{ "cells": [ { "cell_type": "markdown", "id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f", "metadata": {}, "source": [ "### `A` down-regulates `B` , by being the *limiting reagent* in reaction `A + 2 B <-> Y` (mostly forward)\n", "1st-order kinetics. \n", "If [A] is low and [B] is high, then [B] remains high. If [A] goes high, [B] goes low. However, at that point, A can no longer bring B up to any substantial extent.\n", "\n", "Single-bin reaction\n", "\n", "Based on experiment `reactions_single_compartment/down_regulate_2`\n", "\n", "LAST REVISED: May 6, 2024" ] }, { "cell_type": "code", "execution_count": 1, "id": "c1ee6c54-9795-4fca-8972-e5ed4cb84019", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Added 'D:\\Docs\\- MY CODE\\BioSimulations\\life123-Win7' to sys.path\n" ] } ], "source": [ "import set_path # Importing this module will add the project's home directory to sys.path" ] }, { "cell_type": "code", "execution_count": 2, "id": "bea6d4a4", "metadata": { "tags": [] }, "outputs": [], "source": [ "from experiments.get_notebook_info import get_notebook_basename\n", "\n", "from src.modules.chemicals.chem_data import ChemData as chem\n", "from src.life_1D.bio_sim_1d import BioSim1D\n", "\n", "import plotly.express as px\n", "from src.modules.visualization.graphic_log import GraphicLog" ] }, { "cell_type": "code", "execution_count": 3, "id": "cc53849f-351d-49e0-bfa8-22f8d8e22f8e", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-> Output will be LOGGED into the file 'down_regulation_1.log.htm'\n" ] } ], "source": [ "# Initialize the HTML logging\n", "log_file = get_notebook_basename() + \".log.htm\" # Use the notebook base filename for the log file\n", "\n", "# Set up the use of some specified graphic (Vue) components\n", "GraphicLog.config(filename=log_file,\n", " components=[\"vue_cytoscape_2\"],\n", " extra_js=\"https://cdnjs.cloudflare.com/ajax/libs/cytoscape/3.21.2/cytoscape.umd.js\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "23c15e66-52e4-495b-aa3d-ecddd8d16942", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions: 1 (at temp. 25 C)\n", "0: A + 2 B <-> Y (kF = 8 / kR = 2 / delta_G = -3,436.6 / K = 4) | 1st order in all reactants & products\n", "Set of chemicals involved in the above reactions: {'A', 'Y', 'B'}\n", "[GRAPHIC ELEMENT SENT TO LOG FILE `down_regulation_1.log.htm`]\n" ] } ], "source": [ "# Initialize the system\n", "chem_data = chem(names=[\"A\", \"B\", \"Y\"]) # NOTE: Diffusion not applicable (just 1 bin)\n", "\n", "# Reaction A + 2 B <-> Y , with 1st-order kinetics for all species\n", "chem_data.add_reaction(reactants=[(\"A\") , (2, \"B\", 1)], products=[(\"Y\")],\n", " forward_rate=8., reverse_rate=2.)\n", "\n", "chem_data.describe_reactions()\n", "\n", "# Send the plot of the reaction network to the HTML log file\n", "chem_data.plot_reaction_network(\"vue_cytoscape_2\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "be6fabbe-bded-4ff6-b220-5610e73b401f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [5.]\n", " Species 1 (B). Diff rate: None. Conc: [100.]\n", " Species 2 (Y). Diff rate: None. Conc: [0.]\n" ] } ], "source": [ "bio = BioSim1D(n_bins=1, chem_data=chem_data)\n", "\n", "bio.set_uniform_concentration(species_name=\"A\", conc=5.) # Scarce\n", "bio.set_uniform_concentration(species_name=\"B\", conc=100.) # Plentiful\n", "# Initially, no \"Y\" is present\n", "\n", "bio.describe_state()" ] }, { "cell_type": "code", "execution_count": 6, "id": "5562fea2-834e-40a9-9b1d-5ea28a0100bf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ " SYSTEM TIME A B Y caption\n", "0 0 5.0 100.0 0.0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save the state of the concentrations of all species at bin 0 (the only bin in this system)\n", "bio.add_snapshot(bio.bin_snapshot(bin_address = 0))\n", "bio.get_history()" ] }, { "cell_type": "markdown", "id": "0b46b395-3f68-4dbd-b0c5-d67a0e623726", "metadata": { "tags": [] }, "source": [ "### Take the initial system to equilibrium" ] }, { "cell_type": "code", "execution_count": 7, "id": "bcf652b8-e0dc-438e-bdbe-02216c1d52a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.015:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [0.01385228]\n", " Species 1 (B). Diff rate: None. Conc: [90.02770457]\n", " Species 2 (Y). Diff rate: None. Conc: [4.98614772]\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " SYSTEM TIME A B Y caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.850000 93.700000 3.150000 \n", "2 0.002 0.735332 91.470665 4.264668 \n", "3 0.003 0.303911 90.607822 4.696089 \n", "4 0.004 0.131501 90.263002 4.868499 \n", "5 0.005 0.061738 90.123476 4.938262 \n", "6 0.006 0.033369 90.066737 4.966631 \n", "7 0.007 0.021809 90.043618 4.978191 \n", "8 0.008 0.017095 90.034189 4.982905 \n", "9 0.009 0.015172 90.030343 4.984828 \n", "10 0.010 0.014387 90.028774 4.985613 \n", "11 0.011 0.014067 90.028134 4.985933 \n", "12 0.012 0.013936 90.027872 4.986064 \n", "13 0.013 0.013883 90.027766 4.986117 \n", "14 0.014 0.013861 90.027722 4.986139 \n", "15 0.015 0.013852 90.027705 4.986148 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bio.react(time_step=0.0005, n_steps=30, snapshots={\"frequency\": 2, \"sample_bin\": 0}) # At every other step, take a snapshot \n", " # of all species at bin 0\n", "bio.describe_state()\n", "bio.get_history()" ] }, { "cell_type": "markdown", "id": "7dc56592-179d-4e4c-b75a-8eb81dcafe71", "metadata": {}, "source": [ "A, as the scarse limiting reagent, stops the reaction. \n", "When A is low, B is also low." ] }, { "cell_type": "markdown", "id": "962acf15-3b50-40e4-9daa-3dcca7d3291a", "metadata": {}, "source": [ "### Equilibrium" ] }, { "cell_type": "code", "execution_count": 8, "id": "c3afbcc8-bdae-4938-a3f1-ce00d62816f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A + 2 B <-> Y\n", "Final concentrations: [A] = 0.01385 ; [B] = 90.03 ; [Y] = 4.986\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 3.99823\n", " Formula used: [Y] / ([A][B])\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 0.0443 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "bio.reaction_dynamics.is_in_equilibrium(conc=bio.bin_snapshot(bin_address = 0))" ] }, { "cell_type": "markdown", "id": "cbf6c9c7-8cec-400f-9e70-49ff1a9f485c", "metadata": { "tags": [] }, "source": [ "## Plots of changes of concentration with time" ] }, { "cell_type": "code", "execution_count": 9, "id": "665dfff9-e943-44e1-b76d-af363d94c9f8", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=A
