Generating reference models for structurally complex data. Application to the stabilometry medical domain.

OBJECTIVES: We present a framework specially designed to deal with structurally complex data, where all individuals have the same structure, as is the case in many medical domains. A structurally complex individual may be composed of any type of single-valued or multivalued attributes, including time series, for example. These attributes are structured according to domain-dependent hierarchies. Our aim is to generate reference models of population groups. These models represent the population archetype and are very useful for supporting such important tasks as diagnosis, detecting fraud, analyzing patient evolution, identifying control groups, etc. METHODS: We have developed a conceptual model to represent structurally complex data hierarchically. Additionally, we have devised a method that uses the similarity tree concept to measure how similar two structurally complex individuals are, plus an outlier detection and filtering method. These methods provide the groundwork for the method that we have designed for generating reference models of a set of structurally complex individuals. A key idea of this method is to use event-based analysis for modeling time series. RESULTS: The proposed framework has been applied to the medical field of stabilometry. To validate the outlier detection method we used 142 individuals, and there was a match between the outlier ratings by the experts and by the system for 139 individuals (97.8%). To validate the reference model generation method, we applied k-fold cross validation (k = 5) with 60 athletes (basketball players and ice-skaters), and the system correctly classified 55 (91.7%). We then added 30 non-athletes as a control group, and the method output the correct result in a very high percentage of cases (96.6%). CONCLUSIONS: We have achieved very satisfactory results for the tests on data from such a complex domain as stabilometry and for the comparison of the reference model generation method with other methods. This supports the validity of this framework.

[A method for teaching mathematics]. / Sobre um modo de transmissão da matemática.

This paper reports on an experience in teaching mathematics in a graduate course in public health, intermingled with reflections and concepts. Students enrolled in such courses generally feel uneasy about learning mathematics. Without underrating the usual obstacles and even affinities in developing abstractions, the results suggest that a major portion of the difficulties derives not from mathematics itself, but from an oppressive experience in the past. Difficulties also relate to the construction of space in the classroom. Such a perspective involves understanding experiments and life experiences in which the subjects are involved. In short, the work focuses on how to transmit mathematics in the development of a pedagogical approach.

Toxic effects of water eutrophication on pancreatic, hepatic and osteogenic tissues of rats.

Pollution, industrial solvents, concentrations of metals and other environmental agents are widely related to biochemicals values which are used in disease diagnosis of environmental toxicity. A rat bioassay validated for the identification of toxic effects of eutrophication revealed increased serum activities of amylase, alanine transaminase (ALT) and alkaline phosphatase (ALP) in rats that received algae, filtered water and nickel or cadmium from drinking water. Serum Cu-Zn superoxide dismutase activity decreased from its basal level of 40.8 +/- 2.3 to 26.4 U/mg protein, at 7 days of algae and at 48 hr of nickel and cadmium water ingestion. The observation that lipoperoxide concentration was not altered in rats treated with filtered water, while amylase, ALT and ALP were increased in these rats and in those treated with nickel or cadmium, indicated that pancreatic, hepatic and osteogenic lesions by eutrophication were not related to superoxide radicals, and might be due to a novel toxic environmental agent found in filtered and non-filtered algae water.