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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

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Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

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ARCSnake: Reconfigurable snake-like robot with Archimedean screw propulsion for multi-domain mobility

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arXiv 2021年

作者： Richter, Florian Gavrilov, Peter V. Lam, Hoi Man Degani, Amir Yip, Michael C. The Department of Electrical and Computer Engineering University of California San Diego San DiegoCA92093 United States The Department of Mechanical and Aerospace Engineering University of California San Diego San DiegoCA92093 United States The Faculty of Civil and Environmental Engineering The Technion Autonomous Systems Program Technion - Israel Institute of Technology Haifa3200003 Israel

Exploring and navigating in extreme environments, such as caves, oceans, and planetary bodies, are often too hazardous for humans, and as such, robots are possible surrogates. These robots are met with significant locomotion challenges that require traversing a wide range of surface roughnesses and topologies. Previous locomotion strategies, involving wheels or ambulatory motion, such as snake platforms, have success on specific surfaces but fail in others which could be detrimental in exploration and navigation missions. In this paper, we present a novel approach that combines snake-like robots with an Archimedean screw locomotion mechanism to provide multiple, effective mobility strategies in a large range of environments, including those that are difficult to traverse for wheeled and ambulatory robots. This work develops a robotic system called ARCSnake to demonstrate this locomotion principle and tested it in a variety of different terrains and environments in order to prove its controllable, multi-domain, navigation capabilities. These tests show a wide breadth of scenarios that ARCSnake can handle, hence demonstrating its ability to traverse through extreme terrains. Copyright © 2021, The Authors. All rights reserved.

关键词： Screws

Untrained, Physics-Informed Neural Networks for Structured Illumination Microscopy

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arXiv 2022年

作者： Burns, Zachary Liu, Zhaowei Department of Electrical and Computer Engineering University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Material Science and Engineering Program University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Center for Memory and Recording Research University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States

In recent years there has been great interest in using deep neural networks (DNN) for super-resolution image reconstruction including for structured illumination microscopy (SIM). While these methods have shown very promising results, they all rely on data-driven, supervised training strategies that need a large number of ground truth images, which is experimentally difficult to realize. Additionally, such trained methods are prone to overfitting and often fail to generalize well to data that is out of the training distribution making them unsuitable as tools of discovery. For SIM imaging, there exists a need for a flexible, general, and open-source reconstruction method that can be readily adapted to different forms of structured illumination. We demonstrate that we can combine a deep neural network with the forward model of the structured illumination process to reconstruct sub-diffraction images without training data. The resulting physics-informed neural network (PINN) can be optimized on a single set of diffraction limited sub-images and thus doesn’t require any training set. We show with simulated and experimental data that this PINN can be applied to a wide variety of SIM methods by simply changing the known illumination patterns used in the loss function and can achieve resolution improvements that match well with theoretical expectations. © 2022, CC BY.

关键词： Deep neural networks

A flexible head fixation system for optical imaging and electrophysiology in awake mice

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A flexible head fixation system for optical imaging and elec...

Clinical and Translational Biophotonics, Translational 2020

作者： Thunemann, Martin Mächler, Phillip Fomin-Thunemann, Natalie Lu, Yichen Liu, Xin Kuzum, Duygu Devor, Anna Department of Neurosciences University of California San Diego San DiegoCA92093 United States Department of Electrical and Computer Engineering University of California San Diego San DiegoCA92093 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Department of Radiology University of California San Diego San DiegoCA92093 United States Athinoula A. Martinos Center for Biomedical Imaging CharlestownMA02129 United States

ISBN: (纸本)9781943580743

关键词： Electrophysiology

Speckle statistics of biological tissues in optical coherence tomography

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arXiv 2021年

作者： Ge, Gary R. Rolland, Jannick P. Parker, Kevin J. The Institute of Optics University of Rochester RochesterNY14627 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States Department of Biomedical Engineering University of Rochester NY14627 United States Center for Visual Science University of Rochester NY14627 United States

The speckle statistics of optical coherence tomography images of biological tissue have been studied using several historical probability density functions. A recent hypothesis implies that underlying power-law distributions in the medium structure, such as the fractal branching vasculature, will contribute to power-law probability distributions of speckle statistics. Specifically, these are the Burr type XII distribution for speckle amplitude, the Lomax distribution for intensity, and the generalized logistic distribution for log amplitude. In this study, these three distributions are fitted to histogram data from nine optical coherence tomography scans of various biological tissues and samples. The distributions are also compared with conventional distributions such as the Rayleigh, K, and gamma distributions. The results indicate that these newer distributions based on power laws are, in general, more appropriate models and support the plausibility of their use for characterizing biological tissue. Potentially, the governing power-law parameter of these distributions could be used as a biomarker for tissue disease or pathology. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical tomography

Torsional periodic lattice distortions and diffraction of twisted 2D materials

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arXiv 2022年

作者： Sung, Suk Hyun Goh, Yin Min Yoo, Hyobin Engelke, Rebecca Xie, Hongchao Zhang, Kuan Li, Zidong Ye, Andrew Deotare, Parag B. Tadmor, Ellad B. Mannix, Andrew J. Park, Jiwoong Zhao, Liuyan Kim, Philip Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Physics University of Michigan Ann ArborMI48109 United States Department of Physics Sogang University Seoul04107 Korea Republic of Department of Physics Harvard University CambridgeMA02138 United States Aerospace Engineering and Mechanics University of Minnesota MinneapolisMN55455 United States Electrical and Computer Engineering Department University of Michigan Ann ArborMI48109 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Department of Chemistry University of Chicago ChicagoIL60637 United States

Twisted 2D materials form complex moiré structures that spontaneously reduce symmetry through picoscale deformation within a mesoscale lattice. We show twisted 2D materials contain a torsional displacement field comprised of three transverse periodic lattice distortions (PLD). The torsional PLD amplitude provides a single order parameter that concisely describes the structural complexity of twisted bilayer moirés. Moreover, the structure and amplitude of a torsional periodic lattice distortion is quantifiable using rudimentary electron diffraction methods sensitive to reciprocal space. In twisted bilayer graphene, the torsional PLD begins to form at angles below 3.89° and the amplitude reaches 8 pm around the magic angle of 1. 1°. At extremely low twist angles (e.g. below 0.25°) the amplitude increases and additional PLD harmonics arise to expand Bernal stacked domains separated by well defined solitonic boundaries. The torsional distortion field in twisted bilayer graphene is analytically described and has an upper bound of 22.6 pm. Similar torsional distortions are observed in twisted WS2, CrI3, and WSe2/MoSe2 © 2022, CC BY.

关键词： Graphene

Exploring the Non - Hermitian Topology of Exceptional Points in Lasers and Fiber Loops

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Exploring the Non - Hermitian Topology of Exceptional Points...

Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Alexander Schumer Hadiseh Nasari Yuzhou G. N. Liu Gisela Lopez-Galmiche Jason Leshin Helena E. Lopez-Aviles Lei Ding Yousef Alahmadi Absar U. Hassan Qi Zhong Stefan Rotter Patrick LiKamWa Demetrios N. Christodoulides Mercedeh Khajavlkhan Institutefor Theoretical Physics Vienna University of Technology (TU Wien) Vienna Austria Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles USA CREOL The College of Optics & Photonics University of Central Florida Orlando FL USA Center of Excellence for Telecomm Applications Joint Centers of Excellence Program King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia Department of Physics & Astronomy University of Southern California Los Angeles CA USA

ISBN: (纸本)9798350345995

When non-Hermitian eigenvalue surfaces form intertwined Riemann surfaces, the corresponding non-Hermitian singularities, also know as exceptional points (EPs), are located at the center of this specific topology. Various experimental platforms have recently been presented in which interesting new physics associated with these branch point singularities can be explored. In particular, it has been shown that dynamically encircling EPs may lead to a so-called chiral state transfer. The chirality in this context refers to the fact that in such an encircling protocol, the final state at the end of an EP -enclosing loop solely depends on the encircling direction (clockwise or counterclockwise), but not on the initial state. Such a chiral behavior is intrinsically linked to the state vector deviating from adiabatically following the system's eigenstates associated with the eigenvalue surfaces; instead, due to the non-Hermitian violation of the adiabatic theorem, non-adiabatic jumps let the system undergo transitions from lossy eigenstates to eigenstates with gain. Here, we present two experiments that implement concepts that go beyond this established paradigm.

