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arXiv 2024年

作者： Tang, Sophia Zhang, Yinuo Chatterjee, Pranam Department of Biomedical Engineering Duke University United States Management and Technology Program University of Pennsylvania United States Center of Computational Biology Duke-NUS Medical School Singapore Department of Computer Science Duke University United States Department of Biostatistics and Bioinformatics Duke University United States

Peptide therapeutics, a major class of medicines, have achieved remarkable success across diseases such as diabetes and cancer, with landmark examples such as GLP-1 receptor agonists revolutionizing the treatment of type-2 diabetes and obesity. Despite their success, designing peptides that satisfy multiple conflicting objectives, such as target binding affinity, solubility, and membrane permeability, remains a major challenge. Classical drug development and target structure-based design are ineffective for such tasks, as they fail to optimize global functional properties critical for therapeutic efficacy. Existing generative frameworks are largely limited to continuous spaces, unconditioned outputs, or single-objective guidance, making them unsuitable for discrete sequence optimization across multiple properties. To address this, we present PepTune, a multi-objective discrete diffusion model for the simultaneous generation and optimization of therapeutic peptide SMILES. Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures valid peptide structures with bond-dependent masking schedules and penalty-based objectives. To guide the diffusion process, we propose a Monte Carlo Tree Search (MCTS)-based strategy that balances exploration and exploitation to iteratively refine Pareto-optimal sequences. MCTS integrates classifier-based rewards with search-tree expansion, overcoming gradient estimation challenges and data sparsity. Using PepTune, we generate diverse, chemically modified peptides optimized for multiple therapeutic properties, including target binding affinity, membrane permeability, solubility, hemolysis, and non-fouling for various disease-relevant targets. In total, our results demonstrate that MCTS-guided masked discrete diffusion is a powerful and modular approach for multi-objective sequence design in discrete state spaces. © 2024, CC BY-NC-ND.

关键词： Trees (mathematics)

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An Image Inpainting Method Based on Adaptive Fuzzy Switching Median

An Image Inpainting Method Based on Adaptive Fuzzy Switching...

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National Foundation for Science and technology Development Conference on Information and Computer Science (NICS)

作者： Nguyen Van Son Dang N. H. Thanh Ugur Erkan V. B. Surya Prasath Ballistic Research Laboratory Military Weapon Institute Hanoi Vietnam Department of Information Technology Hue College of Industry VN Department of Computer Engineering Karamanoglu Mehmetbey University Karaman Turkey Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Cincinnati USA

ISBN: (数字)9781728151632

ISBN: (纸本)9781728151649

Image inpainting is an important problem of image processing that has many applications. The goal of the image inpainting problem is to restore or fill the corrupted or missing regions of image. In this paper, we propose an image inpainting method based on the adaptive fuzzy switching median. The adaptive fuzzy switching median is to provide an accurate estimation for the values of corrupted pixels when there are many corrupted pixels on image. In the experiments, we implement the proposed method on an open dataset of real natural images. We utilize standard image quality assessment metrics such as the peak signal-to-noise ratio metric and the structured similarity metric to compare the inpainting result of the proposed method with other similar inpainting methods to prove its effectiveness. The proposed inpainting method is also extended to process colorful images.

关键词： Switches Microsoft Windows Measurement Image restoration Image quality Computer science Harmonic analysis

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M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition

arXiv

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arXiv 2024年

作者： Luo, Ting Zhang, Jing Qiu, Yingwei Zhang, Li Hu, Yaohua Yu, Zhuliang Liang, Zhen School of Biomedical Engineering Medical School Shenzhen University Shenzhen518060 China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen518060 China School of Public Health Shenzhen University Medical School Shenzhen China Department of Radiology Nanshan Hospital of Shenzhen University Shenzhen China School of Mathematical Sciences Shenzhen University Shenzhen China School of Intelligent Engineering South China University of Technology Guangdong Guangzhou China Institute for Super Robotics Guangdong Guangzhou China

