OSCR

A longitudinal DNA methylation atlas and its link to brain structure and mental health.

Code ↔ Paper

7 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 7 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Clustering analysis of DNAm data › Step 1: Consensus WGCNA modules ↔ Methylation_Running/(Reply1-1-1)Clustering_direct_WGCNA.R, lines 2 to 57 · score 0.71 · soft thresholding, scale free topology, CpGs, WGCNA, gene, matrices · Report a mismatch
  2. [2] § Materials and methods › Clustering analysis of DNAm data › Step 2: Hierarchical clustering of longitudinal DNAm change ↔ Methylation_Running/(5)Heatmap_and_Clustering.R, lines 2 to 52 · score 0.64 · Hierarchical clustering, flashClust, Euclidean, distance, modules, matrix · Report a mismatch
  3. [3] § Materials and methods › Clustering analysis of DNAm data › Step 2: Hierarchical clustering of longitudinal DNAm change ↔ Methylation_Running/(Reply1-1-1)Clustering_direct_WGCNA.R, lines 59 to 103 · score 0.63 · Hierarchical clustering, flashClust, distance, WGCNA, thresholding, modules · Report a mismatch
  4. [4] § Materials and methods › Clustering analysis of DNAm data › Step 1: Consensus WGCNA modules ↔ Methylation_Running/(2-1)Power_age14.R, the whole file · a weak match · score 0.61 · soft thresholding, scale free topology, WGCNA, gene, matrices, methylated · Report a mismatch
  5. [5] § Materials and methods › Functional and tissue-specific Enrichment analyses ↔ Methylation_Running/(6-2)Tissue_specific_Enrichment_Cluster18.R, lines 1 to 76 · score 0.59 · getNearestTSS, enrichment, probe, Genomes, tissue, Gene · Report a mismatch
  6. [6] § Materials and methods › Robustness of the DNAm pattern ↔ Methylation_Running/(Reply1-10)FirstPC_module.R, the whole file · a weak match · score 0.59 · variance explained, principal component, module, CpG, methylation · Report a mismatch
  7. [7] § Materials and methods › Polygenic risk scores of psychiatric disorders ↔ Methylation_Running/PRS_mental_health/plink_code/(1)PRS_Depression.m, lines 16 to 31 · score 0.55 · PRSice, clumping, GWAS, v1, scores, depressive · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

