Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: An example of multiple machine learning models.

Endocrine-disrupting chemicals (EDCs) pollution is a major global environmental issue. Assessing the multiple toxic effects of EDCs is key to managing their risks. This study successfully developed an EDCs classification and recognition model based on recursive feature elimination and random forest coupling, which passed external validation.

Overexpression of OsSnRK1a through a green tissue-specific promoter improves rice yield by accelerating sheath-to-panicle transport of nonstructural carbohydrates and increasing leaf photosynthesis.

The redistribution of nonstructural carbohydrates (NSCs) in rice (Oryza sativa) sheaths contributes greatly to grain filling. Sucrose nonfermenting-1-related protein kinase 1 (SnRK1) regulates sheath-to-panicle transport of NSCs during rice grain filling; however, it is unknown whether elevated activity of SnRK1 in sheaths improves NSC transport and grain filling. Expression of OsSnRK1a is mainly responsible for regulating SnRK1 activity in rice sheaths.

A multi-framework for bisphenols based on their high performance and environmental friendliness: Design, screening, and recommendations.

Bisphenols (BPs) have gained significant attention due to their extensive use in the production of medical equipment, packaging materials, and everyday commodities. Urgent attention is required for assessing and identifying the risks associated with BP exposure to the environment and human health, as well as developing regulatory strategies. In this paper, 29 common BPs were selected as the research object, high-performance BP substitutes with environmental and human health friendliness characteristics were designed and screened.