dataesr/scientific-paragraphs-categorization huggingface dataset:
The overall query execution time of a query federation engine can be influenced by various factors, such as join type, join order selection, block and buffer size, number of remote requests, intermediate results etc. PolyWeb focuses on querying heterogeneous data models in a federated setting which requires some additional costs apart from the mentioned factors. For example, queries are translated between different query languages (SPARQL, SQL, Apache Drill SQL) on different data models and most importantly, the RDF transformation of heterogeneous query results retrieved from different sources and data sets. PolyWeb performance is still comparable, knowing that additional run-time transformation cost is added. The cost of additional factors can be seen from the query execution time (Fig. 6 ) of PolyWeb-RDF, which is lower than PolyWeb in all the 10 queries.
Oral glucose tolerance test After an overnight fasting, plasma glucose levels were determined with glucometer system Accu-Chek® Performa (Roche Diagnostic, Mannheim, Germany) on a blood sample collected from the mouse tail vein. Then, they received orally 2 g/kg body weight of D-glucose. Ten,  20, 30, 60, 90 and 120 min after, glucose quantification was performed. Area under the curve (AUC) was calculated using GraphPad Prism 5, (GraphPad Software Inc. , La Jolla CA, USA).
First week. During the acquisition phase (4 trials per day for 4 days with a 15-min break between trials), the mouse was placed at the center of the maze in an opaque cylinder for 10 s and then left to freely explore the maze and search for the escape box for up to 3 min. Once the mouse had entered the escape box, it was left there for 1 min. The location of the target was consistent for a given mouse but randomized across mice. On the fifth day (for the probe trial), the maze was turned by 45, and the escape box was removed. The mouse was allowed to move freely for 90 s. A video camera and a computer running Ethovision XT 11. 5 software (Noldus) were used to track and to analyze the animal's displacements during the session. The number of total errors (the number of holes visited before the mouse enter in the target hole) were noted as the indices of learning during the acquisition phase.
The objects had been previously validated to ensure there was no inherent preference for either object (data not shown). Exploratory behaviour was defined as the animal directing its nose towards the object at a distance < 2 cm. Any subjects that failed to complete a minimum of 20 s of exploration during the sample phase were excluded from the analysis. A discrimination index was calculated as the difference between the time spent exploring the novel object minus the time spent exploring the familiar object divided by the sum of both (Antunes and Biala, 2012).
5-mm slices, and 2 averages. The sequence of diffusionweighted image (DWI) was performed in three directions [1, 0, 0], [0, 1, 0], [0, 0, 1] for which 11 b-values were used to characterize signal decay. The b-values were defined as follows  : 1, 10, 20, 30, 40, 60, 80, 100, 400, 600 , 800, and 1000 s/mm 2. The sequence parameters were as follows: FOV = 2 × 2 cm; matrix size: 128 × 128; slice thickness = 1 mm; TR = 3000 ms; TE = 33 ms. Parametric ADC maps were constructed using paravision 5. 1 software (Bruker). Fluid-attenuated inversion and recovery (FLAIR) images were acquired in the coronal plan from optimized RARE pulse sequence with TR = 5000 ms, TE = 33 ms and TI = 1600 ms (inversion), FOV = 1. 5 × 1. 5 cm, matrix size: 150 × 150. The gradient echo sequence with specific parameters TR = 350 ms, TE = 18 ms, FA = 40°, FOV = 1.
We selected vessels showed structures reminiscent of vascular myocytes, had a discernible luminal diameter of 7 ± 3 μm and could be controlled for their integrity for a length of 100-150 μm. Images of vascular responses were acquired with image software (Magix Video Terratec Edition, Berlin, Germany). To measure the diameter, we compared off-lines images acquired at the same focal plane from 1 to 4 sectors. Intraluminal diameters for each experimental condition were obtained as an average of 2 measurements taken. In the different experiments, the number of arterioles studied corresponds to the number of slices used (only one arteriole per slice was examined). Imaging was performed at 20-22 °C and perfused at 7 mL/min.
