dataesr/scientific-paragraphs-categorization huggingface dataset:
This work used global eddy covariance data acquired and shared by the FLUXNET community, including these networks:  AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia , and  USCCC. The ERA-Interim re-analysis data are provided by  ECMWF  and processed by  LSCE. The FLUXNET eddy covariance data processing and harmonization were carried out by the European Fluxes Database Cluster, AmeriFlux Management Project and  Fluxdata  project of FLUXNET, with the support of  CDIAC  and  ICOS Ecosystem Thematic Center  and the  OzFlux, ChinaFlux and AsiaFlux offices.
Data availability The data used in this study are openly available in the following databases: The eddy covariance measurements are obtained from the ICOS (https://www. icos-cp. eu/dataproducts/YVR0-4898) and FLUXNET2015 datasets (https://fluxnet. fluxdata. org/data/ fluxnet2015-dataset/). The CMIP6 data were downloaded from https://esgf-data. dkrz. de/ search/cmip6-dkrz/.
We note here that despite no evidence linking these genes specifically to autism compared to other neurodevelopmental conditions  [5] [6] [7]  , we refer to these genes in this study as "autism-associated genes". In addition, we analyzed 2,492 genes not considered as autism-associated genes, but with evidence for intolerance to LoF variants in reference populations (hereafter referred to as "constrained genes", Extended Data Table  2 , Methods)  12. Second, we identified high-confidence rare LoF variants (frequency less than 1% in each study) that were absent from the reference European population in the Genome Annotation Database (gnomAD; https://gnomad. broadinstitute. org/)  12. We focused this study on LoF variants since 85% of known autism-associated genes are considered as intolerant to LoF variants (Extended Data Fig.
Samples For the SSC, SPARKv1 and SPARKv2 cohorts, we downloaded genetic and clinical data from SFARI Base (https://sfari. org/sfari-base). For the SSC cohort, we selected 10,141 individuals with both whole-exome sequencing (WES) and SNP array data, who were not twins and did not show high number of erroneous variant calls (families filtered out: 12958, 14572 and 11037). For the SPARKv1 cohort, we selected 19,671 individuals with both WES and SNP arrays data, who were not withdrawn, not twins and not showing excessive number of variants or abnormal age, and from families in which both parents were undiagnosed and had available genetic data. For the SPARKv2 cohort, we selected 5,970 individuals with both WES and SNP arrays data, who were not withdrawn, and from families in which both parents were undiagnosed and had available genetic data.
For simplicity, the SPARKv1 and SPARKv2 samples were merged into a SPARK sample. For the UK-Biobank cohort, we downloaded genetic, demographic and brain imaging data from the UK-Biobank database (projects 51869, 40980 and 18584). We selected 200,457 individuals with both WES and SNP arrays data, who were not twins (kinship < 0. 4 from relationship file of UK-Biobank), and who did not report autism-related symptoms (based on ICD10-F84 index or the autism diagnostic questionnaire). For the iPSYCH sample, we downloaded tabular files for each gene of interest from the ASC website (https://asc. broadinstitute. org), and calculated the maximum of the allele numbers per status for all variants, corresponding to 4,811 autistic individuals and 5,214 control individuals.
Autism and constrained gene sets We focused on coding exons of 220 autism-associated genes: genes from the SFARI Gene database with a score of 1 (https://gene. sfari. org/database/human-gene/),102 genes from a recent case-control study of rare variations  8  , and 157 genes robustly associated with autism in multiple independent studies and unrelated individuals by the SPARK committee (http://sparkforautism. org) (Extended Data Table  1 ). Constrained genes were defined based on suggested thresholds of the LOEUF (lossof-function observed/expected upper bound fraction < 0. 35) or the pLI (probability of loss-offunction intolerance > 0. 9), both extracted from the gnomAD website (https://gnomad. broadinstitute. org)  12. The current study focused on autosomal genes, and we filtered out the genes with an evidence of recessive type of inheritance  15.
Admixture We used the 1000genome sequencing data of 2,504 individuals as a reference group of individuals of known ancestry  53. We selected the 1000g SNPs that were present in the SSC, SPARKv1 and SPARKv2 datasets to perform a combined admixture for SFARI-base samples, and 1000g SNPs that were present in the UK-Biobank dataset to perform a separate admixture, using the Admixture v1. 3. 0 tool  54.
Whole exome sequences We downloaded the GRCh37 aligned BAM files of 8,960 SSC participants from SFARI Base (https://sfari. org/sfari-base). We then called the variants using GATK 3. 8 following the BROAD Institute Best Practices  55  , and lifted over all variants to the GRCh38 human genome version. We downloaded the preprocessed GRCh38-based pVCF files of 27,270 SPARKv1 and 16,004 SPARKv2 participants from SFARI Base. All functional-equivalent (FE) GRCh38based pVCF files for 200,642 UK-Biobank participants were downloaded from the UK-Biobank database (projects 51869 and 18584). All variants from SSC, SPARK and UK-Biobank samples were filtered for call rate > 0. 9, genotype quality >= 30, depth > 20, allelic fraction >= 0. 25 (and <= 0. 75 for autosomal variants). Tabular lists of variants from the iPSYCH individuals were downloaded from the ASC website (https://asc.
For the independent regression analyses on autism status in the iPSYCH sample, we performed additional quality control steps on the 236 S-LoFs in autism-associated genes and 1,345 S-LoFs in constrained genes. The initial QC steps for the iPSYCH Danish Blood Spot whole-exome sequencing data has been described previously  58. Briefly, after first round sample-level and variant-level QC, three call rate filters were used subsequently, 1) remove variants with a call rate < 90%, 2) remove samples with a call rate < 95%, and 3) remove variants with a call rate < 95%. Between the sample call rate filter and the final variant call rate filter, one of each pair of related samples (relatedness as a pi-hat value ≥ 0. 2) was removed. Subsequently, we selected for this study the cases diagnosed with autism no later than by the end of 2016.
This gave us a study sample of 4,622 cases and 4,753 controls. We defined rare variants as with an allele count no greater than five across our dataset (n=9,375) and the non-Finnish Europeans from non-psychiatric exome subset of the gnomAD (n=44,779). We matched these S-LoFs to the original S-LoFs and identified 138 out of 236 S-LoFs in autism-associated genes and 767 out of 1,345 S-LoFs in constrained genes in iPSYCH. Replication analyses were based on these S-LoFs.
Developmental brain gene expression and enrichment in autism pathways The developmental brain transcriptome data from 42 specimen and up to 16 brain structures was downloaded from the Allen Brain Atlas BrainSpan database (https://www. brainspan. org/). Only expression RPKM values (Reads Per Kilobase of exon model per Million mapped reads) above 1 were considered for expression analysis. Values for each gene were averaged across four brain regions and eight developmental periods as previously described  20. Brain regions were defined as follows: R1) posterior inferior parietal cortex, primary auditory cortex, primary visual cortex, superior temporal cortex, inferior temporal cortex; R2) primary somatosensory cortex, primary motor cortex, orbital prefrontal cortex, dorsolateral prefrontal cortex, medial prefrontal cortex, ventrolateral prefrontal cortex; R3) striatum, hippocampus, amygdala; R4) mediodorsal nucleus of the thalamus, cerebella cortex.
Developmental periods were defined as follows: P1) early fetal; P2) early mid-fetal; P3) late mid-fetal; P4) late fetal; P5) infancy; P6) childhood; P7) adolescence; P8) Young adult. Note that only one individual was available for P1R4 in the BrainSpan database, the corresponding period/region was therefore not investigated in this study. For the analysis of the correlation between gene expression and autism-OR, we artificially replaced infinite autism-OR values by the highest measurable autism-OR in the gene set, and the Pearson correlation test was performed in the log10 space for both expression and odds ratio of autism-associated genes. We verified that Kendall and Spearman rank-based correlations showed the same trend (data not shown). Functional annotation of synaptic proteins were taken from SynGO  60  and transcription proteins extracted from Gene Ontology term Transcription, DNA templated  61.
Autism polygenic score computation SSC, SPARKv1, SPARKv2 and UK-Biobank imputed genotyping data were filtered separately from variants absent from more than 1% of individuals (geno001 parameter), then variants present in all 4 samples were merged with PLINK 1. 9  62. The polygenic score (PGS) for autism was computed by using the GWAS summary statistics from the Integrative Psychiatric Research Consortium (iPSYCH) and the Psychiatric Genomics Consortium (PGC) 2. To exclude overlap in participants from the test and discovery data in the PGS analysis, the GWAS meta-analysis summary statistics reported  2  were recalculated with the SSC data excluded. We used the SBayesR 63 method of the GCTB tool v2. 02 with the banded linkage disequilibrium (LD) matrix  64  and suggested options (https://cnsgenomics.
com/software/gctb) on the PGC-ASD summary statistics to estimate the posterior statistics of SNP effects. We finally computed the autism-PGS using PLINK 1. 9 based on SBayesR-derived statistics for common SNPs (MAF > 10%). We performed a PCA analysis using PLINK 2. 0, and extracted the four first principal components to control for population structure when using the autism-PGS in regression analyses.
Brain structural anatomy Imaging derived phenotypes (IDP) data were downloaded from the UK-Biobank database (projects 40980 and 18584). A total of 68 metrics for cortical regions, calculated using FreeSurfer and FSL software using the Desikan-Killiany Atlas 65 , were provided for 21,041 individuals with genetic data. Details of the acquisition protocol and imaging processing toolbox are available on the UK Biobank website: https://biobank. ctsu. ox. ac. uk/crystal/crystal/docs/brain_mri. pdf. Three global IDPs were investigated: total cortical volume, total cortical thickness and total cortical surface area. The total brain IDPs were obtained by summing left and right hemisphere global measures.
Statistical analyses Most of the statistical analyses in this work were performed using statistical test implementations from python libraries scipy 66 and statsmodels  67. If not otherwise stated, analyses including adjusting p-values for multiple testing used the Benjamini and Hochberg control for false discovery rate  68. were extracted only for a subset of diagnosed individuals from the SSC and SPARK samples. SNP array data were available for all individuals except from the iPSYCH cohort, which was treated separately (Methods). autism-associated gene, the autism-OR among male individuals is compared to the autismamong female individuals. Some genes were not found mutated among either male or female autistic individuals. The gene-level autism-OR was measured using the sub-sampling procedure described in Methods, randomly selecting 1,596 individuals, i.
different technologies (Illumina Omni1Mv1, n = 1,354, Omni1Mv3, n = 4,626, and Omni2. 5, n = 4,240) were downloaded from the SFARI Base (https://sfari. org/sfari-base), and 15 individuals were removed because they were twins. Arrays from each technology were mapped onto the GRCh37 human genome version separately. We downloaded the pre-processed GRCh37based genotyping files of 26,879 SPARKv1 and 15,904 SPARKv2 participants from the SFARI Base. SSC and SPARK genotyping files were filtered from ambiguous SNPs (A/T & G/C SNPs if MAF > 0. 4, SNPs with differing alleles, SNPs with > 0. 2 allele frequency difference, SNPs not in reference panel) and imputed on the HRC panel version r1. 1 51 on the Michigan servers with default parameters 52. GRCh37-based imputed genotyping files for 200,080 UK-Biobank individuals were downloaded from the UK-Biobank database (projects 51869 and 18584).
ACKNOWLEDGEMENTS This research has been conducted using the Simons Simplex Collection and Simons Powering Autism Research for Knowledge from the  Simons Foundation Autism Research Initiative , and the  UK-Biobank  cohort (projects 51869, 40980 and 18584). This work was supported by a grant from  SFARI  (#: 240059, TB). We are grateful to all of the families at the participating Simons Simplex Collection (SSC) sites, at the participating Simons Searchlight sites, the  Simons Searchlight Consortium , as well as the principal investigators ( A. Beaudet ,  R. Bernier ,  J. Constantino ,  E. Cook ,  E. Fombonne ,  D. Geschwind ,  R. Goin-Kochel ,  E. Hanson ,  D. Grice ,  A. Klin ,  D. Ledbetter ,  C. Lord ,  C. Martin ,  D. Martin ,  R. Maxim ,  J. Miles ,  O. Ousley ,  K. Pelphrey ,  B. Peterson ,  J.
DATA AVAILABILITY Whole-exome and SNP genotyping data from the SSC and SPARK cohorts can be obtained by applying at SFARI Base (https://www. sfari. org/resource/sfari-base/). The UK-Biobank whole-exome, SNP genotyping and brain imaging data can be obtained by applying at the UK-Biobank database (https://www. ukbiobank. ac. uk/). The human neurodevelopmental transcriptome dataset is available on the BrainSpan database (http://www. brainspan. org). Functional annotations can be obtained from SynGO (https://syngoportal. org/) and Gene Ontology (http://current. geneontology. org/annotations/goa_human. gaf. gz). Electronic health records and healthcare claims data used in the present study for the UK-Biobank individuals are not publicly available due to patient privacy concerns. Prevalence and autism-OR measures can be visualized and downloaded on https://genetrek.
Outbreak simulation We used the R package simulacr  [26]  to simulate outbreaks, the reporting process, and the subsequently observed transmission chains. simulacr implements and extends individual-based simulations of epidemics previously used to evaluate transmission tree reconstruction methods  [13, 14, 27]. In its basic form, simulacr implements a Poisson branching process in which the reproduction numbers (R) is combined with the infectious period to determine individual rates of infection. Here, to account for potential heterogeneity in transmission, we have drawn individual values of R from a Gamma distribution fitted to empirical data from the North Kivu EVD epidemic (rate: 1. 2; shape: 2; corresponding mean: 1. 7). The resulting branching process being a combination of Poisson processes with Gamma-distributed rates is therefore a Negative Binomial branching process.
The dates of symptom onset and case notification are generated for each new case using user-provided distributions for the incubation time and reporting delays. Simulations run until any of the set duration of the simulation is reached (here, 365 days). Here, we used parameters values and distributions in line with estimates from the Eastern DRC Ebola outbreak  [12, 28] , the details of which are provided in Table  1. All code used for running these simulations is available from https://github. com/jarvisc1/2020-reporting. Estimand: Reporting. We considered a single estimand π the level of reporting.
This gives an indication of how much the point estimates vary across simulations based on the level of reporting and sample size. Although the method may give unbiased estimates with good coverage under repeated sampling, an imprecise method could lead to large differences from the true value when applied to a single dataset (that is, confidence intervals may cover the true value honestly but are wide). We further explored the impact of bias and precision of the estimator by considering the deviations of the estimates from the true value termed absolute error. The absolute error is defined as the absolute difference between the estimated reporting and its true value, expressed as percentages. For instance, estimates of 43% and 62% for a true reporting of 50% would correspond to absolute errors of 7% and 12%, respectively.
Our approach requires case investigations to fall within the first two categories. In practice, the second and third situations may be easily confused-the third likely being the most frequent. To avoid such confusion, we would recommend recording investigation outcomes as two separate questions: Has a single likely infector been identified? And if yes, is this individual listed amongst reported cases? In our simulations, we assumed for simplicity that all reported cases were successfully investigated, so that the reported outbreak size effectively corresponds to the number of data points available for the estimation. In practice, the actual sample size will be the number of case investigations which led to identifying a single source case (reported, or not). As our method performs better in larger datasets, the data requirement for estimating reporting from transmission chains will involve substantial field work.
TJ, CIJ and WJE receive funding from the  Global Challenges Research Fund (GCRF)  project ' RECAP ' managed through  RCUK  and  ESRC  ( ES/ P010873/1 ). TJ and OPW receive funding from the  UK Public Health Rapid Support Team  funded by the  United Kingdom Department of Health and Social Care. TJ receives funding from the  National Institute for Health Research -Health Protection Research Unit for Modelling Methodology. TPM was funded by the  UK MRC  (grants  MC_UU_12023/21  and  MC_UU_12023/29 ). JAT was jointly funded by the  UK Medical Research Council (MRC)  and the  UK Department for International Development (DFID)  under the  MRC/ DFID  Concordat agreement and is also part of the EDCTP2 programme supported by the  European Union  ( MR/R010161/1 ) The  UK Public Health Rapid Support Team  is funded by the  United Kingdom Department of Health and Social Care.
Characterizing seascape variables and space Sea surface temperature (SST) and salinity (SSS) were retrieved from <http://marine. copernicus. eu/> as the monthly minimum, maximum and average values per year and per reproductive period of each species, respectively, for the period 1987-2015. Salinity data were collected both as surface and as water-column values. Surface and benthic chlorophyll α (Chl) was retrieved from the Bio-ORACLE database  (Tyberghein et al. 2012) , over the same 1987-2015 period, with the same frequency (monthly minimum, maximum and average) and at a resolution of 5 arc-minutes within a 1 km radius of the sample location using the 'raster' package in R  (Hijmans et al. 2013). Chlorophyll is a proxy of biological productivity that decreases from north to south and west to east (Supporting information) and is inversely related to the increase in temperature and salinity in the Mediterranean Sea  (Coll et al.
