dataesr/scientific-paragraphs-categorization huggingface dataset:
As the peptide signal should be absent from the mature peptides, their sequences were removed from the analyzed protein sequences and a size cutoff was applied to select those ranging from 85 to 100 aminoacids, according to the protein analysis results obtained. This reduced the number of sequences to 207. Prediction of the proteases able to cleave after Lys and Phe, as well as their 3D structure, were performed by the I-TASSER online server using the template obtained by searching against the Protein Data Bank database. Five models were generated for each protein with C-scores ranging from (-5) to 2 and those having the highest Cscore, representing the best models, were selected (7 sequences left). Ten PDB structures close to the target were generated for the 7 sequences. The structural similarity between the target model and the 10 closest proteins were ranked by TM-scores  (Zhang and Skolnick, 2005).
, 2005). Indeed, previous researchers have reported that the most active antibacterial peptides against pathogens belong to the prolinerich peptide family  (Otvos, 2002; Cao et al. , 2015). In addition, analysis of the peptide using the PROWL database was in agreement with the BLAST analysis of the amino acid sequence using the GenBank protein database, revealing that the peptide corresponds to the fraction (114-119) of bovine β-casein. Several AMPs generated by bacterial degradation of casein have been reported. For example, S. thermophilus strains degrade β and αcasein resulting in the generation of antibacterial peptides from β-casein (f l93-209) and from α-casein (f l-23)  (Miclo et al. , 2012). Similarly,  Hayes et al. (2006)  reported that the degradation of α-casein by L. acidophilus DPC6026 results in the production of three peptides displaying antibacterial activities against the pathogenic strains Enterobacter sakazakii ATCC 12868 and E.
ACKNOWLEDGMENTS This work was supported by a grant from the  Tunisian Ministry of Higher Education and Scientific Research  within the scope of the laboratory project  LR11ES31. The authors extend their sincere gratitude to  Danielle Slomberg ,  Jason Morris  and  Rachid Hassini  for helpful corrections and comments. Professor Daniele Daffonchio  ( KAUST, SA ), is acknowledged for the very supportive conversations and discussions.
Drosophila suzukii (Matsumura) is one of the most damaging and costly pests to invade temperate horticultural regions in recent history. Conventional control of this pest is challenging, and an environmentally benign microbial biopesticide is highly desirable. A thorough exploration of the pathogens infecting this pest is not only the first step on the road to the development of an effective biopesticide, but also provides a valuable comparative dataset for the study of viruses in the model family Drosophilidae. Here we use a metatransciptomic approach to identify viruses infecting this fly in both its native (Japanese) and invasive (British and French) ranges. We describe eighteen new RNA viruses, including members of the Picornavirales, Mononegavirales, Bunyavirales, Chuviruses, Nodaviridae, Tombusviridae, Reoviridae, and Nidovirales, and discuss their phylogenetic relationships with previously known viruses.
Virus identification and phylogenetic analysis To remove those reads derived from Drosophila, we mapped raw reads against the D. suzukii genome and transcriptome using Bowtie2  (Langmead and Salzberg 2012)  with the '-very-fast' command-line option. We used Trimmomatic  (Bolger, Lohse, and Usadel 2014)  to quality trim and remove adapter sequences from the remaining unmapped raw reads (as pairs) using default parameters, before de novo assembly using Trinity version 2. 2. 0  (Grabherr et al. 2011) , retaining a minimum contig length of 500 nt. All raw unannotated contigs are provided in Supplementary File S1. We concatenated all translations of all open reading frames (ORFs) in each resulting contig, and retained only those with an ORF of 150 codons or greater. These concatenated protein sequences were used to search against a custom database using Diamond  (Buchfink, Xie, and Huson 2015)  with an e-value threshold of 0.
01, retaining a single top hit. The target database comprised all of the viral proteins from the Genbank non-redundant protein database ('nr';  Clark et al. 2016) , and all of the prokaryote, protist, fungal, nematode, hymenopteran, and dipteran sequences from NCBI refseq protein database. Contigs for which the top hit was a virus were imported into Geneious V R 8. 0. 2 sequence analysis software  (Kearse et al. 2012 ) for manual analysis. We grouped putative virus fragments taxonomically according to their initial best Diamond hit, assembled (Geneious) and manually curated them with reference to closest relatives in Genbank, to give the longest viral sequences consistent with the predicted protein content and structure of that virus taxon. To infer phylogenetic relationships, we used RNA-dependent RNA polymerase (RdRp) coding sequences unless otherwise stated.
