{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3e0370c-3c5b-4879-a58d-d2c94832c4f1",
   "metadata": {},
   "source": [
    "# Stochastic Calcium Burst model with GHK currents\n",
    "\n",
    "<div class=\"note\">\n",
    "This chapter is based on the publication \"Anwar H, Hepburn I, Nedelescu H, Chen W, De Schutter E (2013) Stochastic \n",
    "Calcium Mechanisms Cause Dendritic Calcium Spike Variability. The Journal of Neuroscience, 33(40): 15848-15867\".\n",
    "</div>\n",
    "\n",
    "The corresponding python script: [STEPS_Tutorial_CaBurst.py](https://github.com/CNS-OIST/STEPS_Example/tree/master/user_manual/source/API_2/scripts/STEPS_Tutorial_CaBurst.py)\n",
    "\n",
    "This chapter builds on previous chapters by simulating a model that includes both a reaction-diffusion component as well as electrical excitability. As described in [1](#Footnotes), the two are closely coupled and this model contains ion channels where activation is both voltage-dependent and calcium-dependent. In addition, the calcium ions form an important part of the current across the membrane, further coupling the reaction-diffusion component with the electrical excitability. This chapter introduces an important new object: the GHK Current object, which is described in some detail in section [P-type Calcium channel](#P-type-Calcium-channel).\n",
    "\n",
    "In addition, we will also introduce the STEPS parameter system that can automatically generate parameter tables from python scripts.\n",
    "\n",
    "As in previous chapters we will go through the script, looking in some depth at new concepts, but only brief explanations will be offered of things that have been described in previous chapters. \n",
    "\n",
    "\n",
    "## Modelling solution\n",
    "\n",
    "At the start of the script, as usual, we will import some modules, including STEPS modules and some other python modules. We will first declare all important parameters for the module such as physical \n",
    "constants, membrane properties, kinetic properties of the channels, initial conditions and so on.\n",
    "\n",
    "### Parameters\n",
    "\n",
    "As stated earlier, STEPS can automatically generate parameter tables from python scripts. In general, values that are used for setting properties of STEPS objects are registered as model parameters. At the end of a STEPS scripts, one can call e.g. `ExportParameters(sim, '/path/to/fileprefix', method='csv')` and STEPS will automatically generate csv files that contain the values and units of all parameters.\n",
    "\n",
    "We start the script by import relevant modules and declaring a few parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e506ffc-9d4a-443a-8c02-7162a8f9be7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import steps.interface\n",
    "\n",
    "from steps.geom import *\n",
    "from steps.model import *\n",
    "from steps.rng import *\n",
    "from steps.saving import *\n",
    "from steps.sim import *\n",
    "from steps.utils import *\n",
    "\n",
    "import math\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "###########################################################\n",
    "# Simulation Parameters\n",
    "###########################################################\n",
    "\n",
    "SEED = 1234\n",
    "\n",
    "NBRUNS = 5\n",
    "\n",
    "EF_DT = 5.0e-6\n",
    "\n",
    "DT =  2.0e-5\n",
    "ENDT = 0.5\n",
    "\n",
    "###########################################################\n",
    "# Model Parameters\n",
    "###########################################################\n",
    "\n",
    "TEMPERATURE = 34.0 + 273.15\n",
    "Q10 = 3\n",
    "\n",
    "FARADAY = 96485.3365     # C/mol\n",
    "R = 8.3144621            # J/mol K\n",
    "AVOGADRO = 6.02214129e23 # /mol\n",
    "\n",
    "Qt = math.pow(Q10, (TEMPERATURE - (23 + 273.15)) / 10)\n",
    "Qt_mslo = math.pow(Q10, (TEMPERATURE - (25 + 273.15))/10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c25bc6-25d8-4bb7-be39-f8faca593686",
   "metadata": {},
   "source": [
    "Note that since these parameters are standard python floating point numbers, we need to make sure all of them are expressed in SI units.\n",
    "\n",
    "There is however another way to declare parameters that will allow us to specify units, keep track of the name of the parameter, and give additional information about the parameters. This is done by using the `Parameter` class from the `utils` module (see [documentation](API_utils.rst#steps.API_2.utils.Parameter)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c508ded-6468-4430-afc2-62e0328d1d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################\n",
    "# Membrane Parameters\n",
    "#######################################\n",
    "\n",
    "init_pot = Parameter(-60, 'mV', Description='Initial membrane potential')\n",
    "Ra = Parameter(235.7*1.0e-2, 'ohm m', Description='Bulk resistivity')\n",
    "memb_capac = Parameter(1.5e-2, 'F m^-2', Description='Membrane capacitance')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9a8ff0b-2ea3-4600-a3ff-5d5fe24ed36a",
   "metadata": {},
   "source": [
    "The `Parameter` constructor usually takes at least two arguments: the first one is the value of the parameter and the second one is the unit this value is expressed in. As can be seen in the above code,  we can provide non-SI units and values; STEPS will then do the proper conversions automatically. \n",
    "When using the `Parameter` class, STEPS will use the name of the veriable (`init_pot`, `Ra`, etc.) as the name of the parameter in parameter tables. A list of supported units is available in the `Parameter` class [documentation](API_utils.rst#steps.API_2.utils.Parameter).\n",
    "\n",
    "The `Parameter` constructor can then take any keyword argument as additional information about the parameter. Here we supply a description of the parameter that will be displayed in the automatically generated parameter tables.\n",
    "\n",
    "If a `Parameter` object is involved in computations, all other values involved in the computation should be `Parameter`s as well, otherwise STEPS cannot infer the units of the value that results from the computation.\n",
    "Because of this constraint, we do not use the `Parameter` class everywhere:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80ca4297-e574-41fc-91d2-2d8fa887c36b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################\n",
    "# CaP channels parameters\n",
    "#######################################\n",
    "\n",
    "CaP_P = Parameter(2.5e-2, 'um^3 s^-1', Description='CaP single channel permeability')\n",
    "CaP_ro = Parameter(38, 'um^-2', Description='CaP channels density')\n",
    "\n",
    "# Reaction rates\n",
    "\n",
    "vhalfm = -29.458 # mV\n",
    "cvm = 8.429      # mV\n",
    "\n",
    "def minf_cap(mV):\n",
    "    vhalfm = -29.458\n",
    "    cvm = 8.429\n",
    "    return 1 / (1 + math.exp(-(mV - vhalfm) / cvm))\n",
    "\n",
    "def tau_cap(mV):\n",
    "    if mV >= -40:\n",
    "        return 0.2702 + 1.1622 * math.exp(-(mV + 26.798) ** 2 / 164.19)\n",
    "    else:\n",
    "        return 0.6923 * math.exp(mV / 1089.372)\n",
    "\n",
    "alpha_cap = VDepRate.Create(\n",
    "    lambda V: (minf_cap(V * 1e3) / tau_cap(V * 1e3)) * Qt * 1e3\n",
    ")\n",
    "beta_cap = VDepRate.Create(\n",
    "    lambda V: (1 - minf_cap(V * 1e3)) / tau_cap(V * 1e3) * Qt * 1e3\n",
    ")\n",
    "\n",
    "# Initial conditions\n",
    "CaP_p = [0.92402, 0.073988, 0.0019748, 1.7569e-05]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a9b8ea-2317-4ec8-9988-2eca80dd6488",
   "metadata": {},
   "source": [
    "Note that we use `VDepRate.Create(...)` instead of `VDepRate(...)` so that the resulting STEPS object is named after the variable it is attributed to.\n",
    "\n",
    "The other parameters are declared in a similar way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2b8c89b-4111-4db1-a7b4-cb10a7f37970",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################\n",
    "# CaT channels parameters\n",
    "#######################################\n",
    "\n",
    "CaT_P = Parameter(1.65e-2, 'um^3 s^-1', Description='CaT single channel permeability')\n",
    "CaT_ro = Parameter(3.7576, 'um^-2', Description='CaT channels density')\n",
    "\n",
    "# Reaction rates\n",
    "\n",
    "def minf_cat(mV):\n",
    "    vhalfm = -52\n",
    "    cvm = -5\n",
    "    return 1 / (1 + math.exp((mV - vhalfm) / cvm))\n",
    "\n",
    "def taum_cat(mV):\n",
    "    if mV > -90:\n",
    "        return 1 + 1 / (math.exp((mV + 40) / 9) + math.exp(-(mV + 102) / 18))\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "def hinf_cat(mV):\n",
    "    vhalfh = -72\n",
    "    cvh = 7\n",
    "    return 1 / (1 + math.exp((mV - vhalfh) / cvh))\n",
    "\n",
    "def tauh_cat(mV):\n",
    "    return (15 + 1 / (math.exp((mV + 32) / 7)))\n",
    "\n",
    "alpham_cat = VDepRate.Create(lambda V: minf_cat(V * 1e3) / taum_cat(V * 1e3) * 1e3)\n",
    "betam_cat = VDepRate.Create(lambda V: (1 - minf_cat(V * 1e3)) / taum_cat(V * 1e3) * 1e3)\n",
    "\n",
    "alphah_cat = VDepRate.Create(lambda V: hinf_cat(V * 1e3) / tauh_cat(V * 1e3) * 1e3)\n",
    "betah_cat = VDepRate.Create(lambda V: (1 - hinf_cat(V * 1e3)) / tauh_cat(V * 1e3) * 1e3)\n",
    "\n",
    "# Initial conditions\n",
    "CaT_p = [\n",
    "    [0.58661, 0.23687, 0.023912],   # h0\n",
    "    [0.10564, 0.042658, 0.0043063], # h1\n",
    "]\n",
    "\n",
    "#######################################\n",
    "# BK channels parameters\n",
    "#######################################\n",
    "\n",
    "BK_G = Parameter(210, 'pS', Description='BK single channel conductance')\n",
    "BK_ro = Parameter(2.0238, 'um^-2', Description='BK channels density')\n",
    "BK_rev = Parameter(-77, 'mV', Description='BK channel reversal potential')\n",
    "\n",
    "# Reaction rates\n",
    "\n",
    "#Units (1)\n",
    "Qo = 0.73\n",
    "Qc = -0.67\n",
    "\n",
    "#Units (/s)\n",
    "pf0 = 2.39\n",
    "pf1 = 5.4918\n",
    "pf2 = 24.6205\n",
    "pf3 = 142.4546\n",
    "pf4 = 211.0220\n",
    "\n",
    "pb0 = 3936\n",
    "pb1 = 687.3251\n",
    "pb2 = 234.5875\n",
    "pb3 = 103.2204\n",
    "pb4 = 11.6581\n",
    "\n",
    "#Units(/M)\n",
    "k1 = 1.0e6\n",
    "\n",
    "#Units(/s)\n",
    "onoffrate = 1.0e3\n",
    "\n",
    "L0 = 1806\n",
    "\n",
    "#Units (M)\n",
    "Kc = 8.63e-6\n",
    "Ko = 0.6563e-6\n",
    "\n",
    "BK_f = k1*onoffrate*Qt_mslo\n",
    "BKo_b = Ko*k1*onoffrate*Qt_mslo\n",
    "BKc_b = Kc*k1*onoffrate*Qt_mslo\n",
    "\n",
    "BK_f0 = VDepRate.Create(\n",
    "    lambda V: pf0 * Qt_mslo * (math.exp((Qo * FARADAY * V) / (R * TEMPERATURE)))\n",
    ")\n",
    "BK_f1 = VDepRate.Create(\n",
    "    lambda V: pf1 * Qt_mslo * (math.exp((Qo * FARADAY * V) / (R * TEMPERATURE)))\n",
    ")\n",
    "BK_f2 = VDepRate.Create(\n",
    "    lambda V: pf2 * Qt_mslo * (math.exp((Qo * FARADAY * V) / (R * TEMPERATURE)))\n",
    ")\n",
    "BK_f3 = VDepRate.Create(\n",
    "    lambda V: pf3 * Qt_mslo * (math.exp((Qo * FARADAY * V) / (R * TEMPERATURE)))\n",
    ")\n",
    "BK_f4 = VDepRate.Create(\n",
    "    lambda V: pf4 * Qt_mslo * (math.exp((Qo * FARADAY * V) / (R * TEMPERATURE)))\n",
    ")\n",
    "BK_oc_f = [BK_f0, BK_f1, BK_f2, BK_f3, BK_f4]\n",
    "\n",
    "BK_b0 = VDepRate.Create(\n",
    "    lambda V: pb0 * Qt_mslo * (math.exp((Qc * FARADAY * V) / (R * TEMPERATURE)))\n",
    ")\n",
    "BK_b1 = VDepRate.Create(\n",
    "    lambda V: pb1 * Qt_mslo * (math.exp((Qc * FARADAY * V) / (R * TEMPERATURE)))\n",
    ")\n",
    "BK_b2 = VDepRate.Create(\n",
    "    lambda V: pb2 * Qt_mslo * (math.exp((Qc * FARADAY * V) / (R * TEMPERATURE)))\n",
    ")\n",
    "BK_b3 = VDepRate.Create(\n",
    "    lambda V: pb3 * Qt_mslo * (math.exp((Qc * FARADAY * V) / (R * TEMPERATURE)))\n",
    ")\n",
    "BK_b4 = VDepRate.Create(\n",
    "    lambda V: pb4 * Qt_mslo * (math.exp((Qc * FARADAY * V) / (R * TEMPERATURE)))\n",
    ")\n",
    "BK_oc_b = [BK_b0, BK_b1, BK_b2, BK_b3, BK_b4]\n",
    "\n",
    "# Initial conditions\n",
    "BK_p = [\n",
    "    [0.99997,    4.3619e-07, 4.1713e-09, 4.4449e-11, 6.3132e-14],\n",
    "    [2.5202e-05, 1.1765e-06, 6.6148e-08, 2.4392e-09, 4.0981e-11],\n",
    "]\n",
    "\n",
    "#######################################\n",
    "# SK channels parameters\n",
    "#######################################\n",
    "\n",
    "SK_G = Parameter(10, 'pS', Description='SK single channel conductance')\n",
    "SK_ro = Parameter(0.31, 'um^-2', Description='SK channels density')\n",
    "SK_rev = Parameter(-77, 'mV', Description='SK channel reversal potential')\n",
    "\n",
    "# Reaction rates\n",
    "\n",
    "#Units (/s)\n",
    "invc1 = 80\n",
    "invc2 = 80\n",
    "invc3 = 200\n",
    "\n",
    "invo1 = 1000\n",
    "invo2 = 100\n",
    "\n",
    "diro1 = 160\n",
    "diro2 = 1200\n",
    "\n",
    "#Units ( /s M)\n",
    "\n",
    "dirc2 = 200e6\n",
    "dirc3 = 160e6\n",
    "dirc4 = 80e6\n",
    "\n",
    "invc1_t = invc1*Qt\n",
    "invc2_t = invc2*Qt\n",
    "invc3_t = invc3*Qt\n",
    "\n",
    "invo1_t = invo1*Qt\n",
    "invo2_t = invo2*Qt\n",
    "\n",
    "diro1_t = diro1*Qt\n",
    "diro2_t = diro2*Qt\n",
    "\n",
    "dirc2_t = dirc2*Qt/3.0\n",
    "dirc3_t = dirc3*Qt/3.0\n",
    "dirc4_t = dirc4*Qt/3.0\n",
    "\n",
    "# Intital conditions\n",
    "SK_C1_p= 0.96256\n",
    "SK_C2_p= 0.036096\n",
    "SK_C3_p= 0.0010829\n",
    "SK_C4_p= 6.4973e-06\n",
    "\n",
    "SK_O1_p= 0.00017326\n",
    "SK_O2_p= 7.7967e-05\n",
    "\n",
    "#######################################\n",
    "# Leak channels parameters\n",
    "#######################################\n",
    "\n",
    "L_G = Parameter(0.04, 'pS', Description='Leak single channel conductance')\n",
    "L_ro = Parameter(0.25, 'um^-2', Description='Leak channels density')\n",
    "L_rev = Parameter(-61, 'mV', Description='Leak channel reversal potential')\n",
    "\n",
    "#######################################\n",
    "# Ca pump channels parameters\n",
    "#######################################\n",
    "\n",
    "P_ro = Parameter(6.022141, 'um^-2', Description='Ca2+ pump density')\n",
    "\n",
    "# Reaction rates\n",
    "\n",
    "P_f = 3e9\n",
    "P_b = 1.75e4\n",
    "P_k = 7.255e4\n",
    "\n",
    "#######################################\n",
    "# Calcium buffering parameters\n",
    "#######################################\n",
    "\n",
    "# Ca concentrations\n",
    "\n",
    "Ca_oconc = 2e-3\n",
    "Ca_iconc = 45e-9\n",
    "\n",
    "# Mg concentrations\n",
    "\n",
    "Mg_conc = 590e-6\n",
    "\n",
    "# Buffer concentrations\n",
    "\n",
    "iCBsf_conc = 27.704e-6\n",
    "iCBCaf_conc = 2.6372e-6\n",
    "iCBsCa_conc= 1.5148e-6\n",
    "iCBCaCa_conc= 0.14420e-6\n",
    "\n",
    "CBsf_conc= 110.82e-6\n",
    "CBCaf_conc= 10.549e-6\n",
    "CBsCa_conc= 6.0595e-6\n",
    "CBCaCa_conc= 0.57682e-6\n",
    "\n",
    "PV_conc= 3.2066e-6\n",
    "PVCa_conc= 16.252e-6\n",
    "PVMg_conc= 60.541e-6\n",
    "\n",
    "# Diffusion constants\n",
    "\n",
    "DCST = 0.223e-9 # Ca\n",
    "DCB = 0.028e-9  # Calbindin (CB)\n",
    "DPV = 0.043e-9  # Parvalbumin (PV)\n",
    "\n",
    "# Reaction rates\n",
    "\n",
    "CBf_f_kcst = 4.35e7\n",
    "CBf_b_kcst = 35.8\n",
    "\n",
    "CBs_f_kcst = 0.55e7\n",
    "CBs_b_kcst = 2.6\n",
    "\n",
    "PVca_f = 10.7e7\n",
    "PVca_b = 0.95\n",
    "\n",
    "PVmg_f_kcst = 0.8e6\n",
    "PVmg_b_kcst = 25\n",
    "\n",
    "###########################################################\n",
    "# Mesh Parameters\n",
    "###########################################################\n",
    "\n",
    "mesh_file = 'meshes/caburst_cyl80.msh'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4bf8cfc-a05a-4761-8052-c1e4bf1c0e25",
   "metadata": {},
   "source": [
    "## Model specification\n",
    "\n",
    "Since this is a relatively large model we will split its description up into several sections. We first create the species and channels that will be used in the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "434f6690-bfbd-4229-810a-4ed4499db62b",
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################################################\n",
    "# Biochemical model\n",
    "###########################################################\n",
    "\n",
    "mdl = Model()\n",
    "with mdl:\n",
    "    # Species\n",
    "    Pump, CaPump, PV, PVMg, PVCa, Mg = Species.Create()\n",
    "    Ca = Species.Create(valence=2)\n",
    "\n",
    "    # Calbindin\n",
    "    CBs, CBf, CBsCa, CBfCa, CBmob, CBimmob = SubUnitState.Create()\n",
    "    CBsSU, CBfSU, CBmobSU = SubUnit.Create(\n",
    "        [CBs, CBsCa], [CBf, CBfCa], [CBmob, CBimmob]\n",
    "    )\n",
    "    CB = Complex.Create([CBsSU, CBfSU, CBmobSU], statesAsSpecies=True)\n",
    "\n",
    "    # Channels\n",
    "    CaPc, CaPo = SubUnitState.Create()\n",
    "    CaP_SU = SubUnit.Create([CaPc, CaPo])\n",
    "    CaPchan = Channel.Create([CaP_SU, CaP_SU, CaP_SU])\n",
    "    \n",
    "    CaTmc, CaTmo, CaThc, CaTho = SubUnitState.Create()\n",
    "    CaTm_SU = SubUnit.Create([CaTmc, CaTmo])\n",
    "    CaTh_SU = SubUnit.Create([CaThc, CaTho])\n",
    "    CaTchan = Channel.Create([CaTm_SU, CaTm_SU, CaTh_SU])\n",
    "\n",
    "    BK, BKCa, BKopen, BKclose = SubUnitState.Create()\n",
    "    BKCaSU = SubUnit.Create([BK, BKCa])\n",
    "    BKocSU = SubUnit.Create([BKopen, BKclose])\n",
    "    BKchan = Channel.Create([BKCaSU, BKCaSU, BKCaSU, BKCaSU, BKocSU])\n",
    "\n",
    "    SK_C1, SK_C2, SK_C3, SK_C4, SK_O1, SK_O2 = SubUnitState.Create()\n",
    "    SKchan = Channel.Create([SK_C1, SK_C2, SK_C3, SK_C4, SK_O1, SK_O2])\n",
    "\n",
    "    Leak = SubUnitState.Create()\n",
    "    L = Channel.Create([Leak])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b54db78f-cc39-4cc1-bc8e-3b0cd6687df5",
   "metadata": {},
   "source": [
    "All ion channels involve the creation of `SubUnitState`s and `SubUnit`s, as we already saw in the [corresponding chapter](STEPS_Tutorial_Efield.ipynb). There is however one difference compared to previous chapters: the `Ca` species is created with the `valence` keyword parameter. This sets the net elementary electrical charge per calcium ion, which in this example for Ca2+ is +2. This is necessary for using GHK currents as we will see later in this chapter.\n",
    "\n",
    "### Calcium dynamics\n",
    "\n",
    "The following lines of code describe the calcium and calcium buffer reactions and diffusion. Since these are 'ordinary' dynamics with no voltage-dependence we will not look look at this part in detail. A more detailed explanation is offered in [1](#Footnotes) and [2](#Footnotes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b56ec80-544d-4004-8de3-bdb8b1ebc2bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl:\n",
    "    r = ReactionManager()\n",
    "\n",
    "    vsys = VolumeSystem.Create()\n",
    "    with vsys:\n",
    "        # PVCa\n",
    "        PV + Ca <r[1]> PVCa\n",
    "        r[1].K = PVca_f, PVca_b\n",
    "\n",
    "        # PVMg\n",
    "        PV + Mg <r[1]> PVMg\n",
    "        r[1].K = PVmg_f_kcst, PVmg_b_kcst\n",
    "\n",
    "        with CB[...]:\n",
    "            # Fast binding\n",
    "            CBf + Ca <r[1]> CBfCa\n",
    "            r[1].K = CBf_f_kcst, CBf_b_kcst\n",
    "\n",
    "            # Slow binding\n",
    "            CBs + Ca <r[1]> CBsCa\n",
    "            r[1].K = CBs_f_kcst, CBs_b_kcst\n",
    "\n",
    "        diff_Ca   = Diffusion.Create(Ca, DCST)\n",
    "        diff_CB   = Diffusion.Create(CB[:, :, CBmob], DCB)\n",
    "        diff_PV   = Diffusion.Create(PV, DPV)\n",
    "        diff_PVCa = Diffusion.Create(PVCa, DPV)\n",
    "        diff_PVMg = Diffusion.Create(PVMg, DPV)\n",
