• Korean Journal of Radiology
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• v.23 no.10
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• pp.986-997
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• 2022

Objective: Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired metabolic networks in patients with WMHs, and 2) to explore the clinical and imaging features of metabolic redistribution in patients with WMHs. Materials and Methods: This study included 50 patients with WMHs and 70 healthy controls (HCs) who underwent ¹⁸F-fluorodeoxyglucose-positron emission tomography/MRI. Various global property parameters according to graph theory and an individual parameter of brain metabolic network called "individual contribution index" were obtained. Parameter values were compared between the WMH and HC groups. The performance of the parameters in discriminating between the two groups was assessed using the area under the receiver operating characteristic curve (AUC). The correlation between the individual contribution index and Fazekas score was assessed, and the interaction between age and individual contribution index was determined. A generalized linear model was fitted with the individual contribution index as the dependent variable and the mean standardized uptake value (SUV_mean) of nodes in the whole-brain network or seven classic functional networks as independent variables to determine their association. Results: The means ± standard deviations of the individual contribution index were (0.697 ± 10.9) × 10^-3 and (0.0967 ± 0.0545) × 10^-3 in the WMH and HC groups, respectively (p < 0.001). The AUC of the individual contribution index was 0.864 (95% confidence interval, 0.785-0.943). A positive correlation was identified between the individual contribution index and the Fazekas scores in patients with WMHs (r = 0.57, p < 0.001). Age and individual contribution index demonstrated a significant interaction effect on the Fazekas score. A significant direct association was observed between the individual contribution index and the SUV_mean of the limbic network (p < 0.001). Conclusion: The individual contribution index may demonstrate the redistribution of the brain metabolic network in patients with WMHs.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.27.1-27.12
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• 2023

Background: Environmental exposure is characterized by low concentration, chronic, and complex exposure. Traditional epidemiological studies show limitations in reflecting these characteristics since they usually focus on a single or very limited number of exposure factors at a time. In this study, we adopted the methodology of environment-wide association study (EWAS) to figure out the association of human liver function with various environmentally hazardous substances. Methods: We analyzed 2,961 participants from the Korean National Environmental Health Survey Cycle 4 (2018-2020). Using generalized linear model (GLM) analysis, we analyzed the association of 72 variables with 3 liver function indices (aspartate aminotransferase [AST], alanine aminotransferase [ALT], and gamma glutamyl transferase [GGT]). Finally, we visualized our results with Manhattan plot. Results: In GLM analysis, perfluorooctanesulfonate were positively associated with ALT (odds ratio [OR]: 2.2; 95% confidence interval [CI]: 1.39-3.46; p_adjusted = 0.0147) and perfluorodecanoic acid showed positive association with GGT (OR: 2.73; 95% CI: 1.36-5.5; p_adjusted = 0.0256). Plasma mercury showed positive association with GGT (OR: 1.45; 95% CI: 1.14-1.84; p_adjusted = 0.0315). Using a plastic container while keeping food in the refrigerator was associated with elevated GGT compared to using a glass container (OR: 1.51; 95% CI: 1.16-1.95; p_adjusted = 0.0153). 2-ethyl-5-oxohexyl phthalate, showed a negative trend with all 3 indices, with AST (OR: 0.54; 95% CI: 0.39-0.73; p_adjusted = 0.00357), ALT (OR: 0.5; 95% CI: 0.34-0.75; p_adjusted = 0.036), GGT (OR: 0.55; 95% CI: 0.4-0.76; p_adjusted = 0.00697). Bisphenol S and frequent use of sunblock cream showed negative association with ALT (OR: 0.77; 95% CI: 0.66-0.89), and GGT (OR: 0.25; 95% CI: 0.11-0.55), respectively. Conclusions: We conducted an exploratory study on environmental exposure and human liver function. By using EWAS methodology, we identified 7 factors that could have potential association with liver function.

• Clinical Nutrition Research
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• v.12 no.3
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• pp.199-217
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• 2023

