Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the ...Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on Mat Elab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.展开更多
目的 探讨血清可溶性晚期糖基化终末产物受体(soluble receptor for advanced glycation end products,sRAGE)联合内皮细胞特异性分子-1(endothelial cell specific molecules-1,ESM-1)对重症急性胰腺炎(severe acute pancreatitis,SAP...目的 探讨血清可溶性晚期糖基化终末产物受体(soluble receptor for advanced glycation end products,sRAGE)联合内皮细胞特异性分子-1(endothelial cell specific molecules-1,ESM-1)对重症急性胰腺炎(severe acute pancreatitis,SAP)合并应激性高血糖患者预后的预测价值。方法 选取2021年7月至2023年1月临汾市中心医院重症医学科的SAP合并应激性高血糖患者105例,根据患者的预后(对症治疗后28 d生存情况)分为死亡组(39例)和存活组(66例)。分析SAP合并应激性高血糖患者预后的影响因素及血清sRAGE联合ESM-1对患者预后的预测价值。结果 105例患者中男61例、女44例;年龄22~69岁,平均(47.6±8.9)岁。多因素logistic回归分析结果显示,多个器官功能障碍(OR=4.845,95%CI:2.166~8.130,P=0.030)、急性生理和慢性健康评分Ⅱ得分越高(OR=1.872,95%CI:1.207~2.902,P=0.005)、24 h随机空腹血糖越高(OR=1.381,95%CI:1.094~1.743,P=0.007)、sRAGE水平越高(OR=1.017,95%CI:1.007~1.027,P=0.001)、ESM-1水平越高(OR=1.074,95%CI:1.027~1.123,P=0.002)的SAP合并应激性高血糖患者更容易死亡。ROC曲线分析结果显示,血清sRAGE联合ESM-1检测预测SAP合并应激性高血糖患者死亡的AUC为0.882(95%CI:0.804~0.936,P<0.001),血清sRAGE和ESM-1单独预测的AUC分别为0.784(95%CI:0.693~0.859,P<0.001)和0.780(95%CI:0.689~0.855,P<0.001)。结论 SAP合并应激性高血糖患者的血清sRAGE、ESM-1浓度升高与不良预后有关,血清sRAGE联合ESM-1检测对SAP合并应激性高血糖患者预后的预测价值较高。展开更多
数值模拟方法在研究长时间的气候变化上扮演着重要角色。一直以来,数值模式模拟年代际气候变化如太平洋年代际震荡(PDO)的位相转换存在巨大挑战。本文利用自然资源部第一海洋研究所研发的地球系统模式(First Institute of Oceanography-...数值模拟方法在研究长时间的气候变化上扮演着重要角色。一直以来,数值模式模拟年代际气候变化如太平洋年代际震荡(PDO)的位相转换存在巨大挑战。本文利用自然资源部第一海洋研究所研发的地球系统模式(First Institute of Oceanography-Earth System Model Version 2,FIO-ESM v2.0)145年(1870–2014年)历史气候模拟试验结果,结合再分析资料和另外两个地球系统模式结果,分析评估了该模式对太平洋年代际振荡的模拟能力。研究发现,FIO-ESM v2.0能够再现历史时期PDO的空间模态分布特征,其PDO指数具有10~30年的周期变化特征,同时于1960年以后能刻画出与再分析数据结果相近的PDO位相转变特征。研究表明,FIO-ESM v2.0能够较为准确地模拟出PDO的位相转变特征。另外,本文还评估了该模式对大气环流模态的模拟能力及其与PDO之间的关系,以及该模式模拟PDO的可能机制。该模式的PDO与大气环流的阿留申低压模态相关。进一步的分析表明,平流作用和热通量是关键年代际海域海温异常振幅的主要因素,而罗斯贝波西传时间则可能是影响PDO位相转变的关键因素。展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 52022106 and 52172258)the Informatization Plan of Chinese Academy of Sciences (Grant No. CASWX2021SF-0102)。
文摘Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on Mat Elab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.
文摘数值模拟方法在研究长时间的气候变化上扮演着重要角色。一直以来,数值模式模拟年代际气候变化如太平洋年代际震荡(PDO)的位相转换存在巨大挑战。本文利用自然资源部第一海洋研究所研发的地球系统模式(First Institute of Oceanography-Earth System Model Version 2,FIO-ESM v2.0)145年(1870–2014年)历史气候模拟试验结果,结合再分析资料和另外两个地球系统模式结果,分析评估了该模式对太平洋年代际振荡的模拟能力。研究发现,FIO-ESM v2.0能够再现历史时期PDO的空间模态分布特征,其PDO指数具有10~30年的周期变化特征,同时于1960年以后能刻画出与再分析数据结果相近的PDO位相转变特征。研究表明,FIO-ESM v2.0能够较为准确地模拟出PDO的位相转变特征。另外,本文还评估了该模式对大气环流模态的模拟能力及其与PDO之间的关系,以及该模式模拟PDO的可能机制。该模式的PDO与大气环流的阿留申低压模态相关。进一步的分析表明,平流作用和热通量是关键年代际海域海温异常振幅的主要因素,而罗斯贝波西传时间则可能是影响PDO位相转变的关键因素。
