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Applications of nanogenerators for biomedical engineering and healthcare systems

InfoMat 2022年第2期4卷 53-109页

作者： Wanli Wang Jinbo Pang Jie Su Fujiang Li Qiang Li Xiaoxiong Wang Jingang Wang Bergoi Ibarlucea Xiaoyan Liu Yufen Li Weijia Zhou Kai Wang Qingfang Han Lei Liu Ruohan Zang Mark HRümmeli Yang Li Hong Liu Han Hu Gianaurelio Cuniberti College of Electrical Engineering Qingdao UniversityQingdaoChina Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong Institute for Advanced Interdisciplinary Research(iAIR)University of JinanJinanChina College of Electronic Information Qingdao UniversityQingdaoChina Department of Pediatric Surgery The Affiliated Hospital of Qingdao UniversityQingdaoChina College of Physics Qingdao UniversityQingdaoChina Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresdenGermany Center for Advancing Electronics Dresden Technische Universität DresdenDresdenGermany College of Biological Science and Technology University of JinanJinanChina School of Chemistry and Chemical Engineering University of JinanShandongJinanChina College of Energy Soochow Institute for Energy and Materials InnovationsSoochow UniversitySuzhouChina Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province Soochow UniversitySuzhouChina Centre of Polymer and Carbon Materials Polish Academy of SciencesZabrzePoland Institute for Complex Materials IFW DresdenDresdenGermany Institute of Environmental Technology VŠB-Technical University of OstravaOstravaCzech Republic School of Information Science and Engineering University of JinanJinanChina Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinanChina State Key Laboratory of Crystal Materials Center of Bio&Micro/Nano Functional MaterialsShandong UniversityJinanChina College of Chemical Engineering China University of Petroleum(East China)QingdaoChina Dresden Center for Computational Materials Science Technische Universität DresdenDresdenGermany The Dresden Center for Intelligent Materials(DCIM) Technische Universität DresdenDresdenGermany

The dream of human beings for long living has stimulated the rapid development of biomedical and healthcare ***,conventional biomedical and healthcare devices have shortcomings such as short service life,large equipment size,and high potential safety ***,the power supply for conventional implantable device remains predominantly *** emerging nanogenerators,which harvest micro/nanomechanical energy and thermal energy from human beings and convert into electrical energy,provide an ideal solution for self-powering of biomedical *** combination of nanogenerators and biomedicine has been accelerating the development of self-powered biomedical *** article first introduces the operating principle of nanogenerators and then reviews the progress of nanogenerators in biomedical applications,including power supply,smart sensing,and effective ***,the microbial disinfection and biodegradation performances of nanogenerators have been ***,the protection devices have been discussed such as face mask with air filtering function together with real-time monitoring of human health from the respiration and heat ***,the nanogenerator devices have been categorized by the types of mechanical energy from human beings,such as the body movement,tissue and organ activities,energy from chemical reactions,and gravitational potential ***,the challenges and future opportunities in the applications of nanogenerators are delivered in the conclusive remarks.

关键词： biomedical engineering healthcare implantable devices nanogenerators self-powered devices sensors

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A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs

arXiv

引用

arXiv 2023年

作者： Yang, Sikun Zha, Hongyuan School of Computing and Information Technology Great Bay University Dongguan523000 China Great Bay Institute for Advanced Study Great Bay University China Dongguan Key Laboratory for Data Science and Intelligent Medicine Great Bay University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Chinese University of Hong Kong Shenzhen China

Continuously-observed event occurrences, often exhibit self- and mutually-exciting effects, which can be well modeled using temporal point processes. Beyond that, these event dynamics may also change over time, with certain periodic trends. We propose a novel variational auto-encoder to capture such a mixture of temporal dynamics. More specifically, the whole time interval of the input sequence is partitioned into a set of sub-intervals. The event dynamics are assumed to be stationary within each sub-interval, but could be changing across those sub-intervals. In particular, we use a sequential latent variable model to learn a dependency graph between the observed dimensions, for each sub-interval. The model predicts the future event times, by using the learned dependency graph to remove the non-contributing influences of past events. By doing so, the proposed model demonstrates its higher accuracy in predicting inter-event times and event types for several real-world event sequences, compared with existing state of the art neural point processes. © 2023, CC BY.

关键词： Dynamics

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Cell-type composition analysis of scRNA-seq data with deep convolution neural network

Research Square

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Research Square 2020年

作者： Ma, He Fang, Zhihao Liu, Zongbin Chen, Yan College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China College of Engineering Peking University Beijing China Shenzhen Zigzag Biotechnology Ltd Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Background: With the rapid development of single-cell RNA sequencing (scRNA-seq), more large-scale single-cell sequencing data has been generated. Due to the continuous increase of single-cell sequencing data, the analysis of cell-type composition from single-cell transcriptomics has also to face huge challenges. Since the emergence of scRNA-seq technology, the size of sequencing datasets has grown more than 1 million times in just over a decade. Meanwhile, as more gene markers are discovered, the data dimension of single-cell sequencing becomes higher. All of these put forward more stringent requirements on data dimensionality reduction and clustering algorithms. Under the constraints of practical factors such as occurrence of noise and dropouts and the limitation of overhead, it is also required an effective and efficient method that can obtain accurate analysis results in a very short time, and has a competitive algorithm stability. Results: We present scCAE, an effective and efficient method based on convolution autoencoder that can accurately and rapidly analyze cell-type composition from single-cell transcriptomics datasets. Our method achieved the best results in the data sets that simulate the cell differentiation process among existing methods, which achieved the ARI of 69.64% and 68.83% at 10 and 25 clusters tasks. And, in the case of different dropouts, our method also works well. When the sparsity level of data metric is 71%, scCAE can achieved the ARI of 45.29%, which is the highest of the existing methods. In terms of algorithm overhead, our method has also achieved good results by comparing with several existing methods. It takes less time than most methods and takes up much less memory than other algorithms based neural networks. Conclusions: Our method, scCAE, has more accurate and reasonable results in the analysis of cell-types composition. And, because of the design of imputer, it can deal with a large number of dropouts in the data matrix. Becau

关键词： Convolution

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Exploiting interpretable patterns for flow prediction in dockless bike sharing systems

arXiv

引用

arXiv 2020年

作者： Gu, Jingjing Zhou, Qiang Yang, Jingyuan Liu, Yanchi Zhuang, Fuzhen Zhao, Yanchao Xiong, Hui Department of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China George Mason University United States Rutgers University United States Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Henan Institutes of Advanced Technology Zhengzhou University China

Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility. However, the flexibility of these dockless systems comes at the cost of management and operation complexity. Indeed, the imbalanced and dynamic use of bikes leads to mandatory rebalancing operations, which impose a critical need for effective bike traffic flow prediction. While efforts have been made in developing traffic flow prediction models, existing approaches lack interpretability, and thus have limited value in practical deployment. To this end, we propose an Interpretable Bike Flow Prediction (IBFP) framework, which can provide effective bike flow prediction with interpretable traffic patterns. Specifically, by dividing the urban area into regions according to flow density, we first model the spatio-temporal bike flows between regions with graph regularized sparse representation, where graph Laplacian is used as a smooth operator to preserve the commonalities of the periodic data structure. Then, we extract traffic patterns from bike flows using subspace clustering with sparse representation to construct interpretable base matrices. Moreover, the bike flows can be predicted with the interpretable base matrices and learned parameters. Finally, experimental results on real-world data show the advantages of the IBFP method for flow prediction in dockless bike sharing systems. In addition, the interpretability of our flow pattern exploitation is further illustrated through a case study where IBFP provides valuable insights into bike flow analysis. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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AID-DTI: ACCELERATING HIGH-FIDELITY DIFFUSION TENSOR IMAGING WITH DETAIL-PRESERVING MODEL-BASED DEEP LEARNING

arXiv

引用

arXiv 2024年

作者： Fan, Wenxin Cheng, Jian Li, Cheng Ma, Xinrui Yang, Jing Zou, Juan Wu, Ruoyou Liu, Qiegen Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Peng Cheng Laboratory Guangdong Shenzhen China Nan Chang University Jiang Xi Nan Chang China

Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI). Nevertheless, existing methods tend to suffer from Rician noise and detail loss in reconstructing the DTI-derived parametric maps especially when sparsely sampled q-space data are used. This paper proposes a novel method, AID-DTI (Accelerating hIgh fiDelity Diffusion Tensor Imaging), to facilitate fast and accurate DTI with only six measurements. AID-DTI is equipped with a newly designed Singular Value Decomposition (SVD)-based regularizer, which can effectively capture fine details while suppressing noise during network training. Experimental results on Human Connectome Project (HCP) data consistently demonstrate that the proposed method estimates DTI parameter maps with fine-grained details and outperforms three state-of-the-art methods both quantitatively and qualitatively. Copyright © 2024, The Authors. All rights reserved.

关键词： Singular value decomposition

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Nine Million Books and Eleven Million Citations: A Study of Book-Based Scholarly Communication Using OpenCitations

arXiv

引用

arXiv 2019年

作者： Zhu, Yongjun Yan, Erjia Peroni, Silvio Che, Chao Department of Library and Information Science Sungkyunkwan University 25-2 Sungkyunkwan-ro Jongno-gu Seoul Korea Republic of College of Computing and Informatics Drexel University 3141 Chestnut Street PhiladelphiaPA19104 United States Department of Classical Philology and Italian Studies University of Bologna Via Zamboni 32 Bologna40126 Italy Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

Books have been widely used to share information and contribute to human knowledge. However, the quantitative use of books as a method of scholarly communication is relatively unexamined compared to journal articles and conference papers. This study uses the COCI dataset (a comprehensive open citation dataset provided by OpenCitations) to explore books’ roles in scholarly communication. The COCI data we analyzed includes 445,826,118 citations from 46,534,705 bibliographic entities. By analyzing such a large amount of data, we provide a thorough, multifaceted understanding of books. Among the investigated factors are 1) temporal changes to book citations;2) book citation distributions;3) years to citation peak;4) citation half-life;and 5) characteristics of the most-cited books. Results show that books have received less than 4% of total citations, and have been cited mainly by journal articles. Moreover, 97.96% of books have been cited fewer than 10 times. Books take longer than other bibliographic materials to reach peak citation levels, yet are cited for the same duration as journal articles. Most-cited books tend to cover general (yet essential) topics, theories, and technological concepts in mathematics and statistics. Copyright © 2019, The Authors. All rights reserved.

