1
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Matthews SA, Coelho C, Rodriguez Salas EE, Brock EE, Hodge VJ, Walker JA, Wilson LG. Real-time 3D tracking of swimming microbes using digital holographic microscopy and deep learning. PLoS One 2024; 19:e0301182. [PMID: 38669245 PMCID: PMC11051601 DOI: 10.1371/journal.pone.0301182] [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: 12/15/2023] [Accepted: 03/12/2024] [Indexed: 04/28/2024] Open
Abstract
The three-dimensional swimming tracks of motile microorganisms can be used to identify their species, which holds promise for the rapid identification of bacterial pathogens. The tracks also provide detailed information on the cells' responses to external stimuli such as chemical gradients and physical objects. Digital holographic microscopy (DHM) is a well-established, but computationally intensive method for obtaining three-dimensional cell tracks from video microscopy data. We demonstrate that a common neural network (NN) accelerates the analysis of holographic data by an order of magnitude, enabling its use on single-board computers and in real time. We establish a heuristic relationship between the distance of a cell from the focal plane and the size of the bounding box assigned to it by the NN, allowing us to rapidly localise cells in three dimensions as they swim. This technique opens the possibility of providing real-time feedback in experiments, for example by monitoring and adapting the supply of nutrients to a microbial bioreactor in response to changes in the swimming phenotype of microbes, or for rapid identification of bacterial pathogens in drinking water or clinical samples.
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Affiliation(s)
- Samuel A. Matthews
- School of Physics, Engineering and Technology, University of York, Heslington, York, United Kingdom
| | - Carlos Coelho
- School of Physics, Engineering and Technology, University of York, Heslington, York, United Kingdom
| | - Erick E. Rodriguez Salas
- School of Physics, Engineering and Technology, University of York, Heslington, York, United Kingdom
| | - Emma E. Brock
- School of Physics, Engineering and Technology, University of York, Heslington, York, United Kingdom
| | | | - James A. Walker
- Department of Computer Science, Deramore Lane, York, United Kingdom
| | - Laurence G. Wilson
- School of Physics, Engineering and Technology, University of York, Heslington, York, United Kingdom
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2
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Wang Y, Chen H, Xie L, Liu J, Zhang L, Yu J. Swarm Autonomy: From Agent Functionalization to Machine Intelligence. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2312956. [PMID: 38653192 DOI: 10.1002/adma.202312956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/17/2024] [Indexed: 04/25/2024]
Abstract
Swarm behaviors are common in nature, where individual organisms collaborate via perception, communication, and adaptation. Emulating these dynamics, large groups of active agents can self-organize through localized interactions, giving rise to complex swarm behaviors, which exhibit potential for applications across various domains. This review presents a comprehensive summary and perspective of synthetic swarms, to bridge the gap between the microscale individual agents and potential applications of synthetic swarms. It is begun by examining active agents, the fundamental units of synthetic swarms, to understand the origins of their motility and functionality in the presence of external stimuli. Then inter-agent communications and agent-environment communications that contribute to the swarm generation are summarized. Furthermore, the swarm behaviors reported to date and the emergence of machine intelligence within these behaviors are reviewed. Eventually, the applications enabled by distinct synthetic swarms are summarized. By discussing the emergent machine intelligence in swarm behaviors, insights are offered into the design and deployment of autonomous synthetic swarms for real-world applications.
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Affiliation(s)
- Yibin Wang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, China
| | - Hui Chen
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, China
| | - Leiming Xie
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, China
| | - Jinbo Liu
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, China
| | - Li Zhang
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, 999077, China
| | - Jiangfan Yu
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, China
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3
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Azad R, Lenßen P, Jia Y, Strauch M, Bener BA, Merhof D, Wöll D. Modeling the Temperature-Dependent Size Change of Polydisperse Nano-objects using a Deep Generative Model. NANO LETTERS 2024; 24:4447-4453. [PMID: 38588344 DOI: 10.1021/acs.nanolett.4c00267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Modern microscopy techniques can be used to investigate soft nano-objects at the nanometer scale. However, time-consuming microscopy measurements combined with low numbers of observable polydisperse objects often limit the statistics. We propose a method for identifying the most representative objects from their respective point clouds. These point cloud data are obtained, for example, through the localization of single emitters in super-resolution fluorescence microscopy. External stimuli, such as temperature, can cause changes in the shape and properties of adaptive objects. Due to the demanding and time-consuming nature of super-resolution microscopy experiments, only a limited number of temperature steps can be performed. Therefore, we propose a deep generative model that learns the underlying point distribution of temperature-dependent microgels, enabling the reliable generation of unlimited samples with an arbitrary number of localizations. Our method greatly cuts down the data collection effort across diverse experimental conditions, proving invaluable for soft condensed matter studies.
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Affiliation(s)
- Reza Azad
- Institute of Physical Chemistry, RWTH Aachen University, 52074 Aachen, Germany
| | - Pia Lenßen
- Institute of Physical Chemistry, RWTH Aachen University, 52074 Aachen, Germany
| | - Yiwei Jia
- Institute of Imaging and Computer Vision, RWTH Aachen University, 52056 Aachen, Germany
| | - Martin Strauch
- Institute of Imaging and Computer Vision, RWTH Aachen University, 52056 Aachen, Germany
| | - Berk Alperen Bener
- Institute of Physical Chemistry, RWTH Aachen University, 52074 Aachen, Germany
| | - Dorit Merhof
- Institute of Image Analysis and Computer Vision, University of Regensburg, 93040 Regensburg, Germany
| | - Dominik Wöll
- Institute of Physical Chemistry, RWTH Aachen University, 52074 Aachen, Germany
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4
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Nasiri M, Loran E, Liebchen B. Smart active particles learn and transcend bacterial foraging strategies. Proc Natl Acad Sci U S A 2024; 121:e2317618121. [PMID: 38557193 PMCID: PMC11009669 DOI: 10.1073/pnas.2317618121] [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: 10/13/2023] [Accepted: 01/30/2024] [Indexed: 04/04/2024] Open
Abstract
Throughout evolution, bacteria and other microorganisms have learned efficient foraging strategies that exploit characteristic properties of their unknown environment. While much research has been devoted to the exploration of statistical models describing the dynamics of foraging bacteria and other (micro-) organisms, little is known, regarding the question of how good the learned strategies actually are. This knowledge gap is largely caused by the absence of methods allowing to systematically develop alternative foraging strategies to compare with. In the present work, we use deep reinforcement learning to show that a smart run-and-tumble agent, which strives to find nutrients for its survival, learns motion patterns that are remarkably similar to the trajectories of chemotactic bacteria. Strikingly, despite this similarity, we also find interesting differences between the learned tumble rate distribution and the one that is commonly assumed for the run and tumble model. We find that these differences equip the agent with significant advantages regarding its foraging and survival capabilities. Our results uncover a generic route to use deep reinforcement learning for discovering search and collection strategies that exploit characteristic but initially unknown features of the environment. These results can be used, e.g., to program future microswimmers, nanorobots, and smart active particles for tasks like searching for cancer cells, micro-waste collection, or environmental remediation.
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Affiliation(s)
- Mahdi Nasiri
- Institute of Condensed Matter Physics, Department of Physics, Technische Universität Darmstadt, DarmstadtD-64289, Germany
| | - Edwin Loran
- Institute of Condensed Matter Physics, Department of Physics, Technische Universität Darmstadt, DarmstadtD-64289, Germany
| | - Benno Liebchen
- Institute of Condensed Matter Physics, Department of Physics, Technische Universität Darmstadt, DarmstadtD-64289, Germany
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5
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Seckler H, Metzler R, Kelty-Stephen DG, Mangalam M. Multifractal spectral features enhance classification of anomalous diffusion. Phys Rev E 2024; 109:044133. [PMID: 38755826 DOI: 10.1103/physreve.109.044133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 03/19/2024] [Indexed: 05/18/2024]
Abstract
Anomalous diffusion processes, characterized by their nonstandard scaling of the mean-squared displacement, pose a unique challenge in classification and characterization. In a previous study [Mangalam et al., Phys. Rev. Res. 5, 023144 (2023)2643-156410.1103/PhysRevResearch.5.023144], we established a comprehensive framework for understanding anomalous diffusion using multifractal formalism. The present study delves into the potential of multifractal spectral features for effectively distinguishing anomalous diffusion trajectories from five widely used models: fractional Brownian motion, scaled Brownian motion, continuous-time random walk, annealed transient time motion, and Lévy walk. We generate extensive datasets comprising 10^{6} trajectories from these five anomalous diffusion models and extract multiple multifractal spectra from each trajectory to accomplish this. Our investigation entails a thorough analysis of neural network performance, encompassing features derived from varying numbers of spectra. We also explore the integration of multifractal spectra into traditional feature datasets, enabling us to assess their impact comprehensively. To ensure a statistically meaningful comparison, we categorize features into concept groups and train neural networks using features from each designated group. Notably, several feature groups demonstrate similar levels of accuracy, with the highest performance observed in groups utilizing moving-window characteristics and p varation features. Multifractal spectral features, particularly those derived from three spectra involving different timescales and cutoffs, closely follow, highlighting their robust discriminatory potential. Remarkably, a neural network exclusively trained on features from a single multifractal spectrum exhibits commendable performance, surpassing other feature groups. In summary, our findings underscore the diverse and potent efficacy of multifractal spectral features in enhancing the predictive capacity of machine learning to classify anomalous diffusion processes.
