New Fefe-hydrogenase Genes Identified In A Metagenomic Fosmid Library From A Municipal Wastewater Treatment Plant As Revealed By High-throughput Sequencing.

A fosmid metagenomic library was constructed with total community DNA obtained from a municipal wastewater treatment plant (MWWTP), with the aim of identifying new FeFe-hydrogenase genes encoding the enzymes most important for hydrogen metabolism. The dataset generated by pyrosequencing of a fosmid library was mined to identify environmental gene tags (EGTs) assigned to FeFe-hydrogenase. The majority of EGTs representing FeFe-hydrogenase genes were affiliated with the class Clostridia, suggesting that this group is the main hydrogen producer in the MWWTP analyzed. Based on assembled sequences, three FeFe-hydrogenase genes were predicted based on detection of the L2 motif (MPCxxKxxE) in the encoded gene product, confirming true FeFe-hydrogenase sequences. These sequences were used to design specific primers to detect fosmids encoding FeFe-hydrogenase genes predicted from the dataset. Three identified fosmids were completely sequenced. The cloned genomic fragments within these fosmids are closely related to members of the Spirochaetaceae, Bacteroidales and Firmicutes, and their FeFe-hydrogenase sequences are characterized by the structure type M3, which is common to clostridial enzymes. FeFe-hydrogenase sequences found in this study represent hitherto undetected sequences, indicating the high genetic diversity regarding these enzymes in MWWTP. Results suggest that MWWTP have to be considered as reservoirs for new FeFe-hydrogenase genes.

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.