dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Sequence data and both raw and normalized count tables were deposited in the GenBank Geo Submission Omnibus under accession: GSE142509. All genes with significantly DE detected between in vivo vs in vitro time-resolved contrasts (early infection vs lag growth in vitro; middle infection vs exponential growth in vitro; late infection vs stationary growth in vitro) are provided in Supplementary Table  S5. All functionally annotated putative VFs identified from host-specific fuzzy clusters are presented in Supplementary Table  S6. All Supplementary figures, tables, and datasets (i. e. , Supplementary Tables  S5  and S6 ) associated with this study have been deposited in figshare: https://doi. org/10. 25387/g3. 13206221. Open-source R code for statistical analysis is available on github.
2016)  were also captured from in vivo Host all type clusters but the relative expression levels of the Chi-Fimassociated fimbrial and chitin-binding protein genes were orders   S6. distending toxin subcomponents cdtA and cdtB, yenC3 (a YenChomolog also known as rhsA encoded within Rhs2/Is. 5) and putative vegetative insecticidal protein component, vip2 (Table  2 ). Transcripts for known, and putative Yen-TC components, chi1 and yenC3, respectively, were captured within a Host Early in vivo cluster, but only yenC3, not chi1, shared any significant homology against known VFs from the VFDB using BLASTx homology search. In addition to predicted toxins, transcripts for T2SS and T3SSYE2 structural components were also identified as putative VFs from in vivo clusters (Supplementary Table  S6 ). Genes for T3SSYE2 co-located putative effectors were represented from in vivo clusters and found to have significantly greater expression under in vivo conditions compared to in vitro controls, including sicC-like and sicD-like, with the former being more highly expressed across all cell densities and the latter more highly expressed during early infection only.
Figure2Yersinia entomophaga MH96 putative host-specific VFs sharing significant sequence similarity to known VFs from the VF database identified among host-specific in vivo clusters. (A) Median log 2 counts-per-million-fold-change between in vivo and in vitro libraries organized by in vivo cluster type (each line represents a separate host-specific in vivo cluster, refer to Supplementary FigureS7). (B) The proportion of genes organized by virulence function classification among in vivo cluster types. Refer to Supplementary TableS6.
Acknowledgements Research associate,  Mitchell Weston , performed sample collection and RNA extraction for one of the in vitro treatments used in this experiment. We would like to acknowledge  Trystan Whang  and  Elena Kim  for coordinating library preparation, quality control, and sequencing at  Macrogen Korea. Finally ,  AgResearch Ltd. bioinformaticians  Dr. Aurelie Laugraud ,  Dr. Charles Hefer , and  Paul Maclean  helped with job execution on high-performance computing clusters.
Global crop calendar and crop areas We use data on monthly crop-specific growing areas around the year 2000  (1998) (1999) (2000) (2001) (2002)  for 26 crop groups as reported by MIRCA2000  (Portmann et al. , 2010). MIRCA2000 uses global cropland extent and crop harvested areas  (Monfreda et al. , 2008; Ramankutty et al. , 2008)  and also takes (sub-) national agricultural statistics from 402 administrative units into account. The data set provides crop-specific planting and harvest dates using crop calendars from the FAO Global Information and Early Warning System, United States Department of Agriculture and the International Rice Research Institute. MIRCA2000 combines information on crop harvested areas, irrigation, cropping intensities and growing seasons from satellite images and agricultural censuses aiming to minimize errors from inconsistencies between input data and using very little spatial modelling to distribute crop areas across global cropland.
MIRCA2000 is one of the only two global gridded cropland data sets that report monthly growing areas and the most recent data set. The alternative global crop calendar data set  (Sacks et al. , 2010)  does not distinguish between rainfed and irrigated crops and is therefore less suited for our analysis here. The total physical and harvested areas in MIRCA2000 are 1. 60 billion hectares and 1. 31 billion hectares, respectively with 24% of the total harvested area in irrigated agriculture. Compared to other estimates of global cropland total physical land in MIRCA2000 is < 5% below the 1. 66 billion hectares reported in  (Fritz et al. , 2015)  and 4% and 6% above the 1. 53 billion hectares reported as 'Arable land and permanent crops' in FAOStat. An average 0. 44 billion hectares are estimated to be left fallow every year  (Siebert et al.
