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Investigating Relationships between Balance Confidence and Balance Ability in Older Adults

Journal of Aging Research 2021年第1期2021卷 3214366页

作者： Thompson, Lara A. Badache, Mehdi Brusamolin, Joao Augusto Renno Savadkoohi, Marzieh Guise, Jelani De Paiva, Gabriel Velluto Suh, Pius Sanchez Guerrero, Pablo Shetty, Devdas Biomedical Engineering Program Department of Mechanical Engineering School of Engineering and Applied Sciences University of the District of Columbia 4200 Connecticut Ave. NW Washington 20008 DC United States Department of Mechanical Engineering School of Engineering and Applied Sciences University of the District of Columbia 4200 Connecticut Ave. NW Washington 20008 DC United States School of Engineering and Applied Sciences University of the District of Columbia 4200 Connecticut Ave. NW Washington 20008 DC United States Department of Electrical and Computer Engineering School of Engineering and Applied Sciences University of the District of Columbia 4200 Connecticut Ave. NW Washington 20008 DC United States

Increasing balance confidence in older individuals is important towards improving their quality of life and reducing activity avoidance. Here, we investigated if balance confidence (perceived ability) and balance performance (ability) in older adults were related to one another and would improve after balance training. The relationship of balance confidence in conjunction with balance performance for varied conditions (such as limiting vision, modifying somatosensory cues, and also base of support) was explored. We sought to determine if balance confidence and ability, as well as their relationship, could change after several weeks of training. Twenty-seven healthy participants were trained for several weeks during standing and walking exercises. In addition, seven participants with a higher risk of imbalance leading to falls (survivors of stroke) were also trained. Prior to and after training, balance ability and confidence were assessed via the Balance Error Scoring System (BESS) and Activities Specific Balance Confidence (ABC) Scale, respectively. Both groups showed improvements in balance abilities (i.e., BESS errors significantly decreased after training). Balance confidence was significantly higher in the healthy group than in the stroke group;however, ABC results reflected that balance confidence did not significantly increase after training for each. The correlations between balance ability and balance confidence were explored. Encouragingly, healthy participants displayed a negative correlation between BESS errors and ABC (i.e., enhancements in balance confidence (increases in ABC Scale results) were related to improvements in balance ability (decreases in BESS errors)). For the stroke participants, despite improvements in balance ability, our results showed that there was no relation to balance confidence (i.e., no correlation between BESS errors and ABC) in this group. © 2021 Lara A. Thompson et al.

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Robust versus Non-Robust Radiomic features: Machine Learning Based Models for NSCLC Lymphovascular Invasion

Robust versus Non-Robust Radiomic features: Machine Learning...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Seyyed Ali Hosseini Ghasem Hajianfar Elahe Hosseini Stijn Servaes Pedro Rosa-Neto Isaac Shiri Habib Zaidi Department of Medical Physics and Biomedical Engineering Tehran University of Medical Sciences Tehran Iran Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Department of Electrical and Computer Engineering Kharazmi University Tehran Iran Translational Neuroimaging Laboratory The McGill University Research Centre for Studies in Aging Douglas Hospital McGill University Montréal Québec Canada Department of Neurology and Neurosurgery McGill University Montréal Québec Canada Montreal Neurological Institute Montréal Québec Canada Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital Switzerland

The application of radiomic features for predicting and diagnosing lung cancer has been established in previous studies. However, radiomic features might be weak in terms of reproducibility. This study aimed to determine robust features against lung motion in a multicenter study, followed by choosing bold features using various feature selection methods and comparing them with the standard method results without considering the robustness of features. To this end, an in-house developed lung phantom was used. A specific motor was manufactured to simulate motion in 2 orthogonal directions. Lesions and tumors were delineated using a semi-automatic segmentation. In total, 105 radiomic features were extracted. Three different feature selections and five machine learning classifiers were implanted in this study. First, the Intraclass Correlation Coefficient (ICC) was calculated to show the variability of radiomic features and select robust features with an ICC of more than 90%. Next, the selected robust features went through various feature selection methods. Finally, the results of this method were compared with outcomes of regular feature selection without considering the robust features in multiple machine learning classifiers of an imbalanced clinical data set. Our result demonstrated that although considering the robustness of radiomic features has a minor negative impact on prediction accuracy, it has a significant productive impact on sensitivity.

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A multi-class decentralised event-triggered control framework for congestion and emission reduction in freeway networks

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IFAC-PapersOnLine 2018年第9期51卷 291-298页

作者： Pasquale, C. Sacone, S. Siri, S. Ferrara, A. Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

The present work deals with the problem of regulating traffic in freeway networks via ramp metering with the twofold objective of reducing congestion and traffic emissions. In this paper, an answer to this problem is given by proposing a multi-class decentralised control scheme that acts according to an event-triggered logic. Local control algorithms receive local measurements from the freeway network and make a periodic prediction on the local system evolution. On the basis of this information, the local control algorithms communicate suitable parameters to the local feedback controllers, which produce control actions able to jointly reduce the congestion and the traffic emissions in a neighbourhood of the controlled on-ramp. Since these local controllers are of the multi-class type, the resulting ramp metering control actions are distinguished for different classes of vehicles. The results obtained by applying such a decentralised scheme are compared in the paper with those achieved by using a centralised control framework. © 2018

关键词： Traffic congestion Controllers Decentralized control Emission control Control framework Decentralised control Emission reduction Event triggered controls Feedback controller Local controllers Ramp metering control Traffic emissions

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Control Theory Illustrates the Energy Efficiency in the Dynamic Reconfiguration of Functional Connectivity

arXiv

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arXiv 2022年

作者： Deng, Shikuang Li, Jingwei Yeo, B.T. Thomas Gu, Shi School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Research Center Jülich Jülich Germany Institute for Systems Neuroscience Medical Faculty Heinrich-Heine University Düsseldorf Düsseldorf Germany Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Centre for Sleep & Cognition Centre for Translational Magnetic Resonance Research Yong Loo Lin School of Medicine Singapore N.1 Institute for Health Institute for Digital Medicine National University of Singapore Singapore National University of Singapore Singapore Singapore Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA02129 United States

The brain’s functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical functional connectivity that transitions in a non-random order between multiple modes. Yet it remains unexplored how the transition facilitates the entire brain network as a dynamical system and what utility this mechanism for dynamic reconfiguration can bring over the widely used graph theoretical measurements. To address these questions, we propose to conduct an energetic analysis of functional brain networks using resting-state fMRI and behavioral measurements from the Human Connectome Project. Through comparing the state transition energy under distinct adjacent matrices, we justify that dynamic functional connectivity leads to 60% less energy cost to support the resting state dynamics than static connectivity when driving the transition through default mode network. Moreover, we demonstrate that combining graph theoretical measurements and our energy-based control measurements as the feature vector can provide complementary prediction power for the behavioral scores. Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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Cascaded Composite Turbulence and Misalignment: Statistical Characterization and Applications to Reconfigurable Intelligent Surface-Empowered Wireless Systems

arXiv

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arXiv 2021年

作者： Boulogeorgos, Alexandros-Apostolos A. Chatzidiamantis, Nestor Sandalidis, Harilaos G. Alexiou, Angeliki Di Renzo, Marco Digital Systems University of Piraeus Piraeus18534 Greece Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Department of Computer Science and Biomedical Informatics University of Thessaly Lamia35131 Greece Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-surYvette 91192 France

Reconfigurable intelligent surfaces (RISs) empowered high-frequency (HF) wireless systems are expected to become the supporting pillar for several reliability and data-rate hungry applications. Such systems are, however, sensitive to misalignment and atmospheric phenomena including turbulence. Most of the existing studies on the performance assessment of RIS-empowered wireless systems ignore the impact of the aforementioned phenomena. Motivated by this, the current contribution presents a theoretical framework for statistically characterizing cascaded composite turbulence and misalignment channels. More specifically, we present the probability density and cumulative distribution functions for the cascaded composite turbulence and misalignment channels. Building upon the derived analytical expressions and in order to demonstrate the applicability and importance of the extracted framework in different use case cases of interest, we present novel closed-form formulas that quantify the joint impact of turbulence and misalignment on the outage performance for two scenarios, namely cascaded multi-RIS-empowered free space optics (FSO) and terahertz (THz) wireless systems. For the aforementioned scenarios, the diversity order is extracted. In addition, we provide an insightful outage probability upper-bound for a third scenario that considers parallel multi-RIS-empowered FSO systems. Our results highlight the importance of accurately modeling both turbulence and misalignment when assessing the performance of such systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Alignment

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Scanner Independent Deep Learning-Based Segmentation Framework Applied to Mouse Embryos

Scanner Independent Deep Learning-Based Segmentation Framewo...

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IEEE International Ultrasonics Symposium

作者： Orlando Aristizabal Daniel H. Turnbull Jeffrey A. Ketterling Yao Wang Ziming Qiu Tongda Xu Hannah Goldman Jonathan Mamou Skirball Institute NYU School of Medicine New York City USA Lizzi Center for Biomedical Engineering Riverside Research New York City USA Electrical and Computer Engineering New York University New York City USA

ISBN: (数字)9781728154480

ISBN: (纸本)9781728154497

We have applied a deep learning framework, trained on mouse embryo images acquired with a 40 MHz annular array, to volumetric data acquired with a VisualSonics Vevo 3100 commercial scanner using a 40-MHz linear array. The deep learning framework was robust enough to accurately segment out the body and the brain ventricle from the 3D data generated by the commercial scanner. These results show that there is no need to retrain the algorithm with hundreds of new manually segmented datasets.

