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BackgroundCo-infection with HIV and HCV and/or HBV is highly prevalent in intravenous drug users (IDUs). Because of the proximity to the “Golden Triangle”, HIV prevalence among the IDUs is very high in the China-Myanmar border region. However, there are few studies about co-infection with HIV and HCV and/or HBV, especially in the region that belongs to Myanmar.Methods721 IDUs, including 403 Chinese and 318 Burmese, were investigated for their HIV, hepatitis B virus (HBV), and hepatitis C virus (HCV) serological status. Statistical analysis was performed to evaluate the differences of the epidemic situation between the Chinese IDUs and the Burmese IDUs.ResultsAmong the Chinese IDUs and the Burmese IDUs, HCV infection was the most prevalent (69.0% vs 48.1%, P<0.001), followed by HBV (51.6% vs 43.1%, P<0.05) and HIV (33.7% vs 27.0%, P>0.05). Besides, there were more HIV-HBV co-infected IDUs (20.1% vs 11.3%, P<0.005), and HIV-HCV co-infected IDUs (31.8% vs 23.9%, P<0.05) in China than in Myanmar, as well as HIV-HBV-HCV triple infection (19.1% vs 10.4%, P<0.005).ConclusionCo-infection with HIV and HCV and/or HBV is highly prevalent among the IDUs in the China-Myanmar border region. The HIV epidemic appears to be in a downward trend, compared with previous reports. However, all infections were more prevalent among the Chinese IDUs than among the Burmese.

Aboveground biomass is a key indicator of a grassland ecosystem. Accurate estimation from remote sensing is important for understanding the response of grasslands to climate change and disturbance at a large scale. However, the precision of remote sensing inversion is limited by a lack in the ground truth and scale mismatch with satellite data. In this study, we first tried to establish a grassland aboveground biomass estimation model at 1 m 2 quadrat scale by conducting synchronous experiments of unmanned aerial vehicle (UAV) and field measurement in three different grassland ecosystems. Two flight modes (the new QUADRAT mode and the commonly used MOSAIC mode) were used to generate point clouds for further processing. Canopy height metrics of each quadrat were then calculated using the canopy height model (CHM). Correlation analysis showed that the mean of the canopy height model (CHM_mean) had a significant linear relationship with field height (R 2 = 0.90, root mean square error (RMSE) = 19.79 cm, rRMSE = 16.5%, p < 0.001) and a logarithmic relationship with field aboveground biomass (R 2 = 0.89, RMSE = 91.48 g/m 2 , rRMSE = 16.11%, p < 0.001). We concluded our study by conducting a preliminary application of estimation of the aboveground biomass at a plot scale by jointly using UAV and the constructed 1 m 2 quadrat scale estimation model. Our results confirmed that UAV could be used to collect large quantities of ground truths and bridge the scales between ground truth and remote sensing pixels, which were helpful in improving the accuracy of remote sensing inversion of grassland aboveground biomass.