V. Daponte

The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large-scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating these data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited for painless future expansion.

Abstract. This paper presents a model for representing compliance rules related to subsurface objects. Rules expressed in this model can be automatically evaluated (using SHACL or SPARQL) on existing 3D city models expressed in RDF. The main characteristics of the proposed model are (1) its expressiveness, that comes from the use of formal ontologies for representing the rules and the objects they refer to, (2) its integrative nature, given by the interconnection among the proposed ontologies and the connection of these ontologies with CityGML and IFC (in an ontological form), and (3) its multi-geometry aspect. Preliminary results allow to automatically evaluate formally expressed compliance rules for underground objects in a 3D city model, that will considerably ease the task of professionals of the field.

A smart monitoring system for superconducting cable test is proposed with an adaptive current control of a superconducting transformer secondary. The design, based on Fuzzy Gain Scheduling, allows the controller parameters to adapt continuously, and finely, to the working variations arising from transformer nonlinear dynamics. The control system is integrated in a fully digital control loop, with all the related benefits, i.e., high noise rejection, ease of implementation/modification, and so on. In particular, an accurate model of the system, controlled by a Fuzzy Gain Scheduler of the superconducting transformer, was achieved by an experimental campaign through the working domain at several current ramp rates. The model performance was characterized by simulation, under all the main operating conditions, in order to guide the controller design. Finally, the proposed monitoring system was experimentally validated at European Organization for Nuclear Research (CERN) in comparison to the state-of-the-art control system [P. Arpaia, L. Bottura, G. Montenero, and S. Le Naour, “Performance improvement of a measurement station for superconducting cable test,” Rev. Sci. Instrum. 83, 095111 (2012)] of the Facility for the Research on Superconducting Cables, achieving a significant performance improvement: a reduction in the system overshoot by 50%, with a related attenuation of the corresponding dynamic residual error (both absolute and RMS) up to 52%.

We have previously designed and implemented a tree-based ontology to represent glycan structures with the aim of searching these structures with a glyco-driven syntax. This resulted in creating the GlySTreeM knowledge-base as a linchpin of the structural matching procedure and we now introduce a query language, called GlycoQL, for the actual implementation of a glycan structure search.The methodology is described and illustrated with a use-case focused on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spike protein glycosylation. We show how to enhance site annotation with federated queries involving UniProt and GlyConnect, our glycoprotein database.https://glyconnect.expasy.org/glycoql/.

The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating this data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited to painless future expansion. Availability:https://glyconnect.expasy.org/glystreem/wiki

Scientific knowledge embraces all the notions of the scientific subjects, such as mathematics, physics, chemistry and more. This knowledge requires to be visualized to various users and for different tasks. The purpose of this work is to improve the design of scientific knowledge visualization systems through the creation of User interface (UI) prototypes from the scientific knowledge to visualize. To achieve this purpose a methodology is proposed. The formal representation and visualization of this knowledge are the first research questions addressed by two reference models: Scientific Knowledge Model and Visual Model. A mapping language designed to declare abstract representations of the desired UI prototypes associates these two models. Algorithms to transform these abstract representations into concrete UI prototypes have been also developed. The methodology has been tested using various sources and the results, including the real use case of the CERN CMS High Level Trigger configuration management system, are presented.

La construction d’une ontologie des objets de connaissance scientifique, presente ici, s’inscrit dans le developpement d’une approche orientee a la visualisation des connaissances scientifiques. Il est motive par le fait que les concepts d’organisation de la connaissance scientifique (theoreme, loi, experience, preuve, ...) apparaissent dans des ontologies existantes mais qu’aucune de celles-ci n’est centree sur cette thematique et n’en presente une organisation simple et facilement utilisable. Nous presentons la premiere version construite a partir de sources ontologiques (ontologies des objets de connaissance de certains domaines, lexicales et de niveau superieur), de bases de connaissances specialisees et d’interviews avec des scientifiques. Nous avons aligne cette ontologie avec certaines des sources utilisees, ce qui a permis de verifier sa consistance par rapport a ces dernieres. La validation de l’ontologie consiste a l’utiliser pour formaliser des connaissances de diverses sources, ce que nous avons commence a faire dans le domaine de la physique.

The CMS High Level Trigger (HLT) is a collection of software algorithms that run using an optimized version of the CMS offline reconstruction software. The HLT uses Python configuration files each containing hundreds of "modules", organized in "sequences" and "paths". Each configuration usually uses an average of 2200 different modules and more than 400 independent trigger paths. The complexity of the HLT configurations and their large number require the design of a suitable data management system. The work presented here describes the solution designed to manage the considerable number of configurations developed and to assist the editing of new configurations.

Abstract Motivation We have previously designed and implemented a tree-based ontology to represent glycan structures with the aim of searching these structures with a glyco-driven syntax. This resulted in creating the GlySTreeM knowledge-base as a linchpin of the matching procedure and we now introduce a query language, called GlycoQL, for the actual implementation of a glycan structure search. Results The methodology is described and illustrated with a use-case focused on SARS-CoV-2 spike protein glycosylation. We show how to enhance site annotation with federated queries involving UniProt and GlyConnect, our glycoprotein database. Availability currently only available for reviewers at: https://beta.glyconnect.expasy.org/glycoql/ Contact catherine.hayes@unige.ch ; frederique.lisacek@sib.swiss Supplementary information Supplementary data are available at https://glyconnect.expasy.org/glystreem/wiki .

The construction of an ontology of scientific knowledge objects, presented here, is part of the development of an approach oriented towards the visualization of scientific knowledge. It is motivated by the fact that the concepts that are used to organize scientific knowledge (theorem, law, experience, proof, etc.) appear in existing ontologies but that none of these ontologies is centered on this topic and presents them in a simple and easily understandable organization. This ontology has been constructed by 1) selecting concepts that appear in high level ontologies or in ontologies of knowledge objects of specific fields and 2) by interviewing scientists in different fields. We have aligned this ontology with some of the sources used, which has allowed us to verify its consistency with respect to them. The validation of the ontology consists in using it to formalize knowledge from various sources, which we have begun to do in the field of physics.