ADD luminex doe figure
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@ -1946,6 +1946,29 @@ provide these benefits.
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\subsection{study design}
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\subsection{study design}
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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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The first DOE resulted in a randomized 18-run I-optimal custom design where each
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The first DOE resulted in a randomized 18-run I-optimal custom design where each
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DMS parameter was evaluated at three levels: IL2 concentration (10, 20, and 30
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DMS parameter was evaluated at three levels: IL2 concentration (10, 20, and 30
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U/uL), DMS concentration (500, 1500, 2500 carrier/uL), and functionalized
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U/uL), DMS concentration (500, 1500, 2500 carrier/uL), and functionalized
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@ -2206,29 +2229,6 @@ Venn diagram from the venn R package.
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\input{../tables/doe_runs.tex}
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\input{../tables/doe_runs.tex}
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\end{table}
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\end{table}
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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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\begin{figure*}[ht!]
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\begingroup
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\begingroup
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@ -2269,6 +2269,17 @@ Venn diagram from the venn R package.
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% TODO this section header sucks
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% TODO this section header sucks
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\subsection{AI modeling reveals highly predictive species}
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\subsection{AI modeling reveals highly predictive species}
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\begin{figure*}[ht!]
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\begingroup
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\includegraphics{../figures/doe_luminex.png}
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\endgroup
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\caption[Cytokine release profile of T cells from DOE]
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{T cells show robust and varying cytokine responses over time}
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\label{fig:doe_luminex}
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\end{figure*}
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Due to the heterogeneity of the multivariate data collected and knowing that no
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Due to the heterogeneity of the multivariate data collected and knowing that no
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single model structure is perfect for all applications, we implemented an
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single model structure is perfect for all applications, we implemented an
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agnostic modeling approach to better understand these TN+TCM responses. To
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agnostic modeling approach to better understand these TN+TCM responses. To
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