The 4.2 ka event that occurred during the period from 4 500–3 900 a BP was characterized by cold and dry climates and resulted in the collapse of civilizations around the world. The cause of this climatic event, howe...The 4.2 ka event that occurred during the period from 4 500–3 900 a BP was characterized by cold and dry climates and resulted in the collapse of civilizations around the world. The cause of this climatic event, however, has been under debate. We collected four corals(Porites lutea) from Yongxing Island, Xisha Islands, South China Sea, dated them with the U-series method, and measured the annual coral growth rates using X-ray technology. The dating results showed that the coral growth ages were from 4 500–3 900 a BP, which coincide well with the period of the4.2 ka event. We then reconstructed annual sea surface temperature anomaly(SSTA) variations based on the coral growth rates. The growth rate-based SSTA results showed that the interdecadal SSTA from 4 500–3 900 a BP was lower than that during modern times(1961–2008 AD). A spectral analysis showed that the SSTA variations from4 500–3 900 a BP were under the influence of El Nino-Southern Oscillation(ENSO) activities. From 4 500–4 100 a BP, the climate exhibited La Nina-like conditions with weak ENSO intensity and relatively stable and lower SSTA amplitudes. From 4 100–3 900 a BP, the climate underwent a complicated period of ENSO variability and showed alternating El Nino-or La Nina-like conditions at interdecadal time scales and large SSTA amplitudes. We speculate that during the early and middle stages of the 4.2 ka event, the cold climate caused by weak ENSO activities largely weakened social productivity. Then, during the end stages of the 4.2 ka event, the repeated fluctuations in the ENSO intensity caused frequent extreme weather events, resulting in the collapse of civilizations worldwide. Thus, the new evidence obtained from our coral records suggests that the 4.2 ka event as well as the related collapse of civilizations were very likely driven by ENSO variability.展开更多
Recurrent event time data and more general multiple event time data are commonly analyzed using extensions of Cox regression, or proportional hazards regression, as used with single event time data. These methods trea...Recurrent event time data and more general multiple event time data are commonly analyzed using extensions of Cox regression, or proportional hazards regression, as used with single event time data. These methods treat covariates, either time-invariant or time-varying, as having multiplicative effects while general dependence on time is left un-estimated. An adaptive approach is formulated for analyzing multiple event time data. Conditional hazard rates are modeled in terms of dependence on both time and covariates using fractional polynomials restricted so that the conditional hazard rates are positive-valued and so that excess time probability functions (generalizing survival functions for single event times) are decreasing. Maximum likelihood is used to estimate parameters adjusting for right censored event times. Likelihood cross-validation (LCV) scores are used to compare models. Adaptive searches through alternate conditional hazard rate models are controlled by LCV scores combined with tolerance parameters. These searches identify effective models for the underlying multiple event time data. Conditional hazard regression is demonstrated using data on times between tumor recurrence for bladder cancer patients. Analyses of theory-based models for these data using extensions of Cox regression provide conflicting results on effects to treatment group and the initial number of tumors. On the other hand, fractional polynomial analyses of these theory-based models provide consistent results identifying significant effects to treatment group and initial number of tumors using both model-based and robust empirical tests. Adaptive analyses further identify distinct moderation by group of the effect of tumor order and an additive effect to group after controlling for nonlinear effects to initial number of tumors and tumor order. Results of example analyses indicate that adaptive conditional hazard rate modeling can generate useful insights into multiple event time data.展开更多
