dataesr/scientific-paragraphs-categorization huggingface dataset:
OSSE2-OSSE4 represent the range of across swath errors considered for the wide-swath altimetry data. The sections in parentheses represent the following: "Free" is the reference simulation without data assimilation; "3N" denotes the three nadir altimeters; "1S" is one wide-swath; "2S" is two wide-swath; and "LE" is lower radar interferometer error. In sections in brackets represent the order of magnitude of the wide-swath altimetry error selected in the OSSEs with respect to the error of the KaRIN instrument (NASA/CNES SWOT mission). J2, C2, S3 Swath-1 Swath-2 T & S SST Error (cm) OSSE0 (Free) OSSE1 (3N) YES YES YES 1 OSSE2 (3N + 1S) YES YES YES YES 0. 8-2 [4 × KaRIN] OSSE3 (3N + 2S) YES YES YES YES YES 0. 8-2 [4 × KaRIN] OSSE4 (3N + 2S + LE) YES YES YES YES YES 0. 4-1 [2 × KaRIN] inant signature.
Anopheles mosquitoes are vectors of human malaria, but they also harbor viruses, collectively termed the virome. The Anopheles virome is relatively poorly studied, and the number and function of viruses are unknown. Only the o'nyong-nyong arbovirus (ONNV) is known to be consistently transmitted to vertebrates by Anopheles mosquitoes. A systematic literature review searched four databases: PubMed, Web of Science, Scopus, and Lissa. In addition, online and print resources were searched manually. The searches yielded 259 records. After screening for eligibility criteria, we found at least 51 viruses reported in Anopheles, including viruses with potential to cause febrile disease if transmitted to humans or other vertebrates. Studies to date have not provided evidence that Anopheles consistently transmit and maintain arboviruses other than ONNV.
Materials and Methods Four databases were searched in this work: PubMed, Web of Science, Scopus, and Lissa. In addition, manual searches of online and print resources were carried out. Three combinations of keywords were used for searching the PubMed database. These were (i) (("anopheles"  [  Searching article titles proved to be useful because the search terms were compatible across bibliographic databases. Thus, search (iii) above was easy to translate into the Web of Science, Scopus, and Lissa databases, as follows. Web of Science advanced search of all databases, including Web of Science Core Collection, KCI-Korean Journal Database, MEDLINE, Russian Science Citation Index and SciElo Citation Index: TI = Anopheles AND TI = Viruses NOT TI = (Aedes OR Culex); Scopus advanced search: (TITLE (Anopheles AND Viruses) AND NOT TITLE (Aedes OR Culex)); and the French language Lissa: 'Virus d'Anopheles.
Finally, eligibility criteria were applied for inclusion of a virus in the study: (i) virus species and name coherent with the standards of the International Committee on Taxonomy of Viruses (ICTV), (ii) reporting of diagnostic tools to permit independent detection, and (iii) some amount of nucleotide sequence. Reports of a putative virus were ineligible if they met none of these criteria, for example if the report was based only on observation of cytopathic effects on cultured cell lines, or pathology in mice. The Lissa database of scientific literature written in French returned a single article using the above English search terms. However, this article in French (with English keywords) was written in 1957, and was not included in any of the three other databases. In addition, when using French language search terms, the Lissa database identified 13 additional articles (for a total of 14), all in French, that were uniquely identified by Lissa and not by the other three databases.
Discussion Little is known about the Anopheles virome, as evidenced by the relatively small number of scientific publications on the topic. Nevertheless, when the literature summarized here is taken together, it is evident that Anopheles viruses, including among them pathogens of humans and other vertebrates, are abundant in nature but understudied. We found published evidence of at least 51 viruses associated with Anopheles. This number is likely an underestimate, because it does not include publications in journals not indexed by the databases searched. The Anopheles virome appears to be dominated by RNA viruses. RNA viruses are also dominant in Aedes (Yellow fever virus, Zika virus, dengue virus, chikungunya virus) and Culex (Eastern equine encephalitis virus, Rift Valley fever virus, West Nile virus).
