Analysis_Notebooks/TP53_exploration.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:6fb7ffbee9917e07b65ae4f09460ff55a75d894492f93c7bf86d71d246411495"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1 class=\"alert alert-info\">HNSCC HPV- Cohort <small>  <i class=\"icon-globe\"></i>  TP53 Mutation Exploration</small></h1>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Import Data and Packages  \n",
      "For full list of data and packages imported see the [Imports](../Analysis_Notebooks/Imports.ipynb) notebook."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import NotebookImport\n",
      "from Imports import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "TP53 Mutation Clinical Coorelates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53_mut = mut.df.ix['TP53'].ix[keepers_o].dropna().astype(int)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(p53_mut, surv, figsize=(5,4), order=[2,1,0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWkAAAEhCAYAAAC9cFFPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe45e6d0>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "screen_feature(p53_mut>0, kruskal_pandas, clinical.processed.T).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>spread_inferred</th>\n",
        "      <td> 7.65</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>smoker_inferred</th>\n",
        "      <td> 7.39</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>drinker_inferred</th>\n",
        "      <td> 6.45</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>invasion_inferred</th>\n",
        "      <td> 4.91</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 0.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>post_2000</th>\n",
        "      <td> 0.69</td>\n",
        "      <td> 0.41</td>\n",
        "      <td> 1.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "                      H     p     q\n",
        "spread_inferred    7.65  0.01  0.06\n",
        "smoker_inferred    7.39  0.01  0.06\n",
        "drinker_inferred   6.45  0.01  0.07\n",
        "invasion_inferred  4.91  0.03  0.12\n",
        "post_2000          0.69  0.41  1.00\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ecs = clinical.clinical.presenceofpathologicalnodalextracapsularspread\n",
      "ecs.name = 'Extra Capsular Spread'\n",
      "pd.crosstab(p53_mut>0, ecs).T.plot(kind='bar', rot=15)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xe462ed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEwCAYAAABFdpFQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7865350>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "TP53 Mutation Functional Assessment"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Classification by functional domains"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It is important to note, that here a patient with multiple mutation is counted multiple times."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import re as re\n",
      "get_nums = lambda s: re.findall(r'\\d+', s)\n",
      "\n",
      "def is_disruptive(v):\n",
      "    c = v.Variant_Classification\n",
      "    if c != 'Missense_Mutation':\n",
      "        if 'Ins' in c or 'Del' in c:\n",
      "            return 'InDel'\n",
      "        else:\n",
      "            return v.Variant_Classification.split('_')[0]\n",
      "    else:\n",
      "        s = v.Protein_Change\n",
      "        aa = int(get_nums(s)[0])\n",
      "        if int(aa) in range(163,196):\n",
      "            return 'L2'\n",
      "        if int(aa) in range(236, 252):\n",
      "            return 'L3'\n",
      "    return 'other'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53 = FH.get_submaf(run.data_path, cancer.name, ['TP53'], fields='All').ix['TP53']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dd = p53.apply(is_disruptive, 1)\n",
      "dd = dd.replace('Silent',nan).dropna()\n",
      "p53 = p53.ix[dd.index]\n",
      "others = keepers_o.diff(p53.Tumor_Sample_Barcode.ix[dd.index]).intersection(mut.df.columns)\n",
      "dd.index = p53.Tumor_Sample_Barcode.ix[dd.index]\n",
      "dd = pd.concat([pd.Series('WT', others), dd])\n",
      "dd = dd[[i in keepers_o for i in dd.index]]\n",
      "pc = pd.Series(list(p53.Protein_Change), index=p53.Tumor_Sample_Barcode)\n",
      "pc = pd.concat([pd.Series('WT', others), pc])\n",
      "pc = pc[[i in keepers_o for i in pc.index]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s2 = surv.unstack().ix[dd.index]\n",
      "s2.index = range(len(dd))\n",
      "s2 = s2.stack()\n",
      "pats = pd.Series(dd.index, range(len(dd)))\n",
      "dd.index = range(len(dd))\n",
      "pc.index = range(len(dd))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figdir = '/cellar/users/agross/figures/'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.concat([pats, pc, dd, s2[:,'days'], s2[:,'event']],\n",
      "               keys=['patient ID','Functional Class','Protien Change',\n",
      "                     'Days to Death/Censoring', 'Death Indicator'],\n",
      "               axis=1).sort(['patient ID'])\n",
      "df = df.set_index('patient ID')\n",
      "df.to_csv(figdir + 'fig2b.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 107
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Figure 2b"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(3.5,2.7))\n",
      "c={'WT': 'grey', 'Splice':colors[0], 'other': colors[5], 'L3': colors[1], 'L2':colors[2],\n",
      "   'Nonsense': colors[3], 'InDel': colors[4]}\n",
      "draw_survival_curve(dd, s2, colors=c, ax=ax)\n",
      "ax.legend().set_visible(False)\n",
      "prettify_ax(ax)\n",
      "fig.tight_layout()\n",
      "fig.savefig('/cellar/users/agross/figures/fig2b.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAP0AAADDCAYAAABJRRchAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w+Acz7aXXeJSwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf60f890>"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(dd, s2, colors=colors[:6] + ['grey'] + colors[6:], figsize=(4.5,6))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAGxCAYAAAD1d6RSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlclOX6+PHPsA/LoKiIYYmJS27gVmilaGWm5oKZiqZm\nppmZv6xOWSGYS30tPWV5OmqlmeZSbuWux8BMcRdzS80NV1RUVlmG+f0x8jADM6wzzADX+/Wal/M8\nc889FwgX9/Pcm0qn0+kQQggBgIOtAxBCCHsiSVEIIQxIUhRCCAOSFIUQwoAkRSGEMCBJUQghDDjZ\nOgBhmkqlsnUIQlR4pRlxKC1FO6bT6ez2ERkZafMYKmp89hxbZYqvtCQpCiGEAUmKQghhQJKiKJXQ\n0FBbh1Aoe47PnmMDiU+lK8vFt7AalUpVpvsiQlR1pf0dkpaiPUu7CTladm1fx524aK4fPqW8pL15\nk4x9+2wYnBCVkyRFK+jRowd79uwx+drmzZsJDAzEy8uLl156iYyMDPMVJRyFnCwmx6RzadNSDiw7\nrLyUdeIEyZ/PsnToQlR5khQtSKvV8uuvv7J161aT4wwTExMJDw/nyy+/5MKFCyQkJDB16lQbRCqE\nMEeSogUFBQXRr18/tFqtydfXr1/Po48+So8ePfDx8SEiIoLly5eXc5RCiMJIR4sV1K9fn+XLl/Po\no48anX/77bdxdnbm008/BSA1NRUvLy9SUlJwd3c3KqtSqYiKiir2Z7qQSadGnnQYNLHM8QtRGZS2\no0Wm+ZWj5ORk6tWrpxx7eHjg6OjI3bt3CyRFgOjoaOV5QEAAAQEBZuvOxIWYUyl0sGTAQlQg0dHR\nRr8zpSVJsRxpNBqSkpKU49TUVLRaLd7e3ibLl3Q8ViYuZQlPiAotNDTU6Hdm8uTJpapHkmI5Cg4O\nZvHixcrxwYMHadCggclWIkBk5G+gUzNvcwgd4pK4ePsp4k9kKq8vCK4FwHMOsdYNXIgqRDpaylH3\n7t3Zt28f69ev59atW0yZMoUBAwYU/iZVOkM6/2nROO5ETCL77DmL1ilEZSFJ0coWLVpEw4YNAfDx\n8WHx4sWMHz+eevXq4evrS0RERJF1uLtm4t3gEfyb1Sj6A6NUeY/pXrBrZoEimfv2k5OSXOKvRYiq\nQHqf7ZS+56xNgfOZ6c4cWBrCkbVtTF4+R+qijN/g4gkfGCfAhG7dqTbjU1xatrR43ELYC5nmVykV\nvNfoos6izaAS3EPMTIH5jxZdTggBSEeLnRsFzAPSjM66qLPMvuPy/Lomzl6FqAeNztx4rofZOlQe\nHni9PQGv0aNKEKsQlYO0FO3aEGAHsP/+I0/L3ges9qm61FSSZ8q8alE1SVKsUPIup9uP+MNkiR+G\nD+WvFs3L/Em61