OSCR

FLIM quantification of temperature-dependent enzyme-NADH binding dynamics.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches
  1. [1] § Methods › FLIM analysis ↔ DynamicTempCode.m, lines 1–13 · score 0.68 · SPCimage, 249–256, raw, dimensions, deconvoluted, artifact
  2. [2] § Methods › FLIM analysis ↔ CodeDynamicSoln.m, lines 1–25 · score 0.64 · SPCimage, 249–256, raw, artifact, summed, positions
  3. [3] § Methods › FLIM of solutions and cells ↔ DynamicTempCode.m, lines 457–481 · score 0.60 · instrument response function, urea crystals, IRF, cell
  4. [4] § Methods › FLIM of solutions and cells ↔ CodeDynamicSoln.m, lines 27–48 · score 0.59 · instrument response function, urea crystals, IRF, NADH

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 525 lines · 19 KB · no license · 2 matches

  1. %% Import and plot original image w/0 artifact at the end
  3. % Uploading File from SPCimage. (X spatial by Y spatial by Time)
  4. tpsfImage = Convert_ASC_Image('F:\Paper 1\Dynamic Temp\MCF-7\2 MCF7\test_2024_12_26_18_51_binned_raw_data.asc');
  5. irfFile= readmatrix('IRF.txt'); %Loads IRF data for deconvolution
  7. % Artifact produced at the right-hand side, where no viable information is
  8. % Sets artifact pixels as 0 (y positions from 249-256)
  9. tpsfImage(:,249:256,:)= 0;
  10. %tpsfImage(:,196:256,:)= 0;
  12. sumImage = sum(tpsfImage,3); %Sums up dimensions so the intensity image can be created
  13. imagesc(sumImage) %Plots intensities
  16. %% Normalize the data for visualization in imageSegmenter and get the first mask
  17. % Check the intensity range of the summed image
  18. minIntensity = min(sumImage(:));
  19. maxIntensity = max(sumImage(:));
  21. disp(['Min Intensity: ', num2str(minIntensity)]);
  22. disp(['Max Intensity: ', num2str(maxIntensity)]);
  24. % Normalize the image intensity to the range [0, 1]
  25. normalizedImage = sumImage / maxIntensity;
  26. imageSegmenter(normalizedImage);
  28. %% Plotting Mask
  29. croppedImage = maskedImage(:, [1:248]);
  30. imagesc(croppedImage);
  31. %imagesc(maskedImage)
  32. b= colorbar
  33. colormap('parula')
  34. ylabel(b, 'Fluorescence Intensity (a.u)', 'FontSize', 18)
  35. axis image
  36. set(gca, 'XTick', [], 'YTick', []); % This removes the tick marks and labels
  38. clear b
  41. %% Colorbar only
  42. figure;
  43. imagesc([]); % creates an empty axes
  44. colormap('parula');
  45. b = colorbar;
  46. ylabel(b, 'Fluorescence Intensity (a.u)', 'FontSize', 18);
  47. axis off; % hides the axes box and ticks
  48. %% Generate 2D array for timing of laser dwell time (Anna added)
  49. % sumImage= normalizedImage *maxIntensity; %Unnormalizes the intensities
  50. scan_time=[];
  51. time=1; %timing starts at 1
  53. for r=1:height(BW) % Going through each row
  54. for c=1:width(BW) % Going through each column
  55. scan_time(r,c)=time* 0.100; %microsecond dwell time of 100 microseconds
  56. time=time+1;
  57. end
  58. % % flipping row, b/c laser scanning snakes through image (BIDIRECTIONAL)
  59. % if rem(r,2)==0 %if remainder is two (for every alt. row, to flip)
  60. % scan_time(r,:)=flip(scan_time(r,:));
  61. %
  62. % end
  63. end
  65. % Pinpoint timing of region of interest (ROI)
  66. masked_timing= scan_time.*BW; %Filters to have only the time for when there is a cell
  69. clear r c time
  73. % %% Taking into account the bidirectional gathering of data
  74. % % Iterate through each slice in the third dimension
  75. % for k = 1:size(tpsfImage, 3)
