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Tumor-derived interleukin-1 receptor antagonist exhibits immunosuppressive functions and promotes pancreatic cancer

Cell and Bioscience 2023年第1期13卷 1页

作者： Fan, Yu-Ching Fong, Yu-Cin Kuo, Chun-Tse Li, Chia-Wei Chen, Wei-Yu Lin, Jian-da Bürtin, Florian Linnebacher, Michael Bui, Quoc Thang Lee, Kuan-der Tsai, Yuan-Chin Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University and Academia Sinica Taipei Taiwan Graduate Institute of Cancer Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan Institute of Biomedical Sciences Academia Sinica Taipei Taiwan Department of Pathology Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Pathology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Biochemical Science and Technology College of Life Science National Taiwan University Taipei City 10617 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei City 10617 Taiwan Clinic of General Surgery University Medical Center Rostock Schillingallee 35 Rostock 18057 Germany Clinic of General Surgery Molecular Oncology and Immunotherapy University Medical Center Rostock Schillingallee 69 Rostock 18057 Germany International Ph.D. Program for Cell Therapy and Regeneration Medicine (IPCTRM) School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Post-Baccalaureate Medicine College of Medicine Natioanl Chung Hsing University Taichung Taiwan Cell Therapy and Regenerative Medicine Center and Comprehensive Cancer Center Taichung Veterans General Hospital Taichung Taiwan Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan

Background: Pancreatic ductal adenocarcinoma (PdA) is a pernicious disease characterized by an immunosuppressive milieu that is unresponsive to current immunotherapies. Interleukin-1 receptor antagonist (IL-1Ra) is a natural anti-inflammatory cytokine;however, its contribution to cancer pathogenesis and immunosuppression remains elusive. In this research, we investigated the role and mechanism of IL-1Ra in malignant progression of PdA. Results: Through analyzing clinical dataset and examining the pathological tumor tissues and serum samples, we have demonstrated that IL-1Ra expression is elevated in human PdA and positively associated with malignant progression of PdA. To study the biological function of IL-1Ra in tumors, we generated a set of mouse pancreatic cancer cell lines with a knockout (KO) of the Il1rn gene, encoding IL-1Ra, and compared the tumor growth rates in immune-competent and immune-deficient mice. We found that the Il1rn KO cells exhibited greater tumor inhibition in immune-competent mice, highlighting the crucial role of a functional immune system in Il1rn KO-mediated anti-tumor response. Consistently, we found an increase in Cd8⁺ T cells and a decrease in Cd11b⁺Ly6G⁻ immunosuppressive mononuclear population in the tumor microenvironment of Il1rn KO-derived tumors. To monitor the inhibitory effects of IL-1Ra on immune cells, we utilized a luciferase-based reporter Cd4⁺ T cell line and splenocytes, which were derived from transgenic mice expressing ovalbumin-specific T cell receptors in Cd8⁺ T cells, and mice immunized with ovalbumin. We showed that IL-1Ra suppressed T cell receptor signaling and inhibited antigen-specific interferon-γ (IFN-γ) secretion and cytolytic activity in splenocytes. Conclusions: Our findings illustrate the immunosuppressive properties of the natural anti-inflammatory cytokine IL-1Ra, and provide a rationale for considering IL-1Ra-targeted therapies in the treatment of

关键词： IFN-γ Immunosuppression interleukin-1 receptor antagonist Pancreatic adenocarcinoma Patient-derived xenograft Tumor microenvironment

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Revolutionary Energy Harvesting: Gravity-driven Piezocatalysts for Sustainable Hydrogen Production in MoS2@Mo2CTx systems

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Advanced Energy Materials 2024年第39期14卷

作者： Lin, Hsun-Yen Wu, Jyh Ming Department of Materials Science and Engineering National Tsing Hua University 101 Section 2 Kuang Fu Road Hsinchu300 Taiwan Ph.D Program in Prospective Functional Materials Industry National Tsing Hua University 101 Section 2 Kuang Fu Road Hsinchu300 Taiwan High Entropy Materials Center National Tsing Hua University 101 Section 2 Kuang Fu Road Hsinchu300 Taiwan

