Severe fever with thrombocytopenia syndrome virus (SFTSV) is an emerging tick-borne virus causing serious infectious disease with a high case-fatality of up to 50% in severe cases. Currently, no effective drug has been approved for the treatment of SFTSV infection. Here, we performed a high-throughput screening of a natural extracts library for compounds with activities against SFTSV infection. Three hit compounds, notoginsenoside Ft1, punicalin, and toosendanin were identified for displaying high anti-SFTSV efficacy, in which, toosendanin showed the highest inhibition potency. Mechanistic investigation indicated that toosendanin inhibited SFTSV infection at the step of virus internalization. The anti-viral effect of toosendanin against SFTSV was further verified in mouse infection models, and the treatment with toosendanin significantly reduced viral load and histopathological changes in vivo. The antiviral activity of toosendanin was further expanded to another bunyavirus and the emerging SARS-CoV-2. This study revealed a broad anti-viral effect of toosendanin and indicated its potential to be developed as an anti-viral drug for clinical use.

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According to evidence from research and practice, the seasonal demand variation exists in the banking sector and significantly affects the commercial bank's operation. Thus, the bank needs to adopt a seasonal staffing policy to ensure that the human resources can match the customer demands in different seasons. Otherwise, a shortage or surplus of human resource will occur and negatively affect bank operations. In this paper, we develop a seasonal staffing method to help the bank find the optimal staffing policy under seasonality. To capture the characteristics of bank operations, we model the service systems of n branches in a bank as a n-dimensional M/M/c/N queueing system with balking and reneging. Then a profit maximizing model based on the queueing system is constructed, and it is further simplified through linearization so that the model can be solved in a short time period. In addition, we conduct a set of numerical experiments that not only prove the superiority of our method compared with the traditional methods, but also explore the effects of some key factors on the optimal seasonal policy.