The Wide Variety of Acute Disseminated Encephalomyelitis in Children: A Clinical Perspective

Hyunsuk Lim(Hyunsuk Lim),Su-Kyeong Hwang(Su-Kyeong Hwang),Yun-Jeong Lee(Yun-Jeong Lee),Soonhak Kwon(Soonhak Kwon) 대한소아신경학회 2022 대한소아신경학회지 Vol.30 No.4

Acute disseminated encephalomyelitis (ADEM) is an acute demyelinating inflammatory disorder of the central nervous system. It is characterized by encephalopathy, multifocal neurological deficits, and typical magnetic resonance imaging findings of widespread demyelinating lesions, predominantly involving the white matter of the brain and spinal cord. ADEM mainly affects children and is commonly associated with preceding viral and bacterial infections, and, rarely, vaccinations. Despite substantial advances in the understanding of the association of myelin oligodendrocyte glycoprotein antibody with recurrent forms of ADEM or other demyelinating conditions, specific etiologic agents or biological markers have not been identified. Therefore, the diagnosis of ADEM is still based on clinical and radiological findings and the exclusion of other conditions mimicking ADEM. However, a prompt diagnosis and adequate treatment are crucial because diagnostic delays or inappropriate treatment may lead to unwanted neurological sequelae in some children. There is no standardized treatment protocol for ADEM, but the use of corticosteroids, intravenous immunoglobulin, and plasmapheresis has been associated with good clinical outcomes. Adequate treatment has reportedly resulted in favorable outcomes, with full or almost full recovery in most children with ADEM, although some children may develop neurological sequelae, such as cognitive impairment and motor deficits. Further studies are needed to identify biological clues and optimal treatment protocols to minimize the incidence of neurological sequelae.

A Design of Automatic Generation System for Spatial Data of Damage

Hyunsuk Hwang,Changsoo Kim 한국멀티미디어학회 2010 한국멀티미디어학회 국제학술대회 Vol.2010 No.-

As natural disasters occur on a huge scale, systematic data collection work from its damage needs to be operated to gather more detail information such as photos and its position. It needs citizens' participation in collecting damage data to protect people's lives and estates. Thus, a system for executing these works includes functions such as to send photos and related information into a database and then to create geometry area of damage. Therefore, this paper focuses on a design which includes a process which describes how to collect damage data from citizens and how to generate spatial data of the damage automatically.

A System Framework for Managing and Analyzing Disaster History Information

Hyunsuk Hwang,Tanka Nath Sharma,Changsoo Kim 한국멀티미디어학회 2009 한국멀티미디어학회 국제학술대회 Vol.2009 No.-

Hazard maps have recently been constructed to protect people's lives and estates from the damage caused by natural disasters such as flooding, wave-storms, landslides, and windstorms. The hazard maps need previous disaster records and related information such as weather, rainfall sensors, and CCTV information. This paper focuses on the development of a framework to manage and analyze disaster history data. The framework consists of the layers of raw data collection, data management, analysis models, integrated systems, and required services. This paper will introduce the components and implement the procedures needed in constructing a digital hazard map system.

The Study on the Prediction of Driving-Workload Using the DWPT in Curve Section: Local Road and Urban Road

( Yoonsook Hwang ),( Daesub Yoon ),( Hyunsuk Kim ),( Hyunsuk Kim ),( Kyong Ho Kim ) 한국감성과학회 2014 춘계학술대회 Vol.2014 No.-

This study aimed to investigate on whether the DWPT (the subjective Driving-Workload Prediction Tool) could be identified driving-workload according to road characteristics: the local road and the urban road. We had performed statistical analysis using thedata of 26 drivers (male: 15, female: 11; age: 36.54(SD=14.28)) from real driving environment. The DWPT score and EEG data were analyzed. The participants asked to fill out the DWPT Questionnaire before starting driving experiment. EEG data were collected using the FOT (Field Operational Test) method during main driving experiment. The DWPT is the developed questionnaire for predicting on drivers\` subjective driving-workload based on drivers` attitude on driving and their psychological characteristics in previous study. The DWPT is composed of three sub factors: the Situational Inadaptability, the Interpersonal Inadaptability, and the Risk Taking Personality. In this study, we had performed the regression analysis by setting the DWPT as an independent variable. As a result of analysis, the total score of DWPT had predicted driving-workload significantly while driving in the curve at both local and urban roads. However, the sub-factors of DWPT, the Situational Inadaptability, the Interpersonal Inadaptability, and the Risk Taking Personality, had predicted driving-workload inconsistently according to different road types. For details, the situational inadaptability was predicted driving-workload significantly during driving on the curve of both types of road.

들깨의 발아가 들깨유의 산화 및 토코페롤 안정성에 미치는 영향

황현숙(Hyunsuk Hwang),최은옥(Eunok Choe) 한국식품과학회 2011 한국식품과학회지 Vol.43 No.3

본 연구에서는 비발아들깨와 12, 36, 48시간 발아시킨 들깨로부터 유지를 추출한 후 60℃의 빛이 차단된 상태 또는 빛의 존재 하에 4일 동안 저장하면서 가속조건에서의 유지 산화 및 토코페롤 안정성을 평가하였다. 들깨유의 과산화물값과 공액이중산 값은 저장기간이 증가함에 따라 유의하게 증가하였으며, 비발아 들깨유에 비해 발아들깨유에서 낮은 경향을 보였다. 또한 빛이 차단된 조건에 비해 빛 존재 하에서 들깨유의 산화와 토코페롤 분해가 더 많이, 더 빨리 진행되었다. 들깨유 중 12시간 발아시 킨 들깨로부터 추출한 들깨유는 유의하게 낮은 공액이중산값과 토코페롤의 낮은 분해속도 등에 기인하여 가장 높은 자동산화, 광산화 안정성을 보여주었다 uto- and photo-oxidative stability of oil extracted from germinated perilla seeds during storage at 60℃ for 4 days was studied by determining peroxide and conjugated dienoic acid values. Tocopherol contents during oil oxidation were also monitored by high performance liquid chromatography. Perilla oil was oxidized and tocopherols were degraded during storage at 60℃ regardless of the presence of light. Light increased oil oxidation and tocopherol degradation. Seeds germinated for 12 h had increased tocopherol contents in the oil and improved the auto- and photo-oxidative stability of the perilla oil. Tocopherol played a more important role as an antioxidant in the presence of light than in the absence of light.

Hollow Condensate Formation via Spontaneous Coacervate Rearrangement

Hyunsuk CHOI,Sun Young KIM,Yuri HONG,Dong Soo HWANG,Yoo Seong CHOI 한국생물공학회 2023 한국생물공학회 학술대회 Vol.2023 No.4

Vesicle-like Biomolecular Condensate Formation via Spontaneous Coacervate Rearrangement

Hyunsuk CHOI,Sun Young KIM,Yuri HONG,Dong Soo HWANG,Yoo Seong CHOI 한국생물공학회 2023 한국생물공학회 학술대회 Vol.2023 No.10

The study on the prediction of driving-workload using the DWPT in curve section

Yoonsook Hwang,Daesub Yoon,Hyunsuk Kim,Kyong-Ho Kim 대한인간공학회 2014 대한인간공학회 학술대회논문집 Vol.2014 No.5

This study aimed to investigate on whether the DWPT (the subjective Driving-Workload Prediction Tool) could be identified driving-workload according to road characteristics: the local road and the urban road. We had performed statistical analysis using the data of 26 drivers (male: 15, female: 11; age: 36.54(SD=14.28)) from real driving environment. The DWPT score and EEG data were analyzed. The participants asked to fill out the DWPT Questionnaire before starting driving experiment. EEG data were collected using the FOT (Field Operational Test) method during main driving experiment. The DWPT is the developed questionnaire for predicting on drivers" subjective driving-workload based on drivers’ attitude on driving and their psychological characteristics in previous study. The DWPT is composed of three sub factors: the Situational Inadaptability, the Interpersonal Inadaptability, and the Risk Taking Personality. In this study, we had performed the regression analysis by setting the DWPT as an independent variable. As a result of analysis, the total score of DWPT had predicted driving-workload significantly while driving in the curve at both local and urban roads. However, the sub-factors of DWPT, the Situational Inadaptability, the Interpersonal Inadaptability, and the Risk Taking Personality, had predicted driving-workload inconsistently according to different road types. For details, the situational inadaptability was predicted driving-workload significantly during driving on the curve of both types of road. However, the interpersonal inadaptability was tended to predict driving-workload slightly on the curve in only urban road. These results implicate that the density of driving environments (e.g. number of pedestrians and number of other vehicles) may affect driving-workload while curve negotiation. In other words, there are more pedestrians and more vehicles during curve negotiation in urban road than in local road. Therefore, the drivers should be driving more carefully on curve in urban road while interacting with others. These results suggested that the DWPT possibly identify differences of driving environments. The DWPT and the results of study will be applied to the driving-workload management system and adaptive driver intelligent human-vehicle interaction system. These systems could estimate the drivers’ driving-workload and provide intelligent interaction system for drivers by multi-modal interfaces based on the driving-workload.

The comparison of the predicted driving workload with the drivers’ physiological information while curve negotiation in the local road

Yoonsook Hwang,Daesub Yoon,Hyunsuk Kim,Changhyun Jeong 대한인간공학회 2013 대한인간공학회 학술대회논문집 Vol.2013 No.5

Objective: The purpose of this study is to investigate the relationship of driving workload between the DWPT(the subjective Driving-Workload Prediction Tool) developed by ETRI and EEG data collected from real driving environments on curve negotiation in the local road. Background: There are mainly three methods to measure the drivers’ driving workload; the subjective measures using questionnaire, driving performance measures using vehicle information, and drivers’ physiological measures using physiological sensors. However, it is not easy to extract drivers’ characteristics and drivers’ driving attitudes even though if we use above three methods to measure drivers’ driving workload. To overcome these limitations, We had developed the DWPT based on these drivers’ characteristics and attitudes as the part of HVI(Human-Vehicle Interface) project. Method: The total of 27 drivers(male 15, female 12) participated in this study. This experiment was conducted using the FOT(Field Operational Test) method that participants are asked to drive pre-defined path on the real local road. EEG data were collected from the participant while driving. Also, participants are asked to answer the DWPT questionnaire. We had analyzed the collected data in two driving scenarios; curve negotiation and straight road. Results: As the result of correlation analysis, DWPT was not correlated with EEG signal on the straight road. However, the relations between DWPT and EEG signal had positive correlation significantly on curve negotiation. In sub-factor analysis, inadaptability of road circumstances was correlated with EEG data. Conclusion: These results suggested that DWPT developed by ETRI had determined and predicted drivers’ real driving workload on curve negotiation. Application: The DWPT is going to be applied to driving Workload Management System and driver adaptive intelligent Human-Vehicle Interface system as a sub-module in the future intelligent car.

Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort

Yoo Hyunsuk,Kim Eun Young,Kim Hyungjin,Choi Ye Ra,Kim Moon Young,Hwang Sung Ho,Kim Young Joong,Cho Young Jun,Jin Kwang Nam 대한영상의학회 2022 Korean Journal of Radiology Vol.23 No.10

Objective: This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment. Materials and Methods: This retrospective simulation study was conducted using the CXRs of 5887 adults (mean age ± standard deviation, 55.4 ± 11.8 years; male, 4329) from three health screening centers in South Korea using a commercial AI (Lunit INSIGHT CXR3, version 3.5.8.8). Three board-certified thoracic radiologists reviewed CXR images for referable thoracic abnormalities and grouped the images into those with visible referable abnormalities (identified as abnormal by at least one reader) and those with clearly visible referable abnormalities (identified as abnormal by at least two readers). With AI-based simulated exclusion of normal CXR images, the percentages of normal images sorted and abnormal images erroneously removed were analyzed. Additionally, in a random subsample of 480 patients, the ability to identify visible referable abnormalities was compared among AI-unassisted reading (i.e., all images read by human readers without AI), AI-assisted reading (i.e., all images read by human readers with AI assistance as concurrent readers), and reading with AI triage (i.e., human reading of only those rendered abnormal by AI). Results: Of 5887 CXR images, 405 (6.9%) and 227 (3.9%) contained visible and clearly visible abnormalities, respectively. With AI-based triage, 42.9% (2354/5482) of normal CXR images were removed at the cost of erroneous removal of 3.5% (14/405) and 1.8% (4/227) of CXR images with visible and clearly visible abnormalities, respectively. In the diagnostic performance study, AI triage removed 41.6% (188/452) of normal images from the worklist without missing visible abnormalities and increased the specificity for some readers without decreasing sensitivity. Conclusion: This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.