{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elastic network analysis\n",
    "\n",
    "Here we use a Gaussian network model to characterise conformational states of a trajectory.\n",
    "\n",
    "**Last updated:** December 2022 with MDAnalysis 2.4.0-dev0\n",
    "\n",
    "**Minimum version of MDAnalysis:** 1.0.0\n",
    "\n",
    "**Packages required:**\n",
    "    \n",
    "* MDAnalysis (<a data-cite=\"michaud-agrawal_mdanalysis_2011\" href=\"https://doi.org/10.1002/jcc.21787\">Michaud-Agrawal *et al.*, 2011</a>, <a data-cite=\"gowers_mdanalysis_2016\" href=\"https://doi.org/10.25080/Majora-629e541a-00e\">Gowers *et al.*, 2016</a>)\n",
    "* MDAnalysisTests\n",
    "\n",
    "**Optional packages for visualisation:**\n",
    "\n",
    "* [matplotlib](https://matplotlib.org)\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "    \n",
    "**Note**\n",
    "\n",
    "The elastic network analysis follows the approach of (<a data-cite=\"hall_characterization_2007\" href=\"https://doi.org/10.1021/ja071797y\">Hall *et al.*, 2007</a>). Please cite them when using the ``MDAnalysis.analysis.gnm`` module in published work.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:10.907984Z",
     "iopub.status.busy": "2021-05-19T05:58:10.907144Z",
     "iopub.status.idle": "2021-05-19T05:58:11.621995Z",
     "shell.execute_reply": "2021-05-19T05:58:11.622470Z"
    }
   },
   "outputs": [],
   "source": [
    "import MDAnalysis as mda\n",
    "from MDAnalysis.tests.datafiles import PSF, DCD, DCD2\n",
    "from MDAnalysis.analysis import gnm\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test files we will be working with here feature adenylate kinase (AdK), a phosophotransferase enzyme. (<a data-cite=\"beckstein_zipping_2009\" href=\"https://doi.org/10.1016/j.jmb.2009.09.009\">Beckstein *et al.*, 2009</a>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:11.626135Z",
     "iopub.status.busy": "2021-05-19T05:58:11.625593Z",
     "iopub.status.idle": "2021-05-19T05:58:11.919487Z",
     "shell.execute_reply": "2021-05-19T05:58:11.919074Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pbarletta/mambaforge/envs/guide/lib/python3.9/site-packages/MDAnalysis/coordinates/DCD.py:165: DeprecationWarning: DCDReader currently makes independent timesteps by copying self.ts while other readers update self.ts inplace. This behavior will be changed in 3.0 to be the same as other readers. Read more at https://github.com/MDAnalysis/mdanalysis/issues/3889 to learn if this change in behavior might affect you.\n",
      "  warnings.warn(\"DCDReader currently makes independent timesteps\"\n"
     ]
    }
   ],
   "source": [
    "u1 = mda.Universe(PSF, DCD)\n",
    "u2 = mda.Universe(PSF, DCD2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a Gaussian network model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a Gaussian network model to represent a molecule as an elastic network, we can characterise the concerted motions of a protein, and the dominance of these motions, over a trajectory. The analysis is applied to the atoms in the `selection`. If two atoms are within the `cutoff` distance (default: 7 ångström), they are considered to be bound by a spring. This analysis is reasonably robust to the choice of cutoff (between 5-9 Å), but the singular value decomposition may not converge with a lower cutoff.\n",
    "\n",
    "We use the GNMAnalysis class ([API docs](https://docs.mdanalysis.org/stable/documentation_pages/analysis/gnm.html#MDAnalysis.analysis.gnm.GNMAnalysis)) for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:11.923601Z",
     "iopub.status.busy": "2021-05-19T05:58:11.922947Z",
     "iopub.status.idle": "2021-05-19T05:58:16.214927Z",
     "shell.execute_reply": "2021-05-19T05:58:16.215674Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MDAnalysis.analysis.gnm.GNMAnalysis at 0x7f05569e96d0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nma1 = gnm.GNMAnalysis(u1,\n",
    "                      select='name CA',\n",
    "                      cutoff=7.0)\n",
    "nma1.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is saved in `nma1.results`: the time in picoseconds, the first eigenvalue, and the first eigenvector, associated with each frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['eigenvalues', 'eigenvectors', 'times']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(nma1.results.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:16.222738Z",
     "iopub.status.busy": "2021-05-19T05:58:16.221853Z",
     "iopub.status.idle": "2021-05-19T05:58:16.225058Z",
     "shell.execute_reply": "2021-05-19T05:58:16.225568Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98, 98, 98)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len(nma1.results['eigenvalues']), len(nma1.results['eigenvectors']),\n",
    "    len(nma1.results['times']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:16.230286Z",
     "iopub.status.busy": "2021-05-19T05:58:16.229530Z",
     "iopub.status.idle": "2021-05-19T05:58:21.279527Z",
     "shell.execute_reply": "2021-05-19T05:58:21.280142Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MDAnalysis.analysis.gnm.GNMAnalysis at 0x7f0556a1f8e0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nma2 = gnm.GNMAnalysis(u2,\n",
    "                      select='name CA',\n",
    "                      cutoff=7.0)\n",
    "nma2.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike normal mode analysis, Gaussian network model analysis uses only a single eigenvalue to represent the rotation and translation of each frame. The motion with the lowest positive eigenvalue represents the dominant motion of a structure. The frequency of this motion is the square root of the eigenvalue.\n",
    "\n",
    "Plotting the probability distribution of the frequency for the first eigenvector can highlight variation in the probability distribution, which can indicate trajectories in different states.\n",
    "\n",
    "Below, we plot the distribution of eigenvalues. The dominant conformation state is represented by the peak at 0.06."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:21.293277Z",
     "iopub.status.busy": "2021-05-19T05:58:21.292108Z",
     "iopub.status.idle": "2021-05-19T05:58:21.614571Z",
     "shell.execute_reply": "2021-05-19T05:58:21.615117Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histfig, histax = plt.subplots(nrows=2, sharex=True, sharey=True)\n",
    "histax[0].hist(nma1.results['eigenvalues'])\n",
    "histax[1].hist(nma2.results['eigenvalues'])\n",
    "\n",
    "histax[1].set_xlabel('Eigenvalue')\n",
    "histax[0].set_ylabel('Frequency')\n",
    "histax[1].set_ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we plot how the eigenvalue varies with time, we can see that the simulation transitions into the dominant conformation and stays there in both trajectories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:21.626109Z",
     "iopub.status.busy": "2021-05-19T05:58:21.625129Z",
     "iopub.status.idle": "2021-05-19T05:58:21.751749Z",
