Comparison Between Micro- and Macrosurgical Techniques for the Treatment of Localized Gingival Recessions Using Coronally Positioned Flaps and Enamel Matrix Derivative

Background: The aim of this study is to compare the macro- and microsurgery techniques for root coverage using a coronally positioned flap (CPF) associated with enamel matrix derivative (EMD). Methods: Thirty patients were selected for the treatment of localized gingival recessions (GRs) using CPF associated to EMD. Fifteen patients were randomly assigned to the test group (TG), and 15 patients were randomly assigned to the control group (CG). The microsurgical approach was performed in the TG, and the conventional macrosurgical technique was performed in the CG. The clinical parameters evaluated before surgery and after 6 months were GR, probing depth, relative clinical attachment level, width of keratinized tissue (WKT), and thickness of keratinized tissue (TKT). The discomfort evaluation was performed 1 week postoperative. Results: There were no statistically significant differences between groups for all parameters at baseline. At 6 months, there was no statistically significant difference between the techniques in achieving root coverage. The percentage of root coverage was 92% and 83% for TG and CG, respectively. After 6 months, there was a statistically significant increase of WKT and TKT in TG only. Both procedures were well tolerated by all patients. Conclusions: The macro- and microsurgery techniques provided a statistically significant reduction in GR height. After 6 months, there was no statistically significant difference between the techniques regarding root coverage, and the microsurgical technique demonstrated a statistically significant increase in WKT and TKT. J Periodontol 2010;81:1572-1579.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

GEDI: a user-friendly toolbox for analysis of large-scale gene expression data

Abstract Background Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills. Results Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al. Conclusion GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.

The impact of atmospheric particulate matter on cancer incidence and mortality in the city of Sao Paulo, Brazil

This study aimed to verify the impact of inhalable particulate matter (PM10) on cancer incidence and mortality in the city of Sao Paulo, Brazil. Statistical techniques were used to investigate the relationship between PM10 on cancer incidence and mortality in selected districts. For some types of cancer (skin, lung, thyroid, larynx, and bladder) and some periods, the correlation coefficients ranged from 0.60 to 0.80 for incidence. Lung cancer mortality showed more correlations during the overall period. Spatial analysis showed that districts distant from the city center showed higher than expected relative risk, depending on the type of cancer According to the study, urban PM10 can contribute to increased incidence of some cancers and may also contribute to increased cancer mortality. The results highlight the need to adopt measures to reduce atmospheric PM10 levels and the importance of their continuous monitoring.

Homogeneity assessment of a station climate series (1933-2005) in the Metropolitan Area of Sao Paulo: instruments change and urbanization effects

This work assessed homogeneity of the Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG) weather station climate series, using various statistical techniques. The record from this target station is one of the longest in Brazil, having commenced in 1933 with observations of precipitation, and temperatures and other variables later in 1936. Thus, it is one of the few stations in Brazil with enough data for long-term climate variability and climate change studies. There is, however, a possibility that its data may have been contaminated by some artifacts over time. Admittedly, there was an intervention on the observations in 1958, with the replacement of instruments, for which the size of impact has not been yet evaluated. The station transformed in the course of time from rural to urban, and this may also have influenced homogeneity of the observations and makes the station less representative for climate studies over larger spatial scales. Homogeneity of the target station was assessed applying both absolute, or single station tests, and tests relatively to regional climate, in annual scale, regarding daily precipitation, relative humidity, maximum (TMax), minimum (TMin), and wet bulb temperatures. Among these quantities, only precipitation does not exhibit any inhomogeneity. A clear signal of change of instruments in 1958 was detected in the TMax and relative humidity data, the latter certainly because of its strong dependence on temperature. This signal is not very clear in TMin, but it presents non-climatic discontinuities around 1953 and around 1970. A significant homogeneity break is found around 1990 for TMax and wet bulb temperature. The discontinuities detected after 1958 may have been caused by urbanization, as the observed warming trend in the station is considerably greater than that corresponding to regional climate.

Characterization of red wines from South America based on sensory properties and antioxidant activity

BACKGROUND: It is widely accepted that red wines constitute one of the most important sources of dietary polyphenolic antioxidants. However, it is still not known how some variables such as variety, vintage, country of origin, and retail price are associated with the antioxidant activity and sensory profile of South American red wines. In this regard, 80 samples produced in Brazil, Chile and Argentina were assessed in relation to their sensory properties, color and in vitro antioxidant activity, and results were subjected to multivariate statistical techniques. RESULTS: Samples were grouped in clusters, characterized by high, intermediate and low in vitro antioxidant activity, sensory properties and prices. It was possible to observe that wines with high antioxidant activity were associated to high retail prices and overall perception of sensory quality. CONCLUSION: South American wines produced from Vitis vinifera such as Syrah, Malbec and Cabernet Sauvignon had higher in vitro antioxidant activity and also higher sensory quality than wines produced from Vitis labrusca. This result was independent of vintage (2002-2010), corroborating the idea that the same grape varietal, even when produced in different years, displays similar sensory characteristics and antioxidant activity. (C) 2011 Society of Chemical Industry

Comparison of INAA elemental composition data between Lago Grande and Osvaldo archaeological sites in the central Amazon: a first perspective

In this work, 50 ceramic fragments from the Lago Grande and 30 from the Osvaldo archaeological site were compared to assess elemental similarities. The aim is to perform a preliminary comparison between the sites, which are located in the central Amazon, Brazil. The analytical technique employed to obtain the ceramics elemental composition was instrumental neutron activation analysis (INAA). The data set obtained was explored by the multivariate statistical techniques of cluster, principal component and discriminant analysis. The analyzed elements were: Na, Lu, U, Yb, La, Th, Cr, Cs, Sc, Fe, Eu, Ce and Hf. The results showed the existence of at least two compositional groups for Lago Grande and Osvaldo. Each compositional group of Osvaldo archaeological site matches with one group of Lago Grande. Correlated with the archaeological background, the results suggest commercial or cultural exchange in the region, which is an indicative of socio-cultural interactions between those sites.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

The impact of atmospheric particulate matter on cancer incidence and mortality in the city of São Paulo, Brazil

This study aimed to verify the impact of inhalable particulate matter (PM10) on cancer incidence and mortality in the city of São Paulo, Brazil. Statistical techniques were used to investigate the relationship between PM10 on cancer incidence and mortality in selected districts. For some types of cancer (skin, lung, thyroid, larynx, and bladder) and some periods, the correlation coefficients ranged from 0.60 to 0.80 for incidence. Lung cancer mortality showed more correlations during the overall period. Spatial analysis showed that districts distant from the city center showed higher than expected relative risk, depending on the type of cancer. According to the study, urban PM10 can contribute to increased incidence of some cancers and may also contribute to increased cancer mortality. The results highlight the need to adopt measures to reduce atmospheric PM10 levels and the importance of their continuous monitoring.