dataesr/scientific-paragraphs-categorization huggingface dataset:
efsa. europa. eu. • > 6 amino acids long • Stable to chemical modification after food processing • Peptides validated in different papers and evaluated in food matrices similar to those used in ThRAll • Specific for each food at a species level checked by BLAST searching against UniProt Knoweldgebase (unreviewed), and the International Nucleotide Sequence Database Collaboration (INSDC) resources • Proteins extracted from ThRAll incurred matrices (chocolate, broth powder) Ingredients spiked into blank ThRAll matrices • Extracts reduced, alkylated and digested with trypsin and analysed by untargeted high resolution MS/MS analysis • Peptides identified common to spiked and incurred matrices which are > 6 amino acids long Have no missed tryptic cleavages sites Specific for each food at a species level checked by BLAST searching against Uniprot (unreviewed) and INSDC resources 1: references At least three peptides (at least one quantifier and two qualifiers) identified for each allergenic ingredient with relevant selective reaction monitoring transitions Candidate peptide marker list (1) Nwaru, B.
Histological Analysis Histological and re-epithelization scores on hematoxylin-and-eosin (HE) stained colonic sections were calculated as previously described (Vidal-Lletjós, et al. , submitted) after blind microscopic assessment performed by the histological platform Histalim (Montpellier, France). Quantitative evaluation of well-oriented crypts and cell numeration in periodic acid-schiff (PAS) stained 4-µm transversal colon sections was determined using the image analysis software Pannoramic Viewer v. 1. 15. 4 (3DHISTECH, Budapest, Hungary). Paraffin-embedded distal colon samples were cut into 4 µm-thick sections, mounted on positively charged slides and dried. Immunohistochemical stainings of Ki67 and Caspase 3 were performed on the Discovery XT Automated IHC stainer using either Ventana DAB MAP detection kit (Ventana Medical Systems, Tucson, AZ, USA) or the Ventana CHROMO MAP detection kit (Ventana Medical Systems, Tucson, AZ, USA).
Revealing was performed using enhanced chemiluminescence (ECL system, Pierce Biotechnology, Courtaboeuf, France), and bands were quantified by densitometry using the FluorChem FC2 device and the AlphaView software (Cell Biosciences, Santa Clara, CA, USA). GAPDH expression (Ab9484, mouse monoclonal antibody, Abcam, Cambridge, UK) was used to ensure consistent protein loading and transferring.
Evaluation of Adherent Mucosal Microbiota Composition From the scrapped adherent-mucosa, bacterial DNA extracts were obtained by using the PowerFecal DNA Isolation kit (MoBio Laboratories, Carlsbad, CA, USA) according to the manufacturer's protocol. Specific regions of the bacterial 16S rDNA gene were amplified using real-time qPCR. Eubacteria were quantified by real-time qPCR using specific primers (HAD-1: 5 -TGGCTCAGGACGAACGCTGGCGGC-3 and HAD-2: 5 -CCTACTGCTGCCTCCCGTAGGAGT-3 ), annealing at 59 • C for total bacteria. In addition, the V3-V4 region amplification of the 16S rDNA gene was performed and sent to the GenoToul platform (Castanet-Tolosan, France) for MiSeq Illumina sequencing using the MiSeq kit V2 2 × 250 bp. Data quality check and analysis were performed using the INRA-Migale server, as previously published  [21].
Acknowledgments: The authors greatly acknowledge  R. Onifarasoaniaina  and  M. Favier  from the  Cochin HistIM Facility ,  Morgane Dufay  who helped to take care of the animals,  Maxime Evrard  and  Julie Bonnereau  for their involvement in the experiment. Also, they want to thank  Armor Protéines  ( Saint-Brice-en-Cogles, France ) for providing the dietary proteins used to prepare diets.
, 2011). We assume that the use of two different climate products does not affect the analysis much, as both data sets are observation based and were treated with the same perturbation approach (see below). Current cropland patterns were selected in model postprocessing. The grid cell-and crop-specific area was obtained from MIRCA2000  (Portmann et al. , 2010 ) data set at 0. 5 • resolution. The experiment design consisted of separate simulations for five crops (maize, rice, soybean, spring wheat, and winter wheat), under one baseline temperature scenario (T0) and five levels of globally uniform temperature increases (T1, T2, T3, T4, and T6). Moreover, four management settings were simulated, which we call control management setting (T-sensitive growing period & Rainfed) and three adaptive management setting (Fixed growing period & Rainfed, T-sensitive growing period & Irrigation, and Fixed growing period & Irrigation).
