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Adaptive optics two-photon microscopy for in vivo imaging of mouse retina 6

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Adaptive optics two-photon microscopy for in vivo imaging of...

Adaptive Optics and Wavefront Control for Biological systems VI 2020

作者： Qin, Zhongya He, Sicong Chen, Congping Yang, Chao Yung, Jasmine Wu, Wanjie Leung, Christopher K. Liu, Kai Qu, Jianan Y. Biophotonics Research Laboratory Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Center of Systems Biology and Human Health School of Science and Institute for Advanced Study Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Division of Life Science Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781510632592

Non-invasive retinal imaging has greatly facilitated the research of eye disease and neurodegenerative disorders in the central nervous system (CNS). Two-photon microscopy is a powerful tool for in vivo imaging of mouse retina because it provides intrinsic optical sectioning capability and the infrared laser is less likely to excite the photoreceptors. However, the dilated mouse eye has large optical aberrations, which must be corrected to achieve high-resolution or even diffraction-limited imaging. Here, we developed an adaptive optics (AO) two-photon microscope for in vivo imaging of retinal neurons through the eyeball of living mouse. We used the two-photon excited fluorescence signal of retina as the guide star to measure and correct the aberration of mouse eye. After AO correction, the fluorescence signal was increased by at least fivefold and the fine structures such as axons of retinal ganglion cells (RGC) were clearly resolved. To take advantage of the non-invasive high-resolution imaging, we demonstrated functional calcium imaging of RGC responding to the light stimulations. © 2020 SPIE.

关键词： Adaptive optics

Towards new forms of particle sensing and manipulation and 3D imaging on a smartphone for healthcare applications

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arXiv 2020年

作者： Zhu, Jinlong Zhao, Ni Zhou, Renjie Photonic Systems Laboratory Holonyak Micro and Nanotechnology Laboratory Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electronic Engineering Chinese University of Hong Kong Shatin New Territories Hong Kong Department of Biomedical Engineering Chinese University of Hong Kong Shatin New Territories Hong Kong Shun Hing Institute of Advanced Engineering Chinese University of Hong Kong Shatin New Territories Hong Kong

Close to half of the world population have smartphones, while a typical flagship smartphone today has been integrated with more than 20 smart components and sensors, making a smartphone a highly integrated platform that can potentially mimic the five senses of humans. Recent advancement in achieving high compactness, high performance computing, high flexibility, and multiplexed functionality in smartphones have enabled them for many cutting-edge healthcare applications, such as single-molecule imaging, medical diagnosis, and biosensing, which were conventionally done with bulky and sophisticated devices. Most of the current healthcare applications are developed based on using the photon-sensitive components, such as CMOS sensors, flash & fill lights, lens modules, and LED lights in the screen, leaving the rest of the smart and high-performance sensors rarely explored. In this Perspective, we review recent progresses in advanced sensors in modern smartphones and discuss how those sensors have great, as yet unmet, promise to offer widespread and easy-to-implement solutions to many emerging healthcare applications, including nanoscale sensing, point-of-care testing, pollution monitoring, *** Codes 7811(Primary), 9201(Secondary) © 2020, CC-BY-NC-SA.

关键词： Smartphones

Broadening flat-passband DWDM filter design based on ring-assisted silicon asymmetric Mach Zehnder Interferometer

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Broadening flat-passband DWDM filter design based on ring-as...

Asia Communications and Photonics Conference

作者： Shuxian Li Haowen Shu Ming Jin Yuansheng Tao Xingjun Wang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

A silicon based 32-channel DWDM device composed of a modified ring-assisted Mach-Zehnder filter is demonstrated. The 0.5-dB passband is over 78% within a 1.6 nm (200 GHz) channel spacing.

ISBN: (纸本)9781943580705

A silicon based 32-channel DWDM device composed of a modified ring-assisted Mach-Zehnder filter is demonstrated. The 0.5-dB passband is over 78% within a 1.6 nm (200 GHz) channel spacing.

