Dynamic and reversible changes in histone H3-Lys4 methylation and H3 acetylation occurring
Sweden:LPJ-GUESS
Sweden: LPJ-GUESSThe name of the group: Department of Physical Geography and Ecosystem AnalysisName, title and affiliation of Principal investigator: Assoc. Prof. Almut Arneth; Assoc. Prof. Ben Smith Name and affiliation of contact point, incl. detailed address: Almut Arneth & Ben Smith, INES,Sölvegatan12,22362Lund,*****************************.se;*******************.seURL: http://www.nateko.lu.se/INES/Svenska/main.aspPartner institutions: PIK, SMHI/EC-EARTHWhich components:land sfc yesatmospheric chemistry yesProject Description:LPJ-GUESS (Smith et al., 2001; Hickler et al., 2004) is a generalized, process-based model of vegetation dynamics and biogeochemistry designed for regional to global applications. It combines features of the widely used Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM; Sitch et al., 2003) with those of the General Ecosys-tem Simulator (GUESS; Smith et al., 2001) in a single, flexible modeling framework. The models have identical representations of ecophysiological and biogeochemical processes, including the hydrological cycle updates d e-scribed in Gerten et al. (2004). They differ in the level of detail with which vegetation dynamics and canopy stru c-ture are simulated: simplified, computationally efficient representations are used in the LPJ-DGVM, while in GUESS a "gap-model" approach is used which is particularly suitable for continental to regional simulations. Tepre-sentations of stochastic establishment, individual tree mortality and disturbance events ensure representation of suc-cessional vegetation dynamics which is important for vegetation response to extreme events.LPJ-GUESS models terrestrial carbon and water cycle from days to millennia (Sitch et al. 2003; Koca et al. 2006; Morales et al. 2005, 2007) and has been shown to reproduce the CO2 fertilisation effects seen in FACE sites (Hickler et al. in press). It has been widely applied to assess impacts on carbon cycle and veg etation based on scenarios from climate models (Gritti et al. 2006; Koca et al. 2006; Morales et al. 2007; Olesen et al. 2007). In addition it has several unique features that are currently not available in any of the Earth System Models:(1) A process-based description for the main biogenic volatile organic compounds (BVOC) emitted by vegetation. BVOC are crucial for air chemistry and climate models, since they contribute to formation and destruction of trop o-spheric O3 (depending on presence and absence of NOx), constrain the atmospheric lifetime of methane, and are key precursors to secondary organic aerosol formation. LPJ-GUESS is the only land surface model with a mechanistic BVOC representation that links their production to photosynthesis. It also uniquely accounts for the recently disco v-ered direct CO2-BVOC inhibition which has been shown to fundamentally alter future and past emissions compared to empirical BVOC algorithms that neglect this effect (Arneth et al., 2007a,b). (2) The possibility to simulate past and present vegetation description on a tree species (as well as PFT) level (Miller et al., in press, Hickler et al., 2004). This is crucial for simulations of BVOC and other reactive trace gases and allows for a much better represe ntation of vegetation heterogeneity in regional and continental atmospheric chemistry-climate studies (Arneth et al., 2007b), an important aspect since spatial heterogeneity must be accounted for with atmospherically reactive chemical species. (3) LPJ-GUESS accounts for deforestation by early human agriculture throughout the Holocene and the effects on global carbon cycle and atmospheric CO2 concentration (Olofsson & Hickler 2007). We currently investigate the impor-tance of Holocene human deforestation on BVOC and fire trace gas and aerosol emissions, and how these may affect Holocene CH4 levels, and simulations of pre-industrial O3. (4) LPJ-GUESS accounts for deforestation by early human agriculture throughout the Holocene and the effects on global carbon cycle and atmospheric CO2 concentra-tion (Olofsson & Hickler 2007). We currently investigate the importance of Holocene human deforestation on BVOC and fire trace gas and aerosol emissions, and how these may affect Holocene CH4 levels, and simulations of pre-industrial O3. (5) A novel global process-based fire description, SPITFIRE (Thonicke et al., 2007) has been incorporated; it is currently used to study effects of climate change and of human vs. natural ignition on carbon cycle and trace gas emissions in savanna ecosystems. (6) Prognostic schemes for agricultural and forest land use that p a-rameterise farmer and forestmanagement decisions under changing climate and productivity. The agricultural scheme has been implemented and applied at the global scale (Bondeau et al. 2007), the forest management scheme in a prototype form for Sweden (Koca et al. 2006). (7) Incorporation of a permafrost module, wetland processes and methane emissions, as well as vegetation nitrogen cycle is in progress.The vegetation dynamics module of LPJ-GUESS has been coupled to the land surface scheme of the Rossby Centre regional climate model RCA3 (Jones et al. 2004a,b) and is being applied to investigate biophysical feedbacks of land surface changes on climate at the regional scale in Europe. The above listed process descriptions are also applicable and available to global ESMs.(References available on request)。
dynamic 解析
dynamic 解析摘要:1.动态解析的定义和背景2.动态解析的方法和应用3.动态解析的优缺点分析4.我国在动态解析领域的研究现状与展望正文:动态解析是一种重要的数据分析方法,它主要研究数据随时间变化而表现出的规律性。
随着信息技术的飞速发展,动态解析在各个领域得到了广泛应用,如金融、医疗、交通等。
本文将对动态解析的定义、方法和应用进行详细介绍,并分析其优缺点,最后展望我国在动态解析领域的研究现状与发展趋势。
一、动态解析的定义和背景动态解析,顾名思义,是对数据随时间变化的过程进行分析。
它关注的是数据的时间演变特性,通过揭示数据的时间规律,为预测和决策提供支持。
动态解析起源于统计学、时间序列分析等领域,随着科学技术的进步,逐渐发展成为一门独立的研究方向。
二、动态解析的方法和应用1.方法动态解析的方法主要包括:时间序列分析、状态空间模型、卡尔曼滤波、人工神经网络等。
这些方法各有优缺点,适用于不同类型的数据和问题。
2.应用动态解析在各个领域有广泛应用,如金融领域的风险管理、股票预测;医疗领域的疾病传播分析、患者病情监测;交通领域的交通流量预测、路线规划等。
这些应用为各领域的发展提供了有力支持。
三、动态解析的优缺点分析1.优点动态解析能够揭示数据的时间演变特性,为预测和决策提供依据。
同时,随着计算能力的提升和算法的优化,动态解析在处理大规模数据集时表现出较高的准确性和效率。
2.缺点动态解析的缺点主要表现在:对于非线性数据和复杂模型的处理能力较弱;受数据质量和噪声影响较大,可能导致分析结果失真。
四、我国在动态解析领域的研究现状与展望我国在动态解析领域的研究取得了显著成果,不仅在理论研究方面与国际水平相当,还在实际应用中为国家的经济发展、社会进步做出了贡献。
然而,与国际先进水平相比,我国在动态解析领域的研究仍有一定差距。
展望未来,我国应加大投入,培养专业人才,加强国际合作,以期在动态解析领域取得更多突破。
总之,动态解析作为一种重要的数据分析方法,在各个领域具有广泛应用和重要意义。
Application of the AHP in project management
Application of the AHP in project managementKamal M.Al-Subhi Al-Harbi *DepartmentofConstructionEngineeringandManagement,KingFahdUniversityofPetroleum&Minerals,KFUPMBox1468,Dhahran31261,SaudiArabiaReceived 12June 1998;received in revised form 2March 1999;accepted 19May 1999AbstractThis paper presents the Analytical Hierarchy Process (AHP)as a potential decision making method for use in project manage-ment.The contractor prequali®cation problem is used as an example.A hierarchical structure is constructed for the prequali®cation criteria and the contractors wishing to prequalify for a project.By applying the AHP,the prequali®cation criteria can be prioritized and a descending-order list of contractors can be made in order to select the best contractors to perform the project.A sensitivity analysis can be performed to check the sensitivity of the ®nal decisions to minor changes in judgements.The paper presents group decision-making using the AHP.The AHP implementation steps will be simpli®ed by using the `Expert Choice'professional soft-ware that is available commercially and designed for implementing AHP.It is hoped that this will encourage the application of the AHP by project management professionals.#2000Elsevier Science Ltd and IPMA.All rights reserved.Keywords:Analytical hierarchy process;AHP;Project management;Contractor prequali®cation1.IntroductionThe Analytical Hierarchy Process (AHP)is a deci-sion-aiding method developed by Saaty [24±27].It aims at quantifying relative priorities for a given set of alter-natives on a ratio scale,based on the judgment of the decision-maker,and stresses the importance of the intuitive judgments of a decision-maker as well as the consistency of the comparison of alternatives in the decision-making process [24].Since a decision-maker bases judgments on knowledge and experience,then makes decisions accordingly,the AHP approach agrees well with the behavior of a decision-maker.The strength of this approach is that it organizes tangible and intan-gible factors in a systematic way,and provides a struc-tured yet relatively simple solution to the decision-making problems [29].In addition,by breaking a pro-blem down in a logical fashion from the large,descend-ing in gradual steps,to the smaller and smaller,one is able to connect,through simple paired comparison judgments,the small to the large.The objective of this paper is to introduce the appli-cation of the AHP in project management.The paper will brie¯y review the concepts and applications of the multiple criteria decision analysis,the AHP's imple-mentation steps,and demonstrate AHP application on the contractor prequali®cation problem.It is hoped that this will encourage its application in the whole area of project management.2.Multiple criteria decision analysis (MCDA)Project managers are faced with decision environ-ments and problems in projects that are complex.The elements of the problems are numerous,and the inter-relationships among the elements are extremely compli-cated.Relationships between elements of a problem may be highly nonlinear;changes in the elements may not be related by simple proportionality.Furthermore,human value and judgement systems are integral ele-ments of project problems [15].Therefore,the ability to make sound decisions is very important to the success of a project.In fact,Schuyler [28]makes it a skill that is certainly near the top of the list of project management skills,and notices that few of us have had formal train-ing in decision making.0263-7863/00/$20.00#2000Elsevier Science Ltd and IPMA.All rights reserved.P I I :S 0263-7863(99)00038-1International Journal of Project Management 19(2001)19±27/locate/ijproman*Tel.:+966-3-860-3312;fax:+966-3-860-3287.E-mail address:harbi@.sa (K.M.Al-S.Al-Harbi).Multiple criteria decision-making(MCDM)approa-ches are major parts of decision theory and analysis. They seek to take explicit account of more than one criterion in supporting the decision process[5].The aim of MCDM methods is to help decision-makers learn about the problems they face,to learn about their own and other parties'personal value systems,to learn about organizational values and objectives,and through exploring these in the context of the problem to guide them in identifying a preferred course of action [5,12,20,32,34,35].In other words,MCDA is useful in circumstances which necessitate the consideration of di erent courses of action,which can not be evaluated by the measurement of a simple,single dimension[5]. Hwang and Yoon[14]published a comprehensive survey of multiple attribute decision making methods and applications.Two types of the problems that are common in the project management that best®t MCDA models are evaluation problems and design problems. The evaluation problem is concerned with the evaluation of,and possible choice between,discretely de®ned alternatives.The design problem is concerned with the identi®cation of a preferred alternative from a poten-tially in®nite set of alternatives implicitly de®ned by a set of constraints[5].3.The analytical hierarchy process(AHP)Belton[4]compared AHP and a simple multi-attri-bute value(MAV),as two of the multiple criteria approaches.She noticed that both approaches have been widely used in practice which can be considered as a measure of success.She also commented that the greatest weakness of the MAV approach is its failure to incorporate systematic checks on the consistency of judgments.She noticed that for large evaluations,the number of judgments required by the AHP can be somewhat of a burden.A number of criticisms have been launched at AHP over the years.Watson and Freeling[33]said that in order to elicit the weights of the criteria by means of a ratio scale,the method asks decision-makers mean-ingless questions,for example:`Which of these two cri-teria is more important for the goal?By how much?' Belton and Gear[6]and Dyer[9]pointed out that this method can su er from rank reversal(an alternative chosen as the best over a set of X,is not chosen when some alternative,perhaps an unimportant one,is exclu-ded from X).Belton and Gear[7]and Dyer and Wendel [10]attacked the AHP on the grounds that it lacks a ®rm theoretical basis.Harker and Vargas[13]and Perez [19]discussed these major criticisms and proved with a theoretical work and examples that they are not valid. They commented that the AHP is based upon a®rm theoretical foundation and,as examples in the literature and the day-to-day operations of various governmental agencies,corporations and consulting®rms illustrate, the AHP is a viable,usable decision-making tool. Saaty[24±27]developed the following steps for applying the AHP:1.De®ne the problem and determine its goal.2.Structure the hierarchy from the top(the objec-tives from a decision-maker's viewpoint)through the intermediate levels(criteria on which sub-sequent levels depend)to the lowest level which usually contains the list of alternatives.3.Construct a set of pair-wise comparison matrices(size nÂn)for each of the lower levels with one matrix for each element in the level immediately above by using the relative scale measurement shown in Table1.The pair-wise comparisons are done in terms of which element dominates the other.4.There are n nÀ1a judgments required to developthe set of matrices in step3.Reciprocals are auto-matically assigned in each pair-wise comparison.5.Hierarchical synthesis is now used to weight theeigenvectors by the weights of the criteria and the sum is taken over all weighted eigenvector entries corresponding to those in the next lower level of the hierarchy.6.Having made all the pair-wise comparisons,theconsistency is determined by using the eigenvalue, l m x,to calculate the consistency index,CI as fol-lows:gs l m xÀna nÀ1,where n is the matrix size.Judgment consistency can be checked by taking the consistency ratio(CR)of CI with the appropriate value in Table2.The CR is accep-table,if it does not exceed0.10.If it is more,the judgment matrix is inconsistent.To obtain a con-sistent matrix,judgments should be reviewed and improved.7.Steps3±6are performed for all levels in the hier-archy.Table1Pair-wise comparison scale for AHP preferences[24±27] Numerical rating Verbal judgments of preferences 9Extremely preferred8Very strongly to extremely7Very strongly preferred6Strongly to very strongly5Strongly preferred4Moderately to strongly3Moderately preferred2Equally to moderately1Equally preferred20K.M.A.-S.Al-Harbi/International Journal of Project Management19(2001)19±27Fortunately,there is no need to implement the steps manually.Professional commercial software,Expert Choice,developed by Expert Choice,Inc.[11],is avail-able on the market which simpli®es the implementa-tion of the AHP's steps and automates many of its computations.4.Group decision makingThe AHP allows group decision making,where group members can use their experience,values and knowl-edge to break down a problem into a hierarchy and solve it by the AHP steps.Brainstorming and sharing ideas and insights(inherent in the use of Expert Choice in a group setting)often leads to a more complete representation and understanding of the issues.The following suggestions and recommendations are sug-gested in the Expert Choice software manual[11].1.Group decisions involving participants with com-mon interests are typical of many organizational decisions.Even if we assume a group with com-mon interests,individual group members will each have their own motivations and,hence,will be in con¯ict on certain issues.Nevertheless,since the group members are`supposed'to be striving for the same goal and have more in common than in con¯ict,it is usually best to work as a group and attempt to achieve consensus.This mode max-imizes communication as well as each group member's stake in the decision.2.An interesting aspect of using Expert Choice isthat it minimizes the di cult problem of`group-think'or dominance by a strong member of the group.This occurs because attention is focused ona speci®c aspect of the problem as judgments arebeing made,eliminating drift from topic to topic as so often happens in group discussions.As a result,a person who may be shy and hesitant to speak up when a group's discussion drifts from topic to topic will feel more comfortable in speak-ing up when the discussion is organized and attention turns to his area of expertise.Since Expert Choice reduces the in¯uences of group-think and dominance,other decision processes such as the well known Delphi technique may no longer be attractive.The Delphi technique wasdesigned to alleviate groupthink and dominance problems.However,it also inhibits communica-tion between members of the group.If desired, Expert Choice could be used within the Delphi context.3.When Expert Choice is used in a group session,thegroup can be shown a hierarchy that has been prepared in advance.They can modify it to suit their understanding of the problem.The group de®nes the issues to be examined and alters the prepared hierarchy or constructs a new hierarchy to cover all the important issues.A group with widely varying perspectives can feel comfortable with a complex issue,when the issue is broken down into di erent levels.Each member can pre-sent his own concerns and de®nitions.Then,the group can cooperate in identifying the overall structure of the issue.In this way,agreement can be reached on the higher-order and lower-order objectives of the problem by including all the con-cerns that members have expressed.The group would then provide the judgments.If the group has achieved consensus on some judg-ment,input only that judgment.If during the pro-cess it is impossible to arrive at a consensus on a judgment,the group may use some voting techni-que,or may choose to take the`average'of the judgments.The group may decide to give all group members equal weight,or the group members could give them di erent weights that re¯ect their position in the project.All calculations are done automatically on the computer screen.4.The Group Meeting:While Expert Choice is anideal tool for generating group decisions through a cohesive,rigorous process,the software does not replace the components necessary for good group facilitation.There are a number of di erent approaches to group decision-making,some better than others.Above all,it is important to have a meeting in which everyone is engaged,and there is buy-in and consensus with the result.5.Application of the AHP in project managementIn this paper,contractor prequali®cation(an evalua-tion problem)will be used as an example of the possi-bility of using AHP in project management. Prequali®cation is de®ned by Moore[17]and Stephen [30]as the screening of construction contractors by project owners or their representatives according to a predetermined set of criteria deemed necessary for suc-cessful project performance,in order to determine the contractors'competence or ability to participate in the project bid.Another formal de®nition by Clough[8]is that prequali®cation means that the contracting®rmTable2Average random consistency(RI)[24±27]Size of matrix12345678910Random consistency000.580.9 1.121.241.321.411.451.49K.M.A.