NQy1yFERSSXzxXKKOCLAmezdQ44qXIAyHJxYdfj7egWsqnQmnRO7qi6fAwd3jY6\nf9n/QTPvEKJqkJZihTLE5NnTurq4uGQrx445jjAxGNr7mq1JlZ0G0VGWDlCICk+SYoVTsEf6PA8w\nceJG45OujuhCHyi8qswUC8YlROUgSbHCGYWpxBgSOc3ouMu0SSw90JG4Fofg3SCIeh+idPqHEMIs\nGbxtp4oeeNqJLtPeJS3TWTljyTnQzpmZhPboQbt27cjOzkatVlusbiHKgwzeroJGdj6Ou4ujcpyt\ns9x/Z5aLC9HbtnH06FG2bNlisXqFsHfS+1xh/YvwDrUI75A7W+UaY75bSa0rfvqOFgvIkosIUQVJ\nUqyw8s9I8ePZTt1YevInLux7VTn78uEbjF5rOIzHHTb/DbsT9IdtX4Oe3yivJs+dx6xrV60XthB2\nTu4p2qmy7PscErlZef7y4Ru89MuXuDsb1JWhhU/u7wzY+hXo9a3R+80tzuni4kKnTp3o0EHW9xb2\nT+4pCrOWnvQwPuFa+OW1c2amyfOZmZnExMSQmppKTk4Od+7cITU1lYyMDG7fvk1GRgZpaWncvHmT\na9eukZ6ezp07d8jOzub27dskJxe+XFlqaiq3bt0iK8v83G4hrE0un6uAI2tbQ4uDgMG4xKjcZckO\nA22Nynd6pj4xMY3Jwpn8MjMz2bx5M8899xxz586lQYMGNGrUiLVr19K9e3euXLnCyZMncXZ2pl27\ndsTExPDyyy+zcuVK3NzcGDlypNk4t23bRnx8PD179ix0PxohrEmSYhXQ6FAL4CD68Y1pRZSGx0PP\n0b7tP1xtfINTTZuS9MbrHD582NphCmEX5PK5CnDKdgZ6Ym7gtykOnvKjIaom6WixU5bsaAEYtWYw\n6EDloNJvURAdZXqaX1Teat+X/a9z7pFAMib2YufOpIJlbUQ6fERxSEeLKNSA317g41nT9Qcd3tZv\nUeDTQN/7bIbnKHfqnzhDk6GzcLajzo/cDh8hrEGSYhVxL+cecQH7iiyXnemqPPd6O6/XuvX+A3aX\nGIWwBuloqUKynfIltTfP6P89+J1y6vye5wh8cg1gfF+xxV9HafHXUW5tXM+ZM2d47rnnmD17tkV7\nn9euXVtk73NpNzgXorgkKVYRPRYMAmDugsUmXv0x7+khCHwy79D/cm3leU5KDqqkzZw508BKUQph\ne5IUK7ncbVCL4qTNodW1NJrfSCcz3dnk5lgOng74ua/F23syKpUKX19fqlWrhpubG7Vr10atVqPR\naKhWrRoqlQp3d3d8fX1xcnKiWrVquLsX3vOt0WjQaDS4uLiU6msVwhKk99lOlaX3ucu0baRlakv8\nPidtDi/9dYuWvQ/QZlBsIbsG7jdz3voML58jIyNtFoewf9L7LBQjOwcaLSlWXNmO+h+HI2vbsGDg\nWPTJL5rL/tfzlTR1CS5E5SCXz5VQeIcAwjsEKMe91uStqPNrn/UFyhuOa8zvwv7LOKG/n5jb8aLT\nzUOlMr1fTHn65JNPZLyisDhpKYpC7fj6IADJM/O2PFWpip4qaC2G9xtlvKKwBkmKVUyvNT3otaYH\nA9a9wJozq4osn5Otv5hImWecCC/7P2j0uNKoCclz51klZkOdOnUqkBiFsCRJilWA2qng/irp2eks\nPflT8Spw9yiyiC41leSZs0oaWol16NCBiRMnmn39/PnzHD9+3OjcnTt32LVrFwD79u3jxo0bVo1R\nVGySFKuAQU3CzSbGwsztvZh7yRmsa/oR69p+YvTaurafsLFVFP/UfkI5p0tNzV9Fubt27RoXL140\nOpeSkqIkylOnTnHnzh1bhCYqCOloqQL6BIbRJzBMOTbseMnP2SGdrJzi7dyndXTlTMPeNLi+s8wx\nCmEvpKUojLTxW42zW9F/K1v2PgBA1r1sa4ckRLmSlqIwElR7I0EvPaVfSQf4YcjPvPif51Fr3NDl\nzEXloL/kbjMoliNr2xRWVbkxNR96z549ZsudOXNGlh8TZklLURQUHWXytMphtPLc3GyXxNdet0ZE\nBZR1KqAM5xHmSEtRFGSw+GxgpwAcnXNnxwwB8rZLzds6tTY5KTkkz0zl3o5T5RJip06diImJKdOQ\nHBnOI0yRpCgK9fir7fKdMb3Pi4OnA15ve3BvR7mERYcOHUxe+sbGxnLnzh26deumnLt06RKbNm1i\n5MiRLFmyhDNnzpRPkKJCkstnCztw4ABBQUF4enrSo0cPEhMTC5T5/PPP8fPzQ6PR0LVrV06fPm2D\nSPXy90R30a3iJ12YmdJQ2D4vlWlfl8uXLyv/d0ePHiUlJYVz586RmJjI5cuXS1xfRkYGO3fuLHKb\nV2F7leen2A5otVrCwsIYN24cV65cwdfXl/HjxxuV2bNnD7NmzSI2NpYbN27Qtm1bxo4dW65x5h+z\nqHLIu4xMw51vKWxe8xBgB3N7/z/lYSj75N/lPsvFUIMGDWjRooXRuerVq9OxY0cA2rdvX6x6Dh48\nyLlz5wBYtWoVCQkJxMTEcO7cOQ4ePFjiuJKTk9mxYwfXrl0r8XtF+ZKkaEG7du1CrVYzcuRINBoN\nU6ZMYc2aNUabu+cuZ5R7LjMzE41GU65x5h/M7eG3p0BiZNfMQutwdjN9P85zVF4rsrxmuRiqVasW\n/v7+Ruc8PDxo1KgRAA8//HC5xiMqHrmnaEGHDx8mKChIOa5bty5qtZrTp0/TtGlTAB599FHCwsJo\n3LgxAG5ubhw4cKBc4zQ1mNu99iESDr2ZVyg6ShmWY0qbQZc4sLQRWfeyjRal9Xrbw2ietD3MchGi\nJCQpWlBycnKBVp9Go+Hu3bvK8aZNm1i9ejUHDx6kYcOGTJgwgbFjx7J9+/YC9UVFRSnPQ0NDCQ0N\ntVboBZna/tRAUJ9JBPXRTwU8sDSE9iP+APT3Ff0vt+ey/+7yiLLMCtvz5eDBg6xbtw6AH3/Ub9lw\n4cIF5bXSWLFiBZ07d5bxkVYQHR1NdHR0meuRpGhBGo2GpCTj/ZGTkpLw9vZWjn/77TeGDx9OcHAw\nANOmTcPX15fU1FQ8PIwXXjBMivbsyNo2SlLUO6vs7ZKTkoN+UVpXQAM8W/4B5uPi4mKz4TjZ2dnE\nxMRIUrSC/A2H0m5yJvcULSg4OJi4uDjlOD4+nvT0dBo2bKicy8nJISMjQzl2cHDAyckJtbp4843t\nVWa6s8nz+h7pecAVIP8K3raRf/mx8ibjI+2btBQtKCQkhPT0dObPn0///v2JiIigd+/eODvnJYze\nvXszbNgwBg0aRPPmzZkxYwa9evXCwaFi/33SX0LvA+6ZeDWNjL170eVUwy1kKACpi37E5YnHcbZB\nx4e5MY6GfvvtNx544AHatGnDxx9/zJAhQ9ixYwctWrTgypUrPP/88yX6zJs3bzJnzpyyhC3KScX+\nTbQzTk5OrFy5kq+++gp/f3+uX7/O7NmzWbRokdJa7NatG5GRkfTt2xcfHx9OnTrFvHnlO2yl2KJU\nxo/pXmZ7pfXzoBei/zv7cIF9XbIvxqM9f/7+USrpW9ahvRwPyDJewr5IS9HC2rZty5EjR4zODR06\nlKFDhyrHr7/+Oq+/Xj5zhEurV3B3k+fVV7cw6Ex9o97r4vB4IQ39TJiOwMO4PpkMLsnAQGBTGaO1\nvIceeohq1aoB0Lp1a7y8vGjUqBE1a9bEyankvzZubm6WDlFYiSRFgdpJXeSCs7nSHZ1YevIn00nx\n5lXIzgK/TsBuo82u8qQBJwGfMkZtXYZDq3r27Anox0AC1KtXr8T1eXp6WiYwYXVy+SzMrsxtjtkE\nqq0GqzIB/Qyd5Jmp93uf88sm6eO/uBX+/0iee7PkAVcCCQkJxSqXk5PDsmXLilVWq9WyfPnyUsWz\nc+fOAiuWW9K6desKjMywV9JSFMpg7pB9eVudDms6nH6N+ucVilKZvaQ2ReXhQcq8VKOB3LnDdPI/\n119SjwKa339U/h/L9PTitcxBv4VCceh0ulLPo7969SrVq1cv1XuL49y5c8WeYmlr0lIUVuH19gRU\n+cZdmm41gv6Seh7wJpBhpowQ5UOSojDp1pVHCi+Q2yNt6JuWkHAUpnvh1SKZB06dxP9yPP6X43Ht\n0pz0zbUxt8KOqeXIhLAFlU6n09k6CFFQ7sIR5anLtG2kZWoBcHdxZPuHT+e9ON2LXk07Koe/Ht4A\nwNxDPyrnRrd6Ka+8iyd8kLdMVkL3njjUrEHNRT8AU7nsPxfIfxntDmwEit5StSIq7QyLys5aW0OU\n9ndIWopCMbJzoPI8NzkqQqNKVlm+udMeLw3B/YX+Zgornwp0Qn+PcXHJPq8CsOUsGntmb1tDSFIU\nivAOAeZfzL9iTpRO/zA0xnh8piGPQQNx79Xz/lHeDX3T9xlz7zFWLraeXmjP7GnqY+Xv5hPlZu6o\nOEB/Oe3skE6bNccJ6tPURMmxwKeAftiOd2RtCt5TrHz3GHOnFy5YsIAuXboUa7xjTk4OU6dOZdKk\nSUWWzc7O5tNPP+Wjjz4qcWw///wzTZs2pVmzZiV+b3F89dVXhIeHU6NGDaPz9nhLQVqKokzM7RGd\nlaPmwFLzLcdc+iE7O4D9mO+EEaL8SFIUZdJmUEvzifFedqHzpYtW+e4rgn7+e+3atYsuiL6zwHCK\naGEcHR0ZMqSwrSTM69SpEwEBAaV6b3H07du33FeYLy25fBZlEtSnqfEl8jQP5u6dm3ecmVLkKt55\n2gO7gNyBzfOg0P1iKqY6deoUu6xKpSp2sipJ2fx8fX1L9b7iqlu3rlXrtyRpKQrLajOq4LkiVvHO\n83/AaIPjyndfUdg/SYrCslqWvGVnvPNf5WsZiopFkqKwifxTAM3v/Fc57ysK+yVJUVhW7RZFl8H0\n3Oi8nf8Me6Er33hFYd+ko0VYlqOZwcmG86RdPPEKjcLr1ElAf/lsbBTwxf3ncl9RlC9pKQqrirv+\nXMGTuT3SZsl9RWE70lIUZoVEbs535s281w7pX3N3cWRk50CjKYLObk76MYrAgWthBNXeWLDyYvdI\ng/6+4hD02xb8BbxbgveKiignJ4fVq1fTr18/o/OTJ0/Gx8eHcePGWe2zpaUojLi7OJaofFqmlm9/\nP2N0rs2glsrzrBy3vHnS+edKFx6JwfPc+4oHgegSxScqrmPHjpk8n5ycbPK8pUhSFEZGdg4sVWI0\nZHq+c0kZjneU+4qi/MjlszAS3iGg0NVyeq3poTxPOPSm2XJlN4S8zhYhyo8kRWE7So90caaAtc33\n3B19a1I6ZSoLUyvmmDqXlZXF5MmTiYyMtEoccvksypdLSbb6LGzVnMq55mJVY4/rS0pSFOUrNAoc\nXYtZeBRFJ0ZRkdnjwrty+SysLs5wsdkOb4NbNYjfBb2/K7D5VfLceXiNzu1kGYLx5fF04E/guvWD\nFuUid+Hd/Mwtrjt58mScnZ354IMPrBaTtBSFVRiusVjUYrMq57wtCUzPfy5M2/uPJ9CPZzwJFDYG\n8jxws4SfIaoSSYrCKozGKt4fyK1wqwZeeWsKerVOUp7nzX82JRR4AdOX1PfQ32P8DDhj4vVci4E/\nCnld2AOVSsWgQYMKnK9ZsyZt2rSx6mfL5bOwiqA+TYldcND0i0376R/3ebVMIWlPtWLU2uH+wxl9\nAqz8+7pUVSqVioYNGxY4P3bsWKt/trQURYmondS2DgH9fcbcfV322zgWUdlIS1GUyKAm4Xx/9DvL\nVejiWcJ50EWJA0YWUWYNMO3+cxnvKIxJS1GUSJ/AMMtWGBplgUrcyvDe/OMdvwMO3X/+MjAGiCpD\n/aKikaQobKuoDa22R8DJNUVU8iKgKqJMYQzvRZ4Cbt1/fg64BPxThrpFRSOXz8Iiftp1vtA503N7\n67cVcHZzos2glsVfNCLpElR/uIhCvYAYYCXwCjAOCDZTdirQDOiL8dRBIfSkpShKTeWQqTzPv3wY\nYHI/6Kx72UWOWxTCliQpWtiBAwcICgrC09OTHj16kJiYWKDM/v37eeyxx/Dy8qJLly7Ex8fbINKy\n8/DbozzPv3wY6McqmkuMlqUGcseuBQGFbbreEPAzcT53EPj/gPfvP08BrgIngI7IJlpVgyRFC9Jq\ntYSFhTFu3DiuXLmCr68v48ePNypz9+5dnn/+ed566y1u377Nk08+yYgRI2wUcdm41z5U6OtBfZoy\nYvlARq8dwui1JejdjVLlPQ4vhLUjYLoX7Jpp5g21gdxpX28ChV1uDwDa339e3DnYIAtQVB1yT9GC\ndu3ahVqtZuRI/ZCQKVOm8Mgjj5CVlYWzszMAq1at4rHHHmPgwIEATJw4kePHj9ssZrtQnJVzcvd1\nKapjpkR6ob8PmVNUwftkcHhVIC3FfNRqdaEPd3fzq7YcPnyYoKAg5bhu3bqo1WpOnz6tnNuzZw81\na9bkscceo1q1avTv3586deqYqq7qyDcs5/L8usrjysIHSD5yP2ladDwjwLNAC/IGge8HngI+vf/c\nE6ji/zdVkLQU8zlx4kShr6tU5od+JCcno9EY38/SaDTcvXtXOb558ybbtm1j48aNtGjRgg8//JDB\ngwezffv2AvVFRUUpz0NDQwkNDS3eF2Flaic16dnplquww9uoPOabnPesy3Ig+aAGr5alTIhHlsCD\nHaB6/TIGKexddHQ00dHRZa5HkmI+AQEByvPk5GRu376tHKenpzN8+HB2795t8r0ajYakpCSjc0lJ\nSXh7eyvHXl5ehIWF0b69/r5WVFQUNWrUICUlBU9P48tIw6RoTwY1CWfpyZ8smhi93p5A8icfo8sq\n+EdHl1WGC5pD34Onn5mkGIi+U8XQGCB3HnYUoEN2D6wY8jccTK3aXRxy+WzG119/TfXq1alfvz6B\ngYEEBgbSrFkzHnww/8bteYKDg4mLi1OO4+PjSU9PN5rYXq9ePbKyspTjjIwMnJ2dC70stzd9AsNY\n3vMXfu2z3mJ1eo0exQNxu/C/dAH/y/H4Xy6PHnlP9L3RhgLIS4qhQOdyiEPYE2kpmjF9+nRiYmJw\ncHBg3rx5zJ07l4kTJ5pcuSNXSEgI6enpzJ8/n/79+xMREUHv3r2VThaA8PBw2rdvz44dO2jdujVR\nUVH06NEDB4eq9fcpdzB3rrxB3VXr+yDsj0qn05VkM94qw9PTkzt37nDv3j2efPJJDh06RFJSEk2b\nNuXSpUtm37d//35GjBjBP//8Q8eOHVmyZAnr1q1jypQpSofLqlWrePvtt7l+/TpdunRh0aJF+Pj4\nGNWjUqmoCP81IZGbleexk58ttOz3A5YVOkbR2c2JEcsHKseX/fNa5f6vmv+el4qLp76Dp1i92YYz\nX2RVnoqitL9D8mfZjHbt2vHZZ5/h