dataesr/scientific-paragraphs-categorization huggingface dataset:
Evolution of virological assays. The evolution of laboratory assays used for VT detection in databases #2 and #3 was visualized by plotting the yearly proportion of each assay category (i. e. , animal, cellular, immunological and molecular) over time. To weight each category according to the sample size, the yearly proportion of each assay category was calculated relative to the total number of individual mosquitoes tested for VT (i. e. , the relative likelihood of each category over time). Smoothed curves were obtained by kernel density estimation using a Gaussian kernel and the bandwidth selection method proposed by  Sheather and Jones (1991). The association between assay category and VT detection probability was further explored with a subset of databases #2 and #3. The subset consisted of the Aedes-Flavivirus pair, which is the most frequent vector-virus association in the databases (49.
7% and 31. 8% of entries in databases #2 and #3, respectively). Analysis of the relationship between assay category and VT detection probability was restricted to the Aedes-Flavivirus pair to rule out the potential confounding effect of a differential VT probability among arbovirus families. Indeed, some arbovirus families may be vertically transmitted more efficiently than others  (Turell, 1988). If VT of arboviruses with higher VT efficiency were predominantly examined using one particular virological assay, then a spurious association would be detected between this assay and higher VT detection probability. In experimental studies, only three assay categories were compared for the Aedes-Flavivirus pair because the molecular assay category was not represented in database #2. VT occurrence was defined as the proportion of database entries that found evidence of VT.
Summary description of databases The primary database (database #1) includes 257 articles related to arbovirus VT published between 1899 and 2013 (the full list of publications is provided in Supplementary Table  1 ). Two additional databases were derived from database #1 that were restricted to 'experimental' and 'natural' types of studies (database #2 and #3, respectively) and had more stringent inclusion criteria (see material and methods). Database #2 includes a total of 1,119 entries from 94 distinct publications that examined VT in an experimental setting. Overall, VT occurrence was 60. 2% in database #2 (i. e. , evidence of VT was found in 60. 2% of entries). Virological assays consisted of 58. 5% immunological, 23. 6% cellular, 16. 8% animal and 1. 1% molecular assays. The most frequent vector-virus pairs in database #2 were Aedes-Flavivirus, Culex-Flavivirus, Aedes-Orthobunyavirus and Aedes-Alphavirus that are represented by 49.
7%, 22. 6% 18. 9% and 5. 1% of entries, respectively. The remaining 3. 7% consist of 11 less studied vector-virus pairs. The median VT prevalence, estimated as the number of positive pools over the total number of mosquitoes tested, was 0. 15%, 0. 0%, 13. 5% and 0. 0% in Aedes-Flavivirus, Culex-Flavivirus, Aedes-Orthobunyavirus and Aedes-Alphavirus pairs, respectively. Database #3 includes a total of 368 entries from 97 distinct publications that examined VT in a natural setting. Overall, VT occurrence was 37. 5% in database #3 (i. e. , evidence of VT was found in 37. 5% of the entries). Virological assays consisted of 29. 6% animal, 28. 5% immunological, 25. 3% cellular and 16. 3% molecular assays. Aedes-Flavivirus, Culex-Flavivirus, Aedes-Orthobunyavirus and Aedes-Alphavirus pairs are represented by 31.
8%, 21. 2% 16. 8% and 2. 7% of database #3 entries, repectively. The remaining 27. 5% consist of 19 less studied vector-virus pairs. The median VT prevalence was 0. 03%, 0. 0%, 0. 0% and 0. 06% in Aedes-Flavivirus, Culex-Flavivirus, Aedes-Orthobunyavirus and Aedes-Alphavirus pairs, respectively. The 10-to 1000-fold lower estimates of VT prevalence observed in natural studies likely reflect two different processes. First, the infection prevalence in the previous adult generation is generally close to 100% in experimental studies because mothers are experimentally exposed to the arbovirus before VT is assayed in the their progeny. The prevalence of arbovirus infection in wild mosquito populations, by contrast, is typically low and the prevalence of VT infection observed in a given generation actually measures the product of adult population prevalence and VT efficiency.
, 2013). Dengue viruses are primarily transmitted by the mosquito Ae. aegypti, which was largely eliminated from Central and South America during the 1950s and 1960s by the eradication program of the Rockefeller Foundation. Approximately ten years after the end of the eradication program, however, dengue epidemics started to occur in the Americas, quickly followed by hyperendemicity and the emergence of DHF  (Bennett et al. , 2010). The first cases of DHF were reported in Cuba during a DENV-2 epidemic in 1981  (Guzmán et al. , 1999). This epidemiological event did not appear to have an obvious impact on dengue research overall, as shown by a query with the keyword 'dengue' to the ISI Web of Science database (Fig 2. ). By contrast, research on dengue virus VT markedly increased after 1981 (Fig.
