Personalized functional topography-based multisite brain age prediction modeling reveals divergent neurodevelopment in major depression.

Code ↔ Paper

2 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 2 matches

[1] § Materials and Methods › Examining the Associations between Gene Expression and BAG-Related Functional Topography. ↔ 4.gene/s1_brainspan.m, lines 28–98 · score 0.57 · neurodevelopmental processes, axon, synapse, dendrite, BrainSpan, genes
[2] § Materials and Methods › Calculating Individualized Functional Network Topography. ↔ 1.Predict_brain_age/s1.NMF/src/backNMF_u.m, lines 1–70 · score 0.55 · fMRI, spatial maps, NMF, matrix, age

Paper

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The authors' code

MATLAB · 98 lines · 4.5 KB · no license · 1 match

load('D:\study\sub2\brain_age\brain_span\age_cortical_am_hip.mat','age_new_day')
load('D:\study\sub2\brain_age\brain_span\column_pos_cortical_am_hip.mat','column_pos')
data = readmatrix('D:\study\sub2\brain_age\brain_span\expression_matrix_my.csv');
data_new=data(:,column_pos);
filename = 'D:\study\sub2\brain_age\brain_span\columns_metadata.csv';
opts = detectImportOptions(filename);
opts.SelectedVariableNames = opts.VariableNames(7);
strData = readmatrix(filename, opts, 'OutputType', 'string');
brain_new=strData(column_pos);
rowsToDelete = all(data_new == 0, 2);
data_new(rowsToDelete, :) = [];
filename = 'D:\study\sub2\brain_age\brain_span\rows_metadata.csv';
opts = detectImportOptions(filename);
opts.SelectedVariableNames = opts.VariableNames(4); % 选择第一列
strData = readmatrix(filename, opts, 'OutputType', 'string');
gene_new=strData;
gene_new(rowsToDelete)=[];
%%
condi='sub1'
domi_pos=find(brain_new=='OFC'|brain_new=='ITC'|brain_new=='STC');
domi_notpos=find(~(brain_new=='OFC'|brain_new=='ITC'|brain_new=='STC'));
%condi='sub2'
%domi_pos=find(brain_new=='M1C'|brain_new=='V1C'|brain_new=='VFC'|brain_new=='HIP'|brain_new=='AMY'|brain_new=='DFC');
%domi_notpos=find(~(brain_new=='M1C'|brain_new=='V1C'|brain_new=='VFC'|brain_new=='HIP'|brain_new=='AMY'|brain_new=='DFC'));
%%
load('D:\study\sub2\brain_age\brain_span\neurodev_proc.mat');
field = fieldnames(neurodev_proc); % four neurodevelopment processes
for i = 1:length(field)
i
proc = field{i};
gene_proc = getfield(neurodev_proc, proc);
[isMember, indexInB] = ismember(gene_new, gene_proc);
genepos1 = find(isMember);
gene_remain_pos=find(isMember==0);
gene_domi1=data_new(genepos1,domi_pos);
gene_nodomi1=data_new(genepos1,domi_notpos);
Data = zscore( [ gene_domi1'; gene_nodomi1' ] );%region(domi/nodomi)*gene(developmental process gene)
[ ~, PC ] = pca( Data, 'Centered', false );
if corr( PC( :, 1 ), mean( Data, 2 ) ) < 0
PC = -PC;
end
PC = PC(:,1);%region*1
PC_normalized = (PC- min(PC)) ./ (max(PC) - min(PC));
% for trajactory
domAge = log2(age_new_day(domi_pos));%是domi 脑区的那些年龄
notdomAge = log2(age_new_day(domi_notpos));%不是domi 脑区的那些年龄
domPC_minmax = PC_normalized(1:size(gene_domi1,2));% Data = zscore( [ gene_domi1'; gene_nodomi1' ] );%region*gene
notdomPC_minmax = PC_normalized((size(gene_domi1,2)+1):end);
dataTable = table(domAge, domPC_minmax, 'VariableNames', {'age', 'y'});
filename = strcat('D:\study\sub2\brain_age\brain_span\',proc,'_',condi,'_domi_cortical.xlsx');
writetable(dataTable, filename);
dataTable = table(notdomAge, notdomPC_minmax, 'VariableNames', {'age', 'y'});
filename = strcat('D:\study\sub2\brain_age\brain_span\',proc,'_',condi,'_nodomi_cortical.xlsx');
writetable(dataTable, filename);
% for significant analyze
domPC = PC(1:length(domAge));
notdomPC = PC((length(domAge)+1):end);
Index_dom_6_14 = find(domAge >= log2(4281) & domAge <= log2(14866));
Index_notdom_6_14 = find(notdomAge >=log2(4281) & notdomAge <=log2(14866));%here
domPC_6_14 = mean(domPC(Index_dom_6_14));
notdomPC_6_14 = mean(notdomPC(Index_notdom_6_14));
diff_PC(i) = domPC_6_14 - notdomPC_6_14;
nperm = 1000; % permutation number
for j = 1 : nperm
j
myresample = randsample(length(gene_remain_pos),length(genepos1));
myresample=gene_remain_pos(myresample);
domGene_perm = data_new(myresample,domi_pos);
notdomGene_perm = data_new(myresample,domi_notpos);
Dataperm = zscore( [ domGene_perm'; notdomGene_perm' ] );
[ ~, PCperm ] = pca( Dataperm, 'Centered', false );
if corr( PCperm( :, 1 ), mean( Dataperm, 2 ) ) < 0
PCperm = -PCperm;
end
domPCperm = PCperm(1:length(domAge));
notdomPCperm = PCperm((length(domAge) + 1):end);
domPCperm_6_14 = mean(domPCperm(Index_dom_6_14));
notdomPCperm_6_14 = mean(notdomPCperm(Index_notdom_6_14));
diff_PCperm(j,i) = domPCperm_6_14 - notdomPCperm_6_14;
end
if diff_PC(i) > 0
p_perm(i) = length(find(diff_PCperm(:,i) > diff_PC(i))) / nperm;
else
p_perm(i) = length(find(diff_PCperm(:,i) < diff_PC(i))) / nperm;
end
end
Myelin=diff_PCperm(:,1);
Axon=diff_PCperm(:,2);
Dendrite=diff_PCperm(:,3);
Synapse=diff_PCperm(:,6);
%save('D:\study\sub2\draw\permut_all.mat','Myelin','Axon','Dendrite','Synapse')
% save('D:\study\sub2\draw\permut_sub1_cortical.mat','Myelin','Axon','Dendrite','Synapse')
save('D:\study\sub2\draw\permut_sub1.mat','Myelin','Axon','Dendrite','Synapse')

