dataesr/scientific-paragraphs-categorization huggingface dataset:
to semantic representations in chemistry, participants discussed strategies to advance cooperation between chemical databases, including establishing agreement on which database keys to use, how to make databases more interoperable by denser cross-referencing, and harmonizing RDF representations. Also in this track, more work was done on the PIERO ontology for chemical transformations, including improved RDF interoperability and additional data curation. An important development was the definition of a classification based on reaction characteristics. Moving on to larger molecules, in proteomics, participants assessed the scalability of representing the UniProtKB database in OWL. Other participants in the same track worked on ontologizing proteome data. An important resource in this field is jPOST, for which an assessment of available controlled vocabularies and ontologies took place and work on an RDF schema commenced.
; 422-431. Cancer Genome Atlas Research Network, Weinstein JN, Collisson EA, et al. : The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013 [cited 2016 Apr 18]; 45(10): 1113-20. OMIM -Online Mendelian Inheritance in Man. [cited 2016 Dec 13]. Reference Source 223. Orphanet. [cited 2016 Dec 13]. Reference Source 224. HGNC database of human gene names. HUGO Gene Nomenclature Committee. [cited 2016 Dec 13].
Therefore, phenotype annotations for different diseases will collapse in a unique UMLS concept. The integration of the HPO2015 and Hoehndorf2015 data- sets (9,067 and 5,194 UMLS CUIs, respectively) covers 13,596 UMLS concepts of the disease spectrum, of which only 3. 2% (665 UMLS CUIs) are in both datasets. This low overlap is due to the fact that each project mainly focuses on covering dif- ferent disease areas. Whilst the HPO annotation is intended to annotate Mendelian and rare genetic diseases, Hoehndorf et al. 's large-scale literature extraction was focused on broadening the disease class landscape to infectious, environmental, and com- mon diseases. To characterize the disease coverage yielded only by text mining; in Figure 5 we show the top-level DO categories where these novel diseases fall.
As can be seen, these novel findings mostly fall in 'Disease of anatomical entity' (DOID:7) and 'disease of cellular proliferation' (DOID:14566). • HPO2015: This curated DPAs dataset contains 113,203 DPAs between 7,841 diseases and 6,838 phenotypes from OMIM, Orphanet and DECIPHER data sources in which diseases are identified by the corresponding database identifier of provenance, and phenotypes are uniformly identified by HPO identifiers. We normalized 6,220 diseases from the Hoehndorf2015 data- set to 5,194 UMLS CUIs by DOID-UMLS cross-references extracted from DO version 2015-06-04 with which only 75% (4,648) of DOIDs can be mapped to UMLS concepts. This is because 17% (1,088) of diseases are described with obsolete DOIDs and 8% (484 DOIDs) do not map to UMLS. Additionally, not all are 1:1 mappings, some N:1 DOID-CUI mappings exist.
Making tree-of-life knowledge accessible, reusable and convenient. BMC Bioinformatics. 2013 [cited 2013 Sep 17]; 14: 158. PubMed Abstract | Publisher Full Text | Free Full Text 50. Gernhard T, Ford D, Vos R, et al. : PubMed Abstract | Publisher Full Text | Free Full Text 17. Zaaijer S, Columbia University Ubiquitous Genomics 2015 class, Erlich Y: Using mobile sequencers in an academic classroom. eLife. 2016 [cited 2016 Nov 30]; 5: pii: e14258. PubMed Abstract | Publisher Full Text | Free Full Text 18. National Bioscience Database Center. [cited 2016 Dec 4]. Reference Source 19. Database Center for Life Science. [cited 2016 Dec 4]. Reference Source 20. Owen H: Open space technology: a user's guide. Berrett-Koehler Publishers; 2008. Reference Source 21. Home | Global Alliance for Genomics and Health.
[cited 2016 Dec 4]. Reference Source 22. vgteam/vg. [cited 2016 Dec 4]. Reference Source 23. ruby-rdf/rdf-vcf. [cited 2016 Dec 4]. Reference Source 24. Ruby-rdf. github. com by ruby-rdf. [cited 2016 Dec 4]. 49. Stoltzfus A, Lapp H, Matasci N, et al. : Phylotastic!.
