Sun Moon University

This study compared different analytical methods for the quantification of biotin, designated as a fortifying nutrient in the Korean Food Code.Ensuring product quality and safety requires accurate quantification of food additives.On the other hand, the existence of different official biotin quantification methods across countries underscores the importance of identifying the most reliable and suitable approach.The official methods include titration, back titration, and high-performance liquid chromatography (HPLC).These methods differ in their analytical principles, procedural complexity, and sensitivity.The titration method showed 101.240.23%,satisfying the standard specification.It also presented a low standard deviation, reflecting high repeatability.The back titration method yielded a comparable mean value of 101.242.25%,but the larger standard deviation indicated lower precision.HPLC analysis is generally recognized for its superior sensitivity and selectivity.Nevertheless, in this study, even under optimized conditions at 20 C, the relative standard deviation (RSD) values obtained from HPLC exceeded those of the titration method.Furthermore, the lack of a specified column temperature in the official protocol further limits its reliability.Therefore, titration is the most suitable method for biotin quantification in this study.

This study investigated the effects of a high-fat diet (HFD), particulate matter (PM) exposure, and resistance exercise training on circulating lipid profiles, adipokines, inflammatory responses, neurotrophic factors, and blood–brain barrier (BBB) permeability. Forty-eight 10-week-old male C57BL/6 mice were randomly assigned to four groups (n = 12 per group): normal diet (ND), HFD, HFD with PM exposure (HFD + PM), and HFD with PM exposure plus exercise training (HFD + PM + EX). ND and HFD were administered for 16 weeks, whereas PM exposure and exercise training interventions were initiated after 8 weeks of dietary treatment and continued for an additional 8 weeks. PM was administered via tail vein injection three times per week, and resistance exercise training consisted of a ladder-climbing exercise performed five times per week. The results indicated that body weight, total cholesterol (TC), triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C), leptin, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), S100 calcium-binding protein B (S100B), and neuron-specific enolase (NSE) levels were significantly higher in the HFD group than in the ND group (p < 0.05), whereas adiponectin and brain-derived neurotrophic factor (BDNF) levels were significantly lower (p < 0.05). In addition, the HFD + PM group exhibited significantly lower BDNF and vascular endothelial growth factor (VEGF) levels (p < 0.05) and significantly higher S100B and NSE levels (p < 0.05) than the HFD group. In contrast, the HFD + PM + EX group showed significantly lower TG, LDL-C, leptin, and IL-6 levels than the HFD group (p < 0.05). Moreover, compared with the HFD + PM group, the HFD + PM + EX group demonstrated significantly lower TG, LDL-C, leptin, S100B, and NSE levels (p < 0.05) and significantly higher high-density lipoprotein cholesterol (HDL-C), adiponectin, BDNF, and VEGF levels (p < 0.05). Collectively, these findings suggest that an HFD may contribute to dyslipidemia, heightened inflammatory responses, downregulation of neurotrophic factors, and increased BBB permeability and that concurrent PM exposure under HFD conditions may exacerbate adverse alterations in neurotrophic factors and BBB permeability. The results indicate that an HFD induces metabolic and neurovascular alterations, whereas concurrent PM exposure under HFD conditions is associated with additional changes in neurotrophic factors and BBB-related markers. Resistance exercise training attenuated these changes.

To assess the feasibility, acceptability, and preliminary effects of circadian rhythm for sleep (CRS), a mobile digital therapeutic that delivers closed-loop, wearable- and light-sensor-driven circadian coaching for insomnia. Six-week, single-arm feasibility study in adults with short-term or chronic insomnia. CRS provided daily recommendations emphasizing stable wake-up time, morning light exposure, and daytime activity. Feasibility and acceptability outcomes were assessed (completion, passive-sensor data capture/adherence, satisfaction), and the primary clinical outcome (exploratory) was change in Insomnia Severity Index (ISI) from baseline to Week 6; the key secondary clinical outcome (exploratory) was Pittsburgh Sleep Quality Index (PSQI). Objective sleep-wake metrics from wearable device were explored. Twenty-three participants were enrolled; 20 completed the program (87.0%), and 16 comprised the prespecified analysis set based on data-fidelity criteria. Among these 16 participants, valid passive sensor data from the wearable and light sensors were captured on 88.6% of study days. ISI significantly improved from baseline to Week 6 (median 21.0 → 14.0; p < .001 by within-subject analysis), and PSQI improved (mean 10.9 → 7.7; p < .001; partial η2 ≈ 0.50). Objective wearable metrics (total sleep time, time-in-bed, sleep onset, wake time) did not change significantly over time in this short pilot. Satisfaction was favorable (mean 37.9/45). No adverse events occurred. CRS was feasible and acceptable, and was associated with within-subject improvements in subjective insomnia symptoms in this single-arm feasibility study; however, because there was no control group, these findings are preliminary and hypothesis-generating, supporting further evaluation in larger randomized controlled trials. Clinical Research Information Service. https://cris.nih.go.kr. KCT0010801.

