Generalized linear mixed models for the genetic evaluation of binary reproductive traits: a simulation study

The objective of this study was to evaluate the use of probit and logit link functions for the genetic evaluation of early pregnancy using simulated data. The following simulation/analysis structures were constructed: logit/logit, logit/probit, probit/logit, and probit/probit. The percentages of precocious females were 5, 10, 15, 20, 25 and 30% and were adjusted based on a change in the mean of the latent variable. The parametric heritability (h²) was 0.40. Simulation and genetic evaluation were implemented in the R software. Heritability estimates (ĥ²) were compared with h² using the mean squared error. Pearson correlations between predicted and true breeding values and the percentage of coincidence between true and predicted ranking, considering the 10% of bulls with the highest breeding values (TOP10) were calculated. The mean ĥ² values were under- and overestimated for all percentages of precocious females when logit/probit and probit/logit models used. In addition, the mean squared errors of these models were high when compared with those obtained with the probit/probit and logit/logit models. Considering ĥ², probit/probit and logit/logit were also superior to logit/probit and probit/logit, providing values close to the parametric heritability. Logit/probit and probit/logit presented low Pearson correlations, whereas the correlations obtained with probit/probit and logit/logit ranged from moderate to high. With respect to the TOP10 bulls, logit/probit and probit/logit presented much lower percentages than probit/probit and logit/logit. The genetic parameter estimates and predictions of breeding values of the animals obtained with the logit/logit and probit/probit models were similar. In contrast, the results obtained with probit/logit and logit/probit were not satisfactory. There is need to compare the estimation and prediction ability of logit and probit link functions.

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.

Experimental models of autoimmune inflammatory ocular diseases

A inflamação ocular é uma das principais causas de perda visual e cegueira. As uveítes constituem um grupo complexo e heterogêneo de doenças caracterizadas por inflamação dos tecidos intraoculares. O olho pode ser o único órgão envolvido ou a uveíte pode ser parte de uma doença sistêmica. A etiologia é desconhecida em um número significativo de casos, que são considerados idiopáticos. Modelos animais têm sido desenvolvidos para estudar a fisiopatogênese da uveíte autoimune devido às dificuldades na obtenção de tecidos de olhos humanos inflamados para experimentos. Na maioria desses modelos, que simulam as uveítes autoimunes em humanos, a uveíte é induzida com proteínas específicas de fotorreceptores (antígeno-S, proteína ligadora de retinoide do interfotoreceptor, rodopsina, recoverina e fosducina). Antígenos não retinianos, como proteínas associadas à melanina e proteína básica de mielina, são também bons indutores de uveíte em animais. Entender os mecanismos básicos e a patogênese dessas doenças oculares é essencial para o desenvolvimento de novas formas de tratamento das uveítes autoimunes e de novos agentes terapêuticos. Nesta revisão serão abordados os principais modelos experimentais utilizados para o estudo de doenças inflamatórias oculares autoimunes.

Differential regulation of MMP-13 expression in two models of experimentally induced periodontal disease in rats

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

Laurdan Spectrum Decomposition as a Tool for the Analysis of Surface Bilayer Structure and Polarity: a Study with DMPG, Peptides and Cholesterol

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

Applied models to biodegradation kinetics of lubricant and vegetable oils in wastewater

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

Molecular models of NS3 protease variants of the Hepatitis C virus

Background: Hepatitis C virus (HCV) currently infects approximately three percent of the world population. In view of the lack of vaccines against HCV, there is an urgent need for an efficient treatment of the disease by an effective antiviral drug. Rational drug design has not been the primary way for discovering major therapeutics. Nevertheless, there are reports of success in the development of inhibitor using a structure-based approach. One of the possible targets for drug development against HCV is the NS3 protease variants. Based on the three-dimensional structure of these variants we expect to identify new NS3 protease inhibitors. In order to speed up the modeling process all NS3 protease variant models were generated in a Beowulf cluster. The potential of the structural bioinformatics for development of new antiviral drugs is discussed.Results: the atomic coordinates of crystallographic structure 1CU1 and 1DY9 were used as starting model for modeling of the NS3 protease variant structures. The NS3 protease variant structures are composed of six subdomains, which occur in sequence along the polypeptide chain. The protease domain exhibits the dual beta-barrel fold that is common among members of the chymotrypsin serine protease family. The helicase domain contains two structurally related beta-alpha-beta subdomains and a third subdomain of seven helices and three short beta strands. The latter domain is usually referred to as the helicase alpha-helical subdomain. The rmsd value of bond lengths and bond angles, the average G-factor and Verify 3D values are presented for NS3 protease variant structures.Conclusions: This project increases the certainty that homology modeling is an useful tool in structural biology and that it can be very valuable in annotating genome sequence information and contributing to structural and functional genomics from virus. The structural models will be used to guide future efforts in the structure-based drug design of a new generation of NS3 protease variants inhibitors. All models in the database are publicly accessible via our interactive website, providing us with large amount of structural models for use in protein-ligand docking analysis.

Application of abrupt cut-off models in the analysis of the capacitance spectra of conjugated polymer devices

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

Integrable aspects of the scaling q-state Potts models II: finite-size effects

We continue our discussion of the q-state Potts models for q less than or equal to 4, in the scaling regimes close to their critical and tricritical points. In a previous paper, the spectrum and full S-matrix of the models on an infinite line were elucidated; here, we consider finite-size behaviour. TBA equations are proposed for all cases related to phi(21) and phi(12) perturbations of unitary minimal models. These are subjected to a variety of checks in the ultraviolet and infrared limits, and compared with results from a recently-proposed non-linear integral equation. A non-linear integral equation is also used to study the flows from tricritical to critical models, over the full range of q. Our results should also be of relevance to the study of the off-critical dilute A models in regimes 1 and 2. (C) 2003 Elsevier B.V. B.V. All rights reserved.

A class of mixed integrable models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Stabilization of the electroweak scale in 3-3-1 models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Decomposition kinetics of iron (III)-diclofenac compound

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Composting of urban solid residues (USR) by different dispositions - Kinetics of thermal decomposition

The thermal behavior and non-isothermal kinetics of thermal decomposition of three different kinds of composting of the USR like: stack with drilled PVC tubes (ST), revolved stack (SR) and stack with material of structure (SM), from the usine of composing of Araraquara city, São Paulo state, Brazil, within a period of 132 days of composting were studied.Results from TG, DTG and DSC curves obtained on inert atmosphere indicated that the cellulosic fraction present, despite the slow degradation during the composting process, is thermally less stable than other substances originated from that process. Due to that behavior, the cellulosic fraction decomposition could be kinetically evaluated through non-isothermal methods of analysis.The values obtained were: average activation energy, E-a=248, 257 and 259 kJ mol(-1) and pre-exponential factor, logA=21.4, 22.5, 22.7 min(-1), to the ST, SR and SM, respectively.From E-a and logA values and DSC curves, Malek procedure could be applied, suggesting that the SB (Sestak-Berggren) kinetic model is the appropriated one to the first thermal decomposition step.

Titanium(IV)-EDTA complex - Kinetics of thermal decomposition by non-isothermal procedures

This work aims the evaluation of the kinetic triplets corresponding to the two successive steps of thermal decomposition of Ti(IV)-ethylenediaminetetraacetate complex. Applying the isoconversional Wall-Flynn-Ozawa method on the DSC curves, average activation energy: E=172.4 +/- 9.7 and 205.3 +/- 12.8 kJ mol(-1), and pre-exponential factor: logA = 16.38 +/- 0.84 and 18.96 +/- 1.21 min(-1) at 95% confidence interval could be obtained, regarding the partial formation of anhydride and subsequent thermal decomposition of uncoordinated carboxylate groups, respectively.From E and logA values, Dollimore and Malek methods could be applied suggesting PT (Prout-Tompkins) and R3 (contracting volume) as the kinetic model to the partial formation of anhydride and thermal decomposition of the carboxylate groups, respectively.

Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models

As a new modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In this study, the SVR models had been introduced and developed to predict body and carcass-related characteristics of 2 strains of broiler chicken. To evaluate the prediction ability of SVR models, we compared their performance with that of neural network (NN) models. Evaluation of the prediction accuracy of models was based on the R-2, MS error, and bias. The variables of interest as model output were BW, empty BW, carcass, breast, drumstick, thigh, and wing weight in 2 strains of Ross and Cobb chickens based on intake dietary nutrients, including ME (kcal/bird per week), CP, TSAA, and Lys, all as grams per bird per week. A data set composed of 64 measurements taken from each strain were used for this analysis, where 44 data lines were used for model training, whereas the remaining 20 lines were used to test the created models. The results of this study revealed that it is possible to satisfactorily estimate the BW and carcass parts of the broiler chickens via their dietary nutrient intake. Through statistical criteria used to evaluate the performance of the SVR and NN models, the overall results demonstrate that the discussed models can be effective for accurate prediction of the body and carcass-related characteristics investigated here. However, the SVR method achieved better accuracy and generalization than the NN method. This indicates that the new data mining technique (SVR model) can be used as an alternative modeling tool for NN models. However, further reevaluation of this algorithm in the future is suggested.