OSCR

Infant gut microbiomes contribute to metabolic states that impact brain function

Code ↔ Paper

19 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 19 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Gut microbial metabolism in mice is associated with donor cognitive outcomes ↔ FSEA-metabolic-groups.zip/run-fsea.ipynb, lines 203–345 · score 0.95 · gamma glutamyl amino, acyl carnitines, acyl glycines, acetylated peptides, LC PUFAs, long chain polyunsaturated
  2. [2] § Star★Methods › Experimental Model And Study Participant Details › Differential abundance analysis ↔ MaAsLin-bcm-mice-kos-by-BSID-group.zip/code/prep-data.ipynb, lines 75–131 · score 0.90 · log transformed, Sequencing depth, Stool ID, Infant ID, HUMAnN, Infant Age
  3. [3] § Star★Methods › Experimental Model And Study Participant Details › Differential abundance analysis ↔ MaAsLin-bcm-mice-pathways-by-BSID-group.zip/code/prep-data.ipynb, lines 106–159 · score 0.88 · log transformed, Sequencing depth, Stool ID, Infant ID, Infant Age, LinDA
  4. [4] § Results › Rationally designed microbial consortium restores normal phenotypes in LE mice ↔ correlation-microbes-and-metabolites.zip/analyze-microbial-metabolite-correlations-medium.ipynb, lines 911–1001 · score 0.86 · Bifidobacterium breve, Bifidobacterium longum, Bacteroides cellulosilyticus, Bacteroides thetaiotaomicron, Parabacteroides distasonis, CS1
  5. [5] § Results › Rationally designed microbial consortium restores normal phenotypes in LE mice ↔ FBA-correlations-species-amino-acids.zip/plots.ipynb, lines 447–535 · score 0.86 · Bifidobacterium breve, Bifidobacterium longum, Bacteroides cellulosilyticus, Bacteroides thetaiotaomicron, Parabacteroides distasonis, amino acid
  6. [6] § Star★Methods › Experimental Model And Study Participant Details › Microbial community analysis ↔ demographics-table-one.zip/create-table-one.ipynb, lines 81–125 · score 0.83 · household income, birth weight, delivery procedure, gestational age, history, breastfeeding
  7. [7] § Star★Methods › Experimental Model And Study Participant Details › Microbial community analysis ↔ demographic-table-create.zip/create-demographic-table.ipynb, lines 12–99 · score 0.82 · household income, birth weight, delivery procedure, gestational age, breastfeeding, sex
  8. [8] § Results › Early-life microbiomes from 1–4 months-old low-scoring infants impaired rearing behavior ↔ demographic-table-create.zip/create-demographic-table.ipynb, lines 224–265 · score 0.75 · infant fecal samples, typical scoring, low scoring, infant age, high scoring, TS
  9. [9] § Results › Infant gut microbial composition fails to predict cognitive outcomes at two years of age ↔ demographics-table-one.zip/create-table-one.ipynb, lines 81–125 · score 0.71 · household income, infant formula, gestational age, sex, delivery, cognitive
  10. [10] § Star★Methods › Experimental Model And Study Participant Details › Description of the COMBINE cohort study ↔ demographic-table-create.zip/create-demographic-table.ipynb, lines 153–192 · score 0.71 · gross motor, fine motor, receptive, demographics, Bayley, cognition
  11. [11] § Results › Infant gut microbial composition fails to predict cognitive outcomes at two years of age ↔ demographic-table-create.zip/create-demographic-table.ipynb, lines 12–99 · score 0.70 · household income, infant formula, gestational age, sex, delivery, subset
  12. [12] § Star★Methods › Experimental Model And Study Participant Details › Untargeted metabolomics ↔ big_scape/output/html_template/output/html_content/js/chart-4.4.1.umd.js, the whole file · a weak match · score 0.63 · RI, curve, fractions, platform, spaced, split
  13. [13] § Results › Gut microbial metabolism in mice is associated with donor cognitive outcomes ↔ LMM-bcm-mice-gutSMASH-clusters.zip/code/plots.ipynb, lines 183–220 · score 0.62 · fatty acids, mouse gut, amino acid, acetate, pyruvate, peptides
  14. [14] § Results › Rationally designed microbial consortium restores normal phenotypes in LE mice ↔ correlation-microbes-and-metabolites.zip/analyze-microbial-metabolite-correlations-large.ipynb, lines 186–251 · score 0.61 · microbial metabolite correlations, mouse feces, humanized mouse, species, infant
  15. [15] § Results › Rationally designed microbial consortium restores normal phenotypes in LE mice ↔ correlation-microbes-and-metabolites.zip/analyze-microbial-metabolite-correlations-medium.ipynb, lines 201–266 · score 0.61 · microbial metabolite correlations, mouse feces, humanized mouse, species, infant
  16. [16] § Star★Methods › Experimental Model And Study Participant Details › Species-metabolite correlation analysis ↔ correlation-microbes-and-metabolites.zip/analyze-microbial-metabolite-correlations-large.ipynb, lines 186–251 · score 0.56 · mouse colony, metabolite correlation, medians, abundance, Species
  17. [17] § Star★Methods › Experimental Model And Study Participant Details › Species-metabolite correlation analysis ↔ correlation-microbes-and-metabolites.zip/analyze-microbial-metabolite-correlations-medium.ipynb, lines 201–266 · score 0.56 · mouse colony, metabolite correlation, medians, abundance, Species
  18. [18] § Star★Methods › Experimental Model And Study Participant Details › Targeted arginine-polyamine pathway and glutamate cycle method ↔ big_scape/output/html_template/output/html_content/js/sql-asm.js, lines 70–101 · score 0.54 · d8, d4, d7, pH, mL, ng
  19. [19] § Star★Methods › Experimental Model And Study Participant Details › Description of the CHILD cohort study ↔ big_scape/output/html_template/output/html_content/js/chart-4.4.1.umd.js, the whole file · a weak match · score 0.53 · MN, MO, adapter, host, platform, v4

