diff --git a/CITATION.cff b/CITATION.cff
index ffacdee..924462c 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -14,6 +14,9 @@ authors:
- given-names: David
family-names: Bunten
orcid: 'https://orcid.org/0000-0001-6041-3665'
+ - given-names: Vincent
+ family-names: Rubinetti
+ orcid: 'https://orcid.org/0000-0002-4655-3773'
- given-names: Gregory
family-names: Way
orcid: 'https://orcid.org/0000-0002-0503-9348'
diff --git a/docs/presentations/2024-09-18-SBI2-Conference/.gitignore b/docs/presentations/2024-09-18-SBI2-Conference/.gitignore
new file mode 100644
index 0000000..075b254
--- /dev/null
+++ b/docs/presentations/2024-09-18-SBI2-Conference/.gitignore
@@ -0,0 +1 @@
+/.quarto/
diff --git a/docs/presentations/2024-09-18-SBI2-Conference/_extensions/quarto-ext/poster/_extension.yml b/docs/presentations/2024-09-18-SBI2-Conference/_extensions/quarto-ext/poster/_extension.yml
new file mode 100644
index 0000000..764ce84
--- /dev/null
+++ b/docs/presentations/2024-09-18-SBI2-Conference/_extensions/quarto-ext/poster/_extension.yml
@@ -0,0 +1,10 @@
+title: Poster (modified)
+author: Carlos Scheidegger, Dave Bunten
+version: 1.0.0
+quarto-required: ">=1.4.415"
+contributes:
+ formats:
+ typst:
+ template-partials:
+ - typst-template.typ
+ - typst-show.typ
diff --git a/docs/presentations/2024-09-18-SBI2-Conference/_extensions/quarto-ext/poster/typst-show.typ b/docs/presentations/2024-09-18-SBI2-Conference/_extensions/quarto-ext/poster/typst-show.typ
new file mode 100644
index 0000000..1361b23
--- /dev/null
+++ b/docs/presentations/2024-09-18-SBI2-Conference/_extensions/quarto-ext/poster/typst-show.typ
@@ -0,0 +1,74 @@
+// Typst custom formats typically consist of a 'typst-template.typ' (which is
+// the source code for a typst template) and a 'typst-show.typ' which calls the
+// template's function (forwarding Pandoc metadata values as required)
+//
+// This is an example 'typst-show.typ' file (based on the default template
+// that ships with Quarto). It calls the typst function named 'article' which
+// is defined in the 'typst-template.typ' file.
+//
+// If you are creating or packaging a custom typst template you will likely
+// want to replace this file and 'typst-template.typ' entirely. You can find
+// documentation on creating typst templates here and some examples here:
+// - https://typst.app/docs/tutorial/making-a-template/
+// - https://github.com/typst/templates
+
+#show: doc => poster(
+ $if(title)$ title: [$title$], $endif$
+ // TODO: use Quarto's normalized metadata.
+ $if(poster-authors)$ authors: [$poster-authors$], $endif$
+ $if(departments)$ departments: [$departments$], $endif$
+ $if(size)$ size: "$size$", $endif$
+
+ // Institution logo.
+ $if(institution-logo)$ univ_logo: "$institution-logo$", $endif$
+
+ // Footer text.
+ // For instance, Name of Conference, Date, Location.
+ // or Course Name, Date, Instructor.
+ $if(footer-text)$ footer_text: [$footer-text$], $endif$
+
+ // Any URL, like a link to the conference website.
+ $if(footer-url)$ footer_url: [$footer-url$], $endif$
+
+ // Emails of the authors.
+ $if(footer-emails)$ footer_email_ids: [$footer-emails$], $endif$
+
+ // Color of the footer.
+ $if(footer-color)$ footer_color: "$footer-color$", $endif$
+
+ // DEFAULTS
+ // ========
+ // For 3-column posters, these are generally good defaults.
+ // Tested on 36in x 24in, 48in x 36in, and 36in x 48in posters.
+ // For 2-column posters, you may need to tweak these values.
+ // See ./examples/example_2_column_18_24.typ for an example.
+
+ // Any keywords or index terms that you want to highlight at the beginning.
+ $if(keywords)$ keywords: ($for(keywords)$"$it$"$sep$, $endfor$), $endif$
+
+ // Number of columns in the poster.
+ $if(num-columns)$ num_columns: $num-columns$, $endif$
+
+ // University logo's scale (in %).
+ $if(univ-logo-scale)$ univ_logo_scale: $univ-logo-scale$, $endif$
+
+ // University logo's column size (in in).
+ $if(univ-logo-column-size)$ univ_logo_column_size: $univ-logo-column-size$, $endif$
+
+ // Title and authors' column size (in in).
+ $if(title-column-size)$ title_column_size: $title-column-size$, $endif$
+
+ // Poster title's font size (in pt).
+ $if(title-font-size)$ title_font_size: $title-font-size$, $endif$
+
+ // Authors' font size (in pt).
+ $if(authors-font-size)$ authors_font_size: $authors-font-size$, $endif$
+
+ // Footer's URL and email font size (in pt).
+ $if(footer-url-font-size)$ footer_url_font_size: $footer-url-font-size$, $endif$
+
+ // Footer's text font size (in pt).
+ $if(footer-text-font-size)$ footer_text_font_size: [$footer-text-font-size$], $endif$
+
+ doc,
+)
diff --git a/docs/presentations/2024-09-18-SBI2-Conference/_extensions/quarto-ext/poster/typst-template.typ b/docs/presentations/2024-09-18-SBI2-Conference/_extensions/quarto-ext/poster/typst-template.typ
new file mode 100644
index 0000000..cdb9d27
--- /dev/null
+++ b/docs/presentations/2024-09-18-SBI2-Conference/_extensions/quarto-ext/poster/typst-template.typ
@@ -0,0 +1,173 @@
+#let poster(
+ // set variables for use throughout
+ // note: some are referenced from `.qmd` file
+ size: "'36x24' or '48x36''",
+ title: "Paper Title",
+ authors: "Author Names (separated by commas)",
+ departments: "Department Name",
+ univ_logo: "Logo Path",
+ footer_text: "Footer Text",
+ footer_url: "Footer URL",
+ footer_email_ids: "Email IDs (separated by commas)",
+ footer_color: "Hex Color Code",
+ keywords: (),
+ num_columns: "4",
+ univ_logo_scale: "140",
+ univ_logo_column_size: "15",
+ title_column_size: "25",
+ title_font_size: "48",
+ authors_font_size: "36",
+ footer_url_font_size: "40",
+ footer_text_font_size: "40",
+ body
+) = {
+ // initialize template display formatting
+ set text(font: "Lato", size: 32pt)
+ let sizes = size.split("x")
+ let width = int(sizes.at(0)) * 1in
+ let height = int(sizes.at(1)) * 1in
+ univ_logo_scale = int(univ_logo_scale) * 1%
+ title_font_size = int(title_font_size) * 1pt
+ authors_font_size = int(authors_font_size) * 1pt
+ num_columns = int(num_columns)
+ univ_logo_column_size = int(univ_logo_column_size) * 1in
+ title_column_size = int(title_column_size) * 1in
+ footer_url_font_size = int(footer_url_font_size) * 1pt
+ footer_text_font_size = int(footer_text_font_size) * 1pt
+
+ // create overall page output
+ set page(
+ // total dimensions
+ width: width,
+ height: height,
+ // margin on all sides
+ margin:
+ (top: .8in, left: .8in, right: .8in, bottom: 1.8in),
+ // footer section
+ footer: [
+ #set align(center)
+ #set text(42pt)
+ #block(
+ fill: rgb(footer_color),
+ width: 100%,
+ inset: 20pt,
+ radius: 10pt,
+ // adds text to footer
+ [
+ #text(font: "Lato", size: footer_url_font_size, footer_url)
+ #h(1fr)
+ #text(size: footer_text_font_size, smallcaps(footer_text))
+ #h(1fr)
+ #text(font: "Lato", size: footer_url_font_size, footer_email_ids)
+ ]
+ )
+ ]
+ )
+
+ // set math display properties
+ set math.equation(numbering: "(1)")
+ show math.equation: set block(spacing: 0.65em)
+
+ set enum(indent: 10pt, body-indent: 9pt)
+ set list(indent: 10pt, body-indent: 9pt)
+
+ // set the heading numbering system
+ set heading(numbering: "I.A.1.")
+ show heading: it => locate(loc => {
+ let levels = counter(heading).at(loc)
+ let deepest = if levels != () {
+ levels.last()
+ } else {
+ 1
+ }
+
+ // defines how sub-headers display
+ set text(24pt, weight: 400)
+ // sub-header level 1
+ if it.level == 1 [
+ #set text(style: "italic")
+ #v(32pt, weak: true)
+ #if it.numbering != none {
+ numbering("i.", deepest)
+ h(7pt, weak: true)
+ }
+ #it.body
+ // sub-header level 2
+ ] else if it.level == 2 [
+ #v(10pt, weak: true)
+ #set align(left)
+ #set text({ 40pt }, weight: 600, font: "Merriweather", fill: rgb(31, 23, 112))
+ #show: smallcaps
+ #v(50pt, weak: true)
+ #if it.numbering != none {
+ numbering("I.", deepest)
+ h(7pt, weak: true)
+ }
+ #it.body
+ #v(30pt, weak: true)
+ #line(length: 100%, stroke: rgb(200, 200, 200))
+ #v(30pt, weak: true)
+ // all other headers
+ ] else [
+ #set text({ 36pt }, weight: 600, font: "Merriweather", fill: rgb(31, 23, 112))
+ #if it.level == 3 {
+ numbering("☆ 1)", deepest)
+ [ ]
+ }
+ ___#(it.body)___
+ #v(40pt, weak: true)
+ ]
+ })
+
+ // header grid
+ align(left,
+ grid(
+ // rows and cols in the header
+ rows: (auto, auto),
+ columns: (title_column_size, univ_logo_column_size),
+ column-gutter: 5pt,
+ row-gutter: 30pt,
+ // main title
+ text(font: "Merriweather", weight: 1000, size: 48pt, title),
+ grid.cell(
+ image(univ_logo, width: univ_logo_scale),
+ rowspan: 3,
+ align: left,
+ ),
+ // author display
+ text(size: 38pt, authors),
+ // department and notes display
+ text(size: 29pt, emph(departments)),
+ )
+ )
+
+ // spacing between the header and body
+ v(40pt)
+
+ // set main body display
+ show: columns.with(num_columns, gutter: 60pt)
+ // paragraph display properties
+ set par(leading: 10pt,
+ justify: false,
+ first-line-indent: 0em,
+ linebreaks: "optimized"
+ )
+
+ // Configure figures.
+ show figure: it => block({
+ // Display a backdrop rectangle.
+ it.body
+
+ // Display caption.
+ if it.has("caption") {
+ set align(left)
+ v(if it.has("gap") { it.gap } else { 24pt }, weak: true)
+ set text(weight: "bold")
+ it.caption
+ }
+
+ })
+
+ // adds body content to page
+ body
+}
diff --git a/docs/presentations/2024-09-18-SBI2-Conference/abstract.md b/docs/presentations/2024-09-18-SBI2-Conference/abstract.md
new file mode 100644
index 0000000..5c28d52
--- /dev/null
+++ b/docs/presentations/2024-09-18-SBI2-Conference/abstract.md
@@ -0,0 +1,15 @@
+# SBI2 2024 Abstract Submission - coSMicQC
+
+## Author(s):
+
+Dave Bunten, Jenna Tomkinson, Gregory Way
+
+## Title (under 100 characters):
+
+Single-cell Morphology Quality Control (coSMicQC)
+
+## Abstract (under 500 words):
+
+High-dimensional single-cell morphology data from large-scale microscopy drug screening applications help prioritize effective treatments for patients suffering from various diseases and enable the discovery of new biological mechanisms. Image analysis pipelines to process these single-cell data often introduce errors in the segmentation step, where software improperly segments single cells (for example, capturing undersized or overly large portions of a cell compartment) and incorrectly identifies artifacts like dust or other debris as single cells. These errors lead to erroneous single-cell measurements which need to be removed prior to single-cell analyses to help ensure accurate results. Research scientists often use bespoke approaches to filter single cells or aggregate all single cells into bulk profiles, reducing errors' impact and preventing single-cell analysis. This leads to duplicated effort, human error, and a lack of quality control in single-cell feature data which overall may result in a reduced potential for discoveries or inaccurate outcomes.
+
+We introduce a Python package called coSMicQC (Single cell Morphology Quality Control) to improve single-cell morphology analysis. coSMicQC uses high-content morphology measurements to define default thresholds for removing single cells and enables users to customize quality control parameters. Accessible through both command line interface (CLI) and Python application programming interface (API), coSMicQC seamlessly integrates into diverse analytical workflows, including standalone scripts/workflows to interactive Jupyter Notebooks. Notably, the package creates interactive and exportable visualizations that illustrate outlier distributions. The backbone of this package is a novel data format: the CytoDataFrame. CytoDataFrames facilitate real-time exploration of single-cell images within any pandas environment to seamlessly link single-cell morphology measurements with single-cell images. Leveraging real-world datasets, including Joint Undertaking in Morphological Profiling (CPJUMP1) data, we show how our tool empowers researchers to identify technical outliers within single-cell profile features and improve single-cell analysis.
diff --git a/docs/presentations/2024-09-18-SBI2-Conference/figures.ipynb b/docs/presentations/2024-09-18-SBI2-Conference/figures.ipynb
new file mode 100644
index 0000000..ad68c44
--- /dev/null
+++ b/docs/presentations/2024-09-18-SBI2-Conference/figures.ipynb
@@ -0,0 +1,1135 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4fde146d-dacd-4ebf-b7b5-f26b3e28c71b",
+ "metadata": {},
+ "source": [
+ "# Generate figures for poster"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "39b5b7f0-786e-4bb2-8257-25146140b25c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "marker": {
+ "color": "green",
+ "size": 8
+ },
+ "mode": "markers",
+ "name": "Inliers",
+ "type": "scatter",
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+ "legend": {
+ "orientation": "v",
+ "title": {
+ "text": "Legend"
+ },
+ "traceorder": "normal",
+ "x": 1,
+ "xanchor": "right",
+ "y": 0,
+ "yanchor": "bottom"
+ },
+ "template": {
+ "data": {
+ "bar": [
+ {
+ "error_x": {
+ "color": "#2a3f5f"
+ },
+ "error_y": {
+ "color": "#2a3f5f"
+ },
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "bar"
+ }
+ ],
+ "barpolar": [
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+ "marker": {
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+ }
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+ "gridcolor": "white",
+ "linecolor": "white",
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+ },
+ "type": "choropleth"
+ }
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+ "contour": [
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+ "colorbar": {
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+ "ticks": ""
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+ "colorscale": [
+ [
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+ "#0d0887"
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+ [
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+ ],
+ [
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+ [
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+ ],
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+ "contourcarpet": [
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+ "colorbar": {
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+ "type": "contourcarpet"
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+ ],
+ "heatmap": [
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+ [
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+ [
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",
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import plotly.graph_objects as go\n",
+ "\n",
+ "# Generate random data\n",
+ "np.random.seed(0)\n",
+ "\n",
+ "# Inlier data\n",
+ "inlier_x = np.random.normal(loc=0, scale=1, size=50)\n",
+ "inlier_y = np.random.normal(loc=0, scale=1, size=50)\n",
+ "\n",
+ "# Outlier data\n",
+ "outlier_x = np.random.normal(loc=5, scale=1, size=10)\n",
+ "outlier_y = np.random.normal(loc=5, scale=1, size=10)\n",
+ "\n",
+ "# Create figure\n",
+ "fig = go.Figure()\n",
+ "\n",
+ "# Add inliers to the plot\n",
+ "fig.add_trace(\n",
+ " go.Scatter(\n",
+ " x=inlier_x,\n",
+ " y=inlier_y,\n",
+ " mode=\"markers\",\n",
+ " name=\"Inliers\",\n",
+ " marker={\"color\": \"green\", \"size\": 8},\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "# Add outliers to the plot\n",
+ "fig.add_trace(\n",
+ " go.Scatter(\n",
+ " x=outlier_x,\n",
+ " y=outlier_y,\n",
+ " mode=\"markers\",\n",
+ " name=\"Outliers\",\n",
+ " marker={\"color\": \"red\", \"size\": 8},\n",
+ " )\n",
+ ")\n",
+ "\n",
+ "# Update layout\n",
+ "fig.update_layout(\n",
+ " legend_title=\"Legend\",\n",
+ " legend={\n",
+ " \"x\": 1,\n",
+ " \"y\": 0,\n",
+ " \"traceorder\": \"normal\",\n",
+ " \"orientation\": \"v\",\n",
+ " \"xanchor\": \"right\",\n",
+ " \"yanchor\": \"bottom\",\n",
+ " },\n",
+ " width=1200,\n",
+ " height=500,\n",
+ ")\n",
+ "\n",
+ "# Save figure as an image\n",
+ "fig.write_image(\"images/example-outliers.png\")\n",
+ "\n",
+ "fig.show()"
+ ]
+ }
+ ],
+ "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": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/presentations/2024-09-18-SBI2-Conference/figures.py b/docs/presentations/2024-09-18-SBI2-Conference/figures.py
new file mode 100644
index 0000000..77f9f1e
--- /dev/null
+++ b/docs/presentations/2024-09-18-SBI2-Conference/figures.py
@@ -0,0 +1,75 @@
+# ---
+# jupyter:
+# jupytext:
+# text_representation:
+# extension: .py
+# format_name: light
+# format_version: '1.5'
+# jupytext_version: 1.16.2
+# kernelspec:
+# display_name: Python 3 (ipykernel)
+# language: python
+# name: python3
+# ---
+
+# # Generate figures for poster
+
+# +
+import numpy as np
+import plotly.graph_objects as go
+
+# Generate random data
+np.random.seed(0)
+
+# Inlier data
+inlier_x = np.random.normal(loc=0, scale=1, size=50)
+inlier_y = np.random.normal(loc=0, scale=1, size=50)
+
+# Outlier data
+outlier_x = np.random.normal(loc=5, scale=1, size=10)
+outlier_y = np.random.normal(loc=5, scale=1, size=10)
+
+# Create figure
+fig = go.Figure()
+
+# Add inliers to the plot
+fig.add_trace(
+ go.Scatter(
+ x=inlier_x,
+ y=inlier_y,
+ mode="markers",
+ name="Inliers",
+ marker={"color": "green", "size": 8},
+ )
+)
+
+# Add outliers to the plot
+fig.add_trace(
+ go.Scatter(
+ x=outlier_x,
+ y=outlier_y,
+ mode="markers",
+ name="Outliers",
+ marker={"color": "red", "size": 8},
+ )
+)
+
+# Update layout
+fig.update_layout(
+ legend_title="Legend",
+ legend={
+ "x": 1,
+ "y": 0,
+ "traceorder": "normal",
+ "orientation": "v",
+ "xanchor": "right",
+ "yanchor": "bottom",
+ },
+ width=1200,
+ height=500,
+)
+
+# Save figure as an image
+fig.write_image("images/example-outliers.png")
+
+fig.show()
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+---
+title: Single-cell Morphology Quality Control (coSMicQC)
+format:
+ poster-typst:
+ size: "48x36"
+ poster-authors: "Dave Bunten¹\\*, Jenna Tomkinson¹\\*, Vincent Rubinetti¹, Gregory Way¹"
+ departments: "¹Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus\n\n\\*These authors contributed equally to this work."
