{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Example MSM for a ligand binding process" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "-" } }, "source": [ "by Stefan Doerr" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We demonstrate how to use the HTMD code for analysing a protein-ligand binding process\n", "\n", "You can download the data and analysis file from the following links:\n", "\n", "* [Datasets](http://pub.htmd.org/tutorials/ligand-binding-analysis/datasets.tar.gz). Warning: 3GB filesize.\n", "\n", "Alternatively you can download the dataset using `wget`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "os.system('wget -rcN -np -nH -q --cut-dirs=2 -R index.html* http://pub.htmd.org/tutorials/ligand-binding-analysis/datasets/')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Getting started" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "First we import the modules we are going to need for the tutorial:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "\n", "Please cite -- HTMD: High-Throughput Molecular Dynamics for Molecular Discovery\n", "J. Chem. Theory Comput., 2016, 12 (4), pp 1845-1852. \n", "http://pubs.acs.org/doi/abs/10.1021/acs.jctc.6b00049\n", "\n", "You are on the latest HTMD version (unpackaged : /shared/sdoerr/Work/htmdacellera/htmd).\n", "\n" ] } ], "source": [ "%pylab inline\n", "from htmd.ui import *" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Creating a simulation list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the purposes of an analysis, full atom information is required by HTMD. Therefore each simulation trajectory has to be associated with a corresponding PDB file. Additionally, if you plan to run adaptive, each trajectory needs to be associated with an input directory which contains the files used to run the simulations. The `simlist` function allows us to make these associations\n", "\n", "```python\n", "sims = simlist(glob('data/*/'), glob('input/*/structure.pdb'), glob('input/*/'))\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Filtering trajectories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Typically waters are removed from the trajectories as they are not used in the analysis and it helps speed up further calculations. These filtered trajectories are written into a new directory. The advantage of filtering is that if your input structures only differ in the number of water molecules, you can produce a single unique pdb file for all simulations once you remove the waters, which will speed up calculations. This step is not necessary for the analysis and you can skip it if you don't mind the slowdown in calculations. In that case replace in the following commands the fsims variable with sims.\n", "```python\n", "fsims = simfilter(sims, './filtered/', filtersel='not water')\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "In this tutorial due to space limitations we provide only the filtered trajectories which are stored in three separate dataset folders. Therefore we will skip the above two commands and construct the simulation list directly from the filtered trajectories:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating simlist: 100% (230/230) [#################################] eta 00:00 -\n", "Creating simlist: 100% (309/309) [#################################] eta 00:00 |\n", "Creating simlist: 100% (314/314) [#################################] eta 00:01 -\n" ] } ], "source": [ "sets = glob('datasets/*/')\n", "sims = []\n", "for s in sets:\n", " fsims = simlist(glob(s + '/filtered/*/'), 'datasets/1/filtered/filtered.pdb')\n", " sims = simmerge(sims, fsims)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Calculating metrics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To build a Markov state model we need to project the atom coordinates onto a lower dimensional space which can be used for clustering the conformations into a set of states. For protein systems we typically use the binary contact map between the carbon alpha atoms of the protein. This will calculate contacts between all carbon-alpha atoms. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "metr = Metric(sims)\n", "metr.set(MetricDistance('protein and name CA', 'resname MOL and noh', metric='contacts'))\n", "data = metr.project()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we also provide the frame-step in nanoseconds i.e. the time that passes between two consecutive frames in a trajectory. This should be automatically read from the trajectories, but some simulation software does not contain the correct fstep in the trajectories, so it can be useful to manually define it:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.fstep = 0.1" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Removing trajectories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes the set of trajectories can contain trajectories of incorrect length. These are typically corrupted trajectories and are removed.\n", "\n", "The `plotTrajSizes` method plots all trajectory lengths, sorted:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.plotTrajSizes()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The `dropTraj` method has multiple options for removing simulations from the dataset. Here, we use it to remove all trajectories whose length is not equal to the mode length:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-02-08 16:50:43,270 - htmd.metricdata - INFO - Dropped 2 trajectories from 852 resulting in 850\n" ] }, { "data": { "text/plain": [ "array([413, 479])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.dropTraj()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## TICA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TICA is a method that can be used to improve the clustering of the conformations. This is done by projecting the data onto a lower-dimensional space which separates well the metastable minima and places clusters on the transition regions. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "getting output of TICA: 100% (850/850) [###########################] eta 00:00 |" ] } ], "source": [ "tica = TICA(data, 2, units='ns')\n", "dataTica = tica.project(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bootstrapping\n", "\n", "If we want to bootstrap our calculations we can at this point drop a random 20% of the trajectories and do the rest of the analysis multiple times to see if the results are consistent. Alternatively we can keep on using dataTica in the following commands. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataBoot = dataTica.bootstrap(0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clustering conformations\n", "\n", "Once we have cleaned the dataset we proceed to cluster the conformations.\n", "\n", "Here we use the mini-batch kmeans clustering algorithm to procude 1000 clusters." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataBoot.cluster(MiniBatchKMeans(n_clusters=1000))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building the Markov model\n", "\n", "After clustering it is time to build the Markov model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = Model(dataBoot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before constructing the Markov model we need to choose the lag-time at which it will be built. The lag-time is typically chosen by looking at the implied timescale (ITS) plot and selecting a lag-time at which the top timescales start converging. By constructing Markov models at various lag times HTMD creates a plot which shows the slowest implied timescales of each Markov model at various lag times. If a model is Markovian at a specific lag time, the implied timescales should stay unchanged for any higher lag times. Therefore, given an implied timescales plot, the user can monitor the convergence and choose the lag time at which to construct his Markov model, typically the Markov time which is the shortest lag time at which the timescales converge. Too large lag times can reduce the temporal resolution of the Markov model and can create more statistical uncertainty due to fewer transition counts and thus instability in the implied timescales. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "estimating MaximumLikelihoodMSM: 100% (26/26) [####################] eta 00:02 \\08-02-17 16:58:37 pyemma.msm.estimators.implied_timescales.ImpliedTimescales[2] WARNING Some timescales could not be computed. Timescales array is smaller than expected or contains NaNs\n" ] }, { "data": { "image/png": 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awXHXHcfoE1oRn34f/5ZfEQvNIu9UC3knZu4wrQU1Ip/VE1oaR4bzMVpWYx38\nHpZZNYhB50PWThOvX/86yx9Zzu4V1/LUxApWAyO0GMPvHc7oktG8d+V7cPPNaH+6l+JYI16KelU4\nfgD8BDXN+HxgMPBXKeVJPfrWDgEdXFVSwsUXq0OYFi1SB5nkYLt7O2Pmj+HKSVfy2IW5eyQ6uXED\n2xO2g0xBSIpErrUE5aRW6laSEoRsgShA7w3oHH3EQjG8u7x4dnjw7PDQsrWF9f9Yj3ubG3upnSnf\nmcLUuVMpHuaC9b8nvuwx2va+gBYdguOifCzHWjp9toxJIl94CH3sRvMVIYx15JW/iHVmJWLMN8Gs\nPCXBliD3j76fwmPKuXnhVcwUgreBuz66k1s/uJV131vHuG2tMHMm3zY+zV/iV/btAkAhhElKGevx\njYeI9h7HAw+oHW//+Ee48cZOr//mS9/kn+v/yZYbtjCwYOAhLOmRTwglCElx2JYW345agJaOHSUG\n6Vs4ZNsA1EIyHZ2jES2mEW4N42/0twuDZ4cH786UULTtbcu4x2AyUD2rmprrahj71bGYrCYI1MKn\nVxDb1Upb40tgLMDxNSfmIeZulUNKSWxTmNB/6og1FCMMXiyFz5M3JYJhylywVbLskWW8fu3r2J+9\nhB9/YwIvA7P8+xj0x0HMnTqX+efeD0OGsNA7mdNa/9WrPQ4rcCkwlLRZWFLK/+3WpzsM1NTUyKVP\nPqkOYzr9dHUqeydbiqyoX8HUR6Zyy6xbuHP2nYe4pIcfiRo32IoShW1p8e2oHkM6VtQfwnBgWMKS\n8aGodQV6D0Gnt4lH4oR9YcKtYSK+COFWFc/Oi0fjGIwGhFF0OxRCEPGnPbM1TKQ10v7sbIsFO7aZ\nDWYDrsEuCocWUji0ENeQVLxwaCH5VfkYjGlO1F3/hMVziXhPxd/wMIZ8C85vODGWHtjUi9ieGKGF\ndbRtdNLY6GHgmKcouW4emrmUx2Y8hq++jb9u+D7ufCvrgWtfvpKXN7ysjpa95Tai9/0ZixbpVeF4\nG7WAdRlqDBMAKeUfDugTHgJqpk6VSyMRNb6xahWUl3d67dl/PZule5ay9QdbKcwrPISlPHREUCKQ\nFIR0kdhG5pbRAtUrGE5ucRiAPoag05FYOEawJZjTov4o8UicWDhGPBJvNy2idciLR+LEw+raiC9V\necfD8f0XAtWy1+IaPTzWPuN+q8uKtSBh+am4pcCSyi+wYi+xUziskMIhhTgrnZnC0BnRNlh+I2x9\nnFD0twQrHv8mAAAgAElEQVR3XY+xyoTz604Mzq7vD4Wgrg5qa2H3bhXW7daQ69YzYNunjGj8jPHR\nT/EO2og7NJOvPFKB8fznqV3axOMzH2fQzSfw3d+dyS+B8+sWM+OxGcw/dz7fj02BWbMQatJTr63j\nGCSlPKc7DztiqK2FffvgnXe6FI33tr3Hgq0L+MNZf+g3ohEEVgPL02w1meMMNtSq5OGoLQ6S8RHA\nEPp+q2edIxepSYLuIIGmQLsFm1PppBiE3KFMcQh0vRWiMAqMFiNGixGT1dQeN1qNqXjiNYvDgtFi\nVJV1viUjTFbk2WlrgRWzw9xeeUspkXGJFtPQ4pqKdxJKKbE41POM1j484755KXx6ObJ1G0G5gPCu\nGsxjzDi+6kCYM99TSliwAB5+WB20lKzSCvAyg885ns84ns+4XizCW+DlrZHw1BkuFp56EtFh32Xg\n+kWUPjGRWaVzGXT800y+ejKr/riIK6+ewt1jS7lq4PTU0bLXrSFSUgXN2f6FzulOj+MR4H4p5eoD\n+KoOKcnB8eNg7tKbb4bf/a7TazWpMe3RaTQHmtk4b+NReSpeG2rGQrpIrCXVLSxCzZ+eChyLEoYR\nqEFn3Z305SHij+Db48swf6M/QxCSFnKH1JYWOTBajdhL7OQV5WErtnXL8orysDgt3WuN9we0GISb\nILQXgg0qDDVA2zbY+jjSOhS/fwHRHYVYp1uxnakW9KWzYgX8+Mfw3ntwYsVmLqn4hJrIp4xxf0ZZ\n41oiBslHQ+DNmWW8MusYto+ZCcPPpEBM4KanX2HkBjNuV5jQose46QctmKZdjb/8RuaPmU9xTRU3\nLfgmZwjBpSuf5qpXruK9K97j5P97FcuD9/eqq2od6hCr7aizUgQgj+h1HA6HXOp2g6XzIdjnVj/H\n5S9dztMXP80Vk644hKU7MCSq5/A+yme4HNhAqkdeBhyXsKRYDEEXiP6C1CTxaFy1oKMaWkwjHo0T\nC8bw1WeKQtuetox0uDXc4XlGixF7qR17qR1bia093lWe2W7+cpw0KSXEQxBtVRZrTcXTLdysRCEp\nDsG9SjRy+cmMNrTyb9K29S7i9QLbWTbyZmT27XfuVOuU//Y3ON21jCcG3MqQjQsA2DE4n7dOq+al\nSWV8OHIskWGnwvDZYC+lcnk9P3jsRWbFHdzTuprxzU5kaYyXT2/g8XdP4ISLroETX2Dxa0N464a3\nsPzjMm69dDwvxyPMvWcgJw0+iZcqb0ScckqvCseQ3N+t3NmdNzgc1EyYIJeu7ryDFIlHGDt/LAXW\nApZfu/yIPSvDA7wLvJ2wZEdyIEoY0kWiCl0kjgS0uEY0ECXqjxL2hQm5Q4Q8IYLuICFPKCMd9oQz\nXosFY+2CkC0Q3fXZGy1G8qvy281Z5VTxyrS8Sid5hXlHtwhICVoY4kGIBZTFgxAPQMyfsADE0+Ix\nf+br6fGoD6LelCh0Z9Ko0QZ5FZA3AGwVqXheBdgSYV4F0lxBvMWG/yU/mk/D8VUHlrGpRm1Li9rU\n4v77YTSb+NuIXzBx/QtsG+rinuun8+qowdRWTYThZ0LZOACKI0EufXM7Jzz9FjOGGHl8tZdVw03c\n1PAxs7/6HhsCRdSc9QqPzb6Ry/80EmP4ZbTTFvLImSsIuoM8umEeUbuZi9//Ofd8cifb521hSMnw\n3hvjOJIFolOsXbudHlr6ENs923nrv986okRDA1agROItYBHK7eQCzgLOSYSDDlcB+zlSSkKeEG31\niRZ7vY+2+jbaGtqItEWI+qPtohANRIn4Ix3S3RnENVqM5BXlkVeYh63Ihr3ETvGIYkx2E0azEYPJ\ngMFswGAy7DdtyjPhHOBsFwVbse3QCoLUINYGEW+i4vVCxJMW96oKXcZAxpUrR8YSYTyRn5XWYqBF\nE0KQEIOkQKSHWWoqpQBpQRg69rAUAkwOMNnB6EjEE2nnUDAVgDnNLK6OeelmTNUzMirR3Bpxdxyt\nWUvF3RqaJwbSh7AL8q/MxzRQVbuhEMyfD7ffDk5PLQtG/y8nb32CxiYr37nrDP5y6kVox10DJivm\neJSZ8TAX+SOMemYVniff5vgTJB/LSXw/8DdOYAyPVT3O4FN3s7y+jK8E9nHGqgh/vdzEyFev4viL\nP8Pw8cV85U8v8fjpC/jhnR8x9zenI074EXxyJw+teLRHP3u/2la9O7SGW/nNh7/h9GGnc/aIsw93\ncWgCFqDE