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{
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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dina-lab3D/CombFold/blob/master/CombFold.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
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IqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgGcIZPOsJW0oUwR27NiR/cyZM/lSUlLyUPDCwYMHi/L/ubNlS52C7OfOncu1b9++MoF/W+d5ihYteqBAgQLJF3goc56/nStTpkyS1M+ePXtyrVq1khV2RUARUAQUAUVAEVAEFAFFQBFQBBQBRcAuAqoEsIuUjXLbtm3Ldvr06YLHjh3Lj1Cf98CBA6X4WXzXrl11jh49Wv7kyZOl+FsZBPi8CPQFeXPTbA5RAvDm4vf5A/+W3s4h+5/iPS3/H3hT0A0c43cnc+XKdbBcuXKrixQpsos+ThYuXHhH+fLl9+fMmfNUoUKFjtepU0cVBDbmLNGKwIM5zp49mws+yQFf5OCnKJjk/4XHrBw5clzgPc97LuQ9C9+chZ9E2aSPIqAIKAKKgCKgCCgCioAioAgkMAKqBIhgcvfs2ZOdt/SpU6eKHTp0qNKGDRs68/+VkpKSyiOoF0CoL0bzheQnbz6Efnki6PHiqiL8i0KA355G2DvKe4B/H8OCYHvJkiXXIdjtqVmz5uJSpUrtx6LgcOXKlc971rk2FDMEdu7cmQt+K3b8+PEiKJYK8rPsiRMnSvOzHPxXCiVAQXgjHz/ziTJKlAKiCIDgbKIAgC/O8SYH3lP8PIFS6Tg/j+TNm/cASqQ9KJV28TMp8B5FwXQ2ZgPWjhUBRUARUAQUAUVAEVAEFAFFwDMEvJNIPSPJ7Ibmzp0rQlaJ/fv319u7d28LzPqbcftfhbc4glRxhPw8CPwxHQTKgHO8YkVwgndX8eLFN+TLl29NwYIF11asWHEpAt3u3LlzJ6EgUKVATGfKXudYkuRB2VQWfqt++PDhOiiZah85cqQ+An85XEoKw3MFeEXJJBYmOUMVTRkpnX71MMGv5OKfKWJ5Au+cpN4JFAKHUR7t5N3Eu11e3FFWwUM7qlatesQe9VpKEVAEFAFFQBFQBBQBRUARUARMQkCVADZmY86cOSURuMpv2rSpK4JYB4SkGtzAVuRWtQQCf6rQ5eUNvw2SHBWBXhH2UrgNPoKiYge3u6uhfT3KgfnNmjWb36RJkwOOGtTCviOAK0nOVatW1du6dWt7hP4m3PI34ra/MnMpPCdCfyrP+cl3oiCQN8A/EpviGMqkPbigLMPaZCnuA4urV6++EmXSPt8B0Q4UAUVAEXCAAIrTPMThSV0r03nsnH0yc4/KqH7w98G68jO0nfOs32dQxIsFX5Z+tm/fLgrsVAu1NEDIv9P+zo6rWrBOej9Df5de+0JCcK5C5yz0d8EyoeSmpSvcv4N1LyoneyyWmqpYj9MvAuvMQpyPxL03lHfkNjCU1zJaE8LxdnrfR3o8mLZ9CSd2AQvPFM5q4lacJR8uzLLxlgxczqbGVwsAIbgG5yjtd5nRXNnFML31K8gb0r+8F7hQO2G3Qb/K2dkI/erb+HbnzZtXdenSpb259e/JIt2Y2/7KCM/5hHA/hS8/gQkKdvSRjFJgL2bf87nhnV+jRo3pCHPrateurRuRnxMQpu3ly5dXQfjvunv37g4I/p2Sk5OrBG75rVhbmKSegP6/UiAFa5I9+fPnX4ZSaWmFChXmt2jRYiaHW+WfGPKPdq0IKAKWtXbt2mJTp059GGV9R9bPwoHDubg0SXyd4CEwrdAn/87o8JYerMEDfmid4O/kcJmeEHCSdXNJt27dXmvQoMHOrDpXs2bNaoZV5UPsaXXAIG9gXs4EfqYVwlMPzIE3FLJQ4Sl0LkLnIPSgn/b/g22FHviDfYX2GRQc0lMOpG0jo3+npSnIM9JPCtalu+vVqzf80ksvnZBVeSJex80lYasFCxbcwXm6boCXQwXNtPwayku/CYMZ8Hd6iqu0CqyMvgH5vcQVO5knT55FvXr1eo04YYfiFeNI6J44ceIQztU3owwpGmhH9oHgWh9cE+RP6e0Hab/9tOtQ2vlIq4BMS7r8XdpI4T1YtmzZ0ddff/2XkYwv0ro5I20g0eovW7asEsJ/N8yuW06ePLkpgn+TwCHCgonifrght8d52YCrcLss7wB8zHeg8Fjy0UcfTatfv/4MhLrNlSpVyvK3FdGa8NmzZzdftGjR9WPHjhXBvw5KpyIi9JvGc8I/KMIEltxsepUJeFkZLWt/bt12obyYP3LkyPEdO3YcweJ2NFrYaT+KgCKgCIQiwL5dgj28G3tbJxOUp0HaRInKobw4a/xwfpdllQAoZypx5ujJXlJBOdeysFjZR9DoJWChSoA4Ywh4uRxrTS8UOdVMuxwUCxPcOvOyHn4ErFlSCcB3VYO1ph1W0AUMY61jTM26WNMU/1KtRwiOHz++8eLFiwePGTOmFQtySzbrkvJBm3SA8GioFzUTGJ8EkKsl75YtWy4hneFmFo/lQ4cOHTlkyJDv/ehX2/wVATTIjdAk3/bjjz/2YxOpxa9Sg0cGBG2jYQpRKGXne6nIgbviL7/80pVbuGtH8HTq1OkrlAHHjB6EEqcIKAIJhwBrE1tb9lRFqkl7uCgBoKdQwAw+4XC3OyCJXyM4mDQ3dmn3o1yAV1OtTPWJLwQkDhPzV1h42TQlgCApWcdMpCtas8z4ZTPIb+BaU1DcyaOFQ0b9xJyAWAMwZcqUxpim3crtfyc26IZ8LHn89rWO9ZjT6z9kkSiCMqApWDRev359m2eeeebKRo0afXPNNdeoMsDDiVuxYkW1adOm3fb9999fDvYNpel4X6iFfvimOFrnnigDWmAZMPCbb775snPnzt+QoSLL+qR5yDbalCKgCNhDQEzxU02WDHzk3BXb6MGxB0XmxtT5iQU6JvNrLPCIpz7FxNtkWSq9uBvxhK8XtJro+p5RbBIvxmu7DZMZ1/YgnBYklV/en3/+uTNC7oDp06d3RRtTTz7ieBfCnOKQWXm5SeHvdbiRrr1y5cqOTz/99I2tWrX6FFPv8Qh04QKZeElKQrVFwL9sM2fOvByz+fsRmMVfNU9CDfD/KzOKYU3SH/eaFnxv3bCyea158+a/JNpYdTyKgCJgJAKyf5m6T8nhL6sLwEHfWCOZJ0ZEmcqvMYIjbroNxv8wlWAjhM0YgqNZ0DIBP0spAYjun5tgNJ0+//zzm7jt7oqAWzlWgn8wNVtwbgIB+yRoUXAjELPwHGnpiwG9QkcN/Glq4LPeauHChXNxmfiUrAIzNGaAs2WN4CSVvvzyy7vwn78BPI3zH3M2Gnul4Z2yBDi8ffTo0Y1RfLyLVcBXpUuXPmWvtpZSBBQBRcA1ArKfmviEBqMykb5o0aSH82ghrf1kdQRMvAmP1pyock2VAJYlkf4xTb6XABGXE523FgqAqHwUEpiDhx/nkxD0j/Am8f/HiMh/ABok7ZoUOIM1wil+J4E7JHKlmIbnIC1hUeITFOSfQdPBPAhUJfh3AcpLqrii1Jfc8Pn4/9x+Kwigtyp9V8GKoo0oAwhi92HdunUXkFVAg8CFWc7GjRvX49tvv30Yv/+eKAAkGnKWecQni8G2XrJkSUWyHjRAGfJ248aNN2QZAHSgioAiEG0E5OAn0eZNfaJy/jB18AG69HD+/ycovcwUhk+fkheCgMnfc1b/zkxVNmaURSKqH1bCWwIgeJTCN7kXaSJuQ6DugALANwFMJH1mLxlBbx//ewDh5wQp+ETY31+yZMk15FjfybsPGg4VK1ZsP8JgSsAi4DxlzhUvXlz+nfrBShALIq/nQRGQK0S4z4k5eVHaLnjw4MHKKARKUa4AZcrw7+opKSlFiSZflvLyewlUIgoEr59s0F8NMqti1t4MM+9JM2bMeJsb3rVed5Qo7Q0bNuwG4k48znw3jYeAf37hDm+WRwlwD1YBdX744YdXevfu/aNffWm7ioAikKURSE2RlaURMHvw6aX8M5tipU4RSB8B03nZdPr85itTlSBGzEvCKgEw/c9L1PU+BF67FiG5OwJYKa8FsIAJ/0mE8m0oGNYWKVJkG4LOfm7G55cpU2YrQngKvzvO706To9NN/vT0bjL2B74YSSdjIVRlY3x59+/fXwAlQP5t27ZVIP1QaVEK8Dak7wYUq8zYS6AcyO3V1yY+AqS3qEuflUhvV4tg8B8xxpnc8O7zqo94b2f79u35STN5BzffD6E4qR7v4/GCfr7D/PBMf3imLG4lzw4cOPBbL9rVNhQBRUARCEFAFPKmKgGMOPzFmFsUg99PgKnCSoxZxfjuTZ+3rP6tmTw/MactIZUACBjVEP7v3rt371UIwdURwDxdRYgnsA+hfxO3+nsLFiz4S7ly5ebWrl17adGiRQ+LiX/16tWjdvigb2Ei8bEO+llvCw4WH/4iWEFI6r8qpP1rQ77MBghhTVBOlEaGz+UFKIy3AHlS++Ee0Hjjxo0/fvHFFyO6d+8+CbpS3Rqy6kPQyRIIuffu2rXrPjAvl1VxSG/cYuWC4qwFPPNPXHTyXXnllV8oPoqAIqAIeIiAKAFMNQOVYcb88Och1tpU5Ahk9eBtkSMYuxbiwZUjq683ps5RzOcloZQA5LjPM3Xq1Gt4ryNNWReEL8/yroofP2vMVn4uqFq16pT69evPx4f/SNOmTYM387FbgjLouUWLFmJ9sDDwfkNQxLKkbmu2c+fOHuDTHgWJpEQsFCnhItRhFSB54oeQ+q4tLgsNli5d+rHJ2EQ65szqo3DJ/9133z1AAMD7wbiUn33Fa9vCM1jSNCB7wF+/+uqrfNddd90H8ToWpVsRUASMQ8D026+YH/5iPGNZffzpwW+yX3mM2UW7jxCBrPy9ZeWxh2WbhFECLFiwoDKB1+4n8N8NCBgVPDL9P4egIv79i8qWLTsTpcKqNm3azEK4TQqLrIEF2rZtuweyJnBLPXnz5s2158+fPwjXgfYoAmqDWRX+FhE/0EaOPHny1ME14VFiMFQkXsCrpIXbZCAUvpLE2O/ACuUeeFAVAJkgLbEu+L5qr169+onPPvvs/E033fSRrxOjjSsCikBWQsBUocp0BUVW4hFTxqqWAKbMRGLSoYJwYs5rxKOKSOiLuHePGsCkuBeR6v+AQNEdwSu/B82KL/46hP+5BPSb1aFDh8ktW7bc6UG7RjRRq1YtMdVfJe9PP/1UjpgClcmecD3/7s5bN1JlAHNQBheB24iIX+v1118f0aNHj28bNGgQl4oTpxNGEMBrVq5ceR8KkbJO62bF8oGgl7VxJbmPYIGbCRb4U1bEQcesCCgCWQYBPZBnmal2NNBgFihHlbSwEQiYqnA0AhwlIl0EjFD8xbUSAH/rbBMmTLiLDAD3InQ1iSRFXiDI3yna2Mg7B7/+UTfffPPERGfebt267WaM8s4jjkJ7bu+HgEVP4ghUl5t9t+MHwwLETeizb9++uuPHj6+KG8LrDRs2POC2vXioBy+2I33iQ4y9TjzQaxiNLbBMeXTNmjVbSTuZ5axHDJsLJUcRUAT8Q0AtAfzDNl5bFoFAlQDxOXuq1DN73nR+MpmfuFUCYNJeeOjQoX8h2N0N3DxHFHgNoTcZ3+2FBBCcVqpUqTncRk6pVKmSyTmGffnkLr300jk0PAe3ii4IY1cSN+ASlAE1wNeVMkCUMlSteuTIkbswkc9LvIBXGjVqJAqHhHu4/S+D5cPd8FI78XfXxzkCZA3ojiLlEVx6HiVdZpb7/pwjpjUUAUUgAwRMP/iZTp8yVvQR0Nvk6GOuPWYNBEwNDBjzbz4ulQDTpk2rQ+C1J1EAXIXAVdAtD8vtv6T3I6r/mCZNmnzQq1evZW7bSqR6gwYNms54po8cObInsQMuO3r06CVgJcoAV9KtuAegCLiTOSs7atSod6+44orZiYSXjIWYFFcdPny4v9eZKBINp8zGg9Iof1JS0iAURssp935WGruOVRFQBLIMAmoJ8Gt2BFWE/H+Wj7kwkGW+Pu8HqnzsPaZetmhylhgvx+mqLVdCnauePKqEgNCR28L3UADc7FYBIML/2bNnD3H7P54b/38MGDDgb6oA+P0EDR48eMoTTzzxIIEQ7yU2wlsoTCSwoKuHuSqKe8C1ZCj4L0HgriJDgSvrAled+1yJuArNN2zYcCsKgJI+d5XwzcNj5VE8XU96S3WpSPjZ1gEqAr4hYPLBXAVgVQCkZXyT+dW3jzRBGo6H7zkr81dWHnvYTyyuLAHwLe89ffr0/yMdXeuwI8ugAObtyUT5n4/wP7Z27dpjunTpstZtW1mlHnncfyTt3QyCL65AQLsZ/Jq6CcDITW8u5q490eALcWteDsH545o1a0rqxbh9SEtZcPjw4bcxgGZxOwiDCBdXCgJVNieGRH/I0m/ToLlRUhQBRcATBOJBaPBkoNqIIwT0xtIRXFrYJgJZXQg2dfxGWP/EjRLgk08+uXPmzJkPcdvawCbjX1RMbv/xOd5RrFixMaT5e6tr164r3bSTVetUrFhRfLTfmzJlyuy1a9desXXr1gHMhSgD8jjFhHqNSKH3MEqdvCgC3o5nRQDCajt82PuQGtGID9rpXJhYHmVR4d27d3clhsQIYkhsN5FGpUkRUASMRsBkQdtk2qI1qYrBxUgLHqoEiBb3eduPqUKmt6OM39Z0fjKZu7hQAqAAuBth8WmEx8pu+BAT4xRuGBfVqVPnCxQAI+rXr7/PTTtax7J69uy5ElP+VaR0+3bZsmV3IqzdgGVAKafYUKcaioAHCKaXgyCPb5G28IjTNmJdfvv27flwbbgWvqwRa1oSqX+xBsDdp9WqVaskZeWniTQ2HYsioAhEBQGTD34qAKs7QNqPQJUAUVkWfOnE5LVGBqzrjWXpJV0GrG+8EuDjjz++DwXAnxAMKrn5fDH/P4wJ+oROnTq9iun/AjdtaJ2LEahQoYIsKqsQ3v/GbT6y/N67RKh3ihMxGSrt37//PhQBedatW/ca7hmHnbYRy/JLly5td/z48S4aDND7WeB7L7tt27Y+ZKmYSMrAvd73oC0qAopAAiNg+sHXdMEhgVnD2KEpTxg7NZkSZvpaE5+oeke1KgAywdLowICiAODG+SlRAEi6OacPwf/WFSpU6O2rr776YVUAOEUvfHlu74/269fvf6VLl/4r6QTH4HJxInyti0vgTlDx4MGDd6EIeAQ3g+JO68ey/OLFiwej/FArAB8mQawBiBvRCkVAfR+a1yYVAUUgsREw/WCe1QU+0+cn2l+H4hFtxL3rz/RvOavzlnPh0TveML4lY5UAuADciwJALAAqulAACNPPwez/708++eSfuWHeb/xMxCmBKAJOPPzww0OvvfbaewoXLvw+lhdJTofCHJfDr/52rAoe4ua3mNP6sSg/e/bsWsSYaOGCN2NBroWCJoX3eOCV+A7GP9Ba7sCBA42MJ1QJVAQUAdMQMPlgntUP5cIrJs+Pabys9JiNgOm8bDp9fs+uqUoAI/YBI90BUADcjgvAk24sABAcTnO7PJ2b/78R/G+u39yl7f+KAGkEdxPI7VlSOJ49evTorcxdCSfYUL48FgG3km5PXAJedVI3FmVXrlzZGT6rG4u+7fbJt3BMYmHgrrC2TJkyq7CK2YvS4jwuDGVw4ahHFH5JwydZDYy0wJAMFCgB6hOEslCVKlXiOouE3TnTcoqAIpAlEMjqQeCyumCSJZg8iwzSCGEuE6yz+rcm4zdVERDzT8Q4JQBm4b1mzZolWQCqOEWHAIDHqTce///nUAAscVpfy0eGAJHc96MIeG7SpEnHjxw5cpcI9k5aROirRCrC+99///1Td95557tO6kaz7K5du3K8/fbb7Rlf4Wj266QvFACbCxYsOKJz587vtm3bdlN6dbFmqDlnzpw7CMJ3NcoBxzEdnNDjpqy4BKAEaICVSBnqqxLADYhaRxHImgjowdzsec/qgkna2VE8zObXzKjTuTN77kyen5jTZpQSYMKECe1nzJjxf5JCzilPiRl6vnz5vrvqqquewvx/j9P6Wt4bBFAEHKSlf7z22muH9+3bdx+CfR0nJvMEC6y5efPmhz/88MNTt99++2feUOVtK7gs1GJMjnnUWyoybg1l2KHixYt/RiyMfxHE8WxGJTt06LCBOAz/N3r06LxYB9zOmApGi0Y7/QjfEGuiCnxUj/Ib7NSJ5zJkm2DI2SzSccZ8Y4hnHE2kHUVWNvaoHLgQ5UFBl409TizWzpYoUSLu5nrPnj25zpw5k5u1+ky5cuVSTMQbmrKkEoBAu7lYM/PCW+dIW3uKfcBU/jJ9fqLN1qbOk2McyBiVnXhcOWV9K1++fFa3eHGMX6JXSEpKysYZNbukbWetysP/55C1qlSpUhmeVSPERHkwEwCNUQJg/l/o3Xff/Uv+/PnbOJlwYSQOzrsxdf7msssu+48qAJyg51/Zhx566PUPPvhgP+kEb+fw2405sh1/gmB7dRFOH/j++++3XHrppTP8o9JdywhrTalZ011t/2uB9YIePXq8l5kCIEgFaTNPgPPoRYsWteNbau0/dc564CBRhkNFU4SoCRxo/doknBFlszR058DlogC0F+YtgVVDOVxlSiMIFkKIKsjPAoE3PxthTqxLUlt+6qmnLjCHZ/gOTiJoneI9wSaZxBq3h7gb+3gPFSlS5Dg/T/Aeq1y58jmbJGkxnxFgznPj1lQSd5vyzHdNftZGwVYWpWgR5jw/3efgzca3dp55Pf3vf//7aIECBfaVLFlyCy47awmyup3D0A4OzzEVrlm386B8E7ehSrx1GFMNGQf8WpCx5BZtFWO48Mwzz5yGN4+h1DjK3r0P2tcxjo1ly5bdC48eYhxxEX/EZ7ZI27yYproW+th/CmIxV405qXXixInSzEspfpbkZ0nmpihriSiaLrB+nHjuuecOMS8HuSA5JC//f4A1Y3elSpU2FStW7AD8Fqs11fX4ozxX0epO8DBSWGEdy46wlh8eKyhrG/+uxH5W/uTJkyVkL+PNLy/rWz5+5n399ddzBC5+LshehkWf7GWneI+z5h0nW9ch4UFe2c8OsE4clX0My8XjKMDjcb1QhVYGXwn7YWGsgmU/rIvyuC57SmXkvOLwUyHefKxVqXuJnHf+9a9/yR5yiP1wP+8BWauKFi26mbVqLT8PsLe4XTOM/K6itbCE68cIJQDm1TmxAniIw0SncASH/l0UADDRhnr16r3TsWPHj/AbFn9yfQxB4I477viKed0wb968v6II6MdmYFsRwEbRguj7f5g/f/621q1bbzFkSKlksLA1YdEqahJNQVrA+QTC//TGjRvvsksfZecsW7ZsLZu4cUoAcM7NgaM+B44ijEesTIx9Nm3aVICNriICVD05oJPdpCquFuISU5xxFIP9i/FTBMFc/JS1V97sHIzku0jrsyYb13nm5BzvWcZ/BhxOSpwH3iO8R3mTqHuQg/96hK6VCF9b2Cj3NWjQ4JCxIIUQxoEgZzpWQk43+iBumdVLi23w0Bb0FQz+PZub2wjmOx97WGXmvzXxbNpx6KktrlCMrbS4DPFT5tviEPy7aeEgZCUnJ5/jYH1y1apVh9jPdqDsWTNs2LBZ1apVm8e3vJmDcXI05hPhMjfCZQ3G0e6jjz5qD8+JxVNFxiD8W1B4FqWcJW/oA39awqcIoac57B1dvnz5Psaxi0P95qFDh85H0TgXZcBWxnIqGuMI9GHywdz2PhjEC+u4ovBYHeamC+tKW+amBvNQmr8XYF7yBnks7fxQzkJ4E52AKJVO8/NUYP3YAa+v/OabbxahRFyMEmoX/BbN9dXk+Ykim/7WVUSKIS8JZl3ORWaeUrIWsJ81+Oqrr2rw7yrwUln4rATrQRF+Ct/lCexlqYrNjNY4oS1w63uB9U7WiRQsVpIlm1SAF2U/E0vePe+8844oEdewn23ESmovl3oHvBybtuU/ApyPC8I31Uj73ZO1qjMXHzXgmZKBM5CsValEcHa5iBgUSGIZIFmhzgcCWct5R84y2+CHZWPGjJmHQmCxKMnZE53sJSZfkjg973g+gTFXAoh/9dixYx/esmXLfRySCtgdoSwqvKIAeIk0de+j1TZ5ou0OK+HK9e3bd8EPP/zwV/zPxQSor11FAAtFdg6WvWbOnHkPgeH+bUpgOBa3bGxUldIR2oyYOzDezmFuphNiwPb0f//73wMswucFdyd1/S4reiM2kYoISrI2RPOQamtoCG5FRfDnwNSew1J7hP5GktFEDkq8ebghtdVOOoVkHrIHlAV5Ahtn0XTKXRABUoQv5n4fQsDmF198cRmb5RwO96sw195TtWrVWN32ZTj24cOHX/btt9/2EMWIjJNX1m+5BZJX/j9VCRJ4gxvlb8J6SMOhAn6wXNqNNRVLXjmsph5YA2/wd8F98PgXX3wx7YYbbhhmZ9LYswqvX7++2YgRIwZx8OkI9tU52BRFmW2nemoZmVdeoakQ9QvxswoHoQ7EVhmMYk6UOj9Mnjx5JFlYVjCPTg4+tmlAwMzHga35119/PRhe6iLj4C1il3cDvCm3f6Lgyk/dsvxsLIe61atXX0cQ1a0c4qazz49jv55Xo0aNI7aJc18wIYRM9s1mzM8lrC2dUC7Vg7/KgW9euzwW4C/hd2FKeSWOjcRYqc1huzvC2JGFCxfKjeyazz//fHrdunUntWrVapV72G3X1Ns521D5XxBFZm54oTzn8SajRo1qz7/bcf6qJgGe4aF8sg/b5bmMqE3VEiD7BfY0WSskILGcpVIfLjAs9tEU0gIf4f8PcRG07Y033pjPPjYToW8Fe9lu3pgLTemMz0SaQsmMSlA8zunFOA91Hzly5KXsh+0hoDLrFWxjbz8MrFVCt+zLeQOv8EhNeLM7bR74+eefd7MnLmLP/QkF0Y9NmjTZaePrMO78Y4PmqBWJuRKAQIC3wTz3ogAo53DU29iwXkXI/FAVAA6Ri3Lx3r17L+Eg+wxB6PKwuPdM5/YvXYo47BTmUHoD1gRyq/16lMlOtzs2SbnZrWACLenRgCC4lxu3dAMBZkYz5lZ7uDESIcO2Ii4aGAivYFJfDNrEEsCIB6E/B8JNA27lenz33XdtUVLUEcGJg1IhuxuehwPJRr8F5KVNWUObICz0xRJhNzey69gwFyBwL+QQ9QuC5Cb+HfPDNwe9XAg13bFskCwiJsWhOI4JouCTqRKAA0l23Ge6MvfXoHzpztzX4MDq2UFLeJ7Dk/B7EzCqR8aU3r/88st41tBvevXqtchD3rHI5tKWQ/+1rLOXMBe14V/PlICBcRSF3qJg1ojvpj9uXj9MmTLli549e872chwZtGXq4Tys6ffSpUur8Q4hbe5VXHbUEsGfx1PIRLBD0SN8VoRb3joc4HuQlvmqL7/88msO199jUeR4H3FAYEIoaRyMN1zRkxSIisVPKCG44ZYhxlFr1rKuWAC0QQEpvFZaLErsKgHDDczu32W9oN/cvKWQB0rB93VYM7qjkLiRf69GAfCz3AajFFhONirblo52+4+gnKnrTHBInu1N6WHE3pEDhXUP9pHr4aGerCsV0rN6iwBfK7BWlaSNkigvG9FPX/aS2SgDvmGtmoRCILMLIr0gzgT8mCoB0HAXwVzkGg481Z0wCBrKI5gXDkUB8G68+Qk7GWcileXwupQF/AVu9stx0G5gd2xsRBXhk3u4RVo3YMCAiXbr+VWOQ3lpNidHWQ/8oiVtu2Idw2Ip/nVHnfbJd8Reu1MOIkYpAWQcHA6KEkxGbhdXOB2Xl+UR+HKy2bVks+uPMmgACpdaInzHQPDPdFgBC4SqFKrKBt0Vug9wwF+PIDkZAWwK7h9L8AWOmc+53Aix5hfmLejAQ8jLqUy3Lb6fgvgiZnpgYv6rcmC+FcX1dTRSy++DcuBQ3IR5rM3a2Z2AqR8T0HMECvCIXD7ghXIoZYdw03wdfNyQQ9vvfRU8RJxxZBdlCQe4u6ZPn96ZDDCft2nT5nMnbksOyTH5YB60dPndkHDJyIv73I0I/0Mwm27JtMiNqe9P4JBdEF5ohxVKPRR1fbDUGYZVwCisihzvJzYIjrkyMhyNYHFWbqQD5eT7CPrBhLoTyZ+D/w61XJL/D7obSZmgNVKo61dQGXSKM+16gulFTbBF+C/HPPckHlBfFHRdWJLLi+Bv0l4WUApAVo7KrM2V4cnuWAnsge7lWK1MQPCbxBpiSsBgk9eboOVbOJZ3/HcuGqpzHrqHebmSOaru8zaSSl/AKqUMCqtBS5YsaUfffeDjYS1btpyIoii9dSUe40w4ngu3FWKmBOAWrSiC3d/40B3FAWBRPomv4dhLLrnkDVUAuJ322NQbOHDgDwQFeROh/o9sNiKk2Hq4AamHyeIjCxYs2MShZJ2tSj4Vwtxb/DCL+tR8RM2KEgAh5iCCiWMBj+A8YlYlafhKRUSED5Xlhh3hwamlkKeUcGPaWsy+OTD157AmN/+50vq0edqhR40FhMjyHGjLI7i2QnlxNTc/kxEy5AA1Fb/LWGnJPbtx9giq1GYkSnFG7ZH6tDO4PYxVSm9R/ti1aPKCPngtH993G/bNyigiG2Id8H63bt2Wu2kbXm7F3vsH1rJ+8EfxaPKxnOgZRwNcKf6ItUpdFBEvtG/fPhrm526g8quOHEp/JzRMmzatDYL3fawx/eCtktE4UKcdoPA0+0dReKMPMXkaoRhuhtXLWy1atPB63zXaEkD2UtbGybjWfQMmJwI4Bc/LoW5HoQqBoFAf+ncRwEIVAMH/lyalnKy/Kewph3DLW+oXwwXb5exVgjhL/bmQuZR1pDO/Lx0LPnM6zoBVkShiKuEaWIk9rD3C3xUoE6fUr19/LIrRX5y26WF5kxUAMkxfLAFYrzpgyf0kyspL5JwRbYW+9MdbjrPhjfB0a3i7KzR9Tnr4tJdFsqfLmdi1b6aHvJK2qZjzTkyUAGghC8M8j2LScQuTaHtiJLoNwUOmDB48+AlMW3f7ODHatE8IXH755e+iOczDAv5HJy4gCDGdMYd9BG3w45g2Bzdln6jMuFkOR+JPGZXbGaeDk4MLQcUOcqh37AOFdcZ+OexIG6Y90JUXE3dRvkT9wey/LBvLzfiiXYUCsjHrVVwI/2mBChyiJAtBY0ws6yNoDMSffezcuXM/adu2rSthMsLJMPImECVAumsLAdQGI7D+EfxapQ2MFyEOtqsH5rAc38JtWAXU4SD/MopVR9ZRuIYM4Kb5UW4eO7NOXBzhzzYlkRUMjKM4ypTrMUwpS8yYf+MyNiuyVn9X22QhUw6kF/E/frTXoth5nDWmRaz4KxTBwIG+AlZEd02dOrU8N35PY7Xh5a2ryfMjUBwlBsckMk596DFfxqw5+KsZSqZ7sKobCBFlTeAzN2AEhL9i3AT3QLHdlv3sMjJRfdulS5dPkAv2u2kzwet4rgRgvbqac5GsV62iqUROb54CfFxHUpKzLwqPvz1o0KBvQ8qKEkCsXG3LmgnODxcNLya3MRyoB/Dh3sTHXNQJ2Ez2z506dZI0gKoAcAKcQWWJ/Hr+nnvueRVB/m2037YDRIkgiAa4Lx/55bEcDv7fZUUXFUsaMusbq4mjbvy+A8KPLJTGPaIoRNtbVnyxo0kcAkoHNpRXCUjzJ+ZcDuepUd7j+QmYWOYUP2NMzB9AAHubiPZ/4MZPAoZF84mVBUJmY5Tc6ofTFiDC/e34Z/8DzFpF+7Yjg0NPftbOXphC/p0sAtfYnTQi/t+MW8jfJGWr3MjbredXOfmuUUb0mTFjxr/41tr51Y+B7YqS9jdtK4qZIfDXM4E1xihyYZN87HlXMD9/QyH6WxA3D4g0T9t88aDOS1o7D8YZ8yZk3yTg6c244byKAuB2vruyJqxjkQIT2MsKsIa05VLpaSz13sJV67JI23VR33RedjGkjKsQz2cIbnF/F4W4SXzEWiW80FMUlriL3BIyAlG6HvcUhARqLKqHasGNYA7FUQBcCvNUcYIj5Rdj5vHnzp07z3VST8uaiQBa23cxQR/FQmJbGGDRr8Lt5a34sTaN1ai4vRJz+ZgfoNMbP4fIc5hyurKSQPg5TZtGHnpks8fkrDgLfFQ0udx+5SFWyYMIJ29w23CNxCSId+E/Lb/IeMSqAWGyPWZ0zzDeN7AKaByl7ypsYLQo0ZG2m6Mo0S66SeLwfAemp38Er/om8YDQwtrZBsHsKYTI68Ph9d57793LrdljrBEtTRpH4FavC9/anzHprB1uHA7/bqrG7hzjTrXW4kB9OwfqP/PvBibNSyjO0JYT0/HBWOL9CXcUidbtxWO64JSdb8XIfd4J+PjPV0aJ+SJr2L/FmpK5jPsxpbeXyR7NJdFgFKMvvPLKK89hbVzCCU5ZoKwnayEWANegSH4GPqpn4nolNIkyFTnhD1iH3C3zyu/Ecjfqsq5Nnor5Ohh1YNB492dD6epEg8RBdQcKgCcRHKfZBFaLGY4AkYf3NW/e/BWEz+lOSIUXOqDRfgjNr6QWi/rDRmqsFQBgiBLAlSCP8CORiR3HEojGBMjCToaAwmDvu/sSh9xS3Iy/IIIx/TY1caPzEvPA+EqhYLmOLByvYGIerZuUmG9+6eB4kPXotyjD4jfLgedebhhqm8oHHHiaoAh4hLm7JCO+gJ9vw9Ljfv7eyEve8aqtALa9iVXwFJcE4m7lxWMif6WOi/EWwAqnOlYc9xGg8XHMaeuayl/BiZDsBFzeXIdrwJ0eWWQZOz+BMUf9bOwF04e2gdVkY9yYXsGK7j7O2xVM57FIxx8YXy3ki/uwCngVpXbDSNvU+v8fAeLhXIL89jS8VMd0XOCFZgRZfeiFF174FEvOS1m/JLC0PukgENWFDq1keXy6B7Hp2Z4QTBdPc2P8IwqAKTqDiYUAaaKWowj4L6OynYpI3AK4ne2FS4n4tUX94dAdldtoNwODtrMoAVyZ9PNNBvOzu+na9zoof/Iw7/YSzrqkhtuDstyq/od0WbfCZzGJQeCSdC+qiYKlO8E3n8Uq4AEO+r5Gi6cvE2MCHA26A2D+3Anf+T/yXTT1Aly/2pCDL3tkS4K3PU7Mgt9ZcsDP16LQ+oME4/OLBi/aZRy5UERdjiLgDi/aM7UNmS/Wsfoosj9AwfR3UQCYSmtauqC1GLx03cqVK1vHC80R0OnJzWkE/UdUVXyjWcNeQHE+CJ7zdd+MiFAfKksgYS4NriOQ6xsIrn196CLLNYmiuQJKlWcQpqNlLegFxvWwIL0ey06JPRdVWdcL4qPVRtSA4YatILcVj6Glk3zEtsbH4eYCN5Q/XXXVVU/YqqCF4g6Bfv36TSIK76dsVhKZ3tZD2Qrw0yAWJkcuJbYaz6QQi4kwrt/CUSRknsWP0VWuYQ4Kpppop+IBfTkw9/PNEgBzyQrcHjyLAkBMq03KXR8JPziuy9pcn1zhT4DFYwTaMZnXHY8tXAVxpyHw7Cl8CisipD2D0NOGOvY2q3CN+/h3ESz5NrrgJvUIiqyiwa4w3+7IGnmfWAvEwy2gxAhC+SRm5608gstUQS4PY63GnBiXiSUz3IWHeBvxfVyHZYlxqWQ94plgM76lVfOYzt81x9rVim/oRb773vHw3fuBh5wXkB+6cFn0PHECrvSjj5A2TbdqiXj4KGf/CqYdI24o+g3kNPwbiDnvRO2Ag5lfO0zgLpNUR3b4QKKUkwpwKvnlnyJNi0b8tANanJbp27fvGyh7RkO+rQ9ClEhE6e/y448/3hLNIXPQljRdJmvVz7Pg2Y6xEIodGl6pZwv/aGL+24ksW7bcaPd9wR6T3CocFP6FAuBa8LO1PsUCg2j1yfdVGRO6+0ePHv0QQplfihfjeE2sPzCdrUOwxDfAoBt4x40SRIJWEvSrNzeAtwqfIPwXxzrgEQmaZfgh6CK2ZhxNscC4A/7zwnfZVCVAtD5lz/thfrKTZrQ/1gAdPG/crAbjUgmAu0Y7zkUvAmX3ePru/Zj6oNKKdfDvZCMb4EcfcdJmRHst6WQHsy9eHidjjScyI5oXrwYaFSUA/tv5CX5zM6aWNewSjsC1l5zwz5PLeqndOlouPhEgrcuhHj16/CslJWWh3RGgTCrCwjSQDa+93TqRlhNzdJQARqYHlLFB23mEFzHrd/xQT8yzTTTRDo4lD0oAzwV01qZCaLkfwWzsKjDwvH3HE2FAhcDhqSKB5O7HnPIO8rn7sU8YJ6Ax7irccr5JNHSJixA3CoAgyyCglccP8vYvv/zyCdI3vYyiVPI3x9042Ad6oAjoYsCnoCSkgwAuZzUQrAbBa5Gul8atASHDjTslwMKFC+uQBvBfrGOds7oCIDiPARwaEnT0L1hHdNIP2jkCKJSvplZWc490DpS7GjFXBPhxuPsdFDBRV9wAujlwA7AwEZ9dt25dzQTgjrHirha5ytdWqVLlK0y4kuwQH/CtbESgkuvwU4xKkEAOp7lFCWDwBhupEiDmC1JGcw/m+SU4oB3ecFKGQ9Mt5F2/VtLLOKmX6GWFx1mvqxJl90Ful6/wYbxGCgAo+koY/H1nOg1CN7EzGmDZ8jzm2jejKI1LnhZlDHzX2wee0yY9QED4DKuTFihQq3rQnMlNGLlGpQeYuGdgBSDBbLubDGgsaBO5AxfS1rhLPb148eKasaAhxn26PteRtaUT5664siaLMdZx173vSgDM+nKhhbsVc+/ydtARNwD8mjd36tTpxQoVKtj2E7fTtpYxG4E+ffq8jwvIROEBO4/ccmEN0BfTxKhoeDGtlVu1vHZoi1EZ1+4AHB7ECsAe8DEYHPQVYDMq6mXXmH13IrbELbTtVURyL8mLeVsBi4D6WHE9QFAgryPLG3nAjlcFQJBZhH72z2ysjTHnnwgIyIlrTnMUvBUiaEOr+ogACqZaKN8j8RE2dq8Jgc3INSq9acWa7TEsfy6L9/XLL5YVRQAK0h4ES3yU4OSRWrCkJTNu+MQpvsRK6glulZzW0/Lxg4DvSgAOkL3Ird7O7uKEoHWeG+GhDRs2nBc/MCqlXiBQtWrV42QLeIcb91UO2quxbt26qzmQ+B5kCSsFOVkbmx0A2sQSIDX/tMvH2INZQAlQxOW4flcNs+8ymLTewZw2t7s2edV3PLUjhye+x47cCPxB0id6SHvCHpw8xCjLNsXNXWMsCNtmWQAMHzhKpiJbtmxpZziZkZAXN+uT+GxjlXGbRMWPZMCJXlcujbD6uxoXt/t8GKvJ/OL6XIdiqZHdSzkfMM0KTbqeG6/A8VUJQCDAbKQtupNYALY0+hzIrVKlSo1t377924ULFzbZP9kr/LWdNAj07t17eu3atT+GF2xZgciNF3zWKRoHRjSiOU1OEQiUsqC4XVQiqRsNPs6DMOpZ1H7xaSUOgPhMR4P2uO6DW7+cHJ4uw5zyGo8GEnf+th6NW5uxiQBKgFJkY2lqs7gWizICojjFWqMOVngVXXbtdp9y2V1iVsNtpgxzcDPnkqhmSopXNNnKSmAJcDPpA3vE6xiiRTduZVW5lG2glyTRQjw2/fiqBCC4z+UIc7a1xZQ9wG3wUITA3bGBQ3s1AQECQg5l8ZljlxaJZk7O5b4EMvPcZzyUBpQAYgVgsiWAkBvJ4cpkxVsu1gdPsgMsWLCgGrfal6MAUDcAmx8Zh6dSmAbeBHZeCWYm35zYREWL+YUAB8/sKP0qkp1CtXR+gRxhu+y7lcgUUNllM6YrnV0OK7rVWI+vRpmtgQAdwM7a0ggX5buyQJpLB6j8viiu3A2IkWPLjTuijrRyTBHwVQlAjk4JTmTroI3mX6wAxjdp0mRKTBGJUudo0XNgapMXn/bUl4U8Hx+cL5GcuVEp8J/nnru1Xfv2oypVrrQ0e47sJxo0bDDpoYcfevzpp5++mf79SgPmCs1GjRrt6dy582soArbYaSBgDdAFIaWJnfJuy8SBEiDSg1UkCgS3sNqtl4PbDk/4FIVRPxQKrVXDbRf6X8vxnbUgB/WdWN54MQ+qBHAGf5YrzXpbY8+ePXoINXfmC3OGSeT5MXqNwg2yHEqYS2EPXy8/zGU/95SxtvTCGuB29y38rqbJvOLqXEgw95rgVFDPSR5yiYFN+aYEIO/2YJjHthUAB/xD5cuXn1SjRo3D6eH09bBhze+9+57/q1+33mz+npInV+7DA/r1//Luu+5+Fp+oZl5iS0qsXNwq59+4cWMhTIdyEwnXs9sI2sr7009TG185aNCwjh07TClWrNgxeXv16jn+4YceepXndoRyTxZ1xpHv9ddfH9y6detpf3/mr/9bv2L15WePJjepWLRs/v3b9vT+4O33//rss89+0LNXzxEjR45sQ+yGiBcy5qLRI488+rcWzVv9xJyc5k3u0rnruLuZuw8/+LCr3XkiM8SM4sWLTxUXETsPwbBqE39iENpL327qoSVXoroDlC1b1tVGYWduPCqTA/wjnlt4pMKGDRsGoJws6hFdWaYZuZ1lberH7VNfDwYd8VrjAQ3ahMEI8L1XxRJAg1IZOkesB3k5M5RwSZ7JCmeXQ4puNeLa9MdNq43drFvRpc7s3jjHFcMS4HKyBdhOW272iLynjovJCnI5q4+vCMR8HfTiRiddhPC5vZnFyVZuSRH0iAEwpk2bNmPTNjZ8+PDGL7/40rPXXHvtJTkL5judv1iR/GUa1Lpw7szZwj8umDP41