dataesr/scientific-paragraphs-categorization huggingface dataset:
The HMBCS was supported by a grant from the Friends of Hannover Medical School and by the Rudolf Bartling Foundation. Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, the Swedish Cancer Society, The Gustav V Jubilee foundation and Bert von Kantzows foundation. The KBCP was financially supported by the special Government Funding (EVO) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, and by the strategic funding of the University of Eastern Finland. kConFab is supported by a grant from the National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia.
Biospecimen Shared Resource, which is supported by P30 CA68485. The OBCS was supported by research grants from the Finnish Cancer Foundation, the Academy of Finland (grant number 250083, 122715 and Center of Excellence grant number 251314), the Finnish Cancer Foundation, the Sigrid Juselius Foundation, the University of Oulu, the University of Oulu Support Foundation and the special Governmental EVO funds for Oulu University Hospital-based research activities. The Ontario Familial Breast Cancer Registry (OFBCR) was supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR.
The ORIGO study was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The pKARMA study was supported by Märit and Hans Rausings Initiative Against Breast Cancer. The RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318). The SASBAC study was supported by funding from the Agency for Science, Technology and Research of Singapore (A Ã STAR), the US National Institute of Health (NIH) and the Susan G. Komen Breast Cancer Foundation. The SBCS was supported by Yorkshire Cancer Research S295, S299, S305PA and Sheffield Experimental Cancer Medicine Centre. SEARCH is funded by a programme grant from Cancer Research UK [C490/A10124] and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge.
SKKDKFZS is supported by the DKFZ. The SZBCS was supported by Grant PBZ_KBN_122/P05/2004. The TNBCC was supported by: a Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), a grant from the Breast Cancer Research Foundation, a generous gift from the David F. and Margaret T. Grohne Family Foundation, the Stefanie Spielman Breast Cancer fund and the OSU Comprehensive Cancer Center, the Hellenic Cooperative Oncology Group research grant (HR R_BG/04) and the Greek General Secretary for Research and Technology (GSRT) Program, Research Excellence II, the European Union (European Social Fund -ESF), and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) -ARISTEIA.
MBCSG is supported by grants from the  Italian Association for Cancer Research (AIRC)  and by funds from the  Italian  citizens who allocated the 5/1000 share of their tax payment in support of the  Fondazione IRCCS Istituto Nazionale Tumori , according to Italian laws ( INT-Institutional strategic  projects "5x1000"). The MCBCS was supported by the  NIH  grants  CA128978 ,  CA116167 , CA176785 an  NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer  [ CA116201 ], and the  Breast Cancer Research Foundation  and a generous gift from the  David F. and Margaret T. Grohne Family Foundation  and the  Ting Tsung and Wei Fong Chao Foundation. MCCS cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further supported by Australian  NHMRC  grants  209057 ,  251553  and  504711  and by infrastructure provided by  Cancer Council Victoria.
Acknowledgments We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. The HEBCS thanks  Drs Kirsimari Aaltonen ,  Karl von Smitten , and  Tuomas Heikkinen , and research nurses  Irja Erkkilä  and  Virpi Palola  for their help with patient samples and data. The  Finnish Cancer Registry  is gratefully acknowledged for the cancer diagnostic data and  Molecular Medicine Sequencing Laboratory  for the sequencing service. The ABCFS thanks  Maggie Angelakos ,  Judi Maskiell , and  Gillian Dite. The ABCS thanks  Sten Cornelissen ,  Frans Hogervorst ,  Senno Verhoef , and Sanquin, The  Netherlands. The BBCS thanks  Eileen Williams ,  Elaine Ryder-Mills , and  Kara Sargus. The BIGGS thanks  Niall McInerney ,  Gabrielle Colleran ,  Andrew Rowan , and  Angela Jones.
The HEBCS was financially supported by the  Helsinki University Hospital Research Fund ,  Academy of Finland  ( 266528 ), the  Finnish Cancer Society, The Nordic Cancer Union  and the  Sigrid Juselius Foundation. LMP received funding from  Orion-Farmos Research Foundation ,  Maud Kuistila Memorial Foundation ,  Biomedicum Helsinki Foundation ,  Alfred Kordelin Foundation ,  Paulo Foundation  and the  Finnish Cultural Foundation. BCAC is funded by  Cancer Research UK  [ C1287/ A10118 ,  C1287/A12014 ] and by the  European Community´s Seventh Framework Programme  under.
009 0. 012 disease causing pathogenic c. 572+95_96delGG intron 6 171 1 0 0. 003 polymorphism c. 728A>G p. Lys243Arg rs34594234 exon 7 167 4 1 0. 017 0. 008 disease causing unclassified c. 854-103A>G rs10146321 intron 8 104 64 4 0. 209 polymorphism c. 957+125C>A rs10146772 intron 9 48 83 41 0. 480 polymorphism c. 1063G>A p. Ala355Thr rs61758785 exon 11 170 2 0 0. 006 0. 007 polymorphism neutral c. 1094C>G p. Pro365Arg rs28908468 exon 11 140 32 0 0. 093 0. 115 polymorphism NA Counts of a common homozygotes b heterozygotes and c rare homozygotes in this study d Minor-allele frequency (MAF) in this study e MAF in the Finnish population in the ExAC database doi:10. 1371/journal. pone. 0153788. t001 RAD51B in Familial Breast Cancer PLOS ONE | DOI:10. 1371/journal. pone. 0153788 May 5, 2016 6 / 18 Breast Cancer and the Institute of Cancer Research (ICR), London.
03% 1 0. 97% 0. 35 (0. 05-2. 60) 0. 503 OULU dataset Total CC % CT % OR (95% CI) P Controls 273 267 97. 80% 6 2. 20% All female BC cases 452 443 98. 01% 9 1. 99% 0. 90 (0. 32-2. 57) 0. 850 Familial BC (!3 affected) a 83 82 98. 80% 1 1. 20% 0. 54 (0. 06-4. 57) 1 Familial BC (2 affected) b 49 48 97. 96% 1 2. 04% 0. 93 (0. 11-7. 87) 1 Unselected BC 320 313 97. 81% 7 2. 19% 1. 00 (0. 33-3. 00) 0. 993 BELARUS dataset Total CC % CT % OR (95% CI) P Controls 1235 1235 100% 0 0% - BC cases 1900 1897 99. 85% 3 0. 15% - 0. 284 BC = breast cancer; OC = ovarian cancer a Families with !3 BC or OC among firstor second-degree relatives b Two first-degree relatives affected with BC or OC c Includes 33 male BC cases and 4 female BC cases with family history of male BC d The Tampere dataset included one case with homozygous mutation e Male BC cases only doi:10.
Acknowledgements We would like to thank  Dr. Armando Ariza  for his valuable contribution in 1 H-NMR analysis and interpretation,  Dr. Felipe de Jesús González Bravo  for his support in performing elemental analysis from  Department of Chemistry. Q. F. B Elvira Ríos Leal  and  Ing. Gustavo Medina  for their contribution in GC-MS analysis from  Department of Biotechnology. Ing. Sirenia Gonzalez Posos for TEM and Ing. Alvaro Angeles Pascual  for HR-TEM, HAADF-STEM analysis. We would also like to thank  Ing. Marcela Guerrero  for FTIR and  Ing. Adolfo Fuentes Tavira  for XRD analysis. We are highly obliged to  Prof. Sergio Armando Tomás Velázquez  for XPS analysis ( CONACyT  Project No. 205733 ). The authors are thankful for all the facilities provided by  CINVESTAV-IPN, Mexico.
Introduction: The study of local HIV-1 epidemics is essential to formulate tailored prevention policies. Herein, we determined the prevalence of subtypes, transmitted drug resistance (TDR) and the factors that may have led to the self-sustainment of the HIV-1 infection in Cyprus. Materials and methods: 335 HIV-1-infected patients from the Cyprus Reference AIDS Clinic, Larnaca National Hospital diagnosed between 2003 and 2012 were included in this study. HIV-1 pol sequences were subtyped with REGAv3, COMETv1. 0 and manual phylogenetic analysis. TDR was evaluated in 204 naive patients according to the WHO-2009 surveillance list. To determine transmission clusters (TC), maximum likelihood trees were constructed for the most prevalent subtypes by using additional control datasets. TC were identified according to a genetic distance of <0.
Introduction: New trends perceived in the HIV infection reveal a growing and continuously adaptive pandemic, with a recent increase in the participation of the elderly. Studies claim that, as a consequence of advances in treatment, society is aging HIV-positive, which requires to know and understand that elderly population living with the infection. We aimed to analyze the profile of people aged 60 or more reported with HIV in a poor state in northeastern Brazil, and to describe characteristics associated with this profile. Methodology: This is a descriptive, epidemiological study conducted in the database: Notifiable Diseases Information System (SINAN-NET), used by the Ministry of Health of Brazil for the collection and processing of data on notification of diseases throughout the country.
Materials and methods: We implemented HIV, hepatitis B and C testing in the ED of a large tertiary cancer center with approximately 25,000 patients seen annually. Testing information was obtained using billing and laboratory databases. HIV testing was routinely implemented in July 2014, with hepatitis B and C testing added in May 2015. Testing results are described. Results: Testing rates improved considerably after the intervention. Between July 2014 and August 2015, 1. 4% (0. 4% newly diagnosed) of 852 patients screened positive for HIV. Between May and July of 2015, addition of hepatitis B and C screening demonstrated 7. 2% (2. 0% incident) and 3. 3% (1. 0% newly diagnosed) hepatitis B and hepatitis C seropositive rates, respectively. Conclusions: Routine HIV testing is feasible in a comprehensive cancer center ED.
P11 HIV-Infection and related-determinants amongst clinic attendees within rural and urban health facilities in Cameroon Aim: This study was conducted within the framework of the National Program of the Fight against HIV and AIDS. Its aim was to assess the impact of HIV infection on the hospital system in Cameroon. This study identified among others, the prevalence of HIV among patients visiting public and private health facilities as well as their behavior. Materials and methods: The study was conducted within 2015. It was a quantitative transversal study carried out through an administration of a questionnaire to 1500 patients visiting health facilities randomly selected in 6/10 regions of Cameroon. Data was entered on EpiData and analyzed with R. 3. 1. 3. Results: Results from patients revealed an overall HIV prevalence of 7.
e. affecting initiator codons, splice acceptor or donor sites, or causing a frameshift, a stop loss or gain, or a missense with a SIFT score of 0  [17] ), (ii) had a calling quality (QUAL) above 30, (iii) presented a mapping quality (MQ) score of 59 or 60, (iv) had less than 5 % of animals with missing genotypes, and (v) had a minor allele frequency (MAF) higher than 5 % for at least one breed with a minimum of 20 individuals in the dataset (which means that alleles observed only once were not considered). It should be noted that variants with a SIFT score less than 0. 05 are generally considered deleterious. In this study, we chose to retain only missense variants with a SIFT score of 0 to reduce possible artifacts. Furthermore, including missense deleterious variants with a SIFT score between 0.
Gene set enrichment analysis Gene enrichment analysis was performed using Ingenuity Pathway Analysis software (http://www. ingenuity. com/products/ipa/,  [18] ). We focused on "top canonical pathways" with a p value lower than 0. 01 and "diseases and bio functions" annotations with a p value lower than 0. 05. Annotations related to cancer and the general pathways entitled "skin lesion" and "liver lesion" were not considered since their results suffer from a bias. Pathways related to drug metabolism, which were not relevant for this study, were also eliminated. In addition, a unique keyword was assigned to each significantly enriched function annotation, with particular attention paid to the attribution of keywords related to subcellular portions, cell types and organs rather than to general processes.
Estimation of the allelic frequency of the RP1 frameshift mutation The RP1 frameshift mutation was included in the Illumina EuroG10K custom SNP chip, which is routinely used for genomic selection in France. Thus, in addition to the 1000 genome dataset, genotypes for this mutation were available for 53,279 Holstein, 40,548 Montbéliarde, 12,106 Normande, 1634 Abondance, 1005 Red Pied Lowland, 698 Tarentaise, 579 Simmental, 507 Vosgienne, and 296 Brown Swiss animals.
Association with recorded traits At the time of the analysis, Illumina EuroG10K SNP chip genotype data were available for 7439 Normande animals, which had their sire genotyped with the Illumina BovineSNP50 chip. The Illumina EuroG10K SNP chip comprises the RP1 frameshift mutation as well as more than 10,000 common SNPs with the Illumina BovineSNP50 chip. Using Fimpute  [21]  we were able to attribute a genotype for the RP1 polymorphism to 48,715 additional animals that were previously genotyped with the BovineSNP50 chip. The complete dataset comprised 11,986 Normande cows with phenotype information on three coat colour phenotypes (proportion of white areas on the body; proportion of white areas on the face, and brindling intensity) and on 28 traits that are routinely recorded for genetic evaluations (milk yield, fat content, protein content, fat percent, protein percent, cell score, clinical mastitis, milking speed, stature, chest width, body depth, width at pin bone, rump angle, rear legs side view, rear legs rear view, back muscle, fillet muscle, rear muscle, fore udder attachment, rear udder height, udder balance, teat orientation, front teat distance, udder support, udder depth development, interval between calving and first insemination, fertility at insemination of lactating cows, fertility at insemination of heifers).
1 ; for details see "Methods" section). The distribution of these variants was similar in terms of number and type of mutations between breeds in spite of quite different numbers of sequenced animals. This result can be explained by the rather high variant frequencies considered. Interestingly, 89 % (2216/2489) of these polymorphisms were observed in more than one breed and as much as 12 % (308/2489) in all 15 breeds, which indicates (subject to any unregistered crossbreeding event) that the majority of the retained variants existed prior to the splitting of the different cattle populations studied (i. e. at least 500 years ago  [25] ). A total of 1923 genes carried a deleterious mutation of which 566 counted two or more. A screening of phenotype databases revealed that 908 genes (1144 variants) were associated to at least one mammalian phenotype in laboratory animals (MGI database) and 375 (corresponding to 395 variants) with an inherited syndrome in humans (OMIM database).
e. p. R238X mutation in FMO3 for trimethylaminuria or "fishy-off flavor" of milk  [29]  and p. W80X mutation in BCO2 for the "yellow color" of milk and fat  [30] ). These examples validate that such variants which are deleterious to the protein function may exist and segregate at moderate to high frequencies in cattle breeds. To obtain an overall picture of the developmental pathways that are affected by our set of variants, we performed a gene enrichment analysis using the ingenuity pathway analysis (IPA) software  [18]. This revealed an important enrichment for genes related to nervous system development and function and moderate enrichments for a limited number of other diseases, physiological and biological annotations (see Additional file 3: Tables  S3, S4 , and S5). We then analyzed the frequency of the keywords that were assigned to each annotation to gain further insight into the organs, tissues or systems represented (Fig.
IBD analysis reveals a unique and ancestral mutation event As previously mentioned, the frameshift mutation in RP1 is not restricted to the Normande breed. So far, we have identified carriers in at least 12 cattle breeds: nine of the 15 breeds from the 1000 bull genomes dataset used in this study (Holstein, Charolais, Normande, Red Angus, Aberdeen-Angus, Gelbvieh, Beef Booster Composite, Maine-Anjou and Belgian Blue), and the Montbeliarde, Abondance and Vosgienne breeds based on EuroG10K genotyping results (Table  5 ). Since the mutation consists in the insertion of one adenosine in a polynucleotide stretch, which is more prone to mutation than other sites of the genome, we performed an IBD analysis to verify if only one ancestral mutation or multiple independent mutation events accounted for the wide distribution of this variant (see "Methods" section).
In the 1000 bull genomes dataset, we identified a unique fragment of 88. 6 kb (Chr14:23939194-24027957) that encompasses the mutation (Fig. 4 ) and was shared by all carriers (N = 38) but absent in non-carriers from the same breeds. This confirms the existence of a unique ancestral mutation event which, according to the size of the IBD segment, was dated back to approximately 565 generations, i. e. 2800 to 4000 years before present, considering that the generation interval can vary from 5 to 7 years (see "Methods" section). The observation of this old variant at low to moderate frequencies in numerous bovine breeds could be explained by a combination of genetic drift and absence of or a very limited negative counter-selection due to the late onset of the defect. Nevertheless, the high frequency of this mutation observed in Normande cattle (27.
Acknowledgements The authors are grateful to the breeders of the " GAEC de l' Araucaria"  for their hospitality,  Maelle Philippe  ( EVOLUTION ) for providing samples,  Albéric Valais  ( Normande breed Organization ) for providing phenotype information, and the partners of the 1000 bull genomes consortium for the excellent collaboration. This study is part of the  BOVANO  project ( ANR-14-CE19-0011 ) funded by the  French Agence Nationale de la Recherche  and  Apisgene. P. Michot  is recipient of a PhD. Grant from  ALLICE  and  Apis Gène.
