dataesr/scientific-paragraphs-categorization huggingface dataset:
The spore germination rates ranged from 18% to 100%, with an average germination rate of 55%. Additionally, the basidia dissected from F1 intercrosses had a higher average germination rate (64%) compared to basidia dissected from crosses between CBS6039 and CBS6273, which had an average germination rate of 50% (S4 Table ; Student t test, P < 0. 05). We genotyped each of the meiotic progeny from the 76 basidia that were dissected, using the STE3 (Chrom10_17) and SXI2 (Chrom11_08) genes as markers for the P/R and HD loci, respectively (S5 Table ). We then determined the tetrad-type of each basidium with respect to the P/R and HD loci. In cases where the genotyping data of the 2 MAT loci were not sufficient to determine the tetrad type of the basidium, we additionally applied markers from both chromosomes 10 and 11, as well as marker S2-2 that is located on chromosome 2 (S5 Table ).
Both assemblies were evaluated for even coverage of both libraries and checked for contamination using GAEMR (http://software. broadinstitute. org/software/gaemr/). For CBS6039, additional Roche/454 8 kb mate-paired reads as well as PacBio filtered subreads were used for higher order scaffolding using SSPACE-LongRead v1-1  [43] , requiring 5 linking reads (-l 5) and a 200 base gap between scaffolds (-g 200). Genes were predicted and annotated by combining calls from multiple methods. A training set was generated using Genewise and Genemark  [44] , and then GlimmerHmm  [45] , Snap  [46] , and Augustus  [47]  were run to generate ab initio gene models. The best gene model at a given locus was selected from these data sets using EVM  [48] ; conserved genes missing in gene sets were identified using OrthoMCL  [49]  and combined with the EVM set.
Genes matching repetitive elements were then filtered if their coordinates overlapped TransposonPSI (http:// transposonpsi. sourceforge. net/) hits (>30% overlap to CDS, e-value 1e-10), repeat Pfam domains, or RepeatRunner  [50]  proteins. The gene set of CBS6039 was also updated using RNA-seq data. Reads were quality trimmed, jaccard clipped, and normalized using Trinity version 2. 1. 1  [51] ; the filtered reads were then aligned to the genome using STAR version 2. 4. 2a [52]  with parameter-alignIntron-Max 10000. The reads were then assembled into transcripts by providing the STAR aligned bam to Trinity run in the genome-guided mode with parameter-genome_guided_max_intron 10000. Trinity transcripts were aligned to the genome with PASA  [48]  and provided as input to EVM for gene calling as described above.
Identification of intergenic regions We scanned the genome of C. amylolentus (CBS6039) using the Geneious R9 software (http:// www. geneious. com) [54]  to identify the intergenic regions. The largest intergenic (ORF-free) regions were identified on each chromosome. Some of the predicted ORFs were not considered authentic ORFs because they were either transposon-like or dubious in nature. The ORFs that were smaller than 200 amino acids were also not considered for this analysis.
Transposon identification and mapping The LTR-retrotransposons in C. amylolentus centromeric regions were identified using the LTR-finder program (http://tlife. fudan. edu. cn/ltr_finder/, [55] ). Six LTR elements were identified in different centromeres. The sequences of these LTR elements were retrieved from the genome and subjected to sequence analysis for motif/domain analysis using CD-search (http://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi). Next, BLASTn analysis was performed to determine occurrence of full length/traces of these elements in the genome, including centromeres. BLAST results were mapped onto the genome and we found that these elements clustered exclusively at the centromeres, and hence these 6 elements were named Tcen1 through Tcen6.
Construction of LD heat maps To characterize meiotic recombination among genotyped markers on chromosomes 10 and 11, we carried out linkage analysis on all pairwise markers in the F1 population. We estimated the squared correlation coefficient (r 2 ) between markers i and j using the following equation: r 2 ¼ D 2 ij p i ð1 À p i Þp j ð1 À p j Þ where p i and p j are allele frequencies of the 2 markers, and D is defined as the difference between half of the observed frequency of heterozygosity and the expected value, where D ¼ 1 2 p ij þ p ji À p i p j : This analysis was conducted using R "genetics" v 1. 3. 8 packages (https://cran. r-project. org/ web/packages/genetics/index. html) and visualized using the R package of "LDheatmap"  [57].
