ChapterBc science 10 Chapter 3(加拿大bc省科学十年级词汇)

The United Kingdom of Great Britain and Northern IrelandChapter 1: geography, people and language 1. Different Names for Britain and its Parts① The British Isles ② Great Britain ③ England (geographical names)④ The United Kingdom of Great Britain and Northern Ireland (official name)⑤ Britain ⑥ the United Kingdom ⑦ the U.K 2. National Flagthe Union Jack3. The British Isles are made up oftwo large islands-Great Britain (the larger one) and Ireland, and hundreds of small ones. (geography)4. The image of John Bull.5. British National Emblem6. Climate: maritime climateTemperate ( with warm summer ,cool winter) plentiful precipitation (降雨量) Three principal features: frequent fog the large number of rainy days Instability or changeability7. Three political divisions on the island of Great Britain: England, Scotland and Wales. (三个政治区：英格兰、苏格兰和威尔士) Region Capital National emblem People language Famous personsEngland（Southern ） London rose English 50M 83%English (Official)Scotland （north ）Edinburgh （爱丁堡）Thistle （蓟花）Scottish/Scots 5M <9.6% Gaelic (盖尔语)Robert Burns (罗伯特·彭斯)——Auld Lang Syne (友谊地久天长) A Red Red Rose(一朵红红的玫瑰 Wales（west ） Cardiff（加的夫）daffodil (黄水仙) Leek（韭葱）Welsh <3M <2%Welsh (1/4 in Wles)Northern Ireland Belfast（贝尔法斯特）Shamrock（三叶草）Northern Irish1.7M 1.8%English William Butler Yeats（威廉·巴特勒·叶芝）——When youare oldJames Joyce(詹姆斯·乔伊斯)——stream-of-consciousness(意识流)George Bernard Shaw（乔治·萧伯纳）Other people:3%(immigrants)补充：（1）Englandis the largest, most populous and most wealthiest part of the country.The River Thames originates in southwestern England. (英国第二长河)(2) Scotlandhas three natural zones (the Highlands in the north; the Central lowlands; the south Uplands) Capital: Edinburgh 苏格兰位于大不列颠的北部。

加拿大ubc微观经济学教材
加拿大UBC微观经济学教材包括多个版本，以下是部分常用的版本：
1.《微观经济学》（第六版）作者：罗伯特·平狄克、丹尼尔·鲁宾
费尔德。

2.《微观经济学：现代观点》（第九版）作者：弗兰克·H·奈特、
劳埃德·B·诺德豪斯。

3.《微观经济学：基本原理与扩展》（第十二版）作者：安德鲁·马
斯-克莱尔、约瑟夫·E·斯蒂格利茨。

此外，还有一些国际版教材和适用于特定专业领域的教材，如《微观经济学：理论、应用与政策》、《国际微观经济学》等。

在选择教材时，可以根据个人学习情况和兴趣进行选择。

可以通过教授推荐、教材评价网上的评论和学生口碑等多种渠道了解教材的质量和适用性，最终选择适合自己的教材。

Cellular activity and signaling induced by osteoprotegerin in osteoclasts:involvement of receptor activator of nuclearfactor n B ligand and MAPKS.Theoleyre a,1,Y .Wittrant a,1,S.Couillaud a ,P.Vusio b ,M.Berreur a ,C.Dunstan c ,F.Blanchard a ,F.Re´dini a ,D.Heymann a,*aEE 99-01,Pathophysiology of Bone Resorption Laboratory and Therapy of Primitive Bone Tumors,Medicine Faculty,1rue G.Veil,44035Nantes cedex 01,FrancebIFR 26,Institut de Biologie,Nantes Hospital,FrancecAmgen Inc.,One Amgen Center Drive,Thousand Oaks,CA 91320,USA Received 22July 2003;received in revised form 19September 2003;accepted 15October 2003AbstractOsteoprotegerin (OPG)is a decoy receptor for receptor activator of nuclear factor n B ligand (RANKL),an inducer of osteoclastogenesis via its receptor RANK.We recently demonstrated that OPG also exerts a direct effect in osteoclasts by regulating protease expression.Herein,we showed that OPG-induced pro-matrix metalloproteinase-9activity was abolished by ras/MAPK inhibitors in purified osteoclasts.OPG induced the phosphorylation of p38and ERK1/2in RAW264.7cells.Only p38activation was totally abolished by a blocking anti-RANKL antibody or an excess of RANKL.Surface plasmon resonance experiments revealed that RANK,RANKL and OPG are able to form a tertiary complex.These results suggested a potential formation of a tertiary complex RANK–RANKL–OPG on osteoclasts.Thus,OPG is not only a soluble decoy receptor for RANKL but must be also considered as a direct effector of osteoclast functions.D 2003Elsevier B.V .All rights reseved.Keywords:Osteoprotegerin;RANKL;Osteoclast;Metalloproteinase;Signaling1.IntroductionAmong the cellular and molecular parameters involved in the bone resorption process [1–3],receptor activator of nuclear factor n B ligand (RANKL),a member of the tumor necrosis factor family is the main regulator of osteoclast differentiation [4,5].Within the bone system,soluble and membrane forms of RANKL expressed by osteoblasts exert their activities through binding to their receptor RANK on osteoclasts [6,7].The third protagonist,osteoprotegerin (OPG)produced by osteoblasts acts as a decoy receptor for RANKL,preventing it from binding to and activatingRANK [8].It also inhibits the development of osteoclasts [9]and down-regulates the RANKL signaling through RANK [8].The biological effects of OPG on bone cells include the inhibition of terminal stages of osteoclast differentiation,suppression of mature osteoclast activation,and induction of apoptosis [7–9].We recently demonstrated that OPG can also exert a direct biological effect in osteoclasts by inducing proteases and protease inhibitors expression suggesting a more com-plex regulation of bone resorption by OPG than originally described [10].These results are consistent with those obtained by Hakeda et al.[11]who reported a direct effect of OPG on isolated osteoclasts via a 140kDa OPG-binding protein on cell membrane.Whereas OPG exhibits no transmembrane domain,Yun et al.[12]reported the pres-ence of a membrane-bound form of OPG in dendritic cells that may correspond to either a matrix-bound or/and a transmembrane form of the protein.More recently,Standal et al.presented evidence that myeloma cells internalize and0167-4889/$-see front matter D 2003Elsevier B.V .All rights reseved.doi:10.1016/j.bbamcr.2003.10.005*Corresponding author.Tel.:+33-2-4041-2845;fax:+33-2-4041-2860.E-mail address:dominique.heymann@sante.univ-nantes.fr (D.Heymann).1Equally contributed to the Biochimica et Biophysica Acta 1644(2004)1–7degrade OPG through its binding to syndecan-1,the major heparan sulfate proteoglycan expressed on myeloma cells [13].OPG possesses a highly basic heparin-binding domain, making interactions with heparin and heparan sulfates possible,suggesting a new control mechanism for OPG biological activity[14].The purpose of the present study was(i)to further investigate the direct biological effect of OPG on osteoclasts and the signaling pathways associated,(ii)to better charac-terize the receptor(s)involved.2.Experimental procedures2.1.Cell culture2.1.1.Purified osteoclast preparationPurified osteoclasts(purity95%)were obtained from neonatal rabbit bone cells isolated using a previously reported method[15].After purification,the osteoclasts were cultured in serum-free a-MEM for24h in the absence or presence of 50ng/ml human OPG(Amgen Inc.,Thousand Oaks,USA). In some experiments,before addition of OPG,cells were also preincubated for1h in the presence of signaling pathway inhibitors:PI3kinase(Wortmannin:500nM),tyrosine kinase(Genistein:200A M)(Saint Quentin Fallavier,France), protein kinase A(PKI5–24:100nM),PKC and cAMP-dependent protein kinase(H7dihydrochloride:100A M), MAPK extracellular signaling-regulated kinase(ERK)ki-nase(MEK)(PD98059:40A M),Ras(FPT inhibitor II (FPTII):40A M)and p38MAPK(SB203580:0.1,10A M) (Calbiochem,La Jolla,USA).Equivalent dilutions of DMSO (reconstitution solution)alone served as control.A dose–response of the different inhibitors was tested previously and the optimal doses were used in this study.The RAW264.7cells obtained from Prof.Matsumoto (Saitama,Japan)were maintained in a-MEM without red phenol supplemented with10%FBS and differentiated in the presence of50ng/ml human RANKL(Amgen)for10 days.Cells were then cultured in serum-free medium for24 h and incubated for5min in the presence or absence of50 ng/ml RANKL or OPG,with or without4A g/ml anti-human RANKL antibody(R&D System,Abington,UK).In some cases,after serum-free culture period,cells were treated with 50ng/ml RANKL for2h,washed or not before incubation with the combinations described above.The293cells transfected with the full-length cDNA coding for murine RANKL(293-RANKL cells)were a generous gift of Dr.R.Josien(INSERM U437,Nantes, France)and were cultured in DMEM supplemented with 10%FBS.2.1.2.Measurement of matrix metalloproteinase activitiesAfter3days of culture in a-MEM supplemented with10% FBS,purified osteoclasts were cultured for24h in serum-free a-MEM in the presence or absence of50ng/ml OPG.Matrix metalloproteinase(MMP)activities were then detected in cell culture supernatants by gelatin zymography as previously described[15].After the culture period(24h in serum-free a-MEM in the presence or absence of OPG),MMP activities were detected in cell culture supernatants.Electrophoresis gel was composed of10%polyacrylamide gel containing0.2% gelatin.Proteinase activity was apparent as unstained regions.The stained polyacrylamide gels were observed with a video camera that allowed transfer to software for image processing(ImageQuant software program)and isolation of migration bands.Gelatinase activity was expressed as a percentage(shades of grey compared to the control).The same experiment was performed by adding10A M EDTA,a chelating agent of bivalent ions,to inhibit MMP activities.In addition,as protein levels were not modified by the different treatments,the same volume of supernatant was assayed in all experiments,allowing strict comparison of the results.2.1.3.Semi-quantitative reverse transcription-polymerase chain reaction(RT-PCR)Total RNA for cDNA synthesis was isolated from RAW264.7cells and293-RANKL cells by phenol–chloro-form extraction as previously described[12].RT-PCR assays were carried out using the following primer pairs for RANKL (5V-CAGCATCGCTCTGTTCCTGTA-3(sense),5V-TCTCAGTCTATGCCTGAACTTTGAAAGC-3V(anti-sense));RANK(5V-AAGATGGTTCCAGAAGACGGT-3V (sense),5V-CATAGAGTCAGTTCTGCTCGGA-3V); MMP-9(5V-TCTGAGGCCTCTACAGAGTCT-3V(sense), 5V-CTCATGGTCCACCTTGTTCAC-3V(antisense))and 18S(5V-TCAAGAACGAAAGTCGGAGGTTCG-3V (sense)and5V-TTATTGCTCAATCTCGGGTGGCTG-3V (antisense)).2.1.4.Western blot studies and signal transductionRAW264.7cells were lysed in ice-cold lysis buffer(NaCl 150mM,Tris50mM,Nonidet P-401%,sodium deoxy-cholate0.25%,NaF1mM,NaVO41mM,leupeptine10A g/ ml,aprotinin10A g/ml,phenymethylsulfonylfluoride0.5 mM,glycerol10%).Equal amounts of proteins were re-solved on10%SDS-PAGE,and transferred to a PVDF membrane.After two washes in0.05%Tween20/PBS,the membrane was incubated in a saturating solution(0.05% Tween20/3%BSA/PBS)for30min,the levels of phos-phorylated ERK1/2and p38were detected with specific antibodies(Ozyme Saint-Quentin en Yvelines,France)as well as total ERK1/2and p38(R&D System).2.1.5.Surface plasmon resonance studiesThese experiments were performed with the BIACore 2000optical biosensor(BIACore,Uppsala,Sweden).Solu-ble human RANK(sRANK)(Promocell,Heidelberg,Ger-many)was covalently coupled through its primary amino groups to a carboxymethyl dextran flow