• Journal of Genetic Medicine
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• v.19 no.1
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• pp.32-37
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• 2022

Mucopolysaccharidosis type VI (MPS VI) is an autosomal recessive lysosomal disorder caused by the deficiency of arylsulfatase B due to mutations in the ARSB gene. Here, we report the case of a Korean female with a novel variant of MPS VI. A Korean female aged 5 years and 8 months, who is the only child of a healthy non-consanguineous Korean couple, presented at our hospital for severe short stature. She had a medical history of umbilical hernia and recurrent otitis media. Her symptoms included snoring and mouth breathing. Subtle dysmorphic features, including mild coarse face, joint contracture, hepatomegaly, and limited range of joint motion, were identified. Radiography revealed deformities, suggesting skeletal dysplasia. Growth hormone (GH) provocation tests revealed complete GH deficiency. Targeted exome sequencing revealed compound heterozygous mutations in the ARSB genes c.512G>A (p.Gly171Asp; a pathogenic variant inherited from her father) and c.1157C>T (p.Ser386Phe; a novel variant inherited from her mother in familial genetic testing). Quantitative tests revealed increased urine glycosaminoglycan (GAG) levels and decreased enzyme activity of arylsulfatase B. While on enzyme replacement therapy and GH therapy, her height increased drastically; her coarse face, joint contracture, snoring, and obstructive sleep apnea improved; urine GAG decreased; and left ventricular mass index was remarkably decreased. We report a novel variant-c.1157C>T (p.Ser386Phe)-of the ARSB gene in a patient with MPS VI; these findings will expand our knowledge of its clinical spectrum and molecular mechanisms.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.3
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• pp.251-261
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• 2022

Tuna purse seine fishery (TPF) constitute more than 60% of distant water fishery production in Korea based on a statistic of 2018, and 28 ships from four different companies were under operation at the western and central Pacific Ocean. On this research, common risk factors during TPF were investigated via enumeration of five years Korean fisherman's insurance payment statement, followed by some counterplans to diminish the accident rate. The accident rate of TPF on the Pacific Ocean peaked by 43.0% in 2014 and constantly decreased to 23.0% until 2018, presenting an average of 33.6%. Meanwhile, the accident rate on the Indian Ocean reached the highest point 55.1% in 2014 and declined to 11.6% in 2016, having an average of 24.7%. The average accident rate of the Indian Ocean scored 8.9% lower than the rate of the Pacific Ocean, but no statistic significance was observed. Depending on the process of operation, 'casting or hauling of net' was the most frequent part that people received an injury (40.4%). When the accidents were classified by their types, 'falling down' was the most recurrent cause of the injuries (28.5%). At the point of severity, the worst injuries were induced by crush hazard. Considering aforementioned accident frequency and severity, all the factors on the accident type list were divided into three different groups including high risk, moderate risk, and common risk. This study is expected to contribute to the reduction of occupational accidents during the work of fishermen and establishment of a safety management system for distance water fishing vessels.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• Pediatric Infection and Vaccine
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• v.29 no.3
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• pp.166-172
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• 2022

Blau syndrome is a systemic autoinflammatory disease presenting with non-caseating granulomatous dermatitis, chronic uveitis, and arthritis. It is caused by a gain-of-function variant of the nucleotide-binding oligomerization domain protein 2 gene, which leads to the overactivation of inflammatory cytokines and eventually causes autoinflammation. Since the symptoms of Blau syndrome are nonspecific and usually do not appear simultaneously, it is challenging to differentiate Blau syndrome from other inflammatory disorders. This is a case report of a 13-month-old boy who had suffered from recurrent skin rash and fever. The patient was previously misdiagnosed as refractory Kawasaki disease twice and was treated with intravenous immunoglobulin and systemic glucocorticoid, which only resulted in transient improvement of the symptoms. He was eventually diagnosed with Blau syndrome.

• Korean Journal of Agricultural Science
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• v.49 no.1
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• pp.11-18
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• 2022

A disrupted immune system during pregnancy is involved in pregnancy complications, such as spontaneous abortion, preeclampsia, and recurrent pregnancy loss. This study examined the role of toll-like receptor (TLR) 4 and ASC (apoptosis-associated speck-like protein containing a CARD [c-terminal caspase recruitment domain]) in pregnancy complications using a lipopolysaccharide (LPS)-induced miscarriage mice model. Incidences of miscarriage and embryonic resorption were examined at 9.5 days of pregnancy in wild-type (WT), ASC knockout (KO), and TLR4 KO mice after injecting them with LPS. The fetuses and placenta were obtained after sacrifice at 15.5 days of pregnancy. A significantly lower frequency of fetus absorption was found in TLR4 KO mice, whereas corresponding absorption outcomes were strongly induced in the WT and ASC KO mice upon an LPS injection. As expected, TLR4 KO mice were resistant to LPS-induced abortion. A histological analysis of the miscarried placenta showed increasing levels of the eosin staining of spongiotrophoblast cells without any obvious difference between WT and ASC KO mice. These results suggest that TLR4 KO mice are resistant to LPS, which affects pregnancy persistence, whereas WT and ASC KO mice show high miscarriage rates due to LPS. Moreover, the ASC adaptor is not directly involved in LPS-induced miscarriages, and the NLRP3 inflammasome can be activated by other proteins in the absence of ASC.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.147-156
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• 2023

