The Smart Predicting of Algal Concentration for Safer Drinking Water Production with Sensor Data.

To better predict the timely variation of algal blooms and other vital factors for safer drinking water production, a new AI scanning-focusing process was investigated for improving the simulation and prediction of algae counts. With a feedforward neural network (FNN) as a base, nerve cell numbers in the hidden layer and the permutation and combination of factors, etc., were fully scanned to select the best models and highly correlated factors. All the factors involved in the modeling and selection included the date (year/month/day), sensor data (temperature, pH, conductivity, turbidity, UV254-dissolved organic matter, etc.), lab measurements (algae concentration) and calculated CO2 concentration. The new AI scanning-focusing process resulted in the best models with the most suitable key factors, which are named closed systems. In this case study, models with highest prediction performance are the (1) date-algae-temperature-pH (DATH) and (2) date-algae-temperature-CO2 (DATC) systems. After the model selection process, the best models from both DATH and DATC were used to compare the other two methods in the modeling simulation process: the simple traditional neural network method (SP), where only date and target factor as inputs, and a blind AI training process (BP), which considers all available factors as inputs. Validation results show that all methods except BP had comparable results for algae prediction and other water quality factors, such as temperature, pH and CO2, among which DATC displayed an obviously poorer performance through curve fitting with original CO2 data compared to that of SP. Therefore, DATH and SP were selected for the application test, where DATH outperformed SP due to the uncompromised performance after a long training period. Our AI scanning-focusing process and model selection showed the potential for improving water quality prediction by identifying the most suitable factors. This provides a new method to be considered in the enhancing of numerical prediction for the factors in water quality prediction and broader environment-related areas.

Inhibitory role of acyl homoserine lactones in hemolytic activity and viability of Streptococcus pyogenes M6 S165.

Streptococcus pyogenes an adapted human pathogen asymptomatically colonizes the nasopharynx, among other polymicrobial communities. However, information on the events leading to the colonization and expression of virulence markers subject to interspecies and host-bacteria interactions are limited. The interference of acyl homoserine lactones (AHLs) with the hemolytic activity and viability of S. pyogenes M6 S165 was examined. AHLs, with fatty acid side chains ≥12 carbon atoms, inhibited hemolytic activity by downregulating the expression of the sag operon involved in the production of streptolysin S. Inhibitory AHLs upregulated the expression of transcriptional regulator LuxR. Electrophoretic mobility shift assays revealed the interaction of LuxR with the region upstream of sagA. AHL-mediated bactericidal activity observed at higher concentrations (mM range) was an energy-dependent process, constrained by the requirement of glucose and iron. Ferrichrome transporter FtsABCD facilitated transport of AHLs across the streptococcal membrane. The study demonstrates a previously unreported role for AHLs in S. pyogenes virulence.