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Unraveling the Dynamics of Oxytocin in Hypothalamic Neurons.

Code ↔ Paper

9 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 9 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › Trajectory Analysis and Machine Learning Classification › Machine Learning Models ↔ step_CNN.m, lines 64–92 · score 0.89 · fully connected layer, softmax layer, CNN architecture, batch normalization, ReLU, Convolutional
  2. [2] § Materials and Methods › Trajectory Analysis and Machine Learning Classification › Synthetic Dataset ↔ generate_fgn_daviesharte.m, the whole file · a weak match · score 0.78 · fractional Gaussian noise, Hurst exponent, Davies Harte, correlated
  3. [3] § Materials and Methods › Trajectory Analysis and Machine Learning Classification › Synthetic Dataset ↔ generate_synthetic_trajectories.m, the whole file · a weak match · score 0.76 · fractional Brownian Motion, physical constraints, anomalous diffusion, position, matrix, superdiffusion
  4. [4] § Materials and Methods › Trajectory Analysis and Machine Learning Classification ↔ MSD.m, the whole file · a weak match · score 0.76 · power law relationship, Squared Displacement, MSD curve, lag, fitting, particle
  5. [5] § Materials and Methods › Trajectory Analysis and Machine Learning Classification › Synthetic Dataset ↔ generate_synthetic_trajectories.m, the whole file · a weak match · score 0.72 · Davies Harte algorithm, Hurst exponent, Gaussian, fractional, noise, motion
  6. [6] § Materials and Methods › Trajectory Analysis and Machine Learning Classification ↔ MSD.m, the whole file · a weak match · score 0.64 · generalized diffusion coefficient, log fit, intercept, particle, Trajectory
  7. [7] § Results › Machine Learning Classification ↔ main.m, lines 1–9 · score 0.59 · Convolutional Neural Network, Random Forest, machine learning, RF, synthetic, trained
  8. [8] § Materials and Methods › Trajectory Analysis and Machine Learning Classification › Machine Learning Models ↔ main.m, lines 86–106 · score 0.56 · Random Forest, RF model, ensemble, trees, Machine, trained
  9. [9] § Results › Machine Learning Classification ↔ main.m, lines 86–106 · score 0.55 · OOB error, RF model, Bag, prediction, Machine, trained

