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Meng LB, Zou YF, Shan MJ, Zhang M, Qi RM, Yu ZM, Guo P, Zheng QW, Gong T. Computer-assisted prediction of atherosclerotic intimal thickness based on weight of adrenal gland, interleukin-6 concentration, and neural networks. J Int Med Res 2019; 48:300060519839625. [PMID: 31039661 PMCID: PMC7140207 DOI: 10.1177/0300060519839625] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Objective Methods Results Conclusions
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Affiliation(s)
- Ling-Bing Meng
- Neurology Department, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China.,*These authors contributed equally to this work
| | - Yang-Fan Zou
- Department of Neurosurgery, Chinese PLA General Hospital-Sixth Medical Center, Beijing, P.R. China.,*These authors contributed equally to this work
| | - Meng-Jie Shan
- MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China
| | - Meng Zhang
- School of Energy Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, P.R. China
| | - Ruo-Mei Qi
- MOH Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China
| | - Ze-Mou Yu
- Department of Neurology, Peking University First Hospital, Beijing, P. R. China
| | - Peng Guo
- Department of Orthopedics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, P.R. China
| | - Qian-Wei Zheng
- Neurology Department, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China
| | - Tao Gong
- Neurology Department, Beijing Hospital, National Center of Gerontology, Beijing, P.R. China
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Liu Y, Munteanu CR, Fernandez-Lozano C, Pazos A, Ran T, Tan Z, Yu Y, Zhou C, Tang S, González-Díaz H. Experimental Study and ANN Dual-Time Scale Perturbation Model of Electrokinetic Properties of Microbiota. Front Microbiol 2017; 8:1216. [PMID: 28713345 PMCID: PMC5491601 DOI: 10.3389/fmicb.2017.01216] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 06/14/2017] [Indexed: 12/18/2022] Open
Abstract
The electrokinetic properties of the rumen microbiota are involved in cell surface adhesion and microbial metabolism. An in vitro study was carried out in batch culture to determine the effects of three levels of special surface area (SSA) of biomaterials and four levels of surface tension (ST) of culture medium on electrokinetic properties (Zeta potential, ξ; electrokinetic mobility, μe), fermentation parameters (volatile fatty acids, VFAs), and ST over fermentation processes (ST-a, γ). The obtained results were combined with previously published data (digestibility, D; pH; concentration of ammonia nitrogen, c(NH3-N)) to establish a predictive artificial neural network (ANN) model. Concepts of dual-time series analysis, perturbation theory (PT), and Box-Jenkins Operators were applied for the first time to develop an ANN model to predict the variations of the electrokinetic properties of microbiota. The best dual-time series Radial Basis Functions (RBR) model for ξ of rumen microbiota predicted ξ for >30,000 cases with a correlation coefficient >0.8. This model provided insight into the correlations between electrokinetic property (zeta potential) of rumen microbiota and the perturbations of physical factors (specific surface area and surface tension) of media, digestibility of substrate, and their metabolites (NH3-N, VFAs) in relation to environmental factors.
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Affiliation(s)
- Yong Liu
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, Hunan Research Center of Livestock and Poultry Sciences, South-Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, Chinese Academy of SciencesChangsha, China
- RNASA-IMEDIR, Computer Science Faculty, University of A CorunaA Coruña, Spain
| | | | - Carlos Fernandez-Lozano
- RNASA-IMEDIR, Computer Science Faculty, University of A CorunaA Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña, Complexo Hospitalario Universitario de A CoruñaA Coruña, Spain
| | - Alejandro Pazos
- RNASA-IMEDIR, Computer Science Faculty, University of A CorunaA Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña, Complexo Hospitalario Universitario de A CoruñaA Coruña, Spain
| | - Tao Ran
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, Hunan Research Center of Livestock and Poultry Sciences, South-Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, Chinese Academy of SciencesChangsha, China
| | - Zhiliang Tan
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, Hunan Research Center of Livestock and Poultry Sciences, South-Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, Chinese Academy of SciencesChangsha, China
- Hunan Co-Innovation Center of Animal Production Safety, CICAPSChangsha, China
| | - Yizun Yu
- Institute of Biological Resources, Jiangxi Academy of SciencesJiangxi, China
| | - Chuanshe Zhou
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, Hunan Research Center of Livestock and Poultry Sciences, South-Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, Chinese Academy of SciencesChangsha, China
- Hunan Co-Innovation Center of Animal Production Safety, CICAPSChangsha, China
| | - Shaoxun Tang
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, Hunan Research Center of Livestock and Poultry Sciences, South-Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, Chinese Academy of SciencesChangsha, China
- Hunan Co-Innovation Center of Animal Production Safety, CICAPSChangsha, China
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHULeioa, Spain
- IKERBASQUE, Basque Foundation for ScienceBilbao, Spain
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