     "shell.execute_reply": "2021-05-19T05:58:21.752237Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f05540e1ac0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kiq2OgKdFpKov50sAJIQQ9Y7JZKpxj4qoXXW+DlCjpu8In5sG1upLWXr2x2IyEBuqpu3nSglMCCGE8JgEQHUpuAkYzaDZ4MyJak/XA6CoYD+C/FTyTkpgQgghhOckAKpLRiOE6GWw6huhMxwBkD/B/iq1KgGQEEII4TkJgOqa3gfkRiO0vgZQVLCFQMkACSGEEF6TAKiuhbdQt1lHqz1VXwU6KtifID+VAcqXHiAhhBDCYxIA1bUI+/YcWdVvwKr3AEUH+zkCoFzJAAkhhBAekwCoroW3VLeZyVWfh+smaJkGL4QQQnhOAqC65kUGKCrYj2BHBqhENt4TQgghPCQBUF3Te4AyPSuBBdoDIE2DwhJbrQ1PCCGEOBdJAFTXwu0ZoPxTUJRb5amuSmAgM8GEEEIIT0kAVNcCI8A/TN23zwTbn5ZDdkHFPWQyygRAJqMBf7P6z5dbKDPBhBBCCE9IAFQf6FmgzCOs3HuS0a+u5IEFW5xOKbbayMpXQVFUsB9A6VT4YskACSGEEJ6QAKg+sDdCa5nJvLJkL5oGv+9Px2orbW4+naeyPwYDRATpAZAqg0kGSAghhPCMBED1gT0DdOTQHrYcyQRUVufAyTOOU/T+n8ggVf6CMhkg6QESQgghPCIBUH1gzwAlHdjtdHjr0SzH/VNnSvt/dEH+sh2GEEII4Q0JgOoDewYoMP84/mYj43s2A2Db0UzHKafyXARAltK1gIQQQgjhPgmA6oMItRp0c0M61/dvyajOsQBsO1YmA1RmDSCdlMCEEEII75irP0XUtvWng+kHxHGayUNbkmdVPT47jmdTYrVhNhnLbIRasQQm+4EJIYQQnpEMUD0wa81pCjUzJoNGU8MpWkcHE+JvprDExr401QhddhFEnV4Ckx3hhRBCCM9IAFTH1h06xZpDp0khRh3IOoLRaKBbc7U44jZ7I7TLAMhfBUDSBC2EEEJ4RgKgOvaf3/YBUBLaXB2w7wnWo0UEUNoHlJFbCJQLgPwkABJCCCG8IQFQHSqx2mjbJIQQfzNNW3ZQB+27wndvHg7A1mPOGaDoYH/H6/WFEPOkBCaEEEJ4RAKgOmQ2GXlyQlf+fHQkIbGJ6mBmMlAaAO1KyaaoxOa6BOanT4OXDJAQQgjhCQmA6oEQf7NjMUQ9A9QqOojQADNFJTb2nsjhdJ7aByw6RKbBCyGEEDUlAVB9UWZDVACDwUCPFioLtLrMvmCRQWUDICmBCSGEEN6QAKi+cGSAjoKmgp3uzSMAWL4nDYBQfzN+5tL/ZNIELYQQQnhHAqD6IrQZYABrIeSeBEr7gNYnnQYgqkz5CyBQAiAhhBDCKxIA1RdmPwiNV/cdU+FVAFRiL3+VbYAGCNZLYIVSAhNCCCE8IQFQfeIog6mZYC0iA4kIsjieji4XADlKYMWSARJCCCE8IQFQfeKiEVovg0HFDJC+F5iUwIQQQgjPSABUn5SbCg+UC4D8nU7X9wIrKrFRYrXV/viEEEKIc4QEQPVJuQwQlPYBQcUSmN4EDVIGE0IIITwhAVB9EtFS3ZbNANn3BIOKJTB/sxGT0QBAXqEEQEIIIYS7JACqT8IrlsCahQcQHeyHAVuFAMhgMDjKYLIYohBCCOE+c10PQJQR3kLdFmRBQTYEhGHQbHwZ/TZR2jrMsWsrvCTI30ROYYk0QgshhBAekAxQfeIfAoGR6r6eBVr2LIlpSwi3ZRGcsaPCS0q3w5AASAghhHCXZIDqm/AEyD+tGqFPJ8GqV0qfK86rcHrpdhhSAhNCCCHcJRmg+kZvhD64HL6ZbD+oGp2rDoAkAySEEEK4SwKg+kZvhF77JhRmQ8tB0H6MOlaUW+H0QCmBCSGEEB6TAKi+0RdDBAiJg2vmQ4B9LaDi/AqnB9szQPlSAhNCCCHcJgFQfRPRSt0azXDthxDaFCyB6piLEpi+GGKulxkgq32jVSGEEKIxkQCovmk/GvrdAdd8AC0HqmN+werWRQksuAYlsGW70+j2xC98u+mY18MVQgghGiIJgOobsz9cOgs6X1p6zBKkbl2UwIJqUAJbsfck+cVWVu9P92qoQgghREMlAVBD4CiBuWqC9r4ElppVAEBmXrH3YxNCCCEaIAmAGgJHCaxiD5BeAsv3IgBKydYDoCLvxyaEEEI0QBIANQRVlMAcGaBCz0tgqVnqepn5kgESQgjRuEgA1BA4AiAXTdD+9h6gYs8yQMVWG2k5hYBkgIQQQjQ+EgA1BH72AMhFCSzQ4t0ssJM5hWj2GfCZecVomkyHF0II0XhIANQQuDELzNMSWIq9ARqgxKZxxosSmhBCCNFQSQDUENRCCSy1TAAEMhNMCCFE4yIBUEPgRgkst9CzACglyzmbJAGQEEKIxkQCoIagihKYIwPk4UKIJ7KdM0CnpRFaCCFEIyIBUENQtgRWrllZnwafV2z1qJE5pXwJTKbCCyGEaEQkAGoI9BKYZoOSQqenguwLIWoaFBTb3L6k3gNkNhoAmQovhBCicZEAqCHQM0BQYUf4QIvJcT/PgzKYngFqFxsCSA+QEEKIxkUCoIbAZAGjRd0vFwCZjAYCLOo/o7trAdlsmqMHqFPTUEB6gIQQQjQudR4AvfHGGyQmJhIQEEDfvn1ZtWpVpeempKRwww030LFjR4xGI9OnT69wzjvvvMOwYcOIjIwkMjKSUaNGsW7dulr8BGdJFTPB9P3A3A2A0nMLKbFpGA3QPk4FQFmSARJCCNGI1GkAtGDBAqZPn86MGTPYtGkTw4YN4+KLLyY5Odnl+YWFhTRp0oQZM2bQs2dPl+csX76c66+/nmXLlvHHH3/QsmVLxowZw7Fjx2rzo9Q+i31D1GIXU+H1Rmg3S2B6/09saAAxIX6AZICEEEI0LnUaAM2aNYs77riDO++8k86dOzN79mwSEhJ48803XZ7funVrXnvtNSZNmkR4eLjLcz755BOmTJlCr1696NSpE++88w42m41ff/210nEUFhaSnZ3t9FPvWALVrYsAyNMMkN7/0zQ8gIggPQCSDJAQQojGo84CoKKiIjZs2MCYMWOcjo8ZM4Y1a9b47H3y8vIoLi4mKiqq0nNmzpxJeHi44ychIcFn7+8zVS2G6MgAuRcA6Rmg+PAAIgJVb1GWTIMXQgjRiNRZAJSeno7VaiUuLs7peFxcHKmpqT57n4cffpjmzZszatSoSs955JFHyMrKcvwcOXLEZ+/vM1WUwII8LIHpGaC4sAAig6UEJoQQovEx1/UADAaD02NN0yoc89aLL77IZ599xvLlywkICKj0PH9/f/z9/X3ynrWmihJYkIclsFT7Nhjx4QFEBJVmgGw2DaPRN9+9EEIIUZ/VWQYoJiYGk8lUIduTlpZWISvkjZdffpnnnnuOxYsX06NHjxpfr875uZMBcjMAyi7TAxSoMkCaBtkFUgYTQgjRONRZAOTn50ffvn1ZsmSJ0/ElS5YwePDgGl37pZde4umnn+bnn3+mX79+NbpWvWGpYhq8fT+wvELPZoHFhwfiZzYSbA+gZDFEIYQQjUWdlsAeeOABbr75Zvr168egQYOYO3cuycnJTJ48GVC9OceOHePDDz90vGbz5s0AnDlzhpMnT7J582b8/Pzo0qULoMpejz/+OJ9++imtW7d2ZJhCQkIICQk5ux/Ql6oogek7wucVV58B0jTN0QMUH67KghFBfuQW5XM6r4jWBPtowEIIIUT9VacB0MSJE8nIyOCpp54iJSWFbt26sWjRIlq1agWohQ/LrwnUu3dvx/0NGzbw6aef0qpVK5KSkgC1sGJRURFXX3210+ueeOIJnnzyyVr9PLXKnRKYGxmgzLxiCkvUnmGxYarvKSLIwrHMfNkQVQghRKNR503QU6ZMYcqUKS6fmz9/foVj1e14rgdC5xw9A+SiBBbk734PkJ79iQnxw9+sXqc3QsuGqEIIIRqLOt8KQ7hJ7wEqzq3wVJB9Q1R3SmCp2WoGWNPw0llx+mKI0gMkhBCisZAAqKFwlMDyKzwV5G/vAXKjBOZYBTos0HFMXwxRVoMWQgjRWEgA1FBUVQLzYBp8qmMbjNJ1jyLtGaAsKYEJIYRoJCQAaigcK0G7KIF5EACllJkCr9N7gCQDJIQQorGQAKih0PcCc1UCc6wEXX0JzJEBCqvYAyTbYQghhGgsJABqKNwogeW7UwLLdl4DCCAySDZEFUII0bhIANRQVFkCUxmgXI96gMpmgPQSmGSAhBBCNA4SADUUVZbA3MsA5RQUc8Y+U0ymwQshhGjMJABqKKrYC0wPgIqsNoqttkovoWd/wgMtjqwRlE6DzykooaSK1wshhBDnCgmAGoqyCyGWWw27bDBT1Uyw8nuA6cLtARBIH5AQQojGQQKghkIvgWk2sDr36viZjZiNBqDqMpir/h8As8lIaIAKomQqvBBCiMZAAqCGQs8AARRVvhbQofSKz+lSXEyB1zkWQ8yXRmghhBDnPgmAGgqTBYz2UpWLHeGbRahp8jfNW8uT3+/gdG7FQMbVPmA6fSr86VzJAAkhhDj3SQDUkFQxE2zuzf0Y1TkOq01j/pokLnh5Oe+tPkRumf3BKusBAgjXZ4JJD5AQQohGwFz9KaLesARBQZbLEljL6CDevaUfv+9P5+mFO9mdmsNTC3fy3KJddG8RzsA20ew7cQaApmW2wdDpGaBMWQtICCFEIyABUEPimAlWsQSmG9Iuhh+nDuN/64/wxvL9HDmVz6bkTDYlZzrOcZUB0qfCy1pAQgghGgMJgBoSv+oDIACT0cD1/Vtyff+WHDmVx9pDp/jzYAZrD2XQPCKQxJjgCq+R/cCEEEI0JhIANSRVLIZYmYSoIBKigri6b4sqz4sIkgyQEEKIxkOaoBsSN0pg3op0NEFLBkgIIcS5TwKghsRP3xDV9wFQuEyDF0II0YhIANSQWOyztzwogbmrdCFE5wAot7CELzccJa+oxNXLhBBCiAZJeoAaEkvl6wDVlGMhxHJN0M//tJuP/jzM6dwi7hrexufvK4QQQtQFyQA1JI4SWOXbXXgrIlBlgPKKrBSWqP3EbDaNX3akAnDktO+zTkIIIURdkQCoIanFElhogBn7fqpk2WeC7TieTVpOoTomK0QLIYQ4h0gA1JDU4iwwo9FAuL4Yoj3Y+XX3CcfzMj1eCCHEuUQCoIakFmeBQWkjtL6R6rLdaY7nZI8wIYQQ5xIJgBqSWiyBQelU+Mz8YtJyCthyNMvxXJasEC2EEOIcUuMAqKCgwBfjEO6wnJ0MUGZeEct3nwRwlMWkB0gIIcS5xKsAyGaz8fTTT9O8eXNCQkI4ePAgAI8//jjz5s3z6QBFGXoGqJYCIH1D1NN5xY7+n0t7xAMqALLZtFp5XyGEEOJs8yoAeuaZZ5g/fz4vvvgifn5+juPdu3fn3Xff9dngRDl+nu8F5gl9Q9S07EJW7UsH4IY2ebxofptmpJFTKIshCiGEODd4FQB9+OGHzJ07lxtvvBGTyeQ43qNHD3bv3u2zwYlyar0EpjJAv+xIJa/ISlyYP10Of8q15hVcZ1rmmB4vhBBCNHReBUDHjh2jXbt2FY7bbDaKi+WXZK2p7RKYPQA6lqlWmr6wUyyGzMMAxBtOSR+QEEKIc4ZXAVDXrl1ZtWpVheNffPEFvXv3rvGgRCX0afC1XALTXdgpDrKOAhDLadkpXgghxDnDq73AnnjiCW6++WaOHTuGzWbj66+/Zs+ePXz44YcsXLjQ12MUurILIWoaGAw+vbyeAQLwMxsZ0jYKvlYBUJzhNHukBCaEEOIc4VUGaPz48SxYsIBFixZhMBj417/+xa5du/jhhx8YPXq0r8codHoJTLOC1ffZmMgyGaDBbaMJKs6EElUOizOcdnsxxKISG1M/28TclQd8PkYhhBDCF7zeDX7s2LGMHTvWl2MR1dFLYABFuWD29+nl9TV/AEZ2ioWs5NLnDHnkncl26zp/HMzg+y3HWbrrBHcNa4PBx5kqIYQQoqZkJeiGxGQBoz1IKc73+eWjQ/ww23dEHdEp1tH/oyvJSnHrOpuTMwG1s/xpKZsJIYSoh7zKABmNxir/qrdarV4PSFTDEgSFWbUyEyzIz8zL1/TEpmm0iAyCXUecnjfkpLp1nU1HTjvuHzmVR1SwXxVnCyGEEGefVwHQN9984/S4uLiYTZs28cEHH/Dvf//bJwMTlfCzB0BFubVy+ct7Ny99kOUcAJny0qiOpmlsPpLpeHzkdB49EyJ8NDohhBDCN7wKgC677LIKx66++mq6du3KggULuOOOO2o8MFEJx0ww35fAKsh0DoD8C6oPgJIy8sgsU/Y6evosjFMIIYTwkE97gAYMGMDSpUt9eUlRniMAqp0MkBN7Big/tDUAQQUnq33J5jLlL1AlMCGEEKK+8VkAlJ+fz3/+8x9atGjhq0sKV2p5PzAn9gCooGlfAEJL0qt9id4ArW+rcUQyQEIIIeohr0pgkZGRTk3QmqaRk5NDUFAQH3/8sc8GJ1zwZQnsyF+w/UvofzdEt3V+rvAM5KtsjtbiPNj3FZElGdVecpO9/+eibvF8ti6Zo6clAySEEKL+8SoAevXVV50CIKPRSJMmTRgwYACRkZE+G5xwwRclsKyjsPTfsO1/6nFRLlz233Ln2Pt/AsIJaNoJgCacoqDYSoDFhCsFxVZ2HldrBY3vqQdA+dhsGkajrAUkhBCi/vAqALr11lt9PAzhtpqUwIryYM0cWD3bscIzAKlbK56rrwEUnkBgtCprxhoyycovrjQA2nE8ixKbRkyIH+e1jsJkNFBUYuPkmULiwgI8H68QQghRS9wOgLZudfFLshI9evTwajDCDTUpgX1wKRzboO63HAQD/gZf3Appu8BarBZa1GXaV4EOT8AQ2hSAUEM+KVmZxIU1dXn5Tfb+n14JkVhMRuLDAzh6Op+jp/MkABJCCFGvuB0A9erVC4PBgKZpVZ5nMBhkIcTa5G0JrKSwNPi5ah50u0ptqOoXCkU5kL4P4rqUnq+XwCISwD+UPAIIooC8jKOQUEkAZO//6d0yAoAWkYEcPZ3PkVP59G3l2XCFEEKI2uR2AHTo0KHaHIdwl5+XGaAzJ9StyV8FPwaD+onrCkf+hNRt5QKg0hIYwGlTNEHWYxRnHq/0LfQZYL3tCx8mRAbxJ6dkKrwQQoh6x+0AqFUr+RO+XtAzQJ6uBH3GvohhSJwKfHRNu6sA6MQ2YGLpcX0RxHDV/5NtjqG59Vil+4Gl5RRwLDMfgwG6twgHICFKjVUWQxRCCFHfeL0bPMDOnTtJTk6mqKjI6fiECRNqNChRBUcJzMOsip4BCol1