dataesr/scientific-paragraphs-categorization huggingface dataset:
These samples have become available through a recent herpetological survey by one of us (II) and include topotypical P. karstophila, previously unstudied from a molecular perspective. The resulting sequences were combined with those of  Nagy et al. (2012)  and  Koubová et al. (2014)  in MEGA version 5  (Tamura et al. 2011)  to yield an alignment of 664 bp. We performed phylogenetic inference in MEGA under the Maximum Likelihood optimality criterion, with a general time-reversible substitution model with gamma-distributed rates and invariant sites, NNI branch swapping, and assessing robustness of nodes with 500 bootstrap replicates. Newly obtained sequences were submitted to GenBank (accession numbers: KM978078-KM978080).
hordiesi, see  Jackman et al. 2008). All studied species and candidate species of Paroedura included in the cox1 data set were differentiated by very high pairwise uncorrected distances. P. hordiesi differed from its Nosy Hara sister lineage by a p-distance of 16. 3-16. 5%, and from all other Paroedura including the relatively distantly related P. karstophila by >20%. Two samples sequenced from the Tsingy de Namoroka, the type locality of P. karstophila, were sister to each other but showed a substantial divergence of 15. 9% cox1 p-distance, suggesting that possibly two cryptic species may be hiding under the name P. karstophila in this karstic massif. The sample from Ankarafantsika (P. sp. Ankarafantsika in Fig. 1 ), identified in  Jackman et al. (2008)  as P. karstophila, in fact belongs to yet another, undescribed species according to our subsequent comparisons and is only distantly related to P.
Glaw, M. Puente and R. Randrianiaina. GenBank accession numbers for sequences of the holotype  (Jackman et al. 2008 ): EF536213 (ND2), EF536239 (ND4), EF536165 (RAG1) and EF536189 (PDC). Paratypes. All paratypes were collected in the "tsingy" limestone massif at Montagne des Français, Antsiranana province, north Madagascar. Specimens were collected on the tsingy outcrops along the way between the Hotel "Kings Lodge" (12°18'44,8''S, 49°20'22,6''E, 10 m  Diagnosis. Paroedura hordiesi sp. n. is a medium-sized species (SVL up to 58 mm, tail length up to 53 mm), having moderately prominent dorsal tubercles disposed into moderately distinct, and generally regular longitudinal rows and an original tail with no spines. The new species can be easily attributed to the genus Paroedura based on its nested phylogenetic position within the genus  (Jackman et al.
by a cooperation accord between the UADBA and the ZSM. Malagasy authorities are acknowledged for collection and export permits. The research of F. Glaw was supported by the "Deutsche Forschungsgemeinschaft" (grant no. GL 314/1) and the European Association of Zoos and Aquaria (EAZA). Fieldwork of I. Ineich was made possible with the help of Ewe Madagascar for logistical support (Marc Gansuana), La Colas and the Société Aquamas for providing boats, thanks to them. He also wants to thank Lucile Allorge and Thomas Haevermans (MNHN) for scientific organisation, MNHN Labex for providing founds, and the Parc naturel du Namoroka, its guides and the inhabitants of the village of Vilanandro for their useful help.
Acknowledgements We are very grateful to  Parfait Bora ,  Hildegard Enting ,  Michael Franzen ,  Kathrin Glaw ,  Angelika Knoll ,  Steven Megson ,  Zoltan T. Nagy ,  Marta Puente ,  Roger-Daniel Randrianiaina ,  Angelin Razafimanantsoa  and the team of the  Kings Lodge  for their help in the field, to  York Pareik  for logistic support, and to two anonymous reviewers for their helpful comments on the manuscript. Special thanks go to  Freddy Hordies  for his dedication for biodiversity research and conservation and his patience. The research in Madagascar was made possible.
Given the unique history and treatment model for MAT in France, collection of real-world data on the status of treatment provision is of potential value to treatment providers and policymakers alike, in seeking to highlight where current policies or treatment approaches could be improved. We report the results of Project Access France, a national survey of patients in MAT, out-of-treatment opioid users, and treating physicians, which was conducted in 2011 and formed part of a larger European initiative, the European Quality Audit of Opioid Treatment (EQUATOR). 18, 19.