Statistical analysis All values were expressed as mean ± standard error of the mean (SEM). Statistical analyses were performed using R software version 3. 4. 0 (R Core Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www. R-project. org/), and R Studio environment version 1. 0. 143. Multiple inter-group differences were tested for the nature of the diet and its duration by a 2-way ANOVA (Diet × Time) and independently for the weight categories by a 1-way ANOVA followed by, where appropriate, a post-hoc tests based on Tukey's 'Honest Significant Difference' adjustment method. Variance analyses were assessed using "ez" package version 4. 4-0 (Michael A. Lawrence (2016)  ez: Easy Analysis and Visualization of Factorial Experiments.
R package version 4. 4-0. https://CRAN. R-project. org/package=ez). Proportions of weight categories were compared using Pearson's chi-squared test. The a posteriori calculation of Pearson's correlation coefficient (r) or Kendall's rank correlation (tau) was systematically followed by the power calculation using "pwr" package (Stephane Champely (2018) pwr: Basic Functions for Power Analysis. R package version 1. 2-2. https://CRAN. R-project. org/package=pwr). The extrapolation model was generated from normalized values for our datasets following the next equation: y = 100/(1 + 10^(x-f50%) where y = Normalized vascular or cognitive function (%); x = VAT (g) and f50% = the VAT fitted value at half of the function (g). The threshold for statistical significance was set to p < 0. 05.
Visceral adiposity correlates with a reduced endothelial relaxation and both impaired visual recognition memory and flexibility Several statistical correlations were tested in order to authenticate associations between metabolic disturbances, cognitive deficit and vascular dysfunction (Fig. 7A ), and are summarized in Table  4. Significant correlations were found between visceral adipose tissue deposition and NOR index (Kendall's rank coefficient -0. 27, p < 0. 001, power = 0. 96), flexibility (Kendall's rank coefficient 0. 15, p < 0. 05, power = 0. 38), or carbachol response (Pearson's correlation coefficient -0. 46, p < 0. 001, power = 0. 99). Statistical fit models were applied on the same datasets for extrapolation purposes, and were expressed as a percentage of maximal averaged response (bold curves, Fig.
Acknowledgments We warmly thank  Prof Anne Muhr-Tailleux  for her great technical assistance in blood assays (U1011-European Genomic Institute, Lille). We also thank  F. Auger  and  N. Durieux  (from the  University of Lille 's in vivo imaging core facility),  C. Laloux  (from the  SFR   DN2M  functional testing core facility),  M. Tardivel  (from the  Lille Bioimaging Centre ) for her great technical assistance in fluorescent Dextran microscopy technical advice and access to equipment and the  Lille Animal Facility. This work was supported by the  French Ministry for Research and Higher Education  and the  Conseil Régional des Hauts de France , as well as the  VasCog Research Network, Lille, France.
This change of focus was accompanied by the consolidation of the Semantic Web as a reality for knowledge management and sharing. The Linked Data principles and the adoption of standardised knowledge representation formalisms as RDF and OWL are now common choices for publishing resources automatically accessible and processable through the Web. However, despite the general acceptance of these good practices for the release of resources, the overall objective of a shared representation of legal knowledge has not been reached yet. The reuse of legal knowledge in fact requires a wide awareness of the already available resources which model the domain of interest. In order to evaluate a possible reuse, all the actors involved in the ontology building process, i. e. , legal experts as well as developers, need to be constantly up-to-date about the state-of-the-art and they are expected to deepen the ontological commitment and the methodological choices adopted by each resource.
Linked Data Rights ontology The Linked Data Rights (LDR) ontology 3  was developed by the Ontology Engineering Group 4  and it is specifically designed to model the rights which can be exercised on a Linked Data resource. LDR ontology is based on ODRL from which it extends the classes Action, Asset, Policy and Rule in order to model the conditions of use of the Linked Data resources. In detail, LDR defines three subsets of the ODRL Action class in order to represent the actions permitted on a resource protected by the intellectual property right, to use a database of Linked Data and to access a resource via the REST and SPARQL services. Moreover it defines which are the types of Linked Data resources (data-sets, link-sets, ontologies, resources and statements) and which are the types of policy that can be concluded (contract or licence).
