Multivariate approach in the selection of superior soybean progeny which carry the RR gene

Efficiency in the use of genetic variability, whether existing or created, increases when properly explored and analysed. Incorporation of biotechnology into breeding programs has been the general practice. The challenge for the researcher is the constant development of new and improved cultivars. The aim of this experiment was to select progenies with superior characteristics, whether or not carriers of the RR gene, derived from bi-parental crosses in the soybean, with the help of multivariate techniques. The experiment was carried out in a family-type experimental design, including controls, during the agricultural year 2010/2011 and 2011/2012 in Jaboticabal in the Brazilian State of São Paulo. From the F3 generation, phenotypically superior plants were selected, which were evaluated for the following traits: number of days to flowering; number of days to maturity; height of first pod insertion; plant height at maturity; lodging; agronomic value; number of branches; number of pods per plant; 100-seed weight; number of seeds per plant; grain yield per plant. Given the results, it appears possible to select superior progeny by principal component analysis. Cluster analysis using the K-means method links progeny according to the most important characteristics in each group and identifies, by the Ward method and by means of a dendrogram, the structure of similarity and divergence between selected progeny. Both methods are effective in aiding progeny selection.

Multivariate optimization and validation of an analytical methodology by RP-HPLC for the determination of losartan potassium in capsules

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Allometric growth of two species of Ephemeroptera from Neotropical mountains streams

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estabelecimento de condições para ensaio de dissolução de cápsulas de cinarizina empregando planejamento fatorial

This work describes the establishment of dissolution test conditions for 75 mg cinnarizine capsules using a multivariate approach. A 2³ full factorial design was carried out to achieve the best conditions and HCl 0.1 mol L-1 as dissolution medium, basket as apparatus at 100 rpm and collect time at 30 min were considered adequate. The quantification was carried out by spectrophotometry at 251 nm. Both dissolution procedure and analytical method were validated and all parameters were within the acceptable limits. Since there is no official monograph for this pharmaceutical product, this dissolution test could be applied for quality control routine.

Análise químico-farmacêutica de glimepirida comprimidos

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

Abordagem multivariada, perfil composicional e construção de curva de calibração para predição do teor de óleo em Jatropha curcas L

Pós-graduação em Agronomia (Genética e Melhoramento de Plantas) - FCAV

Elicitation of multivariate prior distributions: A nonparametric Bayesian approach

In the context of Bayesian statistical analysis, elicitation is the process of formulating a prior density f(.) about one or more uncertain quantities to represent a person's knowledge and beliefs. Several different methods of eliciting prior distributions for one unknown parameter have been proposed. However, there are relatively few methods for specifying a multivariate prior distribution and most are just applicable to specific classes of problems and/or based on restrictive conditions, such as independence of variables. Besides, many of these procedures require the elicitation of variances and correlations, and sometimes elicitation of hyperparameters which are difficult for experts to specify in practice. Garthwaite et al. (2005) discuss the different methods proposed in the literature and the difficulties of eliciting multivariate prior distributions. We describe a flexible method of eliciting multivariate prior distributions applicable to a wide class of practical problems. Our approach does not assume a parametric form for the unknown prior density f(.), instead we use nonparametric Bayesian inference, modelling f(.) by a Gaussian process prior distribution. The expert is then asked to specify certain summaries of his/her distribution, such as the mean, mode, marginal quantiles and a small number of joint probabilities. The analyst receives that information, treating it as a data set D with which to update his/her prior beliefs to obtain the posterior distribution for f(.). Theoretical properties of joint and marginal priors are derived and numerical illustrations to demonstrate our approach are given. (C) 2010 Elsevier B.V. All rights reserved.

A simple approach to integrate the ecotoxicological and chemical data for the establishment of environmental risk levels

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.

GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance ((1)H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the (1)H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices. (C) 2010 Elsevier B.V. All rights reserved.

Screening Brazilian Commercial Gasoline Quality by ASTM D6733 GC and Pattern-Recognition Multivariate SIMCA Chemometric Analysis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Bayesian and multivariate methods applied to favorability quantification in recôncavo Basin and Ribeira Belt, Brazil

A methodology to define favorable areas in petroleum and mineral exploration is applied, which consists in weighting the exploratory variables, in order to characterize their importance as exploration guides. The exploration data are spatially integrated in the selected area to establish the association between variables and deposits, and the relationships among distribution, topology, and indicator pattern of all variables. Two methods of statistical analysis were compared. The first one is the Weights of Evidence Modeling, a conditional probability approach (Agterberg, 1989a), and the second one is the Principal Components Analysis (Pan, 1993). In the conditional method, the favorability estimation is based on the probability of deposit and variable joint occurrence, with the weights being defined as natural logarithms of likelihood ratios. In the multivariate analysis, the cells which contain deposits are selected as control cells and the weights are determined by eigendecomposition, being represented by the coefficients of the eigenvector related to the system's largest eigenvalue. The two techniques of weighting and complementary procedures were tested on two case studies: 1. Recôncavo Basin, Northeast Brazil (for Petroleum) and 2. Itaiacoca Formation of Ribeira Belt, Southeast Brazil (for Pb-Zn Mississippi Valley Type deposits). The applied methodology proved to be easy to use and of great assistance to predict the favorability in large areas, particularly in the initial phase of exploration programs. © 1998 International Association for Mathematical Geology.

Ecological relevance of sentinels' biomarker responses: A multi-level approach

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comprehensive two-dimensional gas chromatography combined to multivariate data analysis for detection of disease-resistant clones of Eucalyptus

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