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Corrigendum for “Review—Latest Trends and Advancement in Porous Carbon for Biowaste Organization and Utilization” ECS J. Solid State Sci. Technol. 11 011003 2022

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ECS Journal of Solid State Science and technology 2023年第11期12卷 119001-119001页

作者： Kunal Kulkarni Utkarsh Chadha Shreya Yadav D. M. Tarun K. G. Thenmukilan Preetam Bhardwaj Shalu Singh Arghya Chakravorty Chirag Ahuja Srinivasan Latha Arun Kumar Ray Badrish Badoni Nalamala Srinivasa Rao Charanjeet Madan Murali Banavoth Prashant Sonar Department of Analytics School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamil Nadu-632014 India Department of manufacturing School of Mechanical Engineering (SMEC) Vellore Institute of Technology Vellore Tamil Nadu-632014 India School of Bio Sciences & Technology (SBST) Vellore Institute of Technology Vellore Tamil Nadu-632014 India School of Engineering Presidency University Bengaluru-560064 India Mechanical Department Panimalar Engineering College Chennai-600069 India Center for Nanotechnology Research (CNR) School of Electronics Engineering (SENSE) Vellore Institute of Technology Vellore Tamil Nadu-632014 India Department of Chemistry School of Advanced Sciences Vellore Institute of Technology Tamilnadu—632014 India Department of Biotechnology Guru Jambheshwar University of Science and Technology Hisar Haryana-125001 India Department of Mechanical Engineering Hindu College of Engineering Sonipat Haryana-131001 India Department of Physics Bal Ganga Degree College Sendul Kemar Tehri Garhwal Uttrakhand- 249155 India Department of Chemistry M.R.R. Govt. Degree College Udayagiri SPSR Nellore Andhra Pradesh-524226 India Department of Electrical Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana-125001 India Solar Cells and Photonics Research Laboratory School of Chemistry University of Hyderabad Hyderabad Telangana—500046 India School of Chemistry Physics and Mechanical Engineering Queensland University of Technology (QUT) 4001 Brisbane Australia

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Performance and Shelf-Life of Electrospun Ag-doped WO3 Nanofiber-Based SERS Substrates

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IOP Conference Series: Materials Science and engineering 2021年第1期1070卷

作者： Komkrit Juntaracena Tienthong Yuangkaew Mati Horpratum Napat Triroj Papot Jaroenapibal Department of Industrial Engineering Khon Kaen University Khon Kaen 40002 Thailand Department of Electrical Engineering Khon Kaen University Khon Kaen 40002 Thailand National Electronics and Computer Technology Center Pathumthani 12120 Thailand

Recent advances in Ag-doped WO3 nanofibers have provided an opportunity to fabricate high quality and low-cost surface-enhanced Raman scattering (SERS) substrates. This work studied the performance and shelf-life of Ag-doped WO3 nanofiber-based substrates. Methylene blue (MB) molecules were used as analytical references. The prepared substrates were stored in ambient air at room temperature for the duration of 12 months. The Raman enhancement factor (EF) of freshly prepared sample was calculated to be 6.2 × 105. In the first 6 months, a small decrease in EF was observed. The EF was found to significantly decrease after samples were stored for 12 months. After 12 months, the relative standard deviations (RSD) of Raman signals changed from 10.35% to 50.29%, indicating non-uniform Raman enhancements at different locations of the sample. These suggested agglomerations of Ag nanoparticles, which deteriorated the Raman enhancement effect, after samples were stored for a prolonged period.

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Digital twin technology advancing industry 4.0 and industry 5.0 across sectors

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Results in engineering 2025年 26卷

作者： Ocident Bongomin Mwewa Chikonkolo Mwape Nonsikelelo Sheron Mpofu Brendah Kembabazi Bahunde Richard Kidega Ibrahim Luqman Mpungu Godias Tumusiime Cynthia Awuor Owino Yannick Marnaigue Goussongtogue Aregawi Yemane Proscovia Kyokunzire Clement Malanda Jimmy Komakech Dan Tigalana Onesmas Gumisiriza George Ngulube Department of Mechanical Engineering Faculty of Engineering and Survey Ndejje University P.O. Box 7088 Kampala Uganda Department of Manufacturing Textile and Industrial Engineering School of Engineering Moi University P.O. Box 30100-3900 Eldoret Kenya Egypt Solid Waste Management Center of Excellence Faculty of Engineering Ain Shams University Cairo 11566 Egypt Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhoftstraße 1a Witzenhausen 37213 Germany Department of Crop Inspection and Certification Ministry of Agriculture Animal Industry and Fisheries P.O Box 102 Entebbe Uganda Department of Mining Engineering Faculty of Engineering and Technology Busitema University P.O. Box 236 Tororo Uganda Department of Mining and Mineral Processing Engineering School of Mines and Engineering Taita Taveta University P.O. Box 635-80300 Voi Kenya Department of Technical Teacher and Instructor Education School of Education Kyambogo University P.O. Box 1 Kyambogo Kampala Uganda Department of Polymer Textile and Industrial Engineering Faculty of Engineering and Technology Busitema University P.O. Box 236 Tororo Uganda Department of Electrical and Computer Engineering School of Engineering Makerere University P.O. Box 7062 Kampala Uganda Department of Civil and Structural Engineering School of Engineering Moi University P.O. Box 30100-3900 Eldoret Kenya Department of Industrial Engineering School of Mechanical and Industrial Engineering Mekelle University Mekelle Ethiopia Department of Polymer Engineering Graduate School Chonnam National University Gwangju 61186 South Korea Department of Applied Studies Malawi University of Science and Technology P.O. Box 5196 Limbe Malawi College of Education and Human Development Western Michigan University 1903W MI United States Department of Oil and Gas Industry Facility Design and Construction Faculty of Pipeline Construction Ufa State Petroleum Technological University 450064 Street Kosmo

Digital Twin (DT) technology is transforming industrial systems by integrating physical assets with digital models, enabling real-time monitoring, predictive analytics, and process optimization, particularly within the framework of Industry 4.0 (I4.0). As the global industrial landscape shifts toward Industry 5.0 (I5.0), DTs are increasingly being redefined to support human-centric innovation, sustainability, and system resilience. This review examines the evolving role of DTs in bridging the efficiency-driven goals of I4.0 with the inclusive, sustainable objectives of I5.0. It explores ten enabling technologies such as artificial intelligence (AI), internet of things, blockchain, cloud and edge computing, and extended reality, while discussing both the opportunities and challenges posed by I5.0. The study emphasizes key principles of DTs, including real-time synchronization, feedback mechanisms, and lifecycle integration. A detailed sectorial analysis across manufacturing, infrastructure, energy, transportation, mining, agriculture, and healthcare illustrates how DTs are being applied in diverse contexts to enhance operational efficiency, product quality, and decision-making. The mapping of applications by country, sector, and industrial focus reveals growing trends toward I5.0 in areas such as logistics and infrastructure. Common application domains include monitoring, optimization, prediction, and decision support. Despite their potential, DT adoption faces challenges including high implementation costs, data integration issues, cybersecurity concerns, and lack of standardization. The review discusses these barriers alongside the importance of validation and security for trusted deployment. It concludes by identifying future directions, including cognitive twins, industrial metaverse integration, and ethical AI. DTs are positioned as foundational technologies for advancing sustainable, resilient, and human-centered industrial systems.

关键词： Digital twins Industry 4.0 Industry 5.0 Human-centric innovation Sustainability Artificial intelligence Predictive analytics

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Visualizing Functional Network Connectivity Difference between Healthy Control and Major Depressive Disorder Using an Explainable Machine-learning Method

Visualizing Functional Network Connectivity Difference betwe...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ji Ye Chun Mohammad S. E. Sendi Jing Sui Dongmei Zhi Vince D. Calhoun Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Major depressive disorder (MDD) is a complex mental disorder characterized by a persistent sad feeling and depressed mood. Recent studies reported differences between healthy control (HC) and MDD by looking to brain networks including default mode and cognitive control networks. More recently there has been interest in studying the brain using advanced machine learning-based classification approaches. However, interpreting the model used in the classification between MDD and HC has not been explored yet. In the current study, we classified MDD from HC by estimating whole-brain connectivity using several classification methods including support vector machine, random forest, XGBoost, and convolutional neural network. In addition, we leveraged the SHapley Additive exPlanations (SHAP) approach as a feature learning method to model the difference between these two groups. We found a consistent result among all classification method in regard of the classification accuracy and feature learning. Also, we highlighted the role of other brain networks particularly visual and sensory motor network in the classification between MDD and HC subjects.

关键词： Radio frequency Support vector machines Visualization Functional magnetic resonance imaging Machine learning Hospitals Brain modeling

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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High-fidelity laser-free universal control of two trapped ion qubits

arXiv

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arXiv 2021年

作者： Srinivas, Raghavendra Burd, S.C. Knaack, H.M. Sutherland, R.T. Kwiatkowski, A. Glancy, S. Knill, E. Wineland, D.J. Leibfried, D. Wilson, A.C. Allcock, D.T.C. Slichter, D.H. National Institute of Standards and Technology BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Physics Division Physical and Life Sciences Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Electrical and Computer Engineering Department of Physics and Astronomy University of Texas at San Antonio San AntonioTX78249 United States Center for Theory of Quantum Matter University of Colorado BoulderCO80309 United States Department of Physics University of Oregon EugeneOR97403 United States

Universal control of multiple qubits—the ability to entangle qubits and to perform arbitrary individual qubit operations—is a fundamental resource for quantum computation, simulation, and networking. Here, we implement a new laser-free scheme for universal control of trapped ion qubits based on microwave magnetic fields and radiofrequency magnetic field gradients. We demonstrate high-fidelity entanglement and individual control by creating symmetric and antisymmetric two-qubit maximally entangled states with fidelities in the intervals [0.9983, 1] and [0.9964, 0.9988], respectively, at 68% confidence, corrected for state initialization error. This technique is robust against multiple sources of decoherence, usable with essentially any trapped ion species, and has the potential to perform simultaneous entangling operations on many pairs of ions without increasing control signal power or complexity. Copyright © 2021, The Authors. All rights reserved.

关键词： Trapped ions

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Quantum emitters in aluminum nitride induced by heavy ion irradiation

APL Quantum

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APL Quantum 2024年第3期1卷

作者： Alexander Senichev Zachariah O. Martin Yongqiang Wang Owen M. Matthiessen Alexei Lagutchev Han Htoon Alexandra Boltasseva Vladimir M. Shalaev Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University 610 Purdue Mall West Lafayette Indiana 47907 USA Quantum Science Center Department of Energy A National Quantum Information Science Research Center of the U.S. Oak Ridge National Laboratory 1 Bethel Valley Road Oak Ridge Tennessee 37830 USA Materials Science and Technology Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Materials Physics and Applications Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA

The integration of solid-state single-photon sources with foundry-compatible photonic platforms is crucial for practical and scalable quantum photonic applications. This study explores aluminum nitride (AlN) as a material with properties highly suitable for integrated on-chip photonics and the ability to host defect-center related single-photon emitters. We have conducted a comprehensive analysis of the creation of single-photon emitters in AlN, utilizing heavy ion irradiation and thermal annealing techniques. Subsequently, we have performed a detailed analysis of their photophysical properties. Guided by theoretical predictions, we assessed the potential of Zirconium (Zr) ions to create optically addressable spin defects and employed Krypton (Kr) ions as an alternative to target lattice defects without inducing chemical doping effects. With a 532 nm excitation wavelength, we found that single-photon emitters induced by ion irradiation were primarily associated with vacancy-type defects in the AlN lattice for both Zr and Kr ions. The density of these emitters increased with ion fluence, and there was an optimal value that resulted in a high density of emitters with low AlN background fluorescence. Under a shorter excitation wavelength of 405 nm, Zr-irradiated AlN exhibited isolated point-like emitters with fluorescence in the spectral range theoretically predicted for spin-defects. However, similar defects emitting in the same spectral range were also observed in AlN irradiated with Kr ions as well as in as-grown AlN with intrinsic defects. This result is supportive of the earlier theoretical predictions, but at the same time highlights the difficulties in identifying the sought-after quantum emitters with interesting properties related to the incorporation of Zr ions into the AlN lattice by fluorescence alone. The results of this study largely contribute to the field of creating quantum emitters in AlN by ion irradiation and direct future studies emphasizing the ne

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Structuring Nonlinear Wavefront Emitted from Monolayer Transition-Metal Dichalcogenides

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研究（英文） 2020年第4期 392-401页

作者： Xuanmiao Hong Guangwei Hu Wenchao Zhao Kai Wang Shang Sun Rui Zhu Jing Wu Weiwei Liu Kian Ping Loh Andrew Thye Shen Wee Bing Wang Andrea Al Cheng-Wei Qiu Peixiang Lu Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Advanced Science Research Center City University of New York New York 10031 USA Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Department of Physics National University of Singapore 2 Science Drive 3 Singapore 117542 Institute of Materials Research and Engineering Agency for Science Technology and Research 2 Fusionopolis Way Innovis#08-03Singapore 138634 Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Department of Chemistry National University of Singapore 3 Science Drive 3 Singapore 17543 Department of Physics National University of Singapore 2 Science Drive 3 Singapore 117542 Centre for Advanced 2D Materials National University of Singapore Block S14 6 Science Drive 2 Singapore 117546 Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Advanced Science Research Center City University of New York New York 10031 USA Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Hubei Key Laboratory of Optical

The growing demand for tailored nonlinearity calls for a structure with unusual phase discontinuity that allows the realization of nonlinear optical chirality,holographic imaging,and nonlinear wavefront ***-metal dichalcogenide(TMDC)monolayers offer giant optical nonlinearity within a few-angstrom thickness,but limitations in optical absorption and domain size impose restriction on wavefront control of nonlinear emissions using classical light *** contrast,noble metal-based plasmonic nanosieves support giant field enhancements and precise nonlinear phase control,with hundred-nanometer pixel-level resolution;however,they suffer from intrinsically weak nonlinear ***,we report a multifunctional nonlinear interface by integrating TMDC monolayers with plasmonic nanosieves,yielding drastically different nonlinear functionalities that cannot be accessed by either *** a hybrid nonlinear interface allows second-harmonic(SH)orbital angular momentum(OAM)generation,beam steering,versatile polarization control,and holograms,with an effective SH nonlinearityX(2)of-25nm/*** designer platform synergizes the TMDC monolayer and plasmonic nanosieves to empower tunable geometric phases and large field enhancement,paving the way toward multifunctional and ultracompact nonlinear optical devices.

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Direct mapping FBMC based underwater transmission scheme for audio signals

Direct mapping FBMC based underwater transmission scheme for...

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International Conference on Physics, Mechanics of New Materials and Their Applications, PHENMA 2018

作者： Lin, Chin-Feng Li, Chun-Kang Chang, Shun-Hsyung Parinov, Ivan A. Shevtsov, Sergey Department of Electrical Engineering National Taiwan Ocean University Keelung Taiwan Department of Microelectronic Engineering National Kaohsuing University of Science and Technology Kaohsiung Taiwan I. I. Vorovich Mathematics Mechanics and Computer Science Institute Southern Federal University Rostov-on-Don Russia South Center of Russian Academy of Science Rostov-on-Don Russia

ISBN: (纸本)9783030198930

This paper proposes a direct mapping (DM) filter bank multicarrier (FBMC) based underwater transmission scheme for audio signals. Figure 50.1 schematically shows the proposed FBMC scheme, and DM transmission technology, low-density parity-check (LDPC) channel coding, adaptive binary phase shift keying (BPSK) modulation or offset quadrature amplitude modulation (OQAM), FBMC transmission technology, and a power assignment mechanism are integrated into an underwater audio transmission system. The BER performances of the proposed scheme in an underwater channel were explored through simulations, and the power saving ratios for the underwater transmissions of audio signals were discussed. © 2019, Springer Nature Switzerland AG.

关键词： Transmissions

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 — Ex vivo imaging: data processing, comparisons with microscopy, and tractography

arXiv

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arXiv 2024年

作者： Schilling, Kurt G. Howard, Amy F.D. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio van de Looij, Yohan Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Dell' Acqua, Flavio Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Res

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. © 2024, CC BY.

关键词： Data Sharing

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