A screening method for detection of hexavalent chromium levels in soils

A rapid and low cost method to determine Cr(VI) in soils based upon alkaline metal extraction at room temperature is proposed as a semi-quantitative procedure to be performed in the field. A color comparison with standards with contents of Cr(VI) in the range of 10 to 150 mg kg-1 was used throughout. For the different types of soils studied, more than 75% of the fortified soluble Cr(VI) were recovered for all levels of spike tested for both the proposed and standard methods. Recoveries of 83 and 99% were obtained for the proposed and the standard methods, respectively, taking into account the analysis of a heavily contaminated soil sample.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Torque articular e ativação dos musculos biceps femoral e semi-tendineo durante movimentos isocineticos de flexão do joelho em atletas de futebol

Universidade Estadual de Campinas . Faculdade de Educação Física

Mensuração da potência mecânica em esforços de alta intensidade na condição laboratorial de corrida atada e na condição de campo em corrida semi-atada e livre

Universidade Estadual de Campinas . Faculdade de Educação Física