Variable Selection of Generalized Regression Models Based on Maximum Rank Correlation

In this paper, we investigate the variable selection problem of the generalized regression models. To estimate the regression parameter, a procedure combining the rank correlation method and the adaptive lasso technique is developed, which is proved to have oracle properties. A modified IMO （iterative marginal optimization） algorithm which directly aims to maximize the penalized rank correlation function is proposed. The effects of the estimating procedure are illustrated by simulation studies.

Prediction and driving factors of forest fire occurrence in Jilin Province,China

Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.

Assessing Heat-related Mortality Risks in Beijing,China

Objective To obtain the exposure-response relationship for temperature and mortality, and assess the risk of heat-related premature death. Methods A statistical model was developed using a Poisson generalized linear regression model with Beijing mortality and temperature data from October 1st, 2006 to September 30th, 2008. We calculated the exposure-response relationship for temperature and mortality in the central city, and inner suburban and outer suburban regions. Based on this relationship, a health risk model was used to assess the risk of heat-related premature death in the summer （June to August） of 2009. Results The population in the outer suburbs had the highest temperature-related mortality risk. People in the central city had a mid-range risk, while people in the inner suburbs had the lowest risk. Risk assessment predicted that the number of heat-related premature deaths in the summer of 2009 was 1581. The city areas of Chaoyang and Haidian districts had the highest number of premature deaths. The number of premature deaths in the southern areas of Beijing （Fangshan, Fengtai, Daxing, and Tongzhou districts） was in the mid-range. Conclusion Ambient temperature significantly affects human mortality in Beijing. People in the city and outer suburban area have a higher temperature-related mortality risk than people in the inner suburban area. This may be explained by a temperature-related vulnerability. Key words： Temperature; Mortality; Premature death; Health risk; Generalized linear regression model; Climate change

Virtual screening of flavonoids from Jatropha gossypiifolia L.as potential drugs for diabetic complications

Background:Diabetes mellitus is a chronic metabolic disease that is a risk factor for epidemic pathologies.Under hyperglycemic conditions,the enzyme aldose reductase catalyzes the formation of sorbitol in the metabolism of glucose via polyols,leading to the development of diabetic complications.Therefore,inhibitors of this enzyme are therapeutic targets for the prophylaxis and treatment of these conditions.Methods:In this study,a generalized linear regression model was developed to analyze flavonoids-obtained from a database-that have been tested as inhibitors of aldose reductase.In this sense,the molecular descriptors implemented in DRAGON and MATLAB software were used to determine the correlation between the chemical structure of the inhibitors and their pharmacological activity.The model was validated according to the Organisation for Economic Co-operation and Development Standards and subsequently used for the virtual screening of the flavonoids identified in Jatropha gossypiifolia L.Results:The proposed model showed a good fit for its statistical parameters(R2=0.95).In addition,it showed good predictive power(R2 ext=0.94)and robustness(Q2 LOO=0.92).The experimental chemical space wherein the predictions were reliable(domain of application)was also defined.Finally,the model was used to identify 10 flavonoids from Jatropha gossypiifolia L.as candidates for natural drugs.Compounds with a low probability of oral absorption were identified,among which the elagic acid biflavonoid showed the greatest promise(pIC50 predicted=9.75).Conclusion:The Jatropha gossypiifolia L.species harbors flavonoids with high potential as inhibitors of the aldose reductase enzyme,in which the biflavonoid ellagic acid was shown to be the most promising inhibitor of the aldose reductase enzyme,suggesting its possible use in the treatment of the late complications of diabetes mellitus.