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line(data_frame=bio.get_history(), x=\"SYSTEM TIME\", y=[\"A\", \"B\", \"Y\"], \n", " title=\"Changes in concentrations (reaction A + 2 B <-> Y)\",\n", " color_discrete_sequence = ['red', 'blue', 'green'],\n", " labels={\"value\":\"concentration\", \"variable\":\"Chemical\"})\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "448ec7fa-6529-438b-84ba-47888c2cd080", "metadata": { "tags": [] }, "source": [ "# Now, let's suddenly increase [A]" ] }, { "cell_type": "code", "execution_count": 10, "id": "7245be7a-c9db-45f5-b033-d6c521237a9c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.015:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [40.]\n", " Species 1 (B). Diff rate: None. Conc: [90.02770457]\n", " Species 2 (Y). Diff rate: None. Conc: [4.98614772]\n" ] } ], "source": [ "bio.set_bin_conc(bin_address=0, species_index=0, conc=40.)\n", "bio.describe_state()" ] }, { "cell_type": "code", "execution_count": 11, "id": "007161ef-f4d0-4623-92c5-0fe3d2bda98a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SYSTEM TIMEABYcaption
00.0005.000000100.0000000.000000
10.0011.85000093.7000003.150000
20.0020.73533291.4706654.264668
30.0030.30391190.6078224.696089
40.0040.13150190.2630024.868499
50.0050.06173890.1234764.938262
60.0060.03336990.0667374.966631
70.0070.02180990.0436184.978191
80.0080.01709590.0341894.982905
90.0090.01517290.0303434.984828
100.0100.01438790.0287744.985613
110.0110.01406790.0281344.985933
120.0120.01393690.0278724.986064
130.0130.01388390.0277664.986117
140.0140.01386190.0277224.986139
150.0150.01385290.0277054.986148
160.01540.00000090.0277054.986148
\n", "
" ], "text/plain": [ " SYSTEM TIME A B Y caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.850000 93.700000 3.150000 \n", "2 0.002 0.735332 91.470665 4.264668 \n", "3 0.003 0.303911 90.607822 4.696089 \n", "4 0.004 0.131501 90.263002 4.868499 \n", "5 0.005 0.061738 90.123476 4.938262 \n", "6 0.006 0.033369 90.066737 4.966631 \n", "7 0.007 0.021809 90.043618 4.978191 \n", "8 0.008 0.017095 90.034189 4.982905 \n", "9 0.009 0.015172 90.030343 4.984828 \n", "10 0.010 0.014387 90.028774 4.985613 \n", "11 0.011 0.014067 90.028134 4.985933 \n", "12 0.012 0.013936 90.027872 4.986064 \n", "13 0.013 0.013883 90.027766 4.986117 \n", "14 0.014 0.013861 90.027722 4.986139 \n", "15 0.015 0.013852 90.027705 4.986148 \n", "16 0.015 40.000000 90.027705 4.986148 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save the state of the concentrations of all species at bin 0 (the only bin in this system)\n", "bio.add_snapshot(bio.bin_snapshot(bin_address = 0))\n", "bio.get_history()" ] }, { "cell_type": "markdown", "id": "24455d58-a0ea-43fa-b6ad-95c42a8b34b2", "metadata": {}, "source": [ "### Again, take the system to equilibrium" ] }, { "cell_type": "code", "execution_count": 12, "id": "c06fd8d8-d550-4e35-a239-7b91bee32be9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.055:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [0.97997411]\n", " Species 1 (B). Diff rate: None. Conc: [11.98765279]\n", " Species 2 (Y). Diff rate: None. Conc: [44.00617361]\n" ] }, { "data": { "text/html": [ "
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SYSTEM TIMEABYcaption
00.0005.000000100.0000000.000000
10.0011.85000093.7000003.150000
20.0020.73533291.4706654.264668
30.0030.30391190.6078224.696089
40.0040.13150190.2630024.868499
50.0050.06173890.1234764.938262
60.0060.03336990.0667374.966631
70.0070.02180990.0436184.978191
80.0080.01709590.0341894.982905
90.0090.01517290.0303434.984828
100.0100.01438790.0287744.985613
110.0110.01406790.0281344.985933
120.0120.01393690.0278724.986064
130.0130.01388390.0277664.986117
140.0140.01386190.0277224.986139
150.0150.01385290.0277054.986148
160.01540.00000090.0277054.986148
170.01619.34997348.72765125.636175
180.01713.05968136.14706731.926466
190.0189.78362229.59494935.202526
200.0197.74952825.52676037.236620
210.0206.36138522.75047538.624763
220.0215.35570420.73911339.630443
230.0224.59638719.22047840.389761
240.0234.00551018.03872440.980638
250.0243.53503817.09778041.451110
260.0253.15365316.33501141.832494
270.0262.84002115.70774742.146127
280.0272.57907915.18586242.407069
290.0282.35987614.74745642.626272
300.0292.17425414.37621242.811894
310.0302.01600414.05971242.970144
320.0311.88031413.78833343.105833
330.0321.76340013.55450443.222748
340.0331.66223913.35218243.323909
350.0341.57439213.17648843.411756
360.0351.49786613.02343743.488281
370.0361.43102212.88974943.555125
380.0371.37249612.77269643.613652
390.0381.32114612.66999643.665002
400.0391.27601012.57972443.710138
410.0401.23627312.50025143.749875
420.0411.20124012.43018543.784907
430.0421.17031712.36833843.815831
440.0431.14299112.31368643.843157
450.0441.11882012.26534543.867328
460.0451.09742312.22255043.888725
470.0461.07846612.18463643.907682
480.0471.06166012.15102443.924488
490.0481.04675212.12120943.939395
500.0491.03352212.09474843.952626
510.0501.02177412.07125243.964374
520.0511.01133812.05038143.974809
530.0521.00206512.03183543.984083
540.0530.99382212.01534943.992326
550.0540.98649312.00069043.999655
560.0550.97997411.98765344.006174
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" ], "text/plain": [ " SYSTEM TIME A B Y caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.850000 93.700000 3.150000 \n", "2 0.002 0.735332 91.470665 4.264668 \n", "3 0.003 0.303911 90.607822 4.696089 \n", "4 0.004 0.131501 90.263002 4.868499 \n", "5 0.005 0.061738 90.123476 4.938262 \n", "6 0.006 0.033369 90.066737 4.966631 \n", "7 0.007 0.021809 90.043618 4.978191 \n", "8 0.008 0.017095 90.034189 4.982905 \n", "9 0.009 0.015172 90.030343 4.984828 \n", "10 0.010 0.014387 90.028774 4.985613 \n", "11 0.011 0.014067 90.028134 4.985933 \n", "12 0.012 0.013936 90.027872 4.986064 \n", "13 0.013 0.013883 90.027766 4.986117 \n", "14 0.014 0.013861 90.027722 4.986139 \n", "15 0.015 0.013852 90.027705 4.986148 \n", "16 0.015 40.000000 90.027705 4.986148 \n", "17 0.016 19.349973 48.727651 25.636175 \n", "18 0.017 13.059681 36.147067 31.926466 \n", "19 0.018 9.783622 29.594949 35.202526 \n", "20 0.019 7.749528 25.526760 37.236620 \n", "21 0.020 6.361385 22.750475 38.624763 \n", "22 0.021 5.355704 20.739113 39.630443 \n", "23 0.022 4.596387 19.220478 40.389761 \n", "24 0.023 4.005510 18.038724 40.980638 \n", "25 0.024 3.535038 17.097780 41.451110 \n", "26 0.025 3.153653 16.335011 41.832494 \n", "27 0.026 2.840021 15.707747 42.146127 \n", "28 0.027 2.579079 15.185862 42.407069 \n", "29 0.028 2.359876 14.747456 42.626272 \n", "30 0.029 2.174254 14.376212 42.811894 \n", "31 0.030 2.016004 14.059712 42.970144 \n", "32 0.031 1.880314 13.788333 43.105833 \n", "33 0.032 1.763400 13.554504 43.222748 \n", "34 0.033 1.662239 13.352182 43.323909 \n", "35 0.034 1.574392 13.176488 43.411756 \n", "36 0.035 1.497866 13.023437 43.488281 \n", "37 0.036 1.431022 12.889749 43.555125 \n", "38 0.037 1.372496 12.772696 43.613652 \n", "39 0.038 1.321146 12.669996 43.665002 \n", "40 0.039 1.276010 12.579724 43.710138 \n", "41 0.040 1.236273 12.500251 43.749875 \n", "42 0.041 1.201240 12.430185 43.784907 \n", "43 0.042 1.170317 12.368338 43.815831 \n", "44 0.043 1.142991 12.313686 