6OhIcnIyZ86c4dy5c6SkFH7Dv23bthw5coTU1FQ2btyIj48P\nQ4cONeqBDgsL49y5c6SlpbFu3boCCbGyMjeYO5flB3UXIneIjxD5yOWzGQsWLOCDDz7g9OnTREZG\n0qJFCzIzM/nkk09sHVqF9XrcJbb9+AI9Po9m+wdPK+fzX0qbZPElxrDCEB9RGUhSNGPJkiVMnTqV\nhx9+mJYtW9K3b19ycnIKDLkRxafN0Rn9WyIfJJMeE0P2seN4vT4GAJ1OR9qPi/EY+pJSLG3Vatye\neRoHLy/9iR+egic/gIefyqsrqiwr6sie05WdXD6b8ddff9G6dWvatWvHzJkzuX37tiREG7u3fiMp\nCxYYnbsz0XhXt6QZn5FjMIyqxP74BC7uzDs+tAC0+bfgTIOcb8zXce0w/O/Dgud/j4QrhdyT/Kkn\nVID7yJWdtBTNWLZsGZmZmURHR7NmzRoef/xxHnroIQYMGMC4ceNsHZ5dMux0MafztP8pZd1dHBnZ\nOdC6QbV5FXwaFL/81YPgYxDTzZNwoQk8fAajaX4OGejnQtcBnjeuI+0mXN5DAZf3Qt0Q8599ynJD\nnETpSUuxEC4uLnTt2pUpU6Ywffp07ty5w7/+9S9bh2VXDJcPK6m0TK3JJccsqvlAqBZQtjquNCZv\nTxhDd4HCdh8UFZG0FM3Ys2cPmzZtYuPGjRw7doyuXbvy4Ycf0rNnT1uHZlc8/PaQeu0xdDn5LzGL\nJ/+SY3FrjptcgNZweI7hc1PH19s/bvbzVB4eeDX3LP20QVHpyThFM3LXQ3zhhRfo3r07Hh4e5fr5\nFWWcYq81PZTnRc1wCYnczO8fPkW3Gb+TkZW3Ms2ok7eV4TiGYxWvNGpSxD7QpaNyzuGB4VdK9+Yo\ngz2How5YJiBLKNG4y6qhtL9D0lI04+bNm7i5lWVDJFFcbQa1VPaINhyr6PX2BJJnzrJ4YizTXGpD\nuQkyQwvRV2B3gmXqLQ2rLK1WNUlSzMfLy4vk5GSqVatmckUclUpFWpqsq1cark4Oyr+GLcWgPk2V\npGjIa/QovEaPUo5v/+t97v2+nTr79gL6ITlX6j5kNE/6WkgHaq5YhtNDDxWoz+gyu7TjHjO04OqY\n7wtzhNAHbJsUQcZdWogkxXyOHj0KwN9//w1QIS5hK4qYiGcA2PL+U8XqqbaqD5JNn1/RH5q9CM36\n64+3vgdqH3jivfsFFkPWbHDOtzCtqyNELQCGw9ltkD0JGr2HvoPmCrAUFj8Hj70JDZ8z/dlRKojM\nAeWPcT/gC8D8IiTK+0rkMjAW/f7Xk9GPvexR6DuqEkmK+dSrVw+AZ555hoEDBzJgwACaNWtm46gE\ngGvIY6icjFtpnmNeMzr2GByOg5c1x5MOgd82QWA3aHkVWG7wmsEfUFXusY7ir+ydX8F9bywnx+Bf\n+cNvSJKiGbNnz2bt2rU8++yzVKtWjYEDBzJw4EACA608rk6Y5R7WF/ewvsqxSqXC+yPjQdJe494o\n24d0/hjca+Ydt30NHPL9mnT8SN965AfTddRpA5gYj9j1M/B6wPxnv7yjpNEKK5De5yLodDr27dvH\nr7/+yooVK/Dy8uLAAev3OlbG3mdD+VfXMZz/XKJNrkrAaNUdi6zXmIZ+l7/KpnJMY5RVcqzk1KlT\n/Pnnn+zdu5fbt2/TpEkTW4dU6c3tvZjvBywjbo29b+jlfv9R2VTtnQslKZoxZswY6tevT/v27Tl8\n+DDjxo3j8uXLLFmyxNahVUr5lxTLupfNgaVHbBRNSYyi8ibGqknuKZqRmZnJf/7zH5566ilcXEo3\nW0MUX5tBLTmw9IjROMVyXV+x1Ibcf3wNeAAv3z8/Bf1OgTvQ9/YuN/nuwvUBvqJEvc9RxblcvIx+\nL5pfgUjY/iscz4A3jmO8oG7VJEnRjJs3b/Lcc2aGTgiLC+rTVJneV6z1FYWwErl8NsPJyYl169bZ\nOgxRYTgBhsOFnNH/ejlR+raHK/fH9liYCsi9+nGWETn5SEvRjJycHPr27Uu7du2MtihVqVRs2LDB\nhpFVPXFrjuOsdqbps6Y3DVv9r008F9EZNy/XYte5fdafNH++Mb4NaxZduFhey3ecu3VqrzLUWYpL\n7hIN5M5XNkqVb253CRNygfnXS4EkYHQRb3wd/WDy4owHbov+NkVMyWIrAUmKZvTu3ZvevXsXOG9q\n6p+wrnt3M8jRmh8AnXQ1GV0JV/NOvpGKNtOag6PLkTW2aiiNAvOvM4GMYrwxjZINcLfu/5skRTOG\nDx9u6xCEKJ7QKH0yspfEWMFJUjRDrVabPC8LQgi70+Htsq2O899gyM4s2PscNQ6YXbw6yrTvjX2R\nGS1mnD/gKL1cAAAgAElEQVR/Xnmu0+mIj4/ns88+4/nnn2fUqFHm32ghFXFGy4jmr9AnMKxY7yts\nv+jy7n12dnOizaCWJhe3LUrW2bNk/LETz2FDrRBZMWM4cZLMw4fxGDSwdPGYS4p2r/A9uGVGi4UF\nBAQoj/r169OxY0d++OEHIiMjbR2aXVE75bWol578ySJ1FrY3tDWUZaC49vJl7m3YaOGISib7wgXu\nbd1a+njqtocH298/qIwD0UtGkmIJ7N+/n4yM4tw4rjoGNQlXnqdnp1ukzjaDWtokMVZZPb+B3t/d\nP6isM3SKT+4pmuHm5mbU05yTk4NOp2PWrFk2jMr+9AkM4/uj3xVdsBAm11ZsUr1MdZaEkzaHVtfk\nPrFe7gydYYA3pbqnqMyq+QH95l5vFvHm4cDb6GcAFaUt4AbsLKpgqUlSNCErK4uTJ08qx/Hx8fz+\n++88/fTTdOjQwYaRVR7uLo4FNq2ylWxHBw75uRfYAKskyvJeSylsc6/SW13McnXzns43/myVx3/w\nenuC0Srq9kwun/M5cuQIDz30EO+88w4BAQFs3LiR7t27ExcXR9++fdm82cYrRlcSIzsH4u7iWHTB\ncpLtKL8K1qJLTSV5ZmFXWN8BzYtZ2zpgW9mDKoT0PufTtWtXnnjiCSZNmgRAo0aNmDhxIi+//DJb\ntmxhypQp/PHHH1aPo6L0PkPp11Qsb8lz5xXYCKvfyG+V5yu/HWmLsKoMy6xhWXyl/R2SpJiPRqPh\n0qVLaDQarl27hr+/P4mJiXh7e5OdnY2Pjw9JSUlWj0OSYvkobGhQcdz74w9Svv4PNZcvtWRYJZK+\naTNpK1ZQ4/vvbBePmZV6LL+wb/HJkBwLcXZ2xslJf6s1NjaWpk2bKnOfU1JSKkyiEqJC0+XAP1sL\nnv95IBwysw2EhUhSzOeJJ55g1qxZJCcns2TJEjp37qy8NmfOHNq1a2fD6IS9cQ5siKeNOxCcmzfD\nY/gwu4nHInQ5sMTE0n3HlsPO6Vb9aOl9zufLL7/kxRdfZNKkSTRu3Jh///vfAAQHBxMfH8+2bda9\nyVvR5V5Kq53UDGoSXuwZLvZgbu/FJZ7d4ljHD8c6flaOrHBOdeviVLeu3cRT0UlLMZ+AgAD27t1L\ncnIyJ06coO79H7YpU6Zw6tQpWrVqZeMI7Y/hrJZc6dnpFpvhUp4qzjYIwlqkpWiGh4eH0fHzzz9v\no0js36Am4Sw9+VOBGS2WmuFS3qr07BZLMFocoq6Z86Wp675bp+6v/Wid+/uSFEWZ9QkMM7pMNuyN\nFlWEvazpaAFy+WxhBw4cICgoCE9PT3r06EFiYqLZslu3bsXdvWrPMxWVRGiUPjFWAtJStCCtVktY\nWBgRERG8+OKLjB8/nvHjx/Pjjz8WKJuSksKYMWNsEKUQVmBuTUfDKX8ludzNyYapbjAp362MKBXU\naATj/i5dnMUgLUUL2rVrF2q1mpEjR6LRaJgyZQpr1qwhKyurQNn33nuP/v37y7hHIeyMJEULOnz4\nMEFBQcpx3bp1UavVnD592qhcTEwMx48fZ/Toojb0EaKqUsEjJoZzObuDTyOrfrJcPltQcnIyGo3G\n6JxGo+Hu3bvKcVpaGm+88QarVq0qsr6oqCjleWhoKKGhoZYKVQj75uAI/VcUPP9hasFz90VHRxMd\nHV3mj5akaEEajabAvOikpCSjLVIjIiIYOHAgDRs25Ny5c4XWZ5gUhRCFy99wmDx5cqnqkctnCwoO\nDiYuLk45jo+PJz09nYYN8/Yr3rFjB1OnTkWtVvPII4+QkZGBu7s7hw8ftkXIVtdrTQ8GrHuBNWeK\nbhlXNMtjL7D92DVbhyEsTFqKFhQSEkJ6ejrz58+nf//+RERE0Lt3b5ydnZUy+/btU55fuHCBJk2a\nVLrdAdVOaqOB27mzWyralL/iuAOcNvNaWTbEErYjLUULcnJyYuXKlXz11Vf4+/tz/fp1Zs+ezaJF\ni4xai7l0Op3RlgeVxaAm4QWm/lWE2S2W3hdGpgxWTLKeop2qSOspFsbe11o0XE9xbqsHObD0iMWn\n+Y1eO8Si9VUkFXE9Rbl8FuK+0YfirbJh1gJTG3NZmLuLIyM7BxLeIcDqn1XZyeWzqNLsaZ+YskjL\n1PLt72dsHUalIElRVGn2toFWWdjL7ogVnVw+iyotvENAqS85Z244wYM+7rwYUq/Aa4a919a+p2hy\n32xRatJSFKKCuf73DTJSMixaZ0ZKBtf/vmHROq1h8//FcGKbuUFQliFJUYhSeunx+jzTok65f+7u\n7w9yO/5u0QVLIPHiXWIXHLRondZwflc8cSuPW/Uz5PJZlJv8i89WxH1cDPl6u9k6BGEF0lIUVmVq\n/5ZcFXUfF1G5yeBtO1VZBm+vObPK5P4thuxxUHdZFXeaoCUsCK6lPH/5sP3fF7SUojqwZPC2sEv5\n92/JVdn3cXF2c5INsCoouXwWwgraDGpp8bnUonzI/5qwueTMZLxcvGwdhkUF9WlqtdVx1ry3mZDh\nrfB7xBcwnkZY2jGRV48nsHfRIXp/+qxFYsxl6bnPc3svxvsBLwZ+07vMdZkjLUVhcyv+Xm7rEIRQ\nSFIUooLxb1kbN42rRet007jyQEs/i9ZpDU5ujnjX1RRdsCyfYdXahRAW125wsMXrrF7Xm3bhQUUX\ntLFXlg+y+mdIUhQ2t/af1az9Z7XV6q/og8RF+ZLLZ2EThQ3qtjQZJC5KQpKisAlTWxZYU0XYDkHY\nB7l8FjaRO6h7zZlV3EpP5JUWI63yOZV9kLiwPGkpCiGEAWkpCpvyctGQUwnmeIvKQ5KisKmnHnra\n1iEIYUQun4UQwoAkRSEsZO6R/3I3o/AVsa+nXmPRsYXlE5CN7b26x+J1TomdzP8ubLN4vYYkKQph\nIfuu7eGe9l6hZVKyUjiYYNtl/2+nZjJn6ymrf058StkXgMhv3wkHDsVfs3i9hiQpClHFpGdms+2o\ndROLtaTd9SMlzbpb0kpSFEIIA7IdgZ2qLNsR2FpVGLydcOhN5blvq9k2jKSgb8YeUZ6PmdPSonUX\ntY2FbEcghAlqJ3WVmuJnmCDtQT/DiUqHLFx5HwvXd59cPotKrbznWNuCyiHT1iFUKnL5bKfk8rni\nGbnlZaY98Sm13WubLfPPnTN8dWg2X3S23GXuT7vO8+3vZ0jL1FqszoogdnLhWyfI5bMQVVR4hwDC\nOwQUu/yV22mMXbif1W91tF5QwMrTvxAS+pZybIk9Woa/OYuWDbRMGP9umesyR1qKdkpaihXPkRtH\naFy9Ea5ObmbLpGalciHpPE1rNCvHyIyVV1K8mnqVnEaPWq3+opJsaX+HJCnaKUmKwlrSMrI5dOE2\njzeqZfXPutKoCbrUVKvUba2kKB0tQlQx7q5O5ZIQAbzenoDKw6NcPstSpKVoYQcOHGDEiBH8888/\ndOrUiR9//BEfHx+jMj/++CMff/wx169fp0uXLnzzzTfUqVPHqIy0FIUo6NaIV3B/8UXU3Yren1pa\ninZAq9USFhbGuHHjuHLlCr6+vowfP96oTFxcHOPHj+eXX37h6tWr+Pv7M3r0aBtFLITIT1qKFvTH\nH3/w6quvcvLkSQAuXbrEI488QmJiIs7OzgDMmDGDM2fOMG/ePADOnj1Lq1atuHvXeHUVaSkKUVDa\n6jU4N2+Gc8OGRZaVITl24PDhwwQF5e2dW7duXdRqNadPn6Zp06YADBgwAJVKpZQ5cuQItWubH9cm\nhMjj3tdK01gMSFK0oOTkZDQajdE5jUZj1AqsV6+e8nzRokWMHz+eOXPmlFuMQojCSVK0II1GQ1JS\nktG5pKQkvL29jc5dunSJoUOHEh8fz/Lly+natavJ+qKiopTnoaGhhIaGWjpkISqN6OhooqOjy1yP\n3FO0oJ07dzJy5EjlnmJ8fDxNmzY1uqeYmJhImzZt6N+/P1OnTsXFxcVkXXJPUYiykd5nOxASEkJ6\nejrz58/nzp07RERE0Lt3byUhAsyZM4cOHTowY8YMswlRCGE7khQtyMnJiZUrV/LVV1/h7+/P9evX\nmT17NosWLaLh/d6y/fv3s3z5cpydnZWHJEch7IdcPtspuXwWomzk8lkIISxAkqIQQhiQpCiEEAYk\nKQohhAFJikIIYUCSohBCGJCkKIQQBiQpCiGEAUmKQghhQJKiEEIYkKQohBAGJCkKIYQBSYpCCGFA\nkqIQQhiQpCiEEAYkKQohhAFJikIIYUCSohBCGJCkKIQQBiQpCiGEAUmKQghhQJKiEEIYkKQohBAG\nJCkKIYQBSYpCCGFAkqIQQhiQpCiEEAYkKQohhAFJikIIYUCSohBCGJCkKIQQBiQpCiGEAUmKQghh\nQJKiEEIYkKQohBAGJCkKIYQBSYoWduDAAYKCgvD09KRHjx4kJiYWKLN582YCAwPx8vLipZdeIiMj\nwwaRlk10dLStQyiUPcdnz7GBxCdJ0YK0Wi1hYWGMGzeOK1eu4Ovry/jx443KJCYmEh4ezpdffsmF\nCxdISEhg6tSpNoq49Kr6L05Z2HNsIPFJUrSgXbt2oVarGTlyJBqNhilTprBmzRqysrKUMuvXr+fR\nRx+lR48e+Pj4EBERwfLly20YtRDCkCRFCzp8+DBBQUHKcd26dVGr1Zw+fdpsmVatWnHmzBnS0tLK\nNVYhhGkqnU6ns3UQlcX06dM5d+4c8+fPV84FBgby448/0r59ewBGjRpFvXr1+PDDD5Uyzs7OXLx4\nkTp16ijnVCpV+QUuRCVVmvTmZIU4qiyNRkNSUpLRuaSkJLy9vc2WSU1NRavVGpWB0v1nCiHKTi6f\nLSg4OJi4uDjlOD4+nvT0dBo2bGi2zMGDB2nQoAHu7u7lGqsQwjRJihYUEhJCeno68+fP586dO0RE\nRNC7d2+cnZ2VMt27d2ffvn2sX7+eW7duMWXKFAYMGGDDqIUQhiQpWpCTkxMrV67kq6++wt/fn+vX\nrzN79mwWLVqktBZ9fHxYvHgx48ePp169evj6+hIREWHjyIUQCp2wK/v379e1bNlS5+Hhoevevbvu\n1q1btg7JpO7du+tiY2NtHUYBmzZt0jVv3lzn7u6u69y5s+7kyZO2DkmxZs0aXUBAgM7T01P39NNP\n686ePWvrkEw6evSozsXFRXf9+nVbh2KkY8eOOjc3N+XxyiuvWOVzpKVoR4oz+NvWtFotv/76K1u3\nbrW7HvLr16/z4osv8u9//5vExESeeeYZ+vfvb+uwAEhISGDo0KHMnz+f69ev07JlS0aNGmXrsArQ\narWMGjWK7OxsW4dSwLlz50hNTSU9PZ309HS+/fZb63yQVVKtKJUdO3boGjdurBzHx8frPD09dZmZ\nmTaMylizZs10Tk5OOgcHB92ePXtsHY6RZcuW6Z555hnlOCsrS+fg4KC7ffu2DaPSW7lypa5r167K\n8bFjx3Q1atSwYUSmzZgxQ/f+++/rVCqVXbUU09LSdIGBgeXyWdJStCPFGfxta0ePHiUrK4uHHnrI\n1qEUEBoaypw5c5Tjo0eP4urqikajsWFUemFhYWzatAmArKwsFi1apIxdtRenTp1i8eLFREVF2TqU\nAs6ePUtmZiZt2rShRo0a9O/fn4SEBKt8liRFO5KcnFzgF1ij0XD37l0bRVSx1K5dW+nQ2rBhA927\nd2fSpEk4ONjHj7lKpWLDhg2o1WpmzpzJ0KFDbR2SIicnh5EjR/LVV1/h6upq63AKuH37Ng0bNmTR\nokWcPXsWT09Phg0bZpXPksHbdqQ4g79F4e7cucOoUaPYsWMHs2bNIjw83NYhGenevTuZmZmsWrWK\nIUOG0LFjR2rXrm3rsJgzZw6NGzemY8eOysQBnR1NIHjiiSfYtm2bcvzZZ5/h6+tLamoqHh4eFv0s\n+/gTKoDiDf4W5mVmZvLMM8/g5ubG6dOn7Sohzp07l9mzZwPg4ODACy+8gI+PD1euXLFxZHoxMTH8\n+OOPqNVqZSJBQEAAa9eutXFkeuvXr+ePP/5QjrOzs3F0dLRKq1aSoh0pzuBvYd7y5ctxc3Nj0aJF\neHl52TocIw899BCff/45R48eJSMjg++++w5HR0eaNm1q69AA+OWXX7h3757Sswtw4cIFevfubePI\n9BISEhgzZgznzp0jOTmZ999/n7CwMJycLH+xK0nRjpgb/C2KZ//+/ezatQtnZ2fl4eLiQnx8vK1D\n47nnnmPMmDE8++yz1KpVi8WLF7Nu3Tq7vH8H9rcgycsvv8zzzz9Pu3bteOCBB0hNTeWbb76xymfJ\nKjlCCGFAWopCCGFAkqIQQhiQpCiEEAYkKQohhAFJiqJKGzduHB07djQ6l56ezkMPPcR///tfG0Ul\nbEmSoqjSpk2bxunTp1m5cqVy7vPPP8fPz4/Ro0fbMDJhK5IURZWm0Wj47LPPeO+998jKyuLq1avM\nnDmTOXPm0LNnT9zd3QkMDCQ2NlZ5z7vvvkvt2rXx8PCgb9++pKSkADB8+HAmTpxIixYtmDlzJvv3\n76dVq1ao1WpatWrFkSNHbPVlihKQpCiqvCFDhlC3bl1mz57NRx99xKBBg/jggw8IDg7m5s2bfPnl\nl7zwwgtkZ2ezefNmYmJiOHHiBJcuXeLKlSssXLhQqeunn37i559/5u233+a1115j0qRJpKSkMGzY\nMCZMmGC7L1IUmywIIQT6BRGefPJJXF1dOXjwIM2aNWPz5s04ODjQo0cPGjZsyI4dOwgODmbFihV4\ne3sTHx+Ph4cHiYmJgH4WyODBg2nSpAkAKSkpHDp0iODgYN58802GDx9uw69QFJe0FIUAmjVrxtNP\nP83o0aOJj4/n7t27eHh4oFarUavV7N69m0uXLnHv3j2GDBlC48aNeeONN7h9+7ZRPYYrGi1dupS4\nuDiCgoJo1qwZGzduLO8vS5SCtBSFuM/DwwN3d3dq1qxJzZo1uX79uvLa6dOnqVWrFm+88QadO3dm\nypQpAGbX9EtPT+fKlSusXbsWrVbLqlWrGDx4MH379sXNza1cvh5ROtJSFCKfhx9+GH9/f77++msy\nMjLYtWsXjz/+OHfv3iUjI4P09HSysrLYsmULv/32G/fu3QOM1x9UqVSEh4ezfft2VCoVHh4eVK9e\nXRJiBSBJUQgTVqxYwS+//EL16tUZPHgw//nPf6hXrx4ffvgh69evp3r16nz//ffMnDmTr7/+mr/+\n+guVSqWsLuPm5sbChQt5/fXX8fT05L333mPp0qU2/qpEccgqOUIIYUBaikIIYUCSohBCGJCkKIQQ\nBiQpCiGEAUmKQghhQJKiEEIYkKQohBAGJCkKIYQBSYpCCGFAFoSwU/a2GbmwLWtOPKvMP2ul+b5J\nS9GO6XQ6qz8iIyPL5XPK87Mq2+dUlp81nU7HsGHDyu2zSqvSJ8U+ffrw1ltvKccXL17EwcHBaFOi\n6OhoXF1d+fjjj3F2dsbZ2RlHR0ecnJyU46lTp5qsf86cOQUm+h87dgxXV1cSEhIASEtLY8SIEWg0\nGmrXrs2IESNIS0vj2LFjvP7661b4qoWwTwEBAbYOoUiVPil27tzZaH+Nbdu2Ua1aNbZt26ac2717\nNyEhIUyaNImsrCyysrIYOnQo06dPV44/+uijAnUnJyezYMECBg4cqJzTarWMGjWK7Oxs5dzs2bO5\nfPkyly9f5tixY1y6dIkvvviCZs2aceHCBU6ePGmlr14IUVKVPil26tSJQ4cOkZWVBcD//vc//vWv\nfxETE6OUiY2NpXPnzgXeW1QT/Pvvv6d79+5G92RmzZpFx44djd7r4OCAVqtFq9WiUqnIyspCo9EA\n+s2OPvvsszJ9jWURGhpa6T6rsn1Oechd9szaj8mTJxdZplq1arb9XujK66aFjeTk5FCrVi02bNjA\no48+ygMPPEBsbCx9+/Zl/vz5tGnTBj8/P5YtW2b0Q/7yyy/TpEkT3nvvPbN1P/nkk0yZMkV536lT\np+jfvz979+5FrVZz7do1fH19yczMJCQkhMOHDwPQuHFjDh06hJubG3fv3qVBgwYkJCTg4JD3N0ql\nUpXb/SRh36z9s6BSqYiKirJa/YbOnz9f5CV0VFSURb7e0n7fKn1L0cHBgY4dO7J7927++usvPD09\nqVevHk899RRbt27l7Nmz3L17lw4dOpSoXq1Wy969ewkODgb0yXfkyJF89dVXuLq6GpWdNGkSGo2G\nq1evEh8fj7e3NzNmzAD0e3rUqFFDtr8UopSio6MtWl+lT4qgv8yJjY1l+/btdO3aFYCnn36a//3v\nf+zZs4fHHnsMFxeXEtV569YttFqt0tSfM2cOjRs3Nrp0zv133bp1fPDBB9SuXRt/f3/eeecdfvvt\nN6WuWrVqceXKFUt8qULYtfPnz1u8TksnxSoxTjE0NJRZs2aRkpLCK6+8AsATTzzBgAEDqF+/Pl26\ndClxnSqVyuhyNyYmhnXr1rF48WLlXP369fnpp5/QarVkZGQo5x0cHPDy8lKOc3JyTH6G4SVNaGho\npbqHJcyLjo62+C+6vTh58mSxLmknTZpU7Dpv3LhRlpAKqBJJsWXLlqSkpBATE8OSJUsA/c5twcHB\nLF68mE2bNpW4zho1agD6vX09PT355ZdfjF53cHDg/Pnz+Pr6Ehsby//93//RunVr3Nzc+OabbwgL\nC1PKJiQk8MADDxT4jPK6zyPsS/4/gJMnT7ZdMFZg2Jgwx8mp+KnJ0oPPq0RSVKlUdOzYkatXrxrt\ny/vUU0+xd+9eQkJCzL7PHAcHBx577DEOHTrEk08+Weh7P/74Y9566y2aNWuGSqVizJgxyvjElJQU\nbt68ScuWLUv75QlRYTRp0oROnToVWub3338vUUvR0o2HKpEUAVavXl3gXEREBBERESbLL1iwoMg6\nBwwYwIYNG0wmRa1Wqzx3cXFhzpw5zJkzp0C5mJgYevXqVay/nkII65PfxDIYMWIEGzZsMBqoXVIL\nFy40mnEjRGVmjX2vLX2vXZJiGbi7u/Pmm28q9ylL6syZM3h5edGqVSsLRyaEffLz87N4nZZOipV+\n8HZFJYO3Ra7yGLxtT7y9vblz506Z6ynt963K3FMUQpgnf4DzVNqkuHDhQpYvX87GjRuJjo5mzJgx\nnDhxwqKfodPp6NmzJytWrKB169ZcvHjR6PWcnBwyMjI4cOAAo0aN4vjx49SrV48PPviAoUOH8v77\n79OnTx+zvd/CPsTGxjJv3pfFKjtkyEi6dHnKyhFVXNHR0XY/3rbSJsXy8NNPP9G2bVs8PDz4+++/\njV579913lel+gwcP5p133mH48OEcPHiQLl260KVLF8aNG0d4eLjR4hTC/sTExHD16ib69/cutNyG\nDXfZvPkBSYoVXIXvaNmyZQuBgYG4u7vTr18/kpKS2LNnDyNHjmTLli08//zzyr2Ft956Cy8vL5o1\na0ZcXJxSx6effoqvry/e3t5ERkYq5wMCAvjiiy/w8/Nj3759BT571qxZDBs2rMD52NhYNm/erAy6\ndXBwUGa05OTk4OTkhJubG/7+/nh6evLHH39Y+tsiLKxlS3dGjKhZ6KNdOw9bh1lq5bFCjo+Pt923\nEqGCtxTPnz/Piy++yNq1awkJCWH8+PEMGzaM1atX891337F8+XJ+++03oqOjOXXqFCNHjuTWrVtM\nmzaNt956i+3bt7NkyRKWLVvGgQMHUKlU9O7dm7Zt2yrJdNOmTZw8ebLAckZnzpzh5s2bPPzwwwXi\nevfdd5k+fTqOjo4AfPvtt3Tq1Ilx48YB+ilMNWvWBKB79+4sX77c5FjHqmzHjo1s2TIfJ6cssrOd\n6dr1VTp2fM7WYVVaOl0bq3+GSnXA6p9hCRW6pbhkyRL69etHp06dcHV1ZcaMGWzYsIFbt24VWJJc\no9Hwzjvv4OLiQteuXblw4QIAixYtYtKkSTz44IPUrVuXcePG8fPPPyvvmzBhgsn13Xbv3q2skGNo\n69at3L17l549ewKQmZnJoEGDmDFjBmlpaWzYsIHPP/+cM2fOANCqVSt27txp0e9LRbdjx0Y2b57E\n1KkXiYq6ytSpF9m8eRI7dmy0dWiiCNHRyUW8Hl2m18tDhU6Kly9fpn79+sqxRqOhRo0aJlecMRwf\n5ejoqCzCcPHiRcLDw1Gr1ajVal577TUuX76slDW34OXVq1eV1p6hOXPmMGbMGOU4Li6OzMxM3nrr\nLdzc3OjWrRuhoaFs3rwZ0K+QY/h5ArZsmc+0aca9odOm6di6dQTQ9v5jnpl3zzMo09YK5UvKEvFU\nHEUlxaLfH22ZQMqgQl8+161bl3/++Uc5TkpK4tatW/j7+3PggHFT3dxYrFq1avHvf/+bbt26AXD7\n9m0SExOL/GwHB4cCU/Nu3rzJxo0bmTcv7wdeq9UWmPHi4OCgrLxtboUcqLqr5Dg5ZZk87+hoX+Pp\nrCU6OrnMycUWbt3KZs2aO5w7l2G2zIIFC8xOoW3YsKG1QiuRCp0Uw8PDCQ4OZvjw4Tz66KNMnDiR\n7t274+Pjg6OjI6mpqUXW0a9fPz7//HNat26Ng4MDQ4YMITQ0lPfff7/Q9/n5+bF7926jc1u3biUg\nIABfX1/lXJs2bVCr1cyYMYM33niDuLg49uzZw7fffguYXyEHqu4qOdnZzibPa7VVYyxdaKgXoaF5\nS8tNnnzVhtEUn6urivr1XenSRWPy9UWLEgtdpq9WrVrs3bvXWuEVW4VOigEBASxbtozhw4dz9epV\nunbtyg8//ADAY489xr/+9S9eeOEFxo0bV6ClmHs8duxYLly4QIsWLUhLS2PQoEG88847RX52+/bt\njXqqAfbu3Vugw8TZ2ZkNGzbw2muvMWnSJAICAli6dKmSOP/66y8ef/zxUn8PKqOuXV/lww8nGV1C\nf/CBim7dvgeK6mwZdf9RXCUtX1KWiKditJA9PR0JClIzbFgNk68PH36eevXqFXrFI0nRArp162Z0\nCZ2rUaNGXL2a9xf2+PHjyvOQkBDOnj0L6NdtmzVrFrNmzSpQx7lz58x+boMGDahevTp///03jRs3\nBrAVNxoAACAASURBVODf//63ybLNmzc325kSHR3Na6+9ZvZzqqLcXuaIiG9xdMxEq3WhW7eR0vss\nykWFT4q29N577zF//nw+//zzUr3/5s2bXLhwoVQrf1d2HTs+Z1dJcMOGFG7cuF5omSNHUnjqqYrR\nqrMWw8t+06+Hlun18iALQpTR888/z08//WS0vUBxRUZGEhoaanJ7VVkQwn5cunSJLVu2FKtsp06d\naNCggUU/vzwWhCivcYrl+TNd2u+bJEU7JUlR5Kosq+RUr65h1aq15dYalC1OhRClljvZwZqPxMS7\ntv4yi0VainZKWorWc/78eWVGU2EcHR0JCQkp0SZK1lA+l8+V72et1F+XroKrV6+ebs+ePYWWOXfu\nnE6lUumcnJx0Tk5OOhcXF12rVq10mzZtKtZnqFQq3fXr102+FhcXp3v99deNzqWlpekaNmyoW7Zs\nmXLus88+09WuXVvn5eWle+aZZ3SnTp3SZWZm6p599lmdVqstUG8l+K+xW12eeFTX8kEPXcfG3oU+\nNO4uuh07dtg6XKv/LFTWn7XSfl0Vvve5uPdDXF1dSU9PByA5OZnvv/+ePn36sHfvXlq0aFHqz3/3\n3Xf55ptvjM599NFHnD9/Xoltz549zJo1i9jYWOrUqcPkyZMZO3YsW7ZsITQ0lO+++45XX3211DGI\nktFmZfBll1RCAwovF7rM22gDssrMWvcVq3t7kXgnSTmW9RTLmYODA998840yqHrmzJkMHjy4QDkv\nLy/Gjx/P/v37mTJlCitWrAD0S4jNmjWLjIwM/t//+39F7rcbFxdHWlqa0Uo5sbGx7Nu3j/bt2xs1\n3XU6HVlZ+ulrmZmZyjS/YcOG0blz5wqRFHdsXc+Wn2bjpMsgW+VK1/A36fhMD1uHJSxAF1l0mdJQ\nTa540xUrVVIE2L59O2fPnmXLli0MHjyY/v37my3btWtXJkyYAFDoEmLm/Pzzzzz1VN6CohkZGYwZ\nM4alS5cyZswY5a/vY489RlhYmDLI283NTZmbXadOHby8vNi/fz9t29rv5P8dW9ez+evxTGuVN1D+\nw6/1zyUxVk3R5ymytZ1fcVqJtm5NVrqk+NFHH+Hu7k6vXr3IzMwkISHBbFlfX1+Sk/V/yQyXEAOU\nJcQKS4qxsbG88cYbyvHkyZPp27cvTZo0MSq3adMmVq9ezcGDB2nYsCETJkxg7NixbN++HYDWrVuz\nc+dOu06KW36abZQQAaa1+oeIqT3p+Of9E6GREBpV8M3RURBtotVtq/Ip1wqeK4y9xW8nSpMUi1Wv\nJEXLql27NqC/lHZycip0FZrr168rizHkLiGW27rT6XRFzkk2XD7s0KFDbNiwwWiF7tzL599++43h\nw4cr6y9OmzYNX19fUlNT8fDwMLt8mD2tkuOkM73ySRVZuKZcRZ/XPzgfrU+Ydurn4/fjLILhz+2d\nO3fMLseXKyAgoExxlVWlS4olsWHDBtq3bw+UbgkxlUqlLB+2c+dOTpw4odwrzMzMZNeuXezZs0fZ\nwCpXbsJWq9WA+eXD7GmVnGyVq8nzVWThmnIVGnC/BRYaCqFRRd7btpX2dWFIy8LLdP7B+Of48OHD\nJhdnNrR+/XoLRFd6VTIpJicns2DBAtatW0dsbCxQuiXE/Pz8uHXrFqC/3M7dbgCgc+fOjBkzhhdf\nfJFNmzYxbNgwBg0aRPPmzZkxYwa9evVSEmpCQgLNmze34ldcdl3D3+TDr/8xuoT+4FADun30JRR1\nTzE0qmSXgdYu7+kHFD6PuVzjKWl5O1FXU7zLZ8OWYnHvKdpSpUqK5pYHA30niLOzfp0+JycngoKC\n2Lhxo5KMCltCzNxwhfbt23P48OFC7zuCfiWfyMhI+vbty61bt+jWrZuyniLolw8bNcqay1eVXW5n\nSsSyr3DU3kPr6Ea3N8ZJJ4uodGRGSxnkJrP8i82WxO3btwkJCeHkyZNGybeyzjKwB6EhwfSpFkcr\nv8LLjd/uwReL1tl8XF25zGgpxZCc4nS0qCZjFHtxOlEs1dFS2u9bpWoplrcWLVpQo0YNjh49WurL\n38WLFxsN3xHW1/mZ51i9Rcfq24WX83nIlXr16pVPUBWQNXqewfbLh0lLsYxOnjzJrFmzjPZlKa7s\n7Gx69OjBunXrlEv7XNJSFLkq8io5+We0lCdZOqySkaQocsmCEKUjS4cJIeyerXuWi8MqSfH8+fM4\nODjw0UcfGZ1fuHAhzz1nP0vMW8L69ev55JNPANi9ezdNmzbFw8OD9u3bc/DgwQLlv/32Wx555BFA\nvx1Bv379yjXe0tq9ezcP+dejbp0Hi3x8OPFDW4crSkilUpX5Ud27uq2/DIuwWkeLg4MDs2fPZtiw\nYXazn6ul5eTkEBkZSXR0NJmZmfTt25eZM2cSFhbG3LlzGTBgAKdPn1bKX758mffff1/Zya9mzZrU\nqlWLrVu38swzz9jqyyiWS5cuUcOpFi80DC+03F8JcRw9cqycohKW8t9eP5a5jtd+fanIMrbuRCkO\nq10+Ozs789prrxnNDTak1Wp59913qVatGn5+fnz99dd5QTk4MHfuXPz8/PDz82PJkiWAfqzhkCFD\n8Pb2pnbt2ka7523cuJHAwEDUajVhYWGkpaUB+v+EqVOn0rx5c7y8vJQFIABmzJiBn58fnp6eDB8+\nXFnF5syZMzz++OOo1WqCg4ONEpuh3377jaZNm+Lp6cnRo0fx9vZm8ODBqNVqRo0axT///GO09/To\n0aN59dVXje5zvPLKK3z66acl/fbahJuzGzU9fAt9aFxN7/krREVh1XuKkZGRnDhxgl9++aXAa59+\n+imxsbGcO3eO2NhYZs2axdq1a5XXc1e7+e9//8uoUaPIyMhg/vz5ZGRkcOPGDf744w+mT5/OpUuX\nOH/+PC+99BKLFi0iISGB6tWrExERodS1ZMkSNm3axJEjR1iwYAE7duzg6NGjzJ07l2PHjnHx4kXO\nnj3LihUryMnJoUePHgwZMoTExETGjh3LoEGDTH59P//8s7ITX+vWrTlx4gSgb0EuXLiQ5s2b4+Hh\nAegXnPDx8VGmEeZq27Ytx44d48aNG2X7ZlvQts3bGD1gDKNeGMPoAWPYtnmbrUMSduDUzRNlriP/\nPUV7vMdo1aTo4eHBl19+yYQJE5SWW66FCxcyffp0qlevTkBAAO+++y7fffed8rqp1W5SUlK4evUq\n+/fv5+GHH+bMmTPUqVOHpUuXEh4eTocOHfDy8iIiIoKff/4Z0N8ref3116lbty7169enVatWXLx4\nkZSUFNLS0vjzzz9xdnZm06ZN9OvXjz179uDq6sqYMWNQq9W8+uqrJCYmKvtEG4qNjTWax+ng4MCx\nY8dwcXHhjTfeYPjw4QBcu3aN6dOn88UXXxToDVOpVAQFBfHnn39iD7Zt3sa30xbQ+t7jtMl6nNb3\nHv//7Z1nWJTH2sf/u4D07iISVGoQFcUYFEiMWLCABRExggp4SIREiJIYOUcxWGMlFrCBmGAhJnYp\nAhFByYuKXSwokSZiAZHOLmXeDxyeswtLW1ia87uu/fCUmbnn2dl755m55z8I2XgY9+/e72rTKF3M\n04L2O8WGdEenKPbg7dmzZ+PQoUNYu3YthgwZwpzPzc0VEGfV09PDixcvmOOGajeEEHh7e6OgoADO\nzs4oLCzE/Pnz8csvvyA7OxuHDh1CcHCwQNn1qsn1eQF1Cty1tbUwNzeHn58f/P394ejoiIkTJyIo\nKAjZ2dl49OgRI9YA1PX8cnJyBOwFBFVy6hk6dCiqq6uRkJCA6dOnw8rKChs3boSfnx/U1NSEPqOm\nVHK6gj9DT2G8quBk2HjVaThweBdYNFjhg+ZW7g0k5yQ1e4+urm6b8hwxYkR7TBILnbKiJTAwEKam\npliyZAlzTltbG8+fP8dHH30EAHj+/DmjZdgUKSkp+PHHH7Ft2zY8ffoUDg4OOHr0KDgcDpYvX44t\nW7YAqAuKTk1NhYSEhNB8CCF4/PgxrKys8M0336CgoADe3t5YvXo1Fi9ejFGjRuH69evM/Q8fPmzk\nEAFBlZzz588jOTmZmYm2srLC8OHDkZmZiStXriAqKgru7u6ora1FdXU15OTkkJubC1VVVdTW1goN\noO0K6TDShPo+i8UGel8oW7ckISGhW/aghvYbDiudSU1eX33pe0YjtLWIsuhB3HSKU9TR0cGPP/6I\ntWvXMkrVLi4uWL16Nc6ePYvi4mLs2LEDO3bsaDIPQgiOHDkCSUlJ7N69G8rKyiCEoH///oxC9oIF\nC2BoaIgNGzYgJSUFMTExTNqG3Lp1C7t370ZUVBTk5eUhISGBfv36wdLSErm5uThz5gxsbW1x8eJF\nLFmyBOnp6Y3y0NTUxLt376ClpQU9PT0sWrQI06dPh5mZGWJjY5GWlgZLS0uB8cLExER4eHgw449A\nnUpOva4jP10hHcaSAFDV+DwhtbSn2Ek0/APsLtJhMpJ1E23N0VJPseG6Zmlp4ZJ0XYnYWnnDns+K\nFSsEelu+vr4YNWoUdHR0YGZmBi8vL8yaNUto2vo4qPXr1yMzMxN9+/bFsGHDMH36dNja2sLU1BTr\n1q2DnZ0dVFRUkJycjMOHDzdpC4vFwpdffgkzMzMYGxujX79+KC0txerVqyEjI4MzZ85g48aNUFJS\ngq+vL06cOMFMmPBjYWGBO3fuAACGDRuGgIAALFq0CGpqali3bh1OnjwJTU1B1QFCSCN7UlNTYWlp\n2dpHK1bmLp6Dy4XRAufiC6Nh6zYVfeSkmkhFofQe6DK/dhAREYFjx44hPDxc5DwePnwIDw8PXL16\nVeB8Vy69+ivmL5w8fBq11QRsSRYc3OxRWFyIgH/vgutQj2bT3np5Ha81cnAu8mwnWdv76Yxlfq2J\nU3ya/xgf9zVu8rrH+YVttlOcWw9QlZwuYPr06fD390dBQQHU1dVFyiM0NBTff/99B1vWPiZNmYRJ\nUwTHjs6fP487ObeQWfhjs2kreRWYojO12XsoPZPmHKKodMdgbtpTbCeXLl1CUlISs61qW3j//j0W\nLlyICxcuNLrW3Rbp19bWIjs7u1U2aWhoCB1uoIhGT1HJUVFSQWFR83psnbkpFVXJ6WV0N6dI6Tp6\nk0oOdYoUkaFOkVJPb3KKnQkdU2wDmZmZMDY2RkVFRaNrtbW18PX1xW+//QZCCJydnbF9+3ahMY+E\nEEyfPh1//vknpKSksGzZMhw9ehSysrJYtGgRtm7dCgAoKCjAwoULceXKFRgYGCA0NBSffPIJfH19\nYWdnB3Nzc7HXmZ/Q0FDk5OS0eJ+cnBx8fHyajPek9B5EeYVWUlFCUWGRGKzpWj5Ip9gcgYGBiI+P\nx+PHj8HlcjFnzhxmqWJDjh8/jk8//RRycnLYtWsX7ty5g7S0NHC5XEybNg0jRoyAs7MzvL29oaWl\nhZcvXyI8PBz29vbIyMiAl5cXnJyckJiY2Kl19PnRB+qfq0FSunlnl3E+Cy4uLoyqD6X3MuOMTZvT\nXJgd1eY0Xb3RfWugTrEBFy9ehI+PD7Mkz93dHefOnRPqFAMCApg11vHx8fDy8mLiEmfMmIG7d+/C\nwcEB58+fx7Nnz6CkpIQlS5Zg27ZtSE5OhqWlJRQUFHD16lWMHTu28ypJAAM7PUirNB84+zLuVScZ\nRKF0H+gShQZs3rwZtrb/27bz/v37Amun60lPT0d+fj4TkH7mzBlGTScvLw9RUVEwNzfH06dPIScn\nJxDE/cknnzBB3zY2Njhx4oQ4q0ShiEx+akGH5mdlZdUtlzDyQ51iA4YPHw5lZWVUVFRg5cqVOHLk\niNA4wuTk5EYKOQAwf/58fPTRR+ByuRg7diyKi4uhpCSoMaikpISiorqxmJEjRyIpqflF9h1NdU11\np5ZH6bkUpL7r8Dy7u1Okr89CuHr1KlxdXaGjo4Nr164JVQ4XppADAOHh4di7dy+WLl0Kd3d3bNq0\nCcXFgruZFRcXQ0VFBUDXKORUVwtZ3EyhNODl/+Xh6YlnePqHcJFl4H+dgdbi5OQEAwOD9pomVqhT\nbEBycjJmzZqFPXv2wNnZucn72Gy2QIOws7PDtm3bYGhoCFVVVSxatAgrVqzAxx9/jPLycuTl5aF/\n//4AgDt37mDZsmUA6ma7m0JcKjm9L/iid9FdVHL6W2jiY0cDfDxP+HYiEXOiUV3dtreOhIQEXLly\npSPMExsfrFMkhCArK0sgjklDQwOrV6/Gli1bmnWIQJ1CTnJyMnMsLS2NjRs3YufOneDxeAgMDIS1\ntTX69OmDmTNnws/PDzt27EB4eDiqqqpgYWEBoGmFHEB8Kjni2+WX0hF0F5UcFosFsFhgsZtuMW3t\nKU6YMKHbO8UPdkyRx+NBV1cXenp60NPTg76+PqKjo3Hz5k188803kJKSYj7CNpWysLDA3bt3meNd\nu3bh/fv3GDBgAEaOHIlBgwZhw4YNAIDdu3fjxYsX0NLSwr59+3Dq1CkmLuzBgwf47LPPOqfS/0VS\nkqrdUChN8UH2FHV0dJp8bW3tlqP6+vpQVVVFWloajIyMoKmpibNnhSvDqKur4+LFi0KvJSQkwMOj\neeWZjkZS4oP82ikioD5MuFq8qNA4xV7OypUrERwcjO3bt4uUPj8/H1lZWczmV53JP2efQ0K2+a+f\nV8HrJGso3ZW+w0RTf2qO7u4U6drndjJjxgwcP34cioqKbU77008/wcrKCuPHj290TZzrUUNCQlq1\nzE9WVhYrVqygy/y6mO6qktPdl/lRQYheRm9dpE9pO1QQQjRErdcHO9FCoVA6n+4QatQSdEyRIpT3\n798zq26ao0+fPkz8JaXn0tZXaCVlFRS9b15QtqfSo16fMzMz8d133yEpKQlVVVUwNzfH9u3bMXz4\n8GbT+fv74/Xr19i3bx8MDQ1x/PhxmJmZdYhNQUFBUFNTY9Y9A4CtrS3WrFmDMWPGAACMjIyQnZ0t\nkK62thZ37txBYGAg9u7d2yjfrn6lMfjYGG/yC8CWbP5/s+TdG2RmZLS4PS1FdDrj9XnMGuHREU1x\nfd3Ubv/K/UHoKTo6OmLmzJkIDw9HdXU1du7ciSlTpiA7OxtSUk3H3vH/Cz571vSSpbZSUlKCw4cP\nIyUlBQBQU1ODyMhIxMXFCWxPkJaWJpBuxYoVkJaWxpAhQ5CVlYUnT55g8ODBHWZXR1BeWQk95y2Q\nURMeWF7P04NfgcvldpJVlM6iOPM+lHSa72x0Bl0RwtNjxhTLyspw8+ZNfPfdd5CTk4OSkhLWrFmD\nzz77DLm5uUhISMDo0aOxZMkSKCoqwtTUFE+ePAEguK2ojo4Obty4AQCIjIyEvr4+lJWV4ezszIjO\npqSkYPjw4ZCVlcW4ceME9m3mJzQ0FDY2NkzeI0aMwJw5c1BT08SO8gCuXbuGmJgYZpWCq6srtm3b\n1jEPSQjRcfGwd/PCTFdv2Lt5ITqubZuVUz5MirPuiyXfto4pdsUYZI/pKcrLy8PIyAiLFy+Gs7Mz\nzM3NoampiZMnTwKoe7W+desWFixYgKKiIgQGBmLevHm4d++eQD71DiwzMxMuLi6IiIjAsGHDMH/+\nfAQGBsLDwwO2trYICQnBpEmTsGXLFnh4eODUqVONbDp58iTWr1/PHKempgJofkPwFStWYNOmTUyY\ny+TJk+Hp6Yng4OA2L5lqiei4ePgHHgPL1JE55x94DAAwzbrzYyMpPYMabjlqKsvALRLeGainNWFd\nDXnz5k2r03X076G19BinCABxcXHYunUr/Pz88OjRI+jr62P58uXw9PQEACgrK8Pb2xsA4OXlhbVr\n1+Kff/5plA8hBOHh4XBwcGC2AggICEB2djYiIiJgbm6OmTNnAgBWrVoFdXV18Hg89OnTh8mjpqYG\nN27cEJAPa439RUVFmD59OnNOWVkZ6urquH//fpvyag3Bx88IOEQAYJk6wn19CAYk1SnluFvpw318\nY9WSCh6VF/tQKUhNwNu7MXj3+Gqz91laWorVDmVlZTg4OIi1DGH0KKeora2N3bt3AwCKiopw+vRp\neHt7Y9iwYQCAQYMGMfeyWCyoqqri3bt3QmfWcnJyBCYHDA0NYWhoiC1btiA6OhqysrLMtdraWuTl\n5QnkX1BQgJqaGkYCrDUEBQUxDpwfDoeDly9fNnKK7VXJqSbCZxRZbBqM3Z3papUcjVE24JW+g/a4\nBU3ec33dVJF6im1FXKIozdFjnOKFCxfg5+fHiDAoKyvDzc0NERERuHfvHkxMTPDq1f/k87lcLl69\negUdHR2hM1B9+/bFy5cvmePr168jIyMDHA4H9vb2AmrYt2/fhra2tkB6FovVpu59fn4+oqOjcfDg\nwUbXmlqH3d4GIckSPvNGapse86R0Pd1FJUcc9IS1zz1mosXS0hI5OTnYuHEj3r9/j+rqasTGxiIp\nKQmfffYZCCF4/fo1du/ejYqKCmzevBnm5ubgcDgAIOAYWSwW7O3t8ccff+DWrVsoKCjAd999h/Ly\nctjY2CA+Ph5JSUng8Xg4cOAAnJ2dGy11U1evWxNaWlraKvvj4uKgo6MjdBOo5uTD2sNXTrNB7v4h\ncK72zgmE+Lnj2topuLZ2itBXZwCQ7dNj/i8pYkBpUNfPPANds066xzhFdXV1/PXXX7h06RIGDBgA\nNTU1rF+/HmFhYRg5ciSAugmOK1euQE1NDTExMTh69CiAOifY8BXa1NQUW7duhaOjI4yMjDB69Gi