dataesr/scientific-paragraphs-categorization huggingface dataset:
It turns out that the German Texel sample consisted of a case/control study for microphtalmia  [7] , although the case/control status information in this sample is not given in the Sheep HapMap dataset. The consequence of such a recruitment is to bias haplotype frequencies   SNP with significant FLK value at the 5% FDR level, with estimated allele frequencies in all ancestral groups. The number of genes included in each region (1Mb up-or-downstream the position) and the rank of candidate genes within the region is also provided. {: signatures of selection previously identified  [4]. doi:10. 1371/journal. pone. 0103813. t003 in the region associated with the disease, which provokes a very strong differentiation signal between the German Texel and the other Texel populations. Although not related to artificial or natural selection in sheep, this signature illustrates that our method for detecting selection has the potential to identify causal variants in case/control studies, while using haplotype information.
Conclusions The Sheep HapMap dataset is an exceptional resource for sheep genetics studies. In a population genomics context, our study shows that the rich information contained in these data permits to start unraveling the genetic history of sheep populations worldwide. In order to fully exploit this information, we used recent statistical approaches that account for the relationship between populations and the linkage disequilibrium patterns (haplotype diversity). This allowed detecting with confidence more selection signatures and identifying for most of them the selected populations. Among these new selection signatures detected by our study, several result from recent selection and include good positional candidate genes with functions related to pigmentation (KITLG, EDN3), morphology (WNT5A, ALX4, EXT2, HOXA cluster) or production traits (HDAC9).
Methods Selecting populations and animals. Seventy-four breeds are represented in the Sheep HapMap data set, but we only used a subset of these breeds in our genome scan. We removed the breeds with small sample size (v 20 animals), for which haplotype diversity cannot be determined with sufficient precision. Based on historical information, we also removed all breeds resulting from a recent admixture or having experienced a severe recent bottleneck. Focusing on the remaining breeds, we then studied the genetic structure within each population group, in order to detect further admixture events. We performed a standardized PCA of individual based genotype data and applied the admixture software  [16]. In two population groups (AFR and NEU) the different breeds were clearly separated into distinct clusters of the PCA and showed no evidence of recent admixture (Figures  S1  and S2  in File S1).
For each group, we rooted the tree using the Soay sheep as an outgroup. This breed has been isolated on an island for many generations and exhibits a very strong differentiation with all the breeds of the Sheep HapMap dataset, making it well suited to be used as an outgroup. FLK and hapFLK genome scans. The FLK statistic was computed for each SNP within each group. The evolutionary model underlying the FLK statistic assumes that SNP were already polymorphic in the ancestral population. To consider only loci that most likely match this hypothesis, we restricted our analysis within each group to SNP for which estimated ancestral minor allele frequency p 0 was above 5%. Under neutrality, the FLK statistic should follow a x 2 distribution with n{1 degrees of freedom (DF), where n is the number of populations in the group.
5s. This allowed taking into consideration the profile of the hapFLK signal, i. e. if the profile resembles a plateau, the candidate region will be rather broad while very sharp hapFLK peaks will provide a narrower candidate region. We extracted all protein coding genes present in the significant regions using the Ensembl Biomart tool (http://www. ensembl. org/biomart/) for Ovis Aries 3. 1 genome assembly. These full lists are provided as Supporting Information (Dataset S1 and Dataset S2). Within each candidate region, genes were ranked according to their distance from the most significant position of the region (the larger the rank, the larger the distance). The functional candidate genes shown in Table  2  and discussed in the manuscript were chosen based on this rank and/or on their implication in previous association or sweep detection studies.
