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The pathological roles of bacterial DNA have been documented many decades ago. Bacterial DNAs are different from mammalian DNAs; the latter are heavily methylated. Mammalian cells have sensors such as TLR-9 to sense the DNAs with nonmethylated CpGs and distinguish them from host DNAs with methylated CpGs. Further investigation has identified many other types of DNA sensors distributed in a variety of cellular compartments. These sensors not only sense foreign DNAs, including bacterial and viral DNAs, but also sense damaged DNAs from the host cells. The major downstream signalling pathways includeTLR-9-MyD88-IKKa-IRF-7/NF-κB pathways to increase IFN/proinflammatory cytokine production, STING-TBK1-IRF3 pathway to increase IFN-beta, and AIM2-ASC-caspas-1 pathway to release IL-1beta. The major outcome is to activate host immune response by inducing cytokine production. In this review, we focus on the roles and potential mechanisms of DNA sensors and downstream pathways in sepsis. Although bacterial DNAs play important roles in sepsis development, bacterial DNAs alone are unable to cause severe disease nor lead to death. Priming animals with bacterial DNAs facilitate other pathological factors, such as LPS and other virulent factors, to induce severe disease and lethality. We also discuss compartmental distribution of DNA sensors and pathological significance as well as the transport of extracellular DNAs into cells. Understanding the roles of DNA sensors and signal pathways will pave the way for novel therapeutic strategies in many diseases, particularly in sepsis.

This is my undergraduate thesis, which mainly refers to Zhi Y. P. and L. Liu. (2018). although it has successfully passed the graduation defense, there are still many problems in this thesis, so it is not recommended as a reference for academic purposes. Abstract: According to the Heterogeneous Market Hypothesis, a financial market is composed by participants having different trading frequencies. The trading behaviors of investors with various time horizons cause different types of volatility components. Therefore, the various types of investors are concerned about whether the different short-term or long-term component of stock return volatility is more strongly related to past negative return, also named as leverage effect. Therefore, it is meaningful to consider the short-term and long-term leverage effects in volatility prediction. This paper extended the GARCH-MIDAS model proposed by Engle et al. (2013). The short-term and long-term leverage effects in the Shanghai 180 Index and Shenzhen 100 Index series are detected by adding corresponding asymmetric terms in the model. The effect of short-term leverage effect is that negative returns in the past can cause greater short-term fluctuations than positive returns in the future. The effect of long-term leverage effect is that positive returns in the past will increase long-term fluctuations in the future, while negative returns will reduce long-term fluctuations in the future. This paper also finds that the addition of leverage effect can improve the accuracy of the prediction of volatility, which is mainly attributed to the short-term leverage effect. In the estimation method, this paper adopts the rolling window technology. By fixing the monthly window and moving by day, the model can contain more information about long-term fluctuations. This improves the persuasiveness of the model. In the evaluation method, six popular loss functions and success indicators are used to avoid the evaluation errors caused by a single standard. Keywords: volatility prediction; short-term leverage effects; long-term leverage effects; GARCH-MIDAS, As my first academic attempt, there are many problems in this paper. I just want to make a memorial here., {"references":["Zhi Y. P. and L. Liu. Forecasting stock return volatility: A comparison between the roles of short-term and long-term leverage effects [J]. Physica A: Statistical Mechanics and its Applications, 2018, 492: 168-180.","A.J. Patton, Volatility forecast comparison using imperfect volatility proxies, J. Econometrics 160 (2011) 246–256.","F.X. Diebold, R.S. Mariano, Comparing predictive accuracy, J. Bus. Econ. Stat. 13 (1995) 253–263.","Ding, Z., Granger, C., 1996. Modeling Volatility Persistence of Speculative Returns: A New Approach. Journal of Econometrics, 73, 185-215.","Engle, R. F., Lee, G., 1999. 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终于结束了一学期的助教工作,终究还是没有完全实现自己最开始的想法。实际教学中的感觉是:“言的多,而听者少”。尽管如此,还是比较完整的向同学们讲述了 Stata 在各个方面的使用方法。最后整理了一下这学期自己写的一些讲义文档,一共九份。涵盖了 Stata 基本使用、网络数据爬取、计量模型估计、绘图与修图几个方面。

这是我在备考计算机二级 MySQL 科目过程中学习官方教材所做的笔记。之前以博客的形式挂在网站上,现在感觉博客太多了,每次生成博客的时候都要花很长时间,所以就把 MySQL 相关的博客都弄下来合成了这本笔记。除了官方教材的学习笔记之外还有自己在学习 MySQL 的过程中遇到的一些问题的解决过程。, {"references":["DONG W, 2018. pymysql pymysql.err.OperationalError 1045 Access denied 最简单解决办法[EB/OL]. https: //blog.csdn.net/dongweionly/article/details/80273095.","KUMU, 2017. 解决Access denied for user 'root'@'localhost' (using password: NO)[EB/OL]. https://my.oschina.n et/u/2294525/blog/820625.","MAIRR, 2018. Data Grip 使用—> 创建数据库连接[EB/OL]. https://www.cnblogs.com/Mairr/p/8406844.html.","钺览IT, 2018. php7 连接mysql8[EB/OL]. https://www.cnblogs.com/roggeyue/articles/9191884.html."]}

In this paper, according to the previous research and preliminary research results, this paper divides the repo rate and SHIBOR within one year into three parts. The first part includes two kinds of interest rates with a term of less than or equal to one month, which are recorded as term 1; the second part includes two kinds of interest rates with a term of 3-9 (including 3 and 9) months, which are recorded as term 2; the third part is two kinds of interest with a term of one year. Rate, recorded as term 3. In this paper, we use correlation analysis, chart analysis, VAR model and impulse response analysis to compare the benchmark of SHIBOR and inter-bank pledge repo rate in three periods. The results show that the benchmark of SHIBOR series interest rate is strong in term 1, but there are some defects. In term 2, the benchmark of repo rate is better than that of SHIBOR series. The main reason is that the repo rate is more stable and the transaction volume is larger, so the stability, market and continuity are better. Finally, in term 3, the fairness of both is poor, and the trading volume is obviously low, which makes both of them not suitable as benchmark interest rate., {"references":["张流泉. 中国版\"钱荒\"的原因及对策建议[J]. 中国房地产金融, 2013(7):7-9.","史焕平, 李泽成. 我国商业银行\"钱荒\"之谜:基于货币结构视角的分析[J]. 山西大学学报(哲学社会科学版), 2014, 37(5):93-98.","易纲. SHIBOR 成为利率基准的多重意义[J]. 财经, 2009(3):108-108.","刘陆宇. 利率市场化进程中基准利率的选择[J]. 华东经济管理, 2013(9):115-119.","方意, 方明. 中国货币市场基准利率的确立及其动态关系研究[J]. 金融研究, 2012(7):84-97.","梁琪, 张孝岩, 过新伟. 中国金融市场基准利率的培育——基于构建完整基准收益率曲线的实证分析[J]. 金融研究, 2010(9):81-98.","姚余栋, 谭海鸣. 央票利率可以作为货币政策的综合性指标[J]. 经济研究, 2011(s2):63-74.","周林. 中国浮息债券基准利率选择研究[J]. 证券市场导报, 2014(7):65-71.","柏宝春, 孙松, 王晓艺. 货币市场基准利率实证研究——基于 SHIBOR 的分析[J]. 金融与经济, 2009(7):19-22.","刘湘云, 邱乐平. SHIBOR 已成为我国货币市场基准利率了吗?[J]. 金融理论与实践, 2011(1):24-27.","刘喜波, 赵鹏远, 李红梅,等. SHIBOR 作为基准利率的实证研究[J]. 数学的实践与认识, 2008, 38(11):46-49.","徐建华. 我国利率市场化进程中的基准利率问题研究[D]. 山西财经大学, 2011.","康丽华. 我国利率市场化进程中基准利率的选择[D]. 山西财经大学, 2008."]}