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Electrochemical measurement of serotonin by Au-CNT electrodes fabricated on microporous cell culture membranes

Microsystems & Nanoengineering 2020年第1期6卷 494-506页

作者： Ashley A.Chapin Pradeep R.Rajasekaran David N.Quan Liangbing Hu Jens Herberholz William E.Bentley Reza Ghodssi Fischell Department of Bioengineering College ParkMD 20742USA Institute for Systems Research College ParkMD 20740USA Department of Materials Science and Engineering College ParkMD 20740USA Department of Psychology and Neuroscience and Cognitive Science Program College ParkMD 20740USA Institute for Bioscience and Biotechnology Research RockvilleMD 20850USA Robert E.Fischell Institute for Biomedical Devices RockvilleMD 20850USA Department of Electrical and Computer Engineering College ParkMD 20742USA

Gut–brain axis(GBA)communication relies on serotonin(5-HT)signaling between the gut epithelium and the peripheral nervous system,where 5-HT release patterns from the basolateral(i.e.,bottom)side of the epithelium activate nerve *** have been few quantitative studies of this gut-neuron signaling due to a lack of real-time measurement tools that can access the basolateral gut *** vitro platforms allow quantitative studies of cultured gut tissue,but they mainly employ offline and endpoint assays that cannot resolve dynamic molecular-release ***,we present the modification of a microporous cell culture membrane with carbon nanotube-coated gold(Au-CNT)electrodes capable of continuous,label-free,and direct detection of 5-HT at physiological *** characterization of single-walled carbon nanotube(SWCNT)-coated Au electrodes shows increased electroactive surface area,5-HT specificity,sensitivity,and saturation time,which are correlated with the CNT film drop-cast *** microliters of CNT films,with a 10-min saturation time,0.6μA/μM 5-HT sensitivity,and reliable detection within a linear range of 500 nM–10μM 5-HT,can be targeted for high-concentration,high-time-resolution 5-HT *** films(12.5μL)with a 2-h saturation time,4.5μA/μM 5-HT sensitivity,and quantitative detection in the linear range of 100 nM–1μM can target low concentrations with low time *** electrodes achieved continuous detection of dynamic diffusion across the porous membrane,mimicking basolateral 5-HT release from cells,and detection of cell-released 5-HT from separately cultured RIN14B cell ***-integrated cell culture systems such as this can improve in vitro molecular detection mechanisms and aid in quantitative GBA signaling studies.

关键词： release saturation dynamic

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Analog, In-memory Compute Architectures for Artificial Intelligence

arXiv

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arXiv 2023年

作者： Bowen, Patrick Regev, Guy Regev, Nir Pedroni, Bruno Umbria Hanson, Edward Chen, Yiran Neurophos 212 W Main St. #301 DurhamNC27701 United States Center for Metamaterials and Integrated Plasmonics Department of Electrical and Computer Engineering Duke University P.O. Box 90291 DurhamNC27708 United States AlephZero 5141 Beeman Ave. Valley VillageCA91607 United States Department of Electrical and Computer Engineering Ben-Gurion University of the Negev David Ben-Gurion Blvd. 1 Be'er Sheva Israel Department of Bioengineering UC San Diego 9500 Gilman Dr. La Jolla CA92093 United States Duke University Electrical and Computer Engineering Department Science Dr DurhamNC27710 United States

This paper presents an analysis of the fundamental limits on energy efficiency in both digital and analog in-memory computing architectures, and compares their performance to single instruction, single data (scalar) machines specifically in the context of machine inference. The focus of the analysis is on how efficiency scales with the size, arithmetic intensity, and bit precision of the computation to be performed. It is shown that analog, in-memory computing architectures can approach arbitrarily high energy efficiency as both the problem size and processor size scales. © 2023, CC BY-NC-SA.

关键词： Energy efficiency

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Enhancing UAV Spraying Efficiency through Payload Sensor Integration

Enhancing UAV Spraying Efficiency through Payload Sensor Int...

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IEEE Latin American Robotics Symposium, LARS

作者： Celso O. Barcelos Leonardo A. Fagundes-Júnior Daniel C. Gandolfo Martin Saska Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Núcleo de Especialização em Robótica (NERo) Universidade Federal de Viçosa Viçosa MG Brazil Instituto de Automática National University of San Juan San Juan Argentine Department of Cybernetics Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9798331508807

ISBN: (纸本)9798331508814

The use of UAVs has grown in recent years, leading to the prominence of various applications such as monitoring and inspection, search and rescue operations, dam and civil structure inspection, military applications, among others. One of the most relevant applications today is related to Precision Agriculture, which is the subject of this work. Our study analyzes the behavior of an Unmanned Autonomous Vehicle (UAV) used for spraying tasks. The variation of the payload is measured through a load sensor. This information indicates to the UAV the right time to return to the base for refueling or completion of the application. Experimental validation is carried out to assess the functionality of the payload sensor during flight operations. In addition, the dynamic compensation controller improves the navigation of the UAV, even in the presence of payload variations during the entire mission duration.

关键词： Precision agriculture Navigation Spraying Inspection Robot sensing systems Autonomous aerial vehicles Vehicle dynamics Monitoring Payloads Diseases

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Virtual impactor-based label-free bio-aerosol detection using holography and deep learning

arXiv

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arXiv 2022年

作者： Luo, Yi Zhang, Yijie Liu, Tairan Yu, Alan Wu, Yichen 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 Computer Science Department University of California Los AngelesCA90095 United States

Exposure to bio-aerosols such as mold spores and pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and quantification of various bio-aerosols. To address this need, we present a mobile and cost-effective label-free bio-aerosol sensor that takes holographic images of flowing particulate matter concentrated by a virtual impactor, which selectively slows down and guides particles larger than 6μm to fly through an imaging window. The flowing particles are illuminated by a pulsed laser diode, casting their inline holograms on a CMOS image sensor in a lens-free mobile imaging device. The illumination contains three short pulses with a negligible shift of the flowing particle within one pulse, and triplicate holograms of the same particle are recorded at a single frame before it exits the imaging field-of-view, revealing different perspectives of each particle. The particles within the virtual impactor are localized through a differential detection scheme, and a deep neural network classifies the aerosol type in a label-free manner, based on the acquired holographic images. We demonstrated the success of this mobile bio-aerosol detector with a virtual impactor using different types of pollen (i.e., bermuda, elm, oak, pine, sycamore, and wheat) and achieved a blind classification accuracy of 92.91%. This mobile and cost-effective device weighs ~700 g and can be used for label-free sensing and quantification of various bio-aerosols over extended periods since it is based on a cartridge-free virtual impactor that does not capture or immobilize particulate matter. Copyright © 2022, The Authors. All rights reserved.

关键词： Holograms

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Quantum approximation of normalized Schatten norms and applications to learning

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Physical Review A 2022年第5期106卷 052409-052409页

作者： Yiyou Chen Hideyuki Miyahara Louis-S. Bouchard Vwani Roychowdhury Department of Computer Science University of California Los Angeles California 90095 USA Department of Electrical and Computer Engineering University of California Los Angeles California 90095 USA Department of Chemistry and Biochemistry University of California Los Angeles California 90095 USA Department of Bioengineering University of California Los Angeles California 90095 USA Center for Quantum Science and Engineering University of California Los Angeles California 90095 USA California NanoSystems Institute University of California Los Angeles California 90095 USA The Molecular Biology Institute University of California Los Angeles California 90095 USA

Efficient measures to determine the similarity of quantum states, such as the fidelity metric, have been widely studied. In this paper, we address the problem of defining a similarity measure for quantum operations that can be efficiently estimated. Given two quantum operations, U1 and U2, represented in their circuit forms, we first develop a quantum sampling circuit to estimate the normalized Schatten 2-norm of their difference (∥U1−U2∥S2) with precision ε, using only one clean qubit and one classical random variable. We prove a Poly(1ε) upper bound on the sample complexity, which is independent of the size of the quantum system. We then show that such a similarity metric is directly related to a functional definition of similarity of unitary operations using the conventional fidelity metric of quantum states (F): If ∥U1−U2∥S2 is sufficiently small (e.g., ≤ε1+2(1/δ−1)) then the fidelity of states obtained by processing the same randomly and uniformly picked pure state |ψ〉 is as high as needed [F(U1|ψ〉,U2|ψ〉)≥1−ε] with probability exceeding 1−δ. We provide example applications of this efficient similarity metric estimation framework to quantum circuit learning tasks, such as finding the square root of a given unitary operation.

关键词： Machine learning Quantum algorithms Quantum computation

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Investigating Skin Aging Using Jones Matrix Polarization-Sensitive Optical Coherence Tomography

Investigating Skin Aging Using Jones Matrix Polarization-Sen...

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Bio-Optics: Design and Application, BODA 2023 - Part of Optica Biophotonics Congress: Optics in the Life sciences 2023

作者： Maloufi, Sina Zhou, Xin Mirsalehi, Mitra Louie, Daniel C. Lee, Tim K. Tang, Shuo School of Biomedical Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Dermatology and Skin Science Photomedicine Institute University of British Columbia Vancouver Coastal Health Research Institute VancouverBCV5Z 4E8 Canada Cancer Control Research Program BC Cancer VancouverBCV5Z 1L3 Canada

JM-OCT was utilized to present comparison on the effects of intrinsic aging on features quantified by JM-OCT, such as epidermal thickness, local depolarization, and collagen disruption. © 2023 The Author(s)

ISBN: (纸本)9781957171210

关键词： Optical tomography

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Bio-FlatScope: A flat, lensless microscope for fluorescence imaging

Bio-FlatScope: A flat, lensless microscope for fluorescence ...

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Optics and the Brain, BRAIN 2021 - Part of Biophotonics Congress: Optics in the Life sciences 2021

作者： Wu, Jimin Yan, Dong Boominathan, Vivek Adams, Jesse K. Veeraraghavan, Ashok Robinson, Jacob T. Department of Bioengineering Rice University 6100 Main Street HoustonTX77005 United States Applied Physics Program Rice University 6100 Main Street HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Department of Neuroscience Baylor College of Medicine One Baylor Plaza HoustonTX77030 United States Department of Computer Science Rice University 6100 Main Street HoustonTX77005 United States

We developed a lensless microscope with small, inexpensive form factor, large field-of-view (FOV) and cellular resolution for fluorescence imaging. Proof-of-principle measurements with "Bio-FlatScope" are pr... 详细信息

ISBN: (纸本)9781557528209

关键词： Fluorescence imaging

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Weakly Supervised Semantic Segmentation using Out-of-Distribution Data

arXiv

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arXiv 2022年

作者： Lee, Jungbeom Oh, Seong Joon Yun, Sangdoo Choe, Junsuk Kim, Eunji Yoon, Sungroh Department Of Electrical And Computer Engineering Seoul National University Korea Republic of NAVER AI Lab University Of Tübingen Germany Department Of Computer Science And Engineering Sogang University Korea Republic of Interdisciplinary Program In AI AIIS ASRI INMC ISRC And NSI Seoul National University Korea Republic of

