dataesr/scientific-paragraphs-categorization huggingface dataset:
Cloning of Stb6, together with the recent discovery that the matching Z. tritici effector is maintained in fungal populations because avirulent isolates can mate with virulent isolates even on resistant host plants, emphasizes the value of Stb6 for controlling STB disease while also providing new fundamental insights into the molecular control of plant-pathogen interactions. URLs. Wheat TILLING, http://www. wheat-tilling. com/; WebLogo, http://weblogo. berkeley. edu/; GrainGenes, http://wheat. pw. usda. gov/GG3/; The Wheat Portal, https://wheat-urgi. versailles. inra. fr/; Ensembl Fungi, http://fungi. ensembl. org/index. html/; INRA-CNRGV Plant Genomic Resources Center, https://cnrgv. toulouse. inra. fr/en/; Hisat2, https://ccb. jhu. edu/software/hisat2/index. shtml; Hierarchical Genome Assembly Process (HGAP), https://github.
Yeast two-hybrid (Y2H) protein-protein interaction assay. Nucleotide sequences corresponding to the predicted extracellular and intracellular regions of Stb6 (amino acids 25-257 and 281-647, respectively); mature (lacking signal peptide) AvrStb6 from isolates IPO323 (avirulent), IPO88004 (virulent), and RRes16 (virulent); and mature (lacking signal peptide) Z. tritici effector Zt10 (Ensembl Fungi database accession Mycgr3P111505) not known to be recognized by Stb6 were PCR amplified from the plasmid p125:CDS-R containing the full-length Stb6; from the first-strand cDNAs derived from total RNA extracted from the corresponding 6-d-old fungal cultures grown on solid YPD agar medium; and from a cDNA clone  67  , respectively. PCR amplifications were performed with Phusion High-Fidelity DNA Polymerase (Finnzymes).
Acknowledgements We thank the  Biological Resource Centre on Small Grain Cereals (INRA, Clermont-Ferrand, France) , the  National Plant Germplasm System (USDA, US) , the  Germplasm Resources Unit (John Innes Centre, Norwich, UK) , and the breeding companies  KWS, Limagrain, RAGT Seeds, Saaten Union, Senova, Syngenta ,  Agri Obtentions , and  Saatzucht Josef Breun GmbH & Co. KG  for providing seed samples of different wheat species and varieties;  E. Paux  and  H. Rimbert  ( INRA GDEC ) for providing Stb6 expression data at different developmental stages;  A. Doherty ,  A. Huttly , and  C. Sparks  ( Rothamsted Research, Harpenden, UK ) for vectors and wheat transformation;  P. Isaac  ( iDNA Genetics Ltd. , Norwich, UK ) for transgene copy number analyses;  INRA GDEC  facilities for genotyping (GENTYANE) and wheat transformation; and  S.
Data availability. Sequence data that support the findings of this study have been deposited in NCBI GenBank under accession numbers KY485188, KY485190, KY485191, KY485192, KY485193, KY485194, KY485195, KY485196, KY485197, KY485198, KY485199, KY485200, KY485201, KY485202, KY485203, and KY485204, and in the European Nucleotide Archive under accession numbers LT727683 and LT727684.
Ninety-six doubled-haploid lines derived from a CS × Ct cross  49  and 40 doubled-haploid lines derived from an Av × Cad cross  50, 51  were used in pathoassays in conjunction with genotyping assays using publicly available simple-sequencerepeat markers (selected from the GrainGenes database) for production of a lowresolution genetic map around the Stb6 locus. A biparental mapping population derived from a CS × Ct cross comprising 1,962 F 2 individuals was established for fine mapping Stb6. All wheat phenotyping for resistance to the Z. tritici isolate IPO323, which carries AvrStb6 (refs  [6] [7] [8]  ), were done with the attached-seedling-leaf bioassay  52.
Statistical Analysis Student's t-tests followed by a Fischer test on the equality of variance were used to analyze the differences between control and co-culture conditions. The statistical analysis of data was carried out with GraphPad Prism 4 software (GraphPad Software, La Jolla, CA, USA). Differences were considered to be statistically significant when the p-value was lower than 0. 05. For fungal gene expression, experiments were repeated twice using six biological samples for each condition. Otherwise, three samples per condition were used. Graphical values are represented by mean ± standard error of mean (SEM).