cell(CM5,BIA-Core)as recommended by the manufacturer.All binding experiments were performed at a flow rate of10A l/min.TheS.Theoleyre et al./Biochimica et Biophysica Acta1644(2004)1–7 2resonance signal measured on the control flow cell was subtracted from the signal measured on the experimental flow cell.The resulting sensorgrams were analyzed using the BIAEvaluation software(BIACore).2.1.6.Statistical analysisThe mean F S.D.was calculated for all conditions and compared by ANOVA.Differences relative to a probability of two-tailed P<0.05were considered significant.3.Results3.1.OPG enhances proMMP-9activity of purified osteo-clasts by the ras/MAPK pathwayTo determine the signaling pathways by which OPG exerts its effects on osteoclasts,the modulation of OPG-induced proMMP-9activities was studied in the presence of specific inhibitors of different intracellular signaling path-ways.Tyrosine kinase inhibitor(Genistein),PKC and cAMP-dependent protein kinase inhibitor(H7)and Ras inhibitor(FPT inhibitor II)completely abolished OPG-induced stimulation of proMMP-9(Fig.1A).Conversely, PI3kinase inhibitor(Wortmannin)and protein kinase A inhibitor(PKI5–24)did not prevent OPG effects(data not shown).In this context,two synthetic MAPK inhibitors (SB203580and PD98505inhibitors of p38and MEK, respectively)have been studied(Fig.1B).Thus,pretreat-ment with SB203580inhibited the OPG-induced proMMP-9activity in a dose-dependent manner.Similarly,40A M of PD98059completely abolished the OPG-induced proMMP-9activity.Both inhibitors did not affect pro-MMP-9activity of non-stimulated osteoclasts(not shown).These results demonstrated that the ras/MAPK signaling is required for OPG to stimulate osteoclast proMMP-9activity.3.2.OPG induces phosphorylation of ERK1/2and p38indifferentiated RAW264.7cellsTo further explore the MAPK involvement in cellular activities induced by OPG on osteoclasts,we employed the RAW264.7cells.Indeed,this cell line is known to express RANK and differentiate into tartrate-resistant acid phospha-tase(TRAP)and calcitonin receptor-positive cells when cultured in the presence of RANKL[16].Moreover,the large amounts of cellular materials obtained from RAW264.7cell cultures allow to perform signal transduc-tion studies which were impossible on purified osteoclasts. RAW264.7cells were differentiated in the presence of RANKL for10days and then RANKL-and serum-de-prived for24h.OPG signaling studies were then monitored on by Western blot analyses using phosphospecific anti-bodies.Interestingly,treatment with50ng/ml OPG caused phosphorylation of ERK1/2(Fig.2A)and p38(Fig.2B) detectable within2min,whereas total ERK1/2and p38were unchanged.The phosphorylation of ERK1/2induced by OPG reached maximum after5–10min and thereafter gradually slightly decreased(Fig.2A).The phosphorylation of p38reached maximum after10min(Fig.2B).We confirmed that differentiated RAW264.7cells expressed RANK(Fig.3A,lane3)but not RANKL(Fig.3A,lane 2)mRNA transcripts as compared to a positive control (293-RANKL cells,Fig.3A,lane1).Since RANKL is known to activate similar signaling pathways[16,17],we conducted same experiments in the presence of RANKL and confirmed the ability of RANKL to induce both ERK1/ 2and p38MAPKs activation in RAW264.7cells(Fig.3B, lane3compared to the control lane1).Since(i)OPG was classically described as one of the ligands for RANKL and (ii)RAW264.7cells have been differentiated in the pres-ence of50ng/ml of soluble RANKL,similar experiments were performed in the presence of anti-RANKL antibody, to test the hypothesis that OPG could act on osteoclastsvia Fig. 1.Inhibition of OPG-induced proMMP-9activity on purified osteoclasts by ras/MAPK inhibitors.Purified osteoclasts were cultured in the absence or presence of50ng/ml OPG,in association or not with different signaling pathway inhibitors:Genistein200A M(tyrosine kinase inhibitor),H7100A M(PKC-and cAMP-dependent protein kinase inhibitor),FPTII40A M(Ras inhibitor)(A)or PD9805940A M(MEK inhibitor),SB2035800.1and10A M(p38inhibitor)(B).Metalloproteinase activity was determined using a gelatin zymographic assay.Results are the mean F S.D.of five separate cultures tested.**P<0.001***P<0.0001 compared to OPG alone,#P<0.05compared to the untreated control.S.Theoleyre et al./Biochimica et Biophysica Acta1644(2004)1–73RANKL.Anti-RANKL antibody totally abolished the OPG-induced phosphorylation of p38but only slightly inhibited the activation of ERK1/2(Fig.3B ,lane 4com-pared to the OPG condition lane 2).To elucidate the role of RANKL in OPG-induced biological activity,p38activation was monitored on differentiated RAW264.7cells that were RANKL-and serum-deprived for 24h and then further treated with RANKL for 2h.In the first approaches,exten-sive washing were done before signaling analysis to remove free RANKL excess but potentially not specific RANKL bound to membrane RANK.When the RAW264.7cells are not rinsed,the excess of free RANKL abolished the OPG-induced p38phosphorylation (Fig.3C ,lanes 1and 2).This OPG-induced p38activation persisted when free RANKL was removed before OPG treatment (cell cultures washed)(Fig.3C ,lane 3compared to the control lane 1)and was totally inhibited by the addition of blocking anti-RANKL antibody (Fig.3C ,lane 4compared to the OPG condition lane 3).Biological activity of OPG on MMP-9expression was also totally abolished in the presence of blocking anti-RANKL antibody (data not shown).These overall results showed that OPG exerts a cellular activity dependent on the presence of RANKL associated with osteoclasts,demon-strating that OPG is not only a soluble decoy receptor for RANKL as described in the literature [4].3.3.OPG,RANKL and RANK are able to form a hetero-molecular complexAs the above results demonstrated,a biological effect of OPG on osteoclasts,dependent on the presence of RANKL,and as RANKL is the natural ligand of RANK [7],we wondered whether OPG could bind to the complex RANKL–RANK present at the osteoclast membrane.Weinvestigated the molecular interactions between OPG,RANKL and RANK by surface plasmon resonance tech-nique.We confirmed that RANKL bound to immobilized RANK (Fig.4A,B).Analyses of the kinetic association and dissociation curves allowed the calculation of the dissociation constant K d =2.05nM.While OPG did not bind to immobi-lized RANK (Fig.4B),it bound to the preformed RANKL–RANK complex with a K d =5.2nM (Fig.4A),thenconsti-Fig.3.OPG-induced phosphorylation of ERK1/2but not p38is dependent on RANKL in RAW264.7cells.(A)RT-PCR analyses of RANK,RANKL and 18S message levels were performed in RAW264.7cells and in 293-RANKL cells.RNA were isolated from RAW264.7cells differentiated 10days in the presence of 50ng/ml ne 1:RT-PCR reaction for RANKL on a positive control (293-RANKL cells);RT-PCR reactions on RAW264.7cells for RANKL (lane 2)and for RANK (lane 3).(B)ERK1/2and p38phosphorylation was analyzed on differentiated RAW264.7cells treated for 10min with 50ng/ml RANKL or OPG in the presence or absence of 4A g/ml blocking anti-RANKL antibody.(C)The OPG-induced p38activation was analyzed in the presence of absence of free RANKL excess and blocking anti-RANKL antibody.Cell lysates were also subjected to Western blot analysis with anti-ERK1/2and/or anti-p38antibodies to demonstrate equal loading (B andC).Fig.2.OPG induced phosphorylation of ERK1/2and p38in RAW264.7cells.RAW264.7cells were treated with 50ng/ml OPG and collected after the indicated time,followed by immunoblotting using anti-phospho-ERK1/2(A)or -p38(B).Cell lysates were also subjected to Western blot analysis with anti-ERK1/2and anti-p38antibodies to demonstrate equal loading.S.Theoleyre et al./Biochimica et Biophysica Acta 1644(2004)1–74tuting a tertiary complex OPG–RANKL–RANK (Fig.4C).Moreover,the preincubation of OPG with RANKL abolished the capability of RANKL to further bind to immobilized RANK (Fig.4B),confirming the role of decoy receptor for OPG described in the literature.These results allow to explain the OPG biological activity on osteoclasts through the for-mation of a tertiary complex OPG–RANKL–RANK.4.DiscussionThe molecular triad OPG–RANKL–RANK play a piv-otal role in bone resorption regulation [7,9].The results of the present study demonstrate for the first time,that in addition to be a decoy receptor for RANKL,OPG also induces ras-MAPK activation on osteoclasts.Moreover,the OPG-induced phosphorylations on osteoclast-like cells are dependent on RANKL bound to the cell membrane,and OPG–RANKL–RANK can constitute a hetero-molecular complex as demonstrated by surface plasmon resonance technique.First evidence of an OPG binding on eucaryote cells was given by Kwon et al.