-S.Al-Harbi/International Journal of Project Management19(2001)19±2721wishing to bid on a project needs to be quali®ed before it can be issued bidding documents or before it can submit a proposal.Prequali®cation of contractors aims at the elimination of incompetent contractors from the bidding process. Prequali®cation can aid the public and private owner in achieving successful and e cient use of their funds by ensuring that it is a quali®ed contractor who will con-struct the project.Furthermore,because of the skill, capability and e ciency of a contractor,completion of a project within the estimated cost and time is more probable.A number of studies have focused on contractor pre-quali®cation.Lower[16]reviewed the guidelines of the prequali®cation process in di erent States in the US.He also discussed how prequali®cation can provide the owner with appropriate facilities representing an e ec-tive and e cient expenditure of money.Nguyen[18]argued that the prequali®cation process remains largely an art where subjective judgment,based on individual experience,becomes an essential part of the process.Russel and Skibniewski[22]mentioned that the actual process of contractor prequali®cation had received little attention in the past.Russel and Skibniewski[23]tried to describe the contractor prequali®cation process along with the decision-making strategies and the factors that in¯uence the process.They reported®ve methods that they found in use for contractor prequali®cation: dimensional weighting,two-step prequali®cation, dimension-wide strategy,prequali®cation formula,and subjective judgment.In the dimensional weighting method[22],the choice selection criteria and their weights are dependent on the owner.All contractors are ranked on the basis of the criteria.A contractor's total score is calculated by sum-ming their ranks multiplied by the weight of the respec-tive criteria.Then,contractors are ranked on the basis of their total scores,and this rank order of the con-tractors is used for prequali®cation.The problem with this method is deciding the weight of the respective cri-teria,something for which the AHP does provide a methodology.The two-step prequali®cation method[22]is a mod-i®cation of the dimensional weighting method.In the ®rst step,screening of contractors is done on pre-liminary factors.They must get through this step to be eligible for the second phase of prequali®cation.In the second step,the dimensional weighting technique is used for more specialized factors.This method is useful for quick removal of ineligible candidates.This is con-sistent with the`elimination by aspect'method sug-gested by Tversky[31].In dimension-wide strategy method[22],a list of the most important prequali®cation criteria is developed in descending order depending on how important the cri-teria is.Contractors are then evaluated on these factors. If a candidate fails to meet any of the criteria,the can-didate is removed from the prequali®cation process.The method continues until contractors are measured on all criteria[18].The prequali®cation formula method[22]prequali®es contractors on the basis of a formula that calculates the maximum capability of a contractor.The maximum capability is de®ned as the maximum amount of uncompleted work in progress that the contractor can have at any one time.In this method,the contractor's prequali®cation is dependent on the contractors max-imum capability,current uncompleted work and the size of the project under consideration.If the di erence between the contractor's capability and current uncom-pleted work is less than the project works,then the contractor is removed from the bidding process.The previous methods were devised with a common goal to introduce an e cient and systematic procedure for contractor prequali®cation.In some instances,own-ers may base their contractor selection decision on sub-jective judgment and not on a structured approach.The judgment may be in¯uenced by owner biases,such as previous experience with the contractor or how well the contractor's®eld sta operates.Aitah[1]studied the bid awarding system used in Saudi Arabia.He evaluated public building construc-tion projects,and concluded that the projects awarded to the lowest bidder have lower performance quality and schedule delays as compared to the projects which were awarded based on speci®c prequali®cation criteria.Al-Alawi[2]conducted a study on contractor pre-quali®cation for public projects in Bahrain.He surveyed the market and determined the most important criteria in the prequali®cation process,and developed a com-puterized tool for implementing it.Russel[21]analyzed contractor failure in the US and recommended that an owner should have two means of avoiding or minimize the impact of contractor failure: (1)analyzing the contractor quali®cation prior to con-tract award;and(2)monitoring the contractor's per-formance after contract award.Al-Ghobali[3]surveyed the Saudi construction mar-ket and listed a number of factors against which con-tractors should be considered for prequali®cation.This included experience,®nancial stability,past perfor-mance,current workload,management sta ,manpower resources availability,contractor organization,famil-iarity with the project's geographic location,project management capabilities,quality assurance and control, previous failure to complete a contract,equipment resources,purchase expertise and material handling, safety consciousness,claim attitude,planning/schedul-ing and cost control,and equipment repairing and maintenance yard facilities.22K.M.A.-S.Al-Harbi/International Journal of Project Management19(2001)19±276.ExampleA simpli®ed project example of contractor pre-quali®cation will be demonstrated here for illustration purposes.To simplify calculations,the factors that will be used in the project example for prequali®cation are experience,®nancial stability,quality performance, manpower resources,equipment resources,and current workload.Other criteria can be added if necessary, together with a suggestion that a computer be used to simplify calculations.Table3presents a project example for which con-tractors A,B,C,D and E wish to prequalify.An argu-ment could be presented that contractor E is not meeting the minimum criteria.Descriptions presented in Table3under`Contractor E',such as`bad organiza-tion'and`unethical techniques',quali®es him for immediate elimination from the list by the project owner.This is quite consistent with the method`elim-ination by aspect'suggested by Tversky[31].Never-theless,it is the choice of the decision-maker to eliminate contractor E immediately since he/she does not meet the minimum criteria.Contractor E could be left on the list(the choice in this paper for demon-stration purposes)so that he appears at the end of the list of`best contractors in descending order',as will be shown at the end of the example.The matter is safeguarded by checking the consistency of the pair-wise comparison which is a part of the AHP proce-dure.By following the AHP procedure described in the Section5,the hierarchy of the problem can be devel-oped as shown in Fig.1.For step3,the decision-makers have to indicate preferences or priority for each decision alternative in terms of how it contributes to each criter-ion as shown in Table4.Table3ExampleContractor A Contractor B Contractor C Contractor D Contractor EExperience5years experience7years experience8years experience10yearsexperience 15years experienceTwo similar projects One similar project No similar project Two similarprojects No similar projectSpecial procurement experience 1international projectFinancial stability $7M assets$10M assets$14M assets$11M assets$6M assets High growth rate$5.5M liabilities$6M liabilities$4M liabilities$1.5M liabilities No liability Part of a group ofcompaniesGood relationwith banksQuality performance Good organization Average organization Good organization Good organization Bad organizationC.M.personnel C.M.personnel C.M.team Good reputation Unethical techniques Good reputation Two delayed projects Government award Many certi®cates One project terminated Many certi®cates Safety program Good reputation Cost raised insome projectsAverage quality Safety program QA/QC programManpower resources 150labourers100labourers120labourers90labourers40labourers10special skilledlabourers200by subcontract Good skilled labors130bysubcontract260by subcontractAvailability in peaks25special skilledlabourersEquipment resources 4mixer machines6mixer machines1batching plant4mixer machines2mixer machines1excavator1excavator2concrete transferringtrucks1excavator10others15others1bulldozer2mixer machines9others2000sf steel formwork 20others1excavator6000sf wooden formwork15,000sf steel formwork1bulldozer16others17,000sf steel formworkCurrent works load 1big project ending2projects ending(1big+1medium)1medium project started2big projectsending2small projects started2projects in mid(1medium+1small)2projects ending(1big+1medium)1medium projectin mid3projects ending(2small+1medium) K.M.A.-S.Al-Harbi/International Journal of Project Management19(2001)19±2723Then,the following can be done manually or auto-matically by the AHP software,Expert Choice:1.synthesizing the pair-wise comparison matrix (example:Table 5);2.calculating the priority vector for a criterion such as experience (example:Table 5);3.calculating the consistency ratio;4.calculating l m x ;5.calculating the consistency index,CI;6.selecting appropriate value of the random con-sistency ratio from Table 2;and7.checking the consistency of the pair-wise compar-ison matrix to check whether the decision-maker's comparisons were consistent or not.The calculations for these items will be explained next for illustration purposes.Synthesizing the pair-wise comparison matrix is performed by dividing each elementof the matrix by its column total.For example,the value 0.08in Table 5is obtained by dividing 1(from Table 4)by 12.5,the sum of the column items in Table 4(1 3 2 6 1a 2).The priority vector in Table 5can be obtained by ®nding the row averages.For example,the priority of contractor A with respect to the criterion `experience'in Table 5is calculated by dividing the sum of the rows (0X 08 0X 082 0X 073 0X 078 0X 118)by the number of contractors (columns),i.e.,5,in order to obtain the value 0.086.The priority vector for experience,indi-cated in Table 5,is given below.0X 0860X 2490X 1520X 4570X 055P T T T T R Q U U U U S I Now,estimating the consistency ratio is asfollows:Fig.1.Hierarchy of the project example..Table 4Pair-wise comparison matrix for experience Exp.A B C D E A 11/31/21/62B 3121/24C 21/211/33D 62317E1/21/41/31/71Table 5Synthesized matrix for experience a Exp.A B C D E Priority vectorA 0.080.0820.0730.0780.1180.086B 0.240.2450.2930.2330.2350.249C 0.160.1220.1460.1550.1760.152D 0.480.4890.4390.4660.4120.457E0.040.0610.0490.0660.0590.0550X 999al m x 5X 037,gs 0X 00925, s 1X 12,g 0X 0082`0X 1OK.24K.M.A.-S.Al-Harbi /International Journal of Project Management 19(2001)19±270X08613261a2PT TT TRQU UU US0X2491a311a221a4PT TT TRQU UU US0X1521a22131a3PT TT TRQU UU US0X4571a61a21a311a7PT TT TRQU UU US0X05524371PT TT TRQU UU US0X4311X2590X7662X3120X276PT TT TRQU UU USweighted sum m trixPDividing all the elements of the weighted sum matrices by their respective priority vector element,we obtain:0X431 0X086 5X012Y1X2590X2495X056Y0X7660X1525X039Y2X312 0X457 5X059Y0X2760X0555X018QWe then compute the average of these values to obtain l m xl m x5X012 5X056 5X039 5X059 5X01855X037 R Now,we®nd the consistency index,CI,as follows:gs l m xÀnnÀ15X037À55À10X00925 SSelecting appropriate value of random consistency ratio,RI,for a matrix size of®ve using Table2,we®nd RI=1.12.We then calculate the consistency ratio,CR, as follows:g gss0X009251X120X0082 TAs the value of CR is less than0.1,the judgments areacceptable.Similarly,the pair-wise comparison matricesand priority vectors for the remaining criteria can befound as shown in Tables6±10,respectively.In addition to the pair-wise comparison for the deci-sion alternatives,we also use the same pair-wise com-parison procedure to set priorities for all six criteria interms of importance of each in contributing to theoverall goal.Table11shows the pair-wise comparisonmatrix and priority vector for the six criteria.Now,the Expert Choice software can do the restautomatically,or we manually combine the criterionpriorities and the priorities of each decision alternativerelative to each criterion in order to develop an overallpriority ranking of the decision alternative which istermed as the priority matrix(Table12).The calcula-tions for®nding the overall priority of contractors aregiven below for illustration purposes:yver ll priority of ontr tor e0X3720X0860X2930X4250X1560X2690X1510X0390X0840X0870X1440X222 UTable7Pair-wise comparison matrix for quality performance(QP)aQP A B C D E Priority vectorA171/3280.269B1/711/51/440.074C351490.461D1/241/4160.163E1/81/41/91/610.0310X998a l m x 5X38,gs 0X095, s 1X12,g 0X085`0X1OK.Table8Pair-wise comparison matrix for manpower resources(MPR)aMPR A B C D E Priority vectorA11/21/4250.151B211/3570.273C431460.449D1/21/51/4120.081E1/51/71/61/210.0450X999a l m x 5X24,gs 0X059, s 1X12,g 0X053`0X1OK.Table6Pair-wise comparison matrix for®nancial stability(FS)aFS A B C D E Priority vectorA163270.425B1/611/41/230.088C1/3411/350.178D1/223170.268E1/71/31/51/710.0390X998a l m x 5X32,gs 0X08, s 1X12,g 0X071`0X1OK.K.M.A.-S.Al-Harbi/International Journal of Project Management19(2001)19±2725。
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 38, NO. 2, MARCH 2008
A Comprehensive Survey of Multiagent ReinfoN
A
MULTIAGENT system [1] can be defined as a group of autonomous, interacting entities sharing a common environment, which they perceive with sensors and upon which they act with actuators [2]. Multiagent systems are finding applications in a wide variety of domains including robotic teams, distributed control, resource management, collaborative decision support systems, data mining, etc. [3], [4]. They may arise as the most natural way of looking at the system, or may provide an alternative perspective on systems that are originally regarded as centralized. For instance, in robotic teams, the control authority is naturally distributed among the robots [4]. In resource management, while resources can be managed by a central authority, identifying each resource with an agent may provide a helpful, distributed perspective on the system [5].
斯威特柱后衍生系统原理
斯威特柱后衍生系统原理解析1. 背景介绍斯威特柱后衍生系统(Swivel Posterior Derivation System)是一种用于语言模型的后处理技术。
在自然语言处理中,语言模型是指能够预测下一个词或者短语的模型。
斯威特柱后衍生系统通过对已生成的文本进行修正和改进,提高了生成文本的质量和流畅度。
2. 基本原理斯威特柱后衍生系统基于以下两个基本原理来进行文本修正和改进:2.1 后验概率调整在生成文本时,语言模型会根据上下文预测下一个词或短语。
然而,由于训练数据的限制以及模型复杂度等因素,语言模型可能会产生一些不合理或不流畅的词组。
斯威特柱后衍生系统通过计算每个词组出现的条件概率,并与一个经验阈值进行比较,来判断该词组是否需要调整。
如果该词组出现的条件概率低于经验阈值,则认为需要对其进行调整。
具体地,在判断一个词组是否需要调整时,斯威特柱后衍生系统会考虑以下几个因素: - 词组的上下文信息:词组出现的上下文信息对该词组的合理性有很大影响。
如果一个词组在给定的上下文中不够合理,那么可能需要进行调整。
- 词组的频率信息:如果一个词组在训练数据中出现的频率较低,那么它可能是一种不常见或者不合理的表达方式,需要进行调整。
- 词组的语法信息:如果一个词组违反了语法规则,那么它可能需要进行调整。
基于以上因素,斯威特柱后衍生系统可以计算出一个后验概率,用于判断是否需要对该词组进行调整。
如果后验概率低于经验阈值,则认为该词组需要调整。
2.2 调整算法当判断出一个词组需要调整时,斯威特柱后衍生系统会使用一种基于统计和规则的算法来对其进行修正。
具体地,在修正过程中,系统会考虑以下几个因素: - 上下文信息:通过分析上下文中其他相关的词汇和短语,系统可以更好地判断该词组应该如何调整。
- 语法规则:根据语法规则,系统可以调整词组的顺序、形态和结构,使其更符合语言的规范。
- 语义信息:通过分析词组的语义信息,系统可以对其进行合理的替换或修改。
RIC1在拟南芥根的生长发育过程中正调控生长素信号负调控ABA信号
Arabidopsis ROP-interactive CRIB motif-containing protein 1(RIC1)positively regulates auxin signalling and negatively regulates abscisic acid (ABA)signalling during root developmentYUNJUNG CHOI 1,YUREE LEE 1,SOO YOUNG KIM 3,YOUNGSOOK LEE 1,2&JAE-UNG HWANG 11POSTECH-UZH Global Research Laboratory,Division of Molecular Life Sciences,Pohang University of Science andTechnology (POSTECH),Pohang 790-784,Korea,2Division of Integrative Bioscience and Biotechnology,POSTECH,Pohang 790-784,Korea and 3Department of Molecular Biotechnology &Kumho Life Science Laboratory,College of Agriculture and Life Sciences,Chonnam National University,Gwangju 500-757,KoreaABSTRACTAuxin and abscisic acid (ABA)modulate numerous aspects of plant development together,mostly in opposite directions,suggesting that extensive crosstalk occurs between the signal-ling pathways of the two hormones.However,little is known about the nature of this crosstalk.We demonstrate that ROP-interactive CRIB motif-containing protein 1(RIC1)is involved in the interaction between auxin-and ABA-regulated root growth and lateral root formation.RIC1expression is highly induced by both hormones,and expressed in the roots of young seedlings.Whereas auxin-responsive gene induction and the effect of auxin on root growth and lateral root formation were suppressed in the ric1knockout,ABA-responsive gene induction and the effect of ABA on seed germination,root growth and lateral root for-mation were potentiated.Thus,RIC1positively regulates auxin responses,but negatively regulates ABA responses.Together,our results suggest that RIC1is a component of the intricate signalling network that underlies auxin and ABA crosstalk.Key-words :hormone crosstalk;lateral root;RIC protein;root growth;ROP GTPase.INTRODUCTIONAuxin and abscisic acid (ABA)are two major plant growth regulators.In general,auxin promotes the growth of vegeta-tive tissues,whereas ABA suppresses proliferation and confers stress resistance.For example,auxin promotes lateral root initiation,whereas ABA inhibits it.Auxin opens stomata,whereas ABA closes them.Such antagonistic effects of two hormones have been reported to regulate numerous stress responses and developmental and physiological pro-cesses in the plant (Gehring,Irving &Parish 1990;Casimiro et al .2003;Tanaka et al .2006).The interaction between auxin and ABA seems to be more complex during early seedlingdevelopment and primary root elongation than later on.Although both auxin and ABA are necessary for early seed-ling development,exogenously applied ABA,which presum-ably is applied at a significantly greater concentration than the endogenous hormone,inhibits growth.Primary root elon-gation is promoted by nanomolar amounts of both auxin and ABA (Gaither,Lutz &Forrence 1975;Mulkey,Kuzmanoff &Evans 1982),but is inhibited by higher concentrations of these hormones (Pilet &Chanson 1981;Mulkey et al .1982;Eliasson,Bertell &Bolander 1989).The observation that the two hormones function together to regulate many responses indicates that the signalling pathways that transduce the primary hormonal signals to downstream responses may intersect at specific points and/or involve common players.Indeed,the expression of ABA INSENSITIVE 3(ABI3)is activated by both auxin and ABA,and ABI3functions as a positive regulator of ABA-mediated inhibition of seed ger-mination and as a negative regulator of auxin-mediated lateral root formation and ABA-mediated inhibition of primary root growth (Brady et al .2003;Zhang,Garreton &Chua 2005).Given the vast array of responses of plants to auxin and ABA,one would expect that many such points of crosstalk exist;however,this aspect of auxin and ABA signal transduction remains largely unexplored.Rho family GTPases act as molecular switches that mediate diverse cellular responses to multiple extracellular signal including hormones (Bos 2000).ROP (Rho of plants;also called RAC)GTPases represent the sole Rho family of Ras-related G proteins in plants (Yang 2002),and the model plant Arabidopsis contains 11ROP GTPases in its genome (Bischoff et al .1999;Winge et al .2000;Zheng &Yang 2000).Several studies have reported that ROP GTPases play impor-tant roles in auxin-and ABA-related responses (Lemichez et al .2001;Tao,Cheung &Wu 2002;Zheng et al .2002;Bloch et al .2005;Tao et al .2005).Auxin treatment increases the amount of activated ROP GTPase in tobacco (Tao et al .2002)and Arabidopsis (Xu et al .2010;Lin et al .2012)seed-lings.Overexpression of wild-type or constitutively active forms of ROP GTPases stimulates auxin-related phenotypes and auxin-responsive gene expression in Arabidopsis and tobacco (Li et al .2001;Tao et al .2002,2005).Activated ROP GTPases promote the 26S proteasome-dependentCorrespondence:Y.Lee.Fax:+82542792199;e-mail:ylee@postech.ac.kr;J-U.Hwang.Fax:+82542792199;e-mail:thecute@postech.ac.krY.L.and J-U.H.contributed equally to the manuscript.Plant,Cell and Environment (2013)36,945–955doi:10.1111/pce.12028©2012Blackwell Publishing Ltd945degradation of auxin/indole-3-acetic acid(AUX/IAA)pro-teins in tobacco and Arabidopsis(Tao et al.2005).ROP GTPase mutations cause defects in auxin-dependent cell expansion(Fu et al.2005,2009;Xu et al.2010).In contrast to the positive role of ROP GTPases in the auxin response, ROP GTPases appear to be negative regulators of ABA responses.ABA treatment reduces the amount of activated ROP GTPase in Arabidopsis suspension cells and seedlings (Lemichez et al.2001).Expression of constitutively active forms of Arabidopsis ROP2and ROP6reduces sensitivity to ABA during seed germination(Li et al.2001)and stomatal closing(Lemichez et al.2001;Hwang et al.2011).The obser-vation that an Arabidopsis mutant that lacks ROP10expres-sion is hypersensitive to ABA,and that ROP10expression is suppressed by ABA,suggests the existence of an interesting feedback regulation loop in the ABA signalling pathway (Zheng et al.2002).RICs(ROP-interactive CRIB motif-containing proteins) are a unique group of interacting partners of activated ROP GTPases.RIC proteins interact with multiple ROP GTPases via their conserved CRIB motif,and link ROP proteins to diverse target molecules that bind to their variable domains (Yang2002).The11RIC genes present in Arabidopsis are categorized into four phylogenetic groups(Wu et al.2001;Gu et al.2005).However,knowledge on RIC functions is limited; RIC3and RIC4have been shown to regulate[Ca2+]cyt and F-actin dynamics during the polar growth of pollen tubes (Wu et al.2001;Gu et al.2005).RIC7is reported to interact with active ROP2in stomatal guard cells and to suppress light-induced stomatal opening(Jeon et al.2008).In epider-mal cells of the leaf and hypocotyl,RIC1suppresses aniso-tropic cell expansion by regulating microtubule(MT) dynamics(Fu et al.2005,2009;Xu et al.2010).RIC1is expressed in a broad range of tissues(Wu et al. 2001).However,the function of RIC1has been analysed mostly in the development of leaf pavement cells(Fu et al. 2005).In this cell type,RIC1is associated with MTs and regulates their assembly.In the lobe-forming regions of pave-ment cells,RIC1is inactivated by active ROP2,which sup-presses MT assembly,but promotesfine F-actin assembly and thereby induces outgrowth of the region.In contrast,in the neck-forming regions of pavement cells,RIC1is activated by active ROP6and then promotes the assembly of MTs,which limits the expansion of the region and results in the forma-tion of a narrow neck.The cortical MTs in the leaf pavement cells of ric1mutants are randomly organized,resulting in pavement cells with wider necks.This ROP6-RIC1-MT sig-nalling pathway seems to function in both hypocotyl elonga-tion and leaf epidermal cell development(Fu et al.2005, 2009).In pollen tubes,however,RIC1is localized to the apical plasma membrane,where MTs are absent,and over-expression of RIC1suppresses the depolarized tube growth induced by ROP1overexpression(Wu et al.2001).Given its broad expression pattern,RIC1may mediate diverse pro-cesses in the growth and development of plants,which have yet to be elucidated.In this work,we established that RIC1positively regulates the auxin effect and negatively regulates the ABA effect during root growth and lateral root development.These results will advance our current limited understanding on the mode of action of RIC1protein during regulation of plant development by auxin and ABA.MATERIALS AND METHODSPlant materials and growth conditionsSeeds of wild-type,ric1,and ric1/RIC1p:GFP:RIC1Arabi-dopsis thaliana plants(ecotype Ws)were surface sterilized, placed at4°C in the dark for2d,and then sown in half-strength Murashige and Skoog(MS)agar medium.Arabi-dopsis seedlings were grown in a growth chamber with a16h light/8h dark cycle at22°C.Isolation of the RIC1knockout mutantsSeeds of T-DNA insertion mutant for RIC1(ric1; FLAG_075E05)were obtained from Institut National de la Recherche Agronomique(INRA)-Versailles Genomic Resource Center(http://www-ijpb.versailles.inra.fr/en/cra/ cra_accueil.htm).Reverse transcriptase(RT)-PCR analysis using gene-specific primers confirmed that this is a null mutant.Primer information used for RT-PCR is available in Supporting Information Table S1.Complementation of ric1with RIC1p:GFP:RIC1For the ric1complementation assay,the RIC1promoter region(~2kb)and the RIC1open reading frame were indi-vidually obtained by PCR amplification.These genomic DNA fragments and GFP coding sequence were sequentially cloned into a pCR®8/GW/TOPO®vector(Invitrogen,Carls-bad,CA,USA),and then transferred into a pMDC100 gateway vector(Curtis&Grossniklaus2003).The RIC1p:GFP:RIC1construct was transformed into ric1plants by the Agrobacterium-mediatedfloral dipping method (Clough&Bent1998).The phenotypes of the T3seedlings of homozygous ric1/RIC1p:GFP:RIC1lines were observed. RIC1p:GUS expression assayThe genomic DNA fragment containing the promoter region (~2kb)andfirst exon of RIC1was amplified by PCR and fused to the GUS-coding region of the pMDC164vector (RIC1p:GUS).The RIC1p:GUS construct was transformed into wild-type Arabidopsis plants using the Agrobacterium-mediatedfloral dipping method(Clough&Bent1998). RIC1p:GUS expression was observed in T3plants from six independently transformed lines.Briefly,the seedlings of RIC1p:GUS were incubated in GUS staining buffer[100m m Na2HPO4(pH7.2),3m m potassium ferricyanide,3m m potas-sium ferrocyanide,10m m ethylenediaminetetraacetic acid (EDTA),0.1%Triton X-100,and2m m5-bromo-4-chloro-3-indolyl-b-D-glucuronide(Duchefa,Haarlem,The Nether-lands)]at37°C for12h.Chlorophyll was extracted in70% ethanol solution.946Y.Choi et al.©2012Blackwell Publishing Ltd,Plant,Cell and Environment,36,945–955Observation of the cellular localizationof GFP:RIC1Wild-type Arabidopsis plants were stably transformed with a GFP:RIC1construct under the control of CaMV35S pro-moter.In multiple independently transformed lines of Arabidopsis,the subcellular localization of GFP:RIC1was observed by using a Zeiss LSM510Meta Laser scanning microscope(Zeiss,/).To investigate the effects of auxin and ABA on the cellular localization of RIC1,7-day-old seedlings that stably express GFP:RIC1 were incubated in half-strength MS medium containing1m m auxin[naphthalene-1-acetic acid(NAA)]or10m m ABA for1h.Quantification of RIC and ABA-orauxin-responsive gene transcript levelsQuantitative real-time RT-PCR(Q-PCR)was used to quan-tify transcript levels of RIC genes,ABA-responsive genes and auxin-responsive genes.Total RNA was extracted from each sample and then reverse transcribed into cDNA. Q-PCR was carried out using a Takara TP800thermal cycler and Takara SYBR RT-PCR Kit(Takara Bio,Kyoto,Japan), following the manufacturer’s instructions.Transcript levels of RIC s,ABA-responsive genes and auxin-responsive genes were normalized against that of tubulin8. Measurement of lateral root formation and primary root growthTo examine the effects of ABA or auxin on lateral root formation and primary root growth,Arabidopsis seedlings were grown for4d under a16h photoperiod and then trans-ferred to fresh half-strength MS agar plates supplemented with the indicated concentrations of ABA or auxin.After an additional5–7d,the net elongation of primary roots was measured and the number of lateral roots was counted using a stereo microscope(Olympus SZX12,Tokyo,Japan). Seed germination assayFor germination assays,the seeds were placed in the dark at 4°C for2d and then sown on MS medium agar plates con-taining1%sucrose in the presence or absence of0–1.5m m concentrations of ABA.The seeds were incubated under a 16h photoperiod at22°C.Germinated seeds(as determined by cotyledon greening or radicle emergence)were scored every12h for6d.Germination ratio refers to the number of germinated seeds as a proportion of the total number of seeds tested.RESULTSRIC1expression is induced by both auxinand ABAMembers of the ROP small GTPase family are reported to mediate ABA and auxin responses(Li et al.2001;Zheng et al.2002;Tao et al.2005).We hypothesized that RIC proteins might serve an important intermediary in the ROP-mediated ABA and auxin signalling pathways.If this hypothesis was true,then the level of the RIC proteins may be substantially regulated by ABA and auxin.Thus,we tested the effect of auxin and ABA on the expression of10out of the11Arabi-dopsis RIC genes(Fig.1).Arabidopsis seedlings were grown on half-strength MS medium for7d and then treated with 1m m NAA or10m m ABA for1h.Total RNA was isolated from these seedlings and RIC gene transcript levels were examined using quantitative real-time PCR(Q-PCR).Upon NAA and ABA treatment,the transcript level of many RIC s (RIC2,3,4,5,7,9and11)increased,but the increase in RIC1 was the highest.Therefore,we chose to focus our analysis on RIC1.Loss of RIC1expression alters gene induction by auxin and ABAAuxin and ABA induce the expression of sets of genes, which are known as auxin-responsive and ABA-responsive genes,respectively(Brady et al.2003;Zhang et al.2005;Li et al.2009).To examine the involvement of RIC1in auxin and ABA signal transduction,we evaluated the effect of RIC1knockout(ric1)on the expression levels of typical auxin-and ABA-responsive genes.ric1,a T-DNA insertion mutant(Stock No.FLAG_075E05),was obtained from INRA-Versailles Genomic Resource Center(http://www-ijpb.versailles.inra.fr/en/cra/cra_accueil.htm).RT-PCR analy-sis confirmed that the T-DNA insertion into the fourth exon completely blocked the expression of RIC1in ric1(Fig.2a). The small auxin-up RNAs(SAURs)encode short tran-scripts that accumulate rapidly upon auxin treatment(Li et al.2009).IAA6and IAA19are members of INDOLE-3-ACETIC ACID/AUXIN(IAA/AUX)genes andtheir Figure1.Expression of RIC genes were induced by auxin and abscisic acid(ABA).Seven-day-old Arabidopsis seedlings were treated without or with1m m auxin[naphthalene-1-acetic acid (NAA)]or10m m ABA for1h,and total RNA was isolated from seedlings for Q-PCR analysis.The transcript levels of RIC genes were normalized against the transcript level of Tubulin8,which served as the internal control,and are presented as values relative to the untreated control.Data are meansϮSEM of three to eight biological replicates.Asterisks indicate values that are statistically significantly different from the untreated control(***P<0.005;**P<0.001;*P<0.05).RIC1regulates root development947©2012Blackwell Publishing Ltd,Plant,Cell and Environment,36,945–955948Y.Choi et al.©2012Blackwell Publishing Ltd,Plant,Cell and Environment,36,945–955expressions are induced by auxin(Abel,Nguyen&Theologis 1995;Tatematsu et al.2004).Using Q-PCR analysis,we com-pared the transcript levels of SAUR and IAA genes in ric1 seedlings with those in wild-type seedlings(Fig.2b).Under control conditions without NAA treatment,the transcript levels of these genes in ric1seedlings were similar to or slightly higher than those in wild-type seedlings(Fig.2b).In 7-day-old wild-type seedlings,treatment with1m m NAA for 1h induced a two-to fourfold increase in SAUR gene expres-sion(Fig.2b).Interestingly,however,the induction of SAUR genes by1m m NAA was suppressed in ric1seedlings (Fig.2b);SAUR9transcript level increased3.3Ϯ0.1-fold in the wild type,but1.7Ϯ0.1-fold in ric1(t-test,P<0.005); SAUR15transcript level increased3.9Ϯ0.6-fold in the wild type,but1.8Ϯ0.2-fold in ric1(t-test,P<0.01);SAUR23 transcript level increased3.0Ϯ0.3-fold in the wild type,but 1.4Ϯ0.2-fold in ric1(t-test,P<0.005);SAUR62transcript level increased2.5Ϯ0.2-fold in the wild type but1.4Ϯ0.2-fold in ric1(t-test,P<0.001);and SAUR66transcript level increased3.9Ϯ0.4-fold in the wild type,but1.7Ϯ0.2-fold in ric1(t-test,P<0.001).Similarly,induction of IAA6and IAA19upon NAA treatment was suppressed by ric1(Fig.2b bottom panel),whereas the transcript level of IAA6 increased21.9Ϯ1.3-fold in the wild type,but only11.3Ϯ0.2-fold in ric1(P<0.05),and the transcript level of IAA19 increased27.1Ϯ3.3-fold in the wild type,but only13.1Ϯ1.9-fold in ric1(P<0.05).These results indicate that RIC1is involved in the control of the auxin signalling pathway.ABI3,ABI5,responsive to ABA18(RAB18),and respon-sive to dehydration29A(RD29A)and29B(RD29B)are well-characterized ABA-responsive genes that play critical roles in ABA signalling(Parcy et al.1994;Finkelstein& Lynch2000;Lopez-Molina&Chua2000;Hoth et al.2002; Kang et al.2010).To analyse the involvement of RIC1in ABA signalling,we gauged the effects of ABA on expres-sions of these genes in the roots of wild-type and ric1seed-lings(Fig.2c).Seven-day-old seedlings were incubated in half-strength liquid MS medium in the presence or absence of0.5m m ABA for1h.Under control conditions(i.e.in the absence of ABA),transcript levels of ABI3,ABI5,RD29A, RD29B and RAB18were slightly higher in ric1seedlings than in wild-type seedlings,and this difference was further increased after ABA treatment(Fig.2c).Upon ABA treat-ment,ric1seedlings exhibited much higher transcript levels of thosefive ABA-responsive genes,compared with wild-type seedlings(Fig.2c);ABI3transcript level increased 2.4Ϯ0.5-fold in the wild type,but 5.5Ϯ0.9-fold in ric1 (P<0.01);ABI5transcript level increased5.9Ϯ0.4-fold in the wild type,but12.9Ϯ1.9in ric1(P<0.005);RD29A tran-script level increased12.3Ϯ3.9-fold in the wild type,but 17.4Ϯ6.6-fold in ric1(P<0.06);RD29B transcript level increased5.5Ϯ1.2-fold in the wild type,but10.9Ϯ0.8-fold in ric1(P<0.05);and RAB18transcript level increased 4.6Ϯ1.1-fold in the wild type,but8.0Ϯ1.3-fold in ric1 (P<0.01).In summary,RIC1knockout suppressed the induction of auxin-responsive genes by auxin,but promoted the induction of ABA-responsive genes by ABA.These results suggest that RIC1exerts opposite regulatory functions in the auxin and ABA signalling pathways.RIC1is expressed in the roots ofyoung seedlingsTo identify which auxin-and ABA-mediated processes are regulated by RIC1,wefirst determined the tissue-specific and developmental stage-specific expression of RIC1.The genomic DNA region containing the RIC1promoter(~2kb) and thefirst exon was fused to the GUS-coding region (RIC1p:GUS),and introduced into wild-type plants. RIC1p:GUS expression was observed in T3seeds and seed-lings from six independently transformed Arabidopsis lines (Fig.3a,b).In germinating seeds and young seedlings,the RIC1p:GUS signal was evident in roots.In germinating seeds,RIC1p:GUS signal was limited to the embryonic root tip(Fig.3a,left),and in seedlings at1–3d after sowing,RIC1p:GUS extended the expression to other parts of root including differentiation zone,root hairs and root–shoot junction(Fig.3a,right).In the roots of2-week-old plants,RIC1p:GUS signal was detected in root tips and also in maturation zone,where lateral roots grow out(Fig.3b).RIC1p:GUS signal was strongly detected in columella cells from the root tip, (Fig.3b-d).In maturation zone of root,cells surrounding emerged lateral root(Fig.3b-b)and epidermal cells at the base of lateral roots(Fig.3b-c)showed clear RIC1p:GUS signals.These RIC1expression patterns indicate that RIC1is likely to be involved in the regulation of seed germination, early seedling development and root development.In addition to being expressed in the roots,RIC1p:GUS was also expressed in the hypocotyls,petioles,and weakly in the leaves of young seedlings(Fig.3a,b).In Arabidopsis plants of later development stages,expression of RIC1p:GUS was weak except inflowers,where RIC1expression was pre-viously reported(Wu et al.2001;Fu et al.2005,2009;Xu et al.Figure2.ric1knockout mutation altered induction of auxin-and abscisic acid(ABA)-responsive genes.(a)Schematic structure of theRIC1gene(left).The triangle indicates the T-DNA insertion site in ric1.Exons are represented as boxes and introns as lines.RT-PCR analysis using a RIC1-specific primer set(RT-F and RT-R)shows that ric1is a true null mutant(right).Tubulin8was used as an internal control.(b)Expression of SAUR9,SAUR15,SAUR23,SAUR62,SAUR66,IAA6and IAA19in plants treated or not with1m m auxin [naphthalene-1-acetic acid(NAA)].(c)Expression of ABI3,ABI5,RD29A,RD29B and RAB18in plants treated or not with0.5m m ABA.Q-PCR analyses of transcripts of auxin-and ABA-responsive genes were performed using total RNA isolated from the roots of8-day-old seedlings after1h of treatment without or with auxin or ABA.Data were normalized using Tubulin8as an internal control,and are presented as values relative to the untreated wild type(WT).Data are meansϮSEM of four independent experiments.Asterisks indicate values that are significantly different from those of the WT(***P<0.005;**P<0.01;*P<0.05;#P<0.06).RIC1regulates root development949©2012Blackwell Publishing Ltd,Plant,Cell and Environment,36,945–9552010);RIC1p:GUS signal was strongly observed in the anthers and mature pollen grains (Supporting Information Fig.S1).RIC1knockout suppresses the effect of auxin on lateral root formation and primary root elongationAs RIC1is expressed in root (Fig.3)and RIC1expression is up-regulated by auxin (Fig.1),we examined whether auxin-dependent root growth and lateral root formation were affected in the ric1mutant (Fig.4).Arabidopsis seedlings were grown on half-strength MS medium for 4d and then transferred to fresh half-strength MS medium supplemented with various concentrations (0–100n m )of NAA.After 5d,the number of lateral roots (including newly emerged ones)was counted.Auxin promoted the formation of lateral roots in different genotypes,including the wild type,ric1,and ric1/RIC1p:GFP:RIC1(complementation lines,C1and C2;Fig.4a);however,this effect was significantly less in ric1than in the wild type and complementation lines (Fig.4b).InFigure 3.RIC1expression in Arabidopsis plants.(a)One dayafter sowing (DAS),an embryo exhibited RIC1p::GUS signal at the root tip (left,indicated by arrow).Seed coat was removed after GUS staining for observation.A young seedling that had justgerminated but had not yet undergone cotyledon expansion (right;1–3DAS)exhibited relatively stronger RIC1p::GUS signal in the root tip and differentiation zone of the root including the root hairs.Bar =300m m.(b)A 2-week-old seedling displayed RIC1p::GUS signal in the root tip and maturation zone withemerged lateral roots,and,weakly,in the shoot.(b-a )Bar =1cm.(b-b ),(b-c )and (b-d )are enlarged images of maturation zone with an emerged lateral root,a lateral root with GUS staining at the base,and a root tip with GUS staining in columellacells.Figure 4.Auxin responses were reduced in ric1plants.Arabidopsis seedlings were grown vertically on half-strength Murashige and Skoog (MS)medium for 4d,transferred to fresh half-strength MS medium supplemented or not with 0–100n m auxin [naphthalene-1-acetic acid (NAA)],and grown for anadditional 5–7d.(a)Levels of RIC1transcript in wild-type (WT),ric1,and ric1/RIC1p:GFP:RIC1(lines C1and C2)plants.RIC1expression in seedlings was quantified using Q-PCR and presented values relative to that of WT.Data are means ϮSEM of four independent experiments.(b)Number of lateral roots formed in the absence or presence of NAA (means ϮSEM of 28seedlings from four independent experiments),measured 5d after transfer to medium supplemented with or without NAA.Asterisks indicate values that are significantly different from those of the WT atP <0.05.(c)Relative values of lateral root number (mean ϮSEM,n =28).Values in (b)were normalized to the values of non-treated controls.Asterisks indicate values that are significantly different from those of the WT (***P <0.005).(d)Primary root elongation in the absence or presence of NAA (means ϮSEM of 28seedlings from four independent experiments).The net primary root growth was measured 7d after transfer to medium supplemented with or without NAA.Asterisks indicate values that are significantly different from those of the WT (**P <0.05;*P <0.1).(e)Relative values of net primary root elongation (mean ϮSEM,n =28).Values in (d)were normalized to the values ofnon-treated controls.Asterisks indicate values that are significantly different from those of the WT (***P <0.005;*P <0.05).950Y.Choi et al .©2012Blackwell Publishing Ltd,Plant,Cell and Environment,36,945–955the absence of exogenous auxin(0n m NAA),ric1plants produced more lateral roots than did the wild type and ric1 complementation lines(n=28,N=4,P<0.05;Fig.4b). However,in the presence of auxin,lateral root number was less in ric1plants than in the wild type(Fig.4b,P<0.05). Whereas80and100n m NAA increased the number of lateral roots per unit length(cm)of primary root in wild-type seedlings to734and920%of non-treated control values, respectively,the same concentration of NAA increased this number in ric1to466and613%of control values,respec-tively(Fig.4c).This alteration in lateral root formation in ric1 plants was completely reversed by the expression of RIC1 driven by its native promoter(ric1/RIC1p:GFP:RIC1).In two independent ric1/RIC1p:GFP:RIC1lines(C1and C2), lateral root formation was recovered to wild-type levels both in the absence and presence of NAA(Fig.4b,c).The effect of RIC1knockout on primary root elongation was also examined(Fig.4d,e).Seven days after transfer to half-strength MS medium supplemented or not with NAA, the net elongation of primary roots was measured.In medium lacking NAA,ric1mutants had reduced primary root elongation compared with wild-type plants(n=28, N=4,P<0.05);the net primary root elongation of wild-type seedlings was4.9Ϯ0.16cm,while that of ric1seedlings was 4.3Ϯ0.22cm(Fig.4d).NAA(50–100n m)inhibited primary root elongation in both ric1and wild-type seedlings (Fig.4d,e).However,ric1seedlings were less sensitive than the wild type to NAA(n=28,N=4,P<0.1);whereas100n m NAA reduced primary root elongation in wild-type seedlings by34.4%(to3.2Ϯ0.16cm),it inhibited that in ric1seedlings by only19.4%(to3.5Ϯ0.13cm).Primary root elongation in the complementation lines(C1and C2)was similar to that in the wild type,in both the absence and presence of NAA (Fig.4d,e).These results suggest that RIC1participates in auxin-regulated lateral root development and primary root elongation.RIC1knockout enhances the effect of ABA on seed germination,lateral root formation and primary root elongationWe then tested whether RIC1knockout also altered the plant’s response to ABA by comparing seed germination and root development in ric1and wild-type plants treated with ABA(Figs5&6).Seeds were sown on half-strength MS medium after2d of stratification.In the presence of0.5m m ABA,seed germination,as gauged by cotyledon greening, was delayed to a greater extent in ric1than in wild-type seeds (Fig.5a,b);whereas only25%of ric1seedlings exhibited cotyledon greening84h after sowing,66%of wild-type seed-lings exhibited greening(Fig.5b).Similar enhanced sensitiv-ity to ABA in inhibition of seed germination was observed when germination rate was analysed based on radicle emer-gence(Supporting Information Fig.S2).Seed germination rates in the ric1/RIC1p:GFP:RIC1lines were restored to wild-type levels in the presence of0.5m m ABA,confirming that loss of RIC1expression was responsible for the enhanced suppression of seed germination by ABA(Fig.5b).The delayed germination of ric1seeds in the presence of ABA was not due to a defect in seed development,because the germination rate of ric1seeds in the absence of ABA was not significantly different from that of wild type(Fig.5c and Supporting Information Fig.S2b).ABA was reported to inhibit root elongation and lateral root development(Pilet&Chanson1981).We compared primary root elongation and lateral root number in ric1and wild-type plants in the presence and absence ofexogenous Figure5.Inhibition of seed germination by abscisic acid(ABA) was enhanced in the ric1mutant.(a)Representative photographs showing young seedlings of wild type(WT),ric1,and the tworic1/RIC1p:GFP:RIC1lines(C1and C2)in the presence of0.5m m ABA(taken96h after sowing).(b)Seed germination rate in the presence of0.5m m ABA,measured as a percentage of seedlings with green cotyledons at the indicated time points after sowing on half-strength Murashige and Skoog(MS)medium supplemented with ABA.Data are meansϮSEM of three independent experiments.(c)Seed germination rate in the absence of ABA. There was no significant difference between genotypes.RIC1regulates root development951©2012Blackwell Publishing Ltd,Plant,Cell and Environment,36,945–955。
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nutz
Bruno Bouchard
†
Ludovic Moreau June 28, 2012
§
arXiv:1206.6325v1 [math.OC] 27 Jun 2012
We study a stochastic game where one player tries to find a strategy such that the state process reaches a target of controlled-loss-type, no matter which action is chosen by the other player. We provide, in a general setup, a relaxed geometric dynamic programming for this problem and derive, for the case of a controlled SDE, the corresponding dynamic programming equation in the sense of viscosity solutions. As an example, we consider a problem of partial hedging under Knightian uncertainty. Keywords Stochastic target; Stochastic game; Geometric dynamic programming principle; Viscosity solution AMS 2000 Subject Classifications 49N70; 91A23; 91A60; 49L20; 49L25
Universities in Evolutionary Systems(系统变革中的大学)
Universities in Evolutionary Systems of InnovationMarianne van der Steen and Jurgen EndersThis paper criticizes the current narrow view on the role of universities in knowledge-based economies.We propose to extend the current policy framework of universities in national innovation systems(NIS)to a more dynamic one,based on evolutionary economic principles. The main reason is that this dynamic viewfits better with the practice of innovation processes. We contribute on ontological and methodological levels to the literature and policy discussions on the effectiveness of university-industry knowledge transfer and the third mission of uni-versities.We conclude with a discussion of the policy implications for the main stakeholders.1.IntroductionU niversities have always played a major role in the economic and cultural devel-opment of countries.However,their role and expected contribution has changed sub-stantially over the years.Whereas,since1945, universities in Europe were expected to con-tribute to‘basic’research,which could be freely used by society,in recent decades they are expected to contribute more substantially and directly to the competitiveness offirms and societies(Jaffe,2008).Examples are the Bayh–Dole Act(1982)in the United States and in Europe the Lisbon Agenda(2000–2010) which marked an era of a changing and more substantial role for universities.However,it seems that this‘new’role of universities is a sort of universal given one(ex post),instead of an ex ante changing one in a dynamic institutional environment.Many uni-versities are expected nowadays to stimulate a limited number of knowledge transfer activi-ties such as university spin-offs and university patenting and licensing to demonstrate that they are actively engaged in knowledge trans-fer.It is questioned in the literature if this one-size-fits-all approach improves the usefulness and the applicability of university knowledge in industry and society as a whole(e.g.,Litan et al.,2007).Moreover,the various national or regional economic systems have idiosyncratic charac-teristics that in principle pose different(chang-ing)demands towards universities.Instead of assuming that there is only one‘optimal’gov-ernance mode for universities,there may bemultiple ways of organizing the role of univer-sities in innovation processes.In addition,we assume that this can change over time.Recently,more attention in the literature hasfocused on diversity across technologies(e.g.,King,2004;Malerba,2005;Dosi et al.,2006;V an der Steen et al.,2008)and diversity offormal and informal knowledge interactionsbetween universities and industry(e.g.,Cohenet al.,1998).So far,there has been less atten-tion paid to the dynamics of the changing roleof universities in economic systems:how dothe roles of universities vary over time andwhy?Therefore,this article focuses on the onto-logical premises of the functioning of univer-sities in innovation systems from a dynamic,evolutionary perspective.In order to do so,we analyse the role of universities from theperspective of an evolutionary system ofinnovation to understand the embeddednessof universities in a dynamic(national)systemof science and innovation.The article is structured as follows.InSection2we describe the changing role ofuniversities from the static perspective of anational innovation system(NIS),whereasSection3analyses the dynamic perspective ofuniversities based on evolutionary principles.Based on this evolutionary perspective,Section4introduces the characteristics of a LearningUniversity in a dynamic innovation system,summarizing an alternative perception to thestatic view of universities in dynamic economicsystems in Section5.Finally,the concludingVolume17Number42008doi:10.1111/j.1467-8691.2008.00496.x©2008The AuthorsJournal compilation©2008Blackwell Publishingsection discusses policy recommendations for more effective policy instruments from our dynamic perspective.2.Static View of Universities in NIS 2.1The Emergence of the Role of Universities in NISFirst we start with a discussion of the literature and policy reports on national innovation system(NIS).The literature on national inno-vation systems(NIS)is a relatively new and rapidly growingfield of research and widely used by policy-makers worldwide(Fagerberg, 2003;Balzat&Hanusch,2004;Sharif,2006). The NIS approach was initiated in the late 1980s by Freeman(1987),Dosi et al.(1988)and Lundvall(1992)and followed by Nelson (1993),Edquist(1997),and many others.Balzat and Hanusch(2004,p.196)describe a NIS as‘a historically grown subsystem of the national economy in which various organizations and institutions interact with and influence one another in the carrying out of innovative activity’.It is about a systemic approach to innovation,in which the interaction between technology,institutions and organizations is central.With the introduction of the notion of a national innovation system,universities were formally on the agenda of many innovation policymakers worldwide.Clearly,the NIS demonstrated that universities and their interactions with industry matter for innova-tion processes in economic systems.Indeed, since a decade most governments acknowl-edge that interactions between university and industry add to better utilization of scienti-fic knowledge and herewith increase the innovation performance of nations.One of the central notions of the innovation system approach is that universities play an impor-tant role in the development of commercial useful knowledge(Edquist,1997;Sharif, 2006).This contrasts with the linear model innovation that dominated the thinking of science and industry policy makers during the last century.The linear innovation model perceives innovation as an industry activity that‘only’utilizes fundamental scientific knowledge of universities as an input factor for their innovative activities.The emergence of the non-linear approach led to a renewed vision on the role–and expectations–of universities in society. Some authors have referred to a new social contract between science and society(e.g., Neave,2000).The Triple Helix(e.g.,Etzkowitz &Leydesdorff,1997)and the innovation system approach(e.g.,Lundvall,1988)and more recently,the model of Open Innovation (Chesbrough,2003)demonstrated that innova-tion in a knowledge-based economy is an inter-active process involving many different innovation actors that interact in a system of overlapping organizationalfields(science, technology,government)with many interfaces.2.2Static Policy View of Universities in NIS Since the late1990s,the new role of universi-ties in NIS thinking emerged in a growing number of policy studies(e.g.,OECD,1999, 2002;European Commission,2000).The con-tributions of the NIS literature had a large impact on policy makers’perception of the role of universities in the national innovation performance(e.g.,European Commission, 2006).The NIS approach gradually replaced linear thinking about innovation by a more holistic system perspective on innovations, focusing on the interdependencies among the various agents,organizations and institutions. NIS thinking led to a structurally different view of how governments can stimulate the innovation performance of a country.The OECD report of the national innovation system (OECD,1999)clearly incorporated these new economic principles of innovation system theory.This report emphasized this new role and interfaces of universities in knowledge-based economies.This created a new policy rationale and new awareness for technology transfer policy in many countries.The NIS report(1999)was followed by more attention for the diversity of technology transfer mecha-nisms employed in university-industry rela-tions(OECD,2002)and the(need for new) emerging governance structures for the‘third mission’of universities in society,i.e.,patent-ing,licensing and spin-offs,of public research organizations(OECD,2003).The various policy studies have in common that they try to describe and compare the most important institutions,organizations, activities and interactions of public and private actors that take part in or influence the innovation performance of a country.Figure1 provides an illustration.Thefigure demon-strates the major building blocks of a NIS in a practical policy setting.It includesfirms,uni-versities and other public research organiza-tions(PROs)involved in(higher)education and training,science and technology.These organizations embody the science and tech-nology capabilities and knowledge fund of a country.The interaction is represented by the arrows which refer to interactive learn-ing and diffusion of knowledge(Lundvall,Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing1992).1The building block ‘Demand’refers to the level and quality of demand that can be a pull factor for firms to innovate.Finally,insti-tutions are represented in the building blocks ‘Framework conditions’and ‘Infrastructure’,including various laws,policies and regula-tions related to science,technology and entre-preneurship.It includes a very broad array of policy issues from intellectual property rights laws to fiscal instruments that stimulate labour mobility between universities and firms.The figure demonstrates that,in order to improve the innovation performance of a country,the NIS as a whole should be conducive for innovative activities in acountry.Since the late 1990s,the conceptual framework as represented in Figure 1serves as a dominant design for many comparative studies of national innovation systems (Polt et al.,2001;OECD,2002).The typical policy benchmark exercise is to compare a number of innovation indicators related to the role of university-industry interactions.Effective performance of universities in the NIS is judged on a number of standardized indica-tors such as the number of spin-offs,patents and licensing.Policy has especially focused on ‘getting the incentives right’to create a generic,good innovative enhancing context for firms.Moreover,policy has also influ-enced the use of specific ‘formal’transfer mechanisms,such as university patents and university spin-offs,to facilitate this collabo-ration.In this way best practice policies are identified and policy recommendations are derived:the so-called one-size-fits-all-approach.The focus is on determining the ingredients of an efficient benchmark NIS,downplaying institutional diversity and1These organizations that interact with each other sometimes co-operate and sometimes compete with each other.For instance,firms sometimes co-operate in certain pre-competitive research projects but can be competitors as well.This is often the case as well withuniversities.Figure 1.The Benchmark NIS Model Source :Bemer et al.(2001).Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingvariety in the roles of universities in enhanc-ing innovation performance.The theoretical contributions to the NIS lit-erature have outlined the importance of insti-tutions and institutional change.However,a further theoretical development of the ele-ments of NIS is necessary in order to be useful for policy makers;they need better systemic NIS benchmarks,taking systematically into account the variety of‘national idiosyncrasies’. Edquist(1997)argues that most NIS contribu-tions are more focused onfirms and technol-ogy,sometimes reducing the analysis of the (national)institutions to a left-over category (Geels,2005).Following Hodgson(2000), Nelson(2002),Malerba(2005)and Groenewe-gen and V an der Steen(2006),more attention should be paid to the institutional idiosyncra-sies of the various systems and their evolution over time.This creates variety and evolving demands towards universities over time where the functioning of universities and their interactions with the other part of the NIS do evolve as well.We suggest to conceptualize the dynamics of innovation systems from an evolutionary perspective in order to develop a more subtle and dynamic vision on the role of universities in innovation systems.We emphasize our focus on‘evolutionary systems’instead of national innovation systems because for many universities,in particular some science-based disciplinaryfields such as biotechnology and nanotechnology,the national institutional environment is less relevant than the institu-tional and technical characteristics of the technological regimes,which is in fact a‘sub-system’of the national innovation system.3.Evolutionary Systems of Innovation as an Alternative Concept3.1Evolutionary Theory on Economic Change and InnovationCharles Darwin’s The Origin of Species(1859)is the foundation of modern thinking about change and evolution(Luria et al.,1981,pp. 584–7;Gould,1987).Darwin’s theory of natural selection has had the most important consequences for our perception of change. His view of evolution refers to a continuous and gradual adaptation of species to changes in the environment.The idea of‘survival of thefittest’means that the most adaptive organisms in a population will survive.This occurs through a process of‘natural selection’in which the most adaptive‘species’(organ-isms)will survive.This is a gradual process taking place in a relatively stable environment, working slowly over long periods of time necessary for the distinctive characteristics of species to show their superiority in the‘sur-vival contest’.Based on Darwin,evolutionary biology identifies three levels of aggregation.These three levels are the unit of variation,unit of selection and unit of evolution.The unit of varia-tion concerns the entity which contains the genetic information and which mutates fol-lowing specific rules,namely the genes.Genes contain the hereditary information which is preserved in the DNA.This does not alter sig-nificantly throughout the reproductive life-time of an organism.Genes are passed on from an organism to its successors.The gene pool,i.e.,the total stock of genetic structures of a species,only changes in the reproduction process as individuals die and are born.Par-ticular genes contribute to distinctive charac-teristics and behaviour of species which are more or less conducive to survival.The gene pool constitutes the mechanism to transmit the characteristics of surviving organisms from one generation to the next.The unit of selection is the expression of those genes in the entities which live and die as individual specimens,namely(individual) organisms.These organisms,in their turn,are subjected to a process of natural selection in the environment.‘Fit’organisms endowed with a relatively‘successful’gene pool,are more likely to pass them on to their progeny.As genes contain information to form and program the organisms,it can be expected that in a stable environment genes aiding survival will tend to become more prominent in succeeding genera-tions.