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ENH make figure and table captions sane

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Nathan Dwarshuis 2021-09-07 21:09:06 -04:00
parent 89baa4f970
commit 957f09821c
1 changed files with 54 additions and 53 deletions
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tex/thesis.tex
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@ -1426,7 +1426,8 @@ bead-based T cell expansion technology\footnote{adapted from \dmspaper{}}.
\includegraphics{../figures/dms_flowchart.png}
\endgroup
\caption[\Acrshort{dms} Flowchart]{Overview of \gls{dms} manufacturing process.}
\caption[\Acrshort{dms} Manufacturin Flowchart]
{Overview of \gls{dms} manufacturing process.}
\label{fig:dms_flowchart}
\end{figure*}
@ -1468,7 +1469,7 @@ step to remove excess \gls{stp}. They were washed once again in the cell culture
media to be used for the T cell expansion.
\begin{table}[!h] \centering
\caption{Properties of the microcarriers used}
\caption{Microcarrier properties}
\label{tab:carrier_props}
\input{../tables/carrier_properties.tex}
\end{table}
@ -1833,7 +1834,7 @@ that was under-range (`OOR <' in output spreadsheet) was set to zero. All values
that were extrapolated from the standard curve were left unchanged.
\begin{table}[!h] \centering
\caption{Luminex Panel}
\caption{Luminex panel}
\label{tab:luminex_panel}
\input{../tables/luminex_panel.tex}
\end{table}
@ -1894,7 +1895,7 @@ context of pure error). Significance was evaluated at $\upalpha$ = 0.05.
\end{figure*}
\begin{table}[!h] \centering
\caption{\glspl{mab} used for flow cytometry}
\caption{Antibodies used for flow cytometry}
\label{tab:flow_mabs}
\input{../tables/flow_mabs.tex}
\end{table}
@ -1919,7 +1920,7 @@ respective sections. Cells were gated according to \cref{fig:gating_strategy}.
\phantomsubcaption\label{fig:mab_carrier_fitc}
\endgroup
\caption[\gls{dms} Coating]
\caption[\acrshort{dms} Coating]
{\gls{dms} functionalization results.
\subcap{fig:cug_vs_cus}{Bound \gls{stp} surface
density on either \gls{cus} or \gls{cug} microcarriers. Surface density
@ -1977,7 +1978,7 @@ the T cells receive downstream.
\phantomsubcaption\label{fig:dms_snb_decay_curves}
\endgroup
\caption[\gls{dms} Process Parameters]
\caption[\acrshort{dms} Process Parameters]
{Investigation of influential parameters for the \gls{dms} process.
\subcap{fig:dms_qc_doe}{\gls{doe} investigating the effect of initial mass
of microcarriers, reaction temperature, and biotin concentration on
@ -2054,7 +2055,7 @@ water prior to adding it to the microcarrier suspension (which itself is in
\phantomsubcaption\label{fig:dms_biotin_washed}
\endgroup
\caption[\gls{dms} Reaction kinetics]
\caption[\acrshort{dms} Reaction Kinetics]
{Reaction kinetics for microcarrier functionalization.
\subcap{fig:dms_biotin_rxn_mass}{Biotin mass bound per time}
\subcap{fig:dms_biotin_rxn_frac}{Fraction of input biotin bound per time}
@ -2164,7 +2165,7 @@ MATLAB code and output for all the wash step calculations are given in
\phantomsubcaption\label{fig:dms_cells_fluor}
\endgroup
\caption[T cells growing on \glspl{dms}]
\caption[T Cells Growing on \acrshortpl{dms}]
{Cells grow in tight clusters in and around functionalized \gls{dms}.
\subcap{fig:dms_cells_phase}{Phase-contrast image of T cells growing on
\glspl{dms}}
@ -2184,7 +2185,7 @@ MATLAB code and output for all the wash step calculations are given in
\phantomsubcaption\label{fig:dms_expansion_isotype}
\endgroup
\caption[\glspl{dms} selectively expand T cells]
\caption[\acrshortpl{dms} Selectively Expand T Cells]
{T cells are selectively expanded on \gls{dms}.
\subcap{fig:dms_expansion_bead}{T cells expanded with either \glspl{dms} or
bead for 12 days. Significance was assessed using a two-tailed
@ -2221,7 +2222,7 @@ due to the \acd{3} and \acd{28} \glspl{mab}\cite{Waysbort2013}.
\phantomsubcaption\label{fig:apoptosis_bcl2}
\endgroup
\caption[Apoptosis Quantification for \glspl{dms}]
\caption[Apoptosis Quantification for \acrshortpl{dms}]
{\glspl{dms} produce cells with lower apoptosis marker expression on average
compared to bead.
\subcap{fig:apoptosis_annV}{Quantification of apoptosis and necrosis by
@ -2266,7 +2267,7 @@ expansion by lowering apoptosis of the cells in culture.
\phantomsubcaption\label{fig:dms_inside_regression}
\endgroup
\caption[A subset of T cells grow in interior of \glspl{dms}]
\caption[T Cells Growing on Interior of \acrshortpl{dms}]
{A percentage of T cells grow in the interior of \glspl{dms}.
\subcap{fig:dms_inside_bf}{T cells stained dark with \gls{mtt} after
growing on either coated or uncoated \glspl{dms} for 15 days visualized
@ -2279,7 +2280,7 @@ expansion by lowering apoptosis of the cells in culture.
\end{figure*}
\begin{table}[!h] \centering
\caption{Regression for fraction of cells in \gls{dms} at day 14}
\caption{Regression for fraction of cells in \acrshortpl{dms} at day 14}
\label{tab:inside_regression}
\input{../tables/inside_fraction_regression.tex}
\end{table}
@ -2311,7 +2312,7 @@ harvested after \SI{14}{\day}) (\cref{tab:inside_regression}).
\phantomsubcaption\label{fig:dms_exp_mem8}
\endgroup
\caption[\gls{dms} vs bead expansion]
\caption[\acrshort{dms} vs Bead Expansion]
{\gls{dms} lead to superior expansion of T cells compared to beads across
multiple donors.
\subcap{fig:dms_exp_fold_change}{Longitudinal fold change of T cells grown
@ -2357,7 +2358,7 @@ true when observing the CD4+ and CD8+ fractions of the naïve/memory subset
\phantomsubcaption\label{fig:dms_phenotype_cd4}
\endgroup
\caption[Representative flow plots of \ptmem{} and \pth{} T cells]
\caption[Representative Flow Plots of \ptmem{} and \pth{} T Cells]
{Representative flow plots of \ptmem{} and \pth{} T cells at day 14 of
expansion using either beads or \glspl{dms}. For three representative donor
samples, phenotypes are shown for \subcap{fig:dms_phenotype_mem}{\ptmem{}}
@ -2431,7 +2432,7 @@ showing that migration was likely independent of \gls{car} transduction.
\phantomsubcaption\label{fig:car_cd19_endpoint}
\endgroup
\caption[\glspl{dms} lead to efficient CD19 transduction]
\caption[CD19 Transduction]
{\glspl{dms} lead to efficient CD19 transduction.
\subcap{fig:car_cd19_flow}{Representative flow cytometry plot for
transduced or untransduced T cells stained with \gls{ptnl}.}
@ -2451,7 +2452,7 @@ showing that migration was likely independent of \gls{car} transduction.
\phantomsubcaption\label{fig:car_degran_migration}
\endgroup
\caption[\glspl{dms} produce functional \gls{car} T cells]
\caption[\acrshort{car} T Cell Functionality]
{\glspl{dms} produce functional \gls{car} T cells.
\subcap{fig:car_degran_flow}{Representative flow plot for
degenerating T cells.}
@ -2479,7 +2480,7 @@ for bead (\cref{fig:car_bcma_total}).
\phantomsubcaption\label{fig:car_bcma_total}
\endgroup
\caption[BMCA Transduction Results]
\caption[\acrshort{bcma} Transduction]
{\glspl{dms} produce larger numbers of \gls{bcma} \gls{car} T cells compared
to beads.
\subcap{fig:car_bcma_percent}{\ptcarp{} at day 14.}
@ -2500,7 +2501,7 @@ for bead (\cref{fig:car_bcma_total}).
\phantomsubcaption\label{fig:grex_cd4}
\endgroup
\caption[Grex bioreactor results]
\caption[Grex Expansion]
{\glspl{dms} expand T cells robustly in Grex bioreactors.
\subcap{fig:grex_results_fc}{Fold change of T cells over time.}
\subcap{fig:grex_results_viability}{Viability of T cells over time.}
@ -2538,7 +2539,7 @@ area could mean higher signaling and higher differentiation rate to
\includegraphics{../figures/grex_luminex.png}
\endgroup
\caption[Grex luminex results]
\caption[Grex Luminex Results]
{\gls{dms} lead to higher cytokine production in Grex bioreactors.}
\label{fig:grex_luminex}
\end{figure*}
@ -2563,7 +2564,7 @@ tissue-culture plates.
