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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

NATURE NEUROSCIENCE 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, diego Ayad, Fadi Barriere, david A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, luis Cramer, Samuel daniele, Tolomeo dempsey, Elaine diao, Yujian doelemeyer, Arno dopfel, david dvorakova, lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene lambers, Henriette lazari, Alberto lee, Sung-Ho lillywhite, Amanda liu, Yikang liu, Yanyan Y. lopez-Castro, Alejandra lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, lisa Missault, Stephan Moro, Federico Naessens, daphne M. P. Nava-Gomez, laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, lore M. Pereira, Mickael Perez, Pablo d. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, david J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, dorothee P. Barbier, Emmanuel l. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： databases Neural circuits

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eSMARTGreen (ESG): A Scalable IoT-Based Architecture for Multi-Greenhouse Management

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Sustainable Computing: Informatics and Systems 2025年

作者： Fatima Abou-Mehdi-Hassani Atef Zaguia darine Amyed Hassan Ait Bouh Abdelhak Mkhida Moulay Ismail University ENSAM Meknes Laboratory of Mechanics Mechatronics and control(L2MC) Marjane 2 PO Box 15290 Al Mansour Road Agouray 50500 Meknes Morocco Department of Computer Science College of Computers and Information Technology Taif University 11099 Taif 21944 Saudi Arabia Institut Supérieur des Sciences Humaines de Jendouba Université de Jendouba Tunisia System Engineering Department École de Technologie Supérieure University of Quebec Montreal Canada

Concerns about agricultural productivity and sustainability have driven the need for smart greenhouse architectures. However, signifiant challenges remain in ensuring seamless data exchange, interoperability, and efficient management across multiple greenhouses. This paper introduces the eSMARTGreen (ESG) model, a novel IoT-based smart greenhouse architecture designed for scalable multi-greenhouse management. ESG features fault-tolerant, modular, and flexible deployment strategy, integrating a robust decision-making system and an interoperable framework aligned with ISO/IEC 30141 standards. The ESG model was validated through a simulation conducted using CPN Tools across a network of five greenhouses. Performance metrics showed low average latencies (19–25 ms) and reception rates of up to 72%, confirming ESG’s responsiveness and communication efficiency under diverse operational conditions. By facilitating seamless coordination and automation, ESG contributes to greater efficiency and sustainability in smart farming. Future applications of ESG could include predictive maintenance, adaptive climate control, large-scale deployment in agricultural clusters, and integration with renewable energy systems to further enhance sustainability and operational efficiency.

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Automatic Initial Boundary Generation Methods Based on Edge detectors for the level Set Function of the Chan-Vese Segmentation Model and Applications in Biomedical Image Processing 7th

Automatic Initial Boundary Generation Methods Based on Edge ...

引用

7th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2018

作者： Thanh, dang N. H. Hien, Nguyen Ngoc Surya Prasath, V.B. Thanh, le Thi Hai, Nguyen Hoang Department of Information Technology Hue College of Industry Hue Viet Nam Center of Occupational Skills Development Dong Thap University Cao Lãnh Viet Nam Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Cincinnati United States Department of Biomedical Informatics College of Medicine University of Cincinnati Cincinnati United States Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati United States Department of Basic Sciences Ho Chi Minh City University of Transport Ho Chi Minh City Viet Nam Department of Informatics University of Education University of Danang Da Nang Viet Nam

ISBN: (纸本)9789811399190

Image segmentation is an important problem in image processing that has a wide range of applications in medicine, biomedicine and other fields of science and engineering. during the non-learning-based approaches, the techniques based on the partial differential equations and calculus of variation have attracted a lot of attention and acquired many achievements. Among the variational models, the Chan-Vese variational segmentation is a well-known model to solve the image segmentation problem. The level set methods are highly accurate methods to solve this model, and they do not depend on the edges. However, the performance of these methods depends on the level set function and its initial boundary too much. In this paper, we propose automatic initial boundary generation methods based on the edge detectors: Sobel, Prewitt, Roberts and Canny. In the experiments, we prove that among the four proposed initial boundary generation methods, the method based on the Canny edge detector brings the highest performance for the segmentation method. By combining the proposed initial boundary generation method based on the Canny edge detector, we implement the Chan-Vese model to segment biomedical images. Experimental results indicate we obtain improved segmentation results and compare different edge detectors in terms of performance. © Springer Nature Singapore Pte ltd. 2020.

关键词： Image segmentation

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A Hole Repairing Method Based on Edge-Preserving Projection 12th

A Hole Repairing Method Based on Edge-Preserving Projection

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12th International Conference on e-learning and Games, Edutainment 2018

作者： Wang, Yinghui Zhao, Yanni Wang, Ningna Ning, Xiaojuan Shi, Zhenghao Zhao, Minghua lv, Ke Huang, liangyi Institute of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an China Department of Computer Science Shannxi Vocational and Technical College Xi’an China Booking B.V. Herengracht 597 Amsterdam1017 CE Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington D.C. United States

ISBN: (纸本)9783030237110

A point cloud hole repairing method based on edge-preserving projection is proposed in order to maintain sharp features of holes. First, the hole boundary points are acquired by quadrants and angles, then connections among 3d hole boundary points are projected onto the two-dimensional plane using the edge-preserving projection. Secondly, the two-dimensional region obtained from projection is subjected to point collectivization for obtaining filling points, at the same time, the radial-basis interpolation mapping technique is used to construct the hole surface according to the 3d hole boundary point. Finally, the two-dimensional filling points are reflected onto the surface of the constructed hole to complete hole repairing. Experimental results show that our method can effectively repair the hole of the point cloud and restore the sharp features of the hole. © 2019, Springer Nature Switzerland AG.

关键词： Repair

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A Hole Repairing Method Based on Slicing 12th

A Hole Repairing Method Based on Slicing

引用

12th International Conference on e-learning and Games, Edutainment 2018

作者： Zhao, Yanni Wang, Yinghui Wang, Ningna Ning, Xiaojuan Shi, Zhenghao Zhao, Minghua lv, Ke Huang, liangyi Institute of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an China Department of Computer Science Shannxi Vocational and Technical College Xi’an China Booking B.V. Herengracht 597 Amsterdam1017 CE Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington D.C. United States

ISBN: (纸本)9783030237110

The repairing of 3d point cloud holes has an important meaning to ensure the integrity of cloud data. We present a slice-based repairing method for 3d point cloud in this paper. Firstly, the model is horizontal sliced and each slice is projected on a two-dimensional plane, then the band-shaped points obtained during projection are clustered to select boundary points of the hole. Combined with optimal fitting points, the hole repairing point sets in the projection layer are re-sampled based on the cubic B-spline curve to fit boundary points. Finally, all hole repairing point sets in the projection layer are combined in 3d space to finish the entire hole recovery. The experimental results show that the proposed method can effectively repair complex holes for various point cloud models. © 2019, Springer Nature Switzerland AG.

关键词： Curve fitting

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Hyperspectral image classification with attention aided CNNs

arXiv

引用

arXiv 2020年

作者： Hang, Renlong li, Zhu liu, Qingshan Ghamisi, Pedram Bhattacharyya, Shuvra S. School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer Science and Electrical Engineering University of Missouri Kansas CityMO64110 United States Jiangsu Key Laboratory of Big Data Analysis Technology School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Exploration FreibergD-09599 Germany Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States

Convolutional neural networks (CNNs) have been widely used for hyperspectral image classification. As a common process, small cubes are firstly cropped from the hyperspectral image and then fed into CNNs to extract spectral and spatial features. It is well known that different spectral bands and spatial positions in the cubes have different discriminative abilities. If fully explored, this prior information will help improve the learning capacity of CNNs. Along this direction, we propose an attention aided CNN model for spectral-spatial classification of hyperspectral images. Specifically, a spectral attention sub-network and a spatial attention sub-network are proposed for spectral and spatial classification, respectively. Both of them are based on the traditional CNN model, and incorporate attention modules to aid networks focus on more discriminative channels or positions. In the final classification phase, the spectral classification result and the spatial classification result are combined together via an adaptively weighted summation method. To evaluate the effectiveness of the proposed model, we conduct experiments on three standard hyperspectral datasets. The experimental results show that the proposed model can achieve superior performance compared to several state-of-the-art CNN-related models. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Atomically sharp interface enabled ultrahigh-speed, nonvolatile memory devices

arXiv

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arXiv 2021年

作者： Wu, liangmei Wang, Aiwei Shi, Jinan Yan, Jiahao Zhou, Zhang Bian, Ce Ma, Jiajun Ma, Ruisong liu, Hongtao Chen, Jiancui Huang, Yuan Zhou, Wu Bao, lihong Ouyang, Min Pennycook, Stephen J. Pantelides, Sokrates T. Gao, Hong-Jun Institute of Physics Chinese Academy of Sciences P. O. Box 603 Beijing100190 China Univ. of Chinese Acad. of Sciences and Cas Center for Excellence in Topological Quantum Computation Chinese Academy of Sciences P. O. Box 603 Beijing100190 China Songshan Lake Materials Laboratory Dongguan Guangdong523808 China Department of Physics College Park University of Maryland College ParkMD20742 United States Department of Materials Science and Engineering and Centre for Advanced 2D Materials National University of Singapore 117575 Singapore Department of Physics and Astronomy and Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37235 United States

development of memory devices with ultimate performance has played a key role in innovation of modern electronics. As a mainstream technology nonvolatile memory devices have manifested high capacity and mechanical reliability, however current major bottlenecks include low extinction ratio and slow operational speed. Although substantial effort has been employed to improve their performance, a typical hundreds of micro-or even milli-second write time remains a few orders of magnitude longer than their volatile counterparts. We have demonstrated nonvolatile, floating-gate memory devices based on van der Waals heterostructures with atomically sharp interfaces between different functional elements, and achieved ultrahigh-speed programming/erasing operations verging on an ultimate theoretical limit of nanoseconds with extinction ratio up to 1010. This extraordinary performance has allowed new device capabilities such as multi-bit storage, thus opening up unforeseen applications in the realm of modern nanoelectronics and offering future fabrication guidelines for device scale-up. Copyright © 2021, The Authors. All rights reserved.

关键词： light extinction

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Exploration of dark Chemical Genomics Space via Portal learning: Applied to Targeting the Undruggable Genome and COVId-19 Anti-Infective Polypharmacology

Research Square

引用

Research Square 2021年

作者： Cai, Tian Xie, li Chen, Muge liu, Yang He, di Zhang, Shuo Mura, Cameron Bourne, Philip E. Xie, lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal learning provides a practical solution to the out-of-distribution (OOd) problem in statistical machine learning. Here, we have implemented Portal learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal learning is general-purpose and can be further applied to other areas of scientific inquiry. © 2021, CC BY.

关键词： Coronavirus

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Exploration of dark chemical genomics space via portal learning: Applied to targeting the undruggable genome and COVId-19 anti-infective polypharmacology

arXiv

引用

arXiv 2021年

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal learning provides a practical solution to the out-of-distribution (OOd) problem in statistical machine learning. Here, we have implemented Portal learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal learning is general-purpose and can be further applied to other areas of scientific *** Codes 68T07 © 2021, CC BY.

关键词： Genes

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Post Quantum-Resistant Blind Signature Scheme for Consumer Internet of Things Security

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Seno, Mohammed E. Ramesh, Janjhyam Venkata Naga Quraishi, Aadam AlGhamdi, Azzah Prasad, Kdv Nimma, divya Rajesh, Yelisela Berdieva, Uguloy Soni, Mukesh University of Al Maarif College of Sciences Department of Computer Sciences Al Anbar31001 Iraq Graphic Era Hill University Department of CSE Dehradun248002 India Department of CSE Graphic Era Deemed to Be University India Uttarakhand Dehradun248002 India Intervention Treatment Institute HoustonTX United States Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia Symbiosis Institute of Business Management Hyderabad Pune India University of Southern Mississippi United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Dist Andhra Pradesh Vaddeswaram522302 India Tashkent State University of Economics Department of Tax and Taxation Tashkent Uzbekistan Lovely Professional University Division of Research and Development Phagwara India Dr. D. Y. Patil School of Science and Technology Dr. D. Y. Patil Vidyapeeth Tathawade Pune India

To overcome the issue of traditional blind signature schemes being vulnerable to quantum computing attacks during interactions over the consumer electronic Internet of Things (CIoT), a new blind signature scheme called CdBS has been developed. The post quantum digital signature technique CRYSTAlS-dilithium, which was standardized by NIST, serves as the foundation for the quantum-resistant blind signature scheme, CdBS, presented in this study. This system is appropriate for a variety of internet application scenarios, including e-ticketing, anonymous payments, and privacy protection, because it integrates the idea of blind signatures, which is not included in typical digital signature schemes. This scheme is designed to resist quantum computing attacks and is based on the post-quantum digital signature algorithm CRYSTAlS-dilithium, which NIST has selected. The whole technique utilizes the Fiat-Shamir signature structure, which consists of five stages: key creation, blinding, signing, unblinding, and verification. The security of the proposed scheme is dependent on the resolution of the Module learning with Errors (MlWE) problem and the Short Integer Solution (SIS) problem. Additionally, the scheme must satisfy the criteria of correctness, blindness, and unforgeability. According to the test results, the CRYSTAlS-dilithium scheme requires an average of 401.89 μ s for a single round of verification and signing. The CdBS technique takes an average of 657.65 ls for one round of signing and verification since the blinding and unblinding modules are added on top of the original algorithm, increasing the algorithm’s complexity. The software testing results indicate that the average time required to complete a single full blind signature and verification operation is 657.65 microseconds. The suggested system offers a framework for expanding the CRYSTAlS-dilithium digital signature algorithm. © 1975-2011 IEEE.

关键词： Qubits

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