Merge pull request #44 from tsipkens/master

Sync developer with updated paper, etc.
tsipkens · Nov 8, 2023 · 90fc59a · 90fc59a
2 parents d7c1f3e + 0e24b57
commit 90fc59a
Show file tree

Hide file tree

Showing 3 changed files with 41 additions and 37 deletions.
diff --git a/docs/paper/paper.jats b/docs/paper/paper.jats
@@ -113,20 +113,47 @@ a Creative Commons Attribution 4.0 International License (CC BY
 </article-meta>
 </front>
 <body>
-<sec id="introduction">
-  <title>Introduction</title>
+<sec id="summary">
+  <title>Summary</title>
+  <p>The objective of <monospace>atems</monospace> is to provide a suite
+  of open source analysis tools (largely in Matlab) for transmission
+  electron microscopy (TEM) image analysis that are specifically
+  designed for soot and related carbonaceous particles (e.g., tarballs).
+  This codebase started as a manual analysis code by Dastanpour &amp;
+  Rogak
+  (<xref alt="2014" rid="ref-dastanpour2014" ref-type="bibr">2014</xref>),
+  with the first automated methods added by Dastanpour et al.
+  (<xref alt="2016" rid="ref-dastanpour2016" ref-type="bibr">2016</xref>).
+  The current, open source version has been streamlined and expanded to
+  include a larger suite of automated analysis methods from the
+  literature, as detailed in the following section. In this regard, a
+  key contribution of this codebase is to provide open source
+  implementations of multiple analysis methods spanning a range of
+  laboratories. This codebase places these methods in the same
+  framework, with the goal of enabling intercomparisons of analysis
+  routines across a range of data.</p>
+  <fig>
+    <caption><p>Sample TEM image of soot demonstrating the aggregate
+    structure, where <bold>a</bold> is an unlabeled image containing
+    soot aggregates and <bold>b</bold> is that same image with the
+    aggregates
+    labeled.<styled-content id="figU003Asoot"></styled-content></p></caption>
+    <graphic mimetype="image" mime-subtype="png" xlink:href="media/01-sample-image.png" />
+  </fig>
+</sec>
+<sec id="statement-of-need">
+  <title>Statement of need</title>
   <p>Soot, carbon black, and other carbonaceous particles have important
   climate, health, and technological impacts that depend on their
   morphology. These particles have complex shapes composed of a
   collection of small, primary particles in fractal arrangements, as
   shown in <xref alt="[fig:soot]" rid="figU003Asoot">[fig:soot]</xref>a.
-  Transmission electron microscopy (TEM) images of these particles allow
-  for detailed information about particle morphology that is unavailable
-  in other characterization techniques. However, extracting this
-  information requires image analysis across a statistically-significant
-  number of particles, with the quality of conclusions improving as the
-  number of characterized particles increases. For instance, Kelesidis
-  et al.
+  TEM images of these particles allow for detailed information about
+  particle morphology that is unavailable in other characterization
+  techniques. However, extracting this information requires image
+  analysis across a statistically-significant number of particles, with
+  the quality of conclusions improving as the number of characterized
+  particles increases. For instance, Kelesidis et al.
   (<xref alt="2020" rid="ref-kelesidis2020" ref-type="bibr">2020</xref>)
   suggested quantifying at least 400 primary particles per experimental
   condition in a premixed flame to get an accurate average primary
@@ -159,30 +186,6 @@ a Creative Commons Attribution 4.0 International License (CC BY
   et al., 2021</xref>). This limits comparability between laboratories
   (<xref alt="Sipkens et al., 2023" rid="ref-sipkens2023" ref-type="bibr">Sipkens
   et al., 2023</xref>).</p>
-  <p>The objective of <monospace>atems</monospace> is to provide a suite
-  of open source analysis tools (largely in Matlab) for TEM image
-  analysis that are specifically designed for soot and related
-  carbonaceous particles (e.g., tarballs). This codebase started as a
-  manual analysis code by Dastanpour &amp; Rogak
-  (<xref alt="2014" rid="ref-dastanpour2014" ref-type="bibr">2014</xref>),
-  with the first automated methods added by Dastanpour et al.
-  (<xref alt="2016" rid="ref-dastanpour2016" ref-type="bibr">2016</xref>).
-  The current, open source version has been streamlined and expanded to
-  include a larger suite of automated analysis methods from the
-  literature, as detailed in the following section. In this regard, a
-  key contribution of this codebase is to provide open source
-  implementations of multiple analysis methods spanning a range of
-  laboratories. This codebase places these methods in the same
-  framework, with the goal of enabling intercomparisons of analysis
-  routines across a range of data.</p>
-  <fig>
-    <caption><p>Sample TEM image of soot demonstrating the aggregate
-    structure, where <bold>a</bold> is an unlabeled image containing
-    soot aggregates and <bold>b</bold> is that same image with the
-    aggregates
-    labeled.<styled-content id="figU003Asoot"></styled-content></p></caption>
-    <graphic mimetype="image" mime-subtype="png" xlink:href="media/01-sample-image.png" />
-  </fig>
 </sec>
 <sec id="methods">
   <title>Methods</title>

diff --git a/docs/paper/paper.md b/docs/paper/paper.md
@@ -37,14 +37,15 @@ bibliography: paper.bib
 ---
 
