Background: The potential impact of β cell function and insulin sensitivity on adverse pregnancy outcomes in women with gestational diabetes mellitus (GDM) remains uncertain. We aimed to investigate the association b...Background: The potential impact of β cell function and insulin sensitivity on adverse pregnancy outcomes in women with gestational diabetes mellitus (GDM) remains uncertain. We aimed to investigate the association between β cell dysfunction, insulin resistance, and the composite adverse pregnancy outcomes.Methods: This observational study included 482 women diagnosed with GDM during pregnancy. Quantitative metrics on β cell function and insulin sensitivity during pregnancy were calculated using traditional equations. The association of β cell dysfunction and insulin resistance with the risk of the composite adverse pregnancy outcomes was investigated using multivariable-adjusted logistic regression models.Results: Multivariable-adjusted odds ratios (ORs) of adverse pregnancy outcomes across quartiles of homeostatic model assessment for insulin resistance (HOMA-IR) were 1.00, 0.95, 1.34, and 2.25, respectively (P for trend = 0.011). When HOMA-IR was considered as a continuous variable, the multivariable-adjusted OR of adverse pregnancy outcomes was 1.34 (95% confidence interval 1.16-1.56) for each 1-unit increase in HOMA-IR. Multivariable-adjusted ORs of adverse pregnancy outcomes across quartiles of homeostatic model assessment for β cell function (HOMA-β) were 1.00, 0.51, 0.60, and 0.53, respectively (P for trend = 0.068). When HOMA-β was considered as a continuous variable, the multivariable-adjusted OR of adverse pregnancy outcomes was 0.57 (95% CI 0.24-0.90) for each 1-unit increase in HOMA-β. However, other quantitative metrics were not associated with the composite adverse pregnancy outcomes.Conclusions: We demonstrated a significant association of β cell function and insulin sensitivity with the risk of adverse pregnancy outcomes. We have provided additional evidence on the early identification of adverse pregnancy outcomes besides the glycemic values.展开更多
Reconstructing the Holocene megaflood history is a key component of understanding the mechanism of past climate change and assessing the potential impact of future catastrophic events.The Pearl River is the longest wa...Reconstructing the Holocene megaflood history is a key component of understanding the mechanism of past climate change and assessing the potential impact of future catastrophic events.The Pearl River is the longest watercourse in southern China,and its lower reach has been identified as one of the world's most vulnerable regions for flood exposure.However,there is a complete lack of millennial-scale geological records of paleomegafloods for the future prediction of once-in-a-hundred(even once-in-a-thousand)year floods in southern China.Here,we identified a series of paleomegaflood deposits interbedded with wood-rich peat layers in the lower West Pearl River area.All paleoflood layers have been well dated using AMS~(14)C dating method.According to the regional correlation of the flood sequence,sediment characteristics and provenance analysis,there have been at least 7 megafloods corresponding to once-in-a-thousand-year events in the lower reaches of the West Pearl River during the past 6000 years,with an average return period of approximately 855 years.The identified paleomegafloods were coeval with periods of strong El Ni?o-Southern Oscillation(ENSO),indicating that weakening of the Asian summer monsoon,associated with enhanced ENSO variability,may have triggered abnormally high precipitation leading to flooding of exceptional magnitude in southern China.In addition,the most prominent paleomegafloods identified in the lower Pearl River coincided with intervals of lower precipitation and fewer storms in central-eastern China,indicating the intensification of the meridional“tripole”pattern of precipitation across eastern China during the latter half of the Holocene.Increased land use and deforestation over the last 2000 years have resulted in soil loss and rapid degradation of local primeval forest ecosystems,leading to more catastrophic flooding.Large amounts of rice pollen in the uppermost flood layer during the Song Dynasty indicate that this megaflood may have inundated a large area of cultivated land.The periodic occurrence of Holocene megafloods not only caused damage to human existence,but also affected the evolution of local civilization.This study reveals for the first time a series of Holocene millennial-scale megafloods and sheds new light on the importance of atmosphere-ocean interactions in the tropical Pacific and monsoon subtropical climate dynamics for precipitation anomalies in East Asia.Our data yield valuable information for future research into climate extremes and hazard prevention.展开更多
Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumpt...Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumption of computing/communication resources and usually require reliable communications with bounded delay.However,these protocols may be unsuitable for Internet of Things(IoT)networks because the IoT devices are usually lightweight,battery-operated,and deployed in an unreliable wireless environment.Therefore,this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement learning.Specifically,the consensus protocol in this work is designed on the basis of the Proof-of-Communication(PoC)scheme directly in a single-hop wireless network with unreliable communications.A distributed MultiAgent Reinforcement Learning(MARL)algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain system.In this algorithm,each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective performance.Empirical results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed,and the efficiency nearly reaches a centralized optimal solution.展开更多
基金supported by grants from the Shanghai Health and Family Planning Commission(Nos.20184Y0362,20204Y0431)the Shanghai Municipal Education Commission–Gaofeng Clinical Medicine Grant Support(No.20161430)supported by the funding of retrospective studies from Shanghai Sixth People’s Hospital.
