dataesr/scientific-paragraphs-categorization huggingface dataset:
Plant and sequencing material A self-progeny of 76 individuals from the 'Calcutta 4' accession (PT-BA-00051, CRB Plantes Tropicale, Guadeloupe) was produced at the CIRAD research station in Guadeloupe. DNA was extracted, and sequencing libraries were constructed using a genotyping by sequencing (GBS) approach and sequenced on the GeT-PlaGe platform (https://get. genotoul. fr/) using the Illumina Hiseq3000 sequencer. Other banana plant material was obtained either from CRB Plantes Tropicales (Guadeloupe, French West Indies, http:// crb-tropicaux. com/Portail) or from the Bioversity International Musa Germplasm Transit Centre  (Ruas et al. , 2017)  (Supplementary data Table  S1 ). A total of 123 accessions were sequenced to obtain standard Illumina 2 × 150 paired-end reads using the HiSeq4000 platform at Genoscope (http://www.
genoscope. cns. fr). For 11 of these accessions, 5 kb insert mate-pair libraries were constructed and sequenced using the Illumina Hiseq2500 platform at Genoscope (http://www. genoscope. cns. fr). This data set was supplemented by Illumina 5 kb mate-pair sequencing data generated by  Rouard et al. (2018)  for three accessions ('Banksii', 'Maia'Oa' and 'Calcutta 4') and deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under run IDs SRR7013759, SRR7013765 and SRR7013755. The assembly of the M. acuminata ssp. banksii accession 'Banksii' published by these authors, as well as PacBio reads related to this project (SRA ID SRR7013757), were also used. Finally, 'Calcutta 4' and 'Maia'Oa' accessions were sequenced with a MinION nanopore sequencer from Oxford Nanopore Technologies  (Jain et al.
, 2016). As described by  Belser et al. (2018) , a 30× sub-set of the longest nanopore reads was extracted from the total read set. These reads were then assembled using the Smartdenovo assembler (Jue Ruan, https:// github. com/ruanjue/smartdenovo, git-commit 3d9c22e) with -k 17, as advised by the developer for larger genomes, and -c 1 to generate a consensus sequence.
SNP marker segregation analysis Raw GBS sequencing data from the self-progeny were demultiplexed using GBSX version 1. 2  (Herten et al. , 2015)  and adaptors were removed with cutadapt  (Martin, 2011). Variant calling was processed as described by  Garsmeur et al. (2018) , using the VcfHunter package (https://github. com/ SouthGreenPlatform/VcfHunter). Mapping was performed on the M. acuminata reference sequence version 2  (Martin et al. , 2016). Sites with read depths of <10 and >1000 were excluded. Sites with a minor allele frequency of <5 % were considered homozygous. Sites with an alternate allele frequency of >15 % supported by at least three reads were considered heterozygous. Other sites were converted into missing data. single nucleotide polymorphism (SNP) markers were then filtered out based on their segregation ratio in the mapping population: a P-value threshold of 4e-06 was applied with a χ 2 test to compare the observed marker segregation with the expected marker segregation (1:2:1 with a self-population).
Five kilobase discordant mate-pair read analysis To refine the location of the rearrangement breakpoints and to characterize the chromosome structures of 14 accessions, large insert mate-pair reads from the 14 mate-pair sequenced accessions were aligned to the M. acuminata 'DH-Pahang' reference genome sequence (version 2) available on the Banana Genome Hub  (Droc et al. , 2013). They were analysed to identify discordant read clusters according to the methodology described in  Martin et al. (2017) , using Scaffremodler tools  (Martin et al. , 2016)  available on the South Green platform  (Southgreen Collaborators, 2016). Discordant read clusters were viewed using CIRCOS software  (Krzywinski et al. , 2009).
Signature segment junction detection In order to characterize the chromosome structure of the 123 studied Musa accessions, paired reads from 123 accessions were aligned to the complete M. acuminata 'DH-Pahang' reference genome, the 'Calcutta 4' assembly, the 'Maia'Oa' assembly and the 'Banksii' corrected assembly using bowtie2 in very sensitive mode (see next section). Pairs properly mapped with reads on either side of a signature segment junction (SSJ; i. e. the junction between two genomic segments that is specific to one of the chromosome structures; Supplementary data Fig. S1 ) in a flanking window of 1000 bp around the SSJ were counted.
Factorial analysis using SNP data To investigate the distribution of the translocated chromosomes in M. acuminata, a factorial analysis was performed. Paired-end reads (2 × 150) Illumina sequencing data from the 83 M. acuminata diploid accession studied were used to produce a genotype call for each accession using vcfHunter tools  (Garsmeur et al. , 2018 ; https://github. com/SouthGreenPlatform/ VcfHunter). The process_reseq_1. 0. py and VcfPreFilter. 1. 0. py programs were used to (1) align Illumina reads along the M. acuminata 'DH-Pahang' reference genome sequence (version 2)  (Martin et al. , 2016)  using Burrows-Wheeler Alignment version 0. 7. 15; (2) remove duplicates using the MarkDuplicates option of Picard Tools (version 2. 7) (http://broadinstitute. github. io/picard/); (3) locally realign reads around indels using the IndelRealigner tool of the GATK software package (version 3.
6)  (McKenna et al. , 2010) ; (4) count all mapping bases that had a mapping quality ≥10 with the bam-readcount program (https://github. com/genome/bam-readcount); and (5) call a genotype based on the maximum likelihood of the genotype according to a binomial distribution assuming a sequencing error rate of 0. 005. The variant calling file was formatted in VCF format. The VCF file was then filtered using the vcfFilter. 1. 0. py program with the following options: a minimal coverage by accession of 10 (-MinCov 10), a maximal coverage by accession of 119 (-MaxCov 119), a minimal relative allele frequency by accession of 0. 1 (-MinFreq 0. 1), a minimal absolute allele frequency by accession of 3 (-MinAl 3), no missing genotypes in a line allowed (-nMiss 0), filtering out indels (-RmType INDELS) and non-diallelic sites (-RmAlAlt 1:3:4:5).
A sub-set of 3092 polymorphous SNPs dispersed along the 11 chromosomes of the reference sequence was used to calculate a dissimilarity matrix using vcf2dis. 1. 0. py script. The dissimilarity index between two accessions was calculated from the VCF file as the proportion of unmatching alleles between two accessions. The dissimilarity matrix was used to perform a factorial analysis using R version 3. 2. 4 software (http://www. r-project. org). Since the cultivated accessions originated from the wild banana genepool, the factorial analysis was performed with the 32 wild accessions. The 51 cultivated accessions were then projected along the synthetic axes.
Genetic evidence of two large reciprocal translocations in the 'Calcutta 4' accession A self-progeny from the 'Calcutta 4' accession was genotyped by sequencing. A total of 5281 markers segregating in 67 individuals with a 1:2:1 ratio were selected. Pairwise associations between 'Calcutta 4' markers projected on the 11 M. acuminata reference chromosome sequence showed, for most markers physically close on the reference sequence, strong genetic linkage, as expected (Supplementary data Fig. S2A, B ). However, a complete break in genetic linkage was observed within a few chromosomes. Linkage breaks were observed on chromosome 2 at position 29. 2 Mb and on chromosome 8 at position 37. 7 Mb (Fig. 1A ). In addition, a small cluster of markers (four markers) from the distal region of reference chromosome 2 was found in 'Calcutta 4' genetically linked with chromosome 8 markers, whereas a distal part of reference chromosome 8 was found to be linked with chromosome 2 in 'Calcutta 4'.
Cluster 1 linked a distal region of 7. 2 Mb from chromosome 8 (f) to 29. 3 Mb of chromosome 2 (a), resulting in a distinct chromosome structure hereafter referred to as 2T8 (Fig. 2B ). On reference chromosomes 2 and 8, the two discordant read clusters are separated by small chromosome segments of 3 kb (e) and 1. 2 kb (b), respectively, that were not covered by any read sequences in 'Calcutta 4', thus suggesting their absence in this accession (Fig. 2A ). No concordant reads overlapped the breakpoint regions, thus indicating that 'Calcutta 4' is homozygous for this translocation. For chromosomes 1 and 9, the Scaffremodler program could not automatically interpret the discordant read cluster pattern. Therefore, we manually explored discordant read clusters linking those chromosomes and found a complex pattern of interchromosomal and intrachromosomal discordant read clusters (Fig.
Such SSJs were searched at the 2/8 and 1/9 translocation breakpoints in 'Calcutta 4' and in the corresponding region of the 'DH-Pahang' reference genome sequence. Eight SSJs could be identified for translocation 2/8, with two per chromosome structure: a-b and b-c segment junctions for chromosome 2, d-e and e-f segment junctions for chromosome 8, a-x and x-f segment junctions for chromosome 2T8, and d-y and y-c segment junctions for chromosome 8T2 (Fig. 2 ; Supplementary data Table  S3 ). In a first round of analysis, for many accessions we detected SSJs of reference chromosome 2 but did not detect the signatures of reference chromosome 8. We thus compared the d-e-f breakpoint region of 'DH-Pahang' with the corresponding region of two additional accessions, for which long read sequences and sequence scaffolds were available (accession 'Banksii' from ssp.
