dataesr/scientific-paragraphs-categorization huggingface dataset:
Patient 1 The patient had an electrode implanted in the left anterior insula (Figure  5 ). Due to a technical failure, accuracy data in the Go-trials were not recorded. Statistical analyses showed that one contact showed a selective response to a word's emotional category. It should be noted that the other contacts on the same electrode showed similar differences, but these differences failed to reach significance. Three time windows exhibited significant differences between neutral and disgust words, namely an early time window between 186 and 225 ms (t(44) ¼ 3. 77, P ¼ 0. 001), a N400 time window between 437 and 500 ms (t(44) ¼ 2. 46, P ¼ 0. 005) and a late time window between 727 and 865 ms (t(44) ¼ 4. 26, P ¼ 0. 001).
The authors thank  Amel Mosbah  for assistance in data collection and analysis with patients and  Romain Duprat  for assistance in scalp EEG data acquisition. This research was supported by a  French-German ANR/DFG  grant [ ANR-10-FRAL-005-01  awarded to J. C. Z. and A. M. J. ]; and CLC was supported by a  CPER Telius Inserm-Inria  grant.
Study population Data were drawn from the Whitehall II study, an ongoing longitudinal study of 10 308 (67% men) London-based British civil servants aged 35-55 years in 1985. 15 Study inception ( phase 1) took place during 1985-1988 and involved a clinical examination and self-administered questionnaire. Subsequent phases of data collection have alternated between postal questionnaire alone ( phases 2 (1988-1990), 4 (1995-1996), 6 (2001), 8 (2006)  and 10 (2011)), and postal questionnaire accompanied by a clinical examination approximately every 5 years ( phases 3 (1991-1993), 5 (1997-1999), 7 (2002-2004) and 9 (2007-2009)). We utilised CVD risk factors measured at phase 5 ('baseline' for the purposes of our analyses) to assess the risk of developing frailty at phase 9 when the frailty components were first measured.
(2) To examine whether the association between the risk scores and frailty was biased by missing data, we imputed data for missing frailty status and individual CVD risk factors included in the risk scores. This was done for participants eligible at phase 5 and alive at the end of follow-up (n=7412) using the method of multiple imputation by chained equations performed with an SAS-callable software application, IVEware. 23 (3) We tested whether the CVD risk scores also predict 'pre-frailty' in a cohort excluding the frailty cases (see supplementary web appendix). Finally, to explore the extent to which the relationship between the risk scores and frailty was driven by specific CVD risk factors included in the scores, analyses on the risk scores-frailty associations were adjusted individually for each of their risk factors (see supplementary web appendix, table  S1 ).
RESULTS Of the 7870 study members who participated at phase 5, a total of 3895 participants (1037 women) aged 45-69 years constituted the analytic sample (figure  1 ). Compared with participants alive at phase 9 but excluded (owing to non-participation at phases 5 and 9, prior history of CVD at phase 5, and missing data on the CVD risk scores or the frailty scale, total n=3517), persons included in the analytic sample (n=3895) were 1. 1 years younger ( p<0. 0001), less likely to be female (26. 6% vs 34. 5%, p<0. 0001) and less likely to be from the lower socioeconomic group (10. 0% vs 20. 0%, p<0. 0001) (see supplementary web appendix, table  S2 ). In table 2 we present the baseline characteristics of participants according to frailty status at the end of follow-up, on average 10. 5 years (SD=0.
Our sensitivity analysis suggests this is not a major source of bias because the results using the multiple multivariate imputation method were largely similar to those reported in the main analysis. However, we cannot rule out bias arising from attrition not covered by the missingness-at-random assumption. Finally, our study sample consisted of middle-aged civil servants, limiting the generalisability of our findings. These limitations can be compared to the main strength of our study, which resides in the use of prospectively collected data given that previous studies that have examined the association between CVD or its individual risk factors and frailty used cross-sectional data. 2 8 9  Our results suggest a relationship between the CVD risk scores and frailty that is independent of existing CVD.
