Nuclear localization of the p17 protein of avian reovirus is correlated with autophagy induction and an increase in viral replication.

p17 is a nonstructural protein of avian reovirus (ARV) that induces autophagy in infected cells. In the present study, we investigated the effect of p17 and its nuclear localization signal (NLS) on autophagy and viral replication. When Vero cells and DF1 cells were transfected with mutant p17 in which lysine (K) at position 122 and arginine (R) at position 123 were mutated to alanine (A), the expression level of LC3 II decreased dramatically after transfection. The expression of the polypeptide encompassing the first 103 amino acids of p17, a region that did not contain the NLS, did not have a significant effect on autophagy. Moreover, when cells overexpressing mutant p17 were infected with the ARV GX2010/1 strain, the viral titer was significantly decreased compared with the expression of wild-type p17. In general, the NLS of p17 facilitates the induction of autophagy and is correlated with an increase in virus production.

Exploiting Cross-Modal Prediction and Relation Consistency for Semisupervised Image Captioning.

The task of image captioning aims to generate captions directly from images via the automatically learned cross-modal generator. To build a well-performing generator, existing approaches usually need a large number of described images (i.e., supervised image-sentence pairs), requiring a huge effects on manual labeling. However, in real-world applications, a more general scenario is that we only have limited amount of described images and a large number of undescribed images. Therefore, a resulting challenge is how to effectively combine the undescribed images into the learning of cross-modal generator (i.e., semisupervised image captioning). To solve this problem, we propose a novel image captioning method by exploiting the cross-modal prediction and relation consistency (CPRC), which aims to utilize the raw image input to constrain the generated sentence in the semantic space. In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty while using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) prediction consistency: CPRC utilizes the prediction of raw image as soft label to distill useful supervision for the generated sentence, rather than employing the traditional pseudo labeling and 2) relation consistency: CPRC develops a novel relation consistency between augmented images and corresponding generated sentences to retain the important relational knowledge. In result, CPRC supervises the generated sentence from both the informativeness and representativeness perspectives, and can reasonably use the undescribed images to learn a more effective generator under the semisupervised scenario. The experiments show that our method outperforms state-of-the-art comparison methods on the MS-COCO "Karpathy" offline test split under complex nonparallel scenarios, for example, CPRC achieves at least 6 % improvements on the CIDEr-D score.