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arXiv 2020年

作者： Lewis, Jacob S. Perrier, Timothy Barani, Zahra Kargar, Fariborz Balandin, Alexander A. Center University of California Riverside CA 92521 USA Materials Science and Engineering Program Bourns College of Engineering University of California Riverside CA 92521 US Department of Electrical and Computer Engineering Bourns College of Engineering University of California Riverside CA 92521 USA

We review the current state-of-the-art of graphene-enhanced thermal interface materials for the management of heat the next generation of electronics. Increased integration densities, speed, and power of electronic and optoelectronic devices require thermal interface materials with substantially higher thermal conductivity, improved reliability, and lower cost. Graphene has emerged as a promising filler material that can meet the demands of future high-speed and high-powered electronics. This review describes the use of graphene as a filler in curing and non-curing polymer matrices. Special attention is given to strategies for achieving the thermal percolation threshold with its corresponding characteristic increase in the overall thermal conductivity. Many applications require high thermal conductivity of the composites while simultaneously preserving electrical insulation. A hybrid filler graphene and boron nitride approach is presented as possible technology for independent control of electrical and thermal conduction. Reliability and lifespan performance of thermal interface materials is an important consideration towards the determination of appropriate practical applications. The present review addresses these issues in detail, demonstrating the promise of the graphene-enhanced thermal interface materials as compared to alternative technologies. Copyright © 2020, The Authors. All rights reserved.

关键词： III-V semiconductors

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Scalable Metagrating for Efficient Ultrasonic Focusing

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Physical Review Applied 2021年第6期16卷 064014-064014页

作者： Yan Kei Chiang Li Quan Yugui Peng Shahrokh Sepehrirahnama Sebastian Oberst Andrea Alù David A. Powell School of Engineering and Information Technology University of New South Wales Canberra ACT 2610 Australia Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Centre for Audio Acoustics and Vibration University of Technology Sydney Broadway NSW 2007 Australia Physics Program Graduate Center City University of New York New York New York 10016 USA

Acoustic metalenses have been pursued over the past decades due to their pivotal role in a wide variety of applications. Recent research efforts have demonstrated that, at ultrasonic regimes, acoustic levitation can be realized with standing waves, which are created by the interference between incoming and reflected focused waves. However, the conventional gradient-metasurface approach to focus ultrasonic waves is complex, leading to poor scalability. In this work, we propose a design principle for ultrasonic metalenses, based on metagratings—arrays of discrete scatters with coarser features than gradient metasurfaces. We achieve beam focusing by locally controlling the excitation of a single diffraction order with the use of metagratings, with geometry adiabatically varying over the lens aperture. We show that our metalens can effectively focus impinging ultrasonic waves to a focal point with a full width at half maximum of 0.364 of the wavelength. The focusing performance of the metalens is demonstrated experimentally, validating our proposed approach. This metagrating approach to focusing can be adopted for different operating frequencies by scaling the size of the structure, which has coarse features suitable for high-frequency designs, with potential applications ranging from biomedical science to nondestructive testing.

关键词： Acoustic measurements Acoustic metamaterials Acoustic modeling Acoustic wave phenomena Beam optics Ultrasonics Bloch-Floquet theorem Ultrasound techniques

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Field-driven metamorphoses of isolated skyrmions within the conical state of cubic helimagnets

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Physical Review B 2021年第6期104卷 064432-064432页

作者： Andrey O. Leonov C. Pappas Ivan I. Smalyukh Department of Chemistry Faculty of Science Hiroshima University Kagamiyama Higashi Hiroshima Hiroshima 739-8526 Japan Chirality Research Center Hiroshima University Higashi Hiroshima Hiroshima 739-8526 Japan IFW Dresden Postfach 270016 D-01171 Dresden Germany Faculty of Applied Sciences Delft University of Technology Mekelweg 15 2629JB Delft The Netherlands Soft Materials Research Center and Materials Science and Engineering Program University of Colorado Boulder Colorado 80309 USA Department of Physics and Department of Electrical Computer and Energy Engineering University of Colorado Boulder Colorado 80309 USA Renewable and Sustainable Energy Institute National Renewable Energy Laboratory and University of Colorado Boulder Colorado 80309 USA

