Molecular characterization and fumonisin production by Fusarium verticillioides isolated from corn grains of different geographic origins in Brazil

Gibberella moniliformis is most commonly associated with maize worldwide and produces high levels of fumonisins, some of the most agriculturally important mycotoxins. Studies demonstrate that molecular methods can be helpful for a rapid identification of Fusarium species and their levels of toxin production. The purpose of this research was to apply molecular methods (AFLP, TEF-1 alpha partial gene sequencing and PCR based on MAT alleles) for the identification of Fusarium species isolated from Brazilian corn and to verify if real time RT-PCR technique based on FUM1 and FUM19 genes is appropriated to estimate fumonisins B(1) and B(2) production levels. Among the isolated strains, 96 were identified as Fusarium verricillioides, and four as other Fusarium species. Concordant phylogenies were obtained by AFLP and TEF-1 alpha sequencing, permitting the classification of the different species into distinct clades. Concerning MAT alleles, 70% of the F. verricillioides isolates carried the MAT-1 and 30% MAT-2. A significant correlation was observed between the expression of the genes and toxin production r=0.95 and r=0.79 (correlation of FUM1 with FB(1) and FB(2), respectively, P < 0.0001): r=0.93 and r =0.78 (correlation of FUM19 with FB(1) and FB(2). respectively, P < 0.0001). Molecular methods used in this study were found to be useful for the rapid identification of Fusarium species. The high and significant correlation between FUM1 and FUM19 expression and fumonisins production suggests that real time RT-PCR is suitable for studies considering the influence of abiotic and biotic factors on expression of these genes. This is the first report concerning the expression of fumonisin biosynthetic genes in Fusarium strains isolated from Brazilian agricultural commodity. (c) 2010 Elsevier B.V. All rights reserved.

A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.

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.