Understanding the Sargassum phenomenon in the Tropical Atlantic Ocean: From satellite monitoring to stranding forecast - Marianne Debue; Thibault Guinaldo; Julien Jouanno; Malik Chami; Sarah Barbier; Léo Berline; Cristèle Chevalier; Pierre Daniel; Warren Daniel; Jacques Descloitres; Jean-Raphaël Gros-Desormeaux; Christophe Lett; et al. (Marine Pollution Bulletin, 2025)

Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb−1 of s = 13 TeV pp collision data - G. Aad; B. Abbott; K. Abeling; N.J. Abicht; S.H. Abidi; A. Aboulhorma; H. Abramowicz; H. Abreu; Y. Abulaiti; A.C. Abusleme Hoffman; B.S. Acharya; C. Adam Bourdarios; et al. (Science Bulletin, 2024)

Filtering out false Sargassum detections using context features - Witold Podlejski; Jacques Descloitres; Cristèle Chevalier; Audrey Minghelli; Christophe Lett; Léo Berline (Frontiers in Marine Science, 2022)
Since 2011, the distribution extent of pelagic Sargassum algae has substantially increased and now covers the whole Tropical North Atlantic Ocean, with significant inter-annual variability. The ocean colour imagery has been used as the only way to monitor regularly such a vast area. However, the detection is hampered by cloud masking, sunglint, coastal contamination and other phenomena. All together, they lead to false detections that can hardly be discriminated by classic radiometric analysis, but may be overcome by considering the shape and the context of the detections. Here, we built a machine learning model base exclusively on spatial features to filter out false detections after the detection process. Moderate-Resolution Imaging Spectroradiometer (MODIS, 1 km) data from Aqua and Terra satellites were used to generate daily map of Alternative Floating Algae Index (AFAI). Based on this radiometric index, Sargassum presence in the Tropical Atlantic North Ocean was inferred. For every Sargassum aggregations, five contextual indices were extracted (number of neighbours, surface of neighbours, temporal persistence, distance to the coast and aggregation texture) then used by a random forest binary classifier. Contextual features at large-scale were most important in the classifier. Trained with a multi-annual (2016-2020) learning set, the model performs the filtering of daily false detections with an accuracy of ~ 90%. This leads to a reduction of detected Sargassum pixels of ~ 50% over the domain. The method provides reliable data while preserving high spatial and temporal resolutions (1 km, daily). The resulting distribution is consistent with the literature for seasonal and inter-annual fluctuations, with maximum coverage in 2018 and minimum in 2016. This dataset will be useful for understanding the drivers of Sargassum dynamics at fine and large scale and validate future models. The methodology used here demonstrates the usefulness of contextual features for complementing classical remote sensing approaches. Our model could easily be adapted to other datasets containing erroneous detections.

Towards guidance values for the environmental performance of buildings: application to the statistical analysis of 40 low-energy single family houses’ LCA in France - Sébastien Lasvaux; Alexandra Lebert; Fanny Achim; Francis Grannec; Endrit Hoxha; Sylviane Nibel; Nicoleta Schiopu; Jacques Chevalier (The International Journal of Life Cycle Assessment, 2017)

Comparison of generic and product-specific Life Cycle Assessment databases: application to construction materials used in building LCA studies - Sébastien Lasvaux; Guillaume Habert; Bruno Peuportier; Jacques Chevalier (The International Journal of Life Cycle Assessment, 2015)

Suppression of PPARβ, and DHA treatment, inhibit NaV1.5 and NHE-1 pro-invasive activities - Ramez Wannous; Emeline Bon; Ludovic Gillet; Julie Chamouton; Günther Weber; Lucie Brisson; Jacques Goré; Philippe Bougnoux; Pierre Besson; Sébastien Roger; Stephan Chevalier (Pflügers Archiv - European Journal of Physiology, 2015)

Achieving consistency in life cycle assessment practice within the European construction sector: the role of the EeBGuide InfoHub - Sébastien Lasvaux; Johannes Gantner; Bastian Wittstock; Manuel Bazzana; Nicoleta Schiopu; Tom Saunders; Cristina Gazulla; Jo Ann Mundy; Christer Sjöström; Pere Fullana-i-Palmer; Tim Barrow-Williams; Anna Braune; et al. (The International Journal of Life Cycle Assessment, 2014)

Lime Treatment: New Perspectives for the use of Silty and Clayey Soils in Earthen Hydraulic Structures - Gontran Herrier; Daniel Puiatti; Christophe Chevalier; Michel Froumentin; Stéphane Bonelli; Jean-Jacques Fry (WASSERWIRTSCHAFT, 2013)

Classification in LCA: Building of a coherent family of criteria - Jacques Chevalier; Patrick Rousseaux (The International Journal of Life Cycle Assessment, 1999)