Subjects Fifty overweight and obese (BMI: $252,38 Kg/m 2 ), but otherwise healthy subjects (8 males and 42 females), aged from 25 to 65 years, were recruited at the Human Nutrition Research Center (Pitie-Salpetriere Hospital, Paris, France). Forty-five subjects (6 males and 39 females) completed the 7-day dietary records, bioclinical and fecal bacterial data and were included in the present analysis (Figure  1 ). Selection of subjects was based on the absence of inflammatory or infectious diseases, cancer or any history of gastrointestinal problems. Subjects with hepatic, renal or cardiac diseases were excluded. In addition, 17 normal weight healthy female (BMI: ,25. 18 kg/m 2 ) volunteers living in the same area as the obese subjects were recruited as a control group. Fourteen of these completed the dietary records and were included for reference purposes.
No antibiotics were taken within 2-months prior to the start of the study. Subjects were enrolled between October 2008 and December 2009. The Ethical Committee of Hotel-Dieu Hospital, Paris, France, approved the protocol. All the subjects gave written informed consent. This clinical trial was registered before enrolment of participants in the EU Clinical trials Register under the identification number: 2008-001138-28, and in the French agency for the security of medications and health products -changed to ANSM) under the identification number: ID RCB 2008-A00406-49. It was also registered in the ClinicalTrials. gov under the identification number: NCT01314690, later due to some technical problems with the sponsor of the study. The authors confirm that all ongoing and related trials for this study are registered.
Dietary assessment Dietary intake was assessed using an unweighed dietary record for 7-days (unless specified otherwise). The records were selfcompleted and contained pre-defined spaces for the recording of each meal. Subjects were provided with instructions on how to complete the dietary records. On the visit day, the dietitian verified the information with the subject. All records were analyzed by using the computer software program PROFILE DOSSIER X029 (Audit Conseil en Informatique Medicale, Bourges, France), which has a food composition database initially made up of 400 food items representative of the French diet as described previously  [18]. Nutrient intakes were generated for each subject based on the dietary data collected. Information on consumed foods could not be directly extrapolated from the program and were therefore coded manually into 26 food groups.
Food groups were defined based on groups and sub-groups typically used in the reporting of dietary data in large surveys, such as the French Nutrition and Health Survey and program (E ´tude Nationale Nutrition Sante, ENNS)  [19, 20]. Mean food and nutrient intakes in the overweight/obese subjects were calculated for each subject according to the 7-day diary (in 2 subjects only 6 days of data were available). The lean subjects completed a 3-day diary of the same format as those used for overweight and obese subjects. Habitual physical activity (Baecke questionnaire) was also evaluated.
5636. 5 (mean 6 sd) million 35 baselong single reads were determined for each sample (a total of 393 Gb of sequence). By using corona_lite (v4. 0r2. 0), an average of 24. 8614. 3 million reads per individuals were mapped on the reference catalog of 3. 3 million genes  [24] [25]  with a maximum of 3 mismatches. Reads mapping at multiple positions were discarded and an average of 14. 268. 1 million uniquely mapped reads per individuals were retained for estimating the abundance of each reference gene by using METEOR software  [26]. Abundance of each gene in an individual was normalized with METEOR by dividing the number of reads that uniquely mapped to a gene by its nucleotide length. After that, normalized gene abundances were transformed in frequencies by dividing them with the total number of uniquely mapped reads for a given sample.
5 and 5. 5%, respectively. Adiponectin was determined by using ELISA (Bu ¨hlman, Basel, Switzerland). The limit of detection was 0. 019 ng/ml and the intra-and interassay variability was less than 5. 5%. Serum concentrations of 5 cytokines (eotaxin, vascular endothelial growth factor (VEGF), interferon gamma-induced protein 10 (IP10), monocyte chemotactic protein-1 (MCP-1) and macrophage inflammatory protein-1 beta (MIP-1b)) were simultaneously determined by multiplex biochip technology with the human cytokine multiplex panel kits (Bio-Rad, Hercules, CA. USA). A multiplex assay was performed according to the manufacturer's instructions. Multi-analytic profiling was performed on the Luminex-200 system and the Xmap Platform (Luminex Corporation. Austin. TX. USA). Acquired fluorescence data were analyzed using Exponent software with standard curves obtained from serial dilutions of standard cytokine mixtures.
