Aminoacyl-tRNA synthetases (aaRSs) are essential enzymes in translation by linking amino acids onto their cognate tRNAs during protein synthesis. During evolution, aaRSs develop numerous non-canonical functions that expand the roles of aaRSs in eukaryotic organisms. Although aaRSs have been implicated in viral infection, the function of aaRSs during infections with coronaviruses (CoVs) remains unclear. Here, we analyzed the data from transcriptomic and proteomic database on human cytoplasmic (cyto) and mitochondrial (mt) aaRSs across infections with three highly pathogenic human CoVs, with a particular focus on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We found an overall downregulation of aaRSs at mRNA levels, while the protein levels of some mt-aaRSs and the phosphorylation of certain aaRSs were increased in response to SARS-CoV-2 infection. Strikingly, interaction network between SARS-CoV-2 and human aaRSs displayed a strong involvement of mt-aaRSs. Further co-immunoprecipitation (co-IP) experiments confirmed the physical interaction between SARS-CoV-2 M protein and TARS2. In addition, we identified the intermediate nodes and potential pathways involved in SARS-CoV-2 infection. This study provides an unbiased, overarching perspective on the correlation between aaRSs and SARS-CoV-2. More importantly, this work identifies TARS2, HARS2, and EARS2 as potential key factors involved in COVID-19.

Ballistocardiograms (BCG) is an essential signal for vital sign monitoring. Obtaining the beat-to-beat intervals from BCG signal is of great significance for home-care applications, such as sleep staging, heart disease alerting, etc. The current approaches of detecting beat-to-beat intervals from BCG signals are complex. In this paper, we develop a non-invasive BCG monitoring system, and propose an effective and accurate algorithm for beat-to-beat detection. Firstly, a heartbeat shape is adaptively modeled based on a two-step procedure by taking advantage of the J-peak and the K-valley of BCG signals. Then, forward and backward detections with the criteria of both the morphological distance and the cross-correlation are jointly employed to find the position of each BCG signal, and in turn, to determine the beat-to-beat intervals of BCG signals. The proposed method was validated in at least 90 minutes recording from 10 subjects in various setups. The mean absolute beat-to-beat intervals error was 10.72 ms and on an average 97.93% of the beat-to-beat intervals were detected.

For the consensus problem of multi-agent systems with linear dynamics, distributed eventtriggered control strategies are put forward, which can decrease the frequency of information transmissions between agents and the number of control inputs of each agent. In the case that the agent states can be obtained or outputs can be obtained, distributed consensus protocols are designed based on event-triggered state information and event-triggered observer information, respectively. The consensus problem is converted into the stability problem by model transformation. Sufficient conditions for multi-agent systems to achieve consensus are obtained. Meanwhile, it is theoretically proved that the event-triggering conditions will exclude Zeno behavior. Simulation examples verify the effectiveness of obtained results.INDEX TERMS Consensus, multi-agent systems, event-triggering, distributed control, linear dynamics.

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