4\nurpCU1MTYWFhzCx2SEiI0EkWNpuNMWPGMHuttMSNGzeEbk5VWlqK/Pz8FmMtRWGa9QT4L3WGdk4k\nNLIioZ0TibVeC+gkC6VFxBWO01Yn1xVOsUcFbzdHQkICPD098fjx404rMzAwELm5ufj5559FziMy\nMhInTpxAWFiYwPmuDt7WGqgLlfHfQFql8aZd/GQc98XN5CvdXmK+J0ODt0Xjgwje7m4sXrwYFhYW\nWL9+PSRbWPnRFL/++iv+85//dLBl7WeEiQluxe5q8T4lBdk2TTZRuifX101t0/1KyqJ95z1hTLFX\nOUVRJZBERU5ODt7e3jh27BhcXFzanD49PR2KiorM6393IjryfFebQOlEunuvrzPpNa/PvY2ufn2m\ndB+odJhoUOkwCoUiMvWTka35qDXYx7y30aten1tLZmYmjI2NmbXO/FRVVWHZsmU4evQoZGVlsWjR\nImzdulVoPoQQTJ8+HX/++SekpKRaTLdt2zbU1tZi5cqVAABfX1/Y2dkxq2p6K6HBwfht//4W72Ox\n2Ni0e5fYV0pQGvNCS7vlm/6L9ssXIpdDxxR7IHv37sWdO3eQlpYGLpeLadOmYcSIEUK3PD1+/Dg+\n/fRTyMnJYdeuXc2me/LkCfbs2YOlS5cy6b28vODk5ITExMROq19X8H8JiRic9hSTZGSave9AFQ8P\nHz6kTrELSeZyYSEt3dVmdCn09bkB8fHx8PLygqamJgYNGoQZM2Y0GYsYEBDATLAIS1e/+ubbb7+F\niYmJwAoaAPjoo4+goKCAq1ebX2PaGzCQlISltEyzn35SfVrOiCJWknnilYHr7r1EgPYUG3HmzBlm\n+V5eXh6ioqKELrdLT09Hfn4+9PT0WkwXFBSEoKAguLm5NRr4tbGxwYkTJ4QGdndHEqKiELVvHyR4\nPNT06QMbT09Y2dh0tVmUDqCstha51dW40Qp9zKSkpE6wSHSUlZVFTkudYgPqHdv8+fNx4sQJGBgY\nCHVYycnJAgIOrU3XkJEjRyI4OLiDrBcvCVFROLfSFz+8+58M/faVvgBAHWMv4E1tLa7yuHhe07JC\nkq+vr0hlFBUVtcthtZb2hLlRp9gE4eHh2Lt3L5YuXQp3d3ecPy8Yt5eXl4e+ffu2OV1DOBwOcnNz\nO9R2cRG1b5+AQwSAH94VYtu8L2GoVNfQFX2WQ+l7n3aXVbwjACUBvzQ631T+vfn+zkJXUhJfysnD\nR7H52WXtly9E7il25kRLYGCgSOnomGID7OzsmC0LVFVVsWjRImRmZja6j81mC6jktDZdQ5pSyAHq\nVHLqP91hFzQJHk/4+U4Omu/tJHO5CCgpRkBJMX6OjelqczoUOqbYjSGEICsrS2CMT0NDA9LS0ti4\ncSN27twJHo+HwMBAWFtbN0qvqamJ5ORk5ri16RrSnEJOV2jJNUdNH+ETITW9MPC3K7GQlmZmgBUn\nT8Hm2NjOK7vPhz3zDAAgHyAZGRmExWI1+pw6dYrk5eWRWbNmEQUFBaKlpUW+/fZbUl5e3iiP9PR0\noqenxxy3Jp2rqyvZsmWLwLmgoCDi6enZKP/u+NVcjowky4aZkBda2sznu2HDyOXIyGbT/cvJmWxV\nVhFIJ+zjpN6XHDx4sJNq03MQd1sA0OJ3w/9pjz2XL1/uOMNbQFQ7P8ieoo6OTrOvrWfPnm0xD319\nfaiqqiItLQ1GRkbQ1NRsMd3hw4cbnUtISICHh0fLRouJtozx1E+m7N6/H2wuF7XS0rDz8Oh2kyyd\nNW7VEwKRW0tbArJVFRVFLufu3bvd/pl9kE6xo1i5ciWCg4Oxfft2kdLn5+cjKysLEyZ0nb5hW3/Y\nVjY2bXaCEn2kcKCmGieKiyDTTAB3enkZJkq0f6sE6hTbDumkIZD37993SjntgTrFdjB37lyEhYWh\npKQEiiL8e+7ZswebN28Wg2Xdi7U//4wvFy3Cr7/+CldX12bvHT16dOcYRaE0AXWK7eTChQsip+1N\ne280h6amJjQ1NZGYmIjx48d3tTmULqQ1ERldDZUO66Z0tjYkpXsjzp9pb25rojw36hQpFAqFDxq8\nTaFQKHxQp0ihUCh8UKfYzbh16xZGjBgBBQUF2Nra4t27d2Iv09bWFtevXxdb/jExMTAxMYG8vDwm\nTJiAtLQ0sZRz7tw56OrqQlFREdbW1sjIyBBLOfU8fPgQ0tLSePPmjVjyHzduHGRlZZmPu7u7yHm1\npl3FxMTAwMAAioqKWLhwIbitUMsRtaxBgwYJ1G39+vUilQU0335FqlNHRY9T2k91dTUZOHAg8DCZ\nbQAAEZNJREFUCQ4OJkVFRcTV1ZUsWLBArOWdO3eOSElJkevXr4uljFevXhElJSUSFxdHKisryaZN\nm4iJiUmHl/P69WumnLKyMuLj40MmTZrU4eXUU11dTSwtLQmbzSavX78WSxkDBgwgNTU17c6nNe2q\noKCAqKmpkYiICFJQUEAmT55MVq9eLZayKioqiK6ubrvqVF9Wc+1X1DpRp9iNuHLlCjEyMmKOc3Jy\niIKCAuHxeGIpb+jQoURSUpKw2WyxOcXff/+dWFtbM8dVVVWEzWaTwsLCDi3n1KlTZPLkyczxw4cP\nibq6eoeWwc/WrVuJr68vYbFYYnGK5eXlxMDAoEPyak27CgsLI1OnTmWOr169SgwNDcVS1qNHj8jE\niRPbnHdDWmq/otaJvj53I+7evYsRI0Ywx9ra2pCVlWXUdzqa1NRUVFVVYeDAgWLJH6hTRQkKChIo\nU1paGkodvPmRvb09Ll6s29C9qqoKYWFhsLCw6NAy6nn69CmOHj0qVsGO58+fg8fjYdSoUVBXV8fc\nuXNFfk1vTbtqeM/IkSORnp6O8vLyDi/r+fPnePnyJYyNjaGhoYGvvvoKZWVlba5XS+1X1DpRp9iN\nKCkpaeQslJSUUFRU1EUWtZ9+/frB0NAQABAVFQUbGxusWbNGQHato2CxWIiKioKsrCx27NiBRYsW\ndXgZtbW1cHd3x549eyAtxr1MCgsLYWhoiLCwMDx//hwKCgoi7S0OtK5dNVyVJS8vDwkJiTa3vdaU\nVVZWBiMjI8TExODBgwd48eIFfvjhhzaV01pbRKkTXdHSjVBSUkJxcbHAueLi4k5RKhYn79+/x9df\nf40rV64gICAATk5OYivLxsYGPB4Pp0+fxoIFC/DFF1+gX79+HZZ/UFAQjIyM8MUXXzCBwUQMob6f\nf/45/vrrL+Z427Zt0NDQQFlZGeTl5duUV2vaVcN7ysrKUFNT0+a215qyHB0d4ejoyByvX78eU6dO\nxb59+9pUVlttaW2daE+xG2Fqaop79+4xxzk5OaioqGB6Wj0RHo8Ha2tryMjI4NmzZ2JziAcOHMDu\n3bsB1AkAOzg4QE1NrdFmYe0lMTERR44cgaysLOTk5ADUqS6dO3euQ8uJjIwU2NCsuroaEhISIvVO\nW9OuGt5z+/Zt6OvrM3XsyLKOHTuGBw8eMMfV1dVtdvSi2NLqOrV7tJPSYVRVVZGBAweSgwcPksLC\nQuLi4kKcnZ3FXq6Ojo7YJlrCwsLI559/Lpa8+YmKiiIDBgwgDx48IJWVlSQkJIRoa2uTyspKsZYr\nromW0NBQMnToUPL8+XNSXFxMXFxciKOjo0h5taZd8c/U5ufnE2tra7Jq1SqxlLVmzRoyfvx48urV\nK/L27VsyefJk8uOPP4pUN0Kabr+i1ok6xW5GSkoKMTExIXJycmTq1KmkoKBA7GWK0yl6e3sTNptN\nJCUlmY+UlBTJzs7u8LI2bdpEtLS0iKKiIrGysiJ3797t8DIaIs6QHF9fX6Kurk4UFBSIg4NDu9qC\nsHb122+/CcxwR0VFEX19fSIvL0+cnZ1F/kNpqSwej0f+9a9/EWVlZaKiokK++uorUlFRIXLd+Ntv\nR9SJrn2mUCgUPuiYIoVCofBBnSKFQqHwQZ0ihUKh8EGdIoVCofBBnWIPIDMzE2w2G3Z2do2uzZkz\nB2w2G9nZ2W3K09/fH56engAAKSkpvH37tkNsBYC9e/fC2NgYsrKy0NXVxbp16zo0wNnd3V3kzcJc\nXV2xZcsWodcIIbC1tUVpaSk++ugjHDp0SOB6REQEVFRU8Pr1a5HKbono6OgmbRMHwlR4RKn7vXv3\nMGHCBCgrK0NVVRVz5sxBVlZWh9mZmJgIExMTkdImJCTA2Ni4bYlEngendBoZGRlESkqKcDgcUlpa\nypwvKysjHA6H9OnTh2RlZbUpT39/f+Lh4dHRppLIyEhiYGBAUlNTSU1NDblz5w75+OOPyc6dOzu8\nLFEQtvd2PUePHiVr1qwhhNQJWfTv35953lVVVcTIyIjs3r1brPaNHj26U8KwCGlahactdedyuURb\nW5uEhoYSLpdL3rx5Qzw8PIipqanY7W8Nly9fJoMHD25TGtpT7CFISEhg3LhxiIyMZM5FR0dj3Lhx\nAuuIU1JSMHz4cMjKymLcuHFMD7CoqAh2dnaQl5fH2LFj8fLlS2ZvDjabzYgNrFixAv369YO8vDxm\nz56N0tJSAHXCDhs2bMCwYcOgqKgIHx8foXYmJCRgxowZGDp0KNhsNkxNTbFlyxZmuVXDnhr/sY6O\nDnbu3AlNTU1cuXIFEhISAj0Tc3NzHDt2jEmzfv16TJs2jbn+5MkTKCsro7KyEgEBAYwYwcSJEwVW\ntpAmeq0BAQHM+uJ58+ZhyJAhzG6L+/btg5ycHJYuXYro6GgYGBhAVlYW9vb2jMBAZmYmxo8fDwUF\nBWhpaSEwMJDJm81mY//+/VBVVcWbN2+wdetWaGpqQkFBAa6urqiqqgIAODg4CAhoiIuKigpIS0sL\nXYPeXN0b8uzZM5SVlcHNzQ19+vQBh8PB9u3boaGhgcrKykY9Nf5jf39/LFmyBF988QW8vLwwceJE\ngbaxf/9+WFtbIzExEcbGxsjJyQGbzW7UJo4fP47bt2/DzMwMcnJy0NPTw8mTJ0V/OOLxz5SOJCMj\ng8jIyJDw8HCBVQ3Ozs4kPDycyMjIkKysLFJcXEw4HA45d+4cKSsrI2vWrCH29vaEEEI8PT2Jo6Mj\nqaioIDdv3iSKiorE09OTEPK/VRkXL14kZmZmpKCggLx7946MHj2a7NmzhxBCyLhx48jgwYNJTk4O\nef78OVFRUSGJiYmNbP39998Jh8MhO3fuJCkpKY1kzxr21PiPdXR0yJQpUxhZsdGjR5Nff/2VEELI\ny5cviby8PCktLWXSPHr0iMjKyjIBuZs3byYLFy4kaWlpZNCgQSQnJ4eUlZWRWbNmke+//54pb/Pm\nzY3sfvbsGRk4cKDAuSdPnhAVFRVy//59wuFwSHJyMsnIyCDq6urk77//JsXFxWTx4sXEx8eHEELI\n/PnzyaZNm0hVVRVJSkoiEhISpLi4mHnGbm5upKysjDx48IDo6emR/Px8UlBQQMaOHUuOHj1KCCEk\nNTWVDB06tIUW0X5SU1PJwIEDySeffELU1NSIg4ODQBC6sLoLo7S0lKirq5MlS5aQ6OjoRr3chj01\n/uOffvqJKCkpkWvXrhFCCNm7dy+xsrJi7p06dSo5dOgQSUhIYNIIaxMlJSXEwsKCHDlyhFRXV5Pj\nx48zsnG0p9jLsbW1xaVLl8DlcsHj8RAXFwdbW1vmekREBMzNzTFz5kzIyclh1apViI2NBZfLxYUL\nF/Cf//wHMjIyGDVqFGbOnNkof1NTU/zxxx9QVlZGUVER5OXlGdVkFouFb775Btra2tDV1cXIkSOF\njmPOmzcPv/zyC2JjYzF58mSoqKjA3t5e4F7SzPiij48PVFRUANT1mqKjowHUbSU7bdo0gTWyxsbG\n0NXVxeXLlwHUKW/PmzcPGhoaiImJgZaWFgoLCwXq0RTJyckwNTUVOGdkZAQPDw+MHTsWM2bMgLm5\nOcLDw+Hk5ARLS0soKirCz88Pf/75JwDA19cXy5YtQ2VlJdhsNgghKCwsZPLz9fWFnJwcSkpKUF5e\njr///htSUlK4ePEi5syZAwAYMmQI/vnnH7ErI7WkwiOs7sKQl5fH1atXUV1dDW9vb3A4HJiamra6\np2Zra4sxY8YAAGbPno1r166htLQUJSUl+Pvvv2Fvby/QXoS1CQUFBezatQtOTk4oLS2FlJRUuxTr\nqVPsQSgqKsLc3BwXL17EX3/9hTFjxghII2VnZyM6OpoZOFdWVgaPx0NeXh7y8/PB4XCYe3V0dBrl\nX1lZiQULFsDIyAhLly4V+EEDEFCbkZaWRm1trVA7nZ2dERkZiXfv3iEpKQnV1dVwc3MTem9DB1nv\nEIG6SaS4uDjU1tbi7Nmz+PLLLxulr/+RvH79Gk+fPsWUKVNACIGPjw/09fXh5ubWqkmovLw89O3b\nt9H51atXo7i4GH5+fgDqnvH+/fuZZ2xsbIy3b9+ipqYG9+7dg4mJCcaOHYuDBw9CQkJCaN0sLCzg\n5+cHf39/cDgczJ07F69evQJQ9+ejrq6O3NzcFm1uD/UqPEOHDoWysjK2bduGmJgYAV3DhnVvCmNj\nY4SEhODp06fIy8uDi4sLFi5cKHQ7iIbfN79ijaamJkaPHo24uDhmaIi/PQBNt4mEhATo6enB2toa\nZ8+ebfPz4Ic6xR6Gvb09Tp06hdOnT8Pe3l7gGofDgb29PSoqKphPcnIyBgwYgIEDBwrMCObl5Qmk\nJYRg1apVGD9+PNLT0xEREYHhw4e32T4zMzOcPn2aOR45ciQ2bNiAu3fvAqj70dfU1DDXm3NYenp6\n0NHRQWxsLFJSUgR6xfU/rnqneOHCBdjZ2UFSUhI7duyAuro6MjIyEBsbiwkTJrRoN5vNFjq+Vt8z\nrVdW4XA4WL58OfN8S0pKkJycjIqKCnz99de4ePEi7ty5gwMHDjRZ1uPHj2FlZYXbt28jNzcXKioq\nWL16NXO9qT+bjqQ1KjwN6y6MwMBAgagIDQ0NLF++HEOGDMHDhw8bfd85OTnN2lX/fZ4/fx7z5s1r\ndF1Ym0hPT8emTZtw+/Zt3Lhxo137vQDUKfY4Zs2ahaioKERFRWHWrFkC12xsbBAfH4+kpCTweDwc\nOHAAzs7OkJCQgJOTEzZs2ICioiJcv35dwHHVw+VyUVFRgaqqKsTGxuLChQuorKxkrjf8lxf2Gmxj\nYwM/Pz/cvn0bNTU1ePXqFXbs2IHx48cDqFNijo+PR3l5OSIiInDt2rVm6+vg4IAffvgBU6ZMgYyM\nTKPrJiYmYLFY2LVrF9Nr4HK5qKysRFVVFW7cuIHffvuNqUdTr+6ampqteuWyt7fH8ePH8eDBA1RW\nVsLf3x++vr4ghIDH46GsrAzFxcX497//jdraWlRUVDTK4+bNm3B1dUV+fj7k5OQgISGB/v37A6hz\niO/evYOWllaLtrSHN2/ewNPTExkZGSgpKYGvry/s7e0hKdk2idVJkybh0qVLCAkJQXl5ObhcLk6c\nOIGsrCyMGjUK2trayMrKwsOHD5Gbm4vAwEBmgk8Yc+bMQXR0NC5fviw0BA1o3CYqKytRU1ODiooK\nvH37lunZirrxFnWKPYT6hqSurg5TU1MMHToUqqqqAtc0NTURFhaGJUuWQFFRESEhITh16hQAYNWq\nVejbty+0tLTg4uIiMH7EYrHAYrGwatUqREZGQlVVFaGhodixYwcCAwMZ7buGjVlY4/7pp59gb2+P\n2bNnQ15eHmZmZlBSUsKRI0cAAN9++y3Kysqgrq6OoKAgODs7N1tvBwcHPHr0qFGvgb9sBwcHvHnz\nhukRent7IyMjA8rKyvDz80NAQACioqIQGRnJ1LUhFhYWTG+2Ifz3m5qaYt26dbCzs4OKigqSk5MR\nGhoKRUVF/Pzzzxg3bhyGDBkCQ0ND2Nvbw9XVtVF+8+fPh5mZGYyNjdGvXz+UlpYyPcUnT55AV1e3\n0WtjR+Pm5oYZM2bAzMwMWlpaKCsrEyry2pwDA4DBgwfjzJkzCA4OBofDQb9+/RAaGoro6Gj0798f\n+vr68PHxgbm5OT7//HOB71vYd6GlpYWBAwfC3NwcCgoKQu1o2CaGDRsGNzc3DB48GBYWFpg9ezbM\nzMzg4uLS5PfdHFQlh0L5L59++imOHTsGIyOjLrMhKCgIr1+/xrp167rMhg8d2lOkUP7LypUrERwc\n3KU2/P7778xKI0rXQJ0ihfJf5s6di7S0NJSUlHRJ+fHx8ZgwYQIzvkjpGujrM4VCofBBe4oUCoXC\nB3WKFAqFwgd1ihQKhcIHdYoUCoXCB3WKFAqFwgd1ihQKhcIHdYoUCoXCB3WKFAqFwgd1ihQKhcIH\ndYoUCoXCB3WKFAqFwgd1ihQKhcIHdYoUCoXCB3WKFAqFwgd1ihQKhcIHdYoUCoXCB3WKFAqFwsf/\nA8ZHC0+O6tTNAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7683090>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='fx_statistic'>Differing survival rates were observed depending on the TP53 protein domain affected by the mutation or its predicted functional status16 (log-rank P = .03, Extended Data Fig. 2).</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(s2, dd[dd!='WT'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "      <td> 11.9</td>\n",
        "      <td> 0.036</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>other</th>\n",
        "      <td> 78</td>\n",
        "      <td> 30</td>\n",
        "      <td>    4</td>\n",
        "      <td>   2.5</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.476</td>\n",
        "      <td>  0.351</td>\n",
        "      <td> 0.647</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>InDel</th>\n",
        "      <td> 48</td>\n",
        "      <td> 19</td>\n",
        "      <td> 3.53</td>\n",
        "      <td>   1.5</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.421</td>\n",
        "      <td>  0.262</td>\n",
        "      <td> 0.676</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nonsense</th>\n",
        "      <td> 34</td>\n",
        "      <td> 18</td>\n",
        "      <td>  1.6</td>\n",
        "      <td>  1.25</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.333</td>\n",
        "      <td>   0.19</td>\n",
        "      <td> 0.585</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L3</th>\n",
        "      <td> 31</td>\n",
        "      <td> 12</td>\n",
        "      <td> 2.16</td>\n",
        "      <td>   1.5</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.229</td>\n",
        "      <td> 0.0518</td>\n",
        "      <td>     1</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>L2</th>\n",
        "      <td> 30</td>\n",
        "      <td> 18</td>\n",
        "      <td> 1.08</td>\n",
        "      <td> 0.986</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.251</td>\n",
        "      <td>  0.124</td>\n",
        "      <td> 0.512</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Splice</th>\n",
        "      <td> 17</td>\n",
        "      <td> 10</td>\n",
        "      <td> 1.43</td>\n",
        "      <td> 0.767</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.247</td>\n",
        "      <td> 0.0842</td>\n",
        "      <td> 0.724</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>7 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "              Stats          Median Survival              5y Survival                Log-Rank       \n",
        "         # Patients # Events          Median  Lower Upper        Surv   Lower  Upper     chi2      p\n",
        "                                                                                         11.9  0.036\n",
        "other            78       30               4    2.5   NaN       0.476   0.351  0.647                \n",
        "InDel            48       19            3.53    1.5   NaN       0.421   0.262  0.676                \n",
        "Nonsense         34       18             1.6   1.25   NaN       0.333    0.19  0.585                \n",
        "L3               31       12            2.16    1.5   NaN       0.229  0.0518      1                \n",
        "L2               30       18            1.08  0.986   NaN       0.251   0.124  0.512                \n",
        "Splice           17       10            1.43  0.767   NaN       0.247  0.0842  0.724                \n",
        "\n",
        "[7 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Bar Plot of Hazard Ratios for Supplement"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dd = dd.replace('WT', 'aWT')\n",
      "f = get_cox_ph(s2, dd, interactions=False)\n",
      "ci = convert_robj(robjects.r.summary(f)[7])\n",
      "ci.index = map(lambda s: s[7:], ci.index)\n",
      "n = ci.ix[0]*0 +1\n",
      "n.name = 'WT'\n",
      "ci  = ci.append(n)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(7,4))\n",
      "ci = ci.sort('exp(coef)')\n",
      "haz = ci['exp(coef)']\n",
      "b = haz.plot(kind='bar', ax=ax,\n",
      "             yerr=[haz - ci['lower .95'], ci['upper .95'] - haz], ecolor='black', \n",
      "             rot=0, color=['grey', colors[5], colors[4], colors[0], colors[3],\n",
      "                            colors[2], colors[1]])\n",
      "prettify_ax(ax)\n",
      "ax.set_ylabel('Hazard Ratio')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.text.Text at 0x111c8c10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAbIAAAECCAYAAACbuVJpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111f54d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "P-values for Bar Comparisons"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from itertools import combinations"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sig = pd.Series({c: get_cox_ph_ms(s2, dd[dd.isin(c)], interactions=False)['LR']\n",
      "                    for c in combinations(dd.unique(),2)})\n",
      "sig.order()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(aWT, L2)             4.06e-05\n",
        "(aWT, Nonsense)       1.83e-03\n",
        "(aWT, Splice)         2.61e-03\n",
        "(L2, other)           4.88e-03\n",
        "(aWT, L3)             1.18e-02\n",
        "(aWT, InDel)          1.68e-02\n",
        "(aWT, other)          2.74e-02\n",
        "(InDel, L2)           3.58e-02\n",
        "(L3, L2)              4.35e-02\n",
        "(Splice, other)       8.19e-02\n",
        "(Nonsense, other)     1.26e-01\n",
        "(Nonsense, L2)        1.57e-01\n",
        "(InDel, Splice)       2.21e-01\n",
        "(Splice, L3)          2.73e-01\n",
        "(InDel, Nonsense)     3.87e-01\n",
        "(L3, other)           4.57e-01\n",
        "(Nonsense, Splice)    5.59e-01\n",
        "(Nonsense, L3)        5.66e-01\n",
        "(InDel, other)        6.00e-01\n",
        "(Splice, L2)          6.11e-01\n",
        "(InDel, L3)           8.93e-01\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Classification of Mutations via Poeta et. al."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* http://www.ncbi.nlm.nih.gov/pubmed/18094376 \n",
      "* In this paper the authors sequence TP53 for 420 patients.  \n",
      "* They classify a mutation as disruptive or non-disruptive if it:  \n",
      "    a) contains a stop mutation  \n",
