dataesr/scientific-paragraphs-categorization huggingface dataset:
We did not find any sequences in the databank 100% identical to that of group E. This group was new and identified in ticks as well as in Tamias sibiricus collected in the Senart forest, and also in ticks collected in Normandy, another region of North-west France. This strain was introduced in the databank under the number HG529222. 1. γ = β> α γ = β > α γ > β > α γ = β > α α > β = γ α = β > γ.
(Fig 9). Several cases of co-infections were detected, the more frequent implying B. afzelii and B. burgdorferi ss on one hand and B. garinii and B. valaisiana, on the other hand. To further evaluate the polymorphism, the spacer regions of the strains whose Mse1restriction profiles somewhat differ from that of reference strains were sequenced and compared with sequences available in databases. The phylogenetic analyses of rrf-rrl spacer sequences were conducted by neighbour joining. Sequence analysis of B. afzelii identified five subtypes/groups consistent with the existence of various restriction patterns, in 2008, 2009 and 2011 (Fig 9, Table.
Acknowledgments We thank  Office National des Forêts  for granting us free access to the Senart,  Notre-Dame  and Rambouillet forests. We would like to thank  Sylvain Ducroux  and  Leo Castex (ONF)  for the loan of the development data on the Senart forest. We thank  Dr J. L. Chapuis  ( MNHN, Paris, France ) for his help for tick and chipmunk collection in 2008 and 2009.
, 2008a). A Bayesian coalescent framework by a Markov Chain Monte Carlo (MCMC) method was applied to the aligned coreCP dataset using the Bayesian Evolutionary Analysis by Sampling Trees (BEAST v1. 8. 3) program (http://beast. bio. ed. ac. uk/)  (Drummond et al. , 2012) , with an uncorrelated relaxed clock model. Potyviruses were defined as a monophyletic clade with a node calibration at 6600 yrs. The convergence of the parameters to a stationary distribution and the estimation of the time to the most recent common ancestor (TMCRA) for the PRSV and BCMV clusters were assessed with TRACER v1. 6.
Host range of Su03-07 Su03-07 induced a rapid top necrosis on the melon cultivar 'Védrantais', a mosaic and vein banding in cv. 'Ouzbèque', and local lesions and no systemic symptoms in 'WMR29', a PRSV resistant accession (Table  1 ). These symptoms were similar to those reported for MWMV  (Quiot-Douine et al. , 1990 , Lecoq et al. , 2001). Su03-07 induced vein banding in watermelon, cv 'Sugar Baby' and cucumber, cv 'Beit Alpha', and systemic chlorotic spots in Zucchini Squash, cv 'Diamant'. Among non-cucurbit plants, Su03-07 induced chlorotic local lesions without systemic infection on Chenopodium amaranticolor and C. quinoa. No symptoms were observed and no virus was detected by RT-PCR after inoculation of Nicotiana benthamiana, tobacco cv. Xanthi, tomato, pepper, petunia x hybrida, bean and pea (data not shown).
Complete sequences of Su94-54 and Su03-07 The full-length sequences of isolates Su94-54 and Su03-07 were determined to be 10,232 nt and 10,152 nt, respectively, excluding the poly(A) tails (Genbank accession numbers KY623505 and KY623506, respectively). For Su94-54, 5,824,335 HiSeq reads of 21-24 nt mapped to the contig obtained with CLC, with an average coverage of 12,191. Both Su03-07 and Su94-54 had a typical potyvirus genome organization, with a large ORF encoding a 3281 aa and 3295 aa polyprotein, respectively, with a putative frameshift in the P3 coding region yielding a P3N-PIPO protein  (Chung et al. , 2008). The putative cleavage sites in the polyprotein were similar to those of other potyviruses  (Adams et al. , 2005a). The deduced amino-acid sequence of Su03-07 and Su94-54 contained the three conserved motifs involved in aphid transmission of potyviruses  (Pirone and Blanc, 1996) ; "DAG" was present in the N-terminal part of the CP and "PTK" or "PTR" in the HC-Pro protein.
1a ). Indeed, the 5' untranslated region and beginning of P1 coding region of Su94-54 were more similar in length and sequence to that of Su03-07 than to MWMV (Fig. 1b ). The recombination analysis done using the MWMV, Su03-07, and Su94-54 sequences revealed a recombination junction in Su94-54 around position 1080 within the P1 coding region by six of the eight methods used in the RDP4 analysis. There was no evidence for other recombination signals in the dataset. Pairwise comparisons of the full-length nt sequences of Su94-54 and the 24 viruses examined with in the PSRV cluster (see section 2. 4) yielded identities of either less than 70. 2% Comment citer ce document : Desbiez, C. (Auteur de correspondance), Wipf-Scheibel, C. , Millot, P. , Verdin, E. , Dafalla, G. ,  Lecoq, H. (2017). New species in the papaya ringspot virus cluster: insights into the evolution of the PRSV lineage.
Prevalence and molecular variability of PRSV-like viruses in Sudan Two hundred and six virus isolates collected in Sudan between 1994 and 2012 were tested for the presence of WMVBV. Two of these isolates tested positive; Su99-18, a snake melon sample collected in 1999 in Abou Frou about 50 km North from Tayba Block where Su03-07 was collected, and Su12-21, a watermelon sample with strong mosaic and vein banding symptoms collected in 2012 in Wad Abutifa, about 150 km South-East from Tayba Block. Partial sequencing (Genbank accessions KY623546 for Su99-18 and KY623547 for Su12-21) revealed that these isolates shared 92. 3% and 96. 8% nt identity respectively with Su03-07 in the Nib-CP-3NC region. This shows they belong to the same tentative species and that the virus was already present in Sudan at least 18 years ago.
| Data collection and preparation The literature on Fringillidae (153 references, Appendix S1) was used to collect the necessary natural history data (Appendix S2). Fifty-three variables were documented (Appendix S3). For each quantitative variable (measurements, count data) and each species, we used the weighted arithmetic mean value across all available data. Qualitative data (coded as "Yes" or "Yes minus" in Appendix S2) were coded as 1 for the presence of the character, 0 for its absence, 0. 5 for its partial presence). When the same character exhibited different trait modalities (e. g. , foraging height), data were scored according to an arbitrary scale (e. g. , foraging height was scored 1 for ground, 2 for shrubs, 3 for trees). The variance of these numerical values (weighted by their occurrence in literature) was complemented to 1 and was used as synthetic index of specialization (e.
g. , foraging height specialization in Appendix S3). Altitudinal specialization of a given species was measured by dividing its altitudinal range (maximum minus minimum altitude) by the maximum altitude found in our dataset (i. e. , 4,950 m) and complementing to 1 this ratio. The same method based on weighted occurrence was used to measure the intra-specific variability of a behavioral trait when it exhibited different trait modalities (e. g. , breeding dispersion and migration). Missing data (20 ± 23% of total records, Appendix S3) were interpolated according to a nearest-neighbor method  (Holmes & Adams, 2002)  prior to Principal Component Analysis (PCA).
, 2011) , Carpodacus sensu lato  (Tietze, Päckert, Martens, Lehmann, & Sun, 2013) , Haemorhous  (Smith, Bryson, Chua, Africa, & Klicka, 2013) , and Spinus  (Beckman & Witt, 2015). We evaluated the representativeness of the family diversity in our phylogeny by a qualitative comparison against a supermatrix tree. Using the dataset used by  Zuccon et al. (2012)  as starting point, we assembled a supermatrix for three nuclear introns and five mitochondrial genes by comparison with the datasets of other published phylogenies and searching in Genbank for any additional finch sequence (Appendix S4). The supermatrix tree was estimated by maximum likelihood using RAxML version 7. 0. 3 (Stamatakis, 2006) , applying a gene partition, a GTR+Γ+I model, and random starting tree, with α-shape parameters, GTR-rates, and empirical base frequencies estimated and optimized for each partition.
| Phylogenetic principal components analysis (pPCA) The four syndromes (the first principal components of the four original PCAs) were used as continuous variables in a pPCA  (Jombart, Pavoine, Devillard, & Pontier, 2010) , allowing to discern a phylogenetic signal common to all four syndromes. Phylogenetic signal (autocorrelation) was tested on each synthetic variable by Abouheif's test  (Abouheif, 1999; Pavoine, Ollier, Pontier, & Chessel, 2008). These methods were performed using the adephylo package of R (R Core Team, 2016). The reconstruction of ancestral states of the four syndromes suggested that strategy B should be subdivided in two groups. Thereby the first three components of pPCA were used to split the species in three strategies A, B1, and B2 by k-means clustering.
| Association of syndromes with tropical affinity The tropical affinity of species was determined according to geographical records (Appendix S2). Species were considered as having a tropical affinity when more than half of their distribution area was located in the tropics. To test the association of each syndrome with tropical affinity while correcting for phylogenetic autocorrelation, a phylogenetic ANOVA was performed on the variables describing the four syndromes (first principal component of PCA, see section 3), using tropical affinity as a fixed, two-level factor, with the function aov. phylo implemented in the R package geiger  (Harmon, Weir, Brock, Glor, & Challenger, 2008).
| Phylogeny and representativeness of species diversity As expected, the topology of the time-calibrated phylogeny is congruent with that obtained by  Zuccon et al. (2012). Also the topology of the tree generated using the supermatrix, which includes 169 of 204 (82%) species, is largely congruent and recovers the same major clades. Minor differences involve a few nodes with low or no support and the branching order of a few clades. The comparison of the timecalibrated and supermatrix-based trees confirms that the 81 species in the time-calibrated tree have been sampled across the entire family, that all major lineages are represented in the reduced dataset and that the species retained in the subsequent analysis are a fair representation of the family diversity and disparity. This result is a logical consequence of the taxa choice operated by  Zuccon et al.
, 2016;  maps can be browsed in Map of Life, www. mol. org). For each species, range size was calculated (Appendix S1). We then used up-scaled distribution maps to a 100 km grain to calculate the median range size of the species co-occurring in each pixel (i. e. , a 2-D version of "Steven's method";  Gaston et al. , 1998; Sizling et al. , 2009). To provide comparable data with published studies  (Hawkins & Diniz-Filho, 2006; Morueta-Holme et al. , 2013)  we also calculated average range sizes after log-transforming the data.
Environmental predictors The calculation of the climate change velocity (CCV) followed  Sandel et al. (2011)  and  Loarie et al. (2009) , who described CCV as the temporal gradient of temperature change divided by its spatial gradient, resulting in a unit of distance per time. We used mean annual temperatures for current conditions  (averages 1950-2000) , mid-Holocene (6'000 years before present) and Last Glacial Maximum (LGM; 21'000 years before present). LGM data were derived from two coupled Atmosphere-Ocean General Circulation Models (AOGCM), CCSM4 and MIROC 3. 2 (averaged values). Present and LGM data were available from WorldClim (www. worldclim. org; Hijmans et al. , 2005; accessed Aug. 2013 ) at a resolution of 2. 5 arcmin (~5 km). Mid-Holocene data was downloaded from PMIP2 (https://pmip2.
lsce. ipsl. fr/; Braconnot et al. , 2007; accessed Aug. 2013). The PMIP2 data has a lower resolution (2. 5 degree), which misses more localized climatic effects caused, e. g. , by topography. To obtain data at the same resolution as for WorldClim data (2. 5 arcmin), we interpolated the raster. The same interpolation was applied to the PMIP2 current temperature data. We then calculated the difference in current temperature between WorldClim and PMIP2 data and added it to the mid-Holocene data from PMIP2 database to account for elevation effects (and other time-stable anomalies). This method (A. Wilson, pers. comm. ) follows the assumptions that (a) local adiabatic effects on temperatures have not changed much since the mid-Holocene, and (b) that topographies have remained stable. To verify this procedure we repeated all steps to calculate a map for the LGM with data from PMIP2 database and obtained a high correlation with WorldClim data (Pearson's r = 0.
All further GIS manipulations and analyses were carried out in Mollweide World equal area projection at 100 km grain size. Climate data, originally processed at 2. 5 arcmin, were aggregated and projected to this grid. Pleistocene ice extent  (Ehlers et al. , 2011)  was coded as one (ice) and zero (no ice). Elevation range was calculated from a digital elevation model  (Stein et al. , 2015). Furthermore, we used a broad classification of zoogeographic realms from  Holt et al. (2013). We restricted the study region in various ways to reduce unwanted variability and bias. First, all smaller islands were excluded to avoid effects of dispersal limitation of island endemics on range data (as these will not contribute to our understanding of the general drivers of range size). Exceptions were made for the British Isles, Sumatra, Borneo, Madagascar and New Guinea, because they are either large enough to develop range size variability within the island, or were connected to continental regions in the relevant past (i.
Because our dataset contained spatially structured data, ordinary least square (OLS) models are most likely to be biased in significance assessments and possibly also in coefficient estimates  (Bini et al. , 2009). Therefore, we also present results from a spatial simultaneous autoregressive error (SAR) model (function errorsarlm in R-package spdep; neighbourhood distance of 5000 km, based on preliminary trials; residual autocorrelation remained high only over very short distances of <200 km (concluded from correlograms of residuals), which we considered acceptable at our data resolution and extent). By comparing results for OLS and SAR the reader can appreciate the potential effects of spatial structure in our data. Zoogeographic regions differ in their evolutionary history, but it is unclear to what extent this may affect range sizes (e.
Models Eleven models participated in this study; eight crop models and three biogeochemical models (Table1). All models provide data on a daily time step (except CENTURY which uses a monthly time step), consider the complete range of management practices (except AgroC that does not consider nitrogen limitation) and provide simulations for the two considered crops, wheat and maize (except COUP that only simulates wheat). The growing season for the crop models is determined by internal phenological models based on a fixed sowing date, while the three biogeochemical models CENTURY, DailyDayCent and LandscapeDNDC and the crop model STICS also used a fixed harvest date (i. e. fixed length of the growing season). Five of the models determine NPP based on the radiation use efficiency concept (AgroC, APSIM, APSIMmod.
We screened GPS data for positional outliers based on unlikely movement characteristics  72. In addition, we marked the 149 seronegative ibex released using ear tags and/or collars so that individuals could be identified from several hundred meters using binoculars or a telescope. During the snow-free seasons (May-September) of the 2013-2015 period, we then recorded the composition (sex/age classes) of all the ibex groups observed, identity of marked individuals and reported group locations on a 100 × 100 m grid. We recorded most of these observations during the censuses we performed once a month between May and August, during which 1-2 observers travelled on 1 of the 9 tracks in the morning of a single day or 2 consecutive days (to avoid double counting). Using these mark-resight data and an immigration-emigration logit-normal model  73   All the ibex captured have been treated by professionals from the Office National de la Chasse et de la Faune Sauvage according to the ethical conditions detailed in the specific accreditations delivered by the Préfecture de Paris (prefectural decree 2009-014), by the French Minister of Ecology, Sustainable Development and Energy (Ministerial Orders of February 11, 2014) and by the Préfecture de la Haute-Savoie (prefectural decree 2015062-0018) in accordance with the French environmental code (Art.
Acknowledgements We warmly thank all the professionals from the  Office National de la Chasse et de la Faune Sauvage  ( SD74 ,  USF, UFM, Amélie Vaniscotte, Elodie Petit ), the  Laboratoire Départemental d' Analyses Vétérinaires de la Savoie , the  Direction Départementale de la Protection des Populations de la Haute-Savoie , the  Fédération Départementale des Chasseurs de la Haute-Savoie  and  Asters-Conservatoire  d' espaces naturels for their technical support in capturing ibex, analyzing samples and monitoring GPS-collared individuals and population abundance. We gratefully acknowledge  Denise Mirat  for correcting the grammar and two anonymous reviewers for their very helpful comments on previous drafts of this paper.
Statistical Analysis of EAG Data The comparison of the antennal responses elicited by the treatments was carried out using a linear mixed model. The treatments, the sex, and the treatment × sex interaction were considered as fixed effects. The order of the treatment within each antenna was added as a fixed effect quantitative covariate, to take into account a possible change in antenna response to stimulus with time. The sex × succession order was nonsignificant and therefore discarded from the model. Antennae were considered as a random replication effect. The analysis was carried out using SAS procedure MIXED (SAS Institute 2008). The comparison was carried out by computing the differences of least squared mean, and the significance of differences was tested using a t-test, using the procedure MIXED (with the LSMEANS instruction).
Acknowledgments We dedicate this article in to the memory of  Serge Quilici  who initiated the project. We would like to thank  Cedric Lallemand  for technical help in the field, the farmers  Patrick Bernard ,  Stephane Benard ,  Maximin Techer ,  Henri Yu-Sin , and  Isabelle Law-Yat  for giving us access of their coffee field. We also thank  Frederic Chiroleu  for helping us in statistical analysis and  Antoine Franck  for providing us with very high quality pictures. We are grateful to  Andrew Hobson  for proof reading the English version correction of this article. The authors thank the  Plant Protection Platform  ( 3P , IBISA) where EAG and extraction experiments were carried out. We are grateful to the  Institut de Recherche pour le Développement (IRD)  for giving us access of the International Coffee collection.
Acknowledgments: We thank  Olivier Panaud  ( University of Perpignan, France ) and two anonymous reviewers for helpful comments and our colleagues at the  Institute of Research for Development (IRD) and Laboratory of Plant Genome and Development (LGDP)  for stimulating discussions. S. L. is supported by an  ANR  fellowship ( French National Agency for Research). This work was supported by the  IRD , the  FAiD  (Fédération d'aide pour le développement, http://faid. univ-perp. fr/), and the  French National Agency for Research  ( ANR-13-JSV6-0002 ).
Inbred Lines We tested 86 pearl millet [P. glaucum (L) R. Br. ] inbred lines originating from Niger, Togo, Senegal, Zimbabwe, and Cameroon which were used in a previous genetic association study  (Saïdou et al. , 2009). These lines were obtained from the International Crop Research Institute for Semi-Arid Tropics (ICRISAT), the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) and the University of Orsay (Paris XI). The greenhouse phenotyping experiment was carried out in 7 successive blocks of 12 to 14 lines each, with five repeats per line. We indicated the block in which each line was phenotyped in supplementary data (Table  S1 ). To detect variability among phenotyping series, two pearl millet lines (L82 and L114) were repeatedly phenotyped in three different blocks as controls.
Bacterial Diversity Analysis Sequencing of gene encoding 16S rRNA from DNA extracts was performed at MR DNA (Shallowater, TX, USA) with an Ion Torrent PGM system for sequencing (Life Technologies Corp. , Thermo Fischer Scientific, Massachusetts, USA). Samples were barcoded and the V4 variable region of 16S rRNA gene was targeted with 515F/806R primers set (GTGCCAGCMGCCGCGGTAA-GGACTACHVGGGTWTCTAAT)  (Caporaso et al. , 2011). PCR amplification was carried out using the HotStar Taq plus Master Mix Kit (Qiagen, Germantown, MD, USA). The conditions were as follows: 94 • C for 3 min, 28 cycles of 94 • C for 30 s, 53 • C for 40 s, and 72 • C for 1 min. After this, a final elongation step of 72 • C for 5 min was performed. Sequences data analyses were conducted using QIIME software version 1. 8 with standard parameters  (Caporaso et al.
, 2010b). Overall sequences with quality score average <25, short sequences (<200 bp) and sequences containing mismatches as well as homopolymers were removed from the analysis. We conducted a denoising of sequences and a chimera check and filtered mitochondria and chloroplasts sequences using an in-house PHP script (from CEA Cadarache, France). Thus, the multiplex reads were assigned to corresponding sample according to their barcode, and using the Usearch scripts we carried out Operational Taxonomic Unit (OTU) picking by de novo checking method  (Edgar, 2010). Representative sequences of the OTUs were aligned using the PyNAST algorithm with a minimum percent of 80%  (Caporaso et al. , 2010a). These OTUs were taxonomically classified using RDP method and Greengenes database  (DeSantis et al.
, 2006). The phylogenetic position of bacteria in the different rhizosphere lines was characterized using taxa summary QIIME script until genus level (L6). To compare bacterial OTU richness and diversity index in the rhizosphere between inbred lines with these different numbers of sequences, we performed a rarefaction analysis i. e. , a random picking of an equal sequence number in each sample (40,870 reads per sample) by using the opensource bioinformatics pipeline QIIME  (Caporaso et al. , 2010b). α-diversity parameters [number of observed OTUs, qualitative Chao1 index, Faith's Phylogenetic Diversity (PD_whole), and Shannon index] were then calculated on 40,870 randomly picked sequences for each sample. Sequence data are available in NCBI SRA bank under accession number SRR5482172, experiment SRX27088 74.
We pricked out and purified these EPS-producing strains on TSA/10 and then subcultured them in diluted Tryptic Soy Broth medium (TSB/10) during 24-48 h before storage in glycerol at -80 • C. For identification of bacterial strains, we carried out PCR directly on colonies to amplify the gene encoding 16S rRNA using Fd1/Rd1 primers  (Weisburg et al. , 1991) , and sequenced the amplicons at Cogenics, Inc. (Morrisville NC, USA). Then sequences were aligned and 48 consensus sequences were defined and used for an NCBI blast in RDP II database to perform taxonomic assignment. To determine the abundance of these EPS-producing bacterial species (or closely related species) in the rhizosphere of the 9 pearl millet inbred lines, we used the BLAST software (version 2. 3. 0+) from NCBI and we performed a blast data search with the OTU sequences derived from PGM sequencing.