关键词： Bibliographies

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VarBen

引用

The Journal of Molecular Diagnostics 2021年第3期23卷 285-299页

作者： Ziyang Li Shuangsang Fang Rui Zhang Lijia Yu Jiawei Zhang Dechao Bu Liang Sun Yi Zhao Jinming Li National Center for Clinical Laboratories Beijing Hospital National Center of Gerontology Beijing People's Republic of China Graduate School Peking Union Medical College Chinese Academy of Medical Sciences Beijing People's Republic of China Key Laboratory of Intelligent Information Processing Advanced Computer Research Center Institute of Computing Technology Chinese Academy of Medical Sciences Beijing People's Republic of China Beijing Engineering Research Center of Laboratory Medicine Beijing Hospital Beijing People's Republic of China

Next-generation sequencing is increasingly being adopted as a valuable method for the detection of somatic variants in clinical oncology. However, it is still challenging to reach a satisfactory level of robustness and standardization in clinical practice when using the currently available bioinformatics pipelines to detect variants from raw sequencing data. Moreover, appropriate reference data sets are lacking for clinical bioinformatics pipeline development, validation, and proficiency testing. Herein, we developed the Variant Benchmark tool (VarBen), an open-source software for variant simulation to generate customized reference data sets by directly editing the original sequencing reads. VarBen can introduce a variety of variants, including single-nucleotide variants, small insertions and deletions, and large structural variants, into targeted, exome, or whole-genome sequencing data, and can handle sequencing data from both the Illumina and Ion Torrent sequencing platforms. To demonstrate the feasibility and robustness of VarBen, we performed variant simulation on different sequencing data sets and compared the simulated variants with real-world data. The validation study showed that the simulated data are highly comparable to real-world data and that VarBen is a reliable tool for variant simulation. In addition, our collaborative study of somatic variant calling in 20 laboratories emphasizes the need for laboratories to evaluate their bioinformatics pipelines with customized reference data sets. VarBen may help users develop and validate their bioinformatics pipelines using locally generated sequencing data.

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Entanglement depth for symmetric states

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Physical Review A 2016年第4期94卷 042333-042333页

作者： Ji-Yao Chen Zhengfeng Ji Nengkun Yu Bei Zeng State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing China Perimeter Institute for Theoretical Physics Waterloo Ontario Canada Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Institute for Quantum Computing University of Waterloo Waterloo Ontario Canada Department of Mathematics & Statistics University of Guelph Guelph Ontario Canada Canadian Institute for Advanced Research Toronto Ontario Canada

Entanglement depth characterizes the minimal number of particles in a system that are mutually entangled. For symmetric states, there is a dichotomy for entanglement depth: An N-particle symmetric state is either fully separable or fully entangled—the entanglement depth is either 1 or N. We show that this dichotomy property for entangled symmetric states is even stable under nonsymmetric noise. We propose an experimentally accessible method to detect entanglement depth in atomic ensembles based on a bound on the particle number population of Dicke states, and demonstrate that the entanglement depth of some Dicke states, for example the twin Fock state, is very stable even under a large arbitrary noise. Our observation can be applied to atomic Bose-Einstein condensates to infer that these systems can be highly entangled with the entanglement depth that is on the order of the system size (i.e., several thousands of atoms).

关键词： Entanglement detection

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Medical Image Registration and Its Application in Retinal Images: A Review

arXiv

引用

arXiv 2024年

作者： Nie, Qiushi Zhang, Xiaoqing Hu, Yan Gong, Mingdao Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research. © 2024, CC BY-NC-SA.

关键词： Deep learning

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The plant growth simulation algorithm based on phototropism

引用

ICIC Express Letters 2015年第2期9卷 453-459页

作者： Li, Tong Yi, Xiaoxiang School of Business Dalian University of Technology No. 2 Dagong Road New District of Liaodong Bay Panjin China Management College Hangzhou Dianzi University No. 1 Ave. 2 Xiasha Higher Education Zone Hangzhou China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University No. 10 Xuefu Ave Jinzhou New District Dalian China

Plant growth simulation algorithm (PGSA) is a kind of bionic intelligent optimization algorithm. Based on the plant phototropism as heuristic criterion, this algorithm takes the feasible region of different optimization problems as plant growth environment and transforms the changes of the object function into the growth probability (morphactin density) on different growth points. It then makes dynamic model simulating plant rapid growth towards the light (global optimization). PGSA proposed here is finally demonstrated with satisfactory solution by solving basic study (combinatorial optimization, Number Theory, multi-level optimization, integer programming, etc.) and applied study (electric power, logistics, nuke industry, hydraulic power, machine building, industrial engineering, finance engineering, etc.). © 2015, ICIC International.

关键词： Combinatorial optimization

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