dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, the initial terms included in MIMO have been automatically aligned with the same ones existing in other terminologies and ontologies existing in HeTOP. Then the MIMO terms were mapped in the vocabularies wherein its terms were discovered. It allowed expanding MIMO automatically. Nevertheless, health informatics and digital health fields are evolving quickly. Some terms included in MIMO are not discovered in other HeTOP vocabularies. This is the most advanced added value of this research (i. e. , having an up-to-date vocabulary comprising terms related to cutting-edge research and technologies). The alignment and mapping processes are periodically run to ensure the availability of the most enriched version of MIMO; • The possibility of involving a native language speaker and domain expert in the translation process or validation to a specific language without an open system.
Fig. 1  shows an example of a HeTOP response for a query with the term "bibliometrics. " Using the interface in its English version (the user can switch to a different language by using the dropdown list appearing on the top-left side of the screen) and asking for results from all the available terminologies and ontologies (by selecting in the tree of the available terminologies "Terminologies selection") allowing a matrix navigation (among languages and terminologies). The following description sections are available: • Preferred Label: the MeSH Descriptor and its translated versions as provided by native speaker level domain experts; herein, e. g. , "bibliometrics" is translated into 13 languages; • We have developed a specific generic algorithm (currently named WikiMeSH) to propose an extended translation of any thesaurus based on Wikipedia pages  [41].
It expands the MeSH thesaurus, containing over 5,000 health concepts. The DeCS project started during the 1970 s  [44] ; herein, we got "bibliometrics" translated by domain experts to Spanish and Portuguese; • Définition CISMeF: definition of the preferred label, in French, when available, manually translated by CISMeF librarians and health professionals  [38] ; • MeSH definition: definition of the preferred label, in English, extracted from the MeSH [21]; • Wikipedia link: Links to the relevant and available Wikipedia pages in the different languages ran into MIMO to provide the users with more definitions and information as the Wikipedia community writes and shares it. It is critical to point out that Wikipedia's community controls the content of Wikipedia, but this does not ensure that it is validated by medical informatics and digital health vocabularies experts.
Moreover, from a technological perspective, one frequent feedback was that a multilingual ontology focusing on medical informatics and digital health, such as MIMO, is fundamental to the future of health information technology systems. It must support the perpetual inclusion of new technological tools in the health-related process (i. e. , diagnosis, treatment, monitoring, and follow-up). Moreover, one crucial community comment is that MIMO should be opensourced like other knowledge management tools to allow its dissemination, growth, and maintenance. Therefore, MIMO is freely accessible on HeTOP to the entire scientific community and on-demand as an OWL (Web Ontology Language) file and via the HeTOP API. Moreover, it was also important to have clinicians' feedback about potential applications to daily practice.
Moreover, MIMO can be integrated with and into other platforms. To this end, MIMO is accessible as an OWL file and a Web service via the HeTOP API for the HosmartAI consortium to be integrated into the platforms developed. In the context of the HosmartAI project, to support the overall communication of different actors of the projects and the users of its products efficiently and effectively, overtime MIMO is: • Expanding the number of concepts (from nearly-one thousand to a few thousand); • Expanding the number of categories and sub-categories (to a few dozen). For example, robotics, Internet-of-Things, and Human-Computer Interactions, in the context of digital health and artificial intelligence in healthcare (i. e. , hospitals) should be expanded; • Involving native speakers of the different European languages for evaluating, validating, or correcting the automatically imported or translated concepts, as we did for Portuguese, Romanian, and soon Swedish.
The second strength of MIMO is the capability to combine automated validated and domain-expert translations. For languages primarily practiced in Europe and around the World, the automated and validated translations from different sources cover a large part of the MIMO terms and concepts. Nevertheless, native language speakers' domain experts' involvement allows getting an up-to-date terminology and thesaurus in a range of different cases, such as: 1. New terms and concepts that are still not in existing terminologies and ontologies, such as MeSH, 2. For less popular languages with a meager rate or non-existing translations. A third strength of MIMO is that HeTOP hosts it, and this allows: 1. Hosting and maintaining it efficiently over time; 2. Supporting the mapping with other existing terminologies, classifications, and ontologies in health and engineering sciences; 3.