Acknowledgements We thank all participating men and women in the Whitehall II study; all participating Civil Service departments and their welfare, personnel and establishment officers; the  Occupational Health and Safety Agency ; and the  Council of Civil Service Unions. The Whitehall II study team comprises research scientists, statisticians, study coordinators, nurses, data managers, administrative assistants and data entry staff, who make the study possible. Contributors MK and GDB conceived the idea for the study and along with KB developed the objectives and design of the study. KB ran the analyses and acts as guarantor of the paper. KB, MK and GDB drafted the paper. All authors contributed to the interpretation of results and revision of the paper, and approved the final version of the paper.
On the basis of the effect of HCV core on cell polarity, we hypothesized an eventual deregulation of the PtdIns-metabolizing enzymes by HCV to induce loss of polarity. We focus on SHIP2, a PI phosphatase that con-  verts PtdIns(3, 4, 5)P3 to PtdIns(3, 4)P2. First, we examined the localization of SHIP2 in polarized MDCK cysts using a SHIP2 antibody. SHIP2 was found at the basal area at ECM-cell contacts, and, of interest, PtdIns(3,4)P2 was also present at the basal membrane even though some apical dots were observed (Figure  3A ). These data were reinforced by transfection of MDCK cells with a green fluorescent protein (GFP) construct of SHIP2. The efficiency of the transfection was validated by immunoblot (Supplemental Figure  S4A ), and GFP SHIP2 was found mainly at the basal domain at ECM contact (Supplemental Figure  S4, B  and C ).
The localization of PtdIns(3,4)P2 was also examined using a yellow fluorescent protein-tandem pleckstrin homology domain protein (TAPP) probe (Supplemental Figure  S4D ). The probe comprised the pleckstrin homology domain of TAPP protein, which specifically binds to PtdIns(3,4) P2  (Wullschleger et al. , 2011). A signal was observed at the basal membrane; in contrast to data obtained with SHIP2 antibody, however, the signal was most diffuse in the cytoplasm. In addition, we analyzed the membrane localization of SHIP2 through subcellular fractionation using ultracentrifugation. Figure  3B  shows that SHIP2 is enriched in the membrane fraction, with a minor part in the cytosol. Figure  3B  shows a densitometry analysis of Western blot data. These results similar to the subcellular distribution of HCV core presented in Figure  1C.
Next we performed a PIP-Strip overlay assay using lysates from polarized MDCK cells and probed the strip with Dlg1 and Scribble antibodies. The data show that Dlg1 bound mainly to PtdIns(3,4)P2 and to a lesser extent PtdIns(3,4,5)P3 (Figure  5C ). No signal was observed for Scribble (data not shown). To quantify the relative affinity, we next performed a PIP array and spotted each lipid in seven different concentrations (Figure  5D ). Although there is low binding to other PtdIns species, here again PtdIns(3, 4)P2 appeared as the preferential ligand for Dlg1 and could be responsible for its membrane localization.
The opposite data were observed when SHIP2 was reexpressed. These results were highlighted by the 3D reconstruction of the different confocal sections of cysts. These pictures clearly show the recovery of the spherical structure of cysts in the presence of SHIP2. The staining of both SHIP2 and core was quantified using ImageJ software and is presented below the pictures as histograms (Figure  6E ). Furthermore, cells were transfected with SHIP2 phosphatase dead mutant to determine its effect on cell polarization. The transfection efficiency was validated by immunoblot (Supplemental Figure  S6A ), and in such conditions, the mutant was not able to rescue polarity defects induced by HCV core, and even a slight increase of multilumen cysts was observed. Moreover, these cysts presented an increase number of lumens (Supplemental Figure  S6B ), underlining the importance of SHIP2 enzymatic activity to build polarity.
ACKNOWLEDGMENTS We thank  Christian Bréchot  for useful critical discussions of the manuscript;  Sophie Allart  and  Abdelali Jalil , imagery services at the  Institut National de la Santé et de la Recherche Médicale Unité 563, Toulouse , and  Institut Gustave Roussy, Villejuif , respectively; and  Nassima Benzoubir  and  Marion Bourgeade ,  Institut National de la Santé et de la Recherche Médicale Unité 785, Villejuif , for providing the MDCK and Huh7 cell lines stably transfected with core. We thank  Nicholas Leslie  for providing the GFP-SHIP2 and YFP-TAPP constructs and for helpful discussions. This work was supported by grants from the  Association pour la Recherche sur le Cancer  ( ARC/ SUBV/CKLQ6 ) to A. G. D. and the  Fonds de la Recherche Scientifique (Belgium)  to C. E. We acknowledge the support of  Pfizer  to J.
The Human Phenotype Ontology (HPO) project, available at http://www. human-phenotype-ontology. org, provides a structured, comprehensive and welldefined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation.
Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online.
The description of phenotypic variation is a central topic for all of those studies, and therefore, a systematic human and computer interpretable representation is crucial due to the complex nature of human disease. Therefore, the usage of standardized measures for capturing phenotypic abnormalities is of major importance  (3) , but in the past, it has been difficult to agree on common semantic and technical standards as well as the ethical and legal framework for collecting and analyzing human phenotype data  (4). An ontology provides a conceptualization of a domain of knowledge, allowing communication between researchers and makes the information readable and 'understandable' for computers  (5). The Human Phenotype Ontology (HPO) project  (6)  was initiated in 2007 to enable sophisticated integration of phenotype information across scientific fields and databases.
PHENOTYPE ANNOTATION DATA We provide a large set of phenotype annotations, i. e. statements that link a particular term from the HPO to specific diseases or genes. These annotations are made for the most specific term of the HPO, as all of the ancestor terms are implicitly annotated as well. At the time of this writing, we provide 110 301 annotations to 7354 diseases listed in the Online Mendelian Inheritance in Man [OMIM,  (16) ] database, Orphanet  (17)  and DECIPHER  (18). On average, each disease entry has 15 HPO annotations. For Orphanet entries   that are exactly mapped to one OMIM entry, we merge the entries and record the provenance of the annotations. The annotations of OMIM entries are a mixture of manual annotations performed by the HPO team and automated matching of the OMIM Clinical Synopsis to HPO term labels.
The substantial increase in annotation data during since 2008 is shown in Figure  2. Each annotation may have several meta-attributes such as the age of onset, the frequency or a modifier. At the moment, 46 149 annotations have information on the frequency with which individuals with a given disease have a certain phenotypic feature. For instance, 9 of 43 persons with the disease sialidosis type II have cherry red spot of the macula (HP:0010729)  (19). At the moment, the majority of frequency annotations are derived from Orphanet, but a growing number is based on the manual annotation efforts by the HPO team. Furthermore, we provide a set of 303 negative annotations (NOT-modifier), for which patients with this disease are known not to have the clinical feature in question. The frequency and negation information may be important for the differential diagnosis  (20).
A standard strategy for WES data analysis is to compare variants found in multiple affected patients. Especially with autosomal dominant disorders, many unrelated individuals must be analyzed for this strategy to be successful  (23). Therefore, one of the first tasks in WES disease gene discovery projects is to identify multiple patients with the same disease phenotype, which has been extremely successful in identifying novel disease genes even in diseases for which there was little or no previous knowledge about the characteristics of the disease gene. However, many of the Mendelian diseases still waiting to be discovered are very rare or difficult to diagnose clinically. To make progress on elucidating these disorders, it will likely be necessary to combine data from multiple centers to identify a sufficient number of patients with mutations in the same gene and comparable phenotypes-which is widely accepted as a necessary criterion for the identification of a novel disease gene.