Data Protection Ontology The Data Protection Ontology 11  by  Bartolini et al. (2015)  concerns the data protection field, as it is modelled in the GDPR (General Data Protection Regulation 2016/679). The Regulation came into force in May 2018, three years after the ontology published by  Bartolini et al. (2015). However, even if the ontology is not based on the final version of the GDPR text, we decided to include this ontology to enable the interested reader to compare it with other two ontologies modelling the same field, that is GDPRtEXT (see Subsection 2. 4. 2) and PrOnto (see Subsection 2. 4. 4). This ontology is part of a more complex system where it plays the role of a knowledge base used to express data protection requirements as annotations inside a workflow model (e. g. a business process).
GDPRtEXT The GDPRtEXT 12  (GDPR text extensions), by  Pandit et al. (2018) , is one of the most recent ontologies analysed in this survey and it deals with a currently central topic in the privacy domain: the aforementioned General Data Protection Regulation (GDPR). The aim of GDPRtEXT is to represent the GDPR as a Linked Data resource, assigning an URI to each relevant part of the text. To do this, it extends some classes and properties of the ELI ontology (presented in Subsection 2. 5. 3) in order to specify the di↵erent parts in which the GDPR's text is structured (such as articles, recitals, citations and so on) and the properties that hold among them. The ontology also provides more than 200 classes suitable to represent the relevant concepts introduced by the regulation and concerning the data protection field.
The concepts' macro-areas modelled by the ontology are related to the categories of personal data, the concept of consent, the agents involved in the processing of the data, the actions that can be made on data, the rights of the data subject and the obligations of each agent which deals with the data. GDPRtEXT also introduces a special property isDefinedBy which exploits the URI scheme created according to the Linked Data principles in order to link its classes to the relevant part of the text of the GDPR explaining the concepts they represent.
PrivOnto PrivOnto is an ontology developed by  Oltramari et al. (2018)  in the context of the Usable Privacy Policy project 13  and its aim is to model annotated privacy policies explaining the data practices implemented by a website. PrivOnto was built from a corpus of 115 privacy policies of websites belonging to US-based companies. This corpus was annotated by some domain experts who were asked to identify the main categories representing data practices, together with their attributes. The result was a set of ten categories of data practices represented as frames. Each frame has its set of attributes together with the corresponding values, that refer to the fragment of the privacy policy they are taken from. Indeed, PrivOnto allows the modelling, with specific classes, of di↵erent parts of the text and the annotations associated to each of them.
PrOnto Similarly to the Data Protection Ontology and GDPRtEXT (see Subsection 2. 4. 1 and Subsection 2. 4. 2), PrOnto (Privacy Ontology), proposed by  Palmirani et al. (2018) , focuses on the modelling of the knowledge concerning the GDPR. The purpose of PrOnto is not only to support information retrieval, but also to provide a model on which techniques of legal reasoning and compliance checking could be applied. Among its distinctive features, PrOnto focuses on the distinction between agents and roles, with the former able to cover particular roles inside di↵erent contexts and for a limited interval of time. Moreover, PrOnto models the sequence of actions aimed at processing personal data. Specifically, it makes a distinction between a planned sequence of actions named workflow and the real execution of this plan, named workflow execution.
European Legislation Identifier ontology The European Legislation Identifier (ELI) ontology 18 is a model which allows the publication of legal documents of di↵erent European Union countries using a shared and uniform set of metadata in order to enhance interoperability among the national administrations. Nowadays, this resource is used by 11 of the 28 EU countries and by the EU Publication O ce. According to the information published by ELI Task Force (2018), the ELI ontology reflects many of the basic principles of FRBR (Functional Requirements for Bibliographic Records) vocabulary 19 , contextualising them into the legal field. While the FRBR provides the description of a bibliographic record in terms of work, expression, manifestation and item, the ELI ontology describes a legal document through the concepts of legal resource, legal expression and format.
In detail, legal resource refers to the intellectual creation, independently from its translation in more than one language and from the format used for its publishing; it corresponds to the work property in FRBR. The legal expression concept is the realisation of a legal resource using a sequence of signs as, for examples, the alphanumeric characters and it corresponds to the expression property in FRBR. The format refers to the physical means 16 http://docs. oasis-open. org/legalruleml/legalruleml-core-spec/v1. 0/ legalruleml-core-spec-v1. 0. html 17 https://www. oasis-open. org/committees/tc_home. php?wg_abbrev=legalruleml 18 https://publications. europa. eu/en/web/eu-vocabularies/model/-/resource/ dataset/eli 19 https://sparontologies. github. io/frbr/current/frbr. html used to store the legal expression (could be paper or an electronic format) and it corresponds to the manifestation property on FRBR.
