dataesr/scientific-paragraphs-categorization huggingface dataset:
MoonDB 2. 0 (http://moondb. hb. univ-amu. fr/) is a database of predicted and manually curated extreme multifunctional (EMF) and moonlighting proteins, i. e. proteins that perform multiple unrelated functions. We have previously shown that such proteins can be predicted through the analysis of their molecular interaction subnetworks, their functional annotations and their association to distinct groups of proteins that are involved in unrelated functions. In MoonDB 2. 0, we updated the set of human EMF proteins (238 proteins), using the latest functional annotations and protein-protein interaction networks. Furthermore, for the first time, we applied our method to four additional model organisms -mouse, fly, worm and yeast -and identified 54 novel EMF proteins in these species. In addition to novel predictions, this update contains 63 human and yeast proteins that were manually curated from literature, including descriptions of moonlighting functions and associated references.
Prediction of extreme multifunctional proteins The method used to predict extreme multifunctional (EMF) proteins was first described in Chapple et al. (2). Briefly, we perform a large-scale search for EMF proteins by (i) identifying functionally-dissimilar pairs of Biological Process Gene Ontology (GO) terms with PrOnto (7) that uses two metrics of GO functional dissimilarity based on the frequency of co-occurrence of GO term pairs in protein annotations; (ii) clustering the protein interactome into overlapping clusters of proteins using the OCG algorithm (8); (iii) annotating each cluster with functions (Biological Pro-Nucleic Acids Research, 2019, Vol. 47, Database issue D399 cess GO terms) based on the annotations of its constituent proteins; (iv) identifying proteins that belong to at least two clusters and are annotated to dissimilar functions (after having inherited the annotations of their clusters in addition to their own), hereby labeled as EMF proteins.
Database architecture and web interface MoonDB 2. 0 has been developed using the SQLAlchemy (v1. 2. 0) Python (v2. 7. 6) library for data storage. The web interface is mainly written in PHP (v7. 1. 14)  and JQuery (v3. 2. 1) and is powered by the Drupal (v8. 4) Content Management System (CMS). The database was deployed with Docker (v17. 09. 0-ce) to ensure stability. We gathered information on protein domains, publications and diseases from UniProtKB  (11)  in January 2018.
A new dataset of extreme multifunctional proteins In MoonDB 2. 0 we have predicted 292 extreme multifunctional (EMF) proteins in human, and--for the first time--also in mouse, fly, worm and yeast model species (Supplementary Table  S1 ). These have been identified de novo using the latest protein-protein interaction networks and GO term annotations. The power to predict EMF proteins is dependent on the underlying coverage and quality of the protein interactomes and GO term annotations used (Supplementary Table  S1 ). The new human EMF protein dataset and the one in the previous MoonDB version overlap significantly (Supplementary Figure  S1 ). Interestingly, the analysis of the EMF signature on the new set of EMF proteins (as performed in Chapple et al. (2) ), shows that the new set of EMF proteins produces a similar signature in terms of network properties, tissue expression, as well as domain, structural disorder and Eukaryotic linear motif (ELM) presence, among other features (Supplementary Figure  S2 ).
A new user-friendly interface and additional content To provide our visitors with a clear, fast and easy-to-use database, we completely redesigned MoonDB's web interface and added new functionalities. It is now possible to search a MoonDB entry by gene name, UniProtKB identifier (ID) or UniProtKB accession (AC). Moreover, the 'Browse' page (Figure  1 ), which displays all protein entries in MoonDB, can now be filtered through any column, thus allowing searches by species, full name of the protein and its presence in the 'Reference Set'. These filters can also be used in combination with each other to make more advanced queries. Importantly, MoonDB specifies which pairs of dissimilar (i. e. unrelated) functions led us to propose each predicted EMF and curated protein as moonlighting/extrememultifunctional (Figure  2 , under 'MoonDB Dissimilar Functions').