2 • near the North Pole to 0. 5 • at the Equator (Fig. 1 ). Vertically, ORCA05 is discretized into 46 levels with thicknesses that range from 6 m at the surface to 250 m for the deepest ocean level (centred at 5625 m). Model bathymetry is computed from the 2 min bathymetry file ETOPO2 from the National Geophysical Data Center. The numerical characteristics of our ORCA05 configuration follow the lead of  Barnier et al. (2006)  for the ORCA025 configuration with resolutiondependent modifications for the horizontal eddy diffusivity for tracers modified to 600 m 2 s -1 and horizontal eddy viscosity fixed to -4 × 10 11 m 2 s -1. To simulate the advective transport driven by geostrophic eddies, our ORCA05 simulation uses the eddy parameterization scheme of  Gent and McWilliams (1990)  applied with an eddy diffusion coefficient of 1000 m 2 s -1.
The high-resolution geographical information system (GIS) file describing the MARCATS segmentation from LA13 was regridded using the QGIS software  (QGIS Development Team, 2015)  on the ORCA05 model grid in order to sample the model results on its own grid. This regridding technique implies some modifications to the regions initially described in LA13. In the model, the global coastal ocean has a total surface area of 27. 0 × 10 6 km 2 , which is 8 % less than the original value from  Laruelle et al. (2014). Here, the model's total coastal-ocean surface area represents 7. 5 % of the total area of the global ocean. Subsequently we refer to the individual MARCATS regions using the terminology of LA13.
Evaluation dataset To evaluate the total F CO 2 simulated by the model (historical simulation), we compare it to the database from  Laruelle et al. (2014) , hereafter LA14, which provides observation-based estimates for the flux over the MAR-CATS regions. This database was constructed by aggregating 3 × 10 6 coastal-sea surface pCO 2 measurements collected over 1990 to 2011 and included in the Surface Ocean CO 2 Atlas version 2. 0 (SOCAT v2. 0,  Pfeil et al. , 2013; Bakker et al. , 2014). These measurements represent about 30 % of the SOCAT v2. 0 dataset. To compute the flux, LA14 also relied on wind speeds from the multiplatform CCMP wind speed database  (Atlas et al. , 2011) , atmospheric CO 2 from GLOBALVIEW-CO2 (2012), and the flux parameterization from  Wanninkhof (1992)  as modified by  Takahashi et al.
(2009). As sensitivity tests, LA14 also used the flux parameterizations from  Ho et al. (2006)  as well as the original formulation from  Wanninkhof (1992). Thus LA14 computed mean annual F CO 2 estimates for 42 of the 45 MARCATS regions defined in LA13. The remaining MARCATS areas (12: Hudson Bay, 21: Black Sea and 29: Persian Gulf) are devoid of observations in the SOCAT database and were neglected. For the remaining regions, be-cause of the large heterogeneity in both the spatial and temporal coverage of ocean pCO 2 observations, the uncertainties for each of the MARCATS F CO 2 estimates from LA14 vary greatly. For example, only 28 % of the sub-units of MARCATS regions used in LA14 have an estimate for F CO 2 uncertainty of less than 0. 25 mol C m -2 yr -1. The data-based F CO 2 estimate for the Sea of Okhotsk is not taken into account due to the extremely poor data coverage of this region and its strong divergence with the local literature (LA14).
Revelle factor calculation To assess how the capacity of the coastal ocean to absorb anthropogenic carbon differs from open-ocean surface waters, we computed the Revelle factor (R f ,  Sundquist et al. , 1979)  using the CO2SYS MATLAB software  (Van Heuven et al. , 2011(@). CO2SYS was used with the simulated sea surface temperature, salinity, alkalinity, and DIC for model years 1993-2012 while choosing the total pH scale, the K 1 and K 2 constants from  Lueker et al. (2000) , the K SO 4 constant from  Dickson (1990) , and the formulation of the borate-to-salinity ratio from Uppström (1974).
(  2009 ) observation-based database gives a root mean square error (RMSE) of 0. 73 mol C m -2 yr -1 and a correlation coefficient R of 0. 80; likewise, comparison with the Landschützer et al. (  2014 ) observational-based database gives a similar RMSE (0. 70 mol C m -2 yr -1 ) and R (0. 81). Integrating over latitudinal bands, (Table  1 ), the model overestimates carbon uptake for the 90-30 • S region, where it absorbs 1. 50 Pg C yr -1 of total carbon vs. 0. 73-0. 77 Pg C yr -1 from  Takahashi et al. (2009)  and  Landschützer et al. (2014)  observational databases. This overestimate may result from the model simulation still being far from equilibrium (see Sect. 2. 2 paragraph 5 for details). The model also underestimates outgassing in the tropical band, where it releases 0. 13 Pg C yr -1 vs.