[18].These authors identified a new molecule named TNF receptor-like molecule 1(TR1)which exerted direct biological activities including osteoclasto-genesis inhibition and fibroblast proliferation.In fact,this molecule is identical to OPG.Scatchard analyses showed two classes of high and medium affinity receptors for TR1with kD of approximately 45and 320pM.More recently,three different isoforms of the OPG-ligand,RANKL were expressed in osteoblastic cells,two of them as transmem-brane molecules and the third one,as a soluble protein [19].If the expression of membrane-associated RANKL may represent the specific high affinity ‘‘receptor’’for OPG reported by Kwon et al.,the medium affinity receptors need to be further characterized.Standal et al.[13]reported that OPG can bind to heparan sulfate proteoglycans in myeloma cells,evidencing a new potential binding molecule for OPG.Proteoglycans have already been shown to mediate the binding and the internalization of several extracellular ligands [20]with affinity binding compatible to those observed for TR1[18].Other experiments performed by Reiland et al.[21]demonstrated that pervanadate activates intracellular kinases leading to tyrosine phosphorylation of syndecan-1.It has been proposed that cell surface proteo-glycans as syndecans exert putative functions as growth factor co-receptors,influencing the activation of various tyrosine kinase receptor [22].In light of the above data,it can be suggested that OPG binds to membrane RANKL and/or to cell surface heparan sulfate proteoglycans.However,the mechanisms by which OPG exerts its biological activity as well as the nature of its molecular interactions with osteoclasts are not well defined.Hakeda et al.[11]reported the first evidence of a direct biological activity of OPG on isolated osteoclasts via a 140kDa OPG-binding protein.The exact nature of OPG receptors on osteoclasts was not further characterized but RANKL,as one putative OPG receptor has been evinced since it is not expressed by osteoclasts.However,the production of RANKL by contaminating stromal cells cannot be exclud-ed.Direct biological activities of OPG on osteoclasts were recently confirmed by Wittrant et al.[10]who demonstrat-ed that in purified osteoclasts,OPG enhanced proMMP-9activity among several other parameters (TRAP,TIMP,cathepsin K).The present study reveals that OPGstim-Fig.4.Formation of a hetero-molecular complex between RANK,RANKL,and OPG studied by surface plasmon resonance experiments.Binding of 250nM (7.75A g/ml)(A)or 125nM (3.87A g/ml)(B)RANKL and 110nM (10A g/ml)OPG successively (A)or after pre-incubation for 1h at 37j C (B)to RANK was carried out.Schematic diagrams of RANK–RANKL–OPG molecular interactions is shown in (C).Trimer RANKL can bind to the trimer RANK expressed on the osteoclast cell membrane (left),then inducing specific signal transduction (arrow).Dimer OPG could interact on the RANKL–RANK complex to form a tertiary complex that could activate two different signal transduction pathways via this tertiary complex and an undermined molecule (?)(middle).OPG acts as a decoy receptor when it is preincubated with RANKL (right).S.Theoleyre et al./Biochimica et Biophysica Acta 1644(2004)1–75ulates proMMP-9activity of osteoclasts by the ras/MAPK pathway.RAW264.7cells differentiated in the presence of RANKL,express osteoclast markers(RANK,TRAP,calci-tonin receptor,cathepsin K)and allow to demonstrate the OPG-induced ERK1/2and p38phosphorylations.Although these cells did not express RANKL,blocking anti-RANKL antibody totally abolished p38activation and slightly re-duced ERK1/2activation,demonstrating OPG signaling pathways dependent on RANKL.RANKL has been already shown to induce specific signal transduction pathways in osteoclasts through its binding to membrane RANK,espe-cially p38[16,23]and ERK1/2[17,23].In the RAW264.7 cells,soluble RANKL used to induce osteoclastogenesis is bound to its receptor RANK present at the osteoclast cell surface and OPG may bind to the preformed complex RANKL–RANK,then re-inducing p38activation.The potential formation of a tertiary complex RANK–RANKL–OPG is supported by the results of the surface plasmon resonance studies,which revealed that OPG can bind to the preformed complex RANKL–RANK.These results allow to explain the direct OPG biological activity on purified osteoclasts and differentiated RAW264.7cells through the formation of a hetero-molecular complex OPG–RANKL–RANK(Fig.3C).However,the slight inhibition of OPG-ERK1/2activation by an anti-RANKL antibody suggests the potential existence of a second binding site which is still undetermined.In this context, proteoglycans such as syndecan-1that can be phosphory-lated by intracellular tyrosine kinase may be good candi-dates([21],Fig.4C).In addition,OPG-induced MMP-9 activity on purified osteoclasts is abolished by a tyrosine kinase inhibitor(Fig.1),reinforcing this hypothesis.Re-cently,Hotokezaka et al.[17]demonstrated that osteoclasto-genesis is regulated under a balance between ERK and p38 pathways.In their system,ERK pathway negatively regu-lates osteoclastogenesis while p38pathway is involved as a positive control.In our study,OPG-induced ERK1/2phos-phorylation independently of RANKL could strengthen the specific inhibitory activity of OPG on osteoclastogenesis via a similar balance[8].Thus,OPG induces MAPK phosphorylation via RANKL and acts as a decoy receptor.This dual function of OPG could depend on its association sequence of both molecules. Indeed,when OPG binds to RANKL before RANKL binding to membrane RANK,it acts as a decoy receptor (case of stromal cells which expressed membrane RANKL but not RANK or soluble form of RANKL).In the case where RANKL binds to RANK on osteoclast membrane before its interaction with OPG,OPG could form a hetero-tertiary complex on osteoclasts,therefore inducing MAPK phosphorylations.In conclusion,the present study demonstrated for the first time that OPG stimulates proMMP-9activity of osteoclasts via the ras/MAPK pathway involving p38and ERK1/2 phosphorylations.Moreover,OPG-induced MAPK pathway depends on RANKL.These data strengthened by the surface plasmon resonance analyses suggest the potential formation of a tertiary complex RANK–RANKL–OPG on osteo-clasts.These overall results demonstrated that OPG is not only a soluble decoy receptor for RANKL as described in the literature but must be also considered as a direct effector of osteoclast functions.AcknowledgementsThis work was supported by a CReS INSERM no. 4CR06F,by a grant from the French Ministry of Research and Technology(TS/0220044)and by a grant from the Loire-Atlantique Committee of the Ligue Contre le Cancer. 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World History: Patterns of Interaction*Chapter Outline(CIB = Means Chapter in Brief Summary) IntroductionWorld History T hemes, Geography T hemes, T ime, Place, How Do We Know?Rand McNally AtlasStrategies for Taking Standardized TestsStrategies for Studying HistoryTest-taking Strategies and PracticeUnit 14 Million BC-200 BCBeginnings of Civilization Chapter 1 – Prehistory to 2500 BCEThe Peopling of the World1.Human Origins in Africa (CIB)Analyzing Key Concepts: CultureHistory through Art: Cave Paintings2.Humans Try to Control Nature3.Civilization > Case Study Ur in SumerChapter 2 – 3500 BCE – 450 BCEEarly River Valley Civilizations1.City-states in Mesopotamia2.Pyramids on the NileHistory in Depth: Pyramids and MummiesSocial History: Work and Play in Ancient History3.Planned Cities on the IndusScience and Technology: Plumbing in Mohenjo-Daro4.River Dynasties in ChinaChapter 3 – 2000 BCE – 250 BCEPeople and Ideas on the Move1.The Indo-Europeans2.Hinduism and Buddhism Develop3.Seafaring TradersHistory in Depth: Phoenician Trade4.Origins of JudaismAnalyzing Key Concepts: JudaismDifferent Perspectives: The Flood StoryChapter 4 – 1570 BCE – 200 BCEFirst Age of Empires1.The Egyptian and Nubian EmpiresHistory in Depth: Egyptian Influence on Nubian Culture2.The Assyrian Empire3.The Persian EmpireGlobal Impact: Empire Building4.The Unification of ChinaHistory in Depth: The Great Wall of ChinaComparing and Contrasting: Ancient CivilizationsUnit 22000 BCE – 800 CENew Directions in Government and Society Chapter 5 – 2000 BCE – 300 CEClassical Greece1.Cultures of Mountains and Sea2.Warring City-StatesHistory in Depth: Festivals and Sports3.Democracy and Greece’s Golden AgeHistory through Art: Greek Art and Architecture4.Alexander’s Empire5.The Spread of Hellenistic CultureChapter 6 – 500 BCE – 500 CEAncient Rome and Early Christianity1.The Roman Republic2.The Roman EmpireSocial History: Life in a Roman Villa3.The Rise of Christianity4.The Fall of the Roman EmpireDifferent Perspectives: The Fall of the Roman Empire5.Rome and the Roots of Western CivilizationAnalyzing Key Concepts: Western CivilizationScience and Technology: The ColiseumChapter 7 – 400 BCE – 550 CEIndia and China Establish Empires1.India’s