Abstract. The present study aimed to investigate the association of the -579 G>T polymorphism in the DNMT3B promoter with susceptibility to lung cancer. A total of 174 lung cancer patients and 135 healthy controls from the northern part of China were enrolled, and were matched for gender and age. All subjects were genotyped by polymerase chain reaction-restriction-fragment length polymorphism analysis and confirmed by DNA sequencing. Stratification analyses were used to study the subgroups of subjects by age and gender, and evaluate the association between the -579 G>T polymorphism and the genetic susceptibility to lung cancer. The results revealed that individuals with the DNMT3B -579 GT genotype had a significantly decreased risk of lung cancer [odds ratio (OR), 0.517; 95% confidence interval (CI), 0.273-0.981] compared with those with a -579 TT genotype in the studied population. However, the deviation was significant (OR, 0.138, 95% CI, 0.034-0.549) between the risk of lung cancer and the GT and GG genotype, when the smoking factor was considered. The data from this study indicate that the DNMT3B genetic polymorphism varies among various races, ethnic groups and geographical areas. The DNMT3B -579 G>T polymorphism may contribute to the genetic susceptibility to lung cancer.
With the rapid development of 3D scanners, the cultural heritage artifacts can be stored as a point cloud and displayed through the Internet. However, due to natural and human factors, many cultural relics had some surface damage when excavated. As a result, the holes caused by these damages still exist in the generated point cloud model. This work proposes a multi-scale upsampling GAN (MU-GAN) based framework for completing these holes. Firstly, a 3D mesh model based on the original point cloud is reconstructed, and the method of detecting holes is presented. Secondly, the point cloud patch contains hole regions and is extracted from the point cloud. Then the patch is input into the MU-GAN to generate a high-quality dense point cloud. Finally, the empty areas on the original point cloud are filled with the generated dense point cloud patches. A series of real-world experiments are conducted on real scan data to demonstrate that the proposed framework can fill the holes of 3D heritage models with grained details. We hope that our work can provide a useful tool for cultural heritage protection.
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