dataesr/scientific-paragraphs-categorization huggingface dataset:
| Transcriptomes assembly and annotation Each read set was assembled with Trinity v2. 5. 1  (Haas et al. 2013 ) using default parameters and transcripts were clustered using cd-hit v4. 6. 6  (Fu, Niu, Zhu, Wu, & Li, 2012)  using -c 0. 9 -aS 0. 8 -aL 0. 8 parameters (Table  S2 ). To ensure the classification of the sampled individuals, each ribosomal read set were detected with SortMeRNA  (Kopylova, Noé, & Touzet, 2012)  and mapped with bwa v0. 7. 15 using default parameters  (Li & Durbin, 2009)   S2 ). In parallel, homology searches were also included as ORF retention criteria for Transdecoder; the peptide sequences of the longest ORFs were aligned on Oithona nana proteome  (Madoui et al. 2017 ) using DIAMOND v0. 9. 22  (Buchfink, Xie, & Huson, 2014). Protein domain annotation was performed on the final ORF predictions with Interproscan v5.
| Variant functional annotation The variant functional annotation was conducted in two steps. First, the variant sequences were mapped on the previously annotated O. similis transcripts using the "VCF_creator. sh" program of DiscoSNP++. Secondly, a variant annotation was carried out with SNPeff  (Cingolani et al. 2012)  to identify the location of variants within transcripts (i. e. , exon or UTR) and to estimate their effect on the proteins (missense, synonymous or nonsense). The excess of candidate variant annotations was tested for the different classes of SNPEff, for the three sets of variants under selection, psADE, and both, by comparing to the total sets of annotated variants. A significant excess was considered for a hypergeometric test qvalue < 0. 05, after Benjamini-Hochberg correction.
| Functional analysis of transcripts under population-scale ADE and selection The full dataset of variants was positioned on the eight transcriptomes to extract putative functional information, with a total of 25,048 variants (97% of total) successfully mapped on 16,272 transcripts. First, SNPEff was used to estimate the localization of variants inside the transcripts, and an enrichment was estimated for all categories, and for three sets of variants categorized respectively as under selection, psADE and both (Figure  S9 ). Overall, the two first sets showed a significant excess of variants in 3' UTR. Plus, among the variants under psADE, one was categorized as a "stop gained" and one as a "stop retained". However, variants under psADE and selection did not show any excess of specific effect.
Among the 84 loci identified under psADE and selection, 80 were located on O. similis transcripts (Table  S6 ). Amid these transcripts, 64 (76%) were linked to at least one Pfam domain (61 different domains) and 59 (70%) to a functional annotation from the nr database. From this total of 61 Pfam domains, 23 presented a significant excess compared with domains present in the global set of transcripts, corresponding to 21 transcripts (Figure  S10 ). On the latter, three transcripts were involved in nervous system features: omega-amidase NIT2, vang-like 2B protein, and 5-oxoprolinase.
| Functional insights of natural selection and population-scale ADE in Oithona similis By analyzing the functional annotation of the variants with SNPEff, we found a significant excess of variants under selection and variants under psADE located in 3'UTR, but no excess in variants under selection and psADE. Variations in these regions are known to both affect transcription efficiency through mRNA secondary structures, stability, and location  (Matoulkova, Michalova, Vojtesek, & Hrstka, 2012; Mignone, Gissi, Liuni, & Pesole, 2002) , leading to affect the function of the gene. However, no clear pattern was observed among the candidate variants. On the 674 loci under selection, some variants were located in transcripts annotated by homology search (Table  S6 ) as pantothenate kinase, glycine receptors/GABA receptors, and FMRFamide Among the transcripts under psADE and selection, three are of specific interests, as they are also involved in nervous system.