First Empires2.Trade Spreads Indian Religion and CultureHistory through Art: Hindu and Buddhist Art3.Han Emperors in ChinaGlobal Impact: Trade NetworksChapter 8 – 1500 BCE – 700 CEAfrican Civilization1.Diverse Societies in AfricaScience and Technology: African Ironworking2.MigrationCase Study: Bantu-Speaking People3.The Kingdom of AksumChapter 9 – 40,000 BCE – 700 CEThe Americas: A Separate World (CIB)1.The Earliest American2.Early Meso-American CivilizationsHistory through Art: Olmec Sculpture3.Early Civilizations of the AndesHistory in Depth: Narzca LinesComparing and Contrasting: Classical AgesUnit 3500 – 1500An Age of Exchange and EncounterChapter 10 – 600-1200The Muslim World1. The Rise of IslamAnalyzing Architecture: The Dome of the Rock2. Islam Expands3. Muslim CultureScience and Technology: AstronomyWORLD RELIGIONS AND ETHICAL SYSTEMS BuddhismChristianityHinduismIslamJudaism ConfucianismChapter 11 – 500 – 1500Byzantines, Russians, and Turks Interact1. The Byzantine EmpireAnalyzing Key Concepts: Roman Catholicism and Easter Orthodoxy2. The Russian EmpireHistory through Art: Religious Art and Architecture3. Turkish Empires Rise in AnatoliaChapter 12 – 600 – 1350 Empires and East Asia 1. Tang and Song China Social History: Tang and Song Dynasty: People and Technology2. The Mongol ConquestsHistory in Depth: A Mighty Fighting Force3. The Mongol Empire4. Feudal Empires in JapanHistory in Depth: Japanese Samurai5. Kingdoms of Southeast Asia and KoreaChapter 13 – 500 – 1200European Middle Ages 1. Charlemagne Unites Germanic Kingdoms 2. Feudalism in Europe Analyzing Key Concepts: Feudalism3. The Age of Chivalry (CIB)Science and Technology: Castles and Siege Weapons4. The Power of the Church (CIB)Chapter 14 – 800-1500Formation of Western Europe 1. Church Reform and the Crusades (CIB) History in Depth: Gothic Architecture Different Perspectives: The Crusades2. Changes in Medieval Society3. England and France Develop4. The Hundred Years’ War and the Plague (CIB) Global Impact: The Spread of Epidemic DiseaseChapter 15 – 800 – 1500Societies and Empires in Africa1. North and Central African Societies2. Western African Civilizations History through Art: Benin Bronzes3. Eastern City States and Southern Empires Analyzing Architecture: Great ZimbabweComparing and Contrasting: Trade NetworksUnit 4500 – 800Connecting HemispheresChapter 16 – 500 – 1500 People and Empires in America 1. North American Series2. Maya Kings and CitiesHistory through Art: Maya Architecture 3. The Aztecs Control Central Mexico History in Depth: Aztec Calendar 4. The Inca Create a Mountain Empire Social History: Incan MummiesChapter 17 – 1300 – 1600European Renaissance and Reformation1. Italy: Birthplace of the RenaissanceHistory through Art: Renaissance Ideas influence Renaissance Art2. The Northern Renaissance Social History: City Life in Renaissance Europe3. Luther Leads the Reformation Analyzing Key Concepts: Protestantism4. The Reformation Continues Different Perspectives: The ReformationChapter 18 – 1300 – 1700The Muslim World Expands1. The Ottomans Build a Vast Empire2. Cultural BlendingCase Study: The Safavid Empire3. The Mughal Empire in IndiaHistory through Art: Cultural Blending in Mughal IndiaChapter 19 – 1400 – 1800An Age of Explorations and Isolation 1. Europeans Explore the East 2. Science and Technology: The Tools of Exploration3. China Limits European ContactsHistory in Depth: The Forbidden City4. Japan Returns to IsolationChapter 20 – 1492 – 1800The Atlantic World1. Spain Builds an American Empire (CIB) Different Perspectives: The Legacy of Columbus2. Europeans Nations Settle North America3. The Atlantic Slave Trade (CIB)4. The Columbian Exchange and Global Trade (CIB) Global Impact: Food ExchangeAnalyzing Key Concepts: MercantilismComparing and Contrasting: Methods of GovernmentUnit 51500 – 1900Absolutism to RevolutionChapter 21 – 1500 – 1800Absolute Monarchs in Europe1. Spain’s Empire and European AbsolutismAnalyzing Key Concepts: Absolutism2. The Reign of Louis XIVHistory in Depth: The Palace at Versailles3. Central European Monarchs Clash4. Absolute Rulers of RussiaSocial History: Surviving the Russian Winter5. Parliament Limits the English Monarchy Chapter 22 – 1550 – 1789 Enlightenment and Revolution 1. The Scientific Revolution 2. The Enlightenment of Europe Different Perspectives: European Values during the Enlightenment3. The Enlightenment Spreads4. The American RevolutionAnalyzing Key Concepts: DemocracyChapter 23 – 1789 – 1815 The French Revolution and Napoleon 1. The French Revolution Begins 2. Revolution Brings Reform and TerrorScience and Technology: GuillotineDifferent Perspectives: The French Revolution3.Napoleon Forges an Empire (CIB)4.Napoleon’s Empire Collapses (CIB)5.The Congress Vienna (CIB)Chapter 24 – 1789 – 1900Nationalist Revolutions Sweep the Westtin American Peoples Win IndependenceGlobal Impact: Struggling Towards Democracy2.Europe Faces RevolutionAnalyzing Key Systems: Nationalism3.NationalismCase Study: Italy and Germany4.Revolutions in the ArtsHistory through Art: Revolutions in PaintingComparing and Contrasting: Political RevolutionsUnit 61700 – 1914Industrialism and the Race for Empire Chapter 25 – 1700 – 1900The Industrial Revolution1.The Beginnings of RevolutionGlobal Impact: Revolutions in Technology2.Industrialization Spreads3.Reforming the Industrial WorldAnalyzing Key Concepts: Capitalism vs. SocialismDifferent Perspectives: IndustrializationChapter 26 – 1815 – 1914An Age of Democracy and Progress1.Democratic Reform and Activism (CIB)2.Self-Rule for British ColoniesSocial History: Life in Early Australia3.War and Expansion in the United States (CIB)4.Nineteenth-Century Progress (CIB)Science and Technology: Edison’s Inventions Chapter 27 – 1850 – 1914The Age of Imperialism1.The Scramble for Africa2.ImperialismCase Study: NigeriaAnalyzing Key Concepts: ImperialismDifferent Perspectives: Views of Imperialism3.Europeans Claim Muslim Lands4.British Imperialism in India5.Imperialism in Southeast AsiaChapter 28 – 1800 – 1914Transformations around the Globe1. China Resists Outside Influence (CIB)2. Modernization in Japan (CIB)History through Art: Japanese Woodblock Printing3. U.S. Economic ImperialismScience and Technology: Panama Canal4. Turmoil and Change in MexicoComparing and Contrasting: Scientific and Technological ChangesUnit 71900 – 1945The World at WarChapter 29 – 1914 – 1918The Great War 1. Marching Toward War 2. Europe Plunges into War History in Depth: The New Weapons of WarScience and Technology: Military Aviation3. A Global ConflictDifferent Perspectives: Views of War4. A Flawed PeaceChapter 30 – 1900 – 1939Revolution and Nationalism1. Revolutions in RussiaAnalyzing Key Concepts: Communism2. TotalitarianismCase Study: Stalinist RussiaAnalyzing Key Concepts: TotalitarianismHistory through Art: Propaganda3. Imperial China CollapsesHistory in Depth: The Long March4. Nationalism in India and Southwest AsiaChapter 31 – 1819 – 1939Years of Crisis1. Postwar UncertaintySocial History: Labor-saving Devices in the United States2. A Worldwide Depression3. Fascism Rises in Europe Analyzing Key Concepts: Fascism4. Aggressors Invade Nations Chapter 32 – 1939 – 1845World War II 1. Hitler’s Lightening War 2. Japan’s Pacific Campaign 3. The Holocaust4. The Allied VictoryGlobal Impact: Arming for War5. Europe and Japan in RuinsComparing and Contrasting: The Changing Nature of WarfareUnit 81945 – PresentPerspectives on the PresentChapter 33 – 1945 – PresentRestructuring the Post-War World1. Cold War – Super Powers Face Off (CIB)Science and Technology: The Space Race2. Communists Take Power in China (CIB)3. Wars in Korea and Vietnam4. The Cold War Divides the World (CIB)History in Depth: How the Cold War was Fought5. The Cold War Thaws (CIB)Chapter 34 – 1945 – PresentThe Colonies Become New Nations1. The Indian Subcontinent Achieves Freedom2. Southeast Asian Nations Gain Independence Social History: Changing Times in Southeast Asia3. New Nations in Africa4. Conflicts in the Middle EastHistory in Depth: Signs of Hope5. Central Asia StrugglesChapter 35 – 1945 – PresentStruggles fir Democracy 1. Democracy Case Study: Latin America Democracies 2. The Challenge of Democracy in Africa 3. The Collapse of the Soviet Union 4. Change in Central and Eastern EuropeHistory in Depth: Ethnic Groups in the Former Yugoslavia5. China: Reform and ReactionHistory through Art: PhotojournalismChapter 36 – 1945 – PresentGlobal Interdependence 1. The Impact of Science and Technology 2. Global Economic Development Analyzing Key Concepts: Globalization Different Perspective: Economics and the Environment 3. Global Security Issues4. Terrorism > Case Study: September 11, 20015. Cultures Blend in a Global Age > Global Impact: Rock ‘n Roll Comparing and Contrasting: Nation Building。

