Real-Time Nowcasting of Microbiological Water Quality at Recreational Beaches: A Wavelet and Artificial Neural Network-Based Hybrid Modeling Approach.

The number of beach closings caused by bacterial contamination has continued to rise in recent years, putting beachgoers at risk of exposure to contaminated water. Current approaches predict levels of indicator bacteria using regression models containing a number of explanatory variables. Data-based modeling approaches can supplement routine monitoring data and provide highly accurate short-term forecasts of beach water quality. In this paper, we apply the nonlinear autoregressive network with exogenous inputs (NARX) method with explanatory variables to predict Escherichia coli concentrations at four Lake Michigan beach sites. We also apply the nonlinear input-output network (NIO) and nonlinear autoregressive neural network (NAR) methods in addition to a hybrid wavelet-NAR (WA-NAR) model and demonstrate their application. All models were tested using 3 months of observed data. Results revealed that the NARX models provided the best performance and that the WA-NAR model, which requires no explanatory variables, outperformed the NIO and NAR models; therefore, the WA-NAR model is suitable for application to data scarce regions. The models proposed in this paper were evaluated using multiple performance metrics, including sensitivity and specificity measures, and produced results comparable or superior to those of previous mechanistic and statistical models developed for the same beach sites. The relatively high R2 values between data and the NARX models ( R2 values of ∼0.8 for the beach sites and ∼0.9 for the river site) indicate that the new class of models shows promise for beach management.

Proteogenomic Analyses Revealed Favorable Metabolism Pattern Alterations in Rotifer Brachionus plicatilis Fed with Selenium-rich Chlorella.

Organoselenium have garnered attention because of their potential to be used as ingredients in new anti-aging and antioxidation medicines and food. Rotifers are frequently used as a model organism for aging research. In this study, we used Se-enriched Chlorella (Se- Chlorella), a novel organoselenium compound, to feed Brachionus plicatilis to establish a rotifer model with a prolonged lifespan. The results showed that the antioxidative effect in Se-enriched rotifer was associated with an increase in guaiacol peroxidase (GPX) and catalase (CAT). The authors then performed the first proteogenomic analysis of rotifers to understand their possible metabolic mechanisms. With the de novo assembly of RNA-Seq reads as the reference, we mapped the proteomic output generated by iTRAQ-based mass spectrometry. We found that the differentially expressed proteins were primarily involved in antireactive oxygen species (ROS) and antilipid peroxidation (LPO), selenocompound metabolism, glycolysis, and amino acid metabolisms. Furthermore, the ROS level of rotifers was diminished after Se- Chlorella feeding, indicating that Se- Chlorella could help rotifers to enhance their amino acid metabolism and shift the energy generating metabolism from tricarboxylic acid cycle to glycolysis, which leads to reduced ROS production. This is the first report to demonstrate the anti-aging effect of Se- Chlorella on rotifers and to provide a possible mechanism for this activity. Thus, Se- Chlorella is a promising novel organoselenium compound with the potential to prolong human lifespans.