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WoS SCOPUS Document Type Document Title Abstract Authors Affiliation ResearcherID (WoS) AuthorsID (SCOPUS) Author Email(s) Journal Name JCR Abbreviation ISSN eISSN Volume Issue WoS Edition WoS Category JCR Year IF JCR (%) FWCI ⓘ FWCI Update Date WoS Citation SCOPUS Citation Keywords (WoS) KeywordsPlus (WoS) Keywords (SCOPUS) KeywordsPlus (SCOPUS) Language Publication Stage Publication Year Publication Date DOI JCR Link DOI Link WOS Link SCOPUS Link
○ ○ Article ⓘ Bayesian Hybrid Model Search and Averaging for Sparse Gaussian Process Regression ⓘ Gaussian process (GP) regression has been a popular nonparametric Bayesian approach for nonlinear modeling and prediction in the fields of statistics and machine learning. However, when many predictors are considered for the construction of the kernel function, the GP approach provides unacceptable performance in both estimation and prediction. To overcome this limitation, some attempts have been made to exploit a fully Bayesian model selection approach or a penalized likelihood approach. However, the fully Bayesian framework turns out to be extremely expensive in computational terms, and the penalized likelihood method oversimplifies model uncertainties. In this paper, we propose a new sparse GP method that reduces the computational burden of fully Bayesian inference by incorporating a hybrid deterministic-stochastic search approach into Bayesian model averaging. In addition, we develop a scalable extension of the proposed method to high-dimensional massive data settings. The merits of the proposed methods are demonstrated via simulation experiments and real data applications. ⓘ Duan, Weikang; Goh, Gyuhyeong ⓘ Kansas State Univ, Dept Stat, Manhattan, KS USA; Kyungpook Natl Univ, Dept Stat, Daegu, South Korea ⓘ ⓘ 57221924105; 55964615700 ⓘ ggoh@knu.ac.kr; ⓘ STATISTICAL ANALYSIS AND DATA MINING-AN ASA DATA SCIENCE JOURNAL ⓘ STAT ANAL DATA MIN ⓘ 1932-1864 1932-1872 18 2 SCIE COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS;STATISTICS & PROBABILITY ⓘ 2024 3.6 5.1 0 2025-06-11 ⓘ 0 0 Bayesian model averaging; local Gaussian process; sparse Gaussian process; variable selection ⓘ VARIABLE-SELECTION ⓘ Bayesian model averaging; local Gaussian process; sparse Gaussian process; variable selection ⓘ Gaussian distribution; Importance sampling; Linear regression; Logistic regression; Population statistics; Bayesian; Bayesian model averaging; Gaussian process regression; Gaussian Processes; Hybrid model; Local gaussian process; Model averaging; Model search; Sparse Gaussian process; Variables selections; Higher order statistics ⓘ English 2025 2025-04 10.1002/sam.70018 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Boosting the luminescence performance of magneto-mechano-luminescence devices by leveraging self-generated electric potentials ⓘ The reported research on Mechano-luminescence (ML) has primarily focused on enhancing luminescence by optimizing mechanical energy for luminescence by employing various structural deformations of ML composites in response to mechanical stress. In contrast, this study demonstrates the innovative use of ambient magnetic fields to drive a lighting device without any external electrical source. This approach involves the simultaneous application of magnetically induced mechanical vibrations and a self-generated electric potential in the same periodic time. Our magnetically driven lighting device comprises a sheet-shaped ML composite consisting of ZnS: Cu particles embedded within a polydimethylsiloxane (PDMS) elastomer, along with a magnetically vibrating cantilever beam incorporating a 32-mode piezoelectric single crystal fiber composite (SFC). This structure forms a magneto-mechano-electric (MME) generator, capable of simultaneously applying mechanical stress and electrical potential to the ML composite under second harmonic bending vibration. Notably, the MME generator in second harmonic vibration mode responds to ambient magnetic field oscillations, inducing electric potential within the SFC. When the projection part of the MME generator contacts the ML composite, the induced electric potential supplies additional electrons to the ML material. This influx of electrons facilitates greater recombination within the ML composite, thereby enhancing luminescence efficiency. Our results indicate a 240 % improvement in luminescence efficiency when both mechanical and electrical energies are applied simultaneously compared to when only mechanical energy is utilized. When tested in a real-world environment at 60 Hz, the magnetically driven lighting device emits visible light without requiring any additional electrical power source. ⓘ Jung, Ji Yun; Kim, Hyun Soo; Baek, Chang Min; Lee, Seungah; Min, Yuho; Song, Hyun-Cheol; Ryu, Jungho ⓘ Yeungnam Univ, Sch Mat Sci & Engn, Gyongsan 38541, South Korea; Korea Inst Sci & Technol KIST, Elect Mat Res Ctr, Seoul 02792, South Korea; Kyungpook Natl Univ, Dept Mat Sci & Met Engn, Daegu 41566, South Korea; Korea Univ, Dept Mat Sci & Engn, Seoul 02841, South Korea; Sungkyunkwan Univ SKKU, Sch Adv Mat Sci & Engn, Suwon 16419, South Korea; Sungkyunkwan Univ SKKU, KIST SKKU Carbon Neutral Res Ctr, Suwon 16419, South Korea ⓘ ⓘ 58189081100; 56869069300; 59347458600; 57216374588; 36782804100; 57827490000; 57201603501 ⓘ hcsong@kist.re.kr; jhryu@ynu.ac.kr; ⓘ NANO ENERGY ⓘ NANO ENERGY ⓘ 2211-2855 2211-3282 134 SCIE CHEMISTRY, PHYSICAL;MATERIALS SCIENCE, MULTIDISCIPLINARY;NANOSCIENCE & NANOTECHNOLOGY;PHYSICS, APPLIED ⓘ 2024 17.1 5.1 0 2025-05-07 ⓘ 1 1 Magneto-mechano-luminescence (MML); Magneto-mechano-electric (MME); Vibration; Electric potential; Magnetic field ⓘ ZNS; MECHANOLUMINESCENCE; NANOCRYSTALS ⓘ Electric potential; Magnetic field; Magneto-mechano-electric (MME); Magneto-mechano-luminescence (MML); Vibration ⓘ Luminescence of inorganic solids; Luminescent devices; Magnetic levitation; Piezoelectric devices; Piezoelectricity; Surface discharges; Zinc Selenide; Ambients; Fibre composites; Lighting devices; Magnetic-field; Magneto-mechano-electric; Magneto-mechano-luminescence; Mechanical energies; Mechanical stress; Single crystal fiber; Vibration; Microchannels ⓘ English 2025 2025-02 10.1016/j.nanoen.2024.110593 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Dual-controlled piezoelectric composite film with enhanced crystallinity and defect-free via solvent vapor treatment ⓘ A simple, cost-effective solvent vapor annealing (SVA) with a 2-butanone atmosphere-based PVDF/BaTiO3 NPs composite film (treated for 5 h) exhibits a crystallinity and beta-phase fractions of PVDF in the film of 25.6 % and 79.8 %, respectively, which are higher compared to the traditional thermal annealing (TA)-treated composite film. Moreover, the SVA-treated composite films show better control of roughness, pores, defects/cracks than the TA-treated film. The superior properties of SVA-treated composite films were due to the re-orientation of -CH2/CF2 molecular chains by stretching/rotation leads to the recrystallization of PVDF in the composite film. The SVA-treated composite flexible piezoelectric harvester (f-PEH) exhibits 2 folds higher electrical response than the TA-treated f-PEH device upon mechanical bending force. Higher piezoelectric performance was achieved when the pores are minimized and higher crystallinity of film, which are somewhat correlated with the theoretical simulations. Also tested the f-PEH response with periodic water droplets and finger bending forces suggests that f-PEH has potential to work as harvester and sensor. The proposed SVA technique may help to generate novel polymer/composite films with enhanced functional properties for wearable and skin-adaptable energy harvesters and sensor electronics. ⓘ Jang, Haksu; Park, Hyeon Jun; Kim, Gwang Hyeon; Kim, Cheol Min; Alluri, Nagamalleswara Rao; Bae, Bitna; Jeon, Hyomin; Lee, Donghun; Park, Kwi-Il ⓘ Kyungpook Natl Univ, Dept Mat Sci & Met Engn, 80 Daehak Ro, Daegu 41566, South Korea; Kyungpook Natl Univ, Innovat Semicond Educ & Res Ctr Future Mobil, 80 Daehak Ro, Daegu 41566, South Korea; Kyungpook Natl Univ, Res Inst Automot Parts & Mat, 80 Daehak Ro, Daegu 41566, South Korea ⓘ Park, Kwiil/LKN-9445-2024; Alluri, Nagamalleswara Rao/K-1696-2015 ⓘ 59302327400; 58859176900; 59303138700; 59441985400; 56527074500; 59302327300; 59534881000; 59444729800; 35280874200 ⓘ kipark@knu.ac.kr; ⓘ NANO ENERGY ⓘ NANO ENERGY ⓘ 2211-2855 2211-3282 136 SCIE CHEMISTRY, PHYSICAL;MATERIALS SCIENCE, MULTIDISCIPLINARY;NANOSCIENCE & NANOTECHNOLOGY;PHYSICS, APPLIED ⓘ 2024 17.1 5.1 0 2025-05-07 ⓘ 0 0 Solvent vapor annealing; Thermal annealing; Energy harvesting; Piezoelectric; Composite ⓘ POLY(VINYLIDENE FLUORIDE); PERFORMANCE; PHASE ⓘ Composite; Energy harvesting; Piezoelectric; Solvent vapor annealing; Thermal annealing ⓘ Annealing; Molecular orientation; Piezoelectricity; Bending force; Cristallinity; Defect-free; Energy; P.V.D.F; Piezoelectric; Piezoelectric composite; Piezoelectric harvester; Solvent-vapor annealing; Thermal-annealing; Energy harvesting ⓘ English 2025 2025-04 10.1016/j.nanoen.2025.110705 ⓘ 바로가기 바로가기 바로가기 바로가기
○ Article ⓘ A cluster attention-based multiple instance learning network for enhancing histopathological image interpretation ⓘ Background: Histopathological diagnosis involves examining abnormal architectural patterns and cellular-level changes. Whole slide images (WSIs) provide comprehensive digital representations of tissue samples, enabling detailed analysis and interpretation. Annotating the giga-pixel images remains labor-intensive, requiring experts to label abnormal patterns and cellular changes. To address this, Multiple Instance Learning (MIL), a promising weakly supervised approach, enables models to learn from limited annotations while preserving key histopathological features. Method: However, existing MIL-based methods may overlook potential semantic features, limiting their effectiveness. To overcome this limitation, we propose a novel Cluster-Aware Attention-based MIL (CAAMIL) architecture. This approach employs an advanced attention-based module integrated with a clustering method to enhance the interpretability of heterogeneous features. Our approach clusters architectural or cytologic features, making the groups interpretable at the cluster level and reflective of histopathological grades or prognostic indicators. Results: We demonstrated the efficacy of our model in both slide-level and patch-level classification as well as in interpreting tumor and mutation predictions. Experimental results show that our model achieves an AUC score of 0.96 for tumor detection at slide-level and 0.85 at patch-level, outperforming other state-of-the-art MIL-based methods. Conclusion: Our proposed CAAMIL architecture overcomes the limitations of existing MIL methods by effectively clustering features and providing interpretable results. The high accuracy and interpretability of our model make it a promising tool for histopathological diagnosis and tumor detection. © 2025 ⓘ Ko, Seokhwan; Ando, Yu; Kim, Moonsik; Park, Nora Jee-Young; Han, Hyungsoo; Park, Ji Young; Cho, Junghwan ⓘ Department of Pathology, Kyungpook National University Chilgok Hospital, Daegu, South Korea, Department of Biomedical Science, Kyungpook National University, Daegu, South Korea, Clinical Omics Institute, Kyungpook National University, Daegu, South Korea; Department of Biomedical Science, Kyungpook National University, Daegu, South Korea, Clinical Omics Institute, Kyungpook National University, Daegu, South Korea; Department of Pathology, Kyungpook National University Chilgok Hospital, Daegu, South Korea, Department of Pathology, Kyungpook National University School of Medicine, Daegu, South Korea; Department of Pathology, Kyungpook National University Chilgok Hospital, Daegu, South Korea, Department of Pathology, Kyungpook National University School of Medicine, Daegu, South Korea; Department of Physiology, Kyungpook National University School of Medicine, Daegu, South Korea, Clinical Omics Institute, Kyungpook National University, Daegu, South Korea; Department of Pathology, Kyungpook National University Chilgok Hospital, Daegu, South Korea, Department of Pathology, Kyungpook National University School of Medicine, Daegu, South Korea; Clinical Omics Institute, Kyungpook National University, Daegu, South Korea ⓘ ⓘ 57205603624; 57295299200; 57195918515; 57226185359; 7401969388; 57210160197; 59899936300 ⓘ joshua@knu.ac.kr; ⓘ Computers in Biology and Medicine ⓘ COMPUT BIOL MED ⓘ 0010-4825 1879-0534 193 SCIE COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS;ENGINEERING, BIOMEDICAL;MATHEMATICAL & COMPUTATIONAL BIOLOGY;BIOLOGY ⓘ 2024 6.3 5.2 0 2025-06-11 ⓘ 0 ⓘ ⓘ Clustering; Deep learning; Histopathology; Multiple instance learning; Weakly-supervised learning ⓘ Cluster Analysis; Humans; Image Interpretation, Computer-Assisted; Machine Learning; Multiple-Instance Learning Algorithms; Neoplasms; Cluster analysis; Deep learning; Image annotation; Image correlation; Image enhancement; Motion capture; Motion estimation; Multi-task learning; Optical flows; Support vector machines; Clusterings; Deep learning; Histopathological diagnosis; Histopathology; Interpretability; Learning network; Learning-based methods; Multiple-instance learning; Tumour detection; Weakly supervised learning; area under the curve; Article; cancer survival; cluster aware attention based multiple instance learning; controlled study; feature extraction; histopathology; human; human tissue; image processing; information processing; learning; leave one out cross validation; multiple-instance learning algorithm; phenotype; qualitative analysis; quantitative analysis; sampling; tissue structure; Youden index; cluster analysis; computer assisted diagnosis; diagnostic imaging; machine learning; multiple-instance learning algorithm; neoplasm; pathology; procedures; Photointerpretation ⓘ English Final 2025 10.1016/j.compbiomed.2025.110353 ⓘ 바로가기 바로가기 바로가기
○ ○ Article ⓘ Dynamic personalized thermal comfort Model:Integrating temporal dynamics and environmental variability with individual preferences ⓘ Understanding human thermal perception is essential for creating comfortable and energyefficient indoor environments. In this study, we introduce a dynamic deep learning framework, Thermal Comfort Prediction Model using Long Short-Term Memory (TCPM-LSTM) networks, with Reinforcement Learning (RL) to model and predict personalized thermal comfort under varying environmental conditions. Our proposed Personalized Comfort Model with Reinforcement Learning (PCM-RL) captures temporal dynamics and individual differences in thermal sensation, comfort, and preference. PCM-RL shows about a 13.6 % improvement in average reward when using RL with a pre-trained LSTM (TCPM-LSTM) compared to RL without LSTM. This integrated approach allows the RL agent to make more informed decisions, optimizing comfort based on real-time predictions. Moreover, our framework demonstrates more stable learning behavior, with reduced reward variability across episodes, making it a robust tool for personalized comfort management. This study represents a significant step forward in developing intelligent, adaptive systems that optimize human-centric thermal comfort by providing actionable insights for managing indoor environments effectively. ⓘ Abdulraheem, Abdulkabir; Lee, Seungho; Jung, Im Y. ⓘ Kyungpook Natl Univ, Sch Elect & Elect Engn, Daehak Ro 80, Daegu 41566, South Korea ⓘ ⓘ 57929177700; 59541054600; 18037522200 ⓘ aaoabdul@gmail.com; komz18@naver.com; iyjung@ee.knu.ac.kr; ⓘ JOURNAL OF BUILDING ENGINEERING ⓘ J BUILD ENG ⓘ 2352-7102 102 SCIE CONSTRUCTION & BUILDING TECHNOLOGY;ENGINEERING, CIVIL ⓘ 2024 7.4 5.2 2.78 2025-05-07 ⓘ 1 1 Thermal perception; TCPM-LSTM networks; Reinforcement learning; Thermal comfort; Environmental dynamics; Personalized thermal comfort models ⓘ ⓘ Environmental dynamics; Personalized thermal comfort models; Reinforcement learning; TCPM-LSTM networks; Thermal comfort; Thermal perception ⓘ Contrastive Learning; Deep reinforcement learning; Environmental dynamics; Memory network; Personalized thermal comfort model; Prediction modelling; Reinforcement learnings; Short term memory; Thermal; Thermal comfort models; Thermal comfort prediction model using long short-term memory network; Thermal perception; Thermal comfort ⓘ English 2025 2025-05-15 10.1016/j.jobe.2025.111938 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Enhancing building energy through regularized Bayesian neural networks for precise occupancy detection ⓘ Improving building energy efficiency is essential for promoting sustainable construction practices and minimizing operational costs. This study introduces a physics-based framework that integrates domain-specific constraints into a regularized Bayesian Neural Network (BNN) to enhance occupancy detection accuracy, an essential factor for optimizing building energy management. Unlike conventional approaches, our method leverages a unique combination of environmental sensor data (temperature, humidity, light, CO2) and engineered features such as heating degree days (HDD) to improve predictive performance. Additionally, a physics-based regularizer is incorporated within the BNN model to ensure predictions adhere to the fundamental physical principles of the building, enhancing both reliability and uncertainty estimation. Tested on office building data from the University of Mons in Belgium, the proposed framework achieves 96-99 % accuracy across three test cases, outperforming traditional methods like Gradient Boosting Machine, Support Vector Machine, and Na & iuml;ve Bayes. A user-friendly graphical interface was developed to facilitate real-world adoption, enabling facility managers, energy analysts, and building operators to seamlessly implement the approach without extensive technical expertise. By improving the precision of occupancy detection, this research supports more efficient HVAC control, enhanced occupant comfort, and substantial energy savings, an impact well-documented in previous studies that report potential reductions in energy consumption ranging from 20 to 30 %. The findings contribute to the advancement of intelligent building automation, offering a scalable solution for reducing carbon footprints and operational costs while promoting sustainable construction practices. ⓘ Yahaya, Abdullahi; Owolabi, Abdulhameed Babatunde; Suh, Dongjun ⓘ Kyungpook Natl Univ, Dept Convergence & Fus Syst Engn, Sangju 37224, South Korea; Drexel Univ, Dept Civil Architectural & Environm Engn, 3141 Chestnut St, Philadelphia, PA 19104 USA; Kyungpook Natl Univ, Reg Leading Res Ctr Smart Energy Syst, Sangju 37224, South Korea; Kyungpook Natl Univ, Dept Energy Convergence & Climate Change, Daegu 41566, South Korea ⓘ ⓘ 58419707700; 57192210107; 36613529600 ⓘ yahaya@knu.ac.kr; owolabiabdulhameed@gmail.com; dongjunsuh@knu.ac.kr; ⓘ JOURNAL OF BUILDING ENGINEERING ⓘ J BUILD ENG ⓘ 2352-7102 107 SCIE CONSTRUCTION & BUILDING TECHNOLOGY;ENGINEERING, CIVIL ⓘ 2024 7.4 5.2 0 2025-05-07 ⓘ 0 0 Bayesian neural networks; Building energy management; Energy optimization; Occupancy detection; Physics-based regularization; Sustainable building automation ⓘ PERFORMANCE; SYSTEMS; MODEL ⓘ Bayesian neural networks; Building energy management; Energy optimization; Occupancy detection; Physics-based regularization; Sustainable building automation ⓘ Energy saving; Plant management; Power management (telecommunication); Smart homes; Bayesian neural networks; Building automation; Building energy managements; Energy optimization; Occupancy detections; Physic-based regularization; Physics-based; Regularisation; Sustainable building; Sustainable building automation; Energy efficiency ⓘ English 2025 2025-08-01 10.1016/j.jobe.2025.112777 ⓘ 바로가기 바로가기 바로가기 바로가기
