Search Results (30)

Korean rockfish, Sebastes schlegeli, a coastal species, is vulnerable to pollutants such as microplastics and bacteria. While interactions between microplastics and other pollutants have been studied, little is known about microplastic and bacteria interactions. This study examined the effects of combined exposure to polystyrene microplastics in the form of microbeads (MB; 0.2 µm, 5 and 50 beads/L) and Streptococcus iniae (1 × 10⁵ and 1 × 10⁷ CFU/mL) for five days on oxidative stress and apoptosis in Korean rockfish. We assessed the mRNA expression and activity of oxidative stress markers (SOD, CAT, H₂O₂, NO, CYP1A1, GST), plasma LPO levels, and caspase-3 expression in liver tissue. Co-exposure to high MB and S. iniae concentrations significantly elevated oxidative stress and apoptosis markers, suggesting enhanced toxicity. This may result from MB facilitating pathogen transport into the fish, indicating microplastics can act as vectors for bacterial infection in aquatic environments. Full article

The Tsushima leopard cat (Prionailurus bengalensis euptilurus), an endangered feline endemic to Tsushima Island, Japan, faces critical threats due to its small and isolated population. Understanding its demographic history and genetic differentiation from continental populations is essential for conservation planning. In this study, we performed whole-genome sequencing of four Tsushima individuals and applied demographic inference methods, including pairwise sequentially Markovian coalescent (PSMC) and Sequentially Markovian Coalescent (SMC++), to reconstruct the historical effective population size (Ne) and estimate divergence times. PSMC revealed a population expansion between 200,000 and 100,000 years ago, followed by a long-term decline. SMC++ inferred a continuous decline and estimated that the divergence from the Korean leopard cat population occurred approximately 30,000–20,000 years ago. Genetic diversity analysis showed that the Tsushima population has significantly lower heterozygosity and higher inbreeding levels than continental populations. Genetic clustering based on genome-wide SNPs indicated that the Tsushima population is genetically closest to the Korean population, forming a northern cluster distinct from southern populations, such as Borneo and the Malay Peninsula. These findings provide valuable insights into the evolutionary history and genetic status of the Tsushima leopard cat and contribute critical data for the design of future conservation strategies targeting this unique insular lineage. Full article

The categories for call center conversation data are valuably used for reporting business results and marketing analysis. However, they typically lack clear patterns and suffer from severe imbalance in the number of instances across categories. The call center conversation categories used in this study are Payment, Exchange, Return, Delivery, Service, and After-sales service (AS), with a significant imbalance where Service accounts for 26% of the total data and AS only 2%. To address these challenges, this study proposes a model that ensembles meta-information generated through Named Entity Recognition (NER) with machine learning inference results. Utilizing KoBERT (Korean Bidirectional Encoder Representations from Transformers) as our base model, we employed Easy Data Augmentation (EDA) to augment data in categories with insufficient instances. Through the training of nine models, encompassing KoBERT category probability weights and a CatBoost (Categorical Boosting) model that ensembles meta-information derived from named entities, we ultimately improved the F1 score from the baseline of 0.9117 to 0.9331, demonstrating a solution that circumvents the need for expensive LLMs (Large Language Models) or high-performance GPUs (Graphic Process Units). This improvement is particularly significant considering that, when focusing solely on the category with a 2% data proportion, our model achieved an F1 score of 0.9509, representing a 4.6% increase over the baseline. Full article

In the increasingly competitive mobile ecosystem, understanding user behavior is essential to improve targeted sales and the effectiveness of advertising. With the widespread adoption of smartphones and the increasing variety of mobile applications, predicting user behavior has become more complex. This study presents a comprehensive framework for predicting mobile payment behavior by integrating demographic, situational, and behavioral factors, focusing on patterns in mobile application usage. To address the complexity of the data, we use a combination of machine-learning models, including extreme gradient boosting, light gradient boosting machine, and CatBoost, along with Shapley additive explanations (SHAP) to improve interpretability. An analysis of extensive panel data from Korean Android users reveals that incorporating application usage behavior in such models considerably improves the accuracy of mobile payment predictions. The study identifies key predictors of payment behavior, indicated by high Shapley values, such as using social networking services (e.g., KakaoTalk and Instagram), media applications (e.g., YouTube), and financial and membership applications (e.g., Toss and OK Cashbag). Moreover, the results of the SHAP force analysis reveal the individual session-level drivers of mobile purchases. These findings advance the literature on mobile payment prediction and offer practical insights for improving targeted marketing strategies by identifying key behavioral drivers of mobile transactions. Full article

The content in digital intervention therapies for children with attention deficit hyperactivity disorder (ADHD) requires various technical elements to interest and motivate the children. Their structure is often quiz-based, which allows easy access to quantitative assessments. However, in this study, I verify the effectiveness of digital intervention therapy by implementing story-based game content with active participation. In this study, 48 children aged 6 to 13 years diagnosed with ADHD were recruited and assigned to experimental (story-based content) and control (quiz-based content) groups; their attention improvements were compared. The improvement in attention was assessed by comparing the change rate of the Comprehension Attention Test (CAT) and Korean ADHD Rating Scale (K-ARS) scores before and after the intervention. At 4 weeks, the CAT score change rate was significantly different between the groups (p = 0.039, p = 0.040); the CAT score change rate before and after the intervention was significantly greater in the experimental than in the control group (p = 0.038). After adjusting for the baseline, the experimental group showed a significantly greater reduction in the K-ARS impulsivity and total K-ARS scores compared with the control group (p = 0.018, p = 0.012). Therefore, story-based content is more effective than quiz-based content in digital intervention therapy for children with ADHD. Full article

Background/Objectives: Vitamin D deficiency (VDD) is a global health concern associated with metabolic disease and immune dysfunction. Despite known risk factors like limited sun exposure, diet, and lifestyle, few studies have explored these factors comprehensively on a large scale. This cross-sectional study aimed to identify VDD-associated factors in South Korea via an integrative approach of machine learning and statistical analyses using Korea National Health and Nutrition Examination Survey (KNHANES) IX-1 data. Methods: Using the KNHANES dataset, six machine learning algorithms were applied to evaluate VDD (serum 25[OH]D3 < 20 ng/mL)-associated factors through feature importance scores. Thereafter, multivariate linear and logistic regression models were applied to the dataset—stratified by sex and age. Results: Among 583 variables, 17 VDD-associated factors were identified using the CatBoost model, which achieved the highest F1 score. When these factors were assessed through statistical analysis, dietary supplement use emerged as a consistent factor associated with VDD across all subgroups (younger men, younger women, older men, and older women). In younger adults, HDL cholesterol, blood and urinary creatinine, water intake, urban residence, and breakfast frequency were significantly associated with VDD. Additionally, blood urea nitrogen and fasting plasma glucose in men and urinary sodium in women showed sex-specific associations with serum 25(OH)D levels. Conclusions: This study identified key VDD-associated factors in the South Korean population, which varied by age or sex. These findings highlight the multifaceted nature of VDD, influenced by dietary, lifestyle, and biochemical factors and underscore the need for strategies integrating machine learning and statistical analysis. Full article

Background/Objectives: Gastric cancer is a leading cause of cancer-related mortality, particularly in East Asia, with a notable burden in Republic of Korea. This study aimed to construct and develop machine learning models for the prediction of gastric cancer mortality and the identification of risk factors. Methods: All data were acquired from the Korean Clinical Data Utilization for Research Excellence by multiple medical centers in South Korea. A total of 23,717 gastric cancer patients were divided into two groups by cause of mortality (all-cause of 2664 and disease-specific of 1620) and investigated. We used comprehensive data integrating clinical, pathological, lifestyle, and socio-economic factors. Cox proportional hazards analysis was conducted to estimate hazard ratios for mortality. Five machine learning models (random forest, gradient boosting machine, XGBoost, light GBM, and cat boosting) were developed to predict mortality. The models were interpreted by SHAP, one of the explainable AI techniques. Results: For all-cause mortality, the gradient-boosting machine learning model demonstrated the highest performance with an AUC-ROC of 0.795. For disease-specific mortality, the light GBM model outperformed others, achieving an AUC-ROC of 0.867. Significant predictors included the AJCC7 stage, tumor size, lymph node count, and lifestyle factors such as smoking, drinking, and diabetes. Conclusions: This study underscores the importance of integrating both clinical and lifestyle data to enhance mortality prediction accuracy in gastric cancer patients. The findings highlight the need for personalized treatment approaches in the Korean population and emphasize the role of demographic-specific data in predictive modeling. Full article

Severe habitat loss and fragmentation due to extensive road development have escalated wildlife–vehicle collisions (WVCs) as one of the major causes of wildlife mortality. This study, spanning 9 years from 2009 to 2017, presents comprehensive WVC data in South Korea, including species composition, seasonal and regional patterns, and road factors influencing WVCs, aiming to analyze their impact and propose effective mitigation strategies. We collected WVC data with road variables for 9 years from 4561 km of nationwide monitoring road sections and analyzed the data to understand the relationship between WVCs and road characteristics, as well as species-specific patterns. A nationwide survey identified 13,606 WVCs involving 143 terrestrial vertebrate species, and patterns and models of the top seven mammal species (raccoon dog (Nyctereutes procyonoides), Siberian chipmunk (Eutamias sibiricus), Siberian weasel (Mustela sibirica), water deer (Hydropotes inermis), red squirrel (Sciurus vulgaris), Korean hare (Lepus coreanus), and leopard cat (Prionailurus bengalensis)) were presented. Patterns revealed declines in WVCs overall, except for water deer. Although spatial differences in WVCs seemed linked more to wildlife habitats, certain road features correlated both positively or negatively with WVC frequency, highlighting complexities in the effectiveness of preventative measures. For effective mitigation and prevention of WVCs, comprehensive strategies considering species traits, seasonality, and road types should be implemented Full article

Feline cancer is increasingly recognized as a major cause of mortality, yet data on tumor prevalence and behavior in cats, particularly in non-Western regions, remain limited. This study analyzed a decade of feline tumor data in Korea from 2012 to 2022, focusing on age, breed, and anatomical location as predictors of malignancy. Data were collected from 683 cats, with regression analysis applied to determine significant associations. Older cats exhibited a markedly higher risk of malignancy, particularly in mast cell and mammary tumors. Tumors in the mammary gland and alimentary tract had malignancy rates exceeding 90%, underscoring the need for early detection in these regions. Interestingly, squamous cell carcinoma was rare in the skin, in stark contrast to Western studies, likely reflecting differences in environmental exposure. While breed was not a statistically significant predictor, certain breeds, including Persians and Russian Blues, showed a higher frequency of malignancy. These findings highlight the importance of regional tumor research in cats and the need for larger, multicenter datasets that incorporate environmental, genetic, and lifestyle factors. Understanding these influences will help refine veterinary care and improve cancer treatment outcomes in feline populations. Full article

Studies are being conducted on the anatomical structures of various wild animals. Despite the ecological importance of the Korean raccoon dog (Nyctereutes procyonoides koreensis), limited research has been conducted on its anatomical structure. This study is the first to establish a reference range for joint orientation angles in the limbs of the Korean raccoon dog. Joint orientation angles are an unexplored concept not only in Korean raccoon dogs but also in other wildlife. However, they are important in the examination of the skeletal anatomy of humans and companion animals, such as dogs and cats. Because this type of measurement is still emerging in wildlife research, we applied the methodology used in the domestic dog (Canis lupus familiaris). Angles were measured between the mechanical or anatomical axis and the joint orientation lines in the thoracic and pelvic limbs of Korean raccoon dogs. No significant differences were observed between the sexes or between the left and right sides. These findings are consistent with those observed in domestic dogs. Based on this study, a reference range of joint orientation angles could be established for Korean raccoon dogs. Full article

HT074, a multiherbal mixture containing extracts from Inula britannica flowers and Paeonia lactiflora roots, is used in Korean medicine for gastric disorders. This study investigated the protective mechanisms of HT074 against acute reflux esophagitis (RE) in rats. Nitric oxide (NO) production and mRNA expression of antioxidant-related genes (Nrf2, HO-1, SOD, CAT, and GPx2) were evaluated in LPS-induced RAW 264.7 cells. Gastroesophageal reflux (GER) was induced in rats, followed by HT074 (100, 300 mg/kg) or ranitidine (50 mg/kg) administration. Esophageal damage and histological changes were assessed. Gastric pH and protein expression levels of Nrf2, HO-1, SOD, CAT, and GPx-1/2 were measured. HT074 pretreatment reduced NO production and increased the expression of HO-1, CAT, and GPx2 in LPS-induced RAW 264.7 cells. In GER-induced rats, HT074 significantly decreased esophageal lesions and increased the expression of HO-1, SOD, GPx-1/2, and Nrf2. HT074 did not affect gastric pH. These findings suggest that HT074 protects against GER-induced esophagitis by inhibiting NO production and enhancing antioxidant activity. Therefore, HT074 could be a promising therapeutic agent for GER disease. Full article

Apartment buildings are significantly popular among South Korean construction companies. However, design changes present a common yet challenging aspect, often leading to cost overruns. Traditional cost prediction methods, which primarily rely on numerical data, have a gap in fully capitalizing on the rich insights that textual descriptions of design changes offer. Addressing this gap, this research employs machine learning (ML) and natural language processing (NLP) techniques, analyzing a dataset of 35,194 instances of design changes from 517 projects by a major public real estate developer. The proposed models demonstrate acceptable performance, with R-square values ranging from 0.930 to 0.985, underscoring the potential of integrating structured and unstructured data for enhanced predictive analytics in construction project management. The predictor using Extreme Gradient Boosting (XGB) shows better predictive ability (R² = 0.930; MAE = 16.05; RMSE = 75.09) compared to the traditional Multilinear Regression (MLR) model (R² = 0.585; MAE = 43.85; RMSE = 101.41). For whole project cost changes predictions, the proposed models exhibit good predictive ability, both including price fluctuations (R² = 0.985; MAE = 605.1; RMSE = 1009.5) and excluding price fluctuations (R² = 0.982; MAE = 302.1; RMSE = 548.5). Additionally, a stacked model combining CatBoost and Support Vector Machine (SVM) algorithms was developed, showcasing the effective prediction of cost changes, with or without price fluctuations. Full article

Hyperlipidemia, characterized by elevated cholesterol, lipids, and triglycerides in the bloodstream, is linked to hepatic oxidative damage. Doenjang, a traditional Korean condiment made from fermented soybeans, is known for its health benefits, yet its anti-hyperlipidemic effects remain understudied. Our study aimed to assess the hypolipidemic and hepatic protective effects of Doenjang on male ICR mice fed a high-fat cholesterol diet for 8 weeks. Mice were divided into three groups: the normal diet (ND), the high-fat cholesterol diet (HD), and the Doenjang-supplemented HD diet (DS) group. Doenjang supplementation significantly regulated total cholesterol, triglycerides, LDL cholesterol, and HDL cholesterol levels compared to the HD group. It also downregulated lipogenic genes, including PPARγ, FAS, and ACC, and positively influenced the cholesterol metabolism-related genes HMGCR and LXR. Moreover, Doenjang intake increased serum glutathione levels, activated oxidative stress defense genes (NRF2, SOD, GPx1, and CAT), positively modulated inflammation genes (NF-kB and IL6) in hepatic tissue, and reduced malondialdehyde levels. Our findings highlight the effectiveness of traditional Doenjang in preventing diet-induced hyperlipidemia and protecting against hepatic oxidative damage. Full article

Our study harnesses the power of natural language processing (NLP) to explore the relationship between dietary patterns and metabolic health outcomes among Korean adults using data from the Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII). Using Latent Dirichlet Allocation (LDA) analysis, we identified three distinct dietary patterns: “Traditional and Staple”, “Communal and Festive”, and “Westernized and Convenience-Oriented”. These patterns reflect the diversity of dietary preferences in Korea and reveal the cultural and social dimensions influencing eating habits and their potential implications for public health, particularly concerning obesity and metabolic disorders. Integrating NLP-based indices, including sentiment scores and the identified dietary patterns, into our predictive models significantly enhanced the accuracy of obesity and dyslipidemia predictions. This improvement was consistent across various machine learning techniques—XGBoost, LightGBM, and CatBoost—demonstrating the efficacy of NLP methodologies in refining disease prediction models. Our findings underscore the critical role of dietary patterns as indicators of metabolic diseases. The successful application of NLP techniques offers a novel approach to public health and nutritional epidemiology, providing a deeper understanding of the diet–disease nexus. This study contributes to the evolving field of personalized nutrition and emphasizes the potential of leveraging advanced computational tools to inform targeted nutritional interventions and public health strategies aimed at mitigating the prevalence of metabolic disorders in the Korean population. Full article

Accurate weight measurement is critical for monitoring the growth and well-being of cattle. However, the traditional weighing process, which involves physically placing cattle on scales, is labor-intensive and stressful for the animals. Therefore, the development of automated cattle weight prediction techniques assumes critical significance. This study proposes a weight prediction approach for Korean cattle using 3D segmentation-based feature extraction and regression machine learning techniques from incomplete 3D shapes acquired from real farm environments. Firstly, we generated mesh data of 3D Korean cattle shapes using a multiple-camera system. Subsequently, deep learning-based 3D segmentation with the PointNet network model was employed to segment 3D mesh data into two dominant parts: torso and center body. From these segmented parts, the body length, chest girth, and chest width of Korean cattle were extracted. Finally, we implemented five regression machine learning models (CatBoost regression, LightGBM, polynomial regression, random forest regression, and XGBoost regression) for weight prediction. To validate our approach, we captured 270 Korean cattle in various poses, totaling 1190 poses of 270 cattle. The best result was achieved with mean absolute error (MAE) of 25.2 kg and mean absolute percent error (MAPE) of 5.85% using the random forest regression model. Full article