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  1. .gitattributes +66 -0
  2. chapters/collecting-trajectories.log +0 -0
  3. chapters/collecting-trajectories.tex +294 -0
  4. chapters/langevin-equation.log +0 -0
  5. chapters/langevin-equation.tex +367 -0
  6. chapters/preamble.tex +108 -0
  7. chapters/xenopus-equilibrium.tex +71 -0
  8. images/Pulse1-Fourier.pdf +3 -0
  9. images/Pulse1.pdf +3 -0
  10. images/Pulse2-Fourier.pdf +3 -0
  11. images/Pulse2.pdf +3 -0
  12. images/Pulse3-Fourier.pdf +3 -0
  13. images/Pulse3.pdf +3 -0
  14. images/a-logo.png +0 -0
  15. images/coke.jpg +3 -0
  16. images/fourier-1.pdf +3 -0
  17. images/fourier-2.pdf +3 -0
  18. images/fourier-3.pdf +3 -0
  19. images/fourier-4.pdf +3 -0
  20. images/fourier-5.pdf +3 -0
  21. mtrack.png +3 -0
  22. presentation.aux +12 -75
  23. presentation.fdb_latexmk +13 -51
  24. presentation.fls +2 -190
  25. presentation.log +21 -282
  26. presentation.nav +11 -58
  27. presentation.out +2 -6
  28. presentation.pdf +2 -2
  29. presentation.snm +0 -6
  30. presentation.synctex.gz +2 -2
  31. presentation.tex +1 -1
  32. presentation.toc +0 -4
  33. presentation_images/1DCnn.png +0 -0
  34. presentation_images/Derivative_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Fifth_density_stats.png +3 -0
  35. presentation_images/Derivative_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Second_density_stats.png +3 -0
  36. presentation_images/Derivative_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Second_membrane_density_stats.png +3 -0
  37. presentation_images/Derivative_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Sixth_density_stats.png +3 -0
  38. presentation_images/Derivative_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Sixth_membrane_density_stats.png +3 -0
  39. presentation_images/Derivative_Plots_of_PC1_Across_Cell_Types_PC1_Fifth_density_stats.png +3 -0
  40. presentation_images/Derivative_Plots_of_PC1_Across_Cell_Types_PC1_Second_density_stats.png +3 -0
  41. presentation_images/Derivative_Plots_of_PC1_Across_Cell_Types_PC1_Second_membrane_density_stats.png +3 -0
  42. presentation_images/Derivative_Plots_of_PC1_Across_Cell_Types_PC1_Sixth_density_stats.png +3 -0
  43. presentation_images/Derivative_Plots_of_PC1_Across_Cell_Types_PC1_Sixth_membrane_density_stats.png +3 -0
  44. presentation_images/Diffusion_Constant_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Fifth_density_stats.png +3 -0
  45. presentation_images/Diffusion_Constant_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Second_density_stats.png +3 -0
  46. presentation_images/Diffusion_Constant_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Second_membrane_density_stats.png +3 -0
  47. presentation_images/Diffusion_Constant_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Sixth_density_stats.png +3 -0
  48. presentation_images/Diffusion_Constant_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Sixth_membrane_density_stats.png +3 -0
  49. presentation_images/Diffusion_Constant_Plots_of_PC1_Across_Cell_Types_PC1_Fifth_density_stats.png +3 -0
  50. presentation_images/Diffusion_Constant_Plots_of_PC1_Across_Cell_Types_PC1_Second_density_stats.png +3 -0
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+ \section{Collecting Trajectories}
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+
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+ \subsection{Nuclei Segmentation: VollSeg}
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+ \frame{\sectionpage}
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+
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+ \begin{frame}{VollSeg}
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+ \begin{itemize}[<+->]
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+
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+ \item VollSeg is a versatile tool built over StarDist with modular segmentation methodologies.
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+
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+ \item Xenopus: Early stage of development occupies limited space, $\rightarrow$ lots of background signal $\rightarrow$ Normalization issues.
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+
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+ \item Train a ROI and StarDist nuclei model $\rightarrow$ nuclei segmentation in the region of signal.
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+
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+ \end{itemize}
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+
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+ \end{frame}
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+
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+ \begin{frame}{VollSeg}
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+ \href{run:presentation_movies/sixth_nuclei_seg.mp4}{
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+ \fcolorbox{blue!80!black}{blue!10}{% Border and background color
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+ \parbox[c][1.5em][c]{0.6\textwidth}{% Box size
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+ \centering\textbf{\textcolor{blue!90!black}{Nuclei Segmentation (Click)}}% Text styling
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+ }%
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+ }%
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+ }
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+
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+ \end{frame}
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+
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+ \subsection{Mitosis Action Classification: Oneat}
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+ \frame{\sectionpage}
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+ \begin{frame}{Mitosis Action Classification}
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+
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+ \begin{itemize}[<+->]
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+
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+ \item Mitosis is a process that completes in $\delta T$ time-frame $\rightarrow$ action event
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+
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+ \item Oneat: (Osis network for event as action topology) is a network that is jointly trained to find the plane of focus and space-time location of mitotic cells (arbitrary shaped images).
