dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population The dataset employed in the present study was collected from 50 pairs of twins (composed of 42 males and 58 females, mean age 18. 5 years, age range 10-30 years) at the Scientific Institute IRCSS E. Medea of Bosisio Parini (Lecco, Italy). The twin study, which was conducted in collaboration with the Italian Twin Registry [ 26 ], was aimed to explore the genetic and environmental contributions to brain development and behavior. To this end, socio-demographic, psychopathological, behavioral, cognitive, genetic, and neuroimaging information were collected from all participants. Here, only socio-demographic and neuroimaging information was used to pursue the study primary objective. Exclusion criteria were intelligence quotient (IQ) < 70 based on Wechsler intelligence scales for adults/children (WAIS/WISC) [ 27,28 ] diagnosis of autism spectrum disorder, epilepsy or other neurological disorders, history of head trauma, severe visual, auditory, or language comprehension deficits, number of gestation weeks < 34.
The research protocol was approved by the competent Research Ethical Committee in accordance with the 2013 Fortaleza version of the Helsinki Declaration and subsequent amendments. A written informed consent to the study was obtained by all participants of legal age, or their parents in the case of minors. In view of the sexual dimorphism of brain morphology and function, the rs-fMRI data processing was performed only on 46 same-sex twin pairs, of which 19 pairs were monozygotic (MZ) (10 male pairs, 9 female pairs, 16. 7 ± 5. 25 years) and 27 pairs were dizygotic (DZ) (9 male pairs, 18 female pairs, 19. 3 ± 6. 59 years). After quality check (described in the "fMRI pre-processing" section), 3 additional pairs were excluded from the statistical analyses, resulting in a dataset of 43 twin pairs.
MRI analyses The MRI data pre-processing, connectivity feature extraction, and statistical analyses were mainly performed in Matlab R2019b (The MathWorks, Inc. ) using the open-source Statistical Parametric Mapping (SPM) software (version 12, https://www. fil. ion. ucl. ac. uk/spm/software/spm12/) [ 29 ], the open-source Brain Connectivity Toolbox (BCT) (https://sites. google. com/site/bctnet/) [ 30 ], and in-house Matlab scripts including functions from the Statistics and Machine Learning toolbox. The FSL software (version 6, https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/) [ 31 ], was also used for a pre-processing step. The fMRI processing pipelines are illustrated in schematic form in the Supplementary Figure  S1.
fMRI pre-processing The raw fMRI volumes from each subject were imported in SPM12 and spatially realigned to the subject's first volume using a least squares approach followed by a rigid body transformation. The subject's structural T1-weighted image was co-registered to the mean fMRI image through an affine transformation, corrected from intensity biases, and segmented into different tissue types. The forward deformation field parameters estimated in the latter step were used for normalization of the fMRI volumes from the subject's native space to the standard Montreal Neurological Institute (MNI) space. The realigned and normalized fMRI volumes were smoothed using a 3D Gaussian kernel filter with Full Width at Half Maximum (FWHM) equal to 6 mm and imported in the FSL software. Here, the pre-processed fMRI volumes from each subject were temporally high-pass filtered (cut-off frequency of 0.
01 Hz) and entered into a single-subject spatial ICA decomposition using the FSL Multivariate Exploratory Linear Optimized Decomposition into Independent Component (MELODIC) toolbox (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/MELODIC). Artefactual independent components (ICs) were marked using a semi-automatic spatiotemporal tool recently published by our research group and subsequently regressed out from the fMRI volumes [ 32 ]. The resulting fMRI volumes were subjected to the connectivity and statistical analyses described in the next sections. In a final quality check, the subjects with a percentage of noise-related ICs > 75% or characterized by excessive head movements (either translational > 5 mm and/or rotational > 3°) and their siblings were excluded from the analyses, resulting in a dataset composed of 43 twin pairs.
Seed-based FC analysis The SPM Marsbar toolbox (version 0. 44, http://marsbar. sourceforge. net/) was used to obtain the parcellation of the subjects' fMRI volumes in  = 90 regions of interest (ROIs) of the Automated Anatomical Labeling (AAL) atlas [ 33 ], representing the nodes for the functional connectivity (FC) analysis. We selected all AAL ROIs except from the ones located in the cerebellum and vermis, which were not covered by the fMRI volumes in some or all subjects. For each subject, we extracted the mean BOLD time series of the voxels within each ROI (node). Using Matlab in-house scripts, instantaneous statistical dependencies among ROIs were assessed by computing the Pearson correlation coefficients between the BOLD time series of each pair of ROIs, resulting in a  ×  FC adjacency matrix for each participant, whose elements represent the pairwise cross-correlation between the BOLD time series of the corresponding ROIs.