Recurrent event time data and more general multiple event time data are commonly analyzed using extensions of Cox regression, or proportional hazards regression, as used with single event time data. These methods trea...Recurrent event time data and more general multiple event time data are commonly analyzed using extensions of Cox regression, or proportional hazards regression, as used with single event time data. These methods treat covariates, either time-invariant or time-varying, as having multiplicative effects while general dependence on time is left un-estimated. An adaptive approach is formulated for analyzing multiple event time data. Conditional hazard rates are modeled in terms of dependence on both time and covariates using fractional polynomials restricted so that the conditional hazard rates are positive-valued and so that excess time probability functions (generalizing survival functions for single event times) are decreasing. Maximum likelihood is used to estimate parameters adjusting for right censored event times. Likelihood cross-validation (LCV) scores are used to compare models. Adaptive searches through alternate conditional hazard rate models are controlled by LCV scores combined with tolerance parameters. These searches identify effective models for the underlying multiple event time data. Conditional hazard regression is demonstrated using data on times between tumor recurrence for bladder cancer patients. Analyses of theory-based models for these data using extensions of Cox regression provide conflicting results on effects to treatment group and the initial number of tumors. On the other hand, fractional polynomial analyses of these theory-based models provide consistent results identifying significant effects to treatment group and initial number of tumors using both model-based and robust empirical tests. Adaptive analyses further identify distinct moderation by group of the effect of tumor order and an additive effect to group after controlling for nonlinear effects to initial number of tumors and tumor order. Results of example analyses indicate that adaptive conditional hazard rate modeling can generate useful insights into multiple event time data.展开更多
目的 探讨收缩压变异性(SBPV)和心率变异性(HRV)对维持性血液透析(MHD)患者主要不良心血管事件(MACE)发生风险的预测价值。方法 纳入2017年3月—2018年3月在宜昌市中心人民医院肾病内科血液净化中心接受规律治疗的MHD患者120例,根据是...目的 探讨收缩压变异性(SBPV)和心率变异性(HRV)对维持性血液透析(MHD)患者主要不良心血管事件(MACE)发生风险的预测价值。方法 纳入2017年3月—2018年3月在宜昌市中心人民医院肾病内科血液净化中心接受规律治疗的MHD患者120例,根据是否发生MACE分为MACE组(n=59)与无MACE组(n=61)。在患者行血液透析前佩戴Holter,收集24 h心电活动信息,计算均值(MEAN)、RR间期总体标准差(SDNN)、RR间期平均值的标准差(SDANN)和相邻RR间期差值的均方根(r-MSSD)。采用自动血压监测系统记录24 h血压变化,计算白昼收缩压变异性(dSBPV)、夜间收缩压变异性(nSBPV)和24 h收缩压变异性(24 h SBPV)。Logistic回归分析MHD患者MACE发生的危险因素。调整混杂因素后,采用Cox比例风险模型回归分析24 h SBPV和SDNN与MHD患者MACE发生的关系。绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC)、灵敏度、特异度,分析SDNN和收缩压变异性单独及联合对维持性MHD患者发生MACE的预测价值。根据SDNN和24 h SBPV水平将患者分成3组,绘制Kaplan-Meier生存曲线评价不同SDNN和收缩压变异性的MHD患者MACE发生情况。结果 与无MACE组相比,MACE组年龄较大,24 h SBPV、dSBPV、nSBPV较高,SDNN、SDANN较低,差异具有统计学意义(P<0.05)。Logistic回归分析显示,年龄、Kt/V、24 h SBPV、dSBPV、nSBPV、SDNN、SDANN是MHD患者MACE发生的独立危险因素(P<0.05)。调整混杂因素后,多因素COX比例风险模型回归分析,24 h SBPV为MHD患者发生MACE的危险因素,而SDNN为MHD患者发生MACE的保护性因素(P<0.05)。SDNN与收缩压变异性联合预测MHD患者发生MACE的AUC为0.879,预测效能高于单项检测(P<0.05)。组1随访期间累积MACE发生率显著低于组2和组3(19.15%vs 65.12%vs 73.33%,P<0.001)。结论 MHD不良预后患者中24 h SBPV升高,SDNN降低,24 h SBPV和SDNN单独预测MACE的具体价值尚可,两者联合预测效果更佳,可为临床上及早识别及干预MHD患者MACE发生提供参考依据。展开更多
目的探究分析高州市2023—2024年疑似预防接种异常反应(adverse events following immunization,AEFI)监测结果。方法通过中国疾病预防控制信息系统检索收集2023年1月—2024年3月期间的预防接种AEFI监测报告数据,对AEFI监测结果予以描...目的探究分析高州市2023—2024年疑似预防接种异常反应(adverse events following immunization,AEFI)监测结果。方法通过中国疾病预防控制信息系统检索收集2023年1月—2024年3月期间的预防接种AEFI监测报告数据,对AEFI监测结果予以描述性分析。结果2023年1月—2024年3月高州市接种疫苗出现245例AFEI,平均每年报告发生率为15.22/10万剂次。一般反应占比80.00%(196/245)、异常反应占比10.61%(26/245),未出现接种事故以及疫苗质量事故。AFEI个案中男女性别比例为1.36∶1;以<1岁者发生AEFI最多,10~15岁者发生AEFI最少。接种至出现症状的间隔时间以<1 d发生AEFI最多。AFEI报告涉及疫苗36种,前三位是百白破疫苗(无细胞)、麻腮风疫苗、13价肺炎疫苗;报告发生率前三位是带状疱疹疫苗(CHO细胞)、ACYW135流脑疫苗(结合)、冻干甲肝减毒活疫苗;引起一般反应的疫苗前两位是带状疱疹疫苗(CHO细胞)、ACYW135流脑疫苗(结合);引起异常反应的疫苗前两位是麻腮风疫苗、23价肺炎球菌疫苗。结论2023年1月—2024年3月高州市AEFI监测结果符合监测要求,且能在一定程度上说明预防接种疫苗的安全性,但需关注AFEI报告中发生率较高的疫苗,并需加强高州市AEFI监测,提升AEFI监测的及时性与灵敏度。展开更多
基金The National Natural Science Foundation of China under contract No.91428203the Guangxi Scientific Projects under contract Nos AD17129063 and AA17204074the Bagui Fellowship from Guangxi of China
文摘The 4.2 ka event that occurred during the period from 4 500–3 900 a BP was characterized by cold and dry climates and resulted in the collapse of civilizations around the world. The cause of this climatic event, however, has been under debate. We collected four corals(Porites lutea) from Yongxing Island, Xisha Islands, South China Sea, dated them with the U-series method, and measured the annual coral growth rates using X-ray technology. The dating results showed that the coral growth ages were from 4 500–3 900 a BP, which coincide well with the period of the4.2 ka event. We then reconstructed annual sea surface temperature anomaly(SSTA) variations based on the coral growth rates. The growth rate-based SSTA results showed that the interdecadal SSTA from 4 500–3 900 a BP was lower than that during modern times(1961–2008 AD). A spectral analysis showed that the SSTA variations from4 500–3 900 a BP were under the influence of El Nino-Southern Oscillation(ENSO) activities. From 4 500–4 100 a BP, the climate exhibited La Nina-like conditions with weak ENSO intensity and relatively stable and lower SSTA amplitudes. From 4 100–3 900 a BP, the climate underwent a complicated period of ENSO variability and showed alternating El Nino-or La Nina-like conditions at interdecadal time scales and large SSTA amplitudes. We speculate that during the early and middle stages of the 4.2 ka event, the cold climate caused by weak ENSO activities largely weakened social productivity. Then, during the end stages of the 4.2 ka event, the repeated fluctuations in the ENSO intensity caused frequent extreme weather events, resulting in the collapse of civilizations worldwide. Thus, the new evidence obtained from our coral records suggests that the 4.2 ka event as well as the related collapse of civilizations were very likely driven by ENSO variability.
文摘Recurrent event time data and more general multiple event time data are commonly analyzed using extensions of Cox regression, or proportional hazards regression, as used with single event time data. These methods treat covariates, either time-invariant or time-varying, as having multiplicative effects while general dependence on time is left un-estimated. An adaptive approach is formulated for analyzing multiple event time data. Conditional hazard rates are modeled in terms of dependence on both time and covariates using fractional polynomials restricted so that the conditional hazard rates are positive-valued and so that excess time probability functions (generalizing survival functions for single event times) are decreasing. Maximum likelihood is used to estimate parameters adjusting for right censored event times. Likelihood cross-validation (LCV) scores are used to compare models. Adaptive searches through alternate conditional hazard rate models are controlled by LCV scores combined with tolerance parameters. These searches identify effective models for the underlying multiple event time data. Conditional hazard regression is demonstrated using data on times between tumor recurrence for bladder cancer patients. Analyses of theory-based models for these data using extensions of Cox regression provide conflicting results on effects to treatment group and the initial number of tumors. On the other hand, fractional polynomial analyses of these theory-based models provide consistent results identifying significant effects to treatment group and initial number of tumors using both model-based and robust empirical tests. Adaptive analyses further identify distinct moderation by group of the effect of tumor order and an additive effect to group after controlling for nonlinear effects to initial number of tumors and tumor order. Results of example analyses indicate that adaptive conditional hazard rate modeling can generate useful insights into multiple event time data.
文摘Recurrent event time data and more general multiple event time data are commonly analyzed using extensions of Cox regression, or proportional hazards regression, as used with single event time data. These methods treat covariates, either time-invariant or time-varying, as having multiplicative effects while general dependence on time is left un-estimated. An adaptive approach is formulated for analyzing multiple event time data. Conditional hazard rates are modeled in terms of dependence on both time and covariates using fractional polynomials restricted so that the conditional hazard rates are positive-valued and so that excess time probability functions (generalizing survival functions for single event times) are decreasing. Maximum likelihood is used to estimate parameters adjusting for right censored event times. Likelihood cross-validation (LCV) scores are used to compare models. Adaptive searches through alternate conditional hazard rate models are controlled by LCV scores combined with tolerance parameters. These searches identify effective models for the underlying multiple event time data. Conditional hazard regression is demonstrated using data on times between tumor recurrence for bladder cancer patients. Analyses of theory-based models for these data using extensions of Cox regression provide conflicting results on effects to treatment group and the initial number of tumors. On the other hand, fractional polynomial analyses of these theory-based models provide consistent results identifying significant effects to treatment group and initial number of tumors using both model-based and robust empirical tests. Adaptive analyses further identify distinct moderation by group of the effect of tumor order and an additive effect to group after controlling for nonlinear effects to initial number of tumors and tumor order. Results of example analyses indicate that adaptive conditional hazard rate modeling can generate useful insights into multiple event time data.
