Structural basis for selective inhibition of trypanosomatid glyceraldehyde-3-phosphate dehydrogenase: Molecular docking and 3D QSAR studies

The glycolytic enzyme glyceraldehyde-3 -phosphate dehydrogenase (GAPDH) is as an attractive target for the development of novel antitrypanosomatid agents. In the present work, comparative molecular field analysis and comparative molecular similarity index analysis were conducted on a large series of selective inhibitors of trypanosomatid GAPDH. Four statistically significant models were obtained (r(2) > 0.90 and q(2) > 0.70), indicating their predictive ability for untested compounds. The models were then used to predict the potency of an external test set, and the predicted values were in good agreement with the experimental results. Molecular modeling studies provided further insight into the structural basis for selective inhibition of trypanosomatid GAPDH.

Pharmacophore-based 3D QSAR studies on a series of high affinity 5-HT(1A) receptor ligands

5-HT(1A) receptor antagonists have been employed to treat depression, but the lack of structural information on this receptor hampers the design of specific and selective ligands. In this study, we have performed CoMFA studies on a training set of arylpiperazines (high affinity 5-HT(1A) receptor ligands) and to produce an effective alignment of the data set, a pharmacophore model was produced using Galahad. A statistically significant model was obtained, indicating a good internal consistency and predictive ability for untested compounds. The information gathered from our receptor-independent pharmacophore hypothesis is in good agreement with results from independent studies using different approaches. Therefore, this work provides important insights on the chemical and structural basis involved in the molecular recognition of these compounds. (C) 2010 Elsevier Masson SAS. All rights reserved.

Probabilidade de financiamento por cartões de crédito híbridos

Esta pesquisa tem o objetivo de identificar as variáveis e sua influência na propensão à aquisição de crédito pessoal, propondo um modelo estatístico de propensão ao financiamento por cartões de crédito híbridos para maximização de contratação de crédito e otimização dos esforços de marketing. O estudo descritivo pode gerar insights para a compreensão da expansão do crédito ao consumo, sobretudo num contexto de escassez de opções de financiamento e limitação no canal de distribuição. Foram usados dados de uma base de clientes de uma instituição financeira com variáveis sócio demográficas e transacionai, e o modelo matemático foi seguido da validação de sua capacidade preditiva.

Previsão de preços de ativos usando restrições do modelo de valor presente

Motivados pelo debate envolvendo modelos estruturais e na forma reduzida, propomos nesse artigo uma abordagem empírica com o objetivo de ver se a imposição de restrições estruturais melhoram o poder de previsibilade vis-a-vis modelos irrestritos ou parcialmente restritos. Para respondermos nossa pergunta, realizamos previsões utilizando dados agregados de preços e dividendos de ações dos EUA. Nesse intuito, exploramos as restrições de cointegração, de ciclo comum em sua forma fraca e sobre os parâmetros do VECM impostas pelo modelo de Valor Presente. Utilizamos o teste de igualdade condicional de habilidade de previsão de Giacomini e White (2006) para comparar as previsões feitas por esse modelo com outros menos restritos. No geral, encontramos que os modelos com restrições parciais apresentaram os melhores resultados, enquanto o modelo totalmente restrito de VP não obteve o mesmo sucesso.

Previsão de inflação utilizando modelos de séries temporais

Este trabalho compara modelos de séries temporais para a projeção de curto prazo da inflação brasileira, medida pelo Índice de Preços ao Consumidor Amplo (IPCA). Foram considerados modelos SARIMA de Box e Jenkins e modelos estruturais em espaço de estados, estimados pelo filtro de Kalman. Para a estimação dos modelos, foi utilizada a série do IPCA na base mensal, de março de 2003 a março de 2012. Os modelos SARIMA foram estimados no EVIEWS e os modelos estruturais no STAMP. Para a validação dos modelos para fora da amostra, foram consideradas as previsões 1 passo à frente para o período de abril de 2012 a março de 2013, tomando como base os principais critérios de avaliação de capacidade preditiva propostos na literatura. A conclusão do trabalho é que, embora o modelo estrutural permita, decompor a série em componentes com interpretação direta e estudá-las separadamente, além de incorporar variáveis explicativas de forma simples, o desempenho do modelo SARIMA para prever a inflação brasileira foi superior, no período e horizonte considerados. Outro importante aspecto positivo é que a implementação de um modelo SARIMA é imediata, e previsões a partir dele são obtidas de forma simples e direta.

