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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

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

The paper is loaded when this pane is shown.

The authors' code

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

The registry keeps no copy of this file: its repository has no license, so its authors keep all their rights to it. Your browser shows it from its source, with JavaScript.

It can be read at the source: main.m.

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, not copied: shown from their source

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At the source: github.com/ComputBio/oxytocin-dynamics-analysis

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 9 scripts, each with its path and the digest of its content;
  • 9 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 (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: — → 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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}

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