(* Content-type: application/mathematica *) (*** Wolfram Notebook File ***) (* http://www.wolfram.com/nb *) (* CreatedBy='Mathematica 6.0' *) (*CacheID: 234*) (* Internal cache information: NotebookFileLineBreakTest NotebookFileLineBreakTest NotebookDataPosition[ 145, 7] NotebookDataLength[ 338480, 6819] NotebookOptionsPosition[ 330180, 6571] NotebookOutlinePosition[ 330591, 6589] CellTagsIndexPosition[ 330548, 6586] WindowFrame->Normal ContainsDynamic->False*) (* Beginning of Notebook Content *) Notebook[{ Cell[CellGroupData[{ Cell["\<\ Reading from the Protein Data Base and Modeling Protein Folding\ \>", "Title"], Cell["\<\ APAM 1601 Columbia University\ \>", "Subsubtitle"], Cell[CellGroupData[{ Cell["Introduction", "Section"], Cell[TextData[{ "The protein data base is a part of the", ButtonBox[" worldwide protein data bank", BaseStyle->"Hyperlink", ButtonData:>{ URL["http://www.wwpdb.org/"], None}], " whose mission is to maintaina single archive of the structural data for \ the large macromolecules found in living organisms. The", ButtonBox[" US protein databank", BaseStyle->"Hyperlink", ButtonData:>{ URL["http://www.rcsb.org/pdb/Welcome.do"], None}], " was founded in 2000, and it has it's headquarters at Rutgers. The \ worldwide protein data bank was founded in 2003. The data are freely and \ publically available. Protein structure is derived from x-ray diffraction \ patterns and NMR measurements, and the structural data are supplied by \ scientists who make careful and systematic measurements of protein samples. \ The data bank has wonderful tools for visualizing the protein structures. " }], "Text"], Cell[TextData[{ "The purpose of this notbook is to read the sequence of amino acids from the \ protein data bank. The amino acid sequence of a protein is contained in a \ FASTA file (pronounced \"Fast-Aye\") and this format was standardized in 1985 \ as a way to enable fast search algorthyms for proteins with similar \ structures. FASTA also refers to a ", ButtonBox["fast amino acid sequence matching algorithm", BaseStyle->"Hyperlink", ButtonData:>{ URL["http://en.wikipedia.org/wiki/FASTA"], None}], ". " }], "Text"], Cell["\<\ To use this notebook, you need to download a FASTA sequence from a protein of \ your choice from the protein database. A good way to start is to click on the \ \"Search\" tab, and then select a biological category of interest. In this \ notebook, I have downloaded a FASTA sequence for the protein 1AB2, associated \ with human cancer. \ \>", "Text"], Cell["\<\ The name of the FASTA file will be called \"fasta.txt\". You may have \ different filenames, and you will have to change the filename strings in this \ notebook in order to read the file.\ \>", "Text"], Cell[BoxData[ RowBox[{ RowBox[{"Off", "[", RowBox[{"General", "::", "spell1"}], "]"}], ";"}]], "Input", CellLabel->"In[1]:="] }, Closed]], Cell[CellGroupData[{ Cell["Reading the FASTA Sequence", "Section"], Cell[TextData[{ "In order to read a file, you must tell ", StyleBox["Mathematica", FontSlant->"Italic"], " where to find it. This is done with the ", StyleBox["SetDirectory[\[Ellipsis]]", FontWeight->"Bold"], " command. Below, the directory is set to the \"Desktop\" in my home \ directory. You may wish to set the directory path to another directory." }], "Text"], Cell[BoxData["$Path"], "Input", CellLabel->"In[119]:="], Cell[BoxData[ RowBox[{"SetDirectory", "[", "\"\<~/Desktop\>\"", "]"}]], "Input", CellLabel->"In[120]:="], Cell[BoxData[ RowBox[{"Directory", "[", "]"}]], "Input", CellLabel->"In[121]:="], Cell[TextData[{ "The ", StyleBox["FileNames[\[Ellipsis]]", FontWeight->"Bold"], " command is used to list files within the current working directory. The \ FASTA sequence is a plain text file with a \".txt\" extension." }], "Text"], Cell[BoxData[ RowBox[{"FileNames", "[", "\"\<*.txt\>\"", "]"}]], "Input", CellLabel->"In[122]:="], Cell[TextData[{ "The data within a file can be read and stored within ", StyleBox["Mathematica", FontSlant->"Italic"], " as a list. Below, we read the text file, and returning the data as list of \ character strings." }], "Text"], Cell[BoxData[ RowBox[{"fastaStrings", " ", "=", " ", RowBox[{"ReadList", "[", RowBox[{"\"\\"", ",", " ", "String"}], "]"}]}]], "Input", CellLabel->"In[123]:="], Cell["\<\ Notice, the identifier of the protein is listed, \"1AB2\". (1AB2 was measured \ with NMR at Rockefeller University in 1992.) The amino acid sequence is \ represented by the long list of characters after the phrase \"SEQUENCE\". \ This sequence is broken into multiple lines when it is long. Since the FASTA \ file structure is always the same, we can write a simple program that reads \ the file and returns two strings: the protein ID and the amino acid sequence.\ \ \>", "Text"], Cell[CellGroupData[{ Cell[GraphicsData["CompressedBitmap", "\<\ eJzsvfeTJdl1Jpb1Mp8t771vX93VtkxXV3e1q/bee+/9eAcMMMAYgAAGwBCO 8MQOYUiCAGHoQK6n211Ku1KEVn+CXCgUoVCEQlrp6nzH3MyX7/XUDH/RL+yI zH4vK19m3u98x16TB68+f+fmw6vP371+dWj3s1ef3Ll7/bmhXY+fpUNhTRBk zgdBzV8MBfjsgsB2zvGedhf5QPBPB/7pwD8d+P/hwCf5c+iCKHD5pXnXdLwp KLjGQ41B1jWdbHK1m2r5eNhK52SC4D8kfvsx/pz3v6Vfuc4XO93ANwbc4HcG 3cC3B+ha9Cmow2c+ZtvAtwZc72d7+fzWa62u6RTda7rW5YZzLlOb+evEbV7h z5HLNGRc04kmu0UHX7lUceW+L/e5rte6XOuNVm5BcbzowsbwXySu+JJeETfD aQO/M4DL8O+pPckr9n6h17U/anfNp5pdppQJfpO4zPOGXRjws9Ndg6V8jawb /N6gG/7RsBv63pBrPiG//fPEb5/T39bN1rm+d/u0LdiHjB/jcr3Vy6Llagvj hOPAjfALGvj8nBv+vWE38uMRvw19d8jVb6//s8TtntXbZfuybuCbA9pQeVS7 HcQHMZI46dI4vwCRs+hxHFRoudzCiAFgAjqnTzz4/UG7O32TPZ4qN5TjC/1J 4lGeSaBmLc/yPuLvaKU+Ro8+BRGCZQWQW6+0skAgGAho6AdDWb0jfXS5Ebnj r6vcseVKi8kVz8x7NASXpacJRpUWxBZmTdudNmAZJRrUeLCRT/pV4vJPktj+ jmCb0cv3vdfncqPySMv0PKICU2L4nw0Hdu3mk82snjjjF4lrP06A1f2J7rJr Q47Z/iyfMZa4dsejjjJBtF1v89f+4yrXhtwNFrs2jtlvVvE+59oftJfRDPRu OduSfNxHVWhml+z+ZDc3A2eMJ5oF+uFBMwbz+8SbAYHs54lrP6zCGw/F78RQ rNHzcoNeM/y18Tt7hp9VXDvnms81l1spsgTNF5rXfwACzaebqz0lFCoNKrNa QV2r5xVWFLzm2FN2v97tz/ujxLUfJGy12tgYAfqO4zhjndrlmmwNSCbX/wN9 ZPpOx8tab9fF86WfGe2wZzEY8HyjPx11i365yI3+fNSN/P4I2lHtkgtxyyP7 sJ0vZ9voL0Zdy8WWapdcCNn1pu1nW+xydB72cszO+2ni2vc/JLIb/hHXjuza mYz/+40Pifg/5n4ZtWJBG6l9XZ2c09xMB3p6yNAGXV3yp24iWXu7C0ql4IJK oeVSSyX9zzRP/iOeAndavNgFMzMuKJAfqalxwegoYJiYwN/WEUlXrHDBxo0u 2LLFBWvW4MG3bMEZ60mEg4P8ZGf11nAKaaDgj8qByrmOxx0VPCKPoTz6L6oY QS4nT3D4sAump0U0UYQnwlPQE4bJJ4yfDMfwtPQtqvK0pz4kRU2sXS91VQBa t6kuDej/xf/VQYoumJ11wfHjLjh50gVDQ3IqAF69Gg+efOzQnnDzZhds2MDf crzPyjGcOjXlgj4YxTA4rk+F8CL99Bz+pZ5+4KsDFU+vRjj59P+nPT1Amp93 wYkT8vTHjrlgZCRuAYCMnz5rTz8+7jGXY3k5detWFxw9KoDg/717cYvgqFe3 gIPMMlWm7ziebAXcwejPRstage/mJtQA/x/8Xz2AcsEYudt9+1xw6JALDh6U lpw/74LLl4VaIPuqVZ4+DHombhRRZ3JSqI8HP3qU/nbgQIhPeKDt213Q0cH3 O6QPCfjTIoGY0iIB/GmRQExBuTf531VbglrSoOXLXbBzpzTmFDH46lV6hnv3 8JQbNsgvYS42bixUkYjXDOg5Tj90KPANMcnQt8iO4bwGiS4PfEQjCM1INw4a lGrc/2aNg6TAOdxyzx7Rmlu3sta469ddkM2K7s/MFIxaaASUQxoojQAr9+2T RrCs8tacI0dcsHYtLhTs18eEWUo3B+YrbaoQTFSYquutGkz8r2bFYaJB9wMH wK8iP302Fg02WPT16/HwoiOxlZLPO3YAjgqZ5LU5O3fiD4sWmYcK9nw4d9Dx pKNCHvU76tPy+F9M0eAOoP9Xrwa13IwwFoKcIXtYuZUrjViiL2vWSHPQ7NnZ fEoQeVFDKM7Vq6KON26Ioenu1uYsHIsYz3rf7q1oV3FlMd2u/5n/Kwr8d+6g RbJdvCgQw7mcPu2C27cBv7Q3FluQNWdUKgk2EM2mTSJt2IfDh5GAVYgtawqF 5kJh0dT9+yN8EMDECNG/+Y/YcpyTbnnUEaVb/j+ZpCC5Cxe4bdLCAlorrUbr 8cTyd5F0GBaTPxWzAghVmtK0nJAdNglNw3bmjAuuXMlaE3FNIEb/dn6kQFPc AmLWMnNP380t/CzVRDyFCDYmLUw9DAv9q7XzBgasyUG+itC8Tdy2zQVnz3JD MtYcGBFpKdoux+BeQAz6t/0jNhESS0sRv7PztIn/Y1IvtYl529+8KaBDlYxj z1AePTwc1NvvYJkgKONkuRdDA3bvtubIfv9+39C8HcMdoLxLl37khkIp0w2F 8qYa+j/YA9fXy+1FikJaKO65c+IcYC+uXHHB48dZtBYn1cYhZp1dBu4L/sS7 tVxS1LIhOLlwwesk7gCz4CWci80E4AW0zz6LWHTbR8QABjeNAQxzVQzyckcI +9IlaS/aDXV68AB6KI2G8PXnDdZoBGgwONTcjBe20FrQ2L8/Ji9CCZWz6Gwk x27dElY9eRKivTgV0bfea8tHavpTHOi1Vi1x/Duv8BlLg9TFRGIfIdpGumqx 2Gp/RDSK57xzBw0XMKDUemMp/RQkVYClNmlD+lBiSP3yZQ8DaK0wRAYGdB/B L6QNVjx+jKMVWGz+iDToeFjFF8/Xp2nwt/xfCamgxJ9o3ZYtchpEjFQAkQBs wpIlIRwxnn3ZMvkT8ArDBouwEOeaZQCd0bDbt+sMukR7mszl4SeISXDzHTs4 XvSmAJeAbfUmg25x7ZqI5MEDwQznPHyYN8jEC/DFZy1quVoZtTSdbJpQpLrf 6K5AqrS2lEbq3xhVWlslr4Klu3Ahw+GLxhsIMItFCfMRsIA+y5ejlYRaTlAb HZV0AQ8KfxdF3mUMDwtyBFaG9xE0Q2gBo3DvntdInKrP12I/hwhhrZLGFA+p jMsYgtByaDjQw4bf3L+fYwQjEFA4DENQX2+8S5ZKjXcoVad5t1AOqGj+C0MT iohMFd7h3LmsoYmAAUhCsZYuFcSQ3I6NBcJBzVOAJtBG1goG4X+EhvYZEigW Ax9lgN4KcdYgfvhQ7oft4cNcFUvv7QGIWmbH8klnLRtyPtyGFDljrESsA8dC 37K2x+0gHvwNBpjyCQO745mOCrALY4UKsEd+MlIBdk2xJg32b0zX8nlBDXoG Ql25ggYQ4up9QG2py8h5IyMZU3iAjMMQxuRkxHFaGFds5Jvs8cvGxkRIG4qY QSyCtmi4o9F4CtD7yZNMFWfTXgV3bwhAW/gOw13k4P0ILgP3IqSWY2gjaH3k iNw/CC1u4xvNmEX4dHcF/NnumMPr9DxAnYYfIrHzNHP7c/MSUH9kkpA6zAWe GBYSAHAwS5KIpGGdmu3292dgd3EYKgBLvGQJsoacoQ22C/sDfwwKhYJBTU2c RMH2KvPrTALAB9YAvv/w4a4qaGfMWVmkBnN8/LhHFKQGgeWbYI3vCOLg2S5c yCaDZM0nDej+r/RXAF1TqKkAeqEqhgL9p9YA5BLguDA7RhVAo71HjmQ4PskL p3EVxHAwFvgOlNetg8QEztio4BeyR04GmAGrCUu8H1ybMBlAaFrQZeFWjK2A ZO7s7t2YplAMPC59y5i1wDHwBSKDdaEcMm/IWhvo37RRk1BMIwu00xYEji6N LBxiCtk/MWSh13hswlT4Wieo4hiagUcDpfH48NCwrQMqKBjURYtqq5iOLPYZ FE4gr127pHYH2KWGB39Xy6iSTB49Ehsriiy3A4p370IvesyxAGiEoVC0s2fF 6hblgWAGzPnhO7wdZIWHPnFCM9dITAh+jhrFjh1GWliCNLSwGGloEW1VBOIP O9LQSr9kEaouOoKnsHwZkOLpYOdu3oTEHz7E8wFaSellQ2I4Pl6sYhOi5DGQ dvdukGrvXoCOMBUYAWUkb4cPB+L+9BaIEiBZwIxHunkzx0wtr1t0GznQAkjH m9yiGH20ClgjljFzAz7jsxBJckxIEhkiCCA2UR4hkwk26j3gBdPgw1uWg0+p wP0q3UeXrWvyV0YSaDwAwF3u3q1neNVU6QUz1jgoNyLeqanYziYMg/d+qIWi WEMIR7wvSGoAjJEOIKgWTyiUPXMmYxky2oxqCSFcqoJzjz0KtEb4q/ahKNiC JZZGwPgCY1wPTJKrSakQuMKe4zhaf+4c/kbBQCiyaW4usyUI84B55wudHnOE g2nMu17pqugFLI4XFfNfWo4A3sKKQfPwpKJ9dYb99u3+wr6jTOid4HMuaT7E CKO8vnFjjiHPy2FAjg28hOigy6RFzVWw7TX7HGMrIpIwie1ChvErCT0MX8MY KgK/DhkIphmGNBJDhSeEHcxmZxQHjDFouSgott1q4x7r0mRpQv8MwMu6aX80 jL78MjBDYRqAJOwyvI9Ec6Gt0Nxbt4oGLAArqzSWkzpjltiMBzaAOjsLXAjV WtHOubkYWXg+HAORIVMUW4HWtWvIlytQ7rO7A2Vou+XLlF9nrF4IJGF5gSxQ xn2AKrhy+nTG6Iq6GYLRurpgTtkStnG0w7Bm+7KW4GV7sxU96n2f7/NPpfWB XxgoMUHBM8HUzBW2e/dyhmlzPEgjb/w2sgI//A8M5+aKDGEu9mq2Ics5fz5i tLJx+QmWH0IkZKrZAnapqD/AzRkPLfCCNwQXgaSFPZASjn/sYyKtri5fKdVK J3+zwm/9tvoKzMDSp2GW4GHRMAOGeBw048GDjGGGw3qVYpKHLS0cdZVMgxHA odhqQMELZxkhsETiJbQOrcR29Wq+ClASfpUkbIYyAxgYP5h8/BzHACKCvg0b QiCES7zyiiQy9M8qcNrnz9+MW8AkjROwS+G0WE+foMPLaMvQt89W4Vu9YWei FCr4CiseUvAABtUJJRFpXZJQaGOCUGEVnIaMvghqDRdIFXFBGHo3iNoQOEQo ZQwrqxfRv60Wf+ZrKrCCzqWxgm6msFqk6jREh39G2y9p+zFtr9K2l7ZO+uvb VXgnHkRDZRgiK5sMDMRVywSA4ipCMWhQD/qWsWNItmAwCadGQ7KcbEAvGPEq RL9s8jUtQRLnCVSyvfYa7rXdi71S6zLFDBv6JEJD3x3y56n1H9XTQzr8ZUGI 7vpLtoHfou93aZumrRQEb3juRp5kIe9LYmXh72FdYdhAOBtJYPHXnTtchArK a07VCJk17GLXG+MJFwyaEJItxj5gKNf0I+G4QvHyy+X0evFFOZ5UxSomCwP0 0vRCSPIU8NYrtQBbQcH7OX3/Ldqu0dYfBK+bJOG50SLoj1TtAWGDAVOKi4XV iBbaHv3OaWBQD0BSRRGgeMx83GMGU2UpDGRChFtuj4THSJNr61YjV6Y+Hlpk EUXbzbaKsWrkKX+V1LrQnU3AUlRm/UKPfZW2swJN8JpFaGA5NBCadv58o2GS qOI02TPHmMiGtm3e7LMR6CEiZFAvYhuori8rkICq+BlKhceO4eipUynzs8Tg j+GJeYTf6nnGIx1WWGamwJk0j8CtFI/MpO9Vzvyc1eKXfAxAXaEtS99etugA hhhRum1oDqR6/z5Ml6B26JC/jcTgteWoma1H8INmQ3MRjeJ3DQ1BLmn0IQGo GBzdmTOAUuCCqdN7LDbJ4Njrr5ezaf9+CxAKKwsVbPLx6Y/L4lNl01JlR0kt ESzSt9AawQeE+h5t5A6DF01k8P+CTeBNDtoBi0TxZMmOJToRWszgAiNkjdAm JLWnT0saFxQEJ5gZRD24HqwgsEcN4ezZRoPl6FF/WfNAbHU+/nEXvE3+5s03 5bNYIx88laZi5feDbhYIOCV61yG6ERwas+hVcXQlBenj4uv4JBsczn5JQcoZ VMgmQQRsjx/nq5jrsv4scI6QiXifjTsiYLZhdU6exPkCShR39Y8aV+AlAIht b70F7A0PjDfm4dMJjVoomFQ8xhWPjAZJD8XkFBSPn9LX67SR1/Oj1zlAVDyK 1nCEuXBnogoVJrrNfosAE8pDMIQGxuSknAegYNEIjAw+ZYCN54qMV+Lr+AgA gz4+/WlAgl/QPpQEUkueFupGUZPr77/m+vouU8uwD93AwC3X2LjBhWFtsDJh mYpri2U4LhRsKo5WNe1Xc/1Vllused+gbThIjLuHsVQQaw2w+/eFKZcvozkV Nt1X5lHOkNFuCqIGVDivuxv6R5BlRYvBLqtNELAFg9Pqp/RvOBlQIQZPwgl4 u6XaVFCuFItDbmTkeTc19eduevqvim7jxn9Omksf8Yn/X736u3xKR8deOn3Q xxql9SWXX5IvQ3ihEFURnkgg/AxtPw0sAAs5ZD2rNLXR7xziQ00JR+8hYZdB sf37A195BxJAiHDMGJp79sjtm5sRIRBiBUESKCJCQMEEPgGUx/WGh72qwlOm VfXo0YLeDZRbvvyzDFkr7yM3MfELPtbXR54jyFhUACZi8HjSyi0UrKormNLT Yco+Xhashu49YSKf8SCJgbKxwPuSBBjW1QC7DezQfrFXEiE1NHSb3UbKLZot GwgacId2bNoAF6AdGfEhIVsEwSomHY7pI4a6r6nJMptWr/4OiQ7Ahcy08fFv uny+M7YJGDFaVx5dLBSlKmpW70TC+CNFLUow7IYy7L5FCUg+0BwbS2sMAVJA DMghiHrwAAA9fozHwznSy6m1xXzSKMrvrV/jxAlEJARdJB4VsSyCtQ0bno5e Pomebhmydjfdhg0/DfoUuYmJX7q2Nj5Ps9CQJ9yoZ43j19tV4tfzLTqTyFfn 6S6PJHP0gCWN3v3k00IlCYp6AwSGS+21LwjGBk7qBgWNhQ4eRNgmgABIqB5i 4V27Ih5gqaVMHcs4bCoJO5hWyd27Lb0m9jCLiE10b8GHsHKLFr1a5jFGR19k NSV1JfWQ83BOGApmA+Z5d9W7TF3mI9k4ZeCsnj6USDKjKjburgEKliAOJShr DVDEXEuWJB4pJ/qN+BXGqV6d1/x8wXtZ/A6AIyYlHAuGZsJLGE04jiujnA4X QE2VKEXUMhRJV1lnSXc1e83CG/AxeAd4CaCe0/PXrv2hq6sbS3AhtBHgmO8W 2JjXhWI9nXZmPfuhIvfzp1jBO3arSGlFOBYNTfhpLTIMaRO4cwz6bZ1ss7Nx YoTfwzLu24cmC4pjY5UoIuFOmb2wsYMJBTAMEhAOPgO/alX/SyELH8O509O/ ibw1/I3r6jrCJ3XrqUEhtgOF8YIB2HS8qQJATIZLATiXoOMPqtDxWhpA1FUB DEHnDR4MpIS0qpBF0XcAJcU+6SODLuMYjCm552YDb/Nm/1iDdh9QOAVe3d13 mD/iUmNEwDMKWHyHGfhl54W6n5r6jSuVRGU6TcjEu/rtPs5z+bG8599C2qzw WV0ufIp5fJu2YhI+1CQQ7xFwkcGHjoVxidIXmVARjy1bJrdbupS71TPMuJzA jawM/1u/3s6ddYZmIor2xgHKmzCPXTe/46Y3/mUSxEWLXkYQrX0JIZs8Y6qB ODn5p662VkDsUBAbjza60rRPA1ymNRN82B4C9TFWXRmqYhK/Tt/79PTbyVAG 1UgZCiQgIjsRUxZXK1BzxW+bmlRRS1LuRnZjAFrlAz4I/yNT6enxdgCRoIJW eulLbmrjX3rqJU2Z0RbWcXLyT/iMTBWlbVP5Ru2Raz7fzHM1a2prVnnzRMa/ KTQf+aEUWUHcUcUSGog/oe+TZSBmZdgX4j/EhHCwlHwvM/YhroYCw/ds3drC 2EWCF/DDML2xsSE7W8lV8+Y7bv3cn0VV/KalH/l8F8Hzywp4DEYrPOQX53mm YscrHQJRvkZnx2Zd3dY61/NGD8+2LW0oYbRQPBUvrl+CaFEVxVW4bMT+UMLu hQnFPVgGVySdurOzcpkwXJ40hzi0bh2yCIEJEAEqxCnr10c8/qJcL/tTVm7k 9I+8kiHiHRi4kTAHwK27glagn+HbpIxBfRJjJjpf6mT8Ol9W5x7FM5fRhdD1 chcwpGthn3PdH+/GhBNXWFbw3phAi6rorAwGCnYl6PaYtl8k8EO5d1RPv2kW CAbu1Cm5DJwrguaRkRUGBKoqCB0JwaIZMlPRLVsQrQiINo8tiIOx8PmPuYnN f1EGzuTkr9iH4gyrKEI3kbwmzwNJ7Xp+Kn4NRTn32lzfl/pc1xtdrv/L/a71 pswrIvAMR+MhEMzyXmZJNR5odA37GwI/Pf/9YT4Xv9KVAmyiDHTyJ0q+jIL3 hwnwbhh4AO655yR+Qw86Pvf1efAQvCh4OQMPwCHunplB2CjgoQtaA2cDr2// uxWg9K151oNitlRdQdl5ml3wGfX6qA17Ghg4bL3v9vKk3sZDjVZ+SZKvyPvI 9X1OXG3fF/owl9yyQYjBrq6x8157aDr83RRuIN0hPf2aXQIDh5GMPPccTATt 9XsoN/FaLHaOocuYFsNJAEQAOzEhGOp8MPgIlEuxtbfHRCTCTcz8aRlGIGa4 S6YXLk24VotP/HmUrSFLwRk2hh5WEBgSlnQe9jIZEZqKM6oxsaQaDZXFMeTA OB+WMmwOVXv36S8pNHHfTwEJ6zerQF41gDCYAaRLAmnaTP+Eid3S42tqC0Dx HdP2EP3BayC9hRW1iX8ojuI4kASyNjUVv8H/lg4jscE59fXedlLGVsncyz+w 3NjXo+Bb4GOS5yEXtDFIJT3PbKfgLPvuj3W72tnaRBurUbiOfRTOhf53PtfJ E0Mwa6L9bjvn182nm13tTC0vlVCTrdFyl01kzGkenTYDixTdKyYFoJWWwsaN KSlk47Iz0ANqExNqA3SAJSQAROGoIC2c199fzuOJX1Ty+I3PsnKsUF0fHX3Z JOCrggMDt0teq7wdhZYzoJ3Pd7q6zXWJx613xXVF13q1leduwqmjQw2gATyA iKnmABXgAmQCW2ke8lIdFjBpP7fNdm2pwm18b1G0LttjmnFNotrXl0I1FMsJ C0o4lsyiwhKAy2I4du0KtNqBkCgMfSr4VK4itPSICvJTU3+Wsgy/8K7MSozF 9UWzsh5aDNcrriomDFuWRQD42u+0u9bLrYCRUkyBDkP5rI0K3UEz9nT4D1PQ fZG+5/T0ix/SvprVZ68NlZaBqLQviNqDiLCzUpOQiAlBe6k08lREIjex5Tcu fOdLLnjnHfxs7INA7rvsG5nT81AAw4oznS92EkQK3rMdfi2U5Qo1xkR2v9bt t65Xu7irAAvjEBebFMf6rRXDUmw2+awERmU4vhGX7XQ5iIXNq8cRIaTiKMN6 i2ImMRAS5hSmEngCR+AJera2agRUdENDD8oVduNfueHzv++CL37RBe++y8W3 sQTwFUYgQcWs7vOL8q7/a/2u77f7eF0goOq1ncJQlBxjVENGTxGl82QPJwXN hwUglOsVWSRJKWQPqyQfBr47nTegfNWIqWOdBVDZEDTNza02HFGtkfnn6u+z YhANN+BJBnHZU5lFLbz+Q8EN2+7dKz8kCS23w9xRjo++OcAb/DoW0QF85n06 P9FpUZB/lGxP1sNXMPjIMMKAtt1oQyRfrKLg2rl+RPHRLJuDpt8PpBQ+neAj +krSfOyKi+k+XcVkCGC2fTvOo30oHFTzt+Kj47cQ7ayii5Wq4K0JO9S+aS80 AoaKo4VMXZ+Mn9xgbDrc5GGsVRjhm8BO6Hj36925KvpdBqMUxVFl/DE/2I/5 GJLJET3dFlXhLFHi9hjPu3f9ZcdNo0IthsGFiB30WFfDcOTij0lvPYZPPzWb 5CBneB0vdNAFgFuel+Yy3BQ79i4dz3V4tQXeiHoAVaMB9mwnr1MGR01OOayi tgqYrRXSTIe/lgIM35v19DMGGKqGaQImxpl4RU70P49/AFBrDv3MDd3/tsu/ /V4SqEqyoafOZ46wR7y8FuLud3sNHzoVe1k7QxNDJVY990O1XGjhsbLwuCAU BzYW1Nxr53XDgF3Xi10wd16P4akZT0SO99u1xHhMH3ZEKxVJ7FBezGr7Txsm wCmNnVVhgziafBpOy2583y298T3i/YlxNVaLFr1UJdC7icgNq/gxQCBMGiB0 k9htlxjHo4BLE5g9CqAICAWgnj0sQOl8ppNBAlgADeABRAaTQAW4+G1xddEK sbamzrRasyRKryRc7inzEFC/tIcoFq99CHCwta07YnxraFhdYfHHt7znWu+0 uo7nO+heAlDXx7oqAEqurLREww7kCZ0vdPoNoQkai4aTBrLdCnTKS1SWHp/Q tt2T9vvtR1IStEWT2JKnCQKLn0r6PgIG6LlNR2nTm/7c9X99hNcPJBxEkXKu 85XOMmMz9P0hpF6WG9fkaqRy9QKCM+xD/o5iKfm3oEGbn5y2WdZ8Sae+m6LA 7yYCf4OJ05/KIOH6h2++maEVK75YceqiT+9yHS928Kxoar7Y2qzr+UxPWfPR JnNKCckTD6TtrbdbebE8sgmUdUjboQbWdqX/Sb3Epljsvu0PEvQ3NeExqGkO tLT8Yziw+qmnZt3YvUeu87VOBkGBUK9D+68PeBSyvdlqBKhTEJK2IqcgwFYg GMKvtKZruv3pKvxflGg72plu+5Urvu3X9DLDw89URMtr9/88g4bTT6jpFQFR 956ZCluw7ujXXN/X+rAhUvk2PC7h4CM/bEhj2fO+1FmVDQ28j8QWXPS2IFQs EM3Yo5RhIalc2h5+OkEIv4rXArUFM4wdHQcqCLFu3y/Ea1z/nmufPCVGocjL Jwx8Y5QNgfzkXwmO9B3HiRh0QQCSKwMERWuAQclsNVK0qG1IWAV2Gpm6jNVX zRU+qKINmxKNtzhuIW2wxjc1TVc0fv38r7021A9PCBNKHAcY68GAmZl/42Zn /95t2vQ39MN/aeYh4vZHvv3dn+pGKmAcyK/I84K9tAXtvKf0dWMc8/xpeXth 4X431d5vJPL2wx9SA8wEoq8v3d6JbX/m25tr6lmpbh0jvmCr2p9t5zavvvwm EuDZv6efbNr0d27VrZddzxd6XP+3+qXNIbe5/+v9qAdpaTTLxbXW67JKceOx Rtd6qzVowzK0agrN42nDz6iq3qmi8efjdCfYM1Vu6Z97hvK9iZsWxmdbKpo5 tfk3wulr36YcLBzTK2FpZJhwZLwUQiNEdsteOR9xS0O39tLnuPWUmWC9X25m 2902v+iR+fi67XVcgrANUSSai2WU2+63NWuLk4Ngy1ocukY6/MMqog719EPW 9NvrXfDSGWn28xTzvLrbBW0lazp+nC6Ows4tu/59N3z8La0G5DkNbX/czu3G Bh3F/wO3NwXW+Ikj73sjb43X4ilfxfphkPpCltRwOg97mffYeKQRro6MXSxv KD5+pYptCdVCin3AWt9GAH5si2yvHHTBa3NAxDqS16//gwpzvfzyD1zP9nt8 xgptfcP+Bt9yrnV8tU8WM/jict/6jdv/UkT/KYTy0vr+b/b76qR1pKC/XMVO RJDWw4yh9YQCZaOVsk+1frRK69+saD1SyQFrPY/Wl2Xe4nUZjASrVn2jAoYV 537PtU9IqGQ5IHS87yt9gACN4337EyEFNd9DMfTVFdBzRH5q4CWGSxMB/RsG RZ1CkVT8fBUi6NLe5/QSb1aBYjQBhZXYWQ3SULSVKqBYtuztCihWnvyhC4uy 8IZfWjq2A9RsgaLnt3p4TfSJw+97KBa9s1104sUOD0VhPA5drYMXKXnzhWYG gflBV2CjcNwbhagKMVJoLBT9WbdPQi1iNG6v9+fd0vOWrvlMBRqLN3/Wn2fu OWEdSOkEDVjGNRc+pzjIniwlo9H5aqeGQoKaFbmEGPVMtIZDDa7xcCPrBXTG kGg63cQBcfPFZrYaWCSdCKK6lEtaEl6+XMnzm3KkYCa/kULqffreWIYUGZD9 i2MDgu21zS7YOXxH/zy+9f2KEHFk2UteYZblzWZ4XKjtbs3FdxiRjOKyafbv 3PjtVxmRDAeKzBVbgEpxybqoK3It16l9pxpdw4EG13y2WdvdwO0EFmg/sEH7 gRUwA3aMIbAkTIFtcbqI9T/+UnA5rw26XcWRXvjoalRoH3bjx/5QKSP74eEn FcTBGMrWu60wqEFeAVpx8QlHSjFAObf89GMfTSGfUMNqhgSrtioXYC54n8So WY1L/c64HvGbZMPJPlWJk99LqI512X9Y1eneesutOvWTMgSWLHkjhUDEwQ7K HFAfsq3GkmWnHqdAyLqVd55jrWJ7QrEHmdTYfpAGYdkcYUQlAvWKAEyrLbir orei7+tVjMeKBAK7P6JXgclceu27buzcD/2QqZUr3/PnLVHKYWF2LBff81aP 63m7h/uxyIxmFYmxG89WRYIjMEKj5/M9sKgxEhRqgePAoLYKEkXzuRf5XRH8 q78qR2KuChJ3qyHxIbWhc/Yqx5IrLr2fGDz2fsofysgbft8DoYENvMD/CJR7 v9hrfgatfxoi2Mh1gg8eEQSbbCUJi4YqiFgMhnO0dhv883JE8oH0pSYRwfe8 tsBGA31Y7Rg5/TlJIq79rkdkcvLXKUSKXEA0NJIcwWd0LvV+vtdHIpWwRGWw UN60SP0L3l2BmA4INBxsEF8D20ja0XiiUXzM2Wb2yLArZHNVq3JJ5CR0J7ub G839SwHMekbvVqHQXIJC8+Zf9i2q9C+7Rm2QXql7mU+2sE3PxMMPM5k4jLC5 AmGLjBAhfAJ+CQPyZGURNrCLwhZ6JGBWdKsvvWWYSXYKR3TjYxLkUiiDnHpU HzXTmOGFivFmmIa9DQwWAZNnXErsl9j3EGZN58RPA0uc1nBEMCbcMjo2qrix 6F9aksJuRRXsXq/ADqPZl1eSbXk8/e2mgti95UYZiJNb/sKDiIF1aRBhkhUw BbFQRjwG8vUu1/1mNwNFJLNwz/hGAX08+4VSVozhbL3Czon2EY8HYQzJULVc a2kyrTwaLzipoFxS9N+r4pjbE4q3gCm6oefVRHm35PLvcAnLClkbtv26IsrT AZn8K+uKN0MttMI+X0YubKhfoL+NgAjSoETdkQcFY0C4mn8FOqWgkEaxVpJG EpPqFBTCh3mNH/6rclBuVQHlYoIgGcoZXpktBwXfMzVl9rlx8ayRw4MyPvO7 FaC0tMx6UPyEuthmY9QVUyZBFoRzcuzNHrfi/JOycBjAtD1oKwMZpsmAKSkw KH2yqYLZudRcVGAwbsYepwyYhaMZGyy8kL02hAb2v1yB0OKxT1Yg1Nl5sAIh PLXRJq9oJGijCOXd+Jk3q9qiwvKCRwe9kAhsgUuDoUM2iY3LvgboXTapS5pX /GtBx0b8LBTp+KUkYEzS6ByLe9JMqbJ1bVz7TKIDDzcy8mx5YWX6r9zg4P1y /mQDyBS9Q0HhqfzJuWWPL6bQ+Ru36NZx0yc03mgj8OS5KxEGpn5fvWs60yTZ gShT8G/LEVko4tlu7qpqOjQibj3vujZfK7O0XBCeOg3F8RY3GQQPaRBct63O tT/Tzn1DlOkWnsoSCnQ0HNww/yPXf3cSVfLYQA1nXQNFDA17GoJ23lPIv7ve FSc51Qn+urzRCwU1ngYL+BmrltZkIrfo/HtlNFh69dsuLFBGm++pGvoNKnI8 qBDOmcwEejrxP8bO0f9VzYr4pcgtfvGQ6/3cAPssMkXDerliQ+A2nw/c7IXA rTxMEdLuvGvbW+eaTzQFreqJimti86F0sK7hhaIXv1zIAl7HkGlcsiVlPiLi ylU7D6O1k6PfM5liApwC0wOAYIODwcAWfEYRkrKFXBW6RHqsNFFKYU1GhBzy 5NHAbbmI6ED2G07WuJG9Ode5u+SayZi0XGyhOLESqL8uB2qhUGXrh/REZkmG j79VAVSuqccDtXLlb0dVPLQNiU2wiH4u3EHygGiFwft0N/en9/5Wb/RUUskx XWjIj2BcuokBY7iKCtrMucAtPxi53t0UHO+rwzhjhMkVoP2NgHb1g0MZT6oF fJLvnOhdmcKKUsY9z3ussA0PP/Fql/RNBliCWeKjEYxcb/Vsw4Y+NzJM0VON Uvk4B2NZx3AMWKMCtuZYxg3uybn2PbWumfwTWWi6RiVgf1sO2FPCHA/YAm7K d2jvea4CMICYBKyz85AHLFmwsVlhSYZleS8l3CRgGAyNYyj/mvtKx89KMWNX vhS4mdMCVhPv827mTOCmjgVu/cHArd0XuLF5irSmsng/oa4573upFwp35sx9 7avqvkz7oGnpKAcaaZexsWdQvYoicaKy0VeFXVnlGGyWARjqXxADArRE3v+B 1Fq9O6ZWhvc5N0vWfvpk4CYOB27d/sCt2RO41bvoz+PzXKjcJYfwpw10yvSJ AD+J+Neh23Q2cG2DchfF1vo8FwqW7GUCweGllSxcGa8gateD3V928wdlIMM/ pEGGE9DlJjzImNVj5/VWYWReYcV4oXK1lkFu6IRIlhGUhX6RtrYBotm2gNEC 7UA/omGzojR1PHB1LXL7f1+O0kIBlEfp6ppKlHpiEV9VB46YASituPf7bvmd H9Pn3+XYoibM+lUmEUekaZjMZ21d08LqgkejoJigRmTHIt2zHr/TwxaOaCeg 1Lp8beA2ngrcpjOywXVCH1eRPq7aySxjsOicoE6Rwt9zRXmM/1CO1EJRl0fq uY2VSBWiFFI51zV3k1Gybfndn7iO6Qs22Hvp0k99YB7nZ1FTUNBwjBNPxOKG E0aZQGWTKgyPisFdYI6frkyZBtSSACL3iH2+OlAHBChTvOWbA5fReuw/CFBm rhcKwmwhAQYlDdRzG1NASXi65Mq3ABKdhz3d/ja6tmTim62vNzLyQpXBfrd7 9c8Ysobqc/PlZo7ZFTCverEz8IAhsbensTkm/WOeUeQesc+6jSfFSI3NBW7R FDmj0SD4j+WgLBRweVCgU2lQrq6pAKVp+XZjjgele+sdf57pWuWwl5DTOztP VpopykCVJzLxktG52RJ/FqTgDUUHb6R1MOQBP1aftyCl1OgVkPQX+zo2ReuI W2Td3crtYrXAMcAHFwBnCnhh51v7g/8kKNp8wqdEYLaYApvtNHiHl1aAN3Lm CxXgda7oY0/S1wfwJicDNtvpYTOYj/+Wm5gIsNF52OdJV/l3bsWKwA0NIRgh naRMJopsTpzQD4DaBt9JKWMGXxAJJHA2TLs+3sW4JnEuro6jLXHXdUkdZvyA I/AEriu3Cc7AGzpN+Iuah+xO65rlUimcn9KbttnCkerZtNVBo6xcmLwSwUag 0rGxg6tTkEdu3b3X3N6HAW979hGaDx/i1GvX0oN3Qrdjxzfc/fsBNjoP+9Dd uBG4fdSk27dhlrCvp98G7izFBIepbbt2Ie8IXH9/QMm7SKi1NXAFXXdCKY/x 4CwCuBZO5l9GJ2+lWEKzFPfbOJuLk5WQkxMTS79a5pbewC2dFfhJJE0KO3x0 Th4h+G+SsC88PmKT8TzGP+b5mniywCk1AVEucAOrKHA6xEGT28lI7WWUYdDX 3XtV2S8C2fpgNQtjnlC8eNEL5Pbt9DCj0G3e/AcVAjl0SICHKAq8D90xikQO 0K2uXkWTsA/dmjXSJFIJjKsVoK+3sHWWb5XgZ9QwJ5McZIXZvqw3zbXEudXE +zV7cS/sBe0xEsDyLTi2bDOedsUWEQp0ZgMCgJM4KtJZOhP7tf+2XDo19Mjf TEkHI5nq09I5u7JSOiPNKemQFdoQuO3Xmf50e5EK4tmNl/rKpLL63he8mswf o0vyywhENFhGeePG8mFQ+H7vXoaFEiV0hcB3R4/GorlwIWB9IbGpaCJ3jjLj bNbrRl4lseRJsxkuVRf6y/PtrpuY0U1WCkkA0k1IpelEk5cIyjkcwe+FcmKf Zau0nBzl4o2U5q4LkG8G/50gbVXbhUZJ2dpOC5VxbHBxc7dEf8A4p0gjRN5w 904CacpF7m/3SB8nx0UYJ/Vgy5bKUVe3brV4PQgTekAQu5Mn4RzEPEEAp8k2 k+wcvyGtUhWKCvbSBNhZ3lM29mK7W/5Si1v8Uiu7hagJDxC4ZcsCNzoakEsX r1NLMW9Nja5xErIBgIcF8kXdg/WkDW7xNP10Uv7H93E6PjBO4i8EwX8ul8dC w3O8PF6YqZSHzIsvkwecEvLAJPN33Q3ctlsNJIkfqTzybuW9b7k9D/gMt5v+ fveuyOTEicALBZ4hLZSrVxd7oZgemAZAMMRykUuR7RVkgg3ywSlQAnLd3kGU VDCLE4LJ6b7pSIOL2iI3PEzKOS+2jhwQaxc9Iy515Ij8aflyig4XgRLj4zJM W2SHLSE7e8lBTYbc1ozILuJ9/qmyo+N6TsjHEHnhKv99uRxhqdJjL2HRalQ+ tsgcyywtxxdmKuQIQ7nzFrXudqxX0xTgTd6/lXD0ZHDv/cRN3rvidWvnSdxf 5EfKwDKVb7Lfs+etCpleuDBdIVNTNGxXriBuYZkCcJMpNhg6yHXxYqFs3Vgu qFWZdr3U4RY925yQaZYdPGkB+y78loIMOgqBioGEMHEP4ksO9oEugzgET0Oi p2+XL8Pc0V/c8eMSh5RKcuvBNRKaraFLrNlBG7mhVVtp28HHyTaLoFeQhjT3 SKzS0yPcaWsj15RJFE0T60zo4HkbprDQ6EJ7k8dCJcuTGsp0LQ5i4d32Oovg fvW9d8u81Y4Hff7Ug8e8mEllK8Q8P/9sSsw5wvSAiZk3VdsMRJsha6uRXgNH eTdvxkLGZwiZsPdoD52td313W9yyR01u0XOU0LzUUVC5w6D2P25JGNmI+4k3 bRKZk9Ai1eHzFDnt3s1mm2VK4qejkHsEE+/27pW70jk5Fn2WkwA8QAeFnjP0 56k9rBq8X0ciX08+cCPrNfYFN4FMm/R4aDWJmX5bLAltqL1gXMZ9/OPQsFdf DdydO2Q7twRsPRDV1tX51UKRclQlRM7drBLQX0oYboQnaR6c9RVWnWxBAWWe 8i1KkjpH5E7rD4JfQgY16PzNjiG82XEvGc5kmTA+6n9AIcj12BhcupRmSc5t 21ae1m/c+FekfTfTzhasYJbQT8gWeLuADVEOr51KRFHFBFV27pRWYHEL1I6W 3Gl07XdlqmPPvVY3eodo86QFNsLHqf3Pt7n+F9rK4lR05CKxoFvQIxAlQjUW YAaeDzekLeN4MUNhjpKMecxr6tKHSOkDGEBEaDgMEQWl5ISWzWK/kkKnNbSN ruM6A1egYR3AC0Cn14uUMq+9Jnz+1Kdke+EFbrcFCPA5yF6r2JCFatPehiDx SHNnf/zWzYNaYUABsn+lZKJzpC77nyFv+SxZ18ec6bs992PewLCkuYQAbuP9 E2XGZuP9k7FdOi+U4fg49KkL3Wj/k8Roxqw7depg2ryoVRF/nWQO4uWIaZP1 4QHySDSuYV8D3Qw1b3K6RJUOoY4eozD5TqtbfL+Jg2cyNxI3F93QcxTCvdDu CwEd5IEwHwwmRklBlwCBQo4m4FrEyVzkgjm8jxKKN826xOuEfBlYI3zLKJ9g pXCplhbh1NAaGB3wqeDWkeeZQohOXgh9ZYMUAhbqJKJkwxsD+eKLBha4FbJa ARDiFX2T/dSUEKq+Pl5lHLcE2EQ2I5lNZFioSD9lFYcDSyorDjuG96XsEjI7 BPa77+NHxC5Y3ZNxmpVJBP9pfm2+8MHObM9RzbrY55uZ4g2Bwp49b0Q6Bef8 +VlPMJJOVlMASMF4xYeFVnArp05JkzFMRMY0Cq36rja79putSijdo1B5tcl1 0d+6n22XSkWtG32G/RtTCgOR4II0eOH/EbAoxegiZo3kI1MbfyTDE3prFCp7 7Apr18pDkrLSUbAnYms0idISsWcl9B+Hs27RBKlHu4QucJ9iqoVJMFDEH3f9 eswkGC9jUob3BffKK2LU3npLtpdeEr6vXs3salIGISfA4+Pbr8vJNbdA34at RB1cHK+0YIPxiDZZyj7/VAtG/9NfhW0rd8j117Cvl4RTU0+/YYADMStShzh+ 992yP587H3imJcIm3s6cgfl+z5h2+fJY0owZ00ApTUDxv/i9HBBnMzA4GPhY obShlFWyodqN1TjwjR0iBl+TTbMB+fjccqGZBy9jzSlcBlYCNTZwA5cGpSw2 MkO1Zw8TPMt0KiUNFDdn/37mAhMUlgLdNuTwhEqhW7td/B3ZKT1WcMtnKfOd kdpWcy/Zq6LcBQbKG//I2yywjWEQsgHHN94INAMXs4Xo8ckTJhp9wz7i4OsT n5B0kCIte5MLnDO8KEq/KcIt1EVka7QGYY0LXk6NqsD3UMYubP0As9agRGvs EAHOnI6JljBrvKnLNKKtuxp7S5T4HjyIiZZIw1hoYNXc3PvWpX7tWo+ZM02z 8pw6Q4g4HYLculUikebmxJv6SKAIj8Am4xmvMqHHinoMvcXZAf+mJFY31KEa KZHt7ua8mbmGmgdFVRJos5UK2vkhNO9mJiH8IwvBbEKYtEaN1IZdTCO3ic6d oW3tvHTZoOrR2ClRNwoqF7l7muwNPTKulVRA3FHC8TwDAm7ZhnMRYyWJBc1E 2A5KZZReIN/rrzOL6Bh9I7ieJTuyapUUA8hPljTxgmcB2fDn9nZbMMAW5V2o p82Ws2VjljZwF8f9ebMfwsA1Mu8iPo7fIS6l8N2oh34H7ynvi8clbxqpI91w Mnak88e0eFNu3qD/QHTTpl9HPucvVERoABkeCVV9VE1yuXh1MQvkYwsW8mBC TFPEt4IeazoaTzuvU12jzJqvB/diflLip1r+DjeGeB5BD0gG0uH8QNO8CQxS YKOPYkzBjVNysZiCoW5KnVEGJem57dslT+UUQfgFzhoCaq88v2CQjFsvvxyw R0zzC14R3hEsMn4984wYOLqVsI2uhccnfefzgTIKE1AoUi5VuCynIyAbNjQz DP0LLoxxC3VjesZhXs0HzLWZVn4j0ly22Vu3ZiZZlokHooGIICQR04+v6Fma jt2yydyTcwfyrkGUyDeTHvjYSSseZn0ghwIChHrnTkFHEaES8GuOyxBfwxXg 89ycRLeyLjM/DkbDG+fyyi940aazTWU8RKepvoVLX+UUcw6CmJxkFaAmSWAE i4qkEog19yqt8twXD/c3RA63nUK8bF7qWjvYPVK8RGfip5AhGCZ2LOKnT9gw 8YWRd4/YONoHqyKLxXgjFgqxcnxRC8fAY5CM3xILjnF+ws4yvm+O74ur4b6w 0egiLRZVmFlOF4xympoa5e6qM3hKn6/3pQsEbxN6J15RcJj0lMzM/idJm0ap F4l/8pgMeIC/jXLxonvrDlTSbf52TCd0VE/cvxVYHwJqnVP3rvgUVuvWWZ+a ajFMwrbuwEK5TZvedwMDEjKTTbPwVjOCMmcJKmF2ZNKpYkqyNdlWOCeW8iWz uYCryYOr0QTwqM6NkdKNUpS+hBxqFznXxibxfYhuEHWD9fgfWsGv1hQaIdJO mCq2VBnHL1UTQsHGGHMShMJljDkMhFgqMKPcUoU+pIhZJIzGrZPHQF2ct24d 2gpfGrIiGQo/L6dRtbGIlxIGa4GQzF5yA4MFzHbctFCsurWytUSBO7o29j60 rtcIFVQum+P4unuvRone161XfIU8r1Es2ohAXnqOxuIZib/tm9qsN8sOZjla b7vdRg8gxMCicE3HmvSbRluJ6aQyjqjEXUUYu4JuORADHaToqutbLnXGgwc1 iBYbg+AP/EA8lFWJwMdYjFTNwPBbHisNDB02AwM7azSB4wc3SLJGDTglREK0 eRrgpmDj888HytKQL6MWhb7JHvAVi1WosfCSFPYC94XCqLXq1NjUjHhTU/cB VsaSCow8MStjNIFTg19EkXTs3vfCRGfK7gclPgwGHThm/qyWGw7NhdYjQCJD woPGUDed2PsbNz0jr8FasuSN+F15xFAsxGxz1BsPN1LrUNwKZT76eSFUVt0Z rBHmqKjT43C+JlvTozxC+kaGhvmD1kIp2vptkG7gtm1joxIpj+AvYA4lGhLx QcwWCxmPkrEQ/T3NIxz+5CeVR+Xmht+1AP6I/wKlsAEqLSkYlWB5Hj2yEokc g5szKkVKJQTlJvOfCZXuqRwX6rleZxlOtQG9u0bWJqzMijmxMqUPsDJWYUMf bzl9QrcRI6nOJmOhUlntHVWIa5IFshIhOIV3bm72I+ow8nzdyT9zq575lVt9 69ducsdfYnB1iwVvKwvx3LdDDVmelFtrJOKpPdhsUQNeEO9uG/8NJIq6Ih3q meWhGwjSwBg0hr7rU+S5KxZBl1ogljmMIlHCUwYigkeF6YmUMpaekWXA/xYx G2WEf+V80czM+GIxDjZcDXzh7hi5KX4FvpB3qmp6zCtZJz+a80flfFloxI+9 ui/Bl9j0rI1H/NhqRtWinIJmbkQmDh/V9nSqwm67Wta3xwOFMGAICoxxxYso MF2yb3uk8c3Sk3f4ELb2Pm9mikqZ/unHTBe/Pf6V611zs9l4T7LEog48SZk2 TLKt31NfYupEnjq1c7UoB/Qo0RDookWoT1PeackxcnxYOpR+nnvOcwGW3syH FRjxXVN0OlZOBMTboQYk5oAohVKvU8cZp7EAZshSKLg2fMa1QQMc41e6yGPg cJoJ+HmLDsTuVEh6eymInJYyGkpqCPqz2eDLQhR7PdlCPbpmOYLTY5VEWRSv CLYiYWEsjikqQUAYwAwCEZFshDEyfRABQ/qirA+MMdo0PYgSo1LtTvYygN75 R8YGOg/70DWPzfvzGu2J1JqACRneh8yHuh11uv6BHjvV5Cdgt6uJAO3DrPbm lWRMBIIY8m16/QKyWSYrOtaQdRNjWHwQL33PKlMQdlA8ywxIM+XZZ2OmwGyC KcQYZUrk7QWqhhwdCxNwSdzpIacQRBW6GSJaHLMN1qigLEEMnskkY/mQO50+ 9zne6Bv2tXx71AVAPPxm8+a4SAZLjt+gzltTYy9IVzrlkiN63B/o/w8Skc71 tZUs6qrzMrNFQFKRTlFrk7A0GIZCJPPLR42eebeCLrnmvgq6DB19o4IudYPr /Hn+hdqxJQmySg1QRZdAoDbKMVgYWxyiRamAoBz1U9SPMQQtqyNCW/rYtVr+ ZIwBpnA4RAtvUEAJiFvCkJgmcDZxETn0KbPUbCSMtTDklVcCby3ASPBAQlvh CLxykiNIHovKEVQiDBLjCGqKxpG8MgUdN1/8Im90XewL7gtf4GCJQ2lwE9dC G8fH2fp8IkmVvF9xCK/btaW3ES03Jt3T46lKvtTFr9K29W4APMo9ZVbnmTKr 41fYyZV4SkSSL0uvf99f0uoviy99rYIv+Zb+Cr4kzYtxgzwP80OcjxzDrG97 Q2Kjukg8NoZUofSEkToodiLvx7hC3AcTM4k7fYoY0gxYc1Q0YWXgeBBbEtYk 1ZgrwD7JFXBCMx96nJgrkFOyloO4wzhBui904YDaHyZTY0wBI/v7y5kCm5dm Cu707rvClDzvi/zUX/6ybJ/9rLg/5KKwoehgzOeD4BWhywO1LHeDqgvce/+E QUuvbi5nCr7XlDMFvYdzV5IBjJgVRL8wK+bHavtlXPng4U94mgwceK2CJjgn TZNMrlhxXtKsFMyEYC76/voyzwRTY0u71emezUqndKGPz8fzGmbP8WwsNjcZ 9VJRXmd/R1zxQdkfdCC1TFoTDUfomDAEozkQagg3ZA8rpJVdzxCEJlbtpWsY Q1CMM4bwe3SFISAOMsXYo8rIA2NIThny9tsB2w7YQWFIyN/hg4ghWJ7/y2D4 l74knEeZHnaF2Be8kGQJOXE1JMkXTGCN8EjF4ddJhxFJG5bHU15stowAD5bc yINJtCu2IpxZqQRtWDTjurbwoG6KTZ7wVKLGJZvNToAUaaKAPB9kT4rqhZBC 181zuCKvFsxYIaiBwxP0r/QskyHRIAUyfITaK7bqxMZz4jdLTTpDwcxhnoe0 wT6jbg/xIRyBFkMWWWUHmAEfkGQHzlHD4H2N1mh1TL74GrDNqAFLgHKK8QMV BWu+vSwdY6c+85lyfoB18DWIc4wf6PvCsc9/HvZEWAKiw6KgDajqjY1xxCuv Hw8e6h2ei42Ip8hcIuK1Jbw5LklT5Ppaf54tsgCLgjEeIEeuSqCyRtUxqpNF qrGWZr510MJT+JQ0L+B7PsiARMoOLGLBqw/xohYNrm53g6vd0eBK2xpdcVO9 yw5ltQ8rx34GBVkM/d2gc2JX7w5suixogq4lFJFAFyTLLb2qL1lOJhD6wUhD 7GQwvHtJ5DFlxkPDU08PLb7qoCOxHEl68Pt8wYwcS04BYI+HblpK1614jREA 6IxKkgTBKgiBzmcjCUwVCEFkIRxAkpBjcNxNvmEfsUHCE8/McGD7qJwwi6sQ 5gvVCIN0KE2Y05WvwU3alGz1YMXi5Zow64rdK8ryGQStacIguC0nTMFl2nMu N9vqsrPtLprtcOGWbpeZ63U12wZdsBOJzjzexpecQWK9Jxh/jcoJxyU7udxD f5u7jOQADmflTuEQ7AzSejgl8AmzXjFQBTOU6Br+jdoIXJA0oA8UG7CmXCYZ t4JEniwZDldl1ByMj23kZ3zyg/E5lgZLSFICgywxYZf23nviWFA8Rh+r0qgu QSOQOUkjJPmgEW5iNMJlQSNQ1mhkaVrsnyL2T3CFiGDq6jTQVTYJZdI0Wpyg kX+HzNoq4x73xeMebYZt0u5Eancw5hEzlWB3/Iuj69td04qdCHw9h5AnpzmE fLrC6Gyk2HM9Jexbh/hVsSMwJ0oYkKSpS3oM1+yVSITIwj35MimEHwVzWMGT tXuF4TAsqOPWtsTz/sAPOCEUQGHsYU1I9lZ4QQ6KvBb4W8ihpXlWZaJTnRZS wRgeiUgiIHFEoIBmJjagDkoO+4LYVcmSTRACF4wJEbGdUUfjfuu36LBwAqk4 OAGflDQtOEan6TGZHgLzAn/U1aVdA54Tc1U48VwFJ546TNGoAIuCsm5sUWTY ISiCM2xAWrFrGYl+F4JbpULO9e56UlZYW/nkl65r9kqD3bmN5S7btiFjQ57J EDEZ6lqFAHMYObPtGm6EkqsO9lDDUXRT5GHWitNhowEy8ORmIkRTtxgMW4YL 7gajBiTqjDNZaBfkH+oeWahFHEYBRIgWWZB255gCbOnZHuBbhvdFVthPf1rs CEJiczvgAYQGL2c8ACc14cX5WeZBxD+3TIZysryK3aig3ievDMGNENXGWbPU 82yo9K1yeiBq/UGKHn9E35vSJmOBmYG2/kYy+UmaDIQqxCGdpxNRKLuJeYIt LDZaaLro3JcqrEaxY5G/jdnVYE2ncQaPI24m5g5o410PRvxv5RoGmFPHc4nB EPRIYIPp4DnIZ+V/pMjLZmWIeq7oVx7FrAXEF9K1I4RBRIAuoCRhrDyGGKQe 4uBxyE3MhywXfJPyR/Esy/Ln9NrLH5tMUBUSIH01aRMhjAQWfmCjU/IJ14EQ V34rJIDVw5gFzOyWeaHBY9XA21US33tx+NmUMBdWLA+en6k0F/mwL2EuygOQ cnOxUcMArI1oNMBWPzzpPUNU28KmIskFrMlRwYV8iPWhWeJZ3udduGvINR2g aGR+SHgCHpA8t1wKvAWhz8wFjHcVC5LjkIPn8p8RLvSN8TPb2vbwIohRkWLC 2EPOlCpGKnoto/NsijqtniOcRUUUdQf8hPwEpVTVySD5jJABlZMkGR4/NjJk y6SO7Z134gghaRbofnYYvoVO40uRnTEfg+AYlfvGRlstJHiibf1KFVLMxrEB amvwLcNBPGJgoTlZPQkjURFXPImNhE1HxuJAygxUL8Z2cawRr29j921Ztacy 1tjxoJIpI03eaph7qdnU64KeOp7HQRZAyFHiMTDo0YP7gJFAwgJC4LHRl4MZ buRO7EVh6BlBvUAGQMWGAB5FAkfJR1GVsnqFhcLoJETYiPDRGFJihkSssGgF vBLxIoNCRqQMsTjStk9+MmaIph9+g9HKao0jyRCLL4wmYK9dj3yN0CTHwQ3C DNgUhNEYK0tBzeUkYxZ+p26f2n68SvoGXGfA8zc9exaYjWNzxarZFvT+mW2x 9Uhqe1dVsAduJwhqytgzePj1CvbUj0z6+xZMTiS3+nYpmfLfNuMtOmBRzmV3 D7rSTvI9G2hryrOtmTymbCpw1oKAxNiEIAXxDLJlcjH2LhnYCfhpSUKERaik 66g4XxdFRkHRP/8qr89oLEIUiA3BBqJXRA8UhIQao2CksyUkGU1IkPdYwSyT ynXjMCJXFp9gw2+IGaE6HXBXOaXHKi0VccpzzcJeTanNJMHwvfmmGLUMB7pS ioGRhJmSpTq05FL9lcN4q4C9eaxbpYdX9u2k7XnaCEQ3EyDVVOYtsChF5wL5 EOJQ2C1bR6lx6VbPvFD3yQ4jG/RbE+bc2MOfljEP33FcnlrORvGtb4UkvqDR nodSZOHrtZUk2GliZ9d4oMfV7e8VM7eqg8xlDbMOa2YQFxEIMfP4/z0ycgHF 3cZOBTbioS6IjEGBw4c96cCS9vZkx2eW8yAkOMi8EdkODGitQ0maZhUIWc6q bJnJwSZ930Ii3DNNqbT5ojTJmzbwBrYqGUEZr2AuiU2c90fMqyhtz+RNFcGz FowGla9k/lEQv7DCusHJBXII9YJuSK9KCXYtsNSD9WwgZQWbkuxKxkzGLqxT ZuyKlF2NS+dckAnL2FU/PJGyawW2dUEczvGCYd1LpNRCNjRyex/hgTDAplFH iFuoXTM/7Gr39LoGYljNLgq3do8iQXRBdx33PuJRUaEBy0BS2DywDJ3ZXK2J /CJGqAhjZAmIlVd6Qe0HB8srtZg2nqYQii7pY6jup2mVMGC+aGe0QihstMqk Mmt/LIMupShhx5JXQxe7heYgEYgHclGmlVebBcMNvchm7Q1SwXParmrvqP4J fe9LVaox+WavkAqZHe+PVQTjGgG/luqZen6mgmFgEuIs4VZlcrbV7ts65BmW rRJ35S2HphirnGE5jsXCSAp76CBANrUV87vArLzbdp0XcpJL0WOX9vW6pkPd Ltw9iIfePQq3PNXrMvUR5npymRuXAZNgqyaOCmGzBb80BAJyK9PllE2o6AYp NJHxl49DiDhT1mPof7DwKWGneMN4LSMPoug0eRJuzrtDK9/E52V5GkTyutIF IjQCYUEhbPRZaJRl/20j+datkxERsgxP8Lw27EmVEg9GRkwqAC163hJJ65hI kdJpoBqdFphSbx1tCMQwGDhJp6TBssVS6wbXVxgsdH6Wu8MSJ3fpOtHYfAtP yNl1RybmEHeESq1u1z1ZbLlVh2jmhkqu6Wivqz/Y6xoP0/8Hel20mVzhUKMr NctaSnhczKpF7od+CNhcVIVsGhzGkadHPs3MBBVNRzEgzaWEwwNhjEvofEb2 ZTJHj4b5K2T8SV8mfJC/ofs9+Tf0MHAun8HlokRdwFhDBBbW5KxD1ve3gVwU HcqiGNqjKa8yThMHdumuFIB8LbdI3w8pcbJKnDsJ4pTUEQYHqgwN3THcqUYD YRLmZNmGHgmENDA9fuHvmoxrWr6jjCv1I9M+JDLnVugYrTA9ax9/iXliGzqc SNji1HIcNUH2GF0K6gYFut3eRSFKn1hGcMcg+zKE7usPCVXAZpQDEKeH2XjY dVdX5dBaS+aTBhfBfJomkJjR5O5dH55ozQ5C9BFykgJEDeukTgTavMV1ohy7 saSFQamSGBiqU8JdlC56LGKDhjvpgEIbbIqSeIfM1NQ0N3hRm/W4wn9F/P1n GvwsjlPusYBHZzFpckqdfRXUWXjYVptSbGS9DPXEEHJbgmH5Fp3nLDWjdOTd sHjWhXV2gYCfFj0LSOERnKy+8ThkBuXdKrI40w+ulTEId5q/GyiNslwVoE+u vk1bMY1FqZVDpAAZFBTai1xChKcDjxAPYSEacox+WCSCIHAhOdLfikU4w5IO TK1McwghBxwDjnORJ+6MhNAfPYo5lCQLZ1mViRl+ApmbxcJTJTmEGmWklEGf WJpAKC/bjAgyPDYKBPYHcy0oU9VVWIKXtE3wTD9RAmUT/Z1zAY+j8AEGVn06 ruwpJAIgRNxh0mNhrEU6AEqMtUizxza4kJEN/uUlxc7FFRUD5HJRrY1WFxME 9kD8nYukj2H+Dk8BdLvuR271k/dDtUernvzM7bjTyEzC7BictvGUJxKT66EM oeAH7WtQIhVc4cCQC7b0u7Alx92jqEfCGCH0obtbvzHyL+2lpJ9WlpisZLJ+ fcwhy8/AABwjk+AJBLLYMIhkSajCCEn2bofpc65K2I1MTLzW07iT5QzPuAPj JEUsIRC6XhBBF4v2Gt3gZW0QIuXvKYFyiUE48ypwm8S9hr4/LiNQli3UHtrO qFcbTLiVhXpGjQcJItF5O2/bMTtP7FEDu65kfRuurXZgLTFtiUww75Xh6DAq IKN2d4c8X54ShYfpTtScm3z4yNsnLJsKK7jpHJ0MVjVw6oYrIXDmccW5DN5u xaapdKDfBXODLtcU2mqT7NroSZ5oRo+CsM1o1DKTL9ticG6aRZApIhwJO4RF 5oOkb0uiYVRwLMKxzgukckYh4kFOQ5pkrkVZYbaK67K6UGLKns7Xk8qVHQvV rqE1YBomjaCOhHn+sriPjgGUGOfrZYzKsT8bSmTLTcKaWrVEZ9Q6nWE3eIaP 7UyUnqzPt+rA9sXxwPaWj0SqIg/ZaFoxH5OKPjeMTrnmpdOc9Dd04JcylQbe CyZK+CT7tY+/WsGqbXcHmFGYtQVGgT57MNsRrKrjtI1XonkkNogfaXWnKxwc cuGBURdM9rrathpeJI4s43OqhDadV2tHvQoLomxUtEEwpETkznwuj7TJ+i+N UBjbidFhckwIZSG1DMYRQoEeIBPco+9IlzDKCHXvHh0W04M7WeiMTdxc+bD5 mEQyDx2VAxhY/I+GYZxsT4+9rDN4TSWJ4Nh6VbMaIH1ZfJuboO0J9w/xqQfV Ep0xStH+NH2fpq3ofQ4GZxF1XtpUTiJ8jzJlJEKQ/UEkmlcSZRs6yyxT++oZ 17xip+vesI6rSdl8TCKQAsn3bl7nSkg09yA9Rijn1j/6FJNos9YoQSTyckKi Wp+wgUSTNhGttejqD5NZOriYX6qBKRCItC1FQTe7loZ0dJNUHTFBGB0u8Gyb NsGA796Nv2E1F7DOqEW2yWfuoJCWeXxPqc7qTdAt8v2wWtjJaZiTTNDZLgk5 Km2QRFegB2iC7dw5LTsFUhfS5Ry9m8rnZaZqQ4NlzcHHLKLVsOgLRqUGJc5l OnhCiZNNEAfzI3qDeNGBYKjpA8eM2fwm03xE1BTvMomIOHvUk5V6VniudKzd zFxpXrGDeCOfsdQ9jAbmYqEEiInmFLGYzdnwOD03osnN3VvH0TGSccsHUecB PyxZw2fwBnMzMd2zdkeXy+wegaO2WGDHjsDqPTqqv8SAoo4NS4MZSvgfvSKo pKC4oyI2ZtjQ0XgAudR5VOSeAoh0jQJ0qWwi0tVMWymQ81bCNpCMaMEDnDVB RwkU3TZ4WEyAIWbY4rroYEZRulCwuq/ObcjygC6EMH+kyfu9IObE0YQxiXhf YE5sp20bbUfU/mQge6yUem1tWGVKXpNSAm9KqEjYt+sLEHM8fs86wxoXb2Zq tK3ekgEf6MZdayeVJTtdWGjgYVq8yoUM1zJmbH8wUNH9Bg+l3oiOSUF8s1oO tR4ZjaJ7l6shGWhwwakxTsfMp2OWnbVJ+pRrK7osUN0BO5B6QSgwNpAnemFt 6IQOOi+jApwKaAAjQ+fnEqsFWDxLn7Oq9Bg0pkzwhgA8hJ/BaIGcLhQD1iBA gWkjatiENTAF3cN7ecnNvbxYLsadwDrKukbYh0zxDzqW0WNwWzajXIQdvKG3 QoyzkbbagIev8LFbCVMTqak5FUh/2g7+RsQixYGdymM2BFYesDe4IS2ry3n/ hFE2yicVbchj/ExOW80/FaSGjE53GAkrGmNyTYGi59ZVW5HhM9N61q/3TMs1 dPEg9emTfiEpY9r6u5XT+Wbvz3Nyte+JPU7J2x1L0PAZRSOe6IE3fxxfjs7f f12FUahF2+LWGAmIziUEP3A7iHKsDgPjgj4DjBfIK3XAPtAGcQkZlxzTJF9m SEAZmDCMQ8J80JLSBpkSVlZFiZsex6830yOOTEYoSgfwV74SuG9/O3Df+Q4u jn1o3/lbrsqxTOLY174mU5WVNm/qre4Gvv8Lgx28NyoPbSJ3UpO045KERcyc rNsd+G41jiuD62s9Y8AEXc7JMwarBxhjtqgrReiA4h2v5/QoCHWAAJbIYuJk ZcpMvrk/EBNVdC1j213ryq0xeZr7mHS66CJsjg94tt9tpIz+j8vYQ8k+hcZZ nK+PVrB6kU/E4EeR4uk0oQzGp/1DgjowUpAbzD2Ge4A6WFgOcQKiGPglZMVW l9MsGfU5NSVF5gX6N1GzA+3QUYBR0LBG4Aa4IiamgSkJ/mBoCowezod9wJQX dPvCnYLCKHFjkAGm2NE1isqfb34z5kVnghfrJcbUSSaeF8cSvCgqL1APPJXi BfiwW/kyh4c9wocRHfcYL3rrjRK9yyqNCJLZcmOf5Y4odJxDk7dfY5nQRcGL kLVeZ8GpwkQu29jjudC9foPQJEsxzjTmU3BPA2gh7xECLfJu9fWH5bPGuYR4 A7cMQl3/AIOJkEHBhYEKWD6jLDzbLi+R/M8JWgB6LB+HsHDDBvkf1iW15Lom mUWktDzeGL9B2RYjkmCVUGZEgIEVsWyUJWIRG6iaVbl+9auxXLtVrl//elxK qi+X60wVuV5I6HtG41JEJHv1+D4puKhwST/Jlk4ON7q6IC6sQIppyULaJllb 5x09zlgRYPc9nLf/CY5ZZRbyzeh8Kmgj4kt9MQblFjz4B++PDSxQ6V63gSXe snKb61pPOfTIFM+xVfdhBmDjGa7ulRkAlPimTjWWdYEhU+pfJVL22RuCrc3y bon/R49BwRAXJK0zEE9LAYtHWPPryqUwkpCC1Uefoe+nU1I4TN/3qxROq9PO qend1l5yc1117kBUw5YZddUWjfB4kZZUAEjm2NZPRi8KzOW2qzppTf071E2U DPs29t3r45lYPB6bhYGRDAR82yqY4O38eWDThOubnnCNy7bxCmFqhrm+ugNx wI35SKv1G+494pqqPR1WqUFxAlV2X3tZ2urB+3/1GNYrS0dTsJLlkVOeLaXV w1DjMtNH5o58nAgHf7Prp1SktkI4EaaTmopgy7iDcenIAnSxfKIisxTV7i1G bld9js6TY+vjipTvoUMElVYbSMLOs5kaqPahQ2v2XCwxIIy0S5bjhMSa3fab skJPj4wa50lDcIr4XBPlWFK9ExJ19U1TnL9yt+tcN8P/ozy18VTsONFlryWn QErjWTd9YcCLDKMPKI3wwW7QGK8H5rBvVuvcQibxjK6V+o1vkDUTESCOgkOL 8QjeqhKV5HSPAWDnEmKAXhyOpXBUDwnWIgt835wP3WGyy3OdtSacvoQgbDLD 2NaqfZyT6pBgViYp+N55U8aby6o3Iocp7WwS1ERzEIoiIUdMg2xs3yNZlk2D Ca4nI89qG9/i2tduo/RrG8lgF8shLDbylA1oDjRw7sE60RwyY3c+5aEnM+WR z2b8bGZG3hJBOKR1RIEXSEN+SI/zI0qGvvcV8lSQQOS+9S2ZDIVQlfxPS7kQ jlUIIcsZhQkA7uFoEFe/5hJ6kFEJ7NSwEcdn+xuyKgEIplgmgZAtQFoVsBKe ScrWMUJFYeV2/xYQNtubznppwNYAolgrIu7G44LEXf5bBuIg1UblJK/vbWhd McVlkb6NUyyEnsmJDD5Qm3ON3Tx0GLqGCsmGx28EkU3H3LWOx5fKo6k0mgvl emDKDj04QZL7Kg82/jE/7u9RVPidbydDeJkshqQCBRAKJ9rLxTJTIZYIb7hl keyWCN0kskdUpEwiGMN0UCWyua2YV4lsDtL2MOThi2mJ2FqsOEMm2Od5vdHt Wjri8tF5UYjp47GSoHiA2dEIrzIqFogEHSdQFPq7iCXn5i7Jeq46HMV1rlzm BmY2iDjyZLamWULYcs39XEiACm5/MBCL5dJX/VN6sfQ1lIulXs1TmZI8ojtA MkX3/S96l84bWS62YIjVEQIgiOsqF03SrWdVNPdVNPOSi4tochxHm2IcUZHM Bt5XoPBzMBNofi7HliVEZN308OQ29tBEhO812hltM9Cx9k6YUz+QkTf5zGJZ qXlevZv7us+JhOawIPv+J1a6wdWw7bihcmtno2SH2dA9EnePK+Bzx5DcqHl0 1LWO72RJtY7voP/n9fMWl2vp41gQRm7iwSMvueQyPX5FZ5QllrXCxCUlRwEm EfTSnChUkcWWcz94OxYVAgMM5YgiHcPuJVVbIanQXQmkMnIqsGpJ1m1NiGhe AzCWUQbKlU/4GZRvcXZDbNGgK2n90Xfi8hkrVDhgMGpy2aIsroqugA14vYII BwYOgoFq7XusUTNnQ6wq+BNFyhY477ztz5T1+u6IUEhoqmEhr+WGh6gfwCws qFXB9c9MerXqnVjt6obW86IAveONbuXjPw5VPstvv8+BQpl8SvR9th9uSORT q14T8oHB+zwpOUnH/egxzO6P+fFJZu5WLB/pI9A1g8oDABtB+VgN3OmEfCxA hny2yGevQ1Apjc2CrGrSflEzliWudV4tZJRgFZxOWmwYam1iW6bnQadQ5Roc x3nFBhyDOiCK3sBFO5Ef0hh4JAiK4itTLpzG66U9YeGY/DQGl+jhjMgOEdz8 7Vh+SI3xNLmm3sDMosmve4LM45I1rmGJvLi3Z8e9wISHgXndW2+VCw8jmo8u dUExEuEVE8qlwqOoWYRG5pHN5Dqxf6PlQjtWRWhXElmNlSChJodEZeKAORE8 hCqy/ZR8zTaJ0HaJjaS/nQ/MC5pAbMA20pO04ODydUS0LmxR5EgDPfAY9YyB 1AjCISSMtYeTJ1/mhYcAzqTEXWIQXoHTF2iW/cnc2OaLHGlkNI1iS/lIXmMc svDyvJoPC6+5lwXWsmqeth2uaWyeflc/OoW0YPk27pgZPvmOt4y1fat8i30R AhOjLo27oKkgwrMBOA3UrAubRXi1GmpA445PBQhDdIksL7lkTJFJ5KNbUpKD 2iCo2P4UyYnq5URylALtz/ERVsPjvL4U/2SoivAQIKSFB6eivdYBzyUlIqJa iMGHtsAAjCayI+gIj4a/xdon8pNRXyYkbFLxUw08GR/fcVN7JuT18KaBCFHI 88nwH4kakYbh4bFoB+TXNi7xSO3gOkgPwyC3BTKibezhz1JzRH7mjacX4Yo2 F9ygUKqtKCK0oW59pH+3dgbu/YdsOIuqgvQxqYLLygU5WiHIIidNgXgmHtZn ZZ2T6rX2emHmWTUPJ3ze/ihjgvR2dUtBSsTng1iiCGdKqdgEtjFd/9WeKM4b RzT8R44KNYNEsWHVAMzfhCSRa2EIFo//PS6eDW51/rZKkWu4sRpesiqSRJ1m TKWoJzqJKAWX2cFRuMgUESsgyuRrWZZ1w1LIgBGtHVhjMm1csiXVEZGHihqX JVCZ6nXB5dUYvSzytIrG+GDgXjnKjlDtaciO8eYOUcmxpCRzyQjFT05ZGptt CLMrkOlQkWodbOQ+E5w6uv1RTWhyG2hkf3eeJS1y21UlpoTG7X1YKTeA1rss nkmKUAV5qlRVsS+y7liehOopB5Zn42QN771NO0C2m+f1cMhUgNXecTOWGTI1 taXqBEvsUnG8dVCbkCG7ObaL5LaVF0KBDU3oY/e22xXjHjqmz8lPEcBgDPSe RVztc0FcAcyG1Bpi4+culosNlpRcpC7xEryjWpsIXFBXzavLaVPDaXp1QGNN Lybv9kJ3KFPjtlGycp5/KXI6S+csCcrTM7g1G8YfJWpHTVoc79XzUP2elr7b IFJpQQnUUmK0CS8oiYk/kFFZ2nZK4xYvt9BqHRxRSlFeBiFAaPgflXKMj6M8 I1JU6kYmXf2iTSybhEwaFm9yo2e/kKgw5cTNISY5v0rm92HQQI2oUk3Cu13c ErjfuWVxf7lM1pbLJBGXuLXxmMG82r/DbCtnAytMlDszKXfv7iixJHIqj3Oa su3k83bxsRMJXTIZoVgB+5WUEXQJ5XKsxGIDPTHiDzUGSCdrMjovXk3eeAgZ yUgNGCusoEufw0QusFMLHztvxap0RqwlScwn3fBmGNIM9pCECopRqWeMrN4G 1pqExasfnXYrH/28zOKxF1vUgUEILjhIkcTWQatAaam2wnnlVUgkMHdnXoS0 ISmkOAS5kogJoDVTQXlldVdj3h1sEDlEasssO7MOyrMa3V9k6dzn3/VUkQ4G QczJIBa/3BIsH2actPZx54NNQkKPEyyQLIklAuI3Z59iY+eVCAoAJZpKKBG/ 1+ZJUrFkUS3EoFYk5IJtjbY87/IdoywKNWL+//bJ0xoQZt3w5c+74OY6F5yj LP84BQK1WV8qTzqbgoJPFowtWRRaXdmDb2HDRMINJEyWqkLBbaNw4Hg+NDfi TqDE3VFrlQwLzvEzaIggL/vtCfytIIZQ2DyNaEfevwCIO+9PyoxxlYVPrCE3 vDgAYsirMMB1eJ5N/CYAEQYmoG2USqwXBoDXVx74Y4gbEa2QTug98jxRJYk+ TBfG1w2feDuwajgviHPnZYrfSApHl6HyF/yXKn7DbNQrRwKWDM6YLpcA3D3e mJrx9Cx6A2XJ7XEyg7u769xOcuQHWziwUxWQuRknFfKsQn4riFc3tPXRkLTC +GCdF1s716Cm/IkaToATP/1gUQse23k8aiFhm4A4BcMebaAPbYI6ZDVPwjnw /xagge6gfZes0dKkYGcbu83yCOgUf2FU0pIr34zznzvHxSF01wX/t7amvUEK QHAERvPfeySmh0yQ9icHn9HT1yY4aFzqVBtzNmHd4Z2PKpgFBXNp4rf28gt4 Wh0X48E0/sY2Jcutrm2SyhvGdGJ4QlGRBB+ReAqGMW+nJHHxBsNe/ALjggng MBjZgva/ksFoG/J0bVy6hetoDUs2o3QTWPo/9uTnruY4udXR5uA/mePJxM2y hVDzMIsrAvc612pT5hvQkv/VBbo8tGEVaEPlZGyqQ3eYLMYhhbak0B5J/Nb6 k9F3jCADAFo3bd9KXhuMHWLJbPA56QHYfkPrmAQs8npk3SAcuA1nieQBdTUk G/Vt8Rxw4OYN7PCka11z0LVvumCkG37w2y5YKRX4/6iPVswFDBG+LdJjK8hC 3d0VuO/fi7U9aW/nBK1vqrRWqf207Wgd5cz9DRh/TPZXUMGMz3pFpU3dKgwf 0qeNMtKpTi3ncl2ZGTOjyCGV1EuBRijhIjKAYuoaaxmMtqEbIEYr1rPb0fKo wAElRGTWMr7PNa/e6waPfNLg6Nh/3UvqvzYloIS8uVaO2cpDJUoNzm8O3HtX 46A5afy2JeGQNR5AlBMUae0l43Y7g2cRitxTw3hV/Dn7dmSbiLaOqZ4ut0BK 1ieEu4DbUJT8ss9WNkOfz9p9assi17NE5qLCxEV5FWnMjIbls8yKhul52KNq 5Zj/yjIn0vFWMrAZQnTYAqu8lNG+d48joWpWSuYHBN/SekgHHHFt1h0lQhzr rXcnyOSfou/nFIGrisg9b+qzjNJp9bpPxOz7+N8gWbnDzHvWQ4ENVmnxdBzW dC+O1XGJgoFiVf2qza5lzQEBY3ZvsiqcLGwYGP2tdFviRUttvL6lgfHdu7Ga vHdVcmAYlV1JJEIX1ciCM/cZF1GLGxrj3eFvr/E+2Xll1VnUhHTB2yCjLYMC WM/IUuNeT79rnN7NrWpZu991bbvlXU4itw/+Qa+LyHaImDLcEWfCyWaZgL9/ XywCWYZgb3mzAqL4hDarqM06pwKUDglpFlL+nN5+uT6uvbXEiiXNvSakSDrg dX5Ktn8wgzYhW5ra6YZPfbasEiWV4NuJlkWui9i7lXgxQplBWBMM6NNuX8kN 0+6VuAhF3oG9RD5rsyh8+2b6XS6XgahUjUNeZwNiu85trhTb8kQiBAuHWGSx ko+7tkfJ6V5b44J1Mty50LPIt2/TLjd49BOJLDKMM3v69++1fW1k685SjtXZ SAlIs2/fYLukKT/mapIID+1FnkLxRXAo1Ti8UnDzAIfKkTbuotqvZ5inlcIb Mz7n4gaPqLYHPfUySX/XiETzOvAFHWTMSdK45nX73eDhj3teJlv377R1MME3 CbjGErfQEks0Gha6JohLGvD5nzkXqCzjFoPCROXgSLLFBWnx8zMmUneXxHGy PodXmblXjK0l96rmEE+E2Szx62quLqnpWmUTAutl7QuMn7s34YK7GySTRPv1 lY35Zml/48S8a16zj+Puau3/e20/agIwI5soLlrURUSfWYJnGyPDt7grwDf+ vKwH5oBoTpdZTd5oeS87aTqGfeTWDMmxpeRK6wp+lB56HuHjd6ySbeuYVGB7 SB3rC+ROhqtDeSwJZUP8jspVHZjr4ebqsu5iQ57xuap43VL8niier0IFXgus uHRRLD+e6KBdFZS5ukagxIbSyeGlAqcuxIWpsGy4V23hgSYDB16phubfmZkh i3ZtGzGaQqkn+wL3aC8gev4g/nZpixx74SBl6/T5Fe4O/SRXiG6TS/sYRa6f Po1j2OfdDWIl/dW9eUa2l1Clx3tnu3AOoqPQrRvx2NIdsM+5qUXiItFzCO95 aiNjeiIJaRS/pogAaAUar+GqPBnjVWb7WMLK7DUlbioIQvcm8I32JUngDLUz Ms8GY49bVh9wjRius/ma69n5qBpqf6uoITK8PCeoYaOn96gRgu7sjKD2YDfX rzxqL1KgdmVrErWQz3+whxGjY9hn3adOCb8pvnAlutfGJdBPoFV08+PCSjBv abf4KHThwQAQkZGP2mzub+nj+kmzGOpf4B4fwkHM10tKtTt8piB5JYHkHkNy uEkWSiIMI9sfSyB5fa0N0m1YssW1rNrruuZuAr1qQP6NxV5Z6b4yIO+RI324 R7EsumMTguPzlDdd2iyMA8M+cUJw/MSJGMeXD4tMYhwb+NwRfZk1jOOW5cK+ 3WsCt2eNfJ5dJhoO49Faz5ifS8EXL+pNOUIH1xELCt8zGtg/4WMCX3cCPpu5 HpDy8+yXncNwdEJEvN/P4ENPQp4Nt6sbWMvYtU+ccj077pd16s7dLMMPxYcz MzF+j/cwCMbFc+QFr20VDAHz1a3GxTw+s8YSerwB0xtkVz9+3PDLu48dFy+K p7IxGNDUzcv8apQepHipfG7UWGDmLPTm7EXzH3TsUAKknXYN2De4iCfToqWX xpVrWVl/4sFEDNjmgSBJOHR3Ywpc07KtRri+Pc/5W/zbhEuE5hBY9HPs69mW