dataesr/scientific-paragraphs-categorization huggingface dataset:
• Handles Newtonian and non-Newtonian rheologies (Viscous, Visco-plastic and Visco-elasto-plastic). • Database of common rheologies used in geodynamics, which can be personalised / extended by users. • Simple definition of kinematic, stress, and thermal boundary conditions. • Lithostatic pressure calculation. • Thermal equilibrium (steady-state) calculation. • Pseudo Isostasy using a range of kinematic or stress boundary conditions. • Partial melt calculation and associated change in viscosity / heat production. • Simple definition of passive tracers and grid of tracers. • Simple Phase changes • 2-way coupling with the surface processes model pyBadlands  (Salles et al. , 2018). UWGeo comes with a series of examples, benchmarks and tutorial setups that can be used as cookbook recipes. They provide a wide range of teaching materials useful to introduce numerical geodynamic modeling to students.
6. 0 in single-thread mode and default parameter setting. The resulting solution method is denoted as Cplex + reduction in what follows. The extended formulation ILP E is solved via the Branch-and-Price algorithm, initialized with n variables ξ S , where S = {v}, v ∈ V. At each column-generation iteration, linear programs are solved with Cplex 12. 6. 0. The pricing subproblem, formulated as a mincut/max-flow problem, is solved by means of the pre-flow algorithm by Goldberg and Tarjan  [21]. We very rarely observed a branching requiring to solve the subproblem as a MIP (i. e. , introducing incompatibility constraints between vertices). The exploration of the branching tree is performed in a depth-first fashion. The experiments have been performed on a computer with a 3. 40 Ghz 8-core Intel Core i7-3770 processor and 16Gb RAM, running a 64 bits Linux operating system.
Modeling Strategy All the numerical simulations presented in this work are performed using the finite-volume code YALES2  [9] , a Large-Eddy Simulation (LES) and Direct Numerical Simulation (DNS) solver based on unstructured meshes. This code solves the low-Mach number Navier-Stokes equations for turbulent reactive flows using a time-staggered projection method for constant  [10]  or variable density flows  [11]. YALES2 is specifically tailored for solving these low-Mach number equations on massively parallel machines with billion-cell meshes thanks to a highly optimized linear solvers  [12]. Recently, a meso-scale four-way coupling approach for the modeling of solid particles has been implemented in the YALES2 solver. This approach combines DEM approach to represent the solid phase with LES equations solved on an Eulerian unstructured grid for the fluid phase.
The analysis of the time-averaged fields of the solid flux reveals, apart from the progressive intensification of these fields due to the increasing mesh resolution, that this latter did not influence the position of the observed double mixing loop. Figure  13  shows profiles of the time-averaged vertical solid mass flux normalized by the gas mass flowrate, < α p ρ p U p ,3 > xz /ρU f. The profiles are taken at the height z/H = 0. 092 (z = 2. 3 cm) above the bottom of the bed, for which experimental data are available. We notice that the predictions are improved when the finest mesh is used, especially close to the side walls. However, at the center of the bed, the effect of the bottom wall on the axial solid flux is still poorly reproduced even when a finer mesh is used. With the attempt to improve the numerical results, the wall boundary condition type on the gas phase was also investigated.
As previously observed for the particle volume fraction distribution, the position of the centre of the double mixing loop moves upwards when µ w increases (upper pannel). This point is not straightforward when µ p is increased (bottom panel). However, qualitatively speaking, the magnitude of the solid flux exhibits a maximum for µ p = 0. 2 in both sets of simulations. A quantitative analysis of the solid flux may be performed via its vertical profiles at a given height of the bed. Profiles of the time-averaged normalized vertical solid mass flux are depicted in Figure  18a, b  for the two sets of simulations. The profiles are taken at the height z/H = 0. 092 (z = 2. 3 cm) above the bottom of the bed, for which experimental data are available. In all the simulations, two distinct parts for each profile can be observed.
