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Photons Plus Ultrasound: Imaging and Sensing 2025

作者： Ogawa, Ryuhei Sato, Naoto Saito, Atsushi Singh, Mithun Kuniyil Ajith Kawamoto, Hiroaki Sankai, Yoshiyuki Master’s Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Tsukuba Japan Research & Development Division Cyberdyne Inc. Tsukuba Japan Research & Business Development Division Cyberdyne Inc. Rotterdam Netherlands Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781510683860

LED-based photoacoustic imaging (PAI) with linear array ultrasound probes has gained attention due to its cost-effectiveness, safety, and potential for portable applications. However, its low signal-to-noise ratio (SNR) and limited visibility of blood vessel lumens remain challenges. To address these issues, we propose an innovative deconvolution approach that leverages the differential of the LED light pulse to enhance photoacoustic image quality. Our optimized reconstruction algorithm improves blood vessel visualization by applying deconvolution of the PA signals with the differential of the LED pulse, leading to improved lumen visibility and enhanced imaging of peripheral blood vessels in both 2D and potentially 3D. Our method precisely measures the LED pulse profile using the LED arrays integrated into our AcousticX system. The second differential of this pulse is then utilized as the point spread function (PSF) in the deconvolution process. This technique effectively enhances PA signals by emphasizing the leading and trailing edges of the pulse, thereby improving blood vessel visibility and overall image quality. To validate our approach, we conducted experiments using tissue-mimicking phantoms and human volunteers with the AcousticX system. The algorithm was tested offline in MATLAB and in real-time on the AcousticX platform. Results demonstrate substantial artifact suppression, improved SNR, and enhanced image contrast, ultimately enabling clearer visualization of the blood vessels. This advancement improves the competitiveness of LED-based PAI systems compared to laser-based alternatives and broadens their potential for clinical applications, accelerating their translation into medical practice. © 2025 SPIE.

关键词： Ultrasonic imaging

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Cell-specific Nav1.6 knockdown reduced astrocyte-derived Aβ by reverse Na⁺-Ca²⁺ transporter-mediated autophagy in alzheimer-like mice

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Journal of Advanced research 2025年 72卷 451-466页

作者： Wang, Xin Wu, Wei Yang, Guang Yang, Xue-Wei Ma, Xu Zhu, Dan-Dan Ahmad, Kabir Khan, Khizar Wang, Ying-Zi Sui, Ao-Ran Guo, Song-Yu Kong, Yue Yuan, Bo Luo, Tian-Yuan Liu, Cheng-Kang Zhang, Peng Zhang, Yue Li, Qi-Fa Wang, Bin Wu, Qiong Wu, Xue-Fei Xiao, Zhi-Cheng Ma, Quan-Hong Li, Shao Department of Physiology College of Basic Medical Sciences Liaoning Provincial Key Laboratory of Cerebral Diseases National-Local Joint Engineering Research Center for Drug-Research and Development (R&D) of Neurodegenerative Diseases Dalian Medical University Liaoning Dalian 116044 China Department of Thoracic Surgery Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Institute of Neuroscience & Jiangsu Key Laboratory of Neuropsychiatric Diseases Suzhou 215123 China Department of Neurology and Clinical Research Center of Neurological Disease the second affilitated Hospital of Soochow University Jiangsu Suzhou 215021 China Development and Stem Cells Program Monash Biomedicine Discovery Institute and Department of Anatomy and Developmental Biology Monash University Melbourne 3800 VIC Australia