43.843157 \n", "45 0.044 1.118820 12.265345 43.867328 \n", "46 0.045 1.097423 12.222550 43.888725 \n", "47 0.046 1.078466 12.184636 43.907682 \n", "48 0.047 1.061660 12.151024 43.924488 \n", "49 0.048 1.046752 12.121209 43.939395 \n", "50 0.049 1.033522 12.094748 43.952626 \n", "51 0.050 1.021774 12.071252 43.964374 \n", "52 0.051 1.011338 12.050381 43.974809 \n", "53 0.052 1.002065 12.031835 43.984083 \n", "54 0.053 0.993822 12.015349 43.992326 \n", "55 0.054 0.986493 12.000690 43.999655 \n", "56 0.055 0.979974 11.987653 44.006174 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bio.react(time_step=0.0005, n_steps=80, snapshots={\"frequency\": 2, \"sample_bin\": 0}) # At every other step, take a snapshot \n", " # of all species at bin 0\n", "bio.describe_state()\n", "bio.get_history()" ] }, { "cell_type": "code", "execution_count": 13, "id": "2783a665-fca0-44e5-8d42-af2a96eae392", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A + 2 B <-> Y\n", "Final concentrations: [A] = 0.98 ; [B] = 11.99 ; [Y] = 44.01\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 3.74597\n", " Formula used: [Y] / ([A][B])\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 6.351 %\n", "Reaction IS in equilibrium (within 7% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "bio.reaction_dynamics.is_in_equilibrium(conc=bio.bin_snapshot(bin_address = 0), tolerance=7)" ] }, { "cell_type": "code", "execution_count": 14, "id": "58f4f09c-8af6-46b7-bd85-2f6ca194c42a", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=A
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xItJklbt5SO6KqXx55QoHp2xNuXrH3Q9mToHccZ0wzd2NvVA9uZteqHkc/JG9866+zZWR5muV2cSPHmmcB0sXzhIl2cyH9Tw07//Z147puewLCXo7IjIfLycbeji5l2Lut7WdrEypY72HZb7v01LPS5NdIZmY+zMi38rAckSk3dtP9MW5r790yhGR+TYIKvSzt1ANfe2a7eQ8c+Mv1kr2YUfmO/mfSpWsnbEgAAEIQAACEIAABCAAgfARQEQGLPNCl/cGbJpMJ4QEuLQ0hKEzZVcIFFsJ68ogdAIBCEAAAhCAAAQgAAEIQMAGAUSkDUg6NrFeVm2ueGNXVB2ToiY3CdhZ/eXmePQFAb8TKOdye7/PnfohAAEIQAACEIAABCAAAf0IICL1y8RWRYUuNXVrAwRbRdAIAhUm4GSn7QqXxnAQ0I5AObuQazcZCoIABCAAAQhAAAIQgAAEAkEAERmIGJkEBCAAAQhAAAIQgAAEIAABCEAAAhCAAAT0JoCI1DsfqoMABCAAAQhAAAIQgAAEIAABCEAAAhCAQCAIICIDESOTgAAEIAABCEAAAhCAAAQgAAEIQAACEICA3gQQkXrnQ3UQgAAEIAABCEAAAhCAAAQgAAEIQAACEAgEAURkIGJkEhCAAAQgAAEIQAACEIAABCAAAQhAAAIQ0JsAIlLvfKgOAhCAAAQgAAEIQAACEIAABCAAAQhAAAKBIICIDESMTAICEIAABCAAAQhAAAIQgAAEIAABCEAAAnoTQETqnQ/VQQACEIAABCAAAQhAAAIQgAAEIAABCEAgEAQQkYGIkUlAAAIQgAAEIAABCEAAAhCAAAQgAAEIQEBvAohIvfOhOghAAAIQgAAEIAABCEAAAhCAAAQgAAEIBIIAIjIQMTIJCEAAAhCAAAQgAAEIQAACEIAABCAAAQjoTQARqXc+VAcBCEAAAhCAAAQgAAEIQAACEIAABCAAgUAQQEQGIkYmAQEIQAACEIAABCAAAQhAAAIQgAAEIAABvQkgIvXOh+ogAAEIQAACEIAABCAAAQhAAAIQgAAEIBAIAojIQMTIJCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgoDcBRKTe+VAdBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQCQQARGYgYmQQEIAABCEAAAhCAAAQgAAEIQAACEIAABPQmgIjUOx+qgwAEIAABCEAAAhCAAAQgAAEIQAACEIBAIAggIgMRI5OAAAQgAAEIQAACEIAABCAAAQhAAAIQgIDeBBCReudDdRCAAAQgAAEIQAACEIAABCAAAQhAAAIQCAQBRGQgYmQSEIAABCAAAQhAAAIQgAAEIAABCEAAAhDQmwAiUu98qA4CEIAABCAAAQhAAAIQgAAEIAABCEAAAoEggIgMRIxMAgIQgAAEIAABCEAAAhCAAAQgAAEIQAACehNAROqdD9VBAAIQgAAEIAABCEAAAhCAAAQgAAEIQCAQBBCRgYiRSUAAAhCAAAQgAAEIQAACEIAABCAAAQhAQG8CiEi986E6CEAAAhCAAAQgAAEIQAACEIAABCAAAQgEggAiMhAxMgkIQAACEIAABCAAAQhAAAIQgAAEIAABCOhNABGpdz5UBwEIQAACEIAABCAAAQhAAAIQgAAEIACBQBBARAYiRiYBAQhAAAIQgAAEIAABCEAAAhCAAAQgAAG9CSAi9c6H6iAAAQhAAAIQgAAEIAABCEAAAhCAAAQgEAgCiMhAxMgkIAABCEAAAhCAAAQgAAEIQAACEIAABCCgNwFEpN75UB0EIAABCEAAAhCAAAQgAAEIQAACEIAABAJBABEZiBiZBAQgAAEIQAACEIAABCAAAQhAAAIQgAAE9CaAiNQ7H6qDAAQgAAEIQAACEIAABCAAAQhAAAIQgEAgCCAiAxEjk4AABCAAAQhAAAIQgAAEIAABCEAAAhCAgN4EEJF650N1EIAABCAAAQhAAAIQgAAEIAABCEAAAhAIBAFEZCBiZBIQgAAEIAABCEAAAhCAAAQgAAEIQAACENCbACJS73yoDgIQgAAEIAABCEAAAhCAAAQgAAEIQAACgSCAiAxEjEwCAhCAAAQgAAEIQAACEIAABCAAAQhAAAJ6E0BE6p0P1UEAAhCAAAQgAAEIQAACEIAABCAAAQhAIBAEEJGBiJFJQAACEIAABCAAAQhAAAIQgAAEIAABCEBAbwKISL3zoToIQAACEIAABCAAAQhAAAIQgAAEIAABCASCACIyEDEyCQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI6E0AEal3PlQHAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFAEEBEBiJGJgEBCEAAAhCAAAQgAAEIQAACEIAABCAAAb0JICL1zofqIAABCEAAAhCAAAQgAAEIQAACEIAABCAQCAKIyEDEyCQgAAEIQAACEIAABCAAAQhAAAIQgAAEIKA3AUSkC/ls2NrmQi90AYFwEBje0iBbdnRIPJEMx4SZJQTKJFBXE5UB/WuN7xseEICAPQID+9dKVzwpe9q77R1AKwhAQEYObpSN29qEf6FxMkDAHoGGupj0q4/JttZOewfQqqIERg1prOh4DGafACLSPquCLRGRLkCki9AQQESGJmom6hIBRKRLIOkmVAQQkaGKm8m6RAAR6RJIugkNAUSk3lEjIvXNBxHpQjaISBcg0kVoCCAiQxM1E3WJACLSJZB0EyoCiMhQxc1kXSKAiHQJJN2EhgAiUu+oEZH65oOIdCEbRKQLEOkiNAQQkaGJmom6RAAR6RJIugkVAURkqOJmsi4RQES6BJJuQkMAEal31IhIffMJlYhc8eAq+cc/N8qVM6ZmJbJ9R6vMnLtEXlr7pvH+nTfPlePHj860UcddvXCZ8fozp06Qa2dPk8aGuszniEh9T3Aq048AIlK/TKhIbwKISL3zoTo9CSAi9cyFqvQmgIjUOx+q048AIlK/TKwVISL1zScUIvLZ1evkossXGClc/MUzs0RkW3unXLNomUw49nCZcuYkeePtDfLt+bfL9fOmy0H7jxJ17OKly+XWBVdIy8BmuWnpcqMfq8xEROp7glOZfgQQkfplQkV6E0BE6p0P1elJABGpZy5UpTcBRKTe+VCdfgQQkfplgojUOxOzulCISHOy+VZEKvG46JZ7ZP5V0w3RmCsmlXg8YN8RhqRUj1wxqd5DRPrjZKdKPQggIvXIgSr8QwAR6Z+sqFQfAohIfbKgEv8QQET6Jysq1YMAIlKPHApVwYpIffMJvYjMJxbNVY8zL/x81mpJFWPuiklEpL4nN5XpSQARqWcuVKUvAUSkvtlQmb4EEJH6ZkNl+hJAROqbDZXpSQARqWcuZlU6ich83klvevmrM29rOGvG1KzbGTqdCyJy9Tq59/6VWfd9zBWR5541OQM5V0R+5jMiH/1YQmZcmpB+/Z3ipz0EwkegqaFG9nR0SyIZvrmXOuNkUiQSKfVojvM7gVg0IvW1UdnTEff7VKhfcwLJZFIiAflh01AblXhSpKs7oTl1yoOAPgSaG2tkV1u3eP1PtCD9rNEnPSqpBoGaWERqY1Fp6+TfaNXgX2xM9TOtUg/liWbMWSzvb9qaGXLk8CGydOGsvLf8q1Rdahx1ZfDy+1dmbjdYztiIyBLo5bs0u9wVkea/1wcPFvnGNxNy6cw4QrKEbDgkPAT6N9bInva4qH+E8rBHIBmJSARe9mAFsJUhIutisqe9O4CzY0o6EUhKRCKeK4jKzFh9zyQSSURkZXAzSkAINDXWyq62Ls9no/4FyP9f9RwzA1SAQE0sKrU1EWnjfxZXgLbzIZr71To/qIQjzM2Nczc9Vq7JXPT28ro3s/YeKWEYLQ5BRJYQgxf3iFy5UuRbcxPyzNNRo6KWwUmZcVlcLr6kS/r1K6FIDoFAwAlwaXbAA2Z6rhPg0mzXkdJhCAhwaXYIQmaKrhPg0mzXkdJhwAlwabbeAVfi0mxzJeT8edP7vFTZXACnLmmeN//2zMrJXHmZu7LSutmy2ceXzztdrvzeLQZ8c9Xli2tel6sXLjPeGzfmwKzVj8qDPfXcK1lXAZvy1EzQHCffys7r5kzL7JmCiCzhnM8nIt3aNfuJx2PygwU18rdnU0JyUEtKSH5lBkKyhKg4JMAEEJEBDpepeUIAEekJVjoNOAFEZMADZnqeEEBEeoKVTgNMABGpd7iVEJF2L3tWEvGiyxfIZ06dkBGCucfm3gbQdFUjhg2WK2dMNTZOVn1Y5aS6reAddz/Y6z2VjDpGPXJFZO64apz7HnhcvvCZk2TDpi3yyBPPySUXnGUcmytaEZEOznkzMOshVvNswnxp7ZtGk1wrbbXF1hPH7M+6a/bKx1JC8oXnsoXktOnd0tTEpagOYqNpQAkgIgMaLNPyjAAi0jO0dBxgAojIAIfL1DwjgIj0DC0dB5QAIlLvYCshIpUI3Lh5W9Zqw3xU8t0SMFc8qr4O2HdEZvWh6sd63Otvvdfr8u58/ea+ZxWR7R0dMnPuEnGy2Yy1LkSkRue8VUSaZT3yx5SQ/PuLKSE5cJB5yTZCUqPoKKUKBBCRVYDOkL4mgIj0dXwUXyUCiMgqgWdYXxNARPo6PoqvAgFEZBWgOxjSTyJy1PC95JpFy+SBR57qNUPzUms3RKRa8bjolntk/lXTpWVgc16a+RbymaswEZEOTkCvm+YTkeaYf3g4JSTXvNwjJC+ZqS7ZRkh6nQv960kAEalnLlSlLwFEpL7ZUJm+BBCR+mZDZfoSQETqmw2V6UkAEalnLmZVlRCRTi7NXrx0eda9G60rIk0ROeHYw7NWRFoJ21n9qNr3tSKymIhUqx8ffPTpzG7fqj/1nnqoS70RkRqd832JSFWm2uz29w/G5Ac31sjaV3qE5PRL4zL9UoSkRlFSSgUIICIrAJkhAkUAERmoOJlMhQggIisEmmECRQARGag4mUwFCCAiKwC5jCEqISL72qzGeu/FfLtm57s02xR++abthojs69Js856U5541OWvjHURkGSehl4cWE5Hm2EpIPnB/TBYvrJH161JCcsDApKgVkghJLxOib50IICJ1SoNa/EAAEemHlKhRNwKISN0SoR4/EEBE+iElatSJACJSpzR611IJEalGNfcUybcPyX6jhhn3j7QjIs1LonN3qf7ZPQ/JzAs/n7cPO3Iyd