Paper

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The authors' code

Jupyter notebook · 273 lines · 10 KB · CC-BY-4.0 · 4 matches

  1. # %%
  2. import sys
  3. sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
  5. import numpy as np
  6. import pandas as pd
  8. from pandas.api.types import CategoricalDtype
  10. import repo_code.lib_excel as lib_excel
  12. # %% [markdown]
  13. # # Demographic and birth factors
  15. # %%
  16. data_demographics = pd.read_csv('./inputs/MicrobiomeBrainDevelopment_1kD_standardized_demographic_data.csv')
  18. # subset children only
  19. data_demographics = data_demographics[data_demographics.person=='child']
  20. data_demographics['individualID'] = data_demographics['individualID'].astype(int)
  22. # extract variables used for comparing demographic and birth factors between LS, TS, and HS infants
  23. varbs = [
  24. 'individualID',
  25. 'householdID',
  26. 'sex',
  27. 'noOfChildren',
  28. 'noOfAdults',
  29. 'householdIncomebyCurrency',
  30. 'householdLocation',
  31. 'estimatedGestationalAge',
  32. 'birthWeight',
  33. 'deliveryProcedure',
  34. 'feedingBehavior',
  35. 'everBreastfed'
  36. ]
  37. data_demographics = data_demographics.loc[:,varbs]
  39. # rename certain columns for clarity
  40. data_demographics = data_demographics.rename(columns={
  41. 'individualID':'Infant_ID',
  42. 'householdID':'household_ID',
  43. 'householdIncomebyCurrency':'householdIncome'
  44. }
  45. )
  47. # harmonize infant IDs
  48. data_demographics['Infant_ID'] = "CID" + data_demographics['Infant_ID'].astype(str)
  50. # encode categorical variabels as integers
  51. dict_maps = {
  52. 'sex' : {'female':0,'male':1},
  53. 'householdIncome' : {'<= 42000':0,'43000 - 84000':1,'>= 85000':2},
  54. 'householdLocation' : {'Rural (population < 2000)':0,'Urban':1},
  55. 'deliveryProcedure' : {'Vaginal Delivery':0,'Cesarean Section':1},
  56. 'feedingBehavior' : {'Breastfed':0,'Infant formula fed':1,'Combination fed':2},
  57. 'everBreastfed' : {'Yes': 1, 'No': 0}
  58. }
  60. # apply encoding
  61. for variable, mapping in dict_maps.items():
  62. data_demographics[variable] = data_demographics.loc[:,variable].map(mapping)
  64. # convert all values to integer or float, handle NaNs appropriately
  65. data_demographics = data_demographics.fillna(-1).astype({
  66. 'Infant_ID' : 'str',
  67. 'household_ID' : 'Int64',
  68. 'sex' : 'Int64',
  69. 'noOfChildren' : 'Int64',
  70. 'noOfAdults' : 'Int64',
  71. 'householdIncome' : 'Int64',
  72. 'householdLocation' : 'Int64',
  73. 'estimatedGestationalAge' : 'float',
  74. 'birthWeight' : 'Int64',
  75. 'deliveryProcedure' : 'Int64',
  76. 'feedingBehavior' : 'Int64',
  77. 'everBreastfed' : 'Int64',
  78. }).replace(-1,np.nan)
  80. # create meta-data to accompany the above
  81. meta_cols = ['Variable Name','Description','Values','Data Type']
  82. meta_demographics = [
