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IEEE International Symposium on biomedical Imaging

作者： Hossam Magdy Balaha Mayada Elgendy Ahmed Alksas Mohamed Shehata Norah Saleh Alghamdi Fatma Taher Mohammed Ghazal Mahitab Ghoneim Eslam Hamed Fatma Sherif Ahmed Elgarayhi Mohammed Sallah Mohamed Abdelbadie Salem Elsharawy Kamal Harpal Sandhu Ayman El-Baz Department of Bioengineering J.B. Speed School of Engineering University of Louisville Louisville USA Applied Theoretical Physics Research Group Physics Department Faculty of Science Mansoura University Egypt Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia The College of Technological Innovation Zayed University Dubai UAE Electrical Computer and Biomedical Engineering Depatrment Abu Dhabi University UAE Department of Radiology Faculty of Medicine Mansoura University Egypt Otolaryngology Department Faculty of Mediine Mansoura University Egypt Higher Institute of Engineering and Technology Egypt

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

SARS-CoV-2 is the cause of the acute respiratory illness COVID-19. In order to identify whether a COVID-19 patient has normal, moderate, or severe anosmia, this study suggests a computer-assisted diagnostic system. It utilizes fluid-attenuated inversion recovery Magnetic Resonance Imaging and Diffusion Tensor Imaging to extract the olfactory nerve’s appearance, morphology, and diffusivity markers. The suggested CAD system consists of the following stages: (1) extract appearance markers, morphology markers, and diffusivity markers, (2) apply markers fusion, and (3) select the most promising classifier as the basis for the decision and the relevant performance indicators. The current work is unique in that it uses an ensemble of the trained, tuned, and improved ML classifiers to identify OB anosmia using majority voting. From the fusion tests using different folds, stacking boosted the weight accuracy, weighted precision, weighted specificity, weighted sensitivity, and weighted F1-score to 85.30%, 80%, 89.50%, 78.90%, and 78.30% respectively. Regarding the various performance criteria, the LGBM outperforms other ML classifiers.

关键词： COVID-19 Neuroimaging Sensitivity Shape Stacking Olfactory Morphology

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Directly extracting the optical transport coefficients of a turbid medium via time-resolved diffuse reflectance: A Monte Carlo study 10

Directly extracting the optical transport coefficients of a ...

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biomedical Applications of Light Scattering X 2020

作者： Helton, Michael Mycek, Mary-Ann Vishwanath, Karthik Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Department of Physics Miami University OxfordOH45056 United States

ISBN: (纸本)9781510632691

An empirical approach to estimate the reduced scattering (μs') and absorption (μa) coefficients of a turbid medium from time-resolved diffuse reflectance detected at multiple source-detector separations (SDS) is described. The dependence of the temporal point spread function (TPSF) on the medium's optical properties has been previously well studied. Here, we exploit these findings by using the difference in photon arrival-times obtained from a pair of SDS at the (a) peak intensity and (b) longer-time trailing edge of the TPSF to estimate the transport coefficients. This difference may have little dependence on the instrument response functions (IRF). Consequently, real-time quantitation is possible since the method does not depend on non-linear fitting of measured reflectance and/or deconvolving the IRF. The approach uses Monte Carlo simulations to directly translate the difference in arrival times of the temporal reflectance profiles at multiple SDS into the medium's optical properties. Here, we show that a small range in the optical properties could be defined using a pair of time differences in the TPSF. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Table lookup

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Short-Chain Dense Brush Pegylation on Rigid Nanocarriers Overcomes Anti-Peg Antibody Recognition for Immune-Stealth Drug Delivery

SSRN

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SSRN 2025年

作者： Chen, Ting-Yu Chang, Chi-Yuan Wen, Ting-Chun Xu, Li Lin, Yu-Han Peng, Cheng-Liang Yeh, Yi-Qi Chen, Chueh-Hsuan Chuang, Kuo-Hsiang Wu, Si-Han Graduate Institute of Pharmacognosy Taipei Medical University Taipei11031 Taiwan Graduate Institute of Nanomedicine and Medical Engineering Taipei Medical University Taipei11031 Taiwan Department of Isotope Application National Atomic Research Institute Taoyuan Taiwan National Synchrotron Radiation Research Center Hsinchu Science Park Hsinchu30076 Taiwan Research Center for Applied Science Academia Sinica Taiwan PhD Program in Clinical Drug Development of Chinese Herbal Medicine Taipei Medical University Taipei Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan

The immunogenicity of polyethylene glycol (PEG) remains a critical challenge in nanomedicine, as pre-existing anti-PEG antibodies can compromise therapeutic efficacy through accelerated blood clearance (ABC) and life-threatening hypersensitivity. Here, we address this challenge by engineering the PEG architecture, creating short-chain dense brush PEGylation (PEG500) on rigid mesoporous silica nanoparticles (MSNs). Unlike conventional PEG2000-liposomes, MSN-PEG500 evades antibodies via three synergistic mechanisms: (1) short-chain PEG500 reduces epitope accessibility, (2) high grafting density (4.43 chains/nm2, according to TGA) forms an dense steric shield, and (3) the rigid silica surface prevents PEG burial. ELISA studies demonstrated that, at equivalent nanoparticle concentrations, MSN-PEG500 exhibits a 26-fold reduction in anti-PEG IgG (6.3) binding and a 1000-fold reduction in anti-PEG IgM (AGP3) binding compared to Lipodox (PEG2000), both of which target the PEG backbone. Furthermore, radiolabeled MSN-PEG500 achieved prolonged circulation and 25% tumor accumulation (ID/g) at 24 hours. In strongly immunized mice, doxorubicin-loaded MSN-PEG500 preserved therapeutic efficacy and 100% survival, whereas Lipodox induced fatal hypersensitivity. Mechanistic investigations revealed that complement activation drives PEG2000 immunotoxicity, and pretreatment with the C3 inhibitor compstatin effectively mitigated temperature drops from 10°C to 2°C in immunized hosts. By demonstrating that nanoscale control of polymer conformation dictates immunological fate, this work provides a clinically translatable blueprint for developing immune-evasive nanomedicines. © 2025, The Authors. All rights reserved.

关键词： Silica nanoparticles

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Convolutional neural network-based fast seizure detection from video electroencephalograms

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biomedical Signal Processing and Control 2023年第Part2期80卷 000-000页

作者： Chou, Chi-Hsiang Shen, Tsu-Wang Tung, Hsin Hsieh, Peiyuan F. Kuo, Chih-En Chen, Ting-Mao Yang, Chao-Wei Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan The Department of Neurological Institute Taichung Veterans General Hospital Taichung Taiwan Department of Neurology National Yang Ming Chiao Tung University School of Medicine Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Master's Program in Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Applied Mathematics National Chung Hsing University Taichung Taiwan

Objective: Ictal stage detection in electroencephalography (EEG) is important for epilepsy diagnosis. However, it's a laborious and time-consuming task for neurologists, especially with lengthy EEG recordings (such as video-EEG). Here we applied the current deep learning techniques to improve ictal stage detection efficiency. Methods: We enrolled epileptic patients at video-EEG examination beds of our neurology department between Jan 2016 and Apr 2020. Four stages of EEG (interictal, preictal, ictal and postictal) were labeled and confirmed by neurologists. Labeled EEG signals were transformed into second-order Poincaré difference plots. The whole dataset was divided into ‘Training’, ‘Validation’ and ‘Testing’ datasets with the proportions of 45%, 5% and 50% respectively. We applied 4 Convolutional Neural Networks (CNNs), namely, AlexNet, GoogLeNet, ResNet50 and DenseNet201, to learn classifying. The top three CNNs were put into an ensemble model for datasets classification. Results: 30 epileptic patients were included: Male/Female 16/14, mean age 42.8 ± 15.0 (range 22 ∼ 81) years old. Total recorded ictal duration was 47,444 s. Overall accuracies for each of the 4 CNN models (AlexNet, GoogLeNet, ResNet50 and DenseNet201) were 78.0%, 78.3%, 81.7% and 80.8%. We omitted the AlexNet, the other 3 CNNs were put into the ensemble model. The best performance was under test with 1% of the Testing dataset yielded the overall accuracies of 85.9%. The ictal stage accuracy was 97.7%. Conclusion: Our method is a promising computer-aid deep learning approach to detect seizures on video EEG. Significance: For testing phase, selected proportion of testing dataset may influence the performances of deep learning models. © 2022 The Author(s)

关键词： Electroencephalography

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A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics 55

A mechanistically interpretable model of the retinal neural ...

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55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021

作者： Ding, Xuehao Lee, Dongsoo Grant, Satchel Stein, Heike McIntosh, Lane Maheswaranathan, Niru Baccus, Stephen Stanford University Department of Applied Physics Stanford United States Stanford University Neurosciences PhD Program Stanford United States Stanford University Department of Psychology Stanford United States University of Barcelona August Pi i Sunyer Biomedical Research Institute Barcelona Spain Stanford University Neurobiology Department Stanford United States