s1_brainspan.m at commit ce1c5e7, no license · at the source

Overview

Authors: Chenxuan Pang^1,2,3, Xiaoyi Sun^1,2,3,4, Jianlong Zhao^1,2,3, Xinyuan Liang^1,2,3, Lianglong Sun^1,2,3, Qixiang Lin^1,2,3, Jinrong Sun^5,6,7, Xiaowen Lu^5,6,8, Qiangli Dong^5,6,9, Liang Zhang^5,6,10, Xiaoqin Wang^11,12, Dongtao Wei^11,12, Yuan Chen¹³, Bangshan Liu^5,6, Chu-Chung Huang¹⁴, Yanting Zheng¹⁵, Yankun Wu¹⁶, Taolin Chen^17,18,19, Yuqi Cheng²⁰, Xiufeng Xu²¹

and 13 other authors

Qiyong Gong^17,18,19, Tianmei Si¹⁶, Shijun Qiu¹⁵, Ching-Po Lin^22,23,24, Jingliang Cheng¹³, Yanqing Tang²⁵, Fei Wang²⁵, Jiang Qiu^11,12, Peng Xie^26,27, Lingjiang Li^5,6, Yong He^1,2,3,28, DIDA-MDD Working Group, Mingrui Xia^1,2,3

ORCID iDs: Chenxuan Pang, Xiaoyi Sun, Jianlong Zhao, Xinyuan Liang, Qiyong Gong, Shijun Qiu, Yong He, Mingrui Xia