    "        \n",
    "    ssys = SurfaceSystem.Create()\n",
    "    with ssys:\n",
    "        # Ca Pump\n",
    "        Pump.s + Ca.i <r[1]> CaPump.s >r[2]> Pump.s\n",
    "        r[1].K = P_f, P_b\n",
    "        r[2].K = P_k"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba83b1e6-e240-425b-978c-fd906886bc96",
   "metadata": {},
   "source": [
    "### P-type Calcium channel\n",
    "\n",
    "The P-type calcium channel is a different type of ion channel to those we have seen before. In previous chapters we saw \n",
    "Hodgkin-Huxley sodium and potassium channels that conducted an Ohmic current. The sodium and potassium ions in that situation \n",
    "were not explicitly simulated, which was reasonable because those ions were not involved in other processes we were \n",
    "interested in, and we could assume their concentrations inside and outside the cell were not altered significantly during their \n",
    "conduction. However, with calcium we need a different approach. Here calcium is involved in intracellular processes such as \n",
    "potassium channel-activation (as we will see), buffering and diffusion, and so we must simulate the influx of calcium through these\n",
    "P-type channels. Furthermore, the Ohmic approximation is no longer sufficient for our purposes. The large differences between \n",
    "intracellular and extracellular concentration along with large changes in intracellular concentration mean that, in effect, channel \n",
    "conductance has some voltage and concentration dependence and is described much better by the GHK flux equation. The GHK flux equation itself \n",
    "is derived under certain simplifying assumptions that are good approximations for many ion channels, specifically \n",
    "those where channel occupancy and competition are negligible. Please see [3](#Footnotes) for further discussion on the use of the GHK flux \n",
    "equation and the behaviour of the GHK current object in STEPS. It is worth noting that use of the GHK flux equation means that \n",
    "(instead of conductance) we must specify the channel's permeability, which can be more difficult to parameterize. \n",
    "\n",
    "The P-type calcium channel kinetics are described in detail in [1](#Footnotes). The `CaPchan` channel object was created before and contains three identical `CaP_SU` subunits that can either be in closed (`CaPc`) or open (`CaPo`) states.\n",
    "\n",
    "We then declare the voltage-dependent transition reaction between open and closed states. \n",
    "Remember for each of these discrete channels the voltage will be read from the local voltage\n",
    "across the membrane triangle where the channel resides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44004d80-d9c7-4935-91e3-142844d42a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl:\n",
    "    with ssys:\n",
    "        # CaP channel\n",
    "        with CaPchan[...]:\n",
    "            CaPc.s <r[1]> CaPo.s\n",
    "            r[1].K = alpha_cap, beta_cap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b6ab3bf-3b04-4a52-ac58-15121bbd3477",
   "metadata": {},
   "source": [
    "We come to creating our GHK current object. This object will calculate single-channel current for a given\n",
    "channel state by the GHK flux equation:\n",
    "\n",
    "$$\n",
    "I_{s}=P_{s}z_{s}^{2}\\frac{V_{m}F^{2}}{RT}\\frac{[S]_{i}-[S]_{o}exp(-z_{s}V_{m}F/RT)}{1-exp(-z_{s}V_{m}F/RT)}\n",
    "$$\n",
    "\n",
    "where $I_{s}$ is the single-channel current (amps) of ion S, $P_{s}$ is the single-channel permeability of ion S ($m^{3}.s^{-1}$), $z_{s}$ is the valence of ion S, $V_{m}$ is the membrane voltage (volts), $F$ is the Faraday constant, $R$ is the gas constant, $T$ is temperature (Kelvin), $[S]_{i}$ is the intracellular concentration of ion S ($mol.m^{-3}$) and $[S]_{o}$ is the extracellular concentration of ion S ($mol.m^{-3}$).\n",
    "\n",
    "When a GHK current is applied in STEPS it (optionally) results in movement of ions between the 'outer' and 'inner' compartments, the direction of which will depend \n",
    "on the sign of the current and the valence of the ions. \n",
    "\n",
    "Many of the values required for calculating a GHK current are simulation variables, such as concentrations and voltage, simulation constants such as \n",
    "temperature, or fixed constants such as the Faraday constant and the gas constant. Such values are either known or can be found by STEPS during runtime and so are not part of \n",
    "object construction, with the exception of single-channel permeability which we will come to later. Like we saw in [Simulating membrane potential](STEPS_Tutorial_Efield.ipynb), we then create the `GHKCurr` object. There are also optional keyword arguments (`virtual_oconc` and `computeflux`) that we will explain further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "012dcbaf-8fe5-4c33-b253-e3fa57fea220",
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl:\n",
    "    with ssys:\n",
    "        OC_CaP = GHKCurr.Create(\n",
    "            CaPchan[CaPo, CaPo, CaPo], Ca, CaP_P,\n",
    "            computeflux=True,\n",
    "            virtual_oconc=Ca_oconc,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c817d938-413f-4711-8b78-e6c20f7b5f3f",
   "metadata": {},
   "source": [
    "First let's look at the `virtual_oconc` argument. This option allows us to not explicitly model the extracellular ('outer') concentration of the ion, which is useful because\n",
    "often the extracellular compartment is not modelled. This option, rather, allows a fixed 'outer' concentration for the ion to be \n",
    "specified and that number will be used in the GHK flux calculations. The value of the parameter `Ca_oconc` that we declared earlier is 2mM.\n",
    "\n",
    "The other optional argument is `computeflux`. This flag (which defaults to True) tells STEPS whether to model this GHK current process as ion transport \n",
    "or not. If `computeflux` is True, then the calculated GHK current will result in transport of ions between the 'outer' and 'inner' compartments. \n",
    "For example, if over some 0.01ms time step, somewhere on the membrane a mean current of approximately 1.6pA is calculated through a membrane channel to which a GHK current is applied, \n",
    "then for an ion of valence 2+ this means that 50 ions moved from one compartment to the other. The direction of movement depends on the signs of the current\n",
    "and the ion valence. The movement only occurs between surface tetrahedrons surrounding the membrane triangles in which the channels reside and so, for ions \n",
    "where this kind of process occurs, for accuracy it is necessary to model diffusion of these ions at least within the inner compartment \n",
    "and often within both compartments. This can be an expensive computation, particularly where concentrations are in the millimolar range, which shows the value of the `computeflux`\n",
    "flag- if the GHK flux is applied to an ion which does not have any other particularly important effects in the model other than its effect on membrane \n",
    "excitability (a possible example is potassium) then it may be a good labour-saver to clamp 'inner' and 'outer' concentrations of the ion and turn off the transport \n",
    "of ions as an approximation. However, in this model if we set `computeflux` to False then the result would be no intracellular calcium, which is \n",
    "obviously not desirable, and so the `computeflux` flag is set to True, as it usually will be for most ions in most models.  \n",
    "\n",
    "The first positional argument, like for ohmic currents, is the conducting channel state(s). Here, the channel opens when all its subunits are in the open `CaPo` state.\n",
    "\n",
    "The second positional argument is the ion that will carry the current. For calcium (and only for calcium) we used `valence=2` to specify a valence of 2. 'Valence' can be an ambiguous term, but \n",
    "here it means the net elementary electrical charge per ion, which in this example for Ca2+ is +2. Negative valences can of course be specified by using a negative number. It is essential that a valence is set for any ion that will be used for a GHK current in the simulation. If no valence is specified the result will be an error. \n",
    "\n",
    "The third positional argument (`CaP_P`) corresponds to single-channel permeability. Because conductance is not constant for a GHK current (apart from under certain unusual \n",
    "conditions) one value for a conductance parameter does not suffice. However, since single-channel permeability is often rather a difficult parameter\n",
    "to define, STEPS does provide functionality for estimating the permeability. So we have two options for setting single-channel permeability: \n",
    "giving it directly to the GHKCurr constructor; or giving the result of a call to [steps.API_2.model.GHKCurr.PInfo](API_model.rst#steps.API_2.model.GHKCurr.PInfo) to estimate the permeability from data. The first is straightforward and simply means providing single-channel \n",
    "permeability in S.I. units of cubic metres / second. In this model the CaP channel permeability `CaP_P` was set to 2.5e-20 cubic metres / second [4](#Footnotes). If we did not provide the permeability during construction, we could also write\n",
    "\n",
    "```python\n",
    "    OC_CaP.P = CaP_P\n",
    "```\n",
    "\n",
    "The second option, the [steps.API_2.model.GHKCurr.PInfo](API_model.rst#steps.API_2.model.GHKCurr.PInfo) function, requires some explanation. In effect, the conductance of a channel that is modelled \n",
    "by the GHK flux equation varies with \n",
    "voltage (see below figure) with a dependence on the 'outer' and 'inner' concentrations of the ion (in fact conductance is only constant with voltage \n",
    "when these concentrations are equal), as well as weakly on temperature. \n",
    "\n",
    "<figure>\n",
    "<img src=\"images/GHK_K.png\">\n",
    "<figcaption>\n",
    "Figure 1: A single-channel GHK flux in the physiological range for a typical monovalent cation compared to an Ohmic approximation. The GHK flux is calculated with single-channel permeability of 9e-20 cubic metres / second, fixed extracellular concentration of 4mM, fixed intracellular concentration of 155mM and temperature of 20 Celsius. The single-channel Ohmic conductance is 20pS with reversal potential -77mV.\n",
    "</figcaption>\n",
    "</figure>\n",
    "    \n",
    "\n",
    "STEPS is able to estimate single-channel permeability from single-channel conductance, but for STEPS to do so the user must supply \n",
    "information about the conditions under which the conductance was measured, and in theory this should be enough to find the single-channel permeability since it is \n",
    "assumed constant (although there are occasions when permeability too can have some weak voltage dependence [3](#Footnotes), \n",
    "which is, however, currently not possible to model with STEPS). Specifically, the [steps.API_2.model.GHKCurr.PInfo](API_model.rst#steps.API_2.model.GHKCurr.PInfo) function requires arguments of:\n",
    "estimated single-channel conductance [5](#Footnotes) (units: Siemens), one voltage within the range at which conductance was measured (Volts), temperature (Kelvin), 'outer' concentration \n",
    "of the ion (molar), and 'inner' concentration of the ion (molar). Since the valence of the ion is known it is not necessary to supply that information to \n",
    "the [steps.API_2.model.GHKCurr.PInfo](API_model.rst#steps.API_2.model.GHKCurr.PInfo) function. So, for example, for some GHKcurrent object called `K_GHK`, if we measured single-channel conductance \n",
    "as 20pS in a small voltage range around -22mV at 20 degrees Celsius (293.15 Kelvin) with an estimated extracellular ion concentration of 4mM and \n",
    "intracellular concentration of 155mM, then we could create the current with\n",
    "\n",
    "```python\n",
    "    K_Pinfo = GHKCurr.PInfo(g = 20e-12, V = -22e-3, T = 293.15, oconc = 4e-3, iconc = 155e-3)\n",
    "    K_GHK = GHKCurr.Create(Kchan[Ko], K, K_Pinfo)\n",
    "```\n",
    "\n",
    "and the single-channel permeability would be set to approximately 9e-20 cubic metres / second. The behaviour of such a channel is shown in Figure 1.\n",
    "\n",
    "We are now familiar, through aspects discussed so far in this chapter and other chapters, with most of the concepts applied for this model, so \n",
    "a very detailed description is not necessary for most remaining parts of the model. We move on to our other three ion channels in the model.\n",
    "\n",
    "### T-type Calcium channel\n",
    "\n",
    "Like the P-type Calcium channel, transitions between channel states of the T-type Calcium channel are voltage-dependent and we model the calcium current as a GHK current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99676a22-5bf4-41b8-8566-35c05c8af533",
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl:\n",
    "    with ssys:\n",
    "        # CaT channel\n",
    "        with CaTchan[...]:\n",
    "            CaTmc.s <r[1]> CaTmo.s\n",
    "            r[1].K = alpham_cat, betam_cat\n",
    "            \n",
    "            CaThc.s <r[1]> CaTho.s\n",
    "            r[1].K = alphah_cat, betah_cat\n",
    "        OC_CaT = GHKCurr.Create(\n",
    "            CaTchan[CaTmo, CaTmo, CaTho], Ca, CaT_P,\n",
    "            computeflux=True,\n",
    "            virtual_oconc=Ca_oconc,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5388050-5ada-476e-81cc-3635eb6ba7e9",
   "metadata": {},
   "source": [
    "### BK-type Calcium-activated Potassium channel\n",
    "\n",
    "The BK channel in the model undergoes both voltage-dependent and non-voltage dependent processes. This is an example of Channel States interacting with Species through surface reactions. Here we will notice that Channel subunit states (e.g. `BK`) appear alongside Species (`Ca`) in reactions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fbf5a475-e069-4eab-82c9-134d2111da6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl:\n",
    "    with ssys:\n",
    "        # BK channel\n",
    "        with BKchan[..., BKclose]:\n",
    "            BK.s + Ca.i <r[1]> BKCa.s\n",
    "            r[1].K = BK_f, BKc_b\n",
    "\n",
    "        with BKchan[..., BKopen]:\n",
    "            BK.s + Ca.i <r[1]> BKCa.s\n",
    "            r[1].K = BK_f, BKo_b\n",
    "\n",
    "        with BKchan[...]:\n",
    "            BKclose.s <r[1]> BKopen.s\n",
    "\n",
    "            BK_f = CompDepRate.Create(lambda s: BK_oc_f[s.Count(BKCa)], [BKchan])\n",
    "            BK_b = CompDepRate.Create(lambda s: BK_oc_b[s.Count(BKCa)], [BKchan])\n",
    "            r[1].K = BK_f, BK_b\n",
    "        OC_BK = OhmicCurr.Create(BKchan[..., BKopen], BK_G, BK_rev)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa0cf89-ab08-475a-bcbf-c739c1de11bf",
   "metadata": {},
   "source": [
    "The potassium current is modeled with an [steps.API_2.model.OhmicCurr](API_model.rst#steps.API_2.model.OhmicCurr) objects. All states in which the `BKocSU` subunit is in the `BKopen` state are conducting states, demonstrating the support for multiple conducting/permeable states for a channel.\n",
    "\n",
    "Note also that the transition between `BKclose` and `BKopen` is declared with a complex-dependent rate (`CompDepRate`). As we saw in the [multi-state complexes](STEPS_Tutorial_Complexes.ipynb#Expressing-cooperativity-with-complex-dependent-reaction-rates) chapter, the rate of the reaction will be determined depending on the state of the channel. Here, the rate depends on the number of `BKCaSU` subunits that are bound to calcium. We declared tables of reaction rate constants (`BK_oc_f` and `BK_oc_b`) and we access the correct value by counting the number of subunits that have bound calcium (`BK_oc_f[s.Count(BKCa)]`).\n",
    "\n",
    "### SK-type Calcium-activated Potassium channel\n",
    "\n",
    "The SK channel does not have any voltage dependence, and contains two conducting states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7eb322b-435f-4356-9850-0f298a20fbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl:\n",
    "    with ssys:\n",
    "        # SK channel\n",
    "        with SKchan[...]:\n",
    "            ((SK_C1.s + Ca.i <r[1]> SK_C2.s)\\\n",
    "                      + Ca.i <r[2]> SK_C3.s)\\\n",
    "                      + Ca.i <r[3]> SK_C4.s\n",
    "            r[1].K = dirc2_t, invc1_t\n",
    "            r[2].K = dirc3_t, invc2_t\n",
    "            r[3].K = dirc4_t, invc3_t\n",
    "            \n",
    "            SK_C3.s <r[1]> SK_O1.s\n",
    "            SK_C4.s <r[2]> SK_O2.s\n",
    "            r[1].K = diro1_t, invo1_t\n",
    "            r[2].K = diro2_t, invo2_t\n",
    "        OC_SK = OhmicCurr.Create(SKchan[SK_O1|SK_O2], SK_G, SK_rev)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5607b53-360b-4cf7-a112-d325d36a74e3",
   "metadata": {},
   "source": [
    "### Leak channel\n",
    "\n",
    "Although another option for setting the leak would have been to (later) set `sim.membrane.Res`, the leak conductance is described as a leak channel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc4dc3c6-1ebc-4bd5-95f3-e63a771cebc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl:\n",
    "    with ssys:\n",
    "        # Leak current channel\n",
    "        OC_L = OhmicCurr.Create(L[Leak], L_G, L_rev)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68089100-6b41-4c5b-9259-5b0359738b23",
   "metadata": {},
   "source": [
    "## Geometry specification\n",
    "\n",
    "This model is set up with a relatively simple geometry: a cylinder of diameter 2um and 80um length. The membrane across which we compute potential does not include the ends of the cylinder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a0f7f99-b047-42ea-ba59-33baee280314",
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################################################\n",
    "# Mesh and compartmentalization\n",
    "###########################################################\n",
    "\n",
    "mesh = TetMesh.LoadGmsh(mesh_file, 1e-6)\n",
    "\n",
    "with mesh:\n",
    "    cyto = Compartment.Create(mesh.tets, vsys)\n",
    "\n",
    "    ends = [mesh.bbox.min.z, mesh.bbox.max.z]\n",
    "    memb_tris = TriList(tri for tri in mesh.surface if tri.center.z not in ends)\n",
    "    memb = Patch.Create(memb_tris, cyto, None, ssys)\n",
    "    \n",
    "    submemb_tets = TetList()\n",
    "    for tri in memb.tris:\n",
    "        submemb_tets |= tri.tetNeighbs\n",
    "\n",
    "    membrane = Membrane.Create([memb])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85154a3a-a2af-443c-90d1-956258917ad5",
   "metadata": {},
   "source": [
    "We also find the submembrane tetrahedrons, that is all tetrahedrons connected to a membrane triangle from the intracellular side.\n",
    "\n",
    "## Simulation with TetOpSplit\n",
    "\n",
    "### Initialization\n",
    "\n",
    "We first create the random number generator, the partition scheme, and the simulation object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "75c31ad6-8062-46ec-82bb-3a6b338dbdb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model checking:\n",
      "No errors were found\n"
     ]
    }
   ],
   "source": [
    "###########################################################\n",
    "# Simulation\n",
    "###########################################################\n",
    "\n",
    "rng = RNG('mt19937', 512, SEED)\n",
    "\n",
    "part = LinearMeshPartition(mesh, 1, 1, MPI.nhosts)\n",
    "\n",
    "sim = Simulation('TetOpSplit', mdl, mesh, rng, part, calcMembPot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed10aa3-ee07-4870-ad07-be439e1d92c6",