国际经验对中国西部地区绿色发展的启示：政策及实践作者：甄霖杜秉贞刘纪远孙传谆张强来源：《中国人口·资源与环境》2013年第10期摘要在当下全球经济萧条和发展中国家日益增长的巨大需求背景下，中国西部需要绿色发展，国际经验可以起到前车之鉴和抛砖引玉的作用。

以OECD国家为主，基于实地考察调研和座谈、以及文献资料分析，总结了其绿色发展相关政策建议以及典型案例区实践活动在推进区域绿色发展中的作用和经验，涉及政策框架和路线图制定、绩效监测评估、经济建设和环境保护同步发展、生态补偿、绿色就业和扶贫等。

重点分析了OECD国家以往推进绿色经济发展的结构调整与改革的经验，如澳大利亚水资源管理、矿产业开发和生物库的例子；加拿大采用经济手段保护生物多样性的案例；瑞士农业旅游和西班牙绿色工业发展的案例；以及澳大利亚实施“清洁可持续技能培训计划”的经验等。

尽管各个国家由于制度、经济体制、发展水平的差异，在推进绿色发展的具体战略措施和行动方案等方面存在差异，但一些共同认可的经验和成功案例对中国西部绿色发展起步实施具有一定的借鉴和指导作用。

根据这些国际经验分析，本文提出了对中国西部绿色发展的五点启示，主要包括：①制定绿色发展的制度安排和路线图；②开展动态的绩效监测评估与考核，建立切合实际的可量化的考核体系；③建立生态产权或自然资源产权交易市场化的激励机制，并选择典型区先行先试，建立长期稳定有效的生态补偿机制；④利益相关者积极主动参与到绿色发展的进程之中；⑤提供绿色就业机会，提高劳动力技能和素质。

关键词绿色发展；政策；OECD；经验；利益相关者中图分类号X22文献标识码A文章编号1002-2104（2013）绿色发展的主旨是将环境目标以前所未有的规模，纳入经济与发展的主流决策中，是“强调经济增长与环境保护的统一与和谐发展，一种以人为本的可持续发展方式”[1]。

在应对2008年金融危机中，不少国家都开始实施“绿色新政”，并以此来谋划后危机时代的发展，以实现从传统的“黑色发展”转向现代的“绿色发展”。

SCI收录的期刊——石油学科 (2010)1. AAPG BULLETIN 《美国石油地质学家协会通报》美国MonthlyISSN: 0149-1423AMER ASSOC PETROLEUM GEOLOGIST, 1444 S BOULDER AVE, PO BOX 979, TULSA , USA, OK, 74119-36042. BULLETIN OF CANADIAN PETROLEUM GEOLOGY 《加拿大石油地质学通报》加拿大QuarterlyISSN: 0007-4802CANADIAN SOC PETROLEUM GEOLOGISTS, SUITE 600, 640 - 8TH AVE SW, CALGA RY, CANADA, ALBERTA, T2P 1G73. CHEMISTRY AND TECHNOLOGY OF FUELS AND OILS 《燃料与石油化学和工艺学》美国Bimonthly （俄罗斯与拉脱维亚合办的同名期刊的英文翻译版）ISSN: 0009-3092CONSULTANTS BUREAU/SPRINGER, 233 SPRING ST, NEW YORK, USA, NY, 100134. CHINA PETROLEUM PROCESSING & PETROCHEMICAL TECHNOLOGY 《中国炼油与石油化工》中国QuarterlyISSN: 1008-6234CHINA PETROLEUM PROCESSING & PETROCHEMICAL TECHNOLOGY PRESS, NO 18 XU EYUAN RD, BEIJING, PEOPLES R CHINA, 1000835. GEOARABIA 《中东石油地球科学杂志》巴林QuarterlyISSN: 1025-6059GULF PETROLINK, PO BOX 20393, MANAMA, BAHRAIN, 000006. HYDROCARBON PROCESSING 《烃加工》美国MonthlyISSN: 0018-8190GULF PUBL CO, BOX 2608, HOUSTON, USA, TX, 77252-26087. INTERNATIONAL GAS ENGINEERING AND MANAGEMENT 《国际天然气工程与管理》英国MonthlyISSN: 1465-7058INST GAS ENGINEERS & MANAGERS, CHARNWOOD WING, HOLYWELL PARK, ASHBY R D LOUGHBOROUGH, LEICESTERSHIRE, ENGLAND, LE11 3GR8. JOURNAL OF CANADIAN PETROLEUM TECHNOLOGY 《加拿大石油技术杂志》加拿大MonthlyISSN: 0021-9487CANADIAN INST MINING METALLURGY PETROLEUM, 3400 DE MAISONNEUVE BLVD W , STE 855, MONTREAL, CANADA, PQ, H3Z 3B89. JOURNAL OF GEOPHYSICS AND ENGINEERING 《地球物理学与工程学》英国IrregularISSN: 1742-2132IOP PUBLISHING LTD, DIRAC HOUSE, TEMPLE BACK, BRISTOL, ENGLAND, BS1 6 BE10. JOURNAL OF PETROLEUM GEOLOGY 《石油地质学杂志》英国QuarterlyISSN: 0141-6421BLACKWELL PUBLISHING, 9600 GARSINGTON RD, OXFORD, ENGLAND, OXON, OX4 2DQ11. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING《石油科学和石油工程杂志》荷兰MonthlyISSN: 0920-4105ELSEVIER SCIENCE BV, PO BOX 211, AMSTERDAM, NETHERLANDS, 1000 AE12. JOURNAL OF THE JAPAN PETROLEUM INSTITUTE 《日本石油学会志》日本BimonthlyISSN: 1346-8804JAPAN PETROLEUM INST, COSMO HIRAKAWA-CHO BLDG, 3-14, 1-CHOME HIRAKAWA -CHO, CHIYODA-KU, TOKYO, JAPAN, 10213. OIL & GAS JOURNAL 《石油与天然气杂志》美国WeeklyISSN: 0030-1388PENNWELL PUBL CO ENERGY GROUP, 1421 S SHERIDAN RD PO BOX 1260, TULSA, USA, OK, 7411214. OIL & GAS SCIENCE AND TECHNOLOGY-REVUE DE L INSTITUT FRANCAIS DU PETROLE 《石油、天然气的科学与技术；法国石油研究所杂志》法国BimonthlyISSN: 1294-4475EDITIONS TECHNIP, 27 RUE GINOUX, PARIS 15, FRANCE, 7573715. OIL GAS-EUROPEAN MAGAZINE 《欧洲石油气杂志》德国QuarterlyISSN: 0342-5622URBAN-VERLAG GMBH, PO BOX 70 16 06, HAMBURG, GERMANY, D-2201616. OIL SHALE 《油页岩》爱沙尼亚QuarterlyISSN: 0208-189XESTONIAN ACAD PUBLISHERS, KOHTU 6, TALLINN, ESTONIA, EE-1013017. PETROLEUM CHEMISTRY 《石油化学》美国Bimonthly （俄罗斯同名刊(Нефтехимия)的英译版）ISSN: 0965-5441MAIK NAUKA/INTERPERIODICA/SPRINGER, 233 SPRING ST, NEW YORK, USA, NY, 10013-157818. PETROLEUM GEOSCIENCE 《石油地质科学》英国QuarterlyISSN: 1354-0793GEOLOGICAL SOC PUBL HOUSE, UNIT 7, BRASSMILL ENTERPRISE CENTRE, BRASS MILL LANE, BATH, ENGLAND, AVON, BA1 3JN19. PETROLEUM SCIENCE 《石油科学》德国QuarterlyISSN: 1672-5107SPRINGER HEIDELBERG, TIERGARTENSTRASSE 17, HEIDELBERG, GERMANY, D-691 2120. PETROLEUM SCIENCE AND TECHNOLOGY 《石油科学与技术》美国MonthlyISSN: 1091-6466TAYLOR & FRANCIS INC, 325 CHESTNUT ST, SUITE 800, PHILADELPHIA, USA, PA, 1910621. PETROPHYSICS 《岩石物理学》美国BimonthlyISSN: 1529-9074SOC PETROPHYSICISTS & WELL LOG ANALYSTS-SPWLA, 8866 GULF FREEWAY, STE 320, HOUSTON, USA, TX, 7701722. SPE DRILLING & COMPLETION 《石油工程师协会钻井与完井》美国QuarterlyISSN: 1064-6671SOC PETROLEUM ENG, 222 PALISADES CREEK DR,, RICHARDSON, USA, TX, 750823. SPE JOURNAL《石油工程师协会杂志》美国QuarterlyISSN: 1086-055XSOC PETROLEUM ENG, 222 PALISADES CREEK DR,, RICHARDSON, USA, TX, 7508 024. SPE PRODUCTION & OPERATIONS《石油工程师协会生产和操作》美国QuarterlyISSN: 1930-1855SOC PETROLEUM ENG, 222 PALISADES CREEK DR,, RICHARDSON, USA, TX, 7508 025. SPE RESERVOIR EVALUATION & ENGINEERING 《石油工程师协会油藏评估与工程》美国BimonthlyISSN: 1094-6470SOC PETROLEUM ENG, 222 PALISADES CREEK DR,, RICHARDSON, USA, TX, 7508 026. VISION TECNOLOGICA 《技术视野》委内瑞拉Irregular （委内瑞拉油气工业期刊）ISSN: 1315-0855INTEVEP S A, APARTADO POSTAL 76343, CARACAS, VENEZUELA, 1070-A。