Chikungunya emergence in the Indian Ocean region. Chikungunya virus (Togaviridae: Alphavirus) is an arbovirus originating in Africa that primarily circulates between non-human primates and arboreal mosquitoes. In 2004, it emerged in the human population on the coast of Kenya and was subsequently responsible for a vigorous epidemic in the Indian Ocean region between 2005 and 2007. On La Réunion Island, 34. 4% of the population was infected overall, while 1. 4 million cases were reported in 2006 in India  (Pialoux et al. , 2007). The global emergence of chikungunya that followed the 2005 outbreak triggered a renewed scientific interest for chikungunya virus, as shown by a query with the keyword 'chikungunya' to the ISI Web of Science database (Fig. 4 ). Although the normalized yearly number of publications on chikungunya virus VT after 2005 remains relatively low (due to the dramatic increase in the total number of chikungunya publications used for normalization), the frequency of VT publications significantly increased (Fig.
7A ; Kruskal-Wallis test, p = 5. 2 x 10 -5 ; pairwise Wilcoxon tests: p = 6. 4 x 10 -3 and p = 3. 7 x 10 -5 for animal vs. cellular and animal vs. immunological assays, respectively, p = 6. 3 x 10 -1 for cellular vs. immunological assays). Similarly, in natural studies, significantly larger sample sizes are found in studies based on molecular assays (Fig. 7B ; Kruskal-Wallis test, p = 2. 9 x 10 -2 ; pairwise Wilcoxon tests: p = 2. 6 x 10 -2 for animal vs. molecular assays, p = 9. 5 x 10 -1 for animal vs. cellular assays, p = 2. 3 x 10 -1 for animal vs. immunological assays, p = 1. 0 for immunological vs. cellular assays, p = 2. 5 x 10 -1 for molecular vs. cellular assays, and p = 9. 6 x 10 -1 for molecular vs. immunological assays). To determine the respective contributions of sample size and detection assay to changes in VT detection probability, VT occurrence in the databases was analyzed with a model that incorporated both factors and their interaction.
By nature, however, publication bias is difficult to quantify. Therefore, caution must be used to interpret the results, keeping in mind that under reporting of negative results leads to overestimating VT. Although the existence of VT is unequivocal for several mosquito-arbovirus pairs of public health significance, our databases showed that VT is collectively rare. Studies that estimated VT prevalence in a natural situation typically found that <0. 1% of mosquitoes are vertically infected, regardless of the virus family or mosquito species. VT efficiency per se, however, cannot be inferred in field studies because infection prevalence in the adult population is unknown. VT prevalence in experimental studies is 10 to 1000-fold higher, which reflects more closely VT efficiency because the parental generation is usually 100% infected.
Acknowledgements The authors thank members of the  Lambrechts lab  for insightful discussions,  Richard Paul  for critical reading of the manuscript, and two anonymous reviewers for useful comments. S. L. was supported by a doctoral fellowship from  University Pierre and Marie Curie. L. L. received funding from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID ).
Background: Detection of large genomic rearrangements, such as large indels, duplications or translocations is now commonly achieved by next generation sequencing (NGS) approaches. Recently, several tools have been developed to analyze NGS data but the resulting files are difficult to interpret without an additional visualization step. Circos (Genome Res, 19:1639-1645, 2009), a Perl script, is a powerful visualization software that requires setting up numerous configuration files with a large number of parameters to handle. R packages like RCircos (BMC Bioinformatics, 14:244, 2013)  or ggbio (Genome Biol, 13:R77, 2012) provide functions to display genomic data as circular Circos-like plots. However, these tools are very general and lack the functions needed to filter, format and adjust specific input genomic data.
Visualization tools  [1] [2] [3]  have been developed, displaying each variation as a link between positions on a circular ideogram. The most commonly used one, Circos, is very flexible but requires installation of Perl modules, a familiarity with the operating system to run effectively and with the parameters that are used in its configuration files. To circumvent this last difficulty, the variant detection program SVDetect  [4]  provides a script that converts its output into a format readable by Circos together with a limited tutorial set of configuration files. Recently, the RCircos package  [5]  was proposed to obtain Circos-like plots in an R environment  [6]. However, the user cannot zoom into a chromosome and has to program the functions needed to generate input data. Circos, ggbio and RCircos are very powerful tools; however they were designed to manage a wide variety of analyses, and this flexibility leads to rather complex handling.
