A cortical semantic map integrates fractions and integers in the human brain.

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 · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted

[1] § Methods › Voxel tuning ↔ fMRI/Gaussian_fit/gaussianfit_averagegroups_aggruns_figure7CD.ipynb, lines 162 to 273 · score 0.79 · 0.1–2, amplitude, bounded, 0–5, Gaussian, Glasser · Report a mismatch
[2] § Methods › Voxel tuning ↔ fMRI/Gaussian_fit/correlation_between_fractions_integers_figureS7.ipynb, the whole file · a weak match · score 0.68 · smoothed noise, FWHM, IP2, PF, volumes, Glasser · Report a mismatch
[3] § Methods › Voxel tuning ↔ fMRI/Gaussian_fit/correlation-relation_between_fractions_integers_figures8.ipynb, lines 21 to 165 · score 0.64 · smoothed noise, FWHM, IP2, PF, volumes, match · Report a mismatch
[4] § Methods › Representational similarity analysis (RSA) ↔ fMRI/Multiple_regression/searchlight_analysis_allsubs_multipleregression_figure6.py, lines 184 to 273 · score 0.59 · multiple regression, sphere, searchlight, radius, RSA, mask · Report a mismatch
[5] § Methods › Representational similarity analysis (RSA) ↔ fMRI/Multiple_regression/secondlevel_analyses_searchlight_figure6_tableS1.ipynb, lines 104 to 225 · score 0.58 · multiple regression, radius, searchlight, RSA, mask, FDR · Report a mismatch
[6] § Methods › Voxel tuning ↔ fMRI/Gaussian_fit/relation_between_runs_integ_figure7A.ipynb, lines 136 to 192 · score 0.58 · diagonal elements, normalized activation, shuffled, permutation, rows, voxels · Report a mismatch
[7] § Methods › Voxel tuning ↔ fMRI/Gaussian_fit/relation_between_runs_fractions_figure7B.ipynb, lines 142 to 192 · score 0.56 · diagonal elements, normalized activation, shuffled, permutation, voxels, matrix · Report a mismatch
[8] § Methods › Anatomical and functional preprocessing ↔ fMRI/Gaussian_fit/gaussianfit_averagegroups_aggruns_figure7CD.ipynb, lines 162 to 273 · score 0.54 · residual, affine, variance, volume, threshold, square · Report a mismatch
[9] § Results › Individual voxel tuning and numerotopic map ↔ fMRI/Gaussian_fit/relation_between_fractions_integers_figureS7.ipynb, lines 102 to 182 · score 0.51 · preferred integer, normalized activation, Gaussian, S7, preferences, fitted · Report a mismatch