For its part, the FibroScan manufacturer recommends using the M probe for patients with skin-liver capsule distance (SCD) ,25 mm and the XL probe for those with SCD $25 mm. In practice, the Automatic Probe Selection (APS) tool included in recent versions of the device's software automatically measures SCD and indicates the probe to be used as a function of the patient's morphology. According to the manufacturer, following this recommendation enables the use of the same diagnostic cutoffs for both the M and XL probes. Here, as it has never been done, we aimed at evaluating the relevance of these recommendations for clinical practice and at defining the conditions of use for the M and XL probes in a large cohort of patients with biopsy-proven chronic liver diseases.
The latest versions of the FibroScan devices now include the APS tool, which indicates which probe should be used for LSM. In practice, the APS tool indicates "M" or "XL" on the FibroScan screen when the probe is placed on the skin (Figure  s1 , Supplementary Digital Content, http://links. lww. com/CTG/A15). The probe type recommended by the APS tool during the FibroScan examination performed at inclusion was not recorded in the database, but that information was retrievable from the FibroScan acquisition files stored in the device. Therefore, all files from the patients included in the Angers center were retrospectively assessed by the Echosens company, blinded for patient data, to determine which probe was recommended by the APS tool. Because the probe's recommendation can fluctuate along the course of an examination, it was recorded into the device's acquisition file at the time of each measurement.
Statistical analysis Quantitative variables were expressed as mean 6 s. d. or median with first and third quartiles and compared using the Mann-Whitney or the Wilcoxon test, as appropriate. Qualitative variables were expressed as percentages and compared using the Fisher exact test or the McNemar test. Correlations were assessed using the Spearman correlation coefficient (Rs) and agreement using the intraclass correlation coefficient (ICC). Diagnostic accuracy was mainly expressed as the area under the receiver operating characteristics (AUROCs). AUROCs were compared according to Delong et al. (14)  for paired groups. Statistical analyses were performed using SPSS version 18. 0 software (IBM, Armonk, NY) and SAS 9. 1 (SAS Institute, Cary, NC).
Objective: To select, present, and summarize the best papers published in 2018 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers published in 2018 in KRM, based on PubMed and ISI Web Of Knowledge queries. Results: Four best papers were selected among the 962 publications retrieved following the Yearbook review process. The research areas in 2018 were mainly related to the ontology-based data integration for phenotype-genotype association mining, the design of ontologies and their application, and the semantic annotation of clinical texts. Conclusion: In the KRM selection for 2018, research on semantic representations demonstrated their added value for enhanced deep learning approaches in text mining and for designing novel bioinformatics pipelines based on graph databases.
Paper Selection Method We conducted the selection of KRM papers based on a new set of queries. In comparison with the previous editions of the IMIA Yearbook, both PubMed/MELDINE and Web of Knowledge were used to search for KRM articles published in 2018  [1]. We followed the generic method commonly used in all sections of the Yearbook to select the best papers. As for the last four years, the search was performed on MEDLINE by querying PubMed. This year, we also performed an additional query on the ISI Web of Knowledge database (WoL). Our query includes Medical Subject Headings (MeSH) descriptors related to KRM in the context of medical informatics with a restriction to international peer-reviewed journals, including conference proceedings indexed in PubMed. Only original research articles published in 2018 (from 01/01/2018 to 12/31/2018) were considered; we excluded the following publications types: reviews, editorials, comments, and letters to the editors.
Best Papers Selection for 2018 The paper authored by Arguello Casteleiro et al. , and selected as a best paper, aims at automatically identify term variants or acceptable alternative free-text terms for gene and protein names in PubMed biomedical publications  [2]. The use of a domain knowledge ontology, the Cardio Vascular Disease Ontology (CVDO), was associated with the best results. This study led to performance improvements for both Continuous Bag of Words (CBOW) and Skip-gram on a gene/ protein synonym detection task by adding knowledge formalized in the CVDO and without modifying the word embeddings created. Hence, the CVDO supplies context that is effective in inducing term variability for both CBOW and Skip-gram while reducing ambiguity. In an other best paper, Le et al. , presents Spfy, a platform that exploits semantic technologies with a graph database that allows rapid phenotype identification through a novel bioinformatics pipeline, as well as efficient storage and downstream comparative analysis of thousands of genome sequences  [3].
