dataesr/scientific-paragraphs-categorization huggingface dataset:
Good diagnosis sensitivity (99%) and specificity (98%) rates were observed when testing samples from the field. Analytical sensitivity (LOD PCR ) is very close to that reported by Schroeder et al (<100 copies)  [26]. These authors targeted segment 5 (encoding NS1) when developing a real-time RT-PCR assay to detect all seven serotypes. Seven real-time serotyping RT-PCR assays have also been developed and validated that target VP2 encoding gene S2, which is the primary determinant of virus serotype  [6, 27]. In silico analyses have revealed high variability of up to 30-35% between the segment 2 nucleotide sequences of different strains belonging to the same serotype (e. g. a 32% difference between the EHDV-6 of Reunion Island (Accession number HQ848379) and of Guadeloupe (Accession number HQ848380); a 32% difference between EHDV-7 Australia (Accession number AM745048) and Israel (Accession number HM156731)).
Consequently, several selected primer pairs/probes were added into the same mix to be able to amplify all the different strains of EHDV serotypes 1, 2, 6 and 7 (Table  1 ). All seven serotype-specific assays showed good results in terms of sensitivity and specificity. The high variability of the S2 gene underlines the need to verify for news strains that the primers/probe binding site is suitable. It also shows the need to sequence the RNA segments targeted by these real-time RT-PCRs in order to supply and complete the data bank. It might be necessary to design a new set of primers and probe. To date, the traditional method for identifying EHDV from clinical samples is viral isolation on culture cells or embryonated chicken eggs, followed by a vironeutralisation test (VNT) using reference sera  [28].
In most infected areas, Bluetongue and EHD virus co-circulate, and mixed infections are reported. These reports highlight the need for efficient diagnostic tools to quickly detect and identify the causal agent. The pan-EHDV assay developed in this work was able to detect all the strains and biological samples included in the study, representing all seven EHDV serotypes, without any cross reaction with Bluetongue strains. Moreover, the seven serotyping assays gave good results in terms of analytical/diagnostic specificity and sensitivity, making them reliable tools for serotype identification. In conclusion, the development of such molecular assays for EHDV diagnosis is very useful for disease control. It is crucial to have a better knowledge of the worldwide distribution of this virus and to add viral sequences to the data bank.
Acknowledgments This paper has benefited from discussions in several projects:  O2LA  ( Locally Adapted Organisms and Organisations)  funded by the  French Agency for Research, MICMAC Design  (Modelling for Integrated Crop Management, Assessment and Cropping system design) funded by the  French National Research Agency ,  MAELIA (Multi-agent for Environmental Norms impact Assessment ) funded by the  RTRA Sciences and Technologies for Aviation and Space, Cantogether (Crops and ANimals TOGETHER)  funded by the seventh European framework programme ( Food, Agriculture and Fisheries, Biotechnology ) and  Tata-Box  ( Territorial Agroecological Transition in Action : a tool -Box  for designing and implementing a transition to a.
Reference strains and sequences Five reference strains were used in this work along with those isolated in Southeast Asia. Two of these strains are complete genomes: O. Tsutsugamushi strains Boryong (NC 009488) isolated in Korea  (Cho et al. , 2007)  and O. Tsutsugamushi strain Ikeda (NC 010793) from Japan  (Nakayama et al. , 2010). The three other strains are named Karp (AY956315), Kato (M63382) and Gilliam (DQ485289). Information of the time and place of isolation of the reference strains was collected from  Kelly et al. (2009). All sequences have been deposited in Genbank 8 separated sets corresponding to each gene addressed in the work. Accession numbers for each set of sequences are: KJ742066 to KJ742104, KJ742105 to KJ742143, KJ742144 to KJ742182, KJ742183 to KJ742221, KJ742222 to KJ742260, KJ742261 to KJ742299, KJ742300 to KJ742338 and KJ742339 to KJ742371 for adk, lepb, lipa, lipb, secy, sodb, suca and the 56-kda protein gene, respectively.
MLST analysis Identical DNA sequences at a given locus between strains were arbitrarily coded with the same allele number (i. e. each allele or haplotype was given a unique identifier), following MLST conventions  (Urwin and Maiden, 2003). Each unique allelic profile was assigned a Sequence Type or ST (a unique series of allele or haplotype, identifiers). The reference isolates for sequence type (ST) determination in this work was the Cambodian isolate, S005  (Duong et al. , 2013b). MLST data were used to construct dendrogram by cluster analysis using the unweighted pair group method with arithmetic mean (UPGMA) in the ClonalFrame software package  (Didelot and Falush, 2007). Consensus MLST tree was built from combined data from three independent runs with 75% majority rule consensus for inference relatedness (program was run with 50,000 burn-in followed by 50,000 subsequent iterations).
Genetic and DNA polymorphism analyses Genetic similarity and difference matrices were constructed using Muscle in SeaView 4. 2. 6  (Gouy et al. , 2010). DNA sequence polymorphism, subsequent tests and statistical tests were performed using functions from the DnaSP 5. 10. 01 package  (Librado and Rozas, 2009). The numbers of haplotype (alleles) were analyzed. The DNA polymorphism was investigated by calculation of several measures. Nucleotide diversity, Pi (p), the average number of nucleotide differences per site between two sequences was calculated, using the Jukes and Cantor correction. Theta (Watterson's mutation parameter) was calculated for the whole sequence from S. Eta (g) is the total number of mutations, and S is the number of polymorphic sites. K a (the number of non-synonymous substitutions per non-synonymous site), and K s (the number of synonymous substitutions per synonymous site) for any pair of sequences were calculated.
Identification and analyze of sequence types (ST) A total of 44 strains of O. tsutsugamushi (13 from Cambodia, 7 from Vietnam, 19 from Thailand and 5 reference isolates from Genbank) were identified (Table  1 ). Each locus was characterized by a Among the 44 strains studied, each display a unique profile ST (Fig. 1 ) and no clonal complex could be identified by e-burst analysis (data not shown). The degree of recombination was determined using the index of association (IA) measuring the level of linkage between alleles at different loci. IA value determined for O. tsutsugamushi MLST strains was 0. 1199 (p < 0. 001) suggesting a clear clonal population even if no clonal complex are detected.
Acknowledgements This study was supported in part by a grant from the  Institut Pasteur, Paris, France  ( Actions Concertées Inter-Pasteuriennes). Roger Frutos  was supported in part by the  Franco-Thai Hubert Curien  project  PHC 20624VK  and by the project  ANR Biodiv CERo-Path  ( ANR 07 BDIV 012 ). Patimaporn Wongprompitak  was supported by  ERASMUS MUNDUS Action 2 scholarship  through the  MAHEVA  project. The authors certify that there is no conflict of interest of any kind associated to this work. There is no source of funding which could potentially affect the outcome and sincerity of the work. There is no personal or institutional conflict with any other institution or bodies which could affect the results and conclusions presented in this article.
During recent herbarium research on the Malagasy species of Gesneriaceae, a hitherto undetected mixed collection of the type of Streptocarpus betsiliensis Humbert (Perrier de la Bâthie 12482), was observed at the Paris herbarium (P). Of the three sheets present, one, the holotype [P00088746], comprised only Streptocarpus betsiliensis specimens, while the two isotypes, [P00088747, P00088748], were found to comprise a mixture of this species and a closely related and easily confused species, Streptocarpus lanatus MacMaster. As a result of this, the specimens on the mixed sheets were physically separated and the collections were distinguished by the suffixes A and B. They were then remounted to form speciesspecific sheets, rescanned, and the databases at P updated accordingly.
Acknowledgements We are very grateful to the staff of the  herbarium in Paris (P)  for facilitating access to the collections,  Elodie Lerat  for her help with scanning and  Pete Lowry  for providing the photo of Streptocarpus ibityensis. MM would like to thank all those involved in logistical and field support in the early research work in Madagascar, particularly  Albert Randrianjafy  (former Director of  Parc Botanique et Zoologique de Tzimbazaza, PBZT ),  Solo Rapanarivo ,  Guy Rafamatanantsoa ,  Jacky Andriantiana  (all PBZT), and  Lalao Andriamahefarivo  ( Missouri Botanical Garden ). KN would like to acknowledge support from the  Royal Botanic Garden Edinburgh  and the  Sibbald Trust. KN is partly supported by  JSPS KAKENHI  (Grant no  15K18593 ). The  Royal Botanic Garden Edinburgh  is supported by the  Rural and Environment Science and Analytical Services division (RESAS)  in the  Scottish Government.
Introduction A local reduction of genetic variation up-and downstream of the beneficial mutation is caused by the rapid fixation of a beneficial mutation, leaving special patterns of DNA behind, which is commonly referred to as a "selective sweep"  [1]. The study of such signatures of selection can production traits (in the 20th century)  [22]  fosters the interest in detecting selective sweeps in chicken using statistical methods that account for the strong hierarchical structure between these populations. Therefore, this dataset offers an interesting opportunity to evaluate methods that account for population structure in a setting characterized by a strong past selection pressure, high genetic drift and clear population structure, which has never been done before. In this study, FLK  [12]  and hapFLK  [15]  statistics were applied on the same data as in our previous study on selection signatures in commercial chicken  [20] , allowing a comparison between F ST , FLK and hapFLK.
Animals, Data collection and filtering Two sets of samples-commercial egg layers and wild chicken (coded respectively LAY and ANC)-were used in this study. The commercial individuals from Lohmann Tierzucht GmbH originated from three different breeds. One commercial white egg layer breed based on White Leghorn (WL), with three separate lines, and the other two brown egg layer breeds based on White Rock (WR) and Rhode Island Red (RIR), respectively, each with two separate lines per breed. In each of these seven lines, ten individuals were sampled and genotyped. The wild chickens, comprising Red Jungle fowl (Cochin-Chinese) (G. g. gallus) and Red Jungle fowl (Burmese) (G. g. spadiceus) were sampled within the AVIANDIV project. A more detailed list of breeds is presented in Table  1. The ANC group consisted of two subspecies of Gallus gallus that are believed to stem in straight line from wild ancestors of domestic chickens.
Data is publicly available (S1 Dataset). Genotyping was done with three Affymetrix 600K SNP arrays. Overlapping SNPs between the three 600K SNP arrays were removed by the data provider and a total of 1,139,073 SNPs remained. For this study we included only the SNPs that were located on autosomal chromosomes , SNPs that were located on sex chromosomes and linkage groups were removed (62,337 were removed). SNPs with at least one missing value and SNPs with minor allele frequencies lower than 5% (172,344 SNPs) were removed in order to avoid dealing with genotyping errors; this approach was suggested by the data provider. A total of 904,392 SNPs remained after filtering. The entire filtering process was done using the PLINK software (http://pngu. mgh. harvard. edu/purcell/plink/) [23].
Annotation As explained above, regions with extreme FLK and hapFLK values were considered as candidates for selective sweeps. For all the three groups (all layers, white layers and brown layers) the extreme values (the upper and lower 0. 05%) that were within 500 kb of each other were grouped together. For all joined groups gene annotations, QTL annotations and pathway annotations were completed. Gene annotations were done with the biomaRt R package  [28]  based on the Ensembl database  [29]  of Gallus_gallus-4. 0 assembly. Animal QTL database  [30]  was used for QTL annotation, KEGG database for pathway annotation  [31]  and Gene Ontology (GO) database for GO annotation  [32]. Gene enrichment analysis was done with Fisher's exact test  [33]  for all annotated genes in all groups (all layers, white layers and brown layers) separately.
S14 Table. Lists of pathways and gene ontologies under selection with hapFLK with 0. 05% threshold in brown layers. (PDF) S15 Table. Regions detected as putative selective sweeps detected with F ST with upper (U) and lower (L) 1% threshold. LG stands for studies between commercial-layers and non-commercial chickens and BW stands for studies between brown and white layers, respectively. (PDF) S1 Dataset. Compress file of genotyped data in plink format. (RAR).
Acknowledgments This research was funded by the  German Federal Ministry of Education and Research  within the AgroClustEr "Synbreed-Synergistic plant and animal breeding" (Funding ID:  0315528C ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Dr. Preisinger  is the head of Genetic department of LOH-MANN Tierzucht GmbH. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials. We acknowledge support by the  German Research Foundation  and the  Open Access Publication Funds of the Göttingen University.
Introduction Whilst the roots of pest risk modelling extend back to early in the 20 th Century  [1] , modern computer-based pest risk modelling has only been practised for some 30 years  [2, 3]. In that time, there has been a progressive refinement of the spatial distributions of the modelled risks. In the earliest maps, risks were portrayed wherever climate stations were situated  [2]. Following the development of climatic splining techniques  [4] , spatially interpolated results were presented e. g. ,  [5, 6]. Increased computing power, and a thirst for more detailed risk maps saw the development of finer-scaled gridded climate datasets  [7, 8, 9] , and their application to pest risk modelling problems e. g. ,  [10, 11, 12]. Under the International Standards for Phytosanitary Measures (ISPM's), Pest Risk Assessments (PRAs) need to identify the endangered area, "an area where ecological factors favour the establishment of a pest whose presence in the area will result in economically important loss"  [13].
Whilst the standards define the area as ". an officially defined country, part of a country, or all or part of several countries", the Decision-support scheme for quarantine pests of the European and Mediterranean Plant Protection Organisation  [14]  encourages the risk assessor to define the endangered area at a very fine ecological and geographical scale. In order to achieve this, it is not sufficient to use even finer resolution climate datasets. Ecological theory indicates that we need to consider the effects of non-climatic factors as we investigate species niches at finer geographical scales  [15]. Considering the non-climatic factors affecting a species potential distribution can be a challenging prospect. Many factors could affect the potential habitat suitability for a species, and the importance and effect of these factors may often, themselves, depend on climatic factors  [1, 16, 17].
leaf damage, leading to complete senescence and lateral floret development ceased". Using -7. 5°C as a damaging cold stress threshold (TTCS), the stress accumulation rate of -0. 01 week-1 fitted all bar two of the coldest locality records in the northern hemisphere. The outlying records in the Himalayas are found in a region of extremely dissected topography, and the altitude and temperature are so extremely different to the next closest location records that this is likely to be a case of mismatch in either geocoding precision or the climate data. In Argentina, a number of location records for P. hysterophorus in the GBIF database referred to locations that were apparently too cold or too dry for persistence, and for the dry records, did not appear to fall in irrigation areas defined in the irrigation areas database of Siebert et al.
[41]. Searching Google Earth using the locality description of these records revealed that they were incorrectly geocoded, and actually referred to wetter locations found at lower elevations. Dry stress. In the CLIMEX framework, dry stress may not be a factor that affects annual plants directly, because these plants may be able to survive extended periods of drought in the seed life stage. In this case, Dry Stress (in concert with the GIA) acts in such a manner as to ensure that there is a sufficient period within which the soil moisture is sufficient to complete the life cycle. The dry stress accumulation rate was increased to make the westernmost record in Queensland, Australia barely climatically unsuitable. This had the consequence of making some of the records in Pakistan and Western Argentina unsuitable in the absence of irrigation, which was practised there according to the GMIA database of Portmann et al.
[51]. In a small number of cases, location records in Argentina  (17) , Australia (1), India (1) and Pakistan (2) fell in areas that, according to the climate database were extremely xeric and which were not associated with widespread crop irrigation, at least as portrayed in the global irrigated area database we used (see Composite Risk Mapping below). Examining these locations in Google Earth revealed that these records were not able to be related logically to a long-term climatology. The Argentinian records fell in towns or roadsides where there was irrigation or a concentration of rainfall respectively within areas that were extremely sparsely vegetated. The Australian record was within a braided river channel that floods very infrequently due to rain mostly falling further up the catchment.
Climate data The model was fitted initially using the 30' CliMond CM30_1975H_WO_V1. 1 dataset, and subsequently refined with the CM10_1975H_WO_V1. 1  [9]. The CliMond 10' results for 2070 of the A2 SRES climate change scenario run on the CSIRO Mk 3 GCM (CM10_2070_CS_A2_ WO_V1. 1) was chosen because it represented a reasonably extreme scenario that would highlight the sensitivity of the invasion potential for P. hysterophorus.