This approach has been implemented successfully for copy-number variation (CNV) disorders in the International Standards for Cytogenomic Arrays Consortium's publicly available database of CNVs identified during the course of routine clinical microarray testing (http://www. ncbi. nlm. nih. gov/dbvar/studies/nstd37/ ;https://www. iscaconsortium. org). Recognizing that cataloging the phenotype information associated with each CNV would be key in trying to elucidate genotype-phenotype relationships, the group began using HPO terms (as opposed to free text) to describe the phenotypes in a manner that was generalizable (to maintain patient anonymity) as well as easily indexable and searchable for the clinical and research communities  (24). Given the success of this approach, the International Standards for Cytogenomic Arrays has expanded its focus to include sequence variation, and, under the name International Collaboration for Clinical Genomics, will continue to use HPO terms to describe the phenotypes associated with results from additional testing modalities, including WES  (25).
A similar approach is also being used by the DECIPHER project, which enables clinical scientists worldwide to maintain records of phenotype and chromosome rearrangement for their patients and, with informed consent, share this information with the wider clinical research community to find clusters of rare cases having phenotype and structural rearrangement in common  (18). The Deciphering Developmental Disorders project of the Wellcome Trust Sanger Institute has been initiated to use new genomic technologies including especially WES to identify novel etiologies for developmental disorders, and is focused on severe and extreme developmental phenotypes affecting any organ system, which are coded using HPO. An international collaborative study, the Biomedical Research Centres/Units Inherited Diseases Genetic Evaluation consortium, will use the HPO database to record detailed clinical phenotypes of patients with rare inherited disorders (www.
bridgestudy. org). The HPO database that comprises phenotypes related to abnormalities in blood and blood-forming tissues has already facilitated detailed description of the clinical phenotypes of patients with bleeding and platelet disorders (Biomedical Research Centres/Units Inherited Diseases Genetic Evaluation-Bleeding and Platelet Disorders). The homogenization of these clinical phenotypes related to bleeding and platelet disorders will further assist in the clustering of data for detailed bioinformatics analysis of exome sequence data. These patients will be part of the NIHR Bioresource for Rare Diseases. The European Cytogeneticists Association Register of Unbalanced Chromosome Aberrations (ECARUCA, http://www. ecaruca. net), initiated in 2003, is an online database that collects and provides detailed, curated clinical and molecular information on rare unbalanced chromosome aberrations that are considered to be likely causative for the patient's phenotype  (26).
The objective of ECARUCA is to improve the knowledge of rare chromosome aberrations both for medical and research purposes. Currently, the database contains more than 4800 cases with HPO features characterizing these cases, and all these data are publicly available to professionals in genetics. The Nijmegen Genetics Phenotype Database (NGPD, https://www. clinicalfeatures. eu/default. aspx) aims to collect detailed phenotype information of patients with unexplained intellectual disability and/or congenital anomalies using the HPO. The goal of the NGPD is to identify patients who have similar clinical features that are likely due to the same or a related genetic defect. The NGPD currently contains more than 8000 patients with 73 496 HPO features annotated to these patients (median seven features per patient).
Computational approaches are currently being developed for the identification of clusters of phenotypically overlapping patients. Exome sequencing and targeted candidate gene analysis will ultimately provide a diagnosis for many of these patients. Cartagenia (www. cartagenia. com), a genetics software solution provider that services diagnostic laboratories through a set of automated tools for variant interpretation, filtration, reporting and sharing, has standardized the phenotype functions for clinical patient record annotation of its BENCH laboratory platform on HPO. Several advantages come by using HPO: automated genotype-phenotype correlation, advanced search of patients within laboratories but also in external databases (see earlier) and easy sharing of patient phenotype data among different consortia.
See  (13). closely related projects such as the cross-species phenotype ontology Uberpheno  (13)  are available as well. The HPO release (job hpo) is triggered whenever changes in any of the ontology or logical definition files are uploaded. For every build, the OBO Ontology Release Tool (Oort, https://code. google. com/p/owltools/wiki/ OortIntro) is used to generate OBO-and OWL-format versions of the HPO. In addition, the GULO software  (28)  is used to generate a report on the overlap between the hierarchy inferred from the logical definitions and the manually asserted HPO hierarchy. This is used to incrementally improve both the logical definitions and the HPO structure. Annotation data are also integrated in our Hudson build system (Table  4 ). Every HPO release induces a rebuild of the annotation data (job hpo.