Virus genome annotation For viruses with complete, or near complete genomes, we were able to infer genome structure and identify protein functional domains by first identifying ORFs and then comparing these to the Conserved Domain Database with an expected value threshold of 5 Â 10 À3 , and searching the NCBI 'nr' protein database using BLASTp. Only ORFs of 100 amino acids or longer were annotated, unless notable similarity to closely related viruses was evident. ORFs of less than 200 amino acids that were nested completely with larger ORFs were disregarded, unless they displayed high similarity to known proteins.
Viruses with single-stranded negative-sense RNA genomes Five of the viruses we detected are expected to have -ssRNA genomes. Three of these belong to the Bunya-Arena clade of viruses: Notori virus, Ditton virus, and Barming virus (Fig. 4A-C ). Notori and Ditton viruses can be further classified as Phasmaviruses. These were detected in our samples as contigs of around 7 kb in length that represent complete or nearcomplete L-segment (Bishop and Shope 1979) (Fig. 5 ). Barming virus, the third putative Bunya-Arena clade virus we identified, belongs to the Phlebo-like cluster of the clade. It too is known from a contig of just over 6 kb, also representing the L-segment of the Bunyavirus genome, consisting of one ORF containing the viral RdRp (Fig. 5 ). The closest relative of Barming virus was a viral-like sequence identified in the TSA database from Colletotrichum cereale, a plant disease that has been found to cause crown rot anthracnose of turf grass  (Crouch, Clarke, and Hillman 2006).
Known Drosophila viruses We also detected eighteen further viruses previously described from other species of Drosophila. Three known viruses were detected at very high levels (below), and are therefore highly likely to represent infections of D. suzukii. The first of these is Brandeis virus (MF953177), the genome of which is reported here for the first time (Fig. 2 ). Although originally detected by  Webster et al. (2015)  in public D. melanogaster transcriptome datasets (PRJNA159179;  Rodriguez et al. 2012)  and provisionally named, it has not previously been detected in wild flies. It is detected here at high levels (26. 8 per cent of all remapped virus reads) in D. suzukii samples from France in 2013. Brandeis virus belongs to the Hepe-Virga clade of þssRNA viruses and is closely related to Muthill virus, a virus previously detected in associated with D.
Despite applying a detection threshold for very low viral read numbers, there are several sources of error when attempting to analyse patterns of virus sharing among years or sampling locations. For example, barcode switching  (Kircher, Sawyer, and Meyer 2011; Ballenghien, Faivre, and Galtier 2017; Sinha et al. 2017 ) and other sources of crosscontamination between libraries sequenced together on the Illumina platform may allow miss-assignment of reads between the Japanese and British samples from 2016, and also from other drosophilid libraries analysed at the same time. Furthermore, cytochrome oxidase read mapping suggests a small proportion of contaminating reads deriving from D. melanogaster and D. immigrans were present in some of our datasets. For example, in the Japanese sample of 2016 1.
suzukii. suzukii is available in Supplementary Table  S1 ; a table of PCR primers used for virus detection is in Supplementary Table  S2 ; number of reads mapping to all known drosophila viruses and to novel D. suzukii viruses is in Supplementary Table  S3. Coverage depth graphs for novel virus genomes are shown in Supplementary Fig. S1. All raw reads have been submitted to the NCBI sequence read archive under project accession PRJNA402011 (Japan SRR6019484; France SRR6019487; Kent: SRR6019485, SRR6019486, and SRR6019488). All novel virus genomes are submitted separately to the NCBI sequence read archive under the accessions outlined in Table  1.
Acknowledgements We thank  Michelle Fountain ,  Bethan Shaw ,  Adrian Harris ,  Maddie Cannon , and the entomological team at East Malling research for support in collecting British flies. We thank members of the  Nakai Lab at the Tokyo University of Agriculture and Technology  for making us welcome, and the staff at the  TUAT Field Museum Tama hills, Yamagata Prefectural Agricultural Research Centre, Agriculture Centre Kaju Institute, Fukushima and Yamanashi Prefecture Outside Organization Fruit Tree Test Site  for support during field collections. We thank  Edinburgh Genomics  for sequencing, and members of the  Obbard and Vale labs for discussion. This work was funded by a  Wellcome Trust   Research Career Development Fellowship  ( WT085064 ) to D. J. O. and an  Agricultural and Horticultural Development Board  grant to D.
