Using Motion Planning to Study RNA Folding Kinetics

Using Motion Planning to Study RNA Folding Kinetics
Using Motion Planning to Study RNA Folding Kinetics

Using Motion Planning to Study RNA Folding Kinetics?Xinyu Tang?Bonnie Kirkpatrick?Shawna Thomas?Guang Song?Nancy M.Amato?

{xinyut,bkirk,sthomas,gsong,amato}@https://www.360docs.net/doc/fd6916628.html,

Technical Report TR03-005

Parasol Lab

Department of Computer Science

Texas A&M University

September19,2003

Abstract

We propose a novel,motion planning based approach to approximately map the energy landscape of an RNA molecule.Our method is based on the successful probabilistic roadmap motion planners that we have previously successfully applied to protein folding.The key advantage of our method is that it provides a sparse map that captures the main features of the landscape and which can be analyzed to compute folding kinetics.In this paper, we provide evidence that this approach is also well suited to RNA.We compute population kinetics and transition rates on our roadmaps using the master equation for a few moderately sized RNA and show that our results compare favorably with existing methods.

?This research supported in part by NSF Grants ACI-9872126,EIA-9975018,EIA-0103742,EIA-9805823,ACR-0081510,ACR-0113971,CCR-0113974,EIA-9810937,EIA-0079874,and by the DOE.

?Research supported in part by the CRA Distributed Mentor https://www.360docs.net/doc/fd6916628.html,puter Science,Montana State Univ.,Bozeman,MT 59717.

?https://www.360docs.net/doc/fd6916628.html,puter Science,Texas A&M Univ.,College Station,TX77843.

1Introduction

Ribonucleic acid(RNA)molecules perform diverse and important functions such as synthesizing proteins,cat-alyzing reactions,splicing introns,and regulating cellu-lar activities[21].Each RNA molecule has a unique native state–its energetically most stable conforma-tion–that determines how the RNA functions and in-teracts with its environment.The process by which the RNA(re)con?gures itself into its native conformation is called folding.

There are two general,but related,issues for RNA folding:structure prediction and folding kinetics.The structure prediction problem is to predict the structure of the native conformation given the RNA’s nucleotide sequence.Unlike the related protein folding problem, ef?cient algorithms do exist for some forms of RNA structure prediction[22,26].However,they do not provide insight into the folding process or the“energy landscape”which determines the folding kinetics.Each RNA conformation is associated with an energy;the lower its energy,the more stable it is.The energy land-scape can be thought of as adding this energy value to other parameters specifying the conformation.As will described in detail later,the energy landscape encodes information about folding pathways,transition rates,in-termediate states,and population kinetics.It is believed to be shaped like a funnel with the native conforma-tion at the base[6].The size of the landscape grows exponentially with the sequence length,so it is infea-sible to compute the complete landscape.To reduce this complexity,researchers focus on RNA planar sec-ondary structures instead of three-dimensional confor-mations.Although this dramatically reduces the size of the landscape,it remains impractical to compute the complete landscape for sequences longer than about40 nucleotides[9].

There are at least three important reasons to study RNA energy landscapes and folding kinetics.First,a better understanding of the folding process will aid the development of more ef?cient structure prediction algo-rithms.Second,it has recently been discovered that cat-alytic RNA often?uctuate away from their native con-formation to interact with other RNA,proteins,and lig-ands[21],and we cannot model or predict these?uc-tuations without studying energy landscapes.Third,we must study energy landscapes and folding kinetics to un-derstand how and why RNA molecules misfold.

In this paper,we propose a novel,motion planning based approach to approximately map the RNA’s en-ergy landscape.In particular,we develop a probabilistic roadmap(PRM)[12]based approach that?rst samples RNA con?gurations and then connects them together to form a graph,or roadmap.The key advantage of our method is that it provides a sparse representation of the landscape that captures its main features and which can be analyzed to compute folding kinetics.We have pre-viously applied this strategy to protein folding with re-markable success[1,2,19,20],e.g.,our method pre-dicted the subtle folding differences between the struc-turally similary proteins G and L[20].In this paper,we provide evidence that this approach is also well suited to RNA.In particular,we present results such as popula-tion kinetics and transition rates obtained using the mas-ter equation(Section4.1),for a few moderately sized RNA and show that our results compare favorably with existing methods[25].

2Preliminaries and Related Work

2.1RNA Primer

An RNA molecule is a sequence of nucleotides which differs from other RNA molecules in its bases.There are four types of bases:adenine(A),cytosine(C),guanine (G)and uracil(U).The complementary Watson-Crick bases,C-G and A-U,form stable,hydrogen bonds(base pairs)when they form a contact.The wobble pair G-U constitutes another strong base pair.These are the three most commonly considered base pairings[22,27,8], and are also what we consider in our model.

RNA Structure.Tertiary structure is a3D spatial RNA conformation with a set of base pairs.Secondary structure is a planar representation of an RNA confor-mation.Although there are slightly differing de?nitions [4,8],secondary structure is usually considered to be a planar subset of the base pair contacts present(see Table1,case3).Non-planar contacts are often called pseudo knots and are not allowed because they are con-sidered tertiary interactions.Many de?nitions of sec-ondary structure,including the one we adopt,eliminate other types of contacts that are not physically favored. Contacts considered invalid in our secondary structure are de?ned in Table1;this de?nition is also used in [8].Three common representations for RNA secondary structure are shown in Figure1[27].

The tertiary structure gives the most complete rep-resentation of RNA structure.However,the secondary structure is commonly used[26,27,8],because it pro-vides suf?cient information to study folding while dra-

Table 1:De?nition of valid secondary structure for any two

contacts [i ,j ]and [k ,l ]with i

5’3’

. . . ( ( ( ( ( ( . . . . ) ) ) . ) ) ) .

Figure 1:Three representations of the same secondary struc-ture for the sequence GGCGUAAGGAUUACCUAUGCC which denote contact pairs with bonds,arcs,and pairs of brackets,respectively.

matically reducing the RNA conformation space and

the dif?culty of the problem.One justi?cation for this simpli?cation is that research has shown that the RNA folding process is hierarchical,i.e.,secondary structure forms before tertiary structure [21,27].In this work,we focus on the ?rst stage,the formation of secondary structure.

Energy Calculations.Energy calculations are an es-sential component of RNA folding.To represent the energy landscape,we must be able to calculate the en-ergy of each conformation.One commonly used en-ergy function is the Turner or nearest neighbor rules [26].This method involves determining the types of loops that exist in the molecule and looking up their free energy in a table of experimentally determined values.Intuitively,more contacts,especially adjacent contacts,typically yield more stable structures with lower energy.Much work has been done to make these rules more de-tailed and accurate.

RNA (Secondary Structure)Conformation Space.For a given RNA nucleotide sequence,an RNA (sec-ondary structure)conformation is a planar set of valid base pairs.1The secondary structure conformation

1

As we only consider secondary structure in our method,we will usually omit this quali?cation when referring to conformations and conformation space.

C ,of an RNA sequence contains all sets of pairs that meet the criteria in Table 1.The size C grows exponentially as sequence length increases 5].Knowledge of the size of C is used to determine feasibility of enumerating all conformations,or if sampling will be needed..The size of C depends only on the RNA sequence length but also on the itself.Since exact computation of |C|requires enumerating C ,it should be estimated.

Zuker and Sankoff [27]developed a close estimation of |C|using a stochastic approach to account for the ef-fect of the speci?c sequence.Given an RNA sequence of length n ,they calculate the probabilities p A ,p C ,p G ,and p U of the occurrence of each nucleotide,i.e.,the percentage of that nucleotide in the sequence.They then use p =2(p A p U +p C p G )as the probability of two bases making a contact and obtain the approxi-mation |C|≈hn 32

αn ,

where α=(

1+

√1+4√p 2

)2

and

h =

α(1+4

√p )1/4

2√

πp 3/4

.

However,their estimate doesn’t ?t our model because they do not consider the wobble pair G-U or the restric-tion of the minimal hairpin size to 5.We modi?ed this formula to ?t our model by including the wobble pair in the probability p =2(p A p U +p C p G +p U p G ),and then scaling the probability p to p =p ·(n ?3)(n ?4)/n 2to restrict the minimal hairpin size to 5.The estimate results from plugging p in the equations for αand h .As can be seen in Table 2,our estimate can be a sig-ni?cantly better estimate of |C|for our model than the estimate used in [27].Our exact enumeration results match Cupal [8].It can also be seen that |C|grows ex-ponentially with sequence length,and hence it becomes impractical to enumerate all conformations when the se-quence length exceeds 40nucleotides [9]and some type of sampling must be used instead.

Sequence

#nucl Exact Estimation |C|Zuker [27]Ours

(ACGU)285226(ACGU)3

123520647

ACUGAUCGUAGUCAC 151.4.1021.0.103

2.4.102GGCGUAAGGAUUACCUAUGCC

218.6.1036.2.1051.3.104(ACGU)10

401.7.108

1.6.10103.3.109

Table 2:Estimated and actual sizes of C-space for several

RNA sequences.

Figure2:A PRM roadmap in C-space and a query.

2.2PRMs and Protein Folding

Our approach to RNA folding is based on the proba-bilistic roadmap(PRM)technique for motion planning [12].Motion planning determines valid paths to move objects from one con?guration to another.PRM s build graphs(roadmaps)that ideally approximate the topol-ogy of the feasible planning space,and can be used to answer many,varied queries quickly.Brie?y,PRM s work by sampling points from the movable object’s con-formation space(C-space)and retaining those that sat-isfy feasibility requirements(e.g.,collision-free).The movable object’s C-space is the set of all positions and orientations of the movable object,feasible or not[13]. Next,the retained points are connected to form a graph, or roadmap,using some simple local planning method (e.g.,a straight line)to connect nearby points.During query processing,paths connecting the start and goal conformations are extracted from the roadmap using standard graph search techniques(see Figure2).

In previous work,we have used PRM s to study protein folding when the native structure is known[19,2,1,20]. Here,the moving object is the protein,and the main difference from the usual PRM application is that the collision-detection feasibility test is replaced by a pref-erence for low energy conformations.We have obtained very promising results that were validated with experi-mental data for several moderate sized proteins,e.g.,we were able to observe the subtle folding differences in the structurally similar proteins G and L[20].

2.3Related Work

Research on RNA folding falls into two categories: structure prediction and the study of folding kinetics. Structure prediction is commonly solved with dynamic programming.Nussinov introduced a dynamic pro-gramming solution to?nd the conformation with the maximum number of base pairs[15].Zuker and Stiegler

formulated an algorithm to address the minimum energy

problem.Today,Zuker’s MFOLD algorithm is widely

used for structure prediction[22].McCaskill’s algo-

rithm[14]uses dynamic programming to calculate the

partition function Q= s exp(??G(s)/kT)over all secondary structures s,while Chen[4]uses matrices for

approximation.As described in[4],the partition func-

tion is“the sum of Boltzmann factors over all possible

branching patterns in which the chain can be arranged

into helices and intervening regions”.As we will see,

the partition function can also be used to study folding

kinetics.Wuchty extended this algorithm to compute

the density of states at a prede?ned energy resolution

[24].The ViennaRNA package is based on this work,

and implements Zuker and McCaskill’s algorithms as

well as some energy functions[8].

Several approaches other than thermodynamics have

been used to investigate RNA kinetics,e.g.,Gillespie

[10]used a Monte Carlo algorithm to?nd folding path-

ways,and Shapiro et al.[3]used a genetic algorithm to

study RNA folding pathways.

Several methods have been proposed that involve

computations on the folding landscape.Dill[4]used

matrices to compute the partition function over all possi-

ble secondary https://www.360docs.net/doc/fd6916628.html,plete folding landscapes

are approximated by this method.Wuchty modi?ed

Zuker’s algorithm[24]to generate all the secondary

structures within some given energy range of the na-

tive structure.Flammy and Wol?nger[7,23]extended

this algorithm to?nd local minima within some energy

difference of the native state,then to connect them via

energy barriers.The uses energy barrier tree to repre-

sentate the energy landscape.

The master equation can be used to compute the pop-

ulation kinetics of the folding landscape.It uses a ma-

trix of differential equations to represent the probabil-

ity of transition between different conformations.Once

solved,the dominate modes of the solution describe the

general folding kinetics[16,17,11,25].

3RNA Folding with PRMs

In this section,we discuss how to apply PRM s to study

RNA folding.There are two main steps in our approach:

constructing the roadmap and analyzing it.Constructing

the roadmap requires sampling a set of RNA conforma-

tions and computing their energies.Next,we determine

which conformations we should attempt to connect us-

ing a“local planner”,i.e.,a simple method to?nd tran-sitions between RNA conformations.One difference from the protein folding application is that our C-space is not continuous but discrete,and hence our options for making the local connections are more restricted. The local planner also assigns weights to the transitions to re?ect their energetic feasibility.This results in a roadmap(graph)of conformations(nodes)connected by transitions(edges)that represents the energy land-scape and where each pathway is a sequence of confor-mational changes the RNA molecule goes through as it transforms from one conformation to another.

After the roadmap is built,we perform some anal-ysis on it to study the population kinetics and provide insight into the folding process.We can identify transi-tional conformations where the folding process could be trapped or delayed,the folding rate,and representative folding pathways.In this work,we analyze the land-scape via folding pathways and the master equation.

Energy computations are required to measure con-formation feasibility and to calculate the roadmap edge weights(as discussed in Section3.1.2).Our current im-plementation uses a third-party energy function relying on the Turner rules to determine the validity of a point in C-space.This energy function is part of the ViennaRNA package[8].

3.1Roadmap Construction

The goal of roadmap construction is to build an approx-imation of the energy landscape which captures its im-portant features.The quality of our approximation de-pends on our node sampling and connection methods.

3.1.1Node Generation

Our framework currently has three methods for gen-erating RNA conformations:complete base-pair enu-meration(for small RNA),stack-pair enumeration and maximal-contact sampling.

Complete Base-Pair Enumeration.Our discrete RNA C-space makes it possible to enumerate all the conformations for small RNA molecules.However,this is not feasible for molecules with more than around40 nucleotides[9].Let S be the set of all possible base-pair contacts.To generate a valid conformation,we?rst se-lect one contact in S.Then we remove all the contacts from S that would violate the criteria in Table1if com-bined with the already selected contacts.The process of iteratively selecting a valid contact from S and then removing invalid contacts from S continues until S is empty.Each time we select a new contact,we de?ne a new secondary structure.To enumerate the entire space, we enumerate all possible combinations of a valid set of contacts from S as above.Figure3shows the complete enumeration for the RNA sequence ACGUCACGU.

Stack-pair Enumeration.This enumeration con-tains only those conformations containing stack-pair contacts.A stack-pair contact is a set of adjacent base-pair contacts,i.e.,no contacts are isolated from the oth-ers.For example,the contacts in Figure3(c)form a stack,but the contacts in Figure3(f)do not because they are not adjacent.More formally,a stack-pair conforma-tion is a valid conformation that for any contact[i,j], where i

Maximal-Contact Sampling.In this method,nodes are generated in a more‘random’fashion.To get lower energy conformations,we only generate those confor-mations with maximal contacts,i.e.,no more contacts can be added into those conformations without causing a violation(Table1).First,we create a conformation c without any contacts.Then,single contacts are succes-sively added until it is not possible to add a contact and maintain a valid conformation.This method biases the node distribution toward the areas of C-space with more contacts.Since more contacts usually means more sta-bility for the conformation,the energy of these confor-mations is usually lower.Each time a contact is added, it is randomly selected from all currently feasible con-tacts,and the set of feasible contacts is updated.This continues until no more contacts can possibly be added. In Figure3,(a),(d),(e),(g)and(h)are the maximal-contact onformations.

3.1.2Node Connection

After node generation,it would be expensive,and gen-erally not necessary,to make allθ(n2)connections.

.........

(a)

(.......)

(b)

((.....))

(c)

(((...)))

(d)

((...)..)

(e)

(.(...).)(f)

(.....)..(g)((...))..

(h).(.....).

(i).((...)).(j).(...)...

(k)..(...)..(l)

Figure 3:Complete enumeration of all conformations for RNA sequence ACGUCACGU.Conformations (a),(c),(d),(h)

and (j)are the stack-pair conformations.Conformations (a),(d),(e),(g)and (h)are maximal-contact conformations.

Hence,we restrict our attention to connecting nearby conformations.This requires distance metrics to iden-tify nearby conformations for connection and tech-niques for connecting them (i.e.,local planners).

Distance Metrics.The distance metric determines which nodes are close to each other and which are far apart.Here we use base-pair distance (the number of contact pairs that differ between two conformations).This denotes the number of base pairs that have to be opened or closed to transform one conformation into another.Our approach can utilize many other distance metrics such as string edit distance or tree edit distance [18],but we found that base-pair distances perform the best on the RNA we have studied.

Identifying Nodes for Connection.Neighboring roadmap nodes are connected using a local planner.We use two different strategies for determining neighbors.One strategy attempts to connect a node with the k clos-est nodes and the other attempts to connect a node with all the nodes within a ?xed radius r .

Generating Transitional Conformations.Once the neighbors are determined,the local planner connects each pair of nodes by generating transitions between them.To generate a transition from conformation c 1to conformation c 2,we ?rst identify the set O of con-tacts to be opened (i.e.,contacts in c 1and not in c 2)and the set L of contacts to be closed (i.e.,contacts in c 2but not in c 1).See Figure 4(a):contacts q1and q2are in O and contacts p1and p2are in L .To ensure that transitional conformations do not violate our planarity constraint,we construct a con?ict graph G between O and L .G describes which contact pairs cannot exist to-gether in a valid conformation.If one contact p ∈L con?icts with another contact q ∈O ,then p cannot be closed until q is opened,and we have an edge from q

c1: . . . . . ( . ( ( . . . . ) ) ) . . . . c2: . . . . . ( . ( ( . . . ) ) . ) . . . .

q1

q2

p1

p2

(a)

q2

O:

L:(b)

c1: . . . . . ( . ( ( . . . . ) ) ) . . . . c3: . . . . . ( . ( ( . . . ) . ) ) . . . . c4: . . . . . ( . ( ( . . . ) ) . ) . . . . c2: . . . . . ( . ( ( . . . ) ) . ) . . . .

(c)

Figure 4:Transitional node generation.(a)Start and goal

conformations and contact pairs to be opened and closed:q1,q2are in O;p1,p2are in L.(b)Con?ict graph:q1and q2con?ict with p1,q2con?icts with p2.(c)Sequences gener-ated:First open q2and close p2,then open q1and close p1.c3and c4are the two transitional conformations to connect c1and c2,here c4happens to be identical to c2.

to p in G .See Figure 4(b):q1and q2con?ict with p1;q2con?icts with p2.A valid transition is a sequence of transitional conformations that doesn’t violate G .

Our framework can use any strategy to determine the order to open contacts in O and close contacts in L .The most naive method is to ?rst open all the contacts in O and then to close all the contacts in L .This does not violate G ,but it produces high energy transitional con-formations.To ?nd low energy transitions,we want to produce conformations with as many contacts as possi-ble since they usually have lower energy.So,once we open a contact,we close all contacts in L that do not vi-olate G .We use a greedy strategy to determine the order for opening the contacts.In particular,we sort the con-tacts in L according to the number of contacts in O they con?ict with (given by their indegree in L ).We select the contact in L with the smallest number of con?icts

and open all the contacts in O that con?ict with it.We then close all the contacts in L that have no con?icts. See Figure4(c):c3,c4are the two transitional confor-mations generated for the connection.This is repeated until both O and L are empty.This strategy works well for the RNA we have studied.

Edge Weights.Edge weights are assigned to re?ect the transition rate between neighboring conformations, i.e.,the probability the molecule folds from one confor-mation to the other.Thus,edge weights re?ect the ener-getic feasibility for the folding process on this edge. When an edge(q1,q2)is added to the roadmap,it is assigned a weight that depends on the sequence of tran-sitional conformations{q1=c0,c1,c2,...,c n?1,c n= q2}determined by the local planner.For each pair of consecutive conformations c i and c i+1,the probability P i of moving from c i to c i+1

P i= e??E i if?E i>0

1if?E i≤0(1) where?E i=E(c i+1)?E(c i),k is the Boltzmann constant,and T is the temperature of folding.For a de-tailed discussion of different rules to calculate the tran-sition probabilities,please refer to[6].The edge weight w(q1,q2)is calculated as

w(q1,q2)=n?1

i=0?log(P i).(2)

(Negative logs are used since each0≤P i≤1.)By assigning the weights in this manner,we can?nd the most energetically feasible path in our roadmap when performing queries.This is the same method used in our previous work on protein folding.

4Roadmap Analysis

The roadmap is an approximation of the folding land-scape and it can be used to to study individual folding pathways as well as the global folding kinetics.

A folding pathway is a sequence of transitional con-formations the RNA molecules goes through during the folding process.Similar to our previous work on pro-tein folding[1],we can extract folding pathways and compute the free-energy pro?le,energy barriers,and important states of the folding process.From all the folding pathways to the native conformation,we extract the pathway with minimum total weight because this corresponds to the most energetically feasible path in our roadmap.Due to space constraints,no individual pathway results are provided here,but one case study is provided in the appendix.

Beyond the study of speci?c folding pathways,we are interested in the global properties of the energy land-scape.For example,how does the population of con-formations in the landscape vary as a function of time, i.e.,the population kinetics.Folding rates and transition states are also of great interest.These can all be studied using the master equation.

4.1Folding Kinetics and the Master Equation Master equation formalism has been developed for fold-ing kinetics in a number of earlier studies[11,25].The stochastic process of folding is represented as a set of transitions among all n conformations.The time evolu-tion of the population of each conformation,P i(t),can be described by the following master equation:

dP i(t)/dt=

n

i=j(k ji P j(t)?k ij P i(t))(3)

where k ij denotes the transition rate from state i to state j.Thus the change in population P i(t)is the difference between transitions to state i and transitions from state i. The transition rates are computed from the edge weight: K ij=K0e?W ij.K0is the constant coef?cient adjusted according to experimental results.

If we use an n-dimensional column vector p(t)= (P1(t),P2(t),...P n(t)) to denote the population of all n conformational states,then we can construct an n×n matrix M to represent the transitions,where

M ij=k ji i=j

M ii=? i=j k ij i=j(4) The master equation can be represented in matrix form:

d p(t)/dt=M p(t)(5) Th

e solution to the master equation is:

P i(t)= k j N ik eλk t N?1kj P j(0)(6)

where N is the matrix of eigenvectors N i for the ma-trix M in equation4andΛis the diagonal matrix of its eigenvaluesλi.P j(0)is the initial population of confor-mation j.

From equation6,we see that the eigenvalue spectrum is composed of n modes.If sorted by magnitude in as-cending order,the eigenvalues includeλ0=0and sev-eral small magnitude eigenvalues.Since all the eigen-values are negative,the population kinetics will stabilize as time goes by.The population distribution p(t)will converge to the equilibrium Boltzmann distribution,and no mode other than the mode with the zero eigenvalue will contribute to the equilibrium.Thus the eigenmode with eigenvalueλ0=0corresponds to the stable dis-tribution,and its eigenvector corresponds to the Boltz-mann distribution of all conformations in equilibrium.

For the same reason,we see that the large magnitude eigenvalues correspond to the fast folding modes,that is,those modes which fold in a burst.Their contribu-tion to the population will die away very soon.Simi-larly,the smaller the magnitude of the eigenvalue is,the more in?uence its corresponding eigenvector has on the global folding process.Thus,the global folding rates are determined by the slow modes.

For some folders(2-state folders),their folding rate is dominated by only one non-zero slowest mode.If we sort the eigen spectrum by ascending magnitude,there will be one other eigenvalueλ1in addition to eigenvalue λ0,that is signi?cantly smaller in magnitude than all other eigenvalues.Thisλ1corresponds to the folding mode which determines the global folding rate.We will refer it as the master folding mode.Its corresponding eigenvector denotes its contribution to the population of each state.Hence,the large magnitude components of the eigenvector correspond to the states whose popula-tions are most impacted by the master folding mode. These states are the transition states[16,17].

As described above,the equilibrium distribution,i.e., the eigenvector with eigenvalueλ0=0,calculated by the master equation should match the Boltzmann distri-bution.We compared our master equation results to the Boltzmann distribution,and they match exactly.

5Results and Discussion

With our kinetics analysis tools,we are able to evaluate our roadmap-based approximation of the energy land-scape.

Generally,the best way to evaluate an approxima-tion is to compare it to the exact method.Thus,ideally, we should compare the“exact”full base-pair enumera-tion(BPE)roadmap with the“approximate”stack-pair enumeration(SPE)and the maximal contact sampling

RNA Connection BPE SPE MCS

15nt k-closest101010

radius11020

21nt k-closest404040

radius12040

(a)

#Nodes

#Bases RNA BPE SPE MCS

15nt ACUGAUCGUAGUCAC1421533

21nt UAUAUAUCGACACGAUAUAUA5353250602

(b)

Table3:In the tables,BPE,SPE,and MCS denote base-pair enumeration,stack-pair enumeration,and maximal contact sampling,respectively.(a)Parameters used for connection.

(b)Roadmap statistics for RNA sequences studied. (MCS)roadmaps.As previously mentioned,we can af-ford to do a full enumeration for RNA with upto around 40nucleotides[9].Also,there is currently a limit on the size of the master equation we can accurately handle (due to a limitation in our current implementation).For these reasons,we performed the full comparison on a 15nucleotide sequence and a21nucleotide RNA hair-pin sequence.As shown in Table3,we used all three sampling strategies to generate nodes for the roadmap. For the maximal-contact sampling,we tried to generate about twice as many nodes we got with the stack-pair enumeration.We have also processed a41nucleotide RNA using stack-pair enumeration.

Each of the roadmaps was connected using both the k-closest and the radius connection strategies described in Section3.1.2.The parameters for these methods need to be carefully selected.The roadmap generated using complete base-pair enumeration and radius connections with r=1corresponds to a?ne mesh on the energy landscape.Recall that our distance metrics is the base-pair distance,therefore setting r=1creates transitions between all pairs of nodes differing by a single contact. This roadmap is used as a basis for comparison to de-termine appropriate k and r values for the other node generation methods.If we increase k or r,the connec-tions will be more complete and more expensive.We want these parameters to be as small as possible,yet still large enough to capture the important transitions.

For the complete base-pair enumeration,we tested k=5,10,15,20,30,and40.We found the smallest values that closely matched the complete landscape for the15nt and21nt RNA,respectively.For the other sam-

pling strategies,the distance between conformations is usually greater than1,thus,we must use larger values for r and k.To determine the appropriate parameters, we compared the kinetics results using r=1,2,5,10, and20,and k=5,10,15,20,30,and40.For the21nt RNA,k=40always generated a close approximation to the complete energy landscape.Table3gives the pa-rameters used in the results presented here.We only show results for the k-closest-connected roadmaps.

Figure5:The population kinetics of the15nucleotide hair-pin sequence UAUAUAUCGACACGAUAUAUA with the native structure..((((....)))).and a C-space of142conforma-tions.Figures(a)and(b)give a comparison the folding ki-netics of the base-pair enumeration roadmap to the stack-pair enumeration roadmap(15conformations).

5.115nt RNA

We provide detailed results for the15nt RNA.Similar results are available in the appendix for the21nt RNA. Figure5shows the

population kinetics of the four most signi?cant conformations calculated using the base-pair

nu-

the all

and the

so ing

the the the same in Figure6.In addition,the components of the eigenvectors(not shown due to space constraints)are nearly identical.Also in Figure6,we see that the fold-ing rates of the maximal-contact sampling method are farther from the completely enumerated kinetics than the stack-pair kinetics are.We expected this,because the stack-pair pairs encourage the formation of energet-ically stable conformations with stacks.The maximal-contact sampling is more random than the stack-pair pairs,and does not attempt to capture the stability in-herent in stacking pairs.

Figure7(a),(b)compares the folding kinetics of the base-pair enumeration,stack-pair enumeration and the maximal-contact sampling.Figure7(a)shows the equi-librium solutions of the two folding landscapes.They all match very well with the Boltzmann distribution for this molecule.Figure7(b)illustrates the small dif-

Component Indices

C o m p o n e n t M a g n i t u d e s

Figure 7:The folding kinetics of the 15nucleotide hair-pin sequence ACUGAUCGUAGUCAC with the native struc-ture ..((((....)))).and a C-space of 142conformations.Graph (a)compares the biggest 30components of eigenvector N 0and (b)compares the 30biggest components of eigenvector N 1for base-pair enumeration,stack-pair enumeration,and maximal-contact sampling.

ferences in magnitude of the components of the sec-ond eigenvector for all three folding landscapes.Al-though the maximal-contact sampling varies more from the complete base-pair enumeration in eigenvector N 1than in N 0,the differences in magnitude are still rela-tively small.These results indicate that given some spe-ci?c conformations,it’s possible to examine the folding kinetics of these conformations by computing the fold-ing landscape of that set combined with some additional random sampling.This combination will approximate the slow mode eigenvectors.

Conclusion

have demonstrated that the PRM method is promis-for studying RNA folding kinetics.PRM s allow us ef?ciently characterize the folding landscape using roadmaps,and moreover,our roadmaps were suit-for computing the folding kinetics for the RNA we studied so far.Our results also indicate that further on more sophisticated generation and connection will yield better results,and this is the subject ongoing work.If accepted,the ?nal version of this will have more polished results and detailed re-with more interesting RNA molecules.