关键词： Crosstalk Dense wavelength division multiplexing Optical directional couplers Ring resonators Silicon Wavelength division multiplexing

Consistent Discretization of a Class of Predefined-Time Stable systems

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IFAC-PapersOnLine 2020年第2期53卷 628-633页

作者： Esteban Jiménez-Rodríguez Rodrigo Aldana-López Juan D. Sánchez-Torres David Gómez-Gutiérrez Alexander G. Loukianov Relativity6 Inc. Computer Science & Artificial Intelligence Lab Jalisco 45040 México Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza 50009 España Research Laboratory on Optimal Design Devices and Advanced Materials -OPTIMA- Department of Mathematics and Physics ITESO Jalisco 45604 México Multi-agent autonomous systems lab Intel Labs Intel Tecnología de México Jalisco 45019 Mexico Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Jalisco 45138 Mexico Department of Electrical Engineering Cinvestav-Guadalajara Jalisco 45019 México

As the main contribution, this document provides a consistent discretization of a class of fixed-time stable systems, namely predefined-time stable systems. In the unperturbed case, the proposed approach allows obtaining not only a consistent but exact discretization of the considered class of predefined-time stable systems, whereas in the perturbed case, the consistent discretization preserves the predefined-time stability property. All the results are validated through simulations and compared with the conventional explicit Euler scheme, highlighting the advantages of this proposal.

关键词： Predefined-time stability Discrete-time systems Digital implementation Stability of nonlinear systems Fixed-time stability

Experimental demonstration of an electrically tunable broadband coherent perfect absorber based on a graphene–electrolyte–graphene sandwich structure

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Photonics Research 2019年第8期7卷 868-874页

作者： JIN ZHANG XINGZHAN WEI MALIN PREMARATNE WEIREN ZHU Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai200240China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing400714China Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems EngineeringMonash UniversityClaytonVictoria3800Australia

We propose and experimentally demonstrate the operation of an electrically tunable, broadband coherent perfect absorption(CPA) at microwave frequencies by harnessing the CPA features of a graphene–electrolyte–graphene sandwich structure(GSS). Using both a simplified lumped circuit model and full-wave numerical simulation, it is found that the microwave coherent absorptivity of the GSS can be tuned dynamically from nearly 50% to 100% by changing the Fermi level of the graphene. Strikingly, our simplified lumped circuit model agrees very well with the full-wave numerical model, offering valuable insight into the CPA operation of the device. The angle dependency of coherent absorption in the GSS is further investigated, making suggestions for achieving CPA at wide angles up to 80°. To show the validity and accuracy of our theory and numerical simulations, a GSS prototype is fabricated and measured in a C-band waveguide system. The reasonably good agreement between the experimental and the simulated results confirms that the tunable coherent absorption in GSS can be electrically controlled by changing the Fermi level of the graphene.

关键词： GSS electrolyte Experimental demonstration

A perspective of twisted photonic structures

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arXiv 2021年

作者： Chen, Jialin Lin, Xiao Chen, Mingyuan Low, Tony Chen, Hongsheng Dai, Siyuan Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Materials Research and Education Center Department of Mechanical Engineering Auburn University AuburnAL36849 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Moiré superlattices-twisted van der Waals (vdW) structures with small angles-are attracting increasing attention in condensed matter physics, due to important phenomena revealed therein, including unconventional superconductivity, correlated insulating states, and ferromagnetism. Moiré superlattices are typically comprised of atomic layers of vdW materials where the exotic physics arises from the quantum electronic coupling between adjacent atomic layers. Recently, moiré electronics has motivated their photonic counterparts. In addition to vdW materials, twisted photonic systems can also be comprised of metamaterials, metasurfaces, and photonic crystals, mediated by interlayer electromagnetic coupling instead. The interplay between short-ranged interlayer quantum and long-ranged electromagnetic coupling in twisted structures are expected to yield rich phenomena in nano-optics. This perspective reviews recent progress in twisted structures for nanophotonics and outlooks emerging topics, opportunities, fundamental challenges, and potential applications. © 2021, CC BY.

关键词： Van der Waals forces

A novel and efficient tumor detection framework for pancreatic cancer via ct images

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arXiv 2020年

作者： Zhang, Zhengdong Li, Shuai Wang, Ziyang Lu, Yun State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery Affiliated Hospital of Qingdao University Qingdao266003 China

As Deep Convolutional Neural Networks (DCNNs) have shown robust performance and results in medical image analysis, a number of deep-learning-based tumor detection methods were developed in recent years. Nowadays, the automatic detection of pancreatic tumors using contrast-enhanced Computed Tomography (CT) is widely applied for the diagnosis and staging of pancreatic cancer. Traditional hand-crafted methods only extract low-level features. Normal convolutional neural networks, however, fail to make full use of effective context information, which causes inferior detection results. In this paper, a novel and efficient pancreatic tumor detection framework aiming at fully exploiting the context information at multiple scales is designed. More specifically, the contribution of the proposed method mainly consists of three components: Augmented Feature Pyramid networks, Self-adaptive Feature Fusion and a Dependencies Computation (DC) Module. A bottom-up path augmentation to fully extract and propagate low-level accurate localization information is established firstly. Then, the Self-adaptive Feature Fusion can encode much richer context information at multiple scales based on the proposed regions. Finally, the DC Module is specifically designed to capture the interaction information between proposals and surrounding tissues. Experimental results achieve competitive performance in detection with the AUC of 0.9455, which outperforms other state-of-the-art methods to our best of knowledge, demonstrating the proposed framework can detect the tumor of pancreatic cancer efficiently and accurately. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

MONAI: An open-source framework for deep learning in healthcare

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arXiv 2022年

作者： Cardoso, M. Jorge Li, Wenqi Brown, Richard Ma, Nic Kerfoot, Eric Wang, Yiheng Murrey, Benjamin Myronenko, Andriy Zhao, Can Yang, Dong Nath, Vishwesh He, Yufan Xu, Ziyue Hatamizadeh, Ali Zhu, Wentao Liu, Yun Zheng, Mingxin Tang, Yucheng Yang, Isaac Zephyr, Michael Hashemian, Behrooz Alle, Sachidanand Darestani, Mohammad Zalbagi Budd, Charlie Modat, Marc Vercauteren, Tom Wang, Guotai Li, Yiwen Hu, Yipeng Fu, Yunguan Gorman, Benjamin Johnson, Hans Genereaux, Brad Erdal, Barbaros S. Gupta, Vikash Diaz-Pinto, Andres Dourson, Andre Maier-Hein, Lena Jaeger, Paul F. Baumgartner, Michael Kalpathy-Cramer, Jayashree Flores, Mona Kirby, Justin Cooper, Lee A.D. Roth, Holger R. Xu, Daguang Bericat, David Floca, Ralf Zhou, S. Kevin Shuaib, Haris Farahani, Keyvan Maier-Hein, Klaus H. Aylward, Stephen Dogra, Prerna Ourselin, Sebastien Feng, Andrew School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom NVIDIA Corporation Santa Clara and Bethesda United States School of Electrical and Computer Engineering Rice University Houston United States Department of Engineering Science University of Oxford Oxford United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Department of BioAI InstaDeep Ltd London United Kingdom Department of Electrical & Computer Engineering University of Iowa Iowa City United States Mayo Clinic Jacksonville United States Mars Incorporated United States Div. Intelligent Medical Systems German Cancer Research Center Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Medical Physics Guy’s & St Thomas’ NHS Foundation Trust London United Kingdom National Cancer Institute Bethesda United States Kitware Inc. Clifton Park United States Department of Ophthalmology University of Colorado Aurora United States Frederick National Laboratory for Cancer Research Frederick United States Department of Pathology Northwestern University Chicago United States

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare. © 2022, CC BY.

关键词： Clinical research

Asymmetric Heat Transfer with Linear Conductive Metamaterials

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arXiv 2022年

作者： Su, Yishu Li, Ying Qi, Minghong Guenneau, Sebastien Li, Huagen Xiong, Jian Center for Composite Materials and Structures Harbin Institute of Technology Harbin150001 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining314400 China UMI 2004 Abraham de Moivre-CNRS Imperial College London LondonSW7 2AZ United Kingdom Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Asymmetric heat transfer systems, often referred to as thermal diodes or thermal rectifiers, have garnered increasing interest due to their wide range of application possibilities. Most of those previous macroscopic thermal diodes either resort to nonlinear thermal conductivities with strong temperature dependence that may be quite limited by or fixed in natural materials or rely on active modulation that necessitated auxiliary energy payloads. Here, we establish a straightforward strategy of passively realizing asymmetric heat transfer with linear conductive materials. The strategy also introduces a new interrogative perspective on the design of asymmetric heat transfer utilizing nonlinear thermal conductivity, correcting the misconception that thermal rectification is impossible with separable nonlinear thermal conductivity. The nonlinear perturbation mode can be versatilely engineered to produce an effective and wide-ranging perturbation in the heat conduction, which imitates and bypasses intrinsic thermal nonlinearity constraints set by naturally occurring counterparts. Independent experimental characterizations of surface thermal radiation and thermal convection verified that the heat exchange between a graded linear thermal metamaterial and the ambient can be tailored to achieve macroscopic asymmetric heat transfer. Our work is envisaged to inspire conceptual models for heat transfer control, serving as a robust and convenient platform for advanced thermal management, thermal computation, and heat transport. © 2022, CC BY.

关键词： Thermal conductivity