7MaJRafWb0uszrTFKZnnvoxWfCjX4i5OY6aW+4GOayI1OictysirULyd7+JyU0La+T113qEpLFCcka3NA9gUxuN4qUUlwkgIl0GSneBJ4CIDHzETNADAohID6DSZeAJICIDHzETdJkAItJloC53VykRqco2V0a+v2lrZhbW3a3tbFZTqB9TTNqRjqY8tF4GnisiVRtzt22z2Nx7QJobOav3zQeXZrt8gpbbnVMRaR1vxb01ctOiGnnrzYjxtloh+ZVLuqV/U0TGHJ6QQw9LyMhRiMlyM+J4fQggIvXJgkr8QQAR6Y+cqFIvAohIvfKgGn8QQET6Iyeq1IcAIlKfLPJVUkkRqTcJ/aqLJJPqgmEe5RAoR0Sa4/7qlzXGPST/+U5KSFofTU1JGXN4Ug45LGE8HzY6KYeOTsjQoURXTm4cWx0CiMjqcGdU/xJARPo3OyqvHgFEZPXYM7J/CSAi/ZsdlVeHACKyOtztjoqItEuq8u0QkS4wd0NEmmWsfDQmr7wclXXrxLiP5Gvro9Lenr/IgYOScvhYJSnVysmUnDz88CSXdruQKV14RwAR6R1beg4mAURkMHNlVt4SQER6y5feg0kAERnMXJmVdwQQkd6xdaNnRKQbFL3pAxHpAlc3RWS+ct7+R0TWvxqVdWuj8upaMeTkyy+l7i2Z7zFseEpOHnJoUkYfnpQzzozLoBZWT7oQNV24QAAR6QJEuggVAURkqOJmsi4RQES6BJJuQkUAERmquJmsCwQQkS5A9LALRKSHcMvsGhFZJkB1uNcislCJb74RMaTk2leUoIzI669F5JU1hQWl2Y/aDKfQ7x9nAAAgAElEQVSlRaSlRT0npWVwUga1iAweIoawbBmkvk7KoEHq/VRbde9KHhBwgwAi0g2K9BEmAojIMKXNXN0igIh0iyT9hIkAIjJMaTNXNwggIt2g6F0fiEjv2JbbMyKyXIJVFJGFSldy8g0lJV+JyrpXIvL++xHZuiUi27eL7NzR+x6UdhEMHpySlh85MClHHpWwdVisJiJ1tSK1dUmprRWprxfjWb1OvS9SVydy1PiEqEvNeQSfACIy+BkzQ3cJICLd5Ulv4SCAiAxHzszSXQKISHd50lvwCSAi9c4YEalvPohIF7Kp1orIUkvfti0i27dF5MPtItu3p7/+UGT7ttzXSl6mPt+zp9TRgn+ckqvRqEgkop6TxnMk81pEqV/jc+t7Rtv0MapB+nXqvWTmM+MY83hLG7O9MaYaz+yv+ILYqgeipEpXPCFsk1X1KPIWcNXVXXLs8fb+R4OeMwheVYjI4GXKjLwngIj0njEjBI8AIjJ4mTIjbwkgIr3lW27viMhyCXp3PCLSBbZ+E5GlTnnzJiUvI7L9Q5H2toh0dYl0dYp0dol0dqRed3ZK6rkjkn4/mX4/kvos3b6rM/XabN/dHZFEXAw5lUimnxOp52T6OZEQUX/MNqLaZj6LpN43j8n6rHc/au2l2V+qn+zjOzpKpcRxEPA3gWlf6ZbrFnT5exIBqx4RGbBAmU5FCCAiK4KZQQJGABEZsECZjucEEJGeIy5rAERkWfg8PRgR6QLesIhIF1AFrgu1o3mPAI1kSVNTmCrhmS1Ns4WrEq25QjUjXy1SNiNZLWLWFKo9wrb0S+8rFU5Lc53s2N1pzJmHPgTeezci3/xqrYzaOynPvtiuT2FUIohITgIIOCeAiHTOjCMggIjkHICAMwKISGe8Kt0aEVlp4vbHQ0TaZ1WwJSLSBYh0ERoC3CNS36iPHtsgauXzHx5rl7HjuGerLkkhInVJgjr8RAAR6ae0qFUXAohIXZKgDr8QQETqnRQiUt98EJEuZIOIdAEiXYSGACJS36ivmlMrP19WI//xrS65Yna3voWGrDJEZMgCZ7quEEBEuoKRTkJGABEZssCZbtkEEJFlI/S0A0SkyLOr18lFly+Q6+ZMkylnTvKUt5POEZFOaBVoi4h0ASJdhIYAIlLfqB9/LCr/em69jDsqKQ8/wuXZuiSFiNQlCerwEwFEpJ/SolZdCCAidUmCOvxCABGpd1KISJGbli43Qtq4eZtcO3uaNDbUaREaItKFGBCRLkCki9AQQETqG3U8LnLYAY3S1ibywpp2GTacy7N1SAsRqUMK1OA3AohIvyVGvToQQETqkAI1+IkAIlLvtMIuIrfvaJX5P7xLLr3wbFn4k7tl9mXny0H7j9IiNESkCzEgIl2ASBehIYCI1DvqmdPr5He/jsn1N3bJRRdzebYOaSEidUiBGvxGABHpt8SoVwcCiEgdUqAGPxFAROqdVkVF5MaNIuvWVR7IyJEihx2Wd1x1WfYTT/9drpwx1VgZecC+I7S5PBsR6cKpgoh0ASJdhIYAIlLvqH9zX0y+OqNOJk1OyN2/6tC72JBUh4gMSdBM01UCiEhXcdJZSAggIkMSNNN0jQAi0jWUnnRUURG5bJnIxRd7Mo8+O502TeSOO/I2UfJx4kePlOPHjzbuFbl46XK5dcEV0jKwufJ15oyIiHQhAkSkCxDpIjQEEJF6R93aGpGxBzcYRa55vV2am7k8u9qJISKrnQDj+5EAItKPqVFztQkgIqudAOP7jQAiUu/EKioiH3pIZOHCygM54wyROXN6jfvG2xtk0S33yPyrphviUV2mPXPuEpk1Y6ohJqv9QES6kAAi0gWIdBEaAohI/aM+b0q9PLkqKrfe3imfOyeuf8EBrxARGfCAmZ4nBBCRnmCl04ATQEQGPGCm5zoBRKTrSF3tsKIi0tXKy+9sxYOr5OqFy3p1dPEXzzQu1a72AxHpQgKISBcg0kVoCCAi9Y962e01cvW8Wvn8lLj85LZO/QsOeIWIyIAHzPQ8IYCI9AQrnQacACIy4AEzPdcJICJdR+pqh2EVkW3tnXLNomUy4djDs+4JmbtK0lXYDjtDRDoElq85ItIFiHQRGgKISP2j3rwpIkePbTAuy1aXZ8di+tcc5AoRkUFOl7l5RQAR6RVZ+g0yAURkkNNlbl4QQER6QdW9PsMqIpVw/Pb82+X6edOzdsk2BeW5Z02u+uXZiEgXznNEpAsQ6SI0BBCR/oj6Uyc3yJqXInLPfR0y8aSEP4oOaJWIyIAGy7Q8JYCI9BQvnQeUACIyoMEyLc8IICI9Q+tKx2EVka7A87gTRKQLgBGRLkCki9AQQET6I+rFC2vlpoU18uWvdMv3F3T5o+iAVomIDGiwTMtTAohIT/HSeUAJICIDGizT8owAItIztK50jIh0BaMnnSAiXcCKiHQBIl2EhgAi0h9Rv/z3iHz6lAYZNjwpL6xp90fRAa0SERnQYJmWpwQQkZ7ipfOAEkBEBjRYpuUZAUSkZ2hd6RgR6QpGTzpBRLqAFRHpAkS6CA0BRKR/olb3iVT3i/zDY+0ydlzSP4UHrFJEZMACZToVIYCIrAhmBgkYAURkwAJlOp4TQER6jrisARCRZeHz9GBEpAt4EZEuQKSL0BBARPon6m9/q1buvKNGZs3pkivndPun8IBViogMWKBMpyIEEJEVwcwgASOAiAxYoEzHcwKISM8RlzUAIrIsfJ4ejIh0AS8i0gWIdBEaAohI/0S9amVUvvgv9XLEkUn5/aNcnl2t5BCR1SLPuH4mgIj0c3rUXi0CiMhqkWdcvxJAROqdHCJS33w8FZHbd7TKzLlL5KW1b/YiMG7MgXLrgiukZWCzvnRsVoaItAmKZhAQEUSkf06DeFxk7MEN0toaMe4Tqe4XyaPyBBCRlWfOiP4ngIj0f4bMoPIEEJGVZ86I/iaAiNQ7P0Skvvl4KiJvWrrcmPmVM6bqS8CFyhCRLkCki9AQQET6K+rLLqmT366IyfU3dslFF3N5djXSQ0RWgzpj+p0AItLvCVJ/NQggIqtBnTH9TAARqXd6iEh98/FMRKrVkPNuuF1mX3a+HLT/KH0JuFAZItIFiHQRGgKISH9FrSSkkpGTJifk7l91+Kv4gFSLiAxIkEyjogQQkRXFzWABIYCIDEiQTKNiBBCRFUNd0kCIyJKwVeQgRKQLmBGRLkCki9AQQET6K2p1Wba6PFs91rzeLs3NXJ5d6QQRkZUmznhBIICIDEKKzKHSBBCRlSbOeH4ngIjUO0FEpL75eCYi1ZTVpdkH7DtCppw5SV8CLlSGiHQBIl2EhgAi0n9Rn/+Fenni8ajcclunnD0l7r8J+LxiRKTPA6T8qhBARFYFO4P6nAAi0ucBUn7FCSAiK47c0YBhFpHPrl4nF12+IIvXxV88U5vbJnoqIt94e4PcteJPMnvm+dLYUOfopPFTY0Skn9Ki1moTQERWOwHn4//spzXynbm1hoRUMpJHZQkgIivLm9GCQQARGYwcmUVlCSAiK8ub0fxPABGpd4ZhF5GLly7PbBBtbiQ9a8ZUOX786KoH55mI7GvHbDVrds2uevYUAIGqEEBEVgV7WYNu3hSRo8c2GJdlq8uzY7GyuuNghwQQkQ6B0RwCIoKI5DSAgHMCiEjnzDgi3AQQkXrnj4jsEZFt7Z1yzaJlMuHYw7W4YtkzEan3KeludayIdJcnvQWbACLSn/l++pQGefnvEbnnvg6ZeFLCn5PwadWISJ8GR9lVJYCIrCp+BvcpAUSkT4Oj7KoRQERWDb2tgSspIjduFFm3zlZZrjYaOVLksMN6d6kuzc5dEanTZtKISBdOA0SkCxDpIjQEEJH+jPqmhTWyeGGtfPkr3fL9BV3+nIRPq0ZE+jQ4yq4qAURkVfEzuE8JICJ9GhxlV40AIrJq6G0NXEkRuWyZyMUX2yrL1UbTponccUd+EZl7j8iRw4fI0oWz5KD9R7laQymdeS4i890k886b52pxXXopwPIdg4h0iyT9hIEAItKfKa95KSKfOrlBhg1Pygtr2v05CZ9WjYj0aXCUXVUCiMiq4mdwnxJARPo0OMquGgFEZNXQ2xq4kiLyoYdEFi60VZarjc44Q2TOnPwi0roiUrXIXSXpaiEOO/NUROabqNrAZsacxXLZhWdrcW26Q155myMi3aBIH2EhgIj0b9LqPpHqfpG/f7Rdjjgy6d+J+KxyRKTPAqNcLQggIrWIgSJ8RgAR6bPAKLfqBBCRVY+gzwIqKSJ1I5HPxal9XHS5PNszEWneDPPcsyb3Wv2ooNx7/0q5dva0QOymjYjU7duOenQmgIjUOZ2+a7t6Xq0su71GrpzTLbPmcHl2pZJERFaKNOMEiQAiMkhpMpdKEUBEVoo04wSFACJS7yQRkT2b1aikQrEisi/bqlZFLrrlHpl/1XRpGdis99lrozpEpA1INIFAmgAi0r+nwhOPR+X8L9QbqyHVqkgelSGAiKwMZ0YJFgFEZLDyZDaVIYCIrAxnRgkOAUSk3lmGXUTm3iNy3JgD5dYFV2jh4FgR6cL3DiLSBYh0ERoCiEj/Rh2Pi4w9uEFaWyPGfSLV/SJ5eE8AEek9Y0YIHgFEZPAyZUbeE0BEes+YEYJFABGpd55hFpF6JyPimYhUE1/x4CpZfv/KLOvKPSJ1PyWoDwLeEkBEesvX696/ekmd/GZFzNg5W+2gzcN7AohI7xkzQvAIICKDlykz8p4AItJ7xowQLAKISL3zRETqm4+nIlJNm12z9Q2fyiBQDQKIyGpQd2/M3/06JjOn18nEkxJyz30d7nVMTwUJICI5OSDgnAAi0jkzjoAAIpJzAALOCCAinfGqdGtEZKWJ2x/PcxFpvxT/tuTSbP9mR+WVJ4CIrDxzN0dUl2Wry7PVY83r7dLczOXZbvLN1xci0mvC9B9EAojIIKbKnLwmgIj0mjD9B40AIlLvRBGR+uaDiHQhG0SkCxDpIjQEEJH+j/pfz62Xxx+Lyi23dcrZU+L+n5DmM0BEah4Q5WlJABGpZSwUpTkBRKTmAVGedgQQkdpFklUQIlLffBCRLmSDiHQBIl2EhgAi0v9R/3xZjVw1p9aQkEpG8vCWACLSW770HkwCiMhg5sqsvCWAiPSWL70HjwAiUu9MEZH65uO6iNy+o1Vmzl0iXz7vdPnZLx+Wl9a+mXf2Om0dXm48iMhyCXJ8mAggIv2f9uZNETl6bINxWba6PDsW8/+cdJ4BIlLndKhNVwKISF2ToS6dCSAidU6H2nQkgIjUMZWemhCR+ubjuog0p6qE5LwbbpfZl50vB+0/KouA2sDm3vtXyrWzp0ljQ52+dGxWhoi0CYpmEBARRGQwToPTT22Ql16MyN2/6pBJkxPBmJSms0BEahoMZWlNABGpdTwUpykBRKSmwVCWtgQQkdpGYxSGiNQ3n6qIyDfe3iCLbrlH5l81XVoGNutLx2ZliEiboGgGAURkYM6BJYtq5Ac31spFF3fL9Td2BWZeOk4EEaljKtSkOwFEpO4JUZ+OBBCROqZCTToTQETqnA4iUud0qiIiVzy4Sp567hVWROp8ZlAbBDwiwIpIj8BWuNs1L0fkU5MbZNjwpLywpr3Co4drOERkuPJmtu4QQES6w5FewkUAERmuvJlt+QQQkeUz9LIHVkR6Sbe8vl0XkWq144w5i+X9TVsLVjZy+BBZunBWr0u2y5tK9Y5mRWT12DOy/wggIv2XWaGK1X0i1f0if/9ouxxxZDI4E9NsJohIzQKhHF8QQET6IiaK1IwAIlKzQChHewKISL0jCquIVLdCXLx0udy64IqsK5B1WhDouog0T8W+7hGp9+nqvDpEpHNmHBFeAojI4GT/3atq5Y7bauTKOd0yaw6XZ3uVLCLSK7L0G2QCiMggp8vcvCKAiPSKLP0GlQAiUu9kwyoiVSpKOv7jnxvlyhlTjZB0uz2iZyJS71PS3eoQke7ypLdgE0BEBiffJ5+Iynnn1MvYcUn5w2Ncnu1VsohIr8jSb5AJICKDnC5z84oAItIrsvQbVAKISL2TDbOIVAsDZ85dIrNmTJUjRh8o1yxaJhOOPVymnDlJi9AQkS7EgIh0ASJdhIYAIjI4UcfjImMPbpDW1ohxn0h1v0ge7hNARLrPlB6DTwARGfyMmaH7BBCR7jOlx2ATQETqnW8lReTGXRtl3ZZ1FQcysnmkHDbksLzjmpdof/m80+WRJ57Xao8WT0VkX/eLHDfmwF7XrFc8NZcGRES6BJJuQkEAERmsmL82o05+fV9MrpvfJdOmdwdrcprMBhGpSRCU4SsCiEhfxUWxmhBARGoSBGX4hgAiUu+oKikil72wTC7+3cUVBzLt6Glyx+fuyDtuW3unsRLy+Zdf026PFs9EpDlptfzzqLEHy10r/iSzZ54vjQ11ctPS5TLxo0fK8eNHVzwoLwZERHpBlT6DSgARGaxk7/9NTC79Sp1MPCkh99zXEazJaTIbRKQmQVCGrwggIn0VF8VqQgARqUkQlOEbAohIvaOqpIh86PWHZOGfF1YcyBkHnyFzPjGn4LhqVeS996/UajWkKtYzEWndrEYNtOiWe2T+VdONXXt0hVHqWYOILJUcx4WRACIyWKm3tYmMOahREnGRNa+3S3Mzl2e7nTAi0m2i9BcGAojIMKTMHN0mgIh0myj9BZ0AIlLvhCspInUloat7q4iIHDyoWeb/8C6Z940vGSJStx17yj1pEJHlEuT4MBFARAYv7S9NrZeVj0blJ0s75fNfiAdvglWeESKyygEwvC8JICJ9GRtFV5kAIrLKATC87wggIvWODBEp2i4C9ExEWi/NVjvzqMuxD9h3hLFLj9pK/KnnXtFueWip30aIyFLJcVwYCSAig5f6//ysRubNrpXPnROXW2/vDN4EqzwjRGSVA2B4XxJARPoyNoquMgFEZJUDYHjfEUBE6h0ZIjKEIjL3lDS3D39p7ZsycvgQ7W6WWc63ECKyHHocGzYCiMjgJb55U0SOHttgXJatLs+OxYI3x2rOCBFZTfqM7VcCiEi/Jkfd1SSAiKwmfcb2IwFEpN6pISL1zcezFZH6Ttn9yhCR7jOlx+ASQEQGM9szT6uXF1dH5e5fdcikyYlgTrJKs0JEVgk8w/qaACLS1/FRfJUIICKrBJ5hfUsAEal3dIhIffPxTERaN6s5aP9R+hJwoTJEpAsQ6SI0BBCRwYz65h/UyKIFtXLRxd1y/Y1dwZxklWaFiKwSeIb1NQFEpK/jo/gqEUBEVgk8w/qWACJS7+gQkfrmg4h0IRtEpAsQ6SI0BBCRwYz6lTUR+eRJDTJseFJeWNMezElWaVaIyCqBZ1hfE0BE+jo+iq8SAURklcAzrG8JICL1jg4RqW8+nolINWW1Qc3Ejx4px48frS8BFypDRLoAkS5CQwARGdyoJxzTIP98JyIPP9Iu445KBneiFZ4ZIrLCwBkuEAQQkYGIkUlUmAAissLAGc73BBCRekeIiNQ3H09F5Btvb5C7VvxJZs88Xxob6vSlUGZliMgyAXJ4qAggIoMb9zXfrpWfLq2RK2Z3y398i8uz3UoaEekWSfoJEwFEZJjSZq5uEUBEukWSfsJCABGpd9KISH3z8UxEWnfJzjf9cWMOlFsXXCEtA5v1pWOzMkSkTVA0g4CIICKDexr8+cmoTP18vRx+RFL+uJLLs91KGhHpFkn6CRMBRGSY0maubhFARLpFkn7CQgARqXfSiEh98/FMROo7ZfcrQ0S6z5Qeg0sAERncbONxkbEHN0hra8S4T6S6XySP8gkgIstnSA/hI4CIDF/mzLh8AojI8hnSQ7gIICL1zhsRqW8+nonIvnbNfnb1Orn3/pVy7expgbhkGxGp7wlOZfoRQETql4mbFX1jZp3cd29MrpvfJdOmd7vZdWj7QkSGNnomXgYBRGQZ8Dg0tAQQkaGNnomXSAARWSK4Ch2GiKwQ6BKGqYqIVPeOXHTLPTL/qulcml1CaBwCAT8TQET6Ob3itd//25hcenGdnDgpIb9c0VH8AFoUJYCILIqIBhDoRQARyUkBAecEEJHOmXFEuAkgIvXOHxGpbz5VEZErHlwlTz33ijYrItXu3nfc/WBWStfNmSZTzpxkvKfqvXrhMuPrz5w6oVfdrIjU9wSnMv0IICL1y8TNitraRMYc1CiJuMia19uluZnLs8vli4gslyDHh5EAIjKMqTPncgkgIsslyPFhI4CI1DtxRKS++bguItVqxxlzFsv7m7YWnPXI4UNk6cJZctD+o7Qgo0Skelw5Y2qvetRl5IuXLs9srJOvLSJSixgpwicEEJE+CaqMMi84r14eeyQqP17aKed8IV5GTxyqCCAiOQ8g4JwAItI5M46AACKScwACzgggIp3xqnRrRGSlidsfz3URaQ7d1z0i7ZdXmZZ9iUj12QH7jsisjswVk6pCRGRlcmKUYBBARAYjx75m8T931si8/6iVz50Tl1tv7wz+hD2eISLSY8B0H0gCiMhAxsqkPCaAiPQYMN0HjgAiUu9IEZH65uOZiNR3yr0ry70027wsu629U65ZtEwmHHt4RkSqFZ/fnn+7XD9vemZFJyLST2lTa7UJICKrnYD342/eFJGjxzZIY6PIq/9ok1jM+zGDPAIiMsjpMjevCCAivSJLv0EmgIgMcrrMzQsCiEgvqLrXJyLSPZZu94SIzCFqXlo+f950OWL0gYaIPPesyXL8+NFGy3wisrWNnWHdPjHpL7gEmhpqZE9HtyS4daDtkJNJkUjEdnMtGp48MSbPPReRX/8uLqeeRtjlhBKLRqS+Nip7OrjMvRyOHFucQDKZlIjfftgUmFZDbVTiSZGu7kTxidMCAhAwCDQ31siutm7x+m/tIP2s4dQJN4GaWERqY1Fp6+TfaDqeCepnGg89CXgqItXl2TPnLpGX1r7Za/bjxhyYue+ibmjMy7HPOGWCrRWRrXu6dJsC9UBAWwL9G2tkT3tc1D9CedgjkIxEJOIzXosWRuW6a2My/ZKELF7CP87sJZ2/lSEi62Kyp53/6VUOR44tTiApEYl4riCK1+FGC/U9k0gkEZFuwKSP0BBoaqyVXW3e/16j/gXos/+/GppzgIk6I1ATi0ptTUTa+J/FzsBVqHVzv9oKjcQwTgl4KiL7uvei00Ir2d56X0juEVlJ8owVBgJcmh2GlEXWr4vKySfWy8hRSXn40Q7Zay/Ec6nJc2l2qeQ4LswEuDQ7zOkz91IJcGl2qeQ4LqwEuDRb7+S5NFvffDwTkX7ZrEbV+eAjT8mXpnzSSCn30mt2zdb35KUyfxJARPozt1Kq/uyn6+WF56Ky/wFJWXF/h4wYiYwshSMishRqHBN2AojIsJ8BzL8UAojIUqhxTJgJICL1Th8RqW8+oReR5oY0DzzyVCalO2+em7knpHpzxYOr5OqFy4zPP3PqBLl29jRpbKjLtGezGn1PcCrTjwAiUr9MvKqotTUiXzirTta8HJV99k3Kbx9ERpbCGhFZCjWOCTsBRGTYzwDmXwoBRGQp1DgmzAQQkXqnj4jUNx/PRKSacu5lzfpiKK8yRGR5/Dg6XAQQkeHKGxlZft6IyPIZ0kP4CCAiw5c5My6fACKyfIb0EC4CiEi980ZE6puPpyJSXeZ814o/yeyZ52etINQXR2mVISJL48ZR4SSAiAxf7sjI8jJHRJbHj6PDSQARGc7cmXV5BBCR5fHj6PARQETqnTkiUt98PBORfe2YrXDovGu207gQkU6J0T7MBBCR4UxfycjzptTJiy9wmbbTMwAR6ZQY7SEggojkLICAcwKISOfMOCLcBBCReuePiNQ3H89EpL5Tdr8yRKT7TOkxuAQQkcHNttjMdu8WOffz9cjIYqByPkdEOgRGcwgIIpKTAAKlEEBElkKNY8JMABGpd/qISH3zQUS6kA0i0gWIdBEaAojI0ESdd6LISOf5IyKdM+MICLAiknMAAs4JICKdMyt2REe83WiSVP8lk1lfp15J+v3en2eOSx2daWt+LcnU+7n9WsfLHGfpo+/jUvVYx1PVm3UmIzbHM+aaXXNWneZn6fpz55Qarxjd6n9eVxuV+tqotO7prn4xVJBFIBIRmTLuU1DRlICnItK6I/XI4UNk6cJZMmr4XnLNomUy4djDZcqZkzTF4qwsRKQzXrQONwFEZLjzV7NHRjo7BxCRznjRGgKKQBhFpBIe8WRc4omEJJMJ4+uE+i8RT32dfs/8TLVT7yUk/Vki9ZxQfaTbq6+VDOhKdGXejxvtUm3M8eKJbomrftJjqTbZr1U93an28W5jnGKyJCUtcqVHWtzkyAurODEkiwNZUlDq5Okjv5zJL5kKSaQ+5UwvUZU2Mek5ZWRQQfmULZFM4ZR1XI4AsuZQWxORrm51/vQWX3llV4ZRb0mW6sOUbL0ZmTlZays+v6Rhp/JJubSSyyvlzPPIKv5y+zCFIT9BIQCB4BBIXuMDmx0c3I5m4qmINHfNPuOUCbLo1nvkS1NOk4P2HyXPrl4n996/Uq6dPS0Qm9ggIh2dczQOOQFEZMhPgPT0kZH2zwNEpH1WtKw8AfXLe0pwpeWXKaCU0EqLrZTUMkVYWn5Z2ilplSvOEslkWnIpmdaXKEv1Z0oxo59EXOrrItIZj0tHV5fxOlOfRciZQkwJs9RxaXmXlnFmv4acy5pXnjFNcZc1rx4haBV7Rr+KTWZePUIwJflSdWSYmHIwLQ7b422VD5oRIQABVwjUxxokov5Ty7VEcr6W9PvpzyJGy1S79NeZ1zl9SJ62WcflGy8ZUR33GqPnOLMea52WetTx1tryzsmca3lzcgW+B51EoyKxSES64ggvD/CW3eVfp68quw868IaAZyJSbVYz74bbZfZl5xurIK0iUu2mveiWe2T+VdOlZWCzNzOrYK+IyArCZijfE0BE+j5C1yaAjLSHEhFpj5MfW+3q2iWd8Y7Mn454Z8HXxmeJDs1ucvMAACAASURBVOns7jCeO7pTr5VM29O9Wzq7O6VDfW701ykdln67E3HpTnSlV7mpFXM9q9mMlW1pAWesWDNXz6WFWkaMJeKCANP/LGuINUo0EpVYNCrRSCz1dSQmkfRzLJp6Lyrpz8x26h3zM8tx6thoNCYxo8+YcZx6zrxvvk6/Z7wfsbRRXxuv1fE1qrXURGtS8kL9Bm8RMbnCJfOZRbgYOiRLdqQEiVWGZH1ttk32Fj+9BI+lXyVn8taW0kZ55UuqttQ5Yoqm0ubUlxhKiSNjjD7mpDJOceipp6BwSs9JcR0yoF627ezMMDZ598U4k4elnvzHpWsxhVoh3nkYWzO15l96Tma+PYIv9xxSwpAHBIoR4B6RxQhV93PuEVld/n2NXhURyYpIfU8IKoOA1wQQkV4T9lf/yMjieSEiizOy00JJtIysS8u8XHmnJF6PwEtJQevrju72lOxLS0AlBXu/7pGJqWN7v+5KdGYuTbVTu45tlJgwxVRKfCkBFcmSXxkRZpFfWe3S8isjygzRZRFnSl4psaX+S/dhCjCznSHV8rRrrK015Fs8Hkn1kZZzhpBLy7OUYOuRdal6U/PKaqfGyGqnXptizvJZur+IReopgWfUnjWv9LhZ80r1aUi89FiZOafrTcnE9Nhp4WcKJx3PEWryHwHuEem/zKi4ugQQkdXlX2x0RGQxQtX73DMRqaa04sFV8tRzr8i8b3xJfrTs18al2YMHNcvMuUtk6lmTuUdk9XJnZAhUjQAismrotR1Yych//Zd6+duzUdln36Tc97sO45lHikAQRaRawbe7c5fs7tolalWgera+3mO8t1v2dO5OiTxjBWBqJaApAc3XajVfW/fu9Oe95WFb9x5tT6XGmn5SF6uTuli91Mfqjee6aJ3U16jneqmrqTOeC76OqbYN6WNT/fT0lX6t+onVGbKuJlLTs5ItvWqtZ4VbSpj1apNZ4ZaSgn5ZJRTGe0Rqe6JTmG8IICJ9ExWFakIAEalJEAXKQETqm4+nIlJNW61+vOjyBVkE7rx5rhw/frS+VBxWxqXZDoHRPNQEEJGhjr/g5NvbRc47JyUjR4xMym8fREaasHQTkdvat8qH7dtke/rPh+3bZXvHVtnZvkN2du7MKxV3paWjIRy7dlXtm6C5rjkt+FKirz4t+urS4i/z2hB6dT1yMP26IdYgRlslD/sShFkCMUc0xupECUge3hJARHrLl96DSQARGcxcmZV3BBCR3rF1o2dEpBsUvenDcxHpTdl69YqI1CsPqtGbACJS33wiu3dJZM8eSQwdVpUikZH5sXslIls7Ww2BuL1tm+zo+FC2tW8xvv6wY7tsa9sq2w3hqCTjtvT7qXZuPNSquqbaJumv/tQ1yYC6gdJQ2yD9a5ulf21/aaprln61/Y0/eYVhehVhavVfz0rAfK/VODzCRwARGb7MmXH5BBCR5TOkh3ARQETqnTciUt98PBWRatfsjZu3Ze2O3dbeKdcsWiYTjj2cS7P1PS+oDAKeEUBEeoa2rI6bbv2hDLh6rnR87ETZev8fyuqrnIORkb3p2RGR7+96zxCGhjhsVxIx9bUSisbqxZzPtrR9UHJMzXUDpKVhiLQ0tEhL/WBpaRwsgxoGG1831SuRqARjSiiqtv1q+0m/2qaMeBxQP7DksTkQAnYJICLtkqIdBHoIICI5GyDgjAAi0hmvSrdGRFaauP3xPBORpnA896zJvS7DZrMa+wHREgJBI4CI1DPRAd+dK023/NAo7sMf3yZ7zr+gaoUiI0U27X5flFxUfza3bZAdXVvk3Q83WS6H7rk0uiPeXlJW6vLgQVaZqKRiw2AZ3DBEBjW2pGWjkoxKOA4xhOOQhr1KGouDIFBpAojIShNnvCAQQEQGIUXmUEkCiMhK0nY+FiLSObNKHeGZiNy+o1Xm3XC7zL7sfDlo/1FZ83nj7Q2y6JZ7ZP5V06VlYHOl5urZOFya7RlaOg4gAUSknqFaRWRi8BDZ/OzLkhhYvZVrQZaR77X+U97fnZKM6o+Sju/u7Hlvw653HZ8kQxuHpVYmpmWiEopKIA5u2Cv9flosGm1aZET/7L+XHQ/IARDQnAAiUvOAKE9LAohILWOhKI0JICI1DkdEEJH65uOZiGRFpL6hUxkEqkkAEVlN+oXHHvDdedJ0y39lGuy+8GLZsfhHVS3WjzJya/sWefPD11JiUYnG1ndlw+6UbFQCcvOejbaYKok4sv/eMrJpb9lnwD7ykcH7SZ2oS6LVisW9ZGDDIONrdYk090C0hZRGISOAiAxZ4EzXFQKISFcw0kmICCAi9Q4bEalvPp6JSDVldQn2vPm3y9KFszKrItVqyBlzFstlF57NPSL1PS+oDAKeEUBEeoa2rI5NEbn74hnS/46lRl8f/OlJ6Rp/TFn9lnuwkpEXnFcvf/2zXrtpv73zTXlj+2uyfts6eV392b5e1m9fa2szl70ah8rezfvJ8P4jDNE4qmmfzPOIppHykYEHZ2Gzc4/IcjlzPASCRgARGbREmU8lCCAiK0GZMYJEABGpd5qISH3z8VREqmmb4vH9TVszFO68eW6v+0bqi6h4ZVyaXZwRLSBgEkBE6nkumCJy53ULjJ2zm+f/p3SNGSsfrHxKJBaretFTz6mTPz8Rk2HDk/KDm7vk1E/GPa+pPd4mr2171ZCMb2x/1fj6jQ/Xy9qtLxccW+0GfUjLYbLvwAOMFY2jmpVkHGXIxhFNo2S/5gMc142IdIyMAyAgiEhOAgg4J4CIdM6MI8JNABGpd/6ISH3z8VxE6jt19ypDRLrHkp6CTwARqWfGVhG5+yszZeiE8VLz9luy47obZffMr1e9aLUy8t+/mJKR6jHxpLh853vdcsS4RNm1qR2kX9+eWtVoiEe1wvHD9cal1ElJ5u1fbdpy8ODRcnDLoXKo8XyY8Wfv5n0lIpGya7J2gIh0FSedhYQAIjIkQTNNVwkgIl3FSWchIICI1DtkRKS++SAiXcgGEekCRLoIDQFEpJ5Rm5vV7Lx2vuz66jel/slVMuTzp0uyoVE2P/+KxIcNr3rhSkYu+UGN/PS/a0V9rR6nnxmXb327Ww49zL6Q/Gfr2/KX91bJn/+50nhW93LM94hGorJv8wGGbDxk8GE94rFljAysH1QxHojIiqFmoAARQEQGKEymUjECiMiKoWaggBBAROodJCJS33w8FZFq5+yZc5fIS2vf7EVg3JgD5dYFV7Brtr7nBpVBwBMCiEhPsJbdaa6IVB22zPiyNN73S2n/3DmybdldZY/hVgfbtkXkR0tq5OfLaqSjQyQaFTl7SrfMuapb9tuv9wpGJRoz4nHDKvnnzrezSmmINcpBLYcYKxoPSa9yVM8HDjpY6qL1bpVdcj+IyJLRcWCICSAiQxw+Uy+ZACKyZHQcGFICiEi9g0dE6puPpyLypqXLjZlfOWOqvgRcqIwVkS5ApIvQEEBE6hl1PhEZ3bZVhh8zRiK7dsnW3zwsHSdO0qr4zZsixgrJ//tFjXR3idTUiJz/pW656BvvybqOlfLnd1fKX95dJWpjGetD3cfxuJEflU/sM1k+sc9JMn7YcVITrdFqbtZiEJHaRkNhGhNARGocDqVpSwARqW00FKYpAUSkpsGky0JE6puPZyJSrYacd8PtMvuy8zM7ZuuLobzKEJHl8ePocBFAROqZdz4RqSrtv+w2GTjncune/yPywV+fl2Rd9VcI5hJ8+c2t8r2fPilPvf+4JPd/TGSvV7Oa1Mbq5Jhhx8vH95kkJ+47WY4ZcYIWKx3tngmISLukaAeBHgKISM4GCDgngIh0zowjwk0AEal3/ohIffNBRLqQDSLSBYh0ERoCiEg9oy4kIiWZlKGfnCi1q5+X1llzpXXed6s+ge3t2+Sv6h6P7z4uf3nvcVm/bV12TYkakQ3HSd17k+Wso06Say45QYYMaKx63aUWgIgslRzHhZkAIjLM6TP3UgkgIkslx3FhJYCI1Dt5RKS++XgmItWU1aXZB+w7QqacqdflfG7HgYh0myj9BZkAIlLPdAuKSBGpXbtGhk46QZI1NfLBU6uN1ZHVeNy99ufysxdvlTVb/t5r+CP2Okom7nuyfHyfk2TI7oly8w2D5A8Pp3bYHtSSlK99My5f/kqXNDRUo/LyxkRElsePo8NJABEZztyZdXkEEJHl8ePo8BFAROqdOSJS33w8FZFvvL1B7lrxJ5k983xpbKjTl0KZlSEiywTI4aEigIjUM+6+RKSqeODcK6X/T//buE+kul9kJR+/XPe/cvPT8+Wd1n9khh0z5Aj52N6TDPk4YdSJMqB+YK+SXlwdlQXfr5FVK1NCctjwpFw+q1u+9G/dUlNbyRmUNxYisjx+HB1OAojIcObOrMsjgIgsjx9Hh48AIlLvzBGR+ubjmYjsa8dshYNds/U9KagMAl4SQER6Sbf0vouJSLVhzbATxkls8ybZvvRn0vaF80ofzOaRagXkD59dKO/sfMs4ormuWS4+8qvylfFfk5aGwTZ7EXnm6ahcd02tPP+3qHGM2ll71re6Zcq53caO27o/EJG6J0R9OhJAROqYCjXpTgARqXtC1KcbAUSkbolk14OI1Dcfz0SkvlN2vzJWRLrPlB6DSwARqWe2xUSkqrrxvuXSMuMiSQweIpueXyvJpibXJxNPxmXFq/fIkmfmZ3a7HtwwRC45+hvy5SNnSlNt6WOufDQmN15fI39/MWUfDz0sIXPmdcsZn427Pg83O0REukmTvsJCABEZlqSZp5sEEJFu0qSvMBBAROqdMiJS33wQkS5kg4h0ASJdhIYAIlLPqO2ISFX5kM+fLvVPrpLdF8+QHTcucW0ySkD++tVfypJn58s/drxh9Du033CZeczlcuG4S6Qh5t5mMw/9v5gsnF8j619NCcnm5qScclpCzjo7Lqd8Mi71mm0Mjoh07TSjoxARQESGKGym6hoBRKRrKOkoJAQQkXoHjYjUNx/PReSzq9fJRZcvyCJw581z5fjxo/Wl4rAyRKRDYDQPNQFEpJ7x2xWRNW+/JUMnjJdId7d88McnpGv8MWVNSAnI36xfbqyAfGvH60Zfo5r2kcuOuVK+dMSXpS7qjRVMJkV+s6JGFt9YI2+9GcnMobFR5JTT4vLZs+Ny2qfi0q9fWdNz5WBEpCsY6SRkBBCRIQuc6bpCABHpCkY6CREBRKTeYSMi9c3HUxGpJOTipcvl1gVXSMvAZoOC2sBmxpzFctmFZwdmN21EpL4nOJXpRwARqV8mqiK7IlK1bb7x+9K86AbpGjNWPlj1jEikR+TZnZ0SkL9df6+xAvLND18zDttvwEfk68f9h0wdfYHURCuzm4wSkn97NioPPxCVhx6okbf/0TMXtTLy5FPj8tnPJeRTZ3RL//52Z+duO0SkuzzpLRwEEJHhyJlZuksAEekuT3oLPgFEpN4ZIyL1zcczEdnW3inXLFom5541udfqRyUo771/pVw7e1ogdtNGROp7glOZfgQQkfpl4lRERjo7ZOjHjhG1OnLHgptk91cutT2pRDIhv33tXrnpmRsyAvKgQYfIN477lpxz2HkSi6R2uK7W49W1UXnowZghJl/6e89ONnV1IpMmx+WssxPy6TPi0jwgWbESEZEVQ81AASKAiAxQmEylYgQQkRVDzUABIYCI1DtIRKS++XgmItWu2fNuuF1mX3a+HLT/qCwCalXkolvukflXTc+slNQXUfHKEJHFGdECAiYBRKSe54KTFZFqBuo+kep+kWrDms3PvCTxYcP7nFhKQP5KljxzvbyRXgE5evBY+eYJ35LPHjRFohH9tq9+f0NEHnogJg89EJWn/xqTeHpPm5pakYmT1OXbCTnjzLgMHOStlERE6vk9Q1V6E0BE6p0P1elJABGpZy5UpS8BRKS+2ajKEJH65uOZiGRFpL6hUxkEqkkAEVlN+oXHdioiVU8tF18gjb9dIW1Tpsr22+7M27kSkL97XQnIG+T17euNNkcOPVq+efxcOf3As/SEkaeqD7dH5I+/T0nJxx+LSXt7qlFNjcjHT0xdvv2Zs+IyqMV9KYmI9M1pQqEaEUBEahQGpfiGACLSN1FRqCYEEJGaBFGgDESkvvl4JiLVlFc8uEqW37+Se0Tqmz+VQaDiBBCRFUdua8BSRGRs8yYZdsI4iezaJVt/87B0nDgpa6w/vPWAXP+Xb2cE5DEjTpArT7hKTt7vU7Zq0rVRW5vIqpUxeeD+qCEnd+7oua+kkpJT/iUhX7yg27XyEZGuoaSjEBFARIYobKbqGgFEpGso6SgkBBCRegeNiNQ3H09FpJo2u2brGz6VQaAaBBCR1aBefMxSRKTqtf/Sn8jAb8+W7v0/Ih88tVqStalNZl7b/qpMvuto4+uP7T1JvnbsLJm83yeLF+LDFn9+InVPyYcfismG93qk5JAhSTn+owk5+pikHHdCXI46OiFqV26nD0SkU2K0h4AIIpKzAALOCSAinTPjiHATQETqnT8iUt98PBeR+k7dvcq4R6R7LOkp+AQQkXpmXKqIVDdOHDp5gtSuXSOt3/qOtM6+ypjgOfedJs+8/xe59OhvytWfmK/npD2o6sXVUXn0TzF5+q8RWf18VFpbs3cUP3xsQo47ISFHH5uUY49LyEEHJ4pWgYgsiogGEOhFABHJSQEB5wQQkc6ZcUS4CSAi9c4fEalvPp6KyJuWLpeNm7dl7Y5t3jtywrGHy5Qzsy/j0xdT35UhIv2aHHVXgwAishrUi49ZsogUkdrVz8vQT06UZG2dfPDX5+Xutj/L5X+6RIb2Gy5P/ttL0lTbVLyAALZIJkVeWx+V55+LynPPRuSF56Ly6rqoJCzuUd1T8uhjEnLs8Uk55tiE8Sd3R25EZABPDqbkOQFEpOeIGSCABBCRAQyVKXlKABHpKd6yO0dElo3Qsw48E5FsVuNZZnQMAV8TQETqGV85IlLNaNCsr0u/n98hmyd/XMac/qpsa98q//3p/5WzDvmCnhOuUlXq/pIvPBeT5/8Wlef+FjGet2zpWTUZiYgcfEhKSBpy8riEjDtCZFBzrWzZ0VGlqhkWAv4jgIj0X2ZUXH0CiMjqZ0AF/iKAiNQ7L0Skvvl4JiK372iVeTfcLrMvO18O2n9UFoE33t4gi265R+ZfNV1aBjbrS8dmZayItAmKZhAQEUSknqdBuSIyumOHDDv+CJn5ia1y27EiH997ktx7zsN6Tlazqt55OyJ/fzEqf3tGycmoISetj379RD5/TlJGjOqWlsEihx6WNC7pHjnK/R26NUNDORAomQAismR0HBhiAojIEIfP1EsigIgsCVvFDkJEVgy144E8E5GsiHScBQdAIBQEEJF6xlyuiFSzevWuG+SU7d+X+nhEVk59RvYbOVbPyfqgquee7ZGSLzwflXf/mX2vSTWF/v1FDj40YaygPPTQ1ErKgw5JyiGHFr/vpA8QUCIEyiKAiCwLHweHlAAiMqTBM+2SCSAiS0ZXkQMRkRXBXNIgnolIVY3aMXve/Ntl6cJZmVWRajXkjDmL5bILz+YekSVFxkEQ8DcBRKSe+ZUrIuPJuHzq7gmybtsaufYxkVljviY7vr9Qz8n6rCp1j8hEV638+ekueXWtus9kRNatjcr6dRHZtq23oKypFTnggIQcfGjSkJOHHCKGnFSSsqmJVZQ+i59ySySAiCwRHIeFmgAiMtTxM/kSCCAiS4BWwUMQkRWE7XAoT0WkqsUUj+9v2pop7c6b58rx40c7LFXf5lyarW82VKYfAUSkfpmoisoVkbev/rF878k5sn+/fWT9dzdJXVdCPlj5lHSNYVVkuYn3tVnNB5sjxgY461+NyNpXIvLaq1Hjedeu3oJS1TF8REpOjh6TlDM+k1o5WV+flBEjkzJqbyRluVlxvD4EEJH6ZEEl/iGAiPRPVlSqBwFEpB45FKoCEalvPp6LSH2n7l5liEj3WNJT8AkgIvXMuBwR+cGeTXLi/46TXV27jPtCnv6zR6Tp5kXSNf4Y+eCPT4ioHVh4lEyglF2z398QkfWGlFQrKMWQlUpS7tnTdxlKVO69T9IQliMNOSkyclTqfpTqz377IStLDpIDK0oAEVlR3AwWEAKIyIAEyTQqRgARWTHUJQ2EiCwJW0UOQkS6gBkR6QJEuggNAUSknlEP+O48abrlv2Tn92+UXZd+3VGRMx7+kvy/138tnz9kqvzk03dKpL1Nhn30KIm9967suOnHsvvfpznqj8bZBEoRkYUYvvNOxLi8+7X1UXnnbRElLM0/+S7zztfPXnulpKT5Z++9xVhRedwJcdn/AEQl568eBBCReuRAFf4igIj0V15UW30CiMjqZ9BXBYhIffNBRLqQDSLSBYh0ERoCiEg9o86IyOsXyq4ZX7Nd5F/eWyXn/vp0aaptkif/7SUZ2m+4cWzDI3+Qwed93vi64+TTpPVb35HO406w3S8Newi4KSKLcX37HxHZuDEiG96NyvvvR2TDeylZuWlTxNgwR10KbvehhOWQoUkZOjQpgweLqNdDh4sMGZKUIXslZa+hSePrvfYSaR6AwLTLlXb2CCAi7XGiFQSsBBCRnA8QcEYAEemMV6VbIyIrTdz+eIhI+6wKtkREugCRLkJDABGpZ9QZEXndAtk18xu2iuxMdMhJvzhG3tn5lnx/0k3y5SMvzTqu+frvSfOSng1rEJK2sPZqVEkRaafC996NyEYlKdOrKTe8l1pVqSSlEpZbt4q07rQvLM0x1QrLlJhMCUwlKNUf9bW6VLxlUEpg7rMv0tJOTmFvg4gM+xnA/EshgIgshRrHhJkAIlLv9BGR+uaDiHQhG