Validation and evaluation A formal validation of our global multiple cropping data set is not possible due to the lack of global data to compare to but we take the following steps to evaluate accuracy for selected countries, and describe Fig. 2. Illustration of the method for identifying cropping systems in a grid cell with multiple non-overlapping growing periods and a cropping intensity greater than 1. In this example the total harvested area is 612 ha, the total physical area is 541 ha and the cropping intensity is 1. 13. ir is irrigated, rf is rainfed, othersann is short for others annual, a group of crops as defined in MIRCA2000. potential uncertainties and limitations in the data set: (i) review of the literature describing specific cropping systems in different world regions (N = 33, Appendix Table  A2 ) and comparison to systems found in our data set, (ii) listing known issues related to the input data and method used, (iii) grid cell-to-grid cell comparison with national gridded data sets available for China and India, (iv) comparison of total multiple cropping area extent reported in literature and agricultural census statistics and (v) assess the number of alternative cropping systems to the ones identified in the final data set.
For a comparison between these two national maps and our study, we use the 32 crops common in all three data sets and group 24 of them into 5 crop groups: -Cereals: millet, sorghum, maize, barley, rye, wheat, oat -Oil crops: oil palm, soybean, sunflower, groundnut, rapeseed, oilseed -Root crops: cassava, potato, sugar beet, beet -Others perennial crops: citrus, date palm, grapes, sugar cane, other perennial crops -Fodder grasses: alfalfa, fodder grasses The following groups and individual crops were not grouped: pulse, rice, cocoa, coffee, cotton, tobacco, others annual and vegetables. Fallow and non-legume hay are not considered as crops in MIRCA2000 and are therefore excluded from the comparison. A binary classification test between our dataset and the national data set is difficult as they were developed at a different spatial scale and for different, but close baseline years and in both, the national and the global data sets several cropping systems exist in a grid cell.
The rice-cereals irrigated cropping system known to be important in North India is not represented very well (Figure  S7 ) because an additional cropping period for rice (in winter season) and wheat (in summer season) was introduced in MIRCA2000 that deviated from the FAO crop calendar for these states in order to accommodate the irrigated harvested area exceeding the available area equipped for irrigation  (Portmann et al. , 2010). Therefore, our data set reports  Cropping intensity increases from the north-west to the south-east in China with single cropping being prevalent in Northwest China and Northern China and multiple cropping occurring in the Huanghe-Huaihe-Haihe plain and in the middle and lower areas of the Changjiang (Yangtze) river  (Yan et al. , 2013). We estimate the total multiple cropping area and area share in China as 43 Mha (36%).
Double cropping in Mato Grosso in the data set is represented only by small areas of soybeanpulses and soybean-wheat systems. The wheat-soybean system can also be found in southern Brazil with soybean planted in November and wheat harvested in October. Other systems prevalent in South and Central Brazil in the global data set are soybean-pulse in Goiás and maize-pulses in Minas Gerais with the first crop planted in November and the second crop harvested in October. The wheat-maize system prevalent in Mexico seems to be represented well in the global data (Fig. 3 ), it's the second largest cropping system in Mexico with wheat planted in November. We estimate the multiple cropping area in Brazil, Mexico and Argentina as 2. 8 Mha, 0. 7 Mha and 75,000 ha.
We estimate the multiple cropping area in Egypt, Ethiopia, South Africa, Tanzania, Madagascar and Kenya as 2. 1 Mha, 1. 9 Mha, 0. 69 Mha, 0. 56 Mha, 0. 48 Mha, and 0. 24 Mha, respectively. The total multiple cropping area in Africa in our study is 16. 1 Mha, which is 11% of the total physical crop area of 147 Mha. The area share agrees well with a recent estimate for 2014 where double cropping area is 12% of the total physical crop area  (Xiong et al. , 2017) , however, both the double cropping and total crop area are larger in 2014. While we identify several cropping systems and their physical area in each 30 arc minute grid cells, it is likely that there is a higher diversity of cropping systems on the farm scale, in particular in smallholder agriculture and where plot sizes are small. The agreement between our global dataset and the national datasets depends largely on identifying irrigated areas and irrigated cropping sequences correctly as most of them would be multiple cropping systems.