   "metadata": {},
   "source": [
    "We then declare the data to be saved, as seen in previous chapters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f116c191-48d8-405a-8bb2-ecaddcd8411e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = ResultSelector(sim)\n",
    "\n",
    "Currents = rs.SUM(rs.TRIS(memb.tris).OC_CaP.I) << \\\n",
    "           rs.SUM(rs.TRIS(memb.tris).OC_CaT.I) << \\\n",
    "           rs.SUM(rs.TRIS(memb.tris).OC_BK.I) << \\\n",
    "           rs.SUM(rs.TRIS(memb.tris).OC_SK.I)\n",
    "\n",
    "Pot = rs.TET(0, 0, 0).V\n",
    "\n",
    "CaConcs = rs.cyto.Ca.Conc << \\\n",
    "         (rs.SUM(rs.TETS(submemb_tets).Ca.Count) / (AVOGADRO * submemb_tets.Vol * 1e3))\n",
    "\n",
    "BKstates = rs.memb.LIST(*BKchan[...]).Count\n",
    "\n",
    "sim.toSave(Currents, Pot, CaConcs, BKstates, dt=DT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8be9d2b5-6c24-4c45-865e-68140c10e46b",
   "metadata": {},
   "source": [
    "We will save the total currents through the membrane for each GHK and ohmic currents, the membrane potential, the intracellular and submembrane calcium concentration, and the distribution of states of the BK channel.\n",
    "\n",
    "The data will be saved to an HDF5 file, as we saw in a [previous chapter](STEPS_Tutorial_DataSaving.ipynb). We setup the initial state for each run and run `NBRUNS` simulations: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8d1a8e1-4932-4454-8a26-346551805ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "with HDF5Handler('Caburst') as hdf:\n",
    "    sim.toDB(hdf, 'CaBurstSim')\n",
    "\n",
    "    for i in range(NBRUNS):\n",
    "        sim.newRun()\n",
    "\n",
    "        # Setting initial conditions\n",
    "        area = Parameter(memb.Area, 'm^2')\n",
    "\n",
    "        sim.memb.Pump.Count = round(P_ro * area)\n",
    "\n",
    "        for s in CaPchan[...]:\n",
    "            sim.memb.LIST(s).Count = round(CaP_ro*area*CaP_p[s.Count(CaPo)])\n",
    "\n",
    "        for s in CaTchan[...]:\n",
    "            hCnt, mCnt = s.Count(CaTho), s.Count(CaTmo)\n",
    "            sim.memb.LIST(s).Count = round(CaT_ro*area*CaT_p[hCnt][mCnt])\n",
    "\n",
    "        for s in BKchan[...]:\n",
    "            isOpen, nbCa = s.Count(BKopen), s.Count(BKCa)\n",
    "            sim.memb.LIST(s).Count = round(BK_ro*area*BK_p[isOpen][nbCa])\n",
    "\n",
    "        sim.memb.SKchan[SK_C1].Count = round(SK_ro*area*SK_C1_p)\n",
    "        sim.memb.SKchan[SK_C2].Count = round(SK_ro*area*SK_C2_p)\n",
    "        sim.memb.SKchan[SK_C3].Count = round(SK_ro*area*SK_C3_p)\n",
    "        sim.memb.SKchan[SK_C4].Count = round(SK_ro*area*SK_C4_p)\n",
    "        sim.memb.SKchan[SK_O1].Count = round(SK_ro*area*SK_O1_p)\n",
    "        sim.memb.SKchan[SK_O2].Count = round(SK_ro*area*SK_O2_p)\n",
    "\n",
    "        sim.memb.L[Leak].Count = round(L_ro * area)\n",
    "        \n",
    "        sim.cyto.Ca.Conc = Ca_iconc\n",
    "        sim.cyto.Mg.Conc = Mg_conc\n",
    "\n",
    "        sim.cyto.CB[CBs,   CBf,   CBimmob].Conc = iCBsf_conc\n",
    "        sim.cyto.CB[CBsCa, CBf,   CBimmob].Conc = iCBCaf_conc\n",
    "        sim.cyto.CB[CBs,   CBfCa, CBimmob].Conc = iCBsCa_conc\n",
    "        sim.cyto.CB[CBsCa, CBfCa, CBimmob].Conc = iCBCaCa_conc\n",
    "\n",
    "        sim.cyto.CB[CBs,   CBf,   CBmob].Conc = CBsf_conc\n",
    "        sim.cyto.CB[CBsCa, CBf,   CBmob].Conc = CBCaf_conc\n",
    "        sim.cyto.CB[CBs,   CBfCa, CBmob].Conc = CBsCa_conc\n",
    "        sim.cyto.CB[CBsCa, CBfCa, CBmob].Conc = CBCaCa_conc\n",
    "\n",
    "        sim.cyto.PV.Conc = PV_conc\n",
    "        sim.cyto.PVCa.Conc = PVCa_conc\n",
    "        sim.cyto.PVMg.Conc = PVMg_conc\n",
    "\n",
    "        sim.EfieldDT = EF_DT\n",
    "\n",
    "        sim.ALL(Membrane).Potential = init_pot\n",
    "        sim.membrane.VolRes = Ra\n",
    "        sim.membrane.Capac = memb_capac\n",
    "\n",
    "        # Set temperature for ghk reactions\n",
    "        sim.Temp = TEMPERATURE\n",
    "\n",
    "        for j in range(1000):\n",
    "            t = ENDT * j / 999\n",
    "            if MPI.rank == 0:\n",
    "                print(f'run {i}: {t} / {ENDT}s')\n",
    "            sim.run(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "647393d8-14cd-4b92-a673-9d1fe3987ff7",
   "metadata": {},
   "source": [
    "We set a new simulation property, [steps.API_2.sim.Simulation.Temp](API_sim.rst#steps.API_2.sim.Simulation.Temp), which holds the simulation temperature. Currently, this will only influence any GHK flux rates, and \n",
    "will have no influence on any other kinetics. The value for `TEMPERATURE` is in Kelvin and corresponds to 34 degrees Celsius.\n",
    "\n",
    "Note that, since the simulation takes much more computing time than other simulations we saw in previous chapters,  we split the call to `sim.run(ENDT)` to multiple calls interspersed with printouts giving us feedback about the progression of the simulation.\n",
    "\n",
    "### Running the simulation\n",
    "\n",
    "Although the simulation can be run with jupyter notebook, it would take too long to do so and it is thus more suited to parallel runs (see [corresponding chapter](STEPS_Tutorial_MPI.ipynb)). \n",
    "The corresponding python script can be downloaded here: [STEPS_Tutorial_CaBurst.py](https://github.com/CNS-OIST/STEPS_Example/tree/master/user_manual/source/API_2/scripts/STEPS_Tutorial_CaBurst.py)\n",
    "The simulations can then be run with 12 cores by using e.g.:\n",
    "\n",
    "```shell\n",
    "mpirun -n 12 python3 STEPS_Tutorial_CaBurst.py\n",
    "```\n",
    "\n",
    "## Parameters export\n",
    "\n",
    "Finally, after the simulations, we call the `ExportParameters` function to generate parameter tables for our model and simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41330c76-b550-40bc-b076-1bcb3caed52c",
   "metadata": {},
   "outputs": [],
   "source": [
    "if MPI.rank == 0:\n",
    "    ExportParameters(sim, 'CaBurst', method='pdf', \n",
    "        hideColumns=['Defined in', 'valence Units'],\n",
    "        unitsToSimplify=[\n",
    "            'uM', 'uM^-1 s^-1', 'mV', 'um^2 s^-1', 'pS', 'um', 'um^2'\n",
    "        ], numPrecision=5,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a111375-dc9f-4c95-8e70-9ce89fe0050d",
   "metadata": {},
   "source": [
    "First note that only MPI rank 0 calls this function, otherwise all ranks would try to write to the same files.\n",
    "\n",
    "This function takes a STEPS container object as first argument (here we use the full simulation) and a file prefix as second argument. It will then generate files like `CaBurst_Parameters.pdf`, etc.\n",
    "\n",
    "The method keyword argument specifies how the tables will be created, currently `'csv'`, `'tex'` and `'pdf'` are supported. The `'pdf'` method depends on the installation of `pdflatex` and `csv2latex` programs. If these programs are not installed, you can change `method='pdf'` to `method='csv'` in the code above.\n",
    "\n",
    "We will not go over all the available keyword arguments for the `ExportParameters` function, further explanations are available in the [documentation](API_utils.rst#steps.API_2.utils.ExportParameters). The following figure shows some of the tables generated by the function:\n",
    "\n",
    "<figure>\n",
    "<img src=\"images/parameter_export_tables.png\">\n",
    "<figcaption>\n",
    "Figure 2: Examples of tables generated with `ExportParameters`. The simulation table shows the initial state (t = 0) values along with computations that were used to derive them. The reaction table shows all reactions in the model with their associated rate constants. The parameters table shows all explicitely created `Parameter`s along with their description. The diffusion, GHKCurr and OhmicCurr tables respectively show diffusion rules, GHK currents and ohmic currents.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "## Plotting the results\n",
    "\n",
    "The corresponding python script: [STEPS_Tutorial_CaBurst_plot.py](https://github.com/CNS-OIST/STEPS_Example/tree/master/user_manual/source/API_2/scripts/STEPS_Tutorial_CaBurst_plot.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "55c069fd-87f4-49e8-bcb0-0160357f5b65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmwAAAJcCAYAAABE7/iIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAAsTAAALEwEAmpwYAADOvklEQVR4nOzdd5xcVd348c93Zls2u8mmkd5IL0ACSyAUDQQUQaqoID8liA8i2AURfR5FRR9FEEUQReTBghBEkFAE6YI0Ewgp1AQS0pNNT7bPfH9/zJ2ZO7szs1Pu1P2+X69l79x75tyzYebMd04VVcUYY4wxxhQvX6ELYIwxxhhjkrOAzRhjjDGmyFnAZowxxhhT5CxgM8YYY4wpchawGWOMMcYUOQvYjDHGGGOKnAVsxhhjjDFFzgI2Y4wxxpgiZwGbKQoiskBElotIs4hsFpGbRaTBdX2yiPxVRJpEZLeILBORr4uIv4d8q0TkKhF5R0T2i8gaEblNRMalUKZ5IhIUkX0isldE3hKRC7L/a40x5aTY6i8ROc+pt/aJSIurHtsnIvu8+atNvlnAZgpORL4B/BS4HOgPHAmMBR5zKqwJwEvAOuAgVe0PfBxoBOp7yP4e4DTgU07ehwBLgPkpFm+jqtYB/YArgN+JyPQ0/jxjTBkrxvpLVe9Q1Tqn7voITj3mOmdKkNjWVKaQRKQfsBH4rKre7TpfB7xHKEg6HhigqqekmfcJwAPAZFVdlyDNBcA3gVHANuCnqvpb59o84M+qOsqVfhvwBVW9J52yGGPKTzHXX6408+hSj5nSZC1sptCOAmqAe90nVXUf8DBwInACoW+a6ToBeDlRZefYCnyUUAvaBcD1InJo10Qi4hORM4EGYHkGZTHGlJ+SqL9MeagodAFMrzcYaFLVzjjXNgGHAYOc43T1+DxVfcj18BkR+SdwLPCKc26EiOwCgsD7wKdV9a0MymKMKT/FXn+ZMmIBmym0JmCwiFTEqfSGO9e3O8fp2g5MTpZARD4CfM9J5wNqiW1B22hdCcaYBIq9/jJlxLpETaG9ALQBZ7lPOmNAPgI8ATwOfCyDvB8H5ohI3IBLRKqBvwHXAkNVtYFQN4ZkcC9jTO9j9ZfJGwvYTEGp6m7g+8CvROQkEal0pqzfDawH/kToG+RRIvIzERkGICITReTP7qnzcfJ+HHgMuE9EDhORChGpF5GLReSzQBVQTWiwbqfzbfVDuftrjTHlxOovk08WsJmCU9VrgG8T+qa4h+gU+Pmq2qaqq4G5wDhgpYjsJvTNcjGwt4fszyb0rXMhsBtYQWg6/eOquhf4MqHKdSehqfOLPP3jjDFlzeovky+2rIcxxhhjTJGzFjZjjDHGmCJnAZspaV22YHH/rCx02YwxJhmrv0w6rEvUGGOMMabIlf06bIMHD9Zx48YVuhjGmDxZsmRJk6oOKXQ5vGD1lzG9T6I6rOwDtnHjxrF48eJCF8MYkycisrbQZfCK1V/G9D6J6jAbw2aMMcYYU+QsYDPGGGOMKXIWsBljjDHGFDkL2IwxxhhjipwFbMYYY4wxRc4CNmOMMcaYIldyAZuInCQib4nIKhH5VqHLY4wx0HPdJCLVIrLQuf6SiIwrQDGNMSWqpAI2EfEDNwEfAaYD54rI9MKWyhjT26VYN10I7FTVicD1wE/zW0pjTCkrtYVz5wCrVPVdABG5CzgdeD3bjO+/5jqaVg5h6NYV7O87gqbBBwNQu38dInvYXzsjkrZ++z209z2etpqBVLVuRqiiT8s29tQNJFgxEMRPTfM7tNZOon7v+9TtW8eGQRVUBYfTWT2c/nveR8XPoO0r2DTsUPo272THgKmozx+5x4CdbxLwVbK3fhzVbTup3/s+OwdMZtCO19kydA4Ao9Y9zvrRJ0SeU7dvPfvqRnX72+r3rGVvv7Ex53yBdoL+qsjjio5mOitrU/q3qmndTmvNoJTSljPVACJ+tHUZHZUH0O6roJZa2jq2sKeyhfr2IKsqNtEhbSzY9zJ9g+vZ3xBka2UV4u9kc+cIpvs2UCEBdu4cyIqq0UxreZdN1cOoag/yft+BtFW0UBGsZDR7kZq+bGyuo47DqJXGjMstuhuV/gRpIUATlYwmSDs+qlA6ESoIaDMtLEcJUC9HofIurWzEHzyQDrZQxQgC0oqPSjp0I34GEmQfVYymnQ0EfQFQqGYDnQygQ1sJsJdKGUCNTqMiCC3+7XSymyqGUyUNbPE/SU1gMEonfuoJSgd1HQ0M6ljNzE8cxayTz/Lw/57nUqmbTgeuco7vAW4UEVEP9gd87fmneO8nD1G/Zzv99r5PR2Vfnp97Ei2+SgbsH4fgj0kf9DVTWbGUtX0qmCFB3h/Sxjvt7/DZB3fTb89advefQcBXSW3tfiqPH8O6mpEEt7xJTdXbVG8YwRP9ZzGvz+vUzDqab785iRN3KgfUP8uwzkoGjpvMdduHsXvHm1w4cz/+1xYzeOJ5VNaNxv/co0w/dwZPv7aRw48/goVLm5k6fhCzltzMsK1/Zq0eyz+3XcLwA6voN2Qgb720OVLmqhph5lkTqVdh6MhKBrxzE1vkUHYFxzC66TbeHzmT0Y3/j/796wDYtaWZ2j7tdO7cSrDfGOoG1PDo71Ywde5wmrfvgvZ9TDx0CJX3fQYmnwSTToThh3T7t921pZm1y7cxcvAOtuw9gBnHjqStpZN1r+9gwqFDEJFo2q3N1NZXUdUnu4/WYFsbHevWUT1xIm3vvkflsKH4amtRVVa99E+GDRyDr39/3tm4jH70oWP3TsYOmwoVFbz7yD2MHDuDR8bX8rMXvsI37gsy693MXmJ7pw6k/ehxvOYXXt32Kl+9X/EHo3nt76M8eXyAPX4fmyoqOG/3XqrivJxbZQSd2syDdUP4f09spv9+ZV/dUPo0b8cf7Iy9Z9+RbBh5LIO2v87e+tHUtO6gs6KWVRPPYnDTvVQNCNDSMZWW2hHU1O2jrnYb+5qH0BmoZt/+YT3/UdoJ0v3/TwV7CFJBkFrXud10Ugdd3j8AvmArQV8N/areYU/7BOK1f22vfIrp7zzLqCOnc+RVv+65bD0oqb1EReRs4CRV/Zzz+NPAEar6xS7pLgIuAhgzZsxha9f2vPD5TRc/6X2BjUnDsc9dTmVnc9I0T867KU+lKS7V+k8+99ufpJRWRJaoauYRbQZSqZtEZIWTZr3zeLWTpqlLXmnXX/d99VtsbP2QJ3/LiI3PsXHEMSml/cSgr3H39uvTyn/ItqVsGzIr8rjSt4uOYAP9/RvZHRiRcj5DK99iS8eUmHPqC/DFX58IhOr0EXVr2LhvHAAzjh3Bymc3xqQfXvk6Zw36TvTEVbu73afrZ8NnfnwUf/z28wAc/5mpTDtqREzaQSP7cs7/HJHy3xHPhm9+kz2LHmDSC8/zztyj6HvUXMbcdhub713Izm9flVIe513u546fBbIqh1fc9dYHnv06IPzr2Os4YOsSZr5+W8K05aJh19ucd9fFKadPVIeVWgtbSlT1FuAWgMbGxpQi0kMO38qqZ9YyYuNaVo0/BcFPu7+Nhr2r8bGVPfVOZagt0PIIg5onsH3wwQQ6llAbOJB+e9ewvT5I386+7KudQN99a6gIKjUt26jo2MqbY+ro1zmbTn8Fg3ZuIuCvpv+e99g4/GiGbl3M7n7j6aiso726PwADt6/EF9xPW80wfMEO6vatp2nQDBp2rmDL8HkAjNzwLzaM/EDkb6ht3kJz7dBuf1v93vfZWz/GKX8QJPRNoLJjHx2VoW+j/kAbAX91t+f2ad5KS+0BAEgwEGoFdOVhvPPsMT8DYLf/r7Sxm3crhxGo3Iuvs4aJ7KBPw0jqNmV/n6A2E8QPfqW1ejt1LSPpoI1Kov//d/m2stW/Dh9+JnbMYmfNK+wMBKkK9qNNWhgUHE6rby+ifvbLHmq0Lx3Shg8/QQIEfW2IVtBX99JJP3b49qAE6acD6d/RQEUwwI7KZtpkHwOCQ+lfUcdqfY2qQB1VFYq/o5429tG/3U9f2cxxny/q1jVPZVJ/VexthUpv7r99YOqjTPYHBqedvztYA+gINgCkFawB3YI1AAnGtoSEgzWAVUu2dku/qSP9ETVtzR2R4x0b93e7vn1D93Ppana2IwvuD+W1/4UXAWh5++2U8/B7GKtddqGfb/01wOA93a+9ORJuPSn6737dlm3dE+2LHqpUEHR6k3b1n+hdIYvYrobJnuRTagHbBmC06/Eo51zWjrnwHI65MHTc8/fUU1zHx6eU/+lJr54c51yifD+R9r1Lwe4HHqTfR06ife1aNq5cQuB3f6LjnVUpP/+nZ/tYMql7ELn3jZ/w0rfnM7RfTfSkKrTthc3LWPaVa6h8653IpUkf20xFZTCSLFa460MRgf1XNlFbWYH4hPbWTjat3s2IiQ1sXLWLwaPq6GwP8vefv0Jt+1q2ptJU7+gf+Difvnou/Qb3iTm/duV2HvzVawBccvNxMV0xuZeP11pJB2Wp1E3hNOtFpALoD2z34uZSoJ6S0umfyZFcvweDwfzcJwlfXSfvH1DB+kHC4D3d/49vbRDePyBavqn7W7uleWJft1MmA6UWsP0HmCQi4wlVfucAnypskYwX+p/6UQCqJ0xg/IQJcNon4qZrfuUV1n7qvG7nr7gnyAVfFfb3ia3Yag+8jiN+DG9f/RGqKpyATgRq+sG4Y6h8678iaT962k8JBPz4gtC/TyW/OGc2Hxy0B351qJMitrLq+/b9cNDZAFTVVDB2RmhcX/g3wPn/ezRcdTL0r4Dvhj6bO9sDLH54DSv+tYG25tjxG2F/+u8XuPQ3sUHSu69Gv7nmN1gzKUilbloEnA+8AJwNPOnF+DUACRYqdLLXYU6FXx4ZvN+9+j8T7kzp7D6MCwBN+0au16rVY2kpqYBNVTtF5IvAo4RGAd6mqisLXCyTR7WHHsq0N9+g7Z13ePfU02Ku3fDIcD7/sSY6XYNY/dXbkKptXPXASn54+kz8vmgFsXVDbBfJ4IZanrvieCr97pa6IdFxLf93Cqx9LnrpbxfC9DPAn8LbyFWmiio/R54xgSPPmBA5987iLfzz1tiX8gv3rWLumdEuA3U+lOsGdO+6NoWVqG4SkR8Ai1V1EfB74E8isgrYQSio80ThWthK6AM3B0XN218fDmwKEOCIhF5bHQmqufQDNpOpkgrYAFT1YeDhQpfDFFb1pElMfvkl3p4THdxb/+Z6Xv30Gzy65lEue+ayyPm6Cdfxl5d+wl9eep81P4l2Zy//5v8QHjXz6Qtnsvjy6IzbuC54CF75EyxyzXH54aC4A5Vj+Kth1rlJk0xqHMrA4X2564cvR8698uj7MQFbuDWuurbk3ra9Qry6SVW/6zpuBT6eo5t7llVbzcCU0/5j15We3dcr7Q98m6b28cCEmPNt++O3Zr+895NU+/bjlw4mfW8U1bKXX049j4EbJnLqJ8/vln7h1f+J5umMZ9vw9k727WyLnL/32iVsWrWbM756MCN3/RUaL4SKqm55text573Xmph+THT8nnZ00LkxNFh17+OPh052drJ2wQW0vvhiav8IwB9+7tEgNicga08UsGWRdXtVP3Y0TOGtyedExkqbxGzkuClZ/n79mPpG7IouHZs38+FxH+a8ad27TbsaseRfkeMbz7ssSUqXQz8N33wvrXJS2Qf83SvrrgaNrOPim+bFnNu+ITr4I/zhUF3r0ehyUzaCAXtNhD3/ZID7HpvQc0LHf/afw3N7L+SZPRfz1K6LaRbh1rZn2fPOLO64+rWkz33936HA6u8/f5XH/y9aF21aFfoS9/dfLINHvgUv3Bj3+Y/eupKn/vwmu7ZEZ4fv+PMdkeOtP7s2ctycRrDmpXCj3qIj4ocL2bawLZ31ZQvWUmQBmylpIoJUR7sIV807DoBvzYm/CcaGXS1xz9dW9ol7Pn7i1FsgQpRUO0/8fh/HfHxS5PFdV0db3FqdFrZs13gy5Sdo/VIRrVqXxXPrCXpYloi2ONMrCbWwAXR2RO8a3Ls3FyXInC/UhrYhwYRge+nljwVspuRNWfpq3PPPn/u861GoQrz8r92/Mf/Pp/34JcGI2kTOdq0dtL8pcTpw4rXUa7VD5rsmG7r6G9qtS9QkYoO3PZPP0YDR/23FOxA/F8Up1JjLUmcBmyl5iWZM1lfVR45rRtwNwPOru6+i8M4I8KW7rtwM1xIUm5f3kDj1FrZkph8bGucy6fDua+2Z3s0+AKOklBYbceouzUmznkecFrZE/6rWwpY/FrCZsjD8xz9Oer2y/9KE14KSQcDmDhJf+k3ytBksNPzh/5rZ7VzDAaEtU+oabJao6aqEgpScy+7fIp8BSLgaKeYdh3pqYctqWQ+TFgvYTFmoP2F+5k8WSb9L1O3tR5JfV027X2HiYdFBuOHKPFKp2zda00VAep7U0lusaj024+duaD+IH/abxMUv/DLl5yz62XNJrz+x60vQsit6YuXf4ZFvA9C0Ljqp6Pl7V/H2fzbTlsJuBq9P+X/saOi+00MibVX9WHrwpXRUpLZftJs4LWyJqp1AnAudWsWDO7/Drs4RvN4cWze/dvClPHd0atvMmVgWsJmy4KuPdn+6v60+dvZjcdMH9sUuvZ12C1tasvtG2d4SuxyBLZprutpeNannRCYl7U1np5V+3er2pNffbD0elv4leuKv58OLsftlahBe/ef7PPb719n7WPw6y23z8LksnfXllMv4/ugT2DFwOpuGHZnyc8KGTg/VlUFf/HrnH43RuvN/mnYAsL7tINa2NfLc3gt4ak/MVt/s6Tc+7TKYEAvYTFlwBzGtK6PT64fG2Vu1aV8bwd2xa6flNGDLoIWt69NDv51vuhavGVNSeurxLOYuUV9ldIDdly/vfr3V1bj7ib22B1UuWcBmyk7LsuhM0NjWqFCluHl3K6vmxy6Sm1GX6DFf736uvRmu6g//utZ1MrNJB8Mn9g89O7ztUPHW6abg7MVhkvHmW569ygrLAjZTdnb//f4EV0Irf2/f370Lw+fL4K1w6Ge6n3vPWYz3yR9Gz2XYwjbliNCG8YHO8Bi20HnrEjUmdzQnYUny96zmbS/YLCdkeFQKkxkL2EzZCezcGfe8v8/7ANz2XPedCjJqYRsYZyzG41fFSahpzxIFWP9m6O/YvtHpZrBJByYBW9bDO5KTTUd7CNhy/L+vUEtvqK354SlbgdOUD58PgkE61q2Lf93ZxHh/W/c9BT0bw7btje7nNEgmUdbqV0Kb069Z1sTYGYMi326thc10pWIBm1cmNx3ueZ5NrcMYsn01DDwwci7g2t0g3hi2fx95NX33b2RPv3EM2PU2U9+6g2ePuZapb/4pkubJeaHJC75AG0OaXmPGG38A4MU536WqfTeHLv1lJA1Ea6FNQ4/gjWmhHoIZr9/G0K1LUv5b/n3kDyP7zR7/9KXxEzmvx/fbD0s5X9Mza2EzZaPuuOOSJ5DQXpyL10Zb4JaM7wtAR7AjZ+XKtEu03+DQdllvvrg5lI1Tv1u8ZroKWgtbUXu/bTasfR6C0Q3Z29yzv+P872urGcCOQTPorOzLtiGzaa0ZBMD6kfO6pQ36q9kydE7kcXPtUHY1TE5YnvWjonlsGNHzMii3bdoSKWY4WAv75G6baJAvFrCZsjHocxcmvV4zbFG3c3cdMwCACsllY3Nmkw52bwvte9rZFq7k7UPZJGKvjeKnJPr/VMyzRFEY29HpHHavxz7QEn9/ZuM9C9hM2Qg2Nye97qva0e3c+r5HANAnnc3f42nunndEhi1sh31kbLdsACTBekjGmCLWJShzB2lpxWvZNLHHuVG8IKzbLe0LQVGwgM2UjarRo5Ne98dpRQvXfVkPNG7bG/pdOzh6LlI5ZtbC1qeuy+r1VmeahOzFUfw0cWRWIv/74hWzRIpeFixgM2WjcsSIuOcXzFgAQECjY0b6zJ7NxgkzCVc3ns0Mc7fUPXONu1ks7ay6PsUWzjWJ2EuiuLUE+/OPfw5i0fUvs7NzBCuaPwS7N0aub3z51cjx+hHHsn3A1IR5Nfc5IOG1gK+SluroGDP3MYD6Es+Gb+4zJGFrW/hs10mfitCpfvq1hL6o7u4cRkD99nrMEQvYTNmQivjj0I4c3n07Fg0EUJ8/2sKWbRQUcCYt7HbNUF15n3vxtLSznH50/ADUZomarkSDPScyBfNa82m8u7Yv61a385emm3hmzxf404/filxf/GL0Pf325HN47ZAvJcwr6E+8b+xrB13CC3Oja0C6jwFWTTgr7vNaagbx4hFX8e74U7td89cEqXXWiTthf+ywk/fGncyW5vP41NL/YdD+kfy56Wae25t8LLHJnAVspuxNHRj6tnrxQV+NngwECHq5HdWKvzk3+2j0XGcr0Q6D9IOsiqrYb8OaeVam7FnHVKkJkDjwytSuAYlnhibTMSK0Kfyuhokx58cc10RlnyC1qjy9dj1Xbo9d47J94ih2ts8E4KbQMpdsaDsoozIUuxeqc7iSQIosYDNlL+i0Pry7J7THqN8naDCIunY3yLpLdO2/Q7/ffDB6LtCeVQtbN5EuUYvYjDEeEaFvTfe1KQEq+kSXIRkUDHZbuNX9nbemzL80bPMn//v25mEtRAvYTNnb0bqDA3YqH/n2w9R2tDJ+cF8IBFDxIV69yQaM7X5uzwayaWHrqphn/pvCsp0OTFayqp7sC2S+WMBmyt6o+lHc+JsAI3fAL5/+Bau27kODAYKulqqsW61e+WP881luJyUCdQOqY7Oy+tF0YwGb8V7mdU35vR572mM2H9WyBWym7Lm3nRq1v4nTZ42AQKhL1PM32cyPdTmR+SxRCAVp+3a2xeRlXaLG9D7vjTvZk3wUYW/9mMjj3f0n8MyQ/44cJ7Kt40Ae2/XVbud3BkZ1O/fyvnOyL6jpxvYSNWWv68bunUFFAwGCPj+eL+vRNZ/I7D0PukS9y8qUmea6FsQmipa1psGHeJLPhhHHpJy2ok/0RXX39utSft7WjswmPxSzzRXJW9j+UdvOx/dX57QM1sJmyl7XgO2hZZvQQCcqvkjwk3Gr1QlXhX5PPyP0WwMw0PUt1foxTR60VQd6TmQM8Of59T2mGXnUDqadsxFfD0GK28XtXwVglcZfjqiYPVPTwc8akm+xtb+H8c5rKoP8rKGFJl/uvjlZwGbKiq9fv27n/F0Wi6wZeQeb926kVfaSdQvbgc6G85NODP0OdkLMOkleTjpwympbU5kuPJs8Y0yGyv0VWAx/nwVspqzUn3ACFcOHJ01T2W85viA00xQJozIO2Da+Evr90DdCv4NBcAeIHrawZTl/wRhjcqeEK6ZUil4MIw4sYDNlRfx+6Iy/ppCbTyEgwewrmWHOuJIpzoBgDXSZYODhare2cK5JqBi+/5verJRfgaVSpVrAZspK26pVdG7b1mM6XxD2sin6Rs30HdvgzLYae1TodzCQoIUtu7daMBCMTCu3WaKmu1L+uDT5NGfdKUmvt1fWdTu3ZF/8La02tB8cOT6oLTSHcUCwTOunIvizLGAzZaXl1Vd7TFPXrPgUgj7IegxbOBALT+Fs2QGd7dHr4fNZBlk7NzdbC5tJrMom/BtvvDnz09SPbI059+K+T/f4vEmdoS+qOVgsKWWLqzpZVdF9Ak5LkjGe7ShLqrv3yrzo2orqeef4j3Wt/KeAW1TZu9z0OpfdG8AXDAVs4daqjAO28PZWQaeS2LCkSwJvoqxgUKOTDrLKyZSjVF4Txz99KQBPzrspt4XxmL+zhUBFn0IXo9eoHdJOl4n1JWO3P8iS6iAT98b+ATf2b+XyXd1fQ69VdfLP2vgB2MaKILRBG8q/+4QCui0VypaKTp52HsfLM5eshc30OtPXhcawhVrus+xKCtdsmmBZBY8mHWjQVU6L2EwXYl2ixjOl/Vryqnosxn8FC9hMWel/+mlxz2/tH/s43MKW9e4B4fFqwZ7Wwcos/1knhsbI1dRVumaJWsRmYlnAZoy3imFWaFcWsJmysv/5FwAItsQugjhoX2yQE2lhCy+cm/EYtty2sNUPrAEgGFAbw2YSsmXYjEn/fZBK8mKqbi1gM2UlPEM0sHNnzHl/IPat6QuGu0RDsm9hS/R9LLso66VF7wLwzn+2RE8WUw1iioJU7il0EUyZeL/9MG7afB+7O4cCcNPm+wpcotS1+iCQRtSWbPeCNufaLl96UeCeNNOnwwI20yv5gIDPg87Fri1sA8bDQR+PXt+31UmX2Z0CHaFAsLM9QHGOqjDFoH7Ys4UugudmrvgdhywrrQkS5WRD+8xCFyFly6o6eb2yk5WVAfZ1iWr+WNca9znbfEFerImdHfoHV9otfuXevm38ta4trbI8WNvO/bWxz3knzszVTNgsUVOefD1/F/H76sh+0oETiEV2ZtfYNdeam2LTpZt9ZBKq2k4HRUpEBgILgXHAGuATqrozTroAsNx5+L6qxh9wmQFfmfWJhme0GtOTZ2o6eLlL4PWf6g4Ob6vk6ZoOtiTYD/X1qgBdl4zbWqEE0cjSJKsr0x/J1uaDt6uCtDYrNU4+nR69P62FzZSVwZdcAoBUdP8u0jQw9lyb7EMky0H8IqGoKjzpQIPEhFSRyQiZBmwSzdZ9T1NMvgU8oaqTgCecx/G0qOos58ezYK382evd5F+2IZb7VevV1ykL2ExZqTjgAAA0zpiyfntiv4UFnVd/1jsHiD/aJarEtrAFO8M3yShrnxOwhdZhc7LKsJgmZ04H/uAc/wE4I98FsBjemKhsAqTwW8nLNmsL2IyJxxfuouz+Fqnqspi1Zzuo+PzRlrTd70ObawB4ILzrQWY3q6oJtQraB3JRG6qqm5zjzcDQBOlqRGSxiLwoImckykxELnLSLd6WwjZrABLn9W5MNrZ0TGZ/YEChi5F3Xi2blIsq2wI2U1YksvNAz2MPwgvnZv0GFX9sn+WbD0aPwwFbhhHXUR+bCMCBs4dgTWyFIyKPi8iKOD+nu9NpaDuKRNHTWFVtBD4F/EJEJsRLpKq3qGqjqjYOGTIkpfJV9D885b+l/65VKactCvZtpSBeb/kQt2+7rdDF6NE2f/e6frNfu11r7jKOLN7zwDVBIMuX3eaKaP7b/N58obJJB6a8ON2RGuj+Znx1cgWz3442swWchXOzDtjcLWxdBTpiypWu6r7hFrbo0qi2+Xv+qeoJia6JyBYRGa6qm0RkOLA1QR4bnN/visjTwGxgtRflax3/DXjuvZTSzlr2K1ZNOIsNIz/oxa3z5pDXfsVrh3yp0MUwBVJR5aOzPVqv75EgD/TtCG0h1cVbVQF+529llytQ+l2/VuqCwj6fUhuUmGtuD/Rtp0+a8dWN/Vq6nVtbEWSMs7/qu5XezBK1FjZTXsItbBr7Ju6oqWTrgNhAJ7KXaLbxj/gSL5y76bVwosyydoIzVbVVPYrXIuB85/h84P6uCURkgIhUO8eDgaOB170qQDpBvD/YSf/dqQV3xaT/ntIrs/FO/yGx+3bu8xE3WAvrGpC1C+zwK+3S/ZpbQOi2NEhPWnpIX7Zj2ETkKhHZICJLnZ+TXdeuFJFVIvKWiHy4kOU0xUn88btERZXOLm8bT/YSBSdgS1BxVPV10mQasIV+R/YStca1YvQT4EQReQc4wXmMiDSKyK1OmmnAYhF5DXgK+Imqehawpc1eR6bk2Iu2WLtEr1fVa90nRGQ6cA4wAxgBPC4ik1UTNW2YXincJdo1YAtqtxWwK30DCe1O5XGX6JSTofGzcMfZMHhyuAQZZb12+XYAVj63kYYDarMrp8kJVd0OzI9zfjHwOef4eeCgPBctIbUPP2NKTtG1sCVxOnCXqrap6nvAKmBOgctkik2CWaL+jgASjD0XQFHxatKBK2B762HwOd+FImPYMrvHyCmhWVqjpw5E1T5mTXw2rNGUvRJ7jfemddi+KCLLROQ2EQnPKx4JrHOlWe+c6yaTafGmPMSbJdqxcSMAp77cpUvUF3pTZT2I3+cP3c8dJIYDtmBHuGQZZd2nvjL6W7FPZhPXYWOTL78wdMt/Yh437M7NTFF/Z/xtgLIx/r3QrGtfsINBTct7SG3K1ewTx8Q8XlLdmSBl/rxa1Ul7CuHYPo/2Fy1IwNbDFPmbgQnALGATcF26+WcyLd6UiThdooG9++ImDXq1nU+4hS1ewBZZ1iOzt1pkpwNnzoGFayaeUQ3Ju8tnvHF7zOM+rTtysv3TgJ1vep7nmPVPcvzTl+LTIJNW/83z/E3xGX3QoG7nphwxLHL8s4YW3qyKPxrq7s/PzVm5unq8toNfNvT8JaXDo4q7IGPYkk2RdxOR3wHhRa02AKNdl0c554yJSrJwbledPkW86BL1hbemcu553HfAH2oZy7ZLNDJL1CYdmGTsdWHKSRavZy3jRaSLrkvUWcco7ExghXO8CDhHRKpFZDwwCXg53+Uzxa2nhXObjjwmchze6cCzrakiM0Ul1E0K0YAt471EQ7+TLsdqTG9Rxh/GxsX+N8dVjLNErxGRWYT+l60BPg+gqitF5G5Caxd1ApfaDFHTja97l6ivTw0A7w+B/lujjbJBj8YV4HN2Ogh/mLTv6z6GzZMWNrWWFBOXDW00JqScY72iC9hU9dNJrv0I+FEei2NKTfiTyzUjtHnJKwCM2QZsiy6+6d0YNqdLdOea0ON//wIOOTd0HGlhy4wvMoYttPm7V/vcGVOKpKw/jo1Jrui6RI3JhsTZ6cDXp0/ctEqo1cqzvUTb90bPdR3DlnHeoV8atFkHxnsHrP99wmsPTLuJgdtXxr0mwdjX9bQ3/sio9U/nvKmvpnVHTvNPpmHnWwW7d28z75zJBFA2Ovt9fvK/Qyt4jfrAMO6vbUv4vMaxA3qcMZ0vr1R5P4vVAjZTXuKMYauePAmAWz8U+3JXURD1YFkPp4Ut6SzRzO7hc80SBYvXTGIvV4eCqLHVi3tM+5OvdfKJKyu49vQ1CdNsaHibURueiTwe/94DHP/0pRz/9KUc9ur1MWmHb3mJyav+mlnB01CoFrbhG//Noa/dUJB79zaX/uZ4+g3qw88bWrmjvo2fNbQweFQdAKOOHs7bVYm3o7rsw1Oo9MfW8zefd2iP9zxwcN/sCh1HWw6iKwvYTHmJLOvhqtid4G1vl4Y2ddZ792zhXPf2VOEWtmCW37LCPbxBtc4gk1DxBPLFUxJjUhX+YlzsLGAzZUUiy3pEg6e21e8CUNllioo6Y9g825rKvT2VL9wl2h4uWVa32PLubpxBbMbElUlAL2ovKGNKJF6zgM2UmThdopuv/iEAU9cruw+aETmvJG5aT0u4ha2PM3ZiZCP4u2xNlaHm3aGA7/V/bwp9INt0QOOBVOfbuLsgxZbUMAWWSfWXynN8JVKvWsBmykvcLtHQcVBg/6QJkdMqIOJBq1V4WY+KqtDjOf8VbWF75Q9OuTK8SZcN6UqjWjH55vcJrc6Lo0r295h+SCDUGhyU5F9a3LvXVgSiK7r7gvG/iFS6J97kSGX7npzfoyubnVoc+lT6k16P952itqrnxTD6VCXPt1hYwGbKSrwu0XBrmwLqi77kwy1s2Y9h88XuJSq+6Bi2LNXUdsnHIjYTR21VBYurO3msTzsz+jwWOX/Qit9yxMs/6Jb+e007+P5R36elai/Lhvw5Yb5/Ot71EdHybOSwbv/GyPGE1X+PHE9edQ8Dt69k7ov/kzDP9fXx9wMdve4JRmx8Lm553Q5fck3S64mMf+8hjnrhvwGY9M5fmfz2XRnlYwpn2vB+aaX/8ZkHceykwT2mmz26OGaW9sQCNlNewgvnBqLjyQI7dwKhLlH3N+XIGLasdzrwxU46EF+0hS1LfRuqATj8o+PLe0VIk7WgwNLqADImtATCoNa3GdK0jL7NW7qlrVPlrElnAfDChCUJ89wwOPreEOcLTrvTGHHAltBs1Oq2nZE0/mA7s5b/mj5Jlt94ZNof4p6ftPpepr59Z9zyutW47peOIdtepaZtJ8c/fSmjNzzNqI3PMu3NP2WUl0lfex6ija5Tsz51xJiU6vdSGcNWdAvnGpOV8F5Owe7RzdhtwD33Rx6H12HLms8PnW2ugE2iY+miBcso63BdU1Hpoy2mg8qY3EttbnK67yF7FZviYrNEjSkACa/Bo93H5vztqNg3pWezRLsu6yGut9XoI7LMO/RLNbxwbmlULKY8uN8bkQmlXX+XsFRDTfuqlL28/Atm+P27VKpVC9hMeYmzl2jFiOEAvDusS8CGs3CuV8t6dA3YKvtClbMgY7Z7iVq8ZlKW2xdJ18/E0n5JlnbpjTdslqgxhRCnS7Rz4yYAVo3o3sImeDGGzWlhC6/DFi6DSNyWvvTydn7bkgqmB8P71wBQW9UMwAGS2lZKybo9411bNyT0ouy3530Aalq2p1VO7WFmaq74NNDtXJ+WbSk91z3JwmSmuU/uww33q3VQ36qUn1ca4ZqNYTNlJu4sUZe9ff3U7w9V3DUdoZmiWb9ZfX6nCSzcwhaeIu4O2LJvYbNJByaZR7/2Afa2dtLwjzs4d/CX6O/bSEtVJZv7jOTZj1/A3/59Cz+7LTZoefiMB6nbu4W3bvwGG0adzOZh8bvwN9WvZpLzEn5pegUTN3Uwev0TDNzxOnXNm+I+p3HJNeypH0PT7JPZsb8fB767iJfHv0rAl/0ei4cv/l9Eg7x8+HeSpjvqhf/m+blXAyBxdh0ZsHtVSvcbujU0MWPolv+wZejhaZbWALQPr4FVzSmnf/abxyFCt62mUvH8t46nb3Xi8Obl78xnzo+eiDzOVQPbr/u1MDDg48lvfNCT/KyFzZSXOF2iYSpEgjWA419T8GLxXJH4XaLte2Hv5uyydn6HAjZbiM0k1q+mkpENfUCEgRXr8fuC1A1vY+LkGj515OdZO7T7i2d0/7EMGDWH6rYmhjS9ljDvDn/3DbcFYoK17fVdyrN3LaM2Psuw8UMA8AXbCWhTZn9cF/X71lO3fyPVPWwGX9O2M5Im0Vpq8QK5xOxbU76MHljLqAG1DO1Xk/Jzwh0RIxr60L9P4pn6B9TH5pn1sJgE9vtgXWUwaVnSYQGbKS9JZomKwrujo83kQQEk6OGkA9c6bGFNbzvnsls4VzXUOZWrisWYVCUaQpBo94Tw6cK9cu09UwyK+f9CroewZT3sxmEBmykrybpEfUF45KjayOOgz+kSzfbNlGjSgQdEJFTThbtEi7nWM0XKmxdN1m1LBR+Hmej+9qYqF6ktQ1O6LGAz5SXOXqJhFUEYWDs08ji0TIEXXaJdl/WI9wGQ+YeCiISW9UiUtTHGmIzlulr1Kn8L2Ex5ibeXqGNXXxhYOSnyOCjQKhs9WtYj2L2FbeAEGDw5u7xxJpuqjZ4xKZr1qW6nKiT5/LK/z439KJh6VGgpnB21odmRbx7wEv9oDKXZ2S/+x8aTh8R/H407MDQMYcCut1l6oLcfjWPWPwmExqEN3PF6gjShweWVHfH3WB37/iMp329QgnvEk+oM1N6ieUDu5zhOHlrfcyKXcYNCPS7HTT0gpfR9q/xccPS4dIvl2RdtC9hMWUnWJdpWJXQ0Rzew7vRDFYM8HMPWdVkPn2upj+xa2EIRm4VsJgVTT4Grdkcfi+D3+Vl+fvw9PAG+f8tKRv3qVwCMP2Qw8z8zDYD91bv5zdyv8O6gpTwx28e0N9+guSb+a/nZGfE/ToaPrOLSYWfy2S9v5b3h8Z+7v3J33PM9Gb3+KY5/+lKO+9dXmLXsJur2rU+Yxp9gw/oD1zyc8v3q965LOe0BWxNv+dUbtdflNmCbMaJfShMU6mtC5Vjzk1N4+vLjWPOTU5g9JvFeot85eVrkeOUPTuJ7p86Im27NT05hzU9OiTy+8VOzI8cVGcx0jccCNlNeknSJAhz07+hMuEcP9QEezLz0+UIBYjhI9Pmjv+Os/ZSu8HJuGn5gjDElplhqrnTL4UWVW+HR1le2DpspL77EXaIA+zqj3RRtleBJR2O4Ja1rl+i2N92JMs/fJ5GtqYql0jO9WRG/CnPeCm2t3KXOqxmbPd7H9T7xKmCzFjZTViJvxgQtbC9/cGrkWCW8cG4O9xL1gAaVYCC8ZIinWZteweMXTYLs1F6bpgx5EeD5LWAzJo5wl2iCnQ5kwMjIcdCZJVrMy3oABDqCLHtqvY1hM5kpdCBl3fgmD4q5erR12IyJJ8lOBwANfaOjAMItAt60sAXjL5wbSZP9G9aWYTNpOfay0O/aQd2vzfl8t1Ojpw9k5JQGjjprYtJsvzj7iwDs7TIjb2sD1H0wtS14Xhn5Tya/fRezlv4SgEen/D6l5/Vkyjt3RY5HrX865ecdsHVx3PNj1z7C4KZlVHbs63bN39nS7Zx754VRG55J+f693aEnjU37OV+eP6nnRI5fnRudAPDbTx+W0nOu/EioN+bsw0Z1u/bjMw/q8fnzpoR2+Jg1uiGl+6XCAjZTXiJdovG/bvldO4R4tg5btxY2pwzjPwAVqW+r0iPFWitM6sbMDf2O7G3rcvI13U5VVvs542uH0jC0tnt6l8kDQkvV9BszIeZ80CdUjh4deVwzsN25f/fX7MtjHmLUxmcZuOttfjP3K2ytX5v0nqmq2xfdpL3f3jUpP2/Q9vjLdfTf/S4Hr/ht3G2tZr92Q8zj45++lEOW3xx5XN2+J+X792aHHD+agcP7AjDp8KE9pI76ahoB26mHjIjM4jzywDhfYLq46VOH8vkPTmDNT06Ju63Up44Y02MefasrWPOTU/j7pUenXM6eWMBmyopEWtjiz84MuGZtBgXUk62pfKExbKtDa0Kxb2v4Qtabv7tZvGaKnrtfqoi7qDIW0+8W781Yjn+0RxLVXZnu2tfleb3hX94CNlNe/E5rQoIWtk66BnLqzRg2DcJLvwk93vhq6Lf4Eo6lM6bkxY1X4r3v8vctQzN+LyfYGL5XhAEFVoRfQot1i6uEy3qIyLIUnr9NVed7WB5jstLTLFF3wBYU2Mdq+jMybtrUb+paIBfAVxEuTA/bVaWpmEfVFjmrz3IgwZeiolHkxTMmXcnWYfMDJye5LsAib4tjTJZ6mCUaCHZGjj1bhkC6LJAbmXQg3gZZNusgG72wPstNxJK8RTp6T3916D0oVd3HAOWDpDE+1R9oj3ve12V3BF8PC2H7EgzFMCH+Ch+Bztj/L15VaQNqvXudVVfEGffZRZ9KPy0d+f3/naxL9POqujbJzxrgkjyV05jU9LBwbv9N0YHA3s0Sdd5GM84M/R51uHNeiH6AeTVL1CK2DOWsPhORj4vIShEJikhjknQnichbIrJKRL6V6R+SQQEjh2P+7zbG/uWOjLK565S7kl7/88l/Di3w7BgxdxdDv/s/1EyZAufGPveOk9Mrw38mCS9Pjn3tL5mQ/L2QaO/QeIY0vdbt3Oh1j9Ow652YczWuWaAa571Y27KVA7a+wswVt6R8795k7pnRiSr1g5wJWRn2PnT94uCeCZqt+SnsLfqPrxwbOb7783M9u3cyCQM2VX0u3nkRGS0ilydLY0yh9NwlGj0fDAds2XZXhgO2iSeEfg8IT1H3OLiyFraM5bg+WwGcBfwrUQIR8QM3AR8BpgPnisj0DO+Xsb5z51J76KFpP6+uso4Zg+PvoRh2yJBDYh5XVAcZ+ClnI/opH4m5dvCQg5PmtaV/6HeHs83bX+b5uOfo2I+rn34iXitIhh/+cVojR69/Kk5uPbdaznz99xwQJwDs7QShoir6/3DqkcOc894YVFftST4HjeyPL4WFbscN7hs5njN+oCf37klKkw5EZIiIXCIizwJPA6nPvTUmn+J0iVaNHUvfk08CICDR8961sIWDRKe71b35e9c0GRo4oq8X2Ri8r89U9Q1VfauHZHOAVar6rqq2A3cBp2dz38KLE7x4NQSgy+tcbDxaWSj3ScS5lmzSQT2hb42fAiYD9wLjVbX7KnLGFIs4XaKqijjf1N0tbN6NYQsHbOHxDBJ7PkuDRval/5Daop25VAqKoD4bCaxzPV4PHBEvoYhcBFwEMGZMz+s95V34dR3v5ejxSzRet2P+2LejnPJutEivkWzSwVbgZeC/gedUVUXkzPwUy5jMxO0SDUa3n+p0t7B1fU7md3Uy7Lo1lXRPk6Hw5u8mY1nVZyLyODAszqXvqOr9HpURAFW9BbgFoLGx0f6vm7JmvQapS9YleiVQDfwauFJEJiRJa0xxiDdLVDWyoG5nly5RH5XedYl2XcLDs5oolI/ayrnZyKo+U9UTVHVmnJ9Ug7UNwGjX41HOudypd+LL4YckT5emisGDAZCp3bewqp4yOXFxKuu7nWupih63O80HrZWwbVjovdRSER2XtK9Pz2UT1yzOqhzvNODerqrvvtz+rywbrj7RhgNC/0MbhvZNlLog0q1i+1b1PKPUK8kmHfxCVY8kOs7i78AIEblCRBK/K40ppDh7iXZs2MD+558HYO3I6NTvoICf5NvwpCTcohbuEo07hi2b/F3blGaRTW9WBPXZf4BJIjJeRKqAc8j1MiLDDoKLnoF5387o6Xd/9G5umn9Tt/PVkyYx7m/3IBd/mku/EPthNeDccxn+ox8x5exN3Z738FkPxzye8Ng/6XPf7dx58r20bv4oC077DDcuGMKll/gZdvgutp/YwK7qukj6pv7C5Z+Nvd9nvxL72O9ahqNu/0a+f66Pz305Nx+ota1NkePDXv15Tu5RTqTLKkcHzj6As69oZOrceA3XxUOTjMv81+XH8ewVx+etLD1OOnAGyf5YVQ8CGoF+wMM9PM2Ywkiwl2hgWxM+8dFO13XYgt51iXaddOARa1TzTi7qMxE5U0TWA3OBh0TkUef8CBF52LlvJ/BF4FHgDeBuVV2ZzX1TMmIW+JONfEls2qBpzDpgVtxrfWbMQCr8bGuIfXGKCA0fOwtfRfcPuYaahpjHVaNHM23CEYzrN56OncfQXjGbzsOmsbdWkAoINkQDrfBd1g6Nvd++2uRvjm39hT19vXsDJcqpItDq2T16k6Hj+3lQ/xbOmEG1DOxb1XNCj6T0ThaRYYRmOSnwK1X9Tk5LZUyGRCR2hwEXn/jo1FwsnOsEaB0t4RPhwrgTZZx907p97N/VxqgpA6yJzQNe12eqeh9wX5zzG3Et1quqD2NfdhOKt0tnIQfwFXbCgykWxbTBTI9NASLyOUKDdc8CzgZeFJHP5rpgxmTM54vpEo2cxkdHl50OOtjr3Ri2p38c+t3ZJXDzQMveDmfzd/sQyYbVZ+lJ9t7watZy4j3Bi+iT0vR6xVD1ptLCdjkwW1W3A4jIIOB54LZcFsyYjPl8cfc59Pv8dLrGuERnlXvUJRoWcO7h9TvcPr+8YPVZGmwpGeOlYmqtKkWpDLbZDux1Pd7rnDOmKEmCLlFBYvYSjfZcerTTQdfHHi2cO+zA/oyaOiDbbEyI1WcZiPelJjwY+/lpcV6UvgoYkf6OCh898KMAHNge+tLzyLgjAdgRnXtApx/e7WGp4yHbXgVgby0EOxrSLgdAZWfira1Gr3sipTyqW3dmdO9y04Zy0ozYyQW+iujr5oCxoRnEEw/teUsogBOmpZbOC+E693PHjM/bPRNJpYVtFfCSiNxP6Dv+6cAyEfk6gKra9BhTXFxdou4ZPiJCp2vav2df9rpGUZHH3kRX4gt9M7Vvp56w+sxjD10wlQtPuzf25HfTj4FV4dQJp3LX71cyvOp/WY1y/4RjuX/CsdT3iW69+p0fTeK93e8lzOf4py+NHLdUC/vf+BZwWdrlqQi09Zh/T45+8b/ZfMBhvD69d/e6f/038wFYtiW0dvRBHxyJ3x/9QjtgWF8u/U1qsy3X/OQU7wsI3H/p0Zx+07+7nReRnN0zXam0sK0mNAU+/HFxP/AeUO/8pC3ZZskicqWzOfJbIvJh1/nCbJxsSo+7SzQQDdAEobky6Hoc/W9WErawebdwbihas4jNA57XZ71BLrtGu37fUe/emaZYFWFXQSnUrj22sKnq93Nw3/Bmyb91n3Q2Qz4HmAGMAB53rZF0E3AioS1d/iMii1T19RyUzZQ4EYmuiRZ0B2hCIBgN4Dr8AOrdsh5hFTXxz2eau0g0XivCiq6U5Kg+MzmQaNJBsnWxTHErhf91xVzDJmxhE5GrenpyKmniSbJZ8unAXarapqrvEeq+mENZbpxscsbvj+wlGlO5CwRcXaLh4MezWaIHfSL0u09ovBk7VndPkwWL1zKXy/qsN0j2Hsl+0k4sW06jF7D/xRlJ1sL2ORFJtreHEGoNu8rD8owEXnQ9Xu+cgxQ3ToYS2DzZ5FSohc1pWXOPYUMIaICXpgj1zR5+1QtHUcvvjtwJgI2vepZ9MM6sV5OWQtRnxlhsYjyTLGD7HT2P6fhdogv53Cy5K9s8uZfz+aKzRN1dok5gdd1ZsVvVeL6sR8ZpeniqvZKzkVV91lsl634c3388MwfN5PLDL8/qHjUVfo4YP5DPf/BAAD5z9lms/Puf+d/OT3HCtKEM7VfNIifUPmvSWbyy5ZWU827ffiwA/xh7BB9Z+1LyxBoE8TFq/dM534e0N5l7ZnTb3kmNQ1n57EYOOX50kmeUtg9NH8qHZ+Rmu62EAVu2Yz1U9YQMnpZsg+T8bpxsSpd7lmgwtoUtHs+W9fBVhLan8nhrqvBmoqpqXaIZsrFr3qvyV3HnR+/MOh+fT1j4+bmRxx85dCLj7v5fAB44PzQn7ek7+7GnfQ9fP+zr/L+H/1/CvH5z0OlcvDzaHtCxK/T8xUOn9hiwHf/MlzL+G0xih354bOS4tl8Vn/pews6xgvJqbOQtn2nsOVGGvP5kydYi4BwRqRaR8cAkQquS53/jZFO6RKKzRJ2WtoaPfzxxwObVGLaDzwEksgF93DQZZh+tSyxiM/ljO2uYXqeIX/MFCdgSbZbsbIZ8N/A68AhwqaoGCrZxsilNQjTCcVraqidNzN2HT9M7od/te3P4ZtfQn1S8dYkxOWczRE1vlspeokenci4dqnqfqo5S1WpVHaqqH3Zd+5GqTlDVKar6D9f5h1V1snPtR9nc35S3UIuZM0s0PIbN825KlxduDP1+/f64OyyES5UpdwtbEX/5Kwm5qM/KWbEESCXd0lck/4am9KXyKfarFM8ZUxxEoh804d8+yd0YtkTGf8CbfEr5w6r4WH2WiSJ6CdZXxZ870uqvpN1f2eVsqOAdvlQ29TG9WaWz80J9dfG+VhKWTETmAkcBQ8Lbtjj6Af74zzKmCPh83bpExedLGJh5vY5UxOAp8N6/nJtk0cJGeKMD+6aeKavPslREL73r513PI2se4drF18ac/+K8r7G57yCmNr/Mh95+H4Bg+xAG11WxeOiUbvn85Iiz+NZL93Y7b7xz3Ken5jT/B754DKfe+By/Ond21nnNGNGPb588lTNnj/KgZLmRrIWtCqgjFNTVu372AGfnvmjGZMg16SDYGtoPMLBvX+7uN+ao+OffeMCb/J2ILbRwbhE1dZQWq88yUIyvt6F9h3L+jPO7nd9QfwABn59fzjrHdVao9PvQOEMiKiZm/yGfjsqOHNZBRWr60SNymv9Bo/qz5iencOoh2d9HRLjoAxMYUl/tQclyI9myHs8Az4jI7aq6No9lMiY7rkkHu+//OwDbfnkD8t0hCZJn+aHU+Fl4//nu5/dtzi7fiOL70Cw1Vp9lpljGsIV5u6dpfv62RFtsGZOuVDprq0XkFmCcO72qHp+rQhmTDSE6Sl87O0Mng8EcrsOW24VzI5MOrN73gtVnmSjwd4ZcDFsQez+ZEpNKwPZX4DfArUCgh7TGFJ5EZ4lG1mPz+RJ+6ORsDJtXnOKp2vwDD1h9VuaKdi/SImutNKUnlYCtU1VvznlJjPGKe5ZoeNKBJJ4l6sX94ppyMrz1cPI0KWUvRdc1VcKsPktDpS806/KgwQd5kt/Q2qFsad7SY7rqCh9tnYmWyEnPQSP7s2l3a7fzE4b0zTrvvvs2sL9uZM8JCY1h60gww9WYVKSyrMcDInKJiAwXkYHhn5yXzJhMiUS7D8ProiWZJepZHDd0ZuzjQRM9yjj85dyCNg9YfZaG2spa7jrlLn4+7+ee5PfXU//K3077W4/pnrvieB77WnrL4tx/affl9P5+6dH84pxZ3c5rXT2XHpf9+/PQpddz+H9+nFLavs2bqWndHn28L3Z3xeGb/p11eRKZUvNUzvI2+ZNKC1t4Oo57h18FDvS+OMZ4wBn01bJyJXseeTR0zufL3dZU4edX1UHd0NhydE2TSe7hHl7tmqfJgNVnaZoxeIZneQ2oGcCAmgE9phtSXx13tl6ySQeThtZ1OzdrdEPctH6/j2p/9u+lys4WKjtT39a63+73aK0ZBMCQpqUxrXO+YGfW5UlkSOW7vNV6XM7yN/nRY8CmquPzURBjPOPMEl3zMddqDd26RF3j3LyiwdgdFTzdXSFUVovXsmP1WWlK60uVppC2KIYYdC2nvblNcqlsTVUrIv/tzKxCRCaJyEdzXzRjMuOeJRo5JxLTJepzrZXq2ebvGoRuQWGXNBllL6hah6gXrD4zYQUZF2oxmclCKk0A/we0E1olHGADcHXOSmRMtqR761n9iSfGJnEHbJ41W3WZxulVvlbJe8nqM1Ok7I1ukkslYJugqtcAHQCq2oy9skwxizOrss+sWTEtaeLqrvRsDFsw0KUb1KMxbNjWVB6y+qyMHDH8iG7nNFDH+kHJn9dw5plUDIm/kLbX6pzJBYOblpHspaY23sH0IJWArV1E+uA0WYjIBKAtp6UyJhs+X7f+w73//GfCLtGsJeoS9bKFTdXWYfOG1WclzP1F7JVPv8JvT/ht5HH4i1cFffjAU68wZdlrCfMZ8uUvUTGg58kPADfM+liGpQ2pbdnKvGe+wvAtL6f8nA5f6i/Ji4eezcVDPx7n/Mc5qPbByOMDq1+MHJ8z6CvM7//LbuezUVNXGfPY57PKymupzBL9HvAIMFpE7gCOBhbkslDGZEWIrL8W4ffHtrDlZL9v7fIF2qvgzZuWOgNYfVaS4g1bCK8R11WFX6iu6tNThinfu81XlXLaRHwangGa2n2Dkvqazv4Eaf3SGbMtlk86IsdVvv34Q43M+MSb2and/km9nHNlgB4CNgn1Gw0AzgKOJPRq+4qqNuWhbMZkRCJ7OUV1vP8+ItGXu7dj2NxbEbhniXoTXMX5c0wGrD4zhZbsbZzfHRrsi18pShqwqWpQRL6pqncDD+WpTMZkqfukg85duxCiY1Z8eDmGzdGtSzTReLY02dZUnrD6zBS1Un9vW+WUc6k0Wj4uIpeJyGhbGdyUhDiTDsTn65IkR2PYPFosNyZ7XGN3rE7MltVnpkhF39zJFgj2hjXZl6JUArZPApcC/wKWOD+Lc1koY7Li3poqLBiM6fr0tEt059rQ7/1NOekSjf49Vsl6wOqzEnTV3KsY128c/av7d7tWc9BBDP/Rj6iu8DF5aB3XfXxWtzTfPGlK5Hht/VCkpible78+ajo7qr3ZA3To1tBLraKzOc7V1OqL8dUvckTdHUnTjKl6BYCDah+hj28XE2pe4NC+98bca3T1Mmp9O7qcz1xNbbTDzucTTlgw3ZN8TVQqY9i+paoL81QeY7IXZ9BX9bRpCDuiSbxcOHeFszdicxP0cc88827GqMVr2bP6rHQdN+Y4jhsTf2ul8X+9O3L8z699MG6aS+ZN5A3n+OL5l7PGl/qI+BevORuuOZs3pk5L+TmJ1LTtBKBP87Zu11J9e5884KcAvLTvvLjXLxl6ZqS6aajYyGcPuACAOv/2mHQ1vr1ccMCFKd61Z+KaFfqFX9s2WLmQ9FWrqkFi99wzpvjFCdjqj5uXu1miH7gs9Lv/mC4tbN5kL5GF2Lxc5Lf3sfrMFDUPJykVglVNuWdj2Ez5cQU4YarK6t2rXUk8nHQQXl4g6Rg2byYdmKxZfWYKKMV6wIIfE0cq67B90vl9qeucAgd6XxxjshdvL1FfdXVoQ6JIGr/7QZY3TLRwrjcLEYX3EjWesPrMFIF4b+hSj9JKvfzFr8eATVXH56MgxnhGpNssq9ojjwwNM3d4uvl7pAmsy9ZUMfuKZpe7RrpEM8/H5KY+E5GPA1cB04A5qhp3EoOIrAH2AgGgU1UbvS6LKWXROiudhXOLRUWVrZSbaz0GbCLymXjnVfWP3hfHGA/EGcPWdeyXeLkMdzjrHC3rEV5WTlVtDFuWclSfrSC0GO9ve0oIHGcL9RafPrNn0/LqqwA8cYhw71E+bro5ftC0uQGG7fLu3sM3/puath2IRu+3vv9bTG46nLl1f+CFfefHfd7HBl7Bjs7R3hUkRSdcMJ2dm/ZTP6iGp+94C4ABw2o56aKZ7N3eyu6mlryXqbdI5VPrcNfPsYS+SZ6WwzIZk504y3pUjh3LrCGzokly0sKWbC/RbJrYvGmpM0AO6jNVfUNV38q+aKZQxt35l8hxhx/mHPrRhGm//IVURhKlrqZtJ+PXPhJzTiVUgfXx7cZHR7ynMazqbabXPpHWvep83WenpmvKEcM48owJzDh2ZOTcp646kroBNQyf2MDUI4dnfQ8TXypdol9yPxaRBuCuXBXImKz54rewxbZO+WKuZSX8/GAghwvneptnb1Xg+kyBf4qIAr9V1VviJRKRi4CLAMaMGZOnohm3rgtv50bX97L7sbrO2gBWE5LJV4X9gI1rM0Wr9bVlcc/HLuvh5XiLBHtHJTpOk6oS6AzaOmy5kVJ9JiKPA8PiXPqOqt6f4r2OUdUNInIA8JiIvKmq/+qayAnkbgFobGy0/+tlL/y/2L6MmeRSGcP2ANFXlA+YDtyd+BnGFKeELWzZVpSJZoluXJpdvo43X9gcyv4Am3SQrUzrM1U9Idt7q+oG5/dWEbkPmEPMVBhTLHK1NVR+N3g35SaVFrZrXcedwFpVXZ+j8hiTMwkDs6zrUPcsUVdmK+7x8ibGGwWpz0SkL+BT1b3O8YeAH+T6vqY4xasNqp1dEAB214TmpdT6d+OTTgJalaeSmWKWMGATkYnAUFV9psv5o0WkWlVXJ3iqMUXJ51pyY1twCRqsQHyd3rawebT2mtvAEX3ZsXE/1ieauVzWZyJyJvArYAjwkIgsVdUPi8gI4FZVPRkYCtzntPJWAH9R1UcSZmryruGTn2TXQmfXMoXxi+7H37dv5PpNX5vIivb3cnb/q89cwsfeDM0IfXXkY/xg3xLGVr9KpbTSobU9Pv+UhqvTv2mfgdCyo+d0pigk+3T5BbAnzvk9zjVjSoo7MBvu+wDi6/QsZ6B7l2hMksyDwlCw1n2InEnLL8hRfaaq96nqKFWtVtWhqvph5/xGJ1hDVd9V1UOcnxmq+qNs7mm8Vz1xYszjmsmTqRwZnQm5dUQftvfL3Rtw24Bo3irK2OpX03r+uJoljKtZkt5NRx2eXnpTUMkCtqGqurzrSefcuJyVyJhccdW1FfR1nfayhc2V14k56PGyiC1TVp+ZlKjkbgxbsbCxdKUpWcDWkORaH4/LYYxn+hx6KFJZ2e28L+bl7uGyHola2Krru6cxhdKQ5JrVZ8aYopcsYFssIv/V9aSIfA5Is93VmPzx9e2L1NR0O5+zXQIStbB5FKQddNwo/JW+UJeoJzn2SlafGeOwtd1KU7JZol8lNEj2PKIVWiNQBZyZ43IZkzkBDXTfViam61M9XNaDBEGaRwGiTwSfX7rfyqTjq1h9ZlJ09Miju5370LgP8caON7LKt8aZCTpk21Lq9q0DoP/u7hMZDm9pjRxPrvkXS5vPyOq+CdkQi5KSMGBT1S3AUSJyHDDTOf2Qqj6Zl5IZkykR6IxOKBhz++3O6RwFbIk2fPcqeHP2Eu26e4NJndVnJmzl9z+c9PrZk89mzMQzup2/cOaF/PKVX2Z17+r23Xzg2W/gD7QiwLHPXUZlZwuruizJ/NvNWyPHR9X/kebgAN5u/WBW9wbA54dg4jFs//WLD/C7r9rSgMUqla2pngKeykNZjPGMr29fAu3t+Pr1o++RRwBdAjP3fqNZN7DltoWNmK1R7RtxNqw+M32rk3/sVfnir3nm1ZCKikC09ayyM7RRerDL4CT3CFwRpcrX7Mm9e6qTqmq83SfVeMv7RaOMKTBBkOpqAIZ85cvR867KStXLwCeVDd8zv59ApHXNejCMMZmzCqSUWcBmyo8IBIOhQ1/0JR47S9S9r6hHy3p0yde7FrZQE5v1iBpjTO9lAZspT+HoJmYDdvd1Idg+0Dnt4aSDHARv4SFsXbM0xpj02Le+UmYBmyk/InEXvnQHZqo+wtFP1mNTEj3dwzFs4T/H4jVjMtPwyU9SN39+zLnauUcycMECAOrnHw8+Hw2f+HjCPNp3zgHg6YO8fSc+clj8j+KdWse1HYnLk7aahi4nQn/HYYd3MHxC/9DxSWMTPn3WiWNiHo+aOoBD5o/2rnwmqYKMMBSRjwNXAdOAOaq62Dk/DngDeMtJ+qKqXuxcOwy4ndAilw8DX1G1TiIThwgEu88ocO8l6u0rJ4UWtk3LYOiMzHJ3Vl63l7sxmRv+/au6nRv7f/8XOa4cMYJpr69Mmkfb5rOoGvAyd8zzMW9596WDMrV6eGwAeE3HJ/lm5ULuDBzPjYEzOSPQwSTg2PpbsrtRRQ3QFn3s1FdHHt0JUw8LHZ8xgcoaPy/+/d1uTz9w1pCYx6d/dXZ25TFpKVQL2wrgLCDe/OHVqjrL+bnYdf5m4L+ASc7PSbkvpilZzhg2dwAlXScBOBMP8jKGbe+mbG7gmtFqbWzGFFqvfRfal8aCKkjApqpvqOpbPacMEZHhQD9VfdFpVfsjcEauymdKnIhrDJv7tLua9UXWIvJsa6rwvSPHvvjHmWRv9aQxvU54RwLPAsReG2mWh2IcwzZeRF4VkWdE5Fjn3EhgvSvNeudcXCJykYgsFpHF27Zty2VZTTESot2HCVrYtOvCR1ndL4WlPLII2GzdXGMMFD7esiqosHI2hk1EHgeGxbn0HVW9P8HTNgFjVHW7M2bt7yKS9sAfVb0FuAWgsbHRXmO9jIgPnK2p3K1nMeuwhc54dUf3zRMcZ9vCZuuwGVMs8vWhEu4F8Op+/ooE9VCq3wbt07SgchawqeoJGTynDWdEpKouEZHVwGRgAzDKlXSUc86Y7lzrsLkjnJh12DQfY9i86RIVcTpGrLI0puBaNpzDMYe+Czxf6KKk7aNfPIS3XtxM/Yj/hoHj4aXfFrpIJg1F1SUqIkNExO8cH0hocsG7qroJ2CMiR0qomeQzQKJWOtPbibhmVMZfh83TZT1SamHL8h4WsRlTFDr3zGLeyGMKXYzUjT4icthwQC1HnHYg0rgADsxkb1KrgwqpIAGbiJwpIuuBucBDIvKoc+kDwDIRWQrcA1ysqjuca5cAtwKrgNXAP/JbalMy3JMO3Kfp2iXq4f1cd4keejjpIHIr6xM1xpjeqCDrsKnqfcB9cc7/DfhbgucsBmbmuGimHAjxu0Sl69ZUHnWJprTTQXaTDsAmHRhjCsvqoMIqqi5RY7wg7i7RpLNE8ziGzYP8VWOzN8YUikUuJv8sYDNlyD3pIHq2qaUpcqyuSQee3C9y6O4G9aiFzdXEZvGaMYU3sHqAp/m1d+nr2kZom6gtmuF9Bk5ILd0AZxuqPg2ppbc4taAK0iVqTE65Zom6x3y9sOmFyHHMsh5Z94jmeOFch3VHGFMcThj5Ad70ML/t/WMrob8GPshereXRYGNmGX76PvjlwbHnpp3aPd0JV8HYo2Fc/EkUs04YzbAD+7PsqfVsfGdXZmUxnrEWNlN+YnY6iB+Nqfrw9wmtxezpGLZcLJzrZKOq1iVqTBHI/eQf4ZHgHDTTj+iaft3Pzf1i93MV1TDtowmz8fmFCYceEHls3xkLywI2U35SWU7D1R2adeWb8xY21xg2i9iM6YXCvQEWMvVmFrCZ8iMJH0SoZ+PXiA3G3nww/vlsgkKbJmqMMb2eBWym7KTSYrZtX3s0fa5arWJ6R73oErWtqYwx+Wf1TnGwgM2UoZ67RNs73am9XNbDfd7bSQd7t7eyasnWrPMxxnhn/SDv8treZ2Pc8+2a5vzAij7R40knhn73G5FhqVyslb+gLGAz5SdmHFn0cMGMBdEH6uVLP1HAl2gyQpq529dbY4rWt8/3c9Mp2dUnEqwF4N6Dr+PGA97pdr2dyvQyrKyJHh/zDbjsHWgYk0UBM3+q8Y4FbKb8+KIva0mwcK67BvJ00kHMeW9b2IwxRcLV0tRRAa1V2WUnwToAAr5O2ryuK3w+qDug53QpsPa1wrJPEVN+XPFTy2uvJUzWsWeG9zeMOe3R5u/27daYouXN/KVifZNLl9+mkCxgM+XHFRztvn+R63z0sMrvd50u7ha2nE2KMMYUh1JpuiqVcpYp2+nAlJ1EXZzuwCeoPqr6rUyaPo079nw+m24OJ5uqPhUcOHtI5vkYY4pUcX8ps2G0xcFa2EwZit8VGbP5uxL3fGa3S6WFLZtJB6HfanuJGlN0gpLderb/nib065N4UsHAvmkMkPNXw0k/zbwwPbEWtoKygM2Un0QtbOIO2CQyhi1nLWzugG3ozCzv4QSZFrEZUwSUaeds5BNXVmTd/PTLM/zUVCb+KH768nmpv+3/ZysceXFW5THFywI2U35SGOwfCApo6Jtr7lrYXOer4+ztl3L2Tj5Ba2EzpjeTAjdxqTWxFZQFbKb8uKKavkce4TrtuqA+r6Z3kVILmwdCLWwWshlTbop9YpFVO8XBAjZTfly1S/8zz4wc/37F792JXMmzrI3a9iYqSNwypc01hq3I6/VeSUR+JiJvisgyEblPRBoSpDtJRN4SkVUi8q08F9OUqKJ6y1sDW0FZwGbKTqIALKhBdyrCVWHW3247mqPHR33JXZDs8nW89eJmAIIB5fVn429dYwrqMWCmqh4MvA1c2TWBiPiBm4CPANOBc0Vkel5LaUyGxFdUYWOvZct6mDKUwpZQKqhXAVtFdfTY53pLSQrlSMG29xO14JlioKr/dD18ETg7TrI5wCpVfRdARO4CTgdez30JjVeuPmMmQ/vVQO0gOPorsPE+IPm7+69HC3v6Ck394Ip7gnHTjB8wkk0ta7wt7KHnw4YlnmR13P+bxpJ/rGHU1AGe5GcyYwGbKT+uQKn55ZeoO/aYOIl8kf1EV2xfkd39/K5p97YdVW/3WWBhnPMjgXWux+uBI+KkQ0QuAi4CGDMmi/0fjef+35Fjow9O/AH84b6k6d/+8QL+uvfPrjPxAzZfLgaJnXaDZ1nVDajmg5+a4ll+JjMWsJny46r8Wl5dGj+NRrtEX9+eZSOHu4UtJkjzaAybS7/BNT0nMp4TkceBYXEufUdV73fSfAfoBO7I5l6qegtwC0BjY6ONGjLGABawmXKU0h6eHn6jTaWFrbPNk1uNP9h2OigEVT0h2XURWQB8FJivqvGCrA3AaNfjUc45Y5KyiN2EWZ+NKTuBnTujDxIEbIp4t6xHzLi1BLsb7N/qzb1s7G/REZGTgG8Cp6lqc4Jk/wEmich4EakCzgEWJUhrykRm65ZZiGbis4DNlJ09Dz4YfeBL8BJXH55FPwkDNtdx7WBv7mUBWzG6EagHHhORpSLyGwARGSEiDwOoaifwReBR4A3gblVdWagCmzyJ29ja3dDaoUmv7/eF8qmWfVkXKRU1fUNbZfWpT7xllsk/6xI15S1hgCPJLqYn0cxQd8BWk/lOB24WrxUfVZ2Y4PxG4GTX44eBh/NVLpN7fz75z9zz9j1seePelNL/6BM+vnN394kHV8y5gr+987eEz7vkC4dSvWwpE1f8O+OypmP60SOoqPQxaU68YZumUKyFzfROXu504PNHjxNOOvDorWZLjhtTNA4ZcggT+k9IOf1rE3y8Prr7+T4VfZg6cGrC5x0zZQiHH1aVt7e/+IQpRw7HZ+uvFRUL2ExZS7yLgYctbIm6QRMFb2kaPS269pFVn8YY0ztZwGbKWs2MmQmueBmwJWhhS2m2as/cw2D2NLVknI8xxntJJxZYi7jxkAVspqzVf/jD8S+o4Kv2auZmgq5Pj3Y6cFv96jZP8jHGlBgL/no9C9hMeUsy6aCi7i2P7uEO2PzuC67DzN9qKU40M8YUyOYBzjZ31dU9pIQVY9OrC6r89jFtQuyVYMpaojFsmqvRYAnHs9m3Y2PK1XvDhKd+/kkGnHdezHlVZWDNwMjju065i78dE60LrjzfT09qKhOk+eZ78OnkW2OZ8mIBmylviQIl1wzRGr+H2z29/Y8E9848YAt0xN9/0BhTeOExbC0H1Md9m9dW1EaO+1X3Q0XYURd6vLMuixvXDoR+I7PIwJQaC9hMeUu0cK7rpX/Kgad4d793n3Y98GbSQXtrZ8bPNcbkhyT4UpZ4prp3KwuZ3sECNlPmet5LtC3gzT6fAIw41HULb1rYNGiD2IwxprezgM2Ut0RxkisGevDdBxMkysCc/4p/8yxa2KYeNTzz8hhjjCkLFrCZspZ84dyc3NF98/jHaRo4vG8W5THGFMrBh3+Ebxz2jW7nJU6j+YIZCwAIdgwkvMHA4LoeZp3aGLZexfYSNeUt4Ri2XM0S9X7ttWDAukSNKVYaZ92dAy77BoM+9zkAJgAj60ayYd+G7s91VRGnHHhK+uNpq+vgqt1wVf/0nmdKkrWwmfJWLC1sWbCAzZgSIJBs04OuSTO7genNLGAzZS7Rsh45ut3mZT3fO02BTlvWw5iSkuKXNfsqZtJhAZspb0l2OsiJF2503cKbe1RU2dvUmLJikZrJgH0SmLLmnnTgi9keKh/dC97cY8iYek/yMcYUGevlNGkoSMAmIj8TkTdFZJmI3CciDa5rV4rIKhF5S0Q+7Dp/knNulYh8qxDlNiXINelgcJ/BMZdUc/zy96iFTa1H1Jii9Ykpn+BDYz/EZ2d8NqX0XzjkCwSv+zYNH/84J068mqMGfyzHJTTlolCzRB8DrlTVThH5KXAlcIWITAfOAWYAI4DHRWSy85ybgBOB9cB/RGSRqr5egLKbIjf2L39h7ac+FXrgCpp+c8JvOGvRWc4jHwSrwN+aw5JYl6gx5a6+qp7r5l0HwJYU0l8y65LQwXHwPQDOyE3BTNkpyCeBqv5TVcP77bwIjHKOTwfuUtU2VX0PWAXMcX5Wqeq7qtoO3OWkNaYbXx/X3qCugG1E3YiYdIG2YbktiEctbH3797AWkzHGmLJXDF/dPwuEd8weCaxzXVvvnEt0Pi4RuUhEFovI4m3btnlcXFPsqsaPdz2KBk1d10sKtowG4CuHfiUfxTLGGGMylrMuURF5HIjXhPEdVb3fSfMdoBO4w8t7q+otwC0AjY2NNh+nl/HVRFvYYjcbiN/iVSEevg2q6qLHlX28y9cY07t51GJvSlfOAjZVPSHZdRFZAHwUmK/Rpo8NwGhXslHOOZKcNyahYJuHG7un4tDzo8cWsBnTq/gbGgDw1dnMbuO9gkw6EJGTgG8CH1TVZtelRcBfROTnhCYdTAJeJtSvNUlExhMK1M4BPpXfUptS1Ll1K9UHHpi/G+58L3/3MsYUlUEXLMDfrx8NZ9vMT+O9Qs0SvRGoBh5zuqleVNWLVXWliNwNvE6oq/RSVQ0AiMgXgUcBP3Cbqq4sTNFNqZISWnstkQPG2jd3Y4qVVFYy4JxPFroYpkwVJGBT1YlJrv0I+FGc8w8DD+eyXKYMBaOLmNVW1sZcams6jg9O68snpnzCu/sl3GzeGwcfP7rnRMYYY8pOMcwSNSZnNJBk1dlgLedPuqJbIJcV8XuXVxyT5wzNaf7GGGOKkwVspqxVjRub3xtKbt9SiWa6GmOMKW8WsJmyJjnuouxm8OSe0xhjTLrqh7uORyROZ8pWoSYdGJMXGggkv+7VKn2fuR/+eDrMs21ujTHxZTXxadAEuPQ/UFkDNf29K5QpGRawmbKmnckDtoBXEduB8+Cq3d7kZYwx8QyxFvzezLpETVmTqsqk1yt9pTEm7JhPTGLEpIZCF8MYY0yBWMBmylrVqFFxzz/61Q9w7KTBHDp2QJ5LlJlDjh/Nmd84tNDFMMYYUyDWJWrK0oR/PkrbO+8kvD5lWD1/uvCI3Bfkgn9A297c38cYY0xZs4DNlKWqMWOoGjOm0MWAsUcVugTGmN7gmK8VugQmxyxgM8YYY0qZTXjqFWwMmzHGGGNMkbMWNmOMyYKI/Aw4FWgHVgMXqOquOOnWAHuBANCpqo15LKYxpsRZC5sxxmTnMWCmqh4MvA1cmSTtcao6y4I1Y0y6LGAzxpgsqOo/VbXTefgiEH8tGWOMyYJ1iZpe5co5V7K33ZbZMDnzWWBhgmsK/FNEFPitqt4SL5GIXARcBDCmGGY6m6xd84FruHX5rQzvO7znxMYkIOrZZorFqbGxURcvXlzoYhhj8kRElnjd5SgijwPD4lz6jqre76T5DtAInKVxKlYRGamqG0TkAELdqF9S1X8lu6/VX8b0PonqMGthM8aYHqjqCcmui8gC4KPA/HjBmpPHBuf3VhG5D5gDJA3YjDEmzMawGWNMFkTkJOCbwGmq2pwgTV8RqQ8fAx8CVuSvlMaYUmcBmzHGZOdGoB54TESWishvAERkhIg87KQZCjwnIq8BLwMPqeojhSmuMaYUWZeoMcZkQVUnJji/ETjZOX4XOCSf5TLGlBdrYTPGGGOMKXIWsBljjDHGFDkL2IwxxhhjipwFbMYYY4wxRc4CNmOMMcaYIlf2Ox2IyDZgbYrJBwNNOSxOrli5869Uy94byj1WVYfksjD50kvqLyjdslu586u3lDtuHVb2AVs6RGSx11va5IOVO/9KtexW7vJVyv9GpVp2K3d+9fZyW5eoMcYYY0yRs4DNGGOMMabIWcAW65ZCFyBDVu78K9WyW7nLVyn/G5Vq2a3c+dWry21j2Iwxxhhjipy1sBljjDHGFDkL2IwxxhhjilyvDNhE5CQReUtEVonIt+JcrxaRhc71l0RkXAGK2U0K5f6AiLwiIp0icnYhyhhPCuX+uoi8LiLLROQJERlbiHJ2lUK5LxaR5SKyVESeE5HphShnPD2V3ZXuYyKiIlIUU+VT+DdfICLbnH/zpSLyuUKUs5Cs/sqvUq2/oHTrMKu/ElDVXvUD+IHVwIFAFfAaML1LmkuA3zjH5wALS6Tc44CDgT8CZxe6zGmU+zig1jn+Qgn9e/dzHZ8GPFLocqdadiddPfAv4EWgsRTKDSwAbix0WYv838jqr/yWu+jqrzTKXnR1mNVfiX96YwvbHGCVqr6rqu3AXcDpXdKcDvzBOb4HmC8ikscyxtNjuVV1jaouA4KFKGACqZT7KVVtdh6+CIzKcxnjSaXce1wP+wLFMoMnldc4wA+BnwKt+SxcEqmWuzez+iu/SrX+gtKtw6z+SqA3BmwjgXWux+udc3HTqGonsBsYlJfSJZZKuYtRuuW+EPhHTkuUmpTKLSKXishq4Brgy3kqW096LLuIHAqMVtWH8lmwHqT6WvmY0/10j4iMzk/RiobVX/lVqvUXlG4dZvVXAr0xYDNFSkT+H9AI/KzQZUmVqt6kqhOAK4D/LnR5UiEiPuDnwDcKXZYMPACMU9WDgceItiQZU1ClWH9B6dVhvbn+6o0B2wbAHdWOcs7FTSMiFUB/YHteSpdYKuUuRimVW0ROAL4DnKaqbXkqWzLp/nvfBZyRywKloaey1wMzgadFZA1wJLCoCAbu9vhvrqrbXa+PW4HD8lS2YmH1V36Vav0FpVuHWf2VSKEH6uX7B6gA3gXGEx0YOKNLmkuJHbR7dymU25X2dopn0G4q/96zCQ3WnFTo8qZZ7kmu41OBxYUud7qvFSf90xTHoN1U/s2Hu47PBF4sdLmL8N/I6q/8/nsXXf2VRtmLrg6z+ivJPQr9RxboH/Zk4G3nTfYd59wPCH07AqgB/gqsAl4GDix0mVMs9+GE+s33E/pGvbLQZU6x3I8DW4Clzs+iQpc5xXL/EljplPmpZJVKsZW9S9qiqPBS/Df/X+ff/DXn33xqoctchP9GVn/lt9xFWX+lWPairMOs/or/Y1tTGWOMMcYUud44hs0YY4wxpqRYwGaMMcYYU+QsYDPGGGOMKXIWsBljjDHGFDkL2IwxxhhjipwFbMYYY4wxRc4CNlMURGSQiCx1fjaLyAbneJ+I/DoH97tdRN4TkYuTpDlWRF4XkRVe398YUz6s/jL5YOuwmaIjIlcB+1T12hze43bgQVW9p4d045x0M3NVFmNM+bD6y+SKtbCZoiYi80TkQef4KhH5g4g8KyJrReQsEblGRJaLyCMiUumkO0xEnhGRJSLyqIgMT+E+HxeRFSLymoj8K9d/lzGm/Fn9ZbxkAZspNROA44HTgD8DT6nqQUALcIpT6f2K0F6EhwG3AT9KId/vAh9W1UOcvI0xxmtWf5mMVRS6AMak6R+q2iEiywE/8IhzfjkwDpgCzAQeExGcNJtSyPffwO0icjdwr9eFNsYYrP4yWbCAzZSaNgBVDYpIh0YHYQYJvZ6F0KbRc9PJVFUvFpEjgFOAJSJymKpu97Lgxphez+ovkzHrEjXl5i1giIjMBRCRShGZ0dOTRGSCqr6kqt8FtgGjc1xOY4zpyuovk5C1sJmyoqrtInI2cIOI9Cf0Gv8FsLKHp/5MRCYR+ob7BPBaTgtqjDFdWP1lkrFlPUyvZNPijTGlyuqv3sm6RE1vtRv4YU8LTwIPAE15K5UxxvTM6q9eyFrYjDHGGGOKnLWwGWOMMcYUOQvYjDHGGGOKnAVsxhhjjDFFzgI2U3REZIGzv16ziGwWkZtFpMF1fbKI/FVEmkRkt4gsE5Gvi4g/SZ7jRERFZJ/zs0VEfh3ev89Js0ZETnA9PkdEdorIB3P2xxpjyk4u6jDneReKyJsistepwx4WkXrn2u0icrUr7QwR2SQil+XsDzV5ZQGbKSoi8g3gp8DlQH/gSGAsoa1aqkRkAvASsA44SFX7Ax8HGoH6FG7RoKp1wEHAXODSBOU4H7gJOEVVn8nurzLG9Ba5qsOcL44/Bs5V1XpgGrAwQdrZwFPA1ap6rVd/myksmyVqioaI9AM2Ap9V1btd5+uA94ArCG2cPEBVT0kz73FOHpWq2umcu4ZQAHeR83gN8DlCGzT/CDhJVRdn+WcZY3qJHNdhlwHHqOoZCa7fDqwHFgEPA99S1Vsz+DNMkbIWNlNMjgJq6LJ5saruI1QBnQicACRdLDIVIjIC+DDwYpdLXwB+AMy3YM0Yk6Zc1mEvAR8Wke+LyNEiUh0nzRxCG8p/zYK18mMBmykmg4GmcAtYF5uc64Oc40w1icguYAOwn+4V54mEgrjlWdzDGNM75awOU9VngbOAQ4GHgO0i8vMu496OJLSo7j/Szd8UPwvYTDFpAgaLSLw9boc717c7x5karKoNQC3wb+DRLte/AEwGbhURyeI+xpjeJ6d1mKr+Q1VPBQYCpwMLCA3jCLsJWExovNyATO5hipcFbKaYvAC0EfoWGeGM//gIoU2NHwc+lu2NVLUFuB04UkQGuy5tAeYDxwK/zvY+xpheJS91mKoGVfUJ4EnAvU9oAPgU8D7wqDOmzpQJC9hM0VDV3cD3gV+JyEkiUulMFrib0GDaPwHfA44SkZ+JyDAAEZkoIn92T5vviTP+49PAZkLfeN3l2EgoaDtJRK7P/i8zxvQGuazDROR0Z6mhARIyB/ggXcbhqmoHoVmnTcDDItLX+7/UFIIFbKaoqOo1wLeBa4E9RKe/z1fVNlVdTWg5jnHAShHZDfyNUDfA3hRusUtE9hFqSZsLnKZxpkqr6vuEZnOdLSL/m/UfZozpFXJYh+0E/gt4x8n3z8DPVPWOOGVoJ9TK1wo8ICJ9vPnrTCHZsh7GGGOMMUXOWtiMMcYYY4qcBWymbIjIea6tp9w/KwtdNmOM6YnVYSYZ6xI1xhhjjCly8daKKSuDBw/WcePGFboYxpg8WbJkSZOqDil0Obxg9ZcxvU+iOqzsA7Zx48axeLHtMGRMbyEiawtdBq9Y/WVM75OoDiv4GDYR8YvIqyLyoPN4vIi8JCKrRGShiFQ556udx6uc6+MKWnBjjDHGmDwpeMAGfAV4w/X4p8D1qjqR0LozFzrnLwR2Ouevd9IZY4wxxpS9ggZsIjIKOAW41XkshBYrDW/I/QfgDOf4dOcxzvX5ttejMcYYY3qDQo9h+wXwTaDeeTwI2KWqnc7j9cBI53gkodWiUdVOZ3XoQYS234ghIhcBFwGMGTMmV2U3xhhjTIY6OjpYv349ra2thS5KQdTU1DBq1CgqKytTSl+wgE1EPgpsVdUlIjLPy7xV9RbgFoDGxkZbt8QYY4wpMuvXr6e+vp5x48bR2zrMVJXt27ezfv16xo8fn9JzCtklejRwmoisAe4i1BX6S6BBRMKB5Chgg3O8ARgN4FzvT5dNu40xxhhTGlpbWxk0aFCvC9YARIRBgwal1bpYsIBNVa9U1VGqOg44B3hSVc8DngLOdpKdD9zvHC9yHuNcfzLept3GGGOMKQ29MVgLS/dvL4ZZol1dAXxdRFYRGqP2e+f874FBzvmvA98qUPmMMcYYY/KqKAI2VX1aVT/qHL+rqnNUdaKqflxV25zzrc7jic71dwtbalOK7nvnPq5fcn2hi2FKlIiMFpGnROR1EVkpIl9xzg8UkcdE5B3n94AEzz/fSfOOiJwfL40xyQT27mXtBRfQsWlToYtSFvx+P7NmzWLmzJmceuqp7Nq1y5N833vvPY444ggmTpzIJz/5Sdrb27POsygCNmPy5bvPf5fbVtxW6GKY0tUJfENVpwNHApeKyHRCLf5PqOok4Ani9ACIyEDge8ARwBzge4kCO2MS2fPQwzS/8CJNN/+m0EUpC3369GHp0qWsWLGCgQMHctNNN3mS7xVXXMHXvvY1Vq1axYABA/j973/f85N6UOhlPYwxpmSo6iZgk3O8V0TeILTk0OnAPCfZH4CnCQ3vcPsw8Jiq7gAQkceAk4A7c15wY4rc9x9Yyesb93ia5/QR/fjeqTNSTj937lyWLVsGwLx587j22mtpbGykqamJxsZG1qxZw+23386iRYtobm5m9erVnHnmmVxzzTUx+agqTz75JH/5y18AOP/887nqqqv4whe+kNXfYy1sxhiTAWd7vNnAS8BQJ5gD2AwMjfOUyFqSDvc6k+58LxKRxSKyeNu2bd4W2pQNbeuda5flSiAQ4IknnuC0007rMe3SpUtZuHAhy5cvZ+HChaxbty7m+vbt22loaKCiItQmNmrUKDZs2BAvq7RYC5sxxqRJROqAvwFfVdU97tleqqoikvEMdltH0iSy+6GH2HzVVaHj+xcx4qfls0NjOi1hXmppaWHWrFls2LCBadOmceKJJ/b4nPnz59O/f38Apk+fztq1axk9enSui2otbMYYkw4RqSQUrN2hqvc6p7eIyHDn+nBga5ynRtaSdLjXmTSmRxu/cVmhi1B2wmPY1q5di6pGxrBVVFQQDAYBuq2VVl1dHTn2+/10dnbGXB80aBC7du2KnF+/fj0jR3ZrTE+bBWzGGJMiZ//i3wNvqOrPXZfc60S61490exT4kIgMcCYbfMg5Z4wpsNraWm644Qauu+46Ojs7GTduHEuWLAHgnnvu6eHZsUSE4447LvK8P/zhD5x++ulZl9ECNmOMSd3RwKeB40VkqfNzMvAT4EQReQc4wXmMiDSKyK0AzmSDHwL/cX5+EJ6AYIwpvNmzZ3PwwQdz5513ctlll3HzzTcze/Zsmpq6bVneo5/+9Kf8/Oc/Z+LEiWzfvp0LL7ww6/JJuW8W0NjYqIsXLy50MUyROOgPBwGw/PzlBS6JyRURWaKqjYUuhxes/jJub0ydFvN42ptvFKgk3njjjTeYNm1azwnLWLx/g0R1mLWwGWOMMSWm36mnFroIJs8sYDPGGGNKjPj9hS6CyTML2IwxxphS04s3Te+tLGAzxhhjjClyFrAZY4wxxhQ5C9iMMcYYY4qcBWzGGGOM6ZX8fj+zZs1i5syZnHrqqezatcuTfG+88UYmTpyIiGS0jls8FrAZY4wxplcKb021YsUKBg4cGNmaKltHH300jz/+OGPHjvUkP7DN340xxhhTaP/4Fmz2eEHzYQfBR36ScvK5c+eybNkyAObNm8e1115LY2MjTU1NNDY2smbNGm6//XYWLVpEc3Mzq1ev5swzz+Saa67pltfs2bM9+zPCrIXNGGOMMb1aIBDgiSee4LTTTusx7dKlS1m4cCHLly9n4cKFrFu3Lg8ltBY2Y4wxpuQEdu8udBG8lUZLmJdaWlqYNWsWGzZsYNq0aZx44ok9Pmf+/Pn0798fgOnTp7N27VpGjx6d66JaC5sxxhhTajo9Gsje24XHsK1duxZVjYxhq6ioIBgMAtDa2hrznOrq6six3++ns7MzL2UtWMAmIjUi8rKIvCYiK0Xk+87520XkPRFZ6vzMcs6LiNwgIqtEZJmIHFqoshtjjDEF5QQTxhu1tbXccMMNXHfddXR2djJu3DiWLFkCwD333FPg0oUUsoWtDTheVQ8BZgEniciRzrXLVXWW87PUOfcRYJLzcxFwc57La4wxxhQH1UKXoOzMnj2bgw8+mDvvvJPLLruMm2++mdmzZ2e0LMcNN9zAqFGjWL9+PQcffDCf+9znsi5fwcawqaoC+5yHlc5Pslfg6cAfnee9KCINIjJcVTfluKjGGGNMcbGAzRP79u2LefzAAw9EjsMzRgGuvvpqABYsWMCCBQsi5x988MG4+X75y1/my1/+soclLfAYNhHxi8hSYCvwmKq+5Fz6kdPteb2IhDuLRwLuqRjrnXPx8r1IRBaLyOJt27blqvjGGGNMQbS+/nqhi2DyrKABm6oGVHUWMAqYIyIzgSuBqcDhwEDgigzyvUVVG1W1cciQIV4W2RhjjDEm74pilqiq7gKeAk5S1U0a0gb8HzDHSbYBcM+bHeWcM8YYY4wpa4WcJTpERBqc4z7AicCbIjLcOSfAGcAK5ymLgM84s0WPBHbb+DVjjDHG9AaFXDh3OPAHEfETChzvVtUHReRJERkCCLAUuNhJ/zBwMrAKaAYuyH+RjUnTnk3Qvg8GTyp0SYwHROQ24KPAVlWd6ZxbCExxkjQAu5yhHl2fuwbYCwSATlVtzEORjTFlopCzRJcB3TbbUtXjE6RX4NJcl8v0Du9s2cuJ1/+Lpy6bx/jBfXN3o59PDf2+qsxWJe+9bgduBP4YPqGqnwwfi8h1QLL/2cepqq14aoxJW1GMYTPGa61vvc2eRx5JeP3eV0PDHx9ebr3qJnWq+i9gR7xrzjCOTwB35rVQxpiM+f1+Zs2axcyZMzn11FPZtWuXJ/med955TJkyhZkzZ/LZz36Wjo6OrPO0gM2UpfdOP50NX/1awuvi/FZby8h451hgi6q+k+C6Av8UkSUiclGiTGxZImPyJ7w11YoVKxg4cGBka6psnXfeebz55pssX76clpYWbr311qzztM3fTa8kTsRm8Zrx0Lkkb107RlU3iMgBwGMi8qbTYhdDVW8BbgFobGy0V6jpFX768k95c8ebnuY5deBUrpiT+spgc+fOjSyWO2/ePK699loaGxtpamqisbGRNWvWcPvtt7No0SKam5tZvXo1Z555Jtdcc023vE4++eTI8Zw5c1i/fn3Wf4+1sJleSSJtbMZkT0QqgLOAhYnSqOoG5/dW4D6iSxYZk5GOLVsKXYSyEQgEeOKJJzjttNN6TLt06VIWLlzI8uXLWbhwIevWrUuYtqOjgz/96U+cdNJJWZfRWthMr2bNF8YjJwBvqmrcr9Ei0hfwqepe5/hDwA/yWUBTfjrWr6dy6NBCF8MT6bSEeamlpYVZs2axYcMGpk2bxoknntjjc+bPn0///v0BmD59OmvXrmX06NFx015yySV84AMf4Nhjj826rNbCZnqloNMXal2iJh0icifwAjBFRNaLyIXOpXPo0h0qIiNE5GHn4VDgORF5DXgZeEhVE8+KMSYVYh/h2QqPYVu7di2qGhnDVlFRQTAYBKC1tTXmOdXV1ZFjv99PZ2dn3Ly///3vs23bNn7+8597UlZrYTO90q+fXg3AS+9tB2yNNJMaVT03wfkFcc5tJLR2JKr6LnBITgtneh3x5Xhox55N0LobDpia2/sUgdraWm644QbOOOMMLrnkEsaNG8eSJUuYM2cO99xzT9r53XrrrTz66KM88cQT+HzeBNYWnptebfOe1p4TGWNMMfIoEEjo51Ph10fk9h5FZPbs2Rx88MHceeedXHbZZdx8883Mnj2bpqb0l068+OKL2bJlC3PnzmXWrFn84AfZj4CwFjbTqwWD1idqjClNra+/Tp+DDy50MUravn37Yh4/8MADkePwjFGAq6++GoAFCxawYMGCyPkHH3wwbr6JukmzYS1splezcM0YU6q23ejNmmGmNFjAZnq1HUO+xi3Lbil0MYwxJm2BDLrqTOmygM30bhLgV6/+qtClMMYYY5KygM0YY4wxpshZwGbKmu0VaowxphxYwGbKm7PwoTHGGFPKLGAz5a2MAra/XbOYN57fVOhiGGNM2fD7/cyaNYuZM2dy6qmnsmvXLk/yvfDCCznkkEM4+OCDOfvss7stH5IJC9hMWUveJZrH7lIPumY3v7uHJ//4hgeFMcaUmuZXXy10EcpSeGuqFStWMHDgwMjWVNm6/vrree2111i2bBljxozhxhtvzDpPWzjXlLdAIMnFfAZsQRB//u5njCkrQQ9aaIrZ5h//mLY33vQ0z+ppUxn27W+nnH7u3LmRxXLnzZvHtddeS2NjI01NTTQ2NrJmzRpuv/12Fi1aRHNzM6tXr+bMM8/kmmuu6ZZXv379gFCjQUtLCyLZbyNmLWymrGkxBWxZ2LezzaOCGGNMmlp2FboEORcIBHjiiSc47bTTeky7dOlSFi5cyPLly1m4cCHr1q2Lm+6CCy5g2LBhvPnmm3zpS1/KuozWwmbKW7EEbMEA+CszfvrGd3Z6WBhjTOnJ8UbvybzyBzj6Kzm9RTotYV5qaWlh1qxZbNiwgWnTpnHiiSf2+Jz58+fTv39/AKZPn87atWsZPXp0t3T/93//RyAQ4Etf+hILFy7kggsuyKqsBW1hE5EaEXlZRF4TkZUi8n3n/HgReUlEVonIQhGpcs5XO49XOdfHFbL8pvglbWHLZ/2XZQubrU5iTC/nQZdaxlY9Ubh751h4DNvatWtR1cgYtoqKCoLOpLXW1taY51RXV0eO/X5/0n1D/X4/55xzDn/729+yLmuhu0TbgONV9RBgFnCSiBwJ/BS4XlUnAjuBC530FwI7nfPXO+mMSUiTbsCbxyho3+asnm7ryRnTyxUwXusNamtrueGGG7juuuvo7Oxk3LhxLFmyBIB77rknrbxUlVWrVkWOFy1axNSpU7MuY0EDNg0Jj6SsdH4UOB4I/wv9ATjDOT7deYxzfb54MZLPlK9i6RJd93JWT9/4zi5vymGMMSau2bNnc/DBB3PnnXdy2WWXcfPNNzN79mya0tyzVVU5//zzOeiggzjooIPYtGkT3/3ud7MuX8HHsImIH1gCTARuAlYDu1Q13DSyHhjpHI8E1gGoaqeI7AYGAU1d8rwIuAhgzJgxuf4TTBErmha2LLtE3/i3rb9mjOmuff16qkaNKnQxSlbX9dEeeOCByHF4xijA1VdfDcCCBQtYsGBB5PyDDz7YLU+fz8e///1vj0ta+C5RVDWgqrOAUcAcIOt2Q1W9RVUbVbVxyJAh2WZnSph2dCS+mM+22WCylj5jjOlBgu+XO27/Q/wL+bi5yauCB2xhqroLeAqYCzSISLj1bxSwwTneAIwGcK73B7bnt6SmlGhHkbSwBZOVwxhjktO21p4T5ezmFrAVg0LPEh0iIg3OcR/gROANQoHb2U6y84H7neNFzmOc60+qjcY2SSRsYfO14avckceClM8WWb2ZiNwmIltFZIXr3FUiskFEljo/Jyd47kki8pYzy/1b+Su1KQdSUxP/gn0E9hqFbmEbDjwlIsuA/wCPqeqDwBXA10VkFaExar930v8eGOSc/zpglZ5JrjNRl2gQ7eyfv3JYwFYubgdOinP+elWd5fw83PWiM1b3JuAjwHTgXBGZntOSmrKy58GHCndzq7+KQkEnHajqMmB2nPPvEhrP1vV8K/DxPBTNlImkY9jyybpEy4Kq/ivD9R/nAKucug0RuYvQrPfXPSyeKWNt77wT/0I+WtgsYCsKhW5hMyan3LNEg7msdN57Fq7qD7vib1Fikw7K3hdFZJnTZTogzvXIDHeHe/Z7DBG5SEQWi8jibdu25aKspgRpe3vkWGpr83xzC9iKgQVspqy5A7b2QHvsRZ+Hg3hfcWZqrX0+QUEsYCtjNwMTCC3+vQm4LpvMbJa7icfXp0/keNxdd7quWAtbNvx+P7NmzWLmzJmceuqp7Nq1y9P8v/zlL1NXV+dJXhawmbKWeJao4qvycoJxD2uE+KuTXzclS1W3OMsTBYHfEWc4B64Z7g737HdjehR0tkfqc+ih1EyenOebl+8XzvDWVCtWrGDgwIGRram8sHjxYnbu9G4f6IIvnGtMLiUbw6YduZh0kODb7ti5ObiXKQYiMlxVwysbnwmsiJPsP8AkERlPKFA7B/hUnopoykBneLX9LmPW7n5zIV/S/yGnm/7koYfg2bvfpmndvp4TpmHw6DqO/UTqwe3cuXMji+XOmzePa6+9lsbGRpqammhsbGTNmjXcfvvtLFq0iObmZlavXs2ZZ57JNddc0y2vQCDA5Zdfzl/+8hfuu+8+T/4eC9hMWUsasAU9bPWyHdJ6BRG5E5gHDBaR9cD3gHkiMotQtL4G+LyTdgRwq6qe7OzM8kXgUcAP3KaqK/P/F5hSJVVVAPj69o05H8zHpIPJ8SZGl5dAIMATTzzBhRde2GPapUuX8uqrr1JdXc2UKVP40pe+xOjRo2PS3HjjjZx22mkMHz7cszJawGbKmiZc1gPCrWHixZYH4VmgiSpPWyupLKjquXFO/z7OOVR1I3Cy6/HDQLclP4xJRecmpxHXF1tfCdAR7KDKX5W7mz/zUzju27nLH9JqCfNSS0sLs2bNYsOGDUybNo0TTzyxx+fMnz+f/v1DPTTTp09n7dq1MQHbxo0b+etf/8rTTz/taVltDJspawlb2ERDP15Z8beeSuLdvYwxvULHli3s+9e/Ys6JxH5sT12vqNUvGQuPYVu7di2qGhnDVlFRQTAYmmzR2ho7Qa26Oto74/f76eyyZ/Wrr77KqlWrmDhxIuPGjaO5uZmJEydmXVYL2Ex5S7D5e+g7ai4qOWthM8Z4Y80nPsm6iz4fe7LL8Isx28A2/MlebW0tN9xwA9dddx2dnZ2MGzeOJUuWAHDPPfekldcpp5zC5s2bWbNmDWvWrKG2tpZVq1ZlXUYL2ExZSzyGTclJwJZwNpVVqMaY9HRu2dL9pI2XzZnZs2dz8MEHc+edd3LZZZdx8803M3v2bJrCEz4KzMawmbKWaFkPVR/idIl6Oruq61pvkRt6dwtjTO8S2LMn+sDXvZ3FukQzt29f7MzUBx54IHIcnjEKcPXVVwOwYMECFixYEDn/4IMPpn2PTFkLmylr7oVzczrtPSzhFlRWoRpjMrPpO/8dfeBUY+FZo2Bdor2FBWymrLlnicZUaqKIl7NEozdJ77wxxvSgc+eOyHFk0oHfX6DSmEKxgM2UtURj2NyTDjwN2KwlzRjjNXe14vQUuHsMcrpPco715tbBdP92C9hMeUswSxSiy3p0aqI0GUj4Buy9lZIxxjsDzj0ndOBqYSvVMWw1NTVs3769VwZtqsr27dupqalJ+Tk26cCUNXcLW34qNesSNcZ4q+XVVyPHFcOGAaEdD4J79wKlG7CNGjWK9evXs23btkIXpSBqamoYNWpUyuktYDO9WC66ERJ1r5ZmhWqMKQLBaF0V7gqVitDH9/Z6GFGiXwgrKysZP358oYtRMqxL1JS1unnzIsexze55ruBKtEI1xhS3DYOkV3Yp9kYWsJnyFB6Qm2wmlZdbU4W990yCC1ahGmM8EGd5omBOegtMsbGAzZS3pHFSDoKotx/xPk9jjHFUjhgR81jp3TMte5OCBWwiMlpEnhKR10VkpYh8xTl/lYhsEJGlzs/JrudcKSKrROQtEflwocpuyoDkaGuqRKxCNcZ4QCornYNQS9vETTnc/L1+eOj37E/nJn+TlkJOOugEvqGqr4hIPbBERB5zrl2vqte6E4vIdOAcYAYwAnhcRCaraqLNG40pokCpWMphjCknfdugfflKmPNB7zOv6hv6PdAmBhSDgrWwqeomVX3FOd4LvAGMTPKU04G7VLVNVd8DVgFzcl9SU9oSr4tW2W9pPgtijDE5EdyxM7c3KJovvr1bUYxhE5FxwGzgJefUF0VkmYjcJiIDnHMjgXWup60nQYAnIheJyGIRWdxb13cx3XXtNvDVbMzjza3CM8Z4x12fqT9HH+VWbxWVggdsIlIH/A34qqruAW4GJgCzgE3Adenmqaq3qGqjqjYOGTLEy+KaUpOkwgm0jMtfOYwxxkNtGl0UvJn2HN/NArdiUNCATUQqCQVrd6jqvQCqukVVA6oaBH5HtNtzAzDa9fRRzjljUtJ1HbZAc2hcRl1lXT7unod7GGN6DdfyHitWv5Dbe1n1VRQKOUtUgN8Db6jqz13nh7uSnQmscI4XAeeISLWIjAcmAS/nq7ymRKXQpJ+XbV2sa6EsOMM0torICte5n4nIm84wjvtEpCHBc9eIyHJn9vvivBXalCdf9OO79rVVOb6Z1V/FIKtZoiKyLIVk21R1fpzzRwOfBpaLyFLn3LeBc0VkFqFXyBrg8wCqulJE7gZeJzTD9FKbIWoyp4ivFYD9HfsLXBaTL1nWWQC3AzcCf3Sdewy4UlU7ReSnwJXAFQmef5yqNqVaXmMSco1bW7HvHT6Sy3vZF86ikO2yHn7g5CTXhVDLWDeq+hzxN158OFFmqvoj4EfpFND0bu5u0K4tadVDH8hnSfJ4L5NExnUWgKr+y5kk5T73T9fDF4GzsymgMSlxtbAd/F6u6hert4pJtgHb51V1bbIEInJJlvcwJn0iyb8ViiL+1vyVxxSLXNdZnwUWJrimwD9FRIHfquotCe5/EXARwJgxY7Ioiiln7pmhY3LVZrvj3fDdcnQDk46sxrA5rWTdOLsYXJ4sjTF5USz1jHUpFIVc1lki8h1CwzXuSJDkGFU9FPgIcKmIfCBBGW2Wu+lZsn2SvWb1V1HwbNKBiAwRkUtE5FngaWCoV3kb44XYLtF8V0BW4RUbL+ssEVkAfBQ4TxNs7KiqG5zfW4H7sIW/TRbeOnNWHu9m9VcxyHbSQT1wFvApYDJwLzBeVUd5UDZjsldm3wwra/L4rboM5aLOEpGTgG8CH1TV5gRp+gI+Vd3rHH8I+EGm9zTmsWHbmJGvm5VZPVqqsm1h20pozMbVwIGq+g3I+Qp+xmSke8NHvDkvObu5J9nUNVR7kk8vllWdJSJ3Ai8AU0RkvYhcSGjWaD3wmLNkx2+ctCNEJDyJaijwnIi8Rmg5oodU9RHP/irT6zy/8fk83s0CtmKQ7aSDKwltyP5r4E4RSTTY1pgCSbyXaEmSPAaZ5SmrOktVz41z+vcJ0m7EmZGqqu8Ch6RXVGOKhLWwFYVsJx38QlWPJLQxO8DfgREicoWITM62cMbkVF7rIG9u5q+wgC0bVmcZk6Jdrq272/YUrhwmwpNJB6r6rqr+WFUPAhqBfiRZT82YvEn0zVCU/HaJ5u9WpmdWZxnTg4BrpMDLcVegMXnm5SzRYSJyGjAe+JWqTvQqb2NyIdgxsNBFMAVkdZYxppR4ErCJyOcIDaQ9i9Aq3y+KyGe9yNuYrCQZe9G+42gABtZ4ELgN72l4kjWxFROrs0wpajjnk/m72RsJN/wwBZLtpIOwy4HZqrodQEQGAc8Dt3mUvzEZidmaynUsXgdQlbXe5peAjf31jNVZpuT46+oix/2q+gE7Io/3Pvkk9ccf793N3nzIu7yMJ7zqEt0O7HU93uucM6Z3CEdSY49Jft0UC6uzTElTlGvPin6E73n4H97eoLKPt/mZrHnVwrYKeElE7ifU93M6sExEvg6gqj/36D7G9Khj82YIBkMPEm7+7nUA5eTnS7SwrUf3s7jPK1ZnmZK3pcE1ccrrJX8qLGArNl4FbKudn7D7nd/1HuVvTMra338/xZS5iH4soioRVmeZkuOr7xfzOOjuI/O6Fd/WfCw6ngRsqvp9L/Ixxgvic9Vi+YqfwpVlokrTq8rU6lBPWJ1lStGgCxZEHyh0erbOQzxW2RSbrP53i8hVXqQxxlMJvhnGdImKelwd5SkytAa8rFidZUqZVFVFjvd27CXg/gS3FrGyl20L2+dEJNkSyEJoG5irsryPMamLqbjyFUj10MJmkVaxsDrLlLyWzhYA2isLXBCTV9k2qP6O0JiPRD91Thpjio/kMYiyWaLFwuosU/I6g50AMS1sex54wOO7WJ1VbLJqYbNxIKY4uVrYEqzDlrNZojmv5KwSzYbVWaacBK0XtFfJ6ZBFYwoipUrM48DHukSNMXnWmWgVIS9Yr0DRKVjAJiKjReQpEXldRFaKyFec8wNF5DERecf5PcA5LyJyg4isEpFlInJoocpuSoir0mnuaM7HDT3Pce+O1mjuVoca0+uFJ1C1VVkTW2/i1V6iR6dyrotO4BuqOh04ErhURKYD3wKeUNVJwBPOY4CPAJOcn4uAm70ouyk/ktJsKcXb4Cp3y3qse2NHz4lMWjKss4zpRezbYbHxqoXtVymei1DVTar6inO8F3gDGEloxfE/OMn+AJzhHJ8O/FFDXgQaRGS4B2U35cYVsMXsH5rLae/a0xg2byo/m7nvmbTrLGN6FWvOLzpZTToQkbnAUcCQ8JYujn5Ayr3rIjIOmA28BAxV1U3Opc3AUOd4JLDO9bT1zrlNdCEiFxFqhWPMmDGpFsOUi5TWYfP6pj2NYcuc+89p3tvhef69iVd1ljHlzwK2YpNtC1sVoWnwFcROjd8DnJ1KBiJSB/wN+KqqxqyPpKHmkbRfNap6i6o2qmrjkCFD0n26KSfuACrmleR1l2jcm8QvRxZa9rR7kk8vllWdJSK3ichWEVnhOhd33G2c557vpHlHRM735K8xJlesha3oZLusxzPAMyJyu6quTff5IlJJKFi7Q1XvdU5vEZHhqrrJ6fLc6pzfAIx2PX2Uc86YWKm0sOV9lqgpBtnWWcDtwI3AH13nwuNufyIi33IeX+F+kogMBL4HNBJ68S0RkUWqujODMhiTB1aXFRuvxrBVi8gtIvJPEXky/JPsCRIaUPR74A1V/bnr0iIg/O3zfKKbMi8CPuPMFj0S2O3qOjXGxb0OW/QwqMEc3jM/Y9iMZ9KuswBU9V9A11kgicbdun0YeExVdzhB2mPASZkX3/Rmal8MeyVPNn8H/gr8BrgVCKT4nKOBTwPLRWSpc+7bwE+Au0XkQmAt8Ann2sPAycAqoBm4wJOSm/IjCRbOzWkLW/f7xV7P5n420yAHMqmzEkk07tYt0RjcbmwMroHuQVm/U0+NHK9oWtE1eS4KkPt7mLR4FbB1qmpay2yo6nMk/iSaHye9ApdmUDbT2wTjf/5q1yDNy62ptiyP3MVrGrSKMwfSrrNSoaoqkt0LS1VvAW4BaGxstP/5vVTb22/HPK4cNixyfPHjF8d9jqp6MxteFTTb7zHGa151iT4gIpeIyHBnAO5AZ8yGMXm36777XI8STDrI1T6iOdjpIGgBWy54WWdtCS8x1GXcrZuNwTVp0fYuM8JTCcS8ahW7+zPw3r+8yct4xqsWtvCYs8td5xQ40KP8jUlZ56bNcc93a2HLidwu62E842WdFR53+xNix926PQr82DWD9EPAlRncy/QWXd/4CYKxTQNg+M7kadL2xiJv8jGe8iRgU9XxXuRjjCd8robjBJu/S66Ct5yMYTNey7TOEpE7gXnAYBFZT2jmZ9xxtyLSCFysqp9T1R0i8kPgP05WP1BV28LCJCS+2ICt7zHHxE3340/6+dVvnO7LYBD8tpxgufIkYBORWuDrwBhVvUhEJgFTVPVBL/I3Ji2++E1SQdyzRPO8Dlvb3hzcy2Qq0zpLVc9NcCneuNvFwOdcj28Dbsu81KZXcbWwTX19JeKLP4Jpy4D4k6xM+fFqDNv/Ae2EVhCH0NiMqz3K25i0iM/1DTNBC1vOltlIlO2DX83N/UymrM4yxc0VsCUK1rqy5T7Km1cB2wRVvQboAFDVZmwtAlMoqVRuAvnd6SCXa8CZDFidZYpbJoNXLWAra14FbO0i0gfn00pEJgBtHuVtTFrcYz80hXXYxMvP6RxXmH3qK3Oafy9idZYpchnUS8EcfDGceCJU9/M+X5M2r2aJfg94BBgtIncQWhR3gUd5G5MeX/xBt127C3w1udgow77hlgirs0xR6zrpIGzN7jWJn5SLL4w+v7XcFYmsAzYR8QEDgLOAIwl9LfiKqjZlm7cxmfD16RN9ELMMW2wLW1XDYu9vnoOKra25M5fZ9zpWZ5mSkKBL9NS/nxr3PHg0hu3dp7uUw4d9ES0OWXeJqmoQ+KaqblfVh1T1Qav4TCHVHX9c5DiwYzudO0OLFL214y1XKo8roKq67uc8iq7qB9a48vQky17N6ixTEgo1hu2Pp3cph8++KRYJr8awPS4il4nIaNvpwBScq6Lb8uP/5Z25oYmAd7xxhytN9NDbBXXdTXqu4/oRHuVuFadHrM4yxS2TgC0XY9g6mqFjPwQ6ek5rcsqrMWyfdH679/q0nQ5MUcnp5u+RbBPc4wPfyDzLRNtrmWxYnWXKTy5awlY/Gfq97G6YfZ73+ZuUeTWG7VuqutCD8hiTvQSVVt63popZyiOLmagJGu1MZqzOMqVg/3PPpf2cnK7D1tGcu7xNSrwaw3Z5jwmNKbBEC+d6UsmF89BE0VXm94jN0iK2bFmdZUrBlh//b/pPymnA1pK7vE1KbAybKT8Jt/PMQ5doom5XjypSi9c8Y3WWKT/ZjmFL9nxrYSs4G8Nmeo2YLtFcTTpI2MJWZHkaq7NM2bh9vo8FTwSzrx/+c2viay/cBPO+lV3+JiueBGyqOt6LfIzJpaB23fw9fJSjWaIe5euriDaEW7zmDauzTDlpdzZA0WCWFcTO9xJfa9uTXd4ma54EbCLymXjnVfWPXuRvTHpSqbS8jnx6GMOWRaTVt381APWDati/23ZP8oLVWaacaKTHIMt6be/mxNcOuyC7vE3WvBrDdrjr51jgKuA0j/I2JmuqyqenfzrhNQ/vlMJxZnnW9qsC20PeK1ZnmbIRqV2yHcO28t7E1zJZF854yqsu0S+5H4tIA3CXF3kbk7Y4AVjr8uX079c/8ljysQ6buwvWg6BQRGyWqEeszjLlJNLClov6oXYwNDfB0jvho9d7n79JmVctbF3tB3ocIyIit4nIVhFZ4Tp3lYhsEJGlzs/JrmtXisgqEXlLRD6co7KbMqTt7ZGxatXtyqTtW6PX8rHTQTY5OtnYDjE5lVKdZUwxqnY2IQi2tnqf+Vm3hH532rIehebVGLYHiH5S+YDpwN0pPPV24Eag67iR61X12i73mA6cA8wARhCalj9ZVQNZFN30Em3vvQeD6gH40gNB5rz9EBdM8bO/j3gznC2yDlvMyQTHmRGnS0JVI8cmM1nUWcYUnVP+E2rN33nHXxj2P//tbebV9d7mZzLm1bIe7uCqE1irqut7epKq/ktExqV4j9OBu1S1DXhPRFYBc4AX0i2sKXNxmqF23XMP2rgAgIkbQ9erOkPNKh7fPH45vOgSddrDNaiI3wK2LGVUZxlTjBr2hX7vefRR7wO2YKe3+ZmMZRWwichEYKiqPtPl/NEiUq2qqzPM+ovOLK7FwDdUdScwEnjRlWa9cy5euS4CLgIYM2ZMhkUwZaUzcUNspb/Su/skXJw3i4At3CUabmHLPKdeL1d1lohMAdxbXR0IfFdVf+FKMw+4HwivnXCvqv4gk/sZ41btxFSBpibvM/d5WD+arGQ7hu0XQLzFWfY41zJxMzABmAVsAq5LNwNVvUVVG1W1cciQIRkWw5SqeAPzg62t3c4H2g4AoE9FHy/v7i5I/GMPb2HS9gu8r7NQ1bdUdZaqzgIOA5qB++IkfTaczoI1UxL8XnXEmWxlG7ANVdXlXU8658ZlkqGqblHVgLPf3+8IdXsCbABGu5KOcs4ZE0Pb2rudCzQ1RSYXSHi4Wfuw0G9Pgqk467DFu555zohPss3K5KDOimM+sFpV13qUnzGF4+kXWpONbAO2hiTXMvq/LCLDXQ/PBMIzSBcB54hItYiMByYBL2dyD1Petl5zTbdz2tkZZzZoLiKfHI5hC8drNlU0Gw1Jrnn1yXQOcGeCa3NF5DUR+YeIzIiXQEQuEpHFIrJ427ZtHhXJlKOO3bO8yyyYYNhInwHe3cNkJduAbbGI/FfXkyLyOWBJT08WkTsJTRqYIiLrReRC4BoRWS4iy4DjgK8BqOpKQrO4XgceAS61GaImns6tW7udi9clGgmAcraXaPytsDLN08aweSKrOqsnIlJFaAHev8a5/AowVlUPAX4F/D1eHjakw6Sqsv9SAJZM8GAS0uon45+vH5p93sYT2XZOfxW4T0TOI1rZNQJVhFrHklLVc+Oc/n2S9D8CfpR+MU2vF4jG9kHna4rP2XcvL3uJetjCZhFbVr5KFnVWCj4CvKKqW7peUNU9ruOHReTXIjJYVXMwUtz0Bh27D6Gy/2u8MUY4bHWoYujYvJnKYcPSz8xmgxa9rAI2p1I6SkSOA2Y6px9S1QShujGF1+kP/a5wArbYTeEzpHHGsCWcMZpm1uED1zpsJjN5qLPOJUF3qIgMA7aoqorIHEI9HNs9uq/phTRY3e1c6xtvZBawWb1S9Lzamuop4Ckv8jImV8Itaeo6A3naS9SDoNBa2LyTizpLRPoCJwKfd5272Lnfb4CzgS+ISCfQApyjFn0br2W6n+iGrEcEmByz+bqm1+g+hs3DFrawQIf7hvGPM2Rj2Iqbqu4HBnU59xvX8Y2EdnYxJiN72ruuSBOqE7a7NiPQQIZDu+Nt/P4xZ4TSlFNgl016LrRc7SVqTNETDQdAOZp04PnCuc7DoIVsxvQWdccdFzn+4Qs/jLnWuXcaAC9Pjk46eG3L0sxutC/OjOS+zqQXEesyLQIWsP3/9s47Pq7iWvzf2abebFnuDQMu2BiMKaZ3CIQaQyqh8wjOS0gCCQR+CYFHKEl4AWLgUW0SQg29GbBNM9jGxjZuuMlFliyrt11tvfP7Y1errdJKWkmr1fl+PmbvnTt35mjZPXvumTPnCIOGeGk9kuphC924HOZhS8LYUo1KEAYNtgkTADBlZQbb6px1YX189ilA+0YqgH9uWNi9Cd3N0W1tT4kNe6BqI+xa1r2xhaQgBpuQdrQpukiilkQDr8kx2No2HYSMlaS0Hm0eNZMkzhWEwUMMj9bKytipR3XIw5zTlkwhAgNXfuN/XXBOMgcXuogYbELaUXjJ3IT6tX34kxr3HZZ8MjkxbEabwWY2BYYSi00Q0p3OQjU8jTPb+4YYbB6JTE