dataesr/scientific-paragraphs-categorization huggingface dataset:
To assess the extent to which alternative splicing has the potential to impact gene function in Ectocarpus, we used Interproscan  (Jones et al. , 2014)  to compare the domain structures of the predicted protein products of the principal and alternative gene transcripts of the 7,362 genes that exhibited alternative splicing of their coding region. This analysis indicated that, on average, each isoform lacked about 21% of the domains that were detected in the principal transcript. These marked differences between the domain structures of the protein products of principal and alternative transcripts are likely to significantly modify the activities of the alternative protein products. In addition to this genome-wide approach, a more detailed analysis was carried out for four genes that encoded proteins with multiple, repeated copies of small protein domains.
Fig. 4  shows that the alternative transcripts of these genes encode multiple protein variants in which repeated domains are included or excluded from the protein product in different combinations, producing proteins with markedly different domain structures. Together with the genome-wide analysis described above, these analyses suggested that alternative splicing is used in Ectocarpus to combine protein domain modules to generate multiple protein isoforms from individual loci. Analysis of the types of alternative splicing events that give rise to transcript isoforms in Ectocarpus using the program SplAdder  (Kahles et al. , 2016)  indicated that the most common event was the use of an alternative 3' acceptor site (Table  3 ). Intron retention events were relatively rare, representing less than 12% of the detected events.
A screen was also carried out for potential long non-coding RNAs (lncRNAs) using the FEELnc lncRNA prediction pipeline (https://github. com/tderrien/FEELnc) and the RNA-seq data listed in Table  S1. This analysis predicted the presence of 717 lncRNA loci in the Ectocarpus genome (Table  S6 ), corresponding to a total of 952 different transcripts (1. 3 isoforms per locus on average). The mean size of the lncRNA transcripts was 1,708 nucleotides and varied between 200 (the defined minimal size) and 7,988 nucleotides. The lncRNA loci were classified based on their configuration relative to the nearest protein-coding gene in the genome (referred to in the following text as the adjacent gene) and included both loci that were and loci that overlapped with their adjacent gene (Fig. S1 ). About 45% of the lncRNAs were classed as lincRNAs.
S. japonica transcripts were predicted using Stringtie  (Pertea et al. , 2015)  based on the mapping of 220,551,196 million reads of RNA-seq data  (Ye et al. , 2015) , corresponding to several different tissues, to the assembled genome sequence. Based on these data, FEELnc predicted the presence of 2,840 lncRNA loci in the S. japonica genome (Table  S7 ), corresponding to a total of 3,568 different transcripts (1. 3 isoforms per locus on average). The mean size of the S. japonica lncRNA transcripts was 2,036 nucleotides and varied between 200 (the defined minimal size) and 26,887 nucleotides. As with the Ectocarpus lncRNAs, the S. japonica lncRNAs were found to be organised in a range of configurations relative to the adjacent gene on the genome (Fig. S2 ). Comparison of the sets of predicted lncRNAs from Ectocarpus and S.
Extension and improvement of the Ectocarpus genome database The v1 annotation of the Ectocarpus genome has been publically available on the Orcae database  (Sterck et al. , 2012)  since its publication in 2010. We have updated the database by adding the v2 annotation described in this study. In addition, a v2 annotation-based Jbrowse Orcae database, providing an environment for the compilation and analysis of newly generated data before information is definitively incorporated into Orcae, which is the reference database. It is possible for registered users of the Jbrowse genome browser to create private versions in order to upload unpublished and working datasets.
Discussion The objective of the work reported here was to improve the utility of the Ectocarpus genome sequence as a genomic resource. A high-density, RAD-seq-based genetic map was exploited to significantly improve the large-scale assembly of the genome. This approach allowed 90. 5% of the genome sequence to be assembled into 28 pseudo-chromosomes, providing a high quality reference genome for future comparisons with other brown algal genomes focused on synteny and large-scale organisation of chromosomal regions. In addition, extensive RNA-seq data was used to improve 11,108 existing gene models and to identify 2,030 new protein-coding genes. New data available in the public databases has allowed the functional annotation associated with the protein-coding genes to be considerably improved.
