RNF138 Downregulates Antiviral Innate Immunity by Inhibiting IRF3 Activation.

A viral infection activates the transcription factors IRF3 and NF-κB, which synergistically induces type I interferons (IFNs). Here, we identify the E3 ubiquitin ligase RNF138 as an important negative regulator of virus-triggered IRF3 activation and IFN-β induction. The overexpression of RNF138 inhibited the virus-induced activation of IRF3 and the transcription of the IFNB1 gene, whereas the knockout of RNF138 promoted the virus-induced activation of IRF3 and transcription of the IFNB1 gene.

TBK1-stabilized ZNF268a recruits SETD4 to methylate TBK1 for efficient interferon signaling.

Sufficient activation of interferon signaling is critical for the host to fight against invading viruses, in which post-translational modifications have been demonstrated to play a pivotal role. Here, we demonstrate that the human KRAB-zinc finger protein ZNF268a is essential for virus-induced interferon signaling. We find that cytoplasmic ZNF268a is constantly degraded by lysosome and thus remains low expressed in resting cell cytoplasm.

Waste Detection System Based on Data Augmentation and YOLO_EC.

The problem of waste classification has been a major concern for both the government and society, and whether waste can be effectively classified will affect the sustainable development of human society. To perform fast and efficient detection of waste targets in the sorting process, this paper proposes a data augmentation + YOLO_EC waste detection system. First of all, because of the current shortage of multi-objective waste classification datasets, the heavy workload of human data collection, and the limited improvement of data features by traditional data augmentation methods, DCGAN (deep convolution generative adversarial networks) was optimized by improving the loss function, and an image-generation model was established to realize the generation of multi-objective waste images; secondly, with YOLOv4 (You Only Look Once version 4) as the basic model, EfficientNet is used as the backbone feature extraction network to realize the light weight of the algorithm, and at the same time, the CA (coordinate attention) attention mechanism is introduced to reconstruct the MBConv module to filter out high-quality information and enhance the feature extraction ability of the model.