EemAGRPw5JQgdn2bHH/9uLDu/m62j+6Nk8a6WkYQAYTZvGcPcDDB913WK5p9 aAOZZcp0ricRyyXfP+GCZzfyOLlXNdZlw1avUEFFa7UdfhkWjFdGL8aTacBA +1Bm9BxJGjuC7eQKFzycFNjw+f9r77uC69iy65q3+wYkIgcig8AjCRIEEwBG MCcwE8wECBLMBAnm+Mh5fPPCUBM0b+ZJKmnGM54gTVK0x3KVP/xll8vlUA4a 25JsS1aWbMuhHMrlr/ZZO5xz+t4mwS9/6aMvcBuN7tN7rx3PPvsUIks2dE4Q 3xDr3ZVsSNQr2f6+hhZGER0ZTpINrw3sgWw48PtNQ77b5pXv7mfygWxPsVXN AVWSIFs5mRwYcSUbWADziudAwSGmNA7OFZ9iZf4GL3wYlwq6ytdheSJaOZ2D cM4G3C4HgdRFc5yXa1ELv9qjp4m9yLQyJfnz+FKmmaVnBdMX9MQsktbBZiKm 4YL+uGGYWvjEYb7SejH9N35gCfr35HnwMw6sThIUWBMFaAl6aj1jEdJ6cy9L KYgKSTXWRQhaRsxA4I1cMAgKC5SLjPuc5ed0NWgPpeAb9KPa31iJminCTxkV ypwTSl0UymG99C2h6BNLZXZ+J9gHtN2kyIO/vZaIGCkpsZ4V1uPSKiGl4ebJ pSzR9bpVzry40EilJpSALWtZbCfPQcLilWGGxJasvymQXt7pXLf9q81FsJkR frWnl3fau+5ZYTWnMKGJ1BuIDgbAzYb6vG+uOTfKhAdq8RMIhtpE6IefEHyY J0X13hWMZrjpxqG84dM+53fTiYObwyTez2CxkmQ1P83AWIn2eQpzk/pEsCqI KdDqb3Ykq9Q+u5z9w6NL8K4MX+jVq0ZvLmu0CgDZVyybwW3rVhzUsEFm6pI9 vpsX2af/LVHXa4wv97F51Vdn+fgcZeTNb+YFze/2PMgBj8kEmBFRuZGoBJjj AHWnt7CBumrOPx9nlQoqQ5cg/lQqv3uUdQn+ju/Gy1fjrnstWBK7pg588Kx4 ZQqJIyHxSY/EukIJa95J4NGuD4g9uSywdJ5aUUTnMvbJoUh4c2eaWGtaf46K vfG9dnCfjc9goIoJbeyXHcOP5Tqkmz5mgponvKK16gMd7M6AnqFQ9aYoD8Uv fk5uYice50FRUA4UxjlQEOeNQSMKg9IwcGryUfl6q4iqxZvLmmhuubPqPlXV +j8LXH5TVweR+cKtHm4A5cyn6Ai4RFZvVPDUIyiMiRf2PtkzWMTBMyx+75lP 4ubRS4Rq7ONQaLTtYOgwLlSQ5kMpdSGlwJVP3Q0yK2IUh2C2zroDs+xjEQWh IfB7mma4spN/4v8gApi0Ntrgjk/QPBqcJQn6aEOM8sqHKVoWVFXHE1Hj2sCf Ygo5xQDfiOnJn5g0hD91blCoWuUoieP4Uv65VcLxus544akvxn0Tn8aN6yY9 Y8bPgCIoxqzvMfxNuQ5utSgAS9U6bokcT29TzNbjd4tVYA8aAL9PjTpXH06U uqrAKnT2pe1M1X2rKPFOeY8EaV+ze5Px5Q+wo2VeqZS8nOQoEHmn5Tx+3pFA HYYxcst1bMbq/npoBab45CBT/MRScSXms52b8Q64Y9DItYU4qmyIe459TBTv Hv8oblhzjKiUrWq0tZvGNPZf/15RZu/HtGrfQbmKoAeiQyt/AGKZOBKxJH4i 9jxsPNHxkYA0N4lGrTKiNp7ZxR6uQhzeB7wRwBeehUIbsJfIP768jf8HCmWw SxvzBt9UZeF7GDgml6OfZfhaK5dNo7uo54iSJnfYUKKKY1iNYJPRExeMxt3X B9oyGyI+BTZAeV9dzU5yIYl9HNDh69vjjPEmOg99JgM2mP/uGHsc1w7sZfgX 5mtt19tq7INr2O99QZUAL6gkEq7ZbvEKcP3O5Ur8MtIhKgUTm9jlw+9gAAjP QZlhCxqOSrCGvyGUqSho41oh/Pz0LcCzGconKdVnSimdF93yrpx+Sox5Fmig q2nOVcrfjLFxGzvi4IHR6483st07tcxyAEobHMBxrJ85kOfsrs+BCytoyZqh LdwcQ/4wbtt5Oy5vH9Tn0AhA52La+yHeAQ3xzHfUNDSSBl1Hd+gPeJnyoDnQ URVL2Y3G0L7uSEMcHCLvWdgVUfYKHjQixpnd4h/mKSkGD2RWdBJs7sWt5B9K aJ0jZ316mwutj43QNMIjn0nSYSqhniKq88WqtRXMkApP2U8z8s1FzJBNnrNi tzRUUXi8Ebc0nyHj/57hwNU1eIDhjGHB9SHHmatr+BoxEtZhxDUVOUSI5v+Y La3bb6IWPcEW6CDoIp8t0FVaxcUJpAKxpU2M1rjg77Z8p3pppFAqqGj1uXc8 k8gDUQnaXiC5gu6CeXP1MRlCUzUHRC9ISl7QwqijI0yeRQvUt68m3weRJiTt pPhGkLST61xgBVUGaYTaxHWIKDDN97iIeSXdniLb0DDHUqYZtkvyos9IwZVK k907DnaEJQk3Y+bdGmEH31r0vMZW7kBaCT/PDDjmNZTHjWtPizJj5jVvushM mZex5eNzufqavmp2TEOKk85hgvYUm0UDhrv23A3mnJjSPHH0GWsTiiX3iRzW BzoDkSedONrPfhiSTr3ScOTIcGAlDwYMHIHknVjHnEOC6/Ehl2AFRyUxqpvH /nUlb1qTN1nBNxo4x8pXfgVh13pP1pap2QECDi5ms3MLsRdzDOIHLl5cKRwr N97BOgaHcuzcoOMcNGBXdVzRtSYnjEJBjD4tL7LWtjPZxYAqkrZc3i0vh4l1 LBu7Shfftrg7k8KfiQR/uMbrTkK5RIRP1PJjirpMp8byJGhwc5G9wFzonX3s jD2lmjDkHEOaKRjspJyqofMWmoDBhAxmGnCXd4vYUtLQMeK9EZwg5VK0IAtS nkCFF8W06nVRE6h5rwl02VWWtBiR/NFGZo8RFMsuFi6EbaIdy5klvoDhf4/3 k6mjHV5HLxsX+UsqV4g/yhYsETUnBmWOHT23y9DAt2GRi3MsY5Z/s6L5Lhbx b6aIf5rNekbXMf/GROfsYjnjCSSe3cIQkO8G34Z7ecJs/SKe9aqvDIIPivhT 0rk18ju3QnSyKYouFP5IpSeBTP5GfMRLZcQPPOFJl+5JGzRXsoMMnuG4OUxJ UnNOlGPEEg3e4Ti/wkobDJrPPqTwjIeHWHHR+V8wvvbn4vbd95V9HYfeizPZ glCIX3qujb+3eGIn7DPX3aJzMyyGNAGurARqj4soZj1WPmd1aSl4XmDgWMmz r6DcfTmghruUpSFlTQZkC+5IlAWmgnAOGhWs7WygrsXcsS74lnK2pKOz+NJO 8jKvdQgj8iPPyXFfGc65fpDgXf3HotSV1p5Rrh/ceURiyJ/4jniVRZOZrALq M1kSAz6TTy8zHGo3PP2ZePH0Nyyje89+xTqVTWvdBJRyGpNSxZxuWnfGXjcq 10Uep7PCaQjdMeZOwihiQnw2hdMTHqcvihw8DZxT8EQ4PUn0vU/nwP2BgJt+ jImKAXsRnak0d9YzmxHVXdtlbsce0MNDnCEyzq0q3W+p57Y/JT7YtTAjpBmV AXsKlyzEhAjzY2/Q+iJ+QH2DB0x308I58nbBw0fEVeb35dV87spq4XeWYwjl tyhmzhDlOFZThhv0RvPr4oUnvxiC4+YGJNhjj4jbGeF5WevSEp7PpZx1f6p2 cXBuEc2Y5+D3TSaD5blINq0nLub5jMfzK0LKyx75ZkSqT9AzmOd+uYxubojZ 4PnSEE7T14hawGBY33wK058WyXvJ5m9l/uZvJPOTnjm9KPDMy1Ah6E+E0zpT DLi2FXN6oMmo65Ekp03kTxHi5KBwutJlVfWAPoLnxFxnLhsTkKmdH3cdfhkJ lzv3PzNf309wGb1SlWLasH4uHb5OZAFShHaLOwXsJ5nDyvCjIpTHPYYfE4ZP WIZHluGi0jWlfEN4ftbj+ZTwfNLjud+qRJ06jEwS3HZxGXiO2YyHh5I8R52F iVQ1TPm28qJka6PI39oImrlcRnWWWespbrJABIOpwLVk6SuS6dMDHqcjrqgA p3FcXMnMLudAxmf2aVHrUO8AwoIqY5TvZYnJ2bhj70NS4x0H3qW8mBFtqZbP IZ9b4gov2Hp1g/wZ06hHRQtfk3GPKz8jYu9tVtB4rYhYyqfvynEeJ0q5OuG4 qp6Mk+SINMV9ufltp8Bve7pQhblpvmN2vkiYH1J/I2YsYlHkRPtatN+uY6wW GFnGZrV/lT2OeML7WITbcbeMRjvBcCXOT7FqJyEHHerMVb36PKSW761HVsCx +syAYzWZZsPqMnaxfVYDg0hAwF1rq4pbNk2bN2Q+Q5jBZ/wEn5s3nHOLdvIV 8bLZX08I8NKZH9F5XKE7GjfIq10j1XiNzh1m5pATlhXOTzvmQN0q5yc9zh9P 5/yMy0E+CFwwe8fFS8p8EWtlvu/ZKvOzoTtXlsJ8lepTxsLXSLn1/SLml2jy XEKTB4K5C07niM9NnFe/+6oQ7pF3DkR6yBgw557QucuBS1b06BBQWAW//MyA 4CHLcZLiAcfUCpeFcm6cmHPx37nmHiVrigmYcXLhLnydFH1l5yq70iDdXXNu 3bBc1yJ2jNHgMIF46ljgtME1DxMTIq78D4qJgLKmobjuPirUedfUtcLimRzs xjE+znn4OOHwsS5F62t6GN8qU/ChygHpZZyD1ue9FoPvvFbr5xJaH4algqkR SpThue26pDW+6jw6uC469x8JLPyNzro1/sDyQMDiwQZ4d6EiAyGZj4yLqxwy XBQnyDDXzwwlBoz6XzUN8OwXnf8aQaRtzwOU7rn1YkY1QEW8TmUMyXUFZgMB I5LPswKPW/w31RpnPYScdlrjRlJrvA4fEx4+HnnYwHHOw8eNdP2BbVgKRYai wuiPhUYv9JRDf2xpwLnNJsDrM+d2NVHPiiZcvaMpiBdXmvjeRAfLjMHpMN7j /IgnylaneJCKK8zn9HH6TSfULK5KjE7oGx1xTAuULXwob3lRkDMjxuaecx5v CFmfEHkYVPjeU6xr7qxjUE0MulDxgRgXP9M9KfqHdRKnTJc1Fg0vpPJYoIf9 SRc7kMMx9tguoZtL2awW0EOcNjEOIDNZAdRFAdN5BlpEgMqS2NwOEqASvROR BVZUTb0eVTMWVTk7IaaHBJUapd/xgEXL/PlB92VcSL7Xa+KgzGILmBlvM+qt 12gDE3mc7zLW7p0gfq8f7H2fcnqXu834+4L4i4M4h0+DHXP9LXP8zCp8w2c2 /urKIP5owHgW5j7XTIS6tCoIVnr4O7AmiT/f7s0W4W+O7atDcZWXCNagxDRh mBPQzQoWnSbjRTVnmOS0Rm0kcMt6gitrXBJqeiXjT2ZQfI12RULZaU37htgp yA4tSmDvm4S6jBfRAHvz39lksZeqyW7+itVkir1+wR2GftBh75inzI6+HnsT FnvZhAd8V8xXpiiafR64nJCk+WllhKqxyx4QqTKb1RiQhpVwdZZHUVxjWN9f ZmGWA87wpA5G3MxCh7SrPeZJS4L488sd0u4ZNM72MdLygre1XsstRRiQBETx FKyznIiXMq685buvtZxhYitYrdxpDWzNvkXWPRZ/UWSMrCuCxEcigheIgLNW uet9OxUkqNxStE2tsNoOiQ9Gmmi7HCfK/BAa2i+cp9NyDauPxB37nsSLL33L Ak2jqkJDj91Vom333ZJoqmn9pDZhxETpuGDskKjzCTWaWTqlOIMUnaVnc+R8 2gHN6p2T7hz3L2Rn+4SHuzMuzJKZCprViCShfNaDmMxu0Gypw1aOsLWm1oTy 7WbAJoC+hqZ/fYyxDBSZuRIgmzJqDRDL0adRlgZQL/vpHACXF6w9WkRaDn+2 WPvUaLUtvMFcsEZIDqsJ6+km/TmEQ2lVc7UtUPuujJLKd4uTcHt6lYEworNB MrEGo3opSHrnul7ysXfupPzv7GvgpisGqeoVm4keXiKWNWKsKQTPDEQKNyTj kYBHIA/lh1Yq81x362W7CW54Qv2qw4Ad6TnoNkyQoDRLITe/d512TIoH7vyY 9JqJ/bXLJMzpToHccfn9GsNQcJcjwNzyjrOMP83BX06B3jmhSRH08N/XS6Gn 2u2ShV42od32C+TUp/Agh/hhIY2ilS3ew45A9JtxJ0W/CfzygrwLjEQ6jFpT 5N1YyMgz2s4i7yG69oh/pv4sahRGxY9TLYdyE/h20HI3irQcahPBS1/LPd0U B3VlYkH5ugqxny5ozFFy4DxDkJIFslBVg4JJD3mhfIJ56tPpbu9UOrJjYRx8 tJ07Ka1Z4BA4vcoh8PSAnapDyghKrqlCig/KaLMJJPqWXP42wc7Cz2i5nvGP 4wWbL9ttCrB3wNKZX46X3vpV8zbmN3Ou9/SXrVCskKF1etjDsT3QvEKO9N01 77jM6AnFvh4pgiWjx2WJgT3uanjXnhPg2SzxDTYTiVk0yTZQJxrMREBEFuYZ bVlCm8F3Jys6IO5umwXc/S4HuJu9ouqyZE+fLHagMzbW/A2oi+IPlgXxxwOM PKP7FHi4XIGn9W9p5hUTxFJBqiXnv6jA66ouNa+Tg0VeEs9TIoFxhMGn4RKZ 0QkxCBoxzAobnK7jXqzjois7FXR5Vneonni1k4+hVn703j4XWEwM2mDDIu+m EbRGu4kuDRH2FajTw6LPOHgIUat6Rizy0LGq/9r3CXOKPHzXDeG1AgSvvZdR Z67jjPNeD22AxZSKlBnJqSK4yTWqBU+VaEFe2iVBiUXhRUGXj8IJflQChXBg usWKlJtIYRf0XRuhTlCYjQ8uYATiuN5F+DHPYd/u0UL6Tt8ycg4mVzBovjE2 pz1liOOKh83rohFxPFtsTjM2n/eTKwh82hpjmOMHB5Pm2FeK3B4z+CVlEtal FmNz0M0raLs+M0SqIJWp+DzBsIy8nVnvOCEE1Fl8bBWna3XJ8kPxvdqJR5nP ECaYH7WixWERQQUC3UurXUUQMqntVd6QQl8LmvvhM8sTWrIwBSsbzTW25wa0 HmlCD4/Qjvqqy+W6xoC3NvPxiMjioofJIxaPoe8NCgijhKUeT06hzARa33kX 159J6kAtaeBItkDyP1/Q12kCh6PGuk42spN3rQ2eARldA5Z4spMACK1N5652 Wx1ogfe4o/Tcla5SMJpAJAFGo0PNWYAxG39hOYHQAvJFf2ARiQBFEbnBM9MI d1FmmKYtLxchsiwqNdP4XhZJooWvM+4FFVXd87SiB0cmrRpjuJDvm8P8k90s nUqZoPc+twM4YzyelbYX79S5WTwkcZGM8ZMu8AubtZ0V38/TjYJHPqf40uca p68Eh3AO9boBETlMzy0Rlb5P7GaQTczyKBR1AsCzxDYg0aD4WOBweElweMXT hydFul20W63BGyWRe8USww0aEGuMDhaIP4FG6MRTC/j3Ky2BhebRVqsbBZq5 +HibM890QD/2WJX5rNMhMiO4RJKl+NzjRRaF5hujU5Iz9viA8MwB84fGxH9l pUUoUbwhF8TZeU5/NkuxV7H+lOJxXc74PQURdGWx/jzoMiBl8pkVqoHCOtt0 jd0oUpgniDtcPwEKf9McXws4+nRbn0hhEqpAPtouoDWfs2vVicWMFeM2x/HK PYldEEZjoqI8q/ID9blo+q8l4Ar1ifO4okmUbeuOWwpXOvpv/ChuHDk9KChF Zeoy1pZor8ooLVDnTR+l5wRJ1/h9tZhEV9qdDbRsqILwLouL6ZiVf0d116A8 DZmIgpW9bFwVmeCkRYBojvEmoITDkqmWYgCmgTIbn25PgtJEJ8hfEyKfLCxF 33SJ3gzjzy51iMwIImeLEPlqQBFZQWgFGnHsE0QeM8LxiyNB/J6513Fs3jaf Earep1aNurJtRiganOAKWaL8fUUMPM1ihHZVW4RqN3PkPJCu2iMq56ZFJLe0 xvVXSTd8k86JOqX/1o535GqiTO7lFnxjlfrxDhPz9CD2sXEjrSHlWCewsyWn BrgM5tBiDtXmqa5HJ9OJhNuJOLt543n1LBCCF2tU9EjVV1wmt4FGXSmCeJxV YrxHjTz38BO8ikaNEqHPeYfb8cCuDpVQJ0c4hXJebQ7shon9ljNuNgsPP8z6 UaCZjbc3OsiC7Zd7HERhzq9wZCOxdZZygrf6HETP2Bg7G79c5Mz1i8WUyjFn OdEDs+xD8F0TB30BpZ9AaC7+ZIVDIQ5c8gXUxLNRP9pqgWn+5YfrcF8FKOIv BajG5X5heiifT444gE4XARSqC3G4D1CsHOK1hHaaqzagHLX0W2NgattL7unN wPyacBf/VaUuA9YzcuE6H5/dxrHQs1EsmLIrjnQizy/PQ11sN2eotcpmLlPf JNe9c/5rJcCMymuKgMm9E4ZY/gSNERUiazC+W3PcjLxrjDRBY8FuwgHDcVs0 KhTwYtaSulFRT7mxM20WcZl4vBV2vx1/Q0JaIxgTzeQkvD4t/iKmQcyh4fVn uh3W3uuy4bUfpuC4vtB5jZ9fnsSY0XwCsCj+2hq6lshifuYIY/n4B2sD/Jaq DHWWzG/MZ0soPAWp+EOJ/TrptX6B8fcDjSaK1yIiIznWVyfOnQc78tAz4vv8 LNGUIfcN1pew8hYj5F4i5Hk+im8Ma9waCznNEHP6eJ3n0wOzxvVllW8JNfUq AatiqAF+SahFVgcOsTUNBW2aeoQCu6wZRy6PuhTo/nis++AootVkcxDY/rGh IflO7E3X7Oyup9zkXEReFyAm8UpOtJv6g9BnecHYc3ECkZp+2Wcxhv9UfCFd 89mlDmPw7xRf8PPM9yAUJfbUqLzP9BOgjErGZ2SBhfRkdda2zKqKHKbUskGn ob2Dr9M297vrphhTP1Tmn0sxutL6CVfUqd/i4EvFcC89UH094Pnk+YELeqjK EBWCPqig1/obvFgkm5y/Q9lrNmPF420RBQtajChNdvuIqhc0DTGyihF1PJDK AkYUQggtL9UQBdHyIhYrqzpbC+yjY6pfEbWzBFGhRZQxUubtGU6YuEXC2PwU OJk4UFTWy57A5p+ve1ii2TjOtgASCiNjOSPRUngSMI4/G+RUCYigpX5phO1y b4VrS27iCgGSbagKxXR4+PUgOscg+pEyG8tl3wAiZVQoccLXA2cEf858/5A9 NJvYRhkaeVhcEMn3g7e2oDIRWVMQjIRhf6PF/9uBJkdr5Yqjhaa1HFWbhy72 sEKLzJ3nBZzsDRQoObu781XRu1jU0aTZpDyFnhvqGCOb6hkXcK+Ok5PC2BCX Kj7SipdjbJxoZ2wAFTr9+mART7U+73bYuO+5TCY8VWzAc1JsPO8XbBRsNInD mCcyUzBXCayEVBGwsZ76D0qLVk5/rjMvUsg4dQpQYEGYv7TPB8okA+WXlWFp FSpwo6OM545EpGF+OuDg8udx50+JlF8Sha4vEFTlGHtGyWT1dkh4VGoaWB6L IpiqnKcnX+upq1Rj1feSK99N6JTFl79jg85lHqQXMU7MdUM0zKNBcoZCdoOj mYqcaJfTYt2Qd2yVIXWXM0pwbBUNsrXB/MEpEPWz2feusOk0eEV7m41fZtiJ 2+xu5nPnurhK5IGX553soEoRb2o1JNtlwj2Ayc5uoSIE6HmyWMPA0IePzAZk LYzg8wuMsNcN4egXVvM0bluZ0wIYn3ktUqKNb4mjCcbRr7wJR482sNXxdDO4 g6TFV+nbpzRo83s8jumtnT2wSDm/UEVg2K5qMDXz8PMWBHXedY3DJ+IlV7+H DDSBpVE6DflgqWPPGTuQClh4pwMBio3vtooWuRY4czTm+Y86YQLVsqbGgQYI QAy0p0lVS4FUywrjE7QYM9VfFcSd5ZxKKuemDCoiVHNoTjXlMYyFFTiHkjUU fDx4R7VNGL8yGuhxbyDxGAPl/iIGysNF6UApFAHF0zdo00phmrrQXxrkvwMw ao2MwqESOqOQrDKaCzDSI+5XUwBj3RFYmLaqBGCgXaBlPg1U64QEmBs9NXH2 yUZnjeDKjHGhvXafTbM8C0/8VFzRsSIO5s2zFgqp/OZNF4zGwUSAqJZL37ao GpDresT8LPHQsjsFLcgtHwiSxbRXWd1gitga+pY850DHqN8jPvNkmDSmh8pB JWJjntvfayvnSuMcLKrkegqFg012dlF8noDDXWOQXhr2froyHQ6Voj0UDgio jJ4oF5UBBMDPRfCkETXU2jZ0vq91szRpCNjiIeAMI+DXUnyURJSD81U5i4AO E1l/mAuF95+SYn7YUhHP12YZ7n80tPPndyL57D3zZTIxYH5YVq0T9+A9NINO 8Rivw2Kg//oPaD9aXKlTPCsEA0s9DHjxtviwOZoG0rVtkmuHD9Ih8tdexvRT RTFSG8T1WcvqLtEnOTZIxPH2Atenwi35YJnjLhKBd3jyJJW7inIEWvAgjCeB FtPEWgj5zxlFsbrGoQDuJ4aGw+ipBW/g7thK95jTzN1fTwljEjKKuLa1yoYx tE5idy9VZIGjL2oL5nrm9YfGa+hATWBblV1C07zxgpVpZm2OPAX1GvrOfiVu 3303bt4wFYGxELPRi8peQ3lmLWrlpZOXsDZPhB7yjqXyc5QGyqEJYhCIN0L6 PuVTNq7NOlbi6KvUlSDzKWxey5EMoQd7oWIDC6PYIT7WrRSppbzYl1fYeTGf lWreTLBAQcP31zpWIghuyif1uzqIyk5jbqxvc2TY8pMOFF5tXaZC+hvKRjRG kbYNkVbGPx+1PRhJ8GDjyyI73Y/ZNpzCn3g2Dv+iXoFR2vGzhnJPnEPfjbSM 7tz/1DI6L8p7yZXvUFjAPBQh5e/0LecpbzQEDTKhVKlwMsbn7hL5uc5yN6Q9 RbCCH03itUIfts5jK7cUr6LLMMXwIuC6J8g4PIcW8QrwbyqxM71ci5kmneq3 V2dZsr80qCY4jL87QlMJcV3Ohefw1ZSdOAwUhKUhtXXCDni+iKIrS5Wsg+Tu LcHfUN56znrBV8Aww3sQ1BojjXNolodzRdedWdbo+XFslrcWcbF54xRpXhZU 5mL/9R9ajpULx4yjRmoYs+7anCUjpFxM31jVrhGHbdiqWp6mHBHPvdvzbPC/ 8DdVIJd49z3MrDPf+HOz58xpVSQctPX1bGIRvKGE0dxLFaJxiMhKf4ksrXOa vr+WM1Kra5LNxdVpMlwz3MEne976WL0vFqIXK1qE/nrdSeaidk7yQ/MEF4uV r3curwvgUAYLRyxvHS3LzU+CQBdZCjcjOE1kP6FgjdINrVh+1zK0zCsUgwiC obp6ImRPW1g5REReLOc0NIMtrdMm8BHZyUh4aXxg9ds3i+SBeZkUFuY8moON +Nbm8Q0SaaQL+7cQ32ADtza6ONa4N8ijl/BIlSeaFRUrz9ZaFbDfVNZACyII MsRu9MsGmeQWGZgL+7DIx4VK1Dky3WsiKq9FREzkh52Deuy/9r28R/KGoeNw aWyJrWw9iSk685pDRNPVLFNkxzqcqK6s5vUu+Lb8LemszQlgeID5H65TWQgp s4XYxvgT5tGvoyVvpwv6+XhHr2q97gQT9W8rBjU5zhqpRkGuigsxQD5UAMyX F/BJ+4mnp+zuCmXVhOzm0YuB8xsuaZWPXSfXLC/fZukZEj0xBZrxCAI3vlWA N/gGYm7xiLlIroPi+MZQINBkYv7Ucs5ZgYwKzCUVpQXKh4dLlYd0u6ArpEf8 31FiJm11uRIzzbJz947PpsRiarp1/2TYRln/iPUWkdBpkTg+/rui/O2dSr+e NJ1OYyl0go6/1E0OrKUTUhtiGuGY0+f62lI6wSTCNPp0QqO+KEzQ6e+qJMNL 3dtLBjGrZpE9WfHyHIWK4eZL8hWhkDSipILhVS6QQP6pXi5Va4VgcJFQaKUo gnFHIeuAjCqYeYU+QASJ/L6E8ee6rBCOGpu2nZWdxCM5arldrNC2LVPZ+zee NBUrKnyXMjOpcImoIrhLXrHevV43C0lC8GBcdzX7r8dTs8UAmPYAsFSuM35R PGKEIp9xGmZ9HZdzlGf05UL0ci4RCuySqffjtmjBb3vquDjlgNmUbOBXSeRI f2qBHWbtURtQ5bIFgOJYM5kv2wDwfgrjypyGQDZa0asuwnANTavGK2vdC6Eu Trsl6gvhu+xZpi/0O3I55POrRS90w6Mnz2pmySgckPMwHMaXDTT9jggHec3p blJsq1TfpLBqwLu1EgMJr+I3WyNvtqslsBNoYEsxq7oa3P1k25DfFXqe5bey B6US3Qtph46VTtwCrT4yHmG8fwEnY42w6AsNpLzQ4ZQXQgiWfCFjWKv5hcbM UZWz3JorMybv9G/lnZ6lvNMqnSaMiDHagrJaZSaKF1cxb/A6Zgz6OmUCMv91 7qe8Dgz4615nnzlWN761NCVeZ26VOCEvUO2911GVmCwlmvBOOJA7Nx6Kfbnp FF75u5mqt1gWlr7ckPdyO1reWrLk5f7dWyrEM/IWKAzWHcqmWVXYN+wqd294 qYcycHaVEFxf5H6QH8Z65OY81/LiJ3w16JZewY0nila3bFG8l2noYY+1tUyA AwtYAfdUO/0ylxTKdin//i0V5ikZIcIJ3dDnQsAZ9T4lRDklwEEEpB9x7GGz QHOhUPN4g93N5t5GbGHGGliAjTljM2meQLn2Zr50yLxgY45iMH2vt9NWWTKe Pq021uH6g/RY0GqLNJnRzQEPp1jPrctUDf/eW6rhE3I3dP86L2SCpYc/1Kqr 5vJkVy6KpOMnrf2YR0WAZuigTURJH9AGoa85n5UaikPm1OFWzjYaiddZlbnk SMkCR80ny/YGRxbMb+00R3XOzdYcHCqF0WBXCYx+X957IkXxHXZkGRDUIKsK 07uRze2QkAWveqHLiVIz58LwrubNQZYsXQP+gTQ4DEyENjmaADvSxucR1gNx uXk2SnmQQqNCCo0QbSp9gMK9zY5Ge+Txmzsdjd5S1H5fLof79FFQGs+0JpRv jjJbE0w/WhGD/Ejo6uwwsQT5ONeJ29ygXS+XSZOduqwSLKR17UIscx2f291U XEqRp7cF8Y7IrAWKvyCLJt6z0wOFFAo+8Ciongb0nVLQhDwUI6AopJmJt9MM p7HKOvJodV5Mv+ryEvr9B7kcvbc+KaIfiqrVLT0ibBwS2p0Q+mHKZ0i9CmML ECzDNEF5m5cVIkbxlHGRKnkiD65o+Fo65uItXgWnzvgcbDV/NkQ0f1YcgpQ4 QI6yjCtySaHmdAo1MZAtovqNKDA1s3Rql1B0ay/HrpqAmof+/kUBOb7Pm5ew gX8gl485kbUk3euN5JCQbUCcsf1yNFqS1lAoN1pveU6m4Fibs4nXubaQ+6iG rNdAIGidsRar9rZ5JN3RZE4wKRWReqBmzDBJp7rncvnUO4fA4AlDNYGlI+Io peMOc+sVXY6OgGExNAFXvW8RHQHBJ28Q7YNCx5WiAw+IBUUxO6cnquNVNTyj qPKTk+T1VUM/9J/AUggoyWEJieHFgo7GXjDkDM22NzGFQEQDEyZi6OPRDA+f YbylntmBh9eL5jme4oZsdH7qkiqbt4ZlYtWYo4lypSMkfdsSknLVHepzvjzx Rp/zD98g5c89Kd/vkRKcxpgh8fMCl+kxapBIYSTdRiyYGtvUoGrzZq+uRAY8 69nVyMAvyYvYQ8pQNruXTDPomC8BI9wVxHgo7NH5nbkyILrytk0mGHAeIgRb pxRFOxul6PYe3sDUuIu6vTPsMHY50P1msJWJsdeS9/gjuepMikk+qvSjvA6V j0FLdusyg4i8VrgYIJ9BllIP+bCB+bwijKmXI+oBlACbVjwYwBIBM2jMkxOy FatDaAPE+UZb2LKEuRJsml+ojlxyTtcVIACCMI+ST2OIBkkA6ZYFtDlBW51d 1oJ9SXSrHtnGJ94xkCTd3JZ6j/AwFHUIGmJ2EY2Y14rPjrJrEFEPFW2jgYhS NxaWUhKHinddzlDOeG0QiQK/s/FBhKahhSKq8zOoTjLnUKbYLrSp8sRJaBuk TZhoPBay9yDfHBkWFJJ0hcHB/t/YFmD9IjsFhq230FUee59kZAcUdJgvZBNZ jT9+S1O+SwgMt/uhKCL4jdnAtcJDFwLozO206AefWTLtIDJ+Qqj3tyiRswki i9DLKpgwbi7wZKtxsM23pNLkezCF4WgureIxVnhUgiLi3hWlFNZ0foXXCy8r ji9iw5UIWFppxyiiLDY/Ksu5TeOxj5duLqOkxTm9l5D2T97SpOsmcAartJTc F7jLHgtUl85jP5z87VDIrPPSwKp4UhbL462OzJ520MJ2zOog37GHqApKFwjL Rl1QjR0OhDpLnGrUqirkQ6ZkxGmEtg0ivKZyOvWaN49tq+EFAiDy2EqnsdOw i++K3eNJAs9l67d6EgU7z/00eLi9KcNFsLOGNS5bcDNoBLHA2kAVT4OeF+yi pDUiQjuf30l7ZHG8j0uoQ4GtTt2YR9lyX8ypzxQpBrQfkDZ90vabdyvQ5sv4 HVuwji5hiKrynguiQsE/fUsTv1muQzB9N9AG8KUMV4jCuVtTk6QgZq/yGXst QbWYgsCZ6kuttIEpRmBwtNVJvZIPhXy67Bd5k/tF5MNYm2Rs28TgIL0w0mfB RMmomnLb+mou7EkS/8/kbidTbPoJ17gbMn1KBqUEQzsN3TtrxHtJuOxbG1Wm 2aAsKCQNgNgpIlooPhOIBrce6nELtteb58qAMT2LHAWEWO4VLyy3M7oDLA0J mh30+K4bBWD/vJ+e4u28sXMtuj6+LdSEYH8ul8+maEPfG1srggOEt4s1f0r9 2ejSU2zUvUvZ21idsDshxYP1OV+3Z8nDOeIpw0MUP16i/hE+EeHnzwscEWHX Rmpd+uxgC+UNdbHarhRHCUQdKDKdqO/6dJoPELKl2oEc6MJePa9DnZQp/IVc PpVCxNMlFi5P6W245SdliK/M8TlzvBc4hY2sQV8K8XGvZVWU2VCiwlvXtRTa kAKiqEQ9hC46TE8ADvTEgYJX828iYlxIBRpDnx7gZQPWc8yIwDz3EAmp1gS3 9ikFIj8/QcQEBajdW0+To0ClDPL0xlL3cveg1tf9xzeYaFgUnWAdUNUr6EPt 6mqxJlDZH9N7vaInfuTgQKIOAIe6rj1vG36vlwBd3Hqk1a0lwDUwGPtp+f4l akACq60ExTHRSVnHGrktNC6W5YCeSEKDDmKeLacx0lp5o71Knw0OlDi+eiHA RnnqHKQJt18+IzUa/+kNdPwgoOwEXfGOjDYSGuJ6hItYKPlY6BgSHUPC6Ss+ zDj53EtBB9z6TYImSLl4nUQFFNbqHHuthJ0gYSifmHcTIor0h/GFbhfDVMkY 4eMjnMQ0IlQoQN4acvtLpehVoTL+Y0woij75X5xkan5ynlH69IgdEoJvoWoQ yudOT10KMv+zjGIyxb6Aov2Brr101NwhlPzQSbpRMK+sQlIp3ygj1bIuFMIj QTtYnVgojSmBBKEyyJBHQrJDrVwMZJSjvFuBsheYJIDrCNIhYgfpEF6errZU uxIUJw9C2tv7C5NJoOn2qsxnvi4f0Z728MnN/UqtjVRc/iX9KCPIfyrOzec9 BXhErAvUvK5kShNuIafxTF8R2z8WgT/AQj0qSlApichhSZUtYKCOSi0+/Cp9 +NFx1nxHEnhHA0MMk9xwnVCrlZsnhp9JC1ULrwmOAWqP8TvIbKLMDAVKxsY0 JfNFZpySMmRyFssy9rtU0qk9wwbi2E8YV6SRWNYe/Be5/JSQ9BXJPEuryu4L sT2wQeUqWmUllN73lmqgWrhQdFvd+hlVn70Vpd7j0TZf2gsJVYo5H2Qq4Bvs ayb9GYpLephTeRQODNWwQxZwt2No2lGyWob+8ClbGOjgBlIPYv4PKw7m8Tyo o3xEhujEetqZD9uz262m2ygvl1APyCiN8hS3rjj7r3p5QGtVE0pSMXqIUL2D hOfAW1K4lj7zdI/Hcv3xInnZLmjR8LmFq+xrRU6BVORBlMLjbc7LQj5kf4s7 ePaYndkRQfbqGnF+jVpBigY0xbFPfh9vZlrDynXlaFhHhRqwTEmEu62gaU/c HbzznjmnwH7Xa9ghyz7/m8qBGHCmSCmtsinn6uXqD4V2xwThPYJ4zAzsVedb wkUU3NSLDoLBwsQ8CAcNqz8NNM1/wB8okOgfaXXHjkbWtCDeKsVpjjxipd1R 0RTHGkVThKRfVKKVfqCP0i9Ln/nEVtrqQGGPO/QwO88E+++qmwOOwECCbiGR EmKHECHyFNJYsxtCg6AHVni6m7krqhHzZ5TyxVyG/+JraSIZb8tTMKhWAhFw DBOK9lCG7JAh2AkxRRvqJSAxkfhau4E4+Ms+txE9iCCuuMBv9z/kct/Xa3mD mOhbKlGRUp7ooNS+60dRyTMJ2xslLjGj3OyxdTNmCraScKDWR1/qUK19p2OG o3gvcNUA6KTcGFDW3aSz8k6ozGtkrGnLj/8lIlwUH1TRa5WVvNZ4Cop181IM 4FQ7Z5x09RpKyVbMV/+VP/c08bpg0ar6elCwCt4D9fJ6PC2KPFZDTj2iiHId h9awFBtpVhMGU5WXoERa7vwfz/62F2nCIynvotVASIXBOJxpJ42/wH+dal53 yELIA9wgAXtWLMMB5A3Nu+xpDOyLwLY0SBA3Ifcz6p7UvolDpIYxiPFD6xkh uUAMlsUbGdENT5FFg9RSOqmZXYCNdTwjh5y++a+9nr7cLvNCqGr5BylPQYAB xwzphKlOqRnLUXkP1k51FNghhJOxqV60CsvZ2ipWOnr3f+jd/bLa4ojX3nCM fYOWfJd7+dYe76VwHooP7iTqcIyg/KOUW0JfQjVMdrpbdnmT091ySySk0KoT i0gw5YIAzNigf5xyS3AQ/dI49c+3lGwyXaFrH+eq3E4bLmwdojH/3rB5Elvb Ir257l067jzxDXoCvMMBSiOGMfG6vW9aeY5/33+Scl8IMpV9oDVINy/WwGLF 6S6vnGmO+/5T776XxOHDO49740U1A5ZcYbUWes6hskFhvi9IurDIQ+4Ngn9e ctuIsCokjo9gisvcE7FEJ98VLQi04gSdfd4NSpPryo1/VnL7PEWWx8xIz7fz IzBirHW4Ty1z0Fs0pzkmKlnFUpol7PmF1EIopCJQVLkXAs2YZClgxK5BSE8j JGsM3GJznBuWFweGsdLgX3hDOycERTEYFkehB9tm0dBYPIvk6bR5/ZmuQMiR j692SGM/ObQSrJlGOEqf0IioMy6nb3hGgXqi4BwWlGBTuD1i4Mb591AaoyMX tU1epeASOdrfKyh6Aw7QKonwuGZj0Z2PizmC5CJL2m003mSbueEt2i3sbnvy Ze71UeqEJ0UC3qC5XN8hpHeAj7wzkEpvKOaAm1lcDfQlslTsgcZB12Uktebs T7xRn1btGHDZBUJw+IZw9qlNzwzdDOyA2WIc8uedTjtaQU0Yr+KWm+Qn+HN7 WM14kLA3Tp/eUOmYZmMmS7X4BZBQPR/wgq3f8oZ8ykP+PR4yZ2OC0B96kJGx Y0pqIS+FtJKEpN7DvkBGzZ83DZy6ypJReVbIfJAQMU6k0rFDyo7KuKfx56v4 jxv0fwc9veGDRN0ZwPIzMvYcfWb9sVNRFmpYldazHcW0juKb3Vz5MtlFWY68 oNsbcrxBpAfYg5t3jsQmkMZeVFaoR685+y+9kR5Xmx7wnCPGWE6fEY0Ut8Sj ri4UopZC+LaUHt5+x+Cvl3+agWoyUDeUP02Cz1sszxSNCu0b1ycHpruT1wiq MaQqISG0DCZSodBw3O905EK/Fah8jOJyj7nBbdqwYMTb3UJnXFoDu1OT0CtH QqSjOi1/HytLjGxcnWCRIYxpvowMug+pe4xKwGdHBkONUeEwAWb0hpFBTPcH 2qn7aqDN2XRk4NQV+X28hTwQH37qutcFtoo6qKbPiDbjHpgvtOuQwWXJcurg cBxrpyaoJSNUrq6RETglFNIIr/Ho4DHQuamQ6406y5MSoqV17QxXZPR13wEh Zp6+w6fCUK8I6GAbb5Ks8NjGHVFTKVoQurbKc1QTYawzge4fJNJcywDurUyO VdMgkAPMyH1E3/hT9yt+P8D8Pcs5LD7yNgDAlPSVNzTQ4Z3qcENWEi+pKgZB jsxxfeBK5GaZ5Dxa3nwNoTaoWxamDvgwD5aGmpUBPxRxe+4JOooxEaxi86Gp Ljuo892lA0UuCnm8TKAOQY7i5qdCGyQd3mGRjYSs4+VMVpR7+HKkYfRib5QF GeULGeV9ogePcqsoW0y7GBrSiEgn5WSEmKdBlxvMbQPPCwqq4XNEQPXIwPuq ojG28Bgb88kx6j7RFfKKHxGbeYwfCNw5oZ4cIw7jiOeUkl2lKAUMsMb7LBXP cT9jRJH+HDNs6j2hR8BTxIcD3TcvKWAIb34rZeSTHnXn0ydva3VJhG5l0oKf 6UyO8qTTCyJzEU3dQOGjXdrh1oAbNJdTKbqsTSN3ZpmMFkYfk3ToubvYDRyk G6mgkDDhqhyQ0QylDBzZmCfCwSXOfKPoxSgFb5BJxZARgIANCA+8BpW2dSBC EskIBroTlk4josgC7mFF4PxDsEknAXzZ0wmBeqGvQ3UFKYY7ojAuCg9uCHXe l9d9X56jU9xIISbfI0e2z1fWF3sohAq9LojalFh3FrhinOZ3ChyAI1bFLJNP vhU1XNzoQ2ifqKOMAP0jb4j3WX6QGQ3EAtCrAahlIiBHnDDYZ/kYU7accQqR oHzGSgQ3pL/U7b9LGF8y73fUsGt8gWgkQ2ApHiDvtTPPhZk7eW4Bzyx9rSw5 lmcETAAV5klqZOhN3tBzYkg8lKl3QT/hnBl1o33KoXmU+noOPb1OGBVzioqd jlIdMtKiKN3bKUWuKM008e9PSkaaI63yMnA7imPEH6hU8AZhOljtdKywUYcM AzewURIechtNCKkjko5jLTzdMNEg4+TSLFQT6Th3GzX+r0oGyXsPvuTDcPYl yesjAfp1Ro1qzr6SAWctMDC/hE7XTKTS0WqX7cucXKLR4jjbgAcetSXemDbd 3MhDRqqytyIx6jEZdU7E9CVRjUf9VEQT/s1nIAA86llv1LaBb4490cMLXNdI HwA62l3eSHEcb1HohpQqxqTMMNXr7qS0214vtfyTlGG3e8Quk2G/EM2CoT/D CR72Gm/YFd6wpeGlhQQ0erJ/XZZz796wTzY7IgMXIO6QYANMMhb3X6eMdo03 2koh9XuBc/0finXNuLXTQFxSirJEaFXdOA60uNEY9idGupnlPxNvo6TnKC++ oZunjXC/N8Jq+kxOOVenDEubafubtsDqHG7VYZUnhkUldI1JokE9VUeypDo5 pIvekCqEaI+EveeFeIgtu1wpkz+6jJDusHSoh5I90CJSHYGTdmBUlc24G67j umIsO/ztlEE99QaVp88sBee6iG+WxSkIhYaweLp/d62YE2+UdMCdgFUeawns 8FQP4dhUz8PLQJ7NPdd57r1POJ2XqvHGGMoY35OxXZCx3iPN87JEfWojKh0j fBpoGp4m5bGdEZ7u5T6QSrrVtawnkVH+nZJh5Smz9lJ4dkN4CF4+tkKRrE+v 95iKfCPc57yojAmZa1lPqx35+cjJaHFT6fO5aedhseTvOpR3eY/U2mv0HQNC WbT4kSebOGO/s9lpKXxvlCK13y15ZDkt8dfdF+AcIKCDP4qF2lMylEeOET5Y dCoGmmo1Lw23ChO2/5AZzrYmN5R19XaRnyw2ToKixw3FUPEuqU3dM0M3O70k wxR7QLs8tcqrwE4U5nEPk01S6rCHmx7Ss3sqyH38vZRHN3mPrpJNUm4JDhfa R3CpDGatkNGhXGbIHeDQ0PUPUu5b7t23UvbjA8a97KPOAMH1Ls557/AYn3Z7 OL+35fblMmyIz/FAdzbgc2u9++j8IPIX94ueh+9aa/SHKarlivc6Obk3ousT zB0jlK9/3sGU9xv0rkt73lnvebp3zYQ8b8aiJPk8XSTWlfK8Se+6Pyp5Xp7y h/4OsQD/MVZJlr7+s2xL9BRaQg3XyXV/XPKsLPFW9iTT3SwFImnPyJGnXjxn sV3Lq5NkQ6n1VkapMdYH6dzCFLU116qlP/HurS7k1sC1TqyQHnvIqW9xUvGm 2/va809Tbl8e2AVeRvpduzBk/8foG5/r9+5T/Ybn+bNGf+Y9T0PADu918nJv cEZStbbRnP+8qrd83p97z9OIecR7nnbExrN2yLmc987Iljd5z4N1GH7D8/4i 5XlRYCsgA+12OibwOGbpWU6sA42Rph6R56yT3yGlC1gz6FDQ8P0ZXyJlhH5G o5wia7zCAUEGWIoa5k0Bz7gM888spmYCXmiPXMJGGqU718FPDXQ3MOg7ZLKQ q0Z2CHKHFMZlptRfeiM5JCYVM1hL5UU2AFN8d0wbrZcnUhtBM+jVcjf87JN3 RuILvId7jJxZ5LILyDhodsF/smb2QnmBtQQtfhBldote1HusnMvpNUSrFTIc 8+j/6T3maMpjIvl30HKV3MK7Pf+ZF/aC0cheainJ//ZufUzI7d2abjUoo5Hb 5eh2Ed1uhVwaaWUE30lbAYTMICLpUhmcx39QuE0G8n+9f5/w3hF2qYZI1BRo lkKR7/+PN4f5Vyf+6sT//xPBvP8HipjxqA==\ \>"], "Graphics", Evaluatable->False, ImageSize->{482, 350.188}, ImageMargins->{{0, 0}, {0, 0}}, ImageRegion->{{0, 1}, {0, 1}}], Cell[GraphicsData["CompressedBitmap", "\<\ eJy0vQecltW1Lr6ZPgMMAzL0MoxUwaHKgIwwIEMZKUORDg4wSB96RxyadGnS kSZIE1BAiJpEo4maounRxOREz8lJbm7uubknOckpt3z3efZa693voEnO//5+ f/29fPO9Zb97r/Ksstfe3/Dpy+fMWjh9+dyZ0/MGL52+eM7cmcvyBi1ailPJ NZyr8UscK/Ic/044Z/8k+PGp/9v9//Z3A/93csKV4a2D5M3vfe56asKdxrUj OMr1GODcN2L3NfR/pyVcfSf38til9xa5d2K3NrFXPohLe3Fsdi4p4Rb7c119 F+wO/JeScDMcr8ox192NNdXMbuyCS1X+cJn+X5xbHZp6NfZMS2t2iD7zEY6L 7IE8lhMe+5I89gd7Sx2cflQu1bVzufjeS841tHM3nNAT/335niaa4vQquZRj 5+ri+2gcbZ1rZOcGKPnaf76JLJzui2Ogc3XsXE18H4ujQ4y8A5S8LZx7Q5r4 Y7wXlc5YJudK8L2xM+LIuTR8n4ajTYzUbzohN/57/T/RLJuc7Ein0GymUv5e Dl7E0ejzzabjdCGOx5xLt/41VdHi69LJPLl1Lr7mScvN7dx3w7nXpOV/sUtt cLpCLqVbtxs4aX2JygG51UQv45E++NpDHsm1c+3w/Q0ctT//hlS9vcyFTk7y KlGd0nWVJPWcy7BzQ/G9WO6L1LQZvv+wmnT+yTpeoJTupY/NdmRqxIgDQa7T v+ANkfD+PtynqvZjuzQcp7vHqZsub6WUjXIyWL71uL9PB4whLnJhgJlU0ECb UXJr9Haq2O+/+O28fSKOjve8/YCqj9O3l+JIjpF7lSdrwu1wrqadG60iFR94 Igz8jrz6I7tU4URWnKlXepCdQn39UBXgktjAK1WWZOBf9PZGX0B2RauP7RIH 1QpHDQ/Eci7JiV7gv2wTgBp63w4lhPOvSSKFKFTSOTlNxV78BZ1Li3dOjnT/ 5kjWP8PXbHlxhD4xyHxB+v5Tu7RIUaVGjCo1nEBAK9/PiE8pTnCspiIa/00O LSfbvzW0yUV2rqZ0mHKwB8cLsXFW6Pn7cazxrw0DpTT3duEdIie58UH9Mk7h 6tJ5Xgb6MxOHxdFARdoaK2XnxAwZlf4Mjss4LnHgZ/xFdqKhP/hN/m2uDHJG t5rCNMLPdpW22TpQEm0eji2qisCQ2jZIAkKBDnCOEyBMcvVtQOTke46woo8k 015EEHNBBvlJXJFbKakWBWippR3qEdM6dv6QP/hN/n1Jqd0gNihDreJ7BjVZ Odcm4kz1QRVHCmDAGXEuL3AuUrkH8P0bzkP0izKon9ulHUHGohFZb5zBMYz1 s06s1KqYYhBrS+/p/TgntniHytdKR/SrZY3V0ctsppuq4GYnPYxxpokTm9Iu NggOSnTt/3kQlJWb+vZgV+XZ+CAopiNUBsmGFY7CIoPIqg4S/JtAR91b6uiD CNfy3X1xjnziyRfQn1aRjlIt5y7+rcGkhM6xqVy1YDqeK0psMmM8jpb6eCd9 ZJ1SeKOOhWNa4McTUZbq96Qfk2or3jvBSSenO8EROkJpvFzgb2oZOlnTOknX bIqO6whlvCxydO9FMWLB86ILl+4ZPl8kOqt4nyrmc5BKjKhIoEGmE9f8kjLD LBOfobmej+OCnqPkESfEgeSzWdZ5eu50qeg3t1XPJFWUq4f2qMD/LRbRfEYd WJbdn6o0OBZxgYS44WVwqxPHmMf9ziyU9L6Vuyx0+IX/qBX3WOTgeDrHnoob /RIlR51ADqJPlorxCuXfbid6fFJFfJ6SZJnzwBvRohG+D3aiJsVKizRRf0rY ZD3Y5PLgyJPAu5y14TyLyDSLh9YrhdWxv/I3xktqb/gb480RJhzSMZepHJ/T sT7sRB9Xquiv0gE8q81lqD6qglGNqTGtVU7TzXkVSvLlJAGDtJ4x+0+FesbF gCZZHLsf+2fd1fg4a//nx8nO0+FrbHqQIb08oAcl7LwO+KBnVJLdNlpvKVQ+ 7lJ+D3P0M9LjbK5Q8nQIY+YYR8bGTP3t6zsUXAHCNW1ZIobPp10U91XjbeZf HnNTpXiHmOkkiPa+R4wOqCA/50TPb+uAKuy5TNH7ocpkA+cxOJ7AccIPKcBp Q713rqd3hHnd9HR2jNuFyugUIQRbJqJW/T+Nu74KIPu41tPhr4y7nvCb97+o PKb60NBQrbprmxbFUCOp0E103Ll631m+wgaZKueHqWCUemnXPqRLm3yGZrab sr2bZ3UQ94kqUEudGsS/TgJ2bXiMBLlOhrpTuz3ESf/YzP5AAhH5uqKrE/R+ CuoOJeNgFwIPhoVzlDTl2kYf7f5I336xH168H21UBB+7hwQUzYVOotoCJTPl IC3GqtBOsGgtXWS2Uuxffr+orE7/a9T6O9Ncs/2WFHlCqcVzjdiBQt/wNieG ijxvqO+rK+4v00WHVD0OOdHkXG1plT5ieDlS3zJP2fyojTFb2iqL9WSMPsue tNe/1ypfJiqNMgKJxukt3WMBQHM9R01uGahEo0alphWqpYicJi8x352HUO1q nGBpQrCQ/hHWL9W2M4V2HLkB62S93bpX5DRCxIgP4/s1lYp87Q8J/bh+kqJ0 nLbod0raJj4+OcJ16sWTekuZNmO+pUXLM/xjvI+Umey7uddVjzrravfK9Hy+ q06u00r8egppafLIwFgT4jEm/S2ZS5OXT3MW4lcnYS6VQ5SrpY6nQG/vqn/T QaSt4viJz6ecGN8VSgvysY6K8cuO6RHJx9DZj3tWu/XTbBxp2M+JH0u8ImZ3 dJKZIigM1vu36vsLgmM2SS+1dNWxvZOOkM39wL8qsqy1lUOTYlxrG735r5Dx F3apUvtXFQOK3s7UN4jkBu1cPHDI1L87+PvZqwNRoMGeDNfXblH6Fiu7+H2x vk6xknpqMZmRkQqXq6L0lBOOD1YRq9TupEo3k3y6M1nEbHq86+mCtZX6DhrD HiqKrWNKXulCwqhFoETwbKoT87pItxLz50ak2dpCkvauUh/PVkKMs2ZSQ15u gxN3vdBGkS4Gnu3sVEqQIUS+i6b3WaKMlXqecHJCidzGiW/HzMk+3l7preAu 5dMWJeBgJbQ1Q34P1cfHuxAIp8bynckqXX2NcLUFQPkslZgGrb8S+kfOMs5C lkptOy/W3l+i7oeefJ+jbhMXcIJdoRvWTZsoERGUZhrKKykvzCsxdDYdIiQw o0FbQrvdSNs5qDJxUckyTcnA75b9oVKV+3dc9crX2gW3dp8TaDPZna9ooUBF Bq+KUfVJtrLKX7Z8iB0pnsjeY6GztlTZWqqXKUpUiq87saU0YoT8Fb5jE32T I+5pUuic/JdJ/CO7VKbDPePbOaOsygspcULldb2pTFl7W8WpoUrzM0rjVjHp NJ49oLQ+pLTaqdcvantrlC+EF1q2W45pgCTaDCGo+lsMI170onTAiyHnTZjB q1C+s+18ba9K31/LifWr8KRjo8/69k5pf+b5b+W+Vc4v0Rej2asvSTdSpJaK DJuhhaBPXRwoSw9vezXKfhC/pJRNsRwiiXTXUV9CDvEFPdfKfzvjAXKTDoC5 kktKVLoHjNiv6f0X9dX79dwdJ/kTTaKLYuSIsB9XogxQQjHwfFWZy7/pSy/V azeVCUJEoXdOdR5aLp+vGq+3Fuj3TfpZVz95baOSPlVYtV0leJ9/O0ct13o6 6e0bSusaMXfXWDlPxW2xfs+IubZlTiKQwIxvGpKcVQm1w4hIY04/a5gf6V3P 8pAukeOMvzfFJ7trCaayiRtK8+VOMPCMNnlW6XrN0zjJt1pLxMFcJpvzpNV4 yIkOWW6cvu4CPnbHd4mc5xsznarFq54cO2KcsGRZPy8sai+aiEYZ/PDxufq9 sQoUBzFbrzVxQTvXqVAx78WE1EG9VqT30tpS0zhjt1ZmPqiQtDGENoIAM9AE anPE6cZa/pLnrrrqMWWvTwGDErldiHMuTXoZ50ZDL4eRUS1xYRZFLA8fuhtN t2r4E81qxLMYJbzprpePM/fQ82Wl0avK1FdjdpiwTsNGFLms919wokiXVRCu atOpIgt39SD561lTyeKecwTFdk5zJxZj79DnjkRsjnyohnrpul6q0KaOqCz1 VcFxeo29/pUTfSSbKk1xssKMsGX5+Qyhhgo1WkfVO5YU/T24lRPn1vvGDo49 kN1DXG1jR0OVuZoxlc13Zi2CMBQrPeOTsZUucsEiVt51QTqaqrFI//wAliqh TsXofjPiiSpoupwjIU+ofBrIFceeu6zPFcTcOqLQvhiPz8SczN5KPEnE+3EQ C7bF+JEXE+CaKj0yIqHMsPCmz7MkpkG/jHiikw7v2qVMbfmcmqBsP+K2oqk1 Y0TkpBijp10umG+Twj0RU0SbeY5RxE4Vsxr6zF1tkyjI9pfofVSWKy5YnnP6 ne86rcMmLD6rxO4ptx3W2wm2zylTCP8btQkCMLFqu14v0NcfdcJQDi1TX3NU r/F7ihM5Y2KSXhw9HWrMPOUGuWLTwr90ocyAjtgRbeN+JQOdrzT3l7RGSQkt eOiHBLl6xpYqbQb/HYhzTLMr7NltF7SpphL3ts5IpogHYK7ZSxRhARyjS5Ao FZOWYlbL9BqdATYxVTvCZNoGpWWV0qCe0pGsJCbOV3aSBsf1+fPKl/PKKzo0 25RuZPFmzyPla6bcc0D5cVXbOqvvedG3qba4ljgiLPWgktV3IVFZX9/HSJLO C43tNB1mverg55SVTJR9piKSp4/k6VA3Kytpy+nQyAxREsNsUjTOyR7fhdOR FzLMDMg1Jo1QLVbEsf8LWEuTv9eFxHUrZe0LfGNDf5tNiDVUebyrtDzj+RDZ FtNe3kbN3W0JyTRhs7k6Jc7ieSFRd/EYqpSibJ1K2Fm5ckZJwRDPfD5KxTkX fH9O037NiX+/UznB+15Rjh5WCSFXzYV9wQWLeluvUVLpaVCbmIZZpf2qGyPO VZUQ3rtah0vJJJUeNOnOFTaTlf+A459x/BnHV53AM1lNd5reDOM6hqok0Swd F/v+1/g8QUUnOcbn0M/qfE4Olruhdi/JWHbL2Yy70PyKv3bIyzylf6Yzq5hs DilD2evanmFzGu/P95f5BDPBzDozLUSsaqOd7CSu3Sol/PNKREJlUyXIbmUO g1jzPc4o0f9JZYidpl1coW2d0N7wnqeUeM87gYgXlFlk8Hl912V9r/kxr+r7 d+lnF2fBXxIfYhh8W0+xWfPIrWzmfSdxxipnvsV3vZ/BFMQPlBo2O8RXkq7H tD2iRb3WkJtX4Ac1CdMFlJ1CT9jA39gM9z7h7zcsoGikoviKiqNTtt1Vaj3j 2ZrvWc5Jtqf1er7vnopDLLd0V0FPHqopEGYuxRWVlfM6lrsuhG2ZJv9p0qn9 sY684lkiwFtPyMJySkt1MGszT0lEDv3OiVofUw4d06Ze1uZOOE1FHvMYdSXW k2P6NnLroB+Sf2sSvxHHd6hQPRtriq+lXWfeupM++yv/d+Tk3o0YEJwigsl/ wfFzJzK7UClOgaHZJWxOehOmrouftvczSaR03aidUFk3UWU4P8Z0qk+jL2B6 akiJHoqYroxMk9E1VGZytEc9r5J8LJ4WokMehJjZ/laxc/nS+R0uxHVshX4J fYsZyi6rmJrmQkERq4Do3d/UZ3cqL/ZoD6gla5RQVNQqpft0PUeJ+imO7zkG scl8MTv1nLJTdDXVnwbHv4Svr8cGckN7espzPYlspZydVDG97IIbw9sIUEtV QCg8bzvx1tkppl5oRjs68y1FCOoI04ngz2i/Z2hzlDv6H1TDaQC6R05jrK3F N6zycsUBSarl0XuZnyKizWcL/asjTzpW9lqN/zWF/xQZigi9JSYsNjhz+y02 9amjJKJPpJhTlOzndAjZ/kbhfsPqMW5j5SihdqP2sFzFmrlMTmcQsumEs1n6 H/Z8T+1HvkrLQN+PflFU9aSXBs1nqi1iW7/B8ZazcOiUp87pWFvXNQ1UIEp6 U6XjljKCIzupfOZjV2PisV75fUEFzizFK04w5j2lyCyVF4O1Gzokts+0K9Xu UX0XTTwtB32oozFB8DM8N8Ca4uD1kT2PBObST9cS0QyTgx06SlqTetVv1apq lYOv+4/a4kpayEdw760d7u1Cam6oEmOi70SoWoiHpWyjVOXpZacGP1na6uZi UXFbkVIqSyeVj4P6Lioa9Y+wOFL7cF4J9lyMh4d05EzNMIqxKNt8nxXKGPbt Y5WH77hg5l/ScY1S5h32TBDbkmIRtsxynVQpai0icU1bOKWt8Bwxar9SgBSi d0l8opPwCyfSu1mpeEjF4I4L8HlJWXtKRWCDilCpitkFFSd+7u4GHwn+WYNO 0STbSm2GCkMFktm0DMN5kwiaE0aG8Trlei5aGhBV0dD1ljjNbYmLSnqAB3bK DOYqz35ld205v0VZ2tEFX2KKigHdbdpq2ozhfmBBVFY5SxmEIM9ExSClWFm7 XcXukLKS+jfdhRlRnhumrCUhx+qzs5SQJ/VeMoy6RyP+OxWX/yX9kti0njD8 tjLhhIrdMmXYXRWAjcq4SzExs3CDz50lL+76XOZhPU0P7xs6ZIMByoaZGSMx rzP6WKNDK1QSPgOZXFYOdwzHThxP4RjzDlC2jNoRVdORSgyQmPKU5NjnhYOU lRg+CIfN8HeKCUc/pWLWPcJRJwhHgT5GTRmnTd9Uhi9RojTR161uhFe25iun aLAnQlKu9xyy6FmLG6oJR3JcOPRci3hJjwjYEaUmn6cOxEt7ZinzOilViR9b 9T6bD7saEw7GYv9bz1PAjqowmV+aJXLBLtxxYTpuqwuu+22ndZqShDO4j9Qg TSLRn+H4FMdr2pPdfOSc58RNFY2L2mESffNi4ACOtovp/S+mUz96PVi1F/iK YyGOwTjG4Vi8NuEea+aLYCMRoXkgDnNaZqewPKpxIF2oWPcpTel09w8eJp22 La46hFiyCf9tjktJgzBfbLOhk1Q6iNSv6Cdle6b+TW49CRgoLkUP80KRB41Z nrZxSFWrZkg4WlqFaGM1W5Yu2udi2cY+QVgX6HUr5jyvgkxurlZh4bn5KmDL YgLCc0wR0dBwcvJtvTbQBc9xhp4janxFWWv1KydVkHZpX/i3pQjJ57NOFMcg gSJCV/dbep33L3fBkFuce0zbkEDLF2vsZRpgAgShF47lOIZOCrHyLqUBnbMt MyEsq3E9V66nhHI2mhnqM03PZk/asEzE4opIJ7UGjE2kavMkK3W8S0xoGCPS Kcq4R2i0yd7xJlNFne/Gjpf8nVOi+49AWA62CakKC7v5OUXul2sZ8t2AlvfU 0+4RWvK12U7CAEMWHjQrJ/TWKTHBoYofdGE9AZvdpgK0MyYER/WeX6jgkOEJ vU5fwZCqUhl8U4Vmq547pOeP6jmbB5TsVBIvMu48p7deVtmkI7RKH70ek7dX VL74N9Vyh7P5XIWrHOniEZUV3s+QbK62RxKM0q6TO0+W41oVmJ8j51Kr1+hd 1ccKQkVFTHQi7+aCtjxFCS4WLCrpX6Xswn+b/rLU+NZSTBRooimRjVUanzJR aCF+GyFtsA6CODRGB0vaUurvKN95jTEtJZve6jRtz4roiFVrlI+0KOddmIeL W6LToTiAoqJ1nkLz+4RvB7XpT5Rng1RULiqPlmj3biqfb2gXqaCMWl72fGab F53rIM+YR0Pv51kVP5s3ItVNLE/pOzmUFa56foxMbKniSLw5p0OivJ1UEvFe zpdaTTD7Rt4M0fMkBXFy1uSEe3AXyJFJ0gTjRBwnvlY5qz9T6agVl5hgTmYr rY7quTrKni7VF4KUeZE0qXnH3lasjCnXlu5TzEk1B/cF7QbjpqnKZZJ+pz5A cjG+vaRsuRmblrNZVcJ5qd7/lG9D63EzhSzFSpr9ymJj1R1Pft57ycfaHNZm vZ9DP6P3z9Vzy/S5U05U9u+VxfNVfM4peyYp28z7/KqJSn1h11NKrx0uwMkZ F6KfTTHxYX/obv7WCdz8XNskAu7SZhuFWcuVOkybSLmjkkOlm4MhLpwiZGLO 0UI8fhJR2uswqYQ0HcW/xLM5fEOqp2aKjJrNz/OClmRyY1ymbI1VRoxxYbqL vnKWyk2PUHUem+aKHCCKV185ty4uSxnBFeVURIl2gufS/fdIKi4rB45rJ+ht TXS2gg33DIKh7gzFqGtTKyF+LNHvfO6KC2vZ6KjTc6PS0bOz1SVLdZDHXfW2 4kltZlYYXtE2LYhJ1So9t1s5fU0l4A9O4LnEBftlVdHH9J7XVBpopxZqP2gs GHywKG+dPkeFPhHrE7+f1fe/qO/lLMhPHBU8mj287kTfJXUbedI2xX1d33Aa 0DfqvDyVLUSoUGIRvC1e7aBSsXgscOAEZCrLFlBSjKUgvK7QlqcXu1AY61xg O+3HcKWFVczYbJpE3kGyiAZMGybHCgxja9urSVZO9SDH6av5fa0Ldo8aQoze od2UCiWyOMXc5cNQxKuZQiMru+rvLNEkcD/KNxFKJuvryDao1PFV2foa3tvZ hQmeO55omnDTigpy8Koet2I5558rZ7dq25SWX2h6r2Vokp414YzOP+ceGYvT GpYpL7cpkQl9tGqvapO2mQQD7l/pPRTMT7UrFMonlVIXldcykavilBtKLIeq KD10Xig/RtujiHXSvrEv7ZQ7bbQPzR5LuMcvh2riF/3wN3grT+4QOjmRvVrb WqYc26BCtEXHNM+FZcYV+p3KSSWf5r7QOaq2EQIFreQeoUquthgm8vR76rkN Kg1Dvdzc9UJY/giAMlMsxEa9z6o+8pWUlgHorG2w+/SDc6OZxB3+PXNcMAlk 02Df5as+TWtTNnbw3u36Nyf1OOeyT8WCfeD/J5XkfB+BgdI8U+/l8Im3lGTi GXV0jQszwyT9CR0H8/S0kIz833TB+eGsHVcsH3dhNpjvt304nlepaSLSxZTP IpWaDufFZ+JtlJ5y5XiFStYcJRW5aQbnPkjOpBtyjeZsmErDdJWQvaoo2WJr qcsTtflCfb0lo9gdW6f3rDazMoRhdDs4k5oWW9Ub27Ni7V+WmWjhqIHRXmdZ 2DBFSNpKEZMSqLkQos0iOKp1RSVtosxkKVNHQozc4YLtIjbS9FtxBG3fS06w mLzo6UIqbq/+Xa0/baU/s5Qi5LXNSP6TC6VCHfVd/J/OeTMd3wUXatht8rhK zxVr/yv1XpJwvQvTCXTkCT3vOoG2E9reG/68dK+bhB28fNuF1DFRyAoP7ui5 GzHZssI326DGchg2M0pyz1CSJinpOMsw+laQx0LtrpHjqj5LUi1Sclre0MoH bRg8n6okOOnbSvNJxxTh1GPafHqYufzPyVe0kDBFX9LL8zMSrUsunueQyN5X SDZIuK7J0r8hi4SOFHxbKFFH3kAHc5IL8xDnldS8vZ++kffcUlIe899DEsFm jA2l2B2qJiXypt7WRgSLQEJQO6yftEm/Va7m6+upvgQs2jM6AvRWD7iwYonT HrRXdfSTgkxH7B0X3J5XnYAqPz9UgTsW6+Y5z7VAQQtO79p8f7bYWErWiPPy 3IsqIXznWZUu9nm5joeRaj21H02lvJKldzSLVlxxRqlCzJyrQwiFtDI84vFR F4oreO4+5cZ6bW+TXhvpycMuN/XDsJ0QnvLSsirYvrB9QnU5SxFyWna6jUqm JkYHK7We9hwJZbaWI3leWyV8UMBbLIRATfRltsnmxEtxDMQ5n93sPZFGtKpd 4IIBdC8VvLsqGaTwQ5a6yBKyHdPDcoHnnch2qG9JNv+OgkWEobfMuW6rb6Kw MAamuaHj+5zV7aQLF0j5iyow39Chv2JD7yevo5BxsuQDJ9bXUghXlWQUii0u FF7sUk7z3c9BX5Y/Jrlo0xWDmvNKCp47rUNqoBLANljLVPNB+U6Y6R2TKkqY LS3g9bouWPBDOq6DKlX8nqnXW6lEERbpTDKuDzuMSFuyAwiFomlIdoYiY7cm nn5KlTl1zqFv0L7VjyFYgY5VojEha61Q2WZkGLgYypMhBZfns0PA1rQeXO0h SSYUL6fKZR430kJF2p1YU7dVy1Pk9F7t3EMuasXAwgAj3gpFiynn47z9iO/r KiUbyyiYJaJDRCfpZyq5l1RErvjmRIDhxv93JxC1SoX2lnKHBsyq4vgoFwi9 oRK0zwXP5UXlWBFIsztDKHtEpY+uLiV8XSGk4IjcT5tKSR5pi1TSxNbfUVLa 88UJMQZZYYX+wyrQFiVa8WqF0wmfuz4KvOVCVamVRVgi2SK4sCQxLB2nGaYn mmZ7QHyhOL1rIlNXObbXN1/N17IIlx2b7arnFcx3Joynw2dviXaTMkJa/AWw MncUnIUs8ZfHxOahpuorF7lQiX8sTMjZfgDnPRrKOQrKN5wFdje9e2flLS9o N2go6e8ybLaAj9aNimsLsWzh9K91xFZ1YznOYy4U63wbx784CSAvenEJ+WqK AEGEDvFOfN9fQ9qgKWH2iP7J5pjIvKrvmuVUMat8cMY6laddMJC3vMSo6+TE DLyoQzEsOq2SeybW9dNOFaiOBIc1XNC3LSoftJYFSgJ6vDQzmTF5SY9lDT4v L++bvGS5UFF7NsRzJi+rnBi3KSYvDSWo2KjXzblL4nojJVptzgZOBjHTgu6+ rG3xVdNdKKm3Kfnr/jMICOFggr831IvRMn3JBc/pOW2P8dkm7ctu7S9XwBIr /lXf/bQLO26wWpJVsnSZyHhajWUuYM0GF/rNKqEv81wR7P5A3bDhgIcom/C1 ZTP1tEtVLhTFrVKZGXtEYOuSMvp5F5YXMRyhwWqlwsChjfgU7eaEWmaSiFAk RZEk0B2fdH1B32lrr0zerzlbdiI+ww0VPqqoedG0It/T4T3ryRNtgmCLIIZH 8vJtkxcryZfgPXIJDbUiOcmsbqZMTqxTd/1neH5fM8jVMEMbOWeJqx2x++zZ 55zW/meKFbd8JvthQYZNai626cB6obrV1uDk63N0VWlHvukkfjuvcmJlGaYL kmeT7jQSDEvorVTqKvB61mgxJxv0dss0WZduujBna9lsyo8vosPzY47Y/gLR 6oYjelvJ761wRbtwX4A30worJrio340aodpLvG3iktVKiQ1S0U616FKiz4Sn TtiDjVo02j/jVotsfGDTtw11FHG3pZpQ1PrbQmGV/cdzE+7RMRHfn0JoUd7X XJO70a6SoSpVHm87W8xSnUAYW6421IX9w2yyw3hSz4UFSQdiy63YQ/JSFkUn +T110kRK2CT9ZYvI1/NypQddS1CR+0SDP7jQmzNOAhNauoNOn6lXXUrW62dN OwchL6gSkGJvj0BK2h4RWtqsvkQIyVaaZqBEidmTk2R7OlS5IPpWuGcViGyF DoeFWMYhOtIUFq60OqDCI5xOsgmMMqWFTQny+2mVnbJghv6G6LC/lov5nOik VhcdeuFH/bWauvgx4e7fJiBq9sQXlt/xGVnytnYPuDWzEZu1E0NN6qZylW2S qHsj7QKnIC7BYStdFBLeNFk0svOdQMU5U5a06rSiEa7j6ZtkkS1td3sXkIdZ CboNtEh0Kf4njn/D8e/69x/RpbdrCCzTL6TO/hnf/yvofA7xzPS7YhpeqCnW zorodpswNRFhMkEitNOSUXi8vqOtHLS1EAyZNgCkeDvhJs6UWItxEU3KvOEJ 13EHPL6OYbHbRpWmCzkylo1K/MzANDPwg1UE5mRKV8sv896i39N5yH1Qmqqn JLlRRxaJta0tykwJpLUyWSFFH9fP3irvJBctJ12qWvrOXD/8lzxHbL2IHWxz qImcWDLmvkGIFIRCTWpJ71/R1j8neo2rix6Phk5SAVZK0WuSfB6L9fKqC3Pu 5qva1BoZMqUc9zWS8r6vef34yOPW950wnzL5qLZl5aRWWWiB7x0XUDRa/54u Qne/CpjFcGOcLem96an0qgupV76raBX6Ui+Jzrl7QHR3BoY+eLGuSuwFGPxl wnV7AbfWD/u4UR2YnTFTd11l29ZHmaf2IgThoaWQuX4J1xft9H4u4fq/BXUE X0Zmh9IE28/CJmJ2wjMYDQFpki3DoIa/wqIVgObl7Gha6BVw8VKeHCt3oUm8 ov2gJB/LNVTYdGFejRF+U7Q8/A28sYmI61llEBk1XoltJajM7G0Bsz5B85+m yPQSw9nDsc1/0lSsB3iRu1fcktDJpmB50dgwzqFfJG615a1bXYjK28dEjdE7 2X6WU+Q1rR21ihniJVN8vqo9pDj1UZq2CmnIOy445mQlPRhGSKwU+xFv+4En 6zUX1qxQ0Khya3TJXoEww+I7m/61la7bXCgEuISOPp0mKfEWkLOJ9URwyPRf 4e/Bq+UVV/U5xluTB8FPhZU7DJKX5wQZJsrs0S6RrctU+Cze4/mFENAuL0Ew L8rASaRnQPhVlRhxrRTzHEmAEb8UkPImEF15AcdavLJ7FV43x9dQ5PltveYA BnBvfxzrZsjccO1QCmGrxY64IKZ88/1QpWUQsluNRLhesnrhLMt5oI36Irdj b8ia5vRmwjZWCrYIC6ttdxIa8SFUUbGqtFuyFNcNE5n7jl2qpx26G/Ogl4P5 qTCJ07L0XM2Qf6Z89dUBnYRv+WoNSVFykG1w36ihIWfIzh+F0h3NlqiMMGYx 6BYVrOfrsY0wi3IFT2+rG9ZLUhufVI3j92dc8CFejfWHWMKMzSFtl5+2XQjf N1LFbIXeT+1dCSjt8yT4W5xwzWGHW67HuNGfNDBiB3pUDDH8k6Mtlu41kW4T cXeOACDA61kBEbicq0NRfhIUGFGt1tft0i5dh4NyEAHiuO+GnPp1JetSeB2L l0h+/HqdJHNQiXCPqfTRn+mNHrVcC5Q8mnDbu4AaE8T56o9j3UbBIckKKdDX DiW0tizEqHZIlYFI8AYcnmVQhJOt/JZmUWnkUFDlEN4z9aC8p3aPsJ1o2PNb 5I7V1qNZ3CiCFdtpX2XumzZH0jH2ZLaz9YrSM/J9O0LnvmPEo7c0DSP9HbHF AaXDJVt51wmE7FVi7obOnmos51dC7uqC4G0KfZYgyXZRp6meCabt5u5TQ6Sd 6/r+fVC6iY9aJiy4cOcUQmYoFU+4sFCtgaAWL5ulpchxQvG4nqfI9QATt0C8 KhCkdN4CPUbg/MAmdG8fCNsNzK2A3KSz1hSwnK8LpSSvQeDktMh+lf7p5Qm3 AIC4vJEkwjhPwWmeOfq6fUrV/aDXMpD/4d9AMz8BvyHpxztJO7TMS0GZUbuh Deel9v8aNO8keH60T8I9Aqi5Ae09UQTHBs/lMk087U06rKP3hr3jzKuj2A10 YfPN0S4U4POcpYOYWyhU8aN8fATxmgMMH9FeUI3zh4Vw0x9/M+HyhmiiO1XE LUmfF1ELskbV0I3MhseTVzliF6m0NkXZVplBE2CLr2jBDtcKifGLEX+FAdkh c7j7nlEcRs8aYQQLHpPzjO4bTQNcZdNhSDJdoV6OBdt31REDJtV3EeDaVg4v O10tmB6itiMuGHZZHaZK0ClsMGRbPAzWZ8YqbbuBAK2gAOMLBQxHTIYqD4Yk zgCt9mAUB0QiiUDNYPeGzRbAu6Hvo6AzD0RnjU4IHY55C7g2APwGnybUF8ef 0l8Xzt+qYhGVNaUicgP/VfjYG0flZikjfkAAkshDsJyNGGLvQEmJDPhVwjUA ou0vlhhmGh6beStkpSn551yYtb/owgQimdFYGVOuIpLqQtTPobBAkMBMT44l qRch2f0hyt2gSYPxrtljbSMrBgyRpxzfyZLL5zuokk107kGTwBsu+i0KlcD3 DH/5KM1AhhPeU19tX4dj2jMrvpgD/N3UVrfqaRD2humvzy4EFqxsIRa1qLcw exN0dgWitLJJfBYWFscDTSTIyMXnZt1b4qyTz1NBqm+6sHnBVSdW6kU0fzg/ GHvz/qxseasUGkXOJLvPYi1Cz0DI5LJeEgNPwKv7L0iikDG87r8QlgsAtzm2 hTJFm5PJRLZ4hQ8nKCapsjEOHg7RHAE0aovP4WXgbxljDPaj0w3mtx75F2D6 nyCap2F0AZ59gfGLWgqT6DcsMjlBl8YdR1cHyvvmPwtj/idxqNZk+/IpIU1z gRNC2AYdmiWermhfaY/oVnLqoZeqCOEvxQV57aFjk5JpmZjn6qpFe0ORGOV2 hghTkl9MkirSYuXIVMdpznX+AkGLCm0YZopNdQPicz2ZsmKFIl/iwhKns7Hh LLAhpwi5ieSyDUNUqnZNRzxKR7KxndijBiXgUpGUjE3XHmdCcMb0luZpR8bD b5gCzV7bTHJze/wsV5j4vqOCd8XZzLDOo4F7a+YRGUJux+agiNgEo+xYEm6p dv1lqOuuTtL8Q2BiI0FkHsPRXD/g1nhgYHdY3w1Nw0ZstsWslW3ZfHxdPT9Q mc17ibEP65AplMMBVLMheNn1tZoCw+36dbjfv8awYUbHAFQmwFvsvgLYmy9C Tv2g6j6Be7u8BfcMfduTLXBQCmHsAjDqWUvebRGALBEl6S77cNUw2vJzZ5S7 e10oqB6m1++oMrGa0HIFP8ZxAsI4bK+QjqBKM7XZ3pFSXWp+H2SQiRU6te1d 2ICdJLIt/AtcfxHDt+yJFiqCLWIr7jrrS89buKP5u/igJoXtHzYEiaRtft6F Cq+2s8VXHKZPDVcpoURydRhJTo9oyXj07UvAC/h8o7QK+lpEVlECPk+7dM6/ PZrS3gZOzV0nl62SynK403VwuV2SmZ5nVQM9jFNAofFjRfjWtxUG7IWTM3AZ Qr5dsHZAxGfyxTGaDkQ6WFeoyOC2SIVxv1KaI2VAy+DW6iOLVZA4t0GT1Bvv GPSWGOFNAOJmhTJZkA8L2+UrQKMPMXo4VtOhh8VQgl4w5A8/DSHtKcK4FTr3 6DxiasI9CKv7XEeJ8pjReeACUKtCBGG6C7bLhG6xM+scnBqO96iz9YiXvTdm dQSrXED880pSZiPuQiCL9orTcVGFtkLHPJdNT/aNUcadl8kglLGtfTPNZyQ5 Gd5Ql5d4MrnieyTTfsZNVu6UeYNPu0RUWqYqYYxe4DFXkkk3tIdmFvn3rZCe GfcwGusbhJmDtD1FngAqzWkvKDMSJm0UzNDRloFwJ5xtCRTO2W6Yd2PzJLTU BMkNJaJIF/Q97NouCMD8HEGOJUPoXiURmpwm6FnvMBVRTm+IdTYM9oPo0uLW sEg4ZuD2hZDhQzjXD81szBH5k4WEybaN4n7tJZvbDOyb0EmXLwHqe4PK3XG0 w1GK8GMYQr+5wN52I9EkXtlotPinff8NhmsnnoHr8uBmUA2e9PgCYM5+3I9w dtkERuGVnJYgVJa8DH5A16/WkuRdMXo24xlI+mJJb1gFJSWUsNVSiW5LhUzy RukSmYYhnfCqstDWAsYXTVMU9kNJDo6Se1kZz2CLoEbTTymrpRJpyxN/73Nf LlrpzCzrg5oITKK+RJsyt1XZpMr31EHQWUEs01dkdaU2dP/9LtG+vYj4EGu8 u7OtFEP9OwfyhAsrYe/6gWWYLNks8GWbSL0vQB5vZeHPMnBiS+sQ00wBYCwA 9OY/I4Gi+Q+2asxWa823NGO20JDXtwwUN5+YyQhiuwrTNshvtyGSaqpwkiL+ mmrho0LqTYDeto8K7qVCCuoheOlcILMQPvLH37uAW6shwwfRzNM90P2M6js4 nEaH1vYKa0GehvBOhFDOgHAOBqY1XwosHi0RyDZcmwz8G/gULAfe2ytDMkeT IGGtEaY/uB3XSyVw6gfVnYP7H0DoN2om2riMvy+CBMW68BFB7fDlGFodyQqO 0l9h4kSZVxQXpkIPqNiQa4xSpkZcjS0wbqipJxcSqDb5tFTZQHJvUxJ/V8+3 1rYnqkRYrSOr01L13XFx3asC6GIrk8iavf7gt8UeYse6EOjbzBXt3jrVOtj6 R6oLcBleP0UK6txQcxy6KxmYLTvO7FAQUqk40mgIQroMgnSNW1n258Rv8DIX wOsdAVwog5Cd6CLnV8Aa7s8O2G2x4l1niyXuRvtpHUIb8yolDrV80lZVkuJ8 saIp2SGjRptQOVQsLzl9StV2PELpzjg/u0BWmC9APxcUcwGx+hddJadISTul 3q9fOou/O+0Qd/EnaGMK2igtFykrgXTOaiOgwzm8eYiPx1dB+tB+Be6fhmfX rhapaojnHkfbBTukf6sKxXLOwPhWcnYcz006BjDKkz5PPCKlJ8yF9teNMglm XGo7e7Wcp5QxBrOtP1d52g31PJoF5T3bxe8hnexnJ2oFLTPH6CVVbAYd2/Ok 7adwvDKESh9ND9OheVbJwdtJ2rRIOKNIh3Ksv1fa2M6lB3lN/2syHPuNwlT7 udRzkeyGWGKgcpNHLlf1879F2sr06aGVlBguU6y7dzexTov//I/YImre+WDP y1xkz+ua67m2SMZ/JZPYG936NKSm42VOTITHt7eCFNeKoX5GcBHOoan5PXXR PjBjGTBo/4BkC6POKcyweP7BYcy5BcGuP1zyb4xhntf76JbtzRGP4mhLgb9p CPFWAFIXlvLZWaPpIZ8D/G7uIe7n2YwgBLfQnfU4dwJwunCG5AM6iRe8aIjo whjaduZ/4CaMuAnXAB7gLN1SYMpSmX/u+R7oWSaZjuloajheN6VXWD68YDrk FV7qkn4yNcQSvL6FYngZUs3CEJoMF/2YizZ7bhCZnOBCfT51bFI/2ediBa6v 3Sx9ONLaiwrhwka1wGClqdy+YJvsnDEPXXhur4mXiGSFqgJf09CF+ebfx0J4 RpaL7xFsZk3fciZQco5BOMOaB2KCze9L4yKZKhm5xQrEPErs/hSZfKRyrPT9 ebi6gI/CG1TIpTQ0ibJdzd2g39oi1h7RmwaLrsZs3UY3OWxREp9ZsrWY0cIK /LtlEGzIQSK3ug5wjVcCQS/WEjke1Vc3CIM1WdINQoTjQAfJWXsLgHv3loSs C/MI/hcyhklCmeSuhwHk9BfBJqruxNA6DJFnzsLmbESs8xL83a2tQ6nMRPzd fJk4p+cgPYvx3gpI4LWsZFM5cufX6OZUyHeXYpF9evY98Nr87UmcM2Eaouh/ wPU9CTmEgPS+KfI8cKKo2gYYx36vogf1xLlqPwIa20ms//rWguMbJos7/MRc 8YGbQ6OHYiSP/lZmhuYUiTdDf3jwP0M9V8hcGLH3aE8R0IF4vuAuuvoWLPeP xV5Mvor7m4owHo/h9q1MkftVO0So9+l8NHNXm/ZaBmevZ/4bqjssEzxGRu/9 ArAV2bGfrO5qv0ObGpdxOVr7RqWND4NORLLO+c6ce8C+tepj39jaxBEq33Wc 611dwFNSXGLwYJeoqAhdy88XBO/WLbxqKkQ6WyYzXBdrtnZ3yXG52G+p0VQx u9Ax1k2rOXvJBY+G3JAcmrri9SWtsGhdCBM3oLMTILfjSsQFp2BVPIi4DPI+ BOfmt5PZqjUwSwsRu1fNwudpyOodtiNqlh7yQdshhyuyBB2eRnPLksmhqkLX SqaG1uDckNqSmjuIe5/H0LZ018S8kwUd63DuJGzUIjhHDz4ls1WcBCd4MiqY Pk44yAo2qjzRblhztAshbM+uVYngPIqjxwfgFqLdnnhuZF/2ZUwhicv+TcJQ in8Hwzke7imAe2oHcWXX4/09lonjtAK6tRMCPe4KOAyy9QEETINKlvwaAg7h n3oISoJ3FpyQdz4DZVrSXki0epucq8LzG/DO6a+JEnALNG591fOg9Hd/fVEE PlOp7Tw1IuzuTWxcXy4KwuLHX2slRLrYmvLYwWIJFk3ciAE45yVe84qgQJ8S 9rwl8vyGzZ2u5sSU+2/l0SuKYvpC947p6bTYWl3qQ6W/L+gDAwc6/pnO9RJ9 qFQl2qDb/tes6RJntNKzJSLnceOCCaNulJWF106DYAxC/JCe7kKRQep9srg0 5hl5vKX3HOmL1uIuVkHhu21pM6lAr/uWC6Eq4WK1M9dFhFvuCTpDfRg/wPZx l2Is6lWvdWFxQIdhEuctABoPRx9aAX2PZ2sxSX6ItTj9PvKmzJNOeh/XMJ4F cyDET4kDRPrQJZ3Rg2waNoyJTq5bpvvKY0yF6LmvJwfP13aXc/0WSI6PkNQC dqQ/0Hj8KrnWBYLZ+Q3Q4agkqYdUiI3qCvT9OFNmOzi9SOnYiD7vgcU+gHNL J8nznStkbv7B2/Jcr4UQCZ335/W2z+EeCPvDXxPflvNPXuEKpfqhL4S5NxRn 2wAR+k1l3NkAz35JZkfYbr//kHc+h35cawBvCjHyYnhY83hvN7XvCOefnSAr sbhv3InGQt9N/eX71BfA8xuhHUshPKNgxVzfZvRlpyoWecwo5Zriqy1kpqzY WvKLHqSi8qrO2iQdwPZBv+jqUnrpj/9QEwzJcT1SQU+LBw5yMIjOCz+Ry+l9 ZgWZZeoc4J74p6aisLp23cUoV4LrtDoD4Hg9+aRLFCDi37AhvPRe7doBmIFF 6ugvA73Tm8rsLC9ntZWEHf9mzUA9OE/1QdCMlllmFNMw7rolYQi2s6b8SuI+ P44NSqihHp0yLAIhuY+rylUFLbyN53a1iC3w7Ci1fksQI25rG1aub84VbeI+ EOTmVpiUV1KE02z6RWjqyMchnZD4A3j+mWKRJJqreeD8mC3wV+qLD78S7a6B R5afLxa3RoqMprFqhy2DOA/UGbpS/Kvu0OqxePbJXqxDEq3q/gH73PXrfjZ1 SsLVAvTnb5VS31rwHJtiLP8A83Y/tK20UiSNEL+/rWrXUL1vgby70yvQsF+A 6DAJPadBiCokpEETbQ8D3m/BMj0ryRES+SjOH4S3c6ihKO5yWLIn5onVGfwP UA68svS3YommTfbR1A16R3OgLE+VSfHFBQXL0gFCrkeq4A7eb9teBMeXLGCo s2eynLNJ39OqLBNdcOOe80qW5FEzU5rnLVtcmDnJcCFUORurCWJWlcaQUx4f ueo/mDcywnNmq9y9YUy6qRmz7pzXYGRMk8YZ0KzgKbbS0y5WOsk8KeGLqb+2 4Y1U4v1fqHpUs9sIQDbCRqaluUS/fi6xdKk3bpHqNWwo0Q5Cd2/USjCozEy1 adD6ZOBto3KxYbRt/KRNoxTlgQ15T4kXn1xTV94BdzPbiKTE9Y9RKp2HqWij Z77k/HhOfcQoPnpdISeKj1LCPgfEyBfCz8bQxDGrGi13uF/U/JByeVSssOkE XrC1u5g2nzqEr7QcZm09FHgthre0Uny+w2DagWwxcT362m8PIXCeLUrXHQH6 2OPS/nEoVD4U+QGmPeGKdgYp+uDaCgz9Cb87U+EnFAKSLzlLPqlINZKkTXrx nzIC6C2KZ3vNMNtccEfMdU5fI2/C1ewkfzPF2vao17lkvy8f0PEgfLxdMD2D ENA8UiklTY/Dd5tVKNNd/XC+EObqkR+Ipk34ttSmPAy/sfRtOfcdtH2yl8yW kz2PQPOGvwkzvVfcgf15pFFYfjob7e+Hhr6aEuZYbePvHS5sj3QDIPZBa1EB 