N.M.Amato,Ken A.Dill,and https://www.360docs.net/doc/fd6916628.html,ing motion

planning to map protein folding landscapes and analyze folding kinetics of known native structures.JCB ,2003.To appear.Special issue of https://www.360docs.net/doc/fd6916628.html,put.Molecu-lar Biology (RECOMB)2002.N.M.Amato and https://www.360docs.net/doc/fd6916628.html,ing motion planning to

study protein folding pathways.JCB ,9(2):149–168,2002.Special issue of https://www.360docs.net/doc/fd6916628.html,put.Molecular Biology (RECOMB)2001.[3]Wojciech Kasprzak Jin Chu Wu Bruce A.Shapiro,

David Bengali.RNA folding pathway functional inter-mediates:Their prediction and analysis.JMB ,312:27–44,2001.[4]Shi-Jie Chen and Ken A.Dill.RNA folding energy

https://www.360docs.net/doc/fd6916628.html,A ,97:646–651,2000.[5]Jan Cupal,Ivo L.Hofacker,and Peter F.Stadler.Dy-namic programming algorithm for the density of states of RNA secondry https://www.360docs.net/doc/fd6916628.html,puter Science and Bi-ology 96,96:184–186,1996.[6]K.A.Dill and H.S.Chan.From Levinthal to pathways

to funnels:The ,new view of protein folding kinetics.Nat.Struct.Biol.,4:10–19,1997.[7]Christoph Flamm.Kinetic folding of RNA.Disserta-tion ,1998.[8]Ivo L.Hofacker.RNA secondary structures:A tractable

model of biopolymer folding.J.Theor.Biol.,212:35–46,1998.[9] A.Renner P.F.Stadler J.Cupal,C.Flamm.Density of

states,metastable states,and saddle points exploring the energy landscape of an RNA molecule.Proceedings,Fifth InteRNAtional Conference on Intelligent Systems for Molecular Biology ,pages 88–91,1997.

[10] D.T.Gillespie.Exact stochastic simulation of coupled

chemical reactions.JPC,81:2340–2361,1977. [11]N.G.Van Kampen.Stochastic processes in physics and

chemistry.North-Holland Personal Library,1992. [12]L.Kavraki,P.Svestka,https://www.360docs.net/doc/fd6916628.html,tombe,and M.Over-

mars.Probabilistic roadmaps for path planning in high-dimensional con?guration spaces.IEEE Trans.Robot.

Automat.,12(4):566–580,August1996.

[13]https://www.360docs.net/doc/fd6916628.html,tombe.Robot Motion Planning.Kluwer Aca-

demic Publishers,Boston,MA,1991.

[14]John S.McCaskill.The equilibrium partition function

and base pair binding probabilities for RNA secondary structure.Biopolymers,29:1105–1119,1990.

[15]Ruth Nussinov,George Piecznik,Jerrold R.Griggs,

and Danel J.Kleitman.Algorithms for loop matching.

SIAM J.Appl.Math.,35:68–82,1972.

[16]Ivet Bahar S.Banu Ozkan,Ken A.Dill.Fast-folding

protein kinetics,hidden intermediates,and the seuential stabilization model.11:1958–1970,2002.

[17]Ivet Bahar S.Banu Ozkan,Ken https://www.360docs.net/doc/fd6916628.html,puting

the transition state population in simple protein models.

68:35–46,2003.

[18] D.Sankoff and J.B.Kruskal.Time warps,string ed-

its and macromolecules:the theory and practice of se-quence comparison.Addison Wesley,London,1983. [19]G.Song and https://www.360docs.net/doc/fd6916628.html,ing motion planning

to study protein folding pathways.In Proc.Int.Conf.

Comput.Molecular Biology(RECOMB),pages287–296,2001.

[20]G.Song,S.L.Thomas,K.A.Dill,J.M.Scholtz,and

N.M.Amato.A path planning-based study of protein folding with a case study of hairpin formation in protein

G and L.In Proc.Paci?c Symposium of Biocomputing

(PSB),pages240–251,2003.

[21]I.Tinoco and C.Bustamante.How RNA folds.J.Mol.

Biol.,293:271–281,1999.

[22] A.E.Walter, D.H.Turner,J.Kim,M.H.Lyttle,

P.Muller,D.H.Mathews,and M.Zuker.Coaxial stack-ing of helixes enhances binding of oligoribonucleotides and improves predictions of RNA folding.Proc.Natl.

https://www.360docs.net/doc/fd6916628.html,A,91:9218–9222,1994.

[23]Michael Wol?nger.The energy landscape of RNA fold-

ing.2001.

[24]Stefan Wuchty.Suboptimal secondary structures of

RNA.Master Thesis,1998.

[25]W.Zhang and S.Chen.RNA hairpin-folding kinetics.

https://www.360docs.net/doc/fd6916628.html,A,99:1931–1936,2002.[26]M.Zuker,D.H.Mathews,and D.H.Turner.Algorithms

and thermodynamics for RNA secondary structure pre-diction:A practical guide.In J.Barciszewski&B.F.C.

Clark,editor,RNA Biochemistry and Biotechnology, NATO ASI Series.Kluwer Academic Publishers,1999.

[27]M.Zuker and D.Sankoff.RNA secondary structure

and their prediction.Bulletin of Mathematical Biology, 46:591–621,1984.

7APPENDIX:21nt RNA Results

the maximal-contact conformations,however,missed some important conformations.Yet,for those sampled conformations,the values are similar in magnitude to the corresponding components in stack-pair and base-pair roadmaps.This means,that although the random maximal-contact sampling is not accurate enough,it does capture some global properties of the folding pro-cess.Also we found that we can easily increase our approximation accuracy by connecting more conforma-As illustrated above,using the roadmaps generated three different strategies,we compared the kinet-analysis of two RNA molecules which have differ-folding behaviors.The roadmap generated by enu-of base-pairs is the most accurate represen-However,it is not feasible to enumerate any with more than 40nucleotides.The stack-pair are still generated by a form of enumeration,clearly,this much smaller subset of the full enumer-can effectively approximate the energy landscape,to those RNA with different folding behaviors.maximal-contact roadmap does not require enumer-to generate and can be of any size we desire.Al-its approximation is slightly inferior to the stack-roadmap,our preliminary work indicates that this can be improved.Most important,our demonstrates that we can effectively characterize energy landscape using many fewer conformations exist in the complete enumeration.These results that more work into improving our sampling will yield more concise and ef?cient repre-of the energy landscape.Our method is ex-to much longer RNA sequences.

APPENDIX:Folding Pathways Re-sults

to our previous work on protein folding [1],we folding pathways and compute the free-energy energy barriers,and important states of the fold-process.From all the folding pathways to the native we extract the pathway with minimum weight because this corresponds to the most en-feasible path in our roadmap .For a given its energy pro?le shows the energy of each conformation and it is easy for us to ?nd the minima and energy barriers on the pathway.These give an intuitive impression of the folding pro-An example is given in Figure 10.It shows the energy pro?le and folding pathway for a 21nt nucleotide RNA (GGCGUAAGGAUUACCUAUGCC).The molecule began in a a misfolded conformation,and had to overcome a high energy barrier to reach the native conformation as shown in its energy pro?le in Figure 10(a).

如何有效地进行跨文化沟通

如何有效地进行跨文化沟通 由于各国的文化存在着多样性的特点,无论是表层的语言、礼仪,还是中层次的建筑、饮食、礼仪或者处于核心层次的民族价值观、思维等等。这就决定我们在进行跨文化沟通的时候会遇到障碍和冲突,如何能有效地跨文化沟通具有十分重要的意义。 在进行跨文化沟通的时候存在障碍的原因是多种多样的,具体来说,文化差异层面的有 1.价值取向.2.思维模式.3.社会规范;另外也取决于沟通双方是否有培养文化差异的意识。 具体来说,可以用文化维度这个概念对跨文化进行分析,它主要有以下5个维度: 第一维度,个人身份的认同,具体来说就可以分为个人主义文化和集体主义文化两大类。个人主义文化的主要特征有:1.关键单位是个人。个人主义文化重视个人自由。2.对物体空间和隐私有更高的要求。3.沟通倾向于直接、明确和个人化。4.商业看作是一种竞争性的交易。集体主义的特征有 1.关键的单位是集体。个人的行动和决策的起点是群体。2.空间和私隐都没有关系重要。3.沟通时直觉式的、复杂的和根据印象进行的。4.商业是相互关联、相互协作的,认为促成结果的是关系而不是合同。以美国文化和中国文化为例,美国文化是具有典型的个人主义色彩,中国文化具有典型的个人主义色彩。 第二维度,权威指数,指国家或社会与人之间的平等程度。具体来说就是高权距离文化和低权距离文化。高权文化往往会导致沟通受到各种限制,因为高权力距离文化倾向于具有严格的层级权力文化结构,下级往上沟通会严重受阻,著名的“玻璃天花板”现象描述的就是在高权距离文化的影响下,组织对外国工作者的排斥。相反,在低权力距离文化影响的组织中,有权力和没权力的人之间的距离更短,沟通可以向上进行叶可以向下进行,更倾向于扁平化、和更民主的层级结构。低权力距离文化正趋于发展的趋势。 第三维度,性别角色权利。具体来说就性别角色在事业、控制和权力的控制程度。 第四维度,对时间的态度,这侧重于区分对目标的长期投入或短期投入。以美国和日本为例。美国喜欢把经商比喻为“打猎”,日本则把经商比喻为“种植水稻”。这可以看出,美国侧重于短期投入要立竿见影的效果,日本则侧重于长期的投资来获取长线的发展。 第五维度,对不确定性的指数。不确定性指数高的国家对含蓄和不确定性因素的接受和容忍程度高,具体体现在法律发条的伸展度等地方。不确定性指数低的国家,对事物的要求高度精确,喜好制定严格的标准和法律。 在现实交流中,这五个维度往往不会单独出现,而是交叉混合,这也和文化的一体性和交融性有着密切的关系。 综合的来说,我们常遇到跨文化沟通障碍有以下几种: 1.自我文化中心主义。这种障碍原因在于,在与人沟通时,习惯性的从自我的文化观念、价值观念、道德体系作标准来看待他人的行为。这种障碍通常会造成漠不关心距离,例如对沟通对方的要求(如特殊的节假日不工作)不加理睬;回避距离,例如因不了解对方的文化礼仪而回避与沟通对方的交流;蔑视距离,例如因不了解对方的宗教生活而对他的行为就行无理干预与批评。 2.文化霸权主义。在进行跨文化沟通时,沟通双方的地位往往不平等。处于