To feed the core, as illustrated by Figure  2 , data from gene annotation (pathway 2), NM links (pathway 3), reads coverage (pathway 4), intragenomic links (pathway 5) and CNV status (pathway 6) have to be formatted, filtered, scaled and colored according to the user's preferences. First, the karyotype function (pathway 1) can extract data from an input file to build a karyotype file. Then, five specific functions are provided to adapt the data format issued from different prediction tools to a CIRCUS format. Currently, genomic annotations from tabular format and intra-genomic links issued from SVDetect or Pindel  [7]  can be parsed. The writing of new converters from other software results is easy for programmers. In order to reduce computing time, reads coverage and NM links are computed from SAM files with internal calls to SAMtools/BEDtools packages and to a Python script.
The third step, consisting in scaling and coloring the different features, is performed by four other functions which allow to display status, density, significance or other user-defined values. All these functions are linked in the data flow diagram depicted in Figure  2. The core and peripheral functions have many input arguments to allow flexibility; to simplify their use, almost all have default values. At the end of the process, a log file is created showing all arguments used in function calls and the primary data used for the image display. The aims of CIRCUS are to focus on structural genome variations, and to allow non-bioinformaticians to visualize their data in a straightforward way. Therefore, CIRCUS is an R wrapper that uses only a part of Circos functionalities. Ideogram skeleton components (thickness, ticks) are fixed, as well as most of the tracks graphic parameters, depending on the kind of data displayed: coverage is drawn in histogram style, CNVs in heatmap style, annotations in highlights style and links are simple lines with a fixed thickness.
Then, the sizes of the amplified fragments were measured on an ABI3730 DNA analyzer (Applied Biosystems). For that purpose, PCR amplicons were diluted (1800fold for FAM and 600-fold for HEX-labelled amplicons respectively) and multiplexed (Table  1 ) in formamide. LIZ 600 molecular marker (ABI GeneScan 600 LIZ Size Standard, Applied Biosystem) was 100-fold diluted and added for each multiplex. Before loading, diluted amplicons were heated 4 min at 94uC. Allele size was recorded using GeneMarker Demo software V2. 4. 0 (SoftGenetics).
Data analysis Microsatellite analysis was used to investigate the genetic relationships between strains. A dendrogram was built using Bruvo's distance and Neighbor-Joining clustering, by means of the  R program  [61]  and the following packages: polysat v1. 3  [62] , ape  [63] , adephylo  [64] , phyclust  [65]. Bruvo's distance takes into account the peculiar mutational process of microsatellite loci, and is particularly well adapted for populations with mixed ploidy levels  [66]. In order to assess the robustness of tree nodes, multiscale bootstrap resampling associated with an approximately unbiased test  [67]  was performed by means of R and the pvclust package v1. 2-2  [61, 68]. In addition to dendrogram drawing, the software STRUCTURE (V2. 3. 4) was used to delineate clusters of individuals on the basis of their microsatellite genotypes using a Bayesian approach  [69].
The amplicons were separated using a microchip electrophoresis system (MultiNA), and the optimal conditions for microsatellites amplifications were assessed on a panel of twenty strains of T. delbrueckii (data not shown). After optimisation on T. delbrueckii strains, the microsatellites markers were tested on seven additional species of the Torulaspora genus: T. franciscae, T. pretoriensis, T. microellipsoides, T. globosa, T. indica  [54] , T. maleeae  [55] , and T. quercuum  [56]. All these species appear to produce good amplification of several markers, suggesting that some of the microsatellites developed for T. delbrueckii could be useful for the study of population genetics of other Torulaspora species. In particular, the type strain of T. pretoriensis Y-17251 T showed a complex genotype, with three alleles for three loci (TD1B, TD2A, TD6A), two alleles for three loci (TD1A, TD5A, TD8A) and one allele for the remaining two loci (TD1C, TD7A).
In order to definitively determine whether, and to what extent, the genetic variation of T. delbrueckii was related to substrate and/ or geographical origin, an analysis of molecular variance (AMOVA) was performed. We used either the substrate origin (nature, bioprocess, clinical, grape/wine), or the geographical localisation (using continent of isolation) as grouping factors. The substrate origin explained 12. 29% of the total variation of microsatellite dataset, and was strongly significant (p-value,0. 00001), indicating that substrate origin shaped significantly, yet not completely, the T. delbrueckii population structure. By contrast, the geographical localisation, although significantly related to molecular variation (p-value,0. 001), explained less variation (7. 61%). It should be noted that the geographical localisation, when considering only strains from nature, explained 17.
