The EMSO Generic Instrument Module (EGIM): standardized and interoperable instrumentation for ocean observation

The oceans are a fundamental source for climate balance, sustainability of resources and life on Earth, therefore society has a strong and pressing interest in maintaining and, where possible, restoring the health of the marine ecosystems. Effective, integrated ocean observation is key to suggesting actions to reduce anthropogenic impact from coastal to deep-sea environments and address the main challenges of the 21st century, which are summarized in the UN Sustainable Development Goals and Blue Growth strategies. The European Multidisciplinary Seafloor and water column Observatory (EMSO), is a European Research Infrastructure Consortium (ERIC), with the aim of providing long-term observations via fixed-point ocean observatories in key environmental locations across European seas from the Arctic to the Black Sea. These may be supported by ship-based observations and autonomous systems such as gliders. In this paper, we present the EMSO Generic Instrument Module (EGIM), a deployment ready multi-sensor instrumentation module, designed to measure physical, biogeochemical, biological and ecosystem variables consistently, in a range of marine environments, over long periods of time. Here, we describe the system, features, configuration, operation and data management. We demonstrate, through a series of coastal and oceanic pilot experiments that the EGIM is a valuable standard ocean observation module, which can significantly improve the capacity of existing ocean observatories and provides the basis for new observatories. The diverse examples of use included the monitoring of fish activity response upon oceanographic variability, hydrothermal vent fluids and particle dispersion, passive acoustic monitoring of marine mammals and time series of environmental variation in the water column. With the EGIM available to all the EMSO Regional Facilities, EMSO will be reaching a milestone in standardization and interoperability, marking a key capability advancement in addressing issues of sustainability in resource and habitat management of the oceans.This work was funded by the project EMSODEV (Grant agreement No 676555) supported by DG Research and Innovation of the European Commission under the Research Infrastructures Programme of the H2020. EMSO-link EC project (Grant agreement No 731036) provided additional funding. Other projects which supported the work include Plan Estatal de Investigación Científica y Técnica y de Innovación 2017–2020, project BITER-LANDER PID2020- 114732RB-C32, iFADO (Innovation in the Framework of the Atlantic Deep Ocean, 2017–2021) EAPA_165/2016. The Spanish Government contributed through the “Severo Ochoa Centre Excellence” accreditation to ICM-CSIC (CEX2019-000928-S) and the Research Unit Tecnoterra (ICM-CSIC/UPC). UK colleagues were supported by Climate Linked Atlantic Sector Science (CLASS) project supported by NERC National Capability funding (NE/R015953/1).Peer ReviewedArticle signat per 33 autors/es: Nadine Lantéri; Henry A. Ruh; Andrew Gates; Enoc Martínez; Joaquin del Rio Fernandez; Jacopo Aguzzi; Mathilde Cannat; Eric Delory; Davide Embriaco; Robert Huber; Marjolaine Matabos;George Petihakis; Kieran Reilly; Jean-François Rolin; Mike van der Schaar; Michel André; Jérôme Blandin; Andrés Cianca; Marco Francescangeli; Oscar Garcia; Susan Hartman; Jean-Romain Lagadec; Julien Legrand; Paris Pagonis; Jaume Piera; Xabier Remirez; Daniel M. Toma; Giuditta Marinaro; Bertrand Moreau; Raul Santana; Hannah Wright; Juan José Dañobeitia; Paolo FavaliPostprint (published version

The EMSO Generic Instrument Module (EGIM): Standardized and interoperable instrumentation for ocean observation

