Permanent Genetic Resources added to Molecular Ecology Resources Database 1 May 2009-31 July 2009

This article documents the addition of 512 microsatellite marker loci and nine pairs of Single Nucleotide Polymorphism (SNP) sequencing primers to the Molecular Ecology Resources Database. Loci were developed for the following species: Alcippe morrisonia morrisonia, Bashania fangiana, Bashania fargesii, Chaetodon vagabundus, Colletes floralis, Coluber constrictor flaviventris, Coptotermes gestroi, Crotophaga major, Cyprinella lutrensis, Danaus plexippus, Fagus grandifolia, Falco tinnunculus, Fletcherimyia fletcheri, Hydrilla verticillata, Laterallus jamaicensis coturniculus, Leavenworthia alabamica, Marmosops incanus, Miichthys miiuy, Nasua nasua, Noturus exilis, Odontesthes bonariensis, Quadrula fragosa, Pinctada maxima, Pseudaletia separata, Pseudoperonospora cubensis, Podocarpus elatus, Portunus trituberculatus, Rhagoletis cerasi, Rhinella schneideri, Sarracenia alata, Skeletonema marinoi, Sminthurus viridis, Syngnathus abaster, Uroteuthis (Photololigo) chinensis, Verticillium dahliae, Wasmannia auropunctata, and Zygochlamys patagonica. These loci were cross-tested on the following species: Chaetodon baronessa, Falco columbarius, Falco eleonorae, Falco naumanni, Falco peregrinus, Falco subbuteo, Didelphis aurita, Gracilinanus microtarsus, Marmosops paulensis, Monodelphis Americana, Odontesthes hatcheri, Podocarpus grayi, Podocarpus lawrencei, Podocarpus smithii, Portunus pelagicus, Syngnathus acus, Syngnathus typhle,Uroteuthis (Photololigo) edulis, Uroteuthis (Photololigo) duvauceli and Verticillium albo-atrum. This article also documents the addition of nine sequencing primer pairs and sixteen allele specific primers or probes for Oncorhynchus mykiss and Oncorhynchus tshawytscha; these primers and assays were cross-tested in both species.

A multi-GPU algorithm for large-scale neuronal networks

Large-scale simulations of parts of the brain using detailed neuronal models to improve our understanding of brain functions are becoming a reality with the usage of supercomputers and large clusters. However, the high acquisition and maintenance cost of these computers, including the physical space, air conditioning, and electrical power, limits the number of simulations of this kind that scientists can perform. Modern commodity graphical cards, based on the CUDA platform, contain graphical processing units (GPUs) composed of hundreds of processors that can simultaneously execute thousands of threads and thus constitute a low-cost solution for many high-performance computing applications. In this work, we present a CUDA algorithm that enables the execution, on multiple GPUs, of simulations of large-scale networks composed of biologically realistic Hodgkin-Huxley neurons. The algorithm represents each neuron as a CUDA thread, which solves the set of coupled differential equations that model each neuron. Communication among neurons located in different GPUs is coordinated by the CPU. We obtained speedups of 40 for the simulation of 200k neurons that received random external input and speedups of 9 for a network with 200k neurons and 20M neuronal connections, in a single computer with two graphic boards with two GPUs each, when compared with a modern quad-core CPU. Copyright (C) 2010 John Wiley & Sons, Ltd.