0SkCxDpIjQEEJF6Rl3KPSIXPX2d3PzsfBk9eKz86V+fkYj0lk+xTRulacki6fe/P5NIR7sx+Y5Jk6X1qu+xQtLmqaCbiLRTdne3yJYPIrJlS+rPNuNZ5IMPRLZ+oGRl6v2t6v0PItLWZqfXnjb9+0tmVaUhLtUqS2P1pXo/tepSrbg0PtsrKbGYs/5p7X8CiEj/Z8gMKk8AEVl55ozobwKISL3zQ0Tqmw8i0oVsEJEuQKSL0BBAROoZtVMRqVZBTvzFeIknuuWBqU/IUcOO6XNi0a1bpOnmH0j/O38qkbbUjikdE0+S1m9fi5Asckr4UUQ6PcvVJjqGmMyRl0pcKoG5TYnL9GebNjpfbTlQraYcInLEuISxa7j10b8pIgMHJo3Lw1taRPr3T8oA47UYl5Xz8CcBRKQ/c6Pq6hJARFaXP6P7jwAiUu/MEJH65oOIdCEbRKQLEOkiNAQQkXpG7VREqvtCqvtDXjjuErnhpJttTyq6fbv0/9FiabrjNons3pUSkidOktbv/CdCsgDFMIhI2ydQuuGOD9MrLbeJbNlsfq0uD+9Zibl1ixgrLtXqy3Ie9fUiAwb0yElDWjaLISvV103NEenXLymNjWL8Mb7up742n7Pf69evnGo41i4BRKRdUrSDQA8BRCRnAwScEUBEOuNV6daIyEoTtz8eItI+q4ItEZEuQKSL0BBAROoZtRMR+ev1v5Sv/eHLMrhhiPz1wrXGRjVOH9EdO6T/T26W/j+9VaI7d6aE5CcmSevVCMlclohIp2dX7/bmasvt20U+3B6Rna0R2blDpLU1Ih9uF1Gn4M6d6fd2RmTHjoi0tops31aexCxUeUODSGO/pCgpqYSl8az+NCSlsX9aYhpSM7VCs7ExYrQ3ZKcpOdXnxmdp0ZkWoYNaWMWpuCMiy/++oYfwEUBEhi9zZlweAURkefy8PhoR6TXh0vtHRJbOLnMkItIFiHQRGgKISD2jtisid3Xtko/9fIxsa98qP/7Uz+ScQ88ra0JKQva/7SfS/79/JNEPP8wIyV1zvi0dn5hYVt9BORgRWd0kd+9Obb6zc4cSmJJ6VuJSycqdEdm9Oym7dqXuc6nuOtDWlvp6z+70e+rrPamvlQSt1EPdRzOzUjMtLLOkpUWEqsvTDTmqpGhahKYkafZ7pghtbtZfdiIiK3WmMU6QCCAig5Qmc6kEAURkJSiXPgYisnR2Xh+JiHSBMCLSBYh0ERoCiEg9o7YrIr/9+BVy50tL5eN7T5J7z3nYtclE9uyWpttulf63/JdEt21NCcmPTxRDSJ44ybVx/NgRItKPqfVds5Kbppxs3xMRdY/M9vbUe+rrNvM5IzWTKcG5p+c4Q3YqwZnzXik7lpdKWAnJBiUrG0Qa1KXojepZpEGt7DSe01/3Sz2r1/36RYy2KfGZfj/dtqY2KbU1IjW10vNcm5SaGpHaWjGejc9qU8f39UBElpoqx4WZACIyzOkz91IIICJLoVa5YxCRlWPtdCREpFNiedojIl2ASBehIYCI1DNqOyLyxc3Py2eWT5RYtEaeuGC17DfgI65PJtLeJv3vuE2afrxEouqGf0pIfuxE2fWt74RWSCIiXT/NAt9hPJ5nRWZ6taYhOi0SUwnOPbuV6Eyt5swrQpUoTa/qNAVqInvPn6owjUSs0tIiMWtF6upS4jIWS6YkZkZu9rTLvFeTND63Cs/UMam2tXWRtBhN95VHjBY6NiVOzb4tUjVPPVWByKAQsBBARHI6QMAZAUSkM16Vbo2IrDRx++MhIu2zKtgSEekCRLoIDQFEpJ5RD/juPGm65b9k5/dvlF2Xfr1XkUlJymn/d4Ks27ZGvnn8XJnz0e96OpFIR7v0+9+fSdOSRRLbtNEYq/OEj8mur18hnRM+IQm1vXFIHojIkATts2l2dIi0t0WMlZxKYra3R6TdeE4JTfW18dyefs8Qmcmsz9UxqWNFOjoi0tUl0t0lqedu9RxJP6ff71bP6nJ4n8FyUK7aHEmt+MxaFZpeCVp0xWhabsZqsqVsSpL2SFAlVnPfyydqrQI1GhOJRZXYFTG+Tv+Jpt9TwjemxG9um6jZPmkco+bHQ18CiEh9s6EyPQkgIvXMxawKEalvPohIF7JBRLoAkS5CQwARqWfUGRF5fvOaWQAAIABJREFU/ULZNeNrvYpUl2Ory7LVKsjHL3he6qKV+22y/89ul6YlCyW24b1MXV1HjpeOk06WjpNOlc6PfVyS9UWu09QTu62qEJG2MNEopAS6OkW60oIy9ZwSmY21tdLWkZTW3fEewZnTzmib59iMBE1/3tWZzLTrOcZybJeIWoXaI1BTwtRaT19i1WwXpgjValb1R4lMUc+W1+q9SCQpEeM51cZ4TrdR72fes7ZRXVleR6NJ47jMe5YxMv3mHG/229NPMmt8o9bcutTrnBp7+knVoF4b74llzvn66VVj7zlkjR+NZNeT07/JIFNPHgaZDKIiLU210rqnK5WJmU00m0EW0yzevbNMzb1nDlYGBfux5Gw93pqN9XzJOo8sOWRnaZlDOgfreaT64AGBUgggIkuhVrljEJGVY+10JESkU2J52iMiXYBIF6EhgIjUM+q+VkSqjWnUBjVqoxp1X0h1f8hqPPr93/9I/6U/kdo1L/UaXknJzkmnSPvkU6TrqKOrUZ5nYyIiPUNLxwEm4Nd7RKoVonYlZrw7Ip2d5upRyyrSzIpSy2dpMRrvTkqHOka9trTLiFSLQDWlazweMQRvPCGSiKekq/En/bq7O5J6P9HzmfV1Qh3frVa9BviEY2qBIaDuP5ta+ZtaxatW+VpXAZurgdUqYOtK4cxq4WjqVhDWR67oVKLdfPT+rPCxfbXt5VItb/R5XM6BpbbtJXPtjp9z5rg1vpWx8X8ALA/rGGXxF3WuRKQmGpHO7uz7lWT169X4JTK28ihr/lnjZ29i1xdjR+MXyE29XSxH9fnihTnfjIH5SeX/iSAiXcgQEekCRLoIDQFEpJ5R93WPyK/+/iL5zWvL5bMHnyNLT7+r6hOIbt0i9SsflfqVj0j9qsck9t67WTUlBg+WjoknS+fkU6T95NMkvs++Va+5nAIQkeXQ49iwEvCriAxrXkpSqvuOJpPqOWI8JzOvRdSvuMbn1veMtuljVIP069R7kcxnxjHm8ZY2ZntjTDVe+rPU+z3Hm2MYNVnHVCJWuQfzvZwxVA1qbGuNveZlrVl11Wt+PSysNRp1xJOZeowx0uP3zCv/HHo4p+aTGTMpUl9TI20d3YZUNjlkzTk3FwsPK+eCWVoZ2sjSPB/6ysCcQy7nnmxSDNVrdRsIHhCAQHgIqO99HnoSQES6kAsi0gWIdBEaAohIPaMuJCL/8t4qOffXp0tTbZM8+W8vydB+w7WbQM0br0n9449J3eOPSv2Tj0t0x46sGrsPOFA6Jp8inSedIh0nnSKJAQO0m0NfBSEifRUXxWpCABGpSRCU4SsCYb9HpBLiqRW/2at8MyuB1arf7tTnqp2Sm+YK4ew2KfmpHrkixPra0WfWMylHrtjtM7ceR+Nbx3Qyfs53gN1a+64tZ4mhpZ6+jis6f0utdtnUxqKi/p22u727aN5Fx7c5j1612Tw3yhu/h3mfjPs4N3qN78W5of6PTfoRiUbkBwtqfPUzOEzFIiJdSBsR6QJEuggNAUSknlHnE5HdiS6Z+Ivx8s7Ot+SaE2+US8b33sRGu9kkElK3+nljpWTdykek7pmnJdJpuR4wGpXOo442hGTn5FOl84QJklTb62r8QERqHA6laUsAEaltNBSmMYGwi0iNo6E0TQlwj0hNg0mXxT0i9c0HEelCNohIFyDSRWgIICL1jDrfZjU3PztfFj19nYwePFb+8MWnJBaJ6Vl8H1VF2tuk7qm/Sv3jjxirJmv/vjqrtdrkZs+550ti5ChJDB8h3YeNlq6DD5XE0GHazBURqU0UFOIjAohIH4VFqdoQQERqEwWF+IQAIlLvoBCR+uaDiHQhG0SkCxDpIjQEEJF6Rp27WY1aBXnSL46RzkSHPDj1STlq2DF6Fu6wquj27VL/hLqM+zFpeOwRib3zj7w9JAYOlO5Dx0j3IYdK92HpZ/X1Rw5yOGL5zRGR5TOkh/ARQESGL3NmXD4BRGT5DOkhXAQQkXrnjYjUNx9EpAvZICJdgEgXoSGAiNQz6lwRqe4Lqe4PecHYaXLjyT/Ws2gXqlIisu75v0nN669Jzbq1UvPaq1LzxuuiVlIWenQdfoR0H3SIsXqy+5DDpPvQwwxhmWxodKGi3l0gIj3BSqcBJ4CIDHjATM8TAohIT7DSaYAJICL1DhcRqW8+iEgXskFEugCRLkJDABGpZ9RWEXn3aXvLpQ9fIIMbhsif/+1lGVA/UM+iPaxKCcra19YbYjL26jpDVNauXydqx+5Cj/i++0n3wYdK5/hjjPtPGo9oROKD9zIu9Va7eZfyQESWQo1jwk4AERn2M4D5l0IAEVkKNY4JMwFEpN7pIyL1zQcR6UI2iEgXINJFaAggIvWM2hSRG77/nzK+7ifywZ5NcvNpt8m5oy/Qs+AqVRX98EOpWa9WTqYkZY2SlOrrf7xpq6K4uhfl0GESHzY89TxipCSHDpW4EpVDh2feT7S0ZPpDRNpCSyMIZBFARHJCQMA5AUSkc2YcEW4CiEi980dE6psPItKFbBCRLkCki9AQQETqGbUpIr967YlyS/JJOW7EBPntvzyqZ7GaVlW7dk3m0u7ohvck+sFmiW3alHr+YLNEdu9yVHl81N6GrEwOGy6xvUfKnpahKWlpSMzhKXmpvh40yFG/NIZAGAggIsOQMnN0mwAi0m2i9Bd0AohIvRNGROqbDyLShWwQkS5ApIvQEEBE6hn1gO/Olbfu+6EcPTMqkWhMHv3Xv8lBgw7Rs1ifVhXp7JToxg0S27w5JSc3pyRlVD1v3pT1vhNpmayrl0RGUKqVlekVl8NTqy6VrIwPHZ6SlgMG+JQeZUPAGQFEpDNetIaAIoCI5DyAgDMCiEhnvCrdGhFZaeL2x0NE2mdVsCUi0gWIdBEaAohIPaNuvmaenNL1X/K3USJfO/Y/ZN7H/lPPQkNUVc3bb0l082ap3/qB9NuxRfb8832Jbnw/JTG3bDG+NlZa7tntiEp8732ke78DpPPESdnH1TeI2i08MWCgJNXzwIGSbE49K4GZ7P//27v72Djq/I7j3921105ikxAS5wECAYqOQ4WGUg7zcGlCdGoLpdcikVK10tFUuTT02iagRMmhiEaIJgpKQNcWGkWXg6rS0VyFekLkyh/0QnhyodfSQwe05VHk+REnjh/W9m71+83Mena8uzO/9Yw9O/O2FNme+c3v4fX7GcYfz0OHUTsURmAqBQgip1KftptVgCCyWWeOfk+VAEHkVMkHa5cgMpjTVJQiiJyo+g03yOCCy/QvdIVb75DCjTdNtMZIjh8cHZDCSEEKxYIMjxZkuFiQIfuz+t7aPiyF0SEp2NsL6vvikC5fPk5vK8jQyKAur/YNqTJ2veoY9XVhZGisTmdfUdVt7dfHjaq6h0X1jQ8EEIiHwGUyS15d+7G056J5A3Q8RtlcvQjyjEj1jErrKkv7CsvTdlBZvurSugIzM9A/ocGrZ1cWL5olpYsu0reE69BSfa22zZql3xxemj7d+jdNfT1DStPs713bimpbZ+eE+sLBCNQTIIhkfSBgLkAQaW7GEekWIIiM9/wTRMZ3fggiJzo3mUxFDeqXsMKtt0nhjmUy1H2rFG65baIt1Dz+7OAZ/UKJ4xeOyYn+Y9Zn99f9x+Rk/zE5XzgfWR+oGAEEkiXwz7lVcuvav03WoJp8NEGCSJMhOqFl9uxZyfb2SuZcr2TP9Uqm90vJ9Kqvz0n23JeSUZ/VNv25VzJDgybNBC6rr7x0AksVXnZ0Sqm9rWJbUe2fPk1kRocVcOpwU4WcY8GnDjfdoacOPqcH7gcFkyVAEJms+WQ0kyNAEDk5zrSSHAGCyHjPJUFkfOeHIHKic3PokHz543+V1nd6pO3Vn457c2qprV0KN90shdu/bl0xeUu3qG31Po72HS4HizpoVCFj31G97WT/CTl+4agc6Ttk3POO1g5pzeUln2uTfC4vrdm8tOZaJZ91trVJa7ZFb8+3tOntar/+Xh2XbdPft+fapUUdp+ux9qt62+z9TvlpLdMlm83a9ajjrXqc/U77M1q53c94Mpv4AG7NjufkOS+rOffYdulb+xfx7GRKexV2EDkRxuypkzqcVGGlDi3P95bDTBVYZi5c0Fdd6n/99r+BAcn229td20xvKW+03xVXadrhpAooi9Wu3JwxQ0SHmdbVnFa4aV/ZaW9zB6G8KKjRWYn+OILI6I1pIXkCBJHJm1NGFK0AQWS0vhOtnSByooLRHU8QGYKt+xmR6pc0FUjm//1NaXvtgLT83/+Oa6Fw8y0ydPtSGe6+Tc782hLp6X1X3jp8UHqOvCE/P/GfMlIcCdSrznyndE2fL10zFkjX9Hn663kdC2Se3jbf+n7GfJnZxhtVA4FSaFIECCInhdm4EYJIY7JJOyBOQWTYg84MDkimX/1TQaXzWQWZA5LVoeaAHWpaX4v6vq9Pb7PCTXv/BTv8dAJQtf/s2bC7W7e+UkeHqBcHlfJ5kXxeSq15KeVbRext6nvJt46Vsffr7W1tY+XVHytbWkRyOSnlWkRacvr7Utb+bO+TXIuU7H2SzUlJbVfl1DE5Vdbepsrp79V25+uxfbodZ5/+nE3ElaQEkZO6/GksIQIEkQmZSIYxaQIEkZNG3VBDBJENsU3KQQSRAZhf2H9QtuzYq0vevaJbtm5YJdPa8+Uj672sJnvmjLS98ark33hN8m++Lv2f/kIOXl6SA4tFDl