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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Yuma Kanaizuka Takahiro Manabe Jian-Jia Huang Jen-Wen Hung Yumie Ono Electrical Engineering Program Graduate School of Science and Technology Meiji University Japan School of Science and Technology Meiji University Japan Department of Rehabilitation Chang Gung Memorial Hospital-Kaohsiung Medical Center Kaohsiung Taiwan School of Medicine College of Medicine Chang Gung University Taoyuan Taiwan

This study explored directional connectivity networks during hand movement training in stroke patients using 8 conditions combining mirror therapy, robot-assisted bimanual therapy, and object manipulation. The findings revealed that mirror therapy and robot-assisted bimanual therapy decreased interhemispheric inward connectivity to the affected motor cortex in the left-hand paralyzed patient, reducing inhibitory control. Conversely, these interventions increased interhemispheric inward connectivity in the right-hand paralyzed patient, suggesting enhanced excitatory connectivity. The results emphasized the influence of neurorehabilitation methods, hemiparesis severity, and affected side on interhemispheric connectivity. Further research is needed to develop personalized rehabilitation strategies based on directional connectivity measures.

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Respiration and Maturity Assessment of Mango (Mangifera Indica Cv. Arumanis) for Long-Distance Transportation Planning in Indonesia

SSRN

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SSRN 2023年

作者： Kailaku, Sari Intan Arkeman, Yandra Purwanto, Y. Aris Udin, Faqih Agroindustrial Engineering Study Program Graduate School IPB University Bogor16680 Indonesia Research Center for Agroindustry National Research and Innovation Agency Jakarta10340 Indonesia Agroindustrial Technology Department Faculty of Agricultural Technology IPB University Bogor16680 Indonesia Department of Mechanical and Biosystem Engineering Faculty of Agricultural Technology IPB University Bogor16680 Indonesia Center for Tropical Horticulture Studies IPB University Bogor16680 Indonesia

Transportation of mango (Mangifera indica) is a significant challenge in a large archipelago country like Indonesia. This study addresses the quality assurance problem of long-distance transportation of mangoes from Java Island, Indonesia, and recommends the optimal harvest age for different transportation schemes in the Indonesian mango supply chain. The study investigated harvested mangoes at three different maturity levels and evaluated their quality characteristics, including climacteric peak and respiration rate, total soluble solids, acidity, firmness, dry matter concentration, and weight loss. Samples were kept in their respective packaging during storage to imitate transportation conditions, and an Internet of Things (IoT)-based system was used for real-time monitoring and remote access to the storage conditions information. Long-distance transportation cases of six destinations with different distances, modes of transportation, travel durations, and environmental conditions were studied. Mango harvested at 105 and 108 days after flowering is suitable for consumption, while the harvest age of 90-95 days after flowering is recommended for longer transportation durations involving non-refrigerated and refrigerated vehicles. The harvest age of 105 days after flowering is appropriate for long-distance transportation with non-refrigerated vehicles, while 108 days after flowering is only recommended for inter-province transportation with non-refrigerated vehicles with a short duration of transportation. The results emphasize the importance of determining the optimal harvest time based on the climacteric peak and quality changes during storage to ensure that mangoes meet consumer demands and maximize their quality and shelf life. © 2023, The Authors. All rights reserved.

关键词： Fruits

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Contour Detection from Ultrasound Kidney Images with A Coarse-to-Fine Approach

Contour Detection from Ultrasound Kidney Images with A Coars...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Peng, Tao Gu, Yidong Xu, Yanqing Wang, Caishan Zhang, Lei Cai, Jing School of Future Science and Engineering Soochow University Suzhou China UT Southwestern Medical Center Department of Radiation Oncology DallasTX United States The Affiliated Suzhou Hospital of Nanjing Medical University Department of Medical Ultrasound Suzhou Municipal Hospital Jiangsu Suzhou China University of Texas Southwestern Medical Center McDermott Center for Human Growth and Development DallasTX United States The Second Affiliated Hospital of Soochow University Department of Ultrasound Suzhou China Duke Kunshan University Graduate Program of Medical Physics and Data Science Research Center Jiangsu Kunshan China The Hong Kong Polytechnic University Department of Health Technology and Informatics Hong Kong Hong Kong