Providing to the end-user a user-friendly interface that will encourage using MIMO as a day-to-day tool; each MIMO concept has a dedicated URI that can be shared with end-users across the globe. The main strength of MIMO is to be up-to-date with recent and new terms and concepts related to new technologies. However, MIMO has some limitations. Indeed, for non-frequent languages, a native speaker level domain expert with multilingual capabilities is needed to translate from one language to another or validate automated translation (e. g. , based on online services such as Google Translate, Bing Translator). Thus, for this domain expert's timeconsuming involvement, it is necessary to engage them in a community also giving translation peer-review, validation, and enhancing domainrelated knowledge to expand the new terms' translation capabilities.
The methodology empirically implemented for continuous development of MIMO strives to have this controlled vocabulary as a pillar component of an environment supporting health sciences information professionals' different activities, facilitating the mutual understanding of researchers both from academia and industry, and enhancing students' self-learning in a global world with mobility opportunities. It is essential to integrate existing terminological resources in a unified way (e. g. , in UMLS, MeSH, NCIt, SNOMED CT) and to reduce translation-based misunderstanding by involving native language speakers directly (as a part of a working group) or indirectly (via the social platforms. Therefore, MIMO is a timely opportunity to develop and improve scientific, technical, educational, and industrial partnerships in medical informatics and digital health.
Experimental design A computerized experiment was conducted between June and October 2018 at the Montpellier Laboratory for Experimental Economics (LEEM). In total, 172 participants (69 women; average age 24. 48, SD 6. 730) were recruited randomly from the LEEM database (via the ORSEE software). 9 out of 10 subjects had previously participated in a laboratory experiment. We ensured, however, that none had done an experiment with similar parameters. Participants were randomly assigned to a cubicle workstation and told that the experiment was composed of three parts, but that they only received instructions for each part one at a time (after completing a previous part) and that only one, randomly chosen, was to be paid at the end. We run 9 sessions, 3 for each treatment: a control treatment with a neutral emotion induction; a positive emotion treatment and a negative emotion treatment.
We ran an ex-post power analysis using the G � power software which revealed that our sample size (respectively 55, 57 and 60 observations for the three treatments) is sufficient (power = 0. 8) to observe an effect size of 0. 4. While Cohen  [77]  recommends that wherein d = 0. 20 indicates a small effect, d = 0. 50 indicates a medium effect and d = 0. 80 indicates a large effect, Dancey & Reidy  [78]  specify that small and medium effects are more willingly available when evaluating behavioral and psychological constructs as it is the case of constructs investigated herein. We consider an in-between design. In each treatment, participants are matched in pairs but they never learn the identity of the person to whom they are matched with, and instructions were as neutral as possible (for instance, the word "reduce" was used, with no mention of the term "destroy").
Incidental emotion manipulation In Task 1, we induce positive and negative emotions by exposing subjects to slideshows of 15 pictures selected from the Geneva Affective Picture Database (GAPED); see  [79]. A control treatment was run as well where 15 neutral pictures were shown to participants. Emotional states are measured by using the "affective slider" method suggested by Betella and Verschure  [80]. Participants choose a value in between 0 and 99 that reflects best their affective states by moving a mouse pointer over a slider. They have to pick a value for their degree of pleasure (valence) and their degree of excitement (arousal). For valence, the leftmost position represented the most negative value (i. e. , very sad feeling), and the rightmost position represented the most positive value (i.
Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks. Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions.
The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files. The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype.
The first computational cornerstone is the search for causal signatures based on molecular interaction networks created without a priori, in order to avoid bias in the selection of knowledge to be included in the models. Indeed, most computational dynamical models are built using a manual selection of molecules and reactions, which limits the size of the models studied and may bias the analysis towards known molecules instead of exploring all the knowledge in the numerous existing databases  [1]. The second computational cornerstone of identifying causal signatures is the design of methods for computing the controllers of the dynamical response of complex networks combining transcriptional regulation processes, signalling networks and metabolism. Although a wide variety of dynamical modeling formalisms have been developed using either deterministic, stochastic or logical approaches  [2] [3] [4] [5] , the choice of a formalism must be adapted to the nature of the data and the biological question.