  76. % for r = 2:2:size(tpsfImage, 1) % Only even rows
  77. % tpsfImage(r, :, k) = flip(tpsfImage(r, :, k)); % Flip each even row
  78. % end
  79. % end
  81. %% Finding the row and column values for positions= 1 (BW mask) Jocelyn
  82. % Preallocate a cell array to store indices for each row
  83. valOnes = cell(256, 1); % Makes cell array for each row
  85. for r = 1:256 % Loop through each row of the array
  86. columnindex = find(BW(r, :) ~= 0); % Find the column indices of non-zero elements in the current row
  87. % Store the row and corresponding column indices
  88. valOnes{r} = [repmat(r, length(columnindex), 1), columnindex']; % Create a matrix of (row, col) pairs
  89. end
  91. clear columnindex r
  93. %% Getting decay information and time stored (NO BINNING)
  94. % First two columns for row and column indices, next column for information
  96. noBinDecay = []; % Matrix to store decay data without binning
  98. noBinTime = []; %Matrix to store time corresponding to decays without binning
  100. for r = 1:256
  101. if ~isempty(valOnes{r}) % Check if valOnes contains any non-zero elements for this row
  102. rowColPairs = valOnes{r}; % Get the row-column pairs from valOnes
  104. for i = 1:size(rowColPairs, 1)
  105. row = rowColPairs(i, 1); % Extract row index
  106. col = rowColPairs(i, 2); % Extract column index
  108. % Scan_time for each(row, col)
  109. noBinTime = [noBinTime; row, col, scan_time(row, col)];
  110. % Third dimension decay for each (row, col)
  111. thirdDimData = squeeze(tpsfImage(row, col, :))'; % 1x256 vector
  113. % Matrix with row, col, and decay information
  114. noBinDecay = [noBinDecay; row, col, thirdDimData]; % Row, Col, decay data
  115. end
  116. end
  117. end
  119. clear col i r row rowColPairs thirdDimData
  122. %% Binning decay
  123. binNumber = input('How many pixels do you want to average? \n Please type an integer: ');
  125. % Extract the row indices from the first column of noBinDecay
  126. rowIndices = noBinDecay(:, 1);
  128. % Extract the column indices from the second column of noBinDecay
  129. colIndices = noBinDecay(:, 2);
  131. % Find unique row indices
  132. uniqueRowIndices = unique(rowIndices);
  134. % Initialize array to store the final binned data
  135. binDecayTotal = [];
  137. % Loop through each unique row index
  138. for i = 1:length(uniqueRowIndices)
  139. % Find rows in noBinDecay that match the current unique row index
  140. matchingRows = noBinDecay(rowIndices == uniqueRowIndices(i), :);
  142. % Get the number of rows for the current unique row index
  143. numRows = size(matchingRows, 1);
  145. % Check if there are enough rows for the specified binNumber
  146. if numRows >= binNumber
  147. % Determine how many full groups of binNumber can be formed
  148. numGroups = floor(numRows / binNumber); % Number of full groups of binNumber
  150. % Loop through each group
  151. for j = 1:numGroups
  152. % Define the rows for the current group of binNumber within matchingRows
  153. groupRows = (1 + (j-1) * binNumber):(j * binNumber);
  155. % Extract the current column indices for this group
  156. currentColIndices = matchingRows(groupRows, 2); % Get the column indices from the second column
  158. % Sum the information columns for the current group (excluding the first two columns)
  159. summedInfo = sum(matchingRows(groupRows, 3:end), 1); % Summing columns 3 to end
  161. % Store the row index, summed information, and the last column index
  162. binDecayTotal = [binDecayTotal; uniqueRowIndices(i), summedInfo, currentColIndices(end)];
  163. end
  164. end
  165. end
  167. binnedDecay= binDecayTotal(:, 2:257);
  169. indexBinnedDecay= binDecayTotal(:, [1, 258]);
  171. clear colIndices currentColIndices groupRows i j matchingRows numGroups numRows rowIndices summedInfo uniqueRowIndices
  172. clear binDecayTotal
  174. %% Binning time data
  175. binNumber = input('How many pixels do you want to average? \n Please type an integer: ');
  177. % Extract the row indices from the first column of noBinTime
  178. rowIndices = noBinTime(:, 1);
  180. % Extract the column indices from the second column of noBinTime
  181. colIndices = noBinTime(:, 2);
  183. % Find unique row indices
  184. uniqueRowIndices = unique(rowIndices);
  186. % Initialize an array to store the final binned data and corresponding times
  187. binTimeTotal = [];
  189. % Loop through each unique row index
  190. for i = 1:length(uniqueRowIndices)
  191. % Find rows in noBinTime that match the current unique row index
  192. matchingRows = noBinTime(rowIndices == uniqueRowIndices(i), :);
  194. % Get the number of rows for the current unique row index
  195. numRows = size(matchingRows, 1);
  197. % Check if there are enough rows for the specified binNumber
  198. if numRows >= binNumber
  199. % Determine how many full groups of binNumber can be formed
  200. numGroups = floor(numRows / binNumber); % Number of full groups of binNumber
  202. % Loop through each group
  203. for j = 1:numGroups
  204. % Define the rows for the current group of binNumber
  205. groupRows = (1 + (j-1) * binNumber):(j * binNumber);
  207. % Extract the current column indices for this group
  208. currentColIndices = matchingRows(groupRows, 2); % Get the column indices from the second column
  210. % Extract the last row for this group
  211. lastRow = matchingRows(groupRows(end), :); % Get the last row of the group
  213. % Store the row index and the last values of the group
  214. binTimeTotal = [binTimeTotal; lastRow, currentColIndices(end)];
  215. end
  216. end
  217. end
  218. binnedTime= binTimeTotal(:, 3);
  220. indexBinnedTime= binTimeTotal(:, [1,2]);
  222. clear colIndices currentColIndices groupRows i j lastRow matchingRows numGroups numRows rowIndices uniqueRowIndices
  225. %% Normalizes the IRF and produces the alpha 1 Values
  226. irfNorm = irfFile/max(irfFile); %Normalizes the IRF File
  228. size2DbinnedDecay= size(binnedDecay);
  229. nsize= size2DbinnedDecay(1);
  231. % [a1,t1,a2,t2] = Fit_Deconvolution(newdata(1439,:),irfNorm(68:92));
  233. alpha1= zeros([nsize,1]);%Single alpha1 array of nsize
  234. tao1= zeros([nsize, 1]);%Single tao1 array of nsize
  235. alpha2= zeros([nsize,1]);%Single alpha2 array of nsize
  236. tao2= zeros([nsize,1]); %Single tao2 array of nsize
  237. p= 1; %index p starts at 1
  239. %Produces alpha1 values for all data points
  240. for p= 1:nsize
  241. [alpha1(p,1), tao1(p,1), alpha2(p,1), tao2(p,1)]= Fit_Deconvolution(binnedDecay(p,:),irfNorm(68:92));
  242. end
  244. clear p size2DbinnedDecay nsize
  246. %% Calculating alpha1 moving average
  247. binNumber= 8;
  248. alpha1= alpha1*100;
  249. alpha1_mean= movmean(alpha1, binNumber*3);
  251. %% Arranging alpha1 for Heat Map
  252. indexalpha1= [indexBinnedDecay, alpha1_mean];
  253. data = indexalpha1; % Using indexalpha1
  255. % Create a 256x256 array initialized with zeros
  256. heat_alpha1= zeros(256, 256); %Empty