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Affiliation(s)
- Henrik Seckler
- Institute for Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
| | - Ralf Metzler
- Institute for Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
- Asia Pacific Center for Theoretical Physics, Pohang 37673, Republic of Korea
| | - Damian G Kelty-Stephen
- Department of Psychology, State University of New York at New Paltz, New Paltz, New York 12561, USA
| | - Madhur Mangalam
- Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, Nebraska 68182, USA
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6
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Jin C, Sengupta A. Microbes in porous environments: from active interactions to emergent feedback. Biophys Rev 2024; 16:173-188. [PMID: 38737203 PMCID: PMC11078916 DOI: 10.1007/s12551-024-01185-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/27/2024] [Indexed: 05/14/2024] Open
Abstract
Microbes thrive in diverse porous environments-from soil and riverbeds to human lungs and cancer tissues-spanning multiple scales and conditions. Short- to long-term fluctuations in local factors induce spatio-temporal heterogeneities, often leading to physiologically stressful settings. How microbes respond and adapt to such biophysical constraints is an active field of research where considerable insight has been gained over the last decades. With a focus on bacteria, here we review recent advances in self-organization and dispersal in inorganic and organic porous settings, highlighting the role of active interactions and feedback that mediates microbial survival and fitness. We discuss open questions and opportunities for using integrative approaches to advance our understanding of the biophysical strategies which microbes employ at various scales to make porous settings habitable.
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Affiliation(s)
- Chenyu Jin
- Physics of Living Matter Group, Department of Physics and Materials Science, University of Luxembourg, 162 A, Avenue de la Faïencerie, Luxembourg City, L-1511 Luxembourg
| | - Anupam Sengupta
- Physics of Living Matter Group, Department of Physics and Materials Science, University of Luxembourg, 162 A, Avenue de la Faïencerie, Luxembourg City, L-1511 Luxembourg
- Institute for Advanced Studies, University of Luxembourg, 2 Avenue de l’Université, Esch-sur-Alzette, L-4365 Luxembourg
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7
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Caraglio M, Kaur H, Fiderer LJ, López-Incera A, Briegel HJ, Franosch T, Muñoz-Gil G. Learning how to find targets in the micro-world: the case of intermittent active Brownian particles. SOFT MATTER 2024; 20:2008-2016. [PMID: 38328899 PMCID: PMC10900891 DOI: 10.1039/d3sm01680c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 01/29/2024] [Indexed: 02/09/2024]
Abstract
Finding the best strategy to minimize the time needed to find a given target is a crucial task both in nature and in reaching decisive technological advances. By considering learning agents able to switch their dynamics between standard and active Brownian motion, here we focus on developing effective target-search behavioral policies for microswimmers navigating a homogeneous environment and searching for targets of unknown position. We exploit projective simulation, a reinforcement learning algorithm, to acquire an efficient stochastic policy represented by the probability of switching the phase, i.e. the navigation mode, in response to the type and the duration of the current phase. Our findings reveal that the target-search efficiency increases with the particle's self-propulsion during the active phase and that, while the optimal duration of the passive case decreases monotonically with the activity, the optimal duration of the active phase displays a non-monotonic behavior.
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Affiliation(s)
- Michele Caraglio
- Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21A, A-6020, Innsbruck, Austria.
| | - Harpreet Kaur
- Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21A, A-6020, Innsbruck, Austria.
| | - Lukas J Fiderer
- Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21A, A-6020, Innsbruck, Austria.
| | - Andrea López-Incera
- Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21A, A-6020, Innsbruck, Austria.
| | - Hans J Briegel
- Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21A, A-6020, Innsbruck, Austria.
| | - Thomas Franosch
- Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21A, A-6020, Innsbruck, Austria.
| | - Gorka Muñoz-Gil
- Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21A, A-6020, Innsbruck, Austria.
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8
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Dong H, Lin J, Tao Y, Jia Y, Sun L, Li WJ, Sun H. AI-enhanced biomedical micro/nanorobots in microfluidics. LAB ON A CHIP 2024; 24:1419-1440. [PMID: 38174821 DOI: 10.1039/d3lc00909b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology.
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Affiliation(s)
- Hui Dong
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Jiawen Lin
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
| | - Yihui Tao
- Department of Automation Control and System Engineering, University of Sheffield, Sheffield, UK
| | - Yuan Jia
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China
| | - Lining Sun
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Wen Jung Li
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China
| | - Hao Sun
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- Research Center of Aerospace Mechanism and Control, Harbin Institute of Technology, Harbin, China
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9
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Wu Y, Cui Y, Song W, Wei W, He Z, Tao J, Yin D, Chen X, Gao C, Liu J, Liu L, Wu J. Reprogramming the Transition States to Enhance C-N Cleavage Efficiency of Rhodococcus opacusl-Amino Acid Oxidase. JACS AU 2024; 4:557-569. [PMID: 38425913 PMCID: PMC10900486 DOI: 10.1021/jacsau.3c00672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/21/2023] [Accepted: 12/26/2023] [Indexed: 03/02/2024]
Abstract
l-Amino acid oxidase (LAAO) is an important biocatalyst used for synthesizing α-keto acids. LAAO from Rhodococcus opacus (RoLAAO) has a broad substrate spectrum; however, its low total turnover number limits its industrial use. To overcome this, we aimed to employ crystal structure-guided density functional theory calculations and molecular dynamic simulations to investigate the catalytic mechanism. Two key steps were identified: S → [TS1] in step 1 and Int1 → [TS2] in step 2. We reprogrammed the transition states [TS1] and [TS2] to reduce the identified energy barrier and obtain a RoLAAO variant capable of catalyzing 19 kinds of l-amino acids to the corresponding high-value α-keto acids with a high total turnover number, yield (≥95.1 g/L), conversion rate (≥95%), and space-time yields ≥142.7 g/L/d in 12-24 h, in a 5 L reactor. Our results indicated the promising potential of the developed RoLAAO variant for use in the industrial production of α-keto acids while providing a potential catalytic-mechanism-guided protein design strategy to achieve the desired physical and catalytic properties of enzymes.
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Affiliation(s)
- Yaoyun Wu
- School
of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, China
- State
Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China
- School
of Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Yaozhong Cui
- Jiangsu
Xishan Senior High School, Wuxi 214174, China
| | - Wei Song
- School
of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, China
| | - Wanqing Wei
- State
Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China
| | - Zhizhen He
- School
of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, China
| | - Jinyang Tao
- School
of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, China
| | - Dejing Yin
- School
of Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Xiulai Chen
- State
Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China
| | - Cong Gao
- State
Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China
| | - Jia Liu
- State
Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China
| | - Liming Liu
- State
Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China
| | - Jing Wu
- School
of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, China
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10
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Li Y, Zarei Z, Tran PN, Wang Y, Baskaran A, Fraden S, Hagan MF, Hong P. A machine learning approach to robustly determine director fields and analyze defects in active nematics. SOFT MATTER 2024; 20:1869-1883. [PMID: 38318759 DOI: 10.1039/d3sm01253k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Active nematics are dense systems of rodlike particles that consume energy to drive motion at the level of the individual particles. They exist in natural systems like biological tissues and artificial materials such as suspensions of self-propelled colloidal particles or synthetic microswimmers. Active nematics have attracted significant attention in recent years due to their spectacular nonequilibrium collective spatiotemporal dynamics, which may enable applications in fields such as robotics, drug delivery, and materials science. The director field, which measures the direction and degree of alignment of the local nematic orientation, is a crucial characteristic of active nematics and is essential for studying topological defects. However, determining the director field is a significant challenge in many experimental systems. Although director fields can be derived from images of active nematics using traditional imaging processing methods, the accuracy of such methods is highly sensitive to the settings of the algorithms. These settings must be tuned from image to image due to experimental noise, intrinsic noise of the imaging technology, and perturbations caused by changes in experimental conditions. This sensitivity currently limits automatic analysis of active nematics. To address this, we developed a machine learning model for extracting reliable director fields from raw experimental images, which enables accurate analysis of topological defects. Application of the algorithm to experimental data demonstrates that the approach is robust and highly generalizable to experimental settings that are different from those in the training data. It could be a promising tool for investigating active nematics and may be generalized to other active matter systems.
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Affiliation(s)
- Yunrui Li
- Computer Science Department, Brandeis University, USA.
| | - Zahra Zarei
- Physics Department, Brandeis University, USA
| | - Phu N Tran
- Physics Department, Brandeis University, USA
| | - Yifei Wang
- Computer Science Department, Brandeis University, USA.
| | | | - Seth Fraden
- Physics Department, Brandeis University, USA
| | | | - Pengyu Hong
- Computer Science Department, Brandeis University, USA.
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11
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Piven A, Darmoroz D, Skorb E, Orlova T. Machine learning methods for liquid crystal research: phases, textures, defects and physical properties. SOFT MATTER 2024; 20:1380-1391. [PMID: 38288719 DOI: 10.1039/d3sm01634j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Liquid crystal materials, with their unique properties and diverse applications, have long captured the attention of researchers and industries alike. From liquid crystal displays and electro-optical devices to advanced sensors and emerging technologies, the study and application of liquid crystals continue to be of paramount importance in the fields of materials science, chemistry and physics. With the ever-increasing complexity and diversity of liquid crystal materials, researchers face new challenges in understanding their behaviors, properties, and potential applications. On the other hand, machine learning, a rapidly evolving interdisciplinary field at the intersection of computer science and data analysis, has already become a powerful tool for unraveling implicit correlations and predicting new properties of a wide variety of physical and chemical systems and structures. Here we aim to consider how machine learning methods are suitable for solving fundamental problems in the field of liquid crystals and what are the advantages of this artificial intelligence based approach.
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Affiliation(s)
- Anastasiia Piven
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg, Russia.
| | - Darina Darmoroz
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg, Russia.
| | - Ekaterina Skorb
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg, Russia.
| | - Tetiana Orlova
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg, Russia.
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12
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Wang X, Cichos F. Harnessing synthetic active particles for physical reservoir computing. Nat Commun 2024; 15:774. [PMID: 38287028 PMCID: PMC10825170 DOI: 10.1038/s41467-024-44856-5] [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: 08/14/2023] [Accepted: 01/08/2024] [Indexed: 01/31/2024] Open
Abstract
The processing of information is an indispensable property of living systems realized by networks of active processes with enormous complexity. They have inspired many variants of modern machine learning, one of them being reservoir computing, in which stimulating a network of nodes with fading memory enables computations and complex predictions. Reservoirs are implemented on computer hardware, but also on unconventional physical substrates such as mechanical oscillators, spins, or bacteria often summarized as physical reservoir computing. Here we demonstrate physical reservoir computing with a synthetic active microparticle system that self-organizes from an active and passive component into inherently noisy nonlinear dynamical units. The self-organization and dynamical response of the unit are the results of a delayed propulsion of the microswimmer to a passive target. A reservoir of such units with a self-coupling via the delayed response can perform predictive tasks despite the strong noise resulting from the Brownian motion of the microswimmers. To achieve efficient noise suppression, we introduce a special architecture that uses historical reservoir states for output. Our results pave the way for the study of information processing in synthetic self-organized active particle systems.
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Affiliation(s)
- Xiangzun Wang
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, 04105, Leipzig, Germany
| | - Frank Cichos
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany.
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13
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Schmitt MS, Colen J, Sala S, Devany J, Seetharaman S, Caillier A, Gardel ML, Oakes PW, Vitelli V. Machine learning interpretable models of cell mechanics from protein images. Cell 2024; 187:481-494.e24. [PMID: 38194965 DOI: 10.1016/j.cell.2023.11.041] [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: 03/21/2023] [Revised: 09/20/2023] [Accepted: 11/29/2023] [Indexed: 01/11/2024]
Abstract
Cellular form and function emerge from complex mechanochemical systems within the cytoplasm. Currently, no systematic strategy exists to infer large-scale physical properties of a cell from its molecular components. This is an obstacle to understanding processes such as cell adhesion and migration. Here, we develop a data-driven modeling pipeline to learn the mechanical behavior of adherent cells. We first train neural networks to predict cellular forces from images of cytoskeletal proteins. Strikingly, experimental images of a single focal adhesion (FA) protein, such as zyxin, are sufficient to predict forces and can generalize to unseen biological regimes. Using this observation, we develop two approaches-one constrained by physics and the other agnostic-to construct data-driven continuum models of cellular forces. Both reveal how cellular forces are encoded by two distinct length scales. Beyond adherent cell mechanics, our work serves as a case study for integrating neural networks into predictive models for cell biology.
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Affiliation(s)
- Matthew S Schmitt
- James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA; Kadanoff Center for Theoretical Physics, University of Chicago, Chicago, IL 60637, USA
| | - Jonathan Colen
- James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA; Kadanoff Center for Theoretical Physics, University of Chicago, Chicago, IL 60637, USA
| | - Stefano Sala
- Department of Cell & Molecular Physiology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL 60153, USA
| | - John Devany
- James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA
| | - Shailaja Seetharaman
- James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA
| | - Alexia Caillier
- Department of Cell & Molecular Physiology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL 60153, USA
| | - Margaret L Gardel
- James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA.
| | - Patrick W Oakes
- Department of Cell & Molecular Physiology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL 60153, USA.
| | - Vincenzo Vitelli
- James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA; Kadanoff Center for Theoretical Physics, University of Chicago, Chicago, IL 60637, USA.
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14
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Mahmoudabadbozchelou M, Kamani KM, Rogers SA, Jamali S. Unbiased construction of constitutive relations for soft materials from experiments via rheology-informed neural networks. Proc Natl Acad Sci U S A 2024; 121:e2313658121. [PMID: 38170750 PMCID: PMC10786310 DOI: 10.1073/pnas.2313658121] [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: 08/08/2023] [Accepted: 12/03/2023] [Indexed: 01/05/2024] Open
Abstract
The ability to concisely describe the dynamical behavior of soft materials through closed-form constitutive relations holds the key to accelerated and informed design of materials and processes. The conventional approach is to construct constitutive relations through simplifying assumptions and approximating the time- and rate-dependent stress response of a complex fluid to an imposed deformation. While traditional frameworks have been foundational to our current understanding of soft materials, they often face a twofold existential limitation: i) Constructed on ideal and generalized assumptions, precise recovery of material-specific details is usually serendipitous, if possible, and ii) inherent biases that are involved by making those assumptions commonly come at the cost of new physical insight. This work introduces an approach by leveraging recent advances in scientific machine learning methodologies to discover the governing constitutive equation from experimental data for complex fluids. Our rheology-informed neural network framework is found capable of learning the hidden rheology of a complex fluid through a limited number of experiments. This is followed by construction of an unbiased material-specific constitutive relation that accurately describes a wide range of bulk dynamical behavior of the material. While extremely efficient in closed-form model discovery for a real-world complex system, the model also provides insight into the underpinning physics of the material.
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Affiliation(s)
| | - Krutarth M. Kamani
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Champaign, IL61801
| | - Simon A. Rogers
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Champaign, IL61801
| | - Safa Jamali
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA02115
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15
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Schwayer C, Brückner DB. Connecting theory and experiment in cell and tissue mechanics. J Cell Sci 2023; 136:jcs261515. [PMID: 38149871 DOI: 10.1242/jcs.261515] [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] [Indexed: 12/28/2023] Open
Abstract
Understanding complex living systems, which are fundamentally constrained by physical phenomena, requires combining experimental data with theoretical physical and mathematical models. To develop such models, collaborations between experimental cell biologists and theoreticians are increasingly important but these two groups often face challenges achieving mutual understanding. To help navigate these challenges, this Perspective discusses different modelling approaches, including bottom-up hypothesis-driven and top-down data-driven models, and highlights their strengths and applications. Using cell mechanics as an example, we explore the integration of specific physical models with experimental data from the molecular, cellular and tissue level up to multiscale input. We also emphasize the importance of constraining model complexity and outline strategies for crosstalk between experimental design and model development. Furthermore, we highlight how physical models can provide conceptual insights and produce unifying and generalizable frameworks for biological phenomena. Overall, this Perspective aims to promote fruitful collaborations that advance our understanding of complex biological systems.
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Affiliation(s)
- Cornelia Schwayer
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - David B Brückner
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
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16
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McDermott D, Reichhardt C, Reichhardt CJO. Characterizing different motility-induced regimes in active matter with machine learning and noise. Phys Rev E 2023; 108:064613. [PMID: 38243443 DOI: 10.1103/physreve.108.064613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 11/29/2023] [Indexed: 01/21/2024]
Abstract
We examine motility-induced phase separation (MIPS) in two-dimensional run-and-tumble disk systems using both machine learning and noise fluctuation analysis. Our measures suggest that within the MIPS state there are several distinct regimes as a function of density and run time, so that systems with MIPS transitions exhibit an active fluid, an active crystal, and a critical regime. The different regimes can be detected by combining an order parameter extracted from principal component analysis with a cluster stability measurement. The principal component-derived order parameter is maximized in the critical regime, remains low in the active fluid, and has an intermediate value in the active crystal regime. We demonstrate that machine learning can better capture dynamical properties of the MIPS regimes compared to more standard structural measures such as the maximum cluster size. The different regimes can also be characterized via changes in the noise power of the fluctuations in the average speed. In the critical regime, the noise power passes through a maximum and has a broad spectrum with a 1/f^{1.6} signature, similar to the noise observed near depinning transitions or for solids undergoing plastic deformation.
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Affiliation(s)
- D McDermott
- X-Theoretical Design Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - C Reichhardt
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - C J O Reichhardt
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
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17
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Feng K, Chen L, Zhang X, Gong J, Qu J, Niu R. Collective Behaviors of Isotropic Micromotors: From Assembly to Reconstruction and Motion Control under External Fields. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2900. [PMID: 37947744 PMCID: PMC10650937 DOI: 10.3390/nano13212900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023]
Abstract
Swarms of self-propelled micromotors can mimic the processes of natural systems and construct artificial intelligent materials to perform complex collective behaviors. Compared to self-propelled Janus micromotors, the isotropic colloid motors, also called micromotors or microswimmers, have advantages in self-assembly to form micromotor swarms, which are efficient in resistance to external disturbance and the delivery of large quantity of cargos. In this minireview, we summarize the fundamental principles and interactions for the assembly of isotropic active particles to generate micromotor swarms. Recent discoveries based on either catalytic or external physical field-stimulated micromotor swarms are also presented. Then, the strategy for the reconstruction and motion control of micromotor swarms in complex environments, including narrow channels, maze, raised obstacles, and high steps/low gaps, is summarized. Finally, we outline the future directions of micromotor swarms and the remaining challenges and opportunities.
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Affiliation(s)
- Kai Feng
- Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, Hubei Key Laboratory of Material Chemistry and Service Failure, Hubei Engineering Research Center for Biomaterials and Medical Protective Materials, Semiconductor Chemistry Center, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Ministry of Education, Wuhan 430074, China; (K.F.); (L.C.); (X.Z.); (J.Q.)
| | - Ling Chen
- Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, Hubei Key Laboratory of Material Chemistry and Service Failure, Hubei Engineering Research Center for Biomaterials and Medical Protective Materials, Semiconductor Chemistry Center, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Ministry of Education, Wuhan 430074, China; (K.F.); (L.C.); (X.Z.); (J.Q.)
| | - Xinle Zhang
- Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, Hubei Key Laboratory of Material Chemistry and Service Failure, Hubei Engineering Research Center for Biomaterials and Medical Protective Materials, Semiconductor Chemistry Center, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Ministry of Education, Wuhan 430074, China; (K.F.); (L.C.); (X.Z.); (J.Q.)
| | - Jiang Gong
- Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, Hubei Key Laboratory of Material Chemistry and Service Failure, Hubei Engineering Research Center for Biomaterials and Medical Protective Materials, Semiconductor Chemistry Center, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Ministry of Education, Wuhan 430074, China; (K.F.); (L.C.); (X.Z.); (J.Q.)
| | - Jinping Qu
- Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, Hubei Key Laboratory of Material Chemistry and Service Failure, Hubei Engineering Research Center for Biomaterials and Medical Protective Materials, Semiconductor Chemistry Center, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Ministry of Education, Wuhan 430074, China; (K.F.); (L.C.); (X.Z.); (J.Q.)