Pt60m7pN3e583FECawlArn8TKARDjusASM/+dIgNJTRA9RK1iAwE1HYYQgghRH3iVQB08OBBrrjiCrZt2+bUF6RPjZceoFrkbQlM38g0JM75eNPu6jZ1m+oJMhhUQ7Qe6NhLYIUBsZANptwTLi+v9//0KrPvl54BkgBICCFEfePVStDTpk0jMTGREydOEBQUxI4dO1i5ciX9+vVj+fLlPh6icGIJVrfelsBCywVAsV3AYIS89NIsUfZx0GyqXyi4CQDFQep1/vmuAyBH/4+9ARpUDxBASmYBJVabZ+MVQgghapFXAdAff/zBU089RZMmTTAajRiNRoYOHcrMmTOZOnWqr8coyrKospL3JbByAZAlEKLbq/up29StXv4Kbw5G9U9EC1EzvwILK26HYbVpbD2aCUCvMgFQbKg/fiYjJTaN1OwCz8aL2kds8MxfeWP5fo9fK4QQQlTFqwDIarUSEhICQExMDMePq5lBrVq1Ys+ePb4bnaioprPAyvcAQZk+IHsA5GiATnCcYgxXAVBIccUAaF9aDrlFVoL9TLSPDS19jdFAc70P6JTzePOKSrjvs00s+Cu50iH/sPU4x7MKWLTNdd+REEII4S2vAqBu3bo5FkYcMGAAL774Ir///jtPPfUUbdq08ekARTlel8D0AMjFGj5l+4DAeQ0gO7+IZupQSYbqFSpDL3/1aBGBqdyWF5U1Qv+4NYUfthzn2R93YbW5Xlvqz4OnAMg4U+TyeSGEEMJbXgVAjz32GDab6ul45plnOHz4MMOGDWPRokXMmTPHpwMU5XhdAiszDb68OHsAdMI+E8xRAmvpOCUgsjkAgRRAYY7Ty7e4KH/pWkS6ngq/Yu9JALILSth+LKvC64qtNtYn2QOg3KJqF+AUQgghPOHVLLCxY8c67rdp04adO3dy6tSpCrvEi1rgZ88AeVIC0zT3SmAZ+9V+YeXWAAIIDQsnWwsizJCnZpQFhDme2592BoDO8aXHdAlRKmA7WmYxRKtNY/X+0lLa6v3p9Cwzewxg27Es8orUbMKiEhu5RVZC/Gu0aoMQQgjh4FUG6IMPPiA317kEExUV5VXw88Ybb5CYmEhAQAB9+/Zl1apVlZ6bkpLCDTfcQMeOHTEajUyfPt3leV999RVdunTB39+fLl268M0333g8rnpLzwAV5VYoRVUq/zRY7WUkVwFQSJya7aXZ1L5gLkpgEUEWTmiRAFiznVeDPpSu/i20iQmucGl9JljZEtjWo5lk5hU7Hq/eV7Gv6M+DGU6PM84UVvbphBBCCI95FQA9+OCDxMbGct1117Fw4UJKSkq8evMFCxYwffp0ZsyYwaZNmxg2bBgXX3wxycmuG2MLCwtp0qQJM2bMoGfPni7P+eOPP5g4cSI333wzW7Zs4eabb+baa69l7dq1Xo2x3tEXQtSspUFNdfTyV2AkmP0rPm8wQJzeCL2lwjYYAOGBFk5oEQAUZBxzHM/KLybd3qPT2lUA5GI1aL38pWeMNhw+TX6R89pRev+PLiNX+oCEEEL4jlcBUEpKCgsWLMBkMnHdddcRHx/PlClTWLNmjUfXmTVrFnfccQd33nknnTt3Zvbs2SQkJPDmm2+6PL9169a89tprTJo0ifDwcJfnzJ49m9GjR/PII4/QqVMnHnnkEUaOHMns2bMrHUdhYSHZ2dlOP/WWX5kgw90+oMqmwJelN0IfXA4lBYABwpo7nraYjJwyRgNQWGY/sCR79ic21N9liUpvgk7NLqCwRAU5egA0aVArmoUHUGS18VdSacBTbLWxwf5Yv+YpaYQWQgjhQ14FQGazmUsvvZRPPvmEtLQ0Zs+ezeHDhxkxYgRt27Z16xpFRUVs2LCBMWPGOB0fM2aMx4FUWX/88UeFa44dO7bKa86cOZPw8HDHT0JCQqXn1jmTBYz2QKPI0wDIRflLpwdA+39Vt6FNwezndEq2WQVAZfcD08tfiS6yPwDRwX4EWkxoGhzPLCAzr4gt9lWjz+/QhKHtYwCceoK2H8sit8hKRJCFPq1U2S0jV0pgQgghfMerAKisoKAgxo4dy8UXX0z79u1JSkpy63Xp6elYrVbi4pyzEnFxcaSmpno9ntTUVI+v+cgjj5CVleX4OXLkiNfvf1boU+FrIwNUpBqay5a/dLl+KlihzH5gB/X+nyauAyCDwVDaCH06j9X707Fp0CEuhGYRgQxpZw+AyvQB6eWv/q2jaBKiSnZSAhNCCOFLXgdAeXl5fPLJJ4wbN45mzZrx6quvcvnll7N9+/bqX1xG+cZpTdNqPJPM02v6+/sTFhbm9FOv+Xm4Iao7AVB0e7X1hS6iYgBUEKgySKbc0mCyugwQlE6FP3IqnxV7VPnr/A5qi43BbVUAtDMl29HorDdAD2wTTXSIykJJCUwIIYQveTWv+Prrr+eHH34gKCiIa665huXLlzN48GCPrhETE4PJZKqQmUlLS6uQwfFE06ZNfX7NescxE8zNACjHjQDIZIbYzpCyWT12kQEqDoqDU+CXl+Y4dihdZYwSY0IqvXSCvQ8o+VSeo/9nuD0AahLqT6emoexOzWHNgQwu6tbUsf7PwDbRrNynzpcMkBBCCF/yKgNkMBhYsGABx48f5/XXX/c4+AHw8/Ojb9++LFmyxOn4kiVLvLqebtCgQRWuuXjx4hpds95xlMDcXA3anQwQlK4HBE5rAOls9tcHFp4ETUPTNA6d1DNAQZVeVp8J9uuuE6TlFBJgMXJe6yjH88Pal5bB9P6f8EALnZqGEh2sMkCVBUBWm8Y3m47KNHkhhBAe8SoD9Omnn/rkzR944AFuvvlm+vXrx6BBg5g7dy7JyclMnjwZUL05x44d48MPP3S8ZvPmzQCcOXOGkydPsnnzZvz8/OjSpQugdqofPnw4L7zwApdddhnfffcdS5cuZfXq1T4Zc73g6X5gle0EX17THqX3I1pWeNoQGg+A2VYIBZmcLAkkt8iK0VAa5LiizwTbZ18wcVCbaAIsJsfzQ9rF8M6qQ6zen+6YSt8/MQqj0VBaAqukCfrHbSncv2AL/VtH8b/Jg6r+fEIIIYSd10vr/vrrr/z666+kpaU5tsXQvffee25dY+LEiWRkZPDUU0+RkpJCt27dWLRoEa1atQLUdPvyawL17t3bcX/Dhg18+umntGrVytF8PXjwYD7//HMee+wxHn/8cdq2bcuCBQsYMGCAtx+1/vG0BOZuBiiubAaoYgksNCSETC2YCEMu5Jzg0BlVxmoRGYS/2VThfJ3eA6TT+390/ROjsJgMHMvM54sNqgF9YBs14ywq2N4EXUkPkL4K9bqkU2w4fIq+raJcnieEEEKU5VUA9O9//5unnnqKfv36ER8fX6Om5SlTpjBlyhSXz82fP7/CMXf2hLr66qu5+uqrvR5TvedJCaykCPLta+y4UwIzWtR9F03QEYFqNWgVAKVwKEMFNlU1QEPF7ND5HZ2n4wf5menTMpK1h05x0F5SG9hGBTJlS2CumtlTs0qzYG+vOMjcSRIACSGEqJ5XAdBbb73F/Pnzufnmm309HuEOT0pgufbyl9GiVoKuSkA4TPxYbYnhH1rh6XB7ANSRo5CTyqF0tbN8dQFQeKCF0AAzOQUltIwKonV0xXLZsPYxrD10ynF+56ZqJp5eAqtsP7DU7NLS2JJdJzhw8gxtm1TekC2EEEKAl03QRUVF51ZTcUPjSQmsbPnLnUxdx4ug0ziXT4UHWUjDHkTlpFS7BlBZ+p5gwzvEuMwY6usBQWn/D6jsUKC9X8hVo7OeAYoJ8UfT4N1VB6sdixBCCOFVAHTnnXf6rBFaeMGTEliOG6tAuyki0M+xH5jKAFW/BpBuWPsYzEYDV/SuOLsMoHvzcEIDVHZH7//RRVUxEywlqwCAB8d0AOCrDcdIyymo/sMIIYRo1LwqgRUUFDB37lyWLl1Kjx49sFgsTs/PmjXLJ4MTlfCkBOZuA7Qbyu4Ib8tO4XCG+wHQQxd14p4L2xEWYHH5vNlk5K5hbfhu8zEu6R7v9Fx0iB/HMvMrLIZ4prCEnAK1Ee+lPZvxxYajbDh8mvm/J/HQRZ08/nxCCCEaD68CoK1bt9KrVy+ACis/13QVZ+EGRwnMjQyQu1Pg3aD3AAEUnD5GsVXDz2ykWXhgta81GQ2VBj+6qSPbM3Vk+wrHSxuhnUtgqfbsT6i/mRB/M38b3oa7P9rAR38eZsqIdi43ZxVCCCHAywBo2bJlvh6H8ISjBOZOBsi+KrYPMkBBfiZOGfUMkLpuYnSwo1+ntjimwpcrgekBUNPwAABGdY6jTZNgDp7M5fN1ydw5rE2tjksIIUTDVaPNUPfv388vv/xCfr76RezOFHXhA3oJLC+j+nP1DJAPeoAMBgN5/mrmV2B+KoEUuFX+qil9Jlj5tYBS7A3QegBkNBq42x70zFt9iGKr8/pUQgghhM6rACgjI4ORI0fSoUMHxo0bR0qK2h38zjvv5B//+IdPByhciLaXiQ4ugx8fBGtJ5ef6sAcIoDCoKcm2JpiwMsS4g0Q3ZoDVlF4CO1VJBijeHgABXN67OaH+ZlKyChyLJAohhBDleRUA3X///VgsFpKTkwkKKl3TZeLEifz8888+G5yoRKvBMPIJdf+vd+DTayA/0/W5jllgTX3y1uFBfvxq6wPAhcZNJEbXfgBU2Syw1Gy9BFbagxRgMdHCvvDiiWyZDSaEEMI1rwKgxYsX88ILL9CihfOU5vbt23P48GGfDExUwWCAYQ+oRQstQXDgN5g3Gk6VWwNH08pkgGpeAgO1GvQyWy8ARpg2V7kJqq+UlsBcN0GXzQABxIWpnqG0bNkgVQghhGteBUC5ublOmR9deno6/v7+NR6UcFPn8XD7zxDaDNL3wrwxkHeq9PmCLLDagwAfBUDhQRbW2jqTq/kTbzhFO+2Q9xdL2QK51fcxRduboMuXwPQ1gJqGlQuAQtVjyQAJIYSojFcB0PDhw512aDcYDNhsNl566SVGjBjhs8EJN8T3hLt+g8hEyD0Ju74vfU5vgPYPL506X0MRgX4U4sfvNrVxasSR37y7UNpueHs4/K/67VSiyu0HpistgbnOAJ2QBRGFEEJUwqsA6KWXXuLtt9/m4osvpqioiIceeohu3bqxcuVKXnjhBV+PUVQnLB762AOJXT+UHtenwPtgDSBdeKBay+c3W28ADPsWe3ehk7vVbfKf1a5nVHY/sDOFquG7oNjqyAiVL4HFhukZICmBCSGEcM2rAKhLly5s3bqV/v37M3r0aHJzc7nyyivZtGkTbdu29fUYhTs6T1C3B1eo0heUmQLvuwAoIkgFQMusvdSBo+shN93zC+WeVLeaFY5vrvLUsvuB6UGPXt4KsBgdQZmuy5k/ucm0hDQpgQkhhKiE10vlNm3alH//+9++HIuoiZj2ENMR0vfA3sXQ4xqfN0BDaQB0gijSgjsSm7sH9i2BXtd7diE9OAM4+he0HlLl6VHBajuMjNwiWkUHO/p/4sMDK6w+3n3DDPpYTjI5qy0w1LNxCSGEaBS83grDFYPBQEBAAC1btpRm6LrQeTys2qP6gHpcAzn6KtC+mQIPOGVbTrcYQeyePbDvF88DoNxyAVA1Yuz7gemLIaZW0gCNpmEuUI3ggwtWYrPdW+srVQshhGh4vAqAevXq5firW29KLftXuMViYeLEibz99tsEBAS4vIaoBZ3Hw6qXYf9SKMrz6SrQurIBkLnTxbDnLdj/K1iLwVT1Xl9OzpwsvX/0LzVlv4p95KIciyGqvp6USqbAU1KIQbMCcJFxHRk5+TQJr/2p+kIIIRoWr3qAvvnmG9q3b8/cuXPZsmULmzdvZu7cuXTs2JFPP/2UefPm8dtvv/HYY4/5eryiKvE9IbwlFOeptYF8vAo0QESQn+N+XOfBEBQDhdmqmdkT+tj0+1lHqzy9/H5gJyqZAUZR6erPsYZMcvat9mxcQgghGgWvMkDPPvssr732GmPHjnUc69GjBy1atODxxx9n3bp1BAcH849//IOXX37ZZ4MV1TAYoPOl8OcbajZYLfQAJUQG0ishguYRgYQE+EH7MbDlU1UGSxzm/oX0EpjJD6xFKgsUkVDp6THl9gPT9wGrkAEqct7+wm/Pt9BvjPvjEkII0Sh4lQHatm0brVq1qnC8VatWbNu2DVBlMn2PMHEWdR6vbvf+BNnH1f1Q3/UAmU1Gvr1nCK/fqLbDoIM9uNj7i/sX0bTSElji+er26PoqXxJVbj8wvQcornwPULkp9dGHfwab1f2xCSGEaBS8CoA6derE888/T1FR6cq8xcXFPP/883Tq1AmAY8eOERfnu9KLcFPCAAhuoqbCF2SqYz4sgVXQ9kIwmtVK1OW34qhM0RkoURkcOo1Tt9U0QpffD6zsLDDna6sAKMvShCwtiMCiDEj+w71xCSGEaDS8CoBef/11Fi5cSIsWLRg1ahSjR4+mRYsWLFy4kDfffBOAgwcPMmXKFJ8OVrjBaIKO48o8NkNgVO29X0A4tByk7h9wc1VovTnbElyaAUrZAiWVL1wYE2LvATpTSLHVxkn7vmCV9QAVWcL5xXqeOrbjG/fGJYQQotHwqgdo8ODBJCUl8fHHH7N37140TePqq6/mhhtuIDQ0FICbb65+iwNRSzpPgI0fqPvBsWD0Ks51X/O+kLRKbW/hDn0RxJAmENVGBWj5pyB1O7To6/IlZUtgaTmFaBpYTAaig/2cT9RLYH4h/GgbyLWsgJ3fw8UvquBQCCGEoAYLIYaEhDB58mRfjkX4SuJw8A9Ts7N82ABdqZgO6jZjn3vn6xmg4FjVuN2iH+xbrMpg1QRAGblFpGSq8llcWEDFNX7sAZAxIITfbV3JMYQQmpsGh9d41qQthBDinOZ2APT9999z8cUXY7FY+P7776s8d8KECTUemKgBsx90uAi2/a92+390egCU7mYAlFtufaIW55UGQLgOqsvuB7Y/TZW5KswAA0cJzBwQSglmltGfCfymymASAAkhhLBzOwC6/PLLSU1NJTY2lssvv7zS8wwGA1arzLqpcwMnw6EV0OUsBKMx7dRt9jEozAH/0KrP12eABTdRty36qdsqGqH1/cDyi63sTMkGoGn5BmiAQhUA+QWpMXxVeB4T/H5Tq2OPe0nKYEIIIQAPmqBtNhuxsbGO+5X9SPBTTzTvCw/uhd431f57BUaWBjMZ+6s/v3wGqHlfwACZh533CCtHL4PtOK4CINcZIFUC8w8Kw2Q08LutK7aASNV3dPh3tz6OEEKIc59H3bHjxo0jKyvL8fjZZ58lMzPT8TgjI4MuXbr4bHCiAXGUwdwIgBw9QPagKSAcmnRU96tYD0hfDHGnPQCqsAYQlPYA+QfTJMSfEsxktrSvVbR7UfVjE0II0Sh4FAD98ssvFBaWTlV+4YUXOHXqlONxSUkJe/bs8d3oRMMRbS+Dpe+t/lxXe5TpZbBjlQdAegYov1hlGavqAcIvhLgwNXU+LdgeXGUfq35sQgghGgWPAiB949PKHotGzJEBciMAyi0zC0zXwr5mTxV9QNH2tYB0FdYAgjLT4INpEqqez7DZe4XyT1c/NiGEEI1CLS8QIxoNT2aC6U3QIS4CoGMbK926ovyaP1X1AJXNAJ0otu8Gr6+MLYQQotHzKAAyGAwYDIYKx4RwzAQ7daDqvbeKcqHYHqSUDYCadAK/EFXCStvl8qVRZQIgowGalMsIqevrJbBgR4/Q8UJ7oJSf6c4nEUII0Qh4tBCipmnceuut+PurXzwFBQVMnjyZ4OBgAKf+INHIRLRSO7uXFEDWEYhs7fo8vf/HHKgCHp3RBLGdVQns1EFo2q3CS8uWwGJDAzCbXMTvLjJAR/PtgZMEQEIIIew8CoBuueUWp8c33VRxivWkSZNqNiLRMBlNqhE6bacqg1UWAJXdBqN89jAwUt0WZrt8adkSmMv+H3DqAYq1Z4AO5doDp6IcsBaDyVLdpxFCCHGO8ygAev/992trHOJcENO+NABqP9r1OWdcNEDrAsLVbUFWxedwLoG57P8B5xKYvz0AOlNm8cP8TBV8CSGEaNSkCVr4TnR7dVvVTLDyiyCWVU0ApG+HAVVlgCpOgz+ZZ0XzD7NfO7PysQkhhGg0JAASvuPOTLDy22CUVV0AFFzaA9TU1SKImuZUAosM8sNsNKBpYPW3X1umwgshhEACIOFLMT7KAFXSrBzoZyLQospZLjNA1iKwlaj7fsEYjQZiQ1XQVGSp+tpCCCEaFwmAhO/oq0HnplUeaJw5oW696AGC0t6fhKigik/q2R9wzDDTG6HzTfYNWiUDJIQQAg+boIWoUkAYhMZDToraFFXf3qKsM2VmgVV4ffUB0NOXd2PL0Ux6J0RUfFLv/zEHgEn909b7gHIMoUSD9AAJIYQAJAMkfK26MpirbTB0bgRAQ9rFMOWCdq4X4CzT/6PTF0PMxH5MMkBCCCGQAEj4WnWN0I4MUFzF59wIgKpURQB0ymovmUkAJIQQAgmAhK9VNRW+OF8tRgiVlMAi1K3XAVDpFHid3gSdVqIHQJneXVsIIcQ5RQIg4VuOEpiLDJC+CKLJH/R1ecrSA6CiHLCWeP7ehaWLIOr0DFBqoX0KvQ8zQJqm8daKA/yw5bjPrimEEOLskCZo4Vt6CezUQRXEmMr8E8stswu8qx6egDJBUWE2BEV59t4uSmCx+n5g+oaoPmyC3no0i+d/2k14oIXxPZv57LpCCCFqn2SAhG+FNVcbndqKIfOw83OObTAq2YrCZAGLPXjxpgzmogQWF6oCn2MFvs8Ard6fDkBWfjFFJTafXVcIIUTtkwBI+JbRCDH29YDK9wFVtQiiriaN0GV2gtdFBFnwMxnJ1OzHfNgD9Ls9AALIzC/y2XWFEELUPgmAhO85ZoKVC4Cq2gZD55MAqLQEZjAYiA3zLxMAnVZbZtRQQbGV9YdLs0mZecU1vqYQQoizRwIg4XuVBkD2VaBrLQNUsQkaVCN0lr4OkK3YecVoL61POu1U9pIASAghGhYJgITv6VtipO93Pl7VIog6RwCU6fn7uiiBgVoNOg9/rAaz99cuZ3WZ8hfA6TwpgQkhREMiAZDwvdjO6jZ1q3Mmp6ptMHQ+LoEBxIYGAAYKzL7bD0zv/zEZ1Wy2TAmAhBCiQZEASPhebBdo0gmK82DLgtLjHmWAfFcC06fCnzHoAVCm59cuIzOviO3H1fgGtomyH5MSmBBCNCQSAAnfMxjgvDvV/b/eLW06PlNmHaDK+CQAci6BRQT6AZBjKNMIXU6J1cb6pFNsPZrJiewCSqyVT2v/40AGmgbtY0PoGKfWLjotAZAQQjQoshCiqB09JsKSJyB9DySthhbnQaE9qKkqAAqMULc+LIFFBFkAyMIeALnoAfr4z8M8+cNOx2OjAWJC/BnXPZ5/XdoFo7F04Ua9/2dIu5jSa8s0eCGEaFAkAyRqR0AY9Jyo7v/1bukq0Ca/0i0vXL7OBz1A/s4ZoPBAFaSctlW+IeqeEyp7FGgxYTIasGmQllPI/DVJLFh/xOlcvf9naLsYIu0B0OlcyQAJIURDIgGQqD397lC3uxdC6jZ1P7iJ620wdLXQBK0HQBlV7AivZ3D+76KO7H3mYtbNGMn9o9R0/pmLdpGWUwDA0dN5JGXkYTIaGNAmivAgVV6ThRCFEKJhkQBI1J6m3aDlILCVwMqX1LGqFkGEWukB0gOgE8WV7wivNzFHBvthMhqIDQ3gnhFt6dY8jOyCEp6yl8fW7M8AoGeLcEIDLI4MkDRBCyFEw1LnAdAbb7xBYmIiAQEB9O3bl1WrVlV5/ooVK+jbty8BAQG0adOGt956q8I5s2fPpmPHjgQGBpKQkMD9999PQUFBbX0EURW9Gfr4RnVbVf8P1EoGSO/TOVVFCUwPYPRgCcBsMvL8lT0wGmDh1hSW7U5z9P8MbRcDQKQ9AyTrAAkhRMNSpwHQggULmD59OjNmzGDTpk0MGzaMiy++mOTkZJfnHzp0iHHjxjFs2DA2bdrEo48+ytSpU/nqq68c53zyySc8/PDDPPHEE+zatYt58+axYMECHnnkkbP1sURZncc7Z32qmgIP3gdAJUVgtQch5QKgEH8zJqOhdDsMF03QWfkqAIqwBzS6bs3DuWNoIgCPfbvd0f8zxB4A6QGTZICEEKJhqdMAaNasWdxxxx3ceeeddO7cmdmzZ5OQkMCbb77p8vy33nqLli1bMnv2bDp37sydd97J7bffzssvv+w4548//mDIkCHccMMNtG7dmjFjxnD99dezfv36SsdRWFhIdna204/wEbM/9JlU+riqRRChtEG66AxYS9x/n+Iy21tYnAMgg8FAeKCldDsMlxkgFTxFlMkA6e4f3YHmEYEcy8wnI7eIQIuJ3i0jAVUyAygssZFfZHV/vEIIIepUnQVARUVFbNiwgTFjxjgdHzNmDGvWrHH5mj/++KPC+WPHjmX9+vUUF6u/wIcOHcqGDRtYt24dAAcPHmTRokVccskllY5l5syZhIeHO34SEhJq8tFEeX1vBYP9n1p1GSD/sNL7hR4EooX2/h+TH5j9KjwdHmghS9MDoEyn54pKbOTagxe9XFZWkJ+ZZ67o5njcPzEKP7P6PMF+Jsz6atDSCC2EEA1GnQVA6enpWK1W4uLinI7HxcWRmprq8jWpqakuzy8pKSE9XZUmrrvuOp5++mmGDh2KxWKhbdu2jBgxgocffrjSsTzyyCNkZWU5fo4cOVLpucILES2h21XqfvM+VZ9rMpc2MXuyZUUl/T865wxQptNzeuBiMEBoQMUACGBEx1iu6N0cgLFdmzqOGwwGR9lMpsILIUTDUecLIRrKTYnWNK3CserOL3t8+fLlPPvss7zxxhsMGDCA/fv3M23aNOLj43n88cddXtPf3x9/f/+afAxRncvegAsegei21Z8bEK5KYJ70ATkCoFCXT4cHWjii9wAVZoHNCkYTAFn2/p2wAItjby9XXrq6B5MGtaJniwin4xFBFtLPFEoGSAghGpA6C4BiYmIwmUwVsj1paWkVsjy6pk2bujzfbDYTHR0NwOOPP87NN9/MnXeq2Ufdu3cnNzeXu+++mxkzZmA01vnEt8bJ7Ode8AMqAMo+5mEA5HofMF1EUJkMEKhrB9n38bI3QEe6KH+VZTYZHb0/ZclUeCGEaHjqLBrw8/Ojb9++LFmyxOn4kiVLGDx4sMvXDBo0qML5ixcvpl+/flgs6pdQXl5ehSDHZDKhaZojWyTqOb0R2qsMUOUlsBLMFBkrToV3TIEPqtg75I4ImQovhBANTp2mQx544AHeffdd3nvvPXbt2sX9999PcnIykydPBlRvzqRJpTOIJk+ezOHDh3nggQfYtWsX7733HvPmzePBBx90nDN+/HjefPNNPv/8cw4dOsSSJUt4/PHHmTBhAiaT6ax/RuEFb6bCVxMA6bO78kwVd4SvagaYOyJkKrwQQjQ4ddoDNHHiRDIyMnjqqadISUmhW7duLFq0iFatWgGQkpLitCZQYmIiixYt4v777+f111+nWbNmzJkzh6uuuspxzmOPPYbBYOCxxx7j2LFjNGnShPHjx/Pss8+e9c8nvORVAOR6FWhdmD1IOWMMIYITThmg0jWAvAuA9KnwmZIBEkKIBqPOm6CnTJnClClTXD43f/78CsfOP/98Nm7cWOn1zGYzTzzxBE888YSvhijOttrIANnLVNnYM0BlFkPUMzfeZoBkMUQhhGh4pCNY1D81ygBV3gMEkKlV7AHSe3e87QEq3Q5DAiAhhGgoJAAS9U8tBECl+4FVXA1anwXmdQ+Q/dpZMg1eCCEaDAmARP1TkxKYf+XrAAGklwSqA2WaoLMcO8HXLACSDJAQQjQcEgCJ+qcWZ4GllbiYBp+vzwKrWQlMmqCFEKLhkABI1D+OACjT/ddUEwDps8BO6/uBuWiCDvdyFlhEmYUQZa0pIYRoGCQAEvVPLUyDD7CYCLAYy2yIWmYafA1ngekZoBKbxplCD3awF0IIUWckABL1T2CEuvVhCQxUH1Am+karmQAUW23k2IOWCC9ngQVYTPjbd4eXqfBCCNEwSAAk6h89A1ScByVu9tW4EQBFBPpVyADpiyAChAV4vyxWaR+QBEBCCNEQSAAk6h//sNL7hdnuvaaaEhioDFCWIwOkAiA9YAkNMGM2ef8/h9KZYNIILYQQDYEEQKL+MZpKg6DyZbAjf8Ff86B8s3Fh1esAgWpydmSArIVQnO9YuyfSy/KXztEInS8ZICGEaAjqfCsMIVwKCFfZn/Izwb6+C04fgvie0KJf6XFHCazqDFAOgdgwYcQK+afJzFMb5Hq7D5hOpsILIUTDIhkgUT+5mgmWf1oFPwBpu0qPW4tVRgeq6QGyAAYKzKU7wjumwHs5A8xx7SDZD0wIIRoSCYBE/eQqADqxs/R+xv7S+3r2B6rNAAHkGkv7gJy2wcg4ADabV8ONcOwHJhkgIYRoCCQAEvWTywBoR+l9VwGQ0QLmynt59CxNjqF0R3i9ZDXuzFfwnz6w4X2vhquvIZQlGSAhhGgQJAAS9ZPLAGh76f2MA6X33ZgCD6WrQWdTOhU+M68YE1aGpi9Qx5JWezXcSMkACSFEgyIBkKifqguATh0Em1Xdd2MKPJQpU5VZCygzv5iRxo2EFp20X/dAJa+uWrhsiCqEEA2KBECiftIDIH3Xdpu1XONzIWQdVffdzADpPUCnrPqGqKoEdqPp19KTTh2qOMXeDXoGKEumwQshRIMgAZConwIi1K2eATqdpFaGNgdAVFt1TM/WFFW/BhC43hE+4Ewy55u22s8wqKn3uekeD1cWQhRCiIZFAiBRP5Uvgenlryad1A+U9gHpGSD/qktgegYovSTQfu1Mzs/5CYCs5sMhvIU6fuqgx8PVA6Cs/GJsNtkRXggh6jsJgET9VD4ASrUHQE27QbQ9A6TPBHOzB0hvgs7U7OedSWNcyRIA8rrfAlFt1HEv+oAiAlUJTNMgu0DKYEIIUd9JACTqpwoZIPsU+LhuEN1O3XcEQO71AJmMBkIDzGTaZ4Fph38nimxStUgsXS4uEwB5ngHyMxsJ9lOrSstiiEIIUf9JACTqp8pKYHFdXWSA3AuAQJWq9P3ADJpa9PBz6wjCgwPLXNe7mWCyGKIQQjQcEgCJ+qlsAFSQDZmH1eOyGaDMZCgpcrsEBqoPKJPS86yagR9Mo7GYjDUqgYFshyGEEA2JBECiftIDoJJ8SNms7oc2g6AoCIlTwY5mU7PDPMkABfqV7ggP/GbrQ2FQU/XAMbusZlPhM/MlAySEEPWdBECifvIPAwzq/uE16jauq7o1GJzLYB4EQOGBFrLKZIA+sY4s3Qk+sjU1mQrvWAwxVzJAQghR30kAJOono9EeBAGHf1e3TbuVPl+2EdrNdYBABSlFWPgz4Q4Otp7ISlsPxwwuLAE1mgofqZfAZDFEIYSo9yQAEvWXXgY78pe6jaskACrUA6DQai+prwX0S+wdrGj/CDaMjswNUKM+IEcJTJqghRCi3pMASNRfZfuAoLQEBmX6dQ562ANUumu7vm+Xfkxd1/up8HpwJU3QQghR/0kAJOqvwIjS+ya/0qwPlCuBedYDBGrF5ix7piaibAaoBlPhZUd4IYRoOMx1PQAhKqVngEBtf2EqG6jYMzU5KWC1Z1zcmAYfUaZPJyRA/fN39ABBzVaDlmnwQgjRYEgGSNRfZQOgsv0/AIGREBSj7ufZZ2y5kQEKK5MB0gMVpwxQDabCR7g7Db4gC5L/9OjaQgghfEsCIFF/OQVAXSs+X7YkBm6vAwQqS6PP1tIDF6BGU+EdGaCqpsFbS+CDCfDeWNi72KPrCyGE8B0JgET9VTYAatqt4vN6v47OnZWg7UFKdn6xY7aWUwaoBlPh9R6gnMISiq021yetfat0Ycdt//Po+kIIIXxHAiBRf1VVAgMXAZD7s8CKrDZSswqcjjl42QcUXuY6Wa7WAspMhmXPlj7e8zOUFHr0HkIIIXxDAiBRf+kBUEgcBMdUfL5sCcxoBrN/tZcM8jNhNqoVpgtLVJbGaR0gKA2APJwJZjIaCLM3VldYC0jT4McHoTgPWg5W23oU5cCBZR69h8cKz0B+Zu2+hxBCNEASAIn6q0kndZs43PXzZQMgv2C1RUY1DAaDc8kL58yNum6ZNYY8FBlc2mPkZOe3sO8XMFpg/GzofKk6vut7cgqKeezbbXy+LhnNiz3IKlV4Bt4eBq/1VNknIYQQDhIAifqreR+Yuhkm/Nf183qmBtzq/9GFlQl4gvxM+JtNrq/rzVR4+7VPlw2ACrLgp/9T94c9AE06QpfL1OPdP/LGr7v5+M9kHv56Gw9+sZWCYqvH7+vSsmdVEFeQCYsf8801hRDiHCEBkKjfohJVY7IrlkAIszcsu9H/oyvb8xNZdgaY4z1rPhX+6Om80oNL/w1nTqiM1dAH1LGWgyC4CRRksvvPRY5Tv9p4lOvm/smJ7AKP3reC45tUwzUABtj5HRxcXrNrCiHEOUQCINGw6eUqDwKgsiWvCuUvqNFU+AFtogD4z2/7ST9TqPqI1r+nnrx0dmkwZzRBp0sAGKWtpV+rSD6+YwDhgRY2H8lk/H9Wsyn5tEfv7WAtgR+mgWaDbldB/7vV8UUPlS4aKYQQjZwEQKJh0/uAPCiBlV33p3w/EFCjqfB3Dm1Dp6ahnMotYsY329AOrwE01ficOMzp3OPNxgAwxvQXD49tz9D2MXx/7xA6xIWQllPIFW+sYeQry/m/L7fyv/VHOJSe694g1s2FlC2qiXzsTBjxCARFQ/oeWPu2R59HCCHOVRIAiYYtpr269a9+J3hd2ayPywAIvO4D8jMbeeXanpiNBn7ZcYJDW39XT7ToW+Hc53bFkKkF08SQTT/jHgBaRQfz9ZQhXNIjHoADJ3NZsP4ID325lREvL+fJ73dUPYDMI/DbM+r+qH9DaJxaNXvUk+rY8uch54RHn0kIIc5FEgCJhq37NdDtahg4xe2XOJfAXPQAgddT4QG6Ngtn6kgVmOUm/aUONuvjdM6WI5ks3J7OUps9MNr5veO5EH8zr9/Qh02Pj+bdSf2YfH5bzmsdicEA89cksWRnJQGMpsGif0JxLiQMhD63lD7X6yY1hqIcWPqEx59JCCHONRIAiYYtOAaunlehvFQVtzJANZgKD/D3C9rSu1kQHbQkALRmvZ2ef/GX3QBktr5YHdj1A9icV4+ODPZjVJc4Hr64E19MHszdw1RQ9sjX2ziV62K/sT2LYO9P9qn2r4GxzP+8jUYY95K6v+UzSF7r1eeqC09+v4Oxr66suLaSEELUgARAotEpG/RUWAVaV4Op8AAWk5HZF/rjbyghUwtmxvIc3l5xgPm/H2L20r38vj8DP5ORseOvB79QyDkOxzZUec37R3egfWwI6WcKefy77c5PWoth8ePq/uD7ILZTxQu06KcyQQBr5nj1uRz2/AzvXQQndtbsOtXIOFPIh38ksedEDsv3nKzV9xJCNC4SAIlGJ7y6afBQOhU+fT9kHfXqfVoVqL6erbY2fLruCDN/2s2TP+xk9tJ9ANw4sCUJsZHQYax6wfp5UJRX2eUIsJiYdW0vTEYDP25N4Yctx0ufXP++CtaCm6i1hioz8O/qdt9i71eILi6AhfdD8h/ww1SPlwrwxM87UrHZL+/1rDghhHBBAiDR6JTNAFXYBkMX3VZNhy/OhXdGwvHNnr/R8Y0ABCeex/X9E7iyT3Mu6RHP6C5xXNmnOdNHdlDndbtS3W75DGZ1hl9mVNp71L1FOPeMUDPfHv9uO2k5BWqhxeUz1QkXPFJ1Q3hcV2jSGaxFquzmjU0fqYwVwNG/YNsX3l3HDT9uTXHc35icWWvvI4RofCQAEo2OUw9QZSUwkwVu+UEFC2dS4f1xquzjieObAOg76EJmXtmDWdf24vUb+vDOpH7MurZXafDVcRxc/CJEtFSrNv/xX/hPH/jwctj+VYUNU+8d0Y4u8WFk5hVz94cb+O3dRyD/FEdMLRi5rBXPLdpFTkEl6/0YDND9anXfm8CluABWzVL3m/ZQt0uegCI3p+h74GROIX8ezHA83pWSTX6Rj1bJFkI0ehIAiUan7MyviMpKYKACkjt+gTYjVCbo8+vdX0enOB/Sdqn75RqgKzAYYMDf1LYfN/wP2o8FDHBwGXx5O7zSUW2lkboNUFPtZ03sicVkIO3Ifoac/B8AT+Rfx4FThcxdeZARL6/gyw1HsdlclKf0AOjQSshJde/z6PTsT1hzuHWh+o5yjsPvr3l2HTfo5a+eLcJpEupPiU1j+/Esn7+PEKJxkgBINDrhgRbHvqmRwZVkgHQB4XDjF9BnklpZ+aeHYPOn1b/JiR1gK1E9OWHN3RuY0aT6gW78H0zbDMMfUq/NP622tXhrKMwbA7sW0ik2mLdu6subzX7E31DMiajzuOnmu/jvDb1JjAkm/UwhD36xhSveXMPmI5nO7xPZGlr0BzTY/rXrsbjK6JTN/gx7QH03o59Wj39/rdoNV0/nFnHr++uYu9K9xvJF9vLXJT3i6Z0QAUgfkBDCdyQAEo2On9nIP8d2ZPL5bYkNrWSfsbJMFhg/B4ZMV4+XP6+2m6jKMdX/Q7M+bu1SX0Fka7hwBkzfBjd+pTZPNVrgyFpYcCP89zxGps6j56lfAIi7+iUu7NyUS3s045fpw3nk4k4E+5nYciSTy1//namfbeLIqTIN1t2vUbeuymArXoLnmsHXd0NBdunxstmf3jerY10ug1ZDoaRAlcKq8Nqv+1i+5yTP/7Sb/Wlnqjw3LaeAtYdU+Wtc93h6t4xUQ6hJH1DKlgY1/V8IUbskABKN0pQL2vHwxS6milfGYIDz/w+CYiDzsOrNqYq9/6fa8ld1jCZoPwqu/RDu3642Uw0IVzO+Vrygzukx0el9/MxG/nZ+W5Y9eAFX9WmBwQDfbznOha8s5+mFOzmdWwRdLweDSTVql224PrRS7SIPsHWByjod+ati9sfsX/q9XDQTMMCOr2HfEshOgaxjalXqnBOgaSSl5/Lxn4cBsGnw8i97qvzYv2y3l78SImgRGUSflhEAbEw+jebNrLOcVJg3Fj64FM6kef56IcQ5RwIgIdzlFwSD7CtOr55VYeFCJ74KgMoKbQqjnoD7d8JFL6j+m5A4uPBxl6fHhgXwyrU9WXjfUIa1j6HYqjFv9SGGv7SMxYdt0OYCdeK2L9Vt3in4+m+ABh0ugvCWKth7b6zqfyqf/dHF91AlQoBProZZneDVLjC7G7zSAZ5vhe3d0TxtnMujUctobsjg5x2pbKyinLXQXv66tLvaEqR7i3BMRgMnsgtJO7IfPp0Ie35y/7v743UoyVez3w7/7v7rhBDnLAmAhPDEeXeCfzic3A27F7o+p/CM2ngUoFkv34/BPwQGTlblsQd2Q0RClad3bRbOR3cM4MPb+9M5PoycghLu/XQT++Lsq1Bv+0Kt5fP9fSrIiW4HV78Hk1dB1ytBs8KB39S5ZbM/ZV34OFpUWzAYwWgGkx+YA9TjwizaFOzgevMy7s57h88i3gDghZ92u8zmpGUXsC7pFAAXd28KQJCfmU5N1fR+2y8zYO/Pasf74oLqv6+8U7D+vdLHyX9W/xohxDlPAiAhPBEQDv3vUvdXvex6EcDUraphOrSZytrUJqP7/xMe3qEJC+8bykVdm1JktXHz7zHYTP6QsQ9+/IcK6IwWuGoe+AVDYARc/R6/dnqSM1oAu20JJLW80uW1MwhjeMHLjI/6gf2TD8PjJ+GxE2gzUvlH9FvcW3Qfv8bcCEBCwR7CzMWsPXSK5Xsrru780/ZUNA16t1TlL12flpH0MBwg/pjqe+LMCdj8SfUffN07UHRGBWUAh9dUeurv+9NZbw++hBDnNgmAhPDUwClgCVJNtft/rfi8Xv5q3qfic3XMZDQw+7peDEiMIrXQj19t9jGun6duRz3plLVKyshjyvZO9Cl8m8uKnua7bRkVrgnw1cajHDmVz7ZjWUz472q+23wMgKV7M/nqWBhLjEPoctPLEByLQbPxQA+1TtELP+2uMFX/x2322V/28peud0I4/2f+XD0IsQeWv8+uuiG98AysfdP+2f6tbk9sd27utltzIJ0b313LDe+udb3XmmjwNE3jZE5h9SeKRkECICE8FRwNfW9T91e9UvF5xwywXmdtSJ4IsJh455Z+dI4P44vCgaVPtL1QBXd2mqbx6DfbKCyxERIcTCF+fLf5WIWylaZpfLFebRcSHx5AXpGVaZ9v5pGvt/H8T2otpDuGJhIfEeT4Tq5tlk5ogJndqTl8t0Vd81B6Lh/9kcRfjvKXcwA0yLCNIaYdFGlmim76xt6Qnlx1Q/qG+WoZgai2aq2lyNYqO3dkndNpOQXF/POLrQAUldj4cevxitc6h+xJzWHn8YpB4Lnuoz8Pc96zS3nk622u18gSjYoEQEJ4Y/B9qqSSvKZiSaU2GqB9LCzAwge3ncf+8EEctsWSZojh8LBXnEpqX244ypoDGQRYjHxy5wACLEYOpuey7ZjzYoRbjmaxL+0MARYjP00bxtSR7TEY4LN1yRw4mUtUsB+TL7DvrRbfC4Cg9O1MPl8de3rhLgY//xsjXl7O49/tQNPgvNaRNI8ILH0Tm42mfz0PwEfW0ewsbubUkJ6ZW8DR0+X2USsphDX/UfeH3q9m1LUcrB4nO/83e+qHnRzLzMdoX7Hg283nbgCUlV/MlW/8zoT/rmbdocZT7isqsfHf3/YD6t/mjG+3SxDUyNV5APTGG2+QmJhIQEAAffv2ZdWqVVWev2LFCvr27UtAQABt2rThrbfeqnBOZmYm99xzD/Hx8QQEBNC5c2cWLVpUWx9BNEZh8dBL9bTwyww4dUjdz88s3UE+vv4GQKBmic27YyjXWV5jeP5LXDRvL5+uTUbTNNLPFPLsIpW9mT6qA53jwxjdRZWdvt3kHBx8ueEIABd1bUpEkB8PjO7AB7f1JyrYz/769oQF2BecjO+pblM2c/uQRGJD/TmVW0RKVgF+JiMDEqO4f1QH5lxf7rvb+Q2GlC3kG4L4b8llakHEMg3pz706i1GzVpCcUSYI2vyp2sYkrLlaKgCg1SB1e/gPx2lLdp7giw1HMRjgP9f3wWCADYdPO6+bdA5ZsvMEuUVWSmwaUz7ZQGqWG43k54DvNh8jLaeQ0AAzRnuA/vh3271bVkGcE+o0AFqwYAHTp09nxowZbNq0iWHDhnHxxReTnOx6RdlDhw4xbtw4hg0bxqZNm3j00UeZOnUqX31VmgIvKipi9OjRJCUl8eWXX7Jnzx7eeecdmjd3czVeIdw1dLrqBTq+Ef57ngqEDq1Qz0W0UqWyei4xJpivp46gT9tm5BdbefSbbdz14Xoe+2Y7mXnFdIkP486hiQBc3qsZAD9sPY7V/pdzQbGV7+3Zkqv7ls5GG96hCUvuH85ndw3k5oGtSt9QLwum7SLQUMR7t57HP0Z34KM7+rPliTEs+Nsgpo1qT3x4meyPtRh+ewaALS0ncZowtTFqQDgl/e4E4MaiLygotvLJ2sP215So/iCAwVPBbG+A1jNAxzZASSEZZwp55GtV+rp7eBsu6RHP4Lbqv5vex3SuWWTvsTIbDaSfKWLyxxsoLDm391jTNI13Vh0E4J4R7Xj5mp4YDPDJ2mT+9d0OCYLcpGkaby4/wPdbzo0MaZ0GQLNmzeKOO+7gzjvvpHPnzsyePZuEhATefPNNl+e/9dZbtGzZktmzZ9O5c2fuvPNObr/9dl5++WXHOe+99x6nTp3i22+/ZciQIbRq1YqhQ4fSs2fPSsdRWFhIdna2048Q1YpsDXcuVXuF2YrVJqb/s6+HU4/LX+XFhwfy8R0DmDGuM34mI0t3pfHzjlSMBnj+qu6YTer/Joa1b0JEkIWTOYWsOZAOwOKdJ8guKKF5RKAjcNBFh/gzqG00hrIrYYc1V707mhVO7KRb83DuG9meYe2bEOhncj3AjR/AqYMQ3ATbAFX20rfEeCHzAvI1P3oaDzLUuJ1Ff+2m+K/5MP8SOJ0EQdGlaxQBRLdV25NYC9GObeDRb7aRfqaIjnGhPDC6AwCX91J/LH2zqWK/U0OXlV/Mqn1q5t3cSX0JCzCz+UgmT36/o45HVruW7z3J3hNnCPE3c8OAllzZpwUvXa2CoI/+PMxTC3fW9RBr5FB6LrtSav/31vI9J3nh591M/WwTn62reuubhqDOAqCioiI2bNjAmDFjnI6PGTOGNWtcT1P9448/Kpw/duxY1q9fT3GxmlXy/fffM2jQIO655x7i4uLo1q0bzz33HFZr5X/hzJw5k/DwcMdPQkLV66oI4RDXFSZ9Czd9BbFdSo83oAAIwGg0cNfwNnx7zxA6xIUAqnG5R4sIxzl+ZqNjZpZeBvtyg2p+vqpPc4xGA9UyGEqzQCmbqj5X0+Cvd+Gnh9Xj4Q/RvU0zDAY4ejqfOb/u450NOXxmvRCA2X5vscR2F5Yfp8GRPwEDjH5KLWBZ9v1bqjLYTz9+wy87TmAxGZg1sSf+ZhWAXdStKf5mIwdO5rLjHGsUXrLzBMVWjQ5xIVzYKY451/e292sd4dO1Df8XmrWSnp65K1T257rzEhzl2Kv7tuCFq3pgMMD7vyfxlf3fsitbj2bWy5JoxplCZnyzjZGvLGfcnFWO7F5t+aTMv5FHv9nGDw08E1RnAVB6ejpWq5W4uDin43FxcaSmut6hOjU11eX5JSUlpKerv0gPHjzIl19+idVqZdGiRTz22GO88sorPPvss5WO5ZFHHiErK8vxc+TIkRp+OtHotBsFk1fDhP9Az+uh1w11PSKvdGkWxg/3DeX7e4fw6LjOFZ6/vLfKjvyyI5VD6bmObMJVfVu4/yb2RmiOb678nKI8+GayWp/IVgydJ0C/2wgNsNAhVi2IOGvJXgBMQ+4Do4UYThNgKCbZ1BJGPqG2Dul9k9NlD5w8w7enVEkuMEXtC/bQ2E50bRbuOCc0wMKozur/Z77ddG6VwfTZbZd0V+XMCzrG8uCYjgA88f32ihvnNgA2m8bKvSe584O/6PDYTzzy9Vankt62o1n8cTADs9HA7fZyru7afglMH6kyf49/t93lHnWfrUtmwn9/Z9xrq+pNv1Sx1cZ7qw8x4uXlfLI2GZum/l6Y9vkmVu9Lr5X3TMnK57fdJwAY0yUOTYP7F2xm2e6Gu7VMnTdBG8ptFKlpWoVj1Z1f9rjNZiM2Npa5c+fSt29frrvuOmbMmFFpWQ3A39+fsLAwpx8hPGY0qXLLFW9BSGxdj8Zr/mYTPVpEuPzfYd+WanbWmcISpn++CU2D/olRtIoOdv8NHBmgza6fzzgA80bD1s/VStKjn1J7oZnUX+697fuCAVzcrSmTLhoCV88jt/9ULi9+luG5M9ne5g4ILw3KbDaNx77dxuhZK3g3WQU3A8z7+PLu/tw1vE2FIVxm73f6fsvxSrMKDU1WXjGr96tfjpf0KF2gc8oFbRnbNY5iq8bDX22lxFrFFi/V+PjPw4x9dSXby80UrA05BcXMW32IkbNWMOm9dSzdlYbVpvHZuiPc9O5a0s+o9X7m2nt/xvdsRrOyMwvt7r2wHYPbRpNXZOXeTzdSUFwaPP2w5TiPfrNNvV9hCf/6bnutf67qbDh8motmr+SphTvJLiihS3wYn941gEu6x1Ns1bj7o/VsqYVAdsFfR7BpMCAxirdu6stlvZpRYtOY/PEG1h50vT5YfVdnAVBMTAwmk6lCtictLa1ClkfXtGlTl+ebzWaio1X/QXx8PB06dMBkKu0n6Ny5M6mpqRQVyeJmQtSE0WhwBAdbjqpfcld7kv2B0plgabsqbmVx6iC8M0ItVhjcBCZ9D0OmqdKV3SB7r1GHuBB7M6sBulxG8LinadF1MGAobYa2e3nxHj7+U/2l3Kxjf6yWYIK0PPoFui4ZXNAxlvBAC2k5hfxxoGH+n3t5i3emUmzV6BgXSjt7Fg3UH48zr+xBRJCF3ak5zF+TVOk1quqJyi0s4YWfd7PnRA73frqRM4VVLFBZQ1abxvXv/MnTC3dyKD2XUH8ztw5uzSvX9CQ0wMxfSae57L+/s2TnCUdZ6K5hFQNdsC8OOrEXMSF+7E7N4Wl7P9Bvu09w/4LNaJqa4WgxGVi88wQ/b6/dMlNlrDaN//y6j2vf/oMDJ3OJDvZj5pXd+eG+oQxuG8OsiT0Z2i6GvCIrt76/zmU2y1slVhsL/lKVkRsGtMRoNPDyNT0Z2SmWwhIbd3ywnq1HM332fmdLnQVAfn5+9O3blyVLljgdX7JkCYMHD3b5mkGDBlU4f/HixfTr1w+LRf11OGTIEPbv34+tzEaVe/fuJT4+Hj8/Px9/CiEaH70MBhDkZ6qwYnO1whMgMApsJZBWrvn2zzehIAua9oC/rYTEYRVePr5HM96Z1I///W0Qwf5mp+f0GWffbjpOdoHqC/x641HeWK6WJph1bU/m3joAU8sB6gXJf+CKn9nIOL3fycezwYqtNt5ddZABzy09q83HeiAwzsV/r6hgPx6+qBMAry7ZW6HUo2kary7Zy5Dnf+PPSv7a/9/6I+QUqKAnKSOPJ77z/rMdz8yvco2ipbtOsP1YNqEBZp69oht/PjqSJyd05aq+LfhmyhBaRwdxLDOfuz5cj9WmMax9DF2aVZ7Zjw0L4NWJvRwzw2Yu2sXfP95IiU3jsp7xvNHmd17rokqu//puB1n5xV5/Nm8cy8zn+rl/8sqSvVhtGpf1asZvD17A9f1bYrL33vmbTbx1c196tgjndF4xN89bS1J6rk/ef/mek6RkFRAZZOGibip7aDEZef3GPgxqE82ZwhLWNMA/FOq0BPbAAw/w7rvv8t5777Fr1y7uv/9+kpOTmTx5MqB6cyZNKp3BMXnyZA4fPswDDzzArl27eO+995g3bx4PPvig45y///3vZGRkMG3aNPbu3cuPP/7Ic889xz333HPWP58Q56IOcaF0jle/TMZ1j68QhFSrbCN02T6gojzYskDdH/UkhDVz+XKj0cDoLnFEBFX8g6Z/YhQd4kLIL7by9YajbDh8moe/UiWMKRe05co+9myVPh2+in3BLu8Zz2Djdtpuf42CLN/0OazYe5KLZq/kmR93cSK7kPlrkli2p/Z7KCorf5V1bb8EereMILfIytM/Os+KmrVkL6/9uo/jWQXM+GZbhTKZ1abx3u9qLayr+rTAaFDbo3jTQ3U4I5fx/1nNtW//Uel3M2+Veq+bB7bixgGtnP4NtosN4dt7hjC0XYzj2N0uypzlDWvfhCn2BTvfXnmQwhIbozrH8vIwI8Ylj3PxvicZHXmCtJxCXvh5t8efy1vL9qRx8eyVrEs6RbCfiVcn9uS163oTHmipcG6Iv5n3b+tP2ybBpGQVMGb2SmYt3kN+Uc2WOdBnfF3dt4VjsgCUrir/6sSejoVNG5I6DYAmTpzI7Nmzeeqpp+jVqxcrV65k0aJFtGql/opLSUlxWhMoMTGRRYsWsXz5cnr16sXTTz/NnDlzuOqqqxznJCQksHjxYv766y969OjB1KlTmTZtGg8//PBZ/3xCnKseHdeJoe1iuO/Cdt5dQG+ETtlSemzH11CYpZYXaDPCq8saDAZusmeB3vs9ib99tJ4iq40xXeIczb5A6YKIyX9U3NBW02DfUvovu55P/Z7j74avSJp/t0dr5RzPzGfl3pP8siOV7zYf439/HeHOD9Zzy3vrHOWLYe3VL+jHvtlOXlHtlYug8vJXWUajgWcu74bRAD9uTXE0uM/5dR//sa+gHGBRs+MWrHeeKLJkZypHTuUTEWThmcu7MXVkewAe+3Y7hzPcz0Kcyi3i1vf/IsO+F9vzi3ZX6MHaciSTdUmnsJgM3DK4tcvrRAT5Mf+283j44k48dFFHp2CoKveP6kC/VpEADGoTzX9v6IMleTUABjReCfsc0Ph0bfJZWUXbatN48H9byC4ooVdCBIumDeOK3lWXnKOC/fj4zgEMaRdNUYmNOb/tZ9SsFSzaluLVsg7HM/Mdgej1/VtWeD7E31ztmOorg3auLXThA9nZ2YSHh5OVlSUN0ULUhp3fqTWT4nuqUhfAOyPh2Ho1g2vYA15fOqegmAHP/Uqe/a/ezvFhfDm5XLmsOB9mJqgZZvdtVNmm9H2q92jdXMd2JiVGfwzWIkwGjekBT3PFlddzfocmVb7/yr0nueODvyi2Vvy/VrPRwK2DW3PfyPaYjQbGvLqSY5n5/G14Gx5xMevOV259fx3L95zkgdEdHMFJZf79ww7e/z2J6yN20q99Av/4SwVMM8Z1xmIy8OQPO4kJ8WP5P0cQYv9Or35zDesPn+beEe14cGxHSqw2bnhnLeuSTtEzIYIvJw/CYqr67+2CYis3vPMnG5MzHY32WfnFvHhVD649r3Rpkns/3cjCrSlc2ac5s67tVbMvxoUzhSWs3neSCzrGEmAxwafXwd6fHM9/1PIZHt/bhrZNglk0bZhTRqQsTdMotmr4mb3PM2w4fJqr3lxDWICZ9Y+N9uhamqbx8/ZUnvlxF8cy8wEY1j6G56/q4bzNTDVmLdnLnF/3MahNNJ/dPbD6F9QxT35/1/ksMCFEI6RngE7sVHt2pW5TwY/RXGHquqdCAyyOPqWYED/evaVfxTKdJRCa91H3542GZ+Ph7WHw7d9V8GMOhEH3Ypq+hcNtrgfg7rx3ue29P5n80QbHL5TyjpzKY+rnmyi2arSMCqJPywgGt43mwk6xXHdeAj9PH85jl3YhPNBCsL+Zpy/vCsC7qw+x47gPZk7lZ8KPD8K3U8CmAsCsvGLH1GhX/T/lPTC6Az1Csngm/1ku3XovkWTzz7EduWt4G24Y0IrW0UGknyli7grVV7Up+TTrD5/GYjIwaZDKvplNRl69rhfhgRa2HMnkpV/2VPmeVpvG9M83szE5k7AAMx/cfh73jlDZxVeWlJZwjp7O46ftaiLMnUOrL2t5I8TfzEXd4lXwY7OWlknbjwXgxqx3iA9WmbCvNlRe4nv2x110f/KXGjUHL7dnXoZ3aOJxIGUwGLi4ezxLHzifqSPb42c2smpfOhfNXsk3m466lQ1Szc+qCnPDgDLZH5sVkv+EJU/Af/vDzJbO2dwGQgIgIcTZF9ESAiJUBiZtJ6x/Xx3vPN4nSwg8MLoDtw5uzUd3DKj8r922I9VtXgagQWCkWiRx2IMwfRuMfRZDWDxtrnkWLSCCLsbDXG9ezs87Uhn32ip+3++83kpBsZUpn2wkM6+YHi3CWXz/cL6eMoRP7xrIe7eex/NX9aBdbIjTay7sFMcl3eOx2jQe/Xpbzabc718KbwyCv96BzZ/AkbWUWG3M/nUvJTaNTk1DS99/3xLY87PLy4QGWHiiVw4mg4a/oZhXu+zjHnsw4mc28n/2Zul3Vh3iRHYB81arfpwJPZsTGxbguE7ziEBeuKo7AHNXHuSjP5IqHfqzP+7i5x2p+JmMvDOpH+1iQ7l5UCuaRwRyIrvQ0V/0/u9JWG0aQ9tV3dTsMye2q7Ksfxhc9Q6ExmPMOswrLVVZ7LdK1sApsdpYsP4IhSU25q486PXb66WnER29/99EoJ+JB0Z34Jfpw+mVEEFOQQn3L9jCvZ9u4nSu88xom03jZE4hW49m8vP2VF78ZQ8nsguJCvZjTNc4sNng16fh5fbw3li13Uz6HvUdrZvr9Rjriofdi0II4QN6I/TB5ZD0O2z9nzre9zafXD4mxJ8nJ3St+qQhU6FJR7VdRpNOEBzjNN3eISgKwwWPwM//x79DvuFQ0GjWHC9m0nvreHJCV8fMsye/38G2Y1lEBll486a+KoPghifGd2HlvpNsOZrFR38kceuQxOpfVFZhDix+DDbMdzqcvf0nbl8E6w+rbUNutmdnOJMGn9o3h31gF4RWXHakD6UZm/PzFgP/cjy+qFtT+raKtDeYb2WlPbt0x9CK476oWzwPjO7ArCV7+df3O4gO8XfKQhUUW3n82+18YV+F+eVrezKgjVrmIMBi4p9jOzJ9wWbeXH6AS3vEO6Zi3zHMw+/IW0kq0KHlQAgIV8353/yNAUfn04QurDlgoqjEViE7szE50zEj7pcdqZzMKaRJqL9Hb52WXcD2Y2ol8vM7Vl12dUdiTDBfTh7EG8sPMOfXffy4LYU/DmYQG+pPTkEJ2QXFnCksqdASB3CN3vy8dzGssm89FRAO7UZDTHtYPhN2/gDjXgFLQMUL1FOSARJC1A29DLbqFSjKgai2kDj87L2/JRC6Xq6m2oc0cR386M67A2I6Yi7I4MP2K7i8VzOsNo3Hv93Ov77bzsd/Hubzv45gtO8o71aPhX2pjtiwAEdW5aVf9pCW7cFqw+n74c0hpcHPgMlw6asAHP3rB9YfPk2Iv5nXruvFjQPsAdDen9VebJoVkla5vKzhyLrS+6nbIGVr6WODwbFK+LI9J6vNyNx3YTtuHtgKTYPpn2927CN3OCOXK99YwxcbjmI0wJPjuzChp/PMvwk9m9GteRiBhenMe/Ml/AszaB8bwgXV9GH5jB4AtR6qbrtfC836YCo+w4zAr8grsrI+qWIz9PIys9eKrRr/K9c07o7le1UT+u2xe4jZ/IajpFkTZpORqSPb8/WUwbRtEsyp3CJ2p+ZwLDOfnAIV/BgMEBfmT6+ECMZ1b8qUC9oyxZ4BZMun6rbPJPjnAbh6Hgx/SO3xV5gF+5dU/uZlWYtVmfZ4Ndvh1DLJAAkh6oY+FT7f/guk761VByF1yWSBi56Dj6/C/NdcXp1yO+3jOvLSL3v48I/SRRf/MaYjQ9u7MeMo8wi8PVwFfFfN44b+Lflyw1E2H8lk3upD7jVEF+ejLbgJQ+Zhsv2b8XWrR9mY2Y38gym8A3ThEOc313j6hmG0jC6zH9qe0oZeklZB96udr1uQXbo+U8vBkLxGldTiezhO6dsqknHdm7Jom+rHqSojYzAYeHJCV9LPFPLT9lT+9uEGpo/uwOyle8kpKCE62I851/dmSPmZWiWFGPf+zIcB7xPmvwJziY1hlj6cGvZBlbsF+IzNCod/V/f1AMhohIueh/fGMEH7jaXGLqzY24bB5ca+bM9JQGNEu3CW7c/ms3XJTD6/rWPNHncs251GOGd45MwLsLRAlY27XVX9C93Qo0UEP04dxtpDpzAaVNkzNMBMaICZyCA/1w3r+Zmwe5G6f96djpXZMRrVuNbMgW1fqDJ2dTbMV/+m9i1RW9aYPcuO+YpkgIQQdUPPAAGY/KDXjXU2FLe0G6UaYW0lGBY/zj0j2vHWTX0JtJe6RneJ4+/uroWy92cV+O38Fn68H6MBpo5Uf2V/sjbZsYhjVbSfH8FwchcntTBGZj3Gk1uj+H7LcZYc0dhmaw3AvKE5zsFPUR4cWFb6+JCLDNCx9aDZ1C/cYf9Qx7YuUM3qZTw0thNhAWZ6JURwfvuqMzImo4FXJ/ZiQGIUOYUlPL1wJzkFJfRpGcHCqUOdgx+bDVa+DK90hP9NIurYMswGlS0bYdrCZR092HalJk5sV4ty+oVC056lx1sOgB7XYUTjv37/Ydim+yHnhOPp1NNn6HBiEYv9HmJe6jUMCzjA0dP5rLQvK+COYquNVfvSud70GxabPSP4x+sVl2yogQCLifM7NGFY+yb0SoigbZMQYkMDKp+tt/NbsBaqTZ+b9nB+rvs16nbPzyqArkpBNix/Xt0//6E6C35AAiAhRF2JbK36CEBtdhocXafDcctY+6bKe3+CnFQu6taU7+8dwhPjuzB7Yi+M7v6Ff2xD6f2NH8KKF7mgQywd4kI4U1jCJ39WszP7jm8wbHgfm2bgwZJ7OL9vN/5+QVseu6Qzr07sSbO+6q9w88HfnF93aAWU5ENIU7XP2qkDkF1uR2+9/JUwENqOgNBmkH/aOXMEtI4J5veHL2TB3wa69bkDLCbmTurnWETztiGt+fzuQcSHlykX5mfCZxPht6fVe4bGw9D7OXbjCo6YW2LGSsDBxdW+l08k2bM/rQaBqVyxZMIc8gc9QLFmYmjxH9j+2x82fQIbPyL4nUG85vcGHYzHMJbkMzNYrR1U7X/TMtYnnaagsIDbLGU+67ENpf9t6sLmz9Rtz+sqZmqbdoeYjipA2r2w6uv8/hrkpUN0O5X1rUMSAAkh6obBAB3HgckfBk2p69G4J6Y9tDhP3d/9IwDt40K5bUiiZytiH12vbrtcpm6XP4dxyyfcPVxlkN77/VDlCy+eOkTRN/cC8IZ1ApdecSMvX9OT/7uoE3cOa8MVvVsQ3fNide6BXx29RgDsWVT6vvqebOWzQMl/qtuE/mqD357XqcebP6kwlNAAS6Xr4LgSHmjh23sGs+KfF/DE+K7OzcMndsDcC2DfYjAHwIT/wP07YNSTNG/fi4TB9nHs+t7t96sRvf+n1ZCKz5n9CRz7BA9FzWabrTXGwkz4bgp8fy+hecmc0kJYm3AnWIJokbuDMcb1/Lb7BMfLLZ9wKrfI5fIHy/ekMc64ljhOQXAs9LB/9j/+6+MP6aZTB+HInypo7n5txecNhtJS6rYvK79O9nGVyQIY9e/SMlodkQBICFF3JvxHzURq3reuR+K+Tpeq210/ePf6/EzI2KfuX/IqDLUv+vj9VC4L2Ul8eAAncwpdbyNRUkTWRzfhV3KGv2wdsJ3/CNf0S6h4XkJ/VbrJy4CUzeqYzVY69b3jxdDavs9a0srS19mspcFZS/uid/q6TPuXQnYNNwLVNPz3LaLVuqdg2UxY9w5s/xr+ehfeHQWnD0F4S7j9F9VoaywTXHWZYB/Hr1Dou40+XbLZyvT/VNyPTteq60AuL3qab6PvBJM/WkhTXtAmMaRwDoFjH4eBfwfgX0FfgWbj83WlWaAft6Yw4uXlXDJndYUlApbtPsEdZnvG7bw7Yeh0dX/3Qjh1yFef0n36FjVtRkBYJWtJ6f1JB5ermYau/PasykAmDIROl/h8mJ6SAEgIUXdMloZR+ipLb/JMWqXKNJ46vlHdRrZWn33kv6DHRNCsWL6YxNtx39KMdN5eeRBb2XWBUrdz+pNbCT+9nUwtmKWdnuO+UZ1cv4fJAm3OV/f3/1r6vrlpak2bVkNKZ9yVzQCl7VQz8vxCVK8HQHRbtT6SZoOtn3v+eXVH18N7F8GCm2DtW7DieVj0IHx5G/z4DyjOU79g/7aitEG+rLhuEJmoyiz7arkMdmI7FGSq7yG+Z6WnXdAxFismHs8YQ8k/9vLnZct5s/AigkPC6NYsHAZPhYAIWpQkc6VpFZ//dYSsvGL++cUW7vl0o2NT1Se+38Ey+5pCR0/nEXZyAz2NB9FM/tDvdojtrHrQNBusfbt2P3t5Nhts0ctf11d+XnRb9YeMZoUd31Z8PnV7aRZxzDP1YsKDBEBCCOGJ6LbQpLPazX6vF7+Ij9r7f5r3U7cGA0z4r/oFV5JPj+QPWRkwnfszZ7Jh5Q+w7h20t8+Ht4YQeUiV3eY3eYgHJ46sejZUu1Hqdv9SdauXv9qNArOfyvAYTJB5GDLtmYkja9Vti37O2Re9QX3Tx5434p5Ogi9ug3dHqjKKOVD9Uu97mwomWw5Wwc0Fj8BNX0FQlOvrGAylWSBPy2DWEsjNUMsGpO1yLgu64lj/x0X/Txndm4cTGWQhp6CETWk2lu/LBNTKzUajAQIjHNu6/MPyFZk5Zxj+0jK+2HAUg0Ft0HtN3xbYNLXFx47jWSzbc9KR/TH0uFYt0QAw0F4m3vSRyiJ6wmZTvTefXQ9f3Qk/TINfZqhm89OHq37tkT/VvxG/0OqzNnoz9LYvKj639AlAgy6XQ8J5no2/lsg0eCGE8FTn8XByl/pF3HOiZ6/VG6Bb9Cs9ZvaDG75Q66j88V/Mh1Yy3vQnLFf9OAagSDOxxNaXzXHXMO2u26vdW4t29pWuj65zbmLuOE7d+oeq7UCO/qWyQL1vhGR7AJRQbs+nrpfDTw9Bxn41g63jxdV/ztTtqmdl25dqxW8MKpC6cIbae80bnS9Tv8j3Llb7uVmqWG8paTWseFGtNVNYbmZSi/Ng/GsQV8limeWnv1fCZDQwrH0Tvt9ynOV70lyv3Nz/bvjzTeJzUrjRtJT38y+mWXgAsyb2YmCbaIqtNo5n5fP7/gxun/8XvUOzeN1oL0MOLNMb1/ZCFXif3KUa54dMrXJsTpb+C9b8x/VzK16AfnfA8AfVYqDl6dmfrpeBX1DF58vqegX88qj6N3fqEEQlquBz13cqEDdaVMaznpAMkBBCeKqzvQ9o/69qarm7NE1NM4eKfU9GI3QYC7f8wKmbf+Ub23DyNT/2ai15qvhmhlvf5MTYuTwy5S7HJqRVimipZuZoNtjwgSpvGUzQflTpOXoZTF8QUc8AJfR3vpZ/KPSwN79+fiOsfMn1wnyapn7RfXgZvDVE/fK0FUObC9Smt5e/7n3wAypgC2sBxblw4DfX5yT/CR+Mh/mXqFlvZYMf/zDVdH/0L7UO09J/q0CqLJut4gKIVbjAvkrzNxuPsffEGYwGtemogyUQLngYgOl+33FTryh+mj6cgfYVry0mI2/c2Jf2sSGcyC7kvBNfYDJonGk+DOK6lF7HYIBB96j7a99WgYU7fp9TGvwM/yeMfQ5GzFDludbDwFoEa9+E13qq6el5ZRZ2LM4vLWdVVf7ShTYt7ZlaeL/67/B8S/jydnXsvDtUBrWekAyQEEJ4qmkP1ayblax+EesBUXUykyH3pNr0tfxaKmVEte3Hul7Pcv86tYJwt+ZhfDyxF+1iQz0bZ7tRaq+mlS+px60Gqz3PdK2HqZW4D62CnFRV6sBQOtOtrDHPql+IWxfAb8+oIOGKuWorjYwD6vjWBarkBSrY6nIZDL7Xd03uBoPKvq19E3Z+71ySOX0YFk4vDYyMFtVI3e929Ys5IFz1RmUdU9ms3Qth9SzY8Q2c/38qGxTdVs14cqP/RzfMvgbS8Sy1Xk+flpFEBPk5n9TrJvh9DuGnDvBM3HIIHOT0dHighfdvO48b/7uUa0uWAxB8wbSKb9b9Gvj135B9VP13G/A3VWarzJbPYcnj6v7opytmjTQNDi6DpU+qzUyXz1Q//uEQkQCWIBVARrRUpUp3dL9GBZ4Hy6w35R8O7UerMmc9IgGQEEJ4Sv9F/OfrajaYuwGQXv6K61btnknTRnYgJauA3gmR/P2Cth7vBg6oMtifr0ORfdaUXv7SJQxQgUL2UfXLElQgEOBiWwv/ELjibUg8XzUvH1yusjyRiarkofMLhT43q205Ilt5PubqdJmgAqA9P0FJkSofpmyFT66GMydUcNn7JrWIY0TLiq8Pbw7XfQK7FsKif6qZZ99OLvM57Z+95UC3pmk3CfWne/Nwth1T09lHdHKxcanJDBc+phq+1/zHHpQ578HWIjKIBT03Eroxn8zgNkS0G1XxOpYAVVJb9iwsf04Ftm0uUCXKNhdAcJPShQX3LYXv7BmjQfe6LpkZDKq0lniBKlMtfx5O7lbbWpwoMz2/5/UqQ+mO7lerLJytRC0amTBQ7bXn7uvPIgmAhBDCG50vVcHF3p/U3kburGniqv+nEk3DA5h/W/9qz6tSqyGq6bjEXubpeJHz835BKtuTvKZ0jZmEAZVfz2BQvUIt+qnG5rQdKqNlMKoZXD2vh07jwK8WV2tOGKDWxslNg0MrVXDx+U1q9lpsV7juY4hqU/11Ol+qSoCrZ8HhNaq/KS+jtGTWfqzbQzq/QxNHAHR+ZfuUdb1CBT/HN6osy/jZzs9nH6fpNjXDK+LixyufJTX0AfV9b/tS9QPtX+K8B5dfqGokP3NCBSHdr1XZn6oYjWp8Xa9QSwxkHYWsIypjWZyvAjZ3WQJVqbMBkABICCG8kTAAgmLUqrZJq9WqydXRA6Czte6RJQBaD1F9OU06uw4MEoepACjXvlVDVQGQrklHuOtXFTSZA9Vf/aFNfTv2yhhNqvS14X349UlI2636jFoPg4kfV10SKi8gTO3wrss7ZS+BZalMl5tGdo7lv8v20zwikK6VbAqLwaCmf88fp5qYB/5dfY+6355RSwEkDFCBSGVMZtWwPPxBOLlHlQJ3fqeCUc2mAsGiHHVu25Fw2eueZV/8QyC2k/o5x0kAJIQQ3tB/EW/8QPWTVBcAWYvh+GZ1v3n1GSCf6XWDCoD63uL6+dbD1EwgXUs3AiBQf+kP/2fNx+eNLhNUAJS6TT3ueoUqz9V0X6mgqMqn4Vehd8tI3pnUj4SowKqXJmg9BDpeAnt+hCVPwA32suPxzbDZvtP62OfcXyOnSUc4/5/qx2ZTpau8UyqTZSuxlzjdX6m7sal/RTkhhGgo9EURdy2sfm2ZtJ2qFOUfrvZBOlu6XQX/PKh6clxpcZ6aGQUQEgcRtdC342uth6l+F1BTxa96r0431QS1GW6nppVkf5xO/LdqEN/7k8ocappakwdNNRC7UR51yWhUDe7RbdUsvlaDJfiphgRAQgjhrcThqufiTKrad6sqjvJX77PfEBocXXlWwRJQOu09YUC9WKG3WiYLTPoebvxSZUzqYYNtpWLal24Cuvgx1UR/eLXa/2zkE3U6tMamAf2rEUKIesbsX7qh6ec3wPr3K18pufwK0PVJH3t5TN/4tCGI66KmVjeEgK28Cx5W0+yPb4Jv7Jm5QfeqqefirJEASAghauKimaoUZi1S69B8d2/FxfXg7DdAe6LHNfBEZr3YoLJRCIkt3eC0OFeVHvXH4qyRAEgIIWoiIAyu/QhG/VtNT978Mcwbo2YT6Qqy1foqUD8DIGiYmZSGbOA9EGpfFXvEDLXatjirZBaYEELUlMGg/oJv1lstdpe6Ff7bX83AGnq/fbNRDcITKiyAJxopvyCY9J36t9LtqroeTaMkAZAQQvhKm/PVnlffTlHbAWz8QO2grs/6qq/ZH1E3mnRQP6JOSAlMCCF8KbwF3PI93PazWohOs6r9uEACICHqEckACSFEbWg1CG7+Ws3+WvWK2nOq25V1PSohhJ0EQEIIUZta9IXrP63rUQghypESmBBCCCEaHQmAhBBCCNHoSAAkhBBCiEZHAiAhhBBCNDoSAAkhhBCi0ZEASAghhBCNjgRAQgghhGh0JAASQgghRKMjAZAQQgghGh0JgIQQQgjR6EgAJIQQQohGRwIgIYQQQjQ6EgAJIYQQotGRAEgIIYQQjY65rgdQH2maBkB2dnYdj0QIIYQQ7tJ/b+u/x6siAZALOTk5ACQkJNTxSIQQQgjhqZycHMLDw6s8x6C5EyY1MjabjePHjxMaGorBYPDptbOzs0lISODIkSOEhYX59NpCke+49sl3fHbI91z75DuufWfzO9Y0jZycHJo1a4bRWHWXj2SAXDAajbRo0aJW3yMsLEz+x1bL5DuuffIdnx3yPdc++Y5r39n6jqvL/OikCVoIIYQQjY4EQEIIIYRodCQAOsv8/f154okn8Pf3r+uhnLPkO6598h2fHfI91z75jmtfff2OpQlaCCGEEI2OZICEEEII0ehIACSEEEKIRkcCICGEEEI0OhIACSGEEKLRkQDoLHrjjTdITEwkICCAvn37smrVqroeUoM1c+ZMzjvvPEJDQ4mNjeXyyy9nz549TudomsaTTz5Js2bNCAwM5IILLmDHjh11NOKGb+bMmRgMBqZPn+44Jt+xbxw7doybbrqJ6OhogoKC6NWrFxs2bHA8L99zzZSUlPDYY4+RmJhIYGAgbdq04amnnsJmsznOke/YcytXrmT8+PE0a9YMg8HAt99+6/S8O99pYWEh9913HzExMQQHBzNhwgSOHj16dj6AJs6Kzz//XLNYLNo777yj7dy5U5s2bZoWHBysHT58uK6H1iCNHTtWe//997Xt27drmzdv1i655BKtZcuW2pkzZxznPP/881poaKj21Vdfadu2bdMmTpyoxcfHa9nZ2XU48oZp3bp1WuvWrbUePXpo06ZNcxyX77jmTp06pbVq1Uq79dZbtbVr12qHDh3Sli5dqu3fv99xjnzPNfPMM89o0dHR2sKFC7VDhw5pX3zxhRYSEqLNnj3bcY58x55btGiRNmPGDO2rr77SAO2bb75xet6d73Ty5Mla8+bNtSVLlmgbN27URowYofXs2VMrKSmp9fFLAHSW9O/fX5s8ebLTsU6dOmkPP/xwHY3o3JKWlqYB2ooVKzRN0zSbzaY1bdpUe/755x3nFBQUaOHh4dpbb71VV8NskHJycrT27dtrS5Ys0c4//3xHACTfsW/83//9nzZ06NBKn5fvueYuueQS7fbbb3c6duWVV2o33XSTpmnyHftC+QDIne80MzNTs1gs2ueff+4459ixY5rRaNR+/vnnWh+zlMDOgqKiIjZs2MCYMWOcjo8ZM4Y1a9bU0ajOLVlZWQBERUUBcOjQIVJTU52+c39/f84//3z5zj10zz33cMkllzBq1Cin4/Id+8b3339Pv379uOaaa4iNjaV379688847jufle665oUOH8uuvv7J3714AtmzZwurVqxk3bhwg33FtcOc73bBhA8XFxU7nNGvWjG7dup2V7102Qz0L0tPTsVqtxMXFOR2Pi4sjNTW1jkZ17tA0jQceeIChQ4fSrVs3AMf36uo7P3z48FkfY0P1+eefs3HjRv76668Kz8l37BsHDx7kzTff5IEHHuDRRx9l3bp1TJ06FX9/fyZNmiTfsw/83//9H1lZWXTq1AmTyYTVauXZZ5/l+uuvB+Tfcm1w5ztNTU3Fz8+PyMjICuecjd+NEgCdRQaDwemxpmkVjgnP3XvvvWzdupXVq1dXeE6+c+8dOXKEadOmsXjxYgICAio9T77jmrHZbPTr14/nnnsOgN69e7Njxw7efPNNJk2a5DhPvmfvLViwgI8//phPP/2Url27snnzZqZPn06zZs245ZZbHOfJd+x73nynZ+t7lxLYWRATE4PJZKoQ0aalpVWIjoVn7rvvPr7//nuWLVtGixYtHMebNm0KIN95DWzYsIG0tDT69u2L2WzGbDazYsUK5syZg9lsdnyP8h3XTHx8PF26dHE61rlzZ5KTkwH5t+wL//znP3n44Ye57rrr6N69OzfffDP3338/M2fOBOQ7rg3ufKdNmzalqKiI06dPV3pObZIA6Czw8/Ojb9++LFmyxOn4kiVLGDx4cB2NqmHTNI17772Xr7/+mt9++43ExESn5xMTE2natKnTd15UVMSKFSvkO3fTyJEj2bZtG5s3b3b89OvXjxtvvJHNmzfTpk0b+Y59YMiQIRWWcNi7dy+tWrUC5N+yL+Tl5WE0Ov+6M5lMjmnw8h37njvfad++fbFYLE7npKSksH379rPzvdd6m7XQNK10Gvy8efO0nTt3atOnT9eCg4O1pKSkuh5ag/T3v/9dCw8P15YvX66lpKQ4fvLy8hznPP/881p4eLj29ddfa9u2bdOuv/56mdZaQ2VngWmafMe+sG7dOs1sNmvPPvustm/fPu2TTz7RgoKCtI8//thxjnzPNXPLLbdozZs3d0yD//rrr7WYmBjtoYcecpwj37HncnJytE2bNmmbNm3SAG3WrFnapk2bHMu7uPOdTp48WWvRooW2dOlSbePGjdqFF14o0+DPRa+//rrWqlUrzc/PT+vTp49jyrbwHODy5/3333ecY7PZtCeeeEJr2rSp5u/vrw0fPlzbtm1b3Q36HFA+AJLv2Dd++OEHrVu3bpq/v7/WqVMnbe7cuU7Py/dcM9nZ2dq0adO0li1bagEBAVqbNm20GTNmaIWFhY5z5Dv23LJly1z+//Att9yiaZp732l+fr527733alFRUVpgYKB26aWXasnJyWdl/AZN07TazzMJIYQQQtQf0gMkhBBCiEZHAiAhhBBCNDoSAAkhhBCi0ZEASAghhBCNjgRAQgghhGh0JAASQgghRKMjAZAQQgghGh0JgIQQQgjR6EgAJISol5588kl69epVZ+//+OOPc/fdd/vseldffTWzZs3y2fWEEDUjK0ELIc46g8FQ5fO33HIL//3vfyksLCQ6OvosjarUiRMnaN++PVu3bqV169Y+uebWrVsZMWIEhw4dIiwszCfXFEJ4z1zXAxBCND4pKSmO+wsWLOBf//qX047ogYGBhISEEBISUhfDY968eQwaNMhnwQ9Ajx49aN26NZ988gl///vffXZdIYR3pAQmhDjrmjZt6vgJDw/HYDBUOFa+BHbrrbdy+eWX89xzzxEXF0dERAT//ve/KSkp4Z///CdRUVG0aNGC9957z+m9jh07xsSJE4mMjCQ6OprLLruMpKSkKsf3+eefM2HCBKdjF1xwAffeey/33nsvERERREdH89hjj1E2if7GG2/Qvn17AgICiIuL4+qrr3a6xoQJE/jss8+8+9KEED4lAZAQosH47bffOH78OCtXrmTWrFk8+eSTXHrppURGRrJ27VomT57M5MmTOXLkCAB5eXmMGDGCkJAQVq5cyerVqwkJCeGiiy6iqKjI5XucPn2a7du3069