Many of the currently available COVID-19 vaccines and therapeutics are not effective against newly emerged SARS-CoV-2 variants. Here, we developed the metallo-enzyme domain of angiotensin converting enzyme 2 (ACE2)—the cellular receptor of SARS-CoV-2—into an IgM-like inhalable molecule (HH-120). HH-120 binds to the SARS-CoV-2 Spike (S) protein with exceptionally high avidity and confers potent and broad-spectrum neutralization activity against all known SARS-CoV-2 variants of concern. HH-120 was successfully developed as an inhaled formulation that achieves appropriate aerodynamic properties for respiratory system delivery, and we found that aerosol inhalation of HH-120 significantly reduced viral loads and lung pathology scores in golden Syrian hamsters infected by the SARS-CoV-2 wild-type strain and the Delta variant. Our study presents a breakthrough for the inhalation delivery of large biologics like HH-120 (molecular weight ~ 1000kDa) and demonstrates that HH-120 can serve as a highly efficacious, safe, and convenient agent against all SARS-CoV-2 variants. Finally, given the known role of ACE2 in viral reception, it is conceivable that HH-120 will be efficacious against additional emergent coronaviruses.

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Low-light image enhancement has traditionally been tackled by training a heuristically designed neural network architecture. Despite the success of these approaches, the heuristic design pattern inherently not only hinders further optimization of network architectures, but also limits the factors that the designer can take into consideration. As a result, these methods are difficult to achieve a balance between enhancing performance and hardware related performance. In this paper, we equip a basic enhancing algorithm with a neural architecture search technique. This technique helps to automatically search an optimal hardware-aware architecture while also increases neglectable computation burden. In this work, we propose a shrinkage sampling strategy to drastically decrease the computation cost of neural architecture search while improving the quality of search. Extensive experiments on various benchmarks demonstrate that our algorithm achieves state-of-the-art performance with higher speed.