dataesr/scientific-paragraphs-categorization huggingface dataset:
gov, thereby excluding 31. 2 % of trials in the WHO ICTRP  [9]. Our method of classification was based on the processing of the condition field and public and scientific titles only, which are required by the WHO ICTRP  [47]. Thus, our method can be transposed to any of the 16 clinical trial repositories included in the WHO ICTRP up to date, including clinicaltrials. gov. All these sources of registries are fundamental to conduct a worldwide mapping of registered clinical trials to be compared to global health needs. In addition, in our github repository we include codes to analyze clinical trial records downloaded from WHO ICTRP and clinicaltrials. gov websites.
These trials may be considered for manual revision. Because the WHO ICTRP database is large and constantly growing, manual revisions may be expensive. Crowd-sourcing based on the interface for the manual classification we developed could be scaled up to divide the effort needed for revision. In addition, trial registries such as ClinicalTrials. gov could include the GBD classification as a mandatory field in trial records. The classifier we developed could provide an automatic recommendation for classification of newly registered trials by the GBD categories, thus reducing the burden of registration. Another strength of the classifier is that it is based on the UMLS Knowledge Source, a metathesaurus widely used for analyzing biomedical text, which increases the portability and reproducibility of the classification.
The classification method development did not rely on data in the test set. Other approaches such as statistical methods of classification (e. g. support vector machines) may be used to address our objective. However, our knowledge-based classifier may be more resilient to the evolution of clinical trial records. Every year, about 20,000 new clinical trials are registered at WHO ICTRP  [9]. Statistical methods of classification would need new training data to perform classification out of the rule space of a training dataset. Another strength is that our knowledge-based classifier allows understanding the process of classification of trial records (Fig. 1 ), as compared to statistical classifiers. For a public health project, it is of great value understanding the process of data curation  [48, 49].
Limitations Our work has several limitations. First, the quality of the mapping of health research depends on the quality of the registration of clinical trials. Trial registration remains of low quality, but endorsements from WHO are attempting to improve the registration system  [7, 47]. In addition, the misclassification of diseases may be correlated to trial location. For instance, our classifier only supports English language, as MetaMap identifies UMLS concepts in biomedical text written in English. This may increase the misclassification in non-English speaking countries. However, according to the International Standards for Clinical Trial Registries from the WHO, all items of trial records included in the WHO ICTRP (including the condition field and the public and scientific titles) must be available in English language [47].
Conclusion Herein, we presented a knowledge-based classifier to map the health conditions studied in registered clinical trials according to the taxonomy of diseases and injuries from the Global Burden of Diseases 2010 study. The overall performance of the classifier was 81. 9 % sensitivity and 97. 6 % specificity. We applied it to the entire WHO ICTRP database, which characterizes the global burden of disease addressed by the 109,603 clinical trials in the database. This classifier allows for comparing the research effort to the disease burden on a large scale for all diseases and all regions and studying the evolution over time.
Additional files Additional file 1: Includes details on the implementation of MetaMap and IntraMap, prioritization rules, the test set of clinical trials and the classification of the external test set according to the 171 GBD categories. Dataset S1: Expert-based enrichment database for the classification according to the 28 GBD categories. Manual classification of 503 UMLS concepts that could not be mapped to any of the 28 GBD categories. Dataset S2: Expert-based enrichment database for the classification according to the 171 GBD categories. Manual classification of 655 UMLS concepts that could not be mapped to any of the 171 GBD categories, among which 108 could be projected to candidate GBD categories. Table  S1 : Excluded residual GBD categories for the grouping of the GBD cause list in 171 GBD categories.
