ADD figs to aim 2a
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@ -1550,6 +1550,77 @@ provide these benefits.
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\section{introduction}
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\section{introduction}
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\section{methods}
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\section{methods}
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\section{results}
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\section{results}
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\subsection{DOE shows optimal conditions for expanded potent T cells}
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\begin{figure*}[ht!]
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\begingroup
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\includegraphics{../figures/modeling_overview.png}
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\phantomsubcaption\label{fig:mod_overview_flow}
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\phantomsubcaption\label{fig:mod_overview_doe}
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\endgroup
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\caption[Modeling Overview]
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{Overview of modeling experiments.
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\subcap{fig:mod_overview_flow}{Relationship
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between \gls{doe} experiments and AI driven prediction. \glspl{doe} will
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be used to determine optimal process input conditions, and longitudinal
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multiomics data will be used to fit predictive models. Together, these
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will reveal predictive species that may be used for \glspl{cqa} and
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\glspl{cpp}.}
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\subcap{fig:mod_overview_doe}{Overview of the two \gls{doe} experiments; the
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initial \gls{doe} is given by the blue points and the augmented \gls{doe}
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is given by the blue points.}
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}
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\label{fig:mod_overview}
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\end{figure*}
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\begin{figure*}[ht!]
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\begingroup
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\includegraphics{../figures/doe_responses.png}
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\phantomsubcaption\label{fig:doe_responses_mem}
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\phantomsubcaption\label{fig:doe_responses_cd4}
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\endgroup
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\caption[T cell optimization through Design of Experiments]
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{\gls{doe} methodology reveals optimal conditions for expanding T cell
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subsets. Responses vs IL2 concentration, \gls{dms} concentration, and
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functional \gls{mab} percentage are shown for
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\subcap{fig:doe_responses_mem}{total \ptmem{} T cells} and
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\subcap{fig:doe_responses_cd4}{total \pth{} T cells}. Each point represents
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one run.
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}
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\label{fig:doe_responses}
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\end{figure*}
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% DOE regression tables
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% TODO this section header sucks
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\subsection{AI modeling reveals highly predictive species}
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% model summary table
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\begin{figure*}[ht!]
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\begingroup
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\includegraphics{../figures/modeling_flower.png}
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\phantomsubcaption\label{fig:mod_flower_48r}
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\phantomsubcaption\label{fig:mod_flower_cd4}
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\endgroup
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\caption[Data-Driven \gls{cqa} identification]
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{Data-driven modeling using techniques with regularization reveals species
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predictive species which are candidates for \glspl{cqa}. Flower plots are
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shown for \subcap{fig:mod_flower_48r}{CD4:CD8 ratio} and
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\subcap{fig:mod_flower_cd4}{total \ptmemh{} cells}. The left and right
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columns includes models that were trained only on the secretome and
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metabolome respectively. Each flower on each plot represents one model,
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moving toward the center indicates higher agreement between models.}
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\label{fig:mod_flower}
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\end{figure*}
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\section{discussion}
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\section{discussion}
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\chapter{aim 2b}\label{aim2b}
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\chapter{aim 2b}\label{aim2b}
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@ -1615,7 +1686,6 @@ between survival groups.
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\label{fig:mouse_dosing_overview}
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\label{fig:mouse_dosing_overview}
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\end{figure*}
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\end{figure*}
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\begin{table}[!h] \centering
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\begin{table}[!h] \centering
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\caption{Results for \gls{car} T cell \invivo{} dose study}
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\caption{Results for \gls{car} T cell \invivo{} dose study}
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\label{tab:mouse_dosing_results}
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\label{tab:mouse_dosing_results}
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