  83. ['Infant_ID ID', 'Infant ID', '(Numeric Cohort ID)', 'String'],
  84. ['household_ID', 'Household ID', '(Number)', 'Numeric'],
  85. ['sex', 'Sex of infant', '(0 female, 1 male)', 'String'],
  86. ['noOfChildren', 'Number of children in household', '(Number, N/A)', 'Numeric'],
  87. ['noOfAdults', 'Number of adults in household', '(Number, N/A)', 'Numeric'],
  88. ['householdIncomebyCurrency', 'Household income in Euros','(0: <= 42000, 1: 43000 - 84000, 2: >= 85000, N/A)', 'String'],
  89. ['householdLocation', 'Household location', '(0 Rural (population < 2000), 1 Urban, N/A)', 'String'],
  90. ['estimatedGestationalAge', 'Estimated gestational age (weeks)', 'Numeric Range 34.43-42.14', 'Numeric'],
  91. ['birthWeight', 'Birth weight in grams', 'Numeric Range 2030-5250','Numeric'],
  92. ['deliveryProcedure', 'Delivery method', '(0 Vaginal Delivery, 1 Cesarean Section, N/A)', 'String'],
  93. ['feedingBehavior', 'Feed method','(0 Breasfed, 1 Infant formula fed, 2 Combination fed, N/A)','String'],
  94. ['everBreastfed', 'Any breatfeeding ever', '(0 No, 1 Yes)', 'String'],
  95. ]
  96. meta_demographics = pd.DataFrame(meta_demographics,columns=meta_cols)
  98. print(data_demographics.shape, meta_demographics.shape)
  99. meta_demographics
  102. # %% [markdown]
  103. # # Feeding modes
  105. # %%
  106. data_early_life = pd.read_csv('./inputs/1kd_MicrobiomeBrainDevelopment_Early_Life_Data.csv')
  107. meta_early_life = pd.read_csv('./inputs/1kd_MicrobiomeBrainDevelopment_Early_Life_Metadata.csv')
  109. # harmonize infant ID
  110. data_early_life = data_early_life.rename(columns={'Cohort_ID':'Infant_ID'})
  111. data_early_life['Infant_ID'] = "CID" + data_early_life['Infant_ID'].astype(str)
  113. # extract variables used for comparing postnatal factors for LS, TS, and HS infants
  114. varbs = [
  115. 'Infant_ID',
  116. 'd2_FeedMethod',
  117. 'FeedMethod_1m',
  118. 'FeedMethod_2m',
  119. 'FeedMethod_4m',
  120. 'FeedMethod_6m',
  121. 'FeedMethod_9m',
  122. 'FeedMethod_12m',
  123. 'FeedMethod_18m',
  124. 'FeedMethod_24m',
  125. 'Any_breastfeeding_d2',
  126. 'Any_BF_1mth',
  127. 'Any_BF_2mth',
  128. 'Any_BF_4mth',
  129. 'Any_BF_6mth',
  130. 'Any_BF_9mth',
  131. 'Any_BF_12mth',
  132. 'BF_age_stopped_wks',
  133. 'SI_Solids_intro_age_wks'
  134. ]
  135. data_early_life = data_early_life.loc[:,varbs]
  137. # subset meta-data to accompany the above
  138. meta_early_life = meta_early_life[meta_early_life['Variable Name'].isin(varbs)]
  139. meta_early_life = meta_early_life[meta_early_life['Variable Name'] != 'Cohort_ID']
  141. # handle missing values
  142. data_early_life = data_early_life.replace(9999,np.nan)
  143. for idx, row in meta_early_life.iterrows():
  144. meta_early_life.loc[idx,'Values'] = row['Values'].replace('9999 Missing or N/A','N/A').replace('9999 Missing','N/A')
  146. # convert all values except for Infant_ID to integer, handle NaNs appropriately