      "    b) occurs at a binding site AND encodes an amino acid with a different polarity  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lo = pd.read_csv('../Extra_Data/amino_acids.csv', index_col=1)\n",
      "lo = lo.groupby(level=0).first()\n",
      "def is_disruptive(s):\n",
      "    if s.endswith('*'):\n",
      "        return True\n",
      "    if s.endswith('splice'):\n",
      "        return False\n",
      "    if 'fs' in s:\n",
      "        return False\n",
      "    \n",
      "    aa = s[3:-1]\n",
      "    try:\n",
      "        if int(aa) in range(163,196) + range(236, 252):\n",
      "            if lo.Polarity[s[2]] != lo.Polarity[s[-1]]:\n",
      "                return True\n",
      "    except:\n",
      "        pass\n",
      "    return False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53 = FH.get_submaf(run.data_path, cancer.name, ['TP53'], fields='All').ix['TP53']\n",
      "status = pd.concat([combine(p53.Protein_Change.map(is_disruptive), p53.is_silent==0), \n",
      "                    p53.Tumor_Sample_Barcode], axis=1, \n",
      "                   keys=['status','barcode'])\n",
      "status = status.set_index('barcode')['status']\n",
      "status = (status == 'both').groupby(level=0).sum().clip_upper(1.)\n",
      "status = status.ix[mut.df.columns].fillna(-1).map({-1:'WT',0:'Non-Disruptive',1:'Disruptive'})\n",
      "status = status.ix[keepers_o]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(status, surv, colors=colors[:6] + ['grey'] + colors[6:], figsize=(7,5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAfkAAAFpCAYAAACf0FGnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+2Ecm528AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x772c9d0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, status[status.isin(['Non-Disruptive', 'WT'])])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>    </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 8.12</td>\n",
        "      <td> 0.00437</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Non-Disruptive</th>\n",
        "      <td> 140</td>\n",
        "      <td> 61</td>\n",
        "      <td> 2.58</td>\n",
        "      <td> 1.71</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   0.4</td>\n",
        "      <td> 0.299</td>\n",
        "      <td> 0.534</td>\n",
        "      <td>     </td>\n",
        "      <td>        </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>WT</th>\n",
        "      <td>  45</td>\n",
        "      <td> 10</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 4.71</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.664</td>\n",
        "      <td> 0.494</td>\n",
        "      <td> 0.893</td>\n",
        "      <td>     </td>\n",
        "      <td>        </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "                    Stats          Median Survival             5y Survival               Log-Rank         \n",
        "               # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2        p\n",
        "                                                                                             8.12  0.00437\n",
        "Non-Disruptive        140       61            2.58  1.71   NaN         0.4  0.299  0.534                  \n",
        "WT                     45       10             NaN  4.71   NaN       0.664  0.494  0.893                  \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Modified Classification of Mutations via Poeta et. al. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* I find that the polarity of the mutation has no effect\n",
      "* I am also including splice-sites as disruptive \n",
      "* This introduces many more disruptive mutations and results in a larger effect size\n",
      "* In the manuscript we not that \"Notably, patients with mutations predicted as non-disruptive of function nonetheless had worse prognosis than patients with wild-type TP53\", thus we feel that using this modified metric is more conservative for this particular statistic, the p-value would be more extreme for the traditional definition"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def is_disruptive_mod(s):\n",
      "    if s.endswith('*'):\n",
      "        return True\n",
      "    if s.endswith('splice'):\n",
      "        return True\n",
      "    if 'fs' in s:\n",
      "        return False\n",
      "    \n",
      "    aa = s[3:-1]\n",
      "    try:\n",
      "        if int(aa) in range(163,196) + range(236, 252):\n",
      "            return True\n",
      "    except:\n",
      "        pass\n",
      "    return False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53 = FH.get_submaf(run.data_path, cancer.name, ['TP53'], fields='All').ix['TP53']\n",
      "status = pd.concat([combine(p53.Protein_Change.map(is_disruptive_mod), p53.is_silent==0), \n",
      "                    p53.Tumor_Sample_Barcode], axis=1, \n",
      "                   keys=['status','barcode'])\n",
      "status = status.set_index('barcode')['status']\n",
      "status = (status == 'both').groupby(level=0).sum().clip_upper(1.)\n",
      "status = status.ix[mut.df.columns].fillna(-1).map({-1:'WT',0:'Non-Disruptive',1:'Disruptive'})\n",
      "status = status.ix[keepers_o]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(status, surv, colors=colors[:6] + ['grey'] + colors[6:], figsize=(7,5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAfkAAAFpCAYAAACf0FGnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQ/0/DXOCgxKGuSMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10fa63d0>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='stat_non_disruptive'>Notably, patients with mutations predicted as non-disruptive of function nonetheless had worse prognosis than patients with wild-type TP53.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = get_cox_ph(surv, status[status.isin(['Non-Disruptive', 'WT'])]=='Non-Disruptive', interactions=False, \n",
      "               print_desc=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        coef exp(coef) se(coef)    z     p\n",
        "feature 0.79       2.2    0.353 2.24 0.025\n",
        "\n",
        "Likelihood ratio test=5.81  on 1 df, p=0.0159  n= 150, number of events= 52 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "exp(.79), exp(.79) - exp(.79 - .353)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "(2.2033964262559369, 0.65534034919959683)"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "TP53 Mutation Hotspots in HNSCC"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* I also threw in a plot of the frequent mutations as well, can't get alot from it but it shows that they do matter, even though we don't have the power to really say anything else.\n",
      "* When we start looking across multiple cancers, or have 1000 person cohorts, we may be able to extract some meaningfull information by looking at the effects of individual mutations but that is a ways off. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cc = p53.set_index('Tumor_Sample_Barcode').Protein_Change\n",
      "cc = pd.concat([pd.Series('WT', others), cc])\n",
      "cc = cc[cc.isin(true_index(cc.value_counts() > 5))] \n",
      "s2 = surv.unstack().ix[cc.index]\n",
      "s2.index = range(len(cc))\n",
      "s2 = s2.stack()\n",
      "cc.index = range(len(cc))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(cc, s2, colors=['grey'] + colors, figsize=(7,5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}