27TLdhCkie7vwhZvO5ystrMCiSbajm3wRbOXF9vYPmxpIIfUWtX/As2LdnVu r0DBAK1LcEs5G/Omq55raKs9ahlTcMb/5+W+nvG4Ki3RqZNLvArh2rTJW6/E 9eve2lm54Gz0/9w5cRuhYYk1a1zivvsY64umJaVLZSNLf6lBxONWGyUmbrU5 4RqMl8Kq7EJ1dO8LSQ4+S6N4X6mQM/57Ea0rROzMw7SKGrKM9aicBv+ZH9qP /L9ctyNryMPyVE75y087BFNp6seU1/o+oqwdRKltieapelIey7+vYIiDzsJs oJvjy+UeOkV0uE7jvjmQ2kdhE1Z00ukbNH8/Qu9GU0Xiu39Hoqf2ZyApbaXN E03E5nSEdD9wSa7T0Su6LKm01TBZE2hHoAU98M4sBI81kpWXuqSfaQL6F/dq IDWeAMfSmA5+0VCLVZT6CY9AW34ITf+dkJfLR5bXkeiseJ84irw2/QLGj/fN phk+ImPo/OWwrIypCJbtHIMUDfoD3vv3MNsXJYr8oJWMo/3z0FIA7ZIWsd0S 0iSIpoZwsuFhOL5t80KpXiMRz77KMSoQSzE5wfYD5az9KEJcCW1fiDyVmsMq JXOc7fQubeSGrB4tPGf7bC03dY+mL9l9oQJG0RkxIuGVL9g7dpPZs9b3bJ7N 6fNWMe3jNP9Fae+h6trH6I0a17WrS5zAyOl3btgQaR80LfH00y7RG/xdDJXp 1cu7pO385VqidVyfS00j0BKsCbZNEB/UHyngnw/vrO6jBPNMM/DM0PdTJeyK s8fqCvkXqgIyVd/4CxTwPVW+2/o83Yh39Bzzze96FfuR//dt1SbZHT/DYsEj vSQDcqmJzAdfztZr94uZGw0lWAmHq+NLEMhV4gTtgcIVQ5GWYBTLIfAP4r65 EPZJaOsh3N8NEV+39xKuziNiYqhsVA6LDjnX1A6w3+WrIsydXwck/1gKQ2bt gZCz9AfCPhTX9qDNPqfJugdvimcimEuTSqp2z62ucMS51jA1D7yIvnxffFWv mE0lSqvbW7wORnqDv4X3rBWu8H4qFPuUVyqKyJzhNDiapU/As4LpGXRJ2uP4 +v0vyRkO/r2MrwsA5BRouPZlaMYTUrb5MOgyAEf7CmElx26/oEN2db0k/u+W NF9SEc1A2AIHivIeVdLzpHUFndhqtbRt7lG8RfpJZWMpG4M+JnIHqyJODSGa rePl7XRpO3mly/3/qnNU9e/ec18r1bkGMZ37wEVRYg/RuVl6iTEbMyZFRebZ pidq1JDsyQtwp44dE6fz5Zcl22hm256jvl68KEoJZWxvDiTZStPHCWcqJCt1 GbKwaCh/u1ynE5qUIbFfVntFjWypSTCx8pPBXWSaxX4Sg+x8qLaY0ow80cg3 2bMv+zF8SRXxZ6qU9vmxV8VP/D3fcCH3wqN4rJQMtIqtw+Tc1NARwupWfvpf 1+1D0h6H9Ax+i+4hmAAp7gQzV/Aqugc0GgbcWY8hTYJUn8iSxTg9fwJT8S6G 9L6YukV9EI9AK86COefrCjlYRFd6W3M47bhcEF3p+TEtVqtNoNYYKCuoWIjo rDuU56EfSFNNmuP7W5LWpbKxmT/UEsUmJBLfBiglqaQNJwnl6Wjw/g4viIXs CrCo1S74tlSsB7zEP/LP/FGxErxzQrGUVQ1Fd4eWyiTtCoTpV7IkBTIEwNHr 76Bg/w7shO95M0mUlLnWrXVl8pTWbjR4/RtYxAN4f+OZsBPrhLtTlbu+hhfH aNz7bBdx+LgFEp0+TmjbNinms3I1122M6Yl14TepeI3hRsE9WlrXs7jKq+Fm beuovi/+U3MZYfd1zq9ZmbptvZvzV1zUVAkMOSJqKKcXmsUSijGtzTJA4OYM V7UdHtzN/JIZyAq9C0qaKC2VNCct4EtAlZ49o73peZo28xAUIjnZJfrDj7zF ei7chz698opctgOxpeprhriqNJ4URB4tEFXV7Cg+VJ0iSQ9gAPcNFSI2nEyD KKOtK4sS6FXT/6cvMQSjvJkaNh7i9DYnJrlVxjeSRB3bueDEvuZ58omnzmtw ih8f7r+l+n9biwr/NHbcShO+2/PXdSsOqvBhyOokeKFbxyXcgiqo34Pgfw+d zMe53R3l76mI8/rB28zHPQ/BAN7glkFAlcYzMLRuYqS6v4O+QD63gwMDPoUR +xHVPInL0jn/m7cej88FyN8nmsvmqOlsijad3x9+Rxxb2lzaG6oPVYsZycKf AjlelkXu3YAOh9pL9ePzTYWMf0Q79bJF1anKzoXl7FUaxZMj9HJ4jSHmY7CL pbCjtToLE8lYMnR2I5ahJfjz30W/xQe63QDU6AU/uNEodBu8H7dTtJXLn/v/ G7r+j5Bt+LZ9cM/LMPmL68h1asxhtHYcrkkxvvc+L8mXI1xHAurlzZaycMvr cPnDSdx7J5meU5JP4NepvobLllNz05rlGFXlurAZiCk5lZ+ZrV22Cqpe+HmT W/pJcLDffebE/zOuev7Cfk3cNiKhX13jCzXZjKFvokIxhYkaWiBudVknZl/r 6LnaTjcXSZVquuzYUctVV+cMn08dOtTbXs5CeP1du1bUOi8vchZ4mqdMtTkg mNs0noBOnoZX+OKLcktuLpcfihUl0435/tgn1pcOWuvdco4SUCNVaiaYw6iR nGbOBMGgxWqhHnHvtqrbU91l3fwUF3DykKrl15zMC8Xj09eCZv8UceQPRKfF EKfJPlmvxQ6Gsl9WGzxAqqJZLUUbS22ajHDqJnp2O1UUfNFK9CRZ+L0Ezd1O FsVui4G2WCHe4sUmYqh6bhQA2t9AXJDCn0F0odD9PpWQjuEajSa9bdPYrtDe PgCCXj8VI0VNZVuDvgbj0k7a6Pkxjo+kKpveOL8zNOS99Gh96ItQtu/30M+2 sqKZ1OH/yV+g3QSojrvj2p1knltzeDztDom97vuvssiT9af9PoNdg+JPw1EH rkXfP6PZH0nJNCPUfhjepKWw2YvF2ebztP8syekLW70oR7fpTEjYSLvZnPN1 w6SU2n4Q5XS5ZPFnK3OZqOj+phRQWRlfzRjz7Tc36JL9EcfbAKB28AIfzaC9 9cFwsq8RriPvracq+Zy2wfiRMGB7AtBq2gIb5pMOep1USEkO+3e6L45fI522 GRLbjeVB1dHPYjrN3N4vnX1zXmYZQPxej7msked/T+o9o0e7RGGhPJGkakR9 pd7W1AibSho307TBfoIFiHHqlEu0aSP30bXGfbR98v6gyey/GGMTEJ7PLkw1 zbVfjLP9Yk2D/dy882U0ol+5MrvLeSompRnkMmtlyVcjDtXyIydF6LyXOQgG tB/q9W/qp+whFPZFOqb3Zisz+ez3s8VB7eCvhSXJTAIyXmeNfi2dTP+eMo5p HaaFWKZEPOC76JZwUwuu2uZ8Z+9fiaA/rNMQRf8kvnmna/I+/v3A+SSj365M OWUHXXnWDXR5S3O+mTLxYthJFebSA+oLVZnPpDdVLmeLKtPos52TOtNFVWZp r0U6cUM9zLMmKnUgXhKBeImMdkq1WV2cGG24+KNOh4o+HqWgwMIS2QaZqV0m sfri9fOzgypbzE/VmVks51gewPYXKwJ9pPfM0nZs8e+Z2D4NtlmRueVMspbp 81RJ2wvHfuHlqiW6MqpvAWX9sbVkv9BK/GSZxFPNjbb/i+2DG23ZxUi2qReM kOPd66Iam0h7Zc7T16f5c2l+T6NUyaFYqllgwErUn1Qt5zzLpEkS0sKNNjN8 9mxwnanGTAB36oQm7/oEKzX25EnvcItG19PneC8j6g6Q+WeflWmalSvDKGOa nWr/ehu9R+wzncac4hDzEyfFLVIi6wY58d1pxqlaPqmU4Xbf98N2HMp30aYQ zCfQYLcx1XSyzSTVmvguBv5tn4wiBHAWjtaajgB9nZ/qc6/pew0e7DzbIe6z +Krov6MPsEcnMJ430YNXssSUn4U5bJwhRbs07zTZzBrQdnX9mtDFl5r/H0ko dX5d9TNd7BJNdast8knz3fOn/OQ9/T7wpXr462gPOtr+uZ/5566LKSeNO5wV fWYM3O1dbbuW+P7cZZT/X4VCdYVitcXV61mi59w5oceX0Q4coampyax+Jkl7 Zco6jkyQjNvuUKc5BLqMc/D4kwWqz4CI4XCKS9JlfeUCSOdjEL8uN4NO5xaL 3gzXyoXtzYUUJD9d0rUUhSHoNiCpOZB7ySFhwzrgwnk44d+DwCyCY96sSDcw qithrqV6vZnGla/WFnN2EOF6yR/BdjzzOtjxEdgyFuOZqjuEEkOe9yLxnDeF tpyPOn2Ypy97JftY1WqHCz8MPCVsjRX7DYdaFvrGy+ea6JuygiYzdmTAPTeW 96Ks/p9Iu38fkl+HVPhTnCuKa3WqnyFlyRw/H3rIJZKSwnSwedg8OP1z7JhT 9zo5UQ4/ISvLJVat4rDF5eY9BgU1bdY0NZGRIXG34QsewytoxlM9enCi9sgR jxqsj4iWzxBwUlPDc34uKMlv3cQu3r8jzeNCpoTdtCEUKuIDy0FrJMs0Nxka G5N7ITP4YbIvupYfpYgeM3/N3P9GXZtQUypT6IB/RT/fjqkyixOnO4mk1lm4 3l7uoXdxVF9j6n9X2/lhrB2G8QTw7awBhft6YA1k9Bl8hys6FXBworvASe9n xL2lqtHUMt1FvaJZ9ZAAOCj6Z3jm/w1//xqfP5b7un4DfXlNoIAqXPgz0ehm sjyhLyz4WXhURd+S8lXe0gOo0uN7smS/0XT5ZHUfUdfPR31PwvTaTUUSuaHm MnTrEaDTNAQAnc7LjCwDgZk75D1zRwjD0vgr5FI5y17PzJKdmvv9u4AAPNSi f5SNp+aB+MPw9jLg45AFAQRYOrnQydRN8XelaKPf/5ZNHnJwzHwEDEGIshfP LTzk4wryhLnrResSaS+PTiT/C/fxSxK4p862imEAMap2J/D2bfkZNJqQPQMx oH+QQP8s4r+j4MX6Segqer82R7frTA6RuXmJfGcnNSFMflfoOyjXdLUZjdO1 Tw6zQzW0O7Nt28U0ERMXO+gong9gsNhFP+Ac7R5N9MrR49NgVzmHM8S/0NY/ zFYPuaJCHOzjx8VhZxxuzjk/W/kssKj/nTtSgCHf5F/muy9fdgoGd/2EHvPk TLCx3WvXRNfvuy/KB7JAkWn0NNnBiLfpK+r5Wzt2FDSB95BYvVruoYNRu7bE C+zyoKFZsrVPjTRxlmnBWFZ0/06ej0AiDyDRphe0YLs4nVa/WKlw3NXpxlaA gzyN8JmDJmw/ZCuVkiU2kDx5OHcHEvNSjtf9JAvhf4x3vl1XvDq2X+YRQiL8 LpIXfEUB5ZMYKPDzYz33XcUj/v1sFYwh3PfWcyCQ0MRLEMgfAhC/BVWa9h50 AIre61MACGLbDkdxD9Pvs+DYnoTg/kHqIWgol++RIi0CAyfEun9bJrqIm/yb YTevMUzvhfB9xG4JJfz970ro3vuzJM6S0W3gIwSCovcla5DTT9LvkxqREuU+ DGC+rvfHEgbM3CdrsbiM3qdidoqr9wC40n4XnkUY3KaK3Ukyr4+RvI82EAT1 /gfOnai7kCyr9FlQ3e0az/U8ww11ep8B1gA6x92B4SwWaCZSDNb5aXaDlGJb /f6nwGY57p1bX0xM7SxZevXkhkTZnPJEj+HlidTbeRIMnnOiQWQoXf/GYPqE X8pGwTv1PSv9pEiSD+DTBd0JjwcxutG/lNWtuVwT7hfnXGxKJX/byY6rFAD6 AgdiSEG54cwb3Qau/dPdwNPicEGXfk5sl1b6CxrcRwUlLB1mrpCbWjMJx90r Fge4aKavl/+TeCLCDM6RDJTX6pKSudpqZaXoNuemqbCq4wYJVH0m4iSSl3OE DcJHgI50DxN83LwH+56TY95Nmi/Y37pVknpcKst57xdeULio45vkzJylEAgp bGLMGOcLVKJKTK4XYfkDqwQJGy3XukyzS4/PkEkZrkTiAudmQxh3J0U5zzpa H58kORhKAIW3oW1HXB0ekm3K7GKOPMeDS1iHLea1T3x+h3q8YoeUgmUr+7ep zhMHPlLjYbMfrzmLJ0JZxFt67hMXYo1DsJ+HlsNIQY/P8dfxgAn7MJxyxO+H Yfn7fiDaQGei5B1JIxzoJymD4s9wnTmyPzFlkOydhQYSThSdgvJ8R6uajwks 9DsjYYpfoQZH4tF3xUEglNDXJz48DJzo8SUpduHOCY9esXRBsmUL/qiZAiYr SalNowRtWInWFGDdAgwZDsY8AiaNghzeX4+VZAITXQQmuE1uP3hIHZZKvoQD 5Kx46csJNwZEGApQnANVfhJEW4XHx8xHc5/JBB5rf68qTJR8pok8OBWDP5Fe M4/CBCGTHnAws2YMSdTkClDKEjyrCqi9T1MRtP/RSYEEE2/c2u8FehPviINJ 034R4rJsZMLN35Rwx0D4I00FCoBAH2eJH08+z3gVRPu6ELAMlPkYt9bBsYob 2uHYmiuTcHQg7HcJ4z8c0D6UWDZR0WrstT5a4BKbAY+yeawXe9bZL0ylmfCz BSb+ChQiuFvtZ17gQhzzqrOdcrT+JDn8hrPz4s7/5usl1onRsgckqOnVmKjB WOLmTXEyOMmuzoa///nnXeLqVbos8iQn25cscYlGjcIqOVaD6myef/2hQ/aO LH8/Yx2+h5Uz9ECIUleuuMTMmQIV/fsH74qeAqcIIGcZhhRWw0bDNHGa7ojD vYTzPTmyc6UUhFaVION/vRm39F0kRfqcBTyJ0PV8E8081BNL/65ynqXPL6eL EBZSqFdL+YdlFD5Ct95sINpuCMKyjE3s9cd+rSovWYGNr5ajeUKzX6khxThf uk+cDV6330o00PkKunMdRn/lWSkproBF3z5Diiv3QC1bdBYNp84d7yOazG18 mefvhmhj2pcBaDhffC7kHDkXwDmAwWjrDIBmyAmZDxh0RKp66GSMvSwJx1Ot ZR6DCMKZQ0Y0jHaYqC+eLrlLOi9MiLDtxiBtrdYS9FHWvt8NfYXjs7WBkO2R w5LhI/b3A7fKm/LTRb4hxzGrixSMjq4j8yAD/gz9/4aERH3/lXTt8zuupKfn wsmCkcCTYYCnBhMBqGhpKzBlIKKv1TmS+Od04cY0qW4kqNJ9Yb4WEtN/bNPE lLkuUYcTZM0WJUbPHpvo9lBWIpXJKRafvfsA3oaxVHD/x7dlpyj6oc2dzHCy 9G/5FFlxyIqe4ptCw4049x7o+K0sMUL5oMn4y/xVenR/yE1a9BKyCEdvOGJZ uHUAjokNhWx0LbiaKCuUwlFqGGww+8D19kf1eytLSKbIPZkqYZeDY8JZkI0u FpRkBHD8VwURNsmENsOsdgFHLimOJPsfw3L3FsjlxHEkxcMuvQLq+44d6hFk e8iwWhxOIjA2OXw4ilF8vMJ6Ol7n9xs3vGNiGQ4u1WCuMyznSE40AZht2eIS R/3m4/n5hDTGJfEagt275ZMoZXXpEyfic2V2ouzx3MSU8nqJJi1yEvXrx/Ky FsLLDIZmO/fIclCeZ5hff4TP/h7g8BkB0yHc11hW6rHkrN0qwRqGmZwc4HUu ht0KZXgM7UxfLPtPnmkojkmd8CP0o6ATB/IFC1jCatBjv1H+dTQ7eFUcbpwv V2AI+aHaPF5joTmjRa4VeB76MBo27kJLcVCGDRdseUvxxrIfb3NvD8Sg5ecR Dd+SxOgCRAED/16C+j6/Fds77bLoev9/R7uXRAdzO8mGNLxecRzOzlOia7TT xAumMXbPQ3+OSGJ0GAKnVzIVVtHW/n6CJZzPNCzhXAlDA9p8JkF9EvUNBkma CNVdRIuYVckWe0jvhzMA/UG356BQqys475lilexdmFetpCfjxF1hkHRFEph0 UZhXZsUXsyyD0IWJdEv+KNDna/NhAYadkP0Wh/xGniGcPgPo6bFd4Ou5YXJ+ IJ5bWyZQXfyJ7ETKNgoBGU9uS5RszuPyv0Tdpu39gpfRC+Ymcp8DCb9UC8/B tXm9lkxkDGU+rJdU6vXPkp1h+oKkfWcLRDF5sU7NxzScGwr2XcDQ/xvPA1te z5LQ2raPYg0M5342diANRpyn+BVNkTw75ze51UihC9kN/s1w5byTunnex19k ZSnE1hj4EBbpA9G1ytYIqKbkXOgG/UiN6k8VcNg8ox8Lq7vEvBeiyxBBmPvt XKwovq6gzjxFNBbCU8dN59k0PsXJqOWdFmY3GZOwOJ630aFRh8dDBpGHjsft 2wpaGREY8fKBAxLKsLZp4UKXaN5cAaO+j2h4W29dIkCwYho3BoMsgYqaYUcZ /RD7Zi/pkBg3qy8sUGFi+sIuiaL+uZwpbkNqcsKd0sIVGq33JZsAsz6YhRBt DqgAd5PiO66R8RN5Y2WOlLausqlAz+MQzq7Z+gsFbaSAjsvaHn5KXCGe47NT dEkTXX6/PW62YMPs3vIzIMdaiLt0SPFpd74YqW/hvgUTZe70KD53zZaCtw+V sz9RX4ZbJJzS79OGSKlF+9nmH0m6prFMyVLu38UrD0LWD0+RW2iZFsN1OAqd mggZH4vgZhB0bCxCipf6CmSU/EZq/crgIi2C7D+P7oz+trhEnEOhywDnQQKp ZHFdOGe6w2+BeKQw2XCFCs+LHof+USaIBr0Ppb4paVlmcLt/S2AIYjv8IG5B bHCwVH4VhDBEUq3vJg4q38voqeU6F7Fy8AgMv5AOLd/d6RonHIYg1uv2puDH khTBIQudigCLAwCtxXj1Q/sB0eOTuBEsN2cbvhdqr1vAEq7ozRmk+TAS6D30 PfGGHsM9T+Rzxy98DpT9WBE5DYch685lONwWsRzHYhy5rcNmANThu4CKQUD0 TX3FK+QwZ0D6tuTLwucDLuRauaydqeF8dS7eUZ0mfNAwslJqqdMJ1joinTZ3 zNCJmwvYb4/uUN3PVdxgm9dUTC7GJl9p/V72MKVYkhF2ZqKH/b+dREZs5wOn 6RVBLyZuWAHC6ddOAYluuWgn0FZxJHpeztWLJ2EyE+3aeY/EKz4zJBm6vE8L HXlO8SXLOznmDPF+xjxM8BKumPglWLz8st5e11/i7VxoR3+JW4jwsX2g48iR koDNy5PXEZMOwltgd5i4JWS99JK9upbHKvO7uFSIGGVtzZlzT5Ymu1cKSy/o gjJRc/9uKcSwJaKs1Gq2UKW6rcRarODlDGbeBrmH1VvJNYV1TyqLGnSUa1bT yQXUw1YK8HTZIstTKUpzusm7drUV8/GGihi3tLlSW4qd+J2zx1zYRx+WCHIT mjXviPz9gYJLhnCdp37oQnL3JbziYwyyfHASn0+yAjCbYToL43tompyuL6f3 QHkO4VgK92grwqCRf4dP4NSM/QIZxR9Iroi9eaOZpBdWPq3KCD+hAtA7+kO6 R9F0MSu2+30Y0rt+YucTPymcxG/sPWHMSkK4AMjvH8J9e19lxVeyzQ5vAfgP Ro9m3pZsc79fSO+41ofVo0QjbrrCiu0OrPB+8LYrkTdyAseOMUCHnAJNVk9k slknuwGyw6eJpk4FQE/kSpAqvOaGpIG7DxBHqmuRFFv7DSDQlQH/IVWl/f8k kfr6xwVtmMBjjnWFSzSeD+PafUIib9iNRO6EAbqjU/dEm49rJzJpP7i772T1 IHJLZaHk2NfRJpyw2UvAAKDhQITok9tKupn2hkXUjJbsF7YpJvaDLvSr9ypa 2U/hmKMk2/YW+MThfD3P2WRWk7SNhQzc443p5hux4kpiBOMm5ovZj1RzlDKl mIu+OWNMm81spcgmqBRyxQ0Ua+yHxEmPbiEffENB0vnVkq7aYt1Mv3yP/83R XhELiAvUdaZbsrPDKAhP8+f7TK16TtneSyJMEZJ48DnCFWM5ukMXLkh6aNUq wQ5eHzZMp5xq+TklrvUlnNBHIyKW6KISQg5nus0hQxOCTpne8Zo3T0pc6EVt 3x4A1faPSTV0Z85Pysvk8Guotsmi1qZzxe9vKAjCpcXmJnH2NDlLSO5r8LXG pU6RfDKhyo3uiEbcvvCMC+VpbbkVYq7o9tuWW06W2m3eMyy21Jhodckjkcwd Mt/3HRfmmtaj7R+ly9/2479+vgnIdqV7cJ9+6CL4+VBv3Q+9fwYRWtkHsuMD 95bbNEWwgRO+R4dIyPASqDR/nAABI7jpGN4pgOfcW4ywIvjhBmZc6sXllp1O iQvhk0W+MqXgLolthWoGP9OuAQDgZVU8S/hKtabW95eJrs0viB/FSS3mmwTJ Or9B7tG38tf0eOCimpBsWZjMOM87Pv8ucR//fvSn3Mom4cYNlMmwh/+L1MA+ xDzMiCSCEZ2hh/ZKqmv2VViBLCk84ePcvPzp+TIK1thQXDg7CQej2xYRrWlQ 7450VuhIzDifKN3YIVH3+qOyduM/UgV2nqkSvl6BOzwbft+cIbKH9UDwa+ES 2UaIcMIyDxYkzFDIekfhoEjhy7ZzJOwwO1Ps5Wayd13IOPs9AobpDOeZV7bf fWDc954/H8pUmOZep+0y1bvLw2qmXe6g4ssswiJne0tHIFblLCxz0SxLCwVH TlAxUcBRM9rsElvNdcLZJpXcmsTdu1NHNQRK8ZGRqTwgwbI5dG44yT1mjDkp tT0gEWh4O4GGzo9ldwgJBCA6K3wWoJPYs8cmu2tGsEGw4zoUQgfRiM/yu6HS iy+G97ENy2vTYRo+XECSvhjfw8CNIDfF1oxktKSTkF3ow7JtUdYo2XJHTEoT Z/jZbHGSZTgZ2HGHVSaSuN8FIzpmQymO59IJRRrZ5UArdkm5xRdBkd+T8zGJ xgSKpFzudb2+pXuSzXoxiWQ7tFeWQ0KP8BUf6oJsm63SmTBWJnSCk5Qem0TP lMlTPwsO5Ns5Tp75oRos/v0jp22C4y/C43n8W9COF9ENTvYAdpbsE9zgNmlU +PalsgHdiHkCTQSMYXcFf0Z+XRLNvT4N1a5cZ83l4IVfkTIacY0kYcSi+O7f 0nvTBJsYtNGhYfKIRfY9P0qx2I4rWggC/6yuZwcEk4W/kMoc4hSb7P1Zuoeq +6Q1nvZwpC7MUARZK56Xx/mdqWm/Tu0XAl0DgD3Fb8tIC3viE3hT5beW3FlO 2aAf+CigrQLxbIcLgooIfbutSLhSEHhWy0TvYy4x8ecu0WS1KiF34gMcuYpF CffUjkT372eKaSAsvT5E0jHnudXYm+EXoLcViX/Yp7VULJeYcuuerfO1bbZD V4K1YVyKR++nUsXc9neW7RYEodIEsRYoHrynTaVFCu0TRR2cBVcu+hV1Jpc4 oqD7vjxGf40y3dwn25+ZHZA9x7WZmiF5ZAfxc7ECGgfO6IJGsspp4UOyIJmL d0fr53/nGGiyfNndu4NX/erIBa/Dl9oxi5SfH+EmQyT6MTVrGpLU8QhCxCJy EYXWrxcUoWPDaXm6Wfv3S3MM2+hmPfecR6Rovp5oQ6SS4h05R6dI81zRLB3R j5Pr5xEejwUOr1sXQjk6UCzm9SOvVUC65o7mKFk/Vi3HrUtpPCxp/rvpXFmB we0iGM/lPaWw1FASROYxsa3WdDXyBaa4Q2nFMFCxvcAQl0mXFkuxPd2W91Xc duom0lyq41dioCerBvAdK0bT15i1XH517j2Vr+8rzlyHKH+lUQRL22GcP8wK E+6c9CdU0bf+2ENWWN5Dp+sHvimee8//9ssuhKFPI1Q9sFoyJCxDeSdH9gtc N0+++7jogFSuMjc+FVHa9RIpCODnAQR0+zZDk/BM6V2ChSBRqjQzcp16Uh+z SEdzTxmCAUQ+XiNmSEpJ64A+jjJRWwB6Z2bJjB6Bjmvi+RtqhJs2B2W9Xqcb yX4bTOBuKU4XvSmuHjvOyS4iF1cHcEJwFLD1JzAd7U9JmNkdkWnHfuKQPQZQ HIK/B3SXrWKfzpUfQyN+LsarHv03yXYv3wm+FbPaM8m/lqsLAXgrZyWaXShI 5N3NS8y+1ixRwq3ddw5IFMG1rDPrMdk983cZieFcRMvynT+DCJULxCt5H11a CSN6czh5802fWSFysYxMkEvEmBkk2XJSikOJVHuc3MOyn2mKXC2cbclq5fHe 7zqqEkivhWtlsgIMMVVD9e9rk0ypsnFHucIKXau2Mfh4RuFjUgy2+JsyFxSO dDP6yMGyVH3C2k8PG8LY7/QW6LtW++dk78+12h4P2xkjRab5GBgyEz82bNNJ mtzvYlny5DjNdAG/Fhqm+kQQ/Rk76ta1DjfyKMLSHaIb/7ZJPCag6Ett2yaB H5GOQSKv0W/irByDRfpXPEcfjG1bO/TNli6FBZJSJLp0lkPXSwqkqf48uU4Q YxOc5ovoz9+cY/XgfUN1WUKa+FZyToCMINZilWDcfffg1h7cgttGA9uGr5Nr xCquiPH5pTbSzJqm8v17CjD7+8C/zVQhRZT2dbx8R7EADo8L8LFGIC6qwrOX YK6vo50LnOvrIOfebKirhnPlfqsS+qZara+AbafzxS/7vgIWr5mfdhDtjPxA inY5KTcpRxIMJ+AWnPYYfaLS/xoy9Pd9YPCpNXgnguG3Ma5jT8plvPo6XnEa 2DX0HQEgTm9S38ZUCgjRcWEISN+IWyRw6xgGVXSreD/n8zhPx1iNlO76jouS 4Xye/zOkZDtc5OcB8Mcyj0eHidMazSrVWW4q+yN2e1ba9VvtKGxxPoOLBYec AZnaog93pI+lIGXhdOnfYDhRXfD3Qzi3qKFgQX+wqTsAdRW86/VV0taK9WYA 8U5AZb9diaZ7yhJDT/dKtBm8IzF7VXGihKx+4kiipLwyUZfisAvHDzJloxXS 5xMVhf1qX76aB5ZNCCuVyguFHgM7hJVKzFx3UL3KEsgapVyl08VioB0KWwP1 kULeXumlnX4ac+U3Pf5E4s+yZabJ+8Tqhew303h0UUxIkb6ZQ5VD2ajyYPUV fX8Xfedo31JltF7M/Cv6SVX6eHv9u0yfq4w9P4HvqvK4lhF7nDnp30RNhCUS ZXqufQypHnO2IzxtM/+bqYmepk1lgZI1y7l8RnFEooKCCGU5oUqkCO5Yqgcj JtMJXLy2bBmvAbagvsyAE6ToilVVVQcuApuBoAaLHp1yNIHP72zPCpwYkBKp WOi0d68AIRArgmYrdY5ixGRRAwGrUAITRyke/Lt8vFQfUSS32lag6SI3zDec jKEU55/eyCYzvumzA/Se/CI78LW8MhT8rhgtiLRqqMjizMkwywWRt7S3DJdL CXDqQaUKUH1HgUiASq/lB6Cyg7FK6QfyOmowQ2TvzsP4/DCbOJRwFydKte2P AVSrz8le2VMOyNIqqwIoBfrsqJT7B34gK5J5nuhkSMWIjtNsbbnEoOifiPUG VFZBzS0NHiqQTWlZ/GPLNvj//NEBqHw98t9r8zAnD89VbvWRwiUCE4PH4U9L LEpA409F9MoJQPUJwrBhb6rSgTMlx3AAqLrN0Z38EBcvZuwLTg1AyPfQm2HL IIKVny3cITWX4FTzonWJ3rOHJppOGZroMPKYf6cHKzpOA454wCk9mp2oy329 GP2snS0gxOQ2o6JvqapxZd5XVeu/q6o2E7ahb6VUIHVTSekQywFBcVJxDKT/ zqlAghrzO6/5q5WaNYh+hTZynTKdLYSwKTuFtGTZOEyjsUg7eO8z/h0BoTjx ts6FSPAxayM9ZMIMqTL0fXNiK4y6OPuRZgPA8MO6c/RaVayfObrxSnJ8e9rg wdnW6u1j3U6PIJeqWxmtIdNn1d+aoal/Vi09BpAbMkTQ7OGHpSKpRYsIyaIA 3RZ7LFgQ5gcJN4Qy5sIIO8XFElkSqhgBHj2Kx3ED+kckYykmb9Uib49iOAff WoLHS5fknAWNS+UHDvxr+J3BLV+RlxfzcVke43++YqwEjY2e0KptBMxT4Uyt 7B+gi5DFXxiYUyybJFE47QefmN4858JvRhG69nWRfca4mdLN1AjlrmXJqVP3 S5TEmLPREz5SJMP8j2ET5U5yuVd9haQ6eOUgoBu6826KyGjVdPn8JvDhwHAJ CF+Ew3C0XVhhyna+Yhn8PNmWv/gV+IGIgwf+WeY1O51MuLk7JTPEPQCX4dy8 U5KzYkTHaTpGcTMRGI6+JoVMxJUN8DnPw6K1W6MLvi9KbGXOERNPlguT6FB8 qhyBns6Am6EIw/ImCXTxsTNzJO40H4v1VqxVYI2C398a3S6sFAR7VGYAiSrc CmL4YfT0DcldMdqb/xcQbDyOwsX600ujJeB8HCNbAYYPPiFoxeiyxUpQXMvV cvGuJ/DMckSXfa4mei/qlFi6upEKzuN+OLM3TPZ52TyWnU4uTPSH0ahDl4oh YbMZMoXI6TtODfZXlWKAtEER4SrM7TKXeND/fF4RnlmCZ9uK8TFPQUPEUQgR ixjcLdc2v+8V9aaXLuZ7RscOCumfPaKF4JAoxJz8LS8Z6/y5ZdqPaGOyDOlr nRjqsR0Gbkv8OFJNh9jcBQU3AlGjmBNngMaodkZInj2gI2fy7IFoRyX/Mz58 wV69zMqGscFLHOKiXzKJIItIuUbQMMPyY1RG8T11S2E31X80994PcQChYWLg QEk28Tv/hifldyIlhBHamN3nKg/eyzEwucVz/Jt4xBQ7PSs6cyyT2LlT6hOI MYQ1TZophqX6XBvDTn5y3hE4pt5Xsn+PzklqgizD4xfzdPTEGJoSVnmOSbjm zcO6ProQdQcwBU9/HJqRp+siYY877AZBu0hsfUKhiYsRLO1O6bASXIOtt/Rv QhzXTMQLqTrtgrYsEz9BZgLE83qtqXhfM3Dv1fxkW61Ks8wwkDksQlLvl8KE ID2y3SMT7kuAtutNJGPAyIBZ/7UdEd7lSnKDjv2478p6B66hIJys6SEQxHVP OxcIftCVYV23wRaVeGuppOBZx0kIIqasAea8Mo5/M93zISWJaPXISQnxuO6d 2aNen7FcU8PA2oIz8xZJ1p5r4ZitZ3N/VAeu8OfSNV9SzrDwlq/QfIslf6zJ InyxVNR7W8vlB1weB8Z0AofKdJ62n5agj35fMKv4J/Kzm10nS4KsL/BpaKH8 Nuw0OKCPn5afmWveV4bMdf1F40L8xRUB67skmo9/M1EBb2ncRgojnf5WiaUb uiQmrt2cKDlRlMjlDx+9VijayDICrpahVJHr/RB+vCS1gl4diWOdBSu6gVVT IJwtmORhgdOc7jgPI/pGfqIUylTTflu2ENhm84MMoP6HZzUpX+glmMvPmHw6 7b/tiHRYMIw8kGiuV4RFChlZtneCpOgtvz5J/87Sa7ODIzdQhftZbb5JLAeT iB4PxeYs8GHpy6DYNqvlinMbY/FflYs2xYigaatCtos9u+MvQFNtv5S9Tx/J exNCuL8Nt/9n3py5ckKAbflPGOjdO3hX3NKKM448T4jiMzxHaOKsX+fOAk0M CmPQJMiU5lP5TK8zHU/visiGcLSOghMdKltPEwcn9sYcK+sV5xYBmNGEakpd mQuM/KpMASdfkdJdPg+r2LHS+XBzAErvsI8ySwjoxLxupQmNRVUMkPg3gyF6 CB3OhVDwXIFUlvM7Q0jGeVvGyYQfEYii+QNFpVPF8knkGYrnKjaE8s27Co7s Azflv1Bb7mNmfrKi0oCf6V4RkIYN8GCm4vy3uYHbPEmF04HqoXvVMSO0pQTo hrGPfp0VRWFS0KCEE4JMZhPNuKp47EYJ4qJJwQnyW2WTTsvEwmwg6Rw4kQM/ kZm/OCxx3b5VntO1obPJsJ0ZMu7Ww8CQkMN+9fmvCEpeE0iZX09WvXFPClYp cNNotjcciHsJ1448hb4jCOwNYzAYdJ/WRXSCsMO9c0iPzsVinnvp7wQypuV1 qjt981ldEnWnvZkYA+mbDhEr4Wb60I8NME5lrAWb8Gpi5FMzE80/y01Mhf9Q fsQlJq8ckkjjllbMoNd0iXrgzSoWSTM73t0lWreGAkCWWiAOz6er0MKFfXLG HPGFdJ3Qfjq/24952Bbq9qOkHVT9U6XqgS49kZHzPE/G0Miqyl1sD53Zgoxh t7ran5/hq6P3TJMuR2hNOFriz9mvqDmr6MyUgayOtdJd72YZmPzCTwAZFqIv cPYjiy4q6yJIxX2nHQGgMv8yQE3zH7US9epJoRPBhetzmRliOMfaTe60Y8VQ 5AHuTdHMu25V68vGeZmYVKnTscQ7rsmNYZJ3heiKEZRYZ0W3iutdbNsAVp8S dVjgDlctQ0GKXpsWq+q5DD9tyOa4CwiX0zRu7KKaDb+ln4yZP7ZinAzzK4gH RhfIlI0vRR/DKsOUezGMB3dBoRPFLXv8Vv4FdJpCKRWxjZ7M27Y//H1fjGGc 3G93PGDYm2BARblMWO6pKwUK/eAZvVpT8IoO3fRpWuiQIZ4Wf6HojgaAV5ol 3P6O8vePedv3/EQUoxJCFJO9/XW17rzzAmVDEAPOOSLlmz7u+45A0fwNOF5E bDleqkVLPohQi07NrLlAxYFSy1/4MlRmrDhBbAIuULLl0okuQ+E8leGege/p mhpFrBtoeu1+3Vfs14p0pdIFK9PtWiY/9jx4i/7g/ccScpbBMZtXX9SIqPVE pmyOy8I3Do9V3hTs7wKRuqO/bUHVsXvlfgZSfRDGTlyeKFlSlmjcT9f0sMS0 Fc7X6ir3sSSgqEGi0eTnExvgNM0BV1svUgXE9SkHIWfnHkx0nnon0e7WsEQ2 hKrgEZfoAutWAJc7mQujiUj8aRHY2DaUFDhWLXFuDuS0Na0Rs4f0TDopatHS fLNzwv2orjhY9CgyY6jFZDaXvbUJIdTjTtL71GEGLrIzV+SnNFPYcDEfZ46O om4sO8QwtCyuEhnS3DSFITZvMwLrPHjZenqPd/bTRFbwWRRqQEfrE7wcftMl 4/9Sdh7wVVbZ2n9NLyTUUEILkSoQQg2dAAlVIBRDxwCR0Am9dwQjIiBVioCC ooIoI4MdFBULiCL2Ucc7MzrdGWfulDt37v7Wf6+9z3t05mv6O+Sc85bztv3s Z631rLU8dQIdpzvUxa7r40472k21LcSolP8bbqVYmw9CRVowgAMiQKzALXor gQxNm0Yyfzw+UZJ78WLHkZIi8EQFEyxB4GnRIk3RSU6OKO99kVH8XbIo3nnt wUyv/GQLsf9cmrHumbAi5Kx+fb0y+LGgUh5HY1y6oAfR8L4kau41bo3CWlo2 nwLft/UiRcs5e6PwCqXzYw47wC3u8zHBjK31nZMoTrEMTvaqxSyFsaoqcv4h bPXcKAZYqVKL1k/JZ8GJSaVqCx6qoQ8w/trt9m/oen/YwdINvAy0TZ3mYEwo 12tpWpXYxidlvQP1dDZ7KFBZ2MHeAo9iDbW4T62eh4u1zgZggpeHxLyxgl8P 95D1hDqtFEq2QOjOOKFiI76KApcUtfQ49iX79InrfckEX9d1AcI/KIB1+3Wc sxa7/ZoHGG+Tr2bCugN/Efq2xObtKYcwQi5Bjz2CQUPUgJso5nnf17RSPm4x wa7iNT1Mw5VBiFn99oaGJNGCaMwqEDzqL5jVXjCrs7C/bvJ59Ao5rQamoTyL qQNckk1XgfBNcsmGxeroxNhKyjKpYj93kbmxmY21ZOMJzJMBf+CUTuYZXXfI nRqgmk4Z4NPkSlextt9Vm1uXJDPuXPlcW15r5DWHXXVxY3OXG8JinViSzT4q 5Iq/0lSHOQXXfJEu5OZ4HB5wYBGnMbJ9bshnhvDj6wkGUQyGAiNUsGjvoCuG g2VPVSNjQl/fRmFbQfCDbs2xHq++P90fD0KiluoetqhU/c4O9qKF8sQXdlqw DuuQ13DYN9ddldZRqEVK0CD92UhCDqlD2p3Ko9Z4Rxu9WoBKxcTz+Av2YAYi VsA8xGvlTTpZFgFDD2E44GN/gF8YczjZhw5V05BtBb/8VQYsIXa6Kf8mWOvT Zx4TJoRq7dnj5Kl2JZ+kHME7fFzwQWSn7n3EziZy6X8iUiKWi4apTjBloVyK CrlEa2RwdfBCrbgQw2gvepav74todtWnFUIYNP6+f4UwH4j0ENZJeMS+BvJI taBeqaqSgDHW6S5j8kiuZ08aarrhSyk3EjIlV7uzWFPPNNF1zsq4G73WBKsK wqhieU+5vbLvAcKLtmfo786gIK9wnK236Hj/hMMdqBKmAsGHYjmGgreEB5Rp wiDOmIKXlCdROAXIU725gzEnZZ8hx/+uWJXfNFT8qDtHIazzVxGNFgYeXn7S WKxB+nGIYv0+1NWQkAvh6yO/PGasAOoqsZuEzfV5Q0FvMOUlF1rQbDijwCw4 FJjM0XJGHV9VlxiKgT7fybZy5rnC8uYcVAXazdOVcd26TLMchgqqrainXATK 0schGMuoQN0kHGf0nMABWOHHXj0bmEaBY70QZRNM8dwS0/LLqho8eK2hafJr QSq5Ycm121tRhW1UikjbPcXYFdb8u+Bu1Jc6vgtkjLbgRj/bVBNAuZF1wkMh bPhHC02hR6mnm0UZ8piEtaMgKwrGIvtIcYjXKgqlePIxCatGbRuVNRz//4Vm 8SpCNe70eOY6R+13RLiPyHfwTTxfjaO8YRiRj/zgt7yyK4g6dsiZq6kQqRO1 L/DmsVc+jHcb4VgHwmJiNKEPaGvSJOKJGj9eudro0dpcD6xo1Spinf4LsMXY AiwJFsUef1yRcZvQw1tusT/h0wUoiME95wXoYTsKgfMIhoedKpHRFeZYzRdt 8YquwsLIJSNQQLIQn77HXbmVNJtv1kpDp+1kEM1vxSVcU5fj+VGMIhNsY1tl mRFK9PP/DsXOuzn1Wfe1oNYwGUvNdnwfyUCrZ2qGKUGIBFAmoSWYJ5bVlUom WCvW1K1b5RCa4JFXIMsUAIpBgxqKTk8IWE2R3Vyqqg8KEaXLsvl5Oc+H0rVD ID9LYRAEpW9XURQhPk/KcUVvrRwACAFUfcWAG/BqrHek4w3q8UfN7KXqJJ9t nRenkCh7xAT/kKfrE7Fir8vPfpbDRf+0Bf/es9NJ6b9wNqX8+5ow3dnHqATn vhvogE1QrOspQaETsv7jao/arCEKzV1S6adwtcxbz5qE7kLzJvUyWw8KVg+8 ZvYca232nJSbvKOz4O9f5IYKig0XVt33dzqHtK+txgnsheLSxK0GOQTDkmem clUfebY23J1l6gmElYvNkLNHv2MuJmI9YcFYM3XVHWbI+5VNKjS3RkOz/MEk U698p2nwZU0T0+URe11SZUwdFWhK+C4w9XCWC7bkCDzteS0wo74IzKD/kPeP qfGRQpbwAOGHHVqFGRRnHbv5UmlJJEIHtSEYSJAxI4Qa+oWQIZMZNfwfDTQB ORqSMBzvCKzHKwIdPsk6er00NzZKo/ZXy/1056jD8dBl7GFGEMofZWbULjkU crObRSGZb1D3v0Gt+GjUwvKPj+rK/q+oNc7+qWxNMQgYMDNvnjXLLMyAXvi3 +B7zjtRlbkDfvmpaQsp8G0UwCO381q0Wgyxmydf4prBKWSS79RDqZVjRFe5Y 1X/nFfPRtaWIHODL8o44uD2PGr57gbew+01WNxMsEozq6nIG44Wa5KKm7LKU q7FLhTK2R+9jcl3W13Tv3czlg4fa81B94Z5kVVOZBBBEtyOcHI1raqY/MUJb c2aOpgjRLg338xZXNGHuXCE9wwSLbCpInGs2O3QPzHjGcqdpSA1d+ETGfYYh YkUftySHZ5Rg065CGbgutwaktp2MkrWl25jTwt0nyKDfz3EfsR7fsZvV7sNd fliWjbgoAPGfsYAZl26/UIKOciqT5JCnCBsa96gwF7HxxhzTCgNH5bQ/bRXn oYpEli8aixHxbqCphOkKOb70FMJ3ghZ414aIBVv4kB7pWAoRPCcwWNdkMbXn mxiTvYp6RQ0WZYklKch2nnIuX5jOpU+ZPQfrmj3ybG8SHtZB5s1BMm82mSis tI+g66TRZvrKBiaxeYz1KvEoZDdNM21LnjdrK5qZXv0DM18u2DiXP5vdaaip 2nquKZtVzRQixC7SRwhN88Hjjcyei/Em+eOmnKCJERRq+1vGmOBPkGHK5RhS ZQbNFiio/k1gqophdMcdro4IRs/gfRoHAoV6ObTh/X9FWIZmDfPdFh3nEaMK OMkKQvvuahDaj33dNrCvaiECRRV6ithfoeMphIYor1HkO21zahUTMTblIk73 FwSegEV22cwh0DW/aqz6tH6otYpCoMghrnDzR5KvX/d/QKAkKlZaBxbIQ7YN +ADJAYU2bYrwJ5zlABAhOYAGPtS8eeSA2RqsUlGVAhCqLRxasDGhTB6AKIuJ OmL7dnz/qhEl3QfHFnuHEwE00DHuMm58Qow33RSCcaxM33VKGO3uu+qu6apM 0TOSwjQIX103VUyinYIgdW/S2kTdxFh4uIq+H1WiRZC52U/bkc7lPWd/x5eb Oh+EJMnXxgSsnqDYgBgtT8t4zNsYVnrweY5LRivbR0qwv1hzmPGSe5IUq0DV +yHEVs6JJYf6YHfBhruEX3VS7/BinZUj5TVRPWDNUQIT5YJtfSHso+ch5XBU pqMMSuEbvi2zxhnZ9ooczoKTylgOyLgpOqtMCdUD1h5/icVFF5v14lAkULZp qqw3hJ5wRRfBt2JBn+Gva8XH0bLq582x/NSGbKtghFYdykYeMsIoAIu85NtW qES0i3N75cqvdHzL3HOkrklZECjvYQTOIGVHtr8zU/1W8n1yvz+ZWsO+NLUW lpsJu3Wa7DZpq0m4fYJdd/raXLNjp5Zfxh/OJLf3QBW7DRMXQsI+8kTPkauT vmmA6Ve6zBw9nm6fRVBq7kuKUB1/zc2v9Rco7f5LjkCIrRbbpLapJTZ0Lbnq VsmD2bdB2UT+BfUEM7Vbf/vIKJSCvOPy+YdDn6ZuijnmllV31kXi9wHqYhCW pmxsH52LdtBvCMJy/FGSpigL8f8KVG/Y3fPdRfvdF0F0Nd4s+zMAFIeD6VA1 lDky//3dXZQc+5Pfl647cIqMXHxVFFtL9EXx/o9GXYUTd3hwQqkpWBDnhE8A Eu4qFAm33x7t+ImPRiQLKTTGjLfbxVpHObpSegf4o/AFqOQxEfajDnQ8AbAt Mg8RUfXooY0BmPAEwiLXmvrrFGJOasj1yizDI4TML2O4tuWiWDgaDnzsPnGQ +pxU624lEFS/XL4TmKL1HQz/Xhm/RWWO+AjELCyQ+9Zd23BwX9GXewu817IY mwwtK5+Sw6ifRPE7l68o78tprhWvir4P5PPSOwX9fizISLjPoZBc9HeF/lxP Cf1Ns+ToDwjNqb9YhnacgiCpNbBgyn2OFGqVK8jSSUyw1wSFrldWoPMJfJ0/ oE+WQoCwqq4fqs93VZluPoN6cQI094zV/iHn2smQfTkUrFuJ5udaSoW+VrY6 yz/V+sNMy/uJdVF9yhliiLJua1dyKs+JFQb/HiF8DIoFDuFae5nTj5tgpkwd ozrI7gT2R03XbJ0ePzPFc4tNy36zLW0qW9LTVNyTZuoOPGduEsvs1JNVTMWO wCzbnKX1I6jXdC2fQxAo6mC6T9lrCmasM/GF35ri8hkm7/YjZtLSIpM96oJJ rJ5rfQ08Xo8+rY8j+wOFfLoYveL6i7U3a8sIUzRvpf2eDI2444JS77gqAMKL tso4zWgi2yNhwgwaKoB7PcZkNU43W/fLJRx3QB3HINGLMl+/qT4PKzaAL7WI QqFdgfq3vW+aqtyIxVOdD7lyiD5AAQ+GLwqEfv6fbsZkEx84BOwwXodEwQuF oYQH0NEiyVsJ0YE6XgvtIcawM9+RpFngG+7FWloWqwrCz+X1M9crxPf0i3bQ 4y6DZf23EzmwuypuGT6U90IgigKnyMEiY4BTxkXl2mivg38HTkws3Fb+clvx PjVp4sApwRIoJAU4teExgEl2to+60Tck3rIqD1Lkwggwue1jLaixa0CqahRI 4eHGN+U7G/g2ZNEiKuYtQIuniOUED3kKAT3hUepik6mUUwe4Knc1Qc3btNwd LbnIZm6ySOYWeeD7O8DafbMCFEDV2DmJ8tYoCNFK05fgxJgjHrNDUOe4lSXE 2DSIDBWOo7rMnaK6HYpagWnPy7JOJ1UnT2kZzJhyQZe7e4vNLX/XFugDOHOS k1G/K/Srqa3nqUmCcn2el9MZM1MDf9Qw2OB8/+x/9hDZj5zO3Vl6mJfld0dR jmCXohj77v0Od/u9dO7yY73DpBhbxOWG6wF6w8kxr2t/gF7vakdBLQATwhb8 iWbyrAvLsZ3Mvo3x2c4Iz6mQAKqFBV9kWR1FORSSpPBQzHf0AgGwFFVIP9dX +3y2O6e6+W6/MQnlI8zkbQmm5fD7TeKUPLNMZrZqfS7a/fBQ4El84OEsLW1X +koUbLUz+aXbzbbtSaZiWxXT+45tZuyCUtN2wqMmpobY0J1jTZJA0mNPaAla HtCt20PYYgZlNizfOMQktrjTFI1IChV7RxS2Ov1aHl6hncmtVECDANBma3S8 agc3ntoWt8Sa9bt7CnpvUw4gE038JqfJGe94gVDP9ffmmayWDof+EISjOg27 QgGCRb8LqPAQYkkrB1lVLMTE+RDeXwNtZOJxg8k0N6wBEw1QgBOlaeIjkfgI idOtbeejGH6Ce7sgCDsL8vovj0QJ2r3rq6hj/8oDnCINqYoI4zdHIRIi/GGK PrdE222xNjyXrMjrQnGx1mSitrDaXwpDhPZwfnMHfVdC2baS28TOD86qrOpM QpdiE/EyuvZI9rP8jXdQ8+/SYOQ7X5oBG8/vOUIJURHUGh9FCRP0M8qmviOV FZGkl7cuxuf++cp1ERusoRdCCZvJ11w/H5XDz80lwzvkM4FzXWgO3vFaLeUJ VjN9WkDmjBZ5YuCSPIc0B7Enwk7UBqxHPOtyXZ2Orlo9mxaQx0tEdT2qxfis Z2ZKvNes/1ngCNpVd9yxGurT1Fgn2JLTn3NES5ZHK+T7X4z1RerQNhKogvpY Deerqv2E6mCPgS2E3tB2dng3UlyKr6MhDCc1ZzOj1D2PDTVVUMy5BfLrtjTr 7ftMhZw57xnnQBnvoSta1UXMwYlnTY4MsjX3FdnvwBCeLP6OmFtu3+eMO2Mq 7s+P6O/AIOp28DCANbLvSA2QNbRWIYcv0OfHVg2SiXDBdVcN/3fqiCa+QkoE 37FPuSuR3DuWWUOhaoHJG1thRkBWmcpV4W1HgTXAruYp3fmpIxcmrHlxMXxW I8IfvvuHJR3heIQOuZbfkUYkR4LQsZwfFtQk7DY3+F5mnh1YwFEL5w2JUwFL uHXkh7yn2/gfStC9bI9CLa340lJxYaz9U8l6kZEjMt2DD/zlM04brhyOtw4d lG4IRYmJYhuYUBHosBQlJWqp64jmfEpJtjyLb7ZG2NQ7kR1SeN8ypjarCCWJ UBboCd+jNI9oSzGiUpqJZTIprGGD8VRLzqx6S21eZzNLEtXSwVBiNjlhPcPO WIrVtrtaY86Nsdpy90u1MBTUsoeQjCvJWlKXOMQzzrN8MFMLIdCrcYWMyQ9l 67tmqZKx3Rnt0bZDBuTwRSq1QXb3ntzGtttifAelT2/SLHgsqBliQR0WeFoy K8Y5nD+1UgyEA++7UxlyVqsQt3/Qde84pol2mEBD98jp1RdOM0wP5XXZ/3I5 tNaHNK3kgBz+OLFfaAdNfZMO77golVhtZ+SwHmio+EBkH7ER7mEGcHfnL8Ib Q9tEqsZ50RFtOzyPgYhYZYCYZZPFXioQflYsf3tOUVdyl/e1Xcuf5Hn4zwT7 ME66Vwbq2AMm+c6m5qbBX1q/EgP+2Mn6loBkFL1v7n0pzSTLyMrpotWmy6ie fmCL7KeKGXIxUYAjMG1vP27K13a3Jrdv0Qfh4PFN7f+tgtLpWDNzeU+TmrPN ppD4atWnxJ4a/JUCyJnn5Mn8jTyZr/pHr5bdX6rM6afOpJuqvS9a4GC/qO5I Fu4y/k4z5KUkDdDf70Y7E/kfk836i5XF1hIwfDombEQiv3VTvcAkveBLzCWr tfw7t90/3UDOiWIiqVHoQbj2Vvc8tHbgk6hjXWawhBIdUOXLhE/hwmsf5c5p 7X5Cm/VWsd95/jEjyiHsO53/T5Qs5odY4hsuxv+AaCRYFSP+Lafnsc7im292 wBFncQQTB1NH8MTTDuCGa+tfbFmtmox29dxgHCHtZqegEUZOtSgjxxs0MSHh UF7iJwogBFhBawmkderkkITeatVNUGOYWjY3xWmTDfs3Xl0yNdojlo3xEzD9 Nujv13K9xlYhHL2Waau0m7U+NVhDxhr4Mdq1HiBwhZgR7xUOGXzFVYRCdVhu gm2JOrAJlC95SA0ZlItIe5aLzdSDboUXdWDS9ufoTbqP3m+rKwaOQDRq9Fa9 c5a7OJKL3XbZ/d77cmzX5Ld6ysm0ETDJe8oET6Xq+h/KPo8g2XlCweRJ4Uxj 12iSyGLBtWtyveb30HC2rfstYNBwmeaU4ezt9GFEzGjL+u/ShHwKBmBMWAX2 i6oEws5pdxk/1I+5ix5OiES1PK3yHd/x0DuISAshK3bCIDk6Qd/iEpdWIrbR eEHhN6vguakS9DRbX481GeNk9fz/tru4qfMXJnnweyZ5VgPTfOxrZv2lSqbL r2RCkZszeV25pSXxff9i4h5ZYFFl0rl4iyqnzqgZ8tjpNJOanm4q7kkXNPmD eeTxKmb+mnam04QD5hF5ovY+LOu+IPsqVxqR8J9i2gs54zcq3tRHE7TAnPGt M0AT6z0PlKqgGGHZ+MViee5daBIEBeL/HIQJZFxg9Ogk9qW2Ums4UHRI/iue JocEqSqXATSifcG42v7bjfTvHE151D4fVewj7QuLpAQaW2e97iG4YGmITZMw W92bp2Qqzn/d8o+wTQERJLq44NzdFu7ZtzdNC9QZ2MDuPQJJIayEVOZMEFF8 t4j2oKRaaAEBgBf8tb4cCN+hDSR8zWAfMcLbM1pgjnXdK+UHrmDvTRFTxwFK XARQ0AumpDgnSaKFE2Bp4kTlnfRoJF6Fp1gvg9yUhNoaaaraWyNM1frJOQ/U xowwO2prn/W6lenKJKjXBit5uGqcT4/3ASZeC4UVdJTVNsnAPxRosS1EOoAL hqg3AgAEDJqGchhtZdeVZT89Tupo9EVJyE3FYKnoI4NZcKvrVf1ujwDX+zdp 8nrrs+rmbagOmAvJGgTyZb2fc3dyzK0yWoUnnZYzvk2w5PG6oYoNHKGULK2O wJKnm4v9vFiwbLiCQuuVcjrt5BBOaAAJB02TneqbJSW/00fOw9FDQz9gDEQE qQ6RcXJQyfoFWDBmsFbI+kcy6Yt5k5aR2lI/28DUPxV4mh/TmsWsQ2rGLLl3 t03VS4Eekd8o+JWVPacP/NLEviUWxzdyKcf+2Easkgq/NRucG27phjYmmPeC afe1PAm7K5mk8YtMsiwfMH29KZy+1Yacgh9NM8HX9c300/HmkMBEw2FP2zAQ XpcMrKbRn5kGdxwy+58RQiQ4nPobOxS+5PqnrtQQhrW2BE6WPaM8mN5eKMca NdJle3Fu3XLSekFYxkxJNHX8GSUWRWLB9NNqsjacmth8kwkKBX5qdHXBvK/U aKzeQAnnl1FsIC5dlyVlcVzyr9CW2ik6p5DywfpEqWkFxzX1RMcXYeun5olW MVJI4imdpt0BWBgT4RWR1sjWjSosIWWJjrV+SDMJgg3wYy1eUYvdIdneaiHK /VSCKnxYBmO/xe3//xt3JjiChqcWgkJkGgAEU1avVqKC1mXYMCLKSmQQDKK+ IbzIM4IrNz4+vJycLM6gmiF2erzBA5KV5RwiySp3OKiLfM1vhFrQVyJUgnTu hKqocia+esjP4C58hwMlkzKHYl7M6iYUtbLW/cATQd8knCe+JEiscpsB86Kc J7FqQB2sDrvlu/uRvVqnLVKEo+4BuEjEJD50c3n5C2k3YBmBcmaIbQM1sX3G HKFUAiBdd+mAx9VApiw1yMijAiRoug6m5S9Vp61Mew8Lis6YqFECcAatP56C mYITh2uqv6TI+VM6OPiEzxyT5/ohOeypawW/hA/d00UxpZkYRxtvU3m0xZDj Gou3TuNrrlWJy68Hdwhyo13GiIKRdBXG0vNBnZrBJzQ0tkUcsuhPFMKGq30G ucFdzq5siEp4UY3hulsgENnNyNla1nLkfJ3pcaKMm2/zU8euzDU1B79vag+5 YWK+StMChaWvmMpz483S9S1M2bK+JnPlnXIXKpu0ktmmeOFic+vsdXYfo5bc bYYs2GWyi6+Z5PzPTb1rt9j40L2X1Vmy9GF9n+0VfdcrmSYXc82oCQ2s0JV6 DwxCHr4HZUj3+5msWxw+nAiZvd8vptNVy2g6d1Z1P7Iw4IhhUnmeTHK/zfD5 6T553GTIevGzUVR+qM5susJRVLNOrnaKa2HTAirl8qATJ7UNvN1rhLyu9dbH oZl7+pgRazXVW+GlggpHsZ7zU0EAJs7/+FUx2/7kPn/sPnflec+1q3sIcSQp 9Xa5AvfpWdmCBOq0iZAh0GuM28v6sKr+ve4rMuh8EOxb97dp6PT8fwOniQ4q IUUbN6puGBkg4EQO/YABAAOglGoRBsfO5s0hYQKkADPeA2ZsHxsbVeaJsdzI DTKqD2R450tVK55mO49aWFa89z4dngyeAJ4KJjCOB8iCuCEMDKGrhjpsCCYB V2TpJ9TU7yglUl0wYvRoOXXav8n7ra0UW2j/1m2V8JhmAgZyeQ5nKpQdzlS/ TiNtqwTKnXdwBG95ycHReYGzZ5J1N2zmXc8sRwRGNKEdumfZzz11tb4OTlQG Kj5fqBSQBQc52kTbNLV/XKMs+9tr98qXfJZZltpkdLUEzm44zFoahIrB191v vyi86QW5vzP2yx3vqu85Ltt46Kiuc0FwbKLYXSuGKefJ3myCVXnCpfro73sM oxY/aaf1F8Z4xbMNh/9aMQjKRXYp+fu4pK3vRzAn+04tWsZuO153NCxB+15D qTBRbdj9x2FH+yStcGQRUl637pZhuVt/jvprvf4rxmoXb7YXcPqyXibRVtyW 16etTfByTesmricnXX5eLsCloabz1NNmz7FcM6h8n6LgqJ+biRVl5tSTaabP jINmzc6hdvnNX8eamP8JrBIw9WoQmTIzm3Ww6NBq+C6zdGOu2bGvrpkxQwWq nT+y4Sanr69jn/s9R1qYVsWPWBsNowLUIxOSZ7fPrTJWFgw16cfH/gtyEUCp i1Ox30W5OoJcfwtscDSm0zVTd+o5U/M9eT7HXOMqdrjGE48+wgrNHCIRVSc8 lR9VGg70wonIeukOzRS9ImU2bHHuJQ5Nfh2EddUORiEM29CsTo4pRoh87fcd nCVadpTzlg5PIMyi4RK7WUQlbIPYuW5XB8NY17UgLE35int/NaQ3RM1xuxy3 n3IjUYx/h10JVu/EtQR/iCy5lIok50YmrRXPD5/Uf5RsU+lZjWXeP00K2ZQp Kjhct87Ht1LsLoFEglMxxLJ44WN3KW32vMdb8YuNN1eLQvkg7N/kTU6PctF9 57iErOt7P/keTy7AFaZkWGfSSBUcJ8oQTmqghcC1AHi9uUweC2W+OyII+Hiy QhdFv7vIU7cpRwseAVdFZYpwzdXL9IArDQLPJcL0omzSO12T1Il6sZuNYgXV rqc/C+Lwt35tdR2DEgTRmu7RIY2vpXypIlyXDXrIi9OVzC0oV2qSd0UIYL72 gmJ/H8pjcX8TfQjJs71XTuFMfQW/ZuoZPZ+tq9PGt/uzcriNtZFc6+OqMAFZ PMABkBiOfSq0CgG+DwAOGxaAA7UQaXLovKciCF4lCJxXHkbcRle1hMAKMfgK T7u641e1vm271wObTxt0UAc9CMd2OO57yrZlbzsR0V+UsfmQFrJsG+X/raIp x+RyRrIGP25ihh43TeSFYRp/S2fTRJhag6sNNSWM2gRsz34sqRRCuVM48Pa1 ZtLdt5t9B6tYfMuZdMHMPFnJJKJKnnHcpBRqxUH8SLApm7WYP8CuN2j2FrNy a74pnljPVH3KYptaEHUtlD1ts2E+s9DmE5OwDXF+IJMtk/E0SL7Lk+WZTi4C RYjAGzQAVxWFXJ6Wu176I5PYU5YdD8xY9o0YsFVUayWsrnWB2n7A5T4LEhHk OiTPeLvDobtpX6CBe6bAroEWp0T0R4i2W6AabK/aTddfQ9uA5flzl5QqBChe jJAGlMP9pdslu+nuRvuAsKjIAHdgB4PQuwVmQta8E8y4iVqTQfLtv9Pddhcs NF6w3/mqLYEdjPw3yVEnPEPU7AYgcH8PGRJEMA1Moj6fYlppKesj9WneXK81 90dL6Sp1A9OgdFiA7I+KJKxTV/YzRvaTAJaVBv4EVdFIcThfFGS9W05V40hH 3qoRcIOss39qjfteLr4R3h4h9ultw45SYCl0E5oXpvnGqY8AqgZaZE6PtXiW rHiGm2hLUwWp+wQkxvQTcjBZQ2xbsl3ILVXDbXz3sIDHyUxNNQO82C0So7tu 0X10GevwsrKCD71emgv1OS62QdZa7WxNlY6b73al/wUVNlE/c6zrqCtM85FU R83aq3Jx5h5tave4PN5XqlAEwHWwY+JqIyN0mKvLlK3dgUcL/bpN1r8g57Vc TMlX0nU5szXog13S5zFFsyupam6uEnSZIA9I4VJFs4W3uo7Z72gif9tXQ8c3 yGDlUp9pvM1mpV2X9c6ZoEBMzPny+7N2qvObhnM8hdkVDs1aq4XtkbPbRyZY K9fjZFkojWS7wq/kdVk/Q2ttf6s/ay+E/P3WNbauorrp4PyjHFe1QV9qxWd5 FFqKDVjtiTamMbWj2B8TSKvTJuu26ybj6CK51zvNpHum28D/wydTTCpIdnmw dWMlCUqtFPpRKvAxS2j5jtd9vC3DrNs1xNQfeMb03zLEtLnczqx5vo71jPu5 t1bzfmbJ3UX2uNftGuwTKCPzMPegcLKrYXFI4O0jv+9EC2mDZehUpyzJ/E0C JPKMrpIZ5OuobhafO0Z10X5XZC2hdQ4hCKBhPqx0ObBw6i4X1Wb1vKG6AyA8 JGRrY7g97L4jP2mvU0crjuhzg0v+z/xcAYeZJiNvBGUsSUnOc7t4xWJZRGwQ d1JHLZ9So6mZ9/izDd6PD9V45N/lIV61Uby63e2tenXlUcqa+LeG9aeXloZk iHx8EmEx6/BxUQlp8WJ9OMAFzEDvUM8ZIvOD/Fxdj0EdOLkcjvE+G/YkvRBt OmdAPaiqwfd6fEarN+Sm+VZSp84FZp5AU9HBsF0Vh42fAdkSfTq5x7j6mdqo Vp7sDX5ID1iCeghNNdgklkiwv2HY+PJ49YjT69Zbf6AiujmEqLUCG1Pdcv7S j5OK41uyPUTpTwwcqreX/i9YmDjL6IzW6imtd1t7slpwhMUW4ImaKUPwbh36 G2Wbd5O1iSaOsROdXW6aDPsnujshtiD9G5kKSaiRgLCRst7G2igW3CPdSvcx KM5ZkgKJ+S+JBSrbHBymNU8WFSvUrc9VaFo0XIkVpIiiR3jYsSo5Lt+x20uL 8HQBYbcIyRopUDV3tYn0sSOFXqFJYYzimHjugTJkSpV7qF2wfJtcm7oKQz3/ qiZlN7kFcllGyGXpLwjW9x11VPeX7/p+IQi8XOhmkRmzuMhs3xH/PYTCbtsv XGnm3sD0lwly2PIRDvV+Z5GqbMMsUzhLEO4huXU7N5qcGWfM7UsmmIS/xpuW pcctYnUpUId73teBaftT2YegwhoZgmuebGCX9xopCLRfHom7jpjiZZtNw/ZF ZtqKIrusyqBfWSTtcscZ03vmcXP6TIpZtKm3RSqeTpnAbM0mnuAJn/0rQlFE rQmh/H8oYtqhrAVyv7bPMx46omy/DzSQ93fHlvBD3WkfM02S4CF5uLtm9dAw rWpBxJvkaxst97K6JAU5XLkvO1AShMl+NjDJ+CeOsK99rJwgmNrkhzroPoHv DhOR53AmCD6FQoat9PB/adfOaOu2YzQYJRBxswadj8xhGwrp8bkYmPHoExnc anWm2QGPewtuFV1El83BMGqDs5uR8nnlfRaXOGwbH/RF+ZJ3C7Yd5gsbH3SV DCIdR9dsD+VIeLOom7t0qU9/jTVHnhQavSe69V2KFbRhC+K4h/Jxx30fYOqR 8H1cXEhOMQlxfC3MdeqkeHV44a8HV9Y3o4iHw6NcnWKgyvc1knE0XstCsg4e rQHF6srpL6b2fBn/MgUH0wUbigu1qC13OFWs8SwxxB5MVlqENxyPEYnjqB19 Tf4FYqCMXOvKw8k6z8lxdixVVXfrc64wiexjhdiLh4s0EeTgQFVKVgjtmFOh GMV6Twr+LcqnzYHC0xC5ukdVxOAdXYgSsBNXrdVdlMvuOnfQMP+UxSaYdJdC E5BDnhjwSeAPBuLhicMGcsgfo+sBVSaBHV/fG3gC4kj0A57oeoNNiOeLbUkZ RvRAg8rH5FVnqgYHrRT7H+opK3hfLrMcx5BH5fWs2nX5sp9u+6yNiT+JzA5k 0uMXllhbscatX5oDMnomCkbVkge51ygxuka0N81G/sixrxdN/viZpnjhIpMx aapp/3KGmSksIn3Y+1Y5VbxqHJgjj8CCu0uEAc/dXGImLy8xw3bK/o+VaL+t 2y9Z11uzQ+Umc80R0+b2F03V9UdMq2lHTJ+ZR0xy329oY+X2EWdK18tvDf1K Pk00m3b1jNbc3XYjGp6qKjzJbRok83TqM6NM/vyDoSEnjCX5v2UMYYVBol4M TIa8b3jW4QpyhnIPGnK7K/fUAqCNNugtmFehf+sMcc2hhODfM4IousOo5LC4 bbHiVIsS5RxV+X6sBZYIKGEZogcYNCgqlhn2v4sk7oIZDETcULNmuRz8+OjS AR6nPFBNdFsicIR29BRrWFDJ12eDkaCMJGhIpo53UgFaQpISnCWLSgv3O2WQ KATHYmxANrMK2Xz9bra8Hy+vnvfZYqExHrbyFLZqCa/c86SDrQQtP+qDcHK3 hu0USHraoVT1SDNOL/XH0KPBgW/QCZP2TT13yLxz/1OqS8H4wwiEFHKh5OI6 pK8mF72aQkmuUIdtDcLqtEDWnEEyyfTUGGKLRQo/m9rrsn2yh07CvnbUg24p qjVRfTaMCvTCg7WojTrF7srWEkZ39FUtE7uaItPbierqwaomn+tMVoZBbI/n isZVDGeGPWFGlm2QaX+8HE7zCoUQ64OX67dHnrvpu1UY3e+sQtCuYtt7M8ZX hNsthtz5+q6oZXstH+7hq6r8/jxhgYvmaJzyNRJMBDlbPqa/M0z+Dn9AjVOQ FpQFvoAoYA/tQ6ePXZGpriqYvG2yViewiuyrbhtB5tyXNUzKOYHA1th9IYSv 8QJfZ4Y4NxiQuEddVYg7gRm+R6A56F6lRkUCGQOu2v00LVxl0tvvMoMW1jNb d8iYprSAwFhc/m+tO770ToGZkqHaN1eespYdGpv86UcUEu01PmH6TxhhGg9S dXj2Oblfd8m9+KuM3+2yr/OyfcHfTfImC0cxYKQ8RdkjhJRO/rEWzRv3qtV5 lq0rNSWrF5mid2qb0ZfkQX4msNKJ/BkPmcI5+pNILDqXHLGXMmf0cUWqm63n K+e0Ra+IHNSW0AclHpGj37DPnPpRilaoEESIlSm5J2gnSNWC4u/CIvI+VHSx qINttcqhR6vOyt8FrbrnV9WsCq962LROddZ3ZSqa1Z7k0Cstgl5YN3lCFUYI MtZqGYQhS9d7xdffLir6QcElxnbjsEIJlhjHD7jIoKziWZY2KNbdcmgk/PbF yRmCV7yVX3JyRAGhS1OmhP0BiNDhdsL9x4hv2NDbURm2eAiudu+KosYu63qq xXc58j6XCKO8UpwJOPiY/o2XmW/iCYtZemTTPBVL1uo19fTI+whynhbGffmy NvcEqehTEOOSSXBrUgKOv7g10f4+9lhk8Wr56u4nFE2xTqFc1avr5BDDlU3w 6MVMT1fzqQ1DknVHda27t1Eu2nQxkubL32lFGjbw6SUHMjQD5ZhMUytbxnj9 +OzeijqDJhKM1K+F/5YLBSuRX3k/TveC9blI9jp3qKoC4muYoOZorZrU7KBs Vn8+6TVYh6WCGYPPalk2qM2rctArh6g7nNYFH8iprJ4m7+/QlpuHblN8uiBQ /GRrpwEVzHpE4PkN+ZmxFbYufgS36Gq0Sp6zjRNVq4X7nZMaMVNd26sF01b3 UkxGOoa7neMCt2wfrR+rENznyXR7z3WxnOiCh9UVs9iOUcO8T3ySfaOZwI0+ TVju1tnCEucLdXHll3BswYihXLj3wbGm97t9dlDX+RhhqMPftK75ziWHTMaA K6bD/FamSEZ/PAU5ZpaY4XMXmnFTsrTUE42AyeMiaNWpJBS323JMYh422W3x 1OIWeTn3zzPBf2klWVsIvNU2k/ZwuZm6fbwZIQ/fiHENzPDZM20SbutFgv+T 1pmu95Sbcc8JrSvVrBZEFaP2DTVpty0zk1dNMWVrS8zg2RttxgyC9g49W4TA 1dBmeDY/GQKXf4Az8UhPfkWAZYGZvK7ELkckkCxgte5gYCq/rFZTq1U/AK57 5EmbIae6X4j+dnk0ZjW0cS7cKzbj6qqjVJt9RL6S3a7xLjWb2E8G8uC8fwUq MGTAgLBLS1TmCrwIS4yAK2MvIcGpK+NDxQMvqsxeCDw4TXAHAKAASiArviac eXAhuBIgw0F50JFfSnWUEjOQa8XXsBSPYTOQ6t2nuDTXmX/Ko5KsTwlcSt3t us09qdetvlCzyY+RI2el5HO36a5wQ256PjAXr+uRYOKdOBGp3wYaYRmG9dvi bDAFK5CKugLX8XxQ7lRFVf4gj0WTplrhfk4/bSoHRszM0yIl9QsUdQ7WFnIi d/O4LN/QNMbXGIFCsersPmL+19JV4Up8NzFR9QfproUi6MT4BfTwWhFKw8He /EiM7/fJND55gTqVBsrwnj8ZZ5SyoDhVQHyYqIv3j431nlNABVIUaW+eoe4l KyR3oANgES5kF7i/fObK5aoqNH9QgGqhLDsq+1kjINJonYIO7iLIT7ODQeQo USKwPS8yXFi3yU51gicrt+J8eYFNWKvg42a5hPvlEvb/nF11/52NWa3R9Zix CfOhOW3xYJxdnKdQhXQUD5JYf90mVZjke9I1QVPgo3jJOr2MwEmb6ToIejdX y9A3zOt3WHuIu+hhYVmFmbB0rkndJz/790QLMTx/Bf2STHKLdabbndPVBhHa 2/NIgZ3jyhZ3Nk0K15mBezqb22RurH90jClct85U3TNfk/MmyKvLDTPwjrmR mgE8kZt3tDC18o/bMcF+/tXzlG667HR4UXjWwlnC/dobMD5eOcIONJtn1bfa boPDmEm5cgW35XG167led7jJSVuR4VQkUJFKnt3XASpU99CnmialWmqg7mAH AkCVtqf8HryQ8u7zT8hEzSJQd1IPs1dZ2Fm3hgeWZUHYcy7LcjYPLL6WLaAC KrAKZ8LY9MmugAvLyHnnL46munW996qmDYyCxnw/kMRZeQ10yFIir3WKLKZI bKUEQU+QZcKJEFkePa/FizfJ6xW5ezdkoj32skOYFFsVNOcOMcfO6yGAntHt MjHXTp6MpMlVCLnZfsaegdxDuRQyML3L3GkQInUZujVUJOgwUa2kfc6fBMA0 mqWzdp0O/w5gnK+p3vcRZlYPtcigP9x3kGSxQxdbmmSXzvB8X2uCFoBl8FrW cMj1QZffHStgsLFYmcgqsWA2lyg42Oy3irlADXVnITMk4h7tFeu93kDKuea6 6PsQo9BDIPR80EmxBdxBW3BXr7Cy0YPudAq2qN+IyKBtub7dpb0OVUMUFxg6 Av7iL8KHhFIKQKHpGyMbvxFgw3tA5nWK2LoQAFoDCA7IgaIKHzZROZtZ9zsn 2HpU3/u+UCzvLdt22mXTU9ML5PdvF17Yv6szBK+oi7ynlukduE6vLGqryG7k sg0cIIfxqmq7ZbNBszaY1N+na3j7WJkFAFzSA29NN93moYNbYFvu1v2NPHti 7bTfV24GzlhnH3ckB8vEAKt1SPY7+GPKy5miu0pMQjcFmwMPfB9ssEFrtZtr FmwssA8kaTD8HmJTMv15X2uvA5vLdU0CMiW5XMk3l9nM36MvBGGZyNxsU/dU gWmxOhesYYTH+sh9iRvw4OQ29/4zSx0i+XLEhBjNWWcsOYmoFDzmuJdjHPFu zwTi+WqgwFfW54Fm56Ekbe7LY8eFlVAUar6HNAk2A9azHJDGFfvwWXCADov4 kfx8l92baGrUEPbXPzD1HLaMkNeS++yNkk3FBpI1F4oldPqc4sopl+24VijJ c0I6z8uzvVYu3oeCzu++G5h35GKXPeItrDSTd4f6cbDgsPKw5i5cCMzzz6uP h4wXFLv4gvj86KOh1xu0ueu49RG57+KsdQhQQYUEdVK9pdvpZhkuHTVXjVoh +yvp0GtToppJ5tumZdGGlPN4y03dKcOnZ7EOHzgMkTgia3mNuXuVcq00daYu p80HQzfN1pOv0hO0w4yyQ/lepQ4M2zED1XpG6Fgh1ntuqQ5l8qLuXKgw0+O5 MAiMnfRIXhSVSVTKAn2JxhwKr12tHI05jULMgZfcvkajvkyJhwWGp8qBLBxu guX9VJPUeYliDn6yacM1CsmJ2TIfJ02wYKkmsKCGtn3DTygG2JPv5NLlPtNe cg+JAZi7zCoNI4YXhVm44FZv9LnuB3UANdUwemAnAAYOssKd8ruHTO6ILaZS P6cKQGOl6gOb+WhwTA2+KEzmhOk6QqwQYQLNJu5SI03W7zt5qUn5bSWNNTcY ZzVPwz5KMoVTl5lRs0tMZvtS0/+uFabhiXGmPRbNp21N1oudTb3xGs4uuuM2 U2OlkLQBYuxtPGb6fVjNTLtznD2G7pP3mJTen5shs1ea/ncsMUPmrDWZ/c9Z A2z19kJV3P1U9iMANulTMRxWyLMu46P6k0PNuPcqmYTPdMjF1ZtiepesNEVL Z5u+e2tZozSz00LTXm5mY3nuWzxqkSTSVFLAZdwKjX9z72ElHlzw0Vo6sd2C i3fMWMkkZOQDO7jjnH2UIfymswzA2jKA8wVhRworb01PklOWA9X2P+j7TuLH IT13gtWp8N8k5wfHt+28zpa6EIfHq+T9x/KjkWKy2WKJtZdnf2Cpd/DEehPF Zcyety6KpTLs33hbvx8nQ/3KFeEcAg0z5fsl5/QzcPKQQEXBo85+qm1D+e0H qSyIVYi+wjE/+0wPDalQFJpYXw4/caf8XfO4yo4INeDEwpkFd4GnoqqXz6p3 qK1xc7gE7XGtp7dUkcOjCp4F0GJ5M/VMnhK2udW5dGjgSMwLvsPAIR6/pHKI LL7PebwaQqyCUoCYUvbmQNVM6frTpNLgiJkk622Q27G2pTa5mSr22UahRA/I OqsPCo0SEJoxno5umrtSSZP8afAGNOCb2SdHsU0Y1Nuy2d2uIdyqUvUhHyiM sWiSrGiCm5mgWoW8drjg3Zo+GqwbsoWjn7wgaKyoAsTR1ry5YzK40dsKRK7u LU/SDhc1O6V/gUhGN2yGOkqYPw1Xq0jgU7mY7QaEETNCNUArTAdRNxE0rhAl MyF8uGMQfyOTZxtbv6jHd4wVVierDg8xL4Cl/qJ4jycDxHa7bbkpu7exWXr3 EJPZQa5tuzdNQelq03PiWpO4ukKd9VR/kqNI/Fu8mbN2gI2glsk4aXO8gek1 tLvZ84xm8Xc5LSC9p5+6Ycfss7G0BVtGmAyqLH2TZfLnr7OffXpxRr+Xzdod fa3Tp+P4fWbPoYZm0vKZprC4wDSQp7T/zwJz+GVVm+BxzL5nnjn6ZFWzlKCW 8IOJk+LNrLvkuPaMNa1G7rdXL6d7ZzP+eR3qdQ9aPInzzAM93nThOKdc6Qth Iu2fjsANwxi/i5VdKqxEKv6QeJr+TuAnKWtM2UYfcqbpcqQDP9ArkC92QPYI zduxHgaK+bWMqlSC7MZJBlOj2Qw8inq4x53RE6t5/i7c3zg6Tp9kj9DH6L1Q CEDkfe3ajtLUsm4PHFO0O/OxLXgnxRdw6jL4AYS331Z8ee01/XyvXPKPnBPs 8WcD88Crug50Bafw3PPeiZNqJsk+KgRoSoSevHxdAQceCdi89JLcPrEaRx62 3ppIdt12+e2KE3pMAE+ki0ORfk5O9henkqam0Myoal+N0N8hj+3EfA1zbWqs yEM8aoS8mnZXKpOsi4mGPVBNwWaIANBkGb85kxV4ulldVLVCQksYRNaAWuQq nx7QcUmafaONGrqiFfMoof2b5BD6y4jY20HrfSyTp/0d9OMyhg8LKo1O0JJE lI8FUoCWu4sdlampWILblwL7iIOAJl/MOjqsBePgO/52f1TDWrovbY5Et1yW k0LX+iklQPJv0QKVcWqVlSb3cXq4zSk/S5QK2Tv2IHCT3FihBaMHMsNyH4wH giDXRLSAIOg0hli0EBsDKttG8hzpqa26JHRLlti86WoJ/EMhi+653sOD4cV3 1hj7vVpQRNOAtFYTTbPixabB7TNNl/ENTf1b+5gWZ2ublLGvKqw1VC7W/IiJ 27bHdL9aw6R1PGLaLzwiRPOIKZxUqrPc7u4me7ig6bQLAuxydw50Nu0/zrBZ dNknxMbcKqf91GhbPzax4M8Wl2aun2DGLZln8YgSS9aiXyuHNGuuZRQtjxbL ZS0sqxCym5Z/xRyVy8o63Alm372H5cmbs8viWvG2niZH8AcssmiyTsNiQhTC elpONoj3AECypsnVCF758UsXMnsHtkcZUmm2YiAWQ63bHcv1cgBAY59d1dRf Jej5lgbhmkXXyI8qeh0Rwfj2kqVBWNbWgxD1BUY5jEIvXubXiVNYPeJwrYQ4 ckiYkiKCbMTY+HE4maZNtb9srVoRrsQiTl9OSk7w1Vfxrb74oop2gH/sWzBI lphPhL4++6ziE+9fflnfX7yo5Afzi89g1bPvyu6oKhNvXr+hi0/K9XjnI5ut 5GhZut3NaYHFBfs0Eg80rnlA1WJAJa4g+BTReYKZyDHS093RJ2j+L9P41Daa QoLP5omEGFdScUs2l5cAvdM1xvlMORwhcCMQDAgCmjpkKueC8PAdUmqkwRgE VF1C6Q1EMdaAplubapQIhwreCCCG9o9Ew8cJPOxtboJBMVpuFMhAj/iArFMi 8DbpUBDxLQMvL2aZ4GJ9E1yqEyFPoFTHM2FbED7znjL8ewRO1gsiHZHT3t8f Qy3OIlGcxqi0PBw4hFfJilA3tzXBsIUa6qdxOHKB7AqvjLfn6+uggUVpAonX Be66zw0rxmJVUpMR8RG4gTSbdeGoLU+50kojlWpZPHpbndRgkuVAXygO2YoG v3Dax7+55X9WTKPQB7xWKFXn0hLTZ67cbqTEvGYJneslhmHNsWrYyew0aYkY lJNSTO3n5P2qxTJLnTcZD8y0IJI19JzpNXWHWbNzsGkzYqsYe/jy5puc9+uZ 1O9kNn2gn0la/aj1zsS9PtgM/A9Nikvt9Y5pedtJU63gdd3P3QvV1Xypjxn2 bEtbj61ZlwIzeEKBKV1SYE6fSY6A0T33Jpv2o3eauZtGm9S8s6blrMB0+kVg ZmEQ/ySwmY94gRiEDEYLHXSQnaIQ4gHJijuvhrDB4OXxF2SKZDM4VLL7QKWM qUEGA5ZR0gAHRRh2MrtXuU2HEIDQRUZ2Y4jc1DCu7nuhTdpkicz3kGmRrprg MQtFI56lYt21jW359nAzLZMKS18vd+tOcevGB0HTaJiKtc52l3cT0TERSRuh YkQ30tUVTeSJHDjc1nApeA4+YrCJ97iX3xcL89NPFaeuX1c8QrftcYrl4BvY 9eGHcoyfWWZZIrt74b1obKpk8eett/Rn+TkuYJ0mgek6XL1GfIc3iEOrWjUs h4utprpoLpcqfLDbyrqEmbn4mh+upPgzu6MJxsuznTVSMQZahElB9Ir0COZ1 nKzwBmgSZkw/2f9mMVU6zVCha7lQoivyG5uFLh2SMXFKJusPYjV2MBx1mXCL Dge0SgBjWWhjjO8HeVro1d4JCi78Pd0zpE1QpkuZYdFr/r6PbGGZvmcZ5pqv msT6eI2gT3ipZov5tCdHKGEzFUTty0JTHkNeXyAoNFQOf0s3tb4oBoUNyqni RmdooFTA0oIhIm4i3EKRg2cFWod97lJwf65iKBKWgRNoFNtSCIVLZXs+OjqF leazSpB3U9DAFkoxuh9b0e0L1wf3G4UtCmzDStEptDpteo5fagoG1Tbp3dyM zOwME+h5UbMi0rTtWXYTYacFO0zyTxua7EKBtvry5C/O0Ipwck4TptS1T0/Z /Oam5fB9pmz1WJP5YYopf0d12LHvdbe5bjiSgagm36SYzEt5yos4l3mHTfe3 G5rCWXKN9tSOlIMkoYBZ09df8tD00MPpJrbLx6b50H0WZuvKkB8ng6D+nwOT +5lGt7CGMmQQpRMh9y3aXObr96CJvms7IuOVAeDMoTDpTdCp+JwmlGJbkKxi a4N8Jaf2ipsR+YmMwCQ8pjUfsy+z+QYbNosqvIbVy25aypFll1gzJWLWlQSR ugGx3nLZ7g6cuBw5ca53tU2E44aRT0e8DYSZ70ifC6Wx2XS3WaVIjpuXaeOD qhtWtbV1ZTIyXLXHDHsZML/wBXGq4AP1I7gLwA7wc8NxIaJh772n3xFgR5fg IQoahcn3wQdA82e2dvNMuZRn5a7kyUGffTY09/gdOBNQx2eUjexjvrDi3pvV 481x1agRxTfx+LismAR/9rQiJ4l1SLLaz4ftXbBxUtsT8FB1BS5v+vG+2AEd dh4EAjkfI63xNoAsxle2J1EU7sAg6iGD4C4ZIL0r1P6ZKQ/z65V0ktnaVBWI rzbUp2O64FX7lzQQTnL4a3jNd1AhIMQuMM+vz98rGaFHe9IDsm6zKI+2nN7t m5RMkdfrHdYeu3rK8awfql7uJ+TWL5wqp7ZEgEaA57jg3OgSC1sajBd+11yA afhxlQ4ATriKwGJcTNWH6CWGy2DrJTdVfgM4/VyOecyX6ozmcsEzM6eH4EWh vcxpqkf3ZSkZ8ADHtJNK3YgmWD71P2EdW95rA2+95vAkgI73YhPmjV5ngatK G8cNKNGP3JeCNR0uKljy++yLQF8N4cfjZKQdaKGgtrzMGnIVOxtaMKAECp6D 9K5O9SOPdpPyO01C3wxTBl+7lm9yfp5imo/aYTLHBmaZPNbZF/P0HE6WmBqT j9sQfXBsgKm/twwhtvNU1LMzNo9zhTzK7ccfNut39rbXiiIcVCUijY7/8SkT Vwe8csvdwN3nSMnbOqCPPhMFXL64QKAjGaVNHd9jspKFkyGvqU/9njcUtApv aIndaTIC77rsgOu4/RkYr3UxweQobyAQUnhcwQojkYGXToIvRlpjB1rJkVUj FQw88pSEyBOxVLXAra+oq5qkfaHztiQIs47ZtEakNuVC50bGI8apv/CCZo1x TqASCIKhhvMJsgQyQYpAKhDK1uxzyPRCVHyNdd98Ux1XV67IkQBQlfGPm4aH ArPjtJIjFpPfdvWqBTJr6PH+jPxcsVDCF4SzbT9qnd9hR96kRqrVqV0SdjQf 5KbYtp3lQtVTw/ipsFJT71GKRWcS+K7LWNB4bF/9DmOQBJDHkjUZhO/wTz1B ofF0NeZwijBg682DXLl8UiE+xfJg7umsnQVJ9lq6TMFmR6lMME6ODeBQtuSt mgpQvmYsdXgoh4LviGYiPDGPIgmel+BFQmxKrxEWUcGSv9iAqLJfrqfYNIic uH6CQzNN8Jys/5Yc8j1yuoWCXR13u4C8YM+h+lF+pwaKqwVCeja31/fEDbCr cI1jHXKFba7by1qvL6mh2rl0oqVHke8wgt2HUNPjEvvAh+XxiH1Azs50p4d3 jG3unaKHRV0B2+PxbwpPQBM6AMxooKTj9RhX6T89/3VTqQt9SeSFbnruCm5k 67PBMNU/8ktpHRz1cyVCqspzslwu7H1yM2tnmjQZQNn7NB4U6cEnz1JmwSMm 4ZVmthHUhoq6lhLZ/0sFQT9ubBLONzL1RsjvrpJZ5Rdy1b+Wh2fDUv1NQajg l5km41eVTfaGFea+w60ipX9AIbSSMefamRajHjUbtja2v83+PTJVOihjf5tr MPK0G8B/cL8/WmlVg0+0TAn6A1t7BKwQ0tDqKXXbcK4onkfJfgeJIXLyRW27 VPsvWrhqigzFmv1kWB8NaZ51FEVaycbr9pe0KRzHWLlyVBIbyKGVn/h0zdlt CSpL8hLw2Y4DVnJjMlWdXvz/lyBstvMfgTauuOY2ZZNqNhczRKMsW8gWlMG9 hEYAM8/LfYCMjz8Oo/PPPKOwgbmGW5zP8Cbee3cT+yIGB5Itl8uxRbadKsh2 