Estimating the relative order of speciation or coalescence events on a given phylogeny. Evol Bioinform Online. 2007 [cited 2016 Apr 15]; 2: 285-93. PubMed Abstract | Publisher Full Text | Free Full Text 51. Vos RA: Inferring large phylogenies: The big tree problem. Simon Fraser University; 2006 [cited 2017 Mar 6]. Reference Source 52. Vos RA, Mooers AØ: Reconstructing Divergence Times for Supertrees. In: Bininda-Emonds ORP, editor. Phylogenetic supertrees Comb Inf to Reveal Tree Life. Dordrecht: Springer Netherlands; 2004 [cited 2016 Apr 15]. 281-99. Reference Source 53. NESCent: The National Evolutionary Synthesis Center. [cited 2016 Dec 13]. Reference Source 54. Ksepka DT, Parham JF, Allman JF, et al. : The Fossil Calibration Database-A New Resource for Divergence Dating. Syst Biol. 2015 [cited 2016 Apr 15]; 64(5): 853-9.
PubMed Abstract | Publisher Full Text 55. Fossil Calibration Database. [cited 2016 Dec 4]. Reference Source 56. Vos RA, Caravas J, Hartmann K, et al. : BIO::Phylo-phyloinformatic analysis using perl. BMC Bioinformatics. 2011 [cited 2017 Mar 6]; 12: 63. PubMed Abstract | Publisher Full Text | Free Full Text 57. Antonelli A, Hettling H, Condamine FL, et al. : Toward.
inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock Portet F, Reiter E, Hunter J, et al. : Automatic Generation of Textual Summaries from Neonatal Intensive Care Data. Artif Intell Med. Berlin, Heidelberg: Springer Berlin Heidelberg; 2007 [cited 2016 Dec 4]. 227-36. platform for glycoproteomics. Nucleic Acids Res. 2014 [cited 2016 Apr 15]; 42(Database issue): D215-21. Reference Source PubMed Abstract | Publisher Full Text | Free Full Text 195. Vasant D, Chanas L, Malone J, et al. : ORDO: An Ontology Connecting Rare 144. Weininger D: SMILES, a chemical language and information system. 1. Disease, Epidemiology and Genetic Data. [cited 2016 Dec 13]. Introduction to methodology and encoding rules. J Chem Inf Comput Sci. Reference Source American Chemical Society, 1988 [cited 2016 Nov 30]; 28(1): 31-6.
[cited 2016 Dec 4]. Reference Source 154. LODQA: Question-Answering over Linked Open Data. [cited 2016 Dec 4]. Reference Source 155. LODQA: Question-Answering over Linked Open Data. [cited 2018 Mar 16]. Reference Source 156. Enju -An English parser. [cited 2018 Mar 16]. 194. DisGeNET -a database of gene-disease associations. [cited 2016 Dec 4].
Modeling Structural Topic Transitions for Automatic Lyrics Generation. 2014 ; 422-431. Cancer Genome Atlas Research Network, Weinstein JN, Collisson EA, et al. : The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013 [cited 2016 Apr 18]; 45(10): 1113-20. OMIM -Online Mendelian Inheritance in Man. [cited 2016 Dec 13]. Reference Source 223. Orphanet. [cited 2016 Dec 13]. Reference Source 224. HGNC database of human gene names. HUGO Gene Nomenclature Committee. [cited 2016 Dec 13]. Reference Source 225. Nakazato T, Ohta T, Bono H: Reference Source 211. CELEX2 -Linguistic Data Consortium. [cited 2016 Dec 4]. Reference Source 212. leechuck/semantichaiku. [cited 2016 Dec 4]. Reference Source 213. Amyloid beta A4 protein. [cited 2016 Dec 4]. Reference Source 214. PubMed Abstract | Publisher Full Text | Free Full Text 215.
Bioscience. 2015 [cited 2016 May 2]; 65(7): 635. Publisher Full Text 237. The FAIR Data Principles -FOR COMMENT | FORCE11. [cited 2016 Dec 4]. Reference Source 238. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. : The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016; 3: 160018. PubMed Abstract | Publisher Full Text | Free Full Text 239. NFU Data4lifesciences | News | G20 supports FAIR principles. [cited 2016 Dec 4]. PubMed Abstract | Publisher Full Text | Free Full Text 283. Degtyarenko K, de Matos P, Ennis M, et al. : ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res. [cited 2016 May 2] 2008; 36(Database issue): D344-50. PubMed Abstract | Publisher Full Text | Free Full Text 284. Eagle-I Research Resource Ontology -Summary NCBO BioPortal.
