[Distribution Prediction of Soil Heavy Metals Based on Remote Sensing Temporal-Spatial-Spectral Features and Random Forest Model].

Obtaining soil heavy metal content characteristics and spatial distribution is crucial for preventing soil pollution and formulating environmental protection policies. We collected 304 surface soil samples (0-20 cm) in the Changqing district. At the same time, the spectral, temporal, and spatial features of soil heavy metals were derived from multi-remote sensing data; the temporal-spatial-spectral features closely related to soil heavy metals were selected via correlation analysis and used as input independent variables. The measured soil arsenic (As) content was used as the dependent variable to establish a spatial prediction model based on the random forest (RF) algorithm. The results showed the following:the As content in the soils exceeded the background value by 43.17% but did not exceed the risk screening values and intervention values, indicating slight heavy metal pollution in the soil. The accuracy ranking of the spatial prediction models with one feature type from high to low was spatial features (ratio of performance to inter-quartile range (RPIQ)=3.87)>temporal features (RPIQ=2.57)>spectral features (RPIQ=2.50). The spatial features were the most informative for predicting soil heavy metals. The models using temporal-spatial, temporal-spectral, and spatial-spectral features were superior to those using only one feature type, and the RPIQ values were 4.81, 4.21, and 4.70, respectively. The RF model with temporal-spatial-spectral features achieved the highest spatial prediction accuracy (R2=0.90; root mean square error (RMSE)=0.77; RPIQ=5.68). The As content decreased from the northwest to the southeast due to Yellow River erosion and industrial activities. The spatial prediction of soil heavy metals incorporating remote sensing temporal-spatial-spectral features and the random forest model provides effective support for soil pollution prevention and environmental risk control.

Low-Threshold Optical Bistability in the Graphene-Oxide Integrated Asymmetric Nanocavity at Visible Light Frequencies.

Here, we propose an optical bistable device structure with a few layers of graphene oxide integrated in the metal-dielectric-metal based asymmetric nanocavity. Through the light confinement in the nanocavity, the third order nonlinear absorption of graphene oxide can be significantly enhanced, which experimentally delivers low-threshold optical bistability at the visible wavelength of 532 nm with only 267 KW/cm2 intensity. In addition, the switching threshold can be further reduced via increasing the graphene oxide thickness, hence paving a new way for achieving tunable optical bistable devices at visible light frequencies.

Experimental Study on Temperature Effects on NiTi Shape Memory Alloys under Fatigue Loading.

NiTi Shape Memory Alloy (SMA) has been widely studied in the field of structural vibration control, and the results show that the fatigue life of the SMA is a key factor of the vibration control system. In this paper, the fatigue test is carried out in Dynamic Mechanical Analyzer (DMA + 1000) to analyze how the changes of temperature and strain amplitude affecting the main fatigue parameters. The test results show that when the test temperature is higher than Austenite finish temperature (Af), the fatigue properties of SMAs are significantly affected by temperature. With the increase of temperature, the fatigue life becomes shorter and the energy consumption decreases, while the area of hysteresis curve, the stress amplitude, and effective modulus increase.

microRNAs-107 inhibited autophagy, proliferation, and migration of breast cancer cells by targeting HMGB1.

PURPOSE: To investigate the effects of microRNAs-107 (miR-107) on autophagy, proliferation, and migration of breast cancer cells and its mechanism by targeting high mobility group protein B1 (HMGB1). METHODS: Real-time polymerase chain reaction assay was used to detect the expression of miR-107 in breast cancer and its cell lines. In MDA-MB-231 and MDA-MB-453 breast cancer cells, the expression of p62, Beclin1 protein, and the changes of cell proliferation and migration after overexpression of m miR-107 were detected by Western blotting, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, and transwell assays. Target Scan online prediction, dual luciferase reporter gene, and Western blot were used to verify the targeting relationship between miR-107 and HMGB1. The effects of silencing HMGB1 expression on p62, Beclin1 protein expression, cell proliferation, and migration ability were detected. The transfected MDA-MB-453 cells were inoculated into the right axilla of the nude mice, the tumor volume and weight were weighed, and the expression of miR-107, HMGB1, p62, and Beclin1 in the tumor were detected. RESULTS: The expression of miR-107 was downregulated in breast cancer tissues and cell lines (P < 0.01). The expression of p62 protein was upregulated (P < 0.01), while Beclin1 protein was downregulated (P < 0.01) and cell proliferation and migration ability were decreased (P < 0.01) after overexpressing miR-107 in MDA-MB-231 and MDA-MB-453 cells. The results of TargetScan online prediction, dual luciferase reporter gene, and Western blot showed that miR-107 could regulate HMGB1 expression. The expression of p62 protein was upregulated (P < 0.01), while Beclin1 protein was downregulated (P < 0.01) and cell proliferation and migration ability were decreased (P < 0.01) after silencing HMGB1 in MDA-MB-231 and MDA-MB-453 cells. The results of xenograft experiments showed that miR-107 could delay tumor growth and inhibit autophagy. CONCLUSION: miR-107 could inhibit cell autophagy, proliferation, and migration of breast cancer cells by targeting HMGB1.