Hydrogen (H2) is mainly produced using steam methane reforming, electrolysis, and gasification, which require external energy and special catalysts. A new catalyst by combining MoS2 nanoflowers (NFs) with metal carbide/nitride nanosheets (Mo2CTx MXene) to create a nanosheet bending moment. The MoS2@Mo2CTx heterostructures achieve a production rate of 1164.8 µmol g−1 h−1 under an application of mechanical force, 4.01 and 3.06 times higher than Mo2CTx and MoS2 alone, due to enhanced charge transfer from MoS2's piezoelectricity and Mo2CTx's conductivity. This study introduces a pioneering methodology that harnesses gravitational energy as a continuous mechanical force, simulated using a peristaltic pump, to drive the piezocatalytic hydrogen evolution reaction (HER), achieving a notable hydrogen production rate of 454.1 µmol g−1 over 24 hours and demonstrating a sustained capability for hydrogen generation. The theoretical calculation results validate the piezoelectric potential in water-flow-pressure triggered HER systems. The piezocatalytic HER system, assuming powered by the Hoover dam, will produce 290.9 kmoles of hydrogen per ton daily, equivalent to utilizing 19 150 kWh of energy in the electrocatalytic system. The simulated gravity-driven water flow using MoS2@Mo2CTx piezocatalysts for H2 generation demonstrates superior efficiency by eliminating common thermal energy conversion losses, marking a significant breakthrough in sustainable hydrogen production technologies. © 2024 Wiley-VCH GmbH.

关键词： Molybdenum compounds

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Automated Classification of Medical Images Using Convolutional Neural Networks

Automated Classification of Medical Images Using Convolution...

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International Conference on Computing and Networking Technology (ICCNT)

作者： N. Muthuvairavan Pillai S Manimala Aruna Sri Rongali Anchit Gugnani A Senthil Kumar G. V. Sriramakrishnan Computer Science and Business Systems R.M.D Engineering College Kavarapettai Thiruvallur district Computer science and engineering University SJCE JSS Science and technology University Mysore Karnataka India Department Information Technology Emphasis/Information Technology Ph.D University and city with the country University of The Cumberlands US Physiotherapy Amity University Jaipur Rajasthan Artificial Intelligent department Shri Vishnu Engineering College for Women Bhimavaram AP Department Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort.

关键词： Training Image segmentation Accuracy Convolution Medical services Feature extraction Convolutional neural networks Medical diagnostic imaging X-ray imaging Image classification

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Extraction of c- F os Protein Regions from Mouse Brain Slice Images using Multi-channelization, 1 x 1 Convolution and U -Net

Extraction of c- F os Protein Regions from Mouse Brain Slice...

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Ziyue Wang Hibiki Okamura Shijie Luo Yu Hayashi Hotaka Takizawa Master's Program in Computer Science University of Tsukuba Tsukuba Japan Ph.D. Program in Humanics International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering Academic Computing & Communications Center University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This study proposed a novel extraction method of c-Fos protein regions in dAB(3,3‘-diaminobenzidine)-stained mouse brain slice images using the U-Net model combined with the multi-channelization and $1\times 1$ convolution techniques. Our U-Net m odel, called $1\times 1$ conv U-Net, was applied to three dAB- strainedmouse slice images whose resolutions were $1000\mathrm{x}1000$ pixels. The experimental results demonstrated that the $1\times 1$ conv U-Net outperformed the conventional U-Net.

关键词： Proteins Visualization Image resolution Image analysis Convolution Feature extraction Brain modeling Biology Biomedical imaging

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Evaluation of Fatigue and Attention Levels in Multi-target Scenario using CNN

Evaluation of Fatigue and Attention Levels in Multi-target S...

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2020 International Computer Symposium, ICS 2020

作者： Sandeep Vara Sankar, d Ko, Li-Wei National Chiao Tung University International Ph.D Program in Interdisciplinary Neuroscience Dept. of Biological Science and Technology Hsinchu Taiwan National Chiao Tung University Center for Intelligent Drug Systems and Smart Bio-Devices Department of Biological Science and Technology Hsinchu Taiwan

ISBN: (纸本)9781728192550

Fatigue is a behavioral phenomenon that occurs when a user conducts focused mental activity for a prolonged duration resulting in performance errors or lapse. This paper presents an electroencephalogram (EEG)-based fatigue and attention level detection using 2-dimensional convolutional neural networks (2d-CNN). The experimental paradigm was designed to identify and detect a target object from a multi- target scenario. The results show that our proposed model provides a classification accuracy of 86.12%, which is ~18%, ~16% and ~10% higher than the Bayesian linear discriminant analysis (BLdA), support vector machine (SVM) and bootstrap aggregating (bagging tree) algorithms. Further, the decreased session-wise accuracy levels observed for each subject after the 4th experimental session postulates cognitive state disparities were caused due to increased fatigue and dropped attention levels. These biomarkers were assessed by comparing the resting theta and alpha band powers with the rapid serial visual presentation (RSVP) performance of the later sessions (sessions 5 to 7). The results show an inverse relationship between the RSVP classification performance and the resting EEG power, validating that the subjects' performance is affected by the physiological state biomarkers like fatigue and attention for prolonged brain-computer interface (BCI) experiments. Our findings demonstrate that the resting theta and alpha band powers can be considered as indicative measures to interpret mental fatigue and attention deficit problems. © 2020 IEEE.