Furthermore, we provide the set of GO terms associated to the protein (Figure  2 , under 'MoonDB Network Modules'), determined by its participation in network clusters with annotated functions (guilt-by-association principle), and the GO terms directly annotating the protein. This information is pertinent in the context of multifunctionality, since EMF proteins associate with several groups of proteins to perform alternative functions. In addition, since the ability to perform unrelated functions may be correlated with the presence of a protein in unrelated subcellular locations, in MoonDB 2. 0 we identified pairs of unrelated cellular component GO terms associated to each protein with PrOnto (7) (Figure  2 , under 'Protein GO Annotations'). Lastly, to fully describe moonlighting and extreme multifunctional proteins, we further cross-link functional data with other orthogonal information such as the protein association to disease, protein domains and publications associated to the protein.
DISCUSSION AND CONCLUSION The MoonDB 2. 0 database is accessible at http://moondb. hb. univ-amu. fr/ and now contains data for human, mouse, fly, worm and yeast. MoonDB 2. 0 stands out compared to the two other current databases of moonlighting proteins MoonProt  (14)  and Multitask-II (12) because MoonDB 2. 0 combines curated and predicted proteins. We consider our dataset to be more comprehensive as well as highly complementary to other available databases. Whereas other databases are dependent on the available literature, and thus limited to providing information which is already known, our dataset of predictions goes beyond current propositions of moonlighting and provides novel candidates. EMF prediction is large-scale and detection does not require a priori knowledge besides protein interactions and GO term annotations.
Importantly, as protein interactomes and GO term annotations of model organisms will continue to grow towards completion in the following years, MoonDB will be updated every year with EMF predictions made from the latest interactomes and GO term annotations. Both power and reliability will progressively increase with future releases. This will be particularly important in the cases of mouse, which possesses high-quality GO term annotations (average of ∼19 GO terms per protein), but an incomplete protein interactome (<15% of the proteome covered), as well as in fly, whose interactome is better covered (>40% proteome), but GO term annotations are available for less than half of the interactome. Consequently, only few EMF proteins in mouse and fly were detected with our method. However, 5 out of 14 mouse EMF proteins are orthologs of human EMF proteins, suggesting that even when data is limited, the EMF proteins predicted are reliable.
Indeed, the ability for genes to be multifunctional is conserved across orthologs of different organisms  (15)  and some orthologous proteins are known to have moonlighting functions in dif-ferent organisms  (16). Notably, orthologs were also found between human and worm (UBE2I/ubc-9 and SUMO1; 2 out of 6 MoonDB 2. 0 worm entries) and even between the distant human and yeast species (SKP1 gene), although our method does not use ortholog relationships for EMF prediction. Together, these findings further underline the quality of our predictions and designates MoonDB 2. 0 as a valuable data repository for one interested in studying the extreme multifunctionality and moonlighting of proteins, possibly across species. We believe that MoonDB is of interest not only to bioinformaticians working on multifunctionality, but also to any biologist who may profit from knowing whether their protein of study is likely to perform unexpected functions aside from the ones generally known.
Due to the high frequency of EMF proteins involved in multiple diseases, often in comorbidity  (13) , the extensive functional information provided in MoonDB 2. 0 is of interest to help designing therapies that are aware of the several functions of the protein. Importantly, MoonDB 2. 0 is now cross-referenced in the UniProt Knowledgebase (UniProtKB)  (11). We consider that this greatly magnifies the exposure of our database to the general scientific community, as UniProtKB is the reference database for protein-related data and widely used by biologists, biochemists, bioinformaticians and others.
The generic module was called OntoSPM as it focused on SPMs, the actions and processes (considered at various granularity levels, from global procedure to detailed actions completed by the actors). The following lines introduce briefly the essential aspects, namely: 1) the domain covered, 2) the relation to an upper level ontology and philosophical framework and 3) the modular architecture with modules extracted from existing ontologies. In terms of scope, key representation included: roles played by the actors; affected objects (e. g. , anatomy or pathology); instruments and material used; ways of manipulations (e. g. , which hand of the surgeon or of the assistant was involved). A preliminary version of the OntoSPM ontology was presented in 2014  [17] , together with a first version of a software based on this ontology, and dedicated to the annotation of surgical procedures from video recordings (SurgeTrack  [15] ).
