Cell entry of reovirus requires a series of ordered steps, which include conformational changes in outer capsid protein 1 and its autocleavage. The 1N fragment released as a consequence of these events interacts with host cell membranes and mediates their disruption, leading to delivery of the viral core into the cytoplasm. The prototype reovirus strains T1L and T3D exhibit differences in the efficiency of autocleavage, in the propensity to undergo conformational changes required for membrane penetration, and in the capacity for penetrating host cell membranes. To better understand how polymorphic differences in 1 influence reovirus entry events, we generated recombinant viruses that express chimeric T1L-T3D 1 proteins and characterized them for the capacity to efficiently complete each step required for membrane penetration. Our studies revealed two important functions for the central ␦ region of 1. First, we found that 1 autocleavage is regulated by the N-terminal portion of ␦, which forms an ␣-helical pedestal structure. Second, we observed that the C-terminal portion of ␦, which forms a jelly-roll  barrel structure, regulates membrane penetration by influencing the efficiency of ISVP* formation. Thus, our studies highlight the molecular basis for differences in the membrane penetration efficiency displayed by prototype reovirus strains and suggest that distinct portions of the reovirus ␦ domain influence different steps during entry.
We present an electrocardiogram (ECG) -based emotion recognition system using self-supervised learning. Our proposed architecture consists of two main networks, a signal transformation recognition network and an emotion recognition network. First, unlabelled data are used to successfully train the former network to detect specific pre-determined signal transformations in the self-supervised learning step. Next, the weights of the convolutional layers of this network are transferred to the emotion recognition network, and two dense layers are trained in order to classify arousal and valence scores. We show that our self-supervised approach helps the model learn the ECG feature manifold required for emotion recognition, performing equal or better than the fully-supervised version of the model. Our proposed method outperforms the state-of-the-art in ECG-based emotion recognition with two publicly available datasets, SWELL and AMIGOS. Further analysis highlights the advantage of our self-supervised approach in requiring significantly less data to achieve acceptable results.
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