Identification of genetically and oceanographically distinct blooms of jellyfish

Reports of nuisance jellyfish blooms have increased worldwide during the last half-century, but the possible causes remain unclear.Apersistent difficulty lies in identifying whether blooms occur owing to local or regional processes. This issue can be resolved, in part, by establishing the geographical scales of connectivity among locations, which may be addressed using genetic analyses and oceanographic modelling. We used landscape genetics and Lagrangian modelling of oceanographic dispersal to explore patterns of connectivity in the scyphozoan jellyfish Rhizostoma octopus, which occurs en masse at locations in the Irish Sea and northeastern Atlantic. We found significant genetic structure distinguishing three populations, with both consistencies and inconsistencies with prevailing physical oceanographic patterns. Our analyses identify locations where blooms occur in apparently geographically isolated populations, locations where blooms may be the source or result of migrants, and a location where blooms do not occur consistently and jellyfish are mostly immigrant. Our interdisciplinary approach thus provides a means to ascertain the geographical origins of jellyfish in outbreaks, which may have wide utility as increased international efforts investigate jellyfish blooms. © 2013 The Authors.

Application-based workload model for wireless sensor node computing platforms

Wireless sensor node platforms are very diversified and very constrained, particularly in power consumption. When choosing or sizing a platform for a given application, it is necessary to be able to evaluate in an early design stage the impact of those choices. Applied to the computing platform implemented on the sensor node, it requires a good understanding of the workload it must perform. Nevertheless, this workload is highly application-dependent. It depends on the data sampling frequency together with application-specific data processing and management. It is thus necessary to have a model that can represent the workload of applications with various needs and characteristics. In this paper, we propose a workload model for wireless sensor node computing platforms. This model is based on a synthetic application that models the different computational tasks that the computing platform will perform to process sensor data. It allows to model the workload of various different applications by tuning data sampling rate and processing. A case study is performed by modeling different applications and by showing how it can be used for workload characterization. © 2011 IEEE.

PROCÉDÉ DE DÉTECTION DE BIOPRODUITS À L'AIDE D'UN DÉTECTEUR PAR RÉSONANCE PLASMONIQUE DE SURFACE LOCALISÉE DE NANOPARTICULES D'OR

L'invention porte sur un procédé consistant à détecter des bioproduits à l'aide d'une résonance plasmonique de surface localisée (LSPR) de nanoparticules d'or. Ledit procédé peut diagnostiquer des bioproduits sur la base de changements de la longueur d'onde maximale produite par une réaction antigène-anticorps après immobilisation des nanoparticules d'or sur un panneau en verre. Un détecteur utilisant un tel procédé présente une sensibilité élevée, est peu coûteux, permet un diagnostic rapide, et il peut ainsi être appliqué à divers domaines biologiques associés aux contaminants environnementaux, pathogènes et similaires, ainsi qu'au diagnostic de maladies. L'invention porte en outre sur une technologie pour fabriquer un détecteur dont la sensibilité est élevée, le prix faible et la performance rapide, par comparaison avec des procédés classiques utilisant SPR.