dataesr/scientific-paragraphs-categorization huggingface dataset:
Another aliquot was analysed using basic negative ion optimised conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, with 6. 5 mM ammonium bicarbonate at pH 8. The fourth aliquot was analysed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2. 1 × 150 mm, 1. 7 µm) using a gradient consisting of water and acetonitrile with 10 mM ammonium formate, pH 10. 8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slightly between methods but covered 70-1000 m/z. Raw data were extracted, peakidentified and QC processed, using Metabolon's hardware and proprietary software. Peaks were quantified using area-under-the-curve. Shotgun metagenomics.
DNA was quantified using the Qubit HS dsDNA Assay kit on a Qubit 4 fluorometer (Thermo Fisher Scientific, Horsham, UK). Shotgun metagenomics was performed by GenomeScan (Leiden, The Netherlands). The NEBNext® Ultra II FS DNA module (cat# NEB #E7810S/L) and the NEBNext® Ultra II Ligation module (cat# NEB #E7595S/L) were used to process the samples. Fragmentation, A-tailing and ligation of sequencing adapters of the resulting product was performed, according to the procedure described in the NEBNext Ultra II FS DNA module and NEBNext Ultra II Ligation module Instruction Manual. The quality and yield after sample preparation were measured with the fragment analyzer. The size of the resulting product was consistent with the expected size of ~500-700 bp. Clustering and DNA sequencing using the NovaSeq6000 (Illumina inc.
) was performed, according to manufacturer's protocols. A concentration of 1. 1 nM of DNA was used. NovaSeq control software NCS v1. 6 was used. The shotgun metagenomics data were pre-processed using the pre-processing package v0. 2. 2 (https://anaconda. org/fasnicar/preprocessing). In brief, this package concatenates reads, to remove Illumina adapters, discard low-quality (quality < 20 or >2 Ns) or too short reads (<75 bp), remove phiX and rat genome sequences, and finally sorts and splits the reads into R1, R2 and UN sets of reads. Cleaned shotgun metagenomics reads were then processed for taxonomic and pathway profiling. Since there is no gold standard for computational analyses of shotgun metagenomics, we used a combination of approaches. We inferred the taxonomy with the RefSeq database on the metagenomics RAST server  79  , IGGsearch (iggdb_v1.
0. 0_gut database)  80  , MetaPhlAn version 2. 9 (ref. 81 ) and Kaiju 1. 0. 1 (ref. 82 ). In vitro study of bacterial growth. Azoxystrobin, boscalid, chlorpyrifos, imidacloprid and thiabendazole were diluted in dimethylsulfoxide (Merck, Feltham, UK) to obtain stock solutions. Glyphosate was diluted in water. Bacterial strains were provided by the Université de Caen Microbiologie Alimentaire (Caen, France) culture collections (Table  S1 ). The broth dilution method was used to determine how pesticides modify bacterial growth under aerobic conditions. Bacteria from overnight broth cultures were re-suspended to OD 600 nm = 0. 3 (L. rhamnosus) or OD 600 nm = 0. 2 (E. coli) and further diluted 1000-fold in MRS without peptone for L. rhamnosus, or ABTG for E. coli broth, to obtain ~10 5 CFU/ml as confirmed in each experiment by plating cell suspension on agar plates incubated aerobically at 37 °C.
Plates were incubated under aerobic conditions at 37 °C and inspected after 24 and 48 h. All experiments were performed in triplicate. DNA damage. DNA damage was measured as the formation of AP sites in liver genomic DNA. AP sites are common DNA lesions caused by oxidative damage. We used the DNA Damage Assay Kit (AP sites, Colorimetric; Abcam, ab211154; Abcam plc, Cambridge, UK), according to manufacturer's instructions. Statistics and reproducibility. The statistical analysis was performed using R version 4. 0. 0 (ref. 83 ). Metabolome peak area values were median scaled, log transformed and any missing values imputed with sample set minimums, both on a per biochemical basis, and separately for each metabolome dataset. Statistical significance was determined using a Welch's two-sample t test adjusted for multiple comparisons with FDR methods using the R package 'qvalue' version 2.