\includegraphics{../figures/nonstick.png}
\endgroup
\caption[\acrshort{mab} do not detach from \glspl{dms}]
\caption[\acrshort{dms} \acrshort{mab} Detachment]
{\glspl{mab} do not detach from microcarriers onto T cells in a detectable
manner. Plots are representative manufacturing runs harvest after
\SI{14}{\day} of expansion and stained with \anti{\gls{igg}}.
@ -2630,20 +2631,19 @@ this modification as removing conditioned media as the cell are expanding could
disrupt signaling pathways.
\begin{table}[!h] \centering
\caption{Causal Inference on treatment variables only}
\caption{Causal inference on treatment variables}
\label{tab:ci_treat}
\input{../tables/causal_inference_treat.tex}
\end{table}
\begin{table}[!h] \centering
\caption{Causal Inference on treatment variables and control variables}
\caption{Causal inference on all variables}
\label{tab:ci_controlled}
\input{../tables/causal_inference_control.tex}
\end{table}
\begin{table}[!h] \centering
\caption{Causal Inference on treatment variables and control variables (single
donor)}
\caption{Causal inference on all variables (single donor)}
\label{tab:ci_single}
\input{../tables/causal_inference_single.tex}
\end{table}
@ -2657,7 +2657,7 @@ disrupt signaling pathways.
\phantomsubcaption\label{fig:metaanalysis_fx_cd4}
\endgroup
\caption[Meta-analysis effect sizes]
\caption[Meta-analysis Effect Sizes]
{\glspl{dms} exhibit superior performance compared to beads controlling for
many experimental and process conditions. Effect sizes for
\subcap{fig:metaanalysis_fx_exp}{fold change},
@ -3137,7 +3137,7 @@ were true.
\phantomsubcaption\label{fig:il2_mod_flow}
\endgroup
\caption[T cells grown at varying IL2 concentrations]
\caption[T Cells Grown at Varying IL2 Concentrations]
{\glspl{dms} grow T cells effectively at lower IL2 concentrations.
\subcap{fig:il2_mod_timecourse}{Longitudinal cell counts of T cells grown
with either bead or \glspl{dms} using varying IL2 concentrations}
@ -3192,7 +3192,7 @@ at \SI{10}{\IU\per\ml} throughout the remainder of this aim.
\phantomsubcaption\label{fig:doe_response_first_cd4}
\endgroup
\caption[Response plots for first DOE]
\caption[Response Plots for First \acrshort{doe}]
{Response plots from the first \gls{doe} experiment for
\subcap{fig:doe_response_first_mem}{\ptmemp{}} and
\subcap{fig:doe_response_first_cd4}{\pthp{}}. Each point is one run.
@ -3243,7 +3243,7 @@ this bias in our data, these runs were not used.}.
\phantomsubcaption\label{fig:doe_responses_ratio}
\endgroup
\caption[T cell optimization through Design of Experiments]
\caption[T Cell Optimization Through \acrshortpl{doe}]
{\gls{doe} methodology reveals optimal conditions for expanding T cell
subsets. Responses vs IL2 concentration, \gls{dms} concentration, and
functional \gls{mab} percentage are shown for
@ -3257,31 +3257,31 @@ this bias in our data, these runs were not used.}.
\end{figure*}
\begin{table}[!h] \centering
\caption{Total CD62L+CCR7+ T cell response (first order regression)}
\caption{Regression for total \ptmem{} cells (first order)}
\label{tab:doe_mem1.tex}
\input{../tables/doe_mem1.tex}
\end{table}
\begin{table}[!h] \centering
\caption{Total CD62L+CCR7+ T cell response (third order regression)}
\caption{Regression for total \ptmem{} cells (third order)}
\label{tab:doe_mem2.tex}
\input{../tables/doe_mem2.tex}
\end{table}
\begin{table}[!h] \centering
\caption{Total CD4+ T cell response}
\caption{Regression for total \pth{} cells}
\label{tab:doe_cd4.tex}
\input{../tables/doe_cd4.tex}
\end{table}
\begin{table}[!h] \centering
\caption{Linear regression for total \ptmemh{} cells}
\caption{Regression for total \ptmemh{} cells}
\label{tab:doe_mem4.tex}
\input{../tables/doe_mem4.tex}
\end{table}
\begin{table}[!h] \centering
\caption{Linear regression for CD4:CD8 ratio in the \ptmem{} compartment}
\caption{Regression for \ptmem{} CD4:CD8 ratio}
\label{tab:doe_ratio.tex}
\input{../tables/doe_ratio.tex}
\end{table}
@ -3345,7 +3345,7 @@ significant predictors.