关键词： Attenuation Chirality Fiber lasers Lasers Surfaces Waveguides

Fluorinated Graphene-Coated N95 Respirators for Enhanced Protection against Viral Accumulation and Transmission

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Advanced engineering Materials 2025年

作者： He, Fengjie Zhang, Jiongyu Zhai, Shengjie Liu, Changchun Zhao, Hui Department of Mechanical Engineering University of Nevada Las Vegas 4505 S Maryland Parkway Las Vegas89154 United States Department of Biomedical Engineering University of Connecticut Health Center 263 Farmington Avenue FarmingtonCT06030 United States Department of Electrical and Computer Engineering University of Nevada Las Vegas 4505 S Maryland Parkway Las Vegas89154 United States

Highly contagious respiratory infection diseases such as COVID-19 can be transmitted by inhaling virus laden liquid droplets and short-range aerosols, released by an infected person. Particularly, in hospitals, spraying of the respiratory droplets containing pathogens from the conjunctiva or mucus of a susceptible person plays a key role in transferring the infectious diseases. N95 filtering respirators are a critical personal protective equipment. However, due to concerns over the virus accumulation on the N95 respirators, there is an urgent need to make the N95 respirators less contaminated. To address this critical issue, a one-step spray coating approach is developed to coat the fluorinated graphene (FG) nanosheet onto the N95 respirators. The synergistic effect of the FG nanosheet with a low surface energy and the increased surface roughness by FG on the respirator's surface makes it superhydrophobic. For respiratory droplets like saliva and mucus, the FG-coated respirators also show excellent superhydrophobicity. Furthermore, the ability against virus accumulation on the FG-coated respirators is tested by using the mucus droplets laden with SARS-CoV-2. The results show that FG-coated respirators largely increase the virus repel efficiency even under multiple contacts and effectively reduce the virus accumulation. © 2025 The Author(s). Advanced engineering Materials published by Wiley-VCH GmbH.

关键词： Sprayed coatings

Clinical characteristics of hospitalized children with community-acquired pneumonia and respiratory infections: Using machine learning approaches to support pathogen prediction at admission

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Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi 2023年第4期56卷 772-781页

作者： Tu-Hsuan Chang Yun-Chung Liu Siang-Rong Lin Pei-Hsin Chiu Chia-Ching Chou Luan-Yin Chang Fei-Pei Lai Department of Pediatrics Chi Mei Medical Center Tainan City Taiwan. Department of Pediatrics National Taiwan University Hospital College of Medicine National Taiwan University Taipei City Taiwan. Institute of Applied Mechanics National Taiwan University Taipei City Taiwan. Institute of Applied Mechanics National Taiwan University Taipei City Taiwan. Electronic address: ccchou@iam.ntu.edu.tw. Department of Pediatrics National Taiwan University Hospital College of Medicine National Taiwan University Taipei City Taiwan. Electronic address: lychang@ntu.edu.tw. Graduate Institute of Biomedical Electronics and Bioinformatics Taipei City National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei City Taiwan Department of Electrical Engineering National Taiwan University Taipei City Taiwan.

BACKGROUND:Acute respiratory infections (ARIs) are common in children. We developed machine learning models to predict pediatric ARI pathogens at admission. METHODS:We included hospitalized children with respiratory infections between 2010 and 2018. Clinical features were collected within 24 h of admission to construct models. The outcome of interest was the prediction of 6 common respiratory pathogens, including adenovirus, influenza virus types A and B, parainfluenza virus (PIV), respiratory syncytial virus (RSV), and Mycoplasma pneumoniae (MP). Model performance was estimated using area under the receiver operating characteristic curve (AUROC). Feature importance was measured using Shapley Additive exPlanation (SHAP) values. RESULTS:A total of 12,694 admissions were included. Models trained with 9 features (age, event pattern, fever, C-reactive protein, white blood cell count, platelet count, lymphocyte ratio, peak temperature, peak heart rate) achieved the best performance (AUROC: MP 0.87, 95% CI 0.83-0.90; RSV 0.84, 95% CI 0.82-0.86; adenovirus 0.81, 95% CI 0.77-0.84; influenza A 0.77, 95% CI 0.73-0.80; influenza B 0.70, 95% CI 0.65-0.75; PIV 0.73, 95% CI 0.69-0.77). Age was the most important feature to predict MP, RSV and PIV infections. Event patterns were useful for influenza virus prediction, and C-reactive protein had the highest SHAP value for adenovirus infections. CONCLUSION:We demonstrate how artificial intelligence can assist clinicians identify potential pathogens associated with pediatric ARIs upon admission. Our models provide explainable results that could help optimize the use of diagnostic testing. Integrating our models into clinical workflows may lead to improved patient outcomes and reduce unnecessary medical costs.

关键词： Children Community-acquired pneumonia Machine learning Pathogens prediction Respiratory infections