Efferent controls in crustacean mechanoreceptors

International audienceSince the 1960s it has been known that central neural networks can elaborate motor patterns in the absence of any sensory feedback. However, sensory and neuromodulatory inputs allow the animal to adapt the motor command to the actual mechanical configuration or changing needs. Many studies in invertebrates, particularly in crustacea, have described several mechanisms of sensory-motor integration and have shown that part of this integration was supported by the efferent control of the mechanosensory neurons themselves. In this article, we review the findings that support such an efferent control of mechanosensory neurons in crustacea. Various types of crustacean proprioceptors feeding information about joint movements and strains to central neural networks are considered, together with evidence of efferent controls exerted on their sensory neurons. These efferent controls comprise (1) the neurohormonal modulation of the coding properties of sensory neurons by bioamines and peptides; (2) the presynaptic inhibition of sensory neurons by GABA, glutamate and histamine; and (3) the long-term potentiation of sensory-motor synapses by glutamate. Several of these mechanisms can coexist on the same sensory neuron, and the functional significance of such multiple modulations is discussed

Peptoniphilus duodeni sp. nov., a new bacterial species identified in human duodenum

We present here the main characteristics of Peptoniphilus duodeni strain Marseille-P2932 (= CSUR P2932, = DSM 103346) that was isolated from the duodenum of a 60-year-old male. Keywords: Culturomics, Taxonogenomics, Gut microbiota, Human gut, Peptoniphilus duoden

Actinomyces bouchesdurhonensis sp. nov. and Actinomyces mediterranea sp. nov., isolated from human stomach and duodenum

We propose here the main characteristics of the new bacteria Actinomyces bouchesdurhonensis strain Marseille-P2825 (CSUR P2825) isolated from a gastric liquid sample of a 60-year-old man and Actinomyces mediterranea strain Marseille-P3257 (CSUR P3257) isolated from a duodenal liquid sample of a 76-year-old woman. Keywords: Culturomics, Taxonogenomics, Gut microbiota, Actinomyces bouchesdurhonensis, Actinomyces mediterrane

IniRobot: Activités robotiques avec Thymio II pour l’initiation à l’informatique et à la robotique

Des activités robotiques pour l'initiation aux sciences du numériqueUne série d'activités d'initiation aux sciences du numérique, basée sur le robot Thymio 2 et une démarche d'investigation

IniRobot: Activités robotiques avec Thymio II pour l’initiation à l’informatique et à la robotique

Des activités robotiques pour l'initiation aux sciences du numériqueUne série d'activités d'initiation aux sciences du numérique, basée sur le robot Thymio 2 et une démarche d'investigation

Trophic level-based indicators to track fishing impacts across marine ecosystems

Trophic level (TL)-based indicators have been widely used to examine fishing impacts in aquatic ecosystems and the induced biodiversity changes. However, much debate has ensued regarding discrepancies and challenges arising from the use of landings data from commercial fisheries to calculate TL indicators. Subsequent studies have started to examine survey-based and model-based indicators. In this paper, we undertake an extensive evaluation of a variety of TL indicators across 9 well-studied marine ecosystems by making use of model- as well as survey and catch-based TL indicators. Using detailed regional information and data on fishing history, fishing intensity, and environmental conditions, we evaluate how well TL indicators are capturing fishing effects at the community level of marine ecosystems. Our results highlight that the differences observed between TL indicator values and trends is dependent on the data source and the TL cut-off point used in the calculations and is not attributable to an intrinsic problem with TL based indicators. All 3 data sources provide useful information about the structural changes in the ecosystem as a result of fishing, but our results indicate that only model-based indicators represent fishing impacts at the whole ecosystem level.JRC.H.1-Water Resource