Effect of microbial network complexity and stability on nitrogen and sulfur pollutant removal during sediment remediation in rivers affected by combined sewer overflows.

Discovering the complexity and improving the stability of microbial networks in urban rivers affected by combined sewer overflows (CSOs) is essential for restoring the ecological functions of urban rivers, especially to improve their ability to resist CSO impacts. In this study, the effects of sediment remediation on the complexity and stability of microbial networks was investigated. The results revealed that the restored microbial community structure using different approaches in the river sediments differed significantly, and random matrix theory showed that sediment remediation significantly affected microbial networks and topological properties; the average path distance, average clustering coefficient, connectedness, and other network topological properties positively correlated with remediation time and weakened the small-world characteristics of the original microbial networks. Compared with other sediment remediation methods, regulating low dissolved oxygen (DO) shifts the microbial network module hubs from Actinobacteria and Bacteroidetes to Chloroflexi and Proteobacteria. This decreases the positive association of networks by 17%-18%, which intensifies the competitiveness among microorganisms, further weakening the influence and transmission of external pressure across the entire microbial network. Compared with that of the original sediment, the vulnerability of the restored network was reduced by more than 36%, while the compositional stability was improved by more than 12%, with reduced fluctuation in natural connectivity. This microbial network succession substantially increased the number of key enzyme-producing genes involved in nitrogen and sulfur metabolism, enhancing nitrification, denitrification, and assimilatory sulfate reduction, thereby increasing the removal rates of ammonia, nitrate, and acid volatile sulfide by 43.42%, 250.68% and 2.66%, respectively. This study comprehensively analyzed the succession patterns of microbial networks in urban rivers affected by CSOs before and after sediment remediation, which may provide a reference for reducing the impact of CSO pollution on urban rivers in the subsequent stages.

Automatic identification of harmful algae based on multiple convolutional neural networks and transfer learning.

The monitoring of harmful phytoplankton is very important for the maintenance of the aquatic ecological environment. Traditional algae monitoring methods require professionals with substantial experience in algae species, which are time-consuming, expensive, and limited in practice. The automatic classification of algae cell images and the identification of harmful phytoplankton images were realized by the combination of multiple convolutional neural networks (CNNs) and deep learning techniques based on transfer learning in this work. Eleven common harmful and 31 harmless phytoplankton genera were collected as input samples; the five CNNs classification models of AlexNet, VGG16, GoogLeNet, ResNet50, and MobileNetV2 were fine-tuned to automatically classify phytoplankton images; and the average accuracy was improved 11.9% when compared to models without fine-tuning. In order to monitor harmful phytoplankton which can cause red tides or produce toxins severely polluting drinking water, a new identification method of harmful phytoplankton which combines the recognition results of five CNN models was proposed, and the recall rate reached 98.0%. The experimental results validate that the recognition performance of harmful phytoplankton could be significantly improved by transfer learning, and the proposed identification method is effective in the preliminary screening of harmful phytoplankton and greatly reduces the workload of professional personnel.

[Effects of ursolic acid on liver-protection and bile secretion].

OBJECTIVE: To study the liver-protection and promoting bile secretion of ursolic acid. METHODS: Ursolic acid was administrated to the mice with liver injury induced by CCl4 or APIT. ALT, AST levels in serum were examined. The bile flow rate and the concentration of the main components in the bile of rats administrated with ursolic acid were estimated. RESULTS: Ursolic acid could decrease the serum ALT, AST, SB levels in the mice treated with CCl4 or APIT, promote the secretion of bile and increase the concentration of bilirubin in the bile. CONCLUSION: Ursolic acid exhibited significant liver protection and promoting blie secretion.