The oceans are a fundamental source for climate balance, sustainability of resources and life on Earth, therefore society has a strong and pressing interest in maintaining and, where possible, restoring the health of the marine ecosystems. Effective, integrated ocean observation is key to suggesting actions to reduce anthropogenic impact from coastal to deep-sea environments and address the main challenges of the 21st century, which are summarized in the UN Sustainable Development Goals and Blue Growth strategies. The European Multidisciplinary Seafloor and water column Observatory (EMSO), is a European Research Infrastructure Consortium (ERIC), with the aim of providing long-term observations via fixed-point ocean observatories in key environmental locations across European seas from the Arctic to the Black Sea. These may be supported by ship-based observations and autonomous systems such as gliders. In this paper, we present the EMSO Generic Instrument Module (EGIM), a deployment ready multi-sensor instrumentation module, designed to measure physical, biogeochemical, biological and ecosystem variables consistently, in a range of marine environments, over long periods of time. Here, we describe the system, features, configuration, operation and data management. We demonstrate, through a series of coastal and oceanic pilot experiments that the EGIM is a valuable standard ocean observation module, which can significantly improve the capacity of existing ocean observatories and provides the basis for new observatories. The diverse examples of use included the monitoring of fish activity response upon oceanographic variability, hydrothermal vent fluids and particle dispersion, passive acoustic monitoring of marine mammals and time series of environmental variation in the water column. With the EGIM available to all the EMSO Regional Facilities, EMSO will be reaching a milestone in standardization and interoperability, marking a key capability advancement in addressing issues of sustainability in resource and habitat management of the oceans

Développement d'un modèle de "deep learning" pour évaluer la sécurité des patients

International audienc

Développement d'un modèle de "deep learning" pour évaluer la sécurité des patients

International audienc

Réforme pédagogique et « e-learning » pour le concours de première année à la faculté de médecine de Grenoble : satisfaction des étudiants et des enseignants

International audienc

Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018

Purpose In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the individual patient level as accurately as possible is one of the first steps towards improving patient outcomes. Risk assessment can enable healthcare providers to target resources to patients in greatest need through adaptations in processes and procedures. Electronic health data facilitates the application of machine-learning methods for risk analysis. We aim, first to reveal correlations between HAE occurrence and patients’ characteristics and/or the procedures they undergo during their hospitalisation, and second, to build models that allow the early identification of patients at an elevated risk of HAE.Participants 143 865 adult patients hospitalised at Grenoble Alpes University Hospital (France) between 1 January 2016 and 31 December 2018.Findings to date In this set-up phase of the project, we describe the preconditions for big data analysis using machine-learning methods. We present an overview of the retrospective de-identified multisource data for a 2-year period extracted from the hospital’s Clinical Data Warehouse, along with social determinants of health data from the National Institute of Statistics and Economic Studies, to be used in machine learning (artificial intelligence) training and validation. No supplementary information or evaluation on the part of medical staff will be required by the information system for risk assessment.Future plans We are using this data set to develop predictive models for several general HAEs including secondary intensive care admission, prolonged hospital stay, 7-day and 30-day re-hospitalisation, nosocomial bacterial infection, hospital-acquired venous thromboembolism, and in-hospital mortality

The French Virtual Medical University.

International audienceThis paper is the description of a French Virtual Medical University based on the federation of existing or currently being developed resources in several Medical Schools in France. The objectives of the project is not only to share experiences across the country but also to integrate several resources using the New Information and Communication Technologies to support new pedagogical approaches for medical students and also for continuing medical education. The project includes: A virtual Medical Campus using secure access from several sites, The Integration of new interactive resources based on pedagogical methods, Implementation of new indexing and search engines based on medical vocabularies and ontologies, The definition of general and specific portals, the evaluation of the system for ergonomics and contents

Cohort Creation and Visualization Using Graph Model in the PREDIMED Health Data Warehouse

International audienceGrenoble Alpes University Hospital (CHUGA) is currently deploying a health data warehouse called PREDIMED [1], a platform designed to integrate and analyze for research, education and institutional management the data of patients treated at CHUGA. PREDIMED contains healthcare data, administrative data and, potentially, data from external databases. PREDIMED is hosted by the CHUGA Information Systems Department and benefits from its strict security rules. CHUGA’s institutional project PREDIMED aims to collaborate with similar projects in France and worldwide. In this paper, we present how the data model defined to implement PREDIMED at CHUGA is useful for medical experts to interactively build a cohort of patients and to visualize this cohort