Étude des interactions protéine-protéine au sein de polymères d'hémoglobine (application à la mise au point d'un transporteur artificiel d'oxygène)

PARIS7-Bibliothèque centrale (751132105) / SudocSudocFranceF

Intérêt de l'électroconvulsivothérapie dans la schizophrénie

L'électroconvulsivothérapie est une thérapeutique actuellement peu utilisée dans la schizophrénie. De rares études ont tenté d'évaluer son efficacité. Une revue de la littérature permet une synthèse sur: -les modes d'action de l'électroconvulsivothérapie, -les principales indications, -les modalités thérapeutiques de cette méthode, utilisée en curatif ou en entretien. Une étude de l'activité du service d'électroconvulsivothérapie du C.H.U. de Nantes durant l'année 2001, révèle que 15 % des patients bénéficiant de cette thérapeutique sont schizophrènes. La plupart sont résistants aux traitements médicamenteux ou traversent une phase d 'exacerbation délirante aiguë. L'impact cognitif de cette méthode thérapeutique dans la schizophrénie, ainsi que l'individualisation de facteurs prédictifs de réponse, cliniques ou électriques, pourraient être un axe de recherche. Ceci permettrait une atténuation des effets secondaires et une optimisation des résultats thérapeutiques.NANTES-BU Médecine pharmacie (441092101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Motion segmentation and cloud tracking on noisy infrared image sequences

International audienceVolume 3653 Aerial surveillance is an issue of key importance for warship protection. In addition to radar systems, infrared surveillance sensors represent an interesting alternative for remote observation. In this paper, we study such a system and an original approach to the tracking of complex cloudy patterns in noisy infrared image sequences is proposed. We have paid particular attention to robustness with regards to perturbations likely to occur (noise, 'lining effects' . . .). Our approach relies on robust parametric motion estimation and an original regularization scheme allows to handle with the appearance and the disappearance of objects in the scene. Numerous experiments performed on outdoor infrared image sequences underline the efficiency of the proposed method

End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations

International audienceThe characterization of suspended sediment dynamics in the coastal ocean provides key information for both scientific studies and operational challenges regarding, among others, turbidity, water transparency and the development of micro-organisms using photosynthesis, which is critical to primary production. Due to the complex interplay between natural and anthropogenic forcings, the understanding and monitoring of the dynamics of suspended sediments remain highly challenging. Numerical models still lack the capabilities to account for the variability depicted by in situ and satellite-derived datasets. Through the ever increasing availability of both in situ and satellite-derived observation data, data-driven schemes have naturally become relevant approaches to complement model-driven ones. Our previous work has stressed this potential within an observing system simulation experiment. Here, we further explore their application to the interpolation of sea surface sediment concentration fields from real gappy satellite-derived observation datasets. We demonstrate that end-to-end deep learning schemes—namely 4DVarNet, which relies on variational data assimilation formulation—apply to the considered real dataset where the training phase cannot rely on gap-free references but only on the available gappy data. 4DVarNet significantly outperforms other data-driven schemes such as optimal interpolation and DINEOF with a relative gain greater than 20% in terms of RMSLE and improves the high spatial resolution of patterns in the reconstruction process. Interestingly, 4DVarNet also shows a better agreement between the interpolation performance assessed for an OSSE and for real data. This result emphasizes the relevance of OSSE settings for future development calibration phases before the applications to real datasets

Data-Driven Interpolation of Sea Surface Suspended Concentrations Derived from Ocean Colour Remote Sensing Data

International audienceDue to complex natural and anthropogenic interconnected forcings, the dynamics of suspended sediments within the ocean water column remains difficult to understand and monitor. Numerical models still lack capabilities to account for the variabilities depicted by in situ and satellite-derived datasets. Besides, the irregular space-time sampling associated with satellite sensors make crucial the development of efficient interpolation methods. Optimal Interpolation (OI) remains the state-of-the-art approach for most operational products. Due to the large increase of both in situ and satellite measurements more and more available information is coming from in situ and satellite measurements, as well as from simulation models. The emergence of data-driven schemes as possibly relevant alternatives with increased capabilities to recover finer-scale processes. In this study, we investigate and benchmark three state-of-the-art data-driven schemes, namely an EOF-based technique, an analog data assimilation scheme, and a neural network approach, with an OI scheme. We rely on an Observing System Simulation Experiment based on high-resolution numerical simulations and simulated satellite observations using real satellite sampling patterns. The neural network approach, which relies on variational data assimilation formulation for the interpolation problem, clearly outperforms both the OI and the other data-driven schemes, both in terms of reconstruction performance and of a greater ability to recover high-frequency events. We further discuss how these results could transfer to real data, as well as to other problems beyond interpolation issues, especially short-term forecasting problems from partial satellite observations

Corrigendum: Reconstructing global chlorophyll-a variations using a non-linear statistical approach

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tirant, a newly discovered active endogenous retrovirus in Drosophila simulans.

International audienceEndogenous retroviruses have the ability to become permanently integrated into the genomes of their host, and they are generally transmitted vertically from parent to progeny. With the exception of gypsy, few endogenous retroviruses have been identified in insects. In this study, we describe the tirant endogenous retrovirus in a subset of Drosophila simulans natural populations. By focusing on the envelope gene, we show that the entire retroviral cycle (transcription, translation, and retrotransposition) can be completed for tirant within one population of this species

Conformational Changes in Hemoglobin S (βE6V) Imposed by Mutation of the βGlu7–βLys132 Salt Bridge and Detected by UV Resonance Raman Spectroscopy

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