Browsing by Author "Chen, Lin"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content
    (MDPI, 2019-04) Chen, Lin; Ren, Chunying; Li, Lin; Wang, Yeqiao; Zhang, Bai; Wang, Zongming; Li, Linfeng; Earth Sciences, School of Science
    Accurate digital soil mapping (DSM) of soil organic carbon (SOC) is still a challenging subject because of its spatial variability and dependency. This study is aimed at comparing six typical methods in three types of DSM techniques for SOC mapping in an area surrounding Changchun in Northeast China. The methods include ordinary kriging (OK) and geographically weighted regression (GWR) from geostatistics, support vector machines for regression (SVR) and artificial neural networks (ANN) from machine learning, and geographically weighted regression kriging (GWRK) and artificial neural networks kriging (ANNK) from hybrid approaches. The hybrid approaches, in particular, integrated the GWR from geostatistics and ANN from machine learning with the estimation of residuals by ordinary kriging, respectively. Environmental variables, including soil properties, climatic, topographic, and remote sensing data, were used for modeling. The mapping results of SOC content from different models were validated by independent testing data based on values of the mean error, root mean squared error and coefficient of determination. The prediction maps depicted spatial variation and patterns of SOC content of the study area. The results showed the accuracy ranking of the compared methods in decreasing order was ANNK, SVR, ANN, GWRK, OK, and GWR. Two-step hybrid approaches performed better than the corresponding individual models, and non-linear models performed better than the linear models. When considering the uncertainty and efficiency, ML and two-step approach are more suitable than geostatistics in regional landscapes with the high heterogeneity. The study concludes that ANNK is a promising approach for mapping SOC content at a local scale.
  • Thumbnail Image
    Item
    Lovastatin enhances adenovirus-mediated TRAIL induced apoptosis by depleting cholesterol of lipid rafts and affecting CAR and death receptor expression of prostate cancer cells
    (Impact Journals, LLC, 2015-02-20) Liu, Youhong; Chen, Lin; Gong, Zhicheng; Shen, Liangfang; Kao, Chinghai; Hock, Janet M.; Sun, Lunquan; Li, Xiong; Department of Urology, IU school of Medicine
    Oncolytic adenovirus and apoptosis inducer TRAIL are promising cancer therapies. Their antitumor efficacy, when used as single agents, is limited. Oncolytic adenoviruses have low infection activity, and cancer cells develop resistance to TRAIL-induced apoptosis. Here, we explored combining prostate-restricted replication competent adenovirus-mediated TRAIL (PRRA-TRAIL) with lovastatin, a commonly used cholesterol-lowering drug, as a potential therapy for advanced prostate cancer (PCa). Lovastatin significantly enhanced the efficacy of PRRA-TRAIL by promoting the in vivo tumor suppression, and the in vitro cell killing and apoptosis induction, via integration of multiple molecular mechanisms. Lovastatin enhanced PRRA replication and virus-delivered transgene expression by increasing the expression levels of CAR and integrins, which are critical for adenovirus 5 binding and internalization. Lovastatin enhanced TRAIL-induced apoptosis by increasing death receptor DR4 expression. These multiple effects of lovastatin on CAR, integrins and DR4 expression were closely associated with cholesterol-depletion in lipid rafts. These studies, for the first time, show correlations between cholesterol/lipid rafts, oncolytic adenovirus infection efficiency and the antitumor efficacy of TRAIL at the cellular level. This work enhances our understanding of the molecular mechanisms that support use of lovastatin, in combination with PRRA-TRAIL, as a candidate strategy to treat human refractory prostate cancer in the future.