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Biosensors, such as microelectrode arrays that record in vitro neuronal activity, provide powerful platforms for studying neuroactive substances. This study presents a machine learning workflow to analyze drug-induced changes in neuronal biosensor data using complex network measures from graph theory. Microelectrode array recordings of neuronal networks exposed to bicuculline, a GABA $$_A$$ receptor antagonist known to induce hypersynchrony, demonstrated the workflow’s ability to detect and characterize pharmacological effects. The workflow integrates network-based features with synchrony, optimizing preprocessing parameters, including spike train bin sizes, segmentation window sizes, and correlation methods. It achieved high classification accuracy (AUC up to 90%) and used Shapley Additive Explanations to interpret feature importance rankings. Significant reductions in network complexity and segregation, hallmarks of epileptiform activity induced by bicuculline, were revealed. While bicuculline’s effects are well established, this framework is designed to be broadly applicable for detecting both strong and subtle network alterations induced by neuroactive compounds. The results demonstrate the potential of this methodology for advancing biosensor applications in neuropharmacology and drug discovery.
Infineon Technologies AG introduces the XENSIV TLE4960x magnetic switch family. Developed in accordance with ISO 26262, the TLE4960x switches integrate
Robots usually love big, open fields — but most farms are small and chaotic.
Publication date: Available online 25 April 2025Source: Knowledge-Based SystemsAuthor(s): Lakshmi Prasanthi, Sivaneasan Bala Krishnan, K. Venkata Prasad, Prasun Chakrabarti
Silicon Austria Labs (SAL) has developed a proof of concept with AKM's Hall sensor for a traction inverter and DC-DC converter.
The global Photonic Integrated Circuits PIC market is set to experience unprecedented expansion over the next decade Valued at USD 10 2 billion in 2022 the industry is projected to grow at a 29 2 compound annual growth rate CAGR ...
According to SICK, IO-Link as a global standard for smart sensor integration opens up a wide range of possibilities for customers in automation.
Publication date: Available online 28 April 2025Source: Sensors and Actuators A: PhysicalAuthor(s): Georgios Foteinidis, Lampros Koutsotolis, Angelos Ntaflos, Alkiviadis S. Paipetis
ChemElectroChem, EarlyView.
The double-helical design places both electrodes at one end, preventing damage that typically occurs when electrodes are pulled at joints.
Publication date: Available online 25 April 2025Source: Precision EngineeringAuthor(s): Gaurav Kishor, Krishna Kishore Mugada, Raju Prasad Mahto
Photonic sensors & detectors market set for strong growth driven by automation, AI integration, and rising demand across healthcare, automotive, and defense.
The growing demand for real-time, non-invasive, and cost-effective health monitoring has driven significant advancements in portable point-of-care testing (POCT) devices. Among these, optical biosensors have emerged as promising tools for the detection of critical biomarkers such as uric acid (UA) a …
Electrochemical Science Advances, EarlyView.
World Scientific Series on Carbon Nanoscience, Page 89-179. The integration of carbon nanotubes (CNTs) into field-effect transistors (FETs) has unlocked remarkable potential in biosensing technology. This chapter provides a comprehensive overview of CNTFET biosensors, encompassing their fabrication, operational principles and sensing mechanisms, optimization strategies, and diverse applications. First, we describe the various techniques employed in the fabrication of CNTFET biosensors and functionalization of CNTs, elucidating the intricate processes that leverage the unique properties of CNTs to create highly sensitive and selective platforms for biosensing applications. In the following sections, we discuss the operating principles of different CNTFET biosensor configurations and the sensing mechanisms governing CNTFET biosensors. The emphasis is then placed on different strategies to improve biosensing performance based on these sensing mechanisms. Finally, we explore the diverse array of applications for CNTFET biosensors across various fields, including medical diagnostics, health monitoring, environmental pollutants detection, and food analysis. Additionally, recent advances in machine learning-assisted biosensing utilizing CNTFET biosensors are also reviewed. This chapter concludes with challenges and outlooks for future biosensing applications for CNTFETs.