Paper

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The authors' code

MATLAB · 150 lines · 5.5 KB · no license · 3 matches

  1. %% Analysis of Oxytocin (OT) Vesicle Dynamics via Machine Learning
  2. % This script:
  3. % 1. Preprocesses experimental trajectories.
  4. % 2. Generates synthetic trajectories (fBM) for training.
  5. % 3. Trains and evaluates a Random Forest (RF) classifier.
  6. % 4. Trains and evaluates a 1D Convolutional Neural Network (CNN).
  7. % 5. Classifies experimental data and evaluates model consensus.
  9. clearvars; close all; clc;
  11. %% 0. Configuration & Parameters
  12. % Define global parameters
  13. params.numSteps = 80; % Number of frames per trajectory
  14. params.timeStep = 1; % Seconds per frame
  15. params.numTrees = 300; % RF ensemble size
  16. params.numSynthetic = 1e5; % Size of synthetic training set
  17. params.rngSeed = 1; % For reproducibility
  19. % Global parameters used by synthetic generation and training
  20. N_synthetic = 1e5; % Total number of synthetic trajectories
  21. numSteps = 80; % Frames per trajectory
  22. timestep = 1; % Seconds per frame (Delta T)
  24. % Target proportions for training (must sum to 1)
  25. N_nd = 0.35; % 35% Normal Diffusion
  26. N_sub = 0.40; % 40% Subdiffusion
  27. N_sup = 0.25; % 25% Superdiffusion
  29. params.numTrees = 300; % RF ensemble size
  30. params.rngSeed = 1; % For reproducibility
  32. % Create directories if they don't exist
  33. if ~exist('results', 'dir'), mkdir('results'); end
  35. %% 1. Data Loading and Preprocessing
  36. fprintf('--- Step 1: Experimental Data Preparation ---\n');
  38. dataPath = fullfile('data', 'prepared_data.mat');
  39. dimPath = fullfile('data', 'T_dim.mat');
  41. if exist(dataPath, 'file')
  42. fprintf('Loading prepared experimental data...\n');
  43. load(dataPath);
  44. else
  45. fprintf('Running data preparation pipeline...\n');
  46. data_preparation;
  47. end
  49. % Load diameter information (for the size-impact analysis)
  50. if exist(dimPath, 'file')
  51. load(dimPath);
  52. else
  53. error('Vesicle diameter data (T_dim.mat) not found.');
  54. end
  56. valid_D_indices = ~isnan(T.D) & T.D > 0;
  57. logD_real = log10(T.D(valid_D_indices));
  58. logD_mean_real = mean(logD_real);
  59. logD_std_real = std(logD_real);
  61. % Summary Statistics
  62. fprintf('Experimental Dataset: %d trajectories\n', height(T));
  63. fprintf('Mean Alpha: %.3f | Mean D: %.3f\n', mean(T.Alpha, 'omitnan'), mean(T.D, 'omitnan'));
  65. %% 2. Synthetic Data Generation
  66. fprintf('\n--- Step 2: Generating Synthetic Training Set ---\n');
  67. % Target proportions for training: 35% Normal, 40% Subdiffusive, 25% Superdiffusive
  68. propND = 0.35;
  69. propSub = 0.40;
  70. propSup = 0.25;
  72. % Generate fBM trajectories using the Davies-Harte algorithm
  73. % This call assumes 'step_synthetic' uses the proportions and params defined above
  74. step_synthetic;
  76. % --- Step 2.1: Integrate Vesicle Diameter into Training Data ---
  77. % To test if diameter influences classification, we bootstrap real
  78. % diameters into the synthetic training set (size-independent control)
  79. validDiameters = T_dim.diameter(~isnan(T_dim.diameter) & T_dim.diameter > 0);
  80. numSyntheticTotal = height(Training_T);
  82. rng(params.rngSeed);
  83. Training_T.Diameter = randsample(validDiameters, numSyntheticTotal, true);
  84. fprintf('Synthetic training set ready with %d samples.\n', numSyntheticTotal);
  86. %% 3. Machine Learning: Random Forest (RF)
  87. fprintf('\n--- Step 3: Training Random Forest Classifier ---\n');
  89. % Feature Matrix: [Alpha, log10(K_alpha), R-squared, Diameter]
  90. featuresTrain = [Training_T.Alpha, log10(Training_T.D), Training_T.Rsqrt, Training_T.Diameter];
  91. labelsTrain = Training_T.Label;
  93. % Train Random Forest (Bagged Trees)
  94. RF_Model = fitcensemble(featuresTrain, labelsTrain, ...
  95. 'Method', 'Bag', ...
  96. 'NumLearningCycles', params.numTrees, ...
  97. 'Learners', 'Tree');
  99. % Evaluate Out-of-Bag Error
  100. oobError = oobLoss(RF_Model, 'Mode', 'ensemble');
  101. fprintf('RF Out-of-Bag Classification Error: %.2f%%\n', oobError * 100);
  103. % Predict on Experimental Data
  104. % Note: Using T_dim.diameter for consistency with Training_T
  105. featuresReal = [T.Alpha, log10(T.D), T.R2, T_dim.diameter];
  106. T.Predicted_Label_RF = predict(RF_Model, featuresReal);
  108. %% 4. Deep Learning: 1D CNN
  109. fprintf('\n--- Step 4: Training 1D CNN Pipeline ---\n');
  111. % Training logic resides in step_CNN (processes raw MSD curves)
  112. step_CNN;
  114. % Predict experimental trajectories using CNN
  115. step_classification;
  117. %% 5. Results Visualization
  118. fprintf('\n--- Step 5: Generating Publication Figures ---\n');
  120. % Figure A: Alpha Distributions
  121. figure('Name', 'Trajectory Characteristics', 'Color', 'w');
  122. subplot(1,2,1);
  123. histogram(T.Alpha, 20, 'FaceColor', [0.2 0.4 0.6]);
  124. title('Experimental \alpha Distribution');
  125. xlabel('Anomalous Exponent (\alpha)'); ylabel('Frequency');
  126. grid on; axis square;
  128. % Figure B: Predictor Importance (Crucial for the "Size doesn't matter" finding)
  129. subplot(1,2,2);
  130. imp = predictorImportance(RF_Model);
  131. b = bar(imp, 'FaceAlpha', 0.8);
  132. b.FaceColor = 'flat';
  133. b.CData(4,:) = [0.8 0.2 0.2]; % Highlight Diameter in red
  134. set(gca, 'XTickLabel', {'\alpha', 'log_{10}(K_\alpha)', 'R^2', 'Diameter'});
  135. title('Predictor Importance Scores');
  136. ylabel('Importance Score');
  137. grid on; axis square;
  139. % Figure C: Model Consensus (Confusion Matrix)
  140. figure('Name', 'Model Comparison', 'Color', 'w');
  141. cc = confusionchart(categorical(T.Predicted_Label_RF), categorical(T.Predicted_Label_CNN), ...
  142. 'Title', 'Classification Consensus: RF vs. CNN', ...
  143. 'XLabel', 'CNN Prediction', 'YLabel', 'RF Prediction');
  144. cc.DiagonalColor = [0 0.45 0.74];
  145. set(gca, 'FontSize', 11);
  147. %% 6. Data Export
  148. outputFile = fullfile('results', 'classified_trajectories.mat');
  149. save(outputFile, 'T', 'RF_Model', 'params', '-v7.3');
  150. fprintf('\nAnalysis Complete. Results saved to: %s\n', outputFile);