ISBN: (纸本)9798350337020

Ultrasound kidney image segmentation presents significant challenges due to missing or ambiguous boundaries. In this study, we introduce a coarse-to-refinement approach incorporating four novel aspects. Firstly, we leverage the properties of a principal curve (PC) to automatically fine-tune the curve shape and employ a neural network's learning ability to reduce model error. Secondly, a deep fusion learning network is utilized for the coarse segmentation step, incorporating a parallel architecture to enhance deep-learning performance. Thirdly, addressing the limitation of standard PC-based methods in determining the number of vertices automatically, we propose an automatic searching polygon tracking method using a mean shift clustering-based approach to replace the projection and vertex extension step in standard PC-based methods. Lastly, we develop an explainable mathematical map function for the kidney contour, as denoted by the neural network output (i.e., optimized vertices), which aligns well with the ground truth contour. We conducted various experiments to evaluate our method's performance, demonstrating its effectiveness in ultrasound kidney image segmentation. © 2023 IEEE.

关键词： Artificial intelligence Explainable mathematical model Ultrasound kidney segmentation

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HAAQI-Net: A Non-Intrusive Neural Music Audio Quality Assessment Model for Hearing Aids

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1877-1892页

作者： Dyah A. M. G. Wisnu Stefano Rini Ryandhimas E. Zezario Hsin-Min Wang Yu Tsao Taiwan International Graduate Program—Social Network and Human-Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan College of Informatics National Chengchi University Taipei Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Electrical Engineering Chung Yuan Christian University Taiwan

This paper introduces HAAQI-Net, a non-intrusive music audio quality assessment model for hearing aid users. Unlike traditional methods such as Hearing Aid Audio Quality Index (HAAQI), which requires intrusive reference signal comparisons, HAAQI-Net offers a more accessible and computationally efficient alternative. Leveraging a bidirectional long short-term memory architecture with attention mechanisms and features extracted from a pre-trained BEATs model, it can predict HAAQI scores directly from music audio clips and hearing loss patterns. The experimental results demonstrate that, compared to the traditional HAAQI as the reference, HAAQI-Net achieves a linear correlation coefficient (LCC) of 0.9368, a Spearman's rank correlation coefficient (SRCC) of 0.9486, and a mean squared error (MSE) of 0.0064, while significantly reducing the inference time from 62.52 seconds to 2.54 seconds. Furthermore, a knowledge distillation strategy was applied, reducing the parameters by 75.85% and inference time by 96.46%, while maintaining strong performance (LCC: 0.9071, SRCC: 0.9307, MSE: 0.0091). To expand its capabilities, HAAQI-Net was adapted to predict subjective human scores, mean opinion score (MOS), by fine-tuning. This adaptation significantly improved the prediction accuracy. Furthermore, the robustness of HAAQI-Net was evaluated under varying sound pressure level (SPL) conditions, revealing optimal performance at a reference SPL of 65 dB, with the accuracy gradually decreasing as SPL deviated from this point. The advancements in subjective score prediction, SPL robustness, and computational efficiency position HAAQI-Net as a reliable solution for music audio quality assessment, significantly contributing to the development of efficient and accurate models in audio signal processing and hearing aid technology.

关键词： Hearing aids Quality assessment Training Feature extraction Speech processing Auditory system Computational modeling Adaptation models Transformers Multiple signal classification

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Gas vesicle isolated from microorganisms to act as ultrasound contrast agent

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Research on Biomedical engineering 2021年第1期37卷 1-7页

作者： Garrute, Felipe Vianna Machado, João Carlos Biomedical Engineering Program-COPPE Federal University of Rio de Janeiro Horácio Macedo Ave. 2030 Center of Technology Block H Room 327 Rio de JaneiroRJ21941-914 Brazil Post-graduate Program in Surgical Sciences Department of Surgery School of Medicine Federal University of Rio de Janeiro Carlos Chagas Filho Ave. 373 Health Sciences Center Block K Room 49 Rio de JaneiroRJ21941-902 Brazil

Purpose: Ultrasound contrast agent (UCA), used to enhance the contrast in ultrasonography, consists of microbubbles filled with an inert gas and encapsulated with a protein or a lipidic-stabilize membrane. The UCAs are able to resonate when exposed to ultrasound waves and to generate an echo-acoustic signal with increased intensity. Recently, a technique to produce UCA from microorganisms was developed and it consists of lysing microorganism cells to obtain gas vesicle (GV) after cultivation in the laboratory. GVs occur almost exclusively in aquatic microorganisms, as Microcystis aeruginosa (MYC), and provide buoyancy which permit to float towards the water surface. This work implemented a technique to produce GVs from MYC, at the Ultrasound Laboratory, and inspected their properties of acting as UCAs, analyzed through ultrasonic images acquired at 40 MHz, and their structural characteristics such as morphology, diameter, and length. Methods: After the cultivation of MYC, the buoyant cells with GVs were separated from the culture medium and submitted to cell lysis with chemical treatment. Thereafter, the solution with lysed cells was centrifuged, at 300g during 8 h at 4–8 °C, to isolate the GVs (absorbance 0.6, λ = 500 nm). Results: Transmission electron microscopy inspection revealed the MYC-GVs with elliptical morphology and having a diameter between 100 and 150 nm and length near to 200 nm. B-mode ultrasound images of the solutions proved the GV’s ability to generate an echo signal, thus confirming their performance as UCAs. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Cytology

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Nu-Ri Park Min-Joong Kim Jongsu Lee Minah Seo Yong-Sang Ryu Myung-Ki Kim KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA School of Biomedical Engineering Korea University Seoul Republic of Korea Interdisciplinary Program in Precision Public Health Korea University Seoul Republic of Korea

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen (<3%... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Electric potential Accuracy Hydrogen Lasers and electrooptics Optical sensors Electro-optical waveguides

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied Science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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Deep Unsupervised Learning for Biomedical Image Translation from Harmonic Generation Microscopy Image to H&E-stained Image

Deep Unsupervised Learning for Biomedical Image Translation ...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chen, Wei-Ju Liao, En-Yu Tai, Tsung-Ming Liao, Yi-Hua Sun, Chi-Kuang Lee, Cheng-Kuang See, Simon Chen, Hung-Wen International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei Taiwan NVIDIA AI Technology Center NVIDIA Taipei Taiwan Department of Dermatology National Taiwan University Hospital College of Medicine National Taiwan University Taipei Taiwan Institute of Photonics Technologies National Tsing Hua University Hsinchu Taiwan

This work proposes an unsupervised deep learning-based image translation from Harmonic generation microscopy (HGM) to widely used H&E-stained images. The proposed methodology is promising and hopefully will facili... 详细信息

ISBN: (纸本)9781957171258

关键词： Deep learning

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Glucose-mediated mitochondrial reprogramming by cholesterol export at TM4SF5-enriched mitochondria-lysosome contact sites

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Cancer Communications 2024年第1期44卷 47-75页

作者： Ji Eon Kim So-Young Park Chulhwan Kwak Yoonji Lee Dae-Geun Song Jae Woo Jung Haesong Lee Eun-Ae Shin Yangie Pinanga Kyung-hee Pyo Eun Hae Lee Wonsik Kim Soyeon Kim Chang-Duck Jun Jeanho Yun Sun Choi Hyun-Woo Rhee Kwang-Hyeon Liu Jung Weon Lee Department of Pharmacy College of PharmacySeoul National UniversitySeoulRepublic of Korea Research Institute of Pharmaceutical Sciences College of PharmacySeoul National UniversitySeoulRepublic of Korea BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit College of Pharmacy and Research Institute of Pharmaceutical SciencesKyungpook National UniversityDaeguRepublic of Korea Department of Chemistry Seoul National UniversitySeoulRepublic of Korea College of Pharmacy Chung-Ang UniversitySeoulRepublic of Korea Natural Product Informatics Research Center Korea Institute of Science and Technology(KIST)Gangneung-siGangwon-doRepublic of Korea School of Life Sciences Gwangju Institute of Science and Technology(GIST)GwangjuRepublic of Korea Department of Biochemistry College of MedicineDong-A UniversityBusanRepublic of Korea Global AI Drug Discovery Center College of Pharmacy and Graduate School of Pharmaceutical SciencesEwha Womans UniversitySeoulRepublic of Korea Interdisciplinary Program in Genetic Engineering Seoul National UniversitySeoulRepublic of Korea

Background:Transmembrane 4 L six family member 5(TM4SF5)translocates subcellularly and functions metabolically,although it is unclear how intracellu-lar TM4SF5 translocation is linked to metabolic *** is thus of interests to understand how the traffic dynamics of TM4SF5 to subcellular endosomal membranes are correlated to regulatory roles of ***:Here,we explored the metabolic significance of TM4SF5 localization at mitochondria-lysosome contact sites(MLCSs),using in vitro cells and in vivo animal systems,via approaches by immunofluorescence,proximity labelling based proteomics analysis,organelle reconstitution ***:Upon extracellular glucose repletion following depletion,TM4SF5 became enriched at MLCSs via an interaction between mitochondrial FK506-binding protein 8(FKBP8)and lysosomal *** labeling showed molecular clustering of phospho-dynamic-related protein I(DRP1)and certain mitophagy receptors at TM4SF5-enriched MLCSs,leading to mitochondrial fis-sion and ***4SF5 bound NPC intracellular cholesterol transporter 1(NPC1)and free cholesterol,and mediated export of lysosomal cholesterol to mitochondria,leading to impaired oxidative phosphorylation but intact tri-carboxylic acid(TCA)cycle andβ-*** mouse models,hepatocyte Tm4sf5 promoted mitophagy and cholesterol transport to mitochondria,both with positive relations to liver ***:Our findings suggested that TM4SF5-enriched MLCSs regu-late glucose catabolism by facilitating cholesterol export for mitochondrial reprogramming,presumably while hepatocellular carcinogenesis,recapitulating aspects for hepatocellular carcinoma metabolism with mitochondrial repro-gramming to support biomolecule synthesis in addition to glycolytic energetics.

关键词： cholesterol fluorescent imaging glucose catabolism hepatocellular carcinogenesis mem-brane contact sites mitochondria function mitophagy oxidative phosphorylation protein-protein interaction tetraspanin

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The path toward 500 um depletion of AstroPix, a pixelated silicon HVCMOS sensor for space and EIC

arXiv

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arXiv 2024年

作者： Steinhebel, Amanda L. Ott, Jennifer Kroger, Olivia Caputo, Regina Fadeyev, Vitaliy Affolder, Anthony Affolder, Kirsten Deshmukh, Aware Striebig, Nicolas Jadhav, Manoj Suda, Yusuke Fukazawa, Yasushi Metcalfe, Jessica Leys, Richard Peric, Ivan Shin, Taylor K.-W. Violette, Daniel NASA Postdoctoral Program Fellow United States NASA Goddard Space Flight Center GreenbeltMD United States University of California Santa Cruz Santa CruzCA United States Karlsruhe Institute of Technology Karlsruhe Germany Argonne National Laboratory LemontIL United States Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan

The precise reconstruction of Compton-scatter events is paramount for an imaging medium-energy gamma-ray telescope. The proposed AMEGO-X is enabled by a silicon tracker utilizing AstroPix chips - a pixelated silicon HVCMOS sensor novel for space use. To achieve science goals, each 500 × 500 µm2 pixel must be sensitive for energy deposits ranging from 25 - 700 keV with an energy resolution of 5 keV at 122 keV ( © 2024, CC BY.

关键词： Gamma rays

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