目的分析血清碱性磷酸酶(ALP)水平对急性冠脉综合征(ACS)患者预后的影响。方法采用前瞻性队列研究方法,连续入选2015年1月1日至2016年12月31日确诊ACS并行PCI术的患者共2254例,根据入院时血清ALP水平分为两组:低ALP组1805例(ALP≤96.7U&...目的分析血清碱性磷酸酶(ALP)水平对急性冠脉综合征(ACS)患者预后的影响。方法采用前瞻性队列研究方法,连续入选2015年1月1日至2016年12月31日确诊ACS并行PCI术的患者共2254例,根据入院时血清ALP水平分为两组:低ALP组1805例(ALP≤96.7U·L^-1)及高ALP组449例(ALP>96.7 U·L^-1),通过COX回归分析研究血清ALP水平对ACS患者预后的影响。结果与低ALP组相比,高ALP组患者男性(37.9%vs 27.4%,P<0.001)及中重度钙化病变(11.1% vs 6.5%,P=0.001)比例更高。高ALP组患者谷草转氨酶、谷丙转氨酶、白细胞、血小板、脑钠肽数值较低ALP组显著升高(P<0.05)。随访显示高ALP组的术后1年全因死亡率(4.9% vs 2.5%, P=0.007)及心源性死亡率(3.8% vs 2.1%,P=0.039)较低ALP组显著升高。多元COX回归分析显示,血清ALP水平是ACS患者PCI术后1年全因死亡(HR 1.894, 95% CI 1.111-3.228;P=0.019)的独立预测因素。结论合并高水平ALP的ACS患者PCI术后具有更高的全因死亡风险,血清ALP水平是ACS患者预后的独立预测因素。展开更多
Throughfall variability plays a crucial role in regulating hydrological and biogeochemical processes in forest ecosystems. However, throughfall variability and its potential influencing factors remain unclear in the s...Throughfall variability plays a crucial role in regulating hydrological and biogeochemical processes in forest ecosystems. However, throughfall variability and its potential influencing factors remain unclear in the subtropical deciduous forest because of its complex canopy and meteorological conditions. Here, the spatial variability and temporal stability of throughfall were investigated from October 2016 to December 2017 within a deciduous forest in the subtropical hilly regions of eastern China, and the effects of meteorological variables and distance from nearest tree trunk on throughfall variability were systematically evaluated. Throughfall variability during the leafed period was slightly higher than that during the leafless period inferred from the coefficient of variation of throughfall amounts(CVTF), with 13.2%-40.9% and 18.7%-31.9%, respectively. The multiple regression model analysis suggested that the controlling factors of throughfall variability were different in studied periods: Maximum 10-min rainfall intensity, wind speed and air temperature were the dominant influencing factors on throughfall variability during the leafed period, with the relative contribution ratio(RCR) of 25.9%, 18.7% and 8.9%, respectively. By contrast, throughfall variability was affected mainly by the mean rainfall intensity(RCR=40.8%) during the leafless period. The temporal stability plots and geostatistical analysis indicated that spatial patterns of throughfall were stable and similar among rainfall events. Our findings highlight the important role of various meteorological factors in throughfall variability and are expected to contribute to the accurate assessment of throughfall, soil water and runoff within the subtropical forests.展开更多
Extreme rainfall events are rare in inland arid regions, but have exhibited an increasing trend in recent years, causing many casualties and substantial socioeconomic losses. A series of heavy rains that began on July...Extreme rainfall events are rare in inland arid regions, but have exhibited an increasing trend in recent years, causing many casualties and substantial socioeconomic losses. A series of heavy rains that began on July 31st, 2018, battered the Hami prefecture of eastern Xinjiang, China for four days. These rains sparked devastating floods, caused 20 deaths, eight missing, and the evacuation of about 5500 people. This study examines the extreme rainfall event in a historical context and explores the anthropogenic causes based on analysis of multiple datasets (i.e., the observed daily data, the global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), the NCEP/NCAR Reanalysis 1, and the satellite cloud data) and several statistical techniques. Results show that this extraordinarily heavy rainfall was due mainly to the abnormal weather system (e.g., the abnormal subtropical high) that transported abundant water vapor from the Indian Ocean and the East China Sea crossed the high mountains and formed extreme rainfall in Hami prefecture, causing the reservoir to break and form a flood event with treat loss, which is a typical example of a comprehensive analysis of the extreme rainfall event in summer in Northwest China. Also, the fraction of attributable risk (FAR) value was 1.00 when the 2018 July–August RX1day (11.52 mm) was marked as the threshold, supporting the claim of a significant anthropogenic influence on the risk of this extreme rainfall. The results offer insights into the variability of precipitation extremes in arid areas contributing to better manage water-related disasters.展开更多
文摘目的分析血清碱性磷酸酶(ALP)水平对急性冠脉综合征(ACS)患者预后的影响。方法采用前瞻性队列研究方法,连续入选2015年1月1日至2016年12月31日确诊ACS并行PCI术的患者共2254例,根据入院时血清ALP水平分为两组:低ALP组1805例(ALP≤96.7U·L^-1)及高ALP组449例(ALP>96.7 U·L^-1),通过COX回归分析研究血清ALP水平对ACS患者预后的影响。结果与低ALP组相比,高ALP组患者男性(37.9%vs 27.4%,P<0.001)及中重度钙化病变(11.1% vs 6.5%,P=0.001)比例更高。高ALP组患者谷草转氨酶、谷丙转氨酶、白细胞、血小板、脑钠肽数值较低ALP组显著升高(P<0.05)。随访显示高ALP组的术后1年全因死亡率(4.9% vs 2.5%, P=0.007)及心源性死亡率(3.8% vs 2.1%,P=0.039)较低ALP组显著升高。多元COX回归分析显示,血清ALP水平是ACS患者PCI术后1年全因死亡(HR 1.894, 95% CI 1.111-3.228;P=0.019)的独立预测因素。结论合并高水平ALP的ACS患者PCI术后具有更高的全因死亡风险,血清ALP水平是ACS患者预后的独立预测因素。
基金supported by the National Natural Science Foundation of China (Grants No. 41861022, 91647203, 51609145)the Natural Science Foundation of Jiangsu Province (No. BK20161612)+2 种基金“One-Three-Five” Strategic Planning of Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (Grant No. NIGLAS2017GH07)Science Foundation of Nanjing Hydraulic Research Institute (No. Y517009)Jiangsu Planned Projects for Postdoctoral Research Funds (118000003)
文摘Throughfall variability plays a crucial role in regulating hydrological and biogeochemical processes in forest ecosystems. However, throughfall variability and its potential influencing factors remain unclear in the subtropical deciduous forest because of its complex canopy and meteorological conditions. Here, the spatial variability and temporal stability of throughfall were investigated from October 2016 to December 2017 within a deciduous forest in the subtropical hilly regions of eastern China, and the effects of meteorological variables and distance from nearest tree trunk on throughfall variability were systematically evaluated. Throughfall variability during the leafed period was slightly higher than that during the leafless period inferred from the coefficient of variation of throughfall amounts(CVTF), with 13.2%-40.9% and 18.7%-31.9%, respectively. The multiple regression model analysis suggested that the controlling factors of throughfall variability were different in studied periods: Maximum 10-min rainfall intensity, wind speed and air temperature were the dominant influencing factors on throughfall variability during the leafed period, with the relative contribution ratio(RCR) of 25.9%, 18.7% and 8.9%, respectively. By contrast, throughfall variability was affected mainly by the mean rainfall intensity(RCR=40.8%) during the leafless period. The temporal stability plots and geostatistical analysis indicated that spatial patterns of throughfall were stable and similar among rainfall events. Our findings highlight the important role of various meteorological factors in throughfall variability and are expected to contribute to the accurate assessment of throughfall, soil water and runoff within the subtropical forests.
基金This study was sponsored by the Project of Tianshan Innovation Team in Xinjiang(202113050)the Chinese Academy of Sciences President's International Fellowship Initiative(2017VCA0002).
文摘Extreme rainfall events are rare in inland arid regions, but have exhibited an increasing trend in recent years, causing many casualties and substantial socioeconomic losses. A series of heavy rains that began on July 31st, 2018, battered the Hami prefecture of eastern Xinjiang, China for four days. These rains sparked devastating floods, caused 20 deaths, eight missing, and the evacuation of about 5500 people. This study examines the extreme rainfall event in a historical context and explores the anthropogenic causes based on analysis of multiple datasets (i.e., the observed daily data, the global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), the NCEP/NCAR Reanalysis 1, and the satellite cloud data) and several statistical techniques. Results show that this extraordinarily heavy rainfall was due mainly to the abnormal weather system (e.g., the abnormal subtropical high) that transported abundant water vapor from the Indian Ocean and the East China Sea crossed the high mountains and formed extreme rainfall in Hami prefecture, causing the reservoir to break and form a flood event with treat loss, which is a typical example of a comprehensive analysis of the extreme rainfall event in summer in Northwest China. Also, the fraction of attributable risk (FAR) value was 1.00 when the 2018 July–August RX1day (11.52 mm) was marked as the threshold, supporting the claim of a significant anthropogenic influence on the risk of this extreme rainfall. The results offer insights into the variability of precipitation extremes in arid areas contributing to better manage water-related disasters.