Introduction: Nav1.6 is closely related to the pathology of Alzheimer's Disease (AD), and astrocytes have recently been identified as a significant source of β-amyloid (Aβ). However, little is known about the connection between Nav1.6 and astrocyte-derived Aβ. Objective: This study explored the crucial role of Nav1.6 in mediated astrocyte-derived Aβ in AD and knockdown astrocytic Nav1.6 alleviates AD progression by promoting autophagy and lysosome-APP fusion. Methods: A mouse model for astrocytic Nav1.6 knockdown was constructed to study the effects of astrocytic Nav1.6 on amyloidosis. The role of astrocytic Nav1.6 on autophagy and lysosome-APP(amyloid precursor protein) fusion was used by transmission electron microscope, immunostaining, western blot and patch clamp. Glial cell activation was detected using immunostaining. Neuroplasticity and neural network were assessed using patch-clamp, Golgi stain and EEG recording. Behavioral experiments were performed to evaluate cognitive defects. Results: Astrocytic Nav1.6 knockdown reduces amyloidosis, alleviates glial cell activation and morphological complexity, improves neuroplasticity and abnormal neural networks, as well as promotes learning and memory abilities in APP/PS1 mice. Astrocytic Nav1.6 knockdown reduces itself-derived Aβ by promoting lysosome- APP fusion, which is related to attenuating reverse Na⁺-Ca²⁺ exchange current thus reducing intracellular Ca²⁺ to facilitate autophagic through AKT/mTOR/ULK pathway. Conclusion: Our findings unveil the crucial role of astrocyte-specific Nav1.6 in reducing astrocyte-derived Aβ, highlighting its potential as a cell-specific target for modulating AD progression. © 2024

关键词： Alzheimer's disease Amyloid precursor protein Astrocytes Autophagy-lysosome pathway Sodium channels Nav1.6 β-amyloid

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The design and evaluation of an AR-based serious game to teach programming

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Computers and Graphics (Pergamon) 2022年 103卷 1-18页

作者： Sharma, Vandit Bhagat, Kaushal Kumar Huang, Huai-Hsuan Chen, Nian-Shing Department of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur Kharagpur721302 India Advanced Technology Development Centre Indian Institute of Technology Kharagpur Kharagpur721302 India Institute for Research Excellence in Learning Sciences Program of Learning Sciences National Taiwan Normal University Taiwan

The ubiquity of smartphone and tablet devices, combined with the increasing availability of serious games, has enabled students to learn various abstract concepts in an appealing and convenient manner. While several researchers have explored the use of Augmented Reality (AR) in serious games, many of these games have not been critically explained or evaluated. To that end, we employed game-based learning methodologies and Game Learning Analytics (GLA) to systematize the design and evaluation of an AR-based serious game to teach programming. We evaluated our game for usability and effectiveness by conducting a user study on twenty-seven undergraduate students. The evaluation primarily consisted of a learning test conducted twice – before and after playing the game – along with a usability questionnaire that players completed after playing the game. Our results showed that players made significant progress after playing the game. The game helped players improve their basic programming skills, especially for the group having lower prior programming skills. The results highlighted various ways in which GLA can be used to benefit different stakeholders in the game. Based on players’ qualitative responses, we also identified several areas of improvement, most prominently the trade-off between ease of use and game complexity. We provide suggestions and discuss implications for future work. © 2022 Elsevier Ltd

关键词： Serious games

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Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention

Predicting Outcomes in Long COVID Patients with Spatiotempor...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Degan Hao Mohammadreza Negahdar Intelligent Systems Program University of Pittsburgh Pittsburgh USA Research and Early Development Genentech Inc. San Francisco USA

Long COVID is a general term of post-acute sequelae of COVID-19. Patients with long COVID can endure long-lasting symptoms including fatigue, headache, dyspnea and anosmia, etc. Identifying the cohorts with severe long-term complications in COVID-19 could benefit the treatment planning and resource arrangement. However, due to the heterogeneous phenotype presented in long COVID patients, it is difficult to predict their outcomes from their longitudinal data. In this study, we proposed a spatiotemporal attention mechanism to weigh feature importance jointly from the temporal dimension and feature space. Considering that medical examinations can have interchangeable orders in adjacent time points, we restricted the learning of short-term dependency with a Local-LSTM and the learning of long-term dependency with the joint spatiotemporal attention. We also compared the proposed method with several state-of-the-art methods and a method in clinical practice. The methods are evaluated on a hard-to-acquire clinical dataset of patients with long COVID. Experimental results show the Local-LSTM with joint spatiotemporal attention outperformed related methods in outcome prediction. The proposed method provides a clinical tool for the severity assessment of long COVID.

