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03f3efd668
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3f7dcc62b2
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@ -2,7 +2,7 @@ sub createFolderStructure{
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system("bash ./folder-structure.sh");
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}
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# createFolderStructure();
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createFolderStructure();
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$hash_calc_ignore_pattern{aux} =
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'^\\\\gdef\\\\minted@oldcachelist\{,'
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2
Makefile
2
Makefile
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@ -16,6 +16,4 @@ bib:
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glossaries:
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makeglossaries -d build $(SOURCE)
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index:
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makeindex -d build $(SOURCE)
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.PHONY: build
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BIN
figures/lallemand2020-fig1_copy.pdf (Stored with Git LFS)
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figures/lallemand2020-fig1_copy.pdf (Stored with Git LFS)
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figures/tag-definition.pdf (Stored with Git LFS)
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figures/tag-definition.pdf (Stored with Git LFS)
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@ -5,9 +5,8 @@
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\usepackage{tikz}
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\begin{document}
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\definecolor{lammegreen}{HTML}{017d79}
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\definecolor{lammeblue}{HTML}{0050b4}
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\usetikzlibrary{positioning}
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\usetikzlibrary{decorations.pathreplacing}
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@ -22,28 +21,28 @@
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\draw[-,very thick] (-6,0) to (6,0);
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% Gene blocks
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\node[rectangle,fill=orange,minimum height=0.5cm, minimum width=1cm] (orange1) at (-5,0) {};
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\node[rectangle,fill=lammegreen,minimum height=0.5cm, minimum width=1cm] (green1) [right=of orange1] {};
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\node[rectangle,fill=green,minimum height=0.5cm, minimum width=1cm] (green1) [right=of orange1] {};
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\node[rectangle,fill=red,minimum height=0.5cm, minimum width=1cm] (red1) [right=of green1] {};
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\node[rectangle,fill=lammegreen,minimum height=0.5cm, minimum width=1cm] (green2) [right=of red1] {};
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\node[rectangle,fill=lammegreen,minimum height=0.5cm, minimum width=1cm] (green3) [right=of green2] {};
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\node[rectangle,fill=lammegreen,minimum height=0.5cm, minimum width=1cm] (green4) [right=of green3] {};
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\node[rectangle,fill=lammeblue,minimum height=0.5cm, minimum width=1cm] (blue1) [right=of green4] {};
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\node[rectangle,fill=lammeblue,minimum height=0.5cm, minimum width=1cm] (blue2) [right=of blue1] {};
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\node[rectangle,fill=green,minimum height=0.5cm, minimum width=1cm] (green2) [right=of red1] {};
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\node[rectangle,fill=green,minimum height=0.5cm, minimum width=1cm] (green3) [right=of green2] {};
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\node[rectangle,fill=green,minimum height=0.5cm, minimum width=1cm] (green4) [right=of green3] {};
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\node[rectangle,fill=blue,minimum height=0.5cm, minimum width=1cm] (blue1) [right=of green4] {};
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\node[rectangle,fill=blue,minimum height=0.5cm, minimum width=1cm] (blue2) [right=of blue1] {};
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\node[rectangle,fill=orange,minimum height=0.5cm, minimum width=1cm] (orange2) [right=of blue2] {};
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% Homology links
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\draw[-,orange, bend left=40] (orange1) to coordinate[dot] node[nod] {$\mathrm{TAG}_7$} (orange2);
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\draw[-,lammegreen,bend right=40] (green1) to (green2)
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\draw[-,green,bend right=40] (green1) to (green2)
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(green2) to (green3)
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(green3) to (green4);
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\draw[-,lammegreen,bend left=40] (green1) to (green3)
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\draw[-,green,bend left=40] (green1) to (green3)
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(green1) to coordinate[dot] node[nod] {$\mathrm{TAG}_3$}(green4);
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\draw[-,lammegreen,bend right=40] (green1) to (green2)
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\draw[-,green,bend right=40] (green1) to (green2)
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(green2) to (green4);
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\draw[-,lammeblue, bend left=40] (blue1) to coordinate[dot] node[nod] {$\mathrm{TAG}_0$} (blue2);
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\draw[-,blue, bend left=40] (blue1) to coordinate[dot] node[nod] {$\mathrm{TAG}_0$} (blue2);
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\end{tikzpicture}