All GGCM output data are made publicly available at zenodo. org repository. The DOI references are provided in Table  3. For data processing, we used R (R Core Team, 2018) and R-packages for handling netcdf4 Earth's Future 10. 1029/2018EF001130   (Pierce, 2015) , performing computation  (Dowle & Srinivasan, 2017; Wickham, 2011) , and plotting results  (Wickham, 2009).
LC-MS peptide identification and length analysis MS analysis was performed as previously described  (Egger et al. , 2016; Egger et al. , 2017; Kopf-Bolanz et al. , 2014). Briefly, after sample filtration through Microcon columns (Ultracel YM-30, Millipore), the peptides were separated on a Rheos 2200 HPLC (Flux Instruments, Switzerland) equipped with an XTerra MS C18 column (3. 5 μm, 1. 0 mm × 150 mm, Waters). The HPLC was directly coupled to a linear ion trap mass spectrometer (LTQ, Thermo Scientific, Switzerland) using an ESI interface. The signals of an m z -1 between 100 and 1300 were measured in multiple overlapping mass windows. The MSMS spectra of the overlapping mass windows were merged and peptides were identified with a search in Mascot (Matrix Science) using a home built database containing all typical milk proteins of different species.
Peptides from the five main milk proteins (β-casein, α s1 -casein, α s2 -casein, κcasein, and β-lactoglobulin) were monitored. The minimal criteria for peptide identification was an ion score of 20 and being on first place of identification. If questionable, the results were manually verified by checking the fragmentation pattern (peptide coverage and fragmentation pattern). To visualize the peptide abundance, individual amino acids within the identified peptides from the abovementioned dairy proteins were summed up and a color code was applied. The amino acid counting was performed with a home built Perl script extending Mascot search engine, the color codes were adjusted using Microsoft Excel. The colors range from green to red, indicating low and high recurrence of specific amino acids, respectively.
Replication of experiments, statistical analysis and normalization between digestion models Static and dynamic in vitro digestion time course experiments were performed three times independently (n = 3). Samples were analyzed in triplicate by mass spectrometry and in duplicate by HPLC. All samples were separated by SDS-PAGE, and representative gels are shown. The Spearman correlation matrix was calculated with R software using rank order data (Spearman). Normalization between static and dynamic IVD models was performed in order to obtain equal amounts of SMP input. Dilutions of SMP during the different digestion phases were considered for each model. The pig experiment was designed to obtain typical digestion patterns for each sampling section along the digestive tract. Therefore, the pigs were fed three times with SMP, with the inconvenience that the calculation of a precise amount of SMP present in each sampling section at the time of sacrifice is not possible.
Peptide generation during IVD Peptides were analyzed by mass spectrometry in three independent experiments of static and dynamic time course experiments. Peptide identifications were performed for the five most abundant milk proteins (β-casein, β-lactoglobulin, α s1 -casein, α s2 -casein, and κ-casein), considering a minimal length of five amino acids. At first, the peptide patterns of the gastric and intestinal endpoints were compared to the peptide patterns from the pig in vivo data  (Egger et al. , 2017) , by representing the data in a line plot where the protein sequence is shown on the x-axis and the number of times an amino acid was identified within a peptide of the corresponding protein on the y-axis (Fig. 2a ). In the gastric phase, the peptide patterns were very similar, correlation between static and dynamic IVD calculated for αs 1 -casein achieved a factor of 0.
Acknowledgements COST Action  FA1005  INFOGEST (http://www. cost-INFOGEST. eu/) is acknowledged for providing funding for travel, meetings, and conferences. We thank  Urs Kämpfer  from the  Department of Chemistry and Biochemistry, University of Berne  for performing the HPLC analyses. The authors declare that they have no conflict of interest. Contributors: E. L. , M. O. , D. D. , V. G. , and P. R. , designed research; E. L. , M. O. , P. R. , Sch. P. , B. Ch. , and D. D. conducted research; E. L. , P. R. , M. O. , and  B. Ch. , analyzed data and performed statistical analysis; E. L. , P. R. , M. O. wrote the paper; E. L. , M. O. , and P. R. had primary responsibility for final content. St. P. was responsible for animal facility. All authors have read and approved the final manuscript.
MATERIALS AND METHODS The development, architecture, geometry, and yield of five winter wheat cultivars were characterized in field conditions using two contrasting row spacing systems. The five cultivars were chosen to broadly represent a range of characteristics including the ability to cover the soil and the nature of leaf stature (from erectophile to planophile). Data were analyzed to assess differences existing at plant and crop scale between row spacing treatments, and identify possible differences in behavior between cultivars. The data leading to the results presented are provided as Supplementary Data Sheet S2.
