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Jung Kyeongmin,Yoon Joohyun,Ahn Yeeun,Kim Soyeon,Shim Injeong,Ko Hyunwoong,Jung Sang-Hyuk,Kim Jaeyoung,Kim Hyejin,Lee Dong June,Cha Soojin,Lee Hyewon,Kim Beomsu,Cho Min Young,Cho Hyunbin,Kim Dan Say,K 생화학분자생물학회 2023 Experimental and molecular medicine Vol.55 No.-
Irritability is a heritable core mental trait associated with several psychiatric illnesses. However, the genomic basis of irritability is unclear. Therefore, this study aimed to 1) identify the genetic variants associated with irritability and investigate the associated biological pathways, genes, and tissues as well as single-nucleotide polymorphism (SNP)-based heritability; 2) explore the relationships between irritability and various traits, including psychiatric disorders; and 3) identify additional and shared genetic variants for irritability and psychiatric disorders. We conducted a genome-wide association study (GWAS) using 379,506 European samples (105,975 cases and 273,531 controls) from the UK Biobank. We utilized various post-GWAS analyses, including linkage disequilibrium score regression, the bivariate causal mixture model (MiXeR), and conditional and conjunctional false discovery rate approaches. This GWAS identified 15 independent loci associated with irritability; the total SNP heritability estimate was 4.19%. Genetic correlations with psychiatric disorders were most pronounced for major depressive disorder (MDD) and bipolar II disorder (BD II). MiXeR analysis revealed polygenic overlap with schizophrenia (SCZ), bipolar I disorder (BD I), and MDD. Conditional false discovery rate analyses identified additional loci associated with SCZ (number [n] of additional SNPs = 105), BD I (n = 54), MDD (n = 107), and irritability (n = 157). Conjunctional false discovery rate analyses identified 85, 41, and 198 shared loci between irritability and SCZ, BD I, and MDD, respectively. Multiple genetic loci were associated with irritability and three main psychiatric disorders. Given that irritability is a cross-disorder trait, these findings may help to elucidate the genomics of psychiatric disorders.
N ‐(Biphenyl‐3‐ylmethyl)ethanamines as G protein‐biased agonists of 5‐HT 7 R
Kim Doyoung,Lee Jieon,Kwag Rina,Kim Hyunbin,Oh Hyunji,문봉진,Kim Hak Joong,Seong Jihye,Jeon Byungsun,Kang Taek,Choo Hyunah 대한화학회 2022 Bulletin of the Korean Chemical Society Vol.43 No.1
There has been much attention to biased ligands of G protein-coupled receptors (GPCRs) for potential pharmacological benefits. Recently, we reported N- ((6-chloro-2’-methoxy-[1,1’-biphenyl]-3-yl)methyl)ethanamine 1 as G proteinbiased agonist of 5-HT7R, which could be used as a chemical probe for the study on treatment discovery of autism spectrum disorder. Herein, we describe the synthesis of derivatives of the compound 1 and their biological evaluations in both G protein and β-arrestin signaling pathway. Total 16 compounds were synthesized and evaluated, and the compounds 3c, 3f, 3i, and 3p could be called as G protein-biased agonists like the compound 1. Among the four compounds, the compound 3c was the best in efficacy with an Emax value of 73% and the compound 3f was the most potent agonist with an EC50 value of 0.094 μM.
( Hyunbin Kim ),( Mingyu Kim ),( Yonggun Park ),( Sang-Yun Yang ),( Hyunwoo Chung ),( Ohkyung Kwon ),( Hwanmyeong Yeo ) 한국목재공학회 2019 목재공학 Vol.47 No.2
목재의 결점은 생장과정에서 또는 가공 중에 다양한 형태로 발생한다. 따라서 목재를 이용하기 위해서는 목재의 결점을 정확하게 분류하여 용도에 맞는 목재 품질을 객관적으로 평가할 필요가 있다. 하지만 사람에 의한 등급구분과 수종구분은 주관적 판단에 의해 차이가 발생할 수 있기 때문에 목재 품질의 객관적 평가 및 목재 생산의 고속화를 위해서는 컴퓨터 비전을 활용한 화상분석 자동화가 필요하다. 본 연구에서는 SIFT+k-NN 모델과 CNN 모델을 통해 옹이의 종류를 자동으로 구분하는 모델을 구현하고 그 정확성을 분석해보고자 하였다. 이를 위하여 다섯 가지 국산 침엽수종으로부터 다양한 형태의 옹이 이미지 1,172개를 획득하여 학습 및 검증에 사용하였다. SIFT+k-NN 모델의 경우, SIFT 기술을 이용하여 옹이 이미지에서 특성을 추출한 뒤, k-NN을 이용하여 분류를 진행하였으며, 최대 60.53%의 정확도로 분류가 가능하였다. 이 때 k-index는 17이었다. CNN 모델의 경우, 8층의 convolution layer와 3층의 hidden layer로 구성되어있는 모델을 사용하였으며, 정확도의 최대값은 1205 epoch에서 88.09%로 나타나 SIFT+k-NN 모델보다 높은 결과를 보였다. 또한 옹이의 종류별 이미지 개수 차이가 큰 경우, SIFT+k-NN 모델은 비율이 높은 옹이 종류로 편향되어 학습되는 결과를 보였지만, CNN 모델은 이미지 개수의 차이에도 편향이 심하지 않아 옹이 분류에 있어 더 좋은 성능을 보였다. 본 연구 결과를 통해 CNN 모델을 이용한 목재 옹이의 분류는 실용가능성에 있어 충분한 정확도를 보이는 것으로 판단된다. Various wood defects occur during tree growing or wood processing. Thus, to use wood practically, it is necessary to objectively assess their quality based on the usage requirement by accurately classifying their defects. However, manual visual grading and species classification may result in differences due to subjective decisions; therefore, computer-vision-based image analysis is required for the objective evaluation of wood quality and the speeding up of wood production. In this study, the SIFT+k-NN and CNN models were used to implement a model that automatically classifies knots and analyze its accuracy. Toward this end, a total of 1,172 knot images in various shapes from five domestic conifers were used for learning and validation. For the SIFT+k-NN model, SIFT technology was used to extract properties from the knot images and k-NN was used for the classification, resulting in the classification with an accuracy of up to 60.53% when k-index was 17. The CNN model comprised 8 convolution layers and 3 hidden layers, and its maximum accuracy was 88.09% after 1205 epoch, which was higher than that of the SIFT+k-NN model. Moreover, if there is a large difference in the number of images by knot types, the SIFT+k-NN tended to show a learning biased toward the knot type with a higher number of images, whereas the CNN model did not show a drastic bias regardless of the difference in the number of images. Therefore, the CNN model showed better performance in knot classification. It is determined that the wood knot classification by the CNN model will show a sufficient accuracy in its practical applicability.
Anesthetic management for cesarean delivery in a Guillain-Barré syndrome patient -A case report-
Hyunbin Kim,유정희,Jung-Won Hwang,Sang-Hwan Do 대한마취통증의학회 2013 Korean Journal of Anesthesiology Vol.64 No.3
Guillain-Barré syndrome is an acute inflammatory demyelinating polyradiculopathy characterized by progressive motor weakness, areflexia, and ascending paralysis. Guillain-Barré syndrome is extremely rare in pregnant patients, and there are no established guidelines for delivery or safest anesthetic methods. We report a Cesarean delivery in the case of a 32-year old woman who was diagnosed with Guillain-Barré syndrome 18 weeks into gestation. Tracheostomy was performed due to progressive respiratory muscle weakness and respiratory failure, and ventilator support was required in the intensive care unit. The respiratory difficulty was exacerbated by the growth of the fetus, necessitating emergency Cesarean delivery. The delivery was successfully performed under general anesthesia, and the patient recovered without neurological sequelae.
Hyunbin Kim,Yeonjung Han,Yonggun Park,Sang-yun Yang,Hyunwoo Chung,Chang-deuk Eom,Hyun-mi Lee,Hwanmyeong Yeo 한국목재공학회 2017 목재공학 Vol.45 No.6
Predicting the amount and distribution of moisture content within wood allows calculating the various mechanical dynamics of the wood as well as determining the drying time. For boxed-heart wood with a large cross-section, since it is difficult to measure the moisture content of the interior, it is necessary to predict the moisture content distribution. This study predicted the moisture movement in boxed-heart red pine timber, during high temperature drying, by using the three-dimensional finite difference method for the efficient drying process. During drying for 72 h, the predicted and actual moisture content of the tested wood tended to decrease at a similar rate. In contrast, the actual moisture content at 196 and 240 h was lower than predicted because surface checking of the wood occurred from 72 h and excessive water emission was unexpectedly occurred from the checked and splitted surface.
