• Genomics & Informatics
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• v.19 no.4
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• pp.36.1-36.11
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• 2021

Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.829-837
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• 2020

Objective: The aim of this study was to investigate the prognostic value of the maximum standardized uptake value (SUV_max) measured while restaging with F-18 fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) to predict the 3-year post-recurrence survival (PRS) in patients with recurrent gastric cancer after curative surgical resection. Materials and Methods: In total, 47 patients with recurrent gastric cancer after curative resection who underwent restaging with ¹⁸F-FDG PET/CT were included. For the semiquantitative analysis, SUV_max was measured over the visually discernable ¹⁸F-FDG-avid recurrent lesions. Cox proportional-hazards regression models were used to predict the 3-year PRS. Differences in 3-year PRS were assessed with the Kaplan-Meier analysis. Results: Thirty-nine of the 47 patients (83%) expired within 3 years after recurrence in the median follow-up period of 30.3 months. In the multivariate analysis, SUV_max (p = 0.012), weight loss (p = 0.025), and neutrophil count (p = 0.006) were significant prognostic factors for 3-year PRS. The Kaplan-Meier curves demonstrated significantly poor 3-year PRS in patients with SUV_max > 5.1 than in those with SUV_max ≤ 5.1 (3-year PRS rate, 3.5% vs. 38.9%, p < 0.001). Conclusion: High SUV_max on restaging with ¹⁸F-FDG PET/CT is a poor prognostic factor for 3-year PRS. It may strengthen the role of ¹⁸F-FDG PET/CT in further stratifying the prognosis of recurrent gastric cancer.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.89-96
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• 2018

When a fault occurs in a power system, the existing analytic models for the power system fault diagnosis could generate multiple solutions under the condition of one or more protective relays (PRs) and/or circuit breakers (CBs) malfunctioning, and/or an alarm or alarms of these PRs and/or CBs failing. Therefore, this paper presents an improved analytic model addressing the above problem. It takes into account the interaction between the uncertainty involved with PR operation and CB tripping and the uncertainty of the alarm reception, which makes the analytic model more reasonable. In addition, the existing analytic models apply the penalty function method to deal with constraints, which is influenced by the artificial setting of the penalty factor. In order to avoid the penalty factor's effects, this paper transforms constraints into an objective function, and then puts forward an improved immune clonal multi-objective optimization algorithm to solve the optimal solution. Finally, the cases of the power system fault diagnosis are served for demonstrating the feasibility and efficiency of the proposed model and method.

• Journal of Nutrition and Health
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• v.57 no.2
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• pp.211-227
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• 2024

Introduction: Seaweed is a sustainable and underexplored source of bioactive compounds with potent anti-inflammatory activities. However, studies on the interaction between seaweed and genes on inflammation are limited. Purpose: We aimed to evaluate the relationships between seaweed consumption and the polygenic risk scores (PRS) and their interactions with high-sensitivity C-reactive protein (hs-CRP) levels. Methods: Information on seaweed consumption was collected using a food frequency questionnaire, which included laver, kelp, and sea mustard among the items consumed. A total of 31 hs-CRP-related single nucleotide polymorphisms (SNPs) were selected using genome-wide association studies and clumping analysis, and the individual PRS were calculated by weighting the effect size of each allele in the selected SNPs of 39,369 middle-aged (≥40 years) Koreans using the Korean Genome and Epidemiology Study (KoGES)-Health Examinees (HEXA) cohort data. To investigate the interaction between seaweed intake and the PRS on hs-CRP levels >1 mg/L, hazard ratios (HRs) and 95% confidence intervals (CIs) were assessed using multivariable Cox proportional hazards models. Results: During a mean follow-up period of 4.8 years, we recorded 436 patients with elevated hs-CRP levels. Women in the highest tertile of the PRS with the lowest quartile of seaweed intake had an increased incidence of elevated hs-CRP levels compared with women in the lowest tertile of the PRS with the lowest seaweed intake quartile (HR 2.34, 95% CI 1.23-4.45). No significant association was observed among the men. Conclusion: In conclusion, we identified a new interaction between the PRS, seaweed intake, and inflammation in Korean women, and this study suggests that the interaction between the identification of genetic predisposition and dietary seaweed intake may have an impact on determining the risk of developing hyperinflammation in the future.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.383-384
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• 2022

Gas Combustion Unit (GCU) onboard liquefied natural gas carriers handles boil-off to stabilize tank pressure. There are many factors for LNG cargo operators to take into consideration to determine whether to use GCU or not. Gas consumption of main engine and re-liquefied gas through the Partial Re-Liquefaction System (PRS) are good examples of these factors. Human gas operators have decided the operation so far. In this paper, some deep learning neural network models were developed to provide human gas operators with a decision support system. The models consider various factors specially into GCU operation. A deep learning model with Sigmoid activation functions in input layer and hidden layers made the best performance among eight different deep learning models.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.72-77
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• 2002

The development of a Pay Adjustment (PA) procedure for implementing Performance-related Specifications (PRS) is known to be a difficult task faced by most State Highway Agencies (SHAs) due to the difficulty in such areas as selecting pay factor items, modeling the relationship between stochastic variability of pay factor items and pavement performance, and determining an overall lot pay adjustment. This led to the need for an effective way of developing a scientific pay adjustment procedure by incorporating Life Cycle Cost Analysis (LCCA) embedded Monte Carlo approach. In this work, we propose a prototype system to determine a PA specifically using the data in the pavement management information systems at Wisconsin Department of Transportation (WisDOT) as an exemplary to other SHAs. It is believed that the PRS methodology demonstrated in this study can be used in real projects by incorporating the more accurate and reliable performance prediction models and LCC model.