28 affiliations

State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
School of Systems Science, Beijing Normal University, Beijing 100875, China
Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410013, China
Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Changsha, Hunan 410083, China
Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou Mental Health Centre, Yangzhou, Jiangsu 225003, China
Affiliated Wuhan Mental Health Center, Huazhong University of Science and Technology, Wuhan, Hubei 430012, China
Department of Psychiatry, Lanzhou University Second Hospital, Lanzhou, Gansu 730030, China
Mental Health Education and Counseling Center, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing 400715, China
Department of Psychology, Southwest University, Chongqing 400715, China
Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (East China Normal University), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, China
Peking University Sixth Hospital, Peking University Institute of Mental Health, National Health Commission Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing 100191, China
Department of Radiology, Huaxi MR Research Center, Institute of Radiology and Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
Psychoradiology Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
Xiamen Key Lab of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian 361022, China
Affiliated Mental Health Center and Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310013, China
Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei 112304, China
Department of Education and Research, Taipei City Hospital, Taipei 103212, China
Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
Chongqing Key Laboratory of Neurobiology, Chongqing 400016, China
Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
Chinese Institute for Brain Research, Beijing 102206, China

Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 11, article e2519586123

Dates: received 22 July 2025; accepted 10 February 2026; published online 11 March 2026; in print 17 March 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1073/pnas.2519586123 · PMID 41811439 · PMCID PMC12994210 · OpenAlex W7135039044

Open access: hybrid, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), depression (population), developmental (subfield)

Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging, Physiology & signal measures

Keywords: major depressive disorder, brain age, lifespan trajectory, connectome, transcriptome

MeSH: Brain*, Major Depressive Disorder*, Adult, Aging, Case-Control Studies, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neurodevelopment (* major topic)

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: MOST | National Natural Science Foundation of China (NSFC) (82021004, 824B2051, 82402368, T24B2012, 82071998); Beijing Natural Science Foundation (JQ23033); STI 2030-Major Profects (2021ZD0211500, 2022ZD0200500)

Citations: cited by 1 paper (Europe PMC); 81 references in the paper

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 2 matches between paragraphs and lines of code.

ALICE1029/topography_MDD

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: ce1c5e766d572851c68b3b0552e93e90da73c40e, 11 May 2026

Languages: MATLAB (55), Python (4), R (1)

Size: 65 files, 60 scripts

Software Heritage: not archived

Found in: the text, “Materials and Methods”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Tools for NIfTI and ANALYZE image (MATLAB) (16 files), Statistics and Machine Learning Toolbox (12 files), SciPy (4 files), SPM (4 files), Image Processing Toolbox (3 files), BrainSMASH (2 files), GIfTI library for MATLAB (2 files), NumPy (2 files), pandas (2 files), scikit-learn (2 files), cowplot (1 file), ggpubr (1 file), patchwork (1 file), rstatix (1 file), statsmodels (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

