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Inverted scanning microwave microscope for in vitro imaging and characterization of biological cells

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arXiv 2019年

作者： Farina, Marco Jin, Xin Fabi, Gianluca Pavoni, Eleonora Di Donato, Andrea Mencarelli, Davide Morini, Antonio Piacenza, Francesco Hadi, Richard Al Zhao, Yan Pietrangelo, Tiziana Cheng, Xuanhong Hwang, James C.M. Dip. di Ingegneria dell'Informazione Universitá Politecnica Delle Marche Via Brecce Bianche Ancona60131 Italy Dept. of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Dept. of Neuroscience Imaging and Clinical Sciences University G d'Annunzio Chieti-Pescara ChietiI-66013 Italy Alcatera LLC UCLA Anderson Accelerator Los AngelesCA United States Advanced Technology Center for Aging Research Scientific Technological Area IRCCS INRCA Via Birarelli 8 Ancona60121 Italy

This paper presents for the first time an innovative instrument called an inverted scanning microwave microscope (iSMM), which is capable of noninvasive and label-free imaging and characterization of intracellular structures of a live cell on the nanometer scale. In particular, the iSMM is sensitive to not only surface structures, but also electromagnetic properties up to one micrometer below the surface. Conveniently, the iSMM can be constructed through straightforward conversion of any scanning probe microscope, such as the atomic force microscope or the scanning tunneling microscope, with a simple metal probe to outperform traditional SMM in terms of ruggedness, bandwidth, sensitivity and dynamic range. By contrast, the application of the traditional SMM to date has been limited to mainly surface physics and semiconductor technology, because the traditional SMM requires a fragile and expensive probe and is incompatible with saline solution or live biological cells. Copyright © 2019, The Authors. All rights reserved.

关键词： Scanning

A Large Scale Magneto-ionic Fluctuation in the Local Environment of Periodic Fast Radio Burst Source, FRB 20180916B

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arXiv 2022年

作者： McKinven, Ryan Gaensler, B.M. Michilli, D. Masui, K. Kaspi, V.M. Bhardwaj, M. Cassanelli, T. Chawla, P. Dong, F. Adam Fonseca, E. Leung, C. Li, D.Z. Ng, C. Patel, C. Petroff, E. Pearlman, A.B. Pleunis, Z. Rafiei-Ravandi, M. Rahman, M. Sand, K.R. Shin, K. Scholz, P. Stairs, I.H. Smith, K. Su, J. Tendulkar, S. Department of Physics McGill University 3600 rue University MontréalQCH3A 2T8 Canada McGill Space Institute McGill University 3550 rue University MontréalQCH3A 2A7 Canada Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada David A. Dunlap Department of Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Department of Physics Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Mit Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Dept. of Physics and Astronomy 6224 Agricultural Road VancouverBCV6T 1Z1 Canada Lane Department of Computer Science and Electrical Engineering 1220 Evansdale Drive PO Box 6109 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Cahill Center for Astronomy and Astrophysics California Institute of Technology 1216 E California Boulevard PasadenaCA91125 United States Sidrat Research PO Box 73527 RPO Wychwood TorontoONM6C 4A7 Canada Perimeter Institute for Theoretical Physics 31 Caroline Street N WaterlooONN2L 2Y5 Canada Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Mumbai400005 India National Centre for Radio Astrophysics Post Bag 3 Ganeshkhind Pune411007 India

Fast radio burst (FRB) source 20180916B exhibits a 16.33-day periodicity in its burst activity. It is as of yet unclear what proposed mechanism produces the activity, but polarization information is a key diagnostic. Here, we report on the polarization properties of 44 bursts from FRB 20180916B detected between 2018 December and 2021 December by CHIME/FRB, the FRB project on the Canadian Hydrogen Intensity Mapping Experiment the Canadian Hydrogen Intensity Mapping Experiment. In contrast to previous observations, we find significant variations in the Faraday rotation measure (RM) of FRB 20180916B. Over the nine month period 2021 April-2021 December we observe an apparent secular increase in RM of ∼ 50 radm-2(a fractional change of over 40%) that is accompanied by a possible drift of the emitting band to lower frequencies. This interval displays very little variation in the dispersion measure (ΔDM 0.8 pc cm-3) which indicates that the observed RM evolution is likely produced from coherent changes in the Faraday-active medium's magnetic field. Burst-to-burst RM variations appear unrelated to the activity cycle phase. The degree of linear polarization of our burst sample ( 80%) is consistent with the negligible depolarization expected for this source in the 400-800 MHz bandpass of CHIME. FRB 20180916B joins other repeating FRBs in displaying substantial RM variations between bursts. This is consistent with the notion that repeater progenitors may be associated with young stellar populations by their preferential occupation of dynamic magnetized environments commonly found in supernova remnants, pulsar wind nebulae or near high mass stellar companions. © 2022, CC BY.

