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.