dataesr/scientific-paragraphs-categorization huggingface dataset:
These findings indicate that the form of HCC developed by Peruvians with Indigenous American ancestry represents a molecular subtype divergent from the unifying molecular classification of HCCs  [3, 4]. To further characterize this molecular subtype, HCC/ NTL differentially expressed genes (DEGs) extracted from the 56 Peruvian-specific gene sets were scrutinized (AYA: 19,774 and MOA: 14,523). A derivation set was built by randomly selecting 21 Peruvian and 99 non-Indigenous American HCC datasets; and the discovery set was made with the remaining 18 Peruvian and 96 non-Indigenous American HCCs. A 961 gene signature was defined (hereinafter referred to as "Amerind signature"), of which 806 were upregulated and 155 downregulated in Peruvian HCC (Figure  3A  and Supplementary Table  4 ). A major subset (48%) of downregulated DEGs defined hepatocyte identity (n = 74), whereas 16.
There are some limitations to recognize in the present study. Because of the clinical epidemiology of HCC in Peru, we did not have the opportunity to incorporate in our experimental design HCC patients with anthropological backgrounds other than Native American ethnicity. Therefore, it remains difficult to come to a decisive conclusion on the respective contributions of human genome architecture and exposome in the molecular epidemiology of HCC in Peru. The comparison with patients from East Asia, the Middle East, and Western Europe was achieved using publicly available datasets produced in different periods. Although we used up-todate normalization tools to avoid batch effects, we cannot rule out the fact that this comparison is not free of bias  [55]. Nonetheless, the present study stresses the necessity to justly reconsider the potentially prominent roles played both by human genome architecture and biogeography in the molecular epidemiology of cancer affecting underreported minorities and Indigenous patients, notably in low-and middle-income countries  [56].
5°C/sec ramp rate, 98°C for 5 min, and hold at 4°C. Data were analyzed using the QuantaSoft™ software version 1. 7 (Bio-Rad), with autoanalysis settings for duplex experiment. HBV sequences were produced using the BigDye™ Terminator v3. 1 Cycle Sequencing Kit (Applied Biosystems). HBV phylogeny was computed using Kimura two-parameter model and neighborjoining method on the Molecular Evolutionary Genetics Analysis (MEGA) software version 7  [60].
Transcriptome profiling RNA profiling was performed using GeneChip™ Human Transcriptome Array 2. 0 and GeneChip™ WT PLUS Reagent Kit (Applied Biosystems), according to the manufacturer's instructions. Alternatively, transcriptome datasets for comprehensive comparative analyses were obtained from Gene Expression Omnibus (GEO) for HCC/ NTLs. Gene expression data normalization and batch correction were performed using the Robust Multichip Average (RMA) and ComBat algorithm (sva R package), respectively  [55]. These datasets were then collapsed using Fred's Softwares algorithm (collapse_genes-09). A pathway-centric approach using SES was applied to our dataset based on four curated databases by Fred's Softwares: KEGG, Reactome (reactome_2018803), Gene Ontology-Cellular Component (C5_CC), and MSigDB Hallmarks (H), according to the recommended workflow (two-tailed t-test with p-value < 1.
7E-05 as significance level and fold change < 0. 7 and > 1. 5 as thresholds)  [26]. Fred's Software source codes and databases are available at: https://sites. google. com/site/fredsoftwares/products. A gene-centric approach was applied to identify DEGs using limma R package with a q-value of 0. 05, following the recommended workflow  [61]. The identification of the Amerind signature was carried out using the Signature Evaluation Tool (SET) with the lowest error rate at 0. 075, as recommended  [62]. Reference signatures (C6) from the MSigDB version 7. 1 were evaluated on HCC/NTLs using SES  [63].
Methylome profiling DNA methylation profiling was performed with bisulfite treatment using the Infinium ® HumanMethylation450K and MethylationEPIC BeadChip arrays (Illumina), according to the manufacturer's instructions. DNA methylation patterns were normalized using Noob correction method  [64]. DMPs were identified using minfi R package with a q-value of 0. 05, following the recommended workflow  [38]. Stringent criteria were used to account for the most biologically meaningful DMPs, as described previously  [39]. These filtering criteria applied to each DMP consisted in: (1) the mean Δβ value in HCC/ NTL was ≥ 20%; (2) More than 70% of the HCCs had β values greater than 2 standard deviations above the mean methylation level of all 70 NTLs; and (3) the mean Δβ value for NTLs was ≤ 25% (for hypermethylated sites) or ≥ 25% (for hypomethylated sites).
DNA hydroxymethylation profiling was performed with oxidative bisulfite treatment using the Infinium ® MethylationEPIC BeadChip array. 5hmC detection was achieved according to the 5hmCscore method identifying 5hmC positions between the bisulfite-and oxidative bisulfite-treated replicates within each sample, as previously reported  [65]. Briefly, hydroxymethylated CpGs were identified with a 5hmCscore ≥ 0. 3 in at least three samples. 5hmC-scores were determined independently in HCC and NTL tissues. Finally, definite 5hmC were defined as those falling in the third quartile of the 5hmC-score density distribution, which encompassed more than 80% of identified CpGs. Gene list functional enrichment analysis was performed using ToppFun on four curated databases: KEGG, Reactome, Gene Ontology-Cellular Component, and MsigDB Hallmarks, with a Bonferroni q-value of 0.
Quantitative transcript detection qPCR assays were performed as previously described  [11] , with the following modifications: relative gene expression was calculated according to the ΔΔC T method using the geometric mean of five housekeeping gene expression as references: TRIM44, HMBS, LMF2, CIPC, and EME2. These reference genes were selected from the transcriptome profiling of the 39 Peruvian HCC/ NTLs, to which geNorm and NormFinder algorithms were applied  [68]. The list of DNA primer pairs for qPCR is provided in Supplementary Table  7.
ACKNOWLEDGMENTS We are grateful to all patients whose participation was essential to the achievement of this study. We thank  Luis Taxa  from the  INEN Department of Pathology  and scholarship from the  Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica (FONDECYT ) 212-2015-FONDECYT and was supported by a fellowship from  Campus France   941211E ; L. C. was supported by a doctoral fellowship from  IRD  ARTS-2016-878573B; I. L. was supported by a postdoctoral fellowship from the  Fondation ARC pour la Recherche sur le Cancer   PDF20170505624 ; J. J. F. has received funding from the  Agence Nationale de la Recherche (ANR)  under the Investments for the Future program  11-LABX-0068 ; S. C. Z. and E. R. have received funding from the  World Bank Group and FONDECYT-CONCYTEC  under the  Research Infrastructure Improvement program   016-2018-FONDECYT/BM ; and S.
Data availability The datasets generated during this work have been deposited in the GEO repository with accessions GSE111580, GSE136247, GSE136319, GSE136380, and GSE136583. Additional analyzed datasets are available in GEO with accessions GSE141521, GSE17548, GSE37988, GSE45436, GSE56588, GSE62232, and GSE63898; as well as in the database of Genotypes and Phenotypes (dbGaP) with the study accession phs000178. v11. p8 (project ID: TCGA-LIHC). HBV sequences are available in the European Nucleotide Archive (ENA) repository with the secondary study accession ERP023329.
A neural signature of configural face processing is the increased response to faces (or bodies) vs. scattered stimuli in category-specific visual areas of the occipitotemporal cortex  (29, 36). The current results showed an analogous effect during processing of body dyads, where non-facing dyads were taken as the scattered counterpart of facing-dyad configurations. Converging evidence for increased response to facing vs. non-facing dyads was obtained with whole-brain and region of interest (ROI) analyses. The first analysis was based on data collected during the main fMRI experiment, in which 29 subjects in the fMRI scanner viewed upright or inverted images of facing or non-facing body dyads, and, in independent runs, upright or inverted images of single bodies or objects. The whole-brain contrast  The above effect of body positioning in the occipitotemporal cortex was confirmed by an ROI analysis.
Processing of single bodies The body inversion effect. Configural processing are also thought to apply to single bodies, although the underlying mechanisms seem to be functionally and anatomically distinct from face-related processing  (37) (38) (39) , and, in many aspects, less clear  (29, 40). To contribute to this issue, we performed two tests of the body inversion effect as a signature of configural processing. Using fMRI data from runs involving single bodies and non-body objects, we extracted the neural response to upright and inverted stimuli from each ROI. First, in each ROI, we tested whether the difference in the response to upright bodies vs. inverted ones was statistically larger than a general effect of inversion that would be observed for any (non-body) stimulus. We ran separate 2 Category x 2 Orientation ANOVAs for each ROI.
Material and Methods Participants. Thirty subjects took part in the fMRI study (16 female; mean age 24. 8 years, SD = 4. 6). All had normal or corrected-to-normal vision and reported no history of psychiatric or neurological disorders, or use of psychoactive medications. They were screened for contraindications to fMRI and gave informed consent before participation. Of the 30 subjects who enrolled in the fMRI experiment, 23 agreed to come back twice, to take part in two sessions of the behavioral study (13 females; mean age 24. 6 years ± 4. 5 SD). This number was comparable to the sample size used in previous research that used similar task and design  (24). From behavioral and fMRI data analyses, we excluded one subject for whom average performance in the behavioral task was >3 SD above the group mean.
Stimuli Behavioral study. Sixteen grayscale renderings of single human bodies in profile view and various biomechanically possible poses, were created and edited with Daz3D (Daz Productions, Salt Lake City) and the Image Processing Toolbox of MATLAB (The MathWorks Inc, Natick, Massachusetts). As many bodies were obtained by flipping horizontally each unique body, which yielded to a total of 32 single bodies. Sixteen facing dyads were created from the 32 single bodies, and then they were horizontally flipped to create 16 new dyads, for a total of 32 facing dyads. Non-facing dyads were created by swapping the position of the two bodies in each facing dyad (i. e. , the body on the left side was moved to the right side and vice versa). The distance between bodies (i. e. , the distance between the two closest points of the two bodies in a dyad) was matched across facing and non-facing stimuli (meanfacing = 82.
The experiment lasted ~40 min. Stimulus presentation and response collection (accuracy and RTs) were controlled with Psychtoolbox (66) through MATLAB. Each participant took part in two sessions of the same task. The second session took place with a delay ranging from two weeks to one month after the first one (mean interval 19. 8 days ± 6. 1 SD). Autistic-Spectrum Quotient measure. After the second behavioral session, subjects completed the 50items Autism-Spectrum Quotient scale (AQ; see 31), which measures the degree to which an adult with normal intelligence shows traits associated with the autism spectrum, in the domain of social skills, attention-switching, attention to detail, communication, and imagination. Subjects responded to each item using a 4-point rating scale, ranging from "definitely agree" to "definitely disagree".
Stimulus presentation, response collection and synchronization with the scanner were controlled with the Psychtoolbox (66) through MATLAB (The MathWorks Inc, Natick, Massachusetts). Functional localizer task. In addition to the six experimental runs, subjects completed a functional localizer task, with stimuli and task adapted from the fLoc package  (68). During this task, subjects saw 180 grayscale photographs of the following five object classes: 1) body-stimuli (headless bodies in various views and poses, and body parts); 2) faces (adults and children); 3) places (houses and corridors); 4) inanimate objects (various exemplars of cars and musical instruments); 5) scrambled objects. Stimuli were presented over two runs  (5. 27 min each). Each run began with a warm-up block (12 s) and ended with a cool-down block (16 s), and included 72 blocks of four seconds each: 12 blocks for each object class with eight images per block (500 ms per image without interruption), randomly interleaved with 12 baseline blocks featuring an empty screen.
To minimize low-level differences across categories, the view, size, and retinal position of the images varied across trials, and each item was overlaid on a 10. 5° phase-scrambled background generated from another image of the set, randomly selected. During blocks containing images, some images were repeated twice, interleaved by a different image (2-back task). Participants had to press a button when they detected the repetition. Preprocessing of fMRI data. Functional images were preprocessed and analyzed using MATLAB, in combination with SPM12 (69), the CoSMoMVPA toolbox  (70) , the MarsBaR toolbox  (71)  and the MatlabTFCE toolbox  (72). The first four volumes of each run were discarded, taking into account initial scanner gradient stabilization  (73). Preprocessing of the remaining volumes involved despiking, slice time correction, geometric distortions correction using field maps, spatial realignment and motion correction using the first volume of each run as reference.
Analyses Behavioral study Visual categorization task. Separately for the two sessions of the task, we computed the mean proportion of correct responses (accuracy) and the mean RTs for each condition, for each subject. Mean RTs were computed over trials with accurate response and RTs within 2 SD from the individual mean (discarded trials in session 1: 13. 10% ±7. 21 SD; in session 2: 10. 20% ±5. 87 SD). Accuracy and RT data were analyzed with 2 Category (body, chair) x 3 Stimulus (facing dyads, non-facing dyads, single bodies) x 2 Orientation (upright, inverted) repeated-measures ANOVAs. Critical comparisons were tested using pairwise t tests (two-tail). Analyses were repeated using average performance values from the two sessions, and for each session separately. Test-retest reliability. For each subject, we computed the 2BIE on accuracy values [(Uprightinverted)facing-(Upright-inverted)non-facing] for each of the two sessions and compared them (t test, α = 0.
In the first analysis, the effect of the visuo-spatial configuration of bodies within a dyad was assessed, separately for the upright and inverted orientations, with a RFX GLM contrasting facing vs. non-facing dyads. In the second analysis, the effect of stimuli orientation was assessed, separately for facing and non-facing dyadic configurations, with a RFX GLM contrasting upright > inverted orientation. For all univariate analyses, the statistical significance of second-level (group) effects was determined using a voxelwise threshold of p ≤ 0. 001, family-wise error corrected at the cluster level. Definition of regions of interest (ROIs). Individual data recorded during the functional localizer task were entered into a General Linear Model with five regressors for the five object-class conditions (bodies, faces, places, objects and scrambled objects), one regressor for baseline blocks, and six regressors for movement correction parameters as nuisance covariates.
ROIs analyses. Using data recorded during dyad runs of the main fMRI experiment (N = 29), we analyzed in each ROI the effects of seeing upright or inverted facing and non-facing dyads. In particular, we performed a repeated-measures ANOVA with factors: 5 ROI (EBA, FBA, FFA, PPA and EVC) x 2 Stimulus (facing and non-facing dyads) x 2 Orientation (upright and inverted). Next, pairwise t-tests were used to investigate, in each ROI, the two signatures of configural processing: 1) larger response to facing than to non-facing (upright) dyads (α = 0. 05 one-tail), and 2) larger difference between upright and inverted stimuli for facing, than for non-facing dyads, or the neural 2BIE (α = 0. 05, one-tail). Brain-behavior correlation (in ROIs). For the 22 subjects who participated in both the behavioral and fMRI sessions, we computed the (normalized) neural 2BIE, using the PSC extracted from each ROI for each condition (upright and inverted facing and non-facing dyads).
For each ROI, the values representing the individual neural 2BIEs were correlated with the values representing the individual behavioral 2BIE (Pearson correlation, α = 0. 05). This analysis was repeated using data from session 1 of the behavioral task (Results), from session 2 and the average performance across the two sessions (Supplementary Information 7,8). Brain-behavior correlation (across the whole-brain). For the 22 participants who participated in both the behavioral and the fMRI sessions, we used the behavioral-2BIE from the first behavioral session and we measured a neural 2BIE for each voxel in the brain. To do so, we selected all the voxels across the brain with a positive β-weight for all conditions of body dyads and, for the selected voxels, we extracted the β-weights for each condition.
5, dh=0. 1, Connectivity = 26, one-tailed). The same analysis was repeated using values of the behavioral-2BIE based on session 2 of the behavioral task and on the average of the two sessions (see Supplementary information 7,8 for results on these analyses). Test of the single-body inversion effect (BIE). To assess the recruitment of configural processing dfor single bodies, we tested whether the difference in the response to upright bodies vs. inverted ones was statistically larger than a general effect of inversion that would be observed for any (non-body) stimulus. For each of the above ROIs, data recorded during the runs of the main fMRI experiment involving single bodies and objects were analyzed with a repeated-measures ANOVA with factors, 2 Category (body and object) and 2 Orientation (upright and inverted).
Ocean plankton comprise organisms from viruses to fish larvae that are fundamental to ecosystem functioning and the provision of marine services such as fisheries and CO 2 sequestration. The latter services are partly governed by variations in plankton community composition and the expression of traits such as body size at community-level. While community assembly has been thoroughly studied for the smaller end of the plankton size spectrum, the larger end comprises ectotherms that are often studied at the species, or group-level, rather than as communities. The body size of marine ectotherms decreases with temperature, but controls on community-level traits remain elusive, hindering the predictability of marine services provision. Here, we leverage Tara Oceans datasets to determine how zooplankton community composition and size structure varies with latitude, temperature and productivity-related covariates in the global surface ocean.
Latitudinal patterns of zooplankton size structure. Variations in median Equivalent Spherical Diameter (ESD) were explored to examine latitudinal patterns in zooplankton size structure. The most consistent cross-net patterns of median ESD were found for the total zooplankton community, which was driven by the median ESD of calanoid copepods (Fig. 3 ; see Supplementary Fig. S5  for the other groups). The most prominent feature of the copepod median ESD pattern was a sharp decline from the Arctic to the equator, which was more marked in the Bongo (Fig. 3b ) and Régent data (Fig. 3c ) than in the WP2 (Fig. 3a ). In the southern hemisphere, patterns differed across nets: copepod median ESD sharply increased towards the Southern Ocean according to the Régent net, whereas it showed no variations or a slight decrease according to the WP2 and Bongo samples, respectively.
Discussion Here, we provide a homogeneous dataset of zooplankton composition and size structure based on individual measurements of body size and document the shape of the relationships between community-level size structure and key environmental drivers on a macroecological scale. We find that zooplankton communities exhibit larger median size and abundance towards the poles and towards the tropical upwelling regions sampled (Supplementary Doc. S12), a pattern that is largely driven by copepods. The higher contributions of the large-bodied grazing Calanidae relative to the smaller omnivorous-carnivorous Cyclopoida and Poecilostomatoida (i. e. Oithonidae, Oncaeidae and Corycaeidae) drives the latitudinal increase in median size towards the poles, in addition to the observed negative scaling of body length with temperature which is in line with the TSR  9, 17.
Similar reactions may occur within protists  36. We hypothesize that large protists attempt to prevent ROS accumulation by optimizing the distance between the photosymbionts and themselves. Indeed, most symbiont-bearing Foraminifera tend to display large spinose formation, as a support for the symbiont swarms located further away from the central shell  33, 34, 37  , but also enhance prey encounter rates  38. Keeping larger sizes to enhance prey capture and avoid ROS could explain the observed stability in median size and abundance of these organisms in the warmer tropical conditions. The heterogeneity of sampling strategies between surveys usually hinders global scale plankton studies that require the combination of data from multiple oceanographic cruises. The data collected from the Tara Oceans expeditions allow us to examine the in situ properties of plankton communities at a very large spatial scale, thanks to the uniform sampling strategy.
Measurements of environmental covariates. To describe the abiotic habitat associated with each plankton sample, vertical profiles of physical and biogeochemical variables (thereinafter called environmental covariates) were measured by a conductivity temperature depth sensor/rosette (CTD) and Niskin bottles following a published sampling package  50. A detailed description of each method used as well as all metadata used are available on PANGAEA  [51] [52] [53] [54]. Temperature (°C), salinity (psu) and oxygen concentration (µmol kg -1 ) were measured at 10 m depth. Mixed Layer Depth (MLD, m) was estimated based on the 0. 03 kg m -3 sigma differential density relative to the density at 10 m depth  55. Chlorophyll a concentration was estimated from vertical CTD casts. The values derived from the fluorescence composite profiles were integrated from 0 to 200 m (or 100 m depending on seafloor depth), using the trapezoidal method.
Surface backscattering coefficient of particles at 470 nm (bbp470, m -1 ) was calculated from in situ sensor data, corrected with in situ measurements in dark conditions. For both PAR and bbp470, we used the median value around the sampling date and location  [51] [52] [53] [54]. Among all the contextual metadata provided by the TARA consortium  [51] [52] [53] [54]  , the above-mentioned covariates were selected because: (i) they were the most complete across most sampling stations; (ii) presented the most normal-like distribution and because they were collinear with their alternative versions. Finally, distance to coast (km) was added a posteriori to the suite of covariates to help disentangling coastal samples from the open ocean ones and include this geographical effects in our statistical models.