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Sutterella faecalis sp. nov., isolated from human faeces
Byeong Seob Oh,JI-SUN KIM,유승엽,Seoung Woo Ryu,SEUNG-HWAN PARK,강세원,박잠언,Seung-Hyeon Choi,Kook-Il Han,Keun Chul Lee,Mi Kyung Eom,Min Kuk Suh,Han Sol Kim,Dong Ho Lee,Hyuk Yoon,김병용,이제희,Jung-SookLee,이주혁 한국미생물학회 2020 The journal of microbiology Vol.58 No.2
An obligately anaerobic, Gram-stain-negative, non-motile, non-spore-forming, and coccobacilli-shaped bacterial strain, designated KGMB03119T, was isolated from human faeces from a Korean. Phylogenetic analysis based on the 16S rRNA gene sequence revealed that the isolate was a member of the genus Sutterella and most closely related to Sutterlla wadsworthensis KCTC 15691T (96.8% 16S rRNA gene sequence similarity). The DNA G + C content of strain KGMB03119T was 58.3 mol% as determined from its whole genome sequence. Strain KGMB03119T was asaccharolytic, catalase-positive, oxidase- and urease-negative. Furthermore, the isolate was positive for alkaline phosphatase, leucine arylamidase, acid phosphatase, arginine arylamidase, alanine arylamidase, and glycine arylamidase. The major cellular fatty acids (> 10%) of the isolate were C18:1ω9c and C16:0. Methylmenaquinone-5 (MMK-5, 100%) was the predominant isoprenoid quinone in the isolate. Based on the phylogenetic, physiological, and chemotaxonomic characteristics, strain KGMB03119T represents a novel species, for which the name Sutterella faecalis sp. nov. is proposed. The type strain is KGMB03119T (= KCTC 15823T = NBRC 114254T).
Byeong ill Lee,Jeong-hyeon Lim,Min-Ho Park,Seok-Ho Shin,Jin-Ju Byeon,Jangmi Choi,Seo-jin Park,Min-jae Park,Yuri Park,Young G. Shin 대한임상약리학회 2020 Translational and Clinical Pharmacology Vol.28 No.3
Carisbamate is an antiepileptic drug and it also has broad neuroprotective activity andanticonvulsant reaction. In this study, a liquid chromatography-quadrupole time-of-flightmass spectrometric (LC-qTOF-MS) method was developed and applied for the determinationof carisbamate in rat plasma to support in vitro and in vivo studies. A quadratic regression(weighted 1/concentration2), with an equation y = ax2+ bx + c, was used to fit calibrationcurves over the concentration range from 9.05 to 6,600 ng/mL for carisbamate in rat plasma. Preclinical in vitro and in vivo studies of carisbamate have been studied through the developedbioanalytical method. Based on these study results, human pharmacokinetic (PK) profilehas been predicted using physiologically based pharmacokinetic (PBPK) modeling. ThePBPK model was optimized and validated by using the in vitro and in vivo data. The humanPK of carisbamate after oral dosing of 750 mg was simulated by using this validated PBPKmodel. The human PK parameters and profiles predicted from the validated PBPK modelwere similar to the clinical data. This PBPK model developed from the preclinical data forcarisbamate would be useful for predicting the PK of carisbamate in various clinical settings.
A Low-power Real-time Hidden Markov Model Accelerator for Gesture User Interface on Wearable Devices
Hyeon-Gu Do,Seongrim Choi,Jaemin Hwang,Ara Kim,Byeong-Gyu Nam 대한전자공학회 2019 Journal of semiconductor technology and science Vol.19 No.4
A low-power and real-time hidden Markov model (HMM) accelerator is proposed for gesture user interface on wearable smart devices. HMM algorithm is widely used for sequence recognition problems such as speech recognition and gesture recognition thanks to its best-in-class recognition accuracy. However, the HMM algorithm has high computational complexity and requires massive memory bandwidth in sequence matching process. Therefore, there have been studies on hardware acceleration of the HMM algorithm to resolve these issues, but they were focusing on the speech recognition and therefore did not accommodate the motion orientation function required for the gesture recognition problem. The motion orientation function computes the direction of hand movement in gesture sequence and thus involves compute intensive division and arctangent operations. In this paper, we propose an HMM accelerator with a light weight motion orientation module for realizing gesture recognition on wearable devices. Binary search method is exploited in the motion orientation module to avoid the division and arctangent operations associated with calculating orientations for reduced arithmetic complexity. In addition, gesture models are clustered in the gesture database to reduce external memory transactions. Moreover, logarithmic arithmetic is adopted in Viterbi decoder of the HMM algorithm for more reduction in its complexity. Thanks to these proposed schemes, this work achieves 25.6% power reduction compared with a vanilla hardware implementation of the gesture recognizing HMM.
A Self-powered Always-on Vision-based Wake-up Detector for Wearable Gesture User Interfaces
Hyeon-Gu Do,Seongrim Choi,Junsik Woo,Ara Kim,Byeong-Gyu Nam 대한전자공학회 2019 Journal of semiconductor technology and science Vol.19 No.4
Hand gesture recognition is one of the secure natural user interface (NUI) mechanisms on wearable devices since it does not reveal user’s intention in public domain. However, its energy dissipation is very demanding since it requires compute-intensive machine vision processing. Recently, wake-up detectors have been proposed to improve the energy-efficiency of always-on sensing nature of the NUI systems by switching off the main functional blocks while just keeping the wake-up detector alive during idle time. However, vision-based wake-up detectors still require power-consuming vision processing so we propose a self-powered vision wake-up detector to alleviate burdens on limited battery lifetime and thus facilitate always-on wake-up detection for the wearable gesture UIs. Our work has four key features to realize the self-powered wake-up detection; 1) near-threshold imaging-harvesting dualmode CMOS image sensor (CIS) with 0.6 V 3T pixels, 2) subthreshold SRAM with disturb-free 0.3 V 10T bitcells, 3) hand detection engine with skin-color invariant Haar-like filters, and 4) on-die switched capacitor DC-DC converter for lightweight system design. Thanks to these features integrated together, this work achieves self-powered operation of alwayson vision wake-up detection.