9bxGAT0g7ti+0xi6cAvTqJ+YfClkSTlDi3jWClg54PJQhCamMpGuJ/HTky5nVPw5EAtGy7NUy7KNEPaYsYbELaob2ezjsBKmD5+OKVZOnW5CFGmhEnnq2bKIlhE4RBQ/ZR/jLaJb/8ZYf9tLcgTDmYkhiSK6QWYrAJ6UecXZS9mtZDh8SwxTTOkpjWQ1xsgpD+aI2yWlG2eEFpsZ/gTKIe0hYx2IT0I07iyMgl0V5TbOVfx5g8CUahJM4VhMGDNrrlVr/1ZQNvXV3nHTujeDKMPqLn4whJQww2Ie3QcRRdpMHWayuMjpo2QUKF6vGw7R62Hg8lCEKKo7XudhzEtmOP69nk446FG5aDLbtn4whJRQw2If3QGkyJf7STW/ydkNi1XkqcKxabIKQ/mi7psd9eaQ4791RVdX/uq97r0tyJsOHTchY9uSGpYw42xGAT0g8jtsEWaeiYkpkwNyyFh69twtDJez6F7DoQhMGD0bUl0XHVETomxZ7rPvn3Frav6oERKYjBJqQh2uj7ogBGrHQeSfKwBRAPmyAMHnz1dWiHI+H+UVpBnu/SDjHYhLRDG0ZCHrb2GP5kGEAhYxgx8rolI4YtxlSCIKQnjW+82UmPcIvMiFB5qrse+VmXR7cNOaB7YwlJRQw2If1IMPZDZ2zrpfl1+Kv/pPvDBV4dzW4AfF5JtCQI6YyOs9O9I4wI++w3n9zUvcmzCqPbrLL5IBUQg01IPwwj5tNl1C7RZHmqIr1nXmdwxrh9usGOr6sBKF1b3eOxBEFIXZrefa/L9+gIlbeqanX3Jo+lq0zm6LZuUl9pT9pYgw0x2IT0o5O0HiMa/OfBJdGeGlOR9zsbY7Qnbx1TQtgEIb0xWlo67xSpdiLy6xY3dXPyiSdFt9lruzlYNE21zs47CTERg01IO3SctB5RMWxJ87BFLF8EY9iS5GGLvFcMNkFIb7oRf7ZldPg99y7oZsm9nKHRbU17249berjTU/RXt+lXg00p9bRSqkoptSGkbYhS6kOl1LbAa1GgXSmlHlJKbVdKfaOUmtV/kgspTby0HgFN4U36pz7SoIoRw5ZEt5jFJs9ZgpDWJGSvhXeKXBLttcn/enCXR/R52h9qZZd79+lvzb8AODui7RZgsdb6IGBx4BzgO8BBgX/XAY/2kYzCQCNODFsbLqv/tc3D1uNdopEKKKe47UJop57NAcw8fSwAoycX9XgsQRBSmDj6y2qyhnYKv6UXxQmn67ps+ZulvSDH4KNfDTat9adAZNGzC4CFgeOFwIUh7c9qP8uBQqXUyD4RVBhQxCtNFbweuJQ8BRdQYDMu8b/acmIJ1eNZrDZz2HRCaqGU2qWUWq+UWquUWhXjuqwSCAnR/P6imO1Hjjgy7j1Oa3Sbz+jGsmjwgTOEX6zp+jgh7NkYEgMn+qvb9LeHLRbDtdb7AseVwPDA8WigLKTf3kBbFEqp65RSq5RSq6qrZUfdoCNODJsRiDXLDcS8Jn2XqMkSmChGpYOepPVou1USYQ4ETtFaH6a1nh3jmqwSCAnh2ro1Zvvs4bE+Vn60KVpBLFn9ctcnLxgT3dbDPGz2BlfweO+39T0aazCTigZbEO1f7O7yL53W+nGt9Wyt9exhw4b1gmRCSmNoiKG8nF4nKsSISrqHTbV5wHR4e1hb9xF7bcAjqwRCQnQ1TMPTNAOAm68KT79h1CRvd2dPCFV/65aUxe8odEgqGmz725RY4LVtS0o5MDak35hAmyCEow1UDPPG0Ea4Vy3Zu0TbchXFqiWajMkCy7wStJuyaOADpdRqpdR1Ma4ntEogKwRCV9WFs/wHaMPM7uERGxGSWi+5+xiG6KxkkIoG25tAW22My4E3Qtp/GogDOQZoDFk6FYQghsuFstliXgs12KaVJUmJxFsSTbaHTVxsqc7xWutZ+Jc+5ymlTuzOILJCICSiL3TYQ6mZ1r3RJaXMlQl62BLRT6r7yXO1GGxJob/TejwPfAlMVkrtVUpdDdwLnKGU2gacHjgHeBcoBbYDTwA39IPIwkDA60NZLFHNkcsMFyxvf/qsaa3pwYQRBlvbU21YWo+eP+m2F3/v8VBCL6C1Lg+8VgGvAUdFdJFVAiEhfLVdX8r02aPTbYy5/8XEbk5EqRxxRdcECh1eDLakEP2r1odorX8Y59JpMfpqYF7vSiSkBzpupYN4S6JlzWUUZ8XYHZXQdG0GW8SSaKRMPabNYkvCUEJSUUrlACatdXPg+EzgzohubwI/V0q9AByNrBIIXaTHKYjiUb05odm7ixhsySEVl0QFoUdo7TfYRv7PXYz+3weC7UVrd4cZbEnfdBA02Izwdr9QPZ8maK+J8ktBhgOfK6XWASuBd7TW7yulrldKXR/oI6sEQo/oLH7V65gQ1WZfvqLzgfd82Xmf/FGd94mDrAokBzHYhPRDA0pROHcu+d/5TrC5aMPeMCNNhdlTSSgdFZXWI1Ko7o7vfwk6DUX5pRxa61Kt9czAv0O01ncH2h/TWj8WONZa63la60la6xla66hcbYLQEZ09rLXuuTqqrfLOSEdvrIETUCon3NR5nwRZ8+Ee9m1vSNp4gwUx2IT0Qye4JBpxrQcT+l96adNBbYW/EHRoeRdBEAYfneopHZ09111aina7O7kvkU0HITp1b8+eNb74z3Ze/evXPRpjMCIGm5B+aJ3QememJ+TYktmz+QBsuf5Xjz283X/S7eFXvrUTgMrSxuhhBUEYNIStBHSheGj9y50l0O2iUnkyKsxc6APEYBPSD61j1hLtyMOWZcnq0XxASAxb7yTONZlNSRtLEISBh5HAbvOs3/8q+j6Ho+Obqr/trkhCHyIGm5CGaOK52Exx2i2qJxum24LMTOHnSU6cqwLVG8RcEwQhHuN/dBXWQ6eHtXn3V8XpHWDV012f6HXZM9PXiMEmpB06Tgyb8hlxrZ0exbAlUuwzeYUOBEEYpCTiYVMWC8NuvSWsrf5f/0q+MGufS/6YQoeIwSakH4FdopFUVe0knuXUs3JPER42HdEOSUmcGzmdIAiDi0QfLJVJftrTEfm/KqQfcTxsze7muDFsBj0wqHQCS6JJMNhMJsnrIQhCO+OHZke1OdxelHlg/LS7nd7+FmFAMTD+rwpCV4i3JBph52wKLRLUIwdbwBiLqh0VarDFqn7QNYIxbGKvCcKgJHxJVDHvlAOj+kz7wyJMPYrJ7TueuPHTHq5uDC7EYBPSjzhpPUw63GhbfWD7xz8pedhUxNcpVBEZSTDYxMEmCIOaTHN4+iFTnMDWzIkT+0KcpGBI2aqEEYNNSD+0RsXZAKDiHPes0kGbhy1iSbS3PGw9HkkQhIFIUWZR2Hm8fUjm7Oil0lTF8IpGSxQx2IS0Q8cp/m6KCCMLK03VEzPICMRhWDICg8UYKxkeNlPkkqsgCIOJJ9c/GTw23CVxd467vb1UFeWsP4eftzb0WLf5ekvWNEQMNiH9iBPDVtIYnjjXlCyDLTKtR2t9RDtJ2XQgHjZBGNzsd+wHoGXr7WBkxF0SPfj293pHgNkRtUrvGw+PHtujIQ2faLREEYNNSD/ipPWYXB6xhJCs4u+R1dlX/l+sCXowvh/ZJCoIQihKwa/POLjvJrTGKOHXwyoJ4mFLHDHYhPQjjocNiOthSyQhZYfz+UeP005SPWxisAmCAKCUIttmTqiva9s2tDf10mh43T0PFxksiMEmpB8dFH8PNdiUBpMvH+hhHrbggJGT9lZpKrHYBGFw49cFJhXfeX/rcf/F68e066TS887n2+kzqF2woA/kSxzxsCWOGGxC+hGxS3T8v2OXUFFaY/GODdyShF2ive1hU+JhEwShHYViREGMZUpg7bCD+Pcp0d63qnvvY+fF38NTUZE8QdydFJfvgGQWgUl3xGAT0pDwJdGsww4LHod52IC2r4CvJ2k3dEQMW6gcwcOea6WMHEvkqIIgDEKCQRgKvnvoSJ658khmjStM+H7npk3U/aubtUBP+0N0259HwtrnO731srvnRLWtfKsUr0eWRRNBDDYh7Ygs/h5aVy8sD5sGtKn9nu7PGBiwo+Lv3TfYZp09HoBx04Z2ewxBENIPk/J73k+ZXIKlq+WofN2MZzv2F7Hb17/U6a3ZebYoNblrfS1rPyrrniyDjJQ12JRSu5RS65VSa5VSqwJtQ5RSHyqltgVeizobRxiExNklGrwWwL/pwP8V6JVNB66WkD7dH99s9o8rlQ4EYXBRdNllnfRo1zl5GbHLUW0dFftOX3NL7AudYbbGbm/a1/m9CsZMif7Z9rjEw5YIKWuwBThFa32Y1np24PwWYLHW+iBgceBcEMLpYJfoESWzgsehHrYeGWyxPGyGAR/cHi5TN/F5DUwWFdTNUnsv9VBKjVVKLVVKbVJKbVRK/TJGn5OVUo2Bh9C1SqkYa0uC0I72uNuPw2Ji2zcdtGE2Reu85s33sHt4bF3o2r69+4INmxLdFs+QC0Gh2mNxQ5BcbImR6gZbJBcACwPHC4EL+08UIWXpYJfo8OyS4LECdOArkNTEuQA+NzTsDunTfYPQ59OYzaa45baElMAL/EZrPQ04BpinlJoWo99ngYfQw7TWd/atiMJAI/ekk4LH62vWR12PZfxE9GBtcXSBeADt9XRfsBEzotsqv/E/qHbCkedF1zn1SWqPhEhlg00DHyilViulrgu0Dddat/ldK4HhsW5USl2nlFqllFpVXV3dF7IKqURHtUQj0nq0edh6tumgrZZoWHXSKJm6jYHfFpQl0ZRFa71Pa/114LgZ2AyM7l+phIGObezY4LHXiI45C3WqXTp7bNR1gPXDxsQe3BPDYDspwUWri5+I3b7gnI7vUzB8Qn4MUWSraCKkssF2vNZ6FvAd/E+rJ4Ze1H7/cMyfLq3141rr2Vrr2cOGDesDUYWUooMlUSINtmTEsBEnhi2sS/fH12iUUpgC2lmWD1IbpdQE4HBgRYzLc5RS65RS7ymlDolzvzxwCgDoEI9VrFWA0NJUp0+L6b/A1Twrdvu27Ww56ujwpdaJJ8bsG0U8/brny07ui+0V/PaLBOLfhNQ12LTW5YHXKuA14Chgv1JqJEDgtar/JBRSlXjF3yG82ZSsXaLBtB6m6LbgeQ8MtsCfk5nrjxFpbXF3cofQXyilcoH/ADdqrZsiLn8NjNdazwQeBl6PNYY8cApBQvRITB2VQJSESw+Je81oasL+2WehE3ZBuOSy4JZlNNW29tv8A4GUNNiUUjlKqby2Y+BMYAPwJnB5oNvlwBv9I6GQ0nSwS9TqbF/6tPggqR62Xkrr4f97wJbl3wXmdkq8RyqilLLiN9ae01q/Gnlda92ktW4JHL8LWJVSxX0spjCQ6IKHLR5eU+zdo200vh7yM9rLG5o6ktbe4GLL8spenX+gk5IGG/7YtM+VUuuAlcA7Wuv3gXuBM5RS24DTA+eCEE4HS6KHvrIueBwawxYrPqRL8/lHhONubGuM6NMTD5sO7K5qG0qWRFMN5V/neQrYrLV+IE6fEYF+KKWOwq9/a/tOSmGgoeMG8auQ/7YzujArqqcvxPN/22XRlQ+a3n03dMYuShiDOwriGn6iuXpGx6Z3P6G1LgVmxmivBU7re4mEAUUHu0St9vblRL/B5v8KrKhcwSnjTunmfCGbDrICOYa2vh+7T7fG96+2tsWwSVqPlOQ44DJgvVJqbaDt98A4AK31Y8Bc4GdKKS/QCvxAy/9MoQMshYUdXo98Ll12y6lMuOWdqE4/OvsPHJJ7H9vGxPbOB59x80Z2X9hQGvdCYfQmiM52ta58ayf7djRy/i8OS44caUaqetgEoftoHVcxhO4SNWnQJn8NvOc2d7NMi3/CttHbNajH2X45s7DHHjYIKf4uHraUQ2v9udZaaa0PDUnb8a7W+rGAsYbW+h9a60O01jO11sdorb/ob7mF1GTotdcAYB3dvtE4VthGR0ui3951Ni9edwwA9Zn5rBkXP8/8trcn+A+KD+pUthaXlzMe+ITKI34NJ9wEdzTCCb8J79S2677sK3ju0nZ5AzrshkfjPxyXbapj3/aGTuUYjIjBJqQfOhD0FQMVtRkgCU7m0MJ+sXJvxN/QnPDwSqmgwZZAqiNBEAYyJjOYw5cvfUaodyyi+kmsIZTi6APay9k59lwLwKW3Rus8n90NtryERFuzp55tVS3ctP9sOO3/+Rsj64s27wdHHTw3F7YtihpDKcW8x06NO4ejSTZWxUIMNiHtiLVLNP+cc6gYEh4TYtKA0Xl27kRmBPzrlsFAsxADzdUIldFJLxPG0GHOO/GwCUKaEyMO16sTi7P9xWkHcfGs0dgs4T/v2luA4fbvGP3+LdGxbJfk57F5ylQ2T5mKrwMdYw7I9W1lxCbon4Tss3n6TLh/IjgbEpI5Eqc9Okfcoic3sOGTvd0aL10Qg01IP2LsEm3xOVAaWlsagm1KQ0trRhLmC83DlvxqBJuW7cPR6PZ72ZQYbIKQ9sQy2GJsjIqVIPzXZxzMA5ceFnNYw+tPWqtjuObu+Ef78V8XfRtXNLfP/9BbE5leyJod954g9bvDTi3W2CZIdkG7XnY0uWltdrN9VRWfPL+18znSGDHYhPQjhrLb2bQLk4bRde1tSvufOgGunXFtTyYMDEivV2hXJiWbDgQh7YmOw+1RNZY2DFvcS6HGgPeN/8Ttt2ZPQ/fnf/nysNOr/noCF998RFS38m/r8QUMw2d++zlP3/x58Fr1nubuzz/AEYNNSD9i7BLd2bKbAnt4mz9xrr9jpiWzB/O1LbOGeNh6yagymZTEsAlCmqMNI+qhMzyGrXs4980NHq88OP5qwMUfPxfzwXBtWQMPLt4WIlNIH1sCHjZnY9ipNcPMyEkFXPfQSWHt65aU8di8j5l//ZKoIV7681fMv34J21dX8c3SvTTVDJ5ku2KwCelHDA+bVpAZERYRWpoqOZUOQgLNVO98tZRJyZKoIKQ5RnNLVFusGDazqWshGNqbj3PfRQA8cJGJ310ZHcvWxtKFrwHg9Pg4/x+f0+Bws7s2/Kl35c6QJYuSmJXWwqkrjdlstcWXIx6LntjAZy9u5V9/WN7lewcqYrAJ6UeM4u9GDL12/CZNjtsfh2GQrFqiwZ0B7ZdLDoGM6ILH3UEMNkFIb7x1dTS89BLa5QprzzCHxtv69cyRE+Kn6mhj173n8t4vTwiea58/ua5hUuwcEd/gszz5CAAXP/IF3+xt5LA7P+SXL6wN67OjOsSwNCe44/7tXyfWL0EGkz4Ug01IO7THjbKF7/7UcfTS9zZs8F9Pmoet7SsVMt7Yo8CcjN2obUuig0dBCcJgw1tdE7M9S4XXBD196vBOE9G2MXVk6ANjYvcMqynHMDRb9sePGbv99Q3hDT/7Ei54JKypwTsqvM+qp/wpP4QuIwabkHZonwGW8Ke9WB42AGX2J85dXtqDCkGL/xQYLE5aD7MVuln6KtKQVGYx2AQhrYmjq/6+OHznZkVD92K3dAcbDyI54PfvdpjiI4rh0+DwH8P444NNqwrvj+5X+nHiYyaAzzM4AnvFYBPSD58PFRFDFs/DhsWO1orlO2M/1SZE2YrAQYiHzRXyVJpd7A+2Da1+kCCNVeFK2SRpPQRhUNLkDAnC1WDq5q+34QovP3XljeHxY86QxYAfbPmI729ZDEBWHP014ZZ3KKtzsL0qROdd+Q78sQF+v48mRkXf9MqVsOrpsKYLbjyMi26alfgfEsJj//1x8HjTsgpWv7+Lvd/6Kya4WntQJzrFSMlaooLQE7TWUVnCc+M9jGqF/7nFh2HoYOmUhGkMSeQYuungw//X3m7L8b/63GDt2m7U5/4YHlArMWyCkObE+Hq3uFtwmMOTb8fKwdYRu+49F4CyOgfnvPVnALwtk2nUJkqHr+eA/eBT8NPfmHnpXv+O1Ms3+2siV2UX8tvVzwNw5Rm3UpkzNGzsE+5fGjaHX0AFtmz2bQ/fGRrk7V/B7KuCp2Om+Jd8r59/Mo/N+7hLf1soS/8Z7okceWABF98UnTpkICIGm5AWGK2teGtqsI0dC4YRLOPk9Dp5dtOznLA5tpFz4rY6XjzDijJ58WmNKUQJ1lXYySnKICOrg6/J4yeHnKjYu0NNgfu7uSwailKqtzKGCIKQosx5fg5E5PhOMHwtirFDsjli+BGsqvya1rIrAXj9wvsoVXU4bbEHbjPWAJ758B6+c+Ffuzd5ApjNJv+ycBf13BM3fkJWfvRyb1yDcQAiS6JCWlB2/c/YccaZAGjDR6tXs6K0litevZeH1zwc976SFhd5dhNmw40RYQk9f+cKXvvb17FvfPlKeOZcsFe3t6l4BlvA25eEPErKJEuigjBY2Nm4k/e2fRHjisLp6b4+WXD2Alq+vYfCbP/65ym1Q3mhvommnMSswNsspRxSuxMVoyB9h0TqxzsK4Mv5Ud2+f9tRAFz0m8SXSN1OX1QISbohHjZhwNPk9OBY4Y8jO/H+pTzuNVi7t5GbH19OxvB92IZ0fP/T85vYMGoDM/e9wePXncCJBw8LBvvX7m3B6fHhNTS5GSFfl42vxhipEw+b9uG0e6ja1YSj2U3xmFyKxyRWcDk4gyyJCkLaUNtaS7Y1myxLVntjiM10xftXUOeMvaPS7urZA+DTV8xm8oh83lpXAR/621q23cKQiS9yww2lPPJI/PGPf+URjgf+dPQVLB85PfFJf74KHo4wwhb93p8GadoFoMxQMJriMbnB4vBX3HccdRV2LFYTTruH0jXVfLu8skt/6/zrl3DdQyeF5Xtri23rcAUlxRAPm9BvuEpLcaxaFTyvfuQRGv4TvyRKPO57rz1mYU+dA121n7KGthxGiRk30yuaee3t21jyvj9mbG1ZQ/DaeQ99xkm3f9D5INqg0e7j+ZoHmV/5GpXuyf72oIfNy7uPfMNbD69j8YLNvPg/XyUkWyhS6UAQBiaNDg8PfuSvEjBj4QxmLJzByS+dzBFPzOWNteUx74lnrKEtlOSYoW5nt+U5dcpwRhdmcf1Jk/iRZQnDVCPaW8hUfRs1BYl52o6rWM97r9/Ee6/fxM/WvcaEW97hgvnLwvRnGEMnxW7/4Hb4+wz432nQvD9sl31OQQZjpw5h5IGFTJw5jNOumNbFv9TP47/4JOz8yV99ypO/+pS6CnucO6JpqXfy4t0rsTe6Ou/cC4jBNkhxeBwxiwnHwmf4uPWzW9lSt4WdjTv5ojzcRe8pL2fzlKk0L1ka8/57v3yYGQsOp9Hh3+U04ZZ3mHDLO5Secy67f3JZsF/l/MfZcs//4XZ6qdrdlPDf4vL6LZiy0Sfx7td3AXDy3jWM95g4srZrX+5h6/7F+7vep8HRviMrb6+Tq5oz2bej41gIt8vBPW9WUOcdB8Dylh8B0Fy+GYDX33+fuspw5eDxGbi9XbDAlEq47JW90UVlafrEbwhCsvHs3492uzvvmARm3vkB//vRVj7atD/YZvFZsWVWcOOLa6P6u2wFZHiiyz0Z23/JNU1ZXGVfDA8dBg17eiZYzbawU6Vg6aVL+fGN4/nZDe0eqce+E20unF62Onh8/s5lAKwra+DF5z70/yYsjf5NKG+6mprC/xfVHuRvB8OCc2HrB9BaD77Yv1PjpnWydBKDPZtqsTe4wkpeffDUBuoq7AnVKF3/STk1ZS1s/mJfl+dOBgPHF9gPbChvZEKxhXL7XiYPmRxs97W04Fi1iryTT455n/b5aHz9dQouvBBljlFyo7EcGnbD+GOjLj37RSmvV9zLNvuXHDJkKi8UHUvZjIt4YsNT5NnyOGnMSRw18qhgf+fWrez+8U844O23sA4fHjaWvcHFjQ/fyZwLD+SqGVfClvf48P3X+Hjcf/Nm04/wthxEa9nVAKy/40zyMsOTuy5/YwcNlQ4cp27j7dK3ebv07eC19Ze371ja/9e/smzO/1D04Bd8b8Ov4RdrwsZ5buvjoGDRpkounT025nsGsGnqFdQUH8qev6+lalcT//XwSVissUuWaMPAtXUrNY88ynmbSjGUiW0HXcpOzzmcuOd3ZPncXGrPAPuhrDso7pRRONzrufmTm3no6MXBthFev6Kq/mojI7UDqrfGvNdtmPixeRGfcrRfRu2/z7XhbfIA04ZXgN+H3XP2Ax+zo6aV564+ilkjC1j7URkNVQ5K11QTC5OJqDxsFQ2t3P76Bh764eFhy7YLfudXoDc8ekrMBJsvryoD4JIY/0+01tz44lp+eNQ4jjlgaNT1NlbvruPyp7/is9+eQlFOeMDvM8t28v6GSl78rzlx7xeEeNS/8AJ5Z52FpajzbP7g17u7f3o5xddfT+4Jx3fe3+1m+0knk3/eeYz+S4xcYb1Enb3dQLxmpT94/5H8VrTbTfWjjwZ/V5Yd+2cuW+3hyWNuCrv/yprxZGkTe3adDCMeBEctFI7ruiAeJzwwxW8Utclz/ESuOeEAirMy8Vb+il1OL/N//Dxu+xq+nGpixWTFM3+Pv1T63ut+WT2WLFozh7Ljl7fCsX8OXr/sjh/w+3fXAVDNKLx3n8+M9Y9FD7R7mf8f4MoopuUn7zKkuIRlG3YyvCiXnJ+M57zjJ9FS76SxqpXX/3dN9BgxeOuhdVFtteV2nr/TH1Iz77FTeX1NOevLG7n93Knc8eZGKhqdTBiazYtflfHYYX4P4aaKRmbHGN/h9jLnniU89pMjmDMpvt7sLmKwxWHzvia++/DnTJn5EuXur1n545XBOIOKW26h5aPFTProQ2xjxkTdW//CC+y/638wHK0Muewn0YM/cgy4muCOaO/HH9/5ityDvwRgY91mWL2I2ysX8bXdnz7i2U3PhhlL9c8/j9HcTPPixQz50Y/Cxlryz80cVn4aLy592G+wvXIVZ3gcXLvvAvKmgiW3/clqd62D6aMLwu5f/d5uAHKP7/jJw5SRiSujiMqRc6AuWvFluTT5djB8Xhzrv+HibR/z+QHtxuV7f7gW86wZNOX5lU7VLr93beMJp2BriG24hFICGIHYMa81N2afzw5RnLCx3dDxHykUGg34zBkobTB5r+aQ8kMY88wVbMKvfA71GPiAfU8+x5RN/6HUdQxW72zW1p9Ha1Yxw6w7aDUKcTwPh/qGBeeo8ExnfuVrnDX6Hl6sfDDQGv60ePEOgCzWPrCetZ3+peCorqG2PIuFv1/G8ZccxJCROfxtyVaWfFvF+xsqmXtE9OfRMDRmc7TBdvMr3wCxDTa3z+CNtRW8u34f2+4+J648jyzdQYvLy6rd9ZwxLfyB4U9vber071n+xg5GTCxgwqHFnfYV+hZ7oyto9P9s/smYzH23IOPcupXKO/5E84cfMe6pJxO6x9fYSOvq1VTcfDMHL/+y0/7a6/8uNn/wAfTAYNM+H96aWjx7dmMuLsYybBi++nrsGRpTQQHal4XNYsKctQtlrWfBShfkQp6jXR899fF8fveru7lycT2LPvy/oDFg0dEVUrJ0xEOs2w6f3A+1O2DEDCiaAFO/67+2ZwU87d+MhTUHbtoCGYG42bd+EWasAdz+3fYViWybhSanl4t+/Atu/OxqXjnvFea+NZd580bwpxeKKa6NqHIQwjfTr6cpfyJHrr4nrP33L4QbTJbb3uRvU07mrLLNTD69GmuWgfZBa60Na44Ps80ggxoynvI7KY4HmkyKc5WiaXYV+faN5FYsAo6JKce8mX9g/ro748oZyT9v/4IlTc14leYDlcHCL3eRYyjKvGaabD7WvO/3ZjZ/VcvGg8s55ITR/hv3rAB3M2e9aqKx1cMPn1jOrp+PgIxcGDa5gxm7hupRSZ4BwOzZs/WqkDipeLhaXXy18C3coyfS0LiSDVUNZG3NYN2YNZgML/895EK2uj8nw5THmH9/jc+USeVBYzAdaWZHKxQVTMTS1IiprpZJSzfitRbTNDqTvO+ezq7Nm/E4x1GZ8Sn1jq2c1GLGPWoUTZXjyCg0k13bwj7txhhvY+RKB9VDx1CduZIplQcy2reBzTklZHEMRY2VZDevobDFgT72WNa3+jB73RTvN2OYLIzduwSTNmjMm0Br1lA2TfPnuGm21XLoluWUjSoDyxnkNXkprvmGfSPnkO2owsQ2RpWXU9i4gzdPu5Em2y6m7ptIY+GBAAytWY/XkkVj4YHktJQztG4zXksmtUOm0WwspMR1Dg1F/g/lqIrPqRh1PFZ3MybDw87iXZz36RIKmnay6KjpZNquxGfpWi6yvsTkc2OY42cCt+LAQ/QyRV/wl8JWbm7I6rxjBHtyYVyg5F+F2WDczGIKLGae3LCXSV4zs44dhdPu4YMN+2kxaQoNxaj8TJY7HGRquOa0A/nu7DHM/3wH2RkWfnT0OMYOyeaNte1LOXkZFmaOKeD8w0bz1Oc7Oe7AYp5eFh5fc9kx41nybRU/PmYc00bmc83CVfyqzv9Z8M4dyxnThnPTy+uYNiqf6mYXn2ytZubYQv543jRmjUvMywKglFqttY71ADzgSFR/ReL2ubEFPsfa0KDAUdtEZl42rXV1/sTShhtlMvtX2H1elK8Vb/4ImjwtKGViz/Yqvl7of2C64v5jyclv/976DM2+xlasZhMleRlBD66r1YuzxUPe0EzqKlooGpnjT9MAfuPAZIWMXDzl5ez/y18pvGQu3sr9ZB4yjcwpU3CV7sS7v5KWTz6lbsECADY98jLfO3U69XY3Dy7eRrbNzK/OOBir2YSrtJSWpUsBhX3FcuyffgbAmsPzef2H43gg/3q27ijiyIumYd+7lxWPvknu5g18fngTx35TjNXdjMeaw7dT5/K+zeC0ScXs2FHPnbccS+GwTLZO8wfUb7zj+5xz8Pns/tGPcdnyKbvyD5x29Rnc9dCjnLFiDZb6VhoKDmTv6BMZXb0CPF6qh81gz7h6KuonoJQXa+FqplQdgzlgcLWaysky/D/4eU27sOfWY5gOR/kcFDXuom5Iu/HUmFFDgSv2Q82h2W/zjeO7Ma9NzFjOOUX3tTdcswTGBHKSPXoc7I8wukKcCCfcv4Syula+uOVURhX69c6uxl288zt/AfdTP54Xc06AJSf7d34e9dXdrDzytmB7R/cAjD+zmt0fFBO682LsSbXkjmyPGTtx3GjqzWbW7/QbT61K8aZ5OhZPAeW5lezPOJxJu6Zysu1dxhpfs7b5PFbqKzqcNx5n5s/j2ZYHGWFYeCK/kWubwp0aB8ybSo5VceIL7UbZL903UKZLeDXjDn/DJQthwgmQk7jHLZ4OG3AGm1LqbOBBwAw8qbW+t6P+iSq80DXtXkUbsXcSJoFxez7gwNI3gl+WrnLKx/NY2s17O+PUj+d1Wy7Bz+jieyivubW/xcCpDDJ1Yp9hV8M6MgpnUl30Ngv0aWQa8N9N0UZng7OMwsxoT9/fC1rxqIiEnJ3QXwZbZ7pJKZUBPAscAdQC39da7+pozO4YbC9teYm7lvtjOVd/fw1P3PgpJePzqNrdeYxOR4Qurf/prY08s2wXAL89ezI3nHxgmEeujZwCG1fcF1ievKMAMgvhlt1snjI1avyp325m65xj8dWHe332ZxUxe/mnTP/jomDb/XMP5dLZYyn7r+tp+eSTyKH88t6QydxNf+vmXwuXPjCDmlknArB1FBx55Pk0vvEmK2ffSkvuGCbNHsaOVZ2vADiUBjRmk5MMX+yHvWzHfqw2N40W/3cgv2knTfkTuy17KPNGXNR+ctUHMM4fssH8Y6B6c3jnEINtR3ULr6zey2/PmhwWUtH2W7li2H+T7YI1kxROGxyxTXNYqSbbBa7h/wC6brB1xBNnmfhwll/vmAzN3Qt9DGmGR841UVOgOHazwcvH+8sDnrjeoCUTbnnFHx/c3d+ezNYanFl+Q9mq6/Go8AdHM60cmXcN1U3XYsnSbHGeEnOc7w+9keK7v0l43ng6bEAtiSqlzMB84AxgL/CVUupNrXXnay+pQi8ZawC1Q2cwYn/Xdx620ZIbvZyWLFpyYpQnEbrE5Ff2Un5yf0tBwsYaQEbhTABOWlbDufYHaMw/gC2TfxjVL5axBnCOfpH9eZU8u2gXPz2r+8q+t0lQN10N1GutD1RK/QC4D/h+MuYv27aFTZ9/CsA7+97gEMNffujDhf8ExkcZa0PNayi2fE2p6xI85EcOx4bilwAY5pjKcMcMABY98xRK+R/wK9fs5axA361vrWbRzpG0NmcCo8PGsTe6WfTMEwAMaTwYGqHumScYEkMfLHrmCYa4syAn3KDPAT5a+CRnOdoDvXe/t4lF6wvJ312LJY5umVoR/Xd1hU/+9U/GB8bO9EHp7ioyckYF9eSubyqA6CVLIOzB/JuMdzBMLlThHuZU/Cqq64Rd73LArncomGjntfELAJj9tT+2rc3QaLbVk+dO3MscSo1nfPvJllKoC2TgrQZCrwHsbQkeFgBXTx9DbXnsXZTHn/o7xueP5wRHNY+vf4IdY2BH4Cfk0oBt8vpxIxkVY0/HpbdacFWdyV0fLOHQssRK9l27yODaRdEbtG57qb3tks9jx9cdvvbvVIw8Dpu7ibqiKdhzR1Ncsw6fOYP6oikU1X9LfdGUqPvyWsrIdNbSUDSZIVXb2T/8yLDrPrLwLTmEHUeeDB38GW/sv42rE/orO2ZAediUUnOAO7TWZwXObwXQWt8T756U87AJQjc59eN5LDvmLlyZXd8dNdDJMy/mp/PvTqhvf3jYEtFNSqlFgT5fKqUsQCUwTHeghBPVXy/eeRc1FcclLO/s1feR37yHsjGnsO3AuQDk2Cuw54yisGErs9Y+GOwrnvGuk+WoojW7hJKqVUzf9AwAjqwSlh/9x6i+h2x8iuHV/gTdS06ej9nr5KTPfxM8B1g3cikz98X23gwkTv14Hh6T4qIfXoPP7l9GNGfvwDb0E+aUf8vNrxqUDoenzzRz3kqDo7f0nX2yefJP2DdyDusLbuU7G89k75hTOPGz32DxtVtiPdG/bXnlEiEtPGz4H9/KQs73QmBLXghKqeuA6wDGjUts98zY6RupWjMEn7mVjOaN1OePwqaOQBs78OEh35uL17MKZc3CbcrF6ssHXYQyb6HeZiVDjSLT2YyhhuIztlHgnEBDbiUZ5iHg8eG2TsfkWU6r3kOJO5/mvEmYXF4yvLWYDIXLko+yVGN15OPMHEeGbzn4JpHl3U2T2YyyHIDF6yXLtYn8ZhctI0fiMDJozjNjcymsDjOjqtais82o1nwqhuZhMRdSlzcKZbJT1FhLa0YTPtN4cj0aq6sWl60Ej8WFTdfh8dQyqrKWrQfNxjC5KGyyUZe1leGNE8lwK1qym3HknkSGeyNOWx3D6w1arSOoyd9Fhiohp0lhz53NuN1vsWviTEr2l9OaaVBfpMlt2c/wyjq2TC5iROMIHObDabXuJtveijbngbajMqvJbRhHo20v+d5s7FkHU+DbgGrJxGXNwpU1DLOvBavbi6EMvDYbHsswLF4fQ2s+Y9+IWRS2VKDJxm6ppHT0GPbn72Jk8wGYlJdWq5uJ1ZMZ6t2BLhmN12WQ71pJuedshjauxWXOoNFaiTLXYjadh9IGPkseeU1bsPj8SqOiYDNFrS34rDPIcNTjzJuOxzwEs3szBgpsw/F5v6GQQuymKSgDTLqevMbPcGYcjCN3KLmuWlwWG15dSa4xlOaMw7GZPkR7zBhuwDaDLHcd9uwSlM+Nz5zBiKovcfEla2dl48p8ivG7DmL3+POx+SqxuhStGT4M8zDQVWR4qjD5nLRmTsSki7C2foXF5sHnKaE1ayw5jr04syuxeM3kuKExayIuvYdsUy5eSyb5DfXYMyfhtY0gw7sCp2USVl8rOebdOM0KW7ODbMMO9tHYvJXU5oCF8TTnlZDh/RZMB+A1tWCoEVjdLTRaFjPcMZpW616yM02UbPOw7eBZWD3FZDjLaM4eA+YJDG95GZenBLctlwLvfpqzLODwUT6ymtHWURx2wgk9VB29TiK6KdhHa+1VSjUCQ4Ga0E7d0V9TTzue9e/6UyhoNPu9DWSrDPLN2Xg9BZgtLZjcXry+QiwZ9dSd4aPKMgXl20Wx6S2ynLsxiq1Y1BCaShpYNu0QMpUVUJhdz5PtPRCzud3TogG7y4tZKTJt5mDEkcs5Gq0tZGbtxustwGJpX2Iz+5xoTBhmG1aHm2Fryqg/qATDasabbcOXYQGtMXt8WOxuMprttFrMVGdnU1iYiWFoWlxezCZFTmgSa62xtbgwLCZsjU7Mbg/VOQathZkU+oqxOyeTnbsV7cnA0tiA1e6julhhcheR53VjU1XUZs3Ep8rJtGbgchWSl1uNyeTE1NRCVm0rzeOHYjKZyKyz47Bl4rMNwWprxO7KI9PwkFPfhGGy4jYNQeU1k51VhsNqwTXSwYppR5BlM1PjbcLwvADOXMZlOWhxm7BWmLHbqqk55VBsWT5GNy1g95BKts/Jpnr6gWzOfpVMdy75Q+v4rOCfHGWZxjjzCHweE4cOs9JYVkXe2DE4apvxait5Q7PQLjs+axG1dSbGlDRjadoG7hYoOgDyRoS8bwbs+RKGHgiuZhg3B4ZM6PSz5nH6cDS5KSiJDm0wDNhTZ0f5oKXFzcETCqje0cSWr/aTc+EYnNe8ypY6N/8YWcKyHTXsa3By9AFTeGbZIaiD83l3Pmys3cgPJ51O3YnNPOdcBS0zYetSDjecjNiwhYbGBnbOms6BlXayR5TQol1k+fZhV4qhLTk0ZNko3luBuWYrru9cjHdHKfbqcpq8Tey5+FjmtI6l4N8v0ZqZRanFzoLvWvGYoKDhbaqzPuaC4Qfw7RF7qKt/kjVjvVgyDmbc4v0UDivhsIrHKJ1zKV57HZm711E28miMpq/I9OZTN6KEsXsNfBk2LIYFZ8lYvG4weZwUTvAAiRts8RhoHra5wNla62sC55cBR2utfx7vnu4G7QqCMDDpJw9bp7pJKbUh0Gdv4HxHoE9NrDFB9JcgDEbi6bCBlji3HAgNdhkTaBMEQehPEtFNwT6BJdEC/JsPBEEQOmWgGWxfAQcppSYqpWzAD4A3+1kmQRCERHTTm8DlgeO5wJKO4tcEQRBCGVAxbIG4j58Di/BvnX9aa72xn8USBGGQE083KaXuBFZprd8EngL+qZTaDtThN+oEQRASYkAZbABa63eBd/tbDkEQhFBi6Sat9R9Cjp3AJX0tlyAI6cFAWxIVBEEQBEEYdIjBJgiCIAiCkOKIwSYIgiAIgpDiiMEmCIIgCIKQ4ojBJgiCIAiCkOIMqEoH3UEpVQ3sTrB7MRFlYgYIInffM1BlHwxyj9daD+tNYfqKQaK/YODKLnL3LYNF7pg6LO0Ntq6glFrV1yVtkoHI3fcMVNlF7vRlIL9HA1V2kbtvGexyy5KoIAiCIAhCiiMGmyAIgiAIQoojBls4j/e3AN1E5O57BqrsInf6MpDfo4Equ8jdtwxquSWGTRAEQRAEIcURD5sgCIIgCEKKIwabIAiCIAhCijMoDTal1NlKqS1Kqe1KqVtiXM9QSr0YuL5CKTWhH8SMIgG5T1RKfa2U8iql5vaHjLFIQO5fK6U2KaW+UUotVkqN7w85I0lA7uuVUuuVUmuVUp8rpab1h5yx6Ez2kH7fU0pppVRKbJVP4D2/QilVHXjP1yqlrukPOfsT0V99y0DVXzBwdZjorzhorQfVP8AM7AAOAGzAOmBaRJ8bgMcCxz8AXhwgck8ADgWeBeb2t8xdkPsUIDtw/LMB9H7nhxyfD7zf33InKnugXx7wKbAcmD0Q5AauAP7R37Km+Hsk+qtv5U45/dUF2VNOh4n+iv9vMHrYjgK2a61LtdZu4AXggog+FwALA8evAKcppVQfyhiLTuXWWu/SWn8DGP0hYBwSkXup1toROF0OjOljGWORiNxNIac5QKrs4EnkMw5wF3Af4OxL4TogUbkHM6K/+paBqr9g4Oow0V9xGIwG22igLOR8b6AtZh+ttRdoBIb2iXTxSUTuVKSrcl8NvNerEiVGQnIrpeYppXYA9wO/6CPZOqNT2ZVSs4CxWut3+lKwTkj0s/K9wPLTK0qpsX0jWsog+qtvGaj6CwauDhP9FYfBaLAJKYpS6ifAbOAv/S1Lomit52utJwG/A27vb3kSQSllAh4AftPfsnSDt4AJWutDgQ9p9yQJQr8yEPUXDDwdNpj112A02MqBUKt2TKAtZh+llAUoAGr7RLr4JCJ3KpKQ3Eqp04HbgPO11q4+kq0juvp+vwBc2JsCdYHOZM8DpgMfK6V2AccAb6ZA4G6n77nWujbk8/EkcEQfyZYqiP7qWwaq/oKBq8NEf8WjvwP1+vofYAFKgYm0BwYeEtFnHuFBuy8NBLlD+i4gdYJ2E3m/D8cfrHlQf8vbRbkPCjk+D1jV33J39bMS6P8xqRG0m8h7PjLk+CJgeX/LnYLvkeivvn2/U05/dUH2lNNhor86mKO//8h+emPPAbYGvmS3BdruxP90BJAJvAxsB1YCB/S3zAnKfST+dXM7/ifqjf0tc4JyfwTsB9YG/r3Z3zInKPeDwMaAzEs7UiqpJntE35RQeAm+5/cE3vN1gfd8Sn/LnILvkeivvpU7JfVXgrKnpA4T/RX7n5SmEgRBEARBSHEGYwybIAiCIAjCgEIMNkEQBEEQhBRHDDZBEARBEIQURww2QRAEQRCEFEcMNkEQBEEQhBRHDDZBEARBEIQURww2ISVQSg1VSq0N/KtUSpUHjluUUo/0wnwLlFI7lVLXd9DnBKXUJqXUhmTPLwhC+iD6S+gLJA+bkHIope4AWrTWf+3FORYAb2utX+mk34RAv+m9JYsgCOmD6C+htxAPm5DSKKVOVkq9HTi+Qym1UCn1mVJqt1LqYqXU/Uqp9Uqp95VS1kC/I5RSnyilViulFimlRiYwzyVKqQ1KqXVKqU97++8SBCH9Ef0lJBMx2ISBxiTgVOB84F/AUq31DKAVODeg9B7GX4vwCOBp4O4Exv0DcJbWemZgbEEQhGQj+kvoNpb+FkAQush7WmuPUmo9YAbeD7SvByYAk4HpwIdKKQJ99iUw7jJggVLqJeDVZAstCIKA6C+hB4jBJgw0XABaa0Mp5dHtQZgG/s+zwl80ek5XBtVaX6+UOho4F1itlDpCa12bTMEFQRj0iP4Suo0siQrpxhZgmFJqDoBSyqqUOqSzm5RSk7TWK7TWfwCqgbG9LKcgCEIkor+EuIiHTUgrtNZupdRc4CGlVAH+z/jfgY2d3PoXpdRB+J9wFwPrelVQQRCECER/CR0haT2EQYlsixcEYaAi+mtwIkuiwmClEbirs8STwFtATZ9JJQiC0DmivwYh4mETBEEQBEFIccTDJgiCIAiCkOKIwSYIgiAIgpDiiMEmCIIgCIKQ4ojBJgiCIAiCkOL8f/BzojKlTPe0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAEWCAYAAAAuOkCvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAAsTAAALEwEAmpwYAABrUklEQVR4nO2deZwcVbX4v6eX6ZlJZjLZ92QixCxASCAskcUAIpssAor+UAnCQ59A3BBBfcou8EQFifB4qAHEyKIiO0+WgKIICQkJEJYQErIvk2T2pbvq/v6o6u7qZWZ6uqtnPd/PpzNVt27dOtOZPn3uOeeeK8YYFEVRFEVRlO4l0NMCKIqiKIqiDETUCFMURVEURekB1AhTFEVRFEXpAdQIUxRFURRF6QHUCFMURVEURekB1AhTFEVRFEXpAdQIU/osIrJURC7saTkURUmlt342RWS+iGzqaTl6EyKyXkQ+5dNYi0XkOj/GGiioEabkhYj8PxFZJiINIrJVRJ4SkSM7uWeBiPyju2RUFCV/RORIEfmniNSKyG4ReVlEDulpuforXTUQRaRaRIyIhIopl1Jc1AhTuoyIfAf4JXADMBqYBPwaOL0HxVIUxSdEpBJ4HPgVMAwYD1wNtPakXN2NiAR7WoZC6E