  258. % Loop through each row of the 216x3 array to populate resultArray
  259. for i = 1:size(data, 1)
  260. rowIndex = data(i, 1); % Row index from the first column
  261. colIndex = data(i, 2); % Column index from the second column
  262. infoValue = data(i, 3); % Information from the third column
  264. % Store the information in the corresponding position in the result array
  265. heat_alpha1(rowIndex, colIndex) = infoValue;
  267. % Copy the value to previous columns based on binNumber - 1
  268. for b = 1:(binNumber - 1)
  269. previousCol = colIndex - b; % Calculate the previous column index
  270. % Check if the previous column index is valid
  271. if previousCol >= 1
  272. heat_alpha1(rowIndex, previousCol) = infoValue;
  273. end
  274. end
  275. end
  277. clear data rowIndex b i previousCol
  279. %% Heat map of alpha1 PLOT
  281. heat_alpha1(heat_alpha1 ==0 )= NaN; %Sets values of 0 to NaN
  282. figure;
  283. imagesc(heat_alpha1);
  284. c= colorbar; % Add a color bar to indicate the scale
  285. ylabel(c, 'Free NADH (\alpha_1)', 'FontSize', 18)
  287. caxis([min(heat_alpha1(:)) max(heat_alpha1(:))]);
  288. axis image
  289. % set(gca, 'XTick', [], 'YTick', []); % This removes the tick marks and labels
  290. % Adjust colormap
  291. colormap('parula'); % Choose your desired colormap
  292. % Highlight a specific row (e.g., row 5) in red
  293. artifact= 80;
  294. highlightRow = artifact;
  295. hold on;
  296. plot(1:size(heat_alpha1, 2), highlightRow * ones(1, size(heat_alpha1, 2)), 'w', 'LineWidth', 1); % Plot a red line across the row
  298. clear highlightRow c
  300. %% Plotting Free NADH response over Time
  301. artifact= 52;
  302. index = find(binTimeTotal(:,1) == artifact); %Finds the index where the artifact starts
  303. index_Artifact= index(1); %First occurence of the artifact
  304. [max_Value, max_Index ]= max(alpha1_mean(index_Artifact:end)); %Max value and index of where free NADH is highest
  307. laserOnIndex = index_Artifact; % Index where the laser was turned on
  309. % Calculate the response value (mean after laser is turned on)
  310. responsePeriod = alpha1_mean(laserOnIndex:end); % Data after laser turned on
  312. % Plot the time series data
  313. figure;
  314. p1= plot(binnedTime, alpha1) %No smoothing
  315. hold on
  316. %p2= plot(binnedTime, alpha1_mean,'b', 'LineWidth', 3) %alpha1 smoothing for CONTROL
  318. %Highlight the baseline period
  319. p2= plot(binnedTime(1:laserOnIndex-1), alpha1_mean(1:laserOnIndex-1), 'b', 'LineWidth', 3); %Plots baseline period
  320. %
  321. % %Highlight the response period
  322. p3= plot(binnedTime(laserOnIndex:end), alpha1_mean(laserOnIndex:end), 'r', 'LineWidth', 3); %Plots response period
  323. %
  324. % %Add a vertical line to indicate where the laser was turned on
  325. xline(binnedTime(laserOnIndex), '--k', 'LineWidth', 2); % Add a vertical dashed line for when laser starts
  327. xlabel('Time (ms)', 'FontSize', 28)
  328. ylabel('Free NAD(P)H (%)', 'FontSize', 28)
  329. xlim([0 7000])
  330. xticks(0:2000:7000)
  332. ylim([50 100])
  333. yticks(40:15:100)
  334. set(gca, 'FontSize', 23)
  335. % Display the legend (automatically gathers DisplayNames from each plot)
  336. lgd= legend({'Experiment Data', 'Baseline Period','Response Period'}, 'Location', 'northeast');
  337. lgd.FontSize = 25;
  339. %% Plotting Free NADH response over Time CONTROL
  341. % Plot the time series data
  342. figure;
  343. p1= plot(binnedTime, alpha1) %No smoothing
