dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion During the workshop, within the scope of PAPAYA and PoSeID-on project, we presented potential solutions to overcome privacy challenges related to data management and processing for public and private organizations. Additionally, we had the chance to discuss human aspects, particularly end-users, with privacy experts. Inputs from these experts was a helpful feedback for developing the privacy enhancing technologies of the use-cases particularly for the next stages of the project. In terms of PoSeID-on project, the most important inputs raised during discussions were "how the platform should provide trust to its users" and "how PoSeID-on project handles data flow, management and processing to achieve transparency". That is for sure, by considering these inputs, the consortium will have the chance to easily achieve the dissemination goals of the project.
Results The genomes of the two campoplegine ichneumonid wasps were sequenced using high-throughput Illumina HiSeq technology. The datasets were then assembled using either Supernova v. 2. 1. 1  [38]  or Platanus assembler v1. 2. 1  [39] , depending on the species (see "Methods" section). Assembled genomes were then annotated automatically using Augustus v3. 3  [40]  for Campoletis sonorensis and BRAKER1 v1. 10 [41]  for Hyposoter didymator (see "Methods" section). The annotated whole genomes are the first ever produced for ichnovirus-carrying ichneumonids.
Automatic gene annotation for H. didymator (for which RNA-seq datasets were available) and for C. sonorensis (for which no RNA-seq dataset was available) yielded 18,119 and 21,915 transcripts, respectively (Table  2 ). These two genome assemblies and annotations are available at BIPAA website  [46, 47]. Although different software packages were used for gene prediction, the two species have similar gene annotation statistics, except for transcript size, which was longer in H. didymator, and which also showed a higher predicted intron size (Table  2 ). Benchmarking Universal Single-Copy Orthologs (BUSCO)  [48]  analyses indicated a high level of completeness of the two genome assemblies and annotations, with 99% of the BUSCO Insecta protein set (1658 proteins) identified as complete sequences (Fig.
Flanking direct repeated sequences were found for the large majority of HdIV and CsIV loci. All HdIV segment loci, except for Hd45. 1 and Hd45. 2, had DRJs, which varied significantly in size, ranging from 69 to 949 bp   S5. B Segments duplicated in H. didymator genome. Segments Hd23 (Genbank# KJ586309. 1), Hd44 (Genbank# KJ586285. 1) and Hd45 (Genbank# KJ586284. 1), described as part of the packaged HdIV genome in  [36] , have two copies (named Hd(n). 1 and Hd(n). 2) that are either in the same scaffold (Hd23. 1 and Hd23. 2, Hd44. 1, and Hd44. 2) but in different insertion sites or in two different scaffold (Hd45. 1 and Hd45. 2). Nucleotide percentage identity between the two related segment sequences is given on the right part of the figure. By contrast, Hd9 (Genbank# KJ586324. 1), initially described as a separate segment, is located in a genomic locus composed of a tandemly duplicated sequence ("copy 1" and "copy 2" in the diagram).
Note that Supernova recommends 56X total coverage and sequencing deeper than 56X reduces the assembly quality. A full lane of HiSeqX produced several times the sequencing data we needed to reconstruct the genome. Hence, raw reads were divided in four subsets and four separate assemblies were conducted in parallel, with the best one chosen for downstream analyses. We ran Kraken 2  [74]  on the assembly to check for bacterial contamination. All contigs that were classified as bacteria were removed before proceeding with other analyses. We used RepeatMasker [75] to identify repeat regions using the honeybee, Apis mellifera, as the model species. H. didymator transcripts were aligned to the C. sonorensis genome using BLAT  [76]. We created hints files for Augustus from the repeat-masked genome and the BLAT alignments.
Assemblathon2  [42]  was used to calculate metrics of genome assemblies. For annotation, EST reads from venom  [78] , as well as reads obtained using GS FLX (Roche/454), Titanium chemistry from total insects  [78]  and from ovaries  [79] , were mapped to the genome with GMAP  [80] , and Illumina reads published previously  [35] , or from the 1KITE consortium (http://1kite. org/subprojects. html) using STAR  [81] , and new calyx RNA-seq (SRA accession: PRJNA590863) with TopHat2 v2. 1. 0 [82]. BRAKER1 v1. 10 [41]  was used to predict genes in the genome of H. didymator using default settings. Gene annotation was evaluated using BUSCO version 3. 0. 2 [48]  with a reference set of 1658 proteins (conserved in Insecta). The other parasitoid genomes used in this work for comparison purposes were similarly analyzed (genomes available at NCBI for Microplitis demolitor  [31]  (PRJNA251518), Fopius arisanus  [43]  (PRJNA258104), Diachasma alloeum  [44]  (PRJNA306876), and Nasonia vitripennis  [45]  (PRJNA13660); Venturia canescens genome  [22]  available at https://bipaa.
DRJs and breakpoint analysis in H. didymator The proviral integrated segments are circularized and excised by homologous recombination between its extreme DRJs (at left and right extremities of the given segment, named DRJL and DRJR respectively). When the two copies of the DRJ exhibit some punctual differences, the excision site or breakpoint can be identified in a given recombined DRJ sequence with more or less resolution depending on the level of divergence between the DRJ copies. In order to identify and analyze DRJ excision sites of a set of circularized IV sequences in an automatic fashion, we developed the following method called DrjBreak-pointFinder and freely distributed at http:// github. com/ stephanierobin/DrjBreakpointFinder/. The method takes as input a set of circularized sequences (usually obtained by sequencing) and a reference genome.
The segmentation algorithm is classically based on fitting a piecewise constant function with two changepoints to the punctual difference signal (see  [94] ). DrjBreakpointFinder further gathers breakpoint results by proviral segments or DRJ pairs, in order to obtain for each the distribution of potential excision sites observed in a given circular virus sequencing dataset. The output of DrjBreakpointFinder consists of breakpoint region coordinate files along with visual representations for each proviral segment or DRJ pair. In this paper, DrjBreakpointFinder was applied to two circular viral DNA sequencing datasets. Circular DNA was extracted from HdIV particles and sequenced by 454 and Sanger technologies, resulting in 40,343 and 15, 575 reads, respectively  [92]. In addition, the DRJ copy was manually analyzed for a subset of 8 segments (Hd12, Hd16, Hd19, Hd22, Hd24, Hd28, Hd29, and Hd30) that presented only one right and left DRJs in their integrated form.
Authors' contributions FL, SR, and RD were responsible for the assembly of whole genomes and automatic annotations, and AB for database management. ANV annotated manually the endogenous viral sequences. BAW furnished C. sonorensis samples. BFS did the DNA extraction and QC for C. sonorensis. VJ did the H. didymator DNA extractions; XZ and SL supervised whole genome sequencing of this species. SR was responsible for the BUSCO and gene orthology analyses. FL was responsible for transposable element and synteny analyses. GG managed the H. didymator BAC sequencing. CL developed the pipeline for DRJ analyses and SR has performed the DRJ analyses. MR and VJ conducted the FISH experiments. ANV and BFS were responsible for the project conception, funding, and management. ANV wrote the manuscript with large participation of BFS, FL, and SR.
Funding The work on H. didymator was partially supported by the French Agence Nationale de la Recherche (ABC-PaPoGen, ref. ANR-12-ADAP-0001) and AIP-INRA project (GenomeInsect, AIP BIORESSOURCES 2012). The Sackler Institute of Comparative Genomics (AMNH) funded the generation of preliminary data for C. sonorensis and made the final genome sequencing available at reduced cost through their partnership with the New York Genome Center.
Acknowledgements The authors thank  Drs Jing ZHAO  and  Ming TANG , from the  BGI-Shenzhen, China , for their help with H. didymator genome sequencing. We thank  Clotilde GIBARD  and  Gaétan CLABOTS  from the  DGIMI  insectarium for the insects they have furnished, and the quarantine insect platform (PIQ), member of the  Vectopole Sud network , for providing the infrastructure needed for experimentations on insects. The authors acknowledge Séverine CHAMBEYRON and  Christine BRUN  ( IGH, Montpellier ) for their great help in H. didymator.
Availability of data and materials The datasets supporting the conclusions in this article are available at the NCBI under the Bioproject accession numbers PRJNA589497 for Hyposoter didymator  [95]  and PRJNA590982 for Campoletis sonorensis  [96]. The DrjBreakpointFinder method developed in this study is freely distributed at http:// github. com/stephanierobin/DrjBreakpointFinder. All other data are included within this article and its additional files.
The severity of envenomation was assessed according to the classification of the scorpion consensus experts group  [25] : • Grade I: local manifestations. • Grade II: minor manifestations (i. e. non-life-threatening). • Grade III: severe manifestations (i. e. life threatening). • Grade 0: no manifestation / dry sting (an extended classification/adaptation by this research group). The severity of envenomation was also measured using the Poisoning Severity Score  [26]. For the evaluation of pediatric risk of mortality, we used the PRISM Score  [27]. The need for individual patient consent was waived by the local research ethics committee. Our database has been registered at the Commission National de l'Informatique et des Libertés (registration n. 2217032), in compliance with French law on electronic data sources.
Statistical analysis We designed a database with information from patients and scorpions and performed a descriptive analysis using Excel 365 and Stata® Version 15 for Windows. Results were reported as median and interquartile range (25 th -75 th percentiles), mean ± standard deviation, or numbers with percentages. To compare qualitative variables, Fisher's exact test was used. To compare quantitative variables, student's t-test and the Mann-Whitney test were employed. The significance level was set at p ≤ 0. 05. Autoregressive-moving-average model (ARIMA) with ponderation (0,0,0)  [28]  and the coefficient of determination (r²) were utilized to determine the link between environmental condition and number of stings or percentage of general envenomation per month. For multiple analysis, the variable was kept p < 0.
Thanks to the development of high-throughput sequencing technologies, target enrichment sequencing of nuclear ultraconserved DNA elements (UCEs) now allows routine inference of phylogenetic relationships from thousands of genomic markers. Recently, it has been shown that mitochondrial DNA (mtDNA) is frequently sequenced alongside the targeted loci in such capture experiments. Despite its broad evolutionary interest, mtDNA is rarely assembled and used in conjunction with nuclear markers in capture-based studies. Here, we developed MitoFinder, a userfriendly bioinformatic pipeline, to efficiently assemble and annotate mitogenomic data from hundreds of UCE libraries. As a case study, we used ants (Formicidae) for which 501 UCE libraries have been sequenced whereas only 29 mitogenomes are available.
We compared the efficiency of four different assemblers (IDBA-UD, MEGAHIT, MetaSPAdes, and Trinity) for assembling both UCE and mtDNA loci. Using MitoFinder, we show that metagenomic assemblers, in particular MetaSPAdes, are well suited to assemble both UCEs and mtDNA. Mitogenomic signal was successfully extracted from all 501 UCE libraries, allowing us to confirm species identification using CO1 barcoding. Moreover, our automated procedure retrieved 296 cases in which the mitochondrial genome was assembled in a single contig, thus increasing the number of available ant mitogenomes by an order of magnitude. By utilizing the power of metagenomic assemblers, MitoFinder provides an efficient tool to extract complementary mitogenomic data from UCE libraries, allowing testing for potential mitonuclear discordance.
However, this tool requires both closely related reference mitogenomes and good coverage to perform well and also requires UCE and mtDNA assemblies to be conducted separately. Metagenomic assemblers could provide a powerful alternative to assemble both UCE loci and mtDNA simultaneously because they have been designed for efficient de novo assembly of complex read populations by explicitly dealing with uneven read coverage and are computationally and memory efficient. Comparisons based on empirical bulk data sets of known composition  (Vollmers, Wiegand, & Kaster, 2017)  have identified iDbA-UD  (Peng, Leung, Yiu, & Chin, 2012) , MEGAHiT  (Li et al. , 2016)  and METASPADES  (Nurk, Meleshko, Korobeynikov, & Pevzner, 2017)  as the most efficient current metagenomic assemblers. As a case study, we focused on ants (Hymenoptera: Formicidae) for which only 29 mitogenomes were available on GenBank compared to 501 UCE captured libraries as of  March 29, 2018 (Appendix S1).
| De novo assembly of mitogenomic and UCE data with MitoFinder To extract mitogenomic data from UCE libraries, we developed a dedicated bioinformatic pipeline called MitoFinder (Figure  1 ). This pipeline was designed to assemble sequencing reads from target enrichment libraries, extract, and annotate mitochondrial contigs. To evaluate the impact of assembler choice, contigs were assembled with iDbA-UD version 1. 1. 1, MEGAHiT version 1. 1. 3 and METASPADES version 3. 13. 0 within MitoFinder, and with TriniTy version 2. 1. 1 within PHyLUCE using default parameters. Mitochondrial contigs were then identified by similarity search using bLASTn with e-value ≥ 1e-06 against our ant reference mitogenomic database. Each detected mitochondrial contig was then annotated with TbLASTx for protein-coding genes (CDSs) and bLASTn for 16S and 12S rRNAs taking advantage of the geneChecker module of Mi-TOMAkEr (Schomaker-Bastos & Prosdocimi, 2018) that we incorporated into MitoFinder.