+ institution-logo: "./images/header-combined-images.png"
+ footer-text: "Way Lab"
+ footer-url: "https://github.com/WayScience/coSMicQC"
+ footer-emails: "https://www.waysciencelab.com"
+ footer-color: "ADA7FF"
+citation:
+ doi: 10.5281/zenodo.13829960
+---
+
+## Erroneous outliers and analysis
+
+{width=100%}
+
+___Segmentation errors___ during single-cell morphology image analysis such as misidentifying cell compartments or artifacts as cells can lead to inaccurate single-cell measurements and ___erroneous anomalies___ within the data (Figure 1).
+___If single-cell quality control is performed, it often uses bespoke methods___ or aggregate data into bulk profiles to avoid discrepancies caused by anomaly outliers.
+These techniques make it challenging to perform ___quality control___ on the data, impeding the potential for meaningful discoveries.
+
+
+## Single-cell quality control package
+
+ cellprofiler["
CellProfiler"]
+ cellprofiler --> profiles[("🦠 Single-cell
Profiles")]
+ cytotable["
CytoTable"]
+ cytotable --> |find
outliers| cosmicqc["
✨coSMicQC"]
+ cosmicqc --> |further
analysis| pycytominer["
pcytominer"]
+
+ classDef cellprofiler fill:#FFB3CC,color#000,width:0;
+ classDef cytotable fill:#FFEFC2,color:#000;
+ classDef cosmicqc fill:#8431D0,color:#fff;
+ classDef pycytominer fill:#FCDCFF,color:#000;
+ class cellprofiler cellprofiler;
+ class cosmicqc cosmicqc;
+ class cytotable cytotable;
+ class pycytominer pycytominer;
+```
+-->
+{width=85% fig-align="center"}
+
+To address these challenges, we introduce ___`coSMicQC` (Single-cell Morphology Quality Control)___, an open source Python package designed to enhance the accuracy of single-cell morphology analysis.
+__`coSMicQC`__ offers default and customizable thresholds for quality control, integrating seamlessly into both command line and Python API workflows.
+
+## Getting started with coSMicQC
+
+### Installation
+
+```shell
+# pip install from pypi
+pip install coSMicQC
+
+# or install directly from source
+pip install git+https://github.com/WayScience/coSMicQC.git
+```
+
+__`coSMicQC`__ may be installed from PyPI or source.
+
+### Finding outliers
+
+```python
+import cosmicqc
+# find outliers from single-cell profiles
+scdf = cosmicqc.analyze.find_outliers(
+ df="single-cell-profiles.parquet",
+ metadata_columns=[
+ "Metadata_ImageNumber",
+ "Image_Metadata_Plate_x"
+ ],
+ feature_thresholds={
+ "Nuclei_AreaShape_Area": -1},
+)
+```
+
+
+
+```shell
+# CLI interface for coSMicQC find_outliers
+$ cosmicqc find_outliers \
+ --df single-cell-profiles.parquet \
+ --metadata_columns \[Metadata_ImageNumber\] \
+ --feature_thresholds '{"Nuclei_AreaShape_Area": -1}'
+
+Number of outliers: 328 (19.14%)
+Outliers Range:
+Nuclei_AreaShape_Area Min: 734.0
+...
+```
+
+### Visualizing outlier distributions
+
+```python
+import cosmicqc
+# label and show outliers within the profiles
+scdf = cosmicqc.analyze.label_outliers(
+ df="single-cell-profiles.parquet",
+ include_threshold_scores=True,
+).show_report()
+```
+
+{width=100%}
+
+
+
+### Understanding outlier segmentations
+
+```python
+import cosmicqc
+
+# passing image and mask dirs to display images
+cosmicqc.CytoDataFrame(
+ data="single-cell-profiles.parquet",
+ data_context_dir="./image_directory/",
+ data_mask_context_dir="./mask_directory/",
+)
+```
+
+
+
+## Real-world applications
+
+{width=100%}
+
+{width=100%}
+
+{width=100%}
+
+## Acknowledgements
+
+Special thanks goes to the following for their help in contributing to the __coSMicQC__ inspiration, development, or related work.
+
+- ___CU Anschutz CFReT___: Timothy A. McKinsey, Josh Travers
+- ___iNFixion___: Michelle Mattson-Hoss, Herb Sarnoff
+- ___Cold Spring Harbor Laboratory___: Katherine Alexander
+- ___JUMP-Cell Painting Consortium___: Chandrasekaran et al., 2024 (cpg0000)
+- ___St. Jude Children’s Research Hospital___: Adam D. Durbin, Ha Won Lee, Taosheng Chen, and Noha Shendy
diff --git a/docs/presentations/2024-09-18-SBI2-Conference/readme.md b/docs/presentations/2024-09-18-SBI2-Conference/readme.md
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index 0000000..2aa5186
--- /dev/null
+++ b/docs/presentations/2024-09-18-SBI2-Conference/readme.md
@@ -0,0 +1,71 @@
+# 2024 SBI2 Conference Poster
+
+The content here is for creating a poster for 2024 SBI2 conference poster session.
+
+## Poster Details
+
+Poster dimensions will be within (but may not exactly match) SBI2 2023's maximum specifications: `91" wide x 44.75” high`.
+
+## Poster development
+
+We use [Quarto](https://github.com/quarto-dev/quarto-cli)'s [Typst](https://github.com/typst/typst) [integration](https://quarto.org/docs/output-formats/typst.html) through a Quarto extension for posters under [`quarto-ext/typst-templates/poster`](https://github.com/quarto-ext/typst-templates/tree/main/poster).
+Related [Poe the Poet](https://poethepoet.natn.io/index.html) tasks are defined to run processes defined within `pyproject.toml` under the section `[tool.poe.tasks]`.
+
+See the following examples for more information:
+
+```bash
+# preview the poster during development
+poetry run poe poster-preview
+
+# build the poster PDF from source
+poetry run poe poster-render
+```
+
+## Additional notes
+
+- Fonts were sourced locally for rendering within Quarto and Typst:
+ - [Merriweather](https://fonts.google.com/specimen/Merriweather)
+ - [Lato](https://fonts.google.com/specimen/Lato)
+- QR codes with images were generated and saved manually via [https://github.com/kciter/qart.js](https://github.com/kciter/qart.js)
+- [ImageMagick](http://www.imagemagick.org/) was used to form the bottom logos together as one and render the poster pdf as png using the following commands:
+
+```shell
+# append text to qr codes
+magick images/cosmicqc-qr.png -gravity South -background transparent -splice 0x15 -pointsize 40 -font Arial -weight Bold -annotate 0x15 'Scan for coSMicQC!' images/cosmicqc-qr-text.png
+
+# append text to UMAPs and combine them for use as a figure in poster
+magick images/UMAP_localhost230405150001_DMSO_and_TGFRi_no_QC.png -resize 864x864\! images/UMAP_localhost230405150001_DMSO_and_TGFRi_no_QC_resized.png
+magick images/UMAP_localhost230405150001_failing_DMSO_and_TGFRi_w_healthy_DMSO_Merged.pdf -resize 864x864\! images/UMAP_localhost230405150001_failing_DMSO_and_TGFRi_w_healthy_DMSO_Merged.png
+magick images/UMAP_localhost230405150001_DMSO_and_TGFRi_no_QC_resized.png -gravity SouthWest -background transparent -splice 0x15 -pointsize 30 -font Arial-Italic -weight Bold -fill purple -style Italic -annotate +55+25 'Before' images/UMAP_localhost230405150001_DMSO_and_TGFRi_no_QC_with_text.png
+magick images/UMAP_localhost230405150001_failing_DMSO_and_TGFRi_w_healthy_DMSO_Merged.png -gravity SouthWest -background transparent -splice 0x15 -pointsize 30 -font Arial-Italic -weight Bold -fill purple -style Italic -annotate +80+25 'After' images/UMAP_localhost230405150001_failing_DMSO_and_TGFRi_w_healthy_DMSO_Merged_with_text.png
+magick images/UMAP_localhost230405150001_DMSO_and_TGFRi_no_QC_with_text.png images/UMAP_localhost230405150001_failing_DMSO_and_TGFRi_w_healthy_DMSO_Merged_with_text.png +append images/CFReT_UMAP_combined.png
+magick images/CFReT_UMAP_combined.png -density 300 -resize 8000x8000 images/CFReT_UMAP_combined.png
+
+# adjust single cell images from Durbin Lab collaboration
+magick images/normal_phenotype_cosmicqc.png -resize 904x904 -gravity center -background black -extent 904x904 images/normal_phenotype_cosmicqc_resized.png
+magick images/weird_phenotype_cosmicqc.png -resize 904x904 -gravity center -background black -extent 904x904 images/weird_phenotype_cosmicqc_resized.png
+magick images/normal_phenotype_cosmicqc_resized.png images/weird_phenotype_cosmicqc_resized.png +append images/durbin_phenotypes_combined.png
+
+# adjust roc plot for sizing
+magick images/bootstrap_plot.png -density 300 -resize 8000x8000 images/bootstrap_plot_resized.png
+
+# create a transparent spacer
+magick -size 100x460 xc:transparent images/spacer.png
+
+# combine the images together as one using the spacer for separation
+magick -background none images/cosmicqc-qr-text.png images/spacer.png images/waylab.png images/spacer.png images/dbmi.png +append images/header_combined_images.png
+
+# add circles to highlight umap clusters for jump analysis
+magick ../../src/examples/images/umap_comparison_with_and_without_erroneous_outliers_BR00117012.png -fill none -stroke purple -strokewidth 2 \
+-draw "circle 634,317 684,317" \
+-draw "circle 665,64 695,64" \
+-draw "circle 612,465 662,465" \
