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Research Square 2021年

作者： Sebesta, Charles Torres, Daniel Wang, Boshuo Li, Zhongxi Duret, Guillaume Jiang, Kaiyi Xiao, Zhen Zhang, Qingbo Colvin, Vicki Goetz, Stefan M. Peterchev, Angel V. Dierick, Herman Bao, Gang Robinson, Jacob T. Department of Bioengineering Rice University HoustonTX United States Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Psychiatry & Behavioral Sciences School of Medicine Duke University DurhamNC United States Department of Electrical and Computer Engineering School of Engineering Duke University DurhamNC United States Department of Neurosurgery School of Medicine Duke University DurhamNC United States Department of Biomedical Engineering School of Engineering Duke University DurhamNC United States Department of Neuroscience Baylor College of Medicine HoustonTX United States Department of Human and Molecular Genetics Baylor College of Medicine HoustonTX United States Department of Chemistry Brown University ProvidenceRI United States

Precisely timed activation of genetically targeted cells is a powerful tool for studying neural circuits and controlling cell-based therapies. Magnetic control of cell activity or "magnetogenetics" using magnetic nanoparticle heating of temperature-sensitive ion channels enables remote, non-invasive activation of neurons for deep-tissue applications and studies of freely behaving animals. However, the in vivo response time of thermal magnetogenetics is currently tens of seconds, which prevents the precise temporal modulation of neural activity similar to light-based optogenetics. Moreover, magnetogenetics has not provided a means to selectively activate multiple channels to drive behavior. Here we demonstrate that by combining magnetic nanoparticles with a rate-sensitive thermoreceptor (TRPA1-A) it is possible to achieve sub-second behavioral responses in Drosophila melanogaster. Furthermore, by tuning the properties of magnetic nanoparticles to respond to different magnetic field strengths and frequencies, we can achieve fast, multi-channel stimulation, analogous to optogenetic stimulation with different wavelengths of light. These results bring magnetogenetics closer to the temporal resolution and multiplexed stimulation possible with optogenetics while maintaining the minimal invasiveness and deep-tissue stimulation only possible by magnetic control. © 2021, CC BY.

关键词： Cells

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DeepResp: Deep learning solution for respiration-induced B0 fluctuation artifacts in multi-slice GRE

arXiv

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arXiv 2020年

作者： An, Hongjun Shin, Hyeong-Geol Ji, Sooyoen Jung, Woojin Oh, Sehong Shin, Dongmyung Park, Juhyung Lee, Jongho Laboratory for Imaging Science and Technology Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Division of Biomedical Engineering Hankuk University of Foreign Studies Gyeonggi-do Korea Republic of

Respiration-induced B0 fluctuation corrupts MRI images by inducing phase errors in k-space. A few approaches such as navigator have been proposed to correct for the artifacts at the expense of sequence modification. In this study, a new deep learning method, which is referred to as DeepResp, is proposed for reducing the respiration-artifacts in multi-slice gradient echo (GRE) images. DeepResp is designed to extract the respiration-induced phase errors from a complex image using deep neural networks. Then, the network-generated phase errors are applied to the k-space data, creating an artifact-corrected image. For network training, the computer-simulated images were generated using artifact-free images and respiration data. When evaluated, both simulated images and in-vivo images of two different breathing conditions (deep breathing and natural breathing) show improvements (simulation: normalized root-mean-square error (NRMSE) from 7.8 ± 5.2% to 1.3 ± 0.6%;structural similarity (SSIM) from 0.88 ± 0.08 to 0.99 ± 0.01;ghost-to-signal-ratio (GSR) from 7.9 ± 7.2% to 0.6 ± 0.6%;deep breathing: NRMSE from 13.9 ± 4.6% to 5.8 ± 1.4%;SSIM from 0.86 ± 0.03 to 0.95 ± 0.01;GSR 20.2 ± 10.2% to 5.7 ± 2.3%;natural breathing: NRMSE from 5.2 ± 3.3% to 4.0 ± 2.5%;SSIM from 0.94 ± 0.04 to 0.97 ± 0.02;GSR 5.7 ± 5.0% to 2.8 ± 1.1%). Our approach does not require any modification of the sequence or additional hardware, and may therefore find useful applications. Furthermore, the deep neural networks extract respiration-induced phase errors, which is more interpretable and reliable than results of end-to-end trained networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A fuzzy logic controller for tasks scheduling using unreliable cloud resources

