Cyanidin-3-O-glucoside alleviates ethanol-induced liver injury by promoting mitophagy in a Gao-binge mouse model of alcohol-associated liver disease.

BACKGROUND: Alcohol-associated liver disease (ALD) is a leading cause of liver disease-related deaths worldwide. Unfortunately, approved medications for the treatment of this condition are quite limited. One promising candidate is the anthocyanin, Cyanidin-3-O-glucoside (C3G), which has been reported to protect mice against hepatic lipid accumulation, as well as fibrosis in different animal models. However, the specific effects and mechanisms of C3G on ALD remain to be investigated. EXPERIMENTAL APPROACH: In this report, a Gao-binge mouse model of ALD was used to investigate the effects of C3G on ethanol-induced liver injury. The mechanisms of these C3G effects were assessed using AML12 hepatocytes. RESULTS: C3G administration ameliorated ethanol-induced liver injury by suppressing hepatic oxidative stress, as well as through reducing hepatic lipid accumulation and inflammation. Mechanistically, C3G activated the AMPK pathway and enhanced mitophagy to eliminate damaged mitochondria, thus reducing mitochondria-derived reactive oxidative species in ethanol-challenged hepatocytes. CONCLUSIONS: The results of this study indicate that mitophagy plays a potentially important role underlying the hepatoprotective action of C3G, as demonstrated in a Gao-binge mouse model of ALD. Accordingly, C3G may serve as a promising, new therapeutic drug candidate for use in ALD.

Towards Large-Scale and Privacy-Preserving Contact Tracing in COVID-19 Pandemic: A Blockchain Perspective.

Activity-tracking applications and location-based services using short-range communication (SRC) techniques have been abruptly demanded in the COVID-19 pandemic, especially for automated contact tracing. The attention from both public and policy keeps raising on related practical problems, including 1) how to protect data security and location privacy? 2) how to efficiently and dynamically deploy SRC Internet of Thing (IoT) witnesses to monitor large areas? To answer these questions, in this paper, we propose a decentralized and permissionless blockchain protocol, named Bychain. Specifically, 1) a privacy-preserving SRC protocol for activity-tracking and corresponding generalized block structure is developed, by connecting an interactive zero-knowledge proof protocol and the key escrow mechanism. As a result, connections between personal identity and the ownership of on-chain location information are decoupled. Meanwhile, the owner of the on-chain location data can still claim its ownership without revealing the private key to anyone else. 2) An artificial potential field-based incentive allocation mechanism is proposed to incentivize IoT witnesses to pursue the maximum monitoring coverage deployment. We implemented and evaluated the proposed blockchain protocol in the real-world using the Bluetooth 5.0. The storage, CPU utilization, power consumption, time delay, and security of each procedure and performance of activities are analyzed. The experiment and security analysis is shown to provide a real-world performance evaluation.

Contrastive Learning-Based Dual Dynamic GCN for SAR Image Scene Classification.

As a typical label-limited task, it is significant and valuable to explore networks that enable to utilize labeled and unlabeled samples simultaneously for synthetic aperture radar (SAR) image scene classification. Graph convolutional network (GCN) is a powerful semisupervised learning paradigm that helps to capture the topological relationships of scenes in SAR images. While the performance is not satisfactory when existing GCNs are directly used for SAR image scene classification with limited labels, because few methods to characterize the nodes and edges for SAR images. To tackle these issues, we propose a contrastive learning-based dual dynamic GCN (DDGCN) for SAR image scene classification. Specifically, we design a novel contrastive loss to capture the structures of views and scenes, and develop a clustering-based contrastive self-supervised learning model for mapping SAR images from pixel space to high-level embedding space, which facilitates the subsequent node representation and message passing in GCNs. Afterward, we propose a multiple features and parameter sharing dual network framework called DDGCN. One network is a dynamic GCN to keep the local consistency and nonlocal dependency of the same scene with the help of a node attention module and a dynamic correlation matrix learning algorithm. The other is a multiscale and multidirectional fully connected network (FCN) to enlarge the discrepancies between different scenes. Finally, the features obtained by the two branches are fused for classification. A series of experiments on synthetic and real SAR images demonstrate that the proposed method achieves consistently better classification performance than the existing methods.

Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing.

The coexistence of radar and communication systems is necessary to facilitate new wireless systems and services due to the shortage of the useful radio spectrum. Moreover, changes in spectrum regulation will be introduced in which the spectrum is allocated in larger chunks and different radio systems need to share the spectrum. For example, 5G NR, LTE and Wi-Fi systems have to share the spectrum with S-band radars. Managing interference is a key task in coexistence scenarios. Cognitive radio and radar technologies facilitate using the spectrum in a flexible manner and sharing channel awareness between the two subsystems. In this paper, we propose a nullspace-based joint precoder-decoder design for coexisting multicarrier radar and multiuser multicarrier communication systems. The maximizing signal interference noise ratio (max-SINR) criterion and interference alignment (IA) constraints are employed in finding the precoder and decoder. By taking advantage of IA theory, a maximum degree of freedom upper bound for the K+1-radar-communication-user interference channel can be achieved. Our simulation studies demonstrate that interference can be practically fully canceled in both communication and radar systems. This leads to improved detection performance in radar and a higher rate in communication subsystems. A significant performance gain over a nullspace-based precoder-only design is also obtained.