基本教育阶段(6门课程)：课程1：精算科学的数学基础说明：这门课程的目的是为了培养关于一些基础数学工具的知识，形成从数量角度评估风险的能力，特别是应用这些工具来解决精算科学中的问题。

并且假设学员在学习这门课程之前已经熟练掌握了微积分、概率论的有关内容及风险管理的基本知识。

主要内容及概念：微积分、概率论、风险管理（包括损失频率、损失金额、自留额、免赔额、共同保险和风险保费）课程2：利息理论、经济学和金融学说明：这门课程包括利息理论，中级微观经济学和宏观经济学，金融学基础。

在学习这门课程之前要求具有微积分和概率论的基础知识。

主要内容及概念：利息理论，微观经济学，宏观经济学，金融学基础课程3：随机事件的精算模型说明：通过这门课程的学习，培养学员关于随机事件的精算模型的基础知识及这些模型在保险和金融风险中的应用。

在学习这门课程之前要求熟练掌握微积分、概率论和数理统计的相关内容。

建议学员在通过课程1和课程2后学习这门课程。

主要内容及概念：保险和其它金融随机事件，生存模型，人口数据分析，定量分析随机事件的金融影响课程4：精算建模方法说明：该课程初步介绍了建立模型的基础知识和用于建模的重要的精算和统计方法。

在学习这门课程之前要求熟练掌握微积分、线性代数、概率论和数理统计的相关内容。

主要内容及概念：模型－模型的定义－为何及如何使用模型－模型的利弊－确定性的和随机性的模型－模型选择－输入和输出分析－敏感性检验－研究结果的检验和反馈方法－回归分析－预测－风险理论－信度理论课程5－精算原理应用说明：这门课程提供了产品设计，风险分类，定价/费率拟定/建立保险基金，营销，分配，管理和估价的学习。

覆盖的范围包括金融保障计划,职工福利计划，事故抚恤计划，政府社会保险和养老计划及一些新兴的应用领域如产品责任，担保的评估，环境的维护成本和制造业的应用。

该课程的学习材料综合了各种计划和覆盖范围以展示精算原理在各研究领域中应用的一致性和差异性。

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IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 28, NO. 11, NOVEMBER 20091791Distribution of Fiducial Registration Error in Rigid-Body Point-Based RegistrationMehdi Hedjazi Moghari and Purang Abolmaesumi*Abstract—Rigid-body point-based registration is frequently used in computer assisted surgery to align corresponding points, or fiducials, in preoperative and intraoperative data. This alignment is mostly achieved by assuming the same homogeneous error distribution for all the points; however, due to the properties of the medical instruments used in measuring the point coordinates, the error distribution might be inhomogeneous and different for each point. In this paper, in an effort to understand the effect of error distribution in the localized points on the performed registration, we derive a closed-form solution relating the error distribution of each point with the performed registration accuracy. The solution uses maximum likelihood estimation to calculate the probability density function of registration error at each fiducial point. Extensive numerical simulations are performed to validated the proposed solution. Index Terms—Fiducial localization error, fiducial registration error, inhomogeneous and anisotropic zero-mean noise, maximum likelihood, registration, target registration error.I. INTRODUCTIONPOINT-BASED rigid-body registration algorithms derive a transformation that maps preoperative data sets to the physical location of a patient anatomy in intraoperative images. This transformation is generally calculated from a set of points (hereafter referred to as fiducials) which are either attached to the patient anatomy, or are extracted from the imaging data. The fiducials can be easily localized in the preoperative and intraoperative data sets; however, their location is generally perturbed by measurement noise which is referred to as fiducial localization error (FLE) in the literature. Due to FLE, the performed registration between preoperative and intraoperative data sets is usually imperfect, leading to an error in fiducial alignments that is referred to as fiducial registration error (FRE). Another important error in rigid-body point-based registration is called target registration error (TRE). TRE is a distance error, after registration, between a pair of fiducial markers in the preoperative and intraoperative data sets which are not used in the registration process.Manuscript received January 27, 2009; revised May 19, 2009. Current version published October 28, 2009. This work was supported in part by the Canadian Institutes of Health Research (CIHR), and in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). Asterisk indicates corresponding author. M. H. Moghari is with the Harvard Medical School, Boston, MA 02215 USA (e-mail: mhedjazi@). *P. Abolmaesumi is with the Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, V6T 1Z4 Canada (e-mail: purang@ece.ubc.ca). Digital Object Identifier 10.1109/TMI.2009.2024208The properties of FRE and TRE, and their relations with FLE have been under investigation for a few decades. Sibson [1], in 1979, for the first time demonstrated that FLE, FRE, and TRE are related to each other. Inspired by Sibson, Fitzpatrick and West [2], [3], in 1998 and 2001, derived the first closed-form solution that estimated the mean squared value of TRE in terms of FLE and the configuration of the registering data sets. Their derivation was based on the assumption that FLE had an identical and isotropic zero-mean Gaussian distribution. Wiles et al. [4], in 2007, extended the Fitzpatrick and West formulation to estimate the mean squared value of TRE when FLE had an identical and anisotropic zero-mean Gaussian distribution. Their method, similar to the Fitzpatrick and West’s, utilized a least mean squares (LMS) algorithm, such as Horn’s [5] or Arun’s [6], to register the data sets and estimate the characteristics of TRE. Ma et al. [7] and Sielhorst et al. [8], in 2007, derived new formulations that estimated the properties of TRE in the presence of inhomogeneous and anisotropic zero-mean Gaussian FLE. In 2008, Moghari and Abolmaesumi [9] utilized the maximum likelihood (ML) algorithm to derive a general solution that estimated the properties of TRE when FLE had an arbitrary zero-mean distribution. The same authors [10], in 2008, mathematically compared Sielhorst [8], Ma [7], Wiles [4], and Fitzpatrick and West [3] algorithms. They demonstrated that all the algorithms, based on their assumptions for the distribution of FLE, converged to the their proposed ML solution. All the above algorithms generate a formulation correlating TRE to FLE. If the distribution of FLE is available, then the derived formulations can be used to estimate the TRE at a desired target location. But, in real clinical applications, the distribution of FLE is usually unknown and it should be somehow computed. To estimate the characteristics of FLE, it maybe possible to calculate the true value of TRE, and utilize the proposed relationships between FLE and TRE to estimate the properties of FLE. Yet, it is difficult to calculate the actual value of TRE in real clinical situations. To overcome this difficulty, Balachendran and Fitzpatrick [11] proposed to use the characteristics of FRE, rather than TRE, to estimate the properties of FLE. These authors, for the first time, derived a formulation relating the characteristics of FRE and FLE by assuming that FLE had an identical and isotropic zero-mean Gaussian distribution. In this article, we generalize the Balachendran and Fitzpatrick algorithm to estimate the properties of FRE in terms of FLE when FLE has an arbitrary zero-mean distribution. We present novel solutions correlating FRE to FLE when FLE has an identical and isotropic, inhomogeneous and isotropic, identical and anisotropic, and inhomogeneous and anisotropic zeromean Gaussian distribution, respectively.0278-0062/$26.00 © 2009 IEEE1792IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 28, NO. 11, NOVEMBER 2009The rest of this article is organized as follows. Section II briefly describes how the ML algorithm can be used to estimate the registration parameters and their variances. In Section III, the ML algorithm is used to calculate the distribution of FRE in terms of FLE. The numerical simulations supporting the derived formulations are presented in Section IV. Finally, the discussion and conclusions are presented in Sections V and VI, respectively. II. ESTIMATING THE REGISTRATION PARAMETERS BASED ON THE ML ALGORITHM In the remainder of this paper, please note that we use nonbold fonts for scalars, bold lowercase fonts for vectors that are assumed to always be column vectors, and capital bold fonts for matrices. Also, we indicate the number of points in each and dimension of the space containing the points space by by —which is assumed to be 3. While three-dimensional data sets are the subject of this paper, the proposed formulation is . The points in the preoperative data set are still valid for matrix whose columns correspond to the shown by as a position vectors of points in the preoperative coordinate frame. matrix, represents the corresponding points in As a , and , the intraoperative data set. The th columns of represent a pair of corresponding points in the preoperative and intraoperative spaces that are related to each other by the following equation [3], [11]: (1) where is the rotation matrix in terms of . is the vector , and , the rotation angles about , consisting of is the translation and axes1, respectively. , and . models the FLE in both the prevector along operative and intraoperative data sets, i.e., and , which can have a different distribution from a point to another. Also, for simplicity, we assumed that the precise rotation and translation is aligned parameters are very small or zero, and data set along its principal axes and its centroid is set at the origin. Using (1), one can use the ML algorithm to accurately estimate the registration parameters and their variances when FLE has an arbitrary distribution. The registration parameters can be calculated by concurrently solving the following equations [9], [12]: (2) (3) where is the likelihood function generated from the distribution of FLE at the fiducial markers. To estimate the variance of the transformation parameters, one needs to derive the second derivative of the log-likelihood function as follows: (4)1Please note that for consistency with [3] and [11], we changed the notation of  ;  and  defined in our previous paper [9], to R ; R , and R , respectively.(5) (6) Equations (4)–(6) can then be used in the Carmer-Rao bound [13], to obtain the covariance matrix of the registration parameters (7) Since rotation matrix is a nonlinear function in terms of , no closed-form solution has yet been proposed to derive the registration parameters from (2) and (3) in the presence of FLE with an arbitrary distribution. Therefore, (2) and (3) should be numerically calculated to obtain the registration parameters. The results can then be used in (4), (5), and (6) to find the covariance matrices of the registration parameters [14]. On the other hand, if it is assumed that the first-order assumption introduced by Fitzpatrick and West [3] is valid, i.e.,(8)then can be linearized with the accuracy of the first-order Taylor series expansion, and closed-form solutions for calcuand can be obtained. For example, as it is shown lating in [9], if FLE at the th fiducial marker is assumed to be independent Gaussian with distribution , the registration parameters can be derived by solving the following six linear equations:(9)(10) Then, (4)–(6) can be calculated as follows, and substituted in (7) to derive the variance of the registration parameters [9]: (11)(12)MOGHARI AND ABOLMAESUMI: DISTRIBUTION OF FIDUCIAL REGISTRATION ERROR IN RIGID-BODY POINT-BASED REGISTRATION1793FRE at the th fiducial marker can be written as(19) (13) Since and are unbiased estimates and FLE has a zero-mean Gaussian distribution, it can simply be shown from (16) that . Using (16), (19) can be simplified as follows:III. ESTIMATING THE DISTRIBUTION OF FRE BASED ON THE ML ALGORITHM As defined in [11], FRE is the distance error, after registration, between a pair of fiducial markers in the preoperative and intraoperative data sets which are used in the registration process. Therefore, FRE at the th fiducial marker can be mathematically defined as follows: (14) where and are the pair of fiducial markers in the preoperative and intraoperative data sets that are used in the registration; and are the registration parameters estimated from (9) and (10), respectively. By using (1), (14) can be written as (15) Substituting (8) in (15) results to (21) where , and can be computed from (11), (12), (13) which are substituted in (7) as follows. Without loss of generality, if the weighted mean of is set to , then we have the origin such that (22) (16) where . From (9) and (10), one can simply show that (17) Substituting (22), (23), and (24) in (21), one can write and (24) (23) (20) Equation (20) can be rewritten as(18) Equations (17) and (18) illustrate that if FLE has a zero-mean Gaussian distribution, the estimation of the registration paramand are zero. eters is unbiased, i.e., Now, to estimate the distribution of FRE, the covariance mashould be calculated. Covariance matrix of trix of FRE at(25)1794IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 28, NO. 11, NOVEMBER 2009Now, we use (17) and (18) to calculate as follows:andWith more simplification of (29), the covariance matrix of FRE at the th fiducial marker, when FLE has an arbitrary zero-mean Gaussian distribution, can be derived by the following closedform formula:(30) (26) where is the dirac function (27) Also which, by using (22) and (24), can be rewritten as (31) Here, it should be emphasized that if , then (21) cannot be simplified as (31). In that case, (21) should be used to calculate the the covariance matrix of FRE at the th fiducial marker. Finally, the distribution of FRE at the th fiducial marker can be computed from either (31) or (21) and the TRE distribution derived in [15]:(32) , and 2 are the square root of the eigenvalues of , and is the modified Bessel function. Since the mean of FRE at the th fiducial marker is zero, the mean squared value of FRE at can, also, be computed from (31) as where (33) where trace is the summation of diagonal elements of a matrix. Our derivations that calculate the distribution of FRE and its mean squared value when FLE has an arbitrary zero-mean Gaussian distribution is now complete. But, (31) can be further simplified for the special cases where FLE has an identical and isotropic, inhomogeneous and isotropic, and identical and anisotropic Gaussian distributions. In what follows, we make these simplifications and show that our derivations converge to the previous formulations in the literature for these special cases. A. FLE Has an Identical and Isotropic Zero-Mean Gaussian Distribution If FLE has an identical and isotropic zero-mean Gaussian distribution in the data sets, it can be modeled as , where is the identity matrix. In this scenario, every fiducial marker . has the same distribution with covariance matrix , Therefore, If and are centered at the origin, then2The order of  ;  , and  is not theoretically important for solving (32). But, in our performed simulations, we assumed that   .(28) By substituting (26) and (28) in (25), the covariance matrix of FLE at the th fiducial marker can be calculated as(29)where to obtain (29), we should note that metric matrix, andis a skew sym.MOGHARI AND ABOLMAESUMI: DISTRIBUTION OF FIDUCIAL REGISTRATION ERROR IN RIGID-BODY POINT-BASED REGISTRATION1795and (30) can be used to calculateas follows:which exactly matches the one derived by Balachandran and Fitzpatrick [11]. A more interesting result can be found from (38) by computing the average of the mean squared value of FRE over all the fiducial markers as (34)As it is shown in [9], if thenis aligned along its principal axes,(35)where, and . Therefore, by using (35) in (34), one can write(39) which exactly matches the one derived by Sibson [1], when , the dimension of points in data sets, is 3. Finally, the distribution of FRE at the th fiducial marker can be obtained by using the eigenvalues of (37), i.e., (36) (40) where (36) is fully written in (37). From (37), the mean squared value of FRE at the th fiducial marker can be computed as (41) (42) in (32). B. FLE Has an Inhomogeneous and Isotropic Zero-Mean Gaussian Distribution (38) If FLE has an inhomogeneous and isotropic zero-mean is . Here, Gaussian distribution in the data sets, then(37)1796IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 28, NO. 11, NOVEMBER 2009every fiducial marker is perturbed by a different zero-mean Gaussian distribution which is isotropic. In this scenario, if the , and (30) weighted center of is set at origin, then can be employed