dataesr/scientific-paragraphs-categorization huggingface dataset:
4-2). Two samples out of 37 were ignored because of aberrant profiles (kit10 and kit28). In order to quantify the immigration rates under the assumptions of the neutral model, we used the script provided by  Burns et al. (2016). The linear and the non-linear relationships between the OTUs and the ARGs were characterized by the maximal information coefficient (MIC) index  (Reshef et al. , 2011). Briefly, this index compares a X-and an Y-vector of values by quantifying the nonrandomness of points in a two-dimensional space. This index was calculated on all the OTUs with more than 10 counts on average, which represented 104 OTUs. A relationship was considered significant if its MIC value was observed in less than 1% of the 37950 MIC values across 100 randomized datasets. In our case, the MIC threshold values were 0.
ACKNOWLEDGMENTS We would like to thank  Dr. Bernadette Le Normand ,  Benoit Dile , and  Samuel Bouchez  ( Veterinary Cristal Network, France ) for helpful discussions. We would also like to thank  Davi Savietto  ( INRA GenPhyse ),  Patrick Aymard  ( INRA PECTOUL ),  Noemie Deschamps ,  Beatrice Roques , and  Cedric Lacassagne (INTHERES)  for their kind assistance during the experiment. We are also thankful to  Jean-Pierre Goby  ( IUT Perpignan ) for providing a donor doe from his antibiotic-free farm. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  for providing computing and storage resources. We thank  Béatrice Gabinaud  ( INRA ) for her help in the lab and  Sylvie Combes  for her careful proofreading.
Data analysis The effects of landscape parameters in determining fields' visits were examined by fitting the simulated residence times in statistical models having each habitat quality and all possible first-order interactions among them as explanatory variables. The proportion of unvisited pixels was fitted to a generalised linear model incorporating a logit link function and a binomial error term and the same explanatory variables. The relative importance of habitat qualities and their interactions was assessed by McFadden's pseudo r squared  (Long, 1997). Ternary diagrams were produced using the ggtern R package  (Hamilton, 2017)  and the R software (R Core Team, 2011).
Acknowledgements. We warmly thank the three reviewers and  Matthias Foellmer  for their beneficial comments that helped to improve the quality of this article. B. Collard was supported by  INRA  ( French National Institute for Agricultural Research, EA division ), by  CIRAD  ( French Agricultural Research Centre for International Development) , and by the  STRADIV  project (no. 1504-003 ) funded by the  Agropolis Foundation, France. Edited by: Matthias Foellmer Reviewed by: three anonymous referees www. web-ecol. net/19/15/2019/Web Ecol. , 19,  15-26 , 2019.
, 2015). We considered that the research done in WWTPs did not reflect the dilution and degradation processes of receiving waters, thus data from WWTP effluents was excluded to avoid overestimating the pollution of surface waters. However, concentrations in receiving waters were included to reflect the worst case scenario in freshwater ecosystems. The data collected in this study was presented as the mean and maximum concentration values. The classification of pharmaceuticals was based on the PubChem database  (Kim et al. , 2015)  and previous publications (e. g. aus der  Beek et al. , 2015; Hughes et al. , 2012; Liu and Wong, 2013). In the present study, some compounds that are not pharmaceuticals for therapeutic, preventive, and diagnostic purposes were given due to their high detected frequencies together with pharmaceuticals.
In the absence of chronic toxicity data, short-term E(L)C50 data was used. Toxicity data was mainly obtained from the United States EPA ECOTOX database (https://cfpub. epa. gov/ecotox/quick_query. htm). When no data was found in the database, short and long-term toxicity data was obtained from published literature by performing searches in Web of Sciences and Google Scholar using the pharmaceutical and the organism names as keywords. Finally, when no experimental toxicity data was available, acute toxicity values of compounds were calculated using the ECOSAR™v. 1. 11 and QSAR Toolbox 3. 3 by importing chemical name and CAS number. When more than one dataset on toxicity was obtained at the same nutrient level, the one indicating the strongest effect was used. The toxic data that is selected as the toxicological benchmarks for the calculation of the PNECs is shown in Table  S1.
Acknowledgements The present research has been supported by the  SOLUTIONS  project that is funded by the  European Union Seventh Framework Programme  ( FP7-ENV-2013-two-stage Collaborative project)  under grant agreement number (603437) and a personal fellowship to the first author by the  China Scholarship Council. We appreciate the very valuable comments and suggestions of  Dr. Gerd Maack  and  Dr. Peter Carsten  von der Ohe from the  German Federal Environment Agency (UBA) in Germany. The authors thank all scientists and agencies who offered us the data and additional information.
