dataesr/scientific-paragraphs-categorization huggingface dataset:
Our data therefore seem to reinforce the hypothesis that no strong bottleneck occurred during grapevine domestication events, as argued by Myles et al. [14]. Our results could also illustrate the genetic erosion undergone by the wild compartment, probably mainly due to anthropic pressure on natural habitat and biotic stresses, in particular phylloxera and mildews since the middle of the nineteenth century. In addition, the wild sample contains only Western accessions, which could also result in lower diversity. Our LD analysis offers several advantages over previous ones in grapevine. First, the sample size is larger than in Barnaud et al. [38, 39]  and Lijavetzky et al. [44] , with subgroups of sufficient size to prevent any LD overestimation due to small sample size  [111]. Second, we estimated LD separately in each of the three main diversity groups, using adequate samples, and could therefore compare LD extent between subgroups, which had never been reported before.
Acknowledgments This study was supported by: the  DLVitis program  (project  ANR-08-GENM-02 ) funded by the  French ANR  ( Agence National de la Recherche ) and the  French CNIV  ( Comité National Interprofessionnel des Vins d'Appellation d'Origine ); the  SNPGrapMap program  funded by  INRA. SN was supported by an  INRA   postdoctoral fellowship. TL's contribution to this study was part of his PhD thesis funded by  INRA. We thank the  INPT (Institut National Polytechnique de Toulouse ) genomic platform of Genotoul in Toulouse for technical support in the acquisition of genotypic data; the  EPGV (Etude du Polymorphisme des Génomes Végétaux ) group in Evry:  Dominique Brunel  for discussion,  Aurélie Chauveau  and  Rémi Bounon  for performing amplicon production and Sanger sequencing; the  CNG (Centre National de Génotypage) in Evry  for providing access to their robotics and ABI3730 sequencers; the  INRA "Domaine de Vassal" Grape Germplasm Repository  for collection maintenance and phenotyping;  Vincent Thareau  for designing part of the sequencing primers;  Muriel Latreille ,  Audrey Weber  and  Juliette Goarrin  for helping with DNA extraction;  David Desrousseaux  for his help in designing the kinship estimation selection method;  Tristan Marie-Huard  for the R script to compute power;  Timothée Flutre  for useful comments on the manuscript; the two anonymous reviewers for their useful comments on a first version of this manuscript.
Availability of supporting data All plant material is available from the Vassal public repository, and accession information, including ID and passport data, is available on its website [http://www1. montpellier. inra. fr/vassal]. Most datasets supporting the conclusions of this article are included within the article and its additional files, in the SNiPlay repository [http://sniplay. southgreen. fr/ cgi-bin/SNPqueries_v3. cgi], in Lacombe et al. [18] , in the references cited therein, or in the European Vitis database [http://www. eu-vitis. de/index. php]. The SNiPlay repository harbors sequence and variation data for several southern and Mediterranean plant species. Although the remaining SSR and phenotypic data will not be widely released before complete analysis, these data can be provided upon request.
, 1994). Therefore we used the auxin response reporter DR5 driving the expression of Firefly luciferase enzyme -DR5:: LUC  (Moreno-Risueno et al. , 2010). Accordingly to the growth onset, nuclear auxin response could be detected 20 min after auxin application (Figure  1B ). To visualize the auxin-induced acidification and thus approximate the activity of the PM H + -ATPases, we used the genetically encoded apoplastic pH sensor apo-pHusion  (Gjetting et al. , 2012). Similar to the growth initiation and the nuclear auxin signaling, a sudden drop in apoplastic pH could be detected 20 min (20. 11 ± 5 min stdev, n = 12) after auxin application (Figure  2A ; Video 1). We developed a MATLAB-based tool that we named AreaKymo in order to be able to visualize and quantify apoplastic pH changes in an unbiased way.
Samples were automatically imaged each 10 min using the AutoIt program and imaged at 2400DPI. The drug stocks were dissolved in DMSO except for fusicoccin, which was dissolved in methanol. The HS::axr3-1 plants were heatshocked at 37 ˚C for 40 min in aluminumwrapped petri dishes; experiments were started 1. 5 hr after the end of the heat shock. AHA2 d95 hypocotyl segments were transferred to depletion medium containing 1 mM dexamethasone. To image the luciferase activity, ~50 ml of 1 mM D-luciferin dissolved in PBS was added to decapitated hypocotyl segments during the depletion stage (using the depletion medium), and then transferred to the plates with treatments, and imaged. The elongation experiment, pH analysis and luciferase imaging is described in more detail at Bio-protocol  (Li et al.
Image analysis All images were analyzed using Fiji  (Schneider et al. , 2012; Schindelin et al. , 2012). The kymographs were done using the Multiple Kymograph ImageJ plugin (http://www. embl. de/eamnet/html/ body_kymograph. html). To achieve unbiased measurements, the length of hypocotyl segments was measured by thresholding the scanner images, manual clicking on each segment using the Wand tool (Fiji) and recording their Feret's diameter (maximum caliper), this was done for the entire time series. The initial length of the segment was taken as 100%, data was analyzed in Excel. The results in the figures represent one of three repetitions of each experiment, which is based on quantification of 6 hypocotyl segments. For confocal time series, image drift was stabilized using the StackReg Fiji plugin in the 'translation' option.
Luciferase luminescence was measured by selecting the region of interest containing the hypocotyl segment and measuring the mean intensity in this region over time. The apoplastic pH visualized by the apo-pHusion marker was measured both manually and automatically by the AreaKymo MATLAB script. Manual measurement was done by thresholding the cell wall area based on the RFP channel, using this thresholded area as a base for a region of interest (using the 'create selection' command), and then measuring the mean intensity in these regions both for GFP and RFP channels. For the gravitropic experiment, each z-section of the image was measured individually. The AreaKymo script essentially does the same procedure, but does so automatically without the user input, allowing for rapid processing of several hypocotyls at a time.
The user specifies the value of the threshold for the RFP signal (this is set arbitrarily to highlight just the cell wall area) and the desired width of the rectangle that will represent the individual timeframe. The script outputs the visual representation of apoplastic pH and also the values in a form of a series of boxplots. The AreaKymo script is provided in Supplementary file 1. To measure the bending of hypocotyls after gravistimulation, the angle of the upper part of the hypocotyl was measured 6 hr after gravistimulation. Numerical data for the graphs and figures can be found in the Source Data files 1-6, each corresponds to.
Acknowledgements The authors express their gratitude to  Veronika Bierbaum ,  Robert Hauschild  for help with MATLAB,  Daniel von Wangenheim  for the gravitropism assay. We are thankful to  Bill Gray ,  Mark Estelle ,  Michael Prigge ,  Ottoline Leyser ,  Claudia Oecking  for sharing the seeds with us. We thank  Katelyn Sageman-Furnas  and the members of the  Friml lab  for critical reading of the manuscript. The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement n  ˚291734. This work was also supported by the  European Research Council  (project  ERC-2011-StG-20101109-PSDP ).
Acknowledgements We thank two anonymous reviewers for useful comments and criticisms to a previous version of our work. We also thank the  French Research Council (CNRS)  and the  University Paul Sabatier  for support. M. Giurfa  acknowledges in addition the generous support of the  Institut Universitaire de France (IUF). This work is dedicated to the memory of  Josué A. Núñez  (1924Núñez ( -2014)), guide and inspiration during many years.
Experimental Design After preliminary experimental trials, we selected the saturated steam pressure (P) ranged between 0. 24 and 0. 46MPa and the thermal treatment time (t) between 18 and 32s, as DIC operating independent parameters (Table  1 ). Through these DIC independent variables, a 2-parameter 5-level central composite rotatable Design of Experiments (DoE) was defined including 2 2 =4 factorial points, 2*2=4 star-points and 5 repetitions of the central treatment point CTP. Statgraphics for Windows software (5. 1 version)was used to perform statistical analyses of variance (ANOVA) to determine if differences between the effects of independent variables weresignificant (p<0. 05). Pareto chart is introduced to identify the statistical significance of the independent variables on each response, with a vertical line showing if the effects are significant at 95% confidence level.
Moreover, the high value of R 2 as regression coefficient highlights the high ability of the phenomenological CWD model to fit the experimental study. These response values greatly depend on the operating DIC parameters (P and t). Thesignificance of such parameters is to justify, if the histogram, in Pareto chart, crosses the front of significance, translating the threshold at 5%. The improvements on TPC yields allowed us to choose  ∞ from ASE and not from dynamic maceration. DIC ∞ and RM ∞ yields are the same values given in Table  2. RSM statistical analyses show the significance effects of DIC operating parameters as shown in Figure  4. The effective diffusivity values were determined using the method of slopes by plotting experimental data of Ln ). These findings can be attributed to the positive effect given by DIC process in modifying the plant structure, which greatly facilitates mass diffusion.
Conclusion The molecular characterisation data establish that maize DAS-40278-9 contains a single insert consisting of one copy of the aad-1 expression cassette. Bioinformatic analyses of the sequences encoding the newly expressed proteins and other ORFs present within the insert or spanning the junctions between the insert and genomic DNA did not raise safety issues. The stability of the inserted DNA and of the introduced herbicide tolerance trait was confirmed over several generations. The levels of the AAD-1 protein were obtained and reported adequately.
The mixture was gently vortexed and centrifuged at 10,000×g for 10 s. The supernatant (100 μl) was taken, diluted 1:10 in deionized water and stored at -20 °C. Since morphology of lymnaeids is insufficient for the accurate identification of species  (Correa et al. , 2011) , we identified G. cubensis by sequencing the gene internal transcribed spacer 2 (ITS-2)  (Bargues et al. , 2007; Correa et al. , 2011). First, we amplified ITS-2 from one or two individuals taken at random from each sampling site, according to protocols described by  Correa et al. (2011)  with primers Forward News (5'-TGTGTCGATGAAGAACGCAG-3') and Reverse Rixo 2 (5'-TTCTATGCTTAAATTCAGGGG-3'). We then blasted the sequences obtained against sequences of G. cubensis available in GenBank: AM412223, JF514088 and JN614461  Abbreviations: F, forward primer; R, reverse primer.
(  Bargues et al. , 2007 Bargues et al. , , 2011;; Correa et al. , 2011). All the sequences showed 99-100% homology. Finally, we uploaded the sequences obtained to GenBank (see Table  2  for accession numbers). Because of insufficient DNA extracted, we were unable to sequence ITS-2 for individuals from some sites: El Progreso (Colombia), Bahía Honda (Cuba), Fond Barboteaux (Guadeloupe), Ravine Mansarde au Robert (Martinique) and Mesa de Esnujaque (Venezuela). However, the individuals from Cuba, Martinique and Guadeloupe were easily distinguishable from the other lymnaeid that inhabits these regions  (Pointier, 2008; Vázquez et al. , 2014). The individuals from Colombia and Venezuela (Mesa de Esnujaque) were identified based on the morphology of the adult reproductive system  (Pointier et al.
Again, we identified one or two individuals from each species and locality by sequencing ITS-2 as described above and blasting the sequences against those already available in GenBank for G. truncatula (AJ296271;  Bargues et al. , 2001) , G. viator (AM412224;  Bargues et al. , 2007)  and G. neotropica (AM412225;  Bargues et al. , 2007). For each species, the sequences showed 99-100% homology. The sequences were uploaded to GenBank (see Table  2  for accession numbers). Population genetics of G. cubensis and G. neotropica We used individual genotypes to estimate current parameters of population genetics in G. cubensis and G. neotropica: number of alleles, observed and expected heterozygosities (H O and H E ), the inbreeding coefficient F-statistic F IS , and selfing rate (s) as s = 2F IS /(1 + F IS )  (Hartl & Clark, 1997).
ACKNOWLEDGEMENTS We would like to thank  Nicolás Bonel  for his useful comments on earlier drafts of the manuscript and two anonymous reviewers for their critical reviews. M. L. was supported by a doctoral fellowship from  University of Montpellier  and A. A. V. by a grant from  Institute de Recherche pour le Développement (BEST). Fellowships granted by  Erasmus Mundus PRECIOSA  and Méditerranée Infection supported research stays of P. A. at  MIVEGEC (Montpellier, France). This study was financially supported by  IRD ,  CNRS ,  University of Montpellier and Labex Cemeb.
9″N 19 October 2005 31 ND 70°42'58. 1″W San Mateo 10°12'41. 4″N 26 October 2010 10 KT781214; KT781215 67°24'51. 8″W Colombia El Progreso 06°12'01″N 7 September 2009 48 ND 75°35'06″W Ecuador Las Dos Puertas 1°56'1. 1″S 16 October 2014 44 KT461809; KT461817 79°34'38. 1″W Yaguachi 2°5'34. 4″S 15 October 2014 44 KT461814 79°42'29. 8″W G. truncatula Peru Moquegua 17°19'24. 3″S 15 October 2012 3 KU870357 70°59'29″W G. viator Argentina Frias 40°44'07″S 1 February 2004 3 KT781170 66°37'27″W G. neotropica Peru Moquegua 17°19'24. 3″S 15 October 2012 42 KU870349; KU870350 70°59'29″W Ocoña 16°25'16. 8″S 13 October 2012 12 KU870351; KU870352 73°6'55. 8″W Río Lurín 12°6'7. 1″S 7 October 2012 51 KU870353; KU870354 76°47'17. 3″W Venezuela La Linda 10°5'24. 1″N 18 November 2006 9 KT781202 67°47'25. 4″W Fincas 4M Tucacas 10°46'N 19 September 2001 10 KT781217; KT781218 68°24'W Some individuals were identified by sequencing ITS-2; GenBank accession numbers are provided.
E. festucae symB and symC encode membrane associated proteins A tBLASTn search of the E. festucae Fl1 (E894) genome sequence using P. anserina IDC2 and IDC3 as the query sequences identified gene models EfM3. 029010 and EfM3. 029020  (Schardl et al. , 2013) , which we have named symB and symC respectively, as the E. festucae homologues. To confirm the gene structure, each of these sequences was aligned with the P. anserina homologue as well as the corresponding polypeptide sequences from Fusarium graminearum, Magnaporthe oryzae, N. crassa HAM-7  (Maddi et al. , 2012)  and S. macrospora (Supporting Information Figs. S1  and S2 ). The proposed exon-intron structure of each gene was verified by cDNA sequencing. E. festucae SymB, is a 230 amino acid protein that shares 76% identity with P. anserina IDC2 and 75% identity with N.
Acknowledgements Kimberly Green was supported by a  Massey University  PhD and DAAD short-term research scholarship and  Barry Scott  by an  Alexander von Humboldt Research Award. The authors thank  Doug Hopcroft ,  Jianyu Chen ,  Jordan Taylor  and  Matthew Savoian  ( Manawatu Microscopy and Imaging Centre, Massey University ),  Arvina Ram ,  Anne Dettman  and  Yvonne Heilig  for technical assistance. We thank  Lynda Ciuffetti  ( Oregon State University ) for provision of plasmids pCT74 and pCA56. We thank  Chris Schardl  for provision of Clavicipitaceae genome sequences, made available through funding from the  U. S. National Science Foundation  (grant no. EF-0523661 ), the  U. S. Department of Agriculture National Research initiative  (grant no. 2005-35319-16141 ), and the  National Institutes of Health  (grant no.
Bioinformatic analysis E. festucae genes were identified by tBLASTn analysis of the E. festucae Fl1 (E894) genome (http://csbio-l. csr. uky. edu/ef894-2011) with protein sequences obtained from either NCBI GenBank database (http://www. ncbi. nlm. nih. gov/) or the Broad Institute (http://www. broad. mit. edu). Identity and similarity scores were calculated after ClustalW pairwise alignments of sequences  (Thompson et al. , 1994) , using MacVector version 12. 0. 5. The E. festucae genome sequence data  (Schardl et al. , 2013) , as curated by C. L. Schardl at the University of Kentucky is available at http:// csbio-l. csr. uky. edu/ef894-2011/. SignalP (v. 4. 1)  (Nielsen et al. , 1997; Petersen et al. , 2011)  was used for signal peptide detection and TMHMM (v. 2. 0c)  (Sonnhammer et al. , 1998; Krogh et al.
, 2001)  was used for detection of transmembrane domains. Domain analysis was also performed using InterProScan (v. 5)  (Zdobnov and Apweiler, 2001; Quevillon et al. , 2005) , Inter-ProScan lookup service (v. 43. 1), phobius (v. 1. 01) (K€ all  et al. , 2004), SignalP (v. 4. 1)  and TMHMM (v. 2. 0c) plus all software given by default with InterProScan (BlastProDom, FprintScan, HMMPIR, HMMPfam, HMMSmart, HMMTigr, ProfileScan, HAMAP, PatternScan, SuperFamily, Gene3D). The "big-PI Fungal Predictor"  (Eisenhaber et al. , 2004)  was used to check for GPI-lipid anchor modification sites. The symB and symC gene sequences of E. festucae strain Fl1 (E894) are available in the GenBank database (http://www. ncbi. nlm. nih. gov/genbank/index. html) under accession numbers KX827271 and KX827272.
med. umich. edu/I-TASSER/). The primary sequences of proteins of interest were submitted (At2g27760 and MGG_04857) and secondary structures were predicted. Based on secondary structure predictions and primary sequences, the most similar proteins whose 3D structure was determined by NMR or X-ray crystallography were used as template for the model prediction. The model presented in S1B Fig was obtained with the MOD5 yeast protein (PDB accessions: 3epjA and 3ephA) as a template. Similar models were obtained with 3foz (E. coli) and 3a8t (Humulus lupulus) proteins as templates. Afterwards, alignments between the protein of interest and templates were generated by the use of different threading programs (MUSTER, FFAS-3D, SPARKS-X, HHSEARCH I, Neff-PPAS, HHSEARCH, pGenTHREADER, wdPPAS, PROSPECT2).
Statistical analysis and experimental design We analyzed results obtained for symptom quantification (Figs 1B, 3B and 4C) using a generalized linear model with a quasi-Poisson error structure. Significance was determined using a Chi² test. In each experiment, three biological replicates composed of 10 plants were analyzed per strain/condition. The size of the lesion trait was analyzed using a mixed model, with the "leaf" factor as random error, to compare the invasion of the different strains in the different conditions (Figs  1C  and 3C ). Boxplots represent data distribution using the median (indicated by the black line) and approximate quartiles. Each experiment was replicated at least three times. Gene expression data were analyzed using a Student t-test on four biological replicates, with each replicate composed of five to six plants (Figs 2, 5, 7B and S5).
Tissue staining for confocal observation and H 2 O 2 staining Inoculated leaves were harvested, fixed and stained as described by  Ballini et al. , (2013)   [61]. In order to show H 2 O 2 accumulation, we performed a DAB staining as mentioned in Faivre-rampant et al. ,  (2008)   [97]. (4 e -40). (B) The putative tRNA-IPT from Magnaporthe has a predicted structure similar to the yeast MOD5 protein. MOD5 structure (light blue) was determined by X-ray diffraction  [99]. The MOD5 structure used for the alignment corresponds to the 3ephA and 3epjA accessions in the Protein Data Bank (http://www. rcsb. org/pdb/home/home. do). The predicted structural model of tRNA-IPT from M. oryzae (MGG_04857; dark blue) was obtained by threading on the on-line platform I-TASSER, C-score 0,02 (Cf Materials and Methods).
Acknowledgments We particularly thank  Sophien Kamoun  for his scientific advice,  Henri Adreit  and  Romain Gallet  for their help for spore isolation and statistical analysis respectively, and  Marc-Henri Lebrun  and  Florian Frugier  for fruitful discussions. We thank  Nathalie Marnet  and  Solenne Berardocco , from the  IGEPP's Metabolic Profiling and Metabolomic Platform  ( P2M2 ;  Rennes-France ), for their technical support in amino acid and sugar measurements.
This work was partially supported by  Agropolis and Cariplo Fondations  under the reference « Rice Connections »  1201-001. This project is supported by the  French ANR program  " Investissement d'Avenir "  ANR-10-LABX-0001-01  and a  Canadian NSERC   Discovery Grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data Coxiella burnetii infections are known to be enzootic in the cattle population of the Finistère department (northwestern France). In May 2012, 2799 dairy cattle herds (69% of all the cattle herds in Finistère), were tested for antibodies against C. burnetii in bulk tank milk (BTM) using LSI Q fever enzyme-linked immunosorbent assay (ELISA) kit ® (LSI; Lissieu, France). Herds detected as seronegative in 2012 were retested again in May 2013. Data on geographical coordinates and number of cows in herds were also collected. Furthermore, data on the movements of individual cows between dairy cattle herds and through markets of the Finistère department were extracted for the time period of May 2012-2013 from the national register (source: Groupements de Défense Sanitaire de Bretagne, France).
In total, 835 out of 2799 dairy herds were involved (as a source or destination) in 2234 movements of cows (i. e. one movement corresponds to one cow being sold and purchased). In 182 herds, at least one animal during the study period was purchased and was not infected in May 2012. The mean number of animals sold per herd was equal to 4. 7 (n = 474), the mean number of purchased animals was 5. 3 (n = 491), the mean number of partners for selling was 1. 4 and for buying animals was 1. 6. Wind speed data required for dispersion modelling were procured from the open access database of the European Centre for Medium Range Weather Forecasts  [20]. Data on northward and eastward wind components for the Finistère department for the period of May 2012-2013 were extracted. Daily data were converted to weekly averages.
Inter-herd transmission due to cattle trade When an infectious cow moves from one herd to another, it contributes to the spread of C. burnetii within the destination herd (i. e. to the term E i,external in Equation  2 for herd i). All movements of cows between dairy cattle herds in the Finistère department were modelled according to observed trade data on the source and destination herds, the movement date, and the age of moved cows. The only information not present in the data was the animal health status. Hence, while modelling cattle trade, for each individual movement observed between May 2012 and May 2013, an animal in the same lactation year (parity) as the one recorded in the dataset was randomly chosen from the source (selling) herd to move to the destination herd. The probability of trading an infectious cow therefore was related to the proportion of infectious (3) µ = µ death + µ plume source animals in the given lactation year in the source herd.