At present, the two de facto standards used to represent ontologies are RDF and OWL. Connected to this feature, the axioms feature is also considered. The feature refers to the three possible level of axioms planned by OWL 2 specification: class expression axioms, object property axioms and data property axioms. Taking into account the principle of reuse promoted by the Semantic Web, we also considered the ontology design patterns used to represent some parts of knowledge whose modelling was already codified in a standard representa-tion. Finally, the evaluation feature analyses which methods were adopted to evaluate the created knowledge model provided by the ontology. Tables from 5 to 7 classify the analysed ontologies according to the features of this class.
3. As it can be noted, this interface is very di↵erent from the GUI which can be created with LODE. Even if the download links are well visible, the type of licence which regulates the use of each resource is not specified. We found this information in the old Web sites of each resource, before their grouping inside the portal, under the heading "Legal notice". Moreover, in the current interface of the EU vocabularies portal, the title of each tab sometimes does not clarify the information associated with it, and the documentation of the di↵erent resources is not standardized. For example, the documentation of ELI is an xlsx file which must be downloaded and opened with a commercial software in order to be visualized. In contrast, the description of Eurovoc is better organized into expandable windows inside the tab.
Therefore, according to these remarks, some improvement would be desirable to harmonize the way in which the metadata on legal ontologies issued by the EU are organised inside the portal. 22 github. com/essepuntato/LODE 23 publications. europa. eu/en/web/eu-vocabularies Fig. 2 : An excerpt of the NRV GUI, automatically generated using the LODE tool. Concerning the methodological and technological choices made during the development of an ontology, this information is never displayed on the aforementioned GUIs and it could be di cult to find also reading the literature published together with the ontology. However, this information is important for several reasons: first of all, it provides a scientific foundation to the work allowing other researchers to analyse and verify it, secondly, it enables an easy and understandable interpretation of the corresponding literature in which this information is sometimes implicit, even if it is at the basis of the development of the ontology.
RTK-RAS-MAPK Pathway Components Are Synthetic Lethal Interactors with MEK Inhibition To understand common pathways and interactions among synthetic lethal candidates, we performed a meta-analysis of pathway and protein-protein interaction (PPI) enrichment among the synthetic lethal candidates scoring in two or more of the CRISPR-MEKi screens using the Metascape suite of tools (http://metascape. org; STAR Methods;  Tripathi et al. , 2015). We evaluated enrichment of genes within the MSigDB canonical pathways database and observed strong representation of members of the RTK-RAS-MAPK signaling pathways (Figure  2A ). With the exception of KEAP1-mutant NCI-H2030 and NCI-H647, the other eight cell lines demonstrated strong dependence on several well-annotated RTK-RAS-MAPK pathways as well as the MTOR and CDC42 signaling pathways (Figure  2B ).
To understand the potential PPI complexes represented among the MEKi synthetic lethal candidates, we compiled top synthetic lethal interactors from each of the screens into a single gene list and generated a single merged PPI network (Figure  2C ). This network contains the subset of proteins that form physical interactions with at least one other member in the list, based on established interaction databases (STAR Methods). We applied the Molecular Complex Detection (MCODE) algorithm to identify densely connected network components. We observed PPI clusters involving a number of signaling pathways with most notable enrichment of interactions within several different RTK signaling components, RAS-RAF-MAPK pathway members, and CRK-related signaling partners. We identified multiple genes encoding other MAPK pathway members recurrently scoring as potent sensitizers of MEK inhibition, including RAF1,  BRAF, and MAPK1 (Figures 1J, 2D, and 2E).