In their paper, Osumi-Sutherland et al. , use OWLbased (Ontology Web Language) reasoning on Gene Ontology (GO) to generate novel, biologically relevant groupings of GO terms to support mapping with a controlled vocabulary  [4]. The GO term groupings generated by this approach can be used in over-representation analysis to detect cell and tissue type signatures in whole genome expression datasets. Also one of the best papers for 2018, the work of Yu et al. ,  [5]  presents a phenotyping algorithm (Phe-Norm) that does not require expert-labeled samples at the training step. This completely annotation-free 2-step classification method for phenotyping involves an initial normalization step of highly predictive features of the target phenotype, followed by a denoising step to leverage additional information contained in the remaining candidate features.
Semantics for Genomic Data Management In a long article, Al Kawam et al. ,  [15]  tackled fundamental bioinformatics challenges involving semantic representations: genomic data generation, storage, representation, and utilization in conjunction with clinical data. For each aspect, they provided a detailed discussion on the current research directions, outstanding challenges, and possible resolutions. This paper seeks to help narrow the gap between genomic applications, which are being predominantly utilized in research settings, and the clinical adoption of these applications. In differential diagnoses and disease gene prioritization, the Human Phenotype Ontology (HPO) is often used to compare a phenotype profile against gold-standard phenotype profiles of diseases or genes. In his article  [7] , Köhler investigated how this comparison can be improved by exploiting structure and information existing in annotation datasets or full text disease descriptions.
Ontology Design and Documentation Five of the candidate best papers present research in the ontology design domain  [6, [10] [11] [12] 16 ]. This theme is common in the KRM section. While it was declining in recent years, it has become more present in 2018 than in previous years. In each case, articles described the motivation for building the ontology and developed the application that uses it for validation. Traverso et al. ,  [12]  developed a Radiation Oncology Ontology (ROO). This ontology takes into account a few standard ontologies as the Fundational Model of Anatomy (FMA), the National Cancer Institute thesaurus (NCIt), and others terminologies. Authors demonstrated the possible conversion of clinical data following the FAIR principles (Findability, Accessibility, Interoperability, and Reusability), by using a combination of ontologies and Semantic Web (SW) technologies.
This work proposes, using SW technologies based on existing ontologies, to efficiently and easily query data from different sources (relational databases) without knowing a priori their structures. Bibault et al. ,  [10]  developed a Radiation Oncology Structures (ROS) Ontology. This ontology also relies on several standard ontologies as FMA, Radlex, and others. Authors provided annotations of EHR radiation oncology data with ROS concepts and integrated them into their clinical data warehouse. Finally, they showed the utility of the ontology in order to integrate dosimetric data. Jing et al. ,  [6]  described the motivation and the building of OntoKBCF, an ontology for the cystic fibrosis domain. They illustrated the lack of sufficient clinical actionable knowledge that is related to molecular genetic information.
Semantics and Clinical Notes Two candidate best papers presented a research involving semantic formalization associated with NLP approaches to process clinical texts. The first paper by Catling et al. ,  [13]  explored methods for representing clinical text using hierarchical clinical coding ontologies. This study demonstrates that hierarchically-structured medical knowledge can be incorporated into statistical models to produce improved performance for automated clinical coding. However, the authors reported that the data processing was difficult: they used a supervised learning approach with manually-as-signed clinical codes for the training dataset. Consequently, learning good representations of rare diseases in clinical coding ontologies from data alone remains challenging. Viani et al.