This preliminary version of OntoSPM was refined in 2015, in collaboration with the Karlsruhe Institute of Technology (KIT), to make it compatible with the BFO foundational ontology  [74] , as well as with a number of relevant ontologies that could provide terms (classes and object properties) needed in OntoSPM. This work led to an extended version of OntoSPM, composed of modules extracted from several ontologies developed according to the methodology and principles of the Open Biological and Biomedical Ontologies (OBO) Foundry  [75]  and reusing the BFO 2 ontology (Table  1 ). The extraction of the modules was achieved using the Ontofox tool  [83]   2  and based on the MIREOT method  [8]. Actually, this extended version allowed to meet both initial objectives, i. e. creating realistic simulation scenarios for the S3PM project, and supporting other surgical application contexts, as explained in the next section.
Ontology development at the Karlsruhe Institute of Technology The development of medical ontologies at KIT, with its partners in Heidelberg and Rennes, focuses on two key areas: surgical workflows in laparoscopy and annotation of surgical data. To support these, two ontologies are developed: the Ontology for Surgical Process Models in Laparoscopy (LapOntoSPM) and the Ontology for Data Integration in Surgery (ODIS). Both are designed as sub-ontologies of OntoSPM. LapOntoSPM inherits OntoSPM's mechanism to model surgical workflows, and extends it with additional concepts for instruments and actions specific to laparoscopic surgeries. Additionally, it models entire surgeries (e. g. , laparoscopic adrenalectomies, cholecystectomies and pancreatic resections) at the level of phases and steps. ODIS adds means of describing surgical data and algorithms to OntoSPM.
Furthermore, LapOntoSPM was used to segment entire surgical procedures into phases, employing rule-based reasoning and machine learning  [33, 35]. LapOntoSPM successfully aided in this task by enabling the formulation of explicit rules to filter for event indicative of new phases and also helped reduce the number of labeled training samples needed for machine learning. This is especially important in the surgical use case where labeled samples are difficult to acquire. The reliable detection of all relevant phases, as opposed to just critical situations, is an important step towards more sophisticated context-aware assistance during the entire surgery. ODIS provides a language which allows semantic annotation of surgical data, algorithms and the execution of algorithms on the data  [34]. Representation of content types (e.
The model has been applied to several hundreds liver tumor patients, and is currently being extended to applications in renal surgery, including intraoperative process models based on LapOntoSPM. In this context, new methods for large-scale medical data annotation based on crowdsourcing have been developed  [25, 40, 41 ]. An implementation of the system is publicly available  [69]. As a first technical use case, the formalization of biomedical image analysis challenges was chosen. The goal was to establish an ontology that: 1. encourages challenge organizers to formalize challenge design in a structured manner to enhance reproducibility and interpretation of results; 2. enables structured access to information from past biomedical image analysis challenges. The first list comprises a total of more than 50 parameters corresponding to the categories challenge organization, participation conditions, validation objective, study conditions, validation datasets, assessment method and challenge outcome.
Ontology development at the Universidade de Lisboa and the IPCB The ontology development for surgical scenarios in Lisbon at Universidade de Lisboa and the Instituto Politécnico de Castelo Branco (IPCB) has been focusing on orthopedics. An Ontology for Robotic Orthopedic Surgery (OROSU)  [20]  was developed and then applied for hip resurfacing surgery (e. g. , for trimming the femoral head). In this scope, the main goal of the research, related to ontologies, was to build a knowledge based framework for this surgical scenario, along with a formal definition of components and actions to be performed during the surgery. The developed ontology was based, in part, on the 1872-2015 -IEEE Standard Ontologies for Robotics and Automation  [63]. The work was developed under the HIPROB and ECHORD projects, funded by the Portuguese Science Foundation and the EU-FP7, respectively.
From the knowledge based framework, several queries can be performed, to extract information from the system, and also for its validation. Amongst others, is possible to monitor the usage of medical devices, and its actions, in the operating room, and to obtain important data from the system sensors, e. g. , the current drilling position of the femur. To evaluate the developed work in a real scenario, a first part of the work was to set up a medical vision system  [21]  with a robot  [81]  in the loop, in order to compensate some bone movements when the robot is performing surgical procedures. The second part of the work was to develop the OROSU ontology, based on the Hip Resurfacing scenario. During this study, a survey on ontologies for surgical robotics  [19]  was performed, concluding that there was still a large room for improvement, to align medical/surgical ontologies to robotic ontologies.
Here again, while the concept of software modules (or software packages) is well-defined and widely used in software development, the best way to implement this concept in ontology engineering is still lacking of maturity  [10]. As for the OntoSPM initiative, we decided to re-use as much as possible existing resources through dedicated "ontol-ogy modules" containing only an extract of existing resources relevant for our application (e. g. , FMA ontology). However, using existing resources also raises the issue of maintaining the created modules according to the evolution of the initial resource, problem known as "dependance maintenance" in software engineering. For further extending the scope of OntoSPM, the link with existing resources in robotic surgery has also to be strengthened. Discussions are under way in order to link or integrate relevant resources from this domain (e.
Applications based on OntoSPM are able to monitor surgical procedures and provide the right assistance in the right situation based on formalized surgical knowledge  [32]. After the operation is finished, the surgeon benefits from automatically created reports that summarize the procedure in a standardized way. Thereby, OntoSPM enables comparison of the procedure to other institutions  [27]. Furthermore, these reports will augment today's text reports by taking all relevant sensor data and patient information from the procedure into account. Also, applications in surgical training will allow the trainees to benefit from a more standardized feedback. Training will not only be based on sensor information  [36]  but training results will enable comparison of trainees' performance. Because of its shared vocabulary OntoSPM can help to bridge the gap from the training lab to the OR.
Here, applications can monitor the trainee's performance and progress in the OR and subsequently recommend the most appropriate training. Surgical robotics is nowadays dominated by telemanipulators such as the da Vinci Surgical System (Intuitive Surgical Inc. )  [66]. In order to overcome their passive role in the OR and become active assistants, surgical robots need to develop an understanding of the surgical procedure, anatomy, actions and potential adverse events. OntoSPM makes this surgical knowledge machine-interpretable, and thus helps the research community to add cognitive capabilities to surgical robots. In this context, the analysis of surgical processes based on data mining and machine learning techniques would also benefit from OntoSPM. For example, [2] faced the problem of surgical data recorded in different international centers that used different surgical vocabularies to record surgical process.
In this work, the authors manually aligned the vocabularies between the centers. While this ad-hoc solution can be considered for proofof-concept application, it does not scale up and requires important manual work. This is why providing an international resource allowing to share annotated data and results is now mandatory to reach the next level of datadriven and knowledge-driven applications in surgery. Actual surgeons' involvement: Participation of surgeons from different countries and surgical specialties in the OntoSPM collaboration is a prerequisite for its success. Yet, it is essential that their important clinical knowledge can be shared with people trained in computer science or knowledge engineering, in order to insure clinical relevance. The organization of summer schools can definitely help creating a common culture shared by surgical research trainees on knowledge engineering and the use of OntoSPM.
Conclusions The paper presented the OntoSPM Collaborative Action, which serves as a platform developing ontologies in the domain of surgery, focusing on Surgical Process Modeling in the context of Surgical Data Science. Past efforts and contributions from actual members, related to surgical process models and surgical robotics were presented, as well as the currently active collaboration between several European institutions. The authors aim at increasing the size and the scope of this international collaborating network, currently limited to the development of the OntoSPM ontology. Current members and their local clinical partners aim at involving the international clinical community to describe needs, discuss knowledge models dedicated to specific domains of surgery and specific procedures.
Such discussions and models are supposed to feed the reflection on what OntoSPM should cover in terms of scope in the future. Based on the experience and expertise of the action partners and the industry needs, we envision that On-toSPM can evolve to cover data science concepts in a ontological framework, to categorize, learn and assess from experimental surgical data. This framework will enable next-generation surgery, delivering better and safer patient care, thanks to the ability to share surgical knowledge and actual surgical data in semanticallyconsistent machine readable form.