03 m 2. Photographs (0 • and 57 • ) were processed using the SATVA software 1  to determine the ground cover. Additionally, a direct measure of PAIg was made at flowering by dissecting 45 plants (three repetitions of 15 plants) per treatment and scanning all phytoelements. The harvest was conducted mechanically on August 2 (∼2451 • Cd after sowing) and grain yield (Mg/hectare) was measured on an area of 10 m 2 per treatment, without replicates.
The lengths were measured with a ruler, then plants were dissected and blade images were obtained with a flat-bed scanner and were processed using the program Lamina2Shape  (Dornbusch and Andrieu, 2010)  to measure their area and green fraction. To follow the dynamic of the insertion angle of leaf blades i leaf (i) , destructive measurements were carried out on 30 tagged main stems, at seven dates from the start of stem elongation to full senescence. Photographs of stems with their attached leaves were analyzed with the POPCORN software (Weiss and de Solan (see footnote 1), to register the coordinates of points along the stem and the leaf midrib. An R routine was developed to compute the angle of insertion i leaf (i) for each leaf rank, considering only leaves having at least 20% green area.
FUNDING MA was funded by a CIFRE convention between  ARVALIS -Institut du Végétal and ANRT. Research was primarily funded by  INRA  and  ARVALIS -Institut du Végétal  with complementary support from the  Agrobiosphere program of the National Research Agency  ( Wheatamix  project  ANR-13-AGRO-0008 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Confocal laser scanning microscopy For the BIFC assay, images of fluorescent protoplasts were obtained with a Leica TCS-SP2-AOBS spectral confocal laser scanning microscope equipped with a Leica HC PL APO x 20 water immersion objective. YFP and chloroplasts were excited with the 488 nm line of an argon laser (laser power 35%). Fluorescence emission was detected over the range 495 to 595 nm for the YFP construct and 670 to 730 nm for chloroplast autofluorescence. Images were recorded and processed using LCS software version 2. 5 (Leica Microsytems). Images were cropped using Adobe Photoshop CS2 and assembled using Adobe Illustrator CS2 software (Abode, http://www. abode. com). Zhai, Z. , Jung, H. I. , and Vatamaniuk, O. K. (2009). Isolation of protoplasts from tissues of 14-day-old seedlings of Arabidopsis thaliana.
ACKNOWLEDGMENTS The authors would like to thank  Prof. Mark Estelle  ( UCSD, San Diego, CA, USA ) and  Prof Jason Reed  ( UNC, Chapel Hill, NC, USA ) for providing seeds of single and multiple mutants. The authors also thank  Prof. Judy Callis  ( UC, Davis, CA, USA ) for providing Arabidopsis line and the plasmid expressing  HA3-ARF1. We also thank  Hana Martinková  for help with phytohormone analyses. This work was supported by the  Swedish Research Council (VR) , the  Swedish Research Council for Research and Innovation for Sustainable Growth (VINNOVA) , the  K. & A. Wallenberg Foundation , the  Carl Trygger Foundation , the  Carl Kempe Foundation , the  University of Picardie Jules Verne , the  Regional Council of Picardie , the  European Regional Development Fund , and the  Ministry of Education, Youth and Sports of the Czech Republic.
Statistical Analysis The dataset from larval mortality, obtained at 48 h, consisted of 131 observations obtained with the two strains (Bt407WT, ∆plcR), at 25 or 37 • C, and with spores or vegetative cells. Three to 4 biological replications and 4 to 5 doses were used per condition. The LD 50 (dose which kills 50% of totally treated larvae) values were determined by non-linear regression using the statistical software JMP9 (SAS Institute Inc. , Cary, NC, USA). The model used for the regression was a derivative of the Hill equation X n /X n +a n , where x is the dose used and "a" and "n" are the computed parameters. The parameters, determined with their 95% confidence intervals (CI 95 %), represent the LD 50 (a) and the steepness of the curve (n). LD 50 s were considered as significantly different, with p < 0.
The most common fate of duplicated genes is loss through accumulation of deleterious mutations or other mechanisms. This fate is especially likely for loci that are sensitive to dosage or have strong phenotypes when mutated. We identified clusters of orthologs that contained exactly one protein in C. elegans, the two outcrossing, and all three parthenogenetic Panagrolaimus species (1:1:. :1 orthologs), as well as clusters where the parthenogenetic P. sp. PS1159 had more proteins than the obligate outcrossing P. sp. ES5 (parthenogen-excess clusters). Using the database of C. elegans RNAi phenotypes on wormbase. org (https://wormbase. org;  Lee et al. , 2018)  we retrieved phenotypes for 172 of the 703 single-copy one-to-one orthologues and 695 phenotypes for the 1,618 parthenogen-excess clusters.
