Remediation mechanism of high concentrations of multiple heavy metals in contaminated soil by Sedum alfredii and native microorganisms.

Pollution accident of nonferrous metallurgy industry often lead to serious heavy metal pollution of the surrounding soil. Phytoremediation of contaminated soil is an environmental and sustainable technology, and soil native microorganisms in the process of phytoremediation also participate in the remediation of heavy metals. However, the effects of high concentrations of multiple heavy metals (HCMHMs) on plants and native soil microorganisms remain uncertain.

Effects of dissolved organic matter on distribution characteristics of heavy metals and their interactions with microorganisms in soil under long-term exogenous effects.

Long-term waste accumulation (LTWA) in soil not only alters its physical and chemical properties but also affects heavy metals and microorganisms in polluted soil through the dissolved organic matter (DOM) it produces. However, research on the impact of DOM from LTWA on heavy metals and microorganisms in polluted soil is limited, which has resulted in an incomplete understanding of the mechanisms involved in LTWA soils remediation. This study focuses on the DOM generated by waste accumulation and analyses the physicochemical properties, microbial community structure, and vertical distribution of heavy metals in four types of LTWA soils at different depths (0-100 cm).

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.

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.

[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.