This Editorial Guide describes an area of active signaling research. A challenge signaling researchers face is understanding how the information gained by analysis at the cellular and molecular levels can be translated to understanding higher-order organismal physiology and pathophysiology.
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Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings.
Nat Commun
January 2025
Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA, USA.
Intracellular electrophysiology is essential in neuroscience, cardiology, and pharmacology for studying cells' electrical properties. Traditional methods like patch-clamp are precise but low-throughput and invasive. Nanoelectrode Arrays (NEAs) offer a promising alternative by enabling simultaneous intracellular and extracellular action potential (iAP and eAP) recordings with high throughput.
View Article and Find Full Text PDFUltrafast Coherence-Based Power Doppler Estimation Using Nonlinear Compounding With Complementary Subset Transmit.
Ultrasound Med Biol
January 2025
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
Objective: Conventional coherent plane wave compounding (CPWC) and sum-of-square power Doppler (PD) estimation lead to low contrast and high noise level in ultrafast PD imaging when the number of plane-wave angle and the ensemble length is limited. The coherence-based PD estimation using temporal-multiply-and-sum (TMAS) of high-lag autocorrelation can effectively suppress the uncorrelated noises but at the cost of signal power due to the blood flow decorrelation.
Methods: In this study, the TMAS PD estimation is incorporated with complementary subset transmit in nonlinear compounding (DMAS-CST) to leverage the signal coherence in both angular and temporal dimensions for improvement of PD image quality.
Failed stopping transiently suppresses the electromyogram in task-irrelevant muscles.
eNeuro
January 2025
Action Control Lab, Department of Human Physiology, University of Oregon, Eugene, Oregon, USA.
Selectively stopping individual parts of planned or ongoing movements is an everyday motor skill. For example, while walking in public you may stop yourself from waving at a stranger who you mistook for a friend while continuing to walk. Despite its ubiquity, our ability to selectively stop actions is limited.
View Article and Find Full Text PDFDECT sparse reconstruction based on hybrid spectrum data generative diffusion model.
Comput Methods Programs Biomed
January 2025
Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; School of Computer Science and Engineering, Southeast University, Nanjing, China.
Purpose: Dual-energy computed tomography (DECT) enables the differentiation of different materials. Additionally, DECT images consist of multiple scans of the same sample, revealing information similarity within the energy domain. To leverage this information similarity and address safety concerns related to excessive radiation exposure in DECT imaging, sparse view DECT imaging is proposed as a solution.
View Article and Find Full Text PDFA Fine-grained Hemispheric Asymmetry Network for accurate and interpretable EEG-based emotion classification.
Neural Netw
January 2025
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region; Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region. Electronic address:
In this work, we propose a Fine-grained Hemispheric Asymmetry Network (FG-HANet), an end-to-end deep learning model that leverages hemispheric asymmetry features within 2-Hz narrow frequency bands for accurate and interpretable emotion classification over raw EEG data. In particular, the FG-HANet extracts features not only from original inputs but also from their mirrored versions, and applies Finite Impulse Response (FIR) filters at a granularity as fine as 2-Hz to acquire fine-grained spectral information. Furthermore, to guarantee sufficient attention to hemispheric asymmetry features, we tailor a three-stage training pipeline for the FG-HANet to further boost its performance.
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