关键词： Brain computer interface

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Association of Long-Term Ozone Exposure with the Incidence and Progression of Hypertension, diabetes, and Chronic Kidney disease: A National Retrospective Cohort Study

SSRN

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SSRN 2024年

作者： Li, Yu-Ling Chang, Po-Ya Chuang, Ting-Wu Hsieh, Yi-Chen Wang, Bo-Sian Chen, Szu-Ying Chiou, Hung-Yi Institute of Population Health Sciences National Health Research Institutes Miaoli County Zhunan Taiwan Department of Leisure Industry and Health Promotion National Taipei University of Nursing and Health Sciences Taipei Taiwan Department of Molecular Parasitology and Tropical Diseases School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Ph.D Program of Neural Regenerative Medicine College of Medical Science and Technology Taipei Medical University Taipei Taiwan Master Program in Applied Epidemiology College of Public Health Taipei Medical University Taipei Taiwan

Emerging evidence indicates particulate matter and nitrogen oxides are associated with increased risk of transition from healthy state to incident hypertension, diabetes, or chronic kidney disease (CKd) in the dynamic progression of these three chronic diseases. However, across different dynamic disease trajectories the associations of gaseous pollutants such as ozone remain unknown. This study aimed to investigate the long-term effects of exposure to ambient ozone on dynamic progression to multimorbidity, and consequent death from these diseases, with consideration of other air pollutants. This study included 9,256,945 participants from Taiwan’s National Health Insurance Research database between 2006 and 2021. Multimorbidity was defined as the coexistence of CKd and either hypertension or diabetes. The ordinary kriging method was used to estimate daily concentrations of ozone, sulfur dioxide, carbon monoxide, nitrogen dioxide, suspended fine particles, and suspended particles. We performed multi-state models to analyze the association between ambient air pollutants and dynamic progression of hypertension, diabetes, and CKd. during follow-up, 3,555,498 participants experienced hypertension, diabetes, or CKd;656,515 experienced multimorbidity;and 792,555 died. Ozone exposure was significantly associated with incidence of the results in all transitions. The hazard ratios of each IQR (3.57 ppb) increment in ozone for the transition to incident disease were 1.016 [95% confidence interval (CI): 1.014, 1.017], for the transition to death were 1.04 [95% CI: 1.036, 1.043], for the transition to multimorbidity were 1.015 [95% CI: 1.012, 1.017] after adjusting other risk factors. Furthermore, with each IQR increase of ozone, the hazard ratios for transition from the incidence disease to death and from multimorbidity to death were 1.03 [95% CI: 1.026, 1.033] and 1.007 [95% CI: 1.002, 1.013], respectively. Our results suggest that long-term exposure to ozone might be an import

关键词： Health insurance

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diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG systems Review

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IEEE Journal of Biomedical and Health Informatics 2023年第8期27卷 3830-3843页

作者： He, Congying Chen, Yu-Yi phang, Chun-Ren Stevenson, Cory Chen, I-Ping Jung, Tzyy-Ping Ko, Li-Wei Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Applied Arts National Yang Ming Chiao Tung University Hsinchu30010 Taiwan International Ph.D. Program in Interdisciplinary Neuroscience National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San DiegoCA90093 United States National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Biomedical Science and Environment Biology Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung80708 Taiwan

Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FdA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems. © 2023 IEEE.

关键词： Electroencephalography

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Research on data Security and Privacy of Smart Grids

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International Journal of Network Security 2024年第5期26卷 901-910页

作者： Hwang, Min-Shiang Chang, Yung-Ling Lin, Ko-Yu Lin, Iuon-Chang Yang, Cheng-Ying Department of Computer Science & Information Engineering Asia University 500 Lioufeng Rd. Wufeng Taichung Taichung41354 Taiwan Fintech and Blockchain Research Center Asia University 500 Lioufeng Rd. Wufeng Taichung Taichung41354 Taiwan The Ph.D. Program in Artificial Intelligence Asia University Taiwan Department of Management Information Systems National Chung Hsing University 500 Lioufeng Rd. Wufeng Taichung Taichung41354 Taiwan Department of Computer Science University of Taipei Taipei Taiwan

developing a smart grid is a trend nowadays and an indispensable basic necessity of life. In recent years, the electricity demand has risen rapidly due to economic development and quality of life improvement. The power grid in significant cities is unstable due to old equipment, and power outages occur frequently. The smart grid can adjust the grid’s distribution strategy through the consumer’s real-time electricity demand so that the consumer’s electricity consumption data is always transmitted to the grid. Failure to protect the privacy and security of such information will expose consumers’ habits and even make the grid unnecessarily wasteful. The smart grid’s abnormality detection efficiency determines the overall grid’s stability. Failure to detect anomalies or power theft in the grid promptly will not only lead to the selection of the wrong distribution strategy for the smart grid but also reduce the transmission efficiency of the grid. This research ensures the privacy and security of transmitted data to enhance the efficiency of abnormality detection in the smart grid. The research issues are divided into privacy of transmitted data, data security authentication, and anomaly detection efficiency. © (2024), (International Journal of Network Security). All rights reserved.