Paper

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The authors' code

Jupyter notebook · 273 lines · 11 KB · no license · 2 matches

# %%
import numpy as np
import matplotlib.pyplot as plt
import time
from scipy import linalg # Using scipy.linalg.lstsq might offer more options later if needed, but numpy is fine
from scipy.optimize import curve_fit
import os
import sys
import numpy as np
import pandas as pd
import json
import pickle
import nibabel as nib
import time
from nilearn.image import load_img
from nilearn.glm.first_level import FirstLevelModel
from nilearn.glm.first_level import make_first_level_design_matrix
from nilearn.plotting import plot_design_matrix
from nilearn.image import resample_to_img
from nilearn.image import new_img_like
from nilearn import surface
from nilearn import plotting
from nilearn.image import smooth_img
from matplotlib.colors import ListedColormap, BoundaryNorm
from nilearn import plotting
import glob
import os.path as op
from nilearn.image import smooth_img
from scipy.stats import sem # standard error of the mean
import matplotlib.pyplot as plt
from nilearn.maskers import NiftiMasker
from matplotlib import cm
from matplotlib.colors import Normalize
from nilearn.image.image import mean_img
from nilearn.plotting import plot_roi, plot_epi, show
# %%
# Define a Gaussian function
def gaussian(x, a, x0, sigma):
return a * np.exp(-(x - x0)**2 / (2 * sigma**2))
# %% [markdown]
# # Integers
#
# aggregate runs, loop through rois
# %%
r_threshold = 0.5
rois = ["locp005_math", "glasser_bilateral_frontal", "glasser_bilateral_ips", "glasser_bilateral_temporal", "hippocampus", "parahippocampal", "bilateral_PF_IP2"]
for roi in rois:
print(f"\n--- ROI: {roi} ---")
file_path = f"/home_local/Daniela_fmri_analyses/Results/gaussian_fit/group_analysis_aggregate_runs/{roi}/grouplevel_matrix_targetsXvoxels_coords_tmap_mni-space_{roi}_ordercateg_smooth4.npz"
data = np.load(file_path)
# Assuming the first key contains the matrix, print the first row
first_key = data.files[0]
matrix = data[first_key]
tuning_curves = np.zeros((matrix.shape[0], 3))
fitted_gaussians = np.zeros((matrix.shape[0], 6)) #to collect the fitted gaussian model for each voxel
target_tmap_all = np.zeros((matrix.shape[0], 6))
for i_voxel in range(matrix.shape[0]):
#print("Voxel", i_voxel)
target_tmap = matrix[i_voxel, -6:] # all voxels for the integers
target_tmap = (target_tmap - np.min(target_tmap)) /(np.max(target_tmap) - np.min(target_tmap)) # Normalize target_betas between 0 and 1
target_tmap = np.nan_to_num(target_tmap)
target_tmap_all[i_voxel] = target_tmap # to store
x = [0, 1, 2, 3, 4, 5]
high_res_x = np.linspace(0, len(target_tmap) - 1, len(target_tmap))
amplitude, center, size = np.nan, np.nan, np.nan # Initialize outputs
try:
# Attempt to fit the curve
# Fit the Gaussian to target_betas with constrained parameters
# Bounds: amplitude (0 to 1), center (0 to 5), sigma (0.1 to 2)
popt, pcov = curve_fit(
gaussian,
x,
target_tmap,
p0=[1, np.argmax(target_tmap), 1],
bounds=([0, 0, 0.1], [1, 5, 2]) # Lower and upper bounds
)
except RuntimeError as e:
print(f"Curve fitting failed: {e}")
# Outputs remain as NaN or can be set to zero
amplitude, center, size = 0, 0, 0
# Extract the fitted parameters
amplitude, center, size = popt
# Generate the Gaussian fit at the original resolution
# It has 6 datapoints to the 6 conditions
fitted_gaussian = gaussian(x, amplitude, center, size)
fitted_gaussians[i_voxel] = fitted_gaussian # to store
# Generate the high-resolution Gaussian
high_res_gaussian = gaussian(high_res_x, amplitude, center, size)
# Calculate the variance explained (R²)
ss_total = np.sum((target_tmap - np.mean(target_tmap))**2)
ss_residual = np.sum((target_tmap - fitted_gaussian)**2)
r_squared = 1 - (ss_residual / ss_total)
# Ensure R² is non-negative
modified_r_squared = max(0, r_squared)
tuning_curves[i_voxel, 0] = center
tuning_curves[i_voxel, 1] = size
tuning_curves[i_voxel, 2] = modified_r_squared
# Assign values from numTune to the corresponding voxel indices in voxel_ijk
numTune = tuning_curves[:,0] #center
numTune [tuning_curves[:, 2] <= r_threshold] = np.nan # only to have r2 > 0.5
# Load the mask used to extract the matrix (same space & dimensions as reference image)
mask_img = nib.load(f"/home_local/Daniela_fmri_analyses/Results/gaussian_fit/ROIs/{roi}.nii")
mask_data = mask_img.get_fdata()
volume_shape = mask_img.shape
affine = mask_img.affine
# Initialize an empty volume (full brain size)
volume = np.zeros(volume_shape)
# Get the (i, j, k) indices of nonzero voxels (i.e., selected voxels in the mask)
voxel_ijk = np.array(np.nonzero(mask_data)).T # shape: (els,n_vox 3)
# Assume voxel_ijk is already available and numTune contains values to fill
for idx, (i, j, k) in enumerate(voxel_ijk):
volume[i, j, k] = numTune[idx]
