Random regression analyses using B-spline functions to model growth of Nellore cattle

The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

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

Milk yield persistency in Brazilian Gyr cattle based on a random regression model

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic parameters for test-day yield of milk, fat and protein in buffaloes estimated by random regression models

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

Aplicação de técnicas de aprendizado de máquina no reconhecimento de classes estruturais de proteínas

Nowadays, classifying proteins in structural classes, which concerns the inference of patterns in their 3D conformation, is one of the most important open problems in Molecular Biology. The main reason for this is that the function of a protein is intrinsically related to its spatial conformation. However, such conformations are very difficult to be obtained experimentally in laboratory. Thus, this problem has drawn the attention of many researchers in Bioinformatics. Considering the great difference between the number of protein sequences already known and the number of three-dimensional structures determined experimentally, the demand of automated techniques for structural classification of proteins is very high. In this context, computational tools, especially Machine Learning (ML) techniques, have become essential to deal with this problem. In this work, ML techniques are used in the recognition of protein structural classes: Decision Trees, k-Nearest Neighbor, Naive Bayes, Support Vector Machine and Neural Networks. These methods have been chosen because they represent different paradigms of learning and have been widely used in the Bioinfornmatics literature. Aiming to obtain an improvment in the performance of these techniques (individual classifiers), homogeneous (Bagging and Boosting) and heterogeneous (Voting, Stacking and StackingC) multiclassification systems are used. Moreover, since the protein database used in this work presents the problem of imbalanced classes, artificial techniques for class balance (Undersampling Random, Tomek Links, CNN, NCL and OSS) are used to minimize such a problem. In order to evaluate the ML methods, a cross-validation procedure is applied, where the accuracy of the classifiers is measured using the mean of classification error rate, on independent test sets. These means are compared, two by two, by the hypothesis test aiming to evaluate if there is, statistically, a significant difference between them. With respect to the results obtained with the individual classifiers, Support Vector Machine presented the best accuracy. In terms of the multi-classification systems (homogeneous and heterogeneous), they showed, in general, a superior or similar performance when compared to the one achieved by the individual classifiers used - especially Boosting with Decision Tree and the StackingC with Linear Regression as meta classifier. The Voting method, despite of its simplicity, has shown to be adequate for solving the problem presented in this work. The techniques for class balance, on the other hand, have not produced a significant improvement in the global classification error. Nevertheless, the use of such techniques did improve the classification error for the minority class. In this context, the NCL technique has shown to be more appropriated

Extraction of Building Roof Contours From LiDAR Data Using a Markov-Random-Field-Based Approach

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

Search schemes for random optimization algorithms that preserve the asymptotic distribution

Markovian algorithms for estimating the global maximum or minimum of real valued functions defined on some domain Omega subset of R-d are presented. Conditions on the search schemes that preserve the asymptotic distribution are derived. Global and local search schemes satisfying these conditions are analysed and shown to yield sharper confidence intervals when compared to the i.i.d. case.

Strain differentiation of Trichophyton rubrum by random amplification of polymorphic DNA (RAPD)

Trichophyton rubrum é um importante agente causal de dermatomicose. Os métodos de tipagem molecular têm sido recentemente desenvolvidos para responder questões sobre epidemiologia e auxiliar no esclarecimento de recidivas, após o tratamento. As seqüências aleatórias 1- (5'-d[GGTGCGGGAA]-3') e 6- (5'-d[CCCGTCAGCA]-3') foram usadas para tipagem molecular deste fungo por RAPD produzindo variabilidade intraespecífica. Cinco padrões foram observados entre os 10 isolados de T. rubrum, com ambas as seqüências. Foi concluído que a análise por RAPD pode ser utilizada para estudos epidemiológicos.

Identification of a gene encoding adaptin-like protein in the Paracoccidioides brasiliensis genome by random amplified polymorphic DNA analysis

Paracoccidioides brasiliensis isolates are not homogeneous in their patterns of pathogenicity in animals and adhesion to epithelial cells. During this investigation, genotypic differences were observed between two samples of P. brasiliensis strain 18 yeast phase (Pbl 8) previously cultured many times, one taken before (Pb18a) and the other after (Pb18b) animal inoculation. Random amplified polymorphic DNA analysis using the primer OPJ4 distinguished Pb18b from Pbl Ba by one 308 bp DNA fragment, which after cloning and sequencing was shown to encode a polypeptide sequence homologous to the protein beta-adaptin. It is suggested, by comparison to other micro-organisms, that this protein might play an important role in the virulence of P. brasiliensis. This result demonstrates the influence of in vitro subculturing on the genotype of this organism.

