Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Higher incidence of death in multi-vessel coronary artery disease patients associated with polymorphisms in chromosome 9p21

Background: We investigated whether 9p21 polymorphisms are associated with cardiovascular events in a group of 611 patients enrolled in the Medical, Angioplasty or Surgery Study II (MASS II), a randomized trial comparing treatments for patients with coronary artery disease (CAD) and preserved left ventricular function. Methods: The participants of the MASS II were genotyped for 9p21 polymorphisms (rs10757274, rs2383206, rs10757278 and rs1333049). Survival curves were calculated with the Kaplan-Meier method and compared with the log-rank statistic. We assessed the relationship between baseline variables and the composite end-point of death, death from cardiac causes and myocardial infarction using a Cox proportional hazards survival model. Results: We observed significant differences between patients within each polymorphism genotype group for baseline characteristics. The frequency of diabetes was lower in patients carrying GG genotype for rs10757274, rs2383206 and rs10757278 (29.4%, 32.8%, 32.0%) compared to patients carrying AA or AG genotypes (49.1% and 39.2%, p = 0.01; 52.4% and 40.1%, p = 0.01; 47.8% and 37.9%, p = 0.04; respectively). Significant differences in genotype frequencies between double and triple vessel disease patients were observed for the rs10757274, rs10757278 and rs1333049. Finally, there was a higher incidence of overall mortality in patients with the GG genotype for rs2383206 compared to patients with AA and AG genotypes (19.5%, 11.9%, 11.0%, respectively; p = 0.04). Moreover, the rs2383206 was still significantly associated with a 1.75-fold increased risk of overall mortality (p = 0.02) even after adjustment of a Cox multivariate model for age, previous myocardial infarction, diabetes, smoking and type of coronary anatomy. Conclusions: Our data are in accordance to previous evidence that chromosome 9p21 genetic variation may constitute a genetic modulator in the cardiovascular system in different scenarios. In patients with established CAD, we observed an association between the rs2383206 and higher incidence of overall mortality and death from cardiac causes in patients with multi-vessel CAD.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Metal biosorption onto dry biomass of Arthrospira (Spirulina) platensis and Chlorella vulgaris: Multi-metal systems

Binary and ternary systems of Ni2+, Zn2+, and Pb2+ were investigated at initial metal concentrations of 0.5, 1.0 and 2.0 mM as competitive adsorbates using Arthrospira platensis and Chlorella vulgaris as biosorbents. The experimental results were evaluated in terms of equilibrium sorption capacity and metal removal efficiency and fitted to the multi-component Langmuir and Freundlich isotherms. The pseudo second order model of Ho and McKay described well the adsorption kinetics, and the FT-IR spectroscopy confirmed metal binding to both biomasses. Ni2+ and Zn2+ interference on Pb2+ sorption was lower than the contrary, likely due to biosorbent preference to Pb. In general, the higher the total initial metal concentration, the lower the adsorption capacity. The results of this study demonstrated that dry biomass of C. vulgaris behaved as better biosorbent than A. platensis and suggest its use as an effective alternative sorbent for metal removal from wastewater. (C) 2012 Elsevier B.V. All rights reserved.