voxel_mni = nib.affines.apply_affine(affine, voxel_ijk)
# Save NIfTI
output_img = nib.Nifti1Image(volume, affine=affine)
numTune_path = f"/home_local/Daniela_fmri_analyses/Results/gaussian_fit/group_analysis_aggregate_runs/{roi}/grouplevel_numTuning_integers_{roi}_r{r_threshold}_smooth4_norm.nii.gz"
nib.save(output_img, numTune_path)
save_path = f"/home_local/Daniela_fmri_analyses/Results/gaussian_fit/group_analysis_aggregate_runs/{roi}/grouplevel_integers_{roi}_fit_outputs_smooth4_norm.npz"
np.savez(
save_path,
target_tmap_all=target_tmap_all, #save t values after being normalized
fitted_gaussians=fitted_gaussians, #predicted values
tuning_curves=tuning_curves, #preferred centers
voxel_coords=voxel_mni #voxel coordinates
)
# %% [markdown]
# # Fractions
#
# aggregate runs
# %%
r_threshold = 0.5
rois = ["locp005_math", "glasser_bilateral_frontal", "glasser_bilateral_ips", "glasser_bilateral_temporal", "hippocampus", "parahippocampal", "bilateral_PF_IP2"]
for roi in rois:
print(f"\n--- ROI: {roi} ---")
file_path = f"/home_local/Daniela_fmri_analyses/Results/gaussian_fit/group_analysis_aggregate_runs/{roi}/grouplevel_matrix_targetsXvoxels_coords_tmap_mni-space_{roi}_ordercateg_smooth4.npz"
data = np.load(file_path)
# Assuming the first key contains the matrix, print the first row
first_key = data.files[0]
matrix = data[first_key]
tuning_curves = np.zeros((matrix.shape[0], 3))
fitted_gaussians = np.zeros((matrix.shape[0], 6)) #to collect the fitted gaussian model for each voxel
target_tmap_all = np.zeros((matrix.shape[0], 6))
for i_voxel in range(matrix.shape[0]):
#print("Voxel", i_voxel)
target_tmap = matrix[i_voxel, 4:10] # all voxels for the fractions
target_tmap = (target_tmap - np.min(target_tmap)) /(np.max(target_tmap) - np.min(target_tmap)) # Normalize target_betas between 0 and 1
target_tmap = np.nan_to_num(target_tmap)
target_tmap_all[i_voxel] = target_tmap # to store
x = [1/4, 1/3, 1/2, 2/3, 3/2, 5/2]
high_res_x = np.linspace(0, len(target_tmap) - 1, len(target_tmap))
amplitude, center, size = np.nan, np.nan, np.nan # Initialize outputs
try:
# Attempt to fit the curve
# Fit the Gaussian to target_betas with constrained parameters
# Bounds: amplitude (0 to 1), center (0 to 5), sigma (0.1 to 2)
popt, pcov = curve_fit(
gaussian,
x,
target_tmap,
p0=[1, np.clip(np.argmax(target_tmap), 0.25, 2.5), 1],
bounds=([0, 0.25, 0.1], [1, 2.5, 2]) # Lower and upper bounds
)
except RuntimeError as e:
print(f"Curve fitting failed: {e}")
# Outputs remain as NaN or can be set to zero
amplitude, center, size = 0, 0, 0
# Extract the fitted parameters
amplitude, center, size = popt
# Generate the Gaussian fit at the original resolution
# It has 6 datapoints to the 6 conditions
fitted_gaussian = gaussian(x, amplitude, center, size)
fitted_gaussians[i_voxel] = fitted_gaussian # to store
# Generate the high-resolution Gaussian
high_res_gaussian = gaussian(high_res_x, amplitude, center, size)
# Calculate the variance explained (R²)
ss_total = np.sum((target_tmap - np.mean(target_tmap))**2)
ss_residual = np.sum((target_tmap - fitted_gaussian)**2)
r_squared = 1 - (ss_residual / ss_total)
# Ensure R² is non-negative
modified_r_squared = max(0, r_squared)
tuning_curves[i_voxel, 0] = center
tuning_curves[i_voxel, 1] = size
tuning_curves[i_voxel, 2] = modified_r_squared
# Assign values from numTune to the corresponding voxel indices in voxel_ijk
numTune = tuning_curves[:,0] #center
numTune [tuning_curves[:, 2] <= r_threshold] = np.nan # only to have r2 in 0.5
#print("Number tuning", numTune.shape)
# Load the mask used to extract the matrix (same space & dimensions as reference image)
mask_img = nib.load(f"/home_local/Daniela_fmri_analyses/Results/gaussian_fit/ROIs/{roi}.nii")
mask_data = mask_img.get_fdata()
volume_shape = mask_img.shape
affine = mask_img.affine
# Initialize an empty volume (full brain size)
volume = np.zeros(volume_shape)
# Get the (i, j, k) indices of nonzero voxels (i.e., selected voxels in the mask)
voxel_ijk = np.array(np.nonzero(mask_data)).T # shape: (n_voxels, 3)
# Assume voxel_ijk is already available and numTune contains values to fill
for idx, (i, j, k) in enumerate(voxel_ijk):
volume[i, j, k] = numTune[idx]
voxel_mni = nib.affines.apply_affine(affine, voxel_ijk)
# Save NIfTI
output_img = nib.Nifti1Image(volume, affine=affine)
numTune_path = f"/home_local/Daniela_fmri_analyses/Results/gaussian_fit/group_analysis_aggregate_runs/{roi}/grouplevel_numTuning_fractions_{roi}_r{r_threshold}_smooth4_norm.nii.gz"
nib.save(output_img, numTune_path)
save_path = f"/home_local/Daniela_fmri_analyses/Results/gaussian_fit/group_analysis_aggregate_runs/{roi}/grouplevel_fractions_{roi}_fit_outputs_smooth4_norm.npz"
np.savez(
save_path,
target_tmap_all=target_tmap_all,
fitted_gaussians=fitted_gaussians,
tuning_curves=tuning_curves,
voxel_coords=voxel_mni #voxel coordinates
)