R · 103 lines · 3.4 KB · no license · 2 matches

  2. rm(list=ls())
  3. setwd('/Users/dichen/Documents/Methylation_Running/')
  4. #setwd('F:/Methylation_Running/')
  6. library(R.matlab)
  8. num <- 372582
  9. sub <- 506
  10. mat_data <- readMat("./List_ME/substract_19_14_all_CpGs.mat")
  11. substract_19_14_all_CpGs<-mat_data$substract.19.14.all.CpGs
  13. Raw<-substract_19_14_all_CpGs[,-1]
  18. library(WGCNA)
  19. allowWGCNAThreads()
  20. options(stringsAsFactors=FALSE)
  21. gsg <- goodSamplesGenes(as.matrix(Raw), verbose=3)
  22. if (!gsg$allOK) {
  23. Raw <- Raw[gsg$goodSample, gsg$goodGenes]
  24. }
  28. library(flashClust)
  30. #datFT <- readRDS("Removed_Outlier_cut3_Volume_Change.rds")
  31. powers <- c(c(1:10), seq(from=12, to=20, by=2))
  32. nGene <- 90000
  33. sft <- pickSoftThreshold(Raw, powerVector=powers, blockSize = nGene, verbose=5)
  35. saveRDS(sft,file="./WGCNA/Power_information_substract_19_14_all_CpGs.rds")
  36. ########################################
  40. ##############################
  42. sft <- readRDS('./WGCNA/Power_information_substract_19_14_all_CpGs.rds')
  43. pdf(file="./WGCNA/Soft-threshold_substract_19_14_all_CpGs.pdf", width=9, height=5)
  44. par(mfrow=c(1,2)) #Two plots
  45. cex1=0.8 #具体看一下图
  47. plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],xlab="Soft Threshold (power)", ylab="Scale Free Topology Model Fit, signed R^2", type="n", main=paste("Scale independence"))
  48. text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2], labels=powers, cex=cex1, col="red")
  49. abline(h=0.8, col="red")
  52. powers <- c(c(1:10), seq(from=12, to=20, by=2))
  53. nGene <- 90000
  54. plot(sft$fitIndices[,1], sft$fitIndices[,5], xlab="Soft Threshold (power)", ylab= "Mean Connectivity", type="n", main= paste("Mean connectivity"))
  55. text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1, col="red")
  57. dev.off()
  59. #######################
  61. library(WGCNA)
  62. allowWGCNAThreads()
  63. options(stringsAsFactors=FALSE)
  64. #datFT <- readRDS("Removed_Outlier_cut3_Volume_Change.rds")
  65. nGene <- 40000
  68. bwnet <- blockwiseModules(Raw, networkType="unsigned", TOMType="unsigned", power=3,
  69. maxBlockSize = nGene, detectCutHeight=0.995, minModuleSize=50,
  70. reassignThreshold=0, deepSplit=4, mergeCutHeight=0, numericLabels=T,
  71. saveTOMs=T, nThreads=20, verbose=3)
  72. saveRDS(bwnet, file="./WGCNA/substract_19_14_all_CpGs_Power3.rds", compress=T)
  73. #######################
  76. #######################
  77. bwnet <- readRDS('./WGCNA/substract_19_14_all_CpGs_Power3.rds')
  78. Origin <- as.data.frame(bwnet$colors)
  80. Origin$`bwnet$colors`
  81. length(tabulate(Origin$`bwnet$colors`))
  83. write.table(Origin, file="./WGCNA/List_Of_Methylation_Change_Power3_Min50_Merge0_UnsignedTOM_substract_19_14_all_CpGs.txt", quote=F, sep="\t", col.names=F, row.names=F)
  85. ME <- as.data.frame(bwnet$MEs)
  86. write.table(ME, file="./WGCNA/ME_Methylation_Change_Power3_Min50_Merge0_UnsignedTOM_substract_19_14_all_CpGs.txt", quote=F, sep="\t", col.names=T, row.names=F)
  87. ##############################
  88. ##########################
  89. library(flashClust)
  91. r <- cor(ME, method = "pearson", use = "complete.obs")
  93. sampleTree <- flashClust(dist(r), method="average")
  95. pdf(file="./WGCNA/Plot_Hierarchical_clustering_of_Power3_No_Merge_Network_substract_19_14_all_CpGs.pdf", width=12, height=9)
  96. par(cex=0.4)
  97. par(mar=c(0,4,2,0))
  98. plot(sampleTree, main= "WVCNA clusters clustering", sub="", xlab="", cex.lab=0.5, cex.axis=0.5, cex.main=2)
  99. abline (h=1000, col="red")
  100. dev.off()
  102. library(pheatmap)
  103. pheatmap(r, cluster_rows = T,cluster_cols = T,clustering_method = 'average')

(Reply1-1-1)Clustering_direct_WGCNA.R at commit 166fb8a, no license · at the source

