dataesr/scientific-paragraphs-categorization huggingface dataset:
Declaration of interest statement The in vitro study was funded by EUROS company and the in vivo modelling by the BiomecAM chair program on subject-specific musculoskeletal modelling (with the financial support of ParisTech and Yves Cotrel Foundations, Société Générale, Proteor and Covea). The sponsors of this study were not involved in the design, collection, analysis of data or writing the report. CC-BY-NC-ND license. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material.
The majority of parents were European American (86. 3% of fathers, 85. 9% of mothers) followed by African American (5. 4% mothers, 5. 0% fathers), Asian/Asian American (2. 9% mothers, 3. 7% fathers), and other race/ethnicity (2. 1% of mothers and fathers); 3. 7 % of mothers and 2. 9% of fathers identified as Hispanic. Most parents had at least a bachelor's degree (79. 2% of fathers and 83. 9% of mothers), and the median family income was $60,000-$99,999. Mothers and fathers had been married for an average of 5. 77 years (SD = 2. 74). Families were recruited from 2004 to 2008 through advertisements and flyers posted in local obstetric clinics, childbirth classes, pediatricians' offices, and hospitals. Data collection occurred at five times beginning in the mother's last trimester of pregnancy (prenatal) and 1, 4, 8, and 12 months following the birth of the infant sibling.
Measures Data for the current report included assessments of children's attachment security, the parent-child and coparenting relationships, and children's problem behaviors. Because some measures were not collected at all 5 time points, we note below in parentheses when each measure was collected. All available data were used in analyses, which meant that the statistical models of change tested in the LGC and GMM analyses were determined by the number of measurement occasions; more complex models of change were applied in instances with 5 times of measurement (see data analysis section for a detailed discussion). Attachment Q-set (prenatal, 4, and 12 months) The Attachment Q-set (AQS Version 3. 0,  Waters, 1987)  was completed by mothers and fathers at the second home visit conducted at the prenatal, 4-, and 12-month time points to assess children's secure base behavior in the mother-child and father-child relationship.
, San Diego, CA, USA) to analyze tumor or matched normal blood samples (from the same patient) and to reach a minimal yield of 145 or 85 Gb, respectively. Two tumor samples (LMS2T and LMS5T) were sequenced in 20 lanes of HiSeq2000 Flowcell v3 to reach a minimal yield of 560 Gb. Image analysis, base calling and quality scoring of the run were processed using the manufacturer's software Real Time Analysis (RTA 1. 13. 48) and followed by generation of FASTQ sequence files by CASAVA (Illumina Inc. , San Diego, CA, USA). DNA reads were trimmed of the 5ʹ and 3ʹ low-quality bases (PHRED cut-off 20, maximum trimmed size: 30 nucleotides (nt)) and sequencing adapters were removed with Sickle2 (Joshi NA, Fass JN. 2011 available at https:// github. com/ najos hi/ sickle). Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.
33) [Software]. Available at https:// github. com/ najos hi/ sickle) and SeqPrep3 (J. St. John, SeqPrep. (2011) Available at https:// github. com/ jstjo hn/ SeqPr ep), respectively. Then, DNA curated sequences were aligned using bwa v-0. 7. 15  61  with default parameters on the Human Genome version hg38  62  (http:// genome. ucsc. edu/ or https:// www. ncbi. nlm. nih. gov/ grc/ human). Thus, aligned reads were filtered out if their alignment score was less than 20 or if they were duplicated PCR reads, with SAMtools v1. 3. 1  63  and PicardTools v2. 18. 2 ("Picard Toolkit. " 2019. Broad Institute, GitHub Repository. http:// broad insti tute. github. io/ picard/; Broad Institute), respectively. Random breakage model, Hscore, readable genome size. The readable genome is represented as a single interval of length L in base pairs (bp).
Then the missing position was searched among the filtered positions. If it was present in the normal sample, the position was discarded and the breakpoint was completed otherwise. Finally, the third group corresponds to breakpoints with both sides outside the common pool and considered as unique: these were discarded. The sides of breakpoints were sorted according to their chromosomic positions to avoid duplicates. Data for CpG islands, microsatellites, simple repeats, low complexity, retrotransposons, long terminal repeats, self-chains and sequencing gaps were obtained from the UCSC Genome Browser website (http:// genome. ucsc. edu/; genome assembly hg38). All Non-B DNA except RLFS were generated using the non-B DNA research tool from the non-B DNA database  66. RLFS data were generated using QmRLFS-finder  67.
