为探究肠道损伤对断奶仔猪生长性能、血清和空肠炎症因子带来的影响,本试验对相关方面的研究进行了Meta分析。通过计算机对PubMed、Science Direct和Web of Science数据库进行检索,检索肠道损伤对断奶仔猪生长性能、血清和空肠炎症因子...为探究肠道损伤对断奶仔猪生长性能、血清和空肠炎症因子带来的影响,本试验对相关方面的研究进行了Meta分析。通过计算机对PubMed、Science Direct和Web of Science数据库进行检索,检索肠道损伤对断奶仔猪生长性能、血清和空肠炎症因子影响的随机对照试验。检索年限从2000年1月至2023年4月,设定样本纳入及排除标准进行文献筛选、数据提取,纳入总样本34篇,用Review Manager 5.4对纳入文献进行相关分析,效应指标选择标准化均数差(SMD)。结果显示:肠道损伤能够显著降低断奶仔猪平均日增重[SMD=-1.43,95%置信区间(CI)=(-1.71,-1.15),P<0.0001],降低平均日采食量[SMD=-1.08,95%CI=(-1.47,-0.69),P<0.0001],提高料重比[SMD=1.06,95%CI=(0.50,1.62),P=0.0002];肠道损伤显著提高空肠中白细胞介素-1β(IL-1β)[SMD=1.36,95%CI=(0.52,2.20),P=0.001]、肿瘤坏死因子-α(TNF-α)[SMD=0.76,95%CI=(0.22,1.30),P=0.006]、白细胞介素-6(IL-6)[SMD=0.79,95%CI=(0.26,1.32),P=0.004]以及血清中TNF-α[SMD=2.69,95%CI=(1.56,3.81),P<0.0001]的含量。这些结果表明,肠道损伤影响断奶仔猪的生长性能,损害免疫功能。展开更多
Calculating the inter-layer ion diffusion barrier, a crucial metric for evaluating the rate performance of 2D electrode materials, is time-consuming using the transition state search approach. A novel electrostatic po...Calculating the inter-layer ion diffusion barrier, a crucial metric for evaluating the rate performance of 2D electrode materials, is time-consuming using the transition state search approach. A novel electrostatic potential distribution image (EPDI) transfer learning method has been proposed to efficiently and accurately predict the lithium diffusion barriers on metal element-doped transition metal dichalcogenide (TMD) surfaces. Through the analysis of the mean electrostatic potential (MEP) around binding sites, a positive correlation between binding energy and MEP in VIB-TMDs was identified. Subsequently, transfer learning techniques were used to develop a DenseNet121-TL model for establishing a more accurate mapping between the binding energy and electrostatic potential distribution. Trained on training sets containing 33% and 50% transition state search calculation results, which save 66% and 50% of the calculation time, respectively, the model achieves accurate predictions of the saddle point binding energy with mean absolute errors (MAEs) of 0.0444 and 0.0287 eV on the testing set. Based on the prediction of saddle point binding energies, we obtained a diffusion minimum energy profile with an MAE of 0.0235 eV. Furthermore, by analyzing the diffusion data, we observed that the diffusion barrier was lowered by 10% on V-doped TiS2 compared to the stoichiometric surface. Our findings are expected to provide new insights for the high-throughput calculation of ion diffusion on 2D materials.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51974056 and 51474047)the Foundation of the Supercomputing Center of Dalian University of Technology,and the Foundation of the Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province),China.
文摘Calculating the inter-layer ion diffusion barrier, a crucial metric for evaluating the rate performance of 2D electrode materials, is time-consuming using the transition state search approach. A novel electrostatic potential distribution image (EPDI) transfer learning method has been proposed to efficiently and accurately predict the lithium diffusion barriers on metal element-doped transition metal dichalcogenide (TMD) surfaces. Through the analysis of the mean electrostatic potential (MEP) around binding sites, a positive correlation between binding energy and MEP in VIB-TMDs was identified. Subsequently, transfer learning techniques were used to develop a DenseNet121-TL model for establishing a more accurate mapping between the binding energy and electrostatic potential distribution. Trained on training sets containing 33% and 50% transition state search calculation results, which save 66% and 50% of the calculation time, respectively, the model achieves accurate predictions of the saddle point binding energy with mean absolute errors (MAEs) of 0.0444 and 0.0287 eV on the testing set. Based on the prediction of saddle point binding energies, we obtained a diffusion minimum energy profile with an MAE of 0.0235 eV. Furthermore, by analyzing the diffusion data, we observed that the diffusion barrier was lowered by 10% on V-doped TiS2 compared to the stoichiometric surface. Our findings are expected to provide new insights for the high-throughput calculation of ion diffusion on 2D materials.
