检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Kulce, Onur Mengu, Deniz Rivenson, Yair Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States California NanoSystems Institute University of California Los AngelesCA90095 United States

Spatially-engineered diffractive surfaces have emerged as a powerful framework to control light-matter interactions for e.g., statistical inference and the design of task-specific optical components. Here, we report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input (Ni) and output (No), where Ni and No represent the number of pixels at the input and output fields-of-view (FOVs), respectively. First, we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear transformation. In addition to this data-free design approach, we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target transformation. We compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven (deep learning-based) diffractive designs to all-optically perform (i) arbitrarily-chosen complex-valued transformations including unitary, nonunitary and noninvertible transforms, (ii) 2D discrete Fourier transformation, (iii) arbitrary 2D permutation operations, and (iv) high-pass filtered coherent imaging. Our analyses reveal that if the total number (N) of spatially-engineered diffractive features/neurons is ≥ Ni × No, both design methods succeed in all-optical implementation of the target transformation, achieving negligible error. However, compared to data-free designs, deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N i × No . These conclusions are generally applicable to various optical processors that employ spatially-engineered diffractive

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

引用

arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

Self-organization of mouse embryonic stem cells into reproducible pre-gastrulation embryo models via CRISPRa programming

引用

Cell Stem Cell 2025年第6期32卷 895-913.e8页

作者： Lodewijk, Gerrald A. Kozuki, Sayaka Han, Clara J. Topacio, Benjamin R. Lee, Seungho Nixon, Lily Zargari, Abolfazl Knight, Gavin Ashton, Randolph Qi, Lei S. Shariati, S. Ali Department of Biomolecular Engineering University of California Santa Cruz Santa Cruz CA United States Genomics Institute University of California Santa Cruz Santa Cruz CA United States Institute for The Biology of Stem Cells University of California Santa Cruz Santa Cruz CA United States Department of Electrical and Computer Engineering University of California Santa Cruz Santa Cruz CA United States Neurosetta LLC Madison WI United States Wisconsin Institute for Discovery Madison WI United States Department of Biomedical Engineering University of Wisconsin-Madison Madison WI United States Department of Bioengineering Stanford University Stanford CA United States Sarafan ChEM-H Stanford University Stanford CA United States Chan Zuckerberg Biohub San Francisco San Francisco CA United States

Embryonic stem cells (ESCs) can self-organize into structures with spatial and molecular similarities to natural embryos. During development, embryonic and extraembryonic cells differentiate through activation of endogenous regulatory elements while co-developing via cell-cell interactions. However, engineering regulatory elements to self-organize ESCs into embryo models remains underexplored. Here, we demonstrate that CRISPR activation (CRISPRa) of two regulatory elements near Gata6 and Cdx2 generates embryonic patterns resembling pre-gastrulation mouse embryos. Live single-cell imaging revealed that self-patterning occurs through orchestrated collective movement driven by cell-intrinsic fate induction. In 3D, CRISPRa-programmed embryo models (CPEMs) exhibit morphological and transcriptomic similarity to pre-gastrulation mouse embryos. CPEMs allow versatile perturbations, including dual Cdx2-Elf5 activation to enhance trophoblast differentiation and lineage-specific activation of laminin and matrix metalloproteinases, uncovering their roles in basement membrane remodeling and embryo model morphology. Our findings demonstrate that minimal intrinsic epigenome editing can self-organize ESCs into programmable pre-gastrulation embryo models with robust lineage-specific perturbation capabilities. © 2025 The Author(s)

关键词： Collective Motion CRISPRa embryo models embryonic patterning epigenome editing synthetic biology

来源：评论

学校读者我要写书评

暂无评论

Editorial: Autonomy and intelligence in neurorehabilitation robotic and prosthetic technologies

引用

Journal of Medical Robotics Research 2020年第1-2期5卷

作者： Farokh Atashzar, S. Tavakoli, Mahdi Farina, Dario Patel, Rajni V. Dept. of Electrical and Computer Engineering and Department of Mechanical and Aerospace Engineering New York University United States Department of Electrical and Computer Engineering University of Alberta Canada Department of Bioengineering Imperial College London United Kingdom Department of Electrical and Computer Engineering University of Western Ontario Canada

Neurorehabilitation robotic technologies and powered assistive prosthetic devices have shown great potential for accelerating motor recovery or compensating for the lost motor functions of disabled users. The functioning of these technologies relies on a highly-interactive bidirectional flow of information and physical energy between a human user and a robotic system. Thus, key factors are integrity, intelligence and quality of the interaction loops. As a result, research in this field has focused on (a) enhancing the quality and safety of the physical interaction between disabled users and robotic systems while providing a high level of intelligence and adaptability for generating assistive and therapeutic force fields;(b) detecting the user's motor intention with high spatiotemporal resolution to provide bidirectional human-machine interfacing;(c) promoting mental engagement through designing multimodal interactive interfaces and various sensory manipulation strategies. This Special Issue has collected papers that contribute to these three research areas, highlighting the importance of different aspects in human-robot interaction loops for augmenting the performance of neurorehabilitation robotic systems and prosthetic devices. © 2020 World Scientific Publishing Company.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Virtual Staining of Label-Free Tissue in Imaging Mass Spectrometry

arXiv

引用

arXiv 2024年

作者： Zhang, Yijie Huang, Luzhe Pillar, Nir Li, Yuzhu Migas, Lukasz G. Van de Plas, Raf Spraggins, Jeffrey M. Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Mass Spectrometry Research Center Vanderbilt University NashvilleTN37232 United States Delft Center for Systems and Control Delft University of Technology Delft2628 Netherlands Department of Cell and Developmental Biology Vanderbilt University NashvilleTN37232 United States Department of Chemistry Vanderbilt University NashvilleTN37232 United States Department of Biochemistry Vanderbilt University NashvilleTN37232 United States Department of Pathology Microbiology and Immunology Vanderbilt University Medical Center NashvilleTN37232 United States Department of Surgery University of California Los AngelesCA90095 United States

Imaging mass spectrometry (IMS) is a powerful tool for untargeted, highly multiplexed molecular mapping of tissue in biomedical research. IMS offers a means of mapping the spatial distributions of molecular species in biological tissue with unparalleled chemical specificity and sensitivity. However, most IMS platforms are not able to achieve microscopy-level spatial resolution and lack cellular morphological contrast, necessitating subsequent histochemical staining, microscopic imaging and advanced image registration steps to enable molecular distributions to be linked to specific tissue features and cell types. Here, we present a virtual histological staining approach that enhances spatial resolution and digitally introduces cellular morphological contrast into mass spectrometry images of label-free human tissue using a diffusion model. Blind testing on human kidney tissue demonstrated that the virtually stained images of label-free samples closely match their histochemically stained counterparts (with Periodic Acid-Schiff staining), showing high concordance in identifying key renal pathology structures despite utilizing IMS data with 10-fold larger pixel size. Additionally, our approach employs an optimized noise sampling technique during the diffusion model’s inference process to reduce variance in the generated images, yielding reliable and repeatable virtual staining. We believe this virtual staining method will significantly expand the applicability of IMS in life sciences and open new avenues for mass spectrometry-based biomedical research. Copyright © 2024, The Authors. All rights reserved.