Acknowledgments We thank  Dr. Tom Petes ,  Dr. Paul Magwene , and  Dr. Rytas Vigalas  for comments and suggestions during the preparation of the manuscript. The authors would also like to acknowledge the  Genoscope and Broad Institute genomics platform  for generating the Illumina and 454 sequences used for the draft assemblies. MN would like to thank  Ulrich Ku ¨ck  for his support. SS would like to thank  Li Xu  for her support.
Results We obtained samples from 44 adult T-cell leukaemias/lymphoma (ATLs) (from 35 patients, HTLV-1, human disease) and 47 B-cell leukaemias (from 43 individuals, BLV, animal model). The animal sample set included 15 bovine tumours (natural disease) and 32 tumours from BLV-infected sheep, a well-established experimental model for BLV/HTLV-1 (refs 6,7) (Supplementary Data 1). We utilized stranded RNA-seq data (91 tumours) in combination with an improved version of DNA-seq based high-throughput mapping of integration sites (56 tumours) to simultaneously profile proviral integrations, measure clonal abundance and identify virushost transcriptional interactions in tumours  9, 24, [29] [30] [31]. We identified 92 distinct proviral integration sites considering all available tumours (54 genic and 38 intergenic, Supplementary Table  1 ).
1 (ovine), respectively, and proviral genome sequences BLV-YR2 (GenBank: KT122858) and HTLV-1-ATK-1 (GenBank: J02029), respectively. SAMtools and BAMtools were used to sort and index, and separate sense and antisense reads from the STAR output, respectively  54, 55. ENSEMBL v84 was used for host genome annotations and custom annotations for both HTLV-1 and BLV genomes. FeatureCounts 56 , R packages DESeq2 (ref. 57) and DEXSeq  58  were used for read quantification, normalization and differential gene and exon expression analysis, respectively. Integrative Genomic Viewer (IGV) was used for visualization of sequencing alignments on both the host and viral genomes  59. RNA-seq reads stemming from any of the viral LTRs (HTLV-1 and BLV) systematically mapped to both the 5 0 and 3 0 LTR as both are identical in sequence.
Reads were specifically assigned to one or the other LTR based on additional host-specific mapping data and fusion-read information (outlined below): upstream or downstream position of host-mapping mate pair alignment, 3 0 AS RNA-host hybrid read identification (antisense coverage, 3 0 LTR alignment), host-LTR hybrid read identification (sense coverage, 5 0 LTR alignment), LTR-host read identification (sense coverage, 3 0 LTR alignment), host genomic environment (genic, intergenic) and evidence of 5 0 LTR deletion from HTS-based integration mapping data. Detecting hybrid RNAs and insertion sites from RNA-seq data. Mispaired and soft-clipped reads supporting virus-host hybrid transcripts were identified using a custom two-pass alignment scheme as described in ViralFusionSeq  60. RNA-seq paired-end reads were aligned to the host genome using BWA  61  (default parameter except -k 19 and -L 1) and mispaired and soft-clipped reads (minimum 8 soft-clipped nts) were re-aligned to the proviral genome.
Host and proviral alignments of each read were compared, and reads were flagged as fusion reads if one read mapped to the host genome and the other to the proviral genome, or if their soft-clipped breakpoint was localized within a 5 nt window. Fusion reads sharing the same fusion point within a 5nt window were clustered. Fusions were classified as genic/intergenic and concordant/discordant using a combination of BEDTools 2. 16. 2 (ref. 62) and BEDOPS 2. 30 (ref. 63). HTLV-1/BLV integration sites were determined based on identification of chimeric transcripts that encompassed LTR-host boundaries. Hybrid reads generated from 3 0 AS-enriched RNA-seq data (asymptomatic samples) were additionally filtered to exclude both tumourspecific chimeric reads and hybrid reads common to multiple asymptomatic samples to exclude artefacts from sample cross-contamination.
Primer sequences are available in Supplementary Data 5. Paired-end reads were aligned to a host-provirus hybrid genome using BWA. After quality trimming (average base quality Z30) only paired-end reads that fulfilled the following conditions (spanning LTR-host junctions) were retained: Read 1: BLV 5'LTR: 30 nts, BLV 3'LTR: 27nts, HTLV-1 5 0 LTR: 29 nts, HTLV-1 3 0 LTR: 45 nts of the read mapped to the relevant LTR extremity. Read 2: the read mapped to the host genome with r3 mismatches. Duplicates were removed based on reads that showed the same genomic insertion site and identical eight random nt tags. Read numbers were counted for each proviral integration site and reported using in-house R and Perl scripts. Clone abundance in tumours was determined as follows: if both 5 0 and 3 0 LTR flanking sites were identified, % ¼ average 3 0 -5 0 LTR.
If only one flanking site was detected: % ¼ % defined by detected LTR flanking site and provirus identified as either LTR-deleted if deletion is supported by evidence from RNA-seq hybrid read detection (non-canonical virus-host boundary) or full-length if RNA-seq-based data support the presence of LTR-host fusion reads. Identifying hotspots of proviral integration. Identical proviral integration sites (IS) across samples were removed to account for potential cross contamination between samples and the IS that showed the highest read count was retained. 5 0 and 3 0 LTR flanking IS with same proviral orientation located within a 10 bp window were merged. IS distribution across the genome was first examined by counting the number of IS in sliding consecutive bins of 100 kb (50 kb overlap) and bins showing IS numbers 499th percentile of the distribution of IS per bin ('hotbins') were visualized in individual chromosomes.
Hotspots were defined as genic if the median of the IS in the hotspot fell within a gene. Intergenic hotspots defined by simulation were then manually curated to account for poor annotation of the ovine transcriptome (ENSEMBL OAR3. 1 v84) and re-assigned to the genic class if they fell within transcribed regions (i. e. , non annotated exon, 5 0 transcribed region of a mis-annotated gene). This was achieved by visual examination of each predicted hotspot (IS positions and orientation) in IGV combined with the corresponding RNA-seq alignments (RNA-seq data of ovine primary B cells). Predicted hotspots that overlapped multiple genes or novel unannotated transcripts were discarded for further analysis in this study. Of the genes uncovered in the genic hotspot class after curation, 618 showed evidence of 1 hotspot while 56 carried multiple hotspots (2-4).
Simulated expression-matched gene lists were generated according to expression bins that each consisted of a group of 500 genes that most closely matched the listed genes expression level based on average TPM computed using RSEM  65  across all available human leukaemia samples. Additional scores were computed based on the recurrence of paralog genes. We assessed recurrence by P-value counting the frequency of simulated recurrence scores equal to or higher than the observed tumour/asymptomatic recurrence scores, divided by the number of simulated gene lists (N ¼ 100,000). Gene recurrence between genic hotspots of proviral integration in asymptomatic sheep samples and provirus-interacting host genes identified in either asymptomatic or tumour samples, as well as HTLV-1 genic integrations retrieved from the public RID database  50  , was assessed based on the same simulation method.
1 ml Q5High-Fidelity DNA Polymerase (New England BioLabs)). PCR consisted of 35 cycles of 8 s at 98 °C, 20 s at 67 °C and 35 s at 72 °C. Products were visualized on a 2% agarose gel and sequenced by conventional Sanger methods. Proviral load quantification. DNA was isolated using the Qiagen AllPrep DNA/ RNA/miRNA kit and proviral DNA was quantified by real-time PCR using primers targeting either the BLV or HTLV-1 3 0 region and RPS9 or Actin, respectively, for normalization (primers from Integrated DNA Technologies; Supplementary Data 5). Runs were performed in a 50 ml volume containing 1 mg of total DNA, primers and probe (200 nM concentration of each) in 1 Â PCR buffer (Platinum Quantitative PCR SuperMix-UDG) (HTLV-1) or 10 ml containing 50 ng DNA and 1 Â Universal PCR Master Mix, No AmpErase UNGa (ThermoScientific) (BLV).
Acknowledgements This work was supported by the  Fonds de la Recherche Scientifique (FRS) ,  Televie, les Amis de l 0 Institut Bordet , the  International Brachet Stiftung (IBS) , the  Fondation Lambeau-Marteaux , the Ligue contre le cancer, l' Institut National du Cancer (INCA) , the  Canceropole d'ı ˆle de France  and a  Televie Grant  to V. H. M. A. holds a  Postdoctoral Researcher fellowship  of the FRS, A. M. is recipient of a doctoral fellowship from the  Institut National du Cancer (INCA) , N. R. and K. D. are Scientific Research Worker of Televie. We thank  Wouter Coppieters ,  Latifa Karim  and the  GIGA Genomics Platform  for sequencing services and support,  Franck Mortreux  ( ENS Lyon, France ) for providing samples and cell lines used in this work,  Charles Bangham  ( Imperial College, London, UK ) for providing the Tarl2 cell line and  Dominique Bron  ( Institut Jules Bordet, ULB ) for comments on the manuscript.
NRT2 family In legumes, a cDNA encoding a putative high-affinity nitrate transporter of G. max has been cloned  (Amarasinghe et al. 1998). Subsequently, two studies were published describing the identification and molecular characterizations of the NRT2 gene family; one using L. japonicus, the other M. truncatula  (Criscuolo et al. 2012; Pellizzaro et al. 2015). In L. japonicus, interrogation of the genome database led to the identification of four putative LjNRT2 named LjNRT2. 1, LjNRT2. 2, CM0001. 20 and CM0161. 180 (Criscuolo et al. 2012). Using similar approaches,  Pellizzaro et al. (2015)  revealed, for M. truncatula, a small NRT2 gene family composed of three MtNRT2 genes (named MtNRT2. 1, MtNRT2. 2 and MtNRT2. 3), indicating a similar NRT2 family size in legume or non-legume species.
Conclusion Fundamental studies to understand mechanisms that govern nitrate transport and signaling offer prospects to better control nitrogen use efficiency in plants. Mining of extant datasets enables identification of candidate genes and consideration of their possible roles in planta. To date, few studies of legume nitrate transporters have been undertaken, and thus much remains undiscovered, in particular the role of these transporters in nodules. Experimental characterization of nitrate transporters is usually performed under optimal or at least unchallenging conditions. However, plants are continuously subjected to ever changing environmental conditions. Their sessile lifestyle exposes them to unavoidable frequent and severe climatic changes and attacks by herbivores and microorganisms.
Therefore, time of the day and age of the trained wasps were positively correlated with the time interval after training, and may constitute confounding factors for this variable. As a consequence, the effect of the time after training can properly be evaluated only through the comparison of naïve and trained wasps (see "Data analysis" section). Training sessions were planed on series of 5 or 6 successive days, two consecutive series being separated by a break of one or two days, so that the response of the wasps of both strains from all time intervals after training could be assessed each day. Control: Naïve wasps. As a control, the response of individuals that had neither encountered the training odour, nor host kairomone were tested in the olfactometer. Each of these naïve wasps was tested immediately before or after (±10 min) a same-aged trained wasp of the same strain.
Data analysis The response variable that we analysed as a proxy of memory retention following learning was the side chosen by the wasp in the olfactometer (choice outcome: training odour vs. no odour). According to the predictions of a speed-accuracy trade-off  [50] , any difference in choice behaviour can be attributed to the speed at which it is made: slower individuals are expected to make the more accurate choices. Then, the time needed to reach one of the target chambers (choice duration) was also analysed to determine if differences in choice behaviour between the strains could be attributed to differences in the speed of the choice. The choice outcome was analysed using generalised linear models (binomial distribution of error, logit link). The explanatory variables were the strain, the training treatment (naïve vs.
Image Quantitative Analysis We used IMARIS software to compute the maximum thickness (MxT) of formed biofilms. MxT values were extracted for the eight replicas at each sampling time (up to 120 h) when cells were organized forming a clearly differentiable biofilm. The mean values obtained out of the eight replicas were used as the basis for model identification. BIOFILMDIVER  (Mosquera-Fernández et al. , 2014)  was used to obtain the area covered by cells (CA) for each slice. CA corresponds to the ratio between the number of colored pixels and the total number of pixels. In this way, CA = 0% is equivalent to the absence of cells while CA ∼ 100% corresponds to a densely occupied slice. The CA value was computed for the biofilm including both channels (GCh and RCh) but also for both channels separately to analyse when and where non-viable cells appear in the biofilm.
24;  Stewart, 1998). We estimated the unknown parameters for the different candidate models using the time resolved data of biofilm maximum thickness and average nutrient concentration in the bulk. Cell counts and cell dry weight were used to define initial conditions and bounds on the parameters. Details on the parameters to be estimated, bounds and problem formulation are included in the section Theoretical Methods (Table  1 ). Figure  5  presents an overview of the candidate models and the corresponding best fit in terms of least squares error. Model 1 (M1) assumes that biomass diffusivity is constant. Since there is an excess of nutrients in the medium, we assume that G N follows a mass action description G N = C N C B instead of the usual Monod formulation. Note that we also tested the Monod formulation without improvements in the sense of goodness of fit (data not shown).
