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Quantum Mechanical Tunnelling Probes with Redox Cycling for Ultra-Sensitive Detection of Biomolecules

Angewandte Chemie 2025年

作者： Long Yi Tao Jiang Dr. Ren Ren Prof. Ji Cao Prof. Joshua B. Edel Prof. Aleksandar P. Ivanov Prof. Longhua Tang State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou 310027 China Nanhu Brain-Computer Interface Institute Second Affiliated Hospital School of Medicine Hangzhou Zhejiang 311100 China Department of Chemistry Molecular Science Research Hub Imperial College London White City Campus 82 Wood Lane London W12 0BZ UK Institute of Pharmacology and Toxicology Zhejiang Province Key Laboratory of Anti-Cancer Drug Research College of Pharmaceutical Sciences Zhejiang University Hangzhou China

Quantum mechanical tunnelling sensors (QMTs) have emerged as a promising technology for next-generation single-molecule detection. Furthermore, QMT sensors can be combined with redox species resulting in repeated oxidation and reduction (redox cycling).. We developed robust QMT probes with electrode gap distances below 2 nm. Using the generator-collector (GC) mode, we verified that redox cycling of the ferrocyanide/ferricyanide (Fe(CN) 6 3−/4− ) couple occurs both in the tunnelling regime and on the electrode surface. Our findings indicated that the current enhancement is affected by both the gap distance and surface modifications of the probes. These QMT probes exhibited remarkable sensitivity, capable of detecting Fe(CN) 6 3−/4− concentrations down to sub-picomolar levels. Utilising this ability to modulate redox reactions, we adapted the QMT probes to serve as electrochemical sensors for detecting viral proteins. By modifying the electrode surfaces, our functionalised QMT probes achieved sub-pM detection limits with high selectivity in biofluids such as nasopharyngeal secretions. These findings highlight the potential of QMT probes to develop into a new class of electrochemical tunnelling sensors, offering significant advancements in biomedical diagnostics.

关键词： quantum tunnelling sensors redox cycling reaction single-molecular detection biosensor

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Assessment of motor imagery in gamma band using a lower limb exoskeleton

Assessment of motor imagery in gamma band using a lower limb...

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IEEE International Conference on Systems, Man and Cybernetics

作者： M. Ortiz E. Iáñez J. Gaxiola A. Kilicarslan J.L. Contreras-Vidal J. M. Azorín Department of Electrical and Computer Engineering Noninvasive Brain–Machine Interface Systems Laboratory NSF IUCRC BRAIN Center University of Houston Houston United States of America Brain-Machine Interface Systems Lab Miguel Hernández University of Elche Av. Universidad S/N Ed. Innova. Elche España “Biomedical Signal Processing Laboratory” at Cinvestav Monterrey’s Unit (Mexico) and visitor Ph.D student in the Brain-Machine Interface Systems Lab Miguel Hernández University of Elche Spain

The use of a brain-machine interface (BMI) in combination with powered exoskeletons can assist patients with lower limb disabilities to walk again. These neurorobotic systems are commonly based on motor imagery, but their performance may suffer from lack of user engagement in the task or from cognitive load due to multi-tasking. The present paper shows a novel algorithm based on the gamma spectral band, using the Stockwell transform and a set of smoothing filters, to assess the quality of and improve the decoding of motor imagery during the use of a BMI-Rex exoskeleton system. The results computed in a pseudo-online scenario reveal a high accuracy with a very low false positive ratio.

关键词： Exoskeletons Electrodes Feature extraction Task analysis brain-computer interfaces Legged locomotion Filtering algorithms

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Signal Detection,Processing and Challenges of Non-invasive brain-computer interface Technology

Signal Detection,Processing and Challenges of Non-invasive B...

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2019中国智能自动化大会(CIAC 2019)

作者： Xiaoyuan Li Feng Chen Yaohui Jia Xinyu Liu School of Electrical Engineering Zhengzhou University Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou University School of Intelligent Manufacturing Huanghuai University China Shipping Heavy Industry 713 Research Institute

Non-invasive brain-computer interface(BCI) technology is becoming more and more popular in current BCI application study due to its safety,non-invasive and low *** main problem of non-invasive BCI technology is that the spatial resolution of signal is low and it is easy to be drowned by ***,this paper reviews the key technologies and the existing challenges of non-invasive BCI technology from the aspect of signal detection and processing,and discusses its future application ***,it is still a great challenge for non-invasive BCI technology to become the mainstream in the future.

