EEG signal classification for epilepsy diagnosis via optimum path forest - A systematic assessment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

USE OF PROGENY TESTING IN BEEF-CATTLE - PREDICTION OF GENETIC GAIN IN A NELORE CATTLE-BREEDING PROGRAM

Genetic gains predicted for selection, based on both individual performance and progeny testing, were compared to provide information to be used in implementation of progeny testing for a Nelore cattle breeding program. The prediction of genetic gain based on progeny testing was obtained from a formula, derived from methodology of Young and weller (J. Genetics 57: 329-338, 1960) for two-stage selection, which allows prediction of genetic gain per generation when the individuals under test have been pre-selected on the basis of their own performance. The application of this formula also allowed determination of the number of progeny per tested bull needed to maximize genetic gain, when the total number of tested progeny is limited.

Intraspecific variability in the basal metabolic rate: Testing the food habits hypothesis

Several competing hypotheses attempt to explain how environmental conditions affect mass-independent basal metabolic rate (BMR) in mammals. One of the most inclusive and yet debatable hypotheses is the one that associates BMR with food habits, including habitat productivity. These effects have been widely investigated at the interspecific level under the assumption that for any given species all traits are fixed. Consequently, the variation among individuals is largely ignored. Intraspecific analysis of physiological traits has the potential to compensate for many of the pitfalls associated with interspecific analyses and, thus, to be a useful approach for evaluating hypotheses regarding metabolic adaptation. In this study, we investigated the effects of food quality, availability, and predictability on the BMR of the leaf-eared mouse Phyllotis darwini. BMR was measured on freshly caught animals from the field, since they experience natural seasonal variations in environmental factors ( and, hence, variations in habitat productivity) and diet quality. BMR was significantly correlated with the proportion of dietary plants and seeds. In addition, BMR was significantly correlated with monthly habitat productivity. Path analysis indicated that, in our study, habitat productivity was responsible for the observed changes in BMR, while diet per se had no effect on this variable.

Use of progeny testing in beef cattle: Prediction of genetic gain in a Nelore Cattle Breeding Program

Genetic gains predicted for selection, based on both individual performance and progeny testing, were compared to provide information to be used in implementation of progeny testing for a Nelore cattle breeding program. The prediction of genetic gain based on progeny testing was obtained from a formula, derived from methodology of Young and Weiler (J. Genetics 57: 329-338, 1960) for two-stage selection, which allows prediction of genetic gain per generation when the individuals under test have been pre-selected on the basis of their own performance. The application of this formula also allowed determination of the number of progeny per tested bull needed to maximize genetic gain, when the total number of tested progeny is limited.

Structural basis for inhibition of cyclin-dependent kinase 9 by flavopiridol

Flavopiridol has been shown to potently inhibit CDK1 and 2 (cyclin-dependent kinases 1 and 2) and most recently it has been found that it also inhibits CDK9. The complex CDK9-cyclin T1 controls the elongation phase of transcription by RNA polymerase II. The present work describes a molecular model for the binary complex CDK9-flavopiridol. This structural model indicates that the inhibitor strongly binds to the ATP-binding pocket of CDK9 and the structural comparison of the complex CDK2-flavopiridol correlates the structural differences with differences in inhibition of these CDKs by flavopiridol. This structure opens the possibility of testing new inhibitor families, in addition to new substituents for the already known leading structures such as flavones and adenine derivatives. © 2002 Elsevier Science (USA). All rights reserved.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Automatic landslide recognition through Optimum-Path Forest

In this paper we shed light over the problem of landslide automatic recognition using supervised classification, and we also introduced the OPF classifier in this context. We employed two images acquired from Geoeye-MS satellite at March-2010 in the northwest (high steep areas) and north sides (pipeline area) covering the area of Duque de Caxias city, Rio de Janeiro State, Brazil. The landslide recognition rate has been assessed through a cross-validation with 10 runnings. In regard to the classifiers, we have used OPF against SVM with Radial Basis Function for kernel mapping and a Bayesian classifier. We can conclude that OPF, Bayes and SVM achieved high recognition rates, being OPF the fastest approach. © 2012 IEEE.

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

Optimum-Path Forest Applied for Breast Masses Classification

In Computer-Aided Diagnosis-based schemes in mammography analysis each module is interconnected, which directly affects the system operation as a whole. The identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest for further image segmentation. This study aims to evaluate the performance of three techniques in classifying regions of interest as containing masses or without masses (without clinical findings), as well as the main contribution of this work is to introduce the Optimum-Path Forest (OPF) classifier in this context, which has never been done so far. Thus, we have compared OPF against with two sorts of neural networks in a private dataset composed by 120 images: Radial Basis Function and Multilayer Perceptron (MLP). Texture features have been used for such purpose, and the experiments have demonstrated that MLP networks have been slightly better than OPF, but the former is much faster, which can be a suitable tool for real-time recognition systems.

A radial basis function network (RBFN) for function approximation

A radial basis function network (RBFN) circuit for function approximation is presented. Simulation and experimental results show that the network has good approximation capabilities. The RBFN was a squared hyperbolic secant with three adjustable parameters amplitude, width and center. To test the network a sinusoidal and sine function,vas approximated.

An analog implementation of radial basis function network appropriate for transducer linearizer

A circuit for transducer linearizer tasks have been designed and built using discrete components and it implements by: a Radial Basis Function Network (RBFN) with three basis functions. The application in a linearized thermistor showed that the network has good approximation capabilities. The circuit advantages is the amplitude, width and center.