Statistical Analysis Data were analyzed using SAS 9. 2 and SAS Enterprise Guide 4. 2 (SAS Institute Inc. , Cary. NC. USA) and R version 2. 13 Software (http://www. r-project. org). Homeostasis model assessment of insulin resistance (HOMA-IR), insulin sensitivity (HOMA-S%), and b-cell function (HOMA-B%) were calculated by HOMA Calculator from www. dtu. ox. ac. uk/homacalculator. All data are expressed as mean 6 SEM. P values #0. 05 were considered as significant unless specified otherwise. Bonferroni correction was used for the adjustment of the food and nutrient analysis.
Identification of dietary patterns Cluster analysis was employed to derive dietary patterns from the data. Overweight and obese subjects were grouped based on similarities in eating behaviors. Prior to this analysis, all food categories were standardized to a mean of 0 and a standard deviation of 1 in order to ensure that quantities consumed were comparable across different categories. A comparison of two clustering methods for use with dietary data (Ward's Agglomerative Hierarchical Clustering and k-means clustering method) was undertaken as described previously  [27]. K-means was found to be the most appropriate method to use in the conditions of the present study. Group membership is determined by a series of steps. The algorithm starts by a set of selected seeds and an estimation of the centroid (the multidimensional equivalent of the mean).
Each individual is assigned to the nearest centroid and temporary clusters are formed. The seeds are then replaced by the centroid for each group. The process is repeated until no further changes occur in the groups. The k-means function fits a userspecified number of centers (the parameter k), such as that the within-cluster sum of squares from these centers is minimized, based on the Euclidian distance or another chosen distance. Since the number of clusters must be established a prior, several solutions were compared with a varying number of clusters (from 1 to 4). To visualize the food categories that significantly distinguish one pattern from another, a Canonical Discriminate Analysis was implemented through SAS (CANDISC procedure) in order to transform the food categories into two canonical axes that achieve maximum separation between the clusters.
The validity of the dietary pattern analysis depends on the dietary assessment method itself and the completeness and accuracy of the dietary data collected in the study. Indeed, the analysis of dietary patterns requires decisions, judgments and interpretations which may result in biased results, for example the creation of food categories, and the decisions regarding the number of clusters  [3]. Dietary recording in the lean subjects differed from that of the overweight/obese subjects (3 vs 7 days) and compliance with recording might decrease in line with the number of diary days. Furthermore, misreporting of the dietary data may also attenuate any diet and disease relationships and affect any conclusions that can be made  [42]. However, there is no reason to believe that the level of misreporting would differ significantly across these subjects with controlled dietary data collection.
Acknowledgments We thank  Christine Baudoin ,  Valentine Lemoine ,  Patricia Ancel  and  Veronique Pelloux  ( Department of Nutrition, Pitie-Salpetriere Hospital ) who contributed to the clinical investigation study,  Dominique Bonnefont-Rousselot  and  Randa Bittar  ( Department of Metabolic Biochemistry, Pitie-Salpe  ˆtriere hospital) for help with the analysis of plasma lipid profile,  Damien Paineau  and  Pascale Rondeau  ( Danone Research ) for advice and assistance with the dietary statistical analysis. PDC is research associate from the  FRS-FNRS (Belgium) , and is recipient of grant from  FSR  (Fonds speciaux de recherche, UCL) and  ARC  ( Action de Recherche Concertee, Belgium ).
This work was supported by the  National Research Agency  ( Investissements d'avenir   ANR-10-IAHU-05 ,  ANR MICRO-Obes , and  ANR Nutra2sense ),  KOT-Ceprodi Laboratory  ( Florence Massiera  and  Reginald Alouche ) and the association '' Fondation Coeur et Arteres '' Danone Research PhD grant. Other funding came from the  European Commission   FP7  grant  HEALTH-F4-2007-201052  and  HEALTH-F4-2012-305312  ( MetaCardis project ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. KOT-Ceprodi Laboratory  provided funding towards this study. BH and NG were employees of Danone Research at the time that this work was conducted. FHR and RB were contracted to work on behalf of Danone Research at the time that this work was conducted.