0GWm+SpTehRpjSJURkCHANcLEx5s/GmEZjTNQY8xhwi4g0ichwT/+DRGSniBwA3AnMc71ne+Pjici9bp8NIvIjEQm41xa4s+/b3dn4OyJyXDty7SMiz4tIjYjsEpH7RaTKc/37IrJZROpF5N34OCJylYg8JCK/d6+tFpGPi8iVIrJDRDaKyKeL9HYqSm/l4wDGmCXGGMsY02yM+T9jzCpIfG5+H+/cjldmHxF5VUTqROSvIjIsre/57udrj4h8XUQOEZFVIrJXRG73CiMiXxWRNW7fZ0RksueaEZFviMj77mf4Wlcf/NN99oMiUpI23g9cPbFeRM71tC8WkTtE5EkRaQSOEZFTRGSFO9ZGEbkqy+99noh85I75Q8/1gIhcISIfuLrpwfj7kCbPIOApYJyrHxtEZFwn97/k/tzr9p/n0Zm/EJEa4KrOdKPLbPe9rxWRB0Sk1CPbZ0Rkpfv/8k8RmeW5NkdEXnff9wcA733zRWSTq3u3Ab8TkYiI/FJEtrivX4pIJK3/d13du1VEzveMd7KIvO0+a7OIXJb+PvZJjDH60lfOL+BEIAaE2rn+JPCfnvNfAL9yjxcA/0jrfy/wV6ACqAbeAy7w9I8B3wbCwDlALTDMvb4UuNA93hc4HogAI3EU1C/da9OAjcA497wa2Mc9vgpoAU4AQq48HwI/dJ/5H8CHPf2+60tf3fkCKoEa4B7gJGBo2vWrgN97zqsBE9cL7mdzM7A/MAj4U7y/p++dOF/an3Y/g48Ao3C8bjuAT7r9TwfWAjPcz+iPgH96nm1cHVIJ7IfjrXsO+BgwBHgbOM/tO9/VKT93dcUngUZgmnt9satjjsBxUpS69xzgns8CtgNnpP0u/wuUAQe6z5/hXv8m8AowwX3e/wBL2nnP5wOb0travT/9PXfbFri/36Xue1VGB7rRvWc98CowDsfruQb4unttjvt/cRgQBM5z+0eAEmADSf18NhAFrkt7r29y+5fhTOBfcf+fRwL/BK5N63+NO97JQBPu3x6wFTjKPR4KHNTTnxNfPms9LYC++tYLOBfY1sH1c4CX3eMgsA041D1fgMcIc6+3ATM9bV8Dlnr6bwHEc/1V4Mvu8VJcIyyLHGcAK9zjfV1F8ikgnNbvKuBvnvNTgQYg6J5XuIquqqffe33pqztfOEbPYmCT++X4KDDavXYVnRthN3quz3Q/60FP3/Ge6zXAOZ7zPwHfco+fwp2YuecB98t5sntugCM815cD3/ec30JyQhb/oh/kuf4g8F/u8WLg3k7el18Cv0j7vSd4rr8KfME9XgMc57k2FsdQyZjEkt0Ia/f+9Pfcvb4A+KgT+RO60T1fD3zJc34zcKd7fAeukeS5/i6O8Xo0mfr5n6QaYW1Aqef6B8DJnvMTgPWe/s1pv88O4HD3+COc74fKnv5s+PnScKTSVWqAEdJ+fP+vwEwRmYIz+6o1xrzaTt8RODOeDZ62DTgz4TibjfsJ9Fwflz6QiIwWkT+6buo64Pfu+Bhj1gLfwvni2OH2846x3XPcDOwyxliec4DB7fwOitIvMcasMcYsMMZMwPFojcMxQHJlo+d4A85nfYSnLf1zl34e/8xNBm51w2F7gd2AkKonch0LYI8xpjFNNq8+8MqNiBwmIi+IkzJRC3w97fcAZ7IZpylN9r94ZF8DWDi5tLmQz/3p8rerG3OU/7vx57syTMR5v8aRXT972WmMafGcjyNT33vf+xpjTKwdWc7C8Y5tEJEXRWRe+i/eF1EjTOkq/8Jxt5+R7aL7gXsQ+BLwZeA+7+W07rtwZnWTPW2TcMIYccaLiKRd35Ll0Te44x9gjKl0n5+4zxjzB2PMke6zDI6LXFGUHDDGvIPjJdrfbWoEyj1dxmS5baLneBLOZ31XHo/fCHzNGFPleZUZY/6Zx1gAQ90cLK9sXp2Srqf+gOMFnGiMGYITRhVyYyNwUprspcaYzVn6pj+3s/uz9c82Toe6MQf5r097frkxZglOeDCbfu5Ili1k6vts+jwDY8xrxpjTcUKZj+B8z/R51AhTuoQxphb4MbBIRM4QkXIRCYvISSJys9vtXhy3+GmkGmHbgQnxJFnX2/QgcL2IVIiTbPsdnJlanFHAQvcZn8MJkTyZRbQKnDBirYiMB74XvyAi00TkWDcBtAVnZmwX9k4oSv9FRKa7CdIT3POJwBdx8nkAVgJHi8gkcRbrXJllmC+JyEwRKcfJ83nY42HuCncCV4rIfq4sQ1xdUAhXi0iJiBwFfAZ4qIO+FcBuY0yLiBwK/L8uPOdOHP02GUBERorI6e303Q4Md9/PXO7fiaPHPtaJDO3qxhz4X+DrrjdQRGSQOAsVKnAm5DGS+vlM4NBOxlsC/Mj9PUbgfJf8vpN7cP+vzhWRIcaYKFBHP9HhaoQpXcYYcwuOsfQjHEWwEbgEZ3aCMeZlnA/I68YYr+v5eeAtYJuIxGfEl+LMqtcB/8CZdf7Wc8+/gak4M+jrgbONMTVZxLoaOAgnqfYJ4M+eaxHgRneMbTiGXbYvDUVRHOpxkrH/Lc4qwVeAN4HvAhhj/gY8AKzCycF6PMsY9+F4z7bhJLgvzEcQY8xfcDzXf3TDaW/iLBbIl23AHhwPzP04SejvdND/G8A1IlKPYzR0xQNzK44X7f/c+1/BeV8zcGVYAqxzQ3/jOrrfGNOEoxNfdvsf3o4MHenGDjHGLMNZnHQ7znu2FmeCjTGmDTjTPd+Nkw/c2djXActw/m5WA6+7bbnwZWC9+zfwdZz85D6PpIZzFcUfROR54A/GmLsLGGMBTuJ9h0VgFUVRFKUvosXTFN8Rp6r2QWjxVkVRFEVpFw1HKr4iIvcAz+IsL6/vaXkURVEUpbei4UhFURRFUZQeQD1hiqIoiqIoPUCfywkbMWKEqa6u7mkxFEXpRpYvX77LGDOyp+XwA9VhijKw6Eh/9TkjrLq6mmXLlvW0GIqidCMikl6Ju8+iOkxRBhYd6S8NRyqKoiiKovQAaoQpiqIoiqL0AGqEKYqiKIqi9AB9LidMURRFUZTeSTQaZdOmTbS0tPS0KN1OaWkpEyZMIBwO53yPGmGKoiiKovjCpk2bqKiooLq6GhHpaXG6DWMMNTU1bNq0iSlTpuR8n4YjFUVRFEXxhZaWFoYPHz6gDDAAEWH48OFd9gCqEaYoiqIoim8MNAMsTj6/txphiqIoijJQqPkA1i3taSkUFzXCFEVRFGWg8KuD4N7Te1qKohIMBpk9ezb7778/p556Knv37vVl3A8//JDDDjuMfffdl3POOYe2traCx1QjTFEURVGUfkNZWRkrV67kzTffZNiwYSxatMiXcb///e/z7W9/m7Vr1zJ06FB+85vfFDymGmGKoiiKovRL5s2bx+bNmwGYP39+YsuwXbt2Ed/DdfHixZx55pmceOKJTJ06lcsvvzxjHGMMzz//PGeffTYA5513Ho888kjB8mmJCkVRFEVRfOfqx97i7S11vo45c1wlPzl1v5z6WpbFc889xwUXXNBp35UrV7JixQoikQjTpk3j0ksvZeLEiYnrNTU1VFVVEQo5ZtOECRMSxl0hqCdMURRFUZR+Q3NzM7Nnz2bMmDFs376d448/vtN7jjvuOIYMGUJpaSkzZ85kw4Z299z2FfWEKYqiKMpAwxgocimJXD1WfhPPCWtqauKEE05g0aJFLFy4kFAohG3bABn1vCKRSOI4GAwSi8VSrg8fPpy9e/cSi8UIhUJs2rSJ8ePHFyyresIURVEUZaBh7J6WoOiUl5dz2223ccsttxCLxaiurmb58uUAPPzww10aS0Q45phjEvfdc889nH564atM1QhTFEVRlIGGbfW0BN3CnDlzmDVrFkuWLOGyyy7jjjvuYM6cOezatavLY9100038/Oc/Z99996WmpianXLPOEGNMwYN0J3PnzjXx1Q2KogwMRGS5MWZuT8vhB6rDlB7lqiHOzx9uh3Cp78OvWbOGGTNm+D5uXyHb79+R/lJPmKIoiqIMNAZAOLIvoEaYoiiKogw0zMAIR/Z21AhTFEVRlIGGesJ6BWqEKYqiKMpAQ42wXoEaYYqiKIoy0Ohji/L6K2qEKYqiKMpAY4CUqOjtqBGmKIqiKAONfhyODAaDzJ49m/33359TTz2VvXv3+jLu7bffzr777ouI5FVnLBtqhCmKoijKQKMfG2HxbYvefPNNhg0bxqJFi3wZ94gjjuDZZ59l8uTJvowHaoQpiqIoysCjHxthXubNm8fmzZsBmD9/PvFCybt27aK6uhqAxYsXc+aZZ3LiiScydepULr/88qxjzZkzJ3GPXxRtA28RmQjcC4wGDHCXMebWtD4C3AqcDDQBC4wxrxdLJkVRFEVR6J46YU9dAdtW+zvmmAPgpBtz6mpZFs8991xO2wutXLmSFStWEIlEmDZtGpdeeikTJ04sVNpOKaYnLAZ81xgzEzgcuFhEZqb1OQmY6r4uAu4oojyKoiiK0muxjc1lL17Gyh0ri/+wfuwJa25uZvbs2YwZM4bt27dz/PHHd3rPcccdx5AhQygtLWXmzJls2LChGyQtoifMGLMV2Ooe14vIGmA88Lan2+nAvcbZwPIVEakSkbHuvYqiKIoyYNjdsptn1j/Da9te48VzXizuw7rDCMvRY+U38ZywpqYmTjjhBBYtWsTChQsJhULYtvN7t7S0pNwTiUQSx8FgkFgs1i2ydktOmIhUA3OAf6ddGg9s9JxvctvS779IRJaJyLKdO3cWTU5FUZRioDpMyYW61jrAMcaKjt1/PWFxysvLue2227jllluIxWJUV1ezfPlyAB5++OEels6h6EaYiAwG/gR8yxhTl88Yxpi7jDFzjTFzR44c6a+AiqIoRUZ1mJILwUAQgIqSiuI/rB+HI73MmTOHWbNmsWTJEi677DLuuOMO5syZk1eJidtuu40JEyawadMmZs2axYUXXliwfEULRwKISBjHALvfGPPnLF02A97Mtwlum6IoiqIMKFpiToisKlJV/If1YyOsoaEh5fyxxx5LHK9atSpxfN111wGwYMECFixYkGh//PHHs467cOFCFi5c6KOkRfSEuSsffwOsMcb8vJ1ujwJfEYfDgVrNB1MURVEGIm1WGwBBCRb/Yf3YCOtLFNMTdgTwZWC1iKx0234ATAIwxtwJPIlTnmItTomK84soj6IoiqL0WlqtVgBCgaIGqRzUCOsVFHN15D8A6aSPAS4ulgyKoiiK0leoba3txqcVvoF3w55WBg0pQQIdftUrHaAV8xVFURSlF7Cx3ikW0BhtLP7DTGFGWN2uZu658mWeu2eNTwINTNQIUxRFUZRewISKCQAMKx1WnAekGF6FGWG7NjnJ7+/+e1tB4wx01AhTFEVRlF6A5W4lFJAifTX7mAcW0BCkL6gRpiiKoii9ANs1kqTjdOr88RphBYYjP1jZe4sOB4NBZs+ezf7778+pp57K3r17fRn33HPPZdq0aey///589atfJRqNFjymGmGKoiiK0guIe8KcCk9FIMUTVpgRNmHaUACqRpcXNE4xiG9b9OabbzJs2DAWLVrky7jnnnsu77zzDqtXr6a5uZm777674DE7XB0pIo/mMMZuY8yCgiVRFEVRlAGMcb1TfcETFgg6MvZGI8zLvHnzEgVa58+fz89+9jPmzp3Lrl27mDt3LuvXr2fx4sU8+uijNDU18cEHH/DZz36Wm2++OWOsk08+OXF86KGHsmnTpoLl66xExQygo7r8AvhjYiqKoijKAKYvecJibc5YwVD7AbWbXr2Jd3a/U9Bz0pk+bDrfP/T7OfW1LIvnnnuOCy64oNO+K1euZMWKFUQiEaZNm8all17KxIkTs/aNRqPcd9993HrrrV2SPRudGWE/NMZ0uJW7iFxdsBSKoiiKMsDp1pywAlm7fAeQ9Ij1Jpqbm5k9ezabN29mxowZHH/88Z3ec9xxxzFkyBAAZs6cyYYNG9o1wr7xjW9w9NFHc9RRRxUsa4dGmDHmwc4GyKWPoiiKoigdkzDCusMTVmCt1o/eqgE6XiWZq8fKb+I5YU1NTZxwwgksWrSIhQsXEgqFsG3nPWhpaUm5JxKJJI6DwSCxWCzr2FdffTU7d+7kf/7nf3yRtaCcMGPMab5IoSiKoigDmNrmKM+ucWpudY8nrPCK+QCR8m7YYilPysvLue222zjjjDP4xje+QXV1NcuXL+fQQw/l4Ycf7vJ4d999N8888wzPPfccgYA/6xo7G2UeMAH4O/Az4Ja0l6IoiqIoBXLNY2/z3DuOEVa8OmEm+3EezDxirDOM7Y8xVyzmzJnDrFmzWLJkCZdddhl33HEHc+bMYdeuXV0e6+tf/zrbt29n3rx5zJ49m2uuuaZg+TozYccAxwNfBP4f8ASwxBjzVsFPVhRFURQFgKa2GNCN4chCE/NjzlgF2nJFoaGhIeX8scceSxzHV0oCXHfddQAsWLCABQsWJNoff/zxrOO2F6IshA7NbWOMZYx52hhzHnA4sBZYKiKX+C6JoiiKogxQLNsQN4z6QmK+FXXGsnujFdaH6DSYKyIR4BQcb1g1cBvwl+KKpSiKoigDB8s2IH2nYn7cCOvt4cjeToeeMBG5F/gXcBBwtTHmEGPMtcaYzd0inaIoiqL0Yy578TIOuOcAYrZB4p4wP8KRLbXw0PnQ6Ml9si1PhwKNsJgaYX7QWfbfl4CpwDeBf4pInfuqF5G64ounKIqiKH2fptdfZ/P3Lk9UxY/zzPpnAGhojYH4GI587W5468/wsqegqJ+esJjxY5gBT2c5YQFjTIX7qvS8Kowxld0lpKIoiqL0ZTZe9DXqHnsMu74+6/XlG/bga2L+jjXOz2hzss3HxHzbUk+YH+RU4ENEJmVrN8Z85K84iqIoitL/kJDzdWva2jro5GNO2Ef/dn62NSbb/EzMj3vC1AgriFyrrD3hOS4FpgDvAvv5LpGiKIqi9DfCztetnVapPc6sCUN4p8XHnLCyKqj9CMJlyTYfw5EJT1gvtMGCwSAHHHAAsViMKVOmcN9991FVVVXwuBdccAHLli3DGMPHP/5xFi9ezODBgwsaM6eKcMaYAzyvqcChOAn7iqIoiqLkSju1pmxjEjlh6XljeTHtJPegvQKtPuWE9UJPWHzbojfffJNhw4axaNEiX8b9xS9+wRtvvMGqVauYNGkSt99+e8Fj5lWW1xjzOnBYwU9XFEVRlIGAa6wYy8p62XEsud4lX7YUcr1p3hWRRfCE2b3QCPMyb948Nm92CjrMnz+fZcuWAbBr1y6qq6sBWLx4MWeeeSYnnngiU6dO5fLLL886VmWlkwpvjKG5udkXj2WuOWHf8ZwGcEpWbCn46YqiKIoyEHCNHhPLboQ53i8fPWFxQ65irKfJz8T8zldHbrvhBlrXvFPQc9KJzJjOmB/8IKe+lmXx3HPPccEFF3Tad+XKlaxYsYJIJMK0adO49NJLmThxYka/888/nyeffJKZM2dyyy2F796YqyeswvOK4OSInV7w0xVFURRlIGC7BpCd3QizjUHi4Ug/PGHZnuNrYn48J6z3ecKam5uZPXs2Y8aMYfv27Rx//PGd3nPccccxZMgQSktLmTlzJhs2bMja73e/+x1btmxhxowZPPDAAwXLmpMnzBhzdcFPUhRFUZSBSieeMCeq56NhY0fd5xYrHNl5TliuHiu/ieeENTU1ccIJJ7Bo0SIWLlxIKBTCdo3hlrQFEpFIJHEcDAY73CcyGAzyhS98gZtvvpnzzz+/IFk7q5h/VWcD5NJHURRFUQYyCVPFyv7lbhk7UaLCxgePleUaYbbneT6GI+OeMF/S14pEeXk5t912G7fccguxWIzq6mqWL18OwMMPP9ylsYwxrF27NnH86KOPMn369IJl7MwTdmEnlfEF+AJwVcGSKIqiKEp/xXSWmG9DwLVo/DBsOjPCCvWExVdHFjRK8ZkzZw6zZs1iyZIlXHbZZXz+85/nrrvu4pRTTunSOMYYzjvvPOrq6jDGcOCBB3LHHXcULF9nRtj/4uSBddZHURRFUZT26CQcWVPfAsN8zAmz3KKwttfw8scTZmyTXBXZC3PCGhoaUs4fe+yxxPGqVasSx9dddx0ACxYsYMGCBYn2xx9/PGPMQCDAyy+/7LOknRhhmgumKIqiKD4QN4baCUc2RmNE3DCk7UcCfVZPmD8GUzwfzMchByx51QlTFEVRFKULJMKR7RlYJpET5q8nzP9wpNXu76B0FTXCFEVRFKXIxE0e044nrCQoiJ91wrIaYd5QaP7PUE+Yf6gRpihd5a758Pz1PS2Foih9iUQ4MntOWJsdI1Gs1Zc6Ya7x1W7F/PyHbmmMes7UCiuEXCvmjwT+A6j23mOM+WpxxFKUXsyWFc7r2B/2tCSKovQVOknMTwlH+ukJa69OWAHGU8puPWqDFURORhjwV+DvwLNAe39BiqIoiqJkwbS2OgeecKSdYRQVY3Vke3XC8sdrI2o4sjByNcLKjTHfL6okitIXaCefQ1EUJRe8dcIsT6hQxID4mRNWxDphKUZY77PCgsEgBxxwALFYjClTpnDfffdRVVXl2/gLFy7kt7/9bUYpjHzINSfscRE5ueCnKUpfx2rtaQkURenDpBhhGYny3bg6spDE/A62KuoNxLctevPNNxk2bBiLFi3ybexly5axZ88e38bL1Qj7Jo4h1iIi9e6ro0r6itI/iSs2RVGUfOjICPM1JyzuCfN/78gU+Xq3Pca8efPYvHkzAPPnz2fZsmUA7Nq1i+rqagAWL17MmWeeyYknnsjUqVO5/PLLs45lWRbf+973uPnmm32TL9cNvDurmp+BiPwW+Aywwxizf5br83FyzT50m/5sjLmmq89RlG7F1pRIRVG6hvFsBu1NzE8xwiSZE+bL3pFxXdVusdZCKua3M2Qaf3/wPXZtLDxk52XExMEc9fmP59TXsiyee+45Lrjggk77rly5khUrVhCJRJg2bRqXXnopEydOTOlz++23c9pppzF27Ni8ZM9GrjlhiMhpwNHu6VJjTGZd/1QWA7cD93bQ5+/GmM/kKoOi9Di25oQpitI1Ekn5pNYJs+3U8KCvdcLiBp534ujTJNL4ZMwVi+bmZmbPns3mzZuZMWMGxx9/fKf3HHfccQwZMgSAmTNnsmHDhhQjbMuWLTz00EMsXbrUV1lzLVFxI3AIcL/b9E0ROcIYc2V79xhjXhKR6sJFVJRehBphiqJ0EbvNk8bg8YTFjFef+FwxP6snzKdwpCcnrKNhcvVY+U08J6ypqYkTTjiBRYsWsXDhQkKhUMLwbWlpSbknEokkjoPBILFYqq5fsWIFa9euZd999wWgqamJfffdl7Vr1xYka645YScDxxtjfmuM+S1wItC1LcizM09E3hCRp0Rkv/Y6ichFIrJMRJbt3LnTh8cqSp6oEabkgeqwgY3xGGHexPyUEhViQ7E9YX5t4N1HSlSUl5dz2223ccsttxCLxaiurmb58uUAPPzww10a65RTTmHbtm2sX7+e9evXU15eXrABBl2rmF/lOR5S8JPhdWCyMeZA4FfAI+11NMbcZYyZa4yZO3LkSB8erSh5ojlhSh6oDhvYeI2wDuuExUtU+Foxv7iesF5thQFz5sxh1qxZLFmyhMsuu4w77riDOXPmsGvXrp4WDcg9J+ynwAoReQEQnNywKwp5sDGmznP8pIj8WkRGGGN6xzujKNlQT5iiKF0kNScsaQjF7LRwpJ8lKuL5ZkUs1ioB6bhjD5Fev+uxxx5LHK9atSpxfN111wGwYMECFixYkGh//PHOUt4zn5Evua6OXCIiS3HywgC+b4zZVsiDRWQMsN0YY0TkUByvXE0hYypK0VEjTFGULmK3l5ifEo70VMz3NRzp/wbecfkk0OsdYb2eDo0wEZlujHlHRA5ymza5P8eJyDhjzOsd3LsEmA+MEJFNwE+AMIAx5k7gbOA/RSQGNANfML2x9K6ieFEjTFGULmJySMwXv+uExVMn2gtB+hCODPRST1hfojNP2HeAi4BbslwzwLHt3WiM+WJHAxtjbscpYaEofQc1whRF6SKmtZ3E/PZKVPiZE5ZiePmbmC8ByWowGmMQGXgGWj7Gc4dGmDHmIvfwJGNMynpOESnt8tMUpcgYY9jbupehpUOL8wBNzFcUpYuYNs92Z55wZEaxVj9LVMTHNkWomN+BJ6y0tJSamhqGDx8+oAwxYww1NTWUlnbNNMo1Mf+fwEE5tClKj/KDf/yAx9c9zl9P/ysfq/qY/w9QT5iiKF3EG460W9sw0SgSDmfZO9LHnLC4l81rePlcrDUQlIxc/wkTJrBp0yYGYimW0tJSJkyY0KV7OssJGwOMB8pEZA7OykiASqA8HyEVpZh8VP8RANuatqkRpihKr8CbmL/nvvvY84c/MOOtN7OUqPBn9aLz0HiJiiLUCXOHcVZHpo4TDoeZMmVK3mMPNDrzhJ0ALAAmAD/3tNcDPyiSTIqSNxVhZ5tT26el2BmoEaYoShfx5oQBiU28U0pUeIq1+qK/TLbEfH838A4ERFdHFkhnOWH3APeIyFnGmD91k0yKkj+ur7ZoC201J0xRlC6SsjrSxaqtzfCEia/FWjsxwgryhMVLVAycnK9ikWudsD+JyCnAfkCpp/2aYgmmKPmQmKFJVzaD6ALqCVMUpYvEE/OltBTj7lnYvGoV1lRvErePOWHGFDcxP2V1ZN7DKOS4bZGI3AmcA1yK42v4HDC5iHIpSl7EZ5ZFW5WjRpiiKF3EuLXBJJT0e1i1dRmrI8Wv1ZHt1QPzq2K+d3WkWmEFkau74BPGmK8Ae4wxVwPzgJ7ZHl1ROiBRyRk1whRF6SW4xo/t2eqmpLo6pU6YeMKRfj3PeWh7ifkFDN/Lty3qS+RqhMVrhDWJyDggCowtjkiKkj+2u/da8RLz/csJa6pr443nNvo2nqIovRNjZ+oj09yUWaJCfErMbzcZ3/86YeoIK4xc64Q9JiJVwH8Dr+MErv+3WEIpSr5YrpHUF1ZH/uGqV2htijFl9ggqh5f5Nq6iKL0MO9NSiW7dijWmMtkgPm7gnWJsFaFEhadOWCyqVlghdOoJE5EA8JwxZq+7QnIyMN0Y8+OiS6coXcT4ue1HNnw0wuKufCtaJINRUZTeQZZJYfPKNzI8YYnVkQW7l9rJA0sJTfqQmJ9ZJkzpIp0aYcYYG1jkOW81xtQWVSpFyZO4B8wqVimJhBFWeC5EfMuPWJsaYYrSn8kWjiybfWCKnnIMMJ8mkbmEIwsa3lMx35cRBy655oQ9JyJnyUDaCErpk8RnkPHcMN+JG2E+lMCIyxpr09pjitKvyRKONDErJW0i4AlHFmzZpHi/ilAnLL4ASldHFkyu3yRfAx4CWkWkTkTqRaSuiHIpSl7E3ftFT8wPBAseKh6GtC1VYorSr8mWmB+LJvRVOGoY37DXvw28i56Y7/zMtoG30jVyLdZaUWxBFMUP4sZX0RPzffCERd0wZNGq+yuK0iswxoZAIMUYM7FYwgi7/2cW8ADnfncw0RIfV0cGQmmJ+V5d448nTNVXYeRarPW5XNoUpacxflWcbo+EEVa4JyyeV1Ese1FRlF6CbRwjzEsslpG7GrZ8LtYaCHewUtKfEhWaFFYYHXrCRKQUKAdGiMhQktnIlcD4IsumKF0mkZhvipyY70M4MjGkTiUVpX9j24ikJrGbaCxDTwX80gVeT5jVmtle6PDx1ZFBUU9+gXQWjvwa8C1gHLCcpBFWB9xePLEUpet8sLOBprYo0A05YT6uUTFZknYVRelHxMOR3ibLytBTQdunFIWEERaEqP91wmyvJ0wpiA6NMGPMrcCtInKpMeZX3SSTouTFcbe8SPnHGglGuqNOmBphiqLkhrFNxsTNm5gfJxBPUfAtHBkqSmJ+XDzNCSucXBPzfyUinwCqvfcYY+4tklyKkieORihenbD4uP5pHs0JU5R+jp3pCcuWExawfVpY5A1HYhyDSyRt2zWfPGFqhRVETkaYiNwH7AOsBLzfQmqEKb0KKXbF/PjMteBoQXIAzalQlH6OScsJC4ez5oQFjcHRYj6FI4Ph5LkEfSvcmrI6Mu9RFMh978i5wEyj3xZKb8evDXABNr4Kb/8VTrg+2RafSRY4fsyzVZGt4UhF6dcYd3Vk5ckn0/Taa1gNDe3khMVvKPiBzs/4AiJjA+lGWOGrIyWAro4skFyLHb0JjCmmIIqSL+c/fT4PvfeQe+ajEfab4+Fft7dTcbowzeOtkq9zG0Xp5xjHCBv/81uY+veXkGAwe06Yqwp884QFXD9LYvLoV50wd3j1hBVMrp6wEcDbIvIqkFjvaow5rShSKUoWdt1xB6ExY6n67Bkp7cu2L2PZ9mXAjcS3/fB1dWTLXigf5hzHxy3QcIp6jTDNCVOU/o1boiKOhELt5IQ5P/0zwsKp5z57wjQnrHByNcKuKqYQipILO2+9DSDFCMvwIvkZjowT89TZ8Ssc6dm0W1dHKkr/xqSXqAiHsueEJeZ4PnvCErms/iTmJ+uEBdQGK5BcV0e+KCKTganGmGdFpBzwr1qlouRJm92W1lIEI8zyPMP4szrSG47UnDBF6eekVcyXUDhl26I4Qb88YXFScsLwzxMWT8wXNCesQHLdtug/gIeB/3GbxgOPFEkmRcmZVm81aCiOJyxRGwwfPWHeAooFDaUoSm8nPRwZDGKsWIaeChjABP33hKXoLVeOAsORIs5IRVuJPkDINTH/YuAInEr5GGPeB0YVSyhFyZU2K7snzFfFYEU9w2eZUeZB1BOOVE+YovRv0sORKTlhHmMoaIMQ8D8xP/4MY3u2XCssHCkBcVxhqr4KIlcjrNUYk/i2ExG3Apyi9CwZnrB4sVY/9460vUZYtlVGXSdldaQaYYrSv0kPR3pywoKe+VzANo4R5pcnLJiWmG9bmYZZXsMbx7OnuxYVTK5G2Isi8gOgTESOBx4CHiueWIqSSntKqTWWaoRJUXLCPEZYolxFoUaYJzFfM1sVpX+TFo7EzQmzjU3IM190SlT46QmL54R5Jo8JI6yQYq1OjTBBF0cWSq5G2BXATmA1zqbeTwI/KpZQipJBNJq1udlqTm0QnzbA9WJl84T5lxOmJSoUpZ+TJRxpYjFidizVCLNB8DMnLEuJCvEhHOl6wkS0REWh5Fqiogz4rTHmfwFEJOi2NRVLMEXxYremhx0dmqNpRlgxPGF2lpwwSO7HlgfqCVOUgYNJD0cGg+Am5pfYAeK7AfrvCcuSmB+Xo8DVkU5OmNpghZKrJ+w5HKMrThnwrP/iKEp27OZ0Y8uhOea0B8VVNq4nzKbQDXA9miUlHGll79NFvMn46glTlH5OlmKt8ZywiElWewr4npifpUSFL54wd/6pefkFk6sRVmqMaYifuMflxRFJUTIxLS1Z2+NGWEhK4j2dfwudnqUUaPWUqPAm/BeSU6EbeCvKwCFbsVa3TljETgak/CtREd9XKC3/y/iUmO96wqTAcZTcjbBGETkofiIiBwPZXROKUgTs5o6NsAxPWKHupZjnee16wvJ/hq1GmKIMGDLCkZ7E/FJP3fOA8csT1p4RZvuTmO/WCdMSFYWTa07Yt4CHRGQLzoKIMcA5xRJKUdIxLUmb39g24iq0eImKgFs1RXwzwryeMG9OmE8b4KYYYXkPoyhKX8C2nRwql3ix1pgdI2KnhiPjvhFjTOqKyq7QXomKFI9cgXXCRNCC+YWT67ZFr4nIdGCa2/SuMSb7cjUXEfkt8BlghzFm/yzXBbgVOBknwX+BMeb1rgivDBy8njATiyElTvgxboQJQfDkgRU8k2zPE+ZTODKlQKtaYYrSvzG2U9PBRcJhiEaxjZ1qhLnhSHB0mORbiCs9J8ybmB/PCSu0TlggUTJfKYBcw5EAhwCzgIOAL4rIVzrpvxg4sYPrJwFT3ddFwB1dkEUZYNhNjYlj05askp8wwkwQxLMfY6GeMK/hVYRwpLFNotChrYn5itKvyQhHhsPYbW3tJuZDgWkKGSUqXL1le8KRBXnC3G2LRHTbogLJyRMmIvcB+wAria+ldf4H723vHmPMSyJS3cGwpwP3Gucv7RURqRKRscaYrbnIpAws7Pr6xLHx1AxLblsUTOSDgQ95Vl6PV7aK+c5D8h/eNgSCgh0zmhOmKP0d2wZvODISwbRFsew0I8xAfHZWkHGTsW2RzzlhiW2LoNCF6AOdXHPC5gIzjb/fFuOBjZ7zTW5bhhEmIhfheMuYNGmSjyIofQWrPrE4l7XHHseYn/yYqjPOSBhhtjH+esK8Hq9se0emH3d1eBsCAcGIUXf+AEB12ADHthFvOLKkBNPaim1syjOMsGQ4Mm/Sc8JS6oTFw5GFDO/dwFsphFzDkW/iJOP3CMaYu4wxc40xc0eOHNlTYig9iN3g8YQ1N7P1iisBiLnlI5qjUcQzJSu4Tpi3LEXKsU/hSMtd4i2ie0cOAFSHDWzSN/AOlEYwra3ETMwt1uq224CJF1Mt5IEdFGsVnxLzEzlhqr8KIVdP2AjgbRF5FUgsGzPGnFbAszcDEz3nE9w2RcnAbmzM2h7fqPuYVY1sqn+Pdz7utPsajrTaPO1ew6uAYq3GEAgIVkB1mKL0e2yTFo4sxXY9YWGvEebxhBU2kXSVSjAtJyzFE+bPtkWqvgojVyPsqiI8+1HgEhH5I3AYUKv5YEp7tFcnLO4Ju+jpOmAJnz/a+ZMuWjjSp4r5KZ4wtcIUpX+THo4sjUAshh2LEfaoqoANYtycMD8T8xOeMMu/xPy4UanqqyByLVHxooiMxlkhCfCqMWZHR/eIyBJgPjBCRDYBPwHC7nh34mwCfjKwFqdExfn5/ALKwMBuzr5NaczEsrb7aoSlVMz3KSfM9YSJoOFIRennpIcjrZrdAARao6kbeBcrJyzFE+ZHsdbktkVKYeS6OvLzwH8DS3He9l+JyPeMMQ+3d48x5osdjekm+V+cu6jKQMZk8YSZWAzLtghkMWIKXjbtNbzarRPmlycs72EURekLpIUjrfo6AEoa2whb2XPCCvOEpYUj4/rMSeZK7ZPX8En9BZ66YUqXyTUc+UPgkLj3S0RG4mzg3a4Rpih+YqJtmW2trcTsGCVZygYX7Alrr0SFj3XCAgFBAhqOVJR+T1o4cvCRR1L36GOYWJSQlfz8BwwYP9xL7YYj44n5ha1rTKyO9EQk1QTLj1xXRwbSwo81XbhXUQrGWJkGj93W5qwuyhKRLF6JCv8q5kvA3XpNbTBF6dekhyOlrAyAUKtF0PP5dyrmu54wX8OR7rltOUpHClsRZAyJdArnXJVYvuTqCXtaRJ4Blrjn5wBPFUckRcnEWJmWVtwTFsniCSs8HNleYr4/qyOdYq0Bx52vOWGK0r+xDYSTvqJAqWuENUezhCPd1ZGFTCQzSlTEku0SdGd/he74keYKU/Ii18T874nImcCRbtNdxpi/FE8sRUnDssHdby2OaW3Fsq3ieMLarZjvV7FW48eEVFGUvkBaOLJlzRoADly+G2vMiES7k5jvhycsPSfM53CkMQQCqCfMB3IKKYrIFOBJY8x3jDHfwfGMVRdVMkXxYKwYgUgkpc2OFzssRk5YTon5ha0uCgSdmaQqMEXp36SHIwcffRQA700tT80J8y0xv529I+NGWIF5ELZNok6YI2v+og50cs3reojUHaIst01RugfLRspKU5pMa1u7nrCCDZv2SlT4VCfMtuOri7REhaL0ezKKtToTSolZBC2w3EsBYzDGh3TrRE5YesV8yxdPmLPKkmQ2vqqwvMn1fztkjEksT3OPS4ojkqJkYiyLQHl5alubuzoylqkBCt62yBQ3MT++OjIQ0BIVitLvSQtHxr36oaghaEE0BLakhSP98IQF3a/puBFmu3XCCsyDsOOruxOeMFVi+ZKrEbZTRBJbFInI6cCu4oikKFmwLEIjUvfci++9li0x37Z9Wh0pwSw5YeI5znP4eF0dUQWmKP2djNWRJY5x5HjCbGIBcYwwTziyoIlkuhEWnzzaMWfbogIT852aFN7VkfkPNdDJdXXk14H7ReR293wT8OXiiKQomRjLIlhRwYhLLyE0fATbrrrKyQmzYwzKlphf8AbertIKlYJ3ZWZ8Jmlnsfy6gPHMJFWBKUo/xzapRljYydUKxmwnHBl0VE7AkAhHFqVYqx1zV0wWVtUrmZifLNaq5EeuqyM/AA4XkcHueUNRpVKUdCwLCQYZefHFtLz3HtBOTpgxiAT8S8wPRdI8YZaj2OxoYZ4wy5MTplaYovRvbDulonzcExawbIKWIRYg6QnzZXVkejgyzQjzKTFfK7QWTq6eMECNL6XnMJYFQad+TqDUSdC3W5qJBVPDkUEbJBQq3LAxXk9YWsV8P/ZeM8k6YYXai4qi9HISpSEcEp6wqGuEBQU7YFI8YQU/DzwV8+NGmJXMCSswMV80J8wXtOq90jewHU8YgLhGmGmJJ+YnuwVtCErYP09YuDRzA+9A0D0uYCZpue583bZIUfo9Jj0cGQpBIEDQMk5OWNDJCRNvxfyC9EI7dcLsmCtHYTlhdvq2RTqRzBs1wpQ+gYlZEHT+XAPulh92S3PG3pEhC4IS8jcnLMUIszy1dwpbHSmBQMH5sYqi9AFsOzN0Z9tUb2wl2GZjSIYjja/bFqWtjrTanDah4G2LxLNtkZI/OYUjRSQInAJUe+8xxvy8OGIpSirGthC35k0g4Qlrwaq0UkpUBG1Awj6EI10lFopkblsULtwIc5Z4O4pMM/MVpX9jLAsJhTPaZ6yLApsB2Dsode9IX7YtSk/Mt9ogGMGfDbyTrjD15udPrp6wx4AFwHCgwvNSlO4hZiGuJ0zCYQiFsJtb2veE+ZaYX5aZmB8PRxakxPAk5ucvpqIofYBYDAmkft0GKip4+cBkuc14nTB/i7VmM8LCBSfmO54wDUf6Qa6J+ROMMbOKKomidICx7WT1ZxxvmGlpxrItIp5oYcgCW8I+hiMj0FKbbLei7kwSHzxhum2RogwEjG1DKJjSFhwyBLG3UztxKO+WNDFlZytBbzjS72KtxkCs1dFpBSbmxz1hkohHqg7Ll1xN7qdE5NNFlURROiIWS3jCAKSs1PGEmRiRWDIxIWhDQHxYHZnwhJUmPWHGgNXqtEHhOWFB9YQpykDAWLFEOkUcCYcJWoZQS5TmkgBWAIIWYDv9fMkJ866OtC3AJMORBa7ujhebds7zF3Wgk6sn7BXgL+LsuxAlntZnTGXRJFMUD44nLDmTDJSXYzc2ErNjlMYCxLc2DVkAPqyONB5PWDwnLP4zFPeEFbh3pDuT1EKHitLP8SwsiiMlJYRihlBLjJZwkGgIQjYYE3Qztnwo1ioBd9ePmDOBBH/CkRmrI1WH5UuuRtjPgXnAaqOxE6UniMWQQNIICw4a7DHCUj1hxhdPWJbVkZa7fWrYWZ1ZqBILBLVYq6IMBIxlZXrCIhFCDa4nLBwiFoRwDIwJIRSamO8xwgIhR4fFXCMs5ENifmJ1pC6PLJRcw5EbgTfVAFN6ivScisDgwdgNDVjGSg1HWgIEfMwJK/F4wlwjzK9wZEDcOmEFyKkoSu/HStY5jBMoKSHSZghGLVpKAkSDjiffmLh7qYDnxXWTSNIIi+uxYLjgDbwzPGGqxPImV0/YOmCpiDwFtMYbtUSF0l0Yy0rxhAUGDya6ZQsxO5aamB8L0Ib4E44MhJycirgnLGUmCYVoSdu7d6S68hWlX2MsKyMxXyIRBjc7n/3mkoDjCbOSkzJfcsLinjBje8KRkYI38DaG1BIVujoyb3I1wj50XyXuS1G6F8tCPEosWDGYVtcTVhIL0BiBQa1JT5gvifkSTO4TCUkllghH+uEJ06RWRenvpE8iASRSQmWTc9wSChALCpFY0hPmnxHm5oTF4p58P8KRJqVYq3rC8ifXDbyvLrYgitIRxvLW54LAoMFYDc5WpuEoNLlGWMgSxA9PWHyPtUAIrLgnzL9wpNcTplaYovRzYrEMT1jD8y8QX9lWvTNKLAiDWyVhGhVWosJNp/DmhMXTKRKJ+QUMnwhHak5YoeRaMf8FsvyXGWOO9V0iRclGmicsnhOGMZREDXvcCGHQcvZF8yUnLBB0FViaJ8yPnDDLJIodqg2mKP0XY9vOhtdpifnhj32M6Lp1ABywsYUtIyFs+eQJi+e0xieSKasj/UvMT5So0HBk3uQajrzMc1wKnAXE2umrKL4SV2Kk5YQRixGOBQlHbZrcKaUTjvShAKqxnFlkMJxMaI17wsJxI6yAnLCEEhOMpRpMUfotlmMQSZonLHLw7IQR9ptPDudT7253E/Od6wXpsAwjzErmtIZLfUrMT66O1HBk/uQajlye1vSyiLxaBHkUJZMsSixYMRiA8jYIR22aI87MLhT3hPmxbVE8MR/jKLGEJ8yHnDDLEAwKgQBYqsAUpd9irLhBlGqE2aHk1+/qieXMXwuhGP4k5scXEwWCyZywqJuAFi53HWEFJuZ7ti1S8ifXcOQwz2kAOBgYUhSJFCWNbEosMGgQAOUtEIoZmiKONgjZjifMn5ywYHKrJCuauTqywJyweJ0dtcEUpR8Tn0SmlagwEUe32KEgdsBZHRmyIR7jK8wT5i4sEvEYYc3OtXAZfm3grZ6wwsk1HLkc539McMKQHwIXFEsoRUkhixILlJcDUOHqlbgKCMbc1ZGF7mVmLEeJJbb9iGYWa83zGcYYp1hrPBypJSoUpd9i2glH2u55IGaBcUtUxJK6oGBPWMD9eo/nhMWNsFCZDxt4G80J84lcw5FTii2IorRHwhPm2fYjboQNr3MUSUm8tqoliPFxdWTQs/dazJ/EfNs1ugJBLVGhKP2dpP5K/bptXfInT6cwsaC7dyR+JObHkpGDQNhZ4Z0IR5a5OWGFlNghpVirkj8dGmEicqwx5nkROTPbdWPMn4sjlqIkScwkPUpMyhxv1Kha53xVtXDYu8b/cGR8NmnFslTMz9MTZjn3JcORaoUpSr8l5uRnSdrekXEPPzj7RU7cCWVtNuJXYn5cd8VrHUZbnPNwuePlN1b793dCsk6YhiMLpTNP2CeB54FTs1wzgBphSvHJ6glzcsJG7XU+/LsrnPa4EeZLsVavEWZHU1cXQd5GWMITFggUWrhaUZRejrHdD3haTpj8cRHmjAvZcu5JYG+n0VUrFS1tRCl070hPXcVg2JlAej1h3lXf+Qzv1gnTcGThdGiEGWN+4v48v3vEUZRMsnnCAoOccOSovc757gpHG8Td+QXXCTNp4Ugr6tvqSNvyhiPVE6Yo/ZqEJyz169YaUcUXrgzxX3NPgGf/wD9nCPPeMQxtbmEHELMLqALlzQkLlrjhyHhOWKmbrF+IJyx1A++Cc3AHMDlt4C0iN4hIled8qIhcVzSpFMVLwgjLzAkbVZvmCbMEjB+esPTE/FiWOmH5GWHGmxOmxVoVpV/TXmJ+1C0CLQQxJsTeQY5BM6zJCRtaBYQLMxLz456wUBkEAu6euPl5wuL6y1kdGW/MX9SBTk5GGHCSMWZv/MQYswc4uSgSKUoa2RJb40bY6L1gB4Q655Swu8S7KCUq0nPC8tQ88XCk6LZFitLvMbHsdcLiRlhQQmCC7HVKH1LV7BhhhXnCrDRPWJvjCYuv7C4gHBmf4AYCyW2LdIV3/uRqhAVFJBI/EZEyINJBf0XxjyyeMIlEMAEhYCBgG4wILWGItIF/xVrTS1T4tDrSUk+YogwY7E48YRIEE2avk+bKsGYnbFi4J8x9XshrhLmz1UA4WdC1iyTUnnhKVKgOy5tc64TdDzwnIr9zz88H7imOSIqSStITllRiIgKDy6GuMdHWWAqDWg3GiD91wgIhZxYJjhKLtQGSLNaaZ05FIhypdcIUpd+TTX9B0tMlJoQxQVpLhJZIkKFxI6yAnK2UcGSoFGItEGtOplIEQ9DWlNfQcU9YaokK1WH5kpMnzBhzE3A9MMN9XWuMubmz+0TkRBF5V0TWisgVWa4vEJGdIrLSfV3Y1V9A6f9kS8wHsCucqeP2GdUANEYcI8w3T5gEkwZXrNXxhIUiqXli+QztLVER0OXditKfMfHE/FCq/krmhIXAONeaKsoZ2uwYRzFTYGK+xD1hpU55Cm84spCcMFddpSTm6+rIvMnVE4Yx5ingqVz7i0gQWAQcD2wCXhORR40xb6d1fcAYc0mu4yoDkCwlKgBMiWMM7Z46CdjEpF0wadceHiTggxFmOzPJuNKKNjuesGDEs2KyLc+hveFIUQWmKP2ZxLZrqforboRhAiDOcW1FkGENjhFWmCfMyvSEtTUmw5FBt4BrHngT85PhSJ1I5kuHnjARqReRuiyvehGp62TsQ4G1xph1xpg24I/A6X4JrgwcjOVYKemesNCHmwGwI46LfcPI+A0+KAU75ijNeP5XrMX1hJUkvWN5GmHecKSWqFCU/k17nvx4ONKYEJERSwHYUlqb8IQVlhPmqRMWijj6q7UeIpVOWyCUtyfM9uqvRIkKJV86NMKMMRXGmMosrwpjTGUnY48HNnrON7lt6ZwlIqtE5GERmdhF+ZWBgJW94nTDZ44EYMuRnwDgpf2d65FWy786YV5PWGs9lAxO5onF8vSEpVTM16RWRenXtFeiwkp6wuyo83UaGb4PQ1saAB/rhIXLXCOsDiLuEswCVkemLyxyGvMXdaCT6+pIAERklIhMir98eP5jQLUxZhbwN9pJ9heRi0RkmYgs27lzpw+PVfoScU9Y+hLvXRefyeevDCGDhwHJWmFVe1t9zAnzeMKa90JZlf/hSLXC+j2qwwYunZWoMCZA49rvc9LEcxgz+gjKY1GG1pvCc8IS4ciIk7TVVOPxhOWfE2bFHN0aDAUSmfmqw/In12Ktp4nI+8CHwIvAejrPD9sMeD1bE9y2BMaYGmOMu+6fu4GDsw1kjLnLGDPXGDN35MiR2boo/RgTcxNYw9nd+SFxDKUtwxyFMHJ3k38beHs9YS17obTKyQsDf8KR6gkbEKgOG7iYuCc/lH11pLFDQJCvTLuUweveBeA/n7B9XB3p6rDmPTDI/dsLhvLOCbOtuBGW9ISpCsufXD1h1wKHA+8ZY6YAxwGvdHLPa8BUEZkiIiXAF4BHvR1EZKzn9DRgTY7yKAOJdlYXxZVY0C1Zt81xiDFyd1Nh+RSQzKkohicsHo4MarFWRen32PGc1uyeMNs4X8MloQC7Pn0GALM/NP7mhMWJG2HGQP2WvIa2ou4kMhTQYq0+kOvqyKgxpkZEAiISMMa8ICK/7OgGY0xMRC4BngGCwG+NMW+JyDXAMmPMo8BCETkNiAG7gQV5/yZKvyWxxDscTmlPLPE2TntzRNgzqIzqTXXJfIu8H+oqsYQnrCXpCQsEnVBloeFI1xNmqwJTlH5LXH/RTmK+bbtGWDBA435zEtdb48Wh88GOJY2vxA4fwKARzs/aTXkPbVnecKTbqCosb3I1wvaKyGDgJeB+EdkBNHZyD8aYJ4En09p+7Dm+Ergyd3GVgYiJugZVmicsPlM0JjnDHNrYzND3mmmLhTDGJGZqXSaeExYMOz9jzUlPGLgrjvJTksZKXR2pCkxR+jFZdvyA5CTSspz2cChAwKOvXnvlL3xx+hfze6a3Yn58IglQv9X5OfkI+PBFJyQZzLlSlSOvmxPmTcxXFZY/uYYjTweagG8DTwMfAKcWSyhF8WKi8XBkqicsuXoo+We8Z5BTwHXaRpOsw5MP3jo74TJo2u0kspYOSbZF86s4ndg7MlEnTFWYovRXEon5wcxirYIQtRxLpiTo8SwB/3Hj6vwfmp6YHyfeFl8l2daQx9COvgqGNRzpB53VCdtXRI4wxjQaY2xjTMwYcw/wOlDVLRIqA55kOLKdOjt20hN2y5lfBuDq+60C3flpORUN253j0qrkz+a9+Q3t3bYooClhitKvaWfvyJgdIxwIE3U94yVBxxN2yuk3JfrkverQ2KnFWuPsc5zz8yM3pXvPh10eOrE60mM06urI/OnME/ZLIFtR1lr3mqIUneTqyOyeMMtOTh/rKpMLcps2ri/goV4jrAzqtznH8XBk2VBntVFeQ2uJCkUZKCSLtWYm5ocCIaJujlU4JAQEbEn22/AFH8KRW1cm20dMdX5WOzUWefm2Lg/tLVERdEOs8cVGStfpzAgbbYzJ8Im6bdVFkUhR0ojnhGWsjnTr6ERjSSMsZIbwyKePAGD3KZ/P/6HevdfCpUkjLO4JizbDuhfyGzoejtQSFYrS70mGI7N4woJhoq5RUxIMJHKsnj3c2V6o+Y03aHrtta4/1BuOPNBjyMUfMOEQ5+dbf85j6PjqSCFU4pgQsbYCV6MPYDozwqo6uFbWwTVF8Y/2NsC1ogQlSNSCth0nMnP4TEIS4ZU5Byb6bDj//PyeabV5VheVJZdzxz1h8VVGeSTnJxJbA5oTpij9nsSOH1k8YRKizbIRgWBAEon5T54yJtFvw5e/QqympmvPjLU5BVkBKrNsVDP+oNS+XcC7OjJUEnSH0JL5+dKZEbZMRP4jvVFELgSWF0ckRUklkZifFo5sjjVTFiqjNWYRqDuOBz7zgGPYmHJ+cq6jHJr+9Qprps/o+kOtaHJ7orAnp2KwqxznfMn5+a9FXR7a9igx9YQpSv8mseNHemK+FSUcDNNm2YSDTpJ7PNE9GAxzyX8m+79/xJFd02Ox5uSqSBE4/dfwtZey972ua8WDk+HIpCcsqp6wvOnMCPsWcL6ILBWRW9zXi8AFwDeLLp2i4KmzE8puhLXFbEpCzp9yQCBgylkzSVj3reR+8Wumz2D3vffl/lCrLVmUNZ7YGozA4NHO8dTjnZ/PXQ2713Xp97G8q4t0A29F6de0WzHfOIn5bTGbiJtbFU+s2FD/ATuqoOp3v065Z830GVj19Z0/NNqSWppizrkw9sDUPl/yhCKvGgKNuXnbrKh6wvyksw28txtjPgFcjbNV0XrgamPMPGPMtuKLpyienLC01ZEJI8zyGmFCyAwFYPVBVUy8638S/bffcANrps/A2J0oDGNcIyzuCXPyMxg5DQLuR6ZsaLL/bXNg7XM5/z5eJRavE6YFWxWln5LYOzKtTpiVTMwPe/SXl0+/t5Dxv/xFStt7hxxK69q1HT8z2pRqhGVj3+NSz//7Y7DmsY7vwbuBd4BAQAiGA5oTVgA51QkzxrxgjPmV+3q+2EIpipfE6shQphFWGiqlNWoTcZVYUISgXcWw0mHcv+Z+Bh11FNPffouSKVMS970zcz+2XXtd+w+M1x+Le8LitcFG75/a76ra5PHvz3Rmkzns9+ZdXRQKO3LHDTNFUfoXJlGiIrPETjgQJhozTo0wIF7P9VdHPpzod8T271H1j5Sa56z7zKmsmT6D6OaU7ZgdrKizujuUQ9r2T/amnj/wJXjhhg5vSegvV3eFSgLqCSuAXIu1KkqPYWIxCAQyElsTOWEeT5gIWMawu2U3ALPuncXmxi3s89STVD/0YOLePfffz5rpM2hdl6VOTnw7orgnLL7KKG6MefEaYgDXDINd73f4+6TmVMTd+TqTVJR+SSx7Yn6b3UYo4CTmh0OOB6ysxNE1FaHUPK1PP3Ead91xIpN++5uU9rXHfYo102fQ+M9/Jhujzc5Pby5re4g4OmzqCcm2F29yJpTt1EFsa3F0VdjNBwuXBFV/FYAaYUrvJxbLmEUCtMRaHCMsahNx8y0CIti24YXPJ8tHnPTnk/jcY58jMPPjTF/zdsoY604+2VFir3j2o083wo77sbOk+7CvZZfvqlr49PXJ89vnOkqsLfvOXlbMWQ0VCAaSS7zVE6Yo/RK71VlBLZFISntzrJnyUHkiMR9gkDspa2y1WH3eao4Yf0Si/7MfPcvh73+N6atXZTzjo69e4HjGtm71GGFdKGBw7oPwzbRxb5oMv5qbsXIo2hIjVBIgEIx7wtQIKwQ1wpRej2mLZjXCsuWElZcEaY5ajCgbwbIvLUv0fWf3O8z9/Vwefv9hZryzhqn/+HvKWB8tOJ8102c4ia973SKscQ9Y5Vi48FkYNoV2+cQl8KOdqW03jHONsdTtjayYcTa/Ba2zoyj9HNPSCuFwhiesKdrEoPAgZ2FRMOnJB1jx0V4A7vzUnZy/f2qZnVl/OIgdf7uTCYtuz3jW2mOOZc0h82nZG8otHOll6OTM8GTN+3B1FdxzGtQ5ZXraWi3CpUl9HCoJENVwZN6oEab0ekwsllGeAryrI63E6qLKsjB1LU4OWSQYYfV5q7ni0CsS91zzr2s44J4D+Fv9a8x4Zw3T33qTQUcemTLue8ecwpo/jqN+5caurVwMlThesfQQ5Q1jHWNsxf2A4wlL5FOEdXWRovRn7NYWAmleMICmWBPloXJaohZlrgds0jBn79tQMJmg/52Dv8PrX3o95d5Lnr+ET2z6Fuv+eiNVl34jY+wPnx7Fmi9ex4bzFnS+EMlLPDx5euqqTD58EX4+A26cTLS+gZJI0qAMhdUTVghqhCm9HhOLQQdGWGvMJuIaNRWlIepbYin9zp1xLqvPW00kmFSEl790OQfccwCzfj+b8XfdyfRVbzD5/t+n3Lfp5w/xzoyZrJk+g5rfpOZidMpVtXDFxtS2v34DrhqC9cpvCLiesLCrzNrSZFYUpX9gmluQssz8rMZoI+XhcpraLMrcydjwQU4KxH8/825K33AwzOrzVjNjWGqtsCte+RGfHnwXn78iyMQnH814RtO//807M/djzfQZbLzkktyFnnOuo8POStN7LXtpW/M84T1vJrZta9jTwqZ38tvCTVEjTOkD2I2NBMrLM9rjRliLZ3VkZWmYuuZoVg/Wsi8t46VzMgsWzr5vNrOWHMxroxqY8c4aPv7oXRl9dvz3zxLhys3fvQyrtjajTwallY4i+/FumHFaojlmIoQaN8JVQyh//lsANNV1rWq1oih9A7ulhUBpZmiwMdrIoPAgmtssyl1PWCCQ9IDd9PQ7Gfc8eOqDvP7l1zPaEWHe38/k81eGGP/YbQQjmZ6phmefS+iwNdNnYDc1ZY6TzgFnOzrs6y8nmqJ2GSXSDDdVw1VDaNjj5LzFfypdQ40wpddj1dUSrKxMaTPGJIywhtYoFaWOp6yqPIxtYE9TNOtYQ0uHsvq81aw+bzXnzTwv5dolz1/CAfccwOx/fYPqL25l+qO/puL44zPGqHviCd477HDWTJ/B+/OP6dwgCwThnPucnLH5P6DZHkJZwLln0Hanvtj63/8aGnZ2NIqiKH0Qu6mJQFmqEWYbm+ZYM4PCg2hqiyVWRXq5Y+kH1GbRY+GA4xVb9ZVVHD85Uz8d8c/vcNZ3Iryz+IKMVAsv7x50cMIga3n77Xb7ATBmf8ezf8kyWk054UBLRpd7rnyZtx9Z2vE4SgaZ//OK0suw6+oJVlaktMXsGJaxKAuVUd8So8JNFB1d6bj9D7r2b6y/8ZQOx73skMu47JDL2NW8i2MePCbl2qHVE+FfCxk0bxCP3PgCYwaNIbpjB2uP/mSqHNu28d5hh6e0TfzN3Qw+4ggyCJXA/O/T9I9XqagEdkNpoAGA91o+yYzrz2BC5M1k/x9uz22ZuaIovRa7oYFARar+ao45KxgHhQdR1xJjSFnyq/jDn57MlCudumAHXvN/nHnQeH7++dkZ44oIP5//cwBqmmuY/+D8lOs/fvceOAo4KkSkzfDEjNvYfeHFWWX88MyzUs6n/uufBKuqEs8BHM9+aSU1sY3UxKbA8KlQ8z7njbyQe3beDcALT9u88PTzXDjqXCKBZvjh1q6t0hyAqBGm9HqsujpCY8aktDXFHFd6JBihviXG4Ijzp3zyAWO5dMkKAKqveIIXLpvPlBGDOhx/RNkIVp+3GmMMZ/z1DNbVJrchaow2cvzDydnmSf9zKj896qeY2jo+PO10YjszvVcbL7gw5XzYV7/KyEsvScyGm+vaGD15OCysdZZ//6dTTuOve65N3HPxmM/C9aOTg3xzlbN6SVGUPkXTv/+d0dYYdcrXlIXKqG2OUlmazHkVEX53/iGc/7vXAPjz65v58+tOUda1159EKJgZwBpeNpzV561m+fblLHh6Qcb11hLhUx98E64MceT4I/l26WewLrysXZnfn/eJlPPyww9n7NVXEZ40Kdl4qbP6fPCHf2efX77IB63Je+7e4SxCuui6SYTFTbU4+Wdw0HnOZFRJIH1t37q5c+eaZcuWdd5R6Te8d9RRVMw/hrHXXpNo29a4jeMfPp4rD/kvfnDvIL5/4nT+c/4+AGyva+GwG1K3EaosDfH6fx2fVYFl8Pq9mEcv5fGzbuUHr9/Safe/n/N3BjdabLrkUppXrOiwr0FY+slbmVL7b44472AqTjwRY+COb7yQtf+CkV+lJNCYVGRxDl4Ax1/r7GsZDCfXtvdTRGS5MWZuT8vhB6rDBhbxjbdnvLMm0fZh7Yec9shpXHX4DXz3dwGuPGk6X/vkPin31TZHOfDq/8s65n99ZiZfPaI66aXy8q9fwzNXsvlrSznx/77SqXxDGgy/3XIi1p+e6LRvNFTG34/8Gfuu/TOTNj3HqCu+z9AvfpFAJIJl2dx58dJ27/3a6HMIefXYoJFw3mNQOS57Iex+REf6Sz1hSq/Hrq0jUJndnY9xZlUVnro1oytLWXfDyXzsB8mtPupaYuz7w6cS529efULCe5ZBWxMCnPqxUzn1gAUA7G7ZzS3LbuHRDzJXIB31wFHOwYnAiSF+f/LvGU0lw1pCfHD8p1P6RsODMBIkuHMTm799P3z7OwAcCxhg44RjWbtvMjSweOdvE8dnDbuCYaENlARaYPli5+Vl+FT44hKomqyzTUXpJQTKy6n63OdS2uKefEwEiDKkLHP195CyMO9ceyKHXPcs9a2pq6evffxtrn38bY7YdziLzz80UezVGbwGJMj40bNYfd5qIHu4Mk7tYOGsjz8DVzr6UGzDl+v25zN3vJHRNxoeDEBJtA6AHTfexI4bb0pcd/SYsOtzP2T1zrEp9/7P9gfcI5sFIy9kUONO+HVqKgeDx8Bn74TqI5PbxvVz1AhTejV2ayumrY1gZepMKW6EGdv5oHqNMHBWGa2/8RQ+3NXIMT9bmjHu/j95JnH8zLeO5uOjBydnlS1uon1kcKLPsNJhXH/k9Vx/pFMZP2bHOO6h4xLbI3n50pNfSp64iq0sVMaLn32WLb97DFZDSVt9xn0CTNr0PJM2PU9D+VhePfRHKdf/tPvGlPMjBv+GAwY9TVBcBV3zvlOtPxtHXw5HfUfzMxSlGzFtbU5i/pDUhUW7mx29IVY5UMuwQdknTaXhIKuvdrYU2lrbzLyfpm7d/PLaGqZ6JpfTx1Tw8LjNlJUPJxhI1vKKhyvBWdS0vWk7n/nLZ2i1Mlc0moBwb9Vb3HtlUqcOqzP8oPxMKu9yVmaGou2vrBQMIx+6jvkSZPewGaw64D/TegRSJpdDAps5pep6Bod2E27YBvedkX3g+T+AI77Z7/Jk1QhTejXxlYfpiflxI8y2SoC2lJwKL1NGDEok6LfFbI752VI2721O6XPCL1PLVlwbepXTwxWs39bM/uNKUpaNxwkFQrx4zouJ87q2Ou57+z7ufOPOrHI0x5o59KEjGG99nFO5mBs+18jWIc7Hb8HUc/nKjqnseuZJeMHZA25w01aOXeok0VqBMC8e/cuMMV9uuICXGy5IaTvo9VuorF8PwD4nbMNuCxAstShZejPy0s2Zgk08HMYeCAd9GUbtBwFdMK0ofhHd4eSMhkeNSml/7IPHAFi6xlll2BLrvKDq2CFlrL/xFGzb8GFNI8fd8mJGn3e21TN47xLAyYkFOPvgCfzscwcm+ogIYwaNSdlR5O2atznn8XPaffbuSuEy/sKc047nsI1w62ereHdUiKH1hiPfNpz7gk0gLbMpYCxG1LzJsUsvxiC8MD+zwj9ArT2eP+z+dUb7GR+eT6w5SNOOCEOmNDKi4SZKlnawufgR33KMtPJh7ffphagRpvRq7DrH7R1IK1GxrXEbAGGGADszPGHZKAkFePmKYxPnb22p5ZTb/pHRb6TUstUawmm3v5zSPm10BXd++eCsif6VJZVcPPtiLp6dXH1kjOF/V/8vv1rxq0RbWZtjTDaXJD1hi9+/n8UAhwOHp/4eYht+9VI1n5GHaHphKQZhy9hP8O60/5f1d3z9oO8mjpe6q8gnr3mGKeufwEiAoJ2+5P0jYAOlQ/9Ey54Shs+op2JCC6FSi1hLkLLhbv9D/sMJDxxyIQz7mJODZky/z0VTlEKIbtoEQHjChJT2jfVOIecdeyJAKx/rZPGQl0BA2GfkYNbfeAq7Glo5+uYXaEqrWN9qknrk4eWbeHj5ppTr86eN5HsnTGPm2EpEhJnDZyY8ZXHq2uq44d838MS6ZK5YU4kzKd5SuRaAPRXCY4cJjx2WOXkT21DWBot/YSGYxKTSlhA1w2eyev929uJ1eWTK75yDmW5DA4x77x9Me28JWbXOHx8EHgSgap9G2upDlI9qZfDYVkqHRZOq6nP3wNTjwbYgXA7BnjWD1AhTejVWnWOspNcJiyuxkD0C2ElVedfzB/YbNySljEVja4xb/u89Rr22h50mM1H03e31WUObcyZVUT18EMdMH8VpB45LtIsIF826iItmXZRoW/nsR7y8di2PfvEvfPHZz7O5YXOHMpqAcMn8D4EPPQbav92XQ0msjOPeOYHJ9cdkG4INk09gw+QTsl47/N9XUda8k5Y9TjikZk0FNWsqMjv+Ma6IH+lQXoBQeYxwuU3VAaWUHHoa4fkLkCEjnf9D24ZAIOteoIrS34hudj7f4fHjU9rfrHFK0diW8znYb1yqfsuVEYMjvH3NicnnRaNwPUSk4x04lr67k6XvZq9L+PfLj2HisHIqSyq58agbufEoJw3CGMPTv1vFug9qaA01diqbCQhNpfD5KzM/6+UtbxGOLeSLL9o8fGSAW347hH8dfm2WUVLZMu5ItoxLrX1WUb+BoXveo2rv+1TWb6Ak2sDeDxyjtmlHhF1vpg3yxyuBKzt9FkCwFAbNnUXk4E9SMnEckakzCY4aSXDIEMSnqIFqQqVXY9W54ci0Ojsb6zcyqnwUO+scH/iYIYXnOg2KhPjxqTPhgzaYdDDrzzwFYwy/+Nt73Pb82nbvW/HRXlZ8tJe/rNjMwiXtr45c8V/Hs3FrA4GgUDl4ME+f9XTWfk3RJt7b8x5ffurLOcndFmrmqf0fwWsglUYHcdz7X2Fi7fQO733lsKvavRaw2piw+SWqat9neM1bCLmtpI41hYg1QfMLNrzwCNz0SNZ+015+jsDwcVmvKUp/ILp5EwQChNNK7MR5db2bG+aTRznctN05mHwk6893JpjGGO56aR0/fSqzAn82jro5+0ptgO/tdfTst/d7iLFDyhg2qITZE6sQEYwxrNm9hnMeP4fhpcOpaalpd5ymUuf3vfMUJ2/tvG/XAd9MXA/aIU596xLGNEzpVN76isnUV0zmo0mZhWu9TPnwMaZseBpbAgRMbvtpWi1Q949V8I9VGdeqTjyCsb+8O6dxOkKNMKVXE92yBSCjTtim+k1MrJjIE6ud6+2udOwqsTao2wKVzsxVRPjOp6fxnU9PS+nWErV4ZMVmrvjz6myjZGXOtX/jtMYSRhphH08ybTZ+cc6BvHj2cspLgpSGkwm2xhgMhoZoA5vrN/PSppe4fWVmrkVLuJEnZt6R0V4aHcTM7UdQ0TqUGTs+kXHdix0s4aNJn+IjPtXp7yZ2G8N3r2H/t+7GDoQJWm2dGm12a0y37FD6Nbt+7XwGxbP3bVMHSe0F84ibBD+sOtEkInztk/uklMAwxrBqUy0PLNvIH/79UZcf86NH3urg6o3UA9+Yvw8NrTG+e/w0yiNBMDaBgPDn9x/hmleuYu7ouSzbnr1UixWI8cgBv0ycl0YHEQtECZggx649l+o9B3RZ5g+nnMqHU07tsM/InSsobdlNactuRu18nUhbHQayhj9DtZmGWT6oEab0aqIbNiBlZYTSEls31m/kiPFHsHRznb8P3L4a7BgM37fDbqXhIF84dBJfOHRSxjXbNqzctJerHn2LVZtStzQabgk16RmsWfj2A5nLw7MzAbgx65VTZo2lrjnKDWfOZGddlB1Nuxg1RCgNlXHOUyfx4j4PpPQP2EFCdgkHbzqBA7dmD222hwmUsGvEgSz95K867wzURnZx+Sj1gikDj8P+cFjxBv/QXWR0xLc77CYiHDixigMnVnHDZ5MGTdSy2dXQyj/X1vDdh1J10Eira966Xy/9AIB7/7Uh7UopcCMvrAE4u4MRbCS8h0ConuaSGsrGPQTA09Oze59CVgn77jqIqubRzN56bNY+nbFz5JzE8ftTP9dBTxh3hsVn83pKKmqEKb2aljXvUDJxYkr8fVfzLnY272R4ifMlfvmJ09q7veusd5Pxx87Ke4hAQDho0lAevSQ1dyHaZnHXwhc5et5YfvnlGRhjqG2OsquhjZUb9/LoG1t46T3/9o98YtVWAI66KXPT8vYMN4D/A/6vyruC1AIMBNqQYBMBLAaXfsTowW8xZ8t8JtXO6LJsQ1pHsHHrdqZMHN95Z0Xpg2z/afufMYD69/4LgD/95zz/Hz6i40lke4SDAcYOKeOsgydw1sGpiwkWfd0pj3Hgpyay/uyptEQtNu1p4q0tdTzw2kb++UH74cf8CGCiw7Giw6G5mvragzu94zVsIMDfqppB2iAQJVCyi/JJdyMfnUdJ5UrarHLmtpQzd9NJBUn39rKtfPbEzvt1hhphSq8ltmsXTa++SmRm6pd8fJ/H9z9ytvWZ//FRGffmzT9+7lRyHr2/f2O6vPGss5hg3NQqwJmNVpWXUFVewr6jBnN2mtLriLaYzbpdDdz/yke8+N5OrjptJn98dSMvvb+Tlmhu+Q6544ZD7RDGLscCaqOjqa0/hPcEqGru6OYEIQNDbKFVDCEjnFU53Gc5FaX3sPueewCY/If7E20//McPkx0sJ3n84Mk+lVS4qnhV559YlPSKHXGWY+CVhoPsO6qCfUdVcPrs7JMpYwytMZud9a0MjoSoKg+zs76VN7fU8re3t7PkVUcn7jNyEB/sbGT6mAre2ZZZQzF3PAkOpgSsEuzmQTS86yb9NzuyvwC80JneMo6BJMCUaIDagMXHo2FCwO5glMbyyfyw4xFyQo0wpdfy/lFHAzDiouRS5rq2ZPjxidecFX0zxmZZzZcPK/8AzXug+ijfSy+sX72Lfz/q7En58UOzJ+l2hZJQgOljKrn2jKSxeOz00R3c0TVils37OxqYPqYCESFmOYZdc9Ri2YY9HLHPCJat383GPU3c9txaLj9xGjc//S6b9zZzaPWwRMIxOKU93t1eT0ygJhgPxRpGVkR8k1dRehPxrYoAyg86CGMM175ybWLHDTvmGGB/+/bRhT8s1gbXjUye/8fz7ffNg7gHDGDijKFdWkQgIpSGg0wcVp5oG1VZyrGVpRw7fTQ/PTP/iIOX+pYotg2WMbRELXY3trF2RwOHTBnG4JIQlWUhYrbhH+/v4rE3tvCZA8eyva6VnfWt/Pxv73Hs9FGs2VrH1toWj/AQX2P6XokNCNtDscTFFV/zx4OpRpjSK9n83cucOlRAxQnO1j93r76bW1+/FYCmjV8BhF+cc2DhK4usKFw7Inl+8s8KG89DtNXirm8miypOPWQ0kqX4a28jFAwwY2xlyjlARTDAMdMcz+Mn9nXes3MOcfLi2psNK8pAwG5p4d3Zc1LanvnmYXz+nswk8sb3/4t3rzuRSCiYcS1ntr8ND58POz2rHvc/C8Z3HrbriJbGKK8+/iGrX9iUce20b87JckfPU5FWrHtcVRn7j0/1DIaDwjHTR3HM9NTIycLjphZdvo5QI0zpFRjbpvaRv7L1Bz9Iaf/hl4O8f2/qbMmOVWA1zORfVx7L2HxKU7TWw99vgX/8IvPaSf8Nozou65COMQZjG+p3t7B2+Q5eeWRd1n4Tpg/l0xfs13V5FUXpdbR++CE1f1xC7T33Zb1+09kBlpcvz2ivX3NDSn3CFBproPYjQOBPF8IRC+HRS2HEx2HkdFiTuXdtggmHwtm/bfeyMYamujZWPPMRbzy/saNfLSsX35lfsrvSMWqEKUXDGAPG0PzuO2zfspbade+wJ9BG5OFneD9cw6HvdbxK8KazA7w/IdNrNGjLT3jrxhNg3YuwrQm2roJ2trMwBqKmFEMAwSYoMQxCkBhtppyd0X14v+VIRoY/IHrYd3j9wRijXn2Dhj0t7N7SeUHCXPny9fOoHK77NipKb8FYVmKbrqamWlZtW0H9FVczcfX2gsde8O1gohZW2IoQDbby/XWj+CIrCES+xBvf+wwBLFY0nsFpw35CUGIMDuyizZRjmTDvtnySbW1nsu6tCcBfYFt85PPbf+g2YJm/oUiAsy4/mDEfK16+2UBHjMmtAGNvYe7cuWbZsuy1RQYyUTtKSELYxk4UzgtIgF1Nu2mORvlo9/tE6xsYO7KamobdbG6sZ8uKlfDucqZ+IAzdtpW9kSiN44ew36tO3RgBDIIdCNJcOhLBJhYqp6x5J+GoY6BYwRLaSiqxAyGCVhtWMMLGCc6MaUjtWprKR7NrxIE0lY9m1I5l7Bg1l4AdxQ50vcJ9X0EEZh45jjmfnsyQkWp4+YGILDfGtLM7ed+iv+sw73eKiBC1o8TsGLEmm0GDSjHRGDHb5uHXlxMcupHJkRE8+/pS9uwqY1XrH5m+czBTw/tRGm3gjeibfGJNGcObxjN+x2aCVjNibJpLSimxIGi1IIAtAaxgKUGrjZZIFbHwIOoHT2TT+KNpHDQWJLUi3aSP/kZj+RjC0Qa2jS3C6sQ+ylmXH0zV6HJKB/Vf/dwTdKS/+rUR9t4zS/n7S+8xJLSB5sZ1xFqHENojDK0ZTMC2aS4rwwrVUbm3nh0jplPSVk99eTlja2rZPnouLaXJ1VvBWAtD6tbRVlJJw2BnFVt542aaBmkeTF8mFA4Qy7KacNTkCur3tNJc18ao6krmnzuNYWMGEQxredGeYCAaYRs27uDx69P3XFGUTEIlAWJtNsMnDKZmUwMAJ160PxXDS4mUh3Uy2MN0pL/6bThy3Tvv8be/2MC+tLAvcJxzYTDUDU7tu3NE6vmGtOsAVqiU3cNmprSpAZYfg8vbGLvvEN5f1czQ0WXMmhOlZOQESgZXMHykzbo1rew7dzQlZSHCJUGMbZyqxULCy+fXNh+K0ltRA6z3MuMAaDZD2bqunvFTh3DMuVPBNkQqy9m6di/lQyIMHhohVFJA4r0yIOi3RtjIURUErHrsoFO+oLLuQ+oqO9+HqqxpO6N3LGfbmMOIhsppCC5lVF0FFY2GWKiccLSBusElVIT3sHvQdqzAUMZtraN0dICa+iqiwypoqCxn8phDaLU2E6jdSmtVCcP2P5L1O4KMGFXCpNGGcKCCqtIwJc1tlEwaS9QylAVsgiVhIqGgY3BgHNe+BJxtEwIBjG0TixlCrkemLxsjn26n/cC0QuoSkJRtI/ry76wouRKKriAWLmw1WlnTDsKxJuoqq1PaJ294mo0Tj2FwwxbsQJiGwRMYHF1PU3AUlfZqakIHURJpIGB2EzFbMbNH8NIboykrC3DUrBbCFVVMrBzH2OFDKWtaT+uYaRApYUjlIPY2tjJmSAliGajfQkt4JOWDghB2vDF1Oxtp2tNIxchKBg8r63eTqnEfH9rTIih9iKKGI0XkROBWnGqPdxtjbky7HgHuBQ4GaoBzjDHrOxqzv+dTKIqSyUAMRyqK0j/oSH8VLcFFRILAIuAkYCbwRRGZmdbtAmCPMWZf4BfATcWSR1EURVEUpTdRzCzjQ4G1xph1xpg24I/A6Wl9TgfucY8fBo6T/uSXVhRFURRFaYdiGmHjAW9FuE1uW9Y+xpgYUAtkbCgnIheJyDIRWbZzp38bHCuKonQHqsMURclGn1hvb4y5yxgz1xgzd+TIkZ3foCiK0otQHaYoSjaKaYRtBiZ6zie4bVn7iEgIGIKToK8oiqIoitKvKaYR9howVUSmiEgJ8AUgfeOrR4Hz3OOzgedNX6seqyiKoiiKkgdFqxNmjImJyCXAMzglKn5rjHlLRK4BlhljHgV+A9wnImuB3TiGmqIoiqIoSr+nqMVajTFPAk+mtf3Yc9wCfK6YMiiKoiiKovRG+tzekSKyE9jQhVtGALuKJE4xUbm7F5W7++mK7JONMf0io72LOqyv/v+q3N1PX5V9IMjdrv7qc0ZYVxGRZX2x0rbK3b2o3N1PX5a9u+ir75HK3f30VdkHutx9okSFoiiKoihKf0ONMEVRFEVRlB5gIBhhd/W0AHmicncvKnf305dl7y766nukcnc/fVX2AS13v88JUxRFURRF6Y0MBE+YoiiKoihKr0ONMEVRFEVRlB6gXxhhInKiiLwrImtF5Ios1yMi8oB7/d8iUt0DYmYlB9mPFpHXRSQmImf3hIzZyEHu74jI2yKySkSeE5HJPSFnOjnI/XURWS0iK0XkHyIysyfkTKczuT39zhIRIyK9Ysl3Du/3AhHZ6b7fK0Xkwp6Qs6fpqzpM9Vf3ovqr+ym6DjPG9OkXzpZIHwAfA0qAN4CZaX2+AdzpHn8BeKCn5e6C7NXALOBe4OyelrkLch8DlLvH/9kb3vMc5a70HJ8GPN0X5Hb7VQAvAa8Ac/uC3MAC4PaelrUPvE+9Toep/uqVcqv+6v73vCAd1h88YYcCa40x64wxbcAfgdPT+pwO3OMePwwcJyLSjTK2R6eyG2PWG2NWAXZPCNgOucj9gjGmyT19BZjQzTJmIxe56zyng4DesHIll79xgGuBm4CW7hSuA3KVe6DTV3WY6q/uRfVX91N0HdYfjLDxwEbP+Sa3LWsfY0wMqAWGd4t0HZOL7L2Rrsp9AfBUUSXKjZzkFpGLReQD4GZgYTfJ1hGdyi0iBwETjTFPdKdgnZDr38lZbtjnYRGZ2D2i9Sr6qg5T/dW9qP7qfoquw/qDEab0YkTkS8Bc4L97WpZcMcYsMsbsA3wf+FFPy9MZIhIAfg58t6dlyYPHgGpjzCzgbyS9PYrS46j+Kj59XH9BgTqsPxhhmwGv5TnBbcvaR0RCwBCgpluk65hcZO+N5CS3iHwK+CFwmjGmtZtk64iuvt9/BM4opkA50pncFcD+wFIRWQ8cDjzaC5JbO32/jTE1nr+Nu4GDu0m23kRf1WGqv7oX1V/dT/F1WE8nvvmQOBcC1gFTSCbO7ZfW52JSk1of7Gm5c5Xd03cxvSexNZf3fA5OQuPUnpa3i3JP9RyfCizrC3Kn9V9KL0hszfH9Hus5/izwSk/L3Uvfp16nw1R/9Uq5VX91/3tekA7r8V/SpzfqZOA990PzQ7ftGpwZDEAp8BCwFngV+FhPy9wF2Q/BiUM34sx83+ppmXOU+1lgO7DSfT3a0zLnKPetwFuuzC90pCx6k9xpfXuTEuvs/f6p+36/4b7f03ta5l76PvVKHab6q9fJrfqr+9/zgnSYblukKIqiKIrSA/SHnDBFURRFUZQ+hxphiqIoiqIoPYAaYYqiKIqiKD2AGmGKoiiKoig9gBphiqIoiqIoPYAaYYqiKIqiKD2AGmFK0RCR4SKy0n1tE5HN7nGDiPy6CM9bLCIfisjXO+hzlIi8LSJv+v18RVH6D6q/lO5A64Qp3YKIXAU0GGN+VsRnLAYeN8Y83Em/arff/sWSRVGU/oPqL6VYqCdM6XZEZL6IPO4eXyUi94jI30Vkg4icKSI3i8hqEXlaRMJuv4NF5EURWS4iz4jI2Bye8zkReVNE3hCRl4r9eymK0v9R/aX4iRphSm9gH+BY4DTg98ALxpgDgGbgFFeR/Qpn77mDgd8C1+cw7o+BE4wxB7pjK4qi+I3qLyVvQj0tgKIATxljoiKyGggCT7vtq4FqYBqwP/A3EcHtszWHcV8GFovIg8Cf/RZaURQF1V9KAagRpvQGWgGMMbaIRE0yUdHG+RsVnI1/53VlUGPM10XkMOAUYLmIHGyMqfFTcEVRBjyqv5S80XCk0hd4FxgpIvMARCQsIvt1dpOI7GOM+bcx5sfATmBikeVUFEVJR/WX0i7qCVN6PcaYNhE5G7hNRIbg/N3+Enirk1v/W0Sm4sxEnwPeKKqgiqIoaaj+UjpCS1Qo/QZd4q0oSl9F9dfARMORSn+iFri2s2KHwGPArm6TSlEUpXNUfw1A1BOmKIqiKIrSA6gnTFEURVEUpQdQI0xRFEVRFKUHUCNMURRFURSlB1AjTFEURVEUpQf4/xn261qMiipQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "with HDF5Handler('Caburst') as hdf:\n",
    "    Currents, Pot, CaConcs, BKstates =  hdf['CaBurstSim'].results\n",
    "\n",
    "    # Membrane potential\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    for r in range(len(Pot.time)):\n",
    "        plt.plot(Pot.time[r], 1e3 * Pot.data[r,:,0], label=f'Run {r}')\n",
    "    plt.legend()\n",
    "    plt.xlabel('Time [s]')\n",
    "    plt.ylabel('Membrane Potential [mV]')\n",
    "    plt.show()\n",
    "    \n",
    "    # Currents\n",
    "    fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n",
    "    for i in range(len(Currents.data[0,0,:])):\n",
    "        ax = axs[i//2][i%2]\n",
    "        for r in range(len(Currents.time)):\n",
    "            ax.plot(Currents.time[r], 1e12 * Currents.data[r,:,i], label=f'Run {r}')\n",
    "            ax.set_title(Currents.labels[i].split('.')[-2])\n",
    "        ax.set_xlabel('Time [s]')\n",
    "        ax.set_ylabel('Current [pA]')\n",
    "    plt.legend()\n",
    "    \n",
    "    # Calcium\n",
    "    fig, axs = plt.subplots(1, 2, squeeze=True, sharey=True, figsize=(10, 4))\n",
    "    for i in range(len(CaConcs.data[0,0,:])):\n",
    "        for r in range(len(CaConcs.time)):\n",
    "            axs[i].plot(CaConcs.time[r], 1e6 * CaConcs.data[r,:,i], label=f'Run {r}')\n",
    "        axs[i].set_title(['Cytoplasm', 'Submembrane tetrahedrons'][i])\n",
    "        axs[i].set_xlabel('Time [s]')\n",
    "        if i == 0:\n",
    "            axs[i].set_ylabel('Calcium concentration [uM]')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    # BK channel states for run 1\n",
    "    rind = 1\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    totCount = np.sum(BKstates.data[rind,:,:], axis=1)\n",
    "    for c in range(len(BKstates.data[rind, 0, :])):\n",
    "        plt.plot(BKstates.time[rind], 100 * BKstates.data[rind,:,c] / totCount)\n",
    "    plt.legend(BKstates.labels)\n",
    "    plt.xlabel('Time [s]')\n",
    "    plt.ylabel('BK channels states distribution [%]')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84876dba-f326-43dc-8a2a-fe83d86e15d1",
   "metadata": {},
   "source": [
    "## Footnotes \n",
    "\n",
    "1. Anwar H, Hepburn I, Nedelescu H, Chen W, De Schutter E (2013) Stochastic Calcium Mechanisms Cause Dendritic Calcium Spike Variability. The Journal of Neuroscience, 33(40): 15848-15867, doi: 10.1523/​JNEUROSCI.1722-13.2013. \n",
    "2. Anwar H, Hong S, De Schutter E (2012) Controlling Ca2+-activated K+ channels with models of Ca2+ buffering in Purkinje cells. Cerebellum, 11(3):681-93, doi: 10.1007/s12311-010-0224-3. \n",
    "3. Hepburn I, Cannon R and De Schutter E (2013) Efficient calculation of the quasi-static electrical potential on a tetrahedral mesh and its implementation in STEPS. Frontiers in Computational Neuroscience: 7:129, doi: 10.3389/fncom.2013.00129\n",
    "4. The same considerations for converting membrane permeability to single-channel permeability apply as for conductance discussed in [Simulating membrane potentia](STEPS_Tutorial_Efield.ipynb), requiring some estimate of the channel density.\n",
    "5. Since it is assumed that conductance is measured by estimating the slope of an I-V curve over some small voltage range, the conductance will be treated as a slope conductance for the purposes of single-channel permeability estimation.\n",
    "6. We may record voltage from anywhere on the membrane surface or within the 'conduction volume' (here and in most models the conduction volume is the cytosolic compartment). "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}