dataesr/scientific-paragraphs-categorization huggingface dataset:
RT-PCR amplification assays Four RT-PCR assays with different specificities and targeting different parts of the BYDV genome were used in the present study. The position of the amplified regions on the BYDV genome is shown schematically on Fig. 1  while the sequence of the primers used and their annealing temperatures are given in supplementary Table  S3. The RT-PCR1 assay was used to detect viruses belonging to the Luteovirus genus, using polyvalent primers targeting the ORF3 region (primers Luteo1F and YanR-New, adapted from Malmstrom et al. [25] ). The RT-PCR2 assay uses the specific primer pair BYDV-P5-fw and BYDV-39NC-rev2 designed on the reference isolate BYDV-PAV EF521849 sequence. This assay allows the amplification of a hypervariable 820-bp fragment overlapping the 39 end of the ORF5 and the ORF6 of BYDV-PAV isolates.
Determination of the genome sequence of selected BYDV isolates Double-stranded RNAs (dsRNAs) extracted from P. cookii infected by various BYDV isolates were analyzed by 454 pyrosequencing, as described by Candresse et al. [26] , providing partial genomic sequences for isolates K439, K460 and K465. Specific primers designed from these partial sequences were then used to perform Long Distance PCR in order to validate the  pyrosequencing data and to fill the gaps they contained. The specificity of the primers used as well as the annealing conditions are detailed in Table  S3. The 59 Race amplifications were performed as recommended by the kit supplier (Clontech, cat. no. 634923).
Sequence analyses, comparisons and phylogenetic analyses Multiple alignments of nucleotide or amino acid sequences were performed using the ClustalW program  [27]  as implemented in Mega 5. 0  [28]. Pairwise strict nucleotide and amino acid distances were computed using Mega 5. 0 and phylogenetic trees were reconstructed using the Neighbor joining method with bootstrap analysis in Mega 5. 0. The RDP3 program was used to search for potential recombination events in the BYDV genomic sequences obtained during this work  [29].
Kerguelen Islands BYDV isolates comprise three major genetic lineages In order to analyse BYDV diversity, total nucleic acids extracts were submitted to the RT-PCR1 amplification assay. (Fig. 1  and Table  S3 ). In total, the 500 nucleotide-long sequence was determined by direct sequencing of the PCR products obtained from 62 BYDV-isolates (Table  S2 ). The corresponding sequences could be retrieved for 40 GenBank accessions corresponding to members of the Luteoviridae family and were included in the analysis. The phylogenetic tree in Fig. 3  shows the segregation of BYDV sequences from the Kerguelen Islands into three major lineages in the Luteovirus clade, which comprises BYDV-GAV, BYDV-PAV and BYDV-MAV and the tentative species BYDV-SGV. Each Kerguelen Islands lineage is supported by high bootstrap values (84 to 99%).
Genetic diversity of BYDV-PAV-I isolates from Kerguelen Islands The region amplified using the RT-PCR1 polyvalent assay shows high conservation within each of the BYDV-PAV subclusters and therefore does not allow the analysis of withincluster relationships. A selected subset of Ker-I isolates was therefore amplified using the RT-PCR2 assay (Fig. 1  and Table  S3 ) which amplifies an hypervariable 820-bp fragment corresponding to part of the gene encoding the P5 read-through aphid transmission protein (230 C-terminal amino acids) and to the gene coding for the highly variable P6 protein. This sequence was obtained for a total of 32 Ker-I isolates (Table  S2 ) and compared to those of the 23 closest corresponding sequences in GenBank, which all belong to the PAV-I subcluster  [20]. The analysis of the neighbor joining unrooted tree reconstructed (Fig.
The pattern of divergence between the two Ker-II isolates (K439 and divergent K465) is unusual, showing higher divergence in the 59 genome half (in particular 34. 3% amino acid divergence in the P1 protein, Table  S6 ) and comparatively lower divergence levels for the 39 genome part (5. 1% divergence in the CP, overall 13% divergence for the complete P3-P5 read-through protein). Such a pattern suggests that as for other members of the BYDV species complex, some Ker-II isolates might result from recombination events  [18]. A search for potential recombination events involving these two isolates was therefore performed using a dataset consisting of representative full length sequences available to each BYDV strain or species and the RDP3 program  [29]. The results obtained suggest the existence of a recombination event involving the 59 genome end of isolates K439 (typical Ker-II representative) and isolate K460 (Ker-III), up to around position 673 of the K439 sequence, which corresponds to near middle of ORF1 (probability for the absence of recombination evaluated between 10 215 to 10 237 , depending on the particular program considered within RDP3, results not shown).
BYDV-Ker-II and BYDV-Ker-III diversity and evidence for mixed infections Based on the genomic sequences of the K439 and K465 (BYDV-Ker-II) and K460 (BYDV-Ker-III) isolates, primers allowing the specific amplification of members of each cluster were designed, targeting the variable part of the P5 read-through aphid transmission protein gene (RT-PCRs 3 and 4, Table  S3  and Fig. 1 ). A total of 94 P. cookii samples collected in sites where BYDV had been detected were specifically tested for the presence of isolates of these clusters. Table  4  summarizes the infection status data for the 17 samples in which isolates belonging to the Ker-II or Ker-III clusters were identified. Six plants were found to be infected by BYDV-Ker-III, and 16 by BYDV-Ker-II. Mixed infection situations involving isolates belonging to different clusters were also found, since 14 plants contained isolates from more than a single cluster (Table  4 ).
Corresponding sequences from representative Luteoviridae members retrieved from GenBank were included in the analysis and the relevant accession numbers are indicated. The abbreviations used are: PAV: BYDV-PAV; MAV: BYDV-MAV; SGV: BYDV-SGV; RMV: BYDV-RMV; GPV: BYDV-GPV; RSDaV: Rose spring dwarf associated virus; BChV: Beet chlorosis virus; SbDV: Soybean dwarf virus; BLRV: Bean leaf roll virus; PLRV: Potato leaf roll virus; PEMV: Pea enation mosaic virus. The tree was reconstructed by using the neighbor-joining method, using the p-distance model and was bootstrapped with 1,000 replications. Numbers at nodes indicate bootstrap values higher than 70%. The scale bar represents 5% nucleotide divergence. doi:10. 1371/journal. pone. 0067231. g003 (. 10% differences at the amino acid level for any viral gene product)  [17].
Acknowledgments The authors wish to thank EVINCE partners for valuable discussions and  D. Jacob ,  S. Carrere  and  J. Gouzy  for assistance in the bioinformatics analysis of some of the sequence data. Thanks to  C. Queteland ,  L. Chambrin ,  A. Garnier ,  D. Renault ,  M. Laparie ,  F. Le Bouard , and  P. Giraudet  for their help in sampling, to  L. L. Domier  for discussion on Luteoviridae taxonomy, to  W. A. Miller  for valuable discussions and comments on the manuscript and to  M. Lebouvier  for valuable discussions and his remarkable knowledge of the Kerguelen Islands. Our thanks also to  O. Le Gall , who first dreamt of seeking viruses in the Kerguelen Islands.
ACKNOWLEDGMENTS We wish to thank  Professors J. Stevenin  for the purification of SR proteins, insightful suggestions and critical reading of the manuscript,  A. Fry ,  F. Cecconi  and  G. Biamonti  for the generous gift of reagents,  Drs R. Busaà  nd  R. Franco  for help with confocal microscopy and immunohistochemistry,  P. Bielli  for helpful suggestions and critical reading of the manuscript.
Species data We used the largest botanical database yet assembled for the New World (BIEN, the Botanical Information and Ecology Network) with 4 406 875 occurrence records for 84 899 plant species  (Enquist et al. 2009 ; http://bien. nceas. ucsb. edu/bien/). All observations were assigned standardised taxon names using the Taxonomic Name Resolution Service  (Boyle et al. 2013 ) and geographical locations were validated using the Global Administrative Areas data set version 2. 0 (http://www. gadm. org, accessed on 11 May 2011). Cultivated occurrence records were excluded using original cultivated flags and locality descriptions. We projected all records (excluding Greenland) to a Lambert Azimuthal Equal Area co-ordinate system and calculated the range size of each species as the area in km 2 of the convex hull encompassing all their New World records or, for species with < 3 records, as the summed area of all occupied grid cells with a grain size of 10 000 km 2.
Explanatory variables To test hypotheses on the drivers of spatial variation in range-size patterns, we included predictors representing climate stability in time and environmental variation in space (hereafter habitat area). We extracted data on current climate from the 5 arc-minute resolution WorldClim data set  (Hijmans et al. 2005) , projected and aggregated to a 10 000 km 2 resolution. As a measure of present intraannual climatic stability, we used temperature and precipitation seasonality (TSEA and PSEA respectively). Following  Sandel et al. (2011) , longer time climate stability was represented by Late Quaternary climate-change velocity measured as the mean annual temperature velocity since the Last Glacial Maximum (21 000 year ago), corresponding to one of the strongest climatic shifts of the Quaternary.
High values of the final measure correspond to cells that have rare climatic conditions compared to their neighbouring cells. This new climate rarity measure improves the measure used in  Sandel et al. (2011)  by being based on a broad range of climatic variables instead of only two. Elevation range was calculated by projecting 30′-resolution elevation data from the WorldClim data set  (Hijmans et al. 2005)  to equal area 1 km 2 resolution and computing the difference between the minimum and maximum elevation found within each 10 000 km 2 cell. We considered latitude and data on productivity (annual mean NDVI across 1982-2000, downloaded from http://edit. csic. es/ Soil-Vegetation-LandCover. html), but both were correlated with TSEA (Pearson's r = 0. 813 and À0. 703 respectively) and thus excluded from the analyses.
To our knowledge, the next largest data set is from Missouri Botanical Garden, with ~3. 9 mio. records out of the ~10. 9 mio. in BIEN before data checking. Although the mechanisms tested here represent a broad range of hypotheses, other factors might influence patterns of range-size distribution. Besides latitude and productivity (but see Methods), evolutionary history is another proposed driver of range size. Older lineages have been proposed to have larger ranges due to the larger amount of time to spread (the age and area hypothesis,  Willis 1922). However, Willis' hypothesis has been widely rejected due to numerous examples of young lineages with broad ranges and old lineages with narrow distributions (cf. Gaston 1998). Although a phylogenetic analysis is outside the scope of the present study, the data set provides the means for exploring such evolutionary hypotheses in the future.
2012). Finally, the high importance of habitat area raises concerns over increasing habitat losses due to human land use. In summary, creation of the BIEN data set has allowed us, for the first time, to map and analyse macroecological patterns of range-size distributions at spatial scales encompassing the entire New World flora. Importantly, we find that geographical variation in species range sizes is jointly determined by long-and short-term climatic stability, in addition to land area. These results suggest that species' range sizes have been shaped by a combination of processes. Observed patterns of range-size distribution are consistent with the importance of climatic evolution of niche breadth, diversification rates, and effects of areal constraints on range expansions. Importantly, the relative importance of these drivers changes across space.
ACKNOWLEDGEMENTS This study was conducted as a part of the  Botanical Information and Ecology Network (BIEN) Working Group (PIs B. J. E. ,  Richard Condit , B. B. , S. D. , R. K. P. ) supported by the  National Centre for Ecological Analysis and Synthesis , a centre funded by  NSF  (Grant # EF-0553768 ), the  University of California, Santa Barbara , and the  State of California. The BIEN Working Group was also supported by iPlant ( National Science Foundation  # DBI-0735191 ; URL: www. iplantcollaborative. org). We thank all the contributors (see full list in Supporting Information) for the invaluable data provided to BIEN. We further acknowledge support to N. M. -H. by an  EliteForsk Award  and the  Aarhus University Research Foundation. J. -C. S. acknowledges financial support from  Centre for Informatics Research on Complexity in Ecology (CIRCE) , funded by the  Aarhus University Research Foundation  under the  AU Ideas , the  European Research Council  ( ERC Starting Grant  # 310886 'HISTFUNC' , and the  Danish Council for Independent Research -Natural Sciences  (grant # 12-125079 ).
C. V. was supported by a  Marie Curie International Outgoing Fellowship  within the  7th European Community Framework Programme (DiversiTraits project , no. 221060 ). I. S. was funded by grant  P505/11/2387  from the  Grant Agency of the Czech Republic. J. C. D. was supported by the  NSFfunded iPlant Collaborative. Finally, we thank  Diego Nieto-Lugilde , Pedro Abell an and  Constantinos P. Tsirogiannis  for discussions during the development of the climate rarity measure, and  Brody Sandel ,  Jonathan Chase  and three anonymous referees for constructive comments that helped improve this manuscript.
Treatment Planning and Delivery All patients were treated with CyberKnife. Real-time tracking of tumor movements was performed with MultiPlan ® (Accuray) treatment planning software and Synchrony ® (Accuray) respiratory tracking system. Gold seeds measuring 0. 88 mm in diameter by 5 mm in length (Ab Medica, Milan, Italy) were implanted around each lesion. Treatment planning CTs were performed at least 7 days after fiducial placement. Patients were immobilized either in a vacuum mattress or a selfexpanding foam mattress in the treatment position (supine). A spiral CT scan without contrast and a three-phase scan with contrast (arterial, portal, and late phases) were acquired for planning. Slice thickness was 1 mm. 4D-CT was not used. The gross tumor volume (GTV) was contoured on the contrastenhancing disease visible on the partial exhale contrastenhanced CT scan.
From the 139 SNP markers characterized, 133 were polymorphic (96%), showing the large range of molecular diversity represented by the eight lines. The range of polymorphism between the lines and the reference genome (Heinz1706) ranged from 27% to 82% (Supplementary Table  S2 ) The genetic distances among the parents of F 1 hybrids were variable. According to data of  Xu et al. (2013b) , Levovil and Cervil were the two most distant accessions (82% SNP polymorphic), followed by LA0147×Plovdiv 24A (40%), Stupicke Polni Rane×Criollo (34%), and Ferum×LA1420 (27%). Plants were grown during spring 2010 under greenhouse conditions (16/20 °C) in Avignon (South of France). Plants were separated into two blocks; five plants per genotype were included in each block. For proteome, metabolome, and enzymatic measurement, two stages of development, CE and OR, were selected.
1 H-NMR spectra were recorded at 500. 162 MHz on a Bruker Avance III spectrometer (Bruker, Karlsruhe, Germany) using an ATMA inverse 5 mm probe flushed with nitrogen gas and an electronic reference for quantification (ERETIC2). Sixty-four scans of 32 000 data points each were acquired with a 90 ° pulse angle, a 6000 Hz spectral width, a 2. 73 s acquisition time, and a 25 s recycle delay. Two technological replicates were used per biological replicate. Preliminary data processing was conducted with TOPSPIN 3. 0 software (Bruker Biospin, Wissembourg, France). The assignments of metabolites in the 1 H-NMR spectra were made by comparing the proton chemical shifts with values of the MeRy-B metabolomic database  (Ferry-Dumazet et al. , 2011) , by comparison with spectra of authentic compounds recorded under the same solvent conditions, and/or by spiking the samples.
The metabolite concentrations were calculated using AMIX (version 3. 9. 7, Bruker) software. The 144 1 H-NMR spectra of the data set were converted into JCAMP-DX format and deposited with associated metadata into the Metabolomics Repository of Bordeaux  MeRy-B (Ferry-Dumazet et al. , 2011, http ://www. cbib. u-bordeaux2. fr/MERYB/view/project/34, accessed September 2013). LC-QTOF-MS profiling of aqueous methanol-0. 1% formic acid extracts was performed from lyophilized powder (20 mg in 1 ml). For each biological replicate, two extractions were performed and two injections per extract were used. An Ultimate 3000 HPLC (Dionex, Sunnyvale, CA, USA) was used to separate metabolites on a reversed phase C18 column (150 × 2. 0 mm, 3 µm; Phenomenex, Torrance, CA, USA) using a 30 min linear gradient from 3% to 95% acetonitrile in water acidified with 0.
1% formic acid. Metabolites were detected using a QTOF mass spectrometer (Bruker). Electrospray ionization in positive mode was used to ionize the compounds. Scan rate for ions at m/z range 100-1500 was fixed at two spectra per second. Methyl vanillate was spiked in the extraction solvent and used as an internal standard. One sample was used as a quality control sample and injected after every 10 injections. Raw data were processed in a targeted manner using QuantAnalysis 2. 0 software (Bruker). This resulted in eight compounds identified based on accurate mass measurement and comparison with data from  Gomez-Romero et al. (2010). Ascorbic acid was measured using a spectrofluorometric method and values were expressed as total ascorbate (ascorbic acid+dehydroascorbate) as previously described by  Stevens et al.
Proteome analysis Methods for protein extraction, two-dimensional gel electrophoresis (2-DE), and protein identification and classification were as detailed in  Xu et al. (2013a). Briefly, proteins were extracted using the phenol extraction method developed by  Faurobert et al. (2007). Later, proteins were separated by 2-DE. After Coomassie colloidal staining, image analysis was performed with Samespot software (version 4. 1), and the normalized spot volumes were obtained. Protein identification of 424 variable spots was performed at the proteome platform of Le Moulon (Gif-sur-Yvette) using a nano-LC-MS/MS method following the procedure described in  Xu et al. (2013a). The database search was run against the International Tomato Annotation Group (ITAG) Release 2. 3 of predicted proteins (SL2.
40) database (http:// solgenomics. net/, accessed September 2013) with X!Tandem software (http://www. thegpm. org/TANDEM/, version 2010. 12. 01. 1). The Fasta sequence of the identified proteins was employed to reannotate the proteins using the Blast2GO package  (Conesa et al. , 2005). Sequences were compared against the NCBI-nr (version April 9, 2012) database of non-redundant protein sequence using BLASTX with the default settings.
Statistical analysis and inheritance analysis Metabolite contents and enzyme activities were expressed on a dry weight basis to be comparable. All the analyses were performed using R (R Development Core Team, 2012, http://www. R-project. org/, accessed September 2013). Data were submitted to a two-way analysis of variance (ANOVA; P < 0. 05) with genotype, stage, and interaction effect, and then to one-way ANOVA with genotype effect at each stage. In addition, to assess the mode of inheritance of the traits, one-way ANOVA was also performed with genotype effect for each cross (two parental lines and their hybrid) and stage. Means and standard deviations were calculated for each trait (phenotypic traits, metabolite contents, enzyme activities, and protein spot volumes) in each genotype and stage.
Principal component analyses (PCAs) were performed for metabolites, enzymes, and phenotypic traits, as well as for protein spot volumes for both development stages and at each stage, with the 'pcaMethods' package  (Stacklies et al. , 2007). Pearson correlations and P-values were calculated between significantly variable traits at each stage. Correlations were considered to be significant when |r| >0. 7 (P-value <0. 01). Significant correlations were plotted using the R 'corrplot' package (Wei, 2012, http://CRAN. R-project. org/package=corrplot, accessed September 2013). To analyse the relationships among protein spot volumes and other traits, networks were reconstructed and visualized using sPLS correlation regression analysis with the 'mixOmics' package  (Lé Cao et al. , 2009). An arbitrary threshold of 0.
Acknowledgements We thank  Esther Pelpoir  for her help in fruit sampling, and  Caroline Callot  and  Karine Leyre  for technical help. We also thank the experimental team and  Yolande Carretero  for taking care of the plants in the greenhouse. This work was supported by the  ANR   Genomic  project  MAGICTomSNP  project. JX was supported by the  China Scholarship Council.
3 ms) and an excitation intensity of 0. 25 kW/cm 2 at 37uC. The toxin receptor position in each frame was determined from a Gaussian fit to the diffraction pattern of the nanoparticles with a home-made Matlab V8. 2 (Mathworks, Natrick MA) algorithm. The average duration of the e-toxin receptor trajectories is 43006740 frames corresponding to 221638 s for the cholesterol oxidase experiments and 95006850 frames corresponding to 487644 s for the sphingomyelinase experiments. The average duration of the hopping experiments is 24006225 frames corresponding to 123612 s. The mean total photon number per nanoparticle label in each frame is 70 photons, the average signalto-noise ratio is equal to 10, and the average static positioning noise is 30 nm corresponding to an apparent diffusivity due to noise of D noise = 0.
Acknowledgments We are grateful to  M. R. Popoff  for providing the toxin, to  G. Mialon ,  T. Gacoin  and  J. -P. Boilot  for supplying the APTES-coated nanoparticles, and to  M. Vergassola ,  J. Wong-NG ,  M. Dahan ,  C. Bouzigues , and  D. Marguet  for helpful discussions.
Microarray analysis MDA-MB-231 mfp subpopulations were FACS isolated, immediately preserved in RLT buffer (Qiagen) with 10% bmercapto-ethanol, and mRNA extracted for profiling on Affymetrix HG-U219 chips. Data was normalized with the Robust Multichip Average method in the statistical software package, R (version 2. 14. 2). Microarray probes were summarized with a custom chip description file based on Entrez Gene identifiers (version 15. 0. 0) obtained from: http://brainarray. mbni. med. umich. edu. Statistical analysis of differential gene expression was performed with the Limma Bioconductor package. Because the experimental design did not include any technical replicates, a statistical evaluation required selection of two adjacent passages to serve as a replicate pair. Samples from passages 22 and 23 were selected for this purpose as their expression values were most similar based on the principle component analysis, hierarchical clustering, and pairwise expression plots.
To identify genes differentially expressed between EPCR + and EPCR 2 cells, the remaining passages were treated as discrete points in a Limma linear model of the data. Resulting FDR-corrected F-test p-values of 0. 01 and fold changes of 2-fold (log2 ratio = 1) were used to select genes of interest. The moderated F-statistic tests if a given gene is differentially expressed between populations in any of the passages. The selected transcripts were further subjected to hierarchical clustering and subgroups were analyzed for pathway enrichment with Ingenuity Pathway Analysis (IngenuityH Systems, www. ingenuity. com).