Topologically stable field configurations appear in many fields of physics, from elementary particles to condensed matter and cosmology. Deep physical relations and common physical features of a large variety of very different solitonic states in these systems arise from the mathematical similarity of phenomenological equations. During the last decade, chiral liquid crystals and chiral magnets took on the role of model objects for the experimental investigation of topological solitons and the understanding of their nonsingular field configurations. This is directly related to the discovery of particle-like chiral skyrmions, which are also considered as promising ingredients for technological applications. Here we introduce a paradigm of facile skyrmionic networks with mutually orthogonal orientations of constituent isolated skyrmions. On the one hand, such networks are envisioned as a novel concept of spintronic devices based, e.g., on gapless skyrmion motion along each other, and are presumably responsible for precursor phenomena near the ordering temperatures of bulk cubic helimagnets. In particular, we demonstrate an interconversion between mutually orthogonal skyrmions: horizontal skyrmions may swirl into an intermediate spring-like state and subsequently squeeze into vertical skyrmions with both polarities. On the other hand, skyrmion tubes are considered as building blocks for particle-like states with more involved internal structure. A family of target-skyrmions, which includes a so far overlooked type with a multiple topological charge, is formed owing to the tendency to minimize the interaction energy between vertical and horizontal skyrmions. The conical phase serves as a suitable background for considered skyrmion evolution. It substantializes the attracting skyrmion-skyrmion interaction in the skyrmionic networks, shapes their internal structure, and guides the nucleation processes. Alternatively, intricate textural changes of isolated skyrmions result

关键词： Skyrmions Chiral magnets Cholesteric liquid crystals Helimagnets

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基于大语言模型的糖尿病管理:潜力与展望

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science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

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Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs

arXiv

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arXiv 2020年

作者： Zhou, Weimin Bhadra, Sayantan Brooks, Frank J. Li, Hua Anastasio, Mark A. Department of Electrical and Systems Engineering Department of Computer Science and Engineering Washington University in St. Louis St. LouisMO63130 United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Carle Foundation Hospital UrbanaIL61801 United States

It has been advocated that medical imaging systems and reconstruction algorithms should be assessed and optimized by use of objective measures of image quality that quantify the performance of an observer at specific diagnostic tasks. One important source of variability that can significantly limit observer performance is variation in the objects to-be-imaged. This source of variability can be described by stochastic object models (SOMs). A SOM is a generative model that can be employed to establish an ensemble of to-be-imaged objects with prescribed statistical properties. In order to accurately model variations in anatomical structures and object textures, it is desirable to establish SOMs from experimental imaging measurements acquired by use of a well-characterized imaging system. Deep generative neural networks, such as generative adversarial networks (GANs) hold great potential for this task. However, conventional GANs are typically trained by use of reconstructed images that are influenced by the effects of measurement noise and the reconstruction process. To circumvent this, an AmbientGAN has been proposed that augments a GAN with a measurement operator. However, the original AmbientGAN could not immediately benefit from modern training procedures, such as progressive growing, which limited its ability to be applied to realistically sized medical image data. To circumvent this, in this work, a new Progressive Growing AmbientGAN (ProAmGAN) strategy is developed for establishing SOMs from medical imaging measurements. Stylized numerical studies corresponding to common medical imaging modalities are conducted to demonstrate and validate the proposed method for establishing SOMs. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network

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Computational intelligence and neuroscience 2023年第1期2023卷 7717712页

作者： Hameedur Rahman Tanvir Fatima Naik Bukht Rozilawati Ahmad Ahmad Almadhor Abdul Rehman Javed Department of Computer Games Development Faculty of Computing and AI Air University E9 Islamabad Pakistan. Department of Computer Science Faculty of Computing and AI Air University E9 Islamabad Pakistan. Diagnostic Imaging and Radiotherapy Program Therapeutic and Investigation Studies Faculty of Health Sciences University Kebangsaan Malaysia Kuala Lumpur Malaysia. Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka 72388 Saudi Arabia. Department of Cyber Security Air University Islamabad Pakistan. Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon.

Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women's health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.

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Full-field spectral-domain optical coherence microscopy for snapshot three-dimensional imaging

Full-field spectral-domain optical coherence microscopy for ...

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2020 Frontiers in Optics Conference, FiO 2020

作者： Iyer, Rishyashring R. Zurauskas, Mantas Cui, Qi Gao, Liang Smith, R. Theodore Boppart, Stephen A. Beckman Institute for Advanced Science and Technology 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 Bioengineering University of California Los Angeles Los AngelesCA90095 United States Department of Ophthalmology New York Eye and Ear Infirmary of Mount Sinai New YorkNY10003 United States Carle Illinois College of Medicine University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We present a novel imaging setup for full-field spectral-domain optical coherence microscopy (OCM) that generates 3D complex-valued images of the scattered optical field with no moving parts for imaging biological sam... 详细信息

ISBN: (纸本)9781943580804

关键词： Imaging systems

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electrically powered locomotion of dual-nature colloidhedgehog and colloid-umbilic topological and elastic dipoles in liquid crystals

arXiv

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arXiv 2021年

作者： Senyuk, Bohdan Adufu, Richmond E. Smalyukh, Ivan I. Department of Physics University of Colorado BoulderCO80309 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Soft Materials Research Center Materials Science and Engineering Program University of Colorado BoulderCO80309 United States Chemical Physics Program Departments of Chemistry and Physics University of Colorado BoulderCO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado BoulderCO80309 United States

Colloidal particles in liquid crystals tend to induce topological defects and distortions of the molecular alignment within the surrounding anisotropic host medium, which results in elasticity-mediated interactions not accessible to their counterparts within isotropic fluid hosts. Such particle-induced coronae of perturbed nematic order are highly responsive to external electric fields, even when the uniformly aligned host medium away from particles exhibits no response to fields below the realignment threshold. Here we harness the non-reciprocal nature of these facile electric responses to demonstrate colloidal locomotion. Oscillations of electric field prompt repetitive deformations of the corona of dipolar elastic distortions around the colloidal inclusions, which at appropriately designed electric driving synchronize the displacement directions. We observe the colloid-hedgehog dipole accompanied by an umbilical defect in the tilt directionality field (c-field), along with the texture of elastic distortions that evolves with changing applied voltage. The temporal out-of-equilibrium evolution of the director and c-field distortions around particles upon turning voltage on and off is not invariant upon reversal of time, prompting lateral translations and interactions that markedly differ from what is accessible to these colloids under equilibrium conditions. Our findings may lead to both technological and fundamental science applications of nematic colloids as both model reconfigurable colloidal systems and as mesostructured materials with pre-designed temporal evolution of structure and composition. © 2021, CC BY-NC-ND.

关键词： Liquid crystals

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Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma

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CNS Oncology 2024年第1期13卷 2415285页

作者： Oshima, Sonoko Yao, Jingwen Bobholz, Samuel Nagaraj, Raksha Raymond, Catalina Teraishi, Ashley Guenther, Anna-Marie Kim, Asher Sanvito, Francesco Cho, Nicholas S C Eldred, Blaine S Connelly, Jennifer M Nghiemphu, Phioanh L Lai, Albert Salamon, Noriko Cloughesy, Timothy F LaViolette, Peter S Ellingson, Benjamin M UCLA Brain Tumor Imaging Laboratory (BTIL) Center for Computer Vision & Imaging Biomarkers University of California Los Angeles Los Angeles 90024 CA United States Department of Radiological Sciences David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States Department of Bioengineering Henry Samueli School of Engineering & Applied Science University of California Los Angeles Los Angeles 90024 CA United States Department of Radiology Medical College of Wisconsin Milwaukee 53226 WI United States Medical Scientist Training Program David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States UCLA Neuro-Oncology Program David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States Department of Neurology Medical College of Wisconsin Milwaukee 53226 WI United States Department of Neurology David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States Department of Biophysics Medical College of Wisconsin Milwaukee 53226 WI United States Department of Biomedical Engineering Medical College of Wisconsin Milwaukee 53226 WI United States Department of Psychiatry & Biobehavioral Sciences David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States Department of Neurosurgery David Geffen School of Medicine University of California Los Angeles Los Angeles 90024 CA United States

