Chonbuk National University

About: Chonbuk National University is a education organization based out in Jeonju, South Korea. It is known for research contribution in the topics: Apoptosis & Graphene. The organization has 14820 authors who have published 28884 publications receiving 554131 citations.

COMP-Ang1: A designed angiopoietin-1 variant with nonleaky angiogenic activity

Abstract: Angiopoietin-1 (Ang1) has potential therapeutic applications in inducing angiogenesis, enhancing endothelial cell survival, and preventing vascular leakage. However, production of Ang1 is hindered by aggregation and insolubility resulting from disulfide-linked higher-order structures. Here, by replacing the N-terminal portion of Ang1 with the short coiled-coil domain of cartilage oligomeric matrix protein (COMP), we have generated a soluble, stable, and potent Ang1 variant, COMP-Ang1. This variant is more potent than native Ang1 in phosphorylating the tyrosine kinase with Ig and epidermal growth factor homology domain 2 (Tie2) receptor and Akt in primary cultured endothelial cells, enhancing angiogenesis in vitro and increasing adult angiogenesis in vivo. Thus, COMP-Ang1 is an effective alternative to native Ang1 for therapeutic angiogenesis in vivo.

Induction Machine Condition Monitoring Using Neural Network Modeling

Abstract: Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Model-based methods are efficient monitoring systems for providing warning and predicting certain faults at early stages. However, the conventional methods must work with explicit motor models, and cannot be applied effectively for vibration signal diagnosis due to their nonadaptation and the random nature of vibration signal. In this paper, an analytical redundancy method using neural network modeling of the induction motor in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform is used to process the quasi-steady vibration signals to continuous spectra for the neural network model training. The faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results, and it is shown that a robust and automatic induction machine condition monitoring system has been produced