+images/jump_umap_comparison_with_highlights.png
+
+# combine jump umaps together
+magick +append ../../src/examples/images/umap_erroneous_outliers_BR00117012.png ./images/jump_umap_comparison_with_highlights.png images/jump_umap_analyses.png
+
+# convert the poster pdf to png and jpg with 150 dpi and a white background
+magick -antialias -density 300 -background white -flatten poster.pdf poster.png
+magick -antialias -density 300 -background white -flatten poster.pdf poster.jpg
+```
diff --git a/docs/src/examples/cosmicqc_in_a_nutshell.ipynb b/docs/src/examples/cosmicqc_in_a_nutshell.ipynb
index a3b8db4..621efbb 100644
--- a/docs/src/examples/cosmicqc_in_a_nutshell.ipynb
+++ b/docs/src/examples/cosmicqc_in_a_nutshell.ipynb
@@ -943,7 +943,7 @@
"hovertemplate": "Nuclei_AreaShape_Eccentricity
Z-Score: %{x}
Single-cell Count (log): %{y}
",
"legendgroup": "False",
"marker": {
- "color": "#A777F1",
+ "color": "#511CFB",
"opacity": 0.7,
"pattern": {
"shape": ""
@@ -2675,7 +2675,7 @@
"hovertemplate": "Nuclei_AreaShape_Eccentricity
Z-Score: %{x}
Single-cell Count (log): %{y}
",
"legendgroup": "True",
"marker": {
- "color": "#A777F1",
+ "color": "#511CFB",
"opacity": 0.7,
"pattern": {
"shape": "x"
@@ -3554,11 +3554,11 @@
}
}
},
- "image/png": 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MFycm5OCCoRMXY7inhwp+tPb0+Sv8XqW9URCo/UyCh6s37+HR4xd4/uq1WrIp4ZZ2IIVfwYiv7zdkKtpIVQJuWjgUYfDfwR2T561VS1m1UEnrp76CT54fkD0CJQiUIHTw+EVYtHqHWtIp1WmBCQI9uo9SlXxbFw/3c+muaZgnSz5revRXFYd92tczmnupEpTqrZPbZ6gQJyBBoMyLjEkqyWQZtulyUKn0nLtsq6q21Lc/iuc1jEH6MGD0PENFp3advK9dW9ZU95QQ9fT5a6jdyktV6w3s3sTs3yNyL+mPLPmWJc76pr3bUlUnYZoWBGqVhPprW/YYo6oIpTIvUfw4hh8dPnEezbuONFrOa64jA8bMV9Wg/p3ALFWf+pDcUUGgVPLJacbyHSytW8L79v0H5C/fWlWOLpjQSw1NCwK190Qbr/Rb+i//cWHRJE8k+8n25eSO/B8DPosCFKAABShAAQpQgAIUoAAFQr8Ag8BALg3WDjzQV1dpr03Zut1Vdda5PXMNgYG5IFBOLJaTi3csHYnECeOqE4zl9/oKNbmBuSBQ9sSbtnADJs1ZY/ZtPbBuoqpm8isI1JbeWnrVh/VugXIl8kEbj+lpt4EJAqU6qkjVDqpSTarlGrYfanRqsC1Lg4tW64g3bz9YPHHYNMzTlt5aGr82LwEJAuWgD1kaWr9aKXT/38nT+mdJ9aYES6ZNqtCk8lLfZBm4BIrnL99Sy7O1Zar9ujREtXKF1f50Upmp/d7cmLRrZHmvLEfXNy2UXTVzgKo41IJA2c8xVfIkRtdKICdVc3412aOyf5dGfv5c9mkUz6G9mkMOCzHXpMIwS7EmKkjbv/Z/S5mtWBpsj4pA7Tvo1wD0S3v9Cvjls1KhKpWq0mSJdbYMv6rxasvzQ///xHCEFKAABShAAQpQgAIUoAAFKOBMAgwCAxkEdvOaqvbT2750JH5KaFxNWLlxH1XFdXb3HLXc1q/AQFuaqwVOvYbMwLptB7B0al91wIXWzAWBWjglyxKb1SmHVCmSqAM0ZN9C6Zd/QaBUDVZo0Evt61atfGGz76YsMZVDJKS6SQ6W0EIZ7eLABIFyD22PxRGeLbF4zc4AB4HSPzmMYdeKMX5+x0zDPDmIQw7kkHBG9nAz18oWy6OW5doaBGrLSOXgkTlje6iDW0ybBLnnL9/84c9lya7s4WipafeXwFCCw+Ub9qD/qLkWwzVtvOYCOKmAk0o4rQLQUhCoVer5Ffb9kjSh4VAac2OQd1O+O/qDQEyvk4NJStbsgjzZ0mH2mO7qx46qCNS+17KcXL5Ppk3eB3kvpFkKAiXMlCBY/k8LbuUzjWqWQRePGs70vwXsCwUoQAEKUIACFKAABShAAQq4gACDwEAGgdpJsPPG9TQKPiTgyVuulTq1dvPCYRYDA9MgUKr75ETX3u3rGZ2Uay4IlBBPvwRYe2e1MNE0CNTvASfXytLVHKWaq1NyF07sbfGV15ZAntox02hvusAGgdq4ZHlyzBjRjJZPy152UuknQZ2EV/pmeliItrT12JZphtNxTQdkGubJUtYmnYarU2kllLLUbAkCJfSRPRalmk0OjNEOlrD17xQJksKH+zFAlPtoVZ4SSsly88P/nEeTzsPRsn5FtGlsfiza/ngScMlelfqmLf3WDsKwFARaY21prFdv3FP7GYrPjmWjECni9wN19E32EJS9BJvUKotOLaqrH0kQKEtuTcNo/WEh+opA0/dd7mHNHoGT567FpLlrMWtUN7U3oqVmKQjUf071+8i/GDh2gTpUxbSy1tZ3g9dTgAIUoAAFKEABClCAAhSgAAVsFWAQGMggUDtkQZbOyhJarW3fexwd+040Og3Y2opALdCRpYRS6RU1SiS8efte7dcnFVv6w0K0yqzDGycbDip5/fa9Ot1XTnrVgkDpV4bCDdUSXC2Y1Ppaq5WXunbqsE4omCez0Tske8pJNWCcWDGgLXOVyj2tGkr2GBw/a5UKbGw9NVg7TVkeqB2uIr/Wgi35tRZ2Sb83zh9qOIn2zIXrqNlygNEejHLCq5z0KqfQ6g9ikHscPnFBnRxsGua9ePUGv1Vsq54py7tlyafWZGx7Dp5E0d++n2hsbRAoy32rN++nls6aBsS2fkHbeY5HmhRJVQWZ6YnB2rtXKG8WTBna0bB0XK7buXwU3KNHNTxOgsnYMWOow2hkn0kZ55ZFw9XBH9IePnmOYtU6qT/3Xj5aVbBaCgJlzqct2GAU0mkPu//wKcTAv+WvWnhXu3IxdG9T2yjwPHryIhp1/H76sXZYjfxaTkSWk5Gl6lObK1le3qLbSFV9q+1vaOl9tyYIlKXXstehuWpOCfROn7tmWLrt1/dawr5zl26q907fZE69959Qe4f6Z2Tr+8LrKUABClCAAhSgAAUoQAEKUIAClgQYBOqCQAnekidN+IOXe4xohv3dTA8LkUMearceqII0qVr7PW8WFQBJNZM0/ZJha4NA+VzTLiNU5ZI0CcFkr0Gt6YNAObl1256jKrAoUiCbCoM27jioKo6k6YNA7Z6yN5ycPHv/4TN1qrGEFdVb9FPX16xYFBnTJseTZy9x/PQltRedFlhcunYHVZp4qusaVi+tAspdB04aljwGJgiU/has1FbdWx8Eqmf9L/yRU2izZkyF0+evqiBFmv4wlpev3qJEzS6qYkzGmDNLGjx++kIty0yeLKE6mMJcmCeHlcj+ePJcCdxkibecGrz30CkVLml7PFobBGr738n9ShTK4ef3r3HNskanPZu7UDuUQ+71e74sarzv3n/AoX/Oq3dO2upZXkiTMqn6tfZsqa6sXaW4OvTjyMkLav9AbcmvFuLJ+y7zLUGpHAwj1Zf6A00sBYFyGI6cPizzJkGkvHtynzMXr6tn9WpXF3WqFLf4t698T2q1HKDuIUvTJXCNHi0Kzl26gbVb/1af7dupgTrIRWva+OQ053Il8uPOvceQ5c5a0weBfr3v1gSBcr+2vcep91uW3cueh1GjRMbFq7ewdfdRZMuUynCAkF/fa+1EcVna/Hv+rEgYLxYuXLmtQnM5bERCYjnkhY0CFKAABShAAQpQgAIUoAAFKOAoAQaBEgT+b281v9D1BwOYBoHymVev36H/6Lnq5F+tSRAzsm8roz3+/AoMBo1bgMVrvLFz+Wgkih9b3eLz5y+YtXQzjpy4gK9ffVRwJ6fD1m832Gh/MamGksDi7KUbhmfLScMSCEoAdHD9JLjH+F4ZJsGELHfU+qkP3OTzQycsVqfk6pvcS6q1pCJQmunhGhJS/pY7szr5d0ivZqhQsoDFd1erAvtn2/QfloNqy6xNg0Dpd6ueYw2HU8jPO7WohtHTViBFskRYNq2v4ZkS4A2ZsEgFmFqT+WvTqLIKB7Vl1/rwTMJcCXdGTFmqwjCtyXNqVCxi2MvN3GfNDVYbh39f4lmjuyFvdsvLTv/59zJWb95nCMb095RKsw7NqqmDPbQmS4mXr9+jqvkkENVasYLZ4dmhvjqVWq6ZvnCj0QEzMta/OtY3OrhDW5q7bs4gs4GlvHujpi0zHIahPUuCrw7NqqrDMfxrUjkoe0RqwZ92vQR9nh0bIHumVEa3kABSljDr51cCx5t3Hqo/01fG+vW+axW3+qX35uZWls3PWbYFs5dsMbKUd16WVWvvul/fawmmB4yZZ/T3ggxG9nSUgDNpYp4i7N/7wZ9TgAIUoAAFKEABClCAAhSggH0FGATa0VNCijv3HyNOLPcA7wlnqTv7j5yBR/dR6NOhHmpVKma4VJawynMl+EmcIK4h+PPrXrJ0+PWbd0gQN5bRXn9yvYQf9x89Q+SIERAvbkyz+9NJSHnjzkP1nITxvgeXQd0kvLp97zGkfirpT/H93DdP64eM48GjZ6rCTEJMWepqTZNgSqoIY7lHt+lz1tw7MNdIWPni1VtVqSlLqhPFj/PD3OnvL9c/efYKHz99Qvy4sczuwScVfHIScfjw4SHBtSwbDkiTuRFreQ8ldDW3359/95XP3nv4BO8/fFJL0fXLxk0/K2N7+Pi5Wn4sh5L49zxL77t//ZKfy/MkWJfAX8Yn75QtTXwkYH73/qP6vH7Jti334bUUoAAFKEABClCAAhSgAAUoQIHACjAIDKxgEH1eKgSjR42sKrEkeLh28z4Gj1+oquK8V4x2WAAXRMPjbSlAAQpQgAIUoAAFKEABClCAAhSgAAUcLMAg0MHg1j5O2xvO9Ho5kEQOJmGjAAUoQAEKUIACFKAABShAAQpQgAIUoIAtAgwCbdFy4LVy8qocvCBLEn18fJEkcXxkTpcCcWO7O7AXfBQFKEABClCAAhSgAAUoQAEKUIACFKBAaBFgEBhaZpLjoAAFKEABClCAAhSgAAUoQAEKUIACFKCABQEGgXw9KEABClCAAhSgAAUoQAEKUIACFKAABSjgAgIMAl1gkjlEClCAAhSgAAUoQAEKUIACFKAABShAAQowCOQ7QAEKUIACFKAABShAAQpQgAIUoAAFKEABFxBgEOgCk8whUoACFKAABShAAQpQgAIUoAAFKEABClCAQSDfAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKuIAAg0AXmGQOkQIUoAAFKEABClCAAhSgAAUoQAEKUIACDAL5DlCAAhSgAAUoQAEKUIACFKAABShAAQpQwAUEGAS6wCRziBSgAAUoQAEKUIACFKAABShAAQpQgAIUYBDId4ACFKAABShAAQpQgAIUoAAFKEABClCAAi4gwCDQBSaZQ6QABShAAQpQgAIUoAAFKEABClCAAhSgAINAvgMUoAAFKEABClCAAhSgAAUoQAEKUIACFHABAQaBLjDJHCIFKEABClCAAhSgAAUoQAEKUIACFKAABRgE8h2gAAUoQAEKUIACFKAABShAAQpQgAIUoIALCDAIdIFJ5hApQAEKUIACFKAABShAAQpQgAIUoAAFKMAgkO8ABShAAQpQgAIUoAAFKEABClCAAhSgAAVcQIBBoAtMModIAQpQgAIUoAAFKEABClCAAhSgAAUoQAEGgXwHKEABClCAAhSgAAUoQAEKUIACFKAABSjgAgIMAl1gkjlEClCAAhSgAAUoQAEKUIACFKAABShAAQowCOQ7QAEKUIACFKAABShAAQpQgAIUoAAFKEABFxBgEOgCk8whUoACFKAABShAAQpQgAIUoAAFKEABClCAQSDfAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKuIAAg0AXmGQOkQIUoAAFKEABClCAAhSgAAUoQAEKUIACDAL5DlCAAhSgAAUoQAEKUIACFKAABShAAQpQwAUEGAS6wCRziBSgAAUoQAEKUIACFKAABShAAQpQgAIUYBDId4ACFKAABShAAQpQgAIUoAAFKEABClCAAi4gwCDQBSaZQ6QABShAAQpQgAIUoAAFKEABClCAAhSgAINAvgMUoAAFKEABClCAAhSgAAUoQAEKUIACFHABAQaBLjDJHCIFKEABClCAAhSgAAUoQAEKUIACFKAABRgE8h2gAAUoQAEKUIACFKAABShAAQpQgAIUoIALCDAIdIFJ5hApQAEKUIACFKAABShAAQpQgAIUoAAFKMAgkO8ABShAAQpQgAIUoAAFKEABClCAAhSgAAVcQIBBoAtMModIAQpQgAIUoAAFKEABClCAAhSgAAUoQAEGgXwHKEABClCAAhSgAAUoQAEKUIACFKAABSjgAgIMAl1gkjlEClCAAhSgAAUoQAEKUIACFKAABShAAQowCOQ7QAEKUIACFKAABShAAQpQgAIUoAAFKEABFxBgEOgCk8whUoACFKAABShAAQpQgAIUoAAFKEABClCAQSDfAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKuIAAg0AXmGQOkQIUoAAFKEABClCAAhSgAAUoQAEKUIACDAL5DlCAAhSgAAUoQAEKUIACFKAABShAAQpQwAUEGAS6wCRziBSgAAUoQAEKUIACFKAABShAAQpQgAIUYBDId4C5Q1VXAAAgAElEQVQCFKAABShAAQpQgAIUoAAFKEABClCAAi4gwCDQBSaZQ6QABShAAQpQgAIUoAAFKEABClCAAhSgAINAvgMUoAAFKEABClCAAhSgAAUoQAEKUIACFHABAQaBLjDJHCIFKEABClCAAhSgAAUoQAEKUIACFKAABRgE8h2gAAUoQAEKUIACFKAABShAAQpQgAIUoIALCDAIDOQk33/2IZB34McpQAEKUIACFKAABShAAQpQILgFEseJHNxd4PMpQAEKBLkAg8BAEjMIDCQgP04BClCAAhSgAAUoQAEKUMAJBBgEOsEksAsUoECQCzAItJJ4xqKNOHHmChLEjYXKZQsiS/qU6pMMAq0E5GUUoAAFKEABClCAAhSgAAWcWIBBoBNPDrtGAQrYTYBBoJWU0xduQKnCuXHzzkP0Hz0XO5aOQrhwYRkEWunHyyhAAQpQgAIUoAAFKEABCjizAINAZ54d9o0CFLCXAINAGyV9fHyRt1wrbFowFPHjxsSjFx9tvAMvpwAFKEABCoQsgW/4hjAIE7I6zd5SgAIUoAAFbBRIECuSjZ/g5RSgAAVCngCDQBvnbMXGPdjifQSzx3RXn/zq42vjHXg5BShAAQpQIGQJfP7qiwjhw4asTrO3FKAABShAARsFwofj/9bZSMbLKUCBECjAINCGSdssAeDSzZg+ogtix4yuPsk9Am0A5KUUoAAFKEABClCAAhSgAAWcVIBLg510YtgtClDArgIMAq3g/PbtG2Yu3oR//r2EEZ4tET1aFMOnGARaAchLKEABClCAAhSgAAUoQAEKOLkAg0AnnyB2jwIUsIsAg0ArGJ+/fIOCldoiSaJ46oAQaW0bV0GZonlYEWiFHy+hAAUoQAEKUIACFKAABSjg7AIMAp19htg/ClDAHgIMAgOpyIrAQALy4xSgAAUoQAEKUMBJBc6u+hrkPcv4Z/ggfwYfQAEKWCfAINA6J15FAQqEbAEGgYGcPwaBgQTkxylAAQpQgAIUoICTCjgiCHTE0Bk2OkKZzwgNAgwCQ8MscgwUoIB/Ak4RBMoefGHChPGvr075cwaBTjkt7BQFKEABClCAAhQItACDwEAT8gYUCFECDAJD1HSxsxSgQAAFHB4Efvnqgx17j+PClVu4cPUWTp+7hvcfPiJbxlRIlyoZ0qRMhtJFciNa1MgBHJJjP8Yg0LHefBoFKEABClCAAhRwlACDQEdJ8zkUcA4BBoHOMQ/sBQUoELQCDg0CT5+/hn4j5+Dy9bvInik18uXMgETxYyN8uHB4+OQ5zly8Du/9JxA7ZnT07dwQxQvmCNrR2+HuDALtgMhbUIACFKAABShAAScUYBDohJPCLlEgCAUYBAYhLm9NAQo4jYDDgsAZizZi7IyV+KNYXrRtUgVJE8c3i/Di1RvMWboFs5ZsVtcO9/RwGixzHWEQ6NTTw85RgAIUoAAFKECBAAuEliAwwAA2fpB7EdoIxsudToBBoNNNCTtEAQoEgYDDgsBO/SahdJE8KPl7TquGcfbSDfQbORcrZ/S36vrguohBYHDJ87kUoAAFKEABClAgaAUYBNrmyyDQNi9e7XwCDAKdb07YIwpQwP4CDgsCX756i5ju0WwaQUA+Y9MD7HAxg0A7IPIWFKAABShAAQpQwAYBBnQ2YIWySxk2hrIJdbLhBEUQuPfQafj4+CBfzoyIHCmC0YgPHT+HDx8/qW2zbP23spPROX135KyC/YdPW+xn3NjuyJw+pdOPxdoOPnryAh36TkTv9nWRMU1yix/7/PkLTpy5gsvX7+AbgNTJkyBbplSIFNH4nZWbyGGv5y7fxJXrd/H85RskiBsL2TOlQuKEcS0+w9f3G46evIBrt+7jy5eviBc3JlKnSIJUyZNYOyReZycBhwWBduqv092GQaDTTQk7RAEKUIACFKBAKBdgEBjKJ9jC8BgEuu7cO2LkQREEZijcUHXds2N91KxY1DCMG7cfoFz9nur3Cyb0UmEgW8AEjpy8gMYdh2HLouFI9pP5LchevXmH/OVbW3xAiUI5MXZAm4B1wgk/dfveI5Sp0x2zRnVD3hzp/ezhtZv30GXAFHWWQ4J4sdR1EiKm/DkxRvzVEmlSJjV89sHj5+g7YjYOHDuLKJEjqfMd7j54on5erVxh9Ovy/X03bR8+fkbD9kMgKz/lc5EiuqkQUVr1CkXQt1MDfPr8BdlLNsPgns1QsVQBq0UD+jmrHxAKLwyWIHDZul3qJTPX3NzCI1+ODMifKyPcwodzenIGgU4/RewgBShAAQpQgAKhTIBBYCibUBuGwyDQBixearNAUAaBErBsWzwC8u9daf1Hz8Py9bvVrxkE2jxVRh84/M95NOk8HFsWDUOynxL4eTOpCtTaig17MGjcAuxfOwHRo0VRfxwmDNRBpqGlWRMEiknFhr3w5u17TB7SEZnSpVDDP3fpJtr2GQe38OGxft5gRIzgBh8fX9Ru5YXrtx9gdL9WKJgns7r2/YePWLzGG1L9Ku+yuTZq6nLMXroZEwe3V58TZ3nmhh2HcPPOQ/RqVwcfP31GjlLNMbB7E1QuU9DqaQjo56x+QCi8MFiCwB6Dp2PD9oNIl+pnA6m8PLfuPlIJtKTPkhLPH9/T6Bpn9GcQ6Iyzwj5RgAIUoAAFKBBcAgzpgkveNZ7LINA15jm4RhlUQWDD6qUxd/lWQ6XT46cvUaRqBzSrUw5yqKY+CJR/E4+cshSHT1xQVVMSmnRpWVNVXkmbu2wrlm/YjSfPXqnfZ8mQEm0bV0GW/y1plX9Lj5u5EodPnMebtx9UNVeNCkVQvmR+SGA2cc4aTB/RWf17W9qFK7cwaNxCjPD0QKIEcXD6/DWMmLwU/bs2wmbvw+r3RQtkR+3KxdS/1/3qm1R8NesyAmWL5cXx0xex/8gZJIofG509akCW3EqfTp69inw506NJrT8M/ZU+yLXTFqzHybNXkCRRPFQs/ZuykcIg7b7lSuTD8dOXVNiU9tdkqFe1pDp/QMbbqONQ1TfJF2Qp66+//ORnZZr2bklx0oAx83Fo42TEiBYFZy5cx7BJS9CtVU2j5cFbdh1RIdekIR0gVZxiIyHVmi37VX9lyW2fDvUMAZp/4zH3bj988hw9Bk2HVOappbbxYqFCyQJo3aiyoTjKc/hsxIkVA76+vti485AK6GpVKqbmJUIEN3XbV6/fYfjkJdi+97j6fYY0v+DYqYsWKwLXbTuAXkNmYMKg9ihaIJtR92RePLqPQt/ODVG9fGFs9j6Crl5TMLRXc/U+mbb3Hz4hSuSIZr++NVr0x5t377F54TA/v96te43FnoOn1DsQL05Mdd2MkV1x6uwVjJq2XM2x5EaynLhRzTLKSJpfn5M5vnrzngoWtSbztnP/P5g0uIP6I3m/J81Zo95N+b5lTJsCHvUrGL2fwfX3UVA+N1iCQJkoSeq7t65lGJusM6/deiDy5UiPprXLoUW3UYgRPYphgoISITD3ZhAYGD1+lgIUoAAFKECB0CbAIDC0zahzjYdBoHPNR2jrTVAFgb3a1VWBxJET57Fh3hBMmL0aew+dgmfHBqjXdpAhCNQCQlkmLMHL81dvMHPRRhXoTB3WWXHLZ6UyK3WKpGrvwYWrdqgKrd0rxyBa1Mio22YQ7j96qsLBiBEi4Njpi3j4+DmmDO0ICbVkCejBDZPgHj2qup+Eaw3aD8GG+UOQIlki7D/yLzy6j1Y/k6Wh6VL/jCzpf0XxgjlUeOlX36S6K2+5VupzEhJJMLl++0H8e/6a+rOq5X5HmpTJsGLDbtV/qTKTpj1PPiPPkOtnLdmMzh7V0bhmWVU1pt33j2J51b51YichlYR4YQCMn7VKhXWtGlRE7FgxVGhaqnBui6+naRAoe9YVrdZRrU4c7umhPisZRcWGvfFzkgQqKNP6KiFqrUpFESZMGPVcaZq/f+Mx1ymp3Bs7YyXyZEun+n/lxj0VTnVoVlUFosqvWV8V2mbLmEoFoHfuP1bPlveiYJ5MkP33arUcoJbeinXOzGlUGLx2698Wg0CpipT7nN09R43HtOUq4wFxlyW/EpTOX7EN/3rPRrhwYW36+k9fuAHjZq5S76Us+5XQ2bSt2LhHHRirzfP396Ywdv39jwrGs2b4VQW9uw6cwMYdh7BgQm+1N6FfnxsyYZF6n/QH0E6dv169X8e2TMWLV2/wW8W2yJU1rZrPd+8/YvveY8iZJS2a1v7DpvGFtIuDJQiUL1iTWmVRp0oJIy/tv25ISrxq0z6MnbFCleraq8mXQ77M5l5a+dnjZy/Uf60wVw4sfwF99fFBLPfv/yVGawwC7TU7vA8FKEABClCAAqFBgEFgaJhF5x0Dg0DnnZvQ0LOgDAJ/y50JZet2R/8ujVSgIlVK8ePGVMGdVhEo1WbLN+zB3tVjDRV7S9ftgteY+di3ZryqCNOa/NtUDteUii8J95ZM9kSGNMmRuVhjVSXWu309w7VSVScHldgSBA7p1cxQcSU38q9vEdzCq8BOnivPlybVVrKUdIRnS5Qtlkf9mRaUea8YjYTxYqNy4z6q+mv6iC6G/nbqNwlXb9xTYaEWBOrvK1VzBSu1xeh+rVGqcC5V6WjN0mD9O2oaBMrPJCRSIe3qcSoX+Offy6jfbrAhSNP6vn7uIKT8/6pDaXLgS9MuI1R4KAGWf+Px73siYZQEVFIhGC1qJEMALEGgVMqN6d/aENhVaNALebKnU+Za34b1bgGpnpRmzdLg5l1H4smzl1gze6DZrkkln1Qcyjsq10qorIW4/o1F//Onz19hwJh58N5/Qv2xhKk5s6RB3T9LoECujOrP/FviK1nO6zfv8ezla5Sv3xNdPGqoykC/PicVn5aCQO39lGXO+uBY+77YMr6Qdm2wBIG1WnnB18cXy6b1NfKSv8Ck1FfSWUl1h09aqn5tjyYvTb9Rc9Wt5C9ffZNnyrOlzFSaVvoqv5Y/6z5wGnYdOKl+JqcITRjYTv3FII1BoD1mh/egAAUoQAEKUCC0CDAIDC0z6ZzjYBDonPMSWnoVlEFgnSrF0anfZGzbc1QFOpsWDlXLUfVBYMMOQ1Wwp99CS4IwOYxhxfR+SJ/6F1y8ehsjpy5TAZS+zRnTA7mzpTU8QyrH8mZPj9/zZTEsW7UlCNy5fLRa2qs1//qWNHF8FQTqQz+pWitdu5uhak3udf7yTVRr3k8FlzLOrCWaqgq+BPH+e5a2BPTcnrmGIFB/X7mPHMLStVVNyLJrewWBWkWmVoknGYH0d9OCoSp808I2vc3rt++Rr1wrtG/6JxrVKOPveMx9VyTUlSXism+hLHXWmlRfanvuSRAo+/fJoRpaa9ljjPqlVHvOXLwJY6avMAqMrQkC23mOx6Wrd7BtyQizX2MJG2UptgSdHf6aiH8vXMOuFd+fG5B25cZddXLw+cu3sO/wabUUWorEOrWo7megJ8HoyCnL1JJnLbORZ8vSaakCDWgQqFWBSh+KFcyuKg7LFMljtloxIGN15s8ESxAo67L7DJuFwvmzqv0GYsaIhlPnrqrNI6VUVNZkS4nqkRMXApQ2m4LLX7YDxy5QL5mUyeqDQEl7C1VuhzaNK0P+cpY16e09J6gvgvwFLV8o+UJK2an8VxT5siVPlghe3RqrxzAIdObXm32jAAUoQAEKUEAvwJCO70NIF2AQGNJn0Ln7H9RBoBaCaYchyB5z+iBQqq/Chgurwg3TliXDr2p1m5x8K8Up7RpXQYqfE+P123eo1KgPtCBQQqW1W/5Wy2dlOaUEJ7LMsWPzav9VBK6fBPcYlpcGmwaB/vVNFpWaBoH3Hz5FiZpdjIJACTL/bPqXCgKlqi53WQ912qwEMcYtjFryqlUEOiIIlOdL+CdLuJdN7av6LsGbnGorzVwQqJ1GLEGWnArt33jMfQNkafO0BRtUGCZ7QiaMHxuDxy/EvQdPLQaBbXuPw1cfXxUEytJiCRNP7Zxl2FfQmiBQ+9zRzVMRNcr3fSO1JllJztLNDQYTZ6/BlPnrcFD3/gTmGy0BnixDl4NJDm2chK9ffcweFiKFZHfvP0aPtnWQKW1yxI0dE6VqdUGtysX9DQJPnrlsVO2oXxosfZf5W7R6pwonJYSXJgeaFMlvvF9iYMbpjJ8NliBQIGRtuZRE65uU9Q7q0cxQtizlovJfMALbZNNK+QtSEnJZU64PAqUasFXPMTi5fYZhk00p2ZZQUJYuS/Iu/dLW5kuoKP8lR1tDzyAwsLPDz1OAAhSgAAUo4CgBBoGOkuZzgkqAQWBQyfK+IhDUQaA8Qw6ESJYkoQprTIPA3kNn4tA/57BpwTD1b2KtSQD4vSLt++ENCyf2VnvFSdPCHi0IlP33tK2wpOLJc8RsdVCn7OsmYYcsY9Wfrnv05EV12IbpHoGmQaB/fTMX2PkXBEqgKUt8c2VNp06h1TdtzNYEgZrj2jkDkSp5EqteZnNLg+WD2nJgOZDi7oOn2LNqrCEgMxcE7th3XFXKaeGRf+Mx1zkJWSWY1S+PlgM87tx/YnUQKCdQy0nUWuWo/t2YNaob8uZIb9ZFG68URrWsbxxAS6GWnPYry4bF48SZK2pPS8lKZN9LfZMA+tjJi8iXM4PZ50juIsvjTbdpEzsxlABTtijMUqwJ/upYHzUqFlX3efvuA/L80VIF2fp9+8RZCwLl2aafk8/KMm9x0W83N3neOsxZukWtPNV/V+R6OWylVqsBam9M2RMyNLdgCwIFVf5ikjLnDx8/qfJL0/337A0va8RlQ1V9ECh7MMxdtsXo9BpJ1n9JmkhtUCqbY8p/sZEwUJr2X3G0DVYZBNp7lng/ClCAAhSgAAWCSoBBYFDJ8r6OEmAQ6Chp13yOI4JAvaxpECiHQUghSqG8WdQqOTn8QyroJLiYObIrfL99U8GZHLYg1WePnr5QlWTyOQkC0/yaVB0Y0aZRFWRMmxzv3n9Qhy/4+PqqgOjT5y+q4qpS6d/UScKXr99Vp/nKyjn/gkD/+iYBj60VgRIEykEVshpQlofKgSGfP3/FqXNX1JZhEoxZEwR+/vwF2Uo2UwGVHC4h4ZEcImGp+RUEageEXLt1Xy07luXHWtOCQKnck2XXJ85cxpxlW1Sx0bq5g1W46994zPVJwjbZC1JO440bx10tmZXKNf+WBusrAuXk4WLVOqnl1s3rllfVo1IhKPNmKQiU/mhL1mW/vZKFcqrQ2fvvE+rzEr7JUmntIBE5vXj15n1qP8QqfxRC9KhRcO3WPSxatVMNzXT7N2288t7K9mq1qxRXB928f/9RHWYiKzDlXfbsWF9dKkH323cf0bt9XVWtJ/sI1vQYgHBhw6oTqCXPWbV5n6pu1ZYG+/U5bel9jza11ffh0D/n1SEsUnAmQaCsBl26zhsNqpXGL8kS4dbdh2jSabjad1D2HwzNLViDQFn7L2vEP3z4hCSJ46k17+YO6rDXBJgLAuXF27r7qNFJMlIOHC1KZPTt3AAZizTC5CEdDZWJ8l9wKjTsjZ3LRqnw8vX7L/bqHu9DAQpQgAIUcEoBOVArbNgfT5Jzys6yUxYFji/5RCEKhGiBnLUihuj+s/POLRAjipvdOyh72ekPutA/4L8g8Pvpp9IkbJJtraRgxhCi5MmEMf3bqipBOWBz0ty1hr3SJNSTk2Hnju2BjGlToG2fcUb7B8qS2/ZN/jQcbrFo9Q4VMkn4J4GIHOKxcuNebJw/RG2BZXqYh76/lvomAY1UbumX8D549AzFa3RWgZ52IMSla3dQpYknlk75S/37X6qypE8TZq8x2v9N2zdOqwgztzS4W+taaFCtlOrivBXb1AnLMi4JGGXpsaWmBYGHN05G9GhRjC7VDg3RV05qcyNLWRPEi2XYy0+eNax3cyT7KYG6h3/jMdenew+fqsNBJFiUJveUMxUiR46o5lWaVA2mT/OL0R6Bsr+fPG/S4A7qGqn87DF4uuER2rsxa3Q3FVz61b589cHMxRsxe8kWozmQcE5COn2T6rvl6/dgyry1ylpr0mcJTrUCKtNnzV2+Fas37YMErPomVYjN6pZDxAjfv3uy96Wc9qtdJ4HdybNXMWD0PMN3Qg5DkVOD9VWM5j4nh5xIEC5b00mTqsbECeNCqmDlvhKEd+k/2fAs2auy2G850K11TcNhPRZfohD8w2AJAqUSsO/IOVi37YARnRzLPXZAWzVBQdECWhE4qEdTdUS3NNOKwDcMAoNiqnhPClCAAhRwIgEGgU40GYHsyjEGgYEU5MeDWyAXg8DgnoJQ/fzoQRAEBhRMqqEkBIsX292whZV2L6nsk2W3CePHMVpCrP/546cvkCBurB8+K9dI8CMh3U8J4/6wVNOa/lrqmzWfN3eNVLDJybL/f8anOh3ZdAmpNfeVkEruIZVngSkwkpN/xVb23tM3w9LgZaMQOVJEhAkbBu7Rv++1aNoCMh6Zk7Bhw6qgMaBN9t3T5laCMFua9Pnhkxc4dPwspPJPQshcWdP6eQt5D169fovYMWOo6lVrmuw7+OTZC0SKGBHx4rgbKg1NPysHt0hAqy2Rl77dvPMQsWPF8NNc7mH6OfkzOdBFis/8cpWqUxmLfB+0ykdrxhKSrwmWIFDWZUtJpiS4kky7x4iGE/9eVoeFSJPjqAPzxfVrQswFgdoegad2zISbW3j10VK1uqJ+tZKGPQJLF8ltWI/OPQJD8uvOvlOAAhSgAAVcW4BLg117/kPD6Lk0ODTMovOOISiWBjvvaNkzcwLanon6KkbtOnN7BIZGRakyLFGzs6p6lD3/Hj95gdWzvYIkowmNfiFhTMESBOqPoNYjaV+s9XMHGUqX7YEoL7Kvry8GjlugTqLp17khwoULp5Y5yUEiucq0QPfWtdR6ddNTg2VdvJRKy6nBUSJHVKfa8NRge8wK70EBClCAAhQIGQKOCM8cFW44YiwhY1bZy5Aq4KjvSkj1Yb8DJ8AgMHB+oeHTcjLu2UvXMXlIpx+2RZGl3EMnLMakIR1U1WFobs9evFb7BMpy5dju0dX+fAGp0gzNRiF5bMESBErFXYWS+dXmjvom68AlJFwwoZfaGNNeTTtBR38/r26NUaVsIfVHuw6chGy0qbU+HeqhVqVi6rfv3n9UR4jLhp3SMqZJrk6QiR83pvo9Dwux1yzxPhSgAAUoQAHnFHBEeOaocMMRY3HOWWSvQouAo74rocWL47BNgEGgbV68mgIUCJkCwRIEygaW3vtPYOnUv5AiWSK1DvvFqzcYMn4RNnkfxtHNUw1HdDuKVaoG5aSd+HFiGpYI658ta8Zlb0PT5J9BoKNmiM+hAAUoQAEKBI+AI8IzR4UbjhhL8MwSn+oqAo76rriKJ8dpLMAgkG8EBSjgCgLBEgTK5pVy8u77Dx8hJ7NIuCYntkgzdzKNM08Eg0Bnnh32jQIUoAAFKBB4AYZngTfkHShgLwEGgfaS5H3MCTAI5HtBAQq4gkCwBIECKxV2smT3wpXb+PDxE+TE4PIl8iNDml9ClDuDwBA1XewsBShAAQpQwGYBBoE2k/EDFAgyAQaBQUbLGwNgEMjXgAIUcAWBYAsCQwsug8DQMpMcBwUoQAEKUMC8AINAvhkUcC0Bho2uNd/60TIIdN2558gp4EoCDgsCb997hDdvP1hlm/bXZCHmRBoGgVZNKS+iAAUoQAEKhFgBBoEhdurYcQoESIBBYIDYQsWHgjMI/PzhG14+8LXaMULkMIiZKKzV1/NCClCAApqAw4JAOZVXTue1ph3cMAnu0aNac2mwX8MgMNingB2gAAUoQAEKBKkAg8Ag5eXNKeB0AgwCnW5KHNah4AwCH131we6Z76wea4KU4VCkmfP9m/nG7Qd4/Owl8mRL5+9Y3n/4hAgRwiN8uHD+XhtcFzx78RoR3MIjerQohi68ev0OB4+fRZmieYKrW6HiuV+++sDHxweRIkawOJ6geE9OnLmCN2/f4/d8WSAHx376/AVRIkf011W2uDt47CxKF8mtDr0NTN9On7+G5y9fo0j+bP4+194XOCwIvH3vMd6+e29V/9OkZEWgVVC8iAIUoAAFKECBIBdgEBjkxHwABZxKgEGgU02HQzvjKkHg3QdPUKpWV2X797oJiOUeXf169LTlePHqLby6NQ6w+/wV27Dn4CnMHtPd4j0+fPyMnKWbY8Kg9ihawLogRAKbwn+2x/OXb+C9YjQSxosd4H5a+qCv7zdMW7geW7yP4Nqt++rSKJEjoXXDSmhYozTOXLiOmi0H4OzuOSoMcoamn1N9fwrlzYIpQzs6tIvSF3mXhnt6WAx5J85eA++//8Ga2QP97J/pe7L30Gnl36Zx5QCP6c79xyhduxtmjuyKfDkz4NDxc2jaZQQOrJuImO7RLN733KWbqN6iH057z8KXLz42v8P6m0tG9mfTvzC6XysUzJM5wOMJyAcdFgQGpHMh4TOsCAwJs8Q+UoACFKAABQIuwCAw4Hb8JAVCogCDwJA4a/bps6sFgbFjRkflMgXRqUV1BThq6nK8fO2YIFDCtotXbyFJ4viIoau2szSTR09eRKOOQyH9blL7DzSsXto+E29yl6XrdsFrzHxMHdYZ2TOlUiZSAXj+8i307dTAqYNACbcSxv8vIJUAM0G8WEHi5NdNL1y5harN+uLUjplwcwvv57MfP32JN2/fIeUvP/l5jel7smj1TmzdfRQLJvQK8Jg8uo9C6hRJDe/923cfcOvuI6T5Nam/1an6IDBsmLA2v8Omnd625yiGTVqCjfOHWlWRGOBBm3zQYUGgnBBcrkQ+laRb0yTtX7Bqe5B9ua3pgzXXMAi0RonXUIACFKAABUKuAIPAkDt37DkFAiLAIDAgaqHjM64WBA7r3QLdB03D7pVjET9uTKMg8OylGxg2cYlR4CIBSrM65ZEjc2pIpdbkuWuxfe8xvP/wEbmypkXPtnWwZdcRo4rA46cvYcTkpbh++wFKFMqBWpWLI1Pa5OqFqdtmEHq3r4t0qX626vXknf8AACAASURBVAXqO3KOWsL5c5IE2L7nmFEl2dCJi5HspwR49eYtDh47h1qViqFIgWwYN3MlNu08pKoea1Qsgiplf0fkSBEg/Roweh4ePH6unl0kf1b07lBPbVHWzWuqCv+mj+hitl9aRWDXVjWxZI23uqZJrbKoXqGI+rWle2/YfhC7D55C1CiRVKAloWafDvUMFWH3Hz7FkAmLcPjEBWTJkBLVyhVGqcK5/PXRKgK3Lh6OpInj/3D9t2/fsGrTPixavQN3HzyFnMvQqUU1ZMuYCifOXMaY6Stx8eptJEkUF/WqlkSVsoVgqS+1Wnkps+17j6sQrWbFomjVsJKylRBQwkCZ13Bhw6JX+7q4ffcRTp27qsa0ccchpEqeBOnT/IJ//r2MvzrWV/31qx/aeyJZUt02A1VFaMY039+hEr/nVM/XV7FOnrcOnz59Rsfm1X5wkPe6Rov+0G9HJ1WfvQbPwOLJnuqsCktj0weBsqRd/w7Ld8Kv903m3XT88v5IK1u3OxrXLIuq5X73d57tdYHDgkDZI1C+ZIN6NEWalEkt9v/hk+cqgZeXZ9eKMfYaa5Dch0FgkLDyphSgAAUoQAGnEWAQ6DRTwY5QwCECDAIdwuyUD3G1IFDCkNY9xyJdqmTo3b6eURB45OQFNO44DOf2zDXMVcFKbeHVrQkK588Kz+GzceDYGbRtXEUFcxIySRh08uwVQxAoSx/L1OmGzh7VVdC1bfcxrN6yD97LR6sltRkKN8T88b1UsOhfk5ClUOV2ahllkkTxUK5+TxUEpk6RRH20ZY8x2Hf4NEoVzq3CpkxpU2D9tgMqU+jYopp6Xv9Rc9GyfkWUL5kfEghduX5XhVUfPn5C3xFz1LikOnKz9xF09ZqiArHC+bIidcqkKrDTmhYEypJmCf/u3H+CQeMWGMIlS/eeu2wrRkxZCo/6FZA5XUos37Ab/56/hv1rJ0D2zKvYsBeyZvhVPfvG7YeqH9uXjsRPCeNaJNKCwNqVi8E9+n/LW5MliY8KJQtAgqgeg6ejXZM/kS9Hehw8fg4xokfFb7kzokyd7ir4q1K2IG7eeagCqz4d6lvsi8xdyp8Tw6N+RVXJ1tVrqmGJ65ot+9Fn2Cy19DZ8+HDKb83m/WrcmdOnRPGCOZAofhw8ff5S9648MtuP/l0aGd4Tmasx05fjyIkL8PxfeOjm5oY6rb2wZdEwFQS/e/8Ruct6YOqwTmaX20rfdv19Qi1J15ppuGdpbOau1d7hfiPn+vm+afOuH3/ZYt/3mJy2YIOykO+go5rDgsAHj55h8PiF6sAQ+eKVL5Ef2TL+aqgQ/PLlKy5eu4PN3och+wpIwtuvS0Or/+uAo8BMn8MgMLjk+VwKUIACFKCAYwQYBDrGmU+hgLMIMAh0lplwfD9cMQiUMKxB+yGQSrLl6/cYlgZbCgLzZE+v9kYb2L2JWlqsb/o9AqVicOPOQxjVt5W65OtXH7W33qqZA1RFmi1B4I59x9FryEwcXD9RLTeVqrPfcmdCh2ZVDUGgFBxpv9f2lpNwRXIHaas378Ojpy8w3qud+v2TZy8hh0Y8fvpCVbbFiB4FkwZ3wFcfHxWcTZ2/HhKwSSuQKyOkgksq2cztESgh6YBujQ0HP/h1bwmE/j52RoVk0mR5bJGqHbB54TA8ePwMTToNx7xxPVXFoDQJlyqW/g0S8FlqWhBYrGB2RI/638EmqVIkUasspXItSeJ4GNqrudFtZJ++Zet3Yd+a8Ub7HR4+cd5iX2TuFk7srSoKpUlladxY7srI3NJgGfe2vcewaGIfhA37fV9F/bviVz/kOv17Ym5psP5dkEB60tw12LF0lKruM22yDDdalEho3ei/PQbNhXt+jc2vIDB96l/Ud8Kv983c+LW+SUY2e8lm5emo5rAg0DDIv09g5NRlqnxTmpR3Roropso7pUnSLiWl1coX9nd9tqOQLD2HQaAzzAL7QAEKUIACFAg6AQaBQWfLO1PAGQUYBDrjrDimT64YBMpS2OZdRyJenJiIHTOGVUGgVABKRd7G+UOQPFkio8nRhztSgea9/8QPKwJbNqiogjVbgsB2nuNx8swVFC+UUz3vyInz6tTXPavGqcBHKgJlP79mdcqpn8vpxdJHqSLTn0orS6BH92utljB3GTAF2TOlVhWRl6/fVbmE7AuobxLonbl4AxNmrUIENzcsm9bXbBAoyzvbNKoCqfKydG/TIFCelauMBwZ2b6yq2aTSUgvXtH7IEmdZemyp+bc0WJ7Ro01t/PlHIaPbSIAnTZaJ65uEppb6YhoESkXkVx9ftYeiX0GgPgCVZ+nfFb/6Idf5FwRKld/g8YvUwTc1PfqjUpmCaFCtlFmu1r3GqgBZlo5rzb8gUD82v4JAybEsvW/m5l17vlTRtuszXlWFOqo5PAjUBiYv+bWb93D15j21zl+S9ZS/JDacWOQogMA+h0FgYAX5eQpQgAIUoIBzCzAIdO75Ye8oEFIFGDg638y5ahAoQZcEKHKCqizZlP3WZO+2+u0Gm10anC1TKuQv3xrjvNqqZZ76pg935PCRm3ceGC3D1F9rbRD48tVbFKjYRi09jhMrhrqFVO3Jksq5Y3uo/QlNg8BXb96pPq6Y3g9SrWXaKjTohdJF86BVg4rqR7OXbsbRkxdUEPj+w6cfDm5Yv/0Aeg6eoQ7AkL30TE8N1geBlu5tGgjde/gUJWt2UeOQ50o4eWjjJJuLovwLAis37gOp5JQwUN+kSGvfodNYP2+w0Z/L6byW+mIpCBQfOQ33xPYZiBjBTd3XXBCmf1f86od8Vv+eLF7jrVaR6qvnxO33Ku1RqXQByM8tnf4re0nKkmht3uX+9ggCf03+k8X3zVIQKKdsT1u4AUsmezrsL8VgCwIdNsIgfhCDwCAG5u0pQAEKUIACwSzAIDCYJ4CPp0AoFWAQ6HwT66pBoMyEVNxJ9Z7sFSdBoDoApIyHWiore+5t2XVU7YMnv5e99GSpqey7J4d9/JI0ITbtPIysGVLiwLGzhn3f5PCHem0Hq+WoZYrlwavX7yBLfHNmTgMJTqwNAldu3KsOYdCq/7Q3p2GHoaoiUarQTINAuUb2OJR994Z7eiBubHdcunZbBZxSLSb9l2WznZpXU8t/ZQlurJjRVBDYtMsIZE6XAn8Uy4tECeLi6o27GDRuISJEcFOHp5hbGqwPAi3dWwKhddv+xrThXfDp82dMmrsWB46ewfalo/D5yxcUr95ZLbfu0OxPNcxjpy7hy9evPwSupt8eLQiUA04SJ4hj+HHkyBGRMF5sTJqzBnIa8uCezVTge/zUJbx9/wHRokRGk87D1YEd5UsWUMuTDx47iwqlCljsi6UgUFuWPXtMd7UPohxUIofHWqoIPPzPebP9kL0S9e+JvFMtuo1WewJKJWjMGNHUeyhLfiVYlAM3ZF9Bv5pUOv599IyqCtWaPYJA2efS0vtmKQicuXgTZCs9bd9DR/zNyCDQSuUla73VceFaq1WpqPovCwwCrQTkZRSgAAUoQIEQKsAgMIROHLtNAScXYBDofBPkakHgoY2TESPa9/3krty4i0qN+hiCQPkz2eNPgippEv5J5dLkIR3xe74skINAeg2ZoQ4HkSYHeMwY2RV7D53CbtnzbEx39ecSvAyZsFgFi9JkWbEc5CAHO0jAI8GaLM+11CRMlH3+5CAPfZOAUAKgA+smoP1fE9WhI01r/2G45NGTF+g3aq46RERrLeqVVwdmSGDZY9A0tUWZbFcm+wtGjxYFU4Z2VCfrzlqyGfJ5rcn4/+rYAAnixVJLhaWC8uzuOYZ99SQIlINTyhTNY/He2qER2n3FbYSnhzpEQ5p49h4602grNQlSZe8/S00LAk2vKZQ3ixqThHMDx87H2q1/q0tkzMP6tIAceDJ3+VZ1srPW5CATGYulvpgLAn18vxlOAJY9/6bMX6duKfshXrp+RwWM+pOYF6zcbvSu+NUP/XsilaBteo3F/iNn1L2Pb52uTio+ff4aarfy8rMCVBubFuLq3/3zl2+iWvN+OO09S1ViWhqbuWu1d9jS+yZjMx2/1iepIK1btSSqly9scY7t+UMGgVZqduo3CQVyZVKbmkqTjTZlPwUGgVYC8jIKUIACFKBACBVgEBhCJ47dpoCTCzAIdL4JcpUg0BZ52dJLDvlwjxHV7MfevvuAz1++Gp2qa3qhVIQ9e/FaHfIh/4Y2bbLXn6/vN/PdCgOzn7FlDB8/fVbViHFixzBaciuhklRiJYwfB27hw5nt1+u371U1obbE1drn+nVvrTJsypCOePPug59usrRZDlSVpdBS8WYvo8+fv+ClWMSKYXSYho+Pr5qjmDGiqspHfTPti7UGEj5KlaO5OffrHpb6Ydon2bNRQkBpk+etw/4j//q7vFbexWZdR6rDabWDZawdj7XX+fW+mfu8HFLjNWYeti4eYTggxtrnBOY6BoFW6kkQWL18EeTIksboLwkGgVYC8jIKUIACFKBACBVgEBhCJ47dpoCTCzAIdL4JCs4g8MU9X5zc9MFqlFg/hUO2P76fLBvSmyzLvfvw++m8ps0tfHhV0RZamqUlopbG6EpGts61BI6FKrdTS4LlsBb/mhxcKxWc2v6S/l0fVD+X/SGlslT6XfQ3yxWf9u4Dg0ArReUUGjn9Ro4Wz5I+pToqXZLyL199rbwDL6MABShAAQqETIGvvt8QPmwYp+z8gQXW/6PJKQfATlGAAi4rUKBeZJcdu7MO3C18WGftGvsVSgTkdGJZQlowT6ZQMqLgH4ac7Cz7/smejqbVjH71TvYkfPPuPUr87xTq4BjF8dOXIH2X5eSObsESBGplwdp+BNqgpYRSfiabW0r5qzM2KSVt0mk4mtT+Qx15/uTVJ2fsJvtEAQpQgAIUcAmBU8u/uMQ4OUgKUCD0CWStbrz8LvSNMOSNKJ57xJDXafaYAhSggI0CwRIEtu09DunT/IKW9b8f1a21qzfuoWKj3ti5bBQS6U66sXFMQXL546cvET9uTMia9Qbth6j15DmzpOEegUGizZtSgAIUoAAFrBPgsl3rnHgVBSjgfAJcGux8cxKcS4OdT4M9ogAFQquAUwWBslFn8RqdsXqWlzq1x5laqVpd1dHUUj6aK2ta9OvcUFUtco9AZ5ol9oUCFKAABVxNgEGgq804x0uB0CPAIND55pJBoPPNCXtEAQrYX8ChQeCydbvw/sMnrNi4B4kTxkWBnBkNI/ry9Su895/A/UdPsWfVOKMTbOw/bNvvKEuCX75+q04LkqO2tcYg0HZLfoICFKAABShgLwEGgfaS5H0oQAFHCzAIdLS4/89jEOi/Ea+gAAVCvoBDg0Cpqrv7wPxpQBKuFcmfFVX+KIS82dOHGFkGgSFmqthRClCAAhQIhQIMAkPhpHJIFHARAQaBzjfRDAKdb07YIwpQwP4CDg0Cte4Pn7QEyX9OhGrlCtt/RA6+I4NAB4PzcRSgAAUoEGgBR4RnjvoHriPGEmhw3oACFKCAGQFH/T1JfOsFGARab8UrKUCBkCsQLEFgyOX6secMAkPTbHIsFKAABVxDwBHhmaP+geuIsbjGW8FRUoACjhZw1N+Tjh5XSH4eg8CQPHvsOwUoYK1AsASBHz99xt5Dp7D74CncuPXgh77OGt0N0aJGtnYMwXodg8Bg5efDKUABClAgAAIMzwKAxo9QgAIUsLMAg0A7g9rhdgwC7YDIW1CAAk4vECxB4JylWzBy6jJkz5QayX6KD7fw4Y2gurepjciRIjg9nnSQQWCImCZ2kgIUoECgBRwRnjnqH4WOGEugwXkDClCAAqFcwFF/54dyRrsOj0GgXTl5MwpQwEkFgiUIlENDcmdLB69ujZ2UxfpuMQi03opXUoACFAjJAo4Izxz1j0JHjCUkzzX7TgEKUMARAo76O98RYwktz2AQGFpmkuOgAAUsCQRLEFirlRfyZEuHDs2qhvjZYRAY4qeQA6AABShglYAjwjNH/aPQEWOxCpUXUYACFHBhAUf9ne/CxDYPnUGgzWT8AAUoEAIFgiUIXLzGG/OWb8X6eYMRMYJbCGT7r8sMAkP09LHzFKAABawWcER45qh/FDpiLFbD8kIKUIACLirgqL/zXZQ3QMNmEBggNn6IAhQIYQLBEgROmb8OE2evQeb0KREvjvsPZEN7NUeUyJFCBCWDwBAxTewkBShAgUALMDwLNCFvQAEKUIACOgEGgc73OjAIdL45YY8oQAH7CwRbEPjv+et+jmZU35YMAu0/17wjBShAAQoEQoBBYCDw+FEKUIACFPhBgEGg870UDAKdb07YIwpQwP4CwRIE2n8YwXdHVgQGnz2fTAEKUMCRAgwCHanNZ1GAAhQI/QIMAp1vjhkEOt+csEcUoID9BRgEBtKUQWAgAflxClCAAiFEgEFgCJkodpMCFKAABQwCDBttexmCMwj8+P4LHt55icjhYyJMmDAWO/7Z5z3CR/JFwqQxbRsgr6YABSgAIFiCwElz1uDkuat+TsC4AW0RNQr3COQbSgEKUIACziPAINB55oI9oQAFKEAB6wQYBFrnpF0VnEHgjQuPMWvEDmSMX97fTj98ex7uv7xDoy7F/L3W0RfcuP0Aj5+9RJ5s6fx99PsPnxAhQniEDxfO32uD64JnL14jglt4RI8WxdCFV6/f4eDxsyhTNE9wdStUPPfLVx/4+PggUsQIFscTFO/JiTNX8Obte/yeL4vdLfXfAR8fX3z6/AVRIkcM0HN2/X0CcWO7q/M17NmCJQics3QLzly88cM4tu05ipQ/J8bSqX0DDGVPHGvuxYpAa5R4DQUoQIGQL8AgMOTPIUdAAQpQwNUEGATaNuPBGQQ+vPoZe2Z+xPknW5A2bkmEDfNjOPYN3/Dk3RWEC+uG9JlSoUjT/8IpW0Z698ETlKrVVX3k73UTEMs9uvr16GnL8eLVW3h1a2zL7Yyunb9iG/YcPIXZY7pbvMeHj5+Rs3RzTBjUHkULZLPqeRKqFP6zPZ6/fAPvFaORMF5sqz5n60W+vt8wbeF6bPE+gmu37quPy2GmrRtWQsMapXHmwnXUbDkAZ3fP8bd609ZnB/R6/Zzq71EobxZMGdoxoLcN0OekL/IuDff0sBjyygGy3n//gzWzB/r5HNP3ZO+h08q/TePKAeqbfOjO/ccoXbsbZo7sinw5MwT4PvJBc2PVfwcOHT+Hpl1G4MC6iYjpHs3mZ+0+eBLdvKZh04KhiB/XfhXAwRIE+jX6yXPXYteBk1g+rR/ChrVcDm2zYBB9gEFgEMHythSgAAWcTIBBoJNNCLtDAQpQgAL+CjAI9JfI6ILgDAIfXf2K3TPfq/5cebYbyWPlR/iw/1UR+X7zwYsPtyBhYNwoKZHg1/CBDgJjx4yOymUKolOL6uq5o6Yux8vXjgkCJWy7ePUWkiSOjxi6ajtLM3b05EU06jgU0u8mtf9Aw+qlbZtgK69eum4XvMbMx9RhnZE9UyplIhWA5y/fQt9ODZw6CJRwK2H8/wJSCTATxItl5cjtc9mFK7dQtVlfnNoxE25u4f286eOnL/Hm7Tuk/OUnP68xfU8Wrd6JrbuPYsGEXgHurEf3UUidIqnhvQ/wjQCYG6s+CHz77gNu3X2ENL8mDXDlq7yLEtCP7tcqMF01+qxTBYFXbtxFpUZ9sHH+ECRPlshugwzKGzEIDEpd3psCFKCA8wgwCHSeuWBPKEABClDAOgEGgdY5aVc5SxDo++0rbr08ip9iZEWEcFEgv3/58S58vn1BvCipVHftEQQO690C3QdNw+6VY1W1kT4IPHvpBoZNXGIUuEiA0qxOeeTInBpSqSWFPNv3HsP7Dx+RK2ta9GxbB1t2HTGqCDx++hJGTF6K67cfoEShHKhVuTgypU2uxlC3zSD0bl8X6VL9bNVE9R05Ry2z/DlJAmzfc8yokmzoxMVI9lMCvHrzFgePnUOtSsVQpEA2jJu5Ept2HlJVjzUqFkGVsr8jcqQIkH4NGD0PDx4/V88ukj8reneoB/foUdHNa6oK/6aP6GK2X1pFYNdWNbFkjbe6pkmtsqheoYj6taV7b9h+ELsPnlJboUmgJaFmnw71UDBPZvXZ+w+fYsiERTh84gKyZEiJauUKo1ThXP76aBWBWxcPR9LE8X+4/tu3b1i1aR8Wrd6Buw+eIu2vydCpRTVky5gKJ85cxpjpK3Hx6m0kSRQX9aqWRJWyhSz2pVYrL2W2fe9xFXTVrFgUrRpWUrYSAkpAJvMaLmxY9GpfF7fvPsKpc1fVmDbuOIRUyZMgfZpf8M+/l/FXx/qqv371Q3tPJNSs22agqgjNmOb7O1Ti95zq+foq1snz1uHTp8/o2LzaDw7yXtdo0R8HN0xSc601qbwbM22FqgDNnik1PDvWR+oUSSDLl+u2HqiqG+W9kybvvSwVFydzYz197qrhOyD36zV4BhZP9kS4cGEtmpp7h8sWywMJTItU7YAN84cghZ1yMqcKAg+fOI8mnYYzCPT3a84LKEABClDA0QIMAh0tzudRgAIUoEBgBRgE2iboLEGg9Pqr7yfcf3NGVf99+voGX3w/In7U1IYB2SMIlDCkdc+xSJcqGXq3r2cUBB45eQGNOw7DuT1zDc8sWKktvLo1QeH8WeE5fDYOHDuDto2rqIBEQiYJg06evWIIQW7fe4wydbqhs0d1FXRt230Mq7fsg/fy0WpJbYbCDTF/fC8VLPrXJHgsVLmdqopKkigeytXvqYJACWuktewxBvsOn0apwrlV2JQpbQqs33ZABVIdW1RTz+s/ai5a1q+I8iXzQwKhK9fvqrDqw8dP6DtijhqXVEdu9j6Crl5TVNBTOF9WpE6ZVAV2WtOCQFnSLOHfnftPMGjcAkO4ZOnec5dtxYgpS+FRvwIyp0uJ5Rt249/z17B/7QQVOlVs2AtZM/yqnn3j9kPVj+1LR+KnhHEtEmlBYO3KxeAe/b8lqMmSxEeFkgUgAWSPwdPRrsmfyJcjPQ4eP4cY0aPit9wZUaZOdxX8VSlbEDfvPFSBXZ8O9S32ReZOtnXzqF9RbevW1WuqmhuZ5zVb9qPPsFlq6W348OGU35rN+9W4Za+74gVzIFH8OHj6/KXuXXlkth/9uzQyvCcyV2OmL8eRExdUUCfNzc0NdVp7YcuiYSoIfvf+I3KX9cDUYZ0M4aoeTvom++7JknStXb1xDxUb9UazOuVQKG9mLFy1A8dOXcS2JSMRPlxYZCvZDKtmDlDhqbReQ2YgdqwY6OJRw+xY12392zCuc5duonqLfjjtPQvfvsGiqbl3WCpSpcl3sUbFolaFwv59l+TnwRIESgotZbVak3T61Zt3CktejCWTPa3pu1Ncw4pAp5gGdoICFKBAkAswCAxyYj6AAhSgAAXsLMAg0DZQZwoCpecfv77G1ed7ET1iQvzsblwVZq8gUMKwBu2HQCrJlq/fY1gabCkIzJM9vdrfb2D3Jmppsb7pl0VK5dTGnYcwqu/3JY1fv/qovfW0UMWWIHDHvuPoNWQmDq6fqJabSiXWb7kzoUOzqureEqKkSZnU8HttbzkJOLNl/FVds3rzPjx6+gLjvdqp3z959hJyaMTjpy9UZVuM6FEwaXAHfPXxUcHZ1Pnr1R5w0grkygipAJRKNnN7BEpIOqBbYxTJ/32/Q7/uLUHg38fOqJBMmlbttXnhMDx4/EwVRs0b19NweGq/kXNRsfRvkIDPUtOCwGIFsyN61P/2jkyVIolaQi1VdUkSx8PQXs2NbiP79C1bvwv71ow32u9QK9Lyqy8ydwsn9lYVhdKksjRuLHdlZG65rIx7295jWDSxj2EbOP274lc/5N7698Tc0mD9uyCB9KS5a7Bj6ShVgWfahk1agmhRIqF1o//2GBw/axU27TyMbUtGqMvlgBgJnScObo8COTNaDAL9WxqsDwKlUtTS/Jq+w/q+D5+0RL0T+n7b9reb8dXBEgSOmb5ClYDqm5RWyoktRQtkt+smiIHBseazDAKtUeI1FKAABUK+AIPAkD+HHAEFKEABVxNgEGjbjDtTECh7Aspy4Hdfnqm9At0jJkYUt//2erNXECjLI5t3HYl4cWIidswYVgWBUgEoFXnmtvTShztSgea9/4QK6PStZYOKKlizJQhs5zkeJ89cQfFCOdWtjpw4r0593bNqnAp8JESR6imp6pImJ7dKH6WKTH8qrSyBHt2vtVrC3GXAFLUMVCoiL1+/i0gR3dS+gPomgZ4cdDph1ipEcHPDsml9zQaBZet2R5tGVSBLOS3d2zQIlGflKuOBgd0bq2o2qbTUwjWtH7LEWZYeW2r+LQ2WZ/RoUxt//lHI6DYS4EmTZeL6JqGppb6YBoFSEfnVx1ftoehXEKgPQOVZ+nfFr37Idf4FgVLlN3j8InXwTU2P/qhUpiAaVCtllqt1r7EqQJal41qT91SaPiQtWq2jepf+LFvIbkGgVKhaMjV9h/UDmLl4E67evPdDkGvb33D/XR0sQWBAO+uMn2MQ6Iyzwj5RgAIUsL8Ag0D7m/KOFKAABSgQtAIMAm3zdZYg8Ns3Xzz/eAu+vl8RL2oqVRn46uN9RI8YH1Hcvh8EYc8gUIIuCVDkBFVZsin7rUnhTv12g80uDc6WKRXyl2+NcV5t1TJPfdOHO7Ln4M07D4yWYeqvtTYIfPnqLQpUbKOWHseJFUPdQqr2pi3YgLlje6j9CU1DFFlxKH1cMb0f0qf+5YcXoUKDXihdNA9aNaiofjZ76WYcPXlBBYHvP3xSy131bf32A+g5eIY6AEP20jM9NVgfBFq6t2kQeO/hU5Ss2UWNQ54r4eShjZNsPljCvyCwcuM+kEpOCQP1beTUZdh36DTWzxts9OdyOq+lvlgKAsXnz6Z/4cT2GYgYwU3d11wAqn9X/OqHfFb/nixe443N3odVNaLWxO33Ku1RqXQByM8tndAr+/DJkmht3uUesoelHAajnV6sLS+Wpc5Ff8uBYdEGRwAAIABJREFUrMWbYOmUv5ApXQr1SP3SYHNj1Y9LXxF44P/au+/wqKr8j+OflEmDkAChI6Ko2NuKBdafiiusClhRsWCliCKCBVdlxVVQFwUrFlBZCxYsKKjYUBexK3YXEOnSSSBlUmYyv+ecMCGBJMxkJjNzJ+/7PPvHmnvP/Z7XuSB+OOWrn+s0rSsIND7G0izFD8cV1SDQbOpoDghxu0vsNFUDm5y08zHp4ehoqG1MeX62nTbcJqe5zjjlWB2yfxfbJEFgqLI8jwACCDhDgCDQGeNElQgggAAC2wUIAoP7GmIlCNxQ9LsSJOVkVCxpNZfbs0X5JWvVJCVHTVwtwxoEmvbNjDsze8/sFWeCQHsAyMlD7VJZs+feO3O/svvgmf9v9tIzS03NvnvmsI/Ou7W1SysPPaCL5n/9c+X+aObwh4uGj7ezmE4+8Sht2Voos8T3iIO7aq89OgQ8I/CV2Z/YQz/8s//8Jpdce7c9ZNTMQqspRDH7qpl998xBDzktsrRwyQobcJrZYqZ+s2x21OD+dvmvWYLbPLupDQKvuH6CDt5vT5164tFq1yZHvy9dpXEPPKeUFJc9PKWmpcFVg8C62jaB2BvvfqrH/329SkpL9ci0mZr/1U9678X7VFpWpr+dc51dbn3toLNsN7/+fqHKPJ6dAtcdv2x/EGgOOGnfpmXlj9PTU9W2VQs98vTrMqchj//HIBv4fvP9QhUUudU0I12XX/dve2BH31497PLkz77+Wf1696izlrqCQP+y7Kcmjbb7IJqt4F5+86NqS6JNgVUDsy++/bXGOsxeiVWDQPNNDblxot0T0MwEzW7W1H6HZsmvae/sPsfJ7CtY22VmOn761U92Vqj/+vybX+yYm+Cv+xEH2nbMgSMfv3q/nSlrvmGzj+VlA06xs1JvvWeqXa5t9gisqa+vzP64xj0CTcBY1/jWFQSaWbtmNqfZAzMcV1SCwLIyj8yJP2+8O79aH8wU4/v/Nbxyw89wdDBcbTzx3CyLbjbPvH3itMo15wSB4RKmHQQQQCC2BQgCY3t8qA4BBBBAYGcBgsDgvopoBoFrfy/Tx1PdWlfwm10K3DKjYvZR1asiDFxnw8A992utE67YvhdcMD31h0afz56sZk0r2jATdE6/9NbKIND8M7PHnwmqzGXCP7On/+S7RtotvcxBIGZmlDkcxFzmAI8p996gTz7/Xh/NXyATApnLBC93PTTdBovmMv/Nbw5yMAc7mIDHBGtmeW5dlwlizD5/5iCPqpcJCE0ANP+NhzTinw/bsOaK80+tvGXdhlyNvW+aPUTEfw25qK89MMMEljeNe9yeQGtOozXLl812ZY/ePdKerPvkC2/LPO+/TP//OfJitWnV3C4VNjMof/7o6cp99UwQaGZrndzzqDrb9h8W4m/XuE0YM9SelWAu43nL3VPtSbjmMrWZINXs/VfX5R/THe/5v6MPsX0ygdWd9z+jmXM+rWz3nluHyBx4Mu3lOXZWnP8yB5mYvtRVS01BoLfcV3kCsNnz79Fn3rBNmv0QF/6x0gaMVU9ifvaV96p9K7XVUfU7MTNBr775fs378ifb9jdznrAnFf/w6xKdP+yOWmeA+vvmD3GrfvvmZ6ZWU3NN5uZwEZNfmW/FfL9mZt5fjzzYHoJjrh37apbw+n8N/LpomfoPrjgsxEx6q8vUBIE7fsOmfbM8/fizrtWb08apS+cOdX4Hgf4wKkGgSVdNIn31ZWfo6MP3V1azpvrux0V2Oq65zLTUWJ0Z6PWW6+g+w/TWs3fbvQwJAgP91LgPAQQQcLYAQaCzx4/qEUAAgcYoQBAY3KhHMwhcsXCLXnnwJ6UlZ6lF+u61Fu6fGdh+73Sdfc2BwXWwHnebWUzmkI+sZk1qfLqg0K3SMk+1U3V3vNHMCDMHMJhDPsyehDteZq+/8nJfzdUlqMZngulKcUmpnY3YskWzajmDCZXWrNuktq1bypW888pEU9fWgiI7m9C/xDXQ99bWtn+J7KN3jVR+obtWN7O02UygMkuhzYy3cBmVlpYpz1g0b1btMA2Tc5gxym7WxM58rHrtWEugBiZ8NLMcaxrz2tqoq44dazJ7NpoQ0FwmY5r35Y+7PHjWfIuDbrhXB3bdo/JgGX+75jvZuHmL2rZusVMeZcZz0+atNgiu6Qq2r8GYjn/wOa3dsLnykJtA/eu6LypBoFkzb45eNlN0q15m4IaOnhjWpDMcSFXbmDH7Y73z4ZeVf8OxPq/ibza4EEAAAQTiW+CHGWXx3UF6hwACCCAQdwKH9K/+H/Rx18Ewd6h1dlqYWwy8uTUr8vT2i99oj+zuu3xos3uZmrQt1snn1j1LbJcNxcgNZlnuqrUVp/PueLmSk+2Mtni5atorL5C+NSajQDyq3mNCOHPKr1kSbA5r2dVlZluaGZz+/SV3dX80f+7PyD54eaLata7YHzQcV1SCwN4DblC/Xt13Ovp4yfI/ZULCQKYHh6PzwbbxtgkAX3zbTmdtkZ1pHy/zlAfbDPcjgAACCIRZYP6z7jC3SHNVBXzlUkIiJggggAACThPocVG600qOar2uZP5lF9UBaAQvN6cTmyXHxx51UCPobWS6aJbOmn3/zJ6OO85mrK0CsydhfmGRTtp2CnVkKg3+LeYEarNP4RGHdA3+4TqeiEoQ6D9G/MXH/qk9O7WzU11zt+Trrgef11sffqGv3n5MTTKi97cxO3qZ6aPmuOZvf1yoCWOutHsH+C+WBof1e6QxBBBAoF4CLNutFxsPIYAAAgjEuQBLg4Mb4GguDQ6uUu5GAAEE6i8QlSDQrMPvd8ktdsNQM7POrLk3ybi5xowcaI8Fj6XLbAp57OnD7Qao5mQac/k3AiUIjKWRohYEEGisAgSBjXXk6TcCCCCAQF0CBIHBfR8EgcF5cTcCCDhTICpBoKEymyOaI6R/W7xC7uISe/pK35O664CunR0lSRDoqOGiWAQQiFMBgsA4HVi6hQACCCAQkgBBYHB8BIHBeXE3Agg4UyCiQaAJ/8ypQq1bZtsTg6peRe4Suzy4eVamMtJTHaNJEOiYoaJQBBCIggABXRTQeSUCCCCAAAIRFIinsJEgMIIfDq9CAIGoCUQ0CLzw6nFavmqt3n7unmr77Jne/7FijfoO/IfO6XeCbht1cdRAgn0xQWCwYtyPAAKNSYAgsDGNNn1FAAEEEGiMAgSBjXHU6TMCCDhZIGJBoP+I5n+PGWpPc6npun/KK5ry/Gx9/8GTciUnOcKVINARw0SRCCAQJQGCwCjB81oEEEAAAQQiJEAQGCFoXoMAAgiESSBiQeCH877TNWMe1BezJ+80G9DflwU/L5aZNTjz6Tu19x4dw9TFhm2GILBhfWkdAQScLUAQ6Ozxo3oEEEAAAQR2JUAQuCshfo4AAgjElkDEgsDX35mn8Q8+r6/feaxWgbUbNuvE/qP0wuQxOnj/LrElVUs1BIGOGCaKRACBKAkQBEYJntcigAACCCAQIQGCwAhB8xoEEEAgTAIRCwK//XGRBl4zXp+9+YiymjWpsfwvF/ymy0beo09ee0A5LbLC1MWGbYYgsGF9aR0BBJwtQBDo7PGjegQQQAABBHYlQBC4KyF+jgACCMSWQMSCQHNicPe+V9V6GEhZmUcDR9yldRs2a+6MSbGlVEc1BIGOGSoKRQCBKAgQBEYBnVcigAACCCAQQQGCwAhi8yoEEEAgDAIRCwJNrdNf/1DjHnhWRx22ny7q30udOrSRCQB/X7pak/8zU+ZAkYfHj9AJ3Q8LQ9ci0wRBYGSceQsCCDhTgCDQmeNG1QgggAACCAQqQBAYqBT3IYAAArEhENEgsLzcp1dmf6wJj76kIndxNYEW2Zm69dqL1Pv4I2NDJsAqCAIDhOI2BBBolAIEgY1y2Ok0AggggEAjEiAIbESDTVcRQCAuBCIaBPrF8guK9Puy1Vq2cq1SUlzavWMbddm9g9LTUhyHShDouCGjYAQQiKAAQWAEsXkVAggggAACURAgCIwCOq9EAAEEQhCIShAYQr0x9yhBYMwNCQUh0KACBFsNykvjCCCAAAIIIOAwAYJAhw0Y5SKAQKMXIAgM8RMgCAwRkMcRcJgAQaDDBoxyEUAAAQQQQKBBBQgCG5SXxhFAAIGwCxAEViH1eL1KTEhUYmLCTtBmObP5efOszGo/IwgM+zdJgwjEtABBYEwPD8UhgAACCCCAQIQFCAIjDM7rEEAAgRAFCAK3AbqLS3XukLEafGFf9TnpmEpWc6jJ6Dsf19z5C+w/O3j/LnrozmuU0yLL/n+CwBC/QB5HwGECBIEOGzDKRQABBBBAAIEGFSAIbFBeGkcAAQTCLkAQKOnex17S0y++Y3HvuWVItSBw6vS3NGPWx3r2oVvsYSZX3jRJe3RqpztuvIwgMOyfIw0iEPsCBIGxP0ZUiAACCCCAAAKREyAIjJw1b0IAAQTCIUAQKClvS4GKS0t1/rA7NGrwOdWCwLMH3abex3fToAv6WO93P/5Ko8ZO1s8fPa2EhARmBIbjK6QNBBwkQBDooMGiVAQQQAABBBBocAGCwAYn5gUIIIBAWAUiFgQ+/uws/fjbkoCKnzBmqDLS0wK6N5w39R5wg4Zfdma1ILDbyUN15+jLbRhorl8XLVP/wWP12axHlJXZhCAwnANAWwg4QIAg0AGDRIkIIIAAAgggEDEBgsCIUfMiBBBAICwCEQsCn3huln767Y+Air7n1iExEQT6fD4deMKlmnzXSB13zCG29iXLVqvfJbfog5fuU7s2LZXv9gTUJ25CAIH4EPh6ekl8dIReIBCUgE/SzgdpBdUENyOAAAIIxKVAt/NT46ZfmenJcdMXOoIAAgjUJhCxINAJQ1DbjMBxN12hXscdYbuw44zA/KIyJ3SNGhFAIEwCX79AEBgmSppxlIAJAU0YyIUAAggggEB0BLoNaPjAMTPDFZ3O8VYEEEAgggIEgVWwawoCzR6Bfz/hSF1x/qn2TvYIjODXyasQiEEBlgbH4KBQEgIIIIAAAgjEvUAkliC3b5ke9450EAEEEIhYEDhq7CN69+OvAxL3778X0M1huMnj9cpX7lOfgf/Q0IH91Odvx