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]

Geological hazard susceptibility assessment (GSCA) is a crucial tool widely utilized by scholars worldwide for predicting the likelihood of geological disasters. The traditional information quantity model in geological disaster susceptibility evaluation, which superimposes the information quantity of each evaluation factor without considering their weights, often negatively impacts susceptibility zoning results. This paper introduces a method employing random forest (RF) empowerment information quantity to address this issue. The method involves calculating objective weights based on a parameter-optimized random forest model, assigning these weights to each evaluation factor, and then conducting a weighted superimposition of the information. Utilizing the natural discontinuity method, the resulting comprehensive information volume map was segmented. The proposed method was applied in Kang County, Gansu Province, and its performance was compared with that of traditional methods in terms of geological disaster susceptibility zoning maps, zoning of statistical disaster point density, and receiver operating characteristic (ROC) curve accuracy. The experimental findings indicate the superior accuracy and reliability of the proposed method over the traditional approach. [ABSTRACT FROM AUTHOR]

Geological disasters are prevalent during urbanization in the mountainous areas of southwest China due to the complex geographic and fragile geologic conditions. This paper relies on the ArcGIS platform as the model operation carrier and takes Yiliang County of Yunnan Province as the research area. Nine evaluation factors such as slope and elevation were selected, and the risk assessment of geological disasters in Yiliang County is carried out by using the combination weighting method. The results show that: (1) the extremely high-risk areas and high-risk areas are distributed in the central, western, and northeastern parts of Yiliang County, of which 164 disaster points are distributed in the area, accounting for 72.56% of the total disaster points; (2) the elevation, human engineering activities, vegetation coverage, and distance from the river are the four main factors affecting the development of geological disasters in the area; (3) the proportions of extremely high-risk areas, high-risk areas, medium-risk areas, and low-risk areas in the total area of the county were 8.08%, 19.61%, 30.59%, and 41.72%, respectively; (4) the verification of the evaluation results by the receiver operating characteristic (ROC) curve shows that the evaluation accuracy is 80%, and the zoning results are consistent with the spatial and temporal distribution of historical disaster points. The combined weighting method can effectively evaluate the risk of geological disasters in Yiliang County, and the results can be used as a scientific reference for local government departments to carry out relevant work. [ABSTRACT FROM AUTHOR]

An assessment of regional vulnerability to geological disasters can directly indicate the extent and intensity of risks within the study area; thus, providing precise guidance for disaster management efforts. However, in the evaluation of geological disaster susceptibility using a single deterministic coefficient model, the direct superimposition of deterministic coefficient values for each evaluation factor, without considering their objective weights, can impact the accuracy of susceptibility zoning outcomes. To address this limitation, this research proposes a novel approach: geological disaster susceptibility evaluation using a random-forest-weighted deterministic coefficient model. In this method, the objective weight of each evaluation factor is calculated based on a deterministic coefficient model and a parameter-optimized random forest model. By weighting and superimposing the deterministic coefficient values of each evaluation factor, a comprehensive deterministic coefficient map is generated. This map is further divided using the natural breakpoint method to obtain a geological disaster susceptibility zoning map. To validate the accuracy of the evaluation results, partition statistics and the ROC (Receiver Operating Characteristic) curve of the test sample points are utilized. The findings demonstrate that the model performs well in evaluating geological disaster susceptibility in Huize County. The evaluation results are considered reliable and accurate, highlighting the effectiveness of the proposed approach for assessing and zoning geological disaster susceptibility in the region. [ABSTRACT FROM AUTHOR]