annotations). This job pulls the latest manual annotation data (http://svn. code. sf. net/p/obo/svn/phenotype-commons/annotations/OMIM/ by-disease/annotated/) and the latest Orphanet data (http://www. orphadata. org) and constructs one integrated disease annotation file. Again only successful builds are made available, such that e. g. manually curated annotations are automatically checked for consistency before being offered to the public. The simplest check verifies the syntactical correctness of the input files. Another example is that the generation of annotation files fails if there are annotations to obsolete terms, which are terms that have been marked as to be replaced by other HPO terms and thus should not be used for annotation anymore. Another check confirms that annotation onset-modifiers are correctly chosen from the Onset and clinical course subontology.
Once a month, several secondary files are created automatically by the Hudson build system. The job hpo. annotations. monthly creates an MySQL version of the HPO and the annotation data. It also constructs direct gene-to-phenotype mappings, which use known gene-to-disease relations (from morbidmap and Orphanet) and disease-to-phenotype relations from the job hpo. annotations. So for example the gene ATXN10 (Entrez ID 25814) will be associated with Gait ataxia (HP:0002066), because mutations in that gene cause Spinocerebellar ataxia (OMIM:603516), which is annotated to this HPO class. The files are constructed for different phenotype annotation sources (OMIM, Orphanet) and different frequency thresholds. Other jobs generate the data used by the Phenomizer(hpo. annotations. monthly. phenomizer), a precomputed disease-disease similarity matrix (hpo.
org) PhenoDigm (  14 ) as well as PhenomeNET (  12 ) enable searches for novel disease genes based on the analysis of model-organism phenotypes. The HPO has been used to integrate phenotypic information into computational analysis of the distribution of proteins in the postsynaptic density of the human neocortex  (31) , to derive a disease-disease similarity measure for the prediction of novel drug indications  (32)  and to analyze overrepresentation of phenotypes associated with individual protein domains  (33). A summary of tools and applications using data from the HPO project is given in Table  5. The HPO project offers a number of files that are intended to help users use these kinds of data for their own research. A Hudson job (hpo. diseasesimilarity) creates a precomputed disease similarity matrix, which contains all diseases that have annotations to the HPO subontology 'phenotypic abnormality'.
MySQL dump of the HPO database, where MM and YYYY denote the month and year of release. Precomputed disease-disease similarity matrix for all diseases with annotations to HPO's phenotypic abnormality subontology. Symmetric and asymmetric semantic similarity score. Cross-species phenotype ontology (human, mouse, zebrafish). File(s) at URL hp. obo, hp. owl human-phenotype-ontology_xp. obo onet_hpo. tsv, LDDB2HPO-v2. csv, medraMapping. tsv negative_phenotype_annotation. tab phenotype_annotation_hpoteam. tab phenotype_annotation. tab <source >_ <freq >_genes_to_phenotype. txt <source >_ <freq >_phenotype_to_genes. txt MYHPO_MM_YYYY. sql matrices. tar. gz crossSpeciesPheno. obo URL of latest stable release for job (relative to http://compbio. charite. de/hudson/job/) hpo/lastStableBuild/ hpo. annotations/lastStableBuild/ hpo.
Study species Triturus cristatus, the great crested newt (Fig. 1 ), is widely distributed from the United Kingdom to northern France, through southern Scandinavia to central Europe, and into a small part of the Balkans. The species is listed on the Habitats Directive of the European Union (92/43/EEC) and is threatened by exposure to fish, habitat loss, and habitat fragmentation (see  Jehle et al. 2011; Denoe ¨l 2012; Denoe ¨l et al. 2013). Thus far, only eight applicable microsatellite loci for T. cristatus have been published  (Krupa et al. 2002) , which might be an insufficient number to reveal consistent results in population genetic analyses (e. g. , SPOTG software;  Hoban et al. 2013). For the two endemic mountain brook newt species of the genus Calotriton (C. asper, the Pyrenean brook newt and C.