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Utilizing natural compounds as ligands to disrupt the binding of SARS-CoV-2 receptor-binding domain to angiotensin-converting enzyme 2, impeding viral infection

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Phytochemistry Letters 2025年 68卷

作者： Wu, Jingyi Chen, Ting-Hsu Shen, Zi-han Tsai, May-Jywan Leong, Max K. Huang, Yan-qiang Weng, Ching-Feng Guangxi Technology Innovation Cooperation Base of Prevention and Control Pathogenic Microbes With Drug Resistance Youjiang Medical University for Nationalities Baise 533000 China Graduate Institute of Brain and Mind Sciences College of Medicine National Taiwan University Taipei 10051 Taiwan Functional Physiology Section Department of Basic Medical Science Xiamen Medical College Fujian Xiamen 361023 China Neural Regeneration Laboratory Department of Neurosurgery Neurological Institute Taipei Veterans General Hospital Taipei 11217 Taiwan Department of Chemistry National Dong Hwa University Hualien 97401 Taiwan Guangxi Zhuang Autonomous Region Engineering Research Center of Clinical Prevention and Control Technology and Leading Drug for Microorganisms with Drug Resistance in Border Ethnic Areasn Baise 533000 China

Currently, no specific antiviral drug has been definitively proven effective in treating patients with severe coronavirus disease 2019 (COVID-19). Various specific strategies have been employed against COVID-19;however, several potent antiviral candidates, including prodrugs and repurposed drugs, are still under urgent investigation, particularly in the search for molecular targets. This study aimed to evaluate the potential of natural compounds and chemicals against RNA viruses using a three-tiered approach to molecular docking. Binding scores obtained from ChemPLP revealed that natural compounds and repurposed drugs exhibited strong affinities for the binding sites on angiotensin-converting enzyme 2 (ACE2) receptors in host cells, the receptor-binding domain (RBD) site of the RBD-ACE2 complex, PL^pro, and 3CL^pro. Conclusively, these findings suggest that alternative medicines and antiviral drug repurposing strategies may provide promising therapeutic remedies for patients with COVID-19, along with further validation via preclinical and clinical trials. © 2025 The Authors

关键词： ACE2 Arachidonic acid Chloroquine COVID-19 Curcumin Darunavir Hydroxychloroquine RBD Remdesivir

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Observation of Integer and Fractional Quantum Anomalous Hall Effects in Twisted Bilayer MoTe2

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Physical Review X 2023年第3期13卷 031037-031037页

作者： Fan Xu Zheng Sun Tongtong Jia Chang Liu Cheng Xu Chushan Li Yu Gu Kenji Watanabe Takashi Taniguchi Bingbing Tong Jinfeng Jia Zhiwen Shi Shengwei Jiang Yang Zhang Xiaoxue Liu Tingxin Li Key Laboratory of Artificial Structures and Quantum Control (Ministry of Education) Shenyang National Laboratory for Materials Science School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 201210 China Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua University 100084 Beijing China Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of Sciences Beijing 100190 China Hefei National Laboratory Hefei 230088 China Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA

The interplay between strong correlations and topology can lead to the emergence of intriguing quantum states of matter. One well-known example is the fractional quantum Hall effect, where exotic electron fluids with fractionally charged excitations form in partially filled Landau levels. The emergence of topological moiré flat bands provides exciting opportunities to realize the lattice analogs of both the integer and fractional quantum Hall effects without the need for an external magnetic field. These effects are known as the integer and fractional quantum anomalous Hall (IQAH and FQAH) effects. Here, we present direct transport evidence of the existence of both IQAH and FQAH effects in small-angle-twisted bilayer MoTe2. At zero magnetic field, we observe well-quantized Hall resistance of h/e2 around moiré filling factor ν=−1 (corresponding to one hole per moiré unit cell), and nearly quantized Hall resistance of 3h/2e2 around ν=−2/3, respectively. Concomitantly, the longitudinal resistance exhibits distinct minima around ν=−1 and −2/3. The application of an electric field induces topological quantum phase transition from the IQAH state to a charge transfer insulator at ν=−1, and from the FQAH state to a topologically trivial correlated insulator, further transitioning to a metallic state at ν=−2/3. Our study paves the way for the investigation of fractionally charged excitations and anyonic statistics at zero magnetic field based on semiconductor moiré materials.

关键词： Magnetism Phase transitions Topological materials

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Nonreciprocal thermal radiation in ultrathin magnetized epsilon-near-zero semiconductors

arXiv

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arXiv 2022年

作者： Liu, Mengqi Xia, Shuang Wan, Wenjian Qin, Jun Li, Hua Zhao, Changying Bi, Lei Qiu, Cheng-Wei Institute of Engineering Thermophysics MOE Key Laboratory for Power Machinery and Engineering School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore National Engineering Research Center of Electromagnetic Radiation Control Materials University of Electronic Science and Technology of China Chengdu610054 China State Key Laboratory of Electronic Thin-Films and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China Key Laboratory of Terahertz Solid State Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai2000509 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Science Beijing1000049 China

Spectral/angular emissivity e and absorptivity α of an object are widely believed to be identical by Kirchhoff's law of thermal radiation in reciprocal systems, but this introduces an intrinsic and inevitable energy loss for energy conversion and harvesting devices. So far, experimental evidences of breaking this well-known balance are still absent, and previous theoretical proposals are restricted to narrow single-band nonreciprocal radiation. Here we observe for the first time, to our knowledge, the violation of Kirchhoff's law using ultrathin (0.6 has been experimentally demonstrated under a moderate external magnetic field. Moreover, based on magnetized ENZ building blocks supporting asymmetrically radiative Berreman and surface ENZ modes, we show versatile shaping of nonreciprocal thermal radiation: single-band, dual-band, and broadband nonreciprocal emission spectra at different wavebands. Our findings of breaking Kirchhoff's law will advance the conventional understanding of emission and absorption processes of natural objects, and lay a solid foundation for more comprehensive studies in designing various nonreciprocal thermal emitters. The reported recipe of diversely shaping nonreciprocal emission will also breed new possibilities in renovating next-generation nonreciprocal energy devices in the areas of solar cells, thermophotovoltaic, radiative cooling, etc. Copyright © 2022, The Authors. All rights reserved.

关键词： III-V semiconductors

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A self-supervised mixed-curvature graph neural network

arXiv

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arXiv 2021年

作者： Sun, Li Zhang, Zhongbao Ye, Junda Peng, Hao Zhang, Jiawei Su, Sen Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Ifm Lab Department of Computer Science University of California DavisCA United States Department of Computer Science University of Illinois at Chicago IL United States

Graph representation learning received increasing attentions in recent years. Most of the existing methods ignore the complexity of the graph structures and restrict graphs in a single constant-curvature representation space, which is only suitable to particular kinds of graph structure indeed. Additionally, these methods follow the supervised or semisupervised learning paradigm, and thereby notably limit their deployment on the unlabeled graphs in real applications. To address these aforementioned limitations, we take the first attempt to study the self-supervised graph representation learning in the mixed-curvature spaces. In this paper, we present a novel Self-supervised Mixed-curvature Graph Neural Network (SELFMGNN). To capture the complex graph structures, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces, and design hierarchical attention mechanisms for learning and fusing graph representations across these component spaces. To enable the self-supervisd learning, we propose a novel dual contrastive approach. The constructed mixed-curvature space actually provides multiple Riemannian views for the contrastive learning. We introduce a Riemannian projector to reveal these views, and utilize a welldesigned Riemannian discriminator for the single-view and cross-view contrastive learning within and across the Riemannian views. Finally, extensive experiments show that SELFMGNN captures the complex graph structures and outperforms state-of-the-art baselines. © 2021, CC BY-NC-SA.

关键词： Graph neural networks

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Tigc-Net: Transformer-Improved Graph Convolution Network for Spatio-Temporal Prediction

SSRN

引用

SSRN 2022年

作者： Chen, Kai Yang, Chunfeng Zhou, Zhengyuan Liu, Yao Ji, Tianjiao Sun, Weiya Chen, Yang School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China The College of Software Engineering Southeast University Nanjing210096 China Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Beijing Institute of Tracking and Communication Technology Beijing100094 China

Modeling spatio-temporal sequences is an important topic yet challenging for existing neural networks. Most of the current spatio-temporal sequence prediction methods usually capture features separately in temporal and spatial dimensions or employ multiple mutually independent local spatio-temporal graphs to represent a spatio-temporal sequence. The first kind of method mentioned above is difficult to mine the complex spatio-temporal correlations, while the other is limited for the accuracy of long-term predictions. To handle these issues, this paper proposes a Transformer-Improved Graph Convolution Network for spatio-temporal prediction. Specifically, the temporal location encoding method is exploited to derive the spatio-temporal characteristics of the sequence utilizing a spatio-temporal feature fusion network. In addition, a spatio-temporal attention network is developed to enhance the spatio-temporal correlation of the sequence, and the dynamic spatial features of sequence are further extracted through the adaptive graph convolution network. A private dataset and a public dataset are employed to demonstrate the performance of the proposed TIGC-Net. The qualitative and quantitative results show that the proposed TIGC-Net can extract dynamic spatiotemporal properties more effectively, enhance the spatio-temporal correlation of sequences and improve the prediction accuracy compared with four stateof-the-art. © 2022, The Authors. All rights reserved.

