dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank all the healthcare providers who participated in this study. This document is an output of the project ' Communityembedded reproductive healthcare for adolescents in Latin America ' ( GA241615 ), funded by the  European Commission   FP7 Programme. The  International Centre for Reproductive Health (ICRH) of Ghent University, Belgium , coordinates the research consortium. The authors assume full responsibility for this article and the  European Commission  is not responsible for the content. The authors acknowledge  Zoyla Segura ,  Kathya Cordova ,  Arnold Hagens ,  Erica Nelson  and the teams of ICAS, CIES, SG, and  UC  for their contributions to the design and implementation of the research.
Acknowledgements The author acknowledges the support of (1) the  French "Agence Nationale de la Recherche" (ANR) , under grant  ALTERNATIVE  ( ANR-2010 CESA 018 01 ) ''What alternatives to the chemical control of Triatoma infestans ?" and (2) OMS-TDR /  IDRC  under grant  EBS-LAC n°A90281  "An eco-bio-social approach to implement integrated techniques to control T. infestans in Bolivian poor communities" and (3)  Sam Featherston  for correcting the English spelling.
Eligibility criteria for inclusion in the study were as follows: primary surgery, complete information on post-operative chemotherapeutic treatment, high grade serous histopathology subtype, and completed tumor banking informed consent. Patients who died from another disease were censored at time of last follow-up. A gynecologic oncologist reviewed the clinical data for all patients. For the diseasefree progression study, only patients with clinical follow-up of at least 18 months or until disease recurrence were included. The characteristics of the tumors and patient outcome for the sample sets are summarized in Table  1.
Immunoprecipitation of GFP (Green Fluorescent Protein) and GFP-YB-1 constructs were performed as described previously  [21]. Protein bands on western blots were quantified using LI-COR Image Studio software 2. 0 (LI-COR Biosciences, Lincoln, NE). β-actin was used as a control for protein loading. The background signal for each band was determined using an identical area to the target band covering a region in the same lane where no protein signal was observed. Results were determined by calculating a ratio of target protein signal (minus background) over β-actin signal (minus background).
FACS and FITC-Annexin V analyses Cells were transfected with either control siRNA or siRNA against RPS4X. After 72 h, cells were fixed in 50% ethanol overnight. Cells were then washed in phosphatebuffered saline (PBS) and incubated for 30 min at 37°C in a buffer containing propidium iodide and RNAses. Cells were then analyzed on a Beckman-Coulter Epics Elite ESP (Cambridge, MA, USA) flow activated cell sorter. Data were analyzed with the MultiCycle software (Phoenix Flow System, San Diego, CA, USA). To estimate apoptosis and/ or necrosis, we used the FITC Annexin V apoptosis detection kit I (BD Biosiences, Palo Alto, CA). Transfected cells were treated 48 h with the indicated concentration of cisplatin and then harvested to measure apoptosis/necrosis following the manufacturer's instructions.
Statistical analysis The Spearman correlation (two-tailed) and non-parametric Wilcoxon-Mann-Whitney test were used to estimate the correlation with clinicopathological variables and markers as continuous variables. Survival curves were calculated according to Kaplan-Meier method coupled with a logrank test for survival analysis. Since survival times were positively skewed, we took the median as the threshold value for each marker (YB-1 and RPS4X). Univariable and multivariable Cox proportional hazard models were used to estimate the hazard ratio for each marker as continuous variables. All statistical analyses were done using Statistical Package for the Social Sciences software version 16. 0 (SPSS, Inc. ), and statistical significance was set at P < 0. 05. The R software version 2. 10. 1 (http://www.
Acknowledgements Clinical specimens collection was supported by the  Banque de tissus et de données of the Réseau de recherche sur le cancer of the Fonds de recherche du Québec -Santé  which is affiliated with the  Canadian Tumour Repository Network. This work was supported in part by the  Canadian Institutes of Health Research  and the  Cancer Research Society, Inc. to ML. SPT is a scholar of the  Quebec-Clinical Research Organization in Cancer Consortium , financed by the  Pfizer-FRSQ Innovation Award.