Methods In this Phase IV, investigator-blinded trial (EudraCT: 2015-003749-24), healthy adults with Depression Anxiety Stress Scales (DASS-42) stress subscale score >18 and serum magnesium concentration 0. 45 mmol/L-0. 85 mmol/L, were randomized 1:1 to magnesium-vitamin B6 combination (Magne B6 [Mg-vitamin B6]; daily dose 300 mg and 30 mg, respectively) or magnesium alone (Magnespasmyl [Mg]; daily dose 300 mg). Outcomes included change in DASS-42 stress subscale score from baseline to Week 8 (primary endpoint) and Week 4, and incidence of adverse events (AEs).
Outcome variables and assessments The primary outcome variable was change in the DASS-42 stress subscale score from baseline to Week 8. A secondary outcome variable was change in the DASS-42 stress subscale score from baseline to Week 4. Depression and anxiety subscales of DASS-42 were also evaluated during the trial but these data will be reported in separate publications. Subjects completed the DASS-42 questionnaire at screening, baseline, Week 4 and Week 8. The DASS-42 is a 42-item, clinically validated questionnaire that includes three subscales designed to measure negative emotional states of depression, anxiety and stress over the previous week  [18]. The stress component of DASS-42 comprises 14 questions with a 4-point scale for self-reported scoring: 0 = did not apply to me at all; 1 = applied to me to some degree, or some of the time; 2 = applied to me a considerable degree, or a good part of the time; 3 = applied to me very much, or most of the time  [19, 20].
Statistical methods The modified Intention-To-Treat (mITT) population was defined as all subjects with an evaluable DASS-42 stress subscale score at baseline and at least one other time point during the treatment period. The Per Protocol (PP) population was defined as all subjects included in the mITT analysis without any major protocol deviations. Of note, the mITT population comprised some subjects who had a baseline DASS-42 stress subscale score �18. This can be attributed to variations in DASS-42 stress subscale score between screening and baseline (~two weeks occurred between screening and baseline). Therefore, the statistical analysis plan was amended after the database lock based on a decision from the scientific committee, such that the PP population should include all subjects in the mITT without any major protocol violations, and with a DASS-42 stress subscale score >18 at baseline.
Subgroup analyses Based on European Medical Agency (EMA) guidelines for the adjustment of baseline covariates  [23] , the statistical analysis plan was amended after the blinded database lock to include subgroup analyses of primary and secondary efficacy endpoints by baseline stress level, as a significant interaction (p < 0. 05) was present between baseline DASS-42 stress subscale scores and treatment (assessed firstly as a continuous variable and then categorically by class ['normal and moderate stress' versus 'severe and extremely severe stress']).
Acknowledgments Thanks to all the subjects and their families. The authors would like to acknowledge  Patrick Griffin, MSc, of iMed Comms, Macclesfield, UK , an  Ashfield Company , part of  UDG Healthcare  plc for medical writing support that was funded by  Sanofi-Aventis Groupe, Gentilly, France , in accordance with Good Publications Practice (GPP3) guidelines (http://www. ismpp. org/gpp3).
Data Analysis Two litters from the S group were removed from data analysis due to milking problems with the doe. Another litter showed a digesta content weight 2 times higher than the other 9 values of the S group, at 21 and 25 d of age, and this was the only litter with a small faecal excretion during 2 d after trial start (2. 4 g DM at 21 d, and 0. 6 g DM at 22 d old). We assume that this litter already consumed pelleted feed with the doe at 17 d of age. We thus removed it from digesta content analysis and from the milk digestion calculation. Live weight data for litter and doe and data of digestive segment weight of kits were compared for age effect, according to a mono-factorial variance analysis (procedure GLM under SAS). Milk intake and milk digestion data were original and obtained only for the S group, thus we give the mean value associated with the variation coefficient and the minimum and maximum values observed.
Emmanuel Roux This work has been supported by LABEX ITEM ANR-10-LABX-50-01. In May 2017 Annecy Metropolitan Council, in the preamble to a call to tender for a planning project covering Greater Annecy, advocated territorial diagnosis based 'on the mental representations of the territory of policy-makers and technicians  [. ] , on the links of interdependence and solidarity inside the territory [. ] and with neighbouring territories'. The diagnosis should propose, the council added, 'a broad and inclusive approach' and 'a method facilitating collective intelligence and appropriation of knowledge' in order to 'achieve a vision of a shared future'. In December 2017 Grenoble Metropolitan Council announced that it was organizing its first Metropolis-Highland Forum, 'to gain perspective and create links'.
Discussion The summary of the main results is as follows: i) Genetic variation of 16 phenotypic traits revealed two clusters of plant vigour and canopy conductance traits and their association was clarified with PCA analysis and correlation. ii) Using the ultra-high density map, M-QTLs for plant vigour traits predominantly mapped on CaLG04 and these co-mapped with a previously refined "QTLhotspot" region (~300Kb) for drought tolerance traits. Canopy conductance traits were mapped in CaLG03 (TR) and CaLG04 (eTR). iii) The refined "QTL-hotspot" region (Bin-Map) was further sub-divided into a "QTLhotspot-a" and "QTL-hotspotb" regions. While both "QTL-hotspot" sub-regions co-mapped with previous study  [21] , the phenotyping data at a lower level of plant organization gathered here led us to interpret that region 'a' (139.
At the time of eT measurements and transpiration values were estimated from this correction. While this may have induced some error, we made the assumption the method would be correct for genotypic comparison and QTL analysis. Transpiration rate (TR) and evapotranspiration rate (eTR) were calculated by transpiration and evapotranspiration divided by 3D-leaf area and time  [52]. iii) Biomass traits At the end of the experiment (canopy covered maximum in the pot; 35 DAS), shoot samples were harvested and over dried at 65 °C for 48 h. Further, shoot dry weights (SDW) were weighed using gravimetric balance (KERN 3Kg) method. Specific leaf area (SLA) was estimated by leaf area divided by shoot dry weight. Specific leaf weight (SLW) was estimated by 1/SLA (inverse of SLA). QTL analysis-single locus QTL analysis was conducted independently using three genetic maps developed earlier  [18, 20, 21]  and phenotyping data generated in this study.
QTL Cartographer version 2. 5, composite interval mapping (CIM) method was employed  [53]. For ultra-high density bin markers, inclusive composite interval mapping-Additive mapping (ICIM-ADD) method was used for identification of QTLs using IciMapping software (v3. 2;  [54] ). LOD threshold was set by using 1000 permutation and p value ≤0. 05. Constructed linkage map was visualized using Mapchart 2. 2  [55]  software. When the PVE (phenotypic variation explained) was above 10%, QTLs were considered major QTLs (M-QTLs) and PVE below 10% were minor QTLs.
Interactions QTL analysis-multi-loci The QTL interactions influencing the traits were identified using Genotype Matrix Mapping software (GMM; v. 2. 1;  [56] , http://www. kazusa. or. jp/GMM). Using GMM, two and three loci interactions were tested. GMM analysis showed interactions between loci and different linkage groups of plant vigour and canopy conductance related traits. The current study identified allelic interactions that contributed to either a positive (increase) or negative (decrease) effect on the phenotypic value of the trait. In most cases, single locus QTL identified using GMM analysis were similar to those identified with CIM analysis, even though two approaches use different algorithms. In the following text, symbols "AA", "BB" and "stand for alleles originated from the high vigour parent (AA; ICC 4958) and low vigour parent (BB; ICC 1882)" and not distinguished from any parent (-), respectively.
Statistical analysis To find the phenotypic variations and their significance in the population, ANOVA was performed for all observed parameters individually using GENSTAT 14. 0 (VSN International Ltd. , Hemel Hempstead, UK). Similarly, to find the phenotypic variations and their significance in parental lines were analyzed with statistical program package CoStat version 6. 204 (Cohort Software, Monterey, CA, USA). Oneway ANOVA was carried out to test for genotypic difference between the genotypes. Means were compared using Tukey-Kramer test and Least Significant Difference (at P ≤ 0. 05). Normal histograms with frequency distribution analysis for phenotypic traits were done using SPSS 16 desktop version (IBM, SPSS Statistical software). Principal component analysis (PCA) was used to visualize the relationships between traits in a multidimensional space using R software (version 2.
11. 1). To find the trait correlation of all phenotypic traits, simple Pearson correlation was performed using R software (version 2. 11. 1). For QTL and PCA analysis, Best Linear Unbiased Predictors (BLUPs) data were estimated by using GENSTAT 14. 0 were used. The clustering analysis was performed by PCA loadings using R software (version 2. 11. 1). Genotypic and residuals mean square components were obtained from ANOVA through GENSTAT 14. 0, which was used to calculate the broad sense heritability (h 2 ). The broad-sense heritability (h 2 ) was calculated as h 2 = σ 2 G/ (σ 2 G + σ 2 E)  [31, 32] , where σ 2 G is the genetic variance and σ 2 E is the error variance.