4h3UdhqgehO3AOcCM/gS/lC9iNQkgeaAEoSEWycZT4pEMp2r4jfbzVjyLVgcFsx2M2aH\nGbPdTMHAgox0MkxeZ8m3YCuyZYhEXmEeJpup7yr3eERV5DGfmsHTWVwLpyrx9oo7PYxmpmUs8ezW\nlDBEvCotu3HckDCCMIHBlBXPkU5eY7SrVr21RIWmRDqZb7IjpZNo0zCidYOJ1lUgwxYwaAirRFgk\nIk8grAZEnhGRZ0LYBMKasLxEaBHt25Qnj9GTMalmlMRT+TIu24/Yk/E4MuBvFwjpyxQxkScwFBkw\nVhqxHGPBUGTAPMKMId+ApqnTHX72M/DtauHRYXdxSfB+fHti3PqTmfzh9FOIzroZYS3gG9Eg38XK\nxH1hVs1fzLpnFjLi/CitNRfzi3W/pWGEmZt3+zjzm3eimW3ImU/x2m/foX7Is3x9wQfc+70bmDf7\nt8RueQfTyhoGBa9nylU/ZtXvPuW/rprM/JHFzJ5yNY8u14WjS37/ye9pCjRx9+y7D1tXfQ/q7OFX\nUbtGStS2GWejehVno1ZW63SNFtcI7AvsVwza9rahRTtWblaXtd2FUz2rGmdlyq3jrHS2h9b8Ppw4\noUUh4oZwS8Kfnqjgo75UvD0vUfG3x3OIgtbDg15zVtqmHHlmMJhVS9sxFMwusBQmQpcK0/PS8422\nAzqaudOvzK8R3RwluiZKdFsUYqqiNo8yYyg1IMMy00ISzS2R4SgyJHNvZdATBAQ1CBkMFFcZMA8z\nYyg2YCwyYig2YCgyYLDl9mS8+64a+N70hZ+7q+7lWsfviO/28sfra/jVGZNoO/lnUDSMUyN+HhAG\nSja2seie93jy5cWccYmfi799GfPfe5e3Z93J+bGp3F36IENO3EW49BysJz4O9iqmzAjD3mdh73rk\nMb/gzdN/RNlddcy8/QX4z/mce/nfWPfSCcy+8W1eef1yQqf9lqblPdsxo9MxDiHEOyS8JlLKDT38\nag8L+ztzvN5Xz8j7R3LB6At4fs7zh7RscVSP4hHg9UR6Bups4HNQPQx919VMooEonp1pK3J3evDu\nUHHvLi9tDW2qFZiFrdjWQQRyiYLZ3r0VuvtFiyV8456UyybiUelwC0SaE2HC0uPRbp5QYLSByQnm\nfBUmzewEU/5+4mn3mPOVe8ZoS4jG0THOEW+JE90YJboxSqw2BhIMLgPm0WbMY8yYBpsQxu59FqlJ\nZEQJigxLNeUncZSeMIj24/T8AcHWXbBlB2zdLtiyFWVbBI11EewE8OLC5RIUFanDRDsLnU548kn4\n94IIPyl+jFvj/4vZ18AzV07mljOH0HjyT2HQDEZHA9zrkRT/fS2r/rqKhhXbOeNrXsYeex4vfmTm\nRec8wsXf5ce7FnLmnHfBkod5xn2I4Ve1/5belmYK7y/lm8um89dXP8Py/m94a86HnLzqHUzBB2HZ\nD6hru5zHrh2N+Nc3uO0ro6l+9Wp2X/zkwQ+OCyEGoOq0c4DRwOcoIXlPSunv1i90mOjsIKfrXr+O\nx1c8zvrvr2dkcRenBPYiu4HHgScS8XLgW8B3gVGHpARHLtFAFPd2d85tG7w7vfgbM//M2lfnDinE\nNdhF/sCUCCQHfZ0DnGo7h54iNVXZh5sS1pyKR5rT3DOejr782H7+HYQBLMUpsxaDpSQRJq1Itdbb\nK/1ExW92Kn+84cvVrJBSEt+jxCKyMYLWpHqMxgoj5jFmzKPNGAcc3JoLKaG5Wc1m2roVNm+GLVtS\ntnevuk6gMYaNzM5fzKmOJUyJLmawZyWmeISoKY9W+wBarJU0Gquop5LdsUp2RirZ4q9kd7ySeipp\noZir7X/nd7Zf4mrexquXjOdH51Ww7YR5MO4SKnw+fvnmTgr/upraD1czdOwupp5eRnnpDFZunMVj\nG77Fh2e4uPzfE5h70lMMHbeTSMlZWE56Auwdt0kadJOJcfWVBP+ymk+iQe4+fwgnD3uXGU+ehFh2\nA2z+Mx+8fjmrP5/B/LXfwydbabCX9O5eVYkDnGagXO5noNaZLZBSdr5Q4jCSSzg2Nm3kmD8fw/U1\n13P/eff36fvHgDeAR1GD3BpqUHsucCFfnn2atLiGr86He5tb2XY3nm0e3NtV2t+QWeEarUYlCkNy\nbN3Qk9W5WizRsm9W1t7ib84UhHSRiDR37qs3mFXFbu7KJVOY5bJJ5FlLlHvnCJqEcTiQ8UQLP6hM\nC2rtcRlMuJPS8rRWDemXajx7iCklFoXdF9BoVC2c27VLicOuXR3jgUDmPVWVkuOraznduZgpsSWM\naF5MyY6lGP2J01ycTpqOn8gbNS7qnJKqVklVc4SB9X6qdrkp3NWA8HbsRUohEFKycPYIfnRxKctr\nLkdMvY7RC3fyzedWMvSLBQwdtYtRE6tx2I4nGpjNjqZ8/r74Hv417VHKIz/mRzs+4KyvLUBYrZhn\n3AfDv9Vpj3Hmt0poswb5/UN+zhOCor9+k9eu28eMVa9jHipg4fnIvR/w1G//m+g3vs01vzgFRC9u\nq57zJiFKgbOllH/r8c2HgFzCcekLl7Jg6wK2/mAr5Y6+GUHYjupdPIkax6gErga+g9quoz8ipcS7\n00vjmkb2rdvXLhKe7cq1lD62IAwC12AXRcOLKBxW2B4WDi2kaFgRjnJHh8VQ7cTDENgN/p3KArWZ\nlX66SHTl/jGYwVqqzFKSimdYVr7JcdS4dA4FMpqo3AOJij6QqPQDMpXOeq3LcQWhxiiELWUGuwHT\nMBPmUWZEnoFAALxeZa2tqXh6XltLhLZaD/W7Y+ytjdHUEMdIDCNxTImwvCjGoAExqiriVJbFqCyP\nU+XyM8z7BaXbF2NaviTV1TCb1ZEM06ezdfIQXi1r4lX3Ij7e/QlaJ40Mm8lGlbOSKksJVaKAgTEb\nVQET5a0xnilp5N1jT6a86FouePXfXLL5TUYNrqeqejxa5BxigZOp9eax4Iv1rA7ewfrR77JrzMV8\n542xfOeUpxg2fgfRktmYT3wCHNVd/kaXfm08C0auZ9sVO5g8ooQ9Dat59rwTGDZlITOeOhkR9cKC\nE4g07eKx2+by3n9u4+Vhvbit+tFItnAsql3E8Y8fz69P/TW/POWXvfpeceAV1NjFu6i1FOeiehfn\n079mHwSaAzSubqRhdQONaxppXN1I45pGIr5UrWArsVE0vIiiYUUUDi9sjxcNL6KgugCjuZMWY7Q1\nJQrZFtgFwfqO9yRb9JbiRGVfkhCDrLz2/GLl/tFFIAMZS1TwftXK1/xaZjqgwqQg0MX4u7AKhD0h\nAHZB2GCgVRO0aQZ8MUFbzIAvKvCFlbUGodUv8PkEra20W7ZAxDMmu0mq2c0EVjORVUxgNRNYzVg2\nYOYgNu0eO1ZtjDp9OnLaNJaVx3h1+9u8svEV1jSuAWDc8NkMn3YDvqEn02wtwBiPQCyMjAXRIn6i\nkTYi4VZCIQ/BkBt/oIl40MeoPW1c3iQ5q+ETxg/wYTWfSsR/PvHQdBp8Rt5c38ra+vuorX6Gz06s\nZojvIqYsLuOy+Buc9V8LMFjNmGfcC8Ov7tbf7w++cS73j32b18XDbP7Fd7lJGBh1xwz+cnsJNctf\nwTLGAm3b0N6chrvWwFuf38UVL3xXF46kcEgpOeUvp7CpeRNbfrClV0/1exf4EWpF9yDUuMXVQNdt\ngSOfaDDKvnX72kVi35p9NKxuoK0+tVV0XlEeFRMqKJ9QruzYcsqPKSevMGunq3gksf3CHlX5Z4SJ\nuH+XGkhOx2AB+2BwDMlt9kGq96CTgYzLzF5AMEc62SNIiAOdLHuQBghZBAGjkTYh8Esj3pjAGxF4\nIwY8QYE3JPD6BR6/wNMKbo/A4wG3GzweCHe2pCINqxUKCjItPx9cLqjI8zI6vJqhbasZ1LyK8obV\nFNatwez3tt+vVQ9GTJiAmDgBBg0Ck0mZ0dgxnh2aTGqHifHjiThtLNyxkFc3vMqrG1+lzleHEEam\nHHcNJZOuom7AZNYltiYaCkxCdaRCCYs3B7BsbMa2sQn7xibG1q/kBMtHTK7+gsqBRcSC5xL2XYCM\njaPZL3h9l4GV61/GXXgPS86x4ORialaU8JX6/3DshDWMmrKFPFuIWMkZmE58AhyDu/138NAtt3K9\n7U7u2HAt33vuISriUcKr/8a/v/ptbCd9yPSnTlTjQ40fo717OjvWDmLEndt14UgKx+ubXueC5y7g\nz+f9meunXd8rz18H3Ay8iXJB3QnM4eicFRV0B9n7xV72rlBWv6Kepg1N7bOVjFYjZePLMkSiYkIF\nzkqn+sOLtoJ3A7SuB9/mjsIQbur4psKYWF1bBbZKsFd3FIa8ii/duICMJ6aPRlKzfdqnlEZk7ng4\nbdwgkOkSkhJaQ4LmgKDJL2gJGWiJGWiJGGiNCtUDiBpoiwjawoK2EPgCAl8AfH7w+SAe77p1azRC\nYaGyoiJlyXgyLChQIuCyRSgxenDhxSU9OONe7BEP5oBXqYzHo7oXHo/a0W/tWrUNbBKXCyZMSNnE\niXDssSr/AAhGg2xp2cLqxtX8a9O/eHPzm7SGW7E5Kphw/A+xjLuEjUXD2ScMGIBZwPma5PSdHvJX\nN9K0fh/NG5tp3thM08Ymwp42hozdyegpGxkzxYfdPpVo8FSi/tNBFuINwtuNeXz0xTr82q/YfKGb\nQOlXOHGVi4v2vM/EcasYfuw2TOY4MUMRxiEXIQbPgarzetxLXrd4Cce8NZ3rlp7Bg/96j+u0GA9r\nMU6dN5zbn5lKzecvtR8WFd/0JMalVyP+u/u743Zny5EK4A6gSkp5rhBiPHC8lPLxHn2SQ0hSOOJa\nnEkPTSISj7D2e2sxGw+uhdoI/ArllnICPwduQJ1RcaQjpaStvo36FfVKIJar0LMj1dLPr8pnwJQB\nyiYNoHxCOcUjijEYhRKD1oRAeNen4sG0HTUzBCEhChlhIm4tO2yzhKRMLOKKJRZ5JRZ7yZjskJee\nRkssBNNot/a0TEvLtNfjEqI5nh9T4wTEE2H6+3RBOAbNAUFzxKAsZKAlZKA5LGgOGGjxC5rbBM0+\nQZNH0OyFWCx3hWM0plr26Zadl53OFgenM61Oi8Vg2zZYt07Z+vUq3LNHCUIw2PUHFCKhMC41h3Xc\nuEyRGDSoxxVoJB5hu3s7m1s2s6l5E5ubN7OpRYW7W1OiVFw1jVEzbyI0fDbr7KVEhaCiJcj5qxs4\nbnUj5asb8K5KuGbbUupcMszAhNNqGXnsDkocdrTQDKKBM/D5R7Jxn5H1bgMbQ1Z21bYQa/09zZes\nY8+oUzljg41L9ixg0qiVDBpVi8EgCYuBmEfOwTDkEig9Qa2fOUCkplHwcyNnbBjDKy9tYD0wXkp4\n42Y+uPIP1M96iuqbh+Ft9eLxeKhb+BC3PP5JrwrHW6jx3p9JKScJIUzACinlhAP+VH1E9jqOJ1c8\nydWvXc2Ll73InPFzDvi5IeBe4HbU2RXXA7ehzrw+Uon4I+xZsofdn+5m96e72bNkT8b01uKRxVRO\nrWwXisoplTjKHWqAuelz8K5OE4gNatppElM+uMZBwVgoGJeKO4cfMveR1GTGrJzsWTodZu0EuzFI\nezAYUiYMQg12GVHbZSdCYRJgoj0MxFVPoKnNQJMvEfcJWtoETV7BPq+gyS1oaoF9LeDz5a40DQYo\nKYHSUmVlZal4rrySkqwKv6dEImrualIgkrZpk3otyeDBMH48VFcrpXG5ug6dTvVheogmNWpba9nQ\ntKGDOOzw7CAuNXCUQ8FAHMWjKRswGWfpGCyuIcScA2gV+YQ2+6hY3ci41Q2MWt2IbXUjkT0+EBp2\nZ5Di6iiDJpmpGC0pGRKjoDRKXjhEeE8pG3edy6qdE9mzuwl//XY0z3ailqVEK1dTO1lj48xxtOUf\nx3lbo3y1/l2mDF9BRbXaI8LPWKxjLsM0/FIonLjfHyUajeJ2u/F4PLjd7ox4S0tLRvjuktcgJCjL\nr8br9eJubc0eLMqmV4VjiZRymhBihZRySiLvCynl5O68weGgpqZGfvTZR4yeP5qq/CoWfWfRAc33\nlsDfUVt/7AQuAH4HjO3V0h48Ukq8u7ztIlH7WS17v9jb7m4qG1/GwOkDM3oT1gKrmrbqXQNNn0HT\nIhX60hZO2iqVMBQkhMGViNsqe32AWcpEBZ82GJscpO0wYJuYvdMpAjU4m6cGaA02Q2rGjlmAmYxK\nXBgzK/T20JgWGlHCIBLCkC4Uad+F16u8K7t3Q3298rikW2NjKt5ZA9xiURV9V5YUg7Iy1fI/gPq2\nI4GAOvwsl+3bpz7Qhg1qkUOyAhIChg9XAjF+vOoljB+vBprz83uhUCn8ET+bmjexoWkDG5s3pkLP\nTkKuaigdB4VDMRcOxV4yGmNBNVF7GfiMOHf7cO1uxbXLq2y3l6GNdQwLb6Nc1JFf2IbD1UZBsYGi\nAfk4CpxYzQ6EdBKPlFHXMoyt+4aytbGShnoDpbv+Q1nLMpzRLUTK19NYWccX1SY+m3EcmyacQHF4\nMqfuCDJ7zyJq5FJGDN9KflEbmibwxKcQrr4Af8kptIQdGZV9c3MzLS0t7WFLSwsejwePx4PX6yWQ\nPX+4p9jtYNUY7QlhKT4G+3AHDqeD/Px8XnvttV4VjoWoo2PflVJOFULMBO6WUp5ycJ+g76ipqZFf\nu/dr/OS9n7DwqoWcMrTnRf0U+CFq1eNk4A/A6b1bzAMmHolTv6JeicSntez+dDe+PWqeudlhZtCM\nQQw6YRDVJ1QzaMYgbMU2dWOoMU0kFkHLktTitbxyKD1eWclMKJqs1iJ0ExlLzL3P8sET6YafPjFg\n29kOsMKeEACHISMUdoEhz5CaxZM0q+iT7WSCwZQoJG3Xrsy0z9fxPpsts9IvL+9aFPIPZuJXPK6m\nI6WPUKdbMq+lRYlBujh0pmTJLk15OYwZkxKJ8eNh9Gj1AXuAlJJIPEI4HiYcCxOKhTrEfWEfW1q2\nZIjE7mgASseqHWJLx2Evm4JDq8bUaiN/rx9nvY+COh+lO72U7fbi2u2m1FdLadE+iiu8lA+E4nIr\nrmIbjvwCBBVosSq0WBWh8AB2NA1gR4uN7S1GtrUY2NFiYHuLkV0eA+Wx3VzEq5xnfAlt2Ie8Pkbj\n3WMGsm3EFKRjPKX1FRy7YRcj6pZSHNqKsDXhi8ZoaYN9rSaa/DY8ETOetiituf5IuonBYMDhcOB0\nOjOsoKCA/Px8CgoK2l//9IMXeH/cRh5w/oaqU44lz+HgilnH09S4lJuvPocLF5+D65Hf0FzVjJSS\n008/vVeFYypwP+osoDWoox/mSClXHfCn72MmT50sd359J7OqZ/H65a/36N5tqB7Gi6h1GLcDV3J4\nB761uMaeJXvY8s4Wtr+/nT1L9hALqWmHhUMLlUAkhKJiQgUGk0Htf9SyAlqWgXu5cj/5t6sHChMU\nTYHSmQmxmKn2H9pPbSXDUu342ZK5+2e8JY5s7cZCUqsAKwhL2kZzlsS8fYcB4VDz99NDYRedr+3o\nZeJxVflv3Zpp27YpgWhu7nhPebnyxCRt8OBUvLJSve5w9FIBpVRdljVr1MDxmjXKZeR2pwShdT9b\nmCTHETrxbcVLigkW5xMochJ02QkU2AjazATjYQLRAP6on7ZIG/6IH3/Ujy/ShicepQlwG0x4jCZa\njRbaTDYCZjshi4MoAi0WQosGiCeMeBhiYTXrrj0eRkTCWNtiWAMm8rVhuOJDKYpWYvPase2L4Kxv\nw9XgodzbSGmoGbs9QEGxwFVkwemyUFBkxVpYRFiU4wuX09I2gBZfBS3+ItxBNRPMEzTgDgo8IXCH\noanNwB5PAGgBmoFmrOZ9jHWtYaRchDW8mh3GZrbYjezLcyLDJoQnhAwc2AYaQoj2yj0/P79DxV9Q\nUJCRl36d3W7vdqPonUf/zF2jX+SPm25k8tyLAHi+upqHR4zAft809v2/paz46ofErtHQDFqPhKM7\n26ovF0KcAoxBeW43Sil7uJPaoWVv2168IS93nnFnj+77P+BnqC/lNtTMqd76n+8p3t1etr6zla3v\nbGXbe9sIeUIgoKqmiprv1VB9QjXVx1eTX5WvxiValkHLB/BZQijatqUeZh8MJTUw+ntKKIqmgimz\nlajGDLT2qZqaR4lDukBIf9YOoHaBodiAeYhZbexWaFDuIUuaKCQEAjN90gvoKcEgbN+eEoUtW1Lx\nHTvUauMkFgsMG6Y8MTNmZApEdTUMHAh5fXXOrseTEod0a2pCAgEztFS6cI8dStv4fPwFZfjz82hz\nmPHbTPhtRvxWgd8MbSaNNqOGzwg+EcenRQlIjYCUBIUkSCNhmggJQdxoBmzgy4OQDZptYLapvxeL\nA2wl4KiAslKwl4KjDEx5ICWWtgh5nhB53jB5nhCuBj8Dm73YvCHMvhhmfxxrW4iCoI+CgA9X2Et+\nxEd+VMMZC+PUothFGEteBGdBAY6CvdgdreTZt2K152Me7cI41oWgEhmfgMdfzqraCr7YY2FlnZGV\nS0zs9hgIxUKofaeTtrk9LsQ+LOYmDEaV1rQmotFmsge/wlFY2aRO2GzHGwfUWJ8EDAKKnVBoM5Jv\nd+AoLMFRUoXDVYHL5WoXguzQ4XBg6BXfYtdUj5wAvEht63aS4wrn1tfzxNAhBGZ9lwcnr2H6m3+k\n9ILf0DiwsatHdaBT4RBCXNLJS6OFEEgpX+rROx1CGv2NXDX5KiZUdH/8fjeqp3EWaquQjru/9C0R\nf4SdH+5sF4umDWoaa/7AfMZeMpYRZ41g+Ozh2B1tCZF4CbYsh8XL1AK5JM7hShhGzEUWTkUzTkUL\nuAb5QXEAACAASURBVNB8GtIjkXuSq3zbMubzdzZmIAoExmKj2u6hyKgEosiAsdioBOEIIhqFujq1\nzURtreo9ZMfrs9YRFhTAiBFqgfAll8DIkSo9YoQSBuPBdDWjUaVU2RYIIAMBIgEfPn8LbUEvvoAb\nd91WWnZvorlhB81hNy022OcwsLeygoZzhtJ01Sl4C8vwOYuIO8vAMSBVcRutYLSAyariybA9r+uN\nbgzReHuFb/WGlACkmd0TwuEJku8O4vQ2YvfuJs8bxtwaweAN4KAFV5EXV4mXgpJk2IqrxIvdGcBS\nEsE6MILZmtnm1GKlxCPjEzaFeGQc8chYkKrJJqWkLdLGhvomPq/dx8q9TWxorGNH8zqa29zAPgSN\nOI07MYoGpOajqxWKUkI4xwQJm8FAgUEi7QZ8lSUY8wcyRFgZIzyMyW9i1FA3A4rjlOSDiVJE3jFQ\nPYNg2TQixiNzmszw6VMRS6DekJo95orFOLOhkbcmXsHvz7mDuqUvs/iNH2G4umdC1lWP44IuXpPA\nESscAL8+9dc9un4+6kP9mUMjGlJKGlY1sHWBEopdH+0iHoljyjMx5JQhTJ07lRFnj6BsaASx70No\nuA8+/Q/4NqUekj8KWXQa8fKT0MRktNhw4j4L2i4NbaWG5tXU1FBSC/cwJHoKdjU2YCw3tsfb820C\nQ4ESCGE69OIQiUBbW0fz+RLxVo3YnkaadgVoqgvTsjeMuz6MvyWMhTDWNHPlhZlYGGa2K0zpwDCl\nY8OUuSIUO8IUOSLkCeUeIRKBrWFYl4iHw8oiEWWxmLJ4XFkiLuMxfIYYe/Ki7LHH2WOLscehsceh\n0WyDNgv4rInQkkq3WgXx/HIoGKRsYDVMmQjOs8A5IGWO8oxpmaZQjDxviPymVqqamxi3bSuBiAkt\nbCYv5Ccv5MP6/9k77/A4qnt/v2dmtjf1suqyJPdu40I1YJodICEhpNxwLwn8CKTDTXJJJ/eS3JBA\nKgFSLj0Uh25TTDfGVZK7LFmSVVdadWl7m/P7Y+SGCzIlIcm+z3OemZ3ZPTOzK81nzvm2qI41qmOO\n6ZiiSbRIEiWSQI0kUSJJRCSBCMZhJIociZIaiaKHjn+z1cxxMnJHySsLk1UUJKMsgCdnDJdnBIdr\nELt1EEU5MmJbx0bKVIS0VSNseQhzLjJRSSxUSnQkh16fgr8nRt/QKH3BfnoD/fhCjfhj6+kPD9AX\n6GdwdJBgeABdP3EUoQQChzkLmTDm03Pe1jIBq8XQWd0BoVw7w0VZdFeU0VV0FqcHJWcE9zDLuZPS\nSbswW43vZHCsiGH9HEJ5CwlmziOsZp3EX/PfD6vDQcmwRq+994jtH+3uZo3Xi/+0j/Pw5DsoWX07\nuRfcfFJ9/1MGABbUFMjept53fuM4QYxo73MxbBsfFMlokv2v7Kfx6Uaanmk6aNDOm5HHpPMnMem8\nSZQuUDCNvgV9r4H/NQg2I3U7KTkb3bKClLIYPVFBKpKJPiKOnj6yCZSM8VHBgfoAGYohBA4FzO99\nyiiVMqqXRSJHLsNhCIUOtWBw4uuHi8OB6SKNBFU0M5WGI9oU9uLg5LxLJBDTIKZCSoGkzULSaiZl\nMZG0mElZzSTNGkmLydhm1kiaTaTMGjGzQq8lgc8Sx2eK0mOK4tOi+LQwPiVM6G03TVQLDnsRblcZ\npowKVHcZMqOYpKeYuLuQiDOPkC0L21AMd9eY0boDOPpDZIxE8IxGcY/GsY/FsI7GUEeiiNEY5vgw\nxeVtlE7uoHRyO95KH5rJuGNGwxZGBzyMDGQwOuBhdNDD6EAGwWA2oVAO8WQmmsWMZtMw2YwiU9YM\no8CUPVPBnT2CO2MQh3MAu9WPVe3BhA8t2YmS6D/y+oQKtiIjktleAo5SYoqX3sEsfH1murqS7No7\nSsM+P/u7exkc6SMY6SeS6Cea7COpD5/Ub2cSGtmKRgEpClIJcgGPCnaHBa0wG1GcR6q8gKGpkxku\nKSSaEqiRKOZIDHssgTMewxOPk5mIkZGMkpEK49ZDuGQAhxLCbRvDW+nDZDZ+x76xEkaZQThvIUHP\nXBJqxkmd74eJm/7v44zYQ/zu8jVH/N9/Zc5s9jBG9fdPZfdvfGy+4k2WPHzq+2fjABBCrACmAwdn\ndKWUJydRf0MKnAUn9f57Mep7f/0DOJdQX4im1U00Pd1Ey4stJMIJzE4zk86fRPWKaqrOsOBSapG9\njyB9O0k9YSIWryGVmkmKz6LHKtDDR7o1Kh5DGEw1hjgkHQphs0JIVQgnlYNP5oFRCHYbN+NI5NBD\n9ETb24XhwDLxLixcqmq46TscRjuwnpUF1d4QVam9VMYaKA02UDjaQN5AAxmDzSipJBIYtkFHeSHd\nVUVsLz+DIa+DiE0npKQIigQhkSAo4wSJEdJjBPUoIT1KMBkhlIoQTIRIycN92GMcN9fG4QjFmNe3\nG3P6ZtcU3FmTcHjKsLiLKHHkoduySVjchM0OAqqZiBQovUHUjlGcXWO4G8bI6Bojv2uMzK4uHN0B\ntO4xxDGKS5kcJuOG7rGSVRSieEEbhcUt5OY04nK0I4RElypRMZ0x83J053RM6ghm6SOzqJucRDdK\ndB8ieawULodF6KdiEGqD0H5kuBfQQGqAhoxZwFaGtFUwEF+EL+HBN2rGPyro8Sfw9Y7S29NLT18f\n3f119I89Rzg+ctS1HPcrRWDXzDhMJhwWFbsFrFaJ1apjsqVQ7DrCliDl0Im7IeJIMmaHLqeFhpxq\nhG0SWZEqqoJuFsZGmK37mKztp0LdSmZ4GM2cMu5UJ7A/xRJmIgk7sZSDuHTSHL+QeMZ8Au45JL3v\nLhL9w0hhOJ9tJXuIdQ5hLc0+uP2j3T52Tp/O3sVzeOWJIUyrf3lS/b6jcAgh7gTswDLgjxjZNTaf\n1FH+xmgnEXGpYwT3nQIseR+OLaVkoGGAxqcbaXy6ka6NXSDBXexm9r/PZspHyiiraUE2NpDqbiD1\nTDZj8Wr0xPfQUx6CcegPKvRFBQNo9Osq/XGF/rBC35hC/wgEguKIqZv4SQa1mc1GfqBjtQP73G7D\nI8hmMwzAE10eLgoH160pzAM+RNt+wzI93qLtLfS2t9AT7KXXCb1OeM0t6J3kpjfXRq8rlx5TDL8M\nENcTQM944+B936yacZgcOM1OnGYnDrMDpzkD7+HbxtcdZgdWzYqqaMQ1G2GLm6DZSdDsIGhyMGZy\nMGayMWqyMaLZGNEsjKoW9MOe1OIYZtaxYBzv/mFK9wxT0NpLVmsDrtYRrPuHUfePQPTIUYhm03AX\nu412WimuYteh18Vu3F4ndnMbytBb0P8m9K07ZLvSXJC7FJn9BXTHWejqbLSwGfuYjh7UIQl6UhrZ\n5JNyPGo9AfEYMh6HRAqZTBkR6imFZFIwEByjNzCAf6yP3mAf/oAff8BPb6CX3kAv/mAvfYEdJCZY\nUVAADrOCzQJmu45wQNINUQ9EMiCWgeFp4gBpk4SUGCFihjCbHGBxgdmJRfNgVT24k/lkxCpxB7zk\nDTlYMjzAdP8+Jrc1UpK/l4Ky13CXH3JrHRzNwhcopbF3AVLzoJhcqCYHis0FdjfS6UZa3CQVBynh\nQIp/xARBJ4+XYiKmPfg27qGy9PSD208fGCA7GmVk0df4wQUNrPvjqpPqdyJ32KVSyllCiB1Syh8J\nIX6BUWbin4LVGD4Xf8H44383pBIpOt7sODgFNdxiDMUL5xdy1g/PYsqF2eR6tpLcvYVEYwd7XrqI\nNQ3LaexT6Y/o9EVU+kMa/hFBJHb0WQhheEwWFBg3c2+R4evvdB5aHr5+rH12+yFh+ECcm4JBaGhA\ntrQwsr+Bnq4Gmvta6RnpoifaT69dp8cJPS5DIHqKBCOVR0+TCiDXYaHAmUeBs4ApzgIKHAUUugop\ncBYcbLn2XFwWF+bDjL46hjNlD0Za+wNLH9AwvuwdbwfGGkKXmMIJTOEEtlCcwlCC/HCCKaEhssIJ\nMkMJ3GMRXB092Lp6UHr86H39EB3FYotjtsawWOPYM1K4KxRciySOzBQ2dxKzTcdk0dHMOoqSQOgx\n0MfdTw+sx2PQHEc2JpHJPJJJL7qYjm75DroyA10vRQ+60H0SGTwQ65LgoAH4QHT6eOBiKBWiN9iL\nb8xHz2gPPWM9dA110zXko3vQR+9wD4NjfaT0d8hvMo5DKLjMQI6dSEkhyVw3iRwn8Tw35Lgh2w05\nGcgMF0HNTFiYsGLBolgxqzacmg232YW0OklZXCQtdhJmO0pIYuuK4uwMkNE2Qm5bP8U9XRQNdVMw\n2kuWdYiCspcoKO+hYFovNkfU+I2lYDBSTL8+j85kFbHMqYQdNSS9R47KU5wwie+/DEW5VcCLdPTs\no5JDwqFJySU9Pfx50rlsmG1mV6YVht8hJcxhTEQ4DvQWFkJ4Mf43C0/i3N8TQohKDC9Zj5Ty3ecN\nOQ63Y2S2vexdfLZ3ey+1d9ey6y+7iA5HUS0qledUsvTGpUw5W+CIvk5iz2pi62pY17yC1Q0ZPNeo\nsavHSMuRnS0pLBTkV0BNAeTnG+KQn3/kek6OkcTzQ4GU0NmJrK9n//bXqG/bSP1YI/WWYfbkGsIQ\n0zBKHR5W9sSGiUJLDoXuQqZllXGOu+igGBQ6D4lCriP3qBFjFPAf1prHlz6gR5f0jUYZGYwQHIpg\nGopgGwxjG4pgH4xgG4rgGYqQNxihciiCZSyGFoqjhhMQSiDHRwYWW5TM/CGy8obJzBsmqyBKdp4d\nd6Ybq92D4k1AUQQhIggRBSX8tvUoQkTAYgLNijC7kIodSTYykU8ymYtM5aAnstETmchEJnrcQzLq\nZmDYTd+wg2BMEEsKokmIJgUxCTFNIa4pxFXBaCLAQKiLwVAXw8EuRgJdjAY6CQR8hMPdxGI+UqlR\nJoITM3loFKNTRow8RSJdEMgGfwHsq7TTdPYyQrM+QjJvOSW7UlRt9yN0iVQEuiqQQiBHFWRg/GFE\nESiKAEVgEVEytVEylBEKxxopHPaRF/CTFe3HI4dxWgM4XCHs7jAOVwjroqOnDZO6maAoZ8h2LkFL\nNUFTNSFTJbr4R8gO9+Fg0sKF0AG+RMdR+1b6fNxXVkrqlC/xnYt+Dw/unnC/E7kdPSuEyABuBeow\nnnlOrrL52xBC/BlYCfRJKWcctv0CjJkjFfijlPKnUspW4PNCiJMbS02AbcCrwP9ieGJMhHgwzq6H\nd1F7dy2+LT5Ui8q0y6Yx9bIaqub3ofa+SmLvJsJPnsJTDZ/juQYnaxo1ukY0hJCcuhRu/QZccglU\nV3+43FmPIhqF3btJbqujYfdr1HfXUh9toz4rxrYCGLUCZaBKwVSRx5KMyRTnTqLQW0NhTsVBUSh0\nFeIyuw4a5+LAMEa41RBG8shNHCkOB9rIWAytdZjMliEyW4fJahkms3UYT/soroEwucMRxAn8Oywe\nC7YsK55CBU9xkozcAJ6sAVzuUVx2iU01o2FHpPLQk1PREyXoyTKknknXiGDtJo2dvRqKkFg0sGoS\nswpWk/HaokmsGphVY2nRJFYTWGySWEzQNyboDyr0BwV9ocOWYeVgbipdCowsaJ3v0N454lhDJUex\nUChUSkhRkYpRSooiIF9AhgksdpXGUjd15RZ2eHU2ZIZpNY+hZ5ZDzQo8ngsoaSvivA0+yn/ZTt7O\n+xHoZOSM4PSEsLtDOFxh7O4QdlcYuyuM423rBzySgENuTUAqpRCLO4npHuIig6ipgpA9i6Qli6Sa\nQVzxkFAyiKuZRNWif5kppQ+KnPIyPI2CXkv3UfsyEwmW9fXzypz/4MUZPzypfk/Kq0oIYQGsUsqJ\nPdYcv58zMJyZ7jsgHEIIFWgClgNdwBbgU1LKPeP7V010xHG8muNv598xvKi6MFz1ToSv1meMLh7a\nRTwYJ3d6LvOvmc/sS1TMbfeS2Kcz2HseL+w8i+f2Wnlxn4nRiILVIjnvPLjkUsHKlcZU04eGA0WX\nD9TR7Ogg3NHCzv5d1A/voU74qc+X7MwfH0UANqkxy1zM3Py5zJp2NhXlp5KXM5Wwycogh8TgQDvW\ntsNvf0KXuHwBMsfFwds6TH7LMJktw9hbh9EGjvSeMmVZyZ1ixVsDmXlxnFkx7O4IdmcEqy2I2RzA\npAVQZQCiZmTIgR7JQo8XkUqWoydK0ZOlyNSRP0RSJtkT0NncY2Vzh40NjSa6+wyhM5mM/5FE4r0I\n/QgWSxsedxs2ezuq2k4q1UY02k4w2EEodIz082/DajJRaLdTpGnkkMCkhdGdSTQHKE4QLoi7YMxt\nZsyuMWZVCJglIU0npCSIvb3IkaKRUfkRCu0Xk9VZSm7dGKXrOshqHcKTM0LRlF4qFvRTNMlHTkY7\nJuXoqYwUFuLCQ1x4SOAmjoe49BDHQ0y6ietuUtYsdE82STWTpHgv2RXTvBu+8tBKrAmNn1355FH7\nGlwurps/H9Z8CVb87v3zqhJCXA88KKUckVLGhBB2IcR1Uso7Tv4SDKSUbwghyt+2+RSgeXyEgRDi\nYeASjPIX7zu9GHaNqzm+aMTGYux8aCd1f6ijp64HzaYx45MzmHf1PIrnaMja/2Xw0UoeePMW1jRY\neaNVI54SZGdKPvpJY1SxfLl4/1JOvBsiEaivh8bGIwSCjg6G+jvYlhGlvgDqCqHOq9BYWYqcXQXO\nc7HZCsj3TGJKTg22nEoURw4RkwO/EDwI3Hms441HELv7QhT0hcjrC5HdF6KiP4yrL4StL4S5L4Ta\nF4K+EKmBMKQkiprE4QnhzgyRV62TOz9F5iUxPDlhHK4AVssImhhAifshFUVKkKlc9GQZeqKM1FgZ\n+tAs9NQkoolS9Hg+RySKETqKI4ySncKUZSFoM7O13czGvSobtgk2bYVg0LihFRfDacvg1FONNmuW\nQNNA1w1vs2j0UDvwemgoSFvbfjo6WunsbKWzs4WBgVaGh9vp7+8kFAoQi0Ff/7G+NNA0jZzsbApd\nLoosFkqBsnic8rFRXJFBBvNSNBYnqPOOUlckeNNx6IFPkQK7YsWm2rHasrHYcjBbMzFbsyiwZKBZ\nMlAtblSzG1PUjs2nYmpzYKkLU/TbLvKiuymqXEvhdD9lV/vxFrRjNRvyrmMiaJpEn+l8AqYq4mqO\nMSJQPCQUD7pycnmq0vzt8Y7lsL2oEz2SQLEdObcyNRBgytgYbYtvJMrvJtznRKaqrpZSHuxRSjks\nhLgaI1bu/aQIYzx+gC5gkRAiGyNl1FwhxH9JKY+ZR0QIcQ1wDUBp6TtXyroDw3j21bdtl1LSvbmb\nuj/Usesvu0iEE+TPyufC317IrM/MwurUkbtvI3Z/D0+8+T2+/WwuHSMqlWWS678Cl14KS5eKv49N\nIpUyaiBs3nyo7dyJTCbpdkN9IdRO9vDW4nK2f3oyfXnnQHY1ZFWjZU8mlVGOPKxmSQQjK3CmlBQO\nRShoDlHi7yfLH8LlD2L3h7D6g6j+EPhDpPxBEv1hUod5FClqysg6mhkgyxslqyRKxqww7uwQTs8Y\ndvswFvMwJuXoQayUqmEgZjZ6ZDlj0So2N05h3e4CtrU5QQo0FUyKRFPBbAGTTWC2C8wOMDkFZqfA\n7Bpfymxa62H9etixwxACRTHKPVx55SGhONafTzKZpKuri9bWVvbv309LSwv79u1j//79tLW1MXis\nRFaHYTWbKczIoMjppNhqpcxkolxKKhIJKiIRSkZHsfr9tMf81BXCVi+sLbewLT/FgDll2E0yJ5FX\nsBRP4UKys6eSyCwj4MgiqmpEVZUBVUUKgdAlno5Rchr6ydk7QPbeAXL2DlLc1kmh3oMne4z8Ej/5\nM/oouaibDIdx7hKFkFbGsOlUAuYpBExTCJoqkSJdafEfmcKElxfc7QxvaSb7jKlH7f9Ydze3TD16\n+4mYyO1NFUIIOT6nNT6ldOL8Be8jUspB4NoJvO9ujBpLLFiw4ITzbxHg9xhGlurDtjc83sDrP3od\n/w4/JoeJGZ+awfxr5uNd6EUgYf/9JJ55ij27buLbT83i+UYz0yZLXv4rLFsm/rYj8HEjNZs3k9q8\nkZ7t62lv20a7JUqHB1q8LhquWEDb966lr2ASiewqyKqCzEpQzajxFK7+EJm9ASY1B6nYFKawbwuZ\nfSGcfWEUf5CEP0TUHyTkD6Enj445UDQFW5EJ75QAhQtGyPYOGmLgHMVqGcKsDKDKQeO7OxyhHizu\nJC3z0OV04skq9HgxeiSfVMiDPmYjOqRQ16myvs3Em/s1NndqRBICISTVRRKTBRK6IKlDQjemkhIJ\njmjJt83OOByweDF897uGSCxebLgeg1HroK2tjTVr9rFv3z4aGxtpamqiZd8+urq7SZ6gloFZUSgd\nF4QKVaUSqEwkqAyFqNR1PPE4A+E+epU+ehXw2VS6c6zsyDLjcyv0OqAxI4ORnGLIqkZkVePMm4sp\nZwrOjDKCjkwkh2w/7licCt8wk3e0kdE0RHazj4KeVvJGO8mK9eDxjODOGsOdNYq7OoBn8SgWy5EG\n6IjqZcw8m2bT5HGRqCaVHkH80+G1lQEbaGtoOKZwnNnXxx2TJjHxSJyJCcfzwCNCiLvGX/+/8W3v\nN90cWa67eHzbhDmskNMJ3/cghi/+4QF/Qy1DrLpiFdnV2ay4cwUzPzXTqFkB0PsKqY0/Y7jxM9z2\n4oPcvs6KaoKf/Uzyta8JTB/0A5muo+9vZffWNWxufIU23x7axzppt0RpzxB0lVaQmjcT8m+EvJlG\ny6oisy2Ad6uP6au6KGzuJ3PgDVwDa9H6wsiR6DEPFTGriFw7znwnGQVOHLPzceQ78BRAZm4vHnc3\ndksHVtmCEmlChNo4lA9dGOnZbV6wTQLb6ca63Qu2IqTVix73kur3kOzRSXYnSfWmDla+iyehvl/l\nrR4zb7ZobGxSD7onz54hufqLsGwZnHGGICtrYiot5ZFCYrUm6epqGxeGZp5+upHGxkaam5vp7Owk\ndQJxsNrA7gD7eJyK3QlOB9icYHUKMOmgJunVoFfVeNFiwW9V6TfHGBJR9ANWfGehkR48Zwpa3kzU\n3OmksieTdOWjRZM4ewIU+nxUN3ZQ9cYmSgafJC/YR2Z0EJc+jI0xrKYgNodhjPZkj2Kbf/TvGdUz\niWl5xM2T6Vdzial5RMeXYa2EpPLPE+iW5viUTZ0FyYfpDu5n/jH2m6Vkpc/HAyfR50TSqisYU0Dn\njm9ai+HxNDFH8OP3Ww48e5hxXMMwjp+DIRhbgE9LKSfuIzbOiYzjEpiJoZj1HIrdePSyR2l+oZkv\nN33ZyDgLMNqA3Podog2TWb3lRr612kProMblH5f84nZBcfHJntk7cCBt9s6dhHfWsaX5dd4c3sF6\nUw9veVOMetxQMBeRNwt73lyUwrlE8iaT1Kx4OsfwbvUxeXMnFVt7cdf1IIaNm4lqUcmqysKZ78Se\na8eR5zhms+fasdhTiOH68QqAe8arAO4xSsceQLGAezJ4ph1WAXAauKqMxHrj6FGdVHfKEAifsZTh\ncUOzAr1mE51So65b4416hbdqBeGw8YvMnGmIxFlnwRlnGFnAT4axsTH27t1LQ0MDu3fvZs+ePezd\nu5f29naSbx+GHIbNDqkciOcBpfkwfT72WWdgKsxBUVOIRASRjCKSkfFmrCuJCCRCxjJuLDV7Dpa8\nWcjsKQi1DGJZ6CEr1v44zt4AkwabmRPbwXRlLxXuFjLsw9gdIWyOCKp29AgPQNcFsbidWNJFXHeR\nUDwkrHmk3F5i5jxi6oGWk55iSgNAPBblorcu5PL6OVzzjduP+Z4+k4lPnnba+5pWXcewg94phMgC\nit8H0fgLcBaQI4ToAn4gpfyTEOJLwAsYVs0/n6xoTGTEsRbYDdzDIdFof6OdhscbOOvmswzRiPiR\nO35IYoefxuafcNOzFTy7x0xNleTFvxgG7/dMMAg7dxqpsnfupK+xjvUjO1ifEeDNUthaYiE1dzZ4\nL8HjPR1RshiRVYIUCo6eAJO3+pj5tA/v1sexbvWh9xveR4qmkD8rn8JPTMO7wEvRwiJyp+eimo7h\n1ih1oyzswEswuAn2bYKRnXDg59WchjgUnHekSDgqjqoZLnVJyp8i5YuR7EoS6UjS2QodIwodIwqd\nMY2uiIWOUZUOv6C717gJHmDGDLjqqgMjCiN25Z2QUtLT02MIxM6d7K6tpWH3bvbu30/v8PHzIeWo\n4HKCzIbhYhj1AllA9RTsky9Fm3Q+Y6XziViNBwhLPI6QkqiqElXVg5HkSlLH0WcUEHL1BI9e9gZx\n9gRx9naR49lOYYUPb0UPhRU+Cst7sHsNL6VUSmFo2EsgWsxw0E0y4iZlySBly0Q6s5CuLJImwxht\neCV98Cm50/zzYLZYKRk20+M8fv6+3JNMPzHRCoAXY4hMLYbb/VtSyg8itdP7wolGHBdijDTaAQvG\nDe8Pp/yBkD/El/Z8HlPHb0nWPslo9w/49SvL+PkbNqQC3/2e4IYbjOjrk0ZKaG+Ht9462Pa3b+OV\nMsn6UlhXrtJcMwWKFqIULcZechrh3Mno44FwBSmdZes6mPzobkxPN5LoNjxehCLInZ6Ld4H3YMuf\nlY9mPc7zQMRvCMTgJqOw09AWSIwX/jF5IPsUyF4EOYuMCoC2ouO6TuoRnVRXitF9SWrfkmyph13d\nRrW0jlGVntED8QkGimJ4K5WXH2plZcZy5kwjMv64311/PwM7d7Jz/Xq219ezY98+dvp8NI2NMXac\nqSVVMSLnrRmg5Cqk8gWRAhjLTx200BUUnYW75qOkyk7DXzCFoMUOQGEgxIKmHqY3+6ncP4C9J0h8\nKE58ME5sKE5sME58KE5yJA46CKFjdUSwuyLYnGHs+QkshQlySoYp8XZSmN2O1WwU/dGlSoByguYa\ngrbJBM2Tx4Pa/mZmwzT/gnzvz5fT5xzlzk88f8wkpydbAXAiNg6PlHJMCPEFjLiLHwghPrTV/05E\nA4Zx5mYM0QDYfv92emp7+OgDH0F9/RLCLRexdttavrnGQVOfxqWXSH75K0FZ2UkcKB43XGAPe4XK\nMQAAIABJREFUEwp8PpqyYdVsE6vOsVE/61yYdB5a8RJk4TyjWA7glJIFQrAwpTPt9TYsj+6m868N\nhPwhTHYTVRdWUXp6Kd4FXgrmFGB2HOeGEx8er9sx3gY3Q6jd2CdUyJgFZZ82qv9lLwJ3zXGfZKWU\n6AM6kbYkO9brbN4MtY0Kdd0qe/usBwWiKE9SWQXnnAnl5eIIkSgu5ti2oHDYcA+uN2qwRltbadi5\nk50tLWz3+dg1NsZOXafnGB8FjER2h+fQzgXyzVjzc7A4crHZcrFZs7FbsrCYMrEok0kp1YxFnKj9\nUVxrA+R1tVPQsQ2PL4DJH0aEgjg9IRyeIE5P0Ahuc4dxFcVwzIhi90Sxu8JYbWGsliBmNYw4RhSi\njkpIq2DIfBZBUw0BU01aJNL8XSgI57OlrJ/Y/j6slfnvub+JCIcmhCgELsdI/fGh5Z2mqn6FIRgH\nXLTioTiv3PQKRacUMWP+Rvbd/1NuemYOj++0UFkhWb0aLrpoAtNS/f2wYYMhEOvXw9atEI0igT1z\nvKz6WDar8iW71ADMvhLr0v+EjDLMUmcugoVCcAqwQJfY1new59HdNKxqYH9vEM2mUbOihmmXT6P6\noupjC0VsyKj6d1Akth4qEwvG1FL2KVDzZUMksuaBZj/u5ci4JNGdpHFDik3rJbU7FGrbVXb2mIkm\nje8jyy1ZOFfysc/DoiWwcCHk5x/nuxoehu1vK7nX3ExPUxO1fj/bgR3ATgwj1zHHEGag2AHVpdjm\nLMJ8yjK06bOQBV50xYx5KIHVH8XqD+PqCeLyBXC1BHD5xsjqGyJ7eJDMwDB2xzqcnudxZgQNYcgI\n4sqJ4KoJ4fQEsTsCmE3HdhxICathU1DcJEQWMaWc4IHXB5uH5Pi2uJqdFok0HwoKlRJi2i66Nu6m\n6m8kHDdj2B3WSym3jOeO2veej/wBIKV8BnhmwYIFV7993yBwH/BvGA+lAOt/tp6AL8DlD59HYuO3\nuPgP/4/2UYUf/hC++U2B7Z08E7duhVtugSeeMF6bTMh5c9l2/WWsqozw1/h2GkdbINOO95xfYZly\nCTHVzGzgK8BlQsGsSzrXd7D70d2sWbWHYE8QzapRvaKa6ZdPN8TCedjN56iRxDFEIms+VF1jLLPm\ngeX4lmUZkyR7k/TtTbHpTdhYC1uaVOq7VUajxhDBbpHMmSa5dqXklNNh0SJBRYU4csg7MADrGw1h\nOEwcaGmBoSF6MeY5twJbLRZqdZ2eY+RnF0Jgz3OQKHOTmFwGcxeinraS1JwzcfdGmLNlG8ubXmHO\nlidwr78HWzKETYYxW4ySo2ZrHLMljiU3hqk0gdkSRzlBTpKEcBFXM4krWcSVcoJqFnEl87BtmSTU\nLCPYLS0Caf5BKcmfDDxHe18LVZz9nvubiHH8MQ6rbzQe2f1ucgL+XbkLI37ja+Ovx7rGeOvWt5h+\n+XSK7X/muS3X0dSvcs+fJFdedYKOpIQ33jAE48UXISMD+V/fZsupFaxS9vLXfU/ROrwZMaAwe8kN\nzFh4Hbs9ZfQLweXAl4FFGClMXr1/B3se20PAF0C1qFRfZIhFzcqao8Wi46/Q9iD0vc5B99eTFYmo\nIRKRjhTbNups2irY2qSwtVOjdcgQCUVIZkySfOISySln6iw6VWHaNIGmCePau7uNIMNn9hjLhgbY\ns8cQjnH8QlCbl8cWh4OtTid1iQS+wGGJRmJGPIHNZiNvUhGp6gJGphQTnDMHeepKwt5peDpGKd/a\nxrm7XmXpA3+i5tEbKCltx5M9BqUgdUE8YSGZspDQbaSEDV1xkNJySZgdxC0OUqqdlDD2pRRjmVRc\nhiCoWcSVDGRaDNL8C1CxeAE0Q0/y6GSH74aJpBypwYiXy5dSzhBCzAIullL+9/tyBu8jx5uqimOU\nhj0PoxoVwMs3vYzUJef9sAx965e5Z/0teBySyz91nOkWKeG55wzBWL8e8vKI/uTH3LXYxG3b7qBj\naweaonFWzUqWXHofW4oXsU3RyAe+jxH8UqBL9q3Zxz23vkX7G+2GWFxYzbTLp1GzsgaL6zDLeyoK\n3c8aYuFbY6TgdlXDjO9B7mnvLBJSog/rJNuTtNWm2LABtuw1RGJ7j4XY+JRTQbZk0XzJNafrLDlT\nYf58gcOqGzUz9uyB5xrgtnFx2LvXKAJygMxMolOmsHXxYt7Udd4aGKC2rQ1fXx/4/Uecj9lqJrM8\nG2oKCM+qIbhkKZFFK2jPqiSjfZSSrd0sqn2LM164nanqHkpK2iko60WtNtxSx4LZDEWn0RGeTrRg\nDmFHTfqmnybNBMkpKiJrh6DX6ntf+pvIVNUfgP/EeGhHSrlDCPEQ8KETjuNNVT2KUZvhz+Ovu7d0\ns+P+HZz67VNx+2+mt+8LPL3bwr9/VmKzvU04Uil4/HFDMLZtg9JSkr/5FfcuNPGjt35C5+udnFl2\nJl8+/3b2V13EgyYro8AC4H7gE4AaTbLjwR08/osNDDQM4C5xc95t5zH3qrlYPYeVKdNT0PcqtD0E\nnX81PJ6sBVB9HZR/GrIWHNfLSUqJPqgTaUlS93qKDRth0z6NTR0mfGOGIFnNknkzJdd/FJacDosn\nD1MUbEQ0NRq5rH7VaIhDc/OR1aG8Xpg6Fa68kv6SEt6Kx3mju5t1tbVsr60l/jZXPs2iYSu2kax0\nEZleCUvPQq1ejjlQTNb+MSpaW5n+xE5q7vsBxcKH1+ujuKoL50zD8yiRNDMYnkRrcgnhzFmE7DOI\ne/8x6jynSfNhpWTQSa/7OMnSTpKJCIddSrn5bS5cx4+g+pAhMWpuTAXOx7jBvvD1F3DkOTjjC1Hk\nxtU8tOEeoknBF7502DUmEvDAA/DTn0JTE0yejP7nP/HYPAvfX3czTc83cUrRKfz44w/zRMlSvokR\nfPJxjPxXi4DoUIRNv9/C5t9sJuQPUTCngI89+DGmfWLaobgKKQ1bRdtD0PGwEWinuaD0MsPrKf/s\no2Im4JCnU//OJOtf1tmwWbCpWaWu20x4PItrSYHk9LN1LpjVyqme3VQmGlGbG6G+Ef6y1zDqH0DT\nYNIkmDwZVqyAqVORU6bQqGms37GDN954g/XPPUdLS8uRJyLAWmglXq6iTq4mq/Q0ChzzmRxQmTLc\nSuVwK2XPriPX8zhZ+UNk5g9jrYlBzYHrEARiBQwpi+hyzCJon05Iq0in006T5n2mMJhDXUkbqVAU\n1XGCuroTYCLCMSCEmMT4xLoQ4uNwfO/IDxvrMIqI3IUR8Ld71R4613ey8u4LMO+9igRX8MAGDzNr\ndObNU4xssn/6E9x6q+EmOmcO8tFHWTPTwnde+z7bn9zOjLwZPPHJJ/FPvpjrhMCM4W52LUamxuH9\nwzx/+0bq/1RPIpyg6oIqlty4hIqzKw4ZlINtsP8+aH8IxhpBMYH3Iij/DHhXgna0ZT41prP3pSTr\nXtbZUCvY1KrR2G+MJlRFMmeyzo2faGd5QS2zYrW499XCG1vhyaFDneTmGuJwySXG8kCrqEBXVXbs\n2MHatWt5bdUqNmzYwPDbAuk0s4a13IUsyycndxZL3WUs0ceYltxHcUYXWc6Hycz9PVrxId8oXVcI\nxvIIU0CvZQFxRykxSzER1UtUK0wbndOk+RvgTRTxvHM/A5v2kX/2zPfU10SE43qM5IFThBDdwH7g\ns+/pqB8Qx7Jx3A5kY3hTJaNJXvrmS+TPymfu6Vugbg+1+1dT79P45W0Scd+98M1vGmk/Tj0V7ryT\n16fYuOnV7/DWI29RmVnJAx99gHNmXMG1ispTGHlY7sEQjO4t3az6+Qb2rNqDUAUzPz2TJTcsIX/m\nYe5vg1ug4VZjKkrqkHcmTLkBSi4Dy9HTMTIq2fNCgr/cL1n1hkZjv3GTzbDrrJzeyc1n1nKKtY5i\nfy1a/VZ4aNxIrWlGZN3HPgYLFsCsWTBlCmQemUS+s7OTtWvX8vx3v8srr7xyVJZXm8eBvdxLRk4x\nS3JyOdedYpq5HW++j7ySxzGZjcFnSlcZjhUSkZV0aEXEHWXELEVEtCJial56BJEmzd+ZImc58CYd\nexs+eOEY96I6VwjhABQp5TuXIfs78XYbRwvwFHATYAPe/NVGRtpGuPKFj6DsWo6e+VH+b00BZpPk\n3873w9xrYO5ceOwxtlbZ+c4r3+HF+17E6/Jy54o7uWruVbysmpiLUZDoNuArUtJywOD9ejsWt4Ul\nNy5h0VcW4S4aT7sqdcPA3XAr9L0BJjdMuRFqrgfH0Tm8ZVLS8nqCR+6RPPaKynafGRNxrql8nXtn\nvc60+FacTbWILX4jo5eqwvTpcPHFhkjMn28IhfXo4ejY2BhrX17L02ue5tWXX6Vzf+cR+82ZHkpK\ny1mQ7+G8fJ0l+X14S3rx5BzywA7GXAwkK2gVi0g6qonYqwlppWljdZo0H2LKps2G6AN0h9tY+B77\nmohXVQbwOaAcIxgQACnlV97jsT9wfo1xgdcDQX+Qdf+zjpqVNZRn3weDI4zpv+CxbWYuXSHJeui3\nkEiw53c/4Hstv+LxVx8n25bNz5f/nOsWXgcmGzcAvwFmYAS2lHeP8fDVz9D8XLNh8P7Fecz7wrxD\nWXVTMcMrquHnMNYA9hKY+wuo+oIhHochdUn31iSP/EnnsRdVNrWZmEoDn899nr9WvUSF7w2U1hC0\nKTBtGlxwgSESCxbA7NkcCDqJp+K0j7TT2vU6rcOt7Ar0sq7JT8eW/QS3NpPa3QapQwn0FKuDoqJq\nzsgx8/FJo5y1yEdGznbj9HUFf9hLl5hLuz4JmVVDyFJFXMlKV3FLk+YfjNJ5szCtgx616z33NZGp\nqjXARozA3mOn7PwQMorhRXUFUAg8+4PXSEaSXPDf5bDnc8jKa3jyznyGIwrX/EcI/v0Obrq2mp8+\nuwKn2cmPzvoRX1v8NdwWN9uAz2CUIvwq8BMp2ffgTn7/5edIxpJc8KsLWPDFBYcM3vFh2HcnNP4a\nor2QMRuW3A9lnzRsGeNIKelvSPHXP6Z4dLXC3qZBzuZlvu14kbMdL+EO+aAfyKiGf78SzjsPeeaZ\n+LUo+4f30zrcSuvwWlpfvIv9w/tpGeuiy54DBQvA74FNnbDmFSP2YhwhFLyZZSzNtHPRzAQrzugj\nt2AbAKGYC39iJh1iLrHsmURMFWn7Q5o0/ySYTGZKhyz4T5DscKJMRDisUspvvOcj/Y35I0ZR868D\n/p1+6v5Qx8IvLSRz+L9Bs5N03cx960yUFkqWtf8fzWKYn+aPcPn0y/ndRb8j256NDvwcw/CdhTHK\nWOoP8vS1q9n75F5KlpZwyT2XkF09Hk8Raoe9v4SWP0AyBAXLYep9UHDuEU/oMX+KVXcmeeTRGMm9\nG1imv8TvtLVMw3jSx5IF55xDfPnZ7J5fQh291PfWU9fzv+y44zOEEobbKpmVULwYR8XZsOCrhLd1\nwVNPwxOPwsAhj6kMzcWS3FzOmyn56LJhykqNnFWxpJMhMYsm13xGrHMJa+XpkUSaNP/EeEcz6M4c\nQkp5zGSHE2UiwnH/eKnYZ4GDJcSklEPH/8jfhwPG8UlVVfwaOBOYIyUP3PAiFo+Fs6+LQ+0amPtz\n9r1s57VWje/flEL55e38+pICNGWQ28+/nWx7Nl3AlcArwKUYwSz+x3ZzxxdXEw/GWX7rchZ/fTGK\nqsBQvWG/6HgUEFB2BUy9ETJnHzw3fUzHvyHOH+/WaXr+VT4RvotHeRErMXTNROSMxWw8+1rqJrup\nNw1S569nZ++TJJ5JgGLCnj+bsuoLmHHG9wjkTqXLWchYQofnnkf+8C8kXvo6MnzI/FTisrBylpnP\nLIuwdEoAIQLEdScjltnss81lxDKHkFaRTtGdJs2/EIWRAjZW+Inu82OrKXjX/UxEOOLArRgP3geS\n/kig8l0f9QPigHF80oIFV3dg2Dj2rdlH69pWLvjlOVj2XQ3OKvSC67nvceNSri9+ihFfK3+usHLF\njCsodBWyCqNyVQxDMD41GOa569ew+5HdeBd4ufTeS8mdlguhTqj/T+h4xIi9mPw1mPxVcBiFDPWw\nTqIhwe6XEtz/yAj2HQ9xVeouKkQr62uy+P0li6gv1aijh4ah9eiiFoam4PSeQu6SG6jIn0XQU0qf\n2UVYCBoAW5ufylsepGT1IzQ31RNLJgiPX/+MYrjsFLhsIdQUKYRNZYRs1TRplQTMUwhqlWmhSJPm\nX5gipZSEup2OzbuY/AELxw1AlZRy4B3f+SHBD0wCLkykuPuGF8muyWbh8lrYthfOeIrINslD9WbO\nOUOSe8+t/PzcLEL6ENcsuYH/wHCvXYhRYlZ/upHfX/MMkaEIy368jNO+fRqKSMDuW2DX/wA6zPg+\nTPk6mDOQMUl8R4zYrjivrpW8+vJ25nb+kR+qD7OhIs4tpxWypiqL3pxS8Nbg8C7EXjgfV2YlozbD\nVTYIRIEqKTm9ZZiqdduIPPUk27ffw7rODnYflj52QQWsmOfk3EXVeCfPIWSeRFCbxAY1Pz3tlCZN\nmiMoKqoGoLO/mckHi7qePBMRjmY4+FD7D0EIw4hdf+dWBhsH+fST56PsuQgKzkUWrmTt/4TpHFH5\n87L1JG/exG9+mMkZJWfyzYLZbAK+C3xzJMrLX3ue7fduJ39WPp994bMUzC4w8kfVfg2CLVD8UZh3\nG9JaRqI5QXxXkMCeBE9tSdCz7knOjv2WU6p38vgSjS/NzSJYtQytZiVq9QoYFwkdo7j6VGBKUqdi\ney+udR1E3uygZd1e9mmreTS2nX2DxiyhELCgKoNzT5/FacvOxV20gJRiIwm8P+nL0qRJ889K5dJT\nYA/4Uu/tbjER4QgB24QQr3KkjeND646rAp8ajvDnH75OxdkVVBU9CPtGYd5tJJqT3L/eTKZHsqzu\nFzy+wEmHPswXz/oR/4XhbnvBC8388QvPEOgJcPp3T+fM752JGm2F1z5vxGO4p8CyF9Hd5xDdGCVe\nO4p/EJ55s4NE4y9RJ/2F9ZfE+J+l80jVfBdTzcUkvfNBKLil5DwhuBBYHIyjbuqi880OOt/spHND\nJy2hBKHMJprz32DtaBfB8W8822Nm5QVnc8GlV1JQcGiI+Z5q+KZJk+ZfiszcfHKDCv73mOxwIsLx\n5Hj7hyEHqP3xG0SGI1x4Sxli31VQ9f8gYyY9jwVY3eDkpiv2oT74JLd/z0tlZh67y04nKxDDe+OL\nPHh3HTlTc/j845+naI4Hdn8X9t4GigXm/hy94Hqim3VidaN09MGDm/9Ev+n31C0aYcc3zoequ1Cr\nLiTlyEVIyRzgwnGxmJfQaXqigdo7a3l4XTsyJUFA3lwX+vI3WbPrDTY3h2A808e86SWsuPQznH7m\n2ZiOWUIvTZo0aSZO8bCLnoz3ZnmYSOT4ve/pCH9DDnhVlZZPYvNvNzP383PIDf8ENCfM/BGpkRQP\nP6kSSwqui/2STWUqG5Rubjn1Ln6E4IbzH2Dnxi6W3LiEs29ehub/Kzx7I0S6ofzfSFX8jGidi/gT\nEWJxuHf7Tp7zXMXr//0pqH4QvKeAopChJ7lI0bgQOF8IcjHqf9TeXctv/lBHsDdIRkUGp35rKeby\nJp585U5+s3oXg3WGwd5pM3He8mWsvPQKKioq/p5faZo0af7J8AZy2VDeTDIQRXO9u2SHxxUOIcSj\nUsrLhRA7OeRNdRAp5ax3dcQPkANeVZUZlVdrFo3lX03Cjhdg3m1gzSX2VoQH6sycPb2fnGf+jy9/\nuRS3ZQA5+3PkbPFh3tDFBb++gEWftcH65UbRpMy5pKY/SbRhKvFX4qDEeSul8tc1X+TJK9sZ/swr\n4ClmbirJxYrChcACRUM1zof9L+/nlTu20Ph0I1KXVF9UzcIvTqGp6y7+967vsLY+dPD8ayq9rLz0\nk5x77nJs71h+ME2aNGlOnsJkEUP2ZvrfbKDwwrnvqo8TjTi+Or5c+a56/jsSHY1y+i2LsbV9EVw1\nUH09MiXZsjrJrl4bGxfcSWdnlMec7Xxl7te4R7Ny4V1bceUmWTD/Xnj+LjBlkKz+C9H2FSQeSYIW\nZ2iSlV/fs4Em9+U8/7Pr4Yz7KEnEWCUUTtEORVhHR6JsuXcbW+/YymDTILZsG0tvXMrCK3Nprf85\n/+9bn+PVcdcoi0nlrLPO4OJLP87UqVPfU1BOmjRp0rwTRe4K4HU6WvdSyPssHFLKA6nTr5NSfuvw\nfUKI/wW+dfSnPhyoZpUlH6mHHY1w5jOgmkk0xLn/TRMec4SFG3/DTVdUImlj/qk3csdolJKHd/H5\n2x9EbW8nmfNjogPXknhegDmJttjKPZt16v/nszx7xRb6/+0JKDudz+gpfm+x4xo/bk99D1vu2MLO\nB3eSjCQpXlzMpfddwvSzx/Bv+hlfu3Y1964zSnC4nXau+PRnWbFiBW63+4TXkyZNmjTvF+Uz50AA\nuiJtLHqXfUzEOL6co0XiwmNs+9DgKXKgNvwYCs4D7woARjbGeGyHk1/M+QOR+n7uLo3ysZqP8YSz\ngMW/30JFZSMOk5tAcDfJ5nyEVWA900KdbuF31z1PeMoVPPX981A+sgO7xcldwGcVlVQixY5HdrPl\nd1vo2tiFZtOY+ZmZLLx2DoXZ6wlt/wL//ZVabl0N4Rhomsqll36Uz33uc7hcrhNfSJo0adK8zxTP\nno7lNfBr7z7Z4YlsHF8ErgMqhRA7DtvlAta/6yP+DbBahiAZMGwbQpAaSvHkaoVAVPKpntu49yMl\nDCc7+ezSb3KZlNx0Vy1nXN5AsOdxsGRiO8dGoMzCd/8rQGrjx3n1stfwX3Y7LLiG+VLnL0JhEtD8\nfDMvfOMFBhoGyKrO4vzbz2f2p0qwDT6Avvc67rm7m++sUvGNJ2c57bTTuPbaaykqKvq7fj9p0qT5\n10VTNUqHrPQ6/e++jxPsewh4DvgJ8O3Dtgc+jHmqjiDWD1XXQ8Z042VdjAfrzVyV9yy2rkZ+dW0h\nC3MXst27gIItPnJ695CfM5PIQCb2y5zc/5KJ1Rc/ium0/+Cxb1Rhvqwecqr5NnCzUBhtHOChb7zI\nvjX7yJyUyeWPX86UZQKx79fw2r28uiPMNx5xsq0FIEV1dTVf+tKXmDXrQ+dPkCZNmn9BvKNZdGb2\nIXUdoZx8GqIT2ThGMbKTf+o9nN/fB6HCrB8BIFOSpleSvNFq577y23huSQ5NiR7uX3wb/yUEF99d\ny+KL6oiN3s7epOBHHx9h5ugVbL7yFXrO+TLq+b8gW9G4XwiWDkd4+ebX2fLbLWg2jXN/tozFl4+h\n7v9PWLOaJr+J/3w8n6ffDANBcnJyuPrqqzn33HNR3sWPkyZNmjQfBAXRAt7M8hFu6MUx3XvSn5+I\njeMfhgNxHNMqc8BipDpP7E3wwFsmThGbKWt7nc9/vpoiuwX7tI/TPxaj9PFaZt6SzbCvil/e8Rw5\np17Fzz/pxHnJC8ia87gA+FNSp+Purfzm+68SGYqw5LoKzvy3Fiy9V8L6NgYTOfzohYX8//buPLqq\n6l7g+PeXXJJAAgljmOdBCEJAhkAQREQhilC0IloZBCmItTh1Ufpen6/PJwtbSqWiiLMFUcYGBMXI\nE6kCSgizTIIyQ5AhIQmZ9/vjnAshTSA3Jjk3h99nrbu4OWfvc35355qfZ5999n5tcRK5uccICQlh\n5MiRPPDAA4QUsQqfUko5qZGnCXkBSRxJ3EH7Gz1xXFk69pbHvNsyNmfx4fZQFtT/KzurhrI27wDT\ne0znzUAPfRZspVO3RHIvTmTJtoscGP4rtsX2J/SBJeSEhDMbiPv8EMueWkPy7lPEPHiJvg/tperF\nF+BgLkT2Z/GJYYyf+japqZsREQYPHsy4ceOoXbu2cw2hlFLX0LRxeyCeo2cP0r4U9V2VOK6wnoXI\n+ymPz9aC58IR+qYsZvy0KKp6vueubhOZZgz/9fpmeo2+RGZKH3YefI6tTz6HDHiRJhLAmwfOcvTZ\nBJb+XxIxw/bz6LRtBJvDkFkL2j0JrScw+901TJkyBWMM0dHRTJ48mdatWzv82ZVS6tqax3aDHXDC\nHC1VfZcmDkvW1iwWbA1masjfSA4SFoTsZ2znsSwKiaDR5uO0yt9IEA+ybHcO+/uthf77GXrhEuP/\n9ysOfLqIbgOSeGDubmsa9Tqx0PoFaHo/JiCYqVOn8tJLLwEwfvx4HnroIX14TylVKYTXrE1kaiCn\nq5ZuskPXJg6TazjxdQ4b92SyKPBNZoyOIitvB4/HPMUAYMTrW+h9z49kXZzJ1i1vsP7tMfRYksjj\ni2bQuecG6k07gwmsgbScAG2sCRIBcnJyeHTMKObPn09AQADPPfccgwYNcvbDKqWUj5qcr8HJ8NJN\ndujaxJH9XTYffVOFsflzQdJ5teFx4prGsbtOW1JTs7hp7TrqP30HKzcFkdJhNlVbbCL+217Uv+80\n2SHR0Hk60uxB8IRePmZaWhrDhw8nISGB4OBgnn/+eWJiYhz8lEopVTr10+ryVcv9ZJ9Pp0pENZ/q\nunaMaNaWbBZtE56pMpuFIzpwOussU3pO4TWg/wc76d53K1mpo9mwYTVLfzWASfFLqd/4NKkN/0LQ\n8K3QatxVSePMmTP069ePhIQEatSowaxZszRpKKUqrYb5jbhQFc58/Z3Pdd2ZOHLh202GzqcWUzfn\nBH+LzqRjvY40bHkH642h3ztfEXVLFJt+qIXU+yNZPScz/qe3yMgIp8atv/m3wx06dIiePXuSlJRE\nZGQkc+bMoX370oxFUEop/9A4vBUAR37Y63NdVyaO/Ix85icF8Tv5C2v7NWd7xiGm9JzCPBGaJZ6g\nc0QCuenj+WRdEsvvi+CuxBTa37SX9PBHIDDoqmMlJSURExPDDz/8QKtWrXj11Vdp3LixQ59MKaXK\nRrPoaACOZR72ua4rE0deuuHCji/paHYyOy6COtXqMKzTw7wH3DdvMz3uqMnOYy2pLv/BkTuf4Okt\nfyUvN5Dag39/1XESEhLo27cvZ86coUuXLrz88svUqlXLmQ+llFJlqHHUTVTNgdNBvk9MXRt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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.plotTimescales()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After seeing the ITS plot we decided on a lag-time of 50 frames (5ns). Additionally the ITS plot showed us that there is a separation between 4 slow timescales and the rest of the timescales which are fast. Therefore we choose to lump our microstates together into 5 macrostates. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-02-08 16:58:44,634 - htmd.model - INFO - 100.0% of the data was used\n", "2017-02-08 16:58:44,757 - htmd.model - INFO - Number of trajectories that visited each macrostate:\n", "2017-02-08 16:58:44,758 - htmd.model - INFO - [153 112 370 134 226]\n" ] } ], "source": [ "model.markovModel(5, 5, units='ns')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have a Markov model we can plot the free energy surface by projecting it on any of our projected coordinates. For example to plot it on the first two TICA coordinates we call it like this." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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2Yo1ZKsd/FXgP8BT/eFV90TzvfTKwQ1Ufsvf5AnAuCdOuAt1H/pz6w3Q9xtLY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\nrCpOHGZmVhUnDjMzq8r/AysmZ+B4Nl46AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.plotFES(0, 1, temperature=298)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also plot the micro and macrostates on top of the FES by setting `states=True`" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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hlBqK8Vd2X4hjTQa+VEodti4UkSTgDWCWUqpRpqw3q4WglFpuKp7mOCGD3ebk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\nVAB6eqrY2zaZyoo36eir4p9/9rdE+v08v34RH/rKMu8iCxk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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.plotFES(0, 1, temperature=298, states=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the states\n", "\n", "To see what the states look like we use a Matlab integration of VMD. We load the 3 macrostates and add a ligand representation using the ligand atomselection. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.viewStates(ligand='resname MOL and noh')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating the kinetics\n", "\n", "One of the major advantages of Markov state models is that they can provide quantitative results about the kinetics between states.\n", "\n", "Provide the Kinetics constructor with the system temperature and ligand concentration. It automatically then calculates the source and sink states. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-02-08 16:58:45,894 - htmd.kinetics - INFO - Detecting source state...\n", "2017-02-08 16:58:46,489 - htmd.kinetics - INFO - Guessing the source state as the state with minimum contacts.\n", "2017-02-08 16:58:46,490 - htmd.kinetics - INFO - Source macro = 2\n", "2017-02-08 16:58:46,491 - htmd.kinetics - INFO - Detecting sink state...\n", "2017-02-08 16:58:46,493 - htmd.kinetics - INFO - Sink macro = 4\n" ] } ], "source": [ "kin = Kinetics(model, temperature=298, concentration=0.0037)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see the rates between the source and sink states we use the getRates method." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-02-08 16:58:46,498 - htmd.kinetics - INFO - Calculating rates between source: [2] and sink: [4] states.\n", "2017-02-08 16:58:46,594 - htmd.kinetics - INFO - Concentration correction of -3.32 kcal/mol.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "mfpton = 7.24E+02 (ns)\n", "mfptoff = 4.00E+03 (ns)\n", "kon = 3.73E+08 (1/M 1/s)\n", "koff = 2.50E+05 (1/s)\n", "koff/kon = 6.70E-04 (M)\n", "kdeq = 5.49E-04 (M)\n", "g0eq = -4.45 (kcal/mol)\n", "\n" ] } ], "source": [ "r = kin.getRates()\n", "print(r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To plot the free energies and mean first passage times of all state use the plotRates command." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kin.plotRates()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/shared/sdoerr/Software/anaconda3/lib/python3.5/site-packages/pyemma/plots/networks.py:544: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if state_labels == 'auto':\n", "/shared/sdoerr/Software/anaconda3/lib/python3.5/site-packages/matplotlib/figure.py:402: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", " \"matplotlib is currently using a non-GUI backend, \"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Path flux\t\t%path\t%of total\tpath\n", "1.245891146137582e-05\t54.5%\t54.5%\t\t[2 4]\n", "9.695827608462257e-06\t42.4%\t96.9%\t\t[2 3 4]\n", "6.741333557370579e-07\t2.9%\t99.8%\t\t[2 1 4]\n", 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oUZLUAwzAGiYi1rZ/+9RGC9GoRMQNlDFpl9L5ctxc4N2UIDy09aARQ06Dh9bc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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kin.plotFluxPathways()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "" ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }