Enterovirus 71 (EV71) is the primary causative pathogen of hand, foot, and mouth disease (HFMD), affecting children with severe neurological complications. Pyroptosis is a programmed cell death characterized by cell lysis and inflammatory response. Although proinflammatory response has been implicated to play important roles in EV71-caused diseases, the involvement of pyroptosis in the pathogenesis of EV71 is poorly defined. We show that EV71 infection induced caspase-1 activation. Responding to the activation of caspase-1, the expression and secretion of both IL-1β and IL-18 were increased in EV71-infected cells. The treatment of caspase-1 inhibitor markedly improved the systemic response of the EV71-infected mice. Importantly, caspase-1 inhibitor suppressed EV71 replication in mouse brains. Similarly, pyroptosis was activated by the infection of coxsackievirus B3 (CVB3), an important member of the Enterovirus genus. Caspase-1 activation and the increased expression of IL-18 and NLRP3 were demonstrated in HeLa cells infected with CVB3. Caspase-1 inhibitor also alleviated the overall conditions of virus-infected mice with markedly decreased replication of CVB3 and reduced expression of caspase-1. These results indicate that pyroptosis is involved in the pathogenesis of both EV71 and CVB3 infections, and the treatment of caspase-1 inhibitor is beneficial to the host response during enterovirus infection.

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In order to eliminate driving dangers caused by tire surface bubbles, the detection method of bubble defects on tire surfaces based on line lasers and machine vision is studied. Since it is difficult to recognize tire surfaces directly through images, line laser scanning is used to obtain tire images. The filtering method and morphology method are combined to preprocess these images. The gray centroid method is adopted to extract the center of the laser stripe, and then the algorithm to determine the positions of bubble defects on tire surfaces is proposed. According to the geometric characteristics of tire bubbles, the coordinates of starting points, ending points, and rough positions of vertices are determined. Then, the ordinates of the laser center with sub-pixel accuracy near bubble vertices are discretely magnified. The mask made of Gaussian function is convoluted with the magnified region, and the maximum value is obtained. Furthermore, the position of bubble vertices can be accurately extracted. The denoising effects of different methods for images are compared through experiments, and different positions of bubbles are detected. Experimental results show that the detection accuracy of this method is up to 93%, which is much higher than other methods. Experiments verify that the proposed method is effective for detecting tire surface bubbles.

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