Set up of a methodology for participatory plant breeding in bread wheat in France

In Organic Agriculture, cultivation environments and agronomic practices are very diverse. This diversity can be handled with decentralized selection based on the knowledge of farmers and scientists. A collaborative work between associations from Réseau Semences Paysannes and the DEAP team from INRA du Moulon set up an innovative breeding approach on farm based on decentralization and participation of farmers. This approach makes it possible to (i) create new population varieties of bread wheat locally adapted (genetic innovation) (ii) set up an organizational scheme based on decentralization and co construction between actors (societal innovation) and (iii) develop experimental designs, create statistical and data management tools which stimulate these genetic and societal innovations

Learning Multi-Modal Dictionaries

Abstract—Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms. Index Terms—Audiovisual source localization, dictionary learning, multimodal data processing, sparse representation

‘Marseilla massiliensis’ gen. nov., sp. nov., isolated from human right colon

International audienceno abstrac

Corrigendum to “‘Bacteroides mediterraneensis’ sp. nov., a new human-associated bacterium isolated from ileum specimen”

International audienc

Corrigendum to ‘"Mediterranea massiliensis" gen. nov., sp. nov., a new human-associated bacteria isolated from the right and left colon lavage of a 58-year-old patient’

International audienc

“Ihuprevotella massiliensis” gen. nov., sp. nov., isolated from human gut

We report here the main characteristics of “Ihuprevotella massiliensis” strain Marseille-P2826T (CSURP 2826) that was isolated from a human right colon lavage sample

Primer Vector Optimization: Survey of Theory, New Analysis and Applications

In this paper, a summary of primer vector theory is presented. The applicability of primer vector theory is examined in an effort to understand when and why the theory can fail. For example, since the Calculus of Variations is based on "small" variations, singularities in the linearized (variational) equations of motion along the arcs must be taken into account. These singularities are a recurring problem in analyse that employ small variations. Two examples, the initialization of an orbit and a line of apsides rotation, are presented. Recommendations, future work, and the possible addition of other optimization techniques are also discussed