An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

Biochemical dissection of diageotropica and Never ripe tomato mutants to Cd-stressful conditions

In order to further address the modulation of signaling pathways of stress responses and their relation to hormones, we used the ethylene-insensitive Never ripe (Nr) and the auxin-insensitive diageotropica (dgt) tomato mutants. The two mutants and the control Micro-Tom (MT) cultivar were grown over a 40-day period in the presence of Cd (0.2 mM CdCl2 and 1 mM CdCl2). Lipid peroxidation, leaf chlorophyll, praline content, Cd content and antioxidant enzyme activities in roots, leaves and fruits were determined. The overall results indicated that the MT genotype had the most pronounced Cd damage effects while Nr and dgt genotypes might withstand or avoid stress imposed by Cd. This fact may be attributed, at least in part, to the fact that the known auxin-stimulated ethylene production is comprised in dgt plants. Conversely, the Nr genotype was more affected by the Cd imposed stress than dgt, which may be explained by the fact that Nr retains a partial sensitivity to ethylene. These results add further information that should help unraveling the relative importance of ethylene in regulating the cell responses to stressful conditions. (C) 2012 Elsevier Masson SAS. All rights reserved.

Sugarcane Growth Promotion by the Endophytic Bacterium Pantoea agglomerans 33.1

The promotion of sugarcane growth by the endophytic Pantoea agglomerans strain 33.1 was studied under gnotobiotic and greenhouse conditions. The green fluorescent protein (GFP)-tagged strain P. agglomerans 33.1: pNKGFP was monitored in vitro in sugarcane plants by microscopy, reisolation, and quantitative PCR (qPCR). Using qPCR and reisolation 4 and 15 days after inoculation, we observed that GFP-tagged strains reached similar density levels both in the rhizosphere and inside the roots and aerial plant tissues. Microscopic analysis was performed at 5, 10, and 18 days after inoculation. Under greenhouse conditions, P. agglomerans 33.1-inoculated sugarcane plants presented more dry mass 30 days after inoculation. Cross-colonization was confirmed by reisolation of the GFP-tagged strain. These data demonstrate that 33.1:pNKGFP is a superior colonizer of sugarcane due to its ability to colonize a number of different plant parts. The growth promotion observed in colonized plants may be related to the ability of P. agglomerans 33.1 to synthesize indoleacetic acid and solubilize phosphate. Additionally, this strain may trigger chitinase and cellulase production by plant roots, suggesting the induction of a plant defense system. However, levels of indigenous bacterial colonization did not vary between inoculated and noninoculated sugarcane plants under greenhouse conditions, suggesting that the presence of P. agglomerans 33.1 has no effect on these communities. In this study, different techniques were used to monitor 33.1:pNKGFP during sugarcane cross-colonization, and our results suggested that this plant growth promoter could be used with other crops. The interaction between sugarcane and P. agglomerans 33.1 has important benefits that promote the plant's growth and fitness.

Modeling and simulation of severe slugging with mass transfer effects

A mathematical model and numerical simulations are presented to investigate the dynamics of gas, oil and water flow in a pipeline-riser system. The pipeline is modeled as a lumped parameter system and considers two switchable states: one in which the gas is able to penetrate into the riser and another in which there is a liquid accumulation front, preventing the gas from penetrating the riser. The riser model considers a distributed parameter system, in which movable nodes are used to evaluate local conditions along the subsystem. Mass transfer effects are modeled by using a black oil approximation. The model predicts the liquid penetration length in the pipeline and the liquid level in the riser, so it is possible to determine which type of severe slugging occurs in the system. The method of characteristics is used to simplify the differentiation of the resulting hyperbolic system of equations. The equations are discretized and integrated using an implicit method with a predictor-corrector scheme for the treatment of the nonlinearities. Simulations corresponding to severe slugging conditions are presented and compared to results obtained with OLGA computer code, showing a very good agreement. A description of the types of severe slugging for the three-phase flow of gas, oil and water in a pipeline-riser system with mass transfer effects are presented, as well as a stability map. (C) 2011 Elsevier Ltd. All rights reserved.

What can light scattering spectroscopy do for membrane-active peptide studies

Highly charged peptides are important components of the immune system and belong to an important family of antibiotics. Although their therapeutic activity is known, most of the molecular level mechanisms are controversial. A wide variety of different approaches are usually applied to understand their mechanisms, but light scattering techniques are frequently overlooked. Yet, light scattering is a noninvasive technique that allows insights both on the peptide mechanism of action as well as on the development of new antibiotics. Dynamic light scattering (DLS) and static light scattering (SLS) are used to measure the aggregation process of lipid vesicles upon addition of peptides and molecular properties (shape, molecular weight). The high charge of these peptides allows electrostatic attraction toward charged lipid vesicles, which is studied by zeta potential (zeta-potential) measurements. Copyright (c) 2008 European Peptide Society and John Wiley & Sons, Ltd.

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks

Abstract Background Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. Methods The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Results Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). Conclusions An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.