- Key Laboratory of Polymer Processing Engineering, Ministry of Education, Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, National Engineering Research Center of Novel Equipment for Polymer Processing, School of Mechanical and Automotive Engineering, South China University of Technology, Ministry of Education, Guangzhou 510641, China
| | - Ran Niu
- Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, Hubei Key Laboratory of Material Chemistry and Service Failure, Hubei Engineering Research Center for Biomaterials and Medical Protective Materials, Semiconductor Chemistry Center, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Ministry of Education, Wuhan 430074, China; (K.F.); (L.C.); (X.Z.); (J.Q.)
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18
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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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19
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Planells H, Parmar V, Marcus HJ, Pandit AS. From theory to practice: what is the potential of artificial intelligence in the future of neurosurgery? Expert Rev Neurother 2023; 23:1041-1046. [PMID: 37997765 DOI: 10.1080/14737175.2023.2285432] [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] [Received: 09/05/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Affiliation(s)
- Hannah Planells
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Viraj Parmar
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Anand S Pandit
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- High-dimensional Neurology, Institute of Neurology, London, UK
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20
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Sawicki J, Berner R, Loos SAM, Anvari M, Bader R, Barfuss W, Botta N, Brede N, Franović I, Gauthier DJ, Goldt S, Hajizadeh A, Hövel P, Karin O, Lorenz-Spreen P, Miehl C, Mölter J, Olmi S, Schöll E, Seif A, Tass PA, Volpe G, Yanchuk S, Kurths J. Perspectives on adaptive dynamical systems. CHAOS (WOODBURY, N.Y.) 2023; 33:071501. [PMID: 37486668 DOI: 10.1063/5.0147231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/24/2023] [Indexed: 07/25/2023]
Abstract
Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches.
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Affiliation(s)
- Jakub Sawicki
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Rico Berner
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
| | - Sarah A M Loos
- DAMTP, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - Mehrnaz Anvari
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53757 Sankt-Augustin, Germany
| | - Rolf Bader
- Institute of Systematic Musicology, University of Hamburg, Hamburg, Germany
| | - Wolfram Barfuss
- Transdisciplinary Research Area: Sustainable Futures, University of Bonn, 53113 Bonn, Germany
- Center for Development Research (ZEF), University of Bonn, 53113 Bonn, Germany
| | - Nicola Botta
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Computer Science and Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
| | - Nuria Brede
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Computer Science, University of Potsdam, An der Bahn 2, 14476 Potsdam, Germany
| | - Igor Franović
- Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia
| | - Daniel J Gauthier
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
| | - Sebastian Goldt
- Department of Physics, International School of Advanced Studies (SISSA), Trieste, Italy
| | - Aida Hajizadeh
- Research Group Comparative Neuroscience, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Philipp Hövel
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
| | - Omer Karin
- Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
| | - Philipp Lorenz-Spreen
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Christoph Miehl
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Jan Mölter
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstraße 3, 85748 Garching bei München, Germany
| | - Simona Olmi
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Eckehard Schöll
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Alireza Seif
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, USA
| | - Peter A Tass
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California 94304, USA
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Serhiy Yanchuk
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
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21
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El Khiyati Z, Chesneaux R, Giraldi L, Bec J. Steering undulatory micro-swimmers in a fluid flow through reinforcement learning. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2023; 46:43. [PMID: 37306761 DOI: 10.1140/epje/s10189-023-00293-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 04/23/2023] [Indexed: 06/13/2023]
Abstract
This work aims at finding optimal navigation policies for thin, deformable microswimmers that progress in a viscous fluid by propagating a sinusoidal undulation along their slender body. These active filaments are embedded in a prescribed, non-homogeneous flow, in which their swimming undulations have to compete with the drifts, strains, and deformations inflicted by the outer velocity field. Such an intricate situation, where swimming and navigation are tightly bonded, is addressed using various methods of reinforcement learning. Each swimmer has only access to restricted information on its configuration and has to select accordingly an action among a limited set. The optimisation problem then consists in finding the policy leading to the most efficient displacement in a given direction. It is found that usual methods do not converge and this pitfall is interpreted as a combined consequence of the non-Markovianity of the decision process, together with the highly chaotic nature of the dynamics, which is responsible for high variability in learning efficiencies. Still, we provide an alternative method to construct efficient policies, which is based on running several independent realisations of Q-learning. This allows the construction of a set of admissible policies whose properties can be studied in detail and compared to assess their efficiency and robustness.
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Affiliation(s)
| | - Raphaël Chesneaux
- Ecole Nationale Supérieure des Mines de Paris, PSL University, CNRS, Cemef, Sophia-Antipolis, Valbonne, France
| | - Laëtitia Giraldi
- Université Côte d'Azur, Inria, CNRS, Sophia-Antipolis, Valbonne, France
| | - Jérémie Bec
- Université Côte d'Azur, Inria, CNRS, Sophia-Antipolis, Valbonne, France.
- Ecole Nationale Supérieure des Mines de Paris, PSL University, CNRS, Cemef, Sophia-Antipolis, Valbonne, France.
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22
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Liu Y, Zou Z, Pak OS, Tsang ACH. Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination. Sci Rep 2023; 13:9397. [PMID: 37296306 PMCID: PMC10256736 DOI: 10.1038/s41598-023-36305-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/31/2023] [Indexed: 06/12/2023] Open
Abstract
Biological microswimmers can coordinate their motions to exploit their fluid environment-and each other-to achieve global advantages in their locomotory performance. These cooperative locomotion require delicate adjustments of both individual swimming gaits and spatial arrangements of the swimmers. Here we probe the emergence of such cooperative behaviors among artificial microswimmers endowed with artificial intelligence. We present the first use of a deep reinforcement learning approach to empower the cooperative locomotion of a pair of reconfigurable microswimmers. The AI-advised cooperative policy comprises two stages: an approach stage where the swimmers get in close proximity to fully exploit hydrodynamic interactions, followed a synchronization stage where the swimmers synchronize their locomotory gaits to maximize their overall net propulsion. The synchronized motions allow the swimmer pair to move together coherently with an enhanced locomotion performance unattainable by a single swimmer alone. Our work constitutes a first step toward uncovering intriguing cooperative behaviors of smart artificial microswimmers, demonstrating the vast potential of reinforcement learning towards intelligent autonomous manipulations of multiple microswimmers for their future biomedical and environmental applications.
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Affiliation(s)
- Yangzhe Liu
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Zonghao Zou
- Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, 14850, USA
| | - On Shun Pak
- Department of Mechanical Engineering, Santa Clara University, Santa Clara, CA, 95053, USA.
| | - Alan C H Tsang
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
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23
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Huynh H, Kelly TJ, Vu L, Hoang T, Nguyen PA, Le TC, Jarvis EA, Phan H. Quantum Chemistry-Machine Learning Approach for Predicting Properties of Lewis Acid-Lewis Base Adducts. ACS OMEGA 2023; 8:19119-19127. [PMID: 37273580 PMCID: PMC10233689 DOI: 10.1021/acsomega.3c02822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 05/09/2023] [Indexed: 06/06/2023]
Abstract
Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics.
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Affiliation(s)
- Hieu Huynh
- Fulbright
University Vietnam, Ho Chi
Minh 72908, Vietnam
| | - Thomas J. Kelly
- Loyola
Marymount University, Los Angeles, California 90045, United States
| | - Linh Vu
- Fulbright
University Vietnam, Ho Chi
Minh 72908, Vietnam
| | - Tung Hoang
- Independent
Researcher, Palo Alto, California 94303, Unites States
| | - Phuc An Nguyen
- Fulbright
University Vietnam, Ho Chi
Minh 72908, Vietnam
| | - Tu C. Le
- School
of Engineering, STEM College, RMIT University, Melbourne, Victoria 3000, Australia
| | - Emily A. Jarvis
- Loyola
Marymount University, Los Angeles, California 90045, United States
| | - Hung Phan
- Fulbright
University Vietnam, Ho Chi
Minh 72908, Vietnam
- Soka
University of America, Aliso Viejo, California 92656, United States
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24
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Namdeo S, Srivastava VC, Mohanty P. Machine learning implemented exploration of the adsorption mechanism of carbon dioxide onto porous carbons. J Colloid Interface Sci 2023; 647:174-187. [PMID: 37247481 DOI: 10.1016/j.jcis.2023.05.052] [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: 01/16/2023] [Revised: 04/28/2023] [Accepted: 05/08/2023] [Indexed: 05/31/2023]
Abstract
Adsorption of CO2 on porous carbons has been identified as one of the promising methods for carbon capture, which is essential for meeting the sustainable developmental goal (SDG) with respect to climate action, i.e., SDG 13. This research implemented six supervised machine learning (ML) models (gradient boosting decision tree (GBDT), extreme gradient boosting (XGB), light boost gradient machine (LBGM), random forest (RF), categorical boosting (Catboost), and adaptive boosting (Adaboost)) to understand and predict the CO2 adsorption mechanism and adsorption uptake, respectively. The results recommended that the GBDT outperformed the remaining five ML models for CO2 adsorption. However, XGB, LBGM, RF, and Catboost also represented the prediction in the acceptable range. The GBDT model indicated the accurate prediction of CO2 uptake onto the porous carbons considering adsorbent properties and adsorption conditions as model input parameters. Next, two-factor partial dependence plots revealed a lucid explanation of how the combinations of two input features affect the model prediction. Furthermore, SHapley Additive exPlainations (SHAP), a novel explication approach based on ML models, were employed to understand and elucidate the CO2 adsorption and model prediction. The SHAP explanations, implemented on the GBDT model, revealed the rigorous relationships among the input features and output variables based on the GBDT prediction. Additionally, SHAP provided clear-cut feature importance analysis and individual feature impact on the prediction. SHAP also explained two instances of CO2 adsorption. Along with the data-driven insightful explanation of CO2 adsorption onto porous carbons, this study also provides a promising method to predict the clear-cut performance of porous carbons for CO2 adsorption without performing any experiments and open new avenues for researchers to implement this study in the field of adsorption because a lot of data is being generated.
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Affiliation(s)
- Sarvesh Namdeo
- Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India.
| | - Vimal Chandra Srivastava
- Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India.
| | - Paritosh Mohanty
- Department of Chemistry, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India.
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25
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Kollipara PS, Chen Z, Zheng Y. Optical Manipulation Heats up: Present and Future of Optothermal Manipulation. ACS NANO 2023; 17:7051-7063. [PMID: 37022087 PMCID: PMC10197158 DOI: 10.1021/acsnano.3c00536] [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
Optothermal manipulation is a versatile technique that combines optical and thermal forces to control synthetic micro-/nanoparticles and biological entities. This emerging technique overcomes the limitations of traditional optical tweezers, including high laser power, photon and thermal damage to fragile objects, and the requirement of refractive-index contrast between target objects and the surrounding solvents. In this perspective, we discuss how the rich opto-thermo-fluidic multiphysics leads to a variety of working mechanisms and modes of optothermal manipulation in both liquid and solid media, underpinning a broad range of applications in biology, nanotechnology, and robotics. Moreover, we highlight current experimental and modeling challenges in the pursuit of optothermal manipulation and propose future directions and solutions to the challenges.
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Affiliation(s)
- Pavana Siddhartha Kollipara
- Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States
| | - Zhihan Chen
- Materials Science and Engineering program and Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Yuebing Zheng
- Materials Science and Engineering program and Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
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26
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Khadem H, Nemat H, Elliott J, Benaissa M. Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion. Bioengineering (Basel) 2023; 10:bioengineering10040487. [PMID: 37106674 PMCID: PMC10135844 DOI: 10.3390/bioengineering10040487] [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: 03/21/2023] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 04/29/2023] Open
Abstract
Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. One conundrum in developing time-series forecasting models for blood glucose level prediction is to determine an appropriate length for look-back windows. On the one hand, studying short histories foists the risk of information incompletion. On the other hand, analysing long histories might induce information redundancy due to the data shift phenomenon. Additionally, optimal lag lengths are inconsistent across individuals because of the domain shift occurrence. Therefore, in bespoke analysis, either optimal lag values should be found for each individual separately or a globally suboptimal lag value should be used for all. The former approach degenerates the analysis's congruency and imposes extra perplexity. With the latter, the fine-tunned lag is not necessarily the optimum option for all individuals. To cope with this challenge, this work suggests an interconnected lag fusion framework based on nested meta-learning analysis that improves the accuracy and precision of predictions for personalised blood glucose level forecasting. The proposed framework is leveraged to generate blood glucose prediction models for patients with type 1 diabetes by scrutinising two well-established publicly available Ohio type 1 diabetes datasets. The models developed undergo vigorous evaluation and statistical analysis from mathematical and clinical perspectives. The results achieved underpin the efficacy of the proposed method in blood glucose level time-series prediction analysis.
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Affiliation(s)
- Heydar Khadem
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
| | - Hoda Nemat
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
| | - Jackie Elliott
- Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2TN, UK
- Department of Diabetes and Endocrinology, Sheffield Teaching Hospitals, Sheffield S5 7AU, UK
| | - Mohammed Benaissa
- Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S10 2TN, UK
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27
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Zaplotnik J, Pišljar J, Škarabot M, Ravnik M. Neural networks determination of material elastic constants and structures in nematic complex fluids. Sci Rep 2023; 13:6028. [PMID: 37055564 PMCID: PMC10102156 DOI: 10.1038/s41598-023-33134-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/07/2023] [Indexed: 04/15/2023] Open
Abstract
Supervised machine learning and artificial neural network approaches can allow for the determination of selected material parameters or structures from a measurable signal without knowing the exact mathematical relationship between them. Here, we demonstrate that material nematic elastic constants and the initial structural material configuration can be found using sequential neural networks applied to the transmmited time-dependent light intensity through the nematic liquid crystal (NLC) sample under crossed polarizers. Specifically, we simulate multiple times the relaxation of the NLC from a random (qeunched) initial state to the equilibirum for random values of elastic constants and, simultaneously, the transmittance of the sample for monochromatic polarized light. The obtained time-dependent light transmittances and the corresponding elastic constants form a training data set on which the neural network is trained, which allows for the determination of the elastic constants, as well as the initial state of the director. Finally, we demonstrate that the neural network trained on numerically generated examples can also be used to determine elastic constants from experimentally measured data, finding good agreement between experiments and neural network predictions.
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Affiliation(s)
- Jaka Zaplotnik
- Faculty of Mathematics and Physics, University of Ljubljana, 1000, Ljubljana, Slovenia.
- Jožef Stefan Institute, 1000, Ljubljana, Slovenia.
| | - Jaka Pišljar
- Jožef Stefan Institute, 1000, Ljubljana, Slovenia
| | | | - Miha Ravnik
- Faculty of Mathematics and Physics, University of Ljubljana, 1000, Ljubljana, Slovenia
- Jožef Stefan Institute, 1000, Ljubljana, Slovenia
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28
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Patel K, Stark H. Fluid interfaces laden by force dipoles: towards active matter-driven microfluidic flows. SOFT MATTER 2023; 19:2241-2253. [PMID: 36912619 DOI: 10.1039/d3sm00043e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
In recent years, nonlinear microfluidics in combination with lab-on-a-chip devices has opened a new avenue for chemical and biomedical applications such as droplet formation and cell sorting. In this article, we integrate ideas from active matter into a microfluidic setting, where two fluid layers with identical densities but different viscosities flow through a microfluidic channel. Most importantly, the fluid interface is laden with active particles that act with dipolar forces on the adjacent fluids and thereby generate flows. We perform lattice-Boltzmann simulations and combine them with phase field dynamics of the interface and an advection-diffusion equation for the density of active particles. We show that only contractile force dipoles can destabilize the flat fluid interface. It develops a viscous finger from which droplets break up. For interfaces with non-zero surface tension, a critical value of activity equal to the surface tension is necessary to trigger the instability. Since activity depends on the density of force dipoles, the interface can develop steady deformation. Lastly, we demonstrate how to control droplet formation using switchable activity.
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Affiliation(s)
- Kuntal Patel
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany.
| | - Holger Stark
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany.
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29
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Grekov AN, Kabanov AA, Vyshkvarkova EV, Trusevich VV. Anomaly Detection in Biological Early Warning Systems Using Unsupervised Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:2687. [PMID: 36904891 PMCID: PMC10007031 DOI: 10.3390/s23052687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/15/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
The use of bivalve mollusks as bioindicators in automated monitoring systems can provide real-time detection of emergency situations associated with the pollution of aquatic environments. The behavioral reactions of Unio pictorum (Linnaeus, 1758) were employed in the development of a comprehensive automated monitoring system for aquatic environments by the authors. The study used experimental data obtained by an automated system from the Chernaya River in the Sevastopol region of the Crimean Peninsula. Four traditional unsupervised machine learning techniques were implemented to detect emergency signals in the activity of bivalves: elliptic envelope, isolation forest (iForest), one-class support vector machine (SVM), and local outlier factor (LOF). The results showed that the use of the elliptic envelope, iForest, and LOF methods with proper hyperparameter tuning can detect anomalies in mollusk activity data without false alarms, with an F1 score of 1. A comparison of anomaly detection times revealed that the iForest method is the most efficient. These findings demonstrate the potential of using bivalve mollusks as bioindicators in automated monitoring systems for the early detection of pollution in aquatic environments.
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Affiliation(s)
- Aleksandr N. Grekov
- Institute of Natural and Technical Systems, 299011 Sevastopol, Russia
- Department of Informatics and Control in Technical Systems, Sevastopol State University, 299053 Sevastopol, Russia
| | - Aleksey A. Kabanov
- Department of Informatics and Control in Technical Systems, Sevastopol State University, 299053 Sevastopol, Russia
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30
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Scott-Fordsmand JJ, Amorim MJB. Using Machine Learning to make nanomaterials sustainable. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160303. [PMID: 36410486 DOI: 10.1016/j.scitotenv.2022.160303] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/06/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.
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Affiliation(s)
| | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
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31
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Ji F, Wu Y, Pumera M, Zhang L. Collective Behaviors of Active Matter Learning from Natural Taxes Across Scales. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2203959. [PMID: 35986637 DOI: 10.1002/adma.202203959] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/23/2022] [Indexed: 06/15/2023]
Abstract
Taxis orientation is common in microorganisms, and it provides feasible strategies to operate active colloids as small-scale robots. Collective taxes involve numerous units that collectively perform taxis motion, whereby the collective cooperation between individuals enables the group to perform efficiently, adaptively, and robustly. Hence, analyzing and designing collectives is crucial for developing and advancing microswarm toward practical or clinical applications. In this review, natural taxis behaviors are categorized and synthetic microrobotic collectives are discussed as bio-inspired realizations, aiming at closing the gap between taxis strategies of living creatures and those of functional active microswarms. As collective behaviors emerge within a group, the global taxis to external stimuli guides the group to conduct overall tasks, whereas the local taxis between individuals induces synchronization and global patterns. By encoding the local orientations and programming the global stimuli, various paradigms can be introduced for coordinating and controlling such collective microrobots, from the viewpoints of fundamental science and practical applications. Therefore, by discussing the key points and difficulties associated with collective taxes of different paradigms, this review potentially offers insights into mimicking natural collective behaviors and constructing intelligent microrobotic systems for on-demand control and preassigned tasks.
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Affiliation(s)
- Fengtong Ji
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, 999077, China
| | - Yilin Wu
- Department of Physics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, 999077, China
| | - Martin Pumera
- Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 2172/15, Ostrava, 70800, Czech Republic
- Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Li Zhang
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, 999077, China
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32
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Li G, Zhu Y, Guo Y, Mabuchi T, Li D, Huang S, Wang S, Sun H, Tokumasu T. Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers. ACS APPLIED MATERIALS & INTERFACES 2023; 15:5099-5108. [PMID: 36652634 DOI: 10.1021/acsami.2c17198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid-liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.
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Affiliation(s)
- Gaoyang Li
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Yonghong Zhu
- School of Chemical Engineering, Northwest University, Xi'an710069Shaanxi, China
| | - Yuting Guo
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Takuya Mabuchi
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
- Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi980-8577, Japan
| | - Dong Li
- School of Chemical Engineering, Northwest University, Xi'an710069Shaanxi, China
| | - Shengfeng Huang
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Sirui Wang
- Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba263-8522, Japan
| | - Haiyi Sun
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Takashi Tokumasu
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
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33
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Joshi C, Ray S, Lemma LM, Varghese M, Sharp G, Dogic Z, Baskaran A, Hagan MF. Data-Driven Discovery of Active Nematic Hydrodynamics. PHYSICAL REVIEW LETTERS 2022; 129:258001. [PMID: 36608242 DOI: 10.1103/physrevlett.129.258001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 11/14/2022] [Indexed: 06/17/2023]
Abstract
Active nematics can be modeled using phenomenological continuum theories that account for the dynamics of the nematic director and fluid velocity through partial differential equations (PDEs). While these models provide a statistical description of the experiments, the relevant terms in the PDEs and their parameters are usually identified indirectly. We adapt a recently developed method to automatically identify optimal continuum models for active nematics directly from spatiotemporal data, via sparse regression of the coarse-grained fields onto generic low order PDEs. After extensive benchmarking, we apply the method to experiments with microtubule-based active nematics, finding a surprisingly minimal description of the system. Our approach can be generalized to gain insights into active gels, microswimmers, and diverse other experimental active matter systems.
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Affiliation(s)
- Chaitanya Joshi
- Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
- Department of Physics and Astronomy, Tufts University, Medford, Massachusetts 02155, USA
| | - Sattvic Ray
- Department of Physics, University of California at Santa Barbara, Santa Barbara, California 93106, USA
| | - Linnea M Lemma
- Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
- Department of Physics, University of California at Santa Barbara, Santa Barbara, California 93106, USA
| | - Minu Varghese
- Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109 USA
| | - Graham Sharp
- Department of Physics, University of California at Santa Barbara, Santa Barbara, California 93106, USA
| | - Zvonimir Dogic
- Department of Physics, University of California at Santa Barbara, Santa Barbara, California 93106, USA
| | - Aparna Baskaran
- Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
| | - Michael F Hagan
- Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA
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34
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Harrison D, Rorot W, Laukaityte U. Mind the matter: Active matter, soft robotics, and the making of bio-inspired artificial intelligence. Front Neurorobot 2022; 16:880724. [PMID: 36620483 PMCID: PMC9815774 DOI: 10.3389/fnbot.2022.880724] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 10/31/2022] [Indexed: 12/23/2022] Open
Abstract
Philosophical and theoretical debates on the multiple realisability of the cognitive have historically influenced discussions of the possible systems capable of instantiating complex functions like memory, learning, goal-directedness, and decision-making. These debates have had the corollary of undermining, if not altogether neglecting, the materiality and corporeality of cognition-treating material, living processes as "hardware" problems that can be abstracted out and, in principle, implemented in a variety of materials-in particular on digital computers and in the form of state-of-the-art neural networks. In sum, the matter in se has been taken not to matter for cognition. However, in this paper, we argue that the materiality of cognition-and the living, self-organizing processes that it enables-requires a more detailed assessment when understanding the nature of cognition and recreating it in the field of embodied robotics. Or, in slogan form, that the matter matters for cognitive form and function. We pull from the fields of Active Matter Physics, Soft Robotics, and Basal Cognition literature to suggest that the imbrication between material and cognitive processes is closer than standard accounts of multiple realisability suggest. In light of this, we propose upgrading the notion of multiple realisability from the standard version-what we call 1.0-to a more nuanced conception 2.0 to better reflect the recent empirical advancements, while at the same time averting many of the problems that have been raised for it. These fields are actively reshaping the terrain in which we understand materiality and how it enables, mediates, and constrains cognition. We propose that taking the materiality of our embodied, precarious nature seriously furnishes an important research avenue for the development of embodied robots that autonomously value, engage, and interact with the environment in a goal-directed manner, in response to existential needs of survival, persistence, and, ultimately, reproduction. Thus, we argue that by placing further emphasis on the soft, active, and plastic nature of the materials that constitute cognitive embodiment, we can move further in the direction of autonomous embodied robots and Artificial Intelligence.
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Affiliation(s)
- David Harrison
- Department of History and Philosophy of Science, University of Cambridge, Cambridge, United Kingdom,Leverhulme Centre for the Future of Intelligence, Cambridge, United Kingdom,Konrad Lorenz Institute for Evolution and Cognition Research, Vienna, Austria,*Correspondence: David Harrison
| | - Wiktor Rorot
- Human Interactivity and Language Lab, Faculty of Psychology, University of Warsaw, Warsaw, Poland,Wiktor Rorot
| | - Urte Laukaityte
- Department of Philosophy, University of California, Berkeley, Berkeley, CA, United States
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35
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Liu Y, Kelley KP, Vasudevan RK, Zhu W, Hayden J, Maria JP, Funakubo H, Ziatdinov MA, Trolier-McKinstry S, Kalinin SV. Automated Experiments of Local Non-Linear Behavior in Ferroelectric Materials. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2204130. [PMID: 36253123 DOI: 10.1002/smll.202204130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/19/2022] [Indexed: 06/16/2023]
Abstract
An automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function is developed and implemented. Here, the emergence of non-linear electromechanical responses in piezoresponse force microscopy (PFM) is explored. Non-linear responses in PFM can originate from multiple mechanisms, including intrinsic material responses often controlled by domain structure, surface topography that affects the mechanical phenomena at the tip-surface junction, and the presence of surface contaminants. Using an automated experiment to probe the origins of non-linear behavior in ferroelectric lead titanate (PTO) and ferroelectric Al0.93 B0.07 N films, it is found that PTO shows asymmetric nonlinear behavior across a/c domain walls and a broadened high nonlinear response region around c/c domain walls. In contrast, for Al0.93 B0.07 N, well-poled regions show high linear piezoelectric responses, when paired with low non-linear responses regions that are multidomain show low linear responses and high nonlinear responses. It is shown that formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses allows for establishing the preponderant physical mechanisms behind the non-linear behaviors, suggesting that automated experiments can potentially discern between competing physical mechanisms. This technique can also be extended to electron, probe, and chemical imaging.
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Affiliation(s)
- Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Kyle P Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Rama K Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Wanlin Zhu
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - John Hayden
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Jon-Paul Maria
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Dielectrics and Piezoelectrics, Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Hiroshi Funakubo
- Department of Material Science and Engineering, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
| | - Maxim A Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Susan Trolier-McKinstry
- Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
- Center for Dielectrics and Piezoelectrics, Materials Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, 37916, USA
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36
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Abstract
In the last 20 years, active matter has been a highly dynamic field of research, bridging fundamental aspects of non-equilibrium thermodynamics with applications to biology, robotics, and nano-medicine. Active matter systems are composed of units that can harvest and harness energy and information from their environment to generate complex collective behaviours and forms of self-organisation. On Earth, gravity-driven phenomena (such as sedimentation and convection) often dominate or conceal the emergence of these dynamics, especially for soft active matter systems where typical interactions are of the order of the thermal energy. In this review, we explore the ongoing and future efforts to study active matter in space, where low-gravity and microgravity conditions can lift some of these limitations. We envision that these studies will help unify our understanding of active matter systems and, more generally, of far-from-equilibrium physics both on Earth and in space. Furthermore, they will also provide guidance on how to use, process and manufacture active materials for space exploration and colonisation.
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37
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Gu J, Zhang K. Thermodynamics of the Ising Model Encoded in Restricted Boltzmann Machines. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1701. [PMID: 36554106 PMCID: PMC9777808 DOI: 10.3390/e24121701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/13/2022] [Accepted: 11/17/2022] [Indexed: 06/17/2023]
Abstract
The restricted Boltzmann machine (RBM) is a two-layer energy-based model that uses its hidden-visible connections to learn the underlying distribution of visible units, whose interactions are often complicated by high-order correlations. Previous studies on the Ising model of small system sizes have shown that RBMs are able to accurately learn the Boltzmann distribution and reconstruct thermal quantities at temperatures away from the critical point Tc. How the RBM encodes the Boltzmann distribution and captures the phase transition are, however, not well explained. In this work, we perform RBM learning of the 2d and 3d Ising model and carefully examine how the RBM extracts useful probabilistic and physical information from Ising configurations. We find several indicators derived from the weight matrix that could characterize the Ising phase transition. We verify that the hidden encoding of a visible state tends to have an equal number of positive and negative units, whose sequence is randomly assigned during training and can be inferred by analyzing the weight matrix. We also explore the physical meaning of the visible energy and loss function (pseudo-likelihood) of the RBM and show that they could be harnessed to predict the critical point or estimate physical quantities such as entropy.
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Affiliation(s)
- Jing Gu
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan 215300, China
| | - Kai Zhang
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan 215300, China
- Data Science Research Center (DSRC), Duke Kunshan University, Kunshan 215300, China
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38
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Botifoll M, Pinto-Huguet I, Arbiol J. Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook. NANOSCALE HORIZONS 2022; 7:1427-1477. [PMID: 36239693 DOI: 10.1039/d2nh00377e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.
