dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , GenBank, BOLD) fragments have been metabarcoded using a global uncorated reference when more than one pair of overlapping classification) of taxonomy to OTUs The assignment (identification or 6 55 0 2 4 11 4 13 0 0 Degapping Removal of gaps from sequences 1 0 1 1 0 Sequence Dereplication The removal of duplicate reads to retain 58 10 11 19 0 processing only unique sequences in a pool; often the total number of copies of a sequence is recorded in the header of the retained sequence Size sorting The sorting of a fasta file according to a size 10 2 3 4 0 annotation in the header Filtering Quality filtering Removal and/or trimming of sequences 81 11 20 27 0 from a pool based on quality information. Also often converts from fastq to fasta. Similarity filtering Removal of sequences based on similarity to 9 1 4 4 0 an alignment, either based on sequence identity or alignment position Length filtering The removal of sequences from a pool 54 21 17 23 0 that are less than, more than, or fall within or outside of a specified length threshold or thresholds (continuous).
To test this possibility, we analyzed the lipidome of wild-type animals treated with prx-5(RNAi) or control(RNAi). We found 241 upregulated and 322 downregulated lipid clusters in prx-5(RNAi) (Figure  3B ; Table  S1 ). To determine which lipid classes were specifically affected upon prx-5(RNAi), we performed lipid identification based on MS1 annotation and MS2 fragmentation data analysis (MS1, mass spectrometry; MS2, tandem mass spectrometry) and compared results for each individual lipid class. An interesting example is triacylglycerols (TGs). Using the sum of all lipid clusters as a proxy for the total amount of TGs and, therefore, as a proxy for lipid droplet content, no difference was found between prx-5(RNAi) and control(RNAi) animals. Closer examination showed that, of the total 429 identified TGs, 59 were upregulated and 30 downregulated.
We analyzed our RNA-seq and proteomics dataset for Gene Ontology (GO) enrichment using GOrilla  (Eden et al. , 2009)  and REVIGO (REduce and VIsualize Gene Ontology)  (Supek et al. , 2011). This analysis revealed two large clusters of biological processes represented by the terms ''immune response'' and ''lipid metabolic process'' (Figures  6B  and 6D ; Table  S3 ). GO enrichment analysis also revealed that the GO terms ''microbody'' and ''peroxisomes'' were enriched in our RNAseq and proteomics dataset (Table  S3 ). To determine the fraction of peroxisomal proteins that had altered expression upon prx-5(RNAi), we generated a list of 62 C. elegans peroxisomal proteins using literature searches, PeroxisomeDB  (Schl€ uter et al. , 2010)  and WormBase (release WS272)  (Lee et al. , 2018 ; Table  S4 ).
Among the 53 peroxisomal proteins identified in our proteomics dataset, PRX-5 was the only significantly downregulated protein; 13 proteins were significantly upregulated, and the level of the remaining 39 proteins was not changed significantly upon prx-5(RNAi). All upregulated proteins except one were peroxisomal matrix proteins. These proteins include the peroxisomal   S1. catalase CTL-2, which is consistent with upregulation of ctl-2p::GFP. In contrast, we did not observe a significant change in the protein levels of the peroxisomal Lon protease. This result suggests that, in addition to transcriptional regulation, LONP-2 levels are also regulated at the post-transcriptional level. Finally, this analysis revealed that, among the 13 significantly upregulated peroxisomal proteins, 11 proteins are involved in peroxisomal b-oxidation.
Using our list of 62 peroxisomal proteins, we analyzed our RNA-seq dataset (Table  S4 ). Among the 57 corresponding genes identified in our RNA-seq dataset, 60% were upregulated significantly, and expression of the remaining genes was unchanged. Specifically, we found that genes encoding peroxisomal aand b-oxidation enzymes as well as enzymes of the ether-phospholipid biosynthesis pathway were upregulated significantly. In addition, we found that almost all genes encoding peroxins, including prx-5, were upregulated significantly. We speculate that prx-5 transcriptional upregulation is part of the PRS response and acts a compensatory mechanism in response to the decrease in peroxisomal protein import. Such an observation has already been made in the case of the UPR mt , where the gene encoding the mitochondrial protease SPG-7 was upregulated in animals subjected to spg-7(RNAi)  (Nargund et al.
Data and code availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al. , 2019) partner repository (PRIDE: PXD014720). The RNA-seq data has been deposited in NCBI's Gene Expression Omnibus  (Edgar et al. , 2002)  (GEO : GSE156318). The lipidomic data is included in Table  S1. The Fiji script for image analysis is included in the Data S1 file.
Protein quantification Proteins were extracted and digested using the iST Preomics Kit (Germany) with an initial step of sonication with glass beads (Bioruptor, Diagenode). For the mass spectrometry analysis, 5 ml were injected in an RSLCnano system (Thermo) and separated in a 25-cm analytical Aurora C18 nanocolumn (75 mm ID 120 A ˚, 1. 6 mm, Ion Opticks) with a 120-min gradient from 4 to 40% acetonitrile in 0. 1% formic acid. The effluent from the HPLC was directly electrosprayed into a Q Exactive HF (Thermo). The Q Exactive HF instrument was operated in data dependent mode to automatically switch between full scan MS and MS/MS acquisition. Survey full scan MS spectra e4 Cell Reports 34, 108653, January 19, 2021.
Article (from m/z 375-1600) were acquired with resolution R = 60000 at m/z 400 (AGC target of 3x10 6 ). The ten most intense peptide ions with charge states between 3 and 5 were sequentially isolated to a target value of 1x10 5 and fragmented at 27% normalized collision energy with R = 15000. Typical mass spectrometric conditions were: spray voltage, 1. 5 kV; no sheath and auxiliary gas flow; heated capillary temperature, 250 C; ion selection threshold, 33. 000 counts. MaxQuant 1. 5. 2. 8 was used to identify proteins and quantify by LFQ with the following parameters: Database, Uniprot_3AUP000001940_Celegans-; MS tol, 10ppm; MS/MS tol, 10ppm; Peptide FDR, 0. 1; Protein FDR, 0. 01 Min. peptide Length, 5; Variable modifications, Oxidation (M); Fixed modifications, Carbamidomethyl (C); Peptides for protein quantitation, razor and unique; Min.
RT-qPCR The worm pellet in TRIzol (Invitrogen) was thawed and total RNA was extracted after three freeze-thaw cycles with liquid nitrogen/ 37 C heat block following the manufacturer's instructions (Invitrogen). The RNA was purified using the RNeasy Mini Kit (QIAGEN) including an on-column DNase I digest (QUIAGEN) and the quantity and quality was determined using a NanoPhotometer (IMPLEN) and agarose gel electrophoresis. cDNA was synthesized from 600 ng RNA using the ProtoScript II First Strand cDNA Synthesis Kit (NEB) with Oligo-dT primers. Quantitative PCR was performed in three technical replicates per biological cDNA sample using a CFX96 Real-Time PCR Detection System with the CFX Maestro Software (BioRad). Each reaction contained diluted cDNA (1:4 vol/vol) or nuclease free water as control, iTaq Universal SYBR Green Supermix (BioRad) and 400 nM of gene-specific primers (Table  S5 ).
1 mm ID, 1. 6 mm particle size, Waters, Eschborn Germany) and a linear gradient from eluent A (40% H2O / 60% ACN + 10 mM ammonium formate / 0. 1% formic acid) to eluent B (10% ACN / 90% iPrOH + 10 mM ammonium formate / 0. 1% formic acid). The following gradient was used: 68/32 at 0min, 68/32 at 1. 5min, 3/97 at 21min, 3/97 at 25 min, 68/32 at 25. 1 min with a flow rate of 0. 250 ml/min and a temperature of 40 C. Mass spectrometric detection was performed using a Bruker maXis UHR-ToF-MS (Bruker Daltonic, Bermen, Germany) in positive ionization mode using data dependent acquisition to obtain MS1 and MS2 information. Negation ionization mode data was recorded for identification of lipids. Every six samples, a pooled QC was injected to check performance of the UPLC-UHR-ToF-MS system and were used for normalization.
Raw data was processed with Genedata Expressionist for MS 13. 5 (Genedata AG, Basel, Switzerland). Preprocessing steps included noise subtraction, m/z recalibration, chromatographic alignment and peak detection and grouping. Data was exported for Genedata Expressionist for MS 13. 5 Analyst statistical analysis software and as. xlsx for further investigation. Maximum peak intensities were used for statistical analysis and data was normalized on the protein content of the sample and an intensity drift normalization based on QC samples was used to normalize for the acquisition sequence. Lipid features that were detected in all pooled QC samples and had a relative standard deviation (RSD) < 30% were further investigated by statistical analysis. Comparison of prx-5(RNAi) versus control(RNAi) was performed using a Welch test.