One conclusion of this study is that the databases PubMed, Web of Science, Scopus, and Lissa can yield different results for the same specific area of research. The four bibliographic databases are complementary. PubMed and Lissa are free to access without registration, which is not the case for Scopus and Web of Science. Web of Science yielded the most results (58 articles), because it includes articles from many countries. However, PubMed contains the most recent articles. In contrast, the articles found in Lissa are in French and include older works, which can fill gaps not covered by other databases. The Arbocat Arbovirus Catalog indexes arboviruses or potential arboviruses harbored by arthropods, but for some of these viruses, the support is unpublished or no longer available. Finally, it should be noted that a systematic automated search profile can only reveal reports that are indexed using informative combinations of terms.
A limiting factor is thus the quality of index terms in the records. The search parameters presented here could perhaps be modified with additional systematic terms. However, search terms that might appear to be more precise may in fact generate less informative results and lead to diminishing returns. For example, searching of databases with [virus name + Anopheles], can generate large numbers of often low-quality results (e. g. ,  [West Nile virus]  + Anopheles generates 140 records). Such searches require increased levels of labor-intensive manual curation to identify on-target records. Systematic automated searches can be easily re-run. Moreover, the most important biological observation is made when a virus is first reliably reported in at least one Anopheles species. After that, finding the virus in other Anopheles is useful, but hardly surprising, while studies of the biology of host range restriction and mode of transmission of Anopheles viruses are sorely lacking.
The search terms ((Anopheles viruses [Title]) NOT (Aedes [Title] OR Culex [Title])) yielded 36, 43 and 58 records in PubMed, Scopus, and Web of Science databases, respectively, on 4 May 2016 (PRISMA Flow Diagram, Figure S1). The 36 articles found in PubMed produced meaningful outputs, corresponding to recent reports of Anopheles virus. For example, in PubMed, the article number PMID: 27138938 (published 3 May 2016) was returned, but in Web of Science and Scopus the most recent articles were published in November 2015. The alerts in PubMed using criteria (((Anopheles AND viruses) NOT (Aedes OR Culex))) OR ((("Anopheles/virology"[Mesh]) NOT "Culex"[Mesh]) NOT "Aedes"[Mesh]) yielded 14 articles between 15 January and 18 August 2016. These articles have the following numbers: PMID: 26807720, PMID: 25882523, PMID: 26821654, PMID: 25879960, PMID: 25637950, PMID: 26492074, PMID: 26416112, PMID: 26271277, PMID: 27113956, PMID: 25222233, PMID: 27138938, PMID: 26401843, PMID: 27456078, PMID: 26807720.
Acknowledgments: We thank  Catherine Cecilio  of the  Scientific Media and Information Center (CeRIS) ,  Institut Pasteur , for aid with bibliographic methodologies, and  Marie Vazeille ,  Luisa Nardini  and  Constentin Dieme ,  Institut Pasteur , for helpful comments and suggestions on the manuscript. We thank the anonymous reviewers for exceptionally valuable suggestions. This work received financial support to KDV from the  European Commission ,  Horizon   2020 Infrastructures 731060 Infravec2 ;  European Research Council , Support for frontier research, Advanced Grant #323173 AnoPath; and  Agence Nationale de la Recherche Laboratoire d'Excellence  " Integrative Biology of Emerging Infectious Diseases " # ANR-10-LABX-62-IBEID. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
For RNAs without introns, NRT control was performed for each replicate sample to ensure that the Cq was above the Cq obtained from corresponding reverse transcribed RNAs. Experiments were run with six biological replicates of each strain, and biological replicate was run in technical triplicates (Table  S2 ). Normalization genes were selected from a pool of 9 housekeeping genes using geNorm  [27]  (Table  S3 ). RT-qPCR normalization according to the ∆∆Ct method, standard error and 95% confidence interval calculations, and statistical analyses were performed using REST 2009 software (Qiagen, Hilden, Germany)  [28]  (Table  S4 ). RT-qPCR experiments were MIQE compliant  [29].
