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1. Les professionnels de santé face à la pandémie de la maladie à coronavirus (COVID-19) : quels risques pour leur santé mentale ?

Author

Nefize Yalin, Frank Bellivier, Alessandro Colasanti, Bruno Étain, Tim Mantingh, Paul R. A. Stokes, Jean-Pierre Kahn, Chantal Henry, Marion Leboyer, Rashmi Patel, Allan H. Young, King‘s College London, Bethlem Royal Hospital [London], Brighton and Sussex Medical School (BSMS), Hôpitaux Universitaire Saint-Louis, Lariboisière, Fernand-Widal, Optimisation thérapeutique en Neuropsychopharmacologie (OPTeN (UMR_S_1144 / U1144)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Fondation FondaMental [Créteil], IMRB - 'Neuropsychiatrie translationnelle' [Créteil] (U955 Inserm - UPEC), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Hôpital Henri Mondor, Perception et Mémoire / Perception and Memory, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Centre Psychothérapique de Nancy [Laxou] (CPN), This paper represents independent research supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Authors from France acknowledge the support provided by INSERM (Research Protocol C0829), Assistance Publique des Hôpitaux de Paris (Research Protocol GAN12)., Etain, Bruno, and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)

Subjects
MESH: United Kingdom, Identification, MESH: Protective Devices, [SDV.MHEP.PSM] Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, 0302 clinical medicine, MESH: Risk Factors, MESH: Bipolar Disorder, London, Medicine, MESH: Tobacco, Psychotic illness, MESH: Workload, education.field_of_study, MESH: Sleep Wake Disorders, Mania, Psychiatry and Mental health, Clinical Psychology, Characteristics, MESH: Stress Disorders, Post-Traumatic, MESH: Young Adult, Cohort, cardiovascular system, behavior and behavior mechanisms, France, medicine.symptom, MESH: Uncertainty, MESH: Occupational Diseases, medicine.medical_specialty, MESH: Pandemics, Clinical variables, MESH: Sleep, Bipolar disorder, Population, MESH: Social Support, MESH: Mania, Unipolar mania, behavioral disciplines and activities, 03 medical and health sciences, MESH: Infectious Disease Transmission, Patient-to-Professional, MESH: Severe Acute Respiratory Syndrome, mental disorders, Humans, MESH: SARS-CoV-2, education, Psychiatry, MESH: Work Schedule Tolerance, MESH: Helplessness, Learned, MESH: Humans, business.industry, MESH: Influenza Pandemic, 1918-1919, MESH: Retrospective Studies, MESH: London, medicine.disease, United Kingdom, MESH: Quantitative Trait Loci, MESH: Prospective Studies, 030227 psychiatry, MESH: France, Case register, MESH: Suicide, MESH: Pneumonia, Viral, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Matched group, RC0321, MESH: Resilience, Psychological, business, 030217 neurology & neurosurgery
Abstract

Background: Unipolar mania is a putative subtype of bipolar disorder (BD) in which individuals experience recurrent manic but not major depressive episodes. Few studies of unipolar mania have been conducted in developed countries and none in the UK. This study aimed to identify and characterise people with unipolar mania in the UK and France. \ud \ud Methods: People with unipolar mania were ascertained using a South London UK electronic case register and a French BD case series. Each unipolar mania group was compared to a matched group of people with BD who have experienced depressive episodes. \ud \ud Results: 17 people with unipolar mania were identified in South London and 13 in France. The frequency of unipolar mania as a percentage of the BD clinical population was 1.2% for the South London cohort and 3.3% for the French cohort. In both cohorts, people with unipolar mania experienced more manic episodes than people with BD, and in the French cohort were more likely to experience a psychotic illness onset and more psychiatric admissions. Treatment characteristics of people with unipolar mania were similar to people with BD except that the unipolar mania group was less likely to be treated with antidepressants. \ud \ud Limitations: The relatively small number of people with unipolar mania identified by this study limits its power to detect differences in clinical variables. \ud \ud Conclusions: People with unipolar mania can be identified in France and the UK, and they may experience a higher frequency of manic episodes but have similar treatment characteristics to people with BD.