Acknowledgments Data analyses were performed on the computer cluster of the bioinformatics platform  Toulouse Midi-Pyrenees. We would like to thank  Wendy Brand-Williams  for her careful reading of the manuscript. The members of the  International Sheep Genomics Consortium  who contributed samples and expertise towards the design and execution of ovine SNP50k genotyping and next generation sequencing, and/or coauthors of [4] include:  Juan Jose Arranz ,  Universidad de Leon ;  Georgios Banos ,  Aristotle University of Thessaloniki ;  William Barendse ,  CSIRO Livestock Industries; Ahmedn El Beltagy, Animal Production Research Institute ;  Jorn Benenwitz ,  University of Hohenheim ;  Steven Bishop , The  Roslin Institute ;  Simon Boitard ,  INRA ;  Lutz Bunger ,  Scottish Agricultural College ;  Jorge Calvo ,  CITA ;  Antonello Carta ,  Agris Sardegna ;  Ibrahim Cemal ,  Adnan Menderes University ;  Elena Ciani ,  University of Bari; Noelle Cockett, University of Utah ;  Dave Coltman ,  University of Alberta ;  Brian Dalrymple ,  CSIRO Livestock Industries ;  Mariasilvia DAndrea ,  Universit degli Studi del Molise; Ottmar Distl, University of Veterinary Medicine Hannover ;  Cord Drogemuller ,  Institute of Genetics, University of Berne ;  Georg Erhardt ,  Institut fr Tierzucht und Haustiergenetik Justus-Liebig-Universitt Gieen ;  Emma Eythorsdottir ,  Agricultural University of Iceland ;  Kimberly Gietzen ,  Illumina Inc.
2. Two methods that define alternative decompositions for the compound task (attack-player ?npc). The first method provides the behaviour to attack the player with a ranged weapon, while the second one makes the NPC use a close combat weapon. The LISP-like syntax is the one used by SHOP for its input data, where the exclamation mark "!" denotes a primitive task. From a programming viewpoint, logical formulas (preconditions, effects) in operators and methods are generally encoded as lists of atomic propositions evaluated by an inference engine. Pyhop  [7] , a SHOP-like HTN planner coded in Python, follows another approach supposed to be more suited to games. First, world states are represented as Python data structures containing statevariables, an alternative representation for facts in planning, instead of sets of predicates.
Secondly, operators and methods are Python functions taking a state as input, and respectively returning a new state and a sequence of subtasks. Figure  3  provides an insight on what such functions look like. The entire domain is therefore written in Python, and not with a logical language as usual. For SHPE, we decided to follow the same rules for reason of both system simplicity and expectation of a runtime improvement. Indeed, there is no need to code an inference engine and state-variables can be instantly accessed in a structure to read or modify their values, while we have to find a predicate to add or delete it from a state. But unlike Pyhop, we did not choose Python but C++ in order to implement our planning system. First, this is a very commonly used language for video games and we expect our choice to ease the integration of the planner in most game engines.
Algorithm SHOP, Pyhop and SHPE use the same algorithmic principle. They conduct a depth-first, backtracking search in the space of partially decomposed task networks, combined with a forward state-space search. When the planner selects the next task to process, it always picks one that has no predecessor in the task network. Doing so results in constructing the plan in the same order as it will be executed. The planner always has a full description of the current state of the world at its disposal, thus logical expressions in operators and methods can be written with very expressive logical formulas. For instance, the domain modeling language of SHOP2 allows for existential and universal quantifiers, disjunction, implication and even more specific expressions  [6]. Not only does this type of search provide the domain modeler with a powerful way to encode good strategies for decomposing task networks, but it also justifies why operators and methods can be encoded as functions in Pyhop.
Conclusion The ideas introduced with Pyhop  [7]  were put into application in SHPE in order to provide fast planning capabilities to video games. The system has reached the targeted performance: it has been evaluated against various problems with properties similar to FPS games, and was able to solve them in a few milliseconds. In addition, a high-level modeling language can be used to design the planning knowledge required to operate the planner efficiently. To bridge the gap between this high-level language and the C++ encoding expected by the planner, a precompiler should be implemented. This component would enable AI designers to modify the behaviour of game characters, without any skills in low-level C++ programming. But even if this system achieves its initial goal in terms of performance and maintenance, it does not address issues like real-time re-planning in dynamic environments such as games.
