dataesr/scientific-paragraphs-categorization huggingface dataset:
Analogous analyses for infectivity were performed on individual R genes to test whether the observed frequency of infectivity on different R genes in leaf spot populations was significantly different to expected values based on frequencies in the inoculum. All chisquare tests were conducted in R (R Core Team, 2013). Multivariate GLM was used to investigate the effects of resistance, spatio-temporal connectivity, and genetic connectivity on the frequency of infectivity in relation to individual R genes in each experimental plot. Analyses were performed using the R-package mvabund  (Wang, Neuman, Wright, & Warton, 2012). The response variable was binary (infective/noninfective), hence we used a binomial distribution of errors. Otherwise, the models for infectivity against each of the R genes were the same as for analyses of disease severity.
, 2016). Benefits can thus be obtained by better management of stubble loads to reduce transmission of inoculum between cropping seasons, which depends jointly on distance between fields and the size of the source population  (Marcroft et al. , 2004). Transmission between fields can be predicted from spore dispersal  (Bousset et al. , 2015; Marcroft et al. , 2004; Savage et al. , 2013). Thus, spatially explicit models can be used to study and ultimately design combinations of landscapes, cultivar choice and tillage practices promoting resistance durability against blackleg  (Hossard et al. , 2015; Lô-Pelzer et al. , 2010). The number of leaf spots was a poor predictor of canker severity (Figures  2  and 3 ). In part, this might be because we only recorded the occurrence of leaf spots at a single point in time (previous work has shown that the ability to predict canker severity may depend on at what point during an epidemic leaf spot data is collected; Powers, Pirie, Latunde-Dada, & Fitt, 2010).
ACK N OWLED G EM ENTS We thank the staff at the  CSIRO Ginninderra Experiment Station  for their assistance in setting up and managing the field experiment. We thank  Kristy Lam ,  Shamsul Hoque ,  Jaeun Ryu ,  Loup Rimbaud ,  William Thrall ,  John Graham  and  Michael Neave  for technical assistance. This work benefited from the financial support of  INRA-the French National Institute for Agronomical Research , a  CSIRO Sir Frederick McMaster fellowship  to  L. Bousset  (Impact of inoculum carry-over on landscape dynamics of the blackleg canola pathogen) and the  Grains Research & Development Corporation  ( GRDC Grant   CSP00192 ).
Background: The large and constantly evolving HIV-1 pandemic has led to an increasingly complex diversity. Because of some taxonomic difficulties among the most diverse HIV-1 subtypes, and taking advantage of the large amount of sequence data generated in the recent years, we investigated novel lineage patterns among the main HIV-1 subtypes. Results: All HIV full-length genomes available in public databases were analysed (n = 2017). Maximum likelihood phylogenies and pairwise genetic distance were obtained. Clustering patterns and mean distributions of genetic distances were compared within and across the current groups, subtypes and sub-subtypes of HIV-1 to detect and analyse any divergent lineages within previously defined HIV lineages. The level of genetic similarity observed between most HIV clades was deeply consistent with the current classification.
Sequence data All HIV-1 groups M, N, O and P near full-genome sequences with available subtype information were downloaded from the Los Alamos National Laboratory (LANL) database on June 2016. All LANL partial gag, pol and env gene sequences of subtypes A and D were also collected in specific datasets. CRF (circulating recombinant form) sequences as well as clonal or iterative sequences were excluded from the analysis. As the A3 and A4 sub-subtype were not formally retained into the LANL classification at the time of this analysis, all subtype A sequences were manually re-annotated according to the corresponding publications  [15, 16]. The country of sampling was also retrieved for each included sequence.
Alignments After removing LTR regions, sequence datasets were aligned using Mafft version 7 with default settings  [17]  and manually edited using Aliview  [18]. Potential recombinants were identified using the package RDP4  [19]  and excluded. All genome regions that could not be unequivocally aligned (e. g. highly variable env regions) were removed from the alignments. All sequences retained in the final are depicted in Additional file 1: Table  S1.
Pairwise genetic distance calculations Pairwise genetic distances were calculated using HyPhy 2. 2. 4 with the GTR model of nucleotide substitutions and a gamma distribution with 4 parameters  [20]. This model was chosen using jModelTest 2  [21]. The distributions of near full genome pairwise genetic distance within and between known groups, subtypes and sub-subtypes were plotted using R 3. 3. 2 and ggplot2 package  [22]  to describe genetic distance ranges and net genetic divergence observed for intergroup, intersuptypes, intersub-subtypes and intrasub-subtypes comparisons.
