Ecological risk of phenol on typical biota of the northern Chinese river from an integrated probability perspective: the Hun River as an example.

Phenol, known for its bioaccumulative nature and severe toxicity to riverine organisms, poses complex challenges for ecological risk assessment. To tackle this issue, we developed a three-stage incremental assessment method, providing an integrated perspective on phenol toxicity risk for aquatic organisms. The findings indicated that phenol concentrations were generally higher in the aquatic environments of northern rivers, such as the Hun River, Taizi River, and Liao River, compared to those in southern China. The evaluation results at individual points showed that the ecological risk of phenol to aquatic organisms ranked from high to low during rainy, dry, and normal seasons, showing seasonal variation characteristics. Regarding spatial variation along the river, the ecological risk of phenol gradually increased from upper reaches, peaked in the middle reaches, and then decreased in the lower reaches. Considering the different species types, fish face a higher risk of toxic effects of phenol than invertebrates when exposed to phenol over a long period of time, probably due to the bioaccumulative nature of phenol. To address ecological risk control at the watershed scale, there is an urgent need to revise China's current river water quality standards. It is essential to increase the emphasis on ecological risk control for aquatic organisms. Developing more targeted and refined ecological risk control strategies for river phenols is crucial to maintain a healthier and more vibrant river ecosystem.

NMR-based platform for fragment-based lead discovery used in screening BRD4-targeted compounds.

AIM: Fragment-based lead discovery (FBLD) is a complementary approach in drug research and development. In this study, we established an NMR-based FBLD platform that was used to screen novel scaffolds targeting human bromodomain of BRD4, and investigated the binding interactions between hit compounds and the target protein. METHODS: 1D NMR techniques were primarily used to generate the fragment library and to screen compounds. The inhibitory activity of hits on the first bromodomain of BRD4 [BRD4(I)] was examined using fluorescence anisotropy binding assay. 2D NMR and X-ray crystallography were applied to characterize the binding interactions between hit compounds and the target protein. RESULTS: An NMR-based fragment library containing 539 compounds was established, which were clustered into 56 groups (8-10 compounds in each group). Eight hits with new scaffolds were found to inhibit BRD4(I). Four out of the 8 hits (compounds 1, 2, 8 and 9) had IC50 values of 100-260 µmol/L, demonstrating their potential for further BRD4-targeted hit-to-lead optimization. Analysis of the binding interactions revealed that compounds 1 and 2 shared a common quinazolin core structure and bound to BRD4(I) in a non-acetylated lysine mimetic mode. CONCLUSION: An NMR-based platform for FBLD was established and used in discovery of BRD4-targeted compounds. Four potential hit-to-lead optimization candidates have been found, two of them bound to BRD4(I) in a non-acetylated lysine mimetic mode, being selective BRD4(I) inhibitors.

Deep learning: To better understand how human activities affect the value of ecosystem services-A case study of Nanjing.

The value of ecosystem services is affected by increasing human activities. However, the anthropogenic driving mechanisms of ecosystem services are poorly understood. Here, we established a deep learning model to approximate the ecosystem service value (ESV) of Nanjing City using 23 socioeconomic factors. A multi-view analysis was then conducted on feasible impact mechanisms using model disassembly. The results indicated that certain factors had their own significant and independent effects on ESV, such as the proportion of water areas in the land-use structure and the output value of the secondary industry. The proportion of ecological water should be increased as much as possible, whereas the output value of the secondary industry should be reasonably controlled in Nanjing. Other intrinsically related factors were likely to be composited together to affect ESV, such as industrial water consumption and industrial electricity consumption. In Nanjing, simultaneously optimizing socio-economic factors related to city size, resources, and energy use efficiency likely represents an effective management strategy for maintaining and enhancing regional ecological service capabilities. The results of this work suggest that deep learning is an effective method of deepening studies on the prediction of ESV trends and human-driven mechanisms.