  344. hold on
  345. p2= plot(binnedTime, alpha1_mean,'b', 'LineWidth', 3) %alpha1 smoothing for CONTROL
  348. xlabel('Time (ms)', 'FontSize', 28)
  349. ylabel('Free NAD(P)H (%)', 'FontSize', 28)
  351. xlim([0 7000])
  352. xticks(0:2000:7000)
  354. ylim([50 100])
  355. yticks(40:15:100)
  356. set(gca, 'FontSize', 23)
  357. % Display the legend (automatically gathers DisplayNames from each plot)
  358. %legend({'Experiment Data', 'Baseline Period','Response Period'}, 'Location', 'Best');
  360. %% Percent change plot CONTROL
  362. baselineValue= mean(alpha1_mean); %for control stuff
  363. percentChange= ((alpha1_mean-baselineValue)/baselineValue)*100; %Computes percent change
  365. % Plot the percent change
  366. figure;
  367. plot(binnedTime, percentChange,'b', 'LineWidth', 2);
  369. % title('Percent Change of Free NADH (\alpha_1)', 'FontSize', 18)
  370. xlabel('Time (ms)', 'FontSize', 28)
  371. ylabel('Percent Change (%)', 'FontSize', 28)
  372. % Optionally, you can add a legend
  373. % legend('Percent Change', 'Baseline Period', 'Response Period', 'Location', 'Best');
  374. % grid on;
  376. xlim([0 7000])
  377. xticks(0:2000:7000)
  379. ylim([-10 70])
  380. yticks(-10:25:70)
  381. set(gca, 'FontSize', 23)
  384. %% Percent change plot use this one
  386. % startIndex= index_Artifact-10;
  387. startIndex= 1; %We are starting at index 1
  388. index_Artifact= laserOnIndex; %This is the index for our artifact
  389. %baselineValue= mean(alpha1_mean); for control stuff
  390. baselineValue= mean(alpha1_mean(1:index_Artifact-1));
  391. percentChange= ((alpha1_mean-baselineValue)/baselineValue)*100; %Computes percent change
  393. % Plot the percent change
  394. figure;
  395. plot(binnedTime(startIndex:end), percentChange(startIndex:end));
  396. hold on
  397. % Highlight the baseline period
  398. plot(binnedTime(startIndex:laserOnIndex-1), percentChange(startIndex:laserOnIndex-1), 'b', 'LineWidth', 2);
  400. % Highlight the response period
  401. plot(binnedTime(laserOnIndex:end), percentChange(laserOnIndex:end), 'r', 'LineWidth', 2);
  403. % Add a vertical line to indicate where the laser was turned on
  404. xline(binnedTime(laserOnIndex), '--k', 'LineWidth', 2); % Add a vertical dashed line
  406. % title('Percent Change of Free NADH (\alpha_1)', 'FontSize', 18)
  407. xlabel('Time (ms)', 'FontSize', 28)
  408. ylabel('Percent Change (%)', 'FontSize', 28)
  409. % Optionally, you can add a legend
  410. lgd= legend('Percent Change', 'Baseline Period', 'Response Period', 'Location', 'northeast');
  411. lgd.FontSize = 25;
  412. % grid on;
  414. xlim([0 7000])
  415. xticks(0:2000:7000)
  417. ylim([-10 70])
  418. yticks(-10:25:70)
  419. set(gca, 'FontSize', 23)
  420. % max_Value= max(alpha1_mean(index_Artifact:index_Artifact+500))
  421. max_Value= max(alpha1_mean(index_Artifact:end))
  422. totalIncrease= max_Value- baselineValue
  423. percentChangeOverall= ((max_Value-baselineValue)/baselineValue)*100
  424. fprintf('Baseline value: %.3f\n', baselineValue)
  426. %% Bins decays in groups of 8 for solutions
  427. nsize = 65536/8; %Dividing decay data into groups of 8
  428. newdata = zeros([nsize,256]); %Empty array of nsize (8192 summed groups) x 256
  429. time = zeros([nsize,1]); %Single time array of nsize (8192 summed groups)
  430. i = 1; %Index i starts at 1
  431. j = 1; %Index j starts at 1
  432. a=0;
  434. while i < 65530
