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Yang W, Liu Y, Xiao C. Deep metric learning for accurate protein secondary structure prediction. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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2
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Zhang Z, Zhao Y, Wang J, Guo M. DeepRCI: predicting ATP-binding proteins using the residue-residue contact information. IEEE J Biomed Health Inform 2021; 26:2822-2829. [PMID: 34941538 DOI: 10.1109/jbhi.2021.3137840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Adenine-5'-triphosphate (ATP) is a direct energy source for various activities of tissues and cells in the body. The release of ATP energies requires the assistance of ATP-binding proteins. Therefore, the identification of ATP-binding proteins is of great significance for the research on organisms. So far, there are several methods for predicting ATP-binding proteins. However, the accuracies of these methods are so low that the predicted proteins are inaccurate. Here, we designed a novel method, called as DeepRCI (based on Deep convolutional neural network and Residue-residue Contact Information), for predicting ATP-binding proteins. DeepRCI achieved an accuracy of 93.61\% on the test set which was a significant improvement over the state-of-the-art methods.
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3
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Zhang Z, Wang J, Liu J. DeepRTCP: Predicting ATP-Binding Cassette Transporters Based on 1-Dimensional Convolutional Network. Front Cell Dev Biol 2021; 8:614080. [PMID: 33598454 PMCID: PMC7882686 DOI: 10.3389/fcell.2020.614080] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 12/24/2020] [Indexed: 11/13/2022] Open
Abstract
ATP-binding cassette (ABC) transporters can promote cells to absorb nutrients and excrete harmful substances. It plays a vital role in the transmembrane transport of macromolecules. Therefore, the identification of ABC transporters is of great significance for the biological research. This paper will introduce a novel method called DeepRTCP. DeepRTCP uses the deep convolutional neural network and a feature combined of reduced amino acid alphabet based tripeptide composition and PSSM to recognize ABC transporters. We constructed a dataset named ABC_2020. It contains the latest ABC transporters downloaded from Uniprot. We performed 10-fold cross-validation on DeepRTCP, and the average accuracy of DeepRTCP was 95.96%. Compared with the start-of-the-art method for predicting ABC transporters, DeepRTCP improved the accuracy by 9.29%. It is anticipated that DeepRTCP can be used as an effective ABC transporter classifier which provides a reliable guidance for the research of ABC transporters.
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Affiliation(s)
- Zhaoxi Zhang
- School of Computer Science, Inner Mongolia University, Hohhot, China
| | - Juan Wang
- School of Computer Science, Inner Mongolia University, Hohhot, China
- Stage Key Laboratories of Reproductive Regulation & Breeding of Grassland Livestock, Hohhot, China
| | - Jiameng Liu
- School of Computer Science, Inner Mongolia University, Hohhot, China
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Qu Y, Fu Q, Shang C, Deng A, Zwiggelaar R, George M, Shen Q. Fuzzy-rough assisted refinement of image processing procedure for mammographic risk assessment. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106230] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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5
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Ge Y, Zhao S, Zhao X. A step-by-step classification algorithm of protein secondary structures based on double-layer SVM model. Genomics 2020; 112:1941-1946. [DOI: 10.1016/j.ygeno.2019.11.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 10/15/2019] [Accepted: 11/11/2019] [Indexed: 11/26/2022]
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6
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Kong L, Zhang L, Han X, Lv J. Protein Structural Class Prediction Based on Distance-related Statistical Features from Graphical Representation of Predicted Secondary Structure. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666180914110451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Protein structural class prediction is beneficial to protein structure and function analysis. Exploring good feature representation is a key step for this prediction task. Prior works have demonstrated the effectiveness of the secondary structure based feature extraction methods especially for lowsimilarity protein sequences. However, the prediction accuracies still remain limited. To explore the potential of secondary structure information, a novel feature extraction method based on a generalized chaos game representation of predicted secondary structure is proposed. Each protein sequence is converted into a 20-dimensional distance-related statistical feature vector to characterize the distribution of secondary structure elements and segments. The feature vectors are then fed into a support vector machine classifier to predict the protein structural class. Our experiments on three widely used lowsimilarity benchmark datasets (25PDB, 1189 and 640) show that the proposed method achieves superior performance to the state-of-the-art methods. It is anticipated that our method could be extended to other graphical representations of protein sequence and be helpful in future protein research.
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Affiliation(s)
- Liang Kong
- School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao, China
| | - Lichao Zhang
- College of Sciences, Northeastern University, Shenyang, China
| | | | - Jinfeng Lv
- School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao, China
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Zhu XJ, Feng CQ, Lai HY, Chen W, Hao L. Predicting protein structural classes for low-similarity sequences by evaluating different features. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2018.10.007] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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8
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A novel feature selection method to predict protein structural class. Comput Biol Chem 2018; 76:118-129. [DOI: 10.1016/j.compbiolchem.2018.06.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 05/14/2018] [Accepted: 06/30/2018] [Indexed: 01/05/2023]
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9
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Sudha P, Ramyachitra D, Manikandan P. Enhanced Artificial Neural Network for Protein Fold Recognition and Structural Class Prediction. GENE REPORTS 2018. [DOI: 10.1016/j.genrep.2018.07.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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10
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Yang Z, Wang J, Zheng Z, Bai X. A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier. Molecules 2018; 23:E2008. [PMID: 30103521 PMCID: PMC6222536 DOI: 10.3390/molecules23082008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 07/31/2018] [Accepted: 08/07/2018] [Indexed: 12/14/2022] Open
Abstract
Research on cytokine recognition is of great significance in the medical field due to the fact cytokines benefit the diagnosis and treatment of diseases, but the current methods for cytokine recognition have many shortcomings, such as low sensitivity and low F-score. Therefore, this paper proposes a new method on the basis of feature combination. The features are extracted from compositions of amino acids, physicochemical properties, secondary structures, and evolutionary information. The classifier used in this paper is SVM. Experiments show that our method is better than other methods in terms of accuracy, sensitivity, specificity, F-score and Matthew's correlation coefficient.
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Affiliation(s)
- Zhe Yang
- School of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia 010021, China.
| | - Juan Wang
- School of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia 010021, China.
| | - Zhida Zheng
- School of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia 010021, China.
| | - Xin Bai
- School of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia 010021, China.
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11
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Yu B, Li S, Qiu W, Wang M, Du J, Zhang Y, Chen X. Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction. BMC Genomics 2018; 19:478. [PMID: 29914358 PMCID: PMC6006758 DOI: 10.1186/s12864-018-4849-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 06/01/2018] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Apoptosis is associated with some human diseases, including cancer, autoimmune disease, neurodegenerative disease and ischemic damage, etc. Apoptosis proteins subcellular localization information is very important for understanding the mechanism of programmed cell death and the development of drugs. Therefore, the prediction of subcellular localization of apoptosis protein is still a challenging task. RESULTS In this paper, we propose a novel method for predicting apoptosis protein subcellular localization, called PsePSSM-DCCA-LFDA. Firstly, the protein sequences are extracted by combining pseudo-position specific scoring matrix (PsePSSM) and detrended cross-correlation analysis coefficient (DCCA coefficient), then the extracted feature information is reduced dimensionality by LFDA (local Fisher discriminant analysis). Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of the apoptosis proteins. The overall prediction accuracy of 99.7, 99.6 and 100% are achieved respectively on the three benchmark datasets by the most rigorous jackknife test, which is better than other state-of-the-art methods. CONCLUSION The experimental results indicate that our method can significantly improve the prediction accuracy of subcellular localization of apoptosis proteins, which is quite high to be able to become a promising tool for further proteomics studies. The source code and all datasets are available at https://github.com/QUST-BSBRC/PsePSSM-DCCA-LFDA/ .
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Affiliation(s)
- Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China. .,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China. .,School of Life Sciences, University of Science and Technology of China, Hefei, 230027, China.
| | - Shan Li
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Wenying Qiu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Minghui Wang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.,Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Junwei Du
- College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Yusen Zhang
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai, 264209, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 21116, China
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Ni HM, Qi DW, Mu H. Applying MSSIM combined chaos game representation to genome sequences analysis. Genomics 2018; 110:180-190. [DOI: 10.1016/j.ygeno.2017.09.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2017] [Revised: 09/11/2017] [Accepted: 09/19/2017] [Indexed: 10/18/2022]
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13
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Liang Y, Zhang S. Prediction of Apoptosis Protein's Subcellular Localization by Fusing Two Different Descriptors Based on Evolutionary Information. Acta Biotheor 2018. [PMID: 29532347 DOI: 10.1007/s10441-018-9319-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The apoptosis protein has a central role in the development and the homeostasis of an organism. Obtaining information about the subcellular localization of apoptosis protein is very helpful to understand the apoptosis mechanism and the function of this protein. Prediction of apoptosis protein's subcellular localization is a challenging task, and currently the existing feature extraction methods mainly rely on the protein's primary sequence. In this paper we develop a feature extraction model based on two different descriptors of evolutionary information, which contains the 192 frequencies of triplet codons (FTC) in the RNA sequence derived from the protein's primary sequence and the 190 features from a detrended forward moving-average cross-correlation analysis (DFMCA) based on a position-specific scoring matrix (PSSM) generated by the PSI-BLAST program. Hence, this model is called FTC-DFMCA-PSSM. A 382-dimensional (382D) feature vector is constructed on the ZD98, ZW225 and CL317 datasets. Then a support vector machine is adopted as classifier, and the jackknife cross-validation test method is used for evaluating the accuracy. The overall prediction accuracies are further improved by an objective and rigorous jackknife test. Our model not only broadens the source of the feature information, but also provides a more accurate and reliable automated calculation method for the prediction of apoptosis protein's subcellular localization.
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Affiliation(s)
- Yunyun Liang
- School of Science, Xi'an Polytechnic University, Xi'an, 710048, People's Republic of China.
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, People's Republic of China.
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14
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Zhang L, Kong L. iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou's pseudo components. J Theor Biol 2018; 441:1-8. [PMID: 29305179 DOI: 10.1016/j.jtbi.2017.12.025] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 12/18/2017] [Accepted: 12/24/2017] [Indexed: 10/18/2022]
Abstract
Gene recombination is a key process to produce hereditary differences. Recombination spot identification plays an important role in revealing genome evolution and promoting DNA function study. However, traditional experiments are not good at identifying recombination spot with huge amounts of DNA sequences springed up by sequencing. At present, some machine learning methods have been proposed to speed up this identification process. However, the correlations between nucleotides pairs at different positions along DNA sequence is often ignored, which reflects the important sequence order information. For this purpose, this study proposes a novel feature extraction method, called iRSpot-ADPM, based on DNA property in a given DNA sequence. 85 features are selected from the original feature set according to the weights calculated by support vector machine. Five-fold cross validation tests on two widely used benchmark datasets indicate that the proposed method outperforms its existing counterparts on the individual specificity(Spec), Matthews correlation coefficient(MCC) value and overall accuracy(OA). The experimental results show that the proposed method is effective for accurate recombination spot identification. Moreover, it is anticipated that the proposed method could be extended to other biology sequence and be helpful in future research. The datasets and Matlab source codes can be download from the URL: http://stxy.neuq.edu.cn/info/1095/1157.htm.
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Affiliation(s)
- Lichao Zhang
- School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, PR China.
| | - Liang Kong
- School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066004, PR China
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15
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Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising. Oncotarget 2017; 8:107640-107665. [PMID: 29296195 PMCID: PMC5746097 DOI: 10.18632/oncotarget.22585] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 10/30/2017] [Indexed: 02/05/2023] Open
Abstract
Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research.
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Yu B, Lou L, Li S, Zhang Y, Qiu W, Wu X, Wang M, Tian B. Prediction of protein structural class for low-similarity sequences using Chou’s pseudo amino acid composition and wavelet denoising. J Mol Graph Model 2017; 76:260-273. [DOI: 10.1016/j.jmgm.2017.07.012] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Revised: 07/11/2017] [Accepted: 07/12/2017] [Indexed: 11/25/2022]
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17
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Protein secondary structure prediction: A survey of the state of the art. J Mol Graph Model 2017; 76:379-402. [DOI: 10.1016/j.jmgm.2017.07.015] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 07/14/2017] [Accepted: 07/17/2017] [Indexed: 11/21/2022]
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18
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Liang Y, Liu S, Zhang S. Detrended cross-correlation coefficient: Application to predict apoptosis protein subcellular localization. Math Biosci 2016; 282:61-67. [PMID: 27720879 DOI: 10.1016/j.mbs.2016.09.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 09/27/2016] [Accepted: 09/28/2016] [Indexed: 01/02/2023]
Abstract
Apoptosis, or programed cell death, plays a central role in the development and homeostasis of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful for understanding the apoptosis mechanism. The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this paper, we introduce a new position-specific scoring matrix (PSSM)-based method by using detrended cross-correlation (DCCA) coefficient of non-overlapping windows. Then a 190-dimensional (190D) feature vector is constructed on two widely used datasets: CL317 and ZD98, and support vector machine is adopted as classifier. To evaluate the proposed method, objective and rigorous jackknife cross-validation tests are performed on the two datasets. The results show that our approach offers a novel and reliable PSSM-based tool for prediction of apoptosis protein subcellular localization.
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Affiliation(s)
- Yunyun Liang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China.
| | - Sanyang Liu
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China
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