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Shape Anisotropy-Governed High-Performance Nanomagnetosol for In Vivo Magnetic Particle Imaging of Lungs

Small 2024年第5期20卷 2305300-2305300页

作者： Nigam, Saumya Mohapatra, Jeotikanta Makela, Ashley V. Hayat, Hanaan Rodriguez, Jessi Mercedes Sun, Aixia Kenyon, Elizabeth Redman, Nathan A. Spence, Dana Jabin, George Gu, Bin Ashry, Mohamed Sempere, Lorenzo F. Mitra, Arijit Li, Jinxing Chen, Jiahui Wei, Guo-Wei Bolin, Steven Etchebarne, Brett Liu, J. Ping Contag, Christopher H. Wang, Ping Precision Health Program Michigan State University East LansingMI48824 United States Department of Radiology College of Human Medicine Michigan State University East LansingMI48824 United States Department of Physics The University of Texas at Arlington ArlingtonTX76019 United States Michigan State University East LansingMI48824 United States Department of Biomedical Engineering College of Engineering Michigan State University East LansingMI48824 United States Human Biology Program College of Natural Science Michigan State University East LansingMI48824 United States Department of Obstetrics Gynecology and Reproductive Sciences College of Human Medicine Michigan State University East LansingMI48824 United States Department of Materials Science and Engineering National Cheng Kung University Tainan City701 Taiwan Department of Mathematics College of Natural Science Michigan State U niversity East LansingMI48824 United States Department of Electrical and Computer Engineering College of Engineering Michigan State University East LansingMI48824 United States Department of Biochemistry and Molecular Biology College of Natural Science Michigan State University East LansingMI48824 United States Department of Pathobiology and Diagnostic Investigation College of Veterinary Medicine Michigan State University East LansingMI48824 United States Osteopathic Medical Specialties College of Osteopathic Medicine Michigan State University East LansingMI48824 United States Department of Microbiology and Molecular Genetics Michigan State University East LansingMI48824 United States

Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has shown extensive lung manifestations in vulnerable individuals, putting lung imaging and monitoring at the forefront of early detection and treatment. Magnetic particle imaging (MPI) is an imaging modality, which can bring excellent contrast, sensitivity, and signal-to-noise ratios to lung imaging for the development of new theranostic approaches for respiratory diseases. Advances in MPI tracers would offer additional improvements and increase the potential for clinical translation of MPI. Here, a high-performance nanotracer based on shape anisotropy of magnetic nanoparticles is developed and its use in MPI imaging of the lung is demonstrated. Shape anisotropy proves to be a critical parameter for increasing signal intensity and resolution and exceeding those properties of conventional spherical nanoparticles. The 0D nanoparticles exhibit a 2-fold increase, while the 1D nanorods have a > 5-fold increase in signal intensity when compared to VivoTrax. Newly designed 1D nanorods displayed high signal intensities and excellent resolution in lung images. A spatiotemporal lung imaging study in mice revealed that this tracer offers new opportunities for monitoring disease and guiding intervention. © 2023 The Authors. Small published by Wiley-VCH GmbH.

关键词： Nanorods

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Reduce Fingerprint Construction for Positioning IoT Devices Based on Generative Adversarial Nets 1

Reduce Fingerprint Construction for Positioning IoT Devices ...

引用

1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Tarekegn, Getaneh Berie Juang, Rong-Terng Lin, Hsin-Piao Munaye, Yirga Yayeh Belay Adege, Abebe National Taipei University of Technology Department of International Graduate Program in Electrical Engineering and Computer Science Taipei Taiwan Feng Chia University Department of Electronic Engineering Taichung Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei Taiwan Debre Markos University Department of Information Technology Debre Markos Ethiopia

ISBN: (纸本)9781665404839

Fingerprint-based positioning is popular and applicable for Internet of Things (IoT) applications to offer seamless, intelligent and adaptive location-aware services for IoT devices. However, it takes time and cost to build the radio-map. This paper proposed deep convolutional Generative adversarial nets (DCGANs) to minimize the site survey time and cost, and to mitigate signal fluctuations. The radio-map was designed for receiving radio signals from detectable wireless local area network (WLAN) and cellular networks in scalable environments. The proposed fingerprinting-based positioning is a sequential combination of the hybrid support vector machine and long short-term memory algorithms. The experimental results indicate that the proposed method achieves a promising and reasonable positioning performance for IoT devices in scalable wireless environments. © 2020 IEEE.

关键词： Support vector machines

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Advanced Methods for Connectome-Based Predictive Modeling of Human Intelligence: A Novel Approach Based on Individual Differences in Cortical Topography

arXiv

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arXiv 2022年

作者： Anderson, Evan D. Wilcox, Ramsey Nayak, Anuj Zwilling, Christopher Robles-Granda, Pablo Kim, Been Varshney, Lav R. Barbey, Aron K. Neuroscience Program University of Illinois UrbanaIL United States Department of Psychology University of Illinois UrbanaIL United States Department of Electrical & Computer Engineering University of Illinois UrbanaIL United States Beckman Institute University of Illinois UrbanaIL United States Department of Computer Science University of Illinois UrbanaIL United States Google Brain Mountan View CA United States

Individual differences in human intelligence can be modeled and predicted from in vivo neurobiological connectivity. Many established modeling frameworks for predicting intelligence, however, discard higher-order information about individual differences in brain network topology, and show only moderate performance when generalized to make predictions in out-of-sample subjects. In this paper, we propose that connectome-based predictive modeling, a common predictive modeling framework for neuroscience data, can be productively modified to incorporate information about brain network topology and individual differences via the incorporation of bagged decision trees and the network based statistic. These modifications produce a novel predictive modeling framework that leverages individual differences in cortical tractography to generate accurate regression predictions of intelligence scores. Network topology-based feature selection provides for natively interpretable networks as input features, increasing the model's explainability. Investigating the proposed modeling framework's efficacy, we find that advanced connectome-based predictive modeling generates neuroscience predictions that account for a significantly greater proportion of variance in general intelligence scores than previously established methods, advancing our scientific understanding of the network architecture that underlies human intelligence. © 2022, CC BY-SA.

关键词： Forecasting

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Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images 1

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2021

作者： Cho, Minjeong Choi, Yujin Hwang, Donghwi Yie, Si Young Kim, Hanvit Lee, Jae Sung School of Electrical and Electronics Engineering Chung-Ang University Seoul06974 Korea Republic of Department of Biomedical Engineering Kyunghee University College of Electronics and Information 1732 Deogyeong-daero Giheung-gu Gyeonggi-do Yongin-si Korea Republic of Department of Nuclear Medicine Seoul National University College of Medicine Seoul03080 Korea Republic of Department of Biomedical Sciences Seoul National University College of Medicine Seoul03080 Korea Republic of Interdisciplinary Program in Bioengineering Seoul National University Graduate School Seoul Korea Republic of Integrated Major in Innovative Medical Science Seoul National University Graduate School Seoul Korea Republic of Ulsan Korea Republic of Artificial Intelligence Institute Seoul National University Seoul08826 Korea Republic of Brightonix Imaging Inc. Seoul04782 Korea Republic of

ISBN: (数字)9783030982539

ISBN: (纸本)9783030982522

Quantitative positron emission tomography/computed tomography (PET/CT), owing to the functional metabolic information and anatomical information of the human body that it presents, is useful to achieve accurate tumor delineation. However, manual annotation of a Volume Of Interest (VOI) is a labor-intensive and time-consuming task. In this study, we automatically segmented the Head and Neck (H&N) primary tumor in combined PET/CT images. Herein, we propose a convolutional neural network named Multimodal Spatial Attention Network (MSA-Net), supplemented with a Spatial Attention Module (SAM), which uses a PET image as an input. We evaluated this model on the MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation challenge dataset. Our method delivered a competitive cross-validation efficiency, with Dice Similarity Coefficient (DSC) 0.757, precision 0.788, and recall 0.785. When we tested out method on test dataset, we achieved an average DSC and Hausdorff Distance at 95% (HD95) of 0.766 and 3.155 respectively. Our team name is ‘Heck_Uihak’. © 2022, Springer Nature Switzerland AG.

关键词： Tumors

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Unbiased Estimation Using a Class of Diffusion Processes

SSRN

引用

SSRN 2022年

作者： Ruzayqat, Hamza Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Statistical Science University College London LondonWC1E 6BT United Kingdom Department of Mathematics Imperial College London LondonSW7 2AZ United Kingdom

We study the problem of unbiased estimation of expectations with respect to (w.r.t.) π a given, general probability measure on (Rd,B(Rd)) that is absolutely continuous with respect to a standard Gaussian measure. We focus on simulation associated to a particular class of diffusion processes, sometimes termed the Schrödinger-Föllmer Sampler, which is a simulation technique that approximates the law of a particular diffusion bridge process {Xt}t∈[0,1] on Rd, d ∈ N0. This latter process is constructed such that, starting at X0 = 0, one has X1 ∼ π. Typically, the drift of the diffusion is intractable and, even if it were not, exact sampling of the associated diffusion is not possible. As a result, [10, 15] consider a stochastic Euler-Maruyama scheme that allows the development of biased estimators for expectations w.r.t. π. We show that for this methodology to achieve a mean square error of O(Ε2), for arbitrary Ε > 0, the associated cost is O(Ε−5). We then introduce an alternative approach that provides unbiased estimates of expectations w.r.t. π, that is, it does not suffer from the time discretization bias or the bias related with the approximation of the drift function. We prove that to achieve a mean square error of O(Ε2), the associated cost is, with high probability, O(Ε−2|log(Ε)|2+δ), for any δ > 0. We implement our method on several examples including Bayesian inverse problems. © 2022, The Authors. All rights reserved.

关键词： Diffusion

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Reinforcement Learning-Based Edge Caching for IoT in Compute-First Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Asmat, Hamid Din, Ikram Ud Almogren, Ahmad Rodrigues, Joel J. P. C. Department of Information Technology University of Haripur Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Chair of Cyber Security Department of Computer Science Riyadh11633 Saudi Arabia Post-graduation Program in Electrical Engineering Teresina Brazil

6G networks require ultra-low latency and intelligent resource management in IoT environments. Compute-First Networking (CFN), integrating distributed computing and edge caching, addresses backhaul congestion. This paper proposes CaRL, a DRL-based caching framework using TD3 algorithm. CaRL optimizes cache placement at edge nodes by incorporating real-time network state, content popularity, and resource utilization. Simulations show CaRL improves cache hit ratio, reduces latency, and enhances bandwidth efficiency, and ensures secure caching decisions under dynamic network conditions. Unlike existing methods, CaRL adapts to dynamic network conditions by integrating latency, CPU, and memory metrics, demonstrating scalability and adaptability in CFN-driven 6G environments. © 1975-2011 IEEE.

关键词： Edge computing

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Lattice Transformation from 2-D to Quasi 1-D and Phonon Properties of Exfoliated ZrS2 and ZrSe2

arXiv

引用

arXiv 2022年

作者： Alsulami, Awsaf Alharbi, Majed Alsaffar, Fadhel Alolaiyan, Olaiyan Aljalham, Ghadeer Albawardi, Shahad Alsaggaf, Sarah Alamri, Faisal Tabbakh, Thamer A. Amer, Moh R. Center of Excellence for Green Nanotechnologies Joint Centers of Excellence Program King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Department of Mechanical and Aerospace Engineering University of California Los Angeles Los AngelesCA90095 United States National Center for Nanotechnology Materials Science Institute King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States

Recent reports on thermal and thermoelectric properties of emerging 2-Dimensional (2D) materials have shown promising results. Yet, most of these emerging materials are underrepresented or lack a consistent and thorough investigation. Among these materials are Zirconium based chalcogenides such as Zirconium Disulfide (ZrS2) and Zirconium Dieselenide (ZrSe2). Here, we investigate the thermal properties of these Zirconium based materials using confocal Raman spectroscopy. We observed 2 different and distinctive Raman signatures for exfoliated ZrX2 (where X = S or Se). These Raman modes generally depend on the shape of the exfoliated nanosheets, regardless of the incident laser polarization. These 2 shapes are divided into 2D- ZrX2 and quasi 1D- ZrX2. For 2D- ZrX2, Raman modes are in alignment with those reported in literature. However, for quasi 1D-ZrX2, we show that Raman modes are identical to exfoliated ZrX3 nanosheets, indicating a major lattice transformation from 2D to quasi-1D. We also measure thermal properties of each resonant Raman mode for each ZrX2 shape. Based on our measurements, most Raman modes exhibit a linear downshift dependence with temperature. However, for ZrS2, we see an upshift (blueshift) with temperature for A1g mode, which is attributed to non-harmonic effects caused by dipolar coupling with IR-active modes. Moreover, the observed temperature dependence coefficient for some phonon modes of quasi 1D-ZrX2 differ dramatically, which can be caused by the quasi 1D lattice. Finally, we measure phonon dynamics under optical heating for each of 2D-ZrX2 and quasi 1D-ZrX2 and show phonon confinement in quasi 1D-ZrX2 nanosheets. We extract the thermal conductivity and the interfacial thermal conductance for each of 2D-ZrX2 and quasi 1D-ZrX2 nanosheets. Our calculations indicate lower interfacial thermal conductance for quasi 1D-ZrX2 compared to 2D-ZrX2, which can be attributed to the phonon confinement in 1D. Based on our model, we show low thermal con

关键词： Thermal conductivity

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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective

arXiv

引用

arXiv 2024年

作者： Restrepo, David Wu, Chenwei Vásquez-Venegas, Constanza Matos, João Gallifant, Jack Celi, Leo Anthony Bitterman, Danielle S. Nakayama, Luis Filipe Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Departamento de Telematica Universidad del Cauca Cauca Popayán Colombia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Scientific Image Analysis Lab Faculty of Medicine Universidad de Chile RM Santiago Chile Department of Computer Science Faculty of Engineering Universidad de Concepción Biobio Concepción Chile Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Ophthalmology São Paulo Federal University São Paulo São Paulo Brazil

The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes. However, the underrepresentation of diverse groups in the development and application of these models can perpetuate biases, leading to inequitable healthcare delivery. This paper presents a comprehensive scientometric analysis of LLM research for healthcare, including data from January 1, 2021, to July 1, 2024. By analyzing metadata from PubMed and Dimensions, including author affiliations, countries, and funding sources, we assess the diversity of contributors to LLM research. Our findings highlight significant gender and geographic disparities, with a predominance of male authors and contributions primarily from high-income countries (HICs). We introduce a novel journal diversity index based on Gini diversity to measure the inclusiveness of scientific publications. Our results underscore the necessity for greater representation in order to ensure the equitable application of LLMs in healthcare. We propose actionable strategies to enhance diversity and inclusivity in artificial intelligence research, with the ultimate goal of fostering a more inclusive and equitable future in healthcare innovation. © 2024, CC BY.

关键词： Decision making

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Enhanced nonlinearity in a functionalized whispering gallery mode microcavity

Enhanced nonlinearity in a functionalized whispering gallery...

引用

2020 Frontiers in Optics Conference, FiO 2020

作者： Pampel, Steven K. Bae, Kyuyoung Horning, Thomas M. Zohrabi, Mo Grayson, Michael Park, Wounjhang Gopinath, Juliet T. Department of Physics University of Boulder Colorado BoulderCO80309 United States Department of Electrical Computer and Energy Engineering University of Boulder Colorado BoulderCO80309 United States Materials Science and Engineering Program University of Colorado BoulderCO80309 United States

We report a ~3x increase in third-harmonic generation conversion efficiency using indium tin oxide (ITO) nanoparticles on the surface of a silica microsphere. This establishes ITO-microresonators as a promising tool f... 详细信息

ISBN: (纸本)9781943580804

关键词： Microresonators

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