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Quantum Atomic Matter Near Two-Dimensional Materials in Microgravity

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arXiv 2023年

作者： Maestro, Adrian Del Kim, Sang Wook Bigelow, Nicholas P. Thompson, Robert J. Kotov, Valeri N. Department of Physics and Astronomy Min H. Kao Department of Electrical Engineering and Computer Science Institute for Advanced Materials and Manufacturing University of Tennessee KnoxvilleTN37996 United States Department of Physics and Materials Science Program University of Vermont BurlingtonVT05405 United States Department of Physics and Astronomy Institute of Optics Center for Coherence and Quantum Optics University of Rochester RochesterNY14627 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

Novel two-dimensional (2D) atomically flat materials, such as graphene and transition-metal dichalcogenides, exhibit unconventional Dirac electronic spectra. We propose to effectively engineer their interactions with cold atoms in microgravity, leading to a synergy between complex electronic and atomic collective quantum phases and phenomena. Dirac materials are susceptible to manipulation and quantum engineering via changes in their electronic properties by application of strain, doping with carriers, adjustment of their dielectric environment, etc. Consequently the interaction of atoms with such materials, namely the van der Waals / Casimir-Polder interaction, can be effectively manipulated, leading to the potential observation of physical effects such as Quantum Reflection off atomically thin materials and confined Bose-Einstein Condensate (BEC) frequency shifts. Copyright © 2023, The Authors. All rights reserved.

关键词： Transition metals

TargetNet: Functional microRNA target prediction with deep neural networks

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arXiv 2021年

作者： Min, Seonwoo Lee, Byunghan Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of LG AI Research Seoul07796 Korea Republic of Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Interdisciplinary Program in Artificial Intelligence and Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of

Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions. Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (1) relaxed CTS selection criteria accommodating irregularities in the seed region, (2) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments, and (3) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-theart algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets. © 2021, CC BY-NC-ND.

关键词： RNA

ZETA: a library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems

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arXiv 2025年

作者： Rego, Brenner S. Scott, Joseph K. Raimondo, Davide M. Terra, Marco H. Raffo, Guilherme V. Department of Electrical and Computer Engineering University of São Paulo SP São Carlos13566-590 Brazil Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. AtlantaGA30318 United States Department of Engineering and Architecture University of Trieste Trieste34127 Italy Department of Electronics Engineering Graduate Program in Electrical Engineering Federal University of Minas Gerais MG Belo Horizonte31270-901 Brazil

This paper introduces ZETA, a new MATLAB library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems. It features user-friendly implementations of set representations based on zonotopes, namely zonotopes, constrained zonotopes, and line zonotopes, in addition to a basic implementation of interval arithmetic. This library has capabilities starting from the basic set operations with these sets, including propagations through nonlinear functions using various approximation methods. The features of ZETA allow for reachability analysis and state estimation of discrete-time linear, nonlinear, and descriptor systems, in addition to active fault diagnosis of linear systems. Efficient order reduction methods are also implemented for the respective set representations. Some examples are presented in order to illustrate the functionalities of the new library. © 2025, CC BY.

关键词： Digital arithmetic

Knee Injury Diagnostic Device

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Knee Injury Diagnostic Device

IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Julie E. McKenna Tyler R. Hopkins Lucas T. Lavallee Douglas E. Dow Program in Engineering conentration in Computer Eng. Wentworth Institute of Technology Boston Massachussetts USA Program in Electrical Eng. Wentworth Institute of Technology Boston Massachussetts USA Program in Computer Eng. Wentworth Institute of Technology Boston Massachussetts USA Wentworth Institute of Technology Boston Massachussetts USA

ISBN: (数字)9781728196565

ISBN: (纸本)9781728196572

Knee injuries are difficult to accurately diagnose. The manual evaluation relies on many subjective factors such as physician experience, swelling, patient guarding, and the severity of the injury. These factors can lead to an inaccurate or incomplete diagnosis, resulting in less than optimal treatment and recovery. Knee injuries are very common among athletes and can occur during the day to day activities, with many resulting in tears to one or more of the four ligaments. For evaluation, a physician manually manipulates the knee with a series of standard tests. Even though these standard manual tests are considered best practice, they are known to lead to some inaccuracies with upwards of 1 in 8 patients being misdiagnosed due to testing deficiencies. Imaging by MRI is used to support the diagnosis if available, though not available to all patients due to cost and time requirements. This purpose of this project was to develop and test a wearable diagnostic system contained within a sleeve over the knee. Incorporated sensors were used to monitor movement and electromyographic activity to determine quantitative measurements toward a diagnosis. The movement and displacement monitoring subsystems were tested on a constructed model of the lower leg and knee. Preliminary results have shown accurate readings with an average percent error of 1% for range of motion testing and 3% (0.1 to 0.2 mm) for laxity testing. This measurement determined by this system could be reported to a physician who could use when making a diagnosis. Improved diagnosis would guide appropriate treatment and contribute to improved recovery.

关键词： Sensors Knee Injuries Muscles Electromyography Flexible printed circuits Ligaments

Regularity and numerical approximation of fractional elliptic differential equations on compact metric graphs

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arXiv 2023年

作者： Bolin, David Kovács, Mihály Kumar, Vivek Simas, Alexandre B. Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg GothenburgSE-41296 Sweden Department of Differential Equations Budapest University of Technology and Economics Mauegyetem rkp. 3. BudapestH-1111 Hungary Faculty of Information Technology and Bionics Pázmány Péter Catholic University Práter utca 50/a. BudapestH-1083 Hungary Theoretical Statistics and Mathematics Unit Indian Statistical Institute Bangalore Centre 8th Mile Mysore Road Karnataka Bangalore560059 India

The fractional differential equation Lβu = f posed on a compact metric graph is considered, where β > 0 and L = κ2 − ∇(a∇) is a second-order elliptic operator equipped with certain vertex conditions and sufficiently smooth and positive coefficients κ, a. We demonstrate the existence of a unique solution for a general class of vertex conditions and derive the regularity of the solution in the specific case of Kirchhoff vertex conditions. These results are extended to the stochastic setting when f is replaced by Gaussian white noise. For the deterministic and stochastic settings under generalized Kirchhoff vertex conditions, we propose a numerical solution based on a finite element approximation combined with a rational approximation of the fractional power L−β. For the resulting approximation, the strong error is analyzed in the deterministic case, and the strong mean squared error as well as the L2(Γ×Γ)error of the covariance function of the solution are analyzed in the stochastic setting. Explicit rates of convergences are derived for all cases. Numerical experiments for L = κ2 − ∆, κ > 0 are performed to illustrate the *** Codes 35R02, 35A01, 35A02, 60H15, 60H40 Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

MICROBUNDLECOMPUTE: AUTOMATED SEGMENTATION, TRACKING, AND ANALYSIS OF SUBDOMAIN DEFORMATION IN CARDIAC MICROBUNDLES

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arXiv 2023年

作者： Kobeissi, Hiba Jilberto, Javiera Karakan, M. Çağatay Gao, Xining DePalma, Samuel J. Das, Shoshana L. Quach, Lani Urquia, Jonathan Baker, Brendon M. Chen, Christopher S. Nordsletten, David Lejeune, Emma Department of Mechanical Engineering Boston University BostonMA02215 United States Center for Multiscale and Translational Mechanobiology Boston University BostonMA02215 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Photonics Center Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Harvard-MIT Program in Health Sciences and Technology Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA02139 United States Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA02215 United States Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Cardiac Surgery University of Michigan MI48109 United States Department of Biomedical Engineering School of Imaging Sciences and Biomedical Engineering King’s College London King’s Health Partners LondonSE1 7EH United Kingdom

Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present "MicroBundleCompute, " a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, runs without user intervention, requires minimal input parameter setting selection, and is computationally inexpensive. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the *** Codes 92F05, 74A05 © 2023, CC BY-SA.

关键词： Cell culture

No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases.

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arXiv 2024年

作者： Li, Thomas Z. Xu, Kaiwen Krishnan, Aravind Gao, Riqiang Kammer, Michael N. Antic, Sanja Xiao, David Knight, Michael Martinez, Yency Paez, Rafael Lentz, Robert J. Deppen, Stephen Grogan, Eric L. Lasko, Thomas A. Sandler, Kim L. Maldonado, Fabien Landman, Bennett A. Medical Scientist Training Program Vanderbilt University NashvilleTN37235 United States Biomedical Engineering Vanderbilt University NashvilleTN37235 United States Computer Science Vanderbilt University NashvilleTN37235 United States Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Digital Technology and Innovation Siemens Healthineers PrincetonNJ08540 United States Division of Allergy Pulmonary and Critical Care Medicine Department of Medicine Vanderbilt University Medical Center NashvilleTN37232 United States Department of Thoracic Surgery Vanderbilt University Medical Center NashvilleTN37232 United States Biomedical Informatics Vanderbilt University Medical Center NashvilleTN37232 United States Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN37232 United States

Purpose: Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinical settings in which patients would arguably most benefit are scarce. Materials and Methods: This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged 9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions to assess the area under the receiver operating characteristic curve (AUC) of validated models including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence on chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented each model from their published literature, re-training the models if necessary, and curated each cohort from primary data sources. Results: We observed that model performance varied greatly across clinical use cases. No single predictive model emerged as a clear winner across all cohorts, but certain models excelled in specific clinical contexts. Single timepoint chest CT AI performed well in lung screening, but struggled to generalize to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively promising performance on incidentally-detected nodules. However, when applied to nodules that underwent biopsy, all models underperformed. Conclusion: These results underscore the strengths and limitations of 8 validated predictive models and highlight promising directions towards personalized, noninvasive lung cancer diagnosis. © 2024, CC BY-NC-SA.

关键词： Biopsy

Optogenetic Neural Probes: Fiberless, High-Density, Artifact-Free Neuromodulation : (Invited)

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Optogenetic Neural Probes: Fiberless, High-Density, Artifact...

International Electron Devices Meeting (IEDM)

作者： Eunah Ko Kanghwan Kim Mihály Vöröslakos Sungjin Oh György Buzsáki Kensall D. Wise Euisk Yoon Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA Center for BioMicrosystems Brain Science Institute Korea Institute of Science and Technology Seoul Korea Neuroscience Institute Langone Medical Center New York University New York NY USA Department of Biomedical Engineering University of Michigan Ann Arbor MI USA Center for Nanomedicine Institute for Basic Science (IBS) and Graduate Program of Nano Biomedical Engineering (Nano BME) Advanced Science Institute Yonsei University Seoul Korea

ISBN: (纸本)9781665489607

Evolution of “Michigan Probes” has enabled unraveling the connectivity of neurons at cellular resolution and their dynamic interaction within and across brain areas that underlie behavioral and cognitive functions. Recently, micro-LEDs were monolithically integrated for optogenetic neuromodulation at scale without requiring external optical fiber connection. The ‘hectoSTAR’ micro-LED optoelectrode features 256 recording electrodes and 128 stimulation micro-LEDs in four silicon micro-needle shanks, covering a large volume with 1.3-mm × 0.9-mm cross-sectional area located as deep as 6 mm inside the brain. Technical challenges to overcome in scaling of optogenetic probes will be discussed, followed by future direction.

关键词： Optical fibers Electrodes Brain Optogenetics Silicon Recording Optical fiber devices

Correction: Microstructuring of cobalt (Co) thin films by UV laser irradiation

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Applied Physics A 2023年第1期130卷 1-1页

作者： Itapu, Srikanth Borra, Vamsi Selvendran, S. Department of Electronics and Communication Engineering Alliance University Bengaluru India Electrical and Computer Engineering Program Rayen College of Engineering Youngstown State University Youngstown USA Centre for Healthcare Advancement Innovation and Research (CHAIR) School of Electronics Engineering (SENSE) Vellore Institute of Technology Chennai India