ADD link to appendix for derivations
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@ -1669,8 +1669,8 @@ quantified for \gls{stp} protein using the \gls{bca} assay.
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The geometric diffusivity of the microcarriers was determined using a
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The geometric diffusivity of the microcarriers was determined using a
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pseudo-steady-state model. Each microcarrier was assumed to be a porous sphere
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pseudo-steady-state model. Each microcarrier was assumed to be a porous sphere
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with a fixed number of uniformly distributed `\gls{stp} binding sites' equal to
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with a fixed number of uniformly distributed ``\gls{stp} binding sites'' equal
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the number of \gls{stp} molecules experimentally determined to bind to the
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to the number of \gls{stp} molecules experimentally determined to bind to the
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microcarriers. Because the reaction rate between biotin and \gls{stp} is so fast
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microcarriers. Because the reaction rate between biotin and \gls{stp} is so fast
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(it is the strongest non-covalent bond in known existence), we assumed that the
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(it is the strongest non-covalent bond in known existence), we assumed that the
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interface of free biotin shrunk as a function of \gls{stp} diffusing to the
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interface of free biotin shrunk as a function of \gls{stp} diffusing to the
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@ -1679,10 +1679,9 @@ also assumed that the pores in the microcarriers were large enough that the
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interactions between the \gls{stp} and surfaces would be small, thus the
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interactions between the \gls{stp} and surfaces would be small, thus the
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geometric diffusivity could be represented as a fraction of the diffusion
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geometric diffusivity could be represented as a fraction of the diffusion
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coefficient of \gls{stp} in water. This model was given by
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coefficient of \gls{stp} in water. This model was given by
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\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}:
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\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2} (see \cref{sec:appendix_binding}
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for derivations):
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% TODO actually derive these equations, eg state the initial conditions and
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% governing equation
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\begin{equation}
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\begin{equation}
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\label{eqn:stp_diffusion_1}
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\label{eqn:stp_diffusion_1}
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\frac{dr}{dt} = \frac{-D_{app}C_b}{Br(1-r/R)}
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\frac{dr}{dt} = \frac{-D_{app}C_b}{Br(1-r/R)}
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@ -3717,7 +3716,6 @@ group, while the \gls{colb} group visibly lowered CD62L and CD4, indicating
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partial enzymatic cleavage (\cref{fig:collagenase_fx}). Based on this result, we
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partial enzymatic cleavage (\cref{fig:collagenase_fx}). Based on this result, we
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used \gls{cold} moving forward.
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used \gls{cold} moving forward.
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% FIGURE this figure is tall and skinny like me
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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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@ -4867,7 +4865,6 @@ Python, with a subprocess running R in a Docker container to handle the flow
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cytometry data (\cref{fig:meta_overview}). The Postgres database itself was
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cytometry data (\cref{fig:meta_overview}). The Postgres database itself was
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hosted using \gls{aws} using their proprietary Aurora implementation.
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hosted using \gls{aws} using their proprietary Aurora implementation.
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% FIGURE explain what the colors mean
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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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