Statistical analyses Analyses were carried out using the R statistical package (v. 3. 1. 1). The proportion of blood fed females in both mosquito groups was analysed using Pearson's Chi-squared Test. Data collected on egg production was tested for normality and homoscedasticity using the Shapiro test and Bartlett test respectively. The impact of Plasmodium infection and GC on mosquito fecundity (number of eggs laid per female) was analysed using a standard general linear model procedure with a normal error structure. Longevity data were analysed using Cox proportional hazards regression model (coxph, survival package). In this study we are only interested in the impact of Plasmodium infection on the longevity of females after the third blood meal (16 day pfbm). For this end, all unfed females were removed and counted after each blood meal.
Slides were then washed three times in PBS, permeabilized with 0. 1% Triton X-100, washed three times in PBS, and blocked by incubation with 5% FCS. Slides were then incubated overnight at 4 °C with primary antibodies, washed five times with PBS, and then incubated for 1 h at room temperature with fluorochrome-conjugated secondary antibodies. The DNA was stained with DAPI (4',6-diamidino-2-phenylindole) (100 ng/ml in PBS) and slides were mounted in Prolong-Gold medium (Invitrogen). Microscopy and image analysis. Images were acquired with an epifluorescence microscope (Zeiss AxioImager. M2) equipped with Zeiss "Plan-Apochromat" × 40/ 1. 3 and × 63/1. 40 oil objectives, a Coolsnap HQ 2 CCD camera (Photometrics) and Zeiss Axiovision software (version 4. 2). Signals were quantified with ImageJ software (Rasband, W.
S. , ImageJ, US National Institutes of Health, Bethesda, Maryland, USA, http://rsb. info. nih. gov/ij/, 1997-2005). Overlapping profiles for two different antibodies on CFs were measured with the "RGB profiler" tool in ImageJ software. Images showing endogenous Aurora A staining were overexposed to reveal weak signals. Statistics. All statistical analysis were performed with GraphPad Prism v6. 05 (GraphPad Software). For the comparison of PSCS between cell lines, we performed two-tailed unpaired parametric t tests or one-way analysis of variance (ANOVA) followed by Tukey's post hoc analysis, depending on the number of cell lines compared (two or more than two, respectively). When inhibitor was added to cell lines, we performed two-way ANOVA followed by Tukey's post hoc analysis for multiple comparisons.
Acknowledgements We thank  Professor Jonathan Higgins  for his pre-submission critical review of the manuscript, for the experiments he suggested and for the gift of reagents. We would like to thank  Professor Yoshinori Watanabe  ( University of Tokyo, Japan ),  Dr Marie-Bérengère Troadec  ( UMR 6290 CNRS, France ), and  Dr Isabelle Bahon-Riedinger  ( Rennes University Hospital, France ) for providing reagents. We thank  Drs Erwan Watrin ,  Régis Giet , and  Vincent Legagneux  ( IGDR, CNRS, Rennes, France ) for stimulating and helpful discussions. G. F. was supported by a  Ligue Contre le Cancer  fellowship, G. E. -H. and L. M. -J. are investigators at the CNRS. S. M. and C. J. are investigators at INSERM. This work was funded by the  French National Research Agency (ANR, project "EpiCentr") , the  Région Bretagne  ( SAD grant ), the  Cancéropôle Grand Ouest , the  Ligue Contre le Cancer  ( Comité Grand Ouest ) and the  Fondation ARC pour la Recherche sur le Cancer.
Phenotypic Evaluation of Traits Shattering ability, that is the ease with which spikelets are released from the pedicel, was scored subjectively from 0 (florets remaining intact as in the cultivated S. italica) to 3 (freely shattering as in the wild S. viridis) by clasping the panicle in a clenched fist. The phenotypic data used in the QTL mapping are provided in Supplementary Table  S1.
, 2012; Devos et al. , 2017) , the orthologous regions in rice, sorghum, and maize were scanned for the presence of known shattering genes. Comparative Analysis at the DNA Sequence Level of the S. italica and S. viridis Alleles at the Sh1 and qSH1 Loci The sorghum Sh1 (Sobic. 001G152901; Lin et al. , 2012)  and rice qSH1 (LOC_Os01g62920) coding sequences were used in BLASTN searches against the S. italica Yugu1 and S. viridis A10 whole genome sequence assemblies 2 to identify the corresponding orthologs. For each gene, the genomic sequences of the S. italica and S. viridis alleles were aligned and compared. To determine whether the same structural variations were present in B100 as in Yugu1, we exploited the ∼43 Gb of sequence data that were available from a bulk of 48 B100 × A10 RIL lines  (Bennetzen et al.