BP counts and interval sizes were computed for each index. These were then used to compute Hscores under RBM. Sliding windows. For each DNA element (sliding window 0 in Figs. 3, 4, 5 ), each genomic feature was shifted (bedtools) by 100% its length on the positive (+) DNA strand (sliding window + 1 in Figs. 3, 4, 5 ) and on the negative (-) DNA strand (sliding window -1 in Figs. 3, 4, 5) and Hscore was computed. This procedure was repeated by shifting each feature ± 2 × 100% (sliding window + 2, sliding window -2), ± 3 × 100%, until ± 8 × 100%. Heatmap. Hscore was computed with Holm's adjusted P-values procedure. Two patients (LMS78T and LMS131T) had maximal Hscores of 170. 53 and 50. 16 while all the other patients had a maximal Hscore less than 39. Therefore, the heatmap was unexploitable as only these two maximal data points were visible.
For the sake of clarity, Hscores of patients LMS78T and LMS131T were normalized by dividing them by 170. 53 and multiplying them by 39. Ingene/outgene split. A DNA element was considered as inside a gene if overlapped by at least 1 bp the gene interval delimited by its Transcription Start Site (TSS) and Transcription End Site (TES). Gene coordinates were taken from curated RefSeq entries from the UCSC table browser page (https:// genome. ucsc. edu/ cgi-bin/ hgTab les; group = genes and genes prediction; track = NCBI RefSeq; table = RefSeq Curated). Only genes that had expression data in these tumors were considered (for list of genes, see Supplemental Table  S4 ).
Statistical analysis. All statistical tests and the heatmap were carried out using R (R Core Team (2020); R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria;http:// www. r-proje ct. org/ index. html). Leave-one-out cross-validation. Each time one patient was removed from the cohort of patients, thresholds of iTRAC and iRACIN was computed and used to classify the removed patient into Low, Medium, or High. iPART. We consider iPART (Iterative multi-thresholds PARTitioning) to be an unsupervised decision tree (UDT). It is a method that combines the properties and objectives of both unsupervised clustering and decision trees (DT). Hence, iPART looks for thresholds that maximize the differences in groups instead of computing pairwise distances and constructing hierarchical clusters.
Acknowledgements We are grateful to the  French Sarcoma Group  for tumor banks and associated clinical annotations and to  Jean-Baptiste Courrèges. The following French cancer centers also participated in this study:  Centre Paul Papin (Angers), Centre Oscar Lambert (Lille), Institut Paoli Calmettes (Marseille) ,  CHU La Timone (Marseille) ,  CHU Bordeaux and hospital Ambroise Paré (Paris). This work was supported by grant from the  "Instituts Thematiques Multiorganismes" (ITMO) Cancer (INSERM and INCa)  and the  Claudius Regaud Institute. The authors would like to thank the  Centre Nacional d' Anàlisi Genòmica (CNAG, Barcelona, Spain)  for WG sequencing services. We thank  GENOTOUL  for bioinformatics facilities and computer farm.
g. , lungs) died of overstimulation (NETosis) after acute infection and/or were immunosuppressed, as previously reported  (25). Expression of MHC-related molecule CD1a on monocytes is associated with SARS-CoV-2 status, severity, and outcomes. We also assessed whether specific subpopulations of antigen-presenting cells were altered in SARS-CoV-2 + patients and associated with severity because these could secrete multiple inflammatory mediators (cytokines, myeloperoxidases, ROS, etc. ) and present antigen to T cells. Within the monocyte compartment, monocytes bearing CD16 (FCγIIIR) are associated with antigen-presentation, tissue homing, and secretion of proinflammatory cytokine TNF  (26). The proportion of classical CD14 hi CD16 -monocytes was specifically reduced in SARS-CoV-2 + patients compared with HCs and SARS-CoV-2 -patients; in contrast, the frequency of intermediate CD14 hi CD16 + monocytes was specifically augmented in SARS-CoV-2 + patients (Figures  4  and 6A ) but was not significantly associated with severity (Supplemental Figure  3B ) or outcome (data not shown).
In animal models of sepsis, blockade of the PD-1/PD-L1 pathway restored T cell function and was associated with a decrease of the pathogen burden and better survival  (78, (83) (84) (85). Whether such an approach will be beneficial in the context of COVID-19 will soon be determined by an ongoing phase II trial for treatment of patients infected with SARS-CoV-2 with an anti-PD-1 monoclonal antibody (Clinical-Trials. gov NCT04356508). From a clinical point of view, the similarities uncovered between the immune profiles of SARS-CoV-2 + and SARS-CoV-2 - patients suggest that therapeutics targeting general nonspecific inflammatory processes could be tested in multiple severe acute illnesses when lymphopenia and neutrophilia are observed, especially in older patients. Some therapies successfully used in sepsis or other ICU care settings could be applied to COVID-19, as exemplified by the use of steroids in severe COVID-19  (86).
0. 1 and FlowSOM 1. 20. 0 in R version 4. 0. 1, we applied the FlowSOM algorithm (  22 ) on these concatenated files to create a FlowSOM map for each panel. In order to explore the relative proportion of clinically relevant immune cell populations, we used clustering strategies within broad FSC/SSC gates rather than on specific subpopulations. The modal value of clusters, as determined by the PhenoGraph clustering algorithm  (21)  in FlowJo on multiple random samples, was used to determine the number of clusters to input into FlowSOM. A heatmap of each panel's geometric MFI for each cellular cluster was generated with the R package pheatmap 1. 0. 12. UMAPs were generated using the R package UMAP 0. 2. 6. 0. Statistics. Analysis of frequency of populations and their association with SARS-CoV-2 status, severity, and outcome at 30 days were performed by Mann-Whitney U test (for factors with 2 categories) or by Kruskal-Wallis test (for factors with a number of categories >2) followed by Dunn's post hoc test.
Acknowledgments This study was funded by a grant from the CRCHUM foundation and by grant  VR2-173203  from the  Government of Canada  through the  COVID-19 Immunity Task Force  in collaboration with the  Canadian Institutes of Health Research. The BQC-19 is supported by the  Fonds de recherche Québec-Santé (FRQS) ,  Génome Québec , and the  Public Health Agency of Canada. DEK is a  FRQS Merit Research Scholar. NC, MD, and CL receive a FRQS salary award. AF and AP hold a Canada Research Chair. We acknowledge  Dominique Gauchat  and  Philippe St-Onge  from the CRCHUM flow cytometry platform for excellent technical guidance and support.
Statistical analysis Descriptive data are shown as median (interquartile interval) or percentage (number of subjects). Chi-square test or Fisher's test was used to compare qualitative data between groups. Mann-Whitney test and Kruskal-Wallis were applied to analyse non-normally distributed data. A binary logistic regression analysis was performed to identify clinical and demographic characteristics associated with mortality. We included the variables that had differed in descriptive analysis between survivors and non-survivors. Variables with missing data were excluded from this analysis. A two-tailed p value < 0. 05 was considered significant. Statistical analysis was performed using GraphPad Prism and SPSS 85 Statistics version 26. 0. Ethical statement: we obtained the required approval from the Commission Nationale Informatique et Liberté (CNIL) to collect anonymised data.
Ultimately, the ethyl acetate extracts of all three plants and their respective plant part showed greater bioactivity upon further investigation of their in vitro anti-inflammatory and antioxidant activity, as well as their dermal papilla cell proliferation activity and RT-qPCR regulation of several genes involved in the hair cycle  [7, 8]. After these bioassays, first we proceeded to LC-MS/MS analyses and were able to annotate 19 compounds but had not isolated or elucidated any using NMR, nor had the hair growth related biological activities of the compounds been assessed. Feature-Based Molecular Networking (FBMN) provided an ideal option in such a case as it combines feature detection alignments of LC or GC-MS data with classical molecular networking to allow quantitative analysis and mapping of detected features  [9, 10].
Furthermore, the number of DPCs has an impact on hair size and a decrease in their pool leads to gradual hair thinning  [15]. The aim of this study was dual: firstly, to further our investigations of the chemical composition of our three plant species B. pilosa, C. inophyllum and F. berteroana, and secondly to highlight which metabolites or chemical families could be involved in the observed hair growth-related biological activities of the fractions/extracts obtained from the three plants. To do so, we adapted the Bioactivity-Based Molecular Networking workflow proposed by  Nothias et al. , (2018)   [11]  to study features responsible for the hair follicle dermal papilla cells proliferation activity. The raw spectral data obtained from the LC-MS/MS analysis were pre-processed in MZmine2 and quantitatively aligned to compute a featurebased molecular network.
