Molecular mechanism of bovine Gasdermin D-mediated pyroptosis.

Pyroptosis is a form of programmed cell death characterized by cell swelling, pore formation in the plasma membrane, lysis, and releases of cytoplasmic contents. To date, the molecular mechanism of human and murine Gasdermin D-mediated pyroptosis have been fully investigated. However, studies focusing on molecular mechanism of bovine Gasdermin D (bGSDMD)-mediated pyroptosis and its function against pathogenic infection were unclear. In the present study, we demonstrate that bovine caspase-1 (bCaspase-1) cleaves bGSDMD at amino acid residue D277 to produce an N-terminal fragment (bGSDMD-p30) which leads to pyroptosis. The amino acid residues T238 and F239 are critical for bGSDMD-p30-mediated pyroptosis. The loop aa 278-299, L293 and A380 are the key sites for autoinhibitory structure of the full length of bGSDMD. In addition, bCaspase-3 also cleaves bGSDMD at residue Asp86 without inducing cell death. Therefore, our study provides the first detailed elucidation of the mechanism of bovine GSDMD-mediated pyroptosis. The results will establish a significant foundation for future research on the role of pyroptosis in bovine infectious diseases.

Crack Length Measurement Using Convolutional Neural Networks and Image Processing.

Fatigue failure is a significant problem in the structural safety of engineering structures. Human inspection is the most widely used approach for fatigue failure detection, which is time consuming and subjective. Traditional vision-based methods are insufficient in distinguishing cracks from noises and detecting crack tips. In this paper, a new framework based on convolutional neural networks (CNN) and digital image processing is proposed to monitor crack propagation length. Convolutional neural networks were first applied to robustly detect the location of cracks with the interference of scratch and edges. Then, a crack tip-detection algorithm was established to accurately locate the crack tip and was used to calculate the length of the crack. The effectiveness and precision of the proposed approach were validated through conducting fatigue experiments. The results demonstrated that the proposed approach could robustly identify a fatigue crack surrounded by crack-like noises and locate the crack tip accurately. Furthermore, crack length could be measured with submillimeter accuracy.