dataesr/scientific-paragraphs-categorization huggingface dataset:
Data resource access Access to HES APC data is provided by NHS Digital for the NHS, government, researchers and commercial health care bodies. Those requesting an extract of the data must show that their work will support health and social care and improve health. 76 Data cannot be released for solely commercial purposes. Data are requested through the online Data Access Request Service (DARS). Applications are evaluated by the Data Access Advisory Group which check all data requests for patient-level data to evaluate whether there is an appropriate legal basis for data dissemination and that appropriate data security is in place. Details about HES applications and associated costs are available on the DARS website [http://content. digital. nhs. uk/DARS]. NHS Digital carries out audits to check that data users meet obligations regarding the terms and conditions of use, including disclosure control.
77 rofile in a nutshell • Data fields exist for diagnoses, procedures, patient demographics (including ethnicity and area-level deprivation), admission and discharge dates, hospital and other variables. • HES APC data can be linked to outpatient and emergency department attendances as well as datasets external to NHS Digital, including death registrations. • Aggregate data are accessible via the NHS Digital website and individual-level data are available through the NHS Digital Data Access Request Service, subject to approval and a cost recovery charge.
HES APC contains data on all admissions to National Health Service (NHS) hospitals in England, or to independent hospitals where the costs are met by the NHS. It was originally set up for purposes of management and planning of hospital services. Data are now also collected for purposes of reimbursing hospital activity. • HES APC includes all hospital care episodes from the financial year 1989/90 onwards (1 April 1989-31 March 1990). Pseudonymized patient identifiers that allow for longitudinal follow-up of patients are available from 1997/98 onwards. • HES APC data are entered from medical records by clinical coders in each hospital, according to national clinical coding standards. The database is collated and processed centrally by NHS Digital (previously the Health and Social Care Information Centre).
Funding L. W. is supported by funding from the  Department of Health Policy Research Programme  through funding to the  Policy Research Unit in the Health of Children, Young People and Families  (grant reference number  109/0001 ). This is an independent report commissioned and funded by the  Department of Health. The views expressed are not necessarily those of the Department. A. Z. 's PhD studentship is supported by awards to establish the  Farr Institute of Health Informatics Research, London , from the  Medical Research Council, Arthritis Research UK ,  British Heart Foundation ,  Cancer Research UK, Chief Scientist's Office, Economic and Social Research Council, Engineering and Physical Sciences Research Council, National Institute for Health Research, National Institute for Social Care and Health Research and Wellcome Trust  (grant  MR/K006584/1 ).
• HES APC contains data on all admissions to National Health Service (NHS) hospitals in England, or to independent hospitals where the costs are met by the NHS. It was originally set up for purposes of management and planning of hospital services. Data are now also collected for purposes of reimbursing hospital activity. • HES APC includes all hospital care episodes from the financial year 1989/90 onwards (1 April 1989-31 March 1990). Pseudonymized patient identifiers that allow for longitudinal follow-up of patients are available from 1997/98 onwards. • HES APC data are entered from medical records by clinical coders in each hospital, according to national clinical coding standards. The database is collated and processed centrally by NHS Digital (previously the Health and Social Care Information Centre).
Within the T. kodakaraensis genome, genes that code for tryptophan biosynthesis are present in a single locus too  (26). A transcriptomic study showed that the chorismate synthesis is downregulated when T. kodakaraensis is cultivated under high hydrostatic pressure, compared to atmospheric pressure  (27). However, in the same study, the gene trpC, labeled TK0252 in T. kodakaraensis, is upregulated, indicating that the strain could continue to produce tryptophan and compensate the decrease of chorismate production under high pressure. Therefore, the regulation of tryptophan biosynthesis in T. sp. EXT12c under high-pressure conditions requires further investigations. Accession number(s). This genome sequence has been deposited in DDBJ/ENA/ GenBank under the accession no. LT900021. The version described in this paper is the first version.
ACKNOWLEDGMENTS This work was supported by the  "Laboratoire d'Excellence" LabexMER  ( ANR-10-LABX-19 ) and cofunded by a grant from the  French Government  to L. M. under the program " Investissements d'Avenir ," a grant from the  Regional Council of Brittany  to L. M. , and the  Frank R. Lillie Research Innovation Award  from the  Marine Biological Laboratory  to A. M. E.