17. 0 (ref. 84 ). For the shotgun metagenomics, a compositional data analysis approach was used since gut metagenomics datasets are typically zero-inflated  85. We used ALDEx version 2 (ALDEx2) for differential (relative) abundance analysis of proportional data  86. Statistical analysis for taxa abundance was performed on a dataset corrected for asymmetry (uneven sequencing depths) using the inter-quartile log-ratio method, which identifies features with reproducible variance. A multivariate analysis consisting of a non-metric multidimensional scaling (NMDS) plot of Bray-Curtis distances between samples. Statistical significance of the sample clustering was evaluated with a PERMANOVA analysis on the Bray-Curtis distances with adonis() from vegan v2. 4-2. We also used OPLS-DA to evaluate the predictive ability of each omics approach.
OPLS-DA is an extension of PLS methods, which includes an orthogonal component distinguishing the variability corresponding to the experimental perturbation (here the effects of the pesticide mixture) from the portion of the data that is orthogonal; that is, independent from the experimental perturbation. The R package ropls version 1. 20. 0 was used  87. This algorithm uses the non-linear iterative partial least squares algorithm. Prior to analysis, experimental variables were centred and unit-variance scaled. Since PLS-DA methods are prone to overfitting, we assessed the significance of our classification using permutation tests (permuted 1000 times). RNA-seq data were analysed with Salmon  88. This tool was used to quantify transcript abundance by mapping the reads against a reference transcriptome (Ensembl Release Rattus Norvegicus 6.
0 cDNA fasta). Mapping rate was 82. 0 ± 4. 4% on a rat transcriptome index containing 31,196 targets. The Salmon output was then imported in R using the Bioconductor package tximport. We created a transcript database containing transcript counts, which was used to perform a differential gene expression analysis using DESeq2 (ref. 89 ). We finally used goseq to perform a GO analysis accounting for transcript length biases  90. We also compared our transcriptome findings to a list of gene expression signatures collected from various rat tissues after treatments with various drugs, using the drugMatrix toxicogenomics database 91 with EnrichR  92. DNA methylation calls from RRBS data were extracted with Bismark  76. The output from Bismark was then imported in R and analysed with Methylkit  93.
DNA methylation calls were annotated using RefSeq gene predictions for rats (rn6 release) with the package genomation  94. Other annotations were retrieved using the genome-wide annotation for rat tool org. Rn. eg. dbR package version 3. 8. 2. Statistical analysis was performed with logistic regression models fitted per CpG using Methylkit functions. P values were adjusted to Q values using SLIM method  95. Statistical analyses of in vitro tests on bacterial growth were performed using GraphPad Prism version 8. 0. 1 (GraphPad Software, Inc, CA, USA). Differences between treatment groups at different concentrations and the negative control were investigated using Kruskal-Wallis one-way ANOVA with Dunn's multiple comparison post-test. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability Metabolomics raw data are available in Metabolights, with the accession number MTBLS138. Shotgun metagenomics raw data are available from the National Center for Biotechnology Information (NCBI), with BioProject accession no. PRJNA609596. Liver methylation raw data from the RRBS analysis is available at GEO accession no. GSE157551. The raw data from the transcriptomics analysis is available at GEO accession no. GSE157426. All other data are available from the corresponding author (or other sources, as applicable) on reasonable request.
Genome annotation TE-like elements and canonical TE consensus were predicted from the AV20 genome assembly using EDTA  (62)  and TEdenovo pipeline (63, 64) (part of the REPET pipeline). TE-like elements were then annotated using TEannot. Genes were annotated using funannotate (65) as described below. First, repeated elements were masked using BEDTools  (66). Second, part of the available RNA sequencing (RNA-seq) reads were mapped onto the genome, while the other part of RNA-seq reads were used to produce a de novo assembled transcriptome that was subsequently aligned onto the genome as part of the PASA pipeline  (67). Third, PASA annotations, the de novo assembled transcriptome, the metazoan BUSCO database, and the protein UniProt database were used as inputs within funannotate predict function.