‘Natural selection’,thus,is a gradual process selecting the‘fittest’organisms. Finally,there is the unit of evolution,or that which changes over time as the gene pool changes,namely populations.Natural selec-tion produces changes at the level of the population by‘trimming’the set of genetic structures in a population.We would like to point out two central principles of Darwinian evolution.First,its profound indeterminacy since the process of development,for instance the development of DNA,is dominated by time at which highly improbable events happen (Boulding,1991,p.12).Secondly,the process of natural selection eliminates poorly adapted variants in a compulsory manner,since indi-viduals who are‘unfit’are supposed to have no way of escaping the consequences of selection.22We acknowledge that within evolutionary think-ing,the theory of Jean Baptiste Lamarck,which acknowledges in essence that acquired characteris-tics can be transmitted(instead of hereditaryVolume17Number42008©2008The AuthorsJournal compilation©2008Blackwell PublishingThese three levels of aggregation express the differences between ‘what is changing’(genes),‘what is being selected’(organisms),and ‘what changes over time’(populations)in an evolutionary process (Luria et al.,1981,p.625).According to Nelson (see for instance Nelson,1995):‘Technical change is clearly an evolutionary process;the innovation generator keeps on producing entities superior to those earlier in existence,and adjustment forces work slowly’.Technological change and innovation processes are thus ‘evolutionary’because of its characteristics of non-optimality and of an open-ended and path-dependent process.Nelson and Winter (1982)introduced the idea of technical change as an evolutionary process in capitalist economies.Routines in firms function as the relatively durable ‘genes’.Economic competition leads to the selection of certain ‘successful’routines and these can be transferred to other firms by imitation,through buy-outs,training,labour mobility,and so on.Innovation processes involving interactions between universities and industry are central in the NIS approach.Therefore,it seems logical that evolutionary theory would be useful to grasp the role of universities in innovation pro-cesses within the NIS framework.3.2Evolutionary Underpinnings of Innovation SystemsBased on the central evolutionary notions as discussed above,we discuss in this section how the existing NIS approaches have already incor-porated notions in their NIS frameworks.Moreover,we investigate to what extent these notions can be better incorporated in an evolu-tionary innovation system to improve our understanding of universities in dynamic inno-vation processes.We focus on non-optimality,novelty,the anti-reductionist methodology,gradualism and the evolutionary metaphor.Non-optimality (and Bounded Rationality)Based on institutional diversity,the notion of optimality is absent in most NIS approaches.We cannot define an optimal system of innovation because evolutionary learning pro-cesses are important in such systems and thus are subject to continuous change.The system never achieves an equilibrium since the evolu-tionary processes are open-ended and path dependent.In Nelson’s work (e.g.,1993,1995)he has emphasized the presence of contingent out-comes of innovation processes and thus of NIS:‘At any time,there are feasible entities not present in the prevailing system that have a chance of being introduced’.This continuing existence of feasible alternative developments means that the system never reaches a state of equilibrium or finality.The process always remains dynamic and never reaches an optimum.Nelson argues further that diversity exists because technical change is an open-ended multi-path process where no best solu-tion to a technical problem can be identified ex post .As a consequence technical change can be seen as a very wasteful process in capitalist economies with many duplications and dead-ends.Institutional variety is closely linked to non-optimality.In other words,we cannot define the optimal innovation system because the evolutionary learning processes that take place in a particular system make it subject to continuous change.Therefore,comparisons between an existing system and an ideal system are not possible.Hence,in the absence of any notion of optimality,a method of comparing existing systems is necessary.According to Edquist (1997),comparisons between systems were more explicit and systematic than they had been using the NIS approaches.Novelty:Innovations CentralNovelty is already a central notion in the current NIS approaches.Learning is inter-preted in a broad way.Technological innova-tions are defined as combining existing knowledge in new ways or producing new knowledge (generation),and transforming this into economically significant products and processes (absorption).Learning is the most important process behind technological inno-vations.Learning can be formal in the form of education and searching through research and development.However,in many cases,innovations are the consequence of several kinds of learning processes involving many different kinds of economic agents.According to Lundvall (1992,p.9):‘those activities involve learning-by-doing,increasing the efficiency of production operations,learning-characteristics as in the theory of Darwin),is acknowledged to fit better with socio-economic processes of technical change and innovation (e.g.,Nelson &Winter,1982;Hodgson,2000).Therefore,our theory is based on Lamarckian evolutionary theory.However,for the purpose of this article,we will not discuss the differences between these theo-ries at greater length and limit our analysis to the fundamental evolutionary building blocks that are present in both theories.Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingby-using,increasing the efficiency of the use of complex systems,and learning-by-interacting, involving users and producers in an interac-tion resulting in product innovations’.In this sense,learning is part of daily routines and activities in an economy.In his Learning Economy concept,Lundvall makes learning more explicit,emphasizing further that ‘knowledge is assumed as the most funda-mental resource and learning the most impor-tant process’(1992,p.10).Anti-reductionist Approach:Systems and Subsystems of InnovationSo far,NIS approaches are not yet clear and systematic in their analysis of the dynamics and change in innovation systems.Lundvall’s (1992)distinction between subsystem and system level based on the work of Boulding implicitly incorporates both the actor(who can undertake innovative activities)as well as the structure(institutional selection environment) in innovation processes of a nation.Moreover, most NIS approaches acknowledge that within the national system,there are different institu-tional subsystems(e.g.,sectors,regions)that all influence each other again in processes of change.However,an explicit analysis of the structured environment is still missing (Edquist,1997).In accordance with the basic principles of evolutionary theory as discussed in Section 3.1,institutional evolutionary theory has developed a very explicit systemic methodol-ogy to investigate the continuous interaction of actors and institutional structures in the evolution of economic systems.The so-called ‘methodological interactionism’can be per-ceived as a methodology that combines a structural perspective and an actor approach to understand processes of economic evolu-tion.Whereas the structural perspective emphasizes the existence of independent institutional layers and processes which deter-mine individual actions,the actor approach emphasizes the free will of individuals.The latter has been referred to as methodological individualism,as we have seen in neo-classical approaches.Methodological indi-vidualism will explain phenomena in terms of the rational individual(showingfixed prefer-ences and having one rational response to any fully specified decision problem(Hodgson, 2000)).The interactionist approach recognizes a level of analysis above the individual orfirm level.NIS approaches recognize that national differences exist in terms of national institu-tions,socio-economic factors,industries and networks,and so on.So,an explicit methodological interactionist approach,explicitly recognizing various insti-tutional layers in the system and subsystem in interaction with the learning agents,can improve our understanding of the evolution of innovation.Gradualism:Learning Processes andPath-DependencyPath-dependency in biology can be translated in an economic context in the form of(some-times very large)time lags between a technical invention,its transformation into an economic innovation,and the widespread diffusion. Clearly,in many of the empirical case studies of NIS,the historical dimension has been stressed.For instance,in the study of Denmark and Sweden,it has been shown that the natural resource base(for Denmark fertile land,and for Sweden minerals)and economic history,from the period of the Industrial Revolution onwards,has strongly influenced present specialization patterns(Edquist& Lundvall,1993,pp.269–82).Hence,history matters in processes of inno-vation as the innovation processes are influ-enced by many institutions and economic agents.In addition,they are often path-dependent as small events are reinforced and become crucially important through processes of positive feedback,in line with evolutionary processes as discussed in Section3.1.Evolutionary MetaphorFinally,most NIS approaches do not explicitly use the biological metaphor.Nevertheless, many of the approaches are based on innova-tion theories in which they do use an explicit evolutionary metaphor(e.g.,the work of Nelson).To summarize,the current(policy)NIS approaches have already implicitly incorpo-rated some evolutionary notions such as non-optimality,novelty and gradualism.However, what is missing is a more explicit analysis of the different institutional levels of the economic system and innovation subsystems (their inertia and evolution)and how they change over time in interaction with the various learning activities of economic agents. These economic agents reside at established firms,start-upfirms,universities,govern-ments,undertaking learning and innovation activities or strategic actions.The explicit use of the biological metaphor and an explicit use of the methodological interactionst approach may increase our understanding of the evolu-tion of innovation systems.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing4.Towards a Dynamic View of Universities4.1The Logic of an Endogenous‘Learning’UniversityIf we translate the methodological interaction-ist approach to the changing role of universities in an evolutionary innovation system,it follows that universities not only respond to changes of the institutional environment(government policies,business demands or changes in scientific paradigms)but universities also influence the institutions of the selection envi-ronment by their strategic,scientific and entre-preneurial actions.Moreover,these actions influence–and are influenced by–the actions of other economic agents as well.So,instead of a one-way rational response by universities to changes(as in reductionist approach),they are intertwined in those processes of change.So, universities actually function as an endogenous source of change in the evolution of the inno-vation system.This is(on an ontological level) a fundamental different view on the role of universities in innovation systems from the existing policy NIS frameworks.In earlier empirical research,we observed that universities already effectively function endogenously in evolutionary innovation system frameworks;universities as actors (already)develop new knowledge,innovate and have their own internal capacity to change,adapt and influence the institutional development of the economic system(e.g., V an der Steen et al.,2009).Moreover,univer-sities consist of a network of various actors, i.e.,the scientists,administrators at technology transfer offices(TTO)as well as the university boards,interacting in various ways with indus-try and governments and embedded in various ways in the regional,national or inter-national environment.So,universities behave in an at least partly endogenous manner because they depend in complex and often unpredictable ways on the decision making of a substantial number of non-collusive agents.Agents at universities react in continuous interaction with the learn-ing activities offirms and governments and other universities.Furthermore,the endogenous processes of technical and institutional learning of univer-sities are entangled in the co-evolution of institutional and technical change of the evo-lutionary innovation system at large.We propose to treat the learning of universities as an inseparable endogenous variable in the inno-vation processes of the economic system.In order to structure the endogenization in the system of innovation analysis,the concept of the Learning University is introduced.In thenext subsection we discuss the main character-istics of the Learning University and Section5discusses the learning university in a dynamic,evolutionary innovation system.An evolution-ary metaphor may be helpful to make theuniversity factor more transparent in theco-evolution of technical and institutionalchange,as we try to understand how variouseconomic agents interact in learning processes.4.2Characteristics of the LearningUniversityThe evolution of the involvement of universi-ties in innovation processes is a learningprocess,because(we assume that)universitypublic agents have their‘own agenda’.V ariousincentives in the environment of universitiessuch as government regulations and technol-ogy transfer policies as well as the innovativebehaviour of economic agents,compel policymakers at universities to constantly respondby adapting and improving their strategiesand policies,whereas the university scientistsare partly steered by these strategies and partlyinfluenced by their own scientific peers andpartly by their historically grown interactionswith industry.During this process,universityboards try to be forward-looking and tobehave strategically in the knowledge thattheir actions‘influence the world’(alsoreferred to earlier as‘intentional variety’;see,for instance,Dosi et al.,1988).‘Intentional variety’presupposes that tech-nical and institutional development of univer-sities is a learning process.University agentsundertake purposeful action for change,theylearn from experience and anticipate futurestates of the selective environment.Further-more,university agents take initiatives to im-prove and develop learning paths.An exampleof these learning agents is provided in Box1.We consider technological and institutionaldevelopment of universities as a process thatinvolves many knowledge-seeking activitieswhere public and private agents’perceptionsand actions are translated into practice.3Theinstitutional changes are the result of inter-actions among economic agents defined byLundvall(1992)as interactive learning.Theseinteractions result in an evolutionary pattern3Using a theory developed in one scientific disci-pline as a metaphor in a different discipline mayresult,in a worst-case scenario,in misleading analo-gies.In the best case,however,it can be a source ofcreativity.As Hodgson(2000)pointed out,the evo-lutionary metaphor is useful for understandingprocesses of technical and institutional change,thatcan help to identify new events,characteristics andphenomena.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing。
数据仓库设计与建模的缓慢变化维度与多事实表的设计方法(六)
数据仓库设计与建模的缓慢变化维度与多事实表的设计方法一、缓慢变化维度的设计方法在数据仓库设计与建模中,缓慢变化维度是一种常见的设计需求。
缓慢变化维度指的是维度属性随着时间的推移而发生变化的情况。
对于这种情况,我们需要设计合适的数据结构来存储和管理这些变化。
在设计缓慢变化维度时,有三种常见的方法:类型1、类型2和类型3。
类型1的设计方法是覆盖更新,即在维度表中直接更新属性值。
这种方法简单直接,但是不保留历史记录,只能获取到最新的属性值。
类型2的设计方法是新增行版本,即每次维度属性发生变化时,在维度表中新增一行记录。
这种方法可以保留历史记录,但会导致数据冗余,并且需要增加处理复杂性。
类型3的设计方法是增加附加列,即在维度表中新增一列来记录最近的属性值。
这种方法在某些场景下可以简化数据处理,但是无法保留完整的历史记录。
根据具体的业务需求和数据特点,可以选择适合的缓慢变化维度设计方法。
在实际应用中,常常会根据不同的维度属性选择不同的设计方法,灵活应用这些方法可以更好地满足业务需求。
二、多事实表的设计方法在数据仓库设计中,事实表是用于存储度量数据的核心表格。
在某些场景下,一个业务过程或者分析需求涉及到多个度量数据集合,这时就需要设计多事实表来满足需求。
多事实表的设计是通过将不同的度量数据分别存储在不同的事实表中,然后通过公共维度进行关联。
这样可以实现更灵活的数据分析和查询。
在设计多事实表时,需要注意事实表之间的一致性和关联关系。
一致性指的是数据在不同的事实表中应该是一致的,需要进行适当的验证和校验。
关联关系可以通过共享维度进行连接,共享维度可以是业务维度或者公共维度。
在实际应用中,多事实表的设计需要根据具体的业务需求和数据特点进行灵活调整。
根据不同的度量数据类型和数据源,可以选择不同的事实表设计方法,以达到更好的数据效果和查询性能。
三、总结数据仓库设计与建模中的缓慢变化维度和多事实表是常见的设计需求。
对于缓慢变化维度,可以采用类型1、类型2或者类型3的设计方法来满足不同的需求。
多维时间序列特征转换
多维时间序列特征转换多维时间序列特征在数据分析和预测中起着重要的作用。
本文将从多个角度探讨多维时间序列特征的转换方法以及其在实际应用中的意义。
一、多维时间序列特征的转换方法在进行多维时间序列特征转换时,常用的方法包括特征提取和特征变换两种方式。
1. 特征提取特征提取是将原始的多维时间序列数据转换为一组代表性的特征。
常用的特征提取方法有平均值、方差、最大值、最小值等统计特征。
例如,对于某个时间序列数据,可以计算其平均值、方差以及过去一段时间内的最大值和最小值作为特征。
2. 特征变换特征变换是将原始的多维时间序列数据转换为新的特征表示。
常用的特征变换方法包括主成分分析(PCA)、奇异值分解(SVD)等。
通过特征变换,可以将原始的多维时间序列数据转换为具有更好解释性和可解释性的特征。
多维时间序列特征转换在实际应用中具有重要意义,主要体现在以下几个方面:1. 数据降维通过特征提取和特征变换,可以将原始的多维时间序列数据转换为具有更低维度的特征表示。
这样可以减少数据的存储空间和计算复杂度,提高数据处理的效率。
2. 特征选择在进行多维时间序列数据分析时,往往需要选择具有代表性和重要性的特征进行建模和预测。
通过特征转换,可以帮助筛选出对目标变量具有显著影响的特征,提高建模和预测的准确性。
3. 数据可视化通过特征转换,可以将原始的多维时间序列数据转换为二维或三维的特征表示,从而方便进行数据可视化和观察。
通过可视化分析,可以更直观地理解数据的特征和规律,为后续的数据分析和决策提供支持。
4. 数据挖掘多维时间序列特征转换为特征表示后,可以应用各种数据挖掘算法进行模式识别、异常检测等任务。
例如,可以使用聚类算法对转换后的特征进行聚类分析,发现数据中的潜在模式和规律。
三、多维时间序列特征转换的应用案例多维时间序列特征转换在实际应用中有着广泛的应用。
以下是几个典型的应用案例:1. 金融市场预测通过对金融市场的多维时间序列数据进行特征转换,可以提取出代表市场趋势和波动的特征,并用于预测股票价格、汇率等金融指标。
可变单元c-neb方法 -回复
可变单元c-neb方法-回复什么是可变单元cneb方法?可变单元cneb方法(Constrained Natural Extensional Basis, CNEB)是一种计算化学方法,用于研究分子和化学反应的势能面。
在化学反应中,分子会从一个能量极小值的构象转变为另一个能量极小值构象,这种转变称为化学反应路径。
CNEB方法可以用于确定最小能量路径,同时还能够研究该路径上的过渡态以及反应的动力学性质。
CNEB方法的基本原理是通过过渡态理论和多体势能外推法来构建势能面。
过渡态理论指的是研究化学反应路径中过渡态(transition state)的理论,通过确定过渡态的几何结构和能量来描绘反应的进程。
而多体势能外推法是一种根据已知构象和势能计算其他构象的方法。
CNEB方法将这两种方法结合起来,先通过过渡态理论确定反应路径上的过渡态,并通过势能表面外推计算路径上的其他构象。
CNEB方法的关键步骤包括以下几个:1. 选择初始构象:首先需要选择一个初始构象作为化学反应路径的起点。
这通常可以通过分子力学或量子化学计算得到。
2. 猜测中间构象:根据初始构象和目标构象,通过给定的方法猜测中间构象。
这些中间构象作为过渡态的候选构象。
3. 计算过渡态的几何结构:对于每个中间构象,使用过渡态理论计算其几何结构和能量。
常见的过渡态搜索方法有采用内禀反应坐标(IRC)法、能量梯度法等。
4. 多体势能外推:通过已知构象和势能表面计算路径上其他构象的势能。
这需要根据分子力学或量子化学方法计算每个构象的势能。
5. 更新构象:根据计算得到的势能,选择能量最低的构象作为路径上的一个点,并作为下一步计算的初始构象。
然后重复步骤2-5,直到达到收敛条件。
6. 确定最小能量路径:通过计算路径上每个构象的能量,可以确定最小能量路径。
这个路径给出了化学反应的过渡态和构象变化情况。
CNEB方法的优点在于可以通过对构象进行优化和过渡态搜索,找到势能表面上最小能量的化学反应路径。
缓慢变化维
缓慢变化维解决方法维度建模的数据仓库中,有一个概念叫Slowly Changing Dimensions,中文一般翻译成“缓慢变化维”,经常被简写为SCD。
缓慢变化维的提出是因为在现实世界中,维度的属性并不是静态的,它会随着时间的流失发生缓慢的变化。
这种随时间发生变化的维度我们一般称之为缓慢变化维,并且把处理维度表的历史变化信息的问题称为处理缓慢变化维的问题,有时也简称为处理SCD的问题。
缓慢变化维的几种常见解决方法:第一种方法,直接在原来维度的基础上进行更新,不会产生新的记录:1)更新前:emp_rid(代理键) emp_id(自然键) emp_name position101212 12345 Jack Developer更新后:emp_rid(代理键) emp_id(自然键) emp_name position101212 12345 Jack Manager第二种方法,不修改原有的数据,重新产生一条新的记录,这样就可以追溯所有的历史记录:1)更新前:emp_rid(代理键) emp_id(自然键) emp_name position start_date end_date 101212 12345 Jack Developer 2010-2-5 2012-6-12更新后:emp_rid(代理键) emp_id(自然键) emp_name position start_date end_date 201245 12345 Jack Manager 2012-6-12第三种方法,直接在原来维度的基础上进行更新,不会产生新的记录但是只会记录上一次的历史记录:1)更新前:emp_rid(代理键) emp_id(自然键) emp_name position old_position101212 12345 Jack Developer null更新后:emp_rid(代理键) emp_id(自然键) emp_name position old_position101212 12345 Jack Manager Developer。
PDLAMMPS近场动力学
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数据仓库设计与建模的缓慢变化维度与多事实表的设计方法(三)
数据仓库设计与建模的缓慢变化维度与多事实表的设计方法引言:在当今数据驱动的时代,数据仓库的设计与建模成为企业信息化的关键环节。
在数据仓库的设计中,缓慢变化维度与多事实表的设计方法都是至关重要的。
本文将介绍这两个设计方法的概念、优势以及实际应用。
一、缓慢变化维度的设计方法缓慢变化维度是指在数据仓库中,维度数据的变化速度较慢的情况。
缓慢变化维度的设计方法可以分为三种类型:类型一、类型二和类型三。
1. 类型一:覆盖当前值类型一缓慢变化维度是指只保存最新的维度值,并覆盖之前的值。
这种设计方法适用于维度信息变化频率非常低的情况,可以减少数据冗余,但会丢失历史数据。
2. 类型二:添加新的行类型二缓慢变化维度是指在维度表中添加新的行来保存变化的维度值,同时保留历史数据。
这种设计方法适用于维度信息变化频率适中的情况,可以保留历史数据,但会增加数据量和复杂度。
3. 类型三:添加新的列类型三缓慢变化维度是指在事实表中添加新的列来保存变化的维度值。
这种设计方法适用于维度信息变化频率不高且仅影响特定的业务过程的情况,可以简化维度表的设计和查询操作,但会增加事实表的宽度。
二、多事实表的设计方法多事实表是指在数据仓库中,使用多个事实表来存储不同粒度的指标。
这种设计方法可以更好地满足多样化的查询需求和分析目的。
1. 事实表的分层设计多事实表的设计中,可以根据指标的粒度将事实表进行分层。
粗粒度的事实表可以包含汇总的指标,适用于快速的统计查询和报表生成;细粒度的事实表可以包含更详细的指标,适用于更深入的数据挖掘和分析。
2. 事实表的共享维度多事实表的设计中,可以共享相同的维度表,减少维度冗余和降低数据仓库的复杂度。
共享维度表可以包含多个维度层级和属性,以满足不同事实表的查询需求。
3. 事实表的关联多事实表的设计中,可以通过维度表的关联来连接不同的事实表。
这种设计方法可以强化指标之间的关联性,使查询结果更加准确和有价值。
结论:在数据仓库设计与建模中,缓慢变化维度与多事实表的设计方法是非常重要的。
stablediffusionxl例子
很高兴能为您撰写关于“stablediffusionxl例子”的文章。
借助这个主题,我将尽力以深度和广度兼具的方式全面评估,并据此撰写一篇有价值的文章。
1. 什么是stablediffusionxl例子?stablediffusionxl例子是一种在金融领域中常见的模型示例。
它通常用来说明在不同市场环境和条件下,资产价格和波动性是如何变化的。
这个例子的目的是通过对不同因素的影响进行模拟和分析,以帮助投资者和机构更好地理解和预测市场的走势。
2. stablediffusionxl例子的重要性和应用stablediffusionxl例子在金融建模和风险管理中具有重要的应用。
通过对资产价格和波动性的模拟,可以帮助投资者更好地制定交易策略和风险管理方案。
这个例子也在学术研究和金融工程领域中被广泛应用,为理论研究和实践操作提供了有力的支持。
3. 如何进行stablediffusionxl例子的模拟和分析在进行stablediffusionxl例子的模拟和分析时,需要考虑多个因素,包括市场环境、资产特性、交易策略等。
通过对这些因素的综合考虑和模拟,可以得到不同情况下的资产价格和波动性的变化趋势,从而为投资决策和风险管理提供参考。
4. 个人观点和理解对于stablediffusionxl例子,我个人认为它在实践中具有重要的意义。
通过对资产价格和波动性的模拟,可以更好地理解市场的运行规律和特点,从而帮助投资者更好地应对风险和挑战。
对这个例子的研究也有助于提高金融领域的建模和风险管理水平,为相关领域的发展和进步做出贡献。
总结回顾通过对stablediffusionxl例子的探讨,我们可以看到这个例子在金融领域中的重要性和应用。
通过模拟和分析,可以更好地理解市场的特点和规律,并为投资决策和风险管理提供参考。
我个人认为这个例子的研究对于金融领域的发展和进步具有重要意义。
总字数:较3000字5. stablediffusionxl例子的实际案例分析为了更具体地说明stablediffusionxl例子的应用,我们可以通过一个实际的案例来进行分析。
A default mode of brain function
A default mode of brain functionMarcus E.Raichle*†,Ann Mary MacLeod*,Abraham Z.Snyder*,William J.Powers‡,Debra A.Gusnard*§,and Gordon L.Shulman‡*Mallinckrodt Institute of Radiology and Departments of‡Neurology and§Psychiatry,Washington University School of Medicine,St.Louis,MO63110 This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected on April30,1996. Contributed by Marcus E.Raichle,October26,2000A baseline or control state is fundamental to the understanding of most complex systems.Defining a baseline state in the human brain,arguably our most complex system,poses a particular challenge.Many suspect that left unconstrained,its activity will vary unpredictably.Despite this prediction we identify a baseline state of the normal adult human brain in terms of the brain oxygen extraction fraction or OEF.The OEF is defined as the ratio of oxygen used by the brain to oxygen delivered byflowing blood and is remarkably uniform in the awake but resting state(e.g.,lying quietly with eyes closed).Local deviations in the OEF represent the physiological basis of signals of changes in neuronal activity obtained with functional MRI during a wide variety of human behaviors.We used quantitative metabolic and circulatory mea-surements from positron-emission tomography to obtain the OEF regionally throughout the brain.Areas of activation were conspic-uous by their absence.All significant deviations from the mean hemisphere OEF were increases,signifying deactivations,and resided almost exclusively in the visual system.Defining the baseline state of an area in this manner attaches meaning to a group of areas that consistently exhibit decreases from this base-line,during a wide variety of goal-directed behaviors monitored with positron-emission tomography and functional MRI.These decreases suggest the existence of an organized,baseline default mode of brain function that is suspended during specific goal-directed behaviors.F unctional brain imaging studies in normal human subjectswith positron-emission tomography(PET)and functional MRI(fMRI)have consistently revealed expected task-induced increases in regional brain activity during goal-directed behav-iors(for brief reviews see refs.1and2).These changes are detected when comparisons are made between a task state, designed to place demands on the brain,and a control state,with a set of demands that are uniquely different from those of the task state.Researchers have also frequently encountered task-induced decreases in regional brain activity even when the control state consists of lying quietly with eyes closed or passively viewing a stimulus.Whereas cortical increases in activity have been shown to be task specific and,therefore,vary in location depending on task demands,many decreases(Fig.1)appear to be largely task independent,varying little in their location across a wide range of tasks(3).This consistency with which certain areas of the brain participate in these decreases made us wonder whether there might be an organized mode of brain function that is present as a baseline or default state and is suspended during specific goal-directed behaviors.The primary issue this paper will address is whether these unexplained decreases merely arise from unrecognized increases (i.e.,activation in the jargon of functional brain imaging)present only in the‘‘control state.’’Thus,on this argument,any control state,no matter how carefully it is selected,is just another task state with its own unique areas of activation.Unfortunately,in most instances there is insufficient information about the control state to judge whether the observed decrease arose in this manner.We believe conceptual progress has suffered because of our inability to exclude explanations of the above type for regional decreases in brain activity and,more generally,to understand whether a specific level of activity in a given area of the brain can be considered its baseline.At the heart of the problem is the lack of agreed-upon characteristics defining a baseline state.In response to this dilemma we began with a generally accepted, quantitative circulatory and metabolic definition of brain acti-vation(see Background,below).From this definition we speci-fied criteria for a baseline state(i.e.,the absence of activation by this definition).In so doing,we were able to determine that areas consistently exhibiting decreases in activity during specific goal-directed behaviors(3)did so from this baseline state.We believe these findings are consistent with our idea of a baseline or default state of the brain,the functions of which are revealed by those areas whose activities are suspended during many transient, attention-demanding,goal-directed activities.BackgroundAlthough the human brain accounts for only about2%of the body weight,it consumes nearly20%of the oxygen we extract from the air we breathe.This dependence of the brain on oxygen is highlighted by the fact that failure of oxygen delivery to the brain,usually the result of a stoppage of the heart,results in unconsciousness within seconds.An examination of the rela-tionship between oxygen delivery to the brain and flowing blood regionally within the brain(Fig.2)highlights the nature of this dependency.The signal used by PET to map changes in neural activity in the human brain is based on local changes in blood flow(1). Increased neural activity in a local brain region increases blood flow in that region.Scientists have known of this robust rela-tionship for well over100years through repeated demonstrations in laboratory animals and humans(1).It was thought to reflect the changing needs of the brain for oxygen during changing mental activity.Surprisingly,more recently it has been appreciated that these changes in blood flow are accompanied by smaller changes in oxygen consumption(4).This leads to a decrease in the amount of oxygen extracted from blood when blood flow increases and an increase in the amount of oxygen extracted when blood flow decreases.Thus,changes in blood flow accompanying local changes in brain activity are associated with significantly smaller changes in the amount of oxygen used by the brain(1).As a result of these relationships the local blood oxygen content parallels the change in brain activity because the amount of oxygen supplied changes more than the demand(Fig.3).At the present time we do not fully understand why the relationship betweenAbbreviations:OEF,oxygen extraction fraction;fMRI,functional MRI;PET,positron-emission tomography;CBF,cerebral bloodflow;CMRO2,cerebral metabolic rate for oxygen;MPFC,medial prefrontal cortex.†To whom reprint requests should be addressed at:Washington University School of Medicine,4525Scott Avenue,Room2116,St.Louis,MO63110.E-mail: marc@.676–682͉PNAS͉January16,2001͉vol.98͉no.2oxygen delivery and oxygen consumption changes during changes in brain activity (see ref.1for a review),but this phenomenon has had great practical value for our ability to viewchanges in brain activity with fMRI.Because fMRI signal intensity is sensitive to the amount of oxygen carried by hemoglobin (5–7),this change in blood oxygen content at the site of increased brain activity can be detected with fMRI.This phenomenon is the basis for fMRI (8,9)and is usually referred to as the blood oxygen level-dependent (BOLD)signal,following Ogawa and colleagues (6).The relationship of oxygen delivery to oxygen utilization can be measured quantitatively in the human brain with PET as the fraction of available oxygen (i.e.,the arterial oxygen concentra-tion)used by the brain.This measurement is usually referred to as the oxygen extraction fraction (OEF)(10–12).Researchers interested in blood flow and metabolic relationships in the brain have come to appreciate the spatial uniformity of the OEF measured in a resting state (e.g.,lying quietly in a scanner with eyes closed but awake;see Fig.4).This spatial uniformity exists despite considerable variation in resting oxygen consumption and blood flow within gray matter and an almost 4-fold differ-ence between gray and white matter in both oxygen consumption and blood flow (Figs.2and 4).This relationship is altered in the normal brain only when areas briefly change their activity during specific behaviors (4,13).Heretofore the uniformity of the OEF at rest has not been considered in defining a baseline state of the human brain.Here we specifically propose to do so.The brain mean OEF was chosen as the baseline level of activity on the basis of its general uniformity in the eyes closed,resting state.This uniformity suggests that equilibrium has been reached between the local metabolic requirements necessary to sustain a long-term modalFig.1.Regions of the brain regularly observed to decrease their activity during attention demanding cognitive tasks.These data represent a metaanalysis of nine functional brain imaging studies performed with PET and analyzed by Shulman and colleagues (49).In each of the studies included,the subjects processed a particular visual image in the task state and viewed it passively in the control state.One hundred thirty-two individuals contributed to the data in these images.These decreases appear to be largely task independent.The images are oriented with the anterior at the top and the left side to the reader’s left.The numbers beneath each image represent the millimeters above or below a transverse plane running through the anterior and posterior commissures (26).Fig.2.Quantitative maps of blood flow (Upper )and oxygen consumption (Lower )in the subjects from group I while they rested quietly but awake with their eyes closed.The quantitative hemisphere mean values for these images are presented in Table 1.Note the large variation in blood flow and oxygen consumption across regions of the brain.These vary most widely between gray and white matter.Despite this variation,blood flow and oxygen consumption are closely matched,as also reflected in the image of the oxygen extraction fraction (i.e.,the ratio of oxygen consumption to blood flow;see Fig.4).Raichle et al.PNAS ͉January 16,2001͉vol.98͉no.2͉677N E U R O B I O L O G Ylevel of neural activity and the level of blood flow in that region.We propose that this equilibrium state defines a baseline level of local neuronal activity.Consequently,those areas with a reduced OEF relative to the brain mean are defined as activated (i.e.,neural activity is increased above the baseline level).Those areasnot differing from the brain mean OEF are considered to be at baseline.In this scheme,increases in the OEF from the brain mean then define areas of deactivation (i.e.,neural activity is decreased below the baseline level).With these definitions in mind (Fig.3)we used quantitative measurements of the OEF obtained with PET to examine thosebrain areas regularly observed to exhibit reductions in blood flow and blood oxygen level-dependent signal during goal-directed behaviors (3).Our reason for choosing these regions was to test the hypothesis that such decreases occur relative to a baseline state of brain activity (here defined as resting quietly but awake with eyes closed).In other words,decreases in brain activity do not have to be increases simply looked at from the opposite side of the equation.Rather,they are decreases from a true baseline or zero set point.If this hypothesis is correct,the chosen regions should exhibit an OEF similar to that of the rest of the brain in this baseline state.The OEF in these areas was measured in two independent groups of 19normal adults resting quietly with eyes closed.These results were then extrapolated to a more complex control or baseline state (passive visual fixation)in a third group of 11normal adults.MethodsSubjects.Data from three subject groups were used for thisanalysis.The first two groups had served as control subjects in previously published studies from this laboratory (3,14,15).The third group was culled from previously unpublished data.All subjects were recruited from the Washington University com-munity.None had any history of neurological or psychiatric illness.The Human Studies Committee and the Radioactive Drug Research Committee of Washington University approved all rmed consent was obtained in accordance with their guidelines.Group I consisted of 19subjects (eight females)in whom quantitative CBF,CBV,OEF,and the CMRO 2were measured while the subjects rested quietly but awake with eyes closed in a Siemens model 961PET scanner (see below).Their ages ranged from 19to 77years,with a mean age of 43years.Seventeen were right handed.They are described in greater detail else-where (15).Group II consisted of 19subjects (10females)in whom quantitative CBF,CBV,OEF,and CMRO 2were measured while the subjects rested quietly but awake with eyes closed in the PETT VI PET scanner (see below).Their ages ranged from 19to 84years,with a mean age of 42years.Fourteen were right handed.They are described in greater detail elsewhere (14).Group III (previously unpublished)consisted of 11subjectsFig.4.Maps of the fraction of oxygen extracted by the brain from arterial blood (oxygen extraction fraction or OEF expressed as a percentage of the available oxygen delivered to the brain).The data come from 19normal adults (group I,Table 1)resting quietly but awake with their eyes closed.The data were obtained with PET.Despite an almost 4-fold difference in blood flow and oxygen consumption between gray and white matter,the OEF is relatively uniform,emphasizing the close matching of blood flow and oxygen consumption in the resting,awake brain.Areas of increased OEF can be seen in the occipital regions bilaterally (see text for discussion).Fig.3.A schematic representation of the metabolic and circulatory rela-tionships occurring in areas of the brain with transient increases (Activation)or decreases (Deactivation)in the level of neural activity from a baseline or equilibrium state.Typically increases (Right )are characterized by increases in the cerebral blood flow (CBF)and the cerebral blood volume (CBV),with much smaller changes in the cerebral metabolic rate for oxygen (CMRO 2).As a result,there is a fall in the oxygen extraction fraction (OEF)and an increase in the amount of oxygen attached to hemoglobin exiting the brain (HbO 2).This latter change is responsible for the blood oxygen level–dependent (BOLD)signal used in functional magnetic resonance imaging (fMRI).Decreases from baseline (Left )are characterized as the opposite pattern of change.678͉Raichle et al.(five females)in whom qualitative CBF was measured while the subjects rested quietly but awake with eyes closed and again while they passively viewed a visual fixation cross hair in the middle of a television monitor.Five such paired measurements were obtained.Their ages ranged from19to40years,with a mean age of27years.Eight subjects were right handed.There is no record of the handedness of the other three.The data for group III were also acquired with the PETT VI PET scanner(see below).Imaging Methods.The subjects in group I were scanned with a Siemens model961scanner(Siemens Medical Systems,Hoff-man Estates,IL)(16,17).Images were reconstructed by using filtered back projection and scatter correction with a ramp filter at the Nyquist frequency.All images were then filtered with a three-dimensional Gaussian filter to a uniform resolution of 16-mm full width at half-maximum.In the subjects in groups II and III,studies were performed on the PETT VI scanner(18,19).The PETT VI system was used in the low-resolution mode.Images were then filtered with a three-dimensional Gaussian filter to a uniform resolution of 17-mm full width at half-maximum.Quantitative regional OEF,CBF,and CMRO2were measured by using a combination of O15-labeled radiopharmaceuticals as described(11,12,20–22).Qualitative CBF was measured in the subjects in group III.In this instance H215O images consisting of normalized PET counts were used to create maps of the distri-bution of blood flow in the brain(21,23,24).Regions of Interest.The location of each region of interest analyzed in this study was obtained directly from the right hand side of table1of the study by Shulman and colleagues(3).These regions were chosen because they reliably predict the location of areas of the human brain exhibiting reductions in activity,as measured with either PET or fMRI,during the performance of a variety of cognitive tasks(3).Their coordinates are listed in Tables1and2of the present study.The regions are referred to by the Brodmann area originally assigned to them by Shulman et al.(3).Furthermore,a center-of-mass algorithm(25)was used to search the OEF data sets for any significant deviations from the hemisphere mean outside of the specific areas chosen for analysis(preceding paragraph).Data Analysis.A12-mm sphere was centered on the stereotaxic coordinates of each region of interest(see Tables1and2)for each subject’s individual images of CBF,OEF,and CMRO2. Each regional value was divided by the subject’s mean globalTable1.Data obtained from subjects in group I while they rested quietly but awake with their eyes closedArea x y z OEF P CBF P CMRO2PA M31͞7Ϫ5Ϫ4940 1.0100.62 1.374*Ͻ0.0001 1.397*Ͻ0.0001B L40Ϫ53Ϫ39420.9010.070.735*Ͻ0.00010.695*Ͻ0.0001C L39͞19Ϫ45Ϫ67360.9870.660.813*0.00080.805*0.0003D R4045Ϫ57340.9780.26 1.0020.960.9980.97E L lateral8Ϫ2727400.9810.520.9910.740.9860.74F L8͞9Ϫ1141420.9020.040.8740.0060.803*0.001G R8͞9549360.8870.0040.8930.010.813*0.001H L9Ϫ1555260.9430.350.813*0.00010.785*0.002I L10Ϫ195780.9710.060.9400.010.9270.01J M10Ϫ147Ϫ40.9330.01 1.284*Ͻ0.0001 1.203*Ͻ0.0001 K L10͞47Ϫ3345Ϫ60.9300.250.9200.030.8790.04L M32331Ϫ100.9460.20 1.1110.005 1.0580.26M L20Ϫ49Ϫ19Ϫ180.9720.460.821*Ͻ0.00010.814*0.0002 The values for OEF,CBF,and CMRO2are expressed as local-to-global ratios(see Methods).For the19subjects in group I the global values for OEF and CBF (ϮSD)were0.40Ϯ0.09(dimensionless)and46Ϯ8ml͞(minϫ100g),respectively.The mean arterial oxygen content for this group was16.6Ϯ0.16ml͞ml.From these data quantitative images of the CMRO2were created that yielded a mean cerebral hemisphere value of2.94Ϯ0.41ml͞(minϫ100g)or1.31Ϯ0.18mol͞(minϫg).The asterisks denote values that differ significantly from the global mean after correction for multiple comparisons.Table2.Data obtained from subjects in group II while they rested quietly but awake with their eyes closedArea x y z OEF P CBF P CMRO2PA M31͞7Ϫ5Ϫ4940 1.0350.62 1.278*Ͻ0.0001 1.345*Ͻ0.0001B L40Ϫ53Ϫ39420.9030.040.8040.00420.756*0.003C L39͞19Ϫ45Ϫ67360.9970.940.805*Ͻ0.00010.820*0.0004D R4045Ϫ57340.9940.770.9410.040.9570.11E L lateral8Ϫ272740 1.0010.930.9640.050.9960.93F L8͞9Ϫ1141420.7070.040.8930.030.7560.02G R8͞9549360.8710.020.9260.150.8580.02H L9Ϫ1555260.9830.540.876*0.00020.878*0.0009I L10Ϫ19578 1.0420.060.9670.02 1.0120.61J M10Ϫ147Ϫ40.9840.36 1.187*Ͻ0.0001 1.166*Ͻ0.0001 K L10͞47Ϫ3345Ϫ6 1.0370.120.913*Ͻ0.00010.9640.27L M32331Ϫ100.9840.54 1.080*Ͻ0.0001 1.0610.03M L20Ϫ49Ϫ19Ϫ180.9620.350.865*Ͻ0.00010.8770.005 The values for OEF,CBF,and CMRO2are expressed as local-to-global ratios(see Methods).For the19subjects in group II the global values for OEF and CBF (ϮSD)were0.30Ϯ0.09(dimensionless)and48Ϯ10ml͞(minϫ100g),respectively.The mean arterial oxygen content for this group was17.0Ϯ0.02ml͞ml. From these data quantitative images of the CMRO2were created that yielded a mean cerebral hemisphere value of2.17Ϯ0.41ml͞(minϫ100g)or0.97Ϯ0.17mol͞(minϫg).The asterisks denote values that differ significantly from the global mean after correction for multiple comparisons.Raichle et al.PNAS͉January16,2001͉vol.98͉no.2͉679N E U R O B I O L O G Yvalue for that image,creating a local to global ratio for that region.A two-tailed,one-sample Student’s t test was performed to determine whether this ratio differed from a predicted value of1.The level of significance was adjusted for multiple com-parisons.ResultsRegional as well as whole-brain measurement values of CBF, OEF,and CMRO2for groups I and II are presented in Tables 1and2.None of the areas selected for study(3)exhibited an OEF significantly different from the hemispheric mean(com-pare Figs.1and4).The correlation between the regional values of OEF obtained independently on the two groups was excellent (rϭ0.89).By our definition(see Background),therefore,none of these areas was activated in subjects who were awake but resting quietly with their eyes closed(two independent groups of 19subjects each).To complete our analysis,the data sets from groups I and II were automatically searched(25)for any deviations in the OEF above or below the hemisphere mean.No significant decreases in OEF were found signifying areas of activation.In arriving at this determination we had to exclude several‘‘areas’’falling in high-noise areas at the edge of the brain or,in some instances, clearly outside of the brain.However,we did find,bilaterally, areas within extrastriate visual cortices that exhibited a signifi-cantly increased OEF from the hemisphere mean.These changes are readily apparent in Fig.4,and their locations are given in Tables3and4.By our definition these apparent visual areas are deactivated when subjects are awake but resting quietly with their eyes closed.Although none of the areas selected for study appear to be activated by our definition,two areas in both groups(i.e.,M31͞7 and M10)have both resting CBF and CMRO2significantly above the global mean(criteria:PϽ0.0038for both measurements in both groups).Likewise,two additional areas in both groups(i.e., L39͞19and L9)have both resting CBF and CMRO2significantly below the global mean.Resting quietly awake with the eyes closed is much less frequently used as a control state in functional imaging studies than visual fixation of a cross hair on a television monitor or passive viewing of a stimulus.The data presented in Tables1and 2as well as Figs.1and4do not tell us whether areas exhibiting significant reductions in activity during active task performance (3)become more active in the control states of visual fixation than when the eyes are closed.If this were to occur,reduced activity seen in the task state could simply reflect the absence of this increase in activity during fixation and passive viewing. The data from group III resolved this issue.No significant change in blood flow was found for any of the regions listed in Tables1and2when subjects went from the eyes closed and awake but resting state to passively viewing a fixation cross hair in the middle of a television monitor.Furthermore,it should be noted that those areas of extrastriate visual cortex exhibiting deactivation in the eyes closed state(Fig.4)increase their BF when the eyes are opened(details to be published separately). This observation is consistent with the hypothesis that the baseline state of these areas is more nearly approximated when subjects rest quietly with their eyes open.DiscussionOur study represents a comprehensive analysis of the uniformity of the OEF in the normal human brain while adult subjects are awake and resting quietly with their eyes closed.These data affirm the long-held impression of a relatively consistent rela-tionship between oxygen delivery and oxygen consumption in the human brain(Fig.2).Obvious decreases in the OEF from the brain mean,reflecting areas of activation(1),are not apparent in our data when subjects rest quietly with their eyes closed or open.Areas of deactivation(i.e.,increased OEF),primarily in extrastriate visual areas,were clearly apparent from our data (Fig.4and Tables3and4).It is of interest to note that these same increases in OEF were also noted in some of the earliest PET work on normal humans(27),although their possible signifi-cance was not appreciated.Their presence suggests that the baseline state for these areas may well be associated with the eyes being open.This hypothesis receives support from our compar-ison of eyes closed versus passive visual fixation,in which we noted an increase in the blood flow in these areas as subjects opened their eyes.Further work remains,including actual mea-surements of OEF in these areas in the eyes open and closed states.Nevertheless,these data are consistent with our hypoth-esis that the baseline state of these areas is more nearly approx-imated when the eyes are open.More generally our data are consistent with the hypothesis that the brain mean OEF defines a baseline level of neuronal activity.The uniformity of the OEF in the absence of specific goal-directed activities supports our belief that an established equilibrium exists between the local metabolic requirements necessary to sustain a long-term modal level of neural activity and the level of blood flow in a particular region.Deviations from this equilibrium produced by transient changes in this modal level of neural activity manifest themselves as changes in the OEF and provide us with the signals underlying modern functional brain imaging with fMRI.Table3.Areas with maximum absolute deviation of the OEFfrom the hemisphere mean in subjects from group IBrodmann area x y z OEF P11833Ϫ22 1.4910.000719Ϫ23Ϫ796 1.217Ͻ0.00011929Ϫ674 1.201Ͻ0.000118Ϫ29Ϫ9312 1.1940.0011823Ϫ7918 1.1820.00021823Ϫ8110 1.181Ͻ0.00013733Ϫ51Ϫ8 1.1380.00053123Ϫ4740 1.121Ͻ0.00013117Ϫ3350 1.1030.00031921Ϫ8330 1.0980.08Note that these are all increases in the OEF signifying areas of deactivationin the baseline state of resting but awake with eyes closed.With the exceptionof Brodmann area11located in the right gyrus rectus,these areas cluster invisual areas of the occipital and parietal cortices.The conventions used aresimilar to those of Tables1and2.Table4.Areas with maximum absolute deviation of the OEFfrom the hemisphere mean in subjects from group IIBrodmann area x y z OEF P1819Ϫ7910 1.189Ͻ0.000118Ϫ19Ϫ9314 1.169Ͻ0.00011935Ϫ7510 1.163Ͻ0.000119Ϫ27Ϫ798 1.1450.00117Ϫ9Ϫ756 1.1230.0031921Ϫ8128 1.1120.0318Ϫ13Ϫ6930 1.1090.002721Ϫ5932 1.107Ͻ0.000119Ϫ23Ϫ8728 1.104.05717Ϫ6346 1.0730.02Note that these are all increases in the OEF signifying areas of deactivationin the baseline state of resting but awake with eyes closed.These areas clusterin visual areas of occipital and parietal cortices.The conventions used aresimilar to those of Tables1and2.680͉ Raichle et al.Areas consistently observed to decrease their activity in a variety of goal-directed,cognitive activation paradigms (Fig.1)do not exhibit a reduced OEF (i.e.,evidence of activation)in this typical resting state (Tables 1and 2).While a null result such as this (i.e.,the regional OEF does not differ from the hemisphere mean)might be received with some caution,the relatively tight 95%confidence limits for the measurement of OEF (Ϯ3%,based on an analysis of the group I data)make it very unlikely that significant reductions in OEF,of even lesser magnitude than the increases seen in the visual system (Fig.4and Tables 3and 4),would have been missed.Thus,we believe our findings indicate that these localized decreases in activity (Figs.1and 5)(3)occur as decreases from a baseline level of activity rather than the return to baseline of an area of unsuspected activation.The presence of a consistent set of decreases in activity within a select set of brain areas strikingly independent of the goal-directed behaviors with which they are associated suggests to us that specific brain functions unique to the baseline state itself are being temporarily suspended.We posit that areas decreasing their activity in this manner may be tonically active in the baseline state,as distinguished from areas that are transiently activated in support of varying goal-directed activities.Under-standing the exact functions served by such tonically active areas would require much additional work,yet some indication of the directions this research might profitably take is revealed by our current knowledge about two of them.These are midline areas within the posterior cingulate and precuneus and within the medial prefrontal cortex (MPFC)(see Fig.5).The response of posterior cingulate and precuneus neurons to visual stimuli,for example,crucially depends on the physical characteristics of the stimulus (for a recent review see ref.28).Small spots of light to which a monkey may be attending and responding do not elicit neuronal responses in this area.In contrast,large,brightly textured stimuli elicit responses,even if they are totally irrelevant to tasks the animal is performing.These observations are consistent with the fact that elements of the dorsal stream of extrastriate visual cortex [area M in the owl monkey (29)and area PO in the macaque (30)]are part of a network of areas concerned with the representation of the visual periphery.Severe damage to the parietal cortex,when it extends medially to include the precuneus and the posterior cingulate,producesa condition known as Balint’s syndrome (31),the cardinal feature of which is the inability to perceive the visual field as a whole (i.e.,a fixed form of tunnel vision usually referred to as simultanagnosia),despite intact visual fields,during simple confrontation with single small stimuli.Furthermore,patients with Alzheimer’s disease show early reductions of metabolic activity in this area (32)and have been reported to show abnormalities in the processing of extrafoveal information (33).Thus,the posterior cingulate cortex and adjacent precuneus can be posited as a tonically active region of the brain that may continuously gather information about the world around,and possibly within,us.It would appear to be a default activity of the brain with rather obvious evolutionary significance.Detection of predators,for example,should not,in the first instance,require the intentional allocation of attentional resources.These re-sources should be allocated automatically and be continuously available.Only when successful task performance demands focused attention should such a broad information gathering activity be curtailed.The central importance of such a function is underscored by the observation that restoration of conscious-ness from a vegetative state (or at least external awareness,as it can be assessed at the patient’s bedside)is primarily heralded by a restoration of metabolism in parietal areas,including the precuneus (34).Finally,we note the selective vulnerability of the posterior cingulate and precuneus in such conditions as carbon monoxide poisoning [i.e.,acute hypoxia (34)],diffuse brain ischemia (35),and Alzheimer’s disease (32).This vulnerability,in the case of hypoxia and ischemia,has been ascribed to the position of the posterior cingulate and precuneus in the border zone between two of the main arteries supplying blood to the brain.We wonder whether the exceptionally high metabolic rate exhibited by the posterior cingulate and precuneus adds to their vulnerability (Tables 1and 2and Fig.5).This hypothesis receives some support from animal models of schizophrenia (36),based on pharmacologically induced,excitatory amino acid toxicity that preferentially (but without explanation to date)targets this area.Another midline area of the cortex exhibiting prominent decreases in activity during focused attention is MPFC (Figs.1and 5).Because of the large body of data implicating,in particular,the ventral aspects of this area of the brain and its connections in emotional processing within the brain,it hasbeenFig.5.Regions of the brain regularly observed to decrease their activity during attention-demanding cognitive tasks shown in sagittal projection (Upper )as compared with the blood flow of the brain while the subject rests quietly but is awake with eyes closed (Lower ).The data in the top row are the same as those shown in Fig.1,except in the sagittal projection,to emphasize the changes along the midline of the hemispheres.The data in the bottom row represent the blood flow of the brain and are the same data shown in horizontal projection in the top row of Fig.2.The numbers below the images refer to the millimeters to the right (positive)or left (negative)of the midline.Raichle et al.PNAS ͉January 16,2001͉vol.98͉no.2͉681N E U R O B I O L O G Y。
dynamic kinetic resolution
dynamic kinetic resolution动态动力学分解(DynamicKineticResolution,简称DKR)是一种可将不对映的烯丙基化合物分解为对映异构体的技术。
最近几年来,随着科学家们不断地发展和改进,DKR技术在合成有机化学中发挥着越来越重要的作用。
DKR技术最初发展于1960年代,当时,研究人员发现可以利用酶来将不对映的烯丙基化合物分解为对映异构体,直到1981年,Merck公司的研究人员更进一步发展出了第一种动态动力学分解技术,该技术也被称为“选择性氢还原”。
随后,在1986年,科学家们利用微波氢还原技术取得了成功,该技术不仅节省了时间和成本,而且还提高了环境友好性。
在此基础上,人们又开发了许多其他类型的DKR技术,比如以铜为催化剂的DKR、由单层HI形成分子层的DKR和双层HI形成分子层的DKR等。
此外,随着生物催化剂和改性封装压片技术的发展,DKR技术从低分子量化合物开始扩展到药物分子和高分子量类别。
目前,DKR技术已经广泛用于药物研发中,特别是异构化技术。
相比于其他的异构化技术,DKR的优点就在于它能将不对映的烯丙基化合物有效地分解为对映异构体。
其原理是将不对映的烯丙基化合物接受到一种有机或无机催化剂的作用,使得化合物杂质分解为对映异构体,从而达到异构化的效果。
其次,DKR技术还具有催化效率高、产物纯度高、收率较高、环境安全等优点。
首先,DKR技术使用的催化剂是自然界中可以找到的,其性能稳定可靠,而且催化效率非常高,能够大大降低合成的时间和成本。
其次,由于生物降解作用的关系,DKR技术能够大大提高产物的纯度,减少副产物的产生,从而节省时间和成本。
此外,DKR技术对环境污染也小,分解过程没有毒素排放,并且可以大大减少废水的产生,大大提高合成反应的环境友好性。
总而言之,DKR技术的研究和发展催生了有机合成的新技术,它的出现不仅改变了化学反应的方式,而且大大降低了合成制备药物的成本,提高了药物纯度,减少了环境污染。
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Dynamic and Reversible Changes in Histone H3-Lys4Methylation and H3Acetylation Occurring at Submergence-inducible Genes in RiceHiroyuki Tsuji1,3,Hiroaki Saika1,4,Nobuhiro Tsutsumi 1,Atsushi Hirai2and Mikio Nakazono1,*1Laboratory of Plant Molecular Genetics,Graduate School of Agricultural and Life Sciences,The University of Tokyo,1-1-1Yayoi,Bunkyo-ku,Tokyo,113-8657Japan 2School of Agriculture,Meijo University,1-501Shiogamaguchi,Tenpaku-ku,Nagoya,Aichi,468-8502,JapanHistone modifications such as methylation and acetylation in the chromatin surrounding a gene are thought to regulate transcriptional activity.In this study,to determine whether dynamic changes occur in histone modification on the loci of stress-responsive genes in plants,we chose rice submergence-inducible ADH1and PDC1genes.When submerged,the rice ADH1and PDC1genes were activated in a biphasic manner:the first and second inductions occurred after approximately 2and 12h of submergence,respectively.Their expression was transcriptionally induced as shown by increased binding of RNA polymerase II to the ADH1and PDC1loci during submergence.The Lys4residues of the histone H3proteins (H3-K4s)at both the 50-and 30-coding regions of ADH1and PDC1were found to change from a di-methylated state to a tri-methylated state at the first induction period.On the other hand,acetylation of H3increased throughout ADH1and PDC1genes at the later induction period.The methylation and acetylation levels recovered to the initial levels during re-aeration.Treatment of seedlings with a histone deacetylase (HDAC)inhibitor,trichostatin A,increased acetylation of histones H3and association of RNA polymerase II on the ADH1and PDC1loci,thereby increasing transcript levels of ADH1and PDC1.Together,these results showed dynamic and reversible changes of histone H3-K4methylation and H3acetylation in stress-responsive genes in a higher plant in response to the appearance or disappearance of an environmental stress.Keywords:Environmental stress —Gene expression —Histone modification —Rice (Oryza sativa ).Abbreviations:ADH1,alcohol dehydrogenase 1;ChIP,chromatin immunoprecipitation;HAT,histone acetyltransferase;HDAC,histone deacetylase;PDC1,pyruvate decarboxylase 1;RNA pol II,RNA polymerase II;TSA,trichostatin A;qPCR,quantitative PCR.IntroductionChromatin,a highly structured complex of DNA and nuclear proteins,is dynamically modified during several physiological processes including transcription.Modifications mainly occur on the nucleosome,the basic repeated unit of chromatin,which is formed by wrapping approximately 146bp of DNA around a histone octamer (comprised of two of each of the histones H2A,H2B,H3and H4)(Khorasanizadeh 2004).The N-termini of the histones undergo several covalent modifications,such as methylation,acetylation,phosphorylation,ADP-ribosylation and binations of these modifications are believed to generate ‘histone codes’that provide docking sites for proteins that are needed to regulate chromatin-related processes (Strahl and Allis 2000).Histone methylation and acetylation play important roles in gene expression and chromatin states (Kurdistani and Grunstein 2003,Sims et al.2003).Methylation of Lys4of histone H3(H3-K4)is generally associated with transcriptionally active chromatin,and methylation of Lys9of histone H3(H3-K9)generally correlates with transcriptionally repressed states and is often observed in heterochromatin (Sims et al.2003).In particular,it has been suggested that di-methylation of H3-K4correlates with the ‘permissive’state of chromatin,in which genes are either active or potentially active,and tri-methylation of H3-K4is linked to ‘ongoing’transcription (Santos-Rosa et al.2002,Ng et al.2003,Schneider et al.2004).Euchromatic regions,which are actively transcribed regions,contain highly acetylated nucleosome histones,and heterochromatic regions,which are transcriptionally repressed,contain less acetylated nucleosome histones (Chua et al.2001,Kurdistani and Grunstein 2003).Acetylation states are maintained*Corresponding author:E-mail,anakazo@mail.ecc.u-tokyo.ac.jp;Fax,þ81-3-5841-5183.Present address:Nara Institute of Science and Technology,Ikoma,Nara 630-0101Japan.4Present address:National Institute of Agrobiological Sciences,2-1-2Kannondai,Tsukuba,Ibaraki,305-8602Japan.Plant Cell Physiol.47(7):995–1003(2006)doi:10.1093/pcp/pcj072,available online at ßThe Author 2006.Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.All rights reserved.For permissions,please email:journals.permissions@995at Wuhan University Library on December 2, 2010 Downloaded fromby the opposing actions of histone acetyltransferase (HAT)and histone deacetylase(HDAC),and thus are reversible(Kurdistani and Grunstein2003).Unlike histone acetylation,histone methylation has long been considered a stable and irreversible modification.However, recent identification of histone demethylases such as LSD1(Shi et al.2004,Metzger et al.2005)and the JmjC domain-containing proteins(Tsukada et al.2006, Whetstine et al.2006,Yamane et al.2006)suggests that histone methylation is not a permanent modification but a reversible mark.Histone methylation is also removed by the exchange of methylated histones with unmodified histones(Sims et al.2003,Janicki et al. 2004).To our knowledge,however,there have been no reports of dynamic changes of histone methylation in plants.Because plants are sessile,their ability to respond quickly to stressful environmental conditions,such as submergence/waterlogging,cold and drought,is crucial for adaptation and survival.Thus,it is important that gene expression is dynamically controlled in response to environmental changes.The gene expression may be partly regulated by dynamic changes in histone modification(Reyes et al.2002,Loidl2004).If histone modifications play a direct role in gene expression in response to a stress,the modifications should be reversible,i.e.they should depend on the presence or absence of the stress.In higher plants,there is increasing evidence of regulation of gene expression by histone acetylation and methylation(Chua et al.2003, Ausin et al.2004,Bastow2004,Sung and Amasino2004, Zhou et al.2005).However,it is not known whether histone modifications in plants are dynamically and reversibly controlled in response to stress.Although cold exposure in Arabidopsis was found to alter the levels of histone H3acetylation and H3-K9and K27 methylation in a flowering repressor,FLOWERING LOCUS C(FLC)(Bastow2004,Sung and Amasino 2004),the changes were not reversible,i.e.they resulted in a stable and irreversible gene silencing of FLC gene expression.Here we report dynamic and reversible changes in histone H3-K4methylation and H3acetylation of rice submergence-inducible alcohol dehydrogenase1(ADH1) and pyruvate decarboxylase1(PDC1)genes in response to the presence or absence of stress.We chose the two genes as a model system because their expression is reversibly activated or repressed in response to changes of oxygen status(Tsuji et al.2000).Using chromatin immunoprecipi-tation(ChIP)and quantitative real-time PCR(qPCR), we demonstrate the occurrence of dynamic and reversible changes of histone H3-K4methylation and H3 acetylation on chromatin containing the submergence-responsive genes.ResultsEffect of submergence and re-aeration on gene expression We used reverse transcription–quantitative PCR (RT–qPCR)to monitor ADH1and PDC1mRNA levels in rice roots during submergence.Signal intensity was normalized by dividing it by the signal intensity of17S rRNA.Two hours after rice seedlings were completely submerged,transcripts of ADH1and PDC1began to increase(Fig.1).The ADH1and PDC1transcript levels decreased from6to12h,and then increased again.When the submerged plants were re-aerated,ADH1and PDC1 transcripts decreased to the initial levels(Fig.1).The transient decrease of ADH1and PDC1mRNA levels at 12h after submergence was reproducible(n¼5)under our conditions,suggesting that expression of the ADH1and PDC1genes in rice roots was activated in a biphasic manner.Changes of RNA polymerase II associationTo determine whether the increase of ADH1and PDC1 mRNA levels during submergence is due to an increase of transcription rather than to a decrease of mRNA0612182430364248Time (h)RelativemRNAlevelFig.1Representative graphs showing the effects of submergence on transcription of submergence-responsive genes.Real-time RT–PCR analyses of ADH1and PDC1were performed using total RNA extracted from rice roots.Nine-day-old rice seedlings were completely submerged for24h and then re-aerated.Seedling roots were harvested at the indicated times during submergence and following re-aeration(filled circles),and during aerobic control conditions(open circles).The real-time RT–PCR was performed three times for each RNA template.Data show mean valuesÆSD of three separate PCR analyses.The RT–PCR experiments were done three times or more using different RNA samples for the template.Similar results were obtained for each experiment.996Change in histone modifications during submergenceat Wuhan University Library on December 2, Downloaded fromdegradation,we carried out ChIP experiments using an antibody against RNA polymerase II(RNA pol II).The RNA pol II-ChIP,like the conventional run-on transcrip-tion assay,is a method for estimating transcriptional activity in vivo.However,unlike the run-on transcription assay,it does not require the laborious isolation of highly purified nuclei(Sandoval et al.2004).The binding of RNA pol II to all three regions(promoter,50-coding region and 30-coding region)of ADH1and PDC1loci increased during submergence and reverted to the initial levels following re-aeration(Fig.2),suggesting that the in vivo transcription was enhanced under submergence.The promoter regions for ADH1and PDC1(–206to–83nucleotides and–894 to–699nucleotides from the transcription initiation sites, respectively)contain predicted anaerobic responsive elements(AREs),which are cis-elements required for induction of gene expression under anaerobic conditions (Olive et al.1990,Kyozuka et al.1994).Because genomic DNA was cut into0.5–1.0kb fragments in our ChIP assay, the PCR amplification of the ADH1or PDC1promoter regions might detect RNA pol II association or histone modification downstream of the transcription start site(i.e. the50untranslated region)as well as the promoter regions.The RNA pol II association levels(Fig.2),unlike the mRNA levels(Fig.1),did not decrease at12h after submergence.These data suggest that the increases of ADH1and PDC1mRNA are due to induction of transcription,but the biphasic pattern of their mRNA accumulations may be due to some type of post-transcriptional regulation,which decreases the number of transcripts at around12h after submergence. Changes of histone H3-K4di-methylation and tri-methylation To examine whether the histone H3-K4methylation states on the ADH1and PDC1chromatin dynamically change under submerged conditions and following re-aeration,we monitored the di-methylation and tri-methylation states of histone H3-K4of ADH1and PDC1 loci at various time points(Figs.3and4).In all three regions(promoter,50-coding region and 30-coding region)of ADH1,the relative abundance of di-methyl H3-K4decreased after submergence and then increased during re-aeration(Fig.3).Similar results were obtained for the PDC1gene(Fig.3).The relative abundance of tri-methyl H3-K4in the promoter region remained unchanged or slightly decreased during submergence(Fig.4).However,in the50-and 30-coding regions,tri-methylation of H3-K4started to increase at about2h after the start of submergence, continued to increase until the submerged plants were re-aerated(24h after submergence),and then dropped to the initial state after re-aeration(Fig.4).The increase during submergence started at almost the same time that the association of RNA pol II with these genes started to increase(Fig.2).We failed to detect any di-methylation of H3-K9 on any of the genes we examined in this study (data not shown).This may have been because H3 histones with di-methylated K9residues are mainly localized at heterochromatic regions or because the reactivity of the antibody used in our study was too low for our procedure.Changes of histone H3acetylationThe kinetic ChIP analyses showed that H3histones were gradually acetylated at all three regions of the genes under submerged conditions,and the acetylation state of histone H3reverted to the initial levels following re-aeration (Fig.5).In this case,the start of the increase of H3 acetylation(Fig.5)occurred after the start of the increase of ADH1and PDC1transcripts(Fig.1)and the RNA pol II association(Fig.2).This suggests that histoneacetylation123451234567061218243036425101520253035RelativeabundanceofRNApolIIsubmergence re-aeration submergence re-aerationPromotersubmergence re-aeration5′coding3′codingTime (h)Fig.2Representative graphs show-ing the effects of submergence onassociation of RNA pol II with sub-mergence-responsive genes.Nine-day-old rice seedlings were comple-tely submerged for24h and then re-aerated.Seedling roots were harvestedat the indicated times during submer-gence and following re-aeration(filleddiamonds)and during aerobic controlconditions(open diamonds).ChIPassays were performed using an anti-body against RNA pol II.Values arethe meanÆSD of three separate real-time PCR runs in a ChIP experiment.The experiments were done two orthree times with similar results.Change in histone modifications during submergence997at Wuhan University Library on December 2, Downloaded fromR e l a t i v e a b u n d a n c e o f d i -m e t h y l H 3-K 4submergencere-aerationsubmergence re-aerationsubmergencere-aerationPromoter5′coding3′coding612182430364248612182430364248612182430364248Time (h)Fig.3Representative graphs showing the effects of submergence on di-methylation of histone H3-Lys4at submergence-responsive genes.Plants were grown and treated as in Fig. 2.ChIP assays were performed using an antibody against di-methylated histone H3at Lys4.Values are the mean ÆSD of three separate real-time PCR runs in a ChIP experiment.The experi-ments were done two or three times with similar results.submergencere-aeration submergencere-aeration submergencere-aerationPromoter5′coding3′codingTime (h)R e l a t i v e a b u n d a n c e o f t r i -m et h y l H 3-K 4Fig 4Representative graphs showing the effects of submergence on tri-methylation of histone H3-Lys4at submergence-responsive genes.Plants were grown and treated as in Fig. 2.ChIP assays were performed using an antibody against tri-methylated histone H3at Lys4.Values are the mean ÆSD of three separate real-time PCR runs in a ChIP experiment.The experi-ments were done two or three times with similar results.submergencere-aeration submergencere-aerationsubmergencere-aerationPromoter5′coding3′coding6121824303642480612182430364248612182430364248Time (h)R e l a t i v e a b un d a n c e o f a c e t y l H 3Fig.5Representative graphs showing the effects of submergence on acetylation of histone H3at submergence-responsive genes.Plants were grown and treated as in Fig. 2.ChIP assays were per-formed using an antibody against acetylated histone H3.Values are the mean ÆSD of three separate real-time PCR runs in a ChIP experiment.The experiments were done two or three times with similar results.998Change in histone modifications during submergenceat Wuhan University Library on December 2, 2010 Downloaded fromdoes not function at the first induction step of ADH1and PDC1expression.If this is the case,then what is the function of H3acetylation?One possibility is that H3 acetylation enhances the expression of ADH1and PDC1at the late stage,which starts at about12h after submergence. Treatment of seedlings with trichostatin A(TSA),which hyperacetylates histones H3and H4,for6h significantly induced H3acetylation in the50-coding region(Fig.6A)as well as in the promoter and30-coding regions(data not shown)of ADH1and PDC1,even under aerobic conditions. Transcript levels increased with increasing concentration of TSA(Fig.6B).To examine whether increased acetylation leads to enhanced transcription,we investigated the binding of RNA pol II to the ADH1and PDC1loci during TSA treatment.As shown in Fig.6C,RNA pol II was increasingly associated with these loci.These results suggest that increased histone H3acetylation can enhance ADH1and PDC1expression via increased association of RNA pol II.Hence,the increase of histone H3 acetylation observed during submergence may enhance activation of ADH1and PDC1gene expression.DiscussionThe histone H3-K4methylation state of the chromatin at the ADH1and PDC1genes(Figs.3and4)changed dynamically in accordance with the change in transcription (Figs.1and2).This suggests that tri-methylation of H3-K4 is associated with active transcription in plants as well as in mammals and yeast.The changes in the tri-methyl H3-K4 states(Fig.4)at the50-and30-coding regions of ADH1and PDC1were in inverse proportion to the changes in the di-methyl H3-K4states(Fig.3).This suggests that di-methyl H3-K4was converted to tri-methyl H3-K4by histone methyltransferase activity when the ADH1and PDC1genes were activated under submerged conditions. This is shown schematically in Fig.7.When rice plants were re-aerated,H3-K4methylation changed from a tri-methylated state to a di-methylated state at the50-and 30-coding regions of ADH1and PDC1(Figs.3and4).This result suggests that histone methylation is dynamically controlled.If this is the case,how is the methyl turnover of the histone tail regulated?One possible mechanism is that demethylation of tri-methyl H3-K4to di-methyl H3-K4 increases methyl turnover of the histone tail.Several recent studies have found evidence for proteins with demethylase activity,including a histone H3-K4-specific or H3-K9-specific histone demethylase(LSD1)(Shi et al.2004, Metzger et al.2005),and three JmjC domain-containing proteins with specific demethylase activities.The latter include JHDM1(Tsukada et al.2006)and JHDM2A (Yamane et al.2006),which demethylate only di-or mono-methylated histone H3at K36and K9,respectively,and JMJD2that reverses tri-methylated histone H3-K9 or H3-K36to di-but not mono-or unmethylated histone H3(Whetstine et al.2006).Although a demethylase specific to tri-methyl H3-K4has not yet been identified,it is possible that such a demethylase contributes to the reversion of H3-K4methylation states in rice.Another possible mechanism involves histone exchange(Sims et al. 2003,Janicki et al.2004),in which histone H3tri-methylated at K4is first replaced by an unmodified histone H3,and then the unmodified H3is di-methylated by histone methyltransferase.Tri-methylation of H3-K4,which is catalyzed by a histone methyltransferase(e.g.Set1),has been shown to be coupled to transcription by RNA pol II(Sims et al. 2004).Consistent with this evidence,the increase of RNA pol II association(Fig.2)occurred at almost the same time as the increase of histone tri-methylation(Fig.4)when the submergence-inducible genes were activated.These data suggest that the transcription-coupled H3-K4tri-methylation is also used in stress-responsive gene expres-sion in plants.In yeast and chicken,tri-methylation of H3-K4accumulates near the50-coding region of genes (Santos-Rosa et al.2002,Ng et al.2003,Schneider et al. 2004).Because the tri-methyl H3-K4states at the50-and30-coding regions of ADH1and PDC1increased under submergence(Fig.4),tri-methylation of H3-K4in plants may not be limited to the50-coding region and may extend to the30portions of the genes.Changes in gene expression through histone modifica-tions as a result of environmental change have also been observed in winter-annual Arabidopsis lines.In these lines, exposure to prolonged cold attenuates expression of the flowering-repressor FLC through methylation of histone H3-K9and H3-K27at the promoter and first intron of the FLC gene,thereby promoting their flowering when spring comes(Sung and Amasino2004).However,unlike the histone modifications at the ADH1and PDC1loci,those at the FLC locus are irreversible.The acetylation states of histone H3at all three regions of the ADH1and PDC1genes increased under submergence (Fig.5).This was also the case for the acetylation state of histone H4(data not shown).We also found that the times at which ADH1and PDC1transcription started to increase occurred before the start of the increase of histone acetylation at the corresponding genes(Figs.1,2,5).Together,these results suggest that histone acetylation is not required for the initial step of induction of ADH1and PDC1gene expression by submergence.However,the finding that histone acetylation can induce ADH1and PDC1gene expression even under aerobic conditions(Fig.6)suggests that it is involved in enhancing the late phase of ADH1and PDC1expression during submergence.In fact,increased acetylation leadsChange in histone modifications during submergence999at Wuhan University Library on December 2, Downloaded fromto increased RNA pol II association with the ADH1and PDC1loci (Fig.6C).In conclusion,we observed dynamic and reversible changes of the histone methylation and acetylationstates occurring in chromatin at the submergence-responsive genes in response to changes of oxygen availability as a result of submergence and re-aeration.Many questions remain about how histone modifications,such as methylation and acetylation,are involved in the regulation of gene expression in plants in response to environmental stresses.Submergence-responsive genes in rice may be a good model for answering these questions.Materials and MethodsPlant materials and treatmentsRice (Oryza sativa L.,cv.Nipponbare)seedlings were grown in the light at 288C for 9d.For submergence treatment,aerobically grown seedlings were completely submerged in water in the dark at 288C.After 24h of submergence,seedlings were returned to aerobic conditions in the dark at 288C.Aerobic control plants were kept for 48h in the dark.For TSA treatments,seedlings were placed in 1.55%dimethylsulfoxide (DMSO)solution containing various concentrations of TSA (Wako Chemical,Tokyo,Japan)for 6h.RNA extraction and quantitative real-time RT–PCRFor RNA extraction,roots of seedlings were harvested and immediately frozen in liquid nitrogen.RNA was extracted using an RNeasy Plant Mini Kit (Qiagen,Valencia,CA,USA).qPCR was carried out with an ABI PRISM 7700sequence detection system (Applied Biosystems,Foster City,CA,USA)using Quantitect SYBR Green RT–PCR master mix (Qiagen)according to the manufacturer’s protocol.Primers used for the qPCR are listed in Table 1.A cDNA clone or a cDNA fragment of each gene was used to make standard curves for quantification.Experiments were repeated two or more times andsimilar20406080100120ADH1PDC1R e l a t i v e a b u n d a n c e o f a c e t y l H 3(% o f m a x )AB20406080100120140ADH1PDC1R e l a t i v e a b u n d a n c e o f R N A p o l I I (% o f m a x )C255075100125TSA concentrationR e l a t i v e m R N A a m o u n t (% o f m a x)Fig.6Effects of trichostatin A (TSA)on ADH1and PDC1transcription,nucleosome histone acetylation and RNA pol II association in these genes.(A)ChIP analyses using acetylated H3antibodies in the 50-coding regions of ADH1and PDC1.Nine-day-old rice seedlings were treated by 500m M TSA for 6h,and roots were harvested and analyzed.(B)Nine-day-old rice seedlings were treated with the indicated concentrations of TSA for 6h.Relative amounts of ADH1and PDC1mRNA in rice roots,as determined by quantitative real-time RT–PCR.(C)ChIP analyses using RNA pol II antibodies in the 50-coding regions of ADH1and PDC1.Nine-day-old rice seedlings were treated by 500m M TSA for 6h,and roots were harvestedandanalyzed.Submergence-inducible genes(e.g.ADH1, PDC1)submergence re-aeration612182430364248(h)Fig.7Dynamic changes of mRNA levels,histone H3-K4di-methylation (H3di-meK4),histone H3-K4tri-methylation (H3tri-meK4),histone H3acetylation (H3ac)and RNA pol II association (pol II)at submergence-inducible genes (e.g.ADH1and PDC1)during submergence and following re-aeration.1000Change in histone modifications during submergenceat Wuhan University Library on December 2, 2010 Downloaded fromresults were obtained.In each experiment,qPCR was performed three times.Chromatin immunoprecipitation(ChIP)This method determines the relative abundance of histones in particular states that are attached to different regions of particular genes.For example,it might show that di-methylation of H3-K4s in the30fragments of a particular gene decreases during submergence.In brief,chromatin was fixed and broken up into fragments corresponding to500–1,000bp of DNA.Fragments associated with RNA pol II or fragments containing di-methyl H3-K4,tri-methyl H3-K4or acetyl H3were immunoprecipitated, and the numbers of copies of fragments that contain portions (promoter,50and30regions)of the ADH1and PDC1genes were then quantified with real-time PCR.For ADH1,the promoter,50region and30region were defined as nucleotides–206to–83,137to239,and2,891to3,031,respectively,where nucleotide1is the transcription initiation site.The corresponding regions of PDC1were nucleotides–894to–699,206to346,and 2,329to2,470.The detailed procedure is given in the following paragraphs.Unless stated otherwise,all procedures were done at48C. Rice seedlings were harvested and immediately immersed in cross-linking buffer[0.4M sucrose,10mM Tris–HCl(pH8.0),1mM EDTA,1mM phenylmethylsulfonyl fluoride(PMSF),1% formaldehyde]under vacuum at room temperature for30min. Cross-linking was stopped by adding glycine to a final concentra-tion of82mM,and incubation was continued for another5min. After washing the plants in water,the roots were cut off and frozen in liquid nitrogen.The frozen roots were powdered using a Multi-Beads Shocker(Yasui Kikai,Osaka,Japan).The powder was suspended in lysis buffer[50mM HEPES-NaOH(pH7.4),150mM NaCl,1mM EDTA,0.1%(w/v)sodium deoxycholate,10mM sodium butyrate,1%(v/v)Triton X-100,0.1%(w/v)SDS, Complete Tablet(protease inhibitor cocktail;Roche Diagnostics, Germany)]and sonicated so that DNA was broken into approximately500–1,000bp fragments.The mixture was centri-fuged at3,000r.p.m.for5min.The final supernatant was used for immunoprecipitation.For internal standards of ChIP experiments,we extracted cross-linked solutions from yeast(Saccharomyces cerevisiae).Yeast strain AH109(BD Biosciences,San Jose,CA,USA)was cultured in YPDA medium until the OD600reached0.8.Cellular proteins and DNA were cross-linked by addition of formaldehyde [final concentration:1%(v/v)]for15min and then the cross-linking was stopped by adding glycine to a final concentration of82mM.The yeast cells were centrifuged and suspended in lysis buffer,vortexed with glass beads and centrifuged.The supernatant was sonicated.Extracts from rice and yeast were pre-cleaned by rotating with1/100volume of protein A–agarose(50%slurry)(TOYOBO, Tokyo,Japan)in salmon sperm DNA(Roche Diagnostics)for2h. After centrifugation at3,000r.p.m.,extracts from the yeast and rice were mixed at a ratio of1:5(v/v).A4m l aliquot of antibodies against di-methylated histone H3at Lys4(#07-030;Upstate Biotechnology,Lake Placid,NY,USA)or acetylated histone H3at Lys9and Lys14(#06-599;Upstate Biotechnology),8m l of antibodies against tri-methylated histone H3at Lys4(#ab8580; Abcam,Cambridge,UK)or di-methylated histone H3at Lys9 (#07-212;Upstate Biotechnology),or45m l of an antibody against RNA pol II(#sc-900;Santa Cruz Biotechnology,Santa Cruz,CA, USA)was added to600m l of mixed extract.Extracts were incubated overnight with rotation,and1/10volume of proteinTable1List of primers used in this studyName Sequence Experiment AmpliconADH1-RT-fwd TCATCCGCATGGAGAACTA RT–PCR ADH1ADH1-RT-rev CCGTATATCATCATTCACCC RT–PCR ADH1PDC1-RT-fwd GCAACTGCTGGACAAAGAAG RT–PCR PDC1PDC1-RT-rev TGGCAGCCCAGCCAATTTCG RT–PCR PDC117S-RT-fwd TCCTACCGATTGAATGGTCC RT–PCR17S rRNA17S-RT-rev CTTGTTACGACTTCTCCTTCCTC RT–PCR17S rRNAADH1-pro-fwd AAACAGCGGCTGCAATTC ChIP Promoter region of ADH1ADH1-pro-rev GAGGTTTCGCCACTTCCTTC ChIP Promoter region of ADH1ADH1-50-fwd TCAAGTGCAAAGGTCAGTGC ChIP50portion of ADH1coding regionADH1-50-rev CGCCGCTCCAGTAATAAAAT ChIP50portion of ADH1coding regionADH1-30-fwd GCTGGAGGTGGAGAAGTTCA ChIP30portion of ADH1coding regionADH1-30-rev CCAACACCATAATCCCCTGA ChIP30portion of ADH1coding regionPDC1-pro-fwd GCTAGGCGTTACAGCGTAGC ChIP Promoter region of PDC1PDC1-pro-rev CATTCAGTCCAGCCGACAAG ChIP Promoter region of PDC1PDC1-50-fwd CTCCAACGCCGTCATCAAC ChIP50portion of PDC1coding regionPDC1-50-rev TAGTCGAGCAGGGTGAGGTT ChIP50portion of PDC1coding regionPDC1-30-fwd CCAAGAAAGACTGCCTCTGC ChIP30portion of PDC1coding regionPDC1-30-rev TGAGAAGACGAGCAGCAAGA ChIP30portion of PDC1coding regionScACT1-fwd TTTTTCACGCTTACTGCTTTTT ChIP50portion of yeast ACTIN1coding regionScACT1-rev GGGACCGTGCAATTCTTCT ChIP50portion of yeast ACTIN1coding regionChange in histone modifications during submergence1001at Wuhan University Library on December 2, Downloaded from。