People with higher genetic predisposition to obesity are more susceptible to cardiovascular diseases (CVDs) and healthy plant-based foods may be associated with reduced risks of obesity and other metabolic markers. We investigated whether healthy plant-foods-rich dietary patterns might have inverse associations with cardiometabolic risk factors in participants at genetically elevated risk of obesity. For this cross-sectional study, 377 obese and overweight women were chosen from health centers in Tehran, Iran. We calculated a healthy plant-based diet index (h-PDI) in which healthy plant foods received positive scores, and unhealthy plant and animal foods received reversed scores. A genetic risk score (GRS) was developed based on 3 polymorphisms. The interaction between GRS and h-PDI on cardiometabolic traits was analyzed using a generalized linear model (GLM). We found significant interactions between GRS and h-PDI on body mass index (BMI) (p = 0.02), body fat mass (p = 0.04), and waist circumference (p = 0.056). There were significant gene-diet interactions for healthful plant-derived diets and BMI-GRS on high-sensitivity C-reactive protein (p = 0.03), aspartate aminotransferase (p = 0.04), alanine transaminase (p = 0.05), insulin (p = 0.04), and plasminogen activator inhibitor 1 (p = 0.002). Adherence to h-PDI was more strongly related to decreased levels of the aforementioned markers among participants in the second or top tertile of GRS than those with low GRS. These results highlight that following a plant-based dietary pattern considering genetics appears to be a protective factor against the risks of cardiometabolic abnormalities.

• Journal of Preventive Medicine and Public Health
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• v.57 no.2
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• pp.185-196
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• 2024

Objectives: Excess mortality associated with long-term exposure to fine particulate matter (PM_2.5) has been documented. However, research on the disease burden following short-term exposure is scarce. We investigated the cause-specific mortality burden of short-term exposure to PM_2.5 by considering the potential non-linear concentration-response relationship in Korea. Methods: Daily cause-specific mortality rates and PM_2.5 exposure levels from 2010 to 2019 were collected for 8 Korean cities and 9 provinces. A generalized additive mixed model was employed to estimate the non-linear relationship between PM_2.5 exposure and cause-specific mortality levels. We assumed no detrimental health effects of PM_2.5 concentrations below 15 ㎍/m³. Overall deaths attributable to short-term PM_2.5 exposure were estimated by summing the daily numbers of excess deaths associated with ambient PM_2.5 exposure. Results: Of the 2 749 704 recorded deaths, 2 453 686 (89.2%) were non-accidental, 591 267 (21.5%) were cardiovascular, and 141 066 (5.1%) were respiratory in nature. A non-linear relationship was observed between all-cause mortality and exposure to PM_2.5 at lag0, whereas linear associations were evident for cause-specific mortalities. Overall, 10 814 all-cause, 7855 non-accidental, 1642 cardiovascular, and 708 respiratory deaths were attributed to short-term exposure to PM_2.5. The estimated number of all-cause excess deaths due to short-term PM_2.5 exposure in 2019 was 1039 (95% confidence interval, 604 to 1472). Conclusions: Our findings indicate an association between short-term PM_2.5 exposure and various mortality rates (all-cause, non-accidental, cardiovascular, and respiratory) in Korea over the period from 2010 to 2019. Consequently, action plans should be developed to reduce deaths attributable to short-term exposure to PM_2.5.

• Steel and Composite Structures
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• v.50 no.6
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• pp.705-720
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• 2024

Analyzing the collapse behavior of thin-walled steel structures holds significant importance in ensuring their safety and longevity. Geometric imperfections present on the surface of metal materials can diminish both the durability and mechanical integrity of steel shells. These imperfections, encompassing local geometric irregularities and deformations such as holes, cavities, notches, and cracks localized in specific regions of the shell surface, play a pivotal role in the assessment. They can induce stress concentration within the structure, thereby influencing its susceptibility to buckling. The intricate relationship between the buckling behavior of these structures and such imperfections is multifaceted, contingent upon a variety of factors. The buckling analysis of thin-walled steel shell structures, similar to other steel structures, commonly involves the determination of crucial material properties, including elastic modulus, shear modulus, tensile strength, and fracture toughness. An established method involves the emulation of distributed geometric imperfections, utilizing real test specimen data as a basis. This approach allows for the accurate representation and assessment of the diversity and distribution of imperfections encountered in real-world scenarios. Utilizing defect data obtained from actual test samples enhances the model's realism and applicability. The sizes and configurations of these defects are employed as inputs in the modeling process, aiding in the prediction of structural behavior. It's worth noting that there is a dearth of experimental studies addressing the influence of geometric defects on the buckling behavior of cylindrical steel shells. In this particular study, samples featuring geometric imperfections were subjected to experimental buckling tests. These same samples were also modeled using Finite Element Analysis (FEM), with results corroborating the experimental findings. Furthermore, the initial geometrical imperfections were measured using digital image correlation (DIC) techniques. In this way, the response of the test specimens can be estimated accurately by applying the initial imperfections to FE models. After validation of the test results with FEA, a numerical parametric study was conducted to develop more generalized design recommendations for the stainless-steel shell structures with the initial geometric imperfection. While the load-carrying capacity of samples with perfect surfaces was up to 140 kN, the load-carrying capacity of samples with 4 mm defects was around 130 kN. Likewise, while the load carrying capacity of samples with 10 mm defects was around 125 kN, the load carrying capacity of samples with 14 mm defects was measured around 120 kN.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.5
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• pp.345-352
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• 2024

In this paper, a multipatch isogeometric analysis method is developed for a multi-connected NURB patch model and applied to geometrically exact shell element analysis. When connecting different NURBS patches, isogeometric analysis may become inaccurate due to the density of control point meshes and discontinuity between patches. To solve this problem, Nitsche's method is applied to the isogeometric analysis method to ensure the compatibility of the displacement and traction between two patches by using a potential function defined as the product of the displacement difference and traction of the two patches. The final derived governing equation is formed as a symmetric stiffness matrix from this potential function. Since the added system matrices from the compatibility boundary conditions are calculated as a boundary integral between patches, the computational cost does not increase significantly. For the positive definiteness of the system equation, appropriate stability parameters are calculated through generalized eigenvalue analysis, and the stability parameters and solution accuracy are analyzed according to the density of the integration meshes between the two patches. This multipatch isogeometric analysis method is applied to geometrically exact shell elements considering first-order shear deformation, and it is confirmed that by using Nitsche's method in this shell analysis with multiple connected patches results in improved stress continuity as well as displacement continuity between patches.

• Tuberculosis and Respiratory Diseases
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• v.87 no.4
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• pp.532-542
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• 2024

Background: The sequelae of post-coronavirus disease 2019 (COVID-19) pneumonia on lung function, exercise capacity, and quality of life were observed in both short-term and long-term. However, the study about the respiratory and locomotor muscle strength in severe and critically ill COVID-19 survivors are still limited. Therefore, we aimed to examine long-term pulmonary function, functional capacities, and respiratory and locomotor body muscle strength in severe to critically ill post-COVID-19 survivors. Methods: A prospective observational study was conducted in 22 post-COVID-19 pneumonia and healthy adults. Clinical characteristics during admission, pulmonary function, functional capacity, respiratory muscles, and locomotor muscles strength were examined at 1, 3, and 6 months after discharge from the hospital. Results: The generalized linear mixed model showed that percent predicted of forced expiratory volume in the 1 second (%FEV1), percent predicted of forced vital capacity (%FVC), maximum inspiratory pressure (MIP), handgrip strength, 6-minute walk distance, and five times sit to stand (5TSTS) were significantly lower in post-COVID-19 pneumonia patients than in healthy subjects during the follow-up period. The percent predicted of maximal voluntary ventilation (%MVV), and locomotor muscle strength were not different between the two groups throughout the follow-up period. Among post-COVID-19 pneumonia patients, %FEV₁, %FVC, %MVV, 5TSTS, locomotor muscle strength significantly improved at three months compared to baseline at 1 month. Conclusion: Pulmonary function, functional capacity, respiratory, and locomotor muscle strength of survivors from COVID-19 were impaired and recovery was observed after three to six months. These emphasized the need to evaluate the long-term consequences of COVID-19.

• Journal of the Korea Organic Resources Recycling Association
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• v.2 no.2
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• pp.3-17
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• 1994

The inhibitory effect of sodium ion on the anaerobic degradation of food waste was studied by an anaerobic batch toxicity assay and inhibition model. The anaerobic degradation activity of food waste spiked with over $2g\;Na^+/L$ of sodium ion was severely inhibited at the initial stage of the exposure. The inhibition response of anaerobic microorganisms on the sodium ion estimated from the methane production was differed according to the concentration of sodium ion. The relative acclimation time(RAT) and methanation rate(RMR), defined as the ratios of initial lag time and maximum methane production rate of the sample spiked with sodium ion to the control. respectively, were used to evaluate the acclimation and inhibitory effects quantitatively on the anaerobic microorganisms. When sodium ion was increased from $2g\;Na^+/L$ to $20g\;Na^+/L$, the RAT was exponentially increased from 18.9 to 90. but the RMR was linearly decreased from 0.97 to 0.02. The effects of sodium ion for the maximum methanation rate, first order kinetic constant and ultimate methane production were well evaluated by a generalized nonlinear expression model. it could be described by the uncompetitive inhibition mode. The sodium ion concentration causing 50% inhibition of methanation activity was about $11g\;Na^+/L$, and the critical sodium ion beyond to compelete inhibition was 20 to $21g\;Na^+/L$. The presented results could be used to obtain the design or operation parameters of the anaerobic process treating food waste of high salt.

• Korean Journal of Agricultural Science
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• v.14 no.1
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• pp.98-133
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• 1987

The mechanical and rheological properties of agricultural materials are important for engineering design and analysis of their mechanical harvesting, handling, transporting and processing systems. Agricultural materials, which composed of structural members and fluids do not react in a purely elastic manner, and their response when subjected to stress and strain is a combination of elastic and viscous behavior so called viscoelastic behavior. Many researchers have conducted studies on the mechanical and rheological properties of the various agricultural products, but a few researcher has studied those properties of rice plant, and also those data are available only for foreign varieties of rice plant. This study are conducted to experimentally determine the mechanical and the rheological properties such as axial compressive strength, tensile strength, bending and shear strength, stress relaxation and creep behavior of rice stems, and grain detachment strength. The rheological models for the rice stem were developed from the test data. The shearing characteristics were examined at some different levels of portion, cross-sectional area, moisture content of rice stem and shearing angle. The results obtained from this study were summarized as follows 1. The mechanical properties of the stems of the J aponica types were greater than those of the Indica ${\times}$ Japonica hybrid in compression, tension, bendingand shearing. 2. The mean value of the compressive force was 80.5 N in the Japonica types and 55.5 N in the Indica ${\times}$ Japonica hybrid which was about 70 percent to that of the Japonica types, and then the value increased progressively at the lower portion of the stems generally. 3. The average tensile force was about 226.6 N in the Japonica types and 123.6 N in the Indica ${\times}$ Japonica hybrid which was about 55 percent to that of the Japonica types. 4. The bending moment was $0.19N{\cdot}m$ in the Japonica types and $0.13N{\cdot}m$ in the Indica ${\times}$ Japonica hybrid which was 68 percent to that of the Japonica types and the bending strength was 7.7 MPa in the Japonica types and 6.5 MPa in the Indica ${\times}$ Japonica hybrid respectively. 5. The shearing force was 141.1 N in Jinju, the Japonica type and 101.4 N in Taebaeg, the Indica ${\times}$ Japonica hybrid which was 72 percent to that of Jinju, and the shearing strength of Taebaeg was 63 percent to that of Jinju. 6. The shearing force and the shearing energy along the stem portion in Jinju increased progressively together at the lower portions, meanwhile in Taebaeg the shearing force showed the maximum value at the intermediate portion and the shearing energy was the greatest at the portion of 21 cm from the ground level, and also the shearing strength and the shearing energy per unit cross-sectional area of the stem were the greater values at the intermediate portion than at any other portions. 7. The shearing force and the shearing energy increased with increase of the cross-sectional area of the rice stem and with decrease of the shearing angie from $90^{\circ}$ to $50^{\circ}$. 8. The shearing forces showed the minimum values of 110 N at Jinju and of 60 N at Taebaeg, the shearing energy at the moisture content decreased about 15 percent point from initial moisture content showed value of 50 mJ in Jinju and of 30 mJ in Taebaeg, respectively. 9. The stress relaxation behavior could be described by the generalized Maxwell model and also the compression creep behavior by Burger's model, respectively in the rice stem. 10. With increase of loading rate, the stress relaxation intensity increased, meanwhile the relaxation time and residual stress decreased. 11. In the compression creep test, the logarithmic creep occured at the stress less than 2.0 MPa and the steady-state creep at the stress larger than 2.0 MPa. 12. The stress level had not a significant effect on the relaxation time, while the relaxation intensity and residual stress increased with increase of the stress level. 13. In the compression creep test of the rice stem, the instantaneous elastic modulus of Burger's model showed the range of 60 to 80 MPa and the viscosities of the free dashpot were very large numerical value which was well explained that the rice stem was viscoelastic material. 14. The tensile detachment forces were about 1.7 to 2.3 N in the Japonica types while about 1.0 to 1.3 N in Indica ${\times}$ Japonica hybrid corresponding to 58 percent of Japonica types, and the bending detachment forces were about 0.6 to 1.1 N corresponding to 30 to 50 percent of the tensile detachment forces, and the bending detachment of the Indica ${\times}$ Japonica hybrid was 0.1 to 0.3 N which was 7 to 21 percent of Japonica types. 15. The detachment force of the lower portion was little bigger than that of the upper portion in a penicle and was not significantly affected by the harvesting period from September 28 to October 20. 16. The tensile and bending detachment forces decreased with decrease of the moisture content from 23 to 13 percent (w.b.) by the natural drying, and the decreasing rate of detachment forces along the moisture content was the greater in the bending detachment force than the tensile detachment force.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.