波棱瓜子对免疫性肝损伤的免疫炎症调节作用

研究建立Con A致小鼠免疫性肝损伤动物模型,采用免疫组化染色SP法考察不同剂量波棱瓜子总木脂素对小鼠肝脏组织CD4、CD8、CXCL10、ICAM-1、IRF-1、SOCS1、STAT1蛋白的表达,分析波棱瓜子总木脂素对抑制粘附分子和趋化因子肝内蛋白的表达影响,以期研究讨论藏药波棱瓜子总木脂素对免疫性肝损伤的免疫炎症调节作用.实验结果表明,波棱瓜子总木脂素对Con A致免疫性肝损伤小鼠的CD4、ICAM-1、IRF-1蛋白含量无明显差异;而对CD8、CXCL10、SOCS1和STAT1蛋白的含量有不同程度的降低,显示出通过调节T细胞的免疫功能来保护肝脏.由上述结果可知,波棱瓜子总木脂素具有一定的免疫性炎症调节作用,该作用可能是波棱瓜子总木脂素对肝脏免疫性疾病的部分保护机制.     

自然老化作用下聚氯乙烯纳米塑料的形成及邻苯二甲酸酯类添加剂的释放

自然老化作用可以促进环境中纳米塑料的形成以及塑料中添加剂的释放,增强了塑料的生态风险以及人体健康风险。利用室外模拟实验,以典型的聚氯乙烯(PVC)塑料为研究对象,研究了自然老化作用下PVC塑料表面结构特征的变化,纳米塑料的形成以及邻苯二甲酸酯类添加剂的释放特征。结果表明：老化过程使得PVC发生了脱氯化氢和氧化反应,使得C=C键增多,并且形成了新的—OH和C—O键,显著增加了C=C指数、—OH指数和C—O指数。自然老化作用下,光照50 d时,水中纳米颗粒浓度由起始时的7.5×10⁶个/mL增加到9.5×10⁷个/mL;PVC塑料质量由3 200μg减少到2 000μg,质量损失率达到37.50%;体系的主导粒径明显降低,由起始时的约219.3 nm减小到约176.6 nm;三种邻苯二甲酸酯类添加剂在水中浸出浓度的大小顺序为邻苯二甲酸二丁酯(DBP,27.40μg/L)>邻苯二甲酸二(2-乙基己基)酯(DEHP,9.69μg/L)>邻苯二甲酸丁基苄基酯(BBP,5.68μg/L),塑料中添加剂的含量及性质均对其浸出能力具有重要影响。     

Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation: A Semiparametric Reproducing Kernel Hilbert Space Approach

The estimation of high dimensional covariance matrices is an interesting and important research topic for many empirical time series problems such as asset allocation. To solve this dimension dilemma, a factor structure has often been taken into account. This paper proposes a dynamic factor structure whose factor loadings are generated in reproducing kernel Hilbert space (RKHS), to capture the dynamic feature of the covariance matrix. A simulation study is carried out to demonstrate its performance. Four different conditional variance models are considered for checking the robustness of our method and solving the conditional heteroscedasticity in the empirical study. By exploring the performance among eight introduced model candidates and the market baseline, the empirical study from 2001 to 2017 shows that portfolio allocation based on this dynamic factor structure can significantly reduce the variance, i.e., the risk, of portfolio and thus outperform the market baseline and the ones based on the traditional factor model.