39
+
40
+ \item Leveraging the segmentation of nuclei $\rightarrow$ apply trained network to obtain TZYX location of mitotic cells.
41
+ \item Oneat is organism and action event type independent, extensible to Apoptosis, static cell type classification.
42
+
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+ \end{itemize}
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+ \end{frame}
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+ \begin{frame}{Mitosis Action Classification}
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+ \href{run:presentation_movies/all_timepoints_mitosis.mp4}{
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+ \fcolorbox{blue!80!black}{blue!10}{% Border and background color
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+ \parbox[c][1.5em][c]{0.6\textwidth}{% Box size
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+ \centering\textbf{\textcolor{blue!90!black}{Action Classification Video (Click)}}% Text styling
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+ }%
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+ }%
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+ }
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+
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+
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+ \end{frame}
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+
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+ \subsection{Lineage tree correction: TrackMate-Oneat}
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+
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+ \frame{\sectionpage}
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+ \begin{frame}{TrackMate-Oneat}
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+ \begin{itemize}[<+->]
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+
63
+ \item Mitosis Locations $\rightarrow$ a strong prior for track linkage
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+ \begin{alertblock}{Correcting Branch Bifurcations}
65
+ Leveraging prior information to correct the mitotic tracks/lineage tree branch bifurcation
66
+ \end{alertblock}
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+
68
+
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+ \item TrackMate extension $\rightarrow$ TrackMate-Oneat $\rightarrow$ track without branch linking, input Mitosis locations csv $\rightarrow$ updates the tracks xml file
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+
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+ \end{itemize}
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+ \end{frame}
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+
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+ \begin{frame}{TrackMate-Oneat}
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+ \begin{figure}
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+ \centering
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+ % Adjust the width and spacing as needed
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+ \includegraphics[width=1\textwidth]{presentation_images/trackmate-panes-1536x495.png}
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+
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+ \end{figure}
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+ \end{frame}
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+
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+ \begin{frame}{TrackMate-Oneat}
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+ \begin{figure}
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+ \centering
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+ % Adjust the width and spacing as needed
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+
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+ \includegraphics[width=0.8\textwidth]{presentation_images/tm-oneat-panel}
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+ \end{figure}
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+ \end{frame}
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+
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+ \begin{frame}{TrackMate-Oneat}
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+ \href{run:presentation_movies/fifth_data_tracks.mp4}{
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+ \fcolorbox{blue!80!black}{blue!10}{% Border and background color
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+ \parbox[c][1.5em][c]{0.6\textwidth}{% Box size
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+ \centering\textbf{\textcolor{blue!90!black}{Tracks Example1 (Click)}}% Text styling
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+ }%
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+ }%
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+ }
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+
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+ \href{run:presentation_movies/fifth_data_tracks_second.mp4}{
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+ \fcolorbox{blue!80!black}{blue!10}{% Border and background color
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+ \parbox[c][1.5em][c]{0.6\textwidth}{% Box size
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+ \centering\textbf{\textcolor{blue!90!black}{Tracks Example2 (Click)}}% Text styling
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+ }%
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+ }%
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+ }
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+ \end{frame}
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+
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+ \subsection{Membrane Segmentation: VollSeg + CellPose}
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+ \frame{\sectionpage}
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+ \begin{frame}{Membrane Segmentation}
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+
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+ \begin{itemize}[<+->]
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+
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+ \item CellPose: Slice by Slice 3D segmentation, object shape stretched, 3D features washed out.
117
+
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+ \item Mode VollSeg: Autoencoder to enhance membrane edges $\rightarrow$ Nuclei seeds perform 3D watershed.
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+
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+ \item Computer vision methods to remove 'overflows', cellpose regions as mask.
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+ \href{run:presentation_movies/fifth_data_membrane_segmentation.mp4}{
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+ \fcolorbox{blue!80!black}{blue!10}{% Border and background color
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+ \parbox[c][1.5em][c]{0.6\textwidth}{% Box size
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+ \centering\textbf{\textcolor{blue!90!black}{Membrane Segmentation (Click)}}% Text styling
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+ }%
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+ }%
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+ }
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+ \begin{alertblock}{3D membrane leveraged segmentation}
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+ Leveraging nuclei seeds on membrane enhanced signal for 3D shape preserving segmentation
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+ \end{alertblock}
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+
132
+ \end{itemize}
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+
134
+ \end{frame}
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+ \subsection{Tracks: Nuclei $\rightarrow$ Membrane: NapaTrackMater}
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+ \frame{\sectionpage}
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+ \begin{frame}{Transfer Tracks}
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+ \begin{itemize}[<+->]
139
+
140
+ \item Nuclei channel in 3D $\rightarrow$ easy to segment-track-auto-manual correct.
141
+ \item membrane channel in 3D $\rightarrow$ difficult to segment-track, impossible to manually correct.
142
+ \begin{alertblock}{Simply transfer the tracks}
143
+ Using nuclei tracks, membrane segmentation transfer the links, recompute morphological features
144
+ \end{alertblock}
145
+ \item If membrane seg label not found, skip timepoint, mark morphological features as -1 (lost track) but continue.
146
+ \end{itemize}
147
+ \end{frame}
148
+ \begin{frame}{Transfer Tracks}
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+ \href{run:presentation_movies/fifth_data_tracks_membrane.mp4}{
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+ \fcolorbox{blue!80!black}{blue!10}{% Border and background color
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+ \parbox[c][1.5em][c]{0.6\textwidth}{% Box size
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+ \centering\textbf{\textcolor{blue!90!black}{Tracks Example1 (Click)}}% Text styling
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+ }%
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+ }%
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+ }
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+
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+ \href{run:presentation_movies/fifth_data_tracks_second_membrane.mp4}{
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+ \fcolorbox{blue!80!black}{blue!10}{% Border and background color
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+ \parbox[c][1.5em][c]{0.6\textwidth}{% Box size
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+ \centering\textbf{\textcolor{blue!90!black}{Tracks Example2 (Click)}}% Text styling
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+ }%
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+ }%
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+ }
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+ \end{frame}
165
+ \subsection{Point Cloud based morphodynamic features: NapaTrackMater}
166
+ \frame{\sectionpage}
167
+ \begin{frame}{Point Cloud based morphodynamic features}
168
+ \begin{itemize}[<+->]
169
+
170
+ \item Simple TrackMate XML files of nuclei and membrane tracks.
171
+
172
+ \item Input: NapaTrackMater $\rightarrow$ obtain point clouds from segmentation image, compute accurate morphodynamic properties $\rightarrow$ re-create a Master XML file $\rightarrow$ visualize tracks in 3D in Napari: vollseg-napari-trackmate
173
+ \item Shape features: Eccentricity components, surface area, radius, Volume
174
+ \item Dynamic features: Speed, Acceleration, Distance to tissue border, {Radial, Cell-Axis, Motion} angles wrt image co-ordinate frame
175
+
176
+
177
+
178
+ \end{itemize}
179
+ \end{frame}
180
+
181
+ \begin{frame}{Point Cloud based morphodynamic features: NapaTrackMater}
182
+ \href{run:presentation_movies/fifth_data_features.mp4}{
183
+ \fcolorbox{blue!80!black}{blue!10}{% Border and background color
184
+ \parbox[c][1.5em][c]{0.6\textwidth}{% Box size
185
+ \centering\textbf{\textcolor{blue!90!black}{Tracks Example1 (Click)}}% Text styling
186
+ }%
187
+ }%
188
+ }
189
+ \end{frame}
190
+ \subsection{Trajectory Analysis: NapaTrackMater}
191
+ \frame{\sectionpage}
192
+ \begin{frame}{Trajectory Dataframe}
193
+ \begin{itemize}[<+->]
194
+ \item Representation of tracks as feature matrices $F \cross T$.
195
+ \item Dataframe with columns 'Dividing', 'Number Dividing', 't,z,y,x, shape-dynamic features'
196
+ \item Such representation more amenable to Machine Learning based approaches.
197
+ \item Knowing ground truth cell type append column of cell type (Goblet, Basal, Radial) to the dataframe.
198
+ \end{itemize}
199
+ \end{frame}
200
+
201
+ \begin{frame}{Inception Model Prediction}
202
+ \begin{itemize}[<+->]
203
+ \item A totally theoretical physics based network, on ideas of non-equilibrium statistical physics (later slides).
204
+ \item Cell type $\rightarrow$ Track label, Motion type $\rightarrow$ Another Track label
205
+ \item $F$ features along channel dimension, T as other dimension: Input Data for Neural Network(s)
206
+ \item 1D densenet as the model for training a) Cell type/Track type b) Motion type
207
+ \item Not whole track in training, cut out tracklets. Feature not Vision based neural network.
208
+ \begin{alertblock}{Model can Predict!}
209
+ Given a cut-out of tracklet it can reliably predict a) \& b)
210
+ \end{alertblock}
211
+ \end{itemize}
212
+ \end{frame}
213
+
214
+
215
+ \subsection{Time Cross Correlations}
216
+
217
+ \frame{\sectionpage}
218
+ \begin{frame}{Time Cross Correlations: Motivation}
219
+ \begin{itemize}[<+->]
220
+
221
+ \item Equilibrium statistical mechanics $\rightarrow$ thermodynamic properties: partition function of the ensemble
222
+ \item Nonequilibrium systems $\rightarrow$ no unique partition function
223
+ \item System properties obtained via time cross-correlations
224
+ \end{itemize}
225
+ \uncover<+->{\begin{equation*}
226
+ \langle v(t) v(t^\prime) \rangle_\mathrm{time} = \frac{kT}{m}\exp^{-\gamma(t-t^\prime)/m} \label{time-cross}
227
+ \end{equation*}}
228
+
229
+ \uncover<+->{
230
+ \begin{alertblock}{Inception Model}
231
+ Can we design a neural network to learn time-cross correlations $\rightarrow$ predict cell-fate (Basal, Goblet, Radial)/action (Mitosis, Apoptosis) classfication.