The catalogues of Ectocarpus and S. japonica lncRNA loci are expected to serve as important reference sets for future analyses of lncRNA function in the brown algae. A genome-wide SNP resource was also developed as part of this study. This collection of SNPs will be a valuable tool for future genetic analyses using Ectocarpus as a model system  (Cock et al. , 2011; Coelho et al. , 2012a). All of these new and updated resources have been integrated into the Ectocarpus genome database, which has also been improved and extended to facilitate exploitation of the genome data and associated information. With the integration of the new information and resources described here, the Ectocarpus genome represents one of the most extensively annotated genomes within the stramenopile group and, as such, will serve as an important reference genome for future genome analysis projects.
Acknowledgements We thank  Toshiki Uji  for providing RNA-seq data, diverse members of the  Ectocarpus Genome Consortium  for manual annotation of genes through the Orcae database and an anonymous reviewer for comments that led to significant improvement of the manuscript. This work was supported by the  Centre National de la Recherche Scientifique , the  Agence Nationale de la Recherche  (project  Bi -cycle  ANR-10-BLAN-1727 , project  Idealg   ANR-10-BTBR-04-01  and project Sexseaweed  ANR-12-JSV7-0008 ), the  University Pierre et Marie Curie  and the  European Research Council  (grant agreement  638240 ). A. C. was supported by a grant from the  Brittany Region.
The RNA-seq data corresponded to female gametes (127,607,414 reads, accession number SRR2064656), spores (30,552,978 reads, accession number SRR2064654), thalli grown under blue light (11,981,830 reads, accession number SRR371552) or in the dark (12,657,652 reads, accession number SRR371551), young sporophytes grown under blue (13,333,334 reads, accession number SRR496757) or white (17,181,148 reads, accession number SRR496799) light and thalli subjected to heat stress (7,236,840 reads, accession number SRR947066). Orthologous Ectocarpus and S. japonica lncRNA loci were detected by carrying out reciprocal Blastn searches (E-value < 10.
Background Timely assessment of the burden of HIV/AIDS is essential for policy setting and programme evaluation. In this report from the Global Burden of Disease Study 2015 (GBD 2015), we provide national estimates of levels and trends of HIV/AIDS incidence, prevalence, coverage of antiretroviral therapy (ART), and mortality for 195 countries and territories from 1980 to 2015. Methods For countries without high-quality vital registration data, we estimated prevalence and incidence with data from antenatal care clinics and population-based seroprevalence surveys, and with assumptions by age and sex on initial CD4 distribution at infection, CD4 progression rates (probability of progression from higher to lower CD4 cell-count category), on and off antiretroviral therapy (ART) mortality, and mortality from all other causes.
Our estimation strategy links the GBD 2015 assessment of all-cause mortality and estimation of incidence and prevalence so that for each draw from the uncertainty distribution all assumptions used in each step are internally consistent. We estimated incidence, prevalence, and death with GBD versions of the Estimation and Projection Package (EPP) and Spectrum software originally developed by the Joint United Nations Programme on HIV/AIDS (UNAIDS). We used an open-source version of EPP and recoded Spectrum for speed, and used updated assumptions from systematic reviews of the literature and GBD demographic data. For countries with high-quality vital registration data, we developed the cohort incidence bias adjustment model to estimate HIV incidence and prevalence largely from the number of deaths caused by HIV recorded in cause-of-death statistics.
Measurement of disease burden according to geographic units enables comparison with other major conditions, showing where the epidemic remains a dominant cause of health loss and where the burden is still rising in spite of national and global eff orts. Such measurement also enables direct comparison of diff erent HIV/AIDS metrics, emphasising the specifi c needs of each geographic region and allowing for a more targeted response to the epidemic. UNAIDS produces a biannual assessment of incidence of infections, prevalence of people living with HIV, and deaths from HIV/AIDS;  14  the Global Burden of Disease Study (GBD) provides an alternative assessment of these rates. UNAIDS and GBD estimates have increasingly converged at the global level. 2  Nevertheless, estimates diff er substantially in several countries, particularly in middle-income and high-income countries, where GBD estimates are based on data from vital registration systems and UNAIDS estimates are based on prevalence in high-risk groups and estimates of the fraction of the population in these groups.