2010). We performed a principal component analysis (PCA) on 24 environmental variables (Supporting information) collected per geographical position for all individuals that includes SST, SSS and Chl. We used the dudi. pca function in the R package 'ade4' (<www. r-project. org>) and kept the resulting first three principal component (PC) axes to calculate the environmental dissimilarity between samples. Seascape variables were then represented by the three major PCA axes in the dbRDA analysis (Results). To address the effect of space on genetic variation, we estimated Moran's Eigenvector Maps (MEMs) using the dbmem  function in the 'adespatial' R package  (Dray et al. 2022). MEMs represent a spectral decomposition of the spatial relationships among the geographical positions of all samples.
Defining reserve-induced protection status and reserve uniqueness We calculated the in-water geographic distance (i. e. the shortest distance avoiding land) between individuals and the nearest reserve boundaries based on the GPS sampling points using the 'marmap' package (<www. r-project. org>). To reflect the fact that reserves are expected to have protection benefits beyond their boundaries but that this effect is often spatially restricted to a few kilometers  (Manel et al. 2019) , including in at least two of the species considered here  (Benestan et al. 2021) , we defined an individual as being protected when living inside the boundaries of the reserves and its surrounding area within a 5 km buffer. This categorical variable (protected versus not protected) was then transformed into a dummy variable with a value of 0/1.
Building a genomic database We used two different approaches to generate single nucleotide polymorphism (SNP) markers: restriction-site-associated Figure  2. Statistical framework developed to disentangle the influence of seascape, space and protection status on genomic variation in four species (Diplodus sargus, Mullus surmuletus, Palinurus elephas and Serranus cabrilla). The genomic dataset of each species was transformed using principal coordinates analysis (PCoA). All axes were retained for the PCoA. Geographic coordinates were used to estimate Moran's Eigenvector Maps (MEMs) with the dbmem function in the 'adespatial' package  (Borcard and Legendre 2002). Only MEMs with positive eigenvalues were retained. Seascape/environmental variables were transformed using a principal component analysis (PCA) and three principal components (PCs) were kept in the dbRDA.
05 to avoid biases in the genome scan  (Roesti et al. 2012). Individuals with too many missing loci (> 30%) were also filtered out. For each species, SNPs were separated into putatively neutral (hereafter neutral) and outliers (Supporting information) detected by the 'pcadapt' R package  (Luu et al. 2017). 'pcadapt' detects SNPs that contribute disproportionately to discriminant axes of a PCA, an approach that has been successfully used in a variety of contexts  (Cayuela et al. 2020 , Reynes et al. 2021). We first run 'pcadapt' using a large number of PCs (K = 20) to finally retain only two PCs that best explain population structure among individuals. All SNPs were then regressed against the retained PCs and p-values associated to z-scores were assigned to all SNPs. The 1% of SNPs with lowest p-values were considered outliers.
Estimating genetic diversity and pairwise relatedness between individuals Here, we consider the observed heterozygosity within each individual as a measure of genetic diversity. These were estimated using 'vcftools'  (Danecek et al. 2011 ) and the 'hierfstat' (Goudet and Jombart 2015) R package through the het and basics. stats functions, respectively. As a reduction in genetic diversity may be due to inbreeding, we calculated the relatedness among individuals using the Loiselle kinship coefficient as implemented in GenoDive  (Meirmans 2020). We explored the influence of the categorical variable protected/ unprotected on genetic diversity and kinship, respectively. Since heterozygosity and kinship did not follow normality (Shapiro test, p-value < 0. 001), we carried out non-parametric Wilcoxon tests to test for difference in mean genetic diversity in samples considered protected versus not protected for each species.
Seascape, space and reserve influence on genomic patterns We used distance-based redundancy analysis (dbRDA) to build linear models between the pairwise Euclidean genetic distances among individuals transformed by a principal coordinates analysis (PCoA) and three types of explanatory variables: seascape, space and protection status (Fig. 2 ). This step allows the identification of associations between genomic variation and explanatory variables. All axes were retained for the PCoA. A PCA on linear prediction then allows visualization of the genomic environmental associations. Here, the dbRDA was conducted using the dbrda function available in the 'vegan' package on the binary genotype variation obtained from plink  (Purcell et al. 2007 ). We first tested the global model with all positive MEM variables.