z8sqWLzxiSIbJqGsq2iVaEGOojmHBJnPvKg/TTgvLS0qPEC1bRI9bMXtkjj/ NQEMyC4pZE/FxfisNTJfsegAGKJzOZNd0n4LVQ0JRQ9GyWhe1lqX433inpHP gYdq3CadqS0Ylbs6bJ+pB6XhKmEYsvzWw5pVRnojy5ZsUUUisfz5/dTS4hmg zAiFxbD2ACbOAFOKPMc35TgGvKbNz5bJPt+XIftO1UCRqa9ue7FxWF/XupwE qYrKw7yRt4RJvUejSjHTet2rGgEc6CUuuxf0OCJm3YhZavFVrhZqpUEk2mrv jdVUNNCo4fIQhLniOP8tmL7hGNILMj5rafc0j04wKpZHut0S5v9AUQsPOFJJ atP9eLwJVp7SJiMQVKRPoB36baCJaoEE8kiWppIpKi5tweY8WrGqiSafo9d1 N0E0UPS0E/IneozU2CzMUHdHEHyverR3xcKamr8kvzHjebNhX75pPvJxm9xh x1IX2c8gYUaF8YrELxeYdgs3mVLMsu8q2e/GvVcpxhy9GAQtTbtPq5uq3yXS c83EMvTEcsMrjrwjtddVmxuA0o/Z3kNS58vadS35b4FJIrFD7JQx8vBnQRye 133Y7Fr5f91R/VxFxtr0jWKiyTTc53U9hTlCoNJIhxMCVWu3psDxfwlyPpne Nz+qbuNG1AKoG0IRoXq2x1Fltdzf2mVfWsTBO0Tg42eBb5LGPiO1Mfu5w/Mv EOawvcoOtJzZwuYwhstuFxQT3uPeVwmCnGg0amAdTTiPoqVJoBH8BuQBZbCm QCfvOILMeH7krDRz6ZKiEQiFJskF60xlmZQ2yDbXP9ZtsAyvXXNGXYI12kiX a9s2NPwWLHCByKra64dHieLcPKYklxOS4zsuCPeIwD8idKBESLOtx/6IQFjh AnoIhRBUZ4qWNAWKNPtMjC551AYJqZjTX4bMXIWm0rsEUmTqbVNXHblMub4c JMYMdl5bMYweFnuwZqIaPXOEBOycqlADHFFaclcLhYsSZ1AARTUy1SBhuDBN 1JHzallXl12tHhpuh0Z5FWQQUVrDvT6KjzLmxJAYKfu6XMUl4wZabm6vEJZF Ajuv1dBUj83jtGMiOgIUlciYWu7R4sNt9yocAUtAEP2K+l5UjxFx/hpixNUc E9rLnL8nPkAEDu4hrwiECpT3uaaMB1jwjm3WnXtEh/PXcu6/qKMwxvu/yF3+ tqYtkxtMpnzdJuWZ1Cvp/FPOsfOXsCkMQOCQY8ShxnRgew+s1AICSytRUj0G fGcW8p3hQHyQMPeiixFmKptqtUPT0bcGWjV7kY6ncS8oe2EA4I3ZcDbTtqqN 7foTubzN7PBetlV+7vXaNvG+QU/5/ECeSZyxzwySuXPcTxQCyj6KVDXpUr7J LDuea1e1wCbgDVoFf48xN32TaW75vdoXNpbuECrp744wydMxQJ7odYIu9+x1 TXkyfafF6mbvj4U9/FG3ifmnjDPZV3MBysUylmv91IUM3KnZv4/ruumfB2a1 TIJtOymRu+kauCSYJFYDOgfGNM7ltFW6/oA3FQlzPTr5eklkbL0f+ILjDsUS TTt5CgWfTemmCOREFA2RlBFfqNwXC6a7NrUMSNOb7cuUePe47T83L/CIYPdA 4AC4wHuIRAvdJSXjiISKpaYcJy4CXNAlATP92rZttPYceIQHCux6x3XQA78u id17Va7lh58Ebps0y8pgUGwDM8MGhD7xHY8NxNIDGHbl5MmeRlXR+pYRJItR TQsiRkYXVRHgU/xd4FAMkt5PJtxmc2S7eSpFai5Pdon2NHky3vVSi9eauLQA ntlZvRfd3hULaZBwKmR9V52k0AFXg8Xa7YC0sw6yrxeaKrhhzdi81xJ1M8+T ddYs1ax+gKVLikbIASlcvIBhrKsORz0XIiy9g4hM+6METRRBYnlMznD+NsXF c+1dAZPa2hKF+e9Z9wRAn+lfgjyq4yF5iV1673BthTBfrkR7MfbaPK5OHwIH eaf0kLiSUB5OARwbIIdfQVZxx+sWOm64M/wgLA4HBlB4Dvj6uUD4n9JibffH JNsHwBY8OyV7zcoXwgSRWyLoI4Sz9ULyMa9yOy8LuP9RVv8uTTexDd2+1Hw1 lAq4zPkVJOPwLZY3mKtR0PKw4qO1vd3tAff8ZRUYU8BqpssXyvX70RCd+xJ1 RCUIKqQKT4mjeK0gRnIXQYE2rkbnn1TuvVNQZRmVQdrJMkGT5C9jbe2jOOEq feQpLbhCxsJCy5VQYJ05m2wdTffua2oy+l00x07W1Y4IMlKOySN2U+vA1nOC OyVENSwDrQYO1xxPlpWddZpnHb70hcP9W3G/7z9S2abQxP6PgB1UiycYY0KG b8GowLbQPPKo9oWjLUrFGzrAMuRnjj3vWhkKQfBV0JEX4Nuiuh3bclrxizWL jI4jlASS34ixXCue9iZKNnGP/cF2ypRlco6y4xWbdFfkuUcMRHJp0XIN9p7i JO2CSc84Kjj9w41ZJ+oSJvJD/BIbz5Z9DXwb2WRrPoM6lptGqaiYCeCNn0SQ JzYaxNx3KRZ9YGe8YF98Brn462UKgkwa2qtivVEwLcJ4vmAvQAaoCRmzIOhV DUgx8dwhR0MdJYcVUd6DYTHJEee/NQ8T6mC0hAUeYGrUUrzZ1deY7G7xWLke e7I1BIY3/e1Ea3AnggnvJigC8DpF+mzTsiBb9VZLZqqHB9JEQ8Wuzlm1u5FA 5MuKaRCy2DQ3iIQsTHg0bM6IhHCq7H4DCgchMVPk9t0dr7g2UQZgJSFLF2MU oLAPOS0aZ78gbGHCI/RCiPeOdpxV8C8KLrH7j2MdnokpdDZNe0X6s6DgCZjV 8UmtKAKOfRhPnydBksOqp6SoCg7uh4UDNZPvY1PDum+k+iffrLU/726j2Ab3 wdwDoqknmnMuxltntiWTXJk+tr2TolShXC2KEuXJWdbNdwG8teqn54qCiaAT FiI/2/2irs92viOuDNWftKbir0qmOAqMx3YyUeRPlwljrvalcA0Dg5u3aaIv yJzSjGci1uIqEAwrBN148Xws2ad1VmwDmQ5XIehZ45Qa8Lrkhtd3qQpo/3OT 7XZZ7TvNC7U+Hu7hz3U4538dmCflCR4n927y9SSTX5BuO04Cbpt29bQ+qv3H mgoa3WTj5dAulNmPCNJsPxKYh36kfRiQpw8RIy9eQHKg0KQKGZDt+wrgiTG5 8YKFilMMSkYQ+0EvqI9+I5N1c2DWg/GYUhS+Q7j0K9mXjLyZcj6D3xbAHqbn 5lvE1JJJMLW2Hg/Qw1/Cl5QnxlTCjw6KgWZ0vPSliCE9tlVsjrteX9nr4BA2 3gI1+2GfvJKSoqoK+H4v3aMgLcvBIte9krudrgFMUOaNxn8HaZWdU4hIIFBF TGHuXHdR4iKoBq0WXploEcmms1nEAYV4CTpFUk5wx7vv5ZOuT1DPy05Bsn37 /LJY61N3yOb2ER8pyEt0hveFhVH1YIAmF/xTj3GaYkjgxOhL803RHSPMBGBd /i5YlxepgWN1oOhC65SoYRmxzh7HohzpBU3a6Q1S5SVF/KjmhsU7vy8+WVse l6LciJLWrmSfc+39IGeVMkj0l3yiiQn6L1W/EJyr99Uk/6tb8zVTeKXvcZsW lhz3Ok5sPgQKvGdX7JL3uKxYZ3uRa1dZNXQv80p2he9wdR4vUncc66MyyHZn hh1rM3E/UvhseSqIgFJoAzqXUYKu6ys1oXIg8NfzL5rZlhaWwfSlL/G1e2GC 78YJk13nRkLDlc5DFafe8cjE5I4iWQnrWPd1kTqP/TTnLNrAO+LtzaCW98Sz 1pMkDMuxnGRrweLwJpmN2r7WXgOgIM0H59qsWi8yzhntLH7ZBvbkEzl3+2xz bdJlbGHYa258vBkyKrQOxNuwjwA71x5Th1Q1a7ZxGP7/nM/VqWQdSvz/m8As cHpq26D1gq4P1pxys7wQNdX4JNqhYxtj+otX7nZTHoqvbEng31jLzp1lYmjF ue18LewFC6Kg5oLbV27U8PNQcyEqxoercK7b1xG3vMjH8f4d7Hjx5k03aTBC WFPEy4Y1SM4vR/3GG9a2c2CRYJkR9htHCsg4BhULGumpeQIlF4h/E+1nj1cw JTZByx4SsljrhCd/lzS5BIdBiBLQv8fVneSezhhfikTQwNuFFPGhscjNW01c /VLTf0CsmTwlXtuc0jJqSLJZsDD23wGQOrYqaeTc5/gXzdNeb/Rt839xAlEv 5IY8xh/HxXphE3wF2uNf+e+o6OqWBEUzShZBDlZMV58WvGN1guLBR276BcJ4 0kmpIx54c0W8hyZEcsXy94P4QOGqn1CiyXo0EAnGN6ksHpqod5kj1KibcJGt xXrErFsyUHGARH/i/o8JnKUlKKACH8ASFyZJbLnGYiYVy3U5KI9CXQdZyOep 0mQrlvxEoQe7EP9QTFJIuSAteNkRVIHI3jSDuEB6cEGhg9Ve3LoMi9ufCyIq 2/vgn6oRhY1SBcp2oPpU+RfFwNcnqqut9hRZfieGpk7aYox/WEnJzlDnWAej EoSI9pTTnpmiYX1Ny7D0HLIweEiSEuKe101iz6sm9aOmJqa7EoJjF63ZZVK/ TTPHZCQmiqWY9YdY88RTKZZcWH24/E8i7jQ84wOSzfwF8VbWcEIe3qxfppqj L8XonJqpghsxWJafCMzNv1RDD+wBnuimW/CqjgugjuGX8LgahLRqWPZwYKo2 oiGne4yrqEGZckUxaqbAaY9vZPmPHcVKMYOZw2YoDYH+xP7Y0Zx6rqOsIGvN v0RKGcDIPNvhlbAksA31psmjOlgY2ayVYlN+qUd073MRBmhbR4JXciL45hjo WGBLlng8SQ5tQG2Qp57b79znBoFvqqnr5ivgWvIEQi5w6/3NEcLBge/FO9HD GjmGCm1R/bcCXzAzUlwThnVVK05FdPpoR4mtoAnFFeUMP4Wl5EjcEMRjEWfH dyAfq78T6YiRFLEfPczh2ee9izUqGsbaIABThjDUSI8819NbQQpXMSoGayje 5GatKpqQiq4HIzK1peki1AJmFWSOMEVib06YU2AfZMu2uiWoo+zfgB09EBss UvZAbj3FTJrcqYnFLZYIxMgwWymIVSpj4tjQRKCJWikv1NFevoNGaQJLVn85 IkGLdcsV3IgFMpWPd7eamCB2ZG/XvxfNW7oLEcrtOdheyzEpz5N/e9j+Sba3 bxcqUO5XAEBE5AHuZCcxQ8XO+5s8bb/EJJ2sYNf8TgdwGxQw8I/fUqaWEn12 KW9H/7Cusu8/Jqowa4SA6eKisD8vQER4jzCeFZ6/pZyMi47pbGuL71YDEJ89 KdqEKT3IIWgHTPlshRGvOKeZMyw94NkiwJ/4sm/WR/9TuAC1zLv/UQBulS0z EDQdh8eMcnTO85KhptywS0ryvqmvXSxnODhIaarXakRyBOGCmAS9BoBl96u2 TnvZYR242er5ZvT+gwB76uCLttLG8pMCN581tX6s448km8ny3Cf8t5YeuE1M rtjl60xxebGp2BNnsjHzx52NZNLQnXPvPkXNjL8GZusbIb2yEAdarlNfF59h GZhgtCa5+RcCZ38QOPu79sri3PDP1f+9eolBFI57yesWwWM8WCwMtFmLPHXr 7wxMy46Bdlls5ZDSeLpVI9Li2L9ge8UfCLZ9qqfIz6XKOKKUlCuDHSmfDutL lUvQ8E/6fv36EGjw51NFYVwQDlXEqQgxeOp+6vCrShAp0WIxDkOx2C3b7L5X P3+rH8BbWQhv3ipERgqc2T0NjMgyoxHOO52AfgprkoEDhPlMwXff9aQuzsIU bjCc+vI+xlKzuIj43cUkfZU4QpuAGi9AjnClbO23gkpzsZnKbtyQI/g3GMfJ wrpwySbUiTBz6wxjGDNM5BXBuInjTNHpFDNhUrJJa1RkVm7JNV2GjdGQZu2J GpasM8VkkCJoOzc1vpdR1UjIXIsBMgJk8dgJYoVVVm/RvJWgjtostiPa1dBs GniJw7lclztMxPCG/H21OtHJGBCOa01w8bMg1EmgdUAT8WmMU5FWcwHGGurw YjdU+iXydTVVtRDvJuu671TVZbxnPYKO4x/TJgkYnv2OqrepizCqkg3KPYVb 3k+mbpF6T3mt726CvsI3e91lgj4HBB9lNy1nhVKH+DSNEj2foRIJW0h9ksYm Lcmbq99R551C65CxagOV3HX6OMYrFqC3KEmAQmxKeB2uOC6iV1rxc8Ah7jag B74IVPoOo1ZDdr86xfiObSmsUCDwO1Kgdd9WjQr8zplHY2vE2Aaatyi97Kwd z4NizUteti5Lbz9TI6hctkl5Xi2ZgJogv5Wfz5Ubtlku9GU5hc5ZZnp5lqn9 QZbdfu+pLHP0ZJaNJzYWC23DFRVGbT+Ya/rccY955PGqpvSG6jn3CwnLY2xv lin4/DBTdXCiiWt71txzf3tVThRdNHtfdWZoFRMrqFJdEKOz3NstB7WdW/Jy FVDcvV/7kSBooPgaZipFV0j4B/Sq/THiioE2IizyFrjgnxvcSaGth1BdwKq6 YGotAbT9l+zFi0FALk8sOEYTOwKUyDoYouPeCiIFE8reDL1HDFuXXRwpWMLT yTb3nLLkLZLyg6MrS1E9mOIgMUVb7MrDVrW6CrTsXfRqiiL3+f4wQlBFDyIY FXwf+mRU0L+UZnrThHzX1WJwlNUiwFp9pIW+MC0yPWR8sf8GD2PcJQPXSQnH sMV//6bzJELxrl+PhD6BQ8DN2bn+ZoCQ7NEL7WVrV1wqxS7z4nyf10hYk798 vqqt1hLchabI39q1FhDdAVdVo8y70evPVyNLBlxyq61muHOlMYBwr81Z1c0s 2djB7p6kreTGjGErfWywWA7qFFECOpN2Ha9/h09j2aG6sMDF3VVjj0aB8e8F mdRa6/VOmOkCGc1aF/G4IcU42kypRnlFlFa+viq93ksNux1PPiQsUD6/1FS9 b1b5Jft4aJDuBxUaNAn9/oEOArDddJ27BJ6e6qxuOCS0BfI7T7RXNom//c/y XAwTejhvZyj3AE2pr66VrOTfm+W3BfJ+AvLKnPJFvKoLgcpOe12DK8GuYbcp JKY00UgIEgqq5DAHcVng2qjmcDJgf4JxFKbq+VfNP6R0A9+TKGR18u8r1bMY eEEvcZvnQv0E/jfwjzCsdft97HIjP1W8BG4JFICPXH6Si6bK+X/W2gR7t5jg XrGVy3IV6NDNsi50kn0AgDw2r3BBSq8xw418Sle1uCiX55yc7sLdcjQv5ZJd PstFMcZcs81D6429ZuKm6Hu8hik9rprNMuoTPhQM+5X6wOYf7Wz6TFlr+j5T ywJPX3mme4qxkyU0JnXzClN+vJWpRy/ZMedxAzoszLA2LLCa91OVkG94Rbfb eFgV9HU3BmbKi1rABMZCbAHbDJEnxU5WHlOrybfK3LPHj+g0D4q2LJPVp9J2 VNhf7T87YRnHfkG3x3qlPF2MbUNQw+6fq4YNvQYah6RCGGBVWS97tWqB+T0S cQ4dCusucBx7aFTk621lWWT0/MzytdmBWrrTHTJWNjVrqn+/rpz5/Me1ioJy IbfqI+4IGrnvvYh2fECDou9BY6LliiZedtdtt7ItVHE4+pc8JidwaxQ03hr4 CsGRFCdgkPWpRGw9BPUVRaCHaDmAxddf15OV97Egm5yHQ0kv9/d19fjovIKR iYTPpFRes9JgNk+IBElBVpQeTExy5El2k8ohN/QvqArjBUgknIcPKbWVySkY q67SEA6dGZ5gcgbNNfW6rTIJt8jY67HCnHoy0WSfd8hXWds3WxHpHhUbIOT0 Endfj27lSIEeV6eOYYZ4vMO7bhhX1eHqVVRA2YlurkTCs5T2VCL3TjVtCXjC wdogZ5rwftIjDroSVPlf9IwtFm5hb+QjFBXWEIgYb68L2ctroVlLJACgu326 gyLqP2QnL9bWwOorjTSr8p5CjGEtKiEXbL/YkZsF0HJWyOYdxXaWX/y8gcrX h8ouPs4W6pqp/A/go4gDBK/27ZqUbZNFT+iVoWcYSevIZDlbSBvl0XHagTBc SQtMLtBAWT2AFtDs+L67ejVDEPSNMbiSOAZJRrKFJf6p8xFeOfqSAfo4IX27 aNfZNBj2tbpskdu2aRVrneT1VAXPWPIK8/+Q+QpUmyivA+tNyotDTeW+10x6 n2umUv41rYfRYajJkPkmXuaH5bJt298qWoGEyb1vmA0yyrMHKgLSXLC3MJiJ D8jdOF7qYw74AncfyDQ5V9N0qlm80wS/qamLa5rM36qFyVMLA8MAJpwx8xnF 0E2HVGyLsUsVdjT3JUIOaiLIbCF4iS/wE8Wc5Hb6d/mDXNMWFgLBGoGm3hOU 5MFWgb3lAqmthNVm/qdwDfmZtnli379qe1dT5CqbQVevSWCaCNGahr3+lcCn 2OATLmmGD2iCxgJdGKWz4Ct8Ts610OfwLkEB7CkHXrkO+9ZHiCJtQC3u1bXd G2JcD5jEv0SGerinDu72HXZ7WmIRNCca/apYxIofosYksP6AAOeGgyE8Y2hS FdDuHf1bbwemCtWxtnVpkkmXH7golyO5mxYg9cJbEE6A0DM10AobWD8pEQTJ QDc2ASeFTkboMkQQc5vdyDrebmYeQ23jZW6ocoTJJrn9Mi0y/eFPjfVUmnHp x4olDqtNTkf1tyzY2cJ2fu++oWccWW7yC9lzR5jyxzNMZpmA0Nl0s16lLxov SVUqYwsZH1fMo/8EZWWIgOIPJFphc5SfdBHHdJf3/KkLsr6irn+iFuAc2Ldp kcb9cAKeEFv73jKUaoke3qhMerSXZnVfTdfMyIv143ycFy0IKMoeis9Ggo6A 5J8TNdSKpG3o8xpu9bKP1mdd4UCqOIvNv0LW6fyAwhlC0Reb6tiHY8HryImn ChEhWbgXn/kLvHEhmh/RMGiaeu9osQUdI2/bls35b702IBfXAA+cVTbbjus2 a+znsGorWDutL1CPMhU+vZLvCGWwLe9znokB7jDiuKSAH6B3Us748Qm2fWkw pL1zdl1Aq5wtYzNmkD7DMWLYZCFUB2mEqqR/IcbRzzSJkSqVqNHz0UWNk8v+ bnsbXuj6lUZF17yuSZG8jxn0rWn6i8D0+jow5e9rjHKwDIB0wYuMP6hCtsqf VBTWYqPuy17YD1vZ+sstf6/72S00saUcy/bLSpUYk6TmbZNHfsqPXYKljMkh m7Ss3mBqIcsxJAqsNb9iE8Cv04Ai9mf2s2ZH8pJRh4MPOpbj+kJ1l5FV63Oh jQKjPT/T2l0cP1Hf9nkK+sRjK3aqTGTJOxpxbiJ4VkvQpPs3gSmU4xgidK/7 GFXxQkE3bNBIqMWIq2GIwKLQt1pDLOeEGqFVqshtadKElUg/IlDKy8ZDHMvC r5cfRAUbYsN8+NmRXoITHYelewUOUdrVMgesEpC97YByTuYLBjzF/gC8DDIS +ljIzLFOgEmBKgDdK13A9yJujEXObSf4lOCgyQclsEXlb1wUYlGngsWCZJ7C Eerga0Est2qyDXwRPPOVenzXUx/4EZSL0ELYNe5dYddhMAiihVnpIA2+duLR dDNslyDD85mmzoes28V6J9OfyDY5l5Ptpxj7b7ptKBeMGqq+JRpMMaYBNNz4 5BriwiLW6WUWdERoeUqhpbLW8QTLqPAH88DixgyzCT0CBdumK/wQzKAG1UOD 1YR9I9MpRMTUfbilhltZD6ceDWp4/3APxYD5y/XzywJHrzVwnE/2O3af4uat FxT1yg7qsn/K623BntErFQR8j2bMwH5yfMMOOLgT7rltuAk2yOX7eW1NZqIF mYe62pOc518uc+VuGrZF2MdlAurA+Kb3h5IPvH94ADjm7n+MhWLxwBPBtSmX rmIYxKuna1qfFxbcofUDi9CpELvgPUftk8h7/qfrnfoL/Z4kdnyM3KTlchV+ Ifz0Y7mS5zKswx4TDL82tKgjscsiNQXXEQhfEdi0qGby6A4TbtPtU8WYlQ8J v/nCEb0bbUzLL6uaI07v2Us4TOdr2oEwofevzLx1/cxhmbGbCMak/kXjEn1k THf+IDDtPrX7ceZqG9NMsC/97fYmZ/4W0+ZqdfPEs6EKhYiwj3TadE2xopq+ oX/b0QZE8ClThmxH+dtWvt/wiBbrWfy8YtSPnlFpGuHauahgKuu52iiA/N/m t7pe8RrhMHJ+U17R/XjMtlIU2f+SK4HXeZgEWiF/RNVW2x75aRzuvWSA1hEc r39Fr13mtMDMnq2DMSYKkVq+4L/LsW2Q24FS8xXvcJRxrKtWBaZfP996OTYs PBTlbLN4ralr1BDnv/H2T6rWBpLVO3YMbPcOLiNJUijswA9uJUFzoW+Bb1s6 XZZVmxeFbGlBRCZDK3l8e/jwmBL4++67YUNIcIs0T16YocAaKZ4Cc5q+IIcL 5GHkyjqRRhQwM4xf9uUxDErpEjsihNCqgr53+skabhMTKjWnwoydPdjs2N/Q NO6z2NQpH2FGC4YeeNGTx1iLeXsO1f1eJVkLKLsSlPOMlaHSw+EaYUXqAaV1 VHuYYdTioThvi4Jft61ROgN21BgmWCA4M3WF1lJ6W0bX8zLKHuujWAbe0OwG W/OZNjZxU6MK8n7apFDdJk99sHijKnFZb/STqsdjGfthf+x3qux/e3vNpALT SsWOPT5I3Xv/dPbuCy7xOtK0S/jPZkGA80IM58lx93vQKdt2C8420qd6i3DV KreE2GZzPxI03bz6YKf2FSS5daXNuo/3qjlwTqOyfBKrPVGbgBHVxW0HehHp tc1rfstIENBrrohGFJboLq431qfsPtFbdNhsx2dbIvE/tF4QVrCPpnB81KS+ U67RDtwWdoa3zer69FEnFsjGCEkeJaiGYqyHe4zyLOOwI3/hK4pGWd9pDZ9q c21DMD8q/OiRuVDMosO2fjyEa92TSnLIFgAw7P8CCFnX7Mz5K56w6Y+3MQ2H nLMVyvx+KEjF9lPmyGH+QgkTT/xKmkLlvIpBXYvELDGOOo/XActgYBVWJbSR cE6xCEUfmhLsTtuEmf9pYuHiwGDRsHcUr8HwZmJEVwhGj5X9zxfu2bmPLeHj oydpArFjhLKlztYL0cxlvhOvJROWMchfsh+pbUbMFmxIFAq6+5jsRqiUkKND cqJzQWXZvOFDamTT9JSsATGye0cDVbzWVgw0JEMoZtmysL8X1Ay8t+7LDVoW 1Q9eiA5GrndcodrFBoWyUXvIgYoNDdy4IZuJdekiqSziBUZhSAp2eZiBqAFh od0Za81TF7KI2JucEyQRiBPCl+5WpfYiUVcy0uM9SsVXM9n5SyNRcDxoBBPK ZLcTZLc7LrDbBVZgWX9dgZn4o6r2k36XquWZxwdh/PNCLaGzQkfmiRnYs6WW 3yKq2/yIoxli891xWKGjuKrm95PmwefyZSaYkKJ+LVxmJIAeLNZ46gstIspg bMSzeVFaEZIIZVebC/QvdVXZzCMNm7P71auVwYFK1NiAefVwqVp/lwtyaIhY q8KSynaojwnWhdVH81UMyaeoG21T8Fct4DS+EOb3otg9T8rhLpZlp/NYokXW YFtNBmmBKGI2C2XfJ4rRsMRFAs3vhXIRm5n+Ortu+xoARayA+hiAkK3P+qbG GTr/1LnN6quODkmMT2GgoDaED9DfUKGuMRTOSGEwXKkAAOB6za8HKFx4J+X0 xjaNtGAHmPB0MweT0Jwgg6boeEgTeLSzNwReIS//Do+02qK+4dptedaZDP/n O0qb2moS+ydahHKiXdP8G63sMvNNHcvZD2rjTLwiC9a2N42LNNurYneLiF6D 4+HhxubY/bT65R4+Vdus39nHWkpA0ixZnid0q/auwPQ/IkNX5vxN8jnjUU0A tVHIb/U3Z33gOq7+Ss3cbrJOoZw7pixuM6QuYA1C5wyhW8WfqomJpKXokor+ WBb3T/2LOcky1ikRGMuWAdvciQM3nNJyXr5MO9CxVpYPkoFabqEFsW+CmYXD iwTTa8prmDAEy/pGg1N9BScBluRCrauK5Qkogfk46nyxU+p+AHG+TjSxZTKS iI8IibNTUuByICCEPvqJy8ob77x/+WXPguJsSKFsni6ffdIhT1LEYeYzQkEr yBcI9fLLEdDi2UJhhzDRcy2uCyJyhIJJzqkGfgOXo0YpcsXHR9Vki00x2e37 hsi1oqEpmpBj7r6oyNX6uiJSnsBsjY+cZDZedTZkt5PVjiRoTie+XWuF777s VyTnAMFvZa3QwzLfFPBHnWxnnVgrAInVzEzd1v1OojVdrarNotAt6g9bLdsN r3BDOEuz3Gk42PeKQgA2JnpVwoa3Cmi+2UKHc5OdWsIRJNrnSiEuXWqCY/1k Jh6s6o7BMxW1DgnKbBqhsy0ec+rt9xXz+FIV9eIvv0O3Gb03Bv0sc8AYl+EN lbGF6j8Jk/Qt/bkSSvpBJO/It2oNHwYp0Yinra9xQRGpwRINClSslae5XM1K 6mFAyRC8sR5RSkzvKERqPbDcNNjSXY5OTqVRhv2eZxbjjwARE3F3QlheLF9P jJkKdVT1k9Hc+nc6srv+Up7dS4pIc1fmWR+7952AOoyHoPuHJm3TVrPw7vHm rvNqUO17TvOW+L/9hVxbHrFZrxLTbuh8U2fgiyb3tl1m2P8i7DzgrKrOtb+d M31gYChSBRxBaSIC0hxhpBdBGBWk6YAoKAoICAgWQEQH6YjYQUWxAYoidiD2 QrA3YkyummYS05Obm9z9vf/1rLX3wej9OL/NnLPr2nuv9azn7dMvjB/eUcud K5R8mTHDxp4xlMEHGsaTr10Yz665wKEg2+ndjPbLTOBsYQgx2ITNphCjGxWW XoxA+zeFqZ9phGig9dqzfyql25wXfQ9/L0Wk3g9ptIy159HASFXnjxX7EKlJ OLAByON/rLRBnJaAfKCAso6cZpIB2bRb7e2RLyvX3QH51sZf4ZAgJzCgvymz BwmHcEI2jhQNyMah+kr0Z8+2eGz6LPiLTRQvEZ47yBzKJLMNXOJ54fDH6IZ+ 2VUTtT/myGzvPM61YIF3HsuLL8CmMV5wNua+KOVCdihxoFwGGwVEyqe71+aM q8IEP8RiHGyEzE+hzmKw7toby/NXA66xHtBQHvro0anHTXGxrR9by7mg3f28 qgacuDoKBConHvWWSwZrpLfpR1GagCKHWCPHMHsKj8gpUO2k/tQrNhQbdTGq ft2zx5ksdFEIbhJuoY/yuJW4QGDp8+t0vnbCLZeaNZIeiUEPBqGjXztf7vQV H6ZRzwDB0L2qibNgjdy2+k8x1tM7jjadJbwBi0IR5rdMZB08TsyJ9VsrhUMU X8ZkO2l3HP2soWS9pyoVAL/K7v/eIUqONXmHB5N8n+r5E1EfgAjQ6PJ6EgwV MjxrZ9UyRWWH8S/fOOg8O+Xdp0tnRVoAXMLQXSGqIcJh2MSro9nFCe70HjM7 PmFojcuTU3GV5schQ1Sg08UD2au/yzreIGYWgnoWSiFz/tMa2+cdFGaAP5TO aXX/OXGfPS2dsn3bMzKvE7nt8GuaTRmvt4rrDPl53H/FSGeQq/5UZXf4tNrV JyeuPH+xyVsrb+kUz15xTlw1Y1o8aub8eOrVF8S7n8x3wwl1MCExD+8sjftf MT/udOelrvMyvBgDSJlAD4afIDAh+XT7QJkxsKqhP2eIIsfdeLu9AGCkJD7j PUEIkaDcUVeCjvuqwBCjkeyqiwxNJ++i01XtdlGC1unzjAcN+rmhwWHnZ2rb 7H6t+8LprrtOXm4nmpQZXeOf4nE2x2+K4vqwmTop7timCrtE8a9FbPr0iaKB wp3x3pMV7jNgqMoDTrShc+H9unGSb+Q5Ia3EPaD7/XrAAiGMG0XBj/8d2iIf ++nBRUUBJ6q4skeI+vHkB3QN1s/cLe0+xk8EwOC0jDAXsoAhT7OO+YonBtvj WjCecB3a4wtJeqip5doXBEc4Gse4/llgkq+B97DnSuNGG3rEfXHaJl2uYUzk zH3x8gMiPsd8KMW0PcklQ3lMVAAlFmxOPdbE0XVnq0jgg+08y8nIJId7aYI0 BSoN+FinVLRDg7NiMvtFiSy2xidnTupiFOo87Mf+yF8wH8lrrMsJ1AlHTDJg 3eGCW+9whQoBHQY+Ovphr2p1EzcgXUacKTXSHIMt4A26aiSutX18bom98k7g s8nIw3WlSrP+pOdDlMLg98Ur0vzNLjTpQ68vKvF4c1iRAnwHLChrwXf2ZRvg 0+vLKHGDdYXULpOge6M92c5bBTwujCoLeHxtw/IztsVdBw2VqQIRjJIdWHar y+OBL9tbnKsXj+Gfkcl3OvC6x/wI/sKlfM9xQY75cbufaTWjFUXxMbuGxwPf apYDUTD8IEx6y3ZNbJR3iT4wJrj6ZpNNDzg3qV7elavrN1IZXfWkrgZVws+U hKqoiMbOqo7Hz5/pIo+BMdeiW1vFZZMOOUpy2ctOVMhhQ1Q3XmJdsu23Uqvj V0+O+gVrZUKsrFZ6GW6Oafq2h2QSrLZjLnfpphJHMepscP94GbgATMOEgXaT ZQZLlSZZjEebZL073wB0lBGKkrdU0AuwBZQRIQBxXKtwqDWJ3I0Mx/CtxadO 9TUKhyaryZpR8o0ua+PWo865wbusv9dYmExWUSk0v8tG+/gNkpRCmS7ujQag dso4NCo8wnoXWA1IwXeTvAQ2LeLNtssCLzIv3yHQmYZa3Ff4QixnG9r4i+zw aTO0ffKdAguc3rB4kqcV4oxERlM6dzboPVGp/REUUZmxH+LYWWdFvqK7wImZ wxNXD04lbj+CowA8J87DW64Ub6k/V5xnmj3381ZzBohPbnzcB6lyaARyxDTH wmp/kqz2YJKvxFsEYK8ZZiKXgdTC1nE0q8/3sZzSIKNtt7ezykbf+nNgSoKg uhLICLPER2DVcKU7cuHa7URiZs6Ko9v7y1dzzGZFhz/dKaQXS2z/NUvFiYAl bPvkY0Do6X97ymHuH5smB+y4y9vlakljNGRrWluZD95WK41wXGPnGfGSoeI5 wiaXu1RKa1ca9RXJSk51/bGHmVqqw+iKKB8WNQsUCU0P3zFlsg357NRvdNgY LHxqPQ6jg9+TfS4bnfBCAOWQFMsGxC0qa+Lxk5sLoZxP1zcyMrD/PqNDX0t+ OP0dyRV85y+2szGv2lv+ubTV5x9SGOOEt4QtkIh1r0ocoyoPPWj+u1F8jo3C ex9UjzrdRK8h6IwPd47rDz7kbPLgUwjQCTAHYZ81y9d07y98whF89PTxTpRE BHlsd1Hcc8JNcXmvCU43HeKOrjB8G/KxCgfStkXGky40elZlI6u/De7CXypD F1M2elv4FRm46v/der5RtB7EHAVP1vJ49LsaKCyXvChpzOmWTLLrOUjj3QXH E7gIxzEZo9m1UhnDQtiGdoYBCqvAAbNrV2uOQXNDg+NulD80vKl8Vbfu6sDf LBV5kGStiQOyUSpPblO4G/j0YpUbguK6xEFpkNPgtWjMgr4I7hJqi23bFthb xnEmtvMr19se7jIeNHabihDSEmCqrd3Fyp2CryYGSzUGMwhrxYalw+/ifKPu BQnxEOBOkLjwcwCagSo4k0FUEAODzhzEgQ4hpJnEFfSQrPJY6nVLuY5VBc2X sbM8bwK8/PLULYFhPs3Y0nibJ+qsjBIhrcu7KSgFj6QKevhbWoo/jQrCRsCI ZFw4ud3QOSdogoJ130WLenDb4yW7W4J01kiS2HVT4+jqK6Q5urG/ao8hta0b LUAi/+Di2krQDDTOGC91++YeaX5BqmdhYaM5eFuNfN57ahmTWXel0oy65A/v Cj5GXB1Hr3axpg9WbKST3B76v1GLtFzOb/NjQdIcY2G7h8OgctxhhUIfUCsc 5vPDK/xxp5CopX6CKHhxIryhLEJpxF3A21zh5S/VYtTeq5fbUDLWetZBoRCg CF8i3AAEA9WcR98Ad9nj+i2O2555h9Td7E+gFIqluYt5KmsXR8rklbdhrtOG MF3ft0+C1+VvqttA6PFaAq0Qxpo80s8EgsZScJ6Y5KmhGxXbHLbBxkCdrxrG VbPmxxWbL/0PsAJgLrwwis89V327XdW2ePmado5boIngVu/ZVhQPn7XaRawx dd9tIHjGqz5l4RsCUVBm8AoBUgOTGa5+Uo5dxZcayrwUxbneGo7+dsdzNq6s bRVU5LkzpMaoF7cyYKu0dZW2vQG6M2NjI4xCTbsqUWu4ARYSURQfHcVnova5 R3ITlMDwIeS5AKwwtgEXbayvnv2ZcrhOTlLP5MV5f9Epw9Kjx3e0RxkFPSoT jedJRQ6h4EqcGnQKFdwhdaDWLbekVVghMj4FrEemo+MHrNU37kh5FsjU624R qIFbdb2udv7am8TjIpNNnTOWrc/NFfTjcRv8C+CoHTok7qs8jGBMM2EtuHNx JKvAzeAtAPqH6KmgTnK1ElWA0v01iIlcVmT+f70p7pKp0EUfmWqD9RmfPxmB C4MVcYKLl2gdf/m9dKHyMaNKOuBnmx0OdB4cQJJA8OqMJzWQEci2dYyj9wqU RIsih+THoW4q1cY2jJAhDYMaJv8D1rYLz5e7KBYjhuj46wwMrpbaetiBTMCd AQeVn9ThzgfSBl08VVhyuLG+g18J7pQciTsYtfhcvTqlQqzHM+Dxgf6Ygu8H HSICu3/sTY2NRZPCPrhUEriDgye/IX9QJzz6kQbX2/XWXhtHFzVV8i4cx7Dl YdUHTXCDQpMVEKf9gzkQIy4WaFL59fyydafHU6+sigser3IED9tP9dwRrv9i nWE6Q1ILyXNQ0dCH0V73MsJ8jFGqrjZa1+6QPIGWgO0OdT5W3J1NRnHHniYU V/0ybnitiiWecItQB+qFngVBgfGxaGUfF6XT49QyZz+Dzg267I540tzxjkaB GgDf0i26PsjDcfi5Y7tyY2OzXI6gWWhuS3+nHBAMBtyZuLee003O+8hQz4SC vu8E5DE28vfIJXg48ye2/0o3SqxXkkOqyI0aHukEeyaj7P7q2XUX2L22P6Tn onEtXAipntnW0/YvMhStHKTxyrMk6HqI4GVsUBJRGXFC1rJWw7xiQwohi+33 yCMgKOPmAh53qkxSMpsz7jlyMEN+ym8TxEB6mrpEWpHzEHDJvghhIV8Sj31M 6h1FQNNJJ0VZvu7K7cUr894DIekP+imwHo11xjUoP6k9jb8GFlMwZdWqxMB2 1luCFpnqxUNKP1EaozFvOgai/FRYwG7uYILvvDRlKTCwaJnSvQMvwAbpG+AX T9vYGD1Nnj6ysUdS6BgIXmVc44qrdfzbDRN1ENWaKVmBzIxzed9VyuyOlLe6 b07gSgSuwGzOW+Frtb+uEY2zACXEBryZ6oZ7H1T+lrsulREJs/dl88TI7QFv OkeZbRKvcDtkpg3sh+2K3R8TIPA5+eVMKLYqbHG/cr6LLwSmk1vUJT8lOO9T z7EaSasDE0GmAhYxysM6wBiwBa+g5dYXFxsvPH2b8APDP4Z2nFHJCx0KqoKT AVvKa3yvGCLGY/sMnljlBhv+xuxX8bpNVtNVEouJMkyOyEZM5yddq0ERfFQY 0MM+lbH9FLjlndPijq82jfuZvHbzg1HSpTL2zvvYIJ5jg33m3HrxhLnV8ZRF 1fHgC216qbJ7e2lUfMKO8fHAsyucUjf4wZDNCy0/pBx/80y/f8SnnbfEKTgY TQQcAz4DpstUt+A1TeqVRHRQ6c/7NKJWpb0N/mzbRmlwAxLEQkxarUpiPS5W nebBX6rosr1CnKVqHndYZuusAVEdWd+saTNMVur3vhQUk/f5Og136pb5i7RD 6uOB9vjKcFWqk+XGGEeJb9/gbGwxXmpkzaWID8sqYUvGlk6GM5U2OG+w72Ov lBn7UtbbUu1VRR72BUN13EMKNc14EUseUcvwokBeY926XSpkirqnr227/XEX qQJWbHBySVGUOC0Z1OW6CrDWlwcmN+EW+geXw9ie73w4c906Ls3zNyRSq8rc fMUrhq3yFwYcyqbCzWhVI2Kv30uFq0LrZtcbA9liJLvD+85a7yBo/supgJUf lM04Dk6zaXn6Krn2bDpPqmMYjdPZVPA7N9jv0ShfZCNphxGSSR2ORCDK3RB2 TBqQPV6vVOASnbgBudnWPNRWLvenucR9qSL7ThOKVpxhgtrdHmaKBUGE4W09 06WDSmpnUMEUVTLg0H+87X7/maGjyDqvtOn8T+4ZctIEWWrP6dEPY06jFHNc Lr/npIV2zkGeB6EJh9PwO2APaZJxFqLmfMinjwTEfoPt4TaJ2B4lwTQ4AYEx 5P0naJnKiBAYNOsnvZCFPfbYSk50jkWte1a5F46DXePGEg/mr5CiotP4XXHl Jdtcb8b2NN+6akvrltft0QCjTzHO0RO5yvK4KlZvi6vuahdf6X0Ge/5Kep7I 5IWCf+l7j19IZ3Oy4VPrwy0c7kQ/t9f3ubXpwpvi1qN2xSdP2hV3n7IrLql8 Pz73/Gau7AHtmbrwzHjy3GFx3+rlbkZFrKLfgz/X3CO/w4tekDjl6rgb7nSY ruFGB3eZ4InpsPnVei8zLCrylnbbYx5QkxKvSrwIduv7QJt2T30fh97PUUOu 2pwOZgbxyoflrvSQCXgXmtB21uO6GoiNoIASxrkvPuX4QhI87B2pcajMf8SP 10wioLBTXwHTOPfnWBEOTD5AK8lk8be+zh7tiQIllL64Aiy3xs0kWaItc+5V I4PFHnhA0kXqA97BTNT+oQgk60GrYWOERkZ8HAniZlNEkiEvWufbQzuWRz7e zhDg1CiUrnYL+jCTQguU71qki9ps+AptSAhbHceGED/BbHI/DPTwxn1xfQwn NS/IWMCUkvlM39t8ECUmLxQ7XgpLDO4d3v8uPhUpk8vjhg6Pd5XUBC5RExUg gte83TBNaQxNesXWP99VXpACJyWPUR1UqYMO+ma00ikRws6sJpdZHF1jlxze RPGOCHJbOstqhwaI+JFJD8p47WKTbYRPsL+9Q9bzd0wqaStwQtdykYHe8yfH 0cLJcbTXzrNxJLqQtJSqU80Y8BT/J1CRXAqVdrut2cKXdm8sjAIendr5PeFU wzFeXtqndXwIunFJED4Xz0Hrg4sR1VlDFdWbjJpdYZfaPErQ/Caungd7wB35 ud8e1Z6ecXRUntI0u1CT30DJMsERLpRE9LYPrFZlf2BEPxFnuu5zpQRnX1vh RjfjbOaONMlnQCjG4bL7JYKV/lwItWBpO+dLO/tB1enjODyYy/7Ba615A3ZG 16ckMxpmtNVJBhaQ6lBlfN7DlXF5l0rHHltVvRrndNnnngr1Jzp0zLjSz32r r0/yp4JQtAcNNCaxkfcoESYAMM3rpxh6a1418We/hmWwB+18VghLWioiWhr9 Xm3pYcO18z59b2I0afY7aq8rofi0IvWw/OH8dKJtR1GES+RQm8Zn2LVKN0tk BcW5HpSANnay6byqRkOx1Lr1tJ16RtwbkYldPvMjNt8pcVnNElKJdu58JG4V KflfG2GVwwvYDA/1PEU5G3Z5Aa0kXmINGGYNm3NHqrkOZZV50fzmYdnDiUKK 1ZANeo699Iq7hVd8B69yHF4VSmt+tYicWzr4Ns12bcm4hML5sntNSKELHwWX j9nurMBryKBMKPrwHeL9wmqBKKW89s4G+wVN2TDU4GMll/Upo6RwJgEUWUGf 7Ug1iMRFCKn3l2VAg+cyeaFqqbdmRWROiZL8AX+0+7y1lXJWV0VCoxr/HYgB as6aiXOALvmgHVO1TfJOE6HKydtU3u5gD+mBR28y+eYyeftsGCovxWyvIL7v GCzr1487yEuIcFYghiSiaH4J0LvoXKffEcbU8Qrkj6RzDklG4ShwHXgK6McH qxxYEizzp/3F63+K5AhNkVMqO8Mfbr1c1x5sj2iq3UM/Z0y4eBs0ccqD0h/B vcCx0t5ymm52qZ+K6x6BNMhlbc7ZF+d/0zheurXCoQyjkAmLUYxg0cx6SPdf SAVoSKPR0NSx+dN/LjCZZk+6x4R18fxrWro5GX2PqytqAJX/tcxP9GkAKvBt xLyah+RrzGeM7Tvsv2T8jqqekSnvtD+r1c2dM93Wewvi8hM7ujHM4KO/MqbB wKEmTTW01l5gAkcHk8LKv7E+WSX7MH2YLHwMJYxAnQYJJ+Ax5V9LksMBgeQt sKZJH3poLHDCJ7su9HlSKH8K7Czd70ZbpevwU5WDpQnkz7r8gK3uTjO+XjoI 08yjUx7FLLzTAHeA1Zt1bfaLx91wg52Vmu+ZeORIIU3dugk1qgziHJSjm1jT 0SEcjUFC6hXSrDSLVD11g4eeq4w2GdNtNs75ESRi0XW2/cYNwsLga8DrQVUc OB84DSStvE06onKjKuX2OhveEiDHAMgGLKkGEri52l3Xw1Gp2kLbjvOw2MXz AtoJVN2o7acukIQ9xM5x2QZ7dGJP+HaT7c9gyE559tkgAQIezaNnGUo6JKrt YWa2sdzmJvxfafS0/seJ7uim/e4nq1MTGDkdhixS9P+zHQuzEekvRwKay90k bhUlNnoSuPIyPvL8GY5IcQvCE62FriLBCUOlKu691GBmiy5FsNmoZ5N8BCiz x94uJABZQBt8gkCj0zbafHy2lD9IOKQkZvsn5dJMNhZoOCDaK76D3uiyeRLM CPkCdJwb4lvyL3SK5vc8eXlRhjUIDx+2ASDOPv85SqFElMTij+fRGZ8qewBY NN/nOuC04NyJ1J8obsfzQjwDygqP9dBTolgOPKpdDS67YpMLDYLW6zeBu08a wfuffFd6ReaXxm6ycdnNN6mzIFYxB9JpmSODasbpTn8hrowaB+KDagd5oNlB g7QfqSAW/nsQKBxBjKcHoxhSoUuy9LhCHzBNb3IdyUSRd2xCanyiaybRvNB1 xgXz4bFnK8NAmeFHnZ8ab/m96mlFv9F5Sshm8DPfdYyuN/5llCSOIsGUS+nC PnZbRYZR9Q1/0GZdwP4Gl2NmaPw5GnBAbSLMo7yCR0S2kyLHrXg8LGtt/F6z UymO4Vu1Lrf2zWNfUkTlx82MZxVZ1x73nMuYEi+wsXrssX5zrrPvsaraTlFw r71GVmecG+l35LQEjOA3WNx6uok+cv8DRkvo+5HSRl3pf6/0v9t7oKgREBTc YNgv5bVTkSEb8R2bgRvdQlKmj8Db1sEv0UOBb9eLCk21+ymx9zHxQodNhQ5+ aqk0T6ssRsaN5Bt366dT1Pgro+ZaFWCnyM2BzDDMPLxpJFwahyDdaVB1vOmO xklwrmc6ydKCimcGGDNfSbGm7JMozv1MGJT5LIGlhvrJ6hSWyGNIgk6ciz8q SOI4yCaM7BSuRJendtG3odJDIxUvRU4K+/AKhjzplcXkmpytioj4NL3u9wnK NnKmoN6mKiLAwcgu76H8okNfihLrGHhFqjk+bxyvNAXg1X0jhU9UhlhwHszJ MyFrd/9tivEIH6xrJAJ1lQbXyyg31VjJX43xbDo/BxRKXKg/VvEbMKtOBUrq HGchq6szsdnlIz4syztx+3y/0pBz2wXGkz6X9zUweGCQMIto2057HbTluHKA GWU/IRK3Uyir0FPCGrY8vCKvNylteY106fgckSie3BpfaqTveC6dMTFckZ6N EdjrzFRCC2EDI+wFn4tzob2BMkOBS+88Ol67Li9UdXBvhbjTnJ+rDCaARc9H SrpPsQJOOHD6EwPBqZ8o1BWrfzmGLhLGkDm+Mo6PMvSpb2hz9F99RUDrcDON EVVMjFQ2Nd85ZfYk3SaFe2xXl1Pwf+lTMkb8r1/1rxAqd4yTrWr/08bZOxoZ 0DsmY4w4PIFtuBHZXdyyLXXQIZyVlqN6AZrrnq/WH1HpAZfq6fZ0Fkq+hY/V 6+RUPXbtJUvsv3r1HCSR+qixIMmb408P9rJiD0lATlt/XpLi1c3KElCdIpqS qTdKj+nkYSrj9ynLgqvIr6/n1/fQtkJb6tky6kax535wmE2CMLt3L08Vxas9 bpFdhuCzUcbJJtqbLD7Dn/OSwJtKpJtfGaW8imvnePhcrba2XeJ6m42FDY5r oU4i1A7kClpynjk9kF70wEOl8RNPSk0UUGvr8x6N8hxAsbouvo2obZYOSfTL U61zdZlqs/QTrRIN1NbnE9BLS9zcW6ECo7hWdr9UXtI7K5IiEPwkrfAbjdNI D1+yyTpfWg8QV4CeJiM9dpIu0dUW6tkdPl5w/qohyKPKm+WY5FhbjLn6ipHK GIJ+mDhWotzBBGSi0+5E25SAGaVK37DVl9vq/ehnBgnMADH8LLf2k1lQsGBg dopy9n1ZJwWzntUCPoLVSEiFqxEl+vjc1U3RGwDcbZcJ+C67U5CECgpYggvh SM3H1Zd4ScXiqU1BYP9cO/fOcXE08G05GAF39xAkd6/9fk3tJKFfECELvOsC IudRueJiDkX3K2KE7ZjvMCTPM06WqeVtRfWcWvyWl6WAISUIcOQyQxrxaGXb 1q9XZ8KOgjB1tu07xfjvZoOlTtZ7FhrJ2RQ8bBokaHaMjePlW6Ufph+CXtku ATgY4XjS+ZDXsBtqlhlC5pIRih6D/2plLPgpU0Vj6wT5LdTdaRHucTBFzNKF xiqmvmD99Td6Q9MNnup2juLZ63wRZwds9s3OVc2Qv8i/STwFliqvXfBODC5V JIsL7lY8nQBla9cyZO3KJoG4uIdiDzfABaIQfkqIO7UUI9amQm0nf3DN5kiI NlQQ1i+JaY/ErCb78yDWHeUhADVbQYCtPGmf2aeXPyYnWP7yBGdFHqrYZ1aA wFqCOSB4oOAlz5YiW8bZsnaD3KyhlEw8Bm05bLQT19uQlN1K/KjwYc231zBx qxRSPkuUs9wJzWoLydZnodkSv08z/9tuq1M3nXuMvBkMz5SnAGU4Jc8IY0L1 RrhfKPLBOmYTZ8T72BWEVHL7T9XzIFjIfiDe/DUd4qjjqji6oiqgWoH12dz3 DLU+yYmCgc4dSv7dQQvjVkbllv4oyHz5qvTuKudEqqSDQmvm+gxlIXjsOD+B Vu+WJqfLQjd5R66ZlnpR4rGAVSxEqfA0gA/nr31QKaDJTLPdI+B7/kWmhdik zUFwqzdMVR9AOTkOyOm6vzGYzw01T39H8WvM5U/0SmEODRZVCdFwLViQE9Iy vd02jv59lJqOQ1SoVY9CCSkOJ4HZ12r7OpIa5KoYxdx8JWp/pi9N+HMpGSyP 9b7ffBw6fZqGoswxNHx+OHXks3y/f23S4Ghr4i4Z+Lp/GhwXtE/kzwOiUQ7H FZl+SaiWU5TM8gCcyuP5IVHmhiM12u99LKkX41AqVPEC2BjWQMrY/dJgUxsV DVLGBMxZ24MC9wRlPzks9U9I4wu6oUEhggN5wTnvTdSMfO+jzt4fLzLQ7ABz IOKHN0MpQxLEj/2Zi23jrZO3DeGXvs11MFgnw9/e/CRbtu1SkD+SHEUemjRR iLHgjUKEZU5idcyGzxYHHS48hHsC5pa+neZ9QpJE3jnK16rge3FxSHRWorI0 c/zYratz4SsBR83JUfsWrjDktnl8uBBtQECiqX6YN0tuI6nx4Es+hD21LiBX FBCrvo4NRBH0mhBlHysku17XwSbIq9jsDZgJgbNzNtqcVuRy0WlVfnsnd37a 5aCuUxbUlXhtFI83rMt4y4BLTVgpau9sh6oLEfJqhiJriUzWucY7S3rDaOSb sLE6N7gHlQ1InR2sE7qaM4EbFR+JKPg7EZ/Gg4Fy4f7I0NrdI6FX//YjOHmW tdNqmYRTVfvTrfXPl9/lvlgrqmoPKPlBFQSIhA9aJtTlfPjLb4DlxV7BrSUp odCGWYT/C2XBxyAXPui6XL63aur35AQHg+AQ+XUjL3rk+7Ben3EERTiKqNP+ khsywBGNgoyHna7XV/lhNUjB08Sc13FX5gcRIk8Tdt2sUinPB7mnXoIQIZgd N0MQAmdpuAzaXmQC5CBSw4ZafexPbljWW7fM8eIAfADLbwAJWoiDFNosX/LP i2EFDmRCvcuZfhSgpDdgoXkzfeVO1xuhVp18DgHjGklEauoIpKUy3FmBy7MZ Tu98CoIVsNL1OPW+gCNrfbSZ+r/zx8w6rc904M5cKzxab7wjo3FvQYNygeRr GIf0Y+iJ5zpkKfseJEheURs/YqfqcUdVWel2s1AglKG1VxOSdPCWguSXEyTG ACJDPaCEFumhyqi2JEq4CsAw1ZYetlTJ+SDjqQowytsMdjyDbi/8ZeIJG1wU i4suLPBx+4GmuJn1sCjMphe9u5G1rGKJqAgthmqEEpjV21LpCW9Fl+TiDT/n F6YAcmJWNT5+o5v8H39o7R8GEobFVQmQSGuN5PJ5lvb7oH+QIEnQmAcRebY/ L7HgV2QhCU2h7mHn/YpWLRCYYIfjUs6qv132tG2jvGNcY2mb2IaaOxRcwcbl arxcdaR/NlH4rmbh/Wmomcvy+15aEJQnSLY1URsNgRI1gyRqispPD4ddgGsA FkEdvb7iESuKn7tCzGy7Jfo+2hE8YBj4KDNItegKd/reuD9BwCg4fzISq+Sa w1QUOytPyDu3W7iC/ITjLHfj6kJ+BzzcB3DExeN/hGfu8632I32QNSlpcZU/ /cwwxvLd6V1WwEOR5BV5p4SODucgs4bLh6ZXn+AMXMdZ132pUTCkwJ/WFQP2 3gsTdiiUw7AkzPvuOdXLfnaZpNEpP/CSTfbHLlGRrQfyQtToSLyZw/lN+RUC TQQqtKrAXSL3u5fIKOSyLHtdcQo8GyI9NI7hWEJqT/CA1CYF3DAePPMgOjcn KJ1QIyGzjchq3mi/bponIXlxjl2pmy3TbOlly+oNwZm8bnyi8cKVPkjk2GM1 Y/Hcy2xZc4s0cYtsn44bJcphRiPbB/KacdxcH7G/8wlRXrDoUS+w8x3/fJcE qHONEinSQ9ZV5DgGY3iy0p70peO0GWN7uyKVFzlppYIuOM1pb8Y7dhXGm+85 Pi7qfBd1vmVQau1z9fjUGAQqoAZmpPV916aN4/VMDvp+6eN5HCj/3Xc61JKQ ZQqQziqQRPahX8dw+drDHfNg/4NJZjAnUvmh5H6r1kYytazwo4Sgns5L46jn Ie8rbW+/pzV13B7Fk1G0JeAWJjfEm1Mej6OHhidMZeF8eSS9Yw/nj0WCr9i5 jm2Azra/zcbodTaK8tWFHxivWDIQEEjhoQQ9POodbgDvbH4HVRGK8dpdvGdk Iw9ff5LqCEGKXI7M5FTRQ/VT2l1lFDEUuhzoB5VPNwhUzBe1u+p4oC3Xrrk0 P6vyb56TKFC24J/kedIReEbk7PK3QiW+MifxTDVMq/5E2utgX25DyrR2egmA DeuQOhzB+0stvcTYvWju799u0PwqivPfV/fMxcPxv4riwsvWx5m6XWMhxhdO rHZzWp04GvyzAHUab8M09InXJGQ+Ly+FTZxDnUnPmnsU9Ulv9cJU6DNfuEID KEKKXEwDHcQYf8UA50pVg1dfV9Jfktfo3iguxyrcW4+oqFjnckm8b7Jzb/Od bG/SJl93PZM+3rIsvlPH7/+KHwtjPC7yfyikZf/6CAF7xAKwUvd/rXR7JhCe 1GqnbUW+wzfMQl0grtotrk6SW1eRnqv2/wWJfdJX+11IpLn1vg8SC6Stt2bV qqVZrnSsfzxTQkSK/U/SPumRvG4pI8P7ddajsiJiJthjrrzxyIgYLBWw9zQp b4kTEGu89h7ShpsHdSIo8RxsNqgA0+C8gvgUu9Z9u7yO6Y4oaNHjVftCKoCG 8oj8cXEq9S0dooJQL9czyJ8jJXKPJajTjYNcKWPfsUvjBt2WOC0p8eVJ0XOX 0szlQ+n6VlQuNcjRYw0uVxlAd5YKkFnoySi1+mF1ZjThpEPJoL94OMSbAKo3 28DomuK0XN+X1uZ/2vs5vrFQKFNLrw/t00tZcDkoktDYwKsF7PC7Dcx+brc7 b50SeAdnTr4Dl6TYx+t8zK2CTDwOuhs9G7BG7uZ/KU6SZUS/M2b5lZ3vwhUm 9S6Xyt/N70tE+9rdrG1PnhxH0ydr24ZaAjpmk0bjvQHzQChT6klxixQ0nbbo QPp4ce8ENHHvBDTbV+sWL93snS4eV67iozICTICy20E9DrLBtb1HNWmpx4DP A+e7eZQ4oCuc2CqKOjtFc2MGHBbDNoqdcPFZ++VYxId0I9tfkEZqFhkJ3hXX Y1+wk956/JtRXKenkjuhfUE76rSfg/fHDcY+EkdvW/O+PloTpvWCop/Ytuuk Y3/wWQOsT+yVzVsWX7O6Iu7b13AUl8XX/KTJMciwVEmw0Tj9ski9a7kGaQ6w KS7nhFcSA/E90NvI+SVwvxk3WedYjzvhBG+7szmP9LdO2Fjse1M99abaVaJ9 DpD3qB2AJKKkI589fS/MS9rjGaWvRC+1UsrdqHAAUbgtMQ8GUKxOQnZLowCR pe5sg78DiGjcZn0PKHoADEGzibI+ihJxl2gfZwxok+WW+X3giNkVUpNJfeWz NFUGjrkeZuFspGdyHrzBKoiYu9p/l7VQMFkgmGzvIBGvhvh6WyZMMUgclgYD Aofwvr1J4t3CeK29jBM2y/UPPz7yh5PD1+c1Lwxi6zsSVVmOslY1+ACLXp1E JJXtMMtbyn4vGCXlO6O4pL0Bn+03arbqMDMKqYbVaolL+bJwRQ/pfig4T6p/ 5xp+UFU4T39RJqE7KlQND7m0V0bdBNAjef7rHvyYg5FfKLONLMociih8/Xkq OEq+3ofsPJO3yeUcxAo8kS6lMAafLCP0n1CPONUp48WAkmyHb84Fj+hUfA6f 4H1OX5GsSxYotm1YqO1o4HcN8gCYJyET7diBTnJpr7o10T45EDzH5o799vja GwjO72fskZwLM+R/hcAKFw1FTPGQD2p2QI8q8rGns3WkwW88WWF+YB9FSLfZ 9DC1TNuYYigPjbhc3Nb7be3nTfjHQVEGY/IDSDfcJY6GU+vvUsm/S01+MBGk 4BeBlRU4N4TM/0rtHoCP6SnjCF6jv0VRG+fGQE5c8tISbgMjY4BvfzqKH3va 52y7T7Ef9BScO925nifWuHFcf6i1r5/hcd9mccE39Z1PKdchPxPKOtxpS4+r iq+4Qgq7Fr+LxGre9wMSdvUva/Hu5mJv6/xrdsiX62U3wlspVuPcE/7oYfPf sptPv9EjYD2ZFQPacWLVBHWpFEA25ONM8DJ41yPdFx7pWK8GBJkX0Q2QZAF8 GzdOWaMDvH3qlh7weoVNnAlGcIX7dYXDoot9x27ol9EOU9Xisu+Fu0Rlmu8h q6fTmQh0fZGZ5nYDC410Ne0TYC/3u7CXiL8zo9QYEHkMs+eTaR3FrVf5qxW6 SY7JBQwah+njPPUtB9TzBJfWDfFNdSzwSp2ljaHiVMq02N+L7MxnJsww3xk0 l9q2dfb3Wuuqa61rPPGUV9cXu2S/kEC8C0NdK0oIkoQz5BV+9tkk3V3HO1ya PbdQzmzZLhtYEy+Ko2cMB/YakdlTrO9P2fpXPRq2wASpincs+46WJNCpRujI UMblCBcjQlJwGWecIjEjoKH2OmW+4nFQ8JxaWxlo4R0/ayYjIX2dOuLDPBLS N873SFLHp/e5RAvzPfojnDmW2Gm+OFYI1b5U89Ru33s5zd6suXBkJI+LZqFz lAoJQ5NBnVf62sDICFydJo5oZKNJi9cL/f5sTf9V07wAfivtwY0yAOzmne7f bpO4HEUf2Yh87UQV+ZtdbTTKHu4TIzxD2y2QZc5wv401Nhgpx1RyA8M8kcL5 jPdlHXhaKAoh1519+Rq8LVAogmC3zeLak5zdo+VwKQ8BVY7j+/jtOU6d3VdP At79B//gP7Cx/9uGcR3j3Gda7+rYUX0cmx5sDscKasljc2T/VjZ46//Dtikz frziNWdDc80lV9WmA8qi1s/+Xv6QJOe1T8sfDJll03Mqn0fy227WXdfatJW/ 99i4adVuV+9l0p52cZ2vGsVnvG3bDaIarp8VX7uuf9yLsksAB+4+H1o7TlQ7 c2zQNf2v2nI88bwf8TWkdTz1VInPJqd5SMxzkMhATQqA/sttO+ReK1My8gf2 UhjdhFS0xa7P80lw8FCqNHX4kF4/13upkr8hXMrmgPxsYOJN3JHk2ewWNh3t wcbVSfUdlq4Nz1IR+gUOlEAW8IoHgSMRMHmDh5XmKQRmwWIyGmCB7QWLYoEl qhnBZY2bdbLvNdsjWTjCZXAVOdVtT+/5SFwUijZ2AUygEauZB5o3l0nZUUIO GeohFFAPttarIvXkhQJHIKoVojPeJEQz2SubyjQ0w2sUi50J88xVUbzapthB 9nDy1yjl+3MvqGdSv+HjjxPv7O/DR/6Srg+cDMVRSaR++261wLXMHkIr9HXo 93Y2E+pRRavUJ4YiHUJ1swzISYQhsjQFmJ82oGyUUYUYV5PzcakNEbyAgOC6 To6Cxsad+o5TdnJqJbA+WEVwOn+4dxx18d3gsP393IgM+WT/GaneRxxJbv6G d7BpGC+VV3er78oo2fH/ByhhlHuilD/8d5TWFM8XY1wbpaQSmef4zTnBgnDO syKG7wU1XoHL6e0g8h/WJ78kFqqnEmF9W48u8lk7BhXQybaZS2UgGXmlx8pa fJeU/JE92yeNmS2y53nLKD00HpQr/feYCOPofYI5mxXe7C2k7u2F7ve6qGkj NsshrZ3Bcx9ryvDdimli0rnW9n/fZb095NILXHm9jpnkExPy950c5cf52CY+ JEBsbLv8E8Hie8fMuL91j27d5L4VIiCZhsFKNIqueKntv8a6Vos/67cLJLLj hr0ub9T6vBgIne0zZ4+vIW64eRWpcPcKI9u9L5zkWAQcAgLwo51Ne17oGdfu vTs+e878eNihhi5f3iUfCqc7/SpKNZKt5OVJO/GZwKjjZkePVfC6CSGyrrFh 5WRnU/UuW0caNJJxTz3PoKu5KhFkEftMgPYVnj3bMqjJGJtq9qgEXxKa5/oC pN26pacOArKv5Ox+fRcYQ5j1zGBEzqjGEv6oR2cZK9p5bK0XpWQu8MWG/zcw Nveni7LE8/qRyj1HUSLgOgEWrLWHVd4hiq8wxHJFuXp4sORvZ3+pH8TJ4o1S kjClYJdGlUfsNSnVq8BFXsvVvguO9TMAp2Fa6uAR3+5iua1ve41mAeKw4Nv2 WiU6N4pXWY/avFVeQoOMKObbZQ9+dARGJt8BR+rYUHbwuyBJdndfD4cqOnGz O8KtFDjF6qO7/fupbpY6XzUYCaqYZG/IuLyNYq5HGNLV66RsLeyHEAsZYoTy nVEGAg6ygX+N0cZH7HwXzhMQOGeKAdKHOHSkMHMfMZqt9mIuMXB5tq16/z/8 CPi5iYgvNc1xkeG103jNbGREG0i0eElGp6WDI587F5jQPxqowMwNGlQOJqeX y42NZgEezw53hTmdB0vw+SB4E8GXbaQ3dbZmu82aebkBVu++RLGTzw7n18Ee iZWX/UbbrU+cqWQUSLQBF+GDA5cpsHLnONp4sEd0qrXRB4RjMnHayHelyXxm kD36K1R55hvf/nPOVro/2oprXkff2xiBRDfCxN/uqr/EMzCZYTaLIpGxzX7/ Cntli2e62btpd5V5QgtTXiWwooAokjQOYgAOppMHXlThUAAL/xE+ZOGabADZ 6NeqMcF5ANYO7wgQBx+UVE1BwJ07Q1hvmZJOTIni0hcVZjlt0xAH1NMONI4H Qugw/v5GoZauJn1qI3bOaBvudu/4ZdeV22WBIniwRW4zPwSKCZ4wdX4bZdml ixzihThs7sWZkux7uWFFnrGgLj9KjQAhZtsmldQT5T/RsHfYVKoBn8JX/pEO ZlEUfENyA8JOjVLnFacPjsQeu2WhHQJ23wjUS13pyj0qnh2JV1grjzcQudqw qcOUIxAvhTwGek9/jYATTeLc1Uo7Qbkt2DRWYGghcWoJLQxeLSPs9Muss2Cv Pk8dtntf7VPSUSoRmD6ei6RUo8zEFVcEi3OjeATqxPsVdgf2DbYX+a7HOiwm AfeoI02taLCNtkASEaz//7iX79Iczt/mH2vHZiqIcMzcAgd5J8lTfpEfJxee a31rbQp5JHQgiAceOeBNZeMhkOC84xX0Q/BOyP1HhuVJZVInN67Q+AabWlRa r62rHDngGJMyquQ37Np3G8G50c4xc6WOp+RgNuaBb6dn2Zo5HzrIF92492+y viwDIevKQ8bN+lo7p2yWl/9ow5pdY+0e18uR7X9yhVPbJ6eed980lOjeeZ9P +95YeDTJu/vMuUPH8nm5Un+Rl9cOd0VFo3F32VRgU8WsRbav3csddr19lSp/ rZzNacIeBannBBs4zaOZW6erzukZHcTlNgzXJc/riX0uh5UMn2P9uCDEHfrN 2ZtUoPp1uUNW+Ac8t59Ta7Ra2cLlMgW8Tligl9zpp8qoA9jh7U2fKfmnnNxu ek7O+GM+1Qso+7VSGJPNvfod7cM4GGBs8PJXVDn1AZ/+5e67vUGvqYvpPiLv qd3M4MnT4zMeaxqXfar3Tz2MQV8qNAH1PnhG8iDS3XSp8u9/m8c55EX6qJ2r y27VqDLhueAHoa5AUEdXmqtt5EjjVDay7RUrX855NiJrvx35bFdaR+JC9mvQ IPjp/d8Qh1WBHt80C+I6eqBoEImZTXeDL3G1CaQOCyMSbq8stSPOduXZd1JL Vz/DH4OvDVqGwQ7Enff+3YYTrUI+wc5pUWped28/7DcImkrJW2D4ER3tbKIc fcBQ5RyMGzwvEA57JKZsmwrOMy4/yKbIAhS6k8WeQJPCRQ7FHKqRtB9dBaTa CGK+9+71VTecTeSTT9QTAK7vATTfpLquJBiARuVptt1hIupDTyaA5u6il3cW dXdEcrVzm0l/V6uTAK2uAC1MH2uMZ02ZLEADxBguEJNmxyujIWnFqAJIRvSi crI5yLrZ2QQx2/3u5nF0+Y122F7tevlq2UIm+v4JHv0iSl3BKHyB1RSD8GHf AUmM+HWJK5AZLbTjhxSpx3BH+yNxlPf966PJmA+XmmD3dLmAgOG8wu9DsgrC D2ZuMb5QpOs+PVIYxfq1hj/fGlZunJ7jCnFlZCiWe6/HnFxxueU3CF+eHxYc 5aLK94SMb/f0kq9d4S2Dn1vmaj3L7JtSEzpKyVPe8/bgvNQeHJZxLsbrg5Pg JKNs1ig+Xga6Z+zvU01FxEe5cZKIYkzJtIpAiGtXGdBXeOhrqQf/QNO4yXHH x8f0nBkPvnhF3OOeFnFrezD1/qGcOyiZkXDBNuZtcK+v9zG+6VF59wM+ZQY8 5xp+rXtCpSgue0b7MN9j/x1oL6aHHXu+YdtVSZG7oxNgu9e4wWBIG/UVTlsc 972+Kj7eSGDLQ8qzA2HExoznL4QKEldYqEjRAGwtjXr0ZcK6gFtcwhtAA+W9 fz0k1M4GNy1N/FMjBor+sUS/+06SVNWkSRZTGqaBnMqFeU6SYz8wFKDDLyYB 0jzft9dE0SAB3anZpEsxEynQBf9epFdIFomTS/2VvOUVNeSJbrccaCuXavqd G2rkgSrbLgwGIiP3DzhYKN/Bi4V7xL7yrkGf8oFu0CSHBhl3aZZDywShRenp UQKAiJKwQzIcuLklOCkz94zzz9hubpNd5qz7bI46LYpn+JyLA28X+DEVkmKD ukFw9ClTvHOyNH+wugB4gN+HH6pXZoHfd7GP3Vjm7VZyp9dst7HblaOavER4 VrmWVhUonFrYpzgAu9GWixQamcQGlByJh5hB1lUoa/xUW46brNIXoP+8Hn4o Z+Tb1np1Fmq0VQE/siWvteOmDZIjMmQMm8XPfgAQ7+9lvWZ7CogEEN4+xsif MZ22BrL3Gch+nZGGsNQDIgC413fu1rZca2N/3iWptvHyrtg4dI3X+ihb2Ldl CWJdvUbyJPo9PiEFCMpMkoyd9By31O3HdGA4GXg98/b0lCuXJICI2hCZ97ZZ /NK61yu0bpnLibisxlluDiQYmzzBFAxTrhcoJA48Q0fI7NH8cnkIAoY7DWyv IE9utQO8XSMUfvHkKIPsYcouUtZfD4Headu6jN8S9zpnprtDKhIivU7/yJfE eF5guGWn5mI+rtqYAdOc15QjiE/FYTnDkCN6lHIsRyEVAzGpHIvL1ug3khTC 8YKXg2SbgiIBHEjk5UYSW9w3Ii7f2zVuOMpHaX4lUOQ6CDqcE7CF8TmehBvG qx7VpkZeHnOgyC4wFYAqL3CiquhIYGzsD92iw8lI2QzrCJbfhVFOcFNpnByS 4sXBdF2+x0guiWzNZcHj0tIsTA2+s9F/YmQtj2THfgcjJ3hYPSkK7ntHYCSa OwZgLY7q447Cablz1nJWlBp7c2V+PtUDb27Kebk+1ALNYkcZNAAVUv0Ecd8J vPA9Ljw+SszJFBCZolwLHpQyTsIcQez3sOCFVKYpxeCyx4XGke08Z9sL3bFH NRfzxgq0wDuIH9URSQzD7JdVLiCJ3oeDZpNDiB4d7j/JYYP/AEik3UvsWk++ LAn4sof8vThVT6TkOLC+VlfLia2/IWPVTKmQ6g/N/BA4Hj1GNPh6UorapLPa 3vHMIZDHdGiDJCoW5n0/ekmovqJQxx6yF9j57BQUX7UOu8MXrX+nvUmGBnwv GHD9qLvYHcD4N9+h19u1OhiCbblAv5FsL/Xt49YYY4QEf2ioNsqEkVqdpYwE RuBVfB6ZqAKNOP3uGO+doacoOTaf39cxRnmJ6kPziJJiRTvlm0cZEKwZICef Rw2Wtp+XWkPgg9mfr1ooTOTH3fUblxySl5Daf/c5ea7OxvchYn6KiD9tbXt1 V6K546lD27gax0y8ZpafH0e/q6X0l8fPlU4RN25K17o3cUDZzapN5H2Ep7LY pgx7uksHxr33dndIuNFQ76J3VQMDw8aIuePji97Jd62+6JrqeMgsa/32kXED o4Y9DMn6/lTRh9TqmHjYg2FLN3dj46BvA2ShmDWVYPKX+GpXe3yff0N2FsTc UcYDJtoseeLz3eMJt7d2cQPYY9qsszHyhnKdhDwkeOxhZ3G5SBj52EG2ZRkr UO9C+U4WRBn1sLUo/lxqrTj3zyoyRMepe6Mx0HvtUDbnuqJn7fD56O6Gfg4i YXB4ppuVfj8oBoWiK0FU5UDRXzETVw5VzTX/y/3ftm16bKUAsiJo6VLbhRYo 3hQPSHC2nh7AamXpLDfoZq3VqQmwkcc+YHGM/35KJP+BPEEjWIh03TcKcXmR UxXV8fh4trAYCoxzHl707dv7ZrX1g22kMGXQqNS1HcMxAqoMSMJJDCTuuNbu OkMTNbxJM4MN2MdsdBmU4qkzFcSGlynnDYUeKHlEphHOg4o1pEkIoYmLFh1R 7wAoDJAJfAJ/QOORkFkav/uu1gXIvAltzAL5gGFAKQ3xvsFDMlOMpVi8EfXo XDkdDh1GUVQhZq3/RMwcH063orcNYzvDdYY4CwyBRrVErC5yI71cDsUAFcuH 1qdWXpoC5KO26+QxcisESMiLBoh82l7+XpgC/unBBhi43ohRmysFnL/0XSgA ZECTuyvkFwAUYNJY3AnwTNwCt01VOqM+7+uQAQf9tlKZVmGMON+E/CP4NzuL xjNKNXmZ3fl/GWPc3S2ObjR+tubK3EBAqU/0gj23nxyvK+8fmHLVGrvs7RN4 ug9W052BaH3ceFzMaLtmtXAyYCMUcPr9cTTrLqA6xzHYUlXEZDJ739D8FAq1 9fP2EUPwRpNC4RMhe79zlK/rxva6cpe457Mt4invqoaZ+wDq/yiMR81aHA+f ba1eujiuc/dURxLRIM56Q9lGKGmLNjD03ZBNjXkbbGR9KBnvSo0xIc5XNcAR H+paRNTd/IrOM2rrwHj0pZfEVRNaB3SJax1WGSXvTBsPH67xR2RepXXazkgd eFkNSgFriUDMQEoBc4YSVQbXdJE8g9AhBrFowBoukZht/yPAIZ83b54FLJdG Po2Lx0Ro2dxEEQi0ccjIkaCZTtM1hMOlcNc70LSiSPo0JNRJjj8kDgxLotQm wSMa4jd7SojZGVp4omeKtTQZBB1Ruf/OmRpGkrB7RAKdDQ5DkxgQLMwT/EU6 RYnfc3WNu+uh4DuGXHCG9whP4/u8Z8THFi9OYQ6OPtShtoe5Wsk94PgONpH4 j+BJX+HIfcfaxdRp2zSTFrncmMQowQiZaAnQwQMKgZpZbc0aLkFgb6GDNyCP Vn/6qaArqBIPHbLnedilBRmwQBZpHB7wo4Gz57pW2mjv5829JGvFL2bEHCde DGW4ETzCNpaAbWhs19nbXJonKcROscxG9k3G9OfZqZYRE1/OKVS4FjU/FchO 3M0TV2UzyjLhyHJrtTCOjCTDtsbRngEpxmFiAOec516kuZ5Pz2ql/qirDhx9 nK8ACDrNFr8PfiXrB0hS3uc7FL9/Vu5l1QJd57QPdK17pikSDFvDp8e72Jc3 6Nj4MyvaWXVIsHLPvckw9Bhda+2pBNsJG+1premR8r7fNhQ4/axcl17VOePq 8xakhhMWdAlAKFi4eRJXtn0aqHWcgtZheMZQsLKdcM5VOHjVZw2+S7MG+fTe L02rQjWissLTannnF3PQhditH9N5eNwLB5L3nS78DveCaKGHWT2c+g58xtjE yySE7ZffC99RYCeecJOsm1W9py6K2YNuyuRNDwfs6N2+7KndEiJWs3iiQcYw spV+lQIdSeRq/Iw9cFpNkiyYQTb2RdltKcYbRg3kAJMhJsiZJtQcYxNcr2f8 2I6igHSpnh8MmOmXkKPgi8TaQBlFX83WLSRWCr6CDuGW+D5WqNMWeuzCDycc U69eNFLIFoI++iQUh8NBJgygY/13tGe4ZtcI+t02NGxX+FZ28/uBVBhBENdO SAkf5A4i2Di0tJWIHjYPwBZzLUG+zPZr/IOp4+F0ij+tYUh7Y293vWovzh5w p5lRIvwzi4EfsLjPPkuLdGPFAFtASBLivvCuz98+xKNdsS5lp+lhD45alxB9 JkGE38ef+k7V3lpOo0xlXdSMIScM+Ary8RfOSTwc3cmIXMBZQA6D7mFXLUno xjSLzw0KTwM9h26C+BZ6hjyMU5tJ3C3pKGMHusGKIfKNhsjdWqnEIBvrapLj 3Syz9ZfniWIxRVxtHeBG4zRLG6u8E12DOriUnySbwQqjZCuNWp29R6lAGspA S7QdrnsgHeUR5q+QlgwGx/iG+2AM4TK/z0K63UZc/plPJUxd6pM8F70hLpUv MRfcoJ43TYbVfdUiEyRRJFokW6CED8EiQI8rdxAShe+TvDjrrhzHfeqJH1Jp d21LaSQ5Da6BfO70ePzHOrryKvKDzxYKUReOU/G93f2ZoAHAFuuyKRloHT0u cTrsd0hnQBzn8xNbGMhifgoZQQGJQEu2zI9bKiKFxEsOkd+01/dgHG2aGNdv 1SWedlnjuHrR9LjL/SPQk/L6ZzuUN0G38vlOcSuTJvvsUK5bhN78JL9ksUxl 8/TMSWSCpIH6jeyr7Evl28cei0IK+vLN8i1NI5laukx1oXoifi7ZGEfHZhyA k6edXxNXnlPlTk9FW4SYkKmB4Qe+gaPozF3ygm0qADMHUwoeYrzm16KQ8YEU A9zGYtcdwGvwbpXvPl+628o4SbaONmdZFiicC13Dpgu3hBq4beEyKFikzvOq P+Ee+yIsN24cjcrCPYmXDaTiWxSFpOFiY6j3kEL7eozbECk5Qm+/br7/flqU qqnw4JpOB+rjkA6dn4+7SyKhzvLwGAKnK/zhMEqi1s6MJMlyuYUeI43Xdr5S uFeVJHJp5O4Ihs4EFlggqYEJ/UFTx/lRdu4x6LnEpKyBJMvD9hysPFy/YxRX 2NMcZcj18JMGdg9G8ehzQvqVEuU2X5l2FDoFkqwv0ek6A34thrt2dxJTKXW8 2McvzvXWehcti2qL70AZCdKqhgm+3m5OKEYGMOF/wlRqesbRVblez2sDeYp1 +Xk2Z8022Jg7QpW/rzaKMeMWyqPkhPrhq6cDZFkaqTpyaUEx1trE2FfsZfdf KVTDJQ8Xlecr1PXe6CxF3u98P/pnoBb2PtdPVm0D1v/Br85LBUKsuCCWGIoO ASR+0ygAosZtXcmHqPI22v1vn6jotnuHSb0GrgaV2zMj5EGy3va93iTpng+Q HiHBSU6zfpZkQeAGXISSQZ2IwUBmxG5Cq856PhPsFa2WpiyOYBksS1Qp5ybA z9ihmTxmjhUGXm2P69QnBF5cBTAD1P6cH9dZc4JKvva1OWtWhRuCSE/oI6bN 6x6v2tTadUbAiQ7KvnmMpb8JpCaj+7xLyNPAEGDss5parzNQHf8Tx7QSBMPN uM5/O5uDX1fbdc7vIljT31oHNiIw/7W0rlHfyTXxnBuqmL6951tdVxz49B8L NkjjBlMjhRy1MpeZMDPa2jLapvtO53vkgpbjw77YjXF1zHxppUCff/jdpjgo yQRGt8gDE8sQP3JLnMkWHKvrAxAQZvN+5bPaMHP+UevrLOckEkvhbUG6HS0Q 6xVUYIiES7KgEqeS/h63xvmGbfCN6xKlzskcd4LfBgBd4trcznX8/lEoKZcS uWDuaBjW1U5twpiKKVTg/YP9fgIwY9+Ln5OvMfK8HkNdByTMQpgTAC9iFQ8e KojL2k916wCRC2ySe/DNFFAcoA53GJ3tnlPRX+nsIPp0B+N9RT4UF/di2Bdz 1LPPSpY0cpbnydmBA3KA5lctv46Lf/SR0+mmNx9CL/pWpeanKR3i6KmGTFkK 5EuE9Nw6FBUx5tZOFgh86sY1DSXEZXXA/+SSzVlgVVsZcmF5BSacLjTmd09r dS1yTA07YMeM0SBlHYq0/w75z+z/pwxKv87V9sUzXKMSwye2UCS7d7pG+QGh oHB/9Fl6r5+RYB2rftRfqz/pIJNofYETNOvxMRI9OeNFt1t3PSuOdljr/ukN ApDEywfZO59gRMawb1cfXXlhlSSl8e4pPVlFiynbcok9wi1+n8GhAGi59nVR ze8qXAW3FyAL0yzXAHMvu0I6vQvsSQ9ZL5Ukv7HldA8Fs35s+GUUb3dnhXHh 9pXfSMljZvaKuxxIJUQyzRHuELLO1a7tX2iZs6xBjFyuFk7zO0ECmc0v+bEq bhKOiyTgStU+qP3LvD94z1/TFW2Njf6eaDj/qgg26qsQrYFfC1aI69Z1jcfM nRevvLmDy4ud4/1Kz386ckQp18jV4M81HOjvzL9TTahseJfyi7uMUkABWkDI 1AA/bNCcbomkCPmrXyf7fOo+2yIKCU0gRgGsHIpsiRL1WWejCZlx3q/FuRlE Tgwons+penzk/l+XphyoygarTDZYJUDgzAYjIhEvGBLu7pgVRnl07RNJVIMP dvL7YfiARZ2W6gzbpZiVWFZP8ofNjI4IxOAs5zkIlEmCQQskQu0aRPHwKyUv Akv2nO10NolZPwBIEORRw6PZfO+DwnjY9EXxqrv6xQPOuyixbXMc1tbhE/ra rHuNMZAeupuWqeUHhQFq/hAEhqq/wE93iIZYRF98kf1NmrVbwTQQVP/GuXId WOW7JoGsYROuxCZRlrjNBa6lTMBhOW9KGQ4mBjFNfUuONrA2DJtcIrMNto+O p8VR/TNUkI2ecpRP/F1gTC2vvqogEIJ2si2zmfzX2TtZIY+SIcaV1g6297gn E5LtoWMnZJ+sBMFEAKIhFC26JMmDeY89q3daZil1CiRj8nmhu2QtfLQIJtg6 3QNZRjwJ3frnx0eJWgtTbNC38/m6uZzp+HyDR8aBAejcQTV2w3IQ/EVw5riw tlxYrvFZyvGUe/q4OHrOHtzrbXSV2jL24tUCkXnW+/cCUHi2nDlcSc73nmnP 5HUfd7fZw39zH477vuRPIkqW26z+kzK5Ce61c581VSYBDNEYb6F1Lk/Cj4wN Gp/9te1zju0z3wbYCnt3/axp95wTj9pwjrNFkus2yEz16nntUoUSqlpHzP3f KO7yK41cCsZR1xIwa23Uo4kBxDFvR6m3on162agv/a0Kp1CY4LydAq2LnjIw elOVK6usw7uYahz71svyevuPBFQIKmjLKIZHiC/A1bu3ojNoJufLHSe/k2Gf pMCGzoniatCfLuM9JJB14hXfvil+HaGiwYEE/QmC1CIvBRYKAnZF2S6K2nW/ w8EEQRCPvohCdeBRCYfb5ZcRgXv1DEfkeyBpG4DUw1kffwH+MqzQlSEMne5h C7C9ye8D1F3qGzbuSDib7Daz7gw3yXfx6McyIAr5WSJnp6qJhK7QLxysB/tL GjXrt8jhjO0LKjRxsVbgCwvWUnTvoEPl5KVxaa9HDdcK4s33doovv7Jl3PCU a+Kr158Z37a9Rzzz+onx5vt7xf3GV5vsarjW+AR3CxShItE82PdYomh4xxWI x6qO8kDIxv+5Tv+P9xxgZdfO9WDFz4BjLJAwmvj666lxFsj05gMPcrkutINL s1xwgUfrZvHwzR7PLohERrv6p7bGxt8WbJhDhXNoogtb6GWAcQ3P0hjGSDhg sxRl58+VVgcwO/ceIyDj7J3sM2C7DdVU6gGRDXLkjtppWPrBMQnIfXCMyYWl gWzFvIzEwY4PIfJPlikKgQ/+ukE02zlYURZsA5GWG9+ZZ5T09/VTlOJDjqhr 5yoq9nOBZeJNB+ItX5GiHeDJPshPEIOXK2nl/M207oblabs+OEmr/SmC+zAR DqM3yjCJEzNoVxlciJsqGgLv5JCLDzS77gyd8pGecnGZcYOyA7qI5cdE4VD2 YRN46mQbA5doG4kN9vlhEvyG23VBPaphXix0G+NHFbshhfxNLrx9rW/1XRP5 4IuY2byn9cBz7o/ixva+LrS/TQwQlzwmeyc5u9H3ol2GWd1s69s86kcUUFDt GF/iNLfx7g7xzbc1dFMxIwn7/1of1Yw8kW9C5nSb5We9o9yefU1I7XuJtlOW AYBzeRCgWmhUCZLunJAFJ/OEkGCF/afghrXwBY+7PpWCQ60zHRb+B7hFWZzv OP+g8Fue/R/gphRLWUdkgRv072p/9MUe2Ar8xaGF7HeWR9xj/atwE/RZruWT otTKwYJCqjht6zg/YhMqV6QzytvOXb2d8bVtdtvbrO3z7An3cDG6PbZFUTtn JwBfYD8sw6YvjHuMq3GgcvuD3ePMyU/Gwy9eGD//UtO4ataceNtjneJn9h8T v/R6A4dx7Dtv5Zi4z/nLnfgxbXa5UyU0MjFh6Y2pp1CI38I1A9zjWmAr2+Bw 4Co2iHc9l8T2iQD60UcBi4vcNnR5eMmRCAArLfYLTAPwfDuHB0iFO7OPjAjp 8fRSbK5oRCHmPa41Al4ThRw2/u3V0XtGL7/TxuYjhnRNp/kp07bVG6g0bbll EloJvDyRdOXGyS68K402CCX6Kslh8mlOiBYIwPdFQ1kofhHkz1xJd9tt2YyX XDsxtw5yknOMzADxvIUKXUWwC8bIL1vqO44Z35TpO7qqfq8p7Io8UoS5cpwz F9h+fd8wcfZ62UYBNsRclHGYEn7dWPshwGI3RZAM15hzu1Cq/TbZcff1Sduk wAeJ7dgMTrFbuWAV6y7eBoBzexzDQiguFJLvdewcfTaLG469W2oABFnUdsur JDjfNkD3T1svuEMqPDKLuvKDB+S0988crrWzDYz46RZK5WqCq8tXCOQ9VM8e f11P8noqFAWkxCrzkv9uBMzZBKfqmQOJw6z3jmHwbdQ69E9DbN8iE/WadYji Apt+W4zw1X5z9DdY+tuM9KjBwPWhvSjxsEbM2CdNMXYw8DAUGUfQDj5QYCJm UVc85x5hSIlJzI3wBfEOkvXmGNk8FEn0RNmOBNAhNXfGHm6AHfBxccDE/NTl a0uUThoFqfyXhYWJIzC+6UieQ6JgJvhhICyQtQBmhyXg+ig7MbqHrNI0N3tA ObbPiIR+53qIw3vvDP8dEO2ctvI/ULCxrAJrPcba+yi2Z9puqy02LzXiISwX 8M8ycvn2R0KdMXNmx+1Gborzyk52TqF8/8QG7u7nyuN7dpwc97/g2njd1r7x 5EUXiWXVjmfOseHW7LK4UeW2+NoNI9yuTz1d5Kr8rLBpcc9egR2JpTrdJZDK OHG12AEUwAj4BccO8lMBcNg6AbjPPkvwj0QoBFOz+qOPUr8QsNLr8jz+5bpT g424pxhZzPcyMJ0IB8uz79H7GDfT+fSEdGSunyLZv+PRj7xTW0rE/XLrJHMs 2jq0b3iUXH8CGjteQnE7mMWThUK4oD7HpHCCwcyFy+Lo73bsx6S2S93RQK9f 2/K14eofip1pI4dKlsxjwVbqkC1yqvRoXJWyf2Do/Lp5qrgjGAykigOYlgqR QjNC/qiQ8/hr6yGjjEv9/Ng4+qijjKfEU/ymkUTqEjE/gsUAJYykVPbq5bVl 5EOZYCS3XzVJ8rhk263cDoIowQ1UJHXatQ9TTd1ZlE20y429U5f7q08c/4oJ qCOe1G0h/Yf89hwP+AF6v/PucriKvN9ct8/+n9rT/+Q4Ub6TC1SEduR6Gx/V Gjs4D8KGoAQVw+y8m0DmTTzd+9toXsGvHSex4HkPfoAln4QnWeJchxt+HcXd jEWcar2KfFTFf/dgESX7ZwWDljv8Is3AlYT/Efq63Z/fBLrTxygoYtRnUXz1 mxJ6wT56MdFNYCK1alyFznWasUlU1Pw0w1Pb76L1/lzz/bUNPvrbuO4YhMmW kRLsHJ3ywoCBkYf3llmVrwA0bBa+uGpUO/peMpgEqwZYsX9n/jAA6hS1xMNo KaLtQA9uzikxUnnfFr5ZSxzg5TgiUqJwsQsEXlEo2pNq7r4n6dP3AGGO09m1 V3ZPmtwsSmoljzQs2W3sejdxzvZ22l02Kb5vVyfH9Y4dcodiQ32dYKcwe68o 7j1xRfzsgebxu+8XxTfcNihuVzkpSau3+6myeOSY8njBotoJhBEJxtuFsWNh 5nrYWbPDYUMuk1de8VCXSfJAGbwlkWIYINg9BExAW4Psy3pgELnYZGDnxgIt ZDtCyn33JWIxV0JDCATneksscy6W4KtsAqwPG2ykube6OkqFBASG9b6T7HX9 Pcfl4LM39bE9ebt5B58sS22ENpksjCw73b4X6UX3DG+qRFT/4dZx1OVWOYoV tZb+/gSfX5gkKS/ZEF03zFsu6mo0OyCMlLWeD3Z1ROjhRsHaGU0bvUy0jg/m CWV7AmgFbIPE5AAQFjI1T9uu7/z9J0kCThNAkRzlNvLxT5TbMB/+BqsHukLH Kofa0zH2Vr5c53E+cJ+lxtaJ9wgwPyjTGEBDytSB+wg1w0i2QK4/Z774SOBH cj3OM/UhlVUcaayz/UPKSsr+l9ij/ZXNGQ+cKldkPqAZc8aS2XH0mLXtgmVK YIVMTGIUGCkVHNEQTpytY3b0BA/xqWPwlvOUNxtWjy8SwNzoxxzysBGIfBve k6xfVe/2iHG53kLHn0bxmS9Hcelf05kNDWw3Dyh8WoWX38bhGj5QVz1imPat YeEz0gzR10hUiafCRpOi+n2o/FTBjQ7jK77BARuPO078kLR0zU3SumWPMHfS JA8Dm4UqnJP+3caXsXGd7x4d537HIa1Jniga/Ogkj7GvuHYfcvCCH7OSkgGG WvebHwZDlGt1vwOG+R6A0D7AR3HqUGr2tNLO9Ei4Oz01FLLrpR4QjwTD1L6Z nQ+ZRWCYmKaA18YeirmSL31aYcxsvzG0hcZRm01QxH3x+dY9Lx4W1z+twPn2 vPJG/bjx6YAI9aSaxieMuCU++OOj4kzttnHf6mUOZ1AFLlxc6pLOI46yDpEW u8NZM2fGG+5sGz/9tGhZ7doSjQOFg9rxgtmO7RWsM5DKeFc44BBoQ1LmcA6D 4QGBzz/voa3E/eRIwBWI48w4OpWWqkXOZr/M8dFEUA7c8/DhgLS1HLqSSLxs o3/geBUbcNd6W9+dGp+YRUQQV5yvGckvom7VJvHZgxs/XTY1/OEm21h/yQjM LArqKd+3i2dGnY1PD/6tBFM0t4H3bJkgkzTBpy3XtPujxkrSSW2L3zvClONK +uTJtAgSYn/4SxTKO9gljpc2HhmbyAUXvfCsHIJx1btxGWGz8jgJ/nMkUX7d O7gAe1hw+QTtH38pOz3wkTR31IsVLv25HOZsn7V1dG5CINAI4uA8fruy1H/S FG+QxNcGXxLk7OdOtNXXqFlrL7VmGyX5eZGLQXDyL3YMvIFBRWJlT37J00mT dX9iMHXOXOV23mvy87Tr7HuT8JRUKIQADZxQXmorWfhRu+0z7DonoT28WchI ZDMxg8VGJUfdluoP7i2SAThToreGfZE3QlFxcu+ZLOXqBMDvMDTYnEnJCTyZ agwuhzC83hf2UYlo5a0ucXJiPv+loGSkra6eandlc23U0u2F0xwMM9fLL+iL pio01kVEgH537FI65lCtOlSyBiFBRtxpyo6JXCHW5l0kUaOlxPsUAahyX5S4 guB336lTlimULt4sQcYEGFEghDiEyI0C8UP+pyt/KJDPD+uy6mVkexGHoI2a KFxPhKBHlKY4Do5y2HKZgpBV6WLMEfWz6lxQyhWaOPOHUPHkI1GR882INDcE H+h6fpCv8bBIoL09sSKbg5rZ067DdfGEw6h09hwTcQY6EG84z7afvSx+ZE/b +N4HG8VbH2gU55z0VHz88FviB3c0crIzwLLl0S7xuDlTnTYPz2PWYZbAOrxq Xd34mefy41PGLIubtVKVX0ALbGOuAhbffTe4YjZxwjKskhwsAWIBOryZw18A EBjlPNmCdQBG4ByTO9enZ+FsxfyILZBrZbwEjYWaa9GWBGNrx3vtdM1sOROv eV4hUx+isjXn0hus2/N9rDqeE6XzgiaksVzsckvjFifUiRdil/tZJu7yUVE8 tcbQ5vLu6nhT7G8TQ6WTyctsw3bQGHFDiu3wqgHTV2xYvl1XZOdzO/uXpaBg jkfHP5VwLVRNeL5cY4D6TFuh4dlGev5sEHP2LWrzpy0VaTr2CUm5IBsZBEJa +T+XCgpAS5KSgGi/deU9NIyb6lT/ykieBtHY/PciyeJ4FxMFQip4TgFXxAQz 2npu193S/lHW4xRfsJXjQ1b7oS+l6099IgcYIxXDXYO1W0iOH6pcE60GYLK+ 0tjvsKc1dVx8kXJrDbxZp6VJYP7pb+r0/QwI93cx8LR1bddqrgjAiHo3zzC/ 0tbvr+8zuERiJ4Ahwi/BWPP8AH2Wh7/XaY9JAEnQkE9addRRSjIPKhWjuvKG rA5vKn87oRDO8+SRYNFvF5f8wSbd9pq8Wcq+Uunp6Y8Kh/N3yBaCtj1kWuEv SQZcHOULMhK7RAieXoT9QsYJxgHrEJG4Bn5zri0eGXH4w4+fWAqHH419x64V BSWJ8Izbx15YkMXG0khaLZzjUHSukPCUAKoLo7RIBbwg8STz+Z9maGi5s4OK i/w60CtUr1CdR124vk614ntBUd+7+VNgVMI5pmUkegmXD4WlaVLdID63ka8t xhvicaEuYzwoApq9CpROs3Z3neeETfF+G6GkYnBlP3eWxLseL4k//ewoJ0I/ uLuDc4W5fvPgeNzlk+MLrhzjTCiA1+hLZ8TXru4e9xi7NH7oiXZx08pb44Pv lDgg5GUQNEGBNkzUDhRzwDpDK9AN2gbavUp2eBmxIYsAYSpHlzs3Glxu2MZ3 BAqi1AjA5RrE4zDNBoAdOTL1mwEQaadLnVaidQds6UJYCRzYy+Nz3/cd2p5H oZ0nD5UH3gbb/LvYzls+6Gj5t+orOYeU955ogVwbHwW/wQicK6Di76ECFcpG 2O6JLvoovc79rse41DZOz4+AHOoJAoCsw06I/WBbhQTnv8GzMjr3t/b9CzvX H0skdA8yafVPxVLP/ctpFkW5aqUZQsMHNxbMF5hE8HHGWeoPftufS/LCGEHG RoGIxYOYj5VL5LfMB9fAQ12Egz0/o8P9uZT76bZa6w51i5JwCyw5MEGWPu/m uNjdOjLcgNQsI55LVZyVhnH7rFUVP1KO0v39FTZ27FIT19fH0cbh/nI2Zv5U S/kB/2pNfA74tmZ3WqkYZ8xbrdcq/AxhFmLiDbuOlGDcXODH0hORB8FcKUwg KABWiR+eddIyaj1SHGxnmNPvYWEPHqrOGEGXwSaDggOB44/u1N9G+ATZOzzK HnQBsijvbJ9S6RHvQHfG6z5gIRooGOMDJrKvtfM2s77w6HOyqRTBgw4Iz8BC hkA+FSy+VT6BPHCt0De9MM7PT8tTO+p7vD8cGfdnaRlq0BORNpOlmGN35X86 UrX4Lz20sYLFrmFTiKSoF+CwOHUIbOix7Qw/lmb69cxJy7yXXiZNRpxd36zG N76hw96ZDmUDpU0z8Ol1BgY8zf+V10yhO5U/7Bijg6W9khTKUDwQwYVAd9ob l/R4NCj5Mp4+LV47ysUvAjKjLpkWz1k+MHFmgTECnIASoMU6A5mkWhrWtJDZ JeQ+5ret92W4M9k5kxMfGpc6ys9CBmpFXsqllHYzr0sM+efJ8BPYnDuEv0w7 m72yLy/l0mF++zyInYWhcIpmK8RSojZcLhaT8Ta2y4SqRiH5cRx8W/LTJFIs lGzDr3cJHF87ufKPJdr8b9+YTFp3NzF7ZOR2Irk2jcAIYamB0Z36aFQYGh0M rNmIRh74rxt5QpkmuQthZnye68Q2Zd8K1YG1p6LA2Df2N0RsLeUwJDr7mzbY 2TBUVSqR+Dv4Uh2bTLatXKBzAkN0JmAIORb5E50UTcT7hWc9OwrlofYmZZsZ CiGZ7tYs+FHRyeqr1CzKX0x9MrwDhYU6YZApA5Q7FLbluqrZSzwtsiYU+Ic3 4TkhDSyMMAu+F4XXezAR1Fy8l3epyyrL6xM9+TG/zT+sZlkQcTgK3nppfuOa KLgrJ8jk3fPUurr+3F/435Vp1wjVA6LE3fjkcNZQ66FNMPQWCnFgOOP8YTzS Cf770BRVeJw3ZCNNXhpKEZY1Ptc6+w9MW5Hc6BlRSKGerpuWHA/8yffYJFD8 6npsSyEHPzzUcpIS8xLfkKyiEp785DqflWmLRzn8sG1BCxZci1PYqJWURQZi 4EJAFvuElMNYdbkEyfu3bk0qmgW04XVjkkC+rKlJzA9YKgCo0kTfVuiunu3N lyAR6FMdOQ7k5v8Ux+yFCwMKpVZD9RacDLLJzzfe+zdPvGjAVpko0bL92609 7OpLbWwnjGI8stxUqcP5hJTEcSQuxdNmHJNGnTHuYqTeoG52YehmaMc+a5eF SvkCsOwP7hqzrknc/SgS/vjArENy01KN+uQGx5LgrvxgdepsAlj4zFR52Yno SEQwar/HwYbyS+64IzULcxwmhZARamObFHyC4ut5/0y3Rml1rFG+UT9zDRXf 6eVfzaYIZViQg+gH066IjpC3DHTCkKSWGKsqD4Ubz4vPst8NA7JL9EqcuLPK XfughzyHKNlAVP5BqJZXpLn431EyXQUkctmFn4vCA1/ikU6sDrNBj7QuYZyC UGICnenBqiorqn53dGQlrm+z9p/tb2d2gjydgvoLOWq1H/7BaaVAhgLEqWl+ Qf+EhHi5a3CRe50VHqBC7UL+XunRCBkc6bKpP3WgVDV+3QZ/LL9v8seODucu EuixvbW/G9oD+AUMredzlgSkqoiyELAgGa2r1xbGffpknHyNlNas/z12hGGW YdpxQ29xg5/1t23vHo+YPsNhxkuv5Mc/ejnfWVUNboQT9RwueGkusRjwF70/ qjMCSqHBSGzEiBHY72adSKpZMkwbGOagh/M+Ilydlpmw6mRGgV9zp0GjJRhT 0akSFIYxY8QICYMAH5enKa7c9zPSwLHvJ14YXG9LkS35tkyxZRr9xZq0xJpW yXc0IUxae3xvraufdCFYdTffxVA2D8uJc7/IjfPR+f82knYLMPqJ34fCPp/m iGA1lWKM1a8cLV9ksqvx+04j2F0JihogOfDv9gb/kZEyOuQDwnQw8B4EONGl P1lf+HdO6q5BGnSwb+WSvIBtfy2SqFh9k13mmDjq9ybuzxrOdeSgQQUbQZ7O 8tDwOLprjPW+aQH3JJWeS10OQ9XTnrRW8VKIhahFbrg46nudAZqdo+/z9nDu E4DVXKty4BvtfBtNur1iBbbXxEhQecgI1l1yO+ZD0AUt5CFMuVhmlX0dbPpc Lb0XhR67vCERE2LmDAR2V3fgMLhVmAi/QEim0CCyCCjCw6QmE9M4PioYEVFW Y0hkfirKdXqT/CesJzb3dSDmRWnqU5MTc3CkA0JeTYCw4j2FzxOACqvK/DuA ZGsn+yEDhmAGaoqhX3NyXXc7lUF2r5+ql86wyXToXmoho4+23ve1NeVL67nW Y6/cIuAM/iecE7nwKLtmgfXmNifJP3nMnihQNuESTb/DX9+FrNZ1U+n7aZOc 2gERtsT/zvWHoT066CEJuBrhtxf73t7Nny5zRIb3goDAobL8t/5UvJBMNCIb VjPZ0eupZaM4qwHVacUc1HrLo6R0opvdOBwFG4Is+uxVHu2KUyRlQTEG/wuK ttZ+XFf6WaVQcyZRr+X+4gjYzf2dO6RVVVvsHJ31Hpv1mOnsConu/tWGGURD uziOxyFxCQH1gBLmhCtXj4ovvnqUAzVbl+Mc7critifWN7zKODwDJnnD/H3a V70LlgCUWhA7cC3UASbEulevlKAH3LPFn7+58yn2FgK3gNghTd0kn5g6pK4L Fgr2IxCDfRBA9xgFOMl6WbZpYYlh6mW2/+JD6iq1P3HasZxABV/x44blU88Q a8X5dvYyhuXNLeKKiRXx+Gc9DgKbhol1jQMW2D7R1Oo4eqFx4AjuLNgVqFax 08DmlIu0C4f+NA8VmVe95CtZ5d2tVQXiK5uC/5bBXTgHLkg7ULKfsVAexJfe k3GrM1L/ezk1E1Bzvu32blutBfage90OJv4Tn9qmiTu1y5ULOEUqZ986Po6e 9ebapYbt/W402OggkbXXjhzwDq0tKZ4QKYd70+hTQ9TAPo9pPfo8KOw141Vz FgsxoLrHponzfeldphKyCGBtwG2aKmgXT7Xpwobjw8PsmId1rvb3SyTFyYRi SYWtbKAa+J+9xn4Xq2m/DulkB0qC4+XiFsdr+sS/qrG1ZXZtf5kNjC5xsaHq 6FlyRcqHC76VhZjto7i9wdnMK4S2Bf+UuzIf4JLYfaDNybPe3rrgAdV9ZZjx F7MXf4E4YnoRX8iQThWfDTsEm/UMhgto358Uv0YALm4m2bBZ+Pf0XIvIB2Bj 89wZkfgkn9Ue72g7OUO4l9f8ti8dIiQqQgdoTMe4qDDDVPjjFnqQmenPC5pA 5TBJ4I/wmTvHF0QcCgNJvYIY3D76/8NjRuZRzlsrENdi+aP4xEouOVMwhQJX eN9B/danrA+lN/aIYE3F8fk4h4/pZVr7W0pqhucLkbHCYjFCgsRFsR4ndJBb 6xb5pTQ7S9C0zJ7mfsOA/S+XxR99nBtvtzfQctAtmbhR3612tq5jVyXCJAgF kztM9Pxbb6EmDwEWABFAiPm1UfMGSSH5559P/4a63Ui7WA2CeEt/BHvZBik0 fhn8w4MG1naXg0nUMtHZhQVsJJoHmz3CtPdkccYNtoP6LW3d9n26C3Bzo3XB QYfUDLpaqYnH49EY27r6tk/Bq1HK1hv74bU/iEL5znLQ9kBxvHC1f/68m1lR 0MrNtOfZ5d0oq9Z4K/nmwke7TbG3bxjVOUdI+UaxjBJrTxQiImF9a2/rD/ly iLp6uU2D64zHGgwOelDR+aHmV/ApAeBw8Oj7XmpxBWlAJNDqr0UimAbQX9VW brzw+bxFHF2yTLlWdp5lvW5FKmj/obYIKR8C3ACeUbflOhVJbXkYYykl3p+Q 2gvm6opE9tIKqC3s797xasV8p/o0GXqIjLySeoWLHEdsMuecMSOOXuxlnd8w detZkvfJp9xjaxz1Xmn7tNWY7z3dhqQ16xcNhZsPl+lcDNM9vuOjccVdBXYP dq0YHxd23hrn3VDh8HKICZP1GJfN/DH3+pdunaeu4dMSm3AvMKZZ5/dyV6YG eOF/y7yAg8kN1unaGs0vtX6beV+YiBYH+oiOD3MtJk/kJ6x4rCP/BjqZYpuY z35SDiUDrMMW2DXOtQ65epMXx0vjLS/4kuR/FMzV7WinMEJcjc4KmkXOod/5 W57toXCzkAvE+e8oqYHm0K1ZpIS+X/jujXRe37MSX//aJgjnYEqiFqgBQPac A9Xzfhj2SgLEtfBHQ9jreFiDY57p10HUalJ133Ee2mYGYbeOWGOI02Cxoe0E YIToC7MQm78d/WnJqJLjuqw7R+1bFKBxoT3d3fslcu62N9c/FPpsV+OiK8DB m+5qFW+6qzzevKUVVMra8cEHtC2ERaTebHnxq2+Vxa+8USdJC4A6MNe5/9ZN 1IQ42OGeTI4uoAv7p3Oi9IIFiYrJwkKb2Ma+hYWJrgJhmzjI4IEEs8Skeu+9 oYJKqpWkicj7ivzd9CJWESbb3ta0Z705YpmBc5V9X2xINXJBFF9iTa9DvWVD xeutA3Yc4x69EtC8EoWisR62askZufKMOLqlqewVl0YiFyAkItbb/jeqrKeI 69jbEK+5weekNgiW162LVI6zOa5/HO1objJATxmsQLjVhnybT1BHRj34mvWC 941zDSTCbZaoG5/gVLyvpyIfiKgAAfGaI8HTsFflKByEYELYgC8CM9AhjhqT F2hjKGhx1ZxUR7jSYOfxwTp01jUZh3IFCo/FrZfdHTt7SAaI9g9y6CkfsM9N Riq/qSd7ydnrDRFvFCIe7OFPb8BpgmL02fFqCUj09dFyWcatmaQFL56q6hQ0 Y/NE5Wa/ulgvhSdDpDCKVKKGz7xJbTp3Fpyda3SOouM0LtjnPUPnPfVl7Jxv c9F5hkrWF528dIJHOYLTgjRZrsGeMVRr9k0Ud35LCOa1eq5AN+jX/6D8k3FI Hve4vl/lDaQIy/AJpl1Du8SHgvWDjGtMMxZ3/svKAUr/X2tjseNhnX/c0w71 UpOHTbc5/6u2NH7ft3tPuM9mkibBrputMxs0NrtY13f+F+gMYMR01od8J90b pTYMMOOGSHKjlG+avq0tZfa8imCSZJ3b7p5VdP4Po17izsfjC+a9yP1NIQ40 vCkKvza4nrbANwECtJrVCxyEQvaG+ZYROoMgTPg8fBGwbxqFMF9/+mJx4XlR IkR3tbexzd7ixMf9ccxtF6eccvy0ePe+pvFwo7mZU5ytIz598lLv1IE02iQw vERihq4tv2VgvHxjhWOAe15o6WJ333i76Ijcn3gDbdvVwcXFEgWBh16IkeWp kFICw0dFRegctZ0xhH0AQmJoQ8V1XiZGE8QHct5h+SBtxeTJKQoS84vOMJhB 5MjS6T3ey03GA9dZu1rass2Wyw97FLSlge070XM9puFe73iwayhfBNhCsH8h qTDVoproWx5Hi6bZmDbOtv8Yd5pGy/3ctMRA0h5HSxPRS1qdJaMI3nbv5dE9 HmlOCNkzDaVvDKgH99vVTq7NDkBBzSuUcekpw4KDxgXfOD7HgYidfv/pikbg g7cbAifJnPhLLOyQXSnwTakRwhG80eN5u1RfO1WbKDcb/fo8hNYwRT/idXH/ yEa/dkrTWcuDZac9Pqpsq36HehcLp6g40ZIlduqTbMI3nL9wYxqii7MKznjT fFJiXHOesLuvmp/yvf39FHp7+Tw5+p23yGQ27/DN1MKoxJ0Mr7nXozQE9p0o Rdg/+X3u5fGM0JhBnvuFP36//80bhVmg1u+rY3K+MpS0znzTZpVfmWDT7wDr HP+Psu8Ar6pK196ekx5Ig0AILUZaDIQSIAEiBAyEXgKEFjBgJCASmnSkSQtd FLAhoIigIE1hbAMKdrF3x9HRuc71zu/Y5s7ce6es/3vX+629T3Rm/vuT53DO 2Wfvtdfee613vV9PkieV/l2EIemPnmkiK3j8S5bruaKuqKVlrTPVbmynWTU5 vOpQ37FATr39YWZkv03E8MFo+vcEv6ayilc+wiSe0ETCWxUguPN22X4Ej+u8 xbwPuLvFMWmm9yEekj1B7wr81x/Vq0vQ8QvnOoxpl2Uz3wvqRKwi5sEmVSJd y4bmAIa9Xp5XSczr4DAPQjRwr5ONhSRXRBPTFD7hFwzXHdgzttrpEu0AD55u XSLhL5nb0NxifQdGQS8IdjpB92+tsOkUhHiWPbyg3dWeK8Hjw+r6h1ubd9+P MUkj12B8xmqw17mnG5ra3Xk2twpUfpAPq1dNsfkGlm3uY15+JcY8dDrXnD0X 1gVChnRCO0vMBJucyg4QBPhS9adVNyIiAvCFCF2tOWc5HgqxI9hQmL0ac6Ms jDqo2n5KLcxXmTxBzu4y8Fq/y8eRJJAz8T2m0gZC9cfyJdS1XNAu9x0+evuI oQ5/QaEKp35L38vZDiwpLe/JNd6M8qAdHDe7qzDwTDNFlsTsmTLfh46mCEqP lKGjISMu7U2YAlyh8M4KmcTly5nM+BnBoiXLMMpFCEEmwDso5MH5F5Mb+ZUG vxC2sFWf3iOw4QKqXusiQ2Oq8S7l14Up/BXNJWO6+pBq9erRgwy57G6dzVTq +IMIimNcaMG8dbTW5j1BsTSXpArP0L2gToPxAQkSAFPYHY6+6BKUiS629yvN vwxDL74/nE/1NUpZwPJTPYViKRyEkUsVcHepofEmC9An5nLsYAacV4i6oBMW ajaX4fSvOsnOE7rC+AV3EZAIbeqvvcA2bytIXrAzRv5ij8t8l9cEmevxAh3Z InWmyhJbIBxn2lmGOtg/4ZZDztI35JaDOAUgqhHrUWezCAQ+9+hBN5PF0pUc GSBlL+ugkwE46RSPhwWiTAZOl0v0cAn9yD4ApmYKSmadDFzKQEygfMPEBDjv VXiBPPHvnmnxji2FHbIFGmIDG8Z4z1Vf57aSCCULbiDEz/0eHwTMq3k/A6di zxXDrgtOYEbQroHvgk1B47fGAlTI8TDnFQKHleWBY8gwj/4p0DnOUEir9lyW gV3W/x59iXRMWaPNoCdD9btzKAbVmzjdXHg5xSQt7W5KqlaavQ91N3ufi7Ih Da+/lWgdUY6dbWuefTHdLKgdF8JOsobIR7Nxdw+zY18P8+zzSWbsnOnm1OMp ZuiNiwS7rlFVxRrr9WTx6kgSo2885iWBPaJPHzX3xNo4VldoBx6I115LjQRC VhxOgVZBhoWMe+1BxakYa92FVeO+F/nA2srzHPGa7xtgGdJdwU9JuF/IiQon a0ghw52LSLxVxpVDKVCmz36nR8b+gN6vY24MRPHZo8cogvhAVtgZd3+rcwU/ TRbxboog0yGZr8/KZKkSFrSoJ7NNHe5Nh/yZQkeqVqmT/yW6wQqiDHmBujKb 2WkDs++uXR7o1W6XKf+JJisZsIe+Hc4nFqEJyPb0fhNZtUT6fFtu+4Mj+Nu5 PjTPvqwZpW7eIYxIYGjuWlzTnJVwPJ2zgtr/iVX0+QZEgQ1NvYVJlUc9wZBp JMRC/3bMM17f1wOYOjicuUyROh7FLY7LcBgnSNlf7tTOyZqTWGTXtncjkaof GwSLGvwJJ+k7XGphYn1G5y9UJD3swFc/uHjeZdAD2EhBXRfqhPxaIWeSnYhf Y4jFC/yU3E8DwFB5sG3u9EzVRkuufFtsS8GDcZr0Ey9h9sqk2liW31Wm62zE F9xJI0F/BBAK5N2Agotveibzd1TE9ZPxmLSCMAXFG/I/df6l9ulvApPvRzja peogg+iItRDiHSjVQR1FHYnKZTJxu4o4GgOU/kqv09LkE3ZN2stNSSJzNb+s t22InsLhFHLTQY5LdMLjP8eqGGLVXEUbzJoW9ob6EttGnSBFCijYHavMNOcz EkXuNUlfywJXkmv00RY5jpRBXBoegVuBD57xrovAtNE6PzcxonXSolmm9m6Z WF1OmujZpWbCzbPNnA0TTXrvw+b6FVU23UnR5PUh7CTSiny0IkLhpE3myInm FlB27i+yTcR3O8lTwgHg6qMmp6DQypPwGgNmQXGGKmiJSrVgBsKowNMV+S9e PetciMLSpZ4PUmfkwS2UQTJHtU5QcglpImrEMo5GgHujPP8bZFWuP1Mv8hUH OLEMRK2NeCFZ9kl7I3WfRL8dvDrKKVPv9qgHa92KCU3mzKei695WgR7tDVle luTw9+ReNPCBmWzsRMSCyIhqsVCm33a9HHZfGbqDArHzthN1+r/G9ElALKGe 9MxIo0ss9PCQt6xbSX7AjxC2uWIej8Ofc7uFGWB310BP9k5b3yL6ei43AcBA taBug2T52CicIoQvcIw5KRe4swf1VPCDgzQIarX8JibvQ3JSJF8Cdp0VGF4h 8PsH6dUG6X2v94n8wK5Lacw54lRywCtkCr7yVrh3h5ympZ5AaWp/vqDeghES qwBUO5hWKYh1LeKy/03gwos1abfOS0Rrw97wX4pE9fxW8j9gS+GzJFS9H5Rx KGNusqDUJDjrIUP4g4Fh5A+yrAoodR/u628BNKRaTc1qAYnUp0TKA4ERPEwV LJlwgsZMGEFvFKyaIMAxXGZF8/8gXg2W5hJnRfiGiAQYs4U17+wYrVTMAs1D eChzlQQ+bmCUK2V/OU+iLKT575DC5b9ux20ZtC1Jsri2EfGxudyEHhivGMeO eDnw8jzKECBB8Z5T+v9LBMtWDMR2JMDMiEAwFdWQo+S2CK0YKFU/r65WbK6i G143BiRtJlHINrND925M4XC6V5eADdejAcBbdftcglzvMZ5JFtKxRNax98G2 EX86arH1+EWkAIYeJEIhYh4liRSTV7bTZqpbsm20Ra7X3qhvxs2rMv2nzjdR nc8EKCbHZpbuNssPNjfDp42wSe1LSxXAYq1Sq1cvVrsGA4dREtFXWPacMmsT Unc9Rg0olGyojuZQ7eQvaKwc+SFrB88QsW/reflZvsh8RangtiKJZ2Z6ZoqM vCVC4NLvUIR6SwEooW62esip+WwtV5OQ2IrDm3SsJJoRsi42hy0I7nMLiohR mLIwVeL7J4p5s6wvRRpQ3055uLteWy10aqgsIrcQ567aSrWWZV/PMvroPgGr e0RyWlQblJn+0Ppsrq0F51gl6DP2eABqUCi91pkZQxD9eTpfp6OM/S9TaShw f79Lp0fcWeev3irwCnaoNlZV+uiXNWA+zzI3qP6Na4CECIEUJQABSZAeAWs2 E4nsv0Wkx+0CZXdWGO9H6c/DwkAXr+d9wfsO2d5vB6tNVG2iTzRufcORAax1 4+Zs+blqPrsNx7t10s3VBVx2BiJNXxUN+p97DJk/53Fdh1c5dCgCJ35px98r LEKdIJS9HPRGAK3oCMfWYp0uzQXQekA7/rYe94MeJ1OjjcBLX5EA+x+SJVLm 9WqhICi3ingGWARG/lIfPZbGQ+RB8CPOf9b6asgtl28itWZ/7pk6fz96rLsr f/kynsMyfTsJjUsQmMo54jFiErdvovoR+yEJhXpBpxSnGpIloEMTpsluBRes /7P0rQ88rG3mWtkRE3m1QudFC28udEMdq4MJIaec9A+QDtjTzErgPMyhnwuq nxQBaQAdyJhrLUXY5RtUlykQjVEYxJPb6SmpJJYB3kZEYBmauU4/b9KZjM8w SwiQVMjhN5xi6ph3PtTfRseYpGlTLZalFR0x1aummrHz5iqWJdtoKQibbYbu NQOrl5tFW8aYmStH2giGUbOmW/XWyLIYM2CakIWeu01BLYMHEBFVT4EMWnnH px2QrZRz958dANncoyzrlS3P54KsaTUP4xZVP6SxVzYlCUTMixc1GCvOatVQ vwRJ/MGvoa5KbqNwriJmlHYBvub3yTMufp1jqQJL5G47J3C/Gcn0VgxrbFUF 6e6Rkrv78EpZp2o4buf1ZyVDJPFpOIIVXWBPXD8WdUipVr9jgSpu0myZY5wA SPWeqqygMUdWS9SVgQly6zCfH+CozREjH2qjFfP5c2sRCwWPdo0H6aqLRwgf gKsscAkdaikdaXI9M/hC+Y50bvgdSZ5uygvwqFJEx8EnqGDbK2DxbTJzgLwX xwkFjVYfwblbpLdbhsnF9yAuA4taorZ3hd4oGYtTSmX8ZfE4/BVMZMbSG8oo N80UrFs6gGTK5cMULt1MwCoLqmYo4L9QMPmc+3R9WCgSc8BZT8fiXQQjC0h4 eLIYNhQgaQc19sc8Lusi20Q7yBlc8poNbDD1LnlmYKUWtcHaDmb2Z4Jc/FDP tJLx2VDko3YvEi+y7pHTIsIh2RTLcGwIERY2AKFdhdJUVD02O+oiXYNHSnev fZrUzFo693vOHO7wKEwhEM5xnYKwqaIixrxjEKN6E0J9ekNdfJenHhmksdBn QXwDhbvXC6yQQAfIXFoi2dLYad5EYlKuI3kNvSCZR5m+F1tAVEe1hACj8jy/ gLJdKXYpyGyyNy7BwdZ83W2BY2JhTjzInf0dlCUzxH2256q5mqoVDOGGCGab jmddZ+APoi+tRFd0n3RlvUdjUrTFIPApjG/8LvvhxshvcWbqonKLRdUzok3V 0tFm5LwxZoEsXueeaWJKxxb8DILqEWIeOCMwKDiw+GEXbRVrNiOA7lTgIIZ8 maflWdef6KhwjF9YC4650MwjoYsVqdBEglm3jk4b4FW42mE3eaYB8sJf0jsD a6+tIdNwJMhKSl/71IBOMPe0kKfbQVrNFybXAU/yuBcIodt4+BAo7xHCiqaQ 0qhJpY6SRKsDyNsug+lMd05yqK2BXhmVTLIDPRQ8Uu+ag2OOWP9rBIge6EeN +67FMS7QfNGeIAUR/oKAKs4dl7YIcsUhpUsirX2cxeo27m/tIKHbnTluIyEL IiwSiQOWEGcaTqIUaKvxzFINlGBsi4Ui7cnaOkEha/Yalg2DfRBw8ReFC7jj YkgButYssRXlrWiD2GBc9ZXrg8KlmCcoDYwV9VfBvLZiAUQZaFMWCp8a2tj6 fBXL/Ook+zSU9trJ02klvCXHVRqF18HbKjZmmWuFUiU8puj2JoMsQaU2C6++ VqSrFJHEBgja5UOSAEzIMjRIZkSxbG/3AmGmtzz8sntkfxkzcSs4pQE1RZic LxIZ6gmV6i2zZ0CVDAiR/IrlUmbOVEEyxXrbdhKQK36MCq++AwksMA9COZbw H8wVN+YCFWUZ5whCWUCtunjV6W1b99ljIft4qwpGU+BRvo4MgtsMjnhVGhFS 0Byw31W5h/EUUrFzH4F98DMHVjkOrNIUiNzLRTvnqB8F/k/zHAfTmPeYSBGT xwyyE8YHpzUeWZ0tu6wyIhaD+QpEldp6FL3NXBqO4XUF0kmeO5bwBt39Ys+v Kz3hZlo63v3IP5xl699MNDWrB5uyKTChi1gwbOXP4A1JzhEcmt77sNKvGDte E7MrTMXSwWa54BX0/V73lSF3H1oH9yFWV5RBcspiYWGpEz0zW+hxrcBaj/tY VHLdcYWqWKsgmH80Au2Ehx44SleJkN5SmztE83JAFacJpcNaSXqkM65oZpTB 0GmBV7oIPNIqYQlppnG7/qy/Kotp9lcyA77UCYRJi3URj+IJtjf4OppMK+aV BxM2SgCiTaUN+8OlQiUGhKtUIxz8DaAKy3tCoy+jlA49IffrI4hrYReaCccp ZDRzJatODwm8+iNR7sqSQGn/re0lLYyJNO1BWwY0K5suGDxNADE7QLhrB9P0 Z0OCvQaDvE5EOXiq4oLwQuYCoGDucea2gA4fnvu5mlPopwiHota4WZvLWYoZ PmPLZrKtlD4i7Mj5Z64z3rgKjtTtMlI2Nudn+HK65NwV+nD2yuW9nWjit420 7gYFN3im8dPqqY+/b7wg9tshXKbpJSNqkAzEaORbO0yH1mjhXqWCdB2EIKfJ 9y6v66Eie2XdRm+GrI3UiPnNH6ZQac2PKdynZDiff9JUOZ00LPe7izRR8Kql fPJE6dyKJqCiWncvAdalC4r+qzTxqoUUk7SNMifQrelTtvmQaY11Sj5JG42P 2yIEfpllOCEhZhF3xpXvs3epQIdniacOqJxprvxyO536cK7oZJcP7FNhxz2W k9/bG44juK1dMF81U6U1ugFJEjiJLInC2V11CEym3hGTqr3+Plz3T3AQWL8u BHravSVeXQ4HaWmt50qZBvgYCI+KbnG8hDJ9wfK2LgIYywNgRHNMnqnQGrZZ dhEqMHEK7sjypyDjrXmGec6HowmktWo21wdBVG+46dbJZt7GCWbG6kqb6jLa gWCDIQxIUfnSgmBXgGCxH73repnpl9qoVak4W0tPOhycLyx75M0+Dv6M9ck8 OPCwc471nITaxjpmuGrSeIGtt4XkAqYulKGvbEuGS51LuocZoDVVGzQWSXpz ESXfo1mm7eFiW1Yz/x4dPy5mgGHP1rUbPDK60zFuh0v6LXJla6+2+7dc5Zmx B9rAeyLkitlA4YTsbim96fWOVOJDL/kh63D9Rz2q9se9sON4MFsibh5wc/M2 4XWzmEQIf0ghiaS91sniBPge47+vozoMQaPwNrUI9gi3XSjQDJVjiGrAX3Qe Fkr0CPYGcD5UTsycTjHViZJTt8jzm6P7vfdz9EP5V9zQ3UOISB9L15Yvp8oN 8jgqzX5wlaBjO5FaejKNJKRnLFFQSX/h+R7toC9WDAO5AlRBroIN2ZklHfKh 5tZv5CEJ1GXK4tlbSBoI3iZ5OPFQt58jcYM5MEZwqi3U9siqAT3XTg4+ELdB txHu2gn8DRW5Ix37yPRYvI/wN2wy+zSsnMc0VRG35ggBJONztjPzlDN3NrV6 taF3WR8za8KEGSDqr8TDaLlnwy7xHMmt6sqk0GfkHCVm4hxop+Ejnu88guIm KA6FGG58F1wM1GkOC2FpvNELkmgwF7mPjWOI4lZ4HO3xEdBqjEZG+g78dXGw jfOQKtNDir069RXSFWGZwLq3Vasl67ZiPaQsIkVJhMHBvwCQtTEKhbAijNJj CWc+Eq7U64tMW1Sluy6J2OYQdVeEuHudTnohXxWyPr0scs/GjUEms8r9VL8h NmS5gEcF3OI7N0F5XxVpmSCkclm1NR2UVK2McSgIbUqzefJtt8koksc0sgfU wCF337K0h3gNRfesPh7LiqvsXCID96azASJCCF59zHOVoxXp6luL6fXyoI9r YJ3zTwbnx3e4rmEffHbHhlWPB9NA6WV4ygnqwQU9x91/6ytmJwqUdosWMUYB 7azc2sPaRtBexYz8EC4Vq1lclimVgZQpEu3uYwzW8jYMoeQLij4Nj7S3adyq i7l5SbLpN2WOQlt9dd7aSWyCqh544jIQQSWGqCdgB7gh3tsd8GL8aq+VtHs+ WxK4vh6fyCxFxlVxjWcI1GsdiINIH3T1EeIgan/BEDrWqSkKAhyELAr5Fiq5 lGJK5mB/eLiQ0OXZe6uWEzvni+z724y6qrle7xGbfkVssFj4oewzW9rpX8kM wihMPW45+7NUGOmLTahVBMb9IHN8BvHGchxIPzD6wAP0VmKZTaULEf9jbV8x sZ2ww4YydmsQBSh4CHNl/OPO3aO9pYQDH2W/0mVKFcpi21KwL/92zzR7RKVL LHoHqP5tLa9rn2B0AKTU7erJj4U7QV5T5HWrSBwlT9CbrEzEizxQyk+JhwuO cvgMEJxM+ZHRBmgHGIh2oJsrgnvjW5pz5DhrTpeJBDwYXE46JKMSTWVgHQZ1 LKJzE9b2iRPp9IZbVVT0cxT0dGIsr4OCYSeNvqELV2RKJF/WbeUmbbliY4Gn KZ04URp7FGHx6q1IW1/BrkAPKQ8ym2Qo1rWLyH3ZxvMdMSw322EPi3HuIZAE JijOVSlmodU1EfgG9qchCL7LCK4WzHOhPd655sLmXSuPuPZuhmUiuggBS2Wa tNG5h6ACTpk8hkqBvikwcKT2rwN7x8+1tRaIzH73kxgitf9QGc39OSO6nJSR Lp9nFrK3U9MF0EcbL600ypmWXRIpvMDSx8lieIQEf70szAMfJv7dL9g3VRbO 22Ugzz2qydsSrCYO5AtQhFR+kJDx+JBpEtsQd7xunSJljFksy+xUWXrvf4rv N74gh6yVSxJRVwFvroDg6Fdpuc3v1dzkXVtuAS+z+3ybhLBiQUVIAyNHTO5h d1snd2/YazK5ZPLMepbRBvPPy+vmaDP/lhyzdnO2OXkmwYyonhoAHpRjcJuA fh7SbbUAwNExKNQc2E4BQEjDAfDp8rwCXnwk4DFH5aeNOenhMAJ3taQCTdWW oqRPgOWWG+iUO+D+wNsDsQFWyTaJNbiThYhlrWLfIO5mb+JTlSaH75b1Uzji gpuN90Iq19xD1zJ7OfL/stK2LYBplWlfK95BvXCHTJ/5grHr5b1KMLlGXi+2 lGOnyGXXmpJd2SZV+E0nETUKjkVMP1SxqaqiBztcb0BOYO6/aMf8255XzHMg Zwai8Y5TmQfdX/iA4qKMgQmCY50OsD9JgiVtZBilC4Bky9iwmZreIM5B3N2l ONf+ZZar6aQBKdieL6/YjeRciw/QUxZppVGAEy4fk+Tc7VB+QeVdYFvjPxCP 67QjVCbmK/ld+pYgfcj5nAWrcUzpK9KOzOG8yR4FDbhu/FEwVSS5TZvI9eCB PoFVokNOo5apwFb4T4EuQDq4ssE8kvzPkS7MoAnmwwty9uIsrI6KMw+1AIN9 OisojQ8Kfv2jWPka3bbe4VM8O+esFs5PrUAvAKg63eFbDMeBS9vUU8dCXuBX guahqexj4c6PnJJlIVduRclRvtbew8IWNo6sVuFuo7YSZ1JXMX/x3nvTYVe1 8wNIt+HO0gDlpC9jBxlvcRTDaK17wTHIJz4UA57zhO/1m4+eVVmk66N3FK+J 2v2u7NRlefQNZGjuk6XwmLC4rbIsr5Rh3P+AWk8TzQyRXHae8OognkM5h3iI txs1yvPhbomMwCbvE5CwX97bHsJHKUo3toYwmDGOCabGyso24AJqWCeZvB65 ZuXaNFurdeS0UlNxfXNTOraHhcEHH06zNpw797c0tbJ/ytwa033iStNjwkrr ag4QfuZ8vJyjh3xMMW0HLAlSp6EWM2q/Nr2RaYPgtd/pGQGJ1nR1bTicKbZt gMBDULER8hJZYWFHOV0tnukmA3qN8V7JJezhUKRdQoGYSS5bSAPm1dngBbYN QCGeFTjfCpqnLBRuFtn2sX5yphNB6k0UmAAn7SjdazRB9bvZlHdXCh/cKJL7 2baCSULw32/GPuASnbcuRtVl6cOqZVSbwnR5vAdhdS7xxvq9z5OHfH1HU7Wo xPQaN8fEX5NBd9QBOi/Q/+9jbV4P6w+C9W1nkEAOYuP/0fl80Wd21tlX0S8X s0XRzyYfgvQIi+Qaj1Kv4Xtz+Z4nKJtYqqgtf+OXMMuHVQJ+yl7HC6trLZ9b XKSgAgQEqoFGNHSFBKU7LQVVkZDc5sgWJpvbXRPZzWTk1WqhbA2Ecgx5UbW5 iX5iOwhf8Bduc4Lgl/1v0oXhdGV3Qm7YIR8o1yEvUKEBo2DbGPIPkA+2jb94 rsDMT5CvqyOMzbygDjRc/EGfVhAsvGuCFHc/Q7goP6utVTV2VUxJoArO+XY4 H7aF+oC7KNjdilY2227u1JanKLSiq0On+Niy0XMUMDiDC3oYbbfb0NCc5brf DmLMtmd1dzobpwsElQibnPFQE9+SIVKs58iNjHoG+yA3/to4nhYzZsI1cu01 9GPuLqt4n5XM0LCylaJ6c8Jflb4KFfLCAqqXCHUZd9I+8ZjQrwmHIFDKLJjK tB4oWAPnEgi6IHsPnvnnmAetB1ZEDAiHeQj5xOwD5oHizX7Bppoj5mXaeCkc jgpjNbU28aHN+YzFGZGoHbul2xQBMJ0gqgKLLlKPpTVra2q3JdljG18lk7H9 CXs74tsusvXnLjwbRbU+DBidL4Vc2RbYOPD8YjPpXgbXD6jwhuTIcBTZr+Nt pHjADhcZAAjMe1yzVybScrClhniEbJSR8VQO+xC7mRKBfZjtL/0vsM9l7X2u O8+P7ZB5O5yinF0vT1W+LWkhQR/uF5n+RSEKr6TRnmNj7qUfd48T5JBeHykh Bs7fKigkNPMdOfYmGSVLZJQcbUVfNOjhxgonnNvRtCzlE05slEs3y890avZQ HPyTHbPnfDkIs+t5BT3wK6DdY/baz4HAxMgUGw/EgywM+RYR8Id16sFPDviK diBLQ958yvNzsCPHh0U74XIxjzFPh/W5k3sZX8QIQCh60V+/rHQXRVtUFPoP PZfwuSnMmRlSIy9GGKp0QaxFZARMHUNlcOatJ3gC+RA/ankbtGNwX2okXRhv M0X4ijXMpXM/wR0wxRrvZ5o9QO8YRbCjdTDPgR7Ly0QHoAegg7KvjT3XaP+M RDvtxU+Qbm7AwdbpY4OJtTTYjL1gdNhq+xhylgdo37fpw0CDS7LZQm4mItiZ ySiNN2SjgtoBfY6gp62JJhFxo/ysjhsZS4ssqsFVRZCNwJbEhApgHhjqrRM5 DJbKVLhGhmsnGfY9ZHoVzue2NS3pHDjGzzliOy2dvVtGElIKf/ix8K4XPbNf RhnC7Y9eIqAAQBBijzRuI2+QNW+x8/SN8/EMpYrgguLwLCHBcjiXvRzrIBQm j77MIjEaxQ7epfq7DB/PbK03WZpB+UZq5iTXDVgy8kbWmqlzi6wNOOnKIWZK dbY1FZdWzTUx7WptrSj46ux7ON8MnL7ExoHNXZZDcTWVlk5gWXoZh1F2bYBl gELkdUMeIIiaLr0vrKfQ2wEScx5Q0VfxDH53iCYAnlUL3rzWjThVIjzvhkP0 BSm4W5NmN60bD4oY0eu2BvhVsYP5Pxx+wZYB0Rp6RPAtrGQI5gTt7HCGfisP VVBG3TCPLsrfJxDDXmwahHKcLiCWrVwfRC3MXmlyPqHThx3V0APCj7eHP+sg miV9Yx+4jRWPlUFf+biFHQwi+2R7bxeRcC8n8pCTTIBhu/OMg7FGJiRPJ+sA ocQ6bj/KiKscgc7e5z2aRJxqUVax3rLvjCM0MViAOM7pgJD3LIi4AlvpSJQp nfJa0Asz0noKCvE7IkDZFsacJ0nT2WCvcjqEviPeuUsXG29qpdWlDxK2ymp4 NwYdpWyVJF0ccicvPx1KHSFyWYLqoaQAxlxaZGaJOG9h4gO9oi8jYCwuqADi eUFyyygFyYe8CUSyQodMkOu2KOhF0RKBzbDyQbcHI0DXADlhppiuyHlVAFg7 FArR0CpNOBlPpG8a8doq47OvrGpL5MHOlcV/7Fw//9qyp4gHcbu9wNlXhkSb /fqdp+AZo83wQ8k27RDE0UsvN0DdaBU6pMH5iUHlmnxBroUyWQqFKNRkBsLx JAvQZsJJVky9FuRxtv6cbJY+TIeSTgfZzJtyq3vLOvW6yEiP0U3XJlwDA4Pb Ltx3UW3w8ccdasX7qAW9PqADrsH4DioFPZusjQpLiTbxJcwOcwW60jRCa97z zBEIaMI6mv1Lz2w/745pCMnUSqjW+FvL9seMT7ZZm/COCohedAOzcjVzVJ95 spnN7nT6dMhG53zwQcjsO9TCRowdO5fja9pgbgR0OSoG6Go0LnDcQKgUoAua OHzPeUBDrlJUS6cuKzffSaoD+Cp9OYAv+KBc1mIIKMrskrZltqEFthlHMAY8 3ndPZsVVxHECtT5rRv8UJAxGyVN8dlk4YPBFiVPwcEie6GL1DcK82sjQHCtj XC7lbmFjXyeQ1Twr7OqDaLogCmB+Igv7k1eyNCma/STDJDxYaK6dPcb0kuF5 7f0NzOId9U3rPTKFV7PGCghNWOZf5uM0GoTkRmc8qWhzTsfgKIdS8Sb5Os/U W8Dfqu4T0VDWIEuEwGQgbvoGh2QbW9ro/zBGKl4mcqqgZIK8JyPAEsI63kGe RE5Llj5litwUDWT8xrbxe1zTHBlYydL3ShkgTWX/m2U57taN2WdgCUgStlQP BlsY6k4RJBBtuGIFlXQwvsK4HP8qB1m4OY2tCKEnU0u0Gm/cn0HC+1GaE4ZY iKowSqDmKpR/ydDmPG/vA2uaQmRG4MM+RTfARa2id6WClquTGqffO/yksstn drsreOVHKLS1uzo0JG6FFBNrbfO+2i1ZEc6SDfwfFajMlljQqu/snz0U4vBy qkDQtkw5sH5b4w2uJAUqrVVMC1v5zZZR2OVysGlmohWeL9zCWgoQW7t7sMlZ 0desOpluCifWhh2WFcsQ3hbP3buKQLGhMVnXyLl14yRuleXjHJsqu1cvVLq8 XgDsGiQ/EtDZe5Kg9oHc+32PB1kfjwrtuu4kU3AA1JBw8mZpa+lxB2YJPwMz XD5ABcsbAA3loEtL/dgs5A0GrtyoOVcnvxwhVYZtFrcGb9tNCmetfDYG6yrO gLMBEp1FFt/B0AC3aBoLMWAMsHtabyJy/uZ0bGSuHPEYk4YBj4BFwLTodGIa tGbANGjWHFaNFwB5W6TU3hdp6myz14c0MDMwNxdpivLMgDSb1EMkz/PFhDTU nkdGNbCi+hpBmqJFBRK4fEHPAoEHAU0oLf+D5pBcuFSebQPEalG1c7VM7fb0 eBmL/I29ZEzfFYQ5IOQKPhybrjfeneNYjhRMBMRm+r2Cdo2CzO6Qzf7bC8gP fDcgn1TWN/XWjzFJBwb79sQ8WX0bDOW0jV6pU7GTwhjkQjjxDNTtH9tpHnZI Jc/nWuFn7eW5NZBnEfu47oYZfqfHbBfn9P0wXTjSkQXelliQLQichW7uQ236 Be26S0Qv+89AuwVEJLirhIGOgozRbUi2SkqYvKMfRv/nRI/BFdKnwR5lw64W sV1FB4DSPLniTKGRya/YfG36W30LWsmr6EUyyPmb7ff8QNBGjRjIj2z/scwG rCCmnhQnvAh5M4as6WAEsAETCgIU+jmWtXc/uUS2sXYQUfYElsFHBSoxcOrx XqBlK/ORJfD3yPRcGdIgehTGSbAz2Ef7KTjVC6qgIhnmfM9Vt2KnUcG2x2L5 XfZe0MN45TW+YIqzTgwwDar52Du0mbWanIh6vU77GThvy0HLE1wu6waETq/b wya1+ACi4F3og7c8V2afkIatiQH87QyTzpXWUnnWzrofbzxPu6sTbQ/JWHpe MKL9fYGXB8o4IEOkLZ/1BnV8L2mBe6AeBE+AMZStiFRFEqOfop6ryQArJpJY NmjAfbANlNAlwKxREVwkUBfN1fgDlnBwERYYsk4IPfykquMa2Z6Uz68xtXu6 +FnMcdb3tKdvvJ1gMnqJMBfblBl90kopNAKOUNMYugEEG6AaO7x/iXIh5zMM 3RfIG2Q/B3IxdBOBW5qzPEDcvKeGP8fRcIqyo6B+KLWH1OOfhwhev/ELp9hY CQgca1wSbpnVRTXMLw7BE0Lmh01Z5g5/qKqAcnndBOgW3mG8R0oYLg+QW1vG lL+RuXb/SiTwRlyA4BmUsviLqsQbMYCgjIPAvq+UYbc2McC4a3TeQPiTUZry AwEJ4LRkL7fb2Qk69rri5q8V5+KsuTTWYdk5xTioDf9gsSzkzAxPe+zw3+z9 CYGhIaj1Xe3/XxSU39IT/Uh023kHvQqQ2cG2jmZO8zvsRMh/OlZGbdxHNNBW irjYqRP1ru1keFXKtrZtuT88PKyzyV1MzDtfBI40QdIuz1FHkghHLkRnTFCI A+BjfUqgdg2xD0jQi7ZsDYgsP5zfJu3VEoV+AKJFDWekeVcRHJ8zguz+/wDn ciN/QlnCKBdWGcXKhni8cCLpjA21liUiU0u8vjDar/Koy3cWAy8C5nbyKjUD m4+IkOUH2yka2D2xGwwPXVazGZvI4grhdLKm968m8myP860KoHHbI4E2mk00 CXpSvxF+u/w+loB9svAmyZIy6aRi3jmLe9EO64BzNuvpMZ59g8y24j0iTGcD 64igSfbItz9Ut8xdVPF1FRwK3S5P9I4grze8P/JkzTsn4HIOBkxWrwfZA8yN kkNiBjDuAYDTvXsAc/ielOSIXwNbWgEDAVWPIO0ifSVK1qASDjS7x86Qsq15 ju0gj1J7AfcrZCAm1mOXkK5cME+9UDLs8SCZyIx+8nScefW1KJuyEmDnCkpD oVY/JZF0DGBntfzn1DC63Xjz5K7tLhfRtJz0DtsjwQ4VkLeTazm/EFuO6nGE palyLcEhXYB8PZ9kpg9XcBR/LRVfwixdc43nvvH/PQR7FF7iii9U7nAc69W4 cPofmA7B5uz+u4yqj64kQbVV7mUxXbyYJVEfvI7Bs7dNlef/rEi9NlT/U0vz v0MF0RB1785vDVCiGifrw7/XC5wZVtNXDE7dV12lUxxlW2SKXyEEafU9hBn4 uIVdXhAMa9gQ3tG2gadH9OdoslfZtERQJR6Gyz9HFCO9oHhIF5EQjve6E7SN AsJHiqoKnaBzADyrqQDFaMTD4Vg5dizd08IwZOKuC9iF46kvg3Pl2rW0PMSW BhVD4aPgQA+xYLfJtJgjy3ZI+hADGilXXfQqAQ9te1p0BoItRAkcj8zkFpj/ zu0Y59iOtJVWi1buaUAgyRiC6b5W7MQN7WvvCCsOYKW4HPF61+KfKuCujoS+ QYS+JLftCo9JexdGuKsNV8yLiiCWPy2qTCcN/xCIsLlehIdbPIG7v+fKCRI+ XQxFovze3h0vI3ilzLu2O7FNRq8s0ONm8frj7X2IcriHJpaS08HA8OwrOLnw MpmD8IiDUT5a2OQ0gZKaTeWW701ceJPng9+GRgHwgez1OkDg6/QYkqG6WDSA H5ABAIjTwF/9qDD3zHvYewAgUOSsjK7HnmL8KhDm0iWXqTzBIuD5S3ThgNgK lEFSXtTYAPKBJArRC6mKDcAF4AMwoWkcjt1w6CkNqoBHcNxQGibQDCyke06R L8L4mtj1oH1v315F3ZZWcQRnY/QOzUFaRhI5NA+MRbPomatvzWQqZ2llRJQB 6/AxwWSrBbL8xZjqz2PMxKV1cJCfkfYu/xWZb/Wxv6rqwiSNXX1PtAAMUeYK TiaIULW5umUOV6jXBv6+C8Dwb56rYahgmMCAUThvgDhN1GkNcQieAe12E/gG 3E+OD23JQdoqHBaiQvLxUuLklunG+3eB8eXVTC0C2wJk2ClCD1/JpICIMmLG I60wemqgDGymH+g7wuTPMI49bqomn5Uhmf2h4qjc8fg/Eu8Q97AEfsJXKpYi MywE0y8V2CCc/reCWwzTfUAUF8CKE1i+4n8sFPsKPAeJeIa/0BYQA32J5o4O 0CkB+6B4gx/J3YJvi61YoVM8mSRXjmkq+yy8X3v8GSdcUT+KvcAyrMUItp96 k1wZetkhaAOYmCpEbo0IX5m/ZwpdnB8PCCmetskKhsBX4B3wMeYGOrFv2YI2 4C0QZVLV4muLf8UH/iQjPSfu8nnDtJGsWAhbajgCrZxNwb3aR+Jha+NF5LGt 8pyLimYZiapbJ6ZYQSraOZUEr8WKmb6qEHQszXO+HGw/oqCqT98AQaAKKIWb vxroZ0NQbfldsBCs35UVKiyH2SapXsAit3uUL1QShlBqBULclsYVLhY2yiEf JjbcBGoikA+HQpE4bKW7G3Acw3M+dCgos9r0nuAsAk1hVbxFVGS10FLxoOWb 0Vob1dWdCaqwhi3zUyFWxdCwBbWVz9Xdz3mSMHKMJleMGBvdX5tn+ldM8pOk y7vuk1Sn+DTC3ACMNvIWpYeh4wKaJeSQ4cGXDDbTgk9C4GXkadzUZq+Gvmql ZMfYEO/K9LiUZQFiqLCKTCGwOcBu8WqeH+ngec7HQyMd1HxFyzz0PP7mXseC 8ga9jhH20iKLJLAdZBiAnwfst0iXhN+Q5qH8NMVUYBagEttBA7Dg/5cXdsVl qZK2GdDg0WWVRCe0iy+r65pesjMqnLM/BdEb6PcOz3Xb2Mn2dMS+eBGLCE1h AiPzxOkpwj6S6bdzNvW7E2UNra52IAO25nt1zKHIOQRHW/ihtZZ2J9RyX0QV Yj9Hy9K+d2q2sN0Xx+zaFWzDHUBgK1Kv+nKjc3DxImLviyNwx/MClw6nqoN5 pyAIIYgQPdPctqAJ71piUEv30xTPZTIkgEQ7DMrVHxBbEB2o5MDXQIqc9Fcd JMO24XmABThzdNZB5iRF8jWFpcZ8iNu8SOnSZQ4ny8L+V+hVQpJpv1VgKhPh VfJbaxaYbH0HDWAga5UVxrthlBwfpUbZRJ57LMn0YFmVV8sTnngs2rKy2PxT ZkFtuU1bGdX5TGBR3ariS5eDJOlVanQbtlIhO8WCFKZ53yOe2fcCHT3aypO4 IgApvwg0+BTSuUFnpiWuQmA/ghbYDS48QBvUxEApKtTOAA27fDmgVdiG37C/ cKcoxT+wMrAzkDpYBkACz5/3XOFqhz14gaxJq/pbtlm8Lt9MXzbMSsgwMoyv KTdrtubafSHZbt8RK5gVbxK7PWxvfmJSojnwUFNzRcF7pl6P0ybhmpdM8Q2r zGoISQg5AOvaO1kepjC09HL6qiFswMZj7efv+dsCnVyf52G9jHKqOieZgqg9 NjLk5jWyf38bIqK09JyTEqd9HJ2vUBLFCaVLde7kzoLTGlV1OSy96v7Amf5d P6NoardT9H9Dnqdu55nF5cEwcczFqybp/q7mNGgffseqBM0K4AUyKPy9v9Mu vazzLZdSHLwjwLdq71DeBXbj5OtvImDpU8+pzZR4RVEufVr7/pdAPgV6oVAq hE5o2D528mm06SkyUvUCQaf7RCA8a8FQmWxCIFLD3tBB3x/lfjHwgoXF6wx2 f9pK97/i7lNkgGVDix6mcgyyIlZEcCTkIYn7hFcDh1yA39Y9DLcCX4teZ6Eg 0Nwn6dWnOMIUS01/T48eec6LA/LXgghfNmeXiLQpRNFA8I4++3oqWKpOrb1e 7BkUSQysDVFku6M8J2bGRrqIpCqyLfETcVhg6+FRBAaogDM7RRw82hU9nSeG T5OaMIwUQqgLbcfvMEvVaFO1Efh4VS3dkEDNkIWwfjd1WzpGN6v2+4hzk0Rc mFVKrBK880PBgKNC+8NNRWQUXN2GhfDWKJQ9gnPP+gpNpbF6V38zuHqRCXd+ zDe+btDixmgSusDlOXQo8WEvzYc9vGDrhTMJPoOfwTThysWgxgHMloA+yHpA Fch/QBuBvxglWdjVSo2JuFuCl7HqTQJ5EXJj374UNYFkYFbAQMVEn5VBBwfE co67AEXo3ORcPhICinH+R0/Fm6w+SxGdicdzVa0sQ436HLTI1rj0gnlK+n33 uXzjLbvNeN/LInJoIqt4ApqwlMGUChoGlMCShM9pScyki89At/frwb0DkU8q a8rdKxLZdacsW3cIi+p2PCJxpTy4ihrq/D5T9EixWOdjHDIQ3egFAIYafVCw fRcF/GMrIvX+MYGb/6bVkxDK9UO9oMw0ZE38jsNdseh/k8/zUXNBBtyR7qh4 HWBeCtuyMuvfFHs+VdzbK22NjQ0yGQKAMMihc+tLhIAGCdqu2DMOlOIoW/5d 8e33imlnFXBO4TYRAv+gu5x3zCyOAiVgEMuNpv0DEIRle/gEznAMO5fKWMk8 ame8omcC141Ej3InOOVeokL2CXrh2vttK4B9a+FuKqEn5hHeyFAvYQ1rNSTI o40A/A5FAe2FC7iEReztIcN91EQmHrF5VBaiQ3KUV48qOzxe2IycO4UDp+ci 4A6LiUvZFENAwIVCBhQgt9GjYUW9aKIeoO2cXkRCRDQWvuP3tzyvNNJjJIo+ 9+D/CJqPVUiO4hFp+tMSJXuJRNvVXlALYbWC1S7dPVVPfquTZzOCqH+AG+K1 kLc0Qy99reImUoBYu9weCi7gCFb8OEs/0ZHXKaEbbbzhS2TWPCSA2Q8GWT+h CZoXJhwWZt1zk7Dvo3C0a8xD8s6FbMRCFH2jGgw262obm05jdvihqN3v532H Q2i9dkJK1so8k/f+CwX+Utj6JgXxAPleeJfG1IMvkfQ9+za3fwRd2xsMloX5 Ej5yQCQwOXyGeCooFNYCC4cP01AAX48DB3xXEQAiymgR2eiMAjcQ2CVg7ofN AkH62AfGL2w/cOAK88BDKa7lDRs8M20arbMCgQ4BgZ5npM/tpi8xDXpuMI2b pshv0p7cyLMyibaezjANpxwxa19TmEHsee8leFYhm1yuOR9bngBgl5eomAPg DbtIwAOdc04i2AbjKQypcBBB1CoiUo8V0hr6wBQSO/zB0HpjtQyDDKqd8rzA mPil07T1IqX6fQQKIkUc2upYxKAi/P1HA7r7Xn3AwpSdoR82p9g9/izdg1/o wsjWjdWBsh5tPSTtLK4hHXwqO4DBHscCkfZD6c2bDQL3kuPS1rYqv8pAHDzh ICohLAs2K3lgM1YxtD7ugBdYOQGBf9DvLyoowSwLN/tqh3n12MYpr6799aIX CLgOD5/wNMfzn+DPcbUASicoIBEhCQQB94LSHok00Q2X0g5NdPAcVl6wIOCs wHjdgc2NLSaC3PanvIvluO2vaGON0wxIt+3iVc4FLXuDlK9nT5akQX0GPAnI ugPtIF+9GleH4RsSCpjSSW3AWYqHFxUPoe0fod2vpyb2WE7Lz/Q1Uq+q2OKi yr/RRDj8vlebTfECetfCC+Tjt1CEFv86OpYHj59eimywmSTpZ7qHJDkpF/J3 W/1psxd4jmxWMGzqFG2tHI7wd+A74vehkLtaj3egCr8/6421R4HxTgpPtsAa nNYP8vNEGbmTZNTl3S1tCbaXLaSHys6oIH5su7brUa1+7z75LQ15lh6ikCud azyZlkR+k/8Tmc/nxgRic0eZMHC9GM61vGm7LqZBeQ1rdA0OdIAOF+HNDA/A nrJeXXyHwjC2X4Y2d4sq8upbCRWwiOiFWbMo0AIeT54M5GUEPoDIoYY9ACqk yAY/FEi5wMxTKJ4l6CfPDN7Fx08mmAMPJFo8hF4YF25LER7S3VItJYWzV+Mm caZdyVy7K1BYjSYWoF1IBpo8cCjJnH26sRn6u1gTIzNcgFFu1IELsJTseS5k ixeHv/BMgx+jqNT/UtaM38SzyDFoIMgWSNnaCO+7Ic/TPyS9Kdk8ENM5psju 044yVuLRCQzXxx+aqh0jk74TvyPW9Z1OgTYMlKLI/hKYKgAIN3sRGrJkAVq4 lkSzzDPyOqV4QXrftnfatTdc+KZJuknP+6QA4uaJTjY1uGxvkfx2sQXBEWBd tocgCXC83JBt/1mB82vdLgRl0BMaRQBgvKQzbKeCTjRnwgX9CYf7vC/M8PuP vQgtnQZX7vFcLe4LdnL/RduIxNaXbTeUfjakhPiJF5SMP6MY2k3BEilVrvQC U/NLegmg0994vmtg3BErwoccTI/2zHiR/LMKCPcp/6H4KSizVOTiNBGpU9QW 0rrId7ub+LQSdcM5m9iYOAjshM9Jity4uTsFO+8jWF3xNq5E9pDHkQSj9SEF shnoyWf2KU3zAhXhQ7o5TDik0TiAyDEKgau1laQI4Rzr0Dv2d28w8bGTOxgA BlsK9ACV+jkZjqOET7dpp0dnFnyGufVWe4gfQIGYCgftGwLLqioX/EvIsze5 7V1wdYJzO0IkgYgQUuFdBaPdpErjDV1F6jh9lJBaQc65vWlRbRrielyr7V0B FBQOUiLDuHwAGGC0JkZvVrLfauzjup5iUzBV4Bbhc+djNEdLE+07xpvTv8iw WaByREZtfLdDwmgzR4bH6+rPUSA34PRLuJmf2GghsC/AmnNCRtJkWCcAfbDC wj8E/ikgb889p/q9elbcdQo8mOzxc2Ehd7VXFE4wI8ckm+plw0TknCk9ic/G XZbea4yEqpDb2jPv3h0yG7ZmmGbNo03r9k3N4UfSzfMvJZr7H0oPotGwbJRO tr4c0+/PM32+ElHok0am1R/DJuzIFzCp80XTILOpWbW1i8nsuy8InIB94+4b qVND2QPAX8ZUWVl3smxD0XrqAhEI0dUGVniBC8trfMAIpnDVt4CFvxgeaPp/ LYPhf/yeCIlDRFYGQ8HSrZY/9XOm5/DWlxnvmRZBWQSo2pA6zgZRnLVZBhoN OWYmnIlnaibbHOd+kvDNep8JbH6XoNlSwtSxMfhdoTaZnEx4aS95oF0xK8GU YJKFmghOIPAJ/kEx5A3iU1ggO+MrK5oyRuAr3ecH++0HOxcg14EJgVO+ybno dHu2OtcripW4ttfdb/WpkoQXgqsg81c9L/IhQV/4W/39RumvDOBeR5zqMcH6 DTd6JIjssE4rF+mDPEuE5UZ/sNYO5iVOZqbO37H5wUL0FsvgbAF2BWKEqNkS PaVHBrH4FjY1wWa+08CINf50D5R5R7TL0AV20qCGePqcYPMij47aECZb6Wec rr7nDSFadXFAVua5TJsgsZ5f7g9SP+yruYo9MYQiPDswKKwvvS08+THrXfQI vPpv1fKSJzSz9T0kbYAoMCiQN7yDrAE/2p9S88UJHjepOyOyAUtXSC8bCDEb IpA0or8sRbMFzqa6XLPtDoQUoJBJExGYCMZHhZqCHvEsNooT9FzDmwhTLioj ZUwx+X3oL3ezrYdccAi3N/0uUjQLUAU2jsB3lsO+TrGHF6rKAxIgaMI8AMCC tVXASrV3MRbUHDpBzAXA7dsnTcKcFWXyCnNM+Q29zbAJneDDFkJcrZdnWnbo buYvzzaZrfN4G66wa5Jldfnd401q/jqzZH0Xkze4xkyoKTN7D+aagVPKreUE AHXje8hL3NwaJtv8e5JZKMf1GTvBPP9ykkl6JRs1jmXWJLDQ8Jgak9lpgj1f ywF3UtaHvy3A6U5ZOdaMYfJLOGOgxgySGNnggwdoibj6EHlb7iNRzmsksoYW PIORXgkxCYjOQumFedcjN1HIkS/IuXMriQOveq70fLoMt7IJzU3O5VR1KG7E yP3K7YKQb7HIM2AKtQfx9y1hpkAIaafrpaP3FgtCJpJn/M0LLBt/iviMuVml ErQi1bvCgj7QGJpjik5/UlREsMR5j5JoMT8nPWYT6+M6vkG3U77WHL//qRZU /E8bagRDiyJqfa3I9bluziYNwASDUAtyBRexlxXkSnXSXdArADlDSg8ZVFbr f16RAop0IWpp9wpFQrLWWna5ibQZL6v0hI3Maeccl2GehUUCgI5yW01/L92p OQ68s8VN9+vV4gpW6GdwRJQkhfmmXuBZAT0iTCU7Irx2f6c9w2GFilUxjC/D 5hkRnc/whhKeBkXyLLUV+A5riCcH2XAFmhFMMVP3gyS5MyKhLxT1nR6VAbOM QASQQfUlLMNWqmi9C/bfXE3khYGM3VC5G24CwCKbmVAmYZJgUsFhQcY+srJr VDR+6yYTobXNbx2lqT/WH8o2h45olXdBJItG64go7btl04Jh2Rt7PdDWfhF8 kcGPiIMjr2jyzEpLgXwXD5gTsB1mgxMn5CZ+YislYNofPOiZRx4hAEHbD24j AqDSpLDV+GMb85vWWhPzsGE6XmCDbFZjmhfWmNkLs032tcjoBihqw+y69QhD +47kmE7D5pvs4sXWwoCgLZwXoYMQWuFisu/+dJM/ZrW9M0hbXNAn2+zeG2+1 dkmdajUGvjUh6FyWHRXZwsmnHM3l7B5Qy5ozd8gM7i6w86AQlk/bMJclVgfA TfcPfac1OKwhW7LvtJaKqsp0mcUfEAuZfRdPYqwpUloWX6BL2X+pEwW6Aor0 RhbHdZVzBKlnw9RzVpaTpsHNAxLrqzpaXfokbEelrRU7ycBSCBqYB0fkSm4V KXGECLfL5ME/Igj4nar1Ptdp5MDlRc+JUr7ZA3h0vxx+TOvVQyX1K/z8W9tF +uJSOMNfoguQD9scbgXwLfnA0hbrZWjh6yvFk4sKZ/gMMgbBEk4O6zxaM79R aHpZ8UbL79khCxsf6LULQYcGSPMO18klPp8hrcUPoUetH7W68nN0A5m5kLjT ApcEyvJ7tpXyBrfDLSTG3YYsLxB48AJfOa2FXGKY2q5Au3JCgaararPCzAl+ 0W72hhNdBrqf4LwBe/hGp4vKpI18gY86FMTy9H2h3iCXXA6SofO0hYPa9jjq mqoGU2EAJT3ydDnrJBLDgALlHvV1TFBZIVLZ1zEh+Ga/jBhFF6jgh1VCIIvS zOG2Stx1sqxmahadvtA/y6SX3/beGWs23jXAlFRMsQ5iHSc7LAybKaeomv/E Fo2m+xeMgMjOE2yLtRLUkiUUuFyxdmh7fvELQAfqXIWZ/YfeNf6NRrUDKOBl PmKINqmCrAH12dVHLZjGd9xrtt7d1UycU2azauC8MH5CqwRmg21zVxWa9KI7 baazvQfamoJxa8z0ZSPMG2/FEj7q5WnbsrzkycL/UmtLLdJebmlafSjn257l 0hrIEBkrc1Nk2QMzoF+PcpCxCJl1hI/2eZvfMF+PTgmkJfx9JANh8k2EjNdk 3m5cYbxZglxn+tBKiHrE8M7vdZvAQB6lnziWNUZJBJRCnqeTYvYiOvTPWclp fRV9LbDpjbYUevD3R52bu/SwJh6B7d0GddEC+lqgC1wrv1eUQRuwiv6g+/2o bV7WNsEjoCDCSgw70wv6+3j78F73BSz4x9KUyP9hq1yv8/9rbftD/fyOylMo xedxVyjOXerK5h6lEEdTFipsoNvPeX76oIYNNQ0QlPHpPmRo6YNEP0QBmcsQ pQSDt53fL/E2tV/BPEBdTltAPGEvBXYQQkZATBp4nNn5rgBUNG8l2mqkuLHd C+yGHbzAPPdVHfAYYN/qUxGP6QH1CayPoEY79DPAAqLSaH2icN/a7PQgyTwG t2S4tgGvGGcpLtBjNieJzCPTp929lJ/g8iBrcshHjvsdb7GTwW5DCp7+izXB rOYhuzqPBtvuB4goNyYGqp4Gg01m58mWGSB88aSMmPwHgnCpvIP0WEVgpkBH kkWIOCvxwICHiEikTIRrMPLiIATjZlQa6G/rLEBhjSwp+Bar2zDpWzH5v+pd GjJiBylUBS4ze662IgzUOgAnaH7g5QWLHtKLWQf76260uQqRYrJg7BJ7Z5Cj yAJE40l85nJLkjF+BInX5ppW/Rab0nEyeW+4BfVPgponb+ZTjXKwOsoW1FSt CZLA7loSaIuxC6jEm84noR6pBJ7L013qJpGFuevPusYfET5b+ChyVpAaJDKJ NsQW1LRDMrEvVImD8ir/Vp8UAAWlVszXU0Uz3vGhYbJWCe3cPQITX39rwsVz v76g0IYR+Q2deVhUsZhDyP+bAsgX+tv3OqH/rCAC8ID25nadKWjrcfu7lxjJ R1716rqQPqlOCbGUOzDikRcWyyQsZVDCbA2S8tiJj/3gtSQLSPYdnokeqdvw B91aEoHAW+WRf0DRKuOm3WzP5Mn2lrd4Jh163AoRa2WmrNutGuNYm97fDm6h p5HlZJEpOmxLXYZteZSmtPwE+lzMc0giMRFepkgPcShyqEaR8lTqq0UQsI0b fNmL0NHEUmVfqfMaD6fIc/TEQUl/p7fBmcYodMREQMBq/TzQ8ZMYQgTgZbHu gxu6Rn9OImC6bBLYdZv0ZMoIJuzqvFnu8wqa0BA5AkkHlv/2j0Y7AMFXejh7 iS7fF8quTmwuvZO1NP8oAaZ/LO8zoo2RNtRjlpDrb7bZtaBTCQE55JbN+wU3 5R6w6hbneL79WV+Toswj3rpBAYag9oVQAxUyNCyIZx01ytPUOEg1HWXDpX1C mNg+SpM2R7XbYQZXjjNrt7W38gh8DRBBo0HdLoFY/9Gy/La+zew91E04xioL pBeeTyeEJBUQQlKCOkLx2QygBlJl3iDsTBb520pY5Q3ajm230Ez/THsRLUSa OmBVoU6K8D4USWH6kUAxAkXI9fP5UDbNoNX9RRG0ThRBG+KjDvzJgSa+jSqR xnlwh03XUQ8MLYirT/Ku9OAX0kT3k7b2uK9g+TaJWXO+ktM82ZGbYznbj0aM 58se18M3vAB5PtVpjHnwO4se2pUErrNY6/9T0WSztuHS9DMaKM5KK3FkKJ9E gMd5BZNaixghzYeDcY3K4uFFTESNmpd2ZgKrnrDjXQGnKZc1AY4JMh+SkJAC Agu0t/k6LhBKAyF3j049gIwWOs+XdktkkhTIUpy6gwoR+CzBL8TlSQWK4LOs bf5Ud4kLMzKCbZH+6L4bZoAeQIqQTaQaxSnt4LpLgD/D9EZcdpikvkuVOp2x +4AIPuPkHPk3LBJMoqghguK30AuiYsIEUzyhKQ5L4ggQYGTQ5oKtAT1hiwK4 bggULivk0yRZyYTAe4OmGW+o4NeIiTQ2saKXF+MAZMw8Gdb3RbCSONYk7C3j spvI7qXye58YunKhFk7GVF5TgpU1oHttca/no8MjCEbGXYR338o6ADLj8QBA ojWaDxoQaDr27GFOBfyORDSID505k4oTISuJ7lYltGNRCryuhAto613S38S8 WnPgwTSTU7qY8YVvy4mPNjT7j7YLMKQjE8AlFZqxM0eZ6uVlJq3gdsGQhsQQ xKekFxHVHYhgJCS0YU0TKJNscVLpQbWIenfKtrJn5InINK4VuC4UqbGJAM3+ SrqOPzSFYSUHhX4eHs/PdwkYPSFP/71mjG8Zs4VeEIAF6CSu30pN7m3y6P47 isiD92OFmhZQRIuNkyhDVq8nhDg2ArP2pmpuwx/cIV10H/Y5Wso5/ied6wwZ 3uHn0v2tFwhTYBcv6gz5q37/MeBRrqqI0eYAS5CGkL7jeYWdvyoMvahEojEV H8tZoKMddv0gAl7AS0A8ntGRcz2bQ3HvqLFCae8nMYU/jm1nm/YUgtF6lyw1 xnSRYRQHb++/aLvIXJOAno+xPVdBJENEvVbLPJO1wDPNZLBOujPIn+NgRUgI K0pGmSIh1VEjqCbp2tUHkQjNSQALWM3n/wtg6YDNO+yuAz3fTBR2CDJcEWSp /nbZNRNNaQvcAYlfILaMijjtz9FlgPupl8LFtohAZHyHbnqg29aC9GScR53S IC+oOrlBt6/T72X6u1XeyuDJE0Y9Tkj7/DTjDZZB3btS8/reHSQdhWYWuIPc lC4ODy4/nU8lunILSFeJPKVYtLHLwNlyNyqMFx2j4oar5JxoeUuNnG7IwkEm +9YupuYy41WQkcF2S3BpyC02mlkeegA00H/ghc9QucI3x4a15VBzK7/5NxQJ WBoMspURE+3/Vxmv1Ta7raJmkBl5/SCTU9jLRxtEz5XNnGRVI5B44FCGbH72 IWatdEWBbdUkVNm0bbcs4QK4Vi5/2SBqdS3k5FDriu+onYioNMgyTw6hlvUu m7P6nhqwc/hxY1/EEUNJDWqHfFd4AqPW0V0RalKoTAENeBI3z6dzz3XLYWCn 1uI7Va0Clg4UxzpCAxkHdqUPs3X6J5EWQNn4d33H7z2P8fN3OvcAAzUKCd/a zz5JP+/DTEBacChEmL9pk35gRwZFr6fzuR2v//KoLwHN+ExbwnQAs4OLsgBX MQzGsIke9kxaDWtaodYFIKeZ7JNxivt6vwwCfVEBWthDyWGtHHmOVSRtng40 I9OjC1Nmmaa9WXrDrv87nAo3lVcOH5xT+g45JpVTp/GX0t59cpxMq0E1bBei EfQj6B7ONWiiwooMwxIRsaJPkrmITPSPMMeXW1yGUPdK15s/xAuMN2W67VOd 0WA1X5He4KGMD7DInwLjdbeMn2PKYh8HcAMHaX1HyoEupxVgbJL2BiLRDRFJ XZZ6QbKXcdpECr9OEIpReIBNzJRBm7dakG06NbEOUoAVCDPFNkAKBnsHjfkI BwpcRoCQ0Th4wfYBcsiMVOpXGL7rayBC8XZaH3pKwKxCpoKgC9amTvcTXTrd KlMzu6lDl0hwgUIUIAInQAcu0NIArGDjSUr6h+ASbf8PM1iCph2r/RoETYEA DqrMls8cbsszAKf+EeDgNO265LCN+l2s/HfoRI7JGbLB2s9Q2pexNyVk5fDo gz0ty7rly7miWOrQpWOfvYf4Ut/hDb1j8AczjcMbPJrrc36KN0FIlgMPZC/G H/Cm8UReusObu0oS7O7pFJKwK4SjT1MDEzAmPfKLbSmvmzQFoggUEaDNJxRr Ouk4Hu857xKjPIdaQeNsvHEu70Hw96LA5KMy4v7NgkyUgzsc+mdO6GjhDsWP BNjSeiafNyYyCJPFFiHCgnFx6khXoqmp3O7QfYJbFsgkLFumOOFKwtwdhME5 clTBQxEJErIlWhMJJzKPB8l0GTRfi9eO5r6TVnIUwpLs+Z5SYSopKuwN8yNq W7dmXqAAS8Kkgqu9uqpYzN69keAQHURQ4MohQt5h91/to/lqvSw8sBmu/qui Px+SsxyXuCYLFVSQRAoQ52dJaU23ZTxTuNbA1jNdwcXT5nA6VyQM4LLMY95R OORBhlqld7jDQQEcgbEFyRSKRo8hikAtC/WsTU8kCDRWWH7vW7QA/GHNafZg YF6++kiEWvcQE237cCPLcqc8ymTwyGsu6NVwlEJzmUluNdIsur2rrfNlEQ8S /QV+Br7MWTPQLF+TYZY/RKdjzHisEq+9Js+e6lyHM37UmoZtIzZJxF/fkBbX UuWaMpv4uQzbrlzLydrapleQ/5swQQm2XbXZ5PS8tg7GDJk2ycIXcMiLb0Wy AOyV/Q/JbIHIBfnFxxjIUp97XJqBR9jWwipmU4qRGBgkCh7CrqIX3OUgzyyY RgXtdsGh9WMDGerhQkEFQePbxrOE4LIZxnurJeWgJ/Opu8D0dCUNEXiGc+4U krVhFMFFRt8zPUVYaCsgI/D4RRN6Am+/XrpZn0SGbORbWxTlGwUEzD/Mo/d0 LF/QUz0R8f0/dYz/VQcdZr9Tp3yv28FW/lu/4w/i3zNZbP8H3Q6FB8j9u14Q woFzgHmrM0ryIQZi2QoMML8AAAFWH1BygZCUCVZ0g2fljYEogj0WlzUSSvu+ A7gJ8lPr+YpE8pchl9vqjJ4e03qKntaFpGJ0QMx7mqew8tfD2qw8dFkKMwWN hiwgMwGiQOWCuorQ/8JgZDWpyRHUApFrB4gCSY6hGK8uiyHqRCvnt1pu3Fgo Z2/S3ibprp/oe5Yddxrm9S/AJszigzhDgutVDm082IbMq731VLco0FyvILNA twFkwKfAbpxiF8oZcIvxxYJ50uWBAi7T5HP/OYGYj6GO6VPWgqqG9pO06ua9 tBdF+rDg3Vqbz1HNe+U6LCsEGPCZtrJo9hO+n16KQtV+Ubum7TrZGFZrz+ot C+qyPvLQco03p9iU3NHUnHsmw07xW7b1MU+cb2w/33mnDeePVc85GIJgK3IA A9UMUMIedgvBB4iQkBCxTOD0deTZMDP2QoxJsokc5H8Z+leuEVbcz8xaWmAT jtjf5Xuk8mbl2lTTf1g26626cvBWpbvQ1ujqXSY42v0ei6uXX4+qizxfJLJ4 M7ZduSaEjsGXAsQGlmQobpDpF6WmnZm/zW4yGpQhxN8aIU5Lpsr4m8MZ+mk6 U8qd6MMniDwoN90gaz5c8zrKkxdq0kOeyB0DhbQK/B7JZzt/t4PW88UpINBb mXBwDagJyhK+Hw+6o9vieBxEKShr4dAFY8c9ijwPBgQLPjHlrShEulLUAKXX dE7w9Ny9ZWCxNopb3yj+/I9uR6hArR4+R/epVBBAdeYn5YELy8k5qPsBCKDm PEoM6r+HSv9UFx05TBa0QmvW0UHSwpp4OpXKPmdop2xzilcGBOkv58m+X9uE Muhx/QwuhKn8Bu8AlM0ApUQR1wbL3EwVMlAigzVVXtkbOFiAQKVynnRIlf2D FTFKwSgSeHCJu38GPH4IKRS94Htldrb7m+tizr+AHOsZF7J12uIDRTdeCMuA S5zTrKy1GKNSUiPeYxj1oOSFVLVDsQejtr+MuKWyLLaT5bdLa45u65f7KLEC eoMyvRiQEziAWpzZZ11w6bpCiMl9WBW/aayFCSmr+UI/Yj++1SyzdkOSuXam rKz9ZFK0XCFrRJXJv5hpah9p64fLw+h774NtzAuvJMWqjhe2nJtu4s/4fOSI 52uDNRsShJoALqzGY5KdsVH2/7At01JXNM20VnDr5xaKpTSMK8M+0XV1wLA/ Q22Ic8PkTQGrByGo6U3CkNfRppZZbS6/EcN2CvYTQmZPC1t+JDd3vIDr4gVC dgVhNstCMUgEmfzLzE/knBDhOQSQh4ctfNceExZ53xAmHF8lTPJgH048OKoc 6k8p58Q1gljp3P5cDg1R3yWwYN1/60SFL5tzD0EmTJiYHhyhHrBxgb+8UcjY W879frTw4SPK+lmBOhjc5FWdXxhTc3W3hgQ5OPBBhQtdLNJZgGL8Sg/92gv0 P042w/R0NukvPDK/P1tICdkESQ25YGN0X9YZjbMLrCfKCB18gshiUQd244Oc 5WgWRWMg9FoaDzoCWpKowyDR+qwk/IZRmH0AUsKYk0/wkHQZXm0FDDLl0Xe0 4foCL3LX6sMd5rf25Qc7wa22/R3SVoGebiS72a6dbzkyPYReZ4MQtIJOiqd3 yfGczgWYN0W/FylGQpnd0slc/z+wwQD3qyj2QLTJ05czk1Xp/UDEu7Mf4x5P EoZbKqxgURtuAztxCTng+la6iKGVbXegxp7An6w/LRYFrm+Y9sVCwkc3p+4Y Cqwm0xAN4EW5XGoTe9AtLqUv7UghcPwoZ0OC0JRcRCxCTgnhNEk9jlmPD8YM PGo6lm01ex9raU5+ouFEYWvq0dSxciL8n2JKBGccB3G2ZnyGMyvqYFxW7IEH HBxO3G+oSFS/fsA34YBHn1h84/9pA6gtbVKF/eT/FGpQm81224kHeBcEjcQV ZDWqqmljVm8vNDXLunCfK9cQL2IIxgLCvkzU7Q4Zb2lR7uc+a1jZ83NhD1u7 0OTT+fmQs94DqkEVkQnKkpJLMkUFl1fJRTw0lSVSivfRd+15IR/7SjHbfY4x Xz3jmuYG+lWUKoCYO+smUvnvXRB6DJt5RXjJqzL1Vwo3+iBFXVYJHEe7kxyA e0Mw+aMCCIw7633hwoLFBp3BX3qUaf6uu+PvRwWSL/Tz+15dD7gDAo93FDCL kQOOdJpOMJVe1DP+CtaA8Samicgw8EqDbII4mV/q508IHA1Etq6PQQ95B9m3 QG2wzMY5KlKPQgjknrMsAVowyCIFaUYpJ1qDRdKOIEjGKDkMkWexJkZYSz9h KdEA6B6KZzj1btd0HH0BZXgW3x9UH7Dp1Y7aY5o55uHS/ULzAcEC31sEUd24 mBPa+QH/Cit8vSxW/K4WJ7QvWdT8TNHbCEmmgTDB4ipixPhC2mxgGy6XBWuI 4EB/kU6GyGt9BgoEyNC7hrhjI4BkCPadrd5tj2m0kCx6oyZQ+kX7nUZYRYI3 cJgwk6XUMUSzoEaRnLIsV2MWBwUaFFTJzZzhAoTKK6KcOwsQBPMRlCRrtV1s o9rfa4omr/XVNekVK82CV5zLfYzZKY/vrjcD0eXGlzzz+Hv6c4qNit57f54Z Pn2KX28EVATqE0g5eEeGdOfN36ZNgDoFBYHOvV5nOxB9/IeqBcUwoxviEogw TgixNmr8H1sHRSDczF9dYCoXlpma1VYVLPs0pHFbkNiHj/xawIdW6l1FJGs4 jN+hMjnXh3m05+0IsheCkuy/1ng9d4Yg6iBmH8lXIU5eI4xkmYDEhX5MUg1G AcbwngyW033oDwuhxqog4HnXhJ+x9v9SPegfkdG7Qi7/S2SnkAc0WBaMN1v6 kYN2ih+QwfViGxbOXDFOxGWhqidkqH4Qi6FNgBGseRchtjXk2HDDvbe8bnr9 IXqHu+j65RJmfG0ByTdKf+UF3nI4FEDicu4AhG4TQlw7kozFAUwS51aenP2N OA2vlue2qR2x7GU9C0jQC3UAJsZZjbC8q+9JjMjM4x+zQGCrSiByLB20HTQL C3Ufj3QLLCItIkWYTK4Wt3smNFanO/BqEAEl7ABFRmLDB5gqBbGyKAGbiiAC +Nm09Jo6xCjRnkKL2yYonXRI78FICySJ/wsggSdLtBehBs5ixA5oC6gO1M0Y hiUzRRgWhtxcxlaX7UKeZeJ3XEdfeoDEmH4MA+x8iiGGWGCdiEK/+kDvOkgm +K3pLhkDVSmwBI+T8xTU+EACTQcSJTo+H9NYgSSGjuuoTzhQDjksbGPOcLAP /iwjcbk8/0fkp+7r1Wb0uK7VxwkxsvLCSHvoyZY2ZQxqk66S9wuyolTLa9sr jB08LUzliecZT4jJDB96vB85mW0z1CAHDmwy8K+HIDpxIj3ksA1cEhoN+OrD PzopSTElgaGOQDkoTaC2hW0KWAB7C6QdSEdXrg25ECj8lHWLgxhZz3J5Y55p bd9zOrewvTp5rqmNO2xetNyMmVlmKhcM4v6ootRsLpuTCXTVFnsDLr+ZwJ8R dviaog5+Qy9gJJomj3x/RQiZalBMFQah1zqzPCVEqfGC5U/L9+I3WQYT5XIP d2IOneGLac69dXZgFIIiFjlvNtxIrzoEB57ozURlRsfsRzrlvwjRmI2AnDNy lW80DTxhXbHvZXLzVl4vg1vExkuCXCfkrnwcz/EPywJClsD/x23l6MIEQIwQ oAcTfIT054FhUJ4FnhdQuELMrJCh99tUEBviTshmp0kIYgLQCcg2OAHYjU2h 0ZDEyYFOMl1pDwrIXLeO54T+BgyG2ETgAQuBrLJZZBKZ/a2e91x48TkLm2Ao WJYPe36UY/wL9JBJVlPkFCz70G7s1/vobE2VyoxjrBa2er72vlbBrLVeebx1 