Incidence and prevalence estimation We generated incidence and prevalence estimates with the recoded Spectrum model with updated assumptions of on-ART and off -ART mortality and other program data from the UNAIDS country fi les. HIV cause-specifi c deaths from vital registration systems and sample registration systems are among the most reliable sources for estimation of the burden of HIV/AIDS. We used our cohort incidence bias adjustment method to scale the sizes of each incidence cohort on the basis of the raw estimates of HIV mortality from Spectrum, using unadjusted incidence curves and those observed in the vital registration system with proper incompleteness and cause misclassifi cation adjustments. 15 For this procedure, we fi rst ran space-time Gaussian process regression on age-specifi c HIV/AIDS mortality rates after correcting for garbage codes, HIV misclassifi cation, and under-registration by use of formal demographic methods to generate complete time-series estimates by location, sex, year, and age.
We then restructured Spectrum by addition of another compartment such that it could follow groups of people living with HIV/AIDS who were infected in a specifi c year and age group. We then ran the modifi ed program to produce 1000 draws of incidence, prevalence, and mortality for each location and sex combination. From this step, we were able to obtain the proportion of each infection cohort dying in each year and age cell after infection. We then used these proportions to weigh the ratio of the numbers of deaths based on the age-specifi c mortality rates from vital registration and processed by space-time Gaussian process regression, and the population estimated with Spectrum, and those directly from Spectrum. This process greatly improves both the model fi t on mortality data, closer to what the adjusted vital registration suggests, and the incidence mortality ratio.
We recorded coverage in excess of 40% in North America, western Europe, Australasia; the arc of countries in eastern South America, from Guyana to Argentina and Chile; and the corridor of countries from Uganda to South Africa (fi gure 3B). HIV death rates and recent time trends vary greatly across countries (table ). Deaths vary substantially by age, Data in parentheses are 95% uncertainty intervals. New infections and HIV/AIDS deaths are cumulative for the whole of 2015. The number of people living with HIV is the point prevalence (as a count) at the end of 2015. The number of people living with HIV receiving ART and the total number of people living with HIV are year-end point prevalences. We calculated numerators for incidence, prev alence, and mortality rates with counts as previously described.
Our models, in addition to UNAIDS models for estimating HIV incidence and mortality, depend heavily on estimates of prevention of mother-to-child transmission and ART coverage. These numbers are developed by UNAIDS in consultation with national governments, the Global Fund, and PEPFAR. 45 However, the underlying data at the facility or provider level are not available for inspection, critical appraisal, or validation. 7] [48]  If these fi ndings were true in other countries, our estimates of ART coverage could likewise be exaggerated, as could our estimates of deaths from HIV. Data transparency for models used in global health estimation is rapidly increasing. GBD have adopted the GATHER guidelines developed by WHO and other partners. 25 In the future, having input data on ART and prevention of mother-tochild transmission meet the GATHER guideline bar of transparency would be highly benefi cial.
Political sensitivities in some countries have restricted the transparency of UNAIDS on this issue; even the basic incidence and prevalence estimates generated by UNAIDS cannot be released for some countries such as India and Russia because of such issues. Fostering a culture of greater transparency and accountability for HIV prevention and treatment programmes will benefi t everyone concerned with tackling HIV more eff ectively in the future. 0] [51]  Use of more disaggregated assessments of the HIV epidemic will hopefully improve the quality of the results and the relevance to HIV prevention and treatment programmes. Disaggregated assessments of prevalence derived from survey data and surveillance data from antenatal care clinics are feasible. Progress will be needed on the availability of data for prevention of mother-to-child transmission and ART at the local level.