《跨文化沟通》作业评讲

《跨文化沟通》作业评讲 根据重庆电大制定的教学实施意见和本课程考试要求,为帮助同学们学习和跨文化沟通的理论知识、培养沟通能力和技巧,本课程的作业都采用主观性问题。下面就作业要求作一些简单提示提示内容来自重庆电大),供同学们完成作业时参考。 跨文化沟通作业(1)讲评 论述分析:请看下面的一段话,并按照题目要求进行回答。 在奥地利工作的美国子公司人员有时误认为奥地利人不喜欢他们,因为奥地利人对他们总是一本正经。殊不知,奥地利人不象美国人那样随便,待人直呼其名。由于文化习俗不同,海外子公司人员之间产生误解在所难免。许多美国人不理解,为什么法国人和德国人午餐时喝酒?为什么许多欧洲人不愿意上夜班?为什么海外子公司要赞助企业内的职工委员会或向当地幼儿园教师捐款?对美国人来说,这些活动纯粹是浪费,但对于熟悉当地文化的人来说,这些是非常必要的。 一般来说,在不同文化的融合过程中,会由于以下两个因素而受到阻碍。 一、人们不能清楚地认识自己的文化。有句古诗说:“不识庐山真面目,只缘身在此山中”。人们之所以不能真正的认识自己的文化,也是因为他们总是身处于自己的文化之中。由于从小受到特定文化的熏陶,使其认为在其文化背景下发生的事都是理所当然的,是一种世人皆知的道理。当问起他所处的文化有何特征,有何优缺点时,从未接触过其他文化的人是回答不出来的。他们只能说,从来都是这样。并且,当遇到文化差异时,他们就会用自己认为正常的标准去判断,而对其他的文化标准大惑不解。 如果把其他的文化拿来做比较时,这些问题就可以迎刃而解了。没有比较就没有不同。如果我们不能很好的了解自己的文化,也就无所谓进化。因为我们总是认为自己是对的,而无视其他文化的优势之处。因而也就不能从其他的文化吸取有利于自己发展的东西。 二、对其他的文化认识不够。同样的道理,当我们对其他的文化不能很好的理解时,也会对文化的融合造成障碍。如前所述,当外来文化有利于本土文化发展时就会被吸收。要想达到文化融合就要首先发现外来文化有无有利之处。如果对外来文化的理解上发生扭曲,或者了解片面,就不能公正地判断其是否有利于自身的发展。如果一种优势文化被理解为对其有威胁,它很可能就会被拒之门外。 但是由于我们习惯于从自己的文化角度去审视其他的文化就不会很全面。所以在理解其他文化时应该换个角度,从另一个不同的参照系去理解,并且要对其他文化采取一种超然独立的立场,给与足够的重视认识。 问题: 1、为什么会存在文化差异? 2、文化差异对于沟通有何影响?

《葛底斯堡演讲》三个中文译本的对比分析

《葛底斯堡演讲》三个中文译本的对比分析 葛底斯堡演讲是林肯于19世纪发表的一次演讲,该演讲总长度约3分钟。然而该演讲结构严谨,富有浓郁的感染力和号召力,即便历经两个世纪仍为人们津津乐道,成为美国历史上最有传奇色彩和最富有影响力的演讲之一。本文通过对《葛底斯堡演讲》的三个译本进行比较分析,从而更进一步加深对该演讲的理解。 标签:葛底斯堡演讲,翻译对比分析 葛底斯堡演讲是美国历史上最为人们所熟知的演讲之一。1863年11月19日下午,林肯在葛底斯堡国家烈士公墓的落成仪式上发表献词。该公墓是用以掩埋并缅怀4个半月前在葛底斯堡战役中牺牲的烈士。 林肯是当天的第二位演讲者,经过废寝忘食地精心准备,该演讲语言庄严凝练,内容激昂奋进。在不足三分钟的演讲里,林肯通过引用了美国独立宣言中所倡导的人权平等,赋予了美国内战全新的内涵,内战并不仅是为了盟军而战,更是为了“自由的新生(anewbirthoffreedom)”而战,并号召人们不要让鲜血白流,要继续逝者未竞的事业。林肯的《葛底斯堡演讲》成功地征服了人们,历经多年仍被推崇为举世闻名的演说典范。 一、葛底斯堡演说的创作背景 1.葛底斯堡演说的创作背景 1863年7月1日葛底斯堡战役打响了。战火持续了三天,战况无比惨烈,16万多名士兵在该战役中失去了生命。这场战役后来成为了美国南北战争的一个转折点。而对于这个位于宾夕法尼亚州,人口仅2400人的葛底斯堡小镇,这场战争也带来了巨大的影响——战争遗留下来的士兵尸体多达7500具,战马的尸体几千具,在7月闷热潮湿的空气里,腐化在迅速的蔓延。 能让逝者尽快入土为安,成为该小镇几千户居民的当务之急。小镇本打算购买一片土地用以兴建公墓掩埋战死的士兵,然后再向家属索要丧葬费。然而当地一位富有的律师威尔斯(DavidWills)提出了反对意见,并立即写信给宾夕法尼亚州的州长,提议由他本人出资资助该公墓的兴建,该请求获得了批准。 威尔斯本打算在10月23日邀请当时哈佛大学的校长爱德华(EdwardEverett)来发表献词。爱德华是当时一名享有盛誉的著名演讲者。爱德华回信告知威尔斯,说他无法在那么短的时间之内准备好演讲,并要求延期。因此,威尔斯便将公墓落成仪式延期至该年的11月19日。 相比较威尔斯对爱德华的盛情邀请,林肯接到的邀请显然就怠慢很多了。首先,林肯是在公墓落成仪式前17天才收到邀请。根据十九世纪的标准,仅提前17天才邀请总统参加某一项活动是极其仓促的。而威尔斯的邀请信也充满了怠慢,

跨文化沟通

跨文化沟通 姓名:楚辰玺学号:15120299 案例的选择是一个叫做万里的土耳其学生,是我同学的好朋友,所以平时也有接触。他在中国呆了一年,然后走了,现在又在波兰求学。原来我问他说,为什么要离开中国呢。因他是一个熟练掌握七种语言的人,所以我很佩服他。也很不解。他说因为感觉在中国的生活很难受,感觉自己时时刻刻都很委屈。 后来他给我举了个例子,比如他问中国同学,你想要些什么,或者,你会争取奖学金么,或者是他作为领导问自己的手下,你的目标是什么。而以上种种给他的回答大多是摸棱两可,不置可否。后来我通过《跨文化沟通》一书中的高低语境解答了我的困惑。他的本意只是直来直去,比如他只是想知道自己的手下想要到达一个怎么的高度,或者有什么目标。但是他很直白,不含蓄。所以属于低语境的对话,而中国的文化,众所周知,属于高语境文化,是比较含蓄,委婉。他在这样的环境中会觉得自己是被排挤,被隐瞒的一方,觉得中国的人们不够坦诚,给他的印象很不好。i 我问他对于直接询问、回答和间接的委婉的询问和回答在他看来有什么区别,也就是我们所说的高语境文化和低语境文化。他说为什么不直接说呢?他表示不解,觉得即使说错了或者有冒犯也会得到原谅,不会为人们在意。我觉得这里有着中外文化交流之间冲突的一个矛盾点,中式的对话和交流相处在高语境文化下的重要因素是害怕受到惩罚,或者说有规则的舒服,也就是万里经常说的不自由,我认为是谨小慎微,超出了谨慎的程度,成为了一种惯例。 为了了解外国人与中国人的对话,我要了几张他和中国朋友的对话截图。因为万里是去过三个大洲,25个国家,他像我的朋友发出邀请,说一起去欧洲,我朋友是个女生,然后他说可以住在他家里。当时我还记得我朋友的男朋友很不开心。所以对于在我看来他们的思想和行为都是很自由的。他们更注重个人的发展,而不是集体主义。这个论据的寻求在BBC 的一个纪录片《中国老师来了》。着重体现了英国的教育制度与中国传统的教学制度的不同,比如对于纪律的要求,对于整齐划一的广播操的要求。都体现了在中国的教育体制传输的一种集体主义思想。 他们自己会更注重自己个人的发展,比如万里不舒服就很毅然的离开,他对这里的环境虽有眷恋但是并不可以影响自己的追求,还有他去过25个国家,对于自己的国家也没有很强烈的集体主义感。对于个人的发展要求和对自己的愿望的实现更迫切一些,对于一些对于个人无意义的比如集体的跑操活动,还有课堂上的一些硬性要求,为了培养中国学生的整齐划一的执行能力和集体主义的归属感。 在我咨询万里为什么离开中国,他的陈述中有一个高频词汇“自由”,他认为在中国的各种生活都不自由,在他看来,是一种思想自由和行为自由的不想当。他的想法中,除了早操还有固定的上课模式、固定的强制参加的活动、对于体测的要求等等。 而且在我对于所看到聊天记录的分析,他是一个很会适应当地的生活习俗的人,比如节日,前几天,十二月三十一号晚上,他给我的朋友发了元旦快乐。当时的对话是这样的。万里说:“新年快乐”我的朋友W说:“万里!新年快乐!你那里几点了。我这里11:41了。”万里说:“这里还是四点多,还有很长时间,但在中国已经新年了。”W说:“你能想到我,我很开心的。”万里:“嗯,当然想到你。我们是朋友。”加重语气又说:“好朋友。”这里的好朋友三个字引人深思,我朋友和他的交流的时间并不长,也是之前上过一节课,然后在一个小组,所以有了一定的交流。 在这里我想起上课时结合教材老师讲到的,外国学生对于在学校交流的伙伴,在课后就不管不顾不问候,所以这里有些许的疑问,所以我在询问他,是否真的是这样,对于你们在课

财务人员常用的Excel表格使用技巧

财务人员常用的Excel表格使用技巧 1、分数的输入 如果直接输入“1/5”,系统会将其变为“1月5日”,解决办法是:先输入“0”,然后输入空格,再输入分数“1/5”。 2、序列“001”的输入 如果直接输入“001”,系统会自动判断001为数据1,解决办法是:首先输入“'”(西文单引号),然后输入“001”。 3、日期的输入 如果要输入“4月5日”,直接输入“4/5”,再敲回车就行了。如果要输入当前日期,按一下“Ctrl+;”键。 4、填充条纹 如果想在工作簿中加入漂亮的横条纹,可以利用对齐方式中的填充功能。先在一单元格内填入“*”或“~”等符号,然后单击此单元格,向右拖动鼠标,选中横向若干单元格,单击“格式”菜单,选中“单元格”命令,在弹出的“单元格格式”菜单中,选择“对齐”选项卡,在水平对齐下拉列表中选择“填充”,单击“确定”按钮(如图1)。 5、多张工作表中输入相同的内容 几个工作表中同一位置填入同一数据时,可以选中一张工作表,然后按住Ctrl键,再单击窗口左下角的Sheet1、Sheet2......来直接选择需要输入相同内容的多个工作表,接着在其中的任意一个工作表中输入这些相同的数据,此时这些数据会自动出现在选中的其它工作表之中。输入完毕之后,再次按下键盘上的Ctrl键,然后使用鼠标左键单击所选择的多个工作表,解除这些工作表的联系,否则在一张表单中输入的数据会接着出现在选中的其它工作表内。

6、不连续单元格填充同一数据 选中一个单元格,按住Ctrl键,用鼠标单击其他单元格,就将这些单元格全部都选中了。在编辑区中输入数据,然后按住Ctrl键,同时敲一下回车,在所有选中的单元格中都出现 了这一数据。 7、在单元格中显示公式 如果工作表中的数据多数是由公式生成的,想要快速知道每个单元格中的公式形式,以便编辑修改,可以这样做:用鼠标左键单击“工具”菜单,选取“选项”命令,出现“选项”对话框,单击“视图”选项卡,接着设置“窗口选项”栏下的“公式”项有效,单击“确定”按钮(如图2)。这时每个单元格中的分工就显示出来了。如果想恢复公式计算结果的显示,就再设置“窗口选项”栏下的“公式”项失效即可。 8、利用Ctrl+*选取文本 如果一个工作表中有很多数据表格时,可以通过选定表格中某个单元格,然后按下Ctrl +*键可选定整个表格。Ctrl+*选定的区域为:根据选定单元格向四周辐射所涉及到的有 数据单元格的最大区域。这样我们可以方便准确地选取数据表格,并能有效避免使用拖动鼠标方法选取较大单元格区域时屏幕的乱滚现象。 9、快速清除单元格的内容 如果要删除内容的单元格中的内容和它的格式和批注,就不能简单地应用选定该单元格,然后按Delete键的方法了。要彻底清除单元格,可用以下方法:选定想要清除的单元格或 单元格范围;单击“编辑”菜单中“清除”项中的“全部”命令,这些单元格就恢复了本来面目。 10、单元格内容的合并 根据需要,有时想把B列与C列的内容进行合并,如果行数较少,可以直接用“剪切” 和“粘贴”来完成操作,但如果有几万行,就不能这样办了。 解决办法是:在C行后插入一个空列(如果D列没有内容,就直接在D列操作),在D1中输入“=B1&C1”,D1列的内容就是B、C两列的和了。选中D1单元格,用鼠标指

跨文化情景剧剧本

《跨文化沟通》情景剧 第一幕 时间:早上 地点:某跨国公司会客大厅 人物:跨国公司子公司负责人——A日本人(饰演者:) B美国人(饰演者:) C中国人(饰演者:) D阿拉伯人(饰演者:) E泰国人(饰演者:) F英国人总裁(饰演者:) G接引日本人(饰演者:) — (旁白)某跨国大公司出现大危机,来自各国分公司的负责人纷纷赶回英国到总 部会面进行商务会谈。会谈时间定为早上9点, 现在是8点30分。 (CEO坐在办公室,一边批阅文件,一边等待其他子公司的负责人到来) 接引日本人:(“咚咚咚”敲门后开门)先生,有一部分子公司负责人已经到达会 场了。 英国人总裁:好的,谢谢,我这就过去。 接引日本人:(深深鞠躬后,退出办公室,并小声地关上门。) (旁白)此时,各子公司的负责人相继进入会场。 美国人B:(热情地走向第一个到达的日本人A,伸出手)你好,第一次见面。

日本人A:(朝向美国人B,弯腰鞠躬)阁下,你好,请多指教。 美国人B:(手悬在空中,略显尴尬)你好,你好。 日本人A:(见此状,连忙握手)抱歉,先生,实在抱歉。请多指教。(又弯腰鞠躬 一次) 阿拉伯人D:(D进入的同时E也到了,D看见老朋友E,高兴地到E面前,右手扶 住对方的左肩,左手搂抱对方腰部,然后,按 照先左后右的顺序,贴面三次,即左——右—— 左。在贴面的同时)艾赫兰——艾赫兰——艾 赫兰(即“你好”)最近怎么样啊!(后嘴里发 出亲吻的声音) 】 泰国人E:(虽然习惯了,但还是表现出很无奈的样子,后双手合十,举于胸前, 朝向三人,面带微笑)萨瓦滴卡。(转过身面向 D)还好,这把老骨头还能在商场战几年,哈哈 哈。 阿拉伯D:(D搂着E,朝向A、B也热情地迎上去,伸手)(“你好”)你们那边还 好吗 日本人A:(握手)你好啊。我还好。公司还能正常运转。 阿拉伯人D说话时眼睛紧紧盯着日本人A的眼睛,这让日本人A很不自在,很勉 强地看了一眼阿拉伯人,就把头低了下去。 美国人B:(握手)你好,阁下。我们这次来不就是为了让它变得更好吗 中国人C:(看了看手表,嘀咕了一句“还好只是迟到了一点点”后,整理整理服 装进入会场)抱歉,各位,你们好,我来晚了。

大学跨文化沟通重点

P4外在文化:外在文化指文化外显的一面,是可以感知、识别的,或可通过文字记载而获得,一切文化现象,即包括文化行为在内的各种文化事、物,都属于外在文化。 P4内在文化:内在文化指文化内隐的面,从文字记载中不能直接感知和识别,包括人们作出决定、完成任务、衡量事物的重要性和把知识概念化的方式以及怎样对于种种限制作出反应。 P4交互性文化:在沟通的平台上,双方都能对彼此言行中的文化暗示做出反应,并且以此来修正自己的行为,这样就形成了一种交互性文化。 P6文化:文化是群体成员连贯一致的、后天习得的、群体共享的观念,人们藉此决定事情的轻重缓急,就事情的适宜性表明自己的态度,并决定和支配后续的行为。 文化的三个特点: 1、连贯一致的:每一种文化,不管是,去的还是现在的,都具有一致性和完整性,即文化也是一种完整的宇宙观,如果群体成员从自己狭隘的宇宙观出发,就很有可能看不到在自己“统一的、持久的愿景”中所缺少的东西; 2、后天习得的:文化并不是天生的,而是通过学习掌握的。同样如果要了解其他文化,就要通过学习来掌握,不只是浅尝辄止,而要深入学习,并按其行为准则来规范自己的行为; 3、群体共享的观念:文化是为社会所共享的。社会成员在事物的含义的以及这种含义的归因上达成了共识。社会被共同的价值观所驱动,同一文化背景中的人员共享该文化的各种符号、标识。 文化的三个功能: 1、文化决定事情的轻重缓急。 2、文化决定态度,态度是通过学习而形成的,它是对事物的总体评价 3、文化支配行为,人们的行为直接受到价值观的支配,直接来源于对事物价值的判断 P16文化休克:文化休克指的是在一段时间里出现的一系列反应,是一种混乱感、一种心理甚至是生理上的问题,这些问题都是源于在其它文化中求生的欲望引发调整和改变自己的努力所带来的压力。 P18反文化休克:旅居国外的人回到祖国以后,常常会出现一段与在国外相似的调整和适应期,以及伴随而来的一些类似的症状。 P24刻板印象:当我们面对陌生的或者复杂的事物时,我们对其产生的固定的,僵化的印象。P27文化智力:一个人成功地适应新文化环境的能力。 P30跨文化沟通:通常是指不同文化背景的人之间发生的沟通行为。因为地域不同、种族不同等因素导致文化差异,因此,跨文化沟通可能发生在国际间,也能发生在不同的文化群体之间。 问题二P32高语境文化:,有较多的信息量由情景而不是语言方式来进行传达。特点:晦涩的,间接的,暗指的 低语境文化:大多数信息都是通过外在的语言方式来进行传达。特点:明确的,直接的,完全用词语表达 P36 问题三P43语言文化的关系:语言与文化的关系:语言与文化相互交织在一起,相互影响,密不可分。语言能够帮助我们同不同文化背景的人进行沟通,文化认知对语言的运用也十分必要。 1、语言反映环境,语言可以折射出我们的生活环境,我们用语言描述身边的事物。(如“雪”)同时环境影响词汇的发展; 2、语言体现价值观,在与来自其他文化背景的人沟通时,我们要把异国语言文化中的概念用适合国人价值观排序的方式准确翻译出来。进行思想沟通,文化知识与语言知识是同等重

跨文化沟通案例

(一)典型案例: 飞利浦照明公司某区人力资源的一名美国籍副总裁与一位被认为具有发展潜力的中国员工交谈。他很想听听这位员工对自己今后五年的职业发展规划以及期望达到的位置。中国员工并没有正面回答问题,而是开始谈论起公司未来的发展方向、公司的晋升体系,以及目前他本人在组织中的位置等等,说了半天也没有正面回答副总裁的问题。副总裁有些疑惑不解,没等他说完已经不耐烦了。同样的事情之前已经发生了好几次。 谈话结束后,副总裁忍不住想人力资源总监抱怨道:“我不过是想知道这位员工对于自己未来五年发展的打算,想要在飞利浦做到什么样的职位而已,可为什么就不能得到明确的回答呢?”“这位老外总裁怎么这样咄咄逼人?”谈话中受到压力的员工也向人力资源总监诉苦。 (二)案例中的文化差异对沟通产生的影响分析 在该案例中,副总裁是美国籍人,而那位员工则是中国籍。显然,对于出生于两个不同的国度的人,中美之间思维方式、生活习惯、文化背景、教育程度、文化差异等多个方面都存在着显著的差异。正是由于这些文化差异的存在,才使得双方在沟通交流的过程中产生一系列障碍。 案例中“中国员工并没有正面回答问题”,原因可能是多种多样的。 (1)语言障碍、没有理解透彻美国副总裁所说话语的原意。 中文和英文之间存在很大的差异,在我们学习英文的过程中我们可以体会到,对于一个中国人,要完全体会英文背后的文化是很困难的一件事。例如,“pull one's leg”本意是“开玩笑”,但我们很容易就理解成“拉后腿”的意思了。 (2)思维方式明显不相同。 假设这位中国员工从正面直接回答了副总的问题。比如,中国员工回答:“……想在五年之内作到营销部经理的职位。”很显然,按照中国人的传统心理,这样的回答违反了中国人一向谦虚、委婉的心理习惯。太直接反而暴露出自己很有野心,高傲自大的缺陷。谦虚也可以给自己留有后路,万一做不到那个理想的位子,也不至于丢面子,被人耻笑。恰恰相反,美国人一向简单明了,很直接,这也是他们一贯的思维方式。

跨文化沟通复习

《跨文化沟通》考点整理 一、名词解释 1.外在文化 2.内在文化 3.交易性文化 4.文化 5.高语境文化 6.低语境文化 7.个人主义 8.集体主义 9.权力距离 10.不确定性规避 11.不确定性容忍 12.男性化 13.女性化 14.副语言 15.空间语言 二、论述题及简答题考点 1.文化的三个特点(连贯一致、后天习得、群体共享)及三个功能(决定事情的轻重缓急、政治因素就事情的适宜性表明自己的态度、决定和支配后续的行为)P4~8 2.高语境文化与低语境文化P19~20 3.语言反映环境P28~29 4.语言体现价值观P29 5.如何选择正确的语言(语言因素、商业因素、、适当的流利程度)P32~35 6.五个范畴(结合小结P72~73、P91重点复习) 7.副语言P120~121 8.在面对面沟通中的非言语行为习惯(七个方面,重点看“口头沟通中的话语权”、“空间语言”、“沉默”三个部分,注意区分不同文化差异的行为习惯P121~137) 9.如何表达尊重:权势地位、服饰作为权威的象征P140~142 10.绩效奖励P150~152 11.界定问题并解决问题(注意高低语境文化与集体主义和个人主义方面的差异阐述)P176~177 12.冲突管理(注意高低语境文化与集体主义和个人主义方面的的不同理解以及低语境文化对冲突管理方法的排次)P177~180 13.冲突沟通的策略(五点)P180~182 14.谈判要素(四个方面)P190~199 15.谈判的阶段划分(四个阶段)P200~205 《跨文化沟通》论述题及简答题语言归纳 1.文化的三个特点(连贯一致、后天习得、群体共享)及三个功能(决定事情的轻重缓急、就事情的适宜性表明自己的态度、决定和支配后续的行为)P4~8 文化的三个特点:

译文对比分析

话说宝玉在林黛玉房中说"耗子精",宝钗撞来,讽刺宝玉元宵不知"绿蜡"之典,三人正在房中互相讥刺取笑。 杨宪益:Pao-yu,as we saw, was in Tai-yu?s room telling her the story about the rat spirits when Pao-chai burst in and teased him for forgetting the “green wax” allusion on the night of the Feast of Lanterns. 霍克斯: We have shown how Bao-yu was in Dai-yu?s room telling her the story of the magic mice; how Bao-Chai burst in on them and twitted Bao-yu with his failure to remember the …green wax? allusion on the night of the Lantern Festival; and how the three of them sat teasing each other with good-humored banter. 对比分析:杨宪益和霍克斯在翻译“耗子精”采用来了不同的处理方法,前者使用了异化”rat spirits”,后者用的是归化法”magic mice”,使用归化法更受英美读者的亲乃。但是二者同时采用了增译法,增添了the story,原文并没有。在翻译“宝玉不知绿烛之典”的“不知”,英文1用的是“forgetting”,而译文2用的是“with failure to ”,显然译文2更符合英美的表达习惯。 那宝玉正恐黛玉饭后贪眠,一时存了食,或夜间走了困,皆非保养身体之法。幸而宝钗走来,大家谈笑,那林黛玉方不欲睡,自己才放了心。 杨宪益:Pao-yu felt relieved as they laughed and made fun of each other, for he had feared that sleeping after lunch might give Tai-yu indigestion or insomnia that night, and so injure her health. Luckily Pao-chai?s arrival and the lively conversation that followed it had woken Tai-yu up. 霍克斯: Bao-yu had been afraid that by sleeping after her meal Dai-yu would give herself indigestion or suffer from insomnia through being insufficiently tired when she went to bed at night, but Bao-chai?s arrival and the lively conversation that followed it banished all Dai-yu?s desire to sleep and enabled him to lay aside his anxiety on her behalf. 对比分析:译文一对原文语序进行了调整,先说了“放心”,再说“担心”,但并不如不调整顺序的逻辑强。译文二只是用了一个“but”就把原文意思分层了两层,逻辑更加清晰,符合西方人注重逻辑的习惯。原文中的“谈笑”是动词,而两个译文版本都是译的“the lively conversation”,是名词,体现了汉语重动态,英文重静态的特点。 忽听他房中嚷起来,大家侧耳听了一听,林黛玉先笑道:"这是你妈妈和袭人叫嚷呢。那袭人也罢了,你妈妈再要认真排场她,可见老背晦了。" 杨宪益:Just then, a commotion broke out in Pao-yu?s apartments and three of th em pricked up their ears. “It?s your nanny scolding Hai-jen,” announced Tai-yu. “There?s nothing wrong with Hai-jen, yet your nanny is for ever nagging at her. Old age has befuddled her.”

财务人员必备的EXCEL

财务人员必备的EXCEL

财务人员必备的EXCEL 我的天地2010-06-17 14:02:16 阅读54 评论0 字号:大中小订阅 也许你已经在Excel中完成过上百张财务报表,也许你已利用Excel函数实现过上千次的复杂运算,也许你认为Excel也不过如此,甚至了无新意。但我们平日里无数次重复的得心应手的使用方法只不过是Excel全部技巧的百分之一。本专题从Excel中的一些鲜为人知的技巧入手,领略一下关于Excel的别样风情。 一、建立分类下拉列表填充项 我们常常要将企业的名称输入到表格中,为了保持名称的一致性,利用“数据有效性”功能建了一个分类下拉列表填充项。 1.在Sheet2中,将企业名称按类别(如“工业企业”、“商业企业”、“个体企业”等)分别输入不同列中,建立一个企业名称数据库。 2.选中A列(“工业企业”名称所在列),在“名称”栏内,输入“工业企业”字符后,按“回车”键进行确认。 仿照上面的操作,将B、C……列分别命名为“商业企业”、“个体企业”…… 3.切换到Sheet1中,选中需要输入“企业类别”的列(如C列),执行“数据→有效性”命令,打开“数据有效性”对话框。在“设置”标签中,单击“允许”右侧的下拉按钮,选中“序列”选项,在下面的“来源”方框中,输入“工业企业”,“商业企业”,“个体企业”……序列(各元素之间用英文逗号隔开),确定退出。 再选中需要输入企业名称的列(如D列),再打开“数据有效性”对话框,选中“序列”选项后,在“来源”方框中输入公式:=INDIRECT(C1),确定退出。 4.选中C列任意单元格(如C4),单击右侧下拉按钮,选择相应的“企业类别”填入单元格中。然后选中该单元格对应的D列单元格(如D4),单击下拉按钮,即可从相应类别的企业名称列表中选择需要的企业名称填入该单元格中。 提示:在以后打印报表时,如果不需要打印“企业类别”列,可以选中该列,右击鼠标,选“隐藏”选项,将该列隐藏起来即可。 二、建立“常用文档”新菜单 在菜单栏上新建一个“常用文档”菜单,将常用的工作簿文档添加到其中,方便随时调用。 1.在工具栏空白处右击鼠标,选“自定义”选项,打开“自定义”对话框。在“命令”标签中,选中“类别”下的“新菜单”项,再将“命令”下面的“新菜单”拖到菜单栏。 按“更改所选内容”按钮,在弹出菜单的“命名”框中输入一个名称(如“常用文档”)。 2.再在“类别”下面任选一项(如“插入”选项),在右边“命令”下面任选一项(如“超链接”选项),将它拖到新菜单(常用文档)中,并仿照上面的操作对它进行命名(如“工资表”等),建立第一个工作簿文档列表名称。 重复上面的操作,多添加几个文档列表名称。

《傲慢与偏见》译文对比分析

《傲慢与偏见》(节选一) Pride and Prejudice by Jane Austen (An Except from Chapter One) 译文对比分析 节选文章背景:小乡绅贝内特有五个待字闺中的千金,贝内特太太整天操心着为女儿物色称心如意的丈夫。新来的邻居宾利(Bingley)是个有钱的单身汉,他立即成了贝内特太太追猎的目标。 1.It’s a truth u niversally acknowledged, that a single man in possession of a good fortune, must be in want of a wife. 译文一:凡是有钱的单身汉,总想娶位太太,这已经成了一条举世公认的道理。译文二:有钱的单身汉总要娶位太太,这是一条举世公认的真理。 2.However little known the feelings or views of such a man may be on his first entering a neighborhood, this truth is so well fixed in the minds of the surrounding families that he is considered as the rightful property of some one or other of their daughters. 译文一:这样的单身汉,每逢新搬到一个地方,四邻八舍虽然完全不了解他的性情如何,见解如何,可是,既然这样的一条真理早已在人们心中根深蒂固,因此人们总是把他看作是自己某一个女儿理所应得的一笔财产。 译文二:这条真理还真够深入人心的,每逢这样的单身汉新搬到一个地方,四邻八舍的人家尽管对他的性情见识一无所知,却把他视为某一个女儿的合法财产。 3.”My dear Mr. benne” said his lady to him one day ,”have you heard that nether field park is let at last?” 译文一:有一天班纳特太太对她的丈夫说:“我的好老爷,尼日斐花园终于租出去了,你听说过没有?” 译文二:“亲爱的贝特先生”一天,贝纳特太太对先生说:“你有没有听说内瑟费尔德庄园终于租出去了:” 4.Mr. Bennet replied that he had not. 译文一:纳特先生回答道,他没有听说过。 译文二:纳特先生回答道,没有听说过。 5.”But it is,” returned she:” for Mrs. long has just been here, and she t old me all about it.” 译文一:“的确租出去了,”她说,“朗格太太刚刚上这来过,她把这件事情的底细,一五一十地都告诉了我。” 译文二:“的确租出去了,”太太说道。“朗太太刚刚来过,她把这事一五一十地全告诉我了。”

会计人必学必会的Excel表格技巧

会计人必学必会的Excel表格技巧 从Excel中的一些鲜为人知的技巧入手,领略一下关于Excel的别样风情。也许你已经在Excel中完成过上百张财务报表,也许你已利用Excel函数实现过上千次的复杂运算,也许你认为Excel也不过如此,甚至了无新意。但我们平日里无数次重复的得心应手的使用方法只不过是Excel全部技巧的百分之一。 一、建立“常用文档”新菜单 在菜单栏上新建一个“常用文档”菜单,将常用的工作簿文档添加到其中,方便随时调用。 1.在工具栏空白处右击鼠标,选“自定义”选项,打开“自定义”对话框。在“命令”标签中,选中“类别”下的“新菜单”项,再将“命令”下面的“新菜单”拖到菜单栏。按“更改所选内容”按钮,在弹出菜单的“命名”框中输入一个名称(如“常用文档”)。 2.再在“类别”下面任选一项(如“插入”选项),在右边“命令”下面任选一项(如“超链接”选项),将它拖到新菜单(常用文档)中,并仿照上面的操作对它进行命名(如“工资表”等),建立第一个工作簿文档列表名称。重复上面的操作,多添加几个文档列表名称。 3.选中“常用文档”菜单中某个菜单项(如“工资表”等),右击鼠标,在弹出的快捷菜单中,选“分配超链接→打开”选项,打开“分配超链接”对话框。通过按“查找范围”右侧的下拉按钮,定位到相应的工作簿(如“工资.xls”等)文件夹,并选中该工作簿文档。重复上面的操作,将菜单项和与它对应的工作簿文档超链接起来。 4.以后需要打开“常用文档”菜单中的某个工作簿文档时,只要展开“常用文档”菜单,单击其中的相应选项即可。提示:尽管我们将“超链接”选项拖到了“常用文档”菜单中,但并不影响“插入”菜单中“超链接”菜单项和“常用”工具栏上的“插入超链接”按钮的功能。 二、建立分类下拉列表填充项 我们常常要将企业的名称输入到表格中,为了保持名称的一致性,利用“数据有效性”功能建了一个分类下拉列表填充项。 1.在Sheet2中,将企业名称按类别(如“工业企业”、“商业企业”、“个体企业”等)分别输入不同列中,建立一个企业名称数据库。 2.选中A列(“工业企业”名称所在列),在“名称”栏内,输入“工业企业”字符后,按“回车”键进行确认。仿照上面的操作,将B、C……列分别命名为“商业企业”、“个体企业”…… 3.切换到Sheet1中,选中需要输入“企业类别”的列(如C列),执行“数据→有效性”命令,打开“数据有效性”对话框。在“设置”标签中,单击“允许”右侧的下拉按钮,选中“序列”选项,在下面的“来源”方框中,输入“工业企业”,“商业企业”,“个体企业”……序列(各元素之间用英文逗号隔开),确定退出。再选中需要输入

《乡愁》两个英译本的对比分析

《乡愁》两个英译本的对比分析 曹菊玲 (川外2013级翻译硕士教学1班文学翻译批评与鉴赏学期课程论文) 摘要:余光中先生的《乡愁》是一首广为人知的诗歌,曾被众多学者翻译成英文,本文将对赵俊华译本和杨钟琰译本进行对比分析,主要从原作的分析、译文的准确性、译文风格以及对原文意象的把握入手进行赏析,指出两译本的不足和精彩之处。 关键词:《乡愁》;原诗的分析;准确性;风格;意象的把握 1.原诗的分析 作者余光中出生于大陆,抗战爆发后在多地颠沛流离,最后22岁时移居台湾。20多年都未回到大陆,作者思乡情切,于上世纪70年代创作了这首脍炙人口的诗歌,但是当时大陆与台湾关系正处于紧张时期,思而不得的痛苦与惆怅包含于整首诗中。 全诗为四节诗,节与节之间完全对称,四句一节,共十六句,节奏感、韵律感很强,读起来朗朗上口。而且这首诗语言简洁,朴实无华,但是却表达了深刻的内涵。从整体上看,作者采取层层递进的方式,以时空的隔离和变化来推进情感的表达,最后一句对大陆的思念,一下子由个人哀愁扩大到国家分裂之愁,因而带有历史的厚重感。 【原诗】 乡愁 余光中 小时候/乡愁是一枚小小的邮票/我在这头/母亲在那头 长大后/乡愁是一张窄窄的船票/我在这头/新娘在那头 后来啊/乡愁是一方矮矮的坟墓/我在外头/母亲在里头 而现在/乡愁是一湾浅浅的海峡/我在这头/大陆在那头 【译诗】 (1) 赵俊华译 Homesick As a boy, I was homesick for a tiny stamp, —I was here, Mom lived alone over there. When grow up, I was homesick for a small ship ticket. —I was here,

跨文化交流

浅谈“跨文化交流” 摘要:随着经济和现代科技的飞速发展,全球化成为当今世界的一大趋势。跨地域、跨文化的相互了解、相互交流有助于开放自我、开放社会,更好地实现不同地域、不同文化背景间人们的共同进步。改革开放30多年来,越来越多的人走出国门或者参与跨文化的交际,跨文化之间的交流已经成为人们日常生活中一个不可或缺的组成部分。因而掌握与不同文化背景的人打交道时的实际技能,需要通过学习不断得到提高。然而文化上的差异往往会给不同文化背景的人们之间的相互交流带来较大的困难,有时还会造成不必要的误解,阻碍跨文化交流的顺利进行。因此,我们应该对跨文化交流的过程、目的、技巧、影响机制等进行更加深入的研究。本文主要从文化的角度分析了跨文化交流的障碍以及其形成的原因作了分析,并提出了一些避免这些交流障碍,提高交流技巧与能力的观点和方法。 关键词:跨文化交流、价值观、语言文化、行为语言、因素“跨文化交流”一词译自英文的“intercultural communication”,指的是不同文化背景的人之间的交际,也就是不同文化背景的人之间所发生的相互作用。该词是由美国人类文化学者霍尔于1959年在他的《无声的语言》一书中首先提出的。 跨文化交流从理论上说,是来自不同文化的人群,由于不同的文化价值观和处事方式,往往会导致相互间对同一事件产生不同的理

解和看法。随着东西方文化交流越来越频繁,因此需要对不同文化间如何充分发挥跨文化交流的有效性进行深入的研究。 文化仿佛像空气一样,人们平常感觉不到它的存在,但在实际交流中却处处离不开文化。它涉及到人类生活的方方面面,并通过不同层面的种种因素对交流产生影响。在实际的交流过程中,人们通常把自己所熟悉的、习惯性的方式,当作是最正确的、理所当然的思维方式和处事方式。这种把自己的文化模式置于其它文化模式之上的行为,必然会削弱跨文化交际的能力,妨碍跨文化交流的顺利进行。下面我们从观念、语言、行为三个主要因素来分析其对跨文化交流的影响。 价值观的影响 “广义的文化指人类在社会历史发展过程中所创造的物质财富 和精神财富的总和。狭义的文化指人类的精神财富,如科学、教育、文学、艺术等。”文化涵盖的范围很广,包括社会制度、政治与法律、世界观、人生观、价值观、思维方式、习俗、道德、宗教、家庭观念等等。 从文化表面上的不同,我们可以把不同文化深层次的差异归结在不同的观念或称价值观上。所谓价值观,就是判断是非好坏的标准。对于隶属于某个文化的人来说,判断行为的好与不好,都是受这个价值观支配的。不同文化的价值观差异促成了人们认识上的差异。当文化价值系统发挥作用时,它便产生激发力,影响着人们的感觉、

沟通短剧

地点:朝阳医院肝胆外科 人物:王大妈 护士甲 护士乙 实习生 王大妈的女儿 (背景音乐响起,旁白) 医患沟通,是指医疗机构的医务人员在日常诊疗过程中,与患者及家属就伤病、诊疗、健康及相关因素(如费用、服务等),主要以诊疗服务的方式进行的沟通交流,它构成了单纯医技与医疗综合服务实践中十分重要的基础环节,也是医患沟通的主要构成。由于它发生在各医疗机构中的医患个体之间,虽然面广量大,但绝大部分的医患沟通一般范围小、难度小、影响小,不易引起人们的关注。 沟通是一门学问,善于沟通是优秀护士必备的素质, 有效沟通能快速拉近医患距离, 它可以使医护人员快速增进信任; 它可以消除患者的焦虑 它可以浇灭心头的怒火,平息一场纠纷。 我们每天都在和患者打交道, 患者千差万别 沟通需要区别对待 但有一点是共同的, 那就是要用心去交流。 今天,我们肝胆病区来了一位急腹症患者,她是一位 70多岁的王奶奶 王大妈:哎呦!护士!痛死我了! (王大妈的女儿用轮椅推着她,大妈双手捂着肚子)。 护士甲:奶奶,您哪儿不舒服啊? 王大妈的女儿:我妈痛得不行,先叫医生看看! 护士甲:大妈,先到里面躺会吧!我通知医生来看你的。 (护士甲接过病历资料,一边掺扶着大妈到床旁)

护士甲:奶奶,我先给你量体温、测血压吧。 (拿出体温计和血压计) 王大妈的女儿:护士,我妈都痛死了!你先给他打止痛针吧! 护士甲: 您别着急,我知道奶奶肚子很痛, 可是我们在不明确病因的情况下是不能盲目用止痛剂,那样会掩盖病情的 王大妈的女儿:尽快吧。 护士甲(对护士乙) :小李,你先帮奶奶做入院手续,我去通知值班医生。 护士乙(拿着病历资料) :奶奶,我先简要问下您基本的情况,好吗? 护士乙:奶奶,您平时有什么疾病吗? 王大妈:我平时没病,这次就是肚子痛。 护士乙:以前有药物过敏史吗? 王大妈的女儿: 问什么问呀,办事效率怎么那么低, 我妈痛死了还不给她治疗,反而问些无关紧要的话。是不是想耽误我们病情啊?你们付得了责任吗? 护士乙:你们先不要着急……(被打断) 王大妈: 问啥呀,问这问那的, 烦都被你们烦死了, 我都痛死了都没人管, 你们这是什么破医院啊! (伴着阵阵痛苦的呻吟) 护士甲:别激动,好吗?你们的心情我能理解。我们要先了解您的病史,了解清楚后才能决定下 一步的治疗,我们会尽量快点的。 奶奶,住院手续已经帮您办好了。 (旁白)采集完病史后,护士根据开出的医嘱为大妈挂上了液体。 护士甲:

相关文档
最新文档