This function first produced ab initio gene annotations using GeneMark-ES, which were then used along with transcripts and protein data to train Augustus to generate a second set of gene annotations. Fourth, funannotate used 4. Schematic representation of the karyotype of A. vaga. The 12 chromosomes correspond to six pairs of homologous chromosomes (i. e. , a diploid genome structure) sharing the same color. Ancestral genome hybridization (or whole-genome duplication) led to the existence of pairs of homoeologs (or ohnologs), represented in different but similar colors. Gray blocks linking homologous chromosomes and their corresponding numbered tokens depict LOH events produced by HR (see also Fig. 2 ). The length of chromosomes (in Mb) is indicated by the scale on the right. Downloaded from https://www.
science. org at Universite Pierre Et Marie Curie -Paris 6 (Upmc) on October 27, 2021 S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 8 of 10 EVidenceModeler as a weighted approach to combine gene annotations from PASA, GeneMark, and high-quality annotations from Augustus into a single, integrated, gene annotation set. Last, In-terProScan 5  (68)  was used to produce functional annotations to these genes. These functional annotations, along with the BUSCO metazoan database, were then used as input for the funannotate annotate function with default parameters to produce the final functional annotation for the predicted genes. Alienomics, a newly designed pipeline, was used to detect HGTs. This approach combines GC content, coverage, sequence similarity, taxonomic information, expression level, and synteny information to detect both HGTc (i.
e. , alien genes integrated into host scaffolds) and potential contaminants (i. e. , alien genes present on alien scaffolds). Note that our approach can only detect transfers from alien source outside of a given clade (here, metazoans). Viral-like genes were detected by performing a DIAMOND BLASTX search on AV20 scaffolds using all viral proteins extracted from the nr database of the National Center for Biotechnology Information (NCBI) (February 2020) to the exception of Retroviridae and Hepadnaviridae.
Genome analyses Coverage along AV20 scaffolds was computed using read mapping with BWA-MEM. These mappings were used for measuring heterozygosity in three samples (i. e. , GC047403, BXQF, and ERR321927) by genotyping them using GATK (HaplotypeCaller function with -ERC GVCF option). The resulting gvcf files were combined (CombineGVCFs function) and were then jointly genotyped (GenotypeGVCFs function). The variants were then filtered to only retain SNPs using custom bash and perl scripts. Divergence between homoeologous chromosomes was assessed by producing a self-alignment of AV20 genome using nucmer, which was then filtered to only retain genomic alignments between homoeologous regions ranging from 500 to 10,000 bp. MCScanX was used to detect synteny among HGTc, and custom scripts were used to detect strictly homoeologous HGTc synteny blocks stemming from the paleotetraploidization of bdelloids (i.
Reappraisal of the AV13 genome assembly ONT reads were trimmed using porechop (70) and were mapped onto the previous AV13 genome assembly using NGMLR  (71). The AV20 and AV13 genome assemblies were aligned together using Sibelia  (72). This genome alignment was used to localize putative break points, which were then inspected using Tablet (73) to evaluate whether ONT reads confirmed previously reported break points in the AV13 genome assembly. Synteny blocks from the Sibelia alignment between AV20 and AV13 were analyzed using a custom perl script (available at https://github. com/jnarayan81/huntPalindrome) to localize putative palindromes, and circos plots of these locations were inspected to evaluate the existence of palindromes in the AV20 genome.
Water analysis Water was filtered, and 0. 25-L samples were processed after adjusting the pH at 7  0. 2. A solid-phase extraction was performed with an automatic extractor (Smartprep TM , Horizon Technologies). The samples were eluted with acetonitrile (LC-MS/MS grade), and analyzed with an Agilent 1290 liquid chromatograph coupled to an Agilent 6490 QQQ tandem mass spectrometer. Extraction, quantification methods and the list of the targeted chemical compounds are detailed in Supplementary Data (c. f. S1. 1, and tables S1, S2).
2. 9. 2; http://biostatflow. org). Statistical tests were performed on upstream results, using station (CM-Upstream, Cr-Upstream and Nam-Upstream1) as a factor to test the interstation effect. For bioaccumulation and water chemistry results, given the low number of replicates and the absence of significant differences between upstream and downstream locations in the different stations for most chemicals, interstation effect was evaluated on the merged upstream and downstream data from each station. Intrastation effect was evaluated independently for each station using site (upstream/downstream) as a factor for all parameters.