YSipUECpZOpDF2Fi7Sl5VhxnU+BqLsLPqY3Kggjoe9hJC9DF6tMf5v2vVweE Ct1Pzeb4kn+SKlEWWzTM1B5Am9O4BY2KvVJJP1KKQ9DncTUlt7Z50IVz59yO frS9CwSs7b30qEUkj9XLCiiNqPTLD3ljxxjvBhkGpaAlsuamdaHiY/7VAhdb ePoG2g3QGczPwXK+5aPkWAGuBSL3rBos9Fl6PmWBj0C786ji73c3s7yC69tw 67tNo977zOknmpudQoXHz5tuTgli7JbR8ZAIxwvkCacJ/10vKHSdzIYcGQ2t REhuJ/s8/wEToUJyReaZhx9NNtm9Kk12u0Y2ThhdxHNDenB4WyNZNNAIihn4 67ZvHyAQbjGyzeNKMeeRlBa6EtxJBGFCX9JwRAiWHSwjIZmHMY24O3Z5Pp7j dow8uP3JJqdvpUWgQ4+2VltTfTPyhjJTcV2Sjee2TWNFsA/qKJRTNoNMm/5L uA0Ki0tpHuUqZRpH5cEMeJh3DqtGyQMyymrJLIAtA2+lfuYtudM9HqWtqULa frinUONHmO7ltxnKG2QO/iGZeIR4Qbjcoh1471fp/MOK97VOEPAKCDFYqd/R +QevZijaHxV+u6MXBZsX0xkZsGE6lXNQb4CH5G9hnwEXeIeoPVwg9T3ZZ7Ps e7yQxK1jNY0NkwLNGHwXXm1ss//RSbkDkeYmXdq/8QJm9GvtMqBkmTT7i9ac nFANw+4EyEEXAJ2AFSdIPadQJAMr4VGBk48900UG1k3bPcQCch+XigHHFhF+ 5t1Nj5pMIS9z5pD8tO/N/Am2/QB2AvWp4E4m8g5g+v/Kz7SMfISpUDjAuiN3 IDmZJaLtNEOcRLyn6pc4RgHB1w95WZrpPnCjxaXW4AGftrBUqL3GwByuZ3zT m/wTrHEBvp4XQBN04E09l/iKSwEWWOwLvWtHzI5bdWjLJVQMlIk9n1JRh1Nh zGys07CCLrmKuZEwarsvZyH4UplQE1J5CnAeOMR1Ewpf1ZtCFtZ2ay1dJpPy St4iWUibyzVtEpo+TWT1WwSh2sti166CaDNWJsl0mQSjpIfIX3JEkO6gAOS9 8n6vdGNftknM22IOHutgcgauMC0H7DOJE1aabHmy2dsKTfaSMSYVT1lGVqZM 3PoYPfta88nLE20lYvWbHxFCEODX4ppl7KLiReHQMkt+oIIBycHygTJWgJ9m zeyj9BVhSBIBXxXM82azCSlhoAtdUxqOsHnNFgP5H2tPB/zyeaQQqGcydpbJ Od/IwQulxLSBJrnzBpOK55K9IeQELywBSMSI+594NYENZx03B+jC3WTCjrqZ yfz630vFrqutbZPzCIAM2s9aQLAgozcIQP44i4FJnwi0fy/93d9DJvyd3G/3 ZAWZeBIbAI1VzCYyBgAx1S6ryF+d7WcMQeacgGqfSo7IQh27nwtHuwx/FNnn cCFJDxK8dI/nNIBz8+Us9hl/BY8y6d9BefYdBQwLBSjXjqPVGxw5Q1d8tN1X p88J2fePURQ/nHtMLwU9p8X5taLNhwpq+HNKaCDOKenTF/EM6IQe9z+1raf0 PA5FSvV3yCuw9rxMVBn+S5oYpy3zTCPoV7L1XFABwZ1XxKzMLQINnT0zfZ+I WNCpTCctQdp5q036KdEh4FiNDy7z2yCrfLrnoDaQonA3DyqQtAyywKfrpkc8 6sPhSveFXthp28n/Faqkev42n2DGZFCHmNyT/vpT++miBzPNRupdhNNE2cxC aSSuPQ9qKj15dZRnPVjE9qkpwUU2lwWt+Wi2izZmyQJ1jTrLwZRqaYtn8bSe QESvw4JG0sQpudLJ0tnRMkxR3gFeKgD/R6TpAYsJJVj7oPXvu8Q+zlDhQybj XbmYbbmcaWgHjxll4m+sDuHZQa+BkXad8Knb2/BnEEQRmC9/RMVv5skrWKIS k7TlMtOuQ5o5c7a+JTWo1ZmoV6aLgu9S5LxMmlTpDZUmQnF0M0vtR+U1fn+s C9Uo180SArraeHFNfK8XLzbFRIt8PsTBSSu54dkb+QwSVC9+OoARa1peawOS fNOzg5I4+qAgEBmqMQclkHahYs97POS0W6ArEJsg/riCinifItv+JOf+bQNK EN8m6Zovw2uNpvOE8P14xAzEqHdZ85BJ6olkjgJoBMAF4EazpViegTza38mt 21LFyKTf6Qy9Vs454ADPh7ZeRtopeWa9D/Dq4ZcAsxZoEtADbbsEl9ChwOgD 5PpSPzvnvYteoM35nW4DL6jR/r6pxxhFj7nQxbQjP4Gm+jd6bWf1PBg3UNI6 tzoXb/BfnMcJe2R8vMjgojDGF6Y9VvzGnlkiiFC52DNxt1E0T27A7bCgxApa JWJ83qUwAV6FYT4gYu2PhJBD9toUR2KD2iNOH3iX3oerrRqbAxX8C4t9huNB 9RgEcEFfb+kxQK/mCjTQN+XrcRe8in+OL2muRUwHKFR7TBViLOtRswlMoIRB OEXeh1xHLNgio6BwSVgVk9bacLUMgsmwqyUygUrhHJi/fWnQGpURAtRYnVaw PxZjfF6RZtkZb1YLwtySbLUyy/I5tZwoCYP/Yvm8aojxqmUm7ugukC7nx7TA cgBZv/Aoc/IiOuCFdCYXHy6z9FRcJK5wyXCWgSkyoG9vHda0X8CUJLgHaaHr rCtD5thxm33Y3HUo10yaWw4nFj/lTZQs0M3nUYfSpIoX0ZgQAjXL3hgqJKD7 gDa2nV60E16AA/ZzrQlPqjYJD4aozYEVDMELULVgFiFLjS3wcbva0mSfN+V1 SV5tBP3OyntHeT0hN6SxDyCer/IBlXHlbJ6VGzd5u4xu9RZCti7UcnDKDHyu WsNlCsFHsAAzRzqpRjY1vWjmuzATk/1GJ6KmuLT5FZFfTjOC2+UfA79KOv9V NPcDuMydT+/doTopv9W07a6tVPlcdie34XzwOsg/xfJgm2L8+QXl503S44ws nuZPjhU1cHVYbWoBf/H9Uj9jGMA1/yXFEOz7n1q98bc6t57wnO5WVSjxVLSW Kz7h8mApFdYxTBhG/vv2N5zf5miZWEFhGbgRKwLHmlpbL8IZfHJFEixHbGO1 dqVcP+P0I/8xghAhFEHiiCAuOOtbbSJFEa4Flk9Oeefz5jkQqR8k9cULEhCo T4nul6DbXSXDJHtDvEn/LyTpESyszWRqpalnBmYDto2Q977yPS2PuDdPRmyj eI76W/U9Pp5JNRZnymopM6eeMOXmU1R3MoRwAj1nKxF6RspM6Vmt7ESEpeuv YESIS3OJnM+2EIpCSrmMk1MxHG4LSmQ9EennjNzh09KPCpFK9spEHijwdTiV z/6NRKp78Rl2aLQxslZdAIDfhUyxHgkpA6T9zSEvElOAJ4nplHC2i1ycmJZp +kyoNnv3hk3ZmJgAU6JJZVxVH1xio3I/gslbGs1aB1X6kGNiecsQ/RSbyceB 71Fpns8qRsvSeDybv4GfoIQqCIlNz3c78UTkgycziSngJHhlLaG3IwjbS9k0 AaFWH3zenBq46ayQ5uSzcYiu5KkVfx43ftlTzGGUtZ9Qy9IKKLPwBeKSo1GC IfBL+5P09otwUP/dkYFPvSBYpZNaoDsFjiOwuVw1jNl09pbyBlXKc34yK6gU mKJNfZlI5EO3Zq4jtCB3w5XV9Elfou3hdJBMoPYF63Vuca47xuOaC2IPf7bh ERjyjWIGxgO4ASQVl1UJ/WV9Lmp169FNGMNxrwi+uZ6ZggkOzdBMz5TIylck v5XJu83QNtLn/jEfsNJMSgpHeW1tYPaB8QB2Y1vPFc8PjOT0T2Alri6sYL5H VmAowm7fWrwDwsDP+EYddNG6S31tOdku3MSDWh9CAimlns4LHAMV7GOeN/Gf w0iqo0bRakSAAQG0FZ8JJWoWDXOs47hQPO4kF0bMG2yDLjlWT7srEhKyAwvy 1Pk/ydcXTykE+tZzevMcu8D5kQH/A5clNPZfMQptL5bT/5wX6bxsda6HDkWE T4Aap/Z3z6WufDLT5S0M81EA+hjt49lZA6yITuO1uSKG1nt+MbdBr/5gD/+S EY5Qx5E/zPAVjNii1WpNjqLGBOsKzL/ZtSGnrQe1iaQTnPfRLhyz5ADHOOIG 291n6lTL+8Zz+fWNbQqmEafZb6oPxxl4kzl1QMFdhQGnTUUzyGoMVTwEWowN 1wz263GMz5cNRWmK23NOFvE94FjyjxYOjD/s5kQi1416XOVPe4FU9JUeNtcL bDef6MRxjnMpJA/O8PuRTnqMJvUYtVMUowG2RUQpVXLWTzpuixv7fc8K6krt 2uXrFPwJ3t8LQn7U+8yOwbwI/+rPtKMmomzgee8nyoSwKyMYoW4NR4YIBooI PJesn2zbq+Msctpf9o91Yogr+PmPpr3vvXKFHNp4An9LyLFzPOwTFkCCcOim M+kYUixwcJP8OkSmTttt8pKlbXQBGUBxDVR2aPGqWl/VWiiDeo6uhVdtjXKb wbczrlMW0dof685UDJkEM8+7AkoGfyZNWSS8byTEEgUE4ZsjN1ltqQX1JyIA xIkkBQIg49ZQfLlOjr8gRz4fEwAIvBmfJEaGNYEozMh2KEGRtCeY99DDwNQL QcH55KaP5byHKhkOsFeE7MWEbNmHuKBKDsrIQTA53U2o1WQufXuK7BPBk4Bc AnnDZfS1iog7HFp0eBzPovNpmWoPGG+GMLN75VS527gf3sEf7utMPjFyrlOX 8FngWJF3vHJhWU/LnRovx+8plqk8lTf5e7kba6fTz+6knG2a3NGC4zhvD8Rb vpnoCUV7OotWECzPF7pTMfqnWC7RKOoEZrFK7sv5XEofwAYs1e2XUBCLlFTg IIKx+rGOf3jYLqumvQ3eIGtnsYNw9fgxzEeKGQ1GjPuOEYI4ZtQ3+FuIlOLx vvxtbbWGB3UIjL3uDxLSYW3LUQoYf0G6oJxM0Dn9R53f6B+GyWcesfCivmPb q3Kj/3wFweppz/dnszpGLNHwaZPxkt890QzbH0OwWiY3fPsoeQDyWB6JY797 hswSEWVuu421OZ3b0j/EG4S93OPCn8K8PqhmWrjKSzFOmUrUw/2G2iIcQA42 gVEku2Uvjow9RW8BfkeCNmg42tvt9VzLyOTAElB8AhfsE/HtXpn+TfTG/XPQ 8SsAAHTSRwVO45nV3NPCD0ZsbESpTYxmIA+QBp9LhQn3k4PL5OC10uQ1M7E/ QTTknDfB0EMq4AC6wOwtMLXaxnmhm7aCoITcHIdsCA7juAsA0FkwUagsXyjv jcNCUI+EXTK/kTJ294godPYKQo9bhfoexLOLcjRnowzpOVM5fCABQwLqHUXp Fsrcx0Mq5YSpDTl2BdSj+uwa0RCTfI0m2z0dpL20lnPAj0zLrDXMSgDgRPcB OXimnyuYxl1JswqOWyxouCnad4UMxRDd4NhinX+PByE3uQ9HYRp7qln5Ppaq iH+T75v7En3wDvSZWB1oxB1J8hep+kHKligddTdEcSKsxiNTtVWLcjqwVcmN PX0VWnozkYtALh0A4FwHv5Bfo0Kd3NbFs0gLEMR871gqVv+oo/+iznwoJX6s zydjZ73cjmOx7ARc14BQj0tbq6cymGONPkXMwly5Vc/lUveCmdxUkQj3C6Vq Ru8x3vvZgaslVobM6XrReRRwNE4gVoSc0Pc61f7iBTLX33ROOQEHTuvwi3vb owH5Ze3Pt3oDIeBclBs+bDsXOoy8/8vZe8B7VV753tvTD4deBGkiTZAqIEUR UCkqoiBYKYKgqCgiakCNiBGNKAqiQBQLFsSCiigy6caUiUlMT0zGyWTmTrn3 M3dyZ97MO3ln5hbe9X1+a+1nH9TMvK98/p5z/v/93/sp67f6Wg8L+RvncPxN su/zNUp4JIli+TI5LZnb55Zp7eiiyT5/2M/UzjZHNt7TcmQzvZ1rW+qqzAim MsuH8QtfzqUVZQaGoz4aH2dGoZS8lbhLbZUZ/VXiIjnayDlED/ksZ+dLf1IE 78pmu07hrDKjrEbB0p8qkqPzYnGjk+Nrta7WxOuYmjyJCqeKyacIBdelgvhz /Ttgpb1IvCaV5zfm/It0cOYYmf6nz4Vr5cgzXhlYOgm2zYPK6eHVlPXl5kCj oiG8h/XB16A13ms7Mke6llY4VV1HRUigSywqfLWvpZY6K7P5dNnnjEt3zQTy URFak6dKNkorQuTcYWP8/Jm2a/bJBffZep7g/GmQvPGvNyvXpL6LzmyCyE64 V4tELIv3aMiE/cMRRFo0fYf2UHyesug6O2/qp+RiGjnhLiEIwzlR9V11Lfes HlFF9KmXLd5kU0X6NzHHv+kMhyF8RLOJY0lktvuvm0vM50ix6nLPj7lXe5Ni Q/ewcNlB18+pGzUAsb97slIgUU8wGkbMlGXOcUsf2JjOfdwrPY8/Uiy2+/66 0nYFZoTLh2IhKk7e6paObSr+vU6AX/h2Pr/ou3a/5deIIfyuVnWGv3egf7NF nrCfNSv++q2O8nihLv114u3sy/Q97B1eFmK8L/U1fr1eNVTExLtdmJkR0SAi Fr/reGTpmw1HevStYOijIifQ0Ld6YJF7SCOnlleYEbFePC1EdVADSdh5rq9M na84g/w3X0e8v7z/hO3x6I3KHKhtIwL+C38eazevUeIXJ+jCLekwvU6T9hzp fNqempRA2ixyq9pza334G33ZF/wxplSrbDiycdvm/spJs4Tse1b6UfJeP7/v lflSZqMVy0eSk9iCpVqfOVHpaBjtK9epKC46ihOBbFSceLU5sSjTKxDS+CMa euSoDpoM75F6EcTL9+giCYfoc4tEr27SXv7ZSDqNCpmBNupzz85ZYq05Emli +npTzr+IFwkkPI98l6Sp9V3HQg00nJ3ogaa2IzXa9iKd5p6SlffUxGzk3Btn Mvw6MzkeHG6y9qwjxSvG/16nhWLpxElUhzL9U6ecnxdx1KBf00kcCL+xHEq6 HntuvWfDLLlMdTtvk/HRz4/QapEhQwrbiDezYwqV4rjlUdqg/r6keKU2nFfS CFNrhmqUCpzXaX3hYJAfThH1EB6ciuE5xAP0U1cFe+99Q/b+kJXyUQ9+Zu/P SFvsa40gDtHm7YLaqsAgZYbFbexVJkk0OMWxSruL0n5GeUI9XG2kdriLcttS d2zNcfDj4Sb2roKG1hvXZkfPzkvFeeFYeF0uvVeqzJv99aRfD86qCjiAGyXb 2bTTz54ujnVXB9vyG7R2d3LkZjedznTI1ufPPGvlz/rVpH6Xts1LH5STlDzy F5vVf+NohoUakdQNk2r7Rsr1ywKQwx825G98SGzFFB8W3oQfF/k0tu/63zC5 KHdiCpP9fVL33uyn7yME/t3vj3Ojj23/GUap9xhlf7utmOA9ZCGOzEoITMs0 xM1bki3Hbu8wGNUPvC0d/Uc5wLk2lTZ3Fkc6AA5cOGuyikPQRNlxruLUyrsk BSofBY6h9aazZmJIMJjPOClck5MtiEadVOVTXl7AQpMz05JTa0hculGXLhCL GhMshwx3gIu5hmyG6hM72Kbfe61yltWcI05Jxm6S0cWy4EIGXQMeLJojT8Oz wNK1JJj2OUfyOjgRcqvn1TWJxTTpcXFryiGHLtWtYVgRBbb5XWO0t9amMGJd 9iq1V2uQ8280zm8TWewscbDBdpSR42ITWk/1ExuBZu4x5eXhcxF+cd5D4kCQ 0FfDDW2XbrcH33Bu5jwID2ThVwzVX21WoveS20zX6iIPFB6qA3hrH5ciPuQZ 1sIQ2EkcBpaKHpRY7g5xmEFebIVhxjXI0+BS2HzIUPbGDI3aKmchZUbMI3OW FbZgd/dizevjUm1JyBtRQq2zlUOh/zpPQeXqcp7KT/HWVHgKR8Sre2BrngL/ QJt58TSjOduPLxmAfmSrccoJMs2xZLFU572cHTUwAsyf6XanLw80Xt9bPGWP 8dAzX9RQUsbUWmX5UU81y6bxvS7uq/pVf1LGSfbj0figvj5IUTIyj4efpa+T UAcUQNxZnvmLgUEy3WrH/hHnL4gUkv/A+IeOMjqc3OE85fseVjousxR0HbJg 8GGjfCx5UFKJ26Ob/bOzp+v9dutsha6yoX25rSQd7mRiyk3OVszg3LwztRay ZxknMUDQWYZWQ+RrgpnhxijOOugHx630wHBr/tEUWgezIeXvSn/8rRXdqF05 41zFSLMUPFr24CVV7lArBYZgUbsxrELzINGcvMVAM2kDvVdXu5YppqSr63Ul tA0v6XNL8s2UUrL36jJukir+cfJTAwnDm2T68OhLZLBFQzbsh6h/4XvDjQed pxIbidE1tTFIBPrt55s62x7FrDYYBU7JFABbo8yRxYaaZ43x3naOYhnn22hf 7ybeurczQSfqBhqDUUTc6XAwCmNH+zrqUt5H/txma3iwv5gKnglOlsc9Dbi5 Bk1o7mZKFLxHVUcxjDjOjcQU+ZlD5ZCjmN+DSXAt30EFGfSINJKGHEeOl3hF qWngRiZyfe1CEtBqwwxFv4lWVGwinIkVrrINXCgSvpltwC4i8sXJQ+SjXbEl sYh6AtOFWVa3LVfZz8+PF3J+ghvD9uGO2SaUnjY1cZh0EEqTXh6eY8LJQWsE +sRc1VHesi2XFmF10UeDBTnTrLbn7B7nrpR1+tZIxaBxYrNQq+6W95vYGXvv PZqSdUSsDPjwzNedYRDUR0khDk6nAzb8D84c5jqccg82uZ9+VEREWIyiNjOK bydLZWqKXsXBQTg5MJ5edqESwa8kSO11+hSlUZxuQ76lRcM6vgwBEbmiW9y2 bWqgetllRVYOxzp7W18cmbBTxbTt23v0tzWvKJUIoHZPEcaxtreH8w8WaHi+ FDbRTZcurrKJOpEM24HE6XVdTUoArZV+IHvcK3HrVS2LHyXq+vttcDlWLwz3 vzfTr32jMfgCnZqmj/EMwoW6D/Kx/UQ9Or4XIVvs/8ErFCvp//mGGNL2wUcF 040YvzBMEWZM7iuXmTY6UkNJ9YjGFMbbcO+aKPTea0OYb9c+MrjeMw7eS6sJ EXyjyOrCK8YRHrBrN9u9bz6P7+VzMEmIweTH/N42Xr4cDJjtY/TdvaZinL+l NuQv5ZCkrI44yONO2sdOl8ziWdE9wzxuRU2EjL21gw792ieIw/JwdCSn8h5X KJrEJMZfKld5Kiy3n1cbD3vOVuGeWeVlrD8+LySC9kZrLU5RMgrE2uNVRlGv KDxPRinYeVw6naPRucTeSQz31svljN56h2Dzqi32hYPFhe8zTnH/ccx8VxJj y5YIsWgUu+02z8+Tr3ziNjX1/qVxmB/0g0u4lddZCs3lJ0rnxF+FAUU4iSw3 Eu9o7L3zHFlGZ9rm7LbvzDbt5O2uahIDuufkKCzqLKG0+xqlCvxz4cM7Xmjl PRI7cPGPcmTTugiJ8A+O+N/67//LmQcqAW4WDDXCNTgUsFpQln7l13/RMbnI mdXyiYIQBFDbUo6P0x22bFH9PEfGDBlSqV7GsYHve7nGFRXM+FpaWooOn8Aq esZm0iOJFXsk8cBrxAJGxqLgv/Oz7tLr2NRMVqCrbVc6XWvjAKoweSMFhc+5 B5X6EErq47HJpfUNtW4FSG+I7sp9bnZdopO+d7I9d8RI9RPie4hLnLrJK7Il Z0un6v2NUpTeMoK8dDYbJY9OTery31gtDtADI/63PWVXzU+tShasMtluGvNK E1NP9bS393Svi5NyMSDe9Lgg3MHLudJO40Lab8R8vVnx400qzzOK+twsd74e K+Piy85J1thn9xi1PWfEe83tem+zqdZXGfDesHtsmaSCo1kbhGGIG/UZEx2s px6ZL3JMbG3o8Me5/VUmlh3EmtfHdR56elWvE58s9QQYxUlDFE9LJdv28wnj 8PNvyJYIu8Dup128tYZmDDwQtSKUuKKIfoZlrSM2KLw8HcBtas0GW9YV92ts PA3eM/xAQyTrV9tdzDAe/dpFkrhY83Q4+OvOMjQmBI+3FX1qmseF6lLXuqQ5 dTxdeOK7O0aZ+TRLygUreLGt5B67buE2DQOvReTosHTk2P7eQ1aNSsPbO0mO OvSipbB8o+YBFXcja3KuSbIDxkV/XaeRDVbt3JecO+x31SL16uPcsz6aCSP8 e+ccOFLxgmKA3O00Mp5NGiL9c0GRc1ZQmTBSiHnS1eRMX384csNxSZEA+XCJ rVv1WUOb4silD2QHq/dPMb2izJ2lnQLMhQOC+HpzcyVtZXX5GG+uUiuNCa4x pihWVtlGo3KfqiorKZAU19e2cfJor9pWinoxJVAd4hqxAIUa0EAwHnFLQGlt R9dwUW1qXWZ8r9sCfYe9T/C/U2yHpo8n3y4u0k6PiPr+/p+XQ5bfyWSBY6D/ EI5BgSOvcWz4gZvU2QWe2cEXefnF2gSk2Nm1NpKNE6DER6g9ttE+YDr+NluH zSPzkS47B6tMeb3h+YstUlmxH+4cyrftO6NlaL5ipEW/xvXG+V8wBv3EiWId XfW0P3WS4rY3LZYz9tHz9NlYI+4HTDPfZyR2qalrZxnmls6QwZRORXvVzyV5 GUZQH0oIFOONvMv3uBTFpHRF1IrIf94JsyO7LAjRfLFXxcxrUjoSCxs1DN5C LsVEn/E1bRAQ0PVZf0aY6sNfK10THPzOyE56ucjc4c2MVNLk0Ld5+vdMq3rJ EP7LgUdxh/ba2tQC1l3Hp+xQA587HXtbbY0vmSHucNtVtm+2f5etbYguW3hD +Grw03NsTR8yQpu7VsoC7vyFI9UbbKA/tU5ezn22FeuMU/+8sTakLRrutzAS jSU96fNnN266SE5JIkJRk4P6QDAVn19d7LiNa7lx06dHy8EBs33amcEzhRA6 udQIyJtcsEC5KJzXs9mjjbWziyOjH8t8IFLhinySOvYFX5syJZ3APvwTLr36 KLgjG9RVUK/+LuE7n52CNBPkBBUpdDhNsEUVT2RymUv7RtEDoQbuAaQ5KhNv o7SOOMeuLvMOPRBnxRo9ydbZXpOu83t2kBeByCY8nDGRwcZPGHH4pfoZsdw0 VGQKV4RIhoaqO14OadSzmY57QHuvAf3LnkA66v4MXHgIDBsFgYrgfT3krL5r qgD+A2fiMIBNY6VpI+CHzZVy8P0ip0RiUqAo3GGEebupoZ+ze7zTrTUnuH6F knNTQu06A8AeNeOGE1y0qXXPyuhzm5w6O+pKxMdHL/sRifXeKvdlT2pLddWO /DaZnf60TTgttRNs31hjrw/bZ5deK6bQNXfObeyj1WY3LmlRjSp/46PufLZ+ R6cE+qg5o77iMd9t6iXLzyFPlaPG9cQoSwdmU+YM9xhQrlurVrB4FN8cis+o lp3F6oQp/Ivd6CobyS2LNZL/4fh7d5hugyrAPz5bO0vvTd2dj0EYmXlLbh9s Cs4G8ZXU2OFeTWaVvTbYeycZ9M8zFvCsafijbILfbM8Jm5HPLLE0aoZcLhuN udxtPO3867R4M0aq//7vi3w88h9c/qMztHVyIAi7xsZ0xVxFWC92VsH3Pkps os5ZBDjvVFGHL+bSGcYubiqOzE1m1tKEewCxoqjEMmrT+YFeLEpmz6fxh9Lf WPEI1obAAP+BSzhpEt2XlxIntIBq8nVqSjot5bXXpP836j3iqXFmFcaB3Jtl RwxjB0Y8FyqZQedaaTjkg0Z8tIftyfS2umbolDz7O5y1nhSK0mC5hyJ48Fln JyeZqniHEfLNFyoyerjIrOBFU5lu7Gf2t1HhvoFlu/EUePi8qdrrbExv1aki j6VaMcega9R8zzjd43v+LMozZ4xQWU9Snk1bXm7gOtyV6xRdba9E6YMk5V4u tXFVBxUEXrLRucGB3LXl+Dsly4Y8XRN2QnSM4pKhz5XWRYq97nFnwnNO9K/V 8gfymZO5brS577a5j3NWy1pHYeREW/P7bG2XXen7Uq+oRJwuAu9Qa7ciQZTs LXIxe0x2xmATG/sDDRZtZMyfGlP4pjR+xMRgz/VjQgO3lUyCLR7+ZoVJ9MpM YvNq1ek8PlUG+jPGtr7dTZJ3s1fzAhmipoQpiWQTsHvUyORJk8K7+uaO9d+2 bXnR1nuvH4x+9sOsmPsqmpSHsnWuUm7pAUmsqMFYwsVGrk/M0HCNMtYZ19i+ 1N4yqh67VELwPeOyJ8yWmYvA4j1+nz9Hroy5w3MuEUoGUbPwR3zkXAIqxFO5 0ikpilKiwSq+wYecCTLVnYniy8o5GpORxf+hfjaZxjMOpyfGxXJ6VP1xDlAv yawDWfQa+JCXoTjUea/fxqJNvAf75L1e12b4E18cqOvERVpkNJQpDvbZ6nOU bcqkzJ5JZST4T6UWrk52Kc4XEgcP+qyQuBzcEvWmJJD8msvfT0M/2F2BBIII 085JraP1maH588YNbjAKH+sKD+fIMITrJsjbS6LDDWblX2+k9PBUeQwOOkc5 c6QEP37CmxepnVpwhTvnKn2L1KVlRXm+STHfFIEpTxrSjGHOXSTKR0KHZgxX wNxOav7bya2Ic3FviQc0beyAk/bqA3Y3zhCLL/M7JR7UtXS7sIaN0HrbZC4x zPyys06x4sRv4AvLQiVgz3QYeZEsRyY7bq64BiwLTZ+TbCjYB+58j/eSFrgo Zbx3jAwOMAquvmULudOMiu/3knmMMN9j791k7GCDLerGcyK6wHN/l5y2hAEv 6637d56hMDmi8xXTtha8muLZNem4s7bVk4x0H37y3Fu76zPGNWmRNuKKTXXB SFAO4nAvvDGkeaVApt21XjOCYY2cIH/B58fIhma14wDPr1Xg+S3f4rX5MkzO OGaKV5wTcVXhlX3CFZdt9BfzvjdOiak90nybbHm3uTJEubYfxzV+KmzHx5c6 TW9txwOPYy+pDQbO7gei+f24FTpuKUG2MQ5egjbYCx2DZP/vIgshVabqxLey HLPfadK0yScgBkuG1rOFfA1TfCkIgO5zhnZX4LrWC642o7DwVzpmofh6d9HI ens9OUjumJ/4fU85L9eXX5XSVlans+hY5hts4kvs9xVXSwc9x8C7r4+8QQRV KZO7ZizzuXkRMvdup5Fb/GefRZLDhAUBN/03pz0s0KImyo1fE9p2dGvE8Y+X /6QXc4JTkOiIt7lSkMWyhTTxj1BVz/Jin5+/zjNbtX2xunjhaJkBen7SnXjJ OYDs/n5HitNG6hKctBATYJtsgxvzXT0hwAtJ13Vy4NSKWbjnoFQRcPBybYyY wOWIQ5K7baRvIAd/Nlq4xuv84EjxEtqGkL305yjpv0uZNDpbQSDhhbb8Wbvk M/0zIgj8vWFbtX6oqMBo7lQvrb1rmXYC455YoQoVJI2x7JHGgXx2hIEfth2e N6Oc43IjgDH7KycjdhKsEWZzXRwC5VedQBGHREneqkA7ylijevSbQbD16uUX x1YuTd/PYdg4G47XP7pUbp+zGUeXH/OX/t+vKP3xIz4d28OCdVCoobOL9Goe 6Levb625kZvcY0lROoy5NhLn0OBg5ex886AcWA7tr8p6ksJ/TusY0jLTcK+1 DRm6OTzyeaU51gaThOR1cqYeclaARn6jrzTie5Gv3npngXy+dTI6D0/4KLWZ QHUDtqSWX0/0/Xg5dIH/epOl16Ey29V3TIYV2P3mK9xABTjEMdvUtBeOM2Ia I7az20TJs7bI1651i5kCCnvOltm5wuxkznlbbvdfYPu9DE3dfWyO9ySG97ns PuTE2VFZAZENgKJNFUu6bq/+xmfN2rMPoBAR+jNT8U8fR867lq+LpkamLgoz 2IcY3zN18Y0hJnsGqq/xA9cqLLrbVMjHumoJh5yvW4/+ujuVz2RLhB6220Hf EP+HFaGOptY7h73oxkHfSZoIbW04bQqF+3rPd7jKqOFnHWX/xsm4W+YI8X+w Nb3TZNB3bVY3ncWDlybD5hnHAsz/Nrv2T9wG326/P7hAkP5lvRRwjuKktm3H 4k9FfXh/dLRrSbvIc4WUnBa7i/72u6BmFN92tON0+anT3KEK4j9wePL+d/yn d/gsT6VthfjGfMLsAhdza5zmt1YqRXv5c+N1TxrxmvT/kzL8/4hoL+FPbqsO DnLtfDbvdl9YeDlRBHXCpdvpzLpQuimUwCfBz2S3dVPyP2RT10lfw/BKx8/7 SHDrgf42QzKPm21Wztl2vzvHyldx11g8DZpsW4XqaIPMR3jh7/WF/54vPAvQ x5nBTzxLo16XfOCXsqalOnCsot/TfOS4pW4wcb5zrJIPHzHV44rL5MiLxqaE qSbcZyMzkXXbfOUTXG4ze8io9zYOgNl2pJjpLqk2UsdxO5UHv5i2OdGgfs11 0gbw1a1Yky3tVxTtQhkY8VapDGDSlidUJdeSKwNdXal/UWIZXT3K15kQv4fX lKjeWedqm6jf4GdqkWGawJAJcoj9WfcasoZY6juNb+2yt3ebrbLueNHmGlN5 Ri3MO5oy1dfyuDzS5yQmyHaUj7Eow42p3egeD16+rrcNSeN+mCeGIYCegRG+ bAeNc3R41M9scF+zUTxsdHUzVlN3ubxJvEPm0r4C3FMtdsBY2Q9t6W8f7oTT QRH2exxhp07Top19ody699tur7Pt+Kv2+bwVxsS/v+3uw+//cWaB85HZL7HX rj5RSiDjNfK3iPBvdtRGEwmyR17z5xBz4Jl/59yCDID/5RwDnQGZzxip/iKS /64zpXaayjccC7woxV1RRAxPiQjNPqTLi6jweiYB4lFfinhNLopTPp1BjAp+ RMP9KoOArFIxwpVyxoPgqLsiBZYYnmJxdaEq4MALzy85o/j9RYm5zgLF008k qokTiVJW4c3xlx6FnxBRd8lCQRQYnsvHa1NDnfsLZVdQa0v1yGHnuC85h36i yKd2fcfXObjED31xn3JG0VWhJcB+qbG3hyeoFokEO1zbN16idJqIJIw1yt8w Qr1CrzDBcYYhosdSqb9T7jLS29EcUSy8lYCC15hHjxRXXyy+cv4WcQLeR86P eLPI3vlX1cynGqML7/yQZzxG1yMftwT0TxuuQGeU6h9rU9hhPOwew/JK05dv Nuq/x2OmZxnrWGJ88QpDxp39RcHjBmu/ibkmT+zaMiZOII5txyqSzadUld43 ZuzDC8jQIP9wxEG9PUQWDj68hP0v6TA4eMAGe9JBW5kHbxRG/ofj5JuDpaZc 1V3FTzjbnp6Wi51+Z7NYY6Sy/kYJAwiZnP/xDgSkONgmubTXKiMZG+4Lc9SN GAcD9yENUdivjcXN0C9Kzk7zjlmLNWNmB4WmTK1VqoOi5UyyGS5MEp02mr/z mfy796ho0p9UmVGc8LwPErWiVwXsXh0Iz5jhL+iYODJGK4BtV0k/YM5xjNz5 9JT6NFynem1oMLL64pXSvk4uj+GppuGUKXxsZtI5vd2l9PRB2+vieRR3IVuq 5/+AgVb9UxolRCKvL/IIaQnXmO4yKbl+cXhhVu1yGK9xXkfEDNn/nq931EBy Hd4U0iYucQib5NhPRH6iLsMN8BWD9WYj1AmbyIBTWeFwT3qBUojYbzZIzzdh dv5dwhOEjYLb77PYAl4y2E7FQQQQcVkwlSihGGMG97X2/ZVXqJCG1DOWQedA u9B8QWXcSCUUrSFPO5mdJFd1ae8fVGp1LCNrD1NE2vzESxW+OERt7NBrSMUg B3JqL/kD0CW/7GlT7B94xl4459YjxXnHKwr+jNHC43avaaZujPuxhwfesLmN V1ogYx38Ba1D9BMf8nSJlHRyzJMVJ0CtWJhOfszCFLD9xjP4gAUCculUeRAf M7Xr8RbtIoz6pody+Ta9Ms8xHeN7E/Nh1Nf5ji+c6trM017ybmzyicvkXR9/ QFMhGLd3UmuA1wngdx+nHey7vjYMuGpjrs7n5FyqV8YrUe+MooydJZqMkxQR PThnf+vo/t+5Kc0bDvYqwD1db4TjH0HPyT5nOsBHZkvkBZ/y7KI47T8CdkPO 6o+XMvprkmXe1me7TgFx3OYsXAR0ZKun7KH0eb87W8O70WmqKPLoWHwyNZoH eRFBveLyKaC+Mbor5UQa3OpAG1tobSFb/EWXvhwfZATNLGDyP3Fmj5ZDf7tX M6q/Zzz/G91EIkiqmVMUqCGvep89kdOSmdFeYyqPDVJO/sgWzZbmv6CIlWDm xAGHveam7LFCFszpmNocj02xwaWKd6fImDH8BTaGFZdKUuMdSTHICLUfiLTe EBa1uOiZACUAKYU3qhd7SNcHQSwk+4fQZpO+1k2nUv7I+R2G5T+7GHiqURsS AEd7oog8ZU0scFvA6Hj0sJoU+DDW/LzJ/oMTdEYIp5sNedaTDl7Tia/wCEbN 13HXMnL4RUS2mQ2rU/olB2ilRn7R7/eUzOnIzvnmFDXZfXmCYk8kPtA6a7XZ XAuMp11qq3WFjaHLFO0khQiExt60a2bY1Ge4jwkPOJ61m7ur/J1EfgL03/Mg fe+plTG18cB+s9J/X3OtnH4TiPMzNntj+T3l5ccav3ivRahPrH6DLuFrv8Ol s9mrT+SRwnJHv+jnVIwOSVXyFS6I+DnNSRcvEdeja/6jb9zWiudrqws2PFyk +bRUqp/fKsJLkHGPTfOzhP1IthtbBWfzoAoQW2SuBB9AiAzw0HIKle4U6QKJ jtOyKhE1gsjj3qvL0eDxBAGNvfLoU9jtkdwFRlWfvVdzPYjpvSaCdtI5+jta eaGJE4fsnpidDgP5ahFNRD1bti5X5cAjvpEZAAo9Vg9WzQwjr6EThJwzbGYP GnmstN+pnHtyDEko6jMVLT8gZc4USyR/g5pXmyrTENIKElajvLLxaNkkLlVO rve64hXyTow4ryYl8Df58a87cjCNv1MkjpOPdteWuQ+PyWoe/no6KWV3KpJ8 ROyIr/I1bI83QpgzkGnJHXKW7cPqy/JBvQH95G25RF9lAiT3tD25Jpb+fBvz JJvDSS/IPAtjlcHB1GDVnEhS18Gd+w2KTAdDS4Zt/wyb3iImTh4a/6E0BLgH bbZ/bmbe8xdK397UOXvvP2eaw6h7lPqF+ZCsoKfcq1+oef/Fm+VelCtKs8LA 2oI7/g+J6OIg+is9ZlNWgXumLqk4dPeOLnZI/ydmZKcrigz9OUE7zK9aRf5P tWJopRO7k4Li4bQ73iFPAS898xHNQ12CD3NCRVK97ZszrQgb/OtpF64vspO6 twvASf793c4egHxdUZwaBvhAf3+X32+N/233vUosYGLgMboOxKv9JPfX18kM RaeEJbQML+k60B7NCaIJopqqlqoK5fOpV8EmecRYOP7mie3G1OUqkXo5IPr7 CyfES2kBdiQ0v+KLNK8geFZzdOnd19JfX0uXhkn+gD36lHUp/oxZQBH/3WdL bwfgT41Jh1QULSdIAWTLyVSDiXPtCRu9vOWU7ICqkyrc33l825F1sdksA4yR 1WrsHRXK7gI/S9pvuL+xvqNrK/Q8/ECQ2PADEWRPgv4AE7X/Nymyx/1BKRB8 yLDyC/vGcuOszR1qw+eZ2latU3yH1wob1iNTNAQccT1t5ztMkr1V01yCHPYB g4tsfJwqSRt5U0yroYf78M5wkDfJhumzNoM8HTn5lh907T30zUwY9VWZBAwe sYphvnuWDGpc3KCSQoXhtwtBEAajxTbgb3LqFtuCXG9D/OJ0OfzQeIgXXLVY oYutNsP59r0LjIe/Ywi931Tbia87chsysn9vvz9+gdkN23PWTJu8pSjwu2zv 7rJbdO8vtgUzuORBKuxLRTROpIgKv/cci+uTOK9JZ2A1KL600yX7QIdqWz4e nbJIyASitOdv/E7v+l1Ac5yCUyuDvCYdLNYk7sH7J/jtOhc5krewWFEFdVsp 4wjS8MRDkhjoSC2mjNRLtaGPoTfWJCuzKVd8RcdXSIpeSOx1XYeacDK0HS1t M0L7XeaIVAj5p1ZJtbmUGGAvTkjNyP2+c8M3471eyl9lRflsQCGFBXRjuT82 RMmhkUaAmCLchjcNM/xJhzP1u5iZmMehSFM5T88OIK3sNTeZ3c/Me5jabQYL z+1dbD8qZTh3HHC93sU6PSCTTv+8HhUB+a6CKW8l1+9WSUjKIKNfUjpswvPp k9J8vUa6faLkNgbfXqPsU9bmoFyLFpbb4cxMPsA96pq4sY1GRsAOWd5mSA6D 4ESjFiJaw3EN90BNI+strBlm13VuGbxGFRC83Q4/Tn7YYc4q+Zlu8QUJ5sD2 EzP02WNzhW28nghlFjN1g6rP5h2K8Jdt3wcbe/mxvX9yL4b9t90hw1fOFWZP 3adkPVSVZ+2aBR0Zfm3YXOgQmPn/22b337oq6v/dUcYajASesiGMeV8+Om7B 77CPVecdKf6qQQleQIjelJiMf2mv4fsZlnJgWuRSLyovDA0Mz3C7g1a8EScl Ku6cKJvcjuMTonNfMoJypCP2S9f1S5+gmOLKxyRvcNdbfT5kop+/eubU+vT4 B/3zmcWVVcA35+MpY7AsNU4yKMJV1ZpIn0WiB1Qj+BMCoVbooq5ejicxBtCO Bx31C94BhaAB9rkVAmuOMQ4plDbH6xXPKq2VRNY5Zv5ekxQ4OrYdcoo/xfFO gdytZ9ZECv4t9qCr7K0TjAS2nyrHeocTRdnDD2RvF4ys9/U6lhCKVlNaiQAa ioVFCrmmRmU9asP3AcfgdnUdqnDXx70yq0ToIHxKLX23ZGScH4Og4zZoOCwf OlOS5hTmHsoZ8Q+YAFx2jo3IpPdx9jpsu3DKCliEL3etNx1K33U1swGhZt81 1r3J0+KINZ/0Yk0q3W0rNyzOh7edAMAmChixeIwLInlwIHZw2Csl1DfOVXbM 4nsd6pM9RdeDiH9rZtNf1uq4G3Dzu45Kan3GsxZ+2iIb9daF0megq/Dk4FiA mPj9A6fo1GRsoDE6W51R9qyZ3xWOI2yDmfW6vb9qtR8BaWP44TDh/O965aN4 SEcigwfL/x/sOf9snO3vjE39yl4v2uuim1UEMIFVHyLxgvKMzUCTU8iPFaxx 7LbobO5Im4lAe/RNi8PVaeL41wnHv020zN/tq3hviuB8huLljnMV604MrHzT sTw8rUwJoeq5PROLZVWY11ZhntOdo4ImVcWcr/B5J48jYWLBqWu8V2EI9pS7 9gjbU5Oar7YRtyDmc0xDzrdRF3RJPYy6pn5NYWHQNqbFXzgicM9dX4Sc/xOv be+slcbd2c6XA4473sF+50kSjfTlINUOH/qPvOYeYZ5a4zyCRiwYdMjeCU61 g0jxRCASVxkBbb44Z2eEesrqtOpd0cDBDa2ZZRXxTbmgHbGFa5jHBNpTx3PV pnC4Jqo6im9qULpRijQqejqwx9jsZlMTbjKW1OVU7QFmADZ6n4bqMRLsTX2q 2Cb0Ydx6u4F0VarftvfaaOuYzBv9haQzn/VK3ncEdFSM4QdqYm+iNI+8NF69 58r5+CenSyOupWkg9+VAl/fGm/q6Ssoo0SDyv9OBK9RBfstLJosUHI+Q7Qe+ jYgkku46TJF7kJQn+Aus65SfHyn2X6qm0V88nan8vgWvJcdUnPV9gRjdnO6F F+zU2Zej7xZSOBGHw88R6JTw/ovr8R/Z64e18mj+Xw6dbzgCSSQZvLMmYAR5 NvsrlXP8Y3qbWBAcEkF7WsosS7BCX/+nxFW4SAcy7C5yWJwXfqcZTvDQ7ogi Z6+398U4ptLHK66bXBRLj0Ix6hgaet/1OeMZ3h3Qww3GWqafpq61PyU7lqEy 9mFYasCvLuGIGMiBTsBtK66do2EcyG/u7+1/2uajh1f6cAl0x5mjwx260dx4 v0Oor7xvvL3RqOdYI/Ib62WTfmaeWgMP3ZoTTFJH4Z0CCwBJ2HpMwAEkKBdM YYz9fcfVSr8Ea/h5+G6GcG1QeFLR/VwnXjiekYDlMS4VGOMK6ucAGummd6IU W7yOnj/7mF6pQeBbeiraxS1mMnHk9vJ6dXCPFhfMChirbePmlEFZ8pOixAvN 45vrpSVGucTWJjob69jbMz4wIfUnGhHcBDgj9E75KbOwHW6vYaQw3CJ3GLyp IZJGDnzBD+fBxRks/F1Js8HhRYX7z70zxT8k9vx/Ep3ffZ/428BKO2yXMazp j5p9du3cdjTLfPQ3VDqDKs3hvW/bGv8/duvbbS+m22xOP6jzrX7fnscYkzE+ OXG/yv047xJX3TNGMqfYrMbZtVO/pO/xs980ua+jOTLt/7CO/8xXLgU892iY bVSGEccxPeT4W+yil6/ohIYy5IRmTsbXt1yZBuJxeO3REOd3vGVmrc8KhhJF cjyub3TFKmPm9aVEqZxeGWKkUgzNndWTJtNp1bz8OGrLbYkWcUWlV08GrxvS Tdk1GK/9Dt6W9Hv51AAwheRrpkSBn7T6/vfWxLDxCqFtytNTvo3Cm+zXFBKr iTTJaJkbfqH2kzSGKnqPMlYiPFjlW6qEkPUJ1UzfU0lh8aqTKBxF23pwWqQH 815t2DkgNDLAIybXb2OpwRAKxQaLw7gGefx2/K9zQTYaM9MnAyn1wyhSTicq 4i6fp3rjVZ3d+h2Q8+Qfe5Xn+TskY7/moeFIFeE+tLkBSjduyLH+FBXYnl8/ bi7KUADaCIHyCR9xpf2/rf7Gi8W/s30MQJSf1T7jjClOqqnTwcZLfSqdZpTo Anhz35UOXeszqXVv4WgH6BvZOF1YiKKUiFqavrv8uUWR88M/AWTnxvW/dRzb f4uqCKuTBxNjAi0qHbhRk9YDT6SEACungAwiM84nwi01KHH3ulCwSFBILth3 solIQjfTixf03XRC0r+awgED0AAc/veZrk9zL67lEERar+CTPuNZ7yPZW9oJ BWCpde9GryR/1I3EvrkaEvHINAb7sfNREhwu4zB1Adet8/U5ySMQHLKPCFMU bwyWaGTmATCUZvJRk35fr/eDPUXfDHTtN044UpyH6+ptLREnMSHNT35Iv19x j/RzFMDPXyBhlI4veVKrDojZIOpKGnvqOUybQwJ+xAkzH6SWSHhR2lde8B1u SSbii7bet9xuj3xbOmW4pSHl5JLslm3b0EePm3Ok2Ga0se1KrRJnsBLCveq6 1kE1E/ii+4mazm9cynx9pK5hVxY0KhuIMZF4iXRjPEPd94hvmpXn9z6m2/6Z 6XJfPj6X4Ew0aTbFZPq4l/LxslSoEwp9vK302Kv3HSlOfTufDjn+1wIUrAL/ GayCKBHsExV87PeOFJeYDnCJAWX0k5KUr9o2/FONLFP+9W8SRbLUpzzbHDhG g8Uso51dHAvPkn/Dl5xkqLcdnI0C59yK8EAecwBCTVG8JECeGkKueoJeqGJd 55RhXoIe6VSKGt1p2MvSV6XY1HvizStO9jsj4jHynTIuh0MFRSxeKSt6f8gA cQUwifXJ6ZDLPNGwpzb9xW6591qEiqh2p9SOhtBxXimr0+McwTKO1YCe8Ril 7mivyaCJQHAkaA9/XY9rr3WHg0TtEEosyixoH3A/5TFyTKHzhyv2hI01oSZW RSAo4oyUdFbKUQjFkMdf960uplptBJms2I/T0Smcm8N+Pu/KxDHOG2mKl85a nqF0fAXoW7dyDLd6UakWGOv0QVrVL0oNo0hhdIzYg8b4Ft2tDSHbl5UZ90Fu 7AVM2QW4TmOTjvzYc5UUR+DJYadkYbDaqk6TOJr5nM74+XENzFUr3E7CMjQU Dta4a5UM1zObs2WEczt490/bMdzpHzBcvMhn46vBszxPIo1r+AnyUsaZIXaK De2nY44Us17QTqJJIzhpzMcZKqByb20EwjQudNjBF0sB5YSU5VeK6hn+P9vz zjWuVNssvv7BMamJUV1YfNWDQXl93r7/wzoVHJHejQf9t0U+VHRz5di6jNOc +lE5MmzfUVjFE5eOWRgily1qTW27+vgYT1GE+RIcd0EUNan7vi1q21FCOt8K FyIpACGoMKraTyrJiS2HJIbsaY3T19rpzJvAaSfFV7fbHfccoxrmXx2j5iP4 jxGPWIdx7jlfv3F2rmfCzXLCvTkMD2Ujd8rS+VpFZ3hGmY7bLIp9aOaRYtUK yS6oGTF6TG0EJ/IWHa2c0loSNHX3YyXwrCMQWLApTxsnTb184rDxdlnLDR2/ vWecxBHopIBxE5gpHqtPgWWpUJE2dEdRsTPqtN6oZWzN2O9r1+pi3tJN35xR qpWom/x7yEbT88zUbbMml5DMke74ayP3GbZ2f3qymjBHIxjWLGDItIhH4DPi GIhjJ2kI07bqHmiPf3mMjs1IHmSb4junqbMJZgORGeCXyroNiqeaDJhte/N1 M0AuNljNeU8JnH/nWjycAaVg6eeOFAdcYOMT51lMBg/xijaeYHOmOj38ooeO CvmBMeDhx+g7XzXSnPywlhyhnLanUPb/u0U+emP5DcpjXeHSk8yE8B4LjvIy da2YfP85WJ5e2pQjZXUQo6zrpE3uqhSaZo9BQwRZfZHqg80evhG+jnEOGoht YKRjHdAWhe/XNJQD4fKiSjv1innQGIBT8jhmc9HiFCbaAe0Od/c7mlY6hLJZ Bw893Ff+Sx4JOON4zpsXZTae5PQhj8IeSr09C0+iSlk/N6hxO2xh+IacH47U gE81neAk30GOESRXNGNK2T2nKUaSYi87ZYrONJn9jKncEzaG56n4RBhGG/po w5VyUT0/GbjRTKbh2NYwRHj/URgeK36VdmyHXqkdwhe1G9ApycVfuFxxJPga QMQe/EU3fTbJxjZyh5bswh1KNfpV/1Ib4s/vO9njMr+0mxIFF6fC6ub+hQn7 Xf2VwojPleuB7x9q/DSXRuVdkHYHMZFRGimasNQx31Ex1WyD3ssL7NE2nPm2 FOfZff6+i3gGsPyKSbfv2jNWfk5B0y8N0rPuu04JEw/Yd8fa3ycskDIADEme JcYLDOBUZWe0BxXpnrpOMojMlOkOLSgTnxdL2X6ito9l31fE8S754EyuJyVw T+UMFnLsyI8+o5J2/HEoTo2P0JV4XFNfpUUmQfagJIQyc+z/nbRUSBryf1MT /w1SPEj6Fow1JCgV6YPmSNt7GvpDIQMfdmW7QctDAsAgjy6uTg0nSo9w39sy MbFanGaC4jp1j84W4L15m1U7RKFwFX9ERzD60EWGv17iLx94op9b7DFLrlZa AgpsyMEBm93H1MNzOJd46qfNfupmJVwM8GC+TinJiY4QmfLIhCdwlXKq7lEm GL+HRts8qK4KM+dOnwSzMlEsjpPhlVjAjtzSE1aRmg5MlEiiEzD0zVRHeT3/ 8v5imM/Yhv/EFuZ6W+ULTKn+0ZDKGbbe1EyYEwRPlRIaR52UrdGM87xin/3f 9VLarjBYvD1UU/lhjZzSpJfATLEqIrcbck9VWOOMKxjN3G0beKldd4ld81vj Yr/pq+Eg+TB9jxvsx6jYZ79t1mdwk2S2GvOeY/e4aIFScRr76v6vHys4nWhj +mCopsO4CLcfdA2r/4VqqLa+vyA78ty61Pa/ScJ9ggs5IgK4fW4skVYCLdwi RQVoJNpOT18NoE2LnU2tAR4VW+x3V2PK/O2kt1in8Byi1CAR0Lpw4INNDgUj DSU1gN0o5wldl+GmtS3lOZrJCECiEAZkPdBKSJJvNzYLvuQd2y9ATTOS2Rvn Vdfl86oRbFP31IUyCTuAEw1/HXebrND1M6Q44CRJSqIJqrr2KgpqHtQYDk/0 b9Q9Ih2Mdvp9qhmm4gCsKNVCR8Sjb4KbOBqEv5FRXc/P58KFeziFey/JpwTV RqlFBUeNVRy5S6iPRtvrBneaTc2Mq0wgaZBKB45e8PR8PFJElx+ZISyhhCBP BGVbxEEKyI++O/cjDy8WxEcGLVNDNAKm5yYr+/aM55VigauIJIDv9BZZ4T4c dreokCPtMbthQWS28TxivzTtYglOuVCR+senG7UOlbwCJMjMUGEDTOPs+d+3 +d6Y5uamQWf5QSkhgF+kE+dOtB03kPxlvfocA3j+DSrkZqfQbniLSqJeM6Ew y+Zy6Zz66MNDSQ5RRGxpwqV4Oyf/UQCVu1hxhL4oAJ0RF4TUAR11nUXQnbUW KIYInVSLsEWkRtZOiip4zVN/z2mnfiE1XHlJKjlCKoy4RhExMoxboSUpXAmm fODtpB1HodM4s+PvszGebtvz+ExVTiQBdb/6UT/XJwmoduEywYIbuC3jHZJg CrAHtv38JTIi6ciM1XpMwkd9+GIZXjr+YqbAcLqxiWVGFZtN8Z8+X0DT2QFZ CUzW7RYlOlQbkaQCuaOARZZ/0/EezP0EBKWugxvi+DZdM9P3MV5kq912K1Io +wtgWafYBn7eluPGRTkqmxLwLhALxJDpM16unn4REDhep99wssVpttujbI6P 2PXftG152u51u91z0gaNCUG75QSF+4b6FqV0l1e125GtHQhCYlLZcNVYBSf+ azel43/dln7e9orXtCEj6FwjlJdtW77ULzvCunszXtOY5hqlXG7Ct9dgDek2 W+bRtqz/xZZg5k4BmRoEwgVX29LdN1J22S0Xq0oGm2tKV20PXH9gsqcmJVTd UCimhSia59vByRmL//+g6ix+1LD2fF7XvuSdNaH+sHqoTRHMDqrBcmfXSPWC n6ZSzQ0i7JSDMk+7iWKYjoc9rNN6cY9zT8DVY0lpnCGrwED7CblShsdEd7bE Su/Ong2cUbKC9V4N8BK6egdpSqomDWG9N1sx2+r6zhJZ0b4TWxlvY4Qg6QI6 0j9DksI4Ups90w9Psclsmqma0QXXy7t4/G2KbrbVV6KTfnw9TvdAcgbC+L3N YGmYTbqMJWkZltX8ANsIFmJuWqI4se5IkauiSUfDwRGv5ReoieGulPmi1R2t ADoa2HJj0G/bfE+6XQyv7ciqP1fRciCAcPpTg8FNa8QkLypkTKDTDDXOdNrx gtfk1VprYIHnFsUSYiFBOsH5aVns2JQolfPNtts1ytZ/oXweyb/8cs4yCniR czDlAUUwT9xdusuSJ/xlEcr5xsuHdZM8XmK0dFqLEuI4uAMYciQJgX+294yr lU+9K+i6Tlb9eLtm+nDiHTUITxaUgpSrC5WUvuQQIrj3h4SumjBdyFKLxHaQ RmVt1ww08mlwy9eXQJuefnTXskDsMHoCstEuCqU4ZR48KKMIyyXcbhAGJkgS RnMzB0a+sdwqlswKHCyom0fJeq7EGJyRqk4vi4i0a6ptpL7AlPH2E2hLbenO y/r9uOtw6dc6xC7bIIi1EbRwjCa341pZaeoyLJi93Ilas5pU1e/O3pQTWahm D26F3Zh6z06V52P8LGXBXTAb5u+d0VtyWDwdnPN5CQ2+lzySk2QSqf7U03qa dT+GhSJ6wihVbN9jqszE/kpE/GbiMM+kpj4HpikYzgsVChfCa366DeGxF3sa p7Yx7rjIqNIj+kwbrsAWoGbhhQPHWH/hs0pdqF5ojaonPDWNrWTbV87Qeys6 qb/K0+3UvYtngAYUakgYtaVXas190j6ggCCfdpliCJRuz54sLsd4eC4uDtAH qrj/rTaGXbZGn7Uxf6lvpfKzs6fVvKExTTAyO+liP4ACaM1ROSduA+CHTUAR 7a0rtId0RMX5343bzSglDx23r03vLUywmlZIi8V7Qeo5YTeCUqTD/klWIVAq 42xbbkMnmlnp7hlbO4qyp3IZDsAh4gfUPiG8zQi8YQBBBKn169X5xBNYPlgC Rykg1FPFc2hUuLe5jp8YicmhMUe4TH6Zyys98Rq0ySxWJFlTWtB9cU3SFpvy LaBb4JFSExdrOKnqIFf/N/fPIi1qHmGscl941cZL+Un9NpQRK5DcMkLIYOev Sck/dyT00fZxqXO2L/nvF3aQ/cT1II9CJIo+oeqyA3SdFOjwNaagiPhntLdL muOUUo5zNymXfgBGs/y5dIGjOO3zNsIrTKJ8OMAk8RhtQcNx2YXB8ve8Om/R yTOlENKakCKja4/LB2w/Nk+ZKMwa9oh6TyT2QyO+CZfL59Rhcu7EXHc0FOUu OlaaxGWbI79IhPBRkaOQEAmeZZ6AcJ9rG/woHZdM+blspXejeiOOiAgUlvyY W417U4bBl/vXBu7gr5AO37za1mHWNLd05+p95s38r7pam40cIoYNL/tpITsh dfdfmZZubJGPxENpZCJTXJ0AME/77xRm/cRvsdDBFsCj8miTi70LE2ZLhTKc jUXlDOmKkum4mxUfdfcNKHXJNjLpsXPJ7QQMqCAwsg6n6tb8PPYy4aZOa4Ap mmA6ty4iOEAIRUM12Gy/d9lrlIhLh3fuyq4hAtjKi8x9fNNZa8uLki5AOwDi 56DHyuSrKE0qIVkvQRd6E49pNybzJpYeRlB2426jLvMUdZGkRWtsNDsK4K4y Dnur0dY6e+xF0+qi4QPETxFBbrTOrVRrzrqp238OVCP8mA1kmo59GeDAaxLT Ju9vr71Ot8fsNqCsmaZZHnuJ1rm7vq6tkgxDlkF7uCy+1VUyDbmLfAC7YG7q qVpsto9VoHNSP6eQaAPQ6/q8agDvsKHlymmSR4MeY0LaNDayisDkZDqQitZr SoO3g6NwujDCNJ42IB+3zA3Fu7ww+aA/s0l8IjIXokExqXTvdzHI3FEbQwOG sJPzjFbGJtrJTRFYBpbjM55Z/9nZ8guClQccU7Py/qORKEc2J8Hu91W52BEL /6Vmf0EFdmDosP8c6TKva0XmEfJf8x9i77y4AD0ShsjkoRk2DrJtNzbTTdQH 8FONZx0NtXIWeq/KmpA96NxcF20L+Mk9o/N13Ed52fk+KA0KyGq1Q/4m3TbF N/y5LS5zF+XClxhHQC7G0H5SPgopHdzUPXsmWGbaiGNnwR1w3594jhRL3Pbb enlnF0m3aL6tTkM1gUIEY3RnVp2CujMnU3WehEDns4v2AfKhzlejHp3sJZQS VNto69vQXYvfVSgjvWKXO+XR4rCB0jGOc/VI2vvS3TElJHxB9QicxN3xKJT1 WFaC7MzNAic564BMOopoYqWN5ENbp/32/iZTIMdtC5PLr+kthxMijPVeNVJ1 CDjtWXeYBAuFTR+gotP32P0ZYH1tKg/Z8L/SSZl6dy1SB+fqWSrhC1i8Ppns LN7vWlQyea9fftPFova/dHTQ9WLqHwFZR+06gCCQDT5p9Y0qT8LTI58AssKB xvnEnfOtWbz1RUBE7w3xWw8pit1H4YwAFgIKnh0tZuHb3S93iVQvJgmDRGNT r9eMMymAiQ2LGXeWbzfR/GMhjZxAl+S2QeihEaPh++k+88TwUnj5upqj4RZ9 qPus9cc3CzZxHl41uysUzH4bPArWKO0HEfNavWAVmlGPJVlTnLJB3TCvWOEQ U1OTKLEse5m1Ex6ieTIcCnWYmTIzzBgkGWoddQspmbK3n8RYLx2F2lTMOHwd NycxcUdq1nPiGa3FGSsz2Kb9xswc3YJ6sVCj6c6irQolLbhNWss3O6ak3FKA B9CS932vm0ombpZMVELEHGIgWNlbkzVJQyrAsM8MhnuNkvfMVQABnRvZf72T X8qVfkq65nU23Wdn+dkhe3N7UkwCHNoPXaEcD9KtuCYVjxohfNBOqQsLNqtX 0L4TTcItVlAA0M19QEPn+JK3ekoP+TdHlqTr98vGUbSI/YrLGcRapa0vuyGN 0t9rq1xV0IRBj7OEBj5UFuH6oL8MATYMRGmTpX1A9ggl/h0q0g1wbihauyoj s784CnWdRbaQKEjg6JTIhKfKPTHPB+TRR61ATww1soqYEQfrgj3CynFcHH+n C66u+jpGXIqyPZ/VSL4OyYKaQEtVH+Q+kBDCMSOulIeMAuoslZR6qZgBQKmU gK7bhemYWAPddQY2uoLyWNMF6wNsOrLa28p+piFckOn422meduuxQVw1CDRc XqkY/+6o0i2Z3AonzjsqjA+zDfZhy1QTi4UGSXLaLelozttX8sg7uiih7LPO biBoVMGIQKXDM1/WSJO5e8ImPBhg4vZrFI5QHrw7tubktPCU8vqytpLrFg5X NibHx+xMYYVNFxaDzJw5XgRH6uCLdL6xhbvZNMD7l6kehjIH/JcrHHfpCHWb 3lRb1G1m9155rZ8FslceZ47bfNY0zYdNKxgw33sL7hPuHputch5qF56erFNM v95Xlu0mI8HXDCyvJJNk61xE03e7KpWRmhysN5JWqBNZv0xjesxVhuNcNOGb Jo8Kc24e4Nyqw1P9Yz660TkfX6dO9gG//asOY/K3R1RSsEhX61W0dvzl88vl yfTjb9MJsNxva7pHK+x1EvbYHAiciCnuKbAXoTD2HwmWOhu7YQyvxNuvo7oK uSPrK9rdozKaBm7LHkzukSJ4+9AGS3Ucz2RyAD8pzZ8GFnHcdSiz0cehoXUd bC5PyYq/2Rj9ZsinxeuKaanXXopAIP9W9NJZYL0WNkbhSaBORzbqSLfu3vSa xFLmgmWL95asZ7QBRtB0fKl4o6psrrwWFe4lrc1nERWVzWOZWTpbtJr0/06i f8z3x0Zp5CPtNbOdTPYbVkn5YxN3t5fcAIYoIdQ/IHO01KZ62yQu36bu0bPt uu/0yThE0esyW0t/onTyU9fISYpWFWe9XbxRPO0am/WDxmS+3UddXekzxhlx 7VrELpc6/GhmxOfr+0u8wf/YbVpn39dLq4cGsGGlhs4zTn9F1BR2ATD8eYM6 C/9p8jLemA4T2DpLZW8q8alNb9sGvdFHxetIuT84jFg1hgUsvpWkXk1yB9dJ ryAxjZyPz/L20kT65ED+tPLCpYnkOlCo1Tqfc3gg2uo1Rc4cRyDi9ryncHdo rUwFQgb6a2HSsBb591jg/S5Muf+yIrtWl/h7A4vicSFzdpAJcbXUZnpHNppZ S0IvJNQjjtBFG3pm+w0kJz9NpPy29RTElSI5/Ptcz+/YeSkJuqf2m4jp4F0l MqOpb+r1+IrWFmBUzcxPQSZ8oNd1udFUvVe9tlcmCq+VF/Du2qSrDHP6xBNw tUFxuamMwx9sCMtt8h06Y3L9dCmjS1a4zGwr3wlrMtBb1yZW8rjCVdwS7gbt 4eyYfnzCY1RsXVh5daigk783FhXLvINYJ0bIo76jfL+bj5qZ8Z2/twGvXpmb rqU64Ps9ge0sVdW9P0pnMXTvpiUHudipqWTGeNcBU3b32DbeNF0eAlAP08To vKSXDlf9nN37O0a9DxjCe/fRPuGGi2TYhiJrpn9rHOQVu+fNnne0ppybaa12 nzu7ScdP3s235O16ZLqey5xWmZY0xrjG2kv03o32unSYQm84HOn6P8+eP2OL FPCJnaRC3mhz+++228NWabfvmSaRtSyJIR3VBIFeV+Qm7vMdFPUKflP/wq3e SlPxYtBG9ZlBGt7v23Gfg/SNdM3LSbICKgBJYAEpS4oKsQq+i/b6ewcjwQgK 94ZVtv9Ef29Fem+t6l4zE3/sKJASCEaklUd91Yu/E5yIxCBTAGuT7VUrDU4H SmRlnFwqDL2WYQ7azlnXZOeJFqEWp06BnlEWPZdTH4VDkp8AIPUrOyz/Qv/N NdUzuKr9I8RTJJm5VVhRvW8sVduTN8ibddZg/pqfQLd6sPT6+4x2lhpNjNzs FZxUIWzxrtH+4vznL5gIOPNWeUU3nuNFw51EZ60ad2yXRhjxctJmJ9v9nz/Z 8DBQ8KeB4YKpilj3rHjNzvVNLSJzuZ3wCgZOdLxyDQ0JGfuD4/XzX+27Dxge d5ui88+pjPJrkwrD011rWqvvl5gC8FMTl0s+44dVHVDxCzyRwMPh8UqMWfa5 I8UZq01rtGvnPqqzEcgGwUJDAC13KXDZ5miuqGakHU53yhki98NJprned7XO Qv8vhqFb++TjBrnfPbYGJw6W3o9/9OELbJ1xB31GawRbD5fOsHUS0Ydcqu2z dbzCT9i71vjOqc/liuYEljEq+iFZh/enuvQDTF/PtbH9HH/gM+kdG9Lay6uj t3kiNuezjtx6IfmJQscWfS0h0D9rK8ZAVRAeHGopX3WUs1GTHMEwizcL7wK6 It1yYgnMUHU9q6Y273dTCdqyKDedazg+1fPk7IFhuVJoxMGaCAQiygZ45leU r9R1Kr+VCrA3eMfZ/nUp9dDRGwlYuCtxwwE/0FztqYz7DKcIX2e9A706dbT0 XUZD4UgTVeWh0Ftqw+/klm0eelSLW0coYcYJWmB2+tzVdQFcgBplhbw4p3nK bZquA7c2RoI5xUphJXB7ncai+9Q0+iNvP1KMMjBMtfteZLJ3wjJNuqF7dj6R gQuQ7/WNi5a4FwcnbhCIPyxkSv3WgfQ3DOV3ae+/68PF856On0/5rlcmjrRt mdIkv5+4sv3f5O8/tldMY/aTwsfXzPL8/gjFMZbcrzRNymCn9FKb9Gs3KWkM xXG3rdxZoyUIdlUwnLTqPfmIIKaPZwGH0ugditJ+0Zbh7ulSYdEELzB9aojT BTRKF+dr1+ueHcdqM6EDbHt4Ii1T6HRAYI+G/3EOC0nzpz+v07Rm3uCVGi7k iOG/67yVPE2Chn/n791U8cnM8evLVoad5AwAflscjliz3JfWZriFon0KqaI4 x3GM/8ThyvU3pHttTnFioIsWPd6vXenbPdq3VX2eB6X9hBxWV179K0J5kN+7 tsTyOYHAAb4VbEGyK9W8hIx2maMOlUappMlh+7LgxNeS7bTVk4brxOFxjqEU Jz+PCfaqZbvdS3R2+Vfc4cp7qSPpSo0oeSTdUQsuyJnjWJNV87NLMHWceFUJ I7M3eEW8d3NJxVCvZZ9SSjzZk5NQTtrrwrRBNgKERxzk3NUay71j7KuPDmIP DtjjL1mqPXuum46LWTPrE+BNWOULaklAyhLyDSaZqqG3y6Qj1Mv7eMXoDAfr a+ipnNJmJdrRxOSOQiR7mZPXnCK8EsxoWiKz+aZCvd5L19ARk/5kR3ybx9jn V66XA4h9xcph5hTwAdd5Mzjwl2Eb3E38HTa2+v5gOX2uvF+hxwMG7afGa7XP s2n+sr3gfMAgdrFZUJM35kb/VUgzTazSOMs1seYJ2THYrZe6dG7qLv0To3Cx 3f/WCYIC80HXHL2jtQue+zTa7NfZOK+dKiq5bIkMShxKXxkpy5hrMWWpQ1s7 ElXH4L1eZ2fSIi7YDjKY8owLi9xvuXBJKU4qteiwj4l1RYy/43BsBbGu2rtz /Pq3HbbXOqy/VeHCsFqCmZgZw/wZXEu8bGkJ7TJI1dG5R7wu8Ud3K4pdQvLZ JZIfzOkax9TUphKwU6Uxl97Dw0I2seNkf87zfhheUoVjONxC1BamqrXdua0C ixd9h1Mq0PG6FocW30Mv5Xo8xdU2C6nduccUUw6rbc5cs5Gf7Guqi1fOYgVM Mn1s3PMOYZMxD5v+OuLB3B+ZIqenTjAd0XPMKXZ6qp8R1NmyaG04p94JeiWF 37ZFOPtGV6hsp7adKChzOCOm2wDP9R53n6KhVy/J3RkoCkwW8StytB5ToyXg vVQC9pqMBAwUmF59N499NqIyCcf3Oo7Vvho1OtdykXg01tT0BxvF2t/ya/nH Tna3HX3Npj/N2yWqCbdxs/nifCxzSpV5XZ0tLjBONs9Yyg8HyHz+VxdXH9p9 3rUl+203dU6LrqwsN9Gz2ratMQznDFJgntH8l4QnXBmp6ZBhcfAKOWhwMNNx 4hdGUld43Dca+0WdWuSeJHKy1xn2Gn+XyAWV+0V73uMLdKLRu4bTLTb+xSZs xptk2DFS69PTSOXZOmGp8GfQwhEThRjnIscWytnyQhrRcued4DmaIMVxXvDY XiHKW4Tvzb5XcVDA+84jrnFc0iLjl9GBuUGnrvR33sE4tiXslpZVl0JOv14l VstcoKiIL2IEfSU22RXkEeZvSopbpFGTh443tb2XVKd2oG+rC0H4aY5u4Zvy Tq/yls3NAmycDIA8pZ5n0A6JyF7aJCIAIaKxjQhE4tsYutFeBooldpu9drsr vacq4vd+Y8Z7jZDPs2uf7qWq/VSJ/TmJ7Lfr9N6rHbWwBxqZ8t7OSX+6TmA9 WJ+7Vb3YKavSgPalDvIYHtcvt6HqLLzetkLDwDsVHizYE+YvP/ksZXxs1ZGS ySd9s+hb+QqC7FSx7+nOjiGn7k5Se1wkwIdvLWRA0Zj2eWfp/Pt2N4U9f14r jgVHHdeoWa++wF32h6ScpCKod6RdA1dYyNS7TYQ3ZW8sYooaeizOqx2yuyYq JIwoZ1pk8QHZ+Tdp52BBqavRLk0Zjh/pk6nZa72sY8Tdf/dx4/ZpU0kAZ9xU JxLfS62OFPojzIexjHIz19D6g44qONxjNDpzj/Ib4AKrXPqeZgTTt6/sj7d9 I1nG3yQuWDJADhMHzFsrDCM6QYXZikAMPbkEazdltnIN+vTLDkAkI3r0h/4e j8QjjaDHb8CJpvjdEML4Bj7nzIBVfrziD8GMXlVwYEOxU6idWTqrPhfNs30o A3JTCgKkoDQhbbG02s4zXdfYkPUOaBO0svHJ9PEG3LwPItGVkD1hEqFoxzmc /Ew9QysbxYrrwERXQ7sq5nL+DYLqqjMN8Ubrp23T4YB77fP5m8RX2ZmX7O93 jnGeZ+N6ynS8UQdUND/JdMGrVjdEm+ZX2kl0yh2hc1X2ddSlYzdSy1vauaDz 1otEkkm4p40fcRBOC2lCprxYCRJUWdnEdB5SXkdqxdDGMxzaC54XFJGKIknJ phOeBzbRfR+Cgha/mgi8hgvgKSD1ZwgP4y07bANPNWYx4aWIbkqudhMoD3cR O2PtsOIBJO1nOJLk//gTCZcQtSEK9h4JjbM1Gzr7PGu3v/Q+gbDtaIZQ3znK SxMfGuJVirdn4LVxMPJvjb/dXeozzBxmz9cgGW7DorFQqCSQzWmdVOgB1Q92 63bQIvGr92zxDtj1523VgTZDEu/K1io9Tqs9uonLEObB43a2T5VYzg2ONjZi taMGkXdMoPJYicI3nU92d1TfVog3zvf3UIle9GfBHLCG/82nTpt07kG+yBzf 0LkZmOuKMma74yhgJl/oMRWv8NjW/YZqmkQ2GCwkwAAufgJSUm7VyVjXDPZq tORH2i+QRnYKuXGRSENQH/F8jKcOEhLCd0EYCbkZPZBST+lmNfVNJWPrJaqH kMROIs2Tkg7Q4qhnTbedpk5Uh1x5fbETcxLmACr1ya/b/V5vw3Vw0sOJn8Lj Ovgzpq7XNeub88Giw4xiHjhDHUx53pCnue/gxyJ1PZp8psYviu1iy3nGcqfQ bcluwTb6wF+gDZ1pk0cKNVhtaLhFatIHNv53a1XJRFuzOy5IZrjETEfViv53 sjhN+R1lY/0fNp7fN0ts/PkxyjDgdxrtE7vcb+s8zPbqlCdhNzWp7ZXd9kO7 xc+aVW6ayvEriSmpiLqLJxM96qTST+5h/v2hyF1Zg2cjPFNzpMo9wvWO/odw 5iy1JyqkBVkcN1vcCs6EXn2KjeWpbrIfWR+EfIcEoNrkx2kRvr7lrzeL3FoQ SUhIZpvjrnul+QEeNXb+YWeKTSHPOylSBRapiSR3AomJ6YrewKEh/+CY6+j3 eM1/gk9cBw3FHwNfGcLJaT85JBPZDkUlL7u6omJj+qxNREyj6Cs86rH0EeEQ kmoj1yC19XFLho/JcaXrDIDdPLI2pBYYAwvqPX64UBWVpFa4a9XRqTbonnwG XDtlfl5t7rfoVSjpvdQVa0d4AIpP7VCxoWjtIf6fSShpeHd4mqeyTj9KyxEH v+PynOT0y/eAPunadEAdsLn07qUDNbaHJ+qjdHNIDjfOR75vkdUI8vttLCMR EdC8I1FybSqirM0ZD9o+m3KX7DT5Vx8YVJS3OUfcoFgygHDioF/hZ0WxCKaw v2J0VbO9qy2niXwQeBzk9+he0VJDneyePispjplgr02reMBj3REqqH1fSSAp v9Lfv/JGpW7qj9B5pVtSc+xsmQl5OHKuvRNEXWT3RH/t2lBlkl9luwyQxl6t YxbEPEiw5vN10zR4yjYfHZRaF/JcyDnoiGWI07ZyP7FMs9AxiVXV+qncRDZf l9wteyp03JC77kRoqNp7uE4fZXKuC2pms9RH2IVWT+ESjOKVYzLv20jPXycN 6XWn7CR4H/P0ziK384IdXOgEd++iHIdFg+IISN7n+neZjCAUtymOOmCVo2KX +qM2+y1Bya/6i+wbPDBciDcfdBLele8SSSBQ/gtOJ3vKNZBK1VVvVYNqn1TS sNZ/rwLlcJFbxBdFNIUosbLGxxzJ2iEZwgqNWpP9wXjqclhI57y2IvLOouSj Q3sVIm8JIo+TCHPLzZwSSUNz3Dg9lmVcpcSb2yvx+AaxMBT/lFC/X6bLgC0N EVq7Z5yMfSk8tZGsAN8ef7d+4ofHmTqlliNhipYIfSajqrP0KhJeUVpw2KFs Az00ZQgeMFy7sM5L9gY/dtRsu8XoM+FnSUoyXgqUmNH1QI22iO35R9+y5HIt JE3DAZwkKk68hnSslebUX9nJ9Jn5dT/TitrIyRVKSjgthtl8D5qOeM3t0pUe Pe9IMYYSg2fyWT0oG4hTdNc3+snRzUmsqbGk/f5lm9HJD+bxheL21wUsvzbM cKQynVRoTUSIqr1zLDpy4Y870bGAAkHdQHOFvIjqsPm76hyBh5wTtFfTxhEP a1oNRnmnF7LKooyfiPVbFRQo6F0TdmV1RXf6E/0kFaIGaGmoNfUOBPLnJmek RoVRvGBJpMFRZLuwtVLTGgeXBM0TjMLuikrVwTsrNN8gB9rxd6gheMTLSPks Il24XqY91keMQudw6Tyf0GmqJQB4wt6uzXw1IoVv2IzGbVTq5Iy1ssO3mj24 cq6vV63MURjPgaIo7XUsInUM9Gc0yU2G2xfERDdZ6lx1cFopEYIQh+wpuse0 sw3uU+ygpKVHB2uYRDP+qcjggHJ2+98ACrZACg5+yPlJDL/YHdgdbNQ1aNIk f00xNtF8fA4CoGWd+7haMD52phH1HikV19l9lp+joBpUiH80dWaP485atI28 19hNifbfcqrrVWR/gnQg/05T9rWebt8b0Ffma6hR0/dIztQJFOyq+EVJeieZ hHt8jAxo3JaoMagz7xTeLkEa6LN9NYmrVssmhrBTZ5ruaXVvKKTqZ/mQtzr8 H3uKjAs8CNc4wohCRdtJCYlSXUGv+4njYE0i/typ8o+AYUClEDix226pzjIR ftdJ6qerM2iNXmplcjpbLQ8/CbXGT65NecKo3s/72nR0ty+NXx81c7BPD+M5 m9AYSuVicRE+BF+MbpLD4WTEZdO9Sc4kuD94CYdSOvHsAT8s5QENZdcEnR42 2HssQmy1bYoeMWSJ/wqmG2QpPGrYe7mLLPT/WbiBuyf58J/3oeDUHGBUMHqF hjxsrph+HP2Ff+ytBtE8luozRu/jvFS6yXuHI2pPNrE7drUSsPY432Ml02HA ++MMvTIIWT1f6+SdGs7JhSw63GSbfAOreYx0feG0sSFjcnf+b9u93sW9FHrV x+hdlNdGNiyu1Gn26NttRl9wDo3q8ZJv2HPZ34Rdu88XiXANSSmo5YRy5hfC AkR/tsPn+SJr+8iCLRWij9PrpznRX15kX9UY13aaFGmd649iciiCOn0PaVfm clzjbx1T0v/F8REVxagXjb2dSzTnVqvxGm6vqfY6y65bT8eExTKa/hjtE0jA cJ/pMUr8CjhS0QY3GjVddaH8rD0kC/YXWTb0L7Luya35jKguWgFyiMw4whyY BlQJYyb0WNKa7pNzdgcukeOCvDPJa2f7p5+lQcUhxpRZXHebHKUl9fdPEIHU guPvT/vuG99RQma64XmG8ZKz7aG7B4n6X+qhsxLinIMTHz1SnGFqz0wD8EWX aS0W56gBfV5h9ggeos7MFcFCo0eqsuOkuVCkd4QV0KY11d9qvONQO1F19O+F b8Sphw2K/HGLLznpifD1cR95XqhaP6vIro0dvny3+88Dfve3fGEQBvMq1A/l MwKsUJI4EBrobl/zayCQmnJBM/ERoYKXk9L3lI/hPN8uxjQjLNoWkf/5/vyi UHwFsUA+1G8cvKOL1jncxxZK7m0usXBhqMJti+yobVe0jm1wnB5qZqjVNX4d aYYsPtf125B9JRVclEpQit8fm+Ph23tmbAzaqPvN+iQaq5Uw4Fkb4r12St16 wa8HDDT7BwwQTkpIfDD3fsNs6L8p28V3GlCutc869y16fRJCLvZtvImvjE4C gBQRMsV+6Yzm//ijI3NhSw8NKZSiuTbEp4zhf6G9qOeJLuprzueEm/F0Xmq0 PgtY3CboQFFEVZ/rbYyMqtUeZa0CJipJFF0HKceQWcIS4iRLVg97+fEEjdI6 gxbwFLK5qWz9chT3hvgYkjzWkRBrnM6mHJqWeLVTWJIk9rBBg+R6QRTAEuD3 kY6OKjSlcjYRea4wZcwCTA8sPNQBmPV7fpkHgIc6oOCYA51oaRnwe19urqHK eVGhpXxJQCi1/ciZfCkIva1mAogI1Z/m38Fje6XfH0q/2od/wsewMM1Hw0ae ZizhNDMCurqeJLdVxsJYvw4cYBOl8xiXOBY6V7GgF2KcjLRg9GBhgPt+Rtmc 5i3V/cJzsCHNtoRCeAlKKHQW93nTL8dd9PBQW5DVkhVVGJBZyedUPFAsg85D +RZkP6HoGQoRnpJr/ZasxPrKCfV/7uT/O0dI/0K6Nmdcv+8zwTSadL8bxja8 bWNN535IK4F5cvJiEzZjdf33bFX/a7OMuGCWzJ4kSzK7Fq5Rtle/DXUhKWhP dNfYnPjB7Oq76fdh/vX9TrUMbVBoFg2a2jlOAmH7d6HDYduR2LaRfszrA/8J de5rkfaGx+qbjpUVhdyYF7I4q5Pk4PgUNDKy0ckOxTp9xYnsS75g7BFZpK85 Ns4pF1ltoiLV5XUns7v9WvSmH/rC/7Xf5+XIKG3I5yxhICz0oTVnJWia32Jf Jcl1RhGJp46bRgmHr/veg8Emv8YW8dEqSNpm7YynwluumiR1ln0hhEvUY9R+ /f2ZLIJSR8XNodhmjKgMXxihMHaLrflwI9bnGlpjBEN6/ZBs/v9nMNK+tV6V zkBaor8JG0FB0UmYUDzDSKewDC9HLYikv6RTdZHn4699u3lFXWk/pw5Gtddp eaYvDNcd8d9PN+1p8eq0gSWUSZK5ybTMecbfF9nrueHygdAR8U4b8t4eiiDw dzq4q6DvufjntTaFE0wbu+DiupTrX6dK20sHmvbcGS5c7vKYQqqMtiZLkB0V VaGTj3pMwKezINNvQ36pk6y+T77nZMfLfpyBF6t901bfp3N8VQb7I3qKzrEL KH541PdmmVMOGR1/fhRetvptQq97yTFDDA17YbEvCIvz3wopege8DKODDIFJ /urj+4R5vtpHdpqPtMFvP9v364VKpfgo/06fIgvZh/26aFPDvYYV26tw6Zjh Qjga4iLzZolTycxCwhRLcoM/vXX30NKLCVTItux5dVatHjWYTFsnO7kKFbyr 0aCzNVT+CFbaihRYlLd8sVocO6hq0XYrHXOWmn3WfSpG3NR+oYi/imTyzLGt P22k/DEbfGjI4gfGSP3hHz/5O8KcDB3l+aXBRjYTVb77sz7aalju/baaD5zM lEtPzFfH6+Mf+OJOtUevWVITLmgMr11OfKQYzTFj6okTyQnOpTI8vamIeLew I5xkOcEMV4kiGoM1ki4APtBKUM/fdVwccmIFrV/2zRjjC9Ms6QUdd/f1fsYn foZf/tvYozoRXOhYq3zEbQQJCjOI3iMu/tbhwG0wkZ4tcnPPdnLlbfDXIr8E RSYK+2Y7OVO9dL5G0FTd2qlFxYtSL1XtDYcBMnhIUTxSxUFdxkHqZzastPnB 7usOgeS9mCHSR7Ice/Gx8dCRI0XqVcvi08gfBz0u0XSqySeRf0eJ5hk+yRf9 GtwNzUU2y4P8Ux3D1TraQA05S7bApsIWOhTZy9TB31sZrKKDckjOtK8OPtNm bEbIAoNAj+GycG9o1Nb9aaMH/Ar5dWCsv0pD/ptEYs9PUp/tw75XlxrhLl6W 8klqIp9k5Sp9/rx//10v2qxJjpaumjdrAftFPj3nD8OARGWZX2SDcpoTA0SB To3Gi3SAuu9JhNMcFIw7lXFTj0P3l+84wfMVaOPKSluGUQ4+EqTQG/DuwHHI dkIzSrkg0iJr0IugEj4LTRdvEhlNv3MS/2pmaucVEUfNtXmTfAgkW1ws9Yf/ v+urdGsScNq5ZxwoLxxFvbVVpScT/R4n29SfaGXqidQungpnj4aws/0rXeVv wdhd0c9wda3d1tSAi+wh4za0Judek42G5mXJGCfxtSbnMiWC3USqba4YYt0r lNzglA5DwXLsOKVEM0x6qCZWBgaGFiXzrg8xBkHMmiid6eFucv3QqwI4H/Zd W+CU+Rlf25/50j9QpIgAD0BBPFhktxd4e8QGs2ucvoo8iPYg0aMDlRq6hWuz AGgby/2acGKWzpQGcWoeO9MJcG6FVuszrca5sS9UlJ+lRZnsUYYcNvh7XQpt +WIf5naHyXan3W/G4jdLm3/P4QV3+4ZjgxXBR/P1yLj0SM8ZRUVUNiuD7wEf +jSfKiuKr/8v0vhdia7LDcFYvoXFVhHuvLgfFHd22uMMCQqcVxvRrkrNYtvH wpzhVBL+1WXBwHpJobzRyeJqt2xnGbO6FK/+ChHvLWfZQA3gPeeoiVbEvT+Z eLWvW/29MGNKCm5oTcERP98BB/kY5XaI95ipxzxK9QQfiNwIHhsxap5jKsa4 uYLuMnvdsNwMkYtE3St9aGih+EPZy+t8GDCROx13vRS0u76rvvKSk0awKfKA WMXZnZUdgcWApLnPZxMpMqc6tR4usvuvv1Pvm59A3cf7fdjuvoV07UUunXtk x/g1Tt3ShaVbcytM2Wv99xE+DKi+Z5EJAJ27xSmY79/gq/Al35vu2dXPqN8t ZDvyNWb5fhFmqlw/rM71/jm/L/fHTvBhDPCVeCy9nxODnnViV5ZPtyBT9hfl 6qu+CHOK4qGjiH6CT7SEVL0fj7nItttb8L3V4Ny6QerMIn8xcfXa01f7iFuc X7mEfqeLHATTjYuPtN/nnaPEmJepGpymLNIgXoI6LYbqRafp++srINha5Nwb /mYoeyp6egYB7y0qK/w9HFfycaaL7t6+goZ4r1ult0Lk+14W/KApN6rkdbG9 xvnvj9P+KDNHnCjKqvRstzo1NljWXdN6Mbt+WGB83z+1WW89Tey7evBs7n0m Hrq+yFZ6WOzLfMIeGGITkMBA+Qn/+qb0szFQfbETH69bnP5x28C93/Kv8Pu9 /nswn0mFS9NFSUG5s/LVdUVWTr7qt34v0XsNfzC4g/7RTr+MO2JYHEjz0xw6 aA6d/MFTfZr8PvKopaHv30ZHi7TQotSC2xdSB95J991Ysjwnhs1VGLRUYaBX ypZeKXMezveFDsGyuN3KdLvL8+0aAx7TfEYMsUMRLKm064hV418lm2SePxIS HeMQudIee/5C0cq60WaUtVWy5vLmrAsgknqerS182olik9mLF44VTbXJdSSh OkS3szATGd3MHJ/mzp7416H6nouMkqpJaF9QVJSdLkrOZ4FIFonSFSZdlZBp QQvV4MEk3+9AfM/U2omyJ69ZgkPWIVEvVlU9O+bNIqvt5DJ+6JR1RxFGeZGE PNrQ8/4+NPGYTxfui4rAwRVX+z2Dy97olBsSN6dkSgIOz1r6Un+FRhRpcjjg EUW3+KrP9ceFQcmQgCLaC0rdv/iQoB84NCINxo3Wg4BAC8KQR4N5368l2wrU sOkv+H2edjjA8yCs+Wl7Sv9++C7H+qMG+E7oVPqILmpFGhw4jdIBRvss3vcZ gf6p/zFoiLhAC9FvfEcRWiyj0rkl831Uc0LtqNcIV/qLPTwu9rRPznaKZzT4 niHX8Sphx1zh4zi5nxC00IzixbNsU7rKMUaSBLR29bwo4lEXLILEoQGzQaBu 0UKnwyYxv6eLDKPP+CJfnf5u/6nwaZAM5PLgAXekTWgTlFpFCq+IcvB7oy86 +sFP26oLKrmjt/dVdRSik/DFZQOlZE3KCg/Fu0sNWL+qV1fPLctcE/l+0kQO FtHXUWuIQ5F2D+hZ+/z3XzrB3ewEHuo080fLwCRbVuTmhBD6oor3D8x/Na2R D6tNa8CgZAMKwICVwnERQ9NztOxD9RFDQftfV2TDBeMabIATrIVVvrXvFzkP hln/gz9iWSF2NdeHHmHTrCDrd8xBLPARRbWPtkgPF3DvIoe08PbUhwejXqBC /STWB9u6O017a1qKEeUGF5s+XesqVYuIZkHRcewFBsuMwnumquPZLP9oUZH9 GykBdW3Cy+tHUdKtTl1bnHZhUn3628KM1eKuMZqfcLupFRMkvW+enstQN9k1 Sy7T79HpO9JqoV6u29G/ks9f6B4zik8ER8fQVuO9jWkVlGt7vLwdkE8fH/7k