gaussianfit_averagegroups_aggruns_figure7CD.ipynb, no license · at the source

Overview

Authors: Daniela Valério^1,2, Samuel Debray^1,2, Alireza Karami^1,2, Maxime Cauté^1,2, Nicolás Gravel^1,2, Stanislas Dehaene^1,2

ORCID iDs: Daniela Valério

Université Paris Saclay, INSERM, CEA, Cognitive Neuroimaging Unit, NeuroSpin Center, Gif-sur-Yvette, France
Collège de France, PSL University, Paris, France

Institutions: Université Paris Sciences et Lettres (France)

Journal: PLoS biology, volume 24, issue 10, article e3003820

Dates: received 16 March 2026; accepted 18 September 2026; published online 22 October 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pbio.3003820 · PMID 42911907 · PMCID PMC13690230 · OpenAlex W7223218274

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: fMRI (modality), human (organism)

Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging, Machine learning

MeSH: Brain*, Brain Mapping*, Semantics*, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)

Journal subjects: Biology and Life Sciences, Neuroscience, Brain Mapping, Functional Magnetic Resonance Imaging, Medicine and Health Sciences, Diagnostic Medicine, Diagnostic Radiology, Magnetic Resonance Imaging, Research and Analysis Methods, Imaging Techniques, Radiology and Imaging, Neuroimaging, Anatomy, Brain, Hippocampus, Social Sciences, Linguistics, Semantics, Mathematical and Statistical Techniques, Statistical Methods, Multivariate Analysis, Principal Component Analysis, Physical Sciences, Mathematics, Statistics, Algebra, Linear Algebra, Vector Spaces, Neuronal Tuning, Psychology, Behavior, Cerebral Cortex, Temporal Lobe

Topic: Cognitive and developmental aspects of mathematical skills (Developmental and Educational Psychology, Psychology), according to OpenAlex