Lipids with a p value < 0. 05 were considered to be significantly changed. Lipids were putatively annotated on the MS1 level using an in-house developed database for C. elegans lipids and bulk composition from LipidMaps (113), when available. Matching of MS2 spectra against an in-silico database of C. elegans lipids and LipidBlast was performed using the masstrixR package  (Kind et al. , 2013; Kind et al. , 2014)  (M. W. , unpublished data; https://github. com/ michaelwitting/masstrixR) and only hits with a forward and reverse matching score > 0. 75 were considered. Annotations of interesting biological peaks were manually verified and corrected if necessary.
Image analysis and image representation The analysis of two-dimensional images using Fiji/ImageJ2  (Rueden et al. , 2017; Schindelin et al. , 2012)  was automated by a Fiji-implemented macro using the IJ1 Macro language. The analysis of the hsp-6p::GFP, lonp-2p::GFP or ctl-2p::GFP reporter strains was performed as previously described  (Rolland et al. , 2019). For the analysis of the peroxisomal number, the background of the fluorescent images was subtracted with a rolling ball radius of five pixels. Next, a binary mask of the image was generated using the Otsu automated threshold. The mask was inverted and noise was removed by applying the Particle Analyzer with a minimum size of four pixels. The mask was multiplied with the original image and the number of peroxisomes was measured by the 3D Objects counter plug-in for ImageJ.
ACKNOWLEDGMENTS We thank  B. Conradt ,  M. Fransen ,  E. Lambie , and  N. Memar  for comments on the manuscript. We also thank  I. van der Klei  and  D. Leister  for suggestions regarding the project. We also thank  M. Gerstner  for advice regarding statistical analysis of the data. We thank  N. Lebedeva ,  M. Schwarz , and  M. Bauer  for excellent technical support and the  Caenorhabditis Genetics Center  (supported by the  National Institutes of Health National Center for Research Resources ) for strains. Work in S. G. R. 's lab was supported by the  Deutsche Forschungsgemeinschaft  ( RO5352/ 1-1 ). S. G. R. 's work was also supported by the  Institute for Basic Science  ( IBS-R022-A2-2020 ). E. R. is a member of the  Life Science Munich graduate program. Work in A. I. 's lab is partly financed by a grant from the  Deutsche Forschungsgemeinschaft  ( CRC1064/Z2 ).
Work in J. J. E. 's lab is supported by institutional grants from  INSERM ,  CNRS , and  AMU  and an  Agence Nationale de la Recherche program  grant ( ANR-16-CE15-0001-01 ). Z. X. is a recipient of a fellowship from the  Chinese Scholarship Council. Work in M. W. 's lab was supported by the  Deutsche Forschungsgemeinschaft  ( WI4382/11-1 ). C. O. and S. S. are supported by a grant from the  European Research Council  ( ERCStG-714739 IlluMitoDNA ).
Such an approach has been used successfully to obtain exhaustive genotypic characterization of the S-locus in cultivars of the apple tree, Malus domestica  (De Franceschi et al. , 2018) , and in populations of Arabidopsis halleri  (Genete et al. , 2020)  and A. lyrata  (Mable et al. , 2018; Takou et al. , 2021) , two species where molecular polymorphism of the SRK gene had been the focus of several previous studies (though it remained incompletely described). A dedicated bioinformatic pipeline implementing this approach has been developed  (Genete et al. , 2020)  and is available at https://github. com/mathi eu-genet e/NGSge notyp. It computes a series of mapping statistics to help identify the matching reference S-alleles (Figure  5c ) and also uses a de novo assembly approach to detect new S-allele sequences that can in turn enrich the reference database (Figure  5b ).
However, some further difficulties persist in some of these systems. For instance, in S-RNase SI systems of Maloideae (e. g. , apple or pear trees), the pistil expressed gene, S-RNase, has only two short exons separated by an intron highly variable in size and rich in repeat elements, for example,  Dreesen et al. (2010). In such cases, the mapping approach that involves sequential mapping against each of the known reference S-allele sequences (Figure  5 ) may be less successful than in SI systems that have a single large exon covering the pollen-pistil recognition domains. Hence, long-read sequencing technologies may be necessary to allow successful genotyping in those systems. Other developments of the approach could include using a previously established reference database of S-allele sequences to generate multiple targets for gene capture experiments, followed by multiplexed Illumina sequencing, thus allowing the production of a powerful and affordable S-locus genotyping platform for large population surveys.
Alternatively, such a reference database could be used, in combination with the new real-time Oxford Nanopore Technology, to perform selective sequencing of the S-locus alleles. major demographic or genomic (e. g. , polyploidy) events on S-locus diversity and evolution. For instance, based on short-read resequencing data from two populations of A. lyrata, one of which having experienced a strong genetic bottleneck about 70,000 generations ago, no difference in S-allele diversity was observed between the two populations, suggesting strong resilience of the S-locus to demographic events thanks to strong balancing selection  (Takou et al. , 2021). Analyses of population data could also reveal possible shifts in mating systems in peripheral populations, as for instance a shift to a selfing regime, which is considered the most common evolutionary trend in flowering plant reproduction  (Stebbins, 1974).
Strategy to infer S-locus genotypes from individual short-read resequencing data. (a) VISTA plot of haplotypes at the S-locus of Arabidopsis halleri showing extremely low sequence conservation, except for the pistil-expressed gene SRK (adapted from Goubet et al. , 2012). (b) Schematic representation of a database of fasta sequences from previously known alleles at the SRK gene, which are used as references for sequential mapping of short-reads from individual resequencing data. (c) Schematic representation of the results of sequential mapping against control genes (i. e. , single-copy genes with low polymorphism) or against nonmatching versus matching S-alleles. The focal individual is heterozygous for S-allele_2 and S-allele_4, as mapping statistics against these two references are reporting high coverage of the reference sequence (proportion of positions with at least 1 read aligned), low sequence mismatches (between the reads consensus and the reference sequence), and intermediate (about half) read depth as compared to that for control genes (because of heterozygosity at the S-locus).
Funding information Agence Nationale de la Recherche , Grant/ Award Number:  ANR-18-CE02-0020-01 ;  European Research Council , Grant/Award Number:  648321 & 679799 ;  German Research Foundation , Grant/Award Number:  DFG MU 4357/1-1 ;  Natural Sciences and Engineering Research Council of Canada (NSERC)   Discovery Grants Program, Grant/Award Number :  RGPIN-2014-05820.
F Strategy to infer S-locus genotypes from individual short-read resequencing data. (a) VISTA plot of haplotypes at the S-locus of Arabidopsis halleri showing extremely low sequence conservation, except for the pistil-expressed gene SRK (adapted from Goubet et al. , 2012). (b) Schematic representation of a database of fasta sequences from previously known alleles at the SRK gene, which are used as references for sequential mapping of short-reads from individual resequencing data. (c) Schematic representation of the results of sequential mapping against control genes (i. e. , single-copy genes with low polymorphism) or against nonmatching versus matching S-alleles. The focal individual is heterozygous for S-allele_2 and S-allele_4, as mapping statistics against these two references are reporting high coverage of the reference sequence (proportion of positions with at least 1 read aligned), low sequence mismatches (between the reads consensus and the reference sequence), and intermediate (about half) read depth as compared to that for control genes (because of heterozygosity at the S-locus).
Ethics Statements All animal procedures were carried out in accordance with the European Community council directive 2010/63/EU for the care and use of laboratory animals and were approved by our local ethical committee (N • A49007002 for rats) in addition to the French Ministry of Agriculture (authorization APAFIS#19433-2019022511329240 and B49071 for Xenopus). The NC3R's ARRIVE guidelines were followed in the conduct and reporting of all experiments using animals. Four rats were killed to prepare cerebellum membranes (in four independent experiments) to attenuate the effects of individual polymorphism in GABA A R microtransplantation experiments. For TEVC, the data were collected from oocytes collected from 12 distinct Xenopus females. In our animal facility, oocytes were collected twice a week, leading to the use of two different females.
48 h after cDNA injection, oocytes were tested using a two-electrode voltage-clamp amplifier (TEV-200A, Dagan Corporation, Minneapolis, United States), at a holding potential of -60 mV. Data were acquired with a pCLAMP system (Digidata 1440 and pCLAMP 10. 0 software from Axon Instruments). Experiments were performed at room temperature. Control experiments using DMSO (0. 5%) were performed. No change in holding currents were observed, when non-injected or injected oocytes were perfused with SOS solution with 0. 5% DMSO.
Molecular Model Preparation and Docking The models for the three receptors were prepared depending on the closest available template. The β3 homopentamer was directly based on the PDB structure 4COF. For α6β3 and α6β3γ2 heteropentamers, an additional homology modeling step was required and was based on the structure of the α1β3γ2 heteropentamer (6HUG). The sequences of the human α6, β3, and γ2 GABA A R subunits were aligned with those of the template using T-Coffee software  (Notredame et al. , 2000). The model was then prepared by homology modeling using Modeler version 9. 19 software  (Sali and Blundell, 1993)  with default settings. One hundred models were prepared, and the best model, according to the Discrete Optimized Protein Energy function (DOPE), was selected. For the three models, side chains were improved with Scwrl4  (Krivov et al.