In China, the majority of mountainous regions are characterized by complex topography and a delicate, sensitive geological environment. These areas, which exhibit insufficient infrastructure and widespread irrational human engineering activities, are often susceptible to geological hazards such as slope instability and soil mass movements. These geological hazards pose substantial threats to human lives and property, hindering the progress of mountainous areas. Therefore, conducting research on evaluating the vulnerability of slope rock and soil mass movement geological hazards (hereinafter referred to as geological hazards) is of utmost importance for hazard prevention, emergency management, and economic advancement in these regions. This study focuses on Xuanwei City and selects eight factors for evaluation, including elevation, gradient, slope aspect, normalized vegetation index, stratigraphic lithology, distance from faults, distance from rivers, and distance from roads. These factors are chosen based on a comprehensive analysis of the spatial and temporal distribution of geological hazards and hazard incubation conditions. Two paired models, the deterministic coefficient model + logistic regression model (CF+LR) and the information quantity model + logistic regression model (I+LR), were employed to assess the study area quantitatively. The performance of these models was assessed by employing receiver operating characteristic (ROC) curves and calculating the corresponding area under curve (AUC) values. The results indicate that: (1) The AUC values for the coupled CF+LR and I+LR models are 0.799 and 0.772, respectively. These results indicate that both models provide an objective and reliable assessment of the vulnerability to geological hazards, specifically slope rock and soil mass movements, in the study area. (2) Based on the CF+LR model calculations, the geological hazard susceptibility of Xuanwei City can be categorized into four zones: extremely high susceptibility (6.09%), high susceptibility (31.08%), medium susceptibility (32.26%), and low susceptibility (30.57%). (3) The CF+LR model more accurately represents the evaluation results and offers a strong reference value. [ABSTRACT FROM AUTHOR]

The smart museum is a new platform for the appreciation and display of cultural relics across time and space. In the era of 3D scanning technology, computer technology, and network technology, it is necessary to deeply study the smarter and more perfect forms of smart museums. This article, through the analysis of the characteristics of the display level of the smart museum, tries to create a new humanized and intelligent display of cultural relics. [ABSTRACT FROM AUTHOR]

This paper constructs the continuous-Young optimal weighted arithmetic averaging (C-YOWA) operator and the continuous-Young optimal weighted geometric (C-YOWG) operator based on definite integral and Young inequality. A series of special cases and main properties of the proposed aggregation operators are also investigated. In order to integrate heterogeneous interval data and obtain more accurate prediction results, the heterogeneous interval combination prediction (HICP) model based on C-YOWA operator, C-YOWG operator and Theil coefficient is proposed. The HICP model consider not only the existence of both additive and multiplicative interval information, but also the preference information of experts. Finally, the model is applied to the empirical analysis of wind energy prediction. The comparison of results shows that the established model can effectively improve the accuracy of prediction. [ABSTRACT FROM AUTHOR]

Transdermal drug delivery is an attractive, non-invasive treatment. It can avoid first-pass hepatic metabolism and provides the possibility of self-administration. Hydrogels are promising biomaterials due to their important qualities such as biocompatibility and biodegradability. Recently, there has been tremendous growth in the area of hydrogels for transdermal drug delivery. In this work, a new kind of arginine-based poly(ester amide) (Arg-PEA) and polyethylene glycol diacrylamide (PEG-DA) hybrid hydrogel was developed for transdermal drug delivery. The hydrogels not only possess excellent swelling capacity, but also have good mechanical properties, which were then evaluated as drug delivery agents using insulin as a model system. Cytotoxicity testing and in vivo skin irritation tests demonstrated that the hydrogels were biocompatible. Finally, the results indicated that the prepared hydrogels could not only perform transdermal drug delivery, but also might regulate blood glucose levels in a mouse model with streptozotocin-induced diabetes. [ABSTRACT FROM AUTHOR]

The present study aimed to characterize the glucan from C. mollissima Blume fruits and its selenium derivative, then investigate their antitumor activity in vitro. A glucan, designated as CPA, was firstly isolated from the fruits of C. mollissima Blume. Structure analysis indicated that CPA was a linear 1,6-α-D-glucan with the average molecular weight about 2.0 × 10 kDa. The selenylation modification derivative of CPA (sCPA), exhibited a stronger antiproliferative effect on tumor cells than CPA in vitro. CPA and sCPA could induce HeLa cells apoptosis and decrease mitochondrial membrane potential. sCPA could also arrest HeLa cells in S phase, promote reactive oxygen species generation and activate caspase-3 activity in HeLa cells. These results manifest that CPA and sCPA inhibit the proliferation of HeLa cells via different mechanisms, which is meaningful for their potential use as antitumor drugs. [ABSTRACT FROM AUTHOR]