61 files

1.Predict_brain_age/s1.NMF/s0_script_CreatePrepData.m, MATLAB, 13 lines
1.Predict_brain_age/s1.NMF/s1_script_FuncInit_vol.m, MATLAB, 38 lines
1.Predict_brain_age/s1.NMF/s2_script_SelRobustInit_vol.m, MATLAB, 10 lines
1.Predict_brain_age/s1.NMF/s3_script_FuncMvnmfL21p1_func_vol.m, MATLAB, 52 lines
1.Predict_brain_age/s1.NMF/s4_script_SaveResImg_vol.m, MATLAB, 27 lines
1.Predict_brain_age/s1.NMF/s5_name_network.m, MATLAB, 39 lines
1.Predict_brain_age/s1.NMF/src/backNMF_u.m, MATLAB, 218 lines, 1 match
1.Predict_brain_age/s1.NMF/src/backNMF_v.m, MATLAB, 295 lines
1.Predict_brain_age/s1.NMF/src/constructW_vol.m, MATLAB, 52 lines
1.Predict_brain_age/s1.NMF/src/createPrepData.m, MATLAB, 25 lines
1.Predict_brain_age/s1.NMF/src/dataPrepro.m, MATLAB, 62 lines
1.Predict_brain_age/s1.NMF/src/deployFuncInit_vol.m, MATLAB, 70 lines
1.Predict_brain_age/s1.NMF/src/deployFuncMvnmfL21p1_func_vol.m, MATLAB, 68 lines
1.Predict_brain_age/s1.NMF/src/dispSM_func.m, MATLAB, 64 lines
1.Predict_brain_age/s1.NMF/src/func_mMvNMF4fmri_l21p1_ard_woSrcLoad.m, MATLAB, 235 lines
1.Predict_brain_age/s1.NMF/src/func_saveVolRes2Nii.m, MATLAB, 64 lines
1.Predict_brain_age/s1.NMF/src/func_saveVolRes2Nii_idv.m, MATLAB, 67 lines
1.Predict_brain_age/s1.NMF/src/mMultiNMF_l21p1_ard.m, MATLAB, 304 lines
1.Predict_brain_age/s1.NMF/src/mMultiNMF_l21p1_ard_parfor.m, MATLAB, 362 lines
1.Predict_brain_age/s1.NMF/src/mNMF_sp.m, MATLAB, 406 lines
1.Predict_brain_age/s1.NMF/src/mNMF_sp_v.m, MATLAB, 398 lines
1.Predict_brain_age/s1.NMF/src/my_cifti.m, MATLAB, 18 lines
1.Predict_brain_age/s1.NMF/src/my_func_initialization_woLoadSrc.m, MATLAB, 122 lines
1.Predict_brain_age/s1.NMF/src/my_vol.m, MATLAB, 14 lines
1.Predict_brain_age/s1.NMF/src/prepareFuncData_vol_func.m, MATLAB, 34 lines
1.Predict_brain_age/s1.NMF/src/prepareFuncData_vol_func_single.m, MATLAB, 18 lines
1.Predict_brain_age/s1.NMF/src/read_cifti.m, MATLAB, 33 lines
1.Predict_brain_age/s1.NMF/src/read_coor.m, MATLAB, 11 lines
1.Predict_brain_age/s1.NMF/src/read_medial_wall_label.m, MATLAB, 12 lines
1.Predict_brain_age/s1.NMF/src/selRobustInit.m, MATLAB, 83 lines
1.Predict_brain_age/s1.NMF/src/vali_deploy.m, MATLAB, 82 lines
1.Predict_brain_age/s1.NMF/src/vali_func.m, MATLAB, 235 lines
1.Predict_brain_age/s2.brain_age/functions/SVR_all.m, MATLAB, 44 lines
1.Predict_brain_age/s2.brain_age/functions/SVR_all_noco.m, MATLAB, 30 lines
1.Predict_brain_age/s2.brain_age/functions/pca_svr.m, MATLAB, 107 lines
1.Predict_brain_age/s2.brain_age/functions/pca_svr_noco.m, MATLAB, 109 lines
1.Predict_brain_age/s2.brain_age/functions/regress_out.m, MATLAB, 9 lines
1.Predict_brain_age/s2.brain_age/s1_index.m, MATLAB, 102 lines
1.Predict_brain_age/s2.brain_age/s2_predict.m, MATLAB, 225 lines
1.Predict_brain_age/s2.brain_age/s2_predict_loso.m, MATLAB, 83 lines
1.Predict_brain_age/s2.brain_age/s2_predict_mfd.m, MATLAB, 91 lines
1.Predict_brain_age/s2.brain_age/s2_predict_sex.m, MATLAB, 162 lines
1.Predict_brain_age/s2.brain_age/s3_system_predict.m, MATLAB, 218 lines
2.Analyse_subtypes_brain/functions/brainsmash_inter.py, Python, 39 lines
2.Analyse_subtypes_brain/functions/prepare_vars_for_norm_models.m, MATLAB, 22 lines
2.Analyse_subtypes_brain/functions/vars_boot.m, MATLAB, 22 lines
2.Analyse_subtypes_brain/functions/vars_permut.m, MATLAB, 22 lines
2.Analyse_subtypes_brain/functions/x_GRF.m, MATLAB, 114 lines
2.Analyse_subtypes_brain/s1_BAG_topography_correlation.m, MATLAB, 341 lines
2.Analyse_subtypes_brain/s2_trajectory_feature.m, MATLAB, 365 lines
2.Analyse_subtypes_brain/s3_gamlss_test.R, R, 80 lines
3.Analyse_subtypes_clinical/Functions/cca.py, Python, 144 lines
3.Analyse_subtypes_clinical/step1_hamd_subscale_feature_generate.m, MATLAB, 115 lines
3.Analyse_subtypes_clinical/step2_pls_sub_topography.py, Python, 49 lines
3.Analyse_subtypes_clinical/step3_weight.m, MATLAB, 244 lines
3.Analyse_subtypes_clinical/step7_treatment_CSU.m, MATLAB, 178 lines
3.Analyse_subtypes_clinical/step7_treatment_system_CSU.m, MATLAB, 141 lines
4.gene/gen_surrogate_map_for g1z.py, Python, 11 lines
4.gene/s1_brainspan.m, MATLAB, 98 lines, 1 match
4.gene/s2_gene_PLS_analysis.m, MATLAB, 166 lines
README.md, Text, 73 lines