Published
2020

2. Enhancing Perceived Safety in Human–Robot Collaborative Construction Using Immersive Virtual Environments

Author

Sangseok You, Lionel P. Robert, Jeonghwan Kim, SangHyun Lee, Vineet R. Kamat, Okinawa Institute of Science and Technology Graduate University (OIST), Institute for Computational Engineering and Sciences [Austin] (ICES), University of Texas at Austin [Austin], and HEC Research Paper Series

Subjects
0209 industrial biotechnology, Identification, Computer science, 0211 other engineering and technologies, Theoretical models, 02 engineering and technology, Robot Acceptance Safety Model (RASM), Virtual reality, Trust, Human–robot interaction, 020901 industrial engineering & automation, Human–computer interaction, 021105 building & construction, 0502 economics and business, Set (psychology), Masonry, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Construction automation, Civil and Structural Engineering, Perceived safety, Immersive Virtual, business.industry, 05 social sciences, Robotics, Building and Construction, Environment (IVE), Human–Robot Work Collaboration (HRWC), Identification (information), Work (electrical), Control and Systems Engineering, Robot, [SHS.GESTION]Humanities and Social Sciences/Business administration, Intention to Work with Robot, Artificial intelligence, Safety, Immersive virtual environment, business, Humanoid robot, 050203 business & management, Virtual prototyping, Team
Abstract

Advances in robotics now permit humans to work collaboratively with robots. However, humans often feel unsafe working alongside robots. Our knowledge of how to help humans overcome this issue is limited by two challenges. One, it is difficult, expensive and time-consuming to prototype robots and set up various work situations needed to conduct studies in this area. Two, we lack strong theoretical models to predict and explain perceived safety and its influence on human–robot work collaboration (HRWC). To address these issues, we introduce the Robot Acceptance Safety Model (RASM) and employ immersive virtual environments (IVEs) to examine perceived safety of working on tasks alongside a robot. Results from a between-subjects experiment done in an IVE show that separation of work areas between robots and humans increases perceived safety by promoting team identification and trust in the robot. In addition, the more participants felt it was safe to work with the robot, the more willing they were to work alongside the robot in the future.

Published
2018

3. Model selection, identification and validation in anaerobic digestion: a review

Author

Cristina Martin, Jorge Rodríguez, Johan Mailier, Andres Donoso-Bravo, César Arturo Aceves-Lara, Alain Vande Wouwer, Automatic Control Laboratory, Université de Mons (UMons), modelEAU, Département de génie civil et de génie des eaux, Université Laval [Québec] (ULaval), Universidade de Santiago de Compostela [Spain] (USC ), Masdar Institute of Science and Technology [Abu Dhabi], Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), This paper presents research results of the Belgian Network DYSCO (Dynamical Systems, Control, and Optimization), funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian State, Science Policy Office. The scientific responsibility rests with its author(s). This study is also supported by a grant from Belspo (Belgian Science Policy) through its Postdoc fellowships to non-EU researchers program. Johan Mailier is a research fellow supported by the FNRS (Belgian National Fund for Scientific Research)., and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Engineering, Identification, Environmental Engineering, Process (engineering), 020209 energy, engineering, [SDV]Life Sciences [q-bio], 02 engineering and technology, 010501 environmental sciences, water resources, 01 natural sciences, Field (computer science), environmental, Kinetic parameters, Water Purification, Lead (geology), Digestion (alchemy), Biogas, Anaerobic digestion, [SDV.IDA]Life Sciences [q-bio]/Food engineering, 0202 electrical engineering, electronic engineering, information engineering, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Anaerobiosis, environmental sciences, Waste Management and Disposal, 0105 earth and related environmental sciences, Water Science and Technology, Civil and Structural Engineering, Waste management, business.industry, Ecological Modeling, Model selection, Modeling, Uncertainty, Reproducibility of Results, Pollution, Identification (information), Models, Chemical, 13. Climate action, Biochemical engineering, business, Sensitivity analysis
Abstract

