dataesr/scientific-paragraphs-categorization huggingface dataset:
The development of both environmental sequencing to investigate soil organisms  (Taberlet et al. , 2012; Zinger et al. , 2016)  and public databases of historical maps  (Bergès and Dupouey, 2017 ) make such a study of soil community and landscape ecology possible. Indeed, environmental sequencing can target all bacteria and fungi with universal markers, and enable the sequencing of both ectomycorrhizal, saprotrophic or pathotrophic ones. In parallel, public databases are now reporting functional traits for hundreds of soil taxa  (Nguyen et al. , 2016) , and can therefore be used to derive functional diversity from environmental sequencing. Until now, wood associated species, often pathotrophs, are recognized as indicators of old-growth forests  (Parmasto, 2001; Dvořák et al. , 2017; Halme et al.
At this step sequences only occurring once in the dataset were removed, since these likely represented sequencing artifacts. Pairwise distances were then computed using sumaclust function to delineate sequence clusters with similarity of 97% or above (representing operational taxonomic units, OTUs, following  Nilsson et al. , 2008). Taxonomic assignment of OTUs was conducted using reference databases with the ecotag function  (Boyer et al. , 2016) , which uses global alignment of sequences against full-length references. This function compares sampled short sequences with a database of short sequences, and returns not only the most similar sequence but also the taxonomic assignment of other closely similar sequences. With this method, low similarity sequences are assigned to "Fungi", while sequences with a high similarity to several Russulaceae are assigned to "Russulaceae, " or even to an individual species.
For fungi the reference database of short sequences was obtained by running an in silico PCR with the ecoPCR program  (Ficetola et al. , 2010)  on Genbank (release 197; 1  ) using the primer pairs employed here. Chimeras have very bad results with ecotag, as sequences are not assigned to a high taxonomic level. Then, taxonomic assignment yielding the highest similarity score was kept. For Bacteria, taxonomic assignment was not handled with ecotag but conducted using the SILVA database  (Quast et al. , 2012) , with similarity parameters kept at 0. 97. Subsequent dataset processing and analyses were conducted using R (R Core Team, 2019). Based on taxonomy, we removed all non-fungi and non-bacteria sequences, respectively. For bacteria, we also removed chloroplast or mitochondrial sequences. Using the sequenced negative controls, we also removed sequences abundant in the controls and rare in our samples (representing less than 5% of their counts).
Data Analyses All analyses were carried out with the R 3. 6. 1 (R Core Team, 2019) software, using the iNext package  (Hsieh et al. , 2016)  to compute Hill numbers and sample coverage, the lme4  (Bates et al. , 2015)  and MuMin  (Bartoñ, 2019)  packages for the GLMMs, and the vegan package  (Oksanen et al. , 2019)  for multivariate analysis.
Bacterial and Fungal Diversity and Sampling Coverage The sequencing run produced 3 014,848 and 2 137,868 sequences for fungi and bacteria, respectively (without singletons and low-quality sequences). After cleaning the sequences (using negative controls and taxonomic assignment), we detected 3 751 fungal OTUs from 1. 31 × 10 6 sequences and 14 922 bacterial OTUs from 8. 44 × 10 5 sequences. The number of sequences was lower for bacteria as sequences attributed to mitochondria and chloroplasts were removed. Rare OTUs, well identified to fungi and bacteria, represented a minor part of the dataset for fungi (10% of OTUs occurring once, 12% occurring twice), but a larger part of bacteria (22% occurring once, 22% occurring twice). For fungi, 36. 5%, 27. 4% and 9% of OTUs were unambiguously assigned to symbiotrophs, saprotrophs and pathotrophs, respectively.
ACKNOWLEDGMENTS This research was conducted at the  Cevennes National Park  as part of the national action plan to preserve ancient forests and financially supported by  FEDER, FNADT , and EI-PURPAN. We thank  Mathieu Baconnet  and  Sophie Giraud  for their advice,  Patricia Jargeat  for this help with DNA extraction and PCR,  Francois Calatayud  for his assistance with GIS,  Michel Goulard  and  Magali San-Cristobal  for their help with statistics. We finally thank  Alain Roy  and  Julian Donald  for reading our manuscript.
FUNDING This research was financially supported by  FEDER, FNADT , and EI-PURPAN. We also benefitted from the support of " Investissements d'Avenir " grants managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ;  ANAEE-France :  ANR-11-INBS-0001 ,  TULIP :  ANR-10-LABX-0041 ).
| Study design and patients The French multicentre, nationwide CORONADO study (ClinicalTrials. Focusing on the relationship between obesity and COVID-19 severity in T2D, the present analysis excluded individuals with type 1 diabetes, other types of diabetes and those with newly diagnosed diabetes upon admission (n = 406), as well underweight patients (BMI < 18. 5 kg/m 2 ) to avoid interference caused by concomitant severe comorbidities (n = 40) (Figure  1 ). Finally, 1965 CORONADO patients with a medical history of T2D and available data for age, sex and BMI were included in the analysis. Subjects were categorized by four subgroups according to the WHO classification: (a) normal weight (18. 5-24. 9 kg/m 2 ), (b) overweight (25-29. 9 kg/m 2 ), (c) class I obesity (30-34. 9 kg/m 2 ) and (d) class II/III obesity (≥35 kg/m 2 ).
| Data collection Data collection was performed by clinical research associates and physicians in each participating centre. They were instructed to systematically review the medical files of all COVID-19 inpatients, select those with diabetes, extract data from their medical files and, if necessary, contact the patient's general and/or specialist practitioners, regular pharmacist or biomedical laboratory. Collected data included clinical characteristics (age, sex, ethnicity and BMI), classification of diabetes as noted in the medical file by the physician in charge of the patient, duration of diabetes, recent glycaemic control (i. e. the two most recent HbA1c levels determined before admission), microvascular and macrovascular complications and co-morbidities. Full details of data collection are provided in the supporting information.
10 More recently, among 6916 patients with COVID-19, Tartof et al. described a gradually positive association between BMI and risk of death, even after adjustment for obesity-related co-morbidities. 16 The strength of the CORONADO study relies on a large and well-phenotyped population of COVID-19 patients with T2D admitted to medical units or ICUs. Hence, our data further indicate that an association between IMV and BMI already appeared from overweight, while no association was found for death occurring within 7 days. The combined prevalence of overweight and obesity reached 77. 8% in our study population, in agreement with the French epidemiological ENTRED-2 study, in which 80% of people living with T2D were overweight or obese. 17 As expected, the prevalence of obesity decreased with advancing age, as previously reported in the French population of elderly people with T2D.
gov NCT04324736) was a retrospective study designed to describe the phenotypic characteristics and prognosis of patients with diabetes admitted to hospitals for COVID-19 from 10 March to 10 April 2020. The study was conducted in accordance with the declaration of Helsinki and French legislation, and obtained approval from the local ethics committee (IRB/IEC -GNEDS; ref. CORONADOV2), the CER-EES (no. INDS: 1544730) and the CNIL (DR-2020-155/920129). Inclusion criteria were: hospitalization in a dedicated COVID-19 unit with COVID-19 diagnosis confirmed biologically (by SARS-CoV-2 PCR test) and/or clinically/radiologically (i. e. as ground-glass opacity and/or crazy paving on chest computed tomography scan); and a personal history of diabetes or newly diagnosed diabetes upon admission (i. e. HbA1c ≥48 mmol/mol [6.
ACKNOWLEDGEMENTS We thank the sponsor (DRCI  CHU Nantes ), Clinical Project Manager ( Maëva Saignes ) and assistant ( Jeanne Saunier ),  Clinical Research Associates  ( Selma El Andaloussi ,  Joëlle Martin-Gauthier  and  Emily Rebouilleau ) and data managers ( Béatrice Guyomarch  and  Tanguy Roman ). We are grateful to  Peter Tucker  for editing the manuscript. We acknowledge all the medical staff involved in the diagnosis and treatment of patients with COVID-19 at participating centres. We thank all GPs, specialists, pharmacists and biological laboratories in charge of hospitalized patients for providing additional medical information to our investigators. We thank the  Société Francophone du Diabète (SFD)  and  Société Française d'Endocrinologie (SFE)  for disseminating study design and organization and the  Fédération Française des Diabétiques (FFD)  for participating in the study organization.
Identification of a candidate gene Given the unusual symptomatology of the patients somewhat deviating from typical familial HLH, we aimed directly at whole-exome sequencing for the identification of the culprit gene. All family members (patients, both their parents and several siblings in family 2) were whole-exome sequenced. Both families displayed a variant in NCKAP1L segregating with the disease with an autosomal recessive inheritance model i. e. , homozygous in the patient while wild-type or heterozygous in all other family members. Both variants were confirmed by targeted Sanger sequencing (Fig. 1H ). No other deleterious variants were found in genes associated with primary HLH or primary immunodeficiencies  (Picard et al. , 2018). Neither identified variant was present in the Exome Variant Server, ExAC, gnomAD, our in-house and various other databases (Table  S1  reports other variants found in patient 1; no additional variants were found in patient 2.
Impaired actin polymerization in NCKAP1L deficient T cells is associated with impaired synapse and leading edge formation Since the WAVE complex is one of the nucleation promoting factors activating the Arp2/3 complex and allowing the polymerization of F-actin, we sought to study the effect of the loss of NCKAP1L on this tightly regulated machinery. The basal F-actin content in resting CD4 + and CD8 + T cells from Patient 1 did not differ from controls (data not shown). However, abnormal actin polymerization became evident when long-term CD8 T cell lines from the patient were allowed to spread on an anti-CD3coated surface before fixation and F-actin staining. While control cells were able to assemble a dense ring of F-actin around the docking centrosome (marked by pericentrin, PCN), patient T cells exhibited a dysmorphic structure, lacking the characteristic synapse morphology (Fig.
4C ; left panels). Moreover, in line with the NCKAP1L deficiency, the signaling pathway of WAVE complex activation by Rho GTPase was confirmed to be downregulated as well (Fig. 4C , right panels). Together, these data suggest a strong immune activation associating inflammatory and T cell responses to decreased actin cytoskeleton regulation, which is presumably linked to a reduced activation of the WAVE complex pathway. It is interesting to note that in certain genetic forms of HLH, T cell exhaustion has been linked to a less severe form of the disease  (Kogl et al. , 2013). This might explain why, although serious, the disease severity in our two patients has not (yet) required stem cell transplantation. Finally, by equivalent proteomic and transcriptomic profiling of three cases of Familial Hemophagocytic Lymphohistiocytosis type-5, we indeed identified the same deregulated pathways, except IL-1 signaling and fMLP signaling in neutrophils (data not shown), further highlighting a similar pathophysiology of our disease as compared to bona fide genetic HLH.
Exome and targeted sequencing Genomic DNA was isolated from peripheral blood using standard protocols. Exome sequencing libraries were prepared with the Twist Human Core Exome Kit (Twist Bioscience, San Francisco, CA) following the manufacturer's recommendations. Paired-end (2 × 75 bp) sequencing was performed on a NextSeq500 sequencer (Illumina, San Diego, CA). Sequences were mapped to the hg19 reference genome using Isaac Genome Alignment Software 2. 1. 0 (Illumina, San Diego, CA). For each sample, median coverage was at least 60-fold, and over 93% of target sequences were covered at a minimum of 10X. Sequence variants were called using the Isaac Variant Caller 2. 1. 0 (Illumina, San Diego, CA). Annotation was performed with the KGGSeq software package based on dbSNP build 135  (Li et al.
Raw RNA-Seq data have been deposited in the EMBL-EBI ArrayExpress archive (E-MTAB-8524). For each sample, quality control was carried out and assessed with the NGS Core Tools FastQC. Sequence reads were mapped using STAR  (Dobin et al. , 2013). Unmapped reads were remapped with Bowtie2 (Langmead and Salzberg, 2012), using a very sensitive local option to optimize the alignment. The total mapped reads were finally available in BAM (Binary Alignment Map) format for raw read count extraction. Read counts were determined by the HTseq-count tool of the Python package HTSeq  (Anders et al. , 2015)  with default parameters to generate an abundance matrix. Finally, differential analyses were performed using the limma  (Ritchie et al. , 2015)  package of the Bioconductor framework for RNA-Seq data.
Extracted tryptic peptides were analyzed by nanoLC-MS/MS on a nanoUPLC system (nanoAcquityUPLC, Waters Corporation, Milford, MA) coupled to a quadrupole-Orbitrap mass spectrometer (Q-Exactive Plus, Thermo Scientific, San Jose, CA). Chromatographic separation was conducted over a 105-minute linear gradient from 1 to 35% of solvent B (0. 1% formic acid in acetonitrile) at a flow rate of 450 nl/min. A Top 10 method was used with automatic switching between MS and MS/MS modes to acquire high resolution MS/MS spectra. Samples were injected using a randomized injection sequence, and a solvent blank injection was performed after each sample to minimize carry-over. NanoLC-MS/MS data were interpreted to perform label-free extracted ion chromatogram-based differential analysis using MaxQuant (version 1.
6. 0. 16)  (Tyanova et al. , 2016). Peaks were assigned with the Andromeda search engine against a database containing all human entries extracted from UniProtKB-SwissProt and common contaminants  (15-07-2019; 20 409 sequences, Taxonomy ID 9606). No "match between runs" was performed between the samples. The maximum false discovery rate was 1% at both peptide and protein levels with the use of a decoy strategy. Non normalized protein intensity values were exported and used for further statistical analysis. The complete proteomics dataset has been deposited into the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD016191  (Deutsch et al. , 2017). The raw protein intensity data for each sample were first processed using the DEP  (Zhang et al. , 2018)  bioconductor package to assess the quality of the data.
Only proteins that were observed in at least two out of the three replicates were considered. Next, the data were background corrected and normalized by variance stabilizing transformation svn  (Huber et al. , 2002). Missing protein quantification values were imputed by a left-censored imputation based on random draws from a Gaussian distribution centered around a minimal value. Finally, differential analyses were performed using the limma bioconductor package  (Smyth, 2004). Up-and downregulated proteins (patients vs controls) were selected based on the adjusted p-value (< 0. 05) and the fold change (> 1. 5). Pathway analysis of differentially expressed genes at the RNA and protein levels was completed using the Ingenuity Pathway Analysis ® tool (Ingenuity ® Systems, Qiagen, Hilden, Germany).
The Ingenuity knowledge base (genes only) with direct and indirect relationships was used, and only molecules and/or relationships that had been experimentally observed in rat, mouse or human were considered. The volcano plots were generated with R using the ggplot2 library. Gene set enrichment analysis (GSEA) was performed with the GSEA software from the Broad Institute (http://software. broadinstitute. org/gsea/index. jsp) (Mootha et al. , 2003; Subramanian et al. , 2005). The gene sets from the human KEGG and reactome databases were used to compute the enrichment of differentially expressed proteins. The normalized enrichment score (NES), nominal p-value, and false discovery rate (FDR) q-value were assessed using the gene_set mode with 1,000 permutations.
Structural modeling To build a workable structural model of NCKAP1L, we used the crystal structures of NCKAP1 from the Protein Data Bank  (Berman et 2000)  (PDB), as templates. These were PDB ids: 4n78  (Chen et al. , 2014)  and 3p8c  (Chen et al. , 2010). Both PDB structures were solved within the context of the WAVE regulatory complex of Homo sapiens, while the NCKAP1 protein chain (chain B) only shared a root mean square deviation (RMSD) of 0. 264 Å. Therefore, either structure could be used as a modeling template. We indeed used 4n78. The NCKAP1L protein shares a sequence identity of 59. 44% with NCKAP1. In structure modeling, two proteins are expected to share the same structure fold when the sequence identity is higher than 30%. Thus, the structure modeling of NCKAP1 should be reliable.
We used SWISS-MODEL  (Waterhouse et al. , 2018)  to build the wild-type NCKAP1L structure and the NCKAP1L structure without the exon26-encoded region, based on the 4n78 template, and the resulting model was superimposed to its template in the context of the WAVE complex. Mutagenesis analysis was performed using the PYMOL program (Version 2. 0 Schrödinger, n. d. ).
ExAC) and an internal exome database including ~700 exomes. To identify potential causal variants, we further filtered the variants based on a recessive mode of inheritance. Raw exome data (fastq files) are available at NCBI's Sequence Read Archive (SRA) under the accession number PRJNA604669. Conventional capillary Sanger sequencing was performed to validate the genotypes of the identified candidate variants. Mutation-spanning fragments were amplified from genomic DNA using the Expand TM Long Template PCR System (ExoProStar TM Kit (GE Healthcare Life Sciences, Pittsburgh, PA), PCR products were bidirectionally sequenced using the same primers and the Big Dye TM Terminator Kit v3. 1 (ThermoFisher Scientific, Waltham, MA). All targeted sequencing reactions were run on an ABI PRISM 3730xl (ThermoFisher Scientific, Waltham, MA).
ACKNOWLEDGMENTS We thank  Drs Marco Colonna  ( Washington University School of Medicine, St. Louis, MO ),  Taco W. Kuijpers  ( Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands ) and  Philippe Georgel  ( Strasbourg University, Strasbourg, France ) for critical reading of the manuscript,  Ismail Aouadi  for transcriptomics/proteomics pathway analyses, as well as  Alice Bernard ,  Lydie Naegely ,  Antoine Hanauer ,  Ouria Tahar ,  Perrine Spinnhirny ,  Solène Zuttion ,  Michèle Barbié ,  Cornélia Degbé ,  Natalie Féry ,  Alexandra Roux  and  Marcelle Baer  for technical help. We thank the  Center for Chronic Immunodeficiency's (CCI) Biobank (coordination Alexandra Nieters) and Advanced Diagnostic Unit (coordination Ilka Fuchs)  for excellent services. Z. M. is supported by The  Single Cell Gene Expression Atlas  grant  108437/Z/15/Z  from the  Wellcome Trust.
