 1M/H27kEshZVOD6ZhTon1b0G1xf38KjRrBW98V9G1LSeePuRYrrSLxxgFrsOjrZ6MoYEMyjaBxvL + W36xCKosQcdkZqxEwSGkVlQLMLwjn0iOYqb8+xPvoHH6cPjXP+1P36332zzNR1L2M3CeHhjePoPv + B37th0e+5qjzGZdsG7BwRmY1QECfGBv5SwLzI6Y2tK5PFbta34DFU94ccurx4gnv7lGRp4tFkO/s + Fh/vMaa0FaMZSdHOxnZil9XWvwVscvdE8rl5qyvn1yba7sf7yP2xWo1TK8IbQ318ulMt5MmJ2QEZ + m84knGsISOym0eF0SZ7YvSyffsGUdj/94mWbv5i3IBfc9a+Jf3ndGj8xhLfcUCcmnuZw4+sCmK6n + e8L4xP0vzwGbv8PhlifOfnF30WKikSje/QPaRFCln37DFpRf1R/f3UgjEEPfOeHoavcM7PySYrmc + l4osjpxDoOvc5B/Odspm3hJBk4lNbHS4Tml+X214TW0fO3vgqvxenEuYt/Ds7Q8W5/Q6QTnU8vW5 + +SsmJFs9EeeLJnaXjwL4ju+DH1956HOLAV3acfrh9bT1C2Dj0M+Rfbmd8eaXndU8shAOiXDE0i28 + qXW7nRA3btPHG1PdduZ6OPvwx/fW6DXV/F79GkWnUcLq7RL9Lz/2628rpy1d20ARkFUFkVfs6tlZ + L3zcwROsC3Le+GNqJA0itV0cnCR22f/lC73n1RPa/PegP3wGrVX+IsenGtC/51Hzd4h/eMzg5CzA + VWgFbOoeV41rM0swfDof74Ox6EzcM/ehsR4jksuF6AzGavt//MUs7hTOnMUrP7zF2RDDainsxIV9 + /Aknbuf5lNsO+aNz86mIpAQjJZ17mYGmfcm08gJWV0YbSzgldoQtqTPoYMeHCU7IlH55LJiFK2qh + H+HCe235DiV7ZUasMOnEnJLYWY+gE//0lZJdD4AEnNWKG196glSF/bKQjgE7i2mw7nyWfr4erwrM + xUzH56sPe/I4gRI6NbkQg0/TflV0K4aTpDZE3/Twaqt2Jh6I0mD7KyN144cI+o+oIW5zhc56A3UE + lMvQEEmIZMqe4JSBQJou2LWjL13l6t1B9a19Nz4sqsUCXgAD632aFBE1/Vh6bxH+5ks2hg+Ym8Nz + BnPt+iRkH3y4gK+QQHV4jsRZhB58vafiwf1uMj0ovHswD1Uqwew7SOT3/IlF5QDmp1s/tZuf5OJc + 0uD7xNhYm5qyYtnsXv/5M6eOI7paDgqAIQ5n/BgPR4eTJVsX+RqO5LETp3CU450C3btLcWpjtppO + VzOGT9w+SNDaDmDvk+zCi3H7eoFeXwHZ8Aee+MSa3n57Den7Pg3QKD4v8tOfH39FHNBfJMSGnxc9 + vTCRBONrc9o+n1LNsVlwf/NMXzeuooq/KOAdKqVXcTNS6Y5ZXMjvrynRt0Os5Ji6ErQtWE5M0BM6 + YnO0ARYvEP/8wco9owCO9zXEW/4O6NuGNfAdbiHGlzfUFiOUAeKcztP+ej2DRWjqBg6358sTIqqq + bGquEkx3xwRveQRYTrDJwFO9SiTkP426OpY1w1RL6z8/2R304wC2/A+f2m5H//y/LHAK/un5uU0M + 4Q/P7a/QqITlZxu9k3uD5cg4VnznXlaUwX3r3TKCU87LjAY+1ZtETsUypmQy2BayH/eM060fJqn3 + G3RQbRVbPz5xlKwFkgoLHHbGotLv0kPwceqGuEOc9ctnKHO48SsOcsMJl/3to/zy8A0PI/UbOMIO + /q7PzuCdjm9kepAtDIfg9+5BZ+tVF0C9hRG2o3EE37ezJiDIlxp7t9hNN70D4Sx7DbY5iwHrL/95 + ns2Q+AG4gy0vW4FN2zvZ+KIfAHwrCNkwxs/QUFP6dA0B/O5X/kYoXH2472CnFROWa/VW0V//NAbd + e/Pxdkzn2mJrOItCRm4b3i+vtw3hob9ov7ywYm7FcYXyMkCSX/ogHFsj3PyonxFpz7N0FNh8EpWv + iH77F6e7CzODyh3Pb/muQhdsjiawT19t2ulEStn1wwswxPWLSB+h6kmbrSUUkp08CakjhdR6P6W/ + PC0nXBdO5f1aIs4XTOx/BLValPGTw4MrwWmR7XTzT5YAw6Bx8W+/NtN54WAZ9Ry2XoKUsq52z2Fk + ll9PWHiX0h9fObls4GDLA+irF3SovhIDy2JoqL/9GJRc9j6hrV/oOo0S8A/YJtqWp9MtP4TZF+2w + e8vlit/nmQ33jXHCcpsydM2erQ+3/cwk6toL0G2fJ/gOs5BnW9jOphdd8M1fd6Js87/Q51EXnbX4 + bP703m/8ocDIlnKscvPDWdLbYAPc8obHWUvRD9wQDOIsuw1xbMz2S2ukAfwmHO912TyF4yTKASrN + +u1x39OnmkUtluCRiURvdxqtcLJVJf/lr57w8iRAJU1MwG3RJw+uZR/OOm5quOc/CpGMN+3XiH1L + 0NkxGsaWVjlz+hohbD6GMTHusU6XvcLWYuEVPT7LNqDzles4kM3xCetY01N+4xNITyzGtm+U6Zbv + JJBxx8dEtzx1mx8JToY9EKVKFufFXZr2L/9/rlUXjlt9Qex/mQk05bEnbT+0cMsXJ9CzfTgO5yaA + KXy/ieWNpsPkSqRBh8PWRJnSo0OT0gLF3Vpjo0yCkL+zOw/YT/vgraR/AvLVHzk0z0mOJb75qLOv + Mhy889lxsup8cIZPaE5QiqCNg2LkVcrk2AdNQr8ev+W366laN3/0fUxZy7fh4u9XH8n+YT8BRhfT + ed1XEGqmHnr7VGLAMpynAGz7vSlUvWtIsv45w9vVtfH59XAAJRnR4EdPMDE/GVanfJhmuO1H/viC + PPZyJLpoCn7zU32F4h0IfJgL+CRfm3COzZaDhc1YWFFVuZ9n31rhDz/sc9KqrBopJfzgs+ntU/AI + 1742GWhyDiAGdKJw6ycfbnoa39Lsm47dvLPhrRciEitjmdJPRF3UmdjHXigR9SvHnHK43sZhopk2 + 0/4ldh4sEz0h2FdcpwHsR0ALp5k47J5eONyK03r44e1PLzPb/1GCj7LvySmWV9o9sf2Xr+Hjlicu + cRjngBnLBOtbvs5v+T9MpMseO51YUjqOLvO3D7mqe8thB4UxhW0/Q377rLnGUPjxJ3YYXQzXeF1K + KBTmfaILVJ2VG6US0vJCyC2xqn7DTxF8EfPCv/0vb1HZR9t+2RuPY9VvecEEeX53m1AU4Wr9+bH/ + jxMF3H99osAy7h/ifL6fcA1dxofxYBOPP0OhmgcD5hC9biORKu+TDo2p7eCLjT1i5eZVpfJIOhhC + X5/YGqxgvuU4h56Y1uTkml7FvO7mCtLdKSVqdfdVDoyDDovC3BNdL0Z1mZ5KjbjmUGN1epuAeU/3 + FcY38eCx38fSkzqiHRgt/4Rv+rwHtP+UJbAR8Yk9e2I/nK++DZvDZ5rWW8Y5k9f0HLwWJefNuf0F + g51fEoi9b0Lih1Cm06rmNlQli8HW6UrA8pp5BbGvz4W4H8GinHQ9JLD5xKsHP4IFSD73HhCUT0O0 + fSOFvHtgErFYb8AbasSD5WstDcriWSFH/Wz3LGfVLURsUeIk9oZ0TrthgjqkOjk9sjKck/KkwF1L + BSI/BERXqioawMrhjG+i49JZjY4u3AX+kTyOjzpdKrmLEb+7EWJ840/f9pPso8XUM+zlHqVrQB82 + VFyUE8s5XfqZeakKUtu+IB6pboDH+ntFix3GE/+NPxW9eYUArbTO8KlSJ0rR6Q7hp3sbxHo0XErb + yhfQ/polxIhVtefZvurQTJkG3x1XC4mNZQGNnLYQO7/xPV2FewLAcmexHWNY0fmYBvBe9dBbZh+n + rFx0EjyaxY3EZfJymD3nlOhcMSUOD8urnzE9X9BMucZrpbBVP/EhjeGd+Co+xbFG2Ud63qHRunpY + E0sFMMn97sMzp+ZENmWgTmAcNDBM7ZvkTPNO+SVfI7S97h1hHDgTGH0JqW39JOGtM0PiKpIGnkEg + k9OZpA6dKjP+q4d9ktWQf9w0Ez64XsIGa5B0UR2pQPjryiSTRppOqqV20CJcMDGifElZfGh1hAPG + x7HE6pT5dLcZhq1xJtL3cK5WMZwhanwoEUv+COmKil0ESWvW+Dx1K2CW99tGZeCu5FZfOoft+zlC + kWM/yPVIHEDrFutwApGEwyNzpWyXCDsQ34QDude4cMgYPHSoO8NA7rXvh2va5CIQ7Z2FdcpgdYHC + ZYI2KC/EYt9MSFF0HFDRMwbBj7tAV6+pGLTND9bj26xSa6fkSFNdg/guF6kMn7UCNKm1EKv8qv0S + sI0GOeAk3pp/9yoN5tlGX9b8kFhM99VqXAsB4a8nE51NnJ7X7TlAwypk3hL0DFi0tzHA4KPwWC0v + Zcp/jzwDilt9nl6BeaQ8PHoXqFQ+xls9w97a/iei9HUeq/TbpMTX7xOawEXCZxueVOZgpDmUjeaE + ZSd3AP88bBvQa5bgRIzYfrnr0g7d+cNKzO/HdtjEK1p4C+ov9pyQhstSSTba+fh/knYt3cryzPIH + MeAikmTIXW4SBEWcCaICIggkQH79t9jPOzyzM3Qt9xa7O9VVlU70w0VYQ4PtVUhg3GNGuMcoGtTv + XhCFrT2FLQljsD5uFxt64HUOmRglhvhNwV0ZVPCjwTw+B+ZyZQWNH8upn0q6zwphicDlbhvYv6kI + jLbrZ8D4jXcaZt1uIPKyOvBgHPdEYHIHWKCrNtzyR3bWTwMSVnUOHng+x+EpD4dxf0hKxPn8mex6 + kA4C42YHuMoXhpxC1ET8CLAF5uq1hK+K1idaebKRe0hbHAWSZLAp1nRknM89Ef1dC6jOURVUbdmF + cxNbvrQ3jh0AfCDTvHYyxjb8Rb/WU8myrTd2aIZM8TnSk7u374yVTHsdfhdDwZq4A2A9XATuH/6b + vPkeKFP3Hgym3qFehouBqf5ZgPrJOdNLX2j5/H4LFUq4px5Kgd3XfSze9W0mOwglvmprEXy17Q6R + x5mqivtllFcjD+VznP6tD7ZbvsSGn8qu6NH/8mC9gSyGLyMvsGnrjr8Kn0L+qw/q2+bRGF75Ywb8 + bOvYVq9vsF4I05EPW0Jxdh6Mfn+UYmiVvEuqa/4BO+5r2Whf1SX1e1wDNsWuCqeDE//DQ2F/+L7Q + H56WVO0Yk45VAflLnROZL0dAmkyulNcAHKySu+qPU6TayEusnuaN3+brZ+VHkO4UlZoq96tX4awV + 6Npxd2pkwSFfdTkIoGoWe+x+J80fWbbLlJMIH6Gs5Pzw9/fQ55YfjYn88RlXnlLEYoKwxhcHtlBN + y9CZjTEt6/iUzCtvBkh8xEX4vgZ1PovK71+94WJ21FwKzI+JTqZs0CDg9oB9ZCWDh0vEwson5SDO + d6FCrxs7hv3j027922tg3isNNu2mZYuW6Wf0e3QJTrM9AYLfvnQ0Xw8GEZXFyqXm50O44S9+Qk0d + pM9ad+ivvx973zME1+wj1PFCShNeGdl4viEJNrr3o+FjvPiMV8fgD3/xtQdpLTFfFJCwUmlbXw8w + D593gwi4aTRvgnRYhFIxIf9tLjgjw2EQBuJudyi5NS19MTbWX+Sc0XXwU+z6lOVMa5iMrPfBoqb1 + 6pOdeVVMaKRlQ8PqaAzC4yFB+MnJTPF8vm3xuukIameBRv11BSvCPwV+Y/VOC16W/XU+jAEok0cT + At5u60Wk++1AQ3TAp4eMwFq+XwRd1YLgQhhosuq0eSHWm4Q6TR3Uwjebo7/X/+p5eXd+DDvDFqjz + l/8NH5CgNTnWm3DICZWyEbafi0zj7FnXa/POwr/1RQNS1axTMHP+8JjmmsazVYpwCY6v9RECpfkC + AiVjRXH49qgddE5C7o/BQZOlXvHlEfjGeiwDB7ZIbEKuzyGg1PqmSLgIJxzdDsWw2LZSwtH29tTM + CsWfuu+6QjUbKbX78WysOaleSGYtT81+PoI//ELD8D7RA7/uk7k9zDKK1tnBjz1H81Xu7wXs7cCh + N83scybcmxh6++ed8H1P/Xl31kJQa3ZEHQ9pTKSzC2GmFQfqQNUHf58Hle86hZxyPLHZolEAtvjT + M7AWMAdqHqLPYz/SE+mrQey1Dwe/XeZTrN0VMB4q7gW7W/GhpUJe+SK5UQOLbGdQLSeVP39vwRkg + 8VOHS3Za8r/8A/Ny1nHsdeowf29mCpJklxOeQxWYo/IKYV9QgA/N2fMZTzOCgvx9xL5k7HPm4gcH + bbnE4bI9z5aPGb4G6UB4URGHGSqnFpZPbwwlctSHZXqmPyjrn5bi3n4Z7JxzM0zhSaaGnQr1Oh8i + 7188tvqt//09RpVMTfEp1mz5ElOpL/kBB6x95+teuUmgktuaWrMDwJbPAtaX24GeYjc1FnT/tv/y + GVArBqu8KA5w8/6x9T++Zn7XQfhzswe9ZL8gXzvyaKBjeiei/OxnMlo0C5TWa0Lqp7s1X/xJFxCy + G4M8lHeZT6d85cAztHyqEo5PutMlctCUZw5ZrkdWk+t7H8K/+rTO7oMNj7ApkWU/HyFTZzmfGSd7 + ysaXsat9s5xCK0yhJMMSX083d5Dwjo/BPa7vIR8HlLFh9kL4vs/tP37FzNdHAIZqM2qJwS0ZB+JG + +28aR9Te+hG5RnsB9qL3xfpNX2smwkGFNxob1O2lcRjPt/eMpvzpkj1RPGMGy14GPsd+4XrT12FO + TWfbBLT2VNv43yr3cQEu+0NNNXGXgwUvzh3a3Tui1ulhJMLhrs/wvPTZv/7W9cA3YWcFFLu9FNQk + Bt0KP9I5o1EdHnIRys4PPqympTlJ3jmJnF8Ht/jhg/jjEjKdiQqP+4uHwzo+5WImXhpou+1CvS80 + aqFs9eZf/edaFSbLw4tbNOtzQC/Keql3GC3w7/1kZXcDSBLHOPin73z/1+fzy3EzpbdPLhFIPNYs + 29NCudm7jFrlKWNslT8lLMxXQM9+Z+bizXY4cAj3Gnby+eCzzceA6fOe0BDEz+2MO3L+8kfvGFnD + bF4WDwVTeyD8L+D8Pz2Djt5jxHEvXoH4FI82vOXqkepiw9dsTskIVz9aaM6Uic2JU93hVq/4j/8y + 74p+0IvkiT6v62hQVe/tf/1cV19WveLdLlJuu81RqzgTsDltCTzwyh27rOgAQ8cTB7kmU/Glf5yG + cYrOHhw1SSeAZdQfv1HWwrsMfKwp2S9fa8cK4PO3pzRQPTNZd1lugucPUOzSx95gZXdtlS2eWIsd + CzDVd1aYjJcDtdQhTJZU9zPojIQLJWiJjLgOcMDNFjOs58gcBNd8x/DZjQyrp72Ri0PdjCBGzx4H + /t4Hk664NhhXpaC63UdMMoeyhF7QSaGy8bnFF7kWPH8S+ev/bHaPFxW8Xh6PLdHvjSFezADNUymE + c4pUIIWP2YSlh3YhPye9v0zP8vfXH6j9yKeBnFt6B0uRxvTQK3Y+XboshObqtPR0pXEyQuXWAvqL + nxRboM83PBxhcDufcMY/H/58AbBFj9KO8TEYUD3vzo8YijvZxImIKzBE9mkEW/1jqz63/pj/oh9K + XbWivsD5w7xKUYFO7/GL78plO4O+RArKSPyjloLtYTk19x/MjY8Qcte6H1bVVHSIxKqixiMygTil + lYJcKxywfhLdQeCkZwsvjuZiz85uNXOmqwcvkvINITWretMnIxi+4Z3aTHwma8w0G3a3d7v1d5zM + 399zBWq2zASJRTTswnFt0LGSM3xuxiiR2PlSobssefjY9H4ych/Tg40ZfTDOLnkyFyDT//oDPj74 + JVlX5XcG22tq/fFps8wEwJVqiB0PvQFRiuwHodUPZNecPYMF++4MPpVfUyeLW2PU2ZpCEpQVdskt + H+ZgBwowq78TtjOFN8ZTW7fwd0J+KD/260Co1KxwN3oM46Z5J0PgHxQEzt8T9gUesBVhjoOiq7b4 + eN2lhvB4cBzE6njBBgavmk1unwJoDQPVFHBLBAwt849PkpncX8Zcge/vT4+HVbr/+qy2XiEYhjLC + h05y2K6NpwZWzXnCKkHUmC/srKDgdotp0KArEP7iW8UJI8IvgWCa3d7+0yPYtX9WvfNelxQcbU8k + SrVPk7E2QAqi+6UIdz2QhnqKNRVxkJuwcXX6YU2CLoA4bG/YVx+vYXw52n/rb4K2V+/i/SBDZPcm + UZqyGpaHd2+hcLcPIeiXalhu6B1CTXqNoVRvd2zN59sIM4E+MG50fVhcvbkjR9fTUH6kdU0/e7uA + os2PWJsZl09/60mKB4K9h9ANc7aLS+Tt7zeMj86aTJu+V6JU/2C97J/+qhTNiGqEBOz3i16LR/wY + 4UNeAQ1tLLJZF+wMed/rNtGVQ7bpGR2oS1Bgew+9ZFeNmgkb+/kOWf8ecoYuSwebcRcfB76z8j+8 + /+PbYZ9VMluGuiHwZCpGOB8zgVFRuGZ/z0sE6NJ6PN9E4Q+PqKp4mT9UwmuW21Iysd3YOzBjeDTh + +qy+f/rMX4ZhPv/pw3/5XhX/FsFM+BXYbwJpYPY2YX4egg91syoDbFf2EvS7VMaHq5r6IrMSHVZu + Z+KLFqJ8vezuBJr5TsTu49P6c0mkM/C+cYnVauyGn1CgENqR0YTKFzEw5szM0JzfFGzO9y9Y8W4I + YbmfzxRDfQbraJBO2fJD9Uf99qXfm0pwvqp6mCl3mk8JPobw9CjnbRTrlK9P89BBddLa//AY67kM + xNx50zBIX4xpherBzU/b6vMzzFNUeMqjMLLN76oYwTs+gps/Rn0GSp++c5dAQzUZPR0teaCPVDbh + 7qfKOOGz0J+tdq6gLC6AGkrySub9ISmQFBUrjVNll3cv51JCR1dTXIJ4u7/Hv6mwE8zjpmebfJrv + 8KW872uLda97DeNfvYJ0Cuh1/kTDYOD4Dt7iA9MgQH29oBOTUIt2DbaoyW1+cN7Ad30548BXCkOg + itLCephl+gD6CKYoSSJlh88YB+L88NmfP3Sx9iHGFdcAwr1+8V+9Uf8UfcB6bmkGF4t7Y8OHM5id + wXP+6SeLfcZh5XqNgw1zDHrMJQss7LgSuPlFhPPF2F8DdmogussK3fAqp1u80feMUxw0aMd+T5BL + 8E/fWx5z8z//AFjFeqR+47fJuOcAgaZwrbD9rbt6Obw+jXLgUU7NOAkHSZxgC4eQTCS5Om69QFUo + IHpfpg2vQTIrWSyBj1wk1GXct6Z793xHThoH5E8PDjePKNAisoPPtWXUNNh3KdyNDqMP/pOzOWTT + HXrlvPz578mf3oFsSH5Yr7oQLE5/jKB/km2aVk/fJ7o6wP2Bl+808Xa1v/xyS4WbX4St06NOpJxU + FVhevUd9TXuClXymu3LLDyFW2XMCs9BLKwwsv6C4KmxDdJaGg06WHfBjzphBhvUbwsi2WmoGnpHP + XBFlMLIPbbgLLqPfn6+ZAOOGR0ScuJexXPZ1B7d+Rp3gU4I/vSIWNBCwo2UXtq5G6cBHacbhYsl4 + IHWUSXDMIiEEtoWSNXVICAt7crCWnZaE+R1SwSIAlQbbHcPT90k95eP20l891HSXpgHorucf9ch3 + YlT11RXcka/hoIPfep79Vf3zp2nAy7Ix/NJdCIeL/aRebL0Yea6dDR5V7tHQF1eD3bxWAYwvnwQq + p2FgwkmuYOwFCY2uL7rhp6ai6DWF9MjEGpD2zFVgVRyMNTaphvDZHSvINQ8tFDb/enmFkYMadjSx + U7+exvIZVQnVSRKEoPq4bCcOww/eHkcXH2bjBASffiXAdYu86YtgWN7PuoWbPsGBOtbGyB+eOtz6 + PWEq7fy15wv4r9+mzmHPBpdLXzAQdGXzo5e6Z6E9w/Uj33Aen+tkrfhPAW8OSsh78++IFB1KcBK5 + B4H+3mfL4XWrYLGkM/173nnjV/CnFDv8VPLGn5RjFcD6W2oEnJgA1gAsEQLqJcduwBZjuua/Ehoc + jGhJVYfN3DRIgHxtj3qnuvJZNbo2fDS1tfn9DZt679jBKwojekj5D5vL1mthG4wuTk+jxZbwpalw + pv1M9v35yFj5HARwvtk/ssuMeljJ55Ohv/71ttEjmc1v0MJn2u9pFCRfoztUXAVSV6+wWuNd3e9q + n4CXVaSEkXZvDL3FC/ASfXehvNUnO11vNtyXYRtyx4IDc/6eIeTaBlKz1NOEupxXQKwuabgTmlcy + +6V6hqusnv/5g72kzgqar5ZBtQqCZLRPvPdP/+FrdMylOXpCAPRHSkJvNnORao9txLXzcVGJxbCM + 4TMGSSLmOOyhn/RQhSVMfscAm437zuflM3nQP/5wKHvcaswLMQUwSfZCNn/aX1LdyGCwyDzNBFaB + SR74AlxRcgrlo31MBJbxd+WRwyvGgvhlf8+PppqTwp3aNvm2fzAqoRx0tLhazbC8rLkAyqdb/+1n + EHNISwh+vL7hcwQWcT5WStzIEB/9Ftcrt6BRqdyfiQ1haowR9CRWLtVvCdnjJPqLlwwr+PP7N72d + L9tZA3CmRU52pPgYrBK6GZ71cU8YuJXJZB/s6h+f5f/8w9aoTZiP1gs7dsGSjX9CaD+5ih6FawXW + wDypAM5hjA/qVxjWlFcyyF/KO/Us0avXxCzaP38E3zY+s4pT91IOBt5j/7T+fV+5gFV8YjisqJ6L + gHYcbEvBDJej6eSrKKkB8hK9C3mFSzZ/hDaQaiujlh0rbIhn2fnzSwi4Zi8wOcsI4byQgRp9Bep5 + +dxsWCbPZjsrbAHh93BlcI/fd+qr5czWU6WNaIsfOdjBq555yXj9+T9UsxHKScG/FfhrsfbHH+pF + 0kzuj39izb6TZMpEtwJGWjQ4+s6tQQMfy396DxtHNaz/+B4qco2nlui7/iJDSfjb/6K+c8wG9vf8 + LyET/8vvLYQEJtxdw559dZPZK4sMHq5Pis3aZuD3iFAGhes2US7aSt3dVDv62z+h2szKfJanLvrn + j256ASx1/J6Bj2cvlGGtsOn8nCtkHLkd9n3nN2zxMUHoZ+bfflIt3G+nGKUwkekhfXyNVSs/DuR7 + dqI6Se2cZftvAbf9iy2f2T9/E1KuutA/PTIWpiGg5dW4+KQKq09HTeuUZhRjbAXwDMaszW2IHgeJ + 9JsfPMmCb0ICco0oj9gF09/+F+W+KaF2mSXrw9F+ULhywab3+GTTRzPc/XQ5XKzLa1h05eGAv374 + 9U8kXwpNUaDtkzHkmuszWcN2J8O07jxsacejL33WHYHJhU/p0VbZ5s9zDtzhPMJ67R3rneoXAnSY + OVOtugXJYiQwhuupq2maLqwe5/O0Quf+NQjp8wIs3RyXYH1+OuqQ4VBPLVJDtPULGltNbTDJ0mfw + /5go2P3fEwXFNAQhVwHFWOtrmsE5DqVQkI8/tiQGt93akw8UCz/qT/NxsVESXi9EtL46Y8dIr9DN + mTDVHH/219loQrhchZReFLc1lu8+5mAvkCXk9INbSwKvSlA58wlZeO3Ilg8xIGokMhEGfxHY3WEU + w+sznbD5Y/Ew64fKgUmHJWze8OSvh21GV2ofEfXnth2YK99nWJdJQQOz3m4J7nYeBJxxw3osfPMe + HyMVLuHeps/yNiXL9X72EB+f3yHYZ33O0s8ao2t62/pd6dTiuYMrvHH6Geu0XvyF460WTGz3o7a6 + ALaeBk5SLolTUEMKnIFdthUt1FVPnYPOwFIpWoCuz97Bpii4udTt9gRmRCA0FxO9Fi63WIdOV07U + e9ifetVTdAZRavX4lvAfMOcwI1DPwZ4ebgY/0PapFMh5HnV6X4VsmAdZiFDRKjsy1IdDLZhnXUJH + Q/Vp/s71XNL3WQvdzyJQP4F1vssWLUa86n9Dbo6Guv/7fhoBR3x05F+ynIRVRdGFhVgPctuXjntr + htnlY2Lz2ZGcaR/7jszI9rBu9IYvTo86Q4vS2dRgRM1F/rCPAYStS5bC2RnsfhN1qDyaA82wkA6C + tdcKNEqhQ1UZfPyduXd1dK+sBbuuIfnsxVkZ4uMfpJaSsHqZh8sME9bl4e4ZJ8nu6s0ceg9nEZf8 + S8pXr/rOINZ0TAP7fBtI66BVUd9KRSS/a4B0+eYVbKQsJopa6INwOxYz/F7uZ1rY531NpiR9wb46 + FtSMJb+evheiQknmPvTgkomt11Bs9wq3Y1hdeNFflfoWwcSGHk3Cq5bMFN9T8JUDk6bRMxx2kZqF + 6O2EP6yVsWmIVcZ3UF3rjBrh9Z2skv9ukO0WAz7ad92nxSfQ4RYvnD91FYDUeOqQF50TvbxZ6QuW + knjIQ21IM3hi+XL+DjZsEhnh/He0DCG6OzEUo0ChmRW8jdnaayUK5t2J+mftl8xZV5oKVKYfxuYx + HtalqySoe1xH9Va6GCspdAeV/a4MJwl+EsaweEZvoXnh9M1NyQIZ10JcnTt6qdFg7PKq62AYS1+M + y1LwV/FzC+Cw4xnWD8RlYl/ZBHFF9qGFvWqJVIT4DJ9WbFDfW/hhfr96Dw7teMAHhu85O/W+DPvq + vQulJXn60mCNDrBS3aKHZ8ySBQTnBtqi8MZ38hv8RZ1hBbNiEPBhlPbGNJDvHTkWynB4410mZdmq + IKPRKXZ+Hu+v9JEIqDpkEc57ivIlX30JTpHZE96Jb7kwyDAC9udlh4h4ei2+0yKGHzbdsEnTt7HA + ixpCNrUT2Vl2Vq+thwvIJCmjVmD5/ir1dgzVDsXYG9O5Xt7JrEBXH3qyWLDw5yZWK1Q9LYm6+k8Y + 5lgXVFg/fi02L56dDxzJRnjxjkEohKGUk0Mcq6j/CRHVOSIm9Lf7eZDlOQvl7sb85TV1FcivZo6T + pWHJeKOzjsTec6kWyYEvnENDhwBldbgqfVgL5nyRgPhsDCLtgbDlF6Uw/ikU3/TXwVgPlCNQQCbB + 4Vt91b3w6Vb4dsQXVS/3DOxUjtOB9pUeFE+9nEwgOLcoX05XfPhlMyBpcA9AviRXjE9mYcwHmylQ + UO8lNsPk4Qv64efBPF1Vqn4cf1hd+MxAuyr88btbHbYcdU2FB3iLtwkow2deWXuotUOCNa3T/CXO + 9TuqH12L8dfuE0nhx0i52pZBjzgw2Y9lc4XQS51pWWR+srux8Qfz4+tC7+AtDEy5mRmELH1irXrL + OUnk5wumWBqxyV5hzY7TN1QU/3bE3vFxG9iLf9ow+nIOVVuuzMn8vXnwoZYIG9bvk0toO/Mo9mVD + sZQKySgKMIO61x3xHZpGsluMc4dsd7FxOZROzV781YYyrlMa3t1rzgTJ6gDVRxFf/TszVkT3HDRf + k0qjSfzWXWfuW3AZxzdNHOc4rEIxNfA8D4dw9DVx6FDtecrTastw/LYnNn/kSIAwCs4Yl+d2WL/z + c4ah2k/0Xu6WgXWnk4Q2/MVxSNWaSVoxwvR2X6kVl2OytKcZouV9L0ijXUewHJ5eDBXz/MBxvA99 + 0XnlEN6035Mc0h/nz0LYmChhqUq1y+udSyrRWnTcWV8aVhJLuo+cSYrO3UKcufklX9NtAus8n0d8 + CtgnF3VoVOgixd/wvgrysGjKXoByxjTs4nfpL5qyCOgP33TlqRniXrMapFxOhAbHqK+bJ9VWVP1C + ldqGSoc1/3IQ7jjlhR/CmiWC8+sE9HJ7BUfT/AKLzrUyHO8Phz6TsPcXMGsvNMPbgm/4zflLB7jz + jjxulKYb/jBTiUf0Oq8mtmrZzEX3d4vgxZUXmopLywQRizOM782ZxjF3BgL3+bwgPx41+hcf1pqL + h0Kxu/77PHbLhhR2XLFNFJSrz9QaEjhVyi3khi415mP3klH0hQ4+5xYPFsF8hpAXixHHknZLGL13 + Mkq6o0TN5ikbbPolAYqCd03dz7Q3uqo2K5iqvkOE5uINc1G9bTRF9Indp+cZq/lSOlji9EBvJ4Uf + Pu/0HKOtn9GD/9wzwl3PKlreZEcPy1llgvZSTWiL0puGnzOoexy+CXpxkCeCKPQbP1BU9IfHhdmd + k3UvEqgcp/ebtOwx+g3iLx4y7mWPj9mx8NcsUxQ4a61KvYcf5AzAfQo73fxQp676ZA6HIPrjk4Rf + AxtIT66A8HeEOT4cLkG+fIZXg9o1cnFkjJ9h5YAD4dvZvchrwxNBhYkJGyM+hLAXP4BWblHA6h0v + 1CGrkCwD+WZw43fY7opLvSZF8ILN5HyJ7KZhPWNRa2GVGTY1XtZ5WEiJbOQ1qkuvbv/1JfvknOGv + jCV6HJa3PytLNSNlkit6partS4GYjX/4StM3d8xZF6kOvL+XFIf6wR3W+ydvAMavHS7f1jcnQD8Q + mLBfjo/9V/BnN9oHMHSjNz7f8NEfX1exQh94PuKyWoJBzAq+gmtpj9jc6mMeHwqE4rM16PH0O/rS + 3/+boGOQsdqLrH9wYwXLKLkRtPXzNWbRjP74n6kYaPiZdaqDEqoZPkSB5NMuchzgknu39ednzZoL + 8oC88ojazfGdz/5+lEFxPhyoFU3brww9JwLC0TzRjHQ2W1ZpXGHXbL+SkjsfwGzn6IBt/4MGV5k3 + ZsQ/HOgr+wu9PT3Pl5qXp8IUVydyGKWbv0gRL4NNb1DszJov/L1/J6IT9Tc+xPqLrcBOFFvqfwWj + njNvn0FY6yZZe1VPlgrcFbgT+RNBOGjAp1K08B++8Jm1goWVoQR7eID4KKxyvt3Za8M+Qwt+FLM3 + 7MTsxUH/dIlIc/Ha//hOXro+zt95lazJrfeANdcF9UeHA7OsPTx40hcllB+u44/Wdqto46U7rCs9 + qee958xQuEfcVj8HY7lK2ygLKL9UM8qELU/nLQOSqg3FCucOu2c6ZHCY4yNR8Lcd1vYwmspKaEW2 + /j2sh130g7FoDeFbnpJkd5a4CIKkMqgLqJ0vnTJ2ACQvgyxK+UxmN1pCiPaNSJMdEobZLh0PetoO + U+dXv3zGkWhE7HwxsL/xx9EvnHD/uUkyxt7/AAAA//+kXcmWsrwWfSAH0kmSIdJJJ0FAxBkgIiAi + XYA8/V1Y3/Cf3WGtsqxAztln733SNAFYfvz3h0e6YBkBO4T7FTAXJdzwoQDbhkUdWWkaEjlX/YAN + P70Ku4U83PWrmfYPf4F9uXlTikPXXr6KF6KN77gMFBtKY8xP4ISThjjrx6iWGcsrMPndRPRdLgP+ + h5frLD7wOX9x1XpxEwvq3ONG7jvjA2g5HmN0WIpxy7cxpXMqNtARdUC0+7Oy15FdZvRiBIATtM8A + Z7WzjF7sOZqYbX7n6TN7yK+NhOTZY1VWs3BiGDZz46JKfSvL62o1UL0tMwkjsAYDegk52uKDOPMD + 9GMTWB08K2FD/JNwC/q0LDp4r3PsvhX3la42lmJw3scSzvuKpOPlPk+w+cq5e9jqDfG7RwM3fCQW + exbteU865lBziU/Oku2mJGB8ATbtpGCVvq/BIpqLA2c8Fli1xbRf7eGtgw3viEKFM13U4eJA/xJ2 + WK9vEx1/9S6PhTM2o/iU8uJtHQ7ZQ46w/e64fj0Gkge/r8MRazzbB+R7dYUD+qSAKM45DGjgjTsY + OqKGdXbRKZkfjncIH25BdJmTFfbCXxOQ4/CEc1kuAVnVJkThowAu/3h90jV5BzW88HONZcYb01lo + ghWeM7GfuDZj++FYGCqw6m5PNNF16HBo9xbMZqMkP/2zcKuZgZu2tsR5GV9AkZmo4KgOPo5O1sMm + uzO2EKuHdyz/zufpijEHm/7BOo21YIHRXRS1KCqwGwdqxR5vqojOStQQqWE8wILZLH7zh7M4PdPp + x1/esG2JUreVvazJfYUtyzfu/L3twfJ7XkdduMnuD1bKWeVnhclJYIn8lcp06QZHhtme0Tf+IQTr + LrKiX3xPO3CR7Q0vDLhTLEAMvuYVCl6zDDRSW9jLIoP++Ia4PtbLz2+xqe/KNQpu/htjpsMKi2Dp + ovjqffEZ1ak92mzrAW8p3YluepIcVkYG3XmXYgfrMFiTcnbgvhcal3m2brDU+hcCnXveXJAJlNKj + dda3U77DERxKDkw2lhL4rtSK3J7noR9prjPiW/Lpj3/ZTP75MCDphrfrO8VAh7OudbAUixS7AjpV + 62v0ICzbHrpr6e7p5LDehHTr9MIaUYyK3m/3Ds59Xkzj3FqUSehbh+GjBPg8V7VCdzNUxUrVX0R2 + Codyj+vLQ/KkUnzU1qlaUOLuQJ2gcVqub8NmLs+TBC87PsS2/FArdoeAADe/A8d88q1GXzrqyIng + TFTaQXsuoor781eOqv5N19fUdeCH1+fJKrc9OXsHeOQWkNPOtlLy4yd3/3L96dWKaC84Q3RoWCwp + y5DSHjwdWA+cM823+k2J8xUN2GhigLX0ytLlULUM3J7fbcPMsZf6BDqIvyoiVoSzdInYaw5LkeFc + hnaZvWD3O8CnE1H31sUz/V7uwgR5tuPwia2uds+/tz1xlnHC2dkz+4W/SRbk0/mKLf72sekzqhIE + 0LCbVusSg6XbzrBJtTPExsOdQDt1QYvqoCmn/l18lcUt9jFU+tOTOGdbpux6N1Rk5uqZWGrjKZQk + hYh0zoDT+1F/6SzBVP3xk2mY4xDMftNC8RtHALsX/ww4gSoNanczi+VmcQOOlZIGFO9YxLfuwVez + r91iaJ+MkLjuLlCGJaXSzw/Cpk5AOm3xg4rMxtiMB9lma83z0MZfCP4+LTC0X8EBCnO1/uUPTu/F + j/+5MKhVpTsRboDixziTU+NWwQB3dg5vL07BZ4MqPf8UvRDWx3uLHdkafvVhRccP88BhR9d+fAWz + IB5unYPdfHgDgsxEhzeCKiwzdlMNnsC1qDkHOT7J7ZyO94bhAHtOTZfZ/Ec6nvY7cPyU8t/8cp7W + lLDi2RK77o7arSBPHDwsrDFx9W0CtPIN9e/7VcgW9noK2RhcRxmSkzzyyoI7OAHY3TEx1YPRL+2z + imDTDgrWmpQGcxr3M4SnrXHaOpU9KYDuIPsU8+nHRzZ/pICaUsz4tsXzxn8T6FaLQI5M9aYrVhQD + 2a++J3r7gmnXnAYdyOMc48sywGr+vJxtRWHeY3x7oIDUQhwDPl2vWHePesouzDFG0V29EX/ze3pJ + j2WQf9mcSLq07bA65SIUJwfhZHqrAb00RYE2P9alx1NE+XI0E5hcjILEuPsq/Q/fNz7sorN8A8Q7 + vkT0xrubK7yBBZYHNC1YW71PpNk1bYLeowqtVD1gCzRpSmx568ge762742rd5jsBcDCNZglbZuDY + w6ZHAEXAcFlikJQStnPgT++GboDsrtcRBy92MmGtQrY9J5GRwEyZB5Kp1djTOb/uwBbvxDjvFDpn + qEwgOMoT1pZbtR1FGku//CBXxT2mPM569/d74qwDDv744CPpBRdu+Dy/ILQApOhKjPDl07VTkxw+ + ILdOB3palGF7/+LTCSkOnpxkM8U1sYCLbogYmnNUtniO0FPzFLLV14CsbepDeZe62OW+AyU4vRR/ + fpujgNje/PJ//NWR7tv3ifH8q7dYEYoq4PAzMqBVt/tJ4JNvTxL03UH7tFg42fxSthiLEk4Hi5Dz + uxyDeRsPjGCB8H16AjocX/cVira6upmaN2Bh7YMBJ20wsXS9rnYjNZ8WagfLm1DBLsEf3wo6/UqM + z4L6EcHSOTyOGsX4W18qar/cAnJXJsH4eODByjvAhx/lxU7X0+NaceS5d8Hv/298qFrAufFgTtOa + uMzzCRYBxRl4Po2KnNgW0J/fA7691WNDFvT0O3eqDqr8kmETMGdl9ZkyA+9yNshTNXq6zOeDKsYN + Fsnml1Y0ZOtakN/PPdG2/gbbGTcVHFazIuYsoHRl0kCEn0ax/vQ4nfMHhLGdGNP8vT3BmMWh88Pb + 6SwdCzo/iblC3rxIWGsub7BIq+vAVeVll/ecSCFmOHLwdVlnrLiZofBFUUwos15Hom76nnuXYyKC + pKbk1MUemOspLeHncRLdfRBgMLfq0qDYnqpp5j5WSjf+Bud70GAzKUO6eLQNAXqw7M8/2eobM0OO + xE9s+GdBGb3LWQd7lp6mIToFynRbuxJa6T104fxIq01PrqgdLgVxD/KUjvl+58Djta+wFSyhQvdX + cYbfsuJdNuTP4K9ebs/jCmE2KKunTSW8rdmBxBuek9/nj+Tcus1iLj0J7/cGGrHlYyk48unYXOYd + cM5EIxt+K3NtNQ001dLAslh/6Ka9ObD50e7e8A/pd5t/2ER8TY4iL6XM5n9Dkc9aYg0pH1Dq9h38 + 6a/L+GiDtQyBBOyLxP71P8hpVFbY898YH3uXSVftpEhIiqMM695zqujm78MHpz9csb2YYH4YS4Zu + UZoTY/Pnf3oa/vywk3eB9Fef4ctwOuwVRRSsdh8kyL0fbu5qnfpg3L0KEWHsZMSKLLZf3sO6nUF5 + aScQQCXlonZ0YW/tT9jZt/uewMocQHDz3lP/JY+AXlO4g0U4qzgcLyKgLnu00G/+5kSelPVIAk/8 + GnJBtOXlgVmdr8wfXkVmtevJ5r8DM8OTuyeCo2z+ZQdzqplYW7JOmel7DpGRCx+CiTDYa42mAZx0 + uhLz3NQ9cXTNgZewuxOHlTqbrt9hBzZ8x+bwNf/8DXEwOd/l44OvrJIVdnBvzKm7bvqAloY8Q2rl + JT5y4QJm+rFkYF6tnYuC9G5PlLu6ENTG4ac/6WKoBoRjKdwnMbyb/Ry6igSZ08OdXrlf29R3ZhE2 + zyyeRuzUdL47tgpVT7WIn/YzWHud5UDzzGPsvi3X/vOfRr2oyDFTvIAS4dyALb9woHXvYGWSbYfB + 1s/B4zdOxwsjysA6uneXPcUuWI0I61B1z93UyoIe5J42Fb/+hMtj9Ko2fgAh5Q1KAm+WQVHpSwE/ + QvEkNhgqMB/L6l8/z3fSRvnzBxlJVUi0q7791m81wIXPTKK593c16ssTwt2+nLGRzE01nLTSBW6C + 1+kTdbk9k4GXxLMi21gecVTRX/9gTg0Nn4ikK9RWQQkCt5+x/ogEe1JLJ4TFVX9izLyNgNX8V40u + MhWJFq4fm4IkV+FSHn185uA7nW8So0Jtxse//krndW8LSO4aYXOrX8t1aWN4sdUAb/2wakXrI4Ql + XfW/fnFvCv78x+/kHf3rjxRo94Y9Vkl6AnPcRirsQ3fd/Dq2ovIoypBknoiP5a0DND6HIRg4x3BZ + xX0F/E+v7rjP5ldak7LuYdJC/h2l5ESxGAx6LhnQT5oQ2yqnK8umx9HWryFSShOl34cvFf3y/3Js + X/aysKkO99VlxRYSI5tqL7jCc2NFU+0vNSDnkThw6+dgF1xKe73vFffv/ahfuemHjR9DATTLX7+r + 5uJvDJF6mScI7na6Dj3MweZfkb9+9q+/e3CTF1bq8UsHj0s9aNVh/efHr89i9rcDzwQSbnqG/vQA + Lxc9Pom6UjG3+iv98oHoB8Ck3SNadygpT8tWv9Zq1YxwAhMBqsvvvIs938kswdoKeXLsA2APXiL5 + CKnBPM1cWVVUPZjS/3NGgfDfKwp0FlTElq86XbTl3UDFereusIsEOvZO4sJ1lmViL0OQtvv1xSGw + cCO2HlRK515tOJRIRCPSbpJstv4MBrQWeyTa6zZRCpEmQHp+HV1ox7eK20mFLj5OnTPV7F5X+hO3 + eGh6CCoxlKMK2Kf4GiCMgsvEPIcznXV8zaAdKwU+RvLTXvbC2EASDieSk0tmE98QGGgN/c2l77yy + h/TWuADgco9tcxD7eQGRBYynppBTPQ10PVsnGd0PS4G1YgZ2W7h1BxY0hMQ4vUSF3gbXh9SUBiJp + r8xe3+NHhLzqc0QLOldZ2nzp0Mtn3tgdikIhEJ1F2KXd20WJV4FZ3pke8ODDwqkrljb/NDwRygpt + iBQ+imoBB39FzSeS3AlH32DWUGXAqBBYfPxgK6B9+ZggUJUrkQNXp+vN0hyUukVGIvmlpew+7CAs + 18/L/RzoI+A/J62BBy6YiKZdUU/weG/g4oeOewjlR8o0NOmQn7XS1EjWds9qiwex97MWB0boVEyr + ORBe3U6Y+Iu4naLB5TOYyW7Eau2/e77ZGy7ie+OLA0jzdEEPPUbn1+mJ8X592utRtkuoLpMysR/9 + Q2mzNxzwHdkzeVzkLOWPkgmhsH/2RDeFu80Ojm0hRMrtRJfrsWd5+RxCEWkzMTpxb3+tdpCgsWSl + C2/Ip0xeIBneFC/AXpapKXcYBw/ukG9Oy4k1leUWyTPM+8OD+EasAlZ40Aal5f6IVYaXAJPiCwfq + +dyQR4/UdH1OtQE4a08JBv6XzqM2h6gH6YtYZcBWU+8/IJQbU8VZBcR0ObbRDIUxj4h6uEU2E+iz + CyVShkS/wxKssnDKkYDiN068hlVW1+QhvFFrIcocvoO5fhUxYlCrk+zVE0CDHVzhCXsRvh7R2DPF + +vHhUnopeeysJGUqbbWQVbUjSeK3AOj9NBbiNHLbPZfXbY2aWrkwoIlP0vZtBXPQiQwA+/Qx7bWa + CVb8nhw4EXDHJpQjZSa3skG8PmlEL+grSHdMl4vf9L6bGEv69sTafQp0puWEDeZkVVxXNxHqPuqT + mM3uVW3xKyDhXJxcMGrbnq/qqKLGzHkiDweirKLpN6j6PkLyVDOs8N0ukFDglPk066Zj81Z9YUCa + 57073dUmpf1gtVBDUoKxl3D9mjxiGQYz8InLETdgozY1oNr4Jr7uOx1wlIMCnMjhjkPbmMDgI8VF + p2AdsIyxovB9eZ0QQV+J6IdItSm3kw3ENEO57SGSAXfL9BIdau+O1ReMe76NeQ4OoDPcw92r6bIo + xwYxe+JMq3rgAuInovXLP7fXw66i7qUK0Qn7EcHXo5Xyt8vcoKyk08TLu121OqEtAecYVlhqExvw + 3XCfoEWsG9EX4QIWubBioLZB7q76t6/Wac/l8KBGFlHeuWJTfwAZ3J5nWuK7ERCzLkTonA677fYM + t+e8Y1gi4Vye8JOtPEAwO+7g3/P6+dzzWTJHyGpNGctFGFDm8406eGyiGzYOXNUPt9KIoEJyHV9x + b6W0Rp8OKm2+YCnS+XRhm0kV9es+xKbTmz3j36UaxQU0SUbjOSV7SFbYjIfUZUfUpMv92baQwcyE + j+VJU2jc8y4ME0/DZvy92IwUNzq8nx7UPTS7Y7/mQmvBXXzvic493/3MF6oDy7f6csd8PthL0d4j + JKDkjdXncAZTIdx1+HnQA5GtFgccbwoTfGimQ86HTlOGvdq7sGIOLonLVFd4ckqYv3yW87dYDejs + dmJ0Ey1sFYqtrKdQcGC5O9nTYtZlMD4UzUHCazdgYzYa0B+l4w6J+hkRBfoO5U7h7Iqx9F2wqTG9 + zYmXZveHX/iz3eOJj1X9Gx8+G1UVLLnMGehwYS18avWMziz0mm0Fy0hOds3Z83JLy23PXocf+XxQ + 6OBpEfqL91e1T1fRTGpQT45H7Ojwph9ukDhxm5+pqRSbLolShaBFO0LsfhelM+9aAuw8A2Bbei/9 + vGuhAC1aQ5KeMgzmBBciMgW/xtp7J/fc5f00fuOZxKyG1bpL2Bnd8/Q2rSGSbPY6fwowogq46/3B + pmPzlCYI6IES+9znKX8bXA86sX+axPgdAy63viVs76VNTD7bpev7GAuwYFCGn2N9VvjzZY3RyFQX + LCVjby9BzvpIb5OKqJ/+0lP3vh/AU/Mu+KnVYfC9LOGAdkfhiI0r61bcwxJq5OdEn+ZzsafdaIol + FHz5MVFsW9v/fxWI3N0Mp/K1oTzLPBw4348mfjjlxx7159VBPcPp5JzGX5tGtQRRbiMXp9dF6pk4 + VkNYz7gh/uCn6XBjzBaZRqzjZ7FfwPqUxgLg7uz+1WuGL1QXxMpJIspx9+y5vXSX4WeyZHJq8tKe + rbj20esU9+Q5abtqvFxvCfzlT6CrL7qWwRyhqb4TvMVnTwl6MyAUnRBf6PkCfvX5sAsOEb7Ju10/ + r9ScUS0zBrnUN0JHM9+r8CiuYFqei6DUKfJykPCNgKXhjqv5ot062ER7fhLKtLGHw8Jl6MC4LQn3 + OlHqrDd0lEz1mTyKpx+sPHcaEPuVMTGYU1fNr84XkNQWe6ILxEvpxLYFuNyAii0/stJ5eRkZKtNx + nA58lqdr/KYZOFfN091us02HQb2t6P3R7i4l+qOfM2Qm8HPyMP7h2yw+NzyYrg0xr5ejwhkZKuDM + PBVsf3UAaOgRFW7xQHBi0ICOd9GDHGEu+F7eRjodhbyAr3VlMA4OKmAuOGjhGH5kd1kHKZ2PTBjC + 59UtiHPQx2CplW8B328pIMflkVW0NyQZvvJoc2wHjXJl5kxgtc8hsea2Cb7v5a4jWVmav/rL3VM+ + g86VESZ+q7dL7OgzKvJTS6RvebF5Znd3QE/Qh6jn/ZgS8Lp1oohOMzYd5d63z27zEBd7xJqtRn0/ + sUWB6ps3YDxpu/7LyWEMoTFf/uKXvNnAR5JuvrDUWEebaW6PCJa5Bd31+Go2R/SSg+qQVkTu1zeg + dFf4iH00V+ImTl9tP3timF1tfFqzb0V/+Zp9F4WomJT9+g58F1rW5TDtjdQHiw6SQvQs+0E0sXxV + s6GvMixP5xibvshvezACC+Ig6Cbml6/g9WzB9jNxAy6v1rZmk21JkoCdJ+T6iZV8Drosl05ceP5S + +hqcHDi7ZSHJe1XtVf9INfiqYkjk40sPuCh71NCjeTFN3+Ol599s6kGte9sTtZ2TTd7VBQIfNnja + ueTa//4eJW/zO71exeUXHz7kLofYFRoA0qGm3wmGLmNP7/BS2guVxRIW5jvAp3A59kMcOxE4NuEN + Px5tANj34Z2Bz8nH07x+E2WKskcDH5V0xfkTqNXsI9vZ1qgHRIPrkNbLN+TA6zubxAL7O1hKRah/ + 9R3bnvypyJeZVLjpISKtX1GZuSL1YM8Bh5xURw/WVhJXscwNSFx5cW3eMbQMGK8yIGoRvMCCQlGC + r1PSE8ckBZgt5lXDEsoawUVeBKzyBCHc+Pa0SNG2hl+sCnjxDmTqNeedrodx8OH5qZvkdPEDe+3q + KQLgJeQkcICUcr2UtTDM5wQnpJDo6pLSBXb1TDDemUbK8IXjgCiLn1i1JLNiklfpCkKqnMg58So6 + F/GlQc0ZOeRIGa1i4u0wpKFwMHH1eQFz2SmcmPn8E2st+7KHjGYZdIY1w6froQErndoCBLddTJTH + /aGsjPQSgTkwHcnvqp6yhjrnCAeXDmvP9lr98eObyU/kuJ09Pg6tkYP9aHzITR158BY/9xz62pT9 + 8YE5hjqEZ/bN4GcyKykbRq0Ibasspqr81NUaWn0IZ7V+E3/61sG0q8sBGq8iwNcRNcHycETrx5fJ + cR2KYL52/YqY822asvNUV8v9w8oQHCEm0VNtlXl5STkwL/EyrXVw6/uNnwOoX2X3sOX/Mh7WAX7P + 4m763C8c2OrPDh4uvDXxu2SxyW3QfbDl/9S/TrG9Il/It3vnOXw8V41Co9qAf3zACjWl55P9RQVd + /fUmYqQ+nS+vbAdN8dZNPGOXdPDvRgOXZp7xmVUuKXX33x3QIuODz8VzTenS6zF0r6IzVWa20AXY + mQUkCg9YGa6vigTezoKKcJfcynY+9oy+yk7c+OzEGTMFhPPsEqT9vsdW/BboFGZ1i9SAE90lN17p + GkhcDF2nil0+Npee4mpowaw2b2JnErbH5Fp3YpQlT2LP9WRPF6Lqv3pLUtM2laH9nhrAlM6ZZBx2 + 6SyXCoQJVStsHtlP+v1cnQn98FU+P8+UdcTIg3JCLHyyFbVawOOaQ1bY5S773skV+14uOgoTf/Mj + dM/mFEJVWN+vK843vb2O/MGCyrDXXWHjx7RPzBBs+YndQzOAIYSMcfgw/UjMhVEAtxwlGXZf++0e + judbSsuTOP/8iO3vlYrTbD+CsaVfJ7Z5hjaJONmD+8swkXv1pAp9JYoB6ePpYRW6TP/1nmny508c + BTABun0e3QKnxE6wI8riXacc+vmoE+P3/S/BkoE0KiHRla/VM627CNAS6wt+nqtYWVM+bwHa5wvG + 20VRtE+OIdL34YmcNn+AtCXgoHc5yb96FazCHrbQpDMm2UEf0w68tz2S5kvDx9vKpPMMRgfY/J64 + QmsqyvhWkhyKTa3jS++6dntjji2cOcHHTs5cgtWepQgtpZ9it1wrm3SJl0DUS+cJ7lg9YN1F5MBl + 5nfuTjZ3yiIV9wxWmrQdl3/TK8419xDmvkhd7gWaajk/lxJ+ZjMmx951leVw3HnwBMkbn3lgU74v + HwNkeofHd5Z+qk0/5bDl9sm242O25xR5GWIM/k60QEmC+fFSVegcowr/+VH1JW5hxF7vxEQWCKZ2 + WhIonrHvrs22g+4VSRB9C++BMQnllEcdKuCtICVWNn5Dzywri+XV8t0anPpgDl1UwAs7n3F0hzLg + xx40QBDEzRS83ezvRLgJmnA+YCd8V/Z4+lrGX/3UZkalzN7ZN39+kXVcsLKeAwDh6Xh7YFU6SP3M + qqME7/PKbfXmVNF4X1io0uTZfY15DBYmHGb4PpTtdHjhtvoa0TKjAbQGUT6nO52hZoZwJdutLnuZ + o2tf3zxkHQNMtJucK0TdVyv6puluYqjxqVZfWCQkd987+flt9DEcWlEejTu+SWe5X4MJyfBttha2 + Lq5mr2E2dGBOSxMH2sNVNr1TAHBW9hMD3bBa7NXbwZcsRNOyjXddjpIE21wq8INAw2YqVy9Q9zXf + 5FTGTzB5jwsDS3OI8XZdfTDPTyLAoZ5qbDwCidLzfpHQq2bFv3yYkveQQHY7w0b9WIXNYuVtic3h + esWyxz/AoinTDm7vCx+rOaXdFv8iL/Tfab6/35Ti1PORbWgXnGR1VlFSxQYKrpWJHZ/7guW+fHfA + 1iWbHKOO2MvmT4CNX09Q5MqeVb+FD47OvsLq4cbZC5KFCa6zJGNsKiVd4egaUDDewsZn2P43v2Kt + 8g98Hh53Ol6uzwR8vcjFWpfzlE733gG/8eoHnq3WwkoyKOoYEXWVynQqpqsOh4N7INZ0edjLb76f + V6fA/jVf+mWrp3AI5yt5YvGlrHGwGsgIPvdpLzt1MBfFewfnBio4rfZ+xdaPvQOZMUBYni97ZXpk + iwjR5f11d9/vsZ+5Nax/eEPyyYzA/HKvDdzpSTQdBs2iSz4zK2rt9wX7ZZ0HM9C8GO2f5xHLioWU + rf5GP7wlZ5wcq1FoTvDQ2VOE9eWys7/jddaRhuQEOy6OFPLzEzb9SDA6Kjb/84uk8RgS7ZJsDvhl + NH58z525FoHZZvztFp3FdOEPz+6kSMDmBxPH50zw8wdg/o5kl7SP2p4zmuVwaJYOO3Z86zf9I8L8 + LhrECd+KvdRLYaGPPdfEHkdcfQ9HzoNOzpdY23c6pT6LObBLxJ7gIfuAgVOvO/i9RwKR/OmTduXk + ykAerTt2dKEM1p9e3PDDFWjBVYv4GEq4bcL5519y4WMANit8cXx1E0AdTlXFXRO+yeOsCNW6k1oV + PE6tM6GLDIPxc1UnWJS7E1Yqg1Om9P0ID5t/S6Rwtyokqo0d/NWL00XczigIpxr6Ac6m3fysKNeY + uQM2vkF0A5fBKKtnHYjCJ8W/52frbess13Lwhw/2BEfdguTuZDg1FAXMaJ1FuI2HuObHS3/+KWSj + j4TtZ5yD9WOeDHHz07GcQYXOr2254lPzL24HmxEMg/pcIWqrM9aIZvc1+KYy//M3Vcip/WzrqQA+ + BTbJkXfYaq3Mbeco6AzsoPGo/PQd8C9GRBI0UZt6l9CDmQ3EieWyfTrGjr5CqlcD1ud5qKjFpQak + +mvASj+NVYFxNwBBEDySKo/BHofwWcAhXK9TFtc6mN4jEcS8SCmxRD6suJ8f/tMf7ElbwXqyFR9u + fIS4i3QNNnyBMFKUgdgHS+vnY5lKcGlZ7k/v8l9m0qFeiS4xyGsFmx4J4WE/+DgdR1JNjnycEMtV + GNsTy9BNb3Xw1E2eu9udpZ4XXmiFWmxCN7dvWr9mR7sFuwBELnsa9J4sJ3uF2l4riOO7Bzr++gOL + HW8Hg0M/WJfADOHru5rEzNqQLk4hNVB6nDRXxM+eLkfXVMW3Z7PuQRrl7dZl04UXV7hPaOPXa/Ku + E6Tsk+vm3yx955HUEEkzZhiH9yTt7CaN4TvXJ2zcz3k/B/GhgDgb3OlVzdsh6CecwO/Inwm+XUK6 + DKK5imbASCTePv/zZw6bn73lP2eTLd+B8/LXzV+XKaO+oAi7g8ZgdfZGZUjxhTmUrjtiza3eNpFT + wYFeKzfE0sy9PY38wYCVl3VY72VS0RHvfFjcijPJPkm74WvlQJ09VFg5pFNA3P0LQn/R326NT37/ + x8c2/jBNcJdR+vNzy0PDTUWb9GA+nAEDk1v33a5A8NKRze8D2OobNu1KVWZwuwyw8EoBHynzrrZ+ + SYKKT5YRlSv4aowdd/35hxPiqN6z/tMJ4dvsLCwV8NAvRkt1WM3xAYdFcARv9dv6QOKUvVvm76Ra + TmeH+9UD7NzQCqatn4bOhvEmErl0ylg/eBcCXOxd/tOcUuYK3yo0lSdPtC1+ZxcJMSSFdcRyfJ1/ + /qi7Ee5mosq5CuZSPzqIKd3zdAhllM4d/GZQfLYvEvB21S+ns8qB3bI+ieknZb8KqSgBWagDYuTw + 1vd7L14hbR7A3Z9Cs6eHvhygFxQXrFTEBPTEHTz4w3+HPX37JXtaJRSbRifu7XpWCOydGmz++CTu + d49qmcmn/fXPpmV5ZP3f+Dc/Cxumt6242/wKkMCDuz4dN/12osfAn7+lyiWf0mKXMdumFAdfq5Nq + c4G0i+FzOlvExXxCqZO4KzyS6xFfg25S6GtQc3jzaICt6YLsqZfCDl47df2rVzMFWAe/+nE8sV97 + YSyegdfAdCdm89uG+w158IffgZld6BKJYfbn53Rbv3QSTb/+9VuIqV+zavboO0IPvuzIzz9f1HMH + wXPC1oQsOtJ14bcd1/56+/nzFaPeixJu+IetWfrrVxjwlg8O0Wqyo+Oml+B7JyEiXRpZGTY9C2pA + PHxcHrAfWceLf/oa48BfFEo7PkROcjtv9VXpl0jMMhh+Fm3ifnz6h5ft+tTc/VifbW7nf2PAC98v + Pi2hmlKLCwwoJ6OFDdWYbGL5XoGODqqIqpi9Ml6jC4Me3/dMEs182uvC29vznS188vpnOqTbiqBT + RiNs66FVrVboq+LXTL5ERnmXzsL3YADX3X2mPQ0ahbNkb0UikLtNz5yDv/Ghy+c7jSWBgEhGFP71 + 785GpQRrlF0biESYkWsxp/b46xdmJsPg+MpO/YdUsQV++alv/dEpPs4cQLgJyIkNWqVX0peBxvEz + uPvj26t+7wtu/NE9vMkDzORbifCSpXdi3pcoIPxdyGByGpKJP1CULsoCO0DsoHTBxDK/flACDfpq + XeFFI4Wq+2r+6bFtRa5Gl1d+jRB5DAvZ9Gq6GC1Q4Y7zmZ+frswVnWe49S8mQTKtdLjBXIaVl3dE + Y6jRj+NBnKDaeKb78kBks/rz4cBffEm7qbDnDU8BFEeWyPbM9IP6YoT/Z0XB4b9XFGjfVibpaFyU + SSY4hEEk8C4VDsSmovEsISCvLzblxyegQnCK4Pkkylh5Lp9gnkKGQ+PEWxNjjEfAqRzgwLsKDKI/ + 9LWaR/pIYPCOr0R2/FPAzddOhnKFLtiOeVWZF+ssQZfzDkT9gAZQzW0K4L7CET9xqFbjmY1C+HXP + Do51vu8Xu7hYKK45gShHKasWZV0gyi6DhuM8/vRzJeAaOJn6wlcaPPqlHN0Eupx/IPIaAYXYOlsi + U4gErHgMCQjk7roYR9eEuDbm6CpfwxzmZnt218f47hcJfWdxkGhKNAYeFbrQaEUHP3KnORpB8PU8 + 0EFj2E7pGd4+YCt3ZODdeH9xhHvGXqRHzgHt28kTu4TfalGlokNZXz/ctfsudP1WDwsswbfF8uco + BpRVHR/q0U4jZkikgAeRbiB0MnqyjTddBH9WUXfOby67hGbFpDe5E4eYs6aDpFJ70ndFhMZwtxJ8 + eJY2t4d1g37zoYRZQ1eTVSFsbsoBmwIjKUzXhRm6XPZ0Cg903w+vr1qgm9e/sBSNacr0eeyioqcs + Vof1VRGEPi0qlYSf7qWIe/7sljL0kxUT/Yj2ypzOqIA4do/kmUduz5bnSwzBVwUk8s0k5Z7Mt0RL + tNrYsj46mPeZFYM23SGiu6rRdzI5hfCqBROx+5cW8OvxnMP8uw+xxR1PKWNIogQT8aSQI1E4ZZmW + h/G3B/Lq62pAyHaPplUbd2w1Z6anzePYgm0+SLLTD/ZkZU0JX3vkkMuYWdWsdlYM949DTB5fu7KJ + tzwYKKrNAxuY8mBlJDlG3K6uiWeUjs3XoHPgCBidWHy7gvmsaT7qlrOJz+/Krphud3bg+8k/yOnV + JtV6vnADHDjrTE4jqZVfPMC8lFT88A+dzUXPgoNJ09/JmXlfe84vh+00f2+710MqbP7VYBWWnW/j + 81wodLY42ULct4vJ8/4M7TVbygnqEdRckOS4ml+VFYOz4iOMr4mcUlXiRXSU9ldi3M26X9EpzOFn + uycda+5Jmc7rsUXPyeoxtoezwm3uBVohtYn5jq+U+42ffco+Vql36un68Qv0FnBIFPZyq5Z4l6xA + vJdf4pptH6wVX4ToUa4HLD+He89T1rQOu5MYYx0Hdr/6WSkh1y+MzYG+9Wz5cXy4S0OZyLnmKpxa + 3woAMm2Pr1zl9PzklRDdNL9xD69+6MdFjHQkBJqyxU8D+lmjFiret5O76s63pytQPPixBJMoYpH0 + i9c1JfJK7oTtwxACfsMvYNlrSQwHfYNZ+tgNZCbvjXXch8rcdWEO5/IAiPZINZuvPkuGLu/0QSRd + 1QCvT0sGr+wUuvsrn1U8ns0YmhwTkePNWnoqhocJHk1ZnaoGj9V6tQUdetP56DJWOAbrsxBmKFZ3 + eRJq8OzXp9i38IOMmLhzdaDTEu1y6FmZt83Px15Ndy/C72nOcWhamTLl+mcHA3bMiLlTKpsNz3OE + ZsfSXPhcpYDnL6II3VseYixOmj0f3dGCctBZ+LFX0r7LnnoClXuo4GDDk/l9WHZw93ZSrF3CtOef + 6KKix/f82eJ9UCaoGzI0FuuBrex0s8e6jGoo3osvVtd8R7/h66vDqLrmE/SzAKyfMNHhli8Ts42X + 7pYqQd/UMYgbvpV+qVJJQta1BVj/ONt9dh6MUJ7Xd3c4Hq89tUerhv5zSonWuzZlmStNkPq4A7d7 + XNpg2eYfpkwskzzvjwr3w8+csiMJVnpJlz6sMrS+kgs+a3oVLPPwyeA0qVfyIN+vPTCHQEJFmk1Y + jjnbXs5sHsGnxH3di3Av6XyvXAHYoahv8/0GrLlbhl89JG4Td+n8cHwOJKzFY/dyDStG6yMGbfUB + G6l4DJgTtSP4w2eGnc6ANvO6HWbwqvCT5bt+CKpXhHD7MohOetlml6J3wa48CRvYD9Uq7tIMDrAB + 7uEds3QursEKe+FeE+n2VG2mlVeIpnNPJsGtQ/D3sycJexIypzJdl8rrENvdz/gi3GXANl7swLvx + +W4rQEy6ziQUUHHfOeTY7NSUicEYgfhLVSwHxpUu56NQQySCjCiXXdFz+OQK8METbWLyrQN+N0MZ + lRHzIkeoOzZPViuEeZut2PMwG6zdzRNQMzY6zhjRsNm7bzcQPiEmT2E8pAtNUQFLttfxmU4JpYcx + NpBMFBWre8YOWOkRMShXp6PLPZ8NmNLLbQV7OCKsF1Nnrz58Sts9oSqOkF5UvPKeC7TlD/YHQiqi + BqGDYJTsppmtYnsRErcDlanO2JG+oz1OyqHg0cnqyRV+u3TruxkozSTbPcxX1+Zlm0wAHvOVKPfg + FXDsHXnQu/I2drb4ZhPa7aD+SKh7UIYupZf3pUG/fAxJOyszlp4N3OaHHB8eDrZ4zNEd7dLpAA8X + MPtXcYAb/rm7NPXtFbxZWdzqO761xUsZ6OVQ/H2el9cHnWk/xMC/uDvipumq9EzWTeIk7ld8Di5+ + v+5bwAEJVd2/77teVgfFuhyQh5OqypoHsoWY6MyRG8++qnkM2hiJav0gHo7FatAuFUS8ODpY9udz + QIVDFsNJRCu2dvrdprwtzOg1VOXEhVYTfCTvEcNzfDa35yMpqT5LjtbwfnW5wch6miupB4Yr+Uxz + 83KVhb2dOGCRV0AuMGeqmbRjA+T3k2Ib3Xf2/Gi2W3jcx0r03bWqlpx7zai5HQ84I45Pl2N/zqBk + B6nLbXgxW1feFZfS1idYYBHMilwIyH7jM3ES8Kb0PN0kGPeXBmvnzxos7vUbwscXfwjWWb/i9+ue + A40+reR0MKN+8Z+7Cd3fh4EEJn+z//gbD/GEZe92V6g9yjVSTjEgfve9AJ7klxryInGwYgsy4Gbz + xAGctwRbhrtWw4X9DHB4+gLW9xDby8qkEELRJO5uyA1ljcODBPZ7rvjxL3sRX9RC3+ajYANmoF/U + 4tHBWzx52HV5NyVktaJfPXD9i3kEDHNI5d//J+6daar1drJiMSmcHuOp/NJF0bsZMPfw7oLlUylf + yy9LOJD3nriPQxysjfQNf/hMZGIolI+U2ww7o7Tx+clG6Wy2aQnj3ehN6FPu++EqNAUwko0/rqlB + 6bHWLTiEZ41g6Tb042VNZZ40NJhYsd0F69PNMvDiUkBM91v2C6+ZGTyfBJn4CbtXaOILM/zls7wr + o3RlRcmBwVnKsYXzYzDtW8qBg3bzJ/5gRtVSLFCFSRT6G16WyhIpUgkP5feKzf7+qiZRuEfw+XWf + xIWEtcfIlFuwbnfZlY7/Sac+Jzn4vV/dVdt+Zgumhn7dtuRo3ixAL+97A//0Ary86PLhlwkew3hP + 7F2Fg9mxTwmQ5s4luGGLfvWA3MDTsNfwMUmK/r1f20bc8oOc27sYUIa71vDO3C1yU+qmmkbnpUIj + YBKi2pWXzoN2WKHE63ASY/mdklOUSlADz/7Hd1OaGdIOardJJOYC7xU7H44d/MW7MHdFv7bskkHN + Irtp2fB7fRHAiQ0C5wkJ5/OWr/MKhVf9IpFXTmAF/d6AknRusRqewpR4SJChirgXdnZuBti8wzUQ + UZCQUyuYFbMrnRI8YWuSTL3U1SpfsxyGVt+6X6eN6LJf2xq2TMRM3K9+5dx3hkfBnPFv/PPhsXgQ + pjcL68eiBkt2leM/vmyfxZ5S8Wjs4Pph9kSzFqXnFMjuIKtUhykI3oMyTWe2RDHtH8TsPgKYD2jy + 4E0Kk81Bniv6NmMRPkM/+OFFP4nCJUTZqO5x9EKjTc/hYEGgRRaR2CpWZh7eGdhojkce3M4E8y20 + GSivGcGXN3kFs1VeEvTTJ4/1RALaanyH9iX7xhKkJ3vZr0UD97e8I9KpXwJ6u7gJ1Ha1S1TmaVGK + HmIHF6jkrljslmrYGfcWnlr54EJP2ffzBEwR6psiEtUGKH3Ntirc+PC0qMYHjIsuRLAlyQebgswH + dOP7wIXk4/KbXpiehbDC6pV/JsauvGCJTLmDDM4kkuh7HGz6OALjV9xh57DIPbvlIyzu0JkK7lqA + 2UhqEehKmeIt3ilf6r4OXL80iNot12oex8CHW/0j0fTy6PLM7QiMdLkRadMfy1P1cjhcxw+WfO+l + 0Adb10hCr46YRqUp8z0VQnGsMg8fk/WrfLPjpUMs4nhy6k5Ov3qfOwct47snxv01Kt9nreVIvhoD + 8ZiQVWYYmBmE08pMIjxpyngI1QJWe6jiVEvvoLrnSEZEGFIs1WJZcSpHGfjTKxv+9dv77sCG1y5r + xCSgynsukajre3Les3W6drdYBAx1V3x6gCWY7dmQ4bqMMVHevGZz6sOAv3xwxcfFSOkvP25oXxGp + FuWe8ZU8gW/nTrF9jFqb7vqXBy9j7kyDaPbKIn0P8o/fkfOVxHQNF+LAO8kRcY9AVmYfzw2MWXuP + XaFT0gGJ3Q4u0Wzj5AI/PbUOOoNu3euw1cPcHh+wEqExON7Gj0S6JOeHIHpfnyUGpjewHMR7IzaJ + lWHjwlR0Sjw7AhYssQsmAnvaxgFEGYdiF72rvtrwroHcxS/JKQs1SkHb5SDC9UAyI4pSKmtjDXB3 + 7LCh6hOgrbZvudOrtrFTtkSZtvcHH1rOubtGOtI//mveywCfHTr0o/v19Z8+w8c3OQa8ya8JOkro + 6gbxS6iWjNknsNFcDx/9fl/RNDy7AJTteXqVzzVYqCy3qAzGHdn4mTKCdJHRLVhVsr0PpfduHQRd + zPr4GkKqrGnFqMhUm9P2PqtgzpNjjj7FtkIudkqb3h5FjS5XNSKPbT6oIx5ccePX7s59apStRCTB + JScpcVfoKv04P3cga5/ilFp+GBCXUwr0eDDDVF0cnn7vzjFDsAhu+MeHqYdmCWzx7RZMeLXH+WC2 + 8P6VMNbMvQhWeBfWP350uj16hQjPVwHceMe5osBz9mSc+wzMHezcBmhhXyic3gGIPB+nnvStxqH0 + ddApQMeOeqn7WT+cMzCGcMX2drfu0r4fCRxixsLh1+psGiavGXKRO5HjFUz92CVjDaP7Q5vgXtIq + 9ueflRfh+uensIuzZqgePy7B4vRWiG7UCRLurwX/8JKO8w3C5+lyI+7L/KS0T/0ZqoJ4dae4Y1N6 + 384EPL8d7S//1nuGa7jCxcZ5j1W64jGA8HbGnKt04T5YkqfoiMQL3+R/pF3Jtqq8En4gBtJJkiF9 + L0FA1BlgB6j0AfL0d+E+w392h67tVkkq9TWVpGQXPPNVqpsPVPjEwphetzvBdCqiaNYqYtdgV5Gp + zidpdwMXrILB6rcTAjrynOuA7ceOet9jHw/otFiYWAMCOXlX3gp1Ng6JMzQ1EG7OLYOb3sSPsPqC + 1f6mKfzxZRPHfXVbX7sOpNeHQQ6NstDtDioVOTyfYgOJh5wy5/mJcKln+JpOST788mM/qu0Elq/m + cfGVrX/xS+IueeR8sXQTyObXuumz2ltbp4x+/DOoUi2KyRndamg5QTKJ34SC9SFVHdgbp4jY2reo + NryMgLNILsYCz1TEnPYFWPrpTBxntbUNby7/5q8+ptVc3YsJfE7VmQS/+XAfOYTLfcw3vVeCZboN + GTwE6UyshT2BQVMbER0zuZzQedYrdjCWFZ6+AY9dZzsDnjDOBZL8csG386xvXfTWBi5x32DbOR3p + isb2Ce1xWEm64f3iDrd/69mhZkfHfeKX8CKGJU7DzTVnTpqOrAEZJEjQoC3rS+ggmViVxM0Yg+Vg + SRPI4gMJLpu/NrRwXn/6OZg3/Tu8Wv8JHeVWE/8cB/kshmqD0sB0g5e9U2N+uIwJfPYLR4JyjgBR + 9KsLz5m/Yv10NPrpN9+M5H6J/PAPORcNFxFe+vgTiFdHr5Z1uvI//MZO973Q1W3tCbajthA8OFw1 + uuujhIF7u5DEX+N+jk7rAMTX5zUtUf+o1tq7uvBOhZEE49h7FN3WDt4e54D4ofaoSBJYH5hZLIuz + peTi+fC+BOBicZCopyCu5vwolyid8iMJriqio+0yKrAC+4l1e3yB6Wy5meSz9wsJKknXWOv5VeF4 + LzVsyP3H6/o7KcDuXHTYOt88b7x3Vv2LN7Lli2q+ktYE8iU8k1RBD2+5RloNnydZxsqHqWOBeFwI + lEwYieXbXUVv+X4A/UW/4fR0bfM5avs7lJ11P62bf7c8+4WBZOc52A3Huu+bS84Al1GXYL9Oar64 + UVfCj4iUQDQflTebj+wDfnpEkUWRTnv91gHmHeTEpuK5IiA1XdC92WBbX2w+eXAp4Gd9xsSR8r5a + PrUMAftYLHILsssfvgL+aVgBs9ZGv5rR1Ybc+6aQK1Y8b32poQ+jWamI6qiXnJyaMEN16ScBy1pl + TFO8Z0DDJmzAGrcVkIXeZxjfzm9i5gKOSVBGs/QMHuv0eaZKtfrdzYVbviX35/le/fN7q3uItaN/ + Bkt8ZT/gouQB0VvX1WZrdHzo2v2O+C3CdExOpgh/9QvjbU8x/cXDVxWy7U4kVWPTT2NLtPkMGLc9 + jhfujHkIW7aZFqVTKt66kgnUwdoGwst75rwVvz/ww57grx6QT7J5vMBNH2F7z5zixrqGd/Tz53wz + vPQj1LIaSrgJ8e16DuPl02m6NE3miejJo6DLnW9XsP9GWcAx4Yku23YS+F7WT0C547n/1ScAbFKT + bHzOm9J9PKFwaQYSkOGhbfhzgfvFpthdylO+COuog/RTKFhWDn2+hHV9gUlvBliTqzKm+tdnYDhh + hej71MiXlY3hn/66/fTmlesD2MbOFf/yJz1edhnc8HDDq3c8RXwhQb0HVxyYFxiPnt9koI/tI84l + cKb3j/xKodOfIXF7nWjjIa8K9Dg681avsmKBMZLg7+8G617iBZB2Bpsfi718jKqxD14JPO6OHJaX + +yd+k/fmXxlBOKHEPsRCwiYdNJhPENDebLw1nqMVpg9Ag7G5ZpvfdmlgbRwuxGh3DehkysnwGD6O + 2Nr8loVhah7J3CcLmG299x71Jxh/DwyxUaDn3D7xn2CU1U/Av991v+7g8IGhdRrxY+c9Ynq9cyo8 + Sc22x+Yex5RZ+gxauFexhi6KR7mb4KKh9Y7E+3QdnSI+keDAWLeAii+z56blZIP7evaIzKiuRjO5 + KABGjwT7+PPWlj5oU9AeO+FfPe1gSQOUZdxMku7I9J1b+Qw2fCDRmgJv3TWUhQIb3rHcniFYK6FJ + IPElaYJcFFb9xJgfmH3aawAlx9MG49hDsOErwa7xqn71PYkLS26iepN4I1XdDjoJCALBflLtx19A + dPSZaWcdgDZu/i1YLuFIDt9yVw1wgAmwx2nFP39t/dVz1uKuB+x0Hb1ZMjIWbvmHGJbje1zfJD7k + ngLEeu8w3qKZ5YxEa7KJLGuZN1ydQoWP+p3h6Da+KzrV+QAHNUaBqGHWm+eyKuB+JPbPn8gFg6U1 + /NZrGSxTf+i5jY+BLsrqoDTr1fsbD//7KIhaQrsfN30MtvUXPF7+LV+q4M2iH583qn2vUf6M1J8f + QJwqEr3hnjnFXz1AyTK5mplSfyJmp1N8uAcdncEubIA47PJJeIerR2YD2LBy31Gw965xtWTX2UTl + zj9jGV+kfo3sfQR+ej/gMjYeSCANcGYuCZbt0te4TW+hbX1M/DN99evEujXcB1JFTItxwZru4wF8 + n84NYx+E+VbfCvb6LQdB5LCKNy+6KiKYXhisz0Kc0/jzkdGW74LcyPeA/PhLBZIdyVQ7oWOVEAgO + 914InkR+an988cdv8c575IN8CKefHiSy71Y9+fnnYqNgbBvhU+OO4smFuKls4i27vlo2/APvabtT + qKtVuqgiNMHn4Dzxr947/vhHce1PBDcvxuuR64jSxvcxrgs/H3MrX2E3hPCvvtRu+Rj01eMS8PTS + x/TDs0/U96WF3Tws4sn6KCLc9Mj0Vbw1H0q3YqRajn3y00vzeWQG6cePZCaLeipllIV3zyWBkMV7 + sPHlD7TDUdj8+BY8/XNbA75oC/zIRTuebDqXyFOKgqT04uU/Px1GBqvhx4vZugwL5xBohnjFyoYX + c8jGEdrqv8S30zSet3owIkopE++dfwEn3GEKrt7kExdNc790sjRIv/oO/nByT1fFKP6fHQXSf+8o + eEZ2T9zm2VarOctPdHXPV3zwz1VP+UcvAYVylBzmaqfNBzJHqFy8QzCZ0lKRZS8EyAuHPYlk+QNa + szw1QP8MNkn2zpEKl3SdoajaAw4OekD5m6UMqJ+MEitRjvNZznwWcgaF0/4UlN4wHw4DPFlwF4jw + c6joKR4zcHopFr4in/Xo8a2usPmo+4mVqlffy7csgXbTYWyFZy2n5DRtt9i8EVYu9y9Y+crJ4Nik + GjGmbx0vetK6aFxHHUdXPcjZytzfYfAt2mD/eFR0vDqeCgkzzNhYTALWy6Pi4WLXwbQ7nL7eepvH + CabcKOIgDjywzOaBh0ByDSIb/jPnFbsboOsXGTbL1QNCvO4iCHtWIeH+fYwn/hUGyDLCM8ndw75a + Fbuc0PgYAnxHKdDoeS0jdL7JlFyP3clbxpMkI15gAXm46JlTh/OYPQeDI7EIPcZc+dDuyFczjmDr + CwEJd44JTnYeBfN59wbsewAmek9ZSeydf+tnd+gkmDBFjY2LM2oL3zkh3MYjaKWx6VfRaAa494sC + n6+nzOMZ5pQheYq2vp45ydfdXmFRlI0t9tJXTNfj+SxBL1JWoqDmqU3k/mDB0FxjUmT7EKxwfoqo + oPhKTHLowDY+E1qHyxW7NSeAeTdqIbofTJ4Y92brS7KmIgyF8zIR60rjxXNACQ3A+fh81i8eK9++ + AVRMMSQWGfxqJRm6gz6OcLD0mga4VNfviM6NOO0h0/VUlDMV2o+tC8Ost2DZte8Z7gmoCd7ZFhWG + 11yj9sNjktPw4BEvkUVkJuqEHbN9g7W8whmasW2TtM/ZfH634Ta/shWIqqt782LaDOKSfYoLqLUx + bxxeIUoOfUCC0ubz0XPoE+2q4kLcJnpq432NVMQ/2is+qqd7z465PMFbLhyIf6/Lnr84lgRjxnoR + F4ss2L5vQipdK6Jdq9mbxmB1YVM87+ScojgfbxzM4EPiCHGmqK3GvC9DSC+3dBr0hKWjWy08KkT2 + QlLTFeLZFz8BKNqon5qRWmCi1kdEnLFAnFr1V1u1qU6RpbM6KfjTQ+N9lTbopohPErCfkXb39gmR + 0i09cZBB4vESpB/0UWOGWG8B0TXyxRkczq/9xHXcAQj1VoHrxxbj6/Z+mt/KJxpluyResZ6AoDJy + hiCXFKT4JpiyqiSJsHe7DD+eSuPRT+qGcP+GAo6vHUuJmxsd+hbfKZBObFMtv/gvHyoKdqCWwHIk + TxudyHQIfvlkvj/iD4I3W8J4ZQ89b4hMAeq6MohZ7udqCZJDB7OkdbF9GwVt/nqiKikhz2Dc6c94 + ZZjbBfJT+iT2cL14wlfKJoBBxAbT9dJq9L2ddI/WvUfUdLB6gRMWFYlKZGL1gtp+Acy7hAcuzSYR + Phq6YmsN0O/ztnjwlnftylB8f9lpn49jPu9fCQMv+qriw/ug9HT0kg5RzL3wdRavYBGO0IX3Q+VM + C+LbntvPexU+drWNFfV0r5ZoZ/BICNUbtvluT4ktTil4SusDX6TgAxaUySaK1ruI8a1V8mXOxA9Q + 9uqDaKFk9UKb2ywMp7glKjeUoCnStUPMZUqw3SlLThe+YKXaKxBWltzt56eySvCgNXccD/YQr2uV + ZpKzMjI51Eoez8fiNQD2Jb0mkJJbPob8IELfLizsKbchX4dGvCPRfw1YeVlh3p6K6we+OMiT45ZP + 2LU7Zn/zKY7UotTND530W8+PX374xb+jhDq5TvrTo27Bs3C2+/t2i2WQT50DPnAmT5Po9HDX6Pta + NyiNwpkcTtYM5nOnBAjxtUHcE2tXXPlkG8mmSogLii45+8hkFRpO1BFn7sJ4PgWfAeRCTfAddwyd + w/ejgaNyy3H2sVVK1d7k4cpJ7JRZeMhpkck2vFytGWNQS7RF+RKhI88GJHxmhsfemLGQXrxQEPv2 + OPRT5M8rOpyr7UxTZlT8RNUaWjyMJy45pz3dUWrD6+VzIB6XdfkfXqtswBN3cVaPvAYaINNYJKzK + ltcLklvb6Gh83hPMeivmadYUKDjAAF/Zk+oJ6BhL4GPdO+xMuuzRezuyQMuhTuRsyAAXUvcJ29ch + JLicCsDpyrcGgnJKsUpyGXBtNDQwjMUW52G4amssHiAswk7ANvwcevb44iJ0+UhHrGCoUXpm82Kf + slyE44O9aEt5i0J0vdQHosa2VM1SoVyQ9PKC7RbDZ7y0IbhDVd0dgpmWIiAOz7kQ6ZKDo/RdUqE1 + Ux3FviBi/cY28cT1QgKdOtoTJ1EB2OI/ga2jTPgoeWcgvHpXhVOdyyQ04rc3OLqooxCaLnZBjD0h + bEYXnof6SW794FN+24IPGVl/kuurVTzWNl8RfCOISZ6+YiDwhVIi9egr5FHcgDaeiuMHJGenJZ5/ + B9Vwg2yEptPxg5X+OOYrzZ53KCezgu9yovTCvvF5+D6lOb52XwGs9z6z0e/zT4uJ6So4ng5vxfEZ + rI9TqM2SlXaQXk8Qu5w65tSh2QovpSMG0gdbPT2+3RnGqvzefv8A+qdhZ6hQ0EzCHZnySYpPPmLK + ViVpEW99jQ8Gi/hppdMqB3M8fexzBujXvWGt+tjxUgROjY6GGxHbw7ueGJTMwLOCe8DkzF5bV9Fx + YZErAvaF7NnTmBQSBNx1IIF4qeMmXXcuvK56Q4wxZ7R1PrUizKavMI0arPN2Ng0Wesf3Y+Lur7gi + 7b334eHmiliVgzkfXh8vgWr+VrBSr6rHSaoF4XFczWm/z1A/keV4gTAODgErVcrf/wMpnQ/4pksg + Xj4eFGHPH3XiOlen4ssrXKEtyzmJq3H01mrHZ6h3m4xY8gN6i1vaDaRzJ2K8jg0lenkR0WsQJ3KM + DgYQBONd/55nkrR+l69y2cpSE4wIe96z7pdUjUN4CQaCD9FLriatdyBcVISxadWWN0/DuYZ7pu5x + dgKHnF6Z9wcQZpqxt+sHb974qHQvuiqQLpkIaLF1Eduen+gQ2DnHn+4lxFf3TYL6iTTa2jiCwEkI + CUrlWE0vboj2cIgS4n7wtyetmZpSCMsbceMl1KhWqybgR/FALvkpjIWs5E3YzmlHAtzdwRIux1Ra + P0s28eD19lZ4I6mkBU5FLOYjaLPbLROy5WLC+m3UKlK2IQ958xsRhz2VGn2u5goquPAkjd5pvr6f + wxPc3ueGHHQpj6nDaRDFXWBOgjkjsL6f9RPu3/px4rf303WVCsk9QY7I9/NQrU9px0K8JNbUf751 + Nb1vrgs3PAvWWOHAdOyud9DOMyJ+uPUVvBmXEopKaOJ7o7sef36dth1xwxlHAOn9ehZmBuWoU4gc + 5nuPfBEyIeM+nlhrblI1/+I1jKWWqK8xBevE0hlOSuYFrz0KcnrJTra05fNgf6rmeL4coQ7yiZEI + LvBSrd/hy8OEuddEbo9nbX63lwGc9WyHHcEB/XJ12QAaMb5Mt1rJ82WS3AJgQCwia6rm8Vfj/fyX + P3a9r/G28ij++MGuSNOYwD7y4XIMtAlJR0ebyNUP4SNlEuINy4tSmr0T4IrVmajSwuR0y4+gCP0a + B6ulxtO9fTLQANke27fHWNGkubhQP7s5tsK3TxfyQgnkxTgg/sZn5wMRI/jjrw/rC+nscx4PN72B + MRfvvXlnXO5Q/0w2wdZT9OgnueqwqggJRrW95nO6Cja4Zcx32ucgBcThNAbcqOsE1tdtwJKqeQQF + GWjBjEJNm0mDAlh7+5A8OJ/NR7M8deLbj2LiZRnWZhHk0d/634mzEveGq83QerRnHF+r0GN9fuu6 + Zu+GAHBZF08LSgakdLQn2/PnZEeBK7FxP2LsiHe68XcIGq04//BYowe+cZE56hw2guTuUfda3OFT + AwJR/fxN109+YUBpPKtA4Mhbm/bbGYqNT04M+xnBbCjlAKuSc4g+cEI/PWJDhdjdL0RjwEIHxr/5 + 4HQ2r8Fly6cDqrsEim/vNYnwM1arV+wZILRPAZvcufEoaZ4pRPdLRbJUjbQ18udZGql3JvgTOXRq + S3mAvMCDYHXdCtAeNSbgEnX+jU/O9TQbYB9P5U8P0vVcC9kPv7B1s+aKooSJ4NuyvoEoWao3GFou + wQ0PyW9+p51waaAhlTF24+JFa26VErjFO9Zv4OCt53p3gQfN97B3EL18WQZ8l+DNlQhW7GM1LJbk + /uGxubyPFf0kRxMZ8XYL97RT8jUIeAg3/UuyQ1SD4ecH5Kf7Ext1PWhky28o+MopscdB01huUWwQ + ySvEtnq0KU15vwM/PVsoXBuvW3yjSGnLAEV8AHhfcQv4eO5VHFcPrfrDayG8z8HY37WctcrLDLtS + /WDPPVx7Gnb3FFjN0Z/Eb9v1CwCaixwl0oNffpqL+BKCQuS3rmsXR2ODa5LCLZ9gL/ONfhDiZwF7 + Q8lJwndmvJQPr4Dv/YHHtj4n8VhgaZaMMlmxoRnvfK7cmEWZGzpESSSJLtHuwMOCtXbk8HBbbTwM + 2Qx6Q8uJfw52Hv1CTYSyeHBwcl4wnfkLq/7W808/xMPXm2Vky3sFO5Bxq/FqPDvUcG5MDKM/9atq + yAnauXxFbDtR4kVSLQY6p++KnV5SAYvwroaGzZ8nftq94sXD2Pzpe+yFzphP8kfL4Hn4PAOQ+e9q + JVc9QpOP7396bE1BnkoBjq0JBvcjXW3lXIA18N4Be1eXbb70BqKvyBC8w0M13/ZXEUk2GrCOFbWi + kn9K4ZQVEXko9rFfBesqAmCLxab/EsqOuT3Ap1Py2EvJLV6/StTBVUsicm+8CcxnUszQqcP9tGx8 + s5kUNwNj9iqxPXtBJXgKTqBirTzxc5mrJh812y3I38e0mo9Ia3eyM//wmwQ+kXv+UjgFiOQZYp0e + GO2jJ5MqFZ13nITAVGIOCs0M7abB+CjQoydcspsL1znmiFzwojZJx0GVVm1+Y3XDn5k0nA+X3p2x + t/HlPz4nm9UBb/5MRU9H6EPv6PnYa3ZeTq/x5QJFfztzeyFxT+egU6Fzeq9441f5/IngDBbOuk/s + 3llAa91piH54q8mfMZ61J/wAxZp5cpurh7a+b6qNfvmR9W5cteFviULjUAafeGdq3KVyEkjSXUnc + wnD7bt+XCfz5IcorOHmrftQ6tOkprMGTAgSrtTvpiksD30xTqbrN/5Icpt1jq807utzXSEY9TFJi + Nlyd06Boebj9vh/e9mtnHn3kZOud4F26xOO2qQV2jqBj977u8uGdjBCOzZzixEtDuuX/ELinsiTa + OZq07TreAF5LFJB04yuUnq6z1LzrD45vRZ/PeREEMNg1LvaT+EmXS+WksJ1XRLQH4Ks5tV4Mwq85 + w+YoPSitpO+ACssucEbMa7/pkQwlfnrCzsY3Vy+3V9gUpjc96/iZ8877K4HNv8APkXEpEZnnil6D + am98WK7oHKP6zz/yzqDqRyzedWDy7xBjziDVWl7ZGT52H5soY8tqZCecSjgY/ILle/il66GbChh7 + KJpYemC8v8+/4O8pKJmKpZNJSxGphZLgiDl5OXugUYdeeigRZ/Pz2uRBIWIy5YDlJ6Hxn5/aOtqE + DeGexqx8Iz680U+B7eBdadScCh/yrcMSfX62MQGWM0P4fQESXVBbracLa0ItZ/Rp9XOD8q9VkdBU + CzFWfM2m5MA/bcSb9m56bt+3hmRUQTrgGwniU1hRcvqsgIP+EWMFpzHhXycTsRXOsNYKbrz1cWdB + 1dxtopqP1VsZZcfChFGPE7eYhK5MslzQbGMvELhGzul6xxPc8if29+s3Jg83Z0CAjxbRj1cn59X5 + 9oSb/pxQ1Bg5lfBu+vFfrNeGm3fyF37gxJxHgld27Jfi2DOwhsOHGNPUerRT3Cd0GTedds3C5aRM + zxFo3p8PMebl1U9Jkd7Bl4odueSyqk3qdsv1pv+x2guUzudeyuAvfsxd9oxp5MQR1N7iinXIan3/ + RUiHHahkbGXN21vzT16D4PPRiPmQT7EwvLaTJ7NaEJORw4pK7uD++Sdy9ua9zR99/vwfEpxlNV6L + wCygLn2rSRRBDpZYVPSffzyt1v5FecHRdBgrdUQeoM7AwAYvae/Zlr7tmPv28zsZGUivZ0jU9yfy + CAfyC8jG8YhPl/AI/l6fOf9Etg6q+YIeeQPg/UGxI30qQO91FsJNjwctZLqqNQU7Az/9fGfKVz7B + NTJhUzALNlAT98O9RBCyZcL+9GY+Fn6Ywbh22YnX9ly8lPb3CacKjlOe+e++t8VPAq0vv//zk6m6 + 29cSsa5ckHtPvaf1sqzwGW1dDzpliefUauEf3nr72OhnoHr6n37ljJuvLW61Z6G0X9Rp3fyEsZkK + F2zzE+wUxaesVTsQ/OJBN/wYsLD/mnDTlyQiuUzndVDrnx9Mks1fWrTbQ5e+2utL7O+MY+5rZyUk + p6Yl9uZfLedPNcH3xHJYuROnojfmUoPNb8DB9ajH7OZXgtOzZom6rWfeTt0Afiz1TGSoF3S+nK0O + JClJAxqUTzB+9dlF2udsBHy59mCD8c2vXCnGMRC0TY9sfKp4Eu8g9vEQXIsEpPZ8wlEkLd7m1/N/ + fHySGDZfbeVxB6en+yLeVp+Y3dYu0S8ecNPNFWF8O4Gb/4ttkT1rW71Bh2JNAmwCEvSjytU+zE7V + PAHyzmO63q0JbteckJsu5fk6Sjj5rcfNX1RBXx3aFKILracxIq63Hs8PEShUoMQUkKgNG35DyX5d + cUD4Z0yfhpyBTR9gW2ZudBWJP0BVNb/E3/dlvzBW8IRb/QlneHet1t/6dn3VIgfWGrTlUktPIBbG + dRKH60UjKDZMeKvBCSuv4t7P8u3rI3a5B+SHB6sjtsUf3ir25exNwWt+QuFRO8Qxbrt+Jl9flUS6 + nZgNk5e3lhftA6OW07B3lR/xuhPCDhWdc8Sa2IXahu8BlF5O8Kc/l0PX1tDxjfPml49gJVc/gvZq + dzjULYZu9bAJ4hjtiR/yl2oZE5mHm5+PH1ehAzQ54/WvXmBfWUBn247u8MjzwaY3837NX7YK1AJI + xDmdTECAEdZo5eQAZx/8rRa+U0IorPWOWFD+amuK9CdqsSFv/CTyVudNRPDzP+6P0vTW28Q+AXZv + d3KI+AnM88GYYGjgctodAzWf8e4E929C8mmnYD5vb6zmg1QS62lbT2D1FfUODHDZE6vYW9p0PD8k + UPKzj12hucXzw951YL1FO+KJ1i6mQnZJpTuzrpt+WPoxeQAItQMJ8WHj7zReAlPa6gMBv+XXlYGD + D87Bspv49L70c1Idf3cEnLHt4Ue/XB1tq3j704QW8o5pdfQmoND8iVVZ/tCN/ycAHJk6aLvv+ZfP + IrgvVAfjpgur0aePEhrD8RFwpW/kwrTcI9iWBj+9UCNr3M8/+q3f86rH8dq8KxPwCWtO+ylq+6mG + iYy2eiVRY4WjZCJvE3LIFIhyb3S6ZDPHwkcKE2z5NM0JJ+xl6GSph/X5qle8jrM7LK85EzC/+uNv + PH76ubPwEC9Nt6xoaPJ4qvSLBvp5O/HbFHDBql921fzTm52TLT8+Uy3PWLbRr55o4K+jzeK+1FHD + TvJBciYBrJu+BJs/h71i5cB8LNoBijQ7EXftzGqqYSFDFh5fOCdvEFNzSnyU5KJK7qfPWg0/vQi4 + fJikg/fURnyQRRTTKMDaLXjR5SWEH8gb04B/eEfL9sICDJ2MHF751pWIjVOoKl0yreZljIV1Xe8/ + /4i4zjfWOOnSi5BvPXai+sOu1klMbfhORBtf1bmruqGZ71AhpU4Cgt5a23OaiLIqPf7x8c3v62CS + S2pAiaT2i7oPPjDtU4Go5oenIw4k/a/eqx0wR8us0Wew+WtYY3f3ravyAf78BnJYiBELqf9sYJ25 + OtaPkd4LmdCwQDoHzyCWP2O+5nWZwfwkHwL6ZrC29gT5YPOzA8bn3/HPT0O8MQwkYiOqTZteg4qV + Vli581M+fg4fHS7atfzFe76IbJyhpijvm18ux8s+u5lQpGyDjRxouYDig/57TVSSP8EzllcfSM+w + 2epPsjeFZPy/uh6A/95R0NzP7iRw3BeQ7PphUXmv9lg5Lrd8IShRwd3heiwvYhMvTuS4UFm/8TRE + r1Rb9sxSotRnBXL25EJrLpIwwd7vReK/FFZbjM5pYAXPbLBoa0vX/KTZaDmd+ICJRAfQQ9ax0lW7 + 6FgtHozXTtd9CDpKfaw4J0vjXZLokPHBG/vDCvM5JqcCBvBJiUcY5M3et7XhduIl4ENe8bg4oSbi + L8vGGErdW+sO+AC3uCJmYTJ0vPW7D9T6tA5E9ch4y5QbAzztggZjDTyrRfzeJ3hT8TsIg07JV656 + Zuj0aTWsvycZsOIQmejC6xAfmyrwZg/QCaJR0ANaLk3Ohzd/AE04Qazs11e/Rr19h690TwOu29cx + fYchj85ZtxLPdyVt/YAHDz/F6GPTY450WfeTCeUgLEiRdKRa7dPlgsJTaJAsH3f5Nv425JezPnHG + l6P0/KE8GgX4xZZgRlr1KJsA1vo+w2rY1p6wBnaGtDAPiKXWb9Byvd/B4X11A0LeiK6B/zTR+aZW + xF9P15yGGj9Dsbibgbgzbh6LQ85E129gYY/GxKOTlBcwiBsLp4F/88a4PXRA9vFKgtvFArMzVE9U + R6+WXFvJpotAXgmy2PVE1LJ692w8OSraD8w+4KbLHqx4eYTbGftse39DqbNWCTxoS4m1Hc/RhazP + Ekov0cbxAz0Bj5dzBPtzZE8C/+QoeTXXFKb76juxU6/0/CUzSuQ+x4ioiWYBoX1HLpx2e5Yc2pdT + rYkUNpA7iRK5roJTsRdpN8AyPhYkDfmXN6/8nQXCOAw4Lhc7F4q79kTBK0xJYeUA0OFysOEVEXVa + +7eoLU2WMogp1xjjr2NVAkLQhnp2NYjVsbt8CnMxhKnPC1jT1BQMWvdMISecfJyc46TnxI8awvPM + fskjExLAuVwlQT/wDRLCyyefD0otofitGMS9n7WcPd5nFr5SQEngAEcjDHvS4TETXsHDj4yY+xRz + AWFVmsSZ7URbLzhS0Ul2bWKqaaXNxauc4ePcfrDjeud8deumRiqWMpyoHYqX+tBESBn0M0ka5UbZ + 411k0dRm+6A694FGSPFiUXvQa+Lddg5dHTWKkBFpPsGDaVes5cgrbKvvkxiUNzwOLUMJDY7D+LgX + 4nhun9MMPXNvYhkyZc9L7+oDF1B1RO2fn3jpaBAAubx+cQaKkI6nsUzQzvyk+HbDKZ1BU7tIf6fF + tFumc7xcfXVFv/HCK1YAG0+KjOTg+z8AAAD//6RdSbeqMBL+QS5kkhRLZhGQKKDiDhARUJEhAfLr + +3BfL3vXy3fOu/cqSX1ThUqN7cq1M04hjxBOwnCjt44ee259nuiR2CE9aIeGkXnRBBD3/BYH9usd + LXi+neD41LY4Sp98TcRXIIGU2hfq76+ux1VwCeBzngXqXtKqn45qkoOLT1tqwtvI+CbgiLJoaUJm + +Kz3uN9KTp4elBJlg5eaTarfwbNqFOpttu9+RoE2KZ8hsagWZ6+MjdZeh9fWPRMY9kHGiBwVinkj + x2Ce5LCf6xqHcH4cQ8Kf9MrjyK91UeJbJjZG4CJWXb+dsuIHvjabI1qerK+geeRbHHozh9inyyvg + ihPD1vSKssG1QgGqcLMmJhaHlu/UxEpySHyabFJqTAq5hMrVsS50v11vFWkCbti1hj3iIzM//axM + JxvGq5hjK9nqbEFbeoJC+lrUEk8tm/g5ztEr2TyIOBZ1tFRZr4J9v3o4qD8LmuURCyASg2Ft4ks0 + kzPpYPl+fBq+lV/E9aSr4KxHEPTHs8pE/GM6eF2iUxU2ei2MD7VStEjcUbtfLh4pNjcdzuy+x4/f + J8vYxRwruM0HQtXbVzdmurSlwhUhI1ubtkY/WlgH9eYdyOZH/UicVL9Fz/twwmHNYSS8b4oNa/1g + 1fns0GikuwIWbXziB8o8Y5HDMQf7/gtxnM+lMe79RFf0hO2JvBG4nimn5QrYMChWZf5nUL/RZCX6 + UoWa1T1AYuVxLuBP1VN9di7ZvHNOHeorfsRqvbFrMXKiApHiWVE3a4/1lCfeAEu+hb96yRbbVn1l + ekNAb1oyZkN4OS8KqIaLNftzzvhDqDmKKLUG9pJ8YKxQ75KyPUQBPm5cFfH34y0A4XMf8V57PD1i + FA6BQsBxsKsOZS/YtuMDd6/PhHHWEXGqXspQimKI980nzrhryaloem8CHO7S1ph+F3SC/dexsVeY + qGcnvbtC+8UiWfJnYTDVKQc0FHKAn2+FGn2tOS0UHQgBVdWlZkjyVKhCuFB/OX3r4aRsZMiySsH+ + gvie1PtToVDxhfCTvh+Mu3aRqWyfDx17w3PPfnv/pCr61cQ0gnzJaOWBg7rH6YSTlc8EtP2GijiS + gT4dJcomdXeYlO2wuNTA8aHnK+GYghzYJr32fsKIhuQY6RAa1BnKY8+R16WE+bCc//GLUCj6R/nS + 90QzFlFjutjTVemlISXLimdL+/gEALNzoflaz4scvguweBHj41K42TwST0DdfM/widTXrBO4o6ps + Nrd1JkYqR0t7yFpwgFaB0O+/BtkzXVfQOVqnXB9Vj3u6AgHe3dY4WOwJzXv/pKOnUkV07WzXf/Wv + TI+RUtValmxau16As3dMU6k91KLqtIMyvhZGImHHG7P0vQ5yHh1MbG8xY0u9P+VK11bkn55gYq0N + cJ4GjI179DAmctsUcFzK9YQDV3nzXiYnxEe+TotDuqnp3Ms6bDb6RDVDRDUzv5qprM8P268fYlNP + qvIPf8jmk6YR9fyvjYZlCHGqzn22bAs8IHf33WBM3w9E4+rpyOFVMmgUkDH7W2+5TvwukBPuky3r + euyCV3jFhiVsovmKpQGwlZWBfEn1WvS8tAPjZO5pUYpK9rZvXqVY58GidpOcexFJhqo80tnF2JG0 + eih7qVEC+aDTh6R9++V9bVt4V+OFeuqWeKMyDyXsf9wG63ZwQ7Mo5xwCSBbyqj+j0RWbpwphXIVk + To3JoBniHZCNJ4+P3HDpl+qt25BzJ4yfS8zqEWt2AI/N+0iQZj7R3M27BT7ftg52UmojPpxPIXR7 + O6fGx/MNPihEAY65rhLxal+zyW5tDk67DGP3e7+wRU4nG8VylFKcihyaXifKQR/LN3odT2otjob8 + gfN7kej+nrXZtIW7Cij6vcjObOWsm1Ero98uP5JN2qgR/8e3oQcD9n9JUs9S3geIkuueqqH59saU + viuU0N2exu+jkI3RdV8hq8Uqte/Hdz8W11OjXIr9gyTP8w5NRg8T7GLzSELgerSg81lXvIcpU108 + k34Ol2ZQIGlooDymVzR4S94BXwpnvD/Vu1UfDRLKufeV+v5m582bSJaBU7cD1u2D7fFfNTqhWRIW + rLYXL2Pf508HJ/YUqtGwrmnYSB1Y9kKw9/T0iG+QUIF29jRqepUf0d+HfP74IBiWyz1aBOfWoYR+ + VBz4hzj6Zc2iInd3EumqN6Klers2wp+yp97w/LKx4iYAcS9usetXUS8OvRdCan9pAIWU9kxin6tc + xgam6xmjTGA6KVF+yv2Vj2PGK9PJhGeeOcF2dpBHumGTQMpfDtg7AGHj/jjkcHUfNnV1/dLTk17F + f3hJdSJ3HuNa35QV19Cwqb+9ntXir/rTzxgbqWBM94iqyL0Jb7x/+1M9D5quKgK8XwE9xze2OKUn + w5/+LUIF1eTPzzxv/SdYRNFFf3y0q05Ng/Ev1DwRaZ0DWSJYVH3g2Zseyz6BulxONPigBi3GZWMi + RBoR6ydyQX0hyAtszXoI2F+9Pjg3gNndJ4G/GR6IveYpRdXveaIa9Cmjjq1sYLDjHz1mw66nq95G + jB8DXJSL1wuf4+EDPu1uwffYqd7yCa6q8n212b+fH0qhMcF5PS1s30MxYkn4CkG9Cjp9JMcmIite + K1lWKjho11ttPqnSwe485NSob02/7OFlo/52crBFkjsjXqaV8CvlFmNRnHr2p082wQZjY/UrrF/f + Af2Hv3ljGKQsxxSteEG2eJb7cRMtMjgwVlRNs189P7raAeFjf7CnbgOPK3vpA5yV7qkah6dojr6z + D8a+elE1lvz+RxkAcGr3xngmt2xp+M+qDxTAppD/0BpyxfBWup7IB+IbPe5xgT4L0tYpwAdDFO9V + B5yV7LH3AbdejEIlCs6+8fr7h37YP6IYxkSycf7FW2/mNvoEAVQsII9N400arQbF3YVisLE/N288 + 5NDJsrooZKOIO28U6e8KuSUC1RT/HTH1oLhQDg1Hk7tB0HA7KhLMwkOjaXRNGWNQtPAjooIxHhzW + PdtJ34jCKaS6CFBP9fwif/kDdvZcZjDPpzY8cDVgP7tbhvj7fD7oOoJO/bij/SSzZ4viq3PCqaSP + 0RJZZgBcU7Q4qF+aJ2iKvSBV3vqk18wtWvMAgmiUzAH8+WEx3a76z3HW/aB7f/oY7bqThsP37Wiw + e3+q/vGFndsFm/MNP0BnDj19yDxhVNseGuB+vovtCifeEOfPGOW/WMLhbBg9v/5/ZcUzqs9GXc9q + R6+w1hPNNxplw5+eWOuDHlWcebPcowSt/IfPziHol+J8CcBWnCBYVr3aH8BNkCorPlZVt66ZhKaN + YpmpQnb+dsPGK54GJT+xKFiCN0TjQuQP6BtA+FhkNJvseZRQtagCPT+SavWPqf63fwJB46WeZXI7 + QG5VPPVe92PNfNHRFcRNA+GOk1gvcu6X8rtKA4zn37ueWsXYQNNbAbYk9jEofWxOIGnVFLxN+DLq + YKdRikPV4D/9Ow4eX0HXloSu67Xq93cJa34TyGE/sNm+GSUkcddhl1lDNFXIWVAhHGOafcDtp8VR + G0Uw25jaU9dEbLhKBOATbLCvZKxmr1lKkJ7Mexqfnsds5S8fbu/dKZCuvFHPWSOrcE7LFAeHdNMz + 8WQ0Uo9sDx8KLqj/8BO90wMlSN0GhjB8QZeFzMPYFPc3xIs7JwWsk5YGnCxFA3eciSLfrxX2s+LK + llXfgevWj/Xz+9FcfpQAutmOsUoPYT0/erGBKopyvB/ri7cY8atbN9IHe2asejwW0hZBzR+xRuad + x9DuWaDv/Q4ENsODzbpRtFD9nAMNyX1bj9knKAA2S03NdA/GstNVTpnWSeMKynqDdQ63gU+nxjTg + eLvmp6sRA+98F3pUp3e9lDc7BgHLc7Dsnjs0KVpUKEa4bAP0uh/7hemfCura1XC++Q5eK3xKgFUP + 4CBtymh4Vffy7/lSe/e1PLHJzCvwZP7gR3Qy/uVlkAg20AAZcz2RX+kof3rIMGotmtDi+X98iffV + Q0a/ZUdMUBuWUHeAyKBzTVO5fEYD1d6cFC3N9rjI3yN3w5HA1dlcJ9mEAu/e4gAXtjGZ662igh7W + 9NjtLcaO+4HA8dfiVW+nGROaq6OQ4lFh9f259LSEXgZ2uLz/5ZWLqYsbuNV+gtU0O/TCQUIucuu9 + gY35KhmNtZMq8B62jD1/EqK5HdQW0K4MaCA/LtkiOCsenVNCeBI53nAYxY2sCONM9TMus99x+pUo + yh9PbMiRxOavmp0AW/adHuuNH014f9Xh/MBhICc7yfvlTs6Bfr1f8Zq3oTV/cqESrSfVLX2bjcs7 + KpU1T6V//ojT8qFBNzPf4P2RnhlXCoP5p4fxedVDy/uVO/AZUotqn8s6JmLFf+n9k2m8LSBiykm+ + or+8xZCDr0H4UxUqvNvl6ww3M1rzJlX5w3e3Y7vVf9Ql+B4X09tVa+pljBUJlc/zgI9upPeLwFm6 + kuz1DKsf8cmWR5Nd0efb1dT2dqU3OILeKNL0PROk3s9MkIzLCf742R5PZb980E0AnhTPQPFfIxsv + ZDMhz6wv2Gm4myfuh8WHP31/iJzEoJlcEnhtnTNhAldHA1q8AOh1/6N7d51RRyYuBT3JVXwQzseM + Z2mr/vlj0j7FqzELAc3RX759/L3Wqd1nJsMplGKaNPQdfd3n6EMsfiqshqZlsOddc5XuEZ6wv4fP + mv8mArxL9YGDi1ZHa35ewp/ei0X9481HWW2Upk2uq74LmKDcXAeemtvTw7VCrKfR+kbd7/SiR/91 + RGJ4liv4esUH+ys/t8lk+qh7dhoRr0PYD/WycYDJ+URxeSkjfvppOVj2RGiuHU9oktmtQy9YSjKs + epSSCRLpXuVv6qn3GbX3z9tBlfU74gscf6wjohnA9FzuwZeX7zWBl9JBN5sxjqYJodli7QD3KUNE + ZAOJxgsRFuRN8oYawu7izVmz6LDrQi0QbubgTX7hmmjdL1i/Pbxs2Uk/IgciO1NNftB60exRgilT + RbJd9dWwaTY+fJyPvuZxnbc0PCHyI80NvOfle0/nKgV00/dHHITSjy2aYk/IkfUu4IT8sM4IvC9w + 7XcJ1sRfa7DAPp2UNQ8k/DN89u/ouwvgC7IUvJupN1iY3Zs/PUf93yZGnH6e7b/vT/feR4pYz1Ux + WvmXnjnc1PPg/mzEnw8JWc/11/xx5jcAyYcS/rSPUA/pPQUWhxAsVStGc2MefKAvTaWeGZfGrC47 + QIp1fhLRRW4mPqsyUBD5iP/00+rnTOCP+jG49M0STc3FryDeQURX/5Jx3a5Q5VGOE3ow67aemmiy + ISXCkeIzX0XzdLstKB2KHON2MJG48vFumv0h4K3vBc2PrnfQMzc1eiqNNT9opX/1SfXY2dWvc/JT + //Gv5hxIPSoFSdCaJ2CcPc7eemtZjCaGmmBe/S/ZricK6k1aEJamNZq9bkoUskUcdZ7nO1p+m9YG + /fXj6B4M5s1YCDv0933S4lZHjH5UCcJYvAdy+Q7q+YwPAIoVPck2XWqjddSNKxMDW9RnueqJtnA/ + wZqn4ihwzsZ0Ue4B3DDNCCeOnLF8jKqE1D61eM+GIGK3SCHwlw8VuL4h6livUtEzR6J+wVq28kuC + hDa/Yc2hHZoN6qtovqj0T+/0qz5S0bEYJGqbDckmcZsEUB/PET1EP73mJH52FXe3dg9HUe/nvkAd + 3DY5H/xcT8yWwNBURdh0XjDTsO7ZXKUbWPMGvL5bVS/nbabDLs5iauzajg0P6/hB3GKK+C5cn96a + jxDgo/pAD+7M6ikSWxvVtaNRXHOXevjzd/74HakrjShja38Pume76lflEHErvsL7lovUFiQazRbH + O4r0vjL6h8fj62B8wKFWRO/Jp66X2XJ0OMztTI06F7IJ3KWEE5pV7AzlWP99H3htHwl1N3PFmHrg + XVAzqadedoq9Be9esjxKZxIw/dYy+pqlFB7lZhtMaz4rrnoabMrb1Nyd5mh+GmkOO3rt6GHlt+UP + D/PBmPDBbNNs+tRO+NfPCaA4+rWwk14EbTtaEjGRH4htb6IM5fDh8LFsWsTmXlahCesfto7JxpiU + 0RGAqHyML571MqZnfJmgvgg7svtt0549a65T3kHjYlymizH8SrLAGDsNdl5plPFTd5H/+gFYz1M/ + 4ranvkOlWHr05r3sehocEiJvkjb4r/7Km//K/9abHg/jJ5vJKQ0QZ+hXGnxVof6X950uX59Ilhqx + cfX/Sn/BBTbTfW7wy+5jw5ZXNtjjhCGjRnDngPsId2q/ms5Y8zVbSfy9ScRjV3r0Lvj6X56Jne7G + GSxbrib4h7zGq96ImNk+SrCYc/vL0yKCNiGn5BYP2NBvLZp+zzT+y5uDeeJVJCLSA1iP82/1K0ZN + +uMUK7nQqvjx19/wvLSFIa8e1L6zF2OeF7bK73jv1/7vAU3O9tWCEx8UepyVLZv8uNf/+lfYuwav + ev23Cu6Ne1O7e3XoX54B231HpNXvcZ9aDZWtjR5kiZ17zed7J4He/0lkp1HNWJ7tpP7V+/p9Y29I + DD3ZrXqJ7hNZYW3nx/6/PFOLS+axy63vQN7yD7LbBoE3XxsjhHQdCGMdd05PCvs1wMK8OZAjuHns + ePYmyPRtEiBF2qAhbn4Aax5L7ahrI3a6tDn6yy9Q0YnoH97+MMsDocKSt0jGJYQ9b+UY/24hY6tf + +9cfFsZTWXer/4Ztl9xpsq5XzT2SBtq+DfH19cvYDGY3/PEFdk7elo2HUHP/8YMqZlY2vRK5gBAZ + x4BL9+BN5yvxkWw4hO77y4FN9/eJwH4IKD1EJM7+6ev/40QB/O8TBdH3tMF6Eow1o7ZQwf6UyxRb + 6BexT4ls+IqDSkP/Qevp8fx0INTcE+/LQGBTMx0KhdY5o3Gb7XuhpV8CQZVPNF1sKWKe9oqhvZ1r + fGwOFWOQzx9wHFPDNnfdoak060n5/D4nsp2ZnYkRTKm8+U09PhStbSxhr0mAl6DD+6XqvcW6l4vS + +p+Caj0cEfN3BQffz/sVXOXnsedde5bh05wvQeU/cD9hzQ9Rj68f6j9flTEemkZWnDvx8OHiXBGb + 4R2j1zXxqXlwxnpBnmcDXO0sYF9mM1ZopysM1CzpAZd+PWTW4MIljfzgjY+3fpQXJoMv1TkRl8rz + uGXZlnAf5gA/O93JBDX9ubBzmREgK5jZdL9eZGhCjKkd/5SIfaxgQW8n8KlRbderSvvlo1yNqqAX + 4Yz7+eLdJ0W9qRHN9d0um8uvrwP1rir2LX2MJpxPH0UsY4teKsr64dI8Syk7Z9+A+36rTNBPia68 + 3Kal947ZNYu59waypkfBBnio2cjiRiGH15Nqs8vXQ5pvSlCmOsXHULpk4vION0recCGOl8d6T+R6 + z060HviynSJHzBseDZpNTqReX5g9GTKrgF86BDScmqDnT9M4gWXxDY0LlHjCT+5CJTUiEauF/UZM + QAPAJdtdqcqeI5u7RFdhiNGeSJRNBpsEkyh5FDjYyNr13tWzp6J8mfv1jGtvTEfvksBlCl1qvQe/ + 59/PWlfytJOo2Q2Ct9yfUwJucK3pJXSnnkI+N8pU8DuaZziN1jO0OUjTu6NZpkHGlncIaDuOGrai + nYa4Wata5f0wOxrRYdvPyvmUQnuLauoFQoYGh3REWfc7tnfuhs1yUK7vynWIWu+wM8bR+uSKXNtH + ijfJL5vye6DD7W5u8FM4tojbW4oLcSFZNP6EUc/xo3pV+tToqe9tP95kwnCFtyQFZNa/+6ivb10s + 07ep0PNLfkVL2c8hZJadkBpqt/5bL0j064kGu6lFs/+96Mq+ayN6UlgV0d9Db+CxgE7tknuw5SUt + paJfLxNOKDsZ4kO4ECUILKB73nrV3KY8qrB8fITda1/3tOsMTiGz86XHmqqGuNs8bFgwCak6wK3m + U+VkKn+/3/oqmSdEkuEq1+VzDd4b/djzoa6pyq0OE+zvvZfHuc4YQlyWJ+yueDFpueYDVTofp+V4 + yhYc2Sb477OD77uflLHccgRlxQNyOqN3tPgH/rpmModAOXRHJFplPkDYzQk2XqdTz4VKRJT3l9Gg + yU9fj4NJWKBcjDs9aIVqzLM/VUp9SFO8H8nHYI+kI2h5/HpsNMdfPYre5CiP+jFStbAtxBeissjs + Zd6wzaq4FsrlLsk4GzTs/SjLpnYrC7A8+p4eJUvLBKVoADr4KkR+o/XQ+nm2lXnY/uhR+m765fpQ + T8rc357BJv9UjIlN+oGf9PsSIf49slnhG125luDjdZItYqF0LYAen3ywPQ/fjGt+paB8MzkPRE04 + GMJZj6/KZcm2gdwWQcYOwodAuO0FHCX6z1tY34bgTt0USAH+9syMokJZP1/wSri45/n8JSlRmJbU + c7wODbB9CYqVfI5EeFy+PblqGYCtjir2xJ/lLWbwCWT0Tu44deYzms/3KIEisznqIt/yhtO+kdHw + Y2/CjPCdkf4nlRAdxBM+pr8oGg/qPVT++OFAjz9v2hlpCqOILtTdPSLG3fiFwGkzYHq/iq3BUPLs + wEOlR/X++mGM/oxC4fU8o5c4avox/MQtGmTJxK686P0ydQIHrbuJ6fF3cyPuO6zXmH9/DfbZG2Wz + tcMuiOXVon7Ki2y4pXYiTxQSfFaLqBfR2K/4b5dUI5bGlt3slPLVfyc4fHqfaOHrC6dwb3XE9+OM + ouUyyjrA83D5wwM0mElCIP59XWxJ3McY90TQFQ8MgR4+e8gmzkUp/NVT7N1zb/FeeIAexx98k8e+ + 7w57KwDt7Rr0sLlUPTupJSgfW/xi/A7qbG7ubgKj9T7QRMbbbBk/jg7n9ulj9YysTMypYsLjJdaE + rw9XY3HyRFLO0Yfg4IK/2VR0rwAkvPtiX67iiGYVEtD1KQ7UH8o9+sdn02GbUf33vfRi7r0KJZfs + TyAqJ93j1/qA86zmOBRf32hSiGQqKz9iwyxHj1n6iyji451RK1vSmlQWEpDoXvb49lJabxr6U6PY + 8hNIL4k+4700mpTwVpvU4+WIMXu8bGCId3uM53uNJkrUQZGsoMa3c/lkXBM/HFB9boeTyBh6Zm72 + gzKROKDX+Wx7y1KOA9R5R7A6ty8kfETZhbBjCc2u3ztayuu7Uf7WO7ylTiY2Td3AAYY7Tav8VQvd + +/qR8e+XUscbzp7oDZcGtLdj/ONjZsx2oaz4RMONVLHJ38S28svwjmy6FkfL+r67svop8gsNw+M1 + y3WgvEsfHNq8lQni8x3LL5A4fEInj9GD8BmU/W3XkV0RGLU4Cf4Aj+IbEK63pXpZvsEV4Da8MfYl + PeOvQqAihzQ/bHwKyZuWoJYUgK9Br7/SMRbt26WK6vmUnqu4RjNEN1PJFOzS40TebNrwyQBDOfT0 + mZGIjbWvuXCWrSvGm+SQseKwbYH5Yx1sER+ysdSnFNlf3JJN80Q1jUBKleM8kaDB51f2x+fI2UWE + 4l1jRktQiwKQQ/2kRqIfDLF49wAHZGJ8UtDDIN2+bpSZr3bUznxWT/yoxgq3/2zxodbbfmnHn/pX + P9Teny7ebCSNCoZIObKIr322JDHPATO6H/ZWfbUc9pYv30+FGrApcQ3BA1Dl6jxHVBdf+0j0HA1A + eQsXuvdDKRuGPvkof/quuOB9JjSoawHM4kxyNXCixezCUsmMIqBB/tGRcKh2JTxsY0uIuTw99vf7 + 7maXY/zdxdn8UNUTlJ8jT/7weYjXGSONnfVU36qPepSeqQqTXvY43ip11Dqtbiux7+VY/YnGOu9T + E5TPMxWw5TLeG2RidyDOgKnV2dRbkqlyYRddI+pFTl1PWK4/wJ6Fj/eBdugnX2QFYlbI6MG4+N4c + PAxJFuPyRT4H1vdzEhxk+WZxHb1a94MhrniKtNhoAv49DP3Q670u/6sXIbfR8oiUAp2e3Y3aVY0N + TuyLCo6kvAeofHlsKsvERsvoqASsnLL5el5SuIyvkl7wV/ZmOWhPqLn3W7Idtq9oCeeuhCI+0IDP + EskbDzupgXnCm4CHKM+mz3ero7U+AiHeq96oXpAEzZCsHZrqldHcUgV0vTgttioa9cMWRyEcSknA + 3sKrnrA/X68gxtUL22/73ZOpEwT448/oFhhIKE8+IO39I1Svt160+O9Xp+ymScO5dLbYVCXHFhWC + lNBLPH69nzqX/p8+JM9N8ouq41X4oD//oG2LH1vuL9EEOug6xcW5iwavW4L/rlcp7WvGh2IKJnKf + 2FeeYzZrluvKf3pQD/wSLcCLMbCXffurR8R492SDVUchWaSLXws1eSVK6jQLPni2ayx9PHCI55Yv + 1g4bnS1yxgmIZ+6D+queHdl6H4hA6voPb/vZS7MJZKt9Yl9wzmwmIR0QjhfArnOj2aIRTwVyuRhE + GqtXvVgKJJAUfbHq+aaewF7vubyFP+q7J45NLHu7YH4lj7qBbPVi0I42jIn4CKSx0nq+2lMf2cQd + A/l+jdHq32Qw+uRNV/xl0+cr6nDWNg01DnHoTQfUbyCZE4dMpG/RPz7M+CmlRfNE/ap/VOgn9UcL + KlzZzCV4A04WXwhjm2+2tGcYIAHLx/nK5xNMmwXu9ukTAPMk1lugqUrfHC0a/o6RN5PwO4B1X6eg + e5dLNCGEZTDd/EEv31qqB4VItjJvbAe7rl3W0zDeQjg8w54aq76eI/aQ0b4QKHaS/opYuNna8uon + gvsdEJo2eqjKpsNFhMN9jVa/OslmexeodmjKiOrbuYLL2Q+pSRM1W7YpFIi/XXfBVM43g74lK4fx + OVXY6e9jv8jVpYDvYT3KffTiaNkcUfDHZ2T+Mdn408fyJUPXADmei6b961fALzvusMEM5E3dXUnQ + 01uEgI+jNfFezBL4C6FE7i5yNIl9Uco3oRyouu1Ixm7+SwDzQeZgeL4qj167WAAt3fyw9R6GmlR+ + vM6nqlqSufk9mzkGobRtuj029/IbsUnwCajcdqDr/ooGYkUu8O3WoBZ6ikb/PZRE6fjDkZ6l1+wN + //xbhq5UkzuTLZ4RE1QJjo3P5NlkU+Q+dGXFU4oj5cK4+HY9QbA5RfgY2w+P6NepRfxloPiEk4BN + Ep0KpXwPLrWHh+aJpyIDEN75Dxe9fmZn5bbTQebIjar3NOsnJp4LlGquHrzId+sxZzO76Hpx23/6 + VYjXW8o2wl6m+1Z+eSz/xTlwQWniq3/VDVFDzw9cO8MOPv5k9lMPeox+KQnoMdtUxvzcXFMU/94u + 3ue0rKeck6+gNIGHjWJMvdk5G6rS1vt9gNhNiebd6bNRUOZvA3Fid8b6e2rDWKsi2d3OXT/vTW0C + 1Rd22OEzOaO79FCgTDm6WKfTIWNM4jjFwdX8T2/Sy1XewO4VDVT/2//HUWlhq0Yx9Ra+NJgFBxVp + 9+sTu/dJzKbHk3TI7ZdzsOgH5L29DU3R82m42MoSyZgjdpGQmdtfvD6/ekndhijfaUHkpR9Ntvzh + 7+pnsff1gU3fIdmAncp3bGMtMhYdmScQht2depzxZVP9tiWlmkQvkOziV0/zWwj/+dOLyy7Gctx5 + JfzlU7Fhl2yi0z6BwIM9PscqrpkyZQXo3xyvek1b75lMOeBUB9Pbdk+8YaOHOgT9q8PPvNr9y5OU + x+l4pyrbTRHrRcuE+BoLpL+B009bRiSQIg8CeXOW0FIIegLr+mDrqyCPKO9jg5pgIwe75jRFy1K+ + B+D2zZZA0hwyQUT3DvaK/KV/eD9eBVuHYxZa1Al/rjddmmeFnp1AsPUOXYO9j/cAnMnnsLb6eabv + Egnuk1FjW2q1fmEj2cic6mKMXRpkU3hXbaXZqAtWjSPJ/vHFWs/YX/l3KqvrpBiheKWWOjv9vGnk + EyqkqqKH1Z/MJ9FOlFCptEBK1Xf9D09P6S3H5i49Gvzv/EpRV4aUyDGEGT0VEcgBT02Mh62WccXw + lUGcNxhrPYyIhmldIPWSHnD06pgxWYd3C9X+uwvewhnX7DvCdadhzyLta7fJWH8Pbbg++QF7kugj + IW++vqLZpkeP3uvOWECPA8RsabBPbhHqXpJcQinGcbDr7o0xnX47btfY9x6r78crYnvpVEFOX1eq + 6sxD/HKfFyC0yrFqTQ2aeyw2Mr31ONglFmb1MesF9Kf/94fv1xubu5tCWo7vP/2ICDlxsUIFuseq + ghRvRlvFBSEMLsFAbgyROlTMXWUVJvX15xX9+QNY/Sd+GsmCmHkHWzY/oRawW2CwWd7lJZxpel71 + 2OBNBq1teGdFGQjNUBszrpsSfnfTxH/+YkT2hYNw+xNWfV9Ff/Wu/KL5QPUb56K1vgU4rbeYaPxG + rqljnTnlezgwfK5iA4m1XnRgKUVPsbZvIrLqE0WuzSNRVN1g4m3iC7DpJOIC+Lz+l88NKKP4X775 + a8scPmdDC0AN2mzVfzlQYdzTs3400RTeHXu36jeyaW8lW4x6lpVfIHX49FTObM0zJ9gu35Ls3GFf + 8xlvdTBknE+javtiY7TbVEjIIowPClK9SYpNV+HheMNGLgTeYonRAtYVDJr7dwcJDK8zFlzCqDNr + N7aYAfHhSq8agWt2yVhduBuI+YTgA1p8g/72goyCOyqJLC9VzzznAFBKmwKbW6XOJqiVCjnSYuD9 + LzbqIQ/HdJ15tF/f0Uy9iXpoQVRpffr8Pmc0H5pBQt4Cq16aZzaX49DBSTEtuu53b6nJK4W/etv3 + Hq2Xye4DVPUwYMPzCmO8S4ILq54k8ppv/fEB7NDDok6Av/Xqj7hd32ALH0/f9dbfjxKjnTYcaOQZ + RiSm+LyB3zuLqGnGTj0VA5XQbkk87D+WCf3LN1k/Z9S8j1M9Ug66Pz8cfPad2LPDFJh/eWUg6qe+ + Fi39R3bH7GTRWxlcET0q0wLCzCRsUFfP+Hvclai5VS51+CzNpi3OQtk/JR2ORzzURJietrxo1wfZ + anszE257+KDPbWNjfJUDtGCyJPDwFZOgdPeq2cq/68yJkGrN9uK1f/2DP35/eK87+qtnlOjxCaum + xmpa36oYHqACNVGtMmFQ0gT+/OaOxzxjPOkHyBfWU9vcfvrp6D0SyPOzjg8o+dXjM3UX2YmzE7aH + 7eZf/eyy61Bh3z3FaBaFJoEJoi81T8+ft4T4sc4Q2cbBe13/9dq4VZ9CQw3TdmvOlhgHHKUz2XV3 + 0xMsMZqg2G5Dauan719eOMCFHrrg5DY/RK81+IBCx8K+9zZqscE3GU7je6L2NSH1QL+lCeSjHP/t + p1XPC5B/ihyrVelE/MlP3X8/b5eh2vMoe1Vy3eYhdY3DvmfDUx7Q2p/BJvdxjKmsigWqvRlgFcyz + xzpL8qHl6Blr0mv+y88H+O5il+KH+EVsLyUl+ltvu5+v9bIL2Ae+mZRj9YiRN50FZkNs2BURH4fa + mw73m640d3c9oScaNdttHiYMj/yK0z99/+fP1+cfbFf8G9Z8BrKqsgM+N6t66ZenAGOti1TXtTIb + M/eeoLeOzjQAHnriAacr8qswcHArL2gS1C5Eq//+01vGPz235u9rnlfVdDntT9CfNjN2Sj2Mhtd4 + viI6qDq9T1qQrfrYh2VKL/gQGobBV/tvgP7hyVeVolUv5DD9tGjNdw/1or60BVhFXtitcq2msndN + lJWvCZBZyMZn2acofA0F1TdShaYBNyESf+mWuih+InbuLz5a8z+yJcIxI+FF8Hd/eTa+lkPEtFN9 + VVa9HnBUVuofCb8Ehmt9DrahfUBiRWCA2RRE7HYXOWO3yC1ATiSGs4C8DLKUbwKr3wyW+t2yRSCn + VPmpJY/jP/9QCHqqpOHkU/Nl+sbwzAQX0OHDE+Jd+GwejpIkS+leo3s5+nnz7R2f4IKbPY7xWYtE + m20+qGMvA//lcwth2knx625D96emNKj4mDfQb5LiXz9vnJxrg46kuhMheVX9fDt8ASrVOwSb3fUT + tfb1I8Okyq/gunMLNpRmP8kmvaLgbeS7fq59zYFbOjrYbPnOm6z3fQNK+NEwzkiExuEoybArjBsR + Pze3FrhFlSCdOXPNtxKDnWeFQ3/61vPfY0TOhdOi33BN8JGvOvbnB6W/fhK+eErPIuNuwp9fW/fD + v36Qwn1aETvyvql/aeungL5qvT7PMpqNZNChFx93/Jff89yYtTLWyhYfjr+ynwfp7SLVPHdYK9/L + n74KUfp4noKGyo96Hi2Sy9jHA3VGaAzGArWRN63zovv5mGddzeuqgvabnnAO8Qx6rMxCmVgwEuk1 + 1/2fHwCVyzUiP3eet+YHiRJs5s1fP6MWrY9yRfVl/GF7eLyMqddrHXmgCWt/VWf8M9UXGNxxpN75 + HmZU3yXyP3wx1/23XMLjAF7LKnysVMLmfXtJlOb0tWjwUt8ZVYKgBCWmwp8fycjTMAbwB/+Ijfre + ZNPh/tTlP389ZTXOpudlnQFU9AW2fDgY08c1VaXzkhP19956K4d7MpU/vjv/WGowG3cA3UNPA+5k + fep5iM8VPGxtiz2lPyNCeAzyP3/caJb3l1dBFZQbwtZ+DP/WJl15x3VGvkHmG8vytWNY+5dBI4hC + TYJ6K6DKyk38XPlp1X8fQDl/oJfzFHhL/D0nyjG+qPhZciqbvTRa0KgEp+BLHKcexnnn7/7wx32/ + +Gio34EEXJTWf/2EmuW/PEeF7V9xnjSHaH5rkwq61/XUHH/YmLRL+UFNeMRYJ5rkTa/zqMs4Xmec + rf1TTiuwDK2pYWwTQutRD70Emlq8Y/Uelmi+Z4YLHx49qb4pymz1QyvewfzHX2z2BMeEgcEWO8Tc + MOLIngzy0cL0cIpnRlb9Acp4trBTcG7PHXgv/usXU5/cGBv+/AcKXYs6/f3YT8kXt//PiQLlf58o + 