Konkuk University

About: Konkuk University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Apoptosis. The organization has 13405 authors who have published 27027 publications receiving 506313 citations.

Identification and Antimicrobial Activity of Phenylacetic Acid Produced by Bacillus licheniformis Isolated from Fermented Soybean, Chungkook-Jang

Abstract: A bacterial strain, B65-1, which showed strong antimicrobial activity, was isolated from Chungkook-Jang, a traditional Korean fermented-soybean food with antimicrobial properties. Based on carbon utilization pattern and partial 16S rRNA sequence analysis, the B65-1 strain was identified as Bacillus licheniformis.An antibiotic compound, active against bacteria and yeast such as Staphylococcus aureus, Escherichia coli, and Candida albicans, was isolated by various chromatographic procedures from culture filtrates of B. licheniformis B65-1. The purified antibiotic was identified to be phenylacetic acid, with the molecular formula C8H8O2 by analyses of EI-MS and NMR. The phenylacetic acid was detected in fermented soybean made with the strain B65-1 as a starter, but was not present in extracts of nonfermented soybean. Our results indicated that the phenylacetic acid produced by B. licheniformis during fermentation of soybean is one of the main compounds of antimicrobial activity of Chungkook- Jang.

DeepVision: Deepfakes Detection Using Human Eye Blinking Pattern

Abstract: In this paper, we propose a new approach to detect Deepfakes generated through the generative adversarial network (GANs) model via an algorithm called DeepVision to analyze a significant change in the pattern of blinking, which is a voluntary and spontaneous action that does not require conscious effort. Human eye blinking pattern has been known to significantly change according to the person's overall physical conditions, cognitive activities, biological factors, and information processing level. For example, an individual's gender or age, the time of day, or the person's emotional state or degree of alertness can all influence the pattern. As a result, Deepfakes can be determined through integrity verification by tracking significant changes in the eye blinking patterns in deepfakes by means of a heuristic method based on the results of medicine, biology, and brain engineering research, as well as machine learning and various algorithms based on engineering and statistical knowledge. This means we can perform integrity verification through tracking significant changes in the eye blinking pattern of a subject in a video. The proposed method called DeepVision is implemented as a measure to verify an anomaly based on the period, repeated number, and elapsed eye blink time when eye blinks were continuously repeated within a very short period of time. DeepVision accurately detected Deepfakes in seven out of eight types of videos (87.5% accuracy rate), suggesting we can overcome the limitations of integrity verification algorithms performed only on the basis of pixels.