A grouping of 193 GBD categories was defined during the GBD 2010 study to inform policy makers about the main health problems per country. From these 193 GBD categories, we excluded the 22 residual categories listed in the Table. We developed a classifier for the remaining 171 GBD categories. Among these residual categories, the unique excluded categories in the grouping of 28 GBD categories were "Other infectious diseases" and "Other endocrine, nutritional, blood, and immune disorders". Table  S2 : Per-category evaluation of performance of the classifier for the 171 GBD categories plus the "No GBD" category. Number of trials per GBD category from the test set of 2,763 clinical trials. Sensitivities, specificities (in %) and likelihood ratios for each of the 171 GBD categories plus the "No GBD" category for the classifier using the Word Sense Disambiguation server, the expert-based enrichment database and the priority to the health condition field.
(DOCX 84 kb) Additional file 2: Classification towards the 28-class grouping of GBD categories of all interventional trials registered at WHO ICTRP before February 2014. Classification of N = 109,603 clinical trials using the best-performing version of the classifier (using the Word Sense Disambiguation server, the expert-based enrichment database and the priority to the health condition field. (XLSX 12447 kb).
Acknowledgements We thank  Elise Diard  for help with graphics and the interface for manual classification of trials. We thank  Dr Bodenreider  for running IntraMap at the NLM servers for a large amount of UMLS concepts. We thank  Pr Altman ,  Dr Emdin , and  Dr Odutayo  and  Dr Viergever  for sharing their data. Finally, we thank  Laura Smales  for language revision of the manuscript.
This grouping was considered sufficiently informative for a global mapping of health research to a global mapping of health needs Clinical trial record Trial record : NCT00834483 Health condition Scientific title Public title Free text -Knee Osteoarthritis -Hip Osteoarthritis Closure of Total Hip and Use of Knotless Suture for for Closure of Total Hip Use of Knotless Suture Knee Arthroplasties: A and Knee Arthroplasties Prospective-randomized Clinical Trial C0409959 C0029410 UMLS Osteoarthritis, Knee Osteoarthritis of hip ICD10 codes M16 M16. 9 M17 M17. 9 GBD categories Musculoskeletal disorders Classi -fication -fication.
Meteorological data Meteorological conditions were continuously monitored in an open plot in the center of the provenance plot. The conditions included air temperature (°C), relative humidity (%), global incoming solar radiation (W m -2 -Minikin TH ® , EMS, Brno, CZ) and rainfall (mm -MetOne 370 ® , Oregon, USA). The measurements of soil water potential (Ψw) were carried out on each micro-plot at 15, 30 and 50 cm depth (continuously to -1. 1 MPa, using gypsum blocks -Delmhorst Inc. , USA -and MicroLog SP3 datalogger -EMS, Brno, CZ) and also on two micro-plots in the middle, near the meteo measurements. Overall, 36 gypsum blocks were used, 12 for each soil depth. We used the mean values of all of the blocks representing the whole site for the analyses to avoid mosaic heterogeneity between and inside the microplots.
2 ). Cumulative daily and weekly SCI (growth curves) were fitted by the Gompertz (eqn. 3) and SASB (self adjusting sharp beginning -eqn. 4) functions using the software Mini32 ® (EMS Brno, CZ). The Gompertz function is defined in  Rossi et al. (2003)  as (eqn. 3): where y is the daily or weekly cumulative stem circumference increase, t is the time computed in Julian days, A is the upper asymptote of final cumulative stem circum-ference increase, β is the x-axis placement parameter, and k is the rate of change parameter. The SASB growth function below was designed and long-term tested by EMS Brno in its software Mini32 (eqn. 4): where (eqn. 5): and y is the daily or weekly cumulative stem circumference increase, par1 is the starting value (offset, spring level of circumference), par2 is the annual growth, par3 is the time on maximal growth, par4 is the max growth rate, par5 is the virtual starting value (always lower than par1), and t is the time computed in Julian days.
