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Synthetic Biology Construct of Ebola Virus in Bacteria Surrogate Is Stable and Safe for Rapid Detection Studies in a BSL-2 laboratory Setting

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Advances in Microbiology 2022年第1期12卷 25-41页

作者： Nwadiuto Esiobu Douglas Holmes Chad Coarsey Waseem Asghar Bodhi Stone Microbial Biotech Laboratory Biological Sciences Department Florida Atlantic University Boca Raton Florida USA Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton Florida USA

Rapid detection of virulent pathogens during an outbreak is critical for public health advisories and control of the disease in a population. While many molecular techniques for point of care and clinical diagnosis abound, the US experience with the COVID-19 testing in the early stages of the pandemic underscores the critical importance of determining the appropriate target gene(s) with in-built controls that reliably detect pathogens with high sensitivity and specificity. Assays and research for diagnostics and therapy could be slowed during an epidemic because access to the required BSL-3 and BSL-4 laboratories are limited. So, during the 2014 West Africa Ebola outbreak, we tested the hypothesis that using synthetic cDNA of Ebolavirus in a bacteria surrogate (fit for all lab settings), would remain unmutated and safe after several generations, serving as an effective positive control in research settings, self test and point-of-care detection platforms. Primers were designed for the detection and quantification of the nucleoprotein (NP) gene of the 2014 Makona Ebola strain (KR781608.1, 733 - 1332 bp). To test the stability of artificially inserted translation arrest in the Orf of the model gene, it was edited to include three STOP codons in the RNA transcript using SNAP GENE. The segment was then spliced into a high copy number plasmid, cloned into One Shot^TM TOP10 Escherichia coli (Invitrogen), and tested for stability and safety by periodic subculture, extraction and sequencing. Unlike COVID-19, rapid detection of blood-borne etiologies like Ebola requires optimized protocols for blood matrix. Using real-time PCR and newly designed primer pairs, the EBOV surrogate was detected and enumerated in human blood and regular broth and buffers. Based on aligned sequence analysis, the EBOV synthetic NP gene was stable (>99.9999% similarity coefficient) for at least 3 months. Detection sensitivity in broth and blood was at least 100 cells/ml or about 5.8

关键词： Ebola Virus Synthetic Biology Biosafety Point-of-Care Rapid Detection Plasmid Vector

G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors

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arXiv 2024年

作者： Yang, Haoxin Xu, Xuemiao Xu, Cheng Zhang, Huaidong Qin, Jing Wang, Yi Heng, Pheng-Ann He, Shengfeng The School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology State Key Laboratory of Subtropical Building and Urban Science China The Centre for Smart Health The Hong Kong Polytechnic University Hong Kong The School of Future Technology South China University of Technology Guangzhou China The School of Computer Science and Network Security Dongguan University of Technology Dongguan China The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The School of Computing and Information Systems Singapore Management University Singapore

Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent manipulation in pre-trained GANs can lead to changes in ID-irrelevant attributes, adversely affecting data utility due to GAN inversion inaccuracies. This paper introduces G2Face, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility. We utilize a 3D face model to extract geometric information from the input face, integrating it with a pre-trained GAN-based decoder. This synergy of generative and geometric priors allows the decoder to produce realistic anonymized faces with consistent geometry. Moreover, multi-scale facial features are extracted from the original face and combined with the decoder using our novel identity-aware feature fusion blocks (IFF). This integration enables precise blending of the generated facial patterns with the original ID-irrelevant features, resulting in accurate identity manipulation. Extensive experiments demonstrate that our method outperforms existing state-of-the-art techniques in face anonymization and recovery, while preserving high data utility. Code is available at https://***/Harxis/G2Face. Copyright © 2024, The Authors. All rights reserved.