Metabolomics data NMRProcFlow pre-processing generated 418 buckets from 0. 91 to 10 ppm after zeroing the water signal and blank contaminant peaks. The generated data matrix was imported into Biostatflow platform (v. 2. 9. 2; http://biostatflow. org, a web application developed for the statistical analyses of "OMICS" Data, using R scripts). Hence, all multivariate statistical analyses (unsupervised principal component analysis "PCA" and supervised linear kernelorthogonal projection to latent structures "K-OPLS") were performed on this platform. Then, a univariate test (non-parametric Kruskal-Wallis test) was performed to check for significant abundance differences of the identified metabolites between the experimental groups (as described in Supplementary Data S1. 5. ). Interstation effect was tested on CM-Upstream, Cr-Upstream and Nam-Upstream1, and intrastation effect was tested on each station separately using site (upstream/downstream) as a factor.
Blots were developed using SuperSignal West DURA Extended Duration Substrate (Thermo Fisher Scientific) following the manufacturer's instructions. Semiquantitative densitometric analysis of band intensities was performed using the Gels command in FIJI open source software (https://fiji. sc/). The following primary antibodies were used: rabbit anti-cofilin (Cytoskeleton ACFL02; 1:500), rabbit anti-Ser3-phosphorylated cofilin (Cell Signaling Technology #3311; 1:500), and a mouse anti-GAPDH (Abcam ab8245; 1:5000), to use as a control for protein loading. Secondary antibodies were HRPconjugated goat anti-rabbit IgG (G-21234; 1:25000; Life Technologies) and goat anti-mouse IgG (G-21040; 1:25000; Life Technologies). Uncropped images of the Western blot data are provided in Fig. S6.
Chromosome staining for monitoring anaphase-I To evaluate the kinetics of anaphase-I, GV-stage oocytes (uninjected, or expressing LIMK1 or LIMK1 D460A ) were washed from milrinone and allowed to resume meiosis and mature in vitro in a small drop of M16 medium layered with mineral oil (Sigma M8410). Ten hours after milrinone washout, oocytes were stained with Hoechst-33342 (5 µM; Invitrogen H3570) for 10 min in M2 medium, followed by wash, and transferred to glass-bottom dishes (MatTek, Ashland, MA). Oocytes were examined for chromosome configuration using an inverted Leica DMI4000B microscope equipped with a 365nm LED module and a digital monochrome DFC345 FX camera (Leica). Images were aquired using the LAS AF 2. 3. 0 software (Leica). Oocytes were then quickly returned to a 37°C hot block and imaged again at 11 h post-milrinone washout.
F-actin staining and quantification in fixed oocytes F-actin was stained with Alexa Fluor 568-phalloidin (Life Technologies). The density of the cytoplasmic F-actin network in MI (NEBD + 6h) oocytes was evaluated by measuring the mean fluorescence intensity in a 10x10 µm region of interest using the Leica LAS AF Lite 2. 6. 0 software. Care was taken to avoid the spindle region and the elongated cytoplasmic actin rootlets. To calculate the density of elongated microvilli in LIMK1-expressing MI oocytes, a single confocal optical section (thickness 1 µm) obtained at the equator of the oocyte, was analyzed. Elongated microvillar actin bundles were counted across the oocyte perimeter, and their density was expressed as per 10-µm perimeter. Only elongated structures showing bidirectional elongation (outward microvilli + inward rootlet) were considered for the analysis.
Funding This work was supported by an  ATIP  fellowship to GH awarded by the  Centre National de la Recherche Scientifique , and by a research grant awarded to GH by the Ligue contre le cancer Grand Ouest. GH wishes to thank the  Society for Reproduction and Fertility (SRF)  for the award of an  Academic Scholarship. BD  received a PhD scholarship from the  French Ministry of Research and Higher Education , and additional funding from the  Fondation pour la Recherche Médicale.
Impaired QoL has been reported in patients with CAH, mostly in women [reviewed by  Reisch et al. (3) ]. Data on QoL in men with CAH are scarce, although separate analysis of QoL in male and female patients is important since clinical presentation and complications vary greatly between the two sexes. Results are contradictory with some papers describing impaired QoL  (6, 9, 10) , and others equal  (11)  or better  (12)  QoL in men with CAH compared to a control population [recently reviewed by Daae et al. (13) ]. The dsd-LIFE study provides an opportunity to fill this knowledge gap by studying a large European multicenter cohort of men with CAH in which we assessed QoL using the WHOQOL-BREF questionnaire. We hypothesized that QoL would be impaired in men with CAH compared to healthy control populations, and that genotype, medication regimen, and treatment control would be associated with QoL.
