Variabilidade espacial e sazonal da concentração de elementos-traço em sedimentos do sistema estuarino de Santos-Cubatão (SP)

Multi-element analyses of sediment samples from the Santos-Cubatão Estuarine System were carried out to investigate the spatial and seasonal variability of trace-element concentrations. The study area contains a rich mangrove ecosystem that is a habitat for tens of thousands of resident and migratory birds, some of them endangered globally. Enrichments of metals in fine-grained surface sediments are, in decreasing order, Hg, Mn, La, Ca, Sr, Cd, Zn, Pb, Ba, Cu, Cr, Fe, Nb, Y, Ni and Ga, relative to pre-industrial background levels. The maximum enrichment ranged from 49 (Hg) to 3.1 (Ga). Mercury concentrations were greater in the Cubatão river than in other sites, while the other elements showed greater concentrations in the Morrão river. Concentrations of Mn were significantly greater in winter and autumn than in summer and spring. However, other elements (e.g. Cd and Pb) showed the opposite, with greater concentrations in summer and spring. This study suggests that seasonal changes in physical and chemical conditions may affect the degree of sediment enrichment and therefore make the assessment of contamination difficult. Consequently, these processes need to be considered when assessing water quality and the potential contamination of biota.

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.