Interoperability and Data Integration The paper from Maier et al. ,  [14]  reports the experiment of implementing an OMOP/ OHDSI-based pilot within a consortium of eight German University hospitals. Authors evaluated the applicability to support data harmonization and sharing among University hospitals, and they identified potential enhancement requirements. In order to facilitate the work of hospital centers, they provided a virtual machine preconfigured with the OMOP database and the OHDSI tools as well as the jobs to import the data and conduct the analysis. This work is encouraging, even if taking into account important vocabularies for Germany remains to be done. Such a paper shows the difficulties of moving from a model to a real implementation.
Conclusions In the KRM selection for 2018, research on semantic representations demonstrated their added-value for enhanced deep learning approaches in text mining and for designing novel bioinformatics pipelines based on Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes graph database. In addition, the ontology structure can enrich the analyses of whole genome expression data. Finally, semantic representations demonstrated promising results to process phenotypic big data. This work combines semantic modelling (Cardiovascular Disease Ontology, CVDO) and learning algorithms (word embeddings). The authors aim at automatically identifying term variants or acceptable alternative free-text terms for gene and protein names from PubMed biomedical publications.
This work relies on a generic approach to be reused with other medical ontologies. Le KK, Whiteside MD, Hopkins JE, Gannon VPJ, Laing CR Spfy: an integrated graph database for real-time prediction of bacterial phenotypes and downstream comparative analyses Database (Oxford) 2018;2018:1-10 Spfy is a platform that rapidly performs the common reference laboratory tests owing to its database of diverse pre-computed results, and the ability to incorporate user data. This platform handles all analysis tasks by dividing them into subtasks, which are subsequently distributed across a built-in task management process. All results are converted into individual graphs and stored within a large graph database according to previously created ontologies: the Genomic Epidemiology Ontology (GenEpiO), the Feature Annotation Location Description Ontology (FALDO), and the Microbial Typing Ontology (TypOn).
These ontologies provide the relevant metadata for genotypes, location, biomarkers, host, and source. In its presented version, Spfy contains 10,243 Escherichia Coli genomes, for which in-silico serotypes and Shiga-toxin subtypes, as well as the presence of known virulence factors and antimicrobial resistance determinants have been computed. Spfy includes analyses modules that are also self-contained and can be used in existing platforms. This work demonstrates that Spfy, by leveraging semantic technologies with a graph database, facilitates rapid phenotype identification, as well as the efficient storage and downstream comparative analysis of thousands of genome sequences. Osumi-Sutherland DJ, Ponta E, Courtot M, Parkinson H, Badi L Using OWL reasoning to support the generation of novel gene sets for enrichment analysis J Biomed Semantics 2018;9(1):10 The Gene Ontology (GO) consists of over 40,000 terms for biological processes, cell components, and gene product activities linked into a graph structure by over 90,000 relationships.
Yu S, Ma Y, Gronsbell J, Cai T, Ananthakrishnan AN, Gainer VS, Churchill SE, Szolovits P, Murphy SN, Kohane IS, Liao KP, Cai T Enabling phenotypic big data with PheNorm J Am Med Inform Assoc 2018;25(1):54-60 This paper addresses the difficulty to obtain a gold standard to train machine learning processes. The authors introduced a silver standard approach without human solicitation. They present PheNorm, a phenotyping algorithm that does not require expert-labeled samples at the training step. The input for the PheNorm algorithm consists of unlabeled data on a set of potentially informative features, either automatically curated or designed by experts. Online articles about the target phenotype from publicly available knowledge sources, such as Wikipedia and Medscape, are scanned with Natural Language Processing (NLP) software to extract medical concepts recorded in the Unified Medical Language System.
These concepts are potentially related to the target phenotype. Then, narrative notes from the Electronic Heath Record (EHR) database are processed with NLP software, which identifies mentions of the above medical concepts. With such a material, the most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. The authors validated the accuracy of PheNorm with four phenotypes: coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis.