Potential for increasing global crop area As increasing cropping intensity has been an important strategy in the past for increasing harvest area and crop production without expanding physical cropland, we further calculate the potential for increasing cropping intensity on cropland with currently just a single harvest. This intensification potential only refers to the temporal, not the spatial dimension of mixing crops in cropping systems. Based on our crop-specific multiple cropping dataset and different intensification scenarios that reflect the opportunities for growing a second crop and leaving the land fallow for a short period of time before the next agricultural season, we find that a maximum of 395 Mha of global crop area in 2000 is available for this type of intensification (39% of global single cropping area).
Data and code availability statement The multiple cropping data set was created as an R raster object with multiple layers, and is available as NetCDF files, together 128 MB large and available on CSIRO's data repository (https://data. csiro. au/, see data citation below). A preview of the data set is available at geo-wiki. org. The data set was designed to be used together with other global gridded data sets or global crop, agricultural and Earth System models for further analysis that could include (i) quantifying the amount of food produced in multiple cropping systems, (ii) studying climate change impacts on crop production in multiple cropping systems globally, and (iii) studying the biogeochemical interactions and water and carbon fluxes between soil, crop, and the atmosphere in multiple cropping systems.
Acknowledgement We have used the Land Use / Land Cover information from the  Natural Resources Census Project of the National Remote Sensing Centre (NRSC) ,  ISRO based in Hyderabad, India. We thank  Franz Waldner  for reviewing the sections on remote sensing and crop type mapping and  Chris Russell  for helping to publish the multiple cropping data set and R package on bitbucket. We thank  Belay Kassie ,  Jonathan Ojeda ,  Octavio Caviglia ,  Qiangyi Yu  and  Lijun Zuo  for discussing multiple cropping systems in their respective countries with us. This study was funded by the  Commonwealth Scientific and Industrial Research Organisation  and the  Bill & Melinda Gates Foundation.
Conditioned sequencing primer V2 was annealed to the sizeselected SMRTbell. The annealed libraries were then bound to the P6-C4 polymerase using a ratio of polymerase to SMRTbell set at 10:1. After performing a magnetic bead-loading step (OCPW), SMRTbell libraries were sequenced on 48 SMRTcells (RSII instrument at 0. 25nM with a 360-min movie resulting in a total of 61. 8Gb of sequence data (1. 28Gb/SMRTcell). 10X Genomics genome sequencing: Chromium library was prepared according to 10X Genomics' protocol using the Genome Reagent Kits v1. Sample quantity and quality controls were further validated on Qubit, Nanodrop and Femto. Optimal performance has been characterized on input gDNA with a mean length greater than 50 kb. The library was prepared using 3 mg of high molecular weight (HMW) gDNA (cut off at 50kb using BluePippin system).
6Gb of raw sequence data. Genome assembly and annotation: PacBio reads were corrected and trimmed using Canu v1. 5  (Koren et al. 2017). Contigs were then assembled using SMARTdenovo version of May 2017  (Ruan 2019). The draft assembly produced contains 729 contigs with a total genome size of 807. 5 Mbp, an N50 of 3,9 Mbp and a L50 of 59 contigs (Figure  2 ). To improve the assembly base pair quality two polishing steps were run. First, BLASR aligned PacBio reads were processed with Quiver from the Pacific Biosciences SMRT link software v. 4. 0. 0. Second, 10X reads were realigned to the genome using Long Ranger v2. 1. 1 and the alignment file was processed with Pilon v1. 22  (Walker et al. 2014). Third, the same 10X reads were aligned to the genome with BWA-MEM v0. 7. 12-r1039  (Li 2013 ) and the alignment file was processed with ARCS v1.
0. 1  (Yeo et al. 2018)  to scaffold the genome. Both tools were run with default parameters. For genome annotation, the MAKER3 pipeline was employed ((Holt and Yandell 2011); Maker 3. 01. 02-beta in mpi mode to merge data from gene models and cDNA/protein evidences). Maker has been running with entries est_gff, protein_gff and pred_gff in run_evm = 1, est2genome = 0 and protein2genome = 0 mode. No AED cut-off was applied but AED scores have been used to select the best supported transcript for each gene. Transcriptome RNA-seq sequencing and assembly: RNA-seq libraries were prepared according to Illumina's protocols using the Illumina TruSeq Stranded mRNA sample prep kit. Briefly, mRNAs were selected using poly-T beads, reverse-transcribed and fragmented. The resulting cDNAs were then subjected to adaptor ligation.
