Mise en place d'approches bioinformatiques innovantes pour l'intégration de données multi-omiques longitudinales

Les nouvelles technologies «omiques» à haut débit, incluant la génomique, l'épigénomique, la transcriptomique, la protéomique, la métabolomique ou encore la métagénomique, ont connues ces dernières années un développement considérable. Indépendamment, chaque technologie omique est une source d'information incontournable pour l'étude du génome humain, de l'épigénome, du transcriptome, du protéome, du métabolome, et également de son microbiote permettant ainsi d'identifier des biomarqueurs responsables de maladies, de déterminer des cibles thérapeutiques, d'établir des diagnostics préventifs et d'accroître les connaissances du vivant. La réduction des coûts et la facilité d'acquisition des données multi-omiques à permis de proposer de nouveaux plans expérimentaux de type série temporelle où le même échantillon biologique est séquencé, mesuré et quantifié à plusieurs temps de mesures. Grâce à l'étude combinée des technologies omiques et des séries temporelles, il est possible de capturer les changements d'expressions qui s'opèrent dans un système dynamique pour chaque molécule et avoir une vision globale des interactions multi-omiques, inaccessibles par une approche simple standard. Cependant le traitement de cette somme de connaissances multi-omiques fait face à de nouveaux défis : l'évolution constante des technologies, le volume des données produites, leur hétérogénéité, la variété des données omiques et l'interprétabilité des résultats d'intégration nécessitent de nouvelles méthodes d'analyses et des outils innovants, capables d'identifier les éléments utiles à travers cette multitude d'informations. Dans cette perspective, nous proposons plusieurs outils et méthodes pour faire face aux challenges liés à l'intégration et l'interprétation de ces données multi-omiques particulières. Enfin, l'intégration de données multi-omiques longitudinales offre des perspectives dans des domaines tels que la médecine de précision ou pour des applications environnementales et industrielles. La démocratisation des analyses multi-omiques et la mise en place de méthodes d'intégration et d'interprétation innovantes permettront assurément d'obtenir une meilleure compréhension des écosystèmes biologiques.New high-throughput «omics» technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and metagenomics, have expanded considerably in recent years. Independently, each omics technology is an essential source of knowledge for the study of the human genome, epigenome, transcriptome, proteome, metabolome, and also its microbiota, thus making it possible to identify biomarkers leading to diseases, to identify therapeutic targets, to establish preventive diagnoses and to increase knowledge of living organisms. Cost reduction and ease of multi-omics data acquisition resulted in new experimental designs based on time series in which the same biological sample is sequenced, measured and quantified at several measurement times. Thanks to the combined study of omics technologies and time series, it is possible to capture the changes in expression that take place in a dynamic system for each molecule and get a comprehensive view of the multi-omics interactions, which was inaccessible with a simple standard omics approach. However, dealing with this amount of multi-omics data faces new challenges: continuous technological evolution, large volumes of produced data, heterogeneity, variety of omics data and interpretation of integration results require new analysis methods and innovative tools, capable of identifying useful elements through this multitude of information. In this perspective, we propose several tools and methods to face the challenges related to the integration and interpretation of these particular multi-omics data. Finally, integration of longidinal multi-omics data offers prospects in fields such as precision medicine or for environmental and industrial applications. Democratisation of multi-omics analyses and the implementation of innovative integration and interpretation methods will definitely lead to a deeper understanding of eco-systems biology

Learning CAD at university through summaries of the rules of design intent

The ease with which 3D CAD models may be modified and reused are two key aspects that improve the design-intent variable and that can significantly shorten the development timelines of a product. A set of rules are gathered from various authors that take different 3D modelling strategies into account. These rules are then applied to CAD strategic-knowledge learning methodology and included in 3D CAD modelling exercises for students following the degree in mechanical engineering at the University of Burgos (Spain). The experiment was conducted in two groups with a total of 75 students. The design-intent rules were introduced in the different exercises that the teacher explained in both the theoretical and the practical classes. In addition, a summary of the different design rules in each of the practical exercises was explained in the practical classes in only one of the groups. The experimental results, reported in this paper, tested the influence of these summaries on overall improvements in 3D modelling and on the design-intent variable, which is subdivided into four sections: skeleton, structures, alterations and constraints. The use of the summaries of the design intent rules led to statistically significant improvements in 3D modelling in the experimental group, in comparison with the group of students to whom those summaries were not explained

On the effects of the fix geometric constraint in 2D profiles on the reusability of parametric 3D CAD models

