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arXiv 2024年

作者： Aldakheel, Arwa A. Raub, Christopher B. Bui, Hieu Departments of Biomedical Engineering The Catholic University of America 620 Michigan Avenue N.E. WashingtonDC20064 United States Departments of Electrical Engineering and Computer Science The Catholic University ofAmerica 620 Michigan Avenue N.E. WashingtonDC20064 United States

Simple methods to detect biomolecules including specific nucleic acid sequences have received renewed attention since the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus pandemic. Notably, biomolecule detection that uses some form of signal amplification will have some form of amplification-related error, which in the polymerase chain reaction involves mispriming and subsequent signal amplification in the no template control, ultimately providing a limit of detection. To demonstrate the feasibility of the detection of a DNA target sequence without molecular or chemical signal amplification that avoids amplification errors, a gold nanoparticle aggregation assay was developed and tested. Two primers bracketing a 94 base pair target sequence from SARS-CoV-2 were conjugated to 10 nm diameter gold nanoparticles by the salt aging method, with conjugation and primer-target hybridization confirmed by agarose gel electrophoresis and nanospectrophotometry. Upon mixing of both conjugated nanoparticles with target, a surface plasmon resonance shift of 6 nm was observed, and lower electrophoretic mobility of a band containing both DNA fluorescence and gold absorption signals. This did not occur in the presence of a control DNA molecule of the same size and composition as the target but with a randomly scrambled base position. Nanoparticle tracking at 30 frames per second using a sensitive darkfield microscope revealed a lower measured diffusion coefficient of scattering objects in the target mixture than in the control mixture or with bare gold nanoparticles. Stochastic diffusion and reaction models confirmed the accuracy of the diffusion coefficient estimation from nanoparticle tracks, and the presence of dimers, trimers, and tetramers, depending on the stoichiometric amount of target DNA mixed with conjugated detection nanoparticles. These findings support the future development of nanoparticle assays based on freely diffusing nanoparticles and suggest that

关键词： Surface plasmon resonance

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TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter

arXiv

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arXiv 2023年

作者： Chen, Yiqun T. Zou, James Department of Biomedical Data Science Stanford University StanfordCA94305 United States Departments of Biomedical Data Science Electrical Engineering and Computer Science Stanford University StanfordCA94305 United States

Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos. In this work, we introduce TWIGMA (TWItter Generative-ai images with MetadatA), a comprehensive dataset encompassing over 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text, creation date, number of likes), available at https://***/records/8031785. Through a comparative analysis of TWIGMA with natural images and human artwork, we find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. Additionally, we find that the similarity between a gen-AI image and natural images is inversely correlated with the number of likes. Finally, we observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content such as intricate human portraits, whereas their interest in simple subjects such as natural scenes and animals has decreased. Our findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images. © 2023, CC BY.

关键词： Metadata

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Classifying Upper Extremity Motor Function Using Deep Learning and Third-Person Video Data

Classifying Upper Extremity Motor Function Using Deep Learni...

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IEEE International Conference on Big Data

作者： Tan Tran Lin-Ching Chang Peter Lum Department of Electrical Engineering and Computer Science The Catholic University of America Washington DC USA Department of Informatics New Jersey Institute of Technology Newark NJ USA Department of Biomedical Engineering The Catholic University of America Washington DC USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Post-stroke rehabilitation of upper extremity (UE) motor function is essential. Despite the widespread use of UE rehabilitation in clinical settings, assessing the success of these treatments is challenging. Methods for evaluating UE motor function include task performance under clinical supervision, patient self-reports, and data analysis from wearable devices equipped with accelerometers and gyroscopes. In prior research, we demonstrated that machine learning and deep learning models using data from a single wrist-worn accelerometer sensor could accurately differentiate between functional and non-functional UE movements in stroke patients. To overcome the limitations of single wrist-worn accelerometer sensors – challenges in capturing the full context of functional movements – this study presents a new deep learning (DL) based framework designed to classify functional and non-functional arm movements in videos captured from a conventional camera. The system is entirely automated and comprises two DL networks. The first network performs human pose estimation, extracting 2D pose key points of the paretic arm(s) and torso on 2-second sequences of the frames. The second network then uses these 2D pose landmarks to classify the movement sequence as functional or non-functional. This system offers two key benefits for rehabilitation. First, it can automatically generate initial annotations for video frames, significantly reducing the time needed for manual labeling. Second, analyzing the effectiveness of UE rehabilitation through third-person videos allows for objective outcome measurement of UE treatments in stroke survivors' home environments.

