Ku70 plays an important role in the DSBR (DNA double-strand breaks repair) and maintenance of genomic integrity. Genetic variations within human Ku70 have been demonstrated to be associated with increased risk of several types of cancers. In this hospital-based case-control study, we aimed to investigate whether a single nucleotide polymorphism (SNP) in the promoter region (rs2267437) of Ku70 gene is associated with susceptibility to breast cancer in Chinese Han population. A total of 293 patients with breast cancer and 301 age-matched healthy controls were enrolled in this study. The Ku70 -1310C/G polymorphism was determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis. A significant difference in genotype distribution and allele frequency was observed between patients and controls. The CG or GG carries were at higher risk of breast cancer compared with the CC homozygotes (OR=1.43, 95% CI=1.02-2.00, P=0.038 and OR=3.53, 95% CI=1.60-7.80, P=0.002, respectively). Further stratification analysis revealed that G allele was associated with an increased risk of breast cancer among premenopausal women (OR=1.68, 95% CI=1.21-2.33, P=0.002), but not in postmenopausal women (OR=1.33, 5% CI=0.85-2.10, P=0.216). Our study suggests that the Ku70 -1310C/G promoter polymorphism may be a susceptibility factor for breast cancer in Chinese Han population.

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With the rapidly increasing number of electric vehicle users, in many urbans transport networks, there are mixed traffic flows (i.e., electric vehicles and gasoline vehicles). However, limited by driving ranges and long battery recharging, the battery electric vehicle (BEV) drivers’ route choice behaviors are inevitably affected. This paper assumes that in a transportation network, when BEV drivers are traveling between their original location and destinations, they tend to select the path with the minimal driving times and recharging time, and ensure that the remaining charge is not less than their battery safety margin. In contrast, gasoline vehicle drivers tend to select the path with the minimal driving time. Thus, by considering BEV drivers’ battery management strategies, e.g., battery safety margins and en-route recharging behaviors, this paper developed a mixed user equilibrium model to describe the resulting network equilibrium flow distributions. Finally, a numerical example is presented to demonstrate the mixed user equilibrium model. The results show that BEV drivers’ en-route recharging choice behaviors are significantly influenced by their battery safety margins, and under the equilibrium, the travel routes selected by some BEV drivers may not be optimal, but the total travel time may be more optimal.