Acknowledgments We kindly thank the following persons for providing strains and/or advice: Ailsa Hocking ,  Marc-Andre  ´Lachance,  Andreas Podbielski ,  Andrey Yurkov ,  Angelica Ganga ,  Anne Gschaedler ,  Benoit Colonna-Ceccaldi ,  Benoit Divol ,  Celia Quintas ,  Christian Hertel ,  Christian von Wallbrunn ,  David Santo ,  Dennis Sandris Nielsen ,  Erna Storga  ˚rds,  Eveline Bartowsky ,  Gabor Peter ,  Guilherme Martins ,  Hagen Frickmann ,  Harald Claus ,  Hector R Urbina ,  Heide-Marie Daniel , HerveAlexandre,  Hiroshi Fukuhara ,  Huu-Vang Nguyen ,  Irena Macioniene ,  Isabelle Masneuf-Pomarede ,  James Swezey ,  Jane McCarthy ,  Juan JoseRubio Coque ,  Keith Richards ,  Knut J. Heller ,  Kyria Boundy-Mills ,  Lucy Joseph ,  Makoto Kawase ,  Marcela Sangorrin ,  Mark Wilson ,  Matthias Sipiczki ,  Maurizio Ciani ,  Melanie Wieschebrock ,  Meredith Blackwell ,  Michael Arabatzis ,  Monika Coton ,  Morvan Coarer ,  Neil Jolly ,  Noemie Jacques ,  Patrizia Romano ,  Petra Wrent ,  Purificacion Hernandez-Orte ,  Savitree Limtong ,  Serge Casaregola ,  Joana Coulon  and  Aline Lonvaud-Funel.
9 package (DNAstar, USA). Genomic sequences of Cryphonectria parasitica strain EP155, Gaeumannomyces graminis var. tritici strain R3111a, P. oryzae strain 70-15 and M. poae strain ATCC 64411 were retrieved from Broad Institute (www. broadinstitute. org; G. graminis var. tritici, P. oryzae and M. poae) and JGI Genome Portal (http://genomeportal. jgi. doe. gov/; C. parasitica) databases  (Dean et al. 2005).
Phylogenetic analyses Megablast searches of the NCBI's GenBank nucleotide database were used to supplement the sequence data generated in this study, especially to populate the overview LSU phylogeny. Sequences were aligned using the online version of MAFFT (http:// mafft. cbrc. jp/alignment/software/) and the alignments were manually adjusted using MEGA v. 5. 2  (Tamura et al. 2011). Analyses were performed with the individual and combined datasets to test the robustness of each included locus. Phylogenetic trees were reconstructed by Bayesian Inference (BI).
DNA phylogeny We combined the LSU sequences obtained from our Pyricularia/ Magnaporthe species ( Within Magnaporthales, the different clades were not wellresolved using LSU sequences (Fig. 1 ). Therefore, LSU was supplemented with RPB1 sequences to generate a novel phylogenetic tree restricted to species from Magnaporthales. The combined LSU/RPB1 dataset consists of 101 aligned sequences including Cryphonectria parasitica as outgroup. This dataset contains 1 391 characters, of which the LSU dataset contributed 748 characters and the RPB1 dataset contributed 643 characters; 772 characters were constant, while 131 were variable and.
The combined dataset consists of 56 sequences including Barretomyces calatheae as outgroup, since it defines a clade basal to other species from this family (Fig. 2 ). This dataset contains 1 866 characters, of which the ACT dataset contributed 364 characters, the ITS dataset contributed 507 characters and the RPB1 dataset contributed 995 characters: 1 018 characters were constant, 118 were variable and parsimony-uninformative and 730 were parsimony informative (ACT: 94, 34, 236 characters respectively, ITS: 324, 27, 156 characters respectively, and RPB1: 600, 57, 338 characters respectively). A total of 192 equally most parsimonious trees were retained from this analysis (Tree length = 2 587, CI = 0. 563, RI = 0. 821 and RC = 0. 462), the first of which is shown in Fig. 3. The phylogenetic tree delimited 17 species clades, seven of which represent novel species described in in the Taxonomy section.