  147. data_early_life = data_early_life.set_index('Infant_ID').fillna(-1).astype("Int64").replace(-1,np.nan).reset_index()
  149. print(data_early_life.shape, meta_early_life.shape)
  150. meta_early_life
  153. # %% [markdown]
  154. # # Bayley Scores
  156. # %%
  157. data_child_develop = pd.read_csv('./inputs/1kd_MicrobiomeBrainDevelopment_Child_Development_Data.csv')
  158. meta_child_develop = pd.read_csv('./inputs/1kd_MicrobiomeBrainDevelopment_Child_Development_Metadata.csv')
  160. # harmonize infant ID
  161. data_child_develop = data_child_develop.rename(columns={'Cohort_ID':'Infant_ID'})
  162. data_child_develop['Infant_ID'] = "CID" + data_child_develop['Infant_ID'].astype(str)
  164. # extract variables related to 24m BSID test and scores
  165. varbs = [
  166. 'Infant_ID',
  167. 'Chronological_age_wks_24M',
  168. '24m_BSID_completed',
  169. 'BSID_24m_cognitive_comp_score',
  170. 'BSID_24m_receptive_lang_score',
  171. 'BSID_24m_expressive_lang_score',
  172. 'BSID_24m_BSID_language_comp_score',
  173. 'BSID_24m_BSID_fine_motor_score',
  174. 'BSID_24m_gross_motor_score',
  175. ]
  176. data_child_develop = data_child_develop.loc[:,varbs]
  178. # convert all values except for Infant_ID to integer, handle NaNs appropriately
  179. data_child_develop = data_child_develop.set_index('Infant_ID').astype("Int64").replace(-1,np.nan).reset_index()
  181. # subset meta-data to accompany the above
  182. meta_child_develop = meta_child_develop[meta_child_develop['Variable Name'].isin(varbs)]
  183. meta_child_develop = meta_child_develop[meta_child_develop['Variable Name'] != 'Cohort_ID']
  185. # handle missing values
  186. data_child_develop = data_child_develop.replace(9999,np.nan)
  187. for idx, row in meta_child_develop.iterrows():
  188. meta_child_develop.loc[idx,'Values'] = row['Values'].replace('9999 Missing or N/A','N/A').replace('9999 Missing','N/A')
  190. print(data_child_develop.shape, meta_child_develop.shape)
  192. meta_child_develop
  194. # %% [markdown]
  195. # # Combine demographic, feeding, and Bayley tables
  197. # %%
  198. df_data = data_demographics.merge(data_early_life, on = 'Infant_ID', how = 'inner') \
  199. .merge(data_child_develop, on = 'Infant_ID', how = 'inner')
  200. df_meta = pd.concat([
  201. meta_demographics,
  202. meta_early_life,
  203. meta_child_develop
  204. ])
  206. # %% [markdown]
  207. # # Create a table of infant fecal samples
  208. #
  209. # These are samples received by BCM and used to establish humanized-microbiota mouse lines
  211. # %%
  212. table_baylor_samples = pd.read_excel('./inputs/table-leap-infant-samples-HD-v2.xlsx', header=1, dtype={'COMBINE Cohort ID':str})
  214. # map COMBINE Cohort ID to BCM Internal IDs
  215. cid_to_bcm_id = table_baylor_samples.loc[:,['COMBINE Cohort ID','New']]
  216. cid_to_bcm_id = cid_to_bcm_id.rename(columns={'COMBINE Cohort ID':'Infant_ID','New':'BCM_Infant_ID'})
