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She Q, Jin G, Zhu R, Houston M, Xu O, Zhang Y. Upper Limb Cortical-Muscular Coupling Analysis Based on Time-Delayed Back Maximum Information Coefficient Model. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4635-4643. [PMID: 37983151 DOI: 10.1109/tnsre.2023.3334767] [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: 11/22/2023]
Abstract
In musculoskeletal systems, describing accurately the coupling direction and intensity between physiological electrical signals is crucial. The maximum information coefficient (MIC) can effectively quantify the coupling strength, especially for short time series. However, it cannot identify the direction of information transmission. This paper proposes an effective time-delayed back maximum information coefficient (TDBackMIC) analysis method by introducing a time delay parameter to measure the causal coupling. Firstly, the effectiveness of TDBackMIC is verified on simulations, and then it is applied to the analysis of functional cortical-muscular coupling and intermuscular coupling networks to explore the difference of coupling characteristics under different grip force intensities. Experimental results show that functional cortical-muscular coupling and intermuscular coupling are bidirectional. The average coupling strength of EEG → EMG and EMG → EEG in beta band is 0.86 ± 0.04 and 0.81 ± 0.05 at 10% maximum voluntary contraction (MVC) condition, 0.83 ± 0.05 and 0.76 ± 0.04 at 20% MVC, and 0.76 ± 0.03 and 0.73 ± 0.04 at 30% MVC. With the increase of grip strength, the strength of functional cortical-muscular coupling in beta frequency band decreases, the intermuscular coupling network exhibits enhanced connectivity, and the information exchange is closer. The results demonstrate that TDBackMIC can accurately judge the causal coupling relationship, and functional cortical-muscular coupling and intermuscular coupling network under different grip forces are different, which provides a certain theoretical basis for sports rehabilitation.
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Hu C, Li Y, Chen Z, Wang D, Men Z. Research on fault diagnosis of rolling bearing based on multi-sensor bi-layer information fusion under small samples. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2023; 94:115106. [PMID: 37938070 DOI: 10.1063/5.0174359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/17/2023] [Indexed: 11/09/2023]
Abstract
To address the challenge of low fault diagnosis accuracy due to insufficient bearing fault data collected by single-sensor, a rolling bearing fault diagnosis method based on multi-sensor bi-layer information fusion under small samples is proposed. In the first-layer feature fusion, first, aiming at the problem that the number of intrinsic mode functions (IMFs) and the penalty factor in the variational mode decomposition (VMD) is challenging to determine, the Aquila optimizer algorithm is introduced to search for the optimal solution independently. Decomposition of bearing vibration signals acquired by multiple sensors using a parameter optimized the VMD method to obtain IMFs. The 12 time-domain features are then extracted for each IMF, and the maximum information coefficient (MIC) between each IMF time-domain feature and raw signal time-domain features is calculated. Finally, the feature fusion composition ratio is calculated according to the MIC mean of each. In the second layer of data fusion, the fusion composition ratio calculated in the first layer is used as a weight-to-weight and reconstructs the signals of each sensor to constitute a fused signal. Then, the fused signals are input into the fault diagnostic model, and fault pattern recognition and fault severity recognition are performed at the same time. The results show that the accuracy of the method proposed in this paper is higher than that of the comparison method on both the public dataset and the self-built experimental bench dataset, and it is an accurate, stable, and efficient fault diagnosis method.
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Affiliation(s)
- Chaoqun Hu
- College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116000, China
- Department of Locomotive Engineering, Liaoning Railway Vocational and Technical College, Jinzhou 121000, China
| | - Yonghua Li
- College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116000, China
| | - Zhe Chen
- College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116000, China
| | - Denglong Wang
- College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116000, China
| | - Zhihui Men
- College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116000, China
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Yao J, Li H, Yang HY. Predicting adsorption capacity of pharmaceuticals and personal care products on long-term aged microplastics using machine learning. JOURNAL OF HAZARDOUS MATERIALS 2023; 458:131963. [PMID: 37406525 DOI: 10.1016/j.jhazmat.2023.131963] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/13/2023] [Accepted: 06/27/2023] [Indexed: 07/07/2023]
Abstract
We investigated the adsorption mechanism of 66 coexisting pharmaceuticals and personal care products (PPCPs) on microplastics treated with potassium persulfate, potassium hydroxide, and Fenton reagent for 54, 110, and 500 days. The total adsorption capacity (qe) of 66 PPCPs on 15 original microplastics was 171.8 - 1043.7 μg/g, far below that of 177 long-term aged microplastics (7114.0 - 13,114.4 μg/g). Around 69.8% of qe was primarily influenced by the total energy, energy of the highest occupied molecular orbital, and energy gap of PPCPs, calculated using the B3LYP/6-31 G* level. Furthermore, 111 aged microplastics exhibited similar total qe values. Additionally, we developed predictive models based on attenuated total reflectance Fourier transform infrared spectroscopy to predict the individual and total qe on 192 microplastics. These models, including the maximal information coefficient and gradient boosting decision tree regression, exhibited high accuracy with Rtraining2 values of 0.9772 and 0.9661, respectively, and p-values below 0.001. Spectroscopic analysis and machine learning models highlighted surface functional group alterations and the importance of the 1528-1700 cm-1 spectral region and carbon skeleton in the adsorption process. In summary, our findings contribute to understanding the adsorption of PPCPs on microplastics, particularly in the context of long-term aging effects.
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Affiliation(s)
- Jingjing Yao
- Center for Environment and Water Resources, College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China; Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, Changsha 410083, PR China; Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore.
| | - Haipu Li
- Center for Environment and Water Resources, College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China; Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, Changsha 410083, PR China.
| | - Hui Ying Yang
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore.
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Wang Y, Gao X, Ru X, Sun P, Wang J. A hybrid feature selection algorithm and its application in bioinformatics. PeerJ Comput Sci 2022; 8:e933. [PMID: 35494789 PMCID: PMC9044222 DOI: 10.7717/peerj-cs.933] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
Feature selection is an independent technology for high-dimensional datasets that has been widely applied in a variety of fields. With the vast expansion of information, such as bioinformatics data, there has been an urgent need to investigate more effective and accurate methods involving feature selection in recent decades. Here, we proposed the hybrid MMPSO method, by combining the feature ranking method and the heuristic search method, to obtain an optimal subset that can be used for higher classification accuracy. In this study, ten datasets obtained from the UCI Machine Learning Repository were analyzed to demonstrate the superiority of our method. The MMPSO algorithm outperformed other algorithms in terms of classification accuracy while utilizing the same number of features. Then we applied the method to a biological dataset containing gene expression information about liver hepatocellular carcinoma (LIHC) samples obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). On the basis of the MMPSO algorithm, we identified a 18-gene signature that performed well in distinguishing normal samples from tumours. Nine of the 18 differentially expressed genes were significantly up-regulated in LIHC tumour samples, and the area under curves (AUC) of the combination seven genes (ADRA2B, ERAP2, NPC1L1, PLVAP, POMC, PYROXD2, TRIM29) in classifying tumours with normal samples was greater than 0.99. Six genes (ADRA2B, PYROXD2, CACHD1, FKBP1B, PRKD1 and RPL7AP6) were significantly correlated with survival time. The MMPSO algorithm can be used to effectively extract features from a high-dimensional dataset, which will provide new clues for identifying biomarkers or therapeutic targets from biological data and more perspectives in tumor research.
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Affiliation(s)
- Yangyang Wang
- School of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Xiaoguang Gao
- School of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Xinxin Ru
- School of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Pengzhan Sun
- School of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi, China
| | - Jihan Wang
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, Shaanxi, China
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Cao D, Xu N, Chen Y, Zhang H, Li Y, Yuan Z. Construction of a Pearson- and MIC-Based Co-expression Network to Identify Potential Cancer Genes. Interdiscip Sci 2021; 14:245-257. [PMID: 34694561 DOI: 10.1007/s12539-021-00485-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 09/29/2021] [Accepted: 09/30/2021] [Indexed: 11/26/2022]
Abstract
The weighted gene co-expression network analysis (WGCNA) method constructs co-expressed gene modules based on the linear similarity between paired gene expressions. Linear correlations are the main form of similarity between genes, however, nonlinear correlations still existed and had always been ignored. We proposed a modified network analysis method, WGCNA-P + M, which combines Pearson's correlation coefficient and the maximum information coefficient (MIC) as the similarity measures to assess the linear and nonlinear correlations between genes, respectively. Taking two real datasets, GSE44861 and liver hepatocellular carcinoma (TCGA-LIHC), as examples, we compared the gene modules constructed by WGCNA-P + M and WGCNA from four perspectives: the "Usefulness" score, GO enrichment analysis on genes in the gray module, prediction performance of the top hub gene, survival analysis and literature reports on different hub genes. The results showed that the modules obtained by WGCNA-P + M are more biological meaningful, the hub genes obtained from WGCNA-P + M have more potential cancer genes.
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Affiliation(s)
- Dan Cao
- Hunan Engineering and Technology Research Center for Agricultural Big Data Analysis and Decision-Making, Hunan Agricultural University, Changsha, 410128, Hunan, China
- College of Science, Central South University of Forestry and Technology, Changsha, 410004, Hunan, China
| | - Na Xu
- Hunan Engineering and Technology Research Center for Agricultural Big Data Analysis and Decision-Making, Hunan Agricultural University, Changsha, 410128, Hunan, China
| | - Yuan Chen
- Hunan Engineering and Technology Research Center for Agricultural Big Data Analysis and Decision-Making, Hunan Agricultural University, Changsha, 410128, Hunan, China
| | - Hongyan Zhang
- Hunan Engineering and Technology Research Center for Agricultural Big Data Analysis and Decision-Making, Hunan Agricultural University, Changsha, 410128, Hunan, China
| | - Yuting Li
- Hunan Engineering and Technology Research Center for Agricultural Big Data Analysis and Decision-Making, Hunan Agricultural University, Changsha, 410128, Hunan, China
| | - Zheming Yuan
- Hunan Engineering and Technology Research Center for Agricultural Big Data Analysis and Decision-Making, Hunan Agricultural University, Changsha, 410128, Hunan, China.
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