文摘Background: The potential impact of β cell function and insulin sensitivity on adverse pregnancy outcomes in women with gestational diabetes mellitus (GDM) remains uncertain. We aimed to investigate the association between β cell dysfunction, insulin resistance, and the composite adverse pregnancy outcomes.Methods: This observational study included 482 women diagnosed with GDM during pregnancy. Quantitative metrics on β cell function and insulin sensitivity during pregnancy were calculated using traditional equations. The association of β cell dysfunction and insulin resistance with the risk of the composite adverse pregnancy outcomes was investigated using multivariable-adjusted logistic regression models.Results: Multivariable-adjusted odds ratios (ORs) of adverse pregnancy outcomes across quartiles of homeostatic model assessment for insulin resistance (HOMA-IR) were 1.00, 0.95, 1.34, and 2.25, respectively (P for trend = 0.011). When HOMA-IR was considered as a continuous variable, the multivariable-adjusted OR of adverse pregnancy outcomes was 1.34 (95% confidence interval 1.16-1.56) for each 1-unit increase in HOMA-IR. Multivariable-adjusted ORs of adverse pregnancy outcomes across quartiles of homeostatic model assessment for β cell function (HOMA-β) were 1.00, 0.51, 0.60, and 0.53, respectively (P for trend = 0.068). When HOMA-β was considered as a continuous variable, the multivariable-adjusted OR of adverse pregnancy outcomes was 0.57 (95% CI 0.24-0.90) for each 1-unit increase in HOMA-β. However, other quantitative metrics were not associated with the composite adverse pregnancy outcomes.Conclusions: We demonstrated a significant association of β cell function and insulin sensitivity with the risk of adverse pregnancy outcomes. We have provided additional evidence on the early identification of adverse pregnancy outcomes besides the glycemic values.
基金supported by the National Natural Science Foundation of China(Grant Nos.42072205&41301582)the National Key R&D Program of China(Grant No.2022YFF0801501)the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(Grant No.311022010)。
文摘Reconstructing the Holocene megaflood history is a key component of understanding the mechanism of past climate change and assessing the potential impact of future catastrophic events.The Pearl River is the longest watercourse in southern China,and its lower reach has been identified as one of the world's most vulnerable regions for flood exposure.However,there is a complete lack of millennial-scale geological records of paleomegafloods for the future prediction of once-in-a-hundred(even once-in-a-thousand)year floods in southern China.Here,we identified a series of paleomegaflood deposits interbedded with wood-rich peat layers in the lower West Pearl River area.All paleoflood layers have been well dated using AMS~(14)C dating method.According to the regional correlation of the flood sequence,sediment characteristics and provenance analysis,there have been at least 7 megafloods corresponding to once-in-a-thousand-year events in the lower reaches of the West Pearl River during the past 6000 years,with an average return period of approximately 855 years.The identified paleomegafloods were coeval with periods of strong El Ni?o-Southern Oscillation(ENSO),indicating that weakening of the Asian summer monsoon,associated with enhanced ENSO variability,may have triggered abnormally high precipitation leading to flooding of exceptional magnitude in southern China.In addition,the most prominent paleomegafloods identified in the lower Pearl River coincided with intervals of lower precipitation and fewer storms in central-eastern China,indicating the intensification of the meridional“tripole”pattern of precipitation across eastern China during the latter half of the Holocene.Increased land use and deforestation over the last 2000 years have resulted in soil loss and rapid degradation of local primeval forest ecosystems,leading to more catastrophic flooding.Large amounts of rice pollen in the uppermost flood layer during the Song Dynasty indicate that this megaflood may have inundated a large area of cultivated land.The periodic occurrence of Holocene megafloods not only caused damage to human existence,but also affected the evolution of local civilization.This study reveals for the first time a series of Holocene millennial-scale megafloods and sheds new light on the importance of atmosphere-ocean interactions in the tropical Pacific and monsoon subtropical climate dynamics for precipitation anomalies in East Asia.Our data yield valuable information for future research into climate extremes and hazard prevention.
基金This work was partially supported by the National Key Research and Development Program of China(No.2020YFB1005900)the National Natural Science Foundation of China(Nos.62102232,62122042,and 61971269)the Natural Science Foundation of Shandong Province(No.ZR2021QF064).
文摘Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumption of computing/communication resources and usually require reliable communications with bounded delay.However,these protocols may be unsuitable for Internet of Things(IoT)networks because the IoT devices are usually lightweight,battery-operated,and deployed in an unreliable wireless environment.Therefore,this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement learning.Specifically,the consensus protocol in this work is designed on the basis of the Proof-of-Communication(PoC)scheme directly in a single-hop wireless network with unreliable communications.A distributed MultiAgent Reinforcement Learning(MARL)algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain system.In this algorithm,each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective performance.Empirical results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed,and the efficiency nearly reaches a centralized optimal solution.