We then compared the trametinib-treated arm with the control DMSO-treated arm of each screen to yield a "Differential Sensitivity Score," reflecting the preferential dependence of each gene in the presence of trametinib. We prioritized candidates that showed enhanced depletion in the context of MEK inhibition and were also strongly depleted in the trametinib arm of the screen, thus displaying highly negative Differential Sensitivity Scores and Trametinib Sensitivity Scores. STARS analysis v1. 3 was performed in order to collapse guide-level data to genes. STARS analysis was performed as previously described  (Doench et al. , 2016). The code for STARS analysis is written in Python, and is available on the Broad Institute's website: http:// www. broadinstitute. org/rnai/public/software/index.
Meta-analysis of CRISPR-MEKi Screening Data-We used the Metascape suite of tools to perform a meta-analysis of pathway and protein-protein interaction (PPI) enrichment among these synthetic lethal candidates scoring in two or more of the CRISPR-MEKi screens. Metascape is a publicly available web-based pathway enrichment and clustering algorithm (http://metascape. org; STAR Methods)  (Tripathi et al. , 2015). Genes were included in the analysis if they scored in one or more screens with a Differential Sensitivity Score having an FDR ≤ 0. 25 by the STARS algorithm and a Trametinib Sensitivity Score of ≤ -0. 5 or ≥ 0. 5 for the negative selection (MEKi sensitizer) or positive selection (MEKi resistance) analyses, respectively. To specifically interrogate known signaling pathways represented within the MEKi modifier screen data, we focused the enrichment analysis on the MSigDB Canonical Pathways database.
For each given gene list, protein-protein interaction enrichment analysis was conducted with the BioGrid, InWeb_IM and OmniPath databases per the standard metascape algorithm. The resultant networks contain the subset of proteins that form physical interactions with at least one other member in the list. If the network contains between 3 and 500 proteins, the Molecular Complex Detection (MCODE) algorithm  (Bader and Hogue, 2003)  was been applied to identify densely connected network components. The network displays and integrated data representations for each figure were derived from standard plots generated by the Metascape software.
ACKNOWLEDGMENTS This work was supported in part by funds from  Calico Life Sciences  (A. J. A. , K. H. W. , R. S. , A. J. F. , J. S. , and W. C. H. ); the  NIH-National Cancer Institute (NCI)   F32CA243290  (J. J. K. ); the  Fondation pour la Recherche Médicale   SPE2018035027  (E. P. ); the  Lustgarten Foundation  (A. J. A. ); the  Dana-Farber Cancer Institute Hale Center for Pancreatic Cancer Research  (N. S. G. , A. J. A. , and W. C. H. );  NIH-NCI   K00CA212221  (J. R. ); the  Hope Funds for Cancer Research  (A. J. A and S. R. ); the  Doris Duke Charitable Foundation  (A. J. A. ); the  Pancreatic Cancer Action Network (A. J. A. ) ;  American Cancer Society   Postdoctoral Fellowship   PF-17-010-01-CDD  (B. N. ); a  Conquer Cancer Foundation of ASCO   Young Investigator Award  (A. J. A. and S.
Computational analysis to discover meaningful patterns in massive, interlinked datasets is rapidly becoming a routine research activity. The global ocean data system should be designed as an interoperable system of systems that will allow data to be easily findable, accessible, interoperable, and allowing reusability through thematic integrated products and services. The longterm goal is to develop a data system of systems that allows the development of data services at different levels with a guarantee that the best version of the observed data is used at all levels. Data quality, interoperability, and good discoverability can only be assured with a standardized, traceable workflow throughout the lifetime of the datasets. This paper reviews recent developments in technical capacity and requirement setting of a data management system for the Global Ocean Observing System (GOOS).
" SOOS, along with the EMODnet Physics group, is making standardized and aggregated in situ datasets available for exploration and download through SOOSmap (http://www. soos. aq/news/current-news/362-explore-southern-ocean-soosmap). For the long tail of non-standardized data, SOOS is working with Arctic and Antarctic data management groups to investigate the best ways to achieve federated metadata search and to ensure that the EMODnet/SOOSmap infrastructure can be directly linked to virtual labs. SOOS is encouraging scientists in its community to use existing data discovery tools to identify key datasets that should be standardized and federated to publish through SOOSmap. Along this line, SOOS, the Standing Committee on Antarctic Data Management (SCADM), and the Arctic Data Committee (ADC) have founded the POLDER (http://www.