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arXiv 2025年

作者： Shariatpanahi, Bita Nozari, Erfan Daftarian, Soroush Arab, Fahimeh Kheirkhah, Mina Bernhard, Felix P. Khodadadi, Shiva Giltay, Erik J. Hebbrecht, Kaat Hofmann, Stefan G. Hahn, Tim Jamalabadi, Hamidreza Department of Psychiatry and Psychotherapy University of Marburg Germany Department of Mechanical Engineering University of California RiversideCA United States Department of Electrical and Computer Engineering University of California Riverside RiversideCA United States Department of Bioengineering University of California Riverside RiversideCA United States Neuroscience Graduate Program University of California Riverside RiversideCA United States Experimental Therapeutics and Pathophysiology Branch National Institute of Mental Health Bethesda MD United States University of Antwerp Belgium Department of Psychiatry Leiden University Medical Center Netherlands Department of Psychology University of Marburg Germany Institute for Translational Psychiatry University of Münster Germany University of Marburg Germany

Understanding how neural dynamics shape cognitive experiences remains a central challenge in neuroscience and psychiatry. Here, we present a novel framework leveraging state-to-output controllability from dynamical systems theory to model the interplay between cognitive perturbations, neural activity, and subjective experience. We demonstrate that large-scale fMRI signals are constrained to low-dimensional manifolds, where affective and cognitive states are naturally organized. Furthermore, we provide a theoretically robust method to estimate the controllability Gramian from steady-state neural responses, offering a direct measure of the energy required to steer cognitive outcomes. In five healthy participants viewing 2,185 emotionally evocative short videos, our analyses reveal a strong alignment between neural activations and affective ratings, with an average correlation of r ≈ 0.7. In a clinical cohort of 255 patients with major depressive disorder, biweekly Hamilton Rating Scale trajectories over 11 weeks significantly mapped onto these manifolds, explaining approximately 20% more variance than chance (p −10, numerically better than chance in 93% reaching statistical significance in one-third of subjects). Our work bridges dynamical systems theory and clinical neuroscience, providing a principled approach to optimize mental health treatments by targeting the most efficient neural pathways for cognitive change. © 2025, CC BY-NC-SA.

关键词： System theory

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In vivo microelectrode arrays for neuroscience

Nature Reviews Methods Primers

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Nature Reviews Methods Primers 2025年第1期5卷 1页

作者： Williams, Nathaniel P. Voroslakos, Mihaly Shi, Delin Pwint, May Yoon Lanzio, Vittorino Mao, Hongwei Zolotavin, Pavlo Yoon, Euisik Stieglitz, Thomas Xie, Chong Harris, Timothy D. Schwartz, Andrew B. Cui, Xinyan Tracy Department of Bioengineering University of Pittsburgh Pittsburgh PA United States Center for the Neural Basis of Cognition Pittsburgh PA United States Neuroscience Institute and Department of Neurology NYU Grossman School of Medicine New York University New York NY United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Systems Neuroscience Center University of Pittsburgh Pittsburgh PA United States Department of Electrical and Computer Engineering Rice University Houston TX United States Department of Biomedical Engineering University of Michigan Ann Arbor MI United States Department of Mechanical Engineering University of Michigan Ann Arbor MI United States Center for Nanomedicine Institute for Basic Science (IBS) Yonsei University Seoul South Korea Laboratory for Biomedical Microtechnology Department of Microsystems Engineering-IMTEK University of Freiburg Freiburg Germany BrainLinks-BrainTools University of Freiburg Freiburg Germany Rice Neuroengineering Initiative Rice University Houston TX United States Department of Bioengineering Rice University Houston TX United States Janelia Research Campus Howard Hughes Medical Institute Ashburn VA United States Department of Biomedical Engineering Johns Hopkins University Baltimore MD United States

Microelectrode arrays (MEAs) are devices capable of recording extracellular action potentials (spikes) from many neurons simultaneously and with high spatial and temporal resolution. MEAs have emerged as an invaluable tool for understanding how networks of neurons govern complex sensory, motor and decision-making processes. In this Primer, we introduce various in vivo MEA designs and describe their construction, characterization and applications. We describe approaches for effective device implantation and the use of MEA recordings in behavioural experiments. We then discuss strategies for obtaining high-quality and stable electrophysiological recordings, including through mitigating the foreign body reaction. We introduce the reader to spike sorting approaches with a focus on semi-automated sorting of high-channel-count data, as well as to the analysis of sorted spikes and their uses. Finally, we cover future trends and emerging MEA technology designed to expand current capabilities and overcome limitations, with a focus on biomimetic and multifunctional devices. This Primer should provide the reader with a foundation in the fundamental principles of MEA technology for in vivo use. © Springer Nature Limited 2025.