• Geomechanics and Engineering
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• v.30 no.1
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• pp.75-91
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• 2022

This paper aims to show how to use several Machine Learning (ML) methods to estimate the TBM penetration rate systematically (TBM-PR). To this end, 1125 datasets including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), punch slope index (PSI), distance between the planes of weakness (DPW), orientation of discontinuities (alpha angle-α), rock fracture class (RFC), and actual/measured TBM-PRs were established. To evaluate the ML methods' ability to perform, the 5-fold cross-validation was taken into consideration. Eventually, comparing the ML outcomes and the TBM monitoring data indicated that the ML methods have a very good potential ability in the prediction of TBM-PR. However, the long short-term memory model with a correlation coefficient of 0.9932 and a route mean square error of 2.68E-6 outperformed the remaining six ML algorithms. The backward selection method showed that PSI and RFC were more and less significant parameters on the TBM-PR compared to the others.

• Journal of Environmental Impact Assessment
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• v.23 no.2
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• pp.101-111
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• 2014

The research was carried out in order to find climate factors which determine the distribution of Neolitsea sericea, and the potential habitats (PHs) under the current climate and three climate change scenario by using species distribution models (SDMs). Four climate factors; the minimum temperature of the coldest month (TMC), the warmth index (WI), summer precipitation (PRS), and winter precipition (PRW) : were used as independent variables for the model. Three general circulation models under A1B emission scenarios were used as future climate scenarios for the 2050s (2040~2069) and 2080s (2070~2099). Highly accurate SDMs were obtained for N. sericea. The model of distribution for N. sericea constructed by SDMs showed that minimum temperature of the coldest month (TMC) is a major climate factor in determining the distribution of N. sericea. The area above the $-4.4^{\circ}C$ of TMC revealed high occurrence probability of the N. sericea. Future PHs for N. sericea were projected to increase respectively by 4 times, 6.4 times of current PHs under 2050s and 2080s. It is expected that the potential of N. sericea habitats is expanded gradually. N. sericea is applicable as indicator species for monitoring in the Korean Peninsula. N. sericea is necessary to be monitored of potential habitats.

• Journal of Environmental Impact Assessment
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• v.26 no.5
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• pp.291-302
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• 2017

The research was carried out in order to find climate factors which determine the distribution of Eurya japonica, and the potential habitats (PHs) under the current climate and climate change scenario by using species distribution models (SDMs). Four climate factors; the warmth index (WI), the minimum temperature of the coldest month (TMC), summer precipitation (PRS), and winter precipitaion (PRW) : were used as independent variables for the model. Seventeen general circulation models under RCP (Representative concentration pathway) 8.5 scenarios were used as future climate scenarios for the 2050s (2040~2069) and 2080s (2070~2099). Highly accurate SDMs were obtained for E. japonica. The model of distribution for E. japonica constructed by SDMs showed that minimum temperature of the coldest month (TMC) is a major climate factor in determining the distribution of E. japonica. The area above the $-5.7^{\circ}C$ of TMC revealed high occurrence probability of the E. japonica. Future PHs for E. japonica were projected to increase respectively by 2.5 times, 3.4 times of current PHs under 2050s and 2080s. It is expected that the potential of E. japonica habitats is expanded gradually. E. japonica is applicable as indicator species for monitoring in the Korean Peninsula. E. japonica is necessary to be monitored of potential habitats.

• Safety and Health at Work
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• v.12 no.1
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• pp.57-65
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• 2021

Background: The objective of this study was to elucidate the relationships musculoskeletal pains with combined vulnerability in terms of age, gender, and employment status Methods: The fifth European Working Conditions Survey (EWCS) in 2010 (43,816 participants aged 15 years and over) analyzed for European employees and the third Korean Working Conditions Survey (KWCS) in 2011 (50,032 participants aged 15 years and older) analyzed for Korean employees. In this study, three well known vulnerable factors to musculoskeletal pains (older age, female gender, and precarious employment status) were combined and defined as combined vulnerability. Associations of musculoskeletal pains with combined vulnerability were assessed with prevalence ratios (PRs) and 95% confidence intervals (CIs) estimated by Poisson regression models with robust estimates of variance. Results: The prevalences of musculoskeletal pains were lower but the absolute and relative differences between combined vulnerabilities were higher among Korean employees compared with the European employees. Furthermore, the increased risk of having musculoskeletal pains according to combined vulnerability was modestly explained by socioeconomic factors and exposure to ergonomic risk factors, especially in Republic of Korea. Conclusions: The results of this study showed that the labor market may be more unfavorable for female and elderly workers in Republic of Korea. Any prevention strategies to ward off musculoskeletal pains, therefore, should be found and implemented to mitigate or buffer against the most vulnerable work population, older, female, and precarious employment status, in Republic of Korea.