Overview

Authors: Di Chen1,2,3,4, Xinyang Yu4, Wei Cheng2,3, Linbo Wang2,3, Shitong Xiang2,3, Wei Zhang2,3, Tobias Banaschewski5, Arun L W Bokde6, Herta Flor7,8, Antoine Grigis9, Hugh Garavan10, Penny Gowland11, Andreas Heinz12,13, Rüdiger Brühl14, Jean-Luc Martinot15,16, Marie-Laure Paillère Martinot15,17, Eric Artiges15,16, Frauke Nees5,18, Dimitri Papadopoulos Orfanos9, Hervé Lemaître19
and 14 other authorsTomáš Paus20, Luise Poustka21, Sarah Hohmann22, Nathalie Holz5, Christian Bäuchl13, Michael N Smolka13, Nilakshi Vaidya23, Henrik Walter24, Robert Whelan25, Jianfeng Feng2,3,26, Paul M Thompson27, Gunter Schumann28,29, Tianye Jia2,3,4,30,31, Sylvane Desrivières4
ORCID iDs: Di Chen, Xinyang Yu, Wei Cheng, Linbo Wang, Shitong Xiang, Tobias Banaschewski, Arun L W Bokde, Herta Flor, Hugh Garavan, Penny Gowland, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Hervé Lemaître, Tomáš Paus, Sarah Hohmann, Nathalie Holz, Michael N Smolka, Nilakshi Vaidya, Henrik Walter, Robert Whelan, Jianfeng Feng, Gunter Schumann, Tianye Jia, Sylvane Desrivières
31 affiliations
  1. Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
  2. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
  3. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
  4. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
  5. Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
  6. Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
  7. Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
  8. Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
  9. NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
  10. Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT USA
  11. Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, Nottingham, UK
  12. Department of Psychiatry and Psychotherapy, University of Tübingen, and German Center for Mental Health (DZPG), Site Tübingen, Tübingen, Germany
  13. Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
  14. Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
  15. Institut National de la Santé et de la Recherche Médicale, INSERM U1299 “Developmental trajectories & psychiatry”; Université Paris-Saclay, Université Paris Cité, Ecole Normale supérieure Paris-Saclay, CNRS; Centre Borelli, UMR9010 Gif-sur-Yvette, France
  16. Psychiatry Department, Lad-D-Psy, EPS Barthélémy Durand, Etampes, France
  17. AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
  18. Institute of Medical Psychology, Ludwig-Maximilians-Universität (LMU) in Munich, Munich, Germany
  19. Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, 33076 Bordeaux, France
  20. Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hosptalier Universitaire Sainte-Justine, University of Montreal, Montreal, QC Canada
  21. Department of Child and Adolescent Psychiatry and Psychotherapy, Heidelberg University Hospital, Blumenstrasse 8, 69115 Heidelberg, Germany
  22. Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
  23. Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
  24. Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
  25. School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
  26. Department of Computer Science, University of Warwick, Coventry, UK
  27. Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of University of Southern California, Marina del Rey, CA USA
  28. Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
  29. Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
  30. National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing, China
  31. School of Psychology, Southampton University, Southampton, UK
Institutions: Shanghai Medical College of Fudan University (China); King's College London (United Kingdom); Fudan University (China); East China Normal University (China); Heidelberg University (Germany); Central Institute of Mental Health (Germany); Medizinische Fakultät Mannheim (Germany); Trinity College Dublin (Ireland); University of Mannheim (Germany); Commissariat à l'Énergie Atomique et aux Énergies Alternatives (France); Université Paris-Saclay (France); CEA Paris-Saclay - Etablissement de Saclay (France); CEA Paris-Saclay (France); University of Vermont (United States); University of Nottingham (United Kingdom); Deutsches Zentrum für Psychische Gesundheit (Germany); Technische Universität Dresden (Germany); University of Tübingen (Germany); Physikalisch-Technische Bundesanstalt (Germany); École Normale Supérieure Paris-Saclay (France); Centre National de la Recherche Scientifique (France); Inserm (France); Université Paris Cité (France); Trajectoires développementales & psychiatrie (France); Centre Borelli (France); Sorbonne Université (France); Assistance Publique – Hôpitaux de Paris (France); Pitié-Salpêtrière Hospital (France); Institut für Medizinische Psychologie (Germany); Ludwig-Maximilians-Universität München (Germany); Université de Bordeaux (France); Institut des Maladies Neurodégénératives (France); Centre Hospitalier Universitaire Sainte-Justine (Canada); Université de Montréal (Canada); University Hospital Heidelberg (Germany); University Medical Center Hamburg-Eppendorf (Germany); Charité – Universitätsmedizin Berlin (Germany); Humboldt-Universität zu Berlin (Germany); University of Warwick (United Kingdom); University of Southern California (United States); Peking University (China); University of Southampton (United Kingdom)
Journal: Molecular psychiatry, volume 31, issue 8, pages 4535-4549
Dates: received 30 June 2025; accepted 10 March 2026; published online 31 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41380-026-03554-y · PMID 41917495 · PMCID PMC13364678 · OpenAlex W7144108541
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), depression (population)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging, Spectral & time-frequency, Preprocessing
Keywords: Genetics, Psychiatric disorders
MeSH: Brain*, DNA Methylation*, Adolescent, Epigenesis, Genetic, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Mental Disorders, Mental Health, Neurodevelopment, Young Adult (* major topic)
Topic: Epigenetics and DNA Methylation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIDA NIH HHS (R01 DA049238); NIMH NIH HHS (R01 MH085772); cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence; NIA NIH HHS (R56 AG058854); NIBIB NIH HHS (U54 EB020403)
Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

Epigenetic mechanisms are thought to contribute to neurodevelopmental vulnerability for psychiatric disorders, yet longitudinal evidence linking DNA methylation (DNAm) to brain maturation and psychopathology is limited. Using epigenome-wide DNAm (372,582 CpGs) and whole-brain structural MRI data from the IMAGEN cohort (n = 506, ages 14–19), we identified 18 co-regulated DNAm clusters, ten enriched for brain-expressed genes involved in neuronal development and signalling. The clusters showed consistent longitudinal change and were reproducible in independent adult samples (PPMI, n = 513; ADNI, n = 606). Multivariate analyses revealed coordinated coupling between DNAm change and cortical-subcortical maturation, such that greater DNAm reductions in brain-related clusters associated with greater cortical thinning and subcortical volume changes in the fronto-limbic-striatal axis. Increases in depressive symptoms, and frequency of cannabis use and binge drinking were linked to DNAm changes, with mediation models supporting DNAm as a mechanistic bridge between behaviour and brain change. Two clusters (C1 and C7) were associated with depressive and negative psychotic symptoms across adolescence. Associations of these clusters with depressive symptoms replicated in the PPMI dataset. Their coupling with amygdala–striatal maturation suggests that these DNAm signatures index environmentally shaped affective–motivational neurodevelopment.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

dichen27/dnam_pattern

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 166fb8a3c27929ad6ad836fbeb8a7c7713b7d166, 19 June 2025
Languages: MATLAB (113), R (27)
Size: 335 files, 140 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (22 files), pheatmap (10 files), ggplot2 (7 files), limma (5 files), Tools for NIfTI and ANALYZE image (MATLAB) (5 files), WGCNA (4 files), psych (3 files), DPABI (1 file), Image Processing Toolbox (1 file), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
141 files
At the source: github.com/dichen27/DNAm_pattern/tree/main/Methylation_Running/Cluster18

Code availability

Clustering analyses were conducted using the R package “WGCNA” (version 1.72-5). Functional enrichment analyses were performed with the R package “missMethyl” (version 1.34.0). Tissue-specific enrichment was assessed using the R package “TissueEnrich” (version 1.20.0). T1-weighted MRI images were processed with FreeSurfer (version 6.0; http://surfer.nmr.mgh.harvard.edu/). Canonical correlation analysis (CCA) was carried out using the R package “CCA” (version 1.2.2). CpGs corresponding to the 18 DNAm Clusters are available on Github (https://github.com/dichen27/DNAm_pattern/tree/main/Methylation_Running/Cluster18).

Reproduced under the paper's license (CC BY), from the paper cited above.

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;
  • 140 scripts, each with its path and the digest of its content;
  • 7 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.

Data availability

The IMAGEN data is available from a dedicated database at https://imagen2.cea.fr. The PPMI data is available from the website (https://www.ppmi-info.org/). The ADNI data is available from the website (https://adni.loni.usc.edu/).

