The relationship between acute high altitude response (AHAR), cardiac function injury, and high altitude de-adaptation response (HADAR) was assessed. Cardiac function indicators were assessed for 96 men (18 - 35 years...The relationship between acute high altitude response (AHAR), cardiac function injury, and high altitude de-adaptation response (HADAR) was assessed. Cardiac function indicators were assessed for 96 men (18 - 35 years old) deployed into a high altitude (3700 - 4800 m) environment requiring intense physical activity. The subjects were divided into 3 groups based on AHAR at high altitude: severe AHAR (n = 24), mild to moderate AHAR (Group B, n = 47) and non-AHAR (Group C, 25);and based on HADAR: severe HADAR (Group E, n = 19), mild to moderate HADAR (Group F, n = 40) and non-HADAR (Group G, n = 37) after return to lower altitude (1,500 m). Cardiac function indicators were measured after 50 days at high altitude and at 12 h, 15 days, and 30 days after return to lower altitude. Controls were 50 healthy volunteers (Group D, n = 50) at 1500 m. Significant differences were observed in cardiac function indicators among groups A, B, C, and D. AHAR score was positively correlated with HADAR score (r = 0.863, P < 0.001). Significant differ- ences were also observed in cardiac function indicators among groups D, E, F, and G, 12 h and15 days after return to lower altitude. There were no significant differences in cardiac function indicators among the groups, 30 days after return to lower altitude, compared to group D. The results indicated that the severity of HADAR is associated with the severity of AHAR and cardiac injury, and prolonged recovery.展开更多
This paper highlights some recent developments in testing predictability of asset returns with focuses on linear mean regressions, quantile regressions and nonlinear regression models. For these models, when predictor...This paper highlights some recent developments in testing predictability of asset returns with focuses on linear mean regressions, quantile regressions and nonlinear regression models. For these models, when predictors are highly persistent and their innovations are contemporarily correlated with dependent variable, the ordinary least squares estimator has a finite-sample bias, and its limiting distribution relies on some unknown nuisance parameter, which is not consistently estimable. Without correcting these issues, conventional test statistics are subject to a serious size distortion and generate a misleading conclusion in testing pre- dictability of asset returns in real applications. In the past two decades, sequential studies have contributed to this subject and proposed various kinds of solutions, including, but not limit to, the bias-correction procedures, the linear projection approach, the IVX filtering idea, the variable addition approaches, the weighted empirical likelihood method, and the double-weight robust approach. Particularly, to catch up with the fast-growing literature in the recent decade, we offer a selective overview of these methods. Finally, some future research topics, such as the econometric theory for predictive regressions with structural changes, and nonparametric predictive models, and predictive models under a more general data setting, are also discussed.展开更多
文摘The relationship between acute high altitude response (AHAR), cardiac function injury, and high altitude de-adaptation response (HADAR) was assessed. Cardiac function indicators were assessed for 96 men (18 - 35 years old) deployed into a high altitude (3700 - 4800 m) environment requiring intense physical activity. The subjects were divided into 3 groups based on AHAR at high altitude: severe AHAR (n = 24), mild to moderate AHAR (Group B, n = 47) and non-AHAR (Group C, 25);and based on HADAR: severe HADAR (Group E, n = 19), mild to moderate HADAR (Group F, n = 40) and non-HADAR (Group G, n = 37) after return to lower altitude (1,500 m). Cardiac function indicators were measured after 50 days at high altitude and at 12 h, 15 days, and 30 days after return to lower altitude. Controls were 50 healthy volunteers (Group D, n = 50) at 1500 m. Significant differences were observed in cardiac function indicators among groups A, B, C, and D. AHAR score was positively correlated with HADAR score (r = 0.863, P < 0.001). Significant differ- ences were also observed in cardiac function indicators among groups D, E, F, and G, 12 h and15 days after return to lower altitude. There were no significant differences in cardiac function indicators among the groups, 30 days after return to lower altitude, compared to group D. The results indicated that the severity of HADAR is associated with the severity of AHAR and cardiac injury, and prolonged recovery.
基金supported by the National Natural Science Foundation of China(71631004,71571152)the Fundamental Research Funds for the Central Universities(20720171002,20720170090)the Fok Ying-Tong Education Foundation(151084)
文摘This paper highlights some recent developments in testing predictability of asset returns with focuses on linear mean regressions, quantile regressions and nonlinear regression models. For these models, when predictors are highly persistent and their innovations are contemporarily correlated with dependent variable, the ordinary least squares estimator has a finite-sample bias, and its limiting distribution relies on some unknown nuisance parameter, which is not consistently estimable. Without correcting these issues, conventional test statistics are subject to a serious size distortion and generate a misleading conclusion in testing pre- dictability of asset returns in real applications. In the past two decades, sequential studies have contributed to this subject and proposed various kinds of solutions, including, but not limit to, the bias-correction procedures, the linear projection approach, the IVX filtering idea, the variable addition approaches, the weighted empirical likelihood method, and the double-weight robust approach. Particularly, to catch up with the fast-growing literature in the recent decade, we offer a selective overview of these methods. Finally, some future research topics, such as the econometric theory for predictive regressions with structural changes, and nonparametric predictive models, and predictive models under a more general data setting, are also discussed.
