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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 — Ex vivo imaging: added value and acquisition

arXiv

引用

arXiv 2022年

作者： Schilling, Kurt G. Grussu, Francesco Ianus, Andrada Hansen, Brian Howard, Amy F.D. 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 de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell 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. Allan Johnson, G. 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 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 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 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 Re

The value of preclinical diffusion MRI (dMRI) is substantial. 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 including higher signal-to-noise ratio (SNR) and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial 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 represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. 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. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY.

关键词： Image acquisition

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Considerations and Recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 — In vivo small-animal imaging

arXiv

引用

arXiv 2022年

作者： Jelescu, Ileana O. 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 de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell 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. Schilling, Kurt G. Department of Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland 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 Resonance National HIgh Magnetic Field Laboratory TallahasseeFL United States Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany USC Stevens Neuroimaging and Informatics Instit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY-NC-ND.

关键词： Invertebrates

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TOI-2458 b: A mini-Neptune consistent with in situ hot Jupiter formation

arXiv

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arXiv 2024年

作者： Šubjak, Jan Gandolfi, Davide Goffo, Elisa Rapetti, David Jankowski, Dawid Mizuki, Toshiyuki Dai, Fei Serrano, Luisa M. Wilson, Thomas G. Goździewski, Krzysztof Nowak, Grzegorz Jenkins, Jon M. Twicken, Joseph D. Winn, Joshua N. Bieryla, Allyson Ciardi, David R. Cochran, William D. Collins, Karen A. Deeg, Hans J. Garcia, Rafael A. Guenther, Eike W. Hatzes, Artie P. Kabath, Petr Korth, Judith Latham, David W. Livingston, John H. Lund, Michael B. Mathur, Savita Narita, Norio Orell-Miquel, Jaume Palle, Enric Persson, Carina M. Redfield, Seth Schwarz, Richard P. Watanabe, David Ziegler, Carl Astronomical Institute Czech Academy of Sciences Fričova 298 Ondřejov251 65 Czech Republic Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Dipartimento di Fisica Universita degli Studi di Torino via Pietro Giuria 1 TorinoI-10125 Italy Thuringer Landessternwarte Tautenburg Sternwarte 5 Tautenburg07778 Germany NASA Ames Research Center Moffett FieldCA94035 United States Research Institute for Advanced Computer Science Universities Space Research Association WashingtonDC20024 United States Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5 Toruń87-100 Poland Astrobiology Center of NINS 2-21-1 Osawa Mitaka Tokyo181-8588 Japan National Astronomical Observatory of Japan 2-21-2 Osawa Mitaka Tokyo181-8588 Japan Institute for Astronomy University of Hawai'i 2680 Woodlawn Drive HonoluluHI96822 United States Department of Physics University of Warwick Gibbet Hill Road CoventryCV4 7AL United Kingdom SETI Institute Mountain ViewCA94043 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States NASA Exoplanet Science Institute-Caltech/IPAC PasadenaCA91125 United States McDonald Observatory The University of Texas AustinTX United States Center for Planetary Systems Habitability The University of Texas AustinTX United States C. Via Lactea S/N Tenerife La LagunaE-38205 Spain Dept. de Astrofisica Tenerife La LagunaE-38206 Spain Universite Paris-Saclay Universite Paris Cite CEA CNRS AIM Gif-sur-Yvette91191 France Lund Observatory Division of Astrophysics Department of Physics Lund University Box 118 Lund22100 Sweden Astronomical Science Program Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Komaba Institute for Science The University of Tokyo 3-8-1 Komaba Meguro Tokyo153-8902 Japan Chalmers University of Technology Departmen

We report on the discovery and spectroscopic confirmation of TOI-2458 b, a transiting mini-Neptune around an F-type star leaving the mainsequence with a mass of M*= 1.05 ± 0.03Mo, a radius of R*= 1.31 ± 0.03 Ro, an effective temperature of Teff = 6005 ± 50 K, and a metallicity of -0.10 ± 0.05 dex. By combining TESS photometry with high-resolution spectra acquired with the HARPS spectrograph, we found that the transiting planet has an orbital period of ∼3.74 days, a mass of Mp = 13.31±0.99M⊕ and a radius of Rp = 2.83±0.20 R⊕. The host star TOI-2458 shows a short activity cycle of ∼54 days revealed in the HARPS S-index and Hα times series. We took the opportunity to investigate other F stars showing activity cycle periods comparable to that of TOI-2458 and found that they have shorter rotation periods than would be expected based on the gyrochronology predictions. In addition, we determined TOI-2458's stellar inclination angle to be i*= 10.6+13.3 -10.6 degrees. We discuss that both phenomena (fast stellar rotation and planet orbit inclination) could be explained by in situ formation of a hot Jupiter interior to TOI-2458 b. It is plausible that this hot Jupiter was recently engulfed by the star. Analysis of HARPS spectra has identified the presence of another planet with a period of P = 16.55 ± 0.06 days and a minimum mass of Mp sin i = 10.22 ± 1.90M⊕. Using dynamical stability analysis, we constrained the mass of this planet to the range Mc (10, 25)M⊕. © 2024, CC BY.

关键词： Stars

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Change point methods on a sequence of graphs

arXiv

引用

arXiv 2018年

作者： Zambon, Daniele Alippi, Cesare Livi, Lorenzo Faculty of Informatics Università della Svizzera Italiana Lugano Switzerland Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Department of Computer Science College of Engineering Mathematics and Physical Sciences University of Exeter ExeterEX4 4QF United Kingdom

Given a finite sequence of graphs, e.g., coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity in the stochastic process generating the graphs. In order to cover a large class of applications, we consider the general family of attributed graphs where both topology (number of vertexes and edge configuration) and related attributes are allowed to change also in the stationary case. Novel Change Point Methods (CPMs) are proposed, that (i) map graphs into a vector domain;(ii) apply a suitable statistical test in the vector space;(iii) detect the change –if any– according to a confidence level and provide an estimate for its time occurrence. Two specific multivariate CPMs have been designed: one that detects shifts in the distribution mean, the other addressing generic changes affecting the distribution. We ground our proposal with theoretical results showing how to relate the inference attained in the numerical vector space to the graph domain, and vice versa. We also show how to extend the methodology for handling multiple change points in the same sequence. Finally, the proposed CPMs have been validated on real data sets coming from epileptic-seizure detection problems and on labeled data sets for graph classification. Results show the effectiveness of what proposed in relevant application scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Vector spaces

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Algorithms for low-distortion embeddings into arbitrary 1-dimensional spaces

arXiv

引用

arXiv 2017年

作者： Carpenter, Timothy Fomin, Fedor V. Lokshtanov, Daniel Saurabh, Saket Sidiropoulos, Anastasios Dept. of Computer Science & Engineering Ohio State University Department of Informatics University of Bergen Norway Department of Informatics University of Bergen Norway Institute of Mathematical Sciences India Dept. of Mathematics Dept. of Computer Science & Engineering Ohio State University

We study the problem of finding a minimum-distortion embedding of the shortest path metric of an unweighted graph into a "simpler" metric X. Computing such an embedding (exactly or approximately) is a non-trivial task even when X is the metric induced by a path, or, equivalently, into the real line. In this paper we give approximation and fixed-parameter tractable (FPT) algorithms for minimum-distortion embeddings into the metric of a subdivision of some fixed graph H, or, equivalently, into any fixed 1-dimensional simplicial complex. More precisely, we study the following problem: For given graphs G, H and integer c, is it possible to embed G with distortion c into a graph homeomorphic to H? Then embedding into the line is the special case H = K2, and embedding into the cycle is the case H = K3, where Kk denotes the complete graph on k vertices. For this problem we give • an approximation algorithm, which in time f(H) · poly(n), for some function f, either correctly decides that there is no embedding of G with distortion c into any graph homeomorphic to H, or finds an embedding with distortion poly(c);• an exact algorithm, which in time f0(H, c) · poly(n), for some function f0, either correctly decides that there is no embedding of G with distortion c into any graph homeomorphic to H, or finds an embedding with distortion c. Prior to our work, poly(OPT)-approximation or FPT algorithms were known only for embedding into paths and trees of bounded degrees. Copyright © 2017, The Authors. All rights reserved.

关键词： Embeddings

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Evaluation of Cache for Bandwidth Optimization in ICN Through Software-Defined Networks

Evaluation of Cache for Bandwidth Optimization in ICN Throug...

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IEEE Symposium on computers and Communications

作者： Erick B. Nascimento Douglas D. J. de Macedo Edward David Moreno Luis Carlos Erpen de Bona Miriam A. M. Capretz Dept. of Computing Science (DCOMP) Federal University of Sergipe (UFS) Sãao Cristovãao SE Brazil Dept. of Information Science (CIN) Federal University of Santa Catarina (UFSC) Florianóopolis SC Brazil Dept. of Informatics Federal University of Parana (UFPR) Curitiba PR Brazil Dept. of Electrical and Computer Engineering Western University London ON Canada

Traffic reduction in network segments through cache implementations has become a major research topic due to the exponential increase in data requests through the network. Even with high-speed connections, the conventional model still depends on point-to-point communication between two systems. Throughout the world, more connected devices are, accessing services and obtaining information. To support this activity, servers must have massive storage to support creation, retrieval, updated and deletion of large amounts of data. Therefore, in studies of Information Centric Networks (ICN), this model has been widely discussed as the new content distribution model for the Internet. To provide improved network management many approaches are using software-defined networks (SDN) to develop flexible content-based networks. This paper proposes to use cache replication for ICN through SDN to avoid duplicated requests in the same connection. The redundant cache reduced the bandwidth consumed by duplicated requests, from a maximum of 3.20 Gbps to 2.07 Gbps, reducing the bandwidth consumption by 11.3%.

关键词： Cache Controller Networks ICN SDN

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Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

arXiv

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arXiv 2018年

作者： Cheplygina, Veronika De Bruijne, Marleen Pluim, Josien P.W. Biomedical Imaging Group Rotterdam Depts. Radiology and Medical Informatics Erasmus Medical Center Rotterdam Medical Image Analysis Dept. Biomedical Engineering Eindhoven University of Technology Eindhoven Image Sciences Institute University Medical Center Utrecht Utrecht Image Section Dept. Computer Science University of Copenhagen Copenhagen Denmark

Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods that can learn with less/other types of supervision, have been proposed. We give an overview of semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research. Copyright © 2018, The Authors. All rights reserved.

关键词： Medical imaging

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Determination of the structural requirements of μ-Opioid receptor ligands with docking

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AIP Conference Proceedings 2020年第1期2302卷

作者： T. Dzimbova F. Spundzhi R. Mavrevski P. Milanov Neofit Rilski South-West University Dept of Anatomy and Physiology and University Center for Advanced Bioinformatics Research 66 Ivan Mihaylov str. 2700 Blagoevgrad Bulgaria Roumen Tsanev Institute of Molecular Biology Bulgarian Academy of Sciences Acad. Georgi Bonchev str. Bl. 21 1113 Sofia Bulgaria Neofit Rilski South-West University Dept of Communication and Computer Engineering and University Center for Advanced Bioinformatics Research 66 Ivan Mihaylov str. 2700 Blagoevgrad Bulgaria Neofit Rilski South-West University Dept of Informatics and University Center for Advanced Bioinformatics Research 66 Ivan Mihaylov str. 2700 Blagoevgrad Bulgaria Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev str. Bl. 8 1113 Sofia Bulgaria

The μ-opioid receptor (MOR) is an important target in the search of novel analgesics. The MOR agonists, such as morphine, remain the most powerful analgesics available to relieve postoperative and cancer pains. However, these compounds produce adverse effects, including development of tolerance and physical dependence, sedation, constipation, some respiratory depression, nausea, and vomiting. These unwanted effects significantly diminish the patients’ quality of life. Thus, structural requirements of binding to this receptor attract considerable scientific attention. Different classes of compounds were synthesized and biologically tested as ligands of the receptor with different effects. The aim of the present study is to determine the structural requirements of the ligands with the help of docking and subsequent analysis. Ligand preparation was done with Avogadro software. Docking was performed with GOLD 5.2 using all functions available in the program. In vivo biological effects of the compounds were previously tested and already published. Binding to the receptor of four groups of compounds (arginine, kyotorphin, Melanocyte-inhibiting factor - 1 and Tyr-MIF-1 analogues) was analyzed and specific binding sites were determined. The main finding according to the results of docking and in vivo tests was the presence of a relationship between the size of the molecule and the presence of specific functional groups capable of binding the receptor stronger. Smaller molecules could enter the binding site of the receptor easily and could form the ligand-receptor complex with lower energy, which is associated with stronger and prolonged effect. Docking and subsequent mathematical analysis could be promising tools in the design of new and potent ligands of μ-opioid receptors.

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EM-driven multi-objective design of impedance transformers by pareto ranking bisection algorithm

EM-driven multi-objective design of impedance transformers b...

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2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017

作者： Bekasiewicz, Adrian Koziel, Slawomir Cheng, Qingsha S. Bandler, J.W. School of Science and Engineering Reykjavik University Reykjavik Iceland Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland Dept. Electrical and Electronic Eng. Southern University of Science and Technology Shenzhen China Department of Electrical and Computer Engineering McMaster University HamiltonON Canada

ISBN: (纸本)9780996007856

In the paper, the problem of fast multi-objective optimization of compact impedance matching transformers is addressed by utilizing a novel Pareto ranking bisection algorithm. It approximates the Pareto front by dividing line segments connecting the designs found in the previous iterations, and refining the obtained candidate solutions by means of poll-type search involving Pareto ranking. The final Pareto set is obtained using surrogate-based optimization techniques. Our approach is validated using a compact impedance matching transformer and compared to state-of-the-art surrogate-assisted techniques. © 2017 Applied Computational Electromagnetics Society.

关键词： Multiobjective optimization

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Dynamic Human Gait VGRF Reference Profile Generation via Extreme Learning Machine

Dynamic Human Gait VGRF Reference Profile Generation via Ext...

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International Joint Conference on Neural Networks (IJCNN)

作者： Alexandra Vieira Bernardete Ribeiro Heloísa Sobral A. Paulo Coimbra Manuel Crisóstomo João P. Ferreira Tao Liu Dept. of Informatics Engineering CISUC Univ. of Coimbra Coimbra Portugal Dept. of Electrical and Computer Eng. Institute of Systems and Robotics Univ. of Coimbra Coimbra Portugal Dept. of Electrical Eng. Superior Institute of Eng. of Coimbra Institute of Systems and RoboticsUniv. of Coimbra Coimbra Portugal State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China

Vertical Ground Reaction Forces (VGRF) reference profiles of human gait are important in medicine, for the recognition of gait disorders and in the assessment of rehabilitation treatments. A walking human is a dynamic varying system and in spite of the VGRF reference dependence on several patient's variables, doctors traditionally use the same static reference for all patients. The purpose of this study is to find out if an Extreme Learning Machine (ELM) is adequate to generate the dynamic VGRF reference profiles of healthy people depending on the subject's age, weight, height and stride duration. The ELM is compared with two other baseline Computational Intelligence (CI) methods, the Backpropagation Neural Network (BNN) and Multioutput Support Vector Regression (MSVR). Data from 28 healthy males walking at five different stride durations, collected using instrumented shoes, were used to train and test the CI models. The results showed that ELM is a well suited method to generate the dynamic VGRF reference profile for both dominant and non-dominant limbs, showing the lowest root mean square errors for the test set, 0.0201 and 0.0243 (fraction of body weight) for the dominant and non-dominant limbs respectively. This study reveals a promising methodology that can be implemented in real time gait analysis, allowing doctors to find a specific reference gait pattern for the gait analysis of an unhealthy person by specifying the age, weight, height and stride duration.

关键词： Training Biological neural networks Support vector machines Neurons Backpropagation Legged locomotion Adaptation models

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