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Background: This study aimed to investigate the epidemiology of high-risk human papillomavirus (HPV) in the female population in Beijing, China, and identify the relationship between HPV genotypes and host factors. Methods: HPV testing was performed on women aged 15–89 (mean age 38.0 ± 10.9 years) from Beijing in 2020. High-risk HPV genotyping real-time polymerase chain reaction was used to determine HPV genotypes. The overall prevalence, age-specific prevalence, genotype distribution, and the correlation between HPV genotypes and cervical cytology were analyzed. Results: Among the 25,344 study participants, the single and double infection rates were 18.8% (4,777/25,344) and 4.2% (1,072/25,344), respectively. A total of 6,119 HPV-positive individuals were found to have 91.6% negative results for intraepithelial lesion or malignancy (NILM), 5.8% atypical squamous cells of undetermined significance (ASC-US), 0.9% low-grade squamous intraepithelial lesion (LSIL), and 1.7% high-grade squamous intraepithelial lesion (HSIL). In single HPV infections, the HPV16 genotype was highly associated with cervical cytology severity (χ2 trend = 172.487, P < 0.001). Additionally, HPV infection rates increased gradually with age, and statistical differences were observed across age groups (χ2 = 180.575; P < 0.001). High-risk HPV genotypes were highly prevalent in women below 25 years of age and those aged 55–59 years. Cluster analysis revealed that the 13 HPV genotypes could be roughly divided into two groups in a single infection; however, patterns of infection consistent with biological characteristics were not observed. Conclusion: High-risk HPV was found in 24.1% of outpatients, with HPV52, HPV58, HPV16, HPV39, and HPV51 being the most common high-risk genotypes. Single high-risk HPV infection was predominant. HPV16, HPV39, HPV51, and HPV52 were associated with cervical lesion progression. HPV16 infection was especially worrying since it aggravates cervical lesions. Because the infection rates of the 13 HPV genotypes differed by age, the peak HPV infection rate should not guide vaccination, screening, and prevention programs. Instead, these initiatives should be tailored based on the regional HPV distribution characteristics. Moreover, it was determined that Beijing's populace needed to receive treatment for HPV39 infection. [ABSTRACT FROM AUTHOR]

Traffic prediction is the cornerstone of intelligent transportation system. In recent years, graph neural network has become the mainstream traffic prediction method due to its excellent processing ability of unstructured data. However, the network relationship in the real world is more complex. Multiple nodes and various associations such as different types of stations and lines in rail transit always exist at the same time. In an end-to-end model, the training accuracy will suffer if the same weights are assigned to multiple views. Thus, this paper proposes a framework with multi-view and multi-layer attention, which aims to solve the problem of node prediction involving multiple relationships. Specifically, the proposed model maps multiple relationships into multiple views. A graph convolutional neural network of multiple views with multi-layer attention learns the optimal regression of nodes. Furthermore, the model uses an autoencoder module to alleviate the over-smoothing problem during the training phase. With the historical dataset of Beijing rail transit, the experiment proves that the prediction accuracy of the model is generally better than the baseline traffic prediction algorithms. [ABSTRACT FROM AUTHOR]