ORjF0aMHHFdv759J/zjn//8L6bkW9xOGZYLdcZPnHjZ4Kuv6TZt+oxOF86m5C5WsvTmrl06rxhy403bn/zjE/9BWDqMxYKrXOyzZs2qd+eddzzxzTffXl+wcIk8uXPnsapUrw+52ay167d1nb9gcddzZ1POE4SrG2m5XiJDwhK3Y0FDXPSee+7554gRI+6tVqpSjqcuvz9bu9rNrdKFSljli5ax1u/dYi3evLLQl7NHWWtWre81ePDgvk899dSDbCqfFSlSRIR3Rw85WOu9/PIr/xhw6YDL8+bPbhUtXiBX7SalrQvnL1irNizo9/OC6Zd8PvSzO7t06TrusccefXXgwIErMusAZdCJMTxEJe/FDWTYgyC8lpMbylbcTAjP7XBEvM3CogSgqGcKIZvdOikmC0okq3YkdZ3Q6bisCKBeKAE4OHVnY2uBEsAxDX5XkAA8wTeQos/vLh23z7dYdcmSJdejYJxIGldX66DjTmNYIXROhAxT58UORHKgCwZ5Mn0cQh/fe9GjR4+Kublt1zA7OJhYJp7mJgS/XBGkbY354ddEPrBLExdUxTke9eF79uTCyG6/dsuF8rMoKUy7TRZ6cDdqs3DhwsGM6Xm748qgnOkxblx9a1jiehkIOEKIM66e9twUyGrkW38eNuxqXjzsP7UpX07C48aN64/PTWu7xLJIbKtYseLoatWqHQ2t889//vPGa6+++oMchQvkbnLDQKtMw1o5S9etbhWvXilbyvGT1t5VG7LvX7fF2vjT3KrjJ0y4G/OVCigfHm3RosVGu31LOczIK77zzruP9u7T59rTR46UqliiYM4uNUpbBfPmtg4cO1l7/uSxtfccTT73408/de7ZretoBOwXsFo45aQPhPG21157zds7du5p1LJt7xzdLrnGyl+oqFW2XHUrO0vI9q1rrQ1rlliL503OPuq7MdfNnftz82HDhj08aNCgKblz53YknKHBy3nrrbf+SxQAjSvXy/6PwY9abWs2tU6mJFvnL5y3jpw6ZpUtUsoa2KKHdVWbftY3CyYU+Ne3r5//z3P/eZMgjtLXh07G9sEHH/S+avA1759IPlS5zzX1rEZtKlg1G5SyqtUvaZ07c97asGKftXHVgRwLZ2wtM33CtFt37thZnQitd/br129DZv3Ur19/GiZvM7l9vN6O0CZpiwiU05I2fVECsLGJAsA36xknmGdSNpKFxRGfeUSv3WZECRCRuwzWPfk+/PDDnuLfbrfTaJSTTYyxbSWN6i8FChTYgaB9gRu2ihyw69F/DZNuegIBwTpweJI87uOigU+s+uB7PynbQ6FChVbky5dvP/8vLlxVmZvmzFEV0w62GeHEOOTZgHJ3CePYI3TDW5UQsJvCe9UYi5FWGXwT+cDbljthBmOPZC2MCtsxRjF128B3L6/MTXb272qMuzHrVHGTeUxoxYWxQFSA0k4uQgAlQGsUsW1N2huEQNnLWGvW5M+ffxXvHv4trq61k5OTm7DOFDKJn6ElP5ZG3UhF+mXTpk23JzCLOV4H9+3bl+vrr782ev0J8Nou5KNNrJ27sQY+iWKnGK7WVViXarJ+6tpkg6l9UQIQdKsnfduyAhCNIYffqV26dJkQSu8//vGPW//z73+/UrxOtdxNh1xm1er9a0ra82fPWslHjsuVjFWmQW2rQotGVpV2zaxlX47JPnfkpEvvvffeHJiE31CuXLkjNsZvcTNY5fbb7/h49uxZXTvXKp/t+stbW3XLFrcaVyxlFSuU19px4Ki1YucBa9XOgznenbak+eeff94Y5itCSpZ/Va5cOV3XhbT9Tps2rf7NN9/8+u49+5teMvBmq8+lt1l58uW3zjGWlNNyzrSssuWrWZWq1rXadb7UmjTmo+w/jPu83hNPPPECVgd38+e5dsYSLPPll1/2QwFwT5PK9bP/ffAjVovqjaxjyScuakKUASln5T1jDWjWHVuEbNmf/e5/2d54880/4ve7gPlYbqdPhKpeT/3p6TfyFE6pfN0jnaxLrqlv5c2fyzqTcs46eSwltYlq9UpadZqVtdr2qmZVqVki23efLGn90EMPv8KN4o3NmjVLyqifmjVrHhk1atRYFDvdBaJw9PDRl2ZBH4iZ15QKFSrAJN4+zLsoAExWAkRq0u94s/AW4Uxby8ZaEdF6tX379qpsEg1RdEWR7My7gqfE6mYysVDeqVSp0vKSJUvuZzNL4YBXCmVjLbJeDME6Z7BENTaFaAkIxrc7EHoSVgkgShms00YTn+Qz5mU9KWtTFdQoP0qiaGzHvNzO3PXhIGkOM6XDIHwzJziMj8W96tNatWrNYxyHpBhxMYqyTzcnsvhNKAMuh788S7/pFZ9KDBAULkVdtmfyWpY6JHjsIK6AYxo0aPBJmTJlNmBZs5szxQVMlCvAY92Yo3sp1t7l+KNSjdtCtznXI92rojE+I5VjMnCEtBbwT3mTstvwvR5jbx3Lme5N4WfcOfdDY04E7QqkcO4JT9/Hv5uaoggQOlBOtEYGEIW2o4uvaDBfLPtAcVOEb7uEKXOVFgt47RzvXCyGvyKQ/GTWzl3sccfZlwtwGVyR9KU9WD9vY19rEUscbfQd830qokN1egME+HKY8HeyMfjUIhwodzKRkxDafjNB/3r48Ba333Lrf63CBYq0f+gmq2LLxtbZ0wiUaBl/e0TjKEL08bNW4XKlrfYP3WylnEzONn/89H5/ePDBf1HuwXA0sNnWuvvuuz/EDaDTPV2bWHd3a2w1rFDSSj5zzjp77rx16Ngpq1C+3Fbn2hWtHvUqW00ql7Jen7wk59ChQx9C8yTS+1/C9SF/f/mll/62Y8fuVj37D7F697/ZyoUQciblYov7c+fOWudOnbVy5Mpl9eh/k3gIWBPHfNr4pZdefBKB4CaY3FbKPA6p1YcMGfJnus3xj6setZpXbWAlYwGQ2XOGvvs17YpCICXb40P/Vf2TTz6+mfKPhRsbAneFSy7p89rRkwdq3ftEF+uSq+uh1DhnJZ+82Er4LMqGs8dTrIKF81jXP9RKtMX5h7218JLHH3/83/Rxf2b9NG7ceDwbyFV82FeE2/BkweLQ2BErlDa0+WM4+l38PR7MviJZVEy2BMgWqSWACG9sCkb5ACKkzW7ZsuVzl19+ucQ6CX328o+9pOXb8NNPP0mk9BvFB9cFz3peJWAN0EzWz+bNm6/3vIMYN8j6tAkl8rvdu3d/p169ehdZpzFXByDv+2+++WY/h0exTOkXY3Iz7F4OSijYJzCOv+Bqd9E8YWWVRMWprJWr4a8UDn3ivhdxzA2vsSAwVX6v2zShPb775IIFC47t2bPnMygAd4XSxDe1k38PxVpuKymWX2Tfs21VGe2xsS65VQJEm9SE6U9cAUaPHt3KMAuecyivx3br1u0ZUnxvCgFbboI2876PXHAQ+eAF1qXqpkyGxAbAjbQNFwRfoez99UbO+ZNw50Ju0vNx3jLS1USmhzPIaoT+d6+//vrPQ6eL/VpuO9fKC79tWbFixT/Z10xXBDjnOA9reH6riRB6BRtcXbs0Mplzrr322mGh5UcMG/bgmUJ5Cnd48EarbMM61rkUhMpQBUCaxs+dOWNlz5HTan3XNVapejWyEeXuHgTHsJYIf/zjH18UBcDAptWtv1/e3qpZuph19FQKwvA5zOZ/laXO4dN+mn8fP33Gal+zgvXslR2tZlVK5fjs88/vfeWVV64PN04C/V07YeKES2vWbWr1vfx2K2++AqkWABk98rc8efNbvQbcYrXu0M/6YdKkvnNmzxETd1sPB/PmmzZvatilXluraZV6KDTCu/enmtVgGdCrYUesAnrk/H78uIFETg178Pjqq2HXHTmxp9qND7W1OvSpjmLjPKZgGcugogyQv192a2Orbc/quX788cc7sDrINMUQCqIjRYsWnQNP2XK/EDNdblB/NRvx+BF3ALAy9naA4SZsTABZ98E/UneAdmwIxtx4yk0gLlAj01EA/Ma5HTp02MNN4VCEodUes3NEzbFu1yXIolh8uX0iUVa57dNOvSQE5zH9+/d/Ja0CILTylVdeORdF8HR+J0oBIx9uctYjUL6dVgEQSixBY3fXqVNnKOtapjFaYjHAQFyAvCjB3Z5TjOQx2W9RRq7CBPmTtAqAUJxxl5vJ5ci3lL/YjC8Wk5FOnzI/gYw5bigycm5CBmKsYEe2p5pcijQ2yRUAHl0HL3+YRgFwEV9cffXV33KWm2hSsDnBENebdlwQSHAufQIIiJsP82SkOb14UGEhPRuF0xeZTRj8Nh637eGUz/wWNIvPutvNNV3Y+JAKYSbfgz/aZZ6t3PR+HdoYAfFKzZ41u0PBksVyl65XI/Vm/IKNCJViFSAWAU2uH2DlzJv7DAL6A5nN7ZQpUxrMQQFwefMaCPadrLy5cqQK/5k9yWfOWpVLFLb+eXlHK9vpkyW+Hzv2qnD8s2jRQvDIla9lu0us/PkLs2mGj6V1jjJ5UBZ07X2VVbhY2TyvvPLy0yz6tm4BUX405OPNf0Wr3qmm/nYfUXrIglizbDXrwP791ZjHTFP/YG5TeMx33w8uVCR/XgkAWLBIHswbw18kn0MRUKREfmvgjY2soiXynnv9tf9lagkg9GMivQQt8+ZgUKvMxsTBJDcxBNpBv+fm0+oOYJebvC/HvEbkDkBE+wpsbDVMMm+TFJjcyIa1WEFR8Au+3Gvt8L/3yGfYYiHcrtqxLrndQ0wVAha3bdv2g6pVq4ZdPDFDnAY6juLPRGt+hFdwZ1iIO8PMcH1yo7KIw/likw7nQZqhKS/frXEWCuEwzezvMjcokNahfFkYrh0UBd9yiF0Xrlys/h6pdVas6I7nfll3m4J7OZP2Mnh6A1ZSohTN9MHdZRYXNW5v3MM17/jvgQCBVckC1spx5YsrmH455Gh4WPgUhscKmMRjwQHAa8cJ0L4Qa72wAgfub+NMXj8ZU8zPQW4PcOkyFL6FIjjatgJgMtdL8LfQxgjeVWvv/n1Vq7VtZhWuUCbTW/O0RIjLQLVOra1sOXLkGzF8+NWZcT3BC2/AH6hY9VJFrUrFC6fe+Nt5iK9stapa1rq5YyNr3sLFrUit1zazemtWr65ZrEQZq0adptbpMGb5oe2IIqAggQPzoTjYum1raT5KW76n3Kx3Oouyok3NZo4issrBJH/ufCgBqsCW2bJhHlUhs3FJGsP1G9c2rt2orFWnaRnr9Cl7qfykzeRTZ6zGHSpaxcsUzDfm+zG3hcOd26yZBOeaYkcIkkWLQ2MTzKi7hmvX6d8DSgCTF3sZkj1GTn/wYRdVp5h5WT4QmNFVk5hQ1uP2rZqryj5UEl7Gf3IPbj5i9p/pg4CWRFC3XaYJadwC1kfZ5sa001h/YJSNKzGfXxluTuTvKAo2sN5cZMptp140ynDwOU6w3bm8YZUZKAFOwF+bTOMvwYlx5GXv89xtMRpzkFEf8u1jbbIH5V7YuDXt2rVbj3Lel0C3XmAgPt8u24lkn3LZpaNqRu7zWDnm3rNnTwsEaVuXQo5G7LKw8DOuLdu4dc3YxDXQNoLbKmjfZecs55Icx9VYwwsiu9QklpRbXjaSV0KAcLzfsuYW8CIbk+PJCFNB+IZAgPs5N9lSvsNv4oqy22s6Eqk9T5UAEpTN7m0bk3mWRWMWB5CDoYByO5abW+kceQsXtHLlRe7NxA0gvYnImTvVYvhCuNQ1+Nq1O49Zya0dG1rnMIW3+4g5u2QN6NGgqnXiyOFyuD80yqgugnSOxUuWtilesrRVoWJN6zy+93Yfoa1k6YpWkaLFra1bt9ViPLZS0+ETn7o55M/tfI8QS4ACeXDxw+Sd/jJVOnBgzI4QgMFCbqsAvv6OFnWWpFy5c2B5kM06cyalYDhMOMimVK9efSKbh3zQYR/GUYasAl3DFnRYIA6UAJEerCKt7xBRR8Uj2mg5ODVgszcmK0Dg4HQIYSCsmwvC5nnKJTn6xhxB664wa0AVblDquKsdkbLKZZeZVwPfk9ww2Ha74JZWguwZc6sVOjrmZi8mk7bHgqXJQcYflhd9AT6TRhlH7kRTAshwUTZdFGsiM1xRAu4wUUEjNEeimI02LyVCfxKwDeuruia5AoDrGeFRO/gSCHM357iwim87bXlVRi6OwLUmbkfFI2gzovNJBP3aqer4XCcBPyN1v7RDmNMygcsTCai6zU5d9rVTJl6g2KE9WmU8UwIQlVHSbbSxGy0ZBtuMYDcj7UDF6pffXZD88m4OvecCJv0sNJnegAQPFkXy2bpgv4hM1gwrV45UmTwbgnCG0naqrz1+5GTS4VbeOdQ5iHMgi7343dnFApOsJdkQrpdsWekIv9SFkAwCy7autrLnznkaU8VwJoi/zpPkNrdpRREKorgFiH6Hfm1pRjDRnYwgNM8mDmI6XnPDhg3lvPyQ4kQJ4HjBD8EokrpeQp1RW643Wg5O4gpg103J97EIH2PdchDzyF9TaIR54P0j1LFvbhOuQQ/+zjdWjJspt76UJvJaEgpHW1YAIfCFvWn3AGrHTcArhzmY27ZSwHVgN5YyxvmeM47cXCy4iQViIn+lziN7+lluTg/bnVQOvDts7nt2m/SynOs12UsifGrL/u2QTwSkbRb/9RIIrOVNMtOGN0+QCcBWij0EssPQblsBFg1YAwGlqxEg0O150fRvwPFayJqb30QlgPAD+9QxMt7Yyv5GTJULnLP2Gbx+RoPFM+3DuWSaQXOY1pcG6Jp2R8RELu/du/dPactjIremTPGSW3evWW+d2H9YTPvtNmnlwApg5Xc/SCDBM506dZqcWUX6Tw0WccaGH3vadsR//gSBAgMMmeFNEAf8c7Vr1Vx6JOmgtXfPFoIX2h+LlN29c6N16OBeq0GD+svxIbQlLIhgAV3nJCVgdgeKh+yiBCBd4YINy6xK5Stsxt8101t3Fv0jNWvUWbBz82Frx8bDVk5iKth98uTNaU3/fp21d8eRlFYtW4+1U09MzVDsSNRZu0E+GhH0UbIEZLXH8YKf6ABhFVKEtckYKwDBW7RnuAPYyvgh5bk5lFtaWwqzaM0nQ8iBGaWbg5Nj88RojInxHGP9TE2hZ/exq8S0255X5cSqgT3DtqApOepZX4+adFiSw7lYAnAr5UYJ4BWUnreDEuCM3UOsdI7gtFMsJz0nxJsGTReAIhmlUUpXGQhumJX4LopGMigf6p6Q9cNOu5hnn4H/7Z7h7DQZcZlAgMvSSUlJVSNuzNwGHJ0LZW/nNfLbZp86wQ2/bQs8yh42aV8zjUU8UwKQLqknB8JqdjSU+Jocxs/7y/TAID/9vkuvuHzopklzLuxcspKbYkql/ifzJxs35iknTlknDyZhdn8+e/8BAzIVLnv16jU2Z65cp3/4BeHcgbCcg1v2g8dPWV/NW2nlzl/gCBGkM/VNQVjek3I62TqdfJIbAPuCcq5ceaytG1dZhw7ssYi6uglGtqUEePDBB1+vUL7Cuncmf2EdOXnMymmzzxyUW4+iYsa6+VbpUqU3BVIgZgg6yppj99x75ztrluy7MGP8htRyduZeXABOExPg0L6T+O6npPTr13d4uLkN/h2sJ7CB2HIJoE4prD1sK6Xs0qDlYoaALAKu1itcASRTiBvfdd8GK4d6fNvC+gQHCRAlAHWMunUOCGlFsQZwkybM0aHEt4m4uOFT4OzogIoy2ag5keHIgQfeOoGSyfZY2F8OMRbbSoMozUeqEiACd4DwB4doDSSkH76bM8yNbasLFAaSgcL2XMZgSInYpaxPxlkCYLJelTOQaanbTsLPtm5mA+dEW2fZaDIV32RhXGDLuOzT2EwSgfE43mvF3ffXI71ZS6jsbaIEYH8Ln/YsMHgnly0u5z+uq7k6VKc3YkzWK9ldnGCwHZgfZphfunOXzpOLFC68d+HH31gHN26zUv38M2FGuTXPliO7tfjzUdaST0dZFcuVW3nnnXd+l9nM3HfffW8ULFDg4MuTFli7ko5bItzbfbYcPGLtPXLCqlWj5ooWLVpkaj762BN//PehAzvOzvzxW4IcnhEGDtuNjOfc2RRr4c+TrJTTJ87WqFFzWTj3hmCjpNQ7SMrFT1buXHf+uTFvobBIsnLlzPwiJRduB2t2bbLemPiJlTd/vsMPPfTQK4wrrLCNtcV0NLvrvv9subVo+lZSGzIPmeCYPUc2K1eeHNaUb9dan74w50yh/MU3/v0ffx8VFpBAgQEDBsyCx5bZLJ+bQC9t8Fk2bcO0SX5Mitn/CGJCnuWKPpQAVdnMqsaG5Ax7PedECcD3Lwcn424DWfdLHDlyxKmbhZGWACJoIQg7OqDaiekQC77j4HPciYKCcZyEx5JiQWtmfXLoy8mlgZtziqu1wu/xyyGWtShFlDR2+2IuxUIj0ZQAjgUTu3h5VE7WWuMsAVACVOMMZFS2DHjzlKwfDnA3bh/jm8yHEqCkgzHEU1HH35rJ6bCZq9NlypSx/W2yr52GR41T6JnCQG4219/RTmrAopjSNLITrEQ2QW5b1nXt2jXDvMQ33XTTzLvvueefSeu2nl348Uhr94q1qab+8sqNf+qDUiB7zhwk38trnT5+0lo56gdr6VfjLEzwl7/2+usPhgMY389zffr0+Xr9viMX/vztTOs0sQRy015mT95cOa0dh45bjw+bZs3fsv/cZZf2/5gUFPszqzP4yivnd+nc+fvZ08ZY0374OjVFoPj6Z/SIkuAc6Q5/mjTMWrNyodWxQ/upCOUvcjNv+0Om/LvQNXPEvHEXnh/zjnXg6CGrUD4CLdJvULMn1g9y+5+PAIJrd2+y/jbiZevnDYvPXz7wsg+HDBkSNm2Z0E/+6S3/+Mc//nT8sJU07K2F1sJp27CqyIYyICdj/P+slSNndpQLuaxzZ85b08est0a8u+h4gfzF1//rX/8KO09pccKXUiwv7H7QdYhT4cZcORz7mPx323ySziCMPDiH0Bleg5bOoMirXJtfFzFp0sQSgHXQ9sFJlIAmmgRzWCiJEiBRFG1nwNn24UL4ycmNRLT4L2AJcIqx2D5sI2imsH+fNNBsMjs0eXJOiRb+4fqRQ6wTJQAClvhQ2775Cte//t0WAufsXrzYas2DQgSazkVsG1Foe9Cap00ks37Y5k+JieFp7x40JpiePHmyNNnJjFKweDC0YBOOzoXiCmCqO4AoUZ3gEuA3R+N30n68l3WbEuOicRNQoyJatPp2FicOjckEBJwXDrj//ve/b99yyy3lPvnkk6cPbtqRo8WNl1vFqhEtn7SBhcqWtM6cOm0d3rbLOrH3oLV+6s/WmlGTL1StUmXxK6++ev8VV1wRtn3p/80333x8y5at9UbN/blPxaKFrTu7NLTqlS9hJZ85Z50lVoD4/ouFQE4UD7kQamdv3GW9MXmJtXDLvvPXXHPNv2+//fZhzz77bLihWI89/sQ/