Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools.

The overarching hypothesis of this study was that temporal microbial potentiometric sensor (MPS) signal patterns could be used to predict changes in commonly monitored water quality parameters by using artificial intelligence/machine learning tools. To test this hypothesis, the study first examines a proof of concept by correlating between MPS's signals and high algae concentrations in an algal cultivation pond. Then, the study expanded upon these findings and examined if multiple water quality parameters could be predicted in real surface waters, like irrigation canals. Signals generated between the MPS sensors and other water quality sensors maintained by an Arizona utility company, including algae and chlorophyll, were collected in real time at time intervals of 30 min over a period of 9 months. Data from the MPS system and data collected by the utility company were used to train the ML/AI algorithms and compare the predicted with actual water quality parameters and algae concentrations. Based on the composite signal obtained from the MPS, the ML/AI was used to predict the canal surface water's turbidity, conductivity, chlorophyll, and blue-green algae (BGA), dissolved oxygen (DO), and pH, and predicted values were compared to the measured values. Initial testing in the algal cultivation pond revealed a strong linear correlation (R2 = 0.87) between mixed liquor suspended solids (MLSS) and the MPSs' composite signals. The Normalized Root Mean Square Error (NRMSE) between the predicted values and measured values were <6.5%, except for the DO, which was 10.45%. The results demonstrate the usefulness of MPSs to predict key surface water quality parameters through a single composite signal, when the ML/AI tools are used conjunctively to disaggregate these signal components. The maintenance-free MPS offers a novel and cost-effective approach to monitor numerous water quality parameters at once with relatively high accuracy.

Myofibroblast stromal presence and distribution in squamous epithelial carcinomas, oral dysplasia and hyperkeratosis.

PURPOSE: Stromal elements play a key role in growth and development of different neoplasms. Myofibroblasts are the major components and occur in stromal tissue during carcinogenesis processes. The purpose of this study was to review the frequency and the distribution pattern of myofibroblasts(αSMA-positive) in the stroma of squamous epithelial carcinoma and to compare values with those for with oral dysplasia and hyperkeratosis. METHODS: we evaluated αSMA protein frequency in hyperkeratosis (N =18), oral epithelial dysplasia (N=18) and oral squamous cell carcinoma (N=18) using immunohistochemistry. RESULTS: αSMA-positive expression was observed in 67% of OSCC tissue samples with network and spindle patterns, whereas it was seen in 22% with a focal pattern in dysplasia and in 6% with a scanty pattern in hyperkeratosis cases. CONCLUSION: These findings suggest that an increase in number of myofibroblasts and change in their distribution pattern occurs during carcinogenesis which can be an expression of their role in tumor invasive characteristics.