Funding: European Research Council (ERC-2022-ADG 101095866)

Citations: not cited yet (Europe PMC); 117 references in the paper

Research resources: RRID:SCR_001362, distributed with ANTs 2.3.3 [59 RRID:SCR_004757

Abstract

How does the human brain represent the meaning of abstract symbols? Some theories postulate the existence of semantic spaces where concepts occupy positions that reflect their conceptual relationships. In the number domain, psychological evidence suggests that integers are represented along a mental number line which, with education, integrates higher-level number concepts such as fractions. To test this hypothesis, we recorded whole-brain 7T fMRI responses to integer and fraction symbols during an active magnitude comparison task designed to engage magnitude processing. Consistent with predictions, we found both behavioral and neural numerical distance effects. Activation vectors in intraparietal, inferior temporal, prefrontal, hippocampal, and parahippocampal cortices formed a two-dimensional semantic space organized by numerical magnitude and domain (fractions versus integers). Gaussian fits revealed a topographic map of numerical preferences in the anterior inferior parietal cortex, common to both domains. Our results suggest that, when educated adults engage in an explicit magnitude comparison task, their brain can construct an ordered representation that integrates fractions and integers, with a topographic organization in the anterior IPS/IPL.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

OSF jqcna

License: none: the authors keep all their rights

State: the link answers, verified on 25 October 2026

Evidence: files inventoried

Languages: Jupyter (19), R (3), Python (1)

Size: 30 files, 23 scripts

Software Heritage: not checked

Found in: “Data Availability”

Holds: 19 notebooks

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

Tools: NumPy (20 files), Matplotlib (19 files), NiBabel (18 files), Nilearn (17 files), SciPy (16 files), scikit-learn (12 files), pandas (11 files), statsmodels (8 files), reticulate (3 files), ggplot2 (1 file), reshape2 (1 file), seaborn (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 25 October 2026: the link answers (HTTP 200)

25 October 2026: the link answers (HTTP 200)

23 files

Behavioral/BIS_R_data_figure1.R, R, 387 lines
Behavioral/Group_analyses_RTs-errors_efficiencyscore_figure1.ipynb, Jupyter, 440 lines
Behavioral/distance_effect_final_v2_figure1BC.ipynb, Jupyter, 662 lines
Behavioral/efficiency_score_indscal_mds_figure_s1.R, R, 112 lines
fMRI/Gaussian_fit/correlation-relation_between_fractions_integers_figures8.ipynb, Jupyter, 248 lines, 1 match
fMRI/Gaussian_fit/correlation_between_fractions_integers_figureS7.ipynb, Jupyter, 159 lines, 1 match
fMRI/Gaussian_fit/gaussianfit_averagegroups_aggruns_figure7CD.ipynb, Jupyter, 273 lines, 2 matches
fMRI/Gaussian_fit/geodist_nativespace_figureS10.ipynb, Jupyter, 232 lines
fMRI/Gaussian_fit/matrix_creation_aggruns_averagesubs.ipynb, Jupyter, 84 lines
fMRI/Gaussian_fit/relation_between_fractions_integers_figureS7.ipynb, Jupyter, 182 lines, 1 match
fMRI/Gaussian_fit/relation_between_runs_fractions_figure7B.ipynb, Jupyter, 238 lines, 1 match
fMRI/Gaussian_fit/relation_between_runs_integ_figure7A.ipynb, Jupyter, 235 lines, 1 match
fMRI/Multiple_regression/searchlight_analysis_allsubs_multipleregression_figure6.py, Python, 273 lines, 1 match
fMRI/Multiple_regression/secondlevel_analyses_searchlight_figure6_tableS1.ipynb, Jupyter, 225 lines, 1 match
fMRI/PCA/pca_allsubs _figureS3.ipynb, Jupyter, 167 lines
fMRI/PCA/pca_allsubs_figure3.ipynb, Jupyter, 182 lines
fMRI/RSA/mds_inscal_6rois_figures4_5.R, R, 114 lines
fMRI/RSA/rsa_analyses_hipp_parah_final_figure5.ipynb, Jupyter, 401 lines
fMRI/RSA/rsa_analyses_loc_rois_final_figure4.ipynb, Jupyter, 201 lines
fMRI/RSA/rsa_analyses_loc_rois_separatedomains_figure_S5.ipynb, Jupyter, 395 lines
fMRI/RSA/rsa_analyses_locrois_ips_final_figure4.ipynb, Jupyter, 526 lines
fMRI/RSA/rsa_analyses_locrois_itg_final_figure4.ipynb, Jupyter, 518 lines
fMRI/RSA/rsa_analyses_locrois_mfg_final_figure4.ipynb, Jupyter, 512 lines

