The loess area in the northern part of Baoji City, Shaanxi Province, China is a region with frequently landslide occurrences. The main aim of this study is to quantitatively predict the extent of landslides using the ...The loess area in the northern part of Baoji City, Shaanxi Province, China is a region with frequently landslide occurrences. The main aim of this study is to quantitatively predict the extent of landslides using the index of entropy model(IOE), the support vector machine model(SVM) and two hybrid models namely the F-IOE model and the F-SVM model constructed by fractal dimension. First, a total of 179 landslides were identified and landslide inventory map was produced, with 70%(125) of the landslides which was optimized by 10-fold crossvalidation being used for training purpose and the remaining 30%(54) of landslides being used for validation purpose. Subsequently, slope angle, slope aspect, altitude, rainfall, plan curvature, distance to rivers, land use, distance to roads, distance to faults, normalized difference vegetation index(NDVI), lithology, and profile curvature were considered as landslide conditioning factors and all factor layers were resampled to a uniform resolution. Then the information gain ratio of each conditioning factors was evaluated. Next, the fractal dimension for each conditioning factors was calculated and the training dataset was used to build four landslide susceptibility models. In the end, the receiver operating characteristic(ROC) curves and three statistical indexes involving positive predictive rate(PPR), negative predictive rate(NPR) and accuracy(ACC) were applied to validate and compare the performance of these four models. The results showed that the F-SVM model had the highest PPR, NPR, ACC and AUC values for training and validation datasets, respectively, followed by the F-IOE model.Finally, it is concluded that the F-SVM model performed best in all models, the hybrid model built by fractal dimension has advantages than original model, and can provide reference for local landslide prevention and decision making.展开更多
Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid m...Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid model,namely box counting dimension-based kernel logistic regression model,which uses fractal dimension calculated by box counting method as input data based on grid cells mapping unit and terrain mapping unit.The performance of this model was evaluated in the application in Zhidan County,Shaanxi Province,China.Firstly,a total of 221 landslides were identified and mapped,and 11 landslide predisposing factors were considered.Secondly,the landslide susceptibility maps(LSMs) of the study area were obtained by constructing the model on two different mapping units.Finally,the results were evaluated with five statistical indexes,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV) and Accuracy.The statistical indexes of the model obtained on the terrain mapping unit were larger than those based on grid cells mapping unit.For training and validation datasets,the area under the receiver operating characteristic curve(AUC) of the model based on terrain mapping unit were 0.9374 and 0.9527,respectively,indicating that establishing this model on the terrain mapping unit was advantageous in the study area.The results show that the fractal dimension improves the prediction ability of the kernel logistic model.In addition,the terrain mapping unit is a more promising mapping unit in Loess areas.展开更多
目的探讨维持性血液透析(maintenance hemodialysis,MHD)患者Nod样受体蛋白3/白细胞介素-1β(Nod-like receptor protein 3/interleukin 1β,NLRP3/IL-1β)信号通路活化与高密度脂蛋白胆固醇(high density lipoprotein cholesterol,HDL...目的探讨维持性血液透析(maintenance hemodialysis,MHD)患者Nod样受体蛋白3/白细胞介素-1β(Nod-like receptor protein 3/interleukin 1β,NLRP3/IL-1β)信号通路活化与高密度脂蛋白胆固醇(high density lipoprotein cholesterol,HDL-C)水平及动脉粥样硬化(atherosclerosis,AS)发生的关系。方法行MHD治疗的患者83例为研究组,20例健康人为对照组,检测生化指标、HDL-C、血白细胞介素(IL)-1β、IL-18及外周血单核细胞NLRP3mRNA;测定颈动脉内膜中层厚度(intimamedia thickness,IMT)。比较2组间上述指标的差异;分析HDL-C及颈动脉IMT与NLRP3mRNA、IL-1β及IL-18的相关性;明确影响MHD患者HDL-C水平及颈动脉IMT的危险因素。结果研究组MHD患者HDL-C低于对照组(t=-7.048,P<0.001),颈动脉IMT值大于对照组(t=2.634,P<0.001);血HDL-C与NLRP3mRNA(r=-0.682,P<0.001)、IL-1β(r=-0.537,P<0.001)及IL-18(r=-0.426,P<0.001)呈负相关;颈动脉IMT与NLRP3mRNA(r=0.521,P<0.001)、IL-1β(r=0.569,P<0.001)及IL-18(r=0.674,P<0.001)成正相关;NLRP3mRNA活化是HDL-C降低的危险因素(B=-0.390,p<0.001),而NLRP3mRNA、IL-18活化(B=0.069、0.002,均P<0.001)及HDL-C降低(B=-0.106,P<0.001)是MHD患者颈动脉IMT增厚的危险因素。结论NLRP3/IL-1β信号通路参与了MHD患者血HDL-C水平降低及颈动脉IMT增厚。展开更多
基金funded by National Key Research and Development Program of China (Grant No. 2017YFC0504700)
文摘The loess area in the northern part of Baoji City, Shaanxi Province, China is a region with frequently landslide occurrences. The main aim of this study is to quantitatively predict the extent of landslides using the index of entropy model(IOE), the support vector machine model(SVM) and two hybrid models namely the F-IOE model and the F-SVM model constructed by fractal dimension. First, a total of 179 landslides were identified and landslide inventory map was produced, with 70%(125) of the landslides which was optimized by 10-fold crossvalidation being used for training purpose and the remaining 30%(54) of landslides being used for validation purpose. Subsequently, slope angle, slope aspect, altitude, rainfall, plan curvature, distance to rivers, land use, distance to roads, distance to faults, normalized difference vegetation index(NDVI), lithology, and profile curvature were considered as landslide conditioning factors and all factor layers were resampled to a uniform resolution. Then the information gain ratio of each conditioning factors was evaluated. Next, the fractal dimension for each conditioning factors was calculated and the training dataset was used to build four landslide susceptibility models. In the end, the receiver operating characteristic(ROC) curves and three statistical indexes involving positive predictive rate(PPR), negative predictive rate(NPR) and accuracy(ACC) were applied to validate and compare the performance of these four models. The results showed that the F-SVM model had the highest PPR, NPR, ACC and AUC values for training and validation datasets, respectively, followed by the F-IOE model.Finally, it is concluded that the F-SVM model performed best in all models, the hybrid model built by fractal dimension has advantages than original model, and can provide reference for local landslide prevention and decision making.
基金funded by National Key Research and Development Program of China, Ecological Safety Guarantee Technology and Demonstration Channel and Slope Treatment Project in Loess Hilly and Gully Area (Grant No. 2017YFC0504700)。
文摘Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid model,namely box counting dimension-based kernel logistic regression model,which uses fractal dimension calculated by box counting method as input data based on grid cells mapping unit and terrain mapping unit.The performance of this model was evaluated in the application in Zhidan County,Shaanxi Province,China.Firstly,a total of 221 landslides were identified and mapped,and 11 landslide predisposing factors were considered.Secondly,the landslide susceptibility maps(LSMs) of the study area were obtained by constructing the model on two different mapping units.Finally,the results were evaluated with five statistical indexes,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV) and Accuracy.The statistical indexes of the model obtained on the terrain mapping unit were larger than those based on grid cells mapping unit.For training and validation datasets,the area under the receiver operating characteristic curve(AUC) of the model based on terrain mapping unit were 0.9374 and 0.9527,respectively,indicating that establishing this model on the terrain mapping unit was advantageous in the study area.The results show that the fractal dimension improves the prediction ability of the kernel logistic model.In addition,the terrain mapping unit is a more promising mapping unit in Loess areas.
文摘目的探讨维持性血液透析(maintenance hemodialysis,MHD)患者Nod样受体蛋白3/白细胞介素-1β(Nod-like receptor protein 3/interleukin 1β,NLRP3/IL-1β)信号通路活化与高密度脂蛋白胆固醇(high density lipoprotein cholesterol,HDL-C)水平及动脉粥样硬化(atherosclerosis,AS)发生的关系。方法行MHD治疗的患者83例为研究组,20例健康人为对照组,检测生化指标、HDL-C、血白细胞介素(IL)-1β、IL-18及外周血单核细胞NLRP3mRNA;测定颈动脉内膜中层厚度(intimamedia thickness,IMT)。比较2组间上述指标的差异;分析HDL-C及颈动脉IMT与NLRP3mRNA、IL-1β及IL-18的相关性;明确影响MHD患者HDL-C水平及颈动脉IMT的危险因素。结果研究组MHD患者HDL-C低于对照组(t=-7.048,P<0.001),颈动脉IMT值大于对照组(t=2.634,P<0.001);血HDL-C与NLRP3mRNA(r=-0.682,P<0.001)、IL-1β(r=-0.537,P<0.001)及IL-18(r=-0.426,P<0.001)呈负相关;颈动脉IMT与NLRP3mRNA(r=0.521,P<0.001)、IL-1β(r=0.569,P<0.001)及IL-18(r=0.674,P<0.001)成正相关;NLRP3mRNA活化是HDL-C降低的危险因素(B=-0.390,p<0.001),而NLRP3mRNA、IL-18活化(B=0.069、0.002,均P<0.001)及HDL-C降低(B=-0.106,P<0.001)是MHD患者颈动脉IMT增厚的危险因素。结论NLRP3/IL-1β信号通路参与了MHD患者血HDL-C水平降低及颈动脉IMT增厚。
