Dear Editor,Lymphocyte activation gene 3(LAG3),the third established target for immune checkpoint blockade therapy,suppresses T cell function by binding to major histocompatibility complex classⅡ(MHCⅡ).Despite its s...Dear Editor,Lymphocyte activation gene 3(LAG3),the third established target for immune checkpoint blockade therapy,suppresses T cell function by binding to major histocompatibility complex classⅡ(MHCⅡ).Despite its significant therapeutic potential in cancer immunotherapy and the substantial attention it has received from academia and industry,the molecular mechanisms of LAG3-mediated immunosuppression remain poorly understood,primarily because of its unique ligand-binding characteristics and intracellular domains[1].展开更多
This paper propose a comprehensive data-driven prediction framework based on machine learning methods to investigate the lag synchronization phenomenon in coupled chaotic systems,particularly in cases where accurate m...This paper propose a comprehensive data-driven prediction framework based on machine learning methods to investigate the lag synchronization phenomenon in coupled chaotic systems,particularly in cases where accurate mathematical models are challenging to establish or where system equations remain unknown.The Long Short-Term Memory(LSTM)neural network is trained using time series acquired from the desynchronization system states,subsequently predicting the lag synchronization transition.In the experiments,we focus on the Lorenz system with time-varying delayed coupling,studying the effects of coupling coefficients and time delays on lag synchronization,respectively.The results indicate that with appropriate training,the machine learning model can adeptly predict the lag synchronization occurrence and transition.This study not only enhances our comprehension of complex network synchronization behaviors but also underscores the potential and practical applications of machine learning in exploring nonlinear dynamic systems.展开更多
The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of ...The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.展开更多
In 2019,China had over 13.14 million dementia cases,with incidence rates of(56.47–207.08)/100,000[1].Early cognitive impairment—a key dementia symptom—reduces quality of life,increases care dependence,and lowers su...In 2019,China had over 13.14 million dementia cases,with incidence rates of(56.47–207.08)/100,000[1].Early cognitive impairment—a key dementia symptom—reduces quality of life,increases care dependence,and lowers survival in older adults[2].A decline in physical function can also be observed in older adults with increasing age.Grip strength has been shown to be a marker of overall physiological function in older adults.展开更多
跨设备链路聚合组(Multichassis Link Aggregation Group,M-LAG)技术作为一种先进通信技术,在铁路调度信息系统中的应用具有重要意义。通过对M-LAG技术的基本原理及建立过程进行详细论述,并结合实际应用场景,说明现阶段铁路调度信息系统...跨设备链路聚合组(Multichassis Link Aggregation Group,M-LAG)技术作为一种先进通信技术,在铁路调度信息系统中的应用具有重要意义。通过对M-LAG技术的基本原理及建立过程进行详细论述,并结合实际应用场景,说明现阶段铁路调度信息系统对M-LAG技术的迫切需求。将M-LAG技术与传统堆叠技术进行比较,证实M-LAG技术具有更高的灵活性及故障恢复能力、更简化网络管理和更低成本,在提高网络性能和可靠性方面具备巨大潜力。以实际应用场景为例,表明M-LAG技术可实现铁路各站、各设备间的高速、可靠、实时的数据传输,为铁路运输智能化、自动化提供有力技术支持。展开更多
文摘Dear Editor,Lymphocyte activation gene 3(LAG3),the third established target for immune checkpoint blockade therapy,suppresses T cell function by binding to major histocompatibility complex classⅡ(MHCⅡ).Despite its significant therapeutic potential in cancer immunotherapy and the substantial attention it has received from academia and industry,the molecular mechanisms of LAG3-mediated immunosuppression remain poorly understood,primarily because of its unique ligand-binding characteristics and intracellular domains[1].
基金supported by the National Natural Science Foundation of China(No.52174184)。
文摘This paper propose a comprehensive data-driven prediction framework based on machine learning methods to investigate the lag synchronization phenomenon in coupled chaotic systems,particularly in cases where accurate mathematical models are challenging to establish or where system equations remain unknown.The Long Short-Term Memory(LSTM)neural network is trained using time series acquired from the desynchronization system states,subsequently predicting the lag synchronization transition.In the experiments,we focus on the Lorenz system with time-varying delayed coupling,studying the effects of coupling coefficients and time delays on lag synchronization,respectively.The results indicate that with appropriate training,the machine learning model can adeptly predict the lag synchronization occurrence and transition.This study not only enhances our comprehension of complex network synchronization behaviors but also underscores the potential and practical applications of machine learning in exploring nonlinear dynamic systems.
基金supported by the National Key Program for Developing Basic Science(Nos.2022YFF0801702 and 2022YFE0106600)the National Natural Science Foundation of China(Nos.42175060 and 42175021)the Jiangsu Province Science Foundation(No.BK20250200302).
文摘The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.
基金supported by the Shanghai New Three-year Action Plan for Public Health(Grant No.GWV-10.1-XK16)the US National Institute on Aging(RO1-AGO34479).
文摘In 2019,China had over 13.14 million dementia cases,with incidence rates of(56.47–207.08)/100,000[1].Early cognitive impairment—a key dementia symptom—reduces quality of life,increases care dependence,and lowers survival in older adults[2].A decline in physical function can also be observed in older adults with increasing age.Grip strength has been shown to be a marker of overall physiological function in older adults.
文摘跨设备链路聚合组(Multichassis Link Aggregation Group,M-LAG)技术作为一种先进通信技术,在铁路调度信息系统中的应用具有重要意义。通过对M-LAG技术的基本原理及建立过程进行详细论述,并结合实际应用场景,说明现阶段铁路调度信息系统对M-LAG技术的迫切需求。将M-LAG技术与传统堆叠技术进行比较,证实M-LAG技术具有更高的灵活性及故障恢复能力、更简化网络管理和更低成本,在提高网络性能和可靠性方面具备巨大潜力。以实际应用场景为例,表明M-LAG技术可实现铁路各站、各设备间的高速、可靠、实时的数据传输,为铁路运输智能化、自动化提供有力技术支持。
