The cellular endosomal sorting complex required for transport (ESCRT) machinery participates in membrane scission and cytoplasmic budding of many RNA viruses. Here, we found that expression of dominant negative ESCRT proteins caused a blockade of Epstein-Barr virus (EBV) release and retention of viral BFRF1 at the nuclear envelope. The ESCRT adaptor protein Alix was redistributed and partially colocalized with BFRF1 at the nuclear rim of virus replicating cells. Following transient transfection, BFRF1 associated with ESCRT proteins, reorganized the nuclear membrane and induced perinuclear vesicle formation. Multiple domains within BFRF1 mediated vesicle formation and Alix recruitment, whereas both Bro and PRR domains of Alix interacted with BFRF1. Inhibition of ESCRT machinery abolished BFRF1-induced vesicle formation, leading to the accumulation of viral DNA and capsid proteins in the nucleus of EBV-replicating cells. Overall, data here suggest that BFRF1 recruits the ESCRT components to modulate nuclear envelope for the nuclear egress of EBV.

In this paper, we explore the effect of using different convolutional layers, batch normalization and the global average pooling layer upon a convolutional neural network (CNN) based gaze tracking system. A novel method is proposed to label the participant’s face images as gaze points retrieved from eye tracker while watching videos for building a training dataset that is closer to human visual behavior. The participants can swing their head freely; therefore, the most real and natural images can be obtained without too many restrictions. The labeled data are classified according to the coordinate of gaze and area of interest on the screen. Therefore, varied network architectures are applied to estimate and compare the effects including the number of convolutional layers, batch normalization (BN) and the global average pooling (GAP) layer instead of the fully connected layer. Three schemes, including the single eye image, double eyes image and facial image, with data augmentation are used to feed into neural network to train and evaluate the efficiency. The input image of the eye or face for an eye tracking system is mostly a small-sized image with relatively few features. The results show that BN and GAP are helpful in overcoming the problem to train models and in reducing the amount of network parameters. It is shown that the accuracy is significantly improved when using GAP and BN at the mean time. Overall, the face scheme has a highest accuracy of 0.883 when BN and GAP are used at the mean time. Additionally, comparing to the fully connected layer set to 512 cases, the number of parameters is reduced by less than 50% and the accuracy is improved by about 2%. A detection accuracy comparison of our model with the existing George and Routray methods shows that our proposed method achieves better prediction accuracy of more than 6%.

Recently, heterogeneous system architectures are becoming a mainstream for achieving high performance and power efficiency. In particular, many-core graphics processing units (GPUs) have started to play an important role for computing in heterogeneous architectures. However, for application designers, computational workload still needs to be distributed among heterogeneous GPUs manually and remains inefficient. In this work, we propose a MINLP-based method for efficient workload distribution among GPUs by considering the capabilities of GPUs for various applications. Experimental results demonstrate the performance of our proposed method.