Graph Convolutional Network-Enhanced Model for Screening Persistent, Mobile, and Toxic and Very Persistent and Very Mobile Substances.

The global management for persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances has been further strengthened with the rapid increase of emerging contaminants. The development of a ready-to-use and publicly available tool for the high-throughput screening of PMT/vPvM substances is thus urgently needed. However, the current model building with the coupling of conventional algorithms, small-scale data set, and simplistic features hinders the development of a robust model for screening PMT/vPvM with wide application domains.

Impaired barrier integrity of endothelial cells induced by PEGylated black phosphorus nanosheets.

PEGylated black phosphorus nanosheets (PEG-BPNSs) have shown promising applications in biomedicine and potentially interact with the vasculature following iatrogenic exposures. Whether the exposure to PEG-BPNSs could induce toxic effects on endothelial cells that line the blood vessels remains largely unknown. Herein, we investigate the cellular response and transcriptional profiling of human umbilical vein endothelial cells (HUVECs) after the exposure to BPNSs and PEG-BPNSs.

Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model.

As a novel analytical method based on big data, machine learning model can explore the relationship between different parameters and draw universal conclusions, which was used to predict composting maturity and identify key parameters in this study. The results showed that the Stacking model exhibited excellent prediction accuracy. SHapley Additive exPlanations (SHAP) and Partial Dependence Analysis (PDA) were performed to evaluate the importance of different parameters as well as their optimal interval.