This study aims to elucidate the pattern of morphological variation seen in the skull of long-nosed armadillos, with a focus on the nine-banded armadillo. Geometric morphometric data were collected for most Dasypus species using micro-computed tomography (mCT). The main questions asked in the present study are whether different patterns of variation in skull shape can be characterized among and within long-nosed armadillo species, and if those patterns could be linked to factors such as taxonomy, geographical distribution, skull size, or sexual dimorphism. Our ultimate goals are to reconstruct the details of the biogeographic distribution of the widespread nine-banded armadillo at the continental scale and to lay the path for a new integrative taxonomy of long-nosed armadillos. A greater understanding of the morphological diversity and patterns of evolution for long-nosed armadillos is timely to effectively conserve these species, and will also serve to deepen our knowledge of their peculiar evolution and biology  (Loughry & McDonough, 2013).
We analyzed 128 skulls belonging to five Dasypus species (see Table  S1  for a complete list of specimens): Dasypus novemcinctus, Dasypus hybridus, Dasypus septemcinctus, Dasypus kappleri, and Dasypus pilosus (no data was available for Dasypus mazzai and Dasypus sabanicola). With these data we performed a preliminary assessment of the average amounts of cranial variation at the specific level among different populations of Dasypus novemcinctus from French Guiana, Guyana, Suriname, Ecuador, Brazil, Venezuela, Colombia, Costa Rica, Belize, Bolivia, Argentina, Paraguay, Uruguay, Panama, Nicaragua, Honduras, Guatemala, Mexico, Peru, and the USA (Table  S1 ). Juvenile, subadult, and adult specimens were considered in order to take into account the effect of age, size, and differential growth on the dataset.
Several studies  (Hensel, 1872; Russell, 1953; Ciancio et al. , 2012)  showed that long-nosed armadillos possess tooth replacement, as is typical for mammals, and that the eruption of permanent teeth occurs relatively late, as observed in afrotherians  (Asher & Lehmann, 2008). Accordingly, we used eruption of the teeth, suture closure, and size (i. e. , skull length) as criteria to identify adult specimens in our dataset. Specimens were considered as juveniles when they displayed milk teeth and an open suture between the basioccipital and the basisphenoid. Subadults were characterized by erupting permanent teeth. Adult specimens always showed a closed basioccipital/basisphenoid suture and a fully erupted permanent dentition. Except when specified, only adults and subadults specimens were considered in the morphometrical analyses.
Geometric morphometric methods Due to the limitations of the classical qualitative descriptive approach, geometric morphometrics represents a good complementary technique by which to examine intraspecific shape variation. Digital data for all specimens were acquired using X-ray mCT at the University of Montpellier (France), at the Natural History Museum (London, UK), and at the AST-RX platform MNHN (Paris, France). Three-dimensional reconstruction and visualization of the skulls were performed using stacks of digital mCT images with AVIZO v. 6. 1. 1 software (Visualization Sciences Group, Burlington, MA, USA). The mandibles and crania of armadillos were quantified with 10 and 82 anatomical landmarks respectively (Fig. 1 ; Tables  1  and 2 ) using ISE-MeshTools (version 1. 3. 1; Lebrun, 2014).
, 2010). To account for the potentially confounding effects of size allometry on shape of the cranium and mandible at the intraspecific level (i. e. , within Dasypus novemcinctus), size-corrected shapes were obtained as follows: we conducted a multivariate regression of Procrustes residuals on the logarithm of the centroid size, yielding an allometric shape vector (ASV). The ASV represents a direction in shape space, which characterizes mandibular and cranial allometric patterns. All crania and mandibles of Dasypus novemcinctus were then projected on ASVs, the residuals representing the component of skull shape that is independent of size. Shape variation independent of size was visualized by PCA of shape using MORPHOTOOLS. PCres corresponds to principal components of a PCA performed on shape data corrected for allometry.
