{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import os\n",
    "\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.rcParams['font.sans-serif'] = \"Arial\"\n",
    "plt.rcParams['font.family'] = \"sans-serif\"\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "tick_major = 6\n",
    "tick_minor = 4\n",
    "plt.rcParams[\"xtick.major.size\"] = tick_major\n",
    "plt.rcParams[\"xtick.minor.size\"] = tick_minor\n",
    "plt.rcParams[\"ytick.major.size\"] = tick_major\n",
    "plt.rcParams[\"ytick.minor.size\"] = tick_minor\n",
    "\n",
    "font_small = 12\n",
    "font_medium = 13\n",
    "font_large = 14\n",
    "plt.rc('font', size=font_small)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=font_medium)    # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=font_medium)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=font_small)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=font_small)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=font_small)    # legend fontsize\n",
    "plt.rc('figure', titlesize=font_large)   # fontsize of the figure title\n",
    "\n",
    "import matplotlib.colors as clr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Controls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:40:38.619248Z",
     "start_time": "2021-05-02T02:40:30.161499Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1 \n",
      "\t1 \n",
      "Setting temperature to 34.000000 C\n",
      "Setting simulation time step to 0.100000 ms\n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "**********************\n",
      "cADpyr232_L5_TTPC1_0fb1ca4724[0].soma[0]\n",
      "1 \n",
      "1 \n",
      "1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuron import h\n",
    "h.load_file(\"runModel.hoc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Model Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T03:22:53.581851Z",
     "start_time": "2021-05-02T03:22:53.571750Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def init_settings(nav12=1,\n",
    "                  nav16=1,\n",
    "                  dend_nav12=1, \n",
    "                  soma_nav12=1, \n",
    "                  ais_nav12=1, \n",
    "                  dend_nav16=1, \n",
    "                  soma_nav16=1,\n",
    "                  ais_nav16=1, \n",
    "                  axon_Kp=1,\n",
    "                  axon_Kt =1,\n",
    "                  axon_K=1,\n",
    "                  soma_K=1,\n",
    "                  dend_K=1,\n",
    "                  gpas_all=1,\n",
    "                  hcn=1):\n",
    "    \n",
    "    # create default model parameters to avoid loading the model\n",
    "    \n",
    "    h.dend_na12 = 0.026145/2 \n",
    "    h.dend_na16 = h.dend_na12 \n",
    "    h.dend_k = 0.004226 * soma_K\n",
    "\n",
    "\n",
    "    h.soma_na12 = 0.983955/10 \n",
    "    h.soma_na16 = h.soma_na12 \n",
    "    h.soma_K = 0.303472 * soma_K\n",
    "\n",
    "    h.ais_na16 = 4 \n",
    "    h.ais_na12 = 4 \n",
    "    h.ais_ca = 0.000990\n",
    "    h.ais_KCa = 0.007104\n",
    "\n",
    "    h.node_na = 2\n",
    "\n",
    "    h.axon_KP = 0.973538 * axon_Kp\n",
    "    h.axon_KT = 0.089259 * axon_Kt\n",
    "    h.axon_K = 1.021945 * axon_K\n",
    "\n",
    "    h.cell.axon[0].gCa_LVAstbar_Ca_LVAst = 0.001376286159287454\n",
    "    \n",
    "    #h.soma_na12 = h.soma_na12/2\n",
    "    h.naked_axon_na = h.soma_na16/5\n",
    "    h.navshift = -10\n",
    "    h.myelin_na = h.naked_axon_na\n",
    "    h.myelin_K = 0.303472\n",
    "    h.myelin_scale = 10\n",
    "    h.gpas_all = 3e-5 * gpas_all\n",
    "    h.cm_all = 1\n",
    "    \n",
    "    \n",
    "    h.dend_na12 = h.dend_na12 * nav12 * dend_nav12\n",
    "    h.soma_na12 = h.soma_na12 * nav12 * soma_nav12\n",
    "    h.ais_na12 = h.ais_na12 * nav12 * ais_nav12\n",
    "    \n",
    "    h.dend_na16 = h.dend_na16 * nav16 * dend_nav16\n",
    "    h.soma_na16 = h.soma_na16 * nav16 * soma_nav16\n",
    "    h.ais_na16 = h.ais_na16 * nav16 * ais_nav16\n",
    "    \n",
    "    h.hcn = hcn\n",
    "    \n",
    "    h.working()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Stimulation Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:48:59.026404Z",
     "start_time": "2021-05-02T02:48:59.022102Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def init_stim(sweep_len = 800, stim_start = 100, stim_dur = 500, amp = 0.5, dt = 0.01):\n",
    "    # updates the stimulation params used by the model\n",
    "    # time values are in ms\n",
    "    # amp values are in nA\n",
    "    \n",
    "    h(\"st.del = \" + str(stim_start))\n",
    "    h(\"st.dur = \" + str(stim_dur))\n",
    "    h(\"st.amp = \" + str(amp))\n",
    "    h.tstop = sweep_len\n",
    "    h.dt = dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:49:00.722086Z",
     "start_time": "2021-05-02T02:49:00.715276Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def run_model(start_Vm = -72):\n",
    "\n",
    "    h.finitialize(start_Vm)\n",
    "    timesteps = int(h.tstop/h.dt)\n",
    "    \n",
    "    Vm = np.zeros(timesteps)\n",
    "    I = {}\n",
    "    I['Na'] = np.zeros(timesteps)\n",
    "    I['Ca'] = np.zeros(timesteps)\n",
    "    I['K'] = np.zeros(timesteps)\n",
    "    t = np.zeros(timesteps)\n",
    "    \n",
    "    for i in range(timesteps):\n",
    "        Vm[i] = h.cell.soma[0].v\n",
    "        I['Na'][i] = h.cell.soma[0](0.5).ina\n",
    "        I['Ca'][i] = h.cell.soma[0](0.5).ica\n",
    "        I['K'][i] = h.cell.soma[0](0.5).ik\n",
    "        t[i] = i*h.dt / 1000\n",
    "        h.fadvance()\n",
    "        \n",
    "    return Vm, I, t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AP analysis code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:49:02.498832Z",
     "start_time": "2021-05-02T02:49:02.478323Z"
    },
    "code_folding": [
     81,
     90,
     94,
     99,
     113
    ]
   },
   "outputs": [],
   "source": [
    "def detect_spikes(Vm, time, dVdt_thresh = 15, min_spike_len = 0.0002, properties=True):\n",
    "    '''\n",
    "    Method for idenifying spikes based on rates of change in the membrane potential\n",
    "    INPUTS:\n",
    "    Vm: array-like - membrane potential (mV)\n",
    "    time: array-like - time corresponding to Vm (sec)\n",
    "    dVdt_thresh: float - Threshold for determining spike initiation (V/s)\n",
    "    min_spike_len: float - Minimum length of time dVdt must be above dVdt_thresh to be considered a spike (sec)\n",
    "    properties: Bool - If true, returns spike_times and spike_properties. Otherwise returns only spike_properties\n",
    "\n",
    "    Output:\n",
    "    array of spike times\n",
    "\n",
    "    Identification of spike start times:\n",
    "    dVdt is first quanitified from Vm and time\n",
    "    Continuous tretches (runs) of dVdt above dVdt_thresh are identified, and then esured to last longer than min_spike_len\n",
    "\n",
    "    Spike Property measurement:\n",
    "    spike_end is determined by finding the second zero crossing of dVdt after spike dVdt_threshold\n",
    "    First zero crossing is AP peak, second should be end of repolarization phase\n",
    "    Persistent zero crossing (must stay above zero for 3 continous points) is used to make sure that noise in slowly repolarizing spikes doesn't trigger spike end\n",
    "    '''\n",
    "    if len(Vm) != len(time):\n",
    "        raise Exception(\"Vm and time must be the same length\")\n",
    "\n",
    "    #determine stretches where dVdt exceeds dVdt_thresh\n",
    "    dVdt = np.gradient(Vm,time)/1e3\n",
    "    runs = group_consecutives(np.argwhere((dVdt>=dVdt_thresh)).flatten())\n",
    "\n",
    "    #If runs are longer than min_spike_len count as a spike\n",
    "    dt = time[1]-time[0] #sample rate\n",
    "    min_run_len = np.ceil(min_spike_len/dt)\n",
    "    spike_times = []\n",
    "    for run in runs:\n",
    "        if len(run) > min_run_len:\n",
    "            spike_times.append(time[run[0]])\n",
    "    spike_times = np.asarray(spike_times)\n",
    "\n",
    "    if not properties: #just return spike_times\n",
    "        return spike_times\n",
    "\n",
    "    #get spike properties\n",
    "    spike_properties=[]\n",
    "    for spike_time in spike_times:\n",
    "        #find index of spike_time in time\n",
    "        spike_start_idx = np.argwhere(spike_time == time)[0][0]\n",
    "\n",
    "        #find zero crossings of dVdt after spike dVdt_threshold\n",
    "        zero_crosses = find_zero_crossing(dVdt[spike_start_idx:-1])\n",
    "        #make sure zero cross is persistent to account for noise\n",