The progressive block of NMDAR-EPSCs by MK-801 (40 M) allowed estimating the release probability of glutamate (Chavis and Westbrook,  2001). NMDAR-EPSCs were recorded at 40 mV in the presence of 20 M DNQX, and synaptic inputs were activated every 10 s. After a 10 min baseline, MK-801 was added and synaptic stimulation stopped for 15 min. Stimulation was then resumed until total blockade of NMDAR-EPSCs. The progressive block of NMDAR-EPSCs by MK-801 was fitted using a double exponential: y ϭ a*exp(Ϫt/ fast ) ϩ b*exp(Ϫt/ slow ) where y is the normalized response amplitude, a and b are constants, t is the corresponding stimulus number, and fast and slow are the time constants  (Chavis and Westbrook, 2001). Data acquisition and analysis. Because of the strong phenotypes, the experimenters were not blind to the genotypes, but no data were excluded before statistical analysis (GraphPad Software).
Data were recorded on a MultiClamp700B, filtered at 2 kHz, digitized (20 kHz, DigiData 1440A), collected using Clampex 10. 2, and analyzed using Clampfit 10. 2 (all from Molecular Devices). Both area and amplitude of fEPSPs and EPSCs were analyzed. The magnitude of LTP was calculated 25-30 min after TBS as percentage of baseline responses. To perform I-V curves and to test the excitability of MSN, a series of hyperpolarizing and depolarizing current steps were applied immediately after breaking in the cell. Membrane resistance was estimated from the I-V curve around resting membrane potential as previously described  (Kasanetz and Manzoni, 2009). For ES-coupling analysis, 10 traces were recorded for each stimulus. The value of ES-coupling obtained for each animal was calculated by averaging the spiking probability corresponding to each class of EPSP slope.
The tridimensional reconstruction and blind semiautomated analysis were performed with Imaris (Bitplane)  (Iafrati et al. , 2013)  with the following detection settings for dendrites: dendrite starting: 1. 333 m/dendrite seed point: 0. 333 m/threshold: 1; and for spines: seed point: 0. 333 m /maximum length: 2. 666 m/threshold: 0. 5. Statistical analysis. The value n corresponds to the number of cells for morphological experiments and to the number of individual cells or animals in the electrophysiology experiments (maximum 3 cells in a single mice). All values are given as mean Ϯ SEM. Statistical analysis was performed with Prism 6. 0 (Graph-Pad Software). Two sample comparisons were made with the Mann-Whitney t test. Kolmogorov-Smirnov tests were used for comparing the cumulative distributions of amplitude and interevent intervals of mEP-SCs.
1 ). ''Reproduction type'' has two daughter terms: ''Asexual reproduction'' and ''Sexual reproduction'' (Fig. 1 ). We call ''multi-level daughters'' of a trait/ ecological preference term all the terms which have a position below it in the conceptual hierarchy tree. For instance, the multilevel daughters of ''Reproduction type'' are: ''Asexual reproduction'', ''Sexual reproduction'', ''Arrhenotokous'', ''Deuterotokous'' and ''Thelytokous'' (Fig. 1 ). T-SITA has been coupled with a soil invertebrate trait database to improve data management and enrich trait/ecological preference information (see Section 4). To achieve this, a necessary condition is that each term must be typified by a unit. Trait and ecological preference terms are identified by having either a numerical unit or by being ''categorical''.
A Useful Tool for Data Integration, Data Integrity and Navigation among Multiple Data Sources The web of data focuses on the sharing of data on the web. SKOS (Simple Knowledge Organization System) (Isaac 2008) is the W3C standard dedicated to thesaurus representation, based on RDF triples  [28]. SKOS was designed to provide an existing thesaurus standard (ANSI 2005) by providing a common format to adequately manage not only thesauri, but also any knowledge organizational system. T-SITA, built using Thesauform  [13] , is defined through the SKOS format and makes full use of it. As a consequence, T-SITA serves as a stable reference resource, specifically when available as linked data on the web. Additionally, T-SITA has been coupled with a soil invertebrate trait database to enhance data integration and data scientific integrity.