4h+sUQ3o/Z7ZfYoaIVJqpQUQWKVrBohY7zOxbyDLcAc0eR+AkQRMZvTlSP1FuzO57+npzbuk36/uwv49nJlPYqyUFk5FNaKo1dnalDSs/VmfY2Kwi1AlC50CdZdziqyzhBqXOVp2vb8HDkw5iyBrJZK8S0A1EdWLpCT3dY6uzTYagdbFohpx2glgPSsbC0HKS6wlIrfLUDUaee1lYrXHXC13JI64Svrn12H6d3tMtIJieDxcyEg9mKgHfKJoOGEYhegCAyemNaSJYAQWS855MgMr7zQxDpMzfvvPuh7Ny9T57Zvl4untkpu3bv00c8tGZloCBSPcfxzcOvyluHX5O3Dr0m/3PmfSlJqXzsV06JLP9MZPmn1uc52U4pXL/E2p/JiH4xwJy5UpzbZX+2vh6dPUeKc+dKcfYl8V1d9AyBKgIEkfFcFuUrIh/fIX1rvhPPTqa0VwSR8Z94HWIWhkWGC5IpFCQzXBAZsj6r7/X24RHrOZvO/sKwZApDY/vVdl12WJeR0RHJjI6KjIxKZnRERH89IpkR9fXYPv213jZa3iejxbFy9j59rKpnZNQqq7+2jy06dY8av4U9/rMTTQ/1Y3Z0OGqHteqK1fLVpTkrsLX36+32/nIZp3w2q8/3RD1yXH92/bPPBdXOkndfle8rylgnkuPrtNuqWZ/uyFhfgrRb0T/XsWNjqVGft3/qWOXhfK42Ztcx48ZbzTBj29W08BlvleOsdu0xVRtvrT7Wmt8gfbTbGz9mvWPcPPvOm2t9uMvOndUup84NjVtv5TKe9TFuzTpjqVjHVdZ0Rbnxa9/vMVbR/FRTKwLmAgSR5maTeQRB5GRqm7VFEOnjpYLHxYvmy713LdUlvcGk2ua+IvLUwEl5/dBPdejYc+Q1+ehs5a3Zl3cultsv+3W5fdEyueOyZdJVnCFtPW9I61uvS5u6avI/3jabQRErmJyjgskuKV4yV0bnzJVSl/p6jv56aOlyUbeM8YFAHAQIIuMwC+P7QBAZz3lRvSKIjO/cpKVn+m3rKhBVoWbR/uwJRJ2A1B2I6iC1WLLCVztMLZfTwWjRClVVXU6YWg5I1bFWQKpD2OHhsSBVHVsOT61g1mrXDlRHR6RFilIaGZGiCojV8SqcVSFvOcS1A11df7EczFqBrSrvBLzFCb9tPi3rhHEiMBUC+qVk6lEUKvh3An79RwD7jwPlPwKoPxrYj67QV1XbZZw/Grg773kZqZO/louM21/58lId0Naqz7vPU7kOfev2ZQL7Des27osXyt2eYdvjDStZjPpm2na9cahuuOrL5TKSy2VlaLg41kGT+Tftm1/5euvJ71jj/Z6f+DrHG82XrnYC69yZo0xGOnc8PhX/WaLNAAIEkXWQBgYL8ugTe6X7puvKQeTHnx+RR7btkcc3r5arr1goh84dkhfee1k/4/Gtw6/L5+c+qahxQcelctulS63g8dJlcmnnIt9pyZ04LvqFAKdPS/bkccmdOimZU6cke/KE/lpvP3FccqdPSuZ8+G/E1m8ibbOeb1V+zlVbXkQ/zypvvWxH/Q8/31b/OVj6xKDFPq7Vqs/9HCxVn/29tLZKKZcdu/Wq5gmEfZKhn4PlOYGwj+evqL5LbEoLEEROKX/Nxgki4zkvqlcEkfGdG3oWX4GonxGpw1kdYFpXmJYDWudrZ7tTRgWn5atW7fKqjLpJplgUKZUq/9l3z2S826t8X1FGHafKqI9xZUWq1ucc45RXvwIGaFd1vnp9TttjfajaR28byqFqv9V4xnwq23SN03OsLucdWy0/97E1jrHareLqHFuzj565tftg9U8fXGWuXG2p+fCuEefuKs94fOfNtT7GlxVpyarsf2w9VjWsMUfj6qs2roq1Nd5SXzXOBwIIIBCWQPm/s2FVSD1hCRBE1pF0gsj77lkmNy+5Vpf0BpGZrZVp/bwZ82T5lctl+eLl+vM1s68Ja65q1/PFFyInT4qcPi1y9Oj4r/v69AP+ZWjIut3L+ef+vr8/+n7SAgIIxF/gySdF1q2Lfz/pIQIIIIAAAgikW0D9fmM/tsK68rnOP+fq6mplnKDXCVkdVW+IMZXf07Y1K5PpMJlt+Y1tKvvSrG2rKzQffTTd/42M8egJIgMEkfWuiLxx941y1cVXyZ1X3qnDx+vmXhfj6Q7QtUHr+VVVw8pa270BpypXL/SsFoQ6t2XVO0lwThzqlVEnJHwggEDjArt2iaxf3/jxHIkAAggggAACCCCAAAIIIIBADQGCSJ+lYfqMSFYaAgjUF+DWbFYIAmYC3Jpt5kVpBJRA1Ldmo4xAEgV4a3YSZ5UxRSnAy2qi1J143bysZuKGUdVAEOkjO9G3Zkc1cdSLQLMKEEQ268zR76kSIIicKnnabWYBgshmnj36PlUCBJFTJU+7zSpAEBnvmSOIjO/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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line(data_frame=bio.get_history(), x=\"SYSTEM TIME\", y=[\"A\", \"B\", \"Y\"], \n", " title=\"Changes in concentrations (reaction A + 2 B <-> Y)\",\n", " color_discrete_sequence = ['red', 'blue', 'green'],\n", " labels={\"value\":\"concentration\", \"variable\":\"Chemical\"})\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "158e3787-f2d5-4a01-aaa9-6066e93e584c", "metadata": {}, "source": [ "**A**, still the limiting reagent, is again stopping the reaction. \n", "The (transiently) high value of [A] led to a high value of [B]" ] }, { "cell_type": "markdown", "id": "f6619731-c5ea-484c-af3e-cea50d685361", "metadata": { "tags": [] }, "source": [ "# Let's again suddenly increase [A]" ] }, { "cell_type": "code", "execution_count": 15, "id": "d3618eba-a673-4ff5-85d0-08f5ea592361", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.055:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [30.]\n", " Species 1 (B). Diff rate: None. Conc: [11.98765279]\n", " Species 2 (Y). Diff rate: None. Conc: [44.00617361]\n" ] } ], "source": [ "bio.set_bin_conc(bin_address=0, species_index=0, conc=30.)\n", "bio.describe_state()" ] }, { "cell_type": "code", "execution_count": 16, "id": "359b153e-9b63-45a3-851a-6c49259909b7", "metadata": {}, "outputs": [], "source": [ "# Save the state of the concentrations of all species at bin 0 (the only bin in this system)\n", "bio.add_snapshot(bio.bin_snapshot(bin_address = 0))" ] }, { "cell_type": "markdown", "id": "0974480d-ca45-46fe-addd-c8d394780fdb", "metadata": {}, "source": [ "### Yet again, take the system to equilibrium" ] }, { "cell_type": "code", "execution_count": 17, "id": "8fe20f9c-05c4-45a4-b485-a51005440200", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.09:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [24.26245387]\n", " Species 1 (B). Diff rate: None. Conc: [0.51256052]\n", " Species 2 (Y). Diff rate: None. Conc: [49.74371974]\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " SYSTEM TIME A B Y caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.850000 93.700000 3.150000 \n", "2 0.002 0.735332 91.470665 4.264668 \n", "3 0.003 0.303911 90.607822 4.696089 \n", "4 0.004 0.131501 90.263002 4.868499 \n", ".. ... ... ... ... ...\n", "88 0.086 24.262458 0.512569 49.743715 \n", "89 0.087 24.262456 0.512566 49.743717 \n", "90 0.088 24.262455 0.512563 49.743718 \n", "91 0.089 24.262454 0.512562 49.743719 \n", "92 0.090 24.262454 0.512561 49.743720 \n", "\n", "[93 rows x 5 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bio.react(time_step=0.0005, n_steps=70, snapshots={\"frequency\": 2, \"sample_bin\": 0}) # At every other step, take a snapshot \n", " # of all species at bin 0\n", "bio.describe_state()\n", "bio.get_history()" ] }, { "cell_type": "code", "execution_count": 18, "id": "aff608b1-5c78-4070-845a-118afe7c2108", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A + 2 B <-> Y\n", "Final concentrations: [A] = 24.26 ; [B] = 0.5126 ; [Y] = 49.74\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 3.99999\n", " Formula used: [Y] / ([A][B])\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 0.0003695 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "bio.reaction_dynamics.is_in_equilibrium(conc=bio.bin_snapshot(bin_address = 0))" ] }, { "cell_type": "code", "execution_count": 19, "id": "4229e039-b484-4849-a446-59409885deb4", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=A
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line(data_frame=bio.get_history(), x=\"SYSTEM TIME\", y=[\"A\", \"B\", \"Y\"], \n", " title=\"Changes in concentrations (reaction A + 2 B <-> Y)\",\n", " color_discrete_sequence = ['red', 'blue', 'green'],\n", " labels={\"value\":\"concentration\", \"variable\":\"Chemical\"})\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "81a8be4a-f374-494e-b647-184e35707295", "metadata": {}, "source": [ "**A**, again the scarse limiting reagent, stops the reaction yet again \n", "\n", "Note: A can down-regulate B, but it cannot bring it up." ] }, { "cell_type": "markdown", "id": "0f4b82d6-f617-4af9-b4b6-67e03ae891ac", "metadata": {}, "source": [ "# For additional exploration, see the experiment \"reactions_single_compartment/down_regulate_2\"" ] }, { "cell_type": "code", "execution_count": null, "id": "c2a858c2", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }