Factors affecting the health of residents in China: A perspective based on the living environment

Residents’ health is an important factor affecting social development and harmony. Based on 2010 China Family Panel Studies data of the Institute of Social Science Survey, Peking University and using a multi-classification logit regression model, we analyze the factors that affect the health status of residents in China. These factors include environmental pollution, which is a particularly important factor. Our study found that the impacts of residents’ characteristic variables, external living environment, and living habits vary. As residents age, their health status deteriorates. For the General, Less healthy, and Unhealthy groups, an income of less than CNY 10,000 significantly affects health status; however, when their income is greater than CNY 10,000, it no longer has a significant effect. For the Very unhealthy group, this particular threshold value is CNY 3000. At least one of urban–rural classification and residence registration status is significant, indicating that the urban–rural dual structure as well as the household registration system significantly affects residents’ health status. However, the direction of this effect is uncertain. Cooking water significantly affects the Less healthy and Unhealthy groups, and tap water is more conducive to health. Polluting enterprises within a radius of five kilometers mainly affect the Unhealthy group, but the direction of its impact is contrary to expectations. Smoking and drinking significantly affect the health status of the General, Less healthy, and Unhealthy groups. However, the direction of their impact was contrary to expectations. For the Very unhealthy group, drinking has a significant impact on residents’ health status, but the direction of the impact was again the opposite of what we expected. Smoking has no significant impact on the health status of this group. Exercise significantly affects the Less healthy and Unhealthy groups, but its influence has no obvious trend. Our study shows that living habits have a smaller influence on residents’ health status.     

An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

Investigation of protein‐ligand interactions obtained from experiments has a crucial part in the design of newly discovered and effective drugs. Analyzing the data extracted from known interactions could help scientists to predict the binding affinities of promising ligands before conducting experiments. The objective of this study is to advance the CIFAP (compressed images for affinity prediction) method, which is relevant to a protein‐ligand model, identifying 2D electrostatic potential images by separating the binding site of protein‐ligand complexes and using the images for predicting the computational affinity information represented by pIC₅₀ values. The CIFAP method has 2 phases, namely, data modeling and prediction. In data modeling phase, the separated 3D structure of the binding pocket with the ligand inside is fitted into an electrostatic potential grid box, which is then compressed through 3 orthogonal directions into three 2D images for each protein‐ligand complex. Sequential floating forward selection technique is performed for acquiring prediction patterns from the images. In the prediction phase, support vector regression (SVR) and partial least squares regression are used for testing the quality of the CIFAP method for predicting the binding affinity of 45 CHK1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide. The results show that the CIFAP method using both support vector regression and partial least squares regression is very effective for predicting the binding affinities of CHK1‐ligand complexes with low‐error values and high correlation. As a future work, the results could be improved by working on the pose of the ligands inside the grid.     

A Parameter-Elimination Method for Nonlinear Regression with Linear Parameters and Autocorrelated Errors

In this study, we are interested in the problem of estimating the parameters in a nonlinear regression model when the error terms are correlated. Throughout this work, we restrict ourselves to the special case when the error terms follow a pth order stationary autoregressive model (AR(p)). Following the idea of LAWTON and SYLVESTRE (1971) and GALLANT and GOEBEL (1976), a parameter-elimination method is proposed, which has the advantages that it is not sensitive to the initial values and convergence of the procedure may be more stable because of the reduced dimension of the problem. The parameter-elimination method is compared with the methods by GALLANT and GOEBEL (1976) and GLASBEY (1980) by Monte Carlo Simulation, and the results of applying the first two methods to the real data obtained from the Environmental Protection Administration of the Executive Yuan of the Republic of China are presented.     

Refining predictions of climate change impacts on plant species distribution through the use of local statistics

Bioclimate envelope models are often used to predict changes in species distribution arising from changes in climate. These models are typically based on observed correlations between current species distribution and climate data. One limitation of this basic approach is that the relationship modelled is assumed to be constant in space; the analysis is global with the relationship assumed to be spatially stationary. Here, it is shown that by using a local regression analysis, which allows the relationship under study to vary in space, rather than conventional global regression analysis it is possible to increase the accuracy of bioclimate envelope modelling. This is demonstrated for the distribution of Spotted Meddick in Great Britain using data relating to three time periods, including predictions for the 2080s based on two climate change scenarios. Species distribution and climate data were available for two of the time periods studied and this allowed comparison of bioclimate envelope model outputs derived using the local and global regression analyses. For both time periods, the area under the receiver operating characteristics curve derived from the analysis based on local statistics was significantly higher than that from the conventional global analysis; the curve comparisons were also undertaken with an approach that recognised the dependent nature of the data sets compared. Marked differences in the future distribution of the species predicted from the local and global based analyses were evident and highlight a need for further consideration of local issues in modelling ecological variables.     

On partial local smoothing rules for curve estimation

We compare the performances of local and global rules for smoothingparameter choice, in terms of asymptotic mean squared errorsof the resulting estimators. In some instances there is surprisinglylittle to choose between local and global approaches; our analysisidentifies contexts where the differences are small or large.This work motivates development of smoothing rules that forma ‘half-way house’ between local and global smoothing.There, interpolation provides a basis for partial local smoothing.A key result shows that interpolation on even a coarse gridcan produce a very good approximation to full local smoothing.Our theoretical and numerical results lead us to suggest linearinterpolation of a bandwidth obtained by integral approximationson discrete intervals.     

Estimating switchgrass productivity in the Great Plains using satellite vegetation index and site environmental variables

Switchgrass is being evaluated as a potential feedstock source for cellulosic biofuels and is being cultivated in several regions of the United States. The recent availability of switchgrass land cover maps derived from the National Agricultural Statistics Service cropland data layer for the conterminous United States provides an opportunity to assess the environmental conditions of switchgrass over large areas and across different geographic locations. The main goal of this study is to develop a data-driven multiple regression switchgrass productivity model and identify the optimal climate and environment conditions for the highly productive switchgrass in the Great Plains (GP). Environmental and climate variables used in the study include elevation, soil organic carbon, available water capacity, climate, and seasonal weather. Satellite-derived growing season averaged Normalized Difference Vegetation Index (GSN) was used as a proxy for switchgrass productivity. Multiple regression analyses indicate that there are strong correlations between site environmental variables and switchgrass productivity (r = 0.95). Sufficient precipitation and suitable temperature during the growing season (i.e., not too hot or too cold) are favorable for switchgrass growth. Elevation and soil characteristics (e.g., soil available water capacity) are also an important factor impacting switchgrass productivity. An anticipated switchgrass biomass productivity map for the entire GP based on site environmental and climate conditions and switchgrass productivity model was generated. Highly productive switchgrass areas are mainly located in the eastern part of the GP. Results from this study can help land managers and biofuel plant investors better understand the general environmental and climate conditions influencing switchgrass growth and make optimal land use decisions regarding switchgrass development in the GP.     

Estimation of the Spirometric Residual Volume (RV) by a Regression Built from Gower Distances

We estimate the Residual Volume, a spirometric parameter, by use of four continuous and four categorical variables. The estimation is done using distance-based regression, which allows to construct the predicting regression equation from mixed-type explanatory variables. The additionally introduced categorical variables improve essentially the goodness of fit of the regression equation.