tions ranged from 1 to 31 variants (TARA_155 and 206/210) and was significantly higher than expected by chance for all the populations but TARA_155 (Figure4b, hypergeometric test p-value <. 05). It represented a total of 84 unique variants under selection and psADE in at least one population, corresponding to 15% and 12% of variants under psADE and under selection, respectively. Two main different genomic and expression profiles can be observed (FigureS8). First, as illustrated in Figure4c, loci can show an allele under psADE but not fixed in one population (here allele A in TARA_178) and fixed in another (TARA_155). A second observed pattern, more extreme, concerns variants as exampled in Figure4d, which presents an allele F I G U R E 4 Crossing psADE and selection. (a) Scheme representing the method; in yellow the total dataset of SNVs, in green the tested SNVs in one population, in blue SNVs under psADE in this population, in red the SNVs targeted by selection.
d'Avenir" programmes  OCEANOMICS, Grant/Award Number :  ANR , -11-, BTBR and -0008;  FRANCE GENOMIQUE , Grant/Award Number:  ANR-10-INBS-09-08 ;  Veolia Environment Foundation ;  Region Bretagne ,  World Courier ,  Illumina, Cap L'Orient , the  Electricite de France (EDF) Foundation EDF Diversiterre ;  Fondation pour la Recherche sur la Biodiversite ;  Prince Albert II de Monaco Foundation ;  Etienne Bourgois  and the Tara schooner and its captain and crew.
Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0. 00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.
Interestingly, these subjects only appeared to differ in their response to LCD whereas they had similar baseline body weight and glycemic variables. However, the underlying mechanism and physiological changes are not fully understood. Considering the complexity of defining mechanism of action in a clinical cohort, we thus sought to perform a deeper characterization of these subjects using additional omics datasets including metabolomics and proteomics (>1,100 proteins) of plasma, together with transcriptomics (RNA-sequencing) of adipose Tissue (AT) biopsies. We also aimed to investigate further the tissue specific insulin resistance to clarify the contribution of different organs in the obese subjects following LCD intervention. In this report, we acquired and analyzed multi-omics datasets from the Diet, Obesity and Genes (DiOGenes) study, one of the largest weight maintenance intervention of its kind  17.
Material/Subjects and Methods Study design. DiOGenes was a multicenter, randomized controlled dietary intervention study, involving eight European countries  17, 18  (ClinicalTrials. gov number, NCT00390637). The study has been described in detail previously  16, 18  and a CONSORT diagram of the clinical intervention is presented in Fig. 1A. Briefly, 938 overweight/obese, non-diabetic, adults (Body Mass Index (BMI) between 27 and 45 kg/m 2 , blood fasting glucose below 6. 1 mmol/L) underwent an 8-week weight-loss phase using a complete meal replacement low calorie diet (LCD). The LCD provided 800 kcal/day (Modifast, Nutrition et Santé France). Among the 781 participants who completed the LCD, 773 achieved >8% weight loss and were randomized to a 26-week weight maintenance diet. A total of 548 subjects completed the Weight Maintenance Diet (WMD), among which 375 (~70%) had available qc-ed plasma samples at all intervention time-points: baseline (Clinical Intervention Day 1, CID1), after 8-week of LCD (CID2) and after 6-month of weight maintenance (CID3).
ethics. Local ethics committees approved The Visceral Adiposity Index (VAI), a robust index of visceral fat, was derived using the formula proposed by Amato et al. 20. This index is gender-specific and incorporates the following variables: BMI, waist circumference, total TAG and HDL-cholesterol. In this study, it was benchmarked against the Android-gynoid percent fat ratio as computed from dual-energy X-ray absorptiometry (DEXA) data available for 80 DiOGenes subjects from Denmark. Additional indices pertaining to tissue-specific insulin-resistance (IR) were derived. This included the adipose-tissue IR (adipoIR 21 ); the muscle insulin sensitivity and hepatic-IR indices  22  (MISI and HIRI, respectively). AdipoIR corresponds plasma FFA x insulin levels. MISI is derived as the rate of decay of glucose concentration from its peak value to its nadir during Oral Glucose Tolerance Test (OGTT) divided by the mean insulin levels.
