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Bovine coronavirus (BCoV) is a major cause of infectious disease in cattle, causing huge economic losses to the beef and dairy industries worldwide. BCoV can infect humans and multiple other species of animals. A rapid, reliable, and simple test is needed to detect BCoV infection in suspected farms. In this study, we developed a novel multienzyme isothermal rapid amplification (MIRA) and lateral flow dipstick (LFD) combination assay, targeting a highly conserved region of the viral nucleocapsid (N) gene for BCoV detection. The MIRA-LFD assay was highly specific and sensitive, comparable to a published reverse transcription quantitative PCR (RT-qPCR) assay for BCoV detection. Compared with the published RT-qPCR assay, the κ value of the MIRA-LFD assay in the detection of 192 cattle clinical samples was 0.982. The MIRA-LFD assay did not require sophisticated instruments and the results could be observed with eyes. Our results showed that the MIRA-LFD assay was a useful diagnostic tool for rapid on-site detection of BCoV.

Additional file 1: Supplemental Table 1. The primer sequences used in this study. Supplemental Table 2. Relationship between circFOXK2 and clinical characteristics in HCC patients. Supplemental Table 3. Univariate and multivariate Cox-regression analysis of prognostic factors for HCC patients. Supplemental Figure 1. The effects of circFOXK2 on the ability of the progression of HCC cells. Supplemental Figure 2. The IRES site was predicted by circRNADb (http://reprod.njmu.edu.cn/cgi-bin/circrnadb/circRNADb.php). Supplemental Figure 3. FOXK2-142aa binds to LDHA to promote its phosphorylated activity. Supplemental Figure 4. FOX2-142aa positively correlates with the metastasis of HCC in vivo. Supplemental Figure 5. CircFOXK2 act as miRNA sponge for miR-484 in HCC cells. Supplemental Figure 6. The effect of miR-484 inhibitors on the progression of HCC cells. Supplemental Figure 7. circFOXK2 regulates Fis1 expression in HCC in vivo.

River discharge is an important hydrological parameter of river water resources. Especially in small- and medium-scale rivers, data deficiency is the biggest problem for studies of river discharge. In recent years, remote sensing has become a rapid and convenient method to estimate river discharge. However, remote sensing images still have some difficulty generating continuous long-term river discharge. To address this problem, we developed a new method coupling the remote sensing hydrology station method (RSHS) with statistical regression downscaling, using data from optical satellites (Landsat-8, Sentinel-2), radar satellites (Sentinel-1), and un-manned aerial vehicles (UAVs). We applied this method to monitor monthly river discharge for small- and medium-scale rivers from 2016 to 2020 on Yunnan-Guizhou Plateau and evaluated the accuracy of the results. The results show that (1) by applying the newly constructed method, the water body continuity index obtained by Landsat-8 increased by 7% and the average river length percentage in the channel reached 90.7%, a 40% increase; (2) there were only 10 river flow data points, on average, in the 5-year period obtained before this method was applied; after this method was applied, more than 50 river flow data points could be obtained, on average, extending the quantity of data fivefold; in addition, improper extreme values could also be avoided; (3) with better continuity of water body distribution, the images provided steadier river widths. The relative error of daily flow estimation from Landsat-8 images was reduced by 60% and the mean percentage error was reduced by one-fourth. The relative error of the multisource remote sensing composited flow was reduced by 37% with a reduction in the mean percentage error of over a half; (4) in addition, we found that when the threshold difference between water bodies and land in remote sensing images is more than 0.2, the impact of water body recognition error on flow accuracy can be ignored. This method helps to overcome the absence of remote sensing methods for the long-term estimation of flow series in small- and medium-scale rivers, improves the accuracy of remote sensing methods for calculating flow, and provides ideas for regional water resource management and utilization.