Method Based on other published studies  [1, 2] , we present an updated review of the literature on most of the PMP22-related neuropathies, along with descriptions of the main pathological features of these models. We present here a review and a correlative study rather than a detailed scientific presentation. The review of the literature was conducted by searching in Pubmed, Google Scholar, and Sciencedirect with the In our laboratory we have studied the nerves of most of these mutants: we discuss here our personal data (all the micrographs which are presented in this review have been made by the same observer, on at least four blocks of one sensorimotor sciatic nerve of each model). We also discuss the usefulness of these morphological data in the understanding of human nerve lesions examined on pure sensory nerve biopsies (superficial peroneal or sural nerves) and of treatments of hereditary sensorimotor neuropathies such as CMT and HNPP.
2 l/min (normal: 4-8 l/min), a global end diastolic volume index (GEDVI) of 610 ml/m 2 (normal: 680-800 ml/m 2 ), an extravascular lung water index (EVLWI) of 26. 7 ml/kg (normal: 3-7 ml/kg), and a pulmonary vascular permeability index (PVPI) of 6. 3 (normal: 1. 0-3. 0). Chest computed tomography showed vessels enlargement, peribronchial cuffing, bilateral Kerley lines, alveolar edema, and abundant right pleural effusion needing pleural drainage. The protein level in the pleural fluid was 43 g/l and the microbiological culture was sterile. The viral investigations (IgM and RT-PCR in the blood) confirmed an acute infection by Hantavirus. The complete RNA coding sequence of the S RNA segment (GenBank accession no. MG785209) was also generated and compared with those of the other 5 previous hantavirus cases from French Guiana using a Bayesian approach.
Toxicity of Polyunsaturated Fatty Acid ARPE-19 cells were plated into 96-well plates (3 × 10 4 cells/well) and cultured for 24 h to reach confluence before DHA treatment. The cells were treated in medium with 1% FBS (1% FBSM), or with H 2 O 2 prepared in medium with FBS (1%, v/v) to a final concentration of 600 µM. Cells were then treated with DHA, D 2 -DHA and D 4 -DHA, at different concentrations (0-80 µm) for 24 h. Control cells were incubated with DMSO (0. 2%). After 4 h, cell viability was determined in triplicate using the MTT colorimetric assay. Results were expressed in percentage of viable cells normalized with control conditions in the absence of PUFA and H 2 O 2 stress. Sigmoidal doses-responses linked to the toxicity were obtained using GraphPad prism software, allowing to calculate IC 50 values described as the concentration leading to 50% of cell viability.
Protection of Lipophenols against All-trans Retinal ARPE-19 cells were plated into 96-well plates (3 × 10 4 cells/well) and cultured for 24 h to reach confluence before lipophenol treatment. Cells were treated with serum free medium containing lipophenols at different concentrations (0-80 µM) for 1 h. Then all-trans-retinal was added to a final concentration of 25 µM for 4 h (in DMF), before rinsing with medium. Cell viability was determined 16-20 h later, in triplicate samples, using the MTT colorimetric assay. Control cells were incubated with DMSO (0. 2%) and DMF (0. 14%). The data are expressed as the percentage of untreated cells (CTL, without all-trans-retinal).
2. 3. 6. Fluorescence-activated Cell Sorting Analysis FACS was performed with the BD Accuri C6 Flow Cytometer and BD Accuri C6 Software for data acquisition (BD Biosciences). This flow cytometer is equipped with an excitation laser at 488 nm, and a fluorescence detector FL1 533/30 nm. Data were collected to 10,000 events for each sample using a flow rate of 35 µL/min. Parameters obtained for data analysis included: cell size from the forward scatter (FSC), granularity from the side scatter (SSC) and the green fluorescence of the stained cells with C11-Bodipy 581/591. First the SSC-A versus FSC-A dot-plot was used to gate the cellular population and remove the cellular debris. Then the SSC-A versus SSC-H dot-plot was used to remove cell doublets in the previous gate to give a final gate. The cellular population in the last gate designed was plotted on count versus log green fluorescence to give the fluorescence mean intensity of FL1.