Vq66tfG4b9+rIYW7XXIdgnJ+6+yZFFIg/boeiquAKAfOYgEwa+o31thv3rUKFcy7lbrPlixZ0lHU5kqVKh2ZMWPG3ffed9+7I+eP63Tk5NHsfZp0sZpVa2hVKVnBKpK/kLXz8F7iIZyxFm9eYb31w+fW0m2rzvfv3//Dv//97y8NGzYs7JiCBe66667v/vznP9d84b8v//2lP04ucNVdLazaTcpYZSsVtsrwniNd4Z7tx6y9O49ZS2Ztt0a+tyi5YP7iO5599p93Y40xy3ZHgYLwztwlS5ZI3IOwPt7cIFVas2ZNU8quddpPFi1v+mHbFX0oKKvYDVgaxXk/L77BdvuTjSwQNNVulaiUYz0vhRJAvkWJ1xHvzzlwdqoESAnc7ho1doRMUQLYHgvlhb9MvG3OETBNNQrfSIiRQ6wTBaAoDExzBQoZv3ESaSRzE1K3wO7duxsuW7asVjpxP0ItmcJZNQk+QQHkorISF4Z4D3sk+4sdmgnWlpfsT2XhBzvFo1lGlKeO9rJoEme3L+KalUGOEdc2R0Im5U3/BhwLwEF3ALvYRbMce7Sj+aF8ahDzaNIYT315ogQgB3cHIpZWRBsYduww135u4ZeELUgBFAB/vfnmmy/8MGHi1T89+3atAuVL5ShRtZJVtHxp6xyB+U7tP2RtW7nWOn/0ZMqlAy4dfv8D97/A7f5yO21LGXJNnl+8ePFdCKMf/+/HuT1W7txnDWnfwKpVppjVsEJJq3ihfNbOg8eslbsOYi5/0Hr7p8XWmj1HzmFu/9zzzz//7ypVqthiRszYl7377rsPPP3UU++MHv5Wle1b1lhNWnS1KlevZ5Ut92vq8v37t1vJJ09Yc6aNtqZPGWkVKpB31z//+c8nL7vssul2xxNarnPnzmsJjHfb61hFfD1y+D2Tf5mZt2ml+lbNclWtwlgFHEk5YSUdTbJmrZyfkr9koV0PP/zw+/fff//7tWrVytSyIT1a/v3vf7/41FNP5fpi6JdDPnxudv0SZfNZlWuWtEqVL2gVLFDQWr18m7Vj86HkpH1ncnTs2Om7++6797Xrr79+rptxsWEuQwmwhbphlQDssYURAFMVL/rYQsCT9cBWT+4KudpsMVkv5q47X2tdcCpw2rG08pXiNI2L0hdFWxHWfqdmlOEOztEcRmhfMie2DuTBSk5M7qM1KFFKQNdpsXaz2yflzzJ227d5dtuNtJwEqIogDZ2r9SJSmm3UF2WTE6FJ5tERX9qgwbMiWNtlJx2lsfS5GShrW/YtW7bcgkthJ+rLTY34VMoBV6zRZP2S3wXf4PwE4wikFTiC8WykrryizD7D2rmLAMzv8/+T7NCIkCoR24vaKRvlMo6Up073vWiMRfZWlCwlBWP6sx3fIIQ2U9caV/DJmstLXHZXxpeu+nRQyfa+Jm0GLqdVCZABwJ4c+klbUhNmseUXyoF8BzfVq+xO+Keffvq3WbNm/e+Vl1/+67EjRysuXLCg9baZC8u3bdkqW/lCRa3arTv8cvOttzyC0DuddBC2N9Zg/wQo3I5QeePjjz/+1sJFi9vM/XRSuYrFC1sVihW0CuXNbR0+mWJtO5BkbU06eb5cxUpL/+//Hv/vrbfe+i0HLEd93X333RNHjRo1+I03/vfUjz9OuHzZohnZy5SvbBUsVDw1TsDxY4et5FMnrT07N57r1q3rmAceePB/V1555U92cUqvXLt27eR27pHPPvts/NAvht598NChiiPnj29FCsDsffv2sU7muLDl3kfu/3rQFYM+FqXBq6++6rq7//DMnTt3xAfvf/D37dt3dpoz5+fK2epUtPJVqmwVuJDraO8uXad36drls969e01B0ZDktiNyc2//y1/+soFFu5WNNrJzS1l13759eUuXLu3FLVeiLyQJFYVb+INDXI73338/v4FmzhfYnGzzk6nabHDNy8HJKFcLG+tCRkUczYk0wo2uLUVwBDQ5rhpQAjiii3FI1G5HdRwT5qJChEoAFz1GpYpYXdg2iQ4ITaYK2Qkl/ITOPnNUjMwULf3iCNo+iuAp1pC2lAAoWwvajbvlF80ZtHvO4V5mm/ejOQ7mowhvhim/o0mLx33ZPmcE+zXVFSBAn9O1MGHXKC/4JGIlAD40ecaMGWMrb6kcTsh5uxITKEdpmDp27CjRcf8gA+bmvvCCBQsGEofgTwjPZREKP7ruuuumRAJGs2bNdm/btm0wGQ7qfvzJp8/vP3iwG7a6+fcRvX7H0e2bG3XsseDuDh2mDOjfb3zjxo33I4S66g43hYX4dQ3+4IMPnlq1auUla9euaVO7Vpk8v6z85XS2cylbK1coue3lF754jtvy2cQ28EJoTaWTGAuTCZD3E1GW82Le1gALgccxKWuCJcToRx555E+vvvKqq/GkrdS2bdsN+KzdRSC2Ru+9994M4hjk5oZpY6s2TYfjPvECpvyeRJ9GoF/B7cOgcCbeogFkzLVXrlxZBVrVJSD8LIc35Qnfhp8lHG9mHJzEX72on0S5bNuRJYAoAZwctFzS5Lia5BLmIFvIcUVDKzjF2ERLAIFWgiE5gVjiBzgRTJ20HWHZ7BFYAkTYtT/VBWcnrj0yN2I67g812mpmCNhxcXWLoAj0tG977cRnXfaxgm7787HeBSfxR0y0BJB5RgFQwKUSIB6ETDfrh6njcjoWU8fh4ydpv+mIlQCYW5fET6m6ncUS86dkbusXk/PdtYDLzb0EyRn64osvNuWW94oNGzb0x/99DjfZtuIAZAQNQreYmKzcvHnzdT/88MNzKDd6YrUwu127R95GeF8wYez31tNP/ck+shmUxApCGPhZ8r2+iKD8DQt7xdp16i5GgP6kVatWs4m271TLZYsmLBdE+yopyeaNGDFiGMqUGghINWbPnt2kQ4cOdiPuh+0L5cJ5LAIeRdmTm/mWNFU/Xn311a94pQAQAmhrDrf7e+G5yuEI4gBZi9SV4hKgSoBwYP1q7mjy43Txt7BSEgslI90BXADtePwu+nBaJQeKNltWYE4bjkV5p0oAJ4ffaI7HaZYD9p0Lr732mnE3dBIUMNECA8IHZ53wjeGWANFk60Tsy3acG86KxVmfxFzdtMepe5eJ+5i4tuXmzG/6Gci0uY8FPUbyTyyA8KJP2wtQRp1xI1sRRUAZO/6qCGQHOWzYdgXIbICXXHLJyxw+12KKWm3hwoV3ctNd1AtAJPgKVgFNWWx3NWjQYJIoALxoN20b3JZX4nBTEUws0iVuHjhw4Ey/FABp+5bMAcWLF1/AWKtPnTr1ThQfxb0YI3yQ48svv/zvpk2burOYHkABMIr4Cc+QttBxnIHM6CEWwxqw22CHZugoDtb17ZTVMqn+iiY/jhVkYkIJr3jC3yYDE0Pa5KbWuEhV0cLDxFstGTv7lyO/yWAdAy+cJUp1xOeUaPGDzX5kbmwfZANWA7bL26TBq2J6y+YVkmHa4bKtFLwggetMe0zlTUc4gW1eZAoTLS0cjSMLFHbKb7pGZcIUEW+uHACLsEnbuglC03aQ/I5bvGDSRo0a7erWrdvz9H2UW/VeI0eO/Cu3wxEvkCgUrqLNXAiPW3E1cBy93u7YuIF/glvKOiw8x3B3mGi3nhfl6tWrtw8XiM/wAz3Ootdm5syZAyNtlznIxhz8HQuKAYzpCGmNxhH872Fu7T1VAAidKDHECsAuH+WF77KKEOh0cbxo2uF7SRFGUowLloHvKWhylCVDBsc3VoIfiZDCzultS6SftN36iaQEkJgAjhRNgajYjurYBdZtOfl2XabhMmocgfEn4gHOkRIggENEa7tbXtJ6viNge14DSoCIrXd9H1H4DmyPOXxTnpbILS4BnraojSkChiMQ8YIieUs5cNjya6LcIV7XrgBpsSQTwPShQ4e+t3bt2iewSBj4448/SmaAT9xi/sUXX1yL//gVHOx2Imh+26RJk51u28qsHvRWJI5CB4kQjLC8GiXAQj/6yazNFi1azIGOqeRQv4IouD1wqxhXs2ZNVwI7PJCdoId/xAJgAGM6ki9fvgXEaXiyWrVqnsQASG8cuJSs3bVrl0SlzfT2OhAXoPTBgwfzEAPBkZ9stOck1v3B98sQIMpAR1leCRIoSsK0isLgBh48nKd3SM/sb0FhNlSoTa986N/FJ3YbJs5LnGLEwUmUACaaUDodiqlKALmpdbqPmDoWp3MiN+4iOMsbsULdcecZV3AUbyLYjFMFiIf0ZrWmEk0J4EZR46ZOIvKJbcUb6etK2HG7jQOQTFUCSER8dQcwn4Gc8o/T8uYj4CGFTg9vv+sav/zyCGJhb+DF7B0rgDWYvotfumcPioAPuc0uRZrCW1asWHHTV199tRcBdEJoBwi7hdHwZWcRRebOewph8ChC5EWMsW7dusIfffTRlQgaxxGGx3CLPcYzItM0hNl8NW6nC/Nu7tGjx9t+9ZNZu/jsXwCXd4YPH14PbCpiDdCD8sNC65A1oSQWEcIjFwoWLJiMC8FR5vAi3FAA5ESh8SiBGq+AD44XKlRo3uDBg//tpwJAaISfJDbESd6wCiiElNKMQ0yWs4ISwPXhirgUH4Cr8L18zzLvQSWA/JR2w70yNWnLBFkqVPgPCk5BXkr7U/4uB+Vg6iXJxX4CHtzs9Fth3mUsppqrO92cnJZ3Cpeb8sH0V27qmlbHMb6GmmqLRYNjdwDTJiOEHjdrmps60YIgkfJWm4xztOYzkn5sKwHYy0w1VU8Upa7EHzHdJdINrzne1wLnODd9RaOOm/FEg6647CMiJQB++NlGjx4dNl97QGg7TVaAbQTgO+ElUiVLlkxBAH2XG78iWAO0RREwkOj3q0mPt2X69OnVUQrcidvAFQir5YgfUIDAdSe5eZ9y2WWXHXjggQde6tWr19rdu3dn//zzz5/kJjQ3QuwvCEOjvaQxtC1uy0twa/40wepyFS1adG6bNm2W+tVXuHbr1KmzC+uHkWvWrLkWM/5OEyZM2Ij5/nqCBjYneOAtuAxcH2yDPMA7oHUmAv8rWBGsJsDjKdKvFRo7duy91B+CAuAY8zt90KBBLxIDIClc35H+HYXEJuZNLA0yVQKI5pyAOjWI81CBsmsi7TeR6/fu3XsH45M3YR4UbaLZj2id8xEMJ5uZ7cOij/Sm27QLS4Bok+hbfwGze9PmxnGqwwBATvjRN0zTNByJkGnieGR4ToUmp+WjNTfaT+QI2FbWsZeFvWyLnJyotGDkd8lZUSwB3KRJNnI8UZnJ2HTiFG+n5aM1qkj2Ns9ojOhwjIl1CQTv6pLnPtwj/sYEitsXrpybv5N3fu8vv/zyogjXfMQ1UALcS/aApEsvvfQhlAPFUTzkIpichYBvESisEGnyroB2CyVB/yeffPKLiRMn7sCioTEHuj24AUxEOHaUwtAJzQj/lQjIVwmt7j6sAF53UtePsjfccMPXL7zwQjl8p3vMnz//8c8++6wkipOupPfLTtwFC6WJmLxapDasMm3atCoI/deT7u8r0ik+A973owjqC12nEMp/QrHyIlYUSX7QmbZNgjYuQeGzHd4LmyEArEtgfVGONiJVAhjx0UYD30TpIxDt1yRT7SC0TjcmIwUBWRsS6PbEMcaBwIBO59Lvz8txbAMhSN0B/J6W39p3YwlgGo9FDawE78iJEsDUHPZOedNp+WixgMS3cSsTmTqmaGGn/cQpAm4ZPnW43LAW4C2BD3jY4fNxHStbtuzGsAVdFkB4341Q/8GcOXP+iqDdF9/+GtxM5yfqvoXpv4UCIlUJgLBriQIA9wGLMuW/++67RxGEZzKG3bQxsn///jNdkmCrGn1ejUBamlSB33Kj7kmmBFsdZ1IIy4gxxFPoinKiOZYKNevXr2+1b9/eQnmSips8xA2wcLuwsBbI/vXXX9+AZUUX6D+Me8UuTP+/69Sp01dYEURFASD0QItsnmHjS4iQIhp0lD+2LFYixVLrm4VAwA3ERCWAAOXk4OBYQI3WTLiM3u5k7NEaitt+TBuLKyWA28FrPccIuOEXN3UcE+aigirGXYAWqBJ0i7PVgii0EyQmgK3xxqBQJEqAGJCbZbt0uhYae3YyYQYjUgKwIOWUtBp2BsJBMTl//vy+3bALDdzgryNCfUGyBDRCOWGR3k/iEFgsnqnRzrEKsMRqAWWEhRAu5S2sAnKgCOiKsuAdbr59SQcYxIe+GtNXPzA7QTrAZXZwi0YZAuxVXrVqVT1wqykWE1WrVrVQoFjEChBFTyoJ0JtqEcBtv4WixZo8eXJFca/ApeJFLAM+iwadoX1geXAc4f44GQ7Cds048qAYkgBxif7IYqeHspBZhkeEQUzFxMlm5ub2MCr8nsUPpk7mMCrzEUEniTSWCGDwvaqbQ6nOje/TEpMObLsSGZyK1Sk/G8vLWXwvi8kH4KJTY/nHxVhiXiWiG7LABxNeCmOYogRgEbO94LlBhhz1N3JL3ZK89xa30xbxAlJvr6XbYP5j+YlgYCHApv5dTN5Jb2d98sknV44fP36Qm37t1lm9evXl3EiX4cb8p+bNm4+wW8/vcmB207hx42oQONGqW7euRQC2VNxCc0YLZqJMEasK3BisihUrWgsWLCgAjjH5IMEwhQCP2+3ktZYMAh7lfzVVmPSbReK2fXg2lx0eicEAnX43Tg9aURuSS0uAqNHnZ0fsgSbOi4k0+TkNGbXt9BuLFo2m0hWt8Ws/vyLg6DsNKLRNxC5R+FktAf4/d+lZ18QvzQeaIlICSLA9BKyweTVFCCcrQBLvER/GkNrkTz/9VOutt966h9vs4q1atbJKly6dKuxn9sjfpRwuANaePXtKkW7wFmIc+JIihJv2OsQtuIab61MI2asQYg/4hYWTdhlzr0mTJvUlSGGOzp07p1pKZIabKALESoAgclIuN4EhfVWcZDYWXDiO2hHwOKhnF1qd4BKnZRNlM/YM/oA7gGftedyQk/lydGD0mM5wzWXlA4OTOQyHY6z/nkhjiTWW4fp3irXT8uH617/HHgFHa7ooAfSm2tdJk3S3EclEvlKnjQcRcLoWOi2fpZCOiOExsxYFgC1LAITLk7xn/UIXhURJfP0r4NdvYSoeVgEQpEMEXm6ULRGAiYrfAqH4Gj9oJJDe9QgkZRGgV6Gk+MKPPty0iStARdwkihHQz8JdI9Vqws4jVhRkCJC4CrWxCKhtp47XZViwnZhIR8TrIbRnZYHH6yn0vT2+74hcnnwn0H4HJm9kWfabMNQSwD5XaclYIGDytxwLPLJqn8IH9g5cFDRYoe2Un52WjyZ/ZNm9LJoga1/mIBCRYCQpNRiK3TZS/Bw2QnZr/L6LizDrVFsqt8kS3JBFNi9tlPSaTlIYViIewFUoQZKJUTC7du3au73uw217CPDtqJtHYgGEs5wI9iF4SdYAqUNqwcq4OTRx23+E9ZwoAbxY3L1oI8IhZ1rd5M3Vz3Fn2HaCafZ1fv3lIjfft6PbPH/J19bTKGvdzGc0QHTzHbupE42xmIpxNMYe1T7YyxRrfxEXfBXjXzE2GQdT10I33BnzsdgV4NMdHLfGEhTQ1k0bgvlxXt8sAbjFbyW3fiKY2klZGDogKS8+7iLcYrJf181MZlaH1Ht34DZRErx2ktpunNftR9LezJkzu8i4JVCiHdN66UvKkRUgFTMJDrhmzZp6kdDgti502D2A6+LuFuT4r5com5ldXjd+xrAgivnGZzxIsSFQ5yU2uGuvWRcB/ebMmns35wU3daI96nig0S9M9BvLBNmIlACY35dFuA3ray0m5vic7+H1LSYAN/mpYewloJ2bR3zd5SEl3q/h8D16CFJYGiuFQWBwrlatWhMJWPiLR0170gzCfOp4g+O326jI38E6WAWcslvPy3LEVzghpNtoM5vBkXVtkK9FFAGjEXBzwEiUjTlRxmE0gyUgcU75xmn5BIQs4Yakc5pwU6oDUgTiC4GIlAAScI3h2mlDzLZFUPRt0cPEfgX0nCfAn22z9uBUiTC7ZcsWUQCcaN269Xwvp3D69OmPIaxWwtpgB5H3x+JLb9sHzEs6MmqrW7duk8R9QsZv141CykkmAVwBJEjgfuIwLI0GrWn7wLViJ8L96XB9C72kOiy6bt26sEEsw7UVB393I5DFwbCyPIm+rZ1ZHlkFIBERMNn6K5G+Zbf7jdt6icarTnBIFL4xdRwmrxmR8L0THgv246ZOJDRq3RghYEeA94o0CdXv28fPDft6rAGOHDhwwMI6wTbNIiDKrTZ+7ZIa7yDuBFttVw5TcMOGDWUxlb8UQTVb1apVp7Zv395TBYMXdHbp0uUHxn9aBPqcOW15dqTiS1BIa9u2bRJUcWe5cuV2eEGL0zYCgQFtKVWYZ4msayuIZSZ0mL4wmk6f0ynW8hcj4Nv6qUD/hkBW/oZM5K9EPZjrJ6cIOOVtE7/PRJtFt+u/zk2icUIWGY99aTkdQBwIYchf2bLbvWl2g/3tt98+kVv8H/C/vyDCqV2BVuIBSHl569Sps7hGjRqb3PSfXh0CAvaFjhJYGGzGUmGKV+162c4tt9wylRv1ddBqkR4xbDwFmUNx78DNQcqfbdKkyaLy5cvv8pImB23pwnsxWG43MAeQx11RxSTupkwJVgQ8QSCRvn2T97pEwtkTxnPQSERncAf9aNGsjUBW/kZNXjtjzpURLUDcnCehCDgTbhSiAcAcuxBvvnBlI/n73Xff/QYC6slx48almreHUwTI30WYnThxogS6O/zYY489j2m7J5H7EarLcrveXyLuly5deiYpCCdHMjY/6953330v7t2799wPP/yQesOfGW7yN8paU6ZMkXJHrrrqqi+xwvAt1kNm4w5YfNha3MTagddO/IBwUJu+oNjCI9wg9e+KgCJgC4GECdjIaE1f22xNSKCQroNO0IpN2aw+RzL+iM7gsZm23/Wa1efRkGnwlAydU0/hNLexiBYg8softakEEB/yQrxhgwhGAtV11103a8iQIakC7dixY1MFfEn9J7f9QSsE+SnCI7fz1saNG60xY8ZY+/btO3fTTTe9cOWVV86NpP/Qutu3b++IdUELMNpERoDRXrXrRzvPPPPMZ127dv0Kl4jzATxS8Ql1q5D/l7SAK1eutEaPHm0dOXLk9AMPPPB09+7dZ/tBk502CUxZivnME66sZBEQJQ/xC5LDlY3zv+vC/fsJTBRMTBbQEgXjOP/8lfwERsDk798N7Lpm/JqGLaIzuBvgfaiTSHOZSGMJTnUijskHNs6aTUa0ACFbiZ+/XZ/sHIFAgr4i/dxzz/0Tgf5fKALOjhw50ho6dKglpu6ksrMQyK0TJ05YEjdg2rRp1qhRo+T/z9x2223/4nnJS8JINXgFgnMOBM/5WAEY6QoQOt533nnnDz169BixbNmy85MnT7aWLFliHTp0KFWJIukAEfqtSZMmWd9///2pXbt2nX7iiSfueuGFF94rW7asu3QMHoCNZUlheMpOIINzBGc8jtuDF5YAHlDuWxO62PsGrS8N63z5Aqs26hIBFTRdAuewmhuc3dRxSJYWjzICdgNrR5ksx90lyj6WKONwPIHpVDAZC5Np8wL7qLZhR4DKjCDbSoCAxtP3yUPQE6XE399+++15L7744l8QxtsQoO80wmw+BEGrQIECEileTNrP4ac/HUH2ZUzaxxcrVsyzTXbp0qVNUTA04jZ9d4sWLb6J6oy67IzMBYcJYngPrgFnp06devWmTZtyIeCnWlBIJoDTp0+fEUUAT8qwYcO6XHvttctcduVZNQfZKcQYINEVAJ7hmmAN+b7mJBheWXk4WZ1XPNsDPWairD4vHsOpzRmCQKJYAhgCp5KRCQK6hip7pItARJYAtCiClS1LgGgpAYKjvPfeeyfg49714Ycfvv/6669/AYF8JfECNvXs2XMdwexWvfbaa0NIGdfjrrvuGuelAkD6R4h+nL6KYj6/hkCDnrkY+M3DKAKSoH0IVhI1evfuPaZTp05HWrZseSYpKel4xYoVJ2JV0QUaipqgAAhgIUosO4ub8KgorBL9sYNFomOQdnyKSVabcR1vIvG827G4rWcq95iqoDEVr3ihK9IzuAnjlG8tUb63RBlHKF+4GZObOibwotLgEIGILAG4iT1Ff7bMwbmJzccbaYo2R8NDABfa3glU+hs32J0wd3+sZMmSp8gksMBRYzYLk52gC4EGWxGH4EiHDh0+jKW5vE2Sf1eM+ADbiadw2/jx418ntkHF//73v5+hTPmQeAtum/Sl3u7du6uiaAm7WKGQOSfxK3whwrxG9bB48ZyE5Y8YTaGpdLmBIyuPRb83NxyjdbI6Aom0ZkQylxGdwSPpWOsqAnGKgK4dHk5cRFpIhOm9CFhJ4eiRoHKYkpc+fPhwkXBl/fx748aNV5LX/mei2ucicOAfFi1aVM3r/ubOnXtXSkpKafzotyB47vW6/Wi0R6DEXPj/P7Zz587GuFCsRZkxNRr9Ou1D4v3ZrCNWAGGzWNhsy+Riujj+fnZMxsQJbU7KmsyjSpu5CJiq0HDL+27rmThDps6NiVjFC00yp3YtaU0eUyJ9ZybjHAltOkeRoOdf3ZjPS0RKAG67k3mP28EHRUARormXtlPWrzL169c/hDvAp7gG7MKKoc6cOXOuReC1K0iGJevnn3/ufvTo0baM9WTbtm3fa968+dqwlQwsgBVAQzInDGJuU3AHGNOqVavNBpIpJNmdu3MexgTQw5ihzJABWTFfZOMLLqVWETAKAf1+jZqOrEMMZwaLFM/nJc1zJu8F/pbeK/VCX/550ZNy9uzZ7fzGyUWRnj38Zz83642bOv6PJLIeEnFMkSGSoLUjMkUqVKjQicKFC++TiPuh6eQywKoAQmXYdG5+49y0adM9pMH7euHChU/u2bNnIC4CObBSeJYo/hFrZHE1uJUgemVRMKwmzsB6v8fiR/sEBCyDO8NfmKtiuDJMateunZGZDWbNmlV+woQJJe1gAG+eRvGTVdwB7ECSlcpEpOj0EahE8qP0ESZtWhFw5W9s8iHWDW2JJgCavv4lE0t6ZtGiRRfz/QVpDQbCDv4Mzknan/LJhv4ueON/0c0/CoAjBLKOWYplD9cVN/zsYfeeNRXJOCKp69kAtCFFwCkCESkBMBU/gU/2ruPHwxsDiCUA7gAVnRLoR/mBAwfO/oqHzAGP7d+//zKEyYPQ9gGCu2uTcawKevz4448dGeexUqVK/UjwwTV+0O5nm2RMKDB8+PCXcAPohivDGjIbfFa6dOlkP/t02zbpC8uhbAlrWSLafFwzkipXrrzNbV9aL64RSKTNOZHGEtdMpcRHDYFIeD6SulEboHZkHgKcG/ZVr159xDXXXPO+edQZSVGifGuJMo5QJkm0MTkdj9Py0fzAYk5bRLdkBN47S1yADcQFCCs8IxwX2rFjR33MzPNFE+GM+urRo8fwOnXqfI4gmQdlwD3cft+Fu4Jrpcjq1auvIfVgqTNnzmzs06fPqyaM0SkNxDO4GQVADxQ7SVhMfIRLwyKnbUSrPNYn5eirqJ3+iAGxG4uVw3bKapmEQyDmi2wmiDqhzUnZaE+iybT5jYXc7pl4S5tIc5JIYwnyYyKNKZHGEpyfcwULFjzg9+LhsH0T1xmHQ9DiMULA6TfqtHyMhqXdRoqAa6E32DG3rPvRmkqWgHCR/3MjjBXCzDw3ZaV8TB9uuM8dOHDgTdLeVeXn4JUrV96B8LsHor5xShjCc2uUCD0Z2zFS6Y2rXbv2bqdtxLo84685atSom1DWZEOx8yNxAL6KNU2Z9Y/bQlNoDasEEEsA+G4bSoAjHozH1AO/B0OzLIJBVgavsjRWiFe+U1kfcvCmVRaGHkbsHkwyq5O2jSDOv5lUQtdJrFPWd+nSZZfDwZq6mbmhy00dh3BpcUXAKATc8rzp5uZGgRwDYtzOa7RIlWxWsgea9Njda02iOd5occOXburEGy7xTq+Jc2QETRErAdCW7pUDOhxSODMu4cZdMgSUTU5OFksALwSyiJkSYfcsKfD+8c033xTCCqArPv2Dp0+fvhhB47dAeG+99daNxA6otGrVqgYEcslOoLyf69Wrt6h8+fJb8JffKURgBXD16dOni2ARsRQLg7gzHyM4YsHPP//8n8QzqISgtQoFwEflypUL7+MR8Qy4bwAXlPIobcIpnix48xy+fVtxBzB6PO6R8K7mzJkzHwavnrQosRZE+E97kPb7EBJcFNMujnxa5w8Qt+NTaHre4YiNWGgzoNkJbU7KOoRIi4cgkAg4ux2D23p+M5CpdPk97nhoP1HnRuJXyR5o0uP3/hutsZrKM6o4jBYHRLcfU/lNUIg5bRErAYgLkMQBXZQAmT6iBMBUvhx+5yJgyI27EU+lSpWS5s+f/wK34CW4ya9IwMDr+feXCPat3n///bueeOKJppj5F0U4St0QSC14BcFi9kh6xDfffPODrl27Tpk9e3Zd/r6/bt26I2rVqrXfiIE5IIIge/cQE6Etc3mgQYMGX6PcmOOgetSLYgWQ/6OPPipms+MT8N5Bm2XjvVhECwoWE9Xh44YmLExpJwK6imP5Ud3FBEWEiYv+/KySSGPxEye3bScSvok0Fp1PtwhoPbcImKYAcDuOaNRLpLUmkcYSydybjIPJtEWCeUzqRqwEQCA+TJaAIykpKWEzBCBklN+9e3dtRvpLTEabQaetW7deyy3onwkQ+C8E/oFffvnloNGjR1fBP75Ex44dLW4gLW7+U8e3devWPOvWrauyZs2aKs8++2xd/v8oEV6zowiZ1759+69NGpcdWpYsWdJu3LhxQxCUs2PVsYA4AF/YqRfLMqQwrAS9tezQgGLnIFkOVtkpa6NMPLgDRLJAynoQSX0bELouIocyN9lFTB2PAOGENidlXYOsFbM0AibymNvbObf1osEAJuIcjXGH9qEYOEfcZEuArDyfpo/ddPqcfwnOamT18WeKVsRKAAT7IwhaO+ilWbh5ER9ulAD1sQYYjeAsaVaMeTp16rTq66+/HrNixYp/QFRlIsNa3PJbVapUsRDwLcaYSisZBCwi/1uY0FuLFy8uTHaBwihCDjRq1GgagQbjKvgc81Dss88++weuDNVIobewc+fO75vuBiBzsGXLliYoAaqFYx6JB4DLwCasPeIyXWO48fnw97M+tKlNeoOAyUKNNyOM31b0kOHv3CUivokypkjGEUldfznu19ZNFrqjMf6s2IdbnnRbL1oYm06fnziYfHaK+bxElB1AZq1Zs2aHERxnkPPUToaAfPjX1yGye0E/Z9xt2wQIzENwtNLcjluiBJBXBElxZRBFgLzy/2IRUKFCBQvfebEMsLAGyE28gGNu+41VPdIa3o9vfXPGlIQrw7dYRMyLFS1O+t22bVtLaC5ms84WFFWHbJYNVyzRDwWmKwFivmCGYxAHf3c6FqflHZCiRQMIJBLGbsbipo7fzBMJTZHU9Xtc2r7ZCMhef95sEpU6jxHQ9cJjQA1pTuc1k4mIWAkgbXPbugWh7IidCUeALk+qQEnvZtSzdOnSUv/5z3/uX79+fV4EYotAchaCfYY0ovSwUH6IEsSaNm1a4ccee+zPCxYsCJu33pRBE/egD8EOr2Mc53EDmI3bw2em0JYZHWvXrs3P38vz2vmwz0h6QOboqEdjiwd3gEiGqoeeSNBzXtcOD4e26rS8c4rc1Uh05VhmqJg6J+5m0sxabjB2U8fM0f9KVaKNx2SshTYT93pT11mTb1pN5zOlzzkCuhY6xyzDGp4oATCZX0aqwF1yax7ukeBeKAEk+JhRzxtvvPGI0EV6v1QLgMwUAEHCpYzECiCbgHXs2LHypBt80KhBZUAM1hhFsQK4l2wA5XEDWNO7d+/n8ZuPC0sGXAEqMqw6NnFOKl68+GqbZe0UEyE5PJPbacmfMpEujqoE8GdevGg10Q5akfKqF5im14apdDkZr9sxuK3nhLZolk2U8Zg+DtPpc8NzqgRwg5rWUQTMQyDRzk6eIuyJEoDAeXsRJlfbUQLgW18ac+4Wno7Cg8aOHj1aivR42Ro2bGjhI2+7RTIeWPicW8Q4yPftt9/2xT3AtNyyvxvLvHnz7iQbQDPcGw6TDWBE06ZNl9kecIwLkhmgIyTUsEMGvLaN+Vxop6ydMiiwxDQkkQVlo8eGtZFRcUTs8IyHZRLxoO0hPNpUgiKQqAc4/Z7NZ1jTFP6m0RM6g4nCz4kyDvO/LvcUOp0jp+XdUxaHNT1RAhAU7ygZAhaiBAjrU8xBPj8CaAOEZaPiAkyfPr2DZAEQE3/x/bf7iOIDKwhxibAklSDWAW4imNvtLuJyKAB6E/xwiGRzKFWq1CQyGnwScaNRbICAjM3prpidLhHaV2PhsMtOWTtl4kQJEMmCZ/IhQ+JxhF1f7MyjQWWczFWiCkMGTUdCkeKEtxJq4HEwGDdz46ZOHEBhLIlG74WGoaa8adiEKDlxgYARZzpPlAACd8mSJVdy83rIjjUA+ejr4IMvwpwxD5Hya4owL4oAO2MIEi5lCxcubBUoUMA6ePBgRUzsI8644BcojLEIcQvuwXWhAsqOn/r16/efeHEDEEx+/vnnyvyw6wpwAmXONlw7jnuFJ/ydYvhtdKQHl0jrewX179oJBOgMG3zUNwJi37ARG4YXMJB61dSxuDnMuqnjBYx+tGHqWNzS5baeH9hqm/GHgGn7oWn0xN+MKsWJgIDTdd1p+UTAyPYYPFMCIDyvQ0DabqdnblQrLF++vDduAb/m3TPgwaR/JTf51qFDh1IzANh9pCyuBBYZDyz8z7fmy5fPvi+B3U48KkcwwNtQBLRFON6LBcCbpDTc5lHTUWlmw4YNrQhk2MhmZ7vr1av3k82ytoqhBBAhNCsLorZw8quQ4QoYv4YdbNf+ouQ3Jb9v/wLrisn0RR8R7dErBNzyldt6XtGt7SgCWQkB/d7Mne2sPjdZffyZcqZnSoBatWptI8r8Yju36BzmCyQnJ9dF6C5hyneDO8MBCfQn8QCcKAEkXSAm6hbpBQ+3adNmEVYBRioBsADosWjRojsY4zkCOU6oVq3aYlOwt0MHCoBcKI26w19l7JSnzC+lS5deY7OsrWLMtSoBbCHlT6EA/v40HptWnW5OTstHbVTERHF6S2XsWKIGmnkdmTonbulyWy8aM+OENlMtZ6KBU6z6MDEwYKyw8LpfJ7zvdd+ZtsfZ374vcFQp085CEHDDP27qZAnQPVMCIFSe4RZ8KULaETvIcavaYsmSJe3slI1GmZtuuukTYhWkkCLQIq2c7S5xbbBIWyfWAPlRJKzFz9644GVYN+TDCuAuFC9lsHiYOmDAgOdLlCgRV/7V69atq4cCo60oXWw8p7B2WNu1a9cdNsraLhJwBzBSyWN7EJkXNHqhTDAlgFOsnZb3iCXCN5PFD06mzoupdIVnKC2RHgI6n8oXpiKQKLx5gTOGced3Uydd6UoMBGxJVHaHSrq8eQhKtgQvDo5VEOwGkfKtsN32/Sx39dVXD0NAXkbqPGvz5s22FAGiLNi+fbu1evXqA9yELXz44Yff9pNGt23PmjXrnt27d3dAQXOAbABfV61adb/btmJVb+PGjX1QAtSz0z8KgJ2tW7ceZaeskzIBd4AUJ3WiXFZuLyLZkD1dDzwe+3m+NzcKGKc31B6TnWlzTubK5NtAkzGO5nxqX/4g4OQ7CaXAbT1/RhFZq6aOJVFvzE0cl66zkX1D4WqbOOfhaPbr7ybzmtO10Gl5vzA1sl1PD/1t27ZdTIC82XZcAkRYQVjrMHPmzM4mIEMAuXN/+MMfXkpKSkoZN26chel5qiIgPdcA+R0CoYVgbVH2OO4AeW6//fb/4me/z4SxhNKwePHijpISkDk5h7XGd7htzDCNxnD0kM2gHDEXhE/yhSsrf2esq7BK2WKnrJMygewAJisBnAznorJ79uwxWcgUWs+LJYbrAcZ/RZM3MjcmlCaPxym3JNJYnI7d7/KJiK3TtTYRMfCbbyJt30QhyESagjg74VEnZSOdRyf11RLACVpa1gsEYv4teKoEIOL8hcqVK09DSLblEgCCFRG2Je+7Ec9jjz329XXXXfcaQa5Svv/+e4vbZ0tS6UnKQBH65ZX/l5cI19bYsWMPi7Kgf//+r9x5552TjBhECBG4KOTFsuEJhOKKZAGYjRvAf7DW8CxafrTGSyyD3oyljU1XgEMobxb27Nlzr9f0Mf/iQuHmNtprUjJrL5JFJZK6vo4RHr4A/o6xZy1KpIOTcfMTyNrgVAlg3Dh8Zd74aVznxdy50rmJ/twYtXcY7HaVKLx5wWCMo8/95vbolN+cljd35D5QZt/53WbnWAL8QlHJElDERpU8RHuvh099RSLV23IjsNFmREW++OKLP95yyy0po0ePvvmTTz6piIuARZR5i2B6qe1KEEDJBkC6ugO5c+c+cOWVV372/PPPv8Itu2MBJSJCbVSePHnyE9zwipLlEC4AEytWrGhXOWOj9egUwQqg5Pjx4/sgAJa00yMuA/M7der0tZ2yTssksiUAWDi9nXIKX6Tlz7lRAiSQj5/JG5lTJYDwgsnjiZRXtb63CGR1XjF5bXYrKJtueu12XN5yfkhrgfOHb+1HsWGTv2fj5j2K8xLalck4OOUfp+VjBHlsuvXUEkCGcOmll67Inz//FP7XVoANhLbWs2fPHhCb4affK8L/M2+++eYQ3BvGQt/a6dOnH3v//fetoUOHproJYGK/o127dhNffvnle0eOHPmfGjVqJJtEv9ACjV15b0RwOlGzZs2vWrRo8Z1pNNqhByVAd1w0Otq0AkjGCmAxCpnNdtp2Woa2z6IpPuW0XpTLu13wpJ7n64GHYz9LEM4TTtszWAng9GDvdl6dQua4fBZP3egYryhVMJZfojR+k7tx+u3LWHQ+ozujxikpRAlg09U2ukj92lvc8yfYnjf4vBCLOdU+swACnlsCCGZNmjQZzk35ZfxvNRsYlt21a1f/X375ZVzDhg3FgsCI5/rrr5++atWqBQQJrMnbdMaMGf0RQjoj8Gc7cuTIitdee+3Gn37yNA29Z+MmG0AhLBoe4XBeCoXM9L59+/4HV42jnnUQpYbgiRK4ZfRH+K5op0usSnbjjjILqxJJ5ef5kydPHvFJP+l5w+Y0aPJGfgbLG8euLAEXDnMQTjxKxArAlsI3Dobu9vbDbb04gERJ9AEBN+usmzo+kP67Jo0TlqMx6Fj0IUoAzjix6Dpcn6byZji60/5dlABuzo6JuP6bPCan/OZG6eqUd+K2vC83f3369PkZv3lbeeglyB5+963IY9/HNBTr169/En//5Q888MBnw4cPv6ZVq1afQGOeChUqFEPJYUx6w7S4TZw48c+4LbRGs7mfgIdj4lEBIGMi60IHB1YAF+CjhczZUr/4iGCDp+HXkwZr42XoThfIIFxGL5RgnuxGCSCa/fSCe/rFIw7bdTJXps6P+FE6TTfqZNwOIdXiESBg8sHPzbBMHY/Tb9lpeTdYua1jKsZuxxOsZ5xyw6WAGikOftQ3df0/l0AY+zFvprTplH+cljdlnFGhwxclgFBepEiRRfywdXPHAb8MkfZ7LVu2rHJURu2yk969e7935syZDcQEaDRt2rSniWVQ3WVTvlVDmdKd1IuXczA/gUD8Ubdu3b70rTMfG16zZk0R3kHcvtvCWNICEvxwUseOHXf7RRYKlWQ2CeNcP0LG6/qwCH6yFhi7WLJGnEIJ4MYdwKmA6hf7pNeuE7ydlI3mGOT2JCtnbYgm1lmxr0QUMn07d2VFBskqYzY4JoCpe5Mj1uCMcRarU5PPd47GE2Fhk9ddp/zmtHyE0MVXdd82ozZt2nyDP/1yO3CIv/fJkyc7oQS4xE75WJVBqN7cuXPn9zHJSobeLnPnzn348OHDBWJFT9p+casoQTrAh6GvFEqApf369XuxZMmScbmorVy5sj1pATvYvcVNTk6e3bp162hkaHBjLhYtFskuQfTddIYSQOr5th64oSlNneMohI44bUd9/Jwi5ri83J4kihLAzcHHTR3HILuoYCpdLobiuoqpGLhRuJp6kDUVY9dME6honCWAC4urSDHIavXPoQRwfNEQwi8m4+X0O3VaPppjd7oWOi0fzbHEHGffDv1EaF9XokSJHxBKbFkDcJAsu2HDhuvws68bzRlw2hdKgI/IFvAlgvY5UggO/uGHH/6IIsBW/nqnfTktD3ZPkg2gLQLdLuIyjCxWrJjJt6AZDg8+KLJ06dIbuPmtaQcDpmIPsQDECsD3DBNsxMZlgQjBKAdY5LKDWdoy1JP4IK4UCG76c1qHdeSES0sAk5U2TjYn11YeTrF2WF4tARwCFoXibg8WbutFYUiOuzBOiAsZQaIpAdzyjdt6jpkhESokkLLVyb4XtakTJQtKADcxn0znYzf0uakTrblyyj9Oy0drHEb045sSQEZHdP2vsQaQlIFhH7nxRRBpjR/4lbgG5A1bIUYFSIF4dsCAAX9GESBjK7h8+fIhmOBfFSNyfut24cKFXQhg2A9h6SiZAN4dNGjQsFjT5LZ/LEK6gm0Xu1YAuGjMa9SoUVSiNAY2CTcp0dzC4aReDhRArpQAcWAJcJIgl44zM+TNm1eUkCZGU0qIjUkyAxAw1fG8OGHqKJZ1c/AxWdiMInS+duVmXnwlKMLGnZ67TFUACgxu58ZtvQiht13duO8aS7jjBscjMn0+w068BBF2qQQI23YcFjB5Pp2enZyWj9Z0GYGx083IETjt27dfU6ZMmfEIGLa0a3yABXbs2HH1nDlzujjqKMqFixcvfgxf+7/wcz6H4NJkDnicGAFXRpmM37oDs+Lz58+/D5P4EmwSi7p06fJurGiJtF/cGaosWbLkLm59bWUEQFmwCyuA77t27