ISBN: (纸本)9781665458283

The visual system processes stimuli over a wide range of spatiotemporal scales, with individual neurons receiving input from tens of thousands of neurons whose dynamics range from milliseconds to tens of seconds. This poses a challenge to create models that both accurately capture visual computations and are mechanistically interpretable. Here we present a model of salamander retinal ganglion cell spiking responses recorded with a multielectrode array that captures natural scene responses and slow adaptive dynamics. The model consists of a three-layer convolutional neural network (CNN) modified to include local recurrent synaptic dynamics taken from a linear-nonlinear-kinetic (LNK) model [1]. We presented alternating natural scenes and uniform field white noise stimuli designed to engage slow contrast adaptation. To overcome difficulties fitting slow and fast dynamics together, we first optimized all fast spatiotemporal parameters, then separately optimized recurrent slow synaptic parameters. The resulting full model reproduces a wide range of retinal computations and is mechanistically interpretable, having internal units that correspond to retinal interneurons with biophysically modeled synapses. This model allows us to study the contribution of model units to any retinal computation, and examine how long-term adaptation changes the retinal neural code for natural scenes through selective adaptation of retinal pathways. © 2021 IEEE.

关键词： Dynamics

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Artificial touch feedback using microstimulation of human somatosensory cortex to convey grip force from a robotic hand

Artificial touch feedback using microstimulation of human so...

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

作者： Luke E. Osborn Breanne Christie David P. McMullen Tessy M. Thomas Margaret C. Thompson Robert W. Nickl Ambarish S. Pawar Brock A. Wester Gabriela L. Cantarero Pablo A. Celnik Nathan E. Crone Matthew S. Fifer Francesco V. Tenore Johns Hopkins University Applied Physics Laboratory National Institute of Mental Health National Institutes of Health Department of Biomedical Engineering Johns Hopkins University Department of Physical Medicine and Rehabilitation Johns Hopkins University School of Medicine Shirley Ryan AbilityLab Department of Neurology Johns Hopkins University School of Medicine DPM TMT ASP and GLC US Food and Drug Administration

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Invasive brain-machine interfaces can help restore function through the control of external devices while the addition of intracortical microstimulation (ICMS) can elicit sensations of touch and help provide further benefits for individuals living with sensorimotor deficits. However, the extent of tactile information that can be conveyed through ICMS has not been fully explored. In a human participant with spinal cord injury and chronically implanted microelectrode arrays, we used ICMS to the somatosensory cortex to provide grip force feedback in the hands during grasping of objects with varying stiffness with a robotic arm. Using only ICMS-evoked touch sensations, the participant was able to identify between two and three objects with an accuracy of 92% and 67%, respectively. In a compliant grasping task with the goal of grasping a delicate object without crushing it, objects were deformed on average only 2.8 mm with ICMS-based touch feedback compared to 8.7 mm without. These results demonstrate that ICMS-evoked touch sensations to the hands can be used to provide force-based feedback for perceiving object properties and enable more precise grasping during closed-loop control of a robotic limb through a cortical interface.

关键词： Somatosensory Microelectrodes Accuracy Force Force feedback Grasping Robot sensing systems Manipulators Spinal cord injury Object recognition

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Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images

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Physical Review applied 2025年第3期23卷 034035-034035页

作者： Niko R. Reed Danyal Bhutto Matthew J. Turner Declan M. Daly Sean M. Oliver Jiashen Tang Kevin S. Olsson Nicholas Langellier Mark J.H. Ku Department of Physics University of Maryland College Park Maryland USA Quantum Technology Center University of Maryland College Park Maryland USA Joint Quantum Institute University of Maryland College Park Maryland USA Department of Biomedical Engineering Boston University Boston Massachusetts USA A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA Department of Electrical and Computer Engineering University of Maryland College Park Maryland USA The MITRE Corporation McLean Virginia 22102 USA Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College Park Maryland 20742 USA Department of Physics Harvard University Cambridge Massachusetts USA Present address: VideaHealth Inc. Boston MA. Department of Materials Science and Engineering University of Delaware Newark Delaware 19716 USA Department of Physics and Astronomy University of Delaware Newark Delaware 19716 USA Present address: Northrop Grumman Mission Systems Linthicum Maryland 21090 USA.

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high-spatial-frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional images of vector magnetic fields acquired by a quantum diamond microscope . Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower-signal-to-noise-ratio data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources.