Subsequently, the cells were either analyzed (white rectangles) or exposed to white light at 5000 lux (grey rectangles) during 1 h. Fluorescence intensities were determined by FACS with excitation wavelength at 488 nm and emission filter at 530 nm. All data are presented as means ± SD (n = 3 independent experiments, each experiment in triplicate). The data are expressed as the percentage of untreated cells (CTL). Statistical analysis was performed using a one way ANOVA followed by Bonferroni or Dunn post-hoc test; * p < 0. 05, ** p < 0. 01, *** p < 0. 001, versus untreated cells (CTL); # p < 0. 05, ## p < 0. 01, ### p < 0. 001, versus white light-exposed cells; • p < 0. 05, •• p < 0. 01, ••• p < 0. 001, versus natural DHA treated cells. When the cells were stressed by serum starvation (1% FBSM) for 48 h (Figure  2 ), preincubation with natural DHA caused an increase in lipid peroxidation status compared to untreated cells (CTL).
Expertise is essentially clinical and empirical, and the techniques are defended by the surgical teams who created them or who regularly performed them. However, in clinical practice, physicians and physiotherapists are convinced of the effectiveness of these procedures. Moreover, from an ethical point of view, performing prospective, controlled studies is complicated. However, studies comparing techniques could be carried out. The use of databases for case-control studies, as has been done for HO, may be a solution for evaluating surgical procedures, especially since the development of tools such as artificial intelligence (i. e. , database creation from text file reading).
Outcome Measures Self-reported data on previous and current psychiatric diagnoses were collected, including the following: eating disorder, chronic anxiety, chronic depression, attention problems, hyperactivity, eruptive/aggressive behavior, burnout syndrome, schizophrenia, autism, Asperger syndrome or pervasive developmental disorder, and other mental health problems. In addition, participants answered two questions about suicide attempts, "Have you ever thought about committing suicide?" and "Have you ever tried to committing suicide?" both with the answering options "Yes," "No," and "Don't want to answer this question. " The following standardized measures were taken to screen for psychiatric conditions: The Hospital Anxiety and Depression Scale (HADS) is a 14-item self-rating instrument for dimensional and categorical aspects of anxiety and depression  (38).
Furthermore, the relative importance of parameters (i. e. , the amount of explained variance accounted for by a certain parameter) was investigated using the approach of Lindeman et al. (46). The data analysis was performed using the R environment statistics (Version 3. 2. 2) (47) including the package relaimpo and SAS software, Version 9. 4 of the SAS System for Windows  (48). A p value of less than. 05 was considered significant. Because of the explorative nature of the dsd-LIFE study, no corrections for multiple comparisons were performed and the results are to be considered as exploratory.
The dsd-LIFE Group are as follows:  Birgit Kohler , Berlin;  Peggy Cohen-Kettenis  and  Annelou de Vries, Amsterdam; Wiebke Arlt, Birmingham ;  Claudia Wiesemann ,  Gottingen ;  Jolanta Slowikowska-Hilczer ,  Lodz ;  Aude Brac de la Perriere, Lyon ;  Charles Sultan  and  Francoise Paris , Montpellier;  Claire Bouvattier , Paris;  Ute Thyen , Lubeck;  Nicole Reisch , Munich;  Annette Richter-Unruh , Munster;  Hedi Claahsen-van der Grinten ,  Nijmegen ;  Anna Nordenstrom , Stockholm;  Catherine Pienkowski , Toulouse; and  Maria Szarras-Czapnik , Warsaw. We thank the persons who participated in dsd-LIFE and to all staff at the recruiting centers. We also thank the support groups in the different countries that have supported the study. Source of Funding and Conflicts of Interest: This study was funded by the  European Union Seventh Framework Program  (Grant  305373  [FP7/2007-2013], to all authors).
These MS/MS sequences were then used to confirm and identify the putative peptide sequences inferred from the transcriptomic analysis of the T. aethiops venom gland transcriptome. Orbitrap mass spectrometry analysis confirmed the presence of U 5 -PSDTX-Ta1a in this venom, albeit in a low amount. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium  [24]  via the PRIDE  [25]  partner repository with the dataset identifier PXD016337.
Venom Gland Transcriptome and Predictive Precursor Sequences The RNA sequencing of the T. aethiops venom apparatus resulted in the acquisition of 188,907,225 demultiplexed raw reads with a length of 150-bp. They were assembled de novo using Oases graph assembler, which resulted in 16,047 contigs. Among them, 230 transcripts were expressed to a frequency higher than or equal to 100,000 hits and were subsequently addressed using the NCBI blastp program for functional annotations (Table  S1 ). These annotated transcripts were classified into four categories (i. e. , venom peptides, venom proteins, protein maturation, and others; Figure  2 ). This permitted us to deduce that venom peptide toxins have high transcription levels accounting for 40% of the most expressed transcripts by the venom glands (400,732 Reads Per Million (RPM)).
Thus, we identified six putative venom peptide precursors. They shared an average of 52% identity, allowing us to define the following consensus sequence from the alignment of the prepro-regions: MXLSYXXLXLXVXFXLAIXFXPXXXAXAXSVGMADAEXXALAESXANALADAXP. Proportions of addressed functions of the 230 most expressed transcripts (≥100,000 hits) from T. aethiops venom glands. Functional annotations were made with the NCBI blastp program. The category "Others" groups functions involved in cellular metabolism. Contigs coding for venom peptides accounted for 40% of the transcripts expressed by the venom glands. Seventeen percent of the transcripts coded for venom proteins such as venom allergens and phospholipases, and 3% of the transcripts were dedicated to protein maturation. The contigs names and functions are presented in detail in supplementary data (Table  S1 ).
The contigs names and functions are presented in detail in Supplementary Data (Table  S1 ). Tetraponera aethiops peptide precursors seemed to be related to other venom peptide precursors reported for ants. Indeed, this consensus sequence shared 43%, 35%, 52%, and 48% sequence identity with consensus prepro-sequences obtained from studies on Odontomachus monticola  [26] , Myrmecia pilosula  [27] , Myrmecia gulosa  [28] , and Tetramorium bicarinatum venoms  [29] , respectively (Figure  3 ). Unlike prepro-regions, mature regions of these precursors exhibited much more intra-and interspecific amino acid sequence variabilities (Figure  4 ). The complete cDNA sequences of venom peptide precursors from Tetraponera aethiops venom were submitted to GenBank (for accession numbers, see the legend to Figure  4 ).
peptide precursors from Tetraponera aethiops venom were submitted to GenBank (for accession numbers, see the legend to Figure  4 ). Identical residues are highlighted in magenta. Similar residues are highlighted in blue. The consensus sequence of the prepro-region of Tetraponera aethiops venom peptides shared 43%, 35%, 52%, and 48% with the consensus prepro-regions sequences obtained from previous studies on Odontomachus monticola  [26] , Myrmecia pilosula  [27] , Myrmecia gulosa  [28] , and Tetramorium bicarinatum (Superfamily A)  [29] , respectively.
Several venom proteins were also expressed in T. aethiops venom glands including phospholipases and venom allergens accounting for 17% of the venom gland expression (164,978 RPMs). Additionally, 3% of the transcripts relied on protein maturation (33,782 RPMs) with enzymes implicated in the formation of disulfide bonds such as protein disulfide isomerase (3347 RPMs). In keeping with protein maturation, an angiotensin-converting enzyme (521 RPMs) and two types of venom Dipeptidylpeptidase IV (2356 RPMs and 736 RPMs) were identified within the transcripts. These enzymes are likely involved in the processing of bioactive peptides  [30, 31]. RNAseq data are available on the European Nucleotide Archive website (https://www. ebi. ac. uk/ena) under the following study accession number: PRJEB35699.
Identical residues are highlighted in magenta. Similar residues are highlighted in blue. The consensus sequence of the prepro-region of Tetraponera aethiops venom peptides shared 43%, 35%, 52%, and 48% with the consensus prepro-regions sequences obtained from previous studies on Odontomachus monticola  [26] , Myrmecia pilosula  [27] , Myrmecia gulosa  [28] , and Tetramorium bicarinatum (Superfamily A)  [29] , respectively. peptide precursors from Tetraponera aethiops venom were submitted to GenBank (for accession numbers, see the legend to Figure  4 ). Identical residues are highlighted in magenta. Similar residues are highlighted in blue. The consensus sequence of the prepro-region of Tetraponera aethiops venom peptides shared 43%, 35%, 52%, and 48% with the consensus prepro-regions sequences obtained from previous studies on Odontomachus monticola  [26] , Myrmecia pilosula  [27] , Myrmecia gulosa  [28] , and Tetramorium bicarinatum (Superfamily A)  [29] , respectively.