[EN] In order to be reusable, history-based feature-based parametric CAD models must reliably allow for modifications while maintaining their original design intent. In this paper, we demonstrate that relations that fix the location of geometric entities relative to the reference system produce inflexible profiles that reduce model reusability. We present the results of an experiment where novice students and expert CAD users performed a series of modifications in different versions of the same 2D profile, each defined with an increasingly higher number of fix geometric constraints. Results show that the amount of fix constraints in a 2D profile correlates with the time required to complete reusability tasks, i.e., the higher the number of fix constraints in a 2D profile, the less flexible and adaptable the profile becomes to changes. In addition, a pilot software tool to automatically track this type of constraints was developed and tested. Results suggest that the detection of fix constraint overuse may result in a new metric to assess poor quality models with low reusability. The tool provides immediate feedback for preventing high semantic level quality errors, and assistance to CAD users. Finally, suggestions are introduced on how to convert fix constraints in 2D profiles into a negative metric of 3D model quality.The authors would like to thank Raquel Plumed for her support in the statistical analysis. This work has been partially funded by Grant UJI-A02017-15 (Universitat Jaume I) and DPI201784526-R (MINECO/AEI/FEDER, UE), project CAL-MBE. The authors also wish to thank the editor and reviewers for their valuable comments and suggestions that helped us improve the quality of the paper.González-Lluch, C.; Company, P.; Contero, M.; Pérez Lopez, DC.; Camba, JD. (2019). On the effects of the fix geometric constraint in 2D profiles on the reusability of parametric 3D CAD models. 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(Colección Sapientia, Núm. 86). http://cad3dconsolidworks.uji.es .Contero, M., Company, P., Vila, C., & Aleixos, N. (2002). Product data quality and collaborative engineering. IEEE Computer Graphics Applications, 22(3), 32–42. https://doi.org/10.1109/MCG.2002.999786 .Dixon, B. M., & Dannenhoffer, J. F., III. (2014). Geometric sketch constraint solving with user feedback. Journal of Aerospace Information Systems, 11(5), 316–325. https://doi.org/10.2514/1.I010110 .Fudos, I., & Hoffmann, C. M. (1997). A graph-constructive approach to solving systems of geometric constraints. ACM Transactions on Graphics, 16(2), 179–216. https://doi.org/10.1145/248210.248223 .Ge, J. X., Chou, S. C., & Gao, X. S. (1999). Geometric constraint satisfaction using optimization methods. Computer-Aided Design, 31(14), 867–879. https://doi.org/10.1016/S0010-4485(99)00074-3 .González-Lluch, C., Company, P., Contero, M., Camba, J. D., & Colom, J. (2017a). 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A survey on 3D CAD model quality assurance and testing

[EN] A new taxonomy of issues related to CAD model quality is presented, which distinguishes between explicit and procedural models. For each type of model, morphologic, syntactic, and semantic errors are characterized. The taxonomy was validated successfully when used to classify quality testing tools, which are aimed at detecting and repairing data errors that may affect the simplification, interoperability, and reusability of CAD models. The study shows that low semantic level errors that hamper simplification are reasonably covered in explicit representations, although many CAD quality testers are still unaffordable for Small and Medium Enterprises, both in terms of cost and training time. Interoperability has been reasonably solved by standards like STEP AP 203 and AP214, but model reusability is not feasible in explicit representations. Procedural representations are promising, as interactive modeling editors automatically prevent most morphologic errors derived from unsuitable modeling strategies. Interoperability problems between procedural representations are expected to decrease dramatically with STEP AP242. Higher semantic aspects of quality such as assurance of design intent, however, are hardly supported by current CAD quality testers. (C) 2016 Elsevier Ltd. All rights reserved.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund, through the ANNOTA project (Ref. TIN2013-46036-C3-1-R).González-Lluch, C.; Company, P.; Contero, M.; Camba, J.; Plumed, R. (2017). A survey on 3D CAD model quality assurance and testing. Computer-Aided Design. 83:64-79. https://doi.org/10.1016/j.cad.2016.10.003S64798

Qui réalise un frottis cervico-utérin en médecine générale ?

LILLE2-BU Santé-Recherche (593502101) / SudocSudocFranceF

Réponses au défi de la démographie médicale (études des mesures pour revitaliser les zones sous-médicalisées)

LILLE2-BU Santé-Recherche (593502101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Première prescription de contraception en médecine générale (caractéristiques de la consultation de primo prescription de contraception)

LILLE2-BU Santé-Recherche (593502101) / SudocSudocFranceF

A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types

Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics, and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical, or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens, and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g., sparse, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial community data and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modeling to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies