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The massive and rapid transmission of SARS-CoV-2 has led to the emergence of several viral variants of concern (VOCs), with the most recent one, B.1.1.529 (Omicron), which accumulated a large number of spike mutations, raising the specter that this newly identified variant may escape from the currently available vaccines and therapeutic antibodies. Using VSV-based pseudovirus, we found that Omicron variant is markedly resistant to neutralization of sera from convalescents or individuals vaccinated by two doses of inactivated whole-virion vaccines (BBIBP-CorV). However, a homologous inactivated vaccine booster or a heterologous booster with protein subunit vaccine (ZF2001) significantly increased neutralization titers to both WT and Omicron variant. Moreover, at day 14 post the third dose, neutralizing antibody titer reduction for Omicron was less than that for convalescents or individuals who had only two doses of the vaccine, indicating that a homologous or heterologous booster can reduce the Omicron escape from neutralizing. In addition, we tested a panel of 17 SARS-CoV-2 monoclonal antibodies (mAbs). Omicron resists seven of eight authorized/approved mAbs, as well as most of the other mAbs targeting distinct epitopes on RBD and NTD. Taken together, our results suggest the urgency to push forward the booster vaccination to combat the emerging SARS-CoV-2 variants.

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Engineering dynamic systems or materials to respond to biological process is one of the major tasks in synthetic biology and will enable wide promising applications, such as robotics and smart medicine. Herein, a super‐soft and dynamic DNA/dopamine‐grafted‐dextran hydrogel, which shows super‐fast volume‐responsiveness with high sensitivity upon solvents with different polarities and enables creation of electric circuits in response to microbial metabolism is reported. Synergic permanent and dynamic double networks are integrated in this hydrogel. A serials of dynamic hydrogel‐based electric circuits are fabricated: 1) triggered by using water as switch, 2) triggered by using water and petroleum ether as switch pair, 3) a self‐healing electric circuit; 4) remarkably, a microbial metabolism process which produces ethanol triggering electric circuit is achieved successfully. It is envisioned that the work provides a new strategy for the construction of dynamic materials, particularly DNA‐based biomaterials; and the electric circuits will be highly promising in applications, such as soft robotics and intelligent systems.

Estimating statistical uncertainties allows autonomous agents to communicate their confidence during task execution and is important for applications in safety-critical domains such as autonomous driving. In this work, we present the uncertainty-aware imitation learning (UAIL) algorithm for improving end-to-end control systems via data aggregation. UAIL applies Monte Carlo Dropout to estimate uncertainty in the control output of end-to-end systems, using states where it is uncertain to selectively acquire new training data. In contrast to prior data aggregation algorithms that force human experts to visit sub-optimal states at random, UAIL can anticipate its own mistakes and switch control to the expert in order to prevent visiting a series of sub-optimal states. Our experimental results from simulated driving tasks demonstrate that our proposed uncertainty estimation method can be leveraged to reliably predict infractions. Our analysis shows that UAIL outperforms existing data aggregation algorithms on a series of benchmark tasks.