○ Article ⓘ ExPDrug: Integration of an interpretable neural network and knowledge graph for pathway-based drug repurposing ⓘ Precision medicine aims to provide personalized therapies by analyzing patient molecular profiles, often focusing on gene expression data. However, effectively linking these data to actionable drug discovery for clinical application remains challenging. In this paper, we introduce ExPDrug, a neural network (NN) model that integrates biological pathways from transcriptomic data with a biomedical knowledge graph to facilitate pathway-based drug repurposing. ExPDrug enhances disease phenotype prediction by capturing the complex relationships between genes and pathways. Using layer-wise relevance propagation (LRP), the model interprets the contribution of each pathway using relevance scores applied in a random walk-with-restart (RWR) algorithm to prioritize potential drug candidates in the biomedical network. ExPDrug outperforms existing methods in predicting phenotypes for the three diseases and identifying drug candidates, as supported by the literature. This model offers a transformative approach for advancing precision medicine by linking transcriptomic insights directly to clinical drug repurposing, thereby potentially improving treatment strategies for complex diseases. © 2025 Elsevier Ltd ⓘ Kim, Junku; Jang, Hojoong; Park, Youngjun; Jung, Inuk; Jo, Kyuri ⓘ Department of Computer Engineering, Chungbuk National University, Cheongju, South Korea; Department of Computer Engineering, Chungbuk National University, Cheongju, South Korea; Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany; School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea; Department of Computer Engineering, Chungbuk National University, Cheongju, South Korea ⓘ ⓘ 59531217100; 59531386200; 57305122100; 59818450300; 56037547500 ⓘ Kyurijo@chungbuk.ac.kr; ⓘ Computers in Biology and Medicine ⓘ COMPUT BIOL MED ⓘ 0010-4825 1879-0534 187 SCIE COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS;ENGINEERING, BIOMEDICAL;MATHEMATICAL & COMPUTATIONAL BIOLOGY;BIOLOGY ⓘ 2024 6.3 5.2 0 2025-05-07 ⓘ 0 ⓘ ⓘ Biological pathway; Drug repurposing; Gene expression; Knowledge graph; Random walk ⓘ Algorithms; Computational Biology; Drug Repositioning; Humans; Neural Networks, Computer; Precision Medicine; Transcriptome; transcriptome; Biological pathways; Drug candidates; Drug repurposing; Genes expression; Knowledge graphs; Networks/graphs; Neural-networks; Random Walk; Repurposing; Transcriptomics; algorithm; article; drug development; drug repositioning; drug therapy; gene expression; human; nerve cell network; nonhuman; personalized medicine; phenotype; prediction; random walk; artificial neural network; bioinformatics; genetics; personalized medicine; procedures; Knowledge graph ⓘ English Final 2025 10.1016/j.compbiomed.2025.109729 ⓘ 바로가기 바로가기 바로가기
○ ○ Article ⓘ Guidelines for the Treatment of Laryngeal Cancer from the Korean Society of Head and Neck Surgery ⓘ Objectives. Effective treatment of laryngeal cancer requires clinical decision-making that balances survival outcomes with the preservation of essential functions, such as voice and swallowing. This study, led by the Clinical Practice Guideline Committee of the Korean Society of Head and Neck Surgery, aims to establish evidence-based recommendations to guide surgeons in optimizing treatment decisions for patients with laryngeal cancer. Methods. A literature search was performed in the PubMed, Embase, Cochrane Library, and KoreaMed databases by a literature search expert and subsequently reviewed by a panel of 39 committee experts.The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) method was employed to assess the quality of evidence and develop and report the recommendations. The strength of each recommendation reflects the guideline panel's confidence that the benefits of an intervention outweigh its risks for the target population. After drafting the guidelines, Delphi questionnaires were distributed to members of the Korean Society of Head and Neck Surgery and an external reviewer panel. Results. In total, 15 evidence-based recommendations are provided, addressing six critical aspects of laryngeal cancer treatment: early-stage treatment, treatment of locally advanced cancer, neck management, adjuvant therapy, swallowing rehabilitation, and salvage treatment for recurrent tumors. Conclusion. The guidelines provide evidence-based, validated recommendations to support clinicians in making optimal treatment decisions for laryngeal cancer. ⓘ Jang, Jeon Yeob; Kim, Geun-Jeon; Kim, Sang-Yeon; Kim, Min-Su; Lee, Dong Kun; Kwon, Minsu; Ahn, Dongbin; Ban, Myung Jin; Kang, Young; Won, Ho-Ryun; Chang, Jae Won; Lee, Dong Won; Park, Ki Nam; Kim, Yeon Soo; Jung, Ah Ra; Seok, Jungirl; Lee, Hye Ran; Shin, Sung-Chan; Song, Chang Myeon; Lee, Gil Joon; Kwak, Jihye; Jung, Soo Yeon; Kim, Bo Hae; Lee, Dong-Hyun; Choi, Nayeon; Jung, Eun Kyung; Hong, Yong Tae; Kim, Hyun-Bum; Han, Seung Hoon; Kim, Su Il; Kim, Ji Won; Ryu, Yoon-Jong; Kim, Heejin; Park, Sung Joon; Park, Hanaro; Chung, Eun-Jae; Baek, Seung-Kuk; Park, Jun-Ook; Cho, Kwang-Jae ⓘ Ajou Univ, Sch Med, Dept Otorhinolaryngol Head & Neck Surg, Suwon, South Korea; Catholic Univ Korea, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, 222 Banpo Daero, Seoul 06591, South Korea; CHA Univ, CHA Bundang Med Ctr, Dept Otorhinolaryngol Head & Neck Surg, Sch Med, Seongnam, South Korea; Dong A Univ, Dept Otorhinolaryngol Head & Neck Surg, Coll Med, Busan, South Korea; Univ Ulsan, Coll Med, Asan Med Ctr, Dept Otorhinolaryngol Head & Neck Surg, Seoul, South Korea; Kyungpook Natl Univ, Sch Med, Dept Otorhinolaryngol Head & Neck Surg, Daegu, South Korea; Soonchunhyang Univ, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Cheonan, South Korea; ThanQ Seoul Ctr Thyroid Head & Neck Surg & Med, Dept Otorhinolaryngol Head & Neck Surg, Seoul, South Korea; Chungnam Natl Univ, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Daejeon, South Korea; Daegu Catholic Univ, Sch Med, Dept Otorhinolaryngol Head & Neck Surg, Daegu, South Korea; Soonchunhyang Univ, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Bucheon, South Korea; Korea Univ, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Seoul, South Korea; Eulji Univ, Nowon Eulji Med Ctr, Sch Med, Dept Otorhinolaryngol Head & Neck Surg, Seoul, South Korea; Seoul Natl Univ, Seoul Natl Univ Hosp, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Seoul, South Korea; Catholic Kwandong Univ, Int St Marys Hosp, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Incheon, South Korea; Pusan Natl Univ, Pusan Natl Univ Hosp, Sch Med, Dept Otorhinolaryngol Head & Neck Surg, Busan, South Korea; Hanyang Univ, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Seoul, South Korea; Ewha Womans Univ, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Seoul, South Korea; Dongguk Univ, Ilsan Hosp, Dept Otorhinolaryngol Head & Neck Surg, Goyang, South Korea; Sungkyunkwan Univ, Samsung Med Ctr, Sch Med, Dept Otorhinolaryngol Head & Neck Surg, Seoul, South Korea; Chonnam Natl Univ, Hwasun Hosp, Med Sch, Dept Otorhinolaryngol Head & Neck Surg, Hwasun, South Korea; Jeonbuk Natl Univ, Med Sch, Dept Otorhinolaryngol Head & Neck Surg, Jeonju, South Korea; Hallym Univ, Dongtan Sacred Heart Hosp, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Hwaseong, South Korea; Kyung Hee Univ, Kyung Hee Univ Hosp Gangdong, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Seoul, South Korea; Inha Univ, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Incheon, South Korea; Kangwon Natl Univ, Sch Med, Dept Otorhinolaryngol Head & Neck Surg, Chuncheon, South Korea; Hallym Univ, Sacred Heart Hosp, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Anyang, South Korea; Chung Ang Univ, Gwangmyeong Hosp, Coll Med, Dept Otorhinolaryngol Head & Neck Surg, Seoul, South Korea; Sungkyunkwan Univ, Samsung Changwon Hosp, Sch Med, Dept Otorhinolaryngol Head & Neck Surg, Changwon, South Korea ⓘ Kim, Bo Hae/KFR-4626-2024; Ban, Myung/B-4619-2016; Song, Chang Myeon/W-1761-2017; Lee, Dongwon/IAU-6865-2023; Park, Sung Joon/MTF-3122-2025; Kim, Min-Su/AAG-4346-2020; Chang, Jae/R-3511-2019 ⓘ 56720683500; 56532862800; 56014283000; 57203262505; 59899838200; 35215713500; 44761055400; 59941580600; 57201801332; 59818779700; 57226667021; 57202974901; 59602149200; 57207443122; 56921400200; 56487147900; 57220206365; 56611101600; 59469316100; 59753631300; 57393330600; 57189007888; 57051657400; 57216851693; 55601531900; 23397215700; 59941731300; 57222091827; 57192382634; 57213156597; 59087924800; 56054125500; 56984360400; 59941580700; 56645191100; 36810155000; 7201371667; 59924846900; 34867747900 ⓘ junook2000@catholic.ac.kr; entckj@catholic.ac.kr; ⓘ CLINICAL AND EXPERIMENTAL OTORHINOLARYNGOLOGY ⓘ CLIN EXP OTORHINOLAR ⓘ 1976-8710 2005-0720 18 2 SCIE OTORHINOLARYNGOLOGY ⓘ 2024 3.4 5.2 N/A ⓘ 0 0 Guidelines; Laryngeal Cancer; Laryngectomy; Organ Preservation; Neck Dissection ⓘ SQUAMOUS-CELL CARCINOMA; QUALITY-OF-LIFE; TRANSORAL LASER MICROSURGERY; SALVAGE TOTAL LARYNGECTOMY; LYMPH-NODE METASTASIS; EARLY GLOTTIC CANCER; LOCALLY RECURRENT HEAD; ELECTIVE LATERAL NECK; N0 NECK; ORGAN-PRESERVATION ⓘ Guidelines; Laryngeal Cancer; Laryngectomy; Neck Dissection; Organ Preservation ⓘ ⓘ English 2025 2025-05 10.21053/ceo.2025.00009 ⓘ 바로가기 바로가기 바로가기 바로가기
○ Article ⓘ Inflammation dynamics of atopic dermatitis: Phase transition and scaling law of remission time ⓘ Atopic dermatitis (AD) is a prevalent skin disorder affecting individuals globally, with many patients experiencing a range of symptoms. A pronounced clinical phenomenon associated with AD is the cyclic alternation of two distinct phases in time: inflammation and remission, depending on patients’ immune response and skin permeability. Frequent and relatively long inflammatory times lead to symptoms that can severely deteriorate the quality of life for the patient. Through mathematical modeling, we find that patients with similar AD symptoms can be categorized into two phases depending on the skin permeability and immune response that constitute the most clinically relevant parameter plane: the inflammatory time is shorter or longer than the remission time, respectively and the transition between the two phases is of the second-order type. In the parameter plane, a critical threshold curve emerges, which separates the two phases. Computing the frequency and duration of the inflammatory response, we uncover a logarithmic scaling law governing the inflammatory and remission times and discuss its clinical implications. In particular, when the skin condition is managed to be near the phase transition point, the benefits of treatment are more pronounced. However, at this stage, the effectiveness of skincare in reducing flare-ups tends to be less noticeable, making it difficult to evaluate the success of the treatment, largely due to the nature of logarithmic decay in the remission time. Our study provides insights into the mechanisms of AD that can enhance diagnostic accuracy and treatment by understanding the alternation between inflammation and remission periods. © 2025 The Authors ⓘ Kang, Yoseb; Hwang, Jaewoo; Jang, Yong Hyun; Lai, Ying-Cheng; Do, Younghae ⓘ Department of Mathematics, Institute for Future Earth, Pusan National University, Busan, 46241, South Korea; Department of Mathematics, Nonlinear Dynamics & Mathematical Application Center, Kyungpook National University, Daegu, 41566, South Korea; Department of Dermatology, School of Medicine, Kyungpook National University, Daegu, 41944, South Korea; School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, 85287, United States, Department of Physics, Arizona State University, Tempe, 85287, United States; Department of Mathematics, Nonlinear Dynamics & Mathematical Application Center, Kyungpook National University, Daegu, 41566, South Korea ⓘ ⓘ 57325111200; 58815350800; 59681580200; 7401512359; 7103101109 ⓘ yhdo@knu.ac.kr; ⓘ Computers in Biology and Medicine ⓘ COMPUT BIOL MED ⓘ 0010-4825 1879-0534 194 SCIE COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS;ENGINEERING, BIOMEDICAL;MATHEMATICAL & COMPUTATIONAL BIOLOGY;BIOLOGY ⓘ 2024 6.3 5.2 0 2025-06-11 ⓘ 0 ⓘ ⓘ Atopic dermatitis; Logarithm scaling law; Phase transition ⓘ Pathology; Setting; Sublimation; Atopic dermatitis; Immune response; Logarithm scaling law; Parameter plane; Quality of life; Scalings; Second orders; Skin disorders; Skin permeability; Two phase; Article; atopic dermatitis; clinical significance; disease association; human; law; logarithmic scaling law; nonlinear system; oscillation; phase transition; quality of life; remission; skin permeability; Dermatitis ⓘ English Final 2025 10.1016/j.compbiomed.2025.110391 ⓘ 바로가기 바로가기 바로가기