fvwrPffDBB5jNZtauXcucOXN49dVXeffddwFYv349U6dO5amnnmLPnj38/PPPDB8+3On1/fv3Z926dRQWFvr4mxFCeEoCICFEgxEVFcWcOXPo2LEjt99+Ox07diQvL49HH32U9u3b88gjj+Dn58fvv/8OqEyO0Wjk3XffpXv37nTu3Jn333+f5ORkli9f7vI9Dh8+jKZpNGvWrMJzCQkJvPrqq3Ts2JEbb7yR++67j1dffRWA5ORkgoODufTSS2nVqhW9e/dm6tSpTq9v3rw5hYWFpKam+vaLEUJ4TAIgIUSD0bVrV4zG0v/biouLo3v37o7HJpOJ6Oho0tLSANiwYQP79+8nNDTU0VMUFRVFQUEBBw4ccPke+fn5AAQEBFR4buDAgU4N3IMGDWLfvn1YrVZGjx5Nq1ataNOmDTfffDOffPIJeXl5Tq8PDAwEqHBcCHH2SRO0EKLBsFgsTo8NBoPLYzabDQCbzUbfvn355JNPKlyrSZMmLt8jJiYGUKWwys5xJTQ0lI0bN7J8+XIWL17Mv/71L5588kn++usvIiIiADh16lSV7y2EOHskAySEOGf16dOHffv2ERsbS7t27Zx+wsPDXb6mbdu2hIWFsXPnzgrP/fnnnxUet2/fHpPJBIDZbGbUqFG8+OKLbN26laSkJH777TfH+du3b6dFixaOIEsIUXckABJCnLNuvPFGYmJiuOyyy1i1ahWHDh1ixYoVTJs2jaNHj7p8jdFoZNSoUaxevbrCc0eOHOGBBx5gz549fPbZZ/znP/9h2rRpACxcuJA5c+awefNmDh8+zIcffojNZqNjx46O169atYoxY8bUzocVQnhEAiAhxDkrKCiIlStX0rJlS6688ko6d+7M7bffTn5+fpWLEd599918/vnnjlKabtKkSeTn59O/f3/uuece7rvvPsdq0REREXz99ddceOGFdO7cmbfeeovPPvuMrl27AlBQUMA333zDXXfdVXsfWAjhNlkJWgghytE0jYEDBzJ9+nSuv/56QK0D1KtXL2bPnu3VNV9//XW+++47Fi9e7MORCiG8JRkgIYQox/D/7d2xDQAhDARBE1CPiyWjVoJv4DPIbqaAi1eyEGPU3rvOOc8255y11nq2B9zxCgzgR3dXdz/be/mxKnDPCQwAiOMEBgDEEUAAQBwBBADEEUAAQBwBBADEEUAAQBwBBADEEUAAQJwPEMFWSnMCDH8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linefig, lineax = plt.subplots()\n",
    "plt.plot(nma1.results['times'], nma1.results['eigenvalues'], label='DCD')\n",
    "plt.plot(nma2.results['times'], nma2.results['eigenvalues'], label='DCD2')\n",
    "lineax.set_xlabel('Time (ps)')\n",
    "lineax.set_ylabel('Eigenvalue')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DCD and DCD2 appear to be in similar conformation states."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a Gaussian network model with only close contacts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `MDAnalysis.analysis.gnm.closeContactGNMAnalysis` class provides a version of the analysis where the Kirchhoff contact matrix is generated from close contacts between individual atoms in different residues, whereas the `GNMAnalysis` class generates it directly from all the atoms. In this close contacts class, you can weight the contact matrix by the number of atoms in the residues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:21.756636Z",
     "iopub.status.busy": "2021-05-19T05:58:21.756105Z",
     "iopub.status.idle": "2021-05-19T05:58:27.825024Z",
     "shell.execute_reply": "2021-05-19T05:58:27.825519Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MDAnalysis.analysis.gnm.closeContactGNMAnalysis at 0x7f0554052c40>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nma_close = gnm.closeContactGNMAnalysis(u1,\n",
    "                                        select='name CA',\n",
    "                                        cutoff=7.0,\n",
    "                                        weights='size')\n",
    "nma_close.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:27.835704Z",
     "iopub.status.busy": "2021-05-19T05:58:27.834967Z",
     "iopub.status.idle": "2021-05-19T05:58:27.960946Z",
     "shell.execute_reply": "2021-05-19T05:58:27.961555Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(nma_close.results['eigenvalues'])\n",
    "plt.xlabel('Eigenvalue')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-19T05:58:27.976945Z",
     "iopub.status.busy": "2021-05-19T05:58:27.976108Z",
     "iopub.status.idle": "2021-05-19T05:58:28.085441Z",
     "shell.execute_reply": "2021-05-19T05:58:28.086050Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Eigenvalue')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
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    }
   ],
   "source": [
    "ax = plt.plot(nma_close.results['times'], nma_close.results['eigenvalues'])\n",
    "plt.xlabel('Time (ps)')\n",
    "plt.ylabel('Eigenvalue')"
   ]
  },
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   "source": [
    "## References\n",
    "\n",
    "[1] Oliver Beckstein, Elizabeth&nbsp;J. Denning, Juan&nbsp;R. Perilla, and Thomas&nbsp;B. Woolf.\n",
    "Zipping and <span class=\"bibtex-protected\">Unzipping</span> of <span class=\"bibtex-protected\">Adenylate</span> <span class=\"bibtex-protected\">Kinase</span>: <span class=\"bibtex-protected\">Atomistic</span> <span class=\"bibtex-protected\">Insights</span> into the <span class=\"bibtex-protected\">Ensemble</span> of <span class=\"bibtex-protected\">Open</span>↔<span class=\"bibtex-protected\">Closed</span> <span class=\"bibtex-protected\">Transitions</span>.\n",
    "<em>Journal of Molecular Biology</em>, 394(1):160–176, November 2009.\n",
    "00107.\n",
    "URL: <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0022283609011164\">https://linkinghub.elsevier.com/retrieve/pii/S0022283609011164</a>, <a href=\"https://doi.org/10.1016/j.jmb.2009.09.009\">doi:10.1016/j.jmb.2009.09.009</a>.\n",
    "\n",
    "[2] Richard&nbsp;J. Gowers, Max Linke, Jonathan Barnoud, Tyler J.&nbsp;E. Reddy, Manuel&nbsp;N. Melo, Sean&nbsp;L. Seyler, Jan Domański, David&nbsp;L. Dotson, Sébastien Buchoux, Ian&nbsp;M. Kenney, and Oliver Beckstein.\n",
    "<span class=\"bibtex-protected\">MDAnalysis</span>: <span class=\"bibtex-protected\">A</span> <span class=\"bibtex-protected\">Python</span> <span class=\"bibtex-protected\">Package</span> for the <span class=\"bibtex-protected\">Rapid</span> <span class=\"bibtex-protected\">Analysis</span> of <span class=\"bibtex-protected\">Molecular</span> <span class=\"bibtex-protected\">Dynamics</span> <span class=\"bibtex-protected\">Simulations</span>.\n",
    "<em>Proceedings of the 15th Python in Science Conference</em>, pages 98–105, 2016.\n",
    "00152.\n",
    "URL: <a href=\"https://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.html\">https://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.html</a>, <a href=\"https://doi.org/10.25080/Majora-629e541a-00e\">doi:10.25080/Majora-629e541a-00e</a>.\n",
    "\n",
    "[3] Benjamin&nbsp;A. Hall, Samantha&nbsp;L. Kaye, Andy Pang, Rafael Perera, and Philip&nbsp;C. Biggin.\n",
    "Characterization of <span class=\"bibtex-protected\">Protein</span> <span class=\"bibtex-protected\">Conformational</span> <span class=\"bibtex-protected\">States</span> by <span class=\"bibtex-protected\">Normal</span>-<span class=\"bibtex-protected\">Mode</span> <span class=\"bibtex-protected\">Frequencies</span>.\n",
    "<em>Journal of the American Chemical Society</em>, 129(37):11394–11401, September 2007.\n",
    "00020.\n",
    "URL: <a href=\"https://doi.org/10.1021/ja071797y\">https://doi.org/10.1021/ja071797y</a>, <a href=\"https://doi.org/10.1021/ja071797y\">doi:10.1021/ja071797y</a>.\n",
    "\n",
    "[4] Naveen Michaud-Agrawal, Elizabeth&nbsp;J. Denning, Thomas&nbsp;B. Woolf, and Oliver Beckstein.\n",
    "<span class=\"bibtex-protected\">MDAnalysis</span>: <span class=\"bibtex-protected\">A</span> toolkit for the analysis of molecular dynamics simulations.\n",
    "<em>Journal of Computational Chemistry</em>, 32(10):2319–2327, July 2011.\n",
    "00778.\n",
    "URL: <a href=\"http://doi.wiley.com/10.1002/jcc.21787\">http://doi.wiley.com/10.1002/jcc.21787</a>, <a href=\"https://doi.org/10.1002/jcc.21787\">doi:10.1002/jcc.21787</a>."
   ]
  }
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