This work was supported by grants from the  Agence Nationale de la Recherche  ( ANR) -ANR-11-LABX-0070_TRANSPLANTEX  to SB and  ANR-11-IDEX-0004-02 ,  Labex Transimmunom  to  Michelle Rosenzwajg , the  INSERM  ( UMR_S 1109 ), the  Institut Universitaire de France (IUF) ,  MSD-Avenir  grant AUTOGEN, all to SB, the  University of Strasbourg  (including IDEX UNISTRA) to SB, RC and SF, CNRS to SF, the  European regional development fund (European Union ) INTERREG V programs, TRIDIAG and PERSONALIS, to RC and SB, grant 179239 by the  Swiss National Science Foundation  to CL and  German Research Foundation  grant  SFB1160  ( TPA01 ) to SE. Proteomics experiments were supported by the  French Proteomic Infrastructure  ( ProFI ;  ANR-10-INBS-08-03 ) through CC.
( ExAC) and an internal exome database including ~700 exomes. To identify potential causal variants, we further filtered the variants based on a recessive mode of inheritance. Raw exome data (fastq files) are available at NCBI's Sequence Read Archive (SRA) under the accession number PRJNA604669. Conventional capillary Sanger sequencing was performed to validate the genotypes of the identified candidate variants. Mutation-spanning fragments were amplified from genomic DNA using the Expand TM Long Template PCR System (ExoProStar TM Kit (GE Healthcare Life Sciences, Pittsburgh, PA), PCR products were bidirectionally sequenced using the same primers and the Big Dye TM Terminator Kit v3. 1 (ThermoFisher Scientific, Waltham, MA). All targeted sequencing reactions were run on an ABI PRISM 3730xl (ThermoFisher Scientific, Waltham, MA).
fMRI In our experiment, expectations could be violated in 3 main ways: VS, sRPE, and RRE. To identify brain areas associated with these 3 types of surprising events, we ran a 3-level multiple regression analysis by employing a general linear model (GLM). For each monkey, we used a fixed-effects model between sessions and applied the FMRIB's Local Analysis of Mixed Effects (FLAME) 1 + 2 procedure from the FMRIB Software Library (FSL) on the highest hierarchical level (level combining animals). We focus on effects on frontal cortex, striatum, and in the vicinity of the dopaminergic ventral tegmental area and substantia nigra (VTA/SN) in the midbrain because these areas are the ones that have been most frequently related to reward value expectation and PE coding in both human and nonhuman primates  [2, 14, [35] [36] [37] [38] [39] [40].
For this reason, we analyzed data in a volume of interest (VOI) covering frontal cortex and striatum In this way, we attempted to ensure that we were able to detect surprise and PE responses wherever they occurred in the striatum or prefrontal cortex; such responses have previously been reported in several subregions in these structures; moreover, within the striatum it is not clear that the strongest PE/surprise signals can be mapped onto just the ventral striatum, caudate, or putamen. At the same time, the approach increased the statistical power of our analyses to examine neural activity in brain regions that were of a priori interest. We anticipated that the VTA/SN's smaller size would preclude other analysis approaches commonly used in fMRI such as spatial cluster-based statistics that are most beneficial when there are large areas of activation; however, the activity in VTA/SN, as in the striatum and frontal cortex, was so prominent that it could also be identified using a standard cluster-based correction procedure for multiple comparisons corrected across the whole brain.
Thus, in addition, whole-brain cluster-corrected results are reported in S5-S7 Tables and are discussed briefly in the Discussion. We used data from 71 of the 76 sessions we had acquired (fMRI data from 5 sessions were corrupted and unrecoverable). For sRPE, VS, and RRE, our hypotheses necessarily focused on activity that was positively related to each type of surprising event, but we also note other patterns of activity where they existed. To look for evidence of sRPE signals, we first performed an analysis in our VOI in the frontal cortex and striatum and in our ROI of interest in the VTA/SN. The analysis revealed 4 main clusters of activity (cluster p < 0. 05, cluster forming threshold of z > 2. 3; Fig 3A; see S1 Table for cluster locations). While this analysis approach-focused on a priori areas of interest -was our primary one, we note that the same results were evident in a whole-brain clustercorrected analysis (S5 Table ).