dataesr/scientific-paragraphs-categorization huggingface dataset:
The candidates are then visually inspected using Integrative Genomics Viewer (IGV) for the presence of nested polymorphic transposable element (TE) insertion or presence of internal region of same HERV nearby that could result in false positives. After in silico inspection, we identify three strong candidate loci for HERV-K, two for HERV-H, and one for HERV-W (Additional file 2). Two of the three HERV-K candidates have been previously identified and experimentally validated as dimorphic in prior studies  [29, 44, 46]  (Table  1 ). For these two loci, we also identified genomic sequences of the corresponding proviral alleles from the Nucleotide collection (nr/nt) database at the National Centre for Biotechnology Information (NCBI) through homologybased searches (see methods) (Additional file 2).
The results validated that each of the three loci exist as proviral and solo LTR alleles in the human population (Fig. 3a-c , Table  1 , Additional file 4). In addition, we also identified seven FOSMID clones in the nr/nt database at NCBI supporting the presence of proviral alleles (Additional files 2, 5, 6 and 7). Altogether these data strongly support the dimorphic HERV-H and HERV-W calls made through our findprovirus pipeline.
After in silico inspection, we retained 12 HERV-K candidates, 67 HERV-H candidates, and no HERV-W candidate (Additional file 9). In the case of HERV-K, eight of the 12 candidate loci were previously reported to be dimorphic, and some were known to be also insertionally polymorphic, i. e. a pre-integration 'empty' allele has also been reported  [28, 29, 38, 43, 46]  (see Additional file 9). The pipeline predicts four novel HERV-K loci to be dimorphic in the population (Additional file 9). For HERV-H, we observe that many of the predicted solo LTR allele occurs at low frequency in the SGDP dataset, being predicted in only a few individuals (Additional file 9). This might be expected if these alleles arose from relatively recent recombination events. Alternatively, they may represent false positives.
To corroborate the findsoloLTR results, we interrogated the Database of Genomic Variants (DGV)  [62]  to assess whether any of the candidate dimorphic HERV-K or HERV-H loci had been previously predicted as copy number variants in the human population. The DGV systematically catalogs structural variants in human genomes reported in prior studies, but importantly it does not yet include data collected from the SDGP  [58] , thereby potentially serving as independent validation of our predictions from that dataset. We found that two of the four HERV-K candidates and more than half (35 out of 67) of the HERV-H candidates were catalogued in DGV as putative deletion variants (Additional file 9). One of the HERV-K-associated deletions and 20 of the 35 HERV-H-associated deletions were inferred to have breakpoints mapping within the proviral LTRs, consistent with the idea that LTR recombination events caused these deletions (Table  1 ).
Our finding that several HERV-H proviruses are reduced to solo LTR alleles in some individuals argues that haploidy for the internal sequences of these elements is sufficient for normal human development. But that is not to say that such structural variation bears no biological consequences. In fact, one of the dimorphic HERV-H loci we validated at 3p14. 3 is known to drive ESRG, a transcript acting as an early marker of reprogramming of human cells to induced pluripotent stem cells  [63] [64] [65] [66]. Experimental knockdown of the ESRG transcript in human embryonic stem cells leads to a loss of pluripotency and self-renewal  [63]. Thus it is intriguing that we identified a solo LTR allele of ESRG in two individuals from different African populations (Additional file 9, Fig. 3f ). Whether this deletion event impairs ESRG transcription and has any functional consequences for human embryonic development awaits further investigation.
Classification of proviruses and solo LTRs in the reference genome The repeats annotated as LTR5-Hs and HERV-K-int (HER V-K(HML2 family)), as LTR17 and HERV17-int (HERV-W family) and as LTR7 and HERV-H-int (HERV-H family) are extracted from the RepeatMasker annotation of the human reference (GRCh38/hg38) assembly (RepeatMasker open-4. 0. 5 -Repeat Library 20140131 available at http://www. repeatmasker. org/). The extracted RepeatMasker data is parsed to identify potentially full-length proviruses and solo LTRs using the tool "One Code to Find Them All"  [69]. Using a custom script, (https://github. com/jainy/dimorphi-cERV) each copy in the parsed output is further classified as a provirus containing (i) 2 LTRs and internal region (ii) 1 LTR and internal region (iii) only internal region or as a solo LTR.