Phylogenetic Analysis Orthologous and paralogous genes present in fungal genomes were searched by BLAST using the default parameters with the IDC4 protein as queries. Alignment was made with MAFFT  [30]  and manually refined. This alignment was used to construct a phylogenetic tree using the maximum likelihood method (PhyML software  [31] ) and transferred to the iTOL server for visualization  [32]. Bootstrap values are expressed as percentages of 100 replicates.
Malaria is part of the Goal 3: ensure healthy lives and promote well-being for all at all ages, and Target 3. 3: by 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases. More than analysing achievements and providing objectives, the MDG 2015 report  [2]  identifies, for all millennium goals, bottlenecks that must be overcome. One of them is associated with the deficiency of data needed to monitor progress towards the MDGs: "sustainable development demands a data revolution to improve the availability, quality, timeliness and disaggregation of data to support the implementation of the new development agenda"  [2]. If access to malaria diagnostics results is essential, other datasets are required in order to study the different processes involved in malaria epidemics, for example datasets coming from civil registration systems, among others.
Methods Websites where functionalities like consulting, visualizing and analysing spatio-temporal data linked to malaria are analysed. The study focuses on the "tool part" of the websites that is dedicated to data exploration (search/ visualize/analyse). Bing and google search engines are initially used with the keywords "malaria and mapping", "malaria and gis", "malaria and online". In order to include results that are not well indexed and do not show up from the first search method, websites are extracted indirectly from publications (articles, reports, posters) that are returned from the previous searches and from google scholar. Websites where users must register are not included. Completeness is not guaranteed, however Fig. 1  The DIKW pyramid these two research methods offer complementary results that are assumed to be representative of the global trend of how the web is used for searching, visualizing and analysing spatio-temporal data linked to malaria.
What spatial extents and units are used? While malaria fighting is an undeniable global preoccupation that requires world level partnership, in the meantime it is also essential to conduct local studies. What kind of information is revealed by the available online datasets, are they suitable for running local studies? Three expected levels of spatial extents are defined depending on the geographic coverage of a website. At global level, all regions are considered. At continental level, all regions of a continent are represented. The regional level is a sub-continental extent that goes from a locality (a district, a town, etc. ) to a cluster of countries or regions (administrative, geographic, etc. ). Websites are classified depending on the spatial unit used for representing or analysing data.
In eight websites (73%) (a, c, e, f, g, h, i, k), as an intuitive functionality, users can select data of interest and display them on a same map/graph. This functionality corresponds to the first level of analysis (basic) we detailed in the methodology part. The second level of analysis (advance: extract or aggregate information) is available as an intuitive functionality in seven websites (64%). The Malaria Atlas Project website (a) allows to define a region (or points) of interest by uploading a file or selecting a predefined administrative region (country and subnational region) in order to extract information. The IRI Climate and Malaria in Africa website (b) allows to define a region of interest and download the corresponding information. The Health-Map website (e) allows to select a region of interest (town, subnational region, country, continent) and visualize the past year evolution of the corresponding number of disease alerts.
The Worldwide Antimalarial Resistance Network website (g) allows to compare two study results by visualizing them jointly. The VectorBase website (h) allows for aggregating data depending on the zoom level and to visualize study results jointly on a graph. It is also possible to download the selected information. The MalariaGen website (i)  [45, 46]  allows to play with genome information through a "genome browser". Finally, the StatCompiler website (k) allows to explore and personalize graphs by the way of a dynamic menu. The third level of analysis (complex: combine information) is only available as an intuitive functionality in the MalariaGen (i) website: an ergonomic interface allows to build an advanced processing chain in order to process genome information. However, as a non-intuitive functionality, b also allows to run complex analysis through a dedicated programming language named Ingrid.
