Tropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM precipitation products. This study contributes two precipitation downscaling algorithms by exploring the nonstationarity relations between precipitation and various environment factors [daytime surface temperature (LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude, and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of precipitation is achieved with the geographically weighted regression model (GWR) and the backward-propagation artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling. The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within Hubei Province from 0.25° (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through 2010. This paper’s results demonstrate the bias correction is necessary before downscaling. The high-resolution precipitation datasets obtained with the proposed downscaling model with GWR relying on the NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.

Cyclins are essential for cell proliferation, the cell cycle and tumorigenesis in all eukaryotes. UbcH10 regulates the degradation of cyclins in a ubiquitin-dependent manner. Here, we report that UbcH10 is likely involved in tumorigenesis. We found that cancer cells exposed to n-acetyl-leu-leu-norleucinal (ALLN) treatment and UbcH10 depletion exhibit a synergistic therapeutic effect. Abundant expression of UbcH10 drives resistance to ALLN-induced cell death, while cells deficient in UbcH10 were susceptible to ALLN-induced cell death. The depletion of UbcH10 hindered tumorigenesis both in vitro and in vivo, as assessed by colony formation, growth curve, soft agar and xenograft assays. These phenotypes were efficiently rescued through the introduction of recombinant UbcH10. In the UbcH10-deficient cells, alterations in the expression of cyclins led to cell cycle changes and subsequently decreases in tumorigenesis. The tumorigenesis of xenograft tumors from UbcH10-deficient cells treated with ALLN was decreased relative to wild-type cells treated with ALLN in nude mice. On the molecular level, we observed that UbcH10 deficiency enhances the activation of caspase 8 and caspase 3 but not caspase 9 to impair cell viability upon ALLN treatment. Collectively, our results suggest that, as an oncogene, UbcH10 is a potential drug target for the treatment of colorectal cancer.

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.