Results A total of 2017 near full genome sequences were included in the study, representing 204, 1185, 488, 66, 29, 39, 4, 3 and 2 sequences for A, B, C, D, F, G, H, J and K subtypes, respectively. Near full genome pairwise genetic distance distributions showed patterns consistent with the classification at the time, delimiting ranges of distances within which inter-group, inter-subtypes, inter-sub-subtypes and intra-sub-subtypes genetic diversities almost consistently fall (Fig. 1a-d ). Indeed, using these ranges observed in our dataset, two clades, A1 and D, were identified as presenting intra-clade genetic distances inconsistent with the range expected from their respective classification. This highlighted their large diversity and the existence of distinct sub-subtypes within both of them (Fig.
S1 , Tables  S2  and S3 ). The same patterns of phylogenetic tree topology and genetic distance distributions were also observed when analysing the gag, pol and env genes separately. Concerning the A7 clade, as only two near full genome sequences are currently available and as at least three sequences, including two full genome sequences, are required to describe a new sub-subtype  [1] , all available partial pol (1329 bp), gag (1466 bp) and env (2566 bp) genes from subtype A were retrieved from the LANL database (n = 244, 387 and 399 sequences, respectively) and corresponding phylogenies were constructed. Four additional sequences were identified as belonging to the A7 clade, one from the pol tree (DQ273915; Additional file 1: Fig. S2 ) and three from the gag tree (Accession number JF683739, EU673432, EU673431; Additional file 1: Fig.
Discussion The large HIV genetic diversity is a challenging reflection of HIV complex and changing epidemiology leading to an evolving nomenclature and sometime unresolved issues. Given the amount of sequence data regularly and increasingly generated in the recent years, we re-investigated HIV-1 genetic diversity of all subtypes initially to resolve some issues within subtype A. We propose to slightly modify the subtype A classification by dividing it into 6 sub-subtypes, called A1, A2, A3, A4, A6 and A7. We also propose to divide the highly diverse subtype D into 3 sub-subtypes named D1 to D3. Thus, at the time of this work, subtype A was formally divided into only two sub-subtypes A1 and A2 and two other sub-subtypes, A3 and A4, were also proposed but not formally retained in the Los Alamos National Laboratory nomenclature.
Since then, A3, A4 and A6 sub-subtypes have been formally implemented into the Los Alamos National Laboratory HIV database and allow a quick and more accurate description of the subtype A diversity. In this work we also added the description of the A7 sub-subtype that was not previously described. We also enlarged the analysis to all other HIV-1 subtypes and identified evidence of further diversification consistent with distinct sub-subtypes among the subtype D. Thus, 3 sub-subtypes are proposed for this latter subtype, called D1 to D3, allowing a better description of D diversity despite the existence of a few outsider sequences. Their classification may have to continue to evolve in the future depending of the potential acquisition of new sequences. Due to the constant evolution of HIV and regular addition of new sequences in public databases, uncertainties about HIV-1 classification have regularly emerged.
Cordelieres (rsbweb. nih. gov/ij/plugins/track/ track. html), with distance/time measurements taken every 5 min for 3 h. The migration speed, the total distance of migration, the Euclidian distance of migration and the directionality of migration were extracted from the coordinates retrieved from the manual tracking plug-in. Mean values±s. d. and statistical analyses were calculated and plotted using Graphpad Prism (Graphpad Software) and significance was determined using two-tailed Mann-Whitney t-tests (after determining the non-Gaussian distribution of the data using the Shapiro-Wilk normality test).
Funding This work was supported by the  Ligue Contre le Cancer -comitéde l'Oise, INCa ,  ITMO Cancer , and is under the  program 'Investissements d'Avenir ' launched by the  French Government  and implemented by  ANR Labex CelTisPhyBio  ( ANR-11-LBX-0038  and  ANR-10-IDEX-0001-02 PSL ). L. M. is funded by a core grant from  Cancer Research UK  ( A15673 ). Deposited in PMC for immediate release.