These data format (png, jpeg or pdf ) do not allow for researchers to process them, they may be more interested in downloading raw data as they can do in a, b, g, h and k websites. Four websites (c, d, f, g) gather previous studies that focus on a same topic in an implicit goal of exhaustiveness. Users can explore the datasets by choosing a study result, for example: "confirmed resistance", "possible resistance", etc. Those websites do not immediately present indicators, but rather the study that produced them. According to our classification, they are clearly dedicated to the category of researchers, it is unlikely for another category of users to have interest in those resources. The last website that may interest the researchers category is the MalariaGen website (i) that provides a tool for running complex analysis.
Data This review reflects a minority usage of the web for communicating or encouraging multidisciplinary studies: four out of eleven reviewed websites (36%) actually have a multidisciplinary approach. It also reveals that some disciplines are underrepresented (sociology, news) or even absent (population dynamics for example). Working with multidisciplinary data implicates being able to work with different spatial extents and units, parameters that are directly linked to data availability and collecting procedures, study objectives, storage and computing constraints. To facilitate multidisciplinary studies, adequate approaches and tools for representing, modelling and co-analysing the spatially heterogeneous datasets must be developed. Mathematical models for interpolating low spatial resolution or punctual data combined with high spatial resolution remote sensing images is already used in the Malaria Atlas Project website (a), but upstream approaches may be more powerful and facilitate multidisciplinary and/or multiscale analysis.
For example, how to automatically evaluate what spatial extent and unit are adequate to address a specific question? While a 25 km 2 grid can be considered satisfactory for climate scientists, it is still too coarse for mosquitoes breeding habitats mapping and vector control. It behooves to every knowledge area to determine the spatial characteristics they believe suitable for their study. In the context of the web of data, and particularly the semantic web, standardized languages, schemas and tools already provide a framework for knowledge modelling, sharing and automatic inferring. Particularly, for recording the different viewpoints in relation to space scales and units, the OGC standard GeoSPARQL  [47]  and the stRDF/stSPARQL initiative  [48]  provide vocabularies and query languages that can be used.
Inherently, easy sharing and automatic inferring would be possible, facilitating multidisciplinary and/or multiscale analysis. Crossborder multilateral studies, characterized by different viewpoints in relation to a territory (a simple example is the one of administrative divisions) could also be facilitated. It turned out to be challenging to retrieve what is the update frequency of a website and if a dataset is up-todate. When data is systematically and routinely produced, the recommended update frequency corresponds to the time resolution of the data. It may vary from day to year depending on the website: VectorMap (f )  [49]  displays the monthly malaria incidence rate and StatCompiler (k) the annual malaria incidence rate. The first one is to be updated once a month, the other one every year.
Metadata Making scientific data and research more visible and accessible for everyone is in the scope of the "Open science" dynamic. Beyond making the data available on the Internet is also the need for providing keys for self-appropriation of information by every community of users. Metadata is such a tool, by systematically describing the data in a dedicated framework, ideally in a standardized way, the user is able to access key information such as data origin or data processing steps. This review outlines the underutilization of this tool: no website uses a metadata catalogue and it is not possible to access metadata files. Yet, metadata exist as the websites need them for researching, locating and providing information about the data; but they are not visible for the user. In order to promote infrastructures interoperability, in addition to using standardized metadata catalogue, it would be usefull to use systematically a small set of vocabulary terms, generic and easy to use, for describing the datasets: the Dublin Core Metadata Element Set (DCMES)  [50].
Web services This review reveals that the large majority of web platforms provides limited web services; they do not allow for third party solutions to directly access their data and the large majority of websites does not provide tools for running complex analysis online. For example, the upload of data originated from other sources is only possible in two websites. However, we noted that websites are regularly updated in order to provide new tools, mainly for running analysis, more and more web platforms implement innovative technical solutions that allow for every user to take advantage of technology improvements, such as Internet network speed or processing capacity. In the same way as Google Earth Engine (GEE) online platform does  [52] , it is possible to concentrate on a same website both a free and easy access to large heterogeneous datasets and a library for processing data on a cluster of high performance computers.
Still, the two websites that allow running complex analysis (b, i) use non-standardized technologies (programming languages, communication protocols) that makes any processing chain development specific to the platform. If the platform is down, if the company bankrupts or decide to change its terms of use, there could be no chance for reproducing the analysis. Some users still hesitate to enjoy these technologies for those reasons; however the use, or development and publication, of open solutions can guarantee reproducibility and sustainability. The MalariaGen website (i) actually provides the source code of the web application Panoptes  [46, 53] , permitting a local installation of the software. Concerning web services, two main approaches are used to ensure interoperability: the OGC Web Processing Service (WPS) that standardizes "how clients can request the execution of a process, and how the output from the process is handled.
In this regard, the use of standardized metadata and metadata catalogues would permit an easier dataset exploration, but also provide a framework for registering essential information such as data quality (including an "obsolescence" parameter) and information allowing for reproducibility. The use of metadata also turnedout to be a trick for allowing critical data (in respect to privacy) research and localization without allowing its visualization. Another objective is to facilitate multidisciplinary studies. The use of standardized web services for accessing heterogeneous datasets brings multidisciplinary analysis opportunities. However, new challenges are to be faced to reach this objective, developing web platforms for analysing data online seems relevant, for every user to be able to access high performance infrastructures and processing chains, without having to download big volume of distributed data.
, 2011). More recently, segregation analyses in two families indicated that the variant did not segregate in the affected branches  (Santos et al. , 2014). Although a few of the above studies tentatively classified the variant as benign or likely benign, they do not provide robust genetic evidence to justify this conclusion. Conversely, a recent article asserted that the variant was associated with breast cancer, based on a relatively limited case control association study in the Norwegian population  (Møller & Hovig, 2017). As a consequence, to date the classification of c. 68-7T > A reported in databases aggregating information on genomic variations has remained inconclusive. In particular, ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/, last updated: Feb 1, 2018) reports conflicting interpretations classifying the variant as benign (seven entries), likely benign (nine entries) and of uncertain significance (four entries).
Moreover, the BIC (Breast Cancer Information Core, https://research. nhgri. nih. gov/bic/) database presently annotates the variant as of unknown clinical importance, pending classification, while the BRCA Share TM (UMD-BRCA2 mutations database) (https://www. umd. be/BRCA2/) classifies it as likely benign. In the present study, we combined genetic approaches, including a large multicentre case-control study and segregation analysis in a sizable number of families, with qualitative and quantitative analyses of the transcripts, and Mitomycin C growth inhibition test. Our findings provide a robust classification of the BRCA2 c. 68-7T > A variant with respect to its effect on cancer risk, and add evidence that splicing pattern alterations do not necessarily lead to pathogenicity.
Digital PCR The dPCR experiments were performed on a QuantStudio 3D dPCR 20K platform according to the manufacturer's instructions (Applied Biosystem, Foster City, CA). To detect BRCA2 Δ3 transcripts, we used a FAM-labeled custom designed TaqMan assay (Applied Biosystems) specific for the exon 2-4 junction (5 ′ -CAAAGCAG-GAAGGAATG-3 ′ ). To detect ▼3 transcripts, we used a 2 ′ -chloro-7 ′ phenyl-1,4-dichloro-6-carboxy-fluorescein labeled (VIC-labeled) predesigned TaqMan assay (Applied Biosystems, Hs00609076) specific for the exon 3-4 junction (5-AATTAGACTTAG-GAAGGAATGTTCC-3 ′ ). All relative quantification experiments were performed combining Δ3 and ▼3 assays in individual chips. dPCR chips were analyzed in the QuantStudio 3D Analysis Suit Cloud software v2. 0 (Applied Biosystem, Foster City, CA), defining FAM as target.