The coordinates at the boundaries of each copy is then extracted from the parsed output. Each HERV locus is then given a unique identifier depending on the cytoband it belonged to and based on the total number of copies of that family found in each band. The positions of cytoband for GRCh38/hg38 is downloaded (http:// hgdownload. cse. ucsc. edu/goldenpath/hg38/database/cyto-Band. txt. gz). The coordinates of HERV copies marked as proviruses with 2LTRs and internal regions and as solo LTRs are used in the subsequent analysis. For HERV-W, the copies that are generated by retrotransposition mediated by LINE-1 machinery have partial LTRs  [100]  and such copies annotated as pseudogenes  [82]  were excluded from our analysis.
Identification of provirus allele when the reference allele is a solo LTR The findprovirus pipeline identifies solo LTR to provirus variants in the Binary Alignment/Map (bam) format files where paired end reads from whole genome resequencing data are mapped to reference assembly using Burrows-Wheeler Aligner (BWA)  [101]  (Figs. 1d  and 2 ) (https://github. com/jainy/dimorphicERV). The pipeline analyses the coordinates of all solo LTRs obtained from One Code to Find Them All (see methods). The findprovirus pipeline extracts reads mapped to each solo LTR and to a flanking 100-bp region using samtools (version 1. 4. 1) [102]. Only reads that are mapped with a mapping quality of 30 or greater (i. e. mapped with > 99. 99% probability) are collected and the reads are processed to fasta format using SeqKit  [103].
The discordant reads in the solo LTR and in the flanking 100-bp region are identified using samtools  [102]  and the mates of discordant reads are extracted using picard tools (version 2. 9. 2) (http:// broadinstitute. github. io/picard/). Sequence homology of mates of discordant reads to the consensus coding sequence of the respective HERV extracted from the Repbase database  [59]  is tested using BLASTn (version 2. 6. 0, default parameters) and the number of reads with significant hits (e-value < 0. 0001) are counted. Discordant read mates with significant sequence similarity to an internal HERV sequence suggest the presence of a proviral allele in that individual. Two independent, additional approaches are also used to assess the presence of a proviral allele. The first approach measures the average read depth at the solo LTR using samtools and reads with a mapping quality of 20 or more (mapped with > 99% probability) and reads with a base quality of 20 or more (base call accuracy of > 99%) are counted.
The regions of low mappability generate ambiguous mapping and regions of high mappability generate unique mapping. The mappability scores are downloaded for the GRCh37/hg19 version of reference assembly (ftp:// hgdownload. soe. ucsc. edu/gbdb/hg19/bbi/wgEncodeCrgMa pabilityAlign100mer. bw). The downloaded file is processed  [107]  and is converted to bed format  [108]  and scores are lifted over  [109]  to hg38 version. This data is stored in an indexed mysql table. The coordinates of the reference assembly where the informative discordant reads are mapped for each solo LTR are identified using bedtools (version 2. 26. 0)  [110]. The mappability scores for those genomic regions are extracted from the table and the mean of the mappability scores is provided in the output of the pipeline.
Identification of solo LTR allele when the reference allele is a provirus The findsoloLTR pipeline identifies the provirus to solo LTR variants in bam files (Fig. 1e  and 4 , https://github. com/jainy/dimorphicERV). It first calculates the read depth across the provirus using samtools  [102]. Read depth is calculated for reads with a mapping quality of 30 or more and with a base quality score of 20 or more. Similarly, read depth is calculated across 5′ and 3′ flanking 250-bp regions. The pipeline then assesses the percentage of average read depth across the provirus to the average of read depths across the flanks. Presence of two proviral alleles is inferred when the read depth percentage greater than or equal to 50% and read depth percentage lower than 50% is used to infer the presence of solo LTR allele (Fig.