  217. cid_to_bcm_id['BCM_Infant_ID'] = cid_to_bcm_id['BCM_Infant_ID'].str.replace('NS ', 'TS', regex=False).values
  219. # harmonize infant ID
  220. cid_to_bcm_id['Infant_ID'] = "CID" + cid_to_bcm_id['Infant_ID'].astype(str)
  222. cid_to_bcm_id.astype(str)
  224. # %%
  225. table_baylor_samples = pd.read_excel('./inputs/table-leap-infant-samples-HD-v2.xlsx', header=1, dtype={'COMBINE Cohort ID':str})
  227. # map COMBINE Cohort ID to BCM Internal IDs
  228. cid_to_bcm_id = table_baylor_samples.loc[:,['COMBINE Cohort ID','New']]
  229. cid_to_bcm_id = cid_to_bcm_id.rename(columns={'COMBINE Cohort ID':'Infant_ID','New':'BCM_Infant_ID'})
  230. cid_to_bcm_id['BCM_Infant_ID'] = cid_to_bcm_id['BCM_Infant_ID'].str.replace('NS ', 'TS', regex=False).values
  232. # convert table from wide to long, where each row is an infant fecal sample used to established a mouse line
  233. table_samples = table_baylor_samples.loc[:,['COMBINE Cohort ID',2,30,60,120,180,275,365]]
  234. table_samples.rename(columns={'COMBINE Cohort ID':'Infant_ID'},inplace=True)
  235. table_samples.set_index('Infant_ID',inplace=True)
  236. table_samples = table_samples.unstack().dropna().to_frame().reset_index()
  237. table_samples = table_samples.drop([0],axis=1).rename(columns={'level_0':'Infant_Age'})
  238. table_samples = table_samples.loc[:,['Infant_ID','Infant_Age']]
  239. table_samples = cid_to_bcm_id.merge(table_samples,on='Infant_ID')
  241. def get_group(value):
  243. if value.startswith('LS'):
  244. return 'Low-scoring'
  245. elif value.startswith('TS'):
  246. return 'Typical-scoring'
  247. elif value.startswith('HS'):
  248. return 'High-scoring'
  250. table_samples.loc[:,'Group'] = table_samples['BCM_Infant_ID'].apply(lambda x: get_group(x))
  251. cat_group = CategoricalDtype(categories=['Low-scoring','Typical-scoring','High-scoring'],ordered=True)
  252. table_samples['Group'] = table_samples['Group'].astype(cat_group)
  253. table_samples = table_samples.sort_values(['Group','BCM_Infant_ID','Infant_Age'])
  255. def get_stool_id(row,which='BCM_ID'):
  256. return f"{row[which]}-{row['Infant_Age']}"
  258. # harmonize infant ID
  259. table_samples['Infant_ID'] = "CID" + table_samples['Infant_ID'].astype(str)
  261. table_samples['Stool_ID'] = table_samples.apply(lambda row: get_stool_id(row,'Infant_ID'),axis=1)
  262. table_samples['BCM_Stool_ID'] = table_samples.apply(lambda row: get_stool_id(row,'BCM_Infant_ID'),axis=1)
  264. table_samples = table_samples.loc[:,['BCM_Stool_ID','Stool_ID','BCM_Infant_ID','Infant_ID','Infant_Age','Group']]
  265. table_samples
  267. # %%
  268. kwargs_csv = {'sep':'\t','header':True,'index':True}
  270. df_data.to_csv('./outputs/df-data.tsv',**kwargs_csv)
  271. df_meta.to_csv('./outputs/df-meta.tsv',**kwargs_csv)
  272. table_samples.to_csv('./outputs/table-samples.tsv',**kwargs_csv)