Ivqcy5mEFcI/vBnPt1dC3IpJxh6myRvAeUykqwzz+4yc7icmmlb1PF2gauXp 2ThNouAN34TPFO496SZYLHK6W1bkPESsUrykXypa65+HnZTvKnLKeE//Phs6 pmJdneXXtgQ0xsnxATv5C/8+7CY6pCHiXvBxovU96mN629dgrY91uxMKSgVG xCofN1z7GSco3kMVBIaITzJZkDkn+ph3+lze8rXjfnP8GXLWZ6oDH5f7mPoW mdV3L/oGI6LUJsTlgz6Mvv8xCuqOzgavTQL+RJHvF5zsIOPwxvI3fGGg/841 8/z32/06yOwpX6cQ9ffRLsHo6a6eGtwT3cX+g/RpbPREs7ANmxrhDlPWArUT GKDBbks+CJF/PPeNihpFkH1LWk8n/8p758apKH2zkz8EAnwKD2c6PWjLkeKz l6o05u3jjhT3zDdaNKj/oqvOjTvU3Wl5qubF2A+OJFNcNHtPZW9hB8xpSeGC Q7bJyiKfMfmcc+IOcpF/pciuiLv992d8laGycEfMdYpgy0dVrKg4P6uuYq4S MKAJWGpgwtvTE++629+CSIktI5MiHYjo+U99EhE4/oIv1CFfUCzrH/jwWGSU JQTJN31YzO49/5zfx1X0ygM+xTAwN1SGC0PCqzMiUXGpDodMKFIhnYh4gq/E U4X2xLD+OVH93LhksF8yz584IVjEx6hfMcp6mQhcGhHAJwoP2XUR1yx84HEU 9HinxENO9ZuNzuZdqEXC6t7ZR4pROGBQju5OUcenkyhigXGwINU2DZP6wfUv puc0hdfpRb/FZT5ijOXTM52jy7YvKqy/9Xttg1kE7eNRguYnNIDFmuifxFmj f2ls/arr1Bf2rrRSP0xffc8m8mft5dyGbRLP+ZPWEaAxPrhzefuaskbz6XIZ NZdlRWYglzvdfTEjATJDlSVxekWF4gFNNZgd/LRCG9qpQTmcTFAcn9Ntfi08 FDm3zneYHCp8xH/vv6NO/aFQ1zLIFh0kTN6vOslD+i87FaAWg1w6fsIrZ/m0 3nXyR90lMnBGkYuX9vrcikpWdGUO/YNAz/B5v5zo+26R9gXph2cQXOjf6uq7 usxHFRKEmU51WlKfsJJVRNywbCnWKAE0ssi2P7eC5ZS1GMeKzB9qVNPix/sK y5H3hk7CPWgDOcgUgivaa0HWOTTOXWrCsVE6CZ7ZlD9RoDOVZP5mIRWK/drk e1jrGk6dnsDTdMRzp0+l9PZySDKYE/oqy+iYmuyyJXQ2Yb1psyZPvtNf1IQT 5DpX/RoFSR6O9UZmA0rMzKXkRKUatzTYt4vcTHptIdbxhHvQG7LRsdmn/5NE 5mXlyuu+Qaf5fOYWuQ1InyKatOWdkktOD6/Pe1z4IN+opJtFwAlNh/bk4R56 16mRlI1/8aXc4vvfItfYoSInu7NPyAbEB+D7fBH9azKWAQOMeK+TGeT2lI+y NuuXRUU3W+kY6ZKQqveY9N/ouo0i9PPjIyB7edqgnJLdpcgdcVgbyAbnK0z0 9ZwK0eyXxWt5GmRNyizxEMfhNKfs13jQB4wCR5vG3T0kw9gt+BjMCcawyhTd gaYEz2yr9TnTGOpYU4xPMIzcRPLlTfb9xnKZ3imyBDjst3vBaaFB8oml4umw rOsTasqwHdwMToTxVp/JHiX01IQER0if1qIpbIiHva9S274iDZgbisk1w41l nKW9HWh2/M1n5Fadj/re98P8qtF7gzwpFPqY4pN4JkJGbbRLsSOv+0RRgH9R mfQpOWWWP5F+C4uPHxGz2Jf6qSL3wukfNNRRduKzPpUv+q2h9o+K3CaJpZhV ZK88YobIyQEXNV0ktHZVdhZ2DYtf48MCWDf67asZvjv8lm8VkePqbklXIOB4 aFRiX5nM9xWlp+9oMidDzk9XLKtAevgGRrK70lObwn2Ew+OGIvehuutoej5W AjPaz/DZQb/NIf/9fqOBNWeJV09tL8MJ1fK5Lqod2FkvAfhUT1VOHi68fu7x tBvhHT+UFqM2JAmUEJZxUGSPRN/ZE8q64jJoU+QKhooFK4ru9nGKhgrJro6S uWhSyVrfalS4oIMomlAB0a4ZlXGQw9uvom1S+9Xi+3iXxCLHMUVzrnhFVcSs yn586NR22Lf0viJnXMPtL/DrQo98wTdijt9ju19f59s9QP42PgKj6NEIjEjg zIlRGRRP+ci/5TMLwyP4VdSEoUAAEvw/1/o9JxTZbR194SLMMbwS6Mm5RTnn 6DSn8HU+BXZsW/puMTh28oP8Paf0OfFRD6f0dhEnqijjWm0FxY9r7SJCwp7j ow41DnNzV5Ejle/4Ioe7gGLe19po81gFoqFP+HcuWGor1SNnJpxQIeUoVkAK 3pueWxekHeS03h/buyQvdznWiX0ja1HWm3Mdwseou+Hj1E11//4sjiLoDa9D w8H3nDoYGHGfMDdqkKS9/KzI/mluBXEjZ4f59q7Pnt4ZleV+36lNnCOr1BD4 V/1n5Bz19+uivOrlGE6Ry+YQulgn4LGuCJeHq7q9NaVZvg8A7aoi5ylivEb4 660idzE5UmQgIuNRu5F8j/pYmebX/fpXfWwHCu9BrT4mr/kUnqsoL9XEudFO Tufwcee0jRGUfDPNvGTiENFgbaMr5efFR0ReVqWtyokdXZxQhvn6bCxyzViV vtneW33dYCYDfSDNkpKQJUB9zafPz+d9Cy4pfJ51WkaAPwTbs7ak7uf86Yd8 mrGT8o7XxWVVZrm7JFfR18YMVyz85f5xm1zT/p8hcPagm2tyXSRh/6rIhh4r sqRFxundk7MSjQ4+zncaAk8HQvmcRJFeHdknhzuhStD77SJzjiFFpm544kdO VSD9Bd/rSHRkrXDeRdrh5CLz4EAKZtXx8ezucrewvtf45zwPvflyH3eL3+t1 3+nwgQzz7YRSYB3/5HOFfz9SRDHq9pTfwb3wkeBIvNbnyD3g6+Ek7ObvneD3 DSbwBR8fgdNz/fkTfXxszZm+plBg16IY+AkmlhN92bAEwGOvNUYvh0YtceRr 8PmzTkM3VrqyzPbljsKO6tI2+1ClRDD1Q+m+uypbA9Af9anUV1JRUNyqXV24 /lg+3p8+fszfBkTji9YIqP8kBGQv1PaiFGplEIaFg0k0VDLVPoaD+o/jIIqs U8si30TkxSvp5EmeecN0RCAVxvTUwjc0Y6URZ0+5YE7daATbrILGiLLs8inh 3EOJeMU3nxUmqgoQUCaicTCr+GGan/pmfVhksQBxnRK8so1Y6zN+P9YINoep gpWO1HmncMHVkgIM+/3yE3wpl/mm4tSIPtMrfZi3Fzmo9Jrf6pD//a8+rZ1O w3wH6f6S7+ATfi1pUe+nZzKG15Pw7OaE1+Kvd/z+SAa0CfgJ0ubJIh+9MM+v DcmVCXHAfwIKuZNNJprIW60STVZ3cklIlJIecPINGRqcLFYsCFRQKQN/EW0L dARSNhdCFLe8MKdaX+hvbS+yrbaraJ1pDFL2+G1HRsOHo4QEt0YP7Ozvbc+T xKkBe72jkoVcSXXoWF2M7kWlY9OAHPrilggbYo2RmRP9capsmFSFjpUV2F+0 jjvGQiIaIs5E6BVEPO1LEGcu8Nmvy585IzIChYvCl1eXn78xTb3UmvNectH2 dJOI3q8sonm/r2i9Ug5iuIPSkHKGRuhXrDrg/WGR7aywsUI/6+70s7uiwQft FBV34/6iZK39PoFpuRt9XpWEvTSrTCSoauwtJSlvT1c8XvlsQ2Zz0XYrUmZG VkIXQaotlZraiH2+XOTViwI1fg852t834NBRBZ8bS9rNWQDKOan5z5Fy6bKa UJQtGo77/0TKDZ9GymUYIuSCGodpYF0zBUGgf1Pk3Ihqh6cg4LDhYi32VAgY RvUh/EHE9LQTGPNUkpMTaHvtWpiN4fzg971+KxFtNhP/386uPcjL8jof98qy sNwDAUFCYgkDEnQrt6oQ7UIFEla0mIihMhqptFLEKmgiUSFykQS6uLLUEJc7 6LLLRdj8k0nTNDqZzrRjtNPOZKbxj8y0aW5t/ui/luc7z/Od8/uxgtSZddnv 8l7Oed5ze895P7fmytoFLL//rWB8LLsTFueFiOTA8k/KEZebQ80cWcivCBnE eTt2w0fDdoUeWGq+epTxDttsd5oA/n6B99A0QsanzbGDXAuo0r3EHrwW6DB4 LvA8b+czIkQz33vVcegCYrITerFFftViPrvOKuUu3v2ExToAdlHXjnwdGFcL yLSVFoEyHc01EHhxramSSgOBFer5cAqsZplpZBYixgjhreZjQ8JqRVkbLE4c oYTNS/hg0Ng40QnRojNF6NPF1fgArAxrEEDBBwEWJbf6FhP+RtDirjR5MOEv eW8ZGShjAQh+hTbI8IjlIkkAQD5qrtMO8xWYMjokRDGTamA3uvX0c97aRdg4 bUJUock7yFoIYEAP5j7Mj9EWVcBynv8qpW30cEiDC+fFqrPItzqqiy+pFF5e o/CNDrLnrYPDQc5ZxMdE3jvEOcoUgN0EUGzm+18ldp4h07HTAmGBLU94sFgX +9n+ASnFkU7HUxbRlW4S4LjFcRQXLJQu7Av5n8Ltd63MphSss7sWJ6YI2pNE Jll4n7GIise+bhhHtWzqRTYBzx7eD4x3xZFawjgSvEG433GQ/2PhlGGSL03+ 0JavvrTgm9MnsoYFxjGsvkQItKdA+GkS9n3+7fsyLEepc0Zpt+A8mj5X4Pkk eaSfRRZGDWb9ioXve8IicI5pHOEIx8QHaCeQPSrxbLNgZxfviZ0qLO5ORLzB dG5OyBRAck8BGYR6/Npe85yRy7HcXeaSj7ZkXQzzqeVzaO5muwcJw6mJE1iP 2wjXTlkhQ/1vuBXQX6qGwR4tpIdEr+Dp0oOwbvL7kFD38+dOc1UKL22BP1sR OMQ4rgTtusrzGGW31/kbTZcjOwmAMpdrhMVhRYPD3x1rlYdBupnjt8dEESVu DSe78TjWwy8I7OctVLwsph2fvcSG+y+RvTl2yhzcYVsuMBl7EVTDSkd56Lv8 7eBu0G1wRGE9BST2M2+8zsG1j0w+FDL4lIWoFh52Fy1R1Y5x/rxDyFfjWgbI CvISggPiElt3MMgQ0q7h86eJr+4kVIBPhERGmB3KPl+Tr7yXLDYZx7GZoaZs eu44NlRuab1m/m5TqFXEkmFAYMV8g6gFOU+SzD2kGdjUF8jS3o4rl1pRGi0g SAeK45GHilYbRFJ5t+A9ILythOw1YraMbw8iZqcV7U8rrvUFjsu6ljGJWh0m Ueutf8r5gCCv5Ax6gjz4Cv/GMoCaQs7vLFIMz19vvuYRTthk4QtmUAMTKMCd uMRTmbMZ/bpp6zKi/TP4DPYO3+fv4tRLyYoW51Afn1MuUqeFKTHKIqSosMh5 vj7Bhydhr525fr4KtYea6e1WCWdgB8YPjCEYQJAI+csz2MOcz7/PE0zdqaT0 epLvkpjudjgvkwU4wiKp4XFSrd2UDFIKHSXfqy7Mit+ldzjFwlV5zfQtAKcs 8AiteLKYbK3IKA/mAtmokvKzfBw9nWJzcEP+tvi7tBzw6kWOF1poN0nyjMUO ytnIdahEto8flix8ykb+/VjBgdidhMI7WvyAUEfLa3RJKj7t4Xk0kQihfJqd OiSlXrvD/uxsU4GVlYGFLWXbNYUYaaz82OJDJBIIVBo7LT7zjHn8PZGAkAwV 5vXJsU4S8lM+yotsGjY9Iu+APRJI3rVIUjxoUaPexdn1pnuq8sDvBbzXyeFA pe7jO90WZrHzn1waF+/hPtzWhRzPTL6La18iJxEvw1KcY5U5//LJz/O9F0wq c4SIXc+m8OpSrYjlYud0gm1jqoSdaOFKQoSNN1+sj7GlKXq2ztdCl8ksK4Mk RywcoN7YgdlosY0l6wugg3heR1uQ8ZI1poMKPZtB20S4dxObeI1UwcrZY2GO njP3QdfyHtbDVFPRTUQvpSGsSA0OglRgudH9VBkgU0hp9DU74V0BO/xAiq1m PxXoxgCwuQHTAdkdQPqQtPmsUJfQraBI6Y3VVQZGOtn/qJCyRjpJKEu4o7t/ I23QBXyiZ8mllexuc4FUSu8hjvQvWpzfsZEAWG+hfLpIYx1b3OBsgfxDCcjd ZNV2jHR9oSel+L9oEQXT2VYjLFSbAv/ZVGlh7ytKU6Vdt1IoukZOpISgjgUC fbGkoFEfIchmclJm3jxyG7B8oc0gqp/js1haCihj2FssfJ/Pk+iH+LuXwK2B gd4gd/J1i70dTxsNbwxWEWyAM+xZRumf814vW58UUcWE3hlajkpfx8/jzBit j9Nl5pEjZwjcGilGgFKlticJ1DbySZov4VMf1sQIdxSjJD5Zh/93vPdlC4Xx lGkcoRYhbhGBUcrt84TBKYITdD/ItrB5caKgTmmcAPPw5e/ko88lwuFRP1HI MX/a9EXncqRIAPg5edJnIVgnkxAuAnYUzyK0BWG7mLOptVBhwwtQ2oUMyEkZ kP4zlUAEwSZapRmFFQgBjUgcwpPIHZpPgO7jfYRnvk2itpGg3+FMl/H9vsjE 9VB5uC81RVSeVivDo/VyL8BUCOZXOKa3LHaTjliYgH1kjqIf+swQxqNdJZCw 3ewmYXIx6VBGp9uKBX0mXd5obo+hKQht5aK3W1Ro/NBUE+DPjElZl8s4rAcU Z6CXq/UJMkIhLLFwKN5IWcsQkqgG+0HC8QZOax9Jccpijbtcjp0SJf3j+U2E 9xB/ragZNC+UAGcXcbrarEEI8DfG8303FtaejqraZGHpeY26+/erjDUXc4rB zyoxZm9lCA6+HIIwMQ5apfkwhVJgnDc8l9NYaaFbT3MgKMF4hAM7RWQoHoam lI/VTzThumq5jlci8zumM/Wcgiq/cFevprg50vEIe+qEhXnXa7HK48h27w1+ /1c46cWcyU67WZb7yoQ3FpiVecUtxMgai5XdzonNs6hUwshnhg2xkL20CXo8 mgldHLByN6zcAkAzOn7HdShaOVUQQcn4ftJDuTcX3/qq3NvpTz+AWRe73Ruk VqRsSjIyQOo5FqmpwA9KBP46xRQ1cRHrMyaHOMusGjSCcd7PJvXzB2bnrqae ayPG79fuK/af4N4s5ciEP2gkVLuvIO0wq2O8r/Ph8RzUx37iEgRR3TaA9W3O +I20+wjU9BHf7ujGjjWIDktD1fKHLepXz3MMuNdjkTHWF/xRNGMdpzLYQlh3 oWDPKbCGLS6tOksHPT1kUSLXGvFbWBGICuj0EDzyaBooPKj30kB7EvigQrBE 0jatE2NoHKRygqPeRYJDGilHV0eQgGhb0wkGUdxc6poT0etbZJHSr9urRoTR qIWHVeLd7LPKZeH4+iQsBkj7GRYr9MG01Qr0gaij0m7uYLajn2VFHwuL5yeX 7dvpqyF2akZsCFDVvmMaf0FigXeQDFhb2pV4jcQ9SjQd43O72M/rFjsJYMSX LMIJQhyQ2mshKXpLJEbcMk7EoRwYHoG1A+wXGgllPVKGShP4Gudxr0UWxRq7 VZ6hojya/ihSewObWStOtPh0tpNDD3PIkL//RIwiMU5bqsAurNxvhkLYRspA wr+Ny/9aWA3f5xsnObh6ixObMOAjHCE8q79nryhVRvgDuaT7RajmOBirg+/i 30+QKOstwibPJbmrhMYnyRQMT3pLn8VqjDD5eHO4Quv7Oo6MgepSzaXKYajz 9wTXadZ3NWTWVSJzCb3OoXFsBiwoGI6w/iEGnyf4oMofIw0ApDdZxQX6PMBJ HOH7Z8ntC6T/OYLWs+WPFQjBAsCiOGWR9gQAXyz/HbtJ2o//GnnYavHpt07S 9hyJ9Ecc/5JiDjZbpNC48iFGcBo3l4gs5WorIQFHHGsBmuQf0cbPytg9NlW1 bwXvbAvFY62LPT++DF38rOAgTMafWATffYPmWIEAWSgAItyhbaZTtdlms6+0 sxapd0rskXesAvLTVQCcyT6hUFaTMD3G8n136HJixkxOWMkyrYRHg08yY9BP YfreVczI+abzNdxkh7eiLxwwFQYqHCHzu4qpUN6OdqNsPTG3lUwAT6G2dbo/ /v4WSbqS2DnCZ9rZ9p8Zc5IeKzbfT5DlOwk3WQCA6Z9aiAx9LlW7409yRjqy Accw3GKRoAEoLuZUdtpcoS4i75z1qMp0Nh1EAgME21DQ1/BfsBuDIAzkn3IA gHC4Ub/nv08JIIMddKpx67HQ8QhVojACLmDxsdt/Lhb7T823Yd9MxNpGDO4i z/ZanItzVLia5NTIGAS1niZFIRQVqdxGKq0icp4lNqQ1zvLfayziZUd4LZ+n g78fwEzby7JZXNJXrNuLJvozEJtCiguISrlZTKQMS2aUDhfRs9gRXs/bo91Q ANigBXeQJissgLi85Lo/pxzL46TBGD6PuX+XvttIxzM2zA6Y9mwdKQBhvUUA eRXJgDzCdWQLZoMo0z2VOxGyNJ8meYTDJp/zaYsTpKA2tN3pR/2FpQUcIrd2 N5v7XrL9wDR9Jf28xe408mqBWQg+4BVBw4WWdohHOqHk+mOIsj/f4btvc0y7 LJT6Ny2cpDEWEfrGkInawMJjMyyFQFsiWRKm7DISEXIIMhfYlf9wjkR92li3 2V4IbxRnaYvn0yEykQG/z1w8rSV3z/L3nyi6c5+UjKDnWrzc5VyYEBeo838j 6AOxDtm3ybhyO4qiaEWzHyU09iTUneFrOzgWIBdSH7L7Ef6tsiOg815e204q riIEgWRs9QLhAA5irbAxtf18d8Duc0TkalORNF6fo9sbLT5G0Us2bouEz1+Q 8+C0n/DgRgv+v8h8qf0+qTSEpnFGD8SXsrgXmBzOEmhjeUm9Y9x3ko0/5OtY Wo9bRPy1jXHadFpQneI72oeQhYJm16SJPZjOI9UHCHRych5KG0kOtdKanFvg erHJQ15VgBtZVHfxHQz/Itk+yy7D12RyFMurhdtvgwJPn2SvEzlZuLeLuCM3 xeeNy7Dz55MOwBO8aOB8Hf/ezl7w7FeJMQnYVcTYS8QXVpuyzPDuHgIEE9KR Yi0kYsYTDdY/tsrNF52uc+m/W3VNOrQ/uTNdJjc56hh+TIT9yDwu3Jg8QC2Z 94o2fllc+3fTnkm0e9nxp8OCr/MsznzA1LFb/3VeRyhktLkB9iS5sEOytCH2 ABSZ6RMpfOh7+YosjBJmQwJmnWwe6n2QxW5Io/kC2mKMvliZv8Et2WJzFWhR BoNmow8XrK9CWq2DFCMamWq3BDMQE7vAn7OIAI6OgKFcmBsJhZsTNFSgBIn4 DJ8TWrFuRGvADMJ7trlgh4rrMeY6dhSjwcqdQBYsJ9KUx5cOyxT5LKFBprUV 37P3pfU0SVjFGSzIvVwDINlmC+sfihOLdZLCCkNdVSv9BApvk8XGHCYAVwgj h06ZmoE2KCaPn/Mcjo4FeSF2Ysdykm3E3V6LYNQbpg81O+5utyiWws+xousa mZIPph5BqK3EEjAFmPuhIqUP1UwiOLnLJOU6Xr6lhNG9eY27WmF5YGOl9jNz lfNAQfXQNXhniUW6v+80F6zD/8X79Ukc68AUPA6RquweOeyPcE67lTA3xNvB XOdwCEtIzu0Wp+JCRMHRTB9zSfi5WSPeYK5MW9ntWwXiaxT177TKxBLoXmBr NYH/AUEP97sl1pJSDEBH7ErB3uri5BBfxXpCYuR4Xuvis0oJHWsOSbWjdeme YAmpYJNPTHXSC01fUAkRiRV/3JTl4f/Xsczd2vuujZzjDl2rqzw/ZHOs0Xmm 6Lr9yAFUnvf+eXJ1mMWB0wGgAMxCi4/hSAnPi9CLEqLhy3/aXBZ9PQOooRJA bt6B6X57uIv8J4iPCWzueT6uowxUu9FBdvyhySeq/0js1HtyEjhSQ86V5ejj 3BJYwOsKBJ4kXAGWfSQzcrQOWqTDv8xnp/G9X1sYfDCQsnve5IaJUji1/Zxz syYnJR1hhNgrznW9Osqim8M+H+CZQhyWInNkFHfo6BfsZuVzB5DdeNQiM22K RdHnjWTKOPuHKuAMK+FMycNrUFblOcwNfiJpy2XAqVjqe03f4HLNk4WXQAMJ AZtoTmT0ykkUN5RsAoEzl9ewIOB5YC1D+O4wnXZzGVp8e2CC00CGgj4ahzAH bJ5vkD4uTz0WC10BiQ2LGgtX5aAXTJ+hdHTAXEaE5XaiY0TaS9UhDL3Jpsm1 ndez7/GBiFi5jogvWJSYnglEHGALPUmJYkYP80crUqEF9faSxacM+5NujzQy QWK1bmFpbDKJBKvebSohMZKMU7UL/q0gQX2cSKBNpjlWyXP520BfdKlUU+cH TKsvk09YjQ0D87xVPNcJinBZpltl8fZTRPBc8lxW3mry7D/Idzi+SHuVmaHy JbTxN3y+OY0D9/eU1O0PEut8pnL9D411IC3aRDYo+Xk6B4/JdWd2N0axXRbA zu6QT92mcLpP4FULzX686Mt+fG3sLsvVOghMPI91NK2gRWn0YERYtp6NUStO KzHpOgtVcB0pvDZxGz3Wc2IHHS1lUWJK1mmVGaNCRRCti+3t5zXV4biyC+KD WLeSW0gbOGQ5W9lXN4j3LK83aAz1bvcqJt9f9Id2e0ItBrej8+LbVw2l8ecm QxjNY83n0WEyfCOgAot+g8U5H2I4/q4NHaOTAw5zOM6ufIrD/4vdg5wAYBxW NyQ7xLDOYrgNnXWXhkdtBnltzgLHtSZW/4dBgucXWITFsRDg9PphGMF3xeBN NkCjA/oi6faJxP+m1D9sy3ss1f43RxgWPESkDlkruy20z3+TkI3p81hwQpRq MJv4Es+aiR8Ru7PSYWdRbMlOWYozCH+lbDRbHE1fH4WooTJ9NDUSOT1Wzd8r MXiY2m2yKOruKBgZYioKZ2k6NlYaahqPPxdfcLiMO3WR3+ZortfCg4+gzWgd mYxHFbn7lfk+TzMZ8qGMkbqoDcbPzFhu6slSj52VpvLGzIO6SGh046gUXP2p le5QXwOwohxTc2ppp71dxQA/zqL4q6zQka70wOBwdTKFqPIjSIvUzNJ8l9F8 6b9ZuqbTM3qSZIIEfYiIBzxWJhUNnwahpw9IaPhFv4sPJUDgKovijCkTujRN Mo3N4kgVjFhuXr2LJIgqiG0o2+kaQGMslerk3C1puUY1bpwjgzWC2ElvpvAT mcKQR+tVuVAXzhEW/xfstqw7EN8bk8QdlOyrxdTj41iNfP23pNKvNJiGsLxh AcDifraYZImUTCYfN2Vxk3s3UkcS349ywbnUjUPdJNIq6VSu03AdOJO6ICv8 /hdEpo16IzmAZRJw8iFvVyvQ0TeTv9dFNQ6CSXD+cPwpkq63FnMpwQPK3sv+ F5XTKRcoTGooWh06lZdXU2UZgyxFtAILzc+uCoTHjmEJno+mSq0DZ7dfe6eK JOO4WEqS1Hm04ylOf2hQ5UX2ekNxq1nzauHTH3DNbEtUqQ2qrE0e80CJLwq9 DcnDzVPMksd5AbLcFscJHr5GqqQaeVJlu6Y6k4OCIzHC7tAbL/Py5KpDq+B9 oe4TLV6fFi1YDMMW0bX7Bp4+jH44QKNCM+ecmOrpyzmq+KY89dmL9vHn/rJV z32nBn2bxXdt0twVFvtsWqAQ4rB6ITKVdT2zcvpwVmBnrwydnqfvKYAh+lR2 YinFsLLAJyziVp2iVOm91Qww73d9jrs0sjZ23V4sMEd4i3amIhItTxUQv5EL pYO/waWTxTOlAaD4RlY448019nSLUq/xMcb+cl4Bj+r5thUwH5J7AfInXnGq uz/0JpeXQs6DFVQEg6LCdR0nq7x3KIetePV48erUGOFQcTAfTuHBnxjK7HL0 A/rWwzXLbE6Iq+32Ux/9t8So5RbVcUMJRka8VxFG8OQAZ2wru7YdXj1OZXln kqXV/7HHOYnjvEXj3KNxwsmL1PU7NIDxXJZQoG8W0wgpiwFtMH1cohxXfF8i oiiXjSuZY5O4yC4pzvd8UHv12n2c9j1Kg+SWKac9V1P8JEeJ5JAuTvGm0JAy OOclSoWKv8IoaYTAxIMUnG/v+wA7Mg+8UMRBnHfSTJGrhsjBwoaI8sdmBiXR 8R52PvEax8jFAal+SZ78Sx5gnTKYfEDNTHQaaIzME1PRS2tUQsq6b0uD+LgD m2ilF8qB7RtAbrTnrp6zFCO9elf/WdWsjivLzUbY9FqbfWWAtbbyY+K8uonZ 0cT9kgLVinIcX4d0nGq/9BZ+KzWSn4bse4LUvWSd/1d+NDmVCkbdaPbr/AhL DHQ23iT7Tb7LCYNuk3Utbo2JifDWh/j1wZX/bdf9H1uVSHQ=\ \>"], "Graphics", Evaluatable->False, ImageSize->{479, 373}, ImageMargins->{{0, 0}, {0, 0}}, ImageRegion->{{0, 1}, {0, 1}}] }, Closed]], Cell["\<\ Structure views of 1AB2 from the protein data bank. The first is schematic or \ functional. The second view shows the atomic \"back-bone\".\ \>", "Text"], Cell[BoxData[ RowBox[{ RowBox[{"getSequenceFromFile", "[", "filename_String", "]"}], " ", ":=", " ", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", "strings", "}"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"strings", " ", "=", " ", RowBox[{"ReadList", "[", RowBox[{"filename", ",", " ", "String"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"{", RowBox[{ RowBox[{"StringTake", "[", RowBox[{ RowBox[{"First", "[", "strings", "]"}], ",", RowBox[{"{", RowBox[{"2", ",", "5"}], "}"}]}], "]"}], ",", " ", RowBox[{"StringJoin", "[", RowBox[{"Drop", "[", RowBox[{"strings", ",", "1"}], "]"}], "]"}]}], "}"}]}]}], "]"}]}]], "Input", CellLabel->"In[124]:="], Cell[BoxData[ RowBox[{"getSequenceFromFile", "[", "\"\\"", "]"}]], "Input", CellLabel->"In[125]:="] }, Closed]], Cell[CellGroupData[{ Cell["Characterizing Amino Acids", "Section"], Cell[TextData[{ "The chemical properties of the amino acids determine the structures of \ proteins and the actions proteins take in response to their environments. We \ will not discuss this, but a few definitions and characteristics are useful. \ There are", ButtonBox[" international conventions", BaseStyle->"Hyperlink", ButtonData:>{ URL["http://www.chem.qmul.ac.uk/iupac/misc/naabb.html"], None}], " for the symbols and designations for amino acids. " }], "Text"], Cell["\<\ Several common properties are listed in the file called amino acid \ properties.\ \>", "Text"], Cell[BoxData[ RowBox[{ RowBox[{"aminoTable", " ", "=", " ", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "\"\\""}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "71.09", ",", "115", ",", "88.6", ",", "\"\<-\>\"", ",", "6.107", ",", "16.65", ",", "1.401", ",", "1", ",", "\"\\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "103.15", ",", "135", ",", "108.5", ",", "9.3", ",", "5.02", ",", "25.0", ",", "\"\<-\>\"", ",", "0", ",", "\"\\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "115.09", ",", "150", ",", "111.1", ",", "4.5", ",", "2.98", ",", "0.778", ",", "1.66", ",", RowBox[{"-", "0.1"}], ",", "\"\\"", ",", RowBox[{"-", "1"}]}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "129.12", ",", "190", ",", "138.4", ",", "4.6", ",", "3.08", ",", "0.864", ",", "1.46", ",", "0.5", ",", "\"\\"", ",", RowBox[{"-", "1"}]}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "147.18", ",", "210", ",", "189.9", ",", "\"\<-\>\"", ",", "5.91", ",", "2.965", ",", "\"\<-\>\"", ",", "2.3", ",", "\"\<3232AA\>\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "57.05", ",", "75", ",", "60.1", ",", "\"\<-\>\"", ",", "6.064", ",", "24.99", ",", "1.607", ",", "0", ",", "\"\\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "137.14", ",", "195", ",", "153.2", ",", "6.2", ",", "7.64", ",", "4.19", ",", "\"\<-\>\"", ",", "1.3", ",", "\"\<8282D2\>\"", ",", "1"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "113.16", ",", "175", ",", "166.7", ",", "\"\<-\>\"", ",", "6.038", ",", "4.117", ",", "\"\<-\>\"", ",", "2.7", ",", "\"\<0F820F\>\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "128.17", ",", "200", ",", "168.6", ",", "10.4", ",", "9.47", ",", "25", ",", "\"\<-\>\"", ",", "1.9", ",", "\"\<145AFF\>\"", ",", "1"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "113.16", ",", "170", ",", "166.7", ",", "\"\<-\>\"", ",", "6.036", ",", "2.426", ",", "1.191", ",", "2.9", ",", "\"\<0F820F\>\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "131.19", ",", "185", ",", "162.9", ",", "\"\<-\>\"", ",", "5.74", ",", "3.381", ",", "1.34", ",", "2.3", ",", "\"\\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "114.11", ",", "160", ",", "114.1", ",", "\"\<-\>\"", ",", "\"\<-\>\"", ",", "3.53", ",", "1.54", ",", RowBox[{"-", "0.1"}], ",", "\"\<00DCDC\>\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "97.12", ",", "145", ",", "112.7", ",", "\"\<-\>\"", ",", "6.3", ",", "162.3", ",", "\"\<-\>\"", ",", "1.9", ",", "\"\\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "128.14", ",", "180", ",", "143.8", ",", "\"\<-\>\"", ",", "\"\<-\>\"", ",", "2.5", ",", "\"\<-\>\"", ",", "1.3", ",", "\"\<00DCDC\>\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "156.19", ",", "225", ",", "173.4", ",", "12", ",", "10.76", ",", "15", ",", "1.1", ",", "1.1", ",", "\"\<145AFF\>\"", ",", "1"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "87.08", ",", "115", ",", "89", ",", "\"\<-\>\"", ",", "5.68", ",", "5.023", ",", "1.537", ",", "0.2", ",", "\"\\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "101.11", ",", "140", ",", "116.1", ",", "\"\<-\>\"", ",", "\"\<-\>\"", ",", "25", ",", "\"\<-\>\"", ",", "1.1", ",", "\"\\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "99.14", ",", "155", ",", "140", ",", "\"\<-\>\"", ",", "6.002", ",", "8.85", ",", "1.23", ",", "2.2", ",", "\"\<0F820F\>\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "186.12", ",", "255", ",", "227.8", ",", "\"\<-\>\"", ",", "5.88", ",", "1.136", ",", "\"\<-\>\"", ",", "2.9", ",", "\"\\"", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ "\"\\"", ",", "\"\\"", ",", "\"\\"", ",", "163.18", ",", "230", ",", "193.6", ",", "9.7", ",", "5.63", ",", "0.0453", ",", "1.456", ",", "1.6", ",", "\"\<3232AA\>\"", ",", "0"}], "}"}]}], "}"}]}], ";"}]], "Input", CellLabel->"In[9]:="], Cell[BoxData[ RowBox[{"Dimensions", "[", "aminoTable", "]"}]], "Input", CellLabel->"In[10]:="], Cell[BoxData[ RowBox[{ RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{"All", ",", RowBox[{"{", RowBox[{"1", ",", "2", ",", "3"}], "}"}]}], "\[RightDoubleBracket]"}], " ", "//", " ", "TableForm"}]], "Input", CellLabel->"In[11]:="], Cell[BoxData[ RowBox[{ RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{"All", ",", RowBox[{"{", RowBox[{"1", ",", "4", ",", "5", ",", "6"}], "}"}]}], "\[RightDoubleBracket]"}], " ", "//", " ", "TableForm"}]], "Input", CellLabel->"In[12]:="], Cell[BoxData[ RowBox[{ RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{"All", ",", RowBox[{"{", RowBox[{"1", ",", "11", ",", "12", ",", "13"}], "}"}]}], "\[RightDoubleBracket]"}], " ", "//", " ", "TableForm"}]], "Input", CellLabel->"In[13]:="], Cell[CellGroupData[{ Cell["Utility for AminoTable Searches", "Subsection"], Cell["\<\ It will be convenient to have a \"lookup\" utility that can be used to find \ the properties of an amino acid from it's single-letter symbol.\ \>", "Text"], Cell[BoxData[ RowBox[{ RowBox[{"aminoIndex", "[", "aaChar_String", "]"}], " ", ":=", " ", "\[IndentingNewLine]", RowBox[{"Do", "[", RowBox[{ RowBox[{"If", "[", RowBox[{ RowBox[{ RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{"i", ",", "3"}], "\[RightDoubleBracket]"}], "\[Equal]", " ", "aaChar"}], ",", " ", RowBox[{"Return", "[", "i", "]"}]}], "]"}], ",", " ", RowBox[{"{", RowBox[{"i", ",", " ", "2", ",", " ", "21"}], "}"}]}], "]"}]}]], "Input",\ CellLabel->"In[14]:="], Cell[BoxData[ RowBox[{"aminoIndex", " ", "/@", " ", RowBox[{"{", RowBox[{"\"\\"", ",", " ", "\"\\"", ",", " ", "\"\\""}], "}"}]}]], "Input", CellLabel->"In[15]:="] }, Closed]], Cell[CellGroupData[{ Cell["Example View of Amino Acids", "Subsection"], Cell[BoxData[ RowBox[{ RowBox[{"makeColorFromString", "[", "str_String", "]"}], " ", ":=", " ", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{"r", ",", "g", ",", "b"}], "}"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"r", " ", "=", " ", RowBox[{"N", "[", RowBox[{ RowBox[{"ToExpression", "[", RowBox[{"\"\<16^^\>\"", "<>", RowBox[{"StringTake", "[", RowBox[{"str", ",", RowBox[{"{", RowBox[{"1", ",", "2"}], "}"}]}], "]"}]}], "]"}], "/", "255"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"g", " ", "=", " ", RowBox[{"N", "[", RowBox[{ RowBox[{"ToExpression", "[", RowBox[{"\"\<16^^\>\"", "<>", RowBox[{"StringTake", "[", RowBox[{"str", ",", RowBox[{"{", RowBox[{"3", ",", "4"}], "}"}]}], "]"}]}], "]"}], "/", "255"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"b", " ", "=", " ", RowBox[{"N", "[", RowBox[{ RowBox[{"ToExpression", "[", RowBox[{"\"\<16^^\>\"", "<>", RowBox[{"StringTake", "[", RowBox[{"str", ",", RowBox[{"{", RowBox[{"5", ",", "6"}], "}"}]}], "]"}]}], "]"}], "/", "255"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"RGBColor", "[", RowBox[{"r", ",", "g", ",", "b"}], "]"}]}]}], "]"}]}]], "Input", CellLabel->"In[16]:="], Cell[BoxData[ RowBox[{"makeColorFromString", "[", RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{"2", ",", "12"}], "\[RightDoubleBracket]"}], "]"}]], "Input", CellLabel->"In[17]:="], Cell[BoxData[ RowBox[{"aminoGraphics", "=", RowBox[{"Graphics", "[", RowBox[{"Flatten", "[", RowBox[{"Table", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"makeColorFromString", "[", RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{"i", ",", "12"}], "\[RightDoubleBracket]"}], "]"}], ",", RowBox[{"Disk", "[", RowBox[{ RowBox[{"300", " ", RowBox[{"{", RowBox[{ RowBox[{"Mod", "[", RowBox[{ RowBox[{"i", "-", "2"}], ",", "5"}], "]"}], ",", RowBox[{"Floor", "[", FractionBox[ RowBox[{"i", "-", "2"}], "5"], "]"}]}], "}"}]}], ",", RowBox[{ FractionBox["1", "2"], " ", RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{"i", ",", "5"}], "\[RightDoubleBracket]"}]}]}], "]"}], ",", "Black", ",", RowBox[{"Text", "[", RowBox[{ RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{"i", ",", "3"}], "\[RightDoubleBracket]"}], ",", RowBox[{"300", " ", RowBox[{"{", RowBox[{ RowBox[{"Mod", "[", RowBox[{ RowBox[{"i", "-", "2"}], ",", "5"}], "]"}], ",", RowBox[{"Floor", "[", FractionBox[ RowBox[{"i", "-", "2"}], "5"], "]"}]}], "}"}]}]}], "]"}]}], "}"}], ",", RowBox[{"{", RowBox[{"i", ",", "2", ",", "21"}], "}"}]}], "]"}], "]"}], "]"}]}]], "Input", CellLabel->"In[18]:="], Cell[BoxData[ RowBox[{"Show", "[", RowBox[{"aminoGraphics", ",", RowBox[{"AspectRatio", "\[Rule]", "Automatic"}], ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}]}], "]"}]], "Input", CellLabel->"In[19]:="], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"aminoCube", "[", "vol_", "]"}], "[", RowBox[{"{", RowBox[{"x_", ",", "y_", ",", "z_"}], "}"}], "]"}], " ", ":=", " ", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", "side", "}"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"side", " ", "=", " ", RowBox[{"25", " ", RowBox[{ RowBox[{"vol", "^", RowBox[{"(", RowBox[{"1", "/", "3"}], ")"}]}], "/", "2"}]}]}], ";", "\[IndentingNewLine]", RowBox[{"Cuboid", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"x", "-", "side"}], ",", RowBox[{"y", "-", "side"}], ",", RowBox[{"z", "-", "side"}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"x", "+", "side"}], ",", RowBox[{"y", "+", "side"}], ",", RowBox[{"z", "+", "side"}]}], "}"}]}], "]"}]}]}], "]"}]}]], "Input", CellLabel->"In[20]:="], Cell[BoxData[ RowBox[{"aminoGraphics3D", "=", RowBox[{"Graphics3D", "[", RowBox[{"Flatten", "[", RowBox[{"Table", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"makeColorFromString", "[", RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{"i", ",", "12"}], "\[RightDoubleBracket]"}], "]"}], ",", RowBox[{ RowBox[{"aminoCube", "[", RowBox[{ FractionBox["1", "2"], " ", RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{"i", ",", "5"}], "\[RightDoubleBracket]"}]}], "]"}], "[", RowBox[{"300.`", " ", RowBox[{"{", RowBox[{ RowBox[{"Mod", "[", RowBox[{ RowBox[{"i", "-", "2"}], ",", "5"}], "]"}], ",", RowBox[{"Floor", "[", FractionBox[ RowBox[{"i", "-", "2"}], "5"], "]"}], ",", "0.`"}], "}"}]}], "]"}], ",", "Black", ",", RowBox[{"Text", "[", RowBox[{ RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{"i", ",", "3"}], "\[RightDoubleBracket]"}], ",", RowBox[{"300", " ", RowBox[{"{", RowBox[{ RowBox[{"Mod", "[", RowBox[{ RowBox[{"i", "-", "2"}], ",", "5"}], "]"}], ",", RowBox[{"Floor", "[", FractionBox[ RowBox[{"i", "-", "2"}], "5"], "]"}], ",", "0.`"}], "}"}]}]}], "]"}]}], "}"}], ",", RowBox[{"{", RowBox[{"i", ",", "2", ",", "21"}], "}"}]}], "]"}], "]"}], "]"}]}]], "Input", CellLabel->"In[21]:="], Cell[BoxData[ RowBox[{"Show", "[", RowBox[{"aminoGraphics3D", ",", RowBox[{"AspectRatio", "\[Rule]", "Automatic"}], ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}], ",", RowBox[{"Boxed", "\[Rule]", "False"}], ",", RowBox[{"Lighting", "\[Rule]", "None"}]}], "]"}]], "Input", CellLabel->"In[22]:="] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell["Interaction Potential", "Section"], Cell["\<\ The interaction potential represents the attraction or repulsion between \ different types of amino acids. This is beautifully complex chemistry! The \ art of protein folding calculations deals with how best to calculate, \ compute, and characterize the interactions between neighboring amino acids. \ \>", "Text"], Cell[TextData[{ "I do not know how best to characterize this interaction, but I ", StyleBox["invent", FontSlant->"Italic"], " an \"energy\" rule that relates to the degree of hydrophobia and to the \ electrical charge. The interaction energy (called ", StyleBox["J", FontSlant->"Italic"], ") will be \n\t", StyleBox["J", FontSlant->"Italic"], "(", StyleBox["i, j", FontSlant->"Italic"], ") = \[Dash] ", StyleBox["k ", FontSlant->"Italic"], Cell[BoxData[ FormBox[ RowBox[{ RowBox[{ SubscriptBox["A", "i"], SubscriptBox["A", "j"]}], " ", "-", " ", RowBox[{"c", " ", SubscriptBox["Q", "i"], SubscriptBox["Q", "j"]}]}], TraditionalForm]]], "\nwhere ", StyleBox["k", FontSlant->"Italic"], " and ", StyleBox["c", FontSlant->"Italic"], " are constants, ", Cell[BoxData[ FormBox[ SubscriptBox["A", "i"], TraditionalForm]]], " is the degreee of \"hydrophobia\", and ", Cell[BoxData[ FormBox[ SubscriptBox["Q", "i"], TraditionalForm]]], " is the electrical charge. This relationship states that two hydrophobic \ amino acids have ", StyleBox["reduced ", FontSlant->"Italic"], "(chemical) potential energy when they are neighbors because they shield \ each other from water. The second term is more obvious. Two amino acids with \ opposite charges attract and lower their (electrostatic) potential energy \ when they are neighbors." }], "Text"], Cell[TextData[{ "Since there exists 20 amino acids, our interaction matrix will be a 20 x 20 \ array. ", StyleBox["J", FontSlant->"Italic"], "\[LeftDoubleBracket]", StyleBox["i", FontSlant->"Italic"], ", ", StyleBox["k", FontSlant->"Italic"], "\[RightDoubleBracket] represents the energy associated by close proximity \ between the ", StyleBox["i", FontSlant->"Italic"], "th and ", StyleBox["k", FontSlant->"Italic"], "th amino acid. ", "These energies can be either positive (meaning work must be done to bring \ the two amino acid nearby) or negative (meaning the two amino acids are \ attracted to each other)." }], "Text"], Cell["An example (symmetric) interaction matrix generator is", "Text"], Cell[BoxData[ RowBox[{ RowBox[{"aminoJkc", " ", "=", " ", RowBox[{"Table", "[", " ", RowBox[{ RowBox[{ RowBox[{ RowBox[{"-", " ", "k"}], " ", RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{ StyleBox["i", FontColor->RGBColor[1, 0, 0]], ",", "11"}], "\[RightDoubleBracket]"}], " ", RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{ StyleBox["j", FontColor->RGBColor[1, 0, 1]], ",", "11"}], "\[RightDoubleBracket]"}]}], " ", "-", " ", RowBox[{"c", " ", RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{ StyleBox["i", FontColor->RGBColor[1, 0, 0]], ",", "13"}], "\[RightDoubleBracket]"}], " ", RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{ StyleBox["j", FontColor->RGBColor[1, 0, 1]], ",", "13"}], "\[RightDoubleBracket]"}]}]}], ",", " ", RowBox[{"{", RowBox[{ StyleBox["i", FontColor->RGBColor[1, 0, 0]], ",", " ", "2", ",", " ", "21"}], "}"}], ",", " ", RowBox[{"{", RowBox[{ StyleBox["j", FontColor->RGBColor[1, 0, 1]], ",", " ", "2", ",", " ", "21"}], "}"}]}], "]"}]}], ";"}]], "Input", CellLabel->"In[23]:="], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"aminoJ", " ", "=", " ", RowBox[{ RowBox[{"aminoJkc", " ", "/.", " ", RowBox[{"k", " ", "\[Rule]", " ", "1.0"}]}], " ", "/.", " ", RowBox[{"c", " ", "\[Rule]", " ", "1.0"}]}]}], ";"}], " ", RowBox[{"(*", " ", StyleBox[ RowBox[{"for", " ", "example"}], FontColor->RGBColor[0, 1, 0]], " ", "*)"}]}]], "Input", CellLabel->"In[24]:="], Cell[BoxData[{ RowBox[{ RowBox[{"Print", "[", RowBox[{"\"\\"", ",", RowBox[{"Max", "[", "aminoJ", "]"}]}], "]"}], ";"}], "\n", RowBox[{ RowBox[{"Print", "[", RowBox[{"\"\\"", ",", RowBox[{"Min", "[", "aminoJ", "]"}]}], "]"}], ";", RowBox[{"ListDensityPlot", "[", RowBox[{"aminoJ", ",", RowBox[{"ColorFunction", "\[Rule]", "Hue"}], ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}]}], "]"}]}]}], "Input", CellLabel->"In[25]:="] }, Closed]], Cell[CellGroupData[{ Cell["Representing a Protein", "Section"], Cell["\<\ A protein is represented by a sequence of amino acids arranged on a \ two-dimensional lattice. We use the simple \"list of lists\" data structure: \taProtein ==> {{chain of locations}, {sequence of amino acids}} The \"self-avoiding walk\" algorithm from last week, initializes the \ arrangement of a protein. For now, we'll randomly assign the amino acid \ sequence.\ \>", "Text"], Cell[CellGroupData[{ Cell["Self-Avoiding Walk", "Subsubsection"], Cell[BoxData[ RowBox[{ RowBox[{"twist", "[", "chain_List", "]"}], ":=", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{ "n", ",", "iPivot", ",", "fixpart", ",", "movepart", ",", "rotchoice", ",", "newpart", ",", "newchain", ",", "rot"}], "}"}], ",", RowBox[{ RowBox[{"rot", "=", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0", ",", "1", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"-", "1"}], ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", "1"}], "}"}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"-", "1"}], ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", RowBox[{"-", "1"}], ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", "1"}], "}"}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0", ",", RowBox[{"-", "1"}], ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"1", ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", "1"}], "}"}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"1", ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", "1"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", RowBox[{"-", "1"}], ",", "0"}], "}"}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"1", ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", RowBox[{"-", "1"}], ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", RowBox[{"-", "1"}]}], "}"}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"1", ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", RowBox[{"-", "1"}]}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "1", ",", "0"}], "}"}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0", ",", "0", ",", RowBox[{"-", "1"}]}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "1", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"1", ",", "0", ",", "0"}], "}"}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"-", "1"}], ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "1", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", RowBox[{"-", "1"}]}], "}"}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0", ",", "0", ",", "1"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "1", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"-", "1"}], ",", "0", ",", "0"}], "}"}]}], "}"}]}], "}"}]}], ";", RowBox[{"n", "=", RowBox[{ RowBox[{"Length", "[", "chain", "]"}], "-", "1"}]}], ";", RowBox[{"iPivot", "=", RowBox[{"RandomInteger", "[", RowBox[{"{", RowBox[{"1", ",", RowBox[{"n", "-", "1"}]}], "}"}], "]"}]}], ";", RowBox[{"fixpart", "=", RowBox[{"Take", "[", RowBox[{"chain", ",", "iPivot"}], "]"}]}], ";", RowBox[{"movepart", "=", RowBox[{"Take", "[", RowBox[{"chain", ",", RowBox[{"iPivot", "-", RowBox[{"(", RowBox[{"n", "+", "1"}], ")"}]}]}], "]"}]}], ";", RowBox[{"rotchoice", "=", RowBox[{"rot", "\[LeftDoubleBracket]", RowBox[{"RandomInteger", "[", RowBox[{"{", RowBox[{"1", ",", "9"}], "}"}], "]"}], "\[RightDoubleBracket]"}]}], ";", RowBox[{"newpart", "=", RowBox[{ RowBox[{"(", RowBox[{ RowBox[{ RowBox[{ "chain", "\[LeftDoubleBracket]", "iPivot", "\[RightDoubleBracket]"}], "+", RowBox[{ RowBox[{"(", RowBox[{"#1", "-", RowBox[{ "chain", "\[LeftDoubleBracket]", "iPivot", "\[RightDoubleBracket]"}]}], ")"}], ".", "rotchoice"}]}], "&"}], ")"}], "/@", "movepart"}]}], ";", RowBox[{"If", "[", RowBox[{ RowBox[{ RowBox[{"newpart", "\[Intersection]", "fixpart"}], "\[Equal]", RowBox[{"{", "}"}]}], ",", RowBox[{"newchain", "=", RowBox[{"Join", "[", RowBox[{"fixpart", ",", "newpart"}], "]"}]}], ",", "chain"}], "]"}]}]}], "]"}]}]], "Input", CellLabel->"In[27]:="] }, Closed]], Cell[CellGroupData[{ Cell["Protein Definitions", "Subsubsection"], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"newProteinFromFile", "[", RowBox[{"filename_String", ",", RowBox[{"nTwists_:", "50"}]}], "]"}], " ", ":=", " ", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{ "chain", ",", "sequenceLetters", ",", " ", "length", ",", " ", "sequenceNumbers"}], "}"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"sequenceLetters", " ", "=", " ", RowBox[{"getSequenceFromFile", "[", "filename", "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"proteinName", " ", "=", " ", RowBox[{"First", "[", "sequenceLetters", "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"sequenceLetters", " ", "=", " ", RowBox[{"Last", "[", "sequenceLetters", "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"length", " ", "=", " ", RowBox[{"StringLength", "[", "sequenceLetters", "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"chain", " ", "=", " ", RowBox[{"Table", "[", RowBox[{ RowBox[{"{", RowBox[{"i", ",", "0", ",", "0"}], "}"}], ",", " ", RowBox[{"{", RowBox[{"i", ",", " ", "0", ",", " ", RowBox[{"length", "-", "1"}]}], "}"}]}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"chain", " ", "=", " ", RowBox[{"Nest", "[", RowBox[{"twist", ",", " ", "chain", ",", " ", "nTwists"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"sequenceNumbers", " ", "=", " ", RowBox[{"Table", "[", RowBox[{ RowBox[{"aminoIndex", "[", RowBox[{"StringTake", "[", RowBox[{"sequenceLetters", ",", RowBox[{"{", "i", "}"}]}], "]"}], "]"}], ",", RowBox[{"{", RowBox[{"i", ",", "1", ",", "length"}], "}"}]}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"chain", ",", " ", "sequenceNumbers"}], "}"}]}]}], "]"}]}], ";"}]], "Input", CellLabel->"In[28]:="], Cell[BoxData[ RowBox[{"p1", " ", "=", " ", RowBox[{"newProteinFromFile", "[", "\"\\"", "]"}]}]], "Input", CellLabel->"In[126]:="], Cell[BoxData[ RowBox[{"Dimensions", "[", "p1", "]"}]], "Input", CellLabel->"In[127]:="], Cell[BoxData[ RowBox[{ RowBox[{"twistProtein", "[", "p_List", "]"}], " ", ":=", " ", RowBox[{"{", RowBox[{ RowBox[{"twist", "[", RowBox[{"First", "[", "p", "]"}], "]"}], ",", " ", RowBox[{"Last", "[", "p", "]"}]}], "}"}]}]], "Input", CellLabel->"In[31]:="], Cell[BoxData[ RowBox[{ RowBox[{"showProteinText", "[", RowBox[{"p_List", ",", "opts___"}], "]"}], ":=", RowBox[{"Show", "[", RowBox[{ RowBox[{"Graphics3D", "[", RowBox[{"{", RowBox[{ RowBox[{"Line", "[", RowBox[{"First", "[", "p", "]"}], "]"}], ",", RowBox[{"PointSize", "[", "0.04`", "]"}], ",", RowBox[{"Flatten", "[", RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"makeColorFromString", "[", RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{ RowBox[{"Last", "[", "#1", "]"}], ",", "12"}], "\[RightDoubleBracket]"}], "]"}], ",", RowBox[{"Point", "[", RowBox[{"First", "[", "#1", "]"}], "]"}]}], "}"}], "&"}], ")"}], "/@", RowBox[{"Transpose", "[", "p", "]"}]}], "]"}], ",", RowBox[{"Flatten", "[", RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"Text", "[", RowBox[{ RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{ RowBox[{"Last", "[", "#1", "]"}], ",", "3"}], "\[RightDoubleBracket]"}], ",", RowBox[{"First", "[", "#1", "]"}], ",", RowBox[{"{", RowBox[{"0", ",", "0"}], "}"}]}], "]"}], ",", RowBox[{"BaseStyle", "\[Rule]", RowBox[{"{", RowBox[{ RowBox[{"FontWeight", "\[Rule]", "\"\\""}], ",", RowBox[{"FontSize", "\[Rule]", "12"}]}], "}"}]}]}], "}"}], "&"}], ")"}], "/@", RowBox[{"Transpose", "[", "p", "]"}]}], "]"}]}], "}"}], "]"}], ",", "opts", ",", RowBox[{"AspectRatio", "\[Rule]", "Automatic"}]}], "]"}]}]], "Input", CellLabel->"In[32]:="], Cell[BoxData[ RowBox[{ RowBox[{"showProtein", "[", RowBox[{"p_List", ",", "opts___"}], "]"}], " ", ":=", " ", RowBox[{"Show", "[", RowBox[{ RowBox[{"Graphics3D", "[", RowBox[{"{", RowBox[{ RowBox[{ StyleBox["Line", FontColor->RGBColor[1, 0, 1]], "[", RowBox[{"First", "[", "p", "]"}], "]"}], ",", " ", RowBox[{"PointSize", "[", "0.04", "]"}], ",", RowBox[{"Flatten", "[", RowBox[{ RowBox[{ RowBox[{"{", RowBox[{ StyleBox[ RowBox[{"makeColorFromString", "[", RowBox[{"aminoTable", "\[LeftDoubleBracket]", RowBox[{ RowBox[{"Last", "[", "#", "]"}], ",", "12"}], "\[RightDoubleBracket]"}], "]"}], FontColor->RGBColor[1, 0, 1]], ",", " ", RowBox[{ StyleBox["Point", FontColor->RGBColor[1, 0, 1]], "[", RowBox[{"First", "[", "#", "]"}], "]"}]}], "}"}], "&"}], " ", "/@", " ", RowBox[{"Transpose", "[", "p", "]"}]}], "]"}]}], "}"}], "]"}], ",", "opts", ",", RowBox[{"AspectRatio", "\[Rule]", " ", "Automatic"}]}], "]"}]}]], "Input",\ CellLabel->"In[33]:="] }, Closed]], Cell[CellGroupData[{ Cell["Example Proteins", "Subsubsection"], Cell[BoxData[ RowBox[{"showProtein", "[", "p1", "]"}]], "Input", CellChangeTimes->{3.4470625518135433`*^9}, CellLabel->"In[128]:="], Cell[BoxData[ RowBox[{"showProteinText", "[", RowBox[{"p1", "\[LeftDoubleBracket]", RowBox[{"All", ",", RowBox[{"Range", "[", RowBox[{"1", ",", "25"}], "]"}]}], "\[RightDoubleBracket]"}], "]"}]], "Input", CellChangeTimes->{3.447062557697801*^9}, CellLabel->"In[35]:="], Cell[BoxData[ RowBox[{"Show", "[", RowBox[{"GraphicsRow", "[", RowBox[{"{", RowBox[{ RowBox[{"showProtein", "[", RowBox[{"p1", ",", RowBox[{"DisplayFunction", "\[Rule]", "Identity"}]}], "]"}], ",", RowBox[{"showProtein", "[", RowBox[{ RowBox[{"twistProtein", "[", "p1", "]"}], ",", RowBox[{"DisplayFunction", "\[Rule]", "Identity"}]}], "]"}]}], "}"}], "]"}], "]"}]], "Input", CellLabel->"In[36]:="] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell["Measuring Proteins", "Section"], Cell[CellGroupData[{ Cell["Protein end-to-end Length", "Subsection"], Cell[BoxData[ RowBox[{ RowBox[{"lengthProtein", "[", "p_List", "]"}], " ", ":=", " ", RowBox[{ RowBox[{ RowBox[{"N", "[", RowBox[{"Sqrt", "[", RowBox[{ RowBox[{"#1", "^", "2"}], " ", "+", " ", RowBox[{"#2", "^", "2"}], " ", "+", " ", RowBox[{"#3", "^", "2"}]}], "]"}], "]"}], "&"}], " ", "@@", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"First", "[", "p", "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], " ", "-", " ", RowBox[{ RowBox[{"First", "[", "p", "]"}], "\[LeftDoubleBracket]", RowBox[{"Length", "[", RowBox[{"First", "[", "p", "]"}], "]"}], "\[RightDoubleBracket]"}]}], ")"}]}]}]], "Input", CellLabel->"In[37]:="], Cell[BoxData[ RowBox[{"lengthProtein", "[", "p1", "]"}]], "Input", CellLabel->"In[38]:="], Cell[BoxData[ RowBox[{"lengthProtein", "[", RowBox[{"twistProtein", "[", "p1", "]"}], "]"}]], "Input", CellLabel->"In[39]:="], Cell[BoxData[ RowBox[{"lengthProtein", "[", RowBox[{"twistProtein", "[", "p1", "]"}], "]"}]], "Input", CellLabel->"In[40]:="] }, Closed]], Cell[CellGroupData[{ Cell["Center of Mass", "Subsection"], Cell[BoxData[ RowBox[{ RowBox[{"center", "[", "p_List", "]"}], " ", ":=", " ", RowBox[{ RowBox[{"(", RowBox[{"Plus", " ", "@@", " ", RowBox[{"First", "[", "p", "]"}]}], ")"}], "/", RowBox[{"Length", "[", RowBox[{"First", "[", "p", "]"}], "]"}]}]}]], "Input", CellLabel->"In[41]:="], Cell[BoxData[ RowBox[{ RowBox[{"center", "[", "p1", "]"}], " ", "//", " ", "N"}]], "Input", CellLabel->"In[42]:="], Cell[BoxData[{ RowBox[{ RowBox[{"p20", " ", "=", " ", RowBox[{"Nest", "[", RowBox[{"twistProtein", ",", "p1", ",", "20"}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{"showProtein", "[", RowBox[{"p20", ",", " ", RowBox[{"Axes", "\[Rule]", " ", "True"}]}], "]"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"center", "[", "p20", "]"}], " ", "//", " ", "N"}]}], "Input", CellChangeTimes->{3.447062938705584*^9}, CellLabel->"In[116]:="] }, Closed]], Cell[CellGroupData[{ Cell["Mean Square Radius", "Subsection"], Cell[BoxData[ RowBox[{ RowBox[{"meanSquareRadius", "[", "p_List", "]"}], " ", ":=", " ", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{"cm", ",", " ", "del"}], "}"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"cm", " ", "=", " ", RowBox[{"center", "[", "p", "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"del", " ", "=", " ", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{"#", " ", "-", " ", "cm"}], ")"}], "&"}], " ", "/@", " ", RowBox[{"First", "[", "p", "]"}]}]}], ";", "\[IndentingNewLine]", RowBox[{"del", " ", "=", " ", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"#1", "^", "2"}], " ", "+", " ", RowBox[{"#2", "^", "2"}], " ", "+", " ", RowBox[{"#3", "^", "2"}]}], ")"}], "&"}], " ", "@@@", " ", "del"}]}], ";", "\[IndentingNewLine]", RowBox[{ RowBox[{"(", RowBox[{"Plus", " ", "@@", " ", "del"}], ")"}], "/", RowBox[{"Length", "[", RowBox[{"First", "[", "p", "]"}], "]"}]}]}]}], "]"}]}]], "Input", CellLabel->"In[46]:="], Cell[BoxData[ RowBox[{ RowBox[{"Sqrt", "[", RowBox[{"meanSquareRadius", "[", "p1", "]"}], "]"}], " ", "//", " ", "N"}]], "Input", CellLabel->"In[47]:="], Cell[BoxData[ RowBox[{ RowBox[{"Sqrt", "[", RowBox[{"meanSquareRadius", "[", "p20", "]"}], "]"}], " ", "//", " ", "N"}]], "Input", CellLabel->"In[48]:="] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell["Computing the Energy", "Section"], Cell[TextData[{ "It is useful to evaluate the total interaction energy of a particular \ protein. This involves checking every amino acid for neighbors, and summing \ the interaction energies for each pair. (Of course, like the Ising model, \ this algorithm ", StyleBox["double counts", FontSlant->"Italic"], " the pairings. We need to divide by a factor of two.)" }], "Text"], Cell[TextData[{ "In the following, I ", StyleBox["will not ", FontSlant->"Italic"], "distinguish between the \"right\", \"left\", \"up\", and \"down\" sites and \ the \"diagonal\" sites." }], "Text"], Cell[BoxData[ RowBox[{"deltaNeighbors", " ", "=", " ", RowBox[{"DeleteCases", "[", RowBox[{ RowBox[{"Flatten", "[", RowBox[{ RowBox[{"Table", "[", RowBox[{ RowBox[{"{", RowBox[{"i", ",", "j", ",", "k"}], "}"}], ",", " ", RowBox[{"{", RowBox[{"i", ",", RowBox[{"-", "1"}], ",", "1"}], "}"}], ",", RowBox[{"{", RowBox[{"j", ",", RowBox[{"-", "1"}], ",", "1"}], "}"}], ",", RowBox[{"{", RowBox[{"k", ",", RowBox[{"-", "1"}], ",", "1"}], "}"}]}], "]"}], ",", "2"}], "]"}], ",", " ", RowBox[{"{", RowBox[{"0", ",", "0", ",", "0"}], "}"}]}], "]"}]}]], "Input", CellLabel->"In[49]:="], Cell[BoxData[ RowBox[{ RowBox[{"(*", " ", "neighbors", " ", "*)"}], " ", RowBox[{"Flatten", "[", RowBox[{ RowBox[{ RowBox[{ RowBox[{"Position", "[", RowBox[{ RowBox[{"First", "[", "p1", "]"}], ",", " ", RowBox[{ RowBox[{ RowBox[{"First", "[", "p1", "]"}], "\[LeftDoubleBracket]", StyleBox["7", FontColor->RGBColor[1, 0, 1]], "\[RightDoubleBracket]"}], " ", "+", " ", "#"}]}], "]"}], "&"}], " ", "/@", " ", "deltaNeighbors"}], ",", " ", "1"}], "]"}]}]], "Input", CellLabel->"In[50]:="], Cell[BoxData[ RowBox[{ RowBox[{"(*", " ", RowBox[{"eliminate", " ", "covalent", " ", "bonds"}], " ", "*)"}], " ", RowBox[{"Complement", "[", RowBox[{"%", ",", " ", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ StyleBox["7", FontColor->RGBColor[1, 0, 1]], "-", "1"}], "}"}], ",", " ", RowBox[{"{", RowBox[{ StyleBox["7", FontColor->RGBColor[1, 0, 1]], "+", "1"}], "}"}]}], "}"}]}], "]"}]}]], "Input", CellLabel->"In[51]:="], Cell[CellGroupData[{ Cell["Energy Calculators", "Subsubsection"], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"energyOne", "[", RowBox[{"p_List", ",", StyleBox[ RowBox[{ StyleBox["j", FontColor->RGBColor[1, 0, 0]], "_List"}]], ",", StyleBox[ RowBox[{ StyleBox["at", FontColor->RGBColor[0, 1, 0]], "_"}]]}], "]"}], " ", ":=", " ", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{"e", ",", "neighbors"}], "}"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"e", " ", "=", " ", "0.0"}], ";", "\[IndentingNewLine]", RowBox[{"neighbors", " ", "=", " ", RowBox[{"Flatten", "[", RowBox[{ RowBox[{ RowBox[{ RowBox[{"Position", "[", RowBox[{ RowBox[{"First", "[", "p", "]"}], ",", " ", RowBox[{ RowBox[{ RowBox[{"First", "[", "p", "]"}], "\[LeftDoubleBracket]", StyleBox["at", FontColor->RGBColor[0, 1, 0]], "\[RightDoubleBracket]"}], " ", "+", " ", "#"}]}], "]"}], "&"}], " ", "/@", " ", "deltaNeighbors"}], ",", " ", "1"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"neighbors", " ", "=", " ", RowBox[{"Complement", "[", RowBox[{"neighbors", ",", " ", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ StyleBox["at", FontColor->RGBColor[0, 1, 0]], "-", "1"}], "}"}], ",", " ", RowBox[{"{", RowBox[{ StyleBox["at", FontColor->RGBColor[0, 1, 0]], "+", "1"}], "}"}]}], "}"}]}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"e", " ", "+=", " ", RowBox[{"Plus", " ", "@@", " ", RowBox[{ StyleBox["j", FontColor->RGBColor[1, 0, 0]], "\[LeftDoubleBracket]", RowBox[{ RowBox[{ RowBox[{ RowBox[{"Last", "[", "p", "]"}], "\[LeftDoubleBracket]", StyleBox["at", FontColor->RGBColor[0, 1, 0]], "\[RightDoubleBracket]"}], "-", "1"}], ",", RowBox[{ RowBox[{ RowBox[{"Last", "[", "p", "]"}], "\[LeftDoubleBracket]", "#", "\[RightDoubleBracket]"}], "-", "1"}]}], "\[RightDoubleBracket]"}]}]}], "&"}], " ", "/@", " ", "neighbors"}], ";", "\[IndentingNewLine]", RowBox[{"e", "/", "2"}]}]}], "]"}]}], ";"}]], "Input", CellLabel->"In[52]:="], Cell[BoxData[ RowBox[{ RowBox[{"energy", "[", RowBox[{"p_List", ",", StyleBox[ RowBox[{ StyleBox["j", FontColor->RGBColor[1, 0, 0]], "_List"}]]}], "]"}], ":=", " ", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{"etotal", ",", " ", "i"}], "}"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"etotal", " ", "=", " ", "0.0"}], ";", "\[IndentingNewLine]", RowBox[{"Do", "[", " ", RowBox[{ RowBox[{"etotal", " ", "+=", " ", RowBox[{"energyOne", "[", RowBox[{"p", ",", StyleBox["j", FontColor->RGBColor[1, 0, 0]], ",", "i"}], "]"}]}], ",", " ", RowBox[{"{", RowBox[{"i", ",", " ", RowBox[{"Length", "[", RowBox[{"First", "[", "p", "]"}], "]"}]}], "}"}]}], "]"}], ";", "\[IndentingNewLine]", "etotal"}]}], "]"}]}]], "Input", CellLabel->"In[53]:="] }, Closed]], Cell[CellGroupData[{ Cell["Examples", "Subsubsection"], Cell[BoxData[ RowBox[{ RowBox[{"energy", "[", RowBox[{"p1", ",", "aminoJ"}], "]"}], " ", "//", " ", "Timing"}]], "Input",\ CellLabel->"In[54]:="], Cell[BoxData[{ RowBox[{ RowBox[{"elist", " ", "=", " ", RowBox[{"Table", "[", RowBox[{ RowBox[{"energy", "[", RowBox[{ RowBox[{"p1", " ", "=", " ", RowBox[{"twistProtein", "[", "p1", "]"}]}], ",", "aminoJ"}], "]"}], ",", " ", RowBox[{"{", "100", "}"}]}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{"{", RowBox[{ RowBox[{"Mean", "[", "elist", "]"}], ",", " ", RowBox[{"StandardDeviation", "[", "elist", "]"}]}], "}"}]}], "Input", CellLabel->"In[55]:="] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell["Twist Folding Algorithm", "Section"], Cell[TextData[{ "I call one relatively simple algorithm to implement the \"twist folding \ algorithm\", Basically, we twist the protein and check the change in energy. \ If the energy ", StyleBox["decreases", FontSlant->"Italic"], ", we return the new arrangement of the protein. If the energy ", StyleBox["increases", FontSlant->"Italic"], ", then we check a random number. If the random number is less than Exp[- \ \[CapitalDelta]e/T], then we also return the new arrangement." }], "Text"], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"twistFold", "[", RowBox[{"p_List", ",", "j_List", ",", "temp_"}], "]"}], ":=", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{"newp", ",", "\[CapitalDelta]e"}], "}"}], ",", RowBox[{ RowBox[{"newp", "=", RowBox[{"twistProtein", "[", "p", "]"}]}], ";", RowBox[{"\[CapitalDelta]e", "=", RowBox[{ RowBox[{"energy", "[", RowBox[{"newp", ",", "j"}], "]"}], "-", RowBox[{"energy", "[", RowBox[{"p", ",", "j"}], "]"}]}]}], ";", RowBox[{"Which", "[", RowBox[{ RowBox[{"\[CapitalDelta]e", "\[LessEqual]", "0"}], ",", "newp", ",", RowBox[{ RowBox[{"RandomReal", "[", "]"}], "<", SuperscriptBox["\[ExponentialE]", RowBox[{"-", FractionBox["\[CapitalDelta]e", "temp"]}]]}], ",", "newp", ",", "True", ",", "p"}], "]"}]}]}], "]"}]}], ";"}]], "Input", CellLabel->"In[57]:="], Cell[BoxData[{ RowBox[{ RowBox[{"p1", " ", "=", " ", RowBox[{"newProteinFromFile", "[", RowBox[{"\"\\"", ",", "0"}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{"energy", "[", RowBox[{"p1", ",", "aminoJ"}], "]"}]}], "Input", CellLabel->"In[58]:="], Cell[BoxData[ RowBox[{"showProtein", "[", "p1", "]"}]], "Input", CellChangeTimes->{3.4470625398970537`*^9}, CellLabel->"In[60]:="], Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"p100", " ", "=", " ", RowBox[{"Nest", "[", RowBox[{ RowBox[{ RowBox[{"twistFold", "[", RowBox[{"#", ",", "aminoJ", ",", "1.0"}], "]"}], "&"}], ",", " ", "p1", ",", " ", "100"}], "]"}]}], ";"}], ")"}], "//", "Timing"}]], "Input", CellLabel->"In[61]:="], Cell[BoxData[ RowBox[{"energy", "[", RowBox[{"p100", ",", "aminoJ"}], "]"}]], "Input", CellLabel->"In[62]:="], Cell[BoxData[ RowBox[{"showProtein", "[", "p100", "]"}]], "Input", CellChangeTimes->{3.447062541005638*^9}, CellLabel->"In[63]:="], Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"p100", " ", "=", " ", RowBox[{"Nest", "[", RowBox[{ RowBox[{ RowBox[{"twistFold", "[", RowBox[{"#", ",", "aminoJ", ",", "0.1"}], "]"}], "&"}], ",", " ", "p1", ",", " ", "100"}], "]"}]}], ";"}], ")"}], "//", "Timing"}]], "Input", CellLabel->"In[64]:="], Cell[BoxData[ RowBox[{"energy", "[", RowBox[{"p100", ",", "aminoJ"}], "]"}]], "Input", CellLabel->"In[65]:="], Cell[BoxData[ RowBox[{"showProtein", "[", "p100", "]"}]], "Input", CellChangeTimes->{3.447062541981696*^9}, CellLabel->"In[66]:="], Cell[TextData[{ "Finally, a command to apply many (", StyleBox["n", FontSlant->"Italic"], ") twists to a protein\[Ellipsis]" }], "Text"], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"twistFold", "[", "n_Integer", "]"}], "[", RowBox[{"p_List", ",", "j_List", ",", "temp_"}], "]"}], " ", ":=", " ", RowBox[{"Nest", "[", RowBox[{ RowBox[{ RowBox[{"twistFold", "[", RowBox[{"#", ",", "j", ",", "temp"}], "]"}], "&"}], ",", "p", ",", "n"}], "]"}]}]], "Input", CellLabel->"In[67]:="] }, Closed]], Cell[CellGroupData[{ Cell["Single Amino Acid Move Algorithm", "Section"], Cell["\<\ Giordano's textbook describes a protein-folding algorithm where single amino \ acids are moved one at a time. I am not sure whether or not this model is \ more or less physically accurate than the \"twist-folding\" model described \ in the previous section. While the \"twist-folding\" model represents the \ case when the protein's structure is rigid about a pivot point, the \"single \ amino acid move\" algorithm represents the opposite situation. Each amino \ acid is free to adjust its orientation independent of overall structure\ \[LongDash]except its nearest neighbors, covalent bonded.\ \>", "Text"], Cell[CellGroupData[{ Cell["Constructing the Algorithm", "Subsubsection"], Cell["\<\ Let's take an example, and try to move the 3rd amino acid along the protein.\ \>", "Text"], Cell[BoxData[{ RowBox[{ RowBox[{"move", " ", "=", " ", "3"}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"p1", " ", "=", RowBox[{"Part", "[", RowBox[{ RowBox[{"newProteinFromFile", "[", RowBox[{"\"\\"", ",", "0"}], "]"}], ",", "All", ",", " ", RowBox[{"Range", "[", RowBox[{"1", ",", "20"}], "]"}]}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"showProtein", "[", "p1", "]"}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"First", "[", "p1", "]"}], "\[LeftDoubleBracket]", "move", "\[RightDoubleBracket]"}]}], "Input", CellLabel->"In[68]:="], Cell[BoxData[ RowBox[{ RowBox[{"allowedMove", " ", "=", " ", "deltaNeighbors"}], ";"}]], "Input", CellLabel->"In[72]:="], Cell["Let's first delete those moves that are already occupied.", "Text"], Cell[BoxData[ RowBox[{"occupied", " ", "=", " ", RowBox[{ RowBox[{ RowBox[{"MemberQ", "[", RowBox[{ RowBox[{"First", "[", "p1", "]"}], ",", "#"}], "]"}], "&"}], " ", "/@", " ", RowBox[{"(", " ", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"First", "[", "p1", "]"}], "\[LeftDoubleBracket]", "move", "\[RightDoubleBracket]"}], " ", "+", " ", "#"}], ")"}], "&"}], " ", "/@", " ", "allowedMove"}], " ", ")"}]}]}]], "Input", CellLabel->"In[73]:="], Cell[BoxData[ RowBox[{"allowedMove", " ", "=", " ", RowBox[{"Delete", "[", RowBox[{"allowedMove", ",", RowBox[{"Position", "[", RowBox[{"occupied", ",", "True"}], "]"}]}], "]"}]}]], "Input", CellLabel->"In[74]:="], Cell["\<\ Now, let's delete those moves that would break the covalent bonds to the \ adjacent amino acids in the protein.\ \>", "Text"], Cell[BoxData[ RowBox[{"tooFar", " ", "=", " ", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"If", "[", RowBox[{ RowBox[{"move", ">", "1"}], ",", RowBox[{"(", RowBox[{ RowBox[{"Plus", " ", "@@", " ", RowBox[{"Abs", "[", RowBox[{ RowBox[{ RowBox[{"First", "[", "p1", "]"}], "\[LeftDoubleBracket]", RowBox[{"move", "-", "1"}], "\[RightDoubleBracket]"}], " ", "-", " ", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"First", "[", "p1", "]"}], "\[LeftDoubleBracket]", "move", "\[RightDoubleBracket]"}], " ", "+", " ", "#"}], ")"}]}], "]"}]}], " ", "\[GreaterEqual]", " ", "2"}], ")"}], " ", ",", "False"}], "]"}], "||", " ", RowBox[{"If", "[", RowBox[{ RowBox[{"move", " ", "<", " ", RowBox[{"Length", "[", RowBox[{"First", "[", "p1", "]"}], "]"}]}], ",", RowBox[{"(", RowBox[{ RowBox[{"Plus", " ", "@@", " ", RowBox[{"Abs", "[", RowBox[{ RowBox[{ RowBox[{"First", "[", "p1", "]"}], "\[LeftDoubleBracket]", RowBox[{"move", "+", "1"}], "\[RightDoubleBracket]"}], " ", "-", " ", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"First", "[", "p1", "]"}], "\[LeftDoubleBracket]", "move", "\[RightDoubleBracket]"}], " ", "+", " ", "#"}], ")"}]}], "]"}]}], " ", "\[GreaterEqual]", " ", "2"}], ")"}], ",", "False"}], "]"}]}], ")"}], "&"}], " ", "/@", " ", "allowedMove"}]}]], "Input", CellLabel->"In[75]:="], Cell[BoxData[ RowBox[{"allowedMove", " ", "=", " ", RowBox[{"Delete", "[", RowBox[{"allowedMove", ",", RowBox[{"Position", "[", RowBox[{"tooFar", ",", "True"}], "]"}]}], "]"}]}]], "Input", CellLabel->"In[76]:="], Cell["\<\ Only the ends of a \"straight\" protein chain are allowed to move according \ to our rules.\ \>", "Text"] }, Closed]], Cell[CellGroupData[{ Cell["Building the Module", "Subsubsection"], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"aminoFold", "[", RowBox[{"p_List", ",", "j_List", ",", "temp_"}], "]"}], ":=", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{ "\[CapitalDelta]e", ",", "move", ",", "allowedMove", ",", "occupied", ",", "tooFar", ",", "newPoint", ",", "newProtein"}], "}"}], ",", RowBox[{ RowBox[{"move", "=", RowBox[{"RandomInteger", "[", RowBox[{"{", RowBox[{"1", ",", RowBox[{"Length", "[", RowBox[{"First", "[", "p", "]"}], "]"}]}], "}"}], "]"}]}], ";", RowBox[{"allowedMove", "=", "deltaNeighbors"}], ";", RowBox[{"occupied", "=", RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"MemberQ", "[", RowBox[{ RowBox[{"First", "[", "p", "]"}], ",", "#1"}], "]"}], "&"}], ")"}], "/@", RowBox[{ RowBox[{"(", RowBox[{ RowBox[{ RowBox[{ RowBox[{"First", "[", "p", "]"}], "\[LeftDoubleBracket]", "move", "\[RightDoubleBracket]"}], "+", "#1"}], "&"}], ")"}], "/@", "allowedMove"}]}]}], ";", RowBox[{"allowedMove", "=", RowBox[{"Delete", "[", RowBox[{"allowedMove", ",", RowBox[{"Position", "[", RowBox[{"occupied", ",", "True"}], "]"}]}], "]"}]}], ";", RowBox[{"tooFar", "=", RowBox[{ RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"If", "[", RowBox[{ RowBox[{"move", ">", "1"}], ",", RowBox[{ RowBox[{"Plus", "@@", RowBox[{"Abs", "[", RowBox[{ RowBox[{ RowBox[{"First", "[", "p", "]"}], "\[LeftDoubleBracket]", RowBox[{"move", "-", "1"}], "\[RightDoubleBracket]"}], "-", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"First", "[", "p", "]"}], "\[LeftDoubleBracket]", "move", "\[RightDoubleBracket]"}], "+", "#1"}], ")"}]}], "]"}]}], "\[GreaterEqual]", "2"}], ",", "False"}], "]"}], "||", RowBox[{"If", "[", RowBox[{ RowBox[{"move", "<", RowBox[{"Length", "[", RowBox[{"First", "[", "p", "]"}], "]"}]}], ",", RowBox[{ RowBox[{"Plus", "@@", RowBox[{"Abs", "[", RowBox[{ RowBox[{ RowBox[{"First", "[", "p", "]"}], "\[LeftDoubleBracket]", RowBox[{"move", "+", "1"}], "\[RightDoubleBracket]"}], "-", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"First", "[", "p", "]"}], "\[LeftDoubleBracket]", "move", "\[RightDoubleBracket]"}], "+", "#1"}], ")"}]}], "]"}]}], "\[GreaterEqual]", "2"}], ",", "False"}], "]"}]}], "&"}], ")"}], "/@", "allowedMove"}]}], ";", RowBox[{"allowedMove", "=", RowBox[{"Delete", "[", RowBox[{"allowedMove", ",", RowBox[{"Position", "[", RowBox[{"tooFar", ",", "True"}], "]"}]}], "]"}]}], ";", RowBox[{"If", "[", RowBox[{ RowBox[{ RowBox[{"Length", "[", "allowedMove", "]"}], "\[Equal]", "0"}], ",", RowBox[{"Return", "[", "p", "]"}]}], "]"}], ";", RowBox[{"allowedMove", "=", RowBox[{"allowedMove", "\[LeftDoubleBracket]", RowBox[{"RandomInteger", "[", RowBox[{"{", RowBox[{"1", ",", RowBox[{"Length", "[", "allowedMove", "]"}]}], "}"}], "]"}], "\[RightDoubleBracket]"}]}], ";", RowBox[{"newPoint", "=", RowBox[{ RowBox[{ RowBox[{"First", "[", "p", "]"}], "\[LeftDoubleBracket]", "move", "\[RightDoubleBracket]"}], "+", "allowedMove"}]}], ";", RowBox[{"newProtein", "=", RowBox[{"{", RowBox[{ RowBox[{"ReplacePart", "[", RowBox[{ RowBox[{"First", "[", "p", "]"}], ",", "newPoint", ",", "move"}], "]"}], ",", RowBox[{"Last", "[", "p", "]"}]}], "}"}]}], ";", RowBox[{"\[CapitalDelta]e", "=", RowBox[{ RowBox[{"energyOne", "[", RowBox[{"newProtein", ",", "j", ",", "move"}], "]"}], "-", RowBox[{"energyOne", "[", RowBox[{"p", ",", "j", ",", "move"}], "]"}]}]}], ";", RowBox[{"Which", "[", RowBox[{ RowBox[{"\[CapitalDelta]e", "\[LessEqual]", "0"}], ",", "newProtein", ",", RowBox[{ RowBox[{"RandomReal", "[", "]"}], "<", SuperscriptBox["\[ExponentialE]", RowBox[{"-", FractionBox["\[CapitalDelta]e", "temp"]}]]}], ",", "newProtein", ",", "True", ",", "p"}], "]"}]}]}], "]"}]}], ";"}]], "Input", CellLabel->"In[77]:="] }, Closed]], Cell[CellGroupData[{ Cell["Examples", "Subsubsection"], Cell[BoxData[ RowBox[{"energy", "[", RowBox[{"p1", ",", "aminoJ"}], "]"}]], "Input", CellLabel->"In[78]:="], Cell[BoxData[ RowBox[{"showProtein", "[", "p1", "]"}]], "Input", CellChangeTimes->{3.447062490526535*^9}, CellLabel->"In[79]:="], Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"p100", " ", "=", " ", RowBox[{"Nest", "[", RowBox[{ RowBox[{ RowBox[{"aminoFold", "[", RowBox[{"#", ",", "aminoJ", ",", "1.0"}], "]"}], "&"}], ",", " ", "p1", ",", " ", "100"}], "]"}]}], ";"}], ")"}], "//", "Timing"}]], "Input", CellLabel->"In[80]:="], Cell[BoxData[ RowBox[{"energy", "[", RowBox[{"p100", ",", "aminoJ"}], "]"}]], "Input", CellLabel->"In[81]:="], Cell[BoxData[ RowBox[{"showProtein", "[", "p100", "]"}]], "Input", CellChangeTimes->{3.447062507847629*^9}, CellLabel->"In[82]:="], Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"p1000", " ", "=", " ", RowBox[{"Nest", "[", RowBox[{ RowBox[{ RowBox[{"aminoFold", "[", RowBox[{"#", ",", "aminoJ", ",", "1.0"}], "]"}], "&"}], ",", " ", "p100", ",", " ", "900"}], "]"}]}], ";"}], ")"}], "//", "Timing"}]], "Input", CellLabel->"In[83]:="], Cell[BoxData[ RowBox[{"energy", "[", RowBox[{"p1000", ",", "aminoJ"}], "]"}]], "Input", CellLabel->"In[84]:="], Cell[BoxData[ RowBox[{"showProtein", "[", "p1000", "]"}]], "Input", CellChangeTimes->{3.447062512188754*^9}, CellLabel->"In[85]:="], Cell[TextData[{ "Finally, a command to apply many (", StyleBox["n", FontSlant->"Italic"], ") \"amino acid moves\" to a protein\[Ellipsis]" }], "Text"], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"aminoFold", "[", "n_", "]"}], "[", RowBox[{"p_List", ",", "j_List", ",", "temp_"}], "]"}], " ", ":=", " ", RowBox[{"Nest", "[", RowBox[{ RowBox[{ RowBox[{"aminoFold", "[", RowBox[{"#", ",", "j", ",", "temp"}], "]"}], "&"}], ",", "p", ",", "n"}], "]"}]}]], "Input", CellLabel->"In[86]:="] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell["Evolution Examples: aminoMove & twistFold", "Section"], Cell[TextData[{ "This section compares the protein folding evolution using both ", StyleBox["aminoFold", FontWeight->"Bold"], " and ", StyleBox["twistFold", FontWeight->"Bold"], ".", " " }], "Text"], Cell[BoxData[ RowBox[{"energy", "[", RowBox[{"p1", ",", "aminoJ"}], "]"}]], "Input", CellLabel->"In[87]:="], Cell[BoxData[ RowBox[{"Dimensions", "[", "p1", "]"}]], "Input", CellLabel->"In[88]:="], Cell[CellGroupData[{ Cell["Using Nest List", "Subsection"], Cell[TextData[{ "When using ", StyleBox["NestList", FontWeight->"Bold"], ", a complete history of the folding is retained for analysis. For long \ proteins, this will require a lot of computer memory!" }], "Text"], Cell[BoxData[ RowBox[{ RowBox[{"pEvolution", " ", "=", " ", RowBox[{"NestList", "[", RowBox[{ RowBox[{ RowBox[{"twistFold", "[", RowBox[{"#", ",", "aminoJ", ",", "1.0"}], "]"}], "&"}], ",", " ", "p1", ",", " ", "100"}], "]"}]}], ";"}]], "Input", CellLabel->"In[89]:="], Cell[BoxData[ RowBox[{"Dimensions", "[", "pEvolution", "]"}]], "Input", CellLabel->"In[90]:="], Cell[BoxData[{ RowBox[{ RowBox[{"mxX", " ", "=", " ", RowBox[{"Max", "[", RowBox[{"Flatten", "[", RowBox[{"Map", "[", RowBox[{ RowBox[{ RowBox[{"Part", "[", RowBox[{"#", ",", "1"}], "]"}], "&"}], " ", ",", RowBox[{"(", RowBox[{"First", " ", "/@", " ", "pEvolution"}], ")"}], ",", RowBox[{"{", "2", "}"}]}], "]"}], "]"}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"mnX", " ", "=", RowBox[{"Min", "[", RowBox[{"Flatten", "[", RowBox[{"Map", "[", RowBox[{ RowBox[{ RowBox[{"Part", "[", RowBox[{"#", ",", "1"}], "]"}], "&"}], " ", ",", RowBox[{"(", RowBox[{"First", " ", "/@", " ", "pEvolution"}], ")"}], ",", RowBox[{"{", "2", "}"}]}], "]"}], "]"}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"mxY", " ", "=", " ", RowBox[{"Max", "[", RowBox[{"Flatten", "[", RowBox[{"Map", "[", RowBox[{ RowBox[{ RowBox[{"Part", "[", RowBox[{"#", ",", "2"}], "]"}], "&"}], " ", ",", RowBox[{"(", RowBox[{"First", " ", "/@", " ", "pEvolution"}], ")"}], ",", RowBox[{"{", "2", "}"}]}], "]"}], "]"}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"mnY", " ", "=", RowBox[{"Min", "[", RowBox[{"Flatten", "[", RowBox[{"Map", "[", RowBox[{ RowBox[{ RowBox[{"Part", "[", RowBox[{"#", ",", "2"}], "]"}], "&"}], " ", ",", RowBox[{"(", RowBox[{"First", " ", "/@", " ", "pEvolution"}], ")"}], ",", RowBox[{"{", "2", "}"}]}], "]"}], "]"}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"mxZ", " ", "=", " ", RowBox[{"Max", "[", RowBox[{"Flatten", "[", RowBox[{"Map", "[", RowBox[{ RowBox[{ RowBox[{"Part", "[", RowBox[{"#", ",", "3"}], "]"}], "&"}], " ", ",", RowBox[{"(", RowBox[{"First", " ", "/@", " ", "pEvolution"}], ")"}], ",", RowBox[{"{", "2", "}"}]}], "]"}], "]"}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"mnZ", " ", "=", RowBox[{"Min", "[", RowBox[{"Flatten", "[", RowBox[{"Map", "[", RowBox[{ RowBox[{ RowBox[{"Part", "[", RowBox[{"#", ",", "3"}], "]"}], "&"}], " ", ",", RowBox[{"(", RowBox[{"First", " ", "/@", " ", "pEvolution"}], ")"}], ",", RowBox[{"{", "2", "}"}]}], "]"}], "]"}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{ RowBox[{"showProtein", "[", RowBox[{"#", ",", RowBox[{"PlotRange", "\[Rule]", " ", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"mnX", ",", "mxX"}], "}"}], ",", RowBox[{"{", RowBox[{"mnY", ",", "mxY"}], "}"}], ",", RowBox[{"{", RowBox[{"mnZ", ",", "mxZ"}], "}"}]}], "}"}]}], ",", " ", RowBox[{"AspectRatio", "\[Rule]", " ", "Automatic"}]}], "]"}], "&"}], " ", "/@", " ", "pEvolution"}], ";"}]}], "Input", CellLabel->"In[91]:="], Cell[BoxData[ RowBox[{"plot1", "=", RowBox[{"ListPlot", "[", RowBox[{"lengthProtein", "/@", "pEvolution"}], "]"}]}]], "Input", CellLabel->"In[98]:="], Cell[BoxData[ RowBox[{"ListPlot", "[", RowBox[{"meanSquareRadius", "/@", "pEvolution"}], "]"}]], "Input", CellLabel->"In[99]:="], Cell[BoxData[{ RowBox[{"plot2", "=", RowBox[{"ListPlot", "[", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"2", " ", SqrtBox[ RowBox[{"meanSquareRadius", "[", "#1", "]"}]]}], "&"}], ")"}], "/@", "pEvolution"}], ",", RowBox[{"PlotStyle", "\[Rule]", RowBox[{"{", RowBox[{"RGBColor", "[", RowBox[{"1", ",", "0", ",", "0"}], "]"}], "}"}]}]}], "]"}]}], "\n", RowBox[{"Show", "[", RowBox[{"plot1", ",", "plot2", ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}], ",", RowBox[{"AxesLabel", "\[Rule]", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}]}], "]"}]}], "Input", CellLabel->"In[100]:="] }, Closed]], Cell[CellGroupData[{ Cell["aminoFold", "Subsection"], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"evolveAmino", "[", RowBox[{"n_", ",", RowBox[{"nnest_:", "20"}]}], "]"}], "[", RowBox[{"p0_List", ",", "j_List", ",", "temp_"}], "]"}], " ", ":=", " ", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{"pNew", ",", "e", ",", " ", "d"}], "}"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"e", " ", "=", " ", RowBox[{"{", RowBox[{"energy", "[", RowBox[{ RowBox[{"pNew", "=", "p0"}], ",", "j"}], "]"}], "}"}]}], ";", "\[IndentingNewLine]", RowBox[{"d", " ", "=", " ", RowBox[{"{", RowBox[{"lengthProtein", "[", "pNew", "]"}], "}"}]}], ";", "\[IndentingNewLine]", RowBox[{"Do", "[", RowBox[{ RowBox[{ RowBox[{"e", "=", RowBox[{"Append", "[", RowBox[{"e", ",", RowBox[{"energy", "[", RowBox[{ RowBox[{"pNew", "=", RowBox[{ RowBox[{"aminoFold", "[", "nnest", "]"}], "[", RowBox[{"pNew", ",", "j", ",", "temp"}], "]"}]}], ",", "j"}], "]"}]}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"d", " ", "=", " ", RowBox[{"Append", "[", RowBox[{"d", ",", RowBox[{"lengthProtein", "[", "pNew", "]"}]}], "]"}]}]}], ",", RowBox[{"{", "n", "}"}]}], "]"}], ";", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"e", ",", "d"}], "}"}]}]}], "]"}]}]], "Input", CellLabel->"In[102]:="], Cell[CellGroupData[{ Cell["Temp = 1.0", "Subsubsection"], Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"{", RowBox[{"etime1", ",", "dtime1"}], "}"}], " ", "=", " ", RowBox[{ RowBox[{"evolveAmino", "[", "100", "]"}], "[", RowBox[{"p1", ",", "aminoJ", ",", StyleBox["1.0", FontColor->RGBColor[1, 0, 0]]}], "]"}]}], ";"}], ")"}], " ", "//", " ", "Timing"}]], "Input", CellLabel->"In[103]:="], Cell[BoxData[ RowBox[{"ListPlot", "[", RowBox[{"etime1", ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}], ",", RowBox[{"AxesLabel", "\[Rule]", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}]}], "]"}]], "Input", CellLabel->"In[104]:="], Cell[BoxData[ RowBox[{"ListPlot", "[", RowBox[{"dtime1", ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}], ",", RowBox[{"AxesLabel", "\[Rule]", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}]}], "]"}]], "Input", CellLabel->"In[105]:="] }, Closed]], Cell[CellGroupData[{ Cell["Temp = 0.1", "Subsubsection"], Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"{", RowBox[{"etime1", ",", "dtime1"}], "}"}], " ", "=", " ", RowBox[{ RowBox[{"evolveAmino", "[", "100", "]"}], "[", RowBox[{"p1", ",", "aminoJ", ",", StyleBox["0.1", FontColor->RGBColor[1, 0, 0]]}], "]"}]}], ";"}], ")"}], " ", "//", " ", "Timing"}]], "Input", CellLabel->"In[106]:="], Cell[BoxData[ RowBox[{"ListPlot", "[", RowBox[{"etime1", ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}], ",", RowBox[{"AxesLabel", "\[Rule]", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}]}], "]"}]], "Input", CellLabel->"In[107]:="], Cell[BoxData[ RowBox[{"ListPlot", "[", RowBox[{"dtime1", ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}], ",", RowBox[{"AxesLabel", "\[Rule]", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}]}], "]"}]], "Input", CellLabel->"In[108]:="] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell["twistFold", "Subsection"], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"evolveTwist", "[", RowBox[{"n_", ",", RowBox[{"nnest_:", "1"}]}], "]"}], "[", RowBox[{"p0_List", ",", "j_List", ",", "temp_"}], "]"}], " ", ":=", " ", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{"pNew", ",", "e", ",", " ", "d"}], "}"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"e", " ", "=", " ", RowBox[{"{", RowBox[{"energy", "[", RowBox[{ RowBox[{"pNew", "=", "p0"}], ",", "j"}], "]"}], "}"}]}], ";", "\[IndentingNewLine]", RowBox[{"d", " ", "=", " ", RowBox[{"{", RowBox[{"lengthProtein", "[", "pNew", "]"}], "}"}]}], ";", "\[IndentingNewLine]", RowBox[{"Do", "[", RowBox[{ RowBox[{ RowBox[{"e", "=", RowBox[{"Append", "[", RowBox[{"e", ",", RowBox[{"energy", "[", RowBox[{ RowBox[{"pNew", "=", RowBox[{ RowBox[{"twistFold", "[", "nnest", "]"}], "[", RowBox[{"pNew", ",", "j", ",", "temp"}], "]"}]}], ",", "j"}], "]"}]}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"d", " ", "=", " ", RowBox[{"Append", "[", RowBox[{"d", ",", RowBox[{"lengthProtein", "[", "pNew", "]"}]}], "]"}]}]}], ",", RowBox[{"{", "n", "}"}]}], "]"}], ";", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"e", ",", "d"}], "}"}]}]}], "]"}]}]], "Input", CellLabel->"In[109]:="], Cell[CellGroupData[{ Cell["Temp = 1.0", "Subsubsection"], Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"{", RowBox[{"etime2", ",", "dtime2"}], "}"}], " ", "=", " ", RowBox[{ RowBox[{"evolveTwist", "[", "100", "]"}], "[", RowBox[{"p1", ",", "aminoJ", ",", StyleBox["1.0", FontColor->RGBColor[1, 0, 0]]}], "]"}]}], ";"}], ")"}], " ", "//", " ", "Timing"}]], "Input", CellLabel->"In[110]:="], Cell[BoxData[ RowBox[{"ListPlot", "[", RowBox[{"etime2", ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}], ",", RowBox[{"AxesLabel", "\[Rule]", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}]}], "]"}]], "Input", CellLabel->"In[111]:="], Cell[BoxData[ RowBox[{"ListPlot", "[", RowBox[{"dtime2", ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}], ",", RowBox[{"AxesLabel", "\[Rule]", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}]}], "]"}]], "Input", CellLabel->"In[112]:="] }, Closed]], Cell[CellGroupData[{ Cell["Temp = 0.1", "Subsubsection"], Cell[BoxData[ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"{", RowBox[{"etime2", ",", "dtime2"}], "}"}], " ", "=", " ", RowBox[{ RowBox[{"evolveTwist", "[", "100", "]"}], "[", RowBox[{"p1", ",", "aminoJ", ",", StyleBox["0.1", FontColor->RGBColor[1, 0, 0]]}], "]"}]}], ";"}], ")"}], " ", "//", " ", "Timing"}]], "Input", CellLabel->"In[113]:="], Cell[BoxData[ RowBox[{"ListPlot", "[", RowBox[{"etime2", ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}], ",", RowBox[{"AxesLabel", "\[Rule]", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}]}], "]"}]], "Input", CellLabel->"In[114]:="], Cell[BoxData[ RowBox[{"ListPlot", "[", RowBox[{"dtime2", ",", RowBox[{"PlotLabel", "\[Rule]", "\"\\""}], ",", RowBox[{"AxesLabel", "\[Rule]", RowBox[{"{", RowBox[{"\"\\"", ",", "\"\\""}], "}"}]}]}], "]"}]], "Input", CellLabel->"In[115]:="] }, Closed]] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell["Summary", "Section"], Cell[TextData[{ "We developed two algorithms for testing two-dimensional protein folding \ models. Both algorithms are based on the discussion by N. Giordano, but one \ creates more rapid and dramatic folding through structural rotations about a \ pivot. (We called this algorithm the \"twist-folding\" algorithm.)\nWe tested \ both algorithms with a randomly-generated interaction matrix. The time \ evolution of the protein's structure has a very strong temperature \ dependence. \nIt is still very unclear whether or not there exists a ", StyleBox["unique", FontSlant->"Italic"], " configuration corresponding to a particular interaction matrix, ", StyleBox["J", FontSlant->"Italic"], ", and whether there are restrictions on ", StyleBox["J ", FontSlant->"Italic"], "and", StyleBox[" the initial configuration of the protein", FontSlant->"Italic"], " which speed the relaxation to a unique configuration." }], "Text"] }, Closed]] }, Open ]] }, WindowSize->{961, 847}, WindowMargins->{{4, Automatic}, {Automatic, 1}}, PrintingCopies->1, PrintingPageRange->{1, Automatic}, Magnification->1., FrontEndVersion->"6.0 for Mac OS X x86 (32-bit) (May 21, 2008)", StyleDefinitions->"TutorialBook.nb" ] (* End of Notebook Content *) (* Internal cache information *) (*CellTagsOutline CellTagsIndex->{} *) (*CellTagsIndex CellTagsIndex->{} *) (*NotebookFileOutline Notebook[{ Cell[CellGroupData[{ Cell[590, 23, 88, 2, 97, "Title"], Cell[681, 27, 60, 3, 41, "Subsubtitle"], Cell[CellGroupData[{ Cell[766, 34, 31, 0, 86, "Section"], Cell[800, 36, 913, 18, 95, "Text"], Cell[1716, 56, 532, 11, 59, "Text"], Cell[2251, 69, 360, 6, 59, "Text"], Cell[2614, 77, 211, 4, 41, "Text"], Cell[2828, 83, 133, 4, 24, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[2998, 92, 45, 0, 54, "Section"], Cell[3046, 94, 373, 9, 42, "Text"], Cell[3422, 105, 56, 1, 26, "Input"], Cell[3481, 108, 106, 2, 26, "Input"], Cell[3590, 112, 82, 2, 26, "Input"], Cell[3675, 116, 236, 6, 24, "Text"], Cell[3914, 124, 99, 2, 26, "Input"], Cell[4016, 128, 234, 6, 23, "Text"], Cell[4253, 136, 180, 4, 26, "Input"], Cell[4436, 142, 490, 8, 59, "Text"], Cell[CellGroupData[{ Cell[4951, 154, 56698, 932, 359, "Graphics", Evaluatable->False], Cell[61652, 1088, 194505, 3191, 381, "Graphics", Evaluatable->False] }, Closed]], Cell[256172, 4282, 163, 3, 23, "Text"], Cell[256338, 4287, 793, 23, 64, "Input"], Cell[257134, 4312, 113, 2, 26, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[257284, 4319, 45, 0, 54, "Section"], Cell[257332, 4321, 479, 10, 41, "Text"], Cell[257814, 4333, 104, 3, 23, "Text"], Cell[257921, 4338, 6183, 131, 457, "Input"], Cell[264107, 4471, 96, 2, 26, "Input"], Cell[264206, 4475, 258, 7, 26, "Input"], Cell[264467, 4484, 269, 7, 26, "Input"], Cell[264739, 4493, 272, 7, 26, "Input"], Cell[CellGroupData[{ Cell[265036, 4504, 53, 0, 34, "Subsection"], Cell[265092, 4506, 165, 3, 23, "Text"], Cell[265260, 4511, 552, 16, 46, "Input"], Cell[265815, 4529, 188, 5, 26, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[266040, 4539, 49, 0, 34, "Subsection"], Cell[266092, 4541, 1486, 40, 99, "Input"], Cell[267581, 4583, 197, 4, 26, "Input"], Cell[267781, 4589, 1621, 44, 183, "Input"], Cell[269405, 4635, 247, 6, 26, "Input"], Cell[269655, 4643, 958, 29, 64, "Input"], Cell[270616, 4674, 1665, 45, 183, "Input"], Cell[272284, 4721, 338, 7, 45, "Input"] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell[272671, 4734, 40, 0, 54, "Section"], Cell[272714, 4736, 324, 5, 41, "Text"], Cell[273041, 4743, 1418, 48, 144, "Text"], Cell[274462, 4793, 648, 22, 60, "Text"], Cell[275113, 4817, 70, 0, 23, "Text"], Cell[275186, 4819, 1322, 39, 63, "Input"], Cell[276511, 4860, 415, 12, 26, "Input"], Cell[276929, 4874, 521, 14, 63, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[277487, 4893, 41, 0, 54, "Section"], Cell[277531, 4895, 390, 7, 83, "Text"], Cell[CellGroupData[{ Cell[277946, 4906, 43, 0, 33, "Subsubsection"], Cell[277992, 4908, 5078, 145, 205, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[283107, 5058, 44, 0, 33, "Subsubsection"], Cell[283154, 5060, 2021, 51, 207, "Input"], Cell[285178, 5113, 146, 3, 26, "Input"], Cell[285327, 5118, 89, 2, 26, "Input"], Cell[285419, 5122, 282, 8, 28, "Input"], Cell[285704, 5132, 1953, 51, 152, "Input"], Cell[287660, 5185, 1257, 35, 82, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[288954, 5225, 41, 0, 33, "Subsubsection"], Cell[288998, 5227, 134, 3, 26, "Input"], Cell[289135, 5232, 292, 8, 26, "Input"], Cell[289430, 5242, 464, 13, 45, "Input"] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell[289943, 5261, 37, 0, 54, "Section"], Cell[CellGroupData[{ Cell[290005, 5265, 47, 0, 34, "Subsection"], Cell[290055, 5267, 742, 21, 46, "Input"], Cell[290800, 5290, 91, 2, 26, "Input"], Cell[290894, 5294, 130, 3, 26, "Input"], Cell[291027, 5299, 130, 3, 26, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[291194, 5307, 36, 0, 34, "Subsection"], Cell[291233, 5309, 311, 9, 28, "Input"], Cell[291547, 5320, 118, 3, 26, "Input"], Cell[291668, 5325, 463, 12, 63, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[292168, 5342, 40, 0, 34, "Subsection"], Cell[292211, 5344, 1132, 30, 98, "Input"], Cell[293346, 5376, 163, 5, 26, "Input"], Cell[293512, 5383, 164, 5, 26, "Input"] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell[293725, 5394, 39, 0, 54, "Section"], Cell[293767, 5396, 381, 8, 41, "Text"], Cell[294151, 5406, 204, 6, 23, "Text"], Cell[294358, 5414, 734, 22, 45, "Input"], Cell[295095, 5438, 591, 17, 44, "Input"], Cell[295689, 5457, 513, 17, 26, "Input"], Cell[CellGroupData[{ Cell[296227, 5478, 43, 0, 33, "Subsubsection"], Cell[296273, 5480, 2552, 70, 116, "Input"], Cell[298828, 5552, 910, 26, 80, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[299775, 5583, 33, 0, 33, "Subsubsection"], Cell[299811, 5585, 154, 5, 26, "Input"], Cell[299968, 5592, 520, 15, 45, "Input"] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell[300537, 5613, 42, 0, 54, "Section"], Cell[300582, 5615, 500, 11, 60, "Text"], Cell[301085, 5628, 993, 27, 99, "Input"], Cell[302081, 5657, 284, 8, 45, "Input"], Cell[302368, 5667, 133, 3, 26, "Input"], Cell[302504, 5672, 366, 12, 26, "Input"], Cell[302873, 5686, 114, 3, 26, "Input"], Cell[302990, 5691, 133, 3, 26, "Input"], Cell[303126, 5696, 366, 12, 26, "Input"], Cell[303495, 5710, 114, 3, 26, "Input"], Cell[303612, 5715, 133, 3, 26, "Input"], Cell[303748, 5720, 141, 5, 24, "Text"], Cell[303892, 5727, 374, 11, 28, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[304303, 5743, 51, 0, 54, "Section"], Cell[304357, 5745, 618, 9, 78, "Text"], Cell[CellGroupData[{ Cell[305000, 5758, 51, 0, 33, "Subsubsection"], Cell[305054, 5760, 100, 2, 23, "Text"], Cell[305157, 5764, 628, 17, 79, "Input"], Cell[305788, 5783, 124, 3, 26, "Input"], Cell[305915, 5788, 73, 0, 23, "Text"], Cell[305991, 5790, 544, 17, 26, "Input"], Cell[306538, 5809, 233, 6, 26, "Input"], Cell[306774, 5817, 135, 3, 23, "Text"], Cell[306912, 5822, 1792, 47, 96, "Input"], Cell[308707, 5871, 231, 6, 26, "Input"], Cell[308941, 5879, 115, 3, 23, "Text"] }, Closed]], Cell[CellGroupData[{ Cell[309093, 5887, 44, 0, 33, "Subsubsection"], Cell[309140, 5889, 5041, 130, 331, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[314218, 6024, 33, 0, 33, "Subsubsection"], Cell[314254, 6026, 112, 3, 26, "Input"], Cell[314369, 6031, 131, 3, 26, "Input"], Cell[314503, 6036, 366, 12, 26, "Input"], Cell[314872, 6050, 114, 3, 26, "Input"], Cell[314989, 6055, 133, 3, 26, "Input"], Cell[315125, 6060, 369, 12, 26, "Input"], Cell[315497, 6074, 115, 3, 26, "Input"], Cell[315615, 6079, 134, 3, 26, "Input"], Cell[315752, 6084, 155, 5, 24, "Text"], Cell[315910, 6091, 367, 11, 28, "Input"] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell[316326, 6108, 60, 0, 54, "Section"], Cell[316389, 6110, 208, 9, 23, "Text"], Cell[316600, 6121, 112, 3, 26, "Input"], Cell[316715, 6126, 88, 2, 26, "Input"], Cell[CellGroupData[{ Cell[316828, 6132, 37, 0, 34, "Subsection"], Cell[316868, 6134, 218, 6, 23, "Text"], Cell[317089, 6142, 306, 9, 26, "Input"], Cell[317398, 6153, 96, 2, 26, "Input"], Cell[317497, 6157, 3110, 95, 153, "Input"], Cell[320610, 6254, 158, 4, 26, "Input"], Cell[320771, 6260, 133, 3, 26, "Input"], Cell[320907, 6265, 743, 22, 102, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[321687, 6292, 31, 0, 34, "Subsection"], Cell[321721, 6294, 1533, 43, 117, "Input"], Cell[CellGroupData[{ Cell[323279, 6341, 35, 0, 33, "Subsubsection"], Cell[323317, 6343, 409, 13, 26, "Input"], Cell[323729, 6358, 290, 7, 26, "Input"], Cell[324022, 6367, 290, 7, 26, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[324349, 6379, 35, 0, 33, "Subsubsection"], Cell[324387, 6381, 409, 13, 26, "Input"], Cell[324799, 6396, 290, 7, 26, "Input"], Cell[325092, 6405, 290, 7, 26, "Input"] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell[325431, 6418, 31, 0, 34, "Subsection"], Cell[325465, 6420, 1532, 43, 117, "Input"], Cell[CellGroupData[{ Cell[327022, 6467, 35, 0, 33, "Subsubsection"], Cell[327060, 6469, 409, 13, 26, "Input"], Cell[327472, 6484, 290, 7, 26, "Input"], Cell[327765, 6493, 290, 7, 26, "Input"] }, Closed]], Cell[CellGroupData[{ Cell[328092, 6505, 35, 0, 33, "Subsubsection"], Cell[328130, 6507, 409, 13, 26, "Input"], Cell[328542, 6522, 290, 7, 26, "Input"], Cell[328835, 6531, 290, 7, 26, "Input"] }, Closed]] }, Closed]] }, Closed]], Cell[CellGroupData[{ Cell[329186, 6545, 26, 0, 54, "Section"], Cell[329215, 6547, 937, 20, 119, "Text"] }, Closed]] }, Open ]] } ] *) (* End of internal cache information *)