  435. newdata(j,:)=sum(test202212051506binnedrawdata(i:i+7,:),1); %Sums 16 X-val with corresponding y into a 1 dimensional array
  436. if rem((i-256*a),241)==0; %Filters out the last bit of artifact on image
  437. time(j,1)=time(j-1,1)+0.1*2*8;
  438. i=i+8+8;
  439. a=a+1;
  440. else
  441. if j==1;
  442. time(j,1)=0;
  443. else
  444. time(j,1)=time(j-1,1)+0.1*8; %In milliseconds
  445. end
  446. i=i+8; %Adds 16 to i
  448. end
  449. j=j+1; %Adds 1 to j
  450. end
  453. entireTime=time; %Time for entire image (ms)
  455. decayTime = 0:12.5/255:12.5; %In nanoseconds for 1 decay curve
  457. %% Normalizes the IRF and produces the alpha 1 Values
  458. % This is the instrument response function, which is something unique to
  459. % each system, can be obtained imaging urea crystals or YG beads
  461. irfFile= readmatrix('IRF.txt'); %Loads IRF data
  462. irfNorm = irfFile/max(irfFile);
  464. size2DbinnedDecay= size(binnedDecay); %For cells do not need for soln
  465. nsize= size2DbinnedDecay(1); %For cells do not need for soln
  467. alpha1= zeros([nsize,1]);%Single alpha1 array of nsize (8192 summed groups)
  468. tao1= zeros([nsize, 1]);%Single tao1 array of nsize (8192 summed groups)
  469. alpha2= zeros([nsize,1]);%Single alpha2 array of nsize (8192 summed groups)
  470. tao2= zeros([nsize,1]); %Single tao2 array of nsize (8192 summed groups)
  471. p= 1; %index p starts at 1
  474. % for p= 1:nsize
  475. % [alpha1(p,1), tao1(p,1), alpha2(p,1), tao2(p,1)]= Fit_Deconvolution_v1(newdata(p,:),irfNorm(68:92));
  476. % end
  479. for p= 1:nsize
  480. [alpha1(p,1), tao1(p,1), alpha2(p,1), tao2(p,1)]= Fit_Deconvolution_v1(binnedDecay(p,:),irfNorm(68:92));
  481. end
  484. %% Function to deconvolute and fit data/ Function to open up asc file
  485. function [a1Norm,t1,a2Norm,t2] = Fit_Deconvolution(decayCurve,irf)
  486. timeFitting = [1:256]*12.5/256; %Nanoseconds
  487. if max(decayCurve)> 0
  488. deconvDecay = deconvlucy(decayCurve',irf,10); %Deconvolutes data
  489. [~, peakPosition] = max(deconvDecay); %Finds the max peak of decay curve
  490. fittingPart = [deconvDecay(peakPosition:256)' zeros(1,peakPosition-1)];
  492. %Conditions are needed to ensure alpha1 is never above 1
  493. decayFit = fit(timeFitting',fittingPart','exp2','StartPoint',[0.6,-4, 1, -0.8],'Lower',[0.5,-5, 0.03,-1],'Upper',[80,-1.67,60,-0.3]); %Fits data to a 2 exponential form
  494. fittingValues = coeffvalues(decayFit); %Array of all coefficients
  495. a1 = fittingValues(1);
  496. a1Norm = fittingValues(1)/(fittingValues(1) + fittingValues(3) );
  497. t1 = -1/fittingValues(2);
  498. a2 = fittingValues(3);
  499. a2Norm = fittingValues(3)/(fittingValues(1) + fittingValues(3) );
  500. t2 = -1/fittingValues(4);
  501. else
  502. a1Norm = 0;
  503. t1 = 0;
  504. a2Norm = 0;
  505. t2 = 0;
  506. end
  507. % f = fittype('a*exp(b*x) + c*exp(d*x)')
  508. % c = cfit(f,a1,-1/t1,a2,-1/t2)
  509. % plot(c,timeFitting,fittingPart)
  510. % xlabel('Time (ns)');
  511. % ylabel('Decay Amplitude');
  512. % title('Double Exponential Fit');
  513. % legend('Fitted Curve', 'Decay Curve');
  514. % grid on;
  515. end
  518. %Function to open up asc file
  519. function photonDecayImage3D = Convert_ASC_Image(imageDirectory) %Function to read ASC File
  520. PhotonRawDecayImage = importdata(imageDirectory,' ',11);
  521. PhotonDecayImage = PhotonRawDecayImage.data;
  522. photonDecayImage3D = reshape(PhotonDecayImage,[256 256 256]);
  523. photonDecayImage3D = permute(photonDecayImage3D,[2 1 3]);
  524. end