关键词： Deep learning Accelerometers Torso Pose estimation Manuals Stroke (medical condition) Motors Cameras Wearable devices Videos

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Monocular depth estimation based on deep learning for intraoperative guidance using surface-enhanced Raman scattering imaging

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Photonics Research 2025年第2期13卷 550-560页

作者： ANIWAT JUHONG BO LI YIFAN LIU CHENG-YOU YAO CHIA-WEI YANG A.K.M.ATIQUE ULLAH KUNLI LIU RYAN P.LEWANDOWSKI JACK R.HARKEMA DALEN W.AGNEW YU LEO LEI GARY D.LUKER XUEFEI HUANG WIBOOL PIYAWATTANAMETHA ZHEN QIU Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichigan 48824USA Institute for Quantitative Health Science and Engineering Michigan State UniversityEast LansingMichigan 48824USA Department of Biomedical Engineering Michigan State UniversityEast LansingMichigan 48824USA Department of Chemistry Michigan State UniversityEast LansingMichigan 48824USA Department of Pathobiology and Diagnostic Investigation College of Veterinary MedicineMichigan State UniversityEast LansingMichigan 48824USA Department of Periodontics and Oral Medicine University of MichiganAnn ArborMichigan 48104USA Departments of Radiology and Biomedical Engineering University of MichiganAnn ArborMichigan 48109USA Department of Biomedical Engineering School of EngineeringKing Mongkut’s Institute of Technology Ladkrabang(KMITL)Bangkok 10520Thailand

Imaging of surface-enhanced Raman scattering(SERS) nanoparticles(NPs) has been intensively studied for cancer detection due to its high sensitivity, unconstrained low signal-to-noise ratios, and multiplexing detection capability. Furthermore, conjugating SERS NPs with various biomarkers is straightforward, resulting in numerous successful studies on cancer detection and diagnosis. However, Raman spectroscopy only provides spectral data from an imaging area without co-registered anatomic context.

关键词： raman spectroscopy cancer detection surface enhanced raman scattering imaging intraoperative guidance monocular depth estimation anatomic context deep learning sers nanoparticles

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Exact Distribution of the Quantal Content in Synaptic Transmission

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Physical Review Letters 2024年第22期132卷 228401-228401页

作者： Krishna Rijal Nicolas I. C. Müller Eckhard Friauf Abhyudai Singh Ashok Prasad Dibyendu Das Department of Physics Indian Institute of Technology Bombay Powai Mumbai 400076 India Animal Physiology Group Department of Biology University of Kaiserslautern Kaiserslautern Germany Departments of Electrical and Computer Engineering Biomedical Engineering and Mathematical Sciences University of Delaware Newark Delaware 19716 USA Department of Chemical and Biological Engineering Colorado State University Fort Collins Colorado USA

During electrochemical signal transmission through synapses, triggered by an action potential (AP), a stochastic number of synaptic vesicles (SVs), called the “quantal content,” release neurotransmitters in the synaptic cleft. It is widely accepted that the quantal content probability distribution is a binomial based on the number of ready-release SVs in the presynaptic terminal. But the latter number itself fluctuates due to its stochastic replenishment, hence the actual distribution of quantal content is unknown. We show that exact distribution of quantal content can be derived for general stochastic AP inputs in the steady state. For fixed interval AP train, we prove that the distribution is a binomial, and corroborate our predictions by comparison with electrophysiological recordings from MNTB-LSO synapses of juvenile mice. For a Poisson train, we show that the distribution is nonbinomial. Moreover, we find exact moments of the quantal content in the Poisson and other general cases, which may be used to obtain the model parameters from experiments.

关键词： Fluctuations & noise Noise Nonequilibrium statistical mechanics Stochastic processes Master equation Synaptic transmission model

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Delayed Muscle Activity in Stroke Survivors with Upper-Limb Hemiparesis

Delayed Muscle Activity in Stroke Survivors with Upper-Limb ...

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

作者： Danielle R. Lopez Caleb J. Thomson Fredi R. Mino Steven R. Edgely Patrick P. Maitre Marta M. Iversen Jacob A. George Interdepartmental Neuroscience Program University of Utah Salt Lake City UT USA Department of Biomedical Engineering University of Utah Department of Electrical and Computer Engineering University of Utah Department of Physical Medicine and Rehabilitation University of Utah Departments of Electrical and Computer Engineering and Physical Medicine and Rehabilitation University of Utah

Stroke is the leading cause of disability worldwide, and nearly 80% of stroke survivors suffer from upper-limb hemiparesis. Myoelectric exoskeletons can restore dexterity and independence to stroke survivors with upper-limb hemiparesis. However, the ability of patients to dexterously control myoelectric exoskeletons is limited by an incomplete understanding of the electromyographic (EMG) hallmarks of hemiparesis, such as muscle weakness and spasticity. Here we show that stroke survivors with upper-limb hemiparesis suffer from delayed voluntary muscle contraction and delayed muscle relaxation. We quantified the time constants of EMG activity associated with initiating and terminating voluntary hand grasps and extensions for both the paretic and non-paretic hands of stroke survivors. We found that the initiation and termination time constants were greater on the paretic side for both hand grasps and hand extensions. Notably, the initiation time constant during hand extension was approximately three times longer for the paretic hand than for the contralateral non-paretic hand (0.618 vs 0.189 s). We also show a positive correlation between the initiation and termination time constants and clinical scores on the Modified Ashworth Scale. The difficulty stroke survivors have in efficiently modulating their EMG presents a challenge for appropriate control of assistive myoelectric devices, such as exoskeletons. This work constitutes an important step towards understanding EMG differences after stroke and how to accommodate these EMG differences in assistive myoelectric devices. Real-time quantitative biofeedback of EMG time constants may also have broad implications for guiding rehabilitation and monitoring patient *** Relevance— After a stroke, muscle activity changes, and these changes make it difficult to use muscle activity to drive assistive and rehabilitative technologies. We identified slower muscle contraction and muscle relaxation as a key difference i

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Correcting Temporal Inaccuracies in Labeled Training Data for Electromyographic Control Algorithms

Correcting Temporal Inaccuracies in Labeled Training Data fo...

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IEEE International Conference on Rehabilitation Robotics (ICORR)

作者： Aaron T. Wang Connor D. Olsen W. Caden Hamrick Jacob A. George Department of Physical Medicine and Rehabilitation University of Utah Salt Lake City UT USA Department of Electrical and Computer Engineering University of Utah Salt Lake City UT USA Department of Electrical and Computer Engineering Mercer University Macon GA USA Departments of Electrical and Computer Engineering Physical Medicine and Rehabilitation Biomedical Engineering and Mechanical Engineering University of Utah Salt Lake City UT USA

Electromyographic (EMG) control relies on supervised-learning algorithms that correlate EMG to motor intent. The quality of the training dataset is critical to the runtime performance of the algorithm, but labeling motor intent is imprecise and imperfect. Traditional EMG training data is collected while participants mimic predetermined movements of a virtual hand with their own hand. This assumes participants are perfectly synchronized with the predetermined movements, which is unlikely due to reaction time and signal-processing delays. Prior work has used cross-correlation to globally shift and re-align kinematic data and EMG. Here, we quantify the impact of this global re-alignment on both classification algorithms and regression algorithms with and without a human in the loop. We also introduce a novel trial-by-trial re-alignment method to re-align EMG with kinematics on a per-movement basis. We show that EMG and kinematic data are inherently misaligned, and that reaction time is inconsistent throughout data collection. Both global and trial-by-trial re-alignment significantly improved offline performance for classification and regression. Our trial-by-trial re-alignment further improved offline classification performance relative to global realignment. However, online performance, with a human actively in the loop, was no different with or without re-alignment. This work highlights inaccuracies in labeled EMG data and has broad implications for EMG-control applications.

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New Paradigms in Manifold Learning for Knowledge Discovery and Inverse Design of Photonic Nanostructures 19

New Paradigms in Manifold Learning for Knowledge Discovery a...