232
+ \end{alertblock}
233
+ }
234
+
235
+ \end{frame}
236
+
237
+ \begin{frame}{Time Cross Correlations}
238
+
239
+
240
+
241
+ \uncover<+->{
242
+ Consider the vector of morphodynamic properties $\vec{A(t)}$, let us compute time average of this vector
243
+ \begin{equation*}
244
+ \langle \vec{A} \rangle = \frac{1}{\tau}\int_0^\tau dt \vec{A(t)}
245
+ \end{equation*}}
246
+
247
+
248
+ \uncover<+->{
249
+ Subtract the average to get the fluctuation $\delta \vec{A}$
250
+ \begin{equation*}
251
+ \delta \vec{A(t)} = \vec{A(t)} - \langle \vec{A} \rangle
252
+ \end{equation*}}
253
+
254
+
255
+ \uncover<+->{
256
+ The time-averaged product of two fluctuations
257
+ \begin{equation*}
258
+ C(t) = \frac{1}{\tau}\int_0^\tau ds \delta \vec{A}(s)\delta \vec{A}(t + s) \label{cross-corr}
259
+ \end{equation*}
260
+ $C(t)$: cross-correlation of the morphodynamic feature vector.
261
+ }
262
+
263
+
264
+
265
+ \end{frame}
266
+
267
+ \begin{frame}{Neural Network: Learning cross-correlations}
268
+ \begin{itemize}[<+->]
269
+ \item Construct a $(B,F,T)$ dimensional tensor, B: batch dimension, F: channels/features and T: time interval, take all the tracks belonging to a certain cell type $S$.
270
+ \item Inception Model: Customized $1D$ Densenet model with soft attention over time
271
+ \item Convolution operations in Densenet $=$ computing the cross correlations as 1D convolutions are mathematically the same.
272
+ \item Training data: several tracks of $25$ timepoints with $18$ features for each cell type $S$
273
+ \item Data is $Z$ scored to have zero mean and are normalized by the standard deviation of the feature vector (component wise) with $S$ being the ground truth label
274
+ \item Categorical Cross entropy as objective to train the network to classify trajectories as belonging to certain cell type.
275
+ \end{itemize}
276
+ \end{frame}
277
+ \begin{frame}{Neural Network: Learning cross-correlations}
278
+ \begin{columns} % Use columns for better layout
279
+ \begin{column}{0.5\textwidth} % Adjust width as needed
280
+ \includegraphics[width=0.8\linewidth, angle=-90]{presentation_images/1DCNN.png} % Use \linewidth to fit within the column
281
+ \end{column}
282
+
283
+ \begin{column}{0.5\textwidth}
284
+
285
+ \begin{itemize}[<+->]
286
+ \item Input 1D data with features as channels $\rightarrow$ 1D Densenet: a) Convolutional, b) Dense, c) Transition, d) Attention block.
287
+ \item Convolutions compute temporal correlations b/w features
288
+ \item Network output class: cell type, $S$ (Goblet, Basal, Radial)/ action class, $A$ (Mitosis, Non-Mitosis)
289
+ \end{itemize}
290
+ \end{column}
291
+ \end{columns}
292
+ \end{frame}
293
+
294
+
chapters/langevin-equation.log ADDED
The diff for this file is too large to render. See raw diff
 
chapters/langevin-equation.tex ADDED
@@ -0,0 +1,367 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ \section{Langevin Equation for macroscopic properties}
2
+
3
+ \subsection{Brownian Motion}
4
+ \frame{\sectionpage}
5
+
6
+ \begin{frame}{Brownian Particle as real Particle}
7
+ \uncover<+->{\begin{equation*}
8
+ \frac{d}{dt}\left( \frac{\partial L}{ \partial \dot{q}} \right) = \frac{\partial L}{ \partial q} - \frac{\partial R}{ \partial \dot{q}} + \sqrt{2B}\Gamma(t) \label{Lagrange-eqs}
9
+ \end{equation*}}
10
+
11
+ \uncover<+->{\begin{equation*}
12
+ L = \frac{1}{2}m\dot{q}^2 - V(q) \label{Lagrangian-real}
13
+ \end{equation*}}
14
+
15
+ \uncover<+->{\begin{equation*}m \frac{d\dot{q}}{dt} = -\frac{\partial V}{\partial q} -\gamma\dot{q} + \sqrt{2B}\Gamma(t)
16
+ \end{equation*}}
17
+ \uncover<+->{\begin{equation*}\Phi(q,t) = -\frac{\partial V}{\partial q} -\gamma\dot{q}
18
+ \end{equation*}}
19
+ \uncover<+->
20
+ {\begin{equation*}
21
+ \frac{dv}{dt} = \frac{\Phi(q,t)}{m} + \frac{\sqrt{2B}}{m} \Gamma(t),
22
+ \end{equation*}}
23
+ \end{frame}
24
+ \begin{frame}{Obtaining deterministic and random force}
25
+
26
+ \uncover<+->
27
+ {\begin{equation*}
28
+ v(t+dt) - v(t) = \int_{t}^{t+dt} \frac{\Phi(q,t)}{m}dt + \int_{t}^{t+dt} \frac{\sqrt{2B}}{m} \Gamma(t) dt
29
+ \end{equation*}}
30
+
31
+ \uncover<+->
32
+ {\begin{equation*}
33
+ \langle \Gamma(t) \rangle = 0, \quad \langle \Gamma(t) \Gamma(t_1) \rangle = \delta(t - t_1).
34
+ \end{equation*}}
35
+
36
+
37
+ \uncover<+->
38
+ {\begin{equation*}
39
+ \Phi(q,t) = m \frac{ \langle v(t+dt) -v(t) \rangle}{dt}
40
+ \end{equation*}}
41
+
42
+
43
+ \uncover<+->
44
+ {\begin{equation*}
45
+ B = \frac{m^2}{2} \frac{\langle (v(t+dt) - v(t))^2 \rangle}{dt} - \frac{\Phi(q,t) ^2}{2} dt
46
+ \end{equation*}}
47
+
48
+ \end{frame}
49
+
50
+ \begin{frame}{Macroscopic properties as Brownian Particles}
51
+ \uncover<+->{\begin{equation*}
52
+ \vec{a} = ( a_1, \cdots, a_n )
53
+ \end{equation*}}
54
+ \uncover<+->{
55
+ \begin{equation*}
56
+ L = \frac{1}{2}\dot{a}^2 - V(a) \label{Lagrangian-cell}
57
+ \end{equation*}}
58
+ \uncover<+->{
59
+ \begin{equation*}
60
+ \frac{d\dot{a}}{dt} = -\frac{\partial V}{\partial a} -\gamma\dot{a} + \sqrt{2B}\Gamma(t)
61
+ \end{equation*}}
62
+ \uncover<+->{
63
+ \begin{equation*}
64
+ B_i(a,t) = \frac{\langle (a_i(t+dt) - a_i(t))^2 \rangle}{2dt} - \frac{\Phi_i(a,t) ^2}{2} dt \label{diffusion-constant}
65
+ \end{equation*}}
66
+
67
+ \uncover<+->{
68
+ \begin{equation*}
69
+ \Phi_i(a,t) = \frac{ \langle a_i(t+dt) -a_i(t) \rangle}{dt} \label{force-constant}
70
+ \end{equation*}}
71
+
72
+
73
+
74
+ \end{frame}
75
+
76
+
77
+ \begin{frame}{Obtaining deterministic and random force}
78
+ \begin{figure}
79
+ \centering
80
+ % Adjust the width and spacing as needed
81
+ \includegraphics[width=0.3\textwidth]{sixth_phase/nuclei_predicted_morphospaces/Derivative_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Sixth_nuclei_density_stats.png}
82
+ \hspace{0.5cm}
83
+ \includegraphics[width=0.3\textwidth]{sixth_phase/nuclei_predicted_morphospaces/Diffusion_Constant_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Sixth_nuclei_density_stats.png}
84
+
85
+ \end{figure}
86
+ \textbf{Description:} \\
87
+ Mean and Variance for Deterministic and random force, nuclei channel, Sixth Dataset (all principal components).
88
+ \begin{alertblock}{State Transition}
89
+ For each cell type, the deterministic and random force strength decreases with time, system transitions to be less 'fluidic'
90
+ \end{alertblock}
91
+ \end{frame}
92
+
93
+
94
+
95
+ \begin{frame}{Obtaining deterministic and random force}
96
+ \begin{figure}
97
+ \centering
98
+ % Adjust the width and spacing as needed
99
+ \includegraphics[width=0.3\textwidth]{sixth_phase/membrane_predicted_morphospaces/Derivative_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Sixth_membrane_density_stats.png}
100
+ \hspace{0.5cm}
101
+ \includegraphics[width=0.3\textwidth]{sixth_phase/membrane_predicted_morphospaces/Diffusion_Constant_Plots_of_ALL_PCs_Across_Cell_Types_all_PCs_Sixth_membrane_density_stats.png}
102
+
103
+ \end{figure}
104
+ \textbf{Description:} \\
105
+ Mean and Variance for Deterministic and random force, membrane channel, Sixth Dataset (all principal components).
106
+ \begin{alertblock}{State Transition}
107
+ For each cell type, the deterministic and random force strength decreases with time, system transitions to be less 'fluidic'
108
+ \end{alertblock}
109
+ \end{frame}
110
+
111
+
112
+
113
+ \subsection{Ergodic Test: Quantifying $t_{eq}$}
114
+
115
+ \frame{\sectionpage}
116
+
117
+
118
+ \begin{frame}{Ergodic}
119
+ \begin{itemize}[<+->]
120
+
121
+ \uncover<+->{
122
+ \item Time Average:
123
+ \[
124
+ \langle A \rangle_T = \lim_{T \to \infty} \frac{1}{T} \int_0^T A(t) \, dt
125
+ \]}
126
+
127
+ \uncover<+->{
128
+ \item Ensemble Average:
129
+ \[
130
+ \langle A \rangle_E = \frac{1}{N} \sum_{i=1}^{N} A_i
131
+ \]
132
+ where \( A_i \) is the value of the observable for the \( i \)-th system.}
133
+
134
+
135
+ \item
136
+ \uncover<+->{\[
137
+ \text{Ergodicity: } \langle A \rangle_T = \langle A \rangle_E \quad \text{for all relevant observables } A
138
+ \]}
139
+ \end{itemize}
140
+
141
+ \end{frame}
142
+
143
+ \begin{frame}{Ergodicity for cell types: Quantifying $t_{eq}$}
144
+
145
+ \begin{itemize}
146
+ \item \uncover<+->{For each cell track of Basal, Radial, Goblet type.}
147
+ \item \uncover<+->{Use $f_i(t)$: feature values for track i at time t and $T$ in the interval $\Delta t$.}
148
+ \item
149
+ \uncover<+->{
150
+ Ensemble Average
151
+ \begin{equation*}
152
+ \langle f \rangle_{\mathrm{ensemble}, t} = \frac{1}{N}\sum_{i=1}^N f_i(t)
153
+ \end{equation*}
154
+ }
155
+ \item
156
+ \uncover<+->{
157
+ Time Average
158
+ \begin{equation*}
159
+ \langle f \rangle_{\mathrm{time}, i} = \frac{1}{\Delta t} \sum_{t\in T} f_i(t)
160
+ \end{equation*}
161
+ }
162
+ \item
163
+ \uncover<+->{
164
+ Ergodic Test:
165
+ \begin{equation*}
166
+ \Delta f = \arrowvert\sum_i \langle f\rangle_{\mathrm{time}, i} - \sum_t \langle f \rangle_{\mathrm{ensemble},t} \arrowvert \label{ergodic_test}
167
+ \end{equation*}
168
+ }
169
+ \end{itemize}
170
+
171
+
172
+
173
+
174
+ \end{frame}
175
+
176
+ \begin{frame}{Ergodicity for cell types: Quantifying $t_{eq}$}
177
+ \begin{itemize}
178
+ \item \uncover<+->{
179
+ For numerical computation take T = 20, compute $\Delta f$ for each cell type.
180
+ \item $\Delta f(t)$ for $[20,40,\cdots T]$, each cell type $\rightarrow$ discrete plot time and ensemble average.
181
+ \item Initially system for each cell type is in non-equilibrium $\rightarrow$ non-zero gap b/w time-ensemble average.
182
+ \item As system becomes ergodic the gap reduces, $\Delta f_s < \epsilon$, $s$ being cell type.
183
+ \item $t_{eq}$: Critical time when gap reduces to $\epsilon$ for $s$, $t_{eq}(s)$.
184
+ }
185
+ \end{itemize}
186
+
187
+ \end{frame}
188
+
189
+ \begin{frame}{Ergodicity for cell types: Quantifying $t_{eq}$}
190
+
191
+ \begin{figure}
192
+ \centering
193
+ % Adjust the width and spacing as needed
194
+ \includegraphics[width=0.45\textwidth]{sixth_phase/nuclei_predicted_phasespaces/ergodicity_differences_Basal.png}
195
+ \hspace{0.5cm}
196
+ \includegraphics[width=0.45\textwidth]{sixth_phase/nuclei_predicted_phasespaces/ergodicity_differences_Radial.png}
197
+ \hspace{0.5cm}
198
+ \includegraphics[width=0.45\textwidth]{sixth_phase/nuclei_predicted_phasespaces/ergodicity_differences_Goblet.png}
199
+ \end{figure}
200
+
201
+ \vspace{0.5cm} % Add some vertical space for readability
202
+
203
+ \textbf{Description:} \\
204
+ Time and Ensemble average difference plots, Sixth Dataset.
205
+ \end{frame}
206
+
207
+ \subsection{Phase Space}
208
+
209
+ \frame{\sectionpage}
210
+ \begin{frame}{Trajectories in Phase Space}
211
+ \begin{itemize}
212
+
213
+ \item \uncover<+->{The force controls evolution of morphodynamic features}
214
+ \item \uncover<+->{How do we determine the potential of force for each cell type?}
215
+ \item \uncover<+->{Visualizing the evolution of trajectories in phase space would offer the clue.}
216
+ \item \uncover<+->{Phase space is described by each generalized co-ordinate and it's conjugate momentum.
217
+ \begin{equation*}
218
+ \vec{a} = ( a_1, \cdots, a_n )
219
+ \end{equation*}
220
+ \begin{equation*}
221
+ L = \frac{1}{2}\dot{a}^2 - V(a)
222
+ \end{equation*}
223
+ \begin{equation*}
224
+ \vec{p} = \left (\frac{\partial L}{\partial a_1},\cdots,\frac{\partial L}{\partial a_n}\right )
225
+ \end{equation*}
226
+ }
227
+ \end{itemize}
228
+ \end{frame}
229
+
230
+ \begin{frame}{Trajectories in Phase Space: Nuclei}
231
+
232
+ \begin{figure}
233
+ \centering
234
+ % Adjust the width and spacing as needed
235
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Goblet_Eccentricity_Comp_First_dataset_phasespace.png}
236
+ \hspace{0.5cm}
237
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Goblet_Eccentricity_Comp_Third_dataset_phasespace.png}
238
+ \hspace{0.5cm}
239
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Goblet_Speed_dataset_phasespace.png}
240
+ \hspace{0.5cm}
241
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Goblet_Surface_Area_dataset_phasespace.png}
242
+ \end{figure}
243
+
244
+ \vspace{0.5cm} % Add some vertical space for readability
245
+
246
+ \textbf{Description:} \\
247
+ Phase space plots Goblet Cells, nuclei channel, Sixth Dataset.
248
+ \begin{alertblock}{Potential of Force}
249
+ The potential corresponding to deterministic force corresponds to a Harmonic potential ($\frac{1}{2}ka_i^ 2$)
250
+ \end{alertblock}
251
+ \end{frame}
252
+
253
+ \begin{frame}{Trajectories in Phase Space: Nuclei}
254
+
255
+ \begin{figure}
256
+ \centering
257
+ % Adjust the width and spacing as needed
258
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Radial_Eccentricity_Comp_First_dataset_phasespace.png}
259
+ \hspace{0.5cm}
260
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Radial_Eccentricity_Comp_Third_dataset_phasespace.png}
261
+ \hspace{0.5cm}
262
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Radial_Speed_dataset_phasespace.png}
263
+ \hspace{0.5cm}
264
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Radial_Surface_Area_dataset_phasespace.png}
265
+ \end{figure}
266
+
267
+ \vspace{0.5cm} % Add some vertical space for readability
268
+
269
+ \textbf{Description:} \\
270
+ Phase space plots Radial Cells, nuclei channel, Sixth Dataset.
271
+ \begin{alertblock}{Potential of Force}
272
+ The potential corresponding to deterministic force corresponds to a Harmonic potential ($\frac{1}{2}ka_i^ 2$)
273
+ \end{alertblock}
274
+ \end{frame}
275
+
276
+ \begin{frame}{Trajectories in Phase Space: Nuclei}
277
+
278
+ \begin{figure}
279
+ \centering
280
+ % Adjust the width and spacing as needed
281
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Basal_Eccentricity_Comp_First_dataset_phasespace.png}
282
+ \hspace{0.5cm}
283
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Basal_Eccentricity_Comp_Third_dataset_phasespace.png}
284
+ \hspace{0.5cm}
285
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Basal_Speed_dataset_phasespace.png}
286
+ \hspace{0.5cm}
287
+ \includegraphics[width=0.2\textwidth]{sixth_phase/nuclei_predicted_phasespaces/phasespace_Basal_Surface_Area_dataset_phasespace.png}
288
+ \end{figure}
289
+
290
+ \vspace{0.5cm} % Add some vertical space for readability
291
+
292
+ \textbf{Description:} \\
293
+ Phase space plots Basal Cells, nuclei channel, Sixth Dataset.
294
+ \begin{alertblock}{Potential of Force}
295
+ The potential corresponding to deterministic force corresponds to a Harmonic potential ($\frac{1}{2}ka_i^ 2$)
296
+ \end{alertblock}
297
+ \end{frame}
298
+
299
+
300
+ \begin{frame}{Trajectories in Phase Space: Membrane}
301
+
302
+ \begin{figure}
303
+ \centering
304
+ % Adjust the width and spacing as needed
305
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Goblet_Eccentricity_Comp_First_dataset_phasespace.png}
306
+ \hspace{0.5cm}
307
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Goblet_Eccentricity_Comp_Third_dataset_phasespace.png}
308
+ \hspace{0.5cm}
309
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Goblet_Speed_dataset_phasespace.png}
310
+ \hspace{0.5cm}
311
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Goblet_Surface_Area_dataset_phasespace.png}
312
+ \end{figure}
313
+
314
+ \vspace{0.5cm} % Add some vertical space for readability
315
+
316
+ \textbf{Description:} \\
317
+ Phase space plots Goblet Cells, membrane channel, Sixth Dataset.
318
+ \begin{alertblock}{Potential of Force}
319
+ The potential corresponding to deterministic force corresponds to a Harmonic potential of higher order, e.g ($\frac{1}{2}k(a_i^ 2 - b)^ 2$), such a potential has minima at b
320
+ \end{alertblock}
321
+ \end{frame}
322
+
323
+ \begin{frame}{Trajectories in Phase Space: Membrane}
324
+
325
+ \begin{figure}
326
+ \centering
327
+ % Adjust the width and spacing as needed
328
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Radial_Eccentricity_Comp_First_dataset_phasespace.png}
329
+ \hspace{0.5cm}
330
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Radial_Eccentricity_Comp_Third_dataset_phasespace.png}
331
+ \hspace{0.5cm}
332
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Radial_Speed_dataset_phasespace.png}
333
+ \hspace{0.5cm}
334
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Radial_Surface_Area_dataset_phasespace.png}
335
+ \end{figure}
336
+
337
+ \vspace{0.5cm} % Add some vertical space for readability
338
+
339
+ \textbf{Description:} \\
340
+ Phase space plots Radial Cells, membrane channel, Sixth Dataset.