    "        if len(zero_crosses) > 1:\n",
    "            spike_end_idx = spike_start_idx\\\n",
    "                            + zero_crosses[np.argwhere(np.diff(zero_crosses)>3)[0] + 1][0]\n",
    "        else: #Vm ends before spike can repolarize, therefore assigned Vm[-1] as spike end\n",
    "            spike_end_idx = len(Vm)-1\n",
    "\n",
    "        spike_Vm = Vm[spike_start_idx:spike_end_idx]\n",
    "        spike_time = time[spike_start_idx:spike_end_idx] - time[spike_start_idx]\n",
    "        spike_dVdt = dVdt[spike_start_idx:spike_end_idx]\n",
    "        spike = {}\n",
    "        spike['start_idx'] = spike_start_idx\n",
    "        spike['start_time'] = time[spike_start_idx]\n",
    "        spike['end_idx'] = spike_end_idx\n",
    "        spike['end_time'] = time[spike_end_idx]\n",
    "        spike['Vm'] = spike_Vm\n",
    "        spike['time'] = spike_time\n",
    "        spike['thresh'] = spike_Vm[0]\n",
    "        spike['peak_Vm'] = spike_Vm.max()\n",
    "        spike['height'] = np.max(spike_Vm)-spike_Vm[0]\n",
    "        spike['AHP'] = spike_Vm[0]-spike_Vm[-1]\n",
    "        spike['peak_dVdt'] = spike_dVdt.max()\n",
    "        spike['min_dVdt'] = spike_dVdt.min()\n",
    "        try:\n",
    "            half_pnts = find_zero_crossing(spike_Vm - (spike_Vm[0]+(np.max(spike_Vm)-spike_Vm[0])/2))\n",
    "            spike['half_width'] = (half_pnts[1]-half_pnts[0])*dt*1000\n",
    "        except: #For slowly repolarizing spikes this can sometimes fail\n",
    "            spike['half_width'] = np.nan\n",
    "\n",
    "        spike_properties.append(spike)\n",
    "    return spike_times,spike_properties\n",
    "\n",
    "def detect_spike_times(Vm, time, dVdt_thresh = 15, min_spike_len = 0.0002):\n",
    "    '''\n",
    "    Wrapper of detect_spikes to only get spike times\n",
    "    '''\n",
    "    return detect_spikes(Vm, time,\n",
    "                          dVdt_thresh = dVdt_thresh,\n",
    "                          min_spike_len = min_spike_len,\n",
    "                          properties=False)\n",
    "\n",
    "def detect_spike_properties(Vm, time, dVdt_thresh = 15, min_spike_len = 0.0001):\n",
    "    '''\n",
    "    Wrapper of detect_spikes to only get spike properties\n",
    "    '''\n",
    "    return detect_spikes(Vm, time,\n",
    "                          dVdt_thresh = dVdt_thresh,\n",
    "                          min_spike_len = min_spike_len,\n",
    "                          properties=True)[1]\n",
    "\n",
    "def group_consecutives(vals, step=1):\n",
    "    \"\"\"Return list of consecutive lists of numbers from vals (number list).\"\"\"\n",
    "    run = []\n",
    "    result = [run]\n",
    "    expect = None\n",
    "    for v in vals:\n",
    "        if (v == expect) or (expect is None):\n",
    "            run.append(v)\n",
    "        else:\n",
    "            run = [v]\n",
    "            result.append(run)\n",
    "        expect = v + step\n",
    "    return result\n",
    "\n",
    "def find_zero_crossing(x):\n",
    "    '''\n",
    "    returns array of indicies before a zero crossing occur\n",
    "    If your input array starts and stops with zeros, it will find a zero crossing at the beginning, but not at the end\n",
    "    '''\n",
    "    zero_crossings = np.where(np.diff(np.signbit(x)))[0]\n",
    "    return zero_crossings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FI Curve support functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T03:14:42.207959Z",
     "start_time": "2021-05-02T03:14:42.200132Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def FI_curve(stims, iclamp, orig, stim_start = 50, stim_dur = 300, sweep_len = 350, dt = 0.1):\n",
    "             \n",
    "    f = []\n",
    "    i = []\n",
    "    for amp in stims:\n",
    "        i.append(amp)\n",
    "        init_stim(stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len, dt=dt, amp=amp)\n",
    "        iclamp.amp = -0.025 if orig else 0\n",
    "        \n",
    "        Vm, I, t = run_model()\n",
    "        spike_times = detect_spike_times(Vm, t)\n",
    "        f.append(len(spike_times))\n",
    "    \n",
    "    return f, i\n",
    "\n",
    "def FI_curve_plot(stims, iclamp, orig, ax, label='', stim_start = 50, stim_dur = 300, sweep_len = 350, dt = 0.5):\n",
    "    f, i = FI_curve(stims=stims, iclamp=iclamp, orig=orig, stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len, dt=dt)\n",
    "    \n",
    "    ax.set_ylim(0,18)\n",
    "    ax.set_ylabel('Spikes per Epoch ({}ms)'.format(stim_dur))\n",
    "    ax.set_xlabel('Injected Current (nA)')\n",
    "    ax.plot(i, f, linewidth = 1, label=label) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 25% Ri Increase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_path = './ri increase/'\n",
    "if os.path.isdir(plot_path) is False:\n",
    "    os.mkdir(plot_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpas_reduct = 0\n",
    "hcn_reduct = 0.565"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original: 76 MOhm\n",
      "Ri increase: 96 MOhm\n",
      "diff: 19 MOhm (25%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "step_amp = -0.05\n",
    "init_stim(stim_start=600, stim_dur=300, sweep_len=1500, dt=0.2, amp=step_amp)\n",
    "\n",
    "iclamp = h.IClamp(h.cell.soma[0](0.5))\n",
    "iclamp.delay = 300\n",
    "iclamp.dur = 900\n",
    "iclamp.amp = -0.025\n",
    "\n",
    "init_settings()\n",
    "Vm, I, t = run_model()\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(t, Vm, lw=0.7, label='original')\n",
    "start = int(0.6/0.2e-3)\n",
    "end = int(0.9/0.2e-3)\n",
    "original_ri = (Vm[end]-Vm[start])/step_amp\n",
    "original_title = 'original: %d MOhm' %(np.round(original_ri, 2))\n",
    "print(original_title)\n",
    "\n",
    "iclamp.amp = 0\n",
    "init_settings(gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "Vm, I, t = run_model()\n",
    "plt.plot(t, Vm, lw=0.7, label='Ri increase (gpas=0, hcn=0.565)')\n",
    "gpas0_ri = (Vm[end]-Vm[start])/step_amp\n",
    "gpas0_title = 'Ri increase: %d MOhm' %(np.round(gpas0_ri, 2))\n",
    "print(gpas0_title)\n",
    "diff_ri = gpas0_ri-original_ri\n",
    "diff_title = 'diff: %d MOhm (%d%%)' % (np.round(diff_ri, 2), np.round(diff_ri/original_ri*100, 2))\n",
    "print(diff_title)\n",
    "\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel('Voltage (mV)')\n",
    "plt.legend()\n",
    "n50pa_step_title = original_title + ', ' + gpas0_title + ', ' + diff_title\n",
    "plt.title(n50pa_step_title)\n",
    "plt.savefig(plot_path+'n50pa_step.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FI Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(20,8))\n",
    "stims = np.arange(0.1,.51,.02)\n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='original', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "FI_curve_plot(stims, ax, label='Ri increase (gpas=0, hcn=0.565)', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(nav12=0)\n",
    "FI_curve_plot(stims, ax, label='Hom', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(nav12=0, gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "FI_curve_plot(stims, ax, label='Hom + Ri increase (gpas=0, hcn=0.565)', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(plot_path+'fi_curve.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rLStXrszZR+j3FVYXMsbgcrkA8PDI/mN3/n5c5w9Hfv/471wuF+7u7lfcN2vWrIuuRMrMzMRaS2JiYs7phEeOHKF27dp//Yt+Hk9Pzz98Xr83XPh7sGfPnpz9r/7q96xx48YXXYm0YsUKjhw5kvN+RkYGCQkJ1KpV6w+3279/P1999RUPP/xwzjFrLZ6entSsWZNmzZpRvXp1AO677z5uuOEGsrKyeP3113nkkUcoV67cH+5z/ueYm4GaiIiIiIhcXOratcRPCOXcjh1Uum8ENf/9Gm5+fk5niVw17YnkoGHDhtG2bVsef/zxPD9Wjx49+P7773Ou4DVhwoSc/W/O17NnT7777ju2bdsGwKJFi2jWrBlpaWl8++23PPbYYwwfPpyqVauyZMkSsrKyiI+Pp06dOgQEBPDYY4/x6quv5uzZ0759e959910ATp06RadOnZg/f/6fnvf6669n9+7dAJQrV45OnTrlnMa3d+9efvjhh5wBzIkTJ3L2JlqwYAGenp4EBwdfcd+leHh4MGDAgJyNpTdu3MiWLVvo3r37lf7S5+jVq1fO55SUlETPnj3ZuXPnVT1Wu3btSEhIyBmCRUZG0qFDBypUqPCH2/n5+fH888+zevVqIPv3MzU1lbZt23L77bfz9ddf5+yjNGfOHNq0aYO7uztfffVVzue+f/9+vvzyS2677bacx927dy8NGza8qnYRERERkdLMWkvyzz+z7293c+TZ5yjXuxf1l3xHwIgRGiBJsaeVSA776KOPaNas2R9OSboawcHBvPXWW9x4440A1KhRg8jISHbs2PGH2zVu3JjQ0FCGDh2as4Lpq6++omzZsrz44os88cQTvPDCC3h6etK5c2d27dpF5cqVef755+nZsydlypTBw8ODsLAwAKZPn8748eMJDg4mPT2dYcOG8be//e1PfbfffjuPPvooL7/8MpB9BbBRo0bxySefUKtWLYKCgnJWM/n4+DBlyhSeeuopypQpw7x583B3d7+qvkv55JNPGD16NE2bNsUYw5QpU/D/bUlp//79uf/++xk8eHCufw8++ugjHnjgAZo1a4bL5eKZZ56hdevWub7/+Tw9PZkzZw7jx48nJSWFgIAAJk+eDGSvmOrfvz+LFi2iZs2azJo1i3HjxpGenk758uWZO3cuXl5eDBo0iEOHDtGtWzdcLheBgYFEREQAMG3aNO6//36ioqLIysri/fffz7mqWXp6OtHR0Tm3FRERERGRy7NZWZxZsoT4CaGQlUXA2LGUv7EvxkMvu6XkMOefUlSchISE2LVr1/7puC7xXXT17duXV155hbZt2/Laa69x22230bBhQ5KSkmjWrBmLFy/G19eXpk2bkpyc7GhrWFgYderUyRnKlSZRUVFs3ryZt956y+mUK6avfxEREREpbDY9naQFX5MQFoa7vz8B94+jbPfuf9rjVKQYuegfXo1EpdBMmDCB8ePHs2DBAq677jqGDBmCm5sbmZmZPP300zRu3Jh9+/Y5nQlkn+52/mbfpUVycjLTp09n7ty5TqeIiIiIiBRprrQ0Ts3+goSJE/EOCqL6yy/j27aNhkdSomklkoiUGPr6FxEREZGClnX6NCenf07ilCn4tmpJwNixlAkOdjpLJD9pJZKIiIiIiIjI1cpMSCBx0mROzZxJ2e7dCIyaiPe11zqdJVKoNEQSERERERERuYiMI0dIiJxI0oIFlO/fj3pffoFX7dpOZ4k4QkMkERERERERkQuc27OXhPBwzvzwAxVuv41rFnyFZ9WqTmeJOEpDJBEREREREZHfnN2yhfjQMFJXr6bi3+6iwbff4F6hgtNZIkWChkgiIiIiIiJS6qWuXUv8hFDObd9OpZH3UfO1V3Hz83M6S6RIcXM6oDQxxhAfH/+HY1FRUQwcONChIhERERERkdLLWkvyzz+z7293c+SZZynXuxf1v19CwIgRGiCJ/IVCWYlkjDFAFBBrrX37t2MPAqOBMsA6YJS19lxh9IiIiIiIiEjpZbOyOLNkCfETQiEzk4Bx4yh/Y1+Mh07WEbmUAl+JZIxpBPwA3H7esVuBh4FeQBOyB0mPF3RLUZeUlMTdd99N06ZNCQ4O5sknnyQzMxMAHx8fnn32Wdq2bUvjxo2ZNWsWd9xxBw0bNuSGG24gJSXF4XoREREREZGizaanc+rLOewZMJCEiROp8vDDBM2fh//AARogieRCYXyVPASEAwfOO3YP8I61NhHAGHM/4FUILY7r0aMH7u7uOe8nJibSrFkzAB555BECAgKIjY0lPT2dwYMH8/bbb/P0009z7tw5qlevzurVq3njjTcYPXo0W7dupUaNGrRp04b58+dz1113OfVpiYiIiIgDbHo66YcOO50hUgxYUn5ZSUJkJN5B9aj+0kv4tmtL9kkzxcups6c4ee6k0xlygTIeZajuV93pjAJX4EMka+14AGNMn/MOXwdUNcZ8A9QElgNP/tX9jTFjgbEXHq9bt26uG4InBV9B8dWJvTc2V7dbunQplStXznk/KiqKL774AoDFixfzyy+/YIzB29ub+++/n/fff5+nn34agNtuuw2A+vXrExwcTK1atQAICgoiMTExPz8dERERESnCXGlpnJr9BQkTJ2I8PDDn/ZBSRP6a97UNqP3B+5T57Yf4xc2B0weI3BTJd/u/I8AnwOkcuUDLqi35V6d/OZ1R4Jxar+cJ9AZuAs4Ck4DXgMcuvKG1NhQIvfB4SEiIze2T5XbA4zSXy/WHSbjL5SIjIyPnfW9v75y3PT09C7VNRERERJyXdfo0J6d/TuKUKfi2akXtDz+kTHBTp7NEpABtT9xOxKYIoo9EM6ThEBbespCKPhWdzpJSyqkh0hFgjrX2NIAxZirwokMtRUbfvn356KOPeO+990hPTyc0NJTevXs7nSUiIiIiDstMSCBx0mROzZpF2W7dCJwUhXeDBk5niUgB2nB8A+Gx4WxJ2MLwxsP5Z4d/4uepK8aJs5waIn0B3GmMCSd7JdLNwBqHWoqMDz/8kIcffpjg4GDS09O58cYbee6555zOEhERERGHZBw5QkLkRJIWLMB/QH/qffEFXrVrOZ0lIgXEWsuquFWEx4ZzJPkI9zW5j3e6v4O3u/fl7yxSCIy1uT4rLG9PZEwUsMla+7Yxxh14HhgCuAPrgXG/r0zKjZCQELt27do/Hd+6dSuNGjXKn2gRKVb09S8iIiXFuT17SAgLJ/nHH6lwx+1UuvdePKpUcTpLRAqIy7pYemApYbFhnM08y6jgUdwYdCOebtrGRBxx0R3nC20lkrV2xHlvZwEv//afiIiIiIgAaZs3kxAaRuqaNVS8+2/U/+5b3P39nc4SkQKS4crgm73fEB4bThmPMoxpNoYedXrgZtycThP5S06dziYiIiIiIr9JXbuW+AmhnNuxg0r3jaDmf/6Nm6+v01kiUkDOZZ1j3s55TNw8kVpla/F026dpX6P9Hy60JFIUaYgkIiIiIuIAay0py5cTPyGUzBMnCBgzmtoff4Sbl5fTaSJSQFIyUpi1fRZTtkyhSUATXu/yOi2qtnA6SyTXNEQSERERESlENiuLM0uWED8hFLKyCBg3lvJ9+2I89K25SEl18uxJpm2dxqzts2hfsz2f9vqU6ytd73SWyBXTv1QiIiIiIoXApqeTtGABCWHhuPv7U+WRhynbvbtOXxEpwY6lHGPSlknM3zWf3oG9mdp/KnXL13U6S+SqaYgkIiIiIlKAXGlpnJr9BQkTJ+IdFET1l1/Gt20bDY9ESrADpw8QuSmSJfuXcFODm5gzeA7V/Ko5nSWSZxoiiYiIiIgUgKzTpzk5/XMSp0zBt1VLan/4AWWCg53OEpECtD1xOxGbIlh1ZBVDrh/C17d8TUWfik5nieQbDZFERERERPJRZkICiZMmc2rmTMp270Zg1ES8r73W6SwRKUC/nviV8I3hbErYxPDGw3mx/YuU9SrrdJZIvnNzOqA0McYQHx//h2NRUVEMHDjQoaL/zxhDcHAwLVq0oGXLllx//fW0adOGtWvXAvDZZ5/x+uuv/+l+Fzte3Bw+fJjBgwdjrXU6BYATJ07Qr18/GjduTNOmTVm5cuVl71OYf5ZiYmJo06YNjRo1omfPnsTFxf3l7f773/9SvXp1WrRoQYsWLejSpUvOx7788ktat25N06ZNGTBgAAkJCQDExsZStmzZnPu0aNGC7du3c+bMGfr160daWlqhfI4iIiJXKuPIEY6++hq7+w8g68xp6n35BTXfeEMDJJESylrLyiMrGfXtKJ786Uk61+rM4lsXM7LpSA2QpMTSSiTJsXTpUipXrpzz/ttvv83DDz/MqlWruP/++//yPhc7XtyMGTOGl19+ucjsTfDQQw/RpUsXFi9ezIYNGxgwYAA7d+7E19fX6TTS09O5/fbbmTFjBp06deLTTz9l1KhRLFq06E+3XblyJe+++y533XXXH46vXbuW8ePHs2rVKurVq8fjjz/Oc889x2effcbKlSu56667CA0N/dPjDRs2jBdeeIG33367wD4/ERGRK3Vuz14SwsNJ/uEH/G+/jWsWfIVn1apOZ4lIAXFZF0sPLCUsNoy0zDRGB4/mxqAb8XTzdDpNpMBpJVIRkpSUxN13303Tpk0JDg7mySefJDMzEwAfHx+effZZ2rZtS+PGjZk1axZ33HEHDRs25IYbbiAlJeWijxsVFcWIESOuqCUzM5MDBw5QqVIlAF566SXGjx//p9udf7xevXq89NJLdOnShcDAQF544YWc20VGRtKkSROaNWvGDTfcwMGDB1m2bBnNmzenY8eONGvWjHPnzrFgwQLatWtHy5Yt6dSpE6tWrQLg2LFj3HzzzXTo0IGgoCC6d+/O8ePHAfj0009p3rw5bdq0oUuXLmzZsgXIXl10yy230Lp1a5o1a8a///3vv/xcY2JiOH78OG3atAFg165ddO3alaZNm9K7d2969epFVFQU+/btIzAwkHHjxtGiRQuaN2/O8uXLr7qvY8eOf1ht06JFCx566CEyMzP5+uuvGTNmDAAtWrTg2muv5Ztvvrns71tcXBwDBgwgODiYli1bsnXrVgCOHj3KzTffTMOGDWncuDEffvghAN27d+eZZ56ha9eu1KtXj9GjR+NyudiyZcuf2lq0aMHEiRNZs2YN5cuXp1OnTgCMGjWKH374IWcl0flWrlzJtGnTaNasGX379iU2NhaAqVOnMmrUKOrVq5fz5+jJJ5/Muc/WrVtp1aoVbdu2Zc6cOTmPd+eddzJt2jSOHTt22V8LERGRgnZ2yxYOPfY4++++G8/ataj/3bdU+7//0wBJpITKcGWwYPcCbp1/K6GxoYwJHsPcm+YyqP4gDZCk1CgVK5G2NmxU4M/RaNvWXN2uR48euLu757yfmJhIs2bNAHjkkUcICAggNjaW9PR0Bg8ezNtvv83TTz/NuXPnqF69OqtXr+aNN95g9OjRbN26lRo1atCmTRvmz5//p9UeV6pHjx45p9z5+PgwcOBAJk6ceEWPkZyczPLlyzl8+DANGjRg5MiRnD59mqeeeor169dTp04d3n//fV577TWGDh3Kpk2b2LNnD4GBgezcuZNnn32WZcuWERAQwObNm+nVqxe7du1ixowZdOjQgaeeegprLQMGDGDKlCk89thjPPbYY+zbt48aNWowZcoUVqxYQePGjRk+fDiPP/44gwYN4uzZs/Tv358GDRpw5513/qF59uzZfzgNbPjw4dxzzz088MADbN26lZCQEO6++24ADhw4QLdu3ZgwYQKLFy9myJAh7N+//6r6LnaK2tGjR3G5XFSpUiXnWO3atTl06NBlf/337NnDzJkzadCgAY8++ihvv/02ERERPPjgg1x33XXMmzePpKQkOnXqRP/+/QHYvXs3y5YtIzk5mUaNGvHTTz/Ro0cPNmzY8JfPMWPGDOrUqZPzvpeXF1WqVOHw4cMEBATkHE9JSaFhw4Y89dRTdO3alVmzZtGvXz+2bdvGjh07aNasGTfddBP79u0jODiY9957DwA/Pz+GDRvGuHHj2LFjB926daNu3bqEhITg4+NDmzZtWLRoEffdd99lfz1EREQKQuratcRPCOXcjh1Uum8ENV97FTc/P6ezRKSAnMs6x7yd85i4eSI1y9bkybZP0qFGhyJzFoNIYSoVQ6TcDngKw4WnjEVFRfHFF18AsHjxYn755ReMMXh7e3P//ffz/vvv8/TTTwNw2223AVC/fn2Cg4OpVasWAEFBQSQmJv7puR566CF++eUXEhMTSU5OpkWLFnh7exMTE3PJtvXr19O/f3969OhB1Sv8SdpNN90EQK1atahatSqJiYn89NNP9O3bN2fw8NhjjwGwbNky6tSpQ2BgIABLliwhLi6Onj175jyem5sbu3bt4tFHH2X58uW8++677Ny5k02bNtGuXTvc3d2544476NixIwMGDKBv377cddddpKSk8NNPP5GYmJizIio5OZkNGzb8aYi0bds2hg4dCsDJkydZvXo1P//8M0DOnj+/q1ixYs6wrl+/fri7u7Nx48Yr7oPslUipqal/aOnUqRPPPffcn/5Bstb+Yfh4MW3btqVBgwZA9gqm31fxfP/997z55psA+Pv7s2nTppz7DBo0CDc3N8qXL0+DBg1ITExky5YtfzmUfPTRR/H29s5Vn5+fH99++23O+3feeSevvPIKa9asISMjgwULFvDDDz9QtWpVnnzyScaMGcO8efP45JNPcu7TqFEjhgwZwoIFCwgJCQGy/7xv3779sr8WIiIi+claS8ry5cRPCCXz+HECxoym9scf4ebl5XSaiBSQlIwUZm2fxZQtU2gc0JjXu7xOi6otnM4ScVSpGCIVFy6X6w8vzl0uFxkZGTnve3t757zt6Xn55ZIff/wxkD2oWrZsGVFRUbnqaNWqFe+99x4jRoygZcuWOacc5UaZMmVy3jbGYK3Fw8PjD59XWloa+/fvB6Bs2f+/4VxWVhY9e/Zk5syZOccOHjxIzZo1eeqpp1i9ejUjR46kR48eZGRk5GyCPXXqVDZt2sT333/P66+/zpQpUwgPD8/e6G7lypx9hH5fYXUhYwwulwsAD4/sL4nzN9g+fzjy+8d/53K5cHd3v+K+WbNmXXQlUmZmJtZaEhMTc04nPHLkCLVr1/7rX/TznP/n4vdf/9+7z/892LNnT84w869+zxo3bnzRlUgrVqzgyJEjOe9nZGSQkJCQM9T83f79+/nqq694+OGHc45Za/H09KRmzZo0a9aM6tWrA3Dfffdxww03kJWVxeuvv84jjzxCuXLl/nCf8z/H3AzURERE8oPNyuLMkiXETwiFzEwCxo2j/I19MR76NlqkpDp59iTTtk5j1vZZtK/Rnk97fcr1la53OkukSNCeSEVI3759+eijj7DWcu7cOUJDQ+ndu7cjLcOGDaNt27Y8/vjjeX6sHj168P333+dcwWvChAk5+9+cr2fPnnz33Xds27YNgEWLFtGsWTPS0tL49ttveeyxxxg+fDhVq1ZlyZIlZGVlER8fT506dQgICOCxxx7j1Vdfzdmzp3379rz77rsAnDp1ik6dOjF//vw/Pe/111/P7t27AShXrhydOnXKOY1v7969/PDDDzkDmBMnTuTsTbRgwQI8PT0JDg6+4r5L8fDwYMCAATkbS2/cuJEtW7bQvXv3K/2lz9GrV6+czykpKYmePXuyc+fOq3qsdu3akZCQkDMEi4yMpEOHDlSoUOEPt/Pz8+P5559n9erVQPbvZ2pqKm3btuX222/n66+/ztlHac6cObRp0wZ3d3e++uqrnM99//79fPnllzmr8CD796Rhw4ZX1S4iIpJbNj2dU1/OYc+AgSRMnEiVhx8maP48/AcO0ABJpIQ6lnKMN9e8ycC5A4lPi2dq/6m82e1NDZBEzqN/AYuQDz/8kIcffpjg4GDS09O58cYbee655/L8uCNGjLjijbUBPvroI5o1a/aHU5KuRnBwMG+99RY33ngjADVq1CAyMpIdO3b84XaNGzcmNDSUoUOH5qxg+uqrryhbtiwvvvgiTzzxBC+88AKenp507tyZXbt2UblyZZ5//nl69uxJmTJl8PDwICwsDIDp06czfvz4nF/PYcOG8be//e1PfbfffjuPPvooL7/8MgCTJ09m1KhRfPLJJ9SqVYugoKCc1Uw+Pj5MmTKFp556ijJlyjBv3jzc3d2vqu9SPvnkE0aPHk3Tpk0xxjBlyhT8/f0B6N+/P/fffz+DBw/O9e/BRx99xAMPPECzZs1wuVw888wztG7dOtf3P5+npydz5sxh/PjxpKSkEBAQwOTJk4HsFVP9+/dn0aJF1KxZk1mzZjFu3DjS09MpX748c+fOxcvLi0GDBnHo0CG6deuGy+UiMDCQiIgIAKZNm8b9999PVFQUWVlZvP/++zRqlL2vWXp6OtHR0Tm3FRERyW+utDROzf6ChIkT8Q6qR/WXXsK3XVvtfSJSgh04fYDITZEs2b+EmxrcxJeDv6S6X3Wns0SKJHP+aTvFSUhIiF27du2fjm/dujXnBadIbvXt25dXXnmFtm3b8tprr3HbbbfRsGFDkpKSaNasGYsXL8bX15emTZuSnJzsaGtYWBh16tTJGcqVJlFRUWzevJm33nrrLz+ur38REblaWadPc3L65yROmYJvq5YEjB1LmeBgp7NEpADtOLmD8NhwVh1ZxZDrh/C3Rn+jok9Fp7NEioKL/uREK5FEyD7Fbvz48SxYsIDrrruOIUOG4ObmRmZmJk8//TSNGzdm3759TmcC2ae7nb/Zd2mRnJzM9OnTmTt3rtMpIiJSgmQmJJA4aTKnZs6kbPduBEZNxPvaa53OEpEC9OuJXwnfGM6mhE0MbzycF9u/SFmvspe/o4hoJZKIlBz6+hcRkdzKOHKEhMiJJC1YQPn+/QgYNQqvXFzEQkSKJ2st0XHRhMeGc+jMIe5reh83N7gZH48/X3hHRLQSSURERESuUlZSEonTpnFy6jSyTp50OidfuPn6UmHoEK5Z8BWeVas6nSMlRHxaPFO2TGH29tkkZzi7BYL8kcVyjf81jAoeRb+gfni6Xf5q1yLyZyVyiGSt1eaHIqVMcV1VKSJSlGWeOEHipEmcmv0FZW+4gcCpU/CqV8/prPxhjL5flHxzJPkIEzdNZNHeRfQP6s/swbOp4VfD6Sy5gEFf9yJ5VeKGSD4+PiQkJBAQEKC/IERKCWstCQkJ+PhoObKISH5IP3SYxMgIkhYuwn/QIILmzsGzZk2ns0SKnD2n9hCxKYKfDv3E7dfezvyb51O5TGWns0RECkyJGyLVrl2bQ4cOceLECadTRKQQ+fj4UFt7WYiI5Mm53btJCA0jedkyKgwZQv2FX+NRWS+IRS60OWEzEbERrDu2jr81+huLbl1Eea/yTmeJiBS4EjdE8vT0JCgoyOkMERERkWIjLXYTCaGhpK5fT6Xhw6n/3LO4l9cLYpHzWWtZd2wd4bHh7Dq1ixFNRvBqp1fx9fR1Ok1EpNCUuCGSiIiIiFyetZbUNWtImBDKud27CRg5kppvvoFbmTJOp4kUKdZalh9eTnhsOAlpCYwKHsWH13yIl7uX02kiIoVOQyQRERGRUsRaS/KyZSRMCCXr5EkCxo7Bf9AgjJdeEIucL8uVxZL9SwiPDcdiGRM8ht6BvXF3c3c6TUTEMRoiiYiIiJQCNiuL0998Q0JoGBhD5XFjKdenD8ZdL4hFzpeRlcGCPQuI3BRJRe+KPNLqEbrU6qKL9oiIoCGSiIiISInmSk8naf58EsLD8QioTNV//B2/LnpBLHKh1IxU5uycQ9TmKBpUaMBLHV6idbXW+loRETmPhkgiIiIiJZArNZVTs2eTEDkR72uvpeZrr+EbEuJ0lkiRczr9NDO2zWDa1mm0qtqKD3p8QJPKTZzOEhEpkjREEhERESlBspKSODl9OolTpuLbpg21P/6YMk31gljkQvFp8UzdMpUvdn5Bt9rdmNh3ItdUuMbpLBGRIk1DJBEREZESIPPECRInTeLU7C8oe8MNBE6dgvc1ekEscqEjyUeYuGkii/Yuon9Qf2YOnEmtsrWczhIRKRY0RBIREREpxtIPHSYxMoKkhYvwHziQoDlf4llLL4hFLrQnaQ8RsRH8dOgnbrv2NubfPJ/KZSo7nSUiUqxoiCQiIiJSDJ3bvZuE0DCSly2jwp13Un/h13hU1gtikQttTthMRGwE646t466Gd7HwloX4e/s7nSUiUixpiCQiIiJSjKTFbiIhNJTU9eupNPxu6j/3He7lyzudJVKkWGtZd2wd4bHh7Dy1kxFNRvBqp1fx9fR1Ok1EpFjTEElERESkiLPWkrpmDQkTQjm3ezcBI++j5huv4+arF8Qi57PWsvzwcsJjw4lPi2dU01F8eMOHeLl7OZ0mIlIiaIgkIiIiUkRZa0n+6ScSJoSSlZhIwJjR+A8ejPHSC2KR82W5slhyYAnhG8OxWEYHj6Z3YG883PRyR0QkP+lvVREREZEixmZlcfqbb0gIDQNjqDxuLOX69MG4uzudJlKkZGRlsGDPAiI3RVLBuwKPtHqELrW6YIxxOk1EpETSEElERESkiHClp5M0fz4J4eF4VAqg6t8fx69rV70gFrlAakYqc3bOIWpzFPUr1OefHf5JSLUQfa2IiBQwDZFEREREHOZKTeXU7NkkTIzCu0EDar76KmVC9IJY5EKn008zY9sMpm2dRquqrfigxwc0qdzE6SwRkVJDQyQRERERh2QlJXFy+nQSp0zFNySE2h99RJmmekEscqH4tHimbpnKFzu/oFvtbkT2jaR+hfpOZ4mIlDqFMkQy2T9GiwJirbVvX/CxOcARa+34wmgRERERcVpmfDyJkyZxatZsyvboQeCUyXjX1wtikQsdST5C1OYoFu5ZSL+gfswcOJNaZWs5nSUiUmoV+BDJGNMI+BhoB8Re8LEngS7AzILuEBEREXFa+qHDJEZGkLRwEf4DBlDvyy/xqq0XxCIX2pO0h4jYCH469BO3Xnsr82+eT+UylZ3OEhEp9QpjJdJDQDhw4PyDxpjuwI3AZ0DFQugQERERccS53btJCA0jedkyKtx5B/W/XoBHlSpOZ4kUOZsTNhMRG8G6Y+sY1nAYC29ZiL+3v9NZIiLymwIfIv1+mpoxps/vx4wxNYEPyB4ijSvoBhERESk+Mo4fJzFqEinLf8Za63RO3rksWUlJVBp+N/Wf+w738uWdLirVUjJSmL19Ngv3LiQjK8PpHDlPps0kLTONexvfy6udXsXX09fpJBERuUChb6xtjPEEPgcet9bGXe6qI8aYscDYC4/XrVu3YAJFRETEEekHD5IQEcHpxd/gf9Ngarz+Om5eXk5n5QvP2rVxK1PG6YxS7dTZU0zbNo2Z22bSvmZ7nm33LOU8yzmdJReoW74uXu4l4+teRKQkcuLqbCHANcC7vw2QqgPuxhgfa+3oC29srQ0FQv/0ICEhJeBHkyIiInJu507iw8JI+Xk5FYYOof7iRXhUquR0lpQQx1KOMXnLZObtmkfvwN5M7T+VuuX1w0gREZGrUehDJGvtKqDO7+8bY14CKuvqbCIiIqVL2saNxIeGkrbhVyrdcw/VX3gB93JaGSL54+Dpg0RujuS7fd9xU4Ob+HLwl1T3q+50loiISLHmxEokERERKaWstaTGxBA/YQLp+/cTMHIUtd56S6d6Sb7ZcXIHEbERrDqyijuvv5Ovb/maij66houIiEh+MMV1w8qQkBC7du1apzNEREQkF6zLRfKyZcRPmIDr9BkCxozBf+AATAnZ80ic9+uJXwmPDWdz/Gbubnw3d153J2W9yjqdJSIiUhxddPNqrUQSERGRAmMzMzm9+BsSQkPB04PK4+6nXK+eGHd3p9OkBLDWEh0XTURsBIeSD3Ffk/t4q+tb+Hj4OJ0mIiJSImmIJCIiIvnOlZ5O0tx5JISH41GtKlWf/D/8OnfmcldlFckNl3Wx9OBSwjeGk5qZyujg0dwYdCOebp5Op4mIiJRoGiKJiIhIvnGlpHBy1mwSJ07Eu1FDar7+H3xbt3Y6S0qITFcmi/cuJiI2Am8Pb8YGj6VH3R64GTen00REREoFDZFEREQkz7JOnSJx2jROTpuOb7u21PnsU3waN3Y6S0qIc1nnmL9rPpGbIqlZtiZPtn2SDjU6aGWbiIhIIdMQSURERK5axvHjJEZNIunLLynbqyeBU6fifU2Q01lSQqRkpDB7+2wmb5lM44DGvN7ldVpUbeF0loiISKmlIZKIiIhcsfSDB0mIiOD04m/wHzyYoLlz8KxZ0+ksKSFOnT3FtG3TmLltJu1rtOfTXp9yfaXrnc4SEREp9TREEhERkVw7t3Mn8WFhpPz0MxWGDqX+ooV4BAQ4nSUlxLGUY0zeMpl5u+bRO7A3U/pPIbB8oNNZIiIi8hsNkUREROSy0jZuJD40lLQNv1Jp+HCqv/AC7uXKOZ0lJcTB0weJ3BzJd/u+Y3D9wXw5+Euq+1V3OktEREQuoCGSiIiI/CVrLakxq0kIncC5ffsIuG8ktd56C7cyZZxOkxJix8kdRMRGsPLISoZcP4Svb/maij4Vnc4SERGRi9AQSURERP7AulwkL1tGwoRQspKSCBg7Fv+BAzBeXk6nSQnx64lfCY8NZ1P8JoY3Hs4L7V+grFdZp7NERETkMjREEhEREQBsZianF39DQmgoeHpQeew4yvXuhXF3dzpNSgBrLdFx0UTERnDwzEHua3ofb3V9Cx8PH6fTREREJJc0RBIRESnlXOnpJM2dR0J4OB7VqlL1yf/Dr3NnjDFOp0kJ4LIulh5cSvjGcFIyUxgdPJp+Qf3wdPN0Ok1ERESukIZIIiIipZQrJYWTs2aTOHEi3g2vp+Z//o1vSIjTWVJCZLoyWbx3MRGxEXh7eDMmeAw31L0BN+PmdJqIiIhcJQ2RRERESpmsU6dInDaNk9Om49u2LXU++xSfxo2dzpIS4lzWOebvmk/kpkhq+NXgyTZP0qFmB61sExERKQE0RBIRESklMo4fJ3HSJE598SXlevYkcOpUvK8JcjpLSoiUjBRmb5/N5C2TaRTQiP90+Q8tq7Z0OktERETykYZIIiIiJVz6oUMkRERwetFi/AcN4pq5c/CsWdPpLCkhTp09xbRt05i5bSbtarTjk16f0LBSQ6ezREREpABoiCQiIlJCndu5k/iwMFJ++pkKQ4dSf9FCPAICnM6SEuJYyjEmb5nMvF3z6B3Ymyn9pxBYPtDpLBERESlAGiKJiIiUMGmxscRPmEDa/zZQ6Z57qP7CC7iXK+d0lpQQB08fJHJzJN/t+47B9Qfz5eAvqe5X3eksERERKQQaIomISKmWfuAAiVOmknnsmNMp+SIzPp6MuDgCRo6k1ltv4VamjNNJpdrJsyeZvm06u0/tdjolX6RmpLI5YTN3Xn8nC25ZQCWfSk4niYiISCHSEElEREqls9t3kBAWRsovv1BhyJ2U79/f6aR8YXy8KduxI8bLy+mUUu1oylEmbZ7EV7u/om+9vvSt1xdD8b86mbtx5+0ab1PWq6zTKSIiIuIADZFERKRUSduwgfjQMNJiNxJw771Uf+mfuJfVC2LJH/tP72fipoks2b+EWxrcwtyb5lLVt6rTWSIiIiL5QkMkEREp8ay1pK5aRXxoGBkHDlBp9ChqvfsObj4+TqdJCbE9cTvhseHExMUwtOFQFt6ykAo+FZzOEhEREclXGiKJiEiJZV0ukn/8kfgJobhSU6k8dgzl+/fHeHo6nSYlxIbjGwiLDWNrwlbubXIvL3V8CT9PP6ezRERERAqEhkgiIlLi2MxMTi9aRHxoKG7ePgSMG0u5Xr0wbm5Op0kJYK1l1ZFVhMWGEZcSx8imI3m3+7t4u3s7nSYiIiJSoDREEhGREsN17hxJc+eSEB6BZ82aVHv6Gfw6dcSY4r+hsTjPZV38eOBHwmLDSM9KZ1TwKG6sdyMebvp2SkREREoHfdcjIiLFXlZyCqdmziQxKgqfJk2o+eab+LZq6XSWlBAZrgwW711MeGw4fh5+jGs2ju51uuNmtLJNREREShcNkUREpNjKPHmSk1OmcvLzz/Hr0IE6YaH4NGzodJaUEGczzzJ311yiNkVRp1wdnm33LO2qt9PKNhERESm1NEQSEZFiJ+PYcRInTuTU3LmU79Obep9Px6tePaezpIRITk9m5vaZTN06laaVm/JmtzdpXqW501kiIiIijtMQSUREio30AwdICI/g9LffUuHmm7hm/jw8q1d3OktKiJNnTzJ161RmbZ9Fx5odmdB7AtdVvM7pLBEREZEiQ0MkEREp8s5u30FCWBgpK1ZQYdhQ6i9ehEelSk5nSQlxNOUokzZP4qvdX9GnXh+m959OnfJ1nM4SERERKXI0RBIRkSIrbcMG4kPDSIvdSKV77qH6S//EvWxZp7OkhNh/ej8TN01kyf4l3NLgFubeNJeqvlWdzhIREREpsjREEhGRIsVaS2p0NPETQsk4cIBKo0dR6913cPPxcTpNSojtidsJjw0nJi6GoQ2HsvCWhVTwqeB0loiIiEiRpyGSiIgUCdblIvnHH4kPDcOVnEzA2DH4DxiA8fR0Ok1KiA3HNxAWG8bWhK3c0/geXur4En6efk5niYiIiBQbGiKJiIijbGYmpxctIj40FDdvHwLGjaVcr14YNzen06QEsNay6sgqwmLDiEuJY2TTkbzb/V283b2dThMREREpdjREEhERR7jOnSNp7lwSwiPwrFGDak8/g1+njhhjnE6TEsBlXfx44EfCYsM4l3mOUcGj6BfUDw83fesjIiIicrX0nZSIiBSqrOQUTs2cSWJUFD6NG1PzzTfwbdXK6SwpITJcGSzeu5iI2Ah8PXwZ22wsPer0wM1oZZuIiIhIXmmIJCIihSLz5ElOTp3GyenT8evQnjqhE/Bp1MjpLCkhzmaeZd6ueUzcNJHa5WrzdNunaV+jvVa2iYiIiOQjDZFERKRAZRw7TmJUFKfmzKFc714ETp+Gd1CQ01lSQiSnJzNz+0ymbp1K04CmvNH1DVpUbeF0loiIiEiJpCGSiIgUiPQDB0gIj+D0t99S4eabuGb+PDyrV3c6S0qIk2dPMnXrVGZtn0XHmh2Z0HsC11W8zuksERERkRKtUIZIJnsteRQQa6192xhTBvgYaAsYIAZ4yFqbVhg9IiJScM5u30FCWBgpK1ZQYdhQ6i9ehEelSk5nSQlxNOUokzZP4qvdX9GnXh+m9Z9G3fJ1nc4SERERKRUuO0QyxrgBNwDdgNpAFnAI+B74xVprL3P/RmQPjNoBsb8dfu63525G9hBpKvAM8OJVfRYiIuK4tF9/JX5CKGkbN1Lp3nuo/tI/cS9b1uksKSEOnD5A5KZIluxfws0NbmbO4DlU86vmdJaIiIhIqXLJIZIxZhTZA59kYB0Q99t96gN3Ae7GmFettRMv8TAPAeHAgfOO/Qzss9a6fnue/wFNrvaTEBERZ1hrSY2OJn5CKOkH9hMwahS13n0HNx8fp9OkhNieuJ2I2Aii46IZ0nAIX9/yNRV9KjqdJSIiIlIqXXSIZIxZBGwF+llrt1/kNk2BB4wxQ621ff/qNtba8b/dts95x7477zECgceAsVfzCYiISOGzLhfJS5cSPyEU15kzBIwdi//AARhPT6fTSrWjKUdZsHsBZ7POOp2SL7YlbmNLwhbuaXwP/+z4T/w8/ZxOEhERESnVLrUS6SFr7d5L3dlauwl4yBhzzdU8uTGmNTAX+Mha+/VFbjOWvxgw1a2r/Q9ERAqbzczk9OLFJISGYjy9CBg7lnK9e2Hc3Z1OK9X2Ju0lclMkSw8upV+9fgSUCXA6KV/0qtuLd7u/i7e7t9MpIiIiIsIlhkjnD5CMMWWttcnGGHfgNiDBWvvDebfdc6VPbIwZCnwCjLfWTr9ERygQeuHxkJCQS+7FJCIi+cd17hxJc+eREB6OZ/XqVH3yKfw6dyL7ugnilK0JWwmPDWftsbUMaziMhbcsxN/b3+ksERERESmhcrOx9t/IHvb4A28AdwMuY8wH1to3ruZJjTGDgA+BPtbatVfzGCIiUvCyklM4NXMmiVFR+DRuTM0338C3VSuns0q99cfWExYbxo7EHdzb5F5e6fQKvp6+TmeJiIiISAl32SES8CRwszHGExgD9AGOAr+QPVS6Gm+TfVW28PN+iv2Ltfahq3w8ERHJR5knT3Jy6jROTp+OX4f21AmdgE+jRk5nlWrWWn458gthG8M4nnqcUcGj+KDHB3i5ezmdJiIiIiKlRG6GSHWstUuNMT2ANGttDIAxpvyVPJG1dsR5b19/RZUiIlIoMo4dJzEqilNz5lCudy8Cp0/DOyjI6axSLcuVxQ8HfiA8NpwMVwZjgsfQp14fPNxy80+4iIiIiEj+yc13oAeNMbcAdwHfARhjRgE7CzJMREQKT/rBgySER3D6m2/wv2kw18ybi2eNGk5nlWoZWRl8vedrIjdFUt6rPA+2eJCutbviZtycThMRERGRUio3Q6R/AJHAKWCwMaYX2aex3VqAXSIiUgjO7thBQmgYKStWUGHoEOovXoRHpUpOZ5VqaZlpzNk5h6jNUdQrX48X2r9Am+pttIm5iIiIiDjuskMka+33QN3f3zfGHAZqWGszCjJMREQKTtqvvxI/IZS0jRupdM89VP/ni7iXK+d0Vql2Jv0MM7fPZOqWqTSv0px3u71LcJVgp7NERERERHLk5upsZYHhQCDgdt5xrLVPFmCbiIjkI2stqdHRxE8IJf3AfgJGjaLWu+/g5uPjdFqplpCWwLSt05i9Yzada3UmvE84DSo2cDpLRERERORPcnM62xdAPSAGcBVojYiI5DvrcpG8dCnxE0JxnTlDwNix+A8cgPH0dDqtVDuacpSozVEs2L2AfkH9+HzA59QuV9vpLBERERGRi8rNEKkTUNtam1TQMSIikn9sZianFy8mITQU4+lFwLhxlOvVE+Pu7nRaqbYvaR+RmyL58eCP3NrgVubdNI8qvlWczhIRERERuazcDJE2AlUBDZFERIoB17lzJM2dR0J4OJ7Vq1P1yafw69xJGzM7bGvCVsJjw1l7bC1DGw5l4S0L8ff2dzpLRERERCTXcjNEegxYZoxZSPYV2nJoTyQRkaLDlZLCyZmzSIyKwrtRQ2q+8Tq+rVs7nVXqrT+2nrDYMHYk7uCeJvfwSqdX8PX0dTpLREREROSK5WaI9CZwGvABtN5eRKSIyTx5kpNTp3Fy+nR827ejzoTP8GnUyOmsUs1ayy9HfiFsYxjHU48zMngkH/T4AC93L6fTRERERESuWm6GSG2Aqtba1IKOERGR3Ms4dpzEqChOzZlDud69CJw+De+gIKezSrUsVxY/HPiB8NhwMlwZjA4eTd96ffFwy80/tyIiIiIiRVtuvqvdANQBthdsioiI5Eb6wYMkhEdw+ptv8L9pMNfMm4tnjRpOZ5VqGa4MFu5ZSERsBOW8yvFA8wfoVqcbbsbN6TQRERERkXyTmyHSL8DPxpivgUTA/v4B7YkkIlJ4zu7YQUJYOCnLl1Nh6BDqL16ER6VKTmeVammZaczZOYdJmydRt3xdnm//PG2rt9Um5iIiIiJSIuVmiFQVWAS4AZULNkdERC6U9uuvxIeGkfbrr1S65x6qv/gC7uXKOZ1Vqp1JP8PM7TOZumUqzas0551u7xBcJdjpLBERERGRAnXZIZK19r7CCBERkf/PWktqdDTxE0JJP7CfgJGjqPX2W7iVKeN0WqmWkJbAtK3TmL1jNp1rdSasTxjXVrzW6SwRERERkUJxySGSMaYpMAZoAvgCycAWYKq1dm3B54mIlC7W5SJ56VLiJ4TiOnOGgDFj8B84AOOlq3o56WjKUaI2R7Fg9wJurHcj0wdMp065Ok5niYiIiIgUqosOkYwxI4E3genAXCCV7EHS9cBiY8wT1tpJhVIpIlLC2cxMTi9eTEJoKMbTi4Bx4yjXqyfG3d3ptFJtX9I+IjdF8sOBH7j12luZe9NcqvpWdTpLRERERMQRl1qJ9CLQz1q75sIPGGOmALMBDZFERPLAde4cSXPnkRAejmf16lR98in8OnfSxswO25a4jfDYcFbHrWZYw2EsunUR/t7+TmeJiIiIiDjqUkOkCsCGi3xsE6BdXUVErpIrJYWTM2eRGBWFd6OG1HzjdXxbt3Y6q9Rbf2w94bHhbE/czj1N7uHlji/j5+nndJaIiIiISJFwqSHSj0C4MeZFa+3+3w8aY2oD/wG+L+g4EREAm5FB8ooVuJJTnE7JF+l793Dy8xn4tm9HnQmf4dOokdNJV2Vv0l62JGxxOiNfpGelM2/XPI6nHmdk8Eje6/Ee3u7eTmeJiIiIiBQplxoijQTCgV3GmHQgDSgDeALzgbEFnycipZnr7FlOzZlDYngEHtWr41mjhtNJ+cK9YkUCp0/DOyjI6ZSrsjlhMxGxEaw7to621duWiFPvDIY7rr+DG+vdiIfbZS9cKiIiIiJSKl30O2Vr7SngdmOMH3At4Ef25to7rbXJhZMnIqVRVnIyp2bMIGHSJMo0DabmO2/j27Kl01mlmrWWdcfWERYbxu5TuxnRZASvdnoVX09fp9NERERERKSQXPLHrcYYD6AP0ITsK7MlA3WNMd9Za9MKoU9ESpHMkyc5OWUKJz+fgV+nTtQNj8Dn+uuczirVrLUsP7ycsI1hJJ5NZFTwKAZdMwhPd0+n00REREREpJBddIhkjGkHzAESgO1kr0LyBYYCE4wxg/7qym0iIlcq49gxEiMncmrePMr37Uu9GZ/jFRjodFapluXKYsn+JYTHhgMwOng0vQN74+7m7nCZiIiIiIg45VIrkT4FnrfWTrzwA8aYkcBngC4lJCJXLX3/fhLCIzj93XdUuOUWrvlqPp7VqjmdVaplZGWwYM8CIjdFUtG7Io+0eoQutbqUiH2PREREREQkby41RLoOmHyRj00G3sv/HBEpDc5u305CaBgpK1dScdgw6n+zGI+KFZ3OKtVSM1KZs3MOUZujaFChAS91eInW1VpreCQiIiIiIjkuNUTaDIwCQv/iY+OATQVSJCIlVur//kfChFDObt5MpRH3Uv3ll3Ev6+d0Vql2Ov00M7bNYNrWabSu1poPbviAJgFNnM4SEREREZEi6FJDpPuBr4wxzwJbyN4TqQzQEPAC+hV8nogUd9ZaUlauJGFCKBmHDxMwehS1PngfN29vp9NKtfi0eKZumcoXO7+gW+1uTOw7kWsqXON0loiIiIiIFGEXHSJZa/9njGkA3ABcD/iRPUj6L/CjtTa9cBJFpDiyLhdnfviBhAmhuM6mUXnsWMr364fx1FW9nHQk+QgTN01k0d5F9A/qz8yBM6lVtpbTWSIiIiIiUgxcaiUS1tpzwGJgsTGmAtAdOFvwWSJSXNmMDE4vWkR8aBhuZcpQ+f5xlL3hBoybm9NppdqepD1ExEbw06GfuO3a25h/83wql6nsdJaIiIiIiBQjFx0iGWMaA1OA48DzwHfAabJPZUsyxtxorT1QKJUiUuS5zp0jac4cEsIj8Kxdm+rPPYtvhw7amNlhmxM2ExEbwbpj6/hbo7+x6NZFlPcq73SWiIiIiIgUQ5daifRf4GugArAEeM1a+47JfkX4T+AjYHCBF4pIkZaVnMKpmTNIjJqET9Om1Hz7LXxbtnQ6q1Sz1rLu2DrCY8PZdWoXI5qM4NVOr+Lr6et0moiIiIiIFGPGWvvXHzAmyVrrb4ypBhwBvK21mb99zBOIs9Y6di5ESEiIXbt2rVNPL1LqZZ48yckpUzj5+Qz8OnYkYOwYfK6/3umsUs1ay/LDywmPDSchLYFRwaMYeM1AvNy9nE4TEREREZHi46Knk1xqJVKyMaa6tfaoMWbkBQ/SEEjKrzoRKT4yjh0jMXIip+bNo3yfPtSb8TlegYFOZ5VqWa4slhxYQvjGcCyWMcFj6B3YG3c3d6fTRERERESkBLnUECkC+N4Y08ZaO+n3g8aYV4H7gWcKOk5Eio70/ftJCI/g9HffUeHmm7nmq/l4VqvmdFaplpGVwYI9C4jcFEkF7wo80uoRutTqon2oRERERESkQFx0iGStfdEYs99am3bBhzKBu6213xRsmogUBWe37yAhNJSUlSupOGwY9b9ZjEfFik5nlWqpGanM2TmHqM1R1K9Qn392+Cch1UI0PBIRERERkQJ1qT2RqlhrT+TqQYypaq09nq9ll6E9kUQKVtqGDcRPCCVtUywBI0ZQYcgQ3MuWdTqrVDudfpoZ22Ywbes0WlVtxejg0TSp3MTpLBERERERKVmuak+kL4wxS4Awa+2xv3xUY2oADwDdga55KRQR51lrSV21ivgJoWQcOkTA6FHUeu9d3Hx8nE4r1eLT4pm6ZSpf7PyCbrW7Edk3kvoV6judJSIiIiIipcylhkg9gSeAjcaYrcBqIA5wA2oAHYF6wPu/3VZEiinrcnHmhx9ICA3DlZpK5bFjKN+/P8bT0+m0Uu1I8hGiNkexcM9C+gX1Y+bAmdQqW8vpLBERERERKaUutSdSJvC6MeZD4GayVxo1BixwAHgbWPwXeyaJSDFhMzM5vXAh8WFhuPmUIWDcWMr17Ilxc3M6rVTbk7SHyNhIlh1axm3X3sb8m+dTuUxlp7NERERERKSUu9RKJACstanA9N/+uyome7fXKCDWWvu2McYdeAe48beGt621n13t44vIlXGdO0fSnDkkhEfgWasW1Z55Br+OHbUxs8O2JGwhPDacdcfWcVfDu1h4y0L8vf2dzhIREREREQFyMUTKK2NMI+BjoB0Q+9vhccB1QFOgHLDKGLPeWru6oHtESrOs5BROzZxBYtQkfJo0oeZbb+HbqqXTWaXe2qNrCY8NZ+epnYxoMoJXO72Kr6ev01kiIiIiIiJ/UOBDJOAhIJzsU+B+dwsQ+tspcyeNMTOAu8ned0lE8lnmyZOcnDKVk59/jl/HjtQJD8Pn+uudzirVrLUsP7yc8Nhw4tPiGdV0FB/e8CFe7l5Op4mIiIiIiPylAh8iWWvHAxhj+px3uA5w8Lz3DwHNCrpFpLTJOH6cxMiJnJo7l/J9+lBvxud4BQY6nVWqZbmyWHJgCeEbw3HhYkzwGHoH9sbDrTBm+iIiIiIiIlfPqVctbmRv0P07A2T91Q2NMWOBsRcer1u3bsGUiZQQ53bu5MDoMZTr24dr5s/Ds3p1p5NKtYysDL7e8zURmyLw9/bn4ZYP07V2V+1DJSIiIiIixcZlh0jGmJ7AJ0AQ4P77YcBaa90vesdLOwDUPO/9mmSvRvoTa20oEHrh8ZCQEPsXNxcRIG3TZg7efz/Vnn4a/4EDnM4p1dIy05izcw5Rm6O4xv8a/tnhn4RUC9HwSEREREREip3crET6FPgCmA2k59PzzgdGGmMWAGWBocD9+fTYIqVa6vr1HBr/MDVe+RflevZ0OqfUOp1+mpnbZjJ161RaVW3F+93fp0nlJk5niYiIiIiIXLXcDJECrLXP5vPzfgrUB34FvIAJ1tqf8vk5REqdlFWrOPyPJ6j55puU7dzJ6ZxSKSEtgalbpzJ7x2y61e5GZN9I6leo73SWiIiIiIhInuVmiPSjMaaftXZxXp7IWjvivLczgcfy8ngi8kdnflxK3PPPU/vDD/ANCXE6p9Q5knyEqM1RLNyzkH5B/Zg5cCa1ytZyOktERERERCTfXHSIZIyZTfbm1wHAPGPMz8DJ829jrb2zYPNEJDdOL1rE0X//hzoTPqNMcLDTOaXKnqQ9RMZGsuzQMm699lbm3zyfymUqO50lIiIiIiKS7y61EmnTeW/rVDORIurUl3M48f771I2IwOf665zOKTW2JGwhPDacdcfWMazhMBbeshB/b3+ns0RERERERArMRYdI1tqXf3/bGHMdcMRam2yMaQskWWu3F0agiFxc4tRpJEREUHfyJLyDgpzOKRXWHVtHWGwYO0/u5N7G9/Jqp1fx9fR1OktERERERKTAXXZPJGPMXcBnQGdgI9AaeNUYM9JaO7+A+0TkIuJDwzj1xRcETpmCV23tvVOQrLWsOLyC8NhwTqSdYFTTUXzY40O83L2cThMRERERESk0udlY+1/ADdbajQDW2k+NMeuAyYCGSCKFzFrLiQ8+4Mz33xM4ZQqe1ao6nVRiZbmy+P7A94THhpNlsxgTPIbegb3xcMvNX50iIiIiIiIlS25eCVUFNlxwbB1QLd9rROSSrLUc+89/SF27lsDJk/GoVMnppBIpIyuDr/d8TeSmSMp7l2d8i/F0rd0VY4zTaSIiIiIiIo7JzRBpPfAU8Np5x54ge5AkIoXEZmUR989/kr5rN4FRUbiXL+90UomTlpnGnJ1ziNocRVD5IF7s8CIh1UI0PBIRERERESF3Q6SHgIXGmEeBOKAmkAgMKsgwEfn/bEYGR55+hsyEBOpGhOPm5+d0UolyOv00M7fNZOrWqbSs2pL3ur9H08pNnc4SEREREREpUi47RLLWbjbGXAt0IvsUtsNAjLU2o6DjRARc585x+O//gMxM6kz4DDdvb6eTSoyEtASmbp3K7B2z6VqrK5F9I6lfob7TWSIiIiIiIkVSbneHvQ7oA9QGjgGpZJ/mJiIFyJWayqHxD+NWvjy13nsX46WrgeWHuOQ4ojZH8fWer+kX1I8ZA2ZQu1xtp7NERERERESKtMsOkYwx/YDZwFfAfiAIWG6MGWat/aqA+0RKrawzZzh4/wN41alDjVdfwXjoimB5tTdpL5GbIll6cCm3Xnsr82+eT+UylZ3OEhERERERKRZy86r0NeAOa+3i3w/8Nlh6g+zBkojks8yTJzk4egxlmjen2vPPYdzcnE4q1rYmbCU8Npy1x9YyrOEwFt6yEH9vf6ezREREREREipXcDJHqA99ecOxbYEb+54hI5okTHBg5irLdu1Hl73/XlcHyYN2xdYTFhrHz5E7ubXwvr3R6BV9PX6ezREREREREiqXcDJF2kH0ltvnnHRsM7CqQIpFSLOPIEQ7cNxL/m28i4P77NUC6CtZaVhxeQXhsOCfSTjCy6Ug+7PEhXu7aT0pERERERCQvcjNEehaYb4xZwv/fE6k7cHPBZYmUPun793PgvpFUvGc4ASNGOJ1T7GS5svj+wPeEx4aT6cpkTPAY+tTrg4eb9pISERERERHJD5d9dWWtXWKMaQMMAaoCq4DHrLW7CzpOpLQ4t3MnB0aPofJDD1LxzjudzilWMrIy+HrP10RuiqS8d3keavEQXWt3xc1oHykREREREZH8lNsf0R8FdgMpwEEgvsCKREqZtE2bOXj//VR76in8Bw10OqfYSMtMY87OOURtjiKofBAvdniRkGohOgVQRERERESkgFx2iGSMuQGYS/YQ6TAQCLxvjOlnrV1XwH0iJVrq+vUcGv8wNf71MuV69XI6p1g4k36GmdtnMnXLVFpUbcF73d+jaeWmTmeJiIiIiIiUeLlZifQB8Ii1dtLvB4wxo4FPgHYFFSZS0qWsWsXhfzxBzTfeoGyXzk7nFHkJaQlM3TqV2Ttm07VWV8L7hNOgYgOns0REREREREqN3AyRgoCpFxybCLyV/zkipcOZH5cS9/zz1P7gfXzbtHE6p0g7mnKUqM1RLNi9gH5B/ZgxYAa1y9V2OktERERERKTUyc0QaTHwCPDeeceGAd8WSJFICXd60SKO/vs/1JnwGWWCg53OKbL2Ju0lclMkSw8u5dYGtzLvpnlU8a3idJaIiIiIiEiplZshUgXgHWPMOLL3RaoJNAe2GmNW/34ja23bAikUKUFOfTmHE++/T92IcHyuv97pnCJpa8JWwmPDWXtsLUMbDmXhLQvx9/Z3OktERERERKTUy80QaSp/Pp1NRK5Q4tRpJEREUHfSJLyvCXI6p8hZf2w9YbFh7Ejcwb1N7uWVTq/g6+nrdJaIiIiIiIj85rJDpPM31D6fMcZYa23+J4mUPPGhYZz64gsCp0zBq3Ytp3OKDGstvxz5hbCNYRxPPc6o4FF80OMDvNy9nE4TERERERGRC1x0iGSMWWat7X7e+w9aaz857yZJQPkCbBMp9qy1nPjgA84s+Z7AKVPwrFbV6aQiIcuVxQ8HfiA8NpwMVwZjgsfQp14fPNxyszhSREREREREnHCpV2ytLnj/VeD8IZLJ/xyRksNay7H//IfUNWsJnDIZj0qVnE5yXIYrg4V7FhIRG0F5r/I82OJButbuiptxczpNRERERERELuNKfux/4dBIp7KJXITNyuLoSy9xbsdOAidF4V6+dC/aS8tMY87OOUzaPInA8oG80P4F2lRvgzGaRYuIiIiIiBQXVzJE0tBIJBdsRgZHnn6GzPh46kZG4Obn53SSY86kn2Hm9plM3TKV5lWa8063dwiuEux0loiIiIiIiFwFbUAiko9c585x+O//gMxM6kz4DDcfH6eTHJGQlsC0rdOYvWM2nWt1JrxPOA0qNnA6S0RERERERPLgUkMkD2NMP/7/aWwXvu9eoGUixYwrNZVD4x/GrVw5ar33Lsar9F1h7GjKUaI2R7Fg9wL6BfXj8wGfU7tcbaezREREREREJB9caoh0nD9upJ1wwfvHC6RIpBjKOnOGg/c/gFedOtR49RWMR+la5LcvaR+RmyL58eCP3NrgVubdNI8qvlWczhIREREREZF8dNFXutbaeoXYIVJsZZ48ycExYynTLJhqzz+PcSs9VxrblriN8Nhw1hxdw9CGQ1l4y0L8vf2dzhIREREREZECULqWS4jks8wTJzgwchRlu3Wlyj/+UWquNrb+2HrCY8PZnride5rcw786/gtfT1+ns0RERERERKQAaYgkcpUyjhzhwH0jKX/TYCo/8ECJHyBZa/nlyC+EbQzjeOpxRgaP5P0e7+PlXvr2fhIRERERESmNNEQSuQrp+/dz4L6RVBw+nID7RjidU6CyXFn8cOAHwmPDyXBlMDp4NH3r9cXDTX99iIiIiIiIlCaXfRVojHkK+Mham1IIPSJF3rmdOzkwegyVH3yQikPudDqnwGS4Mli4ZyERsRGU9yrPA80foFudbriZ0rPnk4iIiIiIiPx/uVlK8BTwTkGHiBQHaZs3c3Dc/VR76kn8Bw1yOqdAnM08y5ydc4jaHEVg+UBeaP8Cbaq3KfGn64mIiIiIiMil5WaINB341BjzOXAMsL9/wFq7paDCRIqa1PX/49D48VR/+SXK9+7tdE6+O5N+hpnbZzJ1y1SaV2nOO93eIbhKsNNZIiIiIiIiUkTkZoj04G//H3XBcQu452+OSNGUsmoVh//+D2q++QZlu3RxOidfJZ5NZOqWqczeMZvOtToT3iecBhUbOJ0lIiIiIiIiRcxlh0jWWm2AIqXamR+XEvf889T64H382rZ1OiffHE05yqTNk/hq91fcWO9Gpg+YTp1ydZzOEhERERERkSIqV5dXMsZUAG4HagNvA22stUvz+uTGmFuAlwEXkAiMsdbuzuvjiuSX04sWcfS1f1Pns08p06yZ0zn5Yl/SPiI3RfLjwR+5pcEtzLtpHlV8qzidJSIiIiIiIkVcbq7O1gZYBMQCbYAoYL4x5jFrbeTVPrExpgwwFWhurd1ljHkc+BAYcLWPKZKfTn05hxPvv0/dyAh8rr/e6Zw825a4jfDYcNYcXcPQ64ey8JaF+Hv7O50lIiIiIiIixURuViJ9CNxvrf3SGHPSWrvPGNOP7GHSVQ+RyN5PyQC/v4otC5zNw+OJ5JvEqdNIiIig7qRJeF8T5HROnvzv+P8I2xjG9sTt3NPkHv7V8V/4evo6nSUiIiIiIiLFTG6GSA2Bub+9bQGstb8YY6rm5YmttcnGmPuBlcaYBLKHSp3y8pgi+eHMj0tJjIoicMpkvGrXdjonT95d9y5L9i1hZPBI3uvxHt7u3k4niYiIiIhIaXcuGdZNhPVTIDPN6Zr8Ubcj3DrB6YoCl5sh0k6yTzFb8PsBY8wNwI68PLExJhh4EWhsrd1tjHkE+NIY08Jaa8+73Vhg7IX3r1u3bl6eXuSiznz/PZXuG1HsB0jWWhbuWUhEnwjq+ddzOkdEREREREq71ESImQBrwiGoK9z0MZQtIfuzepRxuqBQ5GaI9ASwwBjzI+BrjIkCBgF35vG5+wK/nLeR9sfAe0AAEP/7jay1oUDohXcOCQmxFx4TyStrLSnRqwgYPcrplDzbd3ofBkNg+UCnU0REREREpDQ7HQerPoL/TYXGg2HUdxBQ3+kquQqXHSJZa3/+bdXQMOAIEAe0s9buyuNzrwfGG2OqWWuPATcDe6218Ze+m0jByThwADKz8Aoq3vsgAcTExdCuRjuMMU6niIiIiIhIaZS4F375ADbPhRZ3wQMrwb+W01WSB7lZiQTZw6NoYB9wENh9yVvngrX2R2PMW8AyY0w6kAjclNfHFcmLlFXR+HVoXyIGLzFxMdxQ9wanM0REREREpLQ5tgVWvAe7voc2o+DhdeBX2ekqyQeXHSIZY5oD8wE/4ChQEzhhjBlw3qloV8Va+zHZp7GJFAkpq1ZRtkd3pzPyLMuVxeqjq3mm3TNOp4iIiIiISGlxaC0sfxcOr4X2D8CAd8CnvNNVko/ccnGbz4AIoJq1NhioCsziL/YpEinOrMtFakwMfh06OJ2SZ9sSt1G5TGWq+ubpIooiIiIiIiKXZi3sWQaTBsHsEVC/Bzz6K3R+XAOkEig3p7MFA52ttS4Aa22WMeZfQEKBlokUsnPbtuFesSKe1ao5nZJn0XHRtKvRzukMEREREREpqVwu2LEYlr8DZ09Dl79D8B3g7ul0mRSg3AyRYoBbgC/OO9aV7D2SREqM3/dDKgli4mIY0nCI0xkiIiIiIlLSZGXC5jnZp615eEGXf0DDgeDm7nSZFILcDJGOAzOMMT8Au8jeE6kfEGOMmfX7jay1dxZMokjhSImOpsIdtzudkWfnss7x64lfebv7206niIiIiIhISZFxFn6dDiveB//a0PdVqN8TSsBFiST3cjNE2ga8ct77x4ENBVIj4hCbnk7a+vXUeutNp1Py7Nfjv1K/Qn3Ke+n8YxERERERyaNzybBuIqz6GKo3g1tDoW7JOINDrtxlh0jW2pcLI0TESWm//opXUBDuFSo4nZJn2g9JRERERETyLDURYibAmjAI6gZ3zYIazZyuEoflZiWSSIlXovZDOhrDwy0fdjpDRERERESKo9NxsOoj+N9UaDQIRn4HlRs4XSVFhIZIImTvh1T5oQedzsiz5PRkdp7cSYsqLZxOERERERGR4iRxL/zyAWyeC82HwQO/ZO99JHIeDZGk1HOlpHBu2zZ8W7VyOiXP1h5bS7PKzfDx8HE6RUREREREioNjW2DFe7DrewgZCQ+vA7/KTldJEZWrIZIxpoG1dpcxxhcYDyQAkdZaW6B1IoUgde1afJo2xa1MGadT8iwmLob2NUvGaXkiIiIiIlKADq2F5e/CoTXQ/gEY8Db4+DtdJUXcZYdIxpi/Ay8AFYGPgbaAC2gM/KNA60QKQcrKVfh17OB0Rr6IjovmXx3/5XSGiIiIiIgURdbC3p9h+TuQuAc6PgK3hYOXr9NlUkzkZiXSaKCTMaYMMBRoAxwFNqMhkpQAKdHR1Hj5Jacz8iw+LZ5jqcdoHNDY6RQRERERESlKXC7YsTh75dHZJOjydwi+A9w9nS6TYiY3Q6Tq1totxpgBwHFr7SZjjDvgXcBtIgUuMyGBjCNH8Gna1OmUPIuJiyGkWgjubu5Op4iIiIiISFGQlQmb52QPjzy8oMs/oOFA0GsGuUq5GSJtN8Y8DgwEFhljfICngY0FWiZSCFJjYvANCcF4FP895mPiYmhXo53TGSIiIiIi4rSMs/Dr9OyrrZWvBX1fhfo9wRiny6SYy80r5weAD4Ek4HmgPXAb2ae2iRRrKaui8etQ/DeittYSHRfNiCYjnE4RERERERGnnEuGdRNh1cdQPRhu/gwCS8b+r1I0XHaIZK3dAHQ979AyILiAekQKVcqqVVQcfrfTGXl28MxBMl2ZBPkHOZ0iIiIiIiKFLTURVodm/xfUFe6aCTWaO10lJZBbbm5kjLnXGLPCGLPbGFPbGDPJGFO2oONEClL6oUO4zp7F+9prnU7Js+i4aNrVaIfR8lQRERERkdLjdBx8+xx82BKSDsLI7+COKA2QpMBcdohkjHmK7KuwhQIBwBmgNvBRwaaJFKyUVavwa9++RAxetB+SiIiIiEgpkrgXFjwGn7QDVybcvwJu+hgqN3C6TEq43OyJNA7obq09YIx531qbZIy5A9hewG0iBSp1VTR+nTo6nZFnLutizdE1/F+b/3M6RURERERKisx0iJ0FWxeAK8vpmjxJz3Jx+FQap9MynE7JF542g7oZu1ladiDfVQ7nzNEKMDcOiHM6rVRrWtOfJ/pe73RGgcvNEMkPOP7b278v2UgFivffJFKqWZeLlJgYqv7j706n5NmOkzvw9/anul91p1NEREREpLhLT4X/TYFfPoTK10Lre8HTz+mqq5KYms63m46yYm88bepVokVT/xJxFkIGhvWVW1HWqxy3Oh0jOSr6eTmdUChyM0T6DvjUGPN3wBpjPID/AD8WaJlIATq3cydufn541qrldEqeRR+J1qlsIiIiIpI3Z5NgTThEfwZ12sKQyVCrtdNVV2VffAqf/bSbxZuOckfrjrzw+DVU9/dxOkukRMjNEOkRYCoQT/ZKpBRgOXBXAXaJFKiUVavw61AyLnUZfTSa2669zekMERERESmOkk9AzKewdiJc2wfu/QqqNnK66qpsjTvNJ8t288uueO5uH8iyJ7qXmtUhIoXlskMka+1JYIAxphpQF4iz1h4yxvgWeJ1IAUldFY3/LTc7nZFnGVkZbDi+gTe6vOF0ioiIiIgUJ6cOwsr/wsaZ0PQ2GLsUKtZzuuqqrNt/kk+W7iL2cBKjuwTxn1uDKeudm/USInKlLvuVZYx51lr7b2vtMeDYb8d6AxOAawq4TyTf2YwMUteto8br/3E6Jc82xm+kbrm6+Hv7O50iIiIiIsVB/E5Y8T5s+xpa3QMPxUC54re3prWWFbvi+XjpLg6dTOP+bvX5+G+t8PF0dzpNpETLzXh2lDHGWGtfM8ZUBN4HbgdeK9AykQKSFrsJzzp18KhY0emUPIuJi6F9zfZOZ4iIiIhIURf3Kyx/F/atgLZj4ZH/gW8lp6uumMtl+W7LMT5Ztou09Cwe7FGfgc1q4unu5nSaSKmQmyFSV2CJMeZa4EZgAxBsrd1TkGEiBSUlehV+7UvG4CUmLoZxzcY5nSEiIiIiRdX+VbD8HTi2CTo+DDd9DN5lna66YhlZLhb8eoRPlu3G18udh3o0oHejari5Ff+rrYkUJ7nZE+mwMaYr2Vdp22CtvbHgs0QKTurKVQSMG+t0Rp6lZqSyNXErLau1dDpFRERERIoSa2HXD9nDozNHoPPjMHQaeHg7XXbFzmZkMXvtQT77aQ91K/ny0qAmdGoQgDEaHok44aJDJGPMGsCed8gX6GOM2QCkA1hr2xZonUg+c6WmkrZlC76ti+flSs+39thamgQ0oYxHGadTRERERKQocGXB1gXZwyNXJnT5BzS+GdyL3ybTZ85mMC3mABEr9tK8tj8fDmtJ68Divx2FSHF3qb9NPiq0CpFCkrpuPT6NG+HmW/wvLhgTF0O7Gu2czhARERERp2WmQ+wsWPEe+FSAHs/CtX3BrfjtE5SYkk7UL3uZGnOAzg0qM3lkWxrVKO90loj85qJDJGvtpMIMESkM2fshdXA6I1/ExMXwfPvnnc4QEREREaekp8L/psAvH0LlBjDwPajXBYrhqV5Hk84StnwPX6w7RP/g6sx5oCP1Kvs5nSUiF7jU6Wx7rLXXGGNO8MfT2nJYa6sWWJlIAUhdFU215551OiPPEs8mcjj5ME0qN3E6RUREREQK29kkWBMO0Z9BnbZw52SoXTy3a9gXn8KEn3ezKPYod7SuzbePdaW6v4/TWSJyEZc6ne2e3/5/e2GEiBS0zJMnSd+/nzLBwU6n5NnquNW0rtYaTzdPp1NEREREpLAkn4CYT2HtRLi2N9z7FVRt5HTVVdkad5pPlu3ml13x3N0+kKVPdKeSn5fTWSJyGZc6nW3Fb///yRjjAXQCqgMHgVXW2r9cnSRSVKXGrKZMSGuMV/H/xyk6Llr7IYmIiIiUFkmHYOV/4dcZ0PQ2GLsUKtZzuuqqrNt/kk+W7mLj4SRGdw7i37c0pZyPfjAqUlxcdpt+Y0xzYD5QFogDagInjDEDrLW7C7hPJN+UtP2Q7mp0l9MZIiIiIlKQ4nfCivdh+0JoORweioFy1Z2uumLWWlbsiufjpbs4dDKNcd3q8/HfWuHj6e50mohcodxc6/EzIAJ4zVrrMsa4A/8EQoGeBRknkp9SV0VTcehQpzPy7HDyYVIzU7m2wrVOp4iIiIhIQYj7FZa/C/tWQNux8PB68K3kdNUVc7ks3205xifLdpGansWD3eszqHlNPN2L31XjRCRbboZIwUBna60LwFqbZYz5F5BQoGUi+SjjyBGyzpzB+7rrnE7Js5i4GNrVaIcphlfdEBEREZFL2L8Klr8DxzZBh/Fw08fgXdbpqiuWkeViwa9H+GTZbny93HmwewP6NK6Gm5u+fxUp7nIzRIoBbgG+OO9YVyC6QIpECkDKqmh827XFuBX/n3pEH4mmfc32TmeIiIiISH6wFnb9kD08OnMEOj0GQ6eBh7fTZVfsbEYWs9cdYsJPu6ldsQz/HNSYzg0q64efIiVIboZIx4EZxpgfgF1k74nUD4gxxsz6/UbW2jsLJlEk71Kio/HrUPz3Q7LWEnM0hkdbP+p0ioiIiIjkhSsLti7IHh65MqHz36HJLeCem5doRcuZsxlMizlA5Iq9BNfy54OhLWgdWPxOvxORy8vN31DbgFfOe/84sKFAakQKgLWWlOhVVHnkYadT8mznqZ34evhSq2wtp1NERERE5GpkZcDGWbDiPfApD92fgetuhGK4Yj4xJZ2oX/YyJXo/Xa6twqSRbWlUo7zTWSJSgC47RLLWvlxQT26MCQb+C/gDWcA4a+26gno+KZ3Sd+/Gzcsbrzp1nE7Js9/3QxIRERGRYiY9Ff43BVb+FypdAwPegaCuUAxP9TqadJaw5Xv4Yt0h+gdXZ+6DnahX2c/pLBEpBJccIhljbgG8rLUzjTGVyL5KWwvgK+Af1trMq31iY4wv8B0wylq7yBhzEzANaHi1jynyV1JWRePboWTsIRQTF8PA+gOdzhARERGR3DqbBGvCIfozqNMW7pgEtVs7XXVV9sWnMOHn3SyKPcrtrWvzzWNdqOFfxuksESlEFx0iGWNGAm8B//fboY+AOsDfgfuBF4B/5uG5+wC7rbWLfnv/K2BvHh5P5C+lREdTvl8/pzPyLMOVwbpj6/hXp385nSIiIiIil5N8AmI+hbUT4drecM98qNbY6aqrsjXuNJ8u283ynScY3j6QpU90p5Kfl9NZIuKAS61Eehi4xVr782+rhm4F+ltrfzTGxJK9iigvQ6TrgKPGmAigOXAKeDIPjyfyJzYzk9TVq6nx8ktOp+TZ5vjN1Cpbi0o+2qRQREREAJcLdiyG/SudLsmzjCwXO46d4czZqz7RoUgpk3ma6079zJZKvVjVIJRTXrVgDcAWp9Ou2O4TyWw6cppRnYN47ZamlPPxdDpJRBx0qSHSNdban397uy1ggRUA1tpdxpiqeXxuT6A/0MNaG/Pb6WyLjDGB1tpzv9/IGDMWGHvhnevWrZvHp5fS4OzmzXjWqIFH5cpOp+RZdFy09kMSERERyMqEzXNg+bvg4ZV9RS+34ndFL4C09CzW7T/Jmn2J1KnkR+2KJePUqBRThzkNHyHVuyreQDWng/KgYY3yfHp3a3w83Z1OEZEi4FL/2mQZY7ystelAd2D1b29jjKkCpOTxuY8AW621MQDW2vnGmHDgGmDr7zey1oYCoRfeOSQkxObx+aUUSFkVjV8J2g/pvqb3OZ0hIiIiTsk4C79OhxXvg39t6Psq1O9ZLDdmPnHmHBEr9jJjzQF6NqzGAw9cQ4Oq5ZzOylcdnQ4QESkAlxoi/Qw8YYyZDtwNfHjex54Ffsrjcy8G3jHGtLbWrjPGdCV7tZP2RZJ8kxIdTaV773E6I8/SMtPYnLCZkGohTqeIiIhIYTuXDOsmwqqPoXow3DIBAjs4XXVVDiamEvrzHr769Qg3tajJgvGdqVPJ1+ksERHJpUsNkf4P+AZ4BVgGfAZgjNkD+AGd8/LE1tqjxpibgU+MMX7AOeBWa+3ZvDyuyO9cZ89yduNGfNu0cTolz/537H80qtQIX099kyUiIlJqpCbC6tDs/4K6wl0zoUZzp6uuyq7jZ/hk2W5+3HacoW3qsuTvXalazsfpLBERuUIXHSJZa3caYxoAla21J8770NPA99baxLw++W97LmmTFykQaevX433ddbiXLet0Sp5pPyQREZFS5HQcrPoI/jcVGg2Ekd9B5QZOV12VjYdO8cnS3azdn8iIjvX45//1wL+MNmYWESmuLrkDn7XWAicuODarQItE8knKqmj8OhbPpd4Xio6L5qm2TzmdISIiIgUpcS/88kH2ptnNh8H9K6BCHaerrpi1lpi9iXy8dBe7jiczpss1vDukOb5exXPzbxER+f/0N7mUWCnR0VT9vyeczsizpHNJHDhzgGaVmzmdIiIiIgXh2BZY8R7sWgIhI+Hh9eBX/K4sa61l6fbjfLx0NwnJ53ige31ublkLbw9d1UtEpKTQEElKpKzTp0nfs4cyLVo4nZJnq4+upkXVFni6a+m3iIhIiXJoHSx/Bw6tgfYPwIC3wcff6aorluWyLIqN4+OluwB4qEcD+gfXwN2t+F01TkRELk1DJCmRUlevpkyLFrh5eTmdkmcxcTF0qFEyTssTEREp9ayFvT9nD48SdkOnR+G2cPAqfhfPSM90Mfd/h/h02W4q+Xnx5I3X0+P6qhij4ZGISEmlIZKUSCkrV+HXob3TGfkiOi6a26+73ekMERERyQuXC3Z8kz08OpsEnR+H4DvAo/j9wCs1PZMZqw8StnwPDaqW5fXbmtEuqJKGRyIipYCGSFIipURHU/OtN53OyLOjKUc5fe4011W8zukUERERuRpZmbB5Lqx4F9w8oMs/oNEgcCt++wQlpWUweeU+olbuo029SkwY3ppmtSs4nSUiIoVIQyQpcTKOHSMrIQGfRo2cTsmz6Lho2lRvg5txczpFRERErkTmOdgwHX55H8rVhN6vQIOeUAxX65w4c46IFXuZseYAPRtWY+a49jSoWs7pLBERcYCGSFLipEZH49uuHcat+A9eYuJiaFejndMZIiIiklvnkmFdFKz6CKo1hZs/hcCOTlddlUMnUwn9eQ/zNxzhphY1WTC+M3UqFb+9m0REJP9oiCQlTsqq6BKxH5K1lpi4GB5s/qDTKSIiInI5qYmwOjT7v6CucNdMqNHc6aqrsuv4GT5dtocfth1jaJu6LPl7V6qW83E6S0REigANkaREsdaSEh1NwLixTqfk2d6kvXi6eVK7XG2nU0RERORizhzNXnW0fgo0Gggjv4XK1zpddVViDyXxybJdrN6byIiO9fjpiR74+3o6nSUiIkWIhkhSoqTv3QeAV716jnbkh1Vxq2hXo52udCIiIlIUJe6FlR/CpjnQfCjcvwIq1HG66opZa1m9N5GPl+1mx9EzjOl6De/c2RxfL71MEBGRP9O/DlKipESvwq9DhxIxeImJi6Fvvb5OZ4iIiMj5jm+FFe/Bzu8gZCSMXwtlqzhddcWstSzbfoKPl+7iRPI5HuhWn7B7WuPtUfyuGiciIoVHQyQpUVJXRVOudy+nM/Is05XJ2mNrebHDi06niIiICMChdbDiXTgYA+0fgP5vgY+/01VXLMtlWRQbxyfLdmOt5cEeDejftDoe7sX/giQiIlLwNESSEsNmZZG6ejXVnn/e6ZQ825qwlWq+1ahcprLTKSIiIqWXtbBvOSx/B+J3QadH4NYw8Cp+VyhLz3Qx93+H+OynPVT09eSJPtdxQ8OqJWL1toiIFB4NkaTEOLt1G+6VK+NZrarTKXkWczSG9jWK/xXmREREiiWXC3Z8k73yKO0kdH4cgu8EDy+ny65YanomM1YfJGz5HhpULct/bg2mXVAlDY9EROSqaIgkJUbKqpX4tS8Zg5foI9Hc3fhupzNERERKl6xM2Dw3e3jk5g5d/gGNBme/XcwkpWUwZdU+olbuIySwEhOGt6ZZ7QpOZ4mISDGnIZKUGKmroqn4t7uczsizs5ln2Ri/kZBqIU6niIiIlA6Z52DDdPjlfShXA3q/Ag16QjFcrXPizDkif9nL56sPcEPDqnw+pj3XVivndJaIiJQQGiJJieBKTydtwwZqvf+e0yl5tuHEBq6teC1lvco6nSIiIlKynUuGdVGw6iOo1hRu/hQCOzpddVUOnUwl7Oc9zNtwhMHNa7JgfGfqVCp+ezeJiEjRpiGSlAhp/9uAV4MGuJcv73RKnsXExdCuejunM0REJL8ciIET25yuyLNMa9kWd4bU9EynU/KFX9oR6h/4guOVQtjS7ANO+TeCY8CxA06nXbG1+0/y/dZjDGlThyV/70rVcj5OJ4mISAmlIZKUCCnRq0rMfkgxcTE83vpxpzNERCQvrIVdP2Rf1evMEajXGSh+p0YBZLose+NT2Hb0DL5e7pTzKRnfPia6+bEo8L/Ee9eFJCDplNNJVy2osh8/PdEDf19Pp1NERKSEKxnfBUipl7pyFVUee9TpjDw7nX6a3ad207xKc6dTRETkariyYOuC7OGRKxM6/x2a3ALuxe9brjNnM5gWc4CIFXtpVsufB++pT+vASk5n5asuTgeIiIgUM8XvOxqRC2QlJ3Nu507KtGrldEqerTm6huZVmuPlXvwuISwiUqplpkPsLFjxHvj4Q/dn4Lobwc3N6bIrlpiSTtQve5kSvZ8u11Zh8si2NKpR/E8XFxERkbzTEEmKvdTVa/Bp3gw3b2+nU/IsJi6GdjW0H5KISLGRngr/mwK/fAgB9WHAuxDUtVhe1eto0lnClu/hi3WH6B9cnbkPdqJeZT+ns0RERKQI0RBJir3s/ZA6OJ2RL2LiYvh35387nSEiIpdzNgnWhEP0Z1CnLdw5GWq3drrqquyLT2HCz7tZFHuU21vX5pvHulDDv4zTWSIiIlIEaYgkxV7qqmhqvPaq0xl5djz1OAlnE2hYqaHTKSIicjHJJyDmU1gbCdf2gXvmQ7XGTlddla1xp/l02W6W7zzB8PaBLH2iO5X8dDq1iIiIXJyGSFKsZZ44QcaxY/g0aeJ0Sp7FxMXQplob3N3cnU4REZELJR2Clf+FX2dA01thzFKoFOR01VVZf+Aknyzdxa+HkhjVOYjXbmlKOR9d1UtEREQuT0MkKdZSomPwbdMG4178By/RcdHaD0lEpKiJ3wW/vAdbv4ZWw+HBaChfw+mqK2atZcWueD5ZupsDianc3+0aPrqrFT6exf/fTxERESk8GiJJsZYSvQq/DsV/PyRrLTFxMYwOHu10ioiIAMRthBXvwt6foe1YeOR/4Fv8Lm/vclm+23KMT5btIjU9iwe712dQ85p4uhe/q8aJiIiI8zREkmLLWkvqqmgCRo50OiXP9p/ej8VSr3w9p1NEREq3/auyh0dHY6HDQzD4v+BdzumqK5aR5WLBr0f4dNlufDzdeahHA/o0roabW/G7apyIiIgUHRoiSbGVcfAg/6+9+wyPqzrXPv5fapbk3i33buPeLVds4ASCKaaEDqHHNiUBwiF5088JCcmhJeCCDRiCqQktJIRAOJZxmRl34wa2ZMtVcpObRrLarPfDjHMUx6AyktZs6f5d11x4yt5zS4sljx6v9WxbUkJSz56uo0QtkBMgPS0d48FLQouIeJ61kPkpLH0CTu6HCd+Da16BxGTXyarsVEkZf1yzl+eWZNG5ZQo/vXQAE3u30d8vIiIiUiNURBLPCvr8pI6rH4WXQG6AqV2muo4hItKwhMpg6wfh4lFZCUx6CAZeAfHe+3iUX1TKq/5dvLBsJ4M7Ned31w1jZDfvbb8TERGR2Oa9T0kiEUGfjyaTJ7uOEbWyUBkrc1fygzE/cB1FRKRhKCuBz9+CZU9BcjOY8kPoexHEea9P0NFgMQtXZLPIv4sJvdvw0m1jGNCxmetYIiIiUk+piCSeZEMhCgIB2j/yn66jRO2Lo1/QKrkV7VLbuY4iIlK/lRTC2ldgxe+hVU+Y9gT0mAweXNGae/wUzy/dwR/X7OWbgzrw9szx9GjT2HUsERERqedURBJPKvryS+KbNycxzXuXWT5TICfA2A5jXccQEam/Th2HVS+Afy50Hg3fegk6j3KdqlqyDwd57rMsPtyYy1UjOvPR9yaR1jzFdSwRERFpIFREEk863Q+pPgjkBLim3zWuY4iI1D/Bw+HC0eoXofcFcMv70H6A61TV8kXuCeYszmLp9kPclN6N/33oXFo3aeQ6loiIiDQwKiKJJwX9PlpcdbXrGFErLitm/cH1/M+5/+M6iohI/XF8L6x4Fja8Hm6Ufden4e1rHrR291HmLM5kw97j3D6hB49eMYimyYmuY4mIiEgDpSKSeI4tLqZwzVo6/fa3rqNEbcOhDfRs3pNmSWqCKiIStcOZsPwp2PoXGH4TzPJDM+9te7bWsjzzCLMXZ7I7r4AZ5/bk2RtGkJwY7zqaiIiINHAqIonnFH7+OUnduxPfooXrKFHz5/gZm6Z+SCIiUcn5HJY9CTs/g9F3wf3rINV7l7cPhSyfbD3AnMWZ5BeVMmtKby4b1pHEeO9dNU5ERETqJxWRxHOCPj+N61E/pHuG3eM6hoiIN+32w9InwkWk8ffCZc9Ao6auU1VZaVmIDz7fz5zFWSQnxnPP1F58Y0AH4uK8d9U4ERERqd9ioohkjJkOvGKt9d4nP6lzQb+fNjNnuo4RtfzifLYd3cbwdsNdRxER8Q5rIetTWPpkuPfRxO/BNa9AYrLrZFV2qqSMP63Zy7wlWXRqkcJPLhnApD5tMEbFIxEREYlNzotIxpg+wOOAPjFJhULBIKe2biV15AjXUaK25sAahrQZQnKC937xERGpc6EQfPFBeOVRaTFMehAGXgnxzj/KVFl+USmvBXbx/NKdDOrUnKevHcao7t7bficiIiINj9NPXsaYVGAR8CDwmsss4g0Fq1eTMnAgcSkprqNETf2QREQqoawENv4Rlj0V3qp27iPQ95sQ570+QUeDxSxckc0i/y7G92rNwttGM7Bjc9exRERERCrN9T/fPRe5fe44h3hE0OcntZ70Q/Ln+PnF+F+4jiEi9Ym1kLMBSk+5ThK1otIyDny5kvab5lPUrDs5I37KybTxYAzsPuY6XpWELHy8OZc/rtnLNwd14O2Z4+nRprHrWCIiIiJV5qyIZIyZBZRaa180xnT/mtfdDdx95uNdu3atxXQSq4J+Px1+9lPXMaJ2uPAwB4IHGNB6gOsoIlIfhMpgy3uw9CkoCULjtq4TVVtpyHLoZBEHT5ziZFJb5jT+PttD58AGYMOXruNV29DOLfjoe5NIa+79lbQiIiLScLlciXQrkGqMWQ8kASmRP19srd1/+kXW2vnA/DMPHjVqlK2bmBIrSvPyKNm3j5TBg11HidrKnJWM7DCShDjXiwFFxNNKi+HzN2DZ05DaGs77MfS9MLxax2OO5BexcHk2rwZ2cW7ftsy8oTdDOzRloutgIiIiIvJPzn6DtdaOOf3nyEqkTdbaYa7ySOwrCARIHTkSk+D9wksgN0B6Wv3YliciDhQHYe0fYMUz0LYfXPZ76DbBk8Wj/ccKWbB0B++s3ce0IWm8d88EurXWVi8RERGRWOT938alwQj6/DSuB/2QrLX49/u5ZcAtrqOIiNcUHoNVCyDwHHRNh2sXQSdvXq1y5+Eg8zKy+GhzLteM6szHD0ymfTNdrVJEREQklsVEEclamw00cZ1DYlvQ56PljTe6jhG1vSf3UhIqoWfznq6jiIhX5B8E/xxY8xL0vQi+/Rdo1991qmrZsv8EczIyWZF1hJvTu5Hx/Sm0bJzkOpaIiIiIVEJMFJFEKlK8dx+hggIa9e3jOkrU/Ll+xqSNwXhw24mI1LFju8Nb1j5/CwZfDXcvgZbdXKeqltXZeczJyGLTvuPcNaknj101hCaN9DFERERExEv06U08ocDvo3F6er0ovARyAkzoOMF1DBGJZYe2wfKn4csPYcQtcM9KaNredaoqs9by2fbDzF6cSc7xQmac24s5N44gOTHedTQRERERqQYVkcQT6ks/pJANsTJnJQ+NfMh1FBGJRfvXw7InIXs5jJ0B96+DlJauU1VZKGT5++ZcZmdkUlwaYtaU3lwyJI2E+DjX0UREREQkCioiScyz1hL0+2n7wAOuo0Rt+9HtNGvUjLQmaa6jiEgsyV4OS5+Ag1th/H0wfS4kee8KZSVlId5fv5+5GZk0SU7k/vP6cME57YmL8/4qUhERERFREUk8oGjbduJSU0nq3Ml1lKj5c/yM7TDWdQwRiQXWwvZPwsWj/AMw8QG4/nVIaOQ6WZWdKinjrdV7eG7JDrq1TuW/Lh/E+F6t68UWZBERERH5PyoiScw73Q+pPvDn+Lmi9xWuY4iIS6Ey2PI+LH0SbAgmPQgDpkO89/5KPnmqhEX+3by4fCdDO7fgmRuGM6Kr97bfiYiIiEjleO8TqzQ4QZ+f5pdf5jpG1ErKSlh3cB2/nvhr11FExIXSYvj8TVj2FKS2gvN+DH0vBA+u1jmSX8RLK7JZ5N/FuX3bsuiOsfTr0NR1LBERERGpZSoiSUyzpaUUrFlD2q8edR0lahsPb6Rr0660SG7hOoqI1KXiAlj7B1jxDLTpA5f+DrpP9GTxKOd4IfM/28E7a/cxbUga790zgW6tvde7SURERESqR0UkiWmFGzeS2KkTCa1auY4StUBOgPS0+rEtT0QqofAYrHoeAvOgazpc+wp0GuE6VbXsPBxkXkYWH23O5ZpRnfn4gcm0b5bsOpaIiIiI1DEVkSSmBX31qx/S3UPudh1DRGpb/iHwz4E1C6HvRfDtv0C7/q5TVcuW/SeYk5HJiqwj3JzejYzvT6Fl4yTXsURERETEERWRJKYV+Py0vvMO1zGiVlBSwNa8rQxvN9x1FBGpLcd2h7esff4WDL4a7l4CLbu5TlUta3blMXtxFpv2HefOST147KohNGmkjwwiIiIiDZ0+EUrMChUWUrh5M6mjRrmOErU1B9YwoPUAUhNTXUcRkZp2aBssfxq+/BBG3AL3rISm7V2nqjJrLUu3H2b24kz2Hy9kxrm9mHPjCJIT411HExEREZEYoSKSxKyCNWtJPucc4hp7v2lrICfA2LSxrmOISE3avx6WPQnZy2Hsd+D+dZDivcvbh0KWj7fkMntxFkWlZcya0ptLhqSREB/nOpqIiIiIxBgVkSRmFfjrTz+kQG6AH439kesYIlITdq2ApU/AgS0w/l6YPheSvFfsLikL8ef1+5mTkUmTRgncd15vLjinPXFx3rtqnIiIiIjUDRWRJGYFfX7a//AHrmNE7eipo+w9uZeBbQa6jiIi1WUtZP4jXDw6mQsTH4DrXoOERq6TVdmpkjL+uHoP85bsoFvrVP7r8kGM79UaY1Q8EhEREZGvpyKSxKSyY8cozs4mZcgQ11GiFsgNMKL9CBLjEl1HEZGqCpXB1j+Hi0ehEEx6EAZMh3jv/fV58lQJi/y7eXH5ToZ2bsEzNwxnRFfvbb8TEREREXe89ylYGoRgYCUpI0dgkrx/KelAToCxHdQPScRTSoth41uw7Klwn6OpP4a+F4IHV+vkBYtZuHwni/y7mNy3La/cMYb+HZq5jiUiIiIiHqQiksSkoN9H4/RxrmPUiEBOgOv6Xec6hohURnEBrP0DrHgG2vSBS56G7hM9WTzKOV7Igs928vbavVw8OI337plAt9be690kIiIiIrFDRSSJSQU+Py2vucZ1jKjtz99PsCRIn5Z9XEcRqXmhMjh13HWKGlFUVEBo7as0WrOA0k5jKLr8RcrShoefLCxxG66KDp0s4oVlO/nbplyuGdWZjx+YTPtmya5jiYiIiEg9oCKSxJySnBzKjh+nUb9+rqNE7fRWtjijS2VLPVJaBBteh2VPQ0GeJ1fpnBYCikpCFJWFWMoIXuSH7MjsApnHgQzH6aonJTGea0d3IeP7U2jZ2PtbgkVEREQkdqiIJDEn6POTOnYsJs77hRdfjo+xaeqHJPVEUT6seQl8z0L7gTB9DnQb7zpVtew7VsiCz3bw7rp9XDo0je9M7sWlrVK51HUwEREREZEYpiKSxJxwP6R01zGiZq1lZc5K7h9+v+soItEpPAorF0DgOeg+Aa5/AzoOc52qWrIO5TMvI4uPtxzgutFd+OSBybTTVi8RERERkUpREUliirWWAp+ftvfe6zpK1DKPZZKckEznpp1dRxGpnpO54JsN616BfhfDbX+Dtn1dp6qWTfuOMzcjC/+OI9wyrjtLHp5Ci1Rt9RIRERERqQoVkSSmFO/YgUlMJLFLF9dRohbICZCe5v0VVdIAHc2G5b+HTW/DkGvhO0uhhTfn5MqdecxenMkXuSe4a1JPfnv1EBo30l99IiIiIiLVoU/SElOCPj+p49IxHm7Ue1ogJ8C0ntNcxxCpvINfwLKnYPvfYeRtcO8qaNLOdaoqs9aSse0QcxZncuBEETOn9GL+LSNplBDvOpqIiIiIiKepiCQxJejz0eyii1zHiFppqJTVB1bziwm/cB1FpGL71sDSJ2FPAMbOgG/+BlJauE5VZWUhy0ebcpm9OJOykGXW1F5MG5xGQrz3m/SLiIiIiMQCFZEkZtjSUgpWrSLt5z9zHSVqmw5vomOTjrRKbuU6isjZWQvZy2DpE3B4G4y/H65cAEmprpNVWXFpiPfW72NeRhbNUhJ58D/6cl7/dsTFeX9Fo4iIiIhILFERSWLGqS1bSGzfnoS2bV1HiVogJ8DYtLGuY4j8O2th29/DxaPCPJjwvXDfowTvNZkuLC7jzVW7mf/ZDnq2bcIvrxjEuJ6t68V2WBERERGRWKQiksSM0/2Q6oNAboBbB97qOobI/ykrhS3vhXseGQOTHoJzLoM47/UJOnGqhFd8u1i4PJuR3Vow96aRDO3SwnUsEREREZF6T0UkiRlBv49WN9/iOkbUCksL2Xx4M6Paj3IdRQRKi2DD67DsaWjSHi74OfS+IFxI8pjD+UW8uGwnr63czXn92vHaXWPp276p61giIiIiIg2GikgSE0KnTnFqw+ekPjPadZSorTu4jn6t+pGa6L3eMlKPFAdhzUuw4lloPwCmz4Fu412nqpZ9xwpZ8NkO3l23j0uHpvHBvRPp0krzS0RERESkrqmIJDGhcN06GvXpQ3yTJq6jRM2f41c/JHGn8CisXACB56D7BLj+deg4zHWqask6lM+8jCw+3nKA60Z34ZMHJtOuWbLrWCIiIiIiDZaKSBITgj4/qePHuY5RIwI5AR4e9bDrGNLQnDwA/tmw9g/Q72K47W/Qtq/rVNWyad9x5mZk4d9xhFvGdWfJw1Nokeq9xt8iIiIiIvWNikgSE4J+P+0eesh1jKgdLzrOrhO7GNp2qOso0lAc3QUrfg8b/wRDroHvfAYturpOVS2rsvOYvTiTrTknuGtST3579RAaN9JfUyIiIiIisUKfzsW5shMnKM7MJGX4MNdRorYqdxXD2g0jMT7RdRSp7w5+Acufhm0fwcjb4N5V0KSd61RVZq1lybZDzFmcRe6JU8yc0ovnbh5JowTvXTVORERERKS+UxFJnCtYtYqUYcOIS/L+dhV/jp/0DumuY0h9tm8tLH0C9gRg7Ay4fz2ktHCdqsrKQpa/b85l9uJMSssss6b2YtrgNBLi41xHExERERGRr6AikjgXXOEjdVz9KLwEcgJcNfkq1zGkvrEWdi0PF48OfQnj74crF0CS965QVlIW4r11+5i7JItmyYk8cEFfzuvfjrg44zqaiIiIiIhUQEUkcS7o99Pxscdcx4habjCXY0XH6Neqn+soUl9YC9v+Hi4eFRyBiQ/AkGshwXur9gqLy3hz1W4WLN1JjzaN+eX0QYzr2RpjVDwSEREREfEKFZHEqZIDByk7fJjkAee4jhK1QE6A0R1GE2e0HUeiFCqDze/CsqcAA5MehAGXQ5z3+gSdOFXCK75dLFyezYiuLZh94wiGdWnhOpaIiIiIiFSDikjiVEHAT+qYMZh47/1yfKZAToD0tPqxLU8cKS2CDW+EG2Y3bgcX/Bx6XwAeXK1zOL+Ihct38lpgN1P6teO1u8bSt31T17FERERERCQKTotIxpibgIcBCxQA91trV7vMJHUr6PPXi35I1loCOQFmDJ3hOop4UXEQ1rwMK56B9gPg8tnQbbzrVNWy/1gh8z/bwbvr9nHp0DT+fO9EurTyXu8mERERERH5d86KSMaYfsD/ACOstTnGmIuBd4CurjJJ3bLWEvT5aH3XXa6jRG3n8Z3Ex8XTpWkX11HESwqPwsrnYeVz4aLR9a9Dx2GuU1XLjkP5zFuSxcdbDnDtqC588sBk2jVLdh1LRERERERqkMuVSEXAndbanMj91UAHY0yStbbYYS6pI8XZ2WAtST26u44SNX+On7FpY9UkWCon/yD4ZsPal6HfxXDrh9C2r+tU1bJ5/3HmZGThzzrCLeO6k/H9KbRI9V7jbxERERERqZizIpK1NhvIBjDh37yfBP6sAlLDUeD303jcuHpReAnkBPiP7v/hOkb9VJQPaxbC6heh6KTrNFEpC1kKS8qwpUV8aM7llbhfkbupLWzaBexyHa/KrIWEeMNdk3ry26uG0LiR2uyJiIiIiNRnzj/xG2MaAy8BXYCLzvL83cDdZz7etat2vXld0Oen6fnnuY4RtbJQGasPrOYn437iOkr9UpAHgedg1QLoMRmueh6ae3O74M7D+Sxckc2y7Ye5Yngnpo/uy3lNmuH9//uhRWoiifG6IqGIiIiISEPgurF2V+ADYCsw1VpbeOZrrLXzgflnPj5q1Chb+wmltthQiIJAgPY/+pHrKFHbmreVdqntaJPSxnWU+uFEDviehXWL4JxL4faPoU1v16mq5fO9x5izOIvVu/K4dXx33r1iEs1TEl3HEhERERERqRaXjbWbAhnAy9baX7jKIW6c2rqV+NatSWzfznWUqJ3uhyRRytsJy38Hm9+FodfDzOXQvLPrVFVmrSWwM4/ZizPJPJjPXZN68uS1Q0lNcr7wU0REREREJCouf6u5F+gGXGGMuaLc4+dba484yiR1pMDno3F6uusYNcKf4+fG/je6juFdB7bAsqcg8x8w6na4bw009t6qLmsti788yOzFWRzJL2LmlF5cMbwzSQna6iUiIiIiIvWDy8bavwZ+7er9xa2gz0+L6651HSNqRWVFbDy0kVFTRrmO4j17V8PSJ2HvKkifCdMeh+TmrlNVWVnI8teNOcxZnIkxhllTenHx4DTi47zfMF5ERERERKQ87a+QOhcqLqZw/Xo6PfmE6yhRW39wPb1b9qZpUlPXUbzBWtj5GSx9AvJ2wPj7ww2zk1JdJ6uyotIy3l27j3lLsmjdpBGPXNSfKf3a1ourDYqIiIiIiJyNikhS5wrXryepZ0/im3tv1cmZAjkBxnZQP6QKhUKw7W/hlUenjsOkB2HwtyDee02mC4pLeX3lHp5fuoM+7Zvym6uGMKZHKxWPRERERESk3lMRSepcgd9fb/ohBXICfHfEd13HiF1lpbD5nXDxKCEJJj0E/S+BuHjXyarseEEJf/Bl89KKbMb0aMX8m0cxuLP3C6EiIiIiIiKVpSKS1LngCh9t77/PdYyonSg+QeaxTIa2G+o6SuwpOQUbXgtfba1ZJ7jwl9DrfPDgap1DJ4t4YdlO3li1m/P7t+fN76TTu522L4qIiIiISMOjIpLUqbL8fE5t20bKiBGuo0Rtde5qhrQdQqP4Rq6jxI6ifFizEHyzocNgmD4Puo1znapa9h4tYP5nO3h//X4uH9aRD+6dSJdW3uvdJCIiIiIiUlNURJI6VbBqFSlDhhCXnOw6StQCOQHGpqkfEgAFebByfvjWYzLc8BakDXGdqloyD55kbsYOPv3iANeP6co/HjyXtk1VKBQREREREVERSepUfeuH9OjER13HcOtkLviehXWLoP80uP1jaNPbdapq2bj3OLMXZ7J6Vx63ju/Okoen0jzFe42/RUREREREaouKSFKngj4/af/9X65jRO1QwSEOFR6if6v+rqO4kbcTVvweNr0DQ6+HGcugeWfXqarMWktgZx6zF2eSeTCfuyb15Mlrh5KapB+NIiIiIiIiZ9JvSlJnSg8fpiQnh+SBA11HiZo/x8/oDqOJ9+BVxqJyYAssewoy/wGjbof71kDjNq5TVZm1lsVfHmT24iyO5Bcxc0ovpg/vRKOEBjaeIiIiIiIiVaAiktSZoD9A6ujRmATv/2/X4Poh7V0Dy56EPSshfSZMexySvXd5+7KQ5cONOczJyMJayz1Te3Px4DTi47x31TgREREREZG65v3f5sUzgn4fjcd580pd5VlrCeQGuH3w7a6j1C5rYednsPQJyNsB4++HKxdAkveuUFZcGuLddXuZt2QHrRon8fCFfZnarx3GqHgkIiIiIiJSWSoiSZ0p8PlpfeutrmNEbffJ3YRsiB7NeriOUjtCIdj2Ubh4dOo4THwABn8LEpJcJ6uyguJS3li5hwVLd9CnfVMeu3IwY3q0UvFIRERERESkGlREkjpRvGcPtriYpF69XEeJWiAnQHpaev0rRJSVwuZ3w9vW4hJg8veh/yXgwb5PxwtLeMWXzUsrshndvRXzbx7F4M7e234nIiIiIiISS1REkjoR9PlITa8fhRd/jp8pXaa4jlFzSotg/Wuw/Glo1gm+8d/Q63zw4FgdOlnEi8t38vrK3Zzfvz1v3J1O73ZNXccSERERERGpF1REkjoR9PloMnGS6xhRC9kQK3NX8sjoR1xHiV5RPqx5CXzPQofBMH0edPNmz6q9RwuY/9kO3l+/n8uHdeSDeyfSpZX3ejeJiIiIiIjEMhWRpNbZUIgCf4D2Dz/sOkrUvsj7gpaNWtK+cXvXUaqvIA9WLoCV86HHJLjhTUgb6jpVtWQezGduRhaffnGA60Z35ZMHJ9OuabLrWCIiIiIiIvWSikhS64q2bSO+WTMSO3Z0HSVqp/shedLJ3PCqo3WLoP80uP3v0Ka361TVsnHvceZkZLIqO49bx3dnycNTaZ6S6DqWiIiIiIhIvaYiktS6oM9P6jiPFl7OEMgJ8K2+33Ido2qOZsPy38Gmd2DodfCdpdCii+tUVWatZeXOPGZnZLH9wEnumtSTJ64ZSmqSfoyJiIiIiIjUBf32JbUu6PfR4oorXceonOARCMyDXSv+7aliLOvNXn6zdxfwZN1nq6KCklIOHs2n1and/CP1Yj5sNo+Tu1vA7r3AXtfxqux4YQmnSsqYOaUX028ZSaME7101TkRERERExMtURJJaZYuLKVy9ho6PPeY6ytc7sR9WPAvrX4UBl4cvbx/3r9Njw7FtdN/xDs1H/MBRyMrJPJTPe+v2sf1APhcN7kCn/mNJS2rGHa6DRSkpwTCsS0vi47x31TgREREREZH6QEUkqVWFGzeS2K0rCS1buo5ydkeywlu9trwPw26EmSugeaezvjSwbgNju18QbkYdY6y1+HYcYc7iLHYcSuLuyedzz21dSUnSah0RERERERGpGSoiSa0K+vw0HheDl43P3QTLnoKs/4XRd8J9a6Fx6689JJATYNawWXUUsHJCIcunXxxkTkYmxwtKmDGlF9OHdSIpIc51NBEREREREalnVESSWhX0+2jznRmuY/yfPatg6ROwfy2kz4JLnoLkZhUeFiwJsu3oNoa3G14HIStWWhbirxtzmLM4i4R4wz1Te3PhwA7a6iUiIiIiIiK1RkUkqTWhYJBTW7aSOnKE2yDWwo7FsPRJOLoLJtwP31oIiSmVPsWaA2sY1GYQyQnJtRi0YkWlZby9Zh/zlmTRoVkyP7y4P+f2bYsxKh6JiIiIiIhI7VIRSWpNwZo1pAwYQFxqqpsAoRB8+dfwyqPiApj0IAy6CuITq3wq334fY9PG1kLIygkWlfL6yt0sWLqDc9Ka8fi3hjKmRytneURERERERKThURFJak3Q5yd1XHrdv3FZCWx6O9zzKCE5fKW1ftMgrvp9ggK5AX427mc1GLJyjhUU8/KKXfzBl016z9a88O3RDOrUvM5ziIiIiIiIiKiIJLUm6PfT4Sc/qbs3LDkF6xeFr7bWohtc9GvoORWi3Op1pPAIufm5DGw9sIaCVuzgiVO8sGwnb6zawzcGtOetGePo1bZJnb2/iIiIiIiIyJlURJJaUXr0KCV79pAyeFDtv1nRSVj9IvjmQMdhcOXz0LXmtp6tzF3JyA4jSYir/emyJ6+A5z7L4oMNOVwxvBMffncSnVpUvneTiIiIiIiISG1REUlqRUEgQOrIkZjEqvcfqvyb5EFgHqx6HnpOgZv+BB0G1/jbBHICpKfV7ra8bQdOMjcji8VfHuTGsV359KFzadOkUa2+p4iIiIiIiEhVqIgktaJW+yGd2A++2bBuEQy4DO74BFr3qp33Avw5fm4656ZaOfeGPceYvTiTtbuPctuEHvzi8oE0S67FwpuIiIiIiIhINamIJLUi6PPR8obra/akeTvC/Y42vwfDboCZK6B5p5p9jzPsObmHorIierWouSKVtRbfjiPMWZzFjkP53D25J7+7bjgpSfE19h4iIiIiIiIiNU1FJKlxJfv2EcrPp1GfPjVzwgObw1day/wURt8J962Fxq1r5twVCOQEGNNhDCbK5twQLh59uvUgszMyOV5QwowpvZg+rBNJCdW/apyIiIiIiIhIXVERSWpc0O+ncXo6Ji7K4sieVbD0Cdi/FtJnwbQnIblZzYSspEBOgPEdx0d1jtKyEH/dmMPcjCzijOGeqb25aFAH4uOiL0yJiIiIiIiI1BUVkaTGRdUPyVrYkREuHh3dBRPuh28thMS6v0JZyIZYmbuSB0c+WK3ji0rLeGftPuYtyaJd00Y88s3+TOnbtkZWNYmIiIiIiIjUNRWRpEZZawn6/bT93nerdmAoBF9+GC4eFefDxAdh8NUQ767J9Paj22mS2IS0JmlVOi5YVMrrK3fz/NKd9OvQlP+5eihjerSqpZQiIiIiIiIidUNFJKlRRdu3E5ecTFLnzpU7oKwUNr0Ny56EhGSY9BD0vwSi3QpXA/w5fsamja30648XlPCyL5uXV2ST3rM1z397FIM6Na/FhCIiIiIiIiJ1R0UkqVEFfj+NK7OVreQUrH81fLW15l3gwl9Br/MghrZ6BXICXN778gpfd/DEKV5YtpM3V+/hP85pz1szxtGrbZM6SCgiIiIiIiJSd1REkhoV9PlpfuklX/2CopOweiH4ZkPHYXDlAuha+dU+daUkVMK6g+t4dOKjX/maPXkFPPdZFh9syOGK4Z346/2T6NSi7ns3iYiIiIiIiNQFFZGkxtjSUgpWrybt0V/++5MFeRCYB6ueh55T4KY/QYfBdZ6xsjYd3kSXpl1omdzy357bfuAkczOy+N8vD3LDmK58+tC5tGnSyEFKERERERERkbrjtIhkjJkG/BpoBHwO3GGtPeEyk1TfqU2bSOzYkYRW5ZpIn8gB37OwbhEMuAzu+ARa93IXspLO1g9pw55jzMnIZM2uo9w2oQc/u2wgzVPcNf4WERERERERqUvOikjGmLbAQmCCtXa7MeY3wGPALFeZJDpBn4/G6ZF+SHk7wv2ONr8Hw26AmSugeSen+arCv9/PXUPuwlqLf0ceczIyyTyYz92Te/L0tcNJSYp3HVFERERERESkTrlcifQNYJW1dnvk/lxggzHmHmutdZhLqino89PqyvPh7Tsh81MYfQfctwYat3EdrUoKSgrYmreVE0c7c9X7KzhWUMKMc3sxfXgnkhLcXzVORERERERExAWXRaQuwJ5y9/cCzYCmQIPZ0vbGnZOIPxl0HaNG9NtSyMvDA+xp1ouDnadRlnsY3j9Lf6QYV2xPUFbYkWc/3cM9U3tz0aAOxMfFzlXjRERERERERFwwrhb9GGP+H9DFWjszcj8BKAGaWGuD5V53N3D3WU7RD/iyLrLWgTbAYdchpM5p3BsujX3DpbFvuDT2DZfGvuHS2DdMGveGqz6N/WFr7UVne8LlSqTdQPnOxZ2Ao+ULSADW2vnA/LoMVteMMauttaNc55C6pXFvuDT2DZfGvuHS2DdcGvuGS2PfMGncG66GMvYuG7x8DKQbY/pE7s8A3neYR0REREREREREvoKzlUjW2oPGmNuAPxljkoAs4BZXeURERERERERE5Ku53M6GtfZD4EOXGUREREREREREpGK6XrmIiIiIiIiIiFRIRaTYUK8bh8tX0rg3XBr7hktj33Bp7BsujX3DpbFvmDTuDVeDGHtjrXWdQUREREREREREYpxWIomIiIiIiIiISIVURBIRERERERERkQqpiFSLjDHTjDGfG2O+NMb80RjT7CteZ4wxLxtjvl/usXhjzNPGmC+MMZnGmBl1l1yiEc24Rx4/bIxZX+52Y90kl2hVZuyNMTcZYzZExnaFMWZU5HHNeQ+LZuwjz2nee1Qlx/5eY8xmY8wmY8z7xph2kcc17z0smrGPPKd570GV/ZwXee10Y8zJcvc15z0smrGPPKY571GV/Hn/hDFmd7nxfTPyeP2b99Za3WrhBrQFDgJ9Ivd/A8w5y+vOAf4XCALfL/f4LOBDIAFoCXwBjHH9delW6+PeD9jm+uvQrXbGPjK+OUBa5P7FwO7InzXnPXqrgbHXvPforZJjPxLIBppH7j8OPBf5s+a9R281MPaa9x68VfZzXuS5PkAmkF/uMc15j95qYOw15z16q8Lvdz5g/Fker3fzXiuRas83gFXW2u2R+3OBG40x5ozX3QM8D/zxjMevABZaa0uttUeBN4CbajOw1Ihox308UGaMWRqpdv/UGBNfu5GlhlRm7IuAO621OZH7q4EOxpgkNOe9LNqx17z3rgrH3lq7hvAHz+PGmGSgE3Ak8rTmvXdFO/aa995Uqc95xphUYBHw4BnHa857V7RjrznvXRWOvTGmETAc+E9jzEZjzNvGmK6Rp+vdvFcRqfZ0AfaUu78XaAY0Lf8ia+291trXKnl855oOKTUu2nFPAP4BXARMBi4E7qudqFLDKhx7a222tfavEN7OCDwJ/NlaW/wVx2vOe0O0Y695712V/ZlfYoyZHnl+MrDwa47XvPeGaMde896bKjXuwHOR2+eVOF5z3huiHXvNee+qzNh3JLzL5MfAEMAPvB/5zFfv5r2KSLUnDrBnebysmsebKhwr7kQ17tbaBdba+6y1QWvtMcK/aF5Rg/mk9lR67I0xjYG3gN7AnV9xvOa8d0Q19pr3nlbpsbfWvmetbQP8HPi7MSbuLMdr3ntHVGOvee9ZFY67MWYWUGqtfbESx2vOe0dUY68572kVjr21dqe19mJr7SYb3sP2ONAL6H6W4z0/71VEqj27CVckT+sEHLXWBqt5fEfCVUuJbVGNuzHmZmPMkPIPASU1mE9qT6XGPrK0dQXhvzymRj5InO14zXnviGrsNe89rcKxN8b0NsZMLPeaF4FuhPsiaN57V1Rjr3nvWZX5eX8rMNoYs55wH5SUSJPdjmc5XnPeO6Iae815T6vMz/shxpibzzju9BjXu3mvIlLt+RhIN8b0idyfAbxfhePfB243xiQYY1oA1wHv1WhCqQ3Rjvsg4L8iXfxTgHuBN2s4o9SOCsfeGNMUyADesdZeZ60tLPe05rx3RTv2mvfeVZmf+WnAG8aYNpH7NwKbrLVH0Lz3smjHXvPemyocd2vtGGvtIGvtMMIXUSi01g6z1u5Hc97Loh17zXnvqszP+xDwe2NMj8j9mcDn1tq91MN5ryJSLbHWHgRuA/5kjNkKDAYeMsaMilSnKzIXyAI2AKuAF6y1S2orr9SMGhj3XwB5wEbCe6lXEG7ALTGukmN/L+F/hb7C/OslXlujOe9ZNTD2mvceVZmxt9YuBR4FMiKPXQdMj5xC896jamDsNe89SJ/vGy59xm+4KvnzfhPhHlcfRF5zBXB95BT1bt6b8JY9ERERERERERGRr6aVSCIiIiIiIiIiUiEVkUREREREREREpEIqIomIiIiIiIiISIVURBIRERERERERkQqpiCQiIiL1XrnL7jYoDfXrFhERkdqhIpKIiIjUOWNMhjHm3kq8bpIxJjvK9xpO+HLK1Tl2kDHmKy9la4xpZIz5iTHmC2PMSWPMHmPM08aYJtUOXEOMMZcBb1bwmoeNMbOqcM7zjTHWGPPwGY+3MMZ8ZoxJrmZcERER8QAVkURERCRmWWuXWmu7R3ma5kBiDcT5F8aYBODvwFjgMmttU2AiMAD4c02/XzW04ms+6xljegI3Ac9V4Zx3Ay8As4wx/zy3tfYY8Dbw42olFREREU9QEUlEREScMsb83BizyBjzF2NMvjFmizHmG5HnphhjDpd77WRjzCpjzDFjTMAYM6bcc4OMMUsiK4J2GGNuNMa0A/4GtI6cu7UxJsUY83tjzD5jzH5jzOPGmKTIOeKMMb8yxhwxxuwHrvua6DcAvYGrrbXbAKy1u4AbgWPGmA7GmO6RlTv/XJlkjFltjLk18udsY8z8yPvNjXwv/hL5Huw1xjQzxgyOrNw6ZozZaIy5uNy5so0xPzDGbDPGHI8c29IYMxqYBww3xuR+Rf7/BF611pZFzmWNMfcbY3YZY/IiY5JU7r3aAtOA/wcUA5eccb6XgO8YY1p8zfdMREREPExFJBEREYkF1wBPEV498yHwzJkvMMZ0Bf4CPAq0AR4HPjTGtIoUO/4KfAq0Bq4lvMKmFfBN4Ii1tom19kjkuP7AEGAoMAr4UeRtZgJXAyOAgcC4r8l8EfChtfZU+QettYestVdaa7+qeHOmrkBn4JHI/fMi34+BgAU+Bt6KfM33Aa8aY/qWO3464RVQ/YA+wAxr7SpgBrDOWtvhzDeMfL9uAv50xlPnA4OAdOBC4Kpyz90KfGytPQjMj2Qp/3UfBwKR7CIiIlIPqYgkIiIiscBnrf3UWlsMvEq4GHKmG4DF1tr3rLWl1to/AhsJF30mAI2BX1priyNFlInA/vInMMYY4DbgEWvtEWvtIeBnwF2Rl1wLPGut3WWtPQr89GsytwYOVfcLLudta22htfZE5P46a+2mSFFmGnDQWjsn8jVnAO8RLuic9py19mCkaPURZ//enWkkcMpau+OMx5+21p6MrKxacca57iRcPAJYCEwwxvQ/4/jVwLmVeH8RERHxoATXAURERET412JMCWDO8pquwEXGmGPlHksElgEngBxrbej0E9ba9QDhutE/tQVSgIxyDbMNkBRpCt0B2Ffu9dlfkzkXaH+2J4wx7SIrdirjzBVL5e93BQac8TUnAO+Uu3/m964y/0jYGcg5y+NnPZcxZgrQF3i53PctEbg3cjstB5haifcXERERD1IRSURERLwiB3jTWnvL6Qcil7A/QnhbWpoxJu50ISly1bE1Z5zjCOF+PsNPr8IxxjQGOlhrT0X6IHUr9/pOX5PnI+BxY0yKtbawXKa2wB5jzEVAZuThpHLHtT7jPGde/a38/RzCq7Qmlzt/Z6CQ6FiqtiL9bsJbDH9V7rHxwEvGmB9aa09GHksAyqLMJiIiIjFK29lERETEK94ALo1cZt4YYyYAnwOjCffiOQo8YoxJiDTcfpTwCqUiINkYkxRpIv0q8FjksvSNCfdOeinyHq8A3zPG9DPGNAN+8TV53gJ2AW8ZY3oBRLZ3vUN4dVQGcAA4DtxsjIk3xnybfy1SVeSvQH9jzPWR48+JfK3TK3FsEdDUnLEUK2I3kFaZAMaY1sCVwEJrbe7pG+FtdSf41611acDeypxXREREvEdFJBEREfEEa+12wk2bf0O4MPMH4MFyvZQuBS4ADgOLgDustVsJF5o2A0eMMb2B70Zes5lwwaM54V5IAC8S7vuzFMgCVn1NnjLgG4RXG31qjMknvDrJD1xuw4qBWYS3fB0FphAuDFX2a84j3MB7JuFVVJ8Ac621L1Ti8CWR/x6NbNUrby2AMaZfJc5zC5BtrV13RrYQ4aLbPeUKVWOBf1TinCIiIuJBxtozV1CLiIiIxAZjzFTgDWvtWXsPSfUZY+YCu6y1j9XQ+VoDW4G+1tpjNXFOERERiS1aiSQiIiIxKbJ6ZgBQ2QbVUjWPAbcYY2qqR+YdhK8Ud6yGziciIiIxRkUkERERiVWPAf8NPO46SH1krd0FvAzMiPZcxpgWhPs0/errXykiIiJepu1sIiIiIiIiIiJSIa1EEhERERERERGRCqmIJCIiIiIiIiIiFVIRSUREREREREREKqQikoiIiIiIiIiIVEhFJBERERERERERqZCKSCIiIiIiIiIiUqH/D389hLOw2kD3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(20,8))\n",
    "stims = np.arange(0.1,.51,.02)\n",
    "\n",
    "iclamp = h.IClamp(h.cell.soma[0](0.5))\n",
    "iclamp.delay = 300\n",
    "iclamp.dur = 900\n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=True, ax=ax, label='original', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=False, ax=ax, label='Ri increase (gpas=0, hcn=0.565)', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(nav12=0)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=True, ax=ax, label='Hom', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(nav12=0, gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=False, ax=ax, label='Hom + Ri increase (gpas=0, hcn=0.565)', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(plot_path+'fi_curve_vm_hold.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}