Circulation Research-2007-Kim-919-27[1]
《金融高质量发展研究的文献综述3300字》
金融高质量发展研究的国内外文献综述1. 国外文献综述国外有大部分学者就金融发展与经济增长二者之间的关系进行了研究。
Panicos (1996)等探究了金融发展与经济增长的关系,他们对16个国家的时间序列数据进行研究,研究表明,在相当多的国家,经济增长系统性地导致金融发展,总的来说,金融发展和经济增长之间的关系是双向的[1]。
Simon (2004)等以中国为例进行研究金融开放与地理位置之间的关系,他们认为尽管信息的电子传输会大大减少由于距离而导致的金融信息获取受阻,但地理因素仍然为金融服务主要集中于北京等特定地区的关键性因素[2]。
Arjana (2007)等利用了宏观和行业数据,分析国际金融一体化与金融发展对欧洲经济增长的非线性影响,结果显示出具有显著的非线性效应,认为金融一体化可能不会对增长本身产生积极的影响,其影响取决于国家金融市场的发展、宏观经济的稳定和机构的质量[3]。
Matías (2007)等认为国内金融发展对贸易和资本流动开放的国家的增长影响要小于在两个方面都封闭的国家,在允许资本流动的情况下,非贸易部门的规模对于国内金融体系自由化的机会具有重要作用[4]。
在对金融发展的衡量标准方面,King与Levine(1993)认为金融发展质量的各种衡量标准与实际人均国内生产总值增长率、实物资本积累速度以及经济体使用实物资本的效率提高密切相关[5]。
Pagano(1993)认为金融市场的产生与发展是对“金融发展”概念的重点,要研究金融的发展对经济的增长影响是否存在,以及具体的影响是什么,必须要具体说明相关的特定金融市场[6]。
2. 国内文献综述目前有关金融高质量发展的研究主要分成三方面,学者对于金融高质量发展面临的问题的研究、学者对金融发展质量内涵的界定以及学者关于高质量发展的研究。
对于金融高质量发展面临的问题,诸多学者基于不同的角度进行了详细的讨论。
李伟(2019)提出标准化建设对于金融高质量发展的作用,并就我国金融标准化工作存在的问题提出见解[7]。
考研英语(一)-27
考研英语(一)-27(总分:100.00,做题时间:90分钟)一、Reading Comprehension(总题数:0,分数:0.00)二、(总题数:0,分数:0.00)三、Text 1(总题数:1,分数:20.00)The longest bull run in a century of art-market history ended on a dramatic note with a sale of 56 works by Damien Hirst, Beautiful Inside My Head Forever, at Sotheby"s in London on September 15th 2008. All but two pieces sold, fetching more than £70m, a record for a sale by a single artist. It was a last victory. As the auctioneer called out bids, in New York one of the oldest banks on Wall Street, Lehman Brothers, filed for bankruptcy.The world art market had already been losing momentum for a while after rising bewilderingly since 2003. At its peak in 2007 it was worth some $65 billion, reckons Clare McAndrew, founder of Arts Economics, a research firm—double the figure five years earlier. Since then it may have come down to $50 billion. But the market generates interest far beyond its size because it brings together great wealth, enormous egos, greed, passion and controversy in a way matched by few other industries.In the weeks and months that followed Mr. Hirst"s sale, spending of any sort became deeply unfashionable . In the art world that meant collectors stayed away from galleries and salerooms. Sales of contemporary art fell by two-thirds, and in the most overheated sector, they were down by nearly 90% in the year to November 2008. Within weeks the world"s two biggest auction houses, Sotheby"s and Christie"s, had to pay out nearly $200m in guarantees to clients who had placed works for sale with them.The current downturn in the art market is the worst since the Japanese stopped buying Impressionists at the end of 1989. This time experts reckon that prices are about 40% down on their peak on average, though some have been far more fluctuant. But Edward Dolman, Christie"s chief executive, says: "I"m pretty confident we"re at the bottom."What makes this slump different from the last, he says, is that there are still buyers in the market. Almost everyone who was interviewed for this special report said that the biggest problem at the moment is not a lack of demand but a lack of good work to sell. The three Ds—death, debt and divorce—still deliver works of art to the market. But anyone who does not have to sell is keeping away, waiting for confidence to return.(分数:20.00)(1).In the first paragraph, Damien Hirst"s sale was referred to as "a last victory" because (分数:4.00)A.the art market had witnessed a succession of victories.B.the auctioneer finally got the two pieces at the highest bids.C.Beautiful Inside My Head Forever won over all masterpieces.D.it was successfully made just before the world financial crisis. √解析:[解析] 含义题。
[美]R·格伦·哈伯德《宏观经济学》R.GlennHubbard,AnthonyP
Macroeconomics R. GLENN HUBBARD COLUMBIA UNIVERSITY ANTHONY PATRICK O’BRIEN LEHIGH UNIVERSITY MATTHEW RAFFERTY QUINNIPIAC UNIVERSITY Boston Columbus Indianapolis New York San Francisco Upper Saddle RiverAmsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City So Paulo Sydney Hong Kong Seoul Singapore Taipei TokyoAbout the AuthorsGlenn Hubbard Professor Researcher and Policymaker R. Glenn Hubbard is the dean and Russell L. Carson Professor of Finance and Economics in the Graduate School of Business at Columbia University and professor of economics in Columbia’s Faculty of Arts and Sciences. He is also a research associate of the National Bureau of Economic Research and a director of Automatic Data Processing Black Rock Closed- End Funds KKR Financial Corporation and MetLife. Professor Hubbard received his Ph.D. in economics from Harvard University in 1983. From 2001 to 2003 he served as chairman of the White House Council of Economic Advisers and chairman of the OECD Economy Policy Commit- tee and from 1991 to 1993 he was deputy assistant secretary of the U.S. Treasury Department. He currently serves as co-chair of the nonpar-tisan Committee on Capital Markets Regulation and the Corporate Boards Study Group. ProfessorHubbard is the author of more than 100 articles in leading journals including American EconomicReview Brookings Papers on Economic Activity Journal of Finance Journal of Financial EconomicsJournal of Money Credit and Banking Journal of Political Economy Journal of Public EconomicsQuarterly Journal of Economics RAND Journal of Economics and Review of Economics and Statistics.Tony O’Brien Award-Winning Professor and Researcher Anthony Patrick O’Brien is a professor of economics at Lehigh University. He received a Ph.D. from the University of California Berkeley in 1987. He has taught principles of economics money and banking and interme- diate macroeconomics for more than 20 years in both large sections and small honors classes. He received the Lehigh University Award for Distin- guished Teaching. He was formerly the director of the Diamond Center for Economic Education and was named a Dana Foundation Faculty Fel- low and Lehigh Class of 1961 Professor of Economics. He has been a visit- ing professor at the University of California Santa Barbara and Carnegie Mellon University. Professor O’Brien’s research has dealt with such issues as the evolution of the U.S. automobile industry sources of U.S. economiccompetitiveness the development of U.S. trade policy the causes of the Great Depression and thecauses of black–white income differences. His research has been published in leading journals in-cluding American Economic Review Quarterly Journal of Economics Journal of Money Credit andBanking Industrial Relations Journal of Economic History Explorations in Economic History andJournal of PolicyHistory.Matthew Rafferty Professor and Researcher Matthew Christopher Rafferty is a professor of economics and department chairperson at Quinnipiac University. He has also been a visiting professor at Union College. He received a Ph.D. from the University of California Davis in 1997 and has taught intermediate macroeconomics for 15 years in both large and small sections. Professor Rafferty’s research has f ocused on university and firm-financed research and development activities. In particular he is interested in understanding how corporate governance and equity compensation influence firm research and development. His research has been published in leading journals including the Journal of Financial and Quantitative Analysis Journal of Corporate Finance Research Policy and the Southern Economic Journal. He has worked as a consultantfor theConnecticut Petroleum Council on issues before the Connecticut state legislature. He has alsowritten op-ed pieces that have appeared in several newspapers including the New York Times. iii Brief Contents Part 1: Introduction Chapter 1 The Long and Short of Macroeconomics 1 Chapter 2 Measuring the Macroeconomy 23 Chapter 3 The Financial System 59 Part 2: Macroeconomics in the Long Run: Economic Growth Chapter 4 Determining Aggregate Production 105 Chapter 5 Long-Run Economic Growth 143 Chapter 6 Money and Inflation 188 Chapter 7 The Labor Market 231 Part 3: Macroeconomics in the Short Run: Theory and Policy Chapter 8 Business Cycles 271 Chapter 9 IS–MP: A Short-Run Macroeconomic Model 302 Chapter 10 Monetary Policy in the Short Run 363 Chapter 11 Fiscal Policy in the Short Run 407 Chapter 12 Aggregate Demand Aggregate Supply and Monetary Policy 448 Part 4: Extensions Chapter 13 Fiscal Policy and the Government Budget in the Long Run 486 Chapter 14 Consumption and Investment 521 Chapter 15 The Balance of Payments Exchange Rates and Macroeconomic Policy 559 Glossary G-1 Index I-1ivContentsChapter 1 The Long and Short of Macroeconomics 1WHEN YOU ENTER THE JOB MARKET CAN MATTER A LOT ........................................................ 11.1 What Macroeconomics Is About........................................................................... 2 Macroeconomics in the Short Run and in the Long Run .................................................... 2 Long-Run Growth in the United States ............................................................................. 3 Some Countries Have Not Experienced Significant Long-Run Growth ............................... 4 Aging Populations Pose a Challenge to Governments Around the World .......................... 5 Unemployment in the United States ................................................................................. 6 How Unemployment Rates Differ Across Developed Countries ......................................... 7 Inflation Rates Fluctuate Over Time and Across Countries................................................. 7 Econo mic Policy Can Help Stabilize the Economy .. (8)International Factors Have Become Increasingly Important in Explaining Macroeconomic Events................................................................................. 91.2 How Economists Think About Macroeconomics ............................................. 11 What Is the Best Way to Analyze Macroeconomic Issues .............................................. 11 Macroeconomic Models.................................................................................................. 12Solved Problem 1.2: Do Rising Imports Lead to a Permanent Reductionin U.S. Employment. (12)Assumptions Endogenous Variables and Exogenous Variables in EconomicModels ........................................................................................................ 13 Forming and Testing Hypotheses in Economic Models .................................................... 14Making the Connection: What Do People Know About Macroeconomicsand How Do They KnowIt .............................................................................................. 151.3 Key Issues and Questions of Macroeconomics ............................................... 16An Inside Look: Will Consumer Spending Nudge Employers to Hire................................ 18Chapter Summary and Problems ............................................................................. 20 Key Terms and Concepts Review Questions Problems and Applications Data Exercise Theseend-of-chapter resource materials repeat in all chapters.Chapter 2 Measuring the Macroeconomy 23HOW DO WE KNOW WHEN WE ARE IN ARECESSION ........................................................... 23Key Issue andQuestion .................................................................................................... 232.1 GDP: Measuring Total Production and Total Income ..................................... 25 How theGovernment Calculates GDP (25)Production and Income (26)The Circular Flow of Income (27)An Example of Measuring GDP (29)National Income Identities and the Components of GDP (29)vvi CONTENTS Making the Connection: Will Public Employee Pensions Wreck State and Local Government Budgets.................................................................... 31 The Relationship Between GDP and GNP........................................................................ 33 2.2 Real GDP Nominal GDP and the GDP Deflator.............................................. 33 Solved Problem 2.2a: Calculating Real GDP . (34)Price Indexes and the GDP Deflator (35)Solved Problem 2.2b: Calculating the Inflation Rate ..........................................................36 The Chain-Weighted Measure of Real GDP ....................................................................37 Making the Connection: Trying to Hit a Moving Target: Forecasting with “Real-Time Data” .................................................................................. 37 Comparing GDP Across Countries................................................................................... 38 Making the Connection: The Incredible Shrinking Chinese Economy ................................ 39 GDP and National Income .............................................................................................. 40 2.3 Inflation Rates and Interest Rates ....................................................................... 41 The Consumer Price Index .............................................................................................. 42 Making the Connection: Does Indexing Preserve the Purchasing Power of Social Security Payments ................................................................ 43 How Accurate Is theCPI ............................................................................................... 44 The Way the Federal Reserve Measures Inflation ............................................................ 44 InterestRates .................................................................................................................. 45 2.4 Measuring Employment and Unemployment .. (47)Answering the Key Question ............................................................................................ 49 An Inside Look: Weak Construction Market Persists.......................................................... 50 Chapter 3 The Financial System 59 THE WONDERFUL WORLD OFCREDIT ................................................................................... 59 Key Issue and Question .................................................................................................... 59 3.1 Overview of the Financial System ...................................................................... 60 Financial Markets and Financial Intermediaries ................................................................ 61 Making the Connection: Is General Motors Making Cars or Making Loans .................... 62 Making the Connection: Investing in the Worldwide Stock Market . (64)Banking and Securitization (67)The Mortgage Market and the Subprime Lending Disaster (67)Asymmetric Information and Principal–Agent Problems in Financial Markets...................68 3.2 The Role of the Central Bank in the Financial System (69)Central Banks as Lenders of Last Resort ..........................................................................69 Bank Runs Contagion and Asset Deflation ....................................................................70 Making the Connection: Panics Then and Now: The Collapse of the Bank of United States in 1930 and the Collapse of Lehman Brothers in2008 (71)3.3 Determining Interest Rates: The Market for Loanable Funds and the Market forMoney .......................................................................................... 76 Saving and Supply in the Loanable Funds Market ........................................................... 76 Investment and the Demand for Loanable Funds ............................................................ 77 Explaining Movements in Saving Investment and the Real Interest Rate (78)CONTENTS .。
肌联蛋白基因截断突变致家族性扩张型心肌病的研究进展
[2]Adams JE, Abendschein DR, Jaffe AS. Biochemical markers ofmyocardial injury. Is MB creatine kinase the choice for the 1990s? Circulation, 1993, 88: 750-763.[3] Tate JR. Troponin revisited 2008: assay performance. Clin Chem LabMed, 2008, 46: 1489-1500.[4] Zimmermann R, Baki S, Dengler TJ, et al. Troponin T release afterheart transplantation. Br Heart J, 1993, 69: 395-398.[5] Labarrere CA, Nelson DR, Cox CJ, et al. Cardiac-specific troponin Ilevels and risk of coronary artery disease and graft failure following heart transplantation. JAMA, 2000, 284: 457-464.[6] 薛莉, 王彦卿. 心肌肌钙蛋白 Ⅰ 与慢性心力衰竭. 中国循环杂志,2005, 20: 244.[7] 刘春萍, 陆慰萱, 王孟昭, 等. 急性肺血栓栓塞血浆肌钙蛋白I 的改变及其对预后的评估. 中国循环杂志, 2004, 19: 50-52.[8] Erbel C, Taskin R, Doesch A, et al. High-sensitive troponin Tmeasurements early after heart transplantation predict short-and long-term survival. Transpl Int, 2013, 26: 267-272.[9] Potapov EV, Wagner FD, Loebe M, et al. Elevated donor cardiactroponin T and procalcitonin indicate two independent mechanisms of early graft failure after heart transplantation. Int J Cardiol, 2003, 92: 163-167.[10] Riou B, Dreux S, Roche S, et al. Circulating cardiac troponin T inpotential heart transplant donors. Circulation, 1995, 92: 409-414.[11] Venkateswaran RV, Ganesh JS, Thekkudan J, et al. Donor cardiactroponin-I: a biochemical surrogate of heart function. Eur J Cardiothoracic Surg, 2009, 36: 286-292.[12] Lin KY, Sullivan P, Salam A, et al. Troponin I levels from donors肌联蛋白基因截断突变致家族性扩张型心肌病的研究进展李发有综述,范洁审校扩张型心肌病(DCM) 是一种原因不明的以心腔左心室和(或)双心室扩大、心肌收缩功能减弱为主要特征的异质性心肌病。
企业并购相关研究综述
企业并购相关研究综述袁显平;周满【摘要】为了探明企业并购未来研究方向,运用统计分析法,从文献检索统计、并购理论以及并购绩效研究等视角,对国内外企业并购相关研究进行了统计分析,侧重回顾了国内外具有代表性的研究成果.1990年以来,有关企业并购的英文期刊文献多达1 018篇,其中40篇是探讨中国企业并购的.西方国家并购较早出现,理论相对丰富,大致形成了以西方企业为主的并购动因、并购效应等理论体系,国内则相对较少.学术界多运用事件和财务指标研究法来考察并购绩效,但是证券市场的有效性以及财务指标的可取性有待进一步验证.21世纪以来,企业并购重组已经成为资本市场的一个重要部分,但是国内资本市场发展不够成熟,难以做到资产的有效配置.基于以上研究结论,文章最后提出了需进一步深入研究的方向与主题.【期刊名称】《技术与创新管理》【年(卷),期】2015(036)003【总页数】6页(P258-263)【关键词】企业并购;并购绩效;财务指标;资本市场【作者】袁显平;周满【作者单位】西安科技大学管理学院,陕西西安710054;西安科技大学管理学院,陕西西安710054【正文语种】中文【中图分类】F831西方企业从19世纪到21世纪共经历了5次较大并购浪潮,已有百余年历史,现已建立了完善的并购机制。
国外学者对企业并购进行了大量的理论与实验研究,得出了许多有价值的结论。
美国经济学家Stilger认为:“纵观世界上著名的大企业,几乎没有一家不是通过并购重组发展起来的,也没有哪一家是单纯依靠企业自身积累发展起来的。
”相对西方国家而言,我国资本市场起步较晚,发展不够规范,资本运作经验欠缺,但近些年来企业并购愈演愈烈,不仅国内并购事件众多,众多企业还参与了跨国并购。
据清科数据统计,2014年中国并购市场并购活跃度与规模量双双突破2013年总交易量,企业并购的“黄金时代”已经全面而至。
鉴于我国企业并购事件越来越多,需要经验总结和相关理论支持。
心脏性猝死的一级预防
冠心病已成为西方国家人群发生SCA的主要病因
SCA的原因
Albert CM. Circulation. 2003;107:2096-2101.
SCA的发病情况(美国)
1 U.S. Census Bureau, Statistical Abstract of the United States: 2001. 2 American Cancer Society, Inc., Surveillance Research, Cancer Facts and Figures 2001. 3 2002 Heart and Stroke Statistical Update, American Heart Association. 4 Zheng Z. Circulation. 2001;104:2158-2163.
54岁的爱立信(中国)有限公司总裁杨迈于 2004年4月8日晚,由于心脏病突发在京猝死
直击猝死!(残酷的事实)
全球快餐业巨头麦当劳 公司董事长兼首席执行 官吉姆·坎塔卢波在2004 年4月19日凌晨猝死于家 中,最终死因为心脏病 突发,享年60岁
直击猝死!(残酷的事实)
SCA的高危因素(一)
左室射血分数(LVEF)低下
左室射血分数(LVEF)已成为评估SCA非常重要的独立危险因素1
1 Myerberg RJ,Castellanos A.Cardiac arrest and sudden cardiac death.Braunwald E.Heart Disease,A Textbook of Cardiovascular Medicine.5th ed,Vol.Philadelphia:WB Saunders Co;1997:chapter 24..
Coordination of Supply Chains with risk-averse agents
Coordination of Supply Chainswith Risk-Averse AgentsXianghua Gan,Suresh P.Sethi,and Houmin YanAbstract The extant supply chain management literature has not addressed the issue of coordination in supply chains involving risk-averse agents.We take up this issue and begin with defining a coordinating contract as one that results in a Pareto-optimal solution acceptable to each agent.Our definition generalizes the standard one in the risk-neutral case.We then develop coordinating contracts in three specific cases(1)the supplier is risk neutral and the retailer maximizes his expected profit subject to a downside risk constraint,(2)the supplier and the retailer each maximizes his own mean-variance trade-off,and(3)the supplier and the retailer each maximizes his own expected utility.Moreover,in case(3)we show that our contract yields the Nash Bargaining solution.In each case,we show how we can find the set of Pareto-optimal solutions,and then design a contract to achieve the solutions.We also exhibit a case in which we obtain Pareto-optimal sharing rules explicitly,and outline a procedure to obtain Pareto-optimal solutions. Keywords Capacity•Coordination•Nash bargaining•Pareto-optimality•Risk averse•Supply chain managementX.Gan(*)Department of Logistics and Maritime Studies,The Hong Kong Polytechnic University,Hung Hom,Kowloon,Hong Konge-mail:lgtxgan@.hkS.P.SethiSchool of Management,SM30,The University of Texas at Dallas,800W.Campbell Road, Richardson,TX75080-3021,USAe-mail:sethi@H.YanDepartment of Systems Engineering and Engineering Management,The Chinese University of Hong Kong,Shatin,NT,Hong Konge-mail:yan@.hkT.-M.Choi and T.C.Edwin Cheng(eds.),Supply Chain Coordination under Uncertainty,3 International Handbooks on Information Systems,DOI10.1007/978-3-642-19257-9_1,#Springer-Verlag Berlin Heidelberg20114X.Gan et al. 1IntroductionMuch of the research on decision making in a supply chain has assumed that the agents in the supply chain are risk neutral,i.e.,they maximize their respective expected profits.An important focus of this research has been the design of supply contracts that coordinate the supply chain.When each of the agents maximizes his expected profit,the objective of the supply chain considered as a single entity is unambiguously to maximize its total expected profit.This fact alone makes it natural to define a supply chain to be coordinated if the chain’s expected profit is maximized and each agent’s reservation profit is met.A similar argument holds if each agent’s objective is to minimize his expected cost.In this paper we consider supply chains with risk-averse agents.Simply put,an agent is risk averse if the agent prefers a certain profit p to a risky profit,whose expected value equals p.In the literature,there are many measures of risk aversion; see Szeg€o(2004)for examples.Regardless of the measure used,when one or more agents in the supply chain are risk averse,it is no longer obvious as to what the objective function of the supply chain entity should be.Not surprisingly,the issue of coordination of supply chain consisting of risk-averse agents has not been studied in the supply chain management literature.That is not to say that the literature does not realize the importance of the risk-averse criteria.Indeed,there are a number of papers devoted to the study of inventory decisions of a single risk-averse agent.These include Lau(1980),Bouakiz and Sobel(1992),Eeckhoudt et al. (1995),Chen and Federgruen(2000),Agrawal and Seshadri(2000a),Buzacott et al. (2002),Chen et al.(2007),and Gaur and Seshadri(2005).There also have been a few studies of supply chains consisting of one or more risk-averse u and Lau(1999)and Tsay(2002)consider decision making by a risk-averse supplier and a risk-averse retailer constituting a supply chain.Agrawal and Seshadri(2000b) introduce a risk-neutral intermediary to make ordering decisions for risk-averse retailers,whose respective profits are side payments from the intermediary.Van Mieghem(2003)has reviewed the literature that incorporates risk aversion in capacity investment decisions.While these papers consider risk-averse decision makers by themselves or as agents in a supply chain,they do not deal with the issue of the supply chain coordination involving risk-averse agents.It is this issue of coordination of supply chains consisting of one or more risk-averse agents that is the focus of this paper.That many decision makers are risk-averse has been amply documented in thefinance and economics literature;see, for example,Van Neumann and Morgenstern(1944),Markowitz(1959),Jorion (2006),and Szeg€o(2004).We shall therefore develop the concept of what we mean by coordination of a supply chain,and then design explicit contracts that achieve the defined coordination.For this purpose we use the Pareto-optimality criterion,used widely in the group decision theory,to evaluate a supply chain’s performance.We define each agent’s payoff to be a real-valued function of a random variable representing his profit,and propose that a supply chain can be treated as coordinated if no agent’s payoff can beCoordination of Supply Chains with Risk-Averse Agents5 improved without impairing someone else’s payoff and each agent receives at least his reservation payoff.We consider three specific cases of a supply chain(1)the supplier is risk neutral and the retailer maximizes his expected profit subject to a downside risk constraint,(2)the supplier and the retailer each maximizes his own mean-variance trade-off,and(3)the supplier and the retailer each maximizes his own expected utility.We show how we can coordinate the supply chain in each case according to our definition.In each case we do this byfinding the set of Pareto-optimal solutions acceptable to each agent,and then constructing aflexible contract that can attain any of these solutions.Moreover,the concept we develop and the contracts we obtain generalize the same known for supply chains involving risk-neutral agents.The remainder of the paper is organized as the follows.In Sect.2we review the related literature in supply chain management and group decision theory.In Sect.3 we introduce a definition of coordination of a supply chain consisting of risk-averse agents.In Sect.4we characterize the Pareto-optimal solutions andfind coordinating contracts for the supply chains listed as thefirst two cases.In Sect.5wefirst take up the third case using exponential utility functions for the agents,and design coordinating contracts as well as obtain the Nash Bargaining solution.Then we examine a case in which the supplier has an exponential utility followed by a linear utility.Section6provides a discussion of our results.The paper concludes in Sect.7 with suggestions for future research.2Literature ReviewThere is a considerable literature devoted to contracts that coordinate a supply chain involving risk-neutral agents.This literature has been surveyed by Cachon(2003). In addition,the book by Tayur et al.(1999)contains a number of chapters addressing supply contracts.In light of these,we limit ourselves to reviewing papers studying inventory and supply chain decisions by risk-averse agents.First we review papers dealing with a single risk-averse agent’s optimal inventory decision.Then we review articles dealing with decision making by risk-averse agents in a supply chain.Chen and Federgruen(2000)re-visit a number of basic inventory models using a mean-variance approach.They exhibit how a systematic mean-variance trade-off analysis can be carried out efficiently,and how the resulting strategies differ from those obtained in the standard analyses.Agrawal and Seshadri(2000a)consider how a risk-averse retailer,whose utility function is increasing and concave in wealth,chooses the order quantity and the selling price in a single-period inventory model.They consider two different ways in which the price affects the distribution of demand.In thefirst model,they assume that a change in the price affects the scale of the distribution.In the second model, a change in the price only affects the location of the distribution.They show that in comparison to a risk-neutral retailer,a risk-averse retailer will charge a higher price6X.Gan et al. and order less in thefirst model,whereas he will charge a lower price in the second model.Buzacott et al.(2002)model a commitment and option contract for a risk-averse newsvendor with a mean-variance objective.The contract,also known as a take-or-pay contract,belongs to a class of volumeflexible contracts,where the newsvendor reserves a capacity with initial information and adjusts the purchase at a later stage when some new information becomes available.They compare the performance of strategies developed for risk-averse and risk-neutral objectives. They conclude that the risk-averse objective can be an effective approach when the quality of information revision is not high.Their study indicates that it is possible to reduce the risk(measured by the variance of the profit)by six-to eightfold,while the loss in the expected profit is almost invisible.On the other hand,the strategy developed for the expected profit objective can only be consid-ered when the quality of information revision is high.They show furthermore that thesefindings continue to hold in the expected utility framework.The paper points out a need for modeling approaches that deal with downside risk considerations.Lau and Lau(1999)study a supply chain consisting of a monopolistic supplier and a retailer.The supplier and the retailer employ a return policy,and each of them has a mean-variance objective u and Lau obtain the optimal wholesale price and return credit for the supplier to maximize his utility.However,they do not consider the issue of improving the supply chain’s performance,i.e.,improving both players’utilities.Agrawal and Seshadri(2000b)consider a single-period model in which multiple risk-averse retailers purchase a single product from a common supplier.They introduce a risk neutral intermediary into the channel,who purchases goods from the vendor and sells them to the retailers.They demonstrate that the intermediary, referred to as the distributor,orders the optimal newsvendor quantity from the supplier and offers a menu of mutually beneficial contracts to the retailers.In every contract in the menu,the retailer receives afixed side payment,while the distributor is responsible for the ordering decisions of the retailers and receives all their revenues.The menu of contracts simultaneously(1)induces every risk-averse agent to select a unique contract from it;(2)maximizes the distributor’s profit; and(3)raises the order quantities of the retailers to the expected value maximizing (newsvendor)quantities.Tsay(2002)studies how risk aversion affects both sides of the supplier–retailer relationship under various scenario of relative strategic power,and how these dynamics are altered by the introduction of a return policy.The sequence of play is as follows:first the supplier announces a return policy,and then the retailer chooses order quantity without knowing the demand.After observing the demand, the retailer chooses the price and executes on any relevant terms of the distribution policy as appropriate(e.g.,returning any overstock as allowed).Tsay shows that the behavior under risk aversion is qualitatively different from that under risk neutrality.He also show that the penalty for errors in estimating a channel partner’s risk aversion can be substantial.Coordination of Supply Chains with Risk-Averse Agents7 In a companion paper(Gan et al.2005),we examine coordinating contracts for a supply chain consisting of one risk-neutral supplier and one risk-averse retailer. There we design an easy-to-implement risk-sharing contract that accomplishes the coordination as defined in this paper.Among these supply chain papers,Lau and Lau(1999)and Tsay(2002)consider the situation in which both the retailer and the supplier in the channel are risk averse.However,neither considers the issue of the Pareto-optimality of the actions of the agents.The aim of Agrawal and Seshadri(2000b)is to design a contract that increases the channel’s order quantity to the optimal level in the risk-neutral case by having the risk-neutral agent assume all the risk.Once again,they do not mention the Pareto-optimality aspect of the decision they obtain.Finally since our definition of coordination is based on the concepts used in the group decision theory,we briefly review this stream of literature.From the early fifties to the early eighties,a number of papers and books appeared that deal with situations in which a group faces intertwined external and internal problems.The external problem involves the choice of an action to be taken by the group,and the internal problem involves the distribution of the group payoff among the members. Arrow(1951)conducted one of the earliest studies on the group decision theory, and showed that given an ordering of consequences by a number of individuals,no group ordering of these consequences exists that satisfies a set of seemingly reasonable behavioral assumptions.Harsanyi(1955)presented conditions under which the total group utility can be expressed as a linear combination of individuals’cardinal utilities.Wilson(1968)used Pareto-optimality as the decision criterion and constructed a group utility function tofind Pareto-optimal solutions. Raiffa(1970)illustrates the criterion of Pareto-optimality quite lucidly,and discusses how to choose a Pareto-optimal solution in bargaining and arbitration Valle(1978)uses an allocation function to define Pareto-optimality. Eliashberg and Winkler(1981)investigate properties of sharing rules and the group utility functions in additive and multilinear cases.3Definition of Coordination of a Supply Chainwith Risk-Neutral or Risk-Averse AgentsIn this section we define coordination of a supply chain consisting of agents that are risk neutral or risk averse.We use concepts developed in group decision theory that deals with situations in which a group faces intertwined external and internal problems.The external problem involves the choice of an action to be taken by the group,and the internal problem involves the distribution of the group payoff among the members.In group decision problems,a joint action of the group members is said to be Pareto-optimal if there does not exist an alternative action that is at least as acceptable to all and definitely preferred by some.In other words,a joint action is Pareto-optimal if it is not possible to make one agent better off without makinganother one worse off.We call the collection of all Pareto-optimal actions as the Pareto-optimal set .It would not be reasonable for the group of agents to choose a joint action that is not Pareto-optimal.Raiffa (1970)and LaValle (1978)illustrate this idea quite lucidly with a series of examples.A supply chain problem is obviously a group decision problem.The channel faces an external problem and an internal problem.External problems include decisions regarding order/production quantities,item prices,etc.The internal problem is to allocate profit by setting the wholesale price,deciding the amount of a side payment if any,refund on the returned units,etc.Naturally,we can adopt the Pareto-optimality criterion of the group decision theory for making decisions in a supply chain.Indeed,in the risk-neutral case,the optimal action under a coordinating contract is clearly Pareto-optimal.In general,since the agents in the channel would not choose an action that is not in the Pareto-optimal set,the first step to coordinate a channel is to characterize the set.Following the ideas of Raiffa (1970)and LaValle (1978),we formalize below the definition of Pareto-optimality.Let (O ;F ;P )denote the probability space and N denote the number of agents in the supply chain,N r 2.Let S i be the external action space of agent i ;i ¼1;...;N ,and S ¼S 1ÂÁÁÁÂS N .For any given external joint action s ¼s 1;...;s N ðÞ2S ,the channel’s total profit is a random variable P s ;o ðÞ;o 2O .Let E and V denote the expectation and variance defined on (O ;F ;P ),respectively.Now we define a sharing rule that governs the splitting of the channel profit among the agents.Let Y be the set of all functions from S ÂO to R N .Definition 1.A function u ðs ;v Þ2Q is called a sharing rule if P i u i ðs ;v Þ¼1almost surely.Under the sharing rule u ðs ;o Þ,agent i’s profit is represented byP i ðs ;v ;u ðs ;v ÞÞ¼u i ðs ;v ÞP ðs ;v Þ;i ¼1;...;N :Often,when there is no confusion,we write P ðs ;v Þsimply as P ðs Þ,u ðs ;v Þas u ðs Þ,and P i ðs ;v ;u ðs ;v ÞÞas P i ðs ;u ðs ÞÞ.A supply chain’s external problem is to choose an s 2S and its internal problem is to choose a function u ðs Þ2Y .Thus the channel’s total problem is to choose a pair ðs ;u ðs ÞÞ2S ÂY .Now we define the preferences of the agents over their random profits.Let G denote the space of all random variables defined on O ;F ;P ðÞ.For X ;X 02G ,the agent i ’s preference will be denoted by a real-valued payoff function u i ðÁÞdefined on G .The relation u i ðX Þ>u i ðX 0Þ,u i ðX Þ<u i ðX 0Þand u i ðX Þ¼u i ðX 0Þindicate X is preferred to ,less preferred to ,and equivalent to X 0,respectively.It should be noted that this definition of payoff function allows for ordinal as well as cardinal utility functions.We provide following examples of payoff functions.Example 1.If agent i wants to maximize his mean-variance trade-off,then his payoff function is u i ðX Þ¼E ðX ÞÀl V ðX Þ;X 2G ,for some l >0.Example 2.Assume that agent i maximizes his expected profit under the constraint that the probability of his profit being less than his target profit level a does not exceed a given level b ;0<b b 1.Then his payoff u i can be represented as8X.Gan et al.u iðXÞ¼EðXÞ;if P X b aðÞb b;À1;if P X b aðÞ>b:&Example3.Suppose agent i has a concave increasing utility function g i:R1!R1 of wealth and wants to maximize his expected utility.Then the agent’s payoff function is u iðXÞ¼E g iðXÞ½ ;X2G.Remark1.In Raiffa(1970)and LaValle(1978),each agent is assumed to have a cardinal utility function of profit,and his objective is to maximize his expected utility.However,some preferences,such as the one in Example2,cannot be represented by a cardinal utility function.A point a2R N is said to be Pareto-inferior to or Pareto-dominated by another point b2R N,if each component of a is no greater than the corresponding compo-nent of b and at least one component of a is less than the corresponding component of b.In other words,we say b is Pareto-superior to a or b Pareto-dominates a.A point is said to be a Pareto-optimal point of a subset of R N,if it is not Pareto-inferior to any other point in the subset.With these concepts,we can now define Pareto-optimality of a sharing rule uðsÞand an action pairðs;uðsÞÞ.Definition2.Given an external action s of the supply chain,uÃðsÞis a Pareto-optimal sharing rule,ifðu1ðP1ðs;uÃðsÞÞÞ;ÁÁÁ;u NðP Nðs;uÃðsÞÞÞÞis a Pareto-optimal point of the setfðu1ðP1ðs;uðsÞÞÞ;ÁÁÁ;u NðP Nðs;uðsÞÞÞÞ;u2Y g;where u iðP iðs;uðsÞÞÞis the payoff of the i th agent.Definition3.ðsÃ;uÃðsÃÞÞis a Pareto-optimal action pair if the agents’payoffsðu1ðP1ðsÃ;uÃðsÃÞÞÞ;ÁÁÁ;u NðP NðsÃ;uÃðsÃÞÞÞÞis a Pareto-optimal point of the setfðu1ðP1ðs;uðsÞÞÞ;ÁÁÁ;u NðP Nðs;uðsÞÞÞÞ;ðs;uðsÞÞ2SÂY g:Clearly ifðsÃ;uÃðsÃÞÞis a Pareto-optimal action pair,then uÃðsÃÞis a Pareto-optimal sharing rule given sÃ.We begin now with an examination of the Pareto-optimal set in a supply chain consisting of risk-neutral agents.If an external action maximizes the supply chain’s expected profit,then it is not possible to make one agent get more expected profit without making another agent get less.More specifically,we have the following proposition.Coordination of Supply Chains with Risk-Averse Agents9Proposition1.If the agents in a supply chain are all risk neutral,then an action pairðs;uðsÞÞis Pareto-optimal if and only if the channel’s external action s maximizes the channel’s expected profit.Proof.The proof follows from the fact that in the risk-neutral case,for each s,Xu iðP iðs;uðsÞÞÞ¼XE P iðs;uðsÞÞ¼EXP iðs;uðsÞÞ¼E PðsÞ:Thus,everyðsÃ;uðsÃÞÞ2SÂY is Pareto-optimal provided sÃmaximizes E PðsÃÞ.□Since agents in a supply chain maximize their respective objectives,the agents’payoffs might not be Pareto-optimal if their objectives are not aligned properly.In this case,it is possible to improve the chain’s performance,i.e.,achieve Pareto-superior payoffs.The agents can enter into an appropriately designed contract, under which their respective optimizing actions leads to a Pareto-superior payoff.In the supply chain management literature,a contract is defined to coordinate a supply chain consisting of risk-neutral agents if their respective optimizing external actions under the contract maximize the chain’s expected profit.Then,according to Propo-sition1,a coordinating contract is equivalent to a Pareto-optimal action in the risk-neutral case.It is therefore reasonable to use the notion of Pareto-optimality to define supply chain coordination in the general case.Definition4.Supply Chain Coordination.A contract agreed upon by the agents of a supply chain is said to coordinate the supply chain if the optimizing actions of the agents under the contract1.Satisfy each agent’s reservation payoff constraint.2.Lead to an action pairðsÃ;uÃðsÃÞÞthat is Pareto-optimal.Besides Pareto-optimality of a contract,we have introduced the individual-rationality or the participation constraints as part of the definition of coordination. The constraints ensure that each agent is willing to participate in the contract by requiring that each gets at least his reservation payoff.It is clear that each agent’s reservation payoff will not be less than his status-quo payoff,which is defined to be his best payoff in the absence of the contract.Thus,we need consider only the subset of Pareto-optimal actions that satisfy these participating constraints.The reservation payoff of an agent plays an important role in bargaining,as we shall see in the next section.Now we illustrate the introduced concept of coordination by an example. Example4.Consider a supply chain consisting of one supplier and one retailer who faces a newsvendor problem.Before the demand realizes,the supplier decides on his capacityfirst,and the retailer then prices the product and chooses an order quantity.The supplier and the retailer may enter into a contract that specifies the retailer’s committed order quantity and the supplier’s refund policy for returned items.In this channel,the external actions are the supplier’s capacity selection and the retailer’s pricing and ordering decisions.These are denoted as s.The internal 10X.Gan et al.Coordination of Supply Chains with Risk-Averse Agents11 actions include decision on the quantity of commitment,the refundable quantity, and the refund credit per item.These internal actions together lead to a sharing rule denoted by uðsÞ.Once the contract parameters are determined,the agents in the supply chain choose their respective external actions that maximize their respective payoffs.Ifðs;uðsÞÞsatisfies the agents’reservation payoffs and is Pareto-optimal, then the channel is coordinated by the contract.The definition of coordination proposed here allows agents to have any kind of preference that can be represented by a payoff function satisfying the complete and transitive axioms specified earlier.For example,all of the seven kinds of preferences listed in Schweitzer and Cachon(2000),including risk-seeking preferences,are allowed.Since often in practice,an agent is either risk neutral or risk averse,we restrict our attention to only these two types.Remark2.Our definition applies also to a T-period case.For this,we define the payoff function of player i asu iðP1iðsÃ;uÃðsÃÞÞ;P2iðsÃ;uÃðsÃÞÞ;ÁÁÁ;P T iðsÃ;uÃðsÃÞÞÞ:G T!R1;where P t iðsÃ;uÃðsÃÞÞis agent i’s profit in period t.4Coordinating Supply ChainsEach Pareto-optimal action pairðs;uðsÞÞresults in a vector of payoffsðu1ðP1ðs;uðsÞÞÞ;ÁÁÁ;u NðP Nðs;uðsÞÞÞÞ;where u iðP iðs;uðsÞÞÞis the payoff of the i th agent.LetC¼fðu1ðP1ðs;uðsÞÞÞ;ÁÁÁ;u NðP Nðs;uðsÞÞÞÞjðs;uðsÞÞis Pareto-optimal;ðs;uðsÞÞ2SÂY g;denote the set of all Pareto-optimal payoffs,and let F&C be the subset of Pareto-optimal payoffs that satisfy all of the participation constraints.We shall refer to F as Pareto-optimal frontier.We will assume that F is not empty.To coordinate a supply chain,thefirst step is to obtain the Pareto-optimal frontier F.If F is not a singleton,then agents bargain to arrive at an element in F to which they agree.A coordinating contract is one with a specific set of parameters that achieves the selected solution.A contract is appealing if it has sufficientflexibility.In Cachon(2003),a coordinating contract is said to beflexible if the contract,by adjustment of some parameters,allows for any division of the supply chain’s expected profit among the risk-neutral agents.This concept can be extended to the general case as follows.12X.Gan et al. Definition 5.A coordinating contract isflexible if,by adjustment of some parameters,the contract can lead to any point in F:We shall now develop coordinating contracts in supply chains consisting of two agents:a supplier and a retailer.We shall consider three different cases.In each of these cases,we assume that agents have complete information.In Case1,the supplier is risk neutral and the retailer has a payoff function in Example2,i.e.,the retailer maximizes his expected profit subject to a downside constraint.In Case2, the supplier and the retailer are both risk averse and each maximizes his own mean-variance trade-off.In Case3,the supplier and the retailer are both risk averse and each maximizes his own expected concave utility.We consider thefirst two cases in this section and the third case in Sect.5.In each case,let us denote the retailer’s and the supplier’s reservation payoffs as p r r0and p s r0,respectively.Wefirst obtain F and then design aflexible contract that can lead to any point in F by adjusting the parameters of the contract.4.1Case1:Risk Neutral Supplier and Retailer Averseto Downside RiskWe consider the supplier to be risk neutral and the retailer to maximize his expected profit subject to a downside risk constraint.This downside risk constraint requires that the probability of the retailer’s profit to be higher than a specified level is not too small.The risk neutrality assumption on the part of the supplier is reasonable when he is able to diversify his risk by serving a number of independent retailers,which is quite often the case in practice.When the retailers are independent,the supply chain can be divided into a number of sub-chains,each consisting of one supplier and one retailer.This situation,therefore,could be studied as a supply chain consisting of one risk-neutral supplier and one risk-averse retailer.We say that an action pairðs;uðsÞÞis feasible if the pair satisfies the retailer’s downside risk constraint.We do not need to consider a pairðs;uðsÞÞthat is not feasible since under the pair the retailer’s payoff isÀ1and he would not enter the contract.We denote PðsÞ,P rðs;uðsÞÞ,and P sðs;uðsÞÞas the profits of the supply chain,the retailer,and the supplier,respectively.Other quantities of interest will be subscripted in the same way throughout the chapter,i.e.,subscript r will denote the retailer and subscript s will denote the supplier.Then we have the following result.Theorem1.If the supplier is risk neutral and the retailer maximizes his expected profit subject to a downside risk constraint,then a feasible action pairðs;uðsÞÞis Pareto-optimal if and only if the supply chain’s expected profit is maximized over the feasible set.Proof.ONLY IF:It is sufficient to show that if E PðsÞis not maximal over the feasible set,thenðs;uðsÞÞis not Pareto-optimal.。
TmCalculator 1.0.3 核苷酸序列融解温度计算器说明书
Package‘TmCalculator’October12,2022Type PackageTitle Melting Temperature of Nucleic Acid SequencesVersion1.0.3Date2022-02-20Author Junhui LiMaintainer Junhui Li<****************.cn>Description This tool is extended from methods in Bio.SeqUtils.MeltingTemp of python.The melt-ing temperature of nucleic acid sequences can be calculated in three method,the Wal-lace rule(Thein&Wallace(1986)<doi:10.1016/S0140-6736(86)90739-7>),empirical formu-las based on G and C content(Marmur J.(1962)<doi:10.1016/S0022-2836(62)80066-7>,Schildkraut C.(2010)<doi:10.1002/bip.360030207>,Wet-mur J G(1991)<doi:10.3109/10409239109114069>,Unter-gasser,A.(2012)<doi:10.1093/nar/gks596>,von Ah-sen N(2001)<doi:10.1093/clinchem/47.11.1956>)and nearest neighbor thermodynamics(Bres-lauer K J(1986)<doi:10.1073/pnas.83.11.3746>,Sugi-moto N(1996)<doi:10.1093/nar/24.22.4501>,Allawi H(1998)<doi:10.1093/nar/26.11.2694>,San-taLu-cia J(2004)<doi:10.1146/annurev.biophys.32.110601.141800>,Freier S(1986)<doi:10.1073/pnas.83.24.9373>,Xia T(19 mar-ito S(2000)<doi:10.1093/nar/28.9.1929>,Turner D H(2010)<doi:10.1093/nar/gkp892>,Sugi-moto N(1995)<doi:10.1016/S0048-9697(98)00088-6>,Allawi H T(1997)<doi:10.1021/bi962590c>,Santalu-cia N(2005)<doi:10.1093/nar/gki918>),and it can also be corrected with salt ions and chemi-cal compound(SantaLucia J(1996)<doi:10.1021/bi951907q>,SantaLu-cia J(1998)<doi:10.1073/pnas.95.4.1460>,Owczarzy R(2004)<doi:10.1021/bi034621r>,Owczarzy R(2008)<doi:10.102 BugReports https:///JunhuiLi1017/TmCalculator/issuesLicense GPL(>=2)Depends R(>=2.10)NeedsCompilation noRepository CRANRoxygenNote7.1.2Date/Publication2022-02-2104:10:03UTC12c2s R topics documented:c2s (2)check_filter (3)chem_correction (4)complement (5)GC (6)print.TmCalculator (6)s2c (7)salt_correction (8)Tm_GC (9)Tm_NN (12)Tm_Wallace (15)Index17 c2s convert a vector of characters into a stringDescriptionSimply convert a vector of characters such as c("H","e","l","l","o","W","o","r","l","d")into a single string"HelloWorld".Usagec2s(characters)Argumentscharacters A vector of charactersValueRetrun a stringsAuthor(s)Junhui LiReferencescitation("TmCalculator")Examplesc2s(c("H","e","l","l","o","W","o","r","l","d"))check_filter3check_filter Check andfilter invalid base of nucleotide sequencesDescriptionIn general,whitespaces and non-base characters are removed and characters are converted to up-percase in given method.Usagecheck_filter(ntseq,method)Argumentsntseq Sequence(5’to3’)of one strand of the DNA nucleic acid duplex as string orvector of charactersmethod TM_Wallace:check and return"A","B","C","D","G","H","I","K","M","N","R","S","T","V","W"and"Y"TM_GC:check and return"A","B","C","D","G","H","I","K","M","N","R","S","T","V","W","X"and"Y"TM_NN:check and return"A","C","G","I"and"T"ValueReturn a sequence which fullfils the requirements of the given method.Author(s)Junhui LiReferencescitation("TmCalculator")Examplesntseq<-c("ATCGBDHKMNRVYWSqq")check_filter(ntseq,method= Tm_Wallace )check_filter(ntseq,method= Tm_NN )4chem_correction chem_correction Corrections of melting temperature with chemical substancesDescriptionCorrections coefficient of melting temperature with DMSO and formamide and these corrections are rough approximations.Usagechem_correction(DMSO=0,fmd=0,DMSOfactor=0.75,fmdmethod=c("concentration","molar"),fmdfactor=0.65,ptGC)ArgumentsDMSO Percent DMSOfmd Formamide concentration in percentage(fmdmethod="concentration")or molar (fmdmethod="molar").DMSOfactor Coefficient of Tm decreases per percent DMSO.Default=0.75von Ahsen N (2001)<PMID:11673362>.Other published values are0.5,0.6and0.675.fmdmethod"concentration"method for formamide concentration in percentage and"molar"for formamide concentration in molarfmdfactor Coefficient of Tm decrease per percent formamide.Default=0.65.Several pa-pers report factors between0.6and0.72.ptGC Percentage of GC(%).Detailsfmdmethod="concentration"Correction=-factor*percentage_of_formamidefmdmethod="molar"Correction=(0.453*GC/100-2.88)x formamideAuthor(s)Junhui Licomplement5 Referencesvon Ahsen N,Wittwer CT,Schutz E,et al.Oligonucleotide melting temperatures under PCR conditions:deoxynucleotide Triphosphate and Dimethyl sulfoxide concentrations with comparison to alternative empirical formulas.Clin Chem2001,47:1956-C1961.Exampleschem_correction(DMSO=3)chem_correction(fmd=1.25,fmdmethod="molar",ptGC=50)complement complement and reverse complement base of nucleotide sequencesDescriptionget reverse complement and complement base of nucleotide sequencesUsagecomplement(ntseq,reverse=FALSE)Argumentsntseq Sequence(5’to3’)of one strand of the nucleic acid duplex as string or vector of charactersreverse Logical value,TRUE is reverse complement sequence,FALSE is not.Author(s)Junhui LiReferencescitation("TmCalculator")Examplescomplement("ATCGYCGYsWwsaVv")complement("ATCGYCGYsWwsaVv",reverse=TRUE)6print.TmCalculator GC Calculate G and C content of nucleotide sequencesDescriptionCalculate G and C content of nucleotide sequences.The number of G and C in sequence is divided by length of sequence(when totalnt is TRUE)or the number of all A,T,C,G and ambiguous base.UsageGC(ntseq,ambiguous=FALSE,totalnt=TRUE)Argumentsntseq Sequence(5’to3’)of one strand of the nucleic acid duplex as string or vector of characters.ambiguous Ambiguous bases are taken into account to compute the G and C content when ambiguous is TRUE.totalnt Sum of’G’and’C’bases divided by the length of the sequence when totalnt is TRUE.ValueContent of G and C(range from0to100Author(s)Junhui LiExamplesGC(c("a","t","c","t","g","g","g","c","c","a","g","t","a"))#53.84615GC("GCATSWSYK",ambiguous=TRUE)#55.55556print.TmCalculator Prints melting temperature from a TmCalculator objectDescriptionprint.TmCalculator prints to console the melting temperature value from an object of class TmCalculator.s2c7Usage##S3method for class TmCalculatorprint(x,...)Argumentsx An object of class TmCalculator....UnusedValueThe melting temperature value.s2c convert a string into a vector of charactersDescriptionSimply convert a single string such as"HelloWorld"into a vector of characters such as c("H","e","l","l","o","W","o","r","l","d Usages2c(strings)Argumentsstrings A single string such as"HelloWorld"ValueRetrun a vector of charactersAuthor(s)Junhui LiReferencescitation("TmCalculator")Exampless2c(c("HelloWorld"))8salt_correctionsalt_correction Corrections of melting temperature with salt ionsDescriptionCorrections coefficient of melting temperature or entropy with different operationsUsagesalt_correction(Na=0,K=0,Tris=0,Mg=0,dNTPs=0,method=c("Schildkraut2010","Wetmur1991","SantaLucia1996","SantaLucia1998-1", "SantaLucia1998-2","Owczarzy2004","Owczarzy2008"),ntseq,ambiguous=FALSE)ArgumentsNa Millimolar concentration of NaK Millimolar concentration of KTris Millimolar concentration of TrisMg Millimolar concentration of MgdNTPs Millimolar concentration of dNTPsmethod Method to be applied including"Schildkraut2010","Wetmur1991","SantaLucia1996", "SantaLucia1998-1","SantaLucia1998-2","Owczarzy2004","Owczarzy2008".Firstfourth methods correct Tm,fifth method corrects deltaS,sixth and seventh meth-ods correct1/Tm.See details for the method description.ntseq Sequence(5’to3’)of one strand of the nucleic acid duplex as string or vectorof characters.ambiguous Ambiguous bases are taken into account to compute the G and C content whenambiguous is TRUE.DetailsThe methods are:1Schildkraut C(2010)<doi:10.1002/bip.360030207>2Wetmur J G(1991)<doi:10.3109/10409239109114069>3SantaLucia J(1996)<doi:10.1021/bi951907q>4SantaLucia J(1998)<doi:10.1073/pnas.95.4.1460>5SantaLucia J(1998)<doi:10.1073/pnas.95.4.1460>6Owczarzy R(2004)<doi:10.1021/bi034621r>7Owczarzy R(2008)<doi:10.1021/bi702363u>methods1-4:Tm(new)=Tm(old)+correctionmethod5:deltaS(new)=deltaS(old)+correctionmethods6+7:Tm(new)=1/(1/Tm(old)+correction)Author(s)Junhui LiReferencesSchildkraut C.Dependence of the melting temperature of DNA on salt concentration[J].Biopoly-mers,2010,3(2):195-208.Wetmur J G.DNA Probes:Applications of the Principles of Nucleic Acid Hybridization[J].CRC Critical Reviews in Biochemistry,1991,26(3-4):3Santalucia,J,Allawi H T,Seneviratne P A.Improved Nearest-Neighbor Parameters for Predicting DNA Duplex Stability,[J].Biochemistry,1996,35(11):3555-3562.SantaLucia,J.A unified view of polymer,dumbbell,and oligonucleotide DNA nearest-neighbor thermodynamics[J].Proceedings of the National Academy of Sciences,1998,95(4):1460-1465.Owczarzy R,You Y,Moreira B G,et al.Effects of Sodium Ions on DNA Duplex Oligomers: Improved Predictions ofMelting Temperatures[J].Biochemistry,2004,43(12):3537-3554.Owczarzy R,Moreira B G,You Y,et al.Predicting Stability of DNA Duplexes in Solutions Containing Magnesium and Monovalent Cations[J].Biochemistry,2008,47(19):5336-5353. Examplesntseq<-c("acgtTGCAATGCCGTAWSDBSYXX")salt_correction(Na=390,K=20,Tris=0,Mg=10,dNTPs=25,method="Owczarzy2008",ntseq)Tm_GC Calculate the melting temperature using empirical formulas based onGC contentDescriptionCalculate the melting temperature using empirical formulas based on GC content with different optionsUsageTm_GC(ntseq,ambiguous=FALSE,userset=NULL,variant=c("Primer3Plus","Chester1993","QuikChange","Schildkraut1965","Wetmur1991_MELTING","Wetmur1991_RNA","Wetmur1991_RNA/DNA","vonAhsen2001"),Na=0,K=0,Tris=0,Mg=0,dNTPs=0,saltcorr=c("Schildkraut2010","Wetmur1991","SantaLucia1996","SantaLucia1998-1", "Owczarzy2004","Owczarzy2008"),mismatch=TRUE,DMSO=0,fmd=0,DMSOfactor=0.75,fmdfactor=0.65,fmdmethod=c("concentration","molar"),outlist=TRUE)Argumentsntseq Sequence(5’to3’)of one strand of the nucleic acid duplex as string or vectorof characters.ambiguous Ambiguous bases are taken into account to compute the G and C content whenambiguous is TRUE.userset A vector of four coefficient ersets override value sets.variant Empirical constants coefficient with8variant:Chester1993,QuikChange,Schild-kraut1965,Wetmur1991_MELTING,Wetmur1991_RNA,Wetmur1991_RNA/DNA,Primer3Plus and vonAhsen2001Na Millimolar concentration of Na,default is0K Millimolar concentration of K,default is0Tris Millimolar concentration of Tris,default is0Mg Millimolar concentration of Mg,default is0dNTPs Millimolar concentration of dNTPs,default is0saltcorr Salt correction method should be chosen when provide’userset’.Options are"Schildkraut2010","Wetmur1991","SantaLucia1996","SantaLucia1998-1","Owczarzy2004","Owczarzy2Note that"SantaLucia1998-2"is not available for this function.mismatch If’True’(default)every’X’in the sequence is counted as mismatchDMSO Percent DMSOfmd Formamide concentration in percentage(fmdmethod="concentration")or molar(fmdmethod="molar").Tm_GC11 DMSOfactor Coeffecient of Tm decreases per percent DMSO.Default=0.75von Ahsen N (2001)<PMID:11673362>.Other published values are0.5,0.6and0.675.fmdfactor Coeffecient of Tm decrease per percent formamide.Default=0.65.Several pa-pers report factors between0.6and0.72.fmdmethod"concentration"method for formamide concentration in percentage and"molar"for formamide concentration in molaroutlist output a list of Tm and options or only Tm value,default is TRUE.DetailsEmpirical constants coefficient with8variant:Chester1993:Tm=69.3+0.41(Percentage_GC)-650/NQuikChange:Tm=81.5+0.41(Percentage_GC)-675/N-Percentage_mismatchSchildkraut1965:Tm=81.5+0.41(Percentage_GC)-675/N+16.6x log[Na+]Wetmur1991_MELTING:Tm=81.5+0.41(Percentage_GC)-500/N+16.6x log([Na+]/(1.0+0.7 x[Na+]))-Percentage_mismatchWetmur1991_RNA:Tm=78+0.7(Percentage_GC)-500/N+16.6x log([Na+]/(1.0+0.7x[Na+])) -Percentage_mismatchWetmur1991_RNA/DNA:Tm=67+0.8(Percentage_GC)-500/N+16.6x log([Na+]/(1.0+0.7x [Na+]))-Percentage_mismatchPrimer3Plus:Tm=81.5+0.41(Percentage_GC)-600/N+16.6x log[Na+]vonAhsen2001:Tm=77.1+0.41(Percentage_GC)-528/N+11.7x log[Na+]Author(s)Junhui LiReferencesMarmur J,Doty P.Determination of the base composition of deoxyribonucleic acid from its thermal denaturation temperature.[J].Journal of Molecular Biology,1962,5(1):109-118.Schildkraut C.Dependence of the melting temperature of DNA on salt concentration[J].Biopoly-mers,2010,3(2):195-208.Wetmur J G.DNA Probes:Applications of the Principles of Nucleic Acid Hybridization[J].CRC Critical Reviews in Biochemistry,1991,26(3-4):33.Untergasser A,Cutcutache I,Koressaar T,et al.Primer3–new capabilities and interfaces[J].Nucleic Acids Research,2012,40(15):e115-e115.von Ahsen N,Wittwer CT,Schutz E,et al.Oligonucleotide melting temperatures under PCR conditions:deoxynucleotide Triphosphate and Dimethyl sulfoxide concentrations with comparison to alternative empirical formulas.Clin Chem2001,47:1956-1961.Examplesntseq<-c("ATCGTGCGTAGCAGTACGATCAGTAG")out<-Tm_GC(ntseq,ambiguous=TRUE,variant="Primer3Plus",Na=50,mismatch=TRUE)outout$Tmout$OptionsTm_NN Calculate melting temperature using nearest neighbor thermodynam-icsDescriptionCalculate melting temperature using nearest neighbor thermodynamicsUsageTm_NN(ntseq,ambiguous=FALSE,comSeq=NULL,shift=0,nn_table=c("DNA_NN4","DNA_NN1","DNA_NN2","DNA_NN3","RNA_NN1","RNA_NN2","RNA_NN3","R_DNA_NN1"),tmm_table="DNA_TMM1",imm_table="DNA_IMM1",de_table=c("DNA_DE1","RNA_DE1"),dnac1=25,dnac2=25,selfcomp=FALSE,Na=0,K=0,Tris=0,Mg=0,dNTPs=0,saltcorr=c("Schildkraut2010","Wetmur1991","SantaLucia1996","SantaLucia1998-1", "SantaLucia1998-2","Owczarzy2004","Owczarzy2008"),DMSO=0,fmd=0,DMSOfactor=0.75,fmdfactor=0.65,fmdmethod=c("concentration","molar"),outlist=TRUE)Argumentsntseq Sequence(5’to3’)of one strand of the nucleic acid duplex as string or vectorof characters.ambiguous Ambiguous bases are taken into account to compute the G and C content whenambiguous is TRUE.Default is FALSE.comSeq Complementary sequence.The sequence of the template/target in3’->5’direc-tionshift Shift of the primer/probe sequence on the template/target sequence,default=0.for example:when shift=0,thefirst nucleotide base at5‘end of primer align tofirst one at3‘end of template.When shift=-1,the second nucleotide base at5‘end of primer align tofirst one at3‘end of template.When shift=1,thefirst nucleotide base at5‘end of primer align to second oneat3‘end of template.The shift parameter is necessary to align primer/probeand template/target if they have different lengths or if they should have danglingends.nn_table Thermodynamic NN values,eight tables are implemented.For DNA/DNA hybridizations:DNA_NN1,DNA_NN2,DNA_NN3,DNA_NN4For RNA/RNA hybridizations:RNA_NN1,RNA_NN2,RNA_NN3For RNA/DNA hybridizations:R_DNA_NN1tmm_table Thermodynamic values for terminal mismatches.Default:DNA_TMM1imm_table Thermodynamic values for internal mismatches,may include insosine mismatches.Default:DNA_IMM1de_table Thermodynamic values for dangling ends.DNA_DE1(default)and RNA_DE1dnac1Concentration of the higher concentrated strand[nM].Typically this will be theprimer(for PCR)or the probe.Default=25.dnac2Concentration of the lower concentrated strand[nM].selfcomp Sequence self-complementary,default=False.If’True’the primer is thoughtbinding to itself,thus dnac2is not considered.Na Millimolar concentration of Na,default is0K Millimolar concentration of K,default is0Tris Millimolar concentration of Tris,default is0Mg Millimolar concentration of Mg,default is0dNTPs Millimolar concentration of dNTPs,default is0saltcorr Salt correction method should be chosen when provide’userset’Options are"Schildkraut2010","Wetmur1991","SantaLucia1996","SantaLucia1998-1","SantaLucia1998-2","Owczarzy2004","Owczarzy2008".Note that NA means no salt correction.DMSO Percent DMSOfmd Formamide concentration in percentage(fmdmethod="concentration")or molar(fmdmethod="molar").DMSOfactor Coeffecient of Tm decreases per percent DMSO.Default=0.75von Ahsen N(2001)<PMID:11673362>.Other published values are0.5,0.6and0.675.fmdfactor Coeffecient of Tm decrease per percent formamide.Default=0.65.Several pa-pers report factors between0.6and0.72.fmdmethod"concentration"method for formamide concentration in percentage and"molar"for formamide concentration in molar.outlist output a list of Tm and options or only Tm value,default is TRUE.DetailsDNA_NN1:Breslauer K J(1986)<doi:10.1073/pnas.83.11.3746>DNA_NN2:Sugimoto N(1996)<doi:10.1093/nar/24.22.4501>DNA_NN3:Allawi H(1998)<doi:10.1093/nar/26.11.2694>DNA_NN4:SantaLucia J(2004)<doi:10.1146/annurev.biophys.32.110601.141800>RNA_NN1:Freier S(1986)<doi:10.1073/pnas.83.24.9373>RNA_NN2:Xia T(1998)<doi:10.1021/bi9809425>RNA_NN3:Chen JL(2012)<doi:10.1021/bi3002709>R_DNA_NN1:Sugimoto N(1995)<doi:10.1016/S0048-9697(98)00088-6>DNA_TMM1:Bommarito S(2000)<doi:10.1093/nar/28.9.1929>DNA_IMM1:Peyret N(1999)<doi:10.1021/bi9825091>&Allawi H T(1997)<doi:10.1021/bi962590c> &Santalucia N(2005)<doi:10.1093/nar/gki918>DNA_DE1:Bommarito S(2000)<doi:10.1093/nar/28.9.1929>RNA_DE1:Turner D H(2010)<doi:10.1093/nar/gkp892>Author(s)Junhui LiReferencesBreslauer K J,Frank R,Blocker H,et al.Predicting DNA duplex stability from the base se-quence.[J].Proceedings of the National Academy of Sciences,1986,83(11):3746-3750.Sugimoto N,Nakano S,Yoneyama M,et al.Improved Thermodynamic Parameters and Helix Ini-tiation Factor to Predict Stability of DNA Duplexes[J].Nucleic Acids Research,1996,24(22):4501-5.Allawi,H.Thermodynamics of internal C.T mismatches in DNA[J].Nucleic Acids Research,1998, 26(11):2694-2701.Hicks L D,Santalucia J.The thermodynamics of DNA structural motifs.[J].Annual Review of Biophysics&Biomolecular Structure,2004,33(1):415-440.Freier S M,Kierzek R,Jaeger J A,et al.Improved free-energy parameters for predictions of RNA duplex stability.[J].Proceedings of the National Academy of Sciences,1986,83(24):9373-9377.Xia T,Santalucia,J,Burkard M E,et al.Thermodynamic Parameters for an Expanded Nearest-Neighbor Model for Formation of RNA Duplexes with Watson-Crick Base Pairs,[J].Biochemistry, 1998,37(42):14719-14735.Chen J L,Dishler A L,Kennedy S D,et al.Testing the Nearest Neighbor Model for Canonical RNA Base Pairs:Revision of GU Parameters[J].Biochemistry,2012,51(16):3508-3522.Bommarito S,Peyret N,Jr S L.Thermodynamic parameters for DNA sequences with dangling ends[J].Nucleic Acids Research,2000,28(9):1929-1934.Turner D H,Mathews D H.NNDB:the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure[J].Nucleic Acids Research,2010,38(Database issue):D280-D282.Sugimoto N,Nakano S I,Katoh M,et al.Thermodynamic Parameters To Predict Stability of RNA/DNA Hybrid Duplexes[J].Biochemistry,1995,34(35):11211-11216.Allawi H,SantaLucia J:Thermodynamics and NMR of internal G-T mismatches in DNA.Bio-chemistry1997,36:10581-10594.Santalucia N E W J.Nearest-neighbor thermodynamics of deoxyinosine pairs in DNA duplexes[J].Nucleic Acids Research,2005,33(19):6258-67.Peyret N,Seneviratne P A,Allawi H T,et al.Nearest-Neighbor Thermodynamics and NMR of DNA Sequences with Internal A-A,C-C,G-G,and T-T Mismatches,[J].Biochemistry,1999, 38(12):3468-3477.Examplesntseq<-c("AAAATTTTTTTCCCCCCCCCCCCCCGGGGGGGGGGGGTGTGCGCTGC")out<-Tm_NN(ntseq,Na=50)outout$OptionsTm_Wallace Calculate the melting temperature using the’Wallace rule’DescriptionThe Wallace rule is often used as rule of thumb for approximate melting temperature calculations for primers with14to20nt length.UsageTm_Wallace(ntseq,ambiguous=FALSE,outlist=TRUE)Argumentsntseq Sequence(5’to3’)of one strand of the DNA nucleic acid duplex as string or vector of characters(Note:Non-DNA characters are ignored by this method).ambiguous Ambiguous bases are taken into account to compute the G and C content when ambiguous is TRUE.outlist output a list of Tm and options or only Tm value,default is TRUE.Author(s)Junhui LiReferencesThein S L,Lynch J R,Weatherall D J,et al.DIRECT DETECTION OF HAEMOGLOBIN E WITH SYNTHETIC OLIGONUCLEOTIDES[J].The Lancet,1986,327(8472):93.Examplesntseq=c( acgtTGCAATGCCGTAWSDBSY )#for wallace ruleout<-Tm_Wallace(ntseq,ambiguous=TRUE)outout$OptionsIndexc2s,2check_filter,3chem_correction,4complement,5GC,6print.TmCalculator,6s2c,7salt_correction,8Tm_GC,9Tm_NN,12Tm_Wallace,1517。
急性前庭综合征-中国心脑血管病网
急性前庭综合征田军茹急性前庭综合征(AVS)是一组以急性眩晕为主要症状,或伴恶心呕吐眼震,步态不稳的临床综合征。
急性起病,自发性眩晕可在数秒、数分、数小时之内发展至高峰。
持续时间超过24小时,大多为数天,也有数周者。
也有人称之为急性持续性眩晕(Kerber 2013)。
患者通常难以忍受头动带来的眩晕加重,多不伴其他神经系统体征。
AVS分为外周性和中枢性。
外周性AVS(P-AVS)主要累及外周性前庭结构,如内耳及前庭神经,约占75% 。
中枢性AVS(C-AVS)主要累及中枢性前庭结构,如脑干小脑等处,约占20%。
AVS的主要鉴别诊断AVS定义为≥24小时的持续性眩晕。
持续数秒,数分钟或数小时的发作性眩晕疾病排除在外。
梅尼埃和前庭性偏头痛分别只有12%和27%几率(持续时间>24小时)。
AVS鉴别诊断重点集中在炎症和卒中。
炎症主要为前庭神经元炎和迷路炎,卒中主要为累及小脑以及脑干的卒中。
小脑梗死小脑梗死也叫假性前庭神经元炎(PVN)。
孤立性小脑梗死孤立性小脑梗死如不伴其他神经神经系统症状体征,诊断很具挑战性。
临床可见:①孤立性小结叶梗死;②未累及延髓的PICA梗死;③小脑下部梗死,约占25%,大多累及PICA 内侧支;④小型梗死为大多数;⑤半球哑区为少数。
虽梗死灶大但无明显神经系统体征,称之为无体征梗死;⑥缺血性梗死虽为多数,但小脑小量出血比较局限,也可以孤立性眩晕为主要表现;⑦小脑蚓部周围小量出血可出现CPPV(类似BPPV的位置性眩晕和眼震)和孤立性小结叶梗死。
病史特点起病突然且急骤,在很短时间内达到高峰,大多病人有血管性或卒中性风险因素,发病前3个月或4周内可能有反复短暂性眩晕发作,急性眩晕持续超过24小时,不易在数天内缓解,数天后无改善或有恶化趋势者需严密观察病情发展,以免梗死后的水肿造成严重后果。
临床表现特点1.自发性眼震:小脑梗死呈中枢源性特征。
眼震方向随注视方向不同而改变,或出现纯垂直性,纯旋转性眼震,以及自发性眼震不能被固视抑制。
【供应链金融风险研究国内外文献综述2300字】
供应链金融风险研究国内外文献综述1 国外研究现状从上世纪八十年代开始供应链金融的定义逐步被人们关注,国外涉及到供应链金融的思想观点与实践的应用相对成熟,对其定义的内涵外延比国内更为广泛,包括基于债券、股票等金融衍生商品这类动产质押业务风险研究、供应链金融的契约设计等方面。
M.Theodore,Paul D.Hutchison(2002)提出了供应链风险及其管理的相关概念,现金流管控是供应链金融领域十分关键的内容,供应链风控中的核心就是成功的现金流管控。
Cossin and Hricko(2003)基于企业违约概率与质押物价值,研究了具有价格风险商品作为质押的风险工具,质押物有助于进一步缓释银行信贷风险的作用。
Jimenez and Saurina(2004)研究了资产支持信贷中风险的影响因素包括质押物、银行(借款人)类型以及银行企业的关系等,合理的质押率有效缓释风险暴露,减少银行信贷损失。
Menkhoff,Neuberger and Suwanaporn(2006)的研究表明在不同国家,质押物对风险缓释的作用不同,质押物缓释风险的作用在发展中国家比发达国家显得更为重要。
Martin R(2007)系统分析了供应链资金流管控成本和危机、提升资金流效益的具体情况,指出根据供应链金融可让资金管理更加高效,但要严苛控制相应风险。
Lai and Debo(2009)对有资金局限的供应链存货中的相应问题进行了分析,通过库存契约设计能有效识别供应链上下游风险因子,从而提高供应链库存风险评价的准确性。
Hamadi和Matoussi(2010)根据剖析Logistic模型BP技术评估供应链金融风险的具体状况,表面三层BP神经网络模型在对上市房地产公司风险评价方面具有更好的准确性。
Qin and Ding(2011)分析了供应链金融领域里的风险变化现象,根据相应的风险迁徙模型,基于符合供应链金融条件,降低了借贷与信贷的风险。
张方迤简历
简历张方迤, 医学博士I. 个人信息A. 个人资料:1. 出生日期: 1962年10月13日2. 出生地点: 中国湖北武汉3. 目前国籍: 美国B. 教育程度:1980-1986 北京首都医科大学医学博士毕业C. 医学专科培训经历:2005-2006 住院后研究生--骨科和神经外科--关节和脊柱病学项目--西雅图华盛顿大学2003-2005 住院医师和神经外科总住院医师--西雅图华盛顿大学1999-2003 神经外科住院医师--圣安东尼奥德州大学健康中心1998-1999 普外实习生--圣安东尼奥德州大学健康中心1995-1998 助理研究员--明尼苏达大学神经外科脑血管生理和中风研究实验室1991-1995 博士后研究生--明尼苏达大学神经外科脑血管生理和中风研究实验室l986 - l991 神经外科住院医师--北京天坛医院和神经外科研究所神经外科D. 学术经历:2013 –现在副教授--西雅图华盛顿大学Harborview医疗中心神经外科、骨科、运动医学2010 – 2013 助教--西雅图华盛顿大学Harborview医疗中心骨科、运动医学2009 – 2013 助教--西雅图华盛顿大学Harborview医疗中心神经外科2006 – 2009 助教--德州圣安东尼奥德州大学健康中心神经外科2005 – 2006 教员--西雅图华盛顿大学神经外科1995 – 1998 助教--明尼苏达大学神经外科脑血管生理和中风研究实验室 1991 神经外科主治--北京天坛医院和神经外科研究所神经外科E. 证书及行医许可:1. 学会认证考试: 美国神经外科学会认证医师(# 32071)2. 许可: 华盛顿州内、外科(MD00045051)德州医疗学会内科(M8452)3. E.C.F.M.G. Certificate #0-564-651-8美国外国医学生毕业生教育委员会(无限期有效)F. 荣誉及奖励:1993, 1994 美国心脏协会奖学金--明尼苏达大学1987, 1988 杰出年度医师--北京天坛医院和神经外科研究所II 出版:Wang CC. Wu Z. Zhao J. Li J. Zhang Z. Zhang F. Dong J. Dong W. Long J. Song Y. Wang S. Zhang X. Analysis of the prognostic factors following hypertensive intracerebral hemorrhage. Chinese Journal of Neurosurgery. 6 (suppl):73-76, 1990.高血压患者颅内出血预后因素的分析/中国神经外科杂志Wang CC. Zhao J. Li J. Zhang F. Sylvian fissue arteriovenous malformation: Report of 50 cases and review. Chinese Journal of Neurosurgery. 7(1):1-3, 1991.脑侧裂动静脉血管畸形/中国神经外科杂志Zhang F. Iadecola C. Stimulation of the fastigial nucleus enhances EEG recovery and reduces tissue damage after focal cerebral ischemia. Journal of Cerebral Blood Flow & Metabolism. 12(6):962-70, 1992.局灶性脑缺血后顶核刺激增强脑电波复原和减少组织损害/脑血流量和新陈代谢杂志Zhang F. Iadecola C. Fastigial stimulation increases ischemic blood flow and reduces brain damage after focal ischemia. Journal of Cerebral Blood Flow & Metabolism. 13(6):1013-9, 1993. 局灶性脑缺血后小脑刺激增强缺血性血供和减少脑部损伤/脑血流量和新陈代谢杂志Iadecola C. Zhang F. Xu X. Cerebrovasodilation elicited by fastigial stimulation is preserved under deep halothane anesthesia. American Journal of Physiology. 265(1 Pt 2):R187-94, 1993. 深度氟烷麻醉对小脑刺激引起的脑血管舒张起到保护作用/美国生理学杂志Zhang F. Iadecola C. Nitroprusside improves blood flow and reduces brain damage after focal ischemia. NeuroReport. 4(5):559-62, 1993. 局灶性脑缺血后用硝普盐改善血供和降低脑损伤/神经外科报告Iadecola C. Zhang F. Xu X. Role of nitric oxide synthase-containing vascular nerves in cerebrovasodilation elicited from cerebellum. American Journal of Physiology. 264(4 Pt 2):R738-46, 1993.Iadecola C. Beitz AJ. Renno W. Xu X. Mayer B. Zhang F. Nitric oxide synthase-containing neural processes on large cerebral arteries and cerebral microvessels. Brain Research. 606(1):148-55, 1993.一氧化氮合成酶-包含大脑动脉和微血管的神经系统处理/大脑研究Iadecola C. Xu X. Zhang F. Hu J. el-Fakahany EE. Prolonged inhibition of brain nitric oxide synthase by short-term systemic administration of nitro-L-arginine methyl ester. Neurochemical Research. 19(4):501-5, 1994.通过硝基精氨酸甲酯的短期系统性行为来延迟大脑一氧化氮合成酶的抑制/神经化学研究Iadecola C. Zhang F. Xu X. SIN-1 reverses attenuation of hypercapnic cerebrovasodilation by nitric oxide synthase inhibitors. American Journal of Physiology. 267(1 Pt 2):R228-35, 1994.通过一氧化氮合成酶抑制剂使高碳酸脑血管舒张的SIN-1型逆衰变/美国生理学杂志Zhang F. Iadecola C. Reduction of focal cerebral ischemic damage by delayed treatment with nitric oxide donors. Journal of Cerebral Blood Flow & Metabolism. 14(4):574-80, 1994. 一氧化氮供体的延迟治疗降低大脑缺血性伤害/脑血流量和新陈代谢杂志Iadecola C. Zhang F. Nitric oxide-dependent and -independent components of cerebrovasodilation elicited by hypercapnia. American Journal of Physiology. 266(2 Pt 2):R546-52, 1994. 脑血管舒张的一氧化氮依赖和非依赖成分引起血碳酸过多症/美国生理学杂志Zhang F. White JG. Iadecola C. Nitric oxide donors increase blood flow and reduce brain damage in focal ischemia: evidence that nitric oxide is beneficial in the early stages of cerebral ischemia. Journal of Cerebral Blood Flow & Metabolism. 14(2):217-26, 1994.Zhang F. Xu S. Iadecola C. Role of nitric oxide and acetylcholine in neocortical hyperemia elicited by basal forebrain stimulation: evidence for an involvement of endothelial nitric oxide. Neuroscience. 69(4):1195-204, 1995.Zhang F. Xu S. Iadecola C. Time dependence of effect of nitric oxide synthase inhibition on cerebral ischemic damage. Journal of Cerebral Blood Flow & Metabolism. 15(4):595-601, 1995.Iadecola C. Zhang F. Xu S. Casey R. Ross ME. Inducible nitric oxide synthase gene expression in brain following cerebral ischemia. Journal of Cerebral Blood Flow & Metabolism. 15(3):378-84, 1995. Iadecola C. Zhang F. Xu X. Inhibition of inducible nitric oxide synthase ameliorates cerebral ischemic damage. American Journal of Physiology. 268(1 Pt 2):R286-92, 1995.Iadecola C. Xu X. Zhang F. el-Fakahany EE. Ross ME. Marked induction of calcium-independent nitric oxide synthase activity after focal cerebral ischemia. Journal of Cerebral Blood Flow & Metabolism. 15(1):52-9, 1995.Iadecola C. Zhang F. Permissive and obligatory roles of NO in cerebrovascular responses to hypercapnia and acetylcholine. American Journal of Physiology. 271(4 Pt 2):R990-1001, 1996.Iadecola C. Zhang F. Casey R. Clark HB. Ross ME. Inducible nitric oxide synthase gene expression in vascular cells after transient focal cerebral ischemia. Stroke. 27(8):1373-80, 1996.Zhang F. Casey RM. Ross ME. Iadecola C. Aminoguanidine ameliorates and L-arginine worsens brain damage from intraluminal middle cerebral artery occlusion. Stroke. 27(2):317-23, 1996.Iadecola C. Zhang F. Casey R. Nagayama M. Ross ME. Delayed reduction of ischemic brain injury and neurological deficits in mice lacking the inducible nitric oxide synthase gene. Journal of Neuroscience. 17(23):9157-64, 1997.Zhang F. Eckman C. Younkin S. Hsiao KK. Iadecola C. Increased susceptibility to ischemic brain damage in transgenic mice overexpressing the amyloid precursor protein. Journal of Neuroscience. 17(20):7655-61, 1997.Nogawa S. Zhang F. Ross ME. Iadecola C. Cyclo-oxygenase-2 gene expression in neurons contributes to ischemic brain damage. Journal of Neuroscience. 17(8):2746-55, 1997.Nagayama M. Zhang F. Iadecola C. Delayed treatment with aminoguanidine decreases focal cerebral ischemic damage and enhances neurologic recovery in rats. Journal of Cerebral Blood Flow & Metabolism. 18(10):1107-13, 1998.Zhang F. Iadecola C. Temporal characteristics of the protective effect of aminoguanidine on cerebral ischemic damage. Brain Research. 802(1-2):104-10, 1998.Nogawa S. Forster C. Zhang F. Nagayama M. Ross ME. Iadecola C. Interaction between inducible nitric oxide synthase and cyclooxygenase-2 after cerebral ischemia. Proceedings of the National Academy of Sciences of the USA. 95(18):10966-71, 1998.Zhang F. Slungaard A. Vercellotti GM. Iadecola C. Superoxide-dependent cerebrovascular effects of homocysteine. American Journal of Physiology. 274(6 Pt 2):R1704-11, 1998.Tsekos NV. Zhang F. Merkle H. Nagayama M. Iadecola C. Kim SG. Quantitative measurements of cerebral blood flow in rats using the FAIR technique: correlation with previous iodoantipyrine autoradiographic studies. Magnetic Resonance in Medicine. 39(4):564-73, 1998.Yang G. Feddersen RM. Zhang F. Clark HB. Beitz AJ. Iadecola C. Cerebellar vascular and synaptic responses in normal mice and in transgenics with Purkinje cell dysfunction. American Journal of Physiology. 274(2 Pt 2):R529-40, 1998Iadecola C. Zhang F. Niwa K. Eckman C. Turner SK. Fischer E. Younkin S. Borchelt DR. Hsiao KK. Carlson GA. SOD1 rescues cerebral endothelial dysfunction in mice overexpressing amyloid precursor protein. Nature Neuroscience. 2(2):157-61, 1999.Iadecola C. Salkowski CA. Zhang F. Aber T. Nagayama M. Vogel SN. Ross ME. The transcription factor interferon regulatory factor 1 is expressed after cerebral ischemia and contributes to ischemic brain injury. Journal of Experimental Medicine. 189(4):719-27, 1999.Gerzanich V. Zhang F. West GA. Simard JM. Chronic nicotine alters NO signaling of Ca(2+) channels in cerebral arterioles. [Journal Article] Circulation Research. 88(3):359-65, 2001.Zhang F, Sprague SM, Farrokhi F, Henry MN, Son MG, Vollmer DG HYPERLINK "/pubmed/12405388" Reversal of attenuation of cerebrovascular reactivity to hypercapnia by a nitric oxide donor after controlled cortical impact in a rat model of traumatic brain injury. J Neurosurg. 2002 Oct;97(4):963-9.Kerby JD, Sainz JG, Zhang F, Hutchings A, Sprague S, Farrokhi FR, Son M. HYPERLINK"/pubmed/17505305" Resuscitation from hemorrhagic shock with HBOC-201 in the setting of traumatic brain injury. Shock. 2007 Jun;27(6):652-6.Bransford R. Zhang F. Bellabarba C. Konodi M. Chapman J. Early experience with treatment of thoracic disc herniation using a modified transfacet pedicle-sparing decompression and segmental fusion. J Neurosurg Spine, 2010 Feb;12(2):221-31.Bransford RJ, Zhang F, Bellabarba C, Lee MJ. Treating thoracic-disc herniations: Do we always have to go anteriorly? Evid Based Spine Care J. 2010 May;1(1):21-8.胸椎间盘突出的治疗:是否总是前路?/ Evid基准脊柱养护Bellabarba C, Zhang F, Wagner T. Controversies in TL classifications. What are we actually treating? Some perspectives on the evolution of spine fracture classification systems. Unfallchirurg. 2012 Dec;115(12):1056-60.TL分类的争议。
美敦力起搏器的选择
SAVEPACe试验给我们带来的启示
仅仅有10%的病人植入了带MVP功能的双腔起搏器,而 且这部分病人的随访期也是最短的,所以我们有足够的理由 相信如果植入更多具有MVP功能的起搏器,如果随访时间再 长一些,得到的结果将更加具有显著性。
此次试验中,通过“心脏指南针” (Cardiacompass) 记录的数据成为诊断房颤的标准之 一,说明“心脏指南针”(Cardiacompass)可以
自动诊断功能
10项
临床医生可选择明细 9项
加强型的房性心律失常诊断报告
心脏指南针(Cardiac Compass)
诊断功能
EnPulse™
代表起搏疗法最新 临床和技术进展的
巅峰之作!
EnPulse的主要组成部分
起搏安全网
生理性起搏
ACM心房阈值管理
Enhanced VCM 增强型心室阈植管理
Sensing Assurance 感知保障
心衰风险增加54%
Sweeney MO, et al. Circulation 2003;23:2932-2937
6060
Risk of HFH relative to DDDRRipskatoief nHtFwHirtehlaCtuimve%toVP=0 DDDR patient with Cum%VP=0
RRiisskk ooff HHFFHH rreellaattiivvee ttoo DDDDDDRR ppaattiieenntt wwiitthh CCuumm% %VVPP==00
8080
101000
77
66
44
33
22
11
00
00
2200
40
血红素加氧酶-1与肾脏损伤
血红素加氧酶-1与肾脏损伤麦海星;陈立军;陈彪;游华;张旭【摘要】血红素加氧酶-1(HO-1)作为哺乳动物降解血红素的主要途径之一,其主要降解产物——一氧化碳(CO)、自由铁(Fe2+)和胆绿素——在促进细胞生存、细胞内物质循环及免疫调节中具有重要作用.既往研究提示血红素-HO-1通路是决定急性肾损伤(AKI)易感性及严重性的重要内在因素.诱导HO-1表达能够减轻肾脏缺血-再灌注损伤(IRI)的严重程度,而抑制HO-1的表达会加重IRI.本文综述了国内外有关HO-1在AKI诱导保护机制方面的最新研究进展,以便深入认识HO-1在AKI治疗中的作用.%Heme oxygenase-1 (HO-l) is one of the main pathways to degrade heme in mammals, and the main degradation products are free iron (Fe2+), carbon monoxide (CO), and bilirubin. Heme plays an important role in promoting cell survival, circulation of intracellular substrates, and immune regulation. Previous studies suggest that the heme-HO-1 pathway is an important internal factor in determining the susceptibility and severity of acute kidney injury (AKI). The induction of HO-l expression can attenuate the severity of renal ischemia-reperfusion injury (IRI), and the inhibition of HO-l expression will accentuate the injury of IRI. The present article summarizes the latest advances in research into HO-l regarding the protective mechanism on AKI conducted abroad and at home to learn more information about HO-l in the treatment of AKI.【期刊名称】《解放军医学杂志》【年(卷),期】2012(037)002【总页数】4页(P156-159)【关键词】血红素加氧酶-1;肾疾病;再灌注损伤【作者】麦海星;陈立军;陈彪;游华;张旭【作者单位】100071北京解放军307医院泌尿外科;100071北京解放军307医院泌尿外科;100071北京解放军307医院泌尿外科;100071北京解放军307医院淋巴瘤及头颈部肿瘤科;100853北京解放军总医院泌尿外科【正文语种】中文【中图分类】R692.9临床上引起急性肾损伤(acute kidney injury, AKI)的原因主要是各种因素(包括肾脏原发疾病及系统性疾病)导致的肾脏缺血再灌注损伤(ischemiareperfusion injury, IRI),以及由此导致的急性肾小管坏死和肾脏功能紊乱。
血浆N-末端B型利钠肽原对ICU重症患者预后的预测价值
血浆N-末端B型利钠肽原对ICU重症患者预后的预测价值王婷;王敏【摘要】目的探讨血浆N-末端B型利钠肽原(NT-proBNP)水平对ICU重症患者预后的预测价值.方法回顾2011年10月~2012年4月北京市仁和医院ICU收治的137例患者临床资料,患者入院即时行急性生理和慢性健康状况Ⅱ(APACHEⅡ)评分,并于入ICU第1天、第2天、第3天、第5天、第7天采用酶联免疫吸附法(ELISA)测定血浆NT-proBNP水平,计算患者28 d病死率,分析血浆NT-proBNP 与APACHEⅡ对患者预后的预测价值.结果 137例患者28 d病死率为31.39%(43/137),94例存活(存活组),43例死亡(死亡组).死亡组严重感染患者多,APACHE Ⅱ评分高(P < 0.05).存活组与死亡组血浆NT-proBNP水平均于入ICU第2天到达峰值;死亡组第2天、第3天、第5天和第7天血浆NT-proBNP 水平均显著高于存活组(P < 0.05).多因素分析显示,NT-proBNP> 1 565.2 ng/L 及APACHEⅡ评分均可预测患者28 d病死率(P < 0.05).结论 NT-proBNP> 1 565.2 ng/L及APACHEⅡ评分是ICU重症患者28 d病死率的独立预测因子,NT-proBNP> 1 565.2 ng/L及APACHEⅡ评分越高,提示患者预后不良,可为临床诊断及指导治疗提供借鉴.%Objective To investigate the value of plasma N-terminal probrain natriuretic peptide (NT-pro-BNP) in predicting the prognosis of critically ill patients in ICU. Methods A total of 137 patients in ICU of Beijing Renhe Hospital from October 2011 to April 2012 were evaluated by using acute physiology and chronic health evaluation II (APACHE II), and the level of plasma NT-proBNP was determined on the first day, second day, third day, fifth day and seventh day after admitted into ICU by using ELISA, the 28-day mortality was calculated, then thevalues of plasma NT-proBNP and A-PACHE H score in predicting the prognosis of patients were also analyzed. Results The 28-day mortality of patients was 31.39% (43/137), 94 patients were survival (survival group) and 43 patients were non-survival (non-survival group). The patients in non-survival group were older than the surviva group,suffered more severe infection, and had higher APACHEH score than the survival group (P <0.05). Both survival group and non-survival group had the highest level of plasma NT-proBNP on the second day after admitted into ICU; the level of plasma NT-proBNP on the second day, third day, fifth day and seventh day after admitted into ICU of non-survival group were higher than those of survival group (P < 0.05). Multiplicity analysis showed that, both the level of plasma NT-proBNP>l 565.2 ng/L and APACHE I[ score could predict the 28-day mortality (P < 0.05). Conclusion The level of plasma NT-proBNP> 1 565.2 ng/L and APACHE I score are the independent predictive factors for the 28-day mortality of critically ill patients in ICU, NT-proBNP> 1 565.2 ng/L and higher APACHE II score indicate poor prognosis for critically ill patients, which can provide reference for the clinical diagnosis and guiding treatment.【期刊名称】《中国医药导报》【年(卷),期】2012(009)033【总页数】3页(P54-56)【关键词】重症监护室;N-末端脑钠肽前体;预后;预测【作者】王婷;王敏【作者单位】北京市仁和医院重症监护科,北京,102600;北京市仁和医院重症监护科,北京,102600【正文语种】中文【中图分类】R446.11B型利钠肽(B-type natriuretic peptide,BNP)是一种主要由心室肌细胞分泌的多肽类心脏神经激素,具有扩张血管、拮抗肾素-血管紧张素-醛固酮系统、抑制交感神经系统、促进尿钠排泄、减少水钠潴留等作用[1],是一种重要的心脏标志物,同时也存在于脑组织中。
Stock_Price_Forecasting_Based_on_the_MDT-CNN-CBAM-
Theory and Practice of Science and Technology2022, VOL. 3, NO. 6, 81-90DOI: 10.47297/taposatWSP2633-456914.20220306Stock Price Forecasting Based on the MDT-CNN-CBAM-GRU Model: An Empirical StudyYangwenyuan DengBusiness School, University of New South Wales, Sydney 1466, AustraliaABSTRACTRecently, more researchers have utilized artificial neural network topredict stock price which has the characteristic of time series. This paperproposes the MDT-CNN-CBAM-GRU to forecast the close price of theshares. Meanwhile, three models are set as comparing experiment. CSI300 index and MA 5 are added as new price factors. The daily historicaldata of China Ping An from 1994 to 2020 is utilized to train, validate andtest models. The results of the experiment prove MDT-CNN-CBAM-GRU isthe optimal and GRU has better performance than LSTM. Thus, MDT-CNN-CBAM-GRU can effectively predict the closing price of one stock whichcould be a reference for investing decision.KEYWORDSStock price; Deep learning; Gated Recurrent Unit (GRU); Multi-directionalDelayed Embedding (MDT); Convolutional Block Attention Module(CBAM)1 IntroductionWith the development of Chinese stock market, investors realize the great significance in stock price prediction [1]. Due to the volatility and complexity of stock market, shares prediction contains multi-dimensional variables and massive time-series data [2]. Traditional methods have several shortages such as inefficiency, subjectivity, and poor integrity of inventory content information. To resolve these shortages, artificial intelligence have been introduced to this area. Machine learning such as deep learning, decision trees and logistic regression have emerged in financial data research [3-5].Deep learning is a new branch of machine learning which transfer the low-level feature to high-level feature to simplify learning task [6]. The CNN-LSTM model is a classic model of the deep learning. It has been widely used in different area due to its better performance and prediction accuracy compared with single models [7-8]. Zhao and Xue prove the CBAM module could improve the performance of CNN-LSTM [9]. Cao et al. innovatively applied the multi-directional delayed embedding (MDT) to transform price factor which contributes to the generalization and time-sensitization of forecasting results [10].Based on the CNN-LSTM model, this paper proposes MDT-CNN-CBAM-GRU model. In this experiment, Jupyter notebook is the program platform, and Keras of TensorFlow is used as the neural framework to build model. The experimental data includes the share price factors of ChinaYangwenyuan Deng 82Ping An 1. This experiment will verify the effectiveness of CBAM module and MDT module. Meanwhile, the performance of GRU is compared with LSTM include the time efficiency and prediction errors. Three evaluation indexes are used to present the prediction results.2 Related WorkRecently, machine learning has become a hot spot in financial areas [11]. Artificial neural network (ANN) has been proved as a feasible tool to forecast complex nonlinear statistics while the time efficiency of neural networks is low [12]. In addition, gradient vanishing and local optimal solution affect the further development of ANN model. Based on ANN, recurrent neural network (RNN) was proposed which would memorize short part information of previous stage [13]. In 2014, gated recurrent unit (GRU) is proposed by Cho et al. as a variant of LSTM [14-15]. LSTM and GRU could address the gradient vanishing issue of RNN.Lecun et al. propose the Convolutional Neural Network in 1988 which is a feedforward neural network to solve time series issues [16-17]. CNN-LSTM is widely used in time financial area and further research have been taken to improve it.The first method to improve model is building more complex models. Wang et al. state the CNN-BiSLSTM model has better forecasting accuracy than CNN-LSTM [18]. Kim T and Kim HY prove that CNN-LSTM model combined with stock price features is more effective [19]. Dai et al. proposed a Dual-path attention mechanism with VT-LSTM which improve the model accuracy [20].Price factors selection and pre-processing is another direction to improve models. Zhang et al. add industry factor as model inputs which contributes to better prediction results [21]. The research of Kang et al. proves the self-attention input contributes smaller prediction error [22]. Yu et al. verified that the amount of training samples affects the effectiveness and accuracy of deep learning models [23].3 MDTThe traditional data processing method for the deep learning is the sliding window method [24]. It divides a time series into multiple consecutive subsequences of length along the time step. The two-dimensional time series matrix will be divided it into multiple fixed-size sub-matrices as the inputs of deep learning.The sliding windows fails to consider the correlations of multidimensional time series. To solve this issue, this paper introduces the multi-directional delayed embedding (MDT) tensor processing technology. Shi et al. combine the MDT method and ARIMA model to prove MDT will improve the accuracy of model [25].MDT method will transform daily stock factor vector x=(x1,x2,…,x n),T∈R n into a Hankel matrix M(x) shown in Figure 1:τ1 China Ping An Insurance (Group) Co., Ltd. (hereinafter referred to as "Ping An",) was born in Shekou, Shenzhen in 1988. It is the first joint-stock insurance enterprise in China, and has developed into an integrated, close and diversified comprehensive financial service group integrating financial insurance, banking, investment and other financial businesses.Theory and Practice of Science and Technology The MDT operation can be represented by following formula:M τ(x )=fold (n ,τ)(Cx )Function fold (n ,τ):R τ×(n -τ+1)→R τ×(n -τ+1)is a folding operator that converts vectors into a matrix. Set the Hankel matrix M τ(x )=(v 1,v 2,…v n -τ+1), where v i represents the number i column vector of the Hankel matrix:vi =(xi ,xi +1,…xτ)T 4 CNN-CBAM-GRU(1) CNNCNN is widely used in time series data prediction because of its good performance and time saving. CNN includes pooling layers which transform the data to reduce the feature dimension:l t =tanh (x t *k t +b t )Where l t represent the output of after convolution neural network, x t represents the input vector, k t represents the weight of the convolution kernel, b t is the convolution kernel bias, and tanh is the activation function.(2) CBAMSanghyun et al. introduce the Convolutional Block Attention Module in 2018 which is a simple and effective module which has been widely used in CNN model [26]. The overview of CBAM is presented in Figure 2:The technological process can be concluded as:F 1=Mc (F )⊗F ,F 2=Ms (F 1)⊗F 1,F represents the input which is intermediate feature map F ∈R C ×H ×W . Mc ∈R C ×1×1is a 1D channel attention map and Ms ∈R 1×H ×W is a 2D spatial attention map. ⊗ is the element-wise multiplication which broadcasts the attention values.Channel attention module compress the spatial feature dimension of the input by utilizing the Figure 1 The transformed Hankel matrixFigure 2 The schematic diagram of CBAM 83Yangwenyuan Deng Avg Pooling and Max Pooling at the same time:Mc (F )=σ(MLP (AvgPool (F ))+MLP (MaxPool (F )))=σ(W 1(W 0(F c avg )))+W 1(W 0(F ))Where W 0∈R cr ×c ,W 1∈r cr ×c . the Spatial Attention Module address the issue of where the efficient information area is by aggregating two pooling operations to generate two 2D maps:Mc (F )=σ(f 7×7([AvgPool (F )]))MLP (MaxPool (F ))=σ(f 7×7[F s avg ,F s max ])(3) GRUGRU merge input gate and forget gat into an update gate to improve the efficiency of training while maintain the model accuracy [27]. GRU has two gate structure which respectively are update gate and reset gate. The overview of GRU is presented in Figure 3:1) r t represent the reset gate which controls the amount of the information needed to be forgotten in previous hidden layer h t -1.2) The update gate Z t control the extent to which the information of previous status is brought into current status h ~t .3) W is the weight matrix, b is the bias vector, [h t -1,x t ] represents the connection of the two vectors. σ and tanh are the sigmoid or hyperbolic tangent functions.The process of GRU could be summarized as follow:Z t =σ(W z ⋅h t -1+W z ⋅x t ),rt =σ(W r ⋅ht -1+W r ⋅x t ),h ~t =tanh (W h ~⋅(r t ⊙h t -1)+W h ~⋅x t ),h t =(1-Z t )⊙h t -1+Z t ⊙h t Where ⋅ represents matrix multiplication, and ⊙ represents matrix corresponding elementmultiplication.Figure 3 Gated Recurrent Unit 84Theory and Practice of Science and Technology (4) CNN-CBAM-GRU training and prediction process1) Standardized inputs: Before the MDT process, data of each column have been processed with Z-score normalization:z i =x i -μσ2) Where μ is the mean, σ is the standard deviation. Then, the normalized data will be transferred to Hankel matrices by MDT.3) Network Initialization: initialize the weights and biases of CNN-CBAM-GRU layers.4) CNN layers: through CNN layers, the key features of Hankel matrices are drawn as the input for later layers.5) CBAM module: The CBAM module will further process the features.6) GRU layers: the processed data are used by GRU to predict the close price.7) Output layer: full connection layers utilize the outputs of GRU to calculate the weight of model.8) Prediction result test and circulation: Judge whether the validation loss reduce after training. Return to step 3 until finish all epochs.9) Saving the best model: If validation loss of this epoch is smaller than the previous stored one, save current model as the best model in the experiment folder.10) Load the best model: load the model structure and weight.11) Prediction and denormalization: utilize the weight of best model to predict the test set close price. The prediction result will be denormalized and compared with true value.12) Experiment result: visualize the result and present the evaluation index results.5 Experiments(1) Experimental EnvironmentA notebook computer, equipped with NVIDIA GeForce GTX 1060 6G and Intel 8750H, implements all experiments. Python 3.9 is the programming language. Anaconda with Jupyter notebook is used as the program platform and Keras built in TensorFlow package construct the neuralnetworkFigure 4 The process of model 85Yangwenyuan Deng structure.(2) Experimental DataChina Ping An price factors is used as experimental data and the close price is the forecasting target. The experimental data contain 6000-day price data from 1994 to 2020 downloaded from the Baostock. Total data is divided into 3 parts: 80% for train set and 10% for both validation set and test set. This paper innovatively takes the CSI 300 index and moving average 5 as price factors. There is total 11 parameters to forecast the close price which is presented in Table 1:(3) Model ImplementationEvery model will independently run for 15 times to find the optimal weights. This paper chooses three evaluation indexes, respectively root mean square error (RMSE), mean absolute error (MAE), and R-square (R2) to evaluate the performance of different models. The formulas of them are calculated as follows:MAE =1n i =1n ||||||y ^i-y i ,RMSE =R 2=1-(∑i =1n (y ^i -y i )2)/n (∑i =1n ()y ^i -y i 2)/n ,Where y ^i represent the prediction value of models and y i is the true value. The closer value of MAE and RMSE to 0 indicates the better performance of model. The close value of R 2 to 1 represent the higher accuracy of model.(4) Implementation of MDT-CNN-CBAM-GRUThe pre-setting parameters of MDT-CNN-CBAM-GRU model are listed in Table 2.6 ResultsThe visual results are presented in Figure 5 to Figure 8. Where the orange line with * represent the prediction value of close price and the blue line represent the true value of close price.The evaluation index results of models are presented in Table 3:The average time for each step training is shown in Table 4:Table 1 Stock price factorsDate94-07Amount 1.165176e+07Volume 1385000Turn 0.51547Index 3.84893Open 0.41541PeTTM 12.58321PbMRQ 2.855036PctChg 3.026634Ma50.410159High 0.422353Low 0.42185886Theory and Practice of Science and TechnologyTable 2 Model parametersParametersConvolution layer filters Convolution layer kernel_size Convolution layer activation function MaxPooling2D pool_sizePooling layer paddingPooling layer activation function Dropout layersCBAM_attention reduce axisGRU layerskernel_regularizerNumber of hidden units in GRU layer 1 Number of hidden units in GRU layer 2 GRU layer activation function Dense layers kernel_initializer Dropout layers 2Learning rateTime_stepLoss functionBatch_sizeOptimizerEpochsValue643Relu2SameRelu0.232L2(0.01)12864Relu Random normal0.250.0011Mean square error64Adam200Figure 5 The prediction of CNN-LSTM8788Yangwenyuan Deng ArrayThe prediction of MDT-CNN-GRUFigure 6 The prediction of MDT-CNN-LSTMFigure 7 Theory and Practice of Science and Technology 7 ConclusionThe MDT-CNN-CBAM-GRU proposed has the optimal forecasting accuracy and satisfied time efficiency, which could provide reference for investors investing in share market.Compared with LSTM, GRU has better prediction accuracy and faster speed. However, here are some details to be improved in further research:(1) If time is enough, 30-time independent training for each model will be a better choice.(2) In further research, more experiment of GRU need to be conducted as the GRU has better performance compared with LSTM.(3) The generalization of models needs to be tested in future research by predicting different financial product such as funds, options and other stocks.About the AuthorYangwenyuan Deng, Master of Commerce in Finance of University of New South Wales, and his research field is Finance & Machine Learning.References[1] Meng, S., Fang, H. & Yu, D. (2020). Fractal characteristics, multiple bubbles, and jump anomalies in the Chinese stock market. Complexity, 2020: 7176598.[2] ABU-MOSTAFA, YS. & ATIYA, AF. (1996). Introduction to financial forecasting. Applied intelligence, 6: 205-213.[3] Huang, QP ., Zhou, X., Wei, Y & Gan, JY. (2015). Application of SVM and neural network model in the stock prediction research. Microcomputer and Application, 34: 88-90.[4] Chen, S., Goo, YJ. & Shen, ZD. (2014). A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements. The Scientific World Journal, 2014: 968712.[5] Fang, X., Cao, HY. & Li, XD. (2019). Stock trend prediction based on improved random forest algorithm. Journal of Hangzhou Dianzi University, 39: 25-30.[6] Zhang, QY., Yang, DM. & Hang, JT. (2021). Research on Stock Price Prediction Combined with Deep Learning and Decomposition Algorithm. Computer Engineering and Applications, 57: 56-64.[7] Luo, X. & Zhang, JL. (2020). Stock price forecasting based on multi time scale compound depth neural network. Wuhan Finance, 2020: 32-40.[8] Lu, W., Li, J., Li, Y., Sun, A. & Wang, J. (2020). A CNN-LSTM-Based Model to Forecast Stock Prices. Complexity, 2020: 6622927.[9] Zhao, HR. & Xue, L. (2021). Research on Stock Forecasting Based on LSTM-CNN-CBAM Model. Computer EngineeringTable 3 Evaluation index values of different modelsModelCNN-GRUMDT-CNN-LSTMMDT-CNN-GRUMDT-CNN-CBAM-GRU RMSE 0.32200.15980.09100.0890MAE 0.22680.13200.07940.0639R 20.94180.98660.99590.9959Table 4 Average training timeModelCNN-GRUMDT-CNN-GRUMDT-CNN-LSTMMDT-CNN-CBAM-GRU Time 2s 11ms / step 1s 9ms/ step 2s 10ms/ step 2s 10ms/ step 89Yangwenyuan Deng 90and Applications, 57: 203-207.[10] Cao, CF., Luo, ZN., Xie, JX. & Li, L. (2022). Stock Price Prediction Based on MDT-CNN-LSTM Model. ComputerEngineering and Applications, 58: 280-286.[11] Li, J., Pan, S., Huang, L. & Zhu, X. (2019). A machine learning based method for customer behavior prediction.Tehnicki Vjesnik Technical Gazette, 26: 1670-1676.[12] L¨angkvist, M., Karlsson, L. & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42: 11-24.[13] Sherstinsky, A. (2020). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM)network. Physica D: Nonlinear Phenomena, 404: 132306.[14] Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9: 1735–1780.[15] Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. & Bengio, Y. (2014). Learningphrase representations using RNN encoder-decoder for statistical machine translation. arXiv, 1406: 1078.[16] Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient based learning applied to document recognition.Proceedings of the IEEE, 86: 2278-2324.[17] Hu, Y. (2018). Stock market timing model based on convolutional neural network – a case study of Shanghaicomposite index. Finance& Economy, 4: 71-74.[18] Wang, HY., Wang, JX., Cao, LH., Sun, Q. & Wang, JY. (2021). A Stock Closing Price Prediction Model Based on CNN-BiSLSTM. Complexity, 2021: 5360828.[19] Kim, T. & Kim, HY. (2019). Forecasting stock prices with a feature fusion LSTM-CNN model using differentrepresentations of the same data. PLOS ONE, 14: 0212320.[20] Dai, YR., An, JX. & Tao, QH. (2022). Financial Time-Series Prediction by Fusing Dual-Pathway Attention with VT-LSTM.Computer Engineering and Applications. 6:10.[21] Zhang, YF., Wang, J. Wu, ZH. & L, YF. (2022). Stock movement prediction with dynamic and hierarchical macroinformation of market. Journal of Computer Applications, 6:7.[22] Kang, RX., Niu, BN., Li, X. & Miao, YX. (2021). Predicting Stock Prices Using LSTM with the Self-attention Mechanismand Multi-source Data. Journal of Chinese Computer Systems,12: 9.[23] Yu, SS., Chu, SW., Chan, YK. & Wang, CM. (2019). Share Price Trend Prediction Using CRNN with LSTM Structure.Smart Science, 7: 189-197.[24] Li, XF., Liang, X. & Zhou, XP. (2016). An Empirical Study on Manifold Learning of Sliding Window of Stock Price TimeSeries. Chinese Journal of Management Science, 24: 495-503.[25] SHI, Q., YIN, J. & CAI, J. (2020). Block Hankel tensor ARIMA for multiple short time series forecasting. Proceedings ofthe AAAI Conference on Artificial Intelligence, 34: 5758-5766.[26] Woo, S., Park, J., Lee, JY. & Kweon, S. (2018). CBAM:convolutional block attention module. Proceedings of theEuropean Conference on Computer Vision (ECCV), 2018: 3-19.[27] Dang, JW. & Cong, XQ. (2021). Research on hybrid stock index forecasting model based on CNN and GRU.Computer Engineering and Applications, 57: 167-174.。
高血压患者血清TNF-α、IL-6、C-RP水平检测及意义
高血压患者血清TNF-α、IL-6、C-RP水平检测及意义刘忠仁;谭志辉;黄显南;黄照河【摘要】目的探讨原发性高血压患者血清 TNF-α、IL-6、C-RP水平的变化与高血压的关系.方法选择原发性高血压组86例,正常对照组82例,正常对照组体检时采血,高血压组分别于洛伐他汀治疗前及治疗6周后采血.酶联免疫吸附双抗体夹心法检测TNF-α、IL-6水平,自动生化分析仪检测C-RP水平,高血压组与正常对照组比较,高血压组治疗前后比较.结果高血压组血清IL-6、TNF-α、C-RP水平明显高于对照组,差异均有显著性(P<0.001),高血压组洛伐他汀治疗6周后血清中TNF-α、IL-6、C-RP水平较治疗前明显降低,差异均有显著性(P<0.001).结论炎症可能参与原发性高血压的发生发展,洛伐他汀可抑制原发性高血压血管的慢性炎症反应.【期刊名称】《右江民族医学院学报》【年(卷),期】2011(033)003【总页数】3页(P264-266)【关键词】高血压;肿瘤坏死因子α;白细胞介素6;C反应蛋白质【作者】刘忠仁;谭志辉;黄显南;黄照河【作者单位】右江民族医学院附属医院心内科,广西,百色,533000;右江民族医学院附属医院心内科,广西,百色,533000;右江民族医学院附属医院心内科,广西,百色,533000;右江民族医学院附属医院心内科,广西,百色,533000【正文语种】中文【中图分类】R544.1原发性高血压是以体循环动脉压升高为主要表现,同时伴有血管内皮功能障碍、顺应性下降、血管阻力增加的临床综合征。
其病因和发病机制目前尚未完全明了[1~6],大量的研究表明,TNF-α、IL-6、C-RP等与高血压患者的血压水平相关,认为高血压是一种慢性炎症性疾病,但其触发因素不清楚。
笔者检测高血压患者血清TNF-α、IL-6、C-RP水平并与正常对照组比较,高血压患者洛伐他汀治疗前后比较,以期揭示炎症反应中TNF-α、IL-6、C-RP在高血压发生发展中的作用。
太平洋西部鳓鱼隐蔽种的界定及系统发育关系重建
基因组学与应用生物学,2020年,第39卷,第12期,第5481-5487页研究报告Research Report太平洋西部鳓鱼隐蔽种的界定及系统发育关系重建王倩李晨虹•上海海洋大学,水产种质资源发掘与利用教育部重点实验室,上海,201306* 通信作者,***********************摘要鳓鱼(/fe/m eZongoto)属于鲱形目(Clupeiformes),是锯腹鳓科的鱼类之一,鳓鱼目前在世界上主要分 布在太平洋西部和沙捞越爪哇海海域。
本研究报道了来自丹东和烟台的鳓鱼完整的线粒体基因组序列。
线粒 体基因组长度为16 809 bp,包括13个蛋白编码基因、22个tRNA、2个rRNA、12S rRNA、16S rRNA和控制 区D-l〇〇P基因。
本研究还对上述2个地点的6尾鳓鱼,利用完整的线粒体基因组以及非洲鳓作为外群重建 了系统发育关系。
结果显示,来自丹东和烟台的鳓鱼群体分成两个姊妹关系的单系群。
用物种界定的方法(基 于BEAST的G M Y C模型)对丹东鳓鱼群体有较好的划分,研宄显示丹东鳓鱼群体是太平洋西部的隐蔽种。
由此推断,丹东鳓鱼群体隐蔽种的形成很可能是更新世冰期海平面下降产生了地理隔离以及后续长时间的 基因交流障碍的结果。
关键词鳓鱼,隐蔽种,GMYC,线粒体基因组,系统发育关系Cryptic Species and the Phylogenetic Relationships of the Ilisha elonggta along the Northwestern Pacific CoastWang Qian Li Chenhong*Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, Shanghai, 201306*Corresponsingauthor,***********************DOI: 10.13417/j.gab.039.005481Abstract Ilisha elongate belongs to Clupeiformes,Pristigasteridae.It is mainly distributed in the western Pacific Ocean and the Sarawak Sea.Here we reported the complete mitogenome of Ilisha elongate,which were collected from Dandong and Yantai.The genome sequence is 16 809 bp in length,which comprises 13 protein-coding genes,22 tRNAs,2 rRNAs, 12S rRNA, 16S rNA and a control region.The phylogenetic relationships of6 Ilisha elongate from Dandong and Yantai were reconstructed based on the complete mitochondrial genome and the Ilisha africa as the outgroup.The results showed that the population of Dandong and Yantai were divided into two sisters monophyletic groups.We used BEAST Tree-based methods(GMYC)to infer the putative species group of Dandong.The phylogenetic analyses showed that the Dandong population is a cryptic species.Our findings suggested that species split between them was attributed to geographical isolation during lowing sea levels of ice ages and the barrier of gene flow of the ocean currents during interglacial period in the late Pliocene.Keywords Ilisha elongate,Cryptic species,GMYC,Mitochondrial genome,Phylogenetic relationship鳓鱼(/fc/rn efcngo/fl)属于緋形目(Clupeiformes),印度洋和彼得大帝湾的分布己经越来越少,尤其是是锯腹鳓科的鱼类之一,目前主要分布在太平洋西印度洋的分布,仅有一次记载(/3/部和沙捞越爪哇海海域(Blaberetal., 1998)。
冷环境运动对身体机能的影响
475即将到来的2022年北京冬奥会和冬残奥会,增加了人们参加冰雪运动的热情。
人们在冬季进行体育锻炼或从事冬季运动项目时,经常使身体暴露于冷环境中。
冷环境是在体育运动中经常遇到的特殊环境,对身体各项机能的影响有别于高温环境和常温环境。
一般而言,体育运动中的冷环境是指陆上温度低于12°C 的运动环境[1]。
那么在冷环境下运动或训练,会给身体机能的变化带来哪些独特特点呢?1 冷环境下运动对身体机能的影响1.1 体温调节人类暴露在寒冷环境中会引起特定的急性生理反应,包括寒颤和血管收缩。
这些反应可以减少热量损失以及增加代谢产热以维持机体热量平衡。
人体在大多数冷环境下的产热是由骨骼肌的收缩引起的,人们通过增加体力活动(如体育锻炼)或颤抖来启动这种产热过程。
颤抖是肌肉不自主地重复有节律收缩。
寒颤通常是机体暴露在寒冷环境时立即或在几分钟后发生的,是由皮肤温度降低引起的。
核心温度的下降对颤抖的刺激最大[2-3]。
长期在冷环境中训练,可以使寒颤阈值升高,并改善体温调节能力,从而增加机体的耐寒能力[1]。
Golden 等[4]的研究表明,经常在冷水中训练的游泳运动员能够更好地保持核心温度,这得益于血管收缩反应能力的增强。
冷环境中运动时机体的热量损失取决于运动强度、衣服的隔热效果等因素[5]。
机体在低强度运动时,热量流失较多。
随着运动强度增加,产热增多,可防止体温过低。
研究表明,在环境温度0℃、风速为10 km/h 的冷暴露期间,30%VO 2max (最大摄氧量)的低强度运动会导致体温下降[6],而70%VO 2max 的运动足以防止体温过低。
冷环境下流向皮肤及肢体末端血流量减少,易引发冻伤。
因此,运动时衣着的保暖,特别是肢体末端如头部、手部、足部的保暖很重要。
研究显示,在30%VO 2max 运动中,结合使用聚酯帽、轻质防风夹克和裤子是必要的,这些措施可以提高预热核心温度[7]。
1.2 心肺机能对呼吸道来说,干冷空气的高气压是一个重大的环境压力。
规范使用倍阻剂
β受体阻滞剂在冠心病的全程管理中的应用
急性期
STEMI1 NSTEMI2
目的:减少恶性心律失常,降低病死率
治疗方式:发病后24H内开始使用
出院前
病情稳定期1,2
出院后
稳定性冠心病3
1. 中华心血管病杂志, 2010, 38(8); 675-690 2. 中华心血管病杂志, 2012, 40(5); 353-367 3. 中华心血管病杂志, 2007, 35(3): 195-206
一次,并在未来2-3天
Management of
内剂量翻倍或更换成等
ST-Elevation
剂量的琥珀酸美托洛尔,
Myocardial Infarction 在患者可耐受的前提下,
剂量滴定至200mg
1.2007中国慢性稳定性心绞痛诊断与治疗指南 2. 2013 ACCF/AHA Guideline for the Management of ST-Elevation Myocardial Infarction
• 对BMC-2注册研究的资料进行了分析,该研究是一项大区域、多中心的大型队列研究, 纳入STEMI发作12小时内接受PCI的ACS患者(n=7667),根据PCI术前是否接受β受 体阻滞剂治疗分为2组(未接受BB治疗组n=2898,接受BB治疗组n=4769)。
Valle JA, et al. Am J Cardiol. 2013 ;111(12):1714-20.
专家共识推荐 --β受体阻滞剂在冠心病中的应用要点
中华心血管病杂志.2009.37(3).195-209
ACS患者PCI术前使用β受体阻滞剂不增加心衰和心 源性休克发生风险
- 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。
Hun-Taeg Chung Ki Mo Kim, Hyun-Ock Pae, Min Zheng, Raekil Park, Young-Myeong Kim andTriggered by Endoplasmic Reticulum StressLike Endoplasmic Reticulum Kinase and Inhibits Endothelial Cell Apoptosis−Carbon Monoxide Induces Heme Oxygenase-1 via Activation of Protein Kinase R ISSN: 1524-4571Copyright © 2007 American Heart Association. All rights reserved. Print ISSN: 0009-7330. OnlineTX 72514Circulation Research is published by the American Heart Association. 7272 Greenville Avenue, Dallas,doi: 10.1161/CIRCRESAHA.107.15478120072007, 101:919-927: originally published online September 6,Circulation Research /content/101/9/919located on the World Wide Web at:The online version of this article, along with updated information and services, is/reprintsReprints: Information about reprints can be found online atjournalpermissions@ 410-528-8550. E-mail:Fax:Kluwer Health, 351 West Camden Street, Baltimore, MD 21202-2436. Phone: 410-528-4050. Permissions: Permissions & Rights Desk, Lippincott Williams & Wilkins, a division of Wolters//subscriptions/Subscriptions: Information about subscribing to Circulation Research is online atCarbon Monoxide Induces Heme Oxygenase-1via Activation of Protein Kinase R–Like Endoplasmic Reticulum Kinase and Inhibits Endothelial Cell Apoptosis Triggered byEndoplasmic Reticulum StressKi Mo Kim,Hyun-Ock Pae,Min Zheng,Raekil Park,Young-Myeong Kim,Hun-Taeg ChungAbstract—Carbon monoxide(CO),a reaction product of the cytoprotective heme oxygenase(HO)-1,is antiapoptotic in a variety of models of cellular injury,but the precise mechanisms remain to be established.In human umbilical vein endothelial cells,exogenous CO activated Nrf2through the phosphorylation of protein kinase R–like endoplasmic reticulum kinase(PERK),resulting in HO-1expression.CO-induced activation of PERK was followed by the phosphorylation of eukaryotic translation initiation factor2␣and the expression of activating transcription factor4.However,CO fails to induce X-box binding protein-1expression and activating transcription factor6cleavage.CO had no significant effect on synthesis of endoplasmic reticulum(ER)chaperone proteins such as the78-kDa glucose-regulated proteins78and94.Instead,CO prevented X-box binding protein1expression and activating transcription factor6cleavage induced by ER-stress inducers such as thapsigargin,tunicamycin and homocysteine.CO also prevented endothelial apoptosis triggered by these ER inducers through suppression of C/EBP homologous protein expression,which was associated with its activation of p38mitogen-activated protein kinase.Similarly,endogenous CO produced from endothelial HO-1induced by either exogenous CO or a pharmacological inducer was also cytoprotective against ER stress through C/EBP homologous protein suppression.Our findings suggest that CO renders endothelial cells resistant to ER stress not only by downregulating C/EBP homologous protein expression via p38mitogen-activated protein kinase activation but also by upregulating Nrf2-dependent HO-1expression via PERK activation.Thus,the HO-1/CO system might be potential therapeutics in vascular diseases associated with ER stress.(Circ Res.2007;101:919-927.)Key Words:carbon monoxideⅢendoplasmic reticulum stressⅢapoptosisⅢheme oxygenase-1H eme oxygenase(HO)is a rate-limiting and microsomalenzyme that catalyzes the oxidative degradation of free heme to biliverdin,free iron,and carbon monoxide(CO). There are3distinct isoforms of HO:HO-1,HO-2,and HO-3. The HO-2isoform is constitutively expressed and is present in high concentrations in the brain and testes.In contrast, HO-1is ubiquitously distributed and strongly induced by oxidative,nitrosative,osmotic,and hemodynamic stresses.1–5 Endoplasmic reticulum(ER)stress triggers unfolded pro-tein response(UPR).6–8In the mammalian cells,UPR is a signaling network consisting of3ER-resident sensors:the kinase and endoribonuclease(IRE1),the protein kinase R (PKR)-like ER kinase(PERK),and the basic leucine–zipper activating transcription factor6(ATF6).6–9On ER stress, IRE1is dimerized,activated,and allowed to alternatively splice X-box binding protein(Xbp)-1mRNA by removing a 26-bp intron.This transcription frameshift permits Xbp-1to act as a transcription activator of genes containing upstream ER-stress response elements.Activation of both IRE-1and ATF6increases the expression of ER-resident chaperones such as the78-kDa glucose-regulated protein(GRP)78and GRP94,which facilitate the restoration of proper protein folding within the ER.10UPR-mediated PERK activation impedes protein translation via phosphorylation-dependent inhibition of eukaryotic translation initiation factor(eIF)2␣.11 Independent of its translational regulatory capacity,PERK-dependant signals elicit the activation of the survival tran-scription factor NF-E2–related factor-2(Nrf2)via site spe-cific phosphorylation.12PERK belongs to an eIF2␣kinase family that is responsive to distinct stress signals and includes the interferon-inducible PKR,the hemin-regulated inhibitor kinase(HRI),and the general control of amino acid biosynthesis kinase(GCN2), which responds to uncharged transfer RNAs and adapts cellsOriginal received April22,2007;revision received August9,2007;accepted August29,2007.From the Department of Immunology(K.M.K.,H.-O.P.,M.Z.,R.P.,H.-T.C.),Wonkwang University School of Medicine,Iksan,Chunbug;and Department of Molecular and Cellular Biochemistry(Y.M.K.),School of Medicine,Kangwon National University,Chunchon,Kangwon-Do,Republic of Korea.Correspondence to Dr Hun-Taeg Chung,Department of Immunology,Wonkwang University School of Medicine,Iksan,Chunbug570-749,Republic of Korea.E-mail htchung@wku.ac.kr©2007American Heart Association,Inc.Circulation Research is available at DOI:10.1161/CIRCRESAHA.107.154781to amino acid starvation.13Of these,PERK is localized at ER membrane and required for the cellular response to ER stress.After the eIF2␣kinase activation,eIF2␣phosphorylation inhibits translation of a majority of cellular proteins while paradoxically promoting a cytoprotective gene expression program known as the integrated stress response.10–13Among upregulating genes,activating transcription factor (ATF)4plays an important role in activating genes that promote the linked processes of import and metabolism of thiol-containing amino acids and resistance to oxidative stress and the transcription factor C/EBP homologous protein (CHOP),which induces apoptosis by promoting protein synthesis and oxidative stress.13–15Nrf2activation has been implicated in the promotion of cell survivals following ER stress.16UPR-dependent PERK activation promotes PERK-dependent phosphorylation and nuclear localization of Nrf2,which results in increased transcription of Nrf2target genes of phase II detoxifying enzymes such as HO-1.17,18We recently showed that CO-releasing molecule or CO gas induces HO-1expression via Nrf2activation in rat hepatocytes and human HepG2cells.19In this study,we tried to further examine whether CO could also induce Nrf2activation and HO-1expression in human endothelial cells and,if so,to elucidate the molecular basis of CO-induced Nrf2activation responsible for HO-1expression.In addition,we also investigated the effects of CO on endothelial apoptosis triggered by ER stress.We provide evidence that CO induces Nrf2-dependent HO-1expression via PERK signaling pathway and prevents apoptosis triggered by ER stress via CHOP suppression.Materials and MethodsReagentsTricarbonyl dichlororuthenium (II)dimmer (RuCO),homocysteine (HCys),ruthenium chloride (RuCl 2),CO gas,arsenite,and hemo-globin (Hb)were purchased from Sigma-Aldrich (St Louis,Mo).Thapsigargin (TG),tunicamycin (TM),and HO-1antibody were obtained from Calbiochem (La Jolla,Calif).Lipofectamine 2000was obtained from Invitrogen Life Technology (Grand Island,NY).Antibodies to Nrf-2,phospho (p)-PERK,PERK,p-eIF2␣,eIF2␣,CHOP,ATF4,HRI,GRP78,GRP94,Lamin A,and -actin were purchased from Santa Cruz Biotechnology (Santa Cruz,Calif);and antibodies to GCN2,p-GCN2,PKR,p-PKR,p38,p-p38,extracel-lular signal-regulated (ERK),p-ERK,c-Jun N-terminal kinase (JNK),and p-JNK were from Cell Signaling Technology (Beverly,Mass).Poly(ADP-ribose)polymerase (PARP)and ATF6antibodies were purchased from Biomol (Plymouth Meeting,Pa)and Imgenex (San Diego Calif),respectively.Xbp-1antibody was purchased from Biolegend (San Diego,Calif).The small interfering (si)RNAs against PERK,eIF2␣,CHOP,and HO-1were obtained from Santa Cruz Biotechnology.An in situ cell death detection kit was obtained from Roche (Penzberg,Germany).All other chemicals were ob-tained from Sigma-Aldrich.Detection of CO ReleaseThe release of CO from RuCO was assessed spectrophotometrically by measuring the conversion of deoxymyoglobin to carbonmonoxy myoglobin,as described previously.20The amount of carbonmonoxy myoglobin measured after the reaction revealed that 0.72mol of CO was liberated per 1mol of RuCO.Treatment With CO GasSaturated stock solution of CO was prepared in buffer containing 140mmol/L NaCl,5mmol/L KCl,and 20mmol/L HEPES,pH 7.3,as described previously.19CO stock solution was freshly prepared for everyexperiment.Figure 1.Effects of RuCO on Nrf2activation and HO-1expression in HUVECs.A,Cells were incubated for 2hours (Nrf2activation)or 6hours (HO-1expression)with indicated concentrations of RuCO or RuCl 2in the presence or absence of 50g/mL Hb.CF indicates cytosolic fraction;NF,nuclear fraction.CO gas (20mol/L)was used as a positive control.Western blot analysis (top)for Nrf2and HO-1and densitometry analysis (bottom)of all bands were performed as described in Materials and Methods.Blots shown are repre-sentative of 3independent experiments.B,Cells transfected with HO-1-Luci or control vector were exposed for 3hours to 20mol/L RuCO,20mol/L CO gas,or 20mol/L RuCl 2in the presence or absence of Hb.Cell lysates were assayed for luciferase activity as the fold induction by normalizing the transfection efficiency and dividing values of each experiment relative to the untreated control.Values are means ϮSD from 3independent experiments.*P Ͻ0.05with respect to each untreated group,**P Ͻ0.05.920Circulation Research October 26,2007Cell CultureHuman umbilical vein endothelial cells (HUVECs)from human umbilical cord veins were isolated as described previously 21and used for experiments in passages 3to 8.HUVECs were maintained in EGM-2medium (Cambrex BioScience Inc,Walkersville,Md)in a humidified chamber containing 5%CO 2at 37°C.Cell Viability AssayCell viability was determined by a modified MTT (3-[4,5-dimethyl-thiazol-2-yl]-2,5-diphenyltetrazolium bromide)reduction assay,as described previously.22Absorbance was measured in an ELISA reader at 570nm,with the absorbance at 690nm to correct for background,and viability was expressed as percentage of untreated controls.Plasmid ConstructionsHO-1,the original clone of which was a kind gift from Dr A.M.K.Choi (University of Pittsburgh,Pa),was subcloned into pcDNA3vector.Human HO-1promoter construct was generated by PCR amplification of the target sequence,followed by cloning it into plasmid containing reporter gene.Briefly,an Ϸ4.4-kb fragment of the 5Ј-flanking region of the human HO-1gene including the transcription initiation site (spanning region of Ϫ4384bp to ϩ24bp)was amplified from HeLa cell genomic DNA using PCR primer containing proper restriction enzyme sites for the cloning (5Ј-GCTGAGCTCCAGCCTGTCACACAGCAGTTAGGC-3Јand 5Ј-ACGCTCGAGAGGAGGCAGGCGTTGACTGCC-3Ј).Enzyme-digested fragment was cloned into sac I and Xho I site of the pGL3basic vector containing the firefly luciferase cDNA (Promega,Madison,Wis)to obtain pGL3HO-1/4384-Luci construct.All sequences of pGL3HO-1/4384-Luci were confirmed and verified the presence of the correct and the absence of any other nucleotide changes by DNA sequencing.TUNEL AssayCells were plated in slide chambers.After treatment,cells were fixed with 70%ethanol in PBS.Cells were washed once,permeabilizedbyFigure 2.Effects of RuCO on PERK,eIF2␣,ATF4,HO-1,and Nrf2in HUVECs.A,Cells were exposed to 20mol/L RuCO,10mol/L TG,or 10mol/L TM for indicated periods of time.B,Cells were exposed to 20mol/L RuCO or 20mol/L arsenite (positive control)for 160minutes.C,Cells transfected with siRNA against eIF2␣were exposed to RuCO for 1hour.D,Cells were exposed for 1hour (PERK and eIF2␣phosphorylations)or 6hours (ATF4expression)to RuCO in the presence or absence of 50g/mL Hb.E,Cells trans-fected with ether PERK siRNA or eIF2␣siRNA were exposed to RuCO for 1hour (Nrf2nuclear translocation)or 6hours (HO-1expres-sion).NF indicates nuclear fraction.Western blot analysis and densitometry analysis were performed as described in Materials and Methods.Blots shown are representative of 3independent experiments.Values are means ϮSD from 3independent experiments.*P Ͻ0.05with respect to each untreated group.Kim et al CO Inhibits ER Stress–Induced Apoptosis 921incubating with 100L of 0.1%Triton X-100/0.1%sodium citrate and then washed twice in PBS.The TUNEL reaction was performed at 37°C for 1hour with 0.3nmol of fluorescein isothiocyante-12-dUTP,3nmol of dATP,2L of CoCl 2,25U of terminal deoxynucleotidyl transferase,and TdT buffer (30mmol/L Tris,pH 7.2,140mmol/L sodium cacodylate)in a total reaction volume of 50L.The reaction was stopped with 2L of 0.5mol/L EDTA.Cells were observed under a fluorescence microscope.Preparation of Nuclear Fraction and Cytosolic FractionHUVECs were incubated with or without reagents.They were harvested,washed in ice-cold PBS buffer,and kept on ice for 1minute.The suspension was mixed with buffer A (10mmol/L HEPES,pH 7.5/10mmol/L KCl/0.1mmol/L EGTA/0.1mmol/L EDTA/1mmol/L dithiothreitol/0.5mmol/L phenylmethanesulfonyl fluoride/5g/mL aprotinin/5g/mL pepstatin/10g/mL leupeptin)and lysed by 3freeze–thaw cycles.Cytosolic fractions were obtained by centrifugation at 12000g for 20minutes at 4°C.The pellets were resuspended in buffer C (20mmol/L HEPES,pH 7.5/0.4mol/L NaCl/1mmol/L EGTA/1mmol/L EDTA/1mmol/L dithiothreitol/1mmol/L phenylmethanesulfonyl fluoride/5g/mL aprotinin/5g/mL pepstatin/10g/mL leupeptin)ice for 40minutes and centrifuged at 14000g for 20minutes at 4°C.The resulting super-natant was used as soluble nuclear fractions.Protein content was determined with BCA protein assay reagent (Pierce,Rockford,Ill).Western Blot and Densitometry AnalysesAfter treatment,cells were harvested and washed twice with ice-cold PBS.Cells were lysed with 1ϫLaemmli lysis buffer (2.4mol/Lglycerol/0.14mol/L Tris,pH 6.8/0.21mol/L sodium dodecyl sulfate/0.3mmol/L bromophenol blue)and boiled for 10minutes.Protein content was measured with BCA protein assay reagent (Pierce).The samples were diluted with 1ϫlysis buffer containing 1.28mol/L -mercaptoethanol,and equal amounts of protein (20g of protein)were separated on 7.5%to 12%SDS-PAGE and transferred to poly(vi-nylidene difluoride)membranes.The membranes were blocked with 5%nonfat milk in PBS containing 0.1%Tween 20(PBS-T)for 10minutes and incubated with antibodies to HO-1(1:1000),p-PERK (1:500),p-eIF-2␣(1:1000),Xbp-1(1:500),GRP78(1:1000),GRP94(1:1000),ATF-6(1:1000),CHOP (1:500),p-ERK (1:1000),p-p38(1:1000),p-JNK (1:1000),HRI (1:500),p-GCN2(1:500),or p-PKR (1:500)in PBS-T containing 1%nonfat milk for 3hours.After washing 3times with PBS-T,the membranes were hybridized with horseradish peroxidase–conjugated secondary antibodies for 40minutes.Fol-lowing 5washes with PBS-T,they were incubated with chemilumi-nescent solution for 5minutes and protein bands were visualized on x-ray film.For the densitometry analysis,optical density (the gray-scale value of pixels:0to 255)was measured on the inverted digital images using Scion Image (Scion Corp,Frederick,Md).Transfection of siRNAsPredesigned siRNAs against human HO-1(catalog no.SC-35554),PERK (catalog no.SC-36213),eIF2␣(catalog no.SC-35272),CHOP (catalog no.SC-35437),and control scrambled siRNA (catalog no.SC-37007)were purchased from Santa Cruz Biotech-nology.The sense strands of siRNAs against HO-1,PERK,eIF2␣,and CHOP are as follows:HO-1,UGCUCAACAUCCAGCUCUU,UCCAGCUCUUUGAGGAGUU,CGUGGGCACUGAAGGCUUU,Figure 3.Effects of RuCO on ATF6,GRP78,GRP94,Xbp-1,and apoptosis in HUVECs.A,Cells were exposed for 6hours to indicated concentrations of RuCO or 10mol/L TG (positive control).B,Cells were exposed for 12hours to indicated concentrations of RuCO or 10mol/L TG (positive control).C,Cells were preincubated for 6hours with 20mol/L RuCO in the presence or absence of 50g/mL Hb and were then exposed for 6hours to 10mol/L TG.D,Cells were preincubated for 6hours with RuCO in the presence orabsence of Hb and then exposed for 12hours (apoptosis assay)or 18hours (viability assay)to TG.Western blot analysis for ATF6,GRP 78,GRP 94,and Xbp-1;TUNEL assay for apoptosis;and MTT assay for cell viability were performed as described in Materials and Methods.Blots shown are representative of 3independent experiments.Each bar represents mean ϮSD from 3independent experiments.*P Ͻ0.05.922Circulation Research October 26,2007and AAGCCCUGAGUUUCAAGUA;PERK,CGAGAGCCG-GAUUUAUUGA,GGAUGAAAUUUGGCUGAAA,and CAGA-CACACAGGACAAGUA;eIF2␣,GGCUUGUUAUGGUUAUGAA,CCUCGGUAUGUAAUGACUA,and GAGAGGCUUGAAAGAG-AAA;CHOP,GAAGGCUUGGAGUAGACAA,GGAAAGGUCU-CAGCUUGUA,and GUCUCAGCUUGUAUAUAGA.Cells were transfected with double-stranded siRNAs (40nmol/mL)for 12hours by the Lipofectamine method according to the protocol of the manufacturer (Invitrogen Life)and recovered in fresh media con-taining 10%FBS for 24hours.The interference of HO-1,PERK,CHOP,or eIF2␣expressions was confirmed by immunoblot using anti-HO-1,PERK,CHOP,or eIF2␣antibodies;scrambled siRNA was used as a control.Measurement of Promoter ActivityCells were transiently transfected with the promoter constructs using the transfection reagent Lipofectamine 2000.After harvest,cells were lysed in reporter lysis buffer (Promega).Cell extract (20L)was mixed with 100L of the luciferase assay reagent,and the emitted light intensity was measured using the luminometer AutoLu-mat LB953(EG and G Berthold,Bad Wildbad,Germany).Fold induction was calculated as intensity value from each experimental group divided by value from control group after normalization of transfection efficiency by -galactosidase assay.Statistical AnalysesData are expressed as means ϮSD.Statistical analysis in this study includes ANOVA and the post hoc group comparisons after Bonfer-roni and Scheffe ´procedures.Probability values of Ͻ0.05were considered as significant.ResultsCO Induces Nrf2Activation and HO-1ExpressionIn HUVECs,the CO donor RuCO induced Nrf2nuclear translocation and HO-1expression in a dose-dependent man-ner (Figure 1A).This was further confirmed by our observa-tion that RuCO also enhanced HO-1promoter activity (Fig-ure 1B).The effects of RuCO on Nrf2activation and HO-1expression were abolished when CO gas spontaneously re-leased from RuCO was scavenged by Hb.Moreover,treat-ment of cells with equivalent molar concentrations of ruthe-nium (RuCl 2)did not induce Nrf2activation and HO-1expression (Figure 1A).Our observations,therefore,suggest that CO is only an effector molecule capable of inducing HO-1expression.In agreement with these findings,CO gas induced Nrf2activation and HO-1expression (Figure 1A)and also enhanced HO-1promoter activity (Figure 1B).CO Induces Nrf2-Dependent HO-1Expression via PERK ActivationBecause PERK signaling activates Nrf2,leading to increased expression of Nrf2target genes including HO-1,17,18we examined whether RuCO could phosphorylate PERK in HUVECs.As shown in Figure 2A,RuCO itself phosphory-lated PERK and its downstream eIF2␣in a time-dependent manner,which was similar to the effects of TG or TM.Unlike its effect on PERK phosphorylation,RuCO did notphosphor-Figure 4.Effects of RuCO on TM-induced Xbp-1and ATF6and cell death in HUVECs.A,Cells were preincubated for 6hours with 20mol/L RuCO or 1%dimethyl sulfoxide in the presence or absence of 50g/mL Hb and were then exposed for 6hours to10mol/L TM.B,Cells were preincubated for 6hours with RuCO or dimethyl sulfoxide in the presence or absence of Hb and then exposed to TM for 12hours (apoptosis)or 18hours (cell viability).Western blot analysis for ATF6and Xbp-1,TUNEL assay for apopto-sis,and MTT assay for cell viability were performed as described in Materials and Methods.Blots shown are representative of 3inde-pendent experiments.Each bar represents mean ϮSD from 3independent experiments.*P Ͻ0.05.Kim et al CO Inhibits ER Stress–Induced Apoptosis 923ylate HIR,GCN2,or PKR (Figure 2B).Moreover,RuCO could not induce eIF2␣phosphorylation when the cellular PERK expression was knocked down with a PERK-specific siRNA (Figure 2C),suggesting that eIF2␣phosphorylation by RuCO is mediated mainly via a PERK pathway.In addition,RuCO induced ATF4expression,presumably via PERK-mediated eIF2␣pathway (Figure 2D).We next exam-ined whether PERK activation by RuCO could result in Nrf2-dependent HO-1expression.RuCO could not induce Nrf2nuclear translocation and HO-1expression in the cells transfected with siRNA against PERK (Figure 2E).Blockage of eIF2␣downstream by an eIF2␣-specific siRNA had no effect on Nrf2activation and HO-1expression by RuCO (Figure 2E).Thus,our data clearly indicate that Nrf2is rapidly mobilized from the cytosol to the nucleus in response to RuCO via PERK activation,thereby resulting in HO-1expression.CO Has No Effect on Xbp1and ATF6butSuppresses Xbp1Expression,ATF6Cleavage,and Apoptosis by ER StressGiven that RuCO induces PERK activation in HUVECs,we reasoned that RuCO might also induce other ER-resident transmembrane proteins:IRE1-mediated Xbp1expression and protease-mediated ATF6cleavage.Surpris-ingly,RuCO had no significant effect on Xbp1expression and ATF6cleavage (Figure 3A).In addition,the expres-sion of GRP78and GRP94,2known chaperones in the ER,was not affected by RuCO (Figure 3B).Of note was that CO inhibited TG-induced Xbp-1expression and ATF6cleavage (Figure 3C).Having shown that CO inhibited Xbp-1expression and ATF6cleavage following ER stress by TG (Figure 3C),we thus asked whether CO could prevent apoptosis triggered by ER stress.Treatment of HUVECs with TG resulted in a significant increase in apoptosis that was prevented by the exogenous administration of RuCO (Figure 3D).Consistent with this,RuCO greatly diminished TG-induced cell death (Figure 3D).Similarly,RuCO also prevented Xbp-1expres-sion,ATF6cleavage,and apoptosis induced by other ER-stress inducers,TM (Figure 4)and HCys (Figure 5).These findings prompted us to examine whether RuCO could inhibit proapoptotic CHOP expression by ER stress.The ER-stress inducers markedly increased CHOP expression,which was reversed by RuCO treatment (Figure 6A).We next investi-gated whether PERK activation by RuCO could contribute to its inhibition of TG-induced CHOP expression and found that RuCO-mediated inhibition of CHOP expression was slightly reversed by PERK siRNA but not by eIF2␣siRNA (Figure 6B).This could be explained by a possibility that there would be endogenous CO that was produced by HO-1induction through PERK activation by RuCO.In line with this,CoPP,which can induce cellular HO-1expression to produce endogenous CO,also inhibited TG-induced CHOP expres-sion (Figure 6C),which was further supported by our obser-vation that inhibition of cellular HO-1synthesis by a HO-1–specific siRNA reversed the inhibitory effects of RuCO and CoPP on TG-induced CHOP expression (Figure 6C)and cell death (Figure 6D).These data suggest that both exogenous and endogenous CO may have a capacity of inhibiting CHOP expression by ERstress.Figure 5.Effects of RuCO on HCys-induced Xbp-1and ATF6and cell death in HUVECs.A,Cells were preincubated for 6hours with 20mol/L RuCO or 1%ethanol in the presence or absence of 50g/mL Hb and then were exposed for 6hours to 50mol/L HCys.B,Cells were preincubated for 6hours with RuCO or ethanol in the presence or absence of Hb and then were exposed to HCys for 12hours (apoptosis)or 18hours (cell viability).Western blot analysis for ATF6and Xbp-1,TUNEL assay for apoptosis,and MTT assay for cell viability were performed as described in Materials and Methods.Blots shown are representative of three independent experiments.Each bar represents mean ϮSD from 3independent experiments.*P Ͻ0.05.924Circulation Research October 26,2007CO Inhibits Proapoptotic CHOP Expression via a p38Mitogen-Activated Protein Kinase–Dependent PathwayTo further determine the importance of CHOP in TG-induced apoptosis,we used siRNA methodology to silence the CHOP gene.The siRNA specific for CHOP was transiently trans-fected into HUVECs,which were then subjected to TG treatment.Transfection of CHOP siRNA inhibited the apo-ptotic PARP cleavage even in the presence of TG (Figure 7A,bottom),along with increased cell viability (Figure 7A,top).Notably,a CHOP-specific siRNA mimicked the cytoprotec-tive/antiapoptotic action of RuCO and,in combination with RuCO,did not further enhance cytoprotective effects of RuCO (Figure 7A),thus supporting that CO may prevent TG-induced apoptosis by suppressing CHOP expression.Because CO suppresses endothelial apoptosis mainly through a mechanism that is dependent on the activation of p38mitogen-activated protein kinase (MAPK)pathway,23we finally examined the role(s)of p38MAPK in the effects ofCO with respect to CHOP expression.RuCO increased p38MAPK activation in dose-and time-dependent manner but not the activation of ERK or JNK (Figure 7B).SB203580,a specific inhibitor of p38MAPK,abrogated the inhibitory effects of CO on TG-induced CHOP expression and cell death (Figure 7C),suggesting that CO inhibits CHOP expres-sion via p38MAPK–dependent manner.DiscussionThe HO-1/CO system has been shown to provide significant protection against vascular injury,transplant rejection,hyper-oxic lung injury,and atherosclerotic lesions.3–5CO,a reaction product of HO-1activity,has been shown to have potent antiinflammatory,antiproliferative,and antiapoptotic effects and thereby mimics the cytoprotective effects of HO-1.In our previous study,19we revealed reciprocal feedback relation-ships between HO-1and CO:HO-1produces CO and CO induces HO-1expression in Nrf2-dependent pathway in the liver cells.In the present study,we confirmed that COisFigure 6.Effects of RuCO and RuCO-induced HO-1on TG-,TM-,and HCys-induced CHOP in HUVECs.A,Cells were preincubated for 6hours with 20mol/L RuCO and then were exposed for 6hours to 10mol/L TG,10mol/L TM,or 50mol/L HCys.B,Cells trans-fected with either PERK siRNA or eIF2␣were preincubated for 6hours without or with RuCO and then were exposed to TG for 6hours.C,Normal cells and the cells transfected with siRNA against HO-1were preincubated for 6hours with RuCO or 10mol/LCoPP in the presence or absence of 50g/mL Hb and then exposed to TG for 6hours.D,Normal cells and the cells transfected with PERK siRNA,HO-1siRNA,or eIF2␣siRNA were preincubated for 6hours with or without RuCO and then were exposed to TG for 18hours.Western blot analysis for CHOP,HO-1,ATF6,and Xbp-1and MTT assay for cell viability were performed as described in Materi-als and Methods.Blots shown are representative of 3independent experiments.Each bar represents mean ϮSD from 3independent experiments.*P Ͻ0.05with respect to each untreated group.*P Ͻ0.05.Kim et al CO Inhibits ER Stress–Induced Apoptosis 925capable of inducing Nrf2activation and HO-1expression in human endothelial cells as well.These findings implicate a novel endothelial protective function of CO.Indeed,our results showed that both exogenous and endogenous CO prevents endothelial apoptosis triggered by ER stress.In an effort to understand the possible mechanism(s)responsible for Nrf2activation and HO-1expression by CO,we tested whether CO could induce PERK phosphorylation that has been already reported to activate Nrf2leading to HO-1expression.12Interestingly,CO phosphorylated PERK,and CO-induced PERK activation was followed by eIF2␣phosphorylation and then ATF4expression.Four mammalian eIF2␣kinases have been identified so far.13PKR is activated by double-stranded RNA of viral or synthetic origin.24HRI inhibits protein synthesis in heme-deprived lysates and stressed cells.25A third eIF2␣kinase,GCN2,is specifically activated in response to amino acid deprivation.26Finally,PERK is activated in response to accumulation of misfolded protein in the ER.In our study,we found that CO activates only PERK among four known eIF2␣kinases (Figure 2B).This was further confirmed by our observation that inhibition of cellular PERK synthesis by siRNA completely abolished eIF2␣phosphorylation by CO (Figure 2C).The downstream target of PERK for cell survival was recently identified as Nrf2.12,16In unstressed cells,Nrf2is maintained in cytoplas-mic complexes via association with the cytoskeletal anchor Keap1.It was reported that oxidative stress triggers dissoci-ation of this complex via an uncharacterized mechanism,thereby allowing Nrf2nuclear import,where it promotes expression of phase II detoxifying enzymes.17,18Recently,it was demonstrated that PERK-dependent phosphorylation leads to the nuclear accumulation of Nrf2and increases transcription of Nrf2target genes.12In this study,we found that CO induces Nrf2-dependent HO-1expression via PERK activation.The abovementioned findings raised a question as to whether CO might also activate other ER-bound sensor proteins:Xbp-1and ATF6.Unlike its PERK activation,CO did not activate Xbp-1or ATF6(Figure 3A).Instead,CO blocked Xbp-1and ATF6activation caused by the ER stress response (Figure 3C).This prompted us to study the effects of CO on ER-stress-induced apoptosis.Indeed,CO inhibited endothelial apoptosis triggered by three well-known ER stress inducers:TG,TM,and HCys.These results suggest that the HO-1/CO system upregulates the cell survival path-ways and/or downregulates the cell death pathways,leading to rescue the apoptotic cell from excessive/prolonged UPR by ER stress.To decrease ER protein accumulation and maintain ER function,the UPR attenuates translation via PERK-eIF2␣pathway,increases the folding capacity of the ER by upregu-lation of ER-chaperones via the transcription factors ATF6and Xbp-1,and degrades misfolded proteins via the ER-associated degradation pathway.26However,when ER stress is prolonged or excessive,the proapoptotic transcription factor CHOP is activated.28CHOP expression is alsoupregu-Figure 7.Roles of RuCO-induced p38MAPK activation in TG-induced CHOP expression and cell death in HUVECs.A,Cells trans-fected with siRNA against CHOP were preincubated for 6hours with 20mol/L RuCO and then were exposed for 6hours (CHOP expression),12hours (PARP cleavage),or 18hours (cell viability)to 10mol/L TG.B,Cells were exposed for 30minutes to indicated concentrations of RuCO (top).Cells were exposed to 20mol/L RuCO for the indicated time periods (bottom).C,Cells were preincu-bated for 6hours with RuCO in the presence or absence of 50mol/L SB203580and then were exposed to TG for 6hours (CHOP and HO-1)or 18hours (cell viability).Western blot analysis for PARP,CHOP,HO-1,p-p38,p-ERK1/2,and p-JNK and MTT assay for cell viability were performed as described in Materials and Methods.Blots shown are representative of 3independent experiments.Each bar represents mean ϮSD from 3independent experiments.*P Ͻ0.05.926Circulation Research October 26,2007。