Numerous studies have proven the success of this approach in the animal kingdom, and using various sources of tissue samples (e. g. Lambert 2005 , Clare et al. 2007 , Dawnay et al. 2007 , Hajibabaei et al. 2007 , Borisenko et al. 2008 , Ward et al. 2009 , Shokralla et al. 2010). Today (June 2013), a database, accessible at http://www. boldsystems. org, groups DNA barcode sequence data for more than 133,000 animal species, and offers a powerful identification tool for new specimens  (Ratnasingham and Hebert 2007). DNA barcoding also possesses some inherent limitations  (Valentini et al. 2009 ): it is based on a single locus on the mitochondrial genome so that it is only maternally inherited  (Hartl and Clark 2007) , it can show heteroplasmy  (Kmiec et al. 2006 , Vollmer et al. 2011)  or may exist as nuclear copies.
DNA barcoding also is a valuable and cost effective alternative to the taking of the head or skull of the animals. Third, genetic data collected for DNA barcoding generally include intraspecific variation, which allows downstream population-level analyses including the detection of genetic structure and, in some cases, monitoring population movements. A long-term use of the barcoding approach would therefore clearly increase the significance and the precision of marine mammal stranding monitoring. Migration or movement of populations or groups of a particular species can be highlighted, thus revealing e. g. environmental changes leading to these movements  (Pauls et al. 2012). We evaluated the usefulness of DNA barcoding in the monitoring of marine mammal biodiversity along the coasts of Brittany at three levels: by confirming the taxonomic identification performed by field correspondents, by identifying degraded carcasses or parts of carcasses, and by determining intraspecific variations for two species commonly found off Brittany, the harbour porpoise and the grey seal.
Collection of data and samples The CRMM (Centre de Recherche sur les Mammifères Marins, La Rochelle, France), presently the Joint Service Unit PELAGIS, UMS 3462, University of La Rochelle-CNRS has created the French marine mammal stranding recording program at the beginning of the 70s. The network comprises about 260 field correspondents, members of organizations or volunteers  (Peltier et al. 2013). Since 1995, the LEMM (Laboratoire d'Etude des Mammifères Marins, Océanopolis, Brest, France) has coordinated this network at a regional scale in Brittany, North West of France. Data are collected from the Brittany coastlines, analyzed, and then added to the central database maintained in La Rochelle. The Brittany coasts have been divided into 18 sections covering the whole coastline  (Jung et al.
2009). In each of these areas, correspondents are trained in the analysis of stranded marine mammals. Taxonomic identification and characteristic measurements are performed following a standard procedure. The LEMM therefore compiles standardized data on a large proportion of cetaceans stranded on the Brittany coasts on a yearly basis. Whenever possible, skin, blubber, muscle and teeth samples are also collected in the field from each stranded animal. Samples are then kept in absolute ethanol or dry at -20 °C until analyses. Some harbour porpoise samples, described in the Appendix 1 and in  Alfonsi et al. (2012) , were stranded or by-caught in the Bay of Biscay (Atlantic coast of France).
5 mM. After an initial denaturation step of 10 min at 95 °C, the thermocycle profile consisted of 32 cycles for cetaceans or 35 cycles for pinnipeds at 95 °C for 30 s, 53 °C for 30 s and 72 °C for 60 s, with a final extension at 72 °C for 10 min. For some animals, we also amplified and sequenced another part of the mitochondrial genome including the control region (MCR). For cetaceans, the primers and reaction conditions are described in  (Alfonsi et al. 2012). For pinnipeds, two newly designed primers LMCRHgem 5'-tcatacccattgccagcattat-3' and HMCRHgem 5'-taccaaatgcatgacaccacag-3' amplified a 693 bp fragment from position 16160 to 55 of the Halichoerus grypus complete mitochondrial genome sequence (GenBank acc. no. X72004). PCR reaction conditions were the same as described above for pinnipeds, with the hybridization temperature set to 53 °C.
PCR products were purified using the "MinElute PCR Purification Kit" and sequenced by a commercial sequence facility (Macrogen, Korea). Electropherograms were analyzed and edited manually using the Sequence scanner software (Applied Biosystems), and alignments were produced using CLUSTAL W  (Thompson et al. 1994)  with default settings in Bioedit  (Hall 1999). All sequences were analyzed using the Barcode of Life Data Systems (BOLD) interface (accessible at http://www. boldsystems. org), and were also compared to GenBank data using BLAST  (Benson et al. 2010). DNA sequences and specimen information have been added to two BOLD projects. The first project includes specimens for which the species had been identified without doubt using classical morphological identification, and is referred to as IMMB (Identified Marine Mammals in Brittany).