At the source: doi.org/10.17605/OSF.IO/JQCNA

OSF 267s5

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Languages: R (3)

Size: 8 files, 3 scripts

Software Heritage: not checked

Found in: the references

Holds: 3 notebooks

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

Tools: ggplot2 (3 files), ggpubr (3 files), reshape2 (3 files), tidyverse (3 files), broom (2 files), rstatix (2 files)

Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)

30 September 2026: the link answers (HTTP 200)

3 files

paper_analysis.Rmd, R, 1,122 lines
paper_analysis_exp_2.Rmd, R, 800 lines
supplementary_analyses.Rmd, R, 605 lines

At the source: osf.io/267s5

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

Tracing map

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What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
26 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);
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Data

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Data Availability

All data, code, and outputs to reproduce the main results are publicly available on the Open Science Foundation (OSF): https://doi.org/10.17605/OSF.IO/JQCNA.

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

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Version 1, 25 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 9 MeSH terms, 1 funder, 114 references, 2 RRIDs.

Cite

This paper

Valério, D., Debray, S., Karami, A., Cauté, M., Gravel, N., & Dehaene, S. (2026). A cortical semantic map integrates fractions and integers in the human brain. PLoS biology, 24(10), e3003820. https://doi.org/10.1371/journal.pbio.3003820

BibTeX

@article{valerio2026cortical, author = {Valério, Daniela and Debray, Samuel and Karami, Alireza and Cauté, Maxime and Gravel, Nicolás and Dehaene, Stanislas}, title = {{A cortical semantic map integrates fractions and integers in the human brain}}, journal = {PLoS biology}, year = {2026}, month = oct, volume = {24}, number = {10}, pages = {e3003820}, publisher = {PLOS}, issn = {1544-9173}, doi = {10.1371/journal.pbio.3003820}, url = {https://doi.org/10.1371/journal.pbio.3003820}, pmid = {42911907}, pmcid = {PMC13690230} }

RIS

TY - JOUR AU - Valério, Daniela AU - Debray, Samuel AU - Karami, Alireza AU - Cauté, Maxime AU - Gravel, Nicolás AU - Dehaene, Stanislas TI - A cortical semantic map integrates fractions and integers in the human brain T2 - PLoS biology J2 - PLoS Biol PY - 2026 DA - 2026/10/22 VL - 24 IS - 10 SP - e3003820 SN - 1544-9173 PB - PLOS DO - 10.1371/journal.pbio.3003820 UR - https://doi.org/10.1371/journal.pbio.3003820 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pbio.3003820", "type": "article-journal", "title": "A cortical semantic map integrates fractions and integers in the human brain", "container-title": "PLoS biology", "author": [ { "family": "Valério", "given": "Daniela" }, { "family": "Debray", "given": "Samuel" }, { "family": "Karami", "given": "Alireza" }, { "family": "Cauté", "given": "Maxime" }, { "family": "Gravel", "given": "Nicolás" }, { "family": "Dehaene", "given": "Stanislas" } ], "container-title-short": "PLoS Biol", "volume": "24", "issue": "10", "page": "e3003820", "DOI": "10.1371/journal.pbio.3003820", "PMID": "42911907", "PMCID": "PMC13690230", "ISSN": "1544-9173", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pbio.3003820", "language": "en", "issued": { "date-parts": [ [ 2026, 10, 22 ] ] } }

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