10 cycles of PCR were performed for amplifying the libraries. Quality of the libraries was assessed using a Fragment Analyzer. Quantification was performed by qPCR using the Kapa Library Quantification Kit. RNA-seq libraries were sequenced on an Illumina HiSeq3000 using a paired-end read length of 2x150 pb with the Illumina HiSeq3000 sequencing kits resulting in 95Gb of sequence data (28. 9M reads pairs/library). The read quality of the RNA-seq libraries was evaluated using FastQC  (Andrew S. 2010). De novo and reference-based transcriptome assemblies were produced. Reads were cleaned, filtered and de novo assembled using the DRAP pipeline v1. 91  (Cabau et al. 2017)  with the Oases assembler  (Schulz et al. 2012). Assembled contigs were filtered in order to keep only those with at least one fragment per kilobase of transcript per million reads (FPKM).
In the reference-based approach, all clean reads were mapped to the chromosomal assembly using STAR v2. 5. 1b  (Dobin et al. 2013)  with outWigType and outWigStrand options to output signal wiggle files. Cufflinks v2. 2. 1  (Trapnell et al. 2010 ) was used to assemble the transcriptome. All tissues have been de novo assembled separately and the 1 FPKM cut-off has been set on each library. The min, mean, median and max number of transcripts after the cufflinks assembly of each tissue are respectively 26223, 44992, 47804 and 59234. RAD-library construction: RAD-seq library was built following the Baird et al. (Baird et al. 2008)  protocol with minor modifications. Briefly, between 400 to 500 ng of gDNA per fish were digested with SbfI-HF enzyme (R3642S, NEB). Digested DNA was purified using AMPure PX magnetic beads (Beckman Coulters) and ligated to indexed P1 adapters (1 index per sample) using concentrated T4 DNA ligase (M0202T, NEB).
Identification of genes associated with osmoregulation We listed known O. latipes genes that encode proteins associated with osmoregulation (e. g. , hormones, pumps, transporters, channels, osmolytes-related and cellular junction proteins) based on literatures, and used HMMER version 3. 1b2 (http://hmmer. org/) to identify specific Pfam domains (Pfam 32, (El-Gebali et al. 2019) included in these proteins (Supplemental Table  S1 ). Using our gene model of O. javanicus together with Ensembl gene models of the O. dancena and O. latipes species complex (Hd-rR, HNI-II and HSOK), we counted the number of proteins containing the Pfam domains in each species (Supplemental Table  S2 ).
Data availability All genome and transcriptome information was deposited under the NCBI Bioproject number PRJNA505405. The Illumina sequencing data for the RAD-tag genetic map were deposited in the Sequence Read Archive at NCBI with accession numbers SRX5326271 to SRX5326366. The genomic PacBio sequencing data were deposited in the Sequence Read Archive at NCBI with accession numbers SRX5274121 to SRX5274138 and SRX5274139 to SRX5274169. The 10X genomics Illumina sequencing data were deposited in the Sequence Read Archive at NCBI with accession number SRX5274139. The transcriptome Illumina sequencing data were deposited in the Sequence Read Archive at NCBI with accession numbers SRX5017469 to SRX5017479. The final chromosome assembly and genome annotation were deposited in GenBank at NCBI RWID00000000.
Genome Characteristics To estimate size and other genome characteristics, 10X reads were processed with Jellyfish v1. 1. 11 (Marçais and Kingsford 2011)  to produce 21-mer distribution. The k-mer histogram was uploaded to GenomeScope  (Vurture et al. 2017)  with the max k-mer coverage parameter set to 10,000. Genome size was estimated around 908 Mbp, which is slightly higher than the 850 Mbp (0. 87pg) estimated size reported on the Animal Genome Size Database ("Animal Genome Size Database:: Home"). Furthermore, this analysis estimates that the O. javanicus genome contains 33% of repeat sequences (around 303 Mbp) and has a heterozygosity of 0. 96% (Table  1 ).
