Sungkyunkwan University

About: Sungkyunkwan University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Thin film & Graphene. The organization has 28229 authors who have published 56428 publications receiving 1352733 citations. The organization is also known as: 성균관대학교.

Tissue Adhesive Catechol-Modified Hyaluronic Acid Hydrogel for Effective, Minimally Invasive Cell Therapy

Abstract: Current hyaluronic acid (HA) hydrogel systems often cause cytotoxicity to encapsulated cells and lack the adhesive property required for effective localization of transplanted cells in vivo. In addition, the injection of hydrogel into certain organs (e.g., liver, heart) induces tissue damage and hemorrhage. In this study, we describe a bioinspired, tissue-adhesive hydrogel that overcomes the limitations of current HA hydrogels through its improved biocompatibility and potential for minimally invasive cell transplantation. HA functionalized with an adhesive catecholamine motif of mussel foot protein forms HA-catechol (HA-CA) hydrogel via oxidative crosslinking. HA-CA hydrogel increases viability, reduces apoptosis, and enhances the function of two types of cells (human adipose-derived stem cells and hepatocytes) compared with a typical HA hydrogel crosslinked by photopolymerization. Due to the strong tissue adhesiveness of the HA-CA hydrogel, cells are easily and efficiently transplanted onto various tissues (e.g., liver and heart) without the need for injection. Stem cell therapy using the HA-CA hydrogel increases angiogenesis in vivo, leading to improved treatment of ischemic diseases. HA-CA hydrogel also improved hepatic functions of transplanted hepatocytes in vivo. Thus, this bioinspired, tissue-adhesive HA hydrogel can enhance the efficacy of minimally invasive cell therapy.

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Abstract: This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.