This genome-wide search for susceptibility genes to type 2 diabetes/impaired glucose homeostasis (IGH) was performed on a relatively homogenous Chinese sample with a total number of 257 pedigrees and 385 affected sibpairs. Two regions showed significant linkage to type 2 diabetes/IGH in the Chinese. T ype 2 diabetes is a complex disease that develops in individuals with genetic susceptibility to impaired insulin secretion, as well as to impaired insulin sensitivity, in the presence of appropriate environmental factors, particularly those leading to obesity (1). Marked increases in the prevalence of type 2 diabetes occur in those societies or countries that have experienced tremendous economic development from a starting point of an impoverished economic base (2). Along with the economic development in China, nationwide surveys have revealed an increase in the prevalence of diabetes in the adult population from 0.9 to 2.4% over the years 1980 -1994 (3). In Shanghai, China, the prevalence of diabetes was only 1.0% in 1978 but had reached 9.8% by the turn of the last century, i.e., there has been a 10-fold increase within the last two decades in the Shanghai population alone (4,5).Genetic heterogeneity of type 2 diabetes has been suggested among ethnic groups (6). This may be one of the reasons for the different locations of susceptibility loci reported to be linked to type 2 diabetes among ethnic groups in genome-wide scans (7-22). In the large geographic area of China, 56 ethnic groups are officially recognized, the Han being the largest. The Chinese of Han ethnicity reside throughout China, mostly in the eastern and central regions. Studies of the origin of the East Asian population revealed that, even within the Han ethnic group, considerable genetic heterogeneity might exist according to geographical location (23)(24)(25). Because appropriate definition of a more homogenous sample set is one of the issues for a genome-wide screen for type 2 diabetes susceptibility genes, geographical genetic heterogeneity should be considered when conducting such a study on the Chinese population. Thus, the genome-wide screen reported in this study was performed with Chinese pedigrees recruited from a limited geographic area in China. RESEARCH DESIGN AND METHODSPedigrees for this study were selected from a sampling scheme for the collection of multiplex diabetic families aimed at the genetic studies of type 2 Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals.org. ASP, affected sibpair; IA-2, protein tyrosine phosphatase-like protein; IGH, impaired glucose homeostasis; LOD, logarithm of odds; MLS, maximum likelihood score; MODY, maturity-onset diabetes of the young; NPL, nonparametric linkage.

Studying heavy metal adsorption on soil is important for understanding the fate of heavy metals and properly assessing the related environmental risks. Existing experimental methods and traditional models for quantifying adsorption, however, are time-consuming and ineffective. In this study, we developed machine learning models for the soil adsorption of six heavy metals (Cd(II), Cr(VI), Cu(II), Pb(II), Ni(II), and Zn(II)) using 4420 data points (1105 soils) extracted from 150 journal articles. After a comprehensive comparison, our results showed that the gradient boosting decision tree had the best performance for a combined model based on all the data. The Shapley additive explanation method was used to identify the feature importance and the effects of these features on the adsorption, based on which six independent models were developed for the six metals to achieve better model performance than the combined model. Using these independent models, the global distribution of heavy metal adsorption capacities on soils was predicted with known soil properties. Reversed models, including one combined model for all the six metals and six independent models, were also built using the same data sets to predict the heavy metal concentration in water when the adsorbed amount is known for a soil/sediment.

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