ACKNOWLEDGMENTS This study was supported by a  Research Fellowship  from the  Société Entomologique de France , which awarded the  Germaine Cousin grant  to  G. Gbedjissi  in 2002; as well as by a special foreign student grant to Gbedjissi in 2003 from  EGIDE , partner of the  French Ministry of Foreign Affairs. We thank  Martine Fargier  ( Avignon ) for the English translation of the manuscript;  Rory McDonnell  ( University of California, Riverside, USA ) and  Mike Gormally  ( University of Galway, Ireland ) for reviewing the first manuscript version; and  Antoine Mantilleri  ( Muséum national d'Histoire naturelle, Paris ) for the photos in Fig. 1. This research received support in 2012 from  Synthesys (Synthesis of Systematic Resources ) which is financed by.
Ontologies are not the same as databases or taxonomic keyshowever, ontologies can be used as an enhancement to database systems and keys, both as a standard terminology, allowing different database systems to interoperate, and as a logical extension, allowing domain knowledge to be encoded in a way that enhances query capabilities or data integrity. For example, the GO provides a stable identifier denoting the biological process of apoptosisdifferent databases can use this same identifier for describing genes involved in apoptosis, allowing integration of data from multiple databases covering genes in a variety of species. Furthermore, the knowledge that 'apoptosis' is a kind of 'cell death' is encoded in the ontology, which means queries for genes involved in 'cell death' will return genes described as being involved in apoptosis.
One important type of biological ontology is the anatomy ontology. Anatomy ontologies typically include relationships between structures, such as the relationship of parthood between a hand and a limb. In sum, these relationships form a graph structure that can be used to enhance database queries or bioinformatic analyses. For example, a database query for genes expressed in the limbs can return genes expressed in different parts of the limb (such as the hands) or deeper in the parthierarchy (e. g. , in the distal part of the finger). Anatomical ontologies are also used to standardize characterstate descriptions in evolutionary databases, such as, for example with the Phenoscape knowledge base  [18]. The fundamental unit of these anatomical graph structures are classes (also known as terms).
Each class represents a distinct anatomical feature and is typically assigned a unique identifier that provides a key with which it may be cross-referenced to other ontologies or databases. The open nature of commonly used bio-ontologies allows terminology and definitions to be re-used from other ontologies with which they overlap, reducing duplication of effort and promoting orthogonality. Some anatomy ontologies cover a specific taxonfor example, the Drosophila Anatomy Ontology (DAO)  [23]  Others are applicable to a wide range of taxafor example, the Plant Ontology (PO)  [24]  or the Uberon anatomy ontology  [25] , which covers metazoans. Until now, the major focus of anatomy ontologies has been plants and bilaterians, with no representation of the unique biology of spongeswhilst Uberon includes structures applicable across animals, the focus of the ontology is chordates, with the intention of federating with other metazoan ontologies.
Ontology contents We constructed the Porifera ontology (PORO) as a Web Ontology Language (OWL) ontology using the Thesaurus of Sponge Morphology as a primary source. The ontology primarily focuses on anatomical structures, but includes other kinds of entities of interest to Poriferan biologistsfor example, traits and chemical entities. Each anatomical entity is represented using an OWL class which is uniquely identified by a URI (uniform resource identifier) in the OBO Library "PORO" identifier space. In this paper, we provide examples of classes using short forms of these URIs -for example, PORO_ 0000017 identifies the class 'spicule'. The full URI of this class is "http://purl. obolibrary. org/obo/PORO_0000017", which resolves to an OWL document rendered as a human-readable web page using the OntoBee system.
The phylum Porifera contains over 8,000 accepted species  [1] , but at least 6,000 additional species are thought to exist based on surveys of museum collections  [36]. When integrating morphological and molecular datasets, most studies of sponge systematics find high support for morphological classifications at the species level, indicating the ability of morphological characters to distinguish between sponge species  [29, 34, [37] [38] [39]. However, there is often a lack of resolution at the genus and family levels, suggesting that morphological characters are often homoplasic  [29, 37]. This low phylogenetic resolution within orders is not surprising since there are relatively few morphological characters available for analyses and since these characters can be phenotypically plastic. Recently, Morrow et al.
It is important to note that PORO is not a complete database, knowledge base, or application in its own righthowever, PORO can form the terminological and deductive knowledge backbone of such a system. PORO is complementary to classification aids such as SPON-GIA  [26] , which is a powerful expert system able to infer the species of a sample based on the answers to questions concerning descriptive characters. Indeed, in constructing that system, the authors noted: "The simple work of character definition in the domain model turned out to be a non-trivial task. A thesaurus of terms for sponges that was to contain an important consensus among the European experts in sponge systematics was in the pipeline and its preliminary versions were available to us. However, the current vocabulary of "our" expert was not always standardized according to the previous consensus  [26] ".
Conclusions There are a number of ontologies covering taxa such as plants, fungi and bilaterians, but the Porifera ontology is the first ontology dedicated to a non-bilaterian metazoan. Because many terms used in sponge taxonomy and systematics have Porifera-specific meanings, we created the structure of the Porifera ontology from existing resources, primarily the Thesaurus of Sponge Morphology  [2]. We will revise and expand PORO to accommodate new concepts and relationships as we use the ontology to build character matrices for modern and fossil taxa. By accelerating our ability to describe and understand sponge morphology, we seek to reconcile differences between morphological and molecular approaches to poriferan systematics. We hope that this integrative approach to taxonomy and systematics will inspire investigators working with invertebrate and microbial taxa to add value to their morphological datasets by placing the characters used to describe additional taxonomic groups into an ontological framework.
Bottom-up ontology development When building an anatomy ontology, it is possible to take a 'top-down' approach or a 'bottom-up' approach (or some combination thereof ). With a top-down approach, the creator starts with upper-level categories and gradually introduces more specific classes. With a bottom-up approach, the creator starts with the terms of interest (which are typically more specific) and builds them into a hierarchy, gradually 'working-up' to the root. With PORO we took a bottom-up approach. We started with the online version of the Thesaurus of Sponge Morphology  [2]  and used a Perl script to generate a skeleton ontology in OBO format. This was adjusted using a text editor and OBO-Edit  [49] , and then translated into OWL and edited using the Protégé 4 ontology editor  [50]  (http://protege.
stanford. edu). The translation retained the textual definitions obtained from the thesaurus, as well as annotations on the definitions referencing the source material. These are represented in the OWL ontology as axiom annotations. For Protégé editing, we make use of a number of plugins, including one for annotating images (https://github. com/balhoff/image-depictions-view), OBO-Edit style productivity assistance tools (https://github. com/balhoff/oboactions) and the OBO-Edit Graph plugin for Protégé (http://code. google. com/p/obographview/). The editors of the ontology met through meetings organized by the Phenotype Research Coordination Network (RCN), where the ontology was reviewed and biologists were provided with training in ontology building and reasoning. We make use of the HermiT reasoner as part of the ontology development process  [51].
Acknowledgements We acknowledge the support of the  Phenotype Ontology Research Coordination Network  ( NSF-DEB-0956049 ) for supporting ES and organizing the meetings that brought the developers together. This work was also supported by grants from the  U. S. National Science Foundation ,  Division of Environmental Biology  [grant numbers  0829986  and  1208310  awarded to RWT]. CJM was supported by the Director,  Office of Science, Office of Basic Energy Sciences , of the  U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. We thank the three anonymous reviewers for their detailed comments on a previous version of this manuscript.
The example below, from APN database 6  analysed in Hendriks/Watorek (2008) 7  , shows these uses. (2) 8  a. z tylu placu (Rel) ciagna sie kamienice (Th) behind square-Gen lay buildings-Nom <behind the square there are buildings> b. na srodku placu (Rel) stoi fontanna (Th) at the middle square-Gen is standing fountain-Nom <in the middle of the square there is a fountain> c. obok tej fontanny (Rel) czyta gazete jakis pan (Th) side this-Gen fountain-Gen reads journal certain gentleman-Nom <beside the fountain there is a man reading a newspaper> The introduction of the relatum in topic, and the theme in the focus of the utterance (a), is expressed by bare nouns (placu=square, kamienice=buildings). The information about the theme introduced in the focus of (a) is maintained as the information about the relatum in the topic of (b), using a bare noun (placu=square).