Comparison of selective genotyping strategies for prediction of breeding values in a population undergoing selection

Genomewide marker information can improve the reliability of breeding value predictions for young selection candidates in genomic selection. However, the cost of genotyping limits its use to elite animals, and how such selective genotyping affects predictive ability of genomic selection models is an open question. We performed a simulation study to evaluate the quality of breeding value predictions for selection candidates based on different selective genotyping strategies in a population undergoing selection. The genome consisted of 10 chromosomes of 100 cM each. After 5,000 generations of random mating with a population size of 100 (50 males and 50 females), generation G(0) (reference population) was produced via a full factorial mating between the 50 males and 50 females from generation 5,000. Different levels of selection intensities (animals with the largest yield deviation value) in G(0) or random sampling (no selection) were used to produce offspring of G(0) generation (G(1)). Five genotyping strategies were used to choose 500 animals in G(0) to be genotyped: 1) Random: randomly selected animals, 2) Top: animals with largest yield deviation values, 3) Bottom: animals with lowest yield deviations values, 4) Extreme: animals with the 250 largest and the 250 lowest yield deviations values, and 5) Less Related: less genetically related animals. The number of individuals in G(0) and G(1) was fixed at 2,500 each, and different levels of heritability were considered (0.10, 0.25, and 0.50). Additionally, all 5 selective genotyping strategies (Random, Top, Bottom, Extreme, and Less Related) were applied to an indicator trait in generation G(0), and the results were evaluated for the target trait in generation G(1), with the genetic correlation between the 2 traits set to 0.50. The 5 genotyping strategies applied to individuals in G(0) (reference population) were compared in terms of their ability to predict the genetic values of the animals in G(1) (selection candidates). Lower correlations between genomic-based estimates of breeding values (GEBV) and true breeding values (TBV) were obtained when using the Bottom strategy. For Random, Extreme, and Less Related strategies, the correlation between GEBV and TBV became slightly larger as selection intensity decreased and was largest when no selection occurred. These 3 strategies were better than the Top approach. In addition, the Extreme, Random, and Less Related strategies had smaller predictive mean squared errors (PMSE) followed by the Top and Bottom methods. Overall, the Extreme genotyping strategy led to the best predictive ability of breeding values, indicating that animals with extreme yield deviations values in a reference population are the most informative when training genomic selection models.

Atributos polarimétricos de imagem radar na inferência de parâmetros morfológicos de macrófitas

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

Correlation of properties of Brazilian Haplustalfs with magnetic susceptibility measurements

Magnetic susceptibility (chi, mass specific) is useful for easy indirect estimation of other soil properties at a low cost. The aim of this study was to assess the use of chi as measured with an analytical balance for predicting properties with a substantial influence on the management of Typic Haplustalfs in southern Brazil. To achieve this 48 topsoil samples were taken at the intersection points in a rectangular grid of 20 m x 20 m cells, with 38 of these used for calibration and 10 for validation in regression analyses. The obtained chi values were slightly higher than, and highly correlated (r = 0.970; P < 0.001) with those measured with a susceptibility meter. Highly significant (P < 0.001) correlations were also found between chi and other soil properties relevant to soil classification and management such as clay content (r = 0.68), cation exchange capacity (r = 0.62), P sorption capacity (r = 0.76) and haematite content (r = 0.82). Results from a principal component analysis of eight properties important for soil classification explained 11% of the variance in the data set. The good predictive ability of chi was consistent with current knowledge on the formation pathways for pedogenic ferrimagnets. In summary, chi, which can be readily measured with an analytical balance, has the potential for quantifying soil attributes and may therefore be used in pedotransfer functions.

Linear and Poisson models for genetic evaluation of tick resistance in cross-bred Hereford x Nellore cattle

Cattle resistance to ticks is measured by the number of ticks infesting the animal. The model used for the genetic analysis of cattle resistance to ticks frequently requires logarithmic transformation of the observations. The objective of this study was to evaluate the predictive ability and goodness of fit of different models for the analysis of this trait in cross-bred Hereford x Nellore cattle. Three models were tested: a linear model using logarithmic transformation of the observations (MLOG); a linear model without transformation of the observations (MLIN); and a generalized linear Poisson model with residual term (MPOI). All models included the classificatory effects of contemporary group and genetic group and the covariates age of animal at the time of recording and individual heterozygosis, as well as additive genetic effects as random effects. Heritability estimates were 0.08 ± 0.02, 0.10 ± 0.02 and 0.14 ± 0.04 for MLIN, MLOG and MPOI models, respectively. The model fit quality, verified by deviance information criterion (DIC) and residual mean square, indicated fit superiority of MPOI model. The predictive ability of the models was compared by validation test in independent sample. The MPOI model was slightly superior in terms of goodness of fit and predictive ability, whereas the correlations between observed and predicted tick counts were practically the same for all models. A higher rank correlation between breeding values was observed between models MLOG and MPOI. Poisson model can be used for the selection of tick-resistant animals. © 2013 Blackwell Verlag GmbH.