The IMMB project is a part of the campaign "barcoding mammals of the world". The second project, UMMB (Unidentified Marine Mammals in Brittany), includes specimens only identified to the genus or to higher taxonomic levels. This second project is a part of the campaign "barcoding application". Genetic distances (intraspecific, interspecific and minimal distance to the nearest neighbour) were calculated using the Kimura 2-parameter (K2P) model  (Kimura 1980)  and the MUSCLE alignment algorithm on the BOLD user interface or using the software MEGA5  (Tamura et al. 2011). Neighbour-Joining trees based on the K2P-model were built using the BOLD user interface. DnaSP v5. 10 was used to calculate haplotype and nucleotide diversities  (Librado and Rozas 2009). We used non-metric multidimensional scaling (nMDS) to represent MCR distances graphically and to discriminate closely related species within the Stenella-Tursiops-Delphinus complex  (LeDuc et al.
COI sequencing and analysis from different marine mammal samples DNA was extracted from 92 stranded animals, i. e. from dead cetaceans and pinnipeds, but also from 40 grey seals stranded alive, which were treated in the care center of Océanopolis (Brest, France) and from which a small blood sample was taken and kept at -20 °C. All the samples came from animals stranded at the coasts of Brittany, except for one grey seal (Hgc406), which stranded alive in Spain in 2009 and which  was transported to the care center (Figure  2 ). Our sampling included 12 species of cetaceans, and three species of pinnipeds (Table  2 ). Two species were very common, the harbour porpoise (29 samples) and the grey seal (44 samples), thus allowing intraspecific distance analyses. A COI amplicon was recovered from 89 samples, and good quality sequences of more than 500 bp were obtained for all samples (GenBank accession numbers KF281608-KF281697).
Taxonomic identification of undetermined samples We then determined COI sequences from 10 cetacean samples whose species could not be determined accurately using morphological characters (Figure  4 ), either because only parts of the animal were recovered (Figure  4A ) or because of the highly degraded state of the carcasses (Figure  4C ). COI sequences of good qualities were obtained from all these samples, and three of them were identified unambiguously using the BOLD identification engine: Ms250511 was identified as a Balaenoptera physalus, Ds160111 as a Grampus griseus and Ds290811 as a Phocoena phocoena. The other seven samples were Delphininae, as confirmed by COI sequences. Yet, neither the BOLD identification engine, nor a BLAST search on GenBank allowed a more precise determination.
We therefore sequenced MCR, which is more variable than COI, from six unidentified samples. BLAST searches on GenBank confirmed the COI results: all these samples were Delphininae, but a more precise identification could not be achieved. We constructed a nMDS plot of the distances between MCR sequences of S. coeruleoalba, S. frontalis and D. delphis taken from GenBank: for S. coeruleoalba, we used sequences AM498725, AM498723, AM498721, AM498719, AM498717, AM498715, AM498713, AM498711, AM498709, AM498707 (Mace et al. unpublished), for D. delphis FM211560, FM211553, FM211545, FM211535, FM211527, FM211519, FM211511, FM211503, FM211495  (Mirimin et al. 2009 ) and DQ520121, Figure  3. Neighbour-Joining tree of major species of marine mammals, based on K2P-distances calculated from 507 bp of COI.
The case of the Delphininae Within the Delphininae, species are difficult to discriminate  (Amaral et al. 2007 , 2012 , Viricel and Rosel 2012). In particular, Delphinus delphis, Stenella coeruleoalba and Stenella frontalis show very low interspecific COI distances, which do not allow distinguishing the species accurately. Other mitochondrial loci, such as MCR and cyt b, are neither very effective in this matter  (Amaral et al. 2007, Viricel and Rosel 2012). This is attributed to recent and rapid radiation events in the subfamily, and it leads to problematic results in molecular taxonomic studies  (Kingston et al. 2009 , Amaral et al. 2012 , Viricel and Rosel 2012 , Perrin et al. 2013 ). In our case, these three species produced COI and MCR sequences that did not allow to associate samples with species names, neither with the identification engine on BOLD, nor with a distance tree or a BLAST search on GenBank.
nMDS of genetic distances is known to uncover sample clustering (e. g. Geffen et al. 2004 , Maltagliati et al. 2006 , Alfonsi et al. 2012 , Weckworth et al. 2012 ). As such, nMDS clustering of MCR sequence distances of Delphinus delphis, Stenella coeruleoalba and Stenella frontalis, chosen randomly on GenBank among Atlantic samples, showed that individuals of the three species formed separate groups. Moreover, each individual had a high posterior probability to belong to the right group, except for one sample (i. e. 97. 0% of the assignments were successful), so that all our unidentified samples could be putatively identified to the species level, based on the nMDS plot and its posterior probabilities. Can DNA barcoding increase the accuracy of the data listed by the stranding network? DNA barcoding is informative for animals that belong to species that infrequently strand along the coasts of Brittany, which can involve either species living far off the coasts or living in deep water, but also exotic species.
Contributions of our study to the Barcoding of Life Database This project is part of the collaboration between the Laboratory BioGeMME of the "Université de Bretagne Occidentale" (Brest, France), Océanopolis, a public private company (http://www. oceanopolis. com), the "Parc naturel marin d'Iroise" (http:// www. parc-marin-iroise. com) and the French Stranding Network, coordinated by Pelagis, Université de La Rochelle, France. All the specimens and sequence data described in this manuscript are deposited in BOLD under the institution called "Oceanopolis-BioGeMME" in two projects, UMMB and IMMB. Our mixed institution became the first contributor to BOLD for the Cetacea, as well as for the Phocidae, and these two BOLD projects will be publicly available, and all the sequences published on GenBank.
Acknowledgements The samples in this study were collected by the  French Stranding Network and stored at Océanopolis (Brest) and at Pelagis  ( La Rochelle ). We especially thank all the members of the  LEMM, Christine Dumas  who is in charge of the care center at Océanopolis and  Willly Dabin  and  Olivier Van Canneyt  from  Pelagis. This work is part of a collaboration with ''The  Parc Naturel Marin d'Iroise '', and especially with  Phillipe Le Niliot ,  Cécile Lefeuvre  and all the technicians working on the field. We are indebted to  Paul Marec , who collected the samples and took the photographs of the stranded animals on Ushant Island. Loriane Mendez ,  Anne Moulinet ,  Sandrine Quemener  participated to some experiments during their internships in Bi-oGeMME. Special thanks go to  Chantal Hily-Mazé.
Phenome-Wide Association Studies (PheWAS) investigate whether genetic polymorphisms associated with a phenotype are also associated with other diagnoses. In this study, we have developed new methods to perform a PheWAS based on ICD-10 codes and biological test results, and to use a quantitative trait as the selection criterion. We tested our approach on thiopurine S-methyltransferase (TPMT) activity in patients treated by thiopurine drugs. We developed 2 aggregation methods for the ICD-10 codes: an ICD-10 hierarchy and a mapping to existing ICD-9-CM based PheWAS codes. Eleven biological test results were also analyzed using discretization algorithms. We applied these methods in patients having a TPMT activity assessment from the clinical data warehouse of a French academic hospital between January 2000 and July 2013.
Data after initiation of thiopurine treatment were analyzed and patient groups were compared according to their TPMT activity level. A total of 442 patient records were analyzed representing 10,252 ICD-10 codes and 72,711 biological test results. The results from the ICD-9-CM based PheWAS codes and ICD-10 hierarchy codes were concordant. Crossvalidation with the biological test results allowed us to validate the ICD phenotypes. Iron-deficiency anemia and diabetes mellitus were associated with a very high TPMT activity (p = 0. 0004 and p = 0. 0015, respectively). We describe here an original method to perform PheWAS on a quantitative trait using both ICD-10 diagnosis codes and biological test results to identify associated phenotypes. In the field of pharmacogenomics, PheWAS allow for the identification of new subgroups of patients who require personalized clinical and therapeutic management.
Therefore, for a selected SNP, two groups are composed: one with a specific allele and a control group with other alleles. Thereafter, to search for new associations, all of the phenotypic data (for example, all International Classification of Diseases (ICD) codes) available in the medical records of the patients having the specific allele are screened and compared to those of the control group  [9]. Denny et al. genotyped 6,000 patients in the BioVU data bank at five SNPs with previously reported disease associations and ran a PheWAS on each SNP, based on the ICD-9-CM codes  [7, 10]. They replicated four out of seven known molecular-clinical associations and discovered 19 new potential associations. Following this example, further PheWAS were performed on the SNPs associated with hypothyroidism (FOXE1)  [8] , rheumatoid arthritis  [11] , and on HLA-DRB1*1501, which has been linked to several autoimmune diseases  [12].
5 nmol/h/mL red blood cells (RBC); (ii) nTPMTa; and (iii) vhTPMTa, with an activity above 15. 0 nmol/ h/mL RBC  [43]. We have assessed that TPMTa is stable over time from the patients (n = 51) who underwent more than one TPMTa assay (Table  S2 ). For these patients, only the first measurement was used in the analyses. Data management. An open database connection (ODBC) linking an Oracle database (11 g Enterprise Edition Release 11. 2. 0. 1. 0) of i2b2 CDW (version 1. 3) to R software (version 2. 15. 3) was set up. The dataset containing data from the TPMT cohort (demographic, diagnoses, free text reports, structured questionnaires, biological tests results and drug prescriptions) was imported into R. All further analyses were carried out in R, using the RODBC 1. 3-6 and the ggplot2 0. 9. 3.
1 packages. Time restrictions. The information concerning the treatment was found in the drug prescriptions, in free text reports or in clinical data from structured questionnaires. We extracted prescriptions from the CPOE drug prescriptions with starting dates or directly from free text reports using the brand name and the generic name (IMUREL, AZATHIOPRINE, IMURAN, MERCAPTOPURINE, PURINETHOL) and using the date of report as the starting date. ICD codes analysis. We compared the proportions of cases and controls in the TPMTa groups: (i) vhTPMTa versus other TPMTa and the (ii) lowTPMTa versus other TPMTa. We selected the PheWAS codes with at least 5 occurrences for analysis. Biological test result analyses. Among the biological tests, we focused on 11 routine blood tests widely prescribed during the monitoring of thiopurine treatment: leukocyte count (WBC), neutrophil count, RBC count, hemoglobin, platelet count, mean corpuscular volume (MCV), glycemia, alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma glutamyl-transpeptidase (GGT) (Table  S1 ).
Discussion This study demonstrates the feasibility and benefits of performing a PheWAS on a quantitative trait. Two independent approaches, based on (i) ICD codes and (ii) biological test results, were used to discover pathophysiological features potentially associated with this quantitative trait. In this manner, findings can be cross-validated: the phenotypes extracted from diagnosis codes were confirmed by the biological test results. By this way and using a quantitative trait in the context of pharmacogenomics we discovered new potential associations between TPMTa related to thiopurine treatment and clinical data. To our knowledge, this is the first PheWAS performed using data encoded with ICD-10 classification, as previously published PheWAS were based on ICD-9-CM. The consistency in the results found between the two aggregation methods -the ICD-10-based method and the mapping between ICD-9-CM and ICD-10demonstrates the feasibility of PheWAS using ICD-10.
In our study population and using the ICD code distribution described above, ICD-9-CM based PheWAS codes resulted in more informative phenotypes than the ICD-10 based. Thus, it appears that ICD code aggregation level, i. e. the number of code groups, needs to be optimized according to the size of the population. For example, in a larger population, it may be more appropriate to use a fine grained aggregation based on the 3 digit codes of ICD-10, resulting in more accurate phenotypes. The use of a CDW gives the opportunity to combine data from six heterogeneous sources: demographic data from administrative records, diagnosis codes from the billing system, biological test results, drug prescriptions from the CPOE system, free text reports, and clinical data from structured questionnaires. The clinical interpretation of patient condition by the physician, encoded with ICD codes, and the biological test results, extracted from the laboratory result server, were confronted.
Drug prescriptions were extracted directly from the structured data issued by CPOE, structured questionnaires and from free-text reports. The close relationship between thiopurine drug prescriptions and TPMTa assays for therapeutic management was taken into account by incorporating temporal data for this study. Therefore, we restricted our analyses to the events following the initiation of thiopurine therapy. In addition to patient selection based on TPMTa, biological test results were employed to validate the phenotypes obtained from ICD codes analysis. Thus, we assessed the feasibility of expanding PheWAS to another type of data from the CDW. In that aim, classification algorithms were developed to transform continuous test results into discrete classes using value and frequency thresholds.