Sunchon National University

Abstract Forest tree seeds are mass produced for afforestation and forest restoration programs, but are mostly underutilized beyond propagation. Here, we aimed to evaluate the antioxidant, anti-inflammatory, anticancer, and tyrosinase-inhibitory activities of seed extracts of seven economically important forest tree species in the Republic of Korea to explore their potential as multifunctional natural bioresources. The seed extracts of Alnus japonica, Chamaecyparis obtusa, Cornus kousa, Phellodendron amurense, Pinus densiflora, Prunus sargentii , and Quercus glauca were comparatively assessed using multiple in vitro assays. The results revealed clear species-dependent functional profiles rather than uniform bioactivities across species. Quercus glauca exhibited strong antioxidant activity and significant anti-inflammatory and tyrosinase-inhibitory activities, suggesting multifunctional potential, while C. obtuse presented considerable anticancer activity against several cancer cell lines. Alnus japonica exhibited the highest tyrosinase-inhibitory activity, followed by Q. glauca and C. obtuse ; A. japonica extract also showed a strong antioxidant capacity. Overall, the results demonstrated that forest tree seed extracts possess diverse and complementary bioactivities, supporting their potential as underexplored multifunctional natural materials. By focusing on seed resources generated within existing afforestation systems, we highlight a sustainable approach to valorize forest-derived by-products without additional pressure on natural ecosystems. Nevertheless, as bioactivities were evaluated using crude extracts, further studies are required to identify and elucidate the active compounds and their mechanisms of action.

This study evaluated the nutritional adequacy of commercial dog and cat foods in South Korea by comparing analytically determined nutrient contents with recommended nutrient levels of the National Institute of Animal Science (NIAS) and the Association of American Feed Control Officials (AAFCO). A total of 96 pet food products for puppies (n = 50), adult dogs (n = 18), kittens (n = 17), and adult cats (n = 11) were collected. Nutrients, including crude protein, crude fat, essential amino acids, essential fatty acids, major minerals, and selected trace nutrients, were analyzed using accredited methods stipulated by the Korean Feed Control Act and compared with NIAS and AAFCO recommendations. Most adult dog and cat diets met recommended nutrient levels; however, deficiencies were identified in diets intended for growth. Puppy foods showed inadequate levels of eicosapentaenoic and docosahexaenoic acids (EPA + DHA, 72%), calcium (22%), and phosphorus (42%), as well as imbalanced calcium-to-phosphorus ratios (12%). In kitten diets, insufficient EPA + DHA (41.2%) and taurine (11.8%) were observed. In contrast, 82.3% of products met label-declared guaranteed analysis values for seven mandatory nutrients. These results provide baseline information on the nutritional adequacy and labeling compliance of pet foods across different life stages.

This study investigated the effects of short-duration microwave (MW) exposure on growth, photosynthesis, antioxidant activity, and secondary metabolite accumulation in Agastache rugosa cultivated in a deep flow technique hydroponic system. Plants at 14 and 18 days after transplanting were exposed to MW radiation at 200 W for 5, 10, 15, 20, and 25 s, with untreated plants serving as the control. While most vegetative growth parameters were unaffected, MW exposure for 15–25 s significantly increased flower branch number by 9–15% and flower biomass by 9–24% compared with the control. These treatments also enhanced net photosynthetic rate (by up to 53%), chlorophyll a content (by 12%), and total phenolics (by 43–85%) compared with the control. Antioxidant enzyme activities were markedly elevated, with SOD, POD, and CAT increasing by up to 66%, 49%, and 103%, respectively. MW exposure also promoted phytochemical accumulation: total flavonoids increased by 7–11%, and key bioactive compounds such as chlorogenic acid (up to 7.3-fold), tilianin (up to 53%), and rosmarinic acid (up to 42%) were significantly enhanced. These results indicate that short MW exposures of 15–25 s act as an effective elicitation strategy to improve flower development and phytopharmaceutical quality of A. rugosa under controlled cultivation conditions.