Written informed consent was obtained from all participants. The study was approved by the medical ethics committee at the CharitéUniversitätsmedizin Berlin (reference number EA2/069/13) and the local ethics committees of the other study centers as appropriate for each country. Patients were investigated at their local medical center and treated according to the Endocrine Society guidelines  (1). All patients underwent medical examination and filled out several questionnaires, including the WHOQOL-BREF  (15). Additional data were retrieved from medical records. General patient characteristics and clinical parameters included: country of inclusion, height, weight, BMI, age, age at diagnosis, CYP21A2 genotype, medication use, subjective treatment control, satisfaction with care in childhood, smoking behavior, work, leisure time activities, sports, and educational level.
Reference Populations As matched control groups were not available in the dsd-LIFE study, this study used control groups from the literature. QoL data on female patients with CAH were derived from an earlier DSD-LIFE study on QoL in patients with DSD  (19). This study included a sample of 226 female patients with CAH from France, Germany, the Netherlands, Sweden and the UK. In addition, healthy as well as chronically ill populations from France  (20) , Germany  (21) , the Netherlands  (22) , and the UK  (23)  were used for comparison. Reference populations from the Netherlands and the UK contained both men and women, while France and Germany reported genderspecific QoL scores. For France, data of self-reported healthy (n=5167) and chronically ill (n=1638) adult men, of which 656 were young adults (18-24 years) and 897 were elderly people (65-75 years), were derived from the National Health Barometer 2005, a periodic study by the French National Institute for Preventive and Health Education  (20).
For Germany, data of 925 men from a representative urban sample of the adult general population were available, including 124 young adults (18 to 25 years) and 155 elderly people (≥66 years). Additionally, 261 men and women from this representative urban sample reporting a physical chronic disease were used as a chronically ill reference population  (21). For the Netherlands, data from a healthy control group were used, matched for age and sex ratio (mean age 34. 8 years) to a sample of patients with a mental chronic disease. The matched control group was taken from a pooled data set based on Dutch general population studies (1999-2002)  (22). For the UK, a study including healthy and non-healthy people from all over the UK was available. The healthy people included students and student nurses.
Statistical Analysis SPSS Statistics 25 (SPSS Inc. , Chicago, IL, USA) was used for all analyses. First, descriptive analyses were performed for each variable. After checking missing data and the distributions of the continuous background variables and QoL scores for normality, median and interquartile ranges (IQR = Q1-Q3) were calculated. The overall and country-specific median QoL scores for all 4 domains were compared to the reference populations described above without further statistical analyses. Following, the QoL domain scores (physical, psychological, social relationships, and environment) were compared between male CAH patients with different ages, educational levels, and treatment regiments using the Mann-Whitney-U test. For BMI and glucocorticoid treatment dosages related to the QoL domain scores, Spearman correlation coefficients were calculated.
Although men with CAH may face different complications of their chronic disease and often require lifelong therapy, they do not seem to report a worse QoL on the physical and psychological health domains compared to healthy or chronically ill references. CAH patients from the UK form an exception, which is in line with an earlier study of Arlt et al. who found that only a minority of CAH patients in the UK receive optimal specialist endocrine care  (6). The findings suggest that psychological wellbeing does not seem to be largely affected in men with CAH, which is in contrast to previously reported higher prevalence rates of psychiatric morbidity in men with CAH  (26). Patients with mental health issues, however, might be less likely to participate in studies or fill out questionnaires on QoL, and may be underrepresented in the dsd-LIFE database.
Although we were able to include a large cohort of adult male patients with CAH, our study design was mainly based on descriptive analyses and did not include a reference population. Therefore, we compared our domain scores with scores reported in the literature, although only mean domain scores were available and the German and France cohorts only reported gender-specific QoL scores. This complicated the comparisons with our median domain scores as no statistical analyses were possible. In addition, no data on educational level of the reference populations were available. Although all male patients with CAH were invited to participate in some centers, it is likely that some declined because they do not identify with DSD patient characteristics. Furthermore, all centers involved in the dsd-LIFE study are tertiary care centers.