关键词： Anonymity

Long Duration Battery Sizing, Siting, and Operation Under Wildfire Risk Using Progressive Hedging

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arXiv 2024年

作者： Piansky, Ryan Stinchfield, Georgia Kody, Alyssa Molzahn, Daniel K. Watson, Jean-Paul School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Chemical Engineering Carnegie Mellon University PittsburghPA United States Energy Systems and Infrastructure Analysis Argonne National Laboratory LemontIL United States Lawrence Livermore National Laboratory LivermoreCA United States

Battery sizing and siting problems are computationally challenging due to the need to make long-term planning decisions that are cognizant of short-term operational decisions. This paper considers sizing, siting, and operating batteries in a power grid to maximize their benefits, including price arbitrage and load shed mitigation, during both normal operations and periods with high wildfire ignition risk. We formulate a multi-scenario optimization problem for long duration battery storage while considering the possibility of load shedding during Public Safety Power Shutoff (PSPS) events that de-energize lines to mitigate severe wildfire ignition risk. To enable a computationally scalable solution of this problem with many scenarios of wildfire risk and power injection variability, we develop a customized temporal decomposition method based on a progressive hedging framework. Extending traditional progressive hedging techniques, we consider coupling in both placement variables across all scenarios and state-of-charge variables at temporal boundaries. This enforces consistency across scenarios while enabling parallel computations despite both spatial and temporal coupling. The proposed decomposition facilitates efficient and scalable modeling of a full year of hourly operational decisions to inform the sizing and siting of batteries. With this decomposition, we model a year of hourly operational decisions to inform optimal battery placement for a 240-bus WECC model in under 70 minutes of wall-clock time. Copyright © 2024, The Authors. All rights reserved.

关键词： Charging (batteries)

Exploring Fairness for FAS-assisted Communication systems: from NOMA to OMA

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arXiv 2024年

作者： Yao, Junteng Zhou, Liaoshi Wu, Tuo Jin, Ming Pan, Cunhua Elkashlan, Maged Wong, Kai-Kit The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Yonsei Frontier Laboratory The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

This paper addresses the fairness issue within fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) systems, where a single fixed-antenna base station (BS) transmits superposition-coded signals to two users, each with a single fluid antenna. We define fairness through the minimization of the maximum outage probability for the two users, under total resource constraints for both FAS-assisted NOMA and OMA systems. Specifically, in the FAS-assisted NOMA systems, we study both a special case and the general case, deriving a closed-form solution for the former and applying a bisection search method to find the optimal solution for the latter. Moreover, for the general case, we derive a locally optimal closed-form solution to achieve fairness. In the FAS-assisted OMA systems, to deal with the non-convex optimization problem with coupling of the variables in the objective function, we employ an approximation strategy to facilitate a successive convex approximation (SCA)-based algorithm, achieving locally optimal solutions for both cases. Empirical analysis validates that our proposed solutions outperform conventional NOMA and OMA benchmarks in terms of fairness. © 2024, CC BY.

关键词： Approximation algorithms

Proposal of Axial-gap type Magnetic Multiple Spur Gear for In-wheel Motor System of Electric Vehicle

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Proposal of Axial-gap type Magnetic Multiple Spur Gear for I...

IEEE International Conference on Electric Machines and Drives (IEMDC)

作者： Taiga Kamijo Kohei Aiso Kan Akatsu Yasuaki Aoyama Electrical Engineering and Computer Science Shibaura Institute of Technology Tokyo Japan Department of Electrical Engineering Shibaura Institute of Technology Tokyo Japan Department of Faculty of Engineering Division of Intelligent Systems Engineering Yokohama National University Yokohama Japan Center for Technology Innovation - Materials Hitachi Ltd. Research & Development Group Ibaraki Japan

Magnetic gears have the characteristics of maintenance-free, low noise, and low vibration due to non-contact power transmission. By taking advantage of the non-contact power transmission, the motor system can be made smaller and lighter by further increasing the motor speed. Magnetic Multiple Spur Gear (MMSG) has been proposed, the drive system of MMSG and multiple high-speed motors can reduce the size and weight of the drive system by converting the total power of multiple high-speed motors. Applying this drive system to in-wheel motors will increase the available interior space in electric vehicles. However, there is a risk that the high-speed rotor and low-speed rotor contact with each other due to the radial structure in the in-wheel motor system. In this research, an axial-gap MMSG is proposed to solve the problem and to obtain higher torque density, the performances such as torque density and efficiency are evaluated by finite element analysis.