关键词： Hydrogen

Online Graph-Adaptive Learning with Scalability and Privacy

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arXiv 2018年

作者： Shen, Yanning Leus, Geert Giannakis, Georgios B. Dept. of ECE and the Digital Technology Center University of Minnesota 117 Pleasant Str. SE MinneapolisMN55455 United States Electrical Engineering Mathematics and Computer Science Delft University of Technology CD Delft2826 Netherlands

Graphs are widely adopted for modeling complex systems, including financial, biological, and social networks. Nodes in networks usually entail attributes, such as the age or gender of users in a social network. However, real-world networks can have very large size, and nodal attributes can be unavailable to a number of nodes, e.g., due to privacy concerns. Moreover, new nodes can emerge over time, which can necessitate real-time evaluation of their nodal attributes. In this context, the present paper deals with scalable learning of nodal attributes by estimating a nodal function based on noisy observations at a subset of nodes. A multikernel-based approach is developed which is scalable to large-size networks. Unlike most existing methods that re-solve the function estimation problem over all existing nodes whenever a new node joins the network, the novel method is capable of providing real-time evaluation of the function values on newly-joining nodes without resorting to a batch solver. Interestingly, the novel scheme only relies on an encrypted version of each node’s connectivity in order to learn the nodal attributes, which promotes privacy. Experiments on both synthetic and real datasets corroborate the effectiveness of the proposed methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Function evaluation

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

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arXiv 2022年

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

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio (SNR) and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY.

关键词： Image acquisition

Multi-timescale online optimization of network function virtualization for service chaining

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arXiv 2018年

作者： Chen, Xiaojing Ni, Wei Chen, Tianyi Collings, Iain B. Wang, Xin Liu, Ren Ping Giannakis, Georgios B. Dept. of Communication Science and Engineering Shanghai Institute for Advanced Communication and Data Science Fudan University 220 Handan Road Shanghai China School of Engineering Macquarie University SydneyNSW2109 Australia SydneyNSW2122 Australia Dept. of Electrical and Computer Engineering and Digital Technology Center University of Minnesota MinneapolisMN55455 United States School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia

Network Function Virtualization (NFV) can costefficiently provide network services by running different virtual network functions (VNFs) at different virtual machines (VMs) in a correct order. This can result in strong couplings between the decisions of the VMs on the placement and operations of VNFs. This paper presents a new fully decentralized online approach for optimal placement and operations of VNFs. Building on a new stochastic dual gradient method, our approach decouples the realtime decisions of VMs, asymptotically minimizes the time-average cost of NFV, and stabilizes the backlogs of network services with a cost-backlog tradeoff of [o, 1/o], for any o > 0. Our approach can be relaxed into multiple timescales to have VNFs (re)placed at a larger timescale and hence alleviate service interruptions. While proved to preserve the asymptotic optimality, the larger timescale can slow down the optimal placement of VNFs. A learnand- A dapt strategy is further designed to speed the placement up with an improved tradeoff [o, log2(o)/po]. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 30% and reduce the queue length (or delay) by 83%, as compared to existing benchmarks. Copyright © 2018, The Authors. All rights reserved.

关键词： Virtual machine

Thermal pedestrian detection using block LBP with multi-level classifier

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Thermal pedestrian detection using block LBP with multi-leve...

2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Liu, You-Chen Huang, Shih-Shinh Lu, Ching-Hu Chang, Feng-Chia Lin, Pei-Yu Dept. of Computer and Communication Engineering National Kaohsiung First University of Science and Technology Taiwan Dept. of Electrical Engineering National Taiwan University of Science and Technology Taiwan National Chung-Shan Institute Science and Technology Taiwan Dept. of Information Communication Innovation Center for Big Data and Digital Convergence Yuan Ze University Taiwan

ISBN: (纸本)9781509048977

Pedestrian detection is an important topic in many applications, such as intelligent transportation systems (ITSs) or surveillance. For the purpose of applications used around the clock, the work for detecting pedestrian based on thermal sensors has attracted significant attention. To achieve this, this paper proposes a LBP (local binary pattern) encoded multi-level classifier for detecting pedestrians in thermal images. The proposed method consists of training and detection stages. The training stage firstly encodes the texture information of all training samples in all possible blocks by using LBP. For a particular block, the patches of all positive and negative samples with the same LBP code are collected to train a SVM (support vector machine) classifier for discrimination. HOGs (Histogram of Oriented Gradients) are used for the description of the patch appearance. By considering a set of learned SVMs for a particular block as a weak classifier, a set of discriminative weak classifiers is selected to form a final classifier (strong classifier) by using Adaboost. The experimental results on the constructed dataset demonstrate that the proposed method outperforms those in the literature. © 2017 IEEE.