关键词： brain-computer interface Non-invasive BCI technology Signal detection Pattern recognition

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Start the Engine of Neuroregeneration: A Mechanistic and Strategic Overview of Direct Astrocyte-to-Neuron Reprogramming

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Ageing research reviews 2025年 110卷

作者： Hangyuan Jiang Hengxing Qi Anying Tang Shaohua Hu Jianbo Lai Department of Psychiatry the First Affiliated Hospital Zhejiang University School of Medicine Hangzhou 310003 China Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute) Zhejiang University School of Medicine Zhejiang University Hangzhou 310058 China Edinburgh Medical School: Biomedical Sciences College of Medicine and Veterinary Medicine The University of Edinburgh Edinburgh UK. Zhejiang Key Laboratory of Precision Psychiatry Hangzhou 310003 China Nanhu Brain-computer Interface Institute Hangzhou 311100 China Liangzhu Laboratory Zhejiang University School of Medicine Hangzhou 311121 China. Liangzhu Laboratory Zhejiang University School of Medicine Hangzhou 311121 China Zhejiang Engineering Center for Mathematical Mental Health Hangzhou 310003 China The MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University School of Medicine Hangzhou 310058 China Department of Psychology and Behavioral Sciences Zhejiang University Hangzhou 310058 China Brain Research Institute of Zhejiang University Hangzhou 310058 China. Electronic address: dorhushaohua@***. Department of Psychology and Behavioral Sciences Zhejiang University Hangzhou 310058 China. Electronic address: laijianbo@***.

The decline of adult neurogenesis and neuronal function during aging underlies the onset and progression of neurodegenerative diseases such as Alzheimer's disease. Conventional therapies, including neurotransmitter modulators and antibodies targeting pathogenic proteins, offer only symptomatic improvement. As the most abundant glial cells in the brain, astrocytes outnumber neurons nearly fivefold. However, their proliferative and transdifferentiation potential renders them ideal candidates for in situ neuronal replacement. Direct astrocyte-to-neuron reprogramming offers a promising regenerative approach to restore damaged neural circuits. Herein, we propose a "car start-up" model to conceptualize this process, emphasizing the need to inhibit non-neuronal fate pathways (release the handbrake), suppress transcriptional repressors (release the footbrake), and activate neuron-specific gene expression (step on the gas). Additionally, overcoming metabolic barriers in the cytoplasm is essential for successful lineage conversion. Viral or non-viral vectors deliver reprogramming factors, while small molecules serve as metabolic and epigenetic fuel to boost efficiency. In summary, we review the current evidence supporting direct astrocyte-to-neuron reprogramming as a viable regenerative strategy in the aging brain. We also highlight the conceptual "car start-up" model as a useful framework to dissect the molecular logic of lineage conversion and emphasize its promising therapeutic potential for combating neurodegenerative diseases.

关键词： Astrocyte Direct reprogramming Epigenetics Neurodegenerative diseases Neuron Neuroregeneration

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Adaptive initialization method for K-means algorithm

arXiv

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arXiv 2019年

作者： Yang, Jie Wang, Yu-Kai Yao, Xin Lin, Chin-Teng Computational Intelligence and Brain Computer Interface Lab Centre for AI FEIT University of Technology Sydney Australia Shenzhen Key Laboratory of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CERCIA School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses the random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. Many initialization methods have been proposed, but none of them can dynamically adapt to datasets with various characteristics. In our previous research, an initialization method for K-means based on hybrid distance was proposed, and this algorithm can adapt to datasets with different characteristics. However, it has the following drawbacks: (a) When calculating density, the threshold cannot be uniquely determined, resulting in unstable results. (b) Heavily depending on adjusting the parameter, the parameter must be adjusted five times to obtain better clustering results. (c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets. In the current paper, we proposed an adaptive initialization method for the K-means algorithm (AIMK) to improve our previous work. AIMK can not only adapt to datasets with various characteristics but also obtain better clustering results within two interactions. In addition, we then leverage random sampling in AIMK, which is named as AIMK-RS, to reduce the time complexity. AIMK-RS is easily applied to large and high-dimensional datasets. We compared AIMK and AIMK-RS with 10 different algorithms on 16 normal and six extra-large datasets. The experimental results show that AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Furthermore, AIMK-RS can significantly reduce the complexity of applying it to extra-large datasets with high dimensions. The time complexity of AIMK-RS is O(n). Copyright © 2019, The Authors. All rights reserved.

关键词： K-means clustering

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Risk and adverse events related to lower-limb exoskeletons

Risk and adverse events related to lower-limb exoskeletons

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2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017

作者： He, Yongtian Eguren, David Luu, Trieu Phat Contreras-Vidal, Jose L. Noninvasive Brain-Machine Interface System Laboratory Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

ISBN: (纸本)9781538643778

Since the marketing clearance from the U.S. Food and Drug Administration (FDA), powered lower-limb exoskeletons have been proposed as a promising tool in gait assistance and rehabilitation. However, previous studies often overlooked the potential risk and adverse events related to the device. Without a clear understanding and comprehensive knowledge of adverse events and the risks users face, it is difficult to evaluate the risk-to-benefit ratio of powered exoskeletons. We reviewed all publically available exoskeleton-related adverse events, and compared the result to the risks that were identified by the FDA. Results showed that bone fracture is a serious risk that was not considered in previous literatures. Additionally, skin and soft tissue damage is common across subjects, studies, and devices. It was often treated as an incidental minor event and overlooked. We encourage more effort at keeping the safety of exoskeleton users in future studies. © 2017 IEEE.

关键词： Exoskeleton (Robotics)

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EEG function network analysis of left and right hand motor imagery

EEG function network analysis of left and right hand motor i...

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2017 Chinese Automation Congress, CAC 2017

作者： Zhang, Rui Yan, Yamin Hu, Yuxia Shi, Li Wan, Hong Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electric Engineering Zhengzhou University Zhengzhou China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9781538635247

Network analysis of signals originating from different parts of brain during motor imagery (MI) has gained lots of interest recently. In this paper, we used EEG to construct the brain network during MI, and analyzed the topological characteristics of the EEG function network. It is found that the node degree and clustering coefficient of the right hand MI is higher than the left hand MI on the nodes of right sensorimotor cortex, and the characteristic path length of the network is shorter for right hand MI than left hand MI. As for the right hand MI task, most subjects showed higher node degree and clustering coefficient on the right sensorimotor cortex than the left sensorimotor cortex. We conclude that EEG network based measures, which captures the brain information integrating characteristics during MI, may serve as useful features for classification in MI-BCI application, especially for BCI inefficiency subject. © 2017 IEEE.

关键词： Image analysis

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EEG function network analysis of left and right hand motor imagery

EEG function network analysis of left and right hand motor i...

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Chinese Automation Congress (CAC)

作者： Rui Zhang Yamin Yan Yuxia Hu Li Shi Hong Wan Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou University Zhengzhou China Department of Automation Tsinghua University Beijing China

关键词： Electroencephalography Electrodes Coherence Erbium Synchronization Feature extraction

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Sample Set Reduction Method Based on Neighborhood Non-dominated Crowding-distance Sorting

Sample Set Reduction Method Based on Neighborhood Non-domina...

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International Symposium on Computational Intelligence and Design

作者： Mengmeng Li Zhigang Shang Xiaoyang Shen Caitong Yue Haofeng Wang Yonghui Dong Hong Wan Industrial Technology Research Institute Zhengzhou University Zhengzhou China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou China School of Electrical Engineering Zhengzhou University Zhengzhou China

To remove the redundant samples and reduce sample set size of big data analysis while ensuring classification accuracy, a sample set reduction method based on Neighborhood Non-dominated Crowding-distance Sorting (NNCS) is proposed. The proposed method is based on the idea of neighborhood, in which the distance between the samples of one class and the other classes is taken as the evaluation criterion. Furthermore, optimization algorithm NSGA-II is adapted to select the key samples by non-dominated crowding-distance sorting and the purpose of reducing the sample set is achieved finally. The experimental results on nine UCI standard data sets show that the method has good generalization performance and reduces the sample set size effectively under the premise of fully guaranteed classification accuracy.

关键词： Sample set reduction Key sample Neighborhood Non-dominated sorting Crowding-distance sorting

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Special issue on 2016 International Conference on Intelligence Science and Big Data Engineering

Special issue on 2016 International Conference on Intelligen...

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作者： Zheng, Wei-Shi Gao, Xinbo Li, Yuanqing Lai, Jianhuang School of Data and Computer Science Sun Yat-sen University China State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi'an710071 China School of Automation Science and Engineering South China University of Technology China Guangzhou Key Laboratory of Brain Computer Interface and Applications Guangzhou 510640 China School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China

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