6Id16qs9VfW89zcDuOxsEPCOv2i4Xy42sCMbqDHp5/r36ZQCgmKTBdu6/xhT7u46pX7YJ+pOV99b + duuUOgc7f4kdqdklUR2QuugcsLjVGC/Rewpb38+xdriPHqHZPQGfdzZHMT94Hmf00CLtl+qkqM+q + N5hcWoKJaoZViGg2R0itFFVWXOocxn3P97+oUVohban1/lYeLUujArLl5YDjZz2a0/FRQX7YZNh6 + f3WDO+j2otxA/WFNj4X+7/PD4cORQJSsJZrN02sDbrkXyGbZ//qlpfBBPG8F9DSpJmNl+0sROUwq + UcjtnC2N/Vv7mXxPdt+9gQT92XLwwyWHH98wReIjZLpiXLYhPQYuydrWOTeQxSVPnbFX6iE27gBn + mhyC5TY0xrx/LAXIR1ulj3Q7RV097kvlu92FNExkKVu4oz7A5aO8yO8ShIzzsjZV9FM9UPv1nOqR + FJIro/Rj4sONVkjcz1Kh+NLVo9r12bHhZamC8lsEBdu5qjKhumQNZGOvYexqlbcg3y8B7X4nnN1u + h57XMyuFYS9b6/PH2dIlQwwv3Txg+xVwaDb9SFcew4VSQ4jjehbTgMBPayMakfrVc8LjnYOaDDN9 + LkufcW9BtpXWdDAOzdjIxO1GP0EclQW9sDtE8xKviP9qPaonchIJ4tUMFUsILRxL8Svj2hvtIDVR + RBq6TiXiLm0Dy7UziBxwhcfdNcmGtDrltEj8AyJH7q6CHVwrmvvDPWMWZBIslyamydgr/ehix1Sc + X9zR+PEJPLqfpRx+tKc4eIffnu0yL4EpGu40u9kjYuJPmYBflImqv8ebkUfmp0p4aCxcmNqlFn7w + SpR3AR4Nn4+bMR+/zwHU/U8nXGZgbxKVbYjMorHwvWzeHnfQgwXyBwNqOJPksYL5AvBbJ6CmebTQ + vMRSiqqhlokQfZ7RYs3OCe2S4UBD48R5MykmV0GrNZGln9CTnzqlysM8h9ggoYWmxbd1xeWuC7XM + b4+IsElO6Ng+beyzHBnMirEEvZ4/cZETLeLK9cx4HBaUHllWRmLyOgwAk/PBMR4BjT/IJVCy5ETk + U9qwfrI3MlSLfKHWVnWj+WF4AIP7E6n90RyPY8NTAFfTOxzo1buf5Z440B60J1Y9dEPc3HOuQl1D + oA4XQE33v7CB22E54VPEtF7kfqGJpNP+ir36nmTzK1muSrS7VmQKiitjc374gGrVDdbeptTPydEY + FDhKPj2kmyKbf0zUlUoYhUDkGfV4zTjn6JPgKOCTlxnNH+qnIBK5D85K2daL/iwFxYsfIy7084HN + yexMsNzvPD0Gmevx3oaCrDvPAOvh7R4JVtuloFqvBmuCcuh5b3+UIX8HNr0MgtKzbhom5KeKTl3y + USNR/PGLEg9tF2zwbV8vXy/igJy5gh6pa9bcV3ws4OKNjPXPeWbDqLeLgsNTg/8+H70kjguZl7bU + Ly9XT9iPDx2yvq6pswu9WiybsFAm7YOw9063bE7npoDwmH3weertfjKlV6E4fe7gbHgKGeu18qN4 + uyVZp06ee0EyXIAN4SN6HtIOMRgqQZGiU42dzZxlDDm6Cet+xGZ116J5EJwUHOwSjO/OPpvkG7SA + anKmZn299/QSzT6Ym1Gl/jZu0Lp+sbKxxj11UVZFwzx5NhzMzwXjZ+pmAuew5V/9x0FwNDiS5gR+ + IdHp39+n2FeCf/XE37Z+zW3meaNsv8WeFm+tRzQNnAT2zYZh7w23iNn5OKHSb6vgBPyHLWWbS2Cf + co0ePan3pj88WS6fmLqZjNgs2j+CTmBqOFedyOM7UxZg3uIdmQ/OHE239pqiOldfOBVu0d/+UpXj + 9slh87eT6qX9XoeduaEqDYpiQKz3vwO8WzPBGu5PxvBr61SxEhmo7yu8N3ksvqLH5aLRW0ekfmhN + vQW2iXV8N/gumq7bTwthvWhUzW0/m9to9OHwXrbBfA2wMZuG4sB2k/M04YK8n8lycZXbadoSeLJN + tnAOmwDtUxEHrp6zxbx9WmU8HDvsidGEmC7+QkjvLsbe2frUnWcYBWwPGqbGJd781VsMcVQVZF73 + tzA9y6tiWZRic8UDFhVxCyHiCpzqr6hebIc7KRX++MFEj+eMXaXzRqkV7GHLOtRZv7n/SuiNMsDP + rXQwaNDGiRLOrPj3fJlOjUQpvqZG85dzyOgjRDpYmZ4GEdE6bxGOnwSapVvwRbXiSKx+lqm06Cxh + w6annvliMMH8C2WKjeM+41R78pVd/urwzZ6qXsxt2weGkUvvfjf0s7f5gvJXz4F0H9AC7T6Fh+5x + 1HvIbc1aLtFhq2Q2geLtZvzDMACEV9JSxz/ZvcCPcaVcx0KgHj1XHr1r2QegtZ1gB6UfLRM7XoGF + +g9b/W2sW2wyTkloPOPbqVK85WKLMhwaesI4tbb9NPz2piI8owORf+qOLYOWmzBxyCB8cnLq8dmj + DrJrF2H7+E48cr2xDh7kecQuyGE2lVIE0Gefnl6PAmOE3txCoXZwoWblWDXHXqdBoXLj06A4no3l + aYDzp4+ouVMVNqSfRADlfDlhg7fLen69Tj7sK/FCsfV06y44RQAvOXrhoKJOzXJhshW6yUasPx83 + jxmefUVde6qJwnu+x0P8+A9pZ7KtIA6E4QdiIXPCklkEJCiDuANEFEVkSIA8fR+8vexdr++5KiSp + +v+vkooMnVN4xFz19BoejRULCy3pkMkk3bC68rv+5ROS4jeO5oLxZ1gPBKMgu1nN8g7kF4yZ1UHI + W6ZhNYRyhuYpEYnvj1KE1d4P4Xdgg9/7MOar8jXl2CtVZG3vS2BucwXRWtwxvCykme2bewb7iTEJ + qrtqWPZJ3Co7PyiJeufCgivy0ofj1yZIK7Tt3lYtNpUXHR8kTJpjwZPilCnRI7SIKYSrR2WDOnDS + AxhwlRobC/d0RNAaqYHcQi7oyEyNCjVfl46LUjvNcFXMFzieygz99CRb5giDJ3r5KGFvw7DMqcLA + LxVHdNTMeaDjbahAYs0CVpJaHXhe1lKwr4lA9tHbbGYrAynY9A/x2dqNViE1Q9AAayLF7TuC0Wk9 + Fnrt2yNexkvDMp2N+Pd5ASg6K+K0nVvB85eoxNDCGqx3kG4VcdVFx3g6FsuRcC30hXNJDtHRi4R6 + nFOlq1IXeaf0GdFo0ErIeaxN1Fa2Kcvjhw/dTMvIpicKzkfNCk+6oaFjWC7NRLtbBiG/fxMD7Pdg + qbwHqxxE6CPfPsjR9AFLAD+rvmAm9Z4FXS5yB4mr8SjY9MnypTsdvq71GxneswEU+CcV7vNZCPgY + uMUi6XwFwo9gIUdqPsViK10LRaY+oluCzG0H56qDL8sURL9OXkNjXSkBPstHdIb+ic4me64heZQq + uXFTM6ySV/PQOWoh0maoF5u+yiA9QC8A47waVAeiL/304F7hFDoQootwy78Y+uM1mt66tW4Vgh1y + di88UNUuRkgjO0O2/Ltn1qtZGBGnJTlOzIF1NsfeufsAK0HhGsJOP4VAva410iLogfn5/bZge59E + Vda7sdR7/gWDklfI3llcb77EUg6GCR/I33qZ3pCF3WmvIHcft3TSZFoDj/l26Lx8Xt6ccYYPjYBp + AoqbRzMPR9MHgaBK5Hxj9hE3HWEOvlQeSfA+7xt+yw8yt+5mvOy/WsGnpdtCo/YCFPBfzeMeMw6h + hL8OQm3w9qhZn3QY8V4ScD2XejN/m0rg88EUyLwtDKtenGTFDuInCepAHAjNqhRqacCR6jZch5mz + lho+49uNuKQRm3k6MTw88Zm45XehWMnFLeHlMIfoBO6ax71wPYJANDvi5zEeuu8rLhWmhitJdk+N + LrZ07eCmvwMm87+UlnYQ/P6f3I8PKVqH98lVVE73EJLZ3BgabpGVrnAVYl1wSFd/oC5Qa4qQd8sW + b/2g5/rza0hDOVtQJGUOlJlzH3DmxwP83WBd+Xr6eESrHtKAXfn9hMFNHon68Z7Ncgo0Udne78+v + 0eWpRDZ0dfOK7HzgwPLCNYZOYO+IFh73lC7bbhD8lQH5xTuaNOgsL4nBBC+tb8GaH14MnPiWIk2p + u2GJi3P28xe4q15ZRNPh7YDqzFl/8X7F2m4F7KP3yB4Zkzd/mnmFDwv0gSQpx2iF8TMDLyv6IotI + b2O5XudaUTVsbPFW9KiAfSyPwD8i2+PGYrl0TxFGxe5Kjh92LTa978Jvv94JOj629eA2vZI3sUei + fH8aVvt0dgARS0zsTifNK5yGGhTTV0Mo5U1vTh2/VdKp5APSDLYhCF9llc3Xw0Jbfh7miTmtQDln + O5Sl1Iq4QF1yZXi+c7Txh2GG6iuDdntVUZofmGgU7CSAViZCdKB5ULBWBmLA8k2BNHFKCrrCIpNF + 10kDOfosxToGjgyH5ycn5kE5FcMeCq8//eHU7x1d99dZVwQO5AjBg+XNAbyJAOQvk4TKujOWyE17 + gNSDjWy6f9N1erxleBDEG0rvxPz5u0zRlqrGg2ibkdDK4/MvfmqJEhVUdPcdvK2jS1Tuzg7z7D1j + iL8iCBSu3INuy+dg9GmCAkHgPDwoRxNmQoGQP42kGZ71J4A9g54BPGumR7Vn9YKSfiHkEBf3ZulK + w1asKxzQRSsugIsv2y0NA5CwuPGJdTE1V8nwSUcuabJm1TEvwuC9c9GeuSTRfK87HT7xkAVymJt0 + le4WC6OXIeBd3VUN/qB+hY8nMrf4f9o6RU4zJPf0gSWI5oZI6y6GruE6aA/YNFrE56uFnXJNidYY + izeRnQbl9IpktO+YyVjK52pCvOyuSE+lkFItWF7wPbs8Bps/nGPjBJWfvj+fL1KxcktTwc8XRqhC + XROtL8YIFXziK7Ln22c0MtOgy3LiVMgs3u0w2+/Ogf3dSdF+OV2G+anYL2hc3fsWD2Rv1hmOgWbV + WsSOnYvx42Vg4h4m2h92lcf16iDDc9iySB/ZmS7tJazgCad5ABb24dGff/v5E8sU98NU3QMeKu+X + //O30UpHkQXKIVw3/fMe6Bb//9bjG426gb+qmMM6mQkyOlWndFK4s7R3vtdA+P5O55w6R9n8w8Zf + tGHZ3S4lHHg2JWErt/QXD5R7Fg7okARngN/J7gyv68IFVXOqjbmQFBbYcfQMgKBz3pZvVTmUmAZ5 + GX8d5m99rGHhZR06duKjof5u7OS9YJ/Q9V4ndL2oQw6ilyYg7aYfKBcBpwanwjsiZFSGt8QXrwL9 + J2DRcf8hW35fOyXuHz6xBBDR9dyfQvj8rg7SRGYxCGclKnyR+yvYxaYYzSYUMzCvyxsL+3JnTO3z + 0kI9fIxk088NcWVZBbdLdUf713QqfvENLv6uRoF5XqJl2b17SNpLiPlWcwzy+QwB3PQw2vjnMPrq + wVcOzfdB7Ki9F1sPpf6n39CPz8zPd97DzNzv8drTQ8HW7FGHcfw6ophb9II1kLQCfh+Mf/l9un/G + rZeCCMnhOvfF+NNrsuKDn142ZjEANrAI6APh5V3BcEi351/lBKlHuIu+7qBDZeVXBmm982mWJ8sF + ENSNiYF4UQ2Oe6qiErZLitxcMYY/PxzVfkpuXep6y+nRikpovUakZfUaLeLgVkB8ZxqKnDkzuOYB + ROhOsYHizb8uB8YPYXOQto6HS+vN4SKXf/m3MPyk2J4HA9O9HLDiiZ4hWPFeVrTRY5EJ1m+zPCXf + gdeVcrjGg0TXcneooMVvFWzy8CheoksARS+wgtn2xgJfYJwr5baDeQ9Fq8Ff+M3hz6/Y8XUFs1Iz + PvyE7J5kp0puFoxtHyK2VgiKE9bb8qesdGJtI8cozIJOfRaCInm9yPGsPIaNF9dKy/desPP1QzN/ + X2UJSHmIA4a83GElxTUHy91kyKmJfbp+xbwCe1laiZ5KMxjP+dOHDn22RJvhM1of87pCNcMLcRUP + e7Pgji7w7DIk9/WBmtndKrSkTUJkHkZu6Hj8COBJqD6YMe7K1sPx3sOW7zx0BNilPz0Hf3ra8cO2 + GQqc+6CU9ykJ3FcccW2kPZUPmi9/fmPVgKr/+Ao6aolfCFN7SEGKDAZ359MRkAWVK+QPsbvxt877 + 8TA4GeFArIp+Ir5lMJSrkHyILsm4GcKbyMDCry5ILXTFm30zc0Af+jbJRv8IqJiyFbTHyw7Z3PVr + jCzLjeAM+Iro1gkMK9aEGewnaBKtd/bNeM9cB5IbMgIFd0eDj4khSsdPcSHb5xs/ngQCY1zQ9TxE + G0/lZrDpIeRXbzdaJFqn0HNPC6Z5NkVzLRYMgLEMAua0pxFZ67CG6XDJNv/dDmvctip8XroeuQFb + GT8+C47LJ0JGpwCDFvgcwKDhxmAuuwasgX9U4blfCTlcqo9H+0aA0LoyA0EfTynId8+ZkEtVnRzY + 44Py1eg40MjMGent8zgIzuFVQjO6peT4TJ7N7F6uJfgMqk5KypwHSogrArp6L6JqX5bS5/dow7LQ + XcwIya1ZTueMhUYMGmTwhkipqvsrtJ7GAdkx6KP1Ilol2LVZRmzvNQ9rSkgLP8HYbfmWizBjiTrc + 2w+AtNdeLTjmsFaK2HkUs7fkXKy7VmOVa1AoyDuQOOLlzzCCjcds8aL2lsByUlhXs4qqi5M3i/Jw + MfQz7vXLH8Os2mIAkRZwRB+Smi4JeAXKzMGVFE3PGr12cSGsFENFmoRGOkN1zCCN3T1CzdB6+GV/ + fagUeUjsDyoNnAjfFWJ1JiSyj5IxVl4SQ/5z+WClJOV2knun/+oLwe6tfItx9QMdEul5I+ZJ31Oq + C4+zIoKLGny38SJ30+mgebqImx8a6PvHJ1L8fBN1Vl9g7kc5ln983Umfh0ZgkryFTLry+HSLrEYQ + 1qiDbFUpeNn8FBHWaOsZ4O6Rtl1Dt2r23lfoengRLRBLOnOuWv7eD1GfezrU3eXTwUFNNOLLj9NA + mdtcSresDoKHKSTDaM/kDPaCeUK5pjcAvxjjrHzJl+BjgjZ95NW8Uicr2XjMe6BsDX1oM/dbwPbF + 7K2S1/FwWy/oF98mfqkYSYutC15Wl49oyho6xDtBRof8rAHekTVekbvdLmCYnDXm8jm9fnwDacpi + FhzMRh5o8f5C9EfFDD1jiepPDyKvP1JjnmSdFxlqbvU7sWnWvgThj79gyQddsU74jOGmv0l2+9TG + cgoOIiwdcURIam1jLPRohstefWz69+0tpl/oUJ6zA1Id7EV/823jURsfYY0xGQ+dHFrtGAivaSn+ + 4oF5iPJg10CNLtfXrgfH3Y0lOrcH0QLjZMtPqUGCiyM37/mzjMqKxxodpv5JFzmSfSjQBSJ3/nwo + NeurCnR5+pDgNYfGwkonR7G4IA14mmnDYoZfCIlYYeT0pW6wbXGWFW0pa5Ts1D6iWbmuv+8Lls3v + L4D1VdDW0YLcr3oFc/48yNBOLqegBflc0FuRyfKmLza+NTTL9vtkl8sfBLWBZayJ8Jghuy/RVt8o + B+zlu+zH44mdDwmdC71YoSpQhTjvvVBgrO1mUH4UETOnjGwuyRqhGwAPuYoXGNz1lPeQqZkVOQSc + wLrxR7iLzzKO+JhtFvZ0dOEk4hNeRE/Z3s8aKO/Dt0aWK2G63neTA8edgdEhZ5hobi8GVOZ4dYiT + NYtBxPNU//G/7X0Cesg7X/rphx+Pmn/+6VdvYrHyGSYiAxUOWjajRBdUQ/C3yyx3TMURR9IQpQnq + QyiVTY+Fp2INy+rnNaT93g6Wd8JGuOHeGXQRlFHhPvfNcul6GVQv2CDnYmoe2wbYhFu9509v89rF + ZaAw8ZCozg01LBmyEuYPf8JSHX0j6pz1FFryvkX2Dn7Ag3a3HIqeb5GrHHw9er9MHeTOLIeKlDt7 + i/WyIQCPFyV6+5yatUivK7we6on8/MVcRdRXdkPU4Q7dIm9GB30GWz5Gub2iaDI+sg+vblsSL6BN + NFe6UELtaHf4E6xLQcfk2Mqb/kZHsLMHNpNyBwTmWiM1OS/DSG/arCgOMpC1luZWDyhT+Iv/0ZH9 + eLM2hFDZN8BExlY/5w0wjJC/HQay6X8gbPNVqRRNJT8/+goXuYKbf0VqUKWAL3eHEm7rOxg3PohF + PrVhez3pRL8xn2JOPvL4f3YUcOx/bykA+JIH7YHLm1HqtAoQ7XYMZmsOIp7Z3wModJ8TCcb3TOnJ + yhjgCK8OOc/Ra1hnEF2l2y5etE+yOUzpi+mA96kPSGcnllL1lWRQnJ8GQhFUjJWP7FLOM7xdNC7N + HpWPp1q5a9eGuA33KJaYEU3Y4CtCwX05AUI+7Ay5dD6h7PwQPUrkzoWqd92TsvBuYPYxFOHl5lJy + jB88WOQutAExvjEKwmX0SBWygcJOGoOXprkYI30SDJtrXyC9p2tBh/deVjqhiPHsQhCt8lGXFUPN + LGLvrC9YR2HsQUvzI4lWY2rmKHRzaPtago435HvdrqnPiiSPEmavd9NbTYkXoQdDBVX52wR8a/Kp + 8skeH7LPJdzM+4cSyDkvv4nvPcdmEd6NryBbJ8hdwUwXS5ZnoLDciZwvkw5m9QVVpRUPGglLsAfs + qOk5dG9FiNQS7CmVPk9euQ0vmdw+xguMbLP6wJqLF77yQzGwIYAtjC5ZT7LK3AFyFE8BdAd0QNrL + Mwy2WFcfTg7dNg33T2OW0+4FX/2I0FXS+W3THNWVR9N4yCqqzli6oWYVcH21aI+kDVkxs60c07lE + 7reXQBNIoFX24NQRl91XHvsbH+gmKzna5nngG+lgK7dpQwHE68D6sR0eePvjgzjRBwy04tRSqc/3 + C5bGSGv4A5dgha+yHFXgnQ48r3oBrBiWI7e9mxpCI5EXLPT6guzhEgwsj48q+Hbmk4Q95xp81pIO + siOjkhurfAosVcMMr1blEeco+oCtciNU4kQhJDzwF7De7HwFHkUSph9jP7BTK6kweHcHcuzQakzf + /MLLwqlLkdvOSjEHHnCV3/y2ZP8bsY/39v7u5o1cpl1lLH5ERtjKFycQ1s+XTuVl2tpAwwOKiyUe + aM+HsnLNVJEkHecAbhK7JyR8/SX348vY2hSKJoQNvQWjLT2i+fHuWqh4mkrOhasZqx6JuUIeAsW8 + JMOof7q9DjOJvxL1sscUU/bNK0iUTcwe43nA/vvuy1pnT0gv3IdBNZzF8CsPCNl31qackfgO8He1 + SMpPeopYQbzz0LhcVBTowmFYRa+roX357pGWywNYbkrUwTqMQizBezwsmspk8IxuIjk882rg0DLn + SrV12ap04Tss79qoFX22bsjO5H7g2cZnYHbwPKJyZxzNn6xl4alzL+jKD2BYS3if4T3njmgvhInH + sk6VwzeYjWCeiN2wtXCoYL+eruhwUeSC1uf0pdy7r42OeYIG3i3MXJnPjYY0rBYFF37vLPzsFpeo + T1I3ZJtv0HJojlSgbW289XGFylbCj87IAat6fovQS7QcA5WLClaS+hQoabci73Fq6N/z/c3Pcy1T + LHrdE97J0yARE2geG3ZLqnRmpWF4YJWIsk20Nbr8mMH3C1swfx73UNasp4rM3V5r+Ll2AlB28Q6Z + UuTQ8XuVUuUz+Td0PlC1WeNQqCFgej2AyW4GrHpQUwUI1ArAfIkiIQemrgx6qyGNYWUD53cNwxPZ + n9Hp9I3p3Hx0BhafYUHOFg/o0SufMGVgTE4vz/CEUW1mxSvEC7ne78+BnlGzKpfXniB0uYJiglbr + Kz0PEbpou7aYznZlguu+O2/Pn4P15ZsZHEnyIsiBl4IeWzH+yx/adLtubcMvvrJjM58UL0+NFtMY + zrCoMY/Fy8EA7AdSW3llx5qcdpeCCh866jBbdyW21q82cPWUuMpvfe39nR/91qd0kieTJJnmeots + erz8CJkKqQ9tiZbwe+dhj2KLRJnxoHS5MCGEfZcjbb7w3rzbRQy8RmcTf5aHHfHL5dND3gMDsiJQ + FzxZHjbkmwMid75nolcmKivIXfmILLHKvdGdzjUER8iibf1SYe2/nfDoM4dEebIW9N7sSuCmVULQ + /thFWDsoFXyuuUi0Q+MV82tJZiB3ICNbPG2oopyh8ll2Z3T0wnuziN5JVJrwcyFajV+AhunCKo4N + 8oCP2Ye3vOSYh1er9Eh0KRIg3KjCwPiuU/xSYOwJnhCMMAH4irW1+xhzJiozTBzBxLv4dfBmRVla + hR6KW0ANN6PzXjmpcn7/asRfaNJ0x3hoZWcFK9mvpuPx72O7KvKaGMg6ai9v7l++A6xWkYLR2L3o + 174sqrK7pyQAMj4Cgdz9FFpXW0dHvzhGdJi6Cuo8zNFZYQYwqXxTK5ZwfmB2fEeNoPTkBSkuJXJ2 + rCVaRvdQQQe7W5us+uut3rmAUKIcRJc0NCJWed5meOP0N7Ie3BOskyY8QZYqJilcu496zqGd8qVj + jDJJeHsc93JYePxeDiRyR81j1elVKkU98igkVVJwztPg4T0SzyRvj0UjNIlXAT8Q+oDJBG6Y10E9 + y+wpRcG01AePpwOXQ/Elnkhabm3cigBtWwg4h+w/H4Oup29bwV/+RFiLvPXAJaMSJJcH0n0MB/oy + +hZe1SVBoei7YGWGpoS3oZXRXnaXZosXL3AG2ZlYaoQp3eWn4Je/kN+HR8Br2lEHRr0OCF1n3vu8 + jP4Fn4R3yQU+/Ijfw2ZVsGg8yS+/L2+TjFALBp2o+OxES1raPtjnkk4cdrw0T190W6m+xjFKq68U + Lef+GcM6PIVBzXGzgVfbnmGPUgup6PUxhjH66LAvNA5l1toUc+MRBxxZCAJ597U9jhd8Gw7XJyS6 + fhIjyvEVLwOc5HhRrKFYQhOM8ETnA7mimHgUUtVRAjlTgm5QngN9p28WHvJDQoJDYBabnnnBAsYf + tL8/r5T4uAqh4vAS0j8pa0yp8FTheU0CZKlRQCdknFLlYPg50iNLMNbdVsKqGJ4janK9D3P5uKYK + Dul2bc3RKtjzYwjBlj+RmcV8s3jqq4RqXb5IUlSOR4H5MBV295zw+HRfBV37R6cIU7jHi0HWCOtk + 3NpSpm2wqOe4ICXEGfjph9uFicEqH10RvO/CF/lvU6Wsm39cyNeKg9yYBx6RKzmH5GYywVk/ZdHS + ZUoFC1xjopsd9Wb99mZAUGVvculCEi21I6lQeB0NcpihD7DLdwGIvFkk9vu7i5YCSCHIVP2Ad4pt + eMthTHu4WFmKDt+tTXhbbxf/Gu4buVE/gWUiog1LkbyDz5m+vdX0+xRaxzIkVy33qeCXSg7vuXBE + 9qNJ6HK8PTu4NC+O2BZZjPXcz1CpmqNJ7tb96v3mH3xqpEeH/bLfDmnxKrTm64vopFQGGo8PqJxP + TIfZFWJvTQ/XFm6/l7imDgu6mFSHNiP3+Defn5lUqPJPDylI3ADs/YBhuXs9Mb0/7gW17tAHQ3gX + g2SLT4vevnV4P40eOl+mJ1iMG2wBKw4zsT/yXIw3NFewraVqO+3NRZPwHnzIqHeDBPD4HKhie+Uv + 3yIXn9/NYMSpCHdpNGC5nW/RrCjSC5pRp5LsbbTRsrIXH1YPXSEOrziNsNDcBudXkyDNtY/eHHjU + gZA1LeI16nmg13kJ/uJdNAr1sCb5WilWcT4QfU1zg9bTmst3UhvITV9uMaP0CMHerUvyG69JJLwO + o7ffkCz6FE0vaNMLhN8hJPo0BYD2uWLCXQ8fKC0vDiW/5yGpY6BT01w87qjbWxtMPOAdfsV0/s1/ + M1AOZMvvEa2eiQ1wgE6BXGZGxC0r5CFQXRiIegi8xf5eZTh8wRO5zt4uBPdllIrL8wDpIW2b8eGa + KyxPmYkC5k49sukJQK/YwtxSsd7a1mcHsLt6Ipv+GeYPq/c/vbHNH8lbprgMYWh+WCxy6BqtzJPq + irNKK1LxuSswWR6mkpxDgmKzBcV8lZ0zfHSBRpyufg3r2keq8ud/YrTJnvtRheheXoMlVsmAc2OS + Yeqbu63jxUrnn17T2++KdCmpm8W6VwycERORwI4/dImuhwq+a/eMF//2Adv8UkGgtlOwPicHLNW+ + reFTm3rMGLlXcH70wTBBWMPCLTxE69N96hAz4p3EKwyM+b51xTXzFuDvRZEjLCcghZNU3pCqR7O3 + iITRwfV0AMTagcyg6aEO4OeevUiF2ipaAl9bwW43JQH7uvcePVkh8/c+ovowFMu7oxBGM5/gLChw + M0v+1MGyJEdiPc4PY4lGHwP12Q94jbqMdi/ZDqTBm4xgqbFJl4+iYfhWoItuS30w2DfIRhjh5I28 + 4taDWfS8MxTDY4E5Qt7Gd8eRHoQXuyFHD6keP7weufKbn9r7EnpzGbwZsCwZJhESsUdD0y5Btipl + UKbPdVjC4+gCf4RewJgGD6YygK2CctoQ9AWHhvUOQQg2/YOs5rUMK0mP4c/fobKy5AgX3/ipZMUk + okCKbE/Y8id0758L8pmHC+YHPunKo/M1ZGoHxVvF2e9gGb08dL8d2WjK4voMpc7xEfpoR295YOEJ + E1YoCdrWBz2jYYY1Er9INfW7sbD5UYZ+awVYDo8IrFn76WBbg4pYbv8tpl+89Rtpj45asS/+4tt7 + uBfIOvWo2D6/Vnb5IOFy83dLF1q6IpAqQHutNIfllcwYjPIuDTiVnxuyO3UzfOXXT6AEbultejuH + H2RBZDu2B+Y7x0Mob21Zw03/4vvO7OFxaEJy7OvGGM82KuVy1z5/8a1ZJElkoFKbN3RBkjPw+NO6 + QOTOArHaKjConGoM+Ok1rfzmlCbFwZYPl12APIL3dG68jwurqkJk0+cNV4UVA461vydXeGebmQmh + DBVWOAXzMUq9btP3UH/dBvyc3Mn4y/ebn0WOwfTRNj97+I6fdcBWw2L8/LEc7B8dSoTzSOf7KWfh + tel75FWXTzQXqxzAL8VxsHOuySDcBqtWNp6Bpc1fUe6gn+XPopz/4vuq7voXlNeLQfbeCdI+ydcS + 8sklRZr9DWhfIs+HK9t/A+WdJAORF6775VfkcU8VcO9lzKSPyCKipae6WQM/zLbE9AmYy+AUApFr + V5niq4bMYxw2v++H+dGMibPFS2LovA4pPK/IDv0kwiovu3/+xN941mhI2gqKz3chapm1XtePbCt/ + 5S8KOOHsAyrLrQ0/Io8C2IcTxTuOCUF2bnkMSSkDPGlCrURMfyR7SlM6W18vhD/9Z+RibKyP6Fwq + I6sWyNrW6xjM9zNU0n7dxlemqwUOOdz8DVY+rT2wMHMh+OlR45vrBSsllQm37Z1I1zDfzA98VUHf + fj/odj08m2WLd+DFpxrydLWmlF7VWRGlwQpMIw8M8pZXE/zmm7HUA6CZp7+U1HJN/DVGpWhJaoWw + f91r5ENaDCRxkhH2PIMQErgBDI3uBPBu29dgV2UPuh71pIJv8ygSM1wwXYfD8SUP5uwQt+f2xZKq + +RPevCBGwePtUuH08HtoM2JP/Mhio0cZsK2yxqGBDlifI7KYVFU+9/yF9M1vz8Nh5X/+D2k31inY + GaQ9dKSVEGPbfrT6JV5/vAU57ZoYeIt3EIejRo6XgnokaZZa6cxSI5e3FA9d3Bw7ueptLaD3xy4i + ZzqrUGSuEuacaxZRGobxzw8im9+p0TeQqhieHlG+NV3A0apkdQBXQymwdAuWBkOLZRUMxwydbcn0 + hPQc9sqrx4gcz+seLF/mZSoHPymxKMVGJPDqiVf8dh9s46PSjU/GcDe5HTq0966ZZYPzYfp+9n/r + Z5UXosJnmtUk331bj/TNVMv8h7XI1YwPEU2xmSobb0QOn7d0eh+dEH7z85cc6LL75TcGrmnpkog/ + XIzhcBX7X7wI9svDLjj2dKvg7vQU0N6UnoCcAi2HqiQHBIGz63HTdi2GKH0t4nwSnuYny40hrewI + sxfnUNAniVdFS94MOtzv+kClT88DXXIfWJQEy1uZJ1Dh7++67+Vgtr5GCE7J0CHjnWQRNWXrCc8n + 2JEsE890GfG7lU03SIjncZKBrTWEf3zkyCy1QQGYW8U/pTkx7o9dsTZz1ksyWRNiDtAxqAUyHUSw + tFHcBxldHwLTyuQeWTiGx3e0Jnzr/uXjnUy6iIq3TN8OKZ6R9pZPgN7sR7Y1xz8Re+6szR8QFzq2 + lG/6cTKGspYxcJWFR1qjclv+2NU/nvMXzza+F8AfP9GjTgTLtQK2uI1fwMw6Mdjh3JzBkLiYbPmC + Lm/zgwEjRxHS32YzzBP5ViB6Bw2x1cb0lunmY7jNJ3T85ZuiHk1QX5wjKVdJ8uiNcoycrEOCl0/3 + 8GhzoyJ8eyZHtJTVI+5tEiwHuXohyVLFxmp4dxYIOc2QNYNzMT3cIoOv1rii/am7NGv0VGuwrf/g + e8nFZrl+hieMtQDgr8AfjD64SVuJ6pmTYIDv4se74c+/bjzCmA1Jm+VmfxhRsPG4icdFD3/5KJh1 + 4q2p+nCg1e4k4n+kyFuHQoRg44P/8qWh6FMZ8mcfM6aRUu7Hbzf9EUDLW73lp5c3HoKl7/VtrMww + lPBldZScsxQX9ClaARgXNkaOogQDJvL1CYVTn2JBCBNjgl+5BPItapG9ynw0w8xl/vhlIM6vaAli + OZAfZYswiOTG+9Uj5FS0LnhWX49iuWDpBUbXd1Fa+MDb+JCsVPTdE2c6IDDTJxnBCQIPqbnmRXz6 + Pp6Bq9E73iX9OMzGp5NBItVnFG5+fk3LkgXvO/fF8qsawXyV1RD+9Pcv3uJUfbiKLw7gx0uHgRhZ + B7fvC3h+V//4XQiZqzht+s8ylhP0fHioeC8Q2UMC6PASdfgxZkqKGj+3LbBLDaezL5Ot/uDNb/MQ + /HguSgX1TpcfL7hJOQ0YHNXG8osPQsTfAn6bj/j6Wd2ffkbxpg+W5AQy+LDSkfjvjz6wb1MLJMMj + QSBz3BTNmkpWAK2Wwew9ONPVlBgZ6vP+Rn78eaz2qg42nhW0qYoNfPWrGN7bchdIm99lA8PFkBMc + Acsf0NN147NQsPUX0Y9S7609r59hEpc88s1uLpZImV4gAeMV3aT23EwMowbKjwcoDLqBrvGIK52d + p4KFAAMP/+LTFg/Qvkp9MM6g6iHDMIe/9UgrzinFpuCfyNObZzOTe8/A9vCV//SswO7QGW48AlmZ + 1hvflxyz8EzWGvOrzBd/62njT8R5G3ZEFSVnQG6jCzqCWKOropgpPOReEszhcW1+46/89JS85c/V + TM6rElZaRe5mNHpLS4MZavKQY2UUb9Gqk9H+8TCkV5bizdz5mgJmH6uodCzckI3nAdhVEeZBNwx4 + 42HAvV1DYvz8Lnd6mYB7ZgYyHqLs4WU74rX5ReLUDIyW5OBC0F10TAJneDYUXl4pWPKDi5D+hk0X + sbj+8Q1k8tcxWovjdgRggDzZV+kIFq90Qik6MrtA3vjKehinEF7Fa4X2yaQ1LMtFtvKFXIpubG41 + 3MdaA6UJ3xfkhvMykCLYp5LsPNe/8aVCdLPh/iEwgXhfibd8lAP++QHcMXUY0Ud5fYIZuys55HFi + UPtizBBzQoGfB1oPa5A4LNzqhcTPpClawSo94ZHZK5se1wf2gU+q4nwC/V9e8L1KMRSFkkGnG+tE + /I93aG+xRZrsBeAvf1z4tgq49fto1l+8STTxsNXfGK/Z+IJycV828l7+jq5PEjjwNicEOeF6iHj7 + W9fKVi/DK/OYmg4cZ0fZGSODnI8oDiTQXyb0A65HhmREw6KeL2cYd1WFDl0gDGM3nH0lNq46itra + ou/QpKNyaN4q0bTbg67xWMRgKhKI/MtUG/Pk3QLQ1eUVHc3EoOtqB7OcFNcFHWGNvPmVPG1oSVWH + XPR6FfOml+COzf2f3opm4m/X1nPNnXjw2dKf/vzj3+mml7j2EtnQe29H8lCMvIUc7QDGDe1I8HTG + YTGNJlRUPbRQmEXQ+NbOosMvMW4Bu9s/mhlVTA6/Kr/DfGULzSxWdq2E5psNuC2/UIZ91tB0/QTF + UtRRaq9CCbEuO/gdzCtd73ldS5ZZ+OhIV2mYjMR04GfQU+S61xT8eDE4GEGOzEOw99YjyR04CWGK + ysnti5lt5ACuX1Ugtw6t3pwWuxfMh5ASR1Fws6BlzpRPTjCGohsW1BlOT0X0ypag8xx6C9ZpBhS0 + sAEhRC+E7mk4CtPEGfFKm48WcBeev+f9y880Ylwe/J8tBdx/bynQrjuLWFepbqh00zG4LrxLVGJa + Biua3zM0vOhMnGeWGVQIPQa2ghEHa88akSBd7VY5RTMm5kgkr9OH1gH6XoyCvPE1KiyTN4MCyT1C + R3QxuFLLYgjq0kE6rHAxitCbYX5SbhiWzeSN8ftRw2+W25hZEYiIcyxf8Fre7e2U5zTMn6JbYbYh + 1VMuz8PK3eUZPmreINrdT8Di4UAGsTe16FBmZJjV47WH8SVziYuLF6Xj+WFDDtUE3RdjohOzOK3y + BqYb4FBrjfVRsqsy6OcKGepMhyV40xUy/vtAon7y6VwkXQV3EeIC+Czv3iJqWIb7NY6QF4LdsGaz + xIDHNH3RXVdjj8UlVykHHGukFE5NRJ3PfoVtK2pIlZZzRJESldDvshPS7wmh6/hUbEl4+wy5cU3t + 0ds6+IpuzZCUo8kV9L57QHArjRHZ15CNZi7cNtIHMiYO5CuvNfcfH0TiTQ5YVToOAnRSXZmOkUu8 + HXwViyd3FWTS0xf5N0un3Hev8HLZz2agKG8W4D5hYvDQJIqQddQLvopkF/KJsZVoGzNaL66qKg+v + 3GF65CMwnzYJa429SQwffYz5WGdY0XJmT5ywKgFfSHDboiCeyT4WsoGP2WFVnuouDSC+OQVVb7cO + vu/nCHP7GyrYqhpMWLcWxEyXHiJh7p6V4rzJGfnK6BXcOzsEEOi1TPLKSaN1qIoZHhzIBIudlIBN + 8cuF8bzviMuQii5Ck8dQulsJuW7zAR8EVYTahcPE2d21gR+fignZ6WyR9NlaxlJfriPM1vmDtPDu + AE6dRxWGvqcepaRYAA4OgQ8upzInmrE3PIH7XhnFA6WFEvbSNsLrREJ4P6g3UrizY7COEtbKNp6B + oCs3b4mJg6HfhjLK1qtW8MY376F/GwmJW97+BwAA//+kXcu2qjAS/SAG8pKEIW95SRAQcQaoCIjI + IwHy9b04t4c966Hr3uURktq79q6kCrD0MTFAObghCZ441jHKHQuyrnJHpwhzdJ1BV8PniWtIdq3t + YnXRlssB0iDmfHWn5Gbq4OEpZniZnDJeD9xbkg9iK5Lr8DqA9rx2JQwLh0FaoQ8jeT4uiZysxoBe + ipNSVoY0gkCrJXJWmbAR2tzHEHQiQGpqXcffJJg+vH5bDT1Tt2++qyVof7+PhMd1ixfgfBfY1HGD + JbSXiHEpPyEp6w8yhClt6KEuKug2fIax9qsKjmNdUfLS057uJzjeeE0vATqbD5St38JbWSdhYX6K + ATKuT7UQPFN9ytz5niGtVuxYeGxjAKft80PqQ6fFcuxWUR7UU4vn88PweMI+XDhe4jKQ93gSln54 + Svt+CC7nKB+72JEx/JSehCxPxM0yV2snn8oDQqpTjzq5J34G+7eE8fq+ZqNwsmkE7aSTkaUbccMZ + kxQAQwMKXs3wA7YQ3jEszmtMLgw+FWzzyjvIXYYT3hRh0Lenl2BZF9ET/1i5KVbuzNsgn3uF2CL7 + izfoZTkYRygHW/GpikUai0D2Mv+O8upy96gWvn2ovTsaCEG5jEJzbXIZvQIW2eSZxayRyx0k7AkT + y+uf+iZvWgqLve/+BY3ncchvIwNVOn7Q+YCFYt3xD77Lb0K864Mr6GodlH/4mmQT8BYj5zr5den6 + 4PCVz8UsqliEDLVqZPj20qy3ei1B5Rcl0nq581bdUkJoN1+Al32G9V5oreDh+2zwcYtcwK/OmslU + lAxyu5wcnYSv1pXFiR2JHh/SYrWOCyN7ZH2TUuxflE+PpiFV8KlizuTrcf1wSy1Pp40lgaDz8Vqq + YSq+rKNFSn9L4jUP/A3u648CC5gNr5axJB3SJCAu/7iPdV70FrA/cxRMyPsWy2HSsCyqCkeuVWI3 + XKPNGvh0Qol012oLIcuUWm4HKSKvd8Tp5F6+eUivQ4ZUxZeKGbYqK8vPEaDzJ1yb7bNPLkwLqJP8 + mNXj8glqDTr5KQ7WOX3/4x/ZECOdBD859ejMrhic7aYlZgRruubfA4RXWadBcjOxvv6w20L9GFRk + f9/jakySD9n3pyanj3vy1kKUWPA525DcN1YZhdsc5XIRaRpmX7e4YFd8rSBBZMKCz+mUF6xpgTvf + EfPpfcB6mP0nDAuPwTR8T8Wc7X359/XFw9HePPrGawav4/EZfPnaH7l8ZC35rq4fZC6mWizmFyYw + eguIGNEl9nrgkAXmCmcH4Pq4FmyIKleGrFzirS+WeGXiewQHRiXogouMToj5+HLdnjVkV4hr1t/n + Y8u4uI7kRoVEp9tt76spDQvxraPmcdFhDeX58GbRNZqiWBC3soWnM7yge5mhceG6bwuru38lkej/ + vD2fmWR4dz6o3PlaWGd973p4+5Adr8c/vJL76jih+3Otx7XRPgpw7zZPnhsqYh6n9gAv+Inw3/7n + oMwpwPBLn9jBxx05hzk8IWhpS9wp0mKstscUhh+uwnxxT+n0oJD5i390q8Q3IAcYTFC12QWZPq0K + +sbHHH7DmUX3m8aMa83ovYxdlOK1ry3Kon2yb+3WdcDg10g3/fOsoTP5A+4SBnkCn9gWbN83Czn6 + Oy0WO0Y9DNpcITHvKzFPe1OSdz7BUp5ZYOipvVsgDSKocbaYek/YH0+S+CB7fuZtP/uwT84Ln+gu + v890uSRqKX8ORRc0p8ID65ehNmTVEqFwFFIwmsadgfoVsigeUT3S2+k8QPl3faA9f/RY+eUx8JvH + CoZGRfQNvk8l5DuvIPYseOPyXjwbmlZ8ILbhaoBvPocMXmo6BUdgfcDWU6WUX1SI8Kp95mJ78doC + b9YiIkXhumY6scCACxf1yC0OdMSjGEBgBSJGfnwu9bX/vSP5j39M0ozxalc1BJd6nYhXTWIxK2zv + yswtHEmyNqxHQ1NkjptDYUA9D8RbZJYZ/GyVQu73WfBWK259eft8KaYcJGB7MZUv69WQIje11Gbb + fz+4sdhCHt/F41TLqggbvrqQE8OuYPOujxIk+BAhz5xgQ236TmV3jM5I7SO+oKzWa5A/3k9E2/F/ + Fs1G+xefC9oMb5FFwYXsXb2RjOCeLk+FLnAg2kps3RK8uWwqeExP5EpO4kjiaai+IdzxC5fu7zIu + dXDD8IEdHT0Pwhovw6zw8h+eeS/x4a2vSMxhQ+8isZjxBtb+ccLwJ77v5CF/ErqhM6nhMRtq5LQD + S6dadsSjWZ944srDiW6LGRry14s0Ep1ltxCemBjAdPIMOUWbxGTnL1nw6iexVXQEm4zWSZ7PFxdp + zmh6gmp7C+jD7U7+8Hc9cSz7l+/gzjyn8eof1A0kZkiRl78VurAHroOjJINgucrFSPnPg4foed6I + Vit9sTitW8OisW8oObUm4BiX8nDWCUNOvJLSrf6BGtrNB6D7i783VODDCW6+eCV3rHzGrYi/LDze + flUgzEzt0RtqDFlQ5SiQLhqv0+H1qKRH/KqJ91kauv99FjS0EDE8J67OntfuCURZVpBqCdu4r68C + yyPViPL2mRFbeugDZzz3pPB/ebENd+kJh8/rs/f9TJvtEKYSeKr5EpzPj1Zf1YjFcOieX2K375z+ + wKuq//QHiVY0xgtkrzzgNyYhlquuxaz0VguZ5eAhBWQH/SVmvwySsWlQwITGyF3P1xCirXxj3t2P + 4KlHpwT3R2mTAu2T57vHFAGu3kpyqlJf/8t3oWtrb8y0Rust2n4L/cyXGzp/tlPBzUO4yN5rCJAD + D6I+DfukyvqxBMjWVMXb7u4ngJ7BzCRwjL7YTodEgVPTjgEzWRmgmXRx5VoGb1KyYlisQyZOsPmR + jHiGSQD547d9vf/WH2zP9V3KK9gUdH2DayH0aSTBCpbqfqt79tbkcsnkoSu/5HIcAP28IjGDPfic + EeKuVF9f0ZLLoHra5JJaXLMcTI6R/+LD/sSqzjPl8oT5vYiI0oWuh5eXF4GUCWbc7HxJ5WuywLRp + fBQQVR3545xO0CbREx/uoVKwPy3fQF7Uv0BWbjpdB//lQmoLL6IKKAZEOoks5NY1/5dfT64sY9jD + DiFv3rJxsY9JBqfomZL935t1BE4KA5ozJNjz7y1MfQYK3/SKlMu4TzZlHzwkXh6iM3NyR3q8By28 + xteEINQP+pYez4Y0CC0k9qIE8SauOSt9DveOnEyx08mJKAr0CW8Ro/KCf98PCj7T0aNkpnjt+McE + e/A9I/fOr96WRLEL7COjYPEkbd7HrgYI7w7KyVmrumJjj8omn8slIjf9fG7w4aBa8vrrfyQdc8Xj + PpxYwUiZJHxoRtvbep9iuLKpRyxReY3UhSMj+36Xk5Mmy/ryoCyEc90HKPEz1duflwW0/aWodH9r + 88u+0Sb/KuaC5b/9eLkgA+7P+6dXxmlN6hSyzXQMmCQei2Uqs1BaIg2iQH4+45VjXQk68VBhcXNC + gMtZcf/8AKIMyr3BOPJr8BHkFamPz0+n9UWz/vZrsCzyrVjjp8n++RvkBoy62LjjzIBjkxp4fZVd + sbzFqoNEFYwdD9liSe4IwoxUUkC6VGl4Jom1P/5H5wtxii0yk/zv/wcf8cTrs2HNJRDb8I2U5nMZ + f1hBomTRaMHv/Yg4Wa2DJvKPb7jjTTSSS3t+QkMXNfKYyEdfgw9YQH45PIjueUW8MN93Lu/5J0Lp + 5wTYRf+WkGi8RQLvPNClzo8R3PNvpB4HAH5XQ+D/9B3So73P9N9+LLInh+VjTJrdj6gAM9Q3onnR + SqkLGwb0VNWR5luz3suiYMOuk9SgrVu1Ye2bvshOYVzR8yfz+uKjBw8dm2ECIn+zePu8+xwq6llH + l1dpxVy1+iJ8/bwFIah4Dbv8oC/2xwePt1qxC97gL5n8iB91wKefAkyuFQeQJ9MX3S9jHW848isY + cQYg5zyOweRpNwNaw/QJ9vWmK9N+FbjjJ4aD13j74dAE3uhsIGXnlwWdtAnK0mMjQWsYOn8CLxGC + +Ifw6jk3wG31wEP9yrDE/ulhg+tTLUHhIL0wXz60sXtoQgS8amuRp1yreJnKMPr7ewGxWKT3w8+Y + ILL6gbjWtQejAgwIzvkAkCqV2j5HaB6gMbdvFBwxpuT3FBP4jBoTH1DvetQ3u17OpKDE4ny90vVl + pSzcQlsjxca3OnXrZZG/mfcLUrHm4s04kQA+XrZJTI+wzYYjY5/LguRgRNq5YNNkYcD8M1WUBzd5 + pPI9E6HMagcUXPoIbLYfsdLr5yyoZAJVX+KHYUBupTlykPKjdPf/YGS7JGCO/lGfp/tDBKyr3VHw + vo8jfWyNL1+xwgXt6xbHm/eQRBhw9RX5QI3o5oas/ff7gpi9NvrKqyqG9GGdd/5q//SWK/02q9vx + o9cHGa34+NlqhejF0wFLna+R/Kd/0fLOdO7ZuxG88GpG9M67eTS+XTR5398BZMUlXh+3UyZfS7FC + 2t2fi713CAPT5u2j03IyALcZtIbBdoc7Xloem12fLgj1jkfWm2XADLlXBxLqq2jnt3hBFSfK0m0y + ydXktXFJO2uCb1Pz8dA2z3hpXRtLw+o6+HM/STGtHnwF2eDGIdUMTUpYrVLkw6Jg5EyOXfzzd8Aj + KPB6HAqwvgI2/MNTlA79HbAPGOXy1nUc8bm88+anKv/zUwPh7sh02Q5ZDl+Xtke3LTzF7Mt8GHD3 + f5H6Krt4kV8eBL8KXhByXt9x/XkrhFhfGvIgMjMO9Y9WIAq+NVGmcdPX71vnZXUySpR8gUyx/st7 + eLM2EW8ee2rYdKsi+XeNZ6L51llf7NfPB2L2lYMdX8BSXtoUwqm4IPP0IPGq7nM1dF147PmbXbBy + oxiyaV0O//znzQO1/8e3mLmtXEF3vpd3/kKnSlTpMI9RDVI1rImlPjqwHB23hX7iNIFzn2/eNooB + A3Z+DT6vEAL80IQQpr/Dgeiv4F5s5azYMHX4iZimUcZbOds2FLOPTJzMcuP1gK7uHx4SNTAUwNtd + oADuKjfEvxjHkYxLlkFxIAnSABRjIl+TDSpQFQIy84dmJfc+gCrbfwOwPC46LfSCB3t+gfSrXntD + +BxysPrC8i8e1/73i+DYNjbxDnnQLJcZ9kL/oinykDYXdHK9Hvp+m5NXLKFiJfcqkHmCv0SZHFgQ + 3xkW4Gi1FxzjiOi8MQMIbuxkkQuWcTPvfHV8GM8W6ffrqeDIRVuAVbsFMX/7XLU6lV340G5npMIr + W0xnxtakP72LuI8+cjl7r6VXPclE89jvuBhNocAnkwIsGGpPN0wADyi/zKQYtJ+O3zyBYBOIixxx + jgCdb24Gz5qxojsrN/Fs9rwBRFXjguEEuWZxrSKArnl0ie9oLZ2/p6yHaZQLxOXnwVtn27bhyapK + onrOjW7eYxOh/XSdv/epbwKDoj/+RfbvYxVLuC4LlN75hxjzBQMaRHMG8vs9Il5QCeM6CWcfPA5x + htdtfuocB/sEXvLzO5j5XvC2PV+HHQvk/+pnRvhksA39ldyIfvRWlrxbuMdDwN9umk4P0MLw69gj + Mm5m4G1/fvi6BSzylllt+Cz4pTChgYq0xjzEa92GT6iXxRd/ocyOW+BYARBmagdg9++pNDglOI5i + gC6Fq3qs0RQaVISpIFn7zsHEdm4G0+GgolPbr/G2+1/w0X6F4HJmlwLP5zGA79R+o9tcTvG+vjac + B0snGrH68Q+PQDenLAmyz9Is/PXCy1qvd+Tc/Nrm+wijFu75LHJP+QHMUD36UNIfN+TmmUU/9BxC + edZnBtdHyoE/PQLj7/VD7i/+2ND+LJfHPzw02+4M6PHhYqjRyUXF9XGNsQfqAF4vZ5mo5yhvluez + sSDu7RZpXXRuaKvca2gBXkKofEr67qcZss3E5V5/OHnC33r2N+QHjLHpOq+dXA3kQ/Mlznid9Y2M + AoaN/dWJwqs1JS4b1GDXQ0En4NzbFri1sj+ROWDJoO7+y6bBPIKvAE7JB3QD9CzwXpmM2Ho/gQXm + 9QB62CJ0Xl7siBtcsqDqThAZWATND9ufVL6d97e+kXOzbIcwOxI0T+Tkul4x7Z9lv4sk9O99oKaP + /tYD6e9b6f3pC2n31zGT6RJdzjcQwYjpTyh6CebIwzd6wqDNFJREtKc0VCErjIp3wsePP+x6s1Lg + +KlcEq/D0Iy73wQEBjyQe7iwDTVrJ4L78yJ0G550ecA8g6oSdZjv13L3G7cAZj/rRF5J+Sm6LPgl + 8OKcfKJtfKZv67efACPcmYCTvb2rcBkHsCUII8cXTnFfCkcDoOrqIoWJe4+ubZeCMrQa8uc3LHNW + uuClPOMAiv0LrGKgPOUUnlIsJ7lK2eZoboA5vq/EvbYj3eLFLaX3gVOQu2gXMO37H+Dn00DOYe+6 + ceB+EkwddsIbI7yLKfQmBe7iNVizNfA4vTgkMG8NFulCA/db/Vsk56cLQC8BWzq3+0HwVrcE/elt + Yl83DX4s7CB7qVtvvTfPAe74iFyT0j/8cWFRe9fgbQlRs/mK3kE1riJyGcZfPKKmD+FokwvGNisA + YpPZh+xdv2Gwx7/wY7/532fiYMUc2W1j2L/4Co71O9Kn+OEb8OEeb+gFnteGFe7AhSKX8SgQT5q3 + NMfzBrwsuOP+Wvcxfh/MHEq++g6O88Lp+KEdIsiYToJc8P7odA4mCIPQifb1sPW9PlnKZl7P5ESv + z3j58xs9A874G2EOrNlTTsBeX0RO2Svegpg5gMv9uSJDq/J4z29FuSOlhxLleG6mX9tk8oiNhGR7 + /rv9+Y1//tVezy2wrcs5YPyvE0iN/wbzwPcayJnlitLb6dIsOvwpQFs++1wTdB03tT0m8FM6EjF9 + +tfl+eBC/4EJ8S6zSff3l0GisRZ5XixZ3xaRLwH5+ieUj0HaTK1MDGnXq8SL28ajncJPstkYIrrx + 9dRMe30WHuIzF7BDl8ZbrePtr75CjOLb0AVGpi+Xw2YER3216PLT8gWEkhGgW7dSuoTepB31wy/+ + 86f13X8QYce8HGTcgxD84T8ARyVCziHzPIGrYAXKdyAGi2DdAc0lJoFPh5GQ8Vi6cXuuvyf0MWfs + /m1VbMkUJH/1Csz/1afu3UWRO324kvMmfsel+s4Y7vqVOHu8r3/+VhR8auSz4vKP72Tr3oZ45bK0 + oT9V1SCw5Znoj6/f/PZ6ufT/HCng//eRgooGElHPkkV5at5s6QsVA3m3j9Zw+GZjeA2DhgR6qjak + XGUXlvgqEYvbDjG9QDWA5+f3gw9ClNBNcNwM8kTTcW2ZRKcvBoTw2a850sQHr9O0mHrwOk4BMazB + H1eRb1rZOvUxOSFUe0sNsgo+euNHrAtveJO27aNXhCxD5X6tjf0KUyRdVT4j2tsyCsEyrzaMpxCR + 1O0elPaMFMGFPw7IdZq8+BVSlEJHDjziO/KvWZPTpZat1T0E6xQhsBW/91Oejk+KjJdYxcM1Dhn5 + C0428tRmb2T5EQNQ3HKFRJylxdvJpzbIlqMRDI3nFfN64BkwX+sQi0tbjPRZuxBy3OWKMr99jQI3 + FilkfseV3Cw/b5Z2wPYRfA4dloqFp4vzfobwcYgwssBPLeihBRpsdH8gxWVTC97Q+0WejiUlReGZ + BYdOkQLp9BowrV91vJggrmXNFa8k1p+zNzmqlMPSmTpyOx2KfbQc2GD0mztk5AUYp+J0q6FKcYjs + eybqmyk7Gew79AsO58SJ+47xWkC4SkFG89Q9IWTgBpVUtfDIyd64Xh91JSetcEBa8TsUFN5YCU7X + KkN2UKjN+phjLL+pv5G8d6OCPUoPFj68JEau5ncjL2qjJcdXyw3+vm9D6VmBL5Hu7XVgWNCYuC18 + kMMXBYt1bTjOVTs5l+5fdLHfGuCT1KihUbQMKVjXjRfnnYYwPs7P/fc2HhuoqwQZMYb44IGPN7Nh + JcrM2SnILaTJSIDaiLvFAPFx8+2G2lwtytN63cg9v2V04aQ0gZiKMUr6h+sJqqNmcq12Mv6+WK/Z + ADEtIJ5zi2gzD72t/fVP+XkIO3Rhjr9GKInTwrTyCxJeJZ9yzqqW8n0RITL6PovXTdZziO6qidIL + 33rch587mF7YkFzskzmyH+UQAM9cAQmDl1iQs3hl4LF8aciWVCXmUinnIcaJRm5M0YyYaJMFq7Rs + kYMOM52KL+1gyrQfXDcGpeP1FeayIwMD06/YxRT14wI5fEJ4N+8a4hdzD1NuGVFcm6Yu+FDFstRe + j+TlTanO6bnIQE31Xhi+k6JYjdVhpMPRX5Bj371iP5OzyVx3dEnM6hFdl27LYKlVb5JufjnSYQUD + lCTOREUMXvrcPt6KLGszi3T9VxSs3PopeP6mlGQkt2L+wI0SCKuzjtLmcADbnSMi+F3aM0qXe1Es + hNE6eGVtBqFQ8nU+RB8sM0XTBQdsvLxNqS6tzHXARc7ROXgUqI0kP31GwYfcfgMuV44TVK7JmaBO + vDfLKVg12dFfD3QuFo1upLFdeLBEF130eKJLcXpVMHD0c3BrUOWxEyv6oFd+d+R8skWnl2K2Ib7x + PJ5ljMct4atOvt6ynmR+exjXHz9b0KjKFFkHZOhCQHQLbuZlRhrdxxUTfMrEU5u/g35bJZ2OxLYg + 3RgB+elae1s6XWvZYNgKxfqviFfFPVn/3leZ21fADweRgc9SNTBJrlUjcGboymLS+QRl1tqsX+uW + QufiEpTTVGi2+PXKYbQEFJ3oRxqXs/EpIQmHH3Eu2ebtz2/DWxXExPho13GxWDaQ5y3qEIpPw7gw + 0iOByS1fkPpMan293IUIrueMR8bKBx4PiGmAY2AJxLBCFM9A1jJp8KeZnKTuAqbnakKYT0xGXOmG + wZvrVkkevlywn1A8jWxkh1jWOoUn98/5TQUdupMwHuCJuK/0Pq5P5RjA4dKapFSYmnLxWeTh9Lin + JG3YdaSflXXBvB1eSNnjj+0YvQNZkVCE5mMItje0fMgqvYNKWDGAXrJjB3PbVP/xn/Dohwj6Y2uh + 1H5UHnsJFk0ODa8jpvl9eGt1cwNYPW810u+a3MyS/l5kua9OKKeeEw9b323gR+CNXMroAnjbill4 + bH42umrhL97EOmAgIQol931/rvFql/DR05r4ri2A9UZBBU/T5UWsW87HW1BOCcxRbBGfqYm+rgcG + Qg1WGrE7tY5/d30Ood2RE7FtBoKlyykjj8uoohNW+2Ltq8SA9+tWEEtgSbMUxghhFHzNAPya/dTt + Tc7hF2pGsOavsGBd4RtCkjVfpHTm3ROQjSZIU/9Ngg92R14pP7ys8xrGCxtxoHVUKQNjYexeK2eB + RSkWW06eLYd0uUwB95IuvCzkbosM7lKP8xC+JQA1VUfFc7X1tVHESa7SZ4vvCZNQDpRdIu/4u/MX + oJMWrLV8tx4JlturP3L0ZvvwHnRXFBLjQBdsvKG8VMMbOXGuFQLyQAkd/JWJe1pEsLFllchrLOno + lqtDIThE72Dw/D5IydwqsIZ6F8qf+Bij9IZsj935VKJIKUjkjBfKlZQq//DRNjPJ24zGH+A3RSs5 + CQry+CnVNDg/U5e4FTMBGgRfDYR9ahCNxgqYJeuRAvvW6yis3hbduIxt5dp5AGS2j9ajWYAVMdne + l300lz4utDUT2chFk/ivxabc33pHy3EijsLwdJXj+AnXqr8jQ+nLgj9sSyofAgshy9S8cT0LVfgX + TyQZOJ5uJ9RhOXrzPfH0j9Fs3iazUCyWC7Ejtf3DEwM6Dvzhhw6ot73LIQXarR9RMgx2w38zxoBv + GmzorLhaMaT+2kPzHIzIVe5mvKo62WDo3UbkK0wNNukYuvLNEEhwkERhXH2p4GFgJwk6X+X3OKc3 + z4cEnwTMeSj0tvTD+zDtT3d8PCiqt4GoXGChSCxSAvwt/q3vq5y5YNNCuaCcGdpQTYmA6fJYvKmT + Xiw03g4KJK3Twcz95ifYkpZF+v3iN3NTlR08JLf73/6O6YnhNhgNQ01OSW/FeJ4KTd7fP1EOz8Lj + i9+7BPl1iUkggK++Itds5T8+TlIuAzuf9XCqD1+CkioAW4jmCRr6Q8bSacnAMBxECB1SH5HGmI6+ + WXH+hPSRdbgWp4/XN6tZwmepG3sbqI2u6c0L4NAvLirkrPLWVR826Hjtk1gW54PhbMUpYMHyQ2jn + t/X4axmoOmFPMnUT4tVZ1adUd2VC/uIB61aSyEGKX0SzTOJt50bv4aHpK+QrhIJVXvTlHx+VQuo0 + bPR+RJDAyST53KQFUVCFoew/B2KuZwBoPKY89IefS5L+MejLx6gZWVKKZyCSvIvnfX8ebQeugeCx + 55GQdT9y8jRvJF3uoNj8RQvhKDYH/GpH4OFP49jQX+0buefdCQju+GLhHi+BeMjbYgHzK4BW85iJ + 3QZvj95VksHxwJzwwbhqzbLjAYSariN7zlIPH6Ikgp8hfwWyKjfjEuccAyspPyCligSwUOP3hBNS + PRRMVQQE1hUi8Pd+ApuYdEXDu4fbaCnosko1pSbxDWCWMSHOMQu89SLFKbw/xjsG4P0Ay+8rGSD4 + GAM6PTaw3yNHELoVGokvwSmewJkm8i9bImKdFKAv8xny4KqyGbE+pfHHN3sj7SjD1XWUARVXyT4C + u76RwJq5YmOZwwYlsKnE6B+uzmnvMITZUTH3lkxrM93e5QayujqSK/uovS2DpwCGspiQy6Vymr94 + BPLF3Ed7X5ORPs3d4jdsHmX3TPQm8bt0UPihHp3eZwQWK/PEf/mGdr4kzfY+HCH4w9+8W7G+qvp3 + g4GdJsS9b6f4D5+BWKtFMKS+MfKFdWfhIaD23iZ0GreGoBY6WcoGXK9b9FHcmRCshWGTHe/0OeWJ + JYX8hEhGlZ+3HaIyApc3vwQgh2m8/emvi3g6kvRaWnTYpryCWV0fiSWfKaCWo0jyjzC3YJPCWzGv + TdD/03shG10Bl950H1hXsyZ//EffUbjIT4M9kD2fA+MxqCvIh3ceBerr0CzaprqyhtmFxO5RbRbu + ZmH5/YpSLDXeGJPHeOUhmL2R+B/n7K1O+MmBq7+uSBMn0xNasSjB1IgNSvf1XTbP3iRWEmaEHNyN + 67N6WACrtwGv51EAU3uWeWBUz5SYV37wiEO8Fu75AtLrkMbjZF5coHsfi+juUR2X8CzwoNWPBQns + KqXrU1kD+GqQRhzcfegWxvenZL/eKjIPv2+xSu5+BFkZnIBzzgew7PgIBOFDibPz61S+kxCyn/gR + 9P59jbePNltwvlYhhsxNAWPWD4tUHbFFzB/f6zQjrgEKB2OExPHu/eybP8GqZQmWoLsUyzzMNkyU + Y0uCXzIXq7K6Jfyce4f472Yo1qZKun/6OUDA81aRLtqffiTW96cX/MtfenC4hldiTPoEtiWTLWnn + 9+AwPGGMBU9m5LbftoDZ4kRnxa/YQen0aZBZ5LAg6mfFf/iCdj8g5iDfWPIVqjCQ+drxhCzoFNi/ + 4AvFn/EEiKbV1j/9etr18YRbk4XJx9KQt91sQH1GDqUwPEKkf3jem8TW5yGbHqPgaHUOIBAYFRCX + okNWoG7xP75bLtccnfZC9aKKfQkW3l/J84Knhlif1YdVyxNk5OFcEON6SgGnphKyPeXs8T1qfDlb + fC/Yit8hpjyzbLJ9G3S8jsYdLFy+PSF/STZky1ny33jc9Uowuuug/+ENzI6aSU4NqvRtKXsWWtPl + i0luq3Rlfc6F1tjiAMwO1bedH/75K3euKYuh1DpL3pKORedX+hnpSBRDvvwIJpr2mmIK3mgDPz3Q + AlpOqk5Ux8ng4Qd+SAVxB/7tn9WMOaQ9LluDL1Oqgbv1SjDIGIvy0FEleFhNgjz/Wjb4kR0MuD8v + caEbxvzZKhJ4Qm8H/emF9VldLYjthgSy4tbFUqpLBp8+VFD6O18pf4T6BP/0Z3H71M06dZIEgFKk + RJmtsOAvU6rIy70sSHBMvGITehxBB7MKeojXoSGxtzFg96Ow7Dg/b1ovlQXA7Vtg5vMqxg328wJ2 + PMBL9e7AyjeBDf99dqyk2A7cKMI938Wsxkl09eVTBtmXFv/Ts3y+2Bvwm4dJFFUKKBY/Eoa/r5Bi + TtEHsB7nEEPTPA1IK8Qg3oQp0uBFNI9IuVdkHMEbLQC6rhEcxMoF4yVbO3j+WBs63/E2Lv5Y8X9+ + xj//hSRm0Ut7fv8v38Ia+vryLxwveJGrsdmuixMCABqeeGbW6ctzPUPImOGD2Ob3p1N61VIZ4e+V + aN3LjrdHJlig0UFDLI5XPC4O2wz+vJOCzFU7j/y+X+B9TEaU/u3/P70T5v2G/vCTe0l3FvrBd8bM + cm0b6kRxCB+CMxP1aVIPo9RU/uWD0VoyYAGrBmWOKB5Jue1QTF9hCqU3MBd81z0u3s7at4XW2OGA + ffChjs+N18NdzxOH27yC/sScgbQZl6Ahxkg3ar5c4A+ji5Q/fo2J28Gdj9Hzg4eG8oy4wbFYf0jN + JuIR5os76FxsgtAeLwth3FbOQdrgua5Hbw7OxxQ6DvPDVP+Bgp65IYfe2deJ5lT6yAl3EEFOzlNk + +n0SE5GdMdjzFaKPKPfWRJ+Xv/glunzpxy368PhvfYkhoEhf8jUogbIf6T29z4Qu5B2F0FyeAdJD + PaX0+soy2B6ggLmq32KqMUwkGTx5EX0b8DgdGbWV389bjmzAqqMweuITXvJ+IHfcfcDctEoih3dN + Jr7wFijtiNJCIdIb5Agfv2HD84GFsl8OSHvFGmXJo6tgeGHFPV9XR6G6HUQ4ldODZJraxfPX1Q2o + dhpDzvIRUip9v5as3w+Xv/0fb7pVJtAfO4u482bGnOkGHVwIaYl6HgU6ri1bg+8WB3gI91ubp+ml + wf15kP4ZT3Rl50sA29ivSRQdT6PAGS8IB/58x4PP7Pe/Vw+CVhB4PF6bfbCOdU3l3U8OYNY2++BL + MYGv9XMmTtbext2fWuSxoD+c+TRo1nhOMFg0GSFr6t2Ymv20SDWcH39+WjOdv7IFeMY8BaBsnXGz + f00JjLeHsPRKj830ZcUJjsa3xVw9aCP3x8dypAA83xsjpqNtTFBKxQEFptPo2/v1kOC+n4NqmahO + k/ZQ/+k5pJbRSol4eVpgz4dRVMyVtyanew0EfcwIco4J5TX0DaB4zixi9MKpGO1M6qDuimd03v0m + +iaO8S+fShx+8yYpyQcok8knaRCqxXJTRQn8+YWuk006t/sNwKwXuPtPSJ8uOJbgsxJLdC9dqdj0 + 3wihxhYqUX/7EYqrIgXw237h7gccvSURcASlqZSIWfVbsVTtKYX+Ub8Gx2OGPbqMBQut0xCT88M5 + 6Xyd6grQL96NaOHk6H96B9QkDFCx88MwZnIneT/mHsAVp+PaV6UFmkvH4Zmvf/oyPogGt4GTdzzz + msoFhQJkjbDEx3YMaHU1EmjEWCGB+no1OKgyA2aJjVHUPjK9VwrRBvv7Q2GUuB6R13WA88k2SfY9 + XOi04SyDfAc0fHi9P4D2tQrhX33iLOOgEU6MvEBH9j38SXQbsGYuPEHppQVxvnkVL6dL28L4IRok + lTVzXN9z7AM2BVEQCcYKKm89MRB+PzFSmaMzbko5s7C/cU8UXB1VXzPX7+D3pY5INy0lFsroEoG/ + /OtMUn0U/vSY7kpnYu/1hU38bBjcT1mIbh2ngTWKXww8Sm5CAkZ8jL/6lCnwvG0GMqW9i2N+qTto + xJNC7JHVG/oqxkGMzqKP4sI40xUcOAink31Gf3j/w1KYQpkxbrs+7mKs8kMHuXBQ//JBb56rooUw + qxB6hFfc/KvXTBa6BFPCVIDgq8bKQ2GIRLeFS7FGdHerTs6DZEXex5v4kSb4I2qBlOcxHtdxzgOp + mxQfvXY/diMH8QkfeXHD4uGzNat7Xvk//YesN1/rs7h3ddn9MuTdnav+z99JyeVJvMr1C7Zk/BZK + 7e0YVBveu7IwywYx+/HJn3/O89sxAT1Yj8T7LgHAf37vzifB+h45QDWtNuQKRc9gcBop3vXTAG1H + 5YhylGw67fURqLnSlejfn1DQ4aqn8FYkgPzhKd75/8+fRwUnjyOdgaXJofZeiVb8XvE//3o6uedA + HgHxSP9MfTiNRo/hML+Ktf59n3/1NqTGz6NH8VXj4dq/b0QPXm1Mdr8ddo87gyzM2YC/xhkD4625 + o3MlZTptVJyDVeCfyFC14zhKtu7DsQ558vyZTDHt8QSegvdB+vIIdYGzdQxjqc9RdvvwDW1qrZSZ + iEyYtfauFrv+gHR6DOS15xezvB0SwLZdSzzi6cWksnCSCtyZAT/1Q7HQ00MDfr34qJyuW4P3eg14 + 28YFmVfe9QTtlWfQNM0Bb0T0Y2yllvaH98G669dt3VZeTmB0+sdXS/AcfIBfCiDerr83/KETuHaP + iXjuOnjEvloskDk5xWv8POp//tYxXvmKKHv+thQfRZF2vx+dA+gWgiDeS4g/lR386btFw9dc3v0j + dP+cVcru+lqakO4Ff/VJSlnsHw39JSOnWUqw3NktkJ8xcpHxHt9g9WWUQZ67nPF8ldWGV/m6hc3X + Mcmrr3y6gPnmA/MY2ViSbgGgCqom+UuPOgnsq90IVbZYMiN8dRJkpAGbK9viv3ohOie/mA6XVwv1 + yIjR3/cJ5pTVMORojIJnzO/oohjwEKx2IJnyrdl8Xxr+9B1me+Eb/+2Hv/wIqUfLjrf4dctgyXIn + ZOz5FH9xggy6+ZgT66Bw3tzN+fSXb+3+ATtu6e8HYSMPF4S0+u3R2luf/+qxLr/6+vabigru+Ta6 + 7niz613jn99zHNZEX/f9AisUPvHGWVqxGO90AUMUMejPb9v1UQBPs1+gkH0blO71EHCL3ipCyeUA + 9npFBsWauRFNfKQ6ry1eBIN0epHLnk9spD5BuBarSu6wjRuq9p8Q7voaaak569vF39wjUgWFKId5 + 8HY/i5VPTzZEt3NZgeUy9im8n/IwqJPIo+tfPWI7Zxgf9npK/eW6TJYZ64as8v6Il/HxVWDq3fS/ + 9acDOYjlfsTSJOfUJnRZcglCUay+wfqNezrKl2sLN7FBgeRkvsdlQ99LPy229itXL7DjRwKzH2YC + ULlTgblU1eRuNEOS6QcO0Mf4YOEguFcsV5VEFy6XSrjVoUXupZsXa5SErszrY0X02+9Y4IacOgDA + m0e2zZRgu2m4hZnP9HicGAOsQdhKMAE3j/x7nrN9CY7/z5EC4X8fKTingkQ0UWQbCiM7l7K4F3DS + bk68IfsZQCw8TsSB9zmmS2pGMrNQLgDOkWvW+/bioa9lOrHe7gNsejv00BbDHUIPVsy/KR8C78C6 + KKTVreHe9X7K7uXGRD/UIVi3dDHkS+FPyBEmz9sYWpZwxHUQHL9t423EyEo4fT8pKodB9ChI5xRm + c38ixfGnedxGWEV+FJZKvLeLAK5am4fLPQtR7g4HD9+P9gbdT5rj/EovMTW+jSYP74lHxvlN9HGR + 1Qh2xwFiCMwu3gB4M3KRST2ejgIutpxsIfyuHUeiSncbfLi4GmzSiSI9/9VgXv2qg4L5rjHULxxY + 1PbEwjO+RPtskHgUHqcLlg/5apIMcxXFnpUEMC38J/JwhSjdh17AYiUVcqvbb5xf5+EJbzT7kmtv + 7hCzcrYs1h+PFL0CwFKJZwXg6HpDVvU+0KXkdU1ecKWQvITNSIRkTIE4pR6uhqAYWeHcBrI8eu0+ + i4f1VqV6R5CDgYssn7lQth7uLfy1xweynGYEc9IFCRi6NkXuQ7gWvGUfXNgs2EC285uaVR2WRQ4e + WYxSQzoDNorDWl7yleDsJj1j0rMxlsVEpKS8J5nHV63Cyi7VtuDgCy+PWzDQZOvAZ1gyx19BZxcu + 0DorITnlvlWw+N3YcF9PdL5JYcwdsrsL7VJU0eO6WiPrNliE97N4I5EoJs12bfoFftQnS9SIuwLW + 90YG1s9FQkrC3kZsYMmGThRppHxn0MPenAUSTHKTBGs46xuyU18OXLYg0fsrxYv/ciD0nsUb7fu3 + 4F00YIDULyDO8pU9kl/PNiyV7kIMa9Q9vjelXM5viCClha+Giy8kgQ+QXMhNfhsN2ydhLz/PohOw + Q3rXN2VaXGh61xu6k/7r8ZIh1pB7jSo5cQ9l5NLAy+FpSY4kPo4SWNVbvsDrlX0i5RB96SJlEMM7 + L7/ILfeteBM7h4FP9XNBJ51/7PvppMGfwljIbZx9lsv3Hclc/zpiW4FDs1T7LYzx9XtgztTuBf2V + 4SJfbdtCedzpMf/4tpv80kOXREKXxdyTQAl6zG9F1j7tcC4sXoTYBUzAIKwAWrsJhtfgbpIiZlGz + LPVkQzwlX2IJpeZxzXyHUlejFtmNvjSrAYgPQTT7+NNk75HlMDdAXOmE6Eqn0KWb4ha+nLeDsu8U + jKx8DRfo5VOOQuQ343YpuQAaaLoh96b+4nWqfzW8PUOeOMvFjBc4x5MsGXWC205RCy4peF7WwKML + 2pR6Hne93UXpc3RLchLUJV4JfTHw9VvvyCvEuNle/bztR6Ii5J3l497YkamgVR16crLPdiFc4GBB + 61kQ5LrJKeZK7Q7hKbR+yLQfh2KjV0uCWkwZ4s6GN/JH5/GEisNYSOc0SDeYfOBfPBG3GE4xed5O + NqyCo4NJUQj6lFJFk8vLYUH+2V+8LWCsQT5XnIfyQDaLVfroLbil3JOYufUZhdZ7YDiqskX+4llY + ThkPt2N92fFT17lZsCvo2W6Nok3rm/Z+PmsQHhYWpQH3BdsjWxJIJuFEgqxoPKGj0SSLU+KRrNGX + ceXNxJL1vfGjdzqdR3q5dgZcXj+E8rwk8SKOdyyZQ2AjXYHZyCWdlYL3M8yR3z7cAm+oh/Dj6gWx + 4ucHbJismSy0eUx8pHfekpHbU7b4UxZA5YU9mhTZAItM7El0CqqYT+73CE788/Qv3uhUH/DxFcKa + hJf7pRHYDIXC47na5ObzVrGGc85CfdMUdJaGpOG+mqTBvDRRIISXKV4G/GihQtUg4OJuLRZkK5oc + 7Y2Lpca5xNw3DTXoz9uGvOl3B9zrbZd/60OSfT9hQ8uCv/2NchQ/9DXjlUw+g8OAXFXz4uWQXjaY + Xl5Pgu7CqeBJN0hARFRD+jnWKBWfGy+bn6Qn1wSR8S8+YfFYbHKD8dSQVzT4//jvAL9jQyX5l8Cr + dZiJXs8WWBXymKD0GmhQH+S6+eFZUuAkCB05fVnJo6+oDuRzoGuB1LW3QmCm894oclCxAGO/Yb+u + Y8NzyknEYm+sRzs/nUB9utbEPe6zt9Xm18NjGxnI8+8/QC3oKrDq2o0o+K0CQSVLLQfy0CBlj8f1 + pwMF+qFRoIvC3+iqcUoon2mhI/NY6ICzRS+CkRhdUdBNCV0WZNmwOtoDejYXTDcHeL18tV0LMxcQ + UF5SAgiZ/dZUZIHc2zyrDOBJyy/EK0Q6rqJQtXJJ8zsK8/Hc8PZN9OFHLVlU/qw+/se/O7+Sly8c + vG24EhZaZy1EmSu0o1DKrg2NoGHIXe86sOhylslVokgoYaU1Hp1cbMEf/t5tsdF5xtqq/7B0LlvL + 6jAYviAGIgoNQ+QkJ1sUVJwBIoryIacWevV74b/HTnCR5H3ekKawiYiCZWKFSFr4ANZuolNNM6aO + iVvTg/GJHRq4U+u3TRNIcO8fJTWNOuZLvcNwcP0XMXSPdZNqKA6ExknEW0k2kfhH/kyYE6shiY3t + bqOvL7nKhusfddJVX81mFjvq0eAxDZ66GbWHFGHwJ7Is/lV3SCw2gQ6aRmyydw5NNlm3mKmX8kJo + pEFSMenz56l3SzgupyqqaG5uYaIOSNqPGxkL0bxOp1xd3S8q7haeG80s9hRps/ZIaIeraMzv9xBm + 0zuOjQZtN+H+JiLsSdm/+tubXyWFUDKB6HtcZrOiYQFMtV1htr4ekEhNoQXtc5FGHlgtHx/LIreL + wW28VSlDb2W17cGsD380qHepzy/P1Rvimt6JTl9xVBeTGKqPbeuRA5GCTGzH8xvkd2gS0+4qg8vL + KRJfo964Hdca+tUH5CT6E/+pT7MbaJrryNizB8E3c2eIT17aEOIsop7VudGUkCBVm/YLuGD62Rc3 + YhnA8E4p3v71Y8WT2HTU1Pxu6OVvFyFO0NNUMRX8cYr7wJ9vssbU/at5kkN/e/Gxcu4XRaGyQVzx + 7Ebd+aR78hKfWEmiTzcbR7MBxSzjf/HPQmEq1JEzjYRQlj5bnk9lf3azxBtD4z1hl188j6qT1d1c + hq0ET3ut08TSxorxeNuDMx5LasRzz/uEXnNYUWVND289qXgbeSfECQ9HtvAgy92vCZHanElBRo2L + 0+3cgoRnnVoO+1TfU7BLVV8bPGpyI/MZ7eYW5OCd0XQwu4oflNJWf/kcpK/QZ3ozJeiQ6BHmhelw + Ph2bGEZPFujNfe588bmsdtx4mY6FubB8eu2JCcoJ93S/1GsplJ+92ldbQncoZ/7UE+OlSHmRjj9e + nFqsl5Ds7BW+vJIzGi7KcAH0iUzifS+rbAjqvxBKQfPGt65/fDaya41a2cPET+MKTeo19MDARkGc + XNKR+GqPb3iK0zIyYcpVX1uPGqXjd09y7TEak8hUDR5vJNOfno/NLUmh7PITlrZWmXHi3d8/niSG + nwfVsLkhEz5t1NFF7yJRfI8O2MOppNqHWFxq02MKRTAwanfthMbh5G+VJb/H/udn0qZzQD+UAs3h + K2XTBqwXdLe0oDuq2sZGr1sNHpJ9Gh/HUjV6f5cXKK60C01n9Oy4nqcvWKPdRO3A0AxGxCeAlOfp + KEqOm61X95UJrtpMJM5yd7lr86PD+mC5JD8EoT/fk20MqHXexDf1VcUvkeGgjxgCzd6fNWfiu/ag + e5HLuJVqoZrWyHwBu6Uh0etwm01NfcjR7aZviG+Fm44Zj3YLBZKmf7+PZpY7sOQP3uQCjubw9Bnh + 2q3w8slv8jmd5RnMmvwt+Ya776sPBfVanCR6NI8qGv3bstj2r9zRu8iLalr0WW1lB4/i5rKLpjEM + Srivdgk5SZ3drZNXm6LuYH0oybKN3+98fwt7dE/osYhXaByEQQTIhZ4cZPvN53Oxwj9+plF6gYrh + 4C2oj7OkE4KEIZrsqj+Bk9GGamZPqrkAn6Hn4yuP8hkBYuk1XuLTXJHbcxiM0RLoW1n4i+grVe82 + +bf34Og0ImZveHRd4kIL7GBdqfscBn/S5CH4+WP8ZzmKQcfmdYJ2dTtQbYI92mR20isLv1FTVI4R + O17VAtTkr6e4E0LOCfra8IvfhVe7qd62BWjXpiDOVRGy6ZXMgiKnJ053x2NtsNRpbPRqZp8G/XOM + +DCxGYz2/UfNzeUZTaf9VUJnRcC/ePJZ+Q1rkMMnJ/qWSF3HRcTA8S4uFhxNRHzYXHpQdkKOVcFS + umlz4ybkPLlR7fu5d/PorFN4SUVFXaOu+Xtop1Zd9J+evK+dTZvm7wVsOP/Rw7u7dPxkT42y9A9I + 8PBHgy68rYpieFr4LYiky8BCEEYCWH0Yr445kqGgnUZrYlf1gYuRVAlqJ3I8is2BZOuASQyMRqPk + YYgfY7Rkt4Xj4RUT65TI2Txpf1tYeIkY5s7mNHvEDeTyN6FW4HfdLOe8+OfP/EOcdazdoUbZH16M + aIs+zsPnqwDbwZYazNj7bH/a5si1Vi71jf6vGp9jGKOTUMX4HY05mtYoeMlgVfHyvAOa2T1OVFrX + Dl4n26Hi9hnr4N12T/wpYaro+lUst3yEbFRvQd/R0L+8Ae+sjOhFIGaDrtQBOiC1XeL1kM2VuAoh + j6uG7Jd8677dAcNqc8EUX+c3Z5N2G9HHjmN6dJQMTdfkpKizBDcSqtmEWGoIJbgUbtRWv6HB/ryd + 99NLYvbSEDWgTQzAS0XMYxf/01swj9aGWAf7nk2ZX+aoTLYpvVY+GPwDTww7L6/HzfpeVnM/skT1 + /LNLSVetDL73VyX0lUIIjh5FNV9sOEFeSs9xXvjvXz8i93tGwijcVxIimqb+/HA8TCtj0WMb5Mrq + KEln2Z8H4SOq7NGRUZr522BeVSugIKMixvpsGPOoaFu0+MlfPysbFn8IsXhbU/25jLz++HVllja1 + 1sO1Y3WivmHx/1hQXssIVGsBoOzIf/Wcj3hb9qqJnwKxxNlCm9LNRqiklUEPg3qq5kxxZ9SoB5Fo + x9tU9UzyPHRpthIhC98vdx/raPeoLwRLIvDvUL1KEGhyJvuq0LPJ0kJRLUbvRvzN7PNvT/wX0p1v + QYxwbNCEsrUJLpcn6i56y2KNl+r8DXfU32atT30ne8PiJ3//r5oCMxb/9e+qJd7nbb0T1JHP2v/1 + 5efHi6x+Uv34OGVrY7r2QGKfLyPkK8TMr5Igq3w4ZPFzfC7DlwQGLfbksbxPVngvE4ZLfyX6+j5W + LMO2Bswhm3HWT+D3YD96JKy/Z2JfwgPaRGplqtOsbunPTzNygfwfz1v7wo2YZOQ9/PyE+5AufLoH + 3/yn/9Q70CEavrkggBjrOv5r4zGaCuMkQJBhhWDfev36ZTrwtXskj8o1Iu6m2xqs08zG+0fGFc9m + pQXPfnzJwXC9aBlx1tTHW5aJu/D+R19rp396v79MWiadQ52Bxg1M7TcJ/E0hGB5oll2Tg7ts2Xv1 + iYCej07Gq0Wv3+r7EECqWyu6W/SMSW3Swxln1rhNqrxqjuY1h+/mfsbowwfOH7UTAJUdjZjxFRm0 + 7GoHwEtEavvpjkt8FhPQo0mg/n4/dNNxN23V73dWf/2yTFp4UZVC/0XxbTWjptHmRvXKXKG2rM0G + 60XHg3g50hIW9FiJ3ibYQvMN7+TavexIDB47gLWb6liMko/PbkJYqsn+KlD3gg1/cs5MBB6YLs05 + xAbfdbaguttpGYkWtGiW3XsOFZIq+u99bYajrS79Ulog9vSXfNwiSwu21F54ag5rf/znZ0zPdg0a + 5HMNTdsB3ZMG+/8BAAD//6RdybqqvBJ9IAbSKAlDOulJ6FScASIKKtIFyNPfD/cZ/rM737IhqWat + VUkVe+AenrTpBxObKFq8nC7hHuRWl06sY9gux4j1W8IfUZ+erTnVA9+lW3w7J0TV7TVf2kNdSM+d + 5iCqOkE/HuWUh6OzHYkts6ieZ1614InXJ3IB3RSTauc5f/qJsOlrY7+cIFD9JzONmt3FNIOKI+r1 + l5BNvwVbV7MOxg7vE1kJv3UXIXn98TtsIO5D6XUrYU+efpngtj5cn2Yq3PTvidvyT0XvFvrDp1qp + W2Dzdx5a5hyS4u4ijeXUePrzLyzf6nj9wp6HlaN8sTZ5MyDafKjgPD1loqpRoQ3+CZTw1Rc6uUXn + ql/GIydDyHanqeQOvcZGKV9AYTV6oqbbrdSBlTd855/xHR0Fd0neRgLPoJymw/y5acTpJhUyKudj + tJYvd7S3QWhGNrQTla4fd/66jgOl63rE6j3zcl4zJAumaXUhG/6IZ7NnM5COnUnMmX3W9GaGA+AU + 2OHT6fbpp5rdReDFRhBJ9VDVtFmvE8guPiHGORjpcn9YJQzatMHouovosOm/4Pvwzn/8gu7XgIUp + BSZBtuDQLR/vwU+f087gXK/UyArQAvaE7erOuZM6HhxAuH5H/HB89utPn/r9XrXWIKcfqfN+30+M + rrhSHjhlAWmlPLDlCNsR2h2ZQHDih2n3WZWYKj1ixNsRhuS86edzfKtk2HwXd1o3fWa9AWcGwmtZ + iX04XN2ZyZjhp9dgL2WqeHWvK5Iu+8D8Pb9mBcK3sPiY1STO97BfZdC8f3oU4vr1FtOdvYrg1/j/ + kp7mnKaJ58D0yNyJWT7Ln37Wwm8DbtgOX0dNuL8tBAyZpERbPjwlZ+72hiaHrhiz9aeef/qqcvwY + iFpdQ7f6SiISNqrIj88OL1t2pNOJL4mcPmuwoBSwkDymFqtIH+IVq3MgEZNckLSk+5q8TqMHnWh9 + TlJmMvlUoJaHcv/hCObKQ7xu9QAYIZmd5u1562893wbPYsyV1/iHR+C7jDziuW2lzcgbGPilK5p+ + +stymDIDeH71xMF3X9Oluhwg3PRGonWLAzj2suhAv6UEy7CSNfbFdcbv9wSX1THm7XTx4J5ztemg + pggQFDfGYdMXsWuUuF8PYgUlJQ5L7C+qTPnTekLQAzdnOnQ7W6MpkC3YCLea6Ntgsak5VDzM4OWO + DvE0bfrGkgDON23sbU1nfvYv+d98naQtv8xS46Of/k3kSbj2i89VCSw+xwrjqR1zWm797f70Hewu + PQlK9g2bqe2JPvB+/Lr7zxL+8K6XnNSct9FjkoQ34271g6pe+jRSocp5H+zHYZTPrlIUwLi/LaKw + ipMvT74qfvwQn2b7Q+fLCzNggMTAMuaUmg31ewENy5lRvwAOLOL+8Yax5w8kvXlivwzYrSAzsDdi + 6OJIJ45yKax1CxAHx5JLFWZgQKKuBt74dT9094cDP6JboHkXfcDyTEUI+aVx0cHtHLBQ0X1CZce+ + p7XSur7hKgVB0TcA8Re1opSJ7QxcAb8jFpmjnnS7xju0pC7+7HU+K7A6bO+PioV28XyL928IqryZ + BEXn3alvqj2MKl7a6ou9O2/5Tdr257f/VGBiJZOM8krwybrtYkp9YYWH2uz/6htdf9+d4VCUPEab + PjRfPE+F5xyV2I/Ocs/6J1r86f/yXbL7CklzAcvJupJM3L3qVVyyAnAXPSOK7avu4j/VRHoOt2ja + +G7NEz0opZx/bV3e7H0+hsphD1lV+KLSPaqUTlAOJOfSqrgIOaSxzzN9S1VfRj981S/W3g1gM3U9 + 8ZTxra0dvZ3h2n0rxCvcvR4K+2FIuP+42Cw5PaZF0Hpg49vYG4kRCw6jy9IIWJPcqzGOV/4lQ+lT + 729Ea0QWLN5FZOGmz2LzWls5/zCLRKzzd4wd73GKx15nZbj/lD324cMBY7SvEym2v+wk3ZkkX7+w + ZuFRLFIix8GnnxAVLLDpI1h2DlLcpSYXwKyuWrzxEUriztdB9qUfrFrrnFMu7hIYaktC9Gd0+otn + v3xKbltLnJ/eArb1IcpEJu0Pb797psEKPKKYfjodwctrwsQCzyfo/Ix6UtgedJyG41wP9+TwhrOx + dc0OwUTpn75NNn/6sJm27uewkDY9bWI0ONdz9hhk2PfwjCPPP8erC70W/PR5c8NPfE3CBMLjI8GO + 093dxSHcLKZxJ2AnjY+9sHZNIG76MHaTmw+Wu98V0GHcEPuZWca0suNJSofbRHK+oWD64SmdwM/f + /lKjHWQIbkI/bXpC3f3yxyGoKdnWMx/d4rJC+Xa4EXuLbxx86w001TQkivQt3fmn96ktmIgXD422 + tO9j+X8NPtj/95ECm9xnYs/vXqPHpoLS/jIHWKf3OF4vOGYghPJheoiRTDnNpk8pHfEOm8ndpbQv + 3plkoc4kx9P7kY8cbqBUS/yI9Zetxqyf2hV852GNbQYWGnegtIBhOpdEEY9WLrQUQFCJeog9YW3d + JS+YPbCn8zqtUda4k+FLGbiUCY8DB5F+Vk63Dp7DFBEnij9g2Y93b2sUbRK8u43xmp5mJO2uN4K1 + qX30S14dVelY8C42gZPWfJtYnfTRPETCuUJgAm14looz88b+1xfipzawjMRHooTgOaoB2/OzDvlE + ASQ+v7j4tXZWB78588AGmBm63teTAxWUqyjowZBTZWwDSB9ijZX01bjsfWYMONuLTIqXarhrruxX + +J6XFoEHAnFn858ZcPB1xubAmvW893sRGtPtTE41XHN6bFooHdroRkrBEfsFXi8zGBd5JL7gKZSf + stdZUprhS05gNVw+ruMMBtUznXrrcO95HWhI+gA6E0smUT4074cqmZp7wHI6POM1Cr4BvF9BhX2h + DHOuuhwn6M8txH6wBz0N2+cbtsIrJ1qX2+6Ug6KAewNOGJ3SN5jtQWYk6jaQoLsqxLSGIIK5/RDI + aR+iXGhMWkjSvjXwBRyVnu2+uzc8deERG0LIg/kE1AGyxdEmoc8pYD2bDyjReoNw6eutCRPHrvA7 + DTUOltjvhQwdIFC84knuLarjWfLODdwbrodgroF8XQSFh3G5kkka2Yo2UZQm0MLLkUQdewZLf10a + SPCBI/IS+/V6iK66VFxWi2TM+UTHT9eJQJJTGYf68wJY82wNcH88dYg3LCGvyueHhW9GMCeRXWC+ + vohoSP3+xWHlFja1kIQ3GX5da0du46eNqX7OBlhyiMEIapTOg1FYQNOvCb4JYQoE5MwJ5C5mRI7f + h6etgIaFNJ2rhaR268Z0fbklYErHIerh/nTH67EVIeYqTLLt/9Pd7niGIocNrL1I2w/6sjrg57/x + vX8DWh9GBz6iLztxNYy2Uvc3Abvjm0yNd7jVUxuslQRWKcTb82J6t61V4i7HiCTTo475t9pEMO3D + O5Zt8unHC+uVwPw8d5OAE83lGmV+SjxfPojdkQegLQUMOKnsg8iOIrh0qJ1WPD6qCef3pdbop6Ws + lKF6mprOjHL++ioQ7A/5hZi7RdXW12LyoADbre7TW8m5mp8TWGu7Gsu324FOHqpKeFAYYVovhl7z + gRqrElhtgvXquQdjlcWsdAshQQxOXu7wPOmdlMzGnVi3+uzyaIgm0AaWQDzpG+akBS9G+pLqiEvl + JNaz2JcORFrK4Nsp8mpKBamBpL5nxAyiOOfm09kBZQkum30aGqsvqwWbt3jGCN50wH/v8gpprT3I + sdlmXy9SsoL+UwhESTug0QQhETq6xOHjmr3cNcof2c9eiX4OmXpaVVWV2EHssROtGKyHKDSk0JAL + nKhzQ6f6XA+w+vQ9sfv8mbPvQ+xIA2vo5BffWAjXp+RA8Y4ODITaLL7FAL4e4h3f44OZs0mbvuHP + H+O8RDkl1sTAyxMLRD0sYc8Nj76B3yKWiHPRdzkNXkkp9Z9SwMgY8/5nv2LXfz2cvUjXr/P+/oY1 + EH2sIHPJ6djBDl5fjEd8FzmA+od8DwePARPDb/dOGruroGpFPlHm8ZMv4cd9wvgORoyabN+vb3WI + oC6jHVGZ4FoLnNc6f++P48MnX3YyW8Lnym6NobNzzabVRwas5dkkZ1PUT4FYMRDpXoUqsTxrAjKf + SArhWcUyyi1tgdf7DDM/m6fD/O7d9c3IZyl7HmMk4GumsYNmqHAxUh9frrcc8P2y7g+GKa8kiwyc + z/4dVIB6RMT6/rXNjlvdFp5fMcJH00J98709Jmh+HW2CHlLzX/6Q3lb9wFZ/srS5jb5v+NEQIubx + uWiT1oYG5KO9RM4KKOnf991kFSOqCprG8Wfbg5Fo+ETzswHQy7hU8Dk5KjlSF/aDa4MA3ISCEvlo + Me7c0wcjkddVRDBj9JyvrwcWJnMOkKidfY3nJrWSHH3Hof2zwO46D8oE5/L+RDUrP8Ha8+UqDvdB + QqPgKYCT3kEgbfmKuOd+6Ve4I2eonbeDFVlD82+CKk+qFPWL9er2oINfjCksK2E/tW++ctmjUiIY + e/KIkw1PzPHLKUDKcBoOkbnEM7eYiWRY9ox2gSFrvPZyIExMld/8oeyXq8F3cEDHw0Q1O+rXwn6u + v3iIT+7J1NhclxoIxruHFVvcgzELHir0WCEkqLFsl+3pA0ofUCY4J/4LcOemKn/5m5wNrcq5LjA9 + aUmTEV93dtLPo7c1fmd2PfHcp1JznLlC6RbNITaUL85pZ7MRvDPRG0mZe+z5fhH3UHDU43SIhlPd + 236cggYvPvHHjxXTnfgopfPbmLG32YOArB2E3lNc8BGWBlheYfI83KI1xDf1FAJ28y/JhPhB9H06 + 1CsTarNUkuFAcGDI7td5wDdc1TrBHjU+/XptuwQwtMX4Miqyuxgd3UtBeVxIeAv1filWwZESEkBi + 1GdPm+FhSOHDtwlaK8kHy3wqLSjFtxI939Y5XkyYRmC/81h8C4+J1j6DapaSea9M/Jj5YDgzKQtL + Mh2wSiohXoZmGYDGBOUWrx/54pxhCeGlp9jp2rpfm+bAwMf9fSKaWWv1iHzOEfmGu233HZWcys/W + AUk+psT5xRPX8jx4lWmDFbu148m/Vo3EN8JtAoUWAS5WyADdGBnEAHNJaXUqW5jnzA071jTl9DIe + nlCPdsqGTww6k+fcga/r7Ihu4oH+8huEenMiod1+c/I13o6kjnOIZrFH/V++P5LGx0rqUm0RGdpI + ryDMyd2hHBgb+1lBrRf2k7DsZpcyQ+zB7+14J7plvbXVz5gEuMpHxcfT1frlXws0jZNghx1e+RJb + GhQXpou3Wa5CvWx4GgisN+CY0s6l+y7L4JENZmz1z2rrKCQ/pbJgBATCUekF6cUzf/Ev794wXuTX + AMXsuefIOfCu2mJjp4DoDJ8kI1fgLsdaEeHdUCvipE7o8s6+taCuqA+MwIVqq8NlKmSXDyJWq7Rg + PspyKcFvJGzrywByfGQ8FJ83nfhrbNGZfdTnX/7G5qhUbgs5q4KHU5WTeyJc6ql7r7NEn/6ORFqn + AA6fPg40k1lCB1s9astIFlZK08eNnBXHzLnZlgPIrOxInMgg+Vyn2Qo2vE78rb0gLY9iC68fcdj4 + A9T64InO8EtzA6Mj1N35kDYy9B7xOEEV7Lcj9I8Kslb9wmo00J4u5QfBpTcIlkXP6Hnx5SKQBRYl + G97PP6YQ67DZszy+FhKmcxvvEliA3iIyauWe++FB3/zEaNry1yA7twqeztlxorrq0nXDL2BJzyNx + fUXRlv08JvAmy3ia07tNBYM89INmXFjUF9oKprcoerD03NO0dp8yX0fvrgMSJQUJL/4TzNazfMPF + GL8EY9+g2/pEwBjPKZaP2kebuI7N4OuhS+RcnweteStHBCeFxeQEZz5e4+oYgXnlv0SWedQLylgF + 0jX2HBzY5FOTcX130Lb4CfsnZtQGz7dTqNpXF6OTO8WzfDy0ABQJ2vZTqFetvRqHgc0r4p73jsae + VbjCCvgYl3Vh5VwkaSuwiyzCnsErLse5ii59p6nGeNe9wN/zW7644eK3PzU/n2EfHxeiq/WQU295 + iDAdjgqxbvNUU6QnhvTDD+lQzaC9xS4LH75LyLHJs5p24RTB6kTcafB2BzqGPUnhm+FMfGkt0+U+ + z3kP3eSkE2t3c3Phy0AdGrtgT4I3J1Iid5ElTd+IYqX/cPWfv0loBuR8vWsa+1oejtTLaoyWi69S + wgw5glt++uMTy2XICqiL+Qtxyuz36yUWWMCBMsX+hufpZb2lYLMf9O2VJV/sKHhL++swE+N0jNyR + WYLsj8/JTnV36Tips7TxNbTIp3u8Tmd7w9fVbqL0vfTrJeoMgPefkThbTXQp9fwN53lvE5O/PrSV + X6dMTGZRwd62vhs+2xozywu2b4UAVnuSZnB0fZOoxVYCFHSVhS2+JBPXtLFGFS6LgBj2T6KS3S4e + Nvwg6spFItazwBqNjWIPxeddxwphJ41eA3OAn89+xqbMz/0siMsglZzHTKLGce4qvjRP+vmXCS6L + ++MD8MgZETk257qfHC5SpTBdS6JEDzUXDNaDYj3g70Tw/Zyv9tiq0HuEI/bdvI67bz10ElPt/Ukk + b1pPh+hqgBfbPpDILkX+h0fv10OFC+oyNQ2q+S1RH8moZu8OYLMDl0KVDAu+OON2hEjaqbA4np44 + fGO2p+fGtOBVXhpsyUGXr0boVnBbT8SNFPREc6P3D/8S8yYMdMWkZaGcPPZbfog1rh0XVSIBSRGb + XWV3zCtfhvmuUkiRNTSe41tQgF881+/83u3HyVnB9ElirD4KDoxfbUkkYO4s1OV9S6m3Ch28ECcl + 9tEsXPrOxgaquYyxn3VjPdljpcIWnxIkbfzkG/afVLSCdztRfyjiud/tWKgEpxqbG56l4zp1oE6T + Lxqq86vvRZ1pRHS6lMT4cIm73sclkjZ7wsk5ZPollq8QTjoYCVq97Qg66yGYdoyGtfDAUvp+2CwM + mQ+DcfKyNNqS1ID3bFSRaE0obo/KGcEjp0fk9s0ImNq0ecL6nNmbvmNp3ce2Kii9jCc6HE2orYbP + pdKL3UnYIaZbD+sRsYfbSyCTyOjvfJFbaoD68jDxqeHcmg3N1IPAYB1cjvFSU+HZZmK+FE/iMPYt + X9vTOZV+8dhbD1ib98eDCM97LZhESgdt8WgK4Wy1KpGRGLht/GCTPUGrMEnb+r66AHtgs29iivuv + u/7wM+aeGB/LxQZUWT0LXKhyJdYbCNrSimgPbxgNWPP9JGdDM0ASQbNArKSTtPHl7bs/viffBAdM + zOnTHfZDuWL1gW/x0q6FKDIrP/7wTk42/xL1/e1IlKdauOt0AwXMShRNwmdrRDuypwmcBX/Cx9N+ + BiML1xkiWL6n+Vm7+Rxix5PYXTuSPzxrPcvmoEfunaBM/8SLPNge5LqWx4ahVfFyfmRn+NNTrCQd + 6/aShA48vPvrVpJ13D88ft/lGx7LZ7AKusMfXl6a4XR36/M2W7UMRgpv4vBdjBrdbnxCeocqNpeq + 1eau/bIw9niMGnXWwareUgg76TVgt3vDfAlqKYVh/6yISwefjrAY90A6LDrWG7LEc/PhZ5hkgMfa + i93V1N/NDlx6MSS+O66UIEtgoNmeH0h8aM4//iCfLYjTW/juV/EWIgmjq4FLnLy01TtHpfQ+Fhre + 8nvNSZ9Jhec4mfBPP/zpQ1LXVYhoe2alyynjDVjoqEErGQlYTBhE4JmTgnje7gAWb1bZP/5vXXXk + 9sS8IsjQXY/l2LR6qspRIbFzGUzCsgr9KlvvEl7FdiKWDjs6//TRfJruGx82Y87GagHhueKw4w9M + PL0ZOZH2hu1huS/0WMCnuYBu7BnY0qHz0x8t6cen5FsX5RwzqZG0u94JcS7lnC/hxFVS2E0M+qLM + 0PgXWQ3Q1kOOL3wf1MIEQ1n68TP7rSkxx8rW1mXLSogx+lxO9XM0SA+PjUm2vQ+xHD8S5bMDJ7iX + Ajon7LuUists4dM9WMAi3Kj3F782/lZTzudEqONrQcxaPcXLVc1WKL1xhZadts+Xxn4+IXfZs5ve + TOPph0cDMnFo3yoWYM9MwErfYtbIZRnlekUz34iGqa5IOt1f7m89pQenZ9Mj7XJt9VBbwJAJnsST + nG8+N3W0lz5gmRE/+qeY3P2mA3K7Z6YJv9m6baNHAwL2mpL8ZevxnWGP2/61GD21YKbDfWZ0mN+O + OTHW+E2pDzsZaEePJy5lpLjTBckAqzKuROGuJqD7VU/B89ZD1BP/SBcODwzc8DRa9fxaU6cQUvA+ + lhqWuR3S5rRwLRHvoxC70+P4T28vWUMmeBj7eMYcXeGjvp+RSKmnfTt0KKGF6REBDSNAkWfocD3F + Z7Lp8WCyJ26F29Rm4nQi1eZDOsgwTT2FXBnhpQkTvMrQ0XiB2JfV1MaRLDwsUrPBR/dkurz6gCx0 + GHZHvC1fzJ5vZ8Cuwhirh8iuVwGmK9gxEp38UjDc2Z4HVvzZzw/PLcc7mOHpa5kTv+EpwRgiBuLQ + tYmy4Sl6dXtW/OXjpLopYN3dMxWctE7Dbs6L2lA+CQ9L90SJwe8+8eqdsxJ+rUxC66M4gXnuni2c + zs9lwyfEJfnN8qD8WeWJzwKxHua1aKEestp2q3cFgxKZFUTfqcH4JtnucO3CDP7wFCq5kq7J8m0h + QztM7FtxAUsNEg8IxWoSlYpWzFFOnqVz9PAQs+lfwmHfdsArEdr4eKWNx1rZg8uy366k4Vs+9/xs + QKGcPojuhzqe5nl8w5anh0ma0juYl/28h0FVpfh2ur+0Lb7okD5MBQHrcK/p+A5m+IsX89Xt8rkb + /QlSLy0ncO7DeuMXZ5GETxGbvGf1LOis7Kc3EZvphrrT2qsOKbfUiN57g/KOHCHgCmKL2G/b5T1l + SQGCAtKtftOD4QjXQHSTiz5JL7kEyxyI0Z//n0FJYjrUaifF9S2alqcK3UnQHRY+81TGhft81Mux + ubTSSeUfaL/h9+V9mWe46cvEbH1GW3YHNQNaN4gkfX/NWmCG3IMgfw4Yfb4VoIcDnx6wH1kIHHSi + bf6RwdKVLyTUYUtX2ZpKWJ/HF0GCI9Zk06/hTFsXl8YIaip/dBnaFjtNjVp7+V+9wI3rJxKcvNMW + UbtX4nNMqml3m4N6ieWQgTy+pER3JxbQC1dXIp9oAG/53J33x2Uvubh7TFLdSvW6NwPml3+IMWYj + XVd02Yv651ujqWXtnnWNl3PY8MYEp8x0v5bMIvi6WwW+TekOTNqsGZJyRw2x39ojphqqBnDxxnXD + J8RttbcoHyxeREja9NKZogOC73GysfEUGpeEIypB9UkwUS2uq+nGJ3/fh60k9fufniXtmO7ywz8x + 3U6DQcZNLZxpZ99dX2TVYZJtg8PkRtHmNhYS8b1rvziZEofO3XfXgFt8wNNz828+fj8qCSlFiX/6 + 8ra/HcxHUfqr15HhDqs/f9nWjw7ltdKhyPkG8b4PtF0xMFe4xWu84SuN9kHw/PG9iWz6yeph3oOs + hWw0D1lSD56vZPD2quJp01/q+ae/nlHrEkXWCP3hNaAEl5pgJxnBtKulPSirqsBaZPgxOT8PqagZ + JxadNn44q3dZhT8+bWz8eJ7HeZb016ghEHnfnsLrQ5dUO3fRU7cdTbgJw/nH7/Ad2kgTXpd7KZ4x + tdEOyyimw3bFbeO/GLWnRz+cmYCX3ChtcNFwY78+rFMB6t7osUp29/z7ee5F6FzSnITaUmkrZMIG + bnifWIjGYIjOrCeV7oUSZaunChEoJjhvVzTJ6NF4aceDDL/FqiF6XkfQolrpYCEFHi5oLGs8W6uZ + FMm+hlP+cgCr1oa69FonhXiv5FSPxClVmJoEIB5erHplUycCuV0L2PtO73oRGdDAPlpP2LwJHhWk + 71GXZLFSSbrVR2atv+2hb5SnbbA9D5aXt2+hTZwE2/q56akPnzL0c0vAKrtP+2nzB8hp9YQt9NBj + YTqoeyknwm3z16xejHcq/+rHSMKim//pZbL4VLEZHDM69uWpAL75ivH5Keju2iZyK13sLpnYKrBz + IQlPsnQ3TnAbK+PGHLzeV3jesyv51WuWYt1ZoEXoTNTHF8U89VXnL5/i0nnUy4GCAhRS5P3qMRoR + tYqVSH3LcBweWDBmaazCA7Rv//BwvUcG9Efz8qc/v9ZObqX10BUTD0qcL1X0YP/0ol/9YtxJUwB+ + 8Srd4t8smLiF6TXZIWGLz4tHAwbi+PidRGL2/fIcfQhPOlWwJ10UsN6uxvzDe+RY35x4vXe1IyUm + m2715lP+/eljyXTPUBethK5H5prA7H4KiEcNsx+L9yOSNv+e+C1erKvNqnDf98G0Rnfgzsrp1MKg + cMNpTpkQLDuYPsXffodzNQGSvzH8vwYfHP77SMGjXCOif5lXTNf8lErfZbslO/Y9nS+VvEKyphAj + Daz1APdBBUnp36c3f5Y1zkT0LaW6cySOUo3xlDMhOlzvjjAB/Ig0fod2DYw++xMO97uQcql3hFKx + l3SsO+6jF8C5lqGZXCBa4d1zl080GnDHxgOad+ajprnclpD5Bgd8f7mOy+nXugUWsztMgyB8XPrh + wxUMcHZJXtE+X8g3fUKgTgoS2HrXz8OyZNBbKw2rLXBi1ma0VdpH8E6s55Pp6edbVND5vgSsmOe6 + n0un6qSXMqrEEkUuX/PBcuBweAoEXZyju4gxYWBYendcitq7nl1YFdJzVK+I+zy2kj5by5AYVxEb + NHjmwnwZRFhu4njnO0W+ire2Ac3zKExlJYh0wel3ht0DGXjTDmmtWFYBLEY6kJwkJ7Du79peygWd + JanzHXuqLnwKtNeunJ7yoakX3XtnkuuVZ2LY76juX53Aw8udabFWec+eKy/3EtI5exG3om5O21Pl + SAs+H7HFvwxtaQWMYPuGEvFhdQSsOZYqvAZ3DrvYe4L1g6tyK3cdJyLVMuUWfx1gVHA5Vk5Rnq94 + vfEQCa8KXSbeprMsdWfQqxYimbOnPb0ETQL52TKx4V0UV7jZzSq97ldu4pmX6rJ3os7Qi/Z7kvRF + SlezlGWp89Uv1nZuqPEakt7wiKMSH2Mb5yw7dQnsfPlLrvgDYjrcbAdemkrE+BZecr6/KCUsIEqJ + a0gVHXOoyjAK8xEt5rmuJwSejYT4qCZHd1JjXqeXQeInnxBTFxzAxgFzhhQLIfYVPPa0nuZZWvmB + YHOBn37dIaH57Rc5QjTEi9NwjvQ8eSzOv/wzFvy+muDbcmtyjG0SD9d6ieD1bgnYys2wny0zaOBm + X/h0MizAnc68CgNR0UiIxQQsTIu8w/a95CZ9O22tXvIb8n78QYzlJGB1037e1w7xCUrbFCzKkkJo + w3iPZY23Y37MDoy484sTVuWspMNekztph5ZoYr4vtV7tx/wE1zdzw6YudHToaTFARZRrXM69HHPD + RztLv3hwsaILFeppP//ZM75TrafxFzLiJIUXrHDHnTvVETHgeSdXJHQKJV79i1XCfH3WxOlZ0JOy + BQbEh9uMb+CixMIUwxQK4dao2/xeXTY6XDNoXj7B9Ofvj96K4PgMXrhQayvnjOeHAVUhqvi+1nNM + 0c2yQCcmLTH9HGqTFM1PCe6kPbG/Ft+v8vu5lwIlPmPNX44u92XmDnLhXiYhd0hcLlDvM2SvtTnx + iXmL104ABkgdLcbofv1ofevzExSc1xtfAq+rV7apLMm5OxdiJOYt5yT+vUI+XoNtPZ91E5yjRrrJ + c4eP2l3O2eKw8jBsv7t/+/mCfQmbwmuxdpY/mvDWxQwaSaphU3TteukzpEOcr5C4y5upx6f3ScUH + 9/mieQZGzqUHlpe+l2eAuPkw9BN7ee6l/HL7ElPIcleQPsEqkW90JUff4zXKc2YJFbQdyamVJl5Z + 59XBz0sf8eZfgNNaD0GQszXOlMqPWf2oZVJafhp0gEOa0wuTe3BG80qcJMjclqvsVdpnq46Yejrn + 67bf4DPsI1wg/5KvpHyW0te1rvjiiwoQwv2pksa7EpAoQHo+H07XCaxj403Dq7Dp8rwOLayKvYq9 + wJlp56b9Cj+OQvEVR57Lfz7HM3w4SCFxPRa1AHKGPYzqNyPGB6g133+zClalt8NyXKWxoIXeCq3r + vSLK2EXuqtNtREdv2djrpyhmpzrlJWfWK6LsQjFf0EdNpNsxAdgODCUXpurQgkNqaEQ7eZG2jDe+ + gSQZLHwLDkXPCl/DkBbPWLC5hjwdfbVCUjLtbXwz0oUOpVQnkJWGehJj4PRrLZcqrKf9F3G8OoPZ + OC2WVPMDxqXLvfJusFoRjp3ukBylPhBgCw2AELcSObyF4M++Aym4bPk2qrnLKRug/eCvRH/q69ZI + nh+gRNeKmDOt49F4Egjli+eSsIpcwL1pOUGvG2747jycXpAnfw8v36OLnZ2GNK68XAop6TsOq1ay + 9DNe9inYjzkzNV/mmPMX6Mlw94QESdLXcbnj/sCIp9NYEYV+3JyfvGsGslCzsH4mER1MMQ7gWUAA + K8p0qOf8811Bd00rkkPA5DQwGAdyZdvjPOjONc8er/zBRMWIz5fVB+vH3MmQNvKEAxIX2krKroC3 + 9I2JdRmaegp2uQFtEQJsbvl83eUsD+3P+MEJe5+12bCoCOMVWFhn74E7Psy8gS6NR2LURQHY0D+y + ErwNFBenmHP5V13wcDJXhlx2iQ84owtTaX065cTdI10jF+1jSBt+IDex9nLqjp4MW/5tYQzAued4 + AziwessvYi+XtKfT2y/gsfI+pNyLaixEUaBLltIDtLNHrp6F8Iug/7B2GMETzpeDUu6BN7+G6YDs + D1h4g1pSegs+WHP6T76yTWtJ6HTOicFeDcqHvJFst+Ad7CtNAWblZEZSD7091mKc9kJjnCaID/cZ + O3XQ5XNzWi3pvFMrcg8/Vr5cTtkkjcf9RGwhWcCSGqIFeRd22D6Effy2uGYvMUPlY81rZcrZfhoA + rWABLhcZ11wikBZ+8dRt+Xhxlwv7UKH29VasvIyKUrG7Z0B9ziFOzGMN6PezDSa5ZhVWP+sLLO0C + MqDfMvSHtyY1VROpjdKBoPe9oesA6jccTdyj+c7AfFUYc4asDvk/fDhjXamkL92bRK7WsF+3+PeL + H6QInICuy0FgocGdXexGkdwL3e5swB6JB/KHb6KpTmCjuGTqTntd6/NoeUobniVJc85jSmRLlVwO + 6dhnwxpwHz8MJL0cXWIk71qj7tbIrGX75S/+zMQEMmz7GhEcQqGfKyXew5c0aFgrmzRfbD+IpGrN + it/+0vU9R4Y4C1VFVCsJ63V5ZgzY4jU2WRTEq8Pab+DkBw3fvMaPueFQZLDE+Wuq9Qm5lHqQh/aD + vSJpUB+A7h+xIW7xl8SufK65d37gQSscjtiK/XKzDxzA4/JoiHrXS3d91QkL1/qWo/xhZT0n2tYZ + ePdvvTXWHelsXb8dRHxQE4M/yy5HijwAyu6wEC0S7Xze4j98yl09rSh0KF8JYSdlg67gi3CoKH2U + mgxPBxESrXqEYHwmKQu2+EHKSsjomtr2GZ6V4YnOGx5eXN3dZvsKPjGsVeopd5BTqd5rHqp25qOn + RJZVSdTu/cR+fB6sj3MlSxgZFlYN1QLruJY8/Hw7Bpv3R+fO0VBYP/yBwJdX86W53RxgcImLz9Z6 + 62fUUEeil05ASyYecyqo+gq8s+WRk2zvtCkxoAVCAGLs+XqsLTszaaCczwWJ2zkGa4pujniNiY3R + NBlguXzqVWwNlUN7hm01yhy+PFDUD52emJT5D79CmkosRmLKbY1FQQVqEFQYPc5yLzjWdosK+Fdi + szc+X0z4LKFgR1/iaYPX83wep4cNP2BfP7kxJ9pyAp3gfkJLbX9iaq1tAP09bMj1Mdr93L0vBexV + B02LLeX1XBlkgLsr7018AtaYSP45gDTbu/is36Z4YrpTJqYMIti18qQW0tujkQrCPnCh7oWaHhMu + AqDZGVgvwBSv+8hoAWi9jMRBKmnLqaaRdOzbE1bahw+W7/nWAMYDKlaiXsv50z6upODpl0iURBsM + b/PawIV3i4ldn+d+DXmUAHLWjpMUHGBP4i/7h++IIfYkH4blkMFFHEdyhRdbGzb7A16COeLx+KZR + s+N56TTWR4yjeagpOeYsYLyDis+47gH9ik4AuVCUMea+yGWtkiskIco9YoB7kw9wn1bwXXYuQW95 + py0c26kwjkOJHA1jH89THfC/fItmKxPB8FnMJ9gPR2VaThHIV1LzwR8/UlHoAP6TsMF2xCvG5hfd + 6RhXzgq9y93GRrbv+/l2VTz45C04ifrb1eZ7cZjgS5o04pb8AmgUpQaMrNgh6IDDemSCxxnGiPTE + sEMtF9az/YagRRnRJeEc//Ai2PAYsfoXV6/wcTXgxiexLWa4pjE05MMS7y84Lpt9TuqED6SgSDC5 + 87dao760zNB32xcOysqiwtb+FSL1aKL5sycaPZmVLBIjFwnWseou6JCnwE8WC7shFUHbuqoOGsmZ + EYNA7S47Tisk+xaZ2FKJCNZvOszbLFgH8a8rny9V6yM4Mm1ObDEjNbUvTglNwctJqa4onyWizZA7 + VjYpL85LW8OtDfVLLAliHufqh48NabMfYsuLUi/Q2/PQH5rxt7719Pv7Hz9JxCWtuZciJOLNr0/Y + 2OxhvXFeAbc7aNir2gTQioEt6JQG4DDq65yyg+ZATNRgy1dHQL3dyYBth0KsMZdCGyCWSniXIoHY + 606t+5AqA+zQ5Y03flOPdtQYkiiK+R/+mOTrqwVqEbdEZ52K0q5sCnA7PC/Y1qQhntM1ZEXuvmux + pw1DT0FdBdLTccWJ7c1tFFXWPCWpjIyp2hUzmDrHQyDs3mfEZs6OLr5v8lCWSY2kBuruAsauBN76 + 1NCQv6R6trwmA/JoXqe9En7i0Q2tFRiKRYia3ox41SdtD42xemGvdhSXWvohk37+E4XDBczHJhyk + aa4GchHdb00bRRElGMXHH1/plyIPUunpXxE+CjvHXa87+ARt/0DE3vLdwPnzBPX3c5n2+TYbVWVv + b3GsP8+pzx7Pf/mSDZMRwZhT4uWF2EbKOK/B6TBlGvnxF6evEAk6RwBzvM1upZr+xgWVkni9iu0A + t+9DRLH1WBiV/iz+4nlp+Hn89/4Z/3iQbf/cznNcBKPAmLEqH0m9hrUUgOXkJ8TJFSueb+VJBdXR + uCHppoYx3aWzJ/3yH5ZqGbD8mrHwxJ17dDiEbr6QB9GholcOcQ27p1RdmFTc8g9Ghp/n3Mb3gG6A + N7HveUUXT2QmyFqeg3i1YunyTFJ+n750Hwft0MTjFk/hMl147KjfsF7rLKikMJNc4nvNmBOew+Wh + xnGJlcS+uKzlDSm0tCbCynhSAbvXrA5uehriDlCrBQHu99LJaySy5b967ObAkIyUSYg8g3e8tW3y + AK4uCHv+3tLWsHyUgJ8wId4PT13HvgQ/vHjr6xysp74MYCNZMz5B5MWrGTrrH95L1z7OyasKkNRl + 4w1fNcmL2SPg34DwOx/tmsvJXRZfnOD91g/T4QvdfsOPMuCXu44t/fJyZ87fuiylTk1+8YVA7pDA + BxsNiHK0o+uPj6zTwcbKK5+1eWhYB2jdg0xrNVj5pn8wcGpHY8q027sW7vcpgcivOBwgtaHk01jT + T4/C6Nzf46VXH0jaCQ95etcFpPOtjyIo128Vq+Bs9h0wz42Uj0w4Lfq9dcfmdnKkgxyUWA/Wa89u + 9giS9Pog+pWb+0XhhxWGdDhgJRNf8bjj3BIKp7ba8s+kdc3CpfCnD+nZ99X/+W+nvAGWQ0utqV/v + AnDXPJ8kDadoLJiJDgO96vAtTNV44a+eB9imURB3yvfaUjPGCmGff6ZNz+oJf3s20inJamyW6REI + U7V00hbfyCXNh57e7+9ECtrJIGZ4muh6+a4QtM7LwNpRiOI/Pc1x3gZW6KeP181fgKrZBmL8+OBu + ekwAxDwOCBLTE6D5EUbg8zJGFATJWM9rt/DSAFcXK8q1y7syyVggTFQlunfr4kUzowZs+ijRXLbv + WydIRCjuygErZh/RtXwEhtQ06jboIZXcdj5EmdQ8TWE6VM0xF4jzaeHr2TvTmt7eMR2cvQF8No2x + N7GBy1e3uwNp4VvEs4LSHSVt0KFnryfsR493T9FNtiQ/Ll3sr99rvajPOZXQuU+nlmEtjT65z17k + XaYj+nhd80Xipxk0eq5hjHeMNtbHfQC1QA+IUpfnmg6a0cJDwhJyDfGxXx/wVUJ4BIioD+XSr2Sm + TyD1UNz0HtNdAZZVSZ+SrRltYGjCL/+YyQlOMlkVQF9xxoia/fpgJTPmfCUzeELeDz9oVVBM//7+ + x5+99wu5Ay9MHRRVa8Keuhd6elW1Ff7s00R40QYv8TO45WeitfPWNOzxNkRTmVRiatM7FhZtX8Hr + bMrkuIyPfkj1DgJVcw1Est3U0+F0H2BSYfNPL55154LEzR8QX3AvbV2vCP3wLeIMI82nVfme//i4 + o36Xfv3hIe6zdQkSi2+8nDW7Axu+JNaWj1qX3aniU7gfsGpctlnsbKNCudnv0D72txL/9ejBp2eE + aJnzqp4vlTVDXL+fE7dPrjUtXmoDNSvosOIaO7CsYe8dyqy8Y6M/4759rDSQDgfZxfn5g+kv38LP + 8f7E2v7Q9ksEqvcfP9riJ51ZGRTw9Tj7xAy8rp9XSRVBdp8cLH8VWi9M8Eik5yELJmb2sp5P0Wnj + /+pr2ptIrjkwdgWI41giuoWsfO30fQquwW3resN5PVEXPgMrPxFsXiLL5c68foaKqNaId8iZsj8+ + uenBE527dz1bZtr86Q0bP8/Xgswt/HxbhtjkSGjfz9ITbHouOgDU9HN0CDP4PKQBKeLzNq6G1d4g + LNGdKIAf49WTfBEa0W0gnkc0Op0vW1cltK5YDS9WvwSz0cLjxdAnykOiPW7jt4A/vafspTSnc/N0 + JNEYdli9fBeXIM1vwU//j+Dd0yih6iCdr5U5bXgvXny18kAH+hexJI9xvx7XiWA7OUpUar76MTVW + 609/8zki5CP09iyMkq8yNRu+perQveHQwDdJtvgxX89QBhvfJIr6RPVSNskAtcAIfr+P//juxtdR + 97M/+Tp28Ie3MDO8NGoJUIdJLART/gwZ+rdetfqkBJWBuuHVZoVhY07YJgcdLF0dsdKXw/q0BqiJ + Z9bAEazrcuugCbFGIbck8CRZATFF99tTzr1W8MZsXQVD/Oop466MmF8zeeJ22UUjCbo7EFgfTPyk + lLUfHgKbnoygIJjaapuMDDEkOtFMcf8XD6AWXn18HOlE17238tAqJpbgPenBuOojBNW9htg5ZrLL + b3oCTFeqTEzappQairwXiXUOsbrFj7/6Ur7PF+Jns1VPO/guwGunyPjulbrGP86tKm74HdGxaek6 + +OwgfV3nSlT2dYjbErOlRMKSJbK8G+ny09Nbn8+IkuX3mry6HX/Y/HXa+B5dtvoevHxNF6v3WqPL + JBwMWAwfH7HaznCFfuYqsMXLiZbNPqYP98DAT1AVGEv1dmSb/apQTmtrq7e9tBVX+gwsLMvE7sIM + rNndESG8TRT/9JmlqzNW/O6LlGQFo1BBHZ5v+MNzwtnW6HqM3jq821tXr2po8zVRquS3X1j/3nc1 + Tdqwk/7Hpdn0KAoDYPg+v2Lj1Uz8Qmn3hoLCAFo+lMFksykKCBSrfBRssv99U2ayhz03TXto+rbP + +yzmBUWBifV8vt72HojxJTlIXp76NX06GIaasRV8+8a77PMUw4GfJdHxKov9KmB27w9oW4Y30X9l + AXBpNWEq21z8NqoVEwg+cIAgJbwOboUGr9tAZjv782b19dQogUnWJdu/jk+fzyw3hZhOG7bX7VLm + UtlKoA9jikS+qYsVsUowfRge8jcXEjHRFwJq3ndsk0hcfQ15KM4P03TM1VbemK8vnmbNrEztdUZs + kCM3Fv3QmHNCakVGcqiz6DkpKx6yF4XjOWyQ3hte1e1UqoGBB6o2TWWmzcoVcMbkiJTpurD4vgMp + EPORsz1CtedxeoHQm4dMUayQD/cBHEuShtb+VK9EH+aAaBUY7VLcZ1yRLRMs1yRCYZbFefXp1MeB + b7Qk5nHUnZCvQJQXGXOm60Id+mgo8oXZh/3Cf0FPylZgTGWGZ5XOF/iGd6C94w/0AT4fKgcUaKBa + PO/MbFfLqI3LXILZragQ9uVn1adOGoCE1qX4LwC1660qA5NVd2RJw6KoObmZDZWTPz10K9+uWLQF + DlguNxban+9F1Oqu2Q08p/XnBlZ7dvEd4D6LgBlXKbXa45oGcvKhRwdCn2Y1s1ylG94ryJq8zvwV + jc82gAlR2d70CtGX1gGYtPjKdii3eI8hOQLPDzQk+H/+xbtk3fGYASuVc8eXMchxELZdyUx55pof + B/lhXwO0OctnmV+eAQbm4744UJGvPME4BXfNICyok6lV7xVXAd9KwduPH7+EIDAq6TUmQgxo4r55 + /6cKvC/e6xITMogFo7bGaTz6+a0gjB4VLR/N74YW8b0WrgFcSbMv3WDU0AaT/4bexIJ/3v4CAAD/ + /wMARUfxPboFAgA= headers: Access-Control-Allow-Origin: - "*" Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6ad0f4aeb2d-SJC + - 9854b889e806ed40-SJC Connection: - keep-alive Content-Encoding: @@ -2961,19 +2959,19 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:09 GMT + - Fri, 26 Sep 2025 18:10:55 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=Rd0gt9TfXqlZLXjFPstvY0YaouRu1DfRXfxDnPxqjg0-1758846609-1.0.1.1-8squ.sQD7kC7qkmMgajwmJCImL5X2qb00IlNTY89Jp06omON2UhzBpQhd42t7cZd761rPD9RNnVsA.h06ceixxRGDyAvRs.VxuX61n35Kxg; - path=/; expires=Fri, 26-Sep-25 01:00:09 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=FKjSuuHZ1RSKSldmglwugGjz5MBUIo5gXVsirHHNUXI-1758910255-1.0.1.1-wv2cyPt4Cv7VM3iYWnx1p_Fa.MZpPu78GKYhtFdWIznS0zIhYbSxEQM0yhsLaTiWKwMRqy023AZRdaRRu8z640BMGkPRVhpqLbdG7_psOis; + path=/; expires=Fri, 26-Sep-25 18:40:55 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=vl43iPiy1w9QKAduHjfI0uox8lnWVWyGo03348I7fwI-1758846609890-0.0.1.1-604800000; + - _cfuvid=EkHsf_dJkFMa79kDAh1RBckQHvkelB0UFzY_UvmcDfI-1758910255938-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Transfer-Encoding: - chunked Via: - - envoy-router-5469994578-rqbln + - envoy-router-55b84f9469-5hz5p X-Content-Type-Options: - nosniff alt-svc: @@ -2985,7 +2983,7 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "295" + - "161" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: @@ -2993,7 +2991,7 @@ interactions: strict-transport-security: - max-age=31536000; includeSubDomains; preload x-envoy-upstream-service-time: - - "331" + - "212" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3003,13 +3001,13 @@ interactions: x-ratelimit-remaining-requests: - "199999" x-ratelimit-remaining-tokens: - - "199979820" + - "199979816" x-ratelimit-reset-requests: - 0s x-ratelimit-reset-tokens: - 6ms x-request-id: - - req_40c9c5336e9a4f3e92699d1fe94b3237 + - req_d85fee9165d54dca8b558abeaf6e66ad status: code: 200 message: OK @@ -3174,7 +3172,7 @@ interactions: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6b09df6eb2d-SJC + - 9854b88c28b5ed40-SJC Connection: - keep-alive Content-Encoding: @@ -3182,13 +3180,13 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:10 GMT + - Fri, 26 Sep 2025 18:10:56 GMT Server: - cloudflare Transfer-Encoding: - chunked Via: - - envoy-router-575fcf7dd6-v87z7 + - envoy-router-55b84f9469-hf79m X-Content-Type-Options: - nosniff alt-svc: @@ -3200,7 +3198,7 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "85" + - "136" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: @@ -3208,7 +3206,7 @@ interactions: strict-transport-security: - max-age=31536000; includeSubDomains; preload x-envoy-upstream-service-time: - - "113" + - "179" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3218,13 +3216,13 @@ interactions: x-ratelimit-remaining-requests: - "199999" x-ratelimit-remaining-tokens: - - "199999933" + - "199999930" x-ratelimit-reset-requests: - 0s x-ratelimit-reset-tokens: - 0s x-request-id: - - req_23e4afc064754a9a92c9d08320e1ef20 + - req_6a2c144c256b4f86a6ca220c151654e5 status: code: 200 message: OK @@ -3270,122 +3268,122 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA1R6Ww+ySrPm/fcrVtat80VO0sW64wwC0giKOJlMABUFFTl0A72z//tE3509Mzdc - NJ3QoavqOVT9x7/++uvvtqiv5fj3P3/9/XwM49//47t2ycf873/++p//+uuvv/76j9/z/9t5fRXX - y+Xxrn7bfy8f78t1/vufv7j/Xvm/m/75628mWRw9BLioZ0t85TDfyh3eqyrxF2/Zx0rmHANsnBK/ - nvaem4HluxENpfphTOrFWuDwOLyo/awf/vhUPoLcilxOLwkRa1rt9rKyedsaeSr3czLzNCzB+RwD - rB1Htx9FQ42VDVqnRDq6ERP2ZxSjfutcQvAHkQ08J0sgrt8tNppsQWx72NqKdpAMvNvEtJ9eiHKA - otUdh14/FfRQTK4i6uaWejlJ2GxM+4fiLf0d20GCezob3EqRjMMVa2ar1byUPhvYL7JBndXqUU/t - 5hArgUcWMmncyRjflV4p/SDEBPiIIqo66VUxN9KHqsl9X08v9BaAfAJCj1raF0siHkt0TD86vp20 - LRL7+zmD4Li90OB13dTsGSchqKGZ4atrPtk4U6OBh7Hy8DkeDsZ3fw7heuioLQrXRFxFj1I5yecE - 79pVl9DbuUvhsDfXWLteedSuDVBRd8M9uXcfrVjKZRMq+TbGdCefep+LGdOVj/EYsLo1jglX6aKO - zC1GpL+WG8a6Ml4heIRCyJjnMs7W9ylULmfQS79H/mJw95Vi3PkMpxvxXRC1jgKFBBGP46XXfJYf - 7qqSesoVa48F98JIcgJRo/E0g/6GRIxqXeLXr4AGje8iUXu3nZIF8p2qxt02pvVKeYBz6WTsDEHQ - z31QmYo3PBoC5pzU0zapTTh4oOOL3JuIr6T2AYNVXmhed5rBBarbwizZGd2eUZzwy0a9KujtXLAm - krZYjvxWAuWjVvQ0PjU2yRsI4ba+S3TrqHoidpoTyN07xjSaVL3m9CObFHfOrjgVQGfzwukqrC3F - weoxVFl3sdMGvv+TSFw4JOO1eS3Qh92Cvaop2Vxe9jIk0pWjoSbX/nJ42Q24wp7R0DWfaNLXrxxt - n4McIvsJ9XzLrgeI771HvbrTfO7aFSHoXmnjhDyOhfCNJziaW44mej/003wYBQgugUpzYeOx6aTI - C4wY8SEn5VdDePQPE/hCPJGVJhu+KMt5CMhUW9KgoP3mN8nBs1yN3hy0QTMyDFc5EFXEWbgixrgl - qqAITzeimWtsGc+CJ6cM1vWCfd66I5Yt7gGqKnjT8+bo1CQ4zKDkAqNYu5/lZBLHSgLlgRusReq5 - F1U22yAdXhZ2iijp+ceaz4FH8RXb3SwWUzm8FqCHg0PT9BIbYuyscojfwQ0nr/Wl6JflpSqoOPv4 - 5Eeuz/t3KYdrzW3w2cNqzfB+ypWVHPNU45jps7P1JFBL+0O4qpHO5mh9uSJhtIYQYd7ohbRGOkD6 - aKh7nppkyddaB+htXQizXrM/Obligkr6NVX9bo0WOXw2is7sPfWN1Yxm7XVowdzIH7JeSUs97sy9 - DWwlT2Q+HJJi6tLVAw51s/sTb3wj1blyaWmFPV/X/VnA2aCcnPRKd9dqh5jvYRN47vYg0mfCbB46 - PVIkP5EJTJAazen9vCpR8NlT53DEidBunUBpz51MgxClPs8Nkg7f/MJRPmE0t093BenlOoSb7KH4 - kx/sTXhyvR4Wocv1w7UrAogdllE3725o9u9SpkwvzqBqu51QGaUZp/zy5ZvfhngoJg/VucVh+4nb - ZLmZhqp8wBXo6WJDTexSMxWQTgv2FGOsF2u3W+B2hT028JTUvB45KZLfJsW3jb5loo8qFwxV9fAZ - osAXKbQqvGLugf+cJ+olD0quMGkke5bBCdfnS9kfSI3t3pqNZT+pL6V9ngJqs7Hph6DhODgbQ0BP - wSIWZFh/GkUIKouGp/LSk7u4VHALbgb1dp+Bze1TBcUi2MJO7ks+25+tGKaXYFBjf3mweQtvAudn - nVO1vXi92Nu7FlRhqCl2xXcx74hBwEnFVaiE1SNZmm2QApwWH2uDP9WLEuQ64MZwyNN9fnpid+8V - iBYVqZGHk083/t1TCmNs6WlhJ58ZRKlkS+onqhVYrFnpRo0CRbHBXrxXE7rxPy7sjStQKzEsxHfD - kVM47srh3UzF/l2fx3CTEUqpToxdPwl1o0O/dzWqb4vaX9z6CNCeWxlHu75FrKk7E1Q9c/B1dfaQ - WHPTSsnmcBVK983VYFAuMsgL02goPM4+z/ZNrFytzxkfrc4yhuDDYmUXTya2jl3Ui8KOI3BZVyU+ - z9cjE5R9VEKwvflUjV67QlTZxgSnaxjNl71YDDcuMxV8fpWhwOOEDesVX8EdbV7k+Tb2ifirLy9U - DtRyN0YistelAerkIb5YUVHMFXvYSiYsCVlyzmCfXz348RPvWXnGiM5Zqby0F6LBdqzYLO7fHmrX - s0ANXG4QeWnLQfFyLaWHlbPzmTfXkeI6jwEbx1dZT1e1apTo5t3wFqLAEPPNMQBRyJ54L+akp/wU - lwp4XIyde54WnH0UOqDdzaBJwNOCbvhiAZbGL2pSTfHbDV2n8FKPB1poJ6fgHT4W4DIaITXZZ+qn - ZnPO4D6XgMPzxa7nR6pPypAOJj5EIjZmtm8iaZO7LlmQy/VM0poYjo/TAYenUunZQ9uWYAfZk7o4 - dhi1gtOCDubWpges2cY0bE7e7z7Jot54NpZ66MLrkgR0t3NGgzAlkhUUwR0nAzZ7rtDBBVXPHbLW - DjRZhGZSlcd86aijPHfJ7Gkk+OEhDdqbXk+R6j/g+aZb7OL4jdhoLCYI1ril3vbIDHZI5wHCCWLS - usaHjesVfqDnJboSzrYyNK0NTlWK4bRg86bN9dSL7gBlKKp4p7w+ycSdokF5N6YYMnms2KIhtlKI - 4L7pwbXvaO6FmKDjx43oYXs4GUJZ9rJ8IqOGtS8fennLOQInCSPqGk6ZzP50iyDqhJkan4NqsESg - V6BBUlNz105okZEboDT9TFSjotDT037lwjV+xhSvmoyx3cEHuF1Xe7wl9b5gnzj9U1+xPwtGLaqP - RwBxHr8I6F6LpvncxPCWMQuVe7EU4+LQXHZVOlFb9u8Gu4hap0xuV1MjXc+M+cJ9UBSOLFi/x1Uy - qSyxFS88muE9XkyD6WpQwos8GFULc1sz8eOWyHWXhPqJeWdLYaouHB7pK5z8bs2m4IMiVLSDSZNT - 0tdP7I8AqggeTZxENTg50hqous8eF5TP++kXr5ysb8Nq17uIuZISw1RpTghrEvjkCUiAVX3bkM3o - lQbnB2cbPt0G091WImzZCNsJ7m91TzHsW3+WcdHAuE6BGmc5TUhhui6kqAuw9+EMNuXYbDbh/u7j - ww66fplkosL4+fIboIzNMCgThI1+pqetGLNmOHQA5xkPOOCbbU8+SWAi3ntPRG6LsJ/XK+cBor8P - 6PY2Fz57gsWBodSfEPjm0y+rRlmg25oUG3of9LT3AxMixwHqmeO6J4+pquCZXh7YfX35/Woj2mAH - +ZPqOVczJjS6CmJQMZzyOEHzslFLJXtWA926gp9wKdYGZFkvRI35smHMlOQMKsW7YVOtP2gQNnP6 - 0w+kyccTYuJpbpSMrlPqWPXZ71q5jaH06pha2SZkbGxfMUR6+A5Fv9EQx1O4gtG2B2rX4bqYif6y - QRfXL+xPF1JQTvvk8qG4f/C2bwP0bEJpQu8sf2L/yzenSXMrOO6tkXADGQrhdTx16MKdF4ovNzsR - Eigk1Dr+QKRpTYw//OowzBHGO//us3M1l0qsHItQDiKx7xV/UKEp9W0oTtUrYbaK05++wAEfYbRU - W60E02p29LJwiUFPq2iBnRoJ2OLNJ1reeWqjjRIo+Jg9eWM4W5gAi8Y9Ge908CcGgSSH+9onn422 - Y4tZFybsq4yniRWLyXK/jwFIF+Zi1+sOCSlGeUGG0UshQ++yIGc+WCB5Ki9a+LvImH3+QkA6NBZO - AEc+fbbVA+ARCNSVHi9jxHcWK188++aTxpZq4UwlAjOj5+Nbqlm6yQ9IevoD3RV73C/7yW3gJe+s - kI/pYIx9tcug65ASTnbZ9Uvo3itlh/IQ766cUy9f/IE15aQ/8fSspLba/Or99TyrCRuO7gqW8+aF - w5fwLpYkvsfo5XjRf+HT7/40pdrR4+//bVf3AQbpHodK/AF/lg+xCvfus8aOfsjZeJXBRo9bW+Ad - zi9oifAqQ/fczMg6j1r/2TIqoFD2jyEErZUId5hWcBZmDf/4hnhAowzcXXnhwr9/GEtat0Gyu8FE - 9Js7GrhupaOfHrXG3cGgPJ/JMlixQh3mRIhDYz0ha10O2CkDzZjf5moBs6Bbck+1R8HucXaQU9QG - 9PYYi346VlmDZKdviQK7g78sUn5VvviBy70g15NBTg+k9uqeOimcGaevSQaf6vrEbnmiBltFjytI - QpHg0Jl6Ni8P5MEnoJQ6a2lljDBzAVTBbaaqdEl9xsSmVOLwdaB4f8jR5A22AMtK1umlSVMmNte+ - g5UTqDi7rs794JjvSal3W4Rtf83XYyuRCJJheVCLyLUxxQyp8PTmCH/xxRfk7duV6WdXUlPKV/4s - b+wA8tvZx+purfdEvZ4n0IbohW+2cE2Y+FGvCq9ubbp7ffmedk/DX35T23g8fPbQtFJpY3EVIjGL - ++/3dHSpOZ/6Yzj5rK0HQLTSz1jNtlYhCOwqb0zrtaNGwONEiJebDnw7R9SozI1PDcuT4asnsDbd - vIThvZShhHzzmUpL3X0S00Rho57xV6+wiYp+gzTlscPeVtsU72KVdIp6Xjps+dWOCfXayWFRuoLq - Z+omS1jaAqzlpMZf/c8EPkceKjdwpj/+OGlvrwWOKzns2xshoVI56cpNSHmqa5JvzOfAdSGqcofq - 65Iko+kqKTJDFtEQJzVb5s/Jg0gP3lS9rIlP3rey+vE16vdDxWZda69wFMxtyGPuXU/1GYewPww1 - dp9uWUxVrQWKOPER1cz2Xi9MySRQKXphQx5V9MWTSvnWD6zpyQpN/Jl6cH7e83BSX9qPD6zgxy/1 - jqPJSHRiw+PJMlIW9r2fNpyyoF/8pL21N9jazFW5mssQX/RjyljOXRekDfELh8gLesr1Bx08KRSw - XSHSL+XpWf7OE0rQ39jMe3mEtmGahhs8036ZcykCq14KGuhey+aKdbasrLclVSPZMbjk0XhwseX4 - 668M9fx5nzLFJjtGtfDaJdPUIQ904+N940s3eP24W8AK5xXV1nu1IAZ3B3ArV8TmQIKCG42DrZxv - +YUsJsP9F49KeJ/bAEdapSSzV84ARvWcsWMea7Z84wdejhvR4otPs2i4MRheyeF8p9g1x9sap5wE - 6/n1b5biI3CmBDcke3hvPS5+f0Cj9IsnrH31NR8yz4Wst7Vf/hqTn69XcDpEJ3q6k8kXNjeuRPZE - dezZedj3z7aq/sSf9exKfxbfUMK52bPffoOJIk2hfhtj+LzcHwUv4GhQhFr64N/7+aC7uSIGD0bN - 7UH056mzXHAurRxOWqUU85pWnVK8r5ewG42HMR9vXQNfPwLv7jQwiByODXDKRwyH9qb3XI6D5o8+ - jue4Z0Tevj1g95WE8Un7sMlScgF9+Ra1g/cnGdzPlCkhuRiENUPQL+RiCLDOyiMOLuNQzFvicqjL - 0/QPfx5X+FpC9ExK8sfPa7gYlC/foVgfm3oWLusX+vpdOLzLsjG+5n0O18x943Nl8T5rdocJ2Pux - w8EzMpO5DVmnBGrkUstQeDa8RnWlZIfE+vLzbSFudTkEKzO3+CzYn4SFM3Hh5/9+62XNtgfNRqla - cdS52NAz9dGFMEh1TO1zJtQLyroSduzUY22l2LVgtecH9Msa4/CW0GKOJf+K3OcQ0L2gnRMxK5AK - Tsqvwmlr8An76k8lZ65GzxttRJTnIxm06nUkm/ti+mJTdzaqF7r+4u+7HmVcvJA3n2X6zWf2q38o - eHc9Dirw6l/9lL/+CXW9VO7Z29cCaDv5/vV/9kwYE3sF2i0dQznNZYPcTEOH7dIxQtbXV/Ljs2gW - uBl//Qt/rqTqoQhZePrhIZu59EEgD5BCZkE7F2w6GoLy8/O00n/6hD68CJalnvFWz0K2WGjFQaVr - Dr6mD91fPpleIaU033S/3qsJbzaaidazWVGP6meDmYyt4BC+J+pMXOVPaCV3wNugY4w+XMJ/EtkG - IXhY2BmfGprT5q7DxWp7vPv6GezofWJQe31PwMBNsbRyFSuesvihNPRcwZbdzEFw9C8Y51FrLIXp - ekivnga2MOfUs23fJ/TVa+T5XPGIXOz0BZISYjLi/MJ6b+5jNN+uO+x372cx7ZJxBVK2YzSQbqEh - SuWkKjddP9OLs298NhzVFfzwJzxuA0TeeWoq3/pAr+XQoNmZygzuCP30lovYU8Q5iPFnwniyoR47 - 59PJQSvH2NSFvP7xfyQX2YVwJVGR+O1PgFLf/FCuu7tPslPXwL3Cx3BiQ1svTROZaH8ED5+LViuG - H/86eCud+vaz7OdPJj3kn98SFeanH1K8JeAopoa12v588S/k4HNRUjJLup4IX/8KVFvjsXW5P5KF - vf0Gfnr29OMn60MpybWUHMjXH+6Xx7Fc0Hw41VRT1dDgf3ojPox77Hz2rcFYLMXAlH6L7bNZopc0 - Gwe4K/4O28uBJvTo3WNEK/WM8c9/q9zkCmfnKFNdvfGIfvkPfO+PKPpnqj+i4UZwHtKUKF/9TXGw - WQGPoitWy3htTOU8hrB5cjlhXz9r0uX3CrwnbcknEwQ0bLPPAyK/6DD+wJl1l1c7QYY+LdUkXS+m - Fn9a+J6f7g/taHxg5kIoQiGmO8HYJ7M0yPIf/vxan4VkPAeqqzyec4Zdh1I2+YJBkBZaNtUeC637 - szUSuAUXg/pwqAxKyguH9seVh/1L2PR0OKqg7CzOpye5cxmLnscBLfeV96tnaJGcbIBfPLqZwSek - bWKAOpLnr/4JjR8fBfIJCba4KfDnNkSdfJBuD2qn2ywhv/5Y+vFWRD4xXPCjQhvEk/T51aOknkfq - EjRyUowjAdmI2xN4wVF92NTVNm+/GvDwAPHprahpWxn7xputFGkoEGR0mbHM23sOO0vwqd8lfT9d - n+UiG213CGclEuvl0T9sxRxWE0HMzNAk1IMOp0SfQpmmC6J7bq/Cce+MZEK4qkeBC2RkTcOBul+/ - WWxX0yD//Dk96LRCfNnego7rfYC9SPINcTMdUwBTS0N+5p2e2/iG9/O/f/2VZC6ypER7JR3J87Av - vn5FYcIPD5kVnxL2y2d/qGNsafpcT6Ujv1DBXEptvh7r2TpJHVw2ikPtCcnG4m7qGOWh34TrKvfY - El83A/Dk8KRH1E8JGT9lLIu2csW66pfGSIPHCqWJroeL5WyLIfigGKkNPtItEX2f29gmge1rdceB - Ze0Kcf2xA2WtnheswkzZ+JrPGfriBdZsFBfTjswDZIF0p8dxx/mkGJdFtob1NUTfekV7ThjA94gZ - omVa+/NyAYLe5y4IOfsc9/xhmkvEh5srLeT3xCh9xeTXT/jyD/DZr3/39U9plJapT3c6b8McwJoG - NUbJcFKWScmyF6NO0YzJ6PAxpxRcllL3U6aGOOdeDM1ja+MtarOCZSqNwBbiU/jV8wkRk4+3GQZe - pfZliIrl8qomyKuWYYyvezYdkCPDc20NBEQxTya8z3O5VO32x+9Rpwl8BV5upNQxxsiY3vSUw7zU - Rgjn89Izei8jSNeJTHh3UyfMZYIAP7/DQn1UcKf3WMKKcl64yXHgjzDwC7yz7BnKXz9naurARl8/ - LpTRzqzZ/ryLZbI+dWSWj4k/ydH2hWbn3FHPYTUiwWGzgv4dK4Ra7dbgf/WA3UHC5tfvm2dqvJT3 - rE+h8Lpc+rEJpQUClipYjV5jMq2OnvTLX4ozcUGL3o02OpWYhavd64DmsAQBToRq5N2lRi/Kchwg - fJG9b33WE6G/nTJYk/c+bL/+Kvd4nlplPssSdb2OKz4Lp+uKX6cYYyK9/W//Y5I1oy1xxIa2n9+F - af65L//YDzUJy1AA4y5mdNc/uT/6FO2F+/zzo+o5lNSXUlSfO00Ea1dwJ30TIV71bezL74gt03Hm - lMlta/yHj0S97qIxJQu2+m9/9KuPFKueCuwY4+TP4G4b6AcupumXT863zxjAtx9OVW8yEyavsxb+ - /k0F/Oe//vrrf/0mDF7t5fr8DgaM13n893+PCvxb/Pfwyp/PP2MIZMir69///NcEwt+fvn19xv89 - ts31Pfz9z188/2fW4O+xHfPn/7v+r++n/vNf/wcAAP//AwD0q0Au4CAAAA== + H4sIAAAAAAAAA1R6WROyurrm/f4Vq9YtfUoGJWHdMckMQVDErq4uBgVRRIYEyKnz37vw23W6+8Yq + IRpCkud9hvznv/766+8ub+7F9Pc/f/39fo7T3/9ju1ZmU/b3P3/9z3/99ddff/3n7/P/a3lv83tZ + Pj/Vr/nv5vNT3pe///mL/e8r/7fRP3/9TYSSJWejzeNZOhp32J/0AF2F/QRW3C2RlPeLh6zec5vV + s7oU8nAMiSIzT21hHW6Fo8m2xDAeNRg71ubFR5lmJL+0QjNm8SJKLpPo+P1Kbg0dRlzAAzN5yLtY + Vk50u4qkt/BIMK2rkHKr6WYAy6QIFuMl0PHgiBDO0e6L/BmtYHWyryH18KUiy3HIsK77HQsbpq2Q + XavzgA9RZUm6treIc2xjOkvt8pSkzKyQhgLkYnJgRcnx5RLptao0/ORyHUzap0a8dv9sqLPTI8lP + swWDS3ttpv09qiSiRmcMHl8yjNv7kV5l+iW2uT81C6xNHtJ2xuS0C4Z8Jte3B/TUVlHIAxsIpmyn + MFHfJbEk7tAsctoEEN3GFGWEeTeY8WgHOdo66ME3Z40/PA4Z9B5hT+x5uDfCNVkLSeeWMwosoY+x + /RETWHUdg44F5Nzvcw91gPF1wn31UvLVXG6B9NiLiJjMPLiCu1BVamJjRFonXmLhOCcMcFbE4CcT + H+j6aZ48sHLMB7tCtTWhkesEHrtKI6fBAu46igsjdW8/RaG5ftwxecqehHKWQzHXKu7a5oospfPl + gTSxRwPbFCKGTnPjSKhWD8Ab57IVljrwieIdLcCF97SXCuZeE9SKhjb7+tRCBzkichTdG2gSy7p0 + 0dU33rlm3ND1G+vwN9+haOiAO37SJ/Tfekmi3VvRuDvTvSC64ZRYhziKhUyo7lKifkqE+k+Xr0ft + BuGn7SpyEncKnXsBBnDpD3uitrkasxMvWOLciohcgKjGnLqtmUMAS3QtryqdLSfSoeG+DeTYnBS/ + CW1f8PYOBgyJOWpjFyUrVOeAIifNCkqTvBZhkjMsOU6Xxl3qOOmgyX0pcQjzHtZSahPAN+dDIKgA + NsvnzZxh5bs2CWZdyflDlwfw0esGuqq3S84zl0sBT03NkjNoxmH1nz4PryGrkJKJHDpD1lkh78dc + cNiFJeXet6cF78L1ipeS01xBkPsIpqP+wk/D6Bqqc0EGX+2skCReD2D+KLElKe5LQIWZTnRix4qX + vu0cksyFdsOlzoWVyMkokbrINZjj/f4Mr8v8IXEwmpRIsQKll+ESpH2JGFM1ryBUueGFbIG/5fxu + PRmwV6MjQq4ZD0J+4TIYi+Ideegj5PSdGSt0ddkkUXCJNM593zNYdvMDXaJDAfplTmTpBA8uSndf + yxWUKs3g9v5Q7DhyMx/TKpPUo8gRhRl1d8npu4dVLkWBuPIqnb9H/wmgfsKBpBFtYIPeVeG4Oi9i + fftXvJDLCUNlrEssevfFXbp00uG93O2IrFY7QG3v8pLouT8R88ovYMbFuYOGff9iaUYrJfm6qLBm + 7jOmbz9219v5/oRV4/kku+wUymdMk0mB5VbIqiXVXYG/H6XXXrwT/XnzwfIWdjKEhD4xw1mI0m8V + hVKlfgCWbths3sfj+y7ts+OJIG1CGt8aH0+6vwOR+Pc1cblSSw247S8UOR0CsxHvGSgE/BTsX6nk + UtZRLPjsVhxE0p0F+NDlHjQOKCXyvn4AqlRpKrF9oRFFuxXgAYqOlZKuq0ievRVNWONKBZJ/Y5Fc + fbt4LpxYloSy40k5HSDFTlbr0lzTFTk1mBo6ZdMKT113QlrWxQ0P2U8GxqQj6AILm/LDGlqwmzob + JbLtuXwFUhl6cvFEVlk9ACV96kDeFHTyMLSjxqnFpZWiJGqQN8BFo29ctZLUUo9Yj+MLTLpz3sOE + O3sk4lthmBLGfkm9Gh6JdaPlgCdereB422nEOqkjXXZNBSU/J0ekPdW9u3otF8F99NSIOwpPSvuD + gGF23mVE7hJnYNtc6qCVpk8ib/g3e1DDcMPPgF/AM14N00tgyd1dpBjq3NA66tXffsaf8f4dRrkz + GZh9TIG4J33O8RIpjtT79odEyLm68xJJswjs3Uyc/C0065zML8li6AE52iRr05TZFjz7KiC+WxyB + oB04VtqbDIv0Qtu7bcF5z0OFTUJUA/jDsjucVRjvZ4UoJde4a3o7Qji6lojO71cHViVz9N9+Qxt+ + ABYMFSPFKmaCwxHdNSqLqwgLI1aIFd1uLt+Yr0iq38cbuhn1Mcblh0bSoqb6Nt5w4F6s3kNy0kuU + Hy4Xyt7JXEDaAZdY8O7nrNZ/dehhnZJL9RVyYq17XZIfThGwhn7WRl9/v+D78Ka4B+IpFpDcpXA+ + yCPx0liLhVmTOtge1AA9FD0f5llZHcmU7zFmhEWLv1bYZZBPvj6y/IvTkO66LyR4cERy/HYVXYzA + jADxbZ4ozkl0p3OoniXq3xKSt7UH1vJDQ+n4cIY/+D0LpvyShgA/kCHbnsZ3n4sHdUN+o1swYzCF + 7VpIeVJFSGajJBeyxuhhnu80ctFCDCY1Bhsf4luCZp/Ju3x6JPBClzN5+LOZc83w5KHfNwGRLWUG + 67D7prCOIEQ+vzfi1feiWUJJqqPL4RTQdYkUaz8eRxvvjR07LBNhA2iVQoy0MJL+/XvpyLbE0ZHZ + jJdM6MGeexskOR2MeHkGn+A3n1i87rl4DHaMDseOekSu71OMfW1mpFh+1SiubH3g03C04OnGW3i9 + +CSeP0MlS+9+6bf2fkzXXWDB0C7NP/WTKrx7h9EH2SjYuR8wM09Vh8rBt4n29qlG5anuoJFYF/w9 + gS+d0mjXA03wCryWRQrms83KkluSFen7dKHzW+pG+GCuMtKT3TdehMs8/upTAMO21uh9oYxk0PlD + EljVYPGlZwvSbg7JneuvGvd1c15Msa8gP/R58MTdIYTiaw2JxUtFTFmOhBD2yULMz2NH8YNFd3h1 + SUPMSZ7Byn/TEDCrMhOHJfwwyg9sQXZVIuKoeUrpFLkQBnIbIXcBp5zePrwOU4wUZONQawS1Wz34 + vDHvjQV1YPaFcwRNbqABX3Kri1uDBCKqzJmg/lhrs/5eeqnRo4a4TbLQ9TUpo2Sk0Ypse6ziZTjE + hrSNJ8BfT9fWE+OlsF9VlriKZDcrla0ChEwSE/vB1HSR1dmCbxN/AmEb3/xK3RAU8KyTTF8w/RQn + H0LNfjkk2/CFxUb9+o0Plb2XDeuzfCQwKzM36BbdArMYTMmP/wTMw3YBOYuAh6CjBwxKudA44/k1 + oNHfEFG+C6aLzn5n+Gy7EzEqt3OXWhoqCAMeEhfPSUwkp7Pgy408ZOq+rq0zdywOFlIc9MDnfqBc + d5ehLe5lkowmpfQhljMUaXsjDyk/xe2S9RAO82dEfpnbYLLrUQZb/cFwZYN83oHPEzpw8YjtmXm+ + OP2RhaMIvsHuVH6H7f9WmHcdQUYt+y7Z254Ov/MDEnVsdsOokLCCIXd4IuV5ODXzcXc14Cln3sRk + gobSeI1kCGaZRY+Nz8wb3knKRx+Je0ZuzNJBgYA7qIBo7PlAaSs4KQQQP9ARFl8wOvtTIu3oq8Pv + RL+CeXv/ktzkV4Jq4zr0nW0lcLntImKdCdJWvU0i2JG1DQ4PRgEcJ413CFPvTFy32eWU6xIVNsK1 + RYbJY3d09rdElLrDFznvDoHPVs/Bth6RG+WwoZnQFdAY6xHP8mfM+b17bUFUcgtxypsR84KY74Gc + kAED/4q1xQqrTLIt+4Sso1G7y+1zKiRO5vKAGY7C0J1tKENM726w7rM2pimHEuCODUDu44uGuUF1 + ARnB8smJc+OYGPrMQ5EbeWQv0hv8nh8c4SyhjZ9QoqkPDLf+8EjM0V2T58iKgrxzce2JPp25etBh + kkOWlLuvoNHxUXowecQWMurnOcaplongI2lCwH7KIh8H6q0wOlxact29Q23eUR/Dmx4e0fWOkYut + R/iEE5k5oiTSm5LqQSPp3uglcTb+trYzq0ubXiP5Wu6b+UuyFBySZiSa/EVg+emPDW8DXrZHbbwH + fgpHjKSASlw/LK6pVJIgqAHymslsfvwOim66J+bFv8cvMHTw8MP7EI9y/Ie/bc+LDDx/8kU7nAJg + imuItKkomjlEXQU93/LJpS3fYC4lZYSqpUQBf31Ad8meTxneS2mH9IubUZyGowMc4ZwjpQrv7jqU + zBls48ESsjv3w807HiglOQei/DzGwp6GDGTUk4IsAI+Abw+lCJPTrUXFJ/vSZbzvX6A2TiHmvPvT + xRfCGAD1HYN0eDxrpI9tVbTPvESCnAkBdz/HI9j2B9LJR6bL6NxXeDzFNh6U/Jkv9WdfiC839EiU + vPKB0mD/AtMbffDStzGgNt/fpUfwOqFbNooNXbzrHUhqFZEArzfKy1KQQn5W30imM6ZL2K53ONUg + Rg76DnS+1a4Dl8IkJBA9Jh57gfWg8hAWcjzQxF333KuQhj44E3k3ZgO1bi0Ps6BVSRn6CWWFAvQw + 2+9llN/iG8AfRZglEZcAuf6Bi4nEByHU56QhunButDnoXRl6s39CyA5klz3urrrYzu+SKFXIuLS5 + 8x6cZc5DQSKq+YiKwwydnG1R1o/3eMOTu5TVX2Nbj0cgNHc++O1vYg7w6S4fsy4kM3OZYNXmaFii + p6uCNa8corfG7K76zZeBW/c3FHjqMeeqNDAOpev4xHhNKOa/ZKfCMD2FRJ6ygzs1Q8/DTU8gR0gd + bT6mXQqMuncC4cov9HtOXxYoSJciz0x9ujIe6AB4GD6yCmY/vDrY9FLpR/2f+8Ju/GTw60Y50UTL + ilfxkPBwL5AGbfqfcsEeOOBCqhuxBiUcKHg73R88dfHMx9PQVKrULCJH5NviavRXLySsGsRbWRxP + FE0B2GUoJKjxG0qFu+DAjswtUdcrdgn2iwrel31LrJ1ZUcrvrQzeyWwFO8770PmRkAA2etgg5cQU + +Xo51Z7EjRdE9FqtGxqC/R4uftwim7NkQI3hU0mNcGmRttgMWNl258Drx08D4MkKXVg1Z6AWn3ii + iT1p8DLfVXiIUYrjQamHda9KK/itn8y7n7TVOzme+PPXisxL6CL2GIP8xrdIV54eGI3vS4Uih3lk + fmM8LMfju4BMjD7BvK8fdLFvjgcsKl6Dnx+1KqcuhFKd5ER/vzpKyXpgxFv/LYidUlNjT9XLgY+g + PaEgfI0N3Y2fVHqeapb4Ed/H1LSGABbvo0NcUKiaAERp0zc+Qzb94eJRXCBsSccjWf54ubC3dUPq + QuaO2XlAA7/nxgJyduihhE2keOEbBcJbKc3I10jTLPNN1+HPTzjtE5zP0q2LfviNzqVgNLykn1gJ + vMs3OX7Y1e34Xt/Dunw66Pp5PED3sEsIzUeUI0Me4pzPlsyCq4cVor7OibaaKWLgbdlfySn8zC4n + v9kC8Kur/qk/X3EnVz9+hyy3zsGqlbCAvPCmpFSMWfvVK7ga+RT88Ij78c0PV3zRud3uI7nLpF1q + UBJw/eamobcHc98SA+Z2kXJqY7mXtucL6s0PnVvDecHqxnPIvnSeRmyvfMFKfYOg7jJ1YC9H7wV/ + eBb1/UDHH79Q3FZARzX/UlqAjAevR20Qf5S/8cR0YSodyEHDIjh6OX3mGg+5VL9s8zHmM3Pes2B3 + ZBIU+LelIQePKWDV0xwLQLMo2/dPKDl9R4jaW69m1Sh5AuXFICSHoRiPK1ky+DbHDyoBZMEiuecZ + TgfHR+7O1uOV8Wgv8dPe3PCS1zALQkaqa3pEilLZOXc9ZA50bqONzhtfnznhbsGTUGQkZs83Stvv + 4oBdqlOiTQUctvUbQNXSImK8Ed8sD9Mp4BnSHnkqbzSs1B6eMJsfCPnxQPKVXsATHIWzR7JJuMXs + D19P60UKaCdy8XpzWSwZEauQyADTMJ5ZWYSvzLhg3vF0l6MH0QDa09whOwg+zbjhOfiok0hMp2Po + iGORB8LBGZDyvDrxwu8lWSwiZiJ2NorD8jgsFmT6e02CPRdp/H3Pi3C7HzB4PVByizQVbnwdfz9p + G9PIY1/AT9MFZfLzmC/iTn5KH5O/EmW5n7QlZZ8Yfg2XweIk3P7Mp/Tju7JovAD5VlkILbKbkfdC + SFvFw52FalObKDQk1d3qWwHO9vghRePKMY+7JQSZPFY/va6t30xj4K3czZu+eYCF5Zwe2vJLQ8eh + ZmNeyRwDXsbgiBRxpwD686/AyRuQsfkZM3ezI6hH+ITBzn7lMzmEkfTTAwx8svmswHqGbNKUSLn3 + nbZ0YuoAuTpqyDr7G2EvFBZc6k7AhAn5nCgu30Kne0b4Vbwf8fdGQARujREgeajf+ZrefOaPP4pS + IdB4nFeytNUvcmuGl0tDMItwKrsUKfnDd8eiMnRpzZ8OKav2BeYUF+kPrzEljQXobiQZRNVxRr5a + whi7l0Mvbv0jv/eyZu2f0wo2/x/zzVceuNJdRHg579xg3b1rd8pL8QUL+3MJDsOxa9Yol3VwNl8O + yjZ/ffzDv7peJeYeFsPKCNZT/K2/cpS/OeGPNwzrYFSQ7D6+MbVumIXfR33BtCjUmJ+TuYXj48Yi + tc2f8Zpc8tdv/5HrxWfiUY6KvQhMIcarkMOBfkS4gqGmDdG6NND4664qpBL7IVLPQ6dt+B/BGbg2 + QqddCFp+p52hBgf/Dx8izvcUAJG+bsjZ/Fw2Dpo7TPRJJPo54QDe+A+k2VHF4KTP8RB99x68LeIV + L0r1jUePfkX42u/vyCnMnTbXQhnALd/B7MY3loMgMFCIzQ5/9YkH5IC/T6gxtEdmccyb4fy2Zthe + jt2vvrhzZNgdHHT7RM7P20S/Pz/j0N8jsunpeDEHR4RvbDLB5xHym16sLGmrt8gmH0KX+znGf/AR + Va857uy8xDAwFI0or6mKp7afZnA2Wwdplf0aRiOeoVRyhUseW74xLxE3g27qbeLYMe+uR97q4B8+ + s+achhsxgvDcPWcsCHuf/vgonAOMkTJ/PXfxHCCKIQeexFyeqTb1m4OnnjGDpXhAOdsA9AIGXT94 + 1s+4WYTLHoOfX5SYZwPwMju2UGeCIzG27w32iyfc/Lc//sLsDrIhuXTlMTCVVFsPjyX7+YtEv4/D + QF+nkRE3vR/sakNoVmFZDemdPGfMrSAFs/EdVbh9D/YYr2Dzp2W4G68TFoldNWRdoAhC5hyTTR/S + LS8pxH6VWWKcdCUXTnq2gq88+Uj/nlxNEJJLAp+PWxIcgsAchJetOfBVZl/kPswuXqw2LsCv/7He + 8sOfPrtGzD2Qjt01pmtcydKPj9vSYWnoeepbIMMZk2DKp2bZ8iC4c28mOd4WUVv4OQ4AeD/egRCf + nWYeytv4h89FvD7H49mGqliktzvS9kqh/fAX7KJeDcSktl1cfkAEgma4bPmX63ImfWGIumeNNv8q + Z28870lNzi1Ik7+ETh/zW4BOdS5Io2uUz0N5GuEEi5pc84bd8GBtxSLN7wG3qO98VDJ+hDc9OgZc + 0u7cOcohBpwdecF6WKKBXcmSgi0PI6GlzHT64f2WJyDTr6BL/YeYwM0/JSdVuAy4nC8GtPpqR+TF + BDHZg3WWQsOhRIVwakgBIlbaHWGyrddE+5PnfIS3gbzung7UZ5AH2Wm9BuKmd/AnubWHebzIm98c + 5vT8lme4rBZFli+c6JrsryKs09uExXDKmrmue0fc0bbb8tNwGN4eV0Hq5wkxN728aC8hgydsqoFg + n9aBpo8ihHuZiHj+sI1GmSXh4fVtAGK4TZgL7OQXcNNzwbzxxSl1LjyMkrAJ+M3PWdsZGmDz4wIR + mXozu3apinxH+z/5yfK631pA5Wn48XtApNhmYNX1DB68p60JPzx4PCoBaUf3qq0Wp7VSx7QkEJq1 + HPDdT1e46U+kj8wULxc320MZrpgcL1t+LM6SCi7uhwZ7Y3ce5jKCPPyOSMGTNGsDx8yrBzS7dZCr + 5GrMaS8hhQUFcdBseRd7yYROSh/ZnqCBYYchXiNV+vGpox1/tvxp6sTz2ytQKb86sDz3rA4H8j1u + +ebYjN6J4aFzilPiA8jSVQ+HAMz1siI/abzmT77hVMeaFCfJz4WdYocgqwcDbXqazvz5xErb+0Sa + nkQaLyyrDn5+oY7eOhCEpEyknDvnyNy9Z5cOwe0Fr0wRkcRVvv/2Sw6rVpM//O6Hf3//TgX817/+ + +ut//U4YtF15f28HA6b7Mv3Hfx8V+A/hP8Y2e7//HEPAY1bd//7n3ycQ/v4OXfud/vfUve6f8e9/ + /uK4P2cN/p66KXv/v9f/tXX1X//6PwAAAP//AwDgwOyj4CAAAA== headers: Access-Control-Allow-Origin: - "*" Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6b25d36eb2d-SJC + - 9854b88eeadaed40-SJC Connection: - keep-alive Content-Encoding: @@ -3393,13 +3391,13 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:10 GMT + - Fri, 26 Sep 2025 18:10:56 GMT Server: - cloudflare Transfer-Encoding: - chunked Via: - - envoy-router-8b4d6d9d7-k4dvp + - envoy-router-bc9b5ccf9-774zw X-Content-Type-Options: - nosniff alt-svc: @@ -3411,7 +3409,7 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "141" + - "169" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: @@ -3419,7 +3417,7 @@ interactions: strict-transport-security: - max-age=31536000; includeSubDomains; preload x-envoy-upstream-service-time: - - "164" + - "190" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3429,13 +3427,13 @@ interactions: x-ratelimit-remaining-requests: - "199999" x-ratelimit-remaining-tokens: - - "199999990" + - "199999993" x-ratelimit-reset-requests: - 0s x-ratelimit-reset-tokens: - 0s x-request-id: - - req_eb1d816eec974c4c862f4264e5142061 + - req_c5305f9caf7a41499166e713cf9b44e0 status: code: 200 message: OK @@ -3445,83 +3443,79 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - wellawatteUnknownyearaperspectiveon pages 20-22: Geemi P. Wellawatte, Heta A. - Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of - molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, - doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\nnal - molecule. The counterfactual indicates\\nstructural changes to ethyl benzoate - that would result in the model predicting the molecule\\nto not contain the - \u2018fruity\u2019 scent. The Tanimoto96 similarity between the counterfactual - and\\n2,4 decadienal is also provided. Republished with permission from authors.31\\n\\n\\n - \ The molecule 2,4-decadienal, which is known to have a \u2018fatty\u2019 scent, - is analyzed in Fig-\\n\\nure 5.142,143 The resulting counterfactual, which has - a shorter carbon chain and no carbonyl\\n\\ngroups, highlights the influence - of these structural features on the \u2018fatty\u2019 scent of 2,4 deca-\\n\\ndienal. - To generalize to other molecules, Seshadri et al. 31 applied the descriptor - attribution\\n\\nmethod to obtain global explanations for the scents. The global - explanation for the \u2018fatty\u2019\\n\\nscent was generated by gathering - chemical spaces around many \u2018fatty\u2019 scented molecules.\\n\\nThe resulting - natural language explanation is: \u201CThe molecular property \u201Cfatty scent\u201D - can\\n\\nbe explained by the presence of a heptanyl fragment, two CH2 groups - separated by four\\n\\n\\n 20bonds, and - a C=O double bond, as well as the lack of more than one or two O atoms.\u201D31\\n\\nThe - importance of a heptanyl fragment aligns with that reported in the literature, - as \u2018fatty\u2019\\n\\nmolecules often have a long carbon chain.144 Furthermore, - the importance of a C=O dou-\\n\\nble bond is supported by the findings reported - by Licon et al. 145, where in addition to a\\n\\n\u201Clarger carbon-chain skeleton\u201D, - they found that \u2018fatty\u2019 molecules also had \u201Caldehyde or acid\\n\\nfunctions\u201D.145 - For the \u2018pineapple\u2019 scent, the following natural language explanation - was ob-\\n\\ntained: \u201CThe molecular property \u201Cpineapple scent\u201D - can be explained by the presence of ester,\\n\\nethyl/ether O group, alkene/ether - O group, and C=O double bond, as well as the absence of\\n\\nan Aromatic atom.\u201D31 - Esters, such as ethyl 2-methylbutyrate, are present in many pineap-\\n\\nple - volatile compounds.146,147 The combination of a C=O double bond with an ether - could\\n\\nalso correspond to an ester group. Additionally, aldehydes and ketones, - which contain C=O\\n\\ndouble bonds, are also common in pineapple volatile compounds.146,148\\n\\n\\nDiscussion\\n\\n\\nWe - have shown two post-hoc XAI applications based on molecular counterfactual expla-\\n\\nnations9 - and descriptor explanations.10 These methods can be used to explain black-box\\n\\nmodels - whose input is a molecule. These two methods can be applied for both classification\\n\\nand - regression tasks. Note that the \u201Ccorrectness\u201D of the explanations - strongly depends on\\n\\nthe accuracy of the black-box model.\\n\\n A molecular - counterfactual is one with a minimal distance from a base molecular, but\\n\\nwith - contrasting chemical properties. In the above examples, we used Tanimoto similar-\\n\\nity96 - of ECFP4 fingreprints97 as distance, although this should be explored in the - future.\\n\\nCounterfactual explanations are useful because they are represented - as chemical structures\\n\\n(familiar to domain experts), sparse, and are actionable. - A few other popular examples of\\n\\ncounterfactual on graph methods are GNNExplainer, - MEG and CF-GNNExplainer.69,104,105\\n\\n The descriptor explanation method - developed by Gandhi and White 10 fits a self-explaining\\n\\n\\n\\n 21surrogate - model to explain the black-box model. This is similar to the GraphLIME87 method,\\n\\nalthough - we have the flexibility to use explanation features other than subgraphs. Futher-\\n\\nmore, - we show that natural language combined with chemical descriptor attributions - can\\n\\ncreate explanations useful for chemists, thus enhancing the accessibility - of DL in chemistry.\\n\\nLastly, we examined if XAI can be used beyond interpretation. - Work by Seshadri et al. 31 use\\n\\nMMACE and surrogate model explanations to - analyze the structure-property relationships\\n\\nof scent. They recovered known - structure-property relationships for molecular scent purely\\n\\nfrom explanations, - demonstrating the usefulness of a two step process: fit an accurate model\\n\\nand - then explain it.\\n\\n Choosing among the plethora of XAI methods described - here is still an open question.\\n\\nIt remains to be seen if there will ever - be a consensus benchmark, since this field sits on\\n\\nthe intersection of - human-machine interaction, machine learning, and philosophy (i.e., what\\n\\nconstitutes - an explanation?). Our current advice is to consider first the audience \u2013 - domain\\n\\nexperts or ML experts or non-experts \u2013 and what the explanations - should accomplish. Are\\n\\nthey meant to inform data selection or model building, - how a prediction is used, or how the\\n\\nfeatures can be changed to affect - the outcome. The second consideration is what access you\\n\\nhave to the underlying - model. The ability to have model derivatives or propagate gradients\\n\\nto - the input to models informs the XAI method.\\n\\n\\nConclusion and outlook\\n\\n\\nWe - should seek to explain molecular property prediction models because users are - more\\n\\nlikely to trust explained predictions, and explanations can help assess - if the model is learning\\n\\nt\\n\\n------------\\n\\nQuestion: Are counterfactuals - actionable? [yes/no]\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from wellawatteUnknownyearaperspectiveon + pages 12-14: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew + D. White. A perspective on explanations of molecular prediction models. ChemRxiv, + Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. + This article has 1 citations.\\n\\n------------\\n\\nnterfactual approach, contrastive + approach employ a dual\\n\\noptimization method, which works by generating a + similar and a dissimilar (counterfactuals)\\n\\nexample. Contrastive explanations + can interpret the model by identifying contribution of\\n\\npresence and absence + of subsets of features towards a certain prediction.36,99\\n\\n A counterfactual + x\u2032 of an instance x is one with a dissimilar prediction \u02C6f(x) in classi-\\n\\nfication + tasks. As shown in equation 5, counterfactual generation can be thought of as + a\\n\\nconstrained optimization problem which minimizes the vector distance + d(x, x\u2032) between the\\n\\nfeatures.9,100\\n\\n\\n minimize + \ d(x, x\u2032)\\n (5)\\n + \ such that \u02C6f(x) \u0338= \u02C6f(x\u2032)\\n\\n + \ For regression tasks, equation 6 adapted from equation 5 can be used. Here, + a counter-\\n\\nfactual is one with a defined increase or decrease in the prediction.\\n\\n\\n + \ minimize d(x, x\u2032)\\n (6)\\n + \ such that \u02C6f(x) \u2212\u02C6f(x\u2032) \u2265\u2206\\n\\n + \ Counterfactuals explanations have become a useful tool for XAI in chemistry, + as they\\n\\nprovide intuitive understanding of predictions and are able to + uncover spurious relationships\\n\\nin training data.101 Counterfactuals create + local (instance-level), actionable explanations.\\n\\nActionability of an explanation + suggest which features can be altered to change the outcome.\\n\\nFor example, + changing a hydrophobic functional group in a molecule to a hydrophilic group\\n\\nto + increase solubility.\\n\\n Counterfactual generation is a demanding task as + it requires gradient optimization over\\n\\ndiscrete features that represents + a molecule. Recent work by Fu et al. 102 and Shen et al. 103\\n\\npresent two + techniques which allow continuous gradient-based optimization. Although, these\\n\\nmethodologies + are shown to circumvent the issue of discrete molecular optimization, counter-\\n\\nfactual + explanation based model interpretation still remains unexplored compared to + other\\n\\n\\n\\n 12post-hoc methods.\\n\\n + \ CF-GNNExplainer104 is a counterfactual explanation generating method based + on GN-\\n\\nNExplainer69 for graph data. This method generate counterfactuals + by perturbing the input\\n\\ndata (removing edges in the graph), and keeping + account of perturbations which lead to\\n\\nchanges in the output. However, + this method is only applicable to graph-based models\\n\\nand can generate infeasible + molecular structures. Another related work by Numeroso and\\n\\nBacciu 105 focus + on generating counterfactual explanations for deep graph networks. Their\\n\\nmethod + MEG (Molecular counterfactual Explanation Generator) uses a reinforcement learn-\\n\\ning + based generator to create molecular counterfactuals (molecular graphs). While + this\\n\\nmethod is able to generate counterfactuals through a multi-objective + reinforcement learner,\\n\\nthis is not a universal approach and requires training + the generator for each task.\\n\\n Work by Wellawatte et al. 9 present a model + agnostic counterfactual generator MMACE\\n\\n(Molecular Model Agnostic Counterfactual + Explanations) which does not require training\\n\\nor computing gradients. This + method firstly populates a local chemical space through ran-\\n\\ndom string + mutations of SELFIES106 molecular representations using the STONED algo-\\n\\nrithm.107 + Next, the labels (predictions) of the molecules in the local space are generated\\n\\nusing + the model that needs to be explained. Finally, the counterfactuals are identified + and\\n\\nsorted by their similarities \u2013 Tanimoto distance96 between ECFP4 + fingerprints.97 Unlike the\\n\\nCF-GNNExplainer104 and MEG105 methods, the MMACE + algorithm ensures that generated\\n\\nmolecules are valid, owing to the surjective + property of SELFIES. Additionally, the MMACE\\n\\nmethod can be applied to both + regression and classification models. However, like most XAI\\n\\nmethods for + molecular prediction, MMACE does not account for the chemical stability of\\n\\npredicted + counterfactuals. To circumvent this drawback, Wellawatte et al. 9 propose an-\\n\\nother + approach, which identift counterfactuals through a similarity search on the + PubChem\\n\\ndatabase.108\\n\\n\\n\\n\\n\\n 13Similarity + to adjacent fields\\n\\n\\nTangential examples to counterfactual explanations + are adversarial training and matched\\n\\nmolecular pairs. Adversarial perturbations + are used during training to deceive the model\\n\\nto expose the vulnerabilities + of a model109,110 whereas counterfactuals are applied post-hoc.