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Reversible Data Hiding Based on DNA computing

Computational Intelligence and Neuroscience 2017年第20170101期2017卷 7276084-7276084页

作者： Wang, Bin Xie, Yingjie Zhou, Shihua Zhou, Changjun Zheng, Xuedong Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China Applied Technology College Dalian Ocean University Dalian116300 China

Biocomputing, especially DNA, computing has got great development. It is widely used in information security. In this paper, a novel algorithm of reversible data hiding based on DNA computing is proposed. Inspired by the algorithm of histogram modification, which is a classical algorithm for reversible data hiding, we combine it with DNA computing to realize this algorithm based on biological technology. Compared with previous results, our experimental results have significantly improved the ER (Embedding Rate). Furthermore, some PSNR (peak signal-to-noise ratios) of test images are also improved. Experimental results show that it is suitable for protecting the copyright of cover image in DNA-based information security. © 2017 Bin Wang et al.

关键词： Signal to noise ratio

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Parameter-transferred wasserstein generative adversarial network (PT-WGAN) for low-dose PET image denoising

arXiv

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arXiv 2019年

作者： Gong, Yu Shan, Hongming Teng, Yueyang Tu, Ning Li, Ming Liang, Guodong Wang, Ge Wang, Shanshan College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Key Laboratory of Intelligent Computing in Medical Images Ministry of Education Shenyang110169 China Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States PET-CT/MRI Center Molecular Imaging Center Wuhan University Renmin Hospital Wuhan430060 China Neusoft Medical Systems Co. Ltd Shenyang110167 China

Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However, with the reduction in the radiation dose, the resultant images may suffer from noise and artifacts that compromise diagnostic performance. In this paper, we propose a parameter-transferred Wasserstein generative adversarial network (PT-WGAN) for lowdose PET image denoising. The contributions of this paper are twofold: i) a PT-WGAN framework is designed to denoise lowdose PET images without compromising structural details, and ii) a task-specific initialization based on transfer learning is developed to train PT-WGAN using trainable parameters transferred from a pretrained model, which significantly improves the training efficiency of PT-WGAN. The experimental results on clinical data show that the proposed network can suppress image noise more effectively while preserving better image fidelity than recently published state-of-the-art methods. We make our code available at https://***/90n9-yu/PT-WGAN. Copyright © 2019, The Authors. All rights reserved.

关键词： Positron emission tomography

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Passivity of Reaction–Diffusion Genetic Regulatory Networks with Time-Varying Delays

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Neural Processing Letters 2017年第3期47卷 1115-1132页

作者： Zou, Chengye Wei, Xiaopeng Zhang, Qiang Zhou, Changjun Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China Key Laboratory of Advanced and Intelligent Computing Dalian University Ministry of Education Dalian China

This article investigates the passivity of reaction–diffusion genetic regulatory networks (GRNs) with time-varying delays and uncertainty terms under Dirichlet, Neumann, and Robin boundary conditions. We provide delay-dependent stability criteria by constructing appropriate Lyapunov–Krasovskii functions and linear matrix inequalities, and offer conditions sufficient to ensure the passivity of GRNs. We conducted a comparative analysis of GRNs under these three conditions. Numerical examples of the proposed approaches are provided to illustrate its effectiveness, and represent the three-dimensional figures of the trajectories of the concentrations of mRNA and the proteins of GRNs under Dirichlet boundary conditions.

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DNA self-assembly model to solve compound logic operators problem 11th

DNA self-assembly model to solve compound logic operators pr...

引用

11th International Conference on Bio-inspired computing – Theories and Applications, BIC-TA 2016

作者： Zhou, Shihua Wang, Bin Zheng, Xuedong Zhou, Changjun Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China

ISBN: (纸本)9789811036101

Self-assembly is the process that the component form an ordered form or structure. Because of the biochemical characteristics of DNA molecules, they become a research emphasis in the field of selfassembly. DNA-based self-assembly technology has been widely used in the fields of nanometer machining, molecular circuit, polymer materials, and so on. DNA self-assembly is an effective mechanism that nanometer structure is built bottom-up. In order to overcome the problem that any kind of self-assembled model can only solve the single algorithm, in this paper, a new DNA self-assembly algorithmic model is designed to solve compound logic operators problem. Five types of DNA tiles are designed according to the characteristic of compound operation problem, namely Initial Tile, Process Tile, Operation Tile, End Tile and Boundary Tile. At last, the process of self-assembly are demonstrated by an instance. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Self assembly

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Human motion data editing based on a convolutional automatic encoder and manifold learning

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Entertainment computing 2019年 30卷

作者： Dongsheng Zhou Xinzhu Feng Xin Yang Qiang Zhang Xiaopeng Wei Xiaoyong Fang Deyun Yang Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Ministry of Education Dalian 116622 China College of Computer Science and Technology Dalian University of Technology Dalian 116024 China Research Institute of Human Factors and Safety Engineering Hunan Institute of Technology Hengyang Hunan Province 421002 China School of Information Science and Technology Taishan University Tai’an Shandong Province 271000 China

Technology of human motion capture has been widely used in digital entertainment field. Editing the existing large amount of human motion capture data, correcting and eliminating motion distortion caused by noise and other defects have important value and significance for data reuse. In this paper, data processing is carried out based on convolutional automatic encoder and manifold learning. The popular structure of human motion data was learned by a one-dimensional time domain convolution automatic encoder, in which the hidden unit of the automatic encoder represents motion data. Three constraints were used to overcome the problem that the hidden unit has too much motion editing range. The data to be processed in this paper has no limit on the number of motions. The proposed method can process large data sets in parallel and automatically perform manifold learning without manual labelling and segmentation. In the final, comparative experiments based on a variety of damaged motion data have been carried out. The results showed that the proposed method can effectively reduce the error of the original motion data, and has achieved good results in both objective evaluation and subjective evaluation.

关键词： Human motion data Convolution automatic encoder Manifold learning Motion editing

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Four-Analog Computation Based on DNA Strand Displacement

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ACS omega 2017年第8期2卷 4143-4160页

作者： Chengye Zou Xiaopeng Wei Qiang Zhang Chanjuan Liu Changjun Zhou Yuan Liu Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian 116024 China. Key Laboratory of Advanced and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China.

DNA strand displacement plays an important role in biological computations. The inherent advantages of parallelism, high storability, and cascading have resulted in increased functional circuit realization of DNA strand displacement on the nanoscale. Herein, we propose an analog computation with minus based on DNA strand displacement. The addition, subtraction, multiplication, and division gates as elementary gates could realize analog computation with minus. The advantages of this proposal are the analog computation with negative value and division computation. In this article, we provide the designs and principles of these elementary gates and demonstrate gate performance by simulation. Furthermore, to show the cascade property of gates, we computed a polynomial as an example by these gates.

关键词： Equilibrium Genetics Circuits Addition reactions Reaction rates

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Polymerase chain reaction-based DNA algorithm for maximum clique problem

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Journal of Computational and Theoretical Nanoscience 2016年第7期13卷 4447-4453页

作者： Zhai, Weihua Zheng, Xuedong Zhou, Changjun Wang, Bin Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China

This article discuss a repetitive acceptance sampling plan based on exponentially weighted moving average (EWMA) statistic using a regression estimator under the assumption that quality parameters follow normal distribution. The sampling plan is proposed under two circumstances when standard deviation of normal distribution is known and unknown. The parameters of the proposed sampling plan are investigated and analyzed for both cases under the condition that the given producer's risk and consumer's risk are satisfied. The proposed sampling plan is compared with the existing sampling plans. The proposed sampling plan is more efficient than the existing sampling plans in terms of sample size. The extensive tables are provided for industrial use. © 2016 American Scientific Publishers All rights reserved.

关键词： Normal distribution

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The optimization of DNA encoding based on chaotic optimization particle swarm algorithm

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Journal of Computational and Theoretical Nanoscience 2016年第1期13卷 443-449页

作者： Liu, Yuanyuan Zheng, Xuedong Wang, Bin Zhou, Shihua Zhou, Changjun Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China

DNA sequence encoding is significant for the improvement of reliability and validity of DNA computing. The hybridization reaction between DNA molecules is the most important and core reaction in the field of DNA computing, because its efficiency and accuracy directly affect the result of DNA computing. In view of the disadvantages of precocious phenomena and local optimum of particle swarm optimization (PSO), chaotic mapping is integrated into the traditional particle swarm optimization, namely chaotic optimization particle swarm algorithm (CPSO). The algorithm not only has the property of chaotic with ergodic, randomness and the sensitivity to the initial value, but also has the fast convergence of the PSO. Thus, we use the chaotic optimization particle swarm algorithm to design the DNA sequences. Compared with other algorithms, the simulation results and security analysis of our DNA sequences are more reliable and stable. At same time, our DNA sequences have low similarity and stable melting temperature. Therefore, it is proved that this method is feasible and has better optimization effect. © 2016 American Scientific Publishers All rights reserved.

关键词： DNA sequences

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Logic gates designed with domain label based on DNA strand displacement

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9712 LNCS卷 244-255页

作者： Yang, Qianhao Zhou, Changjun Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China

ISBN: (纸本)9783319409993

The construction of DNA logic gates plays a very significant role in solving NP-complete problems, because DNA computer applied to solving NP-complete problems consists of DNA logic gates. Although AND Gate module and OR Gate module with dual-rail logic constructed by Winfree avoided the instability caused by NOT Gate, the scale of dual-rail logic circuit is two times that of single-rail logic circuit. In this paper, domain t and domain f are applied to representing signal 1 and signal 0 respectively instead of high concentration and low concentration, and AND Gate, OR Gate, NOT Gate with domain label (domain t and f) are constructed. AND Gate, OR Gate, NOT Gate with domain label have good stability and encapsulation, which can be applied to DNA computing in the future. © Springer International Publishing Switzerland 2016.

关键词： DNA

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