关键词： Tissue

来源：评论

学校读者我要写书评

暂无评论

Electrowetting adaptive optical devices for LIDAR

Electrowetting adaptive optical devices for LIDAR

引用

Applied Industrial Optics: Spectroscopy, Imaging and Metrology, AIO 2019

作者： Gopinath, Juliet T. Zohrabi, Mo Supekar, Omkar D. Lim, Wei Yang Ozbay, Baris N. Futia, Gregory L. Restrepo, Diego Gibson, Emily A. Bright, Victor M. Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Department of Mechanical Engineering University of Colorado BoulderCO80309 United States Department of Bioengineering University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Cell and Developmental Biology University of Colorado Anschutz Medical Campus AuroraCO80045 United States

Electrowetting devices are compact, low power, transmissive, and adaptable. We have demonstrated nonmechanical beam steering in a LIDAR system and imaging in a multiphoton microscope, with an electrowetting prism. ... 详细信息

ISBN: (纸本)9781943580651

关键词： Wetting

来源：评论

学校读者我要写书评

暂无评论

Neurophysiological correlates of force control improvement induced by sinusoidal vibrotactile stimulation

Neurophysiological correlates of force control improvement i...

引用

作者： Germer, Carina Marconi Del Vecchio, Alessandro Negro, Francesco Farina, Dario Elias, Leonardo Abdala Neural Engineering Research Laboratory Department of Biomedical Engineering School of Electrical and Computer Engineering University of Campinas Campinas Brazil Neuromechanics and Rehabilitation Technology Group Faculty of Engineering Department of Bioengineering Imperial College London London United Kingdom Department of Clinical and Experimental Sciences Research Centre for Neuromuscular Function and Adapted Physical Activity Teresa Camplani Università Degli Studi di Brescia Brescia Italy Center for Biomedical Engineering University of Campinas Campinas Brazil

Objective. An optimal level of vibrotactile stimulation has been shown to improve sensorimotor control in healthy and diseased individuals. However, the underlying neurophysiological mechanisms behind the enhanced motor performance caused by vibrotactile stimulation are yet to be fully understood. Therefore, here we aim to evaluate the effect of a cutaneous vibration on the firing behavior of motor units in a condition of improved force steadiness. Approach. Participants performed a visuomotor task, which consisted of low-intensity isometric contractions of the first dorsal interosseous (FDI) muscle, while sinusoidal (175 Hz) vibrotactile stimuli with different intensities were applied to the index finger. High-density surface electromyogram was recorded from the FDI muscle, and a decomposition algorithm was used to extract the motor unit spike trains. Additionally, computer simulations were performed using a multiscale neuromuscular model to provide a potential explanation for the experimental findings. Main results. Experimental outcomes showed that an optimal level of vibration significantly improved force steadiness (estimated as the coefficient of variation of force). The decreased force variability was accompanied by a reduction in the variability of the smoothed cumulative spike train (as an estimation of the neural drive to the muscle), and the proportion of common inputs to the FDI motor nucleus. However, the interspike interval variability did not change significantly with the vibration. A mathematical approach, together with computer simulation results suggested that vibrotactile stimulation would reduce the variance of the common synaptic input to the motor neuron pool, thereby decreasing the low frequency fluctuations of the neural drive to the muscle and force steadiness. Significance. Our results demonstrate that the decreased variability in common input accounts for the enhancement in force control induced by vibrotactile stimulation. © 2020 IOP Publ

关键词： Muscle

来源：评论

学校读者我要写书评

暂无评论

Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks

arXiv

引用

arXiv 2022年

作者： Zhang, Yijie Huang, Luzhe Liu, Tairan Cheng, Keyi de Haan, Kevin Li, Yuzhu Bai, Bijie Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Department of Bioengineering University of California Los AngelesCA90095 United States California NanoSystems Institute University of California Los AngelesCA90095 United States Department of Mathematics University of California Los AngelesCA90095 United States Department of Surgery David Geffen School of Medicine University of California Los AngelesCA90095 United States

Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tissue. Standard virtual staining requires high autofocusing precision during the whole slide imaging of label-free tissue, which consumes a significant portion of the total imaging time and can lead to tissue photodamage. Here, we introduce a fast virtual staining framework that can stain defocused autofluorescence images of unlabeled tissue, achieving equivalent performance to virtual staining of in-focus label-free images, also saving significant imaging time by lowering the microscope's autofocusing precision. This framework incorporates a virtual-autofocusing neural network to digitally refocus the defocused images and then transforms the refocused images into virtually stained images using a successive network. These cascaded networks form a collaborative inference scheme: the virtual staining model regularizes the virtual-autofocusing network through a style loss during the training. To demonstrate the efficacy of this framework, we trained and blindly tested these networks using human lung tissue. Using 4× fewer focus points with 2× lower focusing precision, we successfully transformed the coarsely-focused autofluorescence images into high-quality virtually stained H&E images, matching the standard virtual staining framework that used finely-focused autofluorescence input images. Without sacrificing the staining quality, this framework decreases the total image acquisition time needed for virtual staining of a label-free whole-slide image (WSI) by ~32%, together with a ~89% decrease in the autofocusing time, and has the potential to eliminate the laborious and costly histochemical staining process in pathology. Copyright © 2022, The Authors. All rights reserved.

关键词： Tissue

来源：评论

学校读者我要写书评

暂无评论

Data class-specific all-optical transformations and encryption

arXiv

引用

arXiv 2022年

作者： Bai, Bijie Wei, Heming Yang, Xilin Mengu, Deniz Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los Angeles90095 United States Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China University of California Los AngelesCA United States

Diffractive optical networks provide rich opportunities for visual computing tasks since the spatial information of a scene can be directly accessed by a diffractive processor without requiring any digital pre-processing steps. Here we present data class-specific transformations all-optically performed between the input and output fields-of-view (FOVs) of a diffractive network. The visual information of the objects is encoded into the amplitude (A), phase (P), or intensity (I) of the optical field at the input, which is all-optically processed by a data class-specific diffractive network. At the output, an image sensor-array directly measures the transformed patterns, all-optically encrypted using the transformation matrices preassigned to different data classes, i.e., a separate matrix for each data class. The original input images can be recovered by applying the correct decryption key (the inverse transformation) corresponding to the matching data class, while applying any other key will lead to loss of information. The class-specificity of these all-optical diffractive transformations creates opportunities where different keys can be distributed to different users;each user can only decode the acquired images of only one data class, serving multiple users in an all-optically encrypted manner. We numerically demonstrated all-optical class-specific transformations covering A → A, I → I, and P → I transformations using various image datasets. We also experimentally validated the feasibility of this framework by fabricating a class-specific I → I transformation diffractive network using two-photon polymerization and successfully tested it at 1550 nm wavelength. Data class-specific all-optical transformations provide a fast and energy-efficient method for image and data encryption, enhancing data security and privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

来源：评论

学校读者我要写书评

暂无评论

Photonic Crystal Enhanced Emission and Blinking Suppression for Single Quantum Dot Digital Resolution Biosensing of Cancer-associated miRNA Biomarkers

Photonic Crystal Enhanced Emission and Blinking Suppression ...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Yanyu Xiong Qinglan Huang Taylor D. Canady Priyash Barya Shengyan Liu Opeyemi H. Arogundade Caitlin M. Race Xiaojing Wang Lifeng Zhou Xing Wang Manish Kohli Andrew M. Smith Brian T. Cunningham Department of Electrical and Computer Engineering Holonyak Micro and Nanotechnology Laboratory University of Illinois at Urbana−Champaign Urbana Illinois USA Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana−Champaign Urbana Illinois USA Department of Oncology Huntsman Cancer Institute Salt Lake City UT USA Department of Bioengineering University of Illinois at Urbana-Champaign Urbana Illinois USA Cancer Center at Illinois Urbana IL USA

We report 3,000-fold signal enhancement with blinking suppression capability and single QD sensitivity using low-NA lens. Applied toward miRNA detection assays, we achieve single-base mutation selectivity, and 10-atto... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Quantum dot lasers Sensitivity Photonic crystals Lasers and electrooptics Biomarkers Biosensors Electrooptical waveguides

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：