\\n\\nTherefore, + the main difference between adversarial and counterfactual examples are in the\\n\\napplication, + although both are derived from the same optimization problem.100 Grabocka\\n\\net + al. 111 have developed a method named Adversarial Training on EXplanations (ATEX)\\n\\nwhich + improves model robustness via exposure to adversarial examples. While there + are\\n\\nconceptual disparities, we note that\\n\\n------------\\n\\nQuestion: + Are counterfactuals actionable? [yes/no]\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" headers: accept: - application/json @@ -3530,7 +3524,7 @@ interactions: connection: - keep-alive content-length: - - "6498" + - "6294" content-type: - application/json host: @@ -3562,23 +3556,24 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//jFRNbxMxEL3nV4x8TqomTZOQI5UQQnABbgStJvZs1sRfzHjbplX+O/Ju - yKZQJC4r2W/e85uvfR4BKGvUGpRuMGuf3OTuw09Dwbzb2U+z9/lp/0X2Hz/rm7u39eppocaFEbc/ - SOffrCsdfXKUbQw9rJkwU1GdLm9Xq/liMb3uAB8NuULbpTyZx8nsejafTKeT2fWJ2ESrSdQavo0A - AJ67b7EYDD2qNXQy3Y0nEdyRWp+DABRHV24UiljJGLIaD6COIVPoXD9vAsBGSes98mGj1rBRd7EN - mbhGnVt0AsgEhkSz3ZIBFEBdMsStI7ABckNAj5o45Sv42tChI/joSLcOGSRzq3PLJPBgcwPeBuvR - gemMaYKaoweELcqZRrBtcx9ezDJKtmEHuiFvNTpIHBNxtiTdk1IcJIcBi7HeMVNiEgq593ymDnbG - 8NBY3XTRNXrrLDLkCCZ6tKEoEmcZgyRkoTFgMBepl4etgMc9SamBh1aobh3UkaENhrikZ4rtQvTR - 2PpQTkNlLrLYqHHfCSZH96UslejIVDoyvT5hrZCprMcdSbmv0QltwvGytUx1K1gmK7TOXQAYQsx9 - ecpQfT8hx/MYubhLHLfyB1XVNlhpKiaUGMrISI5JdehxBPC9G9f2xQSqxNGnXOW4p+656c3yTS+o - hg0Z4NXNCcwxo7ugzRez8SuKlaGM1snFyCuNuiEzcIf9wNbYeAGMLvL+285r2n3uNuz+R34AtKaU - yVSJyVj9MuUhjKn8Qf4Vdq5zZ1gJ8b3VVGVLXHphqMbW9cut5CCZfFXbsCNObPsNr1N1u5huZ7fL - pdmq0XH0CwAA//8DABEsL8TqBAAA + H4sIAAAAAAAAA4xUTW8bNxC961cMeEoByZDk2I51C4wUMQIUPTRI2ygQRuTs7tRcDksOFQuG/3vB + XVuS8wH0sljwzcx782bIhwmAYWdWYGyHavvoZzcfrm5u/vr49V25/vf9vf62+/0dx8Wnu9u/b6/J + TGuGbP8hq89ZZ1b66ElZwgjbRKhUqy6uLt5cL+bLi8sB6MWRr2lt1NlrmS3ny9ezxWK2nD8ldsKW + slnB5wkAwMPwrRKDo3uzgvn0+aSnnLElszoEAZgkvp4YzJmzYlAzPYJWglIYVD+sA8Da5NL3mPZr + s4K1uZESlFKDVgt6oPvoMWBtKgMmArT1H7eeADNoR3uISXbsCLxY9FPgUDktzTzt6JsK2qFCLm1L + WeFrx7aDhlBLogwWA2wJ0CslcqACtsPQUiUBKWqlpzP4VRLQPVanKxXYjnrOmvbTMZxDCwjd3iWJ + nWzZQlPCqNlDm6TEmoXQiydbPFWeQzx7tk9BVQ2HOsJMkMWXLXvW/Rn80XE+uDCcQY93Vf8L5zIg + lExN8aAivpIOTvDo3dtbePXn29tfoJF02sOzp9I0lICDFlbeEZTgKFVf3dBgcFCClR0lyLEklpIh + kR9d7jjmyqcJOdRwh4pnazMdx53I064OaJOtJKpjX8zX4fF0SRI1JWPd0VC8PwEwBNGRpq7nlyfk + 8bCQXtqYZJu/STUNB87dpvopoS5fVolmQB8nAF+GxS8vdtnEJH3UjcodDXSL5eWbsaA53rUTeH7x + hKoo+hPg/Op8+oOSG0eK7PPJ7TEWbUfumHu8algcywkwOWn8ez0/qj02z6H9P+WPgLUUldwmJnJs + X/Z8DEtUH6OfhR2MHgSbTGnHljbKlOowHDVY/PhOmLzPSv2m4dBSionHx6KJG3fd0OXF1TltzeRx + 8h8AAAD//wMAO9fbajUFAAA= headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6b41a092510-SJC + - 9854b8911b47aaaf-SJC Connection: - keep-alive Content-Encoding: @@ -3586,7 +3581,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:12 GMT + - Fri, 26 Sep 2025 18:10:58 GMT Server: - cloudflare Strict-Transport-Security: @@ -3602,13 +3597,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "1417" + - "1201" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "1456" + - "1220" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3618,13 +3613,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998441" + - "29998492" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 3ms x-request-id: - - req_54a05f4b51024746838d07777e55a053 + - req_3cfefcd009ab4760bcf24c0a2a88ec92 status: code: 200 message: OK @@ -3634,23 +3629,21 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - wellawatteUnknownyearaperspectiveon pages 9-12: Geemi P. Wellawatte, Heta A. - Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of - molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, - doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\nthat - gives subgraph importance for small molecule activity prediction. On the\\n\\nother - hand, similarity maps compare model predictions for two or more molecules based - on\\n\\ntheir chemical fingerprints.83 Similarity maps provide atomic weights - or predicted probabil-\\n\\n\\n 9ity difference - between the molecules by removing one atom at a time. These weights can\\n\\nthen - be used to color the molecular graph and give a visual presentation. ChemInformatics\\n\\nModel + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from wellawatteUnknownyearaperspectiveon + pages 9-12: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew + D. White. A perspective on explanations of molecular prediction models. ChemRxiv, + Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. + This article has 1 citations.\\n\\n------------\\n\\nthat gives subgraph importance + for small molecule activity prediction. On the\\n\\nother hand, similarity maps + compare model predictions for two or more molecules based on\\n\\ntheir chemical + fingerprints.83 Similarity maps provide atomic weights or predicted probabil-\\n\\n\\n + \ 9ity difference between the molecules + by removing one atom at a time. These weights can\\n\\nthen be used to color + the molecular graph and give a visual presentation. ChemInformatics\\n\\nModel Explorer (CIME) is an interactive web based toolkit which allows visualization and\\n\\ncomparison of different explanation methods for molecular property prediction models.84\\n\\n\\nSurrogate models\\n\\n\\nOne approach to explain @@ -3720,7 +3713,7 @@ interactions: connection: - keep-alive content-length: - - "6475" + - "6305" content-type: - application/json host: @@ -3752,24 +3745,24 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//jFRNb9tGEL3rVwz2LBmWYsmxbkUORQrkkNRFD1VADXeH4iTLXXpnKFsw - /N+LXTEW27hALzzsm/dm3nzweQZg2JktGNui2q73iw+/PTj6uP519alt/7i7b748xM+/fP/85/r3 - T5svZp4Zsf5GVn+wrmzsek/KMZxhmwiVsurydv3+/c1ms7wuQBcd+Uw79Lq4iYvV9epmsVwuVtcj - sY1sScwW/poBADyXby4xOHoyWygy5aUjETyQ2b4GAZgUfX4xKMKiGNTML6CNQSmUqvf7/TeJYRee - dwFgZ2ToOkynndnCznyIQ1BKDVod0AM99R4DZncCmAgciU1ckwMUoCfM3gUeWVvoOHCHHhwduTCg - SbEDbQk4sDJ64JALswT1oGcSQq4soSiHA8RBbezoCu5bOgEGeaRUBB4GkiIZG3hsUUE69J5EwbYY - DgQ2Dt4Beh0Jo1KOx3DJGxvg7I9Er+BjKKGlNU+asS56soPHBH0ix7akLHOTeU4xaY3AIAQIjkfp - jjSxhRqFHBTaDy3hjj0m1tMcZLBtbl1OfI+Bu6jxolGTPhJNuQ2HA6U+cVApbRH6eShYCsXa0yh9 - gj7FI7vceeFDq5JtR5CeLDdsx64JBCJHDjQC2pbpWByRcMoexmHszPy8KIk8HXOdldiYKC/M3QgN - Qq7iDg8k+blBL7QLL7uw3++na5ioGQTzFYTB+wmAIUQ9W8oH8HVEXl5X3sdDn2It/6KahgNLWyVC - iSGvt2jsTUFfZgBfy2kN/7gW06fY9Vpp/E4l3fLdu7uzoLlc8wRebkZUo6KfADfr9fwNycqRInuZ - 3KexaFtyF+7lmHFwHCfAbGL853re0j6b53D4P/IXwFrqlVx12fW3whLl391/hb02uhRshNKRLVXK - lPIwHDU4+POfyMhJlLpqstI5pOmr9WZZr9a3t642s5fZ3wAAAP//AwDNfdMglwUAAA== + H4sIAAAAAAAAAwAAAP//jFRNbxs3EL3rVwx4lgRJseNat9RogCBIT+6hqAJhRA61U/Njy5nVRw3/ + 94K7kqUkDtCLAPHNe3zzdobPIwDDzizB2AbVxjZMHj7fPTz88e/u02Hx+5fNx4+l/a29+/Xxy/7D + 059zM66MvPmbrJ5ZU5tjG0g5pwG2hVCpqs7vbn+5n88Wt+97IGZHodK2rU5u8mQxW9xM5vPJYnYi + NpktiVnCXyMAgOf+t1pMjg5mCbPx+SSSCG7JLF+LAEzJoZ4YFGFRTGrGF9DmpJR618+rBLAy0sWI + 5bgyS1iZh9wlpeLRaocB6NAGTFibEsBC4Ehs4Q05QAE6YG1ZYM/aQOTEEQM42nHPAF9yBG0IOLEy + BuBU/ViCTacDCaEaKijKaQu5U5sjTeGxoSNgkj2VXuCfjqSXzB72DSpIxBBIFGyDaUtgcxccYNAT + 4aRU6zFd7s0euPZHolP4lPrSPpGDVizmQLYLWEA4csDCehwDguMTP5IWthD4ieARE8es+YKyQCfk + oJ6R50S9vmPvqVDfNumeKIHu8/kukil8G/oQNNraL25CTa/XEUpSFVHr3yO0Je/YEUhLlj3bUxQy + lAyBBMLeDr660HM0Y4j4VEPXhmL17bsAPhfokqNSO3IVxeSgzXVkGEM4QsyO/bEibSHHvUuZrsx4 + GKZCgXY1jLXYXKgO1f0qvVxPYCHfCdYFSF0IVwCmlHWYtTr7X0/Iy+u0h7xtS97Id1TjObE060Io + OdXJFs2t6dGXEcDXfqu6bxbFtCXHVtean6i/bv5uthgEzWWRr+D5GdWsGK6Am/nN+A3JtSNFDnK1 + msaibchduJc9xs5xvgJGV43/6Oct7aF5Ttv/I38BrKVWya0vH/OtskL1pftZ2WvQvWEjVHZsaa1M + pX4MRx67MDxCRo6iFNee05ZKW3h4iXy7dvee3t/evaONGb2M/gMAAP//AwA8ogPjkgUAAA== headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6b41a046897-SJC + - 9854b8913bd1239e-SJC Connection: - keep-alive Content-Encoding: @@ -3777,7 +3770,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:12 GMT + - Fri, 26 Sep 2025 18:10:58 GMT Server: - cloudflare Strict-Transport-Security: @@ -3793,13 +3786,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "1657" + - "1380" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "1676" + - "1396" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3809,13 +3802,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998447" + - "29998489" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 3ms x-request-id: - - req_f673f4213cce4a4198345c4c37edd91e + - req_e15423fa9282426a934d52ac1b2dd3e3 status: code: 200 message: OK @@ -3825,81 +3818,81 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - wellawatteUnknownyearaperspectiveon pages 12-14: Geemi P. Wellawatte, Heta A. - Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of - molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, - doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\nnterfactual - approach, contrastive approach employ a dual\\n\\noptimization method, which - works by generating a similar and a dissimilar (counterfactuals)\\n\\nexample. - Contrastive explanations can interpret the model by identifying contribution - of\\n\\npresence and absence of subsets of features towards a certain prediction.36,99\\n\\n - \ A counterfactual x\u2032 of an instance x is one with a dissimilar prediction - \u02C6f(x) in classi-\\n\\nfication tasks. As shown in equation 5, counterfactual - generation can be thought of as a\\n\\nconstrained optimization problem which - minimizes the vector distance d(x, x\u2032) between the\\n\\nfeatures.9,100\\n\\n\\n - \ minimize d(x, x\u2032)\\n (5)\\n - \ such that \u02C6f(x) \u0338= \u02C6f(x\u2032)\\n\\n - \ For regression tasks, equation 6 adapted from equation 5 can be used. Here, - a counter-\\n\\nfactual is one with a defined increase or decrease in the prediction.\\n\\n\\n - \ minimize d(x, x\u2032)\\n (6)\\n - \ such that \u02C6f(x) \u2212\u02C6f(x\u2032) \u2265\u2206\\n\\n - \ Counterfactuals explanations have become a useful tool for XAI in chemistry, - as they\\n\\nprovide intuitive understanding of predictions and are able to - uncover spurious relationships\\n\\nin training data.101 Counterfactuals create - local (instance-level), actionable explanations.\\n\\nActionability of an explanation - suggest which features can be altered to change the outcome.\\n\\nFor example, - changing a hydrophobic functional group in a molecule to a hydrophilic group\\n\\nto - increase solubility.\\n\\n Counterfactual generation is a demanding task as - it requires gradient optimization over\\n\\ndiscrete features that represents - a molecule. Recent work by Fu et al. 102 and Shen et al. 103\\n\\npresent two - techniques which allow continuous gradient-based optimization. Although, these\\n\\nmethodologies - are shown to circumvent the issue of discrete molecular optimization, counter-\\n\\nfactual - explanation based model interpretation still remains unexplored compared to - other\\n\\n\\n\\n 12post-hoc methods.\\n\\n - \ CF-GNNExplainer104 is a counterfactual explanation generating method based - on GN-\\n\\nNExplainer69 for graph data. This method generate counterfactuals - by perturbing the input\\n\\ndata (removing edges in the graph), and keeping - account of perturbations which lead to\\n\\nchanges in the output. However, - this method is only applicable to graph-based models\\n\\nand can generate infeasible - molecular structures. Another related work by Numeroso and\\n\\nBacciu 105 focus - on generating counterfactual explanations for deep graph networks. Their\\n\\nmethod - MEG (Molecular counterfactual Explanation Generator) uses a reinforcement learn-\\n\\ning - based generator to create molecular counterfactuals (molecular graphs). While - this\\n\\nmethod is able to generate counterfactuals through a multi-objective - reinforcement learner,\\n\\nthis is not a universal approach and requires training - the generator for each task.\\n\\n Work by Wellawatte et al. 9 present a model - agnostic counterfactual generator MMACE\\n\\n(Molecular Model Agnostic Counterfactual - Explanations) which does not require training\\n\\nor computing gradients. This - method firstly populates a local chemical space through ran-\\n\\ndom string - mutations of SELFIES106 molecular representations using the STONED algo-\\n\\nrithm.107 - Next, the labels (predictions) of the molecules in the local space are generated\\n\\nusing - the model that needs to be explained. Finally, the counterfactuals are identified - and\\n\\nsorted by their similarities \u2013 Tanimoto distance96 between ECFP4 - fingerprints.97 Unlike the\\n\\nCF-GNNExplainer104 and MEG105 methods, the MMACE - algorithm ensures that generated\\n\\nmolecules are valid, owing to the surjective - property of SELFIES. Additionally, the MMACE\\n\\nmethod can be applied to both - regression and classification models. However, like most XAI\\n\\nmethods for - molecular prediction, MMACE does not account for the chemical stability of\\n\\npredicted - counterfactuals. To circumvent this drawback, Wellawatte et al. 9 propose an-\\n\\nother - approach, which identift counterfactuals through a similarity search on the - PubChem\\n\\ndatabase.108\\n\\n\\n\\n\\n\\n 13Similarity - to adjacent fields\\n\\n\\nTangential examples to counterfactual explanations - are adversarial training and matched\\n\\nmolecular pairs. Adversarial perturbations - are used during training to deceive the model\\n\\nto expose the vulnerabilities - of a model109,110 whereas counterfactuals are applied post-hoc.\\n\\nTherefore, - the main difference between adversarial and counterfactual examples are in the\\n\\napplication, - although both are derived from the same optimization problem.100 Grabocka\\n\\net - al. 111 have developed a method named Adversarial Training on EXplanations (ATEX)\\n\\nwhich - improves model robustness via exposure to adversarial examples. While there - are\\n\\nconceptual disparities, we note that\\n\\n------------\\n\\nQuestion: - Are counterfactuals actionable? [yes/no]\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from wellawatteUnknownyearaperspectiveon + pages 20-22: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew + D. White. A perspective on explanations of molecular prediction models. ChemRxiv, + Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. + This article has 1 citations.\\n\\n------------\\n\\nnal molecule. The counterfactual + indicates\\nstructural changes to ethyl benzoate that would result in the model + predicting the molecule\\nto not contain the \u2018fruity\u2019 scent. The Tanimoto96 + similarity between the counterfactual and\\n2,4 decadienal is also provided. + Republished with permission from authors.31\\n\\n\\n The molecule 2,4-decadienal, + which is known to have a \u2018fatty\u2019 scent, is analyzed in Fig-\\n\\nure + 5.142,143 The resulting counterfactual, which has a shorter carbon chain and + no carbonyl\\n\\ngroups, highlights the influence of these structural features + on the \u2018fatty\u2019 scent of 2,4 deca-\\n\\ndienal. To generalize to other + molecules, Seshadri et al. 31 applied the descriptor attribution\\n\\nmethod + to obtain global explanations for the scents. The global explanation for the + \u2018fatty\u2019\\n\\nscent was generated by gathering chemical spaces around + many \u2018fatty\u2019 scented molecules.\\n\\nThe resulting natural language + explanation is: \u201CThe molecular property \u201Cfatty scent\u201D can\\n\\nbe + explained by the presence of a heptanyl fragment, two CH2 groups separated by + four\\n\\n\\n 20bonds, and a C=O double + bond, as well as the lack of more than one or two O atoms.\u201D31\\n\\nThe + importance of a heptanyl fragment aligns with that reported in the literature, + as \u2018fatty\u2019\\n\\nmolecules often have a long carbon chain.144 Furthermore, + the importance of a C=O dou-\\n\\nble bond is supported by the findings reported + by Licon et al. 145, where in addition to a\\n\\n\u201Clarger carbon-chain skeleton\u201D, + they found that \u2018fatty\u2019 molecules also had \u201Caldehyde or acid\\n\\nfunctions\u201D.145 + For the \u2018pineapple\u2019 scent, the following natural language explanation + was ob-\\n\\ntained: \u201CThe molecular property \u201Cpineapple scent\u201D + can be explained by the presence of ester,\\n\\nethyl/ether O group, alkene/ether + O group, and C=O double bond, as well as the absence of\\n\\nan Aromatic atom.\u201D31 + Esters, such as ethyl 2-methylbutyrate, are present in many pineap-\\n\\nple + volatile compounds.146,147 The combination of a C=O double bond with an ether + could\\n\\nalso correspond to an ester group. Additionally, aldehydes and ketones, + which contain C=O\\n\\ndouble bonds, are also common in pineapple volatile compounds.146,148\\n\\n\\nDiscussion\\n\\n\\nWe + have shown two post-hoc XAI applications based on molecular counterfactual expla-\\n\\nnations9 + and descriptor explanations.10 These methods can be used to explain black-box\\n\\nmodels + whose input is a molecule. These two methods can be applied for both classification\\n\\nand + regression tasks. Note that the \u201Ccorrectness\u201D of the explanations + strongly depends on\\n\\nthe accuracy of the black-box model.\\n\\n A molecular + counterfactual is one with a minimal distance from a base molecular, but\\n\\nwith + contrasting chemical properties. In the above examples, we used Tanimoto similar-\\n\\nity96 + of ECFP4 fingreprints97 as distance, although this should be explored in the + future.\\n\\nCounterfactual explanations are useful because they are represented + as chemical structures\\n\\n(familiar to domain experts), sparse, and are actionable. + A few other popular examples of\\n\\ncounterfactual on graph methods are GNNExplainer, + MEG and CF-GNNExplainer.69,104,105\\n\\n The descriptor explanation method + developed by Gandhi and White 10 fits a self-explaining\\n\\n\\n\\n 21surrogate + model to explain the black-box model. This is similar to the GraphLIME87 method,\\n\\nalthough + we have the flexibility to use explanation features other than subgraphs. Futher-\\n\\nmore, + we show that natural language combined with chemical descriptor attributions + can\\n\\ncreate explanations useful for chemists, thus enhancing the accessibility + of DL in chemistry.\\n\\nLastly, we examined if XAI can be used beyond interpretation. + Work by Seshadri et al. 31 use\\n\\nMMACE and surrogate model explanations to + analyze the structure-property relationships\\n\\nof scent. They recovered known + structure-property relationships for molecular scent purely\\n\\nfrom explanations, + demonstrating the usefulness of a two step process: fit an accurate model\\n\\nand + then explain it.\\n\\n Choosing among the plethora of XAI methods described + here is still an open question.\\n\\nIt remains to be seen if there will ever + be a consensus benchmark, since this field sits on\\n\\nthe intersection of + human-machine interaction, machine learning, and philosophy (i.e., what\\n\\nconstitutes + an explanation?). Our current advice is to consider first the audience \u2013 + domain\\n\\nexperts or ML experts or non-experts \u2013 and what the explanations + should accomplish. Are\\n\\nthey meant to inform data selection or model building, + how a prediction is used, or how the\\n\\nfeatures can be changed to affect + the outcome. The second consideration is what access you\\n\\nhave to the underlying + model. The ability to have model derivatives or propagate gradients\\n\\nto + the input to models informs the XAI method.\\n\\n\\nConclusion and outlook\\n\\n\\nWe + should seek to explain molecular property prediction models because users are + more\\n\\nlikely to trust explained predictions, and explanations can help assess + if the model is learning\\n\\nt\\n\\n------------\\n\\nQuestion: Are counterfactuals + actionable? [yes/no]\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" headers: accept: - application/json @@ -3908,7 +3901,7 @@ interactions: connection: - keep-alive content-length: - - "6464" + - "6328" content-type: - application/json host: @@ -3940,24 +3933,23 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA4xUTY/bNhC9+1cMeGoB27Dd9W7gW5AiwObSS5sWqANjRI6kSSiOSg69Nhb73wtK - jq1NUqAXQeCbNx9vPp5nAIad2YGxLarter949+EfRx8+nk+/fay6/tfT058PZ/f4R9V91m0y88KQ - 6jNZ/cpaWul6T8oSRthGQqXidf2wffPm7v5+vRqAThz5Qmt6XdzJYrPa3C3W68VmdSG2wpaS2cHf - MwCA5+FbUgyOTmYHg5vhpaOUsCGzuxoBmCi+vBhMiZNiUDO/gVaCUhiyft4HgL1JueswnvdmB3vz - TnJQijVazeiBTr3HgKWoBBgJ0JZ/rDwBJtCWzpBy01BSeGrZtlATao6UwGKAigC9UiQHKmBbDA0V - EkhWKx2B1IDQR3I8+F3Ce4lAJyxKzoED2JY6ThrP85HOoQGE9uyi9K1UbKHOYczJQxMl94WF0Ikn - mz2VuFd79mwvRiU7DqVFiSCJzxV71vMSXguQoI9yZEfgxaIvKRVFLS08HekbfbRFHUTioJmVjwQY - 3BAqBytHipD6HFlygkh+ZLXcp5KyRuRQqnOouITfi7LFV49R2WaP0Z8hJ6qzL+ZDYB4b8fYRfvrr - 7ePPUEucCiZ1TXEQ7NY0DombVktIFWjlaaL+tWedOK6Z3HJv5uOMRPJ0LHUfkpVIZVbWqwuWE7kD - d9hQKu81+kT78DIdukh1TlhmPmTvJwCGIDoKUcb90wV5uQ64l6aPUqVvqKbmwKk9lP5JKMOcVHoz - oC8zgE/DIuVXu2H6KF2vB5UvNIRb/7Lajg7NbXcn8OayZ0ZF0U+Au81X3iuXB0eK7NNkG41F25K7 - cW+ri9mxTIDZpPDv8/mR77F4Ds3/cX8DrKVeyR1ujf+RWaRy3P7L7Cr0kLBJFI9s6aBMsTTDUY3Z - j3fHpHNS6g41h4ZiH3k8PnV/2N6vq8324cFVZvYy+xcAAP//AwAm4doShQUAAA== + H4sIAAAAAAAAA4xUTW/bMAy951cQOjtFkqZNk9uWDRiwXQYMuyyDQUu0rVZfE+W0QZH/PshO62zN + gF0MSI/vkRQf/TwBEFqJDQjZYpI2mOn282q7/XrX/Xrct/fef3TvP0m7lh++332x70SRGb66J5le + WFfS22Aoae8GWEbCRFl1vrq5W89ni5vbHrBekcm0JqTp0k8Xs8VyOp9PF7MTsfVaEosN/JgAADz3 + 31yiU/QkNjArXm4sMWNDYvMaBCCiN/lGILPmhC6JYgSld4lcX/XzzgHsBHfWYjzsxAZ2Yus7lyjW + KFOHBugpGHSYm2LASKCIZdQVKUAGlBnAyhBUJLFjgtTSoQ+MFCIxuTSEypaslmiAU+xk6iIx1Gi1 + 0RgheVDeonY5H8XERS/BASNTAegUhOj3WhFox7ppE4N2yUPrH0flEH0ma2KQ6KAiQJMokrqCby0x + vW2mQiYF3oHVTls0IFt0DXEuCHsUrDckO0P9lWw17QnyE0bkpF1zKXsBFh8yllqy0DHVnYHaR+ic + opgHojKau7Je6fqQT6c8GCFEUrp/WL7aiWKYUSRDe3SSSpY+Up7VfLZzx/PJRqo7xmws1xlzBqBz + Pg1tZ0/9PCHHVxcZ34ToK/6LKmrtNLdlJGTvsmM4+SB69DgB+Nm7tfvDgCJEb0Mqk3+gPt38erkY + BMW4ICO8Wp3A5BOaM9pyvi4uKJaKEmrDZ44XEmVLauSO64Gd0v4MmJz1/bacS9pD79o1/yM/AlJS + SKTKcZiXwiLlH8i/wl7fuS9YMMW9llQmTTHPQlGNnRl2W/CBE9my1q6hGKIeFrwOpVrXdHuzuqZK + TI6T3wAAAP//AwC93xZd6QQAAA== headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6b3ef9bf9ea-SJC + - 9854b8915fdceb2c-SJC Connection: - keep-alive Content-Encoding: @@ -3965,7 +3957,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:14 GMT + - Fri, 26 Sep 2025 18:10:58 GMT Server: - cloudflare Strict-Transport-Security: @@ -3981,13 +3973,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "3438" + - "1411" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "3465" + - "1433" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -3997,13 +3989,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998450" + - "29998483" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 3ms x-request-id: - - req_ff6cfdc1a6c04061b3e42c8532e6d589 + - req_b3bb14ade7fa453480b466e1fa8801d1 status: code: 200 message: OK @@ -4013,18 +4005,16 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - wellawatteUnknownyearaperspectiveon pages 33-35: Geemi P. Wellawatte, Heta A. - Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of - molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, - doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\n13,\\n\\n - \ 1\u201320.\\n\\n\\n(78) Mastropietro, A.; Pasculli, G.; Feldmann, C.; Rodr\xB4\u0131guez-P\xB4erez, + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from wellawatteUnknownyearaperspectiveon + pages 33-35: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew + D. White. A perspective on explanations of molecular prediction models. ChemRxiv, + Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. + This article has 1 citations.\\n\\n------------\\n\\n13,\\n\\n 1\u201320.\\n\\n\\n(78) + Mastropietro, A.; Pasculli, G.; Feldmann, C.; Rodr\xB4\u0131guez-P\xB4erez, R.; Bajorath, J. Edge-\\n\\n SHAPer: Bond-Centric Shapley Value-Based Explanation Method for Graph Neural\\n\\n Networks. iScience 2022, 25, 105043.\\n\\n\\n(79) White, A. D. Deep learning for molecules and materials. Living Journal of Computa-\\n\\n @@ -4099,7 +4089,7 @@ interactions: connection: - keep-alive content-length: - - "6512" + - "6342" content-type: - application/json host: @@ -4131,25 +4121,19 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA4xUzW4bNxC+6ykGPAQ2sBK0in5i9RQEaJ301CZADlWwGpGzu4y55IYza1sw/Fp9 - gb5YwZUirR2n6IUHfvPzzTc/DyMAZY1ag9I1im5aN3734Zup8vB7RArzaJbvfp13vzV/fPv4IdCV - ypJH2H0lLd+9Jjo0rSOxwR9gHQmFUtR8tXjzZr5c5rMeaIIhl9yqVsbzMJ5NZ/Nxno9n06NjHawm - Vmv4awQA8NC/iaI3dK/WMM2+/zTEjBWp9ckIQMXg0o9CZsuCXlR2BnXwQr5nvd1uv3LwG/+w8QAb - xV3TYNxv1Bo26lNNQPeaYisQqaRIXhMD0y1FdHAX4g1DJJdKBAmgQ+eFYolaOnRA961Dj0kNzsB6 - 7TpjfQVSk43QMYH1wNqSF1taDegNvH0PPbt74Ql8bEknBJ3bZ2AFmmQaPD/PJDWFaIkhlMOAAwJw - 8Sd14qyvKGYwm+bLX+BzCOYOo4FXcG1F1zrom4RNX19moLFjdGD9sWzYIZOB4J/lZri4/ufv0h3C - TheXWV/Hf0iRqn77Hi4+o66FIpAAuklPanU5gU81MR215a6qiAWkRvkhL0ZKIvbKtzHcWkNP8yQe - 1rOtauEMWoxidecwun2igJ2Epu+cIW3ZBj9u8Cb1J/kNVIyEHLz11QSuw13qfZYEP02GCcTgg/TJ - rbbi9oDGRGKGu5qkpvgid9SJJu4cTTYqO4xfJEe36DUVrEOkNIarI5RKLWyDFXH6LtExbfzjxm+3 - 2+FwRypT59QafOfcAEDvgxykSWv15Yg8nhbJhaqNYcfPXFVpveW6OOiQloYltKpHH0cAX/qF7Z7s - oGpjaFopJNxQny5fXeWHgOp8IwbwYnZEJQi6AXA1f529ELIwJGgdD7ZeadQ1mbPv+URgZ2wYAKNB - 4T/yeSn2aQj+T/gzoDW1QqZoIxmrn9Z8NouUjujPzE5C94QVU7y1mgqxFFMzDJXYucN9U7xnoaYo - +z1voz0cubItFst8N1usVmanRo+jfwEAAP//AwDv4UbE7QUAAA== + H4sIAAAAAAAAAwAAAP//jFJNj5swEL3zK6w5hwrYpOxy7KpatdveqqpSWYFjBuJdY7v2sGoU5b9X + hiSQfki9+DBv3vN7M3OIGAPZQMFA7DiJ3qr4/jG/f79/7L59avHz/uNNwj/ILw/vyAn19QesAsNs + n1HQmfVGmN4qJGn0BAuHnDCopvnm9i5Nss3tCPSmQRVonaV4beIsydZxmsZZciLujBTooWDfI8YY + O4xvsKgb/AkFS1bnSo/e8w6huDQxBs6oUAHuvfTENcFqBoXRhHp0Xdf1sze61IdSM1aCH/qeu30J + BSuhhNVUdajwlWuBlRfGYUCTUh9LXdf1UthhO3geculBqQXAtTbEw1zGSE8n5HgJoUxnndn636jQ + Si39rnLIvdHBsCdjYUSPEWNP47CGq/xgnektVWRecPwuzTfrSRDm/cxwdholkCGulrT8TLtSrBok + LpVfDBwEFztsZu68HT400iyAaJH7Tzt/056yS939j/wMCIGWsKmsw0aK68hzm8Nwv/9qu8x5NAwe + 3asUWJFEF3bRYMsHNZ0W+L0n7KtW6g6ddXK6r9ZWzV2Lbzf5DW4hOka/AAAA//8DAFcKWwxoAwAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6bf4d226897-SJC + - 9854b89aa8c0239e-SJC Connection: - keep-alive Content-Encoding: @@ -4157,7 +4141,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:14 GMT + - Fri, 26 Sep 2025 18:10:59 GMT Server: - cloudflare Strict-Transport-Security: @@ -4173,13 +4157,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2418" + - "561" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2433" + - "600" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -4189,13 +4173,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998447" + - "29998489" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 3ms x-request-id: - - req_1fe1450f4e554cada972b5c81f68d9b4 + - req_9cc4d9e8d9744bcfbfde3faa9cf5039d status: code: 200 message: OK @@ -4205,83 +4189,82 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt - from wellawatteUnknownyearaperspectiveon pages 16-20: Geemi P. Wellawatte, Heta - A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations - of molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, - doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\nssion - challenge and is\\n\\nimportant for chemical process design, drug design and - crystallization.133\u2013136 In our previous\\n\\nworks,9,10 we implemented - and trained an RNN model in Keras to predict solubilities (log\\n\\nmolarity) - of small molecules.127 The AqSolDB curated database137 was used to train the\\n\\nRNN - model.\\n\\n In this task, counterfactuals are based on equation 6. Figure - 3 illustrates the generated\\n\\nlocal chemical space and the top four counterfactuals. - Based on the counterfactuals, we ob-\\n\\nserve that the modifications to the - ester group and other heteroatoms play an important role\\n\\nin solubility. - These findings align with known experimental and basic chemical intuition.134\\n\\nFigure - 4 shows a quantitative measurement of how substructures are contributing to - the pre-\\n\\n\\n\\n 16Figure 2: Descriptor - explanations along with natural language explanation obtained for BBB\\npermeability - of Alprozolam molecule. The green and red bars show descriptors that influ-\\nence - predictions positively and negatively, respectively. Dotted yellow lines show - significance\\nthreshold (\u03B1 = 0.05) for the t-statistic. Molecular descriptors - show molecule-level proper-\\nties that are important for the prediction. ECFP - and MACCS descriptors indicate which\\nsubstructures influence model predictions. - MACCS explanations lead to text explanations\\nas shown. Republished from Ref.10 - with permission from authors. SMARTS annotations for\\nMACCS descriptors were - created using SMARTSviewer (smartsview.zbh.uni-hamburg.de,\\nCopyright: ZBH, - Center for Bioinformatics Hamburg) developed by Schomburg et al. 132.\\n\\n\\n\\n\\n\\n - \ 17diction. For example, we see that adding - acidic and basic groups as well as hydrogen bond\\n\\nacceptors, increases solubility. - Substructure importance from ECFP97 and MACCS138 de-\\n\\nscriptors indicate - that adding heteroatoms increases solubility, while adding rings structures\\n\\nmakes - the molecule less soluble. Although these are established hypotheses, it is - interesting\\n\\nto see they can be derived purely from the data via DL and - XAI.\\n\\n\\n\\n\\n\\nFigure 3: Generated chemical space for solubility prediction - using the RNN model. The\\nchemical space is a 2D projection of the pairwise - Tanimoto similarities of the local coun-\\nterfactuals. Each data point is colored - by solubility. Top 4 counterfactuals are shown here.\\nRepublished from Ref.9 - with permission from the Royal Society of Chemistry.\\n\\n\\n\\nGeneralizing - XAI \u2013 interpreting scent-structure relationships\\n\\n\\nIn this example, - we show how non-local structure-property relationships can be learned with\\n\\nXAI - across multiple molecules. Molecular scent prediction is a multi-label classification - task\\n\\nbecause a molecule can be described by more than one scent. For example, - the molecule\\n\\njasmone can be described as having \u2018jasmine,\u2019 \u2018woody,\u2019 - \u2018floral,\u2019 and \u2019herbal\u2019 scents.139 The\\n\\nscent-structure - relationship is not very well understood,140 although some relationships are\\n\\nknown. - \ For example, molecules with an ester functional group are often associated - with\\n\\n\\n 18Figure 4: Descriptor explanations - for solubility prediction model. The green and red bars\\nshow descriptors that - influence predictions positively and negatively, respectively. Dotted\\nyellow - lines show significance threshold (\u03B1 = 0.05) for the t-statistic. The MACCS - and\\nECFP descriptors indicate which substructures influence model predictions. - MACCS sub-\\nstructures may either be present in the molecule as is or may represent - a modification. ECFP\\nfingerprints are substructures in the molecule that affect - the prediction. MACCS descriptor\\nare used to obtain text explanations as shown. - Republished from Ref.10 with permission from\\nauthors. SMARTS annotations for - MACCS descriptors were created using SMARTSviewer\\n(smartsview.zbh.uni-hamburg.de, - Copyright: ZBH, Center for Bioinformatics Hamburg) de-\\nveloped by Schomburg - et al. 132.\\n\\n\\n\\n\\n\\n 19the \u2018fruity\u2019 - scent. There are some exceptions though, like tert-amyl acetate which has a\\n\\n\u2018camphoraceous\u2019 - rather than \u2018fruity\u2019 scent.140,141\\n\\n In Seshadri et al. 31, - we trained a GNN model to predict the scent of molecules and utilized\\n\\ncounterfactuals9 - and descriptor explanations10 to quantify scent-structure relationships. The\\n\\nMMACE - method was modified to account for the multi-label aspect of scent prediction. - This\\n\\nmodification defines molecules that differed from the instance molecule - by only the selected\\n\\nscent as counterfactuals. For instance, counterfactuals - of the jasmone molecule would be false\\n\\nfor the \u2018jasmine\u2019 scent - but would still be positive for \u2018woody,\u2019 \u2018floral\u2019 and \u2018herbal\u2019 - scents.