These policies remain fragile, while support for large-scale conventional agriculture is still predominant. Their challenge is therefore to convince more broadly farmers, consumers and policymakers about the importance of issues such as public health and food security. Sustainable agriculture uses the same supply and marketing channels as conventional agriculture. It is supported by governments concerned by environmental issues, wishing to protect water resources (Mexico, Chile) or to promote a green economy based on ecotourism (Costa Rica). Sustainable agriculture focuses on environmental sustainability, whereas Latin American agroecology also more broadly considers the social pillar. export-oriented family entrepreneurs or companies, which see certification as a commercial opportunity. Sustainable agriculture is a more recent alternative that does not question conventional intensification, but introduces agro-ecological practices generally associated with financial support.
Bacterial strains and genome sequence accessions Pseudomonas baetica a390T originally isolated from the liver of wedge sole fish, D. cuneate, and described by  Lopez et al. (2012) , was obtained from DSMZ (DSM no. 26532) and is considered to be the type strain. Unless stated otherwise, all phenotypic comparisons were performed against P. fluorescens PF01, P. putida KT2440 and P. aeruginosa PA14. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession PKLC00000000. The version described in this paper is version PKLC01000000. Raw sequencing reads were also deposited and can be found with accession numbers SRR6792524 and SRR6792523 for the Nanopore and Ion Torrent reads, respectively.
Combined Genome Assembly of Ion PGM and Nanopore Reads The Ion PGM reads were checked for quality using FastQC and processed with BBDuk in order to remove potential adapter contamination, size exclusion (50> read length <250), and for trimming (phred score < 28)  (Andrews 2014). Basecalling of the Nanopore reads was performed with the software Albacore v2. 1. 3 from ONT, and the barcodes were removed using Porechop. Read length distribution and quality of the Nanopore reads was assessed using NanoPlot. The genome was first assembled with SPAdes, version 3. 11. 1 using default parameters  (Bankevich et al. 2012)  using Ion PGM reads only, then with Unicycler, version v0. 4. 3  (Wick et al. 2017 ) also using default parameters with both Ion PGM and Nanopore reads. For the latter, the genome was polished with Racon  (Vaser et al.
Genomic island prediction and genome visualisation The Ion PGM and the hybrid assemblies were annotated with Prokka (Seemann 2014) using a custom protein database built from the Pseudomonas genus. The annotations were then searched for prophages using PHASTER  (Arndt et al. 2016) , and genomic islands using the IslandViewer and antiSMASH  (Bertelli et al. 2017; Blin et al. 2017). For the latter, the reference genome used for the alignment was that of P. koreensis strain D26 as it was deemed phylogenetically close to P. baetica. CCT maps were created using CCT version 1. 0 using default settings with the exception of the following commands; -t -cct -custom scale blast=F cct blast thickness=60 backboneThickness=10 tickThickness=15 rulerFontSize=100 featureThickness=150 tickLength=30 legend=F details=F.
Phylogenetic analysis Trees were assembled using Mash v1. 1. 1  (Ondov et al. 2016 ) fast genome and metagenome distance estimation using the Min-Hash algorithm, and visualised in FigTree v1. 4. 3  (Rambaut 2012 , also available via GitHub). Reference genomes were obtained from NCBI. Average nucleotide identity (ANI) was performed using PyANI  (Gupta et al. 2016). Data were processed using CLIMB  (Connor et al. 2016).
Sequencing results and assembly The initial Ion Torrent sequencing produced over 3. 5 million barcoded reads with a mean read length of 299 bp. SPAdes assembly resulted in final contig number of 338. The Nanopore sequencing run yielded around 22 000 reads, of which 7771 passed quality control during the basecalling process. The average phred quality score of the reads is 11 and their length distribution was around 9 kbp (see Figure  S1 , Supporting Information). With a genome size of about 6. 6 Mbp, estimated from an initial Ion PGM assembly, we infer a genome coverage of about 9× for the Nanopore sequencing data (60 Mbp), and 80× with Ion PGM data (540 Mbp after QC). The hybrid approach that combines both the dataset of short Ion PGM reads with the long Nanopore reads significantly improved the genome assembly over that using Ion PGM reads only giving 16 contigs over 1000 bp (Table  1 ).
Quast analysis of the combined assembly gives an N50 value of 973739 which is higher than 96% of all draft genomes in The Pseudomonas Genome database. The Ion PGM-only and the hybrid assembly graphs were also inspected visually using Bandage  (Wick et al. 2015)  in order to assess the fragmentation of the genome assemblies and the genomic organisation, including the presence of potential plasmids (see Figures  S2  and S3 , Supporting Information). After gene annotation with Prokka, plasmid-associated genes like recombinases, transposases and integrases were searched for and were not present or were not indicative of being plasmid associated. The high continuity assembly provided by the combinatorial approach of Ion Torrent and Nanopore technologies allowed for a comprehensive analysis of the genome.
Many of the loci referred to throughout this manuscript are labelled around the CCT map in Fig. 2  as well as the results of IslandViewer. The CCT BLAST analysis confirms the close phylogenetic relationship between P. baetica a390 and Irchel 3E19 (Fig. 2 ). Of the nine biosynthetic gene clusters (BGCs) predicted by antiSMASH, four are Nrps (non-ribosomal peptide synthetases) biosynthetic gene clusters. Clusters 3 and 8 had relatively low similarities to the nearest known clusters; however, cluster 6 shared a high similarity with an orfamide BGC with 80% of genes in this cluster sharing homology with the putisolvin biosurfactant BGC. Putisolvin has been implicated in motility (C árcamo-Oyarce et al. 2015) and our preliminary experiments have demonstrated that P. baetica is capable of both swimming and swarming (see  Figure S4, Supporting Information).
FUNDING Work in NPT's lab has been funded by  Biotechnology and Biological Sciences Research Council  grant  BB/K019600/1  and  Chief Scientists Office  grant  TCS/16/24. AB is supported by a  PhD Studentship  funded by the  Industrial Biotechnology Innovation Centre  and the  University of Strathclyde. TS was supported by a PhD studentship funded by the  Iraqi Government. MG was supported by a postdoctoral fellowship from  FWO Vlaanderen  ( 12M4618N ). CL was supported by an SB PhD fellowship from  FWO Vlaanderen  ( 1S64718N ). JB was supported by a grant from  Fundac  ¸ão para a Ci ência e a Tecnologia ( SFRH/BD/104095/2014 ). NPT would like to personally thank all of the contributors to this paper who have made this novel collaboration an open and pleasurable experience. AM and ED were supported by the  Biotechnology and Biological Sciences Research Council -funded  South West Biosciences Doctoral Training Partnership  ( BB/M009122/1 ).
Mass spectra were recorded with a scanning range of 30-600 m/z, at 4. 7 scans s -1 26. Peak finding, peak integration and retention time correction of the GC/MS chromatograms were performed with the XCMS Rpackage. Integrated peaks of the mass (m/z) fragments were normalized across all samples by expressing the peak areas relative to both an internal standard in each sample (ribitol or homatropine), and the exact dry weight of tissue used in each extraction. The XCMS output of integrated peaks was tested for robust integration  26. Each tropane alkaloid was identified by comparison of its mass spectrum to the NIST05 database and to the internal database of the laboratory issuing from Nguyen et al. 25. The retention index of each compound was checked before final annotation. The level of each compound was estimated as % area based on the surface of peaks measured in total ion current  26.
Gene expression analysis by real time PCR RNA was isolated from Arabidopsis thaliana Col-0 inflorescence meristems with small closed buds up to stage 10-11  (19)  using RNAeasy plant mini kit (Qiagen). Complementary DNA (cDNA) was primed with random hexamers on 1g DNase-treated RNA using Superscript III Reverse Transcriptase (Thermo Fisher Scientific). Real time qRT-PCR was performed on a CFX Connect real-time system (BIO-RAD) using Sso Advanced Universal SYBR Green supermix (BIO-RAD) and specific primers listed in Supplementary Table  S2. Three technical replicates were run in parallel per sample. Quantification of gene expression was performed using the comparative CT method with the CFX Connect Manager 3. 1 software. The ACTIN2 (At3g18780) and the EF-1α (At1g09740) genes were used to normalize the qPCR data  (21).
Bioinformatics analysis ChIP-seq data for SEP3  (29)  and AG  (30)  were downloaded from Arrayexpress and mapped and normalized using bowtie1. 2. 1 (31) and MACS2. 1. 1  (32). MNase data  (33)  were mapped using bowtie1. 2. 1. For Segal scores  (34) , 10 000 bp were added to the flanking region of each gene and computed using the web server (https://genie. weizmann. ac. il/software/nucleo prediction. html). Predicted binding sites for SEP3 were computed using the position weight matrices from JASPAR  (35)  and the genes scanned using FIMO from the MEME suite  (36)  with P-value < 10e-4. Nucleic Acids Research, 2018, Vol. 46, No. 10 4969.