关键词： Adaptive boosting

Unsupervised detection of epileptic seizures from EEG signals: A channel-specific analysis of long-term recordings

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Unsupervised detection of epileptic seizures from EEG signal...

2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018

作者： Tsiouris, Kostas M. Konitsiotis, Spiros Markoula, Sofia Rigas, Georgios Koutsouris, Dimitrios D. Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens AthensGR15773 Greece Department of Neurology Medical School University of Ioannina IoanninaGR45110 Greece University Hospital of Ioannina IoanninaGR45110 Greece Unit of Medical Technology and Intelligent Information Systems Dept. of Material Science and Engineering University of Ioannina IoanninaGR45110 Greece Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH University of Ioannina IoanninaGR45110 Greece

ISBN: (纸本)9781538624050

As long-term EEG recordings are becoming more and more frequent in clinical practice, the volume of EEG data that require annotation by clinical experts is growing exponentially, highlighting the need for automated epileptic seizure detection systems. Supervised methodologies based on machine learning techniques have been proposed as a solution, but they require annotated EEG data from multiple patients for training, while unsupervised methodologies, which do not have such limitations, are scarce in the literature. Thus, an unsupervised seizure detection methodology is developed to offer high seizure detection performance with substantial reduction in the time and effort required to inspect large volumes of EEG signals. The ictal rhythmical activity is detected by analyzing the spectral information of each EEG channel independently in order to find intensive accumulation of signal energy over the fundamental delta, theta and alpha frequency bands. The ictal rhythmical activity that is expressed during a seizure is detected using a set of four simple seizure detection conditions. The proposed methodology is validated using the public CHB-MIT EEG database, and the results suggest that an average sensitivity of 95.1% can be obtained with a false detection rate of 10.13 FP/h, while 95% of the each patient's EEG recordings is automatically rejected as non-ictal. © 2018 IEEE.

关键词： Neurophysiology

Corrigendum to "Design and implementation of a low-cost, tabletop MRI scanner for education and research prototyping" [J. Magn. Reson. 310 (2020) 106625]

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Journal of magnetic resonance (San Diego, Calif. : 1997) 2020年 317卷 106764页

作者： Clarissa Zimmerman Cooley Jason P Stockmann Thomas Witzel Cris LaPierre Azma Mareyam Feng Jia Maxim Zaitsev Wenhui Yang Wang Zheng Pascal Stang Greig Scott Elfar Adalsteinsson Jacob K White Lawrence L Wald A. A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA. Electronic address: clarissa@nmr.mgh.harvard.edu. Harvard Medical School Boston MA USA. A. A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Dept. of Radiology Medical Physics Medical Center University of Freiburg Faculty of Medicine University of Freiburg Freiburg Germany. Institute of Electrical Engineering Chinese Academy of Sciences Beijing China. Procyon Engineering San Jose CA USA Department of Electrical Engineering Stanford University Stanford CA USA. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Division of Health Sciences Technology Cambridge MA USA. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA. Harvard Medical School Boston MA USA

Tuning the performance of a computational persistent homology package

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arXiv 2018年

作者： Hylton, Alan Henselman-Petrusek, Gregory Sang, Janche Short, Robert Space Communications and Navigation NASA Glenn Research Center ClevelandOH United States Dept. of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Dept. of Elect. Eng. and Computer Science Cleveland State University ClevelandOH United States Dept. of Mathematics Lehigh University BethlehemPA United States

In recent years, persistent homology has become an attractive method for data analysis. It captures topological features, such as connected components, holes, and voids from point cloud data and summarizes the way in which these features appear and disappear in a filtration sequence. In this project, we focus on improving the performance of Eirene, a computational package for persistent homology. Eirene is a 5000-line open-source software library implemented in the dynamic programming language Julia. We use the Julia profiling tools to identify performance bottlenecks and develop novel methods to manage them, including the parallelization of some time-consuming functions on multicore/manycore hardware. Empirical results show that performance can be greatly improved. Copyright © 2018, The Authors. All rights reserved.

关键词： Open source software