\phantomsubcaption\label{fig:doe_sr_contour_ratio}
\endgroup
\caption[Contour plots for DOE responses]
\caption[Contour Plots for \acrshort{doe} Responses]
{Symbolic regression and contour plots reveal optimal conditions for
\subcap{fig:doe_sr_contour_mem4}{\ptmemh{} cells} and
\subcap{fig:doe_sr_contour_ratio}{CD4:CD8 ratio in the \ptmem{}
@ -3378,7 +3378,7 @@ features of quality early in their expansion process.
\includegraphics{../figures/doe_luminex.png}
\endgroup
\caption[Cytokine release profile of T cells from DOE]
\caption[Cytokine Release Profile of T Cells from \acrshort{doe}]
{T cells show robust and varying cytokine responses over time}
\label{fig:doe_luminex}
\end{figure*}
@ -3394,8 +3394,8 @@ data were collected in plates) (\cref{fig:grex_luminex}).
% TABLE this table looks like crap, break it up into smaller tables
\begin{table}[!h] \centering
\caption[Results for data-driven modeling]
{Results for data-driven modeling using process parameters (PP) with
\caption[Machine Learning Model Results]
{Results for \gls{ml} modeling using process parameters (PP) with
only \gls{nmr} on day 4 (N4), only \gls{nmr} on day 6 (N6), only secretome
on day 6 (S6), or various combindation of each for all seven \gls{ml}
techniques}
@ -3444,7 +3444,7 @@ others showed maximum \rmemh{} predictions (\cref{fig:sr_omics}).
\phantomsubcaption\label{fig:mod_flower_cd4}
\endgroup
\caption[Data-Driven \gls{cqa} identification]
\caption[\acrshort{cqa} Consensus Plots]
{Data-driven modeling using techniques with regularization reveals species
predictive species which are candidates for \glspl{cqa}. Flower plots are
shown for \subcap{fig:mod_flower_48r}{CD4:CD8 ratio} and
@ -3485,7 +3485,7 @@ lactate.
\phantomsubcaption\label{fig:nmr_cors_matrix}
\endgroup
\caption[NMR Day 4 correlations]
\caption[NMR Day 4 Correlations]
{\gls{nmr} features at day 4 are strongly correlated with each other and the
response variables. Highly correlated relationships are shown for
\subcap{fig:nmr_cors_lactate}{lactate},
@ -3750,7 +3750,7 @@ used \gls{cold} moving forward.
\includegraphics{../figures/collagenase.png}
\endgroup
\caption[Effects Collagenase Treatment on T cells]
\caption[Effects of Collagenase Treatment on T cells]
{T cells treated with either \gls{colb}, \gls{cold}, or buffer and then
stained for various surface markers and analyzing via flow cytometry.}
\label{fig:collagenase_fx}
@ -3781,7 +3781,7 @@ are fundamentally altered by changing the number of \glspl{dms} temporally.
\phantomsubcaption\label{fig:add_rem_cd4}
\endgroup
\caption[Endpoint results from adding/removing \gls{dms} on day 4]
\caption[Results of Adding/Removing \acrshort{dms} on Day 4]
{Changing \gls{dms} concentration on day 4 has profound effects on phenotype
and growth.
\subcap{fig:add_rem_growth}{Longitudinal fold change},
@ -3798,7 +3798,7 @@ are fundamentally altered by changing the number of \glspl{dms} temporally.
\includegraphics{../figures/spade_gates.png}
\endgroup
\caption[SPADE Gating Strategy]
\caption[\acrshort{spade} Gating Strategy]
{Gating strategy for quantifying early-differentiated T cells via
\gls{spade}.}
\label{fig:spade_gates}
@ -3814,7 +3814,8 @@ are fundamentally altered by changing the number of \glspl{dms} temporally.
\phantomsubcaption\label{fig:spade_tsne_stem}
\endgroup
\caption[SPADE and tSNE analysis temporally-modified DMS concentration]
\caption[\acrshort{spade} and \acrshort{tsne} Analysis of Temporally Modulated
\acrshort{dms} Cultures]
{Removing \glspl{dms} leads to a higher fraction of potent stem-memory T
cells compared to both adding and not changing the \gls{dms} concentration
at day 4.