Dataset analyzed The two pipelines were run on the publicly available whole genome sequence data generated as part of the SGDP for 279 individuals from 130 populations  [58]. The bam files used for the analysis are generated by aligning 100-bp long paired-end reads to the GRch38/ hg38 version of the human genome using BWA aligner (version 0. 7. 12)  [101]. The bwa-mem alignment allowed a mismatch penalty of 4 (equivalent to 96% identity) and allowed secondary alignments (multi-mapping).
After visual inspection, the candidates are then queried in the DGV  [62]  to identify if any previous studies have reported those loci as a copy number variant (CNV). The CNVs identified in DGV are visually inspected for the concordance of their breakpoints with the two LTRs, which is suggestive of their origin through LTR mediated recombination. The CNVs having one or both breakpoints lie outside the LTRs are also identified. The candidates along with 100-bp flanking sequence are also queried against nr/nt database at NCBI to identify the presence of any BAC/ FOSMID clones containing corresponding the solo LTR or provirus variant.
Analysis of contribution of dimorphic candidate HERVs to annotated genes/transcripts The dimorphic candidate HERV loci are examined individually using the University of California, Santa Cruz (UCSC) genome browser on human GRCh38/hg38 assembly  [112]  (last accessed June 6 2018) to identify any overlap with known NCBI RefSeq protein-coding or non-coding genes (NM_*, NR_*, and YP_*). In addition, to determine the dimorphic candidates that encode an intact env gene, the HERV coordinates are compared with that of intact env Open Reading Frames (ORFs) identified by Heidmann et al. [92]  in the human genome (hg38). In order to find the candidate dimorphic HERV-Hs that are actively transcribed in human embryonic or induced pluripotent stem cells (iPSCs), coordinates of HERV-Hs, which are known to be moderately or highly expressed in hiPSC lines and single cells  [63]  are intersected with coordinates of dimorphic HERV candidates using bedtools v2.
Acknowledgments We would like to thank  W. Scott Watkins  for providing the 279 SGDP bam files for the analysis and  Lynn B. Jorde  for providing the DNA samples as well as equipment and infrastructure for performing this study. We also would like to thank  Aurelie Kapusta  and  Clement Goubert  for many helpful discussions. J. T and C. F declare no competing interests. H. P receives compensation for his work by Geneuro and is inventor on patents owned by  BioMérieux ,  INSERM , or Geneuro, but has transferred all his rights to BioMérieux or to Geneuro under applicable laws for employed inventors.
Footnotes: This table only lists candidates identified by our pipeline and supported by at least one additional piece of evidence: a PCR validation, b alternative allele genomic sequence, c annotation in the Database of Genomic Variants. Other candidates not listed here are in Additional file 2 and Additional file 9. The notations 'K' 'H' and 'W' in the HERV name represent HERV-K(HML2), HERV-H and HERV-W families respectively.
Study Design We conducted a cross-sectional nationwide online survey in French pharmacy faculties to assess psychotropic drug use by pharmacy students from  March 16, 2016 to May 20, 2016 (66 days). The internet address of the survey 1 (Supplementary Data Sheet 1) was sent by email to all the French Pharmacy students (metropolitan France) through the student associations of the French pharmacy faculties and the national association of French pharmacy students [Association Nationale des Etudiants en Pharmacie de France (ANEPF)]. The online survey was also sent to pharmacy students through social networks (Facebook) and though toxicology teachers [Groupement Associé des Enseignants de Toxicologie (GATOX)]. The number of pharmacy students was estimated at 18,000 according to the national numerus clausus for the admission to French pharmacy studies in 2016-2017 (Journal Officiel de la République Française, 2017).
Approximately 20 min were required to complete the online survey. Study data were collected and managed using REDCap electronic data capture tools hosted at CHU Clermont-Ferrand  (Harris et al. , 2009). REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies, providing: (1) an intuitive interface for validated data entry; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for importing data from external sources. Efforts have been done to address potential sources of bias. Selection bias should be limited because the survey has been sent to all the French Pharmacy students. However, there is no data available in France on age, sex and characteristics of these students, so it is difficult to ensure comparability.
Data from  Ghandour et al. (2013)  regarding a Lebanese college, showed that in a weighted lifetime 20. 4% of students used cannabis herb, 2. 8% MDMA and 1. 6% cocaine/crack, which was very close to our study, in which 3. 5% of French pharmacy students had used MDMA in the last 3 months and 2. 0% cocaine. The proportion of smokers and e-cigarette users seems to be low in French pharmacy students (18. 7 and 1. 8%, respectively). In 2016, 29. 4% of French citizens aged 18-75 years smoked cigarettes daily and 2. 5% e-cigarettes (OFDT, 2017c). About 21-23% of French students are daily smokers  (Tavolacci et al. , 2013; Gignon et al. , 2015)  while this figure is 22% for European students  (Beck et al. , 2014). The alcohol consumption of French pharmacy students was about 84. 0% and hazardous alcohol consumption was 9.
Study Limitations The rate of response to the survey was low (14%). This low response rate could have been improved with a longer survey duration. Students of the 1st and the 6th year of pharmacy studies were less represented. During the 1st year of study, the students have a lot of work and competitive exams, so they would probably be less involved in "everyday life in the university. " Students of the 6th year performed an internship of 6 months (community pharmacy or pharmaceutical industry) during the survey schedule. Consequently, students from the 1st and the 6th years would be more difficult to recruit. Selection bias seems to be limited because psychotropic medication and illegal drug users did not seem to be over-represented in comparison to the scientific literature. It is also impossible to know if the population of the study is representative to the French pharmacy students, because there is no data available.
Two tiny pins were left in the foam and were used to secure the insect tether to the apparatus. This foam block was located such that the flight mill's radius was 318. 3 mm, meaning that an insect traveled 2 m per lap. Arm movement was monitored using a computerized system. An infrared emitter and detector (RS Components SAS, Honeywell SPX1189-002 and SPX1189-003) were placed above and below the arm, respectively. They were connected to a PCI board (RS Components SAS, Arcom APCI-IB40) installed in a computer. When the arm cut the beam between the emitter and the detector (which the paper triangles helped ensure), an electrical signal was sent to the PCI board. An application written in C++ detected and time-stamped these signals, and wrote the resulting data in files (named logs below). Insect flight activity was thus precisely recorded.
Data analyses Scripts were developed to analyze the logs recorded by the flight mill computerized system. They divided the log of each flight test into different phases. Active phases (jump and flight phases, see below) occurred when hornets were moving and were separated by rest phases. The scripts also detected and excluded erroneous records, which sometimes appeared. We distinguished jump phases (i. e. , very short flights) from "true" flight phases; the former have no functional significance from the perspective of V. velutina foraging activity and could easily have been artifacts. As no objectively defined limits emerged from the data (S1 File), we arbitrarily defined flight phases as the phases during which hornets covered at least 10 m. Similarly, we arbitrarily defined rest phases as the phases during which hornets seemed not move for at least 5s.
For each phase, the scripts computed distance, duration and speed. All the statistical analyses were performed using R software (version 3. 3. 3) [29]. It was also used to generate data figures. All tests were performed with a 95% confidence level. When the data were normally distributed, means and standard deviations are given. These data were analyzed using standard ANOVAs. When the data were not normally distributed, medians and quantiles/percentiles are given. These data were analyzed using Kruskal-Wallis rank sum tests. As each hornet was tested several times, statistical tests could be biased due to autocorrelation. To deal with this issue, flight test data were summarized per hornet before to be statistically tested. If not usable, we used a mixed model ANOVA including the insect identity as a random effect; if appropriate, flight test rank was also used as covariate; p-values were given for the tested variables.
Such capacities could also partly explain the great invasive potential of Vespidae: at least 2 Vespa species from among the 22 members of the genus are well-established invaders (V. crabro in North America and V. velutina in Europe and Korea), and Vespula germanica is invasive in several places all around the world  [51]. Flight mills can yield abundant information on V. velutina flight capacity and, in particular, allow colony foraging radius to be estimated. However, it also gathers these data under artificial conditions, so it is important to confirm results using field experiments. The flight patterns we saw in the laboratory are consistent with what we know about hornet biology, but field data on flight capacities are lacking. We also need to gather more information about the environmental factors, such as temperature, that may shape V.
Acknowledgments This study was supported by funding from the  French region Centre-Val de Loire , namely the awarding of a  French regional grant for our "FRELON" project  (" Frelon asiatique: etude et lutte ciblee contre une espèce invasive predatrice des abeilles ";  2011-2014 ). The authors thank  E ´ric Velluet , at that time at  Inra in Orleans, France , for developing the application which controls the flight mill systems. The authors finally thank the beekeepers and other people who helped hornet samples, especially  Les Apiculteurs associes (La-Ferte-Saint-Aubin, Loiret, France ) and Jeremy Gevar (IRBI, Tours, Indre-et-Loire, France).
Regeneration is an endogenous process of tissue repair that culminates in complete restoration of tissue and organ function. While regenerative capacity in mammals is limited to select tissues, lower vertebrates like zebrafish and salamanders are endowed with the capacity to regenerate entire limbs and most adult tissues, including heart muscle. Numerous profiling studies have been conducted using these research models in an effort to identify the genetic circuits that accompany tissue regeneration. Most of these studies, however, are confined to an individual injury model and/or research organism and focused primarily on protein encoding transcripts. Here we describe RegenDbase, a new database with the functionality to compare and contrast gene regulatory pathways within and across tissues and research models.
RegenDbase combines pipelines that integrate analysis of noncoding RNAs in combination with protein encoding transcripts. We created RegenDbase with a newly generated comprehensive dataset for adult zebrafish heart regeneration combined with existing microarray and RNA-sequencing studies on multiple injured tissues. In this current release, we detail microRNA-mRNA regulatory circuits and the biological processes these interactions control during the early stages of heart regeneration. Moreover, we identify known and putative novel lncRNAs and identify their potential target genes based on proximity searches. We postulate that these candidate factors underscore robust regenerative capacity in lower vertebrates. RegenDbase provides a systems-level analysis of tissue regeneration genetic circuits across injury and animal models and addresses the growing need to understand how noncoding RNAs influence these changes in gene expression.
9  Studies of these individual models have advanced our understanding of regeneration genetic circuits and their regulation. Among these mechanisms are microRNAs (miRNAs), small noncoding RNAs that are essential regulators of development and adult tissue regeneration. [10] [11] [12] [13]  Comparative studies of multiple models of complex tissue regeneration provide an opportunity to identify signaling pathways that are conserved or unique among models. 14  Conducting these comparisons within a phylogenetic context also allows for analysis of ancestral and derived traits. These comparative studies pose challenges that existing databases have not resolved, including (1) compiling large profiling datasets on complex tissue regeneration; (2) facilitating comparisons of experiments within and across organisms; and (3) integrating noncoding RNAs as another level of regulatory control.
Current database resources used for analyzing gene expression are limited in terms of breadth of organisms, exclusion of noncoding RNAs, and a lack of focus on biological processes. The Limbform resource integrated 829 experiments to predict how temporal and spatial patterns of gene expression are associated with anatomical structures during appendage regeneration. 15  While eight vertebrate species are represented in Limbform, zebrafish and mice are not among them. miRNAs, potent regulators of regeneration, are not represented in this resource. The Regeneration Gene Database (REGene) provides information about 948 genes gathered from the literature using Gene Ontology (GO) annotations and abstracts, but does not present large-scale gene expression datasets from regenerating tissues. 16  Existing gene expression resources such as the Gene Expression Omnibus,  17  Expression Atlas,  18  or Genotype-Tissue Expression Project,  19  while informative, do not specifically focus on regeneration or robustly support comparisons across datasets.