Aim: A radio-pathomic machine learning (ML) model has been developed to estimate tumor cell density, cytoplasm density (Cyt) and extracellular fluid density (ECF) from multimodal MR images and autopsy pathology. In this multicenter study, we implemented this model to test its ability to predict survival in patients with recurrent glioblastoma (rGBM) treated with chemotherapy. Methods: Pre- and post-contrast T₁-weighted, FLAIR and ADC images were used to generate radio-pathomic maps for 51 patients with longitudinal pre- and post-treatment scans. Univariate and multivariate Cox regression analyses were used to test the influence of contrast-enhancing tumor volume, total cellularity, mean Cyt and mean ECF at baseline, immediately post-treatment and the pre- and post-treatment rate of change in volume and cellularity on overall survival (OS). Results: Smaller Cyt and larger ECF after treatment were significant predictors of OS, independent of tumor volume and other clinical prognostic factors (HR = 3.23 × 10^-6, p < 0.001 and HR = 2.39 × 10⁵, p < 0.001, respectively). Both post-treatment volumetric growth rate and the rate of change in cellularity were significantly correlated with OS (HR = 1.17, p = 0.003 and HR = 1.14, p = 0.01, respectively). Conclusion: Changes in histological characteristics estimated from a radio-pathomic ML model are a promising tool for evaluating treatment response and predicting outcome in rGBM. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： MRI rad-path radiopathomic mapping recurrent glioblastoma survival tumor growth rate

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Airr Community Curation and Standardised Representation for Immunoglobulin and T Cell Receptor Germline Sets

SSRN

引用

SSRN 2022年

作者： Lees, William D. Christley, Scott Peres, Ayelet Kos, Justin T. Corrie, Brian Ralph, Duncan Breden, Felix Cowell, Lindsay G. Yaari, Gur Corcoran, Martin Karlsson Hedestam, Gunilla B. Ohlin, Mats Collins, Andrew M. Watson, Corey T. Busse, Christian E. Institute of Structural and Molecular Biology Birkbeck College London United Kingdom Human-Centered Computing and Information Science Institute for Systems and Computer Engineering Technology and Science Porto Portugal Peter O’Donnell Jr School of Public Health Department of Immunology UT Southwestern Medical Center Texas United States Bioengineering Program Faculty of Engineering Bar-Ilan University Ramat Gan Israel Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville Kentucky United States Department of Biological Sciences Simon Fraser University BurnabyBC Canada Fred Hutchinson Cancer Research Center SeattleWA United States Department of Microbiology Tumor and Cell Biology Karolinska Institute Stockholm Sweden Department of Immunotechnology Lund University Lund Sweden School of Biotechnology and Biomolecular Sciences University of New South Wales SydneyNSW Australia Division of B Cell Immunology German Cancer Research Center Heidelberg Germany

Analysis of an individual’s immunoglobulin or T cell receptor gene repertoire can provide important insights into immune function. High-quality analysis of adaptive immune receptor repertoire sequencing data depends upon accurate and relatively complete germline sets, but current sets are known to be incomplete. Established processes for the review and systematic naming of receptor germline genes and alleles require specific evidence and data types, but the discovery landscape is rapidly changing. To exploit the potential of emerging data, and to provide the field with improved state-of-the-art germline sets, an intermediate approach is needed that will allow the rapid publication of consolidated sets derived from these emerging sources. These sets must use a consistent naming scheme and allow refinement and consolidation into genes as new information emerges. Name changes should be minimised, but, where changes occur, the naming history of a sequence must be traceable. Here we outline the current issues and opportunities for the curation of germline IG/TR genes and present a forward-looking data model for building out more robust germline sets that can dovetail with current established processes and provides transparency based on principles of findability, accessibility, interoperability, and reusability. We describe results obtained by applying the data model and associated principles within the AIRR Community, including supporting software and databases. © 2022, The Authors. All rights reserved.

关键词： Genes

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