@ -3884,7 +3885,7 @@ T cells through \gls{a2b1} and \gls{a2b2}, causing them to grow better in the
\phantomsubcaption\label{fig:inegrin_1_cd49}
\endgroup
\caption[Integrin blocking I]
\caption[Integrin Blocking I]
{Blocking with integrin does not lead to differences in memory or growth.
\subcap{fig:inegrin_1_overview}{Experimental overview}
\subcap{fig:inegrin_1_fc}{Fold change of \gls{dms}-activated T cell over
@ -3898,7 +3899,7 @@ T cells through \gls{a2b1} and \gls{a2b2}, causing them to grow better in the
\end{figure*}
\begin{table}[!h] \centering
\caption{Linear regression for day 14 phenotype shown in \cref{fig:integrin_1}}
\caption{Regression for day 14 phenotype shown in \cref{fig:integrin_1}}
\label{tab:integrin_1_reg}
\input{../tables/integrin_1_reg.tex}
\end{table}
@ -3924,7 +3925,7 @@ T cells at day 6, showing that the target we wished to block was present
\phantomsubcaption\label{fig:inegrin_2_mem}
\endgroup
\caption[Integrin blocking II]
\caption[Integrin Blocking II]
{Blocking with integrin does not lead to differences in memory or growth.
\subcap{fig:inegrin_1_fc}{Fold change of \gls{dms}-activated T cell over
time with each blocking condition.}
@ -3936,7 +3937,7 @@ T cells at day 6, showing that the target we wished to block was present
\end{figure*}
\begin{table}[!h] \centering
\caption{Linear regression for day 14 phenotype shown in \cref{fig:integrin_2}}
\caption{Regression for day 14 phenotype shown in \cref{fig:integrin_2}}
\label{tab:integrin_2_reg}
\input{../tables/integrin_2_reg.tex}
\end{table}
@ -3976,7 +3977,7 @@ density.
\phantomsubcaption\label{fig:il15_1_mem}
\endgroup
\caption[IL15 blocking I]
\caption[IL15 Blocking I]
{Blocking IL15Ra does not lead to differences in memory or growth.
\subcap{fig:il15_1_overview}{Experimental overview}
Longitudinal measurements of
@ -4011,7 +4012,7 @@ the markers, and by extension showing no difference in phenotype
\phantomsubcaption\label{fig:il15_2_mem}
\endgroup
\caption[IL15 blocking II]
\caption[IL15 Blocking II]
{Blocking soluble IL15 does not lead to differences in memory or growth.
\subcap{fig:il15_2_overview}{Experimental overview}
Longitudinal measurements of
@ -4217,7 +4218,7 @@ between survival groups.
\end{figure*}
\begin{table}[!h] \centering
\caption{Results for \gls{car} T cell \invivo{} dose study}
\caption{Cells injected for \acrshort{car} T cell \invivo{} dose study}
\label{tab:mouse_dosing_results}
\input{../tables/mouse_dose_car.tex}
\end{table}
@ -4233,7 +4234,7 @@ between survival groups.
\phantomsubcaption\label{fig:mouse_dosing_qc_growth}
\endgroup
\caption[Mouse Dosing T cell Characteristics]
\caption[Mouse Dosing T Cell Characteristics]
{Characteristics of T cells harvested at day 14 injected into NSG
mice at varying doses.
Fractions of T cell subtypes in the day 14 product including
@ -4383,7 +4384,7 @@ at day 14 despite the overall \ptmemp{} decreasing with time as shown elsewhere
\phantomsubcaption\label{fig:mouse_timecourse_qc_mem}
\endgroup
\caption[Mouse Timecourse T cell Characteristics]
\caption[Mouse Timecourse T Cell Characteristics]
{Characteristics of T cells harvested at varying timepoints injected into NSG
mice.
\subcap{fig:mouse_timecourse_qc_growth}{Fold change of T cells (each
@ -4896,7 +4897,7 @@ hosted using \gls{aws} using their proprietary Aurora implementation.
\includegraphics{../figures/metaanalysis.png}
\endgroup
\caption[Meta-analysis overview]
\caption[Meta-analysis Overview]
{Overview of strategy used for meta-analysis. Colors: notebook (pink), input
files (green), analysis framework (blue), data store (cyan), analysis
pipeline (orange).}