The same analyses could not be performed at the interspecific level due to large differences in sample size. Multivariate analyses of variance (MANOVA) were performed on the principal component scores of mandibular and cranial mean shapes in order to assess the effects of  different factors on mandibular and cranial shape variation: clades (species), sex, and geographic distribution (countries). Because our dataset consisted of a relatively large number of variables, PCs accounting for 90% of the interspecific shape variance in the sample were retained for analysis. MANOVAs were performed with Past 2. 06  (Hammer, Harper & Ryan, 2001). Linear discriminant analyses (LDA) of shape coordinates were performed on the same number of PCs to assess a potential discrimination of skull morphology in relation to phylogeny (i.
e. , species) and geography (i. e. , countries). If lumping specimens by country has little biological relevance, we consider that using such artificial boundaries also constitutes a mean to avoid making a priori hypotheses. In all cases, all LDA results were double-checked with post hoc classification methods and PCA results. When a group included only one individual, this specimen was integrated into the dataset as an ungrouped case. A skull from Panama (NMNH 171052) was not complete enough to be considered in these analyses. We then decided to perform similar analyses with a reduced set of landmarks (81 landmarks on the cranium) to enable its morphological comparison with other specimens.
Florent Goussard (Platform AST-RX MNHN, Paris, France) generously provided help and advice on the acquisition of CT scans. Some of the experiments were performed using the m-CT facilities of the Montpellier Rio Imaging (MRI) platform and of the LabEx CeMEB. In compliance with Advantages and Benefits Sharing policy in French Guiana, material from French Guiana has been registred in the collection JAGUARS (http://kwata. net/lacollection-jaguars-pour-l-etude-de-la-biodiversite. html; CITES reference: FR973A) supported by Kwata NGO, Institut Pasteur de la Guyane, DEAL Guyane, and Collectivite Territoriale de la Guyane. This is contribution ISEM 2017-128 of the Institut des Sciences de l'Evolution.
ACKNOWLEDGEMENTS We are grateful to  Christiane Denys ,  Violaine Nicolas , and Geraldine Veron, ( Museum National d'Histoire Naturelle, Paris, France ),  Roberto Portela Miguez ,  Louise Tomsett , and  Laura Balcells  ( British Museum of Natural History, London, UK ),  Eileen Westwig  ( American Museum of Natural History, New York, USA ),  Burton Lim  ( Royal Ontario Museum, Toronto, Canada ),  Nicole Edmison  and  Chris Helgen  ( National Museum of Natural History, Washington, DC, USA ),  Jake Esselstyn  ( Louisiana State University, Museum of Natural Sciences, Baton Rouge, USA ),  Manuel Ruedi  ( Museum d'Histoire naturelle, Geneva, Switzerland ),  Claudia Regina da Silva (Instituto de Pesquisas Cientı ´ficas e Tecnologicas do Estado do Amapa, Macapa, Brazil) ,  Steven van der Mije  ( Naturalis Biodiversity Center, Leiden, Netherland ),  Francois Cazeflis  and  Suzanne Jiquel  ( Institut des Sciences de l'Evolution, Montpellier, France ),  Lucile Dudoignon  ( KWATA association, Cayenne, France ),  Pierre Charles-Dominique  ( CNRS, Montpellier, France ),  Maria-Clara Arteaga ,  Maria Nazareth da Silva  ( Instituto de Pesquisas Cientı ´ficas e Tecnologicas do Estado do Amapa, Macapa, Brazil ) for access to comparative material.
Funding This work has benefited from an " Investissements d'Avenir " grant managed by  Agence Nationale de la Recherche, France  (CEBA,. This research received support from the  Synthesys Project  (http://synthesys3. myspecies. info/),which is financed by the  European Community Research Infrastructure Action  under the  FP7. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Metabolome and Lipidome Analyses Validate the Use of Omics-Based GEMs for Identifying Key Metabolic Features We performed mass-spectrometry-based metabolomics and lipidomics profiling for CHO-K1 and SH-87 cells to validate the key predictions of omics-based global metabolic modeling (STAR Methods). Here, it should be highlighted that, to the best of our knowledge, this is the first study to present the comprehensive CHO lipidome covering 95 lipids (see Data S1). Overall, we identified 18 polar metabolites associated with energy metabolism and N-glycosylation, and 65 lipid compounds to be present at varied levels between the two cell lines (Bonferroni corrected p value <0. 01; Figure  S3 ). Strikingly, 43% of such metabolites with altered levels were also identified as potential biomarkers by the reporter metabolite analysis, indicating a moderate correlation between transcriptomics and metabolomics that has been typically observed  (Cavill et al.
