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Self-Propelled Magnetic Dendrite-Shaped Microrobots for Photodynamic Prostate Cancer Therapy

Angewandte Chemie 2022年第48期134卷

作者： Xia Peng Mario Urso Jan Balvan Michal Masarik Martin Pumera Future Energy and Innovation Laboratory Central European Institute of Technology Brno University of Technology Purkynova 123 61200 Brno Czech Republic Contribution: Data curation (lead) Department of Physiology Faculty of Medicine Masaryk University Kamenice 5 62500 Brno Czech Republic Department of Pathological Physiology Faculty of Medicine Masaryk University Kamenice 5 62500 Brno Czech Republic BIOCEV First Faculty of Medicine Charles University Prumyslova 595 25250 Vestec Czech Republic Department of Chemistry and Biochemistry Mendel University Zemedelska 1 61300 Brno Czech Republic Department of Medical Research China Medical University Hospital China Medical University No. 91 Hsueh-Shih Road 40402 Taichung Taiwan Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava 17. listopadu 2172/15 70800 Ostrava Czech Republic Department of Chemical and Biomolecular Engineering Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul 03722 Korea

Photocatalytic micromotors that exhibit wireless and controllable motion by light have been extensively explored for cancer treatment by photodynamic therapy (PDT). However, overexpressed glutathione (GSH) in the tumor microenvironment can down-regulate the reactive oxygen species (ROS) level for cancer therapy. Herein, we present dendrite-shaped light-powered hematite microrobots as an effective GSH depletion agent for PDT of prostate cancer cells. These hematite microrobots can display negative phototactic motion under light irradiation and flexible actuation in a defined path controlled by an external magnetic field. Non-contact transportation of micro-sized cells can be achieved by manipulating the microrobot's motion. In addition, the biocompatible microrobots induce GSH depletion and greatly enhance PDT performance. The proposed dendrite-shaped hematite microrobots contribute to developing dual light/magnetic field-powered micromachines for the biomedical field.

关键词： Cargo Transportation Micromachines Micromotors Photocatalysis Tumor Cells

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Neural Signatures of Motor Skill in the Resting Brain

Neural Signatures of Motor Skill in the Resting Brain

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ozan Özdenizci Timm Meyer Felix Wichmann Jan Peters Bernhard Schölkopf Müjdat Çetin Moritz Grosse-Wentrup Department of Electrical and Computer Engineering Northeastern University Boston MA United States Max Planck Institute for Intelligent Systems Tübingen Germany Faculty of Science University of Tübingen and Bernstein Center for Computational Neuroscience Tübingen Tübingen Germany FG Intelligente Autonome Systeme Technische Universität Darmstadt Darmstadt Germany Signal Processing and Information Systems Laboratory Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey

Stroke-induced disturbances of large-scale cortical networks are known to be associated with the extent of motor deficits. We argue that identifying brain networks representative of motor behavior in the resting brain would provide significant insights for current neurorehabilitation approaches. Particularly, we aim to investigate the global configuration of brain rhythms and their relation to motor skill, instead of learning performance as broadly studied. We empirically approach this problem by conducting a three-dimensional physical space visuomotor learning experiment during electroencephalographic (EEG) data recordings with thirty-seven healthy participants. We demonstrate that across-subjects variations in average movement smoothness as the quantified measure of subjects' motor skills can be predicted from the global configuration of resting-state EEG alpha-rhythms (8-14 Hz) recorded prior to the experiment. Importantly, this neural signature of motor skill was found to be orthogonal to (independent of) task-as well as to learning-related changes in alpha-rhythms, which we interpret as an organizing principle of the brain. We argue that disturbances of such configurations in the brain may contribute to motor deficits in stroke, and that reconfiguring stroke patients' brain rhythms by neurofeedback may enhance post-stroke neurorehabilitation.

关键词： Electroencephalography End effectors Task analysis Integrated circuits Stroke (medical condition) Planning Visualization

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Application of Surface Plasmon Resonance Sensor with Polypyrrole Chitosan Graphene Oxide layer to Detect the Napropamide 5

Application of Surface Plasmon Resonance Sensor with Polypyr...