Effect of measurement error and autocorrelation on the (X)over-bar chart

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Prevalence of Refractive Errors in a Brazilian Population: The Botucatu Eye Study

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

Condição ocular dos indivíduos facectomizados na região centro-oeste do estado de São Paulo: estudo populacional

OBJETIVO: Avaliar as causas de baixa visão e cegueira em indivíduos facectomizados, de amostra da população de cidades da região centrooeste do estado de São Paulo. Métodos: Estudo transversal, observacional, feito em cinco cidades da região centro-oeste do estado de São Paulo, em amostra domiciliar e baseada nos dados do último Censo Demográfico (IBGE, 1995), com escolha sistemática dos domicílios. Foi considerada para o presente estudo uma subamostra de indivíduos facectomizados, dos quais foram obtidos dados de identificação e exame oftalmológico completo. Os dados foram avaliados por estatísticas descritivas, análise de freqüência de ocorrência e proporção de concordância, com intervalo de confiança de 95%. RESULTADOS: Dos indivíduos amostrados, 2,37% haviam sido submetidos à facectomia. Dos 201 olhos operados, 26,9% apresentavam acuidade visual compatível com cegueira ou deficiência visual. Com a melhor correção óptica, a acuidade visual permaneceu <0,3 em 19,0%. O exame refracional proporcionou melhora da acuidade visual para 27,9% dos indivíduos facectomizados. As causas de baixa visão foram os erros refrativos não corrigidos, opacidade de cápsula posterior (19,4%), ceratopatia bolhosa (8,3%) coriorretinite cicatricial (8,3%), afacia (8,3%), degeneração macular relacionada a idade (5,5%), leucoma (5,5%), glaucoma (5,5%), atrofia de papila (5,5,%), descolamento de retina (2,8%), atrofia de epitelio pigmentado da retina (2,8%) e alta miopia (2,8%). CONCLUSÃO: Apesar da catarata ser causa de cegueira que pode ser evitável, mesmo após a correção cirúrgica porcentagem expressiva de indivíduos permanece com baixa visão, em geral, em decorrência de fatores relacionados ao seguimento pós-operatório negligenciado.

Prevalence and causes of visual impairment in a Brazilian population: The Botucatu Eye Study

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

Prevalência e localização espacial dos casos de tracoma detectados em escolares de Botucatu, São Paulo - Brasil

OBJETIVO: Avaliar a prevalência de tracoma em escolares de Botucatu/SP-Brasil e a distribuição espacial dos casos. MÉTODOS: Foi realizado um estudo transversal, em crianças de 7-14 anos, que frequentavam as escolas do ensino fundamental de Botucatu/SP, em novembro/2005. O tamanho da amostra foi estimado em 2.092 crianças, considerando-se a prevalência histórica de 11,2%, aceitando-se erro de estimação de 10% e nível de confiança de 95%. A amostra foi probabilística, ponderada e acrescida de 20%, devido à possível ocorrência de perdas. Examinaram-se 2.692 crianças. O diagnóstico foi clínico, baseado na normatização da Organização Mundial da Saúde (OMS). Para avaliação dos dados espaciais, utilizou-se o programa CartaLinx (v1.2), sendo os setores de demanda escolar digitalizados de acordo com as divisões do planejamento da Secretaria de Educação. Os dados foram analisados estatisticamente, sendo a análise da estrutura espacial dos eventos calculadas usando o programa Geoda. RESULTADOS: A prevalência de tracoma nos escolares de Botucatu foi de 2,9%, tendo sido detectados casos de tracoma folicular. A análise exploratória espacial não permitiu rejeitar a hipótese nula de aleatoriedade (I= -0,45, p>0,05), não havendo setores de demanda significativos. A análise feita para os polígonos de Thiessen também mostrou que o padrão global foi aleatório (I= -0,07; p=0,49). Entretanto, os indicadores locais apontaram um agrupamento do tipo baixo-baixo para um polígono ao norte da área urbana. CONCLUSÃO: A prevalência de tracoma em escolares de Botucatu foi de 2,9%. A análise da distribuição espacial não revelou áreas de maior aglomeração de casos. Embora o padrão global da doença não reproduza as condições socioeconômicas da população, a prevalência mais baixa do tracoma foi encontrada em setores de menor vulnerabilidade social.

Comparison between human and armadillo Paracoccidioides brasiliensis isolates by random amplified polymorphic DNA analysis

Sixty-three Paracoccidioides brasiliensis isolates obtained from three nine-banded armadillos (Dasypus novem-cinctus), one Amazonian armadillo's and 19 clinical isolates were compared by random amplified polymorphic DNA analysis with the primer OPG-19. The isolates were divided into three major clusters, I, II and III. Coincidences between human and armadillo isolates were observed in clusters I and II. Cluster III consisted only of armadillos' isolates. The results suggested that (I) humans may acquire P. brasiliensis infection by contact with armadillo's environment, (II) there may be P. brasiliensis genotypes peculiar to the animal, and (III) individual armadillos may be infected with P brasiliensis cells with different genotypes.