341
+ \begin{alertblock}{Potential of Force}
342
+ The potential corresponding to deterministic force corresponds to a Harmonic potential of higher order, e.g ($\frac{1}{2}k(a_i^ 2 - b)^ 2$), such a potential has minima at b
343
+ \end{alertblock}
344
+ \end{frame}
345
+
346
+ \begin{frame}{Trajectories in Phase Space: Membrane}
347
+
348
+ \begin{figure}
349
+ \centering
350
+ % Adjust the width and spacing as needed
351
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Basal_Eccentricity_Comp_First_dataset_phasespace.png}
352
+ \hspace{0.5cm}
353
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Basal_Eccentricity_Comp_Third_dataset_phasespace.png}
354
+ \hspace{0.5cm}
355
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Basal_Speed_dataset_phasespace.png}
356
+ \hspace{0.5cm}
357
+ \includegraphics[width=0.2\textwidth]{sixth_phase/membrane_predicted_phasespaces/phasespace_Basal_Surface_Area_dataset_phasespace.png}
358
+ \end{figure}
359
+
360
+ \vspace{0.5cm} % Add some vertical space for readability
361
+
362
+ \textbf{Description:} \\
363
+ Phase space plots Basal Cells, membrane channel, Sixth Dataset.
364
+ \begin{alertblock}{Potential of Force}
365
+ The potential corresponding to deterministic force corresponds to a Harmonic potential of higher order, e.g ($\frac{1}{2}k(a_i^ 2 - b)^ 2$), such a potential has minima at b
366
+ \end{alertblock}
367
+ \end{frame}
chapters/preamble.tex ADDED
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1
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1
+ \section{Experimental Results}
2
+
3
+ \frame{\sectionpage}
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+
5
+ \begin{frame}{Nuclei Channel: Forces}
6
+ \only<-3>{\uncover<+->{\begin{equation*}
7
+ f(t) = \cos(\omega_0 t)e^{-\pi t^2}
8
+ \end{equation*}}
9
+ \uncover<+->{\begin{equation*}
10
+ \widehat{f}(\omega) = \frac{e^{-\frac{(\omega - \omega_0)^2}{4\pi}} + e^{-\frac{(\omega + \omega_0)^2}{4\pi}}}{2\sqrt{2\pi}}
11
+ \end{equation*}}
12
+ \uncover<+->{\begin{equation*}
13
+ \omega = 2\pi\nu
14
+ \end{equation*}}}
15
+ \only<4>{\begin{equation*}
16
+ f(t) = \cos(2\pi\nu_0 t)e^{-\pi t^2}
17
+ \end{equation*}
18
+ \begin{equation*}
19
+ \widehat{f}(\nu) = \frac{e^{-\pi(\nu - \nu_0)^2} + e^{-\pi(\nu + \nu_0)^2}}{2\sqrt{2\pi}}
20
+ \end{equation*}}
21
+ \end{frame}
22
+
23
+ \begin{frame}{Nuclei Channel: $T_{eq}$}
24
+ \begin{equation*}
25
+ f(t) = \cos(2\pi\nu_0 t)e^{-\pi t^2}
26
+ \end{equation*}
27
+ \centering
28
+
29
+ \includegraphics[height = 0.7 \textheight]{images/Pulse1.pdf}
30
+ \end{frame}
31
+
32
+ \begin{frame}{Nuclei Channel: Phase Space}
33
+ \begin{equation*}
34
+ \widehat{f}(\nu) = \frac{e^{-\pi(\nu - \nu_0)^2} + e^{-\pi(\nu + \nu_0)^2}}{2\sqrt{2\pi}}
35
+ \end{equation*}
36
+ \centering
37
+
38
+ \includegraphics[height = 0.65 \textheight]{images/Pulse1-Fourier.pdf}
39
+ \end{frame}
40
+
41
+ \begin{frame}{Membrane Channel: Forces}
42
+ \uncover<+->{\begin{equation*}
43
+ f(t) = e^{i\omega_0 t} = \cos(\omega_0 t) + i \sin(\omega_0 t)
44
+ \end{equation*}}
45
+ \uncover<+->{\begin{equation*}
46
+ \widehat{f}(\omega) = \frac{1}{\sqrt{2\pi}} \int_{-\infty}^{+\infty} e^{i\omega_0 t} e^{-i \omega t} \dd{t}
47
+ \end{equation*}}
48
+ \end{frame}
49
+
50
+ \begin{frame}{Membrane Channel: $T_{eq}$}
51
+ \uncover<+->{\begin{equation*}
52
+ f(t) = e^{i\omega_0 t}
53
+ \end{equation*}}
54
+ \uncover<+->{\begin{equation*}
55
+ e^{i\omega_0 t} = \frac{1}{\sqrt{2\pi}} \int_{-\infty}^{+\infty} \widehat{f}(\omega) e^{i \omega t} \dd{\omega}
56
+ \end{equation*}}
57
+ \uncover<+->{\begin{equation*}
58
+ \widehat{f}(\omega) = \sqrt{2\pi} \dirac{\omega - \omega_0}
59
+ \end{equation*}}
60
+ \end{frame}
61
+
62
+ \begin{frame}{Membrane Channel: Phase Space}
63
+ \begin{equation*}
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+ f(t) = \cos(\omega_0 t) = \frac{e^{i\omega_0t} + e^{-i\omega_0t}}{2}
65
+ \end{equation*}
66
+
67
+ \centering
68
+ \includegraphics[height = 0.65 \textheight]{images/Pulse2.pdf}
69
+ \end{frame}
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+
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+
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