DynamicTempCode.m at commit f5b5071, no license · at the source

Overview

Authors: Jocelyn Martinez1, Anna Theodossiou1, Linghao Hu1, Alex J Walsh1
ORCID iDs: Jocelyn Martinez, Alex J Walsh
  1. Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77840, USA
Institutions: Texas A&M University (United States)
Journal: Biomedical optics express, volume 17, issue 7, pages 3776-3791
Dates: received 21 April 2026; accepted 2 June 2026; published online 18 June 2026
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1364/boe.603469 · PMID 42460335 · PMCID PMC13372349 · OpenAlex W7163700498
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Statistics, Preprocessing, Smoothing, state filtering, decompositions, Connectivity
Topic: Mitochondrial Function and Pathology (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIGMS NIH HHS (R35 GM142990)
Citations: cited by 1 paper (Europe PMC); 46 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

walshlab/Infrared-Temperature-FLIM

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f5b5071b387a6d675ac8673f0bc0e8ac52fc9a7e, 6 February 2026
Languages: MATLAB (3)
Size: 9 files, 3 scripts
Software Heritage: not archived
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Curve Fitting Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/walshlab/Infrared-Temperature-FLIM

The paper's code and data availability statement is in the Data section.

Tracing map

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What the map holds:

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  • 3 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (none stated) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1364/boe.603469.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 1 funder, 44 references.

Cite

This paper

Martinez, J., Theodossiou, A., Hu, L., & Walsh, A. J. (2026). FLIM quantification of temperature-dependent enzyme-NADH binding dynamics. Biomedical optics express, 17(7), 3776-3791. https://doi.org/10.1364/boe.603469

BibTeX

@article{martinez2026flim,
author = {Martinez, Jocelyn and Theodossiou, Anna and Hu, Linghao and Walsh, Alex J},
title = {{FLIM quantification of temperature-dependent enzyme-NADH binding dynamics}},
journal = {Biomedical optics express},
year = {2026},
month = jun,
volume = {17},
number = {7},
pages = {3776--3791},
publisher = {Optica Publishing Group},
issn = {2156-7085},
doi = {10.1364/boe.603469},
url = {https://doi.org/10.1364/boe.603469},
pmid = {42460335},
pmcid = {PMC13372349}
}

RIS

TY - JOUR
AU - Martinez, Jocelyn
AU - Theodossiou, Anna
AU - Hu, Linghao
AU - Walsh, Alex J
TI - FLIM quantification of temperature-dependent enzyme-NADH binding dynamics
T2 - Biomedical optics express
J2 - Biomed Opt Express
PY - 2026
DA - 2026/06/18
VL - 17
IS - 7
SP - 3776
EP - 3791
SN - 2156-7085
PB - Optica Publishing Group
DO - 10.1364/boe.603469
UR - https://doi.org/10.1364/boe.603469
LA - en
ER -

CSL-JSON

{
"id": "10.1364/boe.603469",
"type": "article-journal",
"title": "FLIM quantification of temperature-dependent enzyme-NADH binding dynamics",
"container-title": "Biomedical optics express",
"author": [
{
"family": "Martinez",
"given": "Jocelyn"
},
{
"family": "Theodossiou",
"given": "Anna"
},
{
"family": "Hu",
"given": "Linghao"
},
{
"family": "Walsh",
"given": "Alex J"
}
],
"container-title-short": "Biomed Opt Express",
"volume": "17",
"issue": "7",
"page": "3776-3791",
"DOI": "10.1364/boe.603469",
"PMID": "42460335",
"PMCID": "PMC13372349",
"ISSN": "2156-7085",
"publisher": "Optica Publishing Group",
"URL": "https://doi.org/10.1364/boe.603469",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
18
]
]
}
}

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