create-demographic-table.ipynb, under CC-BY-4.0 · at the source

Overview

Authors: Firas S. Midani1,2, Do-Hun Lee3,4, Younghye Moon3,4, Maggie Seale1, Thomas D. Horvath5,6,7, A. Kyle Ardis1, José Cantú1, Emavieve Coles1, Jason D. Pizzini1, Duolong Zhu1, Sean W. Dooling3, Grace J. Ahern8,9,10, Colleen K. Ardis1, Alisha Beckford1, Nicole M. Ruggiero1, John Shin1, Raphaela Joos8,9, Catherine Stanton8,10, R. Paul Ross8,9, Darlene L.Y. Dai11
and 9 other authorsPiushkumar J. Mandhane12,13, Charisse Petersen11, Stuart E. Turvey11, Mairead E. Kiely14,15, Deirdre M. Murray15,16, Mauro Costa-Mattioli3, Kimberley F. Tolias3,4, Robert A. Britton1,2, Heather A. Danhof1,2
ORCID iDs: Firas S. Midani, Younghye Moon, Maggie Seale, Thomas D. Horvath, A. Kyle Ardis, José Cantú, Emavieve Coles, Jason D. Pizzini, Duolong Zhu, Sean W. Dooling, Colleen K. Ardis, John Shin, Raphaela Joos, Catherine Stanton, R. Paul Ross, Charisse Petersen, Stuart E. Turvey, Mairead E. Kiely, Deirdre M. Murray, Mauro Costa-Mattioli, Kimberley F. Tolias, Robert A. Britton, Heather A. Danhof
16 affiliations
  1. Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
  2. Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, TX, USA
  3. Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
  4. Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX, USA
  5. Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, USA
  6. Department of Pathology, Texas Children’s Hospital, Houston, TX, USA
  7. Department of Pharmacy Practice and Translational Research, University of Houston, Houston, TX, USA
  8. APC Microbiome Ireland, University College Cork, Cork, Ireland
  9. School of Microbiology, University College Cork, Cork, Ireland
  10. Teagasc Food Research Centre, Moorepark, Fermoy, Ireland
  11. Department of Pediatrics, BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada
  12. Department of Pediatrics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
  13. Faculty of Medicine and Health Sciences, UCSI University, Kuala Lumpur, Malaysia
  14. Cork Centre for Vitamin D and Nutrition Research, School of Food and Nutritional Sciences, University College Cork, Cork, Ireland
  15. INFANT Centre, University College Cork, Cork, Ireland
  16. Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
Institutions: Baylor College of Medicine (United States); University of Houston (United States); APC Microbiome Institute (Ireland); University College Cork (Ireland); BC Children's Hospital (Canada); University of British Columbia (Canada); University of Alberta (Canada); UCSI University (Malaysia)
Journal: bioRxiv (preprint)
Dates: published online 10 March 2026
Type: Preprint · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.64898/2026.03.09.710596 · OpenAlex W7134941531
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), developmental (subfield)
Methods: Statistics, Connectivity, fMRI & imaging
Keywords: infant, gut, microbiome, humanized microbiota mice, cognition, neurodevelopment, amino acids, gutbrain axis
Topic: Gut microbiota and health (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Wellcome Leap
Citations: not cited yet (Europe PMC); 80 references in the paper
Research resources: RRID:AB_141637, Anti-GFAP (mouse monoclonal) RRID:AB_1840893, Anti-PSD-95 (mouse monoclonal) RRID:AB_2292909, RRID:AB_2340472, RRID:AB_2762823, RRID:AB_2762826, RRID:AB_2762833, RRID:AB_572263, Anti-Iba1 (rabbit polyclonal) RRID:AB_839504, Anti-Gephyrin (mouse monoclonal) RRID:AB_887719, Anti-VGAT (guinea pig polyclonal) RRID:AB_887869, Anti-VGLUT1 (rabbit polyclonal) RRID:AB_887878