○ ○ Article ⓘ Manipulating subcellular protein localization to enhance target protein accumulation in minicells ⓘ BackgroundMinicells are chromosome-free derivatives of bacteria formed through irregular cell division. Unlike simplified structures, minicells retain all cellular components of the parent cell except for the chromosome. This feature reduces immunogenic responses, making them advantageous for various biotechnological applications, including chemical production and drug delivery. To effectively utilize minicells, it is essential to ensure the accumulation of target proteins within them, enhancing their efficiency as delivery vehicles.ResultsIn this study, we engineered Escherichia coli by deleting the minCD genes, generating minicell-producing strains, and investigated strategies to enhance protein accumulation within the minicells. Comparative proteomic analysis revealed that minicells retain most parent-cell proteins but exhibit an asymmetric proteome distribution, leading to selective protein enrichment. We demonstrated that heterologous proteins, such as GFP and RFP, accumulate more abundantly in minicells than in parent cells, regardless of expression levels. To further enhance this accumulation, we manipulated protein localization by fusing target proteins to polar localization signals. While proteins fused with PtsI and Tsr exhibited 2.6-fold and 2.8-fold increases in accumulation, respectively, fusion with the heterologous PopZ protein resulted in a remarkable 15-fold increase in protein concentration under low induction conditions.ConclusionsThese findings highlight the critical role of spatial protein organization in enhancing the cargo-loading capabilities of minicells. By leveraging polar localization signals, this work provides a robust framework for optimizing minicells as efficient carriers for diverse applications, from therapeutic delivery to industrial biomanufacturing. ⓘ Park, Junhyeon; Polizzi, Karen M.; Kim, Jongmin; Kim, Juhyun ⓘ Kyungpook Natl Univ, Sch Life Sci & Biotechnol, BK21 FOUR KNU Creat Biores Grp, Daegu, South Korea; Imperial Coll London, Dept Chem Engn, London, England; Imperial Coll London, Imperial Coll Ctr Synthet Biol, London, England; Pohang Univ Sci & Technol, Dept Life Sci, Pohang 37673, South Korea ⓘ ⓘ 57405717400; 10039266100; 57205477727; 55829164000 ⓘ juhyunkim@knu.ac.kr; ⓘ JOURNAL OF BIOLOGICAL ENGINEERING ⓘ J BIOL ENG ⓘ 1754-1611 19 1 SCIE BIOCHEMICAL RESEARCH METHODS;BIOTECHNOLOGY & APPLIED MICROBIOLOGY ⓘ 2024 6.5 5.2 0 2025-05-07 ⓘ 0 0 Minicells; Polar localization; PopZ ⓘ ESCHERICHIA-COLI MINICELLS; MEMBRANE-PROTEINS; TRANSPORTER PROP; BACTERIA; DIVISION; ORGANIZATION; POPZ; DYNAMICS; ENZYMES; SYSTEM ⓘ Minicells; Polar localization; PopZ ⓘ cell protein; green fluorescent protein; PopZ protein; recombinant protein; red fluorescent protein; unclassified drug; Article; cell division; cellular distribution; comparative proteomics; controlled study; Escherichia coli; nonhuman; protein engineering; protein expression; protein localization ⓘ English 2025 2025-03-29 10.1186/s13036-025-00495-y ⓘ 바로가기 바로가기 바로가기 바로가기
○ Article ⓘ Mathematical modeling of age-specific intervention strategies for latent tuberculosis infection ⓘ Tuberculosis (TB) remains a leading cause of death from a single infectious agent, with TB deaths in 2021 surpassing those from HIV/AIDS. In alignment with the WHO's End TB strategy, South Korea launched a TB elimination plan in 2006. Recognizing the significance of age-dependent factors, we developed a comprehensive mathematical model to analyze the age-specific transmission dynamics of TB, including latent tuberculosis infection (LTBI), from 2012 to 2021. Our previous research focused on age-specific TB dynamics and the effects of various interventions on reducing active TB cases within different age groups. In this study, we enhance our model to include both short and long latent TB cases, allowing for a more detailed investigation of age-specific LTBI interventions and their impact on TB reduction. Utilizing the Markov Chain Monte Carlo (MCMC) method within a Bayesian inferential framework, we estimated model parameters that closely align with actual data with high accuracy. Our results revealed that non-elderly individuals are more responsive to transmission rates, whereas elderly individuals are more influenced by LTBI treatment. Strengthening LTBI treatment interventions significantly reduce TB infectious cases, particularly among those aged 65 and older. Detection and treatment strategies specifically targeting LTBI in the elderly population, along with primary TB treatments, are essential for TB control. The Korean government aims to reduce the TB incidence rate to below 20 cases per 100,000 by 2027. Our results indicate that this goal can be achieved by enhancing LTBI detection rates and treatment compliance levels. Specifically, our findings suggest that improving LTBI treatment interventions could lower the TB incidence to 10 cases per 100,000 by 2030. This underscores the critical importance of implementing age-specific LTBI detection and treatment interventions to effectively decrease TB incidence in South Korea. © 2024 The Authors ⓘ Lee, Hyosun; Abbas, Wasim; Lee, Sieun; Kim, Sangil; Lee, Sunmi; Kwon, Jinwon ⓘ Department of Applied Mathematics, Kyung Hee University, Yongin, 17104, South Korea; Department of Mathematics, Pusan National University, Busan, South Korea; Department of Mathematics, Pusan National University, Busan, South Korea; Department of Mathematics, Pusan National University, Busan, South Korea; Department of Applied Mathematics, Kyung Hee University, Yongin, 17104, South Korea; BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy and Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu, South Korea ⓘ ⓘ 58945861400; 57871336100; 57972716100; 34978723300; 55716483800; 16202951700 ⓘ sunmilee@khu.ac.kr; ⓘ Computers in Biology and Medicine ⓘ COMPUT BIOL MED ⓘ 0010-4825 1879-0534 184 SCIE COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS;ENGINEERING, BIOMEDICAL;MATHEMATICAL & COMPUTATIONAL BIOLOGY;BIOLOGY ⓘ 2024 6.3 5.2 0 2025-05-07 ⓘ 0 ⓘ ⓘ Age-specific tuberculosis mathematical modeling; Latent tuberculosis infection; Markov Chain Monte Carlo method; WHO's End TB strategy ⓘ Adolescent; Adult; Age Factors; Aged; Bayes Theorem; Child; Child, Preschool; Female; Humans; Infant; Latent Tuberculosis; Male; Middle Aged; Models, Biological; Republic of Korea; Monte Carlo methods; Age-specific tuberculosis mathematical modeling; Causes of death; HIV/AIDS; Infectious agents; Intervention strategy; Latent tuberculosis infection; Markov chain Monte Carlo method; South Korea; Tuberculosis infection; WHO end tuberculosis strategy; age; Article; Bayes theorem; cause of death; disease transmission; groups by age; incidence; latent tuberculosis; Markov chain Monte Carlo method; mathematical model; patient compliance; South Korea; World Health Organization; adolescent; adult; age; aged; biological model; child; drug therapy; epidemiology; female; human; infant; male; middle aged; preschool child; Markov processes ⓘ English Final 2025 10.1016/j.compbiomed.2024.109377 ⓘ 바로가기 바로가기 바로가기
○ Article ⓘ Role of fractional derivatives in pharmacokinetic/pharmacodynamic anesthesia model using BIS data ⓘ In this paper, we investigate a pharmacokinetic/pharmacodynamic model for anesthesia to describe the effects of propofol and the impact of fractional derivatives. Using actual bispectral index data from surgical patients, we demonstrate how fractional-order models can more effectively capture the memory-dependent dynamics of anesthesia than traditional integer-order models. Model parameters are estimated using the trust region reflective algorithm, and numerical simulations employ the Adams-type predictor–corrector method. Comparative analysis across multiple patients reveals that the fractional-order model consistently provides a superior fit to bispectral index data, as indicated by lower prediction errors and reduced Akaike information criterion values. This study primarily aims to demonstrate the advantages of employing fractional derivatives for this specific data set, particularly in accounting for memory effects, which are crucial in capturing the prolonged effects of anesthetic agents. By incorporating actual bispectral index scale data and fractional derivatives, we significantly enhance the relevance and impact of this research, offering a more flexible and accurate model. Our findings highlight the superiority of fractional derivatives in capturing the complex, time-dependent dynamics of anesthetic drug effects, making it a more suitable modeling approach compared to traditional methods, with the potential for improved patient-specific anesthesia management. © 2025 ⓘ Vellappandi, Madasamy; Lee, Sangmoon ⓘ School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea ⓘ ⓘ 57226550675; 59510733500 ⓘ moony@knu.ac.kr; ⓘ Computers in Biology and Medicine ⓘ COMPUT BIOL MED ⓘ 0010-4825 1879-0534 187 SCIE COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS;ENGINEERING, BIOMEDICAL;MATHEMATICAL & COMPUTATIONAL BIOLOGY;BIOLOGY ⓘ 2024 6.3 5.2 0 2025-05-07 ⓘ 0 ⓘ ⓘ Bispectral index; Caputo fractional derivative; Mathematical model; Pharmacokinetics/pharmacodynamics; Predictor-corrector method ⓘ Algorithms; Anesthesia; Anesthetics, Intravenous; Computer Simulation; Electroencephalography; Female; Humans; Male; Models, Biological; Propofol; Anesthetics; Pharmacokinetics; benzodiazepine derivative; narcotic agent; neuromuscular blocking agent; propofol; vasodilator agent; intravenous anesthetic agent; Bispectral index; Caputo fractional derivatives; Fractional derivatives; Fractional-order models; Integer order; Pharmacokinetic pharmacodynamic models; Pharmacokinetic/pharmacodynamic; Predictor-corrector methods; Propofol; Surgical patients; adult; aged; algorithm; anesthesia; Article; bispectral index; comparative study; consciousness; continuous infusion; controlled study; drug clearance; drug distribution; electric potential; empiricism; female; general anesthesia; human; male; mathematical model; middle aged; pharmacodynamic parameters; pharmacokinetic parameters; rate constant; surgical patient; time to maximum plasma concentration; very elderly; biological model; computer simulation; electroencephalography; procedures; Numerical methods ⓘ English Final 2025 10.1016/j.compbiomed.2025.109783 ⓘ 바로가기 바로가기 바로가기
○ ○ Article ⓘ Soil holobiont interplay and its role in protecting plants against salinity stress ⓘ Salinity poses a significant challenge to global agricultural productivity, impacting plant growth, yield, soil fertility, and the composition of soil microbial communities. Moreover, salinity has a significant impact in shifting soil microbial communities and their functional profiles. Therefore, we explored and analyzed the intricate relationships among plant-associated microbes/microbiome, including plant growth-promoting bacteria, arbuscular mycorrhizal fungi (AMF), archaea, and viruses in alleviating salinity stress in plants. In this review, we have highlighted that salinity stress selectively enhances the growth of viruses, while decreasing the abundances of others (Alphaproteobacteria and Betaproteobacteria) and AMF root colonization. These microbes regulate water and nutrient uptake, decrease ionic and osmotic toxicity, enhance the syntheses of antioxidant enzymes (catalase and glutathione S-transferases) and osmolytes (erythrose and galactinol), increase phytohormone (indole-3 acetic acid) production, and activate salinity stress tolerance genes (SOD, APX, and SKOR) in plants. Furthermore, we meticulously examined the significance of soil microbiome and the need for multidisciplinary omics studies on the changes in soil microbiome composition and the relationships of synergistic holobiont in mitigating salinity stress in plants. Such studies will provide insights into the use of microbial components as a sustainable and eco-friendly approach to modulate salinity stress and enhance agricultural productivity. ⓘ Sliti, Amani; Singh, Vineet; Pande, Anjali; Shin, Jae-Ho ⓘ Kyungpook Natl Univ, Dept Appl Biosci, Daegu 41566, South Korea; Natl Inst Plant Genome Res, New Delhi 110067, India; Kyungpook Natl Univ, NGS Core Facil, Daegu 41566, South Korea; Kyungpook Natl Univ, Dept Integrat Biotechnol, Daegu 41566, South Korea ⓘ Pande, Anjali/ABH-2333-2021; shin, Jaeho/K-6792-2013; Singh, Vineet/ABC-7000-2021 ⓘ 58551490600; 57211642447; 57222624154; 57224125922 ⓘ jhshin@knu.ac.kr; ⓘ PEDOSPHERE ⓘ PEDOSPHERE ⓘ 1002-0160 2210-5107 35 1 SCIE SOIL SCIENCE ⓘ 2024 7.3 5.2 7.01 2025-05-07 ⓘ 3 3 agricultural productivity; microbial communities; multiomics approaches; plant-microbe interaction; stress tolerance ⓘ ARBUSCULAR MYCORRHIZAL FUNGI; GROWTH-PROMOTING BACTERIA; SALT-STRESS; TOLERANCE; ARCHAEA; EQUILIBRIUM; ALLEVIATION; RESPONSES; FRUCTANS; HORMONES ⓘ agricultural productivity; microbial communities; multiomics approaches; plant-microbe interaction; stress tolerance ⓘ agricultural production; antioxidant; bacterium; enzyme activity; microbial community; phytohormone; root colonization; salinity tolerance; soil microorganism; virus ⓘ English 2025 2025-02 10.1016/j.pedsph.2024.09.002 ⓘ 바로가기 바로가기 바로가기 바로가기
○ Article ⓘ STCNet: Spatio-Temporal Cross Network with subject-aware contrastive learning for hand gesture recognition in surface EMG ⓘ This paper introduces the Spatio-Temporal Cross Network (STCNet), a novel deep learning architecture tailored for robust hand gesture recognition in surface electromyography (sEMG) across multiple subjects. We address the challenges associated with the inter-subject variability and environmental factors such as electrode shift and muscle fatigue, which traditionally undermine the robustness of gesture recognition systems. STCNet integrates a convolutional-recurrent architecture with a spatio-temporal block that extracts features over segmented time intervals, enhancing both spatial and temporal analysis. Additionally, a rolling convolution technique designed to reflect the circular band structure of the sEMG measurement device is incorporated, thus capturing the inherent spatial relationships more effectively. We further propose a subject-aware contrastive learning framework that utilizes both subject and gesture label information to align the representation of vector space. Our comprehensive experimental evaluations demonstrate the superiority of STCNet under aggregated conditions, achieving state-of-the-art performance on benchmark datasets and effectively managing the variability among different subjects. The implemented code can be found at https://github.com/KNU-BrainAI/STCNet. © 2024 Elsevier Ltd ⓘ Yang, Jaemo; Cha, Doheun; Lee, Dong-Gyu; Ahn, Sangtae ⓘ School of Electronics Engineering, Kyungpook National University, Daegu, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea; Department of Artificial Intelligence, Kyungpook National University, Daegu, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea ⓘ ⓘ 59466797100; 59466931300; 57169003900; 55468016100 ⓘ stahn@knu.ac.kr; ⓘ Computers in Biology and Medicine ⓘ COMPUT BIOL MED ⓘ 0010-4825 1879-0534 185 SCIE COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS;ENGINEERING, BIOMEDICAL;MATHEMATICAL & COMPUTATIONAL BIOLOGY;BIOLOGY ⓘ 2024 6.3 5.2 3.84 2025-05-07 ⓘ 2 ⓘ ⓘ Contrastive learning; Convolutional neural networks; Hand gesture recognition; Subject awareness; Surface electromyography ⓘ Deep Learning; Electromyography; Gestures; Hand; Humans; Signal Processing, Computer-Assisted; Gesture recognition; Palmprint recognition; Convolutional neural network; Cross networks; Environmental factors; Hand-gesture recognition; Learning architectures; Shift-and; Spatio-temporal; Subject awareness; Surface electromyography; Surface EMG; accuracy; Article; classification; controlled study; convolutional neural network; deep learning; feature extraction; Gaussian noise; gesture; hand; muscle fatigue; random forest; recognition; spatial analysis; surface electromyography; temporal analysis; time interval; deep learning; electromyography; human; physiology; procedures; signal processing; Convolutional neural networks ⓘ English Final 2025 10.1016/j.compbiomed.2024.109525 ⓘ 바로가기 바로가기 바로가기
○ ○ Article ⓘ Supplementation of Parachlorella sp. in feed promote the gut microbiome colonization and fecal IgA response of broiler in both early and late period☆ ⓘ This study evaluated the effects of Parachlorella sp. KSN1 (PA) supplementation on the gut microbiota and intestinal immunity of broilers of different ages. A total of 180 Ross 308 broiler chicks were weighed and divided into early (1 to 10 days post hatch) and late (11 to 28 days post hatch) periods, with six replicates of 10 chicks per cage assigned to two dietary groups. The experimental diets included a corn-soybean meal-based control diet and a treatment diet supplemented with 0.5% PA, replacing corn or corn starch, and fed ad libitum for the assigned experimental period. On days 10 and 28, two broilers from each of the six replicate cages, with 7 broilers per cage in each group, were selected and euthanized, and cecal feces and intestinal tissue samples were collected. PA supplementation did not significantly affect broilers growth performance during both the early and the late periods. However, PA supplementation altered the cecal microbiome, with Clostridiaceae and Clostridium exhibiting prominent and consistent changes. In terms of intestinal immunity, PA supplementation significantly increased the number of CD3+ and CD4+ T cells when administered only during the early period. Cecal IgA levels were significantly increased by PA supplementation during both the early and late periods. A significant positive correlation was observed between IgA, Clostridiaceae and Clostridium during the early and late periods. Gene expression analysis identified 40 upregulated genes, including polymeric immunoglobulin receptor (pIgR), and 142 downregulated genes, including marginal zone B and B1 cell specific protein and immunoglobulin lambda-like polypeptide 1 that were associated with the IgA response in PA-treated broilers during the early period. This study demonstrated that PA supplementation promotes gut microbial colonization and intestinal immunity development during the early age of broilers. These findings suggest that the early growth period of broilers is the optimal time for dietary immunomodulation to promote gut health in broilers. ⓘ Ji, Woonhak; Kim, Tae-Yong; Lee, Chae Won; Kim, Z-Hun; Jung, Ji Young; Ban, Byeong Cheol; Kong, Changsu; Kim, Myunghoo ⓘ Pusan Natl Univ, Coll Nat Resources & Live Sci, Dept Anim Sci, Miryang 50463, South Korea; Kyungpook Natl Univ, Dept Anim Sci & Biotechnol, Sangju 37224, South Korea; Hu Evergreen Pharm Corp, 164 Yeorumul Ro, Incheon 21445, South Korea; Nakdonggang Natl Inst Biol Resources NNIBR, Biol Resources Res Dept, Sangju 37242, South Korea; Pusan Natl Univ, Life & Ind Convergence Res Inst, Miryang 50463, South Korea; Kyungpook Natl Univ, Dept Anim Sci, Sangju 37224, South Korea; Kyungpook Natl Univ, Res Inst Innovat Anim Sci, Sangju 37224, South Korea; Pusan Natl Univ, PNU JYS Sci Acad, Future Earth Res Inst, Busan 46241, South Korea ⓘ Kim, Seong-Hun/ABF-2927-2020; Kim, Taeyong/ABG-4877-2020 ⓘ 58929858300; 57222744974; 59873229500; 59818615500; 58915180600; 57995927600; 36027521600; 36611749600 ⓘ changsukong@knu.ac.kr; mhkim18@pusan.ac.kr; ⓘ POULTRY SCIENCE ⓘ POULTRY SCI ⓘ 0032-5791 1525-3171 104 1 SCIE AGRICULTURE, DAIRY & ANIMAL SCIENCE ⓘ 2024 4.2 5.2 7.04 2025-05-07 ⓘ 1 1 Broiler; Microalgae; Gut health; Gut microbiome; Gut immunity ⓘ GROWTH-PERFORMANCE; DIETARY SUPPLEMENTATION; CLOSTRIDIUM-PERFRINGENS; GASTROINTESTINAL-TRACT; INTESTINAL MICROFLORA; IMMUNE-RESPONSE; INNATE IMMUNITY; MICROALGAE; DIVERSITY; BACTERIA ⓘ Broiler; Gut health; Gut immunity; Gut microbiome; Microalgae ⓘ Animal Feed; Animals; Chickens; Diet; Dietary Supplements; Feces; Gastrointestinal Microbiome; Immunoglobulin A; Male; Random Allocation; immunoglobulin A; animal; animal food; diet; dietary supplement; drug effect; feces; Gallus gallus; growth, development and aging; immunology; intestine flora; male; metabolism; microbiology; randomization; veterinary medicine ⓘ English 2025 2025-01 10.1016/j.psj.2024.104572 ⓘ 바로가기 바로가기 바로가기 바로가기
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