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determinant of importance-performance and willingness to participate in Komodo adaptive conservation programs

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Journal for Nature Conservation 2024年 81卷

作者： Sianipar, Imelda Masni Juniaty Lee, Chun-Hung Wang, Hurng-Jyuhn Kim, doo-Chul Suryawan, I Wayan Koko PhD Student in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of International Relations Faculty of Social Science and Political Science Universitas Kristen Indonesia Department of Natural Resources and Environmental Studies College of Environmental Studies and Oceanography National Dong Hwa University Program in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of Rural Environment Management Graduate School of Environmental and Life Science Okayama University Ph.D Program in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of Environmental Engineering Faculty of Infrastructure Planning Universitas Pertamina Jalan Sinabung II Terusan Simprug

This study delves into the evaluation of the Komodo dragons Adaptive Conservation program (KACP) at Komodo National Park in Indonesia through Importance-Performance Analysis (IPA). It aims to understand how the programs components align with community priorities and assess its effectiveness. Utilizing a mixed-methods approach, this research analyzes community perceptions of KACP's critical elements, spanning eco-tourism initiatives to environmental protection strategies. The community is categorized into local advocates, eco-tourism enthusiasts, and eco-community harmonizers, identifying how each group perceives and interacts with various facets of KACP. The findings reveal significant differences in perceptions of importance and performance among these groups, providing insights into areas of strength and those needing improvement. Additionally, the study includes an analysis of demographic influences on these perceptions through Logit and Probit models, aiming to understand the impacts across different community segments. The results highlight the necessity for an adaptive and responsive design in conservation efforts, considering community expectations and ecological impacts. despite offering comprehensive insights, the study acknowledges limitations in the demographic scope and suggests future research directions, such as longitudinal studies and an expanded analysis of variables. These findings offer critical guidance for policymakers and conservation practitioners to enhance conservation programs’ effectiveness and community alignment, emphasizing the need to adapt to the challenges and changes introduced by KACP. © 2024 Elsevier GmbH

关键词： Adaptive capacity Community perception Conservation strategy evaluation Importance-performance analysis Protected areas

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Unveiling Multivariate EEG Features: A Novel Approach to Enhancing AdHd diagnosis Through Visual and Auditory Attention Tests

Unveiling Multivariate EEG Features: A Novel Approach to Enh...

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International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Zuo-Cian Fan Ro-Wei Lin Ching-Shu Tsai Wen-Jiun Chou Chia-Jung Li Zih-Jun Huang Bin-Yu Shih Liang-Jen Wang Li-Wei Ko Department of Biological Science & Technology Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan International Ph.D. Program in Interdisciplinary Neuroscience National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Child and Adolescent Psychiatry Kaohsiung Chang Gung Memorial Hospit Department of Electronics and Electrical Engineering Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

We sought to revolutionize the classification of Attention-deficit/Hyperactivity disorder (AdHd) by pioneering an innovative approach that seamlessly integrated auditory and visual tests with electroencephalogram (EEG) coherence analysis, forming the foundation for our Support Vector Machine (SVM) models. Our primary objective was to achieve precise differentiation between individuals with AdHd and those without, aiming to reshape the landscape of AdHd diagnosis and early intervention. We constructed an SVM model based on CATA coherence, incorporating a carefully selected set of 180 features, and another grounded in CPT coherence with 70 features. Astonishingly, our exploration uncovered that a model incorporating CATA's coherence and Power Spectral density (PSd) as features achieved an astounding accuracy rate of 97.7%, using only half the number of features. This revelation hints at untapped potential for multivariate advancements in the realm of AdHd classification. The landscape of AdHd diagnosis and intervention is perpetually evolving, most notably with the FdA's approval of game therapy for AdHd. Leveraging the transformative power of our findings, we envision the development of user-friendly game therapies for in-home use, empowering both clinicians and parents alike. This marks a paradigm shift in the realm of AdHd intervention, offering objective, data-driven tools for the early management of AdHd. Gamified therapy not only enhances accessibility but also fosters engagement and compliance, all of which are pivotal in the long-term care of individuals with AdHd. In summary, our study represents a significant advancement in our comprehension and classification of AdHd. By fusing auditory and visual tests with EEG coherence analysis, fortified by state-of-the-art machine learning techniques, we have achieved an unprecedented level of accuracy in identifying individuals with AdHd. Our findings, aligned with the FdA-approved game therapy, offer a glimpse into a

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