Sample The original socio-demographic and rs-fMRI dataset was collected from 50 pairs of twins. After exclusion of twin pairs with sex differences (n=4) or low-quality rs-fMRI data during the quality check step (n=3), the resulting sample of 43 twin pairs, including 25 DZ pairs and 18 MZ pairs, was selected for subsequent statistical analyses (schematized in Fig. 1 ). Selected sample's characteristics are detailed in Table  1. Age, sex, and behavioral characteristics based on the Child/Adult Behavior CheckList (CBCL/ABCL) were comparable between MZ and DZ twin pairs.
Discussion In our study, a cross-sectional rs-fMRI dataset from young pairs of twins was analyzed to gain insights into developmental genetic and environmental determinants of brain functional connectome. For the first time, these determinants were comprehensively explored using a multimodal analysis framework, based on multiple brain network features at multiple spatial scales (i. e. , brain-, RSN-, node-, and link-levels) using multiple statistical approaches (including ADE and ACE models). In line with prior knowledge, our preliminary evidence shows that brain FC is characterized by complex region-and feature-specific patterns of genetic-environmental effects throughout development. Of note, we found a good regional consistency of these effects across multiple spatial scales. Indeed, our findings suggest that frontotemporal FC is at least partially under genetic control, since epistatic and additive/dominant genetic influences were found both in selective frontotemporal connections and in the interactions of frontotemporal regions with the rest of the brain.
1 might be causally involved in cue-induced relapse. Using animal model of AUD  [30] , Arc knockout (Arc KO ) murine model, local manipulations of the Arc gene in the mouse amygdala with Clustered Regularly Interspaced Short Palindrome Repeats (CRISPR)/Cas9 system  [31]  delivered by lentiviral vectors, and whole cell patch clamp electrophysiology ex vivo, we identi ed AUD-related behaviors that are regulated by Arc, and Arc-regulated alcohol-induced synaptic plasticity. For translation into humans, we used the IMAGEN cohort database  [32]  to investigate a possible association of ARC genetic variations with alcohol-related behavioral variables and amygdala volume in young adults at a time when alcohol consumption reaches its peak in life.
ARC genetic variations are associated with alcohol consumption in humans To check the contribution of the human ARC gene to the expression of alcohol addiction-related behaviors at a time point when they get established, we interrogated the IMAGEN sample of 1315 19-year old participants  [32] , controlling for gender and site of data collection (Fig. 6 , Supplemental Table  1 ). We found a signi cant effect of the single nucleotide polymorphism (SNP) rs10097505 (variant: G > A) and the methylation of 15 CpG sites on the amount of consumed alcoholic beverages over 12 months. Thereby, the individuals with the AA variant tended to drink more alcohol than those with GG and GA (Fig. 6a , Supplemental Table  2 ). Since alcohol consumption may induce DNA hypomethylation  [43]  (Fig. 3 ), we investigated blood DNA methylation in the IMAGEN cohort using the Illumina 850K BeadChip array.
QuantiTect Reverse Transcription Kit (Qiagen, Cat. No. 205314) was used to quickly prepare gDNA for gene expression analysis. Arc was quanti ed by using the QuantiFast SYBR® Green PCR Kit (Qiagen, Cat No. 204054). The recommended cycling program from this kit was followed for the ampli cation. Arc primer was bought from Qiagen (QuantiTect Primer Assay, Arc: QT00250684). As a control, Gusb expression was analyzed; the Gusb primer was also bought from Qiagen (QuantiTect Primer Assay, Gusb: QT00176715). The ampli cation was conducted with the Step One Plus real-time system (Applied Biosystem). To analyze the fold Arc expression the delta-delta Ct method was used [65]. IntelliCage Behavioral Training. Female mice were trained for long-term alcohol drinking in the IntelliCage System (NewBehavior; https://www.