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5th International Conference on Fundamental and Applied sciences, ICFAS 2018

作者： Sadrolhosseini, Amir Reza Rashid, Suraya Abdul Soleimani, Hassan Shafie, Suhaidi Noor, A.S.M. Mohammadi, Ahmad Institute of Advanced Technology Universiti Putra Malaysia Serdang Selangor43400UPM Malaysia Materials Processing and Technology Laboratory Nanomaterials and Nanotechnology Group Institute of Advanced Technology Universiti Putra Malaysia Serdang Selangor43400UPM Malaysia Department of Fundamental and Applied Science Universiti Teknologi PETRONAS Seri Iskandar3175 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Selangor43400 UPM Malaysia Faculty of Engineering Universiti Putra Malaysia Serdang Selangor43400UPM Malaysia Department of Electrical and Computer Engineering Buein Zahra Technical University Buein Zahra Qazvin Iran

A polypyrrole Chitosan Graphene Oxide (PPy-Chi/GO) composite layer was prepared using the electro-chemical method. The PPy-Chi/GO layer was used to measure the low concentration of napropamide. In this study, the PPy-Chi/GO composite layer was deposited on the gold layer side of a microscope's glass slide in different thicknesses. The morphology, thickness, roughness, and refractive index of the layer were obtained using field effect scanning electron microscopy, profilemeter, atomic force microscopy, and the surface plasmon resonance technique. The structure of the composite layer was investigated using X-Ray diffraction. The peak that were observed from X-ray diffraction was at 9.9°, corresponding to GO, and the broad peak between 25° and 27° was related to polypyrrole and chitosan. The PPy-Chi/GO layer was used to measure the concentration of napropamide dissolved in hexane in the range of 0.01 to 100 ppm using the surface plasmon resonance technique;the limitation of sensor was 0.1 ppm. © Published under licence by IOP Publishing Ltd.

关键词： Polypyrroles

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Managing recurrent virtual network updates in multi-tenant datacenters: A system perspective

arXiv

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arXiv 2019年

作者： Liu, Zhuotao Cao, Yuan Zhang, Xuewu Zhu, Changping Zhang, Fan Electrical and Computer Engineering Department University of Illinois at Urbana-Champaign UrbanaIL61801 United States Google Inc. Mountain ViewCA94043 United States College of Internet of Things Engineering Hohai University Changzhou213022 China Key Laboratory of Unmanned Aerial Vehicle Development and Data Application of Anhui Higher Education Institutes Wanjiang Institute of Technology Anhui243031 China College of Information Science and Electronic Engineering Zhejiang University ZheJiang310027 China

With the advent of software-defined networking, network configuration through programmable interfaces becomes practical, leading to various on-demand opportunities for network routing update in multi-tenant datacenters, where tenants have diverse requirements on network routings such as short latency, low path inflation, large bandwidth, high reliability, etc. Conventional solutions that rely on topology search coupled with an objective function-https://***/project/5beb742041ab9c0e3caec84f to find desired routings have at least two shortcomings: (i) they run into scalability issues when handling consistent and frequent routing updates and (ii) they restrict the flexibility and capability to satisfy various routing requirements. To address these issues, this paper proposes a novel search and optimization decoupled design, which not only saves considerable topology search costs via search result reuse, but also avoids possible sub-optimality in greedy routing search algorithms by making decisions based on the global view of all possible routings. We implement a prototype of our proposed system, OpReduce, and perform extensive evaluations to validate its design goals. Copyright © 2019, The Authors. All rights reserved.

关键词： Network routing

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Load balancing for ultra-dense networks: A deep reinforcement learning based approach

arXiv

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arXiv 2019年

作者： Xu, Yue Xu, Wenjun Wang, Zhi Lin, Jiaru Cui, Shuguang Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering University of California DavisCA95616 United States

—In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense networks (UDNs). Our contribution is three-fold. First, this work proposes a two-layer architecture to solve the large-scale load balancing problem in a self-organized manner. The proposed architecture can alleviate the global traffic variations by dynamically grouping small cells into self-organized clusters according to their historical loads, and further adapt to local traffic variations through intra-cluster load balancing afterwards. Second, for the intra-cluster load balancing, this paper proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB policy under an asynchronous parallel learning framework, without any prior knowledge assumed over the underlying UDN environments. Moreover, the algorithm enables joint exploration with multiple behavior policies, such that the traditional MLB methods can be used to guide the learning process thereby improving the learning efficiency and stability. Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way. Empirical results verify that the proposed framework outperforms the existing MLB methods in general UDN environments featured with irregular network topologies, coupled interferences, and random user movements, in terms of the load balancing performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Brain age acceleration as biomarker of Alzheimer’s disease progression: Functional network connectivity analysis

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Alzheimer's & Dementia 2021年第S1期17卷

作者： Mohammad S.E. Sendi David H Salat Vince D Calhoun School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology Atlanta GA USA Tri-Institutional Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech Atlanta GA USA Neuroimaging Research for Veterans Center VA Boston Healthcare System Boston MA USA Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA Georgia State University Atlanta GA USA

Background The brain age gap, the difference between an individual’s brain predicted age and chronological age, is used as a brain disease biomarker and aging. To date, although previous studies utilized mostly structural magnetic resonance imaging (MRI) data to predict brain age, less work has used functional network connectivity (FNC) from resting-state functional MRI (rs-fMRI) to indicate brain age and its link with Alzheimer’s disease (AD) progression. This study aims to estimate brain age from FNC and introduce it as a biomarker of AD progression. Method This study used 1091 rs-fMRI data and the chronological age at the time of scanning, ranging from 42-95, from the Open Access Series of Imaging Studies (OASIS)-3 cohort. This dataset includes 1021 healthy subjects (HC) and 70 AD patients. We used group independent component analysis to estimate 53 components for the whole-brain. Then, FNC feature, in total 1378, of each subject was calculated by applying the Pearson correlation. A support vector regression (SVR) was trained on 951 HC subjects in which FNC features and the chronological age was served as predictor and prediction output, respectively. We then tested the trained model on 70 HC (age mean± sd: 73.66± 7.47) and 70 AD (age mean± sd: 73.34± 7.51) subjects to predict their brain age. Result The correlation between predicted brain age and the chronological age of HC test data was R=0.73, (p=5.3e -13 , N=70), while the correlation between predicted brain age and AD patients' chronological age was R=0.33 (p=0.004, N=70). This result shows that the model based on HC could better predict the HC age than the AD patients. The brain age gap mean and standard deviation for the test HC and AD group was -2.2581± 5.0879 and 2.0814± 7.4520. This result shows that the brain age gap was increased in AD (Cohen’s d=0.68, p<0.001). Conclusion We proved that FNC, estimated from rs-fMRI, could predict the brain age in HC. Also, we found an acceleration in the brain predict

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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Correction to: Simultaneous observation of auroral substorm onset in Polar satellite global images and ground-based all-sky images

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Earth, Planets and Space 2019年第1期71卷 1-3页

作者： Ieda, Akimasa Kauristie, Kirsti Nishimura, Yukitoshi Miyashita, Yukinaga Frey, Harald U. Juusola, Liisa Whiter, Daniel Nosé, Masahito Fillingim, Matthew O. Honary, Farideh Rogers, Neil C. Miyoshi, Yoshizumi Miura, Tsubasa Kawashima, Takahiro Machida, Shinobu Institute for Space-Earth Environmental Research Nagoya University Nagoya Japan Finnish Meteorological Institute Helsinki Finland Department of Atmospheric and Oceanic Sciences University of California Los Angeles USA Department of Electrical and Computer Engineering and Center for Space Physics Boston University Boston USA Korea Astronomy and Space Science Institute Daejeon South Korea Space Sciences Laboratory University of California Berkeley USA School of Physics and Astronomy University of Southampton Highfield UK Data Analysis Center for Geomagnetism and Space Magnetism Graduate School of Science Kyoto University Kyoto Japan Space and Planetary Physics Lancaster University Bailrigg UK

In the original publication of this article [1], some reference lines are missing in Fig. 5a and Fig. 6b(6). This correction shows the correct figures. The publisher apologizes to the readers and aut...

