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ENH proof appendicies

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Nathan Dwarshuis 2021-09-09 15:01:17 -04:00
parent 891e82b456
commit 8828ee366c
3 changed files with 32 additions and 33 deletions
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25
tables/donor_chars.tex
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@ -1,18 +1,17 @@
\begin{tabular}{@{\extracolsep{5pt}} ccccccc} \begin{tabular}{@{\extracolsep{5pt}} cccccccc}
\\[-1.8ex]\hline \\[-1.8ex]\hline
\hline \\[-1.8ex] \hline \\[-1.8ex]
ID & Demo & Gender & ABO Type & Age (years) & BMI (\si{\kg\per\m\squared}) & HLA-A2 Present \\ ID & Demo & Gender & ABO Type & BMI (\si{\kg\per\m\squared}) & HLA-A2 Present \\
\hline \\[-1.8ex] \hline \\[-1.8ex]
4 & AA & M & NA & 27 & 21.7 & FALSE \\ 4 & AA & M & NA & 21.7 & FALSE \\
24 & C & M & NA & 22 & 23.9 & FALSE \\ 24 & C & M & NA & 23.9 & FALSE \\
29 & AA & M & NA & 40 & 20.9 & TRUE \\ 29 & AA & M & NA & 20.9 & TRUE \\
30 & AA & M & NA & 25 & 20.9 & TRUE \\ 30 & AA & M & NA & 20.9 & TRUE \\
309 & C/PI & F & APOS & 22 & 24.9 & FALSE \\ 309 & C/PI & F & APOS & 24.9 & FALSE \\
338 & C & M & OPOS & 23 & 23.4 & TRUE \\ 338 & C & M & OPOS & 23.4 & TRUE \\
338 & C & M & OPOS & 24 & 23.4 & TRUE \\ 358 & C & F & OPOS & 37.1 & TRUE \\
358 & C & F & OPOS & 52 & 37.1 & TRUE \\ 397 & C & M & OPOS & 31.2 & FALSE \\
397 & C & M & OPOS & 53 & 31.2 & FALSE \\ 512 & C & M & OPOS & 26.5 & FALSE \\
512 & C & M & OPOS & 21 & 26.5 & FALSE \\ 592 & H & M & APOS & 31.8 & FALSE \\
592 & H & M & APOS & 21 & 31.8 & FALSE \\
\hline \\[-1.8ex] \hline \\[-1.8ex]
\end{tabular} \end{tabular}

26
tables/donor_phenotypes.tex
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@ -1,18 +1,18 @@
\begin{tabular}{@{\extracolsep{5pt}} ccccccccc} \begin{tabular}{@{\extracolsep{5pt}} cccccccccc}
\\[-1.8ex]\hline \\[-1.8ex]\hline
\hline \\[-1.8ex] \hline \\[-1.8ex]
ID & \cd{3} & \cd{4} & \cd{8} & \cd{11c} & \cd{14} & \cd{16} & \cd{19} & \cd{56} \\ ID & Age (years) & \cd{3} & \cd{4} & \cd{8} & \cd{11c} & \cd{14} & \cd{16} & \cd{19} & \cd{56} \\
\hline \\[-1.8ex] \hline \\[-1.8ex]
4 & NA & 32.7 & 5.5 & NA & 12.3 & NA & 10.8 & 8.2 \\ 4 & 27 & NA & 32.7 & 5.5 & NA & 12.3 & NA & 10.8 & 8.2 \\
24 & 57.0 & 39.8 & 16.5 & NA & 12.7 & NA & 5.2 & 8.6 \\ 24 & 22 & 57.0 & 39.8 & 16.5 & NA & 12.7 & NA & 5.2 & 8.6 \\
29 & NA & 35.3 & 6.8 & NA & 14.4 & NA & 3.0 & 8.2 \\ 29 & 40 & NA & 35.3 & 6.8 & NA & 14.4 & NA & 3.0 & 8.2 \\
30 & NA & 35.2 & 18.2 & NA & 15.1 & NA & 9.0 & 8.1 \\ 30 & 25 & NA & 35.2 & 18.2 & NA & 15.1 & NA & 9.0 & 8.1 \\
309 & 96.9 & 64.6 & 28.0 & 1.2 & 0.7 & 1.1 & 0.1 & 1.5 \\ 309 & 22 & 96.9 & 64.6 & 28.0 & 1.2 & 0.7 & 1.1 & 0.1 & 1.5 \\
338 & 99.1 & 65.9 & 35.7 & 1.4 & 2.0 & 1.4 & 0.3 & 1.6 \\ 338 & 23 & 99.1 & 65.9 & 35.7 & 1.4 & 2.0 & 1.4 & 0.3 & 1.6 \\
338 & 99.1 & 62.4 & 35.2 & 2.2 & 1.6 & 0.6 & 0.2 & 1.5 \\ 338 & 24 & 99.1 & 62.4 & 35.2 & 2.2 & 1.6 & 0.6 & 0.2 & 1.5 \\
358 & 97.3 & 75.4 & 20.7 & 3.2 & 2.4 & 3.2 & 0.6 & 3.1 \\ 358 & 52 & 97.3 & 75.4 & 20.7 & 3.2 & 2.4 & 3.2 & 0.6 & 3.1 \\
397 & 98.2 & 67.3 & 33.0 & 2.8 & 1.2 & 1.5 & 0.2 & 8.0 \\ 397 & 53 & 98.2 & 67.3 & 33.0 & 2.8 & 1.2 & 1.5 & 0.2 & 8.0 \\
512 & 97.4 & 62.8 & 29.6 & 2.2 & 3.9 & 2.2 & 0.2 & 1.2 \\ 512 & 21 & 97.4 & 62.8 & 29.6 & 2.2 & 3.9 & 2.2 & 0.2 & 1.2 \\
592 & 95.2 & 56.1 & 35.9 & 5.3 & 0.7 & 4.6 & 2.9 & 5.4 \\ 592 & 21 & 95.2 & 56.1 & 35.9 & 5.3 & 0.7 & 4.6 & 2.9 & 5.4 \\
\hline \\[-1.8ex] \hline \\[-1.8ex]
\end{tabular} \end{tabular}