arXiv

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arXiv 2020年

作者： Oikonomou, Panagiotis Kolomvatsos, Kostas Tziritas, Nikos Theodoropoulos, Georgios Loukopoulos, Thanasis Stamoulis, Georgios Computer Science and Engineering Southern Univ. of Science and Technology Shenzhen China Computer Science and Telecomm University of Thessaly Lamia Greece Comp. Science and Biomedical Informatics University of Thessaly Lamia Greece Electrical and Computer Engineering University of Thessaly Volos Greece

The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you-go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the quality of solutions delivered by scheduling algorithms. In this paper, we present a dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS algorithm) for the management of scientific workflows in Cloud. Our model minimizes both the makespan and the monetary cost by dynamically selecting reliable or unreliable virtualized resources. For covering the uncertainty in decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing model of the resources that will host every task. We evaluate the performance of the proposed algorithm using real workflow applications being tested under the assumption of different probabilities regarding the revocation of unreliable resources. Numerical results depict the performance of the proposed approach and a comparative assessment reveals the position of the paper in the relevant literature. Copyright © 2020, The Authors. All rights reserved.

关键词： Fuzzy logic

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Autotuning plug-and-play algorithms for MRI

arXiv

引用

arXiv 2020年

作者： Shastri, Saurav K. Ahmad, Rizwan Schniter, Philip Dept. of Electrical and Computer Engineering Ohio State University ColumbusOH43210 United States Dept. of Biomedical Engineering Ohio State University ColumbusOH43210 United States

For magnetic resonance imaging (MRI), recently proposed "plug-and-play" (PnP) image recovery algorithms have shown remarkable performance. These PnP algorithms are similar to traditional iterative algorithms like FISTA, ADMM, or primal-dual splitting (PDS), but differ in that the proximal update is replaced by a call to an application-specific image denoiser, such as BM3D or DnCNN. The fixed-points of PnP algorithms depend upon an algorithmic stepsize parameter, however, which must be tuned for optimal performance. In this work, we propose a fast and robust auto-tuning PnP-PDS algorithm that exploits knowledge of the measurement-noise variance that is available from a pre-scan in MRI. Experimental results show that our algorithm converges very close to genie-tuned performance, and does so significantly faster than existing autotuning approaches. © 2020, CC BY.

关键词： Magnetic resonance imaging

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Cooperative adaptive learning control for a group of nonholonomic UGVs by output feedback

arXiv

引用

arXiv 2020年

作者： Dong, Xiaonan Stegagno, Paolo Yuan, Chengzhi Zeng, Wei Department of Mechanical Industrial and Systems Engineering University of Rhode Island KingstonRI02881 United States Department of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI02881 United States School of Mechanical and Electrical Engineering Longyan University Longyan364012 China

A high-gain observer-based cooperative deterministic learning (CDL) control algorithm is proposed in this chapter for a group of identical unicycle-type unmanned ground vehicles (UGVs) to track over desired reference trajectories. For the vehicle states, the positions of the vehicles can be measured, while the velocities are estimated using the high-gain observer. For the trajectory tracking controller, the radial basis function (RBF) neural network (NN) is used to online estimate the unknown dynamics of the vehicle, and the NN weight convergence and estimation accuracy is guaranteed by CDL. The major challenge and novelty of this chapter is to track the reference trajectory using this observer-based CDL algorithm without the full knowledge of the vehicle state and vehicle model. In addition, any vehicle in the system is able to learn the knowledge of unmodeled dynamics along the union of trajectories experienced by all vehicle agents, such that the learned knowledge can be re-used to follow any reference trajectory defined in the learning phase. The learning-based tracking convergence and consensus learning results, as well as using learned knowledge for tracking experienced trajectories, are shown using the Lyapunov method. Simulation is given to show the effectiveness of this algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

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Predicting the functional effects of human non-coding variants based on stacking ensemble learning

Predicting the functional effects of human non-coding varian...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zepeng Liu Xiao-Tai Huang Kei Hang Katie Chan Lin Gao School of Computer and Technology Xidian University Xi’an Shaanxi China Guangzhou institute of technology Xidian University Guangzhou Guangdong China Department of Electrical Engineering City University of Hong Kong Hong Kong China Department of Biomedical Sciences City University of Hong Kong Hong Kong China Department of Epidemiology and Center for Global Cardiometabolic Health Brown University Providence Rhode Island United States of America

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Predicting the functional impact of genetic variants in non-coding regions of the human genome can aid in the elucidation of the etiology of diseases or traits. In recent years, an increasing number of methods to predict the impact of sequence variation in non-coding regions of the human genome have been developed. However, most of current studies are limited to predict specific types of non-coding variants. To address this problem, here we propose a non-coding SNVs prediction method based on stacking integration strategy. The method consists of three stacking models built using the same strategy based on different causality assumptions to predict functional, pathogenic, and cancer driver non-coding SNVs, respectively. We demonstrate that our method outperforms the other seven methods. In addition, a comparison of our proposed model with other methods for non-coding de novo mutations in autism spectrum disease reveals that our model has the highest discriminative ability, indicating that its performance is stable and superior in different scenarios.

关键词： Autism Biological system modeling Stacking Redundancy Genomics Predictive models Benchmark testing

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Enhanced prediction of ionic liquid toxicity using a meta-ensemble learning framework with data augmentation

Artificial Intelligence Chemistry

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Artificial Intelligence Chemistry 2025年第1期3卷

作者： Safa Sadaghiyanfam Hiqmet Kamberaj Yalcin Isler Izmir Katip Celebi University Department of Biomedical Technologies Balatcik Campus Cigli 35620 Izmir Turkey International Balkan University Department of Computer Engineering Skopje North Macedonia National Institute of Physics Fan Noli Square Academy of Sciences of Albania Tirana Albania Alanya Alaaddin Keykubat University Department of Electrical and Electronics Engineering Kestel Campus Alanya 07425 Antalya Turkey

Ionic liquids are unique in their properties and potential to be green solvents. Still, the toxicity concern remains, compelling the need for excellent predictive models for safe design and application. This work reports the introduction of a general, robust meta-ensemble learning framework for predicting the toxicity of ionic liquids using molecular descriptors and fingerprints. The proposed model incorporates the Random Forest, Support Vector Regression, Categorical Boosting, Chemical Convolutional Neural Network as a base classifier and an Extreme Gradient Boosting meta-classifier. The framework uses Recursive Feature Elimination for feature selection and GridSearchCV for tuning the best hyperparameters. Without augmentation of the data, the RMSE equals 0.38, MAE equals 0.29, coefficient of determination ( R 2 ) equals 0.87, and Pearson correlation equals 0.94. Data augmentation further improved model performance: RMSE = 0.06, MAE = 0.024, R 2 = 0.99, and a Pearson correlation of 0.99. In addition, this indicates that the data-augmented model outperforms all existing models with prominence in its strength and prediction capacity. Thus, the present framework provides a superior tool for computer-aided molecular design of safer and more effective ionic liquids.

关键词： Ionic liquids (ILs) Machine learning Meta-ensemble learning Data augmentation Recursive Feature Elimination (RFE) Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) Chemception Feature reduction GridSearchCV

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Invited: Drug discovery approaches using quantum machine learning

arXiv

引用

arXiv 2021年

作者： Li, Junde Alam, Mahabubul Sha, Congzhou M. Wang, Jian Dokholyan, Nikolay V. Ghosh, Swaroop Department of Computer Science and Engineering Penn State University University Park United States Department of Electrical Engineering Penn State University University Park United States Department of Engineering Science and Mechanics Penn State University University Park United States Department of Pharmacology Penn State College of Medicine Hershey United States Department of Biochemistry & Molecular Biology Penn State College of Medicine Hershey United States Departments of Chemistry and Biomedical Engineering Penn State University University Park United States

Traditional drug discovery pipeline takes several years and cost billions of dollars. Deep generative and discriminative models are widely adopted to assist in drug development. Classical machines cannot efficiently produce atypical patterns of quantum computers which might improve the training quality of learning tasks. We propose a suite of quantum machine learning techniques e.g., generative adversarial network (GAN), convolutional neural network (CNN) and variational auto-encoder (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively. © 2021, CC BY.