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.

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Zenodo 17652611

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (30 files), NumPy (20 files), Matplotlib (14 files), seaborn (14 files), statsmodels (13 files), SciPy (9 files), tidyverse (5 files), broom (4 files), lme4 (4 files), lmerTest (4 files), scikit-learn (3 files), car (2 files)
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At the source: doi.org/10.5281/zenodo.17652611

victoriapascal/gutsmash

License: AGPL-3.0
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Evidence: files inventoried
Commit: 1651919f7e93d09a995ea080a3c029be84ee48c6, 3 October 2025
Languages: Python (228), JavaScript (3)
Size: 1,247 files, 231 scripts
Software Heritage: archived
Found in: the resources table
Holds: README, license file, environment (setup.cfg, setup.py), tests
Not found: CITATION.cff, continuous integration, documentation
Tools: Biopython (56 files), NumPy (3 files), scikit-learn (3 files), Matplotlib (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
233 files
At the source: github.com/victoriapascal/gutsmash

medema-group/BiG-SCAPE

License: AGPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 6bf7ca79418a34f412822113a1ee99f591e28e9f, 1 April 2026
Languages: Python (141), JavaScript (14)
Size: 246 files, 155 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, license file, CITATION.cff, environment (Dockerfile, environment.yml, pyproject.toml), tests, continuous integration, documentation
Tools: Biopython (21 files), NumPy (7 files), SciPy (3 files), Matplotlib (2 files), scikit-learn (2 files), pandas (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
157 files
At the source: github.com/medema-group/BiG-SCAPE

morgannprice/PaperBLAST

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 640c97d1a3e2bc2adae974ab54b17b7ff7384063, 8 September 2026
Languages: Perl (94), JavaScript (3)
Size: 252 files, 97 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
99 files
At the source: github.com/morgannprice/PaperBLAST

zqfang/GSEApy

License: BSD-3-Clause
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 6e6f0e29ce3b407a7fb19bc6a9a73ee0015263fa, 2 September 2026
Languages: Python (20), Rust (13), Jupyter (2), R (1)
Size: 111 files, 36 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, license file, environment (pyproject.toml, requirements.txt, setup.py, uv.lock), tests, continuous integration, documentation, 2 notebooks
Not found: CITATION.cff
Tools: pandas (14 files), NumPy (11 files), Matplotlib (5 files), NetworkX (2 files), SciPy (2 files), Scanpy (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
38 files
At the source: github.com/zqfang/GSEApy

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:

  • 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 562 scripts, each with its path and the digest of its content;
  • 19 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

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/10.64898/2026.03.09.710596.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, dates, 29 authors, 8 keywords, 1 funder, 79 references, 12 RRIDs.

Cite

This paper

Midani, F. S., Lee, D.-H., Moon, Y., Seale, M., Horvath, T. D., Ardis, A. K., Cantú, J., Coles, E., Pizzini, J. D., Zhu, D., Dooling, S. W., Ahern, G. J., Ardis, C. K., Beckford, A., Ruggiero, N. M., Shin, J., Joos, R., Stanton, C., Ross, R. P., . . . Danhof, H. A. (2026). Infant gut microbiomes contribute to metabolic states that impact brain function. bioRxiv (preprint). https://doi.org/10.64898/2026.03.09.710596