 
-# Introduction
+# Summary
 
-Soot, carbon black, and other carbonaceous particles have important climate, health, and technological impacts that depend on their morphology. These particles have complex shapes composed of a collection of small, primary particles in fractal arrangements, as shown in \autoref{fig:soot}a. Transmission electron microscopy (TEM) images of these particles allow for detailed information about particle morphology that is unavailable in other characterization techniques. However, extracting this information requires image analysis across a statistically-significant number of particles, with the quality of conclusions improving as the number of characterized particles increases. For instance, @kelesidis2020 suggested quantifying at least 400 primary particles per experimental condition in a premixed flame to get an accurate average primary particle diameter from manually drawing elipses (that study counted 800 primary particles). In the broader literature, a few hundred particles per condition seems to be standard, with other authors having employed between 150 and 400 particles per condition [@liati2014; @marhaba2019; @trivanovic2019; @trivanovic2020], depending on the type of analysis. For multiple conditions, this can quickly expand to over 1000 particles. This characterization is often done manually, which at a minimum of several minutes per aggregate, is incredibly labour intensive. Unfortunately, the low contrast (carbonaceous particles on carbon films) and complex particle morphology of common carbonaceous particles makes automated analysis challenging, requiring unique analysis methods over those developed for traditional TEM image analysis of many engineered nanomaterials [@schneider2012imagej]. At the same time, existing automated methods across the literature are typically only applied to data from a single laboratory, with few exceptions [@anderson2017repeatability; @sipkensfrei2021cnn]. This limits comparability between laboratories [@sipkens2023]. 
-
-The objective of `atems` is to provide a suite of open source analysis tools (largely in Matlab) for TEM image analysis that are specifically designed for soot and related carbonaceous particles (e.g., tarballs). This codebase started as a manual analysis code by @dastanpour2014, with the first automated methods added by @dastanpour2016. The current, open source version has been streamlined and expanded to include a larger suite of automated analysis methods from the literature, as detailed in the following section. In this regard, a key contribution of this codebase is to provide open source implementations of multiple analysis methods spanning a range of laboratories. This codebase places these methods in the same framework, with the goal of enabling intercomparisons of analysis routines across a range of data. 
+The objective of `atems` is to provide a suite of open source analysis tools (largely in Matlab) for transmission electron microscopy (TEM) image analysis that are specifically designed for soot and related carbonaceous particles (e.g., tarballs). This codebase started as a manual analysis code by @dastanpour2014, with the first automated methods added by @dastanpour2016. The current, open source version has been streamlined and expanded to include a larger suite of automated analysis methods from the literature, as detailed in the following section. In this regard, a key contribution of this codebase is to provide open source implementations of multiple analysis methods spanning a range of laboratories. This codebase places these methods in the same framework, with the goal of enabling intercomparisons of analysis routines across a range of data. 
 
 ![Sample TEM image of soot demonstrating the aggregate structure, where **a** is an unlabeled image containing soot aggregates and **b** is that same image with the aggregates labeled.\label{fig:soot}](01-sample-image.png){ width=90% }
 
+# Statement of need
+
+Soot, carbon black, and other carbonaceous particles have important climate, health, and technological impacts that depend on their morphology. These particles have complex shapes composed of a collection of small, primary particles in fractal arrangements, as shown in \autoref{fig:soot}a. TEM images of these particles allow for detailed information about particle morphology that is unavailable in other characterization techniques. However, extracting this information requires image analysis across a statistically-significant number of particles, with the quality of conclusions improving as the number of characterized particles increases. For instance, @kelesidis2020 suggested quantifying at least 400 primary particles per experimental condition in a premixed flame to get an accurate average primary particle diameter from manually drawing elipses (that study counted 800 primary particles). In the broader literature, a few hundred particles per condition seems to be standard, with other authors having employed between 150 and 400 particles per condition [@liati2014; @marhaba2019; @trivanovic2019; @trivanovic2020], depending on the type of analysis. For multiple conditions, this can quickly expand to over 1000 particles. This characterization is often done manually, which at a minimum of several minutes per aggregate, is incredibly labour intensive. Unfortunately, the low contrast (carbonaceous particles on carbon films) and complex particle morphology of common carbonaceous particles makes automated analysis challenging, requiring unique analysis methods over those developed for traditional TEM image analysis of many engineered nanomaterials [@schneider2012imagej]. At the same time, existing automated methods across the literature are typically only applied to data from a single laboratory, with few exceptions [@anderson2017repeatability; @sipkensfrei2021cnn]. This limits comparability between laboratories [@sipkens2023]. 
 
 # Methods
 

diff --git a/docs/paper/paper.pdf b/docs/paper/paper.pdf