The mean gray value of the images (arbitrary units), density and size of Arc+ puncta were analyzed by ImageJ (1. 48v) software while the data analysis was performed using scripts in Python  (3. 4. 4v). Hippocampal dissociated cell culture immunostaining was analyzed by All-in-One Fluorescence Microscope BZ-X800 (Keyence) using the 20 X objective. For each transfected neuron the mean gray value was analyzed by ImageJ (1. 48v) software. Arc gene methylation in mice. The amygdala of alcohol-naïve and alcohol trained C57BL/6J mice was quickly dissected on ice. Samples were immediately frozen in liquid nitrogen. Genomic DNA was extracted by Extractme genomic DNA Kit (Blirt, EM13) following the suggested protocol. DNA was analyzed by EpigenDx (96 South Street Hopkinton, MA 01748, www. epigendx. com) for methylation analysis by Targeted NextGen Bisul te Sequencing (tNGBS).
After 2 weeks, the cells were lysed and genomic DNA extracted by Extractme genomic DNA Kit (Blirt, EM13). DNA was used for PCR ampli cation of the Arc gene with a set of primers speci c for the region containing the sequence complementary to gRNA1 (primer forward: 5'ctctgggcctctctagcttc3', primer reverse: 5'cgtccaagttgttctccagc3'). The same procedure was applied to non-transduced cells to obtain non-mutated genomic DNA. The amplicon was sent for Sanger sequencing by Genomed S. A. (Warsaw, Poland). The chromatograms were analyzed with the ICE (Synthego) program which gives NGS quality results with Sanger data. Validation of CRISPR/Cas9 system in vivo. Three weeks after CA1-targeted, bilateral, stereotactic injection of LV coding EV or Arc Crispr (900 nl, 10 7 -10 8 GC/ µl) mice received i. p.
Since the EPSC recorded at +45mV is a composite of AMPA-R-and NMDA-R-mediated current, the amplitude of NMDA response was measured 50 ms after the peak to ensure the absence of the AMPA-R component. All electrophysiological recordings were performed by the experimenter blinded to the mouse genotype. For the analysis of recordings, we averaged the data from multiple cells of one animal, thus showing the overall effect of alcohol on an individual mouse. The number of recorded cells from each experimental group are indicated in the gure legend. Stereotactic Injection of viral vectors. The surgeries were performed at least 7 days before the beginning of the training. Lentiviruses for the CRISPR/Cas9 system with the gRNA1 or the corresponding empty vector were produced at Animal Models Lab at Nencki Institute (Warsaw, PL).
From the IMAGEN cohort, data were only included if behavioral, genetic, methylation and MRI data were available. After quality control and removal of outliers, a sample of 1315 19-years-old participants (52. 8% females) was used for statistical analysis. Local ethics research committees at each IMAGEN site permitted the study. Alcohol-related behaviors were measured using the European School Survey Project on Alcohol and Drugs (ESPAD) questionnaire with the focus on the self-reported quantity of consumed substance, frequency of drunkenness, and binge drinking over the last 12 months. Structural MRI data were acquired with 3T magnetic resonance (MRI) scanners (Siemens, GE and Philips). The same scanning protocols were applied across all IMAGEN sites. Full details of MRI acquisition protocols and quality checks have been previously described  [68].
FreeSurfer software suite (http://surfer. nmr. mgh. harvard. edu/) was utilized to segment MRI brain images and inspect each individual cortex for inaccuracies. Participants with cerebral cortex malformations were excluded from analyses. For this study, values referring to the total volume of four brain regions: Left Amygdala, Left Hippocampus, Right Amygdala, Right Hippocampus, were extracted. Since IMAGEN data was collected at 8 different sites, the effect of site was added as a nuisance covariate in all statistical analyses in addition to total intracranial volume  [32]. The tested variables in the European School Survey Project on Alcohol and Drugs (ESPAD) questionnaire were: 8b -On how many occasions over the last 12 months have you been drunk from drinking alcoholic beverages? -Based on a scale from 0 to 6, where 0 = '0', 1 = '1-2' times; 3 = '6-9', 4 = '10-19', 5 = '20-39', and 6 = '40 or more' times.