Statistical analysis Statistical analyses were performed using the statistical software Statistica ® (Statsoft, Tulsa, OK, USA). Data were tested for normal distribution using Shapiro-Wilk's W test. The significance of differences between the average values of annual SCI (Fig. S3  in Supplementary material) were tested using the Student's t-test for independent samples. Based on the obtained results between all pairs of annual SCI, homogeneous groups at P < 0. 05 were established. Intra-seasonal moving correlation functions (Fig. 4 , Fig. 5 ) were calculated using Spearman's rank correlation coefficients, because a lot of SCI chronologies were not normally distributed. At the same time we supposed the relationships between measured SCI and weather factors to be monotonous (the higher is weather factor, the higher or lower is SCI), though not necessarily linear.
This paper presents MERGILO, a method for reconciling knowledge extracted from multiple natural language sources, and for delivering it as a knowledge graph. The underlying problem is relevant in many application scenarios requiring the creation and dynamic evolution of a knowledge base, e. g. automatic news summarization, human-robot dialoguing, etc. After providing a formal definition of the problem, we propose our holistic approach to handle natural language input -typically independent texts as in news from different sources -and we output a knowledge graph representing their reconciled knowledge. MERGILO is evaluated on its ability to identify corresponding entities and events across documents against a manually annotated corpus of news, showing promising results.
In this paper, we describe in detail and experiment MERGILO, an improved version of the method proposed in  [13]  to handle multiple NL inputs, typically short text inputs such as news, in order to output knowledge graphs representing the integrated knowledge that they express. Integrating knowledge from multiple NL sources is crucial in order to implement intelligent applications requiring the ability to evolve a multi-source and dynamic knowledge base. However, this problem is challenging, considering that natural language can use heterogeneous forms for expressing similar knowledge. To complicate the situation, evaluation is also hard since no gold standards are available and not even universal standards for knowledge representations exist (different representations would require different gold standards).
All discussed approaches focus on knowledge representation, while we focus on knowledge integration  [36]. However, they provide a level of organization of knowledge that makes abstract-level integration possible. Knowledge base integration and ontology matching. A rich overview of ontology matching methods is provided by  [10]. As for knowledge base integration, relevant work includes  [12]  that leverages the interplay between schema and instance matching. Similarly,  [11]  shows a simple greedy iterative algorithm for aligning knowledge bases with millions of entities and facts. These approaches are characterized by the preferred large size of the ontologies/datasets treated (for best performance), which rarely (probably never) derive from text sources. On the contrary, we aim at handling knowledge graphs derived from text sources, and modeled using a frame-semantics-based representation.
Experimental analysis We implemented MERGILO as a Python tool  12  on top of FredLib. 13 We used IBM ILOG CPLEX 12. 6. 1 for solving the Integer Linear Program. A simple baseline method for our problem is to apply existing knowledge extraction tools on a single document built by appending all input documents one after another. However, this approach would produce a sparse representation that misses associations among concepts, entities and events across documents. An alternative approach consists in building multiple knowledge graphs, one per input document, and integrates them by existing knowledge base integration tools. Recent knowledge base integration tools  [11]  are based on graph alignment and hence they are somehow similar to our approach. However, a crucial part of these methods is the definition of rewards and penalties of aligned nodes and edges (node and edge similarity), which relies on similarity between label texts, and often requires manual intervention.
7. We did not perform extensive running time tests on large datasets since our first goal is accuracy. Our implementation is not optimized for efficiency, and contains several bottlenecks (word-similarity computation, rest interfaces among components, etc. ). However, they can be handled by optimizing the implementation for efficiency (pre-computing word-similarity, making all components inprocess, etc. ). The main scalability concern of our method involves the numerical optimization based on ILP, since its resolution is NPcomplete, and therefore the time complexity might be exponential in the worst case. Although the algorithm is exponential in the general case, real scenarios are relatively easy to solve thanks to the sparsity of the graphs and the small set of potential associations across graphs.