, 2012; SRX030717 and SRX030718). The RIL bulk sequencing data had been generated to identify the SNPs used in the construction of the RIL genetic map  (Bennetzen et al. , 2012). The Illumina reads (2 × 75 bp) were aligned against the Yugu1 genome assembly using Bowtie  (Langmead and Salzberg, 2012)  with default parameters. Alignments in the Sh1 and qSH1 regions were inspected visually using the Integrated genome viewer (IGV) v2. 3  (Robinson et al. , 2011 ). 2 phytozome. jgi. doe. gov The structural variants identified in Setaria in Sh1 and qSH1 were further validated by PCR-amplification using primer set SiSh1-91F (5 -GGATCATGCCTTGCACTCCT-3 ) / SiSh1-91R (5 -CATGCATGCACATTTCGGCT-3 ) for Sh1 and qSHIns1_F (5 -GCTTCCTGGAGTGGTCAAAC-3 ) / qSHIns1_R (5 -GG ACGTGGTAGAGCTGCTG-3 ) for qSH1, and genomic DNA of S.
QTL Detection QTL for seed shattering were identified on chromosomes V, VI, and IX in the F 2:3 population and on chromosomes V, VII, and IX in the RIL population (Table  1 ). For all QTL, the A10 allele led to increased shattering. The chromosome IX QTL in the F 2:3 population was located between the rice cDNA sequences C0595 (Genbank accession number AU091626) and C1361 (C98269), which identify loci on the S. italica acc. Yugu1 genome sequence  (Bennetzen et al. , 2012)  at positions 6. 2 and 14. 6 Mb, respectively. This QTL explained 30. 6% of the variation in the across-replicate analysis. The main chromosome IX QTL in the RIL population was located in the region 6. 2 Mb-11. 9 Mb (Figure  1A ) and explained 35. 2% of the variation in the across-replicate analysis (Table  1 ). QTL on chromosome V were also identified in all replicates in both populations although the location of the QTL varied by replicate and population.
Effect of the MITE on Transcript Processing of Sh1 in S. italica Because the MITE in Yugu1 and B100 Sh1 was inserted close to the 3 end of exon 2, we investigated whether its presence affected splicing. Sequencing of four clones from a single PCR reaction using S. viridis accession A10 cDNA as template with primer set sh1-09f-MITE/sh-09r-MITE, which was located in exon 1/exon 3 and hence amplified across the MITE insertion site in exon 2, indicated the presence of two potential 3 splice sites for intron 2 that were separated by 9 bp (Figure  2  and Supplementary Figure  S1 ). Reads corresponding to both splice products were also identified by BLASTN analysis against S. viridis A10 transcriptome data (NCBI SRA Experiment SRX875196), supporting the occurrence of alternative splicing. Sequencing of the same region (two clones from the same PCR reaction) in a S.
italica B100 transcript also showed two alternative splice products that differed by nine basepairs. Furthermore, B100 transcripts lacked exon 2 (Figure  2  and Supplementary Figure  S1 ). The presence of the MITE resulted in the simultaneous splicing of intron 1, exon 2 plus the MITE and intron 2. The transcript data obtained by RT-PCR were confirmed by alignment of available RNA-Seq data from S. italica Yugu1 (NCBI SRA Experiment SRX2832831) against Seita. 9G154300. The alignments further suggested that the MITE presence led to an additional alternatively spliced product in which introns 1 and 2 were retained (Supplementary Figure  S4 ). We were unable to confirm the formation of this putatively alternatively spliced product by RT-PCR with primers located in exon 2 and exon 3. This could be due to the difficulty in amplifying across the GC-rich MITE.
The average frequency in this region of the S. italica SNP alleles in our set of S. viridis germplasm was 67%. The absence of the MITE in Sh1 in the S. viridis germplasm analyzed may be due to (1) a low frequency of the low-shattering Sh1 allele in wild populations, (2) insertion of the MITE early in the domestication process, or (3) a lack in our S. viridis collection of accessions from the center of domestication. Most of the resequencing data was for S. viridis accessions collected in the United States that belong to two major genetic subpopulations. Asian S. viridis germplasm largely forms a third subpopulation  (Huang et al. , 2014; Schröder et al. , 2016)  It is unclear whether qSH1 is the gene that underlies the shattering QTL on Setaria chromosome V. The presence of MITEs near genes can affect transcript levels, and both positive and negative effects have been reported  (Naito et al.