SH-87 specific SVs were identified by comparing calls for SH-87 and CHO-K1 as described previously  (Hillmer et al. , 2011). RNA-PET tags were mapped on the reference CHO-K1 genome. They are clustered by their paired-end positions and produce digital gene expressions for the transcripts. Detecting Vector Integration Sites The Pindel software  (Ye et al. , 2009)  uses a split-read mapping approach to detect breakpoints, short indels and other SVs. The BWA generated BAM files for scaffolds >1000 bp in length were analyzed using Pindel for breakpoints. Each breakpoint prediction in Pindel consists of two sequence fragments: a fragment that matches the current reference scaffold and a fragment that does not match the reference scaffold. In our case, we assume the unmatched sequence represents a translocation event from another scaffold.
Analysis of the Vector Insertion Sites by PCR Of the 6 insertion predicted sites (labeled as A to F), 3 sets of primers were designed for each insertion site with one primer in the genomic DNA region, and the other one in the vector. PCR was carried out to verify product size and to test primers before sequencing. PCR products were obtained for insert regions A to F with 3 sets of primers and the amplicons were in agreement with genome data (Figure  S5 ). Additionally, sequences for insert regions A, B and C, D, E and F are verified to be correct in agreement with the insertion site found in the SH-87 genome.
ACKNOWLEDGMENTS This work was supported by the  Academic Research Fund  ( R-279-000-476-112 ) of the  National University of Singapore, Biomedical Research Council of A*STAR (Agency for Science, Technology and Research) ,  Singapore , and a grant from the  Next-Generation BioGreen 21 Program  (SSAC, no. PJ01109405 ),  Rural Development Administration, Republic of Korea. The authors would also like to thank the  A*STAR Computational Resource Centre  for supporting the high-performance computing facilities and  Denis Bertrand  for assistance with SNP and indel analysis.
Data Resources Raw whole genome shotgun sequencing, DNA-PET, RNA-seq, and RNA-PET data generated in this study have been deposited in SRA under ID code SRA: SRP101907 with Bioproject under the ID code PRJNA378939. The individual SRA accession codes are provided in Data S1. The merged-assembly of CHO-K1 genome is deposited in public repository, Mendeley Data under the DOI: http://dx. doi. org/10. 17632/5ccnnwhszm. 1. e6 Cell Systems 4, 530-542. e1-e6, May 24, 2017.
(a) Delta protrusion area In order to highlight recent deltaic shoreline changes and variations in delta protrusion area, we selected available satellite images of high (pixel size: 1. 5 to 2. 5 m) to moderate (pixel size: 30 to 60 m) resolution made available by the USGS and the French IGN, as well as aerial orthophotographs and, for the Rhône, field data (Table  2 ). The spatial data were chosen to cover the entire ' delta-influenced' shoreline for each year of analysis and with a cloud cover not exceeding 10%. We limited our choice to images taken at low tide and systematically in January of every year to minimize seasonal and tidal distortions (tides induce very little variability in the microtidal context of the Mediterranean and Black Seas). For each delta, the change in protrusion area between 1985 and 2015 was determined.
Data Accessibility Statement • The World Deltas Database Framework: http://www. geol. lsu. edu/WDD. • Landsat satellite images: https://earthexplorer. usgs. gov/. • Wave climate data: http://apps. ecmwf. int/datasets/ data/era40-daily/levtype=sfc/. • Tide data: http://www. psmsl. org/. • The digitized shorelines and geometric processing can be available by contacting MB (besset@cerege. fr).
Acknowledgements We acknowledge the use of Landsat satellite images provided by the  United States Geological Survey (USGS) ,  Spot  satellite images provided by  Centre National d'Etudes Spatiales (CNES) ,  ECMWF ERA-40  and  ERA-Interim  wave datasets provided by the  European Centre for Medium-Range Weather Forecasts (ECMWF) , and the  Permanent Service for Mean Sea Level (PSMSL)  for sea level data. We thank  Guest Editor Irina Overeem  and the two anonymous reviewers for their salient suggestions for improvement of the manuscript.
| Study population, species, and data collection Data were collected in a National Hunting and Wildlife Reserve (1,658 ha, 532-1,124 m above sea level) in the Caroux-Espinouse massif (43°38′N, 2°58′E, 17,000 ha, 130-1,124 m a. s. l. , southern France; Figure  1 ). This low mountain area is characterized by deep valleys indenting plateaux and creating a mosaic of ridges and thalwegs  (Marchand et al. , 2017). Vegetation within the wildlife reserve is an irregular mosaic of beech, chestnut, and coniferous forests with open areas dominated by rocky slopes and broom and heather moorlands  (Marchand et al. , 2015). Human activities are strictly regulated in the wildlife reserve: Hunting is forbidden, and recreational activities are restricted to hiking on a few main trails  (Marchand et al. , 2014a).