Acknowledgements This work was initiated in the context of the  S3PM  project which received a French government support granted to the  CominLabs excellence laboratory  and managed by the  National Research Agency  in the " Investing for the Future" program  under reference  ANR-10-LABX-07-01. The work on ontological modeling at the DKFZ is supported by the  Federal Ministry of Economics and Energy (BMWi)  and the  German Aerospace Center (DLR). The work on ontological modeling at Politecnico (Milano) has received funding from the  European Unions   Horizon 2020 research and innovation programme  under grant agreement No. H2020-ICT-2016-732515. It was also partly supported by  Instituto Politecnico de Castelo Branco  and by  FCT , through IDMEC, under LAETA, project  UID/EMS/50022/2013. T.
Haidegger  is supported through the  New National Excellence Program  of the  Ministry of Human Capacities , his research was partially supported by the  Hungarian   OTKA PD 116121  grant. This work has been partially supported by  ACMIT (Austrian Center for Medical Innovation and Technology) , which is funded within the scope of the  COMET (Competence Centers for Excellent Technologies) program  of the Austrian Government. We acknowledge the financial support of this work by the  Hungarian State  and the  European Union  under the  EFOP-3. 6. 1-16-2016-00010project. The work at ICCAS was funded by the  German Ministry of Education and Research (BMBF).
Synthetic Fibers The material used for synthetic fibers was a rigid opaque polymer (Verowhiteplus RGD 835, Stratasys, Eden Prairie, MN), manufactured at Institut National de la Sante et de la Recherche Medicale (INSERM U930, Tours, France) with a 3D printer. These fibers were designed with FreeCAD software (OpenSource 3D CAD/CAE, 2016), a free and highly programmable 3D modeler. The fibers have a cylindrical shape, a diameter of 0. 03 cm, and a length of 13 cm. They were grouped as a fascicle (group of fibers), with a spacing of 0. 02 cm between them. The fascicle was then attached to an acrylic mold (15 3 15 3 16 cm). After fiber attachment, the mold was filled by deionized water to serve as the matrix. The mold was coupled in a metallic apparatus that enables fixation and control of probe angulation (Figure  2 ).
Comparisons between images of the same position and between the 2 distinct probe positions (longitudinal versus transverse and longitudinal versus oblique) were made for anisotropy analysis. To ensure that the oblique position would make the fascicles parallel to the x 2 -x 3 plane, the angle formed between the fascicles and this plane was measured twice for each image, using ImageJ software (version 1. 4; National Institute of Health, Bethesda, MD). PA was considered as the angle between fascicles and deep aponeurosis, measured twice in each longitudinal probe position. The mean of the PA measurements was correlated with l.
Data Analysis Images were exported in "dicom" format. Subsequently, for the quantitative analysis, a specific MATLAB routine using the l matrix was developed. A rectangular and circular region of interest (ROI) was selected manually in the center of the color map for synthetic (Figure  4 ) and muscle fibers, respectively. Mean shear modulus values were averaged over the largest ROI, avoiding water for synthetic fibers. The ROI area ranged approximately from 1. 4 cm 2 to 2. 0 cm 2 for synthetic fibers. The circular ROI diameter was approximately 0. 8 cm for muscle fibers. As Aixplorer provides elastic modulus (E) values, l values were obtained dividing E by 3 (Equation  2 ). The quality map was generated (Figure  4 ) from the cross-correlation method between the pairs of signals of the shear wave at different times.
The significance level adopted was 0. 05. The statistical program was GraphPad Prism. For the intrasession reliability, the coefficient of variation (CV) and ICC were adopted. The CV was obtained by the following formula: CV 5 standard deviation / mean 3 100. TheICCvalueswereacquired by the statistical software SPSS 20 (SPSS Statistics Viewer; IBM Corporation, Armonk, NY) and can be interpreted as follows: 0. 499, poor; 0. 500 to 0. 699, moderate; 0. 700 to 0. 899, good; and 0. 900 to 1. 000, excellent. 20.