Genotype data were obtained for 582,982 markers utilizing DNA extracted from whole blood and the Illumina (Little Chesterford, UK) HumanHap610 Genotyping BeadChip. SNPs with call values of <98%, minor allele rate <1% or variation from the Hardy-Weinberg equilibrium (P ≤ 1×10 -4 ) were rejected from further examinations. Subjects with an unclear sex code, absent genotypes (failure rate >2%), and outlying heterozygosity (with a frequency of 3 SD from the Mean) were also removed. The identity-bystate resemblance was employed to assess cryptic relatedness for individuals with the PLINK program. Individuals who were closely related to identity-by-descent (IBD > 0. 1875) were excluded from the subsequent analysis. The population was investigated using principal component analysis (PCA) in EIGENSTRAT software.
The four HapMap populations were managed as reference groups in the PCA and participants with atypical ancestry were also eliminated. Genotype data were coded into minor homozygotes, heterozygotes, and major homozygotes. ARC gene. For this study, the candidate gene approach was employed. The information about the selected gene of interest, ARC, was extracted from an online genetic database -(GRCh37. p13; https://www. ncbi. nlm. nih. gov/gene/23237). For the IMAGEN sample, there was only one SNP (rs10097505) located within the ARC gene locus. A total sample of 1315 was extracted from the IMAGEN cohort based on the availability of corresponding ESPAD measures. In the human study, the signi cance of the associations with ESPAD variables of interest was corrected for multiple testing and established using permutations (n = 10000).
Further, they did not replicate this finding in a large sample of 681 subjects from the Autism Brain Imaging Data Exchange (ABIDE) database. Because the studies included in the meta-analysis were generally underpowered, the authors found that the number of significant findings was larger than expected. To date, despite many studies (see  Traut et al. 2017 (9)  for a complete meta-analysis) investigating the cerebellar structure in autism, no consistent atypicalities have been found. Several reasons may explain such discrepant findings. First, it may be that there really are no group differences in the cerebellum between individuals with autism and controls and that previous positive findings are the consequence of a publication bias from a large number of small underpowered studies  (9). Second, there might be differences, but cerebellar morphological alterations in autism are subtle and located only in specific parts of the cerebellum, such as the vermis or Crus I (5,10), which was not investigated in large multicentre studies  (9).
We used the pymatch package (https://github. com/benmiroglio/pymatch) to create a sample of individuals with ASD matched to the neurotypical subjects by age, iq and sex. We subsequently repeated our case control analyses to ensure that the results from the main analyses were not driven by difference between individuals with ASD and controls (see supplementary material 16). In the normative modelling analyses, given the non-parametric distribution of positive and negative deviations in each region of interest, we compared autism and TD with non-parametric Mann-Whitney tests.
049, uncorrected). In addition, Traut et al. (  9 ) studied the cerebellar volume in a larger sample (ABIDE dataset) but did not conduct a parcellation analysis and studied the global volume of the cerebellum. However, the cerebellar cortex can be divided between an anterior part -connected to the sensory motor cortex -and a posterior / cognitive part -connected to the associative cortex. Because of this functional topography, it is critical to study the anatomy of the cerebellum at a lobular level. We did not find a difference in terms of cerebellar sub-volume in individuals with autism compared to neurotypical controls. These results were consistent across two different parcellations methods and a voxel wise analysis. All parcellations were visually inspected by an expert rater blind of the diagnosis.
Heterogeneity focused analysis of verbal IQ the CERES pipeline  (Romero et al. , 2017). All data were processed on a high computing performance cluster in Bordeaux (PC) by the team that developed the CERES pipeline as in the EU-AIMS sample. Next, an expert rater (YE) -blind of the clinical features of each participant -visually assessed the quality of MRI scans in every slice for each spatial plan of the cerebellum. We identified subjects with non-cerebellar voxels labeled as voxels belonging to the cerebellum and vice versa and subjects with parcellation errors within the cerebellar lobules. All images with parcellation defects were excluded from further analyses.
Study population Out of 1453 subjects with structural MRI data and complete clinical assessment (SRS total and subscores, IQ measurement and basic demographic characteristics -age, sex, site of inclusion), we excluded 280 subjects with excessive head motion and low quality of the anatomical image and 279 subjects with parcellation errors. Next we selected only subjects with > 60 SRS2 t-score as described above. The study population is reported in Table  A.