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Affiliation(s)
- Marc Botifoll
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Ivan Pinto-Huguet
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Jordi Arbiol
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
- ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Catalonia, Spain
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39
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Yang X, Nguyen XC, Tran QB, Huyen Nguyen TT, Ge S, Nguyen DD, Nguyen VT, Le PC, Rene ER, Singh P, Raizada P, Ahamad T, Alshehri SM, Xia C, Kim SY, Le QV. Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water. ENVIRONMENTAL RESEARCH 2022; 214:113953. [PMID: 35934147 DOI: 10.1016/j.envres.2022.113953] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/01/2022] [Accepted: 07/19/2022] [Indexed: 05/27/2023]
Abstract
A popular approach to select optimal adsorbents is to perform parallel experiments on adsorbents based on an initially decided goal such as specified product purity, efficiency, or binding capacity. To screen optimal adsorbents, we focused on the max adsorption capacity of the candidates at equilibrium in this work because the adsorption capacity of each adsorbent is strongly dependent on certain conditions. A data-driven machine learning tool for predicting the max adsorption capacity (Qm) of 19 pharmaceutical compounds on 88 biochars was developed. The range of values of Qm (mean 48.29 mg/g) was remarkably large, with a high number of outliers and large variability. Modified biochars enhanced the Qm and surface area values compared with the original biochar, with a statistically significant difference (Chi-square value = 7.21-18.25, P < 0.005). K- nearest neighbors (KNN) was found to be the most optimal algorithm with a root mean square error (RMSE) of 23.48 followed by random forest and Cubist with RMSE of 26.91 and 29.56, respectively, whereas linear regression and regularization were the worst algorithms. KNN model achieved R2 of 0.92 and RMSE of 16.62 for the testing data. A web app was developed to facilitate the use of the KNN model, providing a reliable solution for saving time and money in unnecessary lab-scale adsorption experiments while selecting appropriate biochars for pharmaceutical adsorption.
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Affiliation(s)
- Xiaocui Yang
- Engineering Training Center, Nanjing Vocational University of Industry Technology, Nanjing, Jiangsu, 210023, China
| | - X Cuong Nguyen
- Center for Advanced Chemistry, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam; Faculty of Environmental Chemical Engineering, Duy Tan University, Da Nang, 550000, Vietnam.
| | - Quoc B Tran
- Center for Advanced Chemistry, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam; Faculty of Environmental Chemical Engineering, Duy Tan University, Da Nang, 550000, Vietnam
| | - T T Huyen Nguyen
- Faculty of Environment, The University of Danang-University of Science and Technology, Da Nang, 550000, Vietnam
| | - Shengbo Ge
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Materials Science and Engineering, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China
| | - D Duc Nguyen
- Department of Environmental Energy Engineering, Kyonggi University, Suwon, 442-760, Republic of Korea
| | - Van-Truc Nguyen
- Department of Environmental Sciences, Saigon University, Ho Chi Minh City, 700000, Vietnam
| | - Phuoc-Cuong Le
- Faculty of Environment, The University of Danang-University of Science and Technology, Da Nang, 550000, Vietnam
| | - Eldon R Rene
- Department of Environmental Engineering and Water Technology, IHE Delft Institute for Water Education, PO Box 3015, 2601 DA, Delft, the Netherlands
| | - Pardeep Singh
- School of Advanced Chemical Sciences, Shoolini University, Solan, Himachal Pradesh, 173212, India
| | - Pankaj Raizada
- School of Advanced Chemical Sciences, Shoolini University, Solan, Himachal Pradesh, 173212, India
| | - Tansir Ahamad
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Saad M Alshehri
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Changlei Xia
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Materials Science and Engineering, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China.
| | - Soo Young Kim
- Department of Materials Science and Engineering, Institute of Green Manufacturing Technology, Korea University, Seoul, 02841, Republic of Korea.
| | - Quyet Van Le
- Department of Materials Science and Engineering, Institute of Green Manufacturing Technology, Korea University, Seoul, 02841, Republic of Korea.
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40
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Yamamoto T, Cockburn K, Greco V, Kawaguchi K. Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks. PLoS Comput Biol 2022; 18:e1010477. [PMID: 36067226 PMCID: PMC9481156 DOI: 10.1371/journal.pcbi.1010477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 09/16/2022] [Accepted: 08/09/2022] [Indexed: 11/18/2022] Open
Abstract
Robustness in developing and homeostatic tissues is supported by various types of spatiotemporal cell-to-cell interactions. Although live imaging and cell tracking are powerful in providing direct evidence of cell coordination rules, extracting and comparing these rules across many tissues with potentially different length and timescales of coordination requires a versatile framework of analysis. Here we demonstrate that graph neural network (GNN) models are suited for this purpose, by showing how they can be applied to predict cell fate in tissues and utilized to infer the cell interactions governing the multicellular dynamics. Analyzing the live mammalian epidermis data, where spatiotemporal graphs constructed from cell tracks and cell contacts are given as inputs, GNN discovers distinct neighbor cell fate coordination rules that depend on the region of the body. This approach demonstrates how the GNN framework is powerful in inferring general cell interaction rules from live data without prior knowledge of the signaling involved. During development and homeostasis, cells coordinate with each other to grow, deform, and maintain the tissues. Even with the modern high-throughput cell profiling technologies and high-resolution microscopy, it is still challenging to infer how cell coordination affects the dynamics such as cell fate choice, due to the complexity of the problem and the limited methods to perform perturbation experiments. We here propose a versatile framework of analysis utilizing an interpretable machine learning method based on graph neural network (GNN) which infers the cell-to-cell interaction rules from live images of multicellular dynamics. From the spatiotemporal graphs generated from live images of skin stem cells, we identified previously unaddressed neighbor fate coupling as well as rules consistent with past findings. We further found distinct interaction rules in a different skin region of the body, indicating that our method is useful in probing the diverse mechanism behind the robustness and flexibility in multicellular systems. The GNN framework is applicable for interaction rule discovery for general multicellular dynamics as well as in a wide range of systems where modeling by stochastic interacting agents is effective.
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Affiliation(s)
- Takaki Yamamoto
- Nonequilibrium Physics of Living Matter RIKEN Hakubi Research Team, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- * E-mail: (TY); (KK)
| | - Katie Cockburn
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, United States of America
- Department of Biochemistry and Rosalind & Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada
| | - Valentina Greco
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, United States of America
- Departments of Cell Biology and Dermatology, Yale Stem Cell Center, Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Kyogo Kawaguchi
- Nonequilibrium Physics of Living Matter RIKEN Hakubi Research Team, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
- RIKEN Cluster for Pioneering Research, Kobe, Japan
- Universal Biology Institute, The University of Tokyo, Tokyo, Japan
- * E-mail: (TY); (KK)
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Wang H, Zou B, Su J, Wang D, Xu X. Variational methods and deep Ritz method for active elastic solids. SOFT MATTER 2022; 18:6015-6031. [PMID: 35920447 DOI: 10.1039/d2sm00404f] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Variational methods have been widely used in soft matter physics for both static and dynamic problems. These methods are mostly based on two variational principles: the variational principle of minimum free energy (MFEVP) and Onsager's variational principle (OVP). Our interests lie in the applications of these variational methods to active matter physics. In our former work [H. Wang, T. Qian and X. Xu, Soft Matter, 2021, 17, 3634-3653], we have explored the applications of OVP-based variational methods for the modeling of active matter dynamics. In the present work, we explore variational (or energy) methods that are based on MFEVP for static problems in active elastic solids. We show that MFEVP can be used not only to derive equilibrium equations, but also to develop approximate solution methods, such as the Ritz method, for active solid statics. Moreover, the power of the Ritz-type method can be further enhanced using deep learning methods if we use deep neural networks to construct the trial functions of the variational problems. We then apply these variational methods and the deep Ritz method to study the spontaneous bending and contraction of a thin active circular plate that is induced by internal asymmetric active contraction. The circular plate is found to be bent towards its contracting side. The study of such a simple toy system gives implications for understanding the morphogenesis of solid-like confluent cell monolayers. In addition, we introduce a so-called activogravity length to characterize the importance of gravitational forces relative to internal active contraction in driving the bending of the active plate. When the lateral plate dimension is larger than the activogravity length (about 100 micron), gravitational forces become important. Such gravitaxis behaviors at multicellular scales may play significant roles in the morphogenesis and in the up-down symmetry broken during tissue development.
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Affiliation(s)
- Haiqin Wang
- Physics Program, Guangdong Technion - Israel Institute of Technology, 241 Daxue Road, Shantou, Guangdong, 515063, China.
- Technion - Israel Institute of Technology, Haifa, 32000, Israel
| | - Boyi Zou
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
| | - Jian Su
- Physics Program, Guangdong Technion - Israel Institute of Technology, 241 Daxue Road, Shantou, Guangdong, 515063, China.
| | - Dong Wang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
- Shenzhen International Center for Industrial and Applied Mathematics, Shenzhen Research Institute of Big Data, Shenzhen, Guangdong, 518172, China
| | - Xinpeng Xu
- Physics Program, Guangdong Technion - Israel Institute of Technology, 241 Daxue Road, Shantou, Guangdong, 515063, China.