In the original publication of this article [1], some reference lines are missing in Fig. 5a and Fig. 6b(6). This correction shows the correct figures. The publisher apologizes to the readers and authors for the inconvenience.

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Roadmap on multimode light shaping

arXiv

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arXiv 2021年

作者： Piccardo, Marco Ginis, Vincent Forbes, Andrew Mahler, Simon Friesem, Asher A. Davidson, Nir Ren, Haoran Dorrah, Ahmed H. Capasso, Federico Dullo, Firehun T. Ahluwalia, Balpreet S. Ambrosio, Antonio Gigan, Sylvain Treps, Nicolas Hiekkamäki, Markus Fickler, Robert Kues, Michael Moss, David Morandotti, Roberto Riemensberger, Johann Kippenberg, Tobias J. Faist, Jérôme Scalari, Giacomo Picqué, Nathalie Hänsch, Theodor W. Cerullo, Giulio Manzoni, Cristian Lugiato, Luigi A. Brambilla, Massimo Columbo, Lorenzo Gatti, Alessandra Prati, Franco Shiri, Abbas Abouraddy, Ayman F. Alù, Andrea Galiffi, Emanuele Pendry, J.B. Huidobro, Paloma A. Center for Nano Science and Technology Fondazione Istituto Italiano di Tecnologia Milano Italy Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Data Lab/Applied Physics Vrije Universiteit Brussel Belgium School of Physics University of the Witwatersrand Johannesburg South Africa Department of Physics of Complex Systems Weizmann Institute of Science Rehovot Israel MQ Photonics Research Centre Department of Physics and Astronomy Macquarie University Macquarie ParkNSW Australia Department of Physics and Technology UiT-The Arctic University of Norway Tromsø Norway Laboratoire Kastler Brossel ENS-PSL Research University CNRS Sorbonne Université Collège de France Paris France Tampere University Photonics Laboratory Physics Unit Tampere Finland Institute of Photonics Leibniz University Hannover Germany Optical Sciences Centre Swinburne University of Technology Australia Energie Materiaux Telecommunications Institut National de la Recherche Scientifique Canada Lausanne Switzerland Institute for Quantum Electronics ETH Zurich Zürich Switzerland Max-Planck Institute of Quantum Optics Garching Germany Istituto di Fotonica e Nanotecnologia-CNR Dipartimento di Fisica Politecnico di Milano Milano Italy Dipartimento di Scienza e Alta Tecnologia Università dell'Insubria Como Italy Dipartimento di Fisica Interateneo and CNR-IFN Università e Politecnico di Bari Bari Italy Dipartimento di Elettronica e Telecomunicazioni Politecnico di Torino Torino Italy Istituto di Fotonica e Nanotecnologie IFN-CNR Milano Italy CREOL The College of Optics & Photonics University of Central Florida OrlandoFL United States Department of Electrical and Computer Engineering University of Central Florida OrlandoFL United States Advanced Science Research Center City University of New York United States The Blackett Laboratory Imperial College London London United Kingdom Instituto de Telecomunicações IST-University o

Our ability to generate new distributions of light has been remarkably enhanced in recent years. At the most fundamental level, these light patterns are obtained by ingeniously combining different electromagnetic modes. Interestingly, the modal superposition occurs in the spatial, temporal as well as spatio-temporal domain. This generalized concept of structured light is being applied across the entire spectrum of optics: generating classical and quantum states of light, harnessing linear and nonlinear light-matter interactions, and advancing applications in microscopy, spectroscopy, holography, communication, and synchronization. This Roadmap highlights the common roots of these different techniques and thus establishes links between research areas that complement each other seamlessly. We provide an overview of all these areas, their backgrounds, current research, and future developments. We highlight the power of multimodal light manipulation and want to inspire new eclectic approaches in this vibrant research community. Copyright © 2021, The Authors. All rights reserved.

关键词： Light

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

引用

arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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