[cited 2016 Dec 13]. Reference Source 285. Home -Taxonomy -NCBI. [cited 2016 Dec 4]. Reference Source 286. Dryad Digital Repository -Dryad. [cited 2016 Dec 4]. Reference Source 287. figshare -credit for all your research. [cited 2016 Dec 4]. Reference Source 288. The Dataverse Project -Dataverse. org. [cited 2016 Dec 4]. Reference Source 289. Home -GEO -NCBI. [cited 2016 Dec 13]. Reference Source 290. Martens L, Hermjakob H, Jones P, et al. : PRIDE: the proteomics identifications database. Proteomics. 2005 [cited 2016 Apr 18]; 5(13): 3537-45.
Study selection Medical publication data were selected from open-access articles on the PubMed, Web of Science and Cochrane Library databases. Legal search was performed using keywords in the Ecosia (https://www. ecosia. org/) and Google (https://www. google. com) search engines. The principal search terms used to locate laws, jurisprudence, doctrines and medical articles comprised: copyright, author, citation, article, scientific article, protection, publication, publishing contract, Creative Commons (CC) and Open Access. Articles were selected for relevance and reliability according to the author's reputation in the legal community.
Decomposing the Follow Line Function As an example of further requirements and architecture decomposition, the abstract component of the follow_line function of Listing 23 is detailed in Listing 24. The Follow_Line component type declares an input feature for the light intensity and output features for the left and right wheel power. Such features are typed by the abstract component types of the Physics package (Listing 18). A data access to a robot state internal variable is further added. This variable will be set by the obstacle detection subprogram to stop the robot when an obstacle is detected. abstract Follow_Line features light_intensity : in feature Physics :: Light ; left_motor_power : out feature Physics :: Power ; right_motor_power : out feature Physics :: Power ; state : feature Robot_State ; end Follow_Line ; Listing 24: Follow Line architecture system functions The line following sub function is further decomposed into two sub functions as illustrated by the the Fol-low_Line.
16. The system type Nxt_Brick extends the previously defined Brick system type and adds bus access features (lines 3 to 9) for the brick to be connected to the sensors and actuators of the robot. The corresponding implementation Nxt_Brick. basic (line 12) extends the Brick. basic implementation and adds subcomponents specific to the NXT brick for the input and output circuits and a data bus to connect them to the processor. The input and output circuits take data from the processor as input and  The generic main_processor subcomponent of the extended generic brick system implementation is refined to an ARM_Processor. nxt type as shown in Listing 28 (line 14). The type of this refined subcomponent is an extension of the generic processor component type as shown in Listing 29 below (line 1).
We briefly describe the essence of these properties using our software application example. As shown in Listing 32, we created new data types for our software variables that map to the system variable abstract types that they extend. To these types we added the Data_Representation property from the data modelling annex to specify that our variables will be represented as integers in code. For the robot state internal variable, we have set the Data_Representation as an enumeration with values FORWARD and STOP. The Source_Name property specifies the name of a corresponding data structure in C code located in a C file specified by the Source_Text property. An initial value of FORWARD is set on the data subcomponent of the Carry_Object_Process process of Listing 36. For subprograms such as Compute_Turn_Angle_SW (Listing 33), the Source _Language property has been set to "C" and similar to the Robot_State data component type, properties Source_Name and Source_Text are used to identify corresponding subprograms in C code.
Self Assessment 1. Write a non-functional requirement in ReqSpec for the latency in steering the robot that will constrain the results of analysis in section 8. 6. 7. 2. Add a predicate to verify that the latency is below the limit of 20 ms. 3. Complete the Lego Mindstorm robot product line family by adding AADL system classifiers to the robots library package of Listing 27 for the EV3 version of the Lego Mindstorm brick and robot. 2. 4. Model software requirem ents in ReqSpec derived from the example functional requirements for the system functions of section 8. 6. 4. 5. Perform a latency analysis for the obstacle detection function similar to the one presented in section 8. 6. 7 for steering the robot. First add the missing device driver to the software application system for the sonar sensor and add the required flow annotations and properties.
