In Korea, the agricultural industry has witnessed a growing emphasis on reducing reliance on imported forage by adopting locally available alternatives to enhance sustainability and self-sufficiency. Given the limited information on the potential use of whole-crop rice silage (WCRS) as livestock feed, this study evaluated the effects of total mixed rations (TMR) containing WCRS on the rumen microbiota of Hanwoo heifers. Thirty heifers (body weight = 351 ± 39 kg) were randomly assigned to three TMR diet groups for the early fattening period: oat hay (OH), oat hay with Italian ryegrass silage and corn silage (OIC), and silages from whole-crop rice, Italian ryegrass, and corn (WCRSEF). During the late fattening period, the same heifers (569 ± 40 kg) were reassigned to three other treatment groups: perennial ryegrass (PRG), Italian ryegrass silage and alfalfa hay (IRGA), and silage containing whole-crop rice (WCRSLF). The effects of different feeding diets on the rumen microbial composition of Hanwoo heifers were investigated using high-throughput 16S rRNA gene sequencing. The analysis revealed similar microbial diversity among the treatments across both fattening periods. Bacteroidetes and Firmicutes were the most dominant phyla during early and late fattening periods. Moreover, during the early fattening period, heifers fed WCRS exhibited a higher ratio of Bacteroidetes to Firmicutes, whereas Firmicutes became more predominant in the late fattening period. Hungateiclostridium and Porphyromonas were identified as biomarkers (LDA score > | 2 |; p < 0.05) for the WCRSEF and WCRSLF groups, respectively. Furthermore, the microbial co-occurrence network highlighted different patterns (| r | = 0.85; p < 0.05) between the early and late fattening periods. The results provide significant insights into the effects of WCRS as a substitute for conventional forages such as oat hay, perennial ryegrass, alfalfa, and Italian ryegrass silage. The findings suggest that WCRS can modulate the rumen microbiome of Hanwoo heifers.

Salvia plebeia R. Brown is a medicinal plant containing various bioactive compounds, including flavonoids, and has been reported to exhibit diverse pharmacological effects such as anti-inflammatory, antioxidant, and hepatoprotective activities. However, previous studies on S. plebeia have mainly focused on phytochemical identification and pharmacological evaluation, while biotechnological approaches aimed at enhancing the productivity of major bioactive compounds—particularly metabolic regulation and content improvement through elicitor treatment in in vitro culture systems—remain largely unexplored. In this study, we investigated the effects of elicitor treatments on the accumulation of homoplantaginin, a major bioactive compound in the leaves of in vitro–cultured S. plebeia, as well as the associated changes in anti-inflammatory activity. In vitro–grown plantlets were treated with yeast extract at concentrations of 1, 2, and 5 mg/L and polyethylene glycol (PEG) at concentrations of 1, 2, and 5% (w/v), respectively. Homoplantaginin content was quantitatively analyzed using high-performance liquid chromatography (HPLC), and anti-inflammatory activity was evaluated by measuring nitric oxide (NO) production inhibition in RAW 264.7 macrophage cells. As a result, the homoplantaginin content was significantly increased in the treatment with 2 mg/L yeast extract compared to the control, while the highest NO inhibition activity was observed in the 5% PEG treatment. These findings suggest that elicitor treatment can effectively enhance the production of bioactive compounds and anti-inflammatory activity in in vitro–cultured S. plebeian. Furthermore, this study provides fundamental data supporting the potential industrial application of S. plebeia through further elucidation of metabolic pathways and optimization of culture conditions.

본 연구는 작물 생육을 효율적으로 관리하기 위한 비파괴적 바이오매스 예측 모델을 개발하고자 수행되었 다. 케일과 상추의 상단면 및 측면 이미지를 촬영하여 ImageJ로 엽면적, 가로폭, 세로폭 등의 데이터를 추출하였다. 이를 독립변수, 실측 생체중과 건물중을 종속변수로 하여 회귀 모델을 구축하고, 결정계수(R²)와 평균 제곱근 오차 (RMSE)로 성능을 평가하였다. 엽면적 추정 시, 로제트형 구조의 상추는 상단면과 측면 이미지 모두에서 강한 상관 관계(TV: R2 = 0.964, SV: R2 = 0.95)를 보였던 반면, 직립형 구조의 케일은 측면 이미지에서만 높은 상관관계(R2 = 0.876)를 보여 작물의 형태가 최적의 촬영 각도를 결정함을 보여주었다. 생체중 및 건물중 예측 시, 케일은 측면 이 미지 모델이 예측 정확도가 높았던(DW: R2 = 0.859－0.941, FW: R2 = 0.860－0.946) 반면, 상추는 상단면 이미지 모델이 월등히 안정적이고 높은 예측 정확도(DW: R2 = 0.920－0.968, FW: R2 = 0.870－0.953)를 보였다. 상단면 과 측면 이미지를 결합한 모델은 예측력이 약했던 케일의 정확도를 크게 향상시킨 반면, 단일 촬영 방향으로도 예측 이 잘 되었던 상추의 정확도에는 거의 영향을 미치지 않았다. 최종적으로 케일은 상·측면 결합(SV+TV) 모델이, 상 추는 상면(TV) 단독 모델이 바이오매스 예측을 위한 최적 모델로 선정되어, 작물의 구조적 특성에 맞는 비파괴적 예측 방법이 중요함을 입증했다.