DISCUSSION We identified two reciprocal translocations in the Musa acuminata ssp. burmannicoides accession 'Calcutta 4' compared with the reference genome sequence, i. e. M. acuminata ssp. malaccensis 'DH-Pahang'. One reciprocal translocation involved chromosomes 2 and 8, resulting in chromosomes 2T8 and 8T2. The other one involved chromosomes 1 and 9, resulting in chromosomes 1T9 and 9T1. 'Calcutta 4' was found to be homozygous for both translocations. These translocated chromosome structures were first searched in 13 accessions using 5 kb mate-pair discordant read analysis. In addition, SSJs specific to the distinct chromosome structures were searched in 123 accessions representative of Musa diversity involved in cultivated banana for which pairedend sequence data were available. We found that these methods, particularly SSJ detection, were sensitive to the presence of polymorphism (SNPs and INDELs) and repeated sequences.
ACKNOWLEDGEMENTS We are grateful to the  CRB Plantes Tropicales Antilles CIRAD-INRA (Guadeloupe)  and the  Bioversity International Musa Transit Centre (ITC)  for providing the plant material used in this study. The authors also thank  Remy Habas ,  Jean-Marie Delos ,  Franck Marius  and  Jean-Claude Efile  for helping with plant management, and the  South Green Platform  (http://www. southgreen. fr) for providing computational resources (Galaxy and UMR AGAP HPC Data Center).
FUNDING This work was supported by the  Agropolis Fondation  ' GenomeHarvest  project' (ID  1504-006 ) through the  'Investissements d'Avenir' programme (Labex Agro : ANR-10-LABX-0001-01 ), the  French Government's 'Investissements d'Avenir' FRANCE GENOMIQUE programme  ( ANR-10-INBS-09 ) and the  CGIAR Research Programme on Roots, Tubers and Bananas (RTB).
Availability of supporting data Raw sequence reads for the 'Calcutta 4' selfing population were deposited in the SRA of the NCBI (BioProject: PRJNA532826). The 'Calcutta 4' selfing population genotyping matrix, the breakpoint regions of the 'Calcutta 4' assembly, the 'Maia'Oa' and 'Banksii' long reads including the breakpoint regions of chromosome 8, the Illumina 5 kb reads mapping in the breakpoint regions of chromosomes 1, 2, 8 and 9 of 123 Musa accessions, and the VCF file comprising 3092 SNPs for 83 diploid wild and cultivated Musa acuminata accessions are available in the download section of the Banana Genome Hub (http://banana-genome-hub. southgreen. fr/download) under AF-C4_genotypingmatrix. tab, Calcutta4_breakpoints. fasta, corrected_long_reads_chr08breakpoint_MaiaOaBanksii. fasta, breakpoints_123Musa.
Molecular and genetic analysis Direct sequencing of CACNA1A gene in the index case led to identify a heterozygous missense pathogenic variantNM_023035. 2: c. 835C>T, p. (Arg279Cys) in exon 6 (ClinVar, SCV000778839. 1). This variant segregated in all the affected relatives as well as in three asymptomatic relatives (subjects I. 3, II. 2 and II. 5). The percentage of penetrance in this family is 70% (n=7/10). All of the in silico prediction programs predicted this variant to be deleterious, supported by the high CADD score of 32  [Kircher et al. 2014]. This variant has already been reported in familial episodic ataxia type 2  [Maksemous et al. 2016] , and is absent from GnomAD and ExAc database.
The frequency of these mutations was determined in adult P. waltl immunized on board the Mir space station and in adult P. waltl immunized with the same antigen on Earth. This study showed that the frequency of these mutations is two times lower when animals are immunized on board the space station  [74]. IE, P. waltl immunized on Earth; IS, P. waltl immunized in space; NI, not immunized. Spaceflight-Associated Immune System Modifications DOI: http://dx. doi. org/10. 5772/intechopen. 88880 suspension or 2 G hypergravity) for 3 weeks  [76, 77]. However, the responses from these lymphocytes were not altered after 3 weeks of exposure to the model mimicking socio-environmental stressors encountered in flight  [34]. These data suggest that the lower reactivity in lymphocytes induced by spaceflight is mainly due to gravity change.