\\n\\n\\n\\n\\n\\nFigure 5: Counterfactual for the 2,4 decadienal molecule. - \ The counterfactual indicates\\nstructural changes to ethyl benzoate that would - result in the model predicting the molecule\\nto not contain the \u2018fruity\u2019 - scent. The Tanimoto96 similarity between the counterfactual and\\n2,4 decadienal - is also\\n\\n------------\\n\\nQuestion: Are counterfactuals actionable? [yes/no]\\n\\n\"}]}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from wellawatteUnknownyearaperspectiveon + pages 25-28: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew + D. White. A perspective on explanations of molecular prediction models. ChemRxiv, + Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. + This article has 1 citations.\\n\\n------------\\n\\n2021, 25, 1315\u20131360.\\n\\n\\n + (9) Wellawatte, G. P.; Seshadri, A.; White, A. D. Model agnostic generation + of counter-\\n\\n factual explanations for molecules. Chemical Science 2022, + 13, 3697\u20133705.\\n\\n\\n(10) Gandhi, H. A.; White, A. D. Explaining structure-activity + relationships using locally\\n\\n faithful surrogate models. chemrxiv 2022,\\n\\n\\n(11) + Gormley, A. J.; Webb, M. A. Machine learning in combinatorial polymer chemistry.\\n\\n + \ Nature Reviews Materials 2021,\\n\\n\\n(12) Gomes, C. P.; Fink, D.; Dover, + R. B. V.; Gregoire, J. M. Computational sustainability\\n\\n meets materials + science. Nature Reviews Materials 2021,\\n\\n\\n(13) On scientific understanding + with artificial intelligence. Nature Reviews Physics 2022\\n\\n 4:12 2022, + 4, 761\u2013769.\\n\\n\\n(14) Arrieta, A. B.; D\xB4\u0131az-Rodr\xB4\u0131guez, + N.; Ser, J. D.; Bennetot, A.; Tabik, S.; Barbado, A.;\\n\\n Garcia, S.; + Gil-Lopez, S.; Molina, D.; Benjamins, R.; Chatila, R.; Herrera, F. Explain-\\n\\n + \ able Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities + and Chal-\\n\\n lenges toward Responsible AI. Information Fusion 2019, 58, + 82\u2013115.\\n\\n\\n(15) Murdoch, W. J.; Singh, C.; Kumbier, K.; Abbasi-Asl, + R.; Yu, B. Interpretable machine\\n\\n learning: definitions, methods, and + applications. ArXiv 2019, abs/1901.04592.\\n\\n\\n 25(16) + Boobier, S.; Osbourn, A.; Mitchell, J. B. Can human experts predict solubility + better\\n\\n than computers? Journal of cheminformatics 2017, 9, 1\u201314.\\n\\n\\n(17) + Lee, J. D.; See, K. A. Trust in automation: Designing for appropriate reliance. + Human\\n\\n Factors 2004, 46, 50\u201380.\\n\\n\\n(18) Bolukbasi, T.; Chang, + K.-W.; Zou, J. Y.; Saligrama, V.; Kalai, A. T. Man is to com-\\n\\n puter + programmer as woman is to homemaker? debiasing word embeddings. Advances\\n\\n + \ in neural information processing systems 2016, 29.\\n\\n\\n(19) Buolamwini, + J.; Gebru, T. Gender Shades: Intersectional Accuracy Disparities in\\n\\n Commercial + Gender Classification. Proceedings of the 1st Conference on Fairness,\\n\\n + \ Accountability and Transparency. 2018; pp 77\u201391.\\n\\n\\n(20) Lapuschkin, + S.; W\xA8aldchen, S.; Binder, A.; Montavon, G.; Samek, W.; M\xA8uller, K.-R.\\n\\n + \ Unmasking Clever Hans predictors and assessing what machines really learn. + Nature\\n\\n communications 2019, 10, 1\u20138.\\n\\n\\n(21) DeGrave, A. + J.; Janizek, J. D.; Lee, S.-I. AI for radiographic COVID-19 detection\\n\\n + \ selects shortcuts over signal. Nature Machine Intelligence 2021, 3, 610\u2013619.\\n\\n\\n(22) + Goodman, B.; Flaxman, S. European Union regulations on algorithmic decision-\\n\\n + \ making and a \u201Cright to explanation\u201D. AI Magazine 2017, 38, 50\u201357.\\n\\n\\n(23) + ACT, A. I. European Commission. On Artificial Intelligence: A European Approach\\n\\n + \ to Excellence and Trust. 2021, COM/2021/206.\\n\\n\\n(24) Blueprint for + an AI Bill of Rights, The White House. 2022; https://www.whitehouse.\\n\\n gov/ostp/ai-bill-of-rights/.\\n\\n\\n(25) + Miller, T. Explanation in artificial intelligence: Insights from the social + sciences. Ar-\\n\\n tificial intelligence 2019, 267, 1\u201338.\\n\\n\\n\\n + \ 26(26) Murdoch, W. J.; Singh, C.; Kumbier, + K.; Abbasi-Asl, R.; Yu, B. Definitions, meth-\\n\\n ods, and applications + in interpretable machine learning. Proceedings of the National\\n\\n Academy + of Sciences of the United States of America 2019, 116, 22071\u201322080.\\n\\n\\n(27) + Gunning, D.; Aha, D. DARPA\u2019s Explainable Artificial Intelligence (XAI) + Program.\\n\\n AI Magazine 2019, 40, 44\u201358.\\n\\n\\n(28) Biran, O.; + Cotton, C. Explanation and justification in machine learning: A survey.\\n\\n + \ IJCAI-17 workshop on explainable AI (XAI). 2017; pp 8\u201313.\\n\\n\\n(29) + Palacio, S.; Lucieri, A.; Munir, M.; Ahmed, S.; Hees, J.; Dengel, A. Xai handbook:\\n\\n + \ Towards a unified framework for explainable ai. Proceedings of the IEEE/CVF + Inter-\\n\\n national Conference on Computer Vision. 2021; pp 3766\u20133775.\\n\\n\\n(30) + Kuhn, D. R.; Kacker, R. N.; Lei, Y.; Simos, D. E. Combinatorial Methods for + Ex-\\n\\n plainable AI. 2020 IEEE International Conference on Software Testing, + Verification\\n\\n and Validation Workshops (ICSTW) 2020, 167\u2013170.\\n\\n\\n(31) + Seshadri, A.; Gandhi, H. A.; Wellawatte, G. P.; White, A. D. Why does that molecule\\n\\n + \ smell? ChemRxiv 2022,\\n\\n\\n(32) Das, A.; Rad, P. Opportunities and challenges + in explainable artificial intelligence\\n\\n (xai): A survey. arXiv preprint + arXiv:2006.11371 2020,\\n\\n\\n(33) Machlev, R.; Heistrene, L.; Perl, M.; Levy, + K. Y.; Belikov, J.; Mannor, S.; Levron, Y.\\n\\n Explainable Artificial + Intelligence (XAI) techniques for energy and power systems:\\n\\n Review, + challenges and opportunities. Energy and AI 2022, 9, 100169.\\n\\n\\n(34) Koh, + P. W.; Liang, P. Understanding black-box predictions via influence functions.\\n\\n + \ International Conference on Machine Learning. 2017; pp 1885\u20131894.\\n\\n\\n(35) + Ribeiro, M. T.; Singh, S.; Guestrin, C. \u201D Why should i trust you?\u201D + Explaining the\\n\\n predictions of any classifier. Proceedings of the 22nd + ACM SIGKDD international\\n\\n\\n 27 conference + on knowledge discovery and data \\n\\n------------\\n\\nQuestion: Are counterfactuals + actionable? [yes/no]\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" headers: accept: - application/json @@ -4290,7 +4273,7 @@ interactions: connection: - keep-alive content-length: - - "188751" + - "6350" content-type: - application/json host: @@ -4322,25 +4305,20 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA4xUTW/jNhC9+1cMeLaN2EmcwLfFAgFaLHpoiwJFvZDH5EiaXYrUcoZJjCD/vaDk - 2NovYC866M17b4bz8TIDMOzMFoxtUW3X+8X737+4mv/85+Fgf6N34fDHv9dH/nK/ebj/INbMCyMe - PpHVN9bSxq73pBzDCNtEqFRUV3e39/c3m81qNQBddOQLrel1cRMX66v1zWK1WqyvTsQ2siUxW/hv - BgDwMnxLisHRs9nC1fztT0ci2JDZnoMATIq+/DEowqIY1MwvoI1BKQxZ7/f7TxLDLrzsAsDOSO46 - TMed2cLOvI85KKUarWb0ApgI0Jbq8OAJOIC2BIPas0KsoYuebPaYoE/keAiFoVRZwt8tQZ/iIzty - UHOTEwlgcDCw2fssmlAJ2vgE9htriwGazI6KHNdssWgLaJyYiqZsddDVCGhbpkcCR8KJXPHuKSmT - zEGybQEFOJQOCTmQ6POBPesRYgL0SoUjloIWZs2eZAkPMQE9Y2nyHGyLoRm9SJQSNCnmfqypJaUU - UWMn8ESJQNr4FEooh9pnCpamlpfnkjmg5yZwaOCJtQV67ilxR0HRD9K2pY4teuCgmQtnCX9xxx6T - P86/e7nBPZcSi7mjoFwfz0+F/lwG1jVZLb5vVU9yctTFMPSnBGhLnCDrmDyPrSnAZDoubSkNaAI4 - sixFbbkz83HaEnl6xGCpEhsTlalbXZ2wknLFHTYk5b+mTLvwugv7/X46y4nqLFhWKWTvJwCGEHWc - krJFH0/I63lvfGz6FA/yDdXUHFjaqsxFDGVHRGNvBvR1BvBx2M/81cqZPsWu10rjZxrs1uvr61HQ - XE7CBV6t706oRkU/4V1vTov9tWTlSJG9TJbcWLQtuQv3chEwO44TYDYp/Pt8fqQ9Fs+h+RX5C2At - 9UquuozOj8ISlZv5s7DzQw8JG6H0yJYqZUqlGY5qzH48Z0aOotRVNYeGUp94vGl1X91uVof17d2d - O5jZ6+x/AAAA//8DAD2jiJHcBQAA + H4sIAAAAAAAAAwAAAP//jFJNi9swEL37V4g5x8X2rpuP4y5bKKWwtJQe6sVW5LGjVJaENA5dQv57 + kZ3ETj9gLzrMm/f03swcI8ZA1rBhIHacRGdV/Php+fhh/83S+iGn/vvq+aP5/PD16cuqTZ4PsAgM + s92joAvrnTCdVUjS6BEWDjlhUE2X+WqdJlm+HoDO1KgCrbUU35s4S7L7OE3jLDkTd0YK9LBhPyLG + GDsOb7Coa/wFG5YsLpUOvectwubaxBg4o0IFuPfSE9cEiwkURhPqwXVVVXtvdKGPhWasAN93HXev + BWxYAQUsxqpDhQeuBZZeGIcBTQp9KnRVVXNhh03vecile6VmANfaEA9zGSK9nJHTNYQyrXVm6/+g + QiO19LvSIfdGB8OejIUBPUWMvQzD6m/yg3Wms1SS+YnDd+nyLh8FYdrPBGfnUQIZ4mpOyy+0G8Wy + RuJS+dnAQXCxw3riTtvhfS3NDIhmuf+28y/tMbvU7VvkJ0AItIR1aR3WUtxGntochvv9X9t1zoNh + 8OgOUmBJEl3YRY0N79V4WuBfPWFXNlK36KyT4301tqzXDb7Pl3e4hegU/QYAAP//AwA2CaOqaAMA + AA== headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6b41dcd6803-SJC + - 9854b89f5f33239e-SJC Connection: - keep-alive Content-Encoding: @@ -4348,7 +4326,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:14 GMT + - Fri, 26 Sep 2025 18:10:59 GMT Server: - cloudflare Strict-Transport-Security: @@ -4364,35 +4342,29 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "4191" + - "531" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "4229" + - "637" x-openai-proxy-wasm: - v0.1 - x-ratelimit-limit-input-images: - - "250000" x-ratelimit-limit-requests: - "10000" x-ratelimit-limit-tokens: - "30000000" - x-ratelimit-remaining-input-images: - - "249998" x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29996912" - x-ratelimit-reset-input-images: - - 0s + - "29998486" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - - 6ms + - 3ms x-request-id: - - req_7b7438c5e76a4f2fb878345738ac749a + - req_fbd62d54996942ecb1a50b3b779dd901 status: code: 200 message: OK @@ -4402,13 +4374,11 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt from wellawatteUnknownyearaperspectiveon pages 14-16: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, @@ -4486,7 +4456,7 @@ interactions: connection: - keep-alive content-length: - - "51223" + - "51053" content-type: - application/json host: @@ -4518,24 +4488,23 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA4xUwW4bOQy9+ysIXTYFbMM27DjrW7MFFui5KNCtizFHomeUaqQJyXGcDfLvhcZO - PE27wF4GGD6+Jz6S0tMIwHhnNmBsjWqbNkz++njvqvrw4bA8/v359v7Pfx9W5Zeb+/j+n48fGjPO - jFTekdUX1tSmpg2kPsUTbJlQKavO16ubm+X19XzRA01yFDKtanWyTJPFbLGczOeTxexMrJO3JGYD - X0cAAE/9N5cYHR3NBmbjl0hDIliR2bwmARhOIUcMinhRjGrGF9CmqBT7qne73Z2kuI1P2wiwNdI1 - DfLj1mxga76QjMGmLirxHq12GASQCdBmi1gGmsKnmkDpqNByOnhHAhiBjpgbAQ81Mb2RADq2ASNm - CQHpqopEoUnO7709RzUBQpMC2S7QHwKi3FntmOCKptV0DLbGWFGfqDWBRS7T8TF4C2i9g4pT177L - KPVlgtf80xI3eSA9pwwpuUnJ6COUyOyJ4er29vZd70noTUkYfBXhwWudDRD7hqJigL2PzsdKxuCo - SVGUUX2sQGvUX5pnMeY+tUmGTRy6QVt7OhA4Es/kXpqA3POI1ZNMt2Z8GhdToANGS4XYxJTHNp+d - sU7IFb7BiiTHlTvaxudt3O12w2Vg2neCeRdjF8IAwBiTnsznNfx2Rp5fFy+kquVUyhuq2fvopS6Y - UFLMSyaaWtOjzyOAb/2Cdz/trGk5Na0Wmr5Tf9x8vZ6fBM3lTg3g2fKMalIMA+BmvRr/RrJwpOiD - DG6JsWhrchfu5Uph53waAKOB8V/r+Z32ybyP1f+RvwDWUqvkipbJefuz50saU350/ivttdF9wUaI - D95SoZ44D8PRHrtweg+MPIpSU+x9rIhb9qdHYd8Wq+t5uVit1640o+fRDwAAAP//AwALQkmTHQUA - AA== + H4sIAAAAAAAAA4xU22rbQBB991cM+9IW5GA7Se340aGEUlJKaSmhDma0GkuTrHbF7MixG/LvZWUn + di6Fvgg0Z87RnLnovgdguDBTMLZCtXXj+udfxuefrr7fXVz8nG3ufrWX3yY/Vp9vvuLl+R9nssQI + +Q1ZfWQd2VA3jpSD38JWCJWS6nB8OjkbDkankw6oQ0Eu0cpG+yehPxqMTvrDYX802BGrwJaimcLv + HgDAffdMJfqC1mYKg+wxUlOMWJKZPiUBGAkuRQzGyFHRq8n2oA1eyXdV3889wNzEtq5RNnMzhbm5 + opiBDa1XkiVabdFFQCFAm5xh7ugIflQESmuFRsKKC4qAHmiNyT/cVST0QgJo3Tj0mCQixLYsKSrU + oeAl211UAyDUwZFtHb2LEFVaq61QBrG1FWAEdErCvgStCCxKHtYbxxbQcgGlhLbJkgx1ZQJremlI + 6jSHjpO7EIp+LsgechRhEng/m80+dJ4ivSgJHZce7lirZICEa/KKDpbsC/ZlzKCgOviogrotC/VV + 8yz61KcmxMMmgq3Ql9TZ5jr1kR7No3T5JMoUwfEtwWw22xnJ2bFujuYm2w5PyNEKvaVFtEEoDXE4 + mPuHw5ELLduIaeN869wBgN4H3XpNy3a9Qx6e1suFspGQxxdUs2TPsVoIYQw+rVLU0JgOfegBXHdr + 3D7bTNNIqBtdaLil7nPD8fHJVtDsL2cPnx3vQA2K7oA2GY2zNxQXBSmyiwenYCzaioo9d3832BYc + DoDege/X5bylvfXOvvwf+T1gLTVKxaIRKtg+t7xPE0p/ln+lPfW5K9hEkhVbWiiTpFkUtMTWbY/e + xE1UqhdL9iVJI7y9/GWzOP04zEen43GRm95D7y8AAAD//wMArVhRCQIFAAA= headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6beecc82510-SJC + - 9854b899cde8aaaf-SJC Connection: - keep-alive Content-Encoding: @@ -4543,7 +4512,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:15 GMT + - Fri, 26 Sep 2025 18:11:00 GMT Server: - cloudflare Strict-Transport-Security: @@ -4559,13 +4528,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2862" + - "2306" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2898" + - "2339" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-input-images: @@ -4579,7 +4548,7 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29997688" + - "29997730" x-ratelimit-reset-input-images: - 0s x-ratelimit-reset-requests: @@ -4587,7 +4556,7 @@ interactions: x-ratelimit-reset-tokens: - 4ms x-request-id: - - req_4c09a74983fe4a8798021b74a78a81a8 + - req_cf4d0bac9d3c45e4bb2f7d062a386f44 status: code: 200 message: OK @@ -4597,47 +4566,44 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - wellawatteUnknownyearaperspectiveon pages 5-8: Geemi P. Wellawatte, Heta A. - Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of - molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, - doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\nnct?\\n\\n\\n - \ We present an example evaluation of the SHAP explanation method based on the - above\\n\\nattributes.44 Shapley values were proposed as a local explanation - method based on feature\\n\\nattribution, as they offer a complete explanation - - each feature is assigned a fraction of\\n\\nthe prediction value.44,45 Completeness - is a clearly measurable and well-defined metric, but\\n\\nyields explanations - with many components. Yet Shapley values are not actionable nor sparse.\\n\\nThey - are non-sparse as every feature has a non-zero attribution and not-actionable - because\\n\\nthey do not provide a set of features which changes the outcome.46 - Ribeiro et al. 35 proposed\\n\\na surrogate model method that aims to provide - sparse/succinct explanations that have high\\n\\n\\n 5fidelity - to the original model. In Wellawatte et al. 9 we argue that counterfactuals - are \u201Cbet-\\n\\nter\u201D explanations because they are actionable and sparse. - We highlight that, evaluation of\\n\\nexplanations is a difficult task because - explanations are fundamentally for and by humans.\\n\\nTherefore, these evaluations - are subjective, as they depend on \u201Ccomplex human factors and\\n\\napplication - scenarios.\u201D37\\n\\n\\nSelf-explaining models\\n\\nA self-explanatory model - is one that is intrinsically interpretable to an expert.47 Two com-\\n\\nmon - examples found in the literature are linear regression models and decision trees - (DT).\\n\\nIntrinsic models can be found in other XAI applications acting as - surrogate models (proxy\\n\\nmodels) due to their transparent nature.48,49 A - linear model is described by the equation\\n\\n1 where, W\u2019s are the weight - parameters and x\u2019s are the input features associated with the\\n\\nprediction - \u02C6y. Therefore, we observe that the weights can be used to derive a complete - expla-\\n\\nnation of the model - trained weights quantify the importance of - each feature.47 DT models\\n\\nare another type of self-explaining models which - have been used in classification and high-\\n\\nthroughput screening tasks. - \ Gajewicz et al. 50 used DT models to classify nanomaterials\\n\\nthat identify - structural features responsible for surface activity. In another study by Han\\n\\net - al. 51, a DT model was developed to filter compounds by their bioactivity based - on the\\n\\nchemical fingerprints.\\n\\n\\n\\n \u02C6y + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from wellawatteUnknownyearaperspectiveon + pages 5-8: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. + White. A perspective on explanations of molecular prediction models. ChemRxiv, + Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. + This article has 1 citations.\\n\\n------------\\n\\nnct?\\n\\n\\n We present + an example evaluation of the SHAP explanation method based on the above\\n\\nattributes.44 + Shapley values were proposed as a local explanation method based on feature\\n\\nattribution, + as they offer a complete explanation - each feature is assigned a fraction of\\n\\nthe + prediction value.44,45 Completeness is a clearly measurable and well-defined + metric, but\\n\\nyields explanations with many components. Yet Shapley values + are not actionable nor sparse.\\n\\nThey are non-sparse as every feature has + a non-zero attribution and not-actionable because\\n\\nthey do not provide a + set of features which changes the outcome.46 Ribeiro et al. 35 proposed\\n\\na + surrogate model method that aims to provide sparse/succinct explanations that + have high\\n\\n\\n 5fidelity to the original + model. In Wellawatte et al. 9 we argue that counterfactuals are \u201Cbet-\\n\\nter\u201D + explanations because they are actionable and sparse. We highlight that, evaluation + of\\n\\nexplanations is a difficult task because explanations are fundamentally + for and by humans.\\n\\nTherefore, these evaluations are subjective, as they + depend on \u201Ccomplex human factors and\\n\\napplication scenarios.\u201D37\\n\\n\\nSelf-explaining + models\\n\\nA self-explanatory model is one that is intrinsically interpretable + to an expert.47 Two com-\\n\\nmon examples found in the literature are linear + regression models and decision trees (DT).\\n\\nIntrinsic models can be found + in other XAI applications acting as surrogate models (proxy\\n\\nmodels) due + to their transparent nature.48,49 A linear model is described by the equation\\n\\n1 + where, W\u2019s are the weight parameters and x\u2019s are the input features + associated with the\\n\\nprediction \u02C6y. Therefore, we observe that the + weights can be used to derive a complete expla-\\n\\nnation of the model - trained + weights quantify the importance of each feature.47 DT models\\n\\nare another + type of self-explaining models which have been used in classification and high-\\n\\nthroughput + screening tasks. Gajewicz et al. 50 used DT models to classify nanomaterials\\n\\nthat + identify structural features responsible for surface activity. In another study + by Han\\n\\net al. 51, a DT model was developed to filter compounds by their + bioactivity based on the\\n\\nchemical fingerprints.\\n\\n\\n\\n \u02C6y = \u03A3iWixi (1)\\n\\n\\n Regularization techniques such as EXPO52 and RRR53 are designed to enhance the black-\\n\\nbox model interpretability.54 Although one can argue that \u201Csimplicity\u201D @@ -4682,7 +4648,7 @@ interactions: connection: - keep-alive content-length: - - "6483" + - "6313" content-type: - application/json host: @@ -4714,23 +4680,23 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//jFTBbtswDL3nKwhdekmCOEvaJrdhGAYMw4ZhxQZ0KQxGom21suSJVJqg - 6L8PttM63TpgFx/49J4exUc/jACUNWoNSlcoum7c5N3HX+Z2j/4T3b9fEboft9cf5Hrx9UvxXT6r - ccsI21vS8sSa6lA3jsQG38M6Egq1qtnF8vJycX6eLTugDoZcSysbmSzCZD6bLyZZNpnPjsQqWE2s - 1vBzBADw0H1bi97QXq1hNn6q1MSMJan18yEAFYNrKwqZLQt6UeMB1MEL+c71w8YDbBSnusZ42Kg1 - bNRVRUB7TbERMJZ1YiYGHZIXigVqSegYkIH2jUOPbbsM1kMdHOnkMEITyVjdAtB1ymOobFk5W1Zi - fQlSoYBUdACMBDp4toYiGTjbkgjFs5faW9KYmAYGdtq4dQToDXCDkWkKb4fyC34Tw84aAgQmgVBA - QSgpEvdGNHrQFfqyuwFCEh1qGkONd71ZqiExFclBESIY0pZt8JMen8JVZdv38RKRheHeSgXfKmwc - HWCHLhGP4b6yuuq8++AnveHOuw9y0s50o8b9SCI52qHXlLMOkdrRZLMjlphMbmssidt6gY5p4x9P - ZxypSIxtxHxy7gRA74P079Km6+aIPD7nyYWyiWHLf1BVYb3lKo+EHHybHZbQqA59HAHcdLlNL6Ko - mhjqRnIJd9Rdl72ZXfaCaliVAV5lR1CCoDulrVbjVxRzQ4LW8Un2lUZdkRm4w6JgMjacAKOTvv+2 - 85p237v15f/ID4DW1AiZfNiK145Fan8l/zr2/M6dYcUUd1ZTLpZiOwtDBSbXb7niAwvVeWF9SbGJ - tl/1osmX59l2vry4MFs1ehz9BgAA//8DABK4YQ7zBAAA + H4sIAAAAAAAAAwAAAP//jFTBThsxEL3nK0a+cEmibCAEcquQ4IDKBSpVbVA0a89mXby25RmnRCj/ + Xu0uZUNLpV724Dfv+Y3nzb6MAJQ1agVK1yi6iW5ydbu8url+KO9zcZfOr/fNt9u7r3Tz5aK8/7xQ + 45YRyh+k5TdrqkMTHYkNvod1IhRqVYvl4uKymM3PZx3QBEOupW2jTM7CZD6bn02KYjKfvRLrYDWx + WsH3EQDAS/dtLXpDz2oFnUx30hAzbkmt3ooAVAquPVHIbFnQixoPoA5eyHeuX9YeYK04Nw2m/Vqt + YK0eagJ61pSigLGsMzMx6JC9UKpQS0bHgAz0HB16bNtlsB6a4EhnhwliImN1C0DXKY+BBcX6LUiN + AlLTHjAR6ODZGkpk4KQkEUon72VL0piZBgZ2slg6AvQGOGJimsKn4fgdP6aws4YAgUkgVFARSk7E + vRGNHnSNftvdACGLDg2NocGn3iw1kJmq7KAKCQxpyzb4SY9P4aG27dN4ScjC8NNKDfc1Rkd72KHL + xGP4WVtdd9598JPecOfdBzlqZ7pW434aiRzt0GvasA6J2qkUs7U/HM8wUZUZ2wj57NwRgN4H6Ztv + 0/P4ihze8uLCNqZQ8h9UVVlvud4kQg6+zQZLiKpDDyOAxy6X+V3UVEyhibKR8ETddcV8WfSCaliF + Ab44ewUlCLoj2uliMf5AcWNI0Do+yrbSqGsyA3dYBMzGhiNgdNT333Y+0u57t377P/IDoDVFIbMZ + Uv9RWaL2V/Gvsrd37gwrprSzmjZiKbWzMFRhdv0WK96zULOprN9Sisn2q1zFjbms6HyxPKVSjQ6j + XwAAAP//AwCQPAK30wQAAA== headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6cfba4c6803-SJC + - 9854b8a48e5c239e-SJC Connection: - keep-alive Content-Encoding: @@ -4738,7 +4704,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:16 GMT + - Fri, 26 Sep 2025 18:11:01 GMT Server: - cloudflare Strict-Transport-Security: @@ -4754,13 +4720,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "1132" + - "1660" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "1146" + - "1683" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -4770,13 +4736,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998446" + - "29998488" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 3ms x-request-id: - - req_43ebabb5ac11462e9327db9cbdfd7f82 + - req_f2f09981807c449c9596b557cd969885 status: code: 200 message: OK @@ -4786,221 +4752,27 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - wellawatteUnknownyearaperspectiveon pages 25-28: Geemi P. Wellawatte, Heta A. - Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of - molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, - doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\n2021, - 25, 1315\u20131360.\\n\\n\\n (9) Wellawatte, G. P.; Seshadri, A.; White, A. - D. Model agnostic generation of counter-\\n\\n factual explanations for - molecules. Chemical Science 2022, 13, 3697\u20133705.\\n\\n\\n(10) Gandhi, H. - A.; White, A. D. Explaining structure-activity relationships using locally\\n\\n - \ faithful surrogate models. chemrxiv 2022,\\n\\n\\n(11) Gormley, A. J.; - Webb, M. A. Machine learning in combinatorial polymer chemistry.\\n\\n Nature - Reviews Materials 2021,\\n\\n\\n(12) Gomes, C. P.; Fink, D.; Dover, R. B. V.; - Gregoire, J. M. Computational sustainability\\n\\n meets materials science. - Nature Reviews Materials 2021,\\n\\n\\n(13) On scientific understanding with - artificial intelligence. Nature Reviews Physics 2022\\n\\n 4:12 2022, 4, - 761\u2013769.\\n\\n\\n(14) Arrieta, A. B.; D\xB4\u0131az-Rodr\xB4\u0131guez, - N.; Ser, J. D.; Bennetot, A.; Tabik, S.; Barbado, A.;\\n\\n Garcia, S.; - Gil-Lopez, S.; Molina, D.; Benjamins, R.; Chatila, R.; Herrera, F. Explain-\\n\\n - \ able Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities - and Chal-\\n\\n lenges toward Responsible AI. Information Fusion 2019, 58, - 82\u2013115.\\n\\n\\n(15) Murdoch, W. J.; Singh, C.; Kumbier, K.; Abbasi-Asl, - R.; Yu, B. Interpretable machine\\n\\n learning: definitions, methods, and - applications. ArXiv 2019, abs/1901.04592.\\n\\n\\n 25(16) - Boobier, S.; Osbourn, A.; Mitchell, J. B. Can human experts predict solubility - better\\n\\n than computers? Journal of cheminformatics 2017, 9, 1\u201314.\\n\\n\\n(17) - Lee, J. D.; See, K. A. Trust in automation: Designing for appropriate reliance. - Human\\n\\n Factors 2004, 46, 50\u201380.\\n\\n\\n(18) Bolukbasi, T.; Chang, - K.-W.; Zou, J. Y.; Saligrama, V.; Kalai, A. T. Man is to com-\\n\\n puter - programmer as woman is to homemaker? debiasing word embeddings. Advances\\n\\n - \ in neural information processing systems 2016, 29.\\n\\n\\n(19) Buolamwini, - J.; Gebru, T. Gender Shades: Intersectional Accuracy Disparities in\\n\\n Commercial - Gender Classification. Proceedings of the 1st Conference on Fairness,\\n\\n - \ Accountability and Transparency. 2018; pp 77\u201391.\\n\\n\\n(20) Lapuschkin, - S.; W\xA8aldchen, S.; Binder, A.; Montavon, G.; Samek, W.; M\xA8uller, K.-R.\\n\\n - \ Unmasking Clever Hans predictors and assessing what machines really learn. - Nature\\n\\n communications 2019, 10, 1\u20138.\\n\\n\\n(21) DeGrave, A. - J.; Janizek, J. D.; Lee, S.-I. AI for radiographic COVID-19 detection\\n\\n - \ selects shortcuts over signal. Nature Machine Intelligence 2021, 3, 610\u2013619.\\n\\n\\n(22) - Goodman, B.; Flaxman, S. European Union regulations on algorithmic decision-\\n\\n - \ making and a \u201Cright to explanation\u201D. AI Magazine 2017, 38, 50\u201357.\\n\\n\\n(23) - ACT, A. I. European Commission. On Artificial Intelligence: A European Approach\\n\\n - \ to Excellence and Trust. 2021, COM/2021/206.\\n\\n\\n(24) Blueprint for - an AI Bill of Rights, The White House. 2022; https://www.whitehouse.\\n\\n gov/ostp/ai-bill-of-rights/.\\n\\n\\n(25) - Miller, T. Explanation in artificial intelligence: Insights from the social - sciences. Ar-\\n\\n tificial intelligence 2019, 267, 1\u201338.\\n\\n\\n\\n - \ 26(26) Murdoch, W. J.; Singh, C.; Kumbier, - K.; Abbasi-Asl, R.; Yu, B. Definitions, meth-\\n\\n ods, and applications - in interpretable machine learning. Proceedings of the National\\n\\n Academy - of Sciences of the United States of America 2019, 116, 22071\u201322080.\\n\\n\\n(27) - Gunning, D.; Aha, D. DARPA\u2019s Explainable Artificial Intelligence (XAI) - Program.\\n\\n AI Magazine 2019, 40, 44\u201358.\\n\\n\\n(28) Biran, O.; - Cotton, C. Explanation and justification in machine learning: A survey.\\n\\n - \ IJCAI-17 workshop on explainable AI (XAI). 2017; pp 8\u201313.\\n\\n\\n(29) - Palacio, S.; Lucieri, A.; Munir, M.; Ahmed, S.; Hees, J.; Dengel, A. Xai handbook:\\n\\n - \ Towards a unified framework for explainable ai. Proceedings of the IEEE/CVF - Inter-\\n\\n national Conference on Computer Vision. 2021; pp 3766\u20133775.\\n\\n\\n(30) - Kuhn, D. R.; Kacker, R. N.; Lei, Y.; Simos, D. E. Combinatorial Methods for - Ex-\\n\\n plainable AI. 2020 IEEE International Conference on Software Testing, - Verification\\n\\n and Validation Workshops (ICSTW) 2020, 167\u2013170.\\n\\n\\n(31) - Seshadri, A.; Gandhi, H. A.; Wellawatte, G. P.; White, A. D. Why does that molecule\\n\\n - \ smell? ChemRxiv 2022,\\n\\n\\n(32) Das, A.; Rad, P. Opportunities and challenges - in explainable artificial intelligence\\n\\n (xai): A survey. arXiv preprint - arXiv:2006.11371 2020,\\n\\n\\n(33) Machlev, R.; Heistrene, L.; Perl, M.; Levy, - K. Y.; Belikov, J.; Mannor, S.; Levron, Y.\\n\\n Explainable Artificial - Intelligence (XAI) techniques for energy and power systems:\\n\\n Review, - challenges and opportunities. Energy and AI 2022, 9, 100169.\\n\\n\\n(34) Koh, - P. W.; Liang, P. Understanding black-box predictions via influence functions.\\n\\n - \ International Conference on Machine Learning. 2017; pp 1885\u20131894.\\n\\n\\n(35) - Ribeiro, M. T.; Singh, S.; Guestrin, C. \u201D Why should i trust you?\u201D - Explaining the\\n\\n predictions of any classifier. Proceedings of the 22nd - ACM SIGKDD international\\n\\n\\n 27 conference - on knowledge discovery and data \\n\\n------------\\n\\nQuestion: Are counterfactuals - actionable? [yes/no]\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate - connection: - - keep-alive - content-length: - - "6520" - content-type: - - application/json - host: - - api.openai.com - user-agent: - - AsyncOpenAI/Python 1.109.0 - x-stainless-arch: - - arm64 - x-stainless-async: - - async:asyncio - x-stainless-lang: - - python - x-stainless-os: - - MacOS - x-stainless-package-version: - - 1.109.0 - x-stainless-raw-response: - - "true" - x-stainless-read-timeout: - - "60.0" - x-stainless-retry-count: - - "0" - x-stainless-runtime: - - CPython - x-stainless-runtime-version: - - 3.13.5 - method: POST - uri: https://api.openai.com/v1/chat/completions - response: - body: - string: !!binary | - H4sIAAAAAAAAA4xUTW8bOQy9+1cQuvQyDmw3jlNfCxRG0T0liy52XYxpiTPDVCNNRU4SI8h/X2jG - jd02XexFBz5+PJKPepoAGHZmDcY2qLbt/PT9x2/u7sB/1e/mm82ff797u/n2eLjxn267TfvBFDki - 7u/I6veoCxvbzpNyDCNsE6FSzjpfLa+vL6+u5ssBaKMjn8PqTqeXcbqYLS6n8/l0MTsGNpEtiVnD - PxMAgKfhzRSDo0ezhlnx3dKSCNZk1i9OACZFny0GRVgUg5riBNoYlMLAerfb3UkM2/C0DQBbI33b - YjpszRq25rYhoEdLqVNIVFGiYEkA4SGmr7A/wGfyHh9QlQq4IWnQJS4Ag4PPDStBDPDmj9wpYB2i - KFuoKVDCPCGIFdjYB6VUodUePdBj5zEMqEAVE7TRk+09yRvo+r1nacgBB3jfUMsWPdxYzqSybTFb - LC7gtmEB6euaRAW0Qf3PIpgIehmTHothgi6RYzuQHBYlBXQxj4zR+wNwcGxROdSgDXGCXtmzZgBw - CMO9z5yE60blAjbxge4pFdn9ZaIukkCIOhBiy+oP4FhsLwIPDWlD6SfqI91ThYutKca1JfJ0j8FS - KTYmyuu7OkK5u5JbrEmyuUIvtA3P27Db7c5FkajqBbMmQ+/9GYAhRB3HleX45Yg8vwjQx7pLcS8/ - hZqKA0tTJkKJIYtNNHZmQJ8nAF8Gofc/aNd0Kbadlhq/0lBuvlotxoTmdFtn8Gx5RDUq+jPgerUq - XklZOlJkL2fXYizahtwp9nRa2DuOZ8DkrPFf+byWe2yeQ/1/0p8Aa6lTcuVJiK+5Jcqfz+/cXgY9 - EDZC6Z4tlcqU8jIcVdj78V8wchCltqw41JS6xOPnUHXl8mq+XyxXK7c3k+fJvwAAAP//AwBStwan - JQUAAA== - headers: - Access-Control-Expose-Headers: - - X-Request-ID - CF-RAY: - - 984ea6cfb97d6897-SJC - Connection: - - keep-alive - Content-Encoding: - - gzip - Content-Type: - - application/json - Date: - - Fri, 26 Sep 2025 00:30:16 GMT - Server: - - cloudflare - Strict-Transport-Security: - - max-age=31536000; includeSubDomains; preload - Transfer-Encoding: - - chunked - X-Content-Type-Options: - - nosniff - alt-svc: - - h3=":443"; ma=86400 - cf-cache-status: - - DYNAMIC - openai-organization: - - future-house-xr4tdh - openai-processing-ms: - - "1339" - openai-project: - - proj_RpeV6PrPclPHBb5GlExPXSBj - openai-version: - - "2020-10-01" - x-envoy-upstream-service-time: - - "1356" - x-openai-proxy-wasm: - - v0.1 - x-ratelimit-limit-requests: - - "10000" - x-ratelimit-limit-tokens: - - "30000000" - x-ratelimit-remaining-requests: - - "9999" - x-ratelimit-remaining-tokens: - - "29998444" - x-ratelimit-reset-requests: - - 6ms - x-ratelimit-reset-tokens: - - 3ms - x-request-id: - - req_7218a33009e24858b7e9ca011ac90826 - status: - code: 200 - message: OK - - request: - body: - "{\"messages\":[{\"role\":\"system\",\"content\":\"Provide a summary of - the relevant information that could help answer the question based on the excerpt. - Your summary, combined with many others, will be given to the model to generate - an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - wellawatteUnknownyearaperspectiveon pages 3-5: Geemi P. Wellawatte, Heta A. - Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of - molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, - doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\n - a passive characteristic of a model, whereas explainability\\n\\nis an active - characteristic which is used to clarify the internal decision-making process.\\n\\nNamely, - an explanation is extra information that gives the context and a cause for one - or\\n\\nmore predictions.29 We adopt the same nomenclature in this perspective.\\n\\n - \ Accuracy and interpretability are two attractive characteristics of DL models. - However,\\n\\nDL models are often highly accurate and less interpretable.28,30 - XAI provides a way to avoid\\n\\nthat trade-off in chemical property prediction. - XAI can be viewed as a two-step process.\\n\\nFirst, we develop an accurate - but uninterpretable DL model. Next, we add explanations to\\n\\npredictions. - Ideally, if the DL model has correctly learned the input-output relations, then\\n\\nthe - explanations should give insight into the underlying mechanism.