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Constructing DNA Barcode Sets Based on Particle Swarm Optimization

IEEE/ACM transactions on computational biology and bioinformatics 2018年第3期15卷 999-1002页

作者： Bin Wang Xuedong Zheng Shihua Zhou Changjun Zhou Xiaopeng Wei Qiang Zhang Ziqi Wei the Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China Department of Computing Science University of Alberta Edmonton AB Canada

Following the completion of the human genome project, a large amount of high-throughput bio-data was generated. To analyze these data, massively parallel sequencing, namely next-generation sequencing, was rapidly developed. DNA barcodes are used to identify the ownership between sequences and samples when they are attached at the beginning or end of sequencing reads. Constructing DNA barcode sets provides the candidate DNA barcodes for this application. To increase the accuracy of DNA barcode sets, a particle swarm optimization (PSO) algorithm has been modified and used to construct the DNA barcode sets in this paper. Compared with the extant results, some lower bounds of DNA barcode sets are improved. The results show that the proposed algorithm is effective in constructing DNA barcode sets.

关键词： Bioinformatics DNA Particle Swarm Optimisation Particle Swarm Optimization Next Generation Sequencing DNA Barcode Sets DNA Sequential Analysis Algorithm design And Analysis Next Generation Networking Particle Swarm Optimization Hamming Distance Filtering Algorithms DNA Barcodes Edit Distance Particle Swarm Optimization PSO Lower Bound

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A Review of intelligent Image Processing Method of Pulmonary CT Images

A Review of Intelligent Image Processing Method of Pulmonary...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenjun Tan Pan Liu Hui Guo Jinzhu Yang Xiaoxia Yin Yanchun Zhang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Centre for Applied Informatics College of Engineering and Science Victoria University Melbourne Australia

ISBN: (纸本)9781728118680

Due to the high degree of air pollution, high smoking rate and poor production protection conditions during the industrialization process, the incidence of pulmonary diseases such as pulmonary cancer and chronic obstructive pulmonary disease has been maintained at a high level in the world and has shown an increasing trend, which has become one of the diseases with the highest mortality rate. In recent years, with the rapid development of artificial intelligence technology, the intelligent assisted diagnosis and surgery technology of pulmonary CT images has made great progress, and played an important role in the early diagnosis and precise treatment of pulmonary diseases. This paper mainly discusses the development history of image-assisted diagnosis and surgery technology for pulmonary diseases, as well as the method of segmentation and evaluation of pulmonary tissue and lesion, and the clinical application of pulmonary intelligent assisted diagnosis and surgery systems.

关键词： biological tissues cancer computerised tomography image segmentation lung medical image processing surgery X-Ray Computed Tomography Medical image processing Lung TISSUES (BIOLOGY) Lung Diseases image segmentation Surgery Air contamination intelligent image processing Lung Cancer Chronic Obstructive Airway Disease Mortality

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Experimental Self-Characterization of Quantum Measurements

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Physical Review Letters 2020年第4期124卷 040402-040402页

作者： Aonan Zhang Jie Xie Huichao Xu Kaimin Zheng Han Zhang Yiu-Tung Poon Vlatko Vedral Lijian Zhang National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and Manipulation (Ministry of Education) College of Engineering and Applied Sciences and School of Physics Nanjing University Nanjing 210093 China Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China Department of Mathematics Iowa State University Ames Iowa 50011 USA Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518055 China Atomic and Laser Physics Clarendon Laboratory University of Oxford Parks Road Oxford OX13PU United Kingdom Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117543 Singapore

The accurate and reliable description of measurement devices is a central problem in both observing uniquely nonclassical behaviors and realizing quantum technologies from powerful computing to precision metrology. To date quantum tomography is the prevalent tool to characterize quantum detectors. However, such a characterization relies on accurately characterized probe states, rendering reliability of the characterization lost in circular argument. Here we report a self-characterization method of quantum measurements based on reconstructing the response range—the entirety of attainable measurement outcomes, eliminating the reliance on known states. We characterize two representative measurements implemented with photonic setups and obtain fidelities above 99.99% with the conventional tomographic reconstructions. This initiates range-based techniques in characterizing quantum systems and foreshadows novel device-independent protocols of quantum information applications.

关键词： Quantum information processing Quantum measurements Quantum optics Quantum tomography

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Jacobi Spectral Collocation Method Based on Lagrange Interpolation Polynomials for Solving Nonlinear Fractional Integro-Differential Equations

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Advances in Applied Mathematics and Mechanics 2018年第6期10卷 1440-1458页

作者： Xingfa Yang Yin Yang Yanping Chen Jie Liu State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan UniversityChangsha 410082HunanChina Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtan 411105HunanChina School of Mathematical Sciences South China Normal UniversityGuangzhou 510631GuangdongChina Center for Research on Leading Technology of Special Equipment School of Mechanical and Electric EngineeringGuangzhou UniversityGuangzhou 510006GuangdongChina

In this paper,we study a class of nonlinear fractional integro-differential equations,the fractional derivative is described in the Caputo *** the properties of the Caputo derivative,we convert the fractional integro-differential equations into equivalent integral-differential equations of Volterra type with singular kernel,then we propose and analyze a spectral Jacobi-collocation approximation for nonlinear integro-differential equations of Volterra *** provide a rigorous error analysis for the spectral methods,which shows that both the errors of approximate solutions and the errors of approximate fractional derivatives of the solutions decay exponentially in L^(∞)-norm and weighted L^(2)-norm.

关键词： Spectral method nonlinear fractional derivative Volterra integro-differential equations Caputo derivative

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Dynamic radiomics: A new methodology to extract quantitative time-related features from tomographic images

arXiv

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arXiv 2020年

作者： Che, Fengying Shi, Ruichuan Li, Shuqin Wu, Jian Li, Haoran Chen, Weixing Zhang, Hao Qu, Hui Li, Zhi Cui, Xiaoyu College of Medicine and Biological Information Engineering Northeastern University Liaoning Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Liaoning Shenyang China Department of Medical Oncology The First Affiliated Hospital of China Medical University Liaoning110001 China Shenzhen College of Advanced Technology University of the Chinese Academy of Sciences Beijing100049 China Department of Breast Surgery Liaoning Cancer Hospital and Institute Cancer Hospital of China Medical University Liaoning Shenyang110042 China

The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static characteristics. This study proposes a new dynamic radiomics feature extraction workflow that uses time-dependent tomographic images of the same patient, focuses on the changes in image features over time, and then quantifies them as new dynamic features for diagnostic or prognostic evaluation. We first define the mathematical paradigm of dynamic radiomics and introduce three specific methods that can describe the transformation process of features over time. Three different clinical problems are used to validate the performance of the proposed dynamic feature with conventional 2D and 3D static *** results show that compared with static features, dynamic feature extraction can achieve higher robustness and accuracy for time-dependent tomographic images. We also found that the dynamic features that influence different clinical problems are also quite different. Copyright © 2020, The Authors. All rights reserved.

关键词： Tomography

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N-dimensional matrices operation based on DNA strand displacement

N-dimensional matrices operation based on DNA strand displac...

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International Workshop on advanced Computational Intelligence (IWACI)

作者： Liu Yang Changjun Zhou Hui Lv Qiang Zhang Ministry of Education Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Dalian China Dalian University of Technology Dalian China

In the field of biological computing, the DNA strand displacement technique performs well in computation and programming. DNA strand displacement can be used to simulate calculations. Models based on the characteristics of DNA strand displacement are established to simulate n-dimensional matrices operations in this paper. The input and output of these computational models are analog, which means that the values of the individual elements in the n-dimensional matrices can be expressed directly by the concentration of the input and output DNA strands. Simulation experiment is performed by Visual DSD, and their results with high accuracy are consistent to the results of matrices operations in higher algebra, which prove correctness and validity of the models. The proposed models show calculation ability of DNA strand displacement and can be applied to the field of intelligent computing.

关键词： Handheld computers Computational intelligence

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Accelerating algorithm for biochemical reaction systems with multiple delays 17

Accelerating algorithm for biochemical reaction systems with...

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9th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2017

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

ISBN: (纸本)9781450348799

Delay in the study of biochemical reaction system can't be ignored and the complex time delay systems are integrated to get the multiple time delayed biochemical reactions. An improved acceleration algorithm based on MD-Leaping algorithm for multidelayed biochemical reaction systems is proposed in this paper. The number of the waiting records generated during the operation of the MD-Leaping algorithm slows down its rate. We use a novel method that can reduce the size of the wait queue to add the wait records. The new algorithm greatly reduces the memory requirements without losing accuracy and runs faster. At last, this paper makes a numerical simulation analysis of a multiple time delayed model, and this case further demonstrates the accuracy and efficiency of the proposed algorithm. © 2017 Association for computing Machinery.

关键词： Stochastic systems

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The memory degradation based online sequential extreme learning machine

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Neurocomputing 2018年 275卷 2864-2879页

作者： Quan-Yi Zou Xiao-Jun Wang Chang-Jun Zhou Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian 116622 China School of Computer Science & Technology Dalian University of Technology Dalian 116622 China

In online learning, the contribution of old samples to a model decreases as time passes, and old samples gradually become invalid. Although the Online Sequential Extreme Learning Machine (OS-ELM) can avoid the repetitive training of old samples, invalid samples are still used, which goes against improving the accuracy of an OS-ELM model. The Online Sequence Extreme Learning Machine with Forgetting Mechanism (FOS-ELM) timely discards invalid samples, but it does not consider the differences among valid samples and then has the limitation on boosting the accuracy and generalization. To solve this issue, the Memory Degradation Based OS-ELM (MDOS-ELM) is proposed in this paper. The MDOS-ELM adjusts the weights of the old and new samples in real time by a self-adaptive memory factor, and simultaneously discards invalid samples. The self-adaptive memory factor is determined by two elements. One is the similarity between the new and old samples, and the other is the prediction errors of the current training samples on the previous model. The performance of the proposed MDOS-ELM is validated on both regression and classification datasets which include an artificial dataset and twenty-two real-world dataset. The results demonstrate that the MDOS-ELM model outperforms the OS-ELM and the FOS-ELM models on the accuracy and generalization.

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Implementing Feedforward Neural Network Using DNA Strand Displacement Reactions

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Nano 2020年第1期16卷

作者： Siyan Zhu Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian 116622 P. R. China School of Computer Science and Technology Dalian University of Technology Dalian 116024 P. R. China

The ability of neural networks to process information intelligently has allowed them to be successfully applied in the fields of information processing, controls, engineering, medicine, and economics. The brain-like working mode of a neural network gives it incomparable advantages in solving complex nonlinear problems compared with other methods. In this paper, we propose a feedforward DNA neural network framework based on an enzyme-free, entropy-driven DNA reaction network that uses a modular design. A multiplication gate, an addition gate, a subtraction gate, and a threshold gate module based on the DNA strand displacement principle are cascaded into a single DNA neuron, and the neuron cascade is used to form a feedforward transfer neural network. We use this feedforward neural network to realize XOR logic operation and full adder logic operation, which proves that the molecular neural network system based on DNA strand displacement can carry out complex nonlinear operation and reflects the powerful potential of building these molecular neural networks.

关键词： Molecular programming DNA strand displacement neural network chemical reaction networks

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Observing Geometry of Quantum States in a Three-Level System

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Physical Review Letters 2020年第15期125卷 150401-150401页

作者： Jie Xie Aonan Zhang Ningping Cao Huichao Xu Kaimin Zheng Yiu-Tung Poon Nung-Sing Sze Ping Xu Bei Zeng Lijian Zhang National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and Manipulation (Ministry of Education) College of Engineering and Applied Sciences and School of Physics Nanjing University Nanjing 210093 China Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China Department of Mathematics & Statistics University of Guelph Guelph N1G 2W1 Ontario Canada Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Ontario Canada Department of Mathematics Iowa State University Ames Iowa 50011 USA Department of Applied Mathematics The Hong Kong Polytechnic University 999077 Hong Kong China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong China

In quantum mechanics, geometry has been demonstrated as a useful tool for inferring nonclassical behaviors and exotic properties of quantum systems. One standard approach to illustrate the geometry of quantum systems is to project the quantum state space onto the Euclidean space via measurements of observables on the system. Despite the great success of this method in studying two-level quantum systems (qubits) with the celebrated Bloch sphere representation, it is still difficult to reveal the geometry of multidimensional quantum systems. Here we report the first experiment measuring the geometry of such projections beyond the qubit. Specifically, we observe the joint numerical ranges of a triple of observables in a three-level photonic system, providing a complete classification of these ranges. We further show that the geometry of different classes reveals ground-state degeneracies of a Hamiltonian as a linear combination of the observables, which is related to quantum phases in the thermodynamic limit. Our results offer a versatile geometric approach for exploring the properties of higher-dimensional quantum systems.

关键词： Geometric & topological phases Quantum foundations Quantum measurements Quantum phase transitions

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