- Technion - Israel Institute of Technology, Haifa, 32000, Israel
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42
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A distributed nanocluster based multi-agent evolutionary network. Nat Commun 2022; 13:4698. [PMID: 35948574 PMCID: PMC9365837 DOI: 10.1038/s41467-022-32497-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/02/2022] [Indexed: 11/25/2022] Open
Abstract
As an important approach of distributed artificial intelligence, multi-agent system provides an efficient way to solve large-scale computational problems through high-parallelism processing with nonlinear interactions between the agents. However, the huge capacity and complex distribution of the individual agents make it difficult for efficient hardware construction. Here, we propose and demonstrate a multi-agent hardware system that deploys distributed Ag nanoclusters as physical agents and their electrochemical dissolution, growth and evolution dynamics under electric field for high-parallelism exploration of the solution space. The collaboration and competition between the Ag nanoclusters allow information to be effectively expressed and processed, which therefore replaces cumbrous exhaustive operations with self-organization of Ag physical network based on the positive feedback of information interaction, leading to significantly reduced computational complexity. The proposed multi-agent network can be scaled up with parallel and serial integration structures, and demonstrates efficient solution of graph and optimization problems. An artificial potential field with superimposed attractive/repulsive components and varied ion velocity is realized, showing gradient descent route planning with self-adaptive obstacle avoidance. This multi-agent network is expected to serve as a physics-empowered parallel computing hardware. Designing an efficient multi-agent hardware system to solve large-scale computational problems through high-parallelism processing with nonlinear interactions remains a challenge. Here, the authors demonstrate that a multi-agent hardware system deploying distributed Ag nanoclusters as physical agents enables parallel, complex computing.
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43
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Rassolov G, Tociu L, Fodor E, Vaikuntanathan S. From predicting to learning dissipation from pair correlations of active liquids. J Chem Phys 2022; 157:054901. [DOI: 10.1063/5.0097863] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Active systems, which are driven out of equilibrium by local non-conservative forces, can adopt unique behaviors and configurations. Towards designing such materials, an important challenge is to precisely connect the static structure of active systems to the dissipation of energy induced by the local driving. Here, we use tools from liquid-state theories and machine learning to take on these challenges. We first demonstrate analytically for an isotropic active matter system that dissipation and pair correlations are closely related when driving forces behave like an active temperature. We then extend a nonequilibrium mean-field framework for predicting these pair correlations which, unlike most existing approaches, is applicable even for strongly interacting particles and far from equilibrium, to predict dissipation in these systems. Based on this theory, we reveal analytically a robust relation between dissipation and structure which holds even as the system approaches a nonequilibrium phase transition. Finally, we construct a neural network which maps static configurations of particles to their dissipation rate without any prior knowledge of the underlying dynamics. Our results open novel perspectives on the interplay between dissipation and organization out-of-equilibrium.
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Affiliation(s)
| | - Laura Tociu
- The University of Chicago, United States of America
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44
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Boymelgreen A, Schiffbauer J, Khusid B, Yossifon G. Synthetic electrically driven colloids: a platform for understanding collective behavior in soft matter. Curr Opin Colloid Interface Sci 2022. [DOI: 10.1016/j.cocis.2022.101603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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45
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Autonomous environment-adaptive microrobot swarm navigation enabled by deep learning-based real-time distribution planning. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00482-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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46
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Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI. Sci Rep 2022; 12:7924. [PMID: 35562532 PMCID: PMC9106680 DOI: 10.1038/s41598-022-11997-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 04/12/2022] [Indexed: 12/05/2022] Open
Abstract
With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 70–75% (95% CI 0.48–0.89), and specificity of 71–79% (95% CI 0.52–0.90) on manual optimization, and an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 65–75% (95% CI 0.43–0.89) and specificity of 75–79% (95% CI 0.56–0.90) for automated machine learning. We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation.
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47
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LaChance J, Suh K, Clausen J, Cohen DJ. Learning the rules of collective cell migration using deep attention networks. PLoS Comput Biol 2022; 18:e1009293. [PMID: 35476698 PMCID: PMC9106212 DOI: 10.1371/journal.pcbi.1009293] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 05/13/2022] [Accepted: 03/23/2022] [Indexed: 11/18/2022] Open
Abstract
Collective, coordinated cellular motions underpin key processes in all multicellular organisms, yet it has been difficult to simultaneously express the ‘rules’ behind these motions in clear, interpretable forms that effectively capture high-dimensional cell-cell interaction dynamics in a manner that is intuitive to the researcher. Here we apply deep attention networks to analyze several canonical living tissues systems and present the underlying collective migration rules for each tissue type using only cell migration trajectory data. We use these networks to learn the behaviors of key tissue types with distinct collective behaviors—epithelial, endothelial, and metastatic breast cancer cells—and show how the results complement traditional biophysical approaches. In particular, we present attention maps indicating the relative influence of neighboring cells to the learned turning decisions of a ‘focal cell’–the primary cell of interest in a collective setting. Colloquially, we refer to this learned relative influence as ‘attention’, as it serves as a proxy for the physical parameters modifying the focal cell’s future motion as a function of each neighbor cell. These attention networks reveal distinct patterns of influence and attention unique to each model tissue. Endothelial cells exhibit tightly focused attention on their immediate forward-most neighbors, while cells in more expansile epithelial tissues are more broadly influenced by neighbors in a relatively large forward sector. Attention maps of ensembles of more mesenchymal, metastatic cells reveal completely symmetric attention patterns, indicating the lack of any particular coordination or direction of interest. Moreover, we show how attention networks are capable of detecting and learning how these rules change based on biophysical context, such as location within the tissue and cellular crowding. That these results require only cellular trajectories and no modeling assumptions highlights the potential of attention networks for providing further biological insights into complex cellular systems. Collective behaviors are crucial to the function of multicellular life, with large-scale, coordinated cell migration enabling processes spanning organ formation to coordinated skin healing. However, we lack effective tools to discover and cleanly express collective rules at the level of an individual cell. Here, we employ a carefully structured neural network to extract collective information directly from cell trajectory data. The network is trained on data from various systems, including canonical collective cell systems (HUVEC and MDCK cells) which display visually distinct forms of collective motion, and metastatic cancer cells (MDA-MB-231) which are highly uncoordinated. Using these trained networks, we can produce attention maps for each system, which indicate how a cell within a tissue takes in information from its surrounding neighbors, as a function of weights assigned to those neighbors. Thus for a cell type in which cells tend to follow the path of the cell in front, the attention maps will display high weights for cells spatially forward of the focal cell. We present results in terms of additional metrics, such as accuracy plots and number of interacting cells, and encourage future development of improved metrics.
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Affiliation(s)
- Julienne LaChance
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Kevin Suh
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Jens Clausen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Daniel J. Cohen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
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48
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Lee S, Jo J. Scale-invariant representation of machine learning. Phys Rev E 2022; 105:044306. [PMID: 35590591 DOI: 10.1103/physreve.105.044306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 03/22/2022] [Indexed: 06/15/2023]
Abstract
The success of machine learning has resulted from its structured representation of data. Similar data have close internal representations as compressed codes for classification or emerged labels for clustering. We observe that the frequency of internal codes or labels follows power laws in both supervised and unsupervised learning models. This scale-invariant distribution implies that machine learning largely compresses frequent typical data, and simultaneously, differentiates many atypical data as outliers. In this study, we derive the process by which these power laws can naturally arise in machine learning. In terms of information theory, the scale-invariant representation corresponds to a maximally uncertain data grouping among possible representations that guarantee a given learning accuracy.
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Affiliation(s)
- Sungyeop Lee
- Department of Physics and Astronomy, Seoul National University, Seoul 08826, Korea
| | - Junghyo Jo
- Department of Physics Education and Center for Theoretical Physics and Artificial Intelligence Institute, Seoul National University, Seoul 08826, Korea
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, Korea
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49
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Abstract
Progress in optical manipulation has stimulated remarkable advances in a wide range of fields, including materials science, robotics, medical engineering, and nanotechnology. This Review focuses on an emerging class of optical manipulation techniques, termed heat-mediated optical manipulation. In comparison to conventional optical tweezers that rely on a tightly focused laser beam to trap objects, heat-mediated optical manipulation techniques exploit tailorable optothermo-matter interactions and rich mass transport dynamics to enable versatile control of matter of various compositions, shapes, and sizes. In addition to conventional tweezing, more distinct manipulation modes, including optothermal pulling, nudging, rotating, swimming, oscillating, and walking, have been demonstrated to enhance the functionalities using simple and low-power optics. We start with an introduction to basic physics involved in heat-mediated optical manipulation, highlighting major working mechanisms underpinning a variety of manipulation techniques. Next, we categorize the heat-mediated optical manipulation techniques based on different working mechanisms and discuss working modes, capabilities, and applications for each technique. We conclude this Review with our outlook on current challenges and future opportunities in this rapidly evolving field of heat-mediated optical manipulation.
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Affiliation(s)
- Zhihan Chen
- Materials Science & Engineering Program, Texas Materials Institute, and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Jingang Li
- Materials Science & Engineering Program, Texas Materials Institute, and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Yuebing Zheng
- Materials Science & Engineering Program, Texas Materials Institute, and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
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50
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Schildknecht D, Popova AN, Stellwagen J, Thomson M. Reinforcement learning reveals fundamental limits on the mixing of active particles. SOFT MATTER 2022; 18:617-625. [PMID: 34929723 DOI: 10.1039/d1sm01400e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The control of far-from-equilibrium physical systems, including active materials, requires advanced control strategies due to the non-linear dynamics and long-range interactions between particles, preventing explicit solutions to optimal control problems. In such situations, Reinforcement Learning (RL) has emerged as an approach to derive suitable control strategies. However, for active matter systems, it is an important open question how the mathematical structure and the physical properties determine the tractability of RL. In this paper, we demonstrate that RL can only find good mixing strategies for active matter systems that combine attractive and repulsive interactions. Using analytic results from dynamical systems theory, we show that combining both interaction types is indeed necessary for the existence of mixing-inducing hyperbolic dynamics and therefore the ability of RL to find homogeneous mixing strategies. In particular, we show that for drag-dominated translational-invariant particle systems, mixing relies on combined attractive and repulsive interactions. Therefore, our work demonstrates which experimental developments need to be made to make protein-based active matter applicable, and it provides some classification of microscopic interactions based on macroscopic behavior.
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Affiliation(s)
- Dominik Schildknecht
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Anastasia N Popova
- Applied and Computational Mathematics, California Institute of Technology, Pasadena CA, USA
| | - Jack Stellwagen
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Matt Thomson
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
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