关键词： Signal to noise ratio

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Controlling the spontaneous firing behavior of a neuron with astrocyte

arXiv

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arXiv 2022年

作者： Palabas, Tugba Longtin, Andre Ghosh, Dibakar Uzuntarla, Muhammet Department of Biomedical Engineering Zonguldak Bulent Ecevit University Zonguldak67100 Turkey Department of Physics University of Ottawa OttawaON Canada Physics and Applied Mathematics Unit Indian Statistical Institute Kolkata700108 India Department of Bioengineering Gebze Technical University Kocaeli41400 Turkey

Mounting evidence in recent years suggests that astrocytes, a sub-type of glial cells, not only serve metabolic and structural support for neurons and synapses but also play critical roles in regulation of proper functioning of the nervous system. In this work, we investigate the effect of astrocyte on the spontaneous firing activity of a neuron through a combined model which includes a neuron-astrocyte pair. First, we show that an astrocyte may provide a kind of multistability in neuron dynamics by inducing different firing modes such as random and bursty spiking. Then, we identify the underlying mechanism of this behavior and search for the astrocytic factors that may have regulatory roles in different firing regimes. More specifically, we explore how an astrocyte can participate in occurrence and control of spontaneous irregular spiking activity of a neuron in random spiking mode. Additionally, we systematically investigate the bursty firing regime dynamics of the neuron under the variation of biophysical facts related to the intracellular environment of the astrocyte. It is found that an astrocyte coupled to a neuron can provide a control mechanism for both spontaneous firing irregularity and burst firing statistics, i.e., burst regularity and size. © 2022, CC BY.

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Optical Dipole Structure and Orientation of GaN Defect Single-Photon Emitters

arXiv

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arXiv 2023年

作者： Geng, Yifei Jena, Debdeep Fuchs, Gregory D. Zipfel, Warren R. Rana, Farhan School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States Department of Materials Science and Engineering Cornell University IthacaNY14853 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Meinig School of Biomedical Engineering Cornell University IthacaNY14853 United States

GaN has recently been shown to host bright, photostable, defect single photon emitters in the 600-700 nm wavelength range that are promising for quantum applications. The nature and origin of these defect emitters remain elusive. In this work, we study the optical dipole structures and orientations of these defect emitters using the defocused imaging technique. In this technique, the far-field radiation pattern of an emitter in the Fourier plane is imaged to obtain information about the structure of the optical dipole moment and its orientation in 3D. Our experimental results, backed by numerical simulations, show that these defect emitters in GaN exhibit a single dipole moment that is oriented almost perpendicular to the wurtzite crystal c-axis. Data collected from many different emitters shows that the angular orientation of the dipole moment in the plane perpendicular to the c-axis exhibits a distribution that shows peaks centered at the angles corresponding to the nearest Ga-N bonds and also at the angles corresponding to the nearest Ga-Ga (or N-N) directions. Moreover, the in-plane angular distribution shows little difference among defect emitters with different emission wavelengths in the 600-700 nm range. Our work sheds light on the nature and origin of these GaN defect emitters. Copyright © 2023, The Authors. All rights reserved.

关键词： Defects

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Enhanced Superconducting Qubit Performance Through Ammonium Fluoride Etch

arXiv

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arXiv 2024年

作者： Kopas, Cameron J. Goronzy, Dominic P. Pham, Thang Torres Castanedo, Carlos G. Cheng, Matthew Cochrane, Rory Nast, Patrick Lachman, Ella Zhelev, Nikolay Z. Vallières, André Murthy, Akshay A. Oh, Jin-Su Zhou, Lin Kramer, Matthew J. Cansizoglu, Hilal Bedzyk, Michael J. Dravid, Vinayak P. Romanenko, Alexander Grassellino, Anna Mutus, Josh Y. Hersam, Mark C. Yadavalli, Kameshwar Rigetti Computing BerkeleyCA United States Department of Materials Science and Engineering Northwestern University EvanstonIL United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL United States Department of Physics and Astronomy Northwestern University EvanstonIL United States Department of Physics University of Oregon EugeneOR United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Fermi National Accelerator Laboratory BataviaIL United States Ames Laboratory U.S. Department of Energy AmesIA United States Northwestern University EvanstonIL United States Department of Chemistry Northwestern University EvanstonIL United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL United States

The performance of superconducting qubits is often limited by dissipation and two-level systems (TLS) losses. The dominant sources of these losses are believed to originate from amorphous materials and defects at interfaces and surfaces, likely as a result of fabrication processes or ambient exposure. Here, we explore a novel wet chemical surface treatment at the Josephson junction-substrate and the substrate-air interfaces by replacing a buffered oxide etch (BOE) cleaning process with one that uses hydrofluoric acid followed by aqueous ammonium fluoride. We show that the ammonium fluoride etch process results in a statistically significant improvement in median T1 by ∼ 22% (p = 0.002), and a reduction in the number of strongly-coupled TLS in the tunable frequency range. Microwave resonator measurements on samples treated with the ammonium fluoride etch prior to niobium deposition also show ∼ 33% lower TLS-induced loss tangent compared to the BOE treated samples. As the chemical treatment primarily modifies the Josephson junction-substrate interface and substrate-air interface, we perform targeted chemical and structural characterizations to examine materials’ differences at these interfaces and identify multiple microscopic changes that could contribute to decreased TLS. © 2024, CC BY-NC-ND.

关键词： Fluorine compounds

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