Acknowledgements The authors acknowledge  Dr. Grégoire Hummel  and  Dr. Uladzimir Zhokavets  from  Phenospex  for co-designing the LeasyScan platform with  Dr. Vincent Vadez. Thanks,  Mr. P. V. D Maheswar Rao  and  Mr. M. Anjaiah  for their help in planting, irrigation and crop management practices. Thiyagarajan Thirunalasundari is working as Professor and Head, Department of  Industrial Biotechnology, Bharathidasan University at Tiruchirappalli in India. Pooran M. Gaur  is Principal Scientist (Chickpea Breeding) at ICRISAT in India. He has extensive expertise on chickpea mapping population development and marker assisted breeding. Rajeev K Varshney is Research Program Director -Genetic Gains at ICRISAT in India. Heis internationally recognized for his contribution in genome sequencing of pigeonpea, chickpea, peanut, pearl millet, sesame, mungbean and azuki bean and development of molecular breeding products.
MATERIALS AND METHODS The nr nucleotide GenBank database was blastn searched using seed SSU rRNA gene sequences of characterized microsporidia-like protists, metchnikovellids, deeply branching LB-Microsporidia (clades 2 and 3  Stentiford et al. 2017) , and the phylogenetic diversity of "Cryptomycota" and aphelids in  Karpov et al. (2014). The top 50 matches for each sequence were downloaded, aligned with mafft e-ins-i  (Katoh and Standley 2013) , deduplicated, and a preliminary tree constructed on the basis of which the number of closely related sequences was reduced, retaining the longest possible sequences. The shorted branched LB-Microsporidia Janacekia, Trichonosema, and Bacillidium were selected to represent LB-Microsporidia in order to reduce the possibility of phylogenetic artefacts caused by LBA.
The distribution of microsporidian characters (Table  1 ) was constructed from the literature. OTUs assigned to any of "Opisthosporidia"  (Karpov et al. 2014) ; "Cryptomycota"  (Jones et al. 2011) , "Holomycota"  (Liu et al. 2009) , and "Microsporidia" and otherwise unassigned "fungi" by the taxonomic assignment algorithms of each study were extracted from environmental amplicon sequencing data of tropical forest soils clustered into OTUs by Swarm v2. 1. 5 (Mah e et al. 2015)  and European coastal water and sediment samples  (Logares et al. 2014). We inferred a maximum likelihood tree from our reference database using RaxML v8. 2. 8  (Stamatakis 2014 ). The OTUs were then aligned to the reference database using PaPaRa  (Berger and Stamatakis 2011)  and placed on the tree by RaxML-EPA  (Berger et al.
It is well recognized that SSU rRNA gene data alone are unable to resolve deep phylogenetic relationships. However, although not a substitute for multigene data, the high taxon sampling afforded by SSU provides phylogenetic information currently inaccessible for phylogenomic analyses  (Berney et al. 2004; Cavalier-Smith 2004). We calculated a range of trees of varying taxon samplings (e. g. Fig. 2 ; other data not shown), and although the branching order within the clade was not consistent between them (although many subclades were consistently recovered), the microsporidian clade as marked on Fig. 1  was invariably and strongly recovered. These analyses provide additional evidence for an expanded and strongly supported microsporidian clade, including all of the LB-Microsporidia, (metchnikovellids, the "microsporidian-like" protists discussed above, and almost all of the environmental "crypto/rozellomycotan" diversity indicated in  Lara et al.
Morphological vs. genomic evolution in Microsporidia Corresponding morphological and genomic datasets are available for only a very small proportion of lineages representing SB-Microsporidia and metchnikovellids. Until very recently this applied to only Mitosporidium  (Haag et al. 2014)  and Amphiamblys  (Mikhailov et al. 2017 ), but the addition of nuclear and mitochondrial genomes of Paramicrosporidium saccamoebae  (Quandt et al. 2017 ) have provided several intriguing new perspectives on microsporidian evolution. It is increasingly apparent that, although all SBmicrosporidia exhibit microsporidian-defining morphological characters, their genomic evolution appears far more mosaic  (Quandt et al. 2017) ; Table  1. Rozella and LB-Microsporidia both have horizontally acquired Rickettsia-like NTT ATP/ADP transporters, but metchnikovellids, Mitosporidium and Paramicrosporidium do not (Table  1 ).
Flagella and polar filament evolution in Microsporidia While rozellids have a lifecycle of alternating zoosporic and nonflagellate trophic stages, flagellate (zoosporic) stages are unknown for any lineages in the expanded microsporidian clade, with two intriguing exceptions. Jones et al. (2011)  showed that members of two SB-microsporidia lineages (Laz I and LKM-46, indicated on Fig. 1 ) have zoosporic stages without chitin in their cell walls. Assuming the cells reconstructed from those FISH experiments represent the branches indicated on Fig. 1  and were not false-positive FISH signals, this shows that some SB-Microsporidia do possess flagella at some stage of their life cycle. In this study, we used BLASTP to search for homologs of the specialized epsilon and delta tubulins, intraflagellar transport system, or flagellar-specific motor molecules in the genomic datasets for the putatively earlier-diverging Mitosporidium (or the metchnikovellid Amphiamblys sp.
High diversity of SB-Microsporidia in neotropical soils An important consequence of our analyses is that the taxonomic affiliations of large-scale SSU rRNA amplicon sequencing studies must now be revisited. Most of the sequences annotated as "crypto/rozellomycota" in taxonomically curated databases such as SILVA and PR 2  (Guillou et al. 2013 ) are likely to be SB-Microsporidia. The rozellid annotation should be restricted to the clade indicated on Fig. 1. This is not purely a matter of classification: Microsporidia and rozellids are each monophyletic and distinct in terms of biology and ecology, but are currently conflated in the major sequence databases. Clarifying the composition of the two clades will enable much more accurate and high-resolution analyses, and interpretation of microsporidian diversity and function.
To demonstrate the particular relevance of this more inclusive definition of microsporidia to the annotation and interpretation of environmental sequencing studies, we reevaluated a recent SSU rRNA high-throughput sequencing study of three Neotropical rainforest soils (Mah e et al. 2017), using the Evolutionary Placement Algorithm as implemented in RAxML  (Stamatakis 2014)  to place operational taxonomic units (OTUs) previously annotated as Opisthosporidia, crypto/rozellomycota, or unassigned fungi. We similarly analysed an OTU dataset from European coastal water and sediment samples (BioMarKs:  Logares et al. 2014; Massana et al. 2015). Of the 1,279 candidate tropical soils OTUs, 94% clustered within the microsporidian clade (Fig. 2 , 3). Figure  3  shows the branching positions of clades of OTUs relative to the reference sequences.
OTUs clustering with the metchnikovellid branch are putatively parasites of gregarines. Others grouping nearer Nucleophaga, at the base of the branch leading to metchnikovellids and LB-Microsporidia, may be parasites of amoebae or/and other protists (perhaps including hyperparasites). Mah e et al. (2017)  show that both gregarines and Amoebozoa are highly diverse in their Neotropical soil samples. However, until more lineages are found in nature we will be unable to conclusively determine whether microsporidian diversity branching nearer Paramicrosporidium or Mitosporidium are parasites of protists and arthropods respectively. Although there were far fewer opisthosporidian OTUs in the BioMarKs data, the majority of these also branched within the microsporidian clade (Fig. 3 ). In both datasets a small proportion of OTUs was placed in the rozellid clade (two from soil), and the NAMAKO-37 clade (four from BioMarKs), consistent with the apparently limited diversity of these clades compared to Microsporidia.
Maximum Likelihood phylogeny of SSU rRNA gene V4 region OTUs from tropical soil and European marine (Biomarks) samples. The OTUs are aligned to reference sequence dataset of longand short-branch Microsporidia, metchnikovellids, rozellids, other clades of environmental sequences, and representative aphelids and fungi with a holozoan outgroup. Light and dark grey shading indicates extent of expanded Microsporidia; dark grey box encloses all members of the subclade including long-branch Microsporidia. Green triangles (collapsed clades) represent OTUs from Neotropical soil samples only, 'BM' from Biomarks only, and blue from both.
ACKNOWLEDGMENTS This work was supported by the  Natural Environment Research Council  ( NE/H000887/1  and  NE/H009426/1  to  D. B. ,  NE/P00251X/1  to T. A. W. and D. B. );  Royal Society University Research Fellowships  to B. A. P. and T. A. W. ;  Deutsche Forschungsgemeinschaft  grant ( DU1319/1-1  to M. D); the  Klaus Tschira Foundation  to L. C. ;  Department for Environment, Food , and Rural Affairs (DEFRA) (# C6560  to D. B. and G. D. S); the  French Government's "Investissements d'Avenir" programme OCEANOMICS  ( ANR-11-BTBR-0008 ) to C. B; and  European Commission CORDIS  grant  MSC-IF AlgDates 704566  to G. T.