Acknowledgement This project has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement no. 115300  for the project  EU-AIMS  and no. 777394  for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme , the  European Federation of Pharmaceutical Industries and Associates, Autism Speaks ,  Autistica , and  SFARI. CL is supported by the  Bettencourt Schueller Foundation  ( CCA-INSERM-BETTENCOURT-2018 ) DLF is supported by funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  101025785.
Disclosure Dr. Tillmann  has served as a consultant for  Hoffmann-La Roche. Dr. Baron-Cohen  has served as an author, consultant, or lecturer for Ability Partner,  Clarion Healthcare ,  Expo Medica ,  Eli Lilly, GLGroup, Kompetento, Medice, Prima Psychiatry, Prophase, Roche, Shire , and  System Analytic ; he receives royalties for textbooks and diagnostic tools from  Huber/Hogrefe ,  Kohlhammer , and  UTB. Dr. Charman  has received research grant support from the  Medical Research Council (UK) , the  National Institute for Health Research, Horizon 2020  and the  Innovative Medicines Initiative  ( European Commission ),  MQ ,  Autistica ,  FP7  ( European Commission) , the  Charles Hawkins Fund , and the  Waterloo Foundation ; he has served as a consultant for  F. Hoffmann-La Roche  and Servier; and he has received royalties from  Guilford Publications  and  Sage Publications.
A key issue distinguishing prominent evolutionary models of human life history is whether prolonged childhood evolved to facilitate learning in a skill-and strength-intensive foraging niche requiring high levels of cooperation. Considering the diversity of environments humans inhabit, children's activities should also reflect local social and ecological opportunities and constraints. To better understand our species' developmental plasticity, the present paper compiled a time allocation dataset for children and adolescents from twelve hunter-gatherer and mixed-subsistence forager societies (n = 690; 3-18 years; 52% girls). We investigated how environmental factors, local ecological risk, and men and women's relative energetic contributions were associated with cross-cultural variation in child and adolescent time allocation to childcare, food production, domestic work, and play.
To do so, we used a time allocation dataset comprised of observations for 690 children and adolescents (52% girls) ranging in age from approximately three to eighteen years inhabiting twelve foraging societies from Africa, Asia, Central America, and South America (Fig. 1 ). Using these data, we sought to answer three questions: (1) How do environmental factors influence child and adolescent time allocation? If communities living in harsher environments exhibit more cooperative behaviours  63, 64  , then this association may also apply to the age-graded division of labour. Here, we estimated environmental harshness using three variables. Net Primary Productivity (NPP-gC/m 2 /year) measures the amount of new plant growth produced per year, reflecting habitat productivity. Annual Precipitation (mm) approximates total water inputs, reflecting water availability at the regional scale.
Results Descriptive statistics. Our sample includes 690 children and adolescents (52% girls) ranging in age from approximately three to eighteen years (M = 9. 29 years, SD = 4. 48). An average of 124. 05 observations (SD = 160. 88) were collected per child, totalling 85,597 unique observations. Fifty-five children from BaYaka, Dukha, Mikea, and Savanna Pumé datasets were sampled in more than one year. All other children were observed over a two to twelve months timespan during a single year. Please see the Supplementary Information (SI) for details regarding site-specific data collection procedures. Sampled communities practiced a wide range of subsistence strategies (Table  1 ), with reliance on non-foraged resources representing 5% (Hadza) to 97% (Matsigenka) of diets. While Fig. 1  shows that most participating communities inhabited tropical and subtropical climatic zones, there was considerable variation in NPP, annual mean temperature, and annual rainfall across our sample (Table  1 ).
Children were instead categorized as early juveniles (5-8 years), late juveniles (9-15 years), and young adults (16-25 years). For this table, we held each category at the mean age; early juveniles = 6. 5 years, late juveniles = 12 years, young adults = 20. 5 years. b  The age of children with repeated observations was considered the mean of their age across all years sampled. c Reflects the total density of dangerous mammals per km 2 for each site. Low: n/km 2 < 1. High: n/km 2 > 10. d 1 = At the time of data collection, people usually or always had enough water and the water was of good quality, 2 = At the time of data collection, people usually or always had enough water, but the water was of poor quality, 3 = At the time of data collection, people rarely or never had enough water, but the water was of good quality, 4 = At the time of data collection, people rarely or never had enough water, and the water was of poor quality.