Inovação do método MIA-QSAR: aplicação para doenças tropicais negligenciadas

Multivariate image analysis applied to the quantitative structure-activity relationships (MIA-QSAR) is a 2D QSAR technique that has been presenting promising outcomes for the development of new drug candidates, due to its simplicity, rapidity and low cost. In this way, the present study aims at introducing, consolidating and improving the new dimensions named aug-MIA-QSAR and aug-MIA-QSARcolor, as well as applying them to the study of neglected diseases, in order to obtain new drug targets using chemico-biological interpretation of the MIA molecular descriptors. Four compound data sets with experimental bioactivities against Chagas disease, malaria, dengue and schistosomiasis were evaluated using three approaches: MIA-QSARt, aug-MIA-QSAR and aug-MIA-QSARcolor. In general, representations of atoms as spheres with different colors and sizes proportional to the corresponding van der Waals radii (aug-MIA approaches) improved the predictive ability and interpretability in all data sets. The use of colors proportional to the Pauling´s electronegativity showed that MIA descriptors are capable of identifying periodic properties relevant for the studied activity. Finally, solid colors instead of spotlighted atoms allowed a correct identification of atoms by means of pixel values in the studies for malaria, dengue and schistosomiasis, which were, subsequently, useful for the chemical interpretation related to the bioactivity. It can be inferred that semicarbazones and thiosemicarbazones derivative with a tri-substituted ring in R1 group and a trifluoro methyl group in the R 3 position instead of a chlorine antitripanossoma resulted in higher activity. The antimalarial activity of quinolon-4(1H)imines can be improved if: 1) R1 and R2 are electron donor groups, 2) R3 has long aminoalkyl chains, and 3) R4 possesses substituents with big atomic volume. In the study for dengue, it was found that tetrapeptides with unbranched small size amino acids in the A1 and A4 positions can increase the substrate affinity (Km) to the NS3 protein, and when in A1 and A2 positions, the substrate cleavage rate (kcat). On the other hand, acidic amino acids in the A2 and A4 positions were found to be related with low substrate affinity to the NS3 protein and when present in A1, with low substrate cleavage rate. Finally, the presence of metoxy substituents in R1 (or R2) and R5 in the neolignan backbone can favor their antischistosomal activity.

Análise genética de resistência ao Rhipicephalus (Boophilus) microplus em bovinos cruzados Hereford x Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV

Identification of species of the Euterpe genus by rare earth elements using inductively coupled plasma mass spectrometry and linear discriminant analysis

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

Predição genômica utilizando painéis de marcadores moleculares com diferentes densidades, em bovinos da raça Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV

Fast determination of beef quality parameters with time-domain nuclear magnetic resonance spectroscopy and chemometrics

The noteworthy of this study is to predict seven quality parameters for beef samples using time-domain nuclear magnetic resonance (TD-NMR) relaxometry data and multivariate models. Samples from 61 Bonsmara heifers were separated into five groups based on genetic (breeding composition) and feed system (grain and grass feed). Seven sample parameters were analyzed by reference methods; among them, three sensorial parameters, flavor, juiciness and tenderness and four physicochemical parameters, cooking loss, fat and moisture content and instrumental tenderness using Warner Bratzler shear force (WBSF). The raw beef samples of the same animals were analyzed by TD-NMR relaxometry using Carr-Purcell-Meiboom-Gill (CPMG) and Continuous Wave-Free Precession (CWFP) sequences. Regression models computed by partial least squares (PLS) chemometric technique using CPMG and CWFP data and the results of the classical analysis were constructed. The results allowed for the prediction of aforementioned seven properties. The predictive ability of the method was evaluated using the root mean square error (RMSE) for the calibration (RMSEC) and validation (RMSEP) data sets. The reference and predicted values showed no significant differences at a 95% confidence level.

Seleção genômica para características de carcaça em bovinos da raça Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV