1
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Curion F, Theis FJ. Machine learning integrative approaches to advance computational immunology. Genome Med 2024; 16:80. [PMID: 38862979 PMCID: PMC11165829 DOI: 10.1186/s13073-024-01350-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.
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Affiliation(s)
- Fabiola Curion
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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2
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Solov'yov AV, Verkhovtsev AV, Mason NJ, Amos RA, Bald I, Baldacchino G, Dromey B, Falk M, Fedor J, Gerhards L, Hausmann M, Hildenbrand G, Hrabovský M, Kadlec S, Kočišek J, Lépine F, Ming S, Nisbet A, Ricketts K, Sala L, Schlathölter T, Wheatley AEH, Solov'yov IA. Condensed Matter Systems Exposed to Radiation: Multiscale Theory, Simulations, and Experiment. Chem Rev 2024. [PMID: 38842266 DOI: 10.1021/acs.chemrev.3c00902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
This roadmap reviews the new, highly interdisciplinary research field studying the behavior of condensed matter systems exposed to radiation. The Review highlights several recent advances in the field and provides a roadmap for the development of the field over the next decade. Condensed matter systems exposed to radiation can be inorganic, organic, or biological, finite or infinite, composed of different molecular species or materials, exist in different phases, and operate under different thermodynamic conditions. Many of the key phenomena related to the behavior of irradiated systems are very similar and can be understood based on the same fundamental theoretical principles and computational approaches. The multiscale nature of such phenomena requires the quantitative description of the radiation-induced effects occurring at different spatial and temporal scales, ranging from the atomic to the macroscopic, and the interlinks between such descriptions. The multiscale nature of the effects and the similarity of their manifestation in systems of different origins necessarily bring together different disciplines, such as physics, chemistry, biology, materials science, nanoscience, and biomedical research, demonstrating the numerous interlinks and commonalities between them. This research field is highly relevant to many novel and emerging technologies and medical applications.
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Affiliation(s)
- Andrey V Solov'yov
- MBN Research Center, Altenhöferallee 3, 60438 Frankfurt am Main, Germany
| | | | - Nigel J Mason
- School of Physics and Astronomy, University of Kent, Canterbury CT2 7NH, United Kingdom
| | - Richard A Amos
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, U.K
| | - Ilko Bald
- Institute of Chemistry, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany
| | - Gérard Baldacchino
- Université Paris-Saclay, CEA, LIDYL, 91191 Gif-sur-Yvette, France
- CY Cergy Paris Université, CEA, LIDYL, 91191 Gif-sur-Yvette, France
| | - Brendan Dromey
- Centre for Light Matter Interactions, School of Mathematics and Physics, Queen's University Belfast, Belfast BT7 1NN, United Kingdom
| | - Martin Falk
- Institute of Biophysics of the Czech Academy of Sciences, Královopolská 135, 61200 Brno, Czech Republic
- Kirchhoff-Institute for Physics, Heidelberg University, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany
| | - Juraj Fedor
- J. Heyrovský Institute of Physical Chemistry, Czech Academy of Sciences, Dolejškova 3, 18223 Prague, Czech Republic
| | - Luca Gerhards
- Institute of Physics, Carl von Ossietzky University, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany
| | - Michael Hausmann
- Kirchhoff-Institute for Physics, Heidelberg University, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany
| | - Georg Hildenbrand
- Kirchhoff-Institute for Physics, Heidelberg University, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany
- Faculty of Engineering, University of Applied Sciences Aschaffenburg, Würzburger Str. 45, 63743 Aschaffenburg, Germany
| | | | - Stanislav Kadlec
- Eaton European Innovation Center, Bořivojova 2380, 25263 Roztoky, Czech Republic
| | - Jaroslav Kočišek
- J. Heyrovský Institute of Physical Chemistry, Czech Academy of Sciences, Dolejškova 3, 18223 Prague, Czech Republic
| | - Franck Lépine
- Université Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622, Villeurbanne, France
| | - Siyi Ming
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Andrew Nisbet
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, U.K
| | - Kate Ricketts
- Department of Targeted Intervention, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Leo Sala
- J. Heyrovský Institute of Physical Chemistry, Czech Academy of Sciences, Dolejškova 3, 18223 Prague, Czech Republic
| | - Thomas Schlathölter
- Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
- University College Groningen, University of Groningen, Hoendiepskade 23/24, 9718 BG Groningen, The Netherlands
| | - Andrew E H Wheatley
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Ilia A Solov'yov
- Institute of Physics, Carl von Ossietzky University, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany
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3
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Capone M, Romanelli M, Castaldo D, Parolin G, Bello A, Gil G, Vanzan M. A Vision for the Future of Multiscale Modeling. ACS PHYSICAL CHEMISTRY AU 2024; 4:202-225. [PMID: 38800726 PMCID: PMC11117712 DOI: 10.1021/acsphyschemau.3c00080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 05/29/2024]
Abstract
The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.
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Affiliation(s)
- Matteo Capone
- Department
of Physical and Chemical Sciences, University
of L’Aquila, L’Aquila 67010, Italy
| | - Marco Romanelli
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
| | - Davide Castaldo
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
| | - Giovanni Parolin
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
| | - Alessandro Bello
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
- Department
of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, Modena 41125, Italy
| | - Gabriel Gil
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
- Instituto
de Cibernética, Matemática y Física (ICIMAF), La Habana 10400, Cuba
| | - Mirko Vanzan
- Department
of Chemical Sciences, University of Padova, Padova 35131, Italy
- Department
of Physics, University of Milano, Milano 20133, Italy
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4
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Sinha R, Rocco MV, Daeihagh P, Staples AE. Innovating dialysis through computational modelling of hollow-fibre haemodialysers. Nat Rev Nephrol 2024; 20:269-270. [PMID: 38438536 DOI: 10.1038/s41581-024-00826-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Affiliation(s)
- Ruhit Sinha
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA.
| | - Michael V Rocco
- Section of Nephrology, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Pirouz Daeihagh
- Section of Nephrology, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Anne E Staples
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA
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5
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Smith AM. Decoding immune kinetics: unveiling secrets using custom-built mathematical models. Nat Methods 2024; 21:744-747. [PMID: 38710785 DOI: 10.1038/s41592-024-02265-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Affiliation(s)
- Amber M Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA.
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6
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Xu Y, Cao L, Chen Y, Zhang Z, Liu W, Li H, Ding C, Pu J, Qian K, Xu W. Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation. SMALL METHODS 2024:e2400305. [PMID: 38682615 DOI: 10.1002/smtd.202400305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 04/07/2024] [Indexed: 05/01/2024]
Abstract
Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.
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Affiliation(s)
- Yudian Xu
- Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Linlin Cao
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Yifan Chen
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Ziyue Zhang
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Wanshan Liu
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - He Li
- Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Chenhuan Ding
- Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Jun Pu
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Kun Qian
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Wei Xu
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
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7
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Tuna R, Yi W, Crespo Cruz E, Romero JP, Ren Y, Guan J, Li Y, Deng Y, Bluestein D, Liu ZL, Sheriff J. Platelet Biorheology and Mechanobiology in Thrombosis and Hemostasis: Perspectives from Multiscale Computation. Int J Mol Sci 2024; 25:4800. [PMID: 38732019 PMCID: PMC11083691 DOI: 10.3390/ijms25094800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/19/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
Thrombosis is the pathological clot formation under abnormal hemodynamic conditions, which can result in vascular obstruction, causing ischemic strokes and myocardial infarction. Thrombus growth under moderate to low shear (<1000 s-1) relies on platelet activation and coagulation. Thrombosis at elevated high shear rates (>10,000 s-1) is predominantly driven by unactivated platelet binding and aggregating mediated by von Willebrand factor (VWF), while platelet activation and coagulation are secondary in supporting and reinforcing the thrombus. Given the molecular and cellular level information it can access, multiscale computational modeling informed by biology can provide new pathophysiological mechanisms that are otherwise not accessible experimentally, holding promise for novel first-principle-based therapeutics. In this review, we summarize the key aspects of platelet biorheology and mechanobiology, focusing on the molecular and cellular scale events and how they build up to thrombosis through platelet adhesion and aggregation in the presence or absence of platelet activation. In particular, we highlight recent advancements in multiscale modeling of platelet biorheology and mechanobiology and how they can lead to the better prediction and quantification of thrombus formation, exemplifying the exciting paradigm of digital medicine.
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Affiliation(s)
- Rukiye Tuna
- Department of Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA; (R.T.); (E.C.C.); (Z.L.L.)
| | - Wenjuan Yi
- Department of Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA; (R.T.); (E.C.C.); (Z.L.L.)
| | - Esmeralda Crespo Cruz
- Department of Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA; (R.T.); (E.C.C.); (Z.L.L.)
| | - JP Romero
- Department of Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA; (R.T.); (E.C.C.); (Z.L.L.)
| | - Yi Ren
- Department of Biomedical Sciences, College of Medicine, Florida State University, Tallahassee, FL 32304, USA
| | - Jingjiao Guan
- Department of Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA; (R.T.); (E.C.C.); (Z.L.L.)
- Institute for Successful Longevity, Florida State University, Tallahassee, FL 32304, USA
| | - Yan Li
- Department of Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA; (R.T.); (E.C.C.); (Z.L.L.)
- Institute for Successful Longevity, Florida State University, Tallahassee, FL 32304, USA
| | - Yuefan Deng
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Danny Bluestein
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA;
| | - Zixiang Leonardo Liu
- Department of Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA; (R.T.); (E.C.C.); (Z.L.L.)
- Institute for Successful Longevity, Florida State University, Tallahassee, FL 32304, USA
| | - Jawaad Sheriff
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA;
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8
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Maria Antony AN, Narisetti N, Gladilin E. Linel2D-Net: A deep learning approach to solving 2D linear elastic boundary value problems on image domains. iScience 2024; 27:109519. [PMID: 38595795 PMCID: PMC11002675 DOI: 10.1016/j.isci.2024.109519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/02/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024] Open
Abstract
Efficient solution of physical boundary value problems (BVPs) remains a challenging task demanded in many applications. Conventional numerical methods require time-consuming domain discretization and solving techniques that have limited throughput capabilities. Here, we present an efficient data-driven DNN approach to non-iterative solving arbitrary 2D linear elastic BVPs. Our results show that a U-Net-based surrogate model trained on a representative set of reference FDM solutions can accurately emulate linear elastic material behavior with manifold applications in deformable modeling and simulation.
