Automated diagnosis of human intestinal parasites using optical microscopy images

Intestinal parasitosis constitutes a serious health problem in most tropical countries. The diagnosis of enteroparasites in laboratory routine relies on the examination of stool samples using optical microscopy and the error rates usually range from moderate to high. Approaches based on automatic image analysis have been proposed, but the methods are usually specific for some species, some of them are computationally expensive, and image acquisition and focus are manual. We present a solution to automate the diagnosis of the 15 most common species of enteroparasites in Brazil, using a sensitive parasitological technique, a motorized microscope with digital camera for automatic image acquisition and focus, and fast image analysis methods. The results indicate that our solution is effective and suitable for laboratory routine, in which the exam must be concluded in a few minutes. © 2013 IEEE.

Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.

Binary Bat Algorithm for Feature Selection

Feature selection aims to find the most important information to save computational efforts and data storage. We formulated this task as a combinatorial optimization problem since the exponential growth of possible solutions makes an exhaustive search infeasible. In this work, we propose a new nature-inspired feature selection technique based on bats behavior, namely, binary bat algorithm The wrapper approach combines the power of exploration of the bats together with the speed of the optimum-path forest classifier to find a better data representation. Experiments in public datasets have shown that the proposed technique can indeed improve the effectiveness of the optimum-path forest and outperform some well-known swarm-based techniques. © 2013 Copyright © 2013 Elsevier Inc. All rights reserved.

Parâmetros genéticos entre caracteres quantitativos no girassol como critério de seleção para produtividade de aquênios

Two experiments were conducted in southern Brazil in no-tillage system, in order to estimate genetic parameters and the direct and indirect effects of components for achene yield as a selection criterion in sunflower. We analyzed eight sunflower hybrids at two locations, in a randomized complete block design with four replicates, determined through quantitative descriptors: insertion height of the head, and head stem diameter, weight of 1000 achenes, number of achenes per head, mass by achene head and yield achene. Estimates of genetic parameters were based on combined analysis, decomposing interactions in genetic and environmental components. Considering the coefficient of genetic variation, indirect effects of components and heritability, there are higher possibilities for responses to selection in sunflower achenes by descriptors mass and mass of achenes per head, with its indirect association interrelated pathways for the increase in the achenes of yield.

BCS: A Binary Cuckoo Search algorithm for feature selection

Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques. © 2013 IEEE.

A hybrid image restoration algorithm based on Projections onto Convex Sets and Harmony Search

Image restoration is a research field that attempts to recover a blurred and noisy image. Since it can be modeled as a linear system, we propose in this paper to use the meta-heuristics optimization algorithm Harmony Search (HS) to find out near-optimal solutions in a Projections Onto Convex Sets-based formulation to solve this problem. The experiments using HS and four of its variants have shown that we can obtain near-optimal and faster restored images than other evolutionary optimization approach. © 2013 IEEE.

Optimizing feature selection through binary charged system search

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be inviable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the Charged System Search (CSS), which has never been applied to this context so far. The wrapper approach combines the power of exploration of CSS together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in four public datasets have demonstrated the validity of the proposed approach can outperform some well-known swarm-based techniques. © 2013 Springer-Verlag.

Deciphering the synergism of endogenous glycoside hydrolase families 1 and 9 from Coptotermes gestroi

Termites can degrade up to 90% of the lignocellulose they ingest using a repertoire of endogenous and symbiotic degrading enzymes. Termites have been shown to secrete two main glycoside hydrolases, which are GH1 (EC 3.2.1.21) and GH9 (EC 3.2.1.4) members. However, the molecular mechanism for lignocellulose degradation by these enzymes remains poorly understood. The present study was conducted to understand the synergistic relationship between GH9 (CgEG1) and GH1 (CgBG1) from Coptotermes gestroi, which is considered the major urban pest of São Paulo State in Brazil. The goal of this work was to decipher the mode of operation of CgEG1 and CgBG1 through a comprehensive biochemical analysis and molecular docking studies. There was outstanding degree of synergy in degrading glucose polymers for the production of glucose as a result of the endo-β-1,4-glucosidase and exo-β-1,4-glucosidase degradation capability of CgEG1 in concert with the high catalytic performance of CgBG1, which rapidly converts the oligomers into glucose. Our data not only provide an increased comprehension regarding the synergistic mechanism of these two enzymes for cellulose saccharification but also give insight about the role of these two enzymes in termite biology, which can provide the foundation for the development of a number of important applied research topics, such as the control of termites as pests as well as the development of technologies for lignocellulose-to-bioproduct applications. © 2013 Elsevier Ltd.

Automatic segmentation and classification of human intestinal parasites from microscopy images

Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis. © 2012 IEEE.

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.

Emissão de gases de efeito estufa em áreas de sistema semeadura direta com aportes diferenciados de carbono e nitrogênio

Pós-graduação em Agronomia (Agricultura) - FCA

Modelagem de distorções entre realizações de referenciais geodésicos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise teórica e experimental de um método interferométrico de detecção de fase óptica, auto-consistente e com elevada faixa dinâmica, aplicado à caracterização de atuadores piezoelétricos flextensionais

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Similaridade cariotípica entre Barycholos ternetzi e cinco espécies do gênero Eleutherodactylus do Sudoeste do Brasil (Anura, Leptodactylidae, Eleutherodactylinae)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Simulação computacional do escoamento bifásico com formação de espuma da mistura óleo-refrigerante R134a ao longo de um tubo reto de seção circular constante

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)