Innate immune system is armed by several lines of pattern recognition receptors to sense various viral infection and to initiate antiviral immune response. This process is under a tight control and the negative feedback induced by infection and/or inflammation is critical to maintain immune homoeostasis and to prevent autoimmune disorders, however, the molecular mechanism is not fully understood. Here we report TRIM29, a ubiquitin E3 ligase, functions as an inducible negative regulator of innate immune response triggered by DNA virus and cytosolic DNA. DNA virus and cytosolic DNA stimulation induce TRIM29 expression robustly in macrophages and dendritic cells, although the basal level of TRIM29 is undetectable in those cells. TRIM29 deficiency elevates IFN-I and proinflammatory cytokine production upon viral DNA and cytosolic dsDNA stimulation. Consistently, in vivo experiments show that TRIM29-deficient mice are more resistant to HSV-1 infection than WT controls, indicated by better survival rate and reduced viral load in organs. Mechanism studies suggest that STING–TBK1–IRF3 signaling pathway in TRIM29 KO cells is significantly enhanced and the degradation of STING is impaired. Furthermore, we identify that TRIM29 targets STING for K48 ubiquitination and degradation. This study reveals TRIM29 as a crucial negative regulator in immune response to DNA virus and cytosolic DNA, preventing potential damage caused by overcommitted immune responses.
Intelligent fault diagnosis achieves tremendous success in machine fault diagnosis because of the outstanding data-driven capability. However, the severe imbalanced dataset in practical scenarios of industrial rotating machinery is still a big challenge for the development of intelligent fault diagnosis method. In this paper, we solve this issue by constructing a novel deep learning model incorporated with a transfer learning method based on the Time-GAN and Efficient-Net models. Firstly, the proposed model so called Time-GAN-TL extends the imbalanced fault diagnosis of rolling bearings by using time series generative adversarial network. Secondly, balanced vibration signals are converted into two-dimensional images for training and classification by implementing the Efficient-Net into the transfer learning method. Finally, the proposed method is validated using two-types of rolling bearing experimental data. The high-precision diagnosis results of the transfer learning experiments and the comparison with other representative fault diagnosis classification methods reveal the efficiency, reliability, and generalization performance of the presented model.
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