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Affiliation(s)
- Anto Nivin Maria Antony
- Leibniz Institute of Plant Genetics and Crop Plant Research, OT Gatersleben, Corrensstr. 3, 06466 Seeland, Germany
| | - Narendra Narisetti
- Leibniz Institute of Plant Genetics and Crop Plant Research, OT Gatersleben, Corrensstr. 3, 06466 Seeland, Germany
| | - Evgeny Gladilin
- Leibniz Institute of Plant Genetics and Crop Plant Research, OT Gatersleben, Corrensstr. 3, 06466 Seeland, Germany
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9
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Yao J, Du Z, Yang F, Duan R, Feng T. The relationship between heavy metals and metabolic syndrome using machine learning. Front Public Health 2024; 12:1378041. [PMID: 38686033 PMCID: PMC11057329 DOI: 10.3389/fpubh.2024.1378041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/02/2024] [Indexed: 05/02/2024] Open
Abstract
Background Exposure to high levels of heavy metals has been widely recognized as an important risk factor for metabolic syndrome (MetS). The main purpose of this study is to assess the associations between the level of heavy metal exposure and Mets using machine learning (ML) method. Methods The data used in this study are from the national health and nutrition examination survey 2003-2018. According to the demographic information and heavy metal exposure level of participants, a total of 22 variables were included. Lasso was used to screen out the key variables, and 9 commonly used ML models were selected to establish the associations with the 5-fold cross validation method. Finally, we choose the SHapley Additive exPlanations (SHAP) method to explain the prediction results of Adaboost model. Results 11,667 eligible individuals were randomly divided into two groups to train and verify the prediction model. Through lasso, characteristic variables were selected from 24 variables as predictors. The AUC (area under curve) of the models selected in this study were all greater than 0.7, and AdaBoost was the best model. The AUC value of AdaBoost was 0.807, the accuracy was 0.720, and the sensitivity was 0.792. It is noteworthy that higher levels of cadmium, body mass index, cesium, being female, and increasing age were associated with an increased probability of MetS. Conversely, lower levels of cobalt and molybdenum were linked to a decrease in the estimated probability of MetS. Conclusion Our study highlights the AdaBoost model proved to be highly effective, precise, and resilient in detecting a correlation between exposure to heavy metals and MetS. Through the use of interpretable methods, we identified cadmium, molybdenum, cobalt, cesium, uranium, and barium as prominent contributors within the predictive model.
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Affiliation(s)
- Jun Yao
- Department of Respiratory and Critical Care, Guangyuan Central Hospital, Guangyuan, Sichuan, China
| | - Zhilin Du
- Department of Oncology, Chengdu Seventh People’s Hospital (Affliated Cancer Hospital of Chengdu Medical College), Chengdu, Sichuan, China
| | - Fuyue Yang
- Department of Rheumatology and Immunology, Chengdu Fifth People's Hospital, Chengdu, Sichuan, China
| | - Ran Duan
- Department of Oncology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China
| | - Tong Feng
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
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10
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Sahimi M. Physics-informed and data-driven discovery of governing equations for complex phenomena in heterogeneous media. Phys Rev E 2024; 109:041001. [PMID: 38755895 DOI: 10.1103/physreve.109.041001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Indexed: 05/18/2024]
Abstract
Rapid evolution of sensor technology, advances in instrumentation, and progress in devising data-acquisition software and hardware are providing vast amounts of data for various complex phenomena that occur in heterogeneous media, ranging from those in atmospheric environment, to large-scale porous formations, and biological systems. The tremendous increase in the speed of scientific computing has also made it possible to emulate diverse multiscale and multiphysics phenomena that contain elements of stochasticity or heterogeneity, and to generate large volumes of numerical data for them. Thus, given a heterogeneous system with annealed or quenched disorder in which a complex phenomenon occurs, how should one analyze and model the system and phenomenon, explain the data, and make predictions for length and time scales much larger than those over which the data were collected? We divide such systems into three distinct classes. (i) Those for which the governing equations for the physical phenomena of interest, as well as data, are known, but solving the equations over large length scales and long times is very difficult. (ii) Those for which data are available, but the governing equations are only partially known, in the sense that they either contain various coefficients that must be evaluated based on the data, or that the number of degrees of freedom of the system is so large that deriving the complete equations is very difficult, if not impossible, as a result of which one must develop the governing equations with reduced dimensionality. (iii) In the third class are systems for which large amounts of data are available, but the governing equations for the phenomena of interest are not known. Several classes of physics-informed and data-driven approaches for analyzing and modeling of the three classes of systems have been emerging, which are based on machine learning, symbolic regression, the Koopman operator, the Mori-Zwanzig projection operator formulation, sparse identification of nonlinear dynamics, data assimilation combined with a neural network, and stochastic optimization and analysis. This perspective describes such methods and the latest developments in this highly important and rapidly expanding area and discusses possible future directions.
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Affiliation(s)
- Muhammad Sahimi
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-1211, USA
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11
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Lorenzo G, Heiselman JS, Liss MA, Miga MI, Gomez H, Yankeelov TE, Reali A, Hughes TJ. A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model. CANCER RESEARCH COMMUNICATIONS 2024; 4:617-633. [PMID: 38426815 PMCID: PMC10906139 DOI: 10.1158/2767-9764.crc-23-0449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/15/2023] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer. SIGNIFICANCE Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.
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Affiliation(s)
- Guillermo Lorenzo
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
| | - Jon S. Heiselman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Michael A. Liss
- Department of Urology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Michael I. Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Neurological Surgery, Radiology, and Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hector Gomez
- School of Mechanical Engineering, Weldon School of Biomedical Engineering, and Purdue Institute for Cancer Research, Purdue University, West Lafayette, Indiana
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
- Livestrong Cancer Institutes and Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, The University of Texas at Austin, Austin, Texas
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alessandro Reali
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Thomas J.R. Hughes
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
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12
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Laubenbacher R, Mehrad B, Shmulevich I, Trayanova N. Digital twins in medicine. NATURE COMPUTATIONAL SCIENCE 2024; 4:184-191. [PMID: 38532133 PMCID: PMC11102043 DOI: 10.1038/s43588-024-00607-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/12/2024] [Indexed: 03/28/2024]
Abstract
Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.
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Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - B Mehrad
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | | | - N Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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13
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Sharma A, Lysenko A, Jia S, Boroevich KA, Tsunoda T. Advances in AI and machine learning for predictive medicine. J Hum Genet 2024:10.1038/s10038-024-01231-y. [PMID: 38424184 DOI: 10.1038/s10038-024-01231-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/04/2024] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit limitations in handling potential relationships within the data for more accurate prediction. This review explores a revolutionary shift in predictive modeling through the application of deep learning (DL), specifically convolutional neural networks (CNNs). Using transformation methods such as DeepInsight, omics data with independent variables in tabular (table-like, including vector) form can be turned into image-like representations, enabling CNNs to capture latent features effectively. This approach not only enhances predictive power but also leverages transfer learning, reducing computational time, and improving performance. However, integrating CNNs in predictive omics data analysis is not without challenges, including issues related to model interpretability, data heterogeneity, and data size. Addressing these challenges requires a multidisciplinary approach, involving collaborations between ML experts, bioinformatics researchers, biologists, and medical doctors. This review illuminates these complexities and charts a course for future research to unlock the full predictive potential of CNNs in omics data analysis and related fields.
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Affiliation(s)
- Alok Sharma
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Institute for Integrated and Intelligent Systems, Griffith University, Queensland, Australia.
| | - Artem Lysenko
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
| | - Shangru Jia
- Laboratory for Medical Science Mathematics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Keith A Boroevich
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Laboratory for Medical Science Mathematics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
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14
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Regazzoni F, Pagani S, Salvador M, Dede' L, Quarteroni A. Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks. Nat Commun 2024; 15:1834. [PMID: 38418469 DOI: 10.1038/s41467-024-45323-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 01/19/2024] [Indexed: 03/01/2024] Open
Abstract
Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equations, thus demanding extensive computational resources. In contrast, data-driven approaches leverage deep learning algorithms to describe system evolution in low-dimensional spaces. We introduce an architecture, termed Latent Dynamics Network, capable of uncovering low-dimensional intrinsic dynamics in potentially non-Markovian systems. Latent Dynamics Networks automatically discover a low-dimensional manifold while learning the system dynamics, eliminating the need for training an auto-encoder and avoiding operations in the high-dimensional space. They predict the evolution, even in time-extrapolation scenarios, of space-dependent fields without relying on predetermined grids, thus enabling weight-sharing across query-points. Lightweight and easy-to-train, Latent Dynamics Networks demonstrate superior accuracy (normalized error 5 times smaller) in highly-nonlinear problems with significantly fewer trainable parameters (more than 10 times fewer) compared to state-of-the-art methods.
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Affiliation(s)
| | - Stefano Pagani
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Matteo Salvador
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Luca Dede'
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Alfio Quarteroni
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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15
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Nicolle A, Deng S, Ihme M, Kuzhagaliyeva N, Ibrahim EA, Farooq A. Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview. J Chem Inf Model 2024; 64:597-620. [PMID: 38284618 DOI: 10.1021/acs.jcim.3c01633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.
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Affiliation(s)
- Andre Nicolle
- Aramco Fuel Research Center, Rueil-Malmaison 92852, France
| | - Sili Deng
- Massachusetts Institute of Technology, Cambridge 02139, Massachusetts, United States
| | - Matthias Ihme
- Stanford University, Stanford 94305, California, United States
| | | | - Emad Al Ibrahim
- King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | - Aamir Farooq
- King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
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16
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Hassan J, Saeed SM, Deka L, Uddin MJ, Das DB. Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges. Pharmaceutics 2024; 16:260. [PMID: 38399314 PMCID: PMC10892549 DOI: 10.3390/pharmaceutics16020260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.
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Affiliation(s)
- Jasmin Hassan
- Drug Delivery & Therapeutics Lab, Dhaka 1212, Bangladesh; (J.H.); (S.M.S.)
| | | | - Lipika Deka
- Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK;
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Diganta B. Das
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK
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17
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Beck M, Covino R, Hänelt I, Müller-McNicoll M. Understanding the cell: Future views of structural biology. Cell 2024; 187:545-562. [PMID: 38306981 DOI: 10.1016/j.cell.2023.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 02/04/2024]
Abstract
Determining the structure and mechanisms of all individual functional modules of cells at high molecular detail has often been seen as equal to understanding how cells work. Recent technical advances have led to a flush of high-resolution structures of various macromolecular machines, but despite this wealth of detailed information, our understanding of cellular function remains incomplete. Here, we discuss present-day limitations of structural biology and highlight novel technologies that may enable us to analyze molecular functions directly inside cells. We predict that the progression toward structural cell biology will involve a shift toward conceptualizing a 4D virtual reality of cells using digital twins. These will capture cellular segments in a highly enriched molecular detail, include dynamic changes, and facilitate simulations of molecular processes, leading to novel and experimentally testable predictions. Transferring biological questions into algorithms that learn from the existing wealth of data and explore novel solutions may ultimately unveil how cells work.
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Affiliation(s)
- Martin Beck
- Max Planck Institute of Biophysics, Max-von-Laue-Straße 3, 60438 Frankfurt am Main, Germany; Goethe University Frankfurt, Frankfurt, Germany.
| | - Roberto Covino
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany.
| | - Inga Hänelt
- Goethe University Frankfurt, Frankfurt, Germany.
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18
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Yankeelov TE, Hormuth DA, Lima EA, Lorenzo G, Wu C, Okereke LC, Rauch GM, Venkatesan AM, Chung C. Designing clinical trials for patients who are not average. iScience 2024; 27:108589. [PMID: 38169893 PMCID: PMC10758956 DOI: 10.1016/j.isci.2023.108589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Abstract
The heterogeneity inherent in cancer means that even a successful clinical trial merely results in a therapeutic regimen that achieves, on average, a positive result only in a subset of patients. The only way to optimize an intervention for an individual patient is to reframe their treatment as their own, personalized trial. Toward this goal, we formulate a computational framework for performing personalized trials that rely on four mathematical techniques. First, mathematical models that can be calibrated with patient-specific data to make accurate predictions of response. Second, digital twins built on these models capable of simulating the effects of interventions. Third, optimal control theory applied to the digital twins to optimize outcomes. Fourth, data assimilation to continually update and refine predictions in response to therapeutic interventions. In this perspective, we describe each of these techniques, quantify their "state of readiness", and identify use cases for personalized clinical trials.
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Affiliation(s)
- Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Division of Diagnostic Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ernesto A.B.F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Texas Advanced Computer Center, The University of Texas at Austin, Austin, TX 78712, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Civil Engineering and Architecture, University of Pavia, 27100 Pavia, Italy
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Lois C. Okereke
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Gaiane M. Rauch
- Department of Abdominal Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Aradhana M. Venkatesan
- Department of Abdominal Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
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19
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Li X, Chen F, Ma L. Exploring the Potential of Artificial Intelligence in Adolescent Suicide Prevention: Current Applications, Challenges, and Future Directions. Psychiatry 2024; 87:7-20. [PMID: 38227496 DOI: 10.1080/00332747.2023.2291945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
ObjectiveThe global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various approaches have been used, but with limited success. However, with the rapid advancements in artificial intelligence (AI), new possibilities have emerged. This paper reviews the potentials and challenges of integrating AI into suicide prevention strategies, focusing on adolescents. Method: This narrative review assesses the impact of AI on suicide prevention strategies, the strategies and cases of AI applications in adolescent suicide prevention, as well as the challenges faced. Through searches on the PubMed, web of science, PsycINFO, and EMBASE databases, 19 relevant articles were included in the review. Results: AI has significantly improved risk assessment and predictive modeling for identifying suicidal behavior. It has enabled the analysis of textual data through natural language processing and fostered novel intervention strategies. Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations. The research underscores the potential of AI to enhance future suicide prevention efforts through personalized interventions and integration with emerging technologies. Conclusion: AI possesses transformative potential for adolescent suicide prevention by offering targeted and adaptive solutions, while they also raise crucial ethical and practical considerations. Looking forward, AI can play a critical role in mitigating adolescent suicide rates, marking a new frontier in mental health care.
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20
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Kolk MZH, Ruipérez-Campillo S, Alvarez-Florez L, Deb B, Bekkers EJ, Allaart CP, Van Der Lingen ALCJ, Clopton P, Išgum I, Wilde AAM, Knops RE, Narayan SM, Tjong FVY. Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator. EBioMedicine 2024; 99:104937. [PMID: 38118401 PMCID: PMC10772563 DOI: 10.1016/j.ebiom.2023.104937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/20/2023] [Accepted: 12/12/2023] [Indexed: 12/22/2023] Open
Abstract
BACKGROUND Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. METHODS A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. FINDINGS 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. INTERPRETATION Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. FUNDING This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).
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Affiliation(s)
- Maarten Z H Kolk
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology Zurich (ETHz), Gloriastrasse 35, Zurich, Switzerland; ITACA Institute, Universtitat Politècnica de València, Camino de Vera S/n, Valencia, Spain
| | - Laura Alvarez-Florez
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Brototo Deb
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Erik J Bekkers
- Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands
| | - Cornelis P Allaart
- Department of Cardiology, Amsterdam UMC, Location VU Medical Center, De Boelelaan 1118, Amsterdam, the Netherlands
| | | | - Paul Clopton
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Reinoud E Knops
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fleur V Y Tjong
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands.
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21
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Khan RF, Lee BD, Lee MS. Transformers in medical image segmentation: a narrative review. Quant Imaging Med Surg 2023; 13:8747-8767. [PMID: 38106306 PMCID: PMC10722011 DOI: 10.21037/qims-23-542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/14/2023] [Indexed: 12/19/2023]
Abstract
Background and Objective Transformers, which have been widely recognized as state-of-the-art tools in natural language processing (NLP), have also come to be recognized for their value in computer vision tasks. With this increasing popularity, they have also been extensively researched in the more complex medical imaging domain. The associated developments have resulted in transformers being on par with sought-after convolution neural networks, particularly for medical image segmentation. Methods combining both types of networks have proven to be especially successful in capturing local and global contexts, thereby significantly boosting their performances in various segmentation problems. Motivated by this success, we have attempted to survey the consequential research focused on innovative transformer networks, specifically those designed to cater to medical image segmentation in an efficient manner. Methods Databases like Google Scholar, arxiv, ResearchGate, Microsoft Academic, and Semantic Scholar have been utilized to find recent developments in this field. Specifically, research in the English language from 2021 to 2023 was considered. Key Content and Findings In this survey, we look into the different types of architectures and attention mechanisms that uniquely improve performance and the structures that are in place to handle complex medical data. Through this survey, we summarize the popular and unconventional transformer-based research as seen through different key angles and analyze quantitatively the strategies that have proven more advanced. Conclusions We have also attempted to discern existing gaps and challenges within current research, notably highlighting the deficiency of annotated medical data for precise deep learning model training. Furthermore, potential future directions for enhancing transformers' utility in healthcare are outlined, encompassing strategies such as transfer learning and exploiting foundation models for specialized medical image segmentation.
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Affiliation(s)
- Rabeea Fatma Khan
- Department of Computer Science, Graduate School, Kyonggi University, Suwon, Republic of Korea
| | - Byoung-Dai Lee
- Department of Computer Science, Graduate School, Kyonggi University, Suwon, Republic of Korea
| | - Mu Sook Lee
- Department of Radiology, Keimyung University Dongsan Hospital, Daegu, Republic of Korea
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22
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Wang Y, Liu L, Wang C. Trends in using deep learning algorithms in biomedical prediction systems. Front Neurosci 2023; 17:1256351. [PMID: 38027475 PMCID: PMC10665494 DOI: 10.3389/fnins.2023.1256351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/25/2023] [Indexed: 12/01/2023] Open
Abstract
In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.
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Affiliation(s)
- Yanbu Wang
- School of Strength and Conditioning, Beijing Sport University, Beijing, China
| | - Linqing Liu
- Department of Physical Education, Peking University, Beijing, China
| | - Chao Wang
- Institute of Competitive Sports, Beijing Sport University, Beijing, China
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23
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Jimenez-Mesa C, Arco JE, Martinez-Murcia FJ, Suckling J, Ramirez J, Gorriz JM. Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects. Pharmacol Res 2023; 197:106984. [PMID: 37940064 DOI: 10.1016/j.phrs.2023.106984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/04/2023] [Accepted: 11/04/2023] [Indexed: 11/10/2023]
Abstract
The integration of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms, including deep learning (DL) models, is a promising approach. This integration enhances the precision and efficiency of current diagnostic and treatment strategies while offering invaluable insights into disease mechanisms. In this comprehensive review, we delve into the transformative impact of ML and DL in this domain. Firstly, a brief analysis is provided of how these algorithms have evolved and which are the most widely applied in this domain. Their different potential applications in nuclear imaging are then discussed, such as optimization of image adquisition or reconstruction, biomarkers identification, multimodal fusion and the development of diagnostic, prognostic, and disease progression evaluation systems. This is because they are able to analyse complex patterns and relationships within imaging data, as well as extracting quantitative and objective measures. Furthermore, we discuss the challenges in implementation, such as data standardization and limited sample sizes, and explore the clinical opportunities and future horizons, including data augmentation and explainable AI. Together, these factors are propelling the continuous advancement of more robust, transparent, and reliable systems.
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Affiliation(s)
- Carmen Jimenez-Mesa
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain
| | - Juan E Arco
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain; Department of Communications Engineering, University of Malaga, 29010, Spain
| | | | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK
| | - Javier Ramirez
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, 18010, Spain; Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK.
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Hoffman KW, Tran KT, Moore TM, Gataviņš MM, Visoki E, DiDomenico GE, Schultz LM, Almasy L, Hayes MR, Daskalakis NP, Barzilay R. Allostatic load in early adolescence: gene / environment contributions and relevance for mental health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.27.23297674. [PMID: 37961462 PMCID: PMC10635214 DOI: 10.1101/2023.10.27.23297674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Allostatic load is the cumulative "wear and tear" on the body due to chronic adversity. We aimed to test poly-environmental (exposomic) and polygenic contributions to allostatic load and their combined contribution to early adolescent mental health. Methods We analyzed data on N = 5,035 diverse youth (mean age 12) from the Adolescent Brain Cognitive Development Study (ABCD). Using dimensionality reduction method, we calculated and overall allostatic load score (AL) using body mass index [BMI], waist circumference, blood pressure, blood glycemia, blood cholesterol, and salivary DHEA. Childhood exposomic risk was quantified using multi-level environmental exposures before age 11. Genetic risk was quantified using polygenic risk scores (PRS) for metabolic system susceptibility (type 2 diabetes [T2D]) and stress-related psychiatric disease (major depressive disorder [MDD]). We used linear mixed effects models to test main, additive, and interactive effects of exposomic and polygenic risk (independent variables) on AL (dependent variable). Mediation models tested the mediating role of AL on the pathway from exposomic and polygenic risk to youth mental health. Models adjusted for demographics and genetic principal components. Results We observed disparities in AL with non-Hispanic White youth having significantly lower AL compared to Hispanic and Non-Hispanic Black youth. In the diverse sample, childhood exposomic burden was associated with AL in adolescence (beta=0.25, 95%CI 0.22-0.29, P<.001). In European ancestry participants (n=2,928), polygenic risk of both T2D and depression was associated with AL (T2D-PRS beta=0.11, 95%CI 0.07-0.14, P<.001; MDD-PRS beta=0.05, 95%CI 0.02-0.09, P=.003). Both polygenic scores showed significant interaction with exposomic risk such that, with greater polygenic risk, the association between exposome and AL was stronger. AL partly mediated the pathway to youth mental health from exposomic risk and from MDD-PRS, and fully mediated the pathway from T2D-PRS. Conclusions AL can be quantified in youth using anthropometric and biological measures and is mapped to exposomic and polygenic risk. Main and interactive environmental and genetic effects support a diathesis-stress model. Findings suggest that both environmental and genetic risk be considered when modeling stress-related health conditions.
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Affiliation(s)
- Kevin W. Hoffman
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, US
| | - Kate T. Tran
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
| | - Tyler M. Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
| | - Mārtiņš M. Gataviņš
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
| | - Elina Visoki
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
| | - Grace E. DiDomenico
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
| | - Laura M. Schultz
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, US
| | - Laura Almasy
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, US
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US
| | - Matthew R. Hayes
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US
| | - Nikolaos P. Daskalakis
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ran Barzilay
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, US
- Lifespan Brain Institute of Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, US
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Taneja K, He X, He Q, Chen JS. A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems. COMPUTATIONAL MECHANICS 2023; 73:1125-1145. [PMID: 38699409 PMCID: PMC11060984 DOI: 10.1007/s00466-023-02403-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 09/21/2023] [Indexed: 05/05/2024]
Abstract
This work presents a multi-resolution physics-informed recurrent neural network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter identification of the MSK systems. The MSK application was selected as the model problem due to its challenging nature in mapping the high-frequency surface electromyography (sEMG) signals to the low-frequency body joint motion controlled by the MSK and muscle contraction dynamics. The proposed method utilizes the fast wavelet transform to decompose the mixed frequency input sEMG and output joint motion signals into nested multi-resolution signals. The prediction model is subsequently trained on coarser-scale input-output signals using a gated recurrent unit (GRU), and then the trained parameters are transferred to the next level of training with finer-scale signals. These training processes are repeated recursively under a transfer-learning fashion until the full-scale training (i.e., with unfiltered signals) is achieved, while satisfying the underlying dynamic equilibrium. Numerical examples on recorded subject data demonstrate the effectiveness of the proposed framework in generating a physics-informed forward-dynamics surrogate, which yields higher accuracy in motion predictions of elbow flexion-extension of an MSK system compared to the case with single-scale training. The framework is also capable of identifying muscle parameters that are physiologically consistent with the subject's kinematics data.
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Affiliation(s)
- Karan Taneja
- Department of Structural Engineering, University of California San Diego, La Jolla, CA USA
| | | | - QiZhi He
- Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, Minneapolis, MN USA
| | - Jiun-Shyan Chen
- Department of Structural Engineering, University of California San Diego, La Jolla, CA USA
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Escapil-Inchauspé P, Ruz GA. h-Analysis and data-parallel physics-informed neural networks. Sci Rep 2023; 13:17562. [PMID: 37845265 PMCID: PMC10579276 DOI: 10.1038/s41598-023-44541-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 10/10/2023] [Indexed: 10/18/2023] Open
Abstract
We explore the data-parallel acceleration of physics-informed machine learning (PIML) schemes, with a focus on physics-informed neural networks (PINNs) for multiple graphics processing units (GPUs) architectures. In order to develop scale-robust and high-throughput PIML models for sophisticated applications which may require a large number of training points (e.g., involving complex and high-dimensional domains, non-linear operators or multi-physics), we detail a novel protocol based on h-analysis and data-parallel acceleration through the Horovod training framework. The protocol is backed by new convergence bounds for the generalization error and the train-test gap. We show that the acceleration is straightforward to implement, does not compromise training, and proves to be highly efficient and controllable, paving the way towards generic scale-robust PIML. Extensive numerical experiments with increasing complexity illustrate its robustness and consistency, offering a wide range of possibilities for real-world simulations.
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Affiliation(s)
- Paul Escapil-Inchauspé
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile.
- Data Observatory Foundation, Santiago, Chile.
| | - Gonzalo A Ruz
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile
- Data Observatory Foundation, Santiago, Chile
- Center of Applied Ecology and Sustainability (CAPES), Santiago, Chile
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27
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Procopio A, Cesarelli G, Donisi L, Merola A, Amato F, Cosentino C. Combined mechanistic modeling and machine-learning approaches in systems biology - A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107681. [PMID: 37385142 DOI: 10.1016/j.cmpb.2023.107681] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/14/2023] [Accepted: 06/14/2023] [Indexed: 07/01/2023]
Abstract
BACKGROUND AND OBJECTIVE Mechanistic-based Model simulations (MM) are an effective approach commonly employed, for research and learning purposes, to better investigate and understand the inherent behavior of biological systems. Recent advancements in modern technologies and the large availability of omics data allowed the application of Machine Learning (ML) techniques to different research fields, including systems biology. However, the availability of information regarding the analyzed biological context, sufficient experimental data, as well as the degree of computational complexity, represent some of the issues that both MMs and ML techniques could present individually. For this reason, recently, several studies suggest overcoming or significantly reducing these drawbacks by combining the above-mentioned two methods. In the wake of the growing interest in this hybrid analysis approach, with the present review, we want to systematically investigate the studies available in the scientific literature in which both MMs and ML have been combined to explain biological processes at genomics, proteomics, and metabolomics levels, or the behavior of entire cellular populations. METHODS Elsevier Scopus®, Clarivate Web of Science™ and National Library of Medicine PubMed® databases were enquired using the queries reported in Table 1, resulting in 350 scientific articles. RESULTS Only 14 of the 350 documents returned by the comprehensive search conducted on the three major online databases met our search criteria, i.e. present a hybrid approach consisting of the synergistic combination of MMs and ML to treat a particular aspect of systems biology. CONCLUSIONS Despite the recent interest in this methodology, from a careful analysis of the selected papers, it emerged how examples of integration between MMs and ML are already present in systems biology, highlighting the great potential of this hybrid approach to both at micro and macro biological scales.
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Affiliation(s)
- Anna Procopio
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italia
| | - Giuseppe Cesarelli
- Department of Electrical Engineering and Information Technology, Università degli Studi di Napoli Federico II, Napoli, 80125, Italy
| | - Leandro Donisi
- Department of Advanced Medical and Surgical Sciences, Università della Campania Luigi Vanvitelli, Napoli, 80138, Italy
| | - Alessio Merola
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italia
| | - Francesco Amato
- Department of Electrical Engineering and Information Technology, Università degli Studi di Napoli Federico II, Napoli, 80125, Italy.
| | - Carlo Cosentino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italia.
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He M, Tang B, Xiao Y, Tang S. Transmission dynamics informed neural network with application to COVID-19 infections. Comput Biol Med 2023; 165:107431. [PMID: 37696183 DOI: 10.1016/j.compbiomed.2023.107431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/26/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
Since the end of 2019 the COVID-19 repeatedly surges with most countries/territories experiencing multiple waves, and mechanism-based epidemic models played important roles in understanding the transmission mechanism of multiple epidemic waves. However, capturing temporal changes of the transmissibility of COVID-19 during the multiple waves keeps ill-posed problem for traditional mechanism-based epidemic compartment models, because that the transmission rate is usually assumed to be specific piecewise functions and more parameters are added to the model once multiple epidemic waves involved, which poses a huge challenge to parameter estimation. Meanwhile, data-driven deep neural networks fail to discover the driving factors of repeated outbreaks and lack interpretability. In this study, aiming at developing a data-driven method to project time-dependent parameters but also merging the advantage of mechanism-based models, we propose a transmission dynamics informed neural network (TDINN) by encoding the SEIRD compartment model into deep neural networks. We show that the proposed TDINN algorithm performs very well when fitting the COVID-19 epidemic data with multiple waves, where the epidemics in the United States, Italy, South Africa, and Kenya, and several outbreaks the Omicron variant in China are taken as examples. In addition, the numerical simulation shows that the trained TDINN can also perform as a predictive model to capture the future development of COVID-19 epidemic. We find that the transmission rate inferred by the TDINN frequently fluctuates, and a feedback loop between the epidemic shifting and the changes of transmissibility drives the occurrence of multiple waves. We observe a long response delay to the implementation of control interventions in the four countries, while the decline of the transmission rate in the outbreaks in China usually happens once the implementation of control interventions. The further simulation show that 17 days' delay of the response to the implementation of control interventions lead to a roughly four-fold increase in daily reported cases in one epidemic wave in Italy, which suggest that a rapid response to policies that strengthen control interventions can be effective in flattening the epidemic curve or avoiding subsequent epidemic waves. We observe that the transmission rate in the outbreaks in China is already decreasing before enhancing control interventions, providing the evidence that the increasing of the epidemics can drive self-conscious behavioural changes to protect against infections.
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Affiliation(s)
- Mengqi He
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, China
| | - Biao Tang
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
| | - Yanni Xiao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
| | - Sanyi Tang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, China
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29
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Kelley C, Antic SD, Carnevale NT, Kubie JL, Lytton WW. Simulations predict differing phase responses to excitation vs. inhibition in theta-resonant pyramidal neurons. J Neurophysiol 2023; 130:910-924. [PMID: 37609720 PMCID: PMC10648938 DOI: 10.1152/jn.00160.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/21/2023] [Accepted: 08/21/2023] [Indexed: 08/24/2023] Open
Abstract
Rhythmic activity is ubiquitous in neural systems, with theta-resonant pyramidal neurons integrating rhythmic inputs in many cortical structures. Impedance analysis has been widely used to examine frequency-dependent responses of neuronal membranes to rhythmic inputs, but it assumes that the neuronal membrane is a linear system, requiring the use of small signals to stay in a near-linear regime. However, postsynaptic potentials are often large and trigger nonlinear mechanisms (voltage-gated ion channels). The goals of this work were to 1) develop an analysis method to evaluate membrane responses in this nonlinear domain and 2) explore phase relationships between rhythmic stimuli and subthreshold and spiking membrane potential (Vmemb) responses in models of theta-resonant pyramidal neurons. Responses in these output regimes were asymmetrical, with different phase shifts during hyperpolarizing and depolarizing half-cycles. Suprathreshold theta-rhythmic stimuli produced nonstationary Vmemb responses. Sinusoidal inputs produced "phase retreat": action potentials occurred progressively later in cycles of the input stimulus, resulting from adaptation. Sinusoidal current with increasing amplitude over cycles produced "phase advance": action potentials occurred progressively earlier. Phase retreat, phase advance, and subthreshold phase shifts were modulated by multiple ion channel conductances. Our results suggest differential responses of cortical neurons depending on the frequency of oscillatory input, which will play a role in neuronal responses to shifts in network state. We hypothesize that intrinsic cellular properties complement network properties and contribute to in vivo phase-shift phenomena such as phase precession, seen in place and grid cells, and phase roll, also observed in hippocampal CA1 neurons.NEW & NOTEWORTHY We augmented electrical impedance analysis to characterize phase shifts between large-amplitude current stimuli and nonlinear, asymmetric membrane potential responses. We predict different frequency-dependent phase shifts in response excitation vs. inhibition, as well as shifts in spike timing over multiple input cycles, in theta-resonant pyramidal neurons. We hypothesize that these effects contribute to navigation-related phenomena such as phase precession and phase roll. Our neuron-level hypothesis complements, rather than falsifies, prior network-level explanations of these phenomena.
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Affiliation(s)
- Craig Kelley
- Program in Biomedical Engineering, SUNY Downstate Health Sciences University and NYU Tandon School of Engineering, Brooklyn, New York, United States
| | - Srdjan D Antic
- Institute of Systems Genomics, Neuroscience Department, University of Connecticut Health, Farmington, Connecticut, United States
| | - Nicholas T Carnevale
- Department of Neuroscience, Yale University, New Haven, Connecticut, United States
| | - John L Kubie
- The Robert F. Furchgott Center for Neural and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Cell Biology, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
| | - William W Lytton
- Program in Biomedical Engineering, SUNY Downstate Health Sciences University and NYU Tandon School of Engineering, Brooklyn, New York, United States
- The Robert F. Furchgott Center for Neural and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Neurology, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Neurology, Kings County Hospital Center, Brooklyn, New York, United States
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland, United States
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30
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Zhao Y, Chen Z, Jian X. A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites. Polymers (Basel) 2023; 15:3962. [PMID: 37836011 PMCID: PMC10575166 DOI: 10.3390/polym15193962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/25/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
This study aims to develop a high-generalizability machine learning framework for predicting the homogenized mechanical properties of short fiber-reinforced polymer composites. The ensemble machine learning model (EML) employs a stacking algorithm using three base models of Extra Trees (ET), eXtreme Gradient Boosting machine (XGBoost), and Light Gradient Boosting machine (LGBM). A micromechanical model of a two-step homogenization algorithm is adopted and verified as an effective approach to composite modeling with randomly distributed fibers, which is integrated with finite element simulations for providing a high-quality ground-truth dataset. The model performance is thoroughly assessed for its accuracy, efficiency, interpretability, and generalizability. The results suggest that: (1) the EML model outperforms the base members on prediction accuracy, achieving R2 values of 0.988 and 0.952 on the train and test datasets, respectively; (2) the SHapley Additive exPlanations (SHAP) analysis identifies the Young's modulus of matrix, fiber, and fiber content as the top three factors influencing the homogenized properties, whereas the anisotropy is predominantly determined by the fiber orientations; (3) the EML model showcases good generalization capability on experimental data, and it has been shown to be more effective than high-fidelity computational models by significantly lowering computational costs while maintaining high accuracy.
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Affiliation(s)
- Yunmei Zhao
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China;
| | - Zhenyue Chen
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China;
| | - Xiaobin Jian
- Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
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31
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Zhao Y, Chen Z, Dong Y. Compliance Prediction for Structural Topology Optimization on the Basis of Moment Invariants and a Generalized Regression Neural Network. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1396. [PMID: 37895517 PMCID: PMC10606044 DOI: 10.3390/e25101396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/18/2023] [Accepted: 09/25/2023] [Indexed: 10/29/2023]
Abstract
Topology optimization techniques are essential for manufacturing industries, such as designing fiber-reinforced polymer composites (FRPCs) and structures with outstanding strength-to-weight ratios and light weights. In the SIMP approach, artificial intelligence algorithms are commonly utilized to enhance traditional FEM-based compliance minimization procedures. Based on an effective generalized regression neural network (GRNN), a new deep learning algorithm of compliance prediction for structural topology optimization is proposed. The algorithm learns the structural information using a fourth-order moment invariant analysis of the structural topology obtained from FEA at different iterations of classical topology optimization. A cantilever and a simply supported beam problem are used as ground-truth datasets, and the moment invariants are used as independent variables for input features. By comparing it with the well-known convolutional neural network (CNN) and deep neural network (DNN) models, the proposed GRNN model achieves a high prediction accuracy (R2 > 0.97) and drastically shortens the training and prediction cost. Furthermore, the GRNN algorithm exhibits excellent generalization ability on the prediction performance of the optimized topology with rotations and varied material volume fractions. This algorithm is promising for the replacement of the FEA calculation in the SIMP method, and can be applied to real-time optimization for advanced FRPC structure design.
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Affiliation(s)
- Yunmei Zhao
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China;
| | - Zhenyue Chen
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China;
| | - Yiqun Dong
- Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
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32
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Cen S, Gebregziabher M, Moazami S, Azevedo CJ, Pelletier D. Toward precision medicine using a "digital twin" approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis. Sci Rep 2023; 13:16279. [PMID: 37770560 PMCID: PMC10539386 DOI: 10.1038/s41598-023-43618-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/26/2023] [Indexed: 09/30/2023] Open
Abstract
Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a tenfold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset versus simultaneous onset of brain atrophy.
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Affiliation(s)
- Steven Cen
- Department of Radiology/Neurology, University of Southern California, Los Angeles, USA.
| | - Mulugeta Gebregziabher
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA
| | - Saeed Moazami
- Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, USA
| | - Christina J Azevedo
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, USA
| | - Daniel Pelletier
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, USA
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Chorney HV, Forbes JR, Driscoll M. System identification and simulation of soft tissue force feedback in a spine surgical simulator. Comput Biol Med 2023; 164:107267. [PMID: 37536093 DOI: 10.1016/j.compbiomed.2023.107267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/21/2023] [Accepted: 07/16/2023] [Indexed: 08/05/2023]
Abstract
Surgical simulators are being introduced as training modalities for surgeons. This paper aims to evaluate dynamic models used to convey force feedback from puncturing the soft tissue during a spine surgical simulation. The force feedback of the tissue is treated as a dynamic system. This is done by performing classical system identification across a bandwidth of frequencies on a tissue analogue and fitting that behaviour to dynamic viscoelastic models. The models that are tested are an inverted linear model, the Maxwell model, the Kelvin-Boltzmann (KB) model, and a higher-order blackbox (HO) model. Several error metrics such as percent variance accounted for (%VAF) are determined to measure solution accuracy. The force feedback models are programmed into a surgical simulator and tested with study participants who rated them based on how well the identified models match the behaviour of the rubber tissue analogue. The highest %VAF is 82.64% when the tissue is modelled as the HO model. Statistically significant differences (p < 0.05) are found between all model ratings from participants except between the HO model and the KB model. However, the HO model has the highest percentage (37.8%) of participants who rank its performance as the closest to the tissue analogue compared to the other force feedback models. The more accurately the dynamic behaviour resembles the tissue analogue, the higher the model was rated by study participants. This study highlights the importance of utilizing dynamic signals to generate dynamic models of soft tissue for spine surgical simulators.
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Affiliation(s)
- Harriet Violet Chorney
- The Musculoskeletal Biomechanics Research (MBR) Lab, Department of Mechanical Engineering, McGill University, Montreal, Quebec, Canada; Orthopaedic Research Laboratory (ORL), Research Institute MUHC, Montreal General Hospital, Montreal, Quebec, Canada; The Dynamics, Estimation, and Control in Aerospace and Robotics (DECAR) Group, Department of Mechanical Engineering, McGill University, Montreal, Quebec, Canada
| | - James Richard Forbes
- The Dynamics, Estimation, and Control in Aerospace and Robotics (DECAR) Group, Department of Mechanical Engineering, McGill University, Montreal, Quebec, Canada
| | - Mark Driscoll
- The Musculoskeletal Biomechanics Research (MBR) Lab, Department of Mechanical Engineering, McGill University, Montreal, Quebec, Canada; Orthopaedic Research Laboratory (ORL), Research Institute MUHC, Montreal General Hospital, Montreal, Quebec, Canada.
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Wang LM, Linka K, Kuhl E. Automated model discovery for muscle using constitutive recurrent neural networks. J Mech Behav Biomed Mater 2023; 145:106021. [PMID: 37473576 DOI: 10.1016/j.jmbbm.2023.106021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/18/2023] [Accepted: 07/06/2023] [Indexed: 07/22/2023]
Abstract
The stiffness of soft biological tissues not only depends on the applied deformation, but also on the deformation rate. To model this type of behavior, traditional approaches select a specific time-dependent constitutive model and fit its parameters to experimental data. Instead, a new trend now suggests a machine-learning based approach that simultaneously discovers both the best model and best parameters to explain given data. Recent studies have shown that feed-forward constitutive neural networks can robustly discover constitutive models and parameters for hyperelastic materials. However, feed-forward architectures fail to capture the history dependence of viscoelastic soft tissues. Here we combine a feed-forward constitutive neural network for the hyperelastic response and a recurrent neural network for the viscous response inspired by the theory of quasi-linear viscoelasticity. Our novel rheologically-informed network architecture discovers the time-independent initial stress using the feed-forward network and the time-dependent relaxation using the recurrent network. We train and test our combined network using unconfined compression relaxation experiments of passive skeletal muscle and compare our discovered model to a neo Hookean standard linear solid, to an advanced mechanics-based model, and to a vanilla recurrent neural network with no mechanics knowledge. We demonstrate that, for limited experimental data, our new constitutive recurrent neural network discovers models and parameters that satisfy basic physical principles and generalize well to unseen data. We discover a Mooney-Rivlin type two-term initial stored energy function that is linear in the first invariant I1 and quadratic in the second invariant I2 with stiffness parameters of 0.60 kPa and 0.55 kPa. We also discover a Prony-series type relaxation function with time constants of 0.362s, 2.54s, and 52.0s with coefficients of 0.89, 0.05, and 0.03. Our newly discovered model outperforms both the neo Hookean standard linear solid and the vanilla recurrent neural network in terms of prediction accuracy on unseen data. Our results suggest that constitutive recurrent neural networks can autonomously discover both model and parameters that best explain experimental data of soft viscoelastic tissues. Our source code, data, and examples are available at https://github.com/LivingMatterLab.
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Affiliation(s)
- Lucy M Wang
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, United States.
| | - Kevin Linka
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, United States.
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, United States.
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Tomitaka A, Vashist A, Kolishetti N, Nair M. Machine learning assisted-nanomedicine using magnetic nanoparticles for central nervous system diseases. NANOSCALE ADVANCES 2023; 5:4354-4367. [PMID: 37638161 PMCID: PMC10448356 DOI: 10.1039/d3na00180f] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023]
Abstract
Magnetic nanoparticles possess unique properties distinct from other types of nanoparticles developed for biomedical applications. Their unique magnetic properties and multifunctionalities are especially beneficial for central nervous system (CNS) disease therapy and diagnostics, as well as targeted and personalized applications using image-guided therapy and theranostics. This review discusses the recent development of magnetic nanoparticles for CNS applications, including Alzheimer's disease, Parkinson's disease, epilepsy, multiple sclerosis, and drug addiction. Machine learning (ML) methods are increasingly applied towards the processing, optimization and development of nanomaterials. By using data-driven approach, ML has the potential to bridge the gap between basic research and clinical research. We review ML approaches used within the various stages of nanomedicine development, from nanoparticle synthesis and characterization to performance prediction and disease diagnosis.
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Affiliation(s)
- Asahi Tomitaka
- Department of Computer and Information Sciences, College of Natural and Applied Science, University of Houston-Victoria Texas 77901 USA
| | - Arti Vashist
- Department of Immunology and Nano-Medicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
- Institute of NeuroImmune Pharmacology, Centre for Personalized Nanomedicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
| | - Nagesh Kolishetti
- Department of Immunology and Nano-Medicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
- Institute of NeuroImmune Pharmacology, Centre for Personalized Nanomedicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
| | - Madhavan Nair
- Department of Immunology and Nano-Medicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
- Institute of NeuroImmune Pharmacology, Centre for Personalized Nanomedicine, Herbert Wertheim College of Medicine, Florida International University Miami Florida 33199 USA
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Zhang K, Liu Y, Mei F, Sun G, Jin J. IBGJO: Improved Binary Golden Jackal Optimization with Chaotic Tent Map and Cosine Similarity for Feature Selection. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1128. [PMID: 37628158 PMCID: PMC10453476 DOI: 10.3390/e25081128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/23/2023] [Accepted: 07/26/2023] [Indexed: 08/27/2023]
Abstract
Feature selection is a crucial process in machine learning and data mining that identifies the most pertinent and valuable features in a dataset. It enhances the efficacy and precision of predictive models by efficiently reducing the number of features. This reduction improves classification accuracy, lessens the computational burden, and enhances overall performance. This study proposes the improved binary golden jackal optimization (IBGJO) algorithm, an extension of the conventional golden jackal optimization (GJO) algorithm. IBGJO serves as a search strategy for wrapper-based feature selection. It comprises three key factors: a population initialization process with a chaotic tent map (CTM) mechanism that enhances exploitation abilities and guarantees population diversity, an adaptive position update mechanism using cosine similarity to prevent premature convergence, and a binary mechanism well-suited for binary feature selection problems. We evaluated IBGJO on 28 classical datasets from the UC Irvine Machine Learning Repository. The results show that the CTM mechanism and the position update strategy based on cosine similarity proposed in IBGJO can significantly improve the Rate of convergence of the conventional GJO algorithm, and the accuracy is also significantly better than other algorithms. Additionally, we evaluate the effectiveness and performance of the enhanced factors. Our empirical results show that the proposed CTM mechanism and the position update strategy based on cosine similarity can help the conventional GJO algorithm converge faster.
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Affiliation(s)
- Kunpeng Zhang
- College of Computer Science and Technology, Jilin University, Changchun 130012, China; (K.Z.); (Y.L.); (J.J.)
| | - Yanheng Liu
- College of Computer Science and Technology, Jilin University, Changchun 130012, China; (K.Z.); (Y.L.); (J.J.)
- Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun 130012, China
| | - Fang Mei
- College of Computer Science and Technology, Jilin University, Changchun 130012, China; (K.Z.); (Y.L.); (J.J.)
- Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun 130012, China
| | - Geng Sun
- College of Computer Science and Technology, Jilin University, Changchun 130012, China; (K.Z.); (Y.L.); (J.J.)
- Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun 130012, China
| | - Jingyi Jin
- College of Computer Science and Technology, Jilin University, Changchun 130012, China; (K.Z.); (Y.L.); (J.J.)
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Ma L, Qu S, Song L, Zhang Z, Ren J. A Physics-Informed Generative Car-Following Model for Connected Autonomous Vehicles. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1050. [PMID: 37509998 PMCID: PMC10378484 DOI: 10.3390/e25071050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/04/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
This paper proposes a novel hybrid car-following model: the physics-informed conditional generative adversarial network (PICGAN), designed to enhance multi-step car-following modeling in mixed traffic flow scenarios. This hybrid model leverages the strengths of both physics-based and deep-learning-based models. By taking advantage of the inherent structure of GAN, the PICGAN eliminates the need for an explicit weighting parameter typically used in the combination of traditional physics-based and data-driven models. The effectiveness of the proposed model is substantiated through case studies using the NGSIM I-80 dataset. These studies demonstrate the model's superior trajectory reproduction, suggesting its potential as a strong contender to replace conventional models in trajectory prediction tasks. Furthermore, the deployment of PICGAN significantly enhances the stability and efficiency in mixed traffic flow environments. Given its reliable and stable results, the PICGAN framework contributes substantially to the development of efficient longitudinal control strategies for connected autonomous vehicles (CAVs) in real-world mixed traffic conditions.
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Affiliation(s)
- Lijing Ma
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shiru Qu
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Lijun Song
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Zhiteng Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jie Ren
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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38
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Maria Antony AN, Narisetti N, Gladilin E. FDM data driven U-Net as a 2D Laplace PINN solver. Sci Rep 2023; 13:9116. [PMID: 37277366 DOI: 10.1038/s41598-023-35531-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 05/19/2023] [Indexed: 06/07/2023] Open
Abstract
Efficient solution of partial differential equations (PDEs) of physical laws is of interest for manifold applications in computer science and image analysis. However, conventional domain discretization techniques for numerical solving PDEs such as Finite Difference (FDM), Finite Element (FEM) methods are unsuitable for real-time applications and are also quite laborious in adaptation to new applications, especially for non-experts in numerical mathematics and computational modeling. More recently, alternative approaches to solving PDEs using the so-called Physically Informed Neural Networks (PINNs) received increasing attention because of their straightforward application to new data and potentially more efficient performance. In this work, we present a novel data-driven approach to solve 2D Laplace PDE with arbitrary boundary conditions using deep learning models trained on a large set of reference FDM solutions. Our experimental results show that both forward and inverse 2D Laplace problems can efficiently be solved using the proposed PINN approach with nearly real-time performance and average accuracy of 94% for different types of boundary value problems compared to FDM. In summary, our deep learning based PINN PDE solver provides an efficient tool with various applications in image analysis and computational simulation of image-based physical boundary value problems.
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Affiliation(s)
- Anto Nivin Maria Antony
- Leibniz Institute of Plant Genetics and Crop Plant Research, OT Gatersleben, Corrensstr. 3, 06466, Seeland, Germany.
| | - Narendra Narisetti
- Leibniz Institute of Plant Genetics and Crop Plant Research, OT Gatersleben, Corrensstr. 3, 06466, Seeland, Germany
| | - Evgeny Gladilin
- Leibniz Institute of Plant Genetics and Crop Plant Research, OT Gatersleben, Corrensstr. 3, 06466, Seeland, Germany.
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Barker AD, Alba MM, Mallick P, Agus DB, Lee JSH. An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next. Mol Cell Proteomics 2023:100569. [PMID: 37196763 PMCID: PMC10388583 DOI: 10.1016/j.mcpro.2023.100569] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 04/26/2023] [Accepted: 05/08/2023] [Indexed: 05/19/2023] Open
Abstract
Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.
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Affiliation(s)
- Anna D Barker
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA; Complex Adaptive Systems Initiative and School of Life Sciences, Arizona State University, Tempe, Arizona
| | - Mario M Alba
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA
| | - Parag Mallick
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA; Department of Radiology, Stanford University, Stanford, CA
| | - David B Agus
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA; Keck School of Medicine, University of Southern California, Los Angeles, CA; Viterbi School of Engineering, University of Southern California, Los Angeles, CA
| | - Jerry S H Lee
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA; Keck School of Medicine, University of Southern California, Los Angeles, CA; Viterbi School of Engineering, University of Southern California, Los Angeles, CA
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40
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Du M, Zhang C, Xie S, Pu F, Zhang D, Li D. Investigation on aortic hemodynamics based on physics-informed neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:11545-11567. [PMID: 37501408 DOI: 10.3934/mbe.2023512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Pressure in arteries is difficult to measure non-invasively. Although computational fluid dynamics (CFD) provides high-precision numerical solutions according to the basic physical equations of fluid mechanics, it relies on precise boundary conditions and complex preprocessing, which limits its real-time application. Machine learning algorithms have wide applications in hemodynamic research due to their powerful learning ability and fast calculation speed. Therefore, we proposed a novel method for pressure estimation based on physics-informed neural network (PINN). An ideal aortic arch model was established according to the geometric parameters from human aorta, and we performed CFD simulation with two-way fluid-solid coupling. The simulation results, including the space-time coordinates, the velocity and pressure field, were obtained as the dataset for the training and validation of PINN. Nondimensional Navier-Stokes equations and continuity equation were employed for the loss function of PINN, to calculate the velocity and relative pressure field. Post-processing was proposed to fit the absolute pressure of the aorta according to the linear relationship between relative pressure, elastic modulus and displacement of the vessel wall. Additionally, we explored the sensitivity of the PINN to the vascular elasticity, blood viscosity and blood velocity. The velocity and pressure field predicted by PINN yielded good consistency with the simulated values. In the interested region of the aorta, the relative errors of maximum and average absolute pressure were 7.33% and 5.71%, respectively. The relative pressure field was found most sensitive to blood velocity, followed by blood viscosity and vascular elasticity. This study has proposed a method for intra-vascular pressure estimation, which has potential significance in the diagnosis of cardiovascular diseases.
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Affiliation(s)
- Meiyuan Du
- Key Laboratory of Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China
| | - Chi Zhang
- Key Laboratory of Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China
| | - Sheng Xie
- Department of Radiology, China-Japan Friendship Hospital, Beijing, No. 2 Yinhua East Road, Chaoyang District, Beijing 100029, China
| | - Fang Pu
- Key Laboratory of Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China
| | - Da Zhang
- Department of Physics, Sichuan Cancer Hospital, No. 55 South Renmin Road, Chengdu 610042, China
| | - Deyu Li
- Key Laboratory of Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China
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Cen S, Gebregziabher M, Moazami S, Azevedo C, Pelletier D. Toward Precision Medicine Using a "Digital Twin" Approach: Modeling the Onset of Disease-Specific Brain Atrophy in Individuals with Multiple Sclerosis. RESEARCH SQUARE 2023:rs.3.rs-2833532. [PMID: 37205476 PMCID: PMC10187410 DOI: 10.21203/rs.3.rs-2833532/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a 10-fold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset vs. simultaneous onset of brain atrophy.
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42
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Vodovotz Y. Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins. Trends Immunol 2023; 44:345-355. [PMID: 36967340 PMCID: PMC10147586 DOI: 10.1016/j.it.2023.03.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 04/05/2023]
Abstract
Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.
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Affiliation(s)
- Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Inflammation and Regeneration Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA; Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA 15219, USA.
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43
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Kim N, Lee H, Han G, Kang M, Park S, Kim DE, Lee M, Kim MJ, Na Y, Oh S, Bang SJ, Jang TS, Kim HE, Park J, Shin SR, Jung HD. 3D-Printed Functional Hydrogel by DNA-Induced Biomineralization for Accelerated Diabetic Wound Healing. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2300816. [PMID: 37076933 DOI: 10.1002/advs.202300816] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/26/2023] [Indexed: 05/03/2023]
Abstract
Chronic wounds in diabetic patients are challenging because their prolonged inflammation makes healing difficult, thus burdening patients, society, and health care systems. Customized dressing materials are needed to effectively treat such wounds that vary in shape and depth. The continuous development of 3D-printing technology along with artificial intelligence has increased the precision, versatility, and compatibility of various materials, thus providing the considerable potential to meet the abovementioned needs. Herein, functional 3D-printing inks comprising DNA from salmon sperm and DNA-induced biosilica inspired by marine sponges, are developed for the machine learning-based 3D-printing of wound dressings. The DNA and biomineralized silica are incorporated into hydrogel inks in a fast, facile manner. The 3D-printed wound dressing thus generates provided appropriate porosity, characterized by effective exudate and blood absorption at wound sites, and mechanical tunability indicated by good shape fidelity and printability during optimized 3D printing. Moreover, the DNA and biomineralized silica act as nanotherapeutics, enhancing the biological activity of the dressings in terms of reactive oxygen species scavenging, angiogenesis, and anti-inflammation activity, thereby accelerating acute and diabetic wound healing. These bioinspired 3D-printed hydrogels produce using a DNA-induced biomineralization strategy are an excellent functional platform for clinical applications in acute and chronic wound repair.
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Affiliation(s)
- Nahyun Kim
- Department of Biomedical-Chemical Engineering, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
- Department of Biotechnology, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
| | - Hyun Lee
- Department of Biomedical-Chemical Engineering, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
- Department of Biotechnology, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
| | - Ginam Han
- Department of Biomedical-Chemical Engineering, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
- Department of Biotechnology, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
| | - Minho Kang
- Department of Biomedical-Chemical Engineering, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
- Department of Biotechnology, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
| | - Sinwoo Park
- Department of Biomedical-Chemical Engineering, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
- Department of Biotechnology, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
| | - Dong Eung Kim
- Research Institute of Advanced Manufacturing & Materials Technology, Korea Institute of Industrial Technology, Incheon, 21999, Republic of Korea
| | - Minyoung Lee
- School of Chemical and Biological Engineering, and Institute of Chemical Processes (ICP), Seoul National University, Seoul, 08826, Republic of Korea
- Center for Nanoparticle Research, Institute of Basic Science (IBS), Seoul, 08826, Republic of Korea
| | - Moon-Jo Kim
- Research Institute of Advanced Manufacturing & Materials Technology, Korea Institute of Industrial Technology, Incheon, 21999, Republic of Korea
| | - Yuhyun Na
- Department of Biomedical-Chemical Engineering, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
- Department of Biotechnology, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
| | - SeKwon Oh
- Research Institute of Advanced Manufacturing & Materials Technology, Korea Institute of Industrial Technology, Incheon, 21999, Republic of Korea
| | - Seo-Jun Bang
- Department of Biomedical-Chemical Engineering, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
- Department of Biotechnology, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
| | - Tae-Sik Jang
- Department of Materials Science and Engineering, Chosun University, Gwangju, 61452, Republic of Korea
| | - Hyoun-Ee Kim
- Department of Materials Science and Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jungwon Park
- School of Chemical and Biological Engineering, and Institute of Chemical Processes (ICP), Seoul National University, Seoul, 08826, Republic of Korea
- Center for Nanoparticle Research, Institute of Basic Science (IBS), Seoul, 08826, Republic of Korea
| | - Su Ryon Shin
- Division of Engineering in Medicine, Department of Medicine, Harvard Medical School, and Brigham and Women's Hospital, Cambridge, MA, 02139, USA
| | - Hyun-Do Jung
- Department of Biomedical-Chemical Engineering, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
- Department of Biotechnology, The Catholic University of Korea, Bucheon, 14662, Republic of Korea
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Automated model discovery for human brain using Constitutive Artificial Neural Networks. Acta Biomater 2023; 160:134-151. [PMID: 36736643 DOI: 10.1016/j.actbio.2023.01.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 02/05/2023]
Abstract
The brain is our softest and most vulnerable organ, and understanding its physics is a challenging but significant task. Throughout the past decade, numerous competing models have emerged to characterize its response to mechanical loading. However, selecting the best constitutive model remains a heuristic process that strongly depends on user experience and personal preference. Here we challenge the conventional wisdom to first select a constitutive model and then fit its parameters to data. Instead, we propose a new strategy that simultaneously discovers both model and parameters. We integrate more than a century of knowledge in thermodynamics and state-of-the-art machine learning to build a Constitutive Artificial Neural Network that enables automated model discovery. Our design paradigm is to reverse engineer the network from a set of functional building blocks that are, by design, a generalization of popular constitutive models, including the neo Hookean, Blatz Ko, Mooney Rivlin, Demiray, Gent, and Holzapfel models. By constraining input, output, activation functions, and architecture, our network a priori satisfies thermodynamic consistency, objectivity, symmetry, and polyconvexity. We demonstrate that-out of more than 4000 models-our network autonomously discovers the model and parameters that best characterize the behavior of human gray and white matter under tension, compression, and shear. Importantly, our network weights translate naturally into physically meaningful parameters, such as shear moduli of 1.82kPa, 0.88kPa, 0.94kPa, and 0.54kPa for the cortex, basal ganglia, corona radiata, and corpus callosum. Our results suggest that Constitutive Artificial Neural Networks have the potential to induce a paradigm shift in soft tissue modeling, from user-defined model selection to automated model discovery. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN. STATEMENT OF SIGNIFICANCE: Human brain is ultrasoft, difficult to test, and challenging to model. Numerous competing constitutive models exist, but selecting the best model remains a matter of personal preference. Here we automate the process of model selection. We formulate the problem of autonomous model discovery as a neural network and capitalize on the powerful optimizers in deep learning. However, rather than using a conventional neural network, we reverse engineer our own Constitutive Artificial Neural Network from a set of modular building blocks, which we rationalize from common constitutive models. When trained with tension, compression, and shear experiments of gray and white matter, our network simultaneously discovers both model and parameters that describes the data better than any existing invariant-based model. Our network could induce a paradigm shift from user-defined model selection to automated model discovery.
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Li C, Qian F. Swift progress for robots over complex terrain. Nature 2023; 616:252-253. [PMID: 36944771 DOI: 10.1038/d41586-023-00710-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
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Luo Y, Abidian MR, Ahn JH, Akinwande D, Andrews AM, Antonietti M, Bao Z, Berggren M, Berkey CA, Bettinger CJ, Chen J, Chen P, Cheng W, Cheng X, Choi SJ, Chortos A, Dagdeviren C, Dauskardt RH, Di CA, Dickey MD, Duan X, Facchetti A, Fan Z, Fang Y, Feng J, Feng X, Gao H, Gao W, Gong X, Guo CF, Guo X, Hartel MC, He Z, Ho JS, Hu Y, Huang Q, Huang Y, Huo F, Hussain MM, Javey A, Jeong U, Jiang C, Jiang X, Kang J, Karnaushenko D, Khademhosseini A, Kim DH, Kim ID, Kireev D, Kong L, Lee C, Lee NE, Lee PS, Lee TW, Li F, Li J, Liang C, Lim CT, Lin Y, Lipomi DJ, Liu J, Liu K, Liu N, Liu R, Liu Y, Liu Y, Liu Z, Liu Z, Loh XJ, Lu N, Lv Z, Magdassi S, Malliaras GG, Matsuhisa N, Nathan A, Niu S, Pan J, Pang C, Pei Q, Peng H, Qi D, Ren H, Rogers JA, Rowe A, Schmidt OG, Sekitani T, Seo DG, Shen G, Sheng X, Shi Q, Someya T, Song Y, Stavrinidou E, Su M, Sun X, Takei K, Tao XM, Tee BCK, Thean AVY, Trung TQ, Wan C, Wang H, Wang J, Wang M, Wang S, Wang T, Wang ZL, Weiss PS, Wen H, Xu S, Xu T, Yan H, Yan X, Yang H, Yang L, Yang S, Yin L, Yu C, Yu G, Yu J, Yu SH, Yu X, Zamburg E, Zhang H, Zhang X, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhao S, Zhao X, Zheng Y, Zheng YQ, Zheng Z, Zhou T, Zhu B, Zhu M, Zhu R, Zhu Y, Zhu Y, Zou G, Chen X. Technology Roadmap for Flexible Sensors. ACS NANO 2023; 17:5211-5295. [PMID: 36892156 DOI: 10.1021/acsnano.2c12606] [Citation(s) in RCA: 160] [Impact Index Per Article: 160.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.
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Affiliation(s)
- Yifei Luo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Innovative Centre for Flexible Devices (iFLEX), School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Mohammad Reza Abidian
- Department of Biomedical Engineering, University of Houston, Houston, Texas 77024, United States
| | - Jong-Hyun Ahn
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Deji Akinwande
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Anne M Andrews
- Department of Chemistry and Biochemistry, California NanoSystems Institute, and Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, and Hatos Center for Neuropharmacology, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Markus Antonietti
- Colloid Chemistry Department, Max Planck Institute of Colloids and Interfaces, 14476 Potsdam, Germany
| | - Zhenan Bao
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Magnus Berggren
- Laboratory of Organic Electronics, Department of Science and Technology, Campus Norrköping, Linköping University, 83 Linköping, Sweden
- Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC), SE-100 44 Stockholm, Sweden
| | - Christopher A Berkey
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94301, United States
| | - Christopher John Bettinger
- Department of Biomedical Engineering and Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Jun Chen
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Peng Chen
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Wenlong Cheng
- Nanobionics Group, Department of Chemical and Biological Engineering, Monash University, Clayton, Australia, 3800
- Monash Institute of Medical Engineering, Monash University, Clayton, Australia3800
| | - Xu Cheng
- Applied Mechanics Laboratory, Department of Engineering Mechanics, Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing 100084, PR China
| | - Seon-Jin Choi
- Division of Materials of Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Alex Chortos
- School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Canan Dagdeviren
- Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Reinhold H Dauskardt
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94301, United States
| | - Chong-An Di
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Michael D Dickey
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Xiangfeng Duan
- Department of Chemistry and Biochemistry, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Antonio Facchetti
- Department of Chemistry and the Materials Research Center, Northwestern University, Evanston, Illinois 60208, United States
| | - Zhiyong Fan
- Department of Electronic and Computer Engineering and Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
| | - Yin Fang
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Jianyou Feng
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Xue Feng
- Laboratory of Flexible Electronics Technology, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China
| | - Huajian Gao
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, California, 91125, United States
| | - Xiwen Gong
- Department of Chemical Engineering, Department of Materials Science and Engineering, Department of Electrical Engineering and Computer Science, Applied Physics Program, and Macromolecular Science and Engineering Program, University of Michigan, Ann Arbor, Michigan, 48109 United States
| | - Chuan Fei Guo
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiaojun Guo
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Martin C Hartel
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Zihan He
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - John S Ho
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- The N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore
| | - Youfan Hu
- School of Electronics and Center for Carbon-Based Electronics, Peking University, Beijing 100871, China
| | - Qiyao Huang
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Yu Huang
- Department of Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Fengwei Huo
- Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing 211816, PR China
| | - Muhammad M Hussain
- mmh Labs, Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Ali Javey
- Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, United States
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Unyong Jeong
- Department of Materials Science and Engineering, Pohang University of Science and Engineering (POSTECH), Pohang, Gyeong-buk 37673, Korea
| | - Chen Jiang
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Xingyu Jiang
- Department of Biomedical Engineering, Southern University of Science and Technology, No 1088, Xueyuan Road, Xili, Nanshan District, Shenzhen, Guangdong 518055, PR China
| | - Jiheong Kang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Daniil Karnaushenko
- Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, Chemnitz 09126, Germany
| | | | - Dae-Hyeong Kim
- Center for Nanoparticle Research, Institute for Basic Science (IBS), School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Il-Doo Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Dmitry Kireev
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Lingxuan Kong
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
- National University of Singapore Suzhou Research Institute (NUSRI), Suzhou Industrial Park, Suzhou 215123, China
- NUS Graduate School-Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
| | - Nae-Eung Lee
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Kyunggi-do 16419, Republic of Korea
| | - Pooi See Lee
- School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
- Singapore-HUJ Alliance for Research and Enterprise (SHARE), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Tae-Woo Lee
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Institute of Engineering Research, Research Institute of Advanced Materials, Seoul National University, Soft Foundry, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Fengyu Li
- College of Chemistry and Materials Science, Jinan University, Guangzhou, Guangdong 510632, China
| | - Jinxing Li
- Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Neuroscience Program, BioMolecular Science Program, and Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan 48823, United States
| | - Cuiyuan Liang
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Chwee Teck Lim
- Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore
- Mechanobiology Institute, National University of Singapore, Singapore 117411, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 119276, Singapore
| | - Yuanjing Lin
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Darren J Lipomi
- Department of Nano and Chemical Engineering, University of California, San Diego, La Jolla, California 92093-0448, United States
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Kai Liu
- School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Nan Liu
- Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University, Beijing 100875, PR China
| | - Ren Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Yuxin Liu
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Department of Biomedical Engineering, N.1 Institute for Health, Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore 119077, Singapore
| | - Yuxuan Liu
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Zhiyuan Liu
- Neural Engineering Centre, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China 518055
| | - Zhuangjian Liu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Xian Jun Loh
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
| | - Nanshu Lu
- Department of Aerospace Engineering and Engineering Mechanics, Department of Electrical and Computer Engineering, Department of Mechanical Engineering, Department of Biomedical Engineering, Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Zhisheng Lv
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
| | - Shlomo Magdassi
- Institute of Chemistry and the Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - George G Malliaras
- Electrical Engineering Division, Department of Engineering, University of Cambridge CB3 0FA, Cambridge United Kingdom
| | - Naoji Matsuhisa
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Arokia Nathan
- Darwin College, University of Cambridge, Cambridge CB3 9EU, United Kingdom
| | - Simiao Niu
- Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Jieming Pan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Changhyun Pang
- School of Chemical Engineering and Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Qibing Pei
- Department of Materials Science and Engineering, Department of Mechanical and Aerospace Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Huisheng Peng
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Dianpeng Qi
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Huaying Ren
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, 90095, United States
| | - John A Rogers
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, Illinois 60208, United States
- Department of Materials Science and Engineering, Department of Mechanical Engineering, Department of Biomedical Engineering, Departments of Electrical and Computer Engineering and Chemistry, and Department of Neurological Surgery, Northwestern University, Evanston, Illinois 60208, United States
| | - Aaron Rowe
- Becton, Dickinson and Company, 1268 N. Lakeview Avenue, Anaheim, California 92807, United States
- Ready, Set, Food! 15821 Ventura Blvd #450, Encino, California 91436, United States
| | - Oliver G Schmidt
- Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, Chemnitz 09126, Germany
- Material Systems for Nanoelectronics, Chemnitz University of Technology, Chemnitz 09107, Germany
- Nanophysics, Faculty of Physics, TU Dresden, Dresden 01062, Germany
| | - Tsuyoshi Sekitani
- The Institute of Scientific and Industrial Research (SANKEN), Osaka University, Osaka, Japan 5670047
| | - Dae-Gyo Seo
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Guozhen Shen
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Xing Sheng
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Center for Flexible Electronics Technology, and IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China
| | - Qiongfeng Shi
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
- National University of Singapore Suzhou Research Institute (NUSRI), Suzhou Industrial Park, Suzhou 215123, China
| | - Takao Someya
- Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
| | - Yanlin Song
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing, Beijing 100190, China
| | - Eleni Stavrinidou
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, SE-601 74 Norrkoping, Sweden
| | - Meng Su
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing, Beijing 100190, China
| | - Xuemei Sun
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Kuniharu Takei
- Department of Physics and Electronics, Osaka Metropolitan University, Sakai, Osaka 599-8531, Japan
| | - Xiao-Ming Tao
- Research Institute for Intelligent Wearable Systems, School of Fashion and Textiles, Hong Kong Polytechnic University, Hong Kong, China
| | - Benjamin C K Tee
- Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
- iHealthtech, National University of Singapore, Singapore 119276, Singapore
| | - Aaron Voon-Yew Thean
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Tran Quang Trung
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Kyunggi-do 16419, Republic of Korea
| | - Changjin Wan
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
| | - Huiliang Wang
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Joseph Wang
- Department of Nanoengineering, University of California, San Diego, California 92093, United States
| | - Ming Wang
- Frontier Institute of Chip and System, State Key Laboratory of Integrated Chip and Systems, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, China
- the Shanghai Qi Zhi Institute, 41th Floor, AI Tower, No.701 Yunjin Road, Xuhui District, Shanghai 200232, China
| | - Sihong Wang
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois, 60637, United States
| | - Ting Wang
- State Key Laboratory of Organic Electronics and Information Displays and Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Zhong Lin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
- Georgia Institute of Technology, Atlanta, Georgia 30332-0245, United States
| | - Paul S Weiss
- California NanoSystems Institute, Department of Chemistry and Biochemistry, Department of Bioengineering, and Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Hanqi Wen
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
- Institute of Flexible Electronics Technology of THU, Jiaxing, Zhejiang, China 314000
| | - Sheng Xu
- Department of Nanoengineering, Department of Electrical and Computer Engineering, Materials Science and Engineering Program, and Department of Bioengineering, University of California San Diego, La Jolla, California, 92093, United States
| | - Tailin Xu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518060, PR China
| | - Hongping Yan
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Xuzhou Yan
- School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Hui Yang
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, China, 300072
| | - Le Yang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Department of Materials Science and Engineering, National University of Singapore (NUS), 9 Engineering Drive 1, #03-09 EA, Singapore 117575, Singapore
| | - Shuaijian Yang
- School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Lan Yin
- School of Materials Science and Engineering, The Key Laboratory of Advanced Materials of Ministry of Education, State Key Laboratory of New Ceramics and Fine Processing, and Center for Flexible Electronics Technology, Tsinghua University, Beijing, 100084, China
| | - Cunjiang Yu
- Department of Engineering Science and Mechanics, Department of Biomedical Engineering, Department of Material Science and Engineering, Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania, 16802, United States
| | - Guihua Yu
- Materials Science and Engineering Program and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States
| | - Jing Yu
- School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Shu-Hong Yu
- Department of Chemistry, Institute of Biomimetic Materials and Chemistry, Hefei National Research Center for Physical Science at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Xinge Yu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Evgeny Zamburg
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Haixia Zhang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication; Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing 100871, China
| | - Xiangyu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Xiaosheng Zhang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xueji Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, PR China
| | - Yihui Zhang
- Applied Mechanics Laboratory, Department of Engineering Mechanics; Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing 100084, PR China
| | - Yu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Siyuan Zhao
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Xuanhe Zhao
- Department of Mechanical Engineering, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States
| | - Yuanjin Zheng
- Center for Integrated Circuits and Systems, School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yu-Qing Zheng
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication; School of Integrated Circuits, Peking University, Beijing 100871, China
| | - Zijian Zheng
- Department of Applied Biology and Chemical Technology, Faculty of Science, Research Institute for Intelligent Wearable Systems, Research Institute for Smart Energy, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Tao Zhou
- Center for Neural Engineering, Department of Engineering Science and Mechanics, The Huck Institutes of the Life Sciences, Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Ming Zhu
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore
| | - Rong Zhu
- Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Yangzhi Zhu
- Terasaki Institute for Biomedical Innovation, Los Angeles, California, 90064, United States
| | - Yong Zhu
- Department of Mechanical and Aerospace Engineering, Department of Materials Science and Engineering, and Department of Biomedical Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Guijin Zou
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Xiaodong Chen
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Innovative Center for Flexible Devices (iFLEX), Max Planck-NTU Joint Laboratory for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
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Munch SB, Rogers TL, Symons CC, Anderson D, Pennekamp F. Constraining nonlinear time series modeling with the metabolic theory of ecology. Proc Natl Acad Sci U S A 2023; 120:e2211758120. [PMID: 36930600 PMCID: PMC10041132 DOI: 10.1073/pnas.2211758120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 02/08/2023] [Indexed: 03/18/2023] Open
Abstract
Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a "metabolic time step," our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.
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Affiliation(s)
- Stephan B. Munch
- Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, CA95060
- Department of Applied Mathematics, University of California, Santa Cruz, CA95060
| | - Tanya L. Rogers
- Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, CA95060
| | - Celia C. Symons
- Department of Ecology and Evolutionary Biology, University of California, Irvine, CA92697
| | - David Anderson
- Department of Zoology, University of British Columbia, Vancouver, BCV6T 1Z4, Canada
| | - Frank Pennekamp
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich8057, Switzerland
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Wang N, Zhang Y, Wang W, Ye Z, Chen H, Hu G, Ouyang D. How can machine learning and multiscale modeling benefit ocular drug development? Adv Drug Deliv Rev 2023; 196:114772. [PMID: 36906232 DOI: 10.1016/j.addr.2023.114772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/06/2023] [Accepted: 03/05/2023] [Indexed: 03/12/2023]
Abstract
The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.
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Affiliation(s)
- Nannan Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Yunsen Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Hongyu Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Guanghui Hu
- Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Department of Public Health and Medicinal Administration, Faculty of Health Sciences (FHS), University of Macau, Macau, China.
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Niklas C, Hölle T, Dugas M, Weigand MA, Larmann J. [The digital twin for perioperative medicine-An exciting look into the future of clinical research]. DIE ANAESTHESIOLOGIE 2023; 72:191-194. [PMID: 36695840 PMCID: PMC9876409 DOI: 10.1007/s00101-023-01251-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Affiliation(s)
- Christian Niklas
- Institut für Medizinische Informatik, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Deutschland.
| | - Tobias Hölle
- Klinik für Anästhesiologie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Deutschland
| | - Martin Dugas
- Institut für Medizinische Informatik, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Deutschland
| | - Markus A Weigand
- Klinik für Anästhesiologie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Deutschland
| | - Jan Larmann
- Klinik für Anästhesiologie, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Deutschland
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Cheriet M, Dentamaro V, Hamdan M, Impedovo D, Pirlo G. Multi-speed transformer network for neurodegenerative disease assessment and activity recognition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107344. [PMID: 36706617 DOI: 10.1016/j.cmpb.2023.107344] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/08/2022] [Accepted: 01/07/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Neurodegenerative diseases are the most frequent age-related diseases. This type of disease, if not discovered in the initial stage, will compromise the quality of life of the affected subject. Thus, a timely diagnosis is of paramount importance. One of the most used tasks from neurologists to detect and determine the severity of the disease is analysing human gait. This work presents the dataset named "Beside Gait" containing timeseries of coordinates of extracted body joints of people with neurodegenerative diseases in various stages of the disease as well as control subjects. In addition, the novel Multi-Speed transformer technique will be presented and benchmarked against several other techniques making use of deep learning and Shallow Learning. The objective is to recognize subjects affected by some form of neurodegenerative disease in early stage using a computer vision technique making use of deep learning that can be integrated into a smartphone app for offline inference with the aim of promptly initiate investigations and treatment to improve the patient's quality of life. METHODS The recorded videos were processed, and the skeleton of the person in the video was extracted using pose estimation. The raw time-series coordinates of the joints extracted by the pose estimation algorithm were tested against novel deep neural network architectures and Shallow Learning techniques. In this work, the proposed Multi-Speed Transformer is benchmarked against other deep neural networks such as Temporal Convolutional Neural Networks, Transformers, as well as Shallow Learning techniques making use of feature extraction and different classifiers such as Random Forests, K Nearest Neighbours, Ada Boost, Linear and RBF SVM. The proposed Multi-Speed Transformer architecture has been developed to learn short and long-term patterns to model the various pathological gaits. RESULTS The Multi-Speed Transformer outperformed all other existing models reaching an accuracy of 96.9%, a sensitivity of 96.9%, a precision of 97.7%, and a specificity of 97.1% in binary classification. The accuracy in multi-class classification for detecting the presence of the disease in various stages is 71.6%, the sensitivity is 67.7%, and the specificity is 71.8%. In addition, tests have also been conducted against two other different activity recognition datasets, namely SHREC and JHMDB, in the exact same conditions. Multi-Speed Transformer has demonstrated to beat always all other tested techniques as well as the techniques reviewed in the state-of-the-art with respectively of accuracy 91.8% and 74%. Having those datasets more than two classes, specificity was not computed. CONCLUSIONS The Multi-Speed Transformer is a valuable technique for neurodegenerative disease assessment through computer vision. In addition, the novel dataset "Beside Gait" here presented is an important starting point for future research work on automatic recognition of neurodegenerative diseases using gait analysis.
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Affiliation(s)
- Mohamed Cheriet
- École de Technologie Supérieure, ÉTS, 1100 Notre-Dame St W, Montreal, Quebec H3C 1K3, Canada
| | - Vincenzo Dentamaro
- Department of Computer Science, Università Degli Studi di Bari "Aldo Moro", Via Orabona 4, Bari 70125, Italy.
| | - Mohammed Hamdan
- École de Technologie Supérieure, ÉTS, 1100 Notre-Dame St W, Montreal, Quebec H3C 1K3, Canada
| | - Donato Impedovo
- Department of Computer Science, Università Degli Studi di Bari "Aldo Moro", Via Orabona 4, Bari 70125, Italy
| | - Giuseppe Pirlo
- Department of Computer Science, Università Degli Studi di Bari "Aldo Moro", Via Orabona 4, Bari 70125, Italy
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