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Objective: Smoking and obesity are established risk factors of dyslipidemia, however, the interplay between them has not been well studied. This study aims to explore the joint effect of smoking and body mass index (BMI) on serum lipid profiles. Methods: The study consisted of 9846 Chinese adults (mean age = 49.9 years, 47.6% males, 31.2% ever smokers), based on the China Health and Nutrition Survey. Serum lipid profiles included total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A (APO-A), and apolipoprotein B (Apo-B). The joint effect of smoking and BMI on serum lipids were examined by the four-way decomposition analysis and multivariate linear regression models. Results: The four-way decomposition showed that the interplay between smoking and BMI was complicated. There was only indirect effect (the mediated effect) between smoking and BMI on TC, LDL-C and APO-B. The pure indirect effect was -0.023 for TC, -0.018 for LDL-C, and -0.009 for APO-B. For TG, HDL-C and APO-A, the interaction effect was dominant. The reference interaction (the interactive effect when the mediator is left to what it would be in the absence of exposure) was 0.474 (P < 0.001) for TG, -0.245 (P = 0.002) for HDL-C, and -0.222 (P < 0.001) for APO-A, respectively. The effect of BMI on TG, HDL-C and APO-A were significantly higher in smokers than in nonsmokers (TG: 0.151 in smokers versus 0.097 in nonsmokers, HDL-C: -0.037 versus -0.027, APO-A: -0.019 versus -0.009, P for difference < 0.001 for all). Conclusion: These findings illustrate the joint effects of smoking and BMI on serum lipid profiles. There were significant interaction effects of smoking and BMI on TG, HDL-C and APO-A, while BMI maybe a mediator for the association of smoking with TC, LDL-C and APO-B. The effects between them were rather complex. Smoking cessation is necessary, especially for those overweight. [ABSTRACT FROM AUTHOR]

This paper presents a morphing model of vector geographical data based on Fourier transformation. This model involves three main steps. They are conversion from vector data to Fourier series, generation of intermediate function by combination of the two Fourier series concerning a large scale and a small scale, and reverse conversion from combination function to vector data. By mirror processing, the model can also be used for morphing of linear features. Experimental results show that this method is sensitive to scale variations and it can be used for vector map features’ continuous scale transformation. The efficiency of this model is linearly related to the point number of shape boundary and the interceptive value n of Fourier expansion. The effect of morphing by Fourier transformation is plausible and the efficiency of the algorithm is acceptable. [ABSTRACT FROM AUTHOR]

Regional co-location scoping intends to identify local regions where spatial features of interest are frequently located together. Most of the previous researches in this domain are conducted on a global scale and they assume that spatial objects are embedded in a 2-D space, but the movement in urban space is actually constrained by the street network. In this paper we refine the scope of co-location patterns to 1-D paths consisting of nodes and segments. Furthermore, since the relations between spatial events are usually inversely proportional to their separation distance, the proposed method introduces the “Distance Decay Effects” to improve the result. Specifically, our approach first subdivides the street edges into continuous small linear segments. Then a value representing the local distribution intensity of events is estimated for each linear segment using the distance-decay function. Each kind of geographic feature can lead to a tessellated network with density attribute, and the generated multiple networks for the pattern of interest will be finally combined into a composite network by calculating the co-location prevalence measure values, which are based on the density variation between different features. Our experiments verify that the proposed approach is effective in urban analysis. [ABSTRACT FROM AUTHOR]