Abstract Object detection is a technology that automatically identifies and locates specific objects in images or videos and plays a core role in various fields, such as autonomous driving, security surveillance, and medical imaging. You Only Look Once (YOLO) has gained attention for achieving both accuracy and detection speed in real-time applications; however, during the resolution reduction process, detailed information is lost, and unnecessary signals are mixed in the multi-scale feature fusion stage, resulting in limited detection performance for small objects and complex background scenes. To alleviate these limitations, we propose YOLO-RECAP, which integrates a Content-Aware ReAssembly of FEatures (CARAFE) and Efficient Channel Attention (ECA) modules based on YOLOv11. CARAFE precisely restores boundaries and shapes during the upsampling stage by utilizing position-specific content information, whereas ECA effectively models interchannel interactions to emphasize important signals. For performance verification, VisDrone2019, Store Keeping Unit-110 K (SKU-110 K), Pascal Visual Object Classes (VOC), and Dataset for Object Detection in Aerial Images (DOTA)v1 were used. In addition, Latency is reported under an end-to-end setting that includes pre-processing, inference, and post-processing, to reflect practical deployment conditions. As a result, YOLO-RECAP achieved mAP50 of 0.316, mAP50@95 of 0.184, Latency of 16.5 ms, and 60.6 FPS on VisDrone2019; mAP50 of 0.895, mAP50@95 of 0.572, Latency of 18.2 ms, and 55.0 FPS on SKU-110 K; mAP50 of 0.770, mAP50@95 of 0.561, Latency of 15.6 ms, and 63.9 FPS on Pascal VOC; and mAP50 of 0.281, mAP50@95 of 0.157, Latency of 18.6 ms, and 53.9 FPS on DOTAv1. Qualitative bounding-box visualizations further indicate reduced missed detections and more stable predictions in cluttered and densely populated scenes. As a result, YOLO-RECAP provided a more stable and balanced detection performance than the existing YOLOv11 and recent detection models, especially for small objects and complex backgrounds. This code is available at https://github.com/Heon-ju/YOLO-RECAP.git .

Background/Objectives: Artificial intelligence (AI) is increasingly applied to drug safety evaluation, yet evidence is dispersed across lifecycle stages and tasks. This scoping review aimed to (1) map how AI supports safety- and treatment-related decision types across the drug lifecycle, and (2) examine evaluation strategies used to assess model reliability for clinical or regulatory use. Methods: Using Arksey and O'Malley's framework, we searched a major database for studies published in the past decade that applied AI or machine learning to drug safety or medication-related decisions. After screening, we extracted data on lifecycle stage, decision type, AI methods, data sources, and evaluation strategies. A lifecycle-decision matrix was constructed to characterize application patterns. Results: AI applications were concentrated in real-world clinical care × patient-level safety prediction and post-marketing × safety surveillance, using EHRs, spontaneous reporting systems, and clinical text. Common methods included gradient boosting, deep neural networks, graph neural networks, and natural language processing models. This concentration reflects structural incentives favoring safety-oriented applications with readily available data and lower decision liability. Evidence for treatment optimization, regulatory decision modeling, and evidence synthesis was limited. Most studies used internal validation; external validation and real-world deployment were uncommon, indicating early methodological maturity and limited translational readiness. Conclusions: AI demonstrates strong potential to enhance drug safety-particularly in risk prediction and pharmacovigilance-but its use remains uneven across the lifecycle. By situating AI applications within explicit lifecycle stages and decision contexts, this review clarifies where progress has advanced, where translation has stalled, and why these gaps persist. Limited external validation and minimal real-world testing constrain clinical and regulatory adoption. These findings suggest that external validation and real-world testing may contribute to further advances in AI for drug safety.

Aphids (family Aphididae) are among the most species-rich groups of Sternorrhyncha in the order Hemiptera, and have a complex life cycle that can include several different phenotypes that are perfectly adapted to specific ecological niches. However, because aphids have a small body size, indistinct appearance, and cryptic adult behavior, their species level identification is often difficult and may be time-consuming. To overcome these limitations, DNA barcoding has been employed as an effective tool for species identification. In this study, we conducted a DNA barcoding test based on 566 specimens of Korean Aphididae, representing 125 morphospecies. Based on intraspecific genetic divergence, a threshold of 2% was estimated to efficiently differentiate the morphospecies. Only 87 morphospecies (69.6%) identified across four species delimitation methods (namely, automatic barcode gap discovery, assemble species by automatic partitioning, Poisson-tree-processes or PTP, and Bayesian implementation of the PTP) were consistent with the morphological identifications of the species. This indicates the presence of many cases of cryptic diversity among the other morphospecies, except the abovementioned 87 species. Careful morphological examination of morphospecies exceeding 2.0% intraspecific variability revealed cryptic diversity in three species (Eriosoma yangi, Tuberculatus kuricola, and Greenidea kuwanai). Two morphospecies, Sitobion avenae and Aphis craccivora, also exhibited high intraspecific divergence and comprised a single molecular operational taxonomic unit. Overall, our findings indicate that DNA barcoding can be a powerful tool for identifying species belonging to the family Aphididae, while also revealing cases of cryptic diversity.