\\n\\n In the - remainder of this article, we review recent approaches for XAI of chemical property\\n\\nprediction + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from wellawatteUnknownyearaperspectiveon + pages 3-5: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. + White. A perspective on explanations of molecular prediction models. ChemRxiv, + Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. + This article has 1 citations.\\n\\n------------\\n\\n a passive characteristic + of a model, whereas explainability\\n\\nis an active characteristic which is + used to clarify the internal decision-making process.\\n\\nNamely, an explanation + is extra information that gives the context and a cause for one or\\n\\nmore + predictions.29 We adopt the same nomenclature in this perspective.\\n\\n Accuracy + and interpretability are two attractive characteristics of DL models. However,\\n\\nDL + models are often highly accurate and less interpretable.28,30 XAI provides a + way to avoid\\n\\nthat trade-off in chemical property prediction. XAI can be + viewed as a two-step process.\\n\\nFirst, we develop an accurate but uninterpretable + DL model. Next, we add explanations to\\n\\npredictions. Ideally, if the DL + model has correctly learned the input-output relations, then\\n\\nthe explanations + should give insight into the underlying mechanism.\\n\\n In the remainder + of this article, we review recent approaches for XAI of chemical property\\n\\nprediction while drawing specific examples from our recent XAI work.9,10,31 We show how\\n\\nin various systems these methods yield explanations that are consistent with known and\\n\\nmechanisms in structure-property relationships.\\n\\n\\n\\n\\n\\n 3Theory\\n\\n\\nIn @@ -5063,7 +4835,7 @@ interactions: connection: - keep-alive content-length: - - "6479" + - "6309" content-type: - application/json host: @@ -5095,25 +4867,24 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//jFRNbyM3DL37VxA6tYAdxK7zAd+CoAW8RQ/tFmiAemFzJM4MtxppKlLe - GEH+e6HxxHa2W6AXHfTIx0eKTy8TAMPOrMDYFtV2vZ89fvjbMXY/Ubdf/xH08df9U/hl/fHHx4+/ - /fzBTEtGrD6T1besKxu73pNyDEfYJkKlwjq/u7m/X97ezpcD0EVHvqQ1vc6Wcba4Xixn8/lscT0m - tpEtiVnBnxMAgJfhLBKDo2ezguvp201HItiQWZ2CAEyKvtwYFGFRDGqmZ9DGoBQG1bvd7rPEsAkv - mwCwMZK7DtNhY1awMY8xB6VUo9WMXgATgSOxiStygAI+WvTAJahPpFgaF+AA2hIMVZ4VYg303Hvk - gJUneFjDd08P6++nhUBbOryhID1ZrtkChyLZklzB7y3BwOIiCYSoQzRbVn8AUVSCLy1pSwnsN9Si - LZJK3SlUWYFHoo5CAUBb1FMQe9YDsAAGQNXEVVaCLORAI9AefS71Brnh3OvTw/oKHt5xJKopScl6 - E8daiK0nTNDGL8Chzwo1oeZEAhYDVAS2xdAcy3XRcX0YBhmz9lnLLFhActOQqBylf92zjdk76GN5 - XkbvD4X1PATg+jjyPsU9OxoFNZldmTfEMNbl0BwlDk2gbZn2w9tzIjcKkquNmR63JpGnfWHYio2J - yvbcj1AZ35Y7bEjKdY1eaBNeN2G3213uZKI6CxZLhOz9BYAhxHGzihs+jcjraf99bPoUK/kq1dQc - WNptIpQYyq6Lxt4M6OsE4NPgs/zOOqZPset1q/EvGsrNf7gejWbO1r6AF/cjqlHRXwDLE/KOcutI - kb1cmNVYtC25c+7Z2ZgdxwtgctH4v/V8i/vYPIfm/9CfAWupV3LbPpFj+77nc1ii8vf9V9hp0INg - I5T2bGmrTKk8hqMasz9+S0YOotRtaw5N+Un4+DfV/fbmdl4tbu7uXGUmr5N/AAAA//8DANnzxKCk - BQAA + H4sIAAAAAAAAA4xUwW4bRwy96yuIObWAZFhyHNu6GUYPRg4FggQJEAUSNcPdZTo7sx1yZG8M/3sx + u5Ilpy7Qyx72kY+PnEc+TQAMO7MEYxtU23Z+dvfh6u6Pv+908fEGP/7Zf5L+85d5vfv54erhhs20 + ZMTtD7J6yDqzse08KccwwjYRKhXW+dXl9c38fHF5PQBtdORLWt3p7F2cLc4X72bz+Wxxvk9sIlsS + s4RvEwCAp+FbJAZHj2YJ59PDn5ZEsCazfAkCMCn68segCItiUDM9gjYGpTCo3mw2PySGVXhaBYCV + kdy2mPqVWcLK3MUclFKFVjN6AUwEjsQm3pIDFPDRogcuQV0ixdK4AAfQhmCo8qgQK6DHziMH3HqC + 23v47evt/e/TQqAN9QcUpCPLFVvgUCRbkjP41BAMLC6SQIg6RLNl9T2IohI8NKQNJbBvqEVbJJW6 + U9hmBd4TtRQKANqgvgSxZ+2BBTAAqibeZiXIQg40Au3Q51JvkBuOvX69vT+D21cciSpKUrIO4lgL + sfWECZr4ABy6rFARak4kRbt3sCWwDYZ6LNhGx1U/jDJm7bKWabCA5LomURnF/9r1yNTF8sCM3veF + 9TgG4Gocepfijh3tJdWZXZk4xLCvy6EeRQ5toG2YdgRY3p8TuYOklZmOxknkaVco1mJjomKg61V4 + XoXNZnPqvURVFizWD9n7EwBDiHsHFdd/3yPPLz73se5S3MovqabiwNKsE6HEUDwtGjszoM8TgO/D + PuVXK2K6FNtO1xr/oqHcfPH+YiQ0xxU+hRd7VKOiPwEuri+nb1CuHSmyl5OlNBZtQ+6Ye9xgzI7j + CTA5afzfet7iHpvnUP8f+iNgLXVKbt0lcmxf93wMS1Ru3H+FvQx6EGyE0o4trZUplcdwVGH24/kx + 0otSu6441OVi8HiDqm7tbip6f3l1QVszeZ78AwAA//8DAI1jY2aMBQAA headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6cabf65f9ea-SJC + - 9854b89b2ea0eb2c-SJC Connection: - keep-alive Content-Encoding: @@ -5121,7 +4892,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:16 GMT + - Fri, 26 Sep 2025 18:11:01 GMT Server: - cloudflare Strict-Transport-Security: @@ -5137,13 +4908,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2190" + - "3404" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2225" + - "3417" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -5153,13 +4924,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998448" + - "29998490" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 3ms x-request-id: - - req_6416d2ee65d34675b29cf2f3cd705642 + - req_f5e1b1bffbf541c6b785b4999cfcf76c status: code: 200 message: OK @@ -5169,24 +4940,22 @@ interactions: the relevant information that could help answer the question based on the excerpt. Your summary, combined with many others, will be given to the model to generate an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": - \\\"...\\\",\\n \\\"relevance_score\\\": 0-10,\\n \\\"used_images\\\"\\n}\\n\\nwhere - `summary` is relevant information from the text - about 100 words words. `relevance_score` - is an integer 0-10 for the relevance of `summary` to the question. `used_images` - is a boolean flag indicating if any images present in a multimodal message were - used, and if no images were present it should be false.\\n\\nThe excerpt may - or may not contain relevant information. If not, leave `summary` empty, and - make `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from - wellawatteUnknownyearaperspectiveon pages 1-3: Geemi P. Wellawatte, Heta A. - Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of - molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, - doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\n - A Perspective on Explanations of Molecular\\n\\n Prediction Models\\n\\n\\nGeemi - P. Wellawatte,\u2020 Heta A. Gandhi,\u2021 Aditi Seshadri,\u2021 and Andrew\\n\\n - \ D. White\u2217,\u2021\\n\\n\\n \u2020Department - of Chemistry, University of Rochester, Rochester, NY, 14627\\n\\n\u2021Department - of Chemical Engineering, University of Rochester, Rochester, NY, 14627\\n\\n - \ \xB6Vial Health Technology, Inc., San Francisco, CA 94111\\n\\n\\n - \ E-mail: andrew.white@rochester.edu\\n\\n\\n\\n Abstract\\n\\n\\n + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from wellawatteUnknownyearaperspectiveon + pages 1-3: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. + White. A perspective on explanations of molecular prediction models. ChemRxiv, + Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. + This article has 1 citations.\\n\\n------------\\n\\n A Perspective on Explanations + of Molecular\\n\\n Prediction Models\\n\\n\\nGeemi P. Wellawatte,\u2020 + \ Heta A. Gandhi,\u2021 Aditi Seshadri,\u2021 and Andrew\\n\\n D. + White\u2217,\u2021\\n\\n\\n \u2020Department of Chemistry, University of + Rochester, Rochester, NY, 14627\\n\\n\u2021Department of Chemical Engineering, + University of Rochester, Rochester, NY, 14627\\n\\n \xB6Vial Health + Technology, Inc., San Francisco, CA 94111\\n\\n\\n E-mail: + andrew.white@rochester.edu\\n\\n\\n\\n Abstract\\n\\n\\n \ Chemists can be skeptical in using deep learning (DL) in decision making, due to\\n\\n the lack of interpretability in \u201Cblack-box\u201D models. \ Explainable artificial intelligence\\n\\n (XAI) is a branch of AI which @@ -5256,7 +5025,7 @@ interactions: connection: - keep-alive content-length: - - "6504" + - "6334" content-type: - application/json host: @@ -5288,24 +5057,23 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//jFRNbxs3EL3rVwx4SgDJsBTbSnVrbKBoYSAt4ARBq2BBkbO7E3FJZmbW - sWD4vwfctT6cpkAve+Cbee9x+GYfJwCGvFmBca1V1+Uwu/7jq//yPnygN83db3++++t8yx/p+t3y - 7u9ftp/MtHSkzRd0uu86c6nLAZVSHGHHaBUL63x5+fbtxdXV/HIAuuQxlLYm6+wizRbni4vZfD5b - nD83tokcilnBPxMAgMfhWyxGjw9mBefT/UmHIrZBszoUARhOoZwYK0KiNqqZHkGXomIcXD+uI8Da - SN91lndrs4K1uWsR8MEhZwVP4noRFNAWoReEVAM+5GAp2k1AsKxUkyMbgKJiCNRgdAivPv36+2ug - CK7FjkR5N4U6uV4oNpAidKht8gKBtjjWOBvApT4qcm2d9jYI2OjBozimrIlH4WjLfAU0DYqcGRU8 - YoaAlmPhf3Vz+xqGEUNm9OSGjjO4a1HwIJ053ZNHoCjUtCqFLoEo9057xlnmlJF1B4xh1Gwpj55a - DHk/hlOJKXR2Wxzc3I76Al1iPBodZxY9KPei3xJruzuD6x+vzcVlLJTowQpY0JRCufJe9Ob2pe6m - 1+GJDg+XUCAmHRrIkYYdWO8ZReBbi9oil/rdoGUHluLtbG2mYyQYA97b6LASlxhLNJbPUC/oK+ps - g1KOaxsE1/HpNGKMdS+2JDz2IZwANsak4zRLuD8/I0+HOIfUZE4b+aHV1BRJ2orRSooluqIpmwF9 - mgB8Htamf7EJJnPqslaatjjIzd/MlyOhOW7qCTx/3iqjSW04AS4W+74XlJVHtRTkZPeMs65Ff+w9 - LqrtPaUTYHJy8X/7+Rn3eHmKzf+hPwLOYVb01TEwPytjLL+y/yo7DHowbAT5nhxWSsjlMTzWtg/j - X8bIThS7qqbYlNTT+Kupc3V5Nd8sLpdLvzGTp8l3AAAA//8DANJ9cmlzBQAA + H4sIAAAAAAAAA4xUwU4bQQy95yusOYGUREmAEHJDILWoHKqqVVEbtJrMeHcHZmdGtjdNivLv1e4G + EgqVetnDPD/72X7epx6AclbNQZlSi6mSH1x9Or/68LswZzNH6YHK7/x5FfyKvz3++DJV/YYRlw9o + 5Jk1NLFKHsXF0MGGUAs2WcfnZ7OL8WgyHbVAFS36hlYkGZzGwWQ0OR2Mx4PJaEcsozPIag4/ewAA + T+23kRgsrtUc2jTtS4XMukA1fwkCUBR986I0s2PRQVR/D5oYBEOr+mkRABaK66rStFmoOSzU1xIB + 1wYpCVjHpmZGBlwnr13QS4+gSVzujNMeXBD03hUYDMLR3eXNMbgAUiK0VdYCMQeLmMCjpuBCAUfX + t8fQDoAhjwSmxMqx0GYINwIVhmZ+DFJqgbvLG6hQymi5D1ybEjR3BKM9mFgHQcq1kVp7Bh0sWGRD + LkmkTnLQbbo+GB2em4DrW0iE1pmuVMNLFFfOIrjAriiFm84isFBtpCYcJIoJSTZA6LuUpUs8hI/x + F66Q+m3PbcM2IkOI0lZzxonfgLaWkBl+lSgl0lvlhKBbNc2AhwvV7xZD6HGlg8GMTSRsFjRdhO3h + NgnzmnVjplB7fwDoEKJ0Uhsf3e+Q7YtzfCwSxSX/RVW5C47LjFBzDI1LWGJSLbrtAdy3Dq1fmU4l + ilWSTOIjtuXGk9nOomp/FHt4Nt2BEkX7A9rJ9Bl5lTGzKNp5PnC5MtqUaPfc/Uno2rp4APQO+n4r + 573cXe8uFP+Tfg8Yg0nQZntzvRdG2Pw0/hX2MudWsGKklTOYiUNqdmEx17Xv7lnxhgWrLHehQErk + uqPOU2YvcpyenZ/gUvW2vT8AAAD//wMAhqNMZd0EAAA= headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6d21c6f2510-SJC + - 9854b8a9b8d2aaaf-SJC Connection: - keep-alive Content-Encoding: @@ -5313,7 +5081,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:17 GMT + - Fri, 26 Sep 2025 18:11:01 GMT Server: - cloudflare Strict-Transport-Security: @@ -5329,13 +5097,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "1604" + - "1059" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "1630" + - "1101" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -5345,13 +5113,207 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998442" + - "29998483" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 3ms x-request-id: - - req_c1e7bc43083c416abba2de982eb91913 + - req_9bc4aace153f41c183351c00cd2d5ac2 + status: + code: 200 + message: OK + - request: + body: + "{\"messages\":[{\"role\":\"system\",\"content\":\"Provide a summary of + the relevant information that could help answer the question based on the excerpt. + Your summary, combined with many others, will be given to the model to generate + an answer. Respond with the following JSON format:\\n\\n{\\n \\\"summary\\\": + \\\"...\\\",\\n \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant + information from the text - about 100 words words. `relevance_score` is an integer + 0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or + may not contain relevant information. If not, leave `summary` empty, and make + `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt + from wellawatteUnknownyearaperspectiveon pages 16-20: Geemi P. Wellawatte, Heta + A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations + of molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, + doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\nssion + challenge and is\\n\\nimportant for chemical process design, drug design and + crystallization.133\u2013136 In our previous\\n\\nworks,9,10 we implemented + and trained an RNN model in Keras to predict solubilities (log\\n\\nmolarity) + of small molecules.127 The AqSolDB curated database137 was used to train the\\n\\nRNN + model.\\n\\n In this task, counterfactuals are based on equation 6. Figure + 3 illustrates the generated\\n\\nlocal chemical space and the top four counterfactuals. + Based on the counterfactuals, we ob-\\n\\nserve that the modifications to the + ester group and other heteroatoms play an important role\\n\\nin solubility. + These findings align with known experimental and basic chemical intuition.134\\n\\nFigure + 4 shows a quantitative measurement of how substructures are contributing to + the pre-\\n\\n\\n\\n 16Figure 2: Descriptor + explanations along with natural language explanation obtained for BBB\\npermeability + of Alprozolam molecule. The green and red bars show descriptors that influ-\\nence + predictions positively and negatively, respectively. Dotted yellow lines show + significance\\nthreshold (\u03B1 = 0.05) for the t-statistic. Molecular descriptors + show molecule-level proper-\\nties that are important for the prediction. ECFP + and MACCS descriptors indicate which\\nsubstructures influence model predictions. + MACCS explanations lead to text explanations\\nas shown. Republished from Ref.10 + with permission from authors. SMARTS annotations for\\nMACCS descriptors were + created using SMARTSviewer (smartsview.zbh.uni-hamburg.de,\\nCopyright: ZBH, + Center for Bioinformatics Hamburg) developed by Schomburg et al. 132.\\n\\n\\n\\n\\n\\n + \ 17diction. For example, we see that adding + acidic and basic groups as well as hydrogen bond\\n\\nacceptors, increases solubility. + Substructure importance from ECFP97 and MACCS138 de-\\n\\nscriptors indicate + that adding heteroatoms increases solubility, while adding rings structures\\n\\nmakes + the molecule less soluble. Although these are established hypotheses, it is + interesting\\n\\nto see they can be derived purely from the data via DL and + XAI.\\n\\n\\n\\n\\n\\nFigure 3: Generated chemical space for solubility prediction + using the RNN model. The\\nchemical space is a 2D projection of the pairwise + Tanimoto similarities of the local coun-\\nterfactuals. Each data point is colored + by solubility. Top 4 counterfactuals are shown here.\\nRepublished from Ref.9 + with permission from the Royal Society of Chemistry.\\n\\n\\n\\nGeneralizing + XAI \u2013 interpreting scent-structure relationships\\n\\n\\nIn this example, + we show how non-local structure-property relationships can be learned with\\n\\nXAI + across multiple molecules. Molecular scent prediction is a multi-label classification + task\\n\\nbecause a molecule can be described by more than one scent. For example, + the molecule\\n\\njasmone can be described as having \u2018jasmine,\u2019 \u2018woody,\u2019 + \u2018floral,\u2019 and \u2019herbal\u2019 scents.139 The\\n\\nscent-structure + relationship is not very well understood,140 although some relationships are\\n\\nknown. + \ For example, molecules with an ester functional group are often associated + with\\n\\n\\n 18Figure 4: Descriptor explanations + for solubility prediction model. The green and red bars\\nshow descriptors that + influence predictions positively and negatively, respectively. Dotted\\nyellow + lines show significance threshold (\u03B1 = 0.05) for the t-statistic. The MACCS + and\\nECFP descriptors indicate which substructures influence model predictions. + MACCS sub-\\nstructures may either be present in the molecule as is or may represent + a modification. ECFP\\nfingerprints are substructures in the molecule that affect + the prediction. MACCS descriptor\\nare used to obtain text explanations as shown. + Republished from Ref.10 with permission from\\nauthors. SMARTS annotations for + MACCS descriptors were created using SMARTSviewer\\n(smartsview.zbh.uni-hamburg.de, + Copyright: ZBH, Center for Bioinformatics Hamburg) de-\\nveloped by Schomburg + et al. 132.\\n\\n\\n\\n\\n\\n 19the \u2018fruity\u2019 + scent. There are some exceptions though, like tert-amyl acetate which has a\\n\\n\u2018camphoraceous\u2019 + rather than \u2018fruity\u2019 scent.140,141\\n\\n In Seshadri et al. 31, + we trained a GNN model to predict the scent of molecules and utilized\\n\\ncounterfactuals9 + and descriptor explanations10 to quantify scent-structure relationships. The\\n\\nMMACE + method was modified to account for the multi-label aspect of scent prediction. + This\\n\\nmodification defines molecules that differed from the instance molecule + by only the selected\\n\\nscent as counterfactuals. For instance, counterfactuals + of the jasmone molecule would be false\\n\\nfor the \u2018jasmine\u2019 scent + but would still be positive for \u2018woody,\u2019 \u2018floral\u2019 and \u2018herbal\u2019 + scents.\\n\\n\\n\\n\\n\\nFigure 5: Counterfactual for the 2,4 decadienal molecule. + \ The counterfactual indicates\\nstructural changes to ethyl benzoate that would + result in the model predicting the molecule\\nto not contain the \u2018fruity\u2019 + scent. The Tanimoto96 similarity between the counterfactual and\\n2,4 decadienal + is also\\n\\n------------\\n\\nQuestion: Are counterfactuals actionable? [yes/no]\\n\\n\"}]}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}" + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - "188581" + content-type: + - application/json + host: + - api.openai.com + user-agent: + - AsyncOpenAI/Python 1.109.0 + x-stainless-arch: + - arm64 + x-stainless-async: + - async:asyncio + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.109.0 + x-stainless-raw-response: + - "true" + x-stainless-read-timeout: + - "60.0" + x-stainless-retry-count: + - "0" + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.5 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: !!binary | + H4sIAAAAAAAAA4xUTW/bOhC8+1cseJYN243rxsemH4ceenjvULQODJpcSdtQXJW7TGsE+e8FZcfW + y0uBXnTQcJY7s7N8mAAY8mYDxrVWXdeH6c2n9c271delhOWXz28/3v34Wtv3rz6svlzXd9lUhcH7 + 7+j0iTVz3PUBlTgeYZfQKpaqi/XqzfVivlytB6Bjj6HQml6nVzxdzpdX08ViupyfiC2TQzEb+DYB + AHgYvqXF6PGX2cC8evrToYht0GzOhwBM4lD+GCtCojaqqS6g46gYh64fthFgayR3nU2HrdnA1txw + joqptk6zDQI2IVhXRNl9QLAC2uIB+sT35BEoCjWtClBUBtGUneZkA3TsqSZnC7NwrIKzESjWIWN0 + CB0HdDnYVGr1mJRQKpDs2nKJcMh7CqQH4ATiMOoMPnAC/GWLy1VpA0SzP4DHjqNosooCLf8E19rY + oIAyoCgmaBLnXipoUTGxVe6kKnXbg0/cYIQ9Rw/WOeyVk5w6LfMTHLVSgQ3URIoN/CRtwbXYkbOh + iM9UlM7gH+oo2BQOFbhnVtZPSqBP6GkwFVpq2lAsHJt3FlBss6FIOBJdKDM9+zqDf1sUhJqip9gI + SG4aFD35/ez+oqrJZWqjgXp0JMOMKF4EeRRq4mxrqmNEEga8t9HhThwnLFFZzLfxcRyshHUWW3Id + cwgjwMbIemy4RPr2hDyeQxy46RPv5RnV1BRJ2l0ZA8cSWFHuzYA+TgBuh2XJ/8m/6RN3ve6U73C4 + brm4fn0saC77eYEX8zcnVFltGPFeza+qF0ruPKqlIKONM866Fv2Fe1lPmz3xCJiMhP+/n5dqH8VT + bP6m/AUYoox+d0naS8cSlgfsT8fORg8NG8F0Tw53SpjKMDzWNofj22LkIIrdrqbYYOoTHR+Yut+t + Xi/2y9V67fdm8jj5DQAA//8DAL5dQVtpBQAA + headers: + Access-Control-Expose-Headers: + - X-Request-ID + CF-RAY: + - 9854b8914dffd049-SJC + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Fri, 26 Sep 2025 18:11:03 GMT + Server: + - cloudflare + Strict-Transport-Security: + - max-age=31536000; includeSubDomains; preload + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + alt-svc: + - h3=":443"; ma=86400 + cf-cache-status: + - DYNAMIC + openai-organization: + - future-house-xr4tdh + openai-processing-ms: + - "6509" + openai-project: + - proj_RpeV6PrPclPHBb5GlExPXSBj + openai-version: + - "2020-10-01" + x-envoy-upstream-service-time: + - "6686" + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-input-images: + - "250000" + x-ratelimit-limit-requests: + - "10000" + x-ratelimit-limit-tokens: + - "30000000" + x-ratelimit-remaining-input-images: + - "249998" + x-ratelimit-remaining-requests: + - "9999" + x-ratelimit-remaining-tokens: + - "29996955" + x-ratelimit-reset-input-images: + - 1m0s + x-ratelimit-reset-requests: + - 1m0s + x-ratelimit-reset-tokens: + - 1m0s + x-request-id: + - req_4d1ffd764e2246b9a7a4d3d7cc3e0b39 status: code: 200 message: OK @@ -5360,54 +5322,53 @@ interactions: '{"messages":[{"role":"system","content":"Answer in a direct and concise tone. Your audience is an expert, so be highly specific. If there are ambiguous terms or acronyms, first define them."},{"role":"user","content":"Answer the - question below with the context.\n\nContext:\n\npqac-74bd847d: Counterfactual - explanations are actionable as they suggest which features can be altered to - change the outcome of a prediction. For example, in chemistry, changing a hydrophobic - functional group in a molecule to a hydrophilic group can increase solubility. - Counterfactuals provide local, instance-level explanations that are intuitive - and can uncover spurious relationships in training data. They are particularly - useful in explainable AI (XAI) for chemistry, offering actionable insights into - how predictions can be modified.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, - Aditi Seshadri, and Andrew D. White. A perspective on explanations of molecular - prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, - doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\n\npqac-d9517d89: + question below with the context.\n\nContext:\n\npqac-12c335ad: Counterfactual + explanations are actionable as they provide local, instance-level explanations + that suggest which features can be altered to change the outcome. For example, + in chemistry, changing a hydrophobic functional group in a molecule to a hydrophilic + group can increase solubility. This actionability makes counterfactuals a useful + tool in explainable AI (XAI) for chemistry, as they offer intuitive understanding + and uncover spurious relationships in training data.\nFrom Geemi P. Wellawatte, + Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations + of molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, + doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\n\npqac-0071854a: Yes, counterfactuals are actionable. The text provides an example where counterfactual - explanations suggest modifications to a molecule''s structure (e.g., changes - to the carboxylic acid group) to enable it to permeate the blood-brain barrier + explanations suggest modifications to a molecule''s structure, such as altering + the carboxylic acid group, to enable it to permeate the blood-brain barrier (BBB). These modifications align with experimental findings, demonstrating that - counterfactuals can propose actionable changes to achieve desired molecular - properties.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew - D. White. A perspective on explanations of molecular prediction models. ChemRxiv, - Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. - This article has 1 citations.\n\npqac-07c410de: Counterfactuals are actionable - in the context of molecular prediction models. The provided figures and text - illustrate how counterfactuals can guide modifications to molecular structures - to achieve desired properties, such as increased solubility or altered scent - profiles. For example, changes to ester groups and heteroatoms were shown to - influence solubility predictions, aligning with experimental and chemical intuition. - Similarly, counterfactuals were used to identify structural changes affecting - scent predictions, demonstrating their utility in guiding actionable molecular - design decisions.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, + counterfactuals can propose actionable changes to improve molecular properties + like BBB permeability.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. - This article has 1 citations.\n\npqac-244888b9: Counterfactuals are described - as actionable in the excerpt. They are molecular structures with minimal distance - from a base molecule but with contrasting chemical properties. These explanations - are represented as chemical structures, which are familiar to domain experts, - sparse, and actionable. This makes them useful for understanding and modifying - molecular properties.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, + This article has 1 citations.\n\npqac-ebe7b897: Counterfactuals are actionable + as they provide insights into structural modifications that can influence molecular + properties, such as solubility or scent. For example, the study demonstrates + how changes to ester groups, heteroatoms, or hydrogen bond acceptors can increase + solubility, aligning with chemical intuition. Similarly, counterfactuals for + scent prediction highlight structural changes that alter scent classifications. + These findings suggest that counterfactuals can guide actionable decisions in + chemical design.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. - This article has 1 citations.\n\npqac-945a39b8: The excerpt discusses counterfactuals - as explanations in molecular prediction models, highlighting that they are considered - ''better'' explanations because they are actionable and sparse. Actionable explanations - provide a set of features that can change the outcome, making them useful for - decision-making. This contrasts with Shapley values, which are non-sparse and - not actionable.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and - Andrew D. White. A perspective on explanations of molecular prediction models. - ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. - This article has 1 citations.\n\nValid Keys: pqac-74bd847d, pqac-d9517d89, pqac-07c410de, - pqac-244888b9, pqac-945a39b8\n\n------------\n\nQuestion: Are counterfactuals + This article has 1 citations.\n\npqac-e74f8464: Counterfactual explanations + are described as actionable because they are represented as chemical structures + familiar to domain experts, are sparse, and provide insights into how chemical + properties can be altered. These explanations are based on minimal changes to + a base molecule to achieve contrasting chemical properties, making them useful + for understanding and modifying molecular predictions.\nFrom Geemi P. Wellawatte, + Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations + of molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, + doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\n\npqac-42a817e6: + The excerpt discusses counterfactuals as explanations in molecular prediction + models, stating that they are considered ''better'' explanations because they + are actionable and sparse. Actionable explanations provide a set of features + that can change the outcome, making them useful for decision-making. This contrasts + with Shapley values, which are non-sparse and not actionable.\nFrom Geemi P. + Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective + on explanations of molecular prediction models. ChemRxiv, Unknown year. URL: + https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02. + This article has 1 citations.\n\nValid Keys: pqac-12c335ad, pqac-0071854a, pqac-ebe7b897, + pqac-e74f8464, pqac-42a817e6\n\n------------\n\nQuestion: Are counterfactuals actionable? [yes/no]\n\nWrite an answer based on the context. If the context provides insufficient information reply \"I cannot answer.\" For each part of your answer, indicate which sources most support it via citation keys at the @@ -5429,7 +5390,7 @@ interactions: connection: - keep-alive content-length: - - "5248" + - "5155" content-type: - application/json host: @@ -5461,30 +5422,30 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAAwAAAP//jFXBjhs3DL3vVxBz6gK2sXbttXdvaYAW7aVB00PaJjA4EmeGiUacSNTu - GsH+eyCNHU+6LtCLDQxF6r3HJ/LLFUDFtrqHynSoph/c/PVvn62z6fCu+/vNJvEfP/k2vbujj7/c - +N2bapYzpP5IRk9ZCyP94EhZ/Bg2gVApV11uN7vd+vZ2uS2BXiy5nNYOOl/LfHWzWs+Xy/nq5pjY - CRuK1T38cwUA8KX8Zoje0lN1Dzez05eeYsSWqvtvhwCqIC5/qTBGjopeq9k5aMQr+YL6L4ozMJK8 - UmjQaEIXAQMBmswCa0cL+LOjAwxBHtgSxIEMN2ygF5v/MZ+LoAINoaZAEbRDBYMeTIe+JdCOQJIa - 6QmkAYQhkOVywQx6/MS+zWd6SJGa5KCRAJYMRxY/P8bZQy+OTHIYJvlQhIwL+FkCsM9UDb1kFFPb - UlRApxRyuXOtqCGZgnsGMZkOMEKgwaHJ57qDDTJ0UrOBJvlRFAdtkDREeGTtTkfYsQHxVKRgnzsf - CaK4VLNjPcAPw2c08+26trv11l4v4C337DC4wyyzLwoPEumoWqljMNTydMil0bA93asCVHoDtROx - 8zoge6gxBKYAA4WecLx1Bui49ZlKAUtPAwXuySs6aNhb9m08QrN3m+XW7u6uF/Dev/evL7nCRZlY - A2oymCKN+NtU/HHUE91Lh6DpmB4ILEUOZAtjCspT7U/S2al2EsbW5a+GvObMhl3Oqw/Alrxyc8gs - J+JRVAonydBb6EgpCKr0J8o3W7Ne3li6LiaPlPVx6I+IM+NAQ6BIXslmdKajng2672zz2HHGHgga - 7Nkxhny9lT53pSiumeCAIdKsIDlLOIMmBe0oAPkOvTm+BQ6QdOT+nfOzcq0/ol+t17vdrj417JW1 - PPozW+rSozbiI9tRxpSNIAWpjPefqUNP2omddOVth4OjAzygS0X1aeNz6ZFdIXeaFBOfsI/cdlkF - aZrxBRpHGIpn8pMF8dDJY0ZTXHOYTJOXvjkOk1MX79Yb/PGu3l0v4PcHCujcZfrUNGSUHwhUxMUy - ZyYgM3bOSUMgLV8uDKGiEo8Jr34tJYonoobDYjpkAzUpYp7xPjk3CaD3oqPD8nj/cIw8fxvoTtoh - SB3/lVo17Dl2+/w6xOfhHVWGqkSfrwA+lMWRvtsF1RCkH3Sv8onKdcvVbjcWrM676hxe3d4eoyqK - bpK32axnF0ruLSmyi5PtUxk0Hdlz7nlVYbIsk8DVhPhLPJdqj+TZt/+n/DlgDA1Kdn9eHJeOBcrL - /L+OfRO6AK4ihQc2tFemkJthqcHkxj1bxUNU6vcN+zbbicdl2wz7ze2yXm22W1tXV89XXwEAAP// - AwBVeGxqdQgAAA== + H4sIAAAAAAAAAwAAAP//jFZRb+M2DH7vryD8tAJO0aRpk/at6DBguO0GbN3DbXcIaIm2ucqSJtG5 + 5g797wPtpE7X67CXADJF8tPHj2S+ngAUbIsbKEyLYrroZnfvVnfv/uDF/buff/pyW/1+/euavzTv + nXxfv/9QlOoRqr/IyMHrzIQuOhIOfjSbRCikUeery/X1/HxxtRwMXbDk1K2JMluG2eJ8sZzN57PF + +d6xDWwoFzfw5wkAwNfhVyF6S4/FDZyXhy8d5YwNFTfPlwCKFJx+KTBnzoJeinIymuCF/ID6A+US + TOi9UKrRSI8uAyYCNPoKrBydwd0LO9BjdOhR7RliClu2BC4YdCWw12yGZo625PTITSsZqh3kvmko + C/sGciTDNRuoCaVPBF2wet7HlBYFDHpAJ5Qg9GJCp0A7fFB3aamDlpvW7Y5wAnvogiPTO0wQE1ke + bDBwneG7+Dea2XxhLi4u0ZYwHJcLXM9XdHV6Bj+EBPSIWsBSU+wGCDGFGDJBltQb6RM6MC36RuHk + 3rSAGRJFh0aRIbQ7m0JsQ6Wv6/2IzkGTQh/hM0s73WHHBoInkADsVSqaJ7i+YseyKyGkkZjdGNpg + qsLjTr3QsN3HlADkW+UcKheCnVUJ2UOFKTEliJQ6wkNEdNx4jTYgocdIiTvygg5q9pZ98wZP5+er + +fpyiadn8NF/9PctZXopBBVNopgokxeyyotpqWOD7pk8ylBjx44xKW4bOoU6wJBcAvncJ0XHKreY + SPbAAb2FmLTWGq6X4esZ3GuVNHGOmDKVUAfTZ42gZWfP3VQuTYimZdoSWMqcyB4p63PLpj0QqQok + Ts/aGjFob6N6SQAf/GzMCR1JG2wGxw8Ev7UYHe1gi66nA5W0Wtbr5dXyteRureVRIG73ug1Vfk2v + zXUkckuG80A4+4lgfVDjtcvYkpe9ZI40+6LBJuUODaZXKWunDYrKJbQklAJK6PKgwkGwDXmogreA + xlCUkHI5KLfTEUBDo1ASpj0Vk5A1QjbkBYzTeXTAcaCnolW1vl6dnqmyftlSQudes3EYNBMX5bFO + 9KgqOda020EiR1v08jyJXg4RHCo1ECsh6LwaRc0j2bc/Qh3SSHOWNOpwmjF71v+zYfbHwyMPx7ck + cTymE9V9Rt0SvnfuyIDeBxlLqQvi097y9LwSXGhiClX+l2tRs+fcbnTSBK/jP0uIxWB9OgH4NKye + /sU2KWIKXZSNhAca0s0Xq4sxYDFtu8l8Mb/cWyUIuiO/y/W6/EbIjSVBdvlofxUGTUt28p2WHfaW + w5Hh5Ojhr/F8K/b4ePbN/wk/GQbNk91Me+Vb1xLp34G3rj0TPQAuMqUtG9oIU9JiWKqxd+OmLvIu + C3Wbmn2jEudxXddxY69rurpcXVBVnDyd/AMAAP//AwDDdj6ztwgAAA== headers: Access-Control-Expose-Headers: - X-Request-ID CF-RAY: - - 984ea6ddbdd86803-SJC + - 9854b8bcf8d3239e-SJC Connection: - keep-alive Content-Encoding: @@ -5492,7 +5453,7 @@ interactions: Content-Type: - application/json Date: - - Fri, 26 Sep 2025 00:30:20 GMT + - Fri, 26 Sep 2025 18:11:08 GMT Server: - cloudflare Strict-Transport-Security: @@ -5508,13 +5469,13 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "2687" + - "4303" openai-project: - proj_RpeV6PrPclPHBb5GlExPXSBj openai-version: - "2020-10-01" x-envoy-upstream-service-time: - - "2718" + - "4337" x-openai-proxy-wasm: - v0.1 x-ratelimit-limit-requests: @@ -5524,13 +5485,13 @@ interactions: x-ratelimit-remaining-requests: - "9999" x-ratelimit-remaining-tokens: - - "29998726" + - "29998749" x-ratelimit-reset-requests: - 6ms x-ratelimit-reset-tokens: - 2ms x-request-id: - - req_14f818ee7b934dd68bcdd753a9621e98 + - req_c1255b095cc2469697b35e63e160811e status: code: 200 message: OK