• Finally, the average of the obtained estimates, MSEs and ALs with level 95% (so ν = 0. 05) are computed. All the numerical results are presented in Tables 1-4 for the Rényi entropy, and Tables 5-8 for the q-entropy. All is calculated by the use of the mathematical software Mathcad. Figures  1 2 3 4 provide a graphical approach of the MSEs and ALs of the Rényi entropy estimates, whereas Figures  5 6 7 8 provide a graphical approach of the MSEs and ALs of the q-entropy estimates. Here, some remarks can be formulated about the behavior of the Rényi and q-entropies estimates according to Tables  1 2 3 4 5 6 7 8 , and Figures  1 2 3 4 5 6 7 8 :.
A link function g(y ik ) maps the parameters of interest onto a scale ranging from minus to plus infinity and u i are the trial-specific baselines. For an overview of commonly used link functions in meta-analysis see. 13 All arm-level parameters y ik across studies are collected in a vector y a of length P n i = 1 K i , where superscript a stands for 'arm-level'. With contrast-level data we model trial specific summaries, for example logOR, log risk ratio, mean difference or standardized mean difference. 14 Let y ij be the observed effect size for treatment contrast j in study i. The vector of the estimated contrasts across all studies is denoted as y c and is of length P n i = 1 K i -1 ð Þ. The superscript c indicates the fact that "contrast-level" data are modeled. We will first describe the arm-level (section 2.
7, 19 e performed an empirical comparison of the treatment hierarchies obtained with PreTA, pBV and SUCRA, calculated using parametric bootstrap in a frequentist framework. The agreement between ranking metrics was measured using Kendall's tau. We used a previously described database of NMAs published until 2015 including networks of four or more interventions. 4 We included networks with available outcome data in arm-level format, for which the primary outcome was analysed either as binary or as continuous. We used the effect measure used in the original review. Details about the inclusion criteria of the NMAs included in the database can be found in. 4  The empirical analysis was performed with the use of the nmadb package in R. 20  Results of the empirical analysis are presented in section 5.
Introduction The benefits and risks of low-dose hydrocortisone in patients with septic shock have been investigated in numerous randomised controlled trials and trial-level meta-analyses. Yet, the routine use of this treatment remains controversial. To overcome the limitations of previous meta-analyses inherent to the use of aggregate data, we will perform an individual patient data meta-analysis (IPDMA) on the effect of hydrocortisone with or without fludrocortisone compared with placebo or usual care on 90-day mortality and other outcomes in patients with septic shock. Methods and analysis To assess the benefits and risks of hydrocortisone, with or without fludrocortisone for adults with septic shock, we will search major electronic databases from inception to September 2020 (Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE and Latin American Caribbean Health Sciences Literature), complimented by a search for unpublished trials.
Obtaining individual patient data One reviewer (DA) will contact the primary author and/ or sponsor of all selected trials for potential agreement to share deidentified individual patient data from their trial for the purpose of this patient-level meta-analysis. They will define whether data will be freely available or only after application to and approval by a learnt intermediary and whether we will require a data use agreement. In case of non-response or refusal, we will use published aggregated data and combine them to the IPDMA results in a sensitivity analysis. Data will be stored on a secure server hosted by University of Versailles SQY.
Data items Specifically, with regard to the population of patients for the primary analysis, these will be adult patients with Open access septic shock. Adults will be those 18 years or older at time of randomisation. Septic shock will be defined according to the definition used in each clinical trial. Each included patient will meet at least one of the following criteria 1. Systolic blood pressure <100 mm Hg or mean arterial pressure <65 mm Hg after fluid resuscitation. 2. Lactate >2 mmol/L. 3. Requirement for vasopressors to maintain an adequate blood pressure. The intervention of interest is hydrocortisone, administered intravenously at a dose of less than 400 mg per day, either in divided bolus doses, or as a continuous infusion. We will record the dose, the mode of administration, the duration of administration, and the mode of cessation, either tapered or abruptly ceased.
Data synthesis Baseline patient characteristics will be presented by study and treatment group. For continuous variables, mean and SD or median and IQR will be reported, as appropriate. For categorical variables, the number of observations in each category and corresponding proportions will be reported. Patient characteristics across groups will be contrasted using non-parametric Kruskal-Wallis tests for continuous variables and χ 2 test or Fisher's exact tests for categorical variables. Since earlier and later deaths may stem from qualitatively different processes, to provide a more comprehensive depiction of mortality, length of stay in the ICU or in the hospital will be reported in the overall population as well as in the subpopulation of survivors at day 90. All tests will be two sided and conducted at significance level 0.