关键词： Molecules

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A hybrid volume-surface integral equation method for rapid electromagnetic simulations in MRI

arXiv

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arXiv 2022年

作者： Giannakopoulos, Ilias I. Guryev, Georgy D. Serrallés, José E.C. Paška, Jan Zhang, Bei Daniel, Luca White, Jacob K. Collins, Christopher M. Lattanzi, Riccardo The Bernard and Irene Schwartz Center for Biomedical Imaging Department of Radiology New York University Grossman School of Medicine NY10016 United States The Advanced Imaging Research Center UT Southwestern Medical Center 2201 Inwood Rd DallasTX75390 United States The Research Laboratory of Electronics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

Objective: We developed a hybrid volume surface integral equation (VSIE) method based on domain decomposition to perform fast and accurate magnetic resonance imaging (MRI) simulations that include both remote and local conductive elements. Methods: We separated the conductive surfaces present in MRI setups into two domains and optimized electromagnetic (EM) modeling for each case. Specifically, interactions between the body and EM waves originating from local radiofrequency (RF) coils were modeled with the precorrected fast Fourier transform, whereas the interactions with remote conductive surfaces (RF shield, scanner bore) were modeled with a novel cross tensor train-based algorithm. We compared the hybrid-VSIE with other VSIE methods for realistic MRI simulation setups. Results: The hybrid-VSIE was the only practical method for simulation using 1 mm voxel isotropic resolution (VIR). For 2 mm VIR, our method could be solved at least 23 times faster and required 760 times lower memory than traditional VSIE methods. Conclusion: The hybrid-VSIE demonstrated a marked improvement in terms of convergence times of the numerical EM simulation compared to traditional approaches in multiple realistic MRI scenarios. Significance: The efficiency of the novel hybrid-VSIE method could enable rapid simulations of complex and comprehensive MRI setups. © 2022, CC BY.

关键词： Magnetic resonance imaging

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IPMN RISK ASSESSMENT UNDER FEDERATED LEARNING PARADIGM

arXiv

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arXiv 2024年

作者： Pan, Hongyi Hong, Ziliang Durak, Gorkem Keles, Elif Aktas, Halil Ertugrul Taktak, Yavuz Medetalibeyoglu, Alpay Zhang, Zheyuan Velichko, Yury Spampinato, Concetto Schoots, Ivo Bruno, Marco J. Tiwari, Pallavi Bolan, Candice Gonda, Tamas Miller, Frank Keswani, Rajesh N. Wallace, Michael B. Xu, Ziyue Bagci, Ulas Department of Radiology Northwestern University ChicagoIL United States Department of Radiology Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Internal Medicine Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Department of Radiology and Nuclear Medicine Erasmus Medical Center Rotterdam Netherlands Department of Gastroenterology and Hepatology Erasmus Medical Center Rotterdam Netherlands Department of Biomedical Engineering and Radiology University of Wisconsin MadisonWI United States Division of Gastroenterology and Hepatology Mayo Clinic JacksonvilleFL United States Division of Gastroenterology and Hepatology New York University New YorkNY United States Department of Gastroenterology and Hepatology Northwestern University ChicagoIL United States NVIDIA BethesdaMD United States

Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset. This dataset includes 652 T1-weighted and 655 T2-weighted MRI images, accompanied by corresponding IPMN risk scores from 7 leading medical institutions, making it the largest and most diverse dataset for IPMN classification to date. We assess the performance of DenseNet-121 in both centralized and federated settings for training on distributed data. Our results demonstrate that the federated learning approach achieves high classification accuracy comparable to centralized learning while ensuring data privacy across institutions. This work marks a significant advancement in collaborative IPMN classification, facilitating secure and high-accuracy model training across multiple centers. © 2024, CC BY.

关键词： Federated learning

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