bol0r7t1Cc4YBLljLSwgBdzMv+uMn4EYgK4qmsHt0jLMC5RAji2wpCDE32bag1g6uZke7rgOcna4NaE0nY/WjAqCBhxKEkzUrc0GSfEhYzL0bmLtS8RlQBRYehE6oR19liCjMe4fU+UK6IE4E0U17ZQVnG7hprKbk75x+T1M+YYO9qM3FBLRP1h3PDbyhQg7fMRNiSw3c34hFdw01+06lSsWHE/gQIfYWwifFZftGjR7fjkF49W/6H9oDS5b/v27eI/vw3Fyxe4YZgq9ISFByXAjYyjc9iCgQIIr3Nq1ao12W75SMuhBDgMfaZuFGIJkNvNGFGmiAWBq7pu+nNah006hVt9xzfOKAHk4BS334NTnKJdXtxjsASw5fIVQpvTTTxaw3JzWHJTJ1rjyer9mDo34nZl+xsw3ErLZGVLpPxvFP+wlx1lHzTRqi1SnI2oLwoAl0oAo/gkHTDdfKMmj8n22hnAwnc51wgGdkmE7+AglK4nRd1YhAxbeerxe8p+/PjxbjNnzrz8wIEDvtPnErfUao0bN17BDfRLLMyHDh482HLq1KlPRTs+AO4IjfGhv4KDQjJuAO/hqjAmkjHFsu6IESMuIbXhNbBAQTt0wFN7UcaMQxmzzU55L8pwG32QdkwNtpgT1whX8SlOnz4tLjhGuuGIll6EeYRNx8K8KG0Yl2MLAi94JSu0IVlI+CYE46z6mHhYcnPok/kzcSyR8JWp4xFlrW2rK9a/7HxnTg++keDmpK6pGDsZQ3pljRuXKAEg1NQLCCd4G8fLcsbA0uIIGLu1ajOOX0ImxA1tbvcQJ3zgtqwjudCti6xb4hzWi/m34AhMh4P7rXjLli2HYeo/x64/E2XLbtq06UpS8DVw22e06mHp8EXDhg0/g+azq1atun7cuHHPRKtvLA+KoSx55tixY5VYvBY1a9ZsZLT69rofhP8cuAHcIpYgdtvmXLSwatWq0+yW96JcoUKF9tOO05tPL7oO2wZ45EpJSbGlQEnbGK4kefidKwVCWMI8KEAsjoN8Y46VAGzscnBybEHgAclhm5DDfdhC/7+Am43cQfPOi8p6jsLusMyN89pG1nCLsdt6RoKQIEQZe4jlu8nlUAkgB0Una0U0p/A8+47yfxQQZy+TmAAmKrSzBVxW7KIQc8EnPULB9wCvkWcFu8B6WM7kb9oR/7jNmOUhlkY3FZWNpV27dlsbNGjwAbeNW+2igelvO6LdP0Su+LJ268SiHHEBzlxyySX/RBj9js0wL24M106aNOnmaNACPo9t27atEzdxG8nG8GmVKlUcp1CLBp12+pg8efKNCHnd7JSVMrhhbCf94Tf9+/ffYreOF+XoU9wBTBV6cvGNFXIzTupJqksjlQCSv7dgwYL7y5Yt6zggYyAwoFvtvhso7dbJ5kQQsNtotMuhtBMlgC0rrzS0OdrIoz0uB/2ZfFhyMIzUook0FpPHk9PJ7ZThmVsusCcmGt84/W6iUj5wS22iEiB7IKaQXRyiInfYJSZ1ofjV2nAPrxslgOn874Y+Y5WoTJcj/nGy1jrhmUQp6wjMSAbdvHnzCQjM49jQbAVVE4H61KlTl06ZMuUGboldRTyPhF4ndRlXCoEC/1mhQoXpaBJLEiTwcfz0+zhpw2lZYhC04eb8SvHJJVPBF7169RrvtA1Tyg8dOnQwVhQPM5YydmhiwRZz/B9q1KgR9TETb0FMn/fYoTPaZcAvO+4AhQ4dOuT4u+ZbKwauRioBWMRPY4Gxzw2eCKgnwMVEc3W5PbE9T3LIcnjb4gYux3XA9ggY2wr86rjx6Fdwc1iKPpXaY7wj4EgJEFAW2l4rogyOY8VslOlLmO5QaB+T7E8GDkjcW2zzp4nK74ASYC9Bpt0oAQyckshIwsJPYk8YuR86DX6tlgCZ84LtDzcylrKsmjVrnkQR8D7MtdxuW9wMlyby/XVz587tZLdOrMpVq1ZtD1H5/4zf8iborv7zzz//EauAOn7Qg3VEFdwAnkbhUBwrgMUoAUb70U802pwwYUI7xvMwN4pN7PQXiOK6kJSA71166aV77dTxsgxBCA/RnrgEGPmcOHGiJIoAx0qzo0ePlmZA+U0clGQXcasEKFy4sFjHyJyZ9jiyBJDgYCYqAVjPj6IEcBMjQy0BTONIQw99LmEy8gAbGEtu4tnYVrji4lVAMr+4xMHvaibjHMnYjVuf2AMPsg+YaO0pSi0Jdmnr4SLQ8fnEVsORFTrLedpUC8/IRvZrbUffKfv6WV5TFXw5icHmhN9ymxtSxX6AWC+YIL02oqYEkM65rV6K6djXbIBJdgfEh9mM4Hd/mD59ej27dWJVDpeH1QinH3JY30+AwGa4Bfzfzp07Pc8YQNyBv+3du7ctm8J6AhP+j363x2rMkfRLTIP8WDQ8yAcqmQ3sNrUHJcvoa665Zr7dCl6XE7Mx2jQuSi+LtnXkyJGygUj/joadlJQkbjdGKgHk9gNh3pUlAG46Er/BuNRKYrXBYch2NoaAJUBU12s7DITy7ggYO7UEMDllj6PDkh2MtIwiEIoA33Je3K9sx24JBHs1VQlgqqCQcEzHHpjEXmj77BxFAHLDo072MuOUAHLRgKXFAZeYmWw672pInE+MtQSQDFh2LU+wis2GEtW2wtUVWHFeKeqHytatWw/l9noqk2gLOjkss2H2nDFjxpPcrFe2VSmGhS677LI3iNL/OgLZMRQBHfB1f9JLchYvXtwUxm4HLqck60Lnzp1/8rL9aLWFciT/+PHjH2Dz6CfCq83nAmWnomj52mZ5X4oVK1Zsk+St96XxCBoVRQrfSglM+8W/39GDEqA8YzI1O8B+XG5c3+ajNDLx9iSH3PDZnaTATYttTZnddiMpJ7EaUAK4NU81aiyR4JBAdVUB4vNkyhrNt5zHybcfUAIYJzgJVHz/joO1+gyxl80btUZxbj4F3iYqAfISWNj2BYKJ7gAwzVHw3e0l88RzW+IOwFplT0iL8kDFHYALFFuWAHJuClhSRZnK+OnOtvTl1ZDatm27q379+m+ymK23my0AhizARA7gZv1e0gaaqhH/DaIrrrjiZQR0MdHPsXHjxquHDRvmiSJg6dKldX744Yf/gkcRTHB/JkXhN17NSzTbQQGQFzeA27ds2XIvPFDEQd/bWZimDBw4MKaWD1izbJBNwwHdUSsKPqWJoeFYWYbyoGTUiHTQkawRaOgPEhjQNd7coOykHaMEHFFusqbZPjhh3SE3LbY2PgfwRlSUdehUkSJFdrpsxKgDtssxmFwtq+Nr7PglOwDrbWG7zCNWA5L5xW75aJZzmVc9miQmTF9YxSYjqO4zbCuz4M08KAFs8zP027YaiMbkCZ648R7knLAjGv3FQx/s7WdECWAgr4kSNV8gpXVYKEVZwDnLVbDssI17UyDm+1TUlQCCG0LyVA71nzgxbYIpS3Czfu1XX331EP4gMaHbyZzfcMMNfyhfvvwUxlh8/fr118+fP7+Nk/rplSVI4kPHjx+vj1C0rmfPns8TY2F9pG3Gov6sWbOu2Lx58z3MaVUHbgCHwfIbLElGxYLm0D65ld7Fv40MDsihrNz27dvbOsEIl4zqLJbVHcyFk+YjKsuCfwFBc0Mgyr+rtqgvSiPTLDdSNdQobGytZdwG5mV+jFICSFDAUqVKiULM6WOyO4DTsSRSeaMUZSHAuj0oua3n65zKTShpfUvY7YQ9v6QIWnbLR6tcIJia23XVyLlJw3NG0VipUqXznJv3wD+m3dDmO3nypG23V9MCEAsf58uXbyfnBNfWhtH65qLVjygB6Ms0PksdPhciBVEC2DLxl/hYWMYWN/Fsy1CMWF9sHUD9YDyC6L2Pb/UoPkDb5mQiNOILfws36/egCDACwIywQat4oWPHjn/j75th2Jo//vjj/3GTX90tlgRHbErQt2bUP0LbU9u0abPUbVuxrPfNN98MWLZs2aOYaNd38GEmwydjSTX5Wt++fZNiSb/0TRaI3dCzNtZ0pNc/30ghrE+6IVza1ravXr26J0qAhiaOB5wPk5HhFw5ArqP2IqiKssyoDAHiAsPhvgSKAFvzFDAfNuo2kDFsA9ttLvnG6PXbwZgSZRwOhhzVopHgG0ld3wYp0aoRmmxbwBG0tQJ7pa11wjei02/4QiBtXZS7zbrdFS1adJtproiioMKdsJydWeGCohA6DKNuZgNKgG2BIMJ2hpFeGVMVqK7Gw2XS6YAiwFV9PyuhBChsV+kksSpQuJZx4HLsJ+lGth0zJUCrVq32N2zY8FU+wNlOkIE5G2JOfgfB8a50Ui8WZfFd39i+fftnYcDdMGIL0gbetW3bNsebOVYEokR4BkEtP5rgn1AufBqL8UTa5+zZsxvNmzfvcUyvWjppiw95Pm4kL6AA2Oqknl9l69Spc5QxrKP9FL/6cNuuKFZYIFuSneJyO22sWLGiLBvzAL5D2zdTdtr1qgwHhh0oAVZF0h4WOVsY38ZI2vC6rswTa0IpO2Ztu3fvzsacljDtNpB1ba0oxFxiE7O9xyW98VTNraVFIh1kjVQABJmIPc2REsBQ5jsbiYWWoWMymixcEbeyJyYZRmRurHRtKQEoVxr6bVsNRGOcnA2SOVdvrl69+gmX/SXSupkKAa4nJ9jf3WT9cQmhvWpybuJCpAQBsKvaqcFFS1HWWiPPtnboj0aZmB7ELr/88uUsah+yKIhAZfvhFrkZ/uR34B/f2nalGBXs16/fcEzYn4Z5D5HucBDB8G7aunVrfhQZ2XFt6NSyZctv6tatK8H95IM7RbT/sbfeeusrn376ac8gyQsXLrwDIaAp/95yySWXPN+sWTMjhGEnkP7yyy9VCO74Fw4NjtI9whs7me9J4LjCSX9+lyWf7HxocxWx3m/aUJRVgmcehdf6ZdYXc1Jm4sSJf4e3ejqwyvCb/Ivah67tcvCJpNOmTZuK3/rmSNrwum5AWVOeW76wGxRlimAFVN6kOeLgdBwTyjUR5FU2UUhzQ5ObOl6zk1ftJdph1si5ke8YH+oK69atKxNu4sigk5NAwNVMvMmSwKBYAkj2FTePkXPjZiDRrMNeuF3ORNHsM1xfopxGKKsYrpz8ff/+/fW4yS1vp2y0ykD/YYI9G2nZGS0M0vbDd32Mc6Tbb9s3soPBr1Em2bKqxnK8MWtnMd8ISoCGY6oEEPweeuihoaS6+5iFzXZ6DmEEtDs9uFn/O37yjm6VYzFnAwYMGF67du3/IcxeQBHwp+HDh7917bXX/njddddN3bRpU29cG7pWq1YtT5UqVfLiMtD/448//sPtt9/+3QMPPPDZqFGjbuPG8DLoPoUp2BQUADENiucGP8nqMGbMmH8jbF4mAdHstoHlwyFu3L9GMfKR3TrRKsdcreIQtCVa/Tnth4WvDZYXL//vf/97lW+k26pVq8rAa4U2bNhQeMGCBTVQQN2Ga8b7bN43UtaWf5VTGiItz/yfY3NexcEn4vy9aPrFEsAozTa4V8J1o104nNjIKmJuWdckQYDveDOpAWeFoz2Dv6sA4BI4rWYbAbfWELY7cFtQzi/4qdZhPQ57iUHsHHHTquC2L5/rpaDUNy79qodjNm6dwm/9AJiLS4CHw4y8KeipynmjfriWuLzrghKgqCkKbcGRYIurK1asuDIc7WH+btaERDgYLAFOct44bhqfBYaVG2VSYztD5LzbH2WG7XSsdtpMtDJGRNrv3bv3B6NHj66KIuBGALYVMRsGlTQRvWfOnJkNIecfBMqba/Lk3Hzzza+///77VTDtvQ+XgJtZDC183C3cIgqiHLBKly4tig1rz549FoeD7AhtBXB5EAFtSJkyZQ6ULVv2A8b4+aOPPmryMH9HG7fN1Zjbf3PoGcRYbLtCsPgcY7P7FneK17t3725cED6UALvh1y0sMB1NnRD4qg6xAWpzmzQIWgXDFDZfOdgUhu7KvPLTVPIlCuxBvotlNWvWjFh458Z6Lsqo7Yy3likDZn6KEo/hetJ+zibIZ7qWCsxddhRoV/I9OImh4fsQoecXFJuRHJyMO2C7BM1YYdPleLSazwjIEowgVJP16HqsAebwHaWr5JS4R0OHDr2HNcvWrZfPZP+ueTGjJktRxAraaNMdz/3Vq1fvyBtvvLGUc+Ig+MKYzRuebozb6u1YuD6Fi1i6bpJLliypSVaorqYoAIQPOGOcRQmwgPPcpnjmC68VFCgBJB2lcZYAMk5hey5GWk2fPr07seWmZjR2LMUHIh/2NugzMZLFbN/K+kk95roHOnTo8DyC4jT6OWu3L4TKHHzEvdFA/ue77767HO2QbSHTbh9elhP/ZoTivBzsrU6dOll9+vSxuOW00O7KzYAcDCwEfgvGtrAUsPA9t4jcno3DwmniJ3zFBpDkJT1+t8Wc9B87duyrjO0q5spJdOOzmEv+hJuEKAAiMgX3a4z4j6UQSGaNmEX71YcX7YJ7Nhb0SihUWvF2wMyrPW9DBNDCJm3G6Y0V2ldxoFjiBQ4NGjRYyvcXidDqBRkXtREwbetOvI+/LV++/HcmktwCFiAt6mMoDW9hvmz7EHtO6O8bPIllxQpinhxx2ZfcmiTUzYlLHEyrpnMSpRkRqx7M/PtwgfHnNWvW/M4lCJfBQrhqPYkS90rWCaMCgoZAdJSzy94oQabdBBAgSO58zopuY7H4hWMxTLSv47z3N/at3wX+w8K1Brz+GmfBliadOyReF2fweVyyRRIJPx7WTUc0ckZMRnh2nZbZLyaTdgPuVI2xBH+It3navlCQ5eZMNQAFwHOUdZwu20/a02nb0bz4QZsRlgAyMG65N3/00UevrV27tgjCSnuZazsD5iPOjkVAV5ihBNqhWmya/+P2MOKbQzt9OynDuLIPGTKkL5pSC4WHxe1manVov6gZ+bcoBDhkWxyyLQ4CFhYBuUhL14eCRvnFZzb+ESNGDJEggAgvTZyaMWMRsZg5/Hzw4MFGj5c5nIF2ewhjrOuEF7RseAT4Do6yOc8AY0809I0bNz7w0ksvLcT9oRcbQ4HwFESnBBut+FPewHde8ZNPPpmD+8MafncGF6AKWAC0RrHZC5eYktGhxl4vKFM2g+cke6W1lCKgCKSHgFgCcWC9FcGpxmeffTYPd7/1HL7xmjtZHuu53pxnekoZU9FjHdiPEsCorCumYuUlXVi1rUBpvIxzUkWTBGpoKYe760PsWw2+/PLL+VySbGYfz0tMm7rsbz2Ja9Pc6VnQS9zStiWZFolrM5cLtp896CfmwpwHY/itCeQPCX59yFB3ALEGyEbQv0uwBijEuWkma+cm6D3N76qydjbC4rqb8KNJ34eX8+NlW8YoAWRQt9122w/4w+dBYP4Lm2EruwOVieZjboSJ/R++/fbb4pjSv1u/fv0tdutHo9zrr79+O77YnYlyb7HopHaZWbpXsQogVoJ16aWXWvhvl0ZBcifa1FewmrBtKRGNcaXtg02gMB/mDdD6KB9lTacfIeNeRjT4twYOHPjd448/Hosh2O6TeVzGOOWmWpUAtlELXzCw8fyCid5k/M49U+ixJkwkOOUVWEa0CE9F9EqwoUne8B74/fdgTRDLkvNiOSOvaaZsktIVwWQmVkyLI0TIxEOTKJ7d0OWmToTwafVEQID9sShKwIFYBfQNpH6TW4F8fPf5xE3Q5Ad6D3L4th3LyeSxxBNtxIXa+8EHH/zEjbsoiZxYWPo+TPasAiiuL0OBNZC97CT8nVMCB8o+ZpICIADEYZRYszhTR+puavr675i+cuXKnXv77bcP+c4wEXQgvI/CtBsuVZ1k7ZRzI7xWUC6Ghd+cyh4RkBLXVY1wBwhFkMj432MG/zJpINY4RZbDfUXSnd1NVPR/YHZfw2l9P8ujmGiJdu0UZsmpPi2ZKQCCdIhVAP5KVufOnbOh9S383HPPScwEYx+sHAqB/aOM9c+M0bECAEw2osz5mBSII2rVqmW0skMmAXeFo7gFTGKe1C/SQ66ED1LYnH8GX09cAYKk4Xu7io1juYnabTkgoTSzUH4W5C3M/xunABAcmZtVrM8jPZjuSMwvPeg+oZtwfOgzGA1bFoEZ0B8XOAS+/VycX4rwFucVJYDBU/Iraayl+1DSnnJJqOkuQUbzDoHsJAbWZhP3MuFd9q9s7GMF4OU8sq+ZpgAQ3ODfxVwMTHbJv2mrGc0vEOuYPvm+TeSvUOADa2dO+KywnJv4mV2Up3GkAHA8Lx7x62/NGKcEEMrwiR+Jb/w73Ao7joQPAxRDsz6YACTPkQO9jteAuW2PKJUV8DsqxC3371wAMmtTFAHiGsBCmoyQXcVt/37XA+vquAC8IOZgbAIVnH6ELDab+YjfJQbAx23atDnpN71etY/LxjTmyFNh1Sva4ridjXwr04mB4TZvb7pDr1GjximycExlvkzzp4yLqRJtO+vQVAKazoyQYNMFgAiHp9UNQEB5zMdJQBl4irPMeh+70KYzQQDlyzJcx6bZuUxSINNF4ABWLOO6deu22iN8Yi7MZTIOV7QR9HMXQvZpj/DRZtJHwNXceAmmkUoAblfPkhf+XXyC33JzYIdx82Neeznp9T4hkupfMRexlcPUS2BD28IfqgTCcVMEmxwsPLasAIL1ZZGXYIFouC7gTvC7IBh+0eykXXwarxg5cuTHmDTeKeaNThUA9LUD4eJ9FuR3yQZgZDCSjPAgu8NWAvVMEB92J5hp2fQRgN9P4Es4uWXLlm7Tz2UKLdYAM5gro2NNmMobKGUXwu+f46aRbvRnh3SbaAng5tY55pt4BribSpdDNtHiJiIg8QA4zyw3kTaPaDL6+8FK7nipUqV+Yh72eTTeLNUMF1XTCc493KNBG80rjNGVQhRZZTOXqkdNtwbwaA6zbDNGKgFkNjisJ6MIeJ2AeG8G0ps5miTxReLQ2pbAOw9iov6X9evXxzrXrhx6XR18A0K1pE2/OIqgI0S8L4zvVx4CGj08d+7cF1goOkOnY36i3lYWmnewAHibgIlxKUhjHj2OqVnkPcJZr0V4YTbWFR9jBeBLwClcTbZxg/Iz8+WplUGizxR47WEtHs2a7IXVixxKXK2FhuJs+iHQLmyJMo7geBOJx+zOYVTKsW9vQQmwNoLO4oHXjKYRZexcrCfnqZDmjAvlzIk7xXDS8u50VjPD0q6EbI/6ttOMrIOOeZm4ABu4nNtipwMt4xoBx/PiuqcMKjoW2rwmILP2EK5Ocuh8lUO7W0WA+N+XPHDgwHW4B/wbX/WY5AjHbO4I71roSMFCwZF/lPi8SEaB06dPn0RQ/j6a+GfWl6Tm+Pzzz99EufIMGNdwcfsvFhHbxAKgR48eb6AASDJlbE7pgP61WK98iaC0y2ldLf//EYAfVpMS8LO+ffsu9RMXItt/y0FgoZ99JFLbEgyQdwpBTb/yaFymKgESxRLA9EOpEzZyMyfSfiJh4ASvqJRlPdiBheL+qHSmnaSLQNeuXbcxB2PVGsA+g8C3EvX+W85s39mvZatkzIW5TKh0RRtKgD1YIK9QJZOt+Y/bQkYrAQRVzJ5OoAh4BUXA6yx2roQshNRCCODXc2s9nIiXL6xevboqkbjdHi4cTzYmtGe5RdtBJMtjBPhzpASQoCobN25EB3A6mSim8x137nEFUjDmGzZs2P24AHwIhrehpPhdjmM7XWKlsZoF5g0W4zdxAXCbb9xOV1Epg5/0d/hQiUWA+lC5QJyNZiduAKIA8CLoXKYU0MdyblG+gAe9uglwMeL4qSJWLmTC+AgrCi9jKbg6mBiImmsLLwPHYuqcuN2rTR2PgVNvnySxokL4XIa11jH7teKuZFzwjgRR5tzxHftnXNAbYy44Q/9TCbT9DjEVvHBpCw7HdOxdWQJg6XOOQN0rGaSeaWPMuH52b7wSIKgI6N+//6sI0q8iRG9wAwg31bkwnWq6a9eu+0m3Nxwf9j/iKlDYTVtu6txxxx0fbdu2LTcB9FJjAtiJlipRVlEcWIsXL94P7VuwjHAcKNENrenVIe9mgZkzZ3Yh/+t7ZF74B8qJpmDq5nB2njlczFy+1Lt377dQACR5RWMs20FIOtCiRYs3mbOfdT92NhN8Dwfgp68ICPoelgBR2XAIPvkNh6fvOdAan4XCGZrelhZFSenSpYdx2JzhYcum3tK6Wc9MHYubg6mpY/GQ9bSpSBFgTdjBWcSXmC2R0pbV6nPuOMz7IeOO+QWR6dhz7lzEpeI7nDPW+UCrm/XWBzLSbdL1uk4g5bmcaWMmd0QLoBj2E3O+iQslgEwQm86pAQMG/I8I3//HrfhCt5pPhO98pN1rtWnTpke5zX5+/vz5rbESyOs3E/zxj3+cSTqSMbNmzdq9fPlyK5DTMsNuJc2FKACmTJly7uDBg7mvvvrqD2+77baY3JhDc7vhw4e/hEvFh9AyhEXB1e0/Yz7L3M0i4ONLl1122acIYgnll41SYxka5nfErN1vfkqU9oMKANxBXmrdunXU8tI2adLkEG4BH7EezFelTfrchILkGDcB3xJA6SuCX3odjyTmm5+H35BpvueJhG0ijcVDlotNU6yVcjs4F/e3VRFS4EbhFmGXjqrHDd9deuml8zHd/pS52etohFmosFwecrP96fXXXz/Jp2GbzC+u9yfiJszjAnKanpF84RojeCZulAAyBeSOT7799ts/Q/P5d4Tk8RxSXaeS4+ax9ObNm+/45ptv3hs9evQ/ueXuSnR7X5UBDz744AsM49QPP/yQJIoAMfVPm0NVLAQwk7dOnTplTZs27RxB904SJHHk448/Ps0XNsyk0a1bt5YCnwcnT578Dm4Ad0FrDTsWDOk1KenFMByYgpD8PMqcYTVr1kzIG1j89L5DyfERvKna0zAMiwLgYMAC4D/Eu9gTbf6GDxeUL19+KHR4aeYe7WH40p+kAGNuxmOp8z8OAn5EoDZdCLCLq6nxDdwcMFzfGNkFy0U5tzQlCn+5gMy/KiJMcZs6gkuZpAh70fmJEMDQ6j179vyc9fprfpfsYbMJ0RQ8uwt34k+5pPEqpk284eJ2DbXEJYAgimMZsCtX7HgDKivSG1dKgOAEDRkyZBx+vfcTbO8zPnDXmxGKhJx58+Ztgs/9Q6TxexuB978//vhjn6NHj+bwgxnuvffeFX/6058e4Db8BBkLDn399dcWwQot+rPQrlty+y//j3LCGjNmzHEUAIdQAHzx2muvvUDE9KiYScu4sY6oz83/3ShH3lq6dOnfEeAbQ5vrTZv6SQgV3zGGp+65557xjMm1ZtKPefGyTcynTiNcvg1vSjBLVQRkAC7Y7OXQ8glpIZ/FPC9mQjhm7sNYA76V1IRe8kE8t8X3moyy7wcsM55FOaO5wMNPphuBO3yr7ku4pcdtPfeUhq/p+gBL0673rPBkuS/B93UMk3pZ8+JKES6xAHDj+x6l4Ez3o/+tppw9jZyfAIWR8J0H8Dhrgguy4wRufZPz8ARqJuz5yhkqfGBnz+4tUqTIRygA3kF55aclrYlrZxCuiGjjfDYRawAJSh6XfGW4FUPM18CcTj8qU8pLfnYE6GcwUU/mBn8IQmpJt7QhjOSmbl2E75o7duzouW7duhmY4f+AdvVbt21mVO8vf/nLhKFDh17y+uuv/w1h+9K1a9eeIR9nIRYqWbCsEydOpPDuxBIg+YYbbnjx/vvv/47buKiYSS9YsKAhFgqDuPnvTRaDhmBaROISRPIgXO2knVEEY3kZPDdH0la81MXK4cSWLVte/e67787iPnEfAlX1eKE9GnRymNyGtcvnCJivIoQfiEafGfUhPpU//fTTy1jdlGKzGIi1iq/WQLEcq52+wUCUIT82a9bs3xJA0U4dl2VMVEC72ZClTkSHLJf4abXwCLiZz/CtRlBCFABiXos5/TfE1rmavbEjzUUtNpFb0gPul/NQ5A9D4PQila+J339aeIzjn8zmr1evXusI2vzSsmXL8rO/9qZsXNHvljczqsc5Yy9xfz7jouEN3P9ies7wemwO24tI4Va5cuXTXI6+wzmpCfJAW4d9x6y4WB9LFhO+hWzIVlUgROQ8056Yf6NxqwSQmcTH/uDKlSv/LbfqPL2Z7Hb82rXUijIgJ0xTb/v27fUwf+/LDfzVLB6jiFq+Cu33Cq+4B0sGibh59aOPPnoT/TSaN29eXRQPnfjdOTbYH4k0vwTXgfeInH/wiy++8KrbdNshHkJOLA76oPzoiPDf/fjx4yL85+OgEmm/Z1mEl3LL+s2gQYPeY66iosiIlGiv6uP2cJrUjq98++23p5jje1EENPSq7Thu5xyL8XIxzUMB8Bnf1GETxsIhYRNKgD9PnTpVNst+vPlNoCsGNCSx/k3AAuAF/EyX+Nx/zDe/dMbnVjBJFCWA2/H7ySqR8Ekkdf0a066CBQtOv+666z5FSbyZvf8855ZudFbArw69aJe9fCXK7Q/69OmzyIv2aCMiwcQjGjJrxkTeCTvsa6+9djZWnP/CgvMC59lekZyHw3ZmcAGxwkQB8CkXT29wYeh3rATTeUVkoohoRBZZ+u67776FnFCWs2xVg6c+lTTOMbuI/TaeWFPPEQy+HZebf0OuqWkg3THfc+NaCSAT2qBBA9Hw/R+32F+hrfobt+iDYFLXh3gJeC9++mx6lffv319ZYgXQ3uaXXnppMcL5KMy9lxCE5aAXzPTyyy9/lrad9evXW/JyQPCiiwzbwP2hBa4HTT799NNuCP6dMCOrxDizy9g9eI7zEU4jiOO7t95669hnnnnGgybjrwki3Yv51FvvvPNOEovnnQEtala9aT7OxjyH7+cdDpJj8DPzOtBcRAxCLIcNM2bMeBILoBOsAZfSWPGIGoy/yrv4/L+75JJLXsLyaFMUyI/55pfOGN0KJiYqAdwc+kycEznAuqXLDQa+sj7f2AHWvlRB+vLLL5+BS2BeDqjimdidX5l4UyVnoW3ETvmQvdxLn2q335qv8xPSuPCOcfxjZ/AEkZ6FIuCfKAJOcp4TpXZWOnOclcDMXDR8jFvmB7ieRiONpel8IuuK68vRIM/dfffdn7/yyiuluXB9lHWsvB1ejEUZZI8NKFpHXnHFFX/lUvXMqFGjyvA7kdlUCZDOhMS9EiA4JrR9G0il9xRB9w6hCLiOg3wpdxnsfm1R6oopPMJxGf5ZJjk5uRU35f1ZVDd/9dVXU7jZHoeVwNJYMLnbPjFRL87bfOHChYN+/vlnMUOszMdRJDhWt+2mqbeR9iaigXsDP6w1HrUZ180QB+FLlC5ruPV5mA1KzPSEp7LME/D/H8fN/+tkhVhm6sBxWdmEKeWTzNVW1pBrobOWyzSYpg7xd3Tx/UuskV/YNL9mbt73IOBX3Iw9HULdHOZE0ReXvpLpjD/iGyMfJl/OKBEfYH2gy1WTHJ6TqlSp8puS7ZprrvlhxIgRKaw7Z/lbb9YboxQBcqNKHID3+vXr966rAWdcSZUAHgMa2hyKgJ8nTZr0Z84ce7G+uwq+cu0u6yOZnjbNXnaEb2gemWw+uOuuu0Zgaetp+5k0ZrpLmCc3ezJ+rHpfRXGZE3fhB8G6QrQAttmP7MNrWK8+fvjhh18kI1tqNdYwmR+j1tWQ8bhVcNuEJHyxhFECyFARMnaQz/5hmHQzt9yDU1JSmolVQKTKAGmbNnLATFUI6ldlzZo1rYlHcO3zzz+/htvuhRygNxJ0ZCYm4H6bHYWf0TQlFi1a1AjlSE+UGJU/+uijxoyjNkUqBotFgk1oV2AjC/Bs/Ic+x897PMKEF36DjsdragVuvxcT/+FhbppvwD3gKvBqKekqTaXXC7oY43H4aznuNN9hcv9Fy5YtjY8wi2JPvuG/ffzxx0vIjjGEW7BujKG4V9+JF7h60UYgWM5uFJ2T8U/+7MYbb/wxuGl60X6YNky8OQ+S7JS2uAogFmZeYn4gSYc+ocmNEsC4eZFvjjX/MOvgttBxXnXVVdOw/Du5ZMkS8WHtz1pjhGsA6/dGAty+h2vQm1hwnfJhXXCjdPOBjHSbjFtLgOBosOpas2LFiqexkF0jcbMkwDP8F7GfZ7QmwG4/jOsMZbfgeioXcx/179/fM9ddmzSYzMcyBFECeEIjCsxznGNfJXD5GRQBd9BuPRPORnLWlHTPWCx9gcXCR2nmrRD/dpXW3Ob8R1LMk3mJhICEUgIIEJjqy+b/Kn7u49GC3gyjDkBwbwSDeAl2fvHxPnnypATSu0xMTTAl3oqpzEoUAQuxFtiKZcIUypwpVaqU00Olq/kkAF02BJYc+PiX55b/GsZcHl+Y+hwuqvGRlqPRgn58rHx8KbS9jgX4h6ZNm37MAvyLqwFkgUooRiQuwv9YQOcRy+JG+Kcv/xY3DFO1lK5mBZ6QNEXrCxUqNInvYSS3EvNcNRTDSpi+fkcgnCWYVF515MiRy7hNacg8FfXjG4rmMEUQkdSMCP9LSWU5EtP/US1atIiq8hI3mQuslVFZF6OArdw+mDgWNzS5qeM3xG73bRPnJRmF6Mb0AMM1YD7C2tNY6e3kzHIda03ZWK01kh6U/peh0H/3zjvv/IRbNb/m2O3c+kVP2nZN/B4cjZ1AjklUeH3kyJELyYJ1NRZu/dkDqjK/cX/2ZxznxMqQ+FUzcbH5kjhDExBSRSEQ7cc4hWMaADyda86xp7kgeQWLyd24ud7HPLSDn9woaiOeJwlYyrMV//8J0PUllgqz0mm0GL8rEnFn/jQQ8zXGU+bwByN3rZIuZR01/zx9+vRRvE8Sbb8bwnkJrzdW2svFK8Eyyh4+fLgNwvhV9Lt39uzZW/jdPvzBl6MI2IqAfgGLgQMIRWtIB3gcofkMfxe/6NQFhDbOlS5d+ndmpXv27MkZpBlmz8448iGQ5JZ4Bdzul+NjbMihoeybb75ZjgW+NIf7Urwi+OePNLJ/ZsiLMCEfH8FXprAAj8IKYxYuEkfczVbWqjVw4MD5WJKswkpj4t69e/slJSVJUMjqzJcRN0BuZ4MN+TTvJoT/mRwgRxJgbo5kSnDbXqzrYb2wFRpeJPDoRAJ39uP77hVQBpSK1abnFhM5MPHsIwjZLwj/k7BgmhRjhZ2J6dHkAOnUtF/WcKPiWwT2FDc0ydilnknnApkTNwd7GUvU0ura+S7Zk3fx3U3PqCzBtzaSMeCvs2bNWkVQ2dtYS5uxJ0TNn1v2dNYICVw4hXX7UywUptoZl8syprvRyPpkFP+4xDm12uDBg+egBFhCRqoJBL4egKVsN+a7GvwVkaVsJDS5rct3cYZ3N2fPuZh+T2rcuPEElNkxSzMcWG9N3M+CEHv+raFsEbnlK7JR7MQy4HbcpvuK7OF2Tt3U4yx2EDnqZ+SPEcz/SJQAJzNoR6xfTNrTgmTKXhtzvjERGDf8kGEdclwuxCrgkdWrVw9AayWa0EYchEt6rQwQAgK+9WJ6UogPIjUIBZv5VSy6wpznWHRPYjGwX/LtchA/Ax3nRY0lhxzKH/3yyy83BUybzktb3ECWIJ1gIQQOKZONv+XBlFz8cAoEbiULB36KIsLig/AUu4wag34x9Z/HgeYzLB4mkmYtK6dfcYU5CpPjVByHcDkdk73m8GYvFDtdmeO68IKYn5tomvu7sQZulg/DE5s4PC5B+P8Ok7y5bMyeBM90Ba7HlfCHFeuWX0hHOprvuY3ME4cosS6SG5Wipt6qyFrCvCRxYNrGgWlV2bJlp2DWOwdhY4PHELlpTvhfFESmKL5kM5Z1zakSQATUaASfcoKx0JPRgSizdkToEQxMCYop+95+XuEVp4/Mp2QfkfnxzCfWKREh5WU+lvL9Lc6sDfZSGesHY8eOXUaA4GvY7/sTNLCan2bcAeF/P/0s4nA/AneFCSj1/RaqZF5MVRDL4Xxf4FuIYMrNqsp5TVw6JnEWnsPbgguITuxlXbhYqiMCnPCYH+diL1AQwZ+9TC6ZNpNSewnWZBMJ+jaXiwa/+dQO+cLLsm6a5iMvtMsa6mZfszNui2wUMwievgmL6CW4Yg+mUkt4yTeXk8BadRheXY3V93dYH3+D+3G4QMZicSR7mylnjSC2Mi8xXwMTXgkgaGMVsIMf7+AeMIGNtReCl8QLaIkQ7rufCMyaizdoiiKHq1R/fCLy/+4jY8MP/i5V6JcFOe2iHC1BP70VgIX4GNq3tSzCPyLEfoWvoLFB3mytYAYUInqtMMIMAjbOQqNac/PmzR2x/uiMlUcDsejgFRP0mJhaZQKP3Cof5d3CwXF9mTJlZiNgzsO3fC2m5UkGwOoLCX379l1Lw2txEfgOC5wabHpN8bWULBu1+car8JbmLSDWQb4QEL5RiYx8ivcw3+lGBP81+MgtxMJoOcGSNqGwMyZNJzcHGzh8zoC/xVVJ+DtoFhc0rQyacwd/hprNhZYNXTODh55gG8FycggI+panDUgWDIZ3QqJKBw4L4ZEOlJCDKd/qfOZcXGDkkBEMEiV9izAh7lJySJT/T6tgCPodh/4M7Tu9ccrfZQzBN1g3dc+QPtjbdjD/juNvUG83Y5nFnJSlHTkbBG+QgmMJWgqkxTWIaagfdaipd6i5bOj/px13aJ0cHPiOQctCcV+xPSGBgqJwZzzLGI8INxIULa3peSiP/GaRl6af9HguyGOhvBY6jqDyVuoG8ROFxD7MlqfbtZYjsvmCDRs2LGetmcCecAnrTBewqMtYCnslqIFRMt/hPtpdzvo9qV69euNJqRbuQO10KtItz5wmMTeL6VsUVsJr6c1B2rUgdD5C5zPtdyR9Bv8e/JnW7Da4JgT/HjpXR8BmCd+2KKAS7mGeBfNp8nJB9hF7WRMUAq3gsebMiexjZZkXCRid1ytecwpiwNT/pOxlvCL4r2Ifmw+fLkVRtYGsYG4Ug07JsFU+wMsLWXPlvC4CcOh6GcrDoWtGeutgZv2Frldp16XgvhLc44IZVSDtfBLzuQI8fbNq6dSpk8hXr6K4nEO8tKtwSe4pl6C8nrkgB2SPA7S5Vs6bKLQmYsm40M4EyUUIZ42V/BQljWAle3Xonhxce4JrQuganvbsEJzDtHtd6J6YXjtpSZVzwU54RrCL6WO6T5Yv4GCGXRmBawCmd5fTQRPcBEr70lGCNMrHs5cD1VJMrxZwiPmeIIirAsJrgozQrGEQyLEoCqFqxHRojaDZFncP2ZgrB7T1kv4y2sq7M7KJSPBH3h1i8s+GvIhb/zkIlxvbtGnzm/bKLCT9p4Y1pACbXinmqwYHqUa4AzUQwYPDgChvijFvxfgpioHcvMFI56ECnF0ig0KFCBRiVSRzIoHEjslBiW9UNsgDHJY2YGX0C4el1SgAdqEpN+2WOnW8BEHLLe5LaRQmmQn3QZzs7FlpD/xSN3TTDj2MBdsVq6xT3Co4Fjhx/ZJsHwUDfYQqLUIFwbSHvrQ0pRVagjSm5Y/QculhkU1uzfg+d2Ee6ciqAYEzD8ppsUISJVbaQ2d6c5NW+E1vDEGlSEZ8ntF8yu/P8f0cJzWvK+UVaYMLEBtHXAAzU8qFzktGWKf9fdpDeEYKhqBgKT/PogQ4yi2748M4lxZ5UAQ0QCnQXfYDDo7VUb5KLJlCsq4E+C7cOiI0p8Abouw6wHqxFbfE9Shup2KdMB++3x6uAa//jttD1QD9oTil9+2m13VaXstIoZTuN5KmwdDvNKjoOkaGoySvx2xqeygCsqEEKMk+VpG3DntZM9wUa6AkKi57WMDiVFwHRDEglqehAqedNTm9octedjawlyXL+YL/PwRvHuRbOch5cy172XL2sZXsZ3s4e8b81jSj+SMOV1XoLxJYN4MuYqFK1ND1PHQtzWg9zwzTjL6R0LZS1zWe81x2HsViwvG+5oZXuWTNz4VWU9arzlhAt2etqgPvlOMtyGuXT4Tu03K+gRf2gOv6wEXGXM6bv8APOzh72t7bWDfzs26KNZXwrPBcqAIgdJ/LaP9KK9SnnbP05jD0vJHeXprKI8SI2U3cDlFKxOyxOykxI9DPjgnOVobUPJfz9kMR0C7aPi1+js2Ltvn49iHQzOWgMIFbxHEsynv5f9Fg6RMlBFjA8mGyV4oFtQ5vMzbqmiyMQQFT3FpE4CzC+iaZBiK1GJDFSjZlWYDFjHwXryzCBwi8spf5X4kWdiWHRgl8eThKEMRVN2jC8yJI5Sd+QBHeMrwVsBSogCa6FG8xcelhQGJ2Ka8c4EVAyREQVIIa/FQBKDAXwZtkEfqTZWPk9yexCDqI+4X47+5mXnaxSe4nkvcRNhRjD0pxNZFKrCJgIAKcVYqiHK62bdu2FuwLtVlrygUUjqKEyidCGj9Tb9ZlvZC1IqgsRHFwgPhE63jXsI6vx6R6Dwp9P6L+G4ickuQUAcIWFWTvKsgeVoy3HG95LiTKEdC4BJdC4vYqe5jwW3Avk/1MlN3yU3gweKMa3MtE+JL9LCW4l1H2BGeLfVit7S5SpMh2hP4d8OeBYsWKHcViwY1Lk9NhanmfEMBFoDRn1oZYtjZGsVQP3qnMWVIsmUSZlHoGClmrTsmFhrz87ggKyt2cbbYi9C/mvCnnzn0I/nYVhD6NKDGbzdJKgOCUor0qgibvWpQCA1AGNII5RXOVUBHbHbDvaYTM7WJKScCN6Zj6jGrWrFnMTVYc0J/QRdGw5mUxLYAyQITM8vysys+qxI+ohNY11YRPNmJe0danbsZpBEzBRzbl1Nt9forAL4dF+X8RMk+ghd/HRrwWwXI9P/fwJqGxPM6CrItwBNzF3OVAEZCHG3C5Bc/NN5abby0Xb07mTn7mkJeNMhsKybOsRWc4uP/2otE/Q9DFZHkJ3BXzgDIRQKFVFQFFwAMEsGgsIApHhLVCrCf5sHzIz+1tLtaNFBSFJ3iPs56f5lCdjLLwCEp8XTc8wD2rN0GQwZwoA1L3MX7mgedkLwu+uQL7WU7x4Q7uYexnKexrZ/iZDF+mcKZIRvg/CU/avtXN6rjH6/hRXOZkjSoqCiUuFsVCqwA/8/MzL/xxGoH/MOeaJH4egyeOctZJ5rypfBGFCVclQAjIKAMKcZMnPtltNm3a1IPFSnJg+h43IArzHK4LMYXdyyK+kjGvxd9qLL7ds1icjfG7CjcA/Ts5+davl4VVhMtcchAUIZOfeeRwKJu0/FsETOb4NAdDEfblkHiaBfe0bMr8PCv/xudOF19lKEVAEVAEFAFFQBFQBBQBRSBBEVAlQAYTiylUGXwkB+Lf0l2CtFGsJgoBMblOpEeiU+5mXIsItDEef8Uf5OYA8xs1EUykWdaxKAKKgCKgCCgCioAioAgoAoqAIhBAQJUAYViBgBI5UATUJ4jNdZg91aO4BACpxCsBv1J9nvhpPEOJWRbvWWg9yrsfd4dfMPefye3venJszkQJYGQAMeOBVQIVAUVAEVAEFAFFQBFQBBQBRUARiCMEzJdeDQJz/vz5JTGpLoFSoC0+Lk0xsa6PMqAyv5OUShIoRQJ8xVwpIAK/PNAmQVhOSEAgfLE2YP69guBh6/C72YzJ/3JyEh8wCF4lRRFQBBQBRUARUAQUAUVAEVAEFAFFwGcEVAkQAcALFy4sQaCvsgT8akWk3noEQ6mIQqAS7gNlEMQlCmY+btzz8v+pKVW8VhAEbvflhl+Cu6W+DOcUwv5e+tpAtNVlEmGTwG4bCAq0kzRAuyMYrlZVBBQBRUARUAQUAUVAEVAEFAFFQBGIcwRUCeDhBJIzvCh5wkvs3LmzjuQMJX1PeVwIJIVPYRQEJYnqXl5Sq4hyIBBfQNKDBXOH50BhEMz/Llf5wVzgcpsfzF0r6dsklcYZbvYlwupRbvn3EGV1Iz/38e99tJFEOo1N/PtI9+7ds2z+dg+nVZtSBBQBRUARUAQUAUVAEVAEFAFFIGEQUCWAz1NJxPZcCOY5JB0GLgRlUQyUkvSD8pNo/BJoUAR/SaWSGyVBycC/JVp/MvlTD+Gzf5T/T03phuLgNAL/PvHtJ8L7SXKpHpQ0QM2bN5cAf/ooAoqAIqAIKAKKgCKgCCgCioAioAgoAopAPCGwffv27LyqnImnSVNaFQFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBLIYAv8Pemu73UV+cNgAAAAASUVORK5CYII=)\n"