At the source: github.com/ALICE1029/topography_MDD

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;
60 scripts, each with its path and the digest of its content;
2 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

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Code and data availability statement

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no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1073/pnas.2519586123.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 33 authors, 5 keywords, 11 MeSH terms, 3 funders, 74 references.

Cite

This paper

Pang, C., Sun, X., Zhao, J., Liang, X., Sun, L., Lin, Q., Sun, J., Lu, X., Dong, Q., Zhang, L., Wang, X., Wei, D., Chen, Y., Liu, B., Huang, C.-C., Zheng, Y., Wu, Y., Chen, T., Cheng, Y., . . . Xia, M. (2026). Personalized functional topography-based multisite brain age prediction modeling reveals divergent neurodevelopment in major depression. Proceedings of the National Academy of Sciences of the United States of America, 123(11), e2519586123. https://doi.org/10.1073/pnas.2519586123

BibTeX

@article{pang2026personalized, author = {Pang, Chenxuan and Sun, Xiaoyi and Zhao, Jianlong and Liang, Xinyuan and Sun, Lianglong and Lin, Qixiang and Sun, Jinrong and Lu, Xiaowen and Dong, Qiangli and Zhang, Liang and Wang, Xiaoqin and Wei, Dongtao and Chen, Yuan and Liu, Bangshan and Huang, Chu-Chung and Zheng, Yanting and Wu, Yankun and Chen, Taolin and Cheng, Yuqi and Xu, Xiufeng and Gong, Qiyong and Si, Tianmei and Qiu, Shijun and Lin, Ching-Po and Cheng, Jingliang and Tang, Yanqing and Wang, Fei and Qiu, Jiang and Xie, Peng and Li, Lingjiang and He, Yong and {DIDA-MDD Working Group} and Xia, Mingrui}, title = {{Personalized functional topography-based multisite brain age prediction modeling reveals divergent neurodevelopment in major depression}}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, year = {2026}, month = mar, volume = {123}, number = {11}, pages = {e2519586123}, publisher = {National Academy of Sciences}, issn = {0027-8424}, doi = {10.1073/pnas.2519586123}, url = {https://doi.org/10.1073/pnas.2519586123}, pmid = {41811439}, pmcid = {PMC12994210} }

RIS

TY - JOUR AU - Pang, Chenxuan AU - Sun, Xiaoyi AU - Zhao, Jianlong AU - Liang, Xinyuan AU - Sun, Lianglong AU - Lin, Qixiang AU - Sun, Jinrong AU - Lu, Xiaowen AU - Dong, Qiangli AU - Zhang, Liang AU - Wang, Xiaoqin AU - Wei, Dongtao AU - Chen, Yuan AU - Liu, Bangshan AU - Huang, Chu-Chung AU - Zheng, Yanting AU - Wu, Yankun AU - Chen, Taolin AU - Cheng, Yuqi AU - Xu, Xiufeng AU - Gong, Qiyong AU - Si, Tianmei AU - Qiu, Shijun AU - Lin, Ching-Po AU - Cheng, Jingliang AU - Tang, Yanqing AU - Wang, Fei AU - Qiu, Jiang AU - Xie, Peng AU - Li, Lingjiang AU - He, Yong AU - DIDA-MDD Working Group AU - Xia, Mingrui TI - Personalized functional topography-based multisite brain age prediction modeling reveals divergent neurodevelopment in major depression T2 - Proceedings of the National Academy of Sciences of the United States of America J2 - Proc Natl Acad Sci U S A PY - 2026 DA - 2026/03/11 VL - 123 IS - 11 SP - e2519586123 SN - 0027-8424 PB - National Academy of Sciences DO - 10.1073/pnas.2519586123 UR - https://doi.org/10.1073/pnas.2519586123 LA - en ER -

CSL-JSON

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