Immunomodulatory zinc-based materials for tissue regeneration.

Zinc(Zn)-based materials have contributed greatly to the rapid advancements in tissue engineering. The qualities they possess that make them so beneficial include their excellent biodegradability, biocompatibility, anti-bacterial activity, among and several others. Biomedical materials that act as a foreign body, will inevitably cause host immune response when introduced to the human body. As the osteoimmunology develops, the immunomodulatory characteristics of biomaterials have become an appealing concept to improve implant-tissue interaction and tissue restoration. Recently, Zn-based materials have also displayed immunomodulatory functions, especially macrophage polarization states. It can promote the transformation of M1 macrophages into M2 macrophages to enhance the tissue regeneration and reconstruction. This review covers mainly Zn-based materials and their characteristics, including metallic Zn alloys and Zn ceramics. We highlight the current advancements in the type of immune responses, as well as the mechanisms, that are induced by Zn-based biomaterials, most importantly the regulation of innate immunity and the mechanism of promoting tissue regeneration. To this end, we discuss their applications in biomedicine, and conclude with an outlook on future research challenges.

Administration methods of lipid-based nanoparticle delivery systems for cancer treatment.

As nano medications have developed in the recent four decades, nano-delivery systems have been applied in treating various diseases and are especially common in cancer treatment. Nano-delivery systems could target cancer-associated cells, enhance the accuracy and efficacy of treatment, and reduce systemic side effects. Among the many drugs on nano-carriers, the load system of lipid-based nanoparticles has the brightest prospect due to the high level of biocompatibility, biodegradability, loading capability, and immunogenicity. Previous reviews have comprehensively introduced their effects and progress. However, most of them did not provide great attention to practical applications. This article will focus on different intake methods, which decide the biological process of drugs. This suggests that we can modify lipid-based nano-delivery systems according to how they are capable of prolonging the half-life span and magnifying therapy effects in treating cancer. Besides, we put forth the problems that should be further studied in the future and their probable solutions.

A Topology Control with Energy Balance in Underwater Wireless Sensor Networks for IoT-Based Application.

As part of the IoT-based application, underwater wireless sensor networks (UWSN), which are typically self-organized heterogeneous wireless network, are one of the research hot-spots using various sensors in marine exploration and water environment monitoring application fields, recently. Due to the serious attenuation of radio in water, acoustic or hybrid communication is a usual way for transmitting information among nodes, which dissipates much more energy to prevent the network failure and guarantee the quality of service (QoS). To address this issue, a topology control with energy balance, namely TCEB, is proposed for UWSN to overcome time-delay and other interference, as well as make the entire network load balance. With the given underwater network model and its specialized energy consumption model, we introduce the non-cooperative-game-based scheme to select the nodes with better performance as the cluster-heads. Afterwards, the intra-cluster and inter-cluster topology construction are, respectively, to form the effective communication links of the intra-cluster and inter-cluster, which aim to build energy-efficient topology to reduce energy consumption. With the demonstration of the simulation, the results show the proposed TCEB has better performance on energy-efficiency and throughput than three other representative algorithms in complex underwater environments.

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems.

The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.