Weakly supervised semantic segmentation (WSSS) methods are often built on pixel-level localization maps obtained from a classifier. However, training on class labels only, classifiers suffer from the spurious correlation between foreground and background cues (e.g. train and rail), fundamentally bounding the performance of WSSS. There have been previous endeavors to address this issue with additional supervision. We propose a novel source of information to distinguish foreground from the background: Out-of-Distribution (OoD) data, or images devoid of foreground object classes. In particular, we utilize the hard OoDs that the classifier is likely to make false-positive predictions. These samples typically carry key visual features on the background (e.g. rail) that the classifiers often confuse as foreground (e.g. train), so these cues let classifiers correctly suppress spurious background cues. Acquiring such hard OoDs does not require an extensive amount of annotation efforts;it only incurs a few additional image-level labeling costs on top of the original efforts to collect class labels. We propose a method, W-OoD, for utilizing the hard OoDs. W-OoD achieves state-of-the-art performance on Pascal VOC 2012. The code is available at: https://***/naver-ai/w-ood. © 2022, CC BY-NC-SA.

关键词： Semantic Segmentation

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Increasing the mobility and power-electronics figure of merit of AlGaN with atomically thin AlN/GaN digital-alloy superlattices

arXiv

引用

arXiv 2022年

作者： Pant, Nick Lee, Woncheol Sanders, Nocona Kioupakis, Emmanouil Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

Alloy scattering in random AlGaN alloys drastically reduces the electron mobility and therefore the power-electronics figure of merit. As a result, Al compositions greater than 75% are required to obtain even a two-fold increase of the Baliga figure of merit compared to GaN. However, beyond approximately 80% Al composition, donors in AlGaN undergo the DX transition which makes impurity doping increasingly more difficult. Moreover, the contact resistance increases exponentially with increasing Al content, and integration with dielectrics becomes difficult due to the upward shift of the conduction band. Atomically thin superlattices of AlN and GaN, also known as digital alloys, are known to grow experimentally under appropriate growth conditions. These chemically ordered nanostructures could offer significantly enhanced figure of merit compared to their random-alloy counterparts due to the absence of alloy scattering, as well as better integration with contact metals and dielectrics. In this work, we investigate the electronic structure and phonon-limited electron mobility of atomically thin AlN/GaN digital-alloy superlattices using first-principles calculations based on density-functional and many-body perturbation theory. The band gap of the atomically thin superlattices reaches 4.8 eV, and the in-plane (out-of-plane) mobility is 369 (452) cm2 V-1 s-1. Using the modified Baliga figure of merit that accounts for the dopant ionization energy, we demonstrate that atomically thin AlN/GaN superlattices with a monolayer sublattice periodicity have the highest modified Baliga figure of merit among several technologically relevant ultra-wide band-gap materials, including random AlGaN, β-Ga2O3, cBN, and diamond. Copyright © 2022, The Authors. All rights reserved.

关键词： Aluminum gallium nitride

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State of the Art Models for Fake News Detection Tasks

State of the Art Models for Fake News Detection Tasks

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2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020

作者： Antoun, Wissam Baly, Fady Achour, Rim Hussein, Amir Hajj, Hazem American University of Beirut Electrical and Computer Engineering Department Beirut Lebanon Graduate Program in Computational Science American University of Beirut Beirut Lebanon

ISBN: (数字)9781728148212

ISBN: (纸本)9781728148212

This paper presents state of the art methods for addressing three important challenges in automated fake news detection: fake news detection, domain identification, and bot identification in tweets. The proposed solutions achieved first place in a recent international competition on fake news. For fake news detection, we present two models. The winning model in the competition combines similarity between the embedding of each article's title and the embedding of the top five corresponding google search results. The new model relies on advances in Natural Language Understanding (NLU) end to end deep learning models to identify stylistic differences between legitimate and fake news articles. This second model was developed after the competition and outperforms the winning approach. For news domain detection, the winning model is a hybrid approach composed of named entity features concatenated with semantic embeddings derived from end to end models. For twitter bot detection, we propose to use the following features: duration between account creation and tweet date, presence of a tweet's link, presence of user's location, other tweet's features, and the tweets' metadata. Experiments include insights into the importance of the different features and the results indicate the superior performances of all proposed models. © 2020 IEEE.

关键词： Competition

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