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\end{document}
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42
report.org
42
report.org
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@ -9,8 +9,13 @@
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#+exclude_tags: noexport
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#+options: H:7
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#+options: toc:nil
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#+MACRO: conditional-header (eval (concat "#+header: :results output " (print-to-string org-export-current-backend)))
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# ref. conditional-header https://emacs.stackexchange.com/a/64340/41374
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# ref. https://write.as/dani/writing-a-phd-thesis-with-org-mode
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#+name: acronyms
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| key | abbreviation | full form |
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|------------+--------------+--------------------------------------------|
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@ -48,12 +53,9 @@
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#+end_center
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#+begin_export latex
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{
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\hypersetup{linkcolor=black}
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\tableofcontents
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\listoffigures
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\listoftables
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}
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#+end_export
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[[printglossaries:]]
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@ -152,21 +154,19 @@ In this step, the typical tool involved is =BLAST= (Basic Local Alignment Search
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Several =BLAST= metrics can be used as an homology measure, such as bitscore, identity percentage, E-value or variations of these. The choice of metrics can affect the results of graph clustering in the following step, and we should therefore chose them carefully [cite:@gibbonsEvaluationBLASTbasedEdgeweighting2015].
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**** Identification of gene families
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Based on the homology links between each pair of genes, we construct an undirected weighted graph whose vertices correspond to genes and edges to homology links between them.
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Based on the homology links between each pair of genes, we construct a undirected weighted graph whose vertices correspond to genes and edges to homology links between them.
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We apply a graph clustering algorithm on the graph in order to infer the gene families corresponding to densely connected communities of vertices.
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FTAG Finder proposes three clustering algorithm alternatives: single linkage, Markov Clustering [cite:@vandongenNewClusterAlgorithm1998] or Walktrap [cite:@ponsComputingCommunitiesLarge2005].
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**** Detection of TAGs
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The final step of FTAG Finder consists in the identification of gls:TAG from the gene families and the positions of genes.
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For a given chromosome, the tool seeks genes belonging to the same family and located close to each other. The tool allows a maximal number of genes between the homologous genes, with a parameter set by the user. Ref:fig:tag-definitions is a schematic representation of some possible gls:TAG positioning on a genome associated with their definition in FTAG Finder /Find Tags/ step.
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#+begin_export latex
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\fladdfig{
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\includegraphics[width=.9\linewidth]{./figures/tag-definition.pdf}
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\caption[Schematic representation of TAG definitions]{\label{fig:tag-definitions} Schematic representation of TAG definitions. Several genes are represented on a linear chromosome. The red box represent a singleton gene. Orange boxes represent a TAG with two duplicate genes seperated by 7 other genes ($\mathrm{TAG}_7$). Four green boxes constitute a TAG, the gene at the extremities are seperated by three genes ($\mathrm{TAG}_3$. The two blue boxes represents a TAG with two genes next to each other $\mathrm{TAG}_0$. The bended edges represents the homology links between each pair of genes of a TAG.}}
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#+end_export
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FTAG Finder proposes three clustering algorithm alternatives: single linkage, Markov Clustering [cite:@vandongenNewClusterAlgorithm1998] or Walktrap [cite:@ponsComputingCommunitiesLarge2005].
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**** Detection of TAGs
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The final step of FTAG Finder consists in the identification of gls:TAG from the gene families and the positions of genes.
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For a given chromosome, the tool seeks genes belonging to the same family and located close to each other. The tool allows a maximal number of genes between the homologous genes, with a parameter set by the user. Cref:fig:tag-definitions is a schematic representation of some possible gls:TAG positioning on a genome associated with their definition in FTAG Finder /Find Tags/ step.
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* Objectives for the internship
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** Scientific questions
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The underlying question of FTAG Finder is the study of the evolutionary fate of duplicate genes in Eukaryotes.