The Pyriculariaceae includes eight additional genera, namely Bambusicularia, Barretomyces, Deightoniella, Macgarvieomyces, Neopyricularia, Proxipyricularia, Pseudopyricularia and Xenopyricularia and four novel Pyricularia species. Some previously published and rather broadly defined species of Pyricularia and Magnaporthe clustered outside these families. These include isolate CBS 244. 95, which was originally identified as Pyricularia lauri, and is shown here to represent Rhexodenticula cylindrospora (incertae sedis) (Fig. 1 ). In addition, an isolate deposited at CBS as Pyricularia parasitica (CBS 376. 54, sterile on SNA) clustered in the Chaetothyriales (Fig. 1 ), and sequences of Magnaporthe griffinii (ITS GenBank JQ390311, JQ390312) proved to be distant to the Sordariomycetes (not included).
ACKNOWLEDGEMENTS We thank  Prof. Yukio Tosa  and  Prof. Yong-Hwan Lee  for providing cultures or DNA for phylogenetic analysis. We thank the technical staff,  Arien van Iperen  (cultures),  Marjan Vermaas  (photographic plates), and  Mieke Starink-Willemse  (DNA isolation, amplification and sequencing), as well as  Federico Santoro  and  Alessandro Trotta  (cultures, DNA isolation, amplification and sequencing) for their invaluable assistance.
It is known that the problem of computing hybridization numbers is both NP-hard and APX-hard  [7] , but it is not known whether it is in APX (i. e. whether it admits a polynomial-time approximation algorithm that achieves a constant approximation ratio). Until recently, most research on the hybridization number of two phylogenetic trees had focused on the question of how to exactly compute this value using fixed parameter tractable (FPT) algorithms, where the parameter in question is the hybridization number r of the two trees. For an introduction to FPT we refer to  [8, 9]. For binary trees, algorithmic progress has been considerable in this area, with various authors reporting increasingly sophisticated FPT algorithms  [10] [11] [12] [13]. The fastest algorithms currently implemented are the algorithm available inside the package DENDROSCOPE  [14] , based on  [15] , and the sequence of progressively faster algorithms in the HYBRIDNET family  [11, 16, 17].
New algorithms: CYCLEKILLER and NONBINARYCYCLEKILLER In this article we extend the theoretical work of  [20]  slightly and give it a practical twist to yield a fast approximation algorithm which we have made publicly available as the program CYCLEKILLER. Furthermore, we give an implementation of the algorithm presented in  [21] , available as NONBINARYCYCLEKILLER. The worst-case running time of these approximation algorithms is exponential. However, as we demonstrate with experiments, the running time of our algorithms is in practice extremely fast. For large and/or massively discordant binary trees, CYCLEKILLER is typically orders of magnitude faster than the HYBRIDNET algorithms and the algorithm in DENDROSCOPE. The performance gap between NONBINARYCYCLEKILLER and its exact counterparts is less pronounced, but still significant, especially in its fastest mode of operation.
Practical experiments with binary trees To assess the performance of CYCLEKILLER, a simulation study was undertaken. We generated 3 synthetic datasets, an easy, a medium and a hard one, containing respectively 800, 640 and 640 pairs of rooted binary phylogenetic trees. The easy data set was created by varying two parameters, namely the number of taxa n and the number of rSPR-moves k used to obtain the second tree from the first (note that this number is an upper bound on the actual rSPR distance). The 800 pairs of rooted binary phylogenetic trees were created by varying n in {20, 50, 100, 200} and k in {5, 10,. , 25}, and then creating 40 different instances per each combination of parameters. Each pair (T 1 , T 2 ) of rooted binary phylogenetic trees for a given set of parameters n and k is created as follows: The first tree T 1 on X = {x 1 ,.
, x n } is generated by first creating a set of n leaf vertices bijectively labeled by the set X. Then, two vertices u and v, both with indegree 0, are randomly picked and a new vertex w, along with two new edges (w, u) and (w, v), is created. This is done until only one vertex with no ancestor, the root, is present. The second tree T 2 is obtained from T 1 by applying k rSPR-moves. The medium and the hard data sets were generated in the same way as the easy one, but for different choices of the parameters: n in {50, 100, 200, 300} and k in {15, 25, 40, 55} for the medium one and n in {100, 200, 400, 500} and k in {40, 60, 80, 100} for the hard one. The exact hybridization number has been computed by HYBRIDNET  [11] , available from http://www. cs. cityu. edu. hk/~lwang/software/Hn/treeComp.