The GBD 2015 estimates of prevalence are in line with those from UNAIDS 2014. The high concordance of country-level prevalence estimates between the two series is unsurprising given that they are both based on similar input datasets for prevalence. Much of the diff erence between the two estimates is a result of diff erent assumptions of on-ART and off -ART mortality, background HIV-free mortality rate, initial CD4 distribution, and CD4 progression ratios. Further studies and close collaboration between UNAIDS and GBD are needed to fully understand the relative contribution of each of the aforementioned factors to the diff erence in estimates of incidence, prevalence, and mortality at the country level. This analysis of data for HIV has several further limitations. First, we have used data from the Antiretroviral Therapy Cohort Collaboration to improve the assessment of death rates of patients on ART in high-income countries.
However, we have not been able to incorporate data for the variation in quality of ART programmes such as viral load suppression by country. This information is not widely available. Second, our novel cohort incidence bias adjustment method leverages cause-of-death data to correct past incidence for each cohort. Although our testing shows that the method works fairly well, the information content in the approach for adjustments in the most recent time period is much more restricted than for earlier periods. Estimates of incidence with this approach in the most recent time periods might be biased. Third, we have attempted to propagate multiple sources of uncertainty into our fi nal estimates. Because we did not include uncertainty caused by variation in the quality of ART and might underestimate uncertainty in ART coverage, our fi nal uncertainty intervals could be too narrow.
At the global level, our uncertainty levels might also be too narrow because we assume uncertainty in each country is independent of other countries. Fourth, we have not used the surveillance data for new cases in our analysis. Integration of such information in the future will probably increase the accuracy of incidence and prevalence estimates. Fifth, prevalence and programme data are still sparse in most countries. Prevalence estimates are largely determined by adjusted antenatal clinic data and national surveys. To depict the epidemic in populations accurately, rigorous data collection is needed. For example, ART coverage data should be directly collected in surveys through viral load testing, such as in the Lesotho 2014 Demographic and Health Survey, 54 and questions about ART. Sixth, our study focuses on deaths with HIV/AIDS as the underlying cause of death and does not account for excess mortality from other non-communicable causes of deaths among people living with HIV.
AK's work was supported by the Miguel Servet contract fi nanced by the  CP13/00150  and  PI15/00862  projects, integrated into the National R&D&I and funded by the  ISCIII (General Branch Evaluation and Promotion of Health Research) , and the  European Regional Development Fund (ERDF-FEDER). SML is funded by a  National Institute for Health Research (NIHR)   Clinician Scientist Fellowship  (grant number  NIHR/CS/010/014 ). HJL reports grants from the  NIHR ,  EU Innovative Medicines Initiative ,  Centre for Strategic & International Studies , and  WHO. WM is Program analyst, Population and Development, in the Peru Country Offi ce of the  United Nations Population Fund , which does not necessarily endorse this study. For UOM, funding from the  German National Cohort Consortium  ( O1ER1511D ) is gratefully acknowledged.
Fostering the development of SPARQL interfaces to heterogeneous databases is a key to efficiently expose legacy data as RDF on the Web. To deal with the variety of modern database formats and query languages, this paper describes a two-step approach to translate a SPARQL query into an equivalent target database query. First, given an xR2RML mapping describing how native database entities can be mapped to RDF, a SPARQL query is translated into a pivot abstract query language independent of the database. In a second step, the pivot query is translated into the target database query language, considering the specific database capabilities. The paper focuses on the first step of the query translation, from SPARQL to a pivot query that takes into account join constraints and SPARQL filters, and embeds conditions entailed by matching SPARQL graph patterns with relevant mappings.
