The continuing global spread of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection, has led to an unprecedented global health crisis. Effective and affordable methods are needed to diagnose SARS-CoV-2 infection. In this work, a ratiometric fluorescence probe, Si-Mn:ZnSe nanoparticles, was constructed through the electrostatic interaction between Si dots and Mn:ZnSe QDs, and the fluorescence of Mn:ZnSe QDs has a specifical response to H₂O₂. An immunocomplex was formed by the recognition of capture antibody/spike (S) protein/spike neutralizing antibody/biotinylated second antibody/streptavidin/biotinylated catalase (CAT). In the presence of S protein, CAT effectively catalyzed the decomposition of H₂O₂ in the system, and the fluorescence of Mn:ZnSe QDs was not specifically quenched. Based on this principle, a ratiometric immunoassay of SARS-CoV-2 S protein was established. The sensitivity of the proposed ELISA method was comparable to that of the commercial kit. In addition, this method can effectively distinguish the pseudo-SARS-CoV-2 virus and other pseudovirus. Therefore, this method provided a reliable and potential direction for diagnosing SARS-CoV-2 infection.

Kernel-based home range models are widely-used to estimate animal habitats and develop conservation strategies. They provide a probabilistic measure of animal space use instead of assuming the uniform utilization within an outside boundary. However, this type of models estimates the home ranges from animal relocations, and the inadequate locational data often prevents scientists from applying them in long-term and large-scale research. In this paper, we propose an end-to-end deep learning framework to simulate kernel home range models. We use the conditional adversarial network as a supervised model to learn the home range mapping from time-series remote sensing imagery. Our approach enables scientists to eliminate the persistent dependence on locational data in home range analysis. In experiments, we illustrate our approach by mapping the home ranges of Bar-headed Geese in Qinghai Lake area. The proposed framework outperforms all baselines in both qualitative and quantitative evaluations, achieving visually recognizable results and high mapping accuracy. The experiment also shows that learning the mapping between images is a more effective way to map such complex targets than traditional pixel-based schemes.