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Retromer and sorting nexins (SNXs) transport cargoes from endosomes to the trans-Golgi network or plasma membrane. Recent studies have unveiled the emerging roles for retromer and SNXs in the life cycle of viruses, including members of Coronaviridae, Flaviviridae and Retroviridae. Key components of retromer/SNXs, such as Vps35, Vps26, SNX5 and SNX27, can affect multiple steps of the viral life cycle, including facilitating the entry of viruses into cells, participating in viral replication, and promoting the assembly of virions. Here we present a comprehensive updated review on the interplay between retromer/SNXs and virus, which will shed mechanistic insights into controlling virus infection.

This paper focuses on automatic Cholangiocarcinoma (CC) diagnosis from microscopic hyperspectral (HSI) pathological dataset with deep learning method. The first benchmark based on the microscopic hyperspectral pathological images is set up. Particularly, 880 scenes of multidimensional hyperspectral Cholangiocarcinoma images are collected and manually labeled each pixel as either tumor or non-tumor for supervised learning. Moreover, each scene from the slide is given a binary label indicating whether it is from a patient or a normal person. Different from traditional RGB images, the HSI acquires pixels in multiple spectral intervals, which is added as an extension on the channel dimension of 3-channel RGB image. This work aims at fully exploiting the spatial-spectral HSI data through a deep Convolution Neural Network (CNN). The whole scene is first divided into several patches. Then they are fed into CNN for the tumor/non-tumor binary prediction and the tumor area regression. The further diagnosis on the scene is made by random forest based on the features from patch prediction. Experiments show that HSI provides a more accurate result than RGB image. Moreover, a spectral interval convolution and normalization scheme are proposed for further mining the spectral information in HSI, which demonstrates the effectiveness of the spatial-spectral data for CC diagnosis.

Epidermal growth factor receptor (EGFR) is an efficient target for cancer therapy. In this study, a high-affinity EGFR-antagonistic affibody (ZEGFR) molecule coupled with cisplatin-loaded PEGylated liposomes (LS-DDP) was applied to actively target EGFR+ A431 tumor cells in vitro and in vivo. The LS-DDP coupled with ZEGFR (AS-DDP) had an average size of 140.01 ± 0.84 nm, low polydispersity, a zeta potential of -13.40 ± 0.8 mV, an acceptable encapsulation efficiency of 17.30 ± 1.35%, and released cisplatin in a slow-controlled manner. In vitro, AS-DDP demonstrated a higher amount of platinum intracellular uptake by A431 cells than LS-DDP. The IC50 value of AS-DDP (9.02 ± 1.55 μg/ml) was much lower than that of LS-DDP (16.44 ± 0.87 μg/ml), indicating that the anti-tumor effects of AS-DDP were remarkable due to the modification of ZEGFR. In vivo, the concentration of AS-DDP in the tumor site increased more than 1.76-fold, while an increase in apoptotic cells at 48 h compared to the LS-DDP was also observed, illustrating that AS-DDP possessed excellent tumor-targeting efficiency. As a result, the targeted nano-liposomes achieved greater tumor suppression. Therefore, selective targeting of LS-DDP coupled with ZEGFR enhanced the anti-tumor effects and appeared to be a promising strategy for the treatment of EGFR+ tumors.

Document image classification plays a vital role in the document image processing system. Thus it is of great importance to have a clear understanding of the state-of-the-art of the document image classification field, especially in this deep learning era, which will facilitate the development of effective document image processing systems. In this paper, we provide a comprehensive survey of the progress that has been made in the field of document image classification over the past two decades. We categorize the document images into non-mobile images and mobile images according to the way they are acquired. The existing document image classification methods for these two types of images are reviewed, which are classified as textual-based methods, structural-based methods, visual-based methods and hybrid methods. We further compare the performance of different classification methods on several public benchmark datasets. Finally, we highlight some open issues and recommend promising directions for future research.

The state-of-the-art semantic segmentation solutions usually leverage different receptive fields via multiple parallel branches to handle objects of different sizes. However, employing separate kernels for individual branches may degrade the generalization of the network to objects with different scales, and the computational cost increases with the increase of the number of branches. To tackle this problem, we propose a novel network structure, namely Kernel-Sharing Atrous Convolution (KSAC), where branches with different receptive fields share the same kernel, i.e., let a single kernel ‘see’ the input feature maps more than once with different receptive fields. Experiments conducted on the benchmark PASCAL VOC 2012 dataset show that our proposed sharing strategy can not only boost the network’s generalization and representation abilities but also reduce the computational cost significantly. Specifically, on the validation set, when compared with DeepLabv3+, about 2.7G FLOPs and 12.7G FLOPs are saved for output stride = 16 and 8 respectively. In addition, different from the widely used ASPP structure, our proposed KSAC is able to further improve the mIOU by taking benefit of wider context with larger atrous rates. Finally, our KSAC achieves mIOUs of 88.1%, 45.47% and 80.7% on the PASCAL VOC 2012 test set (Everingham et al., 2009), ADE20K dataset (Zhou et al., 2017) and Cityscapes datasets (Marius et al., 2016), respectively. Our full code will be released on Github: https://github.com/edwardyehuang/iSeg.

This research explores the effect of agency–communion orientation on changes in consumers’ brand attitudes after brand sharing. Two laboratory studies and one field study indicate that when consumers are communion- (vs. agency-) oriented, their attitudes toward a brand can be changed to conform to others’ attitudes after sharing their thoughts about the brand with others. This effect is found to be moderated by contextual differences in message length, where communion- (vs. agency-) oriented consumers are more likely to change their brand attitudes to conform to others’ attitudes if they share their thoughts about the brand in messages of long length. However, this effect is attenuated when they share their thoughts about the brand in messages of short length. The theoretical and practical implications of the findings are discussed.