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Integrated 6G TN and NTN Localization: Challenges, Opportunities, and Advancements

IEEE Communications Standards Magazine

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IEEE Communications Standards Magazine 2025年

作者： Saleh, Sharief Zheng, Pinjun Liu, Xing Chen, Hui Keskin, Musa Furkan Priyanto, Basuki Beale, Martin Ettefagh, Yasaman Seco-Granados, Gonzalo Al-Naffouri, Tareq Y. Wymeersch, Henk Chalmers University of Technology Department of Electrical Engineering Sweden King Abdullah University of Science and Technology (KAUST) Electrical and Computer Engineering Program Division of Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Saudi Arabia Sony Europe BV United Kingdom Universitat Autònoma de Barcelona Department of Telecommunications and Systems Engineering Spain

The rapid evolution of cellular networks has introduced groundbreaking technologies, including large and distributed antenna arrays and reconfigurable intelligent surfaces in terrestrial networks (TNs), as well as aerial and space-based nodes in non-terrestrial networks (NTNs). These advancements enable applications beyond traditional communication, such as high-precision localization and sensing. While integrating TN and NTN enablers will lead to unparalleled opportunities for seamless global localization, such integration attempts are expected to face several challenges. To understand these opportunities and challenges, we first examine the distinctive characteristics of the key 6G enablers, evaluating their roles in localization from both technical and practical perspectives. Next, to identify developments driving TN-NTN localization, we review the latest standardization and industrial innovation progress. Finally, we discuss the opportunities and challenges of TN-NTN integration, illustrating its potential through two numerical case studies. © 2017 IEEE.

关键词： 6G localization low-Earth-orbit (LEO) satellites non-terrestrial network (NTN) reconfigurable intelligent surface (RIS) terrestrial network (TN)

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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Norepinephrine changes behavioral state through astroglial purinergic signaling

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science (New York, N.Y.) 2025年第6748期388卷 769-775页

作者： Chen, Alex B. Duque, Marc Rymbek, Altyn Dhanasekar, Mahalakshmi Wang, Vickie M. Mi, Xuelong Tocquer, Loeva Narayan, Sujatha Legorreta, Emmanuel Marquez Eddison, Mark Yu, Guoqiang Wyart, Claire Prober, David A. Engert, Florian Ahrens, Misha B. Howard Hughes Medical Institute Janelia Research Campus Ashburn VA United States Department of Molecular and Cellular Biology Harvard University Cambridge MA United States Graduate Program in Neuroscience Harvard Medical School Boston MA United States Tianqiao and Chrissy Chen Institute for Neuroscience Division of Biology and Biological Engineering California Institute of Technology Pasadena CA United States Sorbonne Université Paris Brain Institute (Institut du Cerveau Institut National de la Santé et de la Recherche Médicale U1127 Centre National de la Recherche Scientifique Unité Mixte de Recherche 7225 Assistance Publique-Hôpitaux de Paris Campus Hospitalier Pitié-Salpêtrière Paris France Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington VA United States Present address: Allen Institute for Brain Science Seattle WA United States Department of Automation Tsinghua University Beijing China

Both neurons and glia communicate through diffusible neuromodulators;however, how neuron-glial interactions in such neuromodulatory networks influence circuit computation and behavior is unclear. During futility-induced behavioral transitions in the larval zebrafish, the neuromodulator norepinephrine (NE) drives fast excitation and delayed inhibition of behavior and circuit activity. We found that astroglial purinergic signaling implements the inhibitory arm of this motif. In larval zebrafish, NE triggers astroglial release of adenosine triphosphate (ATP), extracellular conversion of ATP into adenosine, and behavioral suppression through activation of hindbrain neuronal adenosine receptors. Our results suggest a computational and behavioral role for an evolutionarily conserved astroglial purinergic signaling axis in NE-mediated behavioral and brain state transitions and position astroglia as important effectors in neuromodulatory signaling.

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Quantitative Analysis of Colonic Epithelial Cell Aging in a Cell-cycle like Model: Changes in Nucleus and Cytoplasm Along the Crypt Axis

Quantitative Analysis of Colonic Epithelial Cell Aging in a ...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Bao, Shunxing Wang, Yaohong Rudravaram, Gaurav Zhang, Rendong Deng, Ruining Yu, Xin Gao, Chenyu Li, Jia Li, Yike Roland, Joseph T. Zuo, Lianrui Liu, Qi Lau, Ken S. Coburn, Lori A. Wilson, Keith T. Landman, Bennett A. Huo, Yuankai Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Anatomical Pathology UT MD Anderson Cancer Center HoustonTX United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center Nashville United States Department of Otolaryngology-Head and Neck Surgery Vanderbilt University Medical Center NashvilleTN United States Department of Surgery Vanderbilt University Medical Center NashvilleTN United States Department of Cell and Developmental Biology Vanderbilt University School of Medicine NashvilleTN United States Vanderbilt University Medical Center Division of Gastroenterology Hepatology and Nutrition Department of Medicine Nashville United States Vanderbilt University Medical Center Vanderbilt Center for Mucosal Inflammation and Cancer Nashville United States Vanderbilt University School of Medicine Program in Cancer Biology Nashville United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Medical Center Department of Pathology Microbiology and Immunology NashvilleTN United States

ISBN: (纸本)9781510686045

Understanding epithelial cell aging in bowel tissue can provide critical insights into gastrointestinal (GI) health, potentially leading to improved diagnosis, treatment, and prevention strategies for various GI conditions. Despite the significance of cellular aging in the GI tract, previous studies have not quantified epithelial cell maturity development along the base-to-surface axis of colon crypts due to the labor-intensive nature of manual quantification and the lack of a clear definition of cellular age. Moreover, the process faces tremendous challenges when tissue is cut in 2D and examines results solely from one or a few biopsy cuts without reconstructing the full 3D space, as the original cell structure is 3D, leading to variability based on the cut angle. This research addresses these challenges by systematically analyzing the relationship between epithelial cell morphology and their position from the base to the surface in colon crypt tissue. Our approach involves using multiplex immunofluorescence (MxIF) imaging for cell nuclei segmentation, identifying the potential full longitudinal cut, and manually annotating the surface and base of the colon crypts. By analyzing the epithelial cells, we create density maps to investigate the relationship between nucleus size and the surface-to-base distance ratio, defining this ratio as cell age. We also examine the nucleus-to-cytoplasm (N/C) ratio in relation to cell age. For the statistical analysis, we use a robust linear regression model to remove outliers and a random effects model for the final regression. Our results show a decreasing trend in both the N/C ratio and nucleus size as epithelial cells move from the base to the surface. These findings provide a quantitative pipeline for single-cell profiling in a spatial manner, facilitating broader analysis of epithelial cell aging and its implications for GI health. © 2025 SPIE.

关键词： Linear regression

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Real-Time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep Learning

arXiv

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arXiv 2025年

作者： Islam, Khayrul Forelli, Ryan F. Han, Jianzhong Bhadane, Deven Huang, Jian Agar, Joshua C. Tran, Nhan Ogrenci, Seda Liu, Yaling Department of Mechanical Engineering Lehigh University BethlehemPA18015 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Coriell Institute for Medical Research CamdenNJ United States Department of Computer Science Lehigh University BethlehemPA18015 United States Cooper Medical School of Rowan University CamdenNJ08103 United States Center for Metabolic Disease Research Temple University Lewis Katz School of Medicine PhiladelphiaPA19122 United States Department of Mechanical Engineering and Mechanics Drexel University PhiladelphiaPA19104 United States Real-time Processing Systems Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Bioengineering Lehigh University BethlehemPA18015 United States

Precise cell classification is essential in biomedical diagnostics and therapeutic monitoring, particularly for identifying diverse cell types involved in various diseases. Traditional cell classification methods such as flow cytometry depend on molecular labeling which is often costly, time-intensive, and can alter cell integrity. To overcome these limitations, we present a label-free machine learning framework for cell classification, designed for real-time sorting applications using bright-field microscopy images. This approach leverages a teacher-student model architecture enhanced by knowledge distillation, achieving high efficiency and scalability across different cell types. Demonstrated through a use case of classifying lymphocyte subsets, our framework accurately classifies T4, T8, and B cell types with a dataset of 80,000 preprocessed images, accessible via an open-source Python package for easy adaptation. Our teacher model attained 98% accuracy in differentiating T4 cells from B cells and 93% accuracy in zero-shot classification between T8 and B cells. Remarkably, our student model operates with only 0.02% of the teacher model’s parameters, enabling field-programmable gate array (FPGA) deployment. Our FPGA-accelerated student model achieves an ultra-low inference latency of just 14.5 µs and a complete cell detection-to-sorting trigger time of 24.7 µs, delivering 12x and 40x improvements over the previous state-of-the-art real-time cell analysis algorithm in inference and total latency, respectively, while preserving accuracy comparable to the teacher model. This framework provides a scalable, cost-effective solution for lymphocyte classification, as well as a new SOTA real-time cell sorting implementation for rapid identification of subsets using in situ deep learning on off-the-shelf computing hardware. © 2025, CC BY.

关键词： Deep learning

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Design and analysis of an adaptive, global strategy for detecting and mitigating distributed DoS attacks in GRID environments

Design and analysis of an adaptive, global strategy for dete...