The majority of the analyses of the evolutionary history of the megadiverse class Insecta are based on the documented taxonomic palaeobiodiversity. A different approach, poorly investigated, is to focus on morphological disparity, linked to changes in the organisms' functioning. Here we establish a hierarchy of the great geological epochs based on a new method using Wagner parsimony and a 'presence/ absence of a morphological type of mouthpart of Hexapoda' dataset. We showed the absence of major rupture in the evolution of the mouthparts, but six epochs during which numerous innovations and few extinctions happened, i. e. , Late Carboniferous, Middle and Late Triassic, 'Callovian-Oxfordian', 'Early' Cretaceous, and 'Albian-Cenomanian'. The three crises Permian-Triassic, Triassic-Jurassic, and Cretaceous-Cenozoic had no strong, visible impact on mouthparts types.
How ver the K-T has been proposed to have been responsible for some extinction, e. g. , at least North American host-specialist herbivores  11. Recent analyses of fossil record at family level show a Late Cretaceous negative net diversification  9. We only register the extinction of the lophioneuridan type of mouthparts. Mouthpart disparity vs. taxa diversification based on phylogenetic studies. By using phylogenetic comparative methods (BAMM model) to avoid problems of incomplete fossil record, Condamine et al. 9  did not recorded any significant Cretaceous or Middle-Late Jurassic diversification shifts among taxa, which are the main times of diversification for mouthparts. They recorded only two (out of eight) significant diversification rate shifts which appears congruent with our mouthpart data 9 : one at the Late Carboniferous concerning beetles, and the other for flies during very Early Jurassic.
Methods The Maximum Parsimony analysis was performed using the software win-paup4b10, using the options BandB and HSearch. The characters are defined as the 'presence vs. absence of taxa bearing a type of mouthpart' (Fig. 1 , Table  S1 ). A character coded with '1' , corresponds to the presence of a fossil that bears the morphological mouthpart type or that belongs to the crown group bearing it. A character coded with '?' corresponds to the presence of a fossil inside the stem group but with its mouthparts not preserved. The analysis using Wagner Parsimony, with the question marks '?' in the matrix identified 45 most parsimonious hierarchies (Fig. 2a ).
Acknowledgements We thank three anonymous referees for their useful comments on the first version of the paper. We also thank  F. Legendre  and  R. Garrouste  ( MNHN, UMR 7205 CNRS ) for the very useful discussion during the preparation of the paper, and  Ioana Cristina Chintauan-Marquier  for her help for Paup analyses. Many thanks to  Oxford University Press  for providing the permissions email for Fig. 2d.
Analysis of standstill compliance Individual cattle and batched sheep movements between Scottish holdings from 01/01/2011 to 31/12/2013 were obtained from the cattle tracing system (CTS) and the Scottish Animal Movement System (SAMS). These datasets were pre-processed separately and subsequently combined for analysis. Under standstill regulations, movements from animal holdings direct to slaughter or to slaughter via a market are exempt from the 13-day standstill rule and were excluded from further analysis. Markets and slaughterhouses were identified through a list of sheep markets and slaughterhouses obtained from historical sheep movement data provided by the Scottish Animal Movement Unit (SAMU). Using the unique county-parish-holding (CPH) number as an identifier, the movement records were subsequently joined with the British Sheep and Goat Inventory to derive premises types, geo-referencing data, and numbers of animals held at each premises.
Cattle movements are registered at the individual animal level and are reported both by the farmer at the holding of origin and the farmer at the holding of destination. After removing births and movements with identical destination and departure location, movement records were joined with location data to retrieve premises types. Movements that could be clearly attributed as being exempt from standstill regulations were excluded from the data. 2 Cattle production types were categorised as described in  Gates and Woolhouse (2015) , whereas only a single category was used for sheep. Finally, the individual movement records were batched in order to calculate holding level standstill exemptions and combined with the batched sheep movements. Standstill periods were calculated as follows: for each unique destination premises we retrieved all subsequent movements leaving this particular holding and calculated the waiting time with respect to the earlier on-movement.