It increased for a given diffusion coefficient when the confinement size decreased for confined trajectories (Fig. 2a ) or when the velocity increases for directed trajectories (Fig. 2b ). The learning procedure was performed with 3 different datasets of trajectories of 2. 1, 3. 1 and 4. 1 s durations. Under our conditions, a similar probability of detection of directed diffusion modes was obtained for 31 and 41 frames. The size of the sliding window was therefore fixed to 31 frames (blue curve in Fig. 2 ), a value at which the probability of detection of 1 μm diameter confinement (a reasonable size of confinement in cell membranes) is still possible and obviously allows detecting smaller segments as compared to 41 frames. Considering 2 directed trajectories T 0 and T 1 with respective diffusion coefficient D 0 and D 1 and velocity V 0 and V 1 , the normalized MSD curves will be similar if: V 1 ¼ V 0 ffiffiffiffiffi ffi D 1 D 0 r ð7Þ Similarly the normalized MSD curves will be similar between 2 confined trajectories T 0 and T 1 with respective diffusion coefficient D 0 and D 1 and confinement diameter L 0 and L 1 , if:.
4b  and c. Duration of confinement and directed segments was slightly underestimated by the BPNN, respectively 4. 6 s and 4. 7 s instead of 5 s. Data from part of the trajectory identified as directed or confined were respectively fitted with the equations 2 and 3 described in the Methods section. The confinement diameter and the velocity were estimated to be 1. 06 μm and 1. 2 μm/s, respectively. These values are in good agreement with the parameters of the simulation. As expected, apparent diffusion coefficients from Brownian segments were estimated around 0. 25 μm 2 /s. No changes in the BPNN-based decision were observed when adding 30 nm positioning noise. We also evaluated the capacity of BPNN to detect small segments within a trajectory. 200 simulated trajectories of 300 frames containing confined segments of various lengths were created and the probability of detection named decision based on BPNN was calculated as a function of the segment length.
Conclusion We demonstrated in this study that our algorithm provides a new conceptual, versatile and useful tool in dissecting complex trajectories, identifying different motion modes and providing diffusion-associated parameters of the identified segments. It requires the adjustment of a minimal set of parameters. Combined with our homemade software named PaTrack, which is freely available on the website of the laboratory  [35] , our algorithm can accurately analyze membrane dynamics of single molecules or larger assemblies, such as viruses  [36].
Tracking procedure All movies or simulated data were analyzed using homemade software named "PaTrack" developed in the Visual C ++ environment (Microsoft, Washington, USA), in which the BPNN-based algorithm has been implemented. This software is available online using the link "http:// www. cbs. cnrs. fr/index. php/en/platforms-facilities/softwares" where it can be downloaded with the complete instructions manual. Single molecule tracking was based on point spread function (PSF) fitting. MSD was computed according to Equation  7. Apparent diffusion coefficient values were determined from a linear fit between the first and fourth points D 1-4 of the MSD versus time lag as previously reported  [3]. Once the associated motion modes were identified by the BPNN, segments were fitted with the following equation: MSD (Δt) = 4DΔt for a simple diffusion, MSD (Δt) = 4DΔt + V 2 (Δt) 2 for directed diffusion and MSD (Δt) = (1/3) L 2 [1-e (-12DΔt/L 2 )] for confined diffusion, where V is the constant drift velocity, L 2 the area of the confined region and D the diffusion coefficient.
Acknowledgements We thank  Dorothée Molle  and  Edouard Bertrand  ( IGMM, Montpellier, France ) for providing the MLV YFP-GAG vector. CE, PR and LF are recipients of PhD scholarships from the  French Ministry for Research and Technology  and PR was supported by a fellowship from the  ANRS (Agence Nationale de Recherches sur le Sida  et les hépatites virales).
Background: To address the lack of standard terminology to describe extracellular RNA (exRNA) data/metadata, we have launched an inter-community effort to extend the Gene Ontology (GO) with subcellular structure concepts relevant to the exRNA domain. By extending GO in this manner, the exRNA data/metadata will be more easily annotated and queried because it will be based on a shared set of terms and relationships relevant to extracellular research. Methods: By following a consensus-building process, we have worked with several academic societies/consortia, including ERCC, ISEV, and ASEMV, to identify and approve a set of exRNA and extracellular vesicle-related terms and relationships that have been incorporated into GO. In addition, we have initiated an ongoing process of extractions of gene product annotations associated with these terms from Vesiclepedia and ExoCarta, conversion of the extracted annotations to Gene Association File (GAF) format for batch submission to GO, and curation of the submitted annotations by the GO Consortium.
As a use case, we have incorporated some of the GO terms into annotations of samples from the exRNA Atlas and implemented a faceted search interface based on such annotations. Results: We have added 7 new terms and modified 9 existing terms (along with their synonyms and relationships) to GO. Additionally, 18,695 unique coding gene products (mRNAs and proteins) and 963 unique non-coding gene products (ncRNAs) which are associated with the terms: "extracellular vesicle", "extracellular exosome", "apoptotic body", and "microvesicle" were extracted from ExoCarta and Vesiclepedia. These annotations are currently being processed for submission to GO. Conclusions: As an inter-community effort, we have made a substantial update to GO in the exRNA context. We have also demonstrated the utility of some of the new GO terms for sample annotation and metadata search.
g. REST Application Programming Interfaces (APIs)). The MWG identified existing biomedical ontologies accessible through the NCBO BioPortal  [37]  as a source of commonly accepted terms for annotating exRNA datasets. Such ontology-based metadata annotation does not only allow semantic retrieval/query of data in ERCC data sources, it also allows DMRR data to be integrated with other data sources with metadata annotated using the same ontologies. For ontologies to be useful for biological applications, it is critical that the relevant ontologies contain meaningful and broadly accepted terms, as well as ensuring that the relationships between the included terms be both accurate and accepted by the pertinent scientific community. For an ontology such as GO which is frequently used for functional enrichment analysis of genomic datasets, it is also important that terms be associated with specific gene products (coding and non-coding RNAs and proteins) in an empirically supported manner.
The Gene Ontology (GO) Consortium (GOC; http:// www. geneontology. org) is a community-based bioinformatics effort. This Consortium develops, maintains and extends two interconnected resources: the Gene Ontology itself, and a database of GO annotations that associate specific gene products with concepts in the Gene Ontology. As of March 2016, there are >42,000 GO terms for describing concepts relevant to gene product function in a species-independent manner, providing not only comprehensive coverage of biological concepts but also community-wide agreement on how those should be used to describe gene functions across all organisms. The GO is organized into three aspects  [38] : these are graph structures comprised of classes for molecular functions, the biological processes these contribute to, the cellular locations where these occur (cellular components), and the relationships connecting these classes.
A 'GO annotation' describes the association between a gene product and an ontology class, as well as references to the evidence supporting the association. In most cases, a GO annotation is a statement about gene function, but because the GO cellular component aspect describes a "cellular and subcellular anatomy" it can have broader applications beyond the originally specified GO annotation usage of "where a gene product is active. " Thus an association of an exRNA to a cellular component term does not necessarily imply a function per se, though a functional role may of course later be discovered. The GOC continuously provides enhancements to the ontology and annotation database, as well as its tools and policies, ensuring that the ontology and annotations are consistent, and accurately reflect the current state of biological knowledge.
Adding gene product annotations The GOC accepts contributions of annotations from external groups, and provides guidance in the 'Documentation' section of its website (http://geneontology. org). The 'Documentation' section includes details of the recommended file format, called GAF (Gene Association Format). Annotations submitted to the GOC undergo automated checks, followed by manual review by GO curators, in this case from the Protein Function Content team at EMBL-EBI. As sources of gene product annotations in the exRNA context, ExoCarta  [45]  and Vesiclepedia  [46]  are online databases that catalogue proteins, RNAs and lipids identified specifically in exosomes and other extracellular vesicle types, respectively. Recently, ExoCarta was updated with ISEV minimal experimental requirements feature for definition of extracellular vesicles.