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arXiv 2018年

作者： Majeed, Hassaan Keikhosravi, Adib Kandel, Mikhail E. Nguyen, Tan H. Liu, Yuming Kajdacsy-Balla, Andre Tangella, Krishnarao Eliceiri, Kevin W. Popescu, Gabriel Lab Department of Bioengineering Beckman Institute of Advanced Science and Technology University of Illinois at Urbana Champaign 405 N. Matthews UrbanaIL61801 United States Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States Lab Department of Electrical and Computer Engineering Beckman Institute of Advanced Science and Technology University of Illinois at Urbana Champaign 405 N. Matthews UrbanaIL61801 United States Biomedical Optics and Biophotonics group Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02142 United States Department of Pathology University of Illinois at Chicago 840 South Wood Street ChicagoIL60612 United States Christie Clinic 1400 West Park Street UrbanaIL61801 United States

Tissue biopsy evaluation in the clinic is in need of quantitative disease markers for diagnosis and, most importantly, prognosis. Among the new technologies, quantitative phase imaging (QPI) has demonstrated promise for histopathology because it reveals intrinsic tissue nanoarchitecture through the refractive index. However, a vast majority of past QPI investigations have relied on imaging unstained tissues, which disrupts the established specimen processing. Here we present color spatial light interference microscopy (cSLIM) as a new whole slide imaging modality that performs interferometric imaging with a color detector array. As a result, cSLIM yields in a single scan both the intrinsic tissue phase map and the standard color bright-field image, familiar to the pathologist. Our results on 196 breast cancer patients indicate that cSLIM can provide not only diagnostic but also prognostic information from the alignment of collagen fibers in the tumor microenvironment. The effects of staining on the tissue phase maps were corrected by a simple mathematical normalization. These characteristics are likely to reduce barriers to clinical translation for the new cSLIM technology. Copyright © 2018, The Authors. All rights reserved.

关键词： Color

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Angular-Momentum Selectivity and Asymmetry in Highly Confined Wave Propagation along Sheath-Helical Metasurface Tubes

arXiv

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arXiv 2018年

作者： Mazor, Yarden Alù, Andrea Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10026 United States Department of Electrical Engineering City College of New York New YorkNY10031 United States

Highly confined surface waves present unique opportunities to enhance light interactions with localized emitters or molecules. Hyperbolic dispersion in metasurfaces allows us to tailor and manipulate surface waves, enhancing the local density of states over broad bandwidths. So far, propagation on this platform was mainly studied in planar geometries, which facilitates the analysis but somehow limits the realm of possibilities. Here we show that "wrapping" hyperbolic metasurfaces into tubes may greatly enrich the wave propagation dynamics along their axis. This system shows strong interaction with fields and sources carrying optical angular momentum, pronounced field asymmetries, and opens pathways to valley-specific excitation and routing. In addition, we demonstrate that various parameter regimes enable strong spin/helicity – momentum locking. Copyright © 2018, The Authors. All rights reserved.

关键词： Surface waves

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Non-reciprocal hyperbolic propagation over moving metasurfaces

arXiv

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arXiv 2018年

作者： Mazor, Yarden Alu, Andrea Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Department of Electrical Engineering City College of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10026 United States

Hyperbolic propagation offers exciting opportunities in nanophotonics, from sub-diffraction imaging to enhanced local density of states. This transport regime is typically induced by strong modulation of conductivity, i.e., with alternating metallic and dielectric material properties. Here, we analyze a moving impedance surface, showing that suitably tailored homogeneous metasurfaces can support one-way hyperbolic propagation when in motion, adding non-reciprocity to hyperbolic propagation phenomena, and without suffering from nonlocal effects stemming from discretization or finite granularity of the surface. Copyright © 2018, The Authors. All rights reserved.

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Molybdenum Disulfide: Scalable Fabrication of Molybdenum Disulfide Nanostructures and their Assembly (Adv. Mater. 43/2020)

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Advanced Materials 2020年第43期32卷

作者： Yun Huang Kang Yu Huaizhi Li Kai Xu Zexi Liang Debora Walker Paulo Ferreira Peer Fischer Donglei (Emma) Fan Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA The Department of Advanced Electron Microscopy Imaging and Spectroscopy International Iberian Nanotechnology Laboratory Braga 4715‐330 Portugal Electrical and Computer Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA Mechanical Engineering Department and IDMEC Instituto Superior Técnico University of Lisbon Av. Rovisco Pais Lisboa 1049‐001 Portugal Max‐Planck‐Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart 70569 Germany Institute of Physical Chemistry University of Stuttgart Pfaffenwaldring 55 Stuttgart 70569 Germany Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA

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Coherently Enhanced Wireless Power Transfer

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Physical Review Letters 2018年第14期120卷 143901-143901页

作者： Alex Krasnok Denis G. Baranov Andrey Generalov Sergey Li Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Department of Physics Chalmers University of Technology 412 96 Gothenburg Sweden Moscow Institute of Physics and Technology Dolgoprudny 141700 Russia Department of Electronics and Nanoengineering Aalto University 02150 Espoo Finland ITMO University St. Petersburg 197101 Russia Photonics Initiative Advanced Science Research Center City University of New York New York 10031 USA Physics Program Graduate Center City University of New York New York 10016 USA Department of Electrical Engineering City College of The City University of New York New York 10031 USA

Extraction of electromagnetic energy by an antenna from impinging external radiation is at the basis of wireless communications and wireless power transfer (WPT). The maximum of transferred energy is ensured when the antenna is conjugately matched, i.e., when it is resonant and it has an equal coupling with free space and its load. This condition, however, can be easily affected by changes in the environment, preventing optimal operation of a WPT system. Here, we introduce the concept of coherently enhanced WPT that allows us to bypass this difficulty and achieve dynamic control of power transfer. The approach relies on coherent excitation of the waveguide connected to the antenna load with a backward propagating signal of specific amplitude and phase. This signal creates a suitable interference pattern at the load resulting in a modification of the local wave impedance, which in turn enables conjugate matching and a largely increased amount of extracted energy. We develop a simple theoretical model describing this concept, demonstrate it with full-wave numerical simulations for the canonical example of a dipole antenna, and verify experimentally in both near-field and far-field regimes.

关键词： Electromagnetism engineering Light propagation, transmission & absorption Photonics

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Glial responses to implanted electrodes in the brain (vol 1, pg 862, 2017)

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NATURE BIOMEDICAL engineering 2018年第1期2卷 52-52页

作者： Salatino, Joseph W. Ludwig, Kip A. Kozai, Takashi D. Y. Purcell, Erin K. Department of Biomedical Engineering Michigan State University East Lansing MI USA. Institute for Quantitative Health Science and Engineering Michigan State University East Lansing MI USA. Department of Neurologic Surgery Mayo Clinic Rochester MN USA. Department of Bioengineering University of Pittsburgh Pittsburgh PA USA. Center for the Neural Basis of Cognition University of Pittsburgh Pittsburgh PA USA. McGowan Institute of Regenerative Medicine University of Pittsburgh Pittsburgh PA USA. Neurotech Center University of Pittsburgh Brain Institute Pittsburgh PA USA. Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA. Neuroscience Program Michigan State University East Lansing MI USA.

The use of implants that can electrically stimulate or record electrophysiological or neurochemical activity in nervous tissue is rapidly expanding. Despite remarkable results in clinical studies and increasing market approvals, the mechanisms underlying the therapeutic effects of neuroprosthetic and neuromodulation devices, as well as their side effects and reasons for their failure, remain poorly understood. A major assumption has been that the signal-generating neurons are the only important target cells of neural-interface technologies. However, recent evidence indicates that the supporting glial cells remodel the structure and function of neuronal networks and are an effector of stimulation-based therapy. Here, we reframe the traditional view of glia as a passive barrier, and discuss their role as an active determinant of the outcomes of device implantation. We also discuss the implications that this has on the development of bioelectronic medical devices.