To explore and independently validate this pattern of absence, we used figmop  (Curran et al. 2014)  to build a gene model based on motifs in the C. elegans gene and screened the P. sp. PS1159 genome. We found one candidate in this genome which was paralogous to the orthologues of mlh-1 in P. sp. ES5, P. superbus, and the outgroup. We identified a Panagrolaimus-specific region at the N-terminal end of the protein, as well as residues specific to the parthenogenetic species. A splits network (not shown) constructed for the proteins also indicates Panagrolaimus-specific divergence. Thus, while the general pattern of presence or absence for orthologues in comparison to the model organism does not allow for inferences about loss or gain of function in the panagrolaims, changes in single genes identifiable in our dataset open avenues for future studies into the molecular underpinnings of parthenogenesis.
Genome Assembly We constructed genome assemblies from Illumina short read libraries. After running first pass assemblies with the CLC Assembly Cell v. 4. 2 we applied the khmer pipeline  (Brown et al. 2012)  to digitally normalise read coverage on ES5, P. superbus, JU765 and PS1159 data. We identified and removed contaminating bacterial sequences using the blobtools approach  (Kumar et al. 2013). The cleaned reads were reassembled using the Velvet assembler  (Zerbino and Birney 2008)  exploring different k-mer sizes. We then employed RNA-Seq derived mRNA predictions (see below) to scaffold the genomes with the SCUBAT pipeline (https://github. com/elswob/SCUBAT). For P. superbus the genome input to SCUBAT originated from a CLC assembly, as this proved better than the tested Velvet assemblies.
2018; Schiffer et al. 2018) , into this analysis, we aimed to gain a more general insight into the origin of parthenogenesis in Nematoda. We extracted all Augustus predicted genes for each species and mapped RNA-Seq read sets against the coding sequences using the CLC mapper (v. 5. 0), requiring a sequence identity of 90% and a read length threshold of 80%. We performed the same mapping approach with genomic reads against repeat masked and unmasked genomes. We used BamTools  (Barnett et al. 2011) (v. 2. 3. 0)  to sort the mappings, SAMtools (v1. 0-13)  (Li et al. 2009 ) to create mpileups, ! 11 VarScan (v2. 3. 6) (Koboldt et al. 2012)  to call variants, and collated the minor variant frequency spectrum (folded site spectrum) for each species. We then implemented a two sample Kolmogorov Smirnov (KS) test available in the Julia  (Bezanson et al.
2012)  programming language to analyse whether the observed variant frequencies are different under the null hypotheses that the frequencies are sampled from the same distribution (Supplementary Table  1 ): we first sampled 20,000 data points from each distribution and directly compared these with the KS test; next we ran 50,000 bootstrap replicates on 20,000 randomly mixed samples per distribution, and then measured the exact p-value in the bootstrapped distribution. For the GRAMPA analysis testing allo-vs. auto-polyploidy we used all ~15k FastME trees from running Orthofinder mid-point rooted with NOTUNG (v2. 8. 1. 7) (Chen et al. 2000). To validate these results we also used a more comprehensive initial dataset including the DL137 and PS1579 proteomes and selected 2313 groups of orthologues from OrthoMCL that had a 2:1 ratio of parthenogenetic to sexual species.
Panagrolaimid and other nematodes We identified Pfam domain annotations in the proteomes of our species set using InterProScan. Statistical differences in abundance of individual Pfam domains was analysed using a two-sided Fisher's exact test (Null Hypothesis Significance Testing -NHST), corrected for multiple testing using the Benjamin-Hochberg algorithm (for false discovery rate) implemented in the R (R Core Team) statistical language. We also analysed transcriptome assemblies from P. sp. DL137 (for which we have no genome) and P. sp. PS1159 (to retrieve proteins potentially missed in the genome-wide annotations). To support and further investigate the results of the NHST with an independent method, we employed classifier analysis in a support vector machine (SVM) framework to address two pivotal questions: (1) Do the patterns of annotation frequency derived from InterProScan correspond to defining differences between species groups? (2) Are the annotations that together form separate and clearly identifiable patterns for each group associated with functions that differ between them? Given a number of samples, each belonging to one of The comparison between the real classifier incorporating true sample-to-class mappings in the training set and the permuted classifier was statistically significant for Panagrolaimidae: T = 7, p < 0.