14
tex/thesis.tex
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@ -4847,7 +4847,7 @@ strongly).
The code used to aggregate all experimental data was written primarily in The code used to aggregate all experimental data was written primarily in
Python, with a subprocess running R in a Docker container to handle the flow Python, with a subprocess running R in a Docker container to handle the flow
cytometry data (\cref{fig:meta_overview}). The Postgres database itself was cytometry data (\cref{fig:meta_overview}). The PostgreSQL database itself was
hosted using \gls{aws} using their proprietary Aurora implementation. hosted using \gls{aws} using their proprietary Aurora implementation.
\begin{figure*}[ht!] \begin{figure*}[ht!]
@ -4888,17 +4888,17 @@ The code is available here: \url{https://github.gatech.edu/ndwarshuis3/mdma}.
\chapter{BINDING KINETICS}\label{sec:appendix_binding} \chapter{BINDING KINETICS}\label{sec:appendix_binding}
The binding kinetics of \gls{stp} or \glspl{mab} were simulated using a The binding kinetics of \gls{stp} or \glspl{mab} were simulated using a
receptor:ligand model, where the free-floating species in question was the receptor/ligand model, where the free-floating species in question was the
ligand which bound to receptors attached to the microcarriers. Each microcarrier ligand which bound to receptors attached to the microcarriers. Each microcarrier
was assumed to be a porous sphere with a fixed number of receptors uniformly was assumed to be a porous sphere with a fixed number of receptors uniformly
distributed throughout its interior matrix. The receptor/ligand reaction was distributed throughout its interior matrix. The receptor/ligand reaction was
assumed to be instantaneous (which is reasonable given that these are reactions assumed to be instantaneous (which is reasonable given that these are reactions
between biotin and \gls{stp} which are extremely strong). From this, we further between biotin and \gls{stp} which are extremely strong). From this, we further
assumed a spherical interface within each microcarrier and aligned at the center assumed a spherical interface aligned at the center within each microcarrier
wherein all receptors in the interior were unbound and all on the exterior were wherein all receptors in the interior were unbound and all on the exterior were
bound. At $\gls{sym:time}=0$ this interface was assumed to start with a radius bound. At $\gls{sym:time}=0$ this interface was assumed to start with a radius
equal to that of the microcarrier, and shrunk down to radius of zero as ligand equal to that of the microcarrier, and shrunk down to radius of zero as ligand
flowed into the porous microcarriers and bound. We assumed the concentration of flowed into the porous microcarrier and bound. We assumed the concentration of
ligand to be zero at the interface and equal to the bulk concentration at the ligand to be zero at the interface and equal to the bulk concentration at the
exterior surface of the microcarrier. Furthermore, we assumed that the interface exterior surface of the microcarrier. Furthermore, we assumed that the interface
moved slowly relative to the diffusion of ligand into the microcarriers, and moved slowly relative to the diffusion of ligand into the microcarriers, and
@ -4981,21 +4981,21 @@ and \cref{eqn:radial_conc_change} yields \cref{eqn:stp_diffusion_1} and
\cref{eqn:stp_diffusion_2}. \cref{eqn:stp_diffusion_2}.
The \gls{stp} binding kinetic profile was fit and calculated using the following The \gls{stp} binding kinetic profile was fit and calculated using the following
MATLAB code. Note that the \inlinecode{geometry} parameter was varied so as to MATLAB code. Note that the \inlinecode{geometry} parameter was varied to
minimize the \inlinecode{SSE} output. minimize the \inlinecode{SSE} output.
\lstinputlisting{../code/diffusion_stp.m} \lstinputlisting{../code/diffusion_stp.m}
The geometric diffusivity from above (the \inlinecode{geometry} variable) was The geometric diffusivity from above (the \inlinecode{geometry} variable) was
used in the below code to obtain the reaction profile for the \gls{mab} binding used in the below code to obtain the reaction profile for the \gls{mab} binding
step. The model is the same except for the parameters which were changes to step. The model is the same except for the parameters which were changed to
reflect the \gls{mab} coating process. reflect the \gls{mab} coating process.
\lstinputlisting{../code/diffusion_mab.m} \lstinputlisting{../code/diffusion_mab.m}
\chapter{WASHING KINETICS CODE}\label{sec:appendix_washing} \chapter{WASHING KINETICS CODE}\label{sec:appendix_washing}
The wash steps for the \gls{dms} were modeled using the following code: The wash steps for the \glspl{dms} were modeled using the following code:
\lstinputlisting{../code/microcarrier_diffusion_washing.m} \lstinputlisting{../code/microcarrier_diffusion_washing.m}