The global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become more serious because of the continuous emergence of variants of concern (VOC), thus calling for the development of broad-spectrum vaccines with greater efficacy. Adjuvants play important roles in enhancing the immunogenicity of protein-based subunit vaccines. In this study, we compared the effect of three adjuvants, including aluminum, nanoparticle manganese and MF59, on the immunogenicity of three protein-based COVID-19 vaccine candidates, including RBD-Fc, RBD and S-trimer. We found that the nanoparticle manganese adjuvant elicited the highest titers of SARS-CoV-2 RBD-specific IgG, IgG1 and IgG2a, as well as neutralizing antibodies against infection by pseudotyped SARS-CoV-2 and its Delta variant. What is more, the nanoparticle manganese adjuvant effectively reduced the viral load of the authentic SARS-CoV-2 and Delta variant in the cell culture supernatants. These results suggest that nanoparticle manganese, known to facilitate cGAS-STING activation, is an optimal adjuvant for protein-based COVID-19 subunit vaccines.

Fang X. PIEZO2 promotes cell proliferation and metastasis in colon carcinoma through the SLIT2/ROBO1/VEGFC pathway [published online as ahead of print on February 8, 2023].

Rotating machinery often produces continuous impact during operation due to the change of load and speed, which shows the characteristics of unsteady state and time-varying. Its working state can not be comprehensively judged by a single vibration state parameter. Therefore, this paper proposes to use acoustic sensors to collect the fault noise signal of rotating machinery, and use the whole column of sensors to detect the fault noise signal. Based on the microphone array, this paper studies the adaptive beamforming algorithm (MVDR) to locate the fault source of rotating machinery in space. The effect of fault source location is verified by simulation and equipment measurement experiments. The acoustic sensor does not in contact with the equipment, which will not damage the generator set, but also provide more effective information for fault source location and fault diagnosis and analysis.

Rotating machines are common equipment in industrial production, which may cause failure for a long time. Because of its convenient use and non-destructive to itself, acoustic detection method is suitable for fault diagnosis of rotating machinery. The convolution neural network model is used to identify several typical rotating machine faults. The repeatability experiments and different training sets show that the method has good universality. A visual fault identification system is built, and the effect of the system is verified by experiments.