Section Knowledge Representation and Management  Arguello Casteleiro M, Demetriou G, Read W, Fernandez Prieto MJ, Maroto N, Maseda Fernandez D, Nenadic G, Klein J, Keane J, Stevens R. Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature. J Biomed Semantics 2018;9(1):13.  Le KK, Whiteside MD, Hopkins JE, Gannon VPJ, Laing CR. Spfy: an integrated graph database for real-time prediction of bacterial phenotypes and downstream comparative analyses. Database (Oxford) 2018;2018:1-10.  Osumi-Sutherland DJ, Ponta E, Courtot M, Parkinson H, Badi L. Using OWL reasoning to support the generation of novel gene sets for enrichment analysis. J Biomed Semantics 2018;9(1):10.  Yu S, Ma Y, Gronsbell J, Cai T, Ananthakrishnan AN, Gainer VS, Churchill SE, Szolovits P, Murphy SN, Kohane IS, Liao KP, Cai T.
Background: With the improvements to text mining technology and the availability of large unstructured Electronic Healthcare Records (EHR) datasets, it is now possible to extract structured information from raw text contained within EHR at reasonably high accuracy. We describe a text mining system for classifying radiologists' reports of CT and MRI brain scans, assigning labels indicating occurrence and type of stroke, as well as other observations. Our system, the Edinburgh Information Extraction for Radiology reports (EdIE-R) system, which we describe here, was developed and tested on a collection of radiology reports. The work reported in this paper is based on 1168 radiology reports from the Edinburgh Stroke Study (ESS), a hospital-based register of stroke and transient ischaemic attack patients.
86, 96. 01,  96. 53 and 92. 61, respectively for the second annotator. Conclusion: Automated reading of such EHR data at such high levels of accuracies opens up avenues for population health monitoring and audit, and can provide a resource for epidemiological studies. We are in the process of validating EdIE-R in separate larger cohorts in NHS England and Scotland. The manually annotated ESS corpus will be available for research purposes on application.
With a specific focus on stroke,  Flynn et al. (2010)  [21] developed a system for analysis of brain scan radiology reports from Tayside, Scotland, i. e. EHR reports which are very similar to the those in the ESS data set  [22]. Their aim was to improve on the coding of the reports which were frequently given generic 'stroke' codes even when a more precise code could be determined by looking at the report. Their method used a keyword matching step looking for affirmative or negative uses of key words from a stroke lexicon. They report results which were acceptably accurate in identifying ischaemic stroke (94. 7% positive predictive value (precision)) on a dataset of 150 reports manually classified as ischaemic stroke. Their method performed less reliably in identifying intracerebral haemorrhage (76.
7% positive predictive value) on a dataset of 150 reports manually classified as intracerebral haemorrhage. The paper does not report sensitivity (recall) scores as the data only contains positive examples of either type. To the best of our knowledge, EdIE-R is the first system that performs named entity extraction, negated entity detection, relation extraction and document level labelling with the goal to classify radiology report with types of stroke, tumours and other information. The extracted entities (positive or negative) and relations are all used to do the final classification (labelling) step. The information captured in and about the reports include a comprehensive set of entities and labels. We provide a detailed evaluation of EdIE-R for all the steps it is designed to perform using standard natural language processing evaluation metrics, including precision, recall and F1-score.
The EdIE-R system EdIE-R is a rule-based text mining system which we developed in tandem with manual data annotation in the form of correction of the system output. The presentation of the data in the BRAT tool, as illustrated in Fig. 2 , is the view that the annotators see, but this is a format that has been derived from the data structure which the system manipulates and outputs, which is an XML data structure. We have developed the system's text analysis components using the LT-XML2 programs, which are the core of our XML rule-based text mining software  [23]. Our most recent software release, the Edinburgh Geoparser  [24] , contains all of our general-purpose components, such as the tokeniser, NER tagger and chunker, which we have adapted to the brain scan report domain in EdIE-R. As shown in Fig.