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The strong noise of satellite-based Time-Variable Gravity (TVG) field is often suppressed by applying the averaging filters. However, how to appropriately compromise the data blurring and de-noising remains as a challenge. In our hypothesis, the optimum spatial averaging filter expects to contain averaging kernels that capture the same amount of orbital samples everywhere, to avoid introducing excessive data blurring. To achieve the goal, we take advantages of the spherical convolution and introduce extra spatial constraints into a Gaussian kernel: (1) its half-width radius adapts to the global inhomogeneity of satellite orbit, and (2) the kernel is reshaped as an ellipsoid to adapt to the regional anisotropy. In this way, we designed optimal filters that contain a spatially-Varying non-isotropic Gaussian-based Convolution (VGC) kernel. The VGC-based filter is compared against three most popular filters through real TVG fields and another closed-loop simulation. In both scenarios, VGC-based filters retain more realistic secular trend and seasonal characteristics, in particular at high latitudes. The spatial correlation between the VGC estimates and the simulated ground truth is found to be 0.95 and 0.86 over Greenland and Antarctica, which is found to be 10% better than other tested filters. Temporal correlations with the ground truth are also found to be considerably better than the other filters over 90% of the globally distributed river basin. Besides, the VGC-based filters provide tolerable efficiency (3.5 s per month) and sufficient accuracy (integral error less than 3%). The method can be extended to the next generation gravity mission as well. Plain Language Summary: Time-Variable Gravity (TVG) fields of the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) need proper filtering to suppress the noise before being applied for intended geophysical studies. Existing filters are generally designed in the spectral domain. Though they are numerically efficient, they can hardly treat the noise in fairness, globally. As a result, the TVG fields may get over-smoothed after applying those filters, particularly in regions with high-latitudes. However, it would be mathematically simple to design a filter by applying a spherical convolution, whose kernels can be easily constrained and tuned in the spatial domain. This study introduces filters with spatially-Varying non-isotropic Gaussian-based Convolution kernel (VGC) that is enforced to comply with the spatial distribution of the TVG noise. The proposed filter is found to preserve a finer spatial resolution of TVG fields, and at the same time, to be able to de-noise them at a comparable level as the existing techniques. Geophysical applications that use GRACE-like TVG fields might have benefits from this practical filtering technique. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping is vital for landslide risk management and urban planning. In this study, we used three statistical models [frequency ratio, certainty factor and index of entropy (IOE)] and a machine learning model [random forest (RF)] for landslide susceptibility mapping in Wanzhou County, China. First, a landslide inventory map was prepared using earlier geotechnical investigation reports, aerial images, and field surveys. Then, the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis. To determine the most effective causal factors, landslide susceptibility evaluations were performed based on four cases with different combinations of factors ("cases"). In the analysis, 465 (70%) landslide locations were randomly selected for model training, and 200 (30%) landslide locations were selected for verification. The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model. Finally, the receiver operating characteristic (ROC) curve was used to verify the accuracy of each model's results for its respective optimal case. The ROC curve analysis showed that the machine learning model performed better than the other three models, and among the three statistical models, the IOE model with weight coefficients was superior. [ABSTRACT FROM AUTHOR]

Silicon-doped materials have been widely used in bone regeneration research; however, a consensus on the safety range of silicon ions has not been reached and its toxicity mechanism remains to be further elucidated. This study aims to explore whether high level of sodium metasilicate can induce toxicity effect in human umbilical vein endothelial cells (HUVEC) and the role of autophagy and apoptosis in its toxic mechanism. HUVEC was treated with different level of high silicon and then investigated with respect to morphologic change, cell viability, immunofluorescence, the level of autophagy, and apoptosis-related protein. Moreover, bafilomycin A1 (Baf A1) was applied to detect whether autophagic flux is disrupted, and 3-methyladenine (3-MA, an autophagy inhibitor) was used to determine the relationship between autophagy and apoptosis. Results demonstrated that high-level silicon induced cell viability to decrease; LC3-II, p62, and apoptosis-related proteins were up-regulated after exposure to high-dose silicon (sodium metasilicate concentration more than 1 mM). There is no significant difference in LC3-II and p62 between Baf A1 and sodium metasilicate-exposed group. Besides, 3-MA further increased the apoptotic rate by inhibiting autophagy after high silicon exposure. Collectively, high concentration of silicon can impair autophagy and induce apoptosis in human umbilical vein endothelial cells, and autophagy may play a protective role in HUVEC apoptosis. Furthermore, silicon concentration used in HUVEC should not be more than 1 mM. [ABSTRACT FROM AUTHOR]