Interspecies Correlations between the Cell Proliferative Activity of the Fractions/Extracts and Their Chemical Constituents Three plants, B. pilosa, C. inophyllum and F. berteroana were studied. The LC-MS/MS data from the ethyl acetate extract of the aerial parts of B. pilosa (BEAE) which yielded BF3 and BF4 after fractionation, the ethanol/water (CEWE) extract of C. inophyllum leaves, the ethyl acetate (CEAE) leaf extract of C. inophyllum which yielded CF3, CF4 and CF5 after fractionation and the ethyl acetate (FEAE) extract of F. berteroana which yielded fractions FF1, FF2, FF3 and FF4, were used for spectral alignment in MZMine. The feature quantification table obtained from MZMine containing 1110 aligned features of the 13 fractions and extracts as well as their respective cell proliferation values, were necessary for spectral data vs biological activity correlation calculations.
berteroana which yielded fractions FF1, FF2, FF3 and FF4, were used for spectral alignment in MZMine. The feature quantification table obtained from MZMine containing 1110 aligned features of the 13 fractions and extracts as well as their respective cell proliferation values, were necessary for spectral data vs biological activity correlation calculations. The mean cell proliferation values at 50 μg. mL -  of the fractions/extracts were first ln-transformed (Table  S1 ), before correlation was calculated. The output results showed that 53 of the 1110 initial features had a significant correlation with the bioactivity (p-value ≤ 0. 05; Table  S2 ). The Pearson correlation coefficients obtained were mostly negative, ranging from -0. 79 to -0. 55. This reveals that the features demonstrated an inverse quantitative relationship with cell proliferation.
1 g) was composed of the 2nd volume of cyclohexane/EtOAc (70/30) and 100% EtOAc, and finally FF4 (2. 3 g) was the fraction eluted with 100% methanol. FEAE was also re-fractioned in open column chromatography to obtain a greater quantity of FF1. 1D and 2D NMR spectra were recorded with a Bruker Avance II+ spectrophotometer at 600 MHz coupled with a Cryoprobe TCI operating under TopSpin 3. 2. PL5. Data were processed on TopSpin 4. -, 471. 1297). Data were consistent with published values  [48]. β-D-Glucopyranoside, (4E)-1-(2-hydroxyethyl)-4-dodecene-6,8,10-triynyl (2) 1 H-NMR (DMSO-d6, 600 MHz) δ in ppm 6. 52 (1H, dt, J = 16. 0, 7. 1), 5. 71 (1H, brd, J = 16. 0), 4. 12 (1H, d, J = 7. 9), 3. 68 (1H, m), 3. 66 (1H, dd, J = 11. 5, 2. 2), 3. 56 (1H, m), 3. 42 (1H, m), 3. 38 (1H, dd, J = 11. 5, 6.
Construction of the Molecular Network The raw MS 2 data files were converted to mzXML via a Bruker script run on Compass Data Analysis version 5. 0 (Bruker Daltonik, Bremen, Germany). The MZmine version 2. 53 was then used for pre-processing. The files were filtered to remove the 0. 00-0. 37 min calibration portion of each analysis. A centroid mass detection of MS 1 at a noise level of 1. 0E2 and MS 2 noise level of 1. 0E2, generated a mass list. The m/z tolerance was set to 0. 01 Da. The feature list was used to build chromatograms using ADAP. The minimum group number of scans was set at 3, the group intensity threshold was set at 3000 and the minimum highest intensity was set at 4000. Deconvolution of the feature lists was done with the wavelets (ADAP) algorithm. The feature lists were deisotoped and the features of the thirteen samples were aligned.
Statistical Analyses A PCA-X analysis was conducted on SIMCA ® version 17 (Umeå, Sweden) on MS 1 quantitative data of the precursors from MS 2 analysis. Data were log-transformed, and center-scaled. The Pearson correlation coefficient between feature intensities and biological activities of the thirteen extracts and fractions was based on the protocol proposed by Nothias et al. , (2018) using a R script (https://github. com/DorresteinLaboratory/Bioactive_Molecular_ Networks/blob/master/Bioactive_Molecular_Networks_v1. 1_MZmine2. r, accessed on 11 February 2021).
Acknowledgments: We wish to thank  Cosmetic Valley  for their financial support. UHPLC-MS/MS was performed on the Mallabar platform (granted by  Provence Alpes Côte d'Azur region, CNRS ,  ANR  and  Total Foundation ) at IMBE. We also wish to greatly thank  Stéphanie Hermitte  and  Claire Chazaud  as well as her team for their expertise in cellular biology experiments. We finally wish to thank l' Institut Louis Mallardé , especially  Philippe Cruchet  of the  Laboratoire de Recherche sur les BioToxines marines  for his availability during the purification of molecules.