Introduction The exposure of legacy data as RDF is an increasingly hot topic as new data integration challenges emerge. Notably, the Web-scale data integration progressively gives birth to the Web of Data thanks to the open publication, in RDF, of data sets on the Web. Two approaches generally apply: legacy data can all be translated into a materialized RDF graph or data can be accessed on-the-fly as a virtual RDF graph using the SPARQL query language. Although the materialization is of interest in some contexts it is hardly a one-fits-all solution in practice, due to the size of the generated graphs. Dynamic access on the other hand scales better and guarantees data freshness. In the last decade, translation methods for relational databases (RDB) have matured, spurred by the publication of the R2RML mapping language  (Das et al.
, 2012). Several methods were proposed to achieve SPARQL access to relational data, either in the context of RDB-backed RDF stores  (Chebotko et al. , 2009; Sequeda and Miranker, 2013; Elliott et al. , 2009)  or using arbitrary relational schemas  (Bizer and Cyganiak, 2006; Unbehauen et al. , 2013a; Priyatna et al. , 2014; Rodríguez-Muro and Rezk, 2015). The mapping of XML data to RDF has been addressed extensively by works such as  (Bischof et al. , 2012)  and  (Bikakis et al. , 2015) , among which the latter proposes a translation from SPARQL to XQuery. Furthermore, extensions of R2RML were proposed such as RML  (Dimou et al. , 2014)  to map heterogeneous data formats (e. g. CSV/TSV, XML or JSON), and xR2RML  (Michel et al. , 2015a)  to enable the mapping of an extensible scope of databases to RDF.
At the same time, new actors, the NoSQL databases, have gained a remarkable success. Initially confined to serve as the core system of Big Data applications for which they were designed, they are being increasingly adopted as general-purpose databases, fostered by their open source licenses and the lightweight, easy-to-start packaging of some of them. Today, this overwhelming success makes them a natural candidate for RDF-based data integration systems, and in particular to feed the Web of Linked Data. In this regard, it shall be necessary to develop SPARQL access methods for heterogeneous databases, that shall vary greatly depending on the target database query languages: for instance RDBs support joins, nested queries and string manipulations, but this is hardly the case of some NoSQL document stores like MongoDB or CouchDB.
Thus, to avoid defining yet another SPARQL translation method for each and every query language, this paper introduces a two-step approach. First, given a set of mappings of the target database to RDF, a SPARQL query is translated into an pivot abstract query by matching SPARQL graph patterns with relevant mappings. This step can be made generic if the mapping language used is generic enough to apply to an extensible set of databases. In a second step, the abstract query is translated into the target database query language, taking into account the specific database capabilities. Our focus, in this paper, is on the first step. Leveraging previous works on R2RML-based SPARQL-to-SQL methods, we define a pivot abstract query language and a method to translate a SPARQL query into an abstract query, utilizing xR2RML to describe the mapping of a target database to RDF.
The method determines a reduced set of mappings matching each SPARQL graph pattern, and takes into account join constraints implied by shared variables and crossreferences denoted in the mappings, and SPARQL filters. Lastly, common query optimization techniques are applied to the abstract query in order to alleviate the work required in the second step. This paper is organized as follows. Section 2 first reviews related works, in particular R2RML-based SPARQL-to-SQL methods that we build upon. Then section 3 describes the xR2RML mapping language, and section 4 presents the main contribution of this paper: the translation of a SPARQL query into an abstract query under xR2RML mappings. Section 5 presents an application of our method to the case of the MongoDB database. Finally we underline current limitations and highlight perspectives in section 6.
Related Works Various methods have been defined to translate SPARQL queries into another query language, that are generally tailored to the expressiveness of the target query language. For instance, SPARQL-to-SQL translation methods harness the ability of SQL to support joins, unions, nested queries and various string manipulation functions. Typically, a conjunction of two basic graph patterns (BGP) results in the inner join of their respective translations; their union results in an SQL UNION ALL clause; the SPARQL OP-TIONAL clause between two BGPs results in a left outer join, and a SPARQL FILTER results in an encapsulating SQL SELECT WHERE clause. Chebotko's algorithm  (Chebotko et al. , 2009)  focuses on the SPARQL-to-SQL query translation in the context of RDB-based triple stores. Priyatna et al.