BibTeX

@article{midani2026infant,
author = {Midani, Firas S. and Lee, Do-Hun and Moon, Younghye and Seale, Maggie and Horvath, Thomas D. and Ardis, A. Kyle and Cantú, José and Coles, Emavieve and Pizzini, Jason D. and Zhu, Duolong and Dooling, Sean W. and Ahern, Grace J. and Ardis, Colleen K. and Beckford, Alisha and Ruggiero, Nicole M. and Shin, John and Joos, Raphaela and Stanton, Catherine and Ross, R. Paul and Dai, Darlene L.Y. and Mandhane, Piushkumar J. and Petersen, Charisse and Turvey, Stuart E. and Kiely, Mairead E. and Murray, Deirdre M. and Costa-Mattioli, Mauro and Tolias, Kimberley F. and Britton, Robert A. and Danhof, Heather A.},
title = {{Infant gut microbiomes contribute to metabolic states that impact brain function}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.09.710596},
url = {https://doi.org/10.64898/2026.03.09.710596}
}

RIS

TY - JOUR
AU - Midani, Firas S.
AU - Lee, Do-Hun
AU - Moon, Younghye
AU - Seale, Maggie
AU - Horvath, Thomas D.
AU - Ardis, A. Kyle
AU - Cantú, José
AU - Coles, Emavieve
AU - Pizzini, Jason D.
AU - Zhu, Duolong
AU - Dooling, Sean W.
AU - Ahern, Grace J.
AU - Ardis, Colleen K.
AU - Beckford, Alisha
AU - Ruggiero, Nicole M.
AU - Shin, John
AU - Joos, Raphaela
AU - Stanton, Catherine
AU - Ross, R. Paul
AU - Dai, Darlene L.Y.
AU - Mandhane, Piushkumar J.
AU - Petersen, Charisse
AU - Turvey, Stuart E.
AU - Kiely, Mairead E.
AU - Murray, Deirdre M.
AU - Costa-Mattioli, Mauro
AU - Tolias, Kimberley F.
AU - Britton, Robert A.
AU - Danhof, Heather A.
TI - Infant gut microbiomes contribute to metabolic states that impact brain function
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/10
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.09.710596
UR - https://doi.org/10.64898/2026.03.09.710596
LA - en
ER -

CSL-JSON

{
"id": "10.64898/2026.03.09.710596",
"type": "article",
"title": "Infant gut microbiomes contribute to metabolic states that impact brain function",
"container-title": "bioRxiv (preprint)",
"author": [
{
"family": "Midani",
"given": "Firas S."
},
{
"family": "Lee",
"given": "Do-Hun"
},
{
"family": "Moon",
"given": "Younghye"
},
{
"family": "Seale",
"given": "Maggie"
},
{
"family": "Horvath",
"given": "Thomas D."
},
{
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},
{
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"given": "José"
},
{
"family": "Coles",
"given": "Emavieve"
},
{
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"given": "Jason D."
},
{
"family": "Zhu",
"given": "Duolong"
},
{
"family": "Dooling",
"given": "Sean W."
},
{
"family": "Ahern",
"given": "Grace J."
},
{
"family": "Ardis",
"given": "Colleen K."
},
{
"family": "Beckford",
"given": "Alisha"
},
{
"family": "Ruggiero",
"given": "Nicole M."
},
{
"family": "Shin",
"given": "John"
},
{
"family": "Joos",
"given": "Raphaela"
},
{
"family": "Stanton",
"given": "Catherine"
},
{
"family": "Ross",
"given": "R. Paul"
},
{
"family": "Dai",
"given": "Darlene L.Y."
},
{
"family": "Mandhane",
"given": "Piushkumar J."
},
{
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"given": "Charisse"
},
{
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"given": "Stuart E."
},
{
"family": "Kiely",
"given": "Mairead E."
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{
"family": "Murray",
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},
{
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}
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"container-title-short": "bioRxiv",
"DOI": "10.64898/2026.03.09.710596",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
10
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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