检索结果-内蒙古大学图书馆

arXiv 2018年

作者： You, Chenyu Li, Guang Zhang, Yi Zhang, Xiaoliu Shan, Hongming Ju, Shenghong Zhao, Zhen Zhang, Zhuiyang Cong, Wenxiang Vannier, Michael W. Saha, Punam K. Wang, Ge Departments of Bioengineering and Electrical Engineering Stanford University StanfordCA94305 United States Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States College of Computer Science Sichuan University Chengdu610065 China Department of Electrical and Computer Engineering University of Iowa Iowa CityIA52246 United States Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Medical School Southeast University Nanjing210009 China Department of Radiology Wuxi No.2 People's Hospital Wuxi214000 China Department of Radiology University of Chicago ChicagoIL60637 United States Department of Electrical and Computer Engineering and Radiology University of Iowa Iowa CityIA52246 United States

Computed tomography (CT) is widely used in screening, diagnosis, and image-guided therapy for both clinical and research purposes. Since CT involves ionizing radiation, an overarching thrust of related technical research is development of novel methods enabling ultrahigh quality imaging with fine structural details while reducing the X-ray radiation. In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from lowresolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this deep imaging process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the CT imaging performance, which limit its real-world applications by imposing considerable computational and memory overheads, we apply a parallel 11 CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. Quantitative and qualitative evaluations demonstrate that our proposed model is accurate, efficient and robust for superresolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three largescale CT datasets, and obtain promising results as compared to the other state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： computerized tomography

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M-indeterminate distributions in quantum mechanics and the non-overlapping wave function paradox

arXiv

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arXiv 2018年

作者： Sala Mayato, R. Loughlin, P. Cohen, L. Departamento de Física and IUdEA Universidad de la Laguna La Laguna Tenerife38203 Spain Departments of Bioengineering Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States Department of Physics Hunter College of the City Universiy of New York 695 Park Ave. New YorkNY10065 United States

We consider the non-overlapping wave function paradox of Aharanov et al., wherein the relative phase between two wave functions cannot be measured by the moments of position or momentum. We show that there is an unlimited number of other expectation values that depend on the phase. We further show that the Wigner distribution is M-indeterminate, that is, a distribution whose moments do not uniquely determine the distribution. We generalize to more than two non-overlapping functions. We consider arbitrary representations and show there is an unlimited number of M-indeterminate distributions. The dual case of non-overlapping momentum functions is also considered. Copyright © 2018, The Authors. All rights reserved.

关键词： Wave functions

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A simple planar focusing collector for concentrated solar power applications

A simple planar focusing collector for concentrated solar po...

引用

Optics for Solar Energy, OSE 2017

作者： Ding, Qing Jacobs, Kyle Choubal, Aakash Mensing, Glennys Zhang, Zhong Tirawat, Robert Zhu, Guangdong Wendelin, Tim Jay Guo, L. Ferreira, Placid Toussaint, Kimani C. University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2102 United States National Renewable Energy Lab GoldenCO80401 United States Affiliate in the departments of Electrical and Computer Engineering and Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We present a simple planar focusing collector, based on binary optics, for concentrated solar power. Fabrication is achieved using 3D two-photon lithography, and subsequent UV nanoimprinting tests for scalability. Opt... 详细信息

ISBN: (纸本)9781943580361

关键词： Solar energy

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

arXiv

引用

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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Extended field-of-view and increased-signal 3D holographic illumination with time-division multiplexing

Extended field-of-view and increased-signal 3D holographic i...

引用

Computational Optical Sensing and Imaging, COSI 2016

作者： Yang, Samuel J. Allen, William E. Kauvar, Isaac Andalman, Aaron S. Young, Noah P. Kim, Christina K. Marshel, James H. Wetzstein, Gordon Deisseroth, Karl Departments of Electrical Engineering Computer Science and Bioengineering Stanford University StanfordCA94305 United States

We both extend the field-of-view and increase the signal in two-photon computer generated holography by using galvanometer mirrors to time-sequentially reposition multiple 3D holograms. We apply this toward 3D two-pho... 详细信息

ISBN: (纸本)9781943580156

关键词： Galvanometers

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

arXiv

引用

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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The need to implement FAIR principles in biomolecular simulations

arXiv

引用

arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein Data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

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Multimodal multiphoton microscopy of carcinogenesis

Multimodal multiphoton microscopy of carcinogenesis

引用

Cancer Imaging and Therapy, CANCER 2016

作者： Boppart, Stephen A. Beckman Institute for Advanced Science and Technology Departments of Electrical and Computer Engineering Bioengineering Medicine University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Adaptively shaping supercontinuum pulses from a photonic crystal fiber source enables multimodal multiphoton microscopy for the longitudinal investigation of structural, metabolic, and molecular changes in carcinogene... 详细信息

ISBN: (纸本)9781943580101

关键词： Photonic crystal fibers

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Quantum bionanophotonics in life science and medicine

Quantum bionanophotonics in life science and medicine

引用

IEEE/LEOS International Conference on Optical MEMS

作者： Luke P. Lee Departments of Bioengineering Electrical Engineering & Computer Science UC Berkeley

Quantum bionanophotonics is making headways into the life sciences, enabling in-vivo interrogation at the molecular level and in-vitro applications for diagnostics and analysis. These and other emerging biomedical app... 详细信息

关键词： Plasmons Biomedical optical imaging Optical sensors Optical imaging Optical scattering Nanobioscience Ultrafast optics

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Multi-modality in gene regulatory networks with slow promoter kinetics

arXiv

引用

arXiv 2017年

作者： Al-Radhawi, M. Ali Vecchio, Domitilla Del Sontag, Eduardo D. Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139-4307 United States Departments of Bioengineering and of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Harvard Program in Therapeutic Science Harvard Medical School BostonMA02115 United States

Phenotypical variability in the absence of genetic variation often reflects complex energetic landscapes associated with underlying gene regulatory networks (GRNs). In this view, different phenotypes are associated with alternative states of complex nonlinear systems: stable attractors in deterministic models or modes of stationary distributions in stochastic descriptions. We provide theoretical and practical characterizations of these landscapes, specifically focusing on stochastic slow promoter kinetics, a time scale relevant when transcription factor binding and unbinding are affected by epigenetic processes like DNA methylation and chromatin remodeling. In this case, largely unexplored except for numerical simulations, adiabatic approximations of promoter kinetics are not appropriate. In contrast to the existing literature, we provide rigorous analytic characterizations of multiple modes. A general formal approach gives insight into the influence of parameters and the prediction of how changes in GRN wiring, for example through mutations or artificial interventions, impact the possible number, location, and likelihood of alternative states. We adapt tools from the mathematical field of singular perturbation theory to represent stationary distributions of Chemical Master Equations for GRNs as mixtures of Poisson distributions and obtain explicit formulas for the locations and probabilities of metastable states as a function of the parameters describing the system. As illustrations, the theory is used to tease out the role of cooperative binding in stochastic models in comparison to deterministic models, and applications are given to various model systems, such as toggle switches in isolation or in communicating populations, a synthetic oscillator and a trans-differentiation network. Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems

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