HIRI is derived as the product of total area under curve (AUC) for glucose and insulin during the first 30 min of the (OGTT). transcriptomics analysis. Abdominal subcutaneous adipose tissue (AT) biopsies were obtained by needle aspiration under local anesthesia after an overnight fast at baseline, at the end of LCD and at the end of weight maintenance. Biopsy samples were stored at -80 °C until total RNA extraction. Gene expression at baseline and upon LCD was quantified using 100-nt long paired-end RNA sequencing with an Illumina HiSeq 2000. This dataset has been previously described  9  and data are available from the Gene Expression Omnibus under accession GSE95640. High throughput quantitative real-time PCR (qPCR) was performed as previously described  9, 23  for specific lipogenesis genes (FADS1/2, ELOVL5, FASN and SCD) and leptin (LEP) and at all three intervention timepoints.
plasma omics analysis. Blood samples were taken after an overnight fasting period at baseline and upon completion of the LCD and WMD interventions. Figure  1C  displays an overview of all omics analyses that were conducted in this study. Lipidomics. LC-MS data generation has been described previously  16, 24. In total, 125 intact lipids including triacylglycerides, phosphocholines, sphingomyelins, cholesterol-esters, cholesterols and diacylglycerides were measured at all three intervention timepoints. Metabolomics. Metabolomics data generation ( 1 H NMR) was also described previously  24, 25  and quantified 18 low-weight metabolites related with obesity and insulin-sensitivity. Candidate mechanistic analyses focus on plasma ketone bodies (acetoacetate (AcAc) and beta hydroxy-isobutyrate (βHB)) quantified at all three intervention timepoints.
Integrative analyses (predictive models) were pursued using all 18 metabolites. Proteomics. Protein levels were measured using a multiplexed aptamer-based proteomic technology developed by SomaLogic Inc (Boulder, CO). This approach uses fluorescently labeled poly-nucleotide aptamers that recognize specific protein epitopes, similar to protein antibodies quantified using relative fluorescence on microarrays  26, 27. In total, 1,129 plasma proteins were measured, both at baseline and upon LCD. Proteomics data are available from the Open Science Framework at this URL https://osf. io/s4v8t/?view_only=90637f2941e14ec986 e5888491fbdbbb. Quantification and statistical analysis. Lipid signature definition. The lipid signature was available from previous work  16  and was computed using Principal Component Analyses (PCA) of the log 2 fold-change for 79 lipids differentially expressed during LCD.
In these plots, ANOVA was used to compare differences between responders and non-responders at each CID; and adjust for gender, age and center. Comparisons between baseline parameters (BMI and Matsuda index) stratified by gender and responder/ non-responder groups were made using ANOVA, followed with a Tukey Honest Significant Test for pairwise comparisons. Pathway analyses. Enrichment analyses were performed using the KEGG  29, 30  , REACTOME  31  and PANTHER  32  databases. Analyses were performed using a Fisher's exact test. For Somalogic (proteomics) analyses, a background correction was implemented (by considering the universe as the 1129 proteins assayed on the array). Multiple testing adjustments were performed with resampling procedures (Monte-Carlo simulations). Significance levels were set at adjusted alpha 5%.
Methodologically, we used the KEGG database  34  and specifically the PathComp methodology  35  to identify the routes between the compounds of interest. We limited our search of paths to a maximum length of 10. Network analysis and visualization of paths and interconnectivities were performed using custom MATLAB (Mathwork Inc. ) scripts and edited using yEd (yWorks GmbH). Predictive models. Data were split into training and testing datasets. The training set included subjects from the two leading DiOGenes intervention centers (Denmark and the Netherlands) and the testing set included all remaining six centers. During weight maintenance, the two leading centers provided food freely to the participants  17, 18. We thus assumed that compliance to the randomized diet was higher for these two centers.
005 and 0. 01 and minimal terminal node size: 5, 10. The performance of a model was evaluated using Receiving Operating Curves (ROC curves). Models were tested using different set of features. The clinical model was based on 19 features (gender, weight, BMI, amount of fat mass, percentage of fat mass, waist circumference, VAI index, HOMA-IR, fasting glucose and insulin, Matsuda index, total cholesterol, HDL, LDL, triglycerides, fatty acids, adipose-IR, liver-IR and muscle-IR). Two sets of omics models were tested, the first set included previous clinical parameters and all features from lipidomics, metabolomics, and proteomics. This resulted in 1246 features. The second omics model, corresponded to a subset of the first model (with only 93 features), and was obtained by selecting features displaying marginal association with the responder status (Kruskal-Wallis analyses using only data from the training set, and with a very liberal significance level at alpha 30%) and also by discarding features that displayed either near-zero variance, were obtained through linear combinations from other features, or were highly correlated with another feature (Pearson R 2 > 95%).