Detection of Horizontal Gene Transfers To detect candidate horizontal gene transfers (HGT), we used Alienness  (Rancurel et al. 2017)  to calculate an Alien Index (AI)  (Gladyshev et al. 2008; Flot et al. 2013). Briefly, all Panagrolaimidae predicted proteins were compared against the NCBI's nr protein database using BLAST  (Altschul et al. 1990)  with an E-value threshold of 1e -3 and no SEG filtering. BLAST hits were parsed to retrieve associated taxonomic information, using the NCBI taxonomy database. For every Panagrolaimidae protein returning at least one hit in both metazoan and non-metazoan species, we calculated the AI: AI = ln(best metazoan E-value + E -200 ) -ln(best non-metazoan E-value + E -200 ) When either no significant metazoan or non-metazoan BLAST hit was found, a penalty Evalue of 1 was automatically assigned.
Codon usage Codon usage for the predicted genes was analysed with GCUA  (McInerney 1998) and codonW (v. 1. 4. 4 ; http://codonw. sourceforge. net). We also used codonW to compare codon usage between genes putatively gained through HGT (see below) and the genomic background of the host nematodes. We used tRNAscan-SE v1. 3. 1 (Lowe and Eddy 1997) to identify tRNA genes, selecting the implemented Cove probabilistic RNA prediction package as the search engine.
Signatures of adaptive evolution We used Crann  (Creevey and McInerney 2003)  to explore signatures of selection in panagrolaim genes. Over 20,000 OrthoMCL derived orthology cluster protein alignments, built using clustal-omega, were reverse-translated into DNA alignments with trimAl and SAMtools. We retained 7,335 alignments that contained at least one parthenogenetic and one male female Panagrolaimus species. Further filtering of short and outlier sequences within each of these gene family alignments, using amino acid versions of the sequence alignments, removed poorly-defined clusters. If the length of any sequence was less than half the length of the overall alignment it was removed. For all remaining sequences in each alignment, two filtering steps were carried out: A distance matrix was constructed from the amino acid versions of the alignments using ProtDist from the PHYLIP  (Felsenstein 1993)  package and the JTT model of evolution was used to construct a neighbour-joining tree with neighbour from the PHYLIP package.
ACKNOWLEDGMENTS The authors are particularly thankful to  Anne-Marike Schiffer Maraun  for providing the SVM classifier code and her help in reporting on the data from this analysis. The authors thank  Isabel Goebel  for generation time measurements and  Janine Beyert  and  Andrea Kraemer-Eis  for help in culturing nematodes. The authors are grateful to the  GenoToul bioinformatics platform Toulouse Midi-Pyrenees  for providing computing resources, as well as to  Nordrhein-Westfalen  for the use of the CHEOPS computing cluster at the  University of Cologne. A. M. B was funded by the  VolkswagenStiftung  in their initiative for evolutionary biology, the  Deutsche Forschungsgemeinschaft  in a grant to T. W. under  SPP1819 , and was supported by the  European Research Council  ( ERC-2012-AdG 322790  to  Max Telford ).
A. M. B. was funded by  Science Foundation Ireland  (Project  RFP/EOB2506 ). E. A. M. -S. thanks  Dr. Ryan Gregory  ( University of Guelph ) for providing access to his laboratory to conduct genome size estimation and  CONACyT  for a Postdoctoral Fellowship (2010), and  UAZ-2016-37096. C. J. C. was funded under the  Science Foundation Ireland (SFI) Stokes lecturer scheme  ( 07/ SK/B1236A ) and the  Biotechnology and Biological Sciences Research Council (BBSRC)   Institute Strategic Program Grant ,  Rumen Systems Biology , ( BB/E/W/10964A01 ). Edinburgh Genomics has core funding from the  UK Natural Environment Research Council  ( UKSBS PR18037 ).
DATA AND CODE AVAILABILITY All sequencing libraries are deposited in the SRA under the BioProject PRJNA374706. All genome assemblies are deposited in ENA under PRJEB32708 (Propanagrolaimus JU765: ERS3438151; Panagrolaimus PS1159: ERS3467865; Panagrolaimus ES5_v2 ERS3467866; Panagrolaimus superbus: ERS3467867; Panagrolaimus PS1579_v2 ERS3468321; Panagrolaimus sp. DAW1: ERS3468322). Transcriptome assemblies are deposited in TSA and can be found under the same BioProject as the raw read data (PRJNA374706). All genome assemblies, as well as gene predictions, and corresponding peptide sequences will be made available through WormBaseParasite shortly.