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Integration of metabolomics and machine learning for precise management and prevention of cardiometabolic risk in Asians

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Clinical Nutrition 2025年 50卷 146-153页

作者： Bi, Xinyan Sun, Lijuan Yeo, Michelle Ting Yun Seaw, Ker Ming Leow, Melvin Khee Shing Singapore Institute of Food and Biotechnology Innovation (SIFBI) Agency for Science Technology and Research (A∗STAR) 31 Biopolis Way Nanos Singapore 138669 Singapore Institute for Human Development and Potential (IHDP) Agency for Science Technology and Research (A∗STAR) Singapore Lee Kong Chian School of Medicine Nanyang Technological University Singapore Department of Endocrinology Tan Tock Seng Hospital Singapore Human Potential Translational Research Programme (HPTRP) Yong Loo Lin School of Medicine National University of Singapore (NUS) Singapore Cardiovascular and Metabolic Program Duke-NUS Medical School Singapore

Rapid changes in dietary patterns have led to a rise in cardiometabolic diseases (CMDs) worldwide, highlighting the urgent need for effective dietary strategies to address the health issues. Compared to Caucasians, Asians are more susceptible to CMDs. Understanding the complex factors driving this increased susceptibility is essential for developing targeted interventions and preventive measures for Asian populations. Metabolomics plays a key role in identifying specific metabolic markers and pathways associated with CMDs, providing insights into disease mechanisms and helping to create individualized risk profiles. However, metabolomics faces several challenges, including difficulties in interpreting results across diverse ethnic groups, limitations in study design, variability in analytical platforms, and inconsistencies in data processing methods. Overcoming these challenges requires the adoption of advanced technologies, standardized approaches, and integration of multi-omics data to maximize the utility of metabolomics in clinical settings. As the volume and complexity of metabolomic data continue to increase, machine learning (ML) algorithms have become essential for effective data integration, interpretation, and knowledge extraction. Advanced ML techniques, such as deep learning and network analysis, can reveal hidden patterns, relationships, and metabolic pathways within large datasets, leading to deeper insights into biological systems and disease processes. By combining metabolomics and ML, we can facilitate early detection, enable personalized interventions, and support the development of targeted nutritional strategies, ultimately improving therapeutic outcomes and reducing the socioeconomic burden of CMDs in this region. © 2025 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism

关键词： Cardiometabolic disease Dietary strategy Machine learning Metabolomics Precision nutrition

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MBSE-Driven Implementation to Optimize Aircraft Maintenance Planning

MBSE-Driven Implementation to Optimize Aircraft Maintenance ...

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IEEE Conference on Aerospace

作者： Nathan Thompson Kyle Blond Steven Berguin Anne Clark Model-Based Acquisitions Georgia Tech Research Institute Smyrna GA Methods & Analysis Development Georgia Tech Research Institute Smyrna GA Air National Guard Program Office Georgia Tech Research Institute Atlanta GA

Condition Based Maintenance Plus (CBM+) is a growing topic among the aviation community, and key to its implementation is the optimal scheduling of maintenance activities. We previously used Model-Based Systems Engineering (MBSE) to architect the CBM+ implementation of a commercial maintenance optimization tool for a notional military aircraft fleet. Challenges in optimally scheduling preventative, corrective, and predictive maintenance (PMx) activities using a letter-check maintenance strategy (e.g., A and C checks) motivated us to research the feasibility of a task-based, progressive maintenance strategy to support CBM+ *** this paper, we discuss our approach in exploring a progressive maintenance strategy, opportunities and CBM+ capabilities that are facilitated by the novel approach, and how MBSE is driving the design and development our maintenance scheduling optimization tool. Special attention is paid to how MBSE identified the systemic issues in the letter-check maintenance strategy that may be solved using a progressive maintenance approach, communicating these conceptual and logical changes among our cross-functional team, and how MBSE unified our continuous agile methodology to revamp our scheduling tool. Future work includes the continued development and testing of the tool guided by MBSE and incorporating enterprise and operational constraints into the tool using MBSE.

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A Prospective Preventive Screening Tool-Pancreatic Cancer Risk Model Developed by AI Technology 1

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11th International Conference on Frontier Computing, FC 2021

作者： Lee, Hsiu-An Chen, Kuan-Wen Hsu, Chien-Yeh National Health Research Institutes The National Institute of Cancer Research Tainan Taiwan Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (数字)9789811680526

ISBN: (纸本)9789811680519

Pancreatic cancer is one of the cancers that are not easy to detect early due to the lack of obvious disease characteristics in the early stage, the tumor is mostly located in the posterior abdominal cavity, and the lack of early diagnosis tools. Therefore, when it is diagnosed, it is often approaching the late stage. In recent years, the studies of pancreatic cancer are mostly single-factor analysis and multi-factor analysis to summarize one or more risk factors, including past diseases, physical signs, family genes and long-term eating habits, etc., and for early evaluation models of pancreatic cancer is less. This study uses Logistic Regression (LR), Deep Neural Networks (DNN), ensemble voting learning (Voting), ensemble stacking learning (Stacking) and other methods to establish different models. Compare the performance between different models. It is hoped that based on the patient's past medical history, the high-risk group can be judged through the model, and whether it has a high probability of suffering from pancreatic cancer within one year, so that the public and doctors are aware of the risk of pancreatic cancer early. In this study, the best model is 19 factors LR’s model. The accuracy is 70%, the sensitivity is 70%, the specificity is 70%, and the AUC is 0.78. The contribution of this study is to use non-invasive factors to identify Chronic Kidney Disease, but it is a preliminary evaluation and ultimately requires doctors to diagnose. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multivariant analysis

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US Department of Defense funded research on treatment of per- and polyfluoroalkyl substance Laden materials (vol 40, pg 44, 2021)

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ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2021年第2期40卷 537-537页

作者： Coyle, C. Ghosh, R. Leeson, A. Thompson, T. Huntsville Engineering & Support Center US Army Corps of Engineers Omaha Nebraska USA Alcoa Pittsburgh Pennsylvania USA Strategic Environmental Research and Development Program and Environmental Security Technology Certification Program US Department of Defense Arlington Virginia USA Science and Engineering for the Environment Seattle Washington USA

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Photographic Aerial Transects of Fort Wainwright, Alaska

Photographic Aerial Transects of Fort Wainwright, Alaska

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作者： Marcot, B. G Jorgenson, M. T Douglas, T. A Nelsen, P. E Engineer Research and Development Center (U.S.) Vicksburg MS STRATEGIC ENVIRONMENTAL RESEARCH AND DEVELOPMENT PROGRAM ALEXANDRIA VA U.S. Army Engineer Research and Development Center Hanover NH

This report presents the results of low-altitude photographic transects conducted over the training areas of US Army Garrison Fort Wainwright, in the boreal biome of central Alaska, to document baseline land-cover conditions. Flights were conducted via a Cessna 180 on two flight paths over portions of the Tanana Flats, Yukon, and Donnelly Training Areas and covered 486 mi (782 km) while documenting GPS waypoints. Nadir photographs were made with two GoPro cameras operating at 5 sec time-lapse intervals and with a handheld digital camera for oblique imagery. This yielded 6,063 GoPro photos and 706 oblique photos. Each image was intersected with a land-cover-classification map, collectively representing 38 of the 44 cover categories.

关键词： Photography Cameras Photographs Department of defense Engineering Low altitude Altitude Flight paths Engineers Fires Vegetation Wildfires Change detection Digital cameras Flight Classification Detection

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Societal, Political, and Psychological Challenges Facing MSM in Nigeria

Journal of Psychosexual Health

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Journal of Psychosexual Health 2024年第1期6卷 98 - 104页

作者： Olubunmi Elizabeth Amoo Olaniyi Felix Sanni Paul Olaiya Abiodun Community and Prevention Program Department APIN Public Health Initiatives Abuja Nigeria Research and Development Department Fescosof Data Solutions Ota Ogun State Nigeria Department of Public Health Texila American University Nicaragua

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