The ParTriCluster Algorithm for Gene Expression Analysis

Analyzing gene expression patterns is becoming a highly relevant task in the Bioinformatics area. This analysis makes it possible to determine the behavior patterns of genes under various conditions, a fundamental information for treating diseases, among other applications. A recent advance in this area is the Tricluster algorithm, which is the first algorithm capable of determining 3D clusters (genes × samples × timestamps), that is, groups of genes that behave similarly across samples and timestamps. However, even though biological experiments collect an increasing amount of data to be analyzed and correlated, the triclustering problem remains a bottleneck due to its NP-Completeness, so its parallelization seems to be an essential step towards obtaining feasible solutions. In this work we propose and evaluate the implementation of a parallel version of the Tricluster algorithm using the filter-labeled-stream paradigm supported by the Anthill parallel programming environment. The results show that our parallelization scales well with the data size, being able to handle severe load imbalances that are inherent to the problem. Further more, the parallelization strategy is applicable to any depth-first searches.     

On the coordinator's rule for Fast Paxos

Fast Paxos is an algorithm for consensus that works by a succession of rounds, where each round tries to decide a value v that is consistent with all past rounds. Rounds are started by a coordinator process and consistency is guaranteed by the rule used by this process for the selection of v and by the properties of process sets called quorums. We show a simplified version of this rule for the specific case where the quorums are defined by the cardinality of these process sets. This rule is of special interest for implementors of the algorithm.     

Tracking human faecal contamination in tropical reservoirs in Puerto Rico

Using a combination of chemical and microbiological (culture-dependent and -independent) approaches, sources of human faecal contamination were identified in two water reservoirs in Puerto Rico – Guajataca and La Plata. Fluorescence from optical brighteners (OB) – commonly found in laundry detergents – was used as an indicator of contamination from septic systems and other household discharges. Traditional indicators of faecal contamination (e.g. Escherichia coli; faecal enterococci) were enumerated, and human faecal contamination was confirmed through detection of Bifidobacterium adolescentis utilizing polymerase chain reaction (PCR)-based analyses. For Guajataca Reservoir, four of 19 sampling sites (21%) were positive for the presence of B. adolescentis under baseflow conditions. The OB fluorescence data suggested that the most likely source for three of these sites was residential, whereas the source of contamination at the remaining site, although of human origin, was probably non-residential. B. adolescentis was present in 83% (19 of 23) of the sampling sites in La Plata. The La Plata sources were more difficult to identify because samples were taken under stormflow conditions, although the presence of OB fluorescence suggested a residential origin in a number of instances. OB fluorescence and traditional bacterial indicators of faecal contamination produced a number of false positive and negative findings for both reservoirs, pointing to the importance of understanding the limitations of these tools for tropical freshwater systems. The results of this study should be useful in developing a weight-of-evidence approach for the identification of potential sources and extent of human faecal contamination in similar tropical reservoirs, a necessary step in the development of management plans to reduce or eliminate these sources.     

Development of regression equations for predicting energy and hygrothermal performance of buildings

Regression equations can be used for predicting indoor air temperature, relative humidity and energy consumption in an easier and more rapid way than building energy simulation tools. The independent variables, that is, the input data, are heating, ventilation and air conditioning (HVAC) power, outdoor temperature, relative humidity and total solar radiation. The present methodology for obtaining the regression equations is based on defining a couple of linear Multiple-Input/Single-Output (MISO) models, since two main outputs are involved, that is, indoor temperature and relative humidity. The methodology has been tested for the low- and high-thermal mass cases of the BESTest model (cases 600 and 900) and the output data is generated by using a building hygrothermal simulation tool. Validation procedures have shown very good agreement between the regression equations and the simulation tool for both winter and summer periods.     

Ultra-deep oxidative desulfurization of diesel fuel by the Mo/Al2O3-H2O2 system: The effect of system parameters on catalytic activity

This work presents the results obtained in the development of Mo/γ-Al₂O₃ catalysts and their evaluation in the oxidative desulfurization (OD) process of diesel fuel using hydrogen peroxide as the oxidizing reagent. The catalysts were prepared by equilibrium adsorption using several molybdenum precursors and aluminas with different acidity values. They were characterized by Raman spectroscopy. The effect of the reaction time, reaction temperature, nature of solvent, concentration of solvent and hydrogen peroxide, content of molybdenum and phosphate in the catalysts were investigated. The results showed that the activity for sulfur elimination depends mainly on the presence of hepta- and octamolybdates species on the catalyst support and the use of a polar aprotic solvent. Likewise, the presence of phosphate markedly increases the sulfur elimination. In this way, it is possible to reduce sulfur level in diesel fuel from about 320 to less than 10 ppmw at 333 K and atmospheric pressure. Additionally, on the basis of the results obtained a mechanistic proposal for this reaction is described, as an oxidation mechanism by nucleophilic attack of the sulfur atom on peroxo species of hepta- and octamolybdates, but a mechanism involving the singlet oxygen presence can be discarded.     

Grouped Approach for the Design of H.264/AVC Motion Estimation Architectures

This letter presents a novel approach for organizing computational resources into groups within H.264/AVC motion estimation architectures, leading to reductions of up to 75% in the equivalent gate count with respect to state‐of‐the‐art designs.     

Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks

The success of hydraulic simulation models of water distribution networks is associated with the ability of these models to represent real systems accurately. To achieve this, the calibration phase is essential. Current calibration methods are based on minimizing the error between measured and simulated values of pressure and flow. This minimization is based on a search of parameter values to be calibrated, including pipe roughness, nodal demand, and leakage flow. The resulting hydraulic problem contains several variables. In addition, a limited set of known monitored pressure and flow values creates an indeterminate problem with more variables than equations. Seeking to address the lack of monitored data for the calibration of Water Distribution Networks (WDNs), this paper uses a meta-model based on an Artificial Neural Network (ANN) to estimate pressure on all nodes of a network. The calibration of pipe roughness applies a metaheuristic search method called Particle Swarm Optimization (PSO) to minimize the objective function represented by the difference between simulated and forecasted pressure values. The proposed method is evaluated at steady state and over an extended period for a real District Metering Area (DMA), named Campos do Conde II, and the hypothetical network named C-town, which is used as a benchmark for calibration studies.     

Contents of volume 49 (1991)

Contents of volume 49 (1991)     

Behavior of the high frequency Raman modes of double-wall carbon nanotubes after doping with bromine or iodine vapors

The effects of two different halogen dopants (bromine and iodine) at different concentrations on the higher frequency modes (the so-called G and G′ bands) of the Raman spectra of double-wall carbon nanotube (DWCNT) “buckypaper” are investigated. The effects of dopants on different DWCNT configurations (metallic inner/semiconducting outer and vice versa) are studied by changing the laser excitation energy. The doping causes the loss of the Breit–Wigner–Fano line shape and the appearance of less metallic behavior. An increase of the relative intensity of the G⁺ band, which is more sensitive for the outer metallic tubes, is clearly observed with increasing Br₂ concentration in the sample. By analysis of the G⁺ band and the G′ band it is possible to measure the changes in the electron–phonon coupling, due to the charge-transfer between the dopant (Br₂ or I₂) and the tubes in the DWCNT. The doping effect causes an upshift of the G⁺ band and a suppression of the contribution of the inner tubes to the G′ band signal and as a consequence, the observed G′ band is dominated by the contribution from the outer tubes.     

Activated Alpha-2 Macroglobulin Improves Insulin Response via LRP1 in Lipid-Loaded HL-1 Cardiomyocytes

Activated alpha-2 Macroglobulin (α₂M*) is specifically recognized by the cluster I/II of LRP1 (Low-density lipoprotein Receptor-related Protein-1). LRP1 is a scaffold protein for insulin receptor involved in the insulin-induced glucose transporter type 4 (GLUT4) translocation to plasma membrane and glucose uptake in different types of cells. Moreover, the cluster II of LRP1 plays a critical role in the internalization of atherogenic lipoproteins, such as aggregated Low-density Lipoproteins (aggLDL), promoting intracellular cholesteryl ester (CE) accumulation mainly in arterial intima and myocardium. The aggLDL uptake by LRP1 impairs GLUT4 traffic and the insulin response in cardiomyocytes. However, the link between CE accumulation, insulin action, and cardiac dysfunction are largely unknown. Here, we found that α₂M* increased GLUT4 expression on cell surface by Rab4, Rab8A, and Rab10-mediated recycling through PI₃K/Akt and MAPK/ERK signaling activation. Moreover, α₂M* enhanced the insulin response increasing insulin-induced glucose uptake rate in the myocardium under normal conditions. On the other hand, α₂M* blocked the intracellular CE accumulation, improved the insulin response and reduced cardiac damage in HL-1 cardiomyocytes exposed to aggLDL. In conclusion, α₂M* by its agonist action on LRP1, counteracts the deleterious effects of aggLDL in cardiomyocytes, which may have therapeutic implications in cardiovascular diseases associated with hypercholesterolemia.