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Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs

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BMC bioinformatics 2018年第Suppl 19期19卷 521页

作者： Yuanlin Ma Zuguo Yu Guosheng Han Jinyan Li Vo Anh Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. yuzuguo@***. School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia. yuzuguo@***. Advanced Analytics Institute Faculty of Engineering & IT University of Technology Sydney P.O Box 123 Broadway NSW 2007 Australia. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia.

BACKGROUND:Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. RESULTS:We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. CONCLUSIONS:The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs iden

关键词： Hibert-Huang transform Network PSI-BLAST profiles Pre-microRNA SVM mRMR

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Topological edge states at singular points in non-Hermitian plasmonic systems

arXiv

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arXiv 2021年

作者： Huang, Yin Shen, Yuecheng Veronis, Georgios Department of Optoelectrics Information Science and Engineering School of Physics and Electronics Central South University Hunan Changsha410012 China State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-Sen University Guangdong Guangzhou510275 China School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States

We introduce non-Hermitian plasmonic waveguide-cavity systems with topological edge states (TESs) at singular points. The compound unit cells of the structures consist of metal-dielectric-metal (MDM) stub resonators side-coupled to an MDMwaveguide. We show that we can realize both a TES and an exceptional point at the same frequency when a proper amount of loss is introduced into a finite three-unit-cell structure. We also show that the finite structure can exhibit both a TES and a spectral singularity when a proper amount of gain is introduced into the structure. In addition, we show that we can simultaneously realize a unidirectional spectral singularity and a TES when proper amounts of loss and gain are introduced into the structure. We finally show that this singularity leads to extremely high sensitivity of the reflected light intensity to variations of the refractive index of the active materials in the structure. TESs at singular points could potentially contribute to the development of singularity-based plasmonic devices with enhanced performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Plasmonics

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Are Electric Vehicles Greener than Hybrid Electric Vehicles in Carsharing? Insights from Large-Scale Multi-Objective simulation-Optimization

SSRN

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SSRN 2024年

作者： Li, Yan Hu, Lu Li, Haobin Chew, Ek Peng Li, Hao Zhu, Juanxiu Department of Industrial Systems Engineering and Management National University of Singapore Singapore Centre of Excellence in Modelling and Simulation for Next Generation Ports National University of Singapore Singapore School of Transportation and Logistics Southwest Jiaotong University Chengdu China School of Traffic and Transportation Engineering Changsha University of Science and Technology Changsha China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle Infrastructure Systems Changsha University of Science and Technology Changsha China School of Management Xihua University Chengdu China

Hybrid electric vehicles (HEVs) are perceived as transitional products bridging the gap between fueled vehicles and electric vehicles (EVs) because people intuitively believe that EVs are more environmentally friendly than HEVs. But is this perception true in the context of carsharing services (CSSs)? This paper pioneers a general large-scale multi-objective simulation-optimization (MOSO) method to explore the values of deploying HEVs in CSSs. We firstly develop a physically logical simulation model, emulating operations of CSSs and capturing mesoscopic dynamics of shared vehicles in a link-based traffic network. This model adopts an event-driven discrete-event mechanism, enhancing efficiency while maintaining high fidelity. Subsequently, we design a simulation-optimization framework aimed at achieving Pareto optimality by jointly optimizing station capacity, fleet size, and trip pricing. The goal is twofold: to maximize operational profits and to minimize carbon emissions, thereby quantitatively analysing the potential of shared HEVs (SHEVs). To tackle the high-dimensional MOSO problem, we propose a general algorithm that incorporates the multiple gradient descent algorithm with the simultaneous perturbation stochastic approximation algorithm. Furthermore, we derive its analytical expression for bi-objective optimization problems. We theoretically prove and practically demonstrate its strong global convergence. The efficiency of this method was validated through large-scale computational experiments conducted in Chengdu, Sichuan Province, involving 66,710 decision variables. These experiments showcased the method's superiority over existing MOSO algorithms. Several groups of sensitivity experiments focusing on vehicle types and traffic scenarios reveal some interesting findings. (1) Regardless of the increase in travel distances, SHEVs, which can be viewed as shared EVs (SEVs) without range anxiety (RA), continue to primarily rely on electricity rather than fuel fo

关键词： Stochastic systems

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Dynamic watermarking scheme based on the Shamir threshold and branch structure

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2008年第SUPPL. 2期38卷 221-225页

作者： Yin, Ke Wang, Zhi-Quan Zhu, Jian-Qi Dong, Gang School of Automation Nanjing University of Science and Technology Nanjing 210094 China Electric Power Simulation and Control Engineering Center Nanjing Institute of Technology Nanjing 211167 China Key Laboratory of Symbol Computation and Knowledge Engineer College of Computer Science and Technology Jilin University Changchun 130012 China

A novel dynamic software watermark scheme based on the Shamir threshold and branch structure is presented. First, we split the watermark into a set of shares using the Shamir threshold scheme. Second, these values are encrypted with the DES block cipher that forms the watermark shares to be embedded into different methods of program according to the dynamic behavior of the branch structure. Our scheme can withstand most semantics-preserving attacks and can retrieve the original watermark based on partial information. simulation tests show that our scheme is very robust, stealthy and has a high price performance rate compared with other methods.

关键词： Semantics

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Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization

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computational biology and chemistry 2015年 57卷 21-8页

作者： Zhi-Qin Zhao Guo-Sheng Han Zu-Guo Yu Jinyan Li Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China. Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia. Electronic address: yuzg1970@***. Advanced Analytics Institute & Centre for Health Technologies University of Technology Sydney Broadway NSW 2007 Australia. Electronic address: jinyan.li@uts.edu.au.

Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request.

关键词： Disease genes and phenotypes Heterogeneous network Laplacian normalization Leave-one-out cross-validation Random walk with restart Shannon information entropy

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Spin-dependent electron transport along hairpinlike DNA molecules

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Physical Review B 2020年第19期102卷 195406-195406页

作者： Pei-Jia Hu Si-Xian Wang Xiao-Hui Gao Yan-Yang Zhang Tie-Feng Fang Ai-Min Guo Qing-Feng Sun Hunan Key Laboratory for Super-microstructure and Ultrafast Process School of Physics and Electronics Central South University Changsha 410083 China School of Physics and Electronic Engineering Guangzhou University Guangzhou 510006 China School of Physical Science and Technology Lanzhou University Lanzhou 730000 China International Center for Quantum Materials School of Physics Peking University Beijing 100871 China Collaborative Innovation Center of Quantum Matter Beijing 100871 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing 100190 China

The chirality-induced spin selectivity (CISS), demonstrated in diverse chiral molecules by numerous experimental and theoretical groups, has been attracting extensive and ongoing interest in recent years. As the secondary structure of DNA, the charge transfer along DNA hairpins has been widely studied for more than two decades, finding that DNA hairpins exhibit spin-related effects as reported in recent experiments. Here, we propose a setup to demonstrate directly the CISS effect in DNA hairpins contacted by two nonmagnetic leads at both ends of the stem. Our results indicate that DNA hairpins present a pronounced CISS effect and the spin polarization could be enhanced by using conducting molecules as the loop. In particular, DNA hairpins exhibit several intriguing features. First, the local spin currents can flow circularly and assemble into a number of vortex clusters when the electron energy locates in the left/right electronic band of the stem. The chirality of vortex clusters in each band is the same and will be reversed by switching the electron energy from the left band to the right one, inducing the sign reversal of the spin polarization. Interestingly, the local spin currents can be greater than the corresponding spin component of the source-drain current. Second, both the conductance and the spin polarization can increase with molecular length as well as dephasing strength, contrary to the physical intuition that the transmission ability of molecular wires should be poorer when suffering from stronger scattering. Third, we unveil the optimal contact configuration of efficient electron transport and that of the CISS effect, which are distinct from each other and can be controlled by dephasing strength. The experimental realization of these results is discussed and the underlying physical mechanism is illustrated.

关键词： Quantum transport Spin filtering DNA Nonequilibrium Green's function

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Multifractal temporally weighted detrended cross-correlation analysis of multivariate time series

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Chaos (Woodbury, N.Y.) 2020年第2期30卷 023134页

作者： Shan Jiang Bao-Gen Li Zu-Guo Yu Fang Wang Vo Anh Yu Zhou Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China. College of Information and Science Technology Hunan Agricultural University Changsha Hunan 410128 China. Faculty of Science Engineering and Technology Swinburne University of Technology PO Box 218 Hawthorn Victoria 3122 Australia. Institute of Future Cities and Department of Geography and Resource Management The Chinese University of Hong Kong Shatin Hong Kong China.

Fractal and multifractal properties of various systems have been studied extensively. In this paper, first, the multivariate multifractal detrend cross-correlation analysis (MMXDFA) is proposed to investigate the multifractal features in multivariate time series. MMXDFA may produce oscillations in the fluctuation function and spurious cross correlations. In order to overcome these problems, we then propose the multivariate multifractal temporally weighted detrended cross-correlation analysis (MMTWXDFA). In relation to the multivariate detrended cross-correlation analysis and multifractal temporally weighted detrended cross-correlation analysis, an innovation of MMTWXDFA is the application of the signed Manhattan distance to calculate the local detrended covariance function. To evaluate the performance of the MMXDFA and MMTWXDFA methods, we apply them on some artificially generated multivariate series. Several numerical tests demonstrate that both methods can identify their fractality, but MMTWXDFA can detect long-range cross correlations and simultaneously quantify the levels of cross correlation between two multivariate series more accurately.

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Spacecraft multi-impulse reachable domain

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Chinese Journal of Aeronautics 2024年

作者： Sai ZHANG Zhen YANG Yazhong LUO College of Aerospace Science and Engineering National University of Defense Technology Changsha 410073 China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions Changsha 410073 China

The concept of the spacecraft Reachable Domain (RD) has garnered significant scholarly attention due to its crucial role in space situational awareness and on-orbit service applications. While the existing research has largely focused on single-impulse RD analysis, the challenge of Multi-Impulse RD (MIRD) remains a key area of interest. This study introduces a methodology for the precise calculation of spacecraft MIRD. The reachability constraints specific to MIRD are first formulated through coordinate transformations. Two restricted maneuvering strategies are examined. The derivation of two extremum conditions allows for determining the accessible orientation range and the nodes encompassing the MIRD. Subsequently, four nonlinear programming models are developed to address two types of MIRD by skillfully relaxing constraints using scale factors. Numerical results validate the robustness and effectiveness of the proposed approach, showing substantial agreement with Monte Carlo simulations and confirming its applicability to spacecraft on various elliptical orbits.

关键词： Orbital maneuver Reachable domain Multiple impulses Space situational awareness Nonlinear programing

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Global diversity and distribution of antibiotic resistance genes in human wastewater treatment systems

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Nature Communications 2025年第1期16卷 1-14页

作者： Zhu, Congmin Wu, Linwei Ning, Daliang Tian, Renmao Gao, Shuhong Zhang, Bing Zhao, Jianshu Zhang, Ya Xiao, Naijia Wang, Yajiao Brown, Mathew R. Tu, Qichao Zhuang, Weiqin Zhou, Hongde Zheng, Wanlin Zhang, Wen Zhang, Qian Zhang, Chiqian Young, Michelle Yang, Min Yan, Tao Xi, Chuanwu Wu, Liyou Wu, Jer-Horng Woo, Sung-Geun West, Stephanie Weaver, Joseph E. Wang, Bei Wakelin, Steve Van Nostrand, Joy D. Tooker, Nicholas B. Sun, Chenxiang Stephens, Kyle de Sousa, Oscarina Viana Smith, Kylie Sidhu, Jatinder Rossetti, Simona de los Reyes, Francis L. Reginatto, Valeria Parameswaran, Prathap Palmer, Andrew Meyers, Arthur J. de Menezes, Francisca Gleire Rodriguez Mendonça-Hagler, Leda C. Liu, Yu Li, Mengyan Li, Zhenxin Li, Yong Lee, Zarraz MP Leal, Cintia Dutra Kumari, Sheena Keucken, Alexander Marcantini, Deborah Garbani Johnson, David R. Hu, Zhiqiang Horn, Harald Harmon, Marc Hale, Lauren Habagil, Moshe Gu, April Z. Griffin, James S. Gómez, Janeth Sanabria Ford, Amanda Etchebehere, Claudia Chen, Si Cabrol, Léa Cabezas, Angela Bux, Faizal Brewster, Rebecca K. Bovio-Winkler, Patricia Bott, Charles B. Bond, Philip Boehnke, Kevin de Abreu Mac Conell, Érika Ferreira de Araujo, Juliana Calabria Agullo-Barcelo, Miriam Acevedo, Dany Ju, Feng Wells, George F. Guo, Jianhua He, Zhili Nielsen, Per H. Wang, Aijie Zhang, Yu Chen, Ting He, Qiang Criddle, Craig S. Wagner, Michael Tiedje, James M. Curtis, Thomas P. Wen, Xianghua Yang, Yunfeng Alvarez-Cohen, Lisa Stahl, David A. Alvarez, Pedro J. J. Rittmann, Bruce E. Zhou, Jizhong School of Biomedical Engineering Capital Medical University Beijing China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical University Beijing China Institute for Environmental Genomics University of Oklahoma Norman OK United States Institute of Ecology Key Laboratory for Earth Surface Processes of the Ministry of Education College of Urban and Environmental Sciences Peking University Beijing China Institute for Food Safety and Health Illinois Institute of Technology Bedford Park IL United States State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing China College of Life and Environmental Sciences Minzu University of China Beijing China Center for Bioinformatics and Computational Biology School of Biological Sciences Georgia Institute of Technology Atlanta GA United States School of Engineering Newcastle University Newcastle upon Tyne United Kingdom Institute for Marine Science and Technology Shandong University Qingdao China Key Laboratory of Coastal Environment and Resources of Zhejiang Province School of Engineering Westlake University Zhejiang Hangzhou China Institute of Advanced Technology Westlake Institute for Advanced Study Zhejiang Hangzhou China Department of Civil and Environmental Engineering Northwestern University Evanston IL United States Australian Centre for Water and Environmental Biotechnology The University of Queensland Brisbane QLD Australia Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China College of Agronomy Hunan Agricultural University Changsha China Department of Chemistry and Bioscience Center for Microbial Communities Aalborg University Aalborg Denmark Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing China State Key Laboratory of Environmental Aquatic Chemistry Research Center for Eco-Environmental Sciences University of Chinese Academy of

Antibiotic resistance poses a significant threat to human health, and wastewater treatment plants (WWTPs) are important reservoirs of antibiotic resistance genes (ARGs). Here, we analyze the antibiotic resistomes of 226 activated sludge samples from 142 WWTPs across six continents, using a consistent pipeline for sample collection, DNA sequencing and analysis. We find that ARGs are diverse and similarly abundant, with a core set of 20 ARGs present in all WWTPs. ARG composition differs across continents and is distinct from that of the human gut and the oceans. ARG composition strongly correlates with bacterial taxonomic composition, with Chloroflexi, Acidobacteria and Deltaproteobacteria being the major carriers. ARG abundance positively correlates with the presence of mobile genetic elements, and 57% of the 1112 recovered high-quality genomes possess putatively mobile ARGs. Resistome variations appear to be driven by a complex combination of stochastic processes and deterministic abiotic factors. © The Author(s) 2025.

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Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives

arXiv

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arXiv 2022年

作者： Yue, Jun Fang, Leyuan Ghamisi, Pedram Xie, Weiying Li, Jun Chanussot, Jocelyn Plaza, Antonio The Department of Geomatics Engineering Changsha University of Science and Technology Changsha410114 China The College of Electrical and Information Engineering Hunan University Changsha410082 China The Peng Cheng Laboratory Shenzhen518000 China Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Vienna1030 Austria The State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China The Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation School of Geography and Planning Sun Yat-Sen University Guangzhou510275 China Université Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble38000 France The Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura Cáceres10003 Spain

In recent years, supervised learning has been widely used in various tasks of optical remote sensing image understanding, including remote sensing image classification, pixel-wise segmentation, change detection, and object detection. The methods based on supervised learning need a large amount of high-quality training data and their performance highly depends on the quality of the labels. However, in practical remote sensing applications, it is often expensive and time-consuming to obtain large-scale data sets with high-quality labels, which leads to a lack of sufficient supervised information. In some cases, only coarse-grained labels can be obtained, resulting in the lack of exact supervision. In addition, the supervised information obtained manually may be wrong, resulting in a lack of accurate supervision. Therefore, remote sensing image understanding often faces the problems of incomplete, inexact, and inaccurate supervised information, which will affect the breadth and depth of remote sensing applications. In order to solve the above-mentioned problems, researchers have explored various tasks in remote sensing image understanding under weak supervision. This paper summarizes the research progress of weakly supervised learning in the field of remote sensing, including three typical weakly supervised paradigms: 1) Incomplete supervision, where only a subset of training data is labeled;2) Inexact supervision, where only coarse-grained labels of training data are given;3) Inaccurate supervision, where the labels given are not always true on the ground. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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