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The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and time-consuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for forecasting algal blooms is a feasible and useful method to overcome these problems. This study explores the potential application of optical measures to enhance algal bloom prediction in terms of prediction accuracy and workload reduction, aided by machine learning (ML) models. Compared to absorption-derived parameters, commonly used fluorescence indices such as the fluorescence index (FI), humification index (HIX), biological index (BIX), and protein-like component improved the prediction accuracy. However, the prediction accuracy was decreased when all optical indices were considered for computation due to increased noise and uncertainty in the models. With the exception of chemical oxygen demand (COD), this study successfully replaced biochemical oxygen demand (BOD), dissolved organic carbon (DOC), and nutrients with selected fluorescence indices, demonstrating relatively analogous performance in either training or testing data, with consistent and good coefficient of determination (R 2 ) values of approximately 0.85 and 0.74, respectively. Among all models considered, ensemble learning models consistently outperformed conventional regression models and artificial neural networks (ANNs). However, there was a trade-off between accuracy and computation efficiency among the ensemble learning models (i.e., Stacking and XGBoost) for algal bloom prediction. Our study offers a glimpse of the potential application of spectroscopic measures to improve accuracy and efficiency in algal bloom prediction, but further work should be carried out in other water bodies to further validate our proposed hypothesis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Many studies have linked ambient fine particulate matter (PM2.5) air pollution to different cardiopulmonary diseases in the general population. However the complex mechanisms underlying PM2.5-induced adverse health effects are not yet to be fully elucidated., Method: In this study, we aimed to identify genes and pathways that may contribute to PM2.5-induced lung toxicity in humans through genome-wide approaches. Human bronchial epithelial (HBE) cells, exposed to various concentrations of PM2.5 collected from Wuhan, China, showed decreased cell viability in a dose-dependent manner. HBE cells were exposed to 200 μg/ml and 500 μg/ml PM2.5 and microarrays were used to obtain a global view of the transcriptomic responses., Results: A total of 970 and 492 genes were identified that significantly changed after 200 μg/ml and 500 μg/ml PM2.5 exposures, respectively. PM2.5 induced a large number of genes involved in inflammatory and immune response, response to oxidative stress, and response to DNA damage stimulus, which might contribute to PM2.5 related cardiopulmonary diseases. Pathway analysis revealed that different dose of PM2.5 triggered partially common disturbed pathways. Flow cytometry assay evidenced that there were statistically significant differences in the G1 phase of cell cycle after low or high-dose PM2.5 exposure when compared to the unexposed controls. Only high-dose PM2.5 significantly increased the proportion of cells in the S phase of cell cycle., Conclusion: We identified many genes and pathways that altered significantly in HBE cells after PM2.5 exposures. These findings are important in providing further understanding of the mechanisms underlying PM2.5-induced adverse health effects., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

In this paper, the hemoglobin (Hb)-collagen microbelt modified electrode with three-dimensional configuration was fabricated via the electrospinning method. Direct electron transfer of the Hb immobilized into the electrospun collagen microbelts was greatly facilitated. The apparent heterogeneous electron transfer rate constant (k(s)) was calculated to be 270.6s⁻¹. The electrospun Hb-collagen microbelt modified electrode showed an excellent bioelectrocatalytic activity toward the reduction of H₂O₂. The amperometric response of the biosensor varied linearly with the H₂O₂ concentration ranging from 5 × 10⁻⁶molL⁻¹ to 30×10⁻⁶molL⁻¹, with a detection limit of 0.37 × 10⁻⁶molL⁻¹ (signal-to-noise ratio of 3). The apparent Michaelis-Menten constant (K(m)(app)) was 77.7 μmolL⁻¹. The established biosensor exhibited fast amperometric response, high sensitivity, good reproducibility and stability., (Copyright © 2011 Elsevier B.V. All rights reserved.)