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Structurally-sensitive multi-scale deep neural network for low-dose CT denoising

arXiv

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arXiv 2018年

作者： You, Chenyu Yang, Qingsong Shan, Hongming Gjesteby, Lars Li, Guang Ju, Shenghong Zhang, Zhuiyang Zhao, Zhen Zhang, Yi Cong, Wenxiang Wang, Ge Departments of Bioengineering and Electrical Engineering Stanford University StanfordCA94305 United States Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Medical School Southeast University Nanjing210009 China College of Computer Science Sichuan University Chengdu610065 China Department of Radiology Wuxi No.2 People's Hospital Wuxi214000 China

Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the x-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of Low-Dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that downgrade CT image quality. In this paper, we propose a novel 3D noise reduction method, called Structurally-sensitive Multi-scale Generative Adversarial Net (SMGAN), to improve the LDCT image quality. Specifically, we incorporate three-dimensional (3D) volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve structural and textural information in reference to normal-dose CT (NDCT) images, and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information, and outperforms competing methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Image quality

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Learning-based regularization for cardiac strain analysis with ability for domain adaptation

arXiv

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arXiv 2018年

作者： Lu, Allen Parajuli, Nripesh Zontak, Maria Stendahl, John Ta, Kevinminh Liu, Zhao Boutagy, Nabil Jeng, Geng-Shi Alkhalil, Imran Staib, Lawrence H. O’Donnell, Matthew Sinusas, Albert J. Duncan, James S. Department of Biomedical Engineering Yale University New HavenCT06511 United States Department of Electrical Engineering Yale University New HavenCT06511 United States College of Computer and Information Science Northeastern University SeattleWA United States Department of Bioengineering University of Washington SeattleWA98015 United States Department of Diagnostic Radiology Yale University New HavenCT06511 United States Department of Internal Medicine and Diagnostic Radiology Yale University New HavenCT06511 United States Departments of Diagnostic Radiology Biomedical Engineering and Electrical Engineering Yale University New HavenCT06511 United States

Reliable motion estimation and strain analysis using 3D+time echocardiography (4DE) for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. However, motion estimation is difficult due to the low-SNR that stems from the inherent image properties of 4DE, and intelligent regularization is critical for producing reliable motion estimates. In this work, we incorporated the notion of domain adaptation into a supervised neural network regularization framework. We first propose an unsupervised autoencoder network with biomechanical constraints for learning a latent representation that is shown to have more physiologically plausible displacements. We extended this framework to include a supervised loss term on synthetic data and showed the effects of biomechanical constraints on the network’s ability for domain adaptation. We validated both the autoencoder and semi-supervised regularization method on in vivo data with implanted sonomicrometers. Finally, we showed the ability of our semi-supervised learning regularization approach to identify infarcted regions using estimated regional strain maps with good agreement to manually traced infarct regions from postmortem excised hearts. Copyright © 2018, The Authors. All rights reserved.

关键词： Echocardiography

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Stretchable Electronics: Nylon Fabric Enabled Tough and Flaw Insensitive Stretchable Electronics (Adv. Mater. Technol. 4/2019)

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Advanced Materials Technologies 2019年第4期4卷

作者： Kyoseung Sim Yang Gao Zhou Chen Jizhou Song Cunjiang Yu Materials Science and Engineering Program University of Houston Houston TX 77204 USA Department of Mechanical Engineering University of Houston Houston TX 77204 USA School of Mechanical and Power Engineering East China University of Science and Technology Shanghai 200237 China Department of Engineering Mechanics Soft Matter Research Center and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou Zhejiang 310027 China Department of Electrical and Computer Engineering Department of Biomedical Engineering University of Houston Houston TX 77204 USA

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The overlapping burden of the three leading causes of disability and death in sub-Saharan African children

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Nature Communications 2022年第1期13卷 1-14页

