Occurrence and significance of ovarian and follicular regression in cachara Pseudoplatystoma fasciatum (Linnaeus, 1766): a histology approach

Estudaram-se os processos de regressão ovariana e atresia folicular em cachara, Pseudoplatystoma fasciatum, mantida em cativeiro, na reprodução não induzida por hormônios. As características macro e microscópicas (diâmetro dos ovócitos e histologia) dos ovários foram descritas a cada 20 dias, em quatro estádios: na regressão inicial (Rg I - os primeiros 20 dias), na regressão intermediária (Rg II - do 21º ao 40º dia), na regressão final (Rg III - do 41º ao 80º dia) e na fase de recuperação ou de repouso II (R II - do 81º ao 150º dia). O experimento foi realizado do final de janeiro (verão-dias longos) a maio (outono-dias curtos). No início do experimento, as amostras apresentaram ovócitos com diâmetros que variaram de 437,5 a 1.187,5mm, sugerindo encontrarem-se nas fases perinucleolar, de maturação final e atrésicos. Aos 150 dias, os diâmetros atingiram os menores valores e pôde-se visualizar a zona radiata rompida e o vitelo reabsorvido. Concomitantemente, houve diminuição abrupta dos valores médios do índice gonadossomático, da temperatura da água, das horas de luz e de chuva. A involução gradual do longo processo foi dinâmica e complexa, afetando o êxito da desova (taxas de fertilização, de eclosão e de sobrevivência de larvas) e, conseqüentemente, o sistema produtivo.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUÇÃO: A malaria é uma doença endêmica na região da Amazônia Brasileira, e a detecção de possíveis fatores de risco pode ser de grande interesse às autoridades em saúde pública. O objetivo deste artigo é investigar a associação entre variáveis ambientais e os registros anuais de malária na região amazônica usando métodos bayesianos espaço-temporais. MÉTODOS: Utilizaram-se modelos de regressão espaço-temporais de Poisson para analisar os dados anuais de contagem de casos de malária entre os anos de 1999 a 2008, considerando a presença de alguns fatores como a taxa de desflorestamento. em uma abordagem bayesiana, as inferências foram obtidas por métodos Monte Carlo em cadeias de Markov (MCMC) que simularam amostras para a distribuição conjunta a posteriori de interesse. A discriminação de diferentes modelos também foi discutida. RESULTADOS: O modelo aqui proposto sugeriu que a taxa de desflorestamento, o número de habitants por km² e o índice de desenvolvimento humano (IDH) são importantes para a predição de casos de malária. CONCLUSÕES: É possível concluir que o desenvolvimento humano, o crescimento populacional, o desflorestamento e as alterações ecológicas associadas a estes fatores estão associados ao aumento do risco de malária. Pode-se ainda concluir que o uso de modelos de regressão de Poisson que capturam o efeito temporal e espacial em um enfoque bayesiano é uma boa estratégia para modelar dados de contagem de malária.

A Simple Approach to Assess VT During a Field Walk Test

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

Antimicrobial use and incidence of multidrug-resistant Pseudomonas aeruginosa in a teaching hospital: an ecological approach

Introduction: Multidrug-resistant Pseudomonas aeruginosa is a major threat in healthcare settings. The use of antimicrobials can influence the incidence of resistant strains by direct and indirect mechanisms. The latter can be addressed by ecological studies. Methods: Our group attempted to analyze the relation between the use of antipseudomonal drugs and the incidence of MDR-PA among 18 units from a 400-bed teaching hospital. The study had a retrospective, ecological design, comprising data from 2004 and 2005. Data on the use of four antimicrobials (amikacin, ciprofloxacin, ceftazidime and imipenem) were tested for correlation with the incidence of MDR-PA (defined as isolates resistant to the four antimicrobials of interest) in clinical cultures. Univariate and multivariate linear regression analyses were performed. Results: Significant correlations were determined between use and resistance for all antimicrobials in the univariate analysis: amikacin (standardized correlation coefficient = 0.73, p = 0.001); ciprofloxacin (0.71, p = 0.001); ceftazidime (0.61, p = 0.007) and imipenem (0.87, p < 0.001). In multivariate analysis, only imipenem (0.67, p = 0.01) was independently related to the incidence of multidrug-resistant strains. Conclusions: These findings share similarities with those reported in individual-based observational studies, with possible implications for infection control.

Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala

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

A QUANTITATIVE APPROACH TO SUBSIDIZE THE PRECAUTIONARY MANAGEMENT of THE SMALL-SCALE FISHERIES IN ITAIPU RESERVOIR, BRAZIL

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bayesian outlier analysis in binary regression

We propose alternative approaches to analyze residuals in binary regression models based on random effect components. Our preferred model does not depend upon any tuning parameter, being completely automatic. Although the focus is mainly on accommodation of outliers, the proposed methodology is also able to detect them. Our approach consists of evaluating the posterior distribution of random effects included in the linear predictor. The evaluation of the posterior distributions of interest involves cumbersome integration, which is easily dealt with through stochastic simulation methods. We also discuss different specifications of prior distributions for the random effects. The potential of these strategies is compared in a real data set. The main finding is that the inclusion of extra variability accommodates the outliers, improving the adjustment of the model substantially, besides correctly indicating the possible outliers.

Use of the Normalized Absorbance Ratio as an Internal Standardization Approach To Minimize Measurement Error in Enzyme-Linked Immunosorbent Assays for Diagnosis of Human Papillomavirus Infection

The serological detection of antibodies against human papillomavirus (HPV) antigens is a useful tool to determine exposure to genital HPV infection and in predicting the risk of infection persistence and associated lesions. Enzyme-linked immunosorbent assays (ELISAs) are commonly used for seroepidemiological studies of HPV infection but are not standardized. Intra-and interassay performance variation is difficult to control, especially in cohort studies that require the testing of specimens over extended periods. We propose the use of normalized absorbance ratios (NARs) as a standardization procedure to control for such variations and minimize measurement error. We compared NAR and ELISA optical density (OD) values for the strength of the correlation between serological results for paired visits 4 months apart and HPV-16 DNA positivity in cervical specimens from a cohort investigation of 2,048 women tested with an ELISA using HPV-16 virus-like particles. NARs were calculated by dividing the mean blank-subtracted (net) ODs by the equivalent values of a control serum pool included in the same plate in triplicate, using different dilutions. Stronger correlations were observed with NAR values than with net ODs at every dilution, with an overall reduction in nonexplained regression variability of 39%. Using logistic regression, the ranges of odds ratios of HPV-16 DNA positivity contrasting upper and lower quintiles at different dilutions and their averages were 4.73 to 5.47 for NARs and 2.78 to 3.28 for net ODs, with corresponding significant improvements in seroreactivity-risk trends across quintiles when NARs were used. The NAR standardization is a simple procedure to reduce measurement error in seroepidemiological studies of HPV infection.

Modeling approach based on experimental results for prediction of measurement errors in energy meters

This paper presents a general modeling approach to investigate and to predict measurement errors in active energy meters both induction and electronic types. The measurement error modeling is based on Generalized Additive Model (GAM), Ridge Regression method and experimental results of meter provided by a measurement system. The measurement system provides a database of 26 pairs of test waveforms captured in a real electrical distribution system, with different load characteristics (industrial, commercial, agricultural, and residential), covering different harmonic distortions, and balanced and unbalanced voltage conditions. In order to illustrate the proposed approach, the measurement error models are discussed and several results, which are derived from experimental tests, are presented in the form of three-dimensional graphs, and generalized as error equations. © 2009 IEEE.

Effects of therapeutic approach on the neonatal evolution of very low birth weight infants with patent ductus arteriosus

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

The zero-inflated Conway-Maxwell-Poisson distribution: Bayesian inference, regression modeling and influence diagnostic

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A note on model uncertainty in linear regression

We consider model selection uncertainty in linear regression. We study theoretically and by simulation the approach of Buckland and co-workers, who proposed estimating a parameter common to all models under study by taking a weighted average over the models, using weights obtained from information criteria or the bootstrap. This approach is compared with the usual approach in which the 'best' model is used, and with Bayesian model averaging. The weighted predictor behaves similarly to model averaging, with generally more realistic mean-squared errors than the usual model-selection-based estimator.

Health related quality of life is differently associated with leisure-time physical activity intensities according to gender: a cross-sectional approach

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multicollinearity and financial constraint in investment decisions: a bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.

Oral impacts on the daily performance of Brazilians assessed using a sociodental approach: analyses of national data

The aim of this study was to analyze the relationship between oral diseases and their impact on the daily performance of adult and elderly Brazilians, verify the association of oral diseases with socioeconomic and demographic features, and compare the standard estimate of need with the sociodental assessment of these same needs. The authors evaluated data from 17,398 Brazilians aged between 35-44 years and 65-74 years, taken from the cross-sectional Brazilian Oral Health Survey (Saúde Bucal Brasil - SBBrasil). Regression models were applied to assess associations among impacts on daily performance and income, schooling, gender, region, use of dental services, health perception and dental disease status. McNemar’s test was applied to compare standard versus impact-related estimates of need. The prevalence ratio of these impacts was associated with the sociodemographic versus health perceptions (p < 0.001) of adults and the elderly. Adults also had impacts associated with loss of periodontal attachment (p < 0.001). The prevalence of normative needs was 95.39% for adults and 99.76% for the elderly, whereas the impact-related estimate of need was 50.92% and 43.71%, respectively. The impacted-related approach had a statistically significant association with the normative estimate of need (p < 0.001). This study showed a relationship between oral impact on daily performance of adults and educational level. Sociodemographic features were also related to the impacts on both adults and the elderly, and to health perception. The impacts among the adults were related to the loss of periodontal attachment. In addition, the authors found a sizable difference between the standard versus the sociodental approach, in that the sociodental assessment needs were lower than the needs identified by the standard estimate of need.