| Guided iterative mitogenomic data assembly with MITObIM For comparison purposes, we also ran MiTObiM  (Hahn et al. , 2013)  to extract mitochondrial sequences from the 501 UCE raw sequencing data. This software is designed to assemble mitochondrial reads using mitochondrial bait such as the CO1 sequence of a related species when available. Then, based on iterative mapping, MiTObiM extends as much as possible the mitochondrial contig previously obtained. For each library, given the scarcity of closely related complete mitochondrial genomes available for ants, the longest CO1 sequence available for the genus, or the most closely related genus, was used as bait for the initial step of MiTObiM. As there is no annotation step in MiTObiM, MitoFinder was used to annotate the resulting MiTObiM contigs.
| DNA barcoding To verify species identification of the 501 ant UCE libraries, CO1 sequences extracted by MitoFinder using METASPADES (mtDNA recovered for all species) were compared with species-level barcode records (3,328,881 CO1 sequences including more than 100,000 ants) through the identification server of the Barcode Of Life Data System version 4  (Ratnasingham & Hebert, 2007). The same CO1 sequences were also compared against the NCBI nucleotide database using MEGAbLAST with default parameters. An identification was considered to be confirmed when the query CO1 sequence had 95% similarity with a reference sequence in BOLD or GenBank with the same identifier.
| Assembly of UCEs As recommended by  Faircloth (2016) , we first relied on TriniTy to assemble UCE contigs using the phyluce_assembly_assemblo_trinity module of PHyLUCE. To assess the impact of assembler choice on UCE loci retrieval, we also used the assemblies obtained with iDbA-UD, MEGAHiT and METASPADES as implemented in MitoFinder. PHyLUCE scripts phyluce_assembly_get_match_counts and phyluce_assembly_get_fastas_from_match_counts were used to match contigs obtained for each sample to the bait set targeting 2,590 UCE loci for Hymenoptera  (Branstetter, Ješovnik, et al. , 2017). The resulting alignments were then cleaned using GbLOCkS  (Castresana, 2000)  with the phyluce_align_get_gblocks_trimmed_ alignments_from_untrimmed script. Finally, loci found in at least 75% of species were selected to create the four corresponding UCE supermatrices using the phyluce_align_get_only_loci_with_ min_taxa script.
| Assembly of UCE data sets De novo assembly of 501 UCE capture sequencing libraries was performed with four different assemblers: iDbA-UD, MEGAHiT and METASPADES via MitoFinder and TriniTy via PHyLUCE. All assemblers provided different numbers of contigs (Table  1 ) ranging from 30,544 (iDbA-UD) to 114,392 (MEGAHiT) on average. The average computational time per assembly was highly variable among assemblers with TriniTy being by far the slowest (35 CPUs, median time per sample: 1 hr:06 min:22 s, total time for all samples: 26. 9 days) and iDbA-UD the fastest (5 CPUs, median time per sample: 0 hr:11 min:01 s, total time for all samples: 4. 4 days), MEGAHiT (5 CPUs, median time per sample: 0 hr:12 min:35 s, total time for all samples: 4. 9 days) being slightly slower, and METASPADES (5 CPUs, median time per sample: 0 hr:25 min:44 s, total time for all samples: 14.
| Barcoding analyses A total of 534 CO1 sequences retrieved from the 501 METASPADES assemblies were used to verify species identification of the UCE libraries (Appendix S3). Similarity searches against BOLD and GenBank allowed us to confirm the species identity in only 312 cases probably because of the limited availability of CO1 barcoding data for these ant species. Moreover, in 42 cases, two or three CO1 sequence fragments were retrieved from the same UCE library. In seven of these cases, the slightly overlapping CO1 fragments probably resulted from poor assembly or erroneous annotation. However, in the 35 remaining cases, the genuine complete CO1 sequence overlapped with shorter contigs assembled from a minority of the reads, suggesting either cross-contaminations, NUMTs, endoparasites or bacterial symbionts.
| Phylogenetic results The ML topologies inferred from the different UCE supermatrices were very similar with an average quartet distance of 0. 005 among assemblers (Appendix S4). However, the percentage of supported nodes (UFBS > 95) differed depending on the assembler: iDbA-UD (91. 37%), METASPADES (89. 96%), MEGAHiT (89. 56%) and TriniTy (85. 85%). In the following, we only discuss the phylogenetic results obtained with MetaSPAdes that provides the most comprehensive assemblies for both UCE and mitochondrial data (Table  1 ). The following 12 well-established subfamilies (with several species in the dataset) were retrieved with maximal UFBS support (100%): Amblyoponinae, Apomyrminae, Dolichoderinae, Dorylinae, Ectatomminae, Formicinae, Heteroponerinae, Myrmeciinae, Myrmicinae, Proceratinae, Ponerinae, and Pseudomyrmecinae (Figure  3a ).
| CON CLUS IONS In this study, we developed the MitoFinder tool to automatically extract and annotate mitogenomic data from raw sequencing data in an efficient way. For the assembly step of our pipeline, we tested four different assemblers and showed that METASPADES is the most efficient and accurate assembler for both UCE and mitochondrial data. Applying MitoFinder to ants, we were able to extract mitochondrial signal from 501 UCE libraries. This demonstrates that mitochondrial DNA can be found as off-target sequences in UCE sequencing data. Interestingly, mtDNA extracted from UCE libraries can also be used to: (a) confirm species identification with barcoding methods, (b) highlight potential sample cross-contamination, and (c) reveal potential cases of mitonuclear discordance caused by hybridization events leading to mitochondrial introgression.
and 30 tribes. Raw sequence reads were downloaded from the NCBI Short Read Archive (SRA) on March 29, 2018 (Appendix S1). For the 501 ant UCE libraries, raw reads were cleaned with TriMMOMATiC version 0. 36 (Bolger, Lohse, & Usadel, 2014) using the following parameters: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:50. A reference database with the 29 complete mitochondrial genomes available for ants on GenBank at the time was constructed.
DATA AVA I L A B I L I T Y S TAT E M E N T The MitoFinder software is available from GitHub (https://github. com/RemiA llio/MitoF inder) and GitLab (https://gitlab. com/RemiA llio/ mitof inder). Annotated mitogenomes and partial mitogenomic contigs containing at least two genes and 1,000 bp have been deposited in GenBank and are available in the Third Party Annotation Section of the DDBJ/ENA/GenBank databases under accession numbers TPA:.
Global thermal inertia and surface roughness maps Remote observations of Bennu during OSIRIS-REx proximity operations were divided into several phases  (20). Detailed Survey was the main global mapping phase, and it was split into two subphases: Baseball Diamond (March and April 2019), optimized for imaging, and Equatorial Stations (late April through early June 2019), optimized for spectroscopy. Targeted observations of potential sample sites at higher spatial resolution took place during the Reconnaissance (Recon) phase of the mission. In the Recon A subphase (October 2019), the spacecraft observed potential sample sites with a spatial resolution about five times higher than in Detailed Survey. The primary datasets presented here were collected in Equatorial Stations, during which OTES and OVIRS measured the thermal emission from the surface of Bennu at ~40-and ~20-m spot sizes and wavelengths of 6 to 50 m and 3.
5 to 4. 0 m, respectively, at seven different local times of day: 3:20 a. m. , 6:00 a. m. , 10:00 a. m. , 12:30 p. m. , 3:00 p. m. , 6:00 p. m. , and 8:40 p. m. (fig. S1 ). The two instrument datasets complement each other well. The OTES wavelength range captures the bulk of the thermal spectral flux from the asteroid and is the primary thermal instrument. OVIRS measurements at shorter wavelengths are more sensitive to hotter facets and have a higher spatial resolution. We carried out thermophysical analysis of these data using a variant of the Advanced Thermophysical Model (ATPM)  (33) (34) (35)  that has been adapted for use on Bennu (see Materials and Methods). On the basis of the apparent absence of fine-grained regolith on the surface  (21)  and the weak temperature dependence of thermal properties of analog meteorite materials  (27, 28) , we began with the assumption that Bennu's thermal properties are constant with both temperature and depth.
Analyses described below validate this assumption in the case of Bennu. We adopted a fractional coverage of hemispherical section craters to represent unresolved surface roughness. The OTES and OVIRS data were then analyzed separately to produce two sets of independently derived maps of thermal inertia and surface roughness for Bennu (Fig. 1 ). We find spatial variations in both thermal inertia and surface roughness on Bennu, and the OTES-and OVIRS-derived maps are in good spatial agreement (Fig. 1 ). Mean thermal inertia values of 300 ± 30 and 320 ± 30 J m -2 K -1 s -1/2 were derived from the OTES and OVIRS observations, respectively. For surface roughness, mean values from OTES and OVIRS were 40° ± 2° and 40° ± 3° RMS slope, respectively. The offset in mean thermal inertia between OTES and OVIRS appears to be systematic in nature, as the distributions of derived values are identical when the OVIRS-derived thermal inertia values are shifted by -20 J m -2 K -1 s -1/2 (fig.