The Time Of Flight TOF Sensor Market Report by The Business Research Company delivers a detailed market assessment covering size projections from 2025 to 2034 This report explores crucial market trends major drivers and market segmentation by key segment categories ...
This post is also available in: עברית (Hebrew)As lithium-ion batteries continue to power a growing number of devices, safety concerns have risen to the forefront. Despite their efficiency and long lifespan, these batteries can be prone to dangerous failures, particularly when they overheat or sustain damage. In response to these risks, a new sensor developed […]
Biosensors are redefining elite sports performance by providing real-time data driven insights into training and recovery helping to prevent injuries and...
This paper claims an innovative solution for intelligent electrical current measurement. The proposed concept involves using a drone as a messenger to…
Arterial pulse wave measurement is beneficial in clinical health assessment and is important for effectively diagnosing different types of cardiovascular disease. Computational pulse signal analysis utilizes sensors and signal processing techniques to understand, classify, and predict disease pulse …
(Bild: AMA) Die Studie „Sensor Trends 2030“ analysiert aktuelle Entwicklungen und Herausforderungen in der Sensorik und zeigt, wie Deutschland seine Technologieführerschaft behaupten kann.
The integration of wearable sensors with artificial intelligence forms the base for analyzing physical activities through digital signal processing, numerical methods, and machine learning. Computational intelligence and communication technologies enable personalized monitoring, training, and rehabilitation, with applications in sports, neurology, and biomedicine. This paper focuses on motion analysis in alpine skiing using real accelerometric, gyroscopic, positioning, and video data to evaluate ski movement patterns. The proposed methodology employs functional transforms to estimate motion patterns and utilizes artificial intelligence for signal segmentation and feature classification related to lower limb movement. Machine learning results indicate differences in energy distribution before and after ski turns and demonstrate the feasibility of classifying associated motion patterns with accuracies of 98.1% and 90.7%, respectively, using a two-layer neural network. The interdisciplinary application of computational intelligence in this domain enhances motion analysis, injury prevention, and performance optimization. This study highlights the unifying role of digital signal processing, which uses similar mathematical tools across various applications.
Purpose: To develop a deep learning-based reconstruction method for highly accelerated 3D time-of-flight magnetic resonance angiography (TOF-MRA) that achieves high-quality reconstruction with robust generalization using extremely limited acquired raw data, addressing the challenge of time-consuming acquisition of high-resolution, whole-head angiograms. Methods: A novel few-shot learning-based reconstruction framework is proposed, featuring a 3D variational network specifically designed for 3D TOF-MRA that is pre-trained on simulated complex-valued, multi-coil raw k-space datasets synthesized from diverse open-source magnitude images and fine-tuned using only two single-slab experimentally acquired datasets. The proposed approach was evaluated against existing methods on acquired retrospectively undersampled in vivo k-space data from five healthy volunteers and on prospectively undersampled data from two additional subjects. Results: The proposed method achieved superior reconstruction performance on experimentally acquired in vivo data over comparison methods, preserving most fine vessels with minimal artifacts with up to 8-fold acceleration. Compared to other simulation techniques, the proposed method generated more realistic raw k-space data for 3D TOF-MRA. Consistently high-quality reconstructions were also observed on prospectively undersampled data. Conclusions: By leveraging few-shot learning, the proposed method enabled highly accelerated 3D TOF-MRA relying on minimal experimentally acquired data, achieving promising results on both retrospective and prospective in vivo data while outperforming existing methods. Given the challenges of acquiring and sharing large raw k-space datasets, this holds significant promise for advancing research and clinical applications in high-resolution, whole-head 3D TOF-MRA imaging.### Competing Interest StatementHao Li receives studentship support from Siemens Healthineers. Iulius Dragonu is an employee of Siemens Healthineers. Peter Jezzard is the Editor-in-Chief of Magnetic Resonance in Medicine. In line with COPE guidelines, Peter Jezzard recused himself from all involvement in the review process of this paper, which was handled by an associate editor. He and the other authors had no access to the identities of the reviewers.
Publication date: 15 May 2025Source: Chemical Engineering Journal, Volume 512Author(s): Anees A. Ansari, Ruichan Lv, Abdul K. Parchur, Marshal Dhayal
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