      ],
      "metadata": {
        "id": "MbEK2RpZcVCU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook runs the CombFold assembly algorithm. The algorithm assembles large protein complexes, by combining PDB files of possible subcomplexes. \"Possible subcomplexes\" are structure models of different combinations of subunits from the target complex.\n",
        "\n",
        "By default, the notebook runs on an example ([PDB: 6YBQ](https://www.rcsb.org/structure/6YBQ)), but by uploading your own PDB files to Google Drive (and creating a subunits.json) you can assemble your complex by changing the path in the \"Run\" cell, as instructed in the cell.\n",
        "\n",
        "\n",
        "**The inputs** are\n",
        "1. subunits.json - a json describing all the subunits (sequences) in the complex.\n",
        "2. pdbs folder - a folder with AlphaFold-Multimer models (PDB files) of different combinations of these subunits.\n",
        "\n",
        "**The output** consists of  several .PDB files which are models of the assembled complex structure.\n",
        "more information is available on https://github.com/dina-lab3D/CombFold.\n"
      ],
      "metadata": {
        "id": "ZbAj512vF4eK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yPmEy4fH4CT8",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Install CombFold (~2 min)\n",
        "\n",
        "!pip -q install biopython\n",
        "!pip -q install py3Dmol\n",
        "!echo Installed python dependencies\n",
        "\n",
        "!wget -qnc -O CombFold-master.zip wget https://github.com/dina-lab3D/CombFold/archive/refs/heads/master.zip\n",
        "!unzip -q CombFold-master.zip\n",
        "!echo Downloaded CombFold, Installing\n",
        "!cd CombFold-master/CombinatorialAssembler && make --silent\n",
        "!echo CombFold Installed!\n",
        "\n",
        "import py3Dmol\n",
        "import os\n",
        "\n",
        "def view_pdb_color_by_chain(pdb_path: str):\n",
        "  pdb_content = open(pdb_path, \"r\").read()\n",
        "  view = py3Dmol.view(width=400, height=300)\n",
        "  view.addModelsAsFrames(pdb_content)\n",
        "\n",
        "  # Get the list of chains in the protein\n",
        "  chains = {i[21] for i in pdb_content.split(\"\\n\") if len(i) > 21}\n",
        "\n",
        "  # Assign a color to each chain\n",
        "  colors = [\"red\", \"blue\", \"green\", \"orange\", \"purple\", \"yellow\", \"pink\", \"brown\", \"black\", \"gray\", \"cyan\", \"magenta\", \"olive\", \"maroon\", \"navy\", \"teal\", \"gold\", \"silver\", \"crimson\"]\n",
        "  colors = 10 * colors\n",
        "\n",
        "  # Set the style for each chain\n",
        "  for i, chain in enumerate(chains):\n",
        "      view.setStyle({'chain': chain}, {'cartoon': {'color': colors[i]}})\n",
        "\n",
        "  view.zoomTo()\n",
        "  view.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title View example elements  {run: \"auto\"}\n",
        "\n",
        "#@markdown  Here we demonstrate the input used to create a model of [PDB: 6YBQ](https://www.rcsb.org/structure/6YBQ). This complex have a total of 12 chains, composed of 2 subunits (unique chains in this case) with 6 copies each.\n",
        "\n",
        "#@markdown  - subunits.json - Defines two subunits (A0 and G0) with 6 copies each (implemented by defining 6 chain_names)\n",
        "\n",
        "#@markdown  - pdb files - Structure models predicted by AlphaFold-Multimer of different subsets of 2 or 3 subunits from the complex. Each combination have multiple models, as many different pairwise interactions can be considered during assembly\n",
        "\n",
        "#@markdown  You can view elements in the input by choosing them and running the cell.\n",
        "\n",
        "element_to_view = 'subunits.json' #@param [\"subunits.json\", 'pdbs/AFM_A0_A0_unrelaxed_rank_1_model_1.pdb', 'pdbs/AFM_A0_A0_unrelaxed_rank_2_model_4.pdb', 'pdbs/AFM_A0_G0_unrelaxed_rank_1_model_2.pdb', 'pdbs/AFM_A0_G0_unrelaxed_rank_2_model_3.pdb', 'pdbs/AFM_G0_G0_unrelaxed_rank_2_model_5.pdb', 'pdbs/AFM_G0_G0_unrelaxed_rank_1_model_4.pdb', 'pdbs/AFM_A0_A0_A0_unrelaxed_rank_1_model_3.pdb', 'pdbs/AFM_A0_A0_A0_unrelaxed_rank_2_model_1.pdb','pdbs/AFM_A0_A0_G0_unrelaxed_rank_2_model_3.pdb', 'pdbs/AFM_A0_A0_G0_unrelaxed_rank_1_model_1.pdb']\n",
        "example_path = \"/content/CombFold-master/example/\"\n",
        "\n",
        "subunits_path = os.path.join(example_path, \"subunits.json\")\n",
        "\n",
        "if element_to_view == 'subunits.json':\n",
        "  print(open(subunits_path, \"r\").read())\n",
        "else:\n",
        "  view_pdb_color_by_chain(os.path.join(example_path, element_to_view))\n"
      ],
      "metadata": {
        "id": "vAk-cPl4p2Jg",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Run (~2 min on example)\n",
        "import os\n",
        "#@markdown The folder path on drive should have\n",
        "#@markdown (1) a file named \"subunits.json\" with a description of the subunits\n",
        "#@markdown and (2) a folder in it named \"pdbs\" containing all pdbs created by AFM.\n",
        "\n",
        "#@markdown  The results will be saved to a new folder named \"assembled\", under the path_on_drive .\n",
        "\n",
        "#@markdown The path should start with /content/drive/MyDrive/ and can be copied by selecting it from the files sidebar with Right-Click->Copy Path.\n",
        "path_on_drive = '/content/CombFold-master/example/' #@param {type:\"string\"}\n",
        "max_results_number = \"5\" #@param [1, 5, 10, 20]\n",
        "create_cif_instead_of_pdb = False #@param {type:\"boolean\"}\n",
        "\n",
        "subunits_path = os.path.join(path_on_drive, \"subunits.json\")\n",
        "crosslinks_path = os.path.join(path_on_drive, \"crosslinks.txt\")\n",
        "pdbs_folder = os.path.join(path_on_drive, \"pdbs\")\n",
        "assembled_folder = os.path.join(path_on_drive, \"assembled\")\n",
        "tmp_assembled_folder = \"/content/tmp_assembled\"\n",
        "\n",
        "if not os.path.exists(crosslinks_path):\n",
        "  crosslinks_path = None\n",
        "\n",
        "if os.path.exists(assembled_folder):\n",
        "  answer = input(f\"{assembled_folder} already exists, Should delete? (y/n)\")\n",
        "  if answer in (\"y\", \"Y\"):\n",
        "    print(\"Deleteing\")\n",
        "    shutil.rmtree(assembled_folder)\n",
        "  else:\n",
        "    print(\"Stopping\")\n",
        "    exit()\n",
        "\n",
        "if os.path.exists(tmp_assembled_folder):\n",
        "  shutil.rmtree(tmp_assembled_folder)\n",
        "\n",
        "import subprocess\n",
        "import sys\n",
        "import shutil\n",
        "sys.path.append(\"/content/CombFold-master/scripts/\")\n",
        "import run_on_pdbs\n",
        "run_on_pdbs.run_on_pdbs_folder(subunits_path, pdbs_folder, tmp_assembled_folder,\n",
        "                               crosslinks_path=crosslinks_path,\n",
        "                               output_cif=create_cif_instead_of_pdb,\n",
        "                               max_results_number=int(max_results_number))\n",
        "\n",
        "shutil.copytree(os.path.join(tmp_assembled_folder, \"assembled_results\"),\n",
        "                assembled_folder)\n",
        "\n",
        "print(\"Results saved to\", assembled_folder)"
      ],
      "metadata": {
        "id": "OgnoSp2WNMPr",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Display Assemled 3D structure models {run: \"auto\"}\n",
        "model_num = \"0\" #@param [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
        "model_num = int(model_num)\n",
        "\n",
        "output_filenames = [i for i in os.listdir(assembled_folder) if i.endswith(\".pdb\") or i.endswith(\".cif\")]\n",
        "assert model_num < len(output_filenames), f\"Only have {len(output_filenames)} models\"\n",
        "output_path = os.path.join(assembled_folder, output_filenames[model_num])\n",
        "\n",
        "view_pdb_color_by_chain(output_path)\n"
      ],
      "metadata": {
        "id": "WwqShmF0_YCY",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}