Emotion decoding using Electroencephalography (EEG)-based affective brain-computer interfaces (aBCIs) is crucial for affective computing but is hindered by EEG’s nonstationarity, individual variability, and the high cost of large-scale labeled data. Deep learning-based approaches, while effective, require substantial computational resources and large datasets, limiting their practicality. To address these challenges, we propose Manifold-based Domain Adaptation with Dynamic Distribution (M3D), a lightweight non-deep transfer learning framework. M3D includes four main modules: manifold feature transformation, dynamic distribution alignment, classifier learning, and ensemble learning. The data undergoes a transformation onto an optimal Grassmann manifold space, enabling dynamic alignment of the source and target domains. This process prioritizes both marginal and conditional distributions according to their significance, ensuring enhanced adaptation efficiency across various types of data. In the classifier learning, the principle of structural risk minimization is integrated to develop robust classification models. This is complemented by dynamic distribution alignment, which refines the classifier iteratively. Additionally, the ensemble learning module aggregates the classifiers obtained at different stages of the optimization process, which leverages the diversity of the classifiers to enhance the overall prediction accuracy. The proposed M3D framework is evaluated on two benchmark EEG emotion recognition datasets using two validation protocols (cross-subject single-session and cross-subject cross-session), as well as on a clinical EEG dataset of Major Depressive Disorder (MDD). Experimental results demonstrate that M3D outperforms traditional non-deep learning methods, achieving an average improvement of 4.47%, while achieving deep learning-comparable performance with significantly lower data and computational requirements. These findings highlight the potential of

关键词： Deep learning

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Preliminary Assessment of Two Simultaneous and Proportional Myocontrol Methods for 3-DoFs Prostheses Using Incremental Learning

Preliminary Assessment of Two Simultaneous and Proportional ...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Fabio Egle Dario Di Domenico Andrea Marinelli Nicolò Boccardo Michele Canepa Matteo Laffranchi Lorenzo De Michieli Claudio Castellini Department Artificial Intelligence in Biomedical Engineering Assistive Intelligent Robotics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Rehab Technologies Lab Italian Institute of Technology Genova Italy Department of Electronics and Telecommunications Politecnico di Torino Turin Italy Bioengineering Lab University of Genova DIBRIS Genova Italy Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT) Genova Italy German Aerospace Center Institute of Robotics and Mechatronics Wessling Germany

Despite progressive developments over the last decades, current upper limb prostheses still lack a suitable control able to fully restore the functionalities of the lost arm. Traditional control approaches for prostheses fail when simultaneously actuating multiple Degrees of Freedom (DoFs), thus limiting their usability in daily-life scenarios. Machine learning, on the one hand, offers a solution to this issue through a promising approach for decoding user intentions but fails when input signals change. Incremental learning, on the other hand, reduces sources of error by quickly updating the model on new data rather than training the control model from scratch. In this study, we present an initial evaluation of a position and a velocity control strategy for simultaneous and proportional control over 3-DoFs based on incremental learning. The proposed controls are tested using a virtual Hannes prosthesis on two healthy participants. The performances are evaluated over eight sessions by performing the Target Achievement Control test and administering SUS and NASA-TLX questionnaires. Overall, this preliminary study demonstrates that both control strategies are promising approaches for prosthetic control, offering the potential to improve the usability of prostheses for individuals with limb loss. Further research extended to a wider population of both healthy subjects and amputees will be essential to thoroughly assess these control paradigms.

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Muscle Activation Patterns Estimation during Repeated Wrist Movements from MRI and sEMG*

Muscle Activation Patterns Estimation during Repeated Wrist ...

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IEEE/RAS-EMBS International Conference on biomedical robotics and Biomechatronics (BioRob)

作者： Enrico Piovanelli Davide Piovesan Shouhei Shirafuji Natsue Yoshimura Yousuke Ogata Jun Ota Department of Precision Engineering The University of Tokyo Bunkyo Hongo Japan Department of Biomedical Gannon University Erie PA USA Research Into Artifacts Center for Engineering (RACE) School of Engineering The University of Tokyo Tokyo Japan Integrative Brain Imaging Center National Center of Neurology and Psychiatry (NCNP) Kodaira Tokyo Japan Institute of Innovative Research Tokyo Institute of Technology Midori-ku Yokohama Japan Precursory Research for Embryonic Science and Technology (PRESTO) Japan Science and Technology Agency (JST) Kawaguchi Saitama Japan

ISBN: (数字)9781728159072

ISBN: (纸本)9781728159089

Functional electrical stimulation (FES) is a widely used method to bypass spinal chord damages and bring the neurological input to muscles. FES methods that allow the activation of both superficial and deep muscles were proposed in the literature. However, even if many studies using surface electromyography (sEMG) for the estimation of muscles' activation were presented, it still remains impossible to access information of deep muscles. Our group proposed a solution that exploits the morphological information of magnetic resonance imaging (MRI) and the sEMG for the estimation of the average muscles' activation pattern during isometric muscle contractions of forearm muscles. With this work we introduce the time dimension to study how the activation patterns changes while performing 3 simple wrist movements. The study involved data collected from a single row of electrodes wrapped around the forearm of a single healthy participant. Morphological information are extracted from the MRI to build a model that is then used to estimate the muscles' activation pattern. The results show that the method is able to provide an estimation of the muscles' activation patterns that explain more than 93% of the input sEMG information and that have a corresponding in anatomical information from the literature.

关键词： Muscles Electrodes Magnetic resonance imaging Estimation Electromyography Wrist Skin

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Fabrication of a bio-instructive scaffold conferred with a favorable microenvironment allowing for superior implant osseointegration and accelerated in situ vascularized bone regeneration via type H vessel formation

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Bioactive Materials 2022年第3期7卷 491-507页

作者： Yijun He Wenhao Wang Shaozhang Lin Yixi Yang Lizhi Song Yihan Jing Lihao Chen Zaopeng He Wei Li Ao Xiong Kelvin W.K.Yeung Qi Zhao Yuan Jiang Zijie Li Guoxian Pei Zhi-Yong Zhang Translational Research Centre of Regenerative Medicine and 3D Printing of Guangzhou Medical University Guangdong Province Engineering Research Center for Biomedical EngineeringState Key Laboratory of Respiratory DiseaseThe Third Affiliated Hospital of Guangzhou Medical UniversityGuangzhou510150PR China Hand and Foot Surgery&Plastic Surgery Affiliated Shunde Hospital of Guangzhou Medical UniversityFoshan528315PR China Department of Bone&Joint Surgery Peking University Shenzhen HospitalShenzhen518036PR China National&Local Joint Engineering Research Center of Orthopaedic Biomaterials Peking University Shenzhen HospitalShenzhen518036PR China Department of Orthopaedics and Traumatology The University of Hong KongPokfulamHong Kong999077PR China Shenzhen Key Laboratory for Innovative Technology in Orthopaedic Trauma The University of Hong Kong Shenzhen HospitalShenzhen518053PR China The Third Affiliated Hospital of Southern University of Science and Technology Southern University of Science and TechnologyShenzhen518055PR China Department of Orthopaedic Surgery The Third Affiliated Hospital of Guangzhou Medical UniversityGuangzhou510150PR China Medical Technology and Related Equipment Research for Spinal Injury Treatment City Key LaboratoryThe Third Affiliated Hospital of Guangzhou Medical UniversityGuangzhou510150PR China