Data Collection Patient characteristics were obtained at hospital admission. These included cardiovascular risk factors and history, and clinical and biological data. Risk scores were calculated (GRACE score and SYNTAX score). Blood samples were collected on admission to measure serum C-reactive protein (CRP), creatinine, creatine kinase peak, troponin Ic peak, NT-proBNP, blood lipids, glucose and hemoglobin. eGFR was calculated using Chronic Kidney Disease-EPIdemiology Collaboration formula (CKD-EPI). Echocardiographic data such as left ventricular ejection fraction (LVEF) were recorded. Finally, in-hospital events were documented, including death, cardiovascular death, re-infarction, stroke, development of heart failure and ventricular tachycardia or fibrillation (VT/VF).
Importantly, we have also shown Chemical synthesis and characterisation. The synthetic procedures and characterization data for all the chemical probes is described in the Supplementary Information. Bacterial culture. Bacterial strains were grown and counterstained as previously described  13, 14. The following bacterial strains were used: Methicillin Resistant S. aureus (ATCC 25923), Methicillin Sensitive S. aureus (ATCC 252), S. pneumoniae (D39 NCTC 7466), H. influenzae (Clinical Isolate), K. pneumoniae (ATCC BAA1706), E. coli (ATCC 25922), A. baumannii (J3433 Clinical Isolate), S. maltophilia (J3270 Clinical Isolate), P. aeruginosa (PA01-ATCC 47085 and J3284-clinical isolate) and GFP fluorescent S. aureus (RN6390-Gfp-EryR). Fungal culture. Aspergillus fumigatus (clinical isolate) was stored in potato glucose agar (Fluka Analytical, Gillingham, UK) at 4 °C.
Data is presented as the proportion of frames over a video sequence containing >80 dots per frame. Ex vivo cystic fibrosis human lung. Whole explant CF lungs were received fresh from theatre (<1 hour) and main the bronchi were identified. Bronchoscopy and suction of the airways was performed prior to instilling NBD-UBI/NBD-UBI dend into anatomically distinct distal bronchopulmonary segments. The bronchoscope was proximally wedged and the S-1500 fiber passed into distal segments. Following imaging in the distal lung (alveolar regions) as described above, a BAL was undertaken, sample was centrifuged at 200 g for 5 minutes to remove erythrocytes and cellular material and was plated for bacterial quantification.
Acknowledgements The authors would like to thank the  NHS Lothian SAHSC Bioresource  for facilitating the studies using ex vivo human lung tissue and the flow cytometry facility at the  Queen's Medical Research Institute. Funding from  Wellcome Trust and Department of Health HICF  ( HICF-0510-069 ), The  Engineering and Physical Sciences Research Council  ( EP/K03197X/1 ) and  EU Commission  ( FP7 CIG333847 ).
STUDY VARIABLES AND MEASUREMENTS The basic data sought for each of the deaths included age, sex, year of death, equipment used, type of diving (recreational or professional), level of training, experience, geographical location of incident dive, previous medical history including dive medical, incident dive trend and cause of death. All available records were examined. These included: Coroners' reports; autopsy and toxicology reports; police and witness statements, equipment reports and gas analyses and medical records. Each diving death was evaluated using chain of events analysis (CEA). 11 The analysis sequentially identified predisposing factors, triggers, the disabling agents, disabling injury(ies) and cause of death. 11 Each death was independently analysed by each author. Working sequentially was intended to minimise bias and reduce variability when assessing the divers' likely actions prior to their death.
STATISTICS Although the data were categorical in nature, the expected small numbers and potentially diverse causative factors were likely to allow only basic statistical analyses which included incidence, mean, standard deviation, medians and interquartile ranges, ratios and percentages. All data were The Tasmanian death rate was calculated as 1. 77 deaths per million population per year. 7 One of the deaths identified had not previously been recorded by Project Stickybeak or DAN AP. For two of the diving deaths more comprehensive documentation (not previously held by DAN, or Project Stickybeak), was identified that allowed full CEA. The locations of where deaths occurred shows a high occurrence rate within 100 km radius of Hobart (Figure  1 ). All were in the marine environment.
, 2018). A total of 243-soybean emergence simulations was performed taking into account three sowing dates, from mid-March to mid-April. To this aim, we first mobilized the STICS soil-crop model  (Brisson et al. , 2003 (Brisson et al. , , 1998) )  to generate soil water content and temperature in the seedbed (0-10 cm) using the most pessimistic IPCC scenario. We then used the data obtained as input variables to feed the SIMPLE crop emergence model  (Constantin et al. , 2015; Dürr et al. , 2001). The emergence courses and final rates, and causes of no-seedling emergence are analyzed and discussed.