文摘目的 探讨收缩压变异性(SBPV)和心率变异性(HRV)对维持性血液透析(MHD)患者主要不良心血管事件(MACE)发生风险的预测价值。方法 纳入2017年3月—2018年3月在宜昌市中心人民医院肾病内科血液净化中心接受规律治疗的MHD患者120例,根据是否发生MACE分为MACE组(n=59)与无MACE组(n=61)。在患者行血液透析前佩戴Holter,收集24 h心电活动信息,计算均值(MEAN)、RR间期总体标准差(SDNN)、RR间期平均值的标准差(SDANN)和相邻RR间期差值的均方根(r-MSSD)。采用自动血压监测系统记录24 h血压变化,计算白昼收缩压变异性(dSBPV)、夜间收缩压变异性(nSBPV)和24 h收缩压变异性(24 h SBPV)。Logistic回归分析MHD患者MACE发生的危险因素。调整混杂因素后,采用Cox比例风险模型回归分析24 h SBPV和SDNN与MHD患者MACE发生的关系。绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC)、灵敏度、特异度,分析SDNN和收缩压变异性单独及联合对维持性MHD患者发生MACE的预测价值。根据SDNN和24 h SBPV水平将患者分成3组,绘制Kaplan-Meier生存曲线评价不同SDNN和收缩压变异性的MHD患者MACE发生情况。结果 与无MACE组相比,MACE组年龄较大,24 h SBPV、dSBPV、nSBPV较高,SDNN、SDANN较低,差异具有统计学意义(P<0.05)。Logistic回归分析显示,年龄、Kt/V、24 h SBPV、dSBPV、nSBPV、SDNN、SDANN是MHD患者MACE发生的独立危险因素(P<0.05)。调整混杂因素后,多因素COX比例风险模型回归分析,24 h SBPV为MHD患者发生MACE的危险因素,而SDNN为MHD患者发生MACE的保护性因素(P<0.05)。SDNN与收缩压变异性联合预测MHD患者发生MACE的AUC为0.879,预测效能高于单项检测(P<0.05)。组1随访期间累积MACE发生率显著低于组2和组3(19.15%vs 65.12%vs 73.33%,P<0.001)。结论 MHD不良预后患者中24 h SBPV升高,SDNN降低,24 h SBPV和SDNN单独预测MACE的具体价值尚可,两者联合预测效果更佳,可为临床上及早识别及干预MHD患者MACE发生提供参考依据。
文摘目的探究分析高州市2023—2024年疑似预防接种异常反应(adverse events following immunization,AEFI)监测结果。方法通过中国疾病预防控制信息系统检索收集2023年1月—2024年3月期间的预防接种AEFI监测报告数据,对AEFI监测结果予以描述性分析。结果2023年1月—2024年3月高州市接种疫苗出现245例AFEI,平均每年报告发生率为15.22/10万剂次。一般反应占比80.00%(196/245)、异常反应占比10.61%(26/245),未出现接种事故以及疫苗质量事故。AFEI个案中男女性别比例为1.36∶1;以<1岁者发生AEFI最多,10~15岁者发生AEFI最少。接种至出现症状的间隔时间以<1 d发生AEFI最多。AFEI报告涉及疫苗36种,前三位是百白破疫苗(无细胞)、麻腮风疫苗、13价肺炎疫苗;报告发生率前三位是带状疱疹疫苗(CHO细胞)、ACYW135流脑疫苗(结合)、冻干甲肝减毒活疫苗;引起一般反应的疫苗前两位是带状疱疹疫苗(CHO细胞)、ACYW135流脑疫苗(结合);引起异常反应的疫苗前两位是麻腮风疫苗、23价肺炎球菌疫苗。结论2023年1月—2024年3月高州市AEFI监测结果符合监测要求,且能在一定程度上说明预防接种疫苗的安全性,但需关注AFEI报告中发生率较高的疫苗,并需加强高州市AEFI监测,提升AEFI监测的及时性与灵敏度。