Reproduced under the paper's license (CC BY), from the paper cited above.

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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 34 authors, 2 keywords, 13 MeSH terms, 5 funders, 80 references.

Cite

This paper

Chen, D., Yu, X., Cheng, W., Wang, L., Xiang, S., Zhang, W., Banaschewski, T., Bokde, A. L. W., Flor, H., Grigis, A., Garavan, H., Gowland, P., Heinz, A., Brühl, R., Martinot, J.-L., Paillère Martinot, M.-L., Artiges, E., Nees, F., Orfanos, D. P., . . . Desrivières, S. (2026). A longitudinal DNA methylation atlas and its link to brain structure and mental health. Molecular psychiatry, 31(8), 4535-4549. https://doi.org/10.1038/s41380-026-03554-y

BibTeX

@article{chen2026longitudinal,
author = {Chen, Di and Yu, Xinyang and Cheng, Wei and Wang, Linbo and Xiang, Shitong and Zhang, Wei and Banaschewski, Tobias and Bokde, Arun L W and Flor, Herta and Grigis, Antoine and Garavan, Hugh and Gowland, Penny and Heinz, Andreas and Brühl, Rüdiger and Martinot, Jean-Luc and Paillère Martinot, Marie-Laure and Artiges, Eric and Nees, Frauke and Orfanos, Dimitri Papadopoulos and Lemaître, Hervé and Paus, Tomáš and Poustka, Luise and Hohmann, Sarah and Holz, Nathalie and Bäuchl, Christian and Smolka, Michael N and Vaidya, Nilakshi and Walter, Henrik and Whelan, Robert and Feng, Jianfeng and Thompson, Paul M and Schumann, Gunter and Jia, Tianye and Desrivières, Sylvane},
title = {{A longitudinal DNA methylation atlas and its link to brain structure and mental health}},
journal = {Molecular psychiatry},
year = {2026},
month = mar,
volume = {31},
number = {8},
pages = {4535--4549},
publisher = {Springer Nature},
issn = {1359-4184},
doi = {10.1038/s41380-026-03554-y},
url = {https://doi.org/10.1038/s41380-026-03554-y},
pmid = {41917495},
pmcid = {PMC13364678}
}

RIS

TY - JOUR
AU - Chen, Di
AU - Yu, Xinyang
AU - Cheng, Wei
AU - Wang, Linbo
AU - Xiang, Shitong
AU - Zhang, Wei
AU - Banaschewski, Tobias
AU - Bokde, Arun L W
AU - Flor, Herta
AU - Grigis, Antoine
AU - Garavan, Hugh
AU - Gowland, Penny
AU - Heinz, Andreas
AU - Brühl, Rüdiger
AU - Martinot, Jean-Luc
AU - Paillère Martinot, Marie-Laure
AU - Artiges, Eric
AU - Nees, Frauke
AU - Orfanos, Dimitri Papadopoulos
AU - Lemaître, Hervé
AU - Paus, Tomáš
AU - Poustka, Luise
AU - Hohmann, Sarah
AU - Holz, Nathalie
AU - Bäuchl, Christian
AU - Smolka, Michael N
AU - Vaidya, Nilakshi
AU - Walter, Henrik
AU - Whelan, Robert
AU - Feng, Jianfeng
AU - Thompson, Paul M
AU - Schumann, Gunter
AU - Jia, Tianye
AU - Desrivières, Sylvane
TI - A longitudinal DNA methylation atlas and its link to brain structure and mental health
T2 - Molecular psychiatry
J2 - Mol Psychiatry
PY - 2026
DA - 2026/03/31
VL - 31
IS - 8
SP - 4535
EP - 4549
SN - 1359-4184
PB - Springer Nature
DO - 10.1038/s41380-026-03554-y
UR - https://doi.org/10.1038/s41380-026-03554-y
LA - en
ER -

CSL-JSON

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"issued": {
"date-parts": [
[
2026,
3,
31
]
]
}
}

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