Field experiments 2. 3. 1. Experimental site and climatic data The experiment was carried out in May 2018 in Auzeville experimental station of INRA (43. 53°N, 1. 58°E). The soil had the following soil granulometry and chemical characteristics at the 0-30 cm soil horizon: 0. 253 g. g -1 clay, 0. 380 g. g -1 silt and 0. 366 g. g -1 sand, 0. 0914 g. g -1 C, 0. 001 g. g -1 N, C/N ratio 8. 31, and pH 8. 57, organic matter 0. 009 g. g -1 , bulk density at sowing depth 1. 0 g. cm -3. After the harvest of wheat in July, conventional tillage was performed with 4-body plough in October 2017 at 30 cm depth. A 9-cm tillage was followed with seedbed cultivator (Kongskilde, tractor New Holland 115) on 13 March 2018 to prepare the seedbed. Soybean (cv. ES Pallador) was sown in 72 m 2 blocks (each of 12 m long and 6 m wide, 4 blocks in total) on 22 May 2018 at 3 cm sowing depth with 40 seeds m -2 and with 50 cm inter-row distance.
Soil temperature and water content were recorded using climate sensors (ECH O 5TM, METER Group, Inc. USA). The sensors were installed in the seedbed following sowing at three soil horizons (3 sensors/depth/block at -3 cm, -5 cm and -10 cm). These sensors delivered hourly temperatures, measured by an onboard thermistor, along with accurate volumetric water content. Data were recorded from sowing until crop emergence. Rainfall data were obtained from an automatic meteorological station installed at the experimental site. The cumulative degree-days (°Cd) were calculated from sowing (time 0) as the sum of the average daily air temperature minus base temperature of soybean.
Climate scenarios and simulations of the seedbed climate We used the RCP 8. 5 emission scenario based on the regionalized climate projections data provided by Drias platform (www. drias-climat. fr). These data were used to generate soil temperature and water content of the seedbed using the STICS soil-crop model  (Brisson et al. 2003). This model daily simulates soil water contents and temperatures, according to daily weather and soil characteristics. Variations in soil moisture were predicted using STICS at 0-2, 2-4, 4-6 and 6-10 cm. We chose Estrées-Mons (49°52′44″N 3°00′27″E) located in Northern France as representative area of North European climate. A detailed description of the study site and soil granulometry and chemical characteristics have been previously published  (Lamichhane et al.
Analysis of simulation results Data were pooled and analyzed by sowing date for each 20-year period. Twenty years considered for each period were treated as replicates. ANOVA was used to determine the potential effect of sowing dates for each period and their interaction both on soil weather and crop germination and emergence parameters. The variability of germination and emergence rates and their duration were analyzed as described previously  (Lamichhane et al. 2019). To this aim, we established three classes of rate or duration, expressed as the frequency of each class over the 20-year period for germination and emergence rates, and their duration. For germination rate, thresholds were poor germination when germination rate was <75% and sufficient germination above 75%. For emergence rate, thresholds were poor emergence when the emergence rate was <50%, and sufficient over 50%.
Model evaluation Predicted vs. observed germination and emergence rates are reported in Figure  3. The model finely predicted germination rate, over germination courses compared with the observed dataset. The predicted final germination rate matched with the observed dataset (98. 6 predicted vs. 100% observed). For germination, the model showed a relatively good fitting quality with EF of 0. 65, RMSE of 0. 10, and CRM of 0. 04. The model prediction was very good also for emergence rate with the final predicted emergence rate almost exactly the same to the observed values (i. e. 87 predicted vs. 88% observed). For emergence, the model showed even a better fitting quality obtaining EF of 0. 70, RMSE of 0. 11, and CRM of -0. 08. The time to reach the maximum germination rate was the same for both predicted and observed values (i.
Simulation study under future climate change A previous study  (Lamichhane et al 2019)  showed that, for early spring sowings, climate change, simulated under the IPPCC 8. 5 scenario, ie the most pessimistic, will become more significant after 2060 in Northern France, with progressively increasing mean seedbed temperatures by +2 °C in February, March and April after 2060. There will be a higher variability of rainfall as well, although with no overall change of its cumulated values with the exception for the mid-April sowing. Indeed, for this latter sowing date, the simulated average cumulated rainfall will be almost two-fold lower compared with the 2000-2018 period. These climatic data were used to feed the SIMPLE model and run the present simulation study on soybean establishment. This study showed no strong risks for a successful establishment of soybean in Northern France in the coming decades for the simulated sowing dates under the chosen scenario.