FUNDING Action Thématique et Incitative sur Programme (ATIP)-Avenir  (to C. Z. );  Grenoble Alliance for Integrated Structural Cell Biology  ( ANR-10-LABX-49-01  to V. H. , A. J. , C. Z. , Q. C. , A. S. , V. C. , F. P. );  Agence Nationale de la Recherche  (project Flopinet to C. Z. , V. H. , A. J. , F. P. , C. C. ) and  Centre National de la Recherche Scientifique  (to S. J. C. ). Funding for open access charge:  ATIP-Avenir ,  ANR.
e. , flying bats). For the analysis of body condition index in flying bats, we used partial correlation with body mass as dependent variable and both time of blood sampling and forearm length (index of body size) as independent variables (Garcı ´a-Berthou 2001). Analyses were run pooling data from 2016 and 2017, as well as for each year, separately. We also used partial correlation to assess whether any of the oxidative status markers was correlated with body condition index (body mass and forearm length both included as independent variables) in flying birds. All analyses were performed using the software STATISTICA 10 (StatSoft. Inc. , Tulsa, OK, USA). Finally, the compute. es package (Del Re 2013) in R (R Core Team 2013) was used to calculate the standardized effect size Hedges' g from test statistics of oxidative status markers.
Mapping and statistical analysis of RNAseq data Read pairs were mapped using the glint software (http://lipmbioinfo. toulouse. inra. fr/download/glint/) with parameters set as follows: matches ≥40 nucleotides, with ≤3 mismatches, only best-scoring hits taken into account. Ambiguous matches (same best score for several read-pairs) were removed. Finally, between 16. 8 and 29. 6 M non ambiguous read-pairs were obtained. Mapped reads were imported into R environment. The package HTSFilter was used to eliminate very lowexpressed genes from the analysis. A total of 4990 genes out the 5307 predicted genes were thus kept in. R package DESeq2 was used to normalize and complete the differential analysis by conducting the built-in Wald test  [12]. The p-values thus obtained were adjusted for multiple comparisons using the false-discovery rate (FDR) method  [13].
The PhcA-dependent regulation pattern in rich medium is mostly conserved in planta In order to compare the set of GMI1000 genes subjected to PhcA regulation in complete medium and in planta, we took the list of 787 and 794 genes up-regulated and down-regulated, respectively, in the phcA mutant in complete medium and looked for their expression pattern in planta using the Khokhani et al. dataset  [7]. First, we observed that a large majority of the PhcA regulated genes in complete medium could not be compared (687 repressed and 511 activated genes) since these genes were absent (i. e no value provided) in the Khokhani et al. [7]  dataset (Fig. 2 ). The PhcA dependent genes expressed in both conditions comprised 383 genes with 100 genes upregulated and 283 genes down-regulated in complete medium (Fig.
In a R. solanacearum GMI1000 phcA mutant strain, (i) 70 up-regulated genes in complete medium are also up-regulated in planta, while 30 genes are down-regulated and (ii) 256 down-regulated genes in complete medium are also down-regulated in planta, while 27 are up-regulated. A large set of genes (687 + 511) is annotated NS in planta, indicating that they are either not differentially expressed or absent from the in planta dataset  [7].
Funding This work was supported by the  'Institut National de la Recherche Agronomique' (INRA) (Plant Health Division  grant  AAP SPE 2015 ) and the  French Laboratory of Excellence  project  TULIP  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ). Rémi Peyraud  was supported by  EMBO (Long-Term Fellowship   ALTF 1627-2011 ) and  Marie Curie Actions  ( EMBOCOFUND2010 ,  GA-2010-267146 ). Anthony Perrier  was supported by  INRA, Plant Health Division (SPE)  and the  'Région Occitanie'  (No. 15000183 ).
This protocol was used to estimate WPw and WPa of rainfed crops in 17 countries for maize and 18 countries for wheat (available at www. yieldgap. org). Estimates of WPw were evaluated against data from the literature and spatial variation in WPw and WPa was investigated. Specific objectives were to evaluate the proposed approach for its ability to: (i) benchmark WPw at local and region scale across environments with contrasting climate and soil, (ii) assess drivers for WPw variation across environments, and (iii) estimate WP gaps and understand their underlying causes to gain insight into opportunities to close them.