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39th Annual Simulation Symposium, 2006

作者： Znati, Taieb Amadei, James Pazehoski, Daniel R. Sweeny, Scott Department of Computer Science Telecommunication Program Department of Electrical and Computer Engineering

ISBN: (纸本)0769525598

Basic protection schemes against Denial-of-Service (DoS) are based on a perimeter-based model, where attacks are dealt with after they occur. This is quite often too late to prevent damage or loss of resources and service. The focus of this paper is to investigate a distributed defense method that can neutralize the attack before it reaches the potential target in mass. In order to effectively implement this method, this paper proposes a progressive, globally deployable sentinel scheme for data sampling, packet inspection, and DoS attack detection and recovery. A simulation framework is developed to study the performance of the proposed scheme. The results show a significant improvement in how the network deals with DoS attacks, in comparison to local DoS detection and prevention schemes. © 2006 IEEE.

关键词： computer crime

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Measurement of peripheral nerve magnetostimulation thresholds of a head solenoid coil between 200 Hz and 88.1 kHz

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IEEE Journal of Translational engineering in Health and Medicine 2025年

作者： Barksdale, Alex C. Ferris, Natalie G. Mattingly, Eli Sliwiak, Monika Guerin, Bastien Wald, Lawrence L. Davids, Mathias Klein, Valerie MIT Department of Electrical Engineering and Computer Science Cambridge MA United States Martinos Center for Biomedical Imaging Charlestown MA United States Harvard University Harvard Graduate Program in Biophysics Cambridge MA United States Harvard-MIT Division of Health Sciences and Technology Boston MA United States Harvard Medical School Boston MA United States

Objective: Magnetic fields switching at kilohertz frequencies induce electric fields in the body, which can cause peripheral nerve stimulation (PNS). Although magnetostimulation has been extensively studied below 10 kHz, the behavior of PNS at higher frequencies remains poorly understood. This study aims to investigate PNS thresholds at frequencies up to 88.1 kHz and to explore deviations from the widely accepted hyperbolic strength-duration curve (SDC). Methods and procedures: PNS thresholds were measured in the head of 8 human volunteers using a solenoidal coil at 16 distinct frequencies, ranging from 200 Hz to 88.1 kHz. A hyperbolic SDC was used as a reference to compare the frequency-dependent behavior of PNS thresholds. Results: Contrary to the predictions of the hyperbolic SDC, PNS thresholds did not decrease monotonically with frequency. Instead, thresholds reached a minimum near 25 kHz, after which they increased by an average of 39% from 25 kHz to 88.1 kHz across subjects. This pattern indicates a significant deviation from previously observed behavior at lower frequencies. Conclusion: Our results suggest that PNS thresholds exhibit a non-monotonic frequency dependence at higher frequencies, diverging from the traditional hyperbolic SDC. These findings offer critical data for refining neurodynamic models and provide insights for setting PNS safety limits in applications like MRI gradient coils and magnetic particle imaging (MPI). Further investigation is needed to understand the biological mechanisms driving these deviations beyond 25 kHz. Clinical impact: These findings call for further basic research into biological mechanisms underlying high frequency PNS threshold trends, and supports refinement of safety guidelines for MRI and MPI systems for clinical implementation. © 2013 IEEE.

关键词： high frequency magnetostimulation magnetic particle imaging (MPI) magnetic resonance imaging (MRI) MRI gradient safety peripheral nerve stimulation

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Accelerating adoption of species-agnostic plant sensors for precision farming

Nature Reviews Electrical Engineering

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Nature Reviews electrical engineering 2025年 58-70页

作者： Abdullah Bukhamsin Khaled Nabil Salama Jürgen Kosel Ikram Blilou Bioengineering Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Center of Excellence — Sustainable Food Security (SFS) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Sensor Systems Division Silicon Austria Labs Villach Austria Plant Science Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Precision farming is an optimized management farming scheme that seeks to link the real-time needs of crops with the nutrients to be administered. Sensing platforms that can monitor the physiological status of crops in situ are key to enabling timely and localized interventions. However, the underdevelopment of plant sensing strategies limits the potential of precision farming. In this Review, we discuss the challenges and advancements in phyto-monitoring, focusing on strategies that are applicable to a wide range of plant species and suitable for field deployment. We explore species-agnostic sensors, including optical and electrochemical sensors, whose operation is based on principles that are widely applicable to all plant species. These platforms enable real-time monitoring of the physiological state of crops by assessing key biomarkers, such as plant hormones, and metabolites such as salicylic acid and reactive oxygen species. Evaluating these systems, we conclude that an integrative sensing approach is necessary to compensate for the limitations of the individual methods and can provide a holistic view of crop health. Cost-effective species-agnostic sensors are thus needed to provide information that can be used to minimize the resource footprint of farming and meet the growing global demand.

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