Annotation results The first annotation step was identifying repetitive DNA content using RepeatMasker v4. 0. 7 ("RepeatMasker Home Page"), Dust  (Morgulis et al. 2006 ) and TRF v4. 09  (Benson 1999). A species-specific de novo repeat library was built with RepeatModeler v1. 0. 11 (Smit and Hubley 2010). Repeated regions were located using RepeatMasker with the de novo and the Zebrafish (Danio rerio) libraries. Bedtools v2. 26. 0  (Quinlan and Hall 2010)  was used to merge repeated regions identified with the three tools and to soft mask the genome. Repeats were estimated to account for 43. 16% (349 Mbp) of our chromosomal assembly. The MAKER3 genome annotation pipeline v3. 01. 02-beta  (Holt and Yandell 2011 ) combined annotations and evidences from three approaches: similarity with known fish proteins, assembled transcripts and de novo gene predictions.
Protein sequences from 11 other fish species (Astyanax mexicanus, Danio rerio, Gadus morhua, Gasterosteus aculeatus, Lepisosteus oculatus, Oreochromis niloticus, Oryzias latipes, Poecilia formosa, Takifugu rubripes, Tetraodon nigroviridis, Xiphophorus maculatus) found in Ensembl were aligned to the masked genome using Exonerate v2. 4  (Slater and Birney 2005). Previously assembled transcripts were used as RNA-seq evidence. A de novo gene model was built using Braker v2. 0. 4  (Hoff et al. 2016)  with wiggle files provided by STAR as hints file for training GeneMark and Augustus. The best supported transcript for each gene was chosen using the quality metric Annotation Edit Distance (AED)  (Eilbeck et al. 2009 ). The genome annotation gene completeness was assessed by BUSCO using the Actinopterygii group (Table  2 ).
Mitochondrial genome and annotation The previously sequenced Oryzias javanicus mitochondrial genome (NC_012981)  (Setiamarga et al. 2009 ) was aligned to the chromosomal assembly using Blat. All hits were supported by a single scaffold. This scaffold was removed from the assembly, circularised and annotated using MITOS  (Bernt et al. 2013). This new Oryzias javanicus mitochondrial genome is 16,789 bp long and encodes 13 genes, 2 rRNAs and 19 tRNAs. Phylogenetic relationship: To precisely determine the phylogenetic position of O. javanicus within the genus Oryzias, we estimated the phylogenetic relationship using published whole genome datasets as references. Reference assemblies and annotations of O. latipes (Hd-rR: ASM223467v1), O. sakaizumii (HNI-II: ASM223471v1), Oryzias sp. (HSOK: ASM223469v1), O.
ACKNOWLEDGMENTS This work was supported by a " Projet Incitatif PHASE department 2015 " grant (Grant ID ACI_PHASE, INRAE) to AH, a  NIBB Collaborative Research Initiative  to KN, NIBB individual Collaboration Research Project to MK, and  Grants-in-Aid for Young Scientists to YT (Grant ID   16K18590 ) and MK (Grant  ID 16K18593 ). AH was additionally funded by the project  AquaCRISPR  ( ANR-16-COFA-0004-01 ). The GeT and MGX core facilities were supported by  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). The GeT core facility was also supported by the  GET-PACBIO program  («  Programme operationnel FEDER-FSE MIDI-PYRENEES ET GARONNE 2014-2020  »).
Basic Safety Messages that are frequently generated from multiple connected vehicles can play a primordial role in providing transport data see credible and reliable information they contain. Otherwise, when considering the way Basic Safety Messages (BSMs) are treated, multiple deficiencies prevent the latter to be capable of constituting a precious data source. As we know, data become more useful the more widely are used, which is the exact opposite of what happens with the BSMs that exist only temporarily, used locally, considered disposable, and are never stored. In this paper, we introduce a data reuse model that retains collected BSMs, stores, and processes them inside the vehicle constituting a continuous data source holding retained snapshots along the roadway. Our model provided a primary data source available on a large scale, considered to be a worthy dataset for machine learning tasks, capable of visualizing different traffic-related indicators to enhance analytics and support decisions-making.
A great deal of the ongoing research under the mentioned program is oriented towards such data-providing applications. In this respect, BSMs frequently generated from multiple connected vehicles can play a primordial role in providing transport data see credible and reliable information that they contain. Otherwise, when considering the way BSMs are treated, multiple deficiencies prevent the latter to be capable of constituting a precious connected vehicle data source. First: presently, the basic safety messages are particularly even exclusively used for the safety purpose. In contrast, a big bundle of its data elements are necessary for a considerable collection of applications not related to safety. At least, the BSM Part 1 data elements can conveniently provide basic vehicle information required by several applications.