The potential translation of bio-inert polymer scaffolds as bone substitutes is limited by the lack of neovascularization upon implantation and subsequently diminished ingrowth of host bone,most likely resulted from the inability to replicate appropriate endogenous crosstalk between *** umbilical vein endothelial cell-derived decellularized extracellular matrix(HdECM),which contains a collection of angiocrine biomolecules,has recently been demonstrated to mediate endothelial cells(ECs)-osteoprogenitors(OPs)*** employed the HdECM to create a PCL(polycaprolactone)/fibrin/HdECM(PFE)hybrid *** hypothesized PFE scaffold could reconstitute a bio-instructive microenvironment that reintroduces the crosstalk,resulting in vascularized bone *** implantation in a rat femoral bone defect,the PFE scaffold demonstrated early vascular infiltration and enhanced bone regeneration by microangiography(μ-AG)and micro-computational tomography(μ-CT).Based on the immunofluorescence studies,PFE mediated the endogenous angiogenesis and osteogenesis with a substantial number of type H vessels and *** addition,superior osseointegration was observed by a direct host bone-PCL interface,which was likely attributed to the formation of type H *** bio-instructive microenvironment created by our innovative PFE scaffold made possible superior osseointegration and type H vessel-related bone *** could become an alternative solution of improving the osseointegration of bone substitutes with the help of induced type H vessels,which could compensate for the inherent biological inertness of synthetic polymers.

关键词： Cell-derived decellularized extracellular matrix Microenvironment Vascularized bone regeneration Type H vessels Osseointegration

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Axial Super-Resolution in Ultrasound Imaging

Axial Super-Resolution in Ultrasound Imaging

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IEEE International Ultrasonics Symposium

作者： Saurav Kumaraswami Shastri Sunil Rudresh Ramkumar Anand Sudarshan Nagesh Dibbyan Mazumder Arun K. Thittai Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bengaluru India Department of Applied Mechanics Indian Institute of Technology Madras Chennai India Zendar Co. Berkeley USA Martinos Center for Biomedical Imaging Harvard Medical School and Massachusetts General Hospital Charlestown USA

A fundamental challenge in non-destructive evaluation using ultrasound is to accurately estimate the thicknesses of different layers or cracks present in the object being probed. This inherently corresponds to localizing the point-sources of the reflections from the received signal. Conventional signal processing techniques cannot resolve reflectors whose spacing is below the axial resolution limit, which is of the order of the wavelength of the probing pulse. The objective of this paper is to demonstrate axial super-resolution capability using both simulated and experimental ultrasound data. We show that the ultrasound reflections could be modelled effectively as FRI signals, which can be sampled at sub-Nyquist rates. The FRI sampling method brings the reconstruction problem within a parametric estimation framework, for which efficient high-resolution spectral estimation techniques are available. We experimentally demonstrate that the proposed technique is able to resolve the thicknesses of layers of custom designed Agarose phantoms that are up to 2.25 times below the conventional resolution limit.

关键词： Signal sampling Ultrasonic imaging Image resolution biomedical imaging biomedical acoustics Phantoms Image reconstruction

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Dispensed transformer network for unsupervised domain adaptation

arXiv

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arXiv 2021年

作者： Li, Yunxiang Li, Jingxiong Dan, Ruilong Wang, Shuai Jin, Kai Zeng, Guodong Wang, Jun Pan, Xiangji Zhang, Qianni Zhou, Huiyu Jin, Qun Wang, Li Wang, Yaqi College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Artificial Intelligence and Biomedical Image Analysis Lab Westlake University Hangzhou China School of Mechanical Electrical and Information Engineering Shandong University Weihai China Department of Ophthalmology Hospital of Zhejiang University Hangzhou China sitem Center for Translational Medicine and Biomedical Entrepreneurship University of Bern Bern Switzerland School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom Department of Human Informatics and Cognitive Sciences Faculty of Human Sciences Waseda University Tokyo Japan Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill United States College of Media Engineering Communication University of Zhejiang Hangzhou China

Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. However, it is costly to perform data annotation that provides ground truth labels for training the supervised algorithms, and the high variance of data that comes from different domains tends to severely degrade system performance over cross-site or cross-modality datasets. To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our novel DTNet contains three modules. First, a dispensed residual transformer block is designed, which realizes global attention by dispensed interleaving operation and deals with the excessive computational cost and GPU memory usage of the Transformer. Second, a multi-scale consistency regularization is proposed to alleviate the loss of details in the low-resolution output for better feature alignment. Finally, a feature ranking discriminator is introduced to automatically assign different weights to domain-gap features to lessen the feature distribution distance, reducing the performance shift of two domains. The proposed method is evaluated on large fluorescein angiography (FA) retinal nonperfusion (RNP) cross-site dataset with 676 images and a wide used cross-modality dataset from the MM-WHS challenge. Extensive results demonstrate that our proposed network achieves the best performance in comparison with several state-of-the-art techniques. © 2021, CC BY.

关键词： Supervised learning

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An integrated rehabilitation system for the upper limb spasticity assessment and treatment: the Rehabotics passive exoskeletal system

An integrated rehabilitation system for the upper limb spast...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Vassiliki Potsika Nikolaos Tachos Athanasios Pardalis Christoforos Papaioannou Ioannis Kouris Theodore Economopoulos Pantelis Syringas Nikolaos Tselikas Orestis Zestas Giorgos Papagiannis Athanasios Triantafyllou Vasilis Protopappas Nikolaos Malizos George Matsopoulos Dimitrios I. Fotiadis Department of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina GR Greece Biomedical Engineering Laboratory National Technical University of Athens Athens Greece CNA Lab - Department of Informatics and Telecommunications University of Peloponnese Tripoli Greece Physiotherapy Department Biomechanics Laboratory University of the Peloponnese Sparta Greece and Physioloft Physiotherapy Center Kifisia Greece Ostracon Ltd Greece Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina GR Greece Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH)

Limb spasticity is caused by stroke, multiple sclerosis, traumatic brain injury and various central nervous system pathologies such as brain tumors resulting in joint stiffness, loss of hand function and severe pain. This paper presents with the Rehabotics integrated rehabilitation system aiming to provide highly individualized assessment and treatment of the function of the upper limbs for patients with spasticity after stroke, focusing on the developed passive exoskeletal system. The proposed system can: (i) measure various motor and kinematic parameters of the upper limb in order to evaluate the patient's condition and progress, as well as (ii) offer a specialized rehabilitation program (therapeutic exercises, retraining of functional movements and support of daily activities) through an interactive virtual environment. The outmost aim of this multidisciplinary research work is to create new tools for providing high-level treatment and support services to patients with spasticity after *** Relevance— This paper presents a new passive exoskeletal system aiming to provide enhanced treatment and assessment of patients with upper limb spasticity after stroke.

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Review: Frontiers of materials science and engineering

Review: Frontiers of materials science and engineering

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作者： Ruan, G.L. Velasco, J.I. Vallés, C. Li, Z.M. Gloria, A. Lin, W.Z. Liu, F. Hubei University of Science and Technology School of Electronic and Information Engineering Xianning China Technical University of Catalonia Department of Materials Science and Metallurgy Terrassa Spain University of Manchester School of Materials Manchester United Kingdom National Research Council of Italy Institute of Composite and Biomedical Materials Naples Italy Oak Ridge National Laboratory Center for Nanophase Materials Sciences Oak RidgeTN United States Wuhan University International School of Software Wuhan China

Materials science and engineering is one of the hot research topics in the world, which is changing the way we live daily. As a result of this, the 2nd Global Conference on Materials Science and engineering was held on November 20-22, 2013. During this event, more than 200 attendees discussed the frontiers of materials science and engineering. This editorial highlights some of these papers that present the new frontiers of materials science research in 2013. © 2014 W. S. Maney & Son Ltd.

关键词： Condensed matter physics

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