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Investigation on Structures of Axial Gap Type Magnetic Multiple Spur Gear for In-Wheel Motor System of Electric Vehicle

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Investigation on Structures of Axial Gap Type Magnetic Multi...

IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

A novel drive system using Magnetic Multiple Spur Gear (MMSG) and multiple high-speed motors is characterized by small size, lightweight, and high efficiency even at high-speed region, it is expected to apply to in-wheel motor systems. However, there is a risk of contact between the rotors when applied to in-wheel motor system. In this study, a novel axial-gap MMSG is proposed to solve this problem, stabilize the air gap between the high-speed and low-speed rotors, and effectively utilize the magnets, and its performance is evaluated by finite element analysis.

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Deep and Dynamic Metabolic and Structural Imaging in Living Tissues

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arXiv 2024年

作者： Liu, Kunzan Cao, Honghao Shashaty, Kasey Yu, Li-Yu Spitz, Sarah Pramotton, Francesca Michela Wan, Zhengpeng Kan, Ellen L. Tevonian, Erin N. Levy, Manuel Lendaro, Eva Kamm, Roger D. Griffith, Linda G. Wang, Fan Qiu, Tong You, Sixian Research Laboratory of Electronics Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Biological Engineering Massachusetts Institute of Technology United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology United States Department of Mechanical Engineering Massachusetts Institute of Technology United States

Label-free imaging through two-photon autofluorescence (2PAF) of NAD(P)H allows for non-destructive and high-resolution visualization of cellular activities in living systems. However, its application to thick tissues and organoids has been restricted by its limited penetration depth within 300 µm, largely due to tissue scattering at the typical excitation wavelength (∼750 nm) required for NAD(P)H. Here, we demonstrate that the imaging depth for NAD(P)H can be extended to over 700 µm in living engineered human multicellular microtissues by adopting multimode fiber (MMF)-based low-repetition-rate high-peak-power three-photon (3P) excitation of NAD(P)H at 1100 nm. This is achieved by having over 0.5 MW peak power at the band of 1100±25 nm through adaptively modulating multimodal nonlinear pulse propagation with a compact fiber shaper. Moreover, the 8-fold increase in pulse energy at 1100 nm enables faster imaging of monocyte behaviors in the living multicellular models. These results represent a significant advance for deep and dynamic metabolic and structural imaging of intact living biosystems. The modular design (MMF with a slip-on fiber shaper) is anticipated to allow wide adoption of this methodology for demanding in vivo and in vitro imaging applications, including cancer research, autoimmune diseases, and tissue engineering. © 2024, CC BY.

关键词： Multimode fibers

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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Editorial

IEEE Transactions on Biometrics, Behavior, and Identity science 2024年第3期6卷 289-289页

作者： Liao, Shengcai Liu, Xiaoming Tistarelli, Massimo Chellappa, Rama Kittler, Josef United Arab Emirates University College of Information Technology Al Ain United Arab Emirates Michigan State University Department of Computer Science and Engineering East LansingMI48824 United States University of Sassari Computer Vision Laboratory Sassari07100 Italy Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218 United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD21218 United States University of Surrey Centre for Vision Speech and Signal Processing GuildfordGU2 7XH United Kingdom

The 2022 International Joint Conference on Biometrics (IJCB 2022) was held at Abu Dhabi, United Arab Emirates, from October 10 to October 13, 2022. This marked the first biometrics conference held in the Middle East. The IJCB 2022 conference combined two major biometrics conferences: the IEEE Biometrics Theory, Applications, and systems (BTAS) conference and the International Conference on Biometrics (ICB). The IJCB 2022 conference was technically co-sponsored by both IAPR TC4 (Technical Committee on Biometrics) and the IEEE Biometrics Council. © 2019 IEEE.

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DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of computer science & Technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network