International audience; Anaerobic digestion enables waste (water) treatment and energy production in the form of biogas. The successful implementation of this process has lead to an increasing interest worldwide. However, anaerobic digestion is a complex biological process, where hundreds of microbial populations are involved, and whose start-up and operation are delicate issues. In order to better understand the process dynamics and to optimize the operating conditions, the availability of dynamic models is of paramount importance. Such models have to be inferred from prior knowledge and experimental data collected from real plants. Modeling and parameter identification are vast subjects, offering a realm of approaches and methods, which can be difficult to fully understand by scientists and engineers dedicated to the plant operation and improvements. This review article discusses existing modeling frameworks and methodologies for parameter estimation and model validation in the field of anaerobic digestion processes. The point of view is pragmatic, intentionally focusing on simple but efficient methods.

Published
2011

4. BMC Genomics

Author

Grace C. Davey, Penelope K. Lindeque, Richard Reinhardt, Christophe Klopp, Pascal Favrel, Dario Moraga, Pierre Boudry, Sylvie Lapegue, Patrick Prunet, Arnaud Huvet, Julien de Lorgeril, Jeanne Moal, Viviane Boulo, Elodie Fleury, Christophe Lelong, Christopher Sauvage, Patrick Wincker, François Moreews, Michel Mathieu, Frédérick Gavory, Arnaud Tanguy, Caroline Fabioux, Charlotte Corporeau, Evelyne Bachère, Jenny P. Shaw, Yannick Gueguen, Laboratoire Environnement Ressources Morbihan Pays de Loire (LERMPL), LITTORAL (LITTORAL), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Physiologie et Ecophysiologie des Mollusques Marins (PE2M), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Ecosystèmes lagunaires : organisation biologique et fonctionnement (ECOLAG), Université Montpellier 2 - Sciences et Techniques (UM2)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS), Adaptation et diversité en milieu marin (ADMM), Institut national des sciences de l'Univers (INSU - CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Brest (UBO), Plymouth Marine Laboratory (PML), Max-Planck-Institut für Molekulare Genetik (MPIMG), Max-Planck-Gesellschaft, Station commune de Recherches en Ichtyophysiologie, Biodiversité et Environnement (SCRIBE), Institut National de la Recherche Agronomique (INRA), Génétique fonctionnelle, agronomie et santé [IFR 140] (GFAS), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), National Diagnostics Centre (NDC), National University of Ireland [Galway] (NUI Galway), Laboratoire de Génétique et Pathologie (LGP), Amélioration génétique, du contrôle des performances et de la santé des mollusques marins (AGSAE), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Systèmes d'Elevage, Nutrition Animale et Humaine (SENAH), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Rennes, Système d'Information des GENomes des Animaux d'Elevage (SIGENAE), Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA), Laboratoire de Physiologie des Invertébrés (LPI), Physiologie Fonctionnelle des Organismes Marins (PFOM), The research presented in this paper was performed within the framework of several research projects funded by: Genoscope (11/AP2006-2007), Marine Genomics Network of Excellence (GOCE-CT-2004-505403), the European project 'Aquafirst' (513692) in the Sixth Framework Program, ANR 'CgPhysiogène' (ANR-06-GANI-0009) and 'Gametogenes' (ANR-08-GENM-041), ANR-06-GANI-0009,CgPhysiogene,Bases moléculaires des fonctions physiologiques de l'huître Crassostrea gigas : interactions hôte/pathogène/milieu(2006), ANR-08-GENM-0041,Gametogenes,Génomiques de la gamétogénèse chez l'huître creuse Crassostrea gigas(2008), Laboratoire Laboratoire Environnement Ressources Morbihan Pays de Loire (LER/MPL), Institut de Recherche pour le Développement (IRD)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS), Adaptation et Biologie des Invertébrés en Conditions Extrêmes (ABICE), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Plymouth Marine Laboratory, Institut National de la Recherche Agronomique (INRA)-IFR140, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Ecole Nationale Supérieure Agronomique de Rennes-Institut National de la Recherche Agronomique (INRA), Unité de Biométrie et Intelligence Artificielle de Toulouse [Castanet-Tolosan] (UBIA), Institut National de la Recherche Agronomique (INRA)-Plateforme bioinformatique du GIS GENOTOUL - Génopole Toulouse Midi-Pyrénées, Laboratoire de Physiologie des Invertébrés [Plouzané] (LPI), Adaptation et diversité en milieu marin (AD2M), Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Recherche Agronomique (INRA), Station biologique de Roscoff (SBR), and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Oyster, genome annotation, génomique fonctionnelle, computer.software_genre, Genome, User-Computer Interface, single nucleotide polymorphisms, Databases, Genetic, crassostrea gigas, structure du génome, Expressed Sequence Tags, base de données, 0303 health sciences, Expressed sequence tag, biology, Database, 04 agricultural and veterinary sciences, Genomics, crustacea, Pacific oyster, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], energy-balance, factor-beta superfamily, DNA microarray, expression des gènes, Biotechnology, lcsh:QH426-470, Sequence analysis, lcsh:Biotechnology, kappa-b, Polymorphism, Single Nucleotide, génomique, 03 medical and health sciences, biology.animal, lcsh:TP248.13-248.65, Genetics, summer mortality, Animals, Crassostrea, 030304 developmental biology, Gene Library, Whole genome sequencing, [SDV.GEN]Life Sciences [q-bio]/Genetics, huître, cell-development, génome, Gene Expression Profiling, linkage maps, Sequence Analysis, DNA, biology.organism_classification, lcsh:Genetics, coquillage, 040102 fisheries, 0401 agriculture, forestry, and fisheries, identification, marine genomics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Microsatellite Repeats
Abstract

Background Although bivalves are among the most-studied marine organisms because of their ecological role and economic importance, very little information is available on the genome sequences of oyster species. This report documents three large-scale cDNA sequencing projects for the Pacific oyster Crassostrea gigas initiated to provide a large number of expressed sequence tags that were subsequently compiled in a publicly accessible database. This resource allowed for the identification of a large number of transcripts and provides valuable information for ongoing investigations of tissue-specific and stimulus-dependant gene expression patterns. These data are crucial for constructing comprehensive DNA microarrays, identifying single nucleotide polymorphisms and microsatellites in coding regions, and for identifying genes when the entire genome sequence of C. gigas becomes available. Description In the present paper, we report the production of 40,845 high-quality ESTs that identify 29,745 unique transcribed sequences consisting of 7,940 contigs and 21,805 singletons. All of these new sequences, together with existing public sequence data, have been compiled into a publicly-available Website http://public-contigbrowser.sigenae.org:9090/Crassostrea_gigas/index.html. Approximately 43% of the unique ESTs had significant matches against the SwissProt database and 27% were annotated using Gene Ontology terms. In addition, we identified a total of 208 in silico microsatellites from the ESTs, with 173 having sufficient flanking sequence for primer design. We also identified a total of 7,530 putative in silico, single-nucleotide polymorphisms using existing and newly-generated EST resources for the Pacific oyster. Conclusion A publicly-available database has been populated with 29,745 unique sequences for the Pacific oyster Crassostrea gigas. The database provides many tools to search cleaned and assembled ESTs. The user may input and submit several filters, such as protein or nucleotide hits, to select and download relevant elements. This database constitutes one of the most developed genomic resources accessible among Lophotrochozoans, an orphan clade of bilateral animals. These data will accelerate the development of both genomics and genetics in a commercially-important species with the highest annual, commercial production of any aquatic organism.

Published
2009