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Quantum Sensing and Nano Electronics and Photonics XIX 2023

作者： Zandehshahvar, Mohammadreza Javani, Mohammad Hadigheh Kiarashi, Yashar Zhu, Muliang Brown, Tyler Bao, Daqian Marzban, Mahmoodreza Pourabolghasem, Reza Adibi, Ali School of Electrical and Computer Engineering Georgia Institute of Technology 778 Atlantic Dr NW GA30332 United States Clark Atlanta University 223 James P Brawley Dr SW AtlantaGA30314 United States Department of Biomedical Informatics Emory School of Medicine 100 Woodruff Circle AtlantaGA30322 United States

ISBN: (纸本)9781510659650

This talk is focused on using the intelligent aspects of machine learning (ML) for both the understanding of the subtle properties of nanophotonic devices and their inverse design to achieve a desired response. It will be shown that by reducing the dimensionality of the problem using manifold learning techniques and simplifying the resulting networks using pruning, the computation complexity of the underlying artificial intelligence (AI) algorithms will be considerably reduced. Furthermore, by optimally defining the loss function (or the metric) for AI algorithms, priceless information about the properties of photonic nanostructures can be uncovered while facilitating the better visualization of the input-output relationship in these nanostructures. In addition, the resulting manifold-learning algorithms can be optimally trained to facilitate the inverse design of such nanostructures while minimizing the structural complexity. This talk will provide the foundation for both knowledge discovery and design in photonic nanostructures using manifold learning and metric learning and their application to the highly desired metaphotonic structures as an example platform. © 2023 SPIE.

关键词： Nanostructures

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Telehealth Uptake into Primary Care During the COVID-19 Pandemic 18

Telehealth Uptake into Primary Care During the COVID-19 Pand...

引用

18th World Congress on Medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Gao, Cheng Malin, Bradley A. Chen, You Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States

ISBN: (纸本)9781643682648

Telehealth is designed to provide health services through the use of electronic information and telecommunication technologies. It has quickly become an important tool to ensure continued care in response to the COVID-19 pandemic while mitigating the risk of viral exposure for patients and providers. This study compared the number of monthly telehealth visits in primary care settings at a large academic medical center from 2019 and 2020. To investigate what health conditions are suitable for telehealth visits, we report on the ten ICD-10 codes with the largest number of telehealth visits. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： COVID-19

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DRASCLR: A SELF-SUPERVISED FRAMEWORK OF LEARNING DISEASE-RELATED AND ANATOMY-SPECIFIC REPRESENTATION FOR 3D MEDICAL IMAGES

arXiv

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arXiv 2023年

作者： Yu, Ke Sun, Li Chen, Junxiang Reynolds, Max Chaudhary, Tigmanshu Batmanghelich, Kayhan School of Computing and Information University of Pittsburgh United States Department of Electrical and Computer Engineering Boston University United States Department of Biomedical Informatics University of Pittsburgh United States

Large-scale volumetric medical images with annotation are rare, costly, and time prohibitive to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature extraction solution for many downstream tasks, as it only uses unlabeled data. Recently, SSL methods based on instance discrimination have gained popularity in the medical imaging domain. However, SSL pre-trained encoders may use many clues in the image to discriminate an instance that are not necessarily disease-related. Moreover, pathological patterns are often subtle and heterogeneous, requiring the ability of the desired method to represent anatomy-specific features that are sensitive to abnormal changes in different body parts. In this work, we present a novel SSL framework, named DrasCLR, for 3D medical imaging to overcome these challenges. We propose two domain-specific contrastive learning strategies: one aims to capture subtle disease patterns inside a local anatomical region, and the other aims to represent severe disease patterns that span larger regions. We formulate the encoder using conditional hyper-parameterized network, in which the parameters are dependant on the anatomical location, to extract anatomically sensitive features. Extensive experiments on large-scale computer tomography (CT) datasets of lung images show that our method improves the performance of many downstream prediction and segmentation tasks. The patient-level representation improves the performance of the patient survival prediction task. We show how our method can detect emphysema subtypes via dense prediction. We demonstrate that fine-tuning the pre-trained model can significantly reduce annotation efforts without sacrificing emphysema detection accuracy. Our ablation study highlights the importance of incorporating anatomical context into the SSL framework. © 2023, CC BY.

关键词： Medical imaging

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