Search

Embryonic and fetal development can be affected during gestation by exposure to xenobiotics that cross the placenta. Liquid crystal monomers (LCMs) are emerging contaminants commonly found in indoor environments; however, whether they can cross the placenta and affect placental development remains unexplored. Here, we develop an evaluation system that integrates human biomonitoring, uterine perfusion in pregnant rats, and placental cells. We find fourteen out of the fifty-six LCMs that are detected in maternal and cord serum samples from ninety-three healthy pregnant women, at median levels of 13.9 and 18.1 ng/mL, respectively. Subsequent explorations of in utero exposure in rats indicate that aromatic amino acid transporter 1 (SLC16A10) mediates transplacental transportation of the LCMs. Placental cells exposed to LCMs exhibit delayed placental development and reduced progesterone release. These findings show that SLC16A10-mediated transplacental transportation of LCMs inhibits placental development and progesterone release, highlighting the importance of gestational exposure to emerging contaminants. Liquid crystal monomers are emerging contaminants; however, whether they can cross the placenta and affect placental development remains unexplored. Here, the authors show SLC16A10- mediated transplacental transportation of LCMs inhibits placental development and progesterone release. [ABSTRACT FROM AUTHOR]

Selecting the most effective prediction model and correctly identifying the main disaster-driving factors in a specific region are the keys to addressing the challenges of geological hazards. Fuyuan County is a typical plateau mountainous town, and slope geological hazards occur frequently. Therefore, it is highly important to study the spatial distribution characteristics of hazards in this area, explore machine learning models that can be highly matched with the geological environment of the study area, and improve the accuracy and reliability of the slope geological hazard risk zoning map (SGHRZM). This paper proposes a hazard mapping research method based on multisource remote sensing data extraction and machine learning. In this study, we visualize the risk level of geological hazards in the study area according to 10 pathogenic factors. Moreover, the accuracy of the disaster point list was verified on the spot. The results show that the coupling model can maximize the respective advantages of the models used and has highest mapping accuracy, and the area under the curve (AUC) is 0.923. The random forest (RF) model was the leader in terms of which single model performed best, with an AUC of 0.909. The grid search algorithm (GSA) is an efficient parameter optimization technique that can be used as a preferred method to improve the accuracy of a model. The list of disaster points extracted from remote sensing images is highly reliable. The high-precision coupling model and the single model have good adaptability in the study area. The research results can provide not only scientific references for local government departments to carry out disaster management work but also technical support for relevant research in surrounding mountainous towns. [ABSTRACT FROM AUTHOR]