From 81a1074b0abd56b2d2990006916f50f734fd3198 Mon Sep 17 00:00:00 2001 From: David Bikard <david.bikard@pasteur.fr> Date: Thu, 1 Feb 2018 16:06:47 +0100 Subject: [PATCH] added explanations to the code --- bad-seed analysis.ipynb | 168 +++++++++++++++++++++++----------------- 1 file changed, 98 insertions(+), 70 deletions(-) diff --git a/bad-seed analysis.ipynb b/bad-seed analysis.ipynb index 4d54f0f..8dff7ff 100644 --- a/bad-seed analysis.ipynb +++ b/bad-seed analysis.ipynb @@ -6,6 +6,7 @@ "source": [ "# Machine learning analysis of the CRISPR screen data\n", "\n", + "In this notebook we use a machine learning approach to understand if some sequence features could explain the unexpected fitness defect produced by some guide RNAs.\n", "\n", "- **guide**: guide RNA sequence\n", "- **pos**: position in the genome\n", @@ -201,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -239,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -248,7 +249,7 @@ "data": { "image/png": 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MMpXBPZecqKtc0s6nrjTL88LCFCwIXCrzgvRsaZ5Lt6vs0hbSucu3oDubN6Sk\naoX8lauljRUDiREvrPg5ESvKOckbqBclofibpHiGBxnfoAgEOeF3bqjqwAMNi3F1w2hMnviV4gfM\nVqGUPk9WkoeFOqjPh+blEovqaFUrVdOeTGsWbqM+dwCptd/FwhVbcPJZN5j2VhmtTmvVmzV6zivE\n98X56rSRSH3vD195sqnfb1emDvVVe0s/TBGA9cLwtXp3m8Xob2DJEKfwFrUJ4aAFz8y7bQfx9rs7\ncO/wxxRt4ybsnIXl8QgGjDiC9sxgHMz2KTEuA8rQv3Qmh6V/31FiwMnkecxdvsXR9Vf9HIYEdQoj\nyjxRRp9hc0uCukcr9kQ9T5+J6rG3DXsG0Wxpe7A7hv4aeKe7uIaKHlO5wgeUthaSUP0oetdk1cNI\n2xvEMUe6BjECRzxXeCCQO1xsvQP0SoVm9dYO4h6itR8AKFmvaPKCOEYNyamtS9HUdie+dvwO7MnW\nYX771diACw21z1uenIbXjp4vddhYtGwb2pOvlO5PLnmt/QZTVG1CanuRzPXFAMIGpEBcJIh9vnhy\nuF8BWlElIyWxgxyHPM/rCmR2lRO1AJTqyeKC2tdL+iMuerVKcTwzbQpIm6NaYZR/n6TkpHqyxI1T\nS2mRfz7H84iEgyUhH+KzpXlJHMuVItG6FMuGzUJ8hLIPJQBiGAogjIv/+NII4j2US9mmYVYJMOL9\nNhIurDX+m1sSONKVLXn9l3uuwaKRjyu9A/JCKuIGUzVQyJHiM8rPTXqUfMHqDcqAihcJdGPEDlkP\nZbWRTM8rUGJUI6BhoSfNPyP9H0nEYxHN8afV5zMa6MZ1/Z7EBS+djQ1tBxTn11NS3AwpbW5JUNcv\nOdI4PL4v8MFvgG2PAV27gJrhwNgfA/UX6RxB4I2te/G71/+F2mwen+cAHAJ+99J29O38As4bN6Tk\n801xoO/5NXhqbSs6Dnejrm81Dg6+HvX7HwLyXcUPBmqAkTcAez4A+gwBaot7kF4YvpWq8XaxtNcp\nDDacQsmZcORmnBLPS0YikapAFoMCglzQUNWBnnwIPfkgqgLFc6uLrAH0KAPaWm4V9XPQFe4BSaE0\n+gy1clsVe6KBiJH80R04a8EqXUNlNNtOfL06d5B+cKv5m3oKthUPI81rptc2R30uLaMk4b3m5FRf\npPsYNiRqyD2afZ9VCrvWOiwVU9WTU2V7JQdgWGgvHm18AjhtItBYmnZDM8ADBlqXeei1LhdMUXWA\n0tyOJ4RS4HKhEwGgagDQc0C5SFByzbQWPNIkWbh7Jh5oWIyIbCL25IOIBrrwyfhL0J4ZjEc6vi0l\nnuthJ4eIpECq+86K4Td37ARcIJXpAAAgAElEQVSA4sQ1IwhqVd6Vo9WbzUhohp5wRQr5MHM/lvM/\nW5cKlWLFIjzhQUI12Nan0VBVmjuoFQKe53lqPpnTPQ69hqYgkZRyEbPjf9Gr20ra5ADA6q4LEDr9\nZHLek1z46NkvFPIIDQIyB4xZRuUb1PODNSz/RYYEO4obXRvBSKblFSAa1WQYsNCr59+UBatMK6ly\nTz/N8i2O9UdH/JKYDhAP70M6k8Mf/v5ZiXdPS0mx4tE3il7vZzntyTTw27OBjtbCK7UAOoFt9wG4\nz9AxzgNwXhBAUPXG6sJ/tO8AQDWAHgAbAEGMUFUZbS1cR3V/4PZPgUCgeN0alMP4ZXqvI0ZBFZEr\nnlpUBbI4kO2LnnxESh+SC9deo75fTeFeggeaR2MSLkcCpdetPqbW7zt7+liFASDPgxy+Lx6r0GZP\n1/ttNF9TjZX8TTdCaWleM1pki5k8aYBosMyu/x7e3nkjEslzAJQv3cdwtEPhHmhyzyuHBNnstmHP\noD7cgRwCCCCPXZkhaB/9M0yWPRfaOswBktymK9dp5RVT9nSSAf7kn/+V2qnAbCpIJcMUVTfQccc3\ntyQkV/66E+owLEQJnZJbbbmglD/xyOm3YuYbxykG8GtHzxfC+pIPSqEewWwnBsosuIvqHwT34SKg\nrTSkWI0d5YSk2JGS2aOBbtwRfxbAg9JrckFwElbijvizGBrqANc2Eogpr9mIUMMB+MapdA+cEcHT\nyHnEwkm049Bet5z/2boU+Pu1ylCezH6hurSKaCFX+bWj56MmHND1IKtxvTCJB5j1wpod/7Qxciid\nMV5IJd8D1PQBrjDY+krOpEcJxjHCdWYGFxWxkSYrBlJe53lgT24Ihp3xkGnLrt7cEkPrxX/rVfNH\ny1O+PDkNP40/S1xfxUgTWgiq1nWZHUtGMaOkxWMR4FAbEMkCsR7lm9V1wORfFf/euwZo+wPQvQ+o\nHgyM+g9gyFn40dJ/lAhkVw58FWf1aRG8gVwAGHoB3gldhj+/146DR3swoLYKXzs5jtNGD9S/yK0v\nA5ufB3I9gpcV+mH4RvYXpwu7md7r9Aw2JogFj2D5CR9i1rL3qMJxJBxAilAIUF451wnUz2F5chp+\nOPhPGBf5TLvmRqoNCxoWg0epMVT9DGnPOhYJoyn2pkJhCnBChXTRuyVXWuWeZ13v94R5BYeAye6p\nosJHUgaDtcLxrOQGmk23AOhKphP5iYTxHOLTuLnu93hh/znSa+VI9zEc7UCpmSDKPfMvG49Fr9bg\nzK2lxpRvd67F5ORo6fegGV14KNPexOsjrkMOVONtbklQoybcSgXxK0xRtYKRhYaysKgVk/vbry7x\nhCIYFXovyhchWWjR5O7b8cx5DygKM8yePraQe3Zj4USjEVR5WDgTif92lBMzRWWGhkpDi5om1hc2\nrSc0wxKN5B7y0A8n0xM8jZwnFg1rKpxOhVg3tyQwd/kWvDxqFhqqekrep95DeJ+U72D2dxWLFIje\npyDHaSr/vQGz49+IsCsXsD8eTy72kz+6A2PmEHJR9FAbxwg5SXLhrj2ZBsaZrBhI8UwkMnU4a+vv\n0DqzGHJqVJmgPTcjlYnVRYhIeeKfjfwZhu25XSHEkMIrSdelhnRP4vmdUJpoz4KW/46VWSCUB/qq\nQ853Ayc2Cf/buhTY+2ugKgVUAcAu4e/Rp2BTv6mK8/08/iucM+gdhVLCH30FHx0Alh7+ofDCYeC5\nNQC/BiVGgxKSOwRFNZ8BICiqWmH4RvYXNwq7md7rHKyU2hWOo2liPW6mpFfwAO6/7IuKOhiAUEDu\nnktOdOw6AEg1FeRjTVdJLRAJdOP2Yc8qFFXSM5w9fSzxXuZeeiKwaUqJshHggJ09dVi4e2ZJ2pD8\nXJpGnsYZwLpv6d+EHLnCR1IGT/uN8P9mFU6z6RZaOJWfSBnPJJnN64gHw9EOGvcgtmUipTBdGluN\n2wYuBlIF+VvD6FJvpg6BA9V4jbaAKle9EC9hfVTNYqWnlQy1YrI8OQ23y5oDp0L11D6JErkUJicf\nlPqOEnvOGS1GQKFpIr0Pm15/KZKQR+sNxdEmLiV0YveaW6TzkXoFkpAvalZ6muqdJxIOSsWV5Bjp\nN2cm7Ky5JYHZz29CMp3Rr2KnIlA7UlpYzfTXE8/74saEZMXL8Txe3JjwrB9sOTD7nPT6Vqr7b9Lm\nQ3tmMLU/py6NM4S89qvyglf2S7/D7uwQYg/leCxivs/dhHklvQdFpY+U063Va1TETL9PEk0Thf7L\nny64CA9feXLJ7zV56o3AaUuQCtUTnwMJ0vlJ9zT7+U2Y/cImQ/dpBNqzmHH6SMo4DIJYNla+pmqE\noM2ePhZhmbVkxqC/lCglHIArB/yv4jVRxdC932DB45cregXk8wqA1GfVaE9fI714zWJ6TXQovDPL\nRYQ2KygVgkXqC7UVFl0+Qbq+AdEwaqtCmLXsPUf7cjdNrDfrc1QQr+ow9gzVJxH/1lA2ViSn4crd\nf8Qp2/9C7EupXn9K9vjoKMP3kUdQkMFEZwOpB674nrjeNn1qTEHUazVjFivXoIYynkl7lG7EQ+tS\nIVrouYDwr82ewIb7IVPuQZR7aMciRfqJRhfFa4U9wbD86EAPWTNGgXLVC/EKjjfSD8AjJk2axG/Y\nsKHcl6FN82iKpYRe/EhO45xXNDeDSDgoLPAfjoB2qAonLE5mr9PMMUiHpeR0yjcl0mcuH/QWFjQs\nLi0qIy76ap4LgHT/eZ7D5zavQCwSFqywKHo0aC0PRO+MkWuncVfzZsmjGOCEJuVdmbzkRdEK3Xr4\nypOpXpcpC1YZ9ijJP/v2uO9oFLogtDiiPWcDmLnGYxktL6L6GapztgFB6VMrUXafMW3Mi8W/FOH1\nBizy7765GPFPf4Hh4Q5Fyxv5HDI7Xrzq0UurqKu+RtL5afdEO4bV36y5JaEozCeuc8Tn8cBIoPog\nMORI8TX1XKeso+LaP/Hev0rnah1/MdF7xvNA4+aXpb8vja1WeLee7LwOc398f+kX3/lvYOWtwK0f\nCUWVTEAbE7TfkAOU7Z7chFhUrLDmhgeVVlclER0lRE0V+nCmQnHc3XaVItSStDfZ2cOMoB7ntDFB\nvScdGUhzbRh3ra5sdVfz5pL2I/L7pz2fZ87bjsmqyAoaeZ5DYIaOXGQlfBfQnY9lgTCes1wEc3be\nqDse9Y5jV/YwPN4NnJt0rE/GX4IAV/p78OBw5o7XNQsbAeR+04prsuHt9mrPKSccx23keX6S3udY\n6K9ZbMae64WRSm58WlieiJ5V10j7CguWYSOhqqT4/TPPvwmh2ATjE5cSOiFa+c6u/ismb7oa8aoO\n4VkVqtRphXBZrWSs9igKNXM4RSsHWmGX/hHtkGAzYWfqptAPNjyKqoAq7I8LA2OuM9eIXAc7hbXK\niVcKkIhWKJD6Wcl7pTVU7cPOHnIhFbvPWK/SbgLT8OfktOLm36j9fCZPvRHNLV9XHG/+ZfYKUZnN\n+bT6u9brrL0cQN3svbRud8nyEZPpDD20NVAFDDkbiG4Bnyq0QNhxNTa0Dsfs6Qnh8zohaEl5BXME\nEEKpsJyTBV6RiuLdNvAhoPXE0jUmWFU4gHbetBpaeO+GtgO61Tg9QS/kUl0RmGQ0BBT7czSbwIKG\nxehTHcLT7VNc6zOuh3o/WnN4As7qu4mgrNL7vWuhnhtyoweylMrnst60L25M4BKVoeRvtbegqdBy\nS3w+JcaU96/D5EuWaLbWkq4xMxgNWh+wE77rQEio4xDGc2jCPJyZnIp1BtZZcT1eNmyWVMxIwkQb\nIRJq+SrIcYoICkXlW9U9kFo7iscS76krHBf68qrgoiOJjgJa8U43qvHSesaDh6JwY6XVC7ECU1TN\nYnOhMdIqoz2ZFsLs1n5XmbtaIJ2vRkRvUyhMkN1rbsHQ4N6SYgSGjkG7Nr3XC/2jmka2AaMKRaDa\nRgnFkAx4nQEQFW0xzLDEG1XYKJpOWwJcRi+pblXhMiIc0BROjqOHBMsFdLP5fKJCMze+BAOCQsGs\nQ/m+uCdxPTa0XYjZ0+90TCnzS9VfMwqKG7lsdiA9w+XJadiIC7F2zrm4kmI9tfyMZdbcpuhINF1Z\n3LRJG64ZYddqTrcT48XO76q39mpdn9FevHrH0cOUIpLPAf2PR3N8ieq+ZM9EpyG8/L6W7v8KZg5a\nqcxRBbDs4Felv2lF8YjCqBj6m6crqmLOvVg0RCwQRHoGf/j7Z7rVOD1DSwiVv0fzqhCKqYX4NOaO\nfI7snS7gttFQvR+t6rkQZ3KbSiPMj/uBJWOofLyV7OM6lc8XvboNF9S+XmIouaR7LtA6AmicgfYk\nucPAbQMfAvBbQf4gFSIs0JMPCRECWjdhoaKrhM58LBuE8dwE/TVVvh7HR1DSkWzmdJMK51lVDEv2\nrtYHDP8eenPM6VxRmmxIeq0356cCTFE1j9mFRrVRNU2YJylTNMEnHosAjRdhzrIWzB72DOLhDuQR\nQBB5JDJ1WLR7Jh41YqlpnIEzfhMDj9JwLcPHIFyb0f5RAMhNtA1eOyAo2kOCe5FHABFOqOIWCXSV\nlswvbBRNTZ9SJ61VAdqIcEBbVIz0IDXqUZo9fSxmP79JsqYtT07D8uQ0BAMcApBb2ZxVyvxQ9des\nguK258Eses/Q0WesY/F3W9h1c7zY+V3F9+WhtUavj3RPAYDgfwSmjavTvgkNTP02fA4IBLWfyRxt\nT4P8vu5p/xEAIVc1yOXBcUFwY65HbfhO1Bf2K2puPEkYDZTmqMoRc+7l3gFSRXIRWoVmeTVO30ET\nni1GZnlhNFTsR803AKSgrPaV+kZngpI+e3ox6olk9NCqfN6eTGPZOHJOoagkxmMRujFl/TXCH40z\ngA03AfnSdl5H8hGcfNYN2vdlJ6rOqQJIPkG+9lD77opOHBuhsK7t5yZ+DyPGSqejzGiyYclrNsOM\n/Q5TVM1iZqGhCIxNpy1B05wZuj08N+BCYjltWuEFEuLkEpUaK8eQY6l/lIjZMJDGGfi/DZ/h4u65\nCusoNa1aZ6OwKkAbFQ5IiwrNIGFFsBCPrfZA8Hxp83d5eIxd65sZr69bmN2oaBtGIim0EfLk2jvb\nga2vAPvfRdPO5fja8IM4nOuDVw+dhh04EecfPxQTMp8B7wjW68aTk3j9wz04lM6gfySseN8Um34G\nZDIA5O0rMsDrPwEmpHBjn23EsvexSBh4p93cuQIh4MSvA5GY9JLR8WIlhNeSkq32Ll9tvpk96Z5S\nPVmiYqVXZVwLU4pIPgcEQvrPRMPToL6vJamfoP9ZSxTPolA/GHe8tFlfGJUTLIgXFEWV1nuYRlCj\nBkHFYTEyy3OjoVWlTEP2EQ31poweENJo9L4ze/pYxD+gfIbPFQ12mQPEjwwIHdHfG+yG79oMCfUT\n6nSkkr67ohPHZrVj+XnUThe0PmzveRr8PYxEQ5alt7yTlaR9ClNULXBo6DfBf/kK5YspQvGEjfOA\n7gAUzdCzhdeHXoGpY+tw10XHY/Gq7djV2YXh/Wpw47nHYerYOiRTPfjR1DH4xcsfoCtbtNvXhAL4\n0dQxSJLOR8CJY8jRu2Z0HkBJ83c5nQfIz4rChCNL0BMMoydvoGdcdITmscVrf3z1duw61GVYOLYj\nHLghWNRWh3AondH12opeRyfCX83mETqNWQVFy/rpWQjwuseBdYulPznUoB+yuAJ/A/A3YDOE/wpM\nKPyHMIR1QvW+OUgbZhpI3IqfiOdQkwWw0sKp+Bww+TrFS3rjxWoIr2mPko6x0Azqe2qkFPaxY1U3\ntV7kcwAXsO1lMzK3RUORpjCqpuBRXf1BAnc9tbvEKGDmOYkFwMTcavnrFZmfZTEE1HOjoVWlTCM8\nVop6ajZx7NalWDn6lmKLPcp3mibWI/UROe9Qfg20+6J2IZDj1/DdMkBKRxKVyECtdqi7nuNCbsgU\nC2WSwrq9UsrU+bLUtmFeYycUvUJgVX8t8KX7X8eeztLcUUZlEO9fgzW3n4sgqZElBTuFeZwq6iMK\n9xfUvi5tBrsydVh84Dv4w96zSj6vtjyKxXq8qhDnZDEjK5VktayfnjyDl28BWn4HjOosfS/aAHz1\nH86cZ8cLwJb7gVQCiNYDmaNA5mDJx9ozg/C9vUtww7TjAACPr96O3Ye6MKx/DW6YdhwuHD/c3Hl7\njgCPTgC+fB/wb/9p6qtWK0mbrnpqs0q7FnbuQWteGJ439w4G/u1GNA/6nmuVYMVrUVesvk2WkhLi\n8iVVbBEdCQz6d+CNJ/DN3Dy8k2ksuTat9JdYJIza6lDJM3ByLS17nlclhOtZreRqpLqt0WMTqywX\nSeWrEZ3yW2VesGYhSQ4441l7FWor4bfzAMPrsdZ4OOPZkmdJKowJAE+O+jmGhEv3tr2ZAbhj//24\n+oxRmDrWeupFCe1/Af71K6BrN1AzDPjCj4C4ULjrzW0deHZdGzqOdKOuT7Wn51bwl9MgPtsTI58g\nyIlOqTJWkjaI0aq/TFG1wGe/vhyhbnLoiIIjrcUcTTlcGOhjvK8XjaPdOew/0q2Y/hyAQX2qURtI\nAV17AdW7+7MxHMkrLe3Sd6r1e5ICADKHga4OkDO0tAgANXVAuK/hb+Q6WxHkSp9hng8gEAgKFQK5\nMFA90NBxU905/F/nUEz5z9/iC0ONX4eccgk5UxaswqlYWeLRSOer8bNdP5ZKyV8aWy0VWZIXRhHb\nn6xITnO9lYPTbRSsHK+5JYGbKd5mT9pZLP8xsOkpYMwRwpuUTcSsAEQSygJVQk8RWfVMeesbx9pZ\nZNLAvGHA+XOBM2eZ+iqtTZeR38XU/HOxHYTZMaluPWPkO5r8fABw1k+Ac+9yZU3SMvaQ2iuVkK4F\nPgviG933YCOv9DSI7RzUOaqAUNly0eUTXFNA3W7x4mcsjRP5mhQeKEzSntJiR8oTjTZmIDKy3lGO\nxfNAIlNHbo/UulTISSXJX+I1OKlsHsOKq6ExRRsPVYOAXLrEYDB3z034ffsUV6+7N7LlxMtRG+wS\n/nDAGOs2rD2Ni4wYEAFSOmFVXXuAUAdKhaQA0G8sUGM/lv0fOw4Se2d3pjNojHyMYLhUiBgYPISW\n9Djid46r0wjZlbNvExDu0vmQGBhR+DdQA/RpBGrM9dPrzPdHbffHMisRkOMDOFo9Bv1iJoSKrj3A\nkVZkunO4OsjhpX/8BV/46hWaXyEtwICB6nMuoVVM4t5Rz+F/DkzFRf1XUQXIaEAoRrURF7p6nYDz\nxQ+shLw1Tax3NEfYNHmh2A0RSnib6VwTUthPvkcQAEJ9kD+6Q+FNBxwsKiUVy8lqf46AnXBVU2Ho\nLraDMDMmtZQ+S78HzwN8HuCC0rU4vf6Q5rAIsRBOyTV2AahFmMuVbIPtyTQ15/6eSyh9Y3UwGk5u\neG3ieeC1u4FDO4uvHW0Dku8Lcy4YBWJfBGrtG52J9B0GfHkeECi2B7Ib2WPk+ZSeYyqaRMXO6Ppk\nNDzWSH4gJWeVB4cLPn4G8y8bX/qmeEyta3AqV5T0XNZ9SyjYNOlR4bVerMQaWnto44EHMWz1un5P\nEhXVBxoeweDQoZLX92X74/adNwMABtZWYdHlXzR7G6Ws/y7Qvbf09eohwOm/Vb62502g9VmguwOo\nrgMarwaGTjV/zj1vAv9aDOQpayvt3IXvVAcKqW+9LBSdKapWuPJZ/c80jwb6Hy19PTwIuMIZr/E3\nNLwSHx93CUBoZFzFAw2ZT0p6NnIAWr9j0MNE9VKorsKBsIN+AN59czFG7PgFhgQ7sDdXh89G/gyT\np95o/CDiRtI/heC+agQOVGPTP9fgm+N6NPMjSJt6TThQtmqy8ViEWkwimm1Hnud1Bch4eJ8neRRu\nVJa1IoyXtWIxnxM8/cGMsXwmK7kmtKImPQfQPGYT1aPsSHVCUQnXaD9Cw7PfxeV8MqNjUkvpAyz8\nHvnCsQLubeFa10QtaiOnEM0RRqkhI8BxaJzzCuKxCOZeak0xVWNUAdVbm0RFLZ3cg3/UPIau6kGo\n6TMQyHQKYXh8HkLN5y4gtQGo2QmE+9m+fgVdh4Cje4WQ+n5x6brsGElJz2d49jMkX34BOCAo29v2\nHMburXvx73lekA6PALv/h8O2D4dg7KGngUwOQLXsCDngL7cAX/ik9IShi4EDbwKZQ0C4PzBkKvDJ\nJ8Anv6Be47Y9h7Hu4/040p1Fn+oQzhgzCGMP1QnHUJHKR9F8/CqMPfAu8AblgBauwTT/Wkx4LgBw\nBFjxPYDjCp7dKgC7gc9+AMSXA7GTnLuGSoD0WyT+jNLnBtTznfhJ6E8lrw8/ksPZtR8qooUyfBCr\nOidJn+e6gfPaj7N9ufzh10vbMgFAD4D2JcW/k/8UUh7Ea+oBsPktYP+F5n/jfy0mjnXquUUGxYG9\nbwrGtdpRvc4YwhRVt6AJkJRqc1bQ8krQqjNynJCAvqBBKPIiKqu6ngx5aAsXIIfUyHGwgbWglAqK\n6bDCf6aQKQCBgvK+5egoTQWAJvTQhE2ny5KTmD19LJKb+2Bg6HDpm+GBmoqsSFc47kl4m196r5a1\nYnE+C1T1Bxq/Any8BDyfQ44P4Lk9U/GbZcMxe7qq+rCVCpsUj2EqFJeEWBKO/A4cJyhKlKquWnj2\nu9hsB+FUSK3e+mD69xDXX5m3zWloc7g+FhEKpZA81QqEtXZq3/ew5pDSwyFW73UyIsWocUxrbZIr\ng0MgPOP56a9j4lduQVPbFCBFWHujA4Emh1OW/vEssPzGokEC9qNUSM/n6uBr+HbuVeBtweg0hucx\nhgOgDgT5FwCIypaaNLD3YY0zRwFkgL2vAXhN9V4hMgA8eHA4DhzG8Jxw/iyAbQDPAZzqvDyAWvAY\ne+hJ4CONUxu6BpvwWZCfixzVA93/F4Bz+DpskgeQ53kpEC7ACW3vnEf2W/DVoKVm/DC4vOTVQDcH\ndIcAPgC+cKEBcDiPfx/nBd8Xvwq8XVQxrdxXHgCncW14Wzbe+VzhpKoxuv8vyPF/NfcsdceS6txq\nru0Eqg1GRlYQTFF1CxdDzkS0vBJPrrkOtw18iOpdE8NAxZw1TU8GrTcqDSMeCy9zOuSCfmH92t41\nAl1HdqMGZGHUrOLphQLWNLEe3f8KAaTHzwnjYdemOtRXEcJVACAYRXTyA65eo4gfeq+KlK1icT4L\n5I4CrU8DfA4cgBCXx+UD38CG1PGY/XyXdH0ArK0ZFI/hwt0zqUYVR3+HQNiSRxXw8HexGOJn14Ml\nR6sKtaXfI1/wUnIG6wpYQHMOxwjjTk1hrf1W3Zv4Hfd9RfVOOU5FpBg1jmndl1wZrOKEZ3w0J/Sq\nbRppo3+mWaRohaI32m6UCun5hJHFAfTHwHuEe/i8LEpLXYyvoU8Q6CntPWo5F04lV3AAumW59CL1\nsQgeOf0DxD/9BYaHO6RUhteOnu+PvGJa/qUm/ip0Q8vbfua87ZicfNA9OY1SUIs7bQleTk7FrGXv\ngUcxp13+W//ZQK654Xx0lTx6746rcCDVQ6wHEpEX7gLAPxcgVqPmeQ7HbV5BPycJrbFkpthXL8M9\nc+yxzoR5wsCS43DceNPEesy/bDzqYxFwECazOBlOPusG3L3rx9jZU0ftOxoP78OAaFh/AtF6o3JB\nAJyQDxceJPx/dJT+ZBIXp1QbAL6Y69K61PjNty4VJvVzAeFfre/KBX1O/IfDFv4MaSFLJNPgURRG\n+0fI7XBikTAiYaVw6KUCVp1Lkt/oOSC0fBj9M6TzpaE0CA/ydJHTGptWaW5JYMqCVWic8wqmLFiF\n5hZKCwIP0bymfFbIWVHNHdFIlMnzmLt8S/ENK2tG4wzhd42Ognz+Pa1RiMJR4S4YtpSj6gf0xpOW\nB8sss6ePLVk3AGE9sfR7eBD6qzmHSeNOTWGtrc4dxNo556J1wUWCZ4OAExEppGdMWpu17kt+HWLI\ncg9f6FVLMxg5aHyWEH9XvqjM0IyhRo2kpOdTFQAi1UUPjngssVhWQ1UHAhwvRGdlOoVCbXLsyDQE\nuSIa6MbcuDK0sT2Zxs3rT8CUrb/D5zavwJlbn8Ly5DTLc9FxSOu2Hm6MGRu8t+ZxvDZmJj4Zfwne\nHvcdXBpbjQtqX8dJO2+1J6fpQdm/0DgDF39xOHgAs87/AtbOObdkjTQiYxhawwny6G0DHwIAzNl5\nI3b21CHPc9jZU4c5O28skaH2ZMmVftszg+nnpEEbS1Xeym9+g1X9dZMyV4ITPYXLhv07MQw4FapH\n9Js7Cd9UYbVyJq1aIC102KhllmSF48JCnhCpGqH88wfDQEcEE7t+jTM+V4O326Po7CoVtCPhADI5\nHllZRcpQgJNaeLy5bS86u7LoVxPC1LFDcFK9Mkfpn4lO3c9Y4p/3E1uPIDwAOOmnwv8faAHa/xfI\nJIFwDIh/FRg40f65y8g/E51YuXkX8fdw5Lm6cE1ffv8W9Dm4AS/1+1LJd3mew/zd1wIAfnqhrLiZ\nQ7/d4lXbieO6X00IN55rP39HZOaac/HxkC9j7dg5jh3TC4yMp/tXbqV+X/GbmTinU2tCVaYT314z\nFX/7/K3454irLB3DcVRrU//cUVzZuR5v9DsNH0/6NQD3x6X6GX9t1B6MSL9leD7Jr28stwOvVs/B\nD3tuwtqqKbjx5MNCKyhe1qubqwJGXu74+jpmz6s4b8sdWPalF3GotlG6N7troPr5LOn3FE7q/gee\nm/K/inNcP+iPiIUI1coDtUCwypm9peU2kOQKngf+nDwHH3QJ46FfTYg4ZkSszEUSQ/rWWDfgtS4F\nNt5U6nHmwkKKRF42ZvzmGWtditTa7yo8h6l8NdL5KgwipRlZqdysOp/88+/GbsXN608oSa84eLQH\nE3/xGr4+sR7vtB6wlH7RqBUhcM7DxR6vBC/mzp46nLn1KcVrpNZjNy24FfNVntcUITLAcKeBY6iC\nNGtPwyhitReaiJVehLq9zEjQFV95eO66E67FsBAlvFVEfX/i5G/fBeytwYzIY1h7cLD2MRgMG/w2\nvAh1XBKX9vSe6ntq3ot+twkAACAASURBVKn+EV7PnYKfZq8r96UcUwxEJ/5R8wP8LPNtPJv7crkv\nh8gIbg/WVM/CT3p+gBfzZ5f7ckxzEvcJXq6+C9f1/ASv50/19NwXBtbjV1WP4YLuhfiIb3DtPL8M\nP4HTuK04q+dR185RSbxz53kY0rfG+gFISgbgb8VDo/2PvL2d9Do4cGZ74YoQPp/OV+N2mVInhsme\nMnIAzl60GuEgh0yuqKeYaSUl9romttMSr3Pd1SAZTPI8h88VQne1ziu2DST1rJfjVf/6SoIpql5Q\nSZYPO9dqRdG1krdBUXzVeQafjL9EKopk+nh//RHwt6XIjU6hq+/ncP9nV2Dp7jNKvjq8fw1ev+Uc\nc9df4PyH3sKuQ6Xte+wcU0HbMmDzvUDqMyA6Ahh/NzDqSvvHNcnLm9pxz/It6MoWjQs1oQB+fumJ\nuHhC3NFznXjPq9T3tvx8uqPnMoreNVX/8QpwnZ+ga9BnQq+4Aql8Ne5p/z5WHjobsUgIa+ec58r1\nvbypHY+88RF2HerC8P41uPm8zzv+u0QWj0eucSp6LvovR4/rNkbGk93x7eb84I7sQfS/TkD39AeR\nPeU7to7lJBvX/AYNOx9AXWAfDvQMxKidh9H91UeQPflq6TNejEvhRCeS80ejI4GLt5S+rrq+4Z3v\n46XquXhr8hOYdP43nb8+wjnFZ7LoxDac+Y9ZSF/7FvJDXaoO27YM1StuQCCdR3pUIc88GAEm/Vdh\nfzH/7MyeH38nG7jyPIcvJ1ZIY8PtveZ/WhK4q/mfePv2aWgYYDKM1yvckjcpEXM0RXV3dgiGzdwj\n/GHWiWHQe1kfi+A3V5+Ki//rbcXn5F7RQK3+MxBlx9fGzCRGFUopC8SChPW44JNnbLUeEzlW+jSb\nhfVRdRsrPQ/LiZ2eYVYqZ5otMCHLdVEXNzranVUsArSKxrrX0LoUaH0GQBBB5FDb/S/8fOhD6M7c\niBf2FxXISDiI278yDrXV1qbHboKSKr5u9ZgKvjBD+K/MPLZqu0JwAICubB6PrdqOK09zNgenXqMY\nzZcf/j/vKvkauKb6WKTwO+eA6DDUnnEbUu/ejppMu2RtXXnobISDHOZeepIzY4LAlaeNdPx3KCEY\nRgA5hF26ByeRrytBQlEfQP7bCc+vOhy0XPXX1fnRLUiQ1VVhVLv87I1WPm5uSeCOt8YgnXkCADAY\nh7Ch5ofYmtiPCV8qXqMn4xIAuj8CggSDZvdHgMYzk67v02rg98A5x9drft4uzS0JzF3xgbTH7TrU\nhefebceZQSASgnvn/uBOAD0AF0BtUNyzuoTXT6G0dTrlbueu5wszgE03ARlygaZ1M4sGPLtzUY9o\nlZC7m8v7x3GjwE15k1bEj0B3Poj57VdD8r+brVRPeV3drSCRTKMzrSzSV+IVNfAMxPER/4DSDSG1\nAzjjWeJYj05+AGu/qe8BJVWwnzauDqu3duiP1UpydpUR/0sXfsVKz0MKTrVAcBWziq6RxY8LCsUi\nZBP03TcXY/Knv8CaER1oH1YIoUgrQygW7p5ZGsZBPH5AWAjkijbfDaE8OgeAR4hP495Rz2Fd7iuO\nPX+/tGZxGzd6pdIgVeoUcbLFhd1rUhRvyeeE6p2NMxBtnKGY52Ily8ltPwI+rOBNKhi21J7Ga9RW\nb5KSSiu8Y3VMuTo/xGqwLhZTAsxVPlYXLskUWnKs/iCBCZe5eplk7FbezxXyCoN6rUfsQSz4koPQ\n0USvwr4dUjsAvkYqeqV43WZbJy3k6+A18etxV90jCPGyOUEp0ORmlfBgQHgICkXVT0qEg/JmCaTK\n8eDAEaLWjuaj2IALiy+YnWOUz8sLDwFAkONK8pKJPeINPIOmifVAm8Z1OjDWLY3NSnN2lRGmqFrF\nSs9DAk62QPAVE+Yhu/57yg1IDil0uHUpTtp5KyJVwmJE6vcq/38xBCSZ64P+4S4EeZXAzOeUEz+1\no9jOQbYGR7PtjuYO+KY1i8sbrZcKudxqSTqnUy0urF4T0cjB5wRFTvZ56b3WpcA7tzu2SZXN2BUI\nK1po+BWSMgAIAlGe5w09M7PP2NX5IVb9dbE9DWCud6daAc8WFNVUmhxh4jqU1k2Gq9SKBhi1ourw\nuio+N3lY4/5DA4G9APIutjCJjoRwEtLrsBeFRUEt7/y+fQqOdGdx76jnEM22l00hLFFU/aZEUOXN\ntkI4rY2xSFLUKE6GWPCIUo4xO8cIn0/lq7Fw90zFx3I8j84upTxH7RFvRObWu04Xxroubhofehmu\ntafhOG4Rx3FbOY57n+O4/+E4LubWucoCzWIkevEMYrsFgpk2LR7SnJyqKO29P9sXB7L9wGu1sNl0\nJyIqi5nYykPN8uQ0nLn1KXxu8wqc8sEf8IuOW8lCmzjxAeE3E63HcmOhw6Xi3WjNYhonWgDpYLQd\nhFM0TazH2jnnljgARNzw5OohXlPrgotKS+jns3SPl9YmZRJaiyVP2vcEQxWhqNLGRp7nyb+dCivP\nmNaS5mh3lvg9U+2XHGhPY+R8ZrzCagVcVFQHRsrUBU+j9YUhJI+qrFWZC+tqLBouaQdTFy60IWv/\nX8vH1WXCPABBpUfV4RZ6akjyzgv7z8EFnzwjFFJs+rQsQnqooKhKFZUdXJ8dgSqjcM6MxcYZwrMX\nfwNSuykAXeG4cp00O8dUn9+dHVJSHRcQDIh/265UTNVeVwkj8pvdtcANHHJ2HQu46VF9DcAdPM9n\nOY57AMAdAG538XzeQgyXQKkXTweaIJBIpjFlwSpt673frH4yBM/XOYrcT0Cn8plG/sKAaBjRqhA1\nT/Hp9imYO5hifRaPO2Ee8Mb3le+5tDG7GaZkCA+sdboeRZeomNBqLUXVwU3KjNfLcQKhigj9tTtm\nrDxj8fWfr9iCg6niM0qmMyVRM6Yja8SQ0IA1JdDo+cw8N3UkSaYgXpxzXBlt1HY8JaKiGpL1pXZh\nXeV5QlijqDx+9BvgS3dbOq4ujTOA2K+A/R8CSHnizfQyXcRMBESAU3lU/aZEUMJzS4oguRkOXMjb\nLMHsHJN9fn1LAq99tBmAcm3N8Txefn8XAKFoVlc2T075MiO/eeg1NTT27KYmHEO4Zurkef6vPM+L\npvb1ANyrsV4ORAuNnhdPB5qgxAH61nu/Wf1kWNqQKBN0V6YO91xyItbOORf1Wk3P9RqyN84Axt0k\n/D/vE6uaW3i00Wp6FF3Ca0+uZfJZemim3lg1gZfCXwmBMJD3v6Jqd8xYfcZNE+sRrSo1VqijZkxH\n1tgM/TV6PjPPTR1JMjxWCx4BHD/EZwYko0ihvzKPqgvr6qF0hh7W2KXThs0u1UOBgSd75s2kyTtO\nGxnNRkCEgipFlboO88A7P3L0Wg1B8ggSe9vDmT3eBQ8kKYJDXDOChPLC2TwPDsCCb3wR9bEIViSn\nYeGBW5AK1Tt2TW5geOxNmCco2nJcjmioVLyKybkWADGGheO46zmO28Bx3IaODgOVXP1E4wyhGBAJ\ng4sFSRAg2MnIQotbyogD4cSWNiTCxE3nq9E++meSAqQpOBmZ+PFCz8Hz3ixbmJEnOKgI+Q1fhFYb\nIZ8XiimRcHCT8kr4IxIMAzn/h/7aHTN2nrERJde0ImyzmJLR85l9bmrDFVchxbaIkIopubCuxmOR\n0rBGUW6vHmL5uIbgc9aMHRZlBFsGIxPnNGv4CRYiE6TQX9L6LLL9ifIpqwbCcx3b49XnKxQENJye\nIENLeWuaWI88pU0mD+WaMvfH9yP6zZ1lDRPXw/DY82M4sk+xFfrLcdzrAIYR3rqT5/k/Fz5zJ4As\nAOKqwvP8EgBLAKGPqp3rKQs23fek8ElaeGsimZYmthPnJuJQOLGlgkKEpP7IhHmYLDuvdripgept\nonW8AvLqbGG3kIhPoIXQlD202ghaob82Kg2qn8m0cXV4cWOiPMW7AiEg60GxHAcK2NgZM3YKpBkJ\nnzUdmiyF/lrzqJo5n625JlNUK6K6vRySourCujp7+lg88vq3ce/wx0qrmjZeY/m4hsjnzBs7bMgI\nltNFTJ7TrOFH9OhJCpN4zHXfIl/Px0uA036lfc1u4/Eeb6fwp17qBG09EnOHKwlTY68cRZwqEFuK\nKs/z52u9z3HcNQAuBnAez1NMJpWOA4uFWhCYsmAVVVlVLAxuLFQO5eBY3pAMTFxNwUnv+4GColoB\nXiBbOFByvdyCZcVXxNZSVAFLmxTpmby4MYFvnFpvrG+b0wTDQM8Rd8/hg1x8ddXpIMcprORaz9qI\nkmtaEbYZ+qt3PsfmfiAE5DOm53K51x4AQJZQTMmFti3Cfd2EhWtCuK7fk4iH9+EINwj90IXvroxg\n69ur3Lt/XiPqg4ZNGcGo4UM+BtadcAuGhYyf06zhR6z6m83JxNTGGXRF1c22QUZxsYUQCTu1EPSU\nN9J6FOCAkYMoXm0fY2js+an1UQXgWjEljuO+AqF40jk8z6f0Pl+xuLBYaPWMVCwMbixUDoYT+9Lr\nJW7KFZBXZxsb1jo/KIllLRLkBHqKqgVoz2T11g5HWywZxotiSmYFY5eEAHHMmZ0XRox2pg17eXse\nVa3zkeb+rGXvYUPbAdzXNN7ciQoeVTNz2Q9rDwB6H1UXvCDCXnk/gPvR3JLAMy8tx0vBOQgg7+79\n57NAqMbcdzyof6AeA0OClLQwyjnNGn5KclRFuCBZKXW5LZRhLI5FK4YgO7UQ9JQ3+Xo0CStxR/xZ\nXPvJnYhnjwKtiYpS4nTHng8Mr5WGm1V/FwOoBvAaJ4RVrOd5/gcunq98OLxxiZP25mXvEd9XLAxO\nb5q9vRKZaB2v1Lwpj/CDkljWIkFOkM9ZViRo+O6ZBDxoT2NGMHZZCLAyL4wKhaYMezZDf9XnE69x\n1rL3EOA45FQBUDyApet3YNKogebmf6HYlplx64e1BwC9j6rLLHp1G/pmAQSBAIQaGK7dv5U1ygMZ\nQT0G2jOD0VBFUFYp5zRr+JGq/qoD/8ZcL+SkqhlzvYG78CdWDUF2KqcbMRw0TaxHU+xN4J0ngFwK\nnblajOPbgHfuEz5QIUqc7thj/VNN45qiyvP8cW4d+1igaWK9FGamxtUiKb0kt5GKGPprQbj2RTia\nR/hBIaqYNjQ0eB0h0ILnz3fPxItiOWYEY5eFALPtxFzzDtospiRHfY0lwnoBHjCvLAVDQC5ratz6\nYe0BUPSoOhwVoUd7Mo3jOKG4TxB5xeuOw1vIUdWSEeRrWnigUBSq54DpyAb1vVppTWLG8CPmQuby\nquKYYh7qx0uKhafGXF/+/FQbWDUE2cnTN2w4kK3fh/O16Bc8UpFKnObY81vrowrA2xWYYQo7C4Nl\nDIYTV6zSZrGYkm/C0QBg33bgqLsVsr/SrxUdh4tCQQ5BbOYbMTTW19XzyinL+HcSrdBfi54/3z0T\nL9rTmDGeuSwE0BQusZ0YoFwbXPMO2sxRlUO6RhqmlaVgFZDPmBq3vjHG5HqE6ye0znCTeCyC/CHh\nnHJF1ZX7z1uo+kuTETrWAtt/DalnQWZ/8TsmIxvUY2B5choA4KfxZzEs1OF4Xh8xR1XktF9VtGKq\nxk6rLcB633RDhoPCOp3nORzORdE3mFK83ivo7VGLLsAUVR9jd2GwjE44sa+UNrOIHi6TXiDfhKOl\nDwKPn+Z6MYcnACFwX8bc/HU4efotitfcNFiUbfw7hZaiatHz57tnEgwXlSa3MJOL77IQQFK4tNqJ\nueYd3PWa8O/rZwGDGmwJ7WauxbSyFAgDuR7D47a5JYFUT6kRsSzGmFwGCFbrf85hZk8fi1+/JIxh\nUVF17f6tpieoZYTWpUollYQJzxhpnr129HycO36WK2udqKgS26T0ssI3dgxBrtcdKazfR/M1yCOI\nfsGjAIBUKI4LCBErFUlvj1p0Aaao+hw/FiTyjdJmBYuhv74JR+vqFJTUf/tPYMx5rp5q7fZ9+NOG\nnTh0NIXfVy3EN0+sxQmy39eqwcKMcuvH8W8YLW+FDc+fr56JF8WUAOO5+C4LAWbaiWm9b8s71roU\n2PoYgCDA8bbzcLXuQY4lZakQ+gvoj1v1eiISi4Qx99ITvR/zuR5lxV+PaJpYj+jRE4DXBUW13k3B\nnM8BhTBjW2y6E5pKqohBz5jXBjkx9DerLqbUCwvf+C4qR05h/T7cI1T77Rc8giwXwd1tVxEjVnyz\nD5rB42rNvQGmqDJM4xulzQpS6K854do/4WiF6x42ARgzzdVTTRkDTJkOQeG6dyFOGKosFW+1sEzF\neuPNouWt6C3hP4X2I77BAyHAaDsxUbh2XCjcdCeQ7wYgm4828rho1+hIyyMToeG0EOTa6pCza4NR\nD5kY+lsGvnxSHHgd+OXlJwGnuFjN20ofVRJGQzNNrG9eGuSCAUrV315Y+MZ3UTlyCs+0c93jAIC+\nkVrc13EzXtg/RfGxinGM0GD9U03BFFWGabxU2hwPLRU3ZZN9VH1jhcwRevu5TSAIgCvxQlsxWFS0\nN94sWqG/Op6/iskBD4ad7UmczwP/Oxs4vNvmgaYAgSlAF4C/rxT+kzP5u8AYZxQArbXBFaEwtQPg\nCwYQTvW6BVwVXE0U2/LEAGrGQ5bLlE1RlQxcOikettcJvYJvRqEZ3uT4OLyRqqhS5lT+6A4sb0n4\nc002gNtGAFvjsnEGOvFV4P116Hf2f+Pp3/6d+LGKcIwwHIEpqgzTzJ4+FrOf34SMbFEPBzjHlTZX\nvG+i4qBl5SdY3JsmCkJM2ZWHXKHAUcjj3CmCwGnFYFHR3nizUBRVYRMfjkn4Ie6IP4uhoQ5wMs9O\nRXmdnfaoHtkNvPsk0DcORAc6d1w5HduA6r6OKap6ip7jQmF0JHA4QX7dIq4JroGw4TQLxw2gJM+p\nGQ+Zg6G/pgV3MWVA49k5sk7ks870BCUZ3gAg1AfIHvV9eGOQFvpLUcDbM4P9uyYbJJfn8a0n/46d\nyZT+h02Q6s7hwNEeKRA8kUzj5mXv4a7mzRhQa8zwk+4RcrP71oT8E83GKBtMUbVLL0u0N4y6EKIL\nhRE1vW+xN609d72qvxoW96aJM8q/KUm9/WQClBdjkCBwWvEyHzObDs8TvRVy4TKBafhzchoi4SDm\nXzYeTY1FhadivM5Ot6fJFgwx5/0MOPkq544r57GJjufVepo3PGEe8Nr3hf8X112/equCIaDHmCBM\nW08eOf0DoPlac+sbbR1XK1IiJM9ZrtsRj6olhVIyqObJ78OhdSKfd8ajWuF5d6GAkKdb4lElKOCp\nfDUW7p7p3zXZIIe7Mlj3yX58saE/jqvrY/t4Ow6ksKW9k1pB/Eh3DuOG1WDkwCjxfTX9ImEcP7yf\nf6LZGGWDKap26IWJ9kZY9Oo2ZFRl3DM5Xlq0nQpbpHnZJmGl1BQagLnnLhZTooUrepSTYvkZSaG/\nBQHKqzFIKJpjJWTwmNl0xEq4Ko+qEeGyorzOJjxmhlCPbzfwoqWOmzTOAD73d6D9aeHv6Cj/KgXB\nKiB/yNBHSevJI6d/gMl7bje/vtHWcS5IDqcleaNzGUc8qpYUyoLipBX668g64VToL1DReXfi4y5R\nVAv3s/OtWYiH96E9MxgLd8+U2uX4ck02iOg9vuLUBlx9xmhbx6IVQlOz61AXXvjhv5k6tq9zahme\nwBRVO/TCRHsjaG2QToYt0rxvd8SfpT93QNuqK27KNEHVg2bMtp6R6HESBXmvxmCQHOJp1pN0zGw6\nooCpEgKNCJcV5XUOhADw1ttcqMl2Cf+6GdrudF5tORh8OoCnga99DAwYXe6roRMw53EvWU+ar7W2\nvtHWaz4neJ+NVIV2qJiSJYVSCv2lC/6OrBNW+qj2QqgeVQBonIErlw2vnDXZIOK9hoL2qz4b7cVs\nVbH3VaV7huc4UJf8GMYDpcaP0BbneCyiaT02y+zpYxEJKzfRSDiIoaEO8hdEa3uqDYCsbUPr0uJn\nOE67pQYtz8vBaqy2npEU+lsQoLwagyYFTi2aJtZj7Zxz0brgIqydc27v3IBEL6PKo6o1d0Ro496X\nXuegWJzMIQ9lVvSouqio+q1SsRUo48t3BENA1z6geTTwXED4V74e62F1faOu46OA05YI/4Ir/k2s\n+utMMSUjc74EA8WUHFkn8lnnPKqtS63/zmWGmqNaoKLWZINkckJYuXjvdjCqgFayYm+W5pYEpixY\nhcY5r2DKglVobiHUFWAYgimqdvBAqfEjWou2k2GLTRPrMf+y8aiPRcABqI9FMP+y8ULhGRJcUNvT\nKqIVrjhhnmBhl+Nw/hftWSSSaf0FTR0a6dUYDIY1rfsMFeL4UnkrjAg8tHHvS4Ve6kvskOInFQtz\nMfQ36HC4cjkQ56LfvWHpBJDapW081MLq+qa1jjfOAJo+Ba7KC//SPLO5HkfGoSUlx0AxJUfWCd4h\nj6qYgmL1dy4zorKWpyiqFbUmG0TyqDqgqBpRQH2r2LtgYBGj5hLJNHgUo+aYsmoNn5tjfY7LzeX9\nilb45qJXtzkaIkMM+YhRnrvRQhlagqoHRSFi0TAOpsiCvW4YsFpR9WoM9gYvlJdQclSNhj4bCnXy\nQyE3MYfPMY+qqKjWOHM8Eg5GB5QNyaPqvaJqKr8++b5QWEyOmdQEq+ubE+t4rgeo6W/88xQ05zxt\nDhsopiQe25aylM8745Wv8DSokI5HFeh94adinREnQn9JtSfCAQ59akJIpjL+TfFxqcZHRRVErACY\nomqHCq90Zwfaou1JsRzac990J7mXm9r6HghqC6ouFoVobkngSJe2N0dzQRMVVdHS79UYdLq6a28n\nT85RBRwSePxSyE0SqB3yUHpSTClY+R5VvqDAeBH6K1OmUqE43m67ConkOQCMGNaOADzhGo2mJthZ\n3+yu42ZCf3WMRsQ5rzWHRxcqXuv0UbUNnytWErJDhadBFfuoahsGehNOelQrtvaESwaWiiqIWAEw\nRdUuFVzpzg08W7Boz92I9b2MVT8XvbpN0X+WBnVBIwnyXozBQKjyhXsvcTuH0C8eDKcV1awHfYKD\nYSBT4QKDFPrrcvaOSpmKZhO4d/hj6Mnlpcqnmoa1cF8AhGdtJjWhXHus0T6qVo1GunOYcz/dwqk+\nqpR+o5WSBhXkREW1zBfiIdmCUu6EogpUqMfZJQNLRRVErABYjirDccpWLKdxhrFCGWXMUbNddEBd\nTMkrtApQMUpxW1H1iwfD6dDfnBfFlCq8PQ3gXTElgjIVDXTjtmHPKF6jrmtDzgB4lSBcKekxBqv+\npt69na5wan5RZw574fl3qlq3B7Ud3CQQ4MBxPvOoulycKiuF/jqjqFYkLtX46I3Ft8oJ86gyehdG\nrO+BUNnaU9AsbXI0FzSpPY39/n6mCPYC4d5L3M4h9IsHQyqmpJxPlvsEZz0qplTp7Wko7Y8ch6JM\nxcP7lH/TDGuxk4DA34DowMpLjzHQR7W5JYFLM+0ASdY3UplYaw4HQh6F/jogBvaCNKhQgNPMUfUU\nD1I7slLor//8VZb3D7O4VOOjYkOhfQpTVBnHHh4UBqIttLaLDnjhcSKhVSmZUQqlmJJj+KWQG6E9\nja0+wWLVX9fb0zg3lj0TquRQqko7DkWZas8Mlv5fNKwRn0MwDOR5obpupZHt1vWoLnp1GyYNG4yG\nKkLLNCOVibXmMBd0N/RX9B46NYYqPA0qwHHIqQt/lQsPUjuyOWdDf53C1v5hFhcNLBUZCu1TmKLK\nOPZwOfTXyEJrWbCVQn/L4FGtdC+Ul7jt8fKLB4PQnsZWxUOvPKoOGao8Fark5D0qpkRQprJcBE92\nXgcOkNYvAMTnMO74LozLZ4TKv5x3ArEjxgMDxZTak2ksxEwsaFiMaKBbej2Vr0bUbmXiQLBYNMsN\npKgP/3nUykEowCGX84mi6kFqh1hMyYk+qk7iecXcCjewHAswRZVx7BFwV+nSW2htWdpyPYIF3Ou2\nFIEQkO3y9pyVjBc5hH7YYAnFlGxVPMx65FF1aP6XrQ2BZAhxWckgKFOhCfMwt3EG5so+NmXBKuJz\nWPNJEuMAwTMY9EbcuKt5M5au3wFR5TgVKzF503+A/7BD6MFt1KBjoJhSPBaRikrdNuwZxMP70J4Z\njCc7r8Ncu5WJuYC7HlW+QnrxekTQT6G/HqR2iEUdnWhP4ySsYi5DDVNUGcceQXdDf11daHP64Wiu\nwNrTmOtb6lVoZrmRiikVFT9bFQ+l9ktuh/46M5bLJlTls960pgEMGURo93sgzQNhCM/bA0W1uSWh\nUFIvja1WejvN5PrlenQNJmIqx/LkNElhjYSDmH/ZeBt3UcDtYkpupydUGMEAh7xfQn89SO3IOVz1\n1zaF/fXj8TvQnhmMhbtnSnMKYBVzj2X8ZUphMLzA5Qq2pAX10thqrDvhWvsV/HIZd8MiaRzr7WnE\n4hapNgB8UeCl/Y5eVWVV43KlyBIkj2pxPlmueNi6FPjgYQA8sHxM8dqdvieDRpfmlgSmLFiFxjmv\nYMqCVWhuSZR8hiY8uS5U5XO+MoLQ7vf/t3f3YXZV9b3Av+u8JJlB4vASiTMhyagYRSOkDLna1FYi\nNl54Lqb4WmMRvRhvxVbbEozltpe2DxKhV6VFro22CveGx9c60mLNFQO2zRVCIFBEiIJDJDMEEmBE\nnZCZc866f+y9z9lnz37fe+299t7fz/NMZrLnzDl7zqyzz/qt9Vu/NTCwyPgio0Gua3fuhz3UuHzp\nTX0puca5hKjI22kbM44Bg4Ib14zg6gtXY2RoAALAyNAArr5wdTqz6aqLKWVVkKsg6rWaPjOqYXcw\nSECrqr+299eakFi24DC2LbseFwzdDoAVc6uOQ2mqRJl9oWwpLgzkLJh0wdDt+OSy6zEQZ1TfKeSW\nCUm4ru+qeqAatbhFVmsI7TKoFDmPy/Y0sdZhW+c+1wbEgt65H94NTNyY7u8UYnuasGtP3YqjZdKp\n6rS0CjC8noc3vHIYeACZXTucM7vO6sRdQWv9ItQCUFY0RXkxpWxSf3MpNhaDVmtUAeVLO7Sq+uuz\nDdY9OE/bNkPZvNv8agAAIABJREFUYKCqQh4dRgqvrnZG1dlR/9Ph/90LUi1xK/i11AaqXh30seUt\nLKty6m/U4haqt6dxk0GlyHlciikBMTrv1rl3FgHC7Cy2Z4BHt8+fVUr6O4UoDBZ27Wlu2xDIjlYp\nm17Pw2tak0agaqV0K+ZMO5+ai1mRt1tdPYfsFYvyYkrqZ1RzKzYWQ72mUdXfDHQDVR1mVD3eR5ct\nOILdW9dnfDKkG33e6cokjw5jEeU161xrArMzwbdLoK+jfrNLRwkwBjAmdkT7nUMU+EjCq4P+wBMz\nWLZYTaBaiBH3qMUt8ghUM6gUOU93exrj9439t7TOUaJ/T0qv1Mckv1OI7IAoa09z2Yag0zaK7WjE\n9Xm4d/6Mu0rOmd1rDs2vyBtqrV93RjXHQFXU1M5EZ5D6m1uxsRjqNdGthFsFWm1Po8u+4KQlvd7p\nyiKPDmPRRF3zl6YM9lHt43exjfo7hyjwkYRXB/25WSiplGyNuE9OH4VEb8TdbT1grs64yujg2vl1\nePNYo+rVzlS+2duq/ib6W1rn6AxUvdISk/xOIV7/ua09DSvLYkpJeMy4q+JcM3oPzsODy/46+lq/\n7oxquEHBMOuZI6s1Cp/6W6QKrg2dqv5moKXT9jRR31+pUhioqpBHh7Fo/GadVas3vTsAKorRuF2E\nLVF/5xB7+yXh1RFfuHCBks6m34j7PFkXCrKLWtxCdUVNt+cijzd7WyDi9bf8k6/eH9yBt85dil7q\nb30QeOnm9H+netNIqex4p1XGLgiVFdnWao2qJ5eq0FHECQA3rhnB7q3rMbHtfOzeuh5nv+HDwMbH\ngHd3jM9ht6YBQl1rlQ221epqiyllkPWh/YCPTa0m0KlSoGqux23qsD2N4/11pjGCK5/8CEb/bii9\ngR8qLA1aaAlxdChYnrPOXlV/Vc3yWhdhL1F+5/Yxpam/Xh301yw/WUn6XugR9zxn4C2jm+Z1eD07\n0irT6ryeC0B5pch5bIGI19+yLWVwB956jdQGjRlV69zX3pD+7+RSqdjJOTN38fBu3LP6Emx86NTs\nB0ncdFpaVf31FOK59pJrtkWE1N9Ig21RqC6mlME+qtoP+NgYM6oK1wRHoGSG3sHankaLGVWg+/46\n/srHcdYDX8CXptbpnWVFmSlA7lABuWySrl3V37yrEue5JsEr9U/l2uLRTebznfB3bs8q3WPSqyjK\nS578PjCZfqcp9J6bGq779i0UMqAo9XdiB3Dne70LDIWdMUqLLRDx+lva+a5PG90ELPkGcPRZYOPt\n/cfT/J3slYp9XkvdNZcTO4A91wEtjYrjdfQqpuTJCvRiDHLlur6xba5pDTEoqCy9VXkxJfWVyXMr\nNhaSfU19oy60CNqyKkBlpf42daj6a1Okdc2UjQK80xWU4tLiiehQlTiDDa09eVX9VD3Lm8bv3J5T\nOqMKeBRF+b9q1vWG3t5Dw3Xfvm+oF5jtK83ZCut1q6LAUFy2oM/tb+nGtwPfngUai1I8QRdR100m\nHCRRUixMtgHNOpiuXLYvCivX9Y1W6m+IQcHQg21RZVZMSW07yqXYWAjOgHCuLfHQE89hfN9krueb\nVaBmpf7WHVV/8y5uWKR1zV7yfg7LpgDvdBRVYNpInutDLRlsaO3Jq+qn6rXFafzOrWP5VKKsN5Wk\n/jpTLEeGBnD1havnX9Q1XPft+4aqopiS2+vWLo/nwlZMyfm3rAv32QnfDnzrGNBQ3L6jrptMMEii\nLH21MMWU4qf+5rq+McI+qsrSW5UXU1IwmFYgbgFhRyJ5ynZCWQVqvX1Ue9dpHYobFmldsxsdnsOy\nKcA7nf50Gj0JlTaiy+xUXrPO9aZ7xymLWd6kv7PiYkqeak1jBF5KwCMAsYvymgg14p7nDLwH35mU\njnmeaQYTfq/PvJ6L7uyk0em1/y2d1yIgRAe+fQyon6DsdAH0BdehJFimoGx2pNMuRoCRYEY1dLZF\nGFGXukQopqQsvVV5MSX129MEynEJkq4zd8pm6B3ctqfRIe021dd9DnR4DstG+YyqEOIyIYQUQpys\n+rHykHT0JO1F86EKO2g4O5Upr2JKec7yhtWezWlG1dovM7jDqWREMehvk0NFYN+ZFBUVNb1en6Ke\nXzv1aRehZ8vtWrPqZ1SjzvIlKI6nrDPcaRWj6m+C7WlitR83cQqxRQhUrXO1VxpOpUOaVTGlvGbm\ncy6Qp+vMXVYFqNy2p9EheE/tdZ8THZ7DslF6hRJCnArgTQBKu4FoktETFYvmQ71INJydylTNZ3sa\nndcWA/kFqn2de//HVzai6PW3yWnNte9Myr0KAlWv122egykBgUjk9WntY0r3CQYQfZYvQXE8ZbMj\nslOMQLU7kBFvrWUq6xvjrDGOkPqrTE1xoGoVU8prZj7nAnluM3c1gdxn7rIqQNXqdNCoCQhbhlRW\ns7lBdF3XHIYuz2GZqB5K+zSAywF8S/Hj5CbJ6ImKDn2oF0kRqhKrVE+5MFCW6Uu5Bar9KZ5+Mh9R\nzLHD4/mGqmKNqo6v24T7ZM7TUlvVGkD01F8g9gCWsjS2oqT+JphRTU2cpS4RZ1SVqNWV1AXo6l6j\ncipVkvMSJGdAuKBRw9LFi7QIkLII1FodOa/KcdHTbnXA5zB9ygJVIcQFACallPcLnzVtQojNADYD\nwPLlxUs9TTJ6oqJDH/pFovvMoUq1FAsDZT2b157NZ5Q/QkCS+YiiLmuu7VSl1en2uk1QLMdpfN8k\nfuu55/DtvYdww8O7vGcRkg4MJVg3GZWy2ZGiFFNKsD1NHK5r4+OsMdYhUBV1oPO8uvvPYB9VX3lu\nUWeyB4Tv++IePP2r2cweO2/ttkSz3j9Ioft2QkXA5zB9id7phBC3AVjq8q0rAPwpgN8Oug8p5XYA\n2wFgbGxMJjmfPCQZPVHRoeeLJIRaw3iT/uYK4OjjyWamsp7NU7yPqqcIAUnmI4oadHjm6eS8/isr\nQpgd6mQzqtYyiLtqc5hFw3sZRBoDQxnP8imZHZHtgqT+Zjco4LWUZuSNl+HsYx+LttRFl9TfMhdT\n0mwJUr1W627ZUgVuM6pAsdNudcHnMF2JelFSynPdjgshVgMYBWDNpi4DcK8QYq2U8lCSx9RNksBQ\nVYeeL5IAzz1ofJ75GSCQbBY069m8Vt4zqsEdzjiviUSVszXr8ACwbf1QgR3AUti6yFoGsXBhC8dg\ntDXXZRBpDAxZbVnl+j/VCpP6m96MexCvpTQfvfN07H7ndmVVf5UpezElzZYy1GtAu1OlQLXTV/GX\nSFdKrlBSygcAvMj6vxDiMQBjUsojKh4vb3EDQ85+5uTQbcZnCSNQBeLPgmY9m5f7GtVwHc4or4nE\nRcU06/AAULNGVVe1ZuIZVWO5g8RCMdcNVHvHbdIYGLJmkDJKR1WiE29GNfOt1JLOqEZI8/ZdShM1\nZb51zPisurCXH+XFlDTYR1WjpQyNWg1tWaFAtS3RqDNQJf1VoBelN85+5qD1LIBFRqBqF2cWNMvZ\nvE7bGAXPZXualIvm2KRSVCxqh0d1AawqBap1j+2eIhgeGsBT078AAMzKZt/xPmkMDOlQ4Ccp2QZq\n0a4DKqrMB4pQhG2eiGneqS6lSSn1N9HAgDP1N+1rllX1twgp5Bmo10Q5Z1Q92k2rI9HIq5AWUQSZ\ntFIp5cqyzqZSAS04wfzCMZoY1Nl126szy71Xu52nPGZUzc5MwpkzN5lXCc5i/75uJ7ACgWoteRXt\nLRtWYXHTeM5mzfFT12UQCfY07d0+u3WTysQophRqj+20Rdh/eR6/NG8Xqe4/mULqb+L9pO2pvyqu\nWd1iSgxWACNQbVnX7bLwaTetdoczqnnJYd/3IuMViqrn1LcYn+2Dp0GdXb+OwugmYONjwLs7xmev\nIDXpxaltpaPpn/obReYbr+/9SKQOcCwdBfuo6iqF1N+Na0bwV+efBgCYQ9N7k/c0BoaSzPLpIsYa\n1Vw2oreuVXGuGxHTvDeuGcHVF67GyNAABODdhsJIYVAw8cCAfUY1YtAeSlUKvoVUrwmULU71azde\nxZRIsSwGykuGVyiqnhetA/BlYNEyoHUwXBpV0iIuaVQrzXNGVWHqb6ZVgid2AHNPu38vzQJYnZYx\nU+GzNVdp1BuptIvzTj8R+A5w5e+swZVj671vmHRdW5JZPl3EWKOay0b0tQSz1zHSvFNbStOeBSAS\nDTQlHhiwV9NWUbSvSoNpITTKOKPq027aHYkmU3+zl+O+70XFVkrVY40gn/v94FlQS9KOQhoj4lY6\nWiOlQDXKDK/C6p2pzoQE8Xu+0yyAVZR9LtNQa6bTLlrmnpGqt18qzRrVaAFGqqmxYSVJs04jzTsu\nq2hdgoGm0JkiXtfhWqO3hMDr2pTkmpX3PqqaqZVxjapPu5lrc0Y1Fzru+665ivSkKPNqjzqLk/qX\ntIhLGhenVoqpv1FneBWv68usqJjf851mB1i2qxOoJt2exir28exBAMcBz9wN4N1pnd183UEXTben\nCVM0J0bqby5V5rv77MZoH3lW827PJb7OhsoU8bsO12q9YFJF0T4WU+rTKGOg6tNu2ndwjWouPPqS\nh1pL8Lqtt7J/7qIiPalqy6Xao86s1L8ogWrSjkIa1UrTTP2Nmn5ShFmoMB18r7/DgpNSrvpboUC1\n1pj/WgpbodTeUZdmgs+jnwdGx9QFJDqn/oYdQIo5Y59LlfkkAxl5bV/STr5fdaiBAb/rcO2C3mCK\niqC9u48qA1XAKqZUskDVp920dt3FfVTz4NKXPNpZiE9M/V5f0TWgov1zFxXpSVVbKtt/lEmYdVNu\nHe21ETeNt0tjRLxbiTJZBwpA9BneIsxChenge/0dzrou3fOx1qhWQc2xPU2U2Xp7R11anaZjatfr\n6DzoEnYAKUbqb25SKLaVuZT2qw4cGPC7Di92bE+TdtCuwz6qGqmLEs6oAp7tptXm9jS5cAweHGot\nwSemfg+3TJ/TvUml++cu2EorIJdqjzoLWm/pVZUNmF/dN+w6zzSqlXYD1RTW8EVd86TzLBQQfg1w\nVtsJVWmNat0RiERZj23vqFt9RCHVrtcJk8ae1/YBYQeQYqT+5iaFfXZTF/T3bc+lVwvAj9912C1T\nIU0dzqja1eslDVQ9tDpM/c2NbaeI1/3oH/qCVEtl++cuKtKTqrZcqj3qrB4wOxh2ViPqOs+kI+Jp\nzqhGneHVeRYKiDZDnEU6YZUCVeeMWZS/hT0VuxuoIt3CVk5Ba9TTqNAdV9glAjGq/uamvsC4dklN\ngoCJHcCeD/b+vr86ANy12Tg/6+/bPpZNdXW/6/DDD0DpfiksptSnlGtUfbQ6EouaDFTzxv55sIr0\npKot0+0/iiAo9TdsRzvrMuMpbELfFXXNk+JiSomlsQY4TZ1OdQLVegNozfb+H/ZvMbEDaP2y938r\n9be+UG1l16DsgDy3Dwg7gFSk1N/GQuDeG40PbTQALO4/9PClAC7t/f+UV6s/Db/r8I+v6E/9TYN9\nScvMi4xjVblOBagLY43qum27KlF0st2RaNaZVJk39s+D8QpVAblUe9RZ3Wd2cGKHsbbQrYPg7Ghn\nXWY8zUAViDazWItYgCpsMZ20qKiKmUSnZVTtrIJaE+j8qvf/MH8L56wl0JtRfc3/UNtWglL/89w+\nwCtwAYwUVevY3FxxAozzPwVM3pP3WfQ8cCV6jc1OAKuv7P13+euyOR+v67CopVsTwPmaO/YsgEXA\n4/8IvPKD6T1OQf34KWPQzJrdKntRG25Powf2z4MV5J2Oksql2qOurA5e26VS6Z7N7kGqW9CT9Sxe\nK+V9VKOIMqOaR+pknltZuKlU6q9jDWKYv4XbrKUVOyy/UOnpBqb+5j077wxc3F5Pc8cDv3gkm/NJ\n6rQ3GR+6mP6cx993BfCGrdmfj5daPd01qm6vOQB48CoGqgD+3yNH5h0rc1GbdqfDqr+aYP/cX0WG\n/IlsvGYHvd7IRd294E7WG9KnPaMaRZQZ1SjFdNJkK1DQLXYF5FMYp0qBqrOYEuD9t7C4zU5aqb+q\nB2K6gy4ebTnr13UQr+vS0z/I/lx0kPT1rNvf10utkW7qr/M1Zw0MPX8wvccosOeed78elLWoTast\n0WDqLxVARXpSRDZeqb9eqX2y410cCchuFi/NfVSjCpqFssszddIpr8I4suL7qAZxm7W0Os5pVLX2\nI4Qx+OSV+qvb7LxrUA+g9YvMTyV3abyedfv7ehF1471HSqPNJuWZKXBq8vsugcWLGq7BalBRm/F9\nk4VM22x1JGdUqRAq0pMisnErphRlbapd0gqyUdZypln1N6oo29PknTppl1dhnCJtH5JUvRm9yJbb\nOlaYAzBZpLYHnXMWlaHD8no9NY/P/lzyltbrWae/rxerWJbspHMtcb7mumvC/zL5fZfAG195Cr65\nb7LvWFBRm/F9k32FcIq0rrXV9k/9LWoATuXDeX+qHmcaa9S1qWEFpah57dfqlcrWPmaek+IZJzdR\ntqfRKbUur9ndTqs4VVmTcm5PEyY1020/22Xm2tTGouzPWWdurycJ4JQ35HE2+dIpW0M1YXbP0mqn\nztdcc8g4/pL3pHP/BXfmqcbzsXTxIggAI0MDuPrC1b7B2bU79/dVawV661p11+pIz31UrQB8cvoo\nJHoB+LgjkCfKAmdUqasyI2h1R6AadW1qGGFS1KLODuSZ+hulmJJOqXV5ze5WaY1qrd5rF1FSM52z\nWndsA/CtbAZi6o3os8B5cXs94VngxDNzPa1c6JStoVotYL/vOOyvuds/ARz6ZC8grjirAu4tf7AO\nLzo+3GCZ1/rVIqxrbXckGh6V6f0C8FL2CUlrvEIRgIqNoDlTf6OuTQ0jTEGhqLMDeab+RlmjCgQX\n08lKXrO7VQpU683eTHuSQlqtY8ZzlsW2PrVGuOwAXdhfTxf81DhWldRyO52yNVTrpv6mvJeqxVqe\nkMb61xKw0mDbHbeti9x5rV8NWteqg7l2x3N7miIH4FQ+DFQJQLFTWCJz7qPoNRpvHh/fN4l123Zh\ndOutWLdtV7jgPUwQ6jkLIN1TJltm6m8jw9RfK43zy+Zz9sx92T12GtxSTOPOkkfR6VQz9TdMu/dK\nDW7PZpfWXqTUXyfrvKvSvuzyej3nwRqISHNG1a5KyxNCqHkFqj5LGbZsWIWBZv9zGLSuVRdtn2JK\nRQ7AqXwqMuRPQSo1gubcnsKtsIs5Sh+7WEKYFDXXgjImt5RJawa4ltGMqjONU0hg8lbjeJE6hnkU\nTum08tnvNg/1Zu+1FNTu/VKDW8eye87qDe/taXRnzbBVNcjQtBBS6ktn7MWUVJAVKvgWguuMasBS\nBuvvW8QlU3Md7+1ptmxY1dfvAYoTgBdFZZbapYAzqgSgYiNozmJKPqP0sWeaw6So9T2uC2fKZHs2\nu9RIwD2NszOnfj/UMqhS6q89jTao3fulBreez3hGVbPU37D7g1rXLQYZ2lCydCbtYkpOVcr6CMFK\ng23ZA9UQSxk2rhnB7q3rMbHtfOzeur4wwYbfjOrGNSO4+sLVGBkaCF1YisKr1FK7FFSkJ0VBKjWC\n5raPqscofeyZ5rAFhazHvbmG3n4BNvaUyfZstoWUnGmcAsYplrHCZtqqFqhas/1B7d4vNXjRbIYz\nqo7taaJsE6VClCJUVipoVdpXASgpPqOimJKdbDNQtbEC1Y49UC1plWkppRGoelT9BYxgtXKBaUbv\nAyxWFQ3f6QgACp3CEpnVAQiR+jc8NIBJl6A01ExzlBS1MKnCWQeqznMSEpAifIXNvDv/eZLt6gQS\n9SYAaXSoa3X/du/XzmeP5bNGNUqQqErQzI39dbTq48YxBhnaULJ0RnkxpRZn5W0abjOqJa0ybf2O\nfvuoVk6G7wOhrxdV7kPZVKQnRWFUZgTNmfrrw2um+TOv/REw/v70LiA+62S7sg5UXdfQNsJV2NSh\n85+nTrs62z5Yr6dvfjC44/vL04Cnn+5/7dUawEmnAUf2AgMnqDtPO/uWOlG3iVLBc+bmwPzX0b1/\nDKDBQFUjiQY0vSgvpsQZVbu6uaSmb41qmPflArJ+R681qlnSZq1mhu8Doa4XVe9D2eTfSomyJkTo\n7Snc1mrc9MZHcPaTHzNHWmXvAuK1pszitwYtTDXL9ly2garznGoN4ORfD3eRTLJNSRlUKfX31LXA\niS8FHt8D/OwH/h/PPAm0lwDHFgHPN4zP7SXG8VodeNkbsznnesRKxap5zdCIusvr6Gjve6QFJdVf\nrSBS1RpVFlPqY8VsfYFqSatMz7WNAl15z6hqtVYzw/eBUNeLqvehbCrSkyJyqDnWqPmYN9M8/v7o\nI29hRseCUoXbs9nvoWo/p+vOBAZPDfdzOnT+81SlQHX0N4E/vLf3/yKkK9lTf3VI7/OauXGrCG71\nozkbpg0lS2es64eqqr+dTnWuUSFYM6ot5/Y0mlaZTsIKxr32Uc2KVms1M3wfCHW9qHofyoZXKaqm\nWiP+SHWcC0gaaSWtY9nuoerkLEDjR4fOf56qFKjaJUhXyjQFrN4AWrPG1zqk93kVobr/CvfXEVDN\n9qWx1JfOdKv+qtxHlUl1FtftaUpqrq1H6q9W2yJm/D4QeL2oeh/Khu90VE31BnDoAeDuv4/+szOn\nALNPzz++4CTv+5t6AoDLbOj0E+HP4ZmfZj+jamefhQqiQ+c/T1Xd+iHmgEzs/YrjqjWBzq+Mr8NW\n6FbNa+bG+TqqmeuYmLZZbqqLKTH1t09NVCdQbWtSTEnJ2u64dHkfsFS9D2XDQJWq6YXLgMf+zfiI\nxe1COgMc/GOP2y/yvqtbvX7Gxarzwt82bfVG+BlV3S76Weu0qhmoxkxXyjwFzJkdoGt6n9vraOUf\nAI/+ZTXbV5WwmFKmrK1akgSq2hQGCqDLGlXttkXU6X2g6n0oGwaqVE2XfA84Oh3/5x//BvDgNuDo\nJDAwArxqK3DqW/1vv29LrxAKANQHgDXX+v+c0+BJ8c85Crd1hrVmqAJUXW4X/SKsX0xDVVN/Y6Yr\nZZ4CliT1P2vO19FTDwFgoFp6WRRTquI1ykO9uz1NjDXBEzswc/fHcMHcFMaWnoxrcBFumT5HbVZI\nAr2qv/kGqpXaFjEOnQLnHPEqRdXUWAgcf0r8nz/9Q8ZHlNsPvLAYQZrXOsO5VcCCwfTvF9DzeUii\nqoFqzHSlzFPAahGyA3RjBS5M2yw35cWUmPprVzdTfzsy4oyq+b422J4BBLBswWFsW3Y9AOCW6XPy\nKQwUoLePav5rlCuzLaKHoszC5yn/VkpUFaObgI2PAe/uGJ+DgjO/7WxU8lpnOHMAaCcY3a9SuXXZ\nqWYnMOZ2Dkq29/BTj7DeWjdWKmgVB0KqRHkxpTaLKdl0Z1TbEQNVl/e1wdoxXL70JgA5FQYKYM0a\n5536W3Vabc+jMaVXKSHEHwgh9gshHhRCXKPysYhKxZp9jLpXaxq81hN2no+W+hv2fstYbr2ia1TH\n901i3VdejNE7P4t1P7sN4yt2h5otd9uv+OoLV6sbWY5SGEw3VnGdCravSmExpUxFWaM6vm8S67bt\nwujWW9H5lfv713DzsPE5j8JAAaxgPO/taarOrzYD9SgLVIUQ5wB4C4DXSClfBeCvVT0WUenkOfvo\ntZ6wsShauqR9RvjrJ0d/vCKrYOpvotHhiR3YeGAddi8/FxOvvRS73/mE2vSnKIXBdGG9nr7zn4z/\nP/WvuZ4OKSY81qimlWnT4RpVOyv1tx2Q+uu8zk3Neb+3ve2k7+dXGMiHlfrbzHl7mqrTansejals\npb8PYJuU8hgASCmfUvhYRMXl1vHIc/bxjKuMdYV29UHghaeHn4VyzgjPPm18diprufUKBqqxR4fz\nyB6IWhgsb33PkenH12W3HICyZ10/7Km/ab5WKpr14aUech9V53XumkMXwe1HagL4yxU3a7nesG2m\n/nJGNV9es+06zsLnSWWg+nIArxdC3CWE+L4Q4my3GwkhNgsh9goh9h4+fFjh6RBpyKvj0TzR/fZZ\nzD56rTM8fjR8oOo2I+wk6qHWLxaOlMYa1YoFqrFHh/PIHqg1kq23TiLOjJj9ObI6xZ3ny7m+mwxu\nqb9pvlaquo7eg1VYKGiNqvN6dsv0OfAK9wZbU2mcWuqs3zHvqr9Vl3lthoJK1JMSQtwGYKnLt64w\n7/sEAK8FcDaArwohXiJlf16FlHI7gO0AMDY2Vv6dlsuuKtuPpMWr41EfMGYb89rs2a0s+j27wqdL\nhpn5lZ1yto1ONdcQxq7cm0f2QD2nGVW3ytc/+D3g8G5g7Q3eP+f5HLlsBUTl0E39tVX9TfO1wn1U\n+1h1pYJSf92uc5NzS7BsgctEi6bLWnSq+ltl3J4nnEStVEp5rpTy1S4f3wJwEMA/SsMeAB0APgvV\nqPDyLABUVF4djNlnYlVPVarWCN+5D/MGrembeGLWrHPGnUB7gY9123ZlXjkw9uiwVztQ2T5ctqfJ\n5PlzzTSQwCOf879O2p8Lqx8tzH94fS0nK4iwz6im+VqRDFTtrKAtKPXX7Tr3mcMXoyUcA3IaL2tp\nabKPKhnB6u6t6zGx7Xzs3rqeQaoLlcMp4wDWA4AQ4uUAFgA4ovDxKG9V2H4k7S1j/DoeUbezUa0e\nIV3SbZ1r333p+yaeWDdQzS71V4cy97Er93qtiVbZPhzb02T2/HnOfEn/6+QZVwFWgqG0Opcy+Oco\nNZkPBLkVU0rztdJpMfXXprs9TUCg6nad+41zP4LGaz+v18Cyj1ab29NQcajsSf0DgH8QQvwQwCyA\n9zrTfqlkyr79iFva3p7Nxtdx35DOuKr/PgF9g7goBWis58NKA19wotGvnnumnCnh9pT3BcuMYxkG\nqn6FjLIcoY21ebuzrWTRPmpNALKb/pjZ8ze43Dtd1+86OboJ+MF7+o9ZfcwCXF+Lvqm9NZB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SoRZcsldRZ7NhtfxwlW0w4WgoISjzTQJ+aW9P2f2wCUTJK2lsbADFXP4hFg/63AP33EdnCR9+2f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\nEhERERERkVYYqBIREREREZFW/j9ejaso14fH5AAAAABJRU5ErkJggg==\n", "text/plain": [ - "<matplotlib.figure.Figure at 0xd2ccf98>" + "<matplotlib.figure.Figure at 0xbfbc5f8>" ] }, "metadata": {}, @@ -284,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -426,7 +427,7 @@ "AAAAAATCTGCCCGTGTCGT -2.405657 " ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -440,12 +441,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This leaves a lot of guides whose effect on the bacteria fitness remains unexplained. We then train a neural network based on the sequence of these targets in the genome to understand it some features can explain the impact these guides have on *E. coli*. We start by training a neural network using a arbitrary 60b sequence window around the target. Several architechtures (dense, locally connected or convolutional) and hyperparamters were tested and manually tuned to optimize the loss of a validation set. Note that the architechture below gives satisfaying performances, sufficient for this study, but could like be further optimized. " + "This leaves a lot of guides whose effect on the bacteria fitness remains unexplained. We then train a neural network based on the sequence of these targets in the genome to understand it some features can explain the impact these guides have on *E. coli*. We start by training a neural network using a arbitrary 60b sequence window around the target. Several architechtures (dense, locally connected or convolutional) and hyperparamters were tested and manually tuned to optimize the loss of a validation set. Note that the architechture below gives satisfying performances but could like be further optimized." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -454,7 +455,7 @@ "((73705, 11), (85392, 11), (5825, 11))" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -514,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -541,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -557,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -571,7 +572,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.11100220680236816 s to get compile\n", + "0.0762026309967041 s to get compile\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", @@ -646,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -655,35 +656,31 @@ "text": [ "Train on 58935 samples, validate on 7367 samples\n", "Epoch 1/100\n", - " - 121s - loss: 0.9470 - mean_squared_error: 0.9436 - val_loss: 0.9447 - val_mean_squared_error: 0.9426\n", + " - 118s - loss: 0.9471 - mean_squared_error: 0.9438 - val_loss: 0.9431 - val_mean_squared_error: 0.9410\n", "Epoch 2/100\n", - " - 115s - loss: 0.9369 - mean_squared_error: 0.9348 - val_loss: 0.9380 - val_mean_squared_error: 0.9357\n", + " - 109s - loss: 0.9312 - mean_squared_error: 0.9289 - val_loss: 0.9420 - val_mean_squared_error: 0.9392\n", "Epoch 3/100\n", - " - 116s - loss: 0.9065 - mean_squared_error: 0.9029 - val_loss: 0.8798 - val_mean_squared_error: 0.8752\n", + " - 107s - loss: 0.8946 - mean_squared_error: 0.8905 - val_loss: 0.8788 - val_mean_squared_error: 0.8727\n", "Epoch 4/100\n", - " - 120s - loss: 0.8633 - mean_squared_error: 0.8575 - val_loss: 0.8396 - val_mean_squared_error: 0.8332\n", + " - 105s - loss: 0.8535 - mean_squared_error: 0.8469 - val_loss: 0.8414 - val_mean_squared_error: 0.8340\n", "Epoch 5/100\n", - " - 112s - loss: 0.8346 - mean_squared_error: 0.8271 - val_loss: 0.8395 - val_mean_squared_error: 0.8311\n", + " - 106s - loss: 0.8283 - mean_squared_error: 0.8202 - val_loss: 0.8354 - val_mean_squared_error: 0.8265\n", "Epoch 6/100\n", - " - 109s - loss: 0.8063 - mean_squared_error: 0.7962 - val_loss: 0.8026 - val_mean_squared_error: 0.7911\n", + " - 108s - loss: 0.8006 - mean_squared_error: 0.7905 - val_loss: 0.8046 - val_mean_squared_error: 0.7927\n", "Epoch 7/100\n", - " - 127s - loss: 0.7814 - mean_squared_error: 0.7697 - val_loss: 0.7862 - val_mean_squared_error: 0.7740\n", + " - 105s - loss: 0.7784 - mean_squared_error: 0.7661 - val_loss: 0.7805 - val_mean_squared_error: 0.7673\n", "Epoch 8/100\n", - " - 135s - loss: 0.7617 - mean_squared_error: 0.7488 - val_loss: 0.7860 - val_mean_squared_error: 0.7724\n", - "Epoch 9/100\n", - " - 110s - loss: 0.7504 - mean_squared_error: 0.7357 - val_loss: 0.7600 - val_mean_squared_error: 0.7450\n", - "Epoch 10/100\n", - " - 122s - loss: 0.7395 - mean_squared_error: 0.7237 - val_loss: 0.7647 - val_mean_squared_error: 0.7484\n", - "Epoch 00010: early stopping\n" + " - 117s - loss: 0.7606 - mean_squared_error: 0.7470 - val_loss: 0.7880 - val_mean_squared_error: 0.7738\n", + "Epoch 00008: early stopping\n" ] }, { "data": { "text/plain": [ - "<keras.callbacks.History at 0x1bccafd0>" + "<keras.callbacks.History at 0x1bd60908>" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -700,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -709,8 +706,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "pearson-r 0.54\n", - "spearman-r 0.55\n" + "pearson-r 0.51\n", + "spearman-r 0.53\n" ] } ], @@ -721,9 +718,16 @@ "print(\"spearman-r {:.2}\".format(spearmanr(model.predict(X_seq[testis]),Y[testis]).correlation))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To understand what information the model used to make predictions we performe in silico experiments. We generate a set of 1000 random sequences and measured the effect on the model prediction of mutating each position along the sequence. This reveals that the model uses the whole 20nt of the guide sequence, and in particular the 5 PAM-proximal bases, but not the surrounding region to make its predictions " + ] + }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -773,11 +777,20 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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68MH07HQQkKW19hH3eVcwm+ze1XB8x2uZlg7360LGxye5mgeAmpKrJSTa16eH\nHnxQUvZVFIIAMbLthM4emNPIthNZYSLTA+uI3e3PzAS6y+a6LJq7bL5gDRQSBOvZVEqHe3s1MjSk\nqWRS1xfTjaDPHzqUaT0JM87WX7892G4zq/qNWVAb3JaPXO0W+VpNgtlk9wod7omNQWtHaeemA0Dt\ncttHcl1Fwe1dfsQJE24PbLGCYLK+RzXsyYqAK1f/9fXFxUy7UDDOXG5ALnQFmrCtVo2GMI1NBSdF\nDo9dy1yhI5VK/w0AjS4IE26Ank0NOb3Wm7de5LsyQrDdXGEGqKZCH/rcgJzrQ13WyYpMpUnavGca\nDYreZQAoTtB64faMlnJoO19PabDd9XeWA7ZS0D8dnGAY9FJvZv0NXECYbkr0LgNAeNFIJFRLRy4E\nE/gU9E+7HxZROsI0ijI3N6sXXzyslpbteZc5d+70ps8DQL1xr3zgXvmj2twrfwC1pJRZ7GZBmEZR\nUqlZdXU9sel1ormONIBG416ho9wTrSrRHgJsJffDZIBZ7I04ARFlOXfutPr6or7LAICqKLedww3Q\nXPkA9ca9jB7yY2YaZWE2GgDyq2R/NVBpwWXyUB5mplG09va9kqRkckqLi9f9FgMAdez0uXOK9vUV\ndfc5oFrcD3pbeU5Ao2FmGkULrgASiUQz158GAJSOw+eoNbQghcfMNKoquNX4IvfxAoAMDq8DjYMw\njariduMAsFFweH39bcQB1B/aPAAAAFA0+quzeQnTxpgjxpjLxph/MMZ0+6gB1VPMDV4AAEB9or86\nm6+Z6Xcl/Y6kb3vaP6pgcfG6ksmpom7wAgDIr5QbvADVQm9/cbz0TFtrvydJxhgfu0eVcIUPAChN\ncGvmA/v3Z10mjxu8oBZwjfTi0DMNAIAn3JoZqH9Vm5k2xvyVpEdyPPUvrbXnStjOgKQBSXr00ccq\nVB1QvunphKanE5KkW7dueq4GyJZIJJRIpMfnzeVlz9UAaxLT00pMT0tibKIxVG1m2lr769baT+b4\nU3SQXt1Owlrbba3t3rWrtVrlIqSgT7oZHT06oJmZec3MzKu1lbGJ2jIwMKD5+XnNz8+rddcu3+Wg\nCLNzc7p89arvMqpu4OhRzc/MaH5mhrGJhkCbB8oyPj6ZuTMiACC82VRKXR0dvssAcuJkxPy8nIBo\njPmspLikVklvGGPettb+po9aAAAAsDlORszP19U8Xpf0uo99AwAAAJXC7cRRVXNzs9q5k35iAADQ\nmOiZRlWlUrPq6OjyXQYAAEBVEKYBAACAkAjTAAAAQEiEaQAAACAkwjQAAAAQEmEaAAAACIkwDQAA\nAIREmAYAAABCIkwDAAAAIRFARhfoAAAgAElEQVSmAQAAgJAI0wAAAEBIhGkAAAAgJMI0AAAAEBJh\nGgAAAAiJMA0AAACERJgGAAAAQiJMAwDg0d72dt8lACgDYRoAAI+ikYjvEgCUgTANAAAAhESYRlW0\nt+/1XQIAAEDVEaZRFZFI1HcJAAAAVUeYBgAAAEIiTAMAAAAhEaYBAACAkAjTAAAAQEiEaQAAasBU\nMqnri4u+ywBQImOt9V1D0T68c6flTlGoRdcWFriLGWrWtfff1969e32XAWzw3nvXuJRqDfrRj36o\nj3zko77L8O6ddy5Za23BiecHtqKYStnb3q75mRnfZQAbdPf2MjZRs7qffVbz8/O+ywA22LevWzMz\njM1aMzYW0/HjMd9lePfRj5q3ilmONg8AAAAgJMI0AAAAEBJhGkDDmkomfZcAAGhwhGkADevawoLv\nEgAADY4wDQAAAIREmAYAAABCIkwDAAAAIRGmAQAAsKnh4X4lk1O+y6hJhGkAAABsqq1tjyKRqO8y\nahJhGgAAAAiJMA0AAACERJgGAAAAQiJMAwAAYANOOiwOYRoAAAAbcNJhcQjTAAAAQEiEaVTFVDLp\nuwQAAICqI0yjKq4tLPguAQAAoOoI0wCaSv/wMEdOAAAVQ5gG0PCmkkn1Dw9Lkva0tSkaiXiuCADQ\nKAjTABpeNBLRnrY232UAABoQYRoAAAAIiTANAAAAhESYBgAAAEIiTAMAAAAhEaYBAAAgSYrHR3X1\n6mXfZdQVwjTKwjV7AQBoHCsr99TR0eW7jLpCmEZZuGYvAABoZl7DtDHmq8aYJWPMuz7rAAAAAMLw\nPTM9JanXcw0AmhQtSgCAcnkN09bab0v6wGcNAJrXtYUF3yUAAOqc75lpAAAAoG7VfJg2xgwYY+aN\nMfM3l5d9l4NNNNuVPRLT0+ru7VV3b68Ym7VnNB7X25cvazQe912KF1nj8+ZN3+UAGYlEQt3d3eru\n7tbyMmMT9a/mw7S1NmGt7bbWdrfu2uW7HGyi2a7sMXD0qOZnZjQ/MyPGZu25t7Kis6dO6d7Kiu9S\nvMgan62tvssBMgYGBjQ/P6/5+Xnt2sXYrEfJ5JTvEmpKzYdpAAAAVNdmATkeH9Xly28rHh+VJC0s\nXNuaouqE70vj/YWkOUkdxpjrxpjP+6wHxZlKJtU/POy7DAAAUCGbBeSVlXs6deqsVlbubV1BdeQB\nnzu31r7gc/8IJxqJcBUEAAAA0eYBoME000mwAAD/CNMAGgpHTQAAW4kwjbJcvnq1aS89hvoxGo/r\n8tWrvssAgLpy9erlzEmHyI8wjbJ0dXQ07aXHUD/urayoq6PDdxkAUFc6Oro46bAIhGlUFVf+AACg\nfozveE3jO17zXUZdIUyjqqKRiPa0tfkuA9ig2e+QCAC5DN99WcN3X/ZdRl0hTKNi6J9GPWn2OyQC\nqG/J5JSGh/t9lwERplFB9E8jrOBydrQFAUBxIpGo2tr2SJKGh/u5xbdHXm/agvoX2zG5+tVxr3Wg\nvgWXs6vWDYHcccoRFACNpq1tjyKRqO8ymhYz0yhL7G6/YnezDzPRi4pSVGu8xHZMShOdiu2YzBqn\nHEEB0Aji8VFdvXrZdxkQYbppbOXhc3pRUYqRbSd09sCcRradyLoedP/wcNF3M8y1bOxuvzR4ZcOH\nPQCoZ0E7xyvbXtXXu89v+f7j8VFdvvw21592EKYbUK7+0xtLS7p9547PsoCcDp7v0uHvPKXR+y9l\nXQ96T1ubopFIUdsoZVkAqAfuCYZuT/TCwjVJ0tPnn9Rn5n9DUvbNVcL0T+cLyLkuk7eyck+nTp3l\n+tMOwnSDcGfm3P7T4LJ0I9tO6Ez3hQ3rFTvzV4zYjkmnNxUozmwqpSe6ujQyNJQ1htze5kKz1MGy\ntBgBaBTuCYa5eqJTqVl1dHRJyr65SqH+6Vxh2w3Ic3OzmfYRLpNXHK8nIBpjeiX9saQPSZq01nLM\nIKRgZm59mAgOmQeHumNKB5Nnenp0Y2lJ33nrLd1YWtLI0FBmW1PJpN68eFGT4+M595Xv1sy5Dqdz\nG2fkE4zDaCSive3tktKz1K07d+rMYHZvszvzPJVMbpiFdpc9e+qUYmNjW/gvAYDKc3ui3ZnnYAZ5\n9+5HtLh4XZIys8eLimWWHRoaybldN2wPD/erp+eZrOdTqVmNjcWq8C9qXN5mpo0xH5J0QtJvSfqE\npBeMMZ/wVU+9C2bm3H5l95D57NxcJtROPv6Gore+nNWr6raEuDPauWYES7k1s7tsKT2waBzu2Dpy\n7JiOHDsmSbp9545uLC1JUiYcz6ZSmfESzFKv/4AYHHlxt1uJoyKMTQC1xO2JDmaeX9n2qmYOfEuv\nbHtVkUhU4+Ppn3sfn27Tl+7/gSTp693n9cq2VyWt9VfnuyZ1uVcB4VrXaZuGaWPMIWPMS873f2OM\neW/1z++Wue8eST+w1r5nrb0v6bSkQ2Vus6m4YSKYmcu6goETMNyQ0jndqtH7L2X1qroB2p1NLrcX\n1a3B3RbBuvEF7687tro6OjLj8Ez3BY1sO7FhneuLi5LWxun6ExSDYO2eBxCcbJiP+2Eyl9F4XGdn\nZjQaj3OtawBbLgi9x44d0bFjRyRl90QHvctPn39Svd/51UxwDhw69HxmJtoN1kF/tdsy4s5yl4tr\nXacVavP4fUnPO9+3SPolST8p6ZSkf1vGvh+V5F5Q9rqkXy5je00nK0ysBtaD57v08IMP6sD+/Xpk\n9+50u4ayQ8ozPT16ZPduzaZSio2NaWRoKCtAj2w7IXVv3F+uw+uFuO0l6aA/KQ1eyZqVDLNdbKGJ\nzvTfm4TVgNu6Ecwgu2PLvd5z//d/O72ss340EsmMhWCcHvza2pge2XZCOiBJc+kV1o3TYIzFdmS3\nHQVjPd+ykjLbHV16KfP/yv33AEA1tE08pC9K0sTv6eurP9MWJfX1RXXx4puS0gG5ry+qVGpEyeTU\nhtnkxcXrmceDYL3+pMKgZcTtry43WLutKHfu3NbS0g1JylljIysUprdZa93A+6a1dlnSsjHmJ8vc\nt8nxmN2wkDEDkgYk6bFHH00/6P5yD74upE6WPTL/aUnSmZMns3qb762saHtLy9pM3uCVrNDbOd2q\naF+fZlNDWeHU/Tv42u2FDnpV3W25favuSWBBf3VYsbv9ih1P39wlfTLkBWniRDpMTXx5bUH3dSr0\nmnl8rxKX7itx6b7U+kndXF5OP1ho+5WsIezrVOKyndOt+lRnp37xfjwzDjMf1MbHswJn8CHJnUFe\nP04/1dmpM4PK25MfCJ6PRiKZD30Hn5vNBOuZ2dnMOA0EY6yYnml3WffvkYnOTL2Tj78h3Xoje3wW\nes0K2aL3NWt83rpVXG3AFkgkEkokEpKk5eWbktKBcr3Fwds5H8/F17Lu8/mWDWaXT548k5l1Pnny\nTNZ6QfhMJqd08eKbGld61jcIpD09z2j37kek1cfXC9o9pLVg/cq2V1cnCb6VfqI7HdJd6TaS8xpe\nKf4kw/Edr0kTr2l8R/oExeAkyGBbmng18+Egl3p5X0tRKEz/tPuNtfYLzretJe1po+uS2p3v90j6\n4fqFrLUJSQlJ6t63Lx223RmyImbL6mnZM1r7ZRgEk5FtJ6Rt6cfcoDv18FcyM8/PHzqUOYmwlFm0\nYFl3u+4sXib0SpmZu9hKuP6o64uLmaDvButA5sRHqbT32NN7lfmUJ6m7t7f47Vey3rCvUwnLXrmw\n+v5PdGbGoTt76x5lyDVe3LGV2VaJgrHjHk0JAr37vGt2bk6HX3xRB/bvL2ofwQdLDV5JB+vVr4tW\nY+9r1vh89tniawOqbGBgQAMD6dG5b1/6k2u+8FJKqPG1rPt8rmW/rtXQLGV6oBclfe77R9TT84wi\nWgvIboB2uWG5kGDZp5+b0oMPPqz9+w9kZqKPK/tkxWKv1LE+QB8/HtPYWGzDto4fjxW1vXp4X0tR\n6ATEvzHGHFv/oDFmUNLFMvf9XUk/Z4z5mDFmm9LtJH9Z5jbr3+CVzC/EoKfU7W12e5+jkUhm9i5X\nmCiFu11XcOc4t4awJ3tNjo9nwnuuet1/D2rQ4BVNPfyVzFGR4FKLbu9zzn78vj499OCDZe3aHTsB\nd7zket695J4r3/h1rxYStEQBQLnc3uePT7et9UGPT1a1FSKVmlVX1xMaGhrJutyd21N97txptbRs\n1/iO19Q28dCGa0oHhu++rMXB2xvCt/tYMCPejArNTL8s6awx5nOS3lp97BeV7p0+XM6OrbU/NsZ8\nQdI3lb403lettdwX0xH0fM6mjmdm4/L9oi83hLrbdb8+fe7cavvI2oxgbCx9CbJYEdudnZtT686d\nFa8XfuQ6kuH2xQdHSNLjZCzU0ZJCMjPIIWX+X+WZuXZbogCgXG7v84ULJRzBqiD3cnfuyYrB18Nj\n9zKzze4x43PnTquvL5r3Bi3J5NTa5flKmD1vNJvOTFtrl6y1T0v6nyVdW/3zr6y1T1lrf1Tuzq21\n37DWPm6t/Vlr7R+Wu71Gc/rcOW1vaZG0FiCqNXvrbtf92m0fyRVi3BpzyTfjjfrmzjbnGwPlht68\n+94k6AYBebMxmVk2z8w1AFSSewm7rdbevldSdugtZQbZDd6BublZvfjiYbW0bPf6b6slRV1n2lr7\n19ba+Oqfv652UUgL2wddSW47Rq4a3BrRPPJ94Co0XqqNgAwAa9xe7CD0uu0lYVozUqlZ9fYezntT\nmGbk9Q6IqH2lzIIHLSEj2044lxw7Xmg1NBDadwCgfgQBu9Q7HjbTZe+K4e0OiCis3JMKqyXfDTCC\nGcrgBhq5bi8OAABqi9u6gdIRpmtYrisU1AL6oOGq1Q99AIDiuFf+QOlo8wBQFlo7AKCxBTPX+/cf\n8F1KTWJmGgAAAHkxc705wjQAAAAQEmEaAAAACIkwjari1swAAKCRcQIiqopbMwMAgEbGzDQAAAAQ\nEmEaFTc7N6fDL76o7S0tvktBE9rb3u67BABAEyFMo2KCm3fMplJ6oqtLI0NDvktCEyrUVnT63Dk+\n6AEAKsZLz7Qx5oikmKSfl9RjrZ33UQcqi5t3oFa5J8IGt70HAKxpb9/ru4S65esExHcl/Y6kCU/7\nB9BE8p0IS0sIAKRFIlHfJdQtL2HaWvs9STLG+Ng9AEgq3BICAEAhNX9pPGPMgKQBSXrs0Uc9VwOJ\na0cHEtPTSkxPS5JuLi97rgbIljU+b93yXA2wJpFIKJFISJKWl296rgYoX9VOQDTG/JUx5t0cfw6V\nsh1rbcJa222t7W7dtata5aIE0UiE/mhJA0ePan5mRvMzM2JsotZkjc/WVt/lABkDAwOan5/X/Py8\ndu1ibKL+VW1m2lr769XaNgAUg55oAEC1cWk8AA2LnmgAQLV5CdPGmM8aY65LekrSG8aYb/qoAwAA\nALktLl5XMjnlu4ya5+tqHq9Let3HvgEAAFDY+Pik7xLqAm0eAAAAkCQlk1NaXLzuu4y6UvOXxgMA\nAMDWiESi3MClRMxMAwAAYFP0T+dHmEZZri8uaiqZ9F0GAACoovHxSWas86DNA2Xh5i0AAKCZMTMN\nAAAAhESYBgAAAEIiTAMAAAAhEaYBAACAkAjTAAAAQEiEaQAAACAkwjQAAAAQEmEaAAAACIkwDQAA\nAIREmAYAAABCIkwDAAAAIRGmURV729t9lwAAAFB1hGlURTQS8V0CkNP1xUVNJZO+ywAANIgHfBcA\nAFtpcnzcdwkAgAbCzDQAAAAQEmEaAAAACIkwDQAAAIREmAYAAABCIkwDAAAAIRGmAQAAgJAI0wAA\nAEBIhGkAAAAgJGOt9V1D0T68c6flNtWoRdcWFriFOmrWewvvq719r+8ygA3ef/+a9u7d67sMIKdL\nly5Za23Biee6ugPi3vZ2zc/M+C4D2KC7t5exiZq1r/dZzczM+y4D2ODZZ7s1P8/YRG0yxrxVzHK0\neQAAAAAhEaYBAACAkAjTAAAAQEiEaQAAACAkwjQAAAAQEmEaAAAACIkwDQAAAIREmAYAoAYkk1Ma\nHu73XQaAEhGmAQCoAZFIVG1te3yXAaBEhGkAAAAgJMI0AAAAEBJhGgAAAAiJMA0AAACERJgGAAAA\nQiJMAwAAACERpgEAAICQCNMAAABASIRpVMVUMum7BAAAgKojTKMqri0s+C4BAACg6gjTAAAAQEiE\naQAAACAkwjQAAAAQEmEaAAAACIkwDQAAAIREmAYAAABCIkwDAAAAIRGmAQAAgJC8hmljzFeNMUvG\nmHd91gEAAACE4XtmekpSr+caAAAAgFC8hmlr7bclfeCzBgAAfEomp3yXAKAMvmemAQBoCsnklIaH\n+zc8vrBwbeuLAVAxD/guoBBjzICkAUl67NFHPVcDrElMTysxPS1Jurm87LkaIJs7PpeXb3muBpIU\niUQJzpKmpxOank5Ikm7duum5GqB8NT8zba1NWGu7rbXdrbt2+S4HyBg4elTzMzOan5kRYxO1xh2f\nu3a1+i4HyDh6dEAzM/OamZlXaytjE/Wv5sM0AAAAUKt8XxrvLyTNSeowxlw3xnzeZz0AAABAKbz2\nTFtrX/C5f1TfVDKpNy9e1OT4uO9SAAAAKo42D1RVNBLRnrY232UAAABUBWEaQEOZSiarsiwAALkQ\npgE0lGsLCxsem0om1T88LEnqHx7OhOhcywIAUArCNICG57Yb7WlrUzQS8VwRsLl8N3gBUHsI0wAA\n1JhIJKq2tj2+ywBQBMI0AAAAEBJhGgAAAAiJMA0AAACERJgG0DBG43G9ffmyRuNx36UAAJoEYRol\ncy8zBtSC4HJ391ZWdPbUKd1bWcl6fjQe1+WrVzc8RvDGVorHR3X16mXfZQCoMMI0Spbvrobu9XuB\nrVTocnf3VlbU1dGx4bFcwRuolpWVe+ro6PJdBoAKI0yjYrh+L7aSe4Tk8tWrzDCjpiSTUyWvw8w1\nUJ8I09iANg7UGveoR/C3e4TkTPcFjWw74a0+YL2FhWuS8t98ZXi4f0PgdmeuCdZA/fAapo0xvcaY\nq8aYHxhjRnzWgjVuSMnVupGr/3T987l6UWkDQSnc8RIc9RiNx3V2Zkaj8XjWOOycbtXo/Zc2bCPX\neMs3i83YrG+5wulWcMNyUEM8PqrLl99WPD6adfOV8R2vaXzHa5KktrY9ikSiebdLSwhQPzYN08aY\njxtjfiXH4582xvxsOTs2xnxI0glJvyXpE5JeMMZ8opxtojLckOK2bgRhI1f/qStfLyptIChFZrxM\ndCq2Y1Ka6NTIthM6e2BOI9tOZI3D5w8d0sjQUGa52I5JSdK1hYUN2+3q6NC9lZWilkX9cMPpVoZq\nNyzfuXNbS0s3tLJyT6dOndXKyr2sZYfvvqzhuy9vWW0Atkahmek/knQ3x+P/ZfW5cvRI+oG19j1r\n7X1JpyUdKnObdW+rWyxyzcaNbDuhM90XNjweJmy4wZy+VuQyGo8rNjam0Xg8a/wHYffg+S4d/s5T\nGr3/kmJ3+6XBK4rd7Vdsx2QmCAfc57MeL7AsV/aof1evXlY8Pqp4fFQzM2cVj49K2tpg3dHRtSFA\nV6Jd49ixIzp27EhZ2wBQPQ8UeH6vtfad9Q9aa+eNMXvL3Pejktx0dl3SL69fyBgzIGlAkh579NH0\ngxOd6b8Hr6x9XUidLBuVFH1cksaL2+aq/uFhPdPTo+itL2cem3r4K3rz4kVNjufZ1kSnopI08WUd\nmf+0JOnMyZPqnG7Vpzo7dWZwLQCPDA2VVE/AnT1MB/QLkoY2fx3c16nQa+bxvUpcuq/EpftS6yd1\nc3k5/WCh7VeyhrCvUxHLHjl2TFJ6PORcdvBKzlUz47CIIxDBsiPbTkjbVh+8tTb+O6dbFe3r02xq\nSP3Dw3pk925Nfe1rio2NaXtLSzoIj40pJun64uKmbRpBuI7leX5k2wnpgCTNSRMn1v6NPsZWhd5X\nd3wuL9+SJLVNPCRJWhy8nfk6F/f5ai1bSKnLfr37vKTz6QcOSNK3pIlX9UVJmvg9/dHDf6yLF9/U\n+Pikhof71dPzjCKRqJLJqU3bLQppm3hI4zukRcUyLRySpInXNL4jPRtdbrtG+t8mfSk+qpWVe2pp\n2a533rkkSTp58kzdva//x8A/UuLSff1965O6detmUesBtaxQmN6+yXP/qMx9mxyP2Q0PWJuQlJCk\n7n370s+7v8jz/FLPqYaWPfjcczr41FPa3tKSN6gGgeYXf+EXdG9lRdtbWnTpnfRnm6yQM3hFk4+/\nId16Qxq8oqlkUtFIRNGJzkwwmUomM8E6E3icZc9o7ZfwlQtrs9JBAB6NKzNztzbDdzxneErPKE4q\ntkNZM4Sxu/2KHT+e9zXL1Lj++UKvr6f3NfMpT1J3b2/x269kvWFfpwLLZh2ZyLWsM/bcMXD7zh3d\nWFrSaDyec8y6IT3XmHXHwDM9PXpk925JynwgdMfZVDKp64uLWc8ffO45HX7xRR3Yv3/z10LS7Nzc\n2rJODVl8/cyowPvqjs99vc9KSgeZgPt1LluxbCGVXPaLEw9Jj0uLmtSfP35GunVGX4rf0FtvfUdL\nSzckKRNUd+9+JBO8C3n6/JPaubNVJwelz33/SCakj43FdPx4TONBIM37US63IJgvKqaPT7epry+q\nV7a9uvbBs3v13y1nv0W8DrXwvj6b+C96Nvj62e6i1wNqVaEw/V1jzDFr7Un3QWPM5yVdKnPf1yW1\nO9/vkfTDMrdZN2ZTqYLLrAWaCxt+gErSwfNdat25U2cGlZnFG5ETOJxfrNFbX84E6yDwyFk2M4u9\nrobMdp2ZO3eWz+2DDmax762kQ3NsbCxrW8HsYb5Zy2gkQk91rSgQ4NzxkgnFijhHH5RzzLoh3T0C\nErzv7hjIe0RlVa7xMptKKTY2ppGhoazWjdm5ObXu3Jl32WB7aFyZmWnJ6VtO9zaPjcUkScePxzQ2\nFtPS0g3dubMxHAaz2MeOHdGPf/z32r//gPr6orp48U1Jygrf7e17JWWH7VIENR6X1NPzjHbvfkSL\nkdyBtZjQD6B6CoXpL0p63RjzP2gtPHcr/Wvys2Xu+7uSfs4Y8zFJ70t6XtLnytxmQwmC7MzsrB5+\n8EEd2L9fM7OzmQAd7evTmxcvSsqexcu3rSC4ZLVbrMoXZDMndo2t5AzIruDELpc7i10oHKF+uOPF\n/XCVdSRidbwEQfbMYPbRCfcISDUEJ8LGxsYywVnKHpNoHpFINNPOce7c6bWZ3tV2DEnZX3enZ33d\nlpDgcndB24X0LS1GbudsEwkeS6VmM2F9bm5WO3e25q3RnY3OepywDNS0TU9AtNb+yFr7tKSvSLq2\n+ucr1tqnrLU3ytmxtfbHkr4g6ZuSvifpa9ZaLqrpCALybCqlw729GhkaUrSvTw89+KCkdKAJAurk\n+PimM2tXLlxIt4Yo/2XENhMcEt/e0qLZubmcl8bLd5LX+pPB0LhOnzuXGSPBeHHHbKHe5q3AmERm\npnfwtobvvrzh76fPP6nPzP+GpNyXsHv6/JPq/c6v6kv3/6Ck/aZSs5v2T3O1D6A+FZqZliRZa78l\n6VuV3rm19huSvlHp7TYKdyY312HwsILZ5mIE4cc9JJ6eqU7P8rknKBJQmtPpc+cU7euTtDa2gjGy\nvoViK49OuL37zELDlWumd25uVi++eHhD60ZwlRBJmWtHB7PNQ0PcHgFAkWEazcsNP3vb2zc8n6u1\nA82llA9nWyloKQmu+AHkEvQ2p1KzWVf2CP7OdZWQP0o+osXF62XtNwjpBHKg/nE78SYU9lA7J2ih\nEHds5frwBdQat4UjV+/zx6fb9KX7f5DV2hGJRMvuYw6uSe3eLdE1NzfL7cSBOkGYbkKF+qtLkatP\nGs3LHVu1+uEr6OsGinHo0PMaGhpRKjWrrq4nSp5JTianNp3Fzne3xEL91QBqB2EaZXFP5iKkNKda\nOKkwF/ckSFettqWgMVViFhtAbSNMo2IIKc2pkkc6Kmk2ldITXV2MSZRlcfF6VW5JPr7jtew7JgKo\nW5yACABAHrluxFIJXAIPaByEaVRVrjvPAUA9ynWCIgDQ5oGyFOqTnk2l1NXRsYUVAQAAbB3CNEo2\nlUzq+uKiJPqkUR/cMevi8n0AgHLR5oGSVeIujEC15ArI+cYs4xgAUC5mplExtXqJNDQXAjJqVaFr\nTgOoT4RpVEytXiINcPGhD75wzWmgMXlp8zDGHJEUk/TzknqstfM+6gDQfCbHx32XAABoIL5mpt+V\n9DuSvu1p/wAAeHPu3Gm1tGz3XQaACvASpq2137PWXvWxbwDNg6t1oFYdOvS8hoZGfJcBoALomQbQ\nsOjhR73iZEWgflQtTBtj/soY826OP4dK3M6AMWbeGDN/c3m5WuUipGY+mSsxPa3u3l519/aKsYla\n447P5eWbvstBHuM7XlPbxEMa3/Fa1uONfLLi9HRCvb3d6u3t1s2bjE3Uv6qdgGit/fUKbSchKSFJ\n3fv22UpsE5XTzCdzDRw9qoGjRyVJ3b29nqsBsrnjc1/vs56rQT7Dd1/W8eMxjY3FfJeyZY4eHdDR\nowOSpGef7fZcDVA+2jxQcbNzczr84oub3mYcAACgEXgJ08aYzxpjrkt6StIbxphv+qgD1TGbSumJ\nri5uMw4AABqel+tMW2tfl/S6j31ja00lk7q+uOi7DAAAgKrwEqbRPKKRCFdUAIAizM3NaufOVt9l\nACgRPdMAANSAVGpWHR1dvssAUCLCNAAAABASYRoAAAAIiTANAAAAhESYBgDAk7m5Wb344mG1tGz3\nXQqAkAjTAAB4kkrNqqvrCQ0NjfguBUBIhGkAALbY4uJ1JZNTvssAUAFcZxoAgC02Pj7puwQAFcLM\nNAAAABASYRoAAAAIiTANAAAAhESYBgAAAEIiTAMAAAAhEaYBAACAkAjTAAAAQEiEaQAAACAkwjQA\nAAAQEmEaAAAACIkwDQAAAIREmAYAAABCIkwDAAAAIRGmAQAAgJAI0wAAAEBIhGkAAAAgJMI0AAAA\nEBJhGgAAAAiJMA0AANke5boAAAdBSURBVACEZKy1vmsomjHmpqS/W/32w5L+c4mbCLNOvaxHjX7X\n2y/pLQ/7DbseNfpdb6trDMZnPbw2YderhxrDrtfINfr62VkPr2nY9aixcuv9Y2tta8GlrLV1+UfS\n/FasUy/rUaP/9eqpXmpsrn9bPb029fKa8m+rzL7qrd56WI8aK7desX9o8wAAAABCIkwDAAAAIdVz\nmE5s0Tr1sh41+l/P134b9b2ohxrDrsfYrPx69VBj2PUaucZKbKNRX9Ow61Fj5dYrSl2dgAgAAADU\nknqemQYAAAD8qubZjdX4I6lX0lVJP5A0UsJ61yT9X5Le1iZndUr6qqQlSe86j+2U9O8l/d+rf/90\nkevFJL2/us+3Jf1369Zpl/QtSd+TdFnS7xWzv03WK7S/7ZIuSvoPq+t9ZfXxj0n6m9X9JSVtK2Kd\nKUl/6+zriTyv54ck/Z+Svl5oX5usU3Bfud7fIt+3XOtt+jpWenwyNsONzXLHZ5ixuZXjsxbGZrXH\nZ5ixGXZ8MjZr92dnLY7NsOMzzNjc6vFZztish/FZybFZ9Fis5Maq/Wf1jfhPkn5G0rbVgfCJEv7T\nfbiI5f6J0te9dP/z/C9a/Q8uaUTSvy5yvZik45vsq03S/tWvd0j6vqRPFNrfJusV2p+R9FOrX//E\n6uA/IOlrkp5fffxPJP2zItaZkvS7Rbye/1zSnzv/efLua5N1Cu4r1/tb5PuWa71NX8dKj0/GZrix\nWe74DDM2t3J81sLYrPb4DDM2w45PxmblxmYlx2etjs2w4zPM2Nzq8VnO2KyH8VmpsVnKn3pr8+iR\n9ANr7XvW2vuSTks6VMkdWGu/LemDdQ8fkvSnq1//qaTDRa5XaF+L1tq3Vr++q/Qn0kcL7W+T9Qrt\nz1pr/5/Vb39i9Y+V9GuS/m2u/W2yTkHGmD2SflvS5Or3ZrN95VqnTAXftwqr6vhkbObcX6jxGWZs\n5lqvTFs5PmvyZ2eYsbm6Xsnjk7FZkqYfm5usV2hfNf+zc6t/r+dar0xb/bu9oHoL049KWnC+v64i\nBtsqK+m8MeaSMWagxP1+xFq7KKUHvKTdJaz7BWPMO8aYrxpjfjrfQsaYvZKeVPoTYtH7W7dewf0Z\nYz5kjHlb6UNX/17pGYFb1tofry6y4TVdv461NtjXH67u6zVjTEuO8v5I0u9L+ofV73cV2leOdQKF\n9pXr/S3mdcw3Lop639YJOz4Zmwo3NnOtV+T4DDM2c60XqMb4rIWxuVkdxQg7Pov+N4YZn4zNLL5/\ndtbb2JQa4GfnFv9ez7VeoNZ/dhal3sK0yfFYUZ+mJP2KtXa/pN+S9JIx5p9Urqy8/o2kn5X0hKRF\nSeO5FjLG/JSklKQvWmvvFLvxHOsV3J+19v+z1j4haY/SMwI/n2PTdrN1jDGflPQHkjol/ZLS/Uv/\nYl1tn5G0ZK295D682b7yrKNC+1oV9v3NtV5R71sOYccnY1Phxmau9QqNzzBjc5P1tNm+HGHe41oY\nm/nqqKai/41hxidjcwPfPzvraWxKDfKzc6t+r2+yngrtb5Xvn51FqbcwfV3pJv3AHkk/LGZFa+0P\nV/9ekvS60gOuWD8yxrRJ0urfS0Xu80erA/YfJJ3MtU9jzE8o/R/nz6y1/67Y/eVar5j9ObXdkjSr\ndJ/Uw8aYB1afyvuaOuv0rh6SstbaFUmncuzrVyT998aYa0oftvs1pT+ZbravDesYY6aL2Fe+97fg\n65hrvVJex3VCjU/G5obaSh6b69YrND7DjM2c61VzfNbC2Nyk9mKVPD6L/TeGGZ+MzZr82Vk3Y3N1\nXw31s3MLfq/nXK+OfnYWpd7C9Hcl/Zwx5mPGmG2Snpf0l4VWMsb8pDFmR/C1pN+Q9G4J+/1LSf/T\n/9/eHaM0FARhHP+2EjsRLAQL8QCewMLCyk6wU7DwFCJ4BG9gpWBh5QGMvWUQtPEkFmMxLxpDdt9m\nTMJ78v9BICDD6O7HupK3a/P+TNJjTdFoshtHkz1TSknSjaQ3M7uu7Zerq+i3kVJaa96vSjqQP5f1\nLOl4Wr9MzftYkJP8eaVfvczswsy2zGxbPk8DMzsp9crUnLb1Ksxv2zhOrWsbx4KZ80k2v78+czYL\ndcV8RrJZqFtIPruQzZbvvdbM+az5GSP5JJudXTt7k82mV+/XzmX+Xi/U9WXtrGMLOtm4qJekQ/kp\n1w9Jl5U1O/ITwqNrYLJ1ku7lHwF8yv9iPpc/E/Qkv4blSdJ6Zd2t/HqWoXzyNydq9uQfhww1dl1L\nW79CXVu/Xfm1NEN5kK7GxudFfi3Rg6SVippB0+tV0p2ak8GZMd3Xz+ndbK9CTbFXbn4rxjFXVxzH\neeaTbMazOY98RrK5jHx2IZvLyGckm9F8ks3urp1dzOZ/Xjv/ms0u53Pe2ax98R8QAQAAgKC+PeYB\nAAAAdAabaQAAACCIzTQAAAAQxGYaAAAACGIzDQAAAASxmQYAAACC2EwDAAAAQWymAQAAgKAvZDIE\n+ymOlF8AAAAASUVORK5CYII=\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x21810da0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def plt_Ei(rec,save_path=\"\"):\n", " fig, axes=plt.subplots(nrows=4, ncols=4, sharex=True, sharey=True, figsize=(12,8))\n", @@ -821,19 +834,19 @@ " fig.savefig(save_path)#, format='eps')\n", " plt.show()\n", "\n", - "#plt_Ei(rec,save_path='figures/mutation_effect_all_60.eps')" + "plt_Ei(rec,save_path=None)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "<matplotlib.figure.Figure at 0x24d7feb8>" + "<matplotlib.figure.Figure at 0x1c631ef0>" ] }, "metadata": {}, @@ -864,12 +877,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Only the guide sequence seems to matter for the prediction. Train again with just the 20nt of the guide." + "Only the guide sequence seems to matter for the prediction. We thus train again using just the 20nt of the guide sequence." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -877,48 +890,54 @@ "output_type": "stream", "text": [ "(73705, 20, 4)\n", - "0.07000017166137695 s to get compile\n", + "0.06240034103393555 s to get compile\n", "Train on 58935 samples, validate on 7367 samples\n", "Epoch 1/100\n", - " - 35s - loss: 0.9469 - mean_squared_error: 0.9450 - val_loss: 0.9410 - val_mean_squared_error: 0.9394\n", + " - 33s - loss: 0.9468 - mean_squared_error: 0.9449 - val_loss: 0.9461 - val_mean_squared_error: 0.9444\n", "Epoch 2/100\n", - " - 32s - loss: 0.9331 - mean_squared_error: 0.9312 - val_loss: 0.9313 - val_mean_squared_error: 0.9293\n", + " - 30s - loss: 0.9365 - mean_squared_error: 0.9349 - val_loss: 0.9412 - val_mean_squared_error: 0.9394\n", "Epoch 3/100\n", - " - 32s - loss: 0.9106 - mean_squared_error: 0.9079 - val_loss: 0.8959 - val_mean_squared_error: 0.8926\n", + " - 29s - loss: 0.9226 - mean_squared_error: 0.9204 - val_loss: 0.9095 - val_mean_squared_error: 0.9066\n", "Epoch 4/100\n", - " - 33s - loss: 0.8768 - mean_squared_error: 0.8728 - val_loss: 0.8608 - val_mean_squared_error: 0.8561\n", + " - 29s - loss: 0.8878 - mean_squared_error: 0.8840 - val_loss: 0.8738 - val_mean_squared_error: 0.8695\n", "Epoch 5/100\n", - " - 34s - loss: 0.8466 - mean_squared_error: 0.8411 - val_loss: 0.8475 - val_mean_squared_error: 0.8413\n", + " - 30s - loss: 0.8655 - mean_squared_error: 0.8605 - val_loss: 0.8585 - val_mean_squared_error: 0.8529\n", "Epoch 6/100\n", - " - 32s - loss: 0.8259 - mean_squared_error: 0.8189 - val_loss: 0.8246 - val_mean_squared_error: 0.8168\n", + " - 29s - loss: 0.8457 - mean_squared_error: 0.8393 - val_loss: 0.8482 - val_mean_squared_error: 0.8411\n", "Epoch 7/100\n", - " - 32s - loss: 0.8018 - mean_squared_error: 0.7933 - val_loss: 0.7964 - val_mean_squared_error: 0.7873\n", + " - 29s - loss: 0.8216 - mean_squared_error: 0.8135 - val_loss: 0.8142 - val_mean_squared_error: 0.8051\n", "Epoch 8/100\n", - " - 33s - loss: 0.7817 - mean_squared_error: 0.7717 - val_loss: 0.8010 - val_mean_squared_error: 0.7903\n", + " - 29s - loss: 0.7925 - mean_squared_error: 0.7825 - val_loss: 0.7956 - val_mean_squared_error: 0.7848\n", "Epoch 9/100\n", - " - 34s - loss: 0.7660 - mean_squared_error: 0.7545 - val_loss: 0.7831 - val_mean_squared_error: 0.7709\n", + " - 29s - loss: 0.7650 - mean_squared_error: 0.7534 - val_loss: 0.7877 - val_mean_squared_error: 0.7754\n", "Epoch 10/100\n", - " - 34s - loss: 0.7521 - mean_squared_error: 0.7395 - val_loss: 0.7618 - val_mean_squared_error: 0.7487\n", + " - 30s - loss: 0.7511 - mean_squared_error: 0.7382 - val_loss: 0.7724 - val_mean_squared_error: 0.7589\n", "Epoch 11/100\n", - " - 33s - loss: 0.7424 - mean_squared_error: 0.7286 - val_loss: 0.7604 - val_mean_squared_error: 0.7461\n", + " - 29s - loss: 0.7388 - mean_squared_error: 0.7247 - val_loss: 0.7674 - val_mean_squared_error: 0.7527\n", "Epoch 12/100\n", - " - 30s - loss: 0.7338 - mean_squared_error: 0.7189 - val_loss: 0.7556 - val_mean_squared_error: 0.7403\n", + " - 29s - loss: 0.7308 - mean_squared_error: 0.7155 - val_loss: 0.7671 - val_mean_squared_error: 0.7512\n", "Epoch 13/100\n", - " - 30s - loss: 0.7275 - mean_squared_error: 0.7115 - val_loss: 0.7489 - val_mean_squared_error: 0.7325\n", + " - 29s - loss: 0.7219 - mean_squared_error: 0.7054 - val_loss: 0.7523 - val_mean_squared_error: 0.7354\n", "Epoch 14/100\n", - " - 31s - loss: 0.7227 - mean_squared_error: 0.7058 - val_loss: 0.7505 - val_mean_squared_error: 0.7331\n", + " - 29s - loss: 0.7136 - mean_squared_error: 0.6961 - val_loss: 0.7493 - val_mean_squared_error: 0.7313\n", "Epoch 15/100\n", - " - 30s - loss: 0.7155 - mean_squared_error: 0.6977 - val_loss: 0.7492 - val_mean_squared_error: 0.7309\n", - "Epoch 00015: early stopping\n" + " - 30s - loss: 0.7087 - mean_squared_error: 0.6902 - val_loss: 0.7421 - val_mean_squared_error: 0.7230\n", + "Epoch 16/100\n", + " - 30s - loss: 0.7036 - mean_squared_error: 0.6839 - val_loss: 0.7409 - val_mean_squared_error: 0.7208\n", + "Epoch 17/100\n", + " - 30s - loss: 0.6986 - mean_squared_error: 0.6780 - val_loss: 0.7499 - val_mean_squared_error: 0.7289\n", + "Epoch 18/100\n", + " - 29s - loss: 0.6933 - mean_squared_error: 0.6717 - val_loss: 0.7424 - val_mean_squared_error: 0.7204\n", + "Epoch 00018: early stopping\n" ] }, { "data": { "text/plain": [ - "<keras.callbacks.History at 0x1cb2fcf8>" + "<keras.callbacks.History at 0x22aa3ba8>" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -948,21 +967,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "pearson-r 0.55\n" + "pearson-r 0.56\n", + "spearman-r 0.53\n" ] }, { "data": { - "image/png": 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+xHEEqVLMVD1WBjn1wMMYiAtD3XcpjWGQltvz0dLC4I84CTcHxXZBueKTF7AB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\nEKwyWiwdglVGi6VDsMposXQI/x9LkHwkrMKJCQAAAABJRU5ErkJggg==\n", 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7v8tXv/rVTdn3ajS8iAOTdQYKmR8UJimHZlvsHS4S9BLOuwfyPHp4Hr8X2YMs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\n", "text/plain": [ - "<matplotlib.figure.Figure at 0x19696eb8>" + "<matplotlib.figure.Figure at 0x1c6312e8>" ] }, "metadata": {}, @@ -971,6 +991,7 @@ ], "source": [ "print(\"pearson-r {:.2}\".format(pearsonr(model2.predict(X_seq2[testis]),Y[testis])[0][0]))\n", + "print(\"spearman-r {:.2}\".format(spearmanr(model.predict(X_seq[testis]),Y[testis]).correlation))\n", "fig, ax=plt.subplots(1,1,figsize=(3,3))\n", "ax.scatter(y_scaler.inverse_transform(Y[testis]),y_scaler.inverse_transform(model2.predict(X_seq2[testis])),alpha=0.2)\n", "ax.set_xlim(-10,2)\n", @@ -982,7 +1003,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -994,9 +1015,9 @@ }, { "data": { - "image/png": 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/pKljfqtHPiLpsaf11ukP6bdL/jXdbvfZS+a+a/p8bx2/PSu83jr+QkXjN+XsPzE4qHG1\ntTn7L7TdYus37X84/TOfguH4xhtv1E9/+lP95je/0V/91V/pzJkzRW+0kAkTJujkyZPp5aGhoaxg\nLEnBYPDc1dKi0ahtu9eozxm/1xeJRBQI5KnvzpjektKH3sdKCgxju3e+9oXcebnJ/di1SVJN5x/q\nIzVH1fkRSc/88OzI7YpfpdvXXBDW1CuPJZd69Nbpg1LgOwXaX8tpN+1fOjsyHIsd08svHxzR6HVL\nS1BNTQvzvrcbNvxYUuGR3ztfbzLWJ0n19YH8n52hPfV46rM993lOl++bbGnqMz/MWL5ICjxS9Pop\nsVhEwUCeXnfnf+WMfE7NfdaI/ThjTmc+qdG5Rz4i6Zmnc0bn1lywQ1PGnJA+Ikn/oSOnJ2lKYE2B\n9pdy2lOjXxfW1elYX592bNmSM3oXbGnRsb4+rWxpyakvtf6xWExbN28ueNLXm2+8kbf9/smWpjzz\ndMbyJE0J3Ff09lPvX6HRs5PHj2vr5s3p0c/pkyZlfc6B+noFA4H0tIrU8rnt+ZaH89yRLKdEYrG8\nj6fem5qaGtvPLt/nViy79U1998cbNihsWTrU26urP/3p3NHZO/9L0gef3VQp+28r+bdnd1TCyev7\nm9eu1CUNDdrT3a0F8+ad7Vuj+PmZ+nbr+0uy+uzj7e1qHUbfLLTfYusz7d9Uf0okFst5TLIJx7fe\neqtuvfVW/ed//qe2b9+uV199VQ8++KAWL16sSy+9tNBqtubPn6/nn39eixYt0r59+0a8HaCS2B36\nN00L+P8OXmV7wptp2oLphDnT/qWz0zq8ugpHMfXZTeswSU27yHz/RlMprnbRHAppTmNj/vDsskdO\n/mXWF9gjBaYNpL5AhzutwOmhY9P6pkPrTuszKfbQc6FD136WnlaQfG/zTSvwmulvp5hpD25NB0pN\nK9nT3Z13WolpTq/TOeV+nzPsdFqQ8YS8z3zmM/rMZz6j3/3ud9qxY4dWr16tcDg8glKlq6++Wi++\n+KKWLl2qM2fO6IEHHhjRdoBKYxfg7NpM4TDV3tZ2m227Xbj0O1P4dRLeTe+fidtzmv3O7S9Q07xS\nUzg1rW8K76bX5/a8XVN9ftZwx8uuXw3CbW6G32L3Xyi8m35YOe2bxZwwWM6KvpTbBRdcoGXLlmnZ\nsmUj3llNTY3a2tpGvD5QqewCnNORWcsKq7f3UMHwaAqXTkZeR4Ob4ddtlfDjwwnTF2gqXGYeGh7N\n7RdzUpeT+opd36+ja3ZKUbuXRzWqnenzdfr5O/3b9ho3AQHKXDHhsbFxTsE5t6Zw6XX49Hr/dtwe\nGa70kedUuLytrc2VcOl0dMtU32js33Ro3iuVPjJY7Uyfr9d/O14jHANlzs/hsdK5PTJcCSPPTsJf\nKcKl1+HUq0PzXl8qC7Dj9VEXwjEAwDVuhz/T9r08acptpkPXdgHD6ZxTr8MLzLz+4eeE05Frp/2T\ncAygqnk9pxruchp+U5fzKhQwTAHEzYBiOnTt5tQIpl34Xzn/8HPKaf8kHAOoakxL8ZYpfHrNdNKY\nKYCMRjgv19E/oFwRjgHAI5V+wl0xuGKBvWoe/YO/efnDze1pPYRjAPBIJZxwB6A8OZ0S5OUPN7en\n9RCOAQC+xbQCwB1uTwkqZ4RjAIBvVfMXNABv1HhdAAAAAOAXhGMAAAAgiWkVAABUGW7iARRGOAYA\nG5YVVm/vIW4SgorCTTyAwgjHAGAjFGpWY+McBQJBr0sBAJRAyecc79q1S3/3d39X6t0CAAAARiUN\nx2vXrtVDDz2koaGhUu4WAAAAPpJ5DXO/Kem0ivnz52vhwoXaunVrKXcLAAAAH/HzNcxdCcfbt2/X\n5s2bsx574IEHtGjRIu3Zs8d23Wg0WrAtkUjYtnuN+pzxe32SFI8Xrm9wMGHb7iU/1yaVf339/XFP\n2yUpGo8XbEsMDtq2e436Rm53V5d6fvUrdWzdqoVNTXmfE+/vL1i/XdtotEv0TTf5ub5i+qZJKfpn\nPq6E4yVLlmjJkiUjWjcYLHzSSzQatW33GvU54/f6IpGI7UlZ8XjUtydt+bk2qfzrq68PeNoei0UU\nDAQKtkfjcdt2r1HfyAVbWrSwqcm2vkB9fcF2u7bRaI/EYvRNF/m5vmL6pkkp+mc+3AQEAFxkWWH1\n9ByQZYW9LgUAUAQu5QYALgqFmrk+MgCUkZKPHC9YsEDf/e53S71bAAAwDH6+mgDgJkaOAQBADj9f\nTQBwE3OOAQAAgCTCMQAAAHzFy2k9TKsAAACAr3g5rYeRYwBwgEu1AUBlYeQYABxw+1JtmeGbS8IB\ngPsIxwDgY1wnGQBKi2kVAABUKK5VDAwfI8cAAFQorlUMDB8jxwAAAKgoTo6aMHIMAB7ihDsAGH1O\njpoQjgHAQ5xwBwD+wrQKAAAAIIlwDAAAho0rYcBLbvY/plUAAIBh40oY8JKb/Y+RYwAAACCJcAwA\nAAAkEY4BAACAJMIxAAAAkEQ4BgCgCnG1CSA/rlYBAEAV4moTQH6MHAMAAABJhGMAAAAgiXAMAAAA\nJBGOAQAAgCTCMQAAAJBEOAYAAACSzjtz5swZr4tIiUQiXpcAAACAKnHFFVfkPOa76xxPm5ZbZEo8\nHlUgECxhNcNDfc74vb5YLJL3jyglGo0qGPRn/X6uTaI+pyKRiK6YNq1gezQeVzAQKGFFw0N9zvi5\nvkgsRt90EfU5E4nF8j7OtAoAAAAgiXAMAAAAJBGOAQAAgCTCMQAAAJBEOAYAAACSCMcAAABAEuEY\nAADkCFuWli5frrBleV0KUFKEYwAAkKM5FNLc2bPVHAp5XQpQUoRjAAAAIIlwjKpgWWEtX75UlhUe\nUTsAAKgOhGNUhVCoWbNnz1Uo1DyidgAAUB0IxwAAAEAS4RgAAABIIhwDAAAASYRjAAAAIIlwDAAA\nACQRjgEAAFBW3LyDI+EYAAAAZcXNOzgSjgEAAIAkwjEAAACQRDgGAAAAkgjHqAiWFdby5UtlWWGv\nSwEAAGWMcIyKEAo1a/bsuQqFmr0uBQAAlDHCMQAAAHzFzUu1mRCOAQCoQk7Dh5fhBZXPzUu1mRCO\nAQCoQk7Dh5fhBXAT4RgAAAAl5ecjD4RjAAAAlJSfjzyUPBzv379fy5YtK/VuUea4VBsAACiF2lLu\nbOPGjfrJT36i8ePHl3K3qAChULO6u/dxqTYAAOCqko4cz5gxQx0dHaXcJQAAAFC0ko4cX3PNNTp8\n+LDtc+LxaMG2wcGEbbvXqM8ZU339/XFP2yUpGi3cnkgkbNu95OfaJOobDdF4vGBbYnDQtt1r1OeM\nXX27u7r07M6dWnTttVrY1JTTHu/vt31tTtsl+qabyr2+UvS/kShpOC5GIBAs2BaPR23bvUZ9zpjq\nq68PeNoei0UUDBZuj0ajtu1e8nNtEvU5FYlEFAwECrZH43Hbdq9RnzN29QVbWnSsr08rW1rytgfq\n621fm9P2SCxG33RRudfnZv8LW5a27NihpYsXFzzpLxKL5X2cq1UAAACgoji5GgbhGAAAAEgqeThu\naGjQtm3bSr1b+JxlhbV69V1cqg0AhiFsWbpr9Wpf3kgBKFeMHMMXQqFmzZw5i0u1AcAwNIdCmjVz\npi9vpACUK8IxAAAAkEQ4BgAAAJJ8dyk3AADgrXUdHUoMDOhAT49a29s1rq5Oa1au9LosoCQIxwAA\nIEtiYECtq1all1vb2z2sBuUobFl6bNs2fe3668tuTjzhGAAAAKOqORTSrr17XQnGbh/ZIByj4nV0\nrNPAQEI9PQfU3t6qurpxWrlyjddlAQBQlZyGW7ePbBCOUfEGBhJatao1vdze3lrwuQAAwF1+n7bD\n1SpQ9To61qm9vTU9stzRsc7rkgDfCFuWli5fzk0mUHW4wYq9Sv6/gXCMkrCssJYvX+rLO+ClRpY3\nbNiiVataNTCQ8LokwDeaQyHNnT277E6oqRSVHECccvu94QYr9ir5/wamVaAkQqFmdXfv4w54QBnh\ncl7eaw6FtK+723cBxA99w/TehC1LW3bs0NLFi/M+x9QO73jdvwjHKHuccAe4o5h5gQSM6uT3OaOp\ncCVJ+7q7daCnJydc+fWHx2gp579Nr/sX4bhIlhXWjh1btHjx0ryjn5YV1rZtj+n66782otFR0/ar\nmSn8csId4I6v1/9Ieux7GcuTJK3Keo7T0TtgJEwji16HKz+o9PDvpqoJx8WEW7t207SAUKhZe/fu\nKtjudPte8zK8E34Bbzzaf11WwHi0vV2tGe3FHPosJjyX640CiuHloX0nh6aL+WHkpa+OeVpTJp6Q\nLpekn+nI6UmSPnhtpvpN702q/VBvb8VOKfKyb7rdv5xOyyhpOB4aGlJra6sOHjyosWPHau3atbr4\n4otLsu9iwm2hdtPIZaq9t/dQwcP65R5+TfU7Cc/FvH8ARsYufK7r6NC+7m69feSIPjplii6bN6/k\n1xp180YBpWAKEKYfB26O7jn5fEw/jLxmqs/UbgrX6fYrJemlnHbJ/XDp9lEXN/um6ceF0/5lCtdO\n/28qGI7vvPNOrV+/flgbM9m9e7dOnTqlrVu3at++fVq3bp06OztHdR/5FBtuC7WvHPO4pk48lvwj\nsvTW6Ysk5Wm/UpJ+kdPudP+l4DS8O1k/NTIcj0cVCAQZGQZGkV34/OqYpzXl8hPJpf/WkdP7lRkA\nfn34sFrb23Wgp0dzZ8/WuLq6rPWdjs4Vw+/TMtwMt26elOT1CU/FsPvsTX3T1F5suI7G4woGAgWP\nmkj55zQXM+fZ6Q8rP0uF09T7N9rTWtz+8VYwHB8/fnwUd3NWJBLRVVddJUm67LLL9Oqrrxa13uD/\nadD0cb/X1ORyX2Kiau84XPR+Vww9rOkTf58Ot+8MTdZQRng1td/52ufV0HCJenoOaPbsuWfDa8b2\n73zt80okEvrNb36lj33sDzVv3mVZ7abtm8K35Hxag5M50X4I706sqn9SUx/7bsbyRZKvxkAAd8Q7\nP6dAzXE98hFJjz2t+NCHFVjxi3T76tcWKJFIZI8cZ6z/xPr1CluWDvT06LJ583K+pFe/tkCXNDRk\nBZDM9U2jc6bRpWIChtu8nPZhGv1ycujfD3Ny7d7beOfn1FxzXM2XS3rzZ4p3fiur75r65hPJwb3W\n9vas15ny68OHtXT58qy+f257a3u79nR3a8G8eTnh+q+HNqth4nvpvh0f+rDyjjwb+r6Uv2+n/nZb\nJ0p67Hs5f7um98/tHz/l8OPKiYLhuK+vT9/5znfytt11110j2tl7772nCRMmpJfHjBmjwcFB1dZ+\nUEY8Hs1d8YZdOiFpcDCh2tpxySdmP6+ra7d27nxW1167SE1NC3PW/9G57ZnrG9rvueduSVJn58O6\n6aaWnDpT7Zn1xYex/X945VOaNm2aensPaebMWRo7tk43Z7Rv2rRRp04NaGDgXe3du0v79/9cN998\nS9GvP7X+mTNDeddPtff2HlJb221n95/R/rXT/6SLJ57MCO8fUjy+WOfq74/n/fxM20+tNziYUDwe\nzdmO0+XOoyGtWHH7B8udD2vFMNbPFI3mf1ySEomEbbuX/FybRH2jIRqP5zz2zf2f1LRp09Rz6JBm\nz5qlurFjdUvG8+6+5x7jduY0Nur+xsa8bXffc492d3Xp3e5uXTR9uhY2NWU95+GjId2+YsUHy52d\nuj2j/caaH2raxHfTh65jgxcqGl+a254MGOe2b9y0SdGDB3X06FFNnjxZwTlzdMvNN2fVuLurS8/u\n3KlF116rhU1NuW+cwZzGRs3Ys0dzGhvzvseSFO/vz9u2cdMmDZw6pUO9vbqtre3s+59Rn6n91vHb\ns0bmbx1/oaLxm9LLfSdO6PYVK7R4cFDjamv1cGdnVh3n1pW5bNc2GsuZ7N6boTNntGvvXv18//6s\n157qu4d6ezVr5sycvivZ903p7Ge/p7tbHVu35nz2pr6f6ttDv/xl3r6tGyyd+z/C0Yz2b77yKSUG\nBrL7Zkb7Daf/WTMm9qf79lunP5TVt4t9/YX6ZqpvpJzbN0x9L6XQ52rafmq9xOCgovH4sPuPqe+P\nZv/Mp2A4HjdunGbOnFn0hooxYcIEnTx5Mr08NDSUFYwlKRAIFlw/ddj9XKmRzbq6C9XXd0xbtuzI\nGdlsaQmqpaXwrxpTu2WFFYsd08svHyw48lqoPtP2N2z4cXL7W/TpT1+ds/1V47dp+qTfSx+RpF+c\nHXkOZP9waWkJqq/vWN59rLmpl+SDAAAgAElEQVQgrKljjiXX/4+zI9MZ62e3/yKnXXfG9JbOngiX\nOjEukLH91Psfix3T5s1bc0aWa2sv0Jo1renl9vbWrPepvj6gQCCYfv9Sy+e2e7WcEotFFAwW7p/R\naNS23Ut+rk2iPqcikYiCgUDO4z/esEFhy9Kh3l5d/elPuzLyGWxp0cqWlrxtJ48f19bNm9Mjy9Mn\nTcqqs/X9JVmHXh9vb1drnvaUc9vXXLBDU65MTQt5XUdOH9CUQPb//cGWFh3r6ytYYzHTNmpravK+\nv6nRs2OxmLZu3pwzenZBba1a13xQT2t7e9Z21lywQ1PGnEj+33t2Xmtm/abXH6ivVzAQSL9/qeWU\n+ydbmvLM0xnLkzQlcF/Wuuduq9hlu21nisRied+7u8b/ixomvZf+XooPfViBjNee6rtbYrER9127\nvlns+gubmvLWb/LjDRvsn3Dnf+X0vakZzabXnxpZfmyqpGe25Iwsmz4fU9809W2n/aPY/xtSCvX9\nkS6nRGKxnMckm3A8efJk/cVf/EWh5hGZP3++nn/+eS1atEj79u3TpZdeOirbNU1bGA2hULOrJ9PZ\nbb/2jsP6vs20CtO0h9S0kO7uPZo3b0HeaSF200ZMTFeTME1rSLVPNbQXWt8ppl2gkjWHQprT2Dii\nL3inijm0bXfo2jRv1DStw+nVNEzTFpxeMcHteZNubt/pthvueFmSdFtbmx657z7l653NoVBZzrct\nlun12bV/4+BndElDQ9bfTmbfN/1tOD2hrdi+XWjOtun/Bq8VDMef+MQnRn1nV199tV588UUtXbpU\nZ86c0QMPPDAq262943DOyGalsQvPpjnL69c/IUlqa7st7/uTard7/ywrrJ6eA7KscO7ItiFctvff\nmBOeM/8UUu2ZJ+Tlay+0vtNwa9o+AHekviBva2vL+wVp+gI1zTs1hVcT00lFpgBiajcx/Tgod6mj\nGmHLqugQ7AanfztOf9z4/WomTi8VVzAc33333Y4Ky6empkZtbW2jvl3JPrxVumLCnWWF1dt7qOD7\nY3r/7MK51+HS6/0DKCwVXt0KQHaja06v05z6gg2ml7O/YIsJ73btpvDr99E1p7w8qlHtnF6Jptjt\nFzoq5Dan4b1ibgLi9rQHPzt8+Ndqb2/NmhZxrlCoWY2NcwrOia7m9w+Ae7w8NG4KAKZDx6ZDw045\nDb+m8I7K5mTk3enIcrE/7AqNbDvl9lGVignH1ayYaRFuMoXzYsI7gOpk+oJ3MvJsCgCm0TGvR79M\nnIR3v98BD2Z+Pp+g3PdPOIZjpnDudXgH4F+mL3g3R55No2PFjH65PW3ELX6fMwrnyrVvjganI8uE\nY3guNbKceTUNABgNdgFhNA7NmsJ7pQaUSj9ZsBI4+WFZ7p+v05FlwnGFKOcTEk1X0wCAkbILCKU4\nNOw0PDsJ104CTrWfLFgN7PpWtX++hOMKUckn1DFnGUClcnKtWxMnAafaw1E18PN1pL0euSYcw/eY\nswwAufx+wiAwUl7/OCMco+wxsgygGrl9uSygWhGOUfZMI8uEZwAjxV3cgOpT43UBqAyZJwT6zfr1\nT2jVqlbNnj1Xq1a1auXK4dzAFUAlyzwpKZ/mUEjf+cd/JBgD5zD97WT+sCw3jBxjVJhOCCznq2kA\nqFx+PikJ8LNiTiYt19uDE45REpV8NQ0AAFA5CMeoesxJBlBtvL5UFuBnhGNUPS4VB6DaeH2pLPif\n23d39PPdI0sejnft2qWdO3fqoYceKvWuAQAAUAS35+P7eb5/ScPx2rVr9cILLygYDJZytwAAAEBR\nShqO58+fr4ULF2rr1q0FnxOPRwu2DQ4mbNu9Rn3ODA0N2tbX3x/3tF2SotHC7YlEwrbdS36uTaK+\n0RCNxwu2JQYHbdu9Rn3ODA4N2dYX7+8v2G7XNhrtEn3TTZVeXyn6Zz6uhOPt27dr8+bNWY898MAD\nWrRokfbs2WO7biBQeFQ5Ho/atnuN+pypqam1ra++PuBpeywWsT3qEY1GfXtUxM+1SdTnVCQSsb1c\nUjQe9/XllKjPmdqaGtv6AvX1Bdvt2kajPRKL0TddVOn1Oel/YcvSsVhMB19+ueD0jUgslvdxV8Lx\nkiVLtGTJEjc2DQAAANhyMqeZO+TBFywrrN7eQ768wx4AAKgeXMoNvhAKNauxcY6vp30AAIDKV/Jw\nvGDBAi1YsKDUuwUAAACMmFYBAECFyrzRAoDiMK0CAIAK5ecbLQB+xcgxAABlKmxZOtTby8gwqo6b\nR0UYOQYAoEw1h0Ka09jo62vdAm5w86gII8cAAGDYmM+MSsXIMcqCZYXV03NAlhVWKNTsdTkAUPWY\nz4xKRThGWQiFmgnFAADAdUyrAAAAgK94OW2HkWMAAAD4ipfTdhg5BgAAAJIIxwAAAEAS4RgAAABI\nIhyjImRe6g0AAGCkOCEPFYFLvQEAgNHAyDEAAACQRDgGAAA5uD00qhXTKgAAQA5uD41qxcgxAAAA\nkEQ4BgCgCjFtAsiPaRUAAFQhpk0A+TFyDAAAACQRjgEAAIAkwjGqAnfQAwAAxWDOMaoCd9ADAADF\nYOQYAAAASCIcAwAAAEmEYwAAACDpvDNnzpzxuoiUSCTidQkAAACoEldccUXOY747Ie+KadMKtkXj\ncQUDgRJWMzzU54zf64vEYmXbP/1cm0R9TkVisbz/wadEo1EFg8ESVjQ81OeMn+uLRCKaNq1w34zH\nowoE/Fm7RH1O+b2+WCz/oCzTKgAAAIAkwjEAAACQRDgGAAAAkgjHqAhhy9LS5csVtiyvSwEAAGWM\ncIyK0BwKae7s2WoOhbwuBQAAlDHCMQAAAJBEOAYAAACSCMcAAABAEuEYAAAASCIcAwAAAEmEYwAA\nACCJcAwAAABfsaywli9fKssKl3zfhGMAcBE3qAGA4QuFmjV79lyFQs0l3zfhGABcZLpBDeEZQCWy\nrLBWr77Lk5FfpwjHAOAh7u4IoBKFQs2aOXOWJyO/ThGOAQAAgCTCMSAObcO/6JsAKpGXJ9yZEI4B\ncWgb/kXfBFCJvDzhzoRwjLLA6BkAACgFwjHKAqNnAACgFAjHAAAAQBLhGAAAAGXFzRP6CMcAAAAo\nK26e0Ec4RlXghD6MVNiydNfq1fQdAKgShGNUBU7ow0g1h0KaNXMmfQcAqgThGAAAAEgiHAOAA0zZ\nAYDKQjgGAAeYsgMA/uPkahYlD8f79+/XsmXLSr1bAAAAVAknV7MoaTjeuHGj7r33Xg0MDJRytygD\nXBEAAAD4QW0pdzZjxgx1dHRo9erVBZ8TjccLtiUGB23bvUZ9IzensVEz9uzRnMbGgjXG+/tt63e7\nXSrf/unn2iT/1zc4NORp3yuqb0ajBdsSiYRtu9eozxm/1xePF65tcDBh2+416nNmaGjQtr7+/rin\n7YWUNBxfc801Onz4sO1zgoFAwbZoPG7b7jXqc6a2psa2vkB9vaftkVisbPunn2uT/F9fWfTNYLBg\nezQatW33GvU54+f6IpGIAoHCtcXjUdt2r1GfMzU1tbb11dcHPG2PxSJ5H+eEPAAAACCJcAwAAAAk\nEY4BAACApJLOOZakhoYGbdu2rdS7BYCSW9fRocTAgA709Ki1vV3j6uq0ZuVKr8sCANgoeTgGUF3C\nlqUtO3Zo6eLFVXejjMTAgFpXrUovt7a3e1gNgHJiWWHt2LFFixcvHdG1estZR8c6DQwk1NNzQO3t\nraqrG6eVK9eUbP+EY5Q9Ruf8rTkU0r7ubt8G42oO7wD8KxRqVnf3vrIMxqlw29t7KG+4NYXfgYGE\nVq1qTS+3t7eqlAjHKHuMzsEJv4d3APAju5HtVLhNXWru3HDrdfg1IRwDQAGpoxKHens5KgEAGcp5\nZNuEcAwABaSOSqRuUsJRCQCofIRjAJ4KW5Ye27ZNX7v+eqY2nIP59ABQelznuETClqWly5crbFle\nl+KJan/9KKw5FNKsmTMJxnmkRq63bNig1lWrlBgY8LokoGpYVlirV98lywq7tv3ly5e6tn2MHCPH\nJVLtJ/1U++uvZHZXe/DDyKebV6Pww+sD4I5QqFl79+5ybU6t3Zxdry9lVu0Ix6PE7UPDbl9uysvL\nWZlOeiKA+JvdD59SXEnE1Hfd/GHm9PXRt4Hy5eZ1iEfjag6m+py2e8ntHw+E41HSHApp1969rgVL\n0xe8KSCYwrvT7TthOunJ7YBFQPE3L8OvU1+v/5H02PcylidJ+qAvcxlCwDtOw5+bV2tYVf+kpj72\n3YzliyS1Dmsbpvqctjvh9PW5fSk45hz7hNM5uc2hkObOnl0wIDid12nafjnPKa72eZ1ufnZnw+Hc\n9L+v1/8oq31dR4da29vTRw3WdXTkbMPU9/zs0f7rpK8dSP97tP86r0sCkBQKNWv27Lm+GxWVpPb+\nG/XW195N/2vvvzHnOW7PiXbibPi9UJ985kpNfexCrap/Mqvd9PpS66f+nbu+SUfHOrW3t6ZHljs6\n1g1rfUaOi+T2tAM/j34Vw8/1V/rIsNtHDeyY3ttH+6/LGhl9tL09a2zA60ulmepPjfwG08vZI78A\nKpPpDm/FsBuZHo1pAW7PiXaivf/GnJuADOd/ztT66eVhru90ZJlwXCS7AOH2nFnT+sW2V+uNDCr9\n0LUp3Lo55cf03v768GG1trfrQE+P5s6erXF1dVntpvA5Wn2/ULup/lS4T4X3c8M9gMpkusNbMeym\nJZjC2+HDv06PfM6ePVd1deOGvX+MHOF4FDidM+v0C7zY9kL1OQ0YXjPN64QzdiPTpvf+ifXrJZ3t\nk5l9NMUUPker7xdqd4q+B5QnpyO3pvVN7aY5t+vXPyEpOWKaEaLP3X6hkW2uduFM0eH4nXfe0enT\np3Xeeedp6tSpI9rZ0NCQWltbdfDgQY0dO1Zr167VxRdfPKJtlZLb4fCrY57WlIknpMsl6Wc6cnqS\npNxDux8sZ38BF9teaHTOFCBM9Tnl9NC16dB9NXN6VCPVLkn7urt1oKcnq331awt0SUND1sjwcP77\nTY0s7+nu1oJ58wqOLH+wPLK+X6jdKfoe4E8dHevU3b1PR468rSlTPqp58y7LCocrxzyuqROPJb/X\nLL11+iJJueF1ano5O7yaRn5N27/ztc+roeGSrJHh4Xyrprd/pST9Imf7pvrc/nFQ7mzDcU9Pj9ra\n2vTkk0/qpptu0oUXXqh33nlH3/rWt3TVVVcNe2e7d+/WqVOntHXrVu3bt0/r1q1TZ2encb11HR3a\n192tX/3mN/rDj31Ml82bV9KRy78e2qyGie+lw2F86MPKF14LhTvTF7QpYIxWe2YAyWw31ec0ABQ7\nL7WcD117dZe3Yo86jPSohqnvm0aGpbPvzYGeHoUtK+e9Sa1/W1tb3vVNfdvUN03tfh/59Xt9gJfs\n5vSuHPO4pl5+LL381un/1HDCqWnOrGnk17T99eufkGWF1dNzQPPmXVbwUmo9PQdkWeGc9tT2u7v3\naN68BTnbN9XnNDy7fbUIr9mG4/b2dn3jG9+QJAUCAf3gBz/QG2+8oXvvvXdE4TgSiaTXu+yyy/Tq\nq6/mPCcaj+c8dmPND7Xm8neTX9CvKzb4/xSNLx3Wvnd3denZnTu16NprtbCpKatt46ZNih48qKNH\nj2ry5MkKzpmjW26++YMn3GApKunhzk7dvmKFJOloRp3ffOVTmjZtmnoOHdLsWbNUN3asbsnTfqi3\nV7Nmzsxpv/uee7S7q0vvdnfrounTtbCpKet9KKZdyfpabrop531Mtb/1yCN52x8+Gkq/rtR2bs9o\n39/To9va2rLqz1x/46ZNGjh1Sod6e3VbW9vZ15fx/vWdOJGz/cz14/39isbjSgwOKhqPp5fPbfdq\nOVO+xz+0/UtqHvNbNV8p6c2X9Naj/1u/XfKvWc+x63+mdru2G2t+qGkT302H19jghVl/G7eO3571\nw+3W8RcqGr8pp10F2nWDpY5z9n/0nPdgd1eX9nR3q2Pr1ryvbU5jo+5vbJSU//3b3dWlnl/9Ku/6\npr5t6pum9mL7fubf9nD6jmn/pvVN9WWKRqN5H5ekRCJh2+416nPG7/XF4/lr6+rarWef/akWLfqi\nmpoW5m3fufNZXXvtopz2TZs26tSpAQ0MvKu9e3dp//6f6+abb0m3/8Mrn9LAQCL9vT5nTlA3Z9Rx\nzz13q6trt7q739X06RepqWlhVp09Pfv1v/5XSPH4EQUCUzRnTjCr/R+S3+u9vYc0c+YsjR1bN6zt\nS1Jj4xw1Nt5f8D2ya7/nnrslSY888pZuuqkl5zmm+v5u/PezwvPfjb9Q8XhLevnEiT6tWHF7ermz\n8+Gs7ff3x4taHhxMKB6P5rT39OxXW9ttWfUNZ/um+k3rm9iG4/fff19/9Ed/JEmaOHGiJOniiy/W\n4OBg0TvI9N5772nChAnp5TFjxmhwcFC1tR+UEQwEctb7m9c/mzvyec7zTGfsB1tadKyvTytbWnLa\n1lywQ1OuPJFcel1HTh/QlED24YGwZelYLKaDL7+cs/0fb9gg6ezo1yP33Zez/VS73ehasKUlb23F\nttvVl2p/84038rafPH5cWzdvTo/OTZ80KetzMNW/5oIdmjLmhPQRSXpJR05Pynr/7p9sacozT2cs\nT9KUwAfvU6C+XsFAID26mVoe7vpuLadEYrH8/fO1K237Z2p098K6Oh3r69OOLVtyjnwU6p+mdVvf\nX5L1mTze3q7WjH2b/nZS7Vkjs+e8Rqd90yTY0qKFTU1539uUQp+JqW+a2ovt+4X+tk19x7T/Ue2b\nwWDO4ynRaNS23WvU54yf64tEIgoEcmtLjUzW1NSor++YtmzZkXfObF3dhXnbV43fpumTfp/83vmF\n3hmarKHAd9LtGzb82FhbS0tQLS35j0Kn1k+NHOdrt6ywYrEt+vSnr8478mu3/dFgWWG98cabevnl\ngzn7T9VfaM5y+/tfzXr8ofZWrcp4nfX1gazXPdzl48dPavPmremR7UmTpme1m+ozbd9p/SmxWCTn\nMckQjgcyrvf66KOPfrBS7cjO45swYYJOnjyZXh4aGipqW0+sX6+wZWlPd7cumzev4C1qpfzzIk2H\nnle/tkCJREJvHzmij06Zcnbaxjk1NIdCtofLw5alQ729eQ8dp9oLHVoeDab6mkMhzWlszPvFWsyh\ncTtOD2073b7XTP3TNDXBrn+a5nubrgZhmraQqv1AT0/e2iud074PYGRMc2ZXDD2s6RN/n56z+87Q\nZA1ltNfecVjf9/gObqFQs6eXUQuFmtXYOCdv6HPKNC2j2BMK29puyxt+3eb0JiO2yXTKlCl65ZVX\n9MlPfjL92CuvvKKAzQiPnfnz5+v555/XokWLtG/fPl166aVFr2sX7pyeUJb6gnTCrr5Ue7UFj2KZ\nTspy22jM67T7/BvueNn2yIZd/zTNuR2NcOf3vun2D0snTD9OTJhTjGplmjNbe8dhvWXYhtfhtJKZ\nrjPs9DrEJk4vZee0Pttw/I1vfENf//rXdeWVV+riiy9WX1+fXnrpJW1IHiocrquvvlovvviili5d\nqjNnzuiBBx4Y0XbO5fSEN5jZBRSnAcE0uumU2yccFsMugNrt3+kJb6l2u6MaflfMURuvwrPbR12A\nSpU6Ie1sOM5/Qhrc4/frKJsuZec223A8ffp0bd++XV1dXTp8+LA+8YlP6I477lB9ff2IdlZTU6O2\ntrYRrWunFKNr1c4uoJjeX6fh2alyDyCm8OdkSk0l8HN4dsrrvx3ATW5OC4D91S68Dp9+Z5zwO27c\nOC1atKgUtYzYaIyuVTs33x9+nNgzBSC/T3vwu3J+//jbATBSTqadmEaW/T7y7FTF3CHP6ehateP9\n8Q4BCADgJ6aRZa9Hnt0O5xUTjgl31asSDj1zZAMAgOKYwrnT8Fwx4RjVqxJGXvlxBwDA6HA6sl0z\nuuWgWmWOfALlhL4LAMjEyDFGhZsjn5UwbQL+xag9ACAT4Rgl4WROrdNpE4RrAACy2V3qrdoRjlES\nXo7OVcKcZFQmfrgB8Ap3GCyMcAwAHuGHGwD4D+EYVY/ROwAAslXztAvCMaoeo3ewE7YsHert5RrU\nAKpKNU+7IBwDgI3mUEhzGhsVDAS8LgUAfKOSR5YJx/AFRucAACgflTyyXPJwvGvXLu3cuVMPPfRQ\nqXcNH2N0DgAA+EFJ75C3du1aPfTQQxoaGirlbgHANdxhDwAqS0lHjufPn6+FCxdq69atpdwtALiG\nO+wBQGVxJRxv375dmzdvznrsgQce0KJFi7Rnzx7bdaPxeMG2xOCgbbvXqM8ZU33x/n5P26Xy7Z9+\nrk2q/PpK0jej0YJtiUTCtt1r1OeM3+uLxwvXNjiYsG33GvU5MzQ0WLC+rq7d6u7eo61bO9TUtDDv\nc/r747avz2l7Ia6E4yVLlmjJkiUjWtduzmk0Hvf1nFTqc8ZUX6C+3tP2SCxWtv3Tz7VJlV9fSfpm\nMFiwPRqN2rZ7jfqc8XN9kUhEgUDh2uLxqG2716jPmZqa2oL1tbQE1dKy0nb9+vqA7etz2h6LRfI+\nXtI5xwAAAKh8lhVWb+8hWVbY61KGjUu5AYCLMk/YY24ygGoRCjWrsXGOr0e2Cyl5OF6wYIEWLFhQ\n6t0CgCc4YQ8AygvTKgAAAFBRMu/gN1xMqwAAAEBZMd2+2skd/AjHAAAAKCtu3r6aaRUA4GPcgQ9A\nNXIyLcIpRo4BwMc4oQ9ANXJzZNiEkWMAAAAgiXAMAAAAJBGOAQAAgCTCMaoCJzUBAIBicEIeqgIn\nNQEAgGIwcoyywMgvAAAoBUaOURYY+QUAAKXAyDEAeIijIgDgL4wcoyJkBgxGmFFOOCoCAP5COEZF\nIGAAAIDRwLQKAAAAIIlwDAAAACQRjgEAAIAkwjEAAACQRDgGxOW0AADAWVytAhBXuwAAAGcxcgwA\nAAAkEY4BAACAJMIxAAAAkEQ4BgAAAJIIxwAAAEAS4RgAAABIIhwDAAAASeedOXPmjNdFpEQiEa9L\nAAAAQJW44oorch7z3U1Arpg2rWBbNB5XMBAoYTXDQ33O+L2+SCxWtv3Tz7VJ1OdUJBbTtGm5/8Gn\nxONRBQLBElY0PNTnjJ/ri8UiecNHSjQaVTDoz9ol6nPK7/UVGpRlWgUAAACQRDgGAAAAkgjHAAAA\nQBLhGAAAAEgiHAMAAABJhGMAAAAgiXAMAAAAJBGOAQAAgCTCMQAAAJBEOAYAAACSCMcAAABAEuEY\nkBS2LC1dvlxhy/K6FAAA4CHCMSCpORTS3Nmz1RwKeV0KAADwEOEYAAAASCIcAwBQhSwrrOXLl8qy\nwl6XAvgK4RgAgCoUCjVr9uy5CoWavS4F8BXCMQAAAJBEOEZF4GoTAABgNBCOURG42gQAABgNhGMA\nAAAgiXCMqsC0CwAAUAzCMaoC0y4AAEAxCMcAAABAEuEYAAAASCIcAwAAAEmEYwDwECeLolxx+2lU\nKsIxAHiIk0VRrrj9NCpVycPx/v37tWzZslLvFgAAADAqaTjeuHGj7r33Xg0MDJRytwBQtph2Aa8w\nbQLVqqTheMaMGero6CjlLgHAU07DLdMu4BWmTaBa1ZZyZ9dcc40OHz5s+5xoPF6wLTE4aNvuNepz\nxq6+3V1denbnTi269lotbGrK+5x4f7/t63PaLpVv//RzbVJl1zensVEX7d2rOY2NBbcxGn0zHo8W\nbBscTNi2e436nHFSX39/3HZdp+2SFI0Wbk8kErbtXqM+Z/xeXyElDcfFCAYCBdui8bhtu9eozxm7\n+oItLTrW16eVLS0F1w/U19u+PqftkVisbPunn2uT3K0vbFnasmOHli5ePOLRV6f1laJvBgLBgu3x\neNS23WvU54xdfZYV1o4dW7R48dK8I8D19QHb1+a0PRaLKBgs3B6NRm3bvUZ9zvi9vkgkkvdxrlYB\noKIxLQHVjKkRwPARjgEAAICkkofjhoYGbdu2rdS7BQCg4lhWWKtX38UVJYBRxMgxfCFsWbpr9Wou\nVwWgqpgul2ZqD4WaNXPmLKZNAKOIcAxfaA6FNGvmTOaFAqgqpjnBzBkGSo9wDKCqma5DzFENAKgu\nhGMAVc10NQvTUQ3uYAcAlYVwDAAOcKk4AKgshGMUpZhDz4yeAQCAckc4RlGKOfTM6BkAZDNdbQKA\n//ju9tEAMBx2t4de19GhxMCADvT0qLW9XePq6rRm5UqPKkU1CoWa1d29j6tNAGWEcIySsAswgBPN\noZD2dXfn7VeJgQG1rlqVXm5tby9laQCAMkQ4RknYBRinGB1EpaJvA0DpEY5R9kyjgwQMeMVp32Pk\nu7J1dKzTwEBCPT0H1N7eqrq6cVq5co3XZRWlnGsHTAjHkFTZ0x4Iz+XNzb7p9mdPuK18lhXWjh1b\ntHjx0mHPKx4YSGjVqtb0cnt7a1a7lwHUtG9T7UA5IxxDkv20h0oPjwQYb4UtS49t26avXX993v7n\nZErO1+t/JD32vYzlSZI++KyL/eF0qLe3Ivs+nHPzhDsvAyjhF9WMcAwjt0deCSDVrTkU0q69e0cU\nfk1979H+67L67qPt7WodxvZTfT8ajysYCJTlDyfLCmvbtsd0/fVf44oJAFCEkobjoaEhtba26uDB\ngxo7dqzWrl2riy++uKh1TaNLsOfm++c0PFdCAKF/jozTH0ZOR/1NI8tuM/1tOK0vdWhckrq796mn\n5wDzQkvIz/Ny/Vwb4DVjON66dauuu+461dbW6uWXX9brr7+uL3/5yyPa2e7du3Xq1Clt3bpV+/bt\n07p169TZ2WlcL975OTXXHFfzlZLefEnxzm8psOIXI6rBK17P6XUyOuf0C7rSpy1UQv/0ylfHPK0p\nE09IV0rSSzpyepKkD8Kh0/D468OH1drergM9PZo7e7bG1dVl7d/pyLJTpr8Np/WtHPO4pk48lnx/\nf6G3Tl8k6YMA1NGxTt3d+3TkyNuaMuWjmjfvspyA5GRObbUzTU1YVf+kpj723Yzli6RhfMKpgNvb\ne2jYAZdpE0BhtuG4o3y/1iAAAA+RSURBVKNDr7/+uv78z/9ctbW1+uhHP6onnnhCx48f12233Tbs\nnUUiEV111VWSpMsuu0yvvvpqUet94+BndElDg/Z0d2vBvHlnvyCHvXdnTOHWbuQw9QUvSfu6u3Wg\npydndMzN8Bzv/JwCNcf1yEckPfa04kMfzgpvTg9Nez365rVi+qfXP46cKKbvj7R99WsLbN+7dHi+\nXJJ+lhOeTX3zifXrJZ0NnZnPK1aqbwfTy9l92+2RX6fufO3zami4RN3dezRv3oKz4SmjfcXQw5p+\n+e/Ty+8M7dVQxidQ0/mH+mrNUX31cklvWnqnc7KGVvyqZPVL3odzu2kpptFXU/ht778xJ6Bm9g7T\n+qmAG49HFQgERzXgmvbtNNgDfmYbjn/+859r27ZtOu+88yRJDQ0N+u53v6ulS5eOKBy/9957mjBh\nQnp5zJgxGhwcVG3tB2VE4/Gc9e6+5x7t7urS0C9/qYumT9fCpqac5+3u6tKzO3dq0bXXamFTU842\nnLR/aPuX1Dzmt2q+XNKbP9Nbj/5v/XbJv6bbN27apIFTpzR05ox27d2rn+/fr1tuvjndfsPpf9aM\nif3pL/i3Tn9I0fjSdPv5T12j5vH96e2/uf4enfzK/x1W/Xa+uf+TmjZtmnoOHdLsWbNUN3asbsl4\n//pOnNDtK1aklx/u7Mx6f/f39Oi2tjYd6u3VrJkzVTd2bFb7w0dDOevfntF+6/jtWQHh1vEXKhq/\nKb0c7+9XNB5XYnBQ0Xg8vXyuYh8f7eVMI+mfpv4j2X++o9G3f/rss/riokU57Rs3bVL04EEdPXpU\nkydPVnDOnKy+a6rd1HdN65veu2++8ilNmzYtq+/dMoy+mVLoMy22bycGBzWutjanb5v+dkx/G6PZ\nN+PxaM5j99xzt7q6duuXvxzS9OkXqalpYfbzbtilH3Xt1s6dz+raaxepqWmhlNH+D8n/O3p7D2nm\nzFkaO7ZON2e0b9q0UQcPRtP9Z86coG6++ZasGrrO3f45urp269lnf6pFi76Y075p00adOjWggYF3\ntXfvLu3f//MRbd+u3aSxcY727JmhxsY5Oe/xiRN9WrHi9vRyZ+fDWc/pPBrKaV+R0d7fH896/rnL\nxa4/OJhQPB7NWb/Qdkdj36b2TNFo/sclKZFI2LZ7jfqc8Xt9hdiG4/r6+nQwTvmDP/gDnX/++SPa\n2YQJE3Ty5Mn08tDQUFYwlqRgIJB33WBLixY2NeVtT43eXFhXp2N9fdqxZUvOyGywpUXH+vq0sqVl\n2Ov/zWtX6pKGhqxDs2sy6rhr/L+oYdJ70kck6T/OjswGMsa/7vyvnNGzqZkF5GnPFO/8nFbWHNfK\nKyX99iXF/+X/5By2t1v/xxs2SJJua2vTI/fdl/P6759sacozT2csT9KUwH056xcafTt5/Li2bt6c\nfn+mT5qU9Tm1vr8ka73H29vVmtEeqK9XMBBIzzlOLaekPp9jsZi2bt6cMzpnqv/c7Z27bFo/JRKL\njah/mvpPav18/dPUN4ttr6mpydt+1/h/UcOV7yWXXld86GBW3zXWfud/Sfqgb8wY4Wsv9N6Z+p6p\nPeXcz7zY9U190+vllEgspkAgmPO4JLW0BNXUtNC2vaUl/zzvDRt+LMsKKxbbok9/+uqckdM1F4Q1\n9cpjyaXX9Nbpg1LgO+n21MhqXd2F6us7pi1bdmSNrKbaa2pq8rbX1l6gNWta08vt7a1Zr8O0/dTr\n6+s7VvA12o1Mp7b/xhtvavPmrTkjw/dNtjT1mR9mLF8kBR5JL9fXB7LqPXf5+PGT2rx5q3p6Dmj2\n7LmaNGm67fMLLadGjs9tT9Ufix3Lqb/YbY90OSUWiygYzN/3pLPB2a7da9TnjN/ri0QieR+3Dcfj\nxo1TX1+fpk+fnn6sr68vJzAXa/78+Xr++ee1aNEi7du3T5deeumItnMu06HX1LSC1omSHvtezrQC\n0/pPrF+vsGXpQE+PLps3Lyd8NtzxsqQPwme++NQcCtkeTrdrTx22zwoYeda3u9xV2LJ0qLdXYcsq\neGjbbvt2TIeuTfM+TYeu3Z6X6fa8U1P/sTs0b+qbRbcXmNPbcMfLtj+sipmWkHpt+fqW6bX7Xarv\nZk778GL/hf52SiEUai44neHO1z6vRCKRPWc5oz095/lySbJy5jyb5kSbDt2btl/s6yt0KTZTfaZp\nESbr1z/xwXoZ20k5fPjXam9vTYfnurpxw9g684qBkbINx6tWrdLXv/51ffazn9X06dMVi8X0wgsv\n6Nvf/vaIdnb11VfrxRdf1NKlS3XmzBk98MADI9rOuUzhzhQuiwmHpnBrFz6dchKuMuuf09iYd9TJ\n6bzMYuq3234qnKZG50Y7nHo971Oy7z92AdcU3E3tpjm9ptok+/BbzPqmdj9L9d3b2tpc+dsw9U23\n/zadSoW7QlJznjPDXeb/TKlwmTlndlWe9vTyOe2m7Tu9IoNpzrYpvDoNt6bwDMAdtuH44x//uJ5+\n+mk999xzOnLkiObNm6fbbrsta97wcNTU1KitrW1E69oxfYGYwuVojG7Zhc/RMNJwBe+vSGDipD7T\nyOJohDuvw60pnHvJ6Q8vv/dNp5yGO1O4NG3f6chyavttbbfl3b5p/26H29TI+tT0MifFAaPBeCm3\niRMnqrm5/C/fU8mjW5X+BVvp7AJuseHXryOLo8HpyLabTH97fpgW4Wep8Js5MpvJabg0jTyX+7V+\nTSPvAEamYu6Q5+fRJbeNxhewn98/P0yLcJNdwHU657caOA3Pbr5/1fDjxcSywurpOSDLCufM6zWN\nzDrdvmnkeTTmLAOoPBUTjst55Nep0fgCLufRuUpg9/46nfPr5nz4clDJR43Kgd0JfW5v3zTyXMwJ\ndZYVVm/vobzh2++cXIvY7fnUgJ9VTDiGu9wMEF5fEcAP7N5fp++92/PhATfZjQyXQijUrMbGOQUv\nhee0Pjdfn134NwVnr+dTA14iHGNUODl07fYVAQC3MKfYfW6OPI/G6KfT+tweWS/E6WXogEpGOMao\n8PLQNQEFXmFOsXNuT1soZs6zm6OfXo98Axg+wnGFqOaTspzehASAd0zTFkZj+16G0lKMfBe62geA\nkSEcVwhOKiqM0T0AlWg0rvYBIFeN1wUAgJ9lXu0DKLXMaRkASoORYwCw4ebVPpjyAxOvp4UA1Yhw\nDAAeYcqP9zhhDsC5CMfwPUbXALiFkVkA5yIcw/fcHl0jfMPPqvlKNADgBcIxqh6HtuFnXIkGI8Ut\nnoGRKXk43rVrl3bu3KmHHnqo1LsGAKBqcItnYGRKGo7Xrl2rF154QcGgOxd7BwAAZowqA4WVNBzP\nnz9fCxcu1NatW0u5WwAAkIFRZaAwV8Lx9u3btXnz5qzHHnjgAS1atEh79uyxXTcajxdsSwwO2rZ7\njfqcGRwasq0v3t/vabtUvv3Tz7VJ5V+fXd/Z3dWlPd3d6ti6VQubmoa9frHi8WjBtsHBhG2716jP\nmaGhQdv6+vvjBdvt2kajXZKi0cLtiUTCtt1r1OeM3+srxJVwvGTJEi1ZsmRE69pdaD8aj7tyIf7R\nQn3O1NbU2NYXqK/3tD0Si5Vt//RzbVL512fXd4ItLVrZ0mK7fVPfM4nEYgoECk9Xi8ejtu1eoz5n\nampqbeurrw8UbLdrG432WCxiO5UyGo36eqol9Tnj9/oikUjex7l9NHyBW/SiXGVeag2oJNy6GtWK\nS7nBF9y8RS/gJi61hkrFDVJQrUoejhcsWKAFCxaUercAAACAEdMqAADAsDHtApWKaRUAAGDYmHaB\nSsXIMQAAAJBEOAYAoExZVli9vYeY2gCMIqZVAABQpkKhZjU2zvH1dZiBcsPIMQAAAJBEOAYAAACS\nCMcAAABAEuEYAAAASCIcAwAAAEmEYwAAACCJcIyKELYsHejpUdiyRtQOAAAgcZ1jlInMcNscCuW0\nN4dCeR8vth0AAEAiHKNMEG4BAEApMK0CADzElB8A8BdGjgHAQxwVAQB/YeQYAIAKZVlh9fQckGWF\nvS4FKBuMHAMAUKFCoWaFQs1elwGUFUaOAQAAgCTCMQAAAJBEOAYAAACSCMcAAABAEuEYAAAASCIc\nAwAAAEmEYwAAACCJcAyIW/gCAICzuAkIIG7hCwAAzjrvzJkzZ7wuIiUSiXhdAgAAAKrEFVdckfOY\nr8IxAAAA4CXmHAMAAABJhGMAAAAgiXAMAAAAJJVFOB4aGtJ9992nlpYWLVu2TG+88YbXJeVobm7W\nsmXLtGzZMv393/+91+VIkvbv369ly5ZJkt544w19+ctf1g033KD7779fQ0NDHleXXV93d7euuuqq\n9Hv47LPPelxdceibI0f/dB/9c2Tom/9/e/fvSl8cx3H8efwocpOB0a1rMCBJYmI73e1OBlLKvf+A\nQUQR3bruZJEMMsmgTGZKt5BBnXTd/AsyOjeR3O9yujqpb9dxz/m49XpMzvaK5/C+3K7wqc3g1Gf4\nGuKj3M7Pz3l/f+fk5ATHccjn8+zv75ueVfX29gbA0dGR4SVfDg4OODs7o729HYDt7W0WFxeZmJhg\nY2ODi4sLbNv+M/tKpRILCwuk02ljm4JQm8Goz2ioz59Tm9FQm8Goz2g0xG+O7+7umJycBGBkZIRi\nsWh4kd/j4yOvr6+k02nm5+dxHMf0JOLxOLu7u9Xnh4cHxsfHAZiamuL6+trUNOD7vmKxyOXlJXNz\nc6ytreG6rsF1tVObwajPaKjPn1Ob0VCbwajPaDTEcey6LrFYrPrc3NzMx8eHwUV+bW1tZDIZDg8P\n2draYmlpyfi+ZDJJS8vXHwYqlQqWZQHQ0dHBy8uLqWnA933Dw8MsLy9zfHxMb28ve3t7BtfVTm0G\noz6joT5/Tm1GQ20Goz6j0RDHcSwWo1wuV58/Pz9933zTEokEqVQKy7JIJBJ0dXXx/PxsepZPU9PX\nj7pcLtPZ2WlwzXe2bTM0NFT9ulQqGV5UG7VZH+ozHOrz99RmONRmfajPcDTEcTw6OkqhUADAcRz6\n+/sNL/I7PT0ln88D8PT0hOu69PT0GF7lNzAwwO3tLQCFQoGxsTHDi/wymQz39/cA3NzcMDg4aHhR\nbdRmfajPcKjP31Ob4VCb9aE+w/F3Xqb9h23bXF1dMTMzQ6VSIZfLmZ7kMz09zerqKrOzs1iWRS6X\n+1OvgAFWVlZYX19nZ2eHvr4+ksmk6Uk+m5ubZLNZWltb6e7uJpvNmp5UE7VZH+ozHOrz99RmONRm\nfajPcOjfR4uIiIiIeBribRUiIiIiIlHQcSwiIiIi4tFxLCIiIiLi0XEsIiIiIuLRcSwiIiIi4tFx\nLCIiIiLi0XEsIiIiIuL5Bw8qaSO/P1mcAAAAAElFTkSuQmCC\n", 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Fi/O+1ns9OpepzyY824yu2Y5uVmtk2O34BZWpdlPfNJ3V+MWxY9kLBjPtuUx9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\nsAeCzHgrtxkzZqijI9hXGNciIPjNrb5yPqBNp65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"text/plain": [ - "<matplotlib.figure.Figure at 0x24d03160>" + "<matplotlib.figure.Figure at 0x29249780>" ] }, "metadata": {}, @@ -1013,7 +1034,7 @@ "\n", "randomset = np.array(randomset)\n", "rec=mutationEffects(randomset,model=model2)\n", - "plt_Ei(rec,save_path='figures/mutation_effect_all_20.png')" + "plt_Ei(rec,save_path=None)" ] }, { @@ -1060,6 +1081,13 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When doing this in silico mutational analysis one can also see that the effect of individual mutations depends a lot on the sequence context (Fig. 3b and Supplementary Fig. 4). For instance, a mutation at position 18 to a G can have a positive or a negative impact on the fitness depending on the rest of the sequence. This suggests important interactions between bases. To analyze these interactions, we mutated in silico every pair of bases and compared the effect of individual mutations to that of pairs of mutations. An interaction is observed if the effect of a pair of mutation is not simply the sum of the effects of individual mutations " + ] + }, { "cell_type": "code", "execution_count": 26, -- GitLab