作者： Reiner, Robert C. Welgan, Catherine A. Troeger, Christopher E. Baumann, Mathew M. Weiss, Daniel J. Deshpande, Aniruddha Blacker, Brigette F. Miller-Petrie, Molly K. Earl, Lucas Bhatt, Samir Abolhassani, Hassan Abosetugn, Akine Eshete Abu-Gharbieh, Eman Adekanmbi, Victor Adetokunboh, Olatunji O. Aghaali, Mohammad Aji, Budi Alahdab, Fares Al-Aly, Ziyad Alhassan, Robert Kaba Ali, Saqib Alizade, Hesam Aljunid, Syed Mohamed Almasi-Hashiani, Amir Al-Mekhlafi, Hesham M. Altirkawi, Khalid A. Alvis-Guzman, Nelson Amare, Azmeraw T. Amini, Saeed Amugsi, Dickson A. Ancuceanu, Robert Andrei, Catalina Liliana Ansari, Fereshteh Anvari, Davood Appiah, Seth Christopher Yaw Arabloo, Jalal Aremu, Olatunde Atout, Maha Moh’d Wahbi Ausloos, Marcel Ausloos, Floriane Ayanore, Martin Amogre Aynalem, Yared Asmare Azene, Zelalem Nigussie Badawi, Alaa Baig, Atif Amin Banach, Maciej Bedi, Neeraj Bhagavathula, Akshaya Srikanth Bhandari, Dinesh Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bhutta, Zulfiqar A. Bijani, Ali Birhanu, Tesega Tesega Mengistu Bitew, Zebenay Workneh Boloor, Archith Brady, Oliver J. Butt, Zahid A. Car, Josip Carvalho, Felix Casey, Daniel C. Chattu, Vijay Kumar Chowdhury, Mohiuddin Ahsanul Kabir Chu, Dinh-Toi Coelho, Camila H. Cook, Aubrey J. Damiani, Giovanni Daoud, Farah Gela, Jiregna Darega Darwish, Amira Hamed Daryani, Ahmad Das, Jai K. Davis Weaver, Nicole Deribe, Kebede Desalew, Assefa Dharmaratne, Samath Dhamminda Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Dorostkar, Fariba Dubljanin, Eleonora Duko, Bereket Dwyer-Lindgren, Laura Effiong, Andem El Sayed Zaki, Maysaa El Tantawi, Maha Enany, Shymaa Fattahi, Nazir Feigin, Valery L. Fernandes, Eduarda Ferrara, Pietro Fischer, Florian Foigt, Nataliya A. Folayan, Morenike Oluwatoyin Foroutan, Masoud Frostad, Joseph Jon Fukumoto, Takeshi Gaidhane, Abhay Motiramji Gebrekrstos, Hailemikael Gebrekidan G. K. Gebremeskel, Leake Gebreslassie, Assefa Ayalew Gething, Peter W. Gezae, Kebede Embaye Ghadiri, Keyghobad Ghashghaee, Ahmad Golechha, Mahaveer Gubar Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Malaria Atlas Project University of Oxford Oxford United Kingdom Imperial College London London United Kingdom Department of Laboratory Medicine Karolinska University Hospital Huddinge Sweden Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Public Health Debre Berhan University Debre Berhan Ethiopia Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Population Health Sciences King’s College London London United Kingdom Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Stellenbosch South Africa Department of Global Health Stellenbosch University Cape Town South Africa Department of Epidemiology and Biostatistics Qom University of Medical Sciences Qom Iran Faculty of Medicine and Public Health Jenderal Soedirman University Purwokerto Indonesia Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center Department of Veterans Affairs St Louis MO United States Institute of Health Research University of Health and Allied Sciences Ho Ghana Department of Information Systems College of Economics and Political Science Sultan Qaboos University Muscat Oman Infectious and Tropical Disease Research Center Hormozgan University of Medical Sciences Bandar Abbas Iran Department of Health Policy and Management Kuwait University Safat Kuwait International Centre for Casemix and Clinical Coding National University of Malaysia Bandar Tun Razak Malaysia Department of Epidemiology Arak University of Medical Sciences Arak Iran Medical Research Center

Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (under 5), primarily in sub-Saharan Africa (SSA). The spatial burden of each of these diseases has been estimated subnationally across SSA, yet no prior analyses have examined the pattern of their combined burden. Here we synthesise subnational estimates of the burden of LRIs, diarrhoea, and malaria in children under-5 from 2000 to 2017 for 43 sub-Saharan countries. Some units faced a relatively equal burden from each of the three diseases, while others had one or two dominant sources of unit-level burden, with no consistent pattern geographically across the entire subcontinent. Using a subnational counterfactual analysis, we show that nearly 300 million DALYs could have been averted since 2000 by raising all units to their national average. Our findings are directly relevant for decision-makers in determining which and targeting where the most appropriate interventions are for increasing child survival. © 2022, The Author(s).

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