Data analysis We will consider as the primary analysis, the comparison between hydrocortisone (with or without fludrocortisone) and placebo (or no treatment) on 90-day mortality for patients with septic shock. Prespecified secondary analyses will include all possible pairwise comparisons, namely, hydrocortisone versus placebo, hydrocortisone plus fludrocortisone versus placebo, hydrocortisone plus fludrocortisone versus hydrocortisone. In order to increase the robustness of the results, we will perform two different statistical approaches, that is, a one stage conventional meta-analysis and machinelearning targeted maximum likelihood analysis. As suggested by different studies comparing one-stage to two-stage approaches,  21 22  the conventional will be performed using a one-stage meta-analysis.
In one-stage meta-analysis, all data from all studies are aggregated and the primary outcome is analysed simultaneously by adopting a single statistical model that accounts for potential heterogeneity across studies. 23 Analyses will rely on generalised linear mixed effect models (GLMM) where both the intercept and the treatment effect will be treated as random variables with the study as the subject (ie, a random study intercept and a random study-by-treatment Open access interaction). For the primary outcome and for binary secondary outcomes, we will use a GLMM with a logit link function. Continuous secondary outcomes will be analysed using a GLMM with an identity link function. Our estimates of the average treatment effect will be adjusted for study (random effect), age, predicted mortality from SAPS2 or APACHE 2, SOFA, admission type (medical, elective surgery or emergent surgery), infection site infection type (hospital vs community-acquired infection) and type of pathogen, baseline and increment in cortisol levels post corticotrophin, lactate levels and need for mechanical ventilation (fixed effects).
Methods to assess bias We will assess for the potential for publication bias or small study bias by inspection of funnel plots and the use of Egger's test. The potential bias introduced by the studies that could not be included in the analyses will be evaluated  32  by performing a two-stage meta-analysis aggregating the results obtained on shared data and treatment effect estimates published for unshared data, if data permit. Specifically, the available IPD will first be reduced to aggregated data using the modelling methods described above. Then, these aggregated data will be pooled with published aggregated data into a weighted average. 33 34 Heterogeneity will be assessed by using an estimate of τ 2 generated from the one stage and two-stage models.
The SL  42  is an ensemble machine-learning algorithm that automatically constructs an optimal weighted combination estimator based on a collection of supplied candidate estimators. The SL yields an estimator that is mathematically guaranteed to perform essentially as well as or better than the best candidate among the ones it is built on-this is significant since in practice which of the candidate estimators behaves best in a given problem and dataset is not known to the analyst. 42 In the context of IPDMA, as compared with GLMM, this approach may avoid any strong assumption about the functional form of the relationship between outcome and explanatory variables. It may help leverage the advantages of all candidate learners such as GLMM. Finally, it may allow accounting for potential high-order interactions by including in the library highly flexible algorithms such as random forests.
e. the first stage of the PLS-PM algorithm). Hanafi (2007)  points out that there are two main procedures for the computation of scores in the first stage of the PLS-PM algorithm: the original procedure as proposed by  Wold (1982 Wold ( , 1985)) , and extended by  Hanafi (2007)  (called here Hanafi-Wold's procedure), and an alternative procedure introduced by  Lohmöller (1989). As demonstrated by  Hanafi (2007) , the advantage of Hanafi-Wold's procedure is that it is monotonically convergent, it reaches a stable solution faster and has a better performance in terms of convergence speed compared to Lohmöller's procedure. This latter procedure does not converge monotonically, but is implemented in most PLS-PM software applications, such as PLS-Graph (Chin 2003), PLS-GUI  (Hubona 2015) , SmartPLS (Ringle et al 2015), XLSTAT's PLSPM package (Addinsoft XLSTAT 2019), among others.
To become user-driven and more useful for decision-making, the current evidence synthesis ecosystem requires significant changes (Paper 1. Future of evidence ecosystem series). Reviewers have access to new sources of data (clinical trial registries, protocols, clinical study reports from regulatory agencies or pharmaceutical companies) for more information on randomized control trials. With all these new available data, the management of multiple and scattered trial reports is even more challenging. New types of data are also becoming available: individual patient data and routinely collected data. With the increasing number of diverse sources to be searched and the amount of data to be extracted, the process needs to be rethought. New approaches and tools, such as automation technologies and crowdsourcing, should help accelerate the process.