Efforts to employ smart home sensors to monitor the indoor activities of elderly single residents have been made to assess the feasibility of a safe and healthy lifestyle. However, the bathroom remains an area of blind spot. In this study, we have developed and evaluated a new edge computer device that can automatically detect water usage activities in the bathroom and record the activity log on a cloud server. Three kinds of sound as flushing, showering, and washing using wash basin generated during water usage were recorded and cut into 1-second scenes. These sound clips were then converted into a 2-dimensional image using MEL-spectrogram. Sound data augmentation techniques were adopted to obtain better learning effect from smaller number of data sets. These techniques, some of which are applied in time domain and others in frequency domain, increased the number of training data set by 30 times. A deep learning model, called CRNN, combining Convolutional Neural Network and Recurrent Neural Network was employed. The edge device was implemented using Raspberry Pi 4 and was equipped with a condenser microphone and amplifier to run the pre-trained model in real-time. The detected activities were recorded as text-based activity logs on a Firebase server. Performance was evaluated in two bathrooms for the three water usage activities, resulting in an accuracy of 96.1% and 88.2%, and F1 Score of 96.1% and 87.8%, respectively. Most of the classification errors were observed in the water sound from washing. In conclusion, this system demonstrates the potential for use in recording the activities as a lifelog of elderly single residents to a cloud server over the long-term.

• Korean Journal of Clinical Pharmacy
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• v.33 no.3
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• pp.178-185
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• 2023

Objective: Atopic dermatitis (AD) is a chronic, recurrent inflammatory skin disease. Both tralokinumab and dupilumab have been recommended in the European Guideline for the treatment of adult patients with severe AD. In Korea, dupilumab has been approved for patients with moderate to severe AD, and reimbursed for those with severe AD. Since there is no clinical trial directly comparing tralokinumab and dupilumab, we conducted indirect comparison to assess the clinical usefulness in patients with AD. Methods: We selected clinical trials for indirect comparison through a systematic literature review. Individual patient data were available for the tralokinumab clinical trial, and aggregated data were available for the dupilumab clinical trial. Therefore, we employed the Matching-Adjusted Indirect Comparison (MAIC) method. The treatment efficacy was assessed based on whether patients achieved a 75% reduction on the Eczema Area and Severity Index (EASI 75) after drug administration. Results: The difference in the proportion of patients achieving EASI 75 between tralokinumab and dupilumab was 4.7% (95% CI: -7.9 to 17.3). Considering the non-inferiority margin for the EASI 75 achievement rate is -10%, tralokinumab is deemed non-inferior to dupilumab as the lower bound of the CI for the difference in the EASI 75 achievement rate between tralokinumab and dupilumab was within -10%. Conclusion: We conducted a MAIC analysis comparing tralokinumab and dupilumab based on EASI 75 achievement. The findings of this study show that tralokinumab is non-inferior to dupilumab and can be implemented in Korean clinical settings with a therapeutic position comparable to dupilumab.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.145-145
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• 2022

Availability of abundant water resources data in developing countries is a great concern that has hindered the adoption of deep learning techniques (DL) for disaster prevention and mitigation. On the contrary, over the last two decades, a sizeable amount of DL publication in disaster management emanated from developed countries with efficient data management systems. To understand the current state of DL adoption for solving water-related disaster management in developing countries, an extensive bibliometric review coupled with a theory-based analysis of related research documents is conducted from 2003 - 2022 using Web of Science, Scopus, VOSviewer software and PRISMA model. Results show that four major disasters - pluvial / fluvial flooding, land subsidence, drought and snow avalanche are the most prevalent. Also, recurrent flash floods and landslides caused by irregular rainfall pattern, abundant freshwater and mountainous terrains made India the only developing country with an impressive DL adoption rate of 50% publication count, thereby setting the pace for other developing countries. Further analysis indicates that economically-disadvantaged countries will experience a delay in DL implementation based on their Human Development Index (HDI) because DL implementation is capital-intensive. COVID-19 among other factors is identified as a driver of DL. Although, the Long Short Term Model (LSTM) model is the most frequently used, but optimal model performance is not limited to a certain model. Each DL model performs based on defined modelling objectives. Furthermore, effect of input data size shows no clear relationship with model performance while final model deployment in solving disaster problems in real-life scenarios is lacking. Therefore, data augmentation and transfer learning are recommended to solve data management problems. Intensive research, training, innovation, deployment using cheap web-based servers, APIs and nature-based solutions are encouraged to enhance disaster preparedness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.491-493
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• 2022

Flood damage due to torrential rains and typhoons is occurring in many parts of the world. In this paper, we propose a water level prediction model using water level, precipitation, and humidity data, which are key parameters for flood prediction, as input data. Based on the LSTM and GRU models, which have already proven time-series data prediction performance in many research fields, different input datasets were constructed using the ASOS(Automated Synoptic Observing System) data and AWS(Automatic Weather System) data provided by the Korea Meteorological Administration, and performance comparison experiments were conducted. As a result, the best results were obtained when using ASOS data. Through this paper, a performance comparison experiment was conducted according to the input data, and as a future study, it is thought that it can be used as an initial study to develop a system that can make an evacuation decision in advance in connection with the flood risk determination model.