Data Analysis In electrophysiology, the amplitude of each current response was expressed as a% of the response to GABA EC 50. The EC 50 and the Hill coefficient (nH) were determined by nonlinear regression (Figure  2B ) using the Langmuir equation with variable slope. We excluded data (i) in case of potential drift (>0. 6 mV) after pulling out the electrodes from the oocytes and (ii) when current amplitudes were < 10 nA or > 2 µA. When required, current densities were calculated using membrane capacitance. GraphPad Prism 7. 02 (GraphPad Software, San Diego, United States) was used for all graphs and statistical analyses. Normality of data distribution was validated using Shapiro-Wilk test to choose a parametric or a non-parametric test. Statistical significance tests between groups were performed using variance analysis (one-way ANOVA) followed by Tukey's post hoc test for comparison of all groups or non-parametric Kruskal-Wallis procedure, followed by the post hoc Dunn's test when appropriate.
Antagonist Activity of Fipronil on Ternary Receptors To compare the inhibitory effects of FPN on extrasynaptic and synaptic receptors, we expressed ternary α6β3δ and α6β3γ2S combinations. FPN alone (10 µM) did not induce any current. We analyzed the effects of FPN (10 µM) with increasing concentrations of GABA (Figures  4A, C ). For this purpose, we first applied 10 -9 to 10 -4 M GABA in the absence of FPN, which served as the control. This was followed by another set of experiments where the same concentrations of GABA were applied in the presence of 10 µM FPN. We measured the current density of each oocyte recorded. The concentration-response relationships for GABA in the absence and presence of FPN are shown in Figure  4. Our data show that the FPN effect is similar between the GABA A R combination: for both receptors, the addition of FPN did not modify EC 50 values, whereas the maximal effect was significantly decreased (Figures  4B, D  and Table  1 ).
(A,C) Concentration-response curves for α6β3γ2S and α6β3δ GABA A Rs. Data were best fitted by non-linear regression to the Hill equation with variable slope. (B,D) Comparison of EC 50 and GABA-currents densities obtained with α6β3γ2S (B) and α6β3δ (D) GABA A Rs, in control (CTRL) and in the presence of FPN (10 µM). For α6β3γ2S, pEC 50 was 5. 83 ± 0. 21 without FPN and 5. 77 ± 0. 28 with FPN; E max was 11. 6 ± 0. 6 pA/pF without FPN and 3. 4 ± 0. 2 with FPN. For α6β3δ, pEC 50 was 6. 24 ± 0. 12 without FPN and 6. 33 ± 0. 10 with FPN; E max was 1. 8 ± 0. 3 pA/pF without FPN and 0. 8 ± 0. 2 with FPN. The number of recorded oocytes is indicated inside the bars. For both receptors, the normality of maximal current density distribution was validated using Shapiro-Wilk test and data were analyzed with unpaired t-test.
Introduction Pharmaco-epidemiology (PE) studies the conditions and consequences of health products, i. e. drugs or medical devices usage at the population scale in real situations using methodologies developed in general epidemiology. Modern PE relies on administrative databases to perform such studies on care trajectories, i. e. on patient-centered sequences of drugs deliveries, medical procedures and hospitalizations. The use of medico-administrative databases (MADB) is useful in PE studies, since data are readily available and cover a large population. The problem with MADB is the semantic gap between raw data and the epidemiological question. On the one side, epidemiologists are looking for medical events. For instance, they would like to identify patients suffering from venous thromboembolism (VTE).
On the other side, raw data are related to reimbursements of medical acts or drug deliveries. There is no exploitable diagnosis available in administrative databases and no clinical results related to medical acts or exams. The challenge for epidemiologists is to define phenotypes of medical events  (Hong et al. , 2019) , i. e. a combination of information available in the database that reveals an occurrence of a medical event. For instance, a patient having a lower limbs doppler ultrasonography exam and few days after a delivery of anticoagulant drugs for 3 to 6 or 12 months is probably suffering from VTE. As MADB record medical exam and drugs deliveries, the above description may be used as a proxy of VTE. The Semantic Web offers a relevant framework for representing complex data patterns and linking them with domain knowledge.