关键词： Deep learning Embryo Three-dimensional displays Transducers Pipelines Mice Arrays

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Depth-Awareness in a System for Mixed-Reality Aided Surgical Procedures 15th

Depth-Awareness in a System for Mixed-Reality Aided Surgical...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Sylos Labini, Mauro Gsaxner, Christina Pepe, Antonio Wallner, Juergen Egger, Jan Bevilacqua, Vitoantonio Institute for Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Electrical and Information Engineering Polytechnic University of Bari Bari Italy Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria

ISBN: (纸本)9783030267650

computer-assisted surgery is a trending topic in research, with many different approaches which aim at supporting surgeons in the operating room. Existing surgical planning and navigation solutions are often considered to be distracting, unintuitive or hard to interpret. In this work, we address this issue with an approach based on mixed reality devices like Microsoft HoloLens. We assess the depth sensing capabilities of Microsoft HoloLens, and the potential benefit they could bring to computer-assisted surgery applications. © 2019, Springer Nature Switzerland AG.

关键词： Mixed reality

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Prescient: A platform for the rapid evaluation of single cell produced antibody success using integrated microfluidic-enabled technology 23

Prescient: A platform for the rapid evaluation of single cel...

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23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019

作者： Wippold, Jose A. Wang, Han Tingling, Joseph Leibowitz, Julian Defigueiredo, Paul Han, Arum Department of Biomedical Engineering Texas A&M University College Station United States Department of Electrical and Computer Engineering Texas A&M University College Station United States Department of Biomedical Engineering Tsinghua University Beijing China Department of Microbial Pathogenesis and Immunology Texas A&M Health Science Center Bryan United States

ISBN: (纸本)9781733419000

Identifying antibodies (Abs) that neutralize infectious agents is the first step for developing therapeutics against those infectious agents. Conventional discovery methods rely on time-consuming and biased approaches that cannot identify the entire repertoire of neutralizing Abs (nAbs). Here we present PRESCIENT, a single-cell resolution droplet microfluidic platform that can identify nAbs through a direct functional assay. The major innovation realized throughout this work is the first high throughput system for the functional discovery of nAbs against infectious agents. Thus, PRESCIENT represents a revolutionary advancement towards nAb-based therapeutics and vaccine development that can address several global health priorities. © 2019 CBMS-0001.

关键词： Antibodies

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Virtual brightfield and fluorescence staining for Fourier ptychography via unsupervised deep learning

arXiv

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arXiv 2020年

作者： Wang, Ruihai Song, Pengming Jiang, Shaowei Yan, Chenggang Zhu, Jiakai Guo, Chengfei Bian, Zichao Wang, Tianbo Zheng, Guoan School of Automation Hangzhou Dianzi University Hangzhou310018 China Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States Department of Biomedical Engineering University of Connecticut StorrsCT06269 United States

Fourier ptychographic microscopy (FPM) is a computational approach geared towards creating high-resolution and large field-of-view images without mechanical scanning. To acquire color images of histology slides, it often requires sequential acquisitions with red, green, and blue illuminations. The color reconstructions often suffer from coherent artifacts that are not presented in regular incoherent microscopy images. As a result, it remains a challenge to employ FPM for digital pathology applications, where resolution and color accuracy are of critical importance. Here we report a deep learning approach for performing unsupervised image-to-image translation of FPM reconstructions. A cycle-consistent adversarial network with multiscale structure similarity loss is trained to perform virtual brightfield and fluorescence staining of the recovered FPM images. In the training stage, we feed the network with two sets of unpaired images: 1) monochromatic FPM recovery, and 2) color or fluorescence images captured using a regular microscope. In the inference stage, the network takes the FPM input and outputs a virtually stained image with reduced coherent artifacts and improved image quality. We test the approach on various samples with different staining protocols. High-quality color and fluorescence reconstructions validate its effectiveness. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Approximate Optimal Event-Triggered Control of Discounted-Cost Nonlinear Systems

Approximate Optimal Event-Triggered Control of Discounted-Co...

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第三十八届中国控制会议

作者： YANG Xiong HE Haibo School of Electrical and Information Engineering Tianjin University Department of Electrical Computer and Biomedical Engineering University of Rhode Island

In this paper, we investigate an event-triggered optimal control problem of discounted-cost nonlinear systems. Initially,we present the event-triggered Hamilton-Jacobi-Bellman equation(ET-HJBE) associated with the solution of the optimal control problem. Meanwhile, the event-triggering condition is proposed. Then, within the framework of reinforcement learning, we utilize a unique critic network to approximately solve the ET-HJBE. The weight vector used in the critic network is tuned via the combination of the gradient descent method and the experience replay technique. An advantage of the weight tuning rule is that the historical state data can be fully used. Moreover, by employing the classic Lyapunov approach, we prove that all the signals in the closed-loop system are uniformly ultimately bounded. Finally, we verify the effectiveness of the present event-triggered control method through simulations of the continuous stirred tank reactor system.

关键词： Approximate optimal control Discounted-cost Event-triggered control Reinforcement learning

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