main.m at commit 64345ee, no license · at the source

Overview

Authors: Beatriz Aznar‐Escolano1, Vera Egorova2, José Villanueva1, Luis Miguel Gutiérrez1, Virginia González‐Vélez3, Amparo Gil2, Sandra Jurado1
ORCID iDs: Vera Egorova, José Villanueva, Amparo Gil, Sandra Jurado
  1. Instituto de Neurociencias, CSIC‐UMH, San Juan de Alicante, Spain
  2. Depto. de Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, Santander, Spain
  3. Departamento de Ciencias Básicas, UAM Azcapotzalco, Mexico City, Mexico
Institutions: Consejo Superior de Investigaciones Científicas (Spain); Instituto de Neurociencias (Spain); Universidad de Cantabria (Spain); Universidad Autónoma Metropolitana (Mexico)
Journal: Traffic (Copenhagen, Denmark), volume 27, issue 2, article e70034
Dates: received 7 November 2025; accepted 26 March 2026; published online 6 April 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1111/tra.70034 · PMID 41942291 · PMCID PMC13053160 · OpenAlex W7151044107
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: cellular / molecular (subfield)
Methods: Machine learning, Complexity, Statistics, fMRI & imaging, Single-unit activity, calcium imaging, Spectral & time-frequency
Keywords: anomalous diffusion, hypothalamic neurons, large dense‐core vesicles (LDCVs), machine learning, neuropeptide trafficking, oxytocin, single‐particle tracking (SPT)
MeSH: Hypothalamus*, Neurons*, Oxytocin*, Animals, Dense Core Vesicles, Machine Learning, Protein Transport (* major topic)
Topic: Neuroendocrine regulation and behavior (Social Psychology, Psychology), according to OpenAlex
Funding: CONAHCyT-Mexico (CVU 202622); Ministerio de Ciencia Innovación y Universidades (PID2020-113878RB-I00/AEI/10.13039/501100011033, PID2020-114824GB-I00, PID2020‐114824GB‐I00, PID2023-146390NB-I00, PID2020‐113878RB‐I00/AEI/10.13039/501100011033, PID2023‐146390NB‐I00)
Citations: not cited yet (Europe PMC); 67 references in the paper
Research resources: RRID:SCR_001622

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) 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 9 matches between paragraphs and lines of code.

ComputBio/oxytocin-dynamics-analysis

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 64345ee151a46bf138c12facde7ecf7faea69554, 6 April 2026
Languages: MATLAB (9)
Size: 11 files, 9 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), Deep Learning Toolbox (1 file), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/ComputBio/oxytocin-dynamics-analysis

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

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  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Code and data availability statement

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Read it in the paper: doi.org/10.1111/tra.70034.

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Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 7 keywords, 7 MeSH terms, 2 funders, 61 references, 1 RRID.

Cite

This paper

Aznar‐Escolano, B., Egorova, V., Villanueva, J., Gutiérrez, L. M., González‐Vélez, V., Gil, A., & Jurado, S. (2026). Unraveling the Dynamics of Oxytocin in Hypothalamic Neurons. Traffic (Copenhagen, Denmark), 27(2), e70034. https://doi.org/10.1111/tra.70034

BibTeX

@article{aznarescolano2026unraveling,
author = {Aznar‐Escolano, Beatriz and Egorova, Vera and Villanueva, José and Gutiérrez, Luis Miguel and González‐Vélez, Virginia and Gil, Amparo and Jurado, Sandra},
title = {{Unraveling the Dynamics of Oxytocin in Hypothalamic Neurons}},
journal = {Traffic (Copenhagen, Denmark)},
year = {2026},
month = jun,
volume = {27},
number = {2},
pages = {e70034},
publisher = {Wiley},
issn = {1398-9219},
doi = {10.1111/tra.70034},
url = {https://doi.org/10.1111/tra.70034},
pmid = {41942291},
pmcid = {PMC13053160}
}

RIS

TY - JOUR
AU - Aznar‐Escolano, Beatriz
AU - Egorova, Vera
AU - Villanueva, José
AU - Gutiérrez, Luis Miguel
AU - González‐Vélez, Virginia
AU - Gil, Amparo
AU - Jurado, Sandra
TI - Unraveling the Dynamics of Oxytocin in Hypothalamic Neurons
T2 - Traffic (Copenhagen, Denmark)
J2 - Traffic
PY - 2026
DA - 2026/06/01
VL - 27
IS - 2
SP - e70034
SN - 1398-9219
PB - Wiley
DO - 10.1111/tra.70034
UR - https://doi.org/10.1111/tra.70034
LA - en
ER -

CSL-JSON

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"id": "10.1111/tra.70034",
"type": "article-journal",
"title": "Unraveling the Dynamics of Oxytocin in Hypothalamic Neurons",
"container-title": "Traffic (Copenhagen, Denmark)",
"author": [
{
"family": "Aznar‐Escolano",
"given": "Beatriz"
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"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

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