ABSTRACT The explosive growth of Internet of Things (IoT) devices has demanded the urgency of robust data security to protect private information. The random numbers generated by pseudo‐random number generators (PRNGs) play a crucial role in cryptographic algorithms. However, these sequences have become increasingly predictable due to advances in computing power and machine learning. Therefore, the development of true random number generators (TRNGs), which exploit the intrinsic hardware‐level randomness as an entropy source, is important for future data security. In this study, we present TRNG circuits that harness the inherent stochasticity of a multilayer hBN volatile memristor. The fabricated device exhibits highly stable threshold switching with low set/hold voltages and a significantly high on/off ratio. We implement a spike generator by integrating the threshold switching memristor with passive components. Digitization with a fixed‐reference comparator directly converts output spikes into bitstreams that successfully pass NIST randomness tests without postprocessing. The practical utility of the TRNG is demonstrated through XOR‐based encryption and decryption of black‐and‐white and grayscale images, resulting in noise‐like encrypted data and perfect recovery of the originals using identical keys. These results establish 2D hBN‐based threshold switching memristors as compelling hardware entropy sources for secure, next‐generation electronic systems.

Endosymbiotic bacteria influence the ecology and evolution of insects through complex associations within host cells. To explore how these relationships vary among environments and taxa, we examined 1028 insect specimens from 14 orders across Korea for infections by three representative endosymbionts (Wolbachia, Rickettsia, and Spiroplasma). Overall, 33.8% of specimens were infected, with single infections predominating and co-infections remaining relatively less common. Weak-to-modest but statistically significant associations were detected between several symbiont pairs (Rickettsia-Spiroplasma, Wolbachia-Spiroplasma, and Wolbachia-Rickettsia). Infection rates exhibited no significant variation among host orders except for Spiroplasma, and Wolbachia infections were more frequently detected in terrestrial than in aquatic insects. These results indicate that endosymbiont infection patterns might be shaped by factors operating at multiple biological scales, including host taxonomy and habitat types. As this study relied on polymerase chain reaction detection, infection frequencies should be interpreted as comparative rather than absolute measures. This survey provides baseline data that might help characterize regional patterns of endosymbiont distributions and their variation across taxonomic and ecological contexts.

Student dropout undermines both student success and institutional performance. In recent years, various machine-learning models have been developed to predict dropout risk in higher education; however, existing studies remain constrained by four major gaps: (i) limited prediction horizons, (ii) first-year focus, (iii) inadequate handling of class imbalance, and (iv) lack of calibrated interpretability. To overcome these limitations, this study proposes a portable, generalizable machine-learning framework for long-term dropout prediction that systematically integrates modular components addressing each gap. Specifically, (i) rolling multi-semester windows generate horizon-specific targets several semesters in advance, (ii) an all-cohort student–semester dataset eliminates the first-year restriction by representing every enrolled cohort, (iii) a resampling–threshold optimization coupling mitigates class imbalance to improve sensitivity to rare events, and (iv) probability calibration with SHAP (SHapley Additive exPlanations)-based interpretability ensures reliable and transparent model outputs. Empirical evaluation on a university registry dataset demonstrates that the framework produces well-calibrated, temporally stable, and interpretable dropout-risk estimates. Because it relies solely on ubiquitous registry variables and off-the-shelf algorithms, the framework is inherently portable and generalizable across higher-education institutions and suitable for integration into early-warning systems that support timely, data-driven student advising.

This study aimed to develop a machine-learning-based predictive model for winter traffic-accident risk using meteorological variables and analyze the influence of weather factors on accident occurrence. The study area consisted of five cities in Gyeonggi-do, South Korea—Dongducheon, Paju, Yangpyeong, Icheon, and Suwon—where automated surface observing system (ASOS) stations are located. Winter traffic-accident data from 2015 to 2023 were collected from the traffic accident analysis system (TAAS), and meteorological observations were obtained from the Korea Meteorological Administration. The dependent variable was constructed based on accidentoccurrence levels; ten meteorological variables, including temperature, solar radiation, wind speed, humidity, and cloud cover, were the independent variables. Softmax Regression, Random Forest, and XGBoost models were employed for prediction, and their performances were compared in terms of prediction accuracy and feature importance. The results revealed that the Random Forest model demonstrated the highest predictive performance across all regions. In addition, solar radiation, wind speed, and cloud cover significantly influenced accident occurrence beyond the widely assumed impact of temperature alone. These findings highlight the potential of machine-learning models as decision-support tools for winter road-safety management and proactive accident-prevention policies.