关键词： Convolution

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Guaranteed feasible control allocation using model predictive control

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control Theory and Technology 2019年第3期17卷 252-264页

作者： Mehdi NADERI Ali KHAKI SEDIGH Tor Arne JOHANSEN Center of Excellence in Industrial Control Faculty of Electrical Engineering K. N. Toosi University of Technology Seyyed Khandan Bridge Shariati Ave. Tehran Iran Center for Autonomous Marine Operation and Systems Department of Engineering Cybernetics Norwegian University of Science and Technology N7491 Trondheim Norway

This paper proposes a guaranteed feasible control allocation method based on the model predictive control. Feasible region is considered to guarantee the determination of the desired virtual control signal using the pseudo inverse methodology and is described as a set of constraints of an MPC problem. With linear models and the given constraints, feasible region defines a convex polyhedral in the virtual control space. In order to reduce the computational time, the polyhedral can be approximated by a few axis alig ned hypercubes. Employing the MPC with rectangular constraints substantially reduces the computational complexity .In two dimensions, the feasible region can be approximated by a few rectangles of the maximum area using numerical geometry techniques which are considered as the constraints of the MPC problem. Also, an active MPC is defined as the controller to minimize the cost function in the control horizon. Finally, several simulation examples are employed to illustrate the effectiveness of the proposed techniques.

关键词： control allocation feasible region actuator constraints model predictive control

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A Dual-Chaining Watermark Scheme for Data Integrity Protection in Internet of Things

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computers, Materials & Continua 2019年第3期58卷 679-695页

作者： Baowei Wang Weiwen Kong Wei Li Neal NXiong Jiangsu Engineering Centre of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologySchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Engineering Research Center of Electronic Information and Control from Fujian Provincial Education Department Fuzhou350108China Department of Mathematics and Computer Science Northeastern State UniversityOklahomaUSA.

Chaining watermark is an effective way to verify the integrity of streaming data in wireless network environment,especially in resource-constrained sensor networks,such as the perception layer of Internet of Things ***,in all existing single chaining watermark schemes,how to ensure the synchronization between the data sender and the receiver is still an unsolved *** the synchronization points are attacked by the adversary,existing data integrity authentication schemes are difficult to work properly,and the false negative rate might be up to 50 *** the additional fixed group delimiters not only increase the data size,but are also easily detected by *** this paper,we propose an effective dual-chaining watermark scheme,called DCW,for data integrity protection in smart campus IoT *** proposed DCW scheme has the following three characteristics:(1)In order to authenticate the integrity of the data,fragile watermarks are generated and embedded into the data in a chaining way using dynamic grouping;(2)Instead of additional fixed group delimiters,chained watermark delimiters are proposed to synchronize the both transmission sides in case of the synchronization points are tampered;(3)To achieve lossless integrity authentication,a reversible watermarking technique is *** experimental results and security analysis can prove that the proposed DCW scheme is able to effectively authenticate the integrity of the data with free distortion at low cost in our smart meteorological Internet of Things system.

关键词： Dual-chaining reversible watermark integrity authentication Internet of things.

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Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps 24

Binding in hippocampal-entorhinal circuits enables compositi...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Christopher J. Kymn Sonia Mazelet Anthony Thomas Denis Kleyko E. Paxon Frady Friedrich T. Sommer Bruno A. Olshausen Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Department of Electrical and Computer Engineering UC Davis Davis Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden and Intelligent Systems Lab Research Institutes of Sweden Kista Sweden Intel Labs Santa Clara Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Intel Labs Santa Clara Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Herbert Wertheim School of Optometry & Vision Science UC Berkeley Berkeley

ISBN: (纸本)9798331314385

We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions. Code is available at https://***/SoniaMaz8/Hippocampal_enthorinal_circuit

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Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 64-74页

作者： Zhengguo Sheng Saskia Pfersich Alice Eldridge Jianshan Zhou Daxin Tian Victor C.M.Leung the Department of Engineering and Design University of Sussex Infineon Technologies AG the Department of Music University of Sussex Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety ControlSchool of Transportation Science and EngineeringBeihang University the Department of Electrical and Computer Engineering The University of British Columbia

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity *** scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ *** and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination *** tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.

关键词： Acoustic sensor networks edge computing energy efficiency

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