Multivariate curve resolution analysis applied to time-resolved synchrotron X-ray Absorption Spectroscopy monitoring of the activation of copper alumina catalyst

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)

Why is it so difficult to identify a single indicator of water stress in plants? A proposal for a multivariate analysis to assess emergent properties

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

Multivariate analysis for animal selection in experimental research

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

Deriving vegetation indices for phenology analysis using genetic programming

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

Barriers related to non-adherence in a mammography breast-screening program during the implementation period in the interior of São Paulo State, Brazil

Mammography is the best exam for early diagnosis of breast cancer. Developing countries frequently have a low income of mammography and absence of organized screening. The knowledge of vulnerable population and strategies to increase adherence are important to improve the implementation of an organized breast-screening program. A mammography regional-screening program was implemented in a place around 54.238 women, aged 40-69 years old. It was proposed to perform biannual mammography free of cost for the women. We analyze the first 2 years of the implementation of the project. Mammography was realized in 17.964 women. 42.1% of the women hadn't done de mammography in their lives and these women were principally from low socio-economic status (OR=2.99), low education (OR=3.00). The best strategies to include these women were mobile unit (OR=1.43) and Family Health Program (OR=1.79). The incidence of early breast tumors before the project was 14.5%, a fact that changed to 43.2% in this phase. Multivariate analysis showed that the association of illiterate and the mobile unit achieve more women who had not performed mammography in their lives. The strategies to increase adherence to mammography must be multiple and a large organization is necessary to overpass the barriers related to system health and education.

Multivariate analysis for selecting animals for experimental research

Background Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate.

Morphological patterns and community organization in leaf-litter ant assemblages

We present a new approach to determine the number and composition of guilds, using the hyperdiverse leaf-litter ant fauna as a model, based on appropriate morphological variables and species co-occurrence null models to describe the complex assemblages of interacting Species Community structure at the 1-m(2) scale. We obtained 18 linear morphometric measures from 949 workers of 171 leaf-litter ant species (18762 measurements) surveyed in four Atlantic Forest localities to test whether the assemblages are morphologically structured; the morphological characters were selected to indicate diet and foraging habits. Principal components analysis was used to characterize the morphospace and to describe the guild structure (number of species and composition). The guild proportionality assembly rule (significant tendency toward constant proportion of species in guilds) was assessed at the 1-m(2) scale. Our analysis indicates that the division of leaf-litter ants into guilds is based mainly on microhabitat distribution in the leaf-litter, body size and shape, eye size, and phylogeny. The same guild scheme applied to four more sites shows that different Atlantic Forest areas have the same leaf-fitter ant guilds. The guild proportionality assembly rule was confirmed for most guilds, Suggesting that there are guild-specific limitations on species coexistence within assemblages; on the other hand, in a few cases the variance in guild proportion was greater than expected under the null assumptions. Other studies on ant functional group classification are partially supported by our quantitative morphological analysis. Our results, however, imply that there are more compartments than indicated in previous models, particularly among cryptic species (confined to soil and litter) and tropical climate specialists. We argue that a general null model for the analysis of species association based oil morphology can reveal objectively defined groups and may thus contribute to a robust theory to explain community structure in general and have important consequences on studies of litter ant community ecology in particular.

Prevalence of hepatitis B virus infection and carriage after nineteen years of vaccination program in the Western Brazilian Amazon

Introduction: Reductions in the prevalence of hepatitis B virus (HBV) infection and carriage, decreases in liver cancer incidence, and changes in patterns of liver dysfunctions are described after hepatitis B vaccination. Methods: We conducted a population-based seroprevalence study aimed at estimating the HBV prevalence and risk of infection in the rural area of Labrea following nineteen years of HBV vaccination. Results: Half of the subjects showed total anti-HBc of 52.1% (95% CI 49.6-54.7). The HBsAg prevalence was 6.2% (95% CI 5.1-7.6). Multivariate analysis showed an inverse association between HBV infection and vaccination (OR 0.62; 95% CI 0.44-0.87). HBsAg remained independently associated with past hepatitis (OR 2.44; 95% CI 1.52-3.89) and inversely to vaccination (OR 0.43; 95% CI 0.27-0.69). The prevalence of HBeAg among HBsAg-positive individuals was 20.4% (95% CI 12.8-30.1), with the positive subjects having a median age of 11 years (1-46) p=0.0003. Conclusions: We demonstrate that HBV infection is still an important public health issue and that HBV vaccination could have had better impact on HBV epidemiology. If we extrapolate these findings to other rural areas in the Brazilian Amazon, we can predict that the sources of chronic infected patients remain a challenge. Future studies are needed regarding clinical aspects, molecular epidemiology, surveillance of acute cases, and risk groups.

Detecting an abnormality in a recovery boiler using dynamic multivariate data analysis with parallel coordinate plots

Data visualization techniques are powerful in the handling and analysis of multivariate systems. One such technique known as parallel coordinates was used to support the diagnosis of an event, detected by a neural network-based monitoring system, in a boiler at a Brazilian Kraft pulp mill. Its attractiveness is the possibility of the visualization of several variables simultaneously. The diagnostic procedure was carried out step-by-step going through exploratory, explanatory, confirmatory, and communicative goals. This tool allowed the visualization of the boiler dynamics in an easier way, compared to commonly used univariate trend plots. In addition it facilitated analysis of other aspects, namely relationships among process variables, distinct modes of operation and discrepant data. The whole analysis revealed firstly that the period involving the detected event was associated with a transition between two distinct normal modes of operation, and secondly the presence of unusual changes in process variables at this time.

Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis

Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission (P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

Quantitative Chemical Profile and Multivariate Statistical Analysis of Alembic Distilled Sugarcane Spirit Fractions

Concentrations of 39 organic compounds were determined in three fractions (head, heart and tail) obtained from the pot still distillation of fermented sugarcane juice. The results were evaluated using analysis of variance (ANOVA), Tukey's test, principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). According to PCA and HCA, the experimental data lead to the formation of three clusters. The head fractions give rise to a more defined group. The heart and tail fractions showed some overlap consistent with its acid composition. The predictive ability of calibration and validation of the model generated by LDA for the three fractions classification were 90.5 and 100%, respectively. This model recognized as the heart twelve of the thirteen commercial cachacas (92.3%) with good sensory characteristics, thus showing potential for guiding the process of cuts.

IDENTIFICATION OF ETHANOLIC WOOD EXTRACTS USING ELECTRONIC ABSORPTION SPECTRUM AND MULTIVARIATE ANALYSIS

IDENTIFICATION OF ETHANOLIC WOOD EXTRACTS USING ELECTRONIC ABSORPTION SPECTRUM AND MULTIVARIATE ANALYSIS. The application of multivariate analysis to spectrophotometric (UV) data was explored for distinguishing extracts of cachaca woods commonly used in the manufacture of casks for aging cachacas (oak, cabretiva-parda, jatoba, amendoim and canela-sassafras). Absorbances close to 280 nm were more strongly correlated with oak and jatoba woods, whereas absorbances near 230 nm were more correlated with canela-sassafras and cabretiva-parda. A comparison between the spectrophotometric model and the model based on chromatographic (HPLC-DAD) data was carried out. The spectrophotometric model better explained the variance data (PC1 + PC2 = 91%) exhibiting potential as a routine method for checking aged spirits.

Biochemical Analysis of a Castor Bean Leaf Extract and its Insecticidal Effects Against Spodoptera frugiperda (Smith) (Lepidoptera: Noctuidae)

Plant extracts represent a great source of molecules, with insecticidal activity, which are used for pest control in several crop production systems. This work aimed to evaluate the toxicity of an aqueous extract of leaves of castor bean against larvae of Spodoptera frugiperda (Smith) (Lepidoptera: Noctuidae) in search for different classes of molecules with insecticidal activities by using in vitro assays. The effects of the castor bean leaf extract on the food utilization, development, and survival of S. frugiperda larvae was evaluated by feeding the larvae an artificial diet supplemented with different concentrations of the extract (0%, 1%, 2.5%, 5%, and 10% w/v). The effects observed were dose-dependent, and the highest concentration evaluated (10% w/v) was the one the most affected food utilization by altering the nutritional indices, as well as larval weight gain, development time, and survivorship. In vitro assays to detect saponins, lectins, and trypsin inhibitors in the castor bean leaf extract were performed, but only trypsin inhibitors were detected. No preference for the diet source was detected in S. frugiperda by feeding the larvae in choice experiments with diets containing different concentrations of the castor bean extract tested. The data obtained indicate the existence of a potential molecule in the tested extract of castor bean to be used as an alternative insecticide to be integrated in the management of S. frugiperda.

Multivariate analysis in the maximum strength performance

This study performed an exploratory analysis of the anthropometrical and morphological muscle variables related to the one-repetition maximum (1RM) performance. In addition, the capacity of these variables to predict the force production was analyzed. 50 active males were submitted to the experimental procedures: vastus lateralis muscle biopsy, quadriceps magnetic resonance imaging, body mass assessment and 1RM test in the leg-press exercise. K-means cluster analysis was performed after obtaining the body mass, sum of the left and right quadriceps muscle cross-sectional area (Sigma CSA), percentage of the type II fibers and the 1RM performance. The number of clusters was defined a priori and then were labeled as high strength performance (HSP1RM) group and low strength performance (LSP1RM) group. Stepwise multiple regressions were performed by means of body mass, Sigma CSA, percentage of the type II fibers and clusters as predictors' variables and 1RM performance as response variable. The clusters mean +/- SD were: 292.8 +/- 52.1 kg, 84.7 +/- 17.9 kg, 19249.7 +/- 1645.5 mm(2) and 50.8 +/- 7.2% for the HSP1RM and 254.0 +/- 51.1 kg, 69.2 +/- 8.1 kg, 15483.1 +/- 1 104.8 mm(2) and 51.7 +/- 6.2 %, for the LSP1RM in the 1RM, body mass, Sigma CSA and muscle fiber type II percentage, respectively. The most important variable in the clusters division was the Sigma CSA. In addition, the Sigma CSA and muscle fiber type II percentage explained the variance in the 1RM performance (Adj R-2 = 0.35, p = 0.0001) for all participants and for the LSP1RM (Adj R-2 = 0.25, p = 0.002). For the HSP1RM, only the Sigma CSA was entered in the model and showed the highest capacity to explain the variance in the 1RM performance (Adj R-2 = 0.38, p = 0.01). As a conclusion, the muscle CSA was the most relevant variable to predict force production in individuals with no strength training background.