For instance, the Pathway Commons database is a set of 23 databases supporting BioPAX format and describing 4,700 signalling and regulatory pathways, representing 2. 3 million interactions  [11] , whose biological entities are linked to other biological resources. It integrates information from databases such as the Pathway Interaction Database (PID)  [12]  and the Kyoto Encyclopedia of Genes and Genomes (KEGG)  [13]. Although all these databases are described according to the same ontology, combining them is actually difficult due to the diversity of their content in terms of biological compounds and reaction types. To overcome this limitation, a first strategy is to interpret the content of BioPAX resources as interaction graphs  [14]. However, this prevents any dynamical analysis on the models.
A second strategy is to extract specific, non-exhaustive information from the datasources, as done in the Omnipath framework  [15] , which uses conversion rules to extract only binary interactions from a subset of the BioPAX databases, which can then be manually enriched with generic Boolean rules  [16]. All of these initiatives show that combining BioPAX data sources to analyze their combined dynamics is currently out of reach. Nevertheless, a key step towards this goal is to develop methods to compare the dynamics of BioPAX models. To this ends, the BioASF framework  [17]  relies on Petri nets to simulate BioPAX models. However, it requires defining the simulation rules manually and this approach can only be applied to small-scale models. The goal of the present paper is to show that the Cadbiom framework  [6] , a dynamical abstraction based on guarded transitions  [18] , can be used to allow the identification and the comparison of causal regulators in large-scale models, formalized in the BioPAX language.
Initially, the Cadbiom tool  [6]  was designed to analyze the large-scale PID signalling network and to explore the causal effects of TGFβ associated molecular compounds. We have very significantly refactored the Cadbiom approach to interpret all regulation, transformation and control processes of the BioPAX ontology into a single dynamical framework, which integrates the concepts of consumption, production, control and competition between resources. Applied to the PID, KEGG and ACSN databases, our approach has allowed us to extract and curate the relevant information from the BioPAX description of the databases in order to interpret them in a unified dynamical model and to compare their content despite their initial heterogeneity. The comparison of the resulting model structures highlighted their high level of complementarity.
In addition, Cadbiom explores the dynamical model and identifies the controllers with respect to an expected phenotype (e. g. , the activation of a gene). This allows for a comparison of the context of the databases at a dynamical level, through the comparison of controllers. As a biological application, we searched for putative regulators of genes that characterize the Epithelio-Mesenchymal Transition (EMT), a critical process in tumor progression whereby epithelial cells transdifferentiate into mesenchymal cells characterized by motility features  [19, 20]. Together our data show specific patterns for the regulation of epithelial or mesenchymal marker genes and demonstrate how the combination of patterns provides new signatures for intermediate states of EMT.
Data sources in BioPAX format BioPAX (Biological Pathway Exchange) is a standard format that aims to enable the integration, exchange, visualization and analysis of biological pathway data to facilitate the understanding of complex biological processes. The BioPAX ontology consists of classes specifically designed for use in systems biology, representing metabolic and signalling pathways, molecular and genetic interactions and gene regulatory networks. This ontology is compliant with the Linked Open Data initiative. The knowledge representation uses the Resource Description Framework (RDF) standard, relying on typed relations between entities, themselves typed using a controlled vocabulary associated with an ontology based on constraints and hierarchies between classes. Pathway Commons is a web resource for biological pathway data  [11]  that integrates numerous databases of molecular interactions in several formats including the BioPAX format.
We performed a systematic analysis of the content of two BioPAX models corresponding to the Pathway Interaction Database (PID)  [12] , and the Kyoto Encyclopedia of Genes and Genomes (KEGG)  [13]  database. We used the PID V9 and KEGG V10, each file being freely downloadable on the Pathway Commons website at https://www. pathwaycommons. org/ archives/PC2. We also used the Atlas of Cancer Signalling Network (ACSN) database  [21]  that is available as a catalog of 13 molecular maps formalized in the SBGN-ML format. Each map refers to biomolecule and reaction identifiers that are specific to each file. The maps are curated to homogenize the identifiers. The list of unified identifiers is then used to curate each individual map, so that the identifiers are prefixed by the name of the map to which they belong.
Methods to curate BioPAX models The three BioPAX models were curated according to the following principles in order to homogenize and curate the databases (see details in S1 Appendix): • We re-focused the analysis of the ACSN database to entities having the types Proteins, Complexes and SmallMolecules and their associated interactions. • We removed classes (generic entities) and nested classes when they were not involved in Bio-PAX reactions or controllers. When the deletion took place, the member entities inherited the properties ModificationFeatures and Location from their parent class. • We processed the databases to group all duplicated entities together, so that each group of similar entities eventually appeared in the processed model as a single entity with a unique identifier. • The controls of type Modulation that are present at low frequency were not considered.
The Cadbiom framework The Cadbiom framework and its suite (GNU GPL License) is written in Python and hosted on the PyPI (Python Package Index) platform for easy deployment. The installation procedure, documentation for users and developers, sources and examples illustrating the interpretation processes of large-scale databases are detailed at http://cadbiom. genouest. org/doc/cadbiom/ index. html and in S1 Appendix. In order to adapt to large-scale model analysis, the former library developed in  [6]  was modified as follows: (i) time-consuming functions are rewritten in C language, (ii) an architecture implementing multiprocessing technology is integrated into the command line module, (iii) the cryptominisat solver is upgraded. Overall, these changes increase the capabilities of Cadbiom such that the performance of the current implementation is 2000 times faster for the same query on a single CPU than the original implementation.
An important parameter of the Cadbiom framework is the maximum number of trajectories computed by the solver for each query. Prior to this study, we tested three values for this parameter (400, 900 and 5000) on a selection of genes. We noticed that extending the analysis to 900 trajectories did not significantly change the lists of trajectory entities compared to the computation of 400 trajectories. We therefore retained the value of 400 for the full study because it corresponded to the best compromise between the completeness of the trajectory computation and execution time analysis. In addition, the biopax2cadbiom package was developed as a stand-alone module. It is compatible with the BioPAX Level 3 specifications, which offers the possibility to define entities under several states (including a generic state).
The tool executes queries in the SPARQL query language to automatically create Python classes based on BioPAX specifications. Objects instantiated from these classes are then used in the translation operations of the BioPAX simultaneously P and M; it is never triggered in the context of obtaining C because of the production of the inhibitor M. A cycle of 3 biomolecules I, J, K, constitutes a strongly connected component. This cycle is resolved by arbitrarily adding a virtual node named cycle_initiation_node on the first of the lexicographically sorted nodes (i. e. , I); this has the effect of adding I to the system boundary entities. https://doi. org/10. 1371/journal. pcbi. 1010175. g003 formalism to the Cadbiom format. Documentation is available at http://cadbiom. genouest. org/doc/biopax2cadbiom/index.
Curation of PID, KEGG and ACSN BioPax models We performed a systematic content analysis of 2 databases available on Pathway Commons, PID (cell signaling) and KEGG (metabolism and signaling reactions), as well as the ACSN database (Cancer signaling network), which are the largest reaction databases available in the BioPAX format. In order to homogenize the three databases, we performed some curation to handle the generic entities, the nested classes and the duplicated entities (see detail in S1 Appendix). The characteristics of the models are presented in Table  1. The effect of the curation processes is very limited; we observed a slight increase in the PID (11,124 vs 10,526) model and no changes in the KEGG and ACSN models. As shown in Table  1 , the PID and ACSN BioPAX databases contain few SmallMolecules and the reactants of reactions are mainly Proteins and Complexes.
This type of content is representative of signaling pathways with proteins involved in either signalling reactions or control of these reactions. In contrast, the KEGG BioPAX file contains proportionally more SmallMolecules (1,585) than the other databases but does not contain ComplexAssembly reactions. There are as many BiochemicalReaction (1,786) as Control reactions (1,782) (only from the class Catalysis) suggesting that each reaction is effectively catalyzed by a single biomolecule. This is because the KEGG BioPAX file consists specifically of small molecules and corresponds to the metabolism part of the entire KEGG database, with no description of the signalling part.
Interpreting BioPAX models into guarded transitions The BioPAX format is compliant with the guarded transition formalism because BioPAX classes can be interpreted as entities, transitions or conditions. We used this mapping to implement a complex rewriting strategy inspired by the seminal paper  [6]  to automatically interpret any Bio-PAX model into a guarded transition model. The main principle introduced in  [6]  is to rewrite a reaction r: A + B ! C + D controlled by the catalyzer E by four guarded transitions t 1 : A h[B] ! C, t 2 : B h[A] ! C, t 3 : A h[B] ! D, and t 4 : B h[A] ! D. Since all these guarded transitions share the same event h, they are linked by the constraint that they must be activated simultaneously. As detailed in the Material and Methods section, this principle was adapted to the content of each BioPAX database in order to capture their main specificities and homogenize their analyses.