Pintura Naïve: características e análises - quatro exemplos em São Paulo

Pós-graduação em Artes - IA

Modelos para dados grupados e sensurados aplicados à área biológica: comparação usando fator de Bayes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Aquatic weed automatic classification using machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

What Role Should Government Regulation Play in Ecological Restoration? Ongoing Debate in São Paulo State, Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Características produtivas de novilhas submetidas à restrição alimentar e dieta suplementada com vitaminas D e E

O objetivo deste trabalho foi avaliar os efeitos da restrição alimentar e de dieta de suplementação com vitaminas D e E sobre o desempenho e características de carcaça de novilhas Canchim. Vinte e quatro fêmeas, com peso vivo inicial médio de 251,95±18,49 kg, foram distribuídas em quatro tratamentos: alimentação em quantidade restrita e não restrita, com e sem suplementação de vitaminas D e E. Utilizou-se o delineamento inteiramente casualizado, em arranjo fatorial 2x2, com seis repetições. Os animais com alimentação restrita receberam, por 48 dias, 70% da dieta fornecida ao grupo de alimentação não restrita. Após o período de restrição (peso médio de 300,10 kg), eles voltaram a receber ração à vontade por mais 77 dias, até atingirem peso de abate de 380 kg. As avaliações foram feitas ao final dos períodos de restrição e de realimentação. Não foi observado efeito da restrição alimentar sobre o desempenho animal. Novilhas que não receberam suplementação vitamínica apresentaram maiores índices de fragmentação miofibrilar, luminosidade e mastigabilidade, e menor força de cisalhamento. A restrição alimentar não afeta o desempenho nem as características de carcaça das novilhas Canchim, e a suplementação com vitaminas D e E não melhora a qualidade da carne.

Michael Faraday: o caminho da livraria à descoberta da indução eletromagnética

Estudando o trabalho experimental sobre eletromagnetismo realizado por Michael Faraday no início do século XIX, encontramos vários elementos que poderiam ser utilizados no Ensino de Ciências. Um conhecimento histórico sobre o trabalho experimental desenvolvido por Faraday e que o levou à descoberta da indução eletromagnética pode transmitir aos estudantes uma concepção mais adequada do processo de desenvolvimento da Ciência. No entanto, isso só pode ser feito utilizando-se um estudo detalhado e bem fundamentado do processo histórico ocorrido, deixando de lado as simplificações e os mitos que costumam ser apresentados.

Bayesian binary regression model: an application to in-hospital death after AMI prediction

Um modelo bayesiano de regressão binária é desenvolvido para predizer óbito hospitalar em pacientes acometidos por infarto agudo do miocárdio. Métodos de Monte Carlo via Cadeias de Markov (MCMC) são usados para fazer inferência e validação. Uma estratégia para construção de modelos, baseada no uso do fator de Bayes, é proposta e aspectos de validação são extensivamente discutidos neste artigo, incluindo a distribuição a posteriori para o índice de concordância e análise de resíduos. A determinação de fatores de risco, baseados em variáveis disponíveis na chegada do paciente ao hospital, é muito importante para a tomada de decisão sobre o curso do tratamento. O modelo identificado se revela fortemente confiável e acurado, com uma taxa de classificação correta de 88% e um índice de concordância de 83%.

Pattern recognition theory in nonlinear signal processing

A body of research has developed within the context of nonlinear signal and image processing that deals with the automatic, statistical design of digital window-based filters. Based on pairs of ideal and observed signals, a filter is designed in an effort to minimize the error between the ideal and filtered signals. The goodness of an optimal filter depends on the relation between the ideal and observed signals, but the goodness of a designed filter also depends on the amount of sample data from which it is designed. In order to lessen the design cost, a filter is often chosen from a given class of filters, thereby constraining the optimization and increasing the error of the optimal filter. To a great extent, the problem of filter design concerns striking the correct balance between the degree of constraint and the design cost. From a different perspective and in a different context, the problem of constraint versus sample size has been a major focus of study within the theory of pattern recognition. This paper discusses the design problem for nonlinear signal processing, shows how the issue naturally transitions into pattern recognition, and then provides a review of salient related pattern-recognition theory. In particular, it discusses classification rules, constrained classification, the Vapnik-Chervonenkis theory, and implications of that theory for morphological classifiers and neural networks. The paper closes by discussing some design approaches developed for nonlinear signal processing, and how the nature of these naturally lead to a decomposition of the error of a designed filter into a sum of the following components: the Bayes error of the unconstrained optimal filter, the cost of constraint, the cost of reducing complexity by compressing the original signal distribution, the design cost, and the contribution of prior knowledge to a decrease in the error. The main purpose of the paper is to present fundamental principles of pattern recognition theory within the framework of active research in nonlinear signal processing.

A Bayesian approach for assessing the stability of genotypes

Several statistical models can be used for assessing genotype X environment interaction (GEI) and studying genotypic stability. The objectives of this research were to show how (i) to use Bayesian methodology for computing Shukla's phenotypic stability variance and (ii) to incorporate prior information on the parameters for better estimation. Potato [Solanum tuberosum subsp. andigenum (Juz. & Bukasov) Hawkes], wheat (Triticum aestivum L.), and maize (Zea mays L.) multi environment trials (MET) were used for illustrating the application of the Bayes paradigm. The potato trial included 15 genotypes, but prior information for just three genotypes was used. The wheat trial used prior information on all 10 genotypes included in the trial, whereas for the maize trial, noninformative priors for the nine genotypes was used. Concerning the posterior distribution of the genotypic means, the maize MET with 20 sites gave less disperse posterior distributions of the genotypic means than did the posterior distribution of the genotypic means of the other METs, which included fewer environments. The Bayesian approach allows use of other statistical strategies such as the normal truncated distribution (used in this study). When analyzing grain yield, a lower bound of zero and an upper bound set by the researcher's experience can be used. The Bayesian paradigm offers plant breeders the possibility of computing the probability of a genotype being the best performer. The results of this study show that although some genotypes may have a very low probability of being the best in all sites, they have a relatively good chance of being among the five highest yielding genotypes.

Development of an open case-based decision-support system for diagnosis in oral pathology

Making diagnoses in oral pathology are often difficult and confusing in dental practice, especially for the lessexperienced dental student. One of the most promising areas in bioinformatics is computer-aided diagnosis, where a computer system is capable of imitating human reasoning ability and provides diagnoses with an accuracy approaching that of expert professionals. This type of system could be an alternative tool for assisting dental students to overcome the difficulties of the oral pathology learning process. This could allow students to define variables and information, important to improving the decision-making performance. However, no current open data management system has been integrated with an artificial intelligence system in a user-friendly environment. Such a system could also be used as an education tool to help students perform diagnoses. The aim of the present study was to develop and test an open case-based decisionsupport system.Methods: An open decision-support system based on Bayes' theorem connected to a relational database was developed using the C++ programming language. The software was tested in the computerisation of a surgical pathology service and in simulating the diagnosis of 43 known cases of oral bone disease. The simulation was performed after the system was initially filled with data from 401 cases of oral bone disease.Results: the system allowed the authors to construct and to manage a pathology database, and to simulate diagnoses using the variables from the database.Conclusion: Combining a relational database and an open decision-support system in the same user-friendly environment proved effective in simulating diagnoses based on information from an updated database.

A Bayesian Analysis of Spectral ARMA Model

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Skin test and tick immune status in susceptible and resistant cattle in Brazil

A cutaneous hypersensitivity test (CHT) was used to correlate host resistance to ticks and type of reaction elicited to unfed larval extract-ULE of the cattle tick Boophilus microplus in European and Indian cattle. Twenty calves were separated into four groups of five animals each: naïve or preinfested Indian or European cattle. CHT was induced by intradermal inoculation of 0.1 ml of ULE cattle tick B. microplus (50 μg protein) in the calf ear. Ear thickness was measured using calipers before and 10 min, 1, 2, 6, 18, 24, 48, 72, 96, and 144 h postinoculation (PI). Preinfested European calves showed only an immediate type reaction with maximum response (75% increase in ear thickness) at 10 min PI. On the other hand, preinfested Indian calves presented an immediate response with maximum reaction (70% increase in ear thickness) between 10 min and one hour PI, and a delayed type reaction at 72 h PI (60% increase in ear thickness). These results point out the crucial role of the cellular immune response of cattle in the expression of resistance to cattle tick B. microplus. Skin test might be useful in the ranking of cattle according to the susceptibility/resistance to ticks.

A Bayesian approach for constructing genetic maps when markers are miscoded

The advent of molecular markers has created opportunities for a better understanding of quantitative inheritance and for developing novel strategies for genetic improvement of agricultural species, using information on quantitative trait loci (QTL). A QTL analysis relies on accurate genetic marker maps. At present, most statistical methods used for map construction ignore the fact that molecular data may be read with error. Often, however, there is ambiguity about some marker genotypes. A Bayesian MCMC approach for inferences about a genetic marker map when random miscoding of genotypes occurs is presented, and simulated and real data sets are analyzed. The results suggest that unless there is strong reason to believe that genotypes are ascertained without error, the proposed approach provides more reliable inference on the genetic map.

A Bayesian approach to estimate the accuracy of in-house ELISA assay to measure rabies antibodies from compulsory vaccinated dogs and cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)