Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

Integration of different models may improve the performance of landslide susceptibility assessment, but few studies have tested it. The present study aims at exploring the way to integrating different models and comparing the results among integrated and individual models. Our objective is to answer this question: Will the integrated model have higher accuracy compared with individual model? The Lvliang mountains area, a landslide-prone area in China, was taken as the study area, and ten factors were considered in the influencing factors system. Three basic machine learning models (the back propagation (BP), support vector machine (SVM), and random forest (RF) models) were integrated by an objective function where the weight coefficients among different models were computed by the gray wolf optimization (GWO) algorithm. 80 and 20% of the landslide data were randomly selected as the training and testing samples, respectively, and different landslide susceptibility maps were generated based on the GIS platform. The results illustrated that the accuracy expressed by the area under the receiver operating characteristic curve (AUC) of the BP-SVM-RF integrated model was the highest (0.7898), which was better than that of the BP (0.6929), SVM (0.6582), RF (0.7258), BP-SVM (0.7360), BP-RF (0.7569), and SVM-RF models (0.7298). The experimental results authenticated the effectiveness of the BP-SVM-RF method, which can be a reliable model for the regional landslide susceptibility assessment of the study area. Moreover, the proposed procedure can be a good option to integrate different models to seek an "optimal" result. Keywords: landslide susceptibility, random forest, integrated model, causal factor, GI

Association of Mitochondrial DNA Polymerase γ Gene <i>POLG1</i> Polymorphisms with Parkinsonism in Chinese Populations

Background: Mitochondrial DNA polymerase gamma (POLG1) mutations were associated with levodopa-responsive Parkinsonism. POLG1 gene contains a number of common nonsynonymous SNPs and intronic regulatory SNPs which may have functional consequences. It is of great interest to discover polymorphisms variants associated with Parkinson's disease (PD), both in isolation and in combination with specific SNPs.Materials and Methods: We conducted a case-control study and genotyped twenty SNPs and poly-Q polymorphisms of POLG1 gene including in 344 Chinese sporadic PD patients and 154 healthy controls. All the polymorphisms of POLG1 we found in this study were sequenced by PCR products with dye terminator methods using an ABI-3100 sequencer. Hardy-Weinberg equilibrium and linkage disequilibrium (LD) for association between twenty POLG1 SNPs and PD were calculated using the program Haploview.Principal Results: We provided evidence for strong association of four intronic SNPs of the POLG1 gene (new report: c.2070-12T>A and rs2307439: c.2070-64G>A in intron 11, P = 0.00011, OR = 1.727; rs2302084: c.3105-11T>C and rs2246900: c.3105-36A>G in intron 19, P = 0.00031, OR = 1.648) with PD. However, we did not identify any significant association between ten exonic SNPs of POLG1 and PD. Linkage disequilibrium analysis indicated that c.2070-12T>A and c.2070-64G>A could be parsed into one block as Haplotype 1 as well as c.3105-11T>C and c.3105-36A>G in Haplotype 2. In addition, case and control study on association of POLG1 CAG repeat (poly-Q) alleles with PD showed a significant association (P = 0.03, OR = 2.16) of the non-10/11Q variants with PD. Although intronic SNPs associated with PD didn't influence POLG1 mRNA alternative splicing, there was a strong association of c.2070-12T>A and c.2070-64G>A with decreased POLG1 mRNA level and protein levels.Conclusions: Our findings indicate that POLG1 may play a role in the pathogenesis of PD in Chinese populations.</p

Analyzing the prices of the most expensive sheet iron all over the world: Modeling, prediction and regime change

The private car license plates issued in Shanghai are bestowed the title of "the most expensive sheet iron all over the world", more expensive than gold. A citizen has to bid in an monthly auction to obtain a license plate for his new private car. We perform statistical analysis to investigate the influence of the minimal price $P_{\min}$ of the bidding winners, the quota $N_{\rm{quota}}$ of private car license plates, the number $N_{\rm{bidder}}$ of bidders, as well as two external shocks including the legality debate of the auction in 2004 and the auction regime reform in January 2008 on the average price $P_{\rm{mean}}$ of all bidding winners. It is found that the legality debate of the auction had marginal transient impact on the average price in a short time period. In contrast, the change of the auction rules has significant permanent influence on the average price, which reduces the price by about 3020 yuan Renminbi. It means that the average price exhibits nonlinear behaviors with a regime change. The evolution of the average price is independent of the number $N_{\rm{bidder}}$ of bidders in both regimes. In the early regime before January 2008, the average price $P_{\rm{mean}}$ was influenced only by the minimal price $P_{\min}$ in the preceding month with a positive correlation. In the current regime since January 2008, the average price is positively correlated with the minimal price and the quota in the preceding month and negatively correlated with the quota in the same month. We test the predictive power of the two models using 2-year and 3-year moving windows and find that the latter outperforms the former. It seems that the auction market becomes more efficient after the auction reform since the prediction error increases.Comment: 10 pages including 5 figures and 4 table