New circulating Enterovirus (EV) strains often emerge through recombination. Upsurges of recombinant non-polio enteroviruses (NPEVs) associated with neurologic manifestations such as EVA71 or Echovirus 30 (E30) are a growing public health concern in Europe. Only a few complete genomes of EVs circulating in Spain are available in public databases, making it difficult to address the emergence of recombinant EVs, understand their evolutionary relatedness and the possible implication in human disease. We have used metagenomic (untargeted) NGS to generate full-length EV genomes from CSF samples of EV-positive aseptic meningitis cases in Southern Spain between 2015 and 2018. Our analyses reveal the co-circulation of multiple Enterovirus B (EV-B) types (E6, E11, E13 and E30), including a novel E13 recombinant form.
Complete genome analysis. After quality trimming, we assembled the reads using MetaSPAdes v3. 12, and used Diamond to query the contigs against the non-redundant protein database (NCBI). For each sample, we obtained a large contig corresponding to a near-complete enterovirus genome, or fragments that could be assembled into a genomic scaffold. We did not detect co-infection with other pathogens, including those that have been found in the CSF of patients with meningoencephalitis such as Dengue virus, Chikungunya virus or West Nile virus  18. Results of the metagenomic analysis can be found in Table  2. De novo assembly resulted in the reconstruction of complete or near-complete EVs genome for all 12 samples. All viruses presented high sequence coverage throughout the genome as shown in Supplementary Fig.
Recombination analysis. Considering the prevalence of recombination in the evolutionary history of enteroviruses, we next looked for evidence of recombination in the study strains. Recombination analyses were carried out using a combination of six methods implemented in RDP5. 5  23. We detected multiple events of recombination in the evolutionary history of the EV types studied. This was also evidenced when comparing the topologies of phylogenies build using the 3 coding regions (P1, P2 and P3) of the sequences from this study and representative EV-B reference genomes available in GenBank (Fig. 2 ). The phylogenetic analysis using the P1 capsid coding region showed that the E6, E11, E13 and E30 sequences obtained from this study cluster together with their respective reference genome which coincides with what we observed using the partial VP1 region (Fig.
Samples were tested for the presence of EVs using a qualitative multiplexed PCR (FilmArray ® Meningitis/Encephalitis Panel, BioFire Diagnostics) or RT-PCR (Xpert EV ® , Cepheid). In all CSF samples, bacterial, herpes simplex virus types 1 and 2, and varicella-zoster virus infections of the CNS were excluded by culture or PCR. Bacterial screening was performed by culture on chocolate agar, blood agar and thioglycollate broth, while herpes simplex virus and varicella zoster virus screening was performed by a qualitative multiplexed PCR (FilmArray ® Meningitis/Encephalitis Panel, BioFire Diagnostics) or RT-PCR (RealCycler ® herpesvirus type 1 + herpesvirus type 2 + varicella-zoster virus, Progenie Molecular). Sample remnants were stored at -45 °C. Clinical, epidemiological and laboratory data were collected retrospectively from medical records.
Recombination analysis. Recombination analyses were carried out on the study strain using a background of randomly sampled EV genomes retrieved from GenBank on May 2021, using a combination of six methods implemented in RDP5. 5  23  (RDP, GENECONV, MaxChi, Bootscan, SisScan and 3SEQ) and we considered recombination signals detected by more than three methods for breakpoint identification. Except for specifying that sequences are linear, all settings were kept to their defaults. In addition, similarity plot and bootscanning analysis were performed by using the SimPlot program, version 3. 5. 1, with a 400-nt window moving in 40-nt steps and using a Kimura 2-parameter method with a transitions-transversions ratio of 2 with 1000 resampling.
Phylogenetic analysis. We performed phylogenetic analysis of the 12 genomes from our dataset, together with published genomes available on NCBI GenBank retrieved on May 2021. Because the majority of the reported sequences from strains circulating in Spain consisted of partial VP1 sequences, we first performed phylogenetic analysis based on the alignment of partial 3'-half VP1 region (E6, E30, and E13) or in the 5'-half VP1 region (E11). Nt sequence alignment was performed by using ClustalW multiple alignment program  39  within the BioEdit sequence Alignment Editor package, version 7. 0. 9. 0. Maximum-likelihood trees were constructed using IQ-TREE2 (v. 2. 0. 6)  40  with 1000 bootstrap replicates  41. We constructed the trees using the best-fit model as determined by ModelFinder 42 implemented in IQ-TREE2.