| Microsatellite genotyping We used 262 hair samples from individuals trapped within a time period  (2010) (2011) (2012) (2013) (2014) , that is short in comparison with the mouflon generation period (4. 21 years,  Hamel et al. , 2016) , preventing us from the existence of any temporal genetic structure. Analyzes were performed on individuals of both sexes having a fixed home range (in theory no longer expected to disperse), that is females two or more years old and males four or more years old (e. g. ,  Dubois et al. , 1992 Dubois et al. , , 1993;; Dupuis et al. , 2002). Genotyping was performed by the Antagene laboratory Table  S1 ) and two markers for sex identification (Amelogenin and genotyping error rates were estimated to be <2% in our dataset  (Queney et al. , unpublished data). Individuals were genotyped between one and four times to obtain at least 13 markers with no missing data.
| Genotyping errors MICROCHECKER v. 2. 2. 3  (Van Oosterhout et al. , 2004)  was used to detect null alleles. A Correspondence analysis (CA) was performed to identify and exclude outlier individuals. Data were checked for the presence of twin genotypes using the matching option in GenAlex v. 6. 501 (Peakall & Smouse, 2006 , 2012). When detected, one of the two twin genotypes was randomly deleted to prevent bias in subsequent analyzes.
| Genetic structure of the population We performed sex-specific analyzes (genetic and spatial), as different genetic and socio-spatial structures between the sexes were expected due to (i) their different utilization of space  (Marchand et al. , 2015 (Marchand et al. , , 2017)) , (ii) the segregation of males and females most of the year  (Cransac et al. , 1998) , and (iii) the philopatric behavior of females and much more mobile behavior of males  (Dubois et al. , 1992 (Dubois et al. , , 1993 (Dubois et al. , , 1996;; Dupuis et al. , 2002). We first used a DAPC (Discriminant Analysis of Principal Component, library adegenet of R software,  Jombart, Devillard, & Balloux, 2010; Jombart 2008)  to identify the most probable number of genetic clusters (K) in our population and assign each individual to a given cluster.
| Basic population genetics Allele frequencies, number of alleles per locus (N a ), allelic richness (A r , calculated using the rarefaction method (El Mousadik &  Petit, 1996)  to avoid bias due to variable sample sizes among different sociospatial units, and expected heterozygosity (He sensu Nei's gene diversity,  Nei, 1973)  were determined within each socio-spatial unit for both sexes using FSTAT v. 2. 9. 3. 2 software  (Goudet, 1995 (Goudet, , 2001)). Observed heterozygosity was determined using R software, package hierfstat  (Goudet & Jombart, 2015). Departures from Hardy-Weinberg Equilibrium (HWE) for each locus were tested using 10,000 randomizations in FSTAT. Similarly, F is values per locus and socio-spatial units were calculated, and their significance levels as compared to zero were assessed by randomizations of alleles among individuals within socio-spatial units.
| Measurement of history footprint on the current population We tested for the occurrence of recent bottlenecks (less than 15 generations ago, founder events) using the approach implemented in Bottleneck v. 1. 2. 02 software  (Cornuet & Luikart, 1996; Piry, Luikart, & Cornuet,1999 ) (within each socio-spatial unit when relevant). When a population has faced a bottleneck, H e is expected to be larger than expected heterozygosity under migration-drift equilibrium (H eq ) and a heterozygosity excess is detectable  (Piry et al. , 1999). Following the recommendations of  Piry et al. (1999)  for microsatellite datasets of less than 20 loci, we used the onestep stepwise mutation (SMM) and the two-phase (TPM) models of evolution. The TPM was parameterized with a variance among multiple steps of 12, 95% single step mutations and 5% multistep mutations.
| Genetic data Among the genotyped samples, 237 individuals with fixed home range and known socio-spatial unit had at least 13 successfully genotyped markers. Two outliers and five pairs of twins were identified among these individuals. Data on the 230 remaining individuals (69 males and 161 females) were considered in subsequent analyzes and were assigned to a socio-spatial unit (see below). Comparison of the observed genotypes with the distribution of randomized genotypes generated with the program MICROCHECKER revealed that there were no null alleles in the dataset.