8jlqpgWPuX52Xpl9if21OCM9FQNHT2RU4PDYE4TCDhVgCDQqSNH3QgggAACCCDgZAGCQCePHrUjgEAsCUQsCPzoswVa9eeGgPp+Tr8TlJoSuWnZ5hRgM9Ov6jX7mbts4FdYVKzr//Wo/vvFD/bHB3bdQw+NG6HWOdn2//+5yR1Qn7gJAQTiQ4AgMD7GkV4ggAACCCCAgLMECAKdNV5UiwACsSsQsSAwdgkCq2xLfqHKyjzKaZFV7QGCwMD8uAuBeBEgCIyXkaQfCCCAAAIIIOAkAYJAJ40WtSKAQCwLRC0IzN2Sr4/mL9DqtRvVs8fhOqBrZ7314Rdq2byZjj58/1g2Iwh0zOhQKALhFyAIDL8pLSKAAAIIIIAAArsSIAjclRA/RwABBAITiEoQuGb9ZvW7+GYVuYttlXffPFh9e3XXfY+9rJlz5umjV+9XclJSYD2I8l3MCIzyAPB6BCIsQBAYYXBehwACCCCAAAIImC2azqrYw70hLw4LaUhd2kYAgVgRiEoQOHnaTM2dv0AP3DFct983TX1P6m6DwF8WLtM5Q8ZqzvR/a7f2rWPFqM46CAIdMUwUiUDYBAgCw0ZJQwgggAACCCCAQMACBIEBU3EjAgggUKdAVILAnv1HatAFfTTg9BM1+IZ7K4PALVsL1b3fVXrxsdt00L57OGLoCAIdMUwUiUDYBAgCw0ZJQwgggAACCCCAQMACBIEBU3EjAgggEHtB4IBhd+jwA/fWDcPOqxYEfv39/3TJtXfrk9ce2OlQjlgdR4LAWB0Z6kKgYQQIAhvGlVYRQAABBBBAAIG6BAgC+T4QQACB8AhEZUbg1Olv6fFnZ+nO0ZfrpTfm2mXBe3XuoNHjHldWs6Z6YfKY8PQuAq0QBEYAmVcgEEMCBIExNBiUggACCCCAAAKNRoAgsNEMNR1FAIEGFohKEOjxenXTuCf0ztwvq3WvY7tWmnzXterSuUMDdzt8zRMEhs+SlhBwggBBoBNGiRoRQAABBBBAIN4ECALjbUTpDwIIREsgKkGgv7M/L1yq/y1eoYJCtzp1bKNj/nKA0tNSomVRr/cSBNaLjYcQcKwAQaBjh47CEUAAAQQQQMDBAgSBDh48SkcAgZgSiEoQ+Nvi5XY24Nl9jlenDttPB37iuVlq1TJbZ5x8bEwh1VUMQaBjhopCEQiLAEFgWBhpBAEEEEAAAQQQCEqAIDAoLm5GAAEEahWIShB4y91T9euiZXplyr+UlJRYWdz01z/UuAee1TdznnDMzECCQH51IdC4BAgCG9d401sEEEAAAQQQiA0BgsDYGAeqQAAB5wtEJQjsd/HN6te7h644/9Rqghs25en4s67Va0/eoa5ddnOELkGgI4aJIhEImwBBYNgoaQgBBBBAAAEEEAhYgCAwYCpuRAABBOoUiEoQeO6Q27V/1866bdTF1Yr79sdFGnjNeM165i7t2amdI4aOINARw+TYIiMROkXiD1WOHYAaCo/EmMSTF31BAAEEEEAAAQTCIRCJP7O2b5kejlJpAwEEEIhpgagEgfc88oKemfGupk8eowO77mGXB6/fmKd/TnhS3/64WJ+9+bBcruSYhvMXRxDoiGFybJGRCJ0i8Ycqxw4AQWA8DR19QQABBBBAAAEHC0Tiz6wEgQ7+QCgdAQQCFohKELhla6HOuPxWrduQq4z0NHVsl6NFf6yyRd9982D17dU94A5E6sYXZn6oXxctr3zdgNN7av99OosgMFIj0DjfQxAYe+MeiTGJvV5TEQIIIIAAAgggEF0BgsDo+vN2BBCIH4GoBIGGr8hdrJfe+Eg//W+p3MUl6rxbW/X52zE6oGvnmNQdNfYR9eh2kPbdq5Otr2P7VsrKbEIQGJOjFT9FRSJ0isQfquJnRKRIjEk8edEXBBBAAAEEEEAgHAKR+DMrMwLDMVK0gQACsS4QtSAw1mF2rM8Egef0PUF/OaSrXMlJlT9mRqDTRtJZ9UYidIrEH6qcpV53tZEYk3jyoi8IIIAAAggggEA4BCLxZ1aCwHCMFG0ggECsC0Q1CNy4eYtuu/dpXT/0XO0R44eDjH/wef22eLnWb8zVIft30Z2jL1dKiktlnvJYH2Pqc7DA/GfdDV59j4vYFDkY5EiMSTD1cC8CkRDw+aSEhEi8iXcggAACCCBQs0Ak/szqSk6EHwEEEIh7gagGgStWr9fJF9xoDw0x4ZoTLp/Pp8tH/VuXn3+qenQ7UOvzip1QNjU6VOCHGWUNXvkh/V0N/o54ekEkxiSevOgLAggggAACCCAQDoGEHf5GqtznUWHpJhWV5So1OVOl3gIlJbjULK29XIlptb6y3OeVuyxPhWWb7H1eX8WftzNT2+jUIW3DUSptIIAAAjEtQBAY4PCYU41b52TL6y3XxSPu0rWDztYRh3Rlj8AA/bitfgKRWIYaiWUW9et9bD4ViTGJzZ5TFQIIIIAAAgggED2BqkGg1+dRUekmuT15ynC1VIaruQ308opXSkpQdlrHGsNAGwJ68myAmJacqSYpreTzebWlZI1MsHj+iEOj10HejAACCERIgCAwQOjeA25QdrOmyi8sUrdD99XY6y6R+ZcRewQGCMht9RKIROhEEBjc0ERiTIKriLsRQAABBBBAAIH4F/AHgSYENEFesWeLMlwtbAiYmFCxh3tZuVt5xatkVnE1T+9ULQzcOQTMsTMIzeUpL7Fh4HnXHBj/kPQQAQQavUBUg0CzR+Cd9z+rEVecFfN7BJp/meRtLVBqiksZ6dunmhMENvpfQw0KEInQiSAwuCGMxJgEVxF3I4AAAggggAAC8S9ggkC7HLhsk9xlO4eAfoGy8uJtYWB5ZRhYEQJuUWHpRqUlN1OTFBMCJldDM2HgyUNaxj8kPUQAgUYvENUgMB70CQLjYRRjtw+RCJ0IAoMb/0iMSXAVcTcCCCCAAAIIIBD/Ama2X4k3v84Q0K9gQr284tU2ODTLhE04WFC6XunJ2TWGgP7nThrUNP4h6SECCDR6gagFgblb8vXR/AVavXajevY4XAd07ay3PvxCLZs309GH7++YgSEIdMxQObLQSIROBIHBfRqRGJPgKuJuBBBAAAEEEEAg/gXWFvyixESXmmzbE9C/HLi2npvwb2vJGpV6CpWQkGSXEDdNyVHiDjMBqz5PEBj/3xE9RAABKSpB4Jr1m9Xv4ptV5K44cffumwerb6/uuu+xlzVzzjx99Or9Sk6q2Och1i+CwFgfIWfXF4nQiSAwuG8kEmMSXEXcjQACCCCAAAIIxL/AsrzPlebKUsv0zkpJarLLDvvks7MANxYukSspXTkZe9kDQuq6CAJ3ycoNCCAQBwJRCQInT5upufMX6IE7huv2+6ap70ndbRD4y8JlOmfIWM2Z/m/t1r61I3gJAh0xTI4tMp5Cp0gEjvHk5diPlsIRQAABBBBAAIEGENhaukZlXrdtOSutQ42nAld9bZEnV5uLlisztbW85WX2IBFzgEhKYkat1REENsDA0SQCCMScQFSCwJ79R2rQBX004PQTNfiGeyuDwC1bC9W931V68bHbdNC+e8QcVk0FEQQ6YpgcW2Q8BVsEgY79DCkcAQQQQAABBBCIuoBP5fKUF9vTfROUqOy0DkpOTK2xLrcnTxsKl6hFeid7srDXV6atJWvt8y3Sd5crMb3G5wgCoz7MFIAAAhEQiEoQOGDYHTr8wL11w7DzqgWBX3//P11y7d365LUHlNMiKwLdD/0VBIGhG9JC7QIEgcF9HfHkFVzPuRsBBBBAAAEEEIhvAXNqsFnua2YF5hWvsqf+mpmBO4aBJgRcV7BQORld1CSlhQ0NzXPe8lJtKflTnvJStUjrZJcLV72KvfnqO7RdfCPSOwQQQEBR2iNw6vS39Pizs3Tn6Mv10htz7bLgvTp30OhxjyurWVO9MHmMYwaHINAxQ+XIQuMp2GJGoCM/QYpGAAEEEEAAAQRiQsAEgebyh4G5xSuUnJC6LQxMsT/zh4CtmuxlZwImqOKZistnQ8CK04TL7DJh/8xAEwJuLPxdl153bEz0lSIQQACBhhSIyoxAj9erm8Y9oXfmflmtbx3btdLku65Vl84dGrLPYW2bIDCsnDS2gwBBYHCfRDx5Bddz7kYAAQQQQAABBOJbwB8E+ntZ6i3SZvdypSRlqFlqO7sH4IbCxWqR0dmeLFw9BNxu4ykvUW7xKhsMmuXF3nKPNrmX2hOFz756n/hGpHcIIIBAtGYE+uV/XrhU/1u8QgWFbnXq2EbH/OUApadV/G2OUy6CQKeMlDPrjKdgixmBzvwGqRoBBBBAAAEEEIgFgR2DQFNTqbdQm9zLlJiQpFJPoZ3l1yTFhICJdZZcVl6sXPdKeX2lKi8vU5OUVmqW2ka9B2fHQlepAQEEEGhQgajMCGzQHkW4cYLACIM3stcRBAY34PHkFVzPuRsBBBBAAAEEEIhvgZqCQHOAyNaSddpSvFrpyVk2CExKTKl1NqBfyDxXWLrJLhM2ewza04STMtRrUGZ8I9I7BBBAIJIzAs2egD/+tiQg9AljhiojPS2ge6N9E0FgtEcgvt8fT8EWMwLj+1uldwgggAACCCCAQEMK7BgElvu8dk/A/JJ1dq+/Em+B0l1ZapbaVkkJLvOfujWWY0LAYk++8kvWKkFJ9kRhV1Kafe6UIa0asgu0jQACCMSEQMRmBD7x3Cz99NsfAXX6nluHEAQGJMVN8S5AEBjcCMeTV3A9524EEEAAAQQQQCC+BaoGgf4QsKBkg9JdzdU0paU9CGSze5lSkpoqK82EgTtvObU9BFxfGf6ZtvLcK5WYmKzzrjkovhHpHQIIIBDJGYHxqs2MwHgd2djoVzwFW8wIjI1viioQQAABBBBAAAEnCviDQH8IWFi6UWnJWWqSkqOkhGTbJf+egalJTexpwhUzAysufwhYULJeyUlpykxpo+TEirDQHDRi9gy84NrDnUhDzQgggEBQAhGbERhUVQ66mSDQQYPlwFIJAoMbtHjyCq7n3I0AAggggAACCMS3gAkCt4eAm5SW3KxaCOjvvQkDK08TTmuv5ISU7SFg6Qa7J6A5GGTHGYMmDGRpcHx/Q/QOAQQqBKISBN4x6Rl9+tVPNY5BaopLx3c/VKf+7Rh17bJbzI8TQWDMD5GjC4ynYIsZgY7+FCkeAQQQQAABBBCIqoDHV2Jn/BWWblZacmaNIeD2MLBIucUrlJxYsfefp7xElTMBU9vYcLCm66RBTaPaR16OAAIIREIgKkHgbfc+rXlf/qhTTzymso/FJSWa/vqH6tnjMLlLSvX5N79o4tir1Pv4bpFwqPc7CALrTceDAQgQBAaAVOWWePIKrufcjQACCCCAAAIIxLfAhqLFtoMVIWCryuXAtfXahIZ5xX/aRcHmciWl1zgTsOrzBIHx/Q3ROwQQqBCIShB44dXj1OPIA3XlwNOqjcOosZOVkpKsu28erFvveVLLVq7Vcw/fEtNjRRAY08Pj+OLiKdhiRqDjP0c6gAACCCCAAAIIRE1gWd7nSnNlqWV6Z6UkNdllHT75VFC6XhsLl9gQMCdjLxsi1nURBO6SlRsQQCAOBKISBB57+nBdcOZJGjqwXzXC/8x4V1Ofn615Mx/Sm+/N1x2TntXX7zwW08wEgTE9PI4vjiAwuCGMJ6/ges7dCCCAAAIIIIBAfAvkFi+X11emJLvHX9tal/f6FYo9W5VbvFLm4BCzt6C5zAEirsS0WqEIAuP7G6J3CCBQIRCVIHDo6Pv0y8Jl+vDliUpJ2X6Sk5kpuGFTnt59YYKef+0D3T/lFYJAvtRGLRBPwRYzAhv1p0znEUAAAQQQQACBkATK5VGpt0j5pevl2hYG7njgR2UI6M3XpqKlauJqqczU1vLZHEPrAAAgAElEQVSWl2pryVr74+y0jvbAkJougsCQhoiHEUDAIQJRCQJ/+HWJzh92hzLS09Tzr4epRXYzff39//Tb4uWaMOZKnXLiUbr+X49q7frNLA12yIdEmQ0jQBAYnGs8eQXXc+5GAAEEEEAAAQTiW8CcGuxTuUo8BTbUs3v+pbRRUmL1gz+KvfnaWPi7MlPbKDOltRITkmWWCZd5i7S1ZJ1Fykprv9PMwBJvofoMbRPfiPQOAQQQiNaMQCP/0/+WavK0mfrmh4Uqchdrnz076px+J+jcfj2VmJigpSvW2NmCHdrmxMRATXl+tr77abHa5DTXGaccq0P272LrYmlwTAxP3BYRT8EWMwLj9jOlYwgggAACCCCAQIMLmCDQXP4wcEvJGqUkZdhlwkkJFavMir1b7Z6Amaltt4WASZV1bQ8DzczABGWntbenCpvLhICb3Us1cOTRDd4PXoAAAghEWyAqMwKj3en6vP+J52ap9/FH2gNMbp84Te+/eJ+SkhIJAuuDyTMBCxAEBkxlb4wnr+B6zt0IIIAAAggggEB8C/iDwIow0GdnBuYVr1Racpad/ecpL9aGwsVqltpOTVNaKTFhewjol6kIA93aUrxaiYnJykptb/cP3OReqrTkZjpn+H7xjUjvEEAAgWjOCDT6ZtbfqjUbdxqIY47YX8lJO//GHQsj5vWW6+g+w/TWs3erdU42QWAsDEoc1xBPwRYzAuP4Q6VrCCCAAAIIIIBAAwtUDQKrhoGb3cvsMuHisi32MJCKEDDRzvqr6fLPDMwtXiX5fPL4SpTham4DxL8Pbt7AvaB5BBBAIPoCUZkR+PPCpbpu7GStWrOhRoHPZj2irMxdHwkfDb4Zsz/WOx9+qacmjbavX59XHI0yeGcjEfhhRlnc9PSQ/tsPBmqoTsWTV0MZ0S4CCCCAAAIIIOBEgR2DQH8fCko3aFPRMjVJaakW6Z3snoCBXEVluTIholle3Dx9d7tn4N+uaBrIo9yDAAIIOFogKkHg8Fse0KI/VulfN16mdq1bypVcffZfm1Yt7D6BsXa9bQLAF9/WExOuV4vsTFtemac81sps9PXMf9bd6A0AQAABBMIp4CuX7OQKLgQQQAABBKIksFMQ6PPJHAyS614hc3pwiTffLvVtmmoOCKljdZnPZ08fziteZZcFe31l22YEtlXPy7Kj1DteiwACCEROICpBYM/+I9W/7/G6cuBpketpCG/y+XyaOv0tffvjQnuqcWbTjMrWOCwkBNgGejSeltM2EBHNIoAAAggggAACCCDgKIHqewRWnB6cV7za7u23fY/AqqcF17ZHYJG2FP+5bY/ADir3eezMwNTkTPYIdNQXQbEIIFBfgagEgaPHPa6yMq8mjh1W37oj+tzmvHwde/pwdWzXyh4QYq7hl52pk3sexR6BER2JwF5GEBiYE3chgAACCCCAAAIIIOAUgaqnBhd78rW1ZI1Sk5raEHD7qcH5204NblPjqcGl3kJtLVmrRCXa/QSTE1Nt980/3+RepoEjj3IKB3UigAAC9RaIShD4yec/aNg/Junh8SPUtlWLnYrfZ8/dKgO3evcsQg8yIzBC0EG8hiAwCCxuRQABBBBAAAEEEEDAAQImCPSpXCYEzC9ZZ/f2qxoC+rtglgtvLFxif5ZpDw5JtqcMm+XA+SVrlWBDwPaVIaD/ORMGnjq0jQMkKBEBBBAITSAqQaDZI3Du/AW1Vh7Lh4XsWDRBYGgfYEM8TRDYEKq0iQACCCCAAAIIIIBA9ATK5bFhXkHpeiUnpqmZnQmYUmNBJgzcvO0AEXOKsKe81IaHZhf6qjMBd3z4pEEcFhK9EebNCCAQKYGoBIHLV63T1vzCWvu43z67Kzmpjg1eI6UTwHsIAgNAivAtBIERBud1CCCAAAIIIIAAAgg0sEBu8XJ5fR47k6+uENBfhgkD89wr7cxBcyiIlKDsKsuBayqXILCBB5HmEUAgJgSiEgTGRM/DVARBYJggw9gMQWAYMWkKAQQQQAABBBBAAIEYEFiW97nSXFlqmd5ZKUlNdlmRWQ5sZg+aZcKupHTlZOyltOTMOp8jCNwlKzcggEAcCEQsCNyUu1X/+32FDj9oH23YlKv8AnetfPvu1Yk9AuPg44pWFwgCoyXPexFAAAEEEEAAAQQQaBiBDUWLbcOpyU1llvv6Dwip7W1mzz9zqrD/ciWlKTO1rZJrWU5s7iMIbJixo1UEEIgtgYgFgR9/9r2uuvl+vf3cPbr30RfZIzC2voO4qoYgMK6Gk84ggAACCCCAAAIIICCPr8Se7ltYukmpyZlqmpJTaxho9hLcXLxcLruXYDt5y0u1tXSdXImpdYaBBIF8aAgg0BgEIhYEbs7L12+Ll2+bEZingsKiWn27dmFGYGP4+BqqjwSBDSVLuwgggAACCCCAAAIIREfAnBps9vpze/LqDANNWLjJvcwuH85KNacDp1SeNlxQusHOCLQzAxOrHzRSVu7WKUNaRadzvBUBBBCIoEDEgsAI9imir2KPwIhyB/QygsCAmLgJAQQQQAABBBBAAAHHCJgg0FwVYeAWFZZuUFpyMzWxy4ST7c9KvIXa7F6m1KSmykprV+1UYZ/KVezJrzh1OGHbzMBtYaAJAXPdK3XBtYc7xoNCEUAAgfoKRDQIfO3t/8rMDLx8wCny/0b+3iff6JXZH2vDpjz1Oam7Ljnn747ZH9CgEwTW99NruOcIAhvOlpYRQAABBBBAAAEEEIiGgP+/H/1hYLFni/JL1ivdlW2XCXvKS7XJvdQeCGKWA9e0h6AJA0s8Bdpask5mz0Bz+rAJFk0ImJTo0nnXHBSNrvFOBBBAIKICEQsCtxYU6Zg+w3TuaT31z5EDbSdXrF6nky8YrRbZmcppkaVFf6zSVZeeoWEXnxZRhFBeRhAYil7DPEsQ2DCutIoAAggggAACCCCAQLQEqgaBNgxUudxlecovWWuXARd7ttpQ0IR7FSFgxQzCHS//zEDzXGJCsg0QK0LBtiwNjtbg8l4EEIioQMSCwC8X/KbLRt6jN6eNU5fOHWwnxz/4nJ5/7QN98PJEtW3VXPc++pKmvTxH3703Rakp5jfv2L8IAmNvjAgCY29MqAgBBBBAAAEEEEAAgVAEdgwCTVsm1MsvWae84j+V7spS87TdlJSYooRaQkD/+81zhaWblVe8yh4okp2+m1KSMtRrUGYoJfIsAggg4AiBiAWBs9//XKPHPa7vP3hSruQki3PGZbeqeVamnpo02v7/735arIuGj9PsZ+7SHp3aOQKQIDD2hokgMPbGhIoQQAABBBBAAAEEEAhFoKYg0B4MUrRUCQmJMicFt0jvpCYpOUpQYp2vKisvVq57hTzlJSr3edQ0pZWdEdh7cHYoJfIsAggg4AiBiAWBb743X/8YP0UL3puilBSXSkrLdHivQXa/wFFDzrFYq9ZsUO8BN+iFyWN08P5dHAFIEBh7w0QQGHtjQkUIIIAAAggggAACCIQisGMQaII/czBIij0YpK1KvW5tKFysFumd1SSlZa2zAk34l1u80paSndbRBoGbiv6wAeLZV+8TSok8iwACCDhCIGJB4IKfF+vCq8dp6r036JgjDtC7H3+lUWMn6+HxI3RC98Ms1rwvf9TQ0RP13ov3qkPbnJgC9Pl88paXKzmpYjaj/yIIjKlhssUQBMbemFARAggggAACCCCAAAKhCPiDQJ98KvMW2Rl9yUnpykptr+Rtp/+6PXlaX7hIORldlOFqUS0MNM+ZEHBL8Wp7QEjz9N3kSky3JRV787WxcIkuve6voZTIswgggIAjBCIWBJaX+3TWFWPsgSC9j++meV/+pFYtszTrP3dVnhJ80/gnNOu9z/T9+1PlclUcAR8rl6lr0pQZmjtjUrWSCAJjZYS210EQGHtjQkUIIIAAAggggAACCIQiYIJAfwiYV7za7gVYNQT0t109DGxulwn7Q8CtJWvkLS9T8/ROdm/AqpcJA/sOdcb2VKE48iwCCCAQsSDQUP+5dqNuvedJmYNDTjz2cA26oK8O2ncPOwq/Llqm/oPH6oyTj9Wdoy+PmZExJxsPuv5eu2y5TavmBIExMzK1F0IQ6IBBokQEEEAAAQQQQAABBIIQMAd8mL39TJiXmJC0LQRMrbEFEwZuLPrDHh5iZgZ6fWX2dGFzQnDVmYA7PnzSoKZBVMStCCCAgDMFIhoE1kVUVuZRkbtEqakupaWmxIymx+vVxs1bNPfTBZo6fTZBYMyMDEGgA4aCEhFAAAEEEEAAAQQQCIvA1tK1dkmwORE4K62DkhNrDgH9Lyvy5CnXvVxNU1rLW14qT3nxtpmAFcuBa7oIAsMyVDSCAAIxLhAzQWCMO+mduV9qwqMvEgTG+kCxR6ADRogSEUAAAQQQQAABBBAITmBZ3udKd2XZw0BSkprs8mGzHLigdL3d+8+VlK6cjL2UlpxZ53MEgbtk5QYEEIgDAYLAAAextiAw3+0JsAVui5TA19NLIvUq3oMAAgg0EgGfpIRG0le6iQACCCAQiwJrC35RYmKymrha2OW+ZnlwXVeZORik5E+VeYqUkJCodFdzNU3JUVJC7XvRn3DZrgPGWLShJgQQQCAYAYLAALVqCwLnPlkQYAv1v63bgLqnvde/5cg/+fULhHSRV+eNCCCAQKgCJgQ0YSAXAggggAAC0REoK3erxFOgIk/uLsNAs5egOVDEng6c1tHuLVhQsl5priw1TWlVaxhIEBidseWtCCAQWQGCwAC9awsC33siP8AW6n/bgWfF1gnK9e+JxEEeoejxLAIIIIAAAggggAACjVPAnBpc7vOosGyT3GVblOFqbv+XuMMMv4oQcKV8Pl/lwSAmEHR7tqiwdKNdHtzEzgx0VYP0lJfo5CEtGycuvUYAgUYlQBC4i+E2/wLxeLya89FXmjRlht6dPkEJiQlKTqqYik4QGNyvF4LA4Ly4GwEEEEAAAQQQQAABBCQTBJqrrjDQzBrMLV5l7zMnBrsS0yrpqoaBqcmZ25YJV4SBJgQ0y4jPu+YgqBFAAIG4FyAI3MUQ/750tU679JZqd/Xt1V133zyYILAevzwIAuuBxiMIIIAAAggggAACCDRyAX8QuD0M3Cx3Wa4yXC3tzECvr0y57hV270BzqnDVENBPt/PMwFby+bw2BDQ/O3/EoY1cme4jgEBjECAIDHGUmREYHCBBYHBe3I0AAggggAACCCCAAALbZwRuD/U8KizdpKKyXKW5mqnEk6/ExBRlpbaTK9HssV7zIVcVYWCefdaEhV5fqb03M7WN+gxtCzUCCCAQ9wIEgSEOMUFgcIAEgcF5cTcCCCCAAAIIIIAAAgjsHAQaE7NMeGvJGhWUbpQrKaNiOXBSuhJ2cdK9CQMLSjcov2SdkhJdyk7rqNTkpuo1qBnUCCCAQNwLEASGOMQEgcEBEgQG58XdCCCAAAIIIIAAAgggUHMQWOZ1a7N7uQ0EzdLg5umdlOFqscsg0OwJaE4VLvUWyucrt7MBm6bkqPfgbKgRQACBuBcgCAxxiAkCgwMkCAzOi7sRQAABBBBAAAEEEEBg5yDQnA6c616ppMRkZaa0Vqm3SFuK/7RhYLoru9Yw0FNeqq0lf8pbXmYDQO+2WYUmCDzzqi5QI4AAAnEvQBAY4hATBAYHSBAYnBd3I4AAAggggAACCCCAQPUg0J4O7F6lxIREZaW1V7I9HdinwrLN2ly0TK2a7KX05J1n9/lDwLLyEmWndVBKUhP7nLtsi3KLV+iSUT2gRgABBOJegCAw7oeYDiKAAAIIIIAAAggggAACCCCAAAIIICARBPIVIIAAAggggAACCCCAAAIIIIAAAggg0AgECAIbwSDTRQQQQAABBBBAAAEEEEAAAQQQQAABBAgC+QYQQCAuBTxerzZu3iJfuU+tc5orKSkxLvtJp3YWcBeXKjdvq9q2bqnExASIGpmA+bWfnJTUyHrduLpbWlqm3C0Fap2TrYQEfo03ptH3+Xzylpfza7wxDbqkLfmFKikps7/muRBAAAEEQhcgCAzdkBYcIPDJ5z9o2D8mafJdI3XcMYc4oGJKDEXgpTfm6l+Tnqlsok2r5nrwzmt0YNc9QmmWZx0gMPyWBzR3/gJbaYvsTJ3+92N13dBzHFA5JYZDYMXq9Tr5ghv1/ov3qn3bnHA0SRsxJGBCoEefeVOPPP165a/xh8dfq0P255TPGBqmBi1l1nufadKUGZo7Y1KDvofGY0PA/IXuwGvGa/mqdbagLru316AL+qhvr+6xUSBVIIAAAg4VIAh06MBRduACC5es1IVXj1ORu5ggMHA2R99p/kMhO6up/nJwV5nZQdffPlkej1dPTRrt6H5R/K4FHn7qdfU6vps6dWitL779VVfdfL9efPSfOmi/PXf9MHc4WmDAsDv0469LbB8IAh09lLUWv+Dnxfbf588+dLMO2ndPPfjka3rrw8/1wUsTmf0bn0Ne2asVq9dp0PX3atWaDTJ/uUcQGOcDvq176zfmaeaceerXu4eapKfp2Vfe09MvzdF/X39Q6WkpjQOBXiKAAAINIEAQ2ACoNBk7Ahs25encobdr1OBzdPvE/+jef17JjMDYGZ6IVXL9vx5VeblPE8cOi9g7eVFsCPTsP1LnndZTgy/sGxsFUUWDCZj/YFy7fpNMIEgQ2GDMUW34vsde1m+/L9fUe2+wdZgxP+Hsa/XKlNu13967R7U2Xt6wAv7tPuZ+ukBTp88mCGxY7pht3QTBvQfcYP8y4PCD9onZOikMAQQQiHUBgsBYHyHqq7eA2SfskhF36dijDtbVl52hbicPJQist6YzH3zzvfky/9Gw6I+Vmjj2Ku27VydndoSq6yVglhKdcuFoZgLXS8+ZD63bkCsT/hIEOnP8dlW1+Uud5llNdcuIiypvPeD4S/g1viu4OPr5O3O/1IRHXyQIjKMxDaYrr78zT7fe86TmzXzIbv/BhQACCCBQPwGCwPq58VQUBbzecj314tu1VnDisX9R545tZf6DwVxmFqA5MIAgMIqDFqZXf/vjIn3306IaW2uelamz+xxX7Wf3T3lF5pn1G3N1x42X68jD9g1TJTQTaQGz3Hvths01vnb/fTqrR7cDq/2ssKhYF159p5o2ydC0+2/isJhID1iY3hfI7/d7dmpX+TaCwDDBx2gzg2+4V127dKq276f5d/vY6y/RqSceHaNVU1Y4BQgCw6nprLYWL12l84fdqYv797Z/wc+FAAIIIFB/AYLA+tvxZJQEzPIQszyotuv0v/9VJhQyy4VMMGT2FDHXf2a8q+O7H6p+vXqo9/HdolQ9rw1FYN6XP+qzb36psQnzN8NmA+marsefnaXnXn3P/g0ylzMFnn/tA7s3VE3X4QftrZP+74jKH5nZwCPGPKi16zfrmQdvtvtFcjlTIJDf77t22a2ycwSBzhznQKs2f8Fnfq+/+ZoLKx9hRmCgevFxH0FgfIxjsL1YvXajLho+Tt0O3VfjbxrEX+4FC8j9CCCAwA4CBIF8EnEpYA4Gee7V96v17YGpr6rPSceoz9+OscuFuRqPwHuffKORtz2sHz58UslJSY2n442wp1sLinTNrQ/K7S7R4/++jhCwkX0DBIHxPeDmLwEXLlmhJyZcbzvKHoHxPd419Y4gsPGN+e9LV+vSkXer518P15iRA/lzXOP7BOgxAgg0gABBYAOg0mRsCrA0ODbHpSGqmjxtpnoceZDMTKFNuVvtMvH01BRODW4I7Bhqs8hdovOG3m5Pip50+9Vq2iTdVpeYmKh2rVvEUKWU0hACZR6vPSzk7+ffqLefu0ft2+bIlUzw3xDW0Wpz+6nBt9iTwB+Y+ore/vALTg2O1oBE8L0+n08ej1dzPvpKk6bM0LvTJyghMYFQKIJjEI1XLVyyUmdePsYu/R9++Zn23+fmykhPtat/uBBAAAEE6idAEFg/N55yoABBoAMHrZ4l33L3VM2c82nl04cduLfuvmWwOrZrVc8WecwJAv7ZYDvWapYSsizcCSMYWo3m93gzG9x/Me6hecbi0yYMevjp1/XYM29uCwPS9MSE62R+j+eKbwEzK+y0S2+p1sm+vbrr7psHx3fHG3nvzAxQ/57fVSkY+0b+YdB9BBAIWYAgMGRCGkAAgVgUKC0t0/pNeWqakc7y0FgcIGpCAAEE6ilQXFKqzblb1bZ1S3sYGBcCCCCAAAIIIIBA4AIEgYFbcScCCCCAAAIIIIAAAggggAACCCCAAAKOFSAIdOzQUTgCCCCAAAIIIIAAAggggAACCCCAAAKBCxAEBm7FnQgggAACCCCAAAIIIIAAAggggAACCDhWgCDQsUNH4QgggAACCCCAAAIIIIAAAggggAACCAQuQBAYuBV3IoAAAggggAACCCCAAAIIIIAAAggg4FgBgkDHDh2FI4AAAggggAACCCCAAAIIIIAAAgggELgAQWDgVtyJAAIIIIAAAggggAACCCCAAAIIIICAYwUIAh07dBSOAAIIIIAAAggggAACCCCAAAIIIIBA4AIEgYFbcScCCCCAAAIIIIAAAggggAACCCCAAAKOFSAIdOzQUTgCCCCAAAIIIIAAAggggAACCCCAAAKBCxAEBm7FnQgggAACCCCAAAIIIIAAAggggAACCDhWgCDQsUNH4QgggAACCCCAAAIIIIAAAggggAACCAQuQBAYuBV3IoAAAggggAACCCCAAAIIIIAAAggg4FgBgkDHDh2FI4AAAggggAACCCCAAAIIIIAAAgggELgAQWDgVtyJAAIIIIAAAggggAACCCCAAAIIIICAYwUIAh07dBSOAAIIINDYBT76bIF85b4aGVq2yNIh+3epk6i83KevFvymJcv/VFmZR61ysrXPnh219x4dGzst/UcAAQQQQAABBBBAIC4FCALjcljpFAIIIIBAYxA44PhLau3m/x19iB69e2StP3cXl+qSEXfp54VLlZGeprRUlzbn5dv7z+l3gm4bdXFjIKSPCCCAAAIIIIAAAgg0KgGCwEY13HQWAQQQQCCeBDxer3w7TAh86oW39eCTr2ri2GHqffyRtXb3vsde1lMvvq2Hx4/QsUcdrOSkJOUXFGnW+59r2cq1uvmaC+KJir4ggAACCCCAAAIIIICAJIJAPgMEEEAAAQTiRGDelz9p6Oj7NOSivrrm8rPq7NW5Q25XfmGR3n7unjrv83rL9dKbH+mNOZ/qjxVrtHvHNvrbsX/R0IH97HOz3vvMBoqL/lhllxVfPuBU9TnpGPszM+tw0PUTbD2r127UvC9/VHazphp30xU2dDSB5Yeffqd1G3J11GH76carBmjfvTrFyWjQDQQQQAABBBBAAAEEYk+AIDD2xoSKEEAAAQQQCFpg+ap1OnvQbTrysH314B3XKCkpsc42nnhulh6Y+qqGX3amTuvdQ+3atKzxfv/MweO7H6pex3XToiUrNe3lOfrl42l668MvdOMdj6lHtwP19xOO1JyPvtL8r3/WhDFX6pQTj7Jh39F9htl2W2Rnqtuh+ymrWRPdOuIinT/sDuVtLdD5Z/5NLbIy9dyr79ugce6MicpsmhF0/3kAAQQQQAABBBBAAAEEdi1AELhrI+5AAAEEEEAgpgUKCt06Z8hYW+NLj91WLUgz4VqRu7iy/uZZmerQNkcbN2/Rvyb9Rx/O+87+zOwTeMQhXXXhWSfZYM9c5p7jzhyx056B6zfmqXVOtk65cLR97pUpt1e2f8Zlt6qktMzONPQHgeee1lP/uPp8uVzJ9j5zyMnVNz+g6ZPHVB5oYmYUmmcfuGO4nXHIhQACCCCAAAIIIIAAAuEXIAgMvyktIoAAAgggEDEBc/LviDEP6ovvfrOBnFm6W/W68OpxWvDz4sp/dOYp/6c7brys8v8vXrrKnhz866Ll+u8XP9gDQy4fcIpGDTlHXy74TZeNvEcPjRuhnj0Oq9auCfsO7zVIV5x/qkYO7l/5s4mPv6wnX3hbC96bYgNBMyPQP0PQf9Njz7yph556TfvtvXvlc16v1y4vHn3VAA3s3ztifrwIAQQQQAABBBBAAIHGJEAQ2JhGm74igAACCMSdwORpM/XItJl6YsL1lTP5qnZyzfrNKi4uqfxHZtltTousGh2KS0o1dPRE/bJwmT6f/Yg+/+YX+/+n3X+Tuh26b7VnCouKdeQpQzXiirM0+MK+lT979Jk39PBTr+ubOU/I4/HUGATeP+UVTXl+th67Z9ROdezesa06dWgdd+NEhxBAAAEEEEAAAQQQiAUBgsBYGAVqQAABBBBAoB4CH8z7ViPGPKQbrjxPl5z796Ba+OTzH/TXIw/aaS/Ba//5sN7/7zf6/oMntWbdRp18wWgNuqCPrh10dmX75gARswfhsacPV5fOHWxQ6L8uGj5ey1au0byZD1UuDd5xRuAb787XzXdN0RtPj9Nee3SoVrfP51NCQkJQfeFmBBBAAAEEEEAAAQQQCEyAIDAwJ+5CAAEEEEAgpgTMSbs9+4+0h3Dceu1FO9WWlpqq4445pNaaTYhnZgaawzr22XM3FRUV64vvftXU6W/pvNN6aszIgfZZcwrxtz8u1qXnnazexx1hD/QwsxBff+pOO6vPzO4zpwKbff3MCcBm2a9ZKmyWDPv3CNwxCDSzCfte/A+lpaZo9FXnq/NubbVs5Vq98e6n6turu07oXn0ZckzBUwwCCCCAAAIIIIAAAg4WIAh08OBROgIIIIBA4xX4bfFye0pwbVebVs01d8akWn9uTv597a3/asnyP6vdc+XA0zTowj5KTXHZf24ODBn/4HN69+OvK+878djD7cnEpaVluvexl/X8a+9X/uyis3tp1OD+SklxyRxictSpV+60R6C52QSKd056xu5D6L/MnoHjbrpCXbvs1ngHlp4jgAACCCCAAAIIINCAAgSBDYhL0wgggAACCMS6gLu4VBs25crMIGzVMqvWZbnm4I8Nm/LsLEIzk6/qZdBA9tgAAAS3SURBVNpYu36T2rZuqfS06j/bVf/NvoQmbDSnGTfJSNvV7fwcAQQQQAABBBBAAAEEQhAgCAwBj0cRQAABBBBAAAEEEEAAAQQQQAABBBBwigBBoFNGijoRQAABBBBAAAEEEEAAAQQQQAABBBAIQYAgMAQ8HkUAAQQQQAABBBBAAAEEEEAAAQQQQMApAgSBThkp6kQAAQQQQAABBBBAAAEEEEAAAQQQQCAEAYLAEPB4FAEEEEA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+ "image/png": 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