Several venom proteins were also expressed in T. aethiops venom glands including phospholipases and venom allergens accounting for 17% of the venom gland expression (164,978 RPMs). Additionally, 3% of the transcripts relied on protein maturation (33,782 RPMs) with enzymes implicated in the formation of disulfide bonds such as protein disulfide isomerase (3347 RPMs). In keeping with protein maturation, an angiotensin-converting enzyme (521 RPMs) and two types of venom Dipeptidylpeptidase IV (2356 RPMs and 736 RPMs) were identified within the transcripts. These enzymes are likely involved in the processing of bioactive peptides  [30, 31]. RNAseq data are available on the European Nucleotide Archive website (https://www. ebi. ac. uk/ ena) under the following study accession number: PRJEB35699.
Discussion Defensive venoms such as those of bees or fishes are arguably comprised of conserved toxins acting primarily to trigger pain and being less complex in composition than predatory venoms  [1]. Figure  6. Of Tetraponera aethiops mature venom peptides with similar venom peptides from ant venoms  [28, 29, 34, 36]. Alignments were generated with the Muscle program in Seaview version 4. 6. 1 and edited using BOXSHADE version 3. 2. Conserved residues are highlighted in cyan, identical residues are highlighted in magenta and similar residues are highlighted in blue.
5 kV, and the sheath gas and auxiliary gas were set at 50 and 10 psi, respectively. The acquisition range was from 100 to 2000 m/z. The area value of each peak corresponding to a peptide was manually integrated using the peak ion extraction function in Xcalibur software (version 4. 0, ThermoFisher Scientific, Courtaboeuf, France). The relative peak area indicates the contribution of each peptide to all the peptides identified in the venom, providing a measure of relative abundance.
Peptides were analyzed in the Orbitrap cell, at a resolution of 120,000, with a mass range of m/z 350-1550. Fragments were obtained through high collision-induced dissociation (HCD) activation with a collisional energy of 27%. Data were acquired in the Orbitrap cell in a Top20 mode, at a resolution of 17,500. For the identification step, all MS and MS/MS data were processed with an in-house Peaks software (BSI, version 6. 0) to perform de novo sequencing. The mass tolerance was set to 10 ppm for precursor ions and 0. 02 Da for fragments. The following modifications were allowed: oxidation (Met) and pyroglutamic acid (Glu). De novo peptide sequences with Average Local Confidence (ALC) higher than 60% were used for the peptide identifications.
Contigs Quantification The read pairs were assembled twice with drap (version 1. 9. 1)  [48]  using the de Bruijn graph assembler called oases (parameter: -dbg oases). The assembly metrics were produced with the assemblathon_stats. pl scripts. Raw reads were aligned on the contigs with bwa mem (version 0. 7. 12-r1039)  [49]  using the default parameters and the alignment files were sorted, compressed, and indexed with samtools view, sort, and index (version: 1. 3. 1) using the default parameters  [50]. The quantification files were generated with samtools idxstats (version: 1. 3. 1), giving us the length of each contig in base pairs along with the number of hits, corresponding to the number of sequences from RNAseq reads which aligned on a given contig. To calculate the expression rate of transcripts discovered in the venom gland transcriptome, the appellation Reads Per Million (RPM) was chosen over the traditionally used Transcripts count Per Million (TPM) value.
Precursor Identification and Mature Sequences RNAseq data were translated using a translate program command lines (emboss package, command line: transeq) in order to obtain the potential Open Reading Frames. Then, the fragments of sequences obtained during the de novo Orbitrap mass spectrometry-based sequencing were aligned against these data by using the command-line NCBI BLAST program (ncbi-blast-2. 6. 0+ package, command line: blastp, parameter: -matrix PAM30) with adapted parameters for short sequences, allowing us to find the complete peptide sequences and the name of the contigs on which they aligned. The masses of mature peptide sequences, obtained from these different approaches, were systematically verified using the peptide mass program from ExPASy portal (https://expasy. org) and compared to those obtained through mass spectrometry.
The isoelectric points, net charges and percentage of hydrophobic amino acids were calculated using ExPASy (https://web. expasy. org/compute_ pi/), PepCalc (https://pepcalc. com/), and Peptide Property Calculator V3. 1 (https://www. biosyn. com/ peptidepropertycalculator/peptidepropertycalculator. aspx). Secondary structure predictions were conducted with the PEP-FOLD3 server  [35]. Signal sequences and transmembrane domains were predicted with the phobius program available at http://phobius. sbc. su. se/. Sequence similarities were searched for using the NCBI BLAST program presented in the Uniprot server with the default parameters. Alignments were achieved and sequence identity percentages were calculated with the EMBL-EBI server and the MUSCLE program with the default parameters  [52]. They were then edited using Seaview version 4.