The BETSI database will be soon in production. It is a relational database defined under the database management system PostgreSQL (http://www. postgresql. org/docs/9. 1/static/reference. html), which contains soil invertebrate data on traits and ecological preferences. The linkage of the thesaurus and the database presents huge advantages. First, each insertion of trait data in the BETSI database is under the control of T-SITA, which improves data quality in the database. Indeed, a trait term must be present in T-SITA in order to insert related data in the BETSI database, so data contributors to the BETSI database must consult T-SITA before inputting data. It guides the data integration and limits scientific misunderstanding. Concerning the data integration, it allows integrating trait data from two trait terms that represent the same concept (see section 1).
For instance, sclerotization of the body is called ''integument sclerotization'' or ''cuticule sclerotization'' in the literature. As both terms are defined as synonyms in T-SITA, data inserted from these two terms will be identified in the database as belonging to the same trait concept. As a consequence, a data query concerning the sclerotization of the body will return data from both terms. Concerning the data interpretation, contributors to the database have to associate their with a term coming from a limited set of trait terms. Each of them represents a trait concept which does not overlap with other trait concepts in the thesaurus. Two trait terms representing the same concept are synonyms. This can resolve the problems of scientific misunderstanding mentioned in section 1. Data contributors who want to insert raw data describing the ''body colour'' trait for carabid beetles and the ''body pigmentation'' trait for earthworms will realize that both data apply to the same concept.
As a consequence, they will insert the data under the same trait term ''body colour'' in T-SITA and not under the trait term ''body pigmentation'' which refers to a different concept. Second, in the web page describing the trait/ecological preference information in T-SITA, the dynamic link with the database enriches information about a given trait/ecological preference by providing statistics coming from the BETSI database (Fig. 2 ). It displays for each trait or ecological preference (as soon as a unit has been allocated to them, see section 2), the number of raw data observations input into the database and how many species they concern (Fig. 2 ). In addition, the statistics are aggregated according to the level of the tree hierarchy. When clicking on an above term (no unit, see Section 2), the number of raw data observations input into the database and how many species they concern are aggregated from its trait/ecological preference daughter terms.
Conclusion Harmonization of trait data for soil invertebrates requires a handbook to answer questions such as: what is really to be understood by this trait term, how can I measure it? T-SITA forms the first step in this huge task by being, to our knowledge, the first initiative to deal with the semantics of traits and ecological preferences for soil invertebrates. It reflects the agreement of a scientific expert community to fix the semantic properties (e. g. definition) of approximately 100 traits and ecological preferences. In addition, T-SITA has been successfully linked with a fully operational database on soil invertebrate traits. Such a link enhances data integration and improves data scientific integrity. The future of T-SITA depends on the tool used to build it (Thesauform), which allows improvements in the current content by (i) performing other complete procedures (edit, validate, supervise), (ii) including other scientific experts and (iii) including new trait/ecological preference properties such as methods of measurement.
Data sampling and data analysis The data set for this study was obtained from the race website of the 'Ironman World Championship'  [6]. All competitors who finished within the top ten in the 'Ironman World Championship' Hawaii between 1998 and 2013 were analyzed regarding their change in performance, the sex difference in performance and the transition times. Before 1998, transition times were not separately recorded in the race results. Therefore, data before 1998 could not be included into data analysis. To determine the sex difference in performance, overall race times and split times of the annual top and of the annual top ten women and men were taken from the race results of the organizer. Unfortunately, data from  [6]  do not allow to differentiate the two transition times (i. e. swimming to cycling and cycling to running) during the whole period 1998-2013.
Split times in swimming, cycling, and running as well as overall race times and transition times in top ten women and men and differences in transition times between 'Ironman Hawaii' and 'Ironman 70. 3' in top ten men and women were compared using multiple t-tests with Holm-Sidak correction for multiple comparisons. Statistical analyses were performed using IBM SPSS Statistics (Version 22, IBM SPSS, Chicago, IL, USA) and GraphPad Prism (Version 6. 01, GraphPad Software, La Jolla, CA, USA). Significance was accepted at P < 0. 05 (two-sided for ttests). Data in the text and figures are given as mean ± standard deviation (SD).
Objectives: To conduct a systematic review to determine whether there is an association between metabolic syndrome (MetS) and lower urinary tract symptoms (LUTS) or overactive bladder (OAB) in women. Methods: We systematically reviewed English language observational studies on the effect of MetS (or component factors) on the presence of OAB or LUTS in women. We searched PubMed, Web of Science and The Cochrane Library with no date restrictions, checked reference lists and undertook citation searches in PubMed and Google Scholar. Studies were assessed for risk of bias. Because of heterogeneity, results were not pooled, but are reported narratively. Results: Of 27 included studies, only three looked at the link between MetS and OAB. The rest looked at links between OAB and components of MetS such as obesity or insulin resistance (n = 10), between MetS and urinary symptoms (n = 3) and between urinary symptoms and components of MetS, such as obesity (n = 14).
Identification of studies To identify studies for the review, we searched the following electronic databases: PubMed, Web of Science (including SCI, SSCI, HCI), Google Scholar and the Cochrane Library. In addition, we used 'lateral searching' techniques such as checking reference lists of relevant papers, and using the 'Cited by' option on Google Scholar and Scopus, and the 'Related articles' option on PubMed. Methodological search filters were not used. Searches were limited to published English language studies. There were no date restrictions. Database searches were run in August 2012 and lateral searching was updated in October 2013; search terms can be seen in Box 1.
Data extraction, critical appraisal and analysis For studies that met the inclusion criteria, data were extracted onto a predesigned, and piloted, form. Data included the following: type of study design (e. g. cross-sectional, case-control), characteristics of participants (including age, sex, ethnicity), number of participants, country of origin, outcomes relating to MetS or OAB (including how they were measured) and estimation of association between MetS, or components of MetS, and OAB. The methodological quality of studies was assessed using criteria based on those of the Centre for Evidence Based Management. The core quality assessment principles are summarised in Table  2. Owing to sub- Diagnosis of the MetS requires any three of the following: • Waist circumference > 38 inches (88 cm) • Blood pressure of 130 mmHg systolic OR 85 mmHg diastolic, or greater OR on antihypertensive.
Study quality Of the observational studies, 13 were judged to be free from selection bias because they involved a population-based sample or large samples from general practice or a HMO database. In seven studies, the response rate was not clear, but of the others, 11 were judged to have a satisfactory response rate (≥ 50%). Quality assessment scores can be seen in Table  5  and a summary of quality assessment domains for the 25 crosssectional studies in Figure  2.
Abstraction of the Product Variants ExtractorPL takes as input the source code of a set of product variants. To analyze and compare these variants, each product is first abstracted into a SoCPs. To this end, ExtractorPL builds the SoCPs associated to each product by using the general model proposed within FeatureHouse, called Feature Structure Tree (FST)  [4]. An FST represents the essential software artifacts as a tree. Each node of an FST has a name and a type. The choice of FSTs is based on the following two criteria. First, an FST is a language-independent model. Second, FSTs include general composition operators. From the FSTs associated to each product variant, ExtractorPL represents them as a SoCPs. The construction primitives (CPs) used to decompose each FST depend on the type of nodes within each FST.
Feature Code Generation In addition to the feature identification, ExtractorPL aims at extracting a full compositional implementation of an SPL from the source code of product variants. It means that our approach includes the generation of the code units associated to each feature once the SPL is extracted. As a result, one is able to build tailored software products by automatically generating the source code of the features selected. To perform the code generation, ExtractorPL uses the FSTs of the features obtained in the previous step. Then, it generates their code units by using the pretty printers proposed by FeatureHouse. Indeed, the FeatureHouse framework includes a set of pretty printers for different programming languages such as C or Java. These printers produce the code from FSTs.