Irrigation An irrigation scenario of 2. 5 mm day -1 was applied as a top-up to natural rainfall. Under this scenario, in any week in which average daily precipitation did not meet this threshold, the difference was assumed to be added to the rainfall inputs to the soil moisture model. Actual irrigation rates depend on a variety of factors, including the crops, their stage of growth and climatic factors such as wind flux, temperature, and humidity. The selected rate accords with indicative low-end rates  [54]. The irrigation scenario was run on the global CM10_1975H_WO_V1. 1 dataset.
Habitat factors We compared two methods for identifying habitat types that are suitable for invasion by P. hysterophorus. The first, loosely termed an expert assessment, reflects the current standard practice within the EPPO pest risk assessment framework, while the second is an objective inferential method. In the expert assessment, the habitat types listed in the CORINE database  [55]  were considered by the EPPO Expert Working Group while performing the PRA for P. hysterophorus, and classified as either suitable or unsuitable for P. hysterophorus based upon consideration of the habitat types where it has been reported in the literature, and where the panel members had observed it in the field. The CORINE database was selected because it is preferred by the EPPO due to its fine spatial resolution.
Notably, the spatial coverage of the CORINE database is limited to Europe. The assessors used a consensus method to decide on suitable land use factors, drawing upon published descriptions and personal observations of P. hysterophorus occupying different habitat types. In the inferential method, the distribution points in Fig  2  were spatially intersected with a global habitat dataset; habitat types with one or more point records were listed. This list was used to identify the subset of habitat types in Europe that was considered suitable. Because the geographical coverage of the CORINE database is limited to Europe, the FAO Land Use Systems of the World version 1. 1 [www. fao. org/nr/lada/] was used to identify suitable habitat types. This database has a moderately coarse spatial resolution (5 arc minutes) which is equivalent to a map scale of approximately 1:10 000 000.
This is coarser than the CORINE database, which summarises the spatial data at a scale of 1:100 000 (equivalent to a raster resolution of approximately 50 m). The attraction of the FAO dataset is that it has a global coverage, enabling risks to be projected globally. For both the CORINE and FAO datasets, the suitable habitat classes were spatially intersected with the CLIMEX model of climate suitability to create composite climate and land use/ habitat risk maps and statistics.
The habitat classes considered at greatest risk (by area) are disturbed (urban, cropping and pastures). Perhaps also of cultural and economic significance is the threat to olive groves (100% of the plantations are at risk), vineyards (90%) and fruit and berry plantations (77%) may be threatened. Under the inferential FAO habitat model 29 land use classes were identified as being at risk in Europe, including cropping and pasture areas (Table  3 , Fig  6 ). However, grazed forests and shrublands were also identified as being at risk (Table  3 ). The total area of suitable habitat in Europe modelled as at risk using the FAO dataset and the inferred habitat suitability classes was 1. 6 million ha, nearly twice that from the CORINE dataset based on the expert opinion. The global risk patterns based on the inferential FAO model are similar to those for the expert-based system applied to Europe (Table  4 , Fig  7B ).
Habitat factors Irrigation has an important effect on extending the range of P. hysterophorus, particularly in Saharan Africa, the Middle East and Central Australia. Conversely, within Europe, restricting    4. A real summary of composite global invasion risk from Parthenium hysterophorus by land use system class according to the FAO Land Use Systems of the World database, considering climate with irrigation scenarios applied according to the GMIAV5 database  [41]. Habitat classes are listed in descending order of area at risk under the current climate scenario. the endangered area by using habitat types refines the area at risk considerably within the climatic range. These analytical elements could aid in refining economic impact analyses, and also perhaps in informing surveillance and rapid responses to incursion detections.
The spatial analysis of the distribution data for P. hysterophorus using the FAO dataset was revealing; expanding the range of habitat types beyond those identified by the expert assessment process. The association between the open water and wetland land use classes and P. hysterophorus was surprising given that P. hysterophorus does not grow in waterlogged situations. However, P. hysterophorus does grow on floodplains  [56] , so it is likely that the location records   fall within riparian zones within the coarse open water and wetland land use classes. Similarly, during the expert deliberations, forested areas were discounted as suitable habitat on the grounds that P. hysterophorus reportedly grows poorly under shaded conditions, and would therefore be unable to persist. The FAO dataset comparison underscores the fact that forests (particularly those that are actively managed) are frequently a mosaic of different seral stages, and that ruderals such as P.