Segmentation study of nanoparticle topological structures based on synthetic data.

Nanoparticles exhibit broad applications in materials mechanics, medicine, energy and other fields. The ordered arrangement of nanoparticles is very important to fully understand their properties and functionalities. However, in materials science, the acquisition of training images requires a large number of professionals and the labor cost is extremely high, so there are usually very few training samples in the field of materials. In this study, a segmentation method of nanoparticle topological structure based on synthetic data (SD) is proposed, which aims to solve the issue of small data in the field of materials. Our findings reveal that the combination of SD generated by rendering software with merely 15% Authentic Data (AD) shows better performance in training deep learning model. The trained U-Net model shows that Miou of 0.8476, accuracy of 0.9970, Kappa of 0.8207, and Dice of 0.9103, respectively. Compared with data enhancement alone, our approach yields a 1% improvement in the Miou metric. These results show that our proposed strategy can achieve better prediction performance without increasing the cost of data acquisition.

Trends and associated factors of HIV, HCV and syphilis infection among different drug users in the China-Vietnam border area: an 11-year cross-sectional study (2010-2020).

BACKGROUND: Data on recent human immunodeficiency virus (HIV), hepatitis C virus (HCV) and syphilis prevalence among drug users in the Southwest China are sparse despite the high burden of drug use. This study aims at assessing the prevalence trends and related factors of HIV, HCV and syphilis infection among different drug users in the China-Vietnam border area. METHODS: A continuous cross-sectional survey was conducted among drug users from 2010 to 2020 in the China-Vietnam border area. Chi-square trend tests were used to assess the trend of HIV, HCV and syphilis prevalence and the proportion for drug type used by drug users. Multivariate logistic regression was used to identify associated factors of HIV, HCV and syphilis infection in different drug users. RESULTS: In this study, a total of 28,951 drug users were included, of which 27,893 (96.45%) male, 15,660 (54.09%) aged 13-34 years, 24,543 (84.77%) heroin-only users, 2062 (7.12%) synthetic drug-only (SD-only) users and 2346 (8.10%) poly-drug users. From 2010 to 2020, the proportion of heroin-only users decreased from 87.79% to 75.46%, whereas SD-only users and poly-drug users increased from 5.16% to 16.03%, and from 7.05% to 8.52%, respectively. The prevalence of HIV, HCV, and syphilis during the study period declined from 12.76%, 60.37% and 5.72% to 4.35%, 53.29% and 4.53%, respectively, among heroin-only users and declined from 18.30%, 66.67% and 15.69% to 6.95%, 27.81% and 5.35%, respectively, among poly-drug users; however, the prevalence of HIV and HCV among SD-only users increased from 0.89% and 8.93% to 2.84% and 18.75%, respectively. Having ever injected drugs and needle sharing were common associated factors for both HIV and HCV infection among poly-drug users and heroin-only users. Aged ≥ 35 years old was an associated factor for HIV, HCV and syphilis infection among the SD-only users. Female drug users were at high risk of contracting syphilis among three different drug users. CONCLUSIONS: The prevalence of HIV, HCV and syphilis among heroin-only users and poly-drug users decreased during the study period. However, the prevalence of HIV and HCV among SD-only users increased. Comprehensive intervention strategies, particularly focusing on the SD-only users are needed in order to bring down the disease burden in this population in the China-Vietnam border areas.