Ageing is often accompanied with a decline in immune system function,resulting in immune ageing.Numerous studies have focussed on the changes in different lymphocyte subsets in diseases and immunosenescence.The change...Ageing is often accompanied with a decline in immune system function,resulting in immune ageing.Numerous studies have focussed on the changes in different lymphocyte subsets in diseases and immunosenescence.The change in immune phenotype is a key indication of the diseased or healthy status.However,the changes in lymphocyte number and phenotype brought about by ageing have not been comprehensively analysed.Here,we analysed T and natural killer(NK)cell subsets,the phenotype and cell differentiation states in 43,096 healthy individuals,aged 20–88 years,without known diseases.Thirty-six immune parameters were analysed and the reference ranges of these subsets were established in different age groups divided into 5-year intervals.The data were subjected to random forest machine learning for immune-ageing modelling and confirmed using the neural network analysis.Our initial analysis and machine modelling prediction showed that na.ve T cells decreased with ageing,whereas central memory T cells(Tcm)and effector memory T cells(Tem)increased cluster of differentiation(CD)28-associated T cells.This is the largest study to investigate the correlation between age and immune cell function in a Chinese population,and provides insightful differences,suggesting that healthy adults might be considerably influenced by age and sex.The age of a person's immune system might be different from their chronological age.Our immune-ageing modelling study is one of the largest studies to provide insights into‘immune-age’rather than‘biological-age’.Through machine learning,we identified immune factors influencing the most through ageing and built a model for immune-ageing prediction.Our research not only reveals the impact of age on immune parameter differences within the Chinese population,but also provides new insights for monitoring and preventing some diseases in clinical practice.展开更多
The analysis of transient linear viscoelastic response of asphalt concrete (AC) is important for engineering applications. The traditional transient response of AC is analyzed in the time domain by performing compli...The analysis of transient linear viscoelastic response of asphalt concrete (AC) is important for engineering applications. The traditional transient response of AC is analyzed in the time domain by performing complicated convolution integral. The frequency domain approach allows one to determine the transient responses by performing simple multi- plication instead of the complicated convolution integral, and it does not require the time derivative of the input excitation, and thus, the approach could greatly reduce the analysis complexity. This study investigated the frequency domain approach in calculating the transient response by utilizing the discrete Fourier transform technique. The accuracy and effectiveness of the frequency domain approach were verified by comparing the analytical and calculated responses for the standard 3-parameter Maxwell model and by comparing the time and frequency domain solutions for AC. The effect of aliasing of the frequency domain approach can effectively reduce by selecting a small sampling interval for the time domain excitation function. A sampling interval is acceptable as long as the amplitude of the Fourier transformed excitation is close to 0 more than half of the sampling rate. The results show that the frequency domain approach provides a simple and accurate way to perform linear viscoelastic analysis of AC.展开更多
基金supported by National Key Research and Development Program of China(2020YFA0803502 to Z.Y.)National Natural Science Foundation of China(32030036 and 31830021 to Z.Y.)+6 种基金the 111 Project(B16021 to Z.Y.)Natural Science Foundation of China(81971301 and 32050410285 to O.J.L.)Guangzhou Planned Project of Science and Technology(202002020039 to O.J.L.)Guangdong Basic and Applied Basic Research Foundation(2021A1515110734 to Z.R.)Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology,The First Affiliated Hospital,Sun Yat-sen University,Guangzhou,China(2013A061401007,2017B030314018,2020B1212060026)Guangdong Provincial International Cooperation Base of Science and Technology(Organ Transplantation)The First Affiliated Hospital,Sun Yat-sen University,Guangzhou,China(2015B050501002,2020A0505020003).
文摘Ageing is often accompanied with a decline in immune system function,resulting in immune ageing.Numerous studies have focussed on the changes in different lymphocyte subsets in diseases and immunosenescence.The change in immune phenotype is a key indication of the diseased or healthy status.However,the changes in lymphocyte number and phenotype brought about by ageing have not been comprehensively analysed.Here,we analysed T and natural killer(NK)cell subsets,the phenotype and cell differentiation states in 43,096 healthy individuals,aged 20–88 years,without known diseases.Thirty-six immune parameters were analysed and the reference ranges of these subsets were established in different age groups divided into 5-year intervals.The data were subjected to random forest machine learning for immune-ageing modelling and confirmed using the neural network analysis.Our initial analysis and machine modelling prediction showed that na.ve T cells decreased with ageing,whereas central memory T cells(Tcm)and effector memory T cells(Tem)increased cluster of differentiation(CD)28-associated T cells.This is the largest study to investigate the correlation between age and immune cell function in a Chinese population,and provides insightful differences,suggesting that healthy adults might be considerably influenced by age and sex.The age of a person's immune system might be different from their chronological age.Our immune-ageing modelling study is one of the largest studies to provide insights into‘immune-age’rather than‘biological-age’.Through machine learning,we identified immune factors influencing the most through ageing and built a model for immune-ageing prediction.Our research not only reveals the impact of age on immune parameter differences within the Chinese population,but also provides new insights for monitoring and preventing some diseases in clinical practice.
基金sponsored by Inner Mongolia Transportation Research Project(NJ-2014-X)Shanxi Transportation Research Project(2015-1-22)National Natural Science Foundation of China(51208080)
文摘The analysis of transient linear viscoelastic response of asphalt concrete (AC) is important for engineering applications. The traditional transient response of AC is analyzed in the time domain by performing complicated convolution integral. The frequency domain approach allows one to determine the transient responses by performing simple multi- plication instead of the complicated convolution integral, and it does not require the time derivative of the input excitation, and thus, the approach could greatly reduce the analysis complexity. This study investigated the frequency domain approach in calculating the transient response by utilizing the discrete Fourier transform technique. The accuracy and effectiveness of the frequency domain approach were verified by comparing the analytical and calculated responses for the standard 3-parameter Maxwell model and by comparing the time and frequency domain solutions for AC. The effect of aliasing of the frequency domain approach can effectively reduce by selecting a small sampling interval for the time domain excitation function. A sampling interval is acceptable as long as the amplitude of the Fourier transformed excitation is close to 0 more than half of the sampling rate. The results show that the frequency domain approach provides a simple and accurate way to perform linear viscoelastic analysis of AC.