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@ -180,17 +180,14 @@ Another objective of my internship will be to port FTAG Finder on a workflow man
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We will have to make a choice for the tool we will use.
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The two main options being Snakemake and Nextflow. Snakemake is a python powered workflow manager based on rules /à la/ GNU Make [cite:@kosterSnakemakeScalableBioinformatics2012]. Nextflow is a groovy powered workflow manager, which rely on the data flows paradigm [cite:@ditommasoNextflowEnablesReproducible2017]. Both are widely used in the bioinformatics community, and their use have been on the rise since they came out in 2012 and 2013 respectively [cite:@djaffardjyDevelopingReusingBioinformatics2023].
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#+begin_export latex
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\flstop
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#+begin_export html
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<h3>Bibliography</h3>
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#+end_export
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* References
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:PROPERTIES:
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:UNNUMBERED: t
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:END:
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#+print_bibliography:
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#+begin_export latex
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\printbibliography[heading=none]
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\flstop
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#+end_export
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#+begin_export latex
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@ -198,8 +195,7 @@ The two main options being Snakemake and Nextflow. Snakemake is a python powered
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\clearpairofpagestyles
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#+end_export
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* Summary
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** Summary
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:PROPERTIES:
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:UNNUMBERED: t
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:END:
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@ -220,10 +216,6 @@ Principle: construct vertex communities based on where an agent would get stuck
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# LocalWords: speciation Subfunctionalization Neofunctionalization
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# LocalWords: Pseudogenization
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# Local Variables:
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# eval: (progn (org-babel-goto-named-src-block "startup") (org-babel-execute-src-block) (outline-hide-sublevels 1))
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# End:
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* Setup :noexport:
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#+name: startup
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@ -233,3 +225,7 @@ Principle: construct vertex communities based on where an agent would get stuck
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#+RESULTS: startup
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: Loaded ./setup.el
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# Local Variables:
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# eval: (progn (org-babel-goto-named-src-block "startup") (org-babel-execute-src-block) (outline-hide-sublevels 1))
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# End:
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BIN
report.pdf (Stored with Git LFS)
BIN
report.pdf (Stored with Git LFS)
Binary file not shown.
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@ -40,7 +40,7 @@ See: [[https://emacs.stackexchange.com/a/41456/41374]]
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\\makeindex
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\\makeglossaries "
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;("\\part{%s}" . "\\part*{%s}")
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("\\chapter{%s}" . "\\chapter*{%s}")
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("\\chapter{%s}" . "\\chapter{%s}")
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("\\section{%s}" . "\\section*{%s}")
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("\\subsection{%s}" . "\\subsection*{%s}")
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("\\subsubsection{%s}" . "\\subsubsection*{%s}")
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@ -52,8 +52,6 @@
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natbib=true
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]{biblatex}
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\renewcommand\bibname{References}
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\RequirePackage{doi}
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\RequirePackage{xurl}
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\RequirePackage{orcidlink}
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@ -65,7 +63,7 @@
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]{doclicense}
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\RequirePackage[
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%nameinlink,
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nameinlink,
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noabbrev
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]{cleveref}
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@ -85,8 +83,7 @@
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urlcolor=primaryLink,
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linkcolor=primaryLink,
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anchorcolor=primaryLink,
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citecolor=primaryCite,
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%linktoc=page
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citecolor=primaryCite
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}
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\newcommand*{\glsplainhyperlink}[2]{%
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\begingroup%
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@ -103,14 +100,6 @@
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\renewcommand{\Gls}[1]{{\hypersetup{hidelinks}%
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\oldGls{#1}}}%
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\let\oldglspl=\glspl
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\renewcommand{\glspl}[1]{{\hypersetup{hidelinks}%
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\oldglspl{#1}}}%}
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\let\oldGlspl=\Glspl
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\renewcommand{\Glspl}[1]{{\hypersetup{hidelinks}%
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\oldGlspl{#1}}}%}
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%\renewcommand*{\glstextformat}[1]{\begingroup\hypersetup{hidelinks}#1\endgroup}
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%\renewcommand*{\glsdohyperlink}[2]{%
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