2 resultados para Français (Langue)--Français parlé--Étude et enseignement (Secondaire)--Matériel didactique
em Archimer: Archive de l'Institut francais de recherche pour l'exploitation de la mer
Resumo:
Le présent rapport est une nouvelle version du rapport intitulé « Estimation de l’exploitation halieutique des navires de pêche français ». Il restitue les résultats des traitements des « données VMS » en offrant une précision spatiale accrue en matière d’occupation de l’espace marin, d’origine géographique des débarquements, et de dépendance des navires à certains secteurs de pêche.
Resumo:
The marine diatom Haslea ostrearia [1] produces a water-soluble blue-pigment named marennine [2] of economic interest. But the lack of knowledge of the ecological conditions, under which this microalga develops in its natural ecosystem, more especially bacteria H. ostrearia interactions, prevents any optimization of its culture in well-controlled conditions. The structure of the bacterial community was analyzed by PCR-TTGE before and after the isolation of H. ostrearia cells recovered from 4 localities, to distinguish the relative part of the biotope and the biocenose and eventually to describe the temporal dynamic of the structure of the bacterial community at two time-scales. The differences in genetic fingerprints, more especially high between two H. ostrearia isolates (HO-R and HO-BM) showed also the highest differences in the bacterial structure [3] as the result of specific metabolomics profiles. The non-targeted metabolomic investigation showed that these profiles were more distinct in case of bacteria-alga associations than for the H. ostrearia monoculture Here we present a Q-TOF LC/MS metabolomic fingerprinting approach [3]: - to investigate differential metabolites of axenic versus non axenic H. ostrearia cultures. - to focus on the specific metabolites of a bacterial surrounding associated with the activation or inhibition of the microalga growing. The Agilent suite of data processing software makes feature finding, statistical analysis, and identification easier. This enables rapid transformation of complex raw data into biologically relevant metabolite information.