In the present study, we perform a more comprehensive omics characterization of these two groups. Namely, we took advantages of other DiOGenes omics datasets, that encompassed transcriptomics, proteomics and metabolomics profiles (Fig. 1C ). We also sought to study possible gender-specific differences and to pursue a deeper investigation from the lipid signature. Previous analyses of clinical and physiological parameters had shown that these two groups (responders and non-responders) are comparable at baseline in term of weight and insulin sensitivity (Fig. 1D ). Upon LCD, both groups had achieved significant weight loss (>8% initial body weight); yet the responder group had significantly higher weight loss (mean difference +2% improved weight loss with 95% CI (0. 97, 2. 34), two-sided t-test p = 2.
Characterization based on proteomics revealed alteration in ApoE. We took advantages of a large proteomics dataset (1,129 proteins) quantified from fasting plasma samples at baseline and after LCD  41. We first assessed whether differences between the two groups could be observed in their baseline protein levels. Following adjustment for multiple-testing (FDR < 5%), four somamers (probes) had significant differences in baseline levels between responders and non-responders. All these somamers pertained to the ApoE protein (Supplemental Table  6 ). We observed that responders had higher baseline ApoE protein levels compared to non-responders (Fig. 5 ). During LCD, responders had significant down-regulation in ApoE levels while non-responders displayed no significant changes. We evaluated changes in protein levels during LCD and whether these changes associated with the lipid signature.
Acknowledgements We are thankful to  Dr. François-Pierre Martin  for fruitful DXA discussions and to  Dr. Sami Trabelsi  for help with lipoprotein analyses. The study was founded by the  European Commission ,  Food Quality and Safety Priority of the Sixth Framework Program  ( FP6-2005-513946 ), and  Nestlé Institute of Health Sciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The relevance of such refinement is illustrated in a larger perspective in Fig. 4C , where the distribution of the distances obtained before and after the paramagnetic NMR structure determination is compared with all the Co complexes in the Cambridge Structural Database (CSD) featuring the same Co pattern as in SOD. The original SOD ensemble covers a large portion of the possible combinations of r1 and r2 values (369 structural motifs), and thus a correspondingly large part of the chemical activity space for Co II complexes, while the NMR refined interval discriminates a much smaller subset of 29 structural motifs (if we consider the constraints obtained from Fig. 4A-B ), and an even smaller subset of only 2 structures if we consider the range delimited by the three selected structures (C,E,F).
Quantum chemistry modeling Molecular models were built from the X-ray structure of the thermostable mutant of human Cu,Zn-SOD (PDB ID 1SOS). 27 he crystal unit cell of 1SOS contains ten protein chains (five dimers) labelled A-J. PNMR calculations were performed consistently for all of them, thus exploiting the structural variation naturally occurring in the crystal. Two molecular models of the Co II site (substituted for Zn II ) were built for each chain (see Fig. S2 ): a larger one (m1, 86 atoms) for structure optimization, hyperfine coupling and orbital shielding calculations and a smaller one (m0, 32 atoms) for subsequent high-level ab initio calculations of g-and D-tensors. Each model is named after the corresponding chain in the PDB structure. The larger model m1 consists of the Co II ion in place of the native Zn II , two backbone segments between Cα atoms of residues 71-72 and 79-83, and side chains of metal-binding residues His63, His71, His80, and Asp83.
ASSOCIATED CONTENT Supporting Information NMR pulse sequence schemes, NMR acquisition parameters additional  13  C and 1 H spectra, EPR parameters for the Co II center of CoSOD, selected structure parameters of the metal coordination in SOD, reference isotropic shielding, calculated paramagnetic NMR shifts with PBE0 and PBE50 hyperfine coupling, assignment of 1 H,  13  C, and  15  N paramagnetic NMR shifts and shift anisotropies, Cartesian coordinates of molecular models (PDF). The Supporting Information is available free of charge on the ACS Publications website. The NMR raw data have been deposited at: [will be added on publication] as detailled in the SI.
ACKNOWLEDGMENT This work was inspired by  Ivano Bertini , to whom our gratitude goes. The work was co-funded by the  European Research Council  ( ERC-2015-CoG GA 648974  " P-MEM-NMR "), the  People Programme of the European Union 's  FP7  ( FP7-PEOPLE-2012-ITN GA 317127  "pNMR"), the  Agence Nationale de la Recherche  ( 10-BLAN-713-01 ),  Fondazione CR Firenze, Egide  (programme Galilée  22397RJ ), the  Università Italo-francese  (programma Galileo  11/12 ),  CNRS  ( IR-RMN FR3050 ), the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany´s Excellence Strategy -EXC 2008/1 -390540038, the  Swiss National Centre of Competence in Research (NCCR) Chemical Biology , as well as by the  EC-project iNext (infrastructure for NMR, EM , and X-rays for Translational Research, GA 653706).
Study design The design was a cross-sectional analysis of prospective data collection from patients presenting a first episode of stroke in French Guiana described in detail in  [17]. Consecutive patients aged > 18 years admitted for an acute stroke, confirmed by neuroimaging, and who were able to be interviewed either personally or via a next of kin were eligible. Exclusion criteria were a history of symptomatic stroke, presence of other short-term lifethreatening diseases and inability to contact patients (or support persons) by telephone during follow-up. Between June 2011 and October 2014, 301 patients were recruited in emergency or medicine departments of three hospitals in French Guiana (Cayenne, Saint-Laurent du Maroni, and Kourou). Among them, patients with Subarachnoid Hemorrhage (SAH) (n = 3) were excluded.
Collected data Demographic data, preexisting conditions, pre-stroke modified Rankin scale (mRS), admission NIHSS score, which categorizes stroke severity upon patient arrival, and mechanism of stroke were collected at the time of inclusion. The mRs and NIHSS scores were also collected after discharge. Anamnestic information included medications, history of ischemic vascular diseases, known and suspected cardiovascular risk factors. Patients, or their next of kin, were interviewed on their socio economic status, on the geographic origin of their parents or ancestors, and on their marital status employment status, education, housing conditions, wealth indicators, type of health insurance and deprivation defined by an EPICES score > 30. 17. They were also interviewed on healthcare access including the travel time to the nearest primary care physician (PCP), the travel time to the nearest hospital, health insurance, the number of consultations with a PCP, a dentist or a specialist physician within the previous year, or a previous hospitalization > = 24 h in the 12 preceding months.
Availability of data and materials Researchers wishing to access the data should first obtain permission from the Commission Nationale Informatique et Libertés. For this, the research proposal must be submitted to the CNIL following the indication on their website (CNIL https://www. cnil. fr/en/home). Once the approval of the CNIL has been obtained by the researchers, the anonymized and encrypted database will be sent by the research organization of CIC-EC (cicec@ch-cayenne. fr). If you have any questions, it is possible to contact Marilyne ABIVEN (director of the indirect right of access service department of the CNIL) at mabiven@cnil. fr.
Human and animal brain studies bring converging evidence of a possible role for the cerebellum and the cerebro-cerebellar system in impulsivity. However, the precise nature of the relation between cerebro-cerebellar coupling and impulsivity is far from understood. Characterizing functional connectivity (FC) patterns between large-scale brain networks that mediate different forms of impulsivity, and the cerebellum may improve our understanding of this relation. Here, we analyzed static and dynamic features of cerebro-cerebellar FC using a highly sampled resting-state functional magnetic resonance imaging (rs-fMRI) dataset and tested their association with two widely used self-reports of impulsivity: the UPPS-P impulsive behavior scale and the behavioral inhibition/approach systems (BIS/BAS) in a large group of healthy subjects (N = 134, ≈ 1 hr of rs-fMRI/subject).