딥러닝 기반 화력발전 보일러 출구 NOx 농도 선행 예측 모델
조현빈(Hyunbin Jo),강동협(Donghyup Kang),박성민(Seongmin Park),이종욱(Jongwuk Lee),류창국(Kang Y. Huh) 한국연소학회 2022 KOSCOSYMPOSIUM논문집 Vol.2022 No.11
In this study, we developed a deep learning model to forecast the NOx and oxygen concentration, and gas temperature at the boiler exit of a coal-fired power plant. The target boiler is a 500 MWe tangential firing boiler, which is one of 20 units often referred to as standard coal power plant. From the database of the power plant, 73 raw items of operation data with one-minute frequency were collected for a period of approximately 5 months. Through the feature selection procedure, the raw data items were condensed into 19 features which include coal feeder throughput to burners, air flow rate, and burner tilt. The features were then used to establish two types of data segments: segment #1 for current operation status and segment #2 for recent histories measured at the boiler exit. Considering the large fluctuations, the histories of the recent values at the boiler exit values were averaged over 5 min. After evaluating different prediction models with respect to the nature of the data segments, suitable models were applied in the form of ensemble model to forecast the boiler exit values 1 min in advance. When compared to measured data, the prediction quality was sufficiently high with a mean square error of 0.0123 for NOx emission.
Sungbok Kim,Hyunbin Kim 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10
This paper presents the positional uncertainty assessment and the complex obstacle detection of an ultrasonic sensor ring with overlapped beam pattern. Due to beam overlap between adjacent ultrasonic sensors, the entire sensing range of an individual ultrasonic senor can be divided into three smaller sensing subzones. First, the concept of collision free region of each ultrasonic sensor with reference to the center of an ultrasonic sensor ring is introduced. Second, the effective beam width of an overlapped ultrasonic sensor ring is defined to assess the positional uncertainty in obstacle detection. Third, high resolution single obstacle detection can be made based on the combination of two adjacent ultrasonic sensors detecting a given obstacle. Fourth, high resolution multiple obstacle detection can be made based on the inequality relationships among the obstacle distances by three consecutive ultrasonic sensors.
Mask R-CNN기법을 활용한 목재 표면 옹이 구획화
김현빈 ( Hyunbin Kim ),정현우 ( Hyunwoo Chung ),김민규 ( Mingyu Kim ),박용건 ( Yonggun Park ),양상윤 ( Sang-yun Yang ),여환명 ( Hwanmyeong Yeo ) 한국목재공학회 2019 한국목재공학회 학술발표논문집 Vol.2019 No.2
목재 품질의 객관적 평가 및 목재 생산의 고속화를 위해서는 컴퓨터 비전을 활용한 목재 표면 화상분석 자동화가 필요하다. 딥러닝(Deep Learning) 기술은 최근 컴퓨터 비전을 통한 화상 분석 및 패턴인식 분야에서 높은 정확도와 속도로 인해 그 활용도가 높아지고 있다. 따라서 본 연구에서는 딥러닝 기술 중 화상의 구획화에 높은 성능을 보이는 알려진 합성곱 신경망(Convolutional Neural Network)을 이용하여 목재 표면 옹이를 구획화하고, 그 종류를 분류하였다. 본 연구에서 사용한 목재 재면 사진은 낙엽 송, 잣나무, 소나무, 삼나무, 편백, 더글라스 퍼, 라디에타 파인에서 획득한 938개의 제재목 사진을 사용 하였다. 제재목 사진에서 추출한 옹이 이미지는 1,172개로, 4 가지 종류로 분류하였다. 옹이의 종류와 위치에 대한 데이터베이스를 통해 제재목 표면의 옹이를 구획화하여 표시하고, 그 종류를 분류하는 알고리즘 학습을 진행하였다. 학습에 사용한 Mask R-CNN(Regions with Convolutional Neural Network) 모델은 resnet101을 이용하여 Feature Pyramid Network를 토대로 옹이 위치 예측 학습과 옹이 종류 분류 학습을 동시에 진행하였다. 목재 표면의 옹이 구획화 학습을 진행한 결과, 옹이 종류별 이미지의 편차가 존재하며, 옹이의 크기가 다양함에 불구하고 높은 정확도로 목재 표면의 옹이 탐지가 가능하였다. 200번의 반복학습결과, 학습이 반복될수록 학습 이미지셋에 과적합하는 현상이 발생하여 목재 문양이 옹이로 탐지되는 경우가 발생하였다. 하지만 높은 정확도로 분류가 가능하였기 때문에 다양한 옹이 형태를 추가로 학습시킨다면 더 높은 정확도로 옹이 구획화가 가능할 것으로 기대된다.