복어는 동아시아 요리의 고급 원료로 상업적 가치가 높 은 수산자원이나, 야생 복어의 테트로도톡신은 치명적인 식중독 사건을 꾸준히 유발하고 있다. 자주복은 한국, 일 본, 중국에서 인기 있는 복어 종으로, 테트로도톡신이 없 는 복어 생산을 위해 양식되는 주된 어종이다. 따라서 양 식 자주복과 자연산 자주복의 구별은 식품 안전과 규제측면에서 매우 중요하다. 본 연구에서는 국내 온라인 및 오프라인 시장에서 판매되는 100개의 자주복 제품을 대상 으로 ‘양식 및 자연산’ 여부를 PCR 기반 방법을 이용하 여 확인하였으며, PCR 결과를 제품의 표시사항 정보와 비 교하였다. PCR 분석에는 자연산 자주복과 양식 자주복의 유전적 다양성 차이를 보이는 6개의 마커를 이용하였다. PCR 분석 결과 모든 양식 자주복 제품에서는 6개 마커에 서 모두 완전한 증폭 패턴을 보였으나, 자연산 자주복 제 품에서는 0 - 5개 마커에서 만 무작위 증폭 패턴을 나타냈 다. 따라서 6개 유전 마커의 증폭 패턴을 이용한 분석법 은 향후 자주복 제품의 허위표시 모니터링 및 테트로도톡 신 유무의 신속 검사에 널리 활용될 수 있을 것이다. 또 한 본 연구 결과는 현행 복어 종 판별을 보완할 수 있는 기초자료를 제공해 줄 수 있다.

This study proposes a mobile-based lightweight deep learning model (Lite-MCC) capable of reconstructing three-dimensional (3D) spatial structures from a single RGB image. Conventional 3D reconstruction models require multi-view inputs or point cloud data and depend on large-scale computational resources, which limits their real-time applicability in practical environments. To address this limitation, the proposed Lite-MCC model simplifies the existing Multiview Compressive Coding (MCC) architecture, enabling accurate 3D reconstruction using only a single image. The model adopts a parallel structure consisting of a Vision Transformer (ViT-Tiny) and a Geometry Encoder to extract visual and spatial features simultaneously, while a Transformer Decoder generates the corresponding 3D point cloud. Furthermore, depth map–based input transformation and ONNX-based optimization are employed to achieve efficient real-time inference on edge devices. Experimental results on the CO3D dataset demonstrate that Lite-MCC reduces computational cost by 87% and memory usage by 65%, while maintaining a Chamfer Distance of 0.045, comparable to the original MCC model. These results indicate that the proposed method provides a promising direction for lightweight AI models enabling low-cost, real-time 3D recording and visualization.

Transition metal/porous carbon composite is good electrode candidate since porous carbon provides high surface porosity which promotes the access of electrolyte ions, and transition metal enables redox reactions to improve specific capacitance and energy density. In this study, iron/carbon nanofiber (CNF) composite electrodes were prepared by grafting ferrocenecarboxaldehyde to the CNFs which were fabricated by electrospinning and thermal treatment of polyacrylonitrile (PAN). The presence of iron on the CNF surface was confirmed by SEM/EDS, ICP-MS and XPS. Electrochemical performance was evaluated using a three-electrode cell with 1 M Na2SO4 as an electrolyte. Iron-grafted CNFs exhibited a high specific capacitance of 358 F g− 1 and an energy density of 49.7 Wh kg− 1 at 0.5 A g− 1, which is significantly higher than those for untreated CNFs (68 F g− 1 and 9.4 Wh kg− 1). This demonstrates that this iron/CNF composite is promising candidate for supercapacitor electrode with outstanding energy storage performance.