This may be due to the methods used to estimate symptoms that remain imprecise, increasing variability in the dataset. The possibility to estimate pathogen multiplication in this experiment could have made it possible to evidence G P * G P interactions. However, we could show that the specific viral isolate can affect the outcome of co-infection with a positive effect on bacterial symptoms only observed for some viral isolates, in particular the Tanzanian isolate Tz11 (congruent results in the different experiments, see Figures  3A, 4B ). Similarly, no G * P G P interactions have been observed for the effect of coinfecting bacterial strains on nematode survival  (Bose and Schulte, 2014) , nevertheless, the interaction mechanism depends on the particular bacterial strain considered. As in our study, the molecular mechanisms involved would consequently depend on the particular pathogen genotype considered.
ACKNOWLEDGMENTS We are grateful to  Drissa Silué  (AfricaRice) and  Boris Szurek (IRD)  for providing Xoc strains from Tanzania and Madagascar. We thank  Prof. Adam Bogdanove  for donating the BLS256 and derivative mutant strains. The team and collaborators of  Denis Fargette  is acknowledged for RYMV isolates from  Burkina Faso , Tanzania and Madagascar. We are grateful to  Jamel Aribi  and  William Ribière  for their help during greenhouses experiments, to  Agnès Pinel-Galzi  and  Martine Bangratz  for their advice concerning ELISA method and to the following fieldworkers:  Manaka Douanio ,  Amadou Diallo ,  Mustapha Koala , and  Bouda Zacharia. The master's students  Rony Midahuen  and  Linh Lan Azria-Luong  contributed to preliminary experiments on virusbacteria co-infection and to the design of quantitative PCR method to estimate bacterial load.
FUNDING This study was facilitated by the formal partnership between  IRD  and  INERA within the international lab "LMI Patho-Bios :  Monitoring Plant Pathogens in West Africa. " This  project is supported by the  French Agropolis Foundation  ( Labex Agro-Montpellier :  ANR-10-LABX-0001-01 ), with two projects (reference IDs  1403-041  and  1504-004 ).
Site The experimental data used for model calibration were collected from an on-station field trial during 2013 growing season at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Bamako, Mali Republic (12. 52 0 N and -8. 07 0 W). Daily climatic condition was monitored during 2013 growing season using automatic weather station (AWS) installed within the station (<500 m to the experimental site). The data observed include rainfall, solar radiation, maximum and minimum temperature, relative humidity, and wind speed and direction. Long-term (1970 Long-term ( -2010) )  daily climatic records were obtained to establish comparison with the cropping year at the station. The record shows that 2013 total rainfall (1190 mm) was a little above the long-term average  and classified as a wet year.
Validation data For model validation we used the results of field experiments carried out between 2000 and 2008 for two locations (Bamako and Cinzana, Mali). The details of these experiments have been reported by  Clerget et al. (2004 Clerget et al. ( , 2007 Clerget et al. ( , 2008)). The agronomic practices and relevant observations used for this study are presented in Table  2.
Sensitivity analysis A sensitivity analysis was carried out on the three models used (APSIM, DSSAT and Samara) from the data used for calibration. Five (5) model parameters were changed by adding or subtracting 10% to the calibrated values and the effect on the flowering date, maximum LAI, final above ground biomass (AGB) and grain yield were calculated. Similar to  Zuidema et al. (2005) , such an analysis will identify parameters that have a strong influence on modelled output, in this case sorghum production, and therefore need to be estimated accurately.
Acknowledgements This work constitutes part of doctoral research studies funded by  WASCAL Graduate research programme. Experimental work was supported by  ICRISAT ,  Mali  and funded through the  CGIAR Cooperative Research Programs ,  Dryland Systems  and  Dryland Cereals. The authors thank  Dr. Neil Huth  for his assistance during calibration of the varieties in APSIM and also  Dr. Benoit Clerget  for permission to use part of his data reported on sorghum physiology project experiments in  West Africa  for the validation of the models.
Statistical analysis Statistical analyses were performed using R software, version 3. 0. 3 [50]. To obtain a general overview of the production of volatile compounds during the fermentation for each stage of fermentation (T1 and T2), principal component analysis (PCA) was performed using the FactoMineR package  [51]. The limma package  [52]  was used to import and normalize the global microarray data (quantile method for normalization between arrays). For each studied time of CO 2 released (T1 and T2) and based on this normalized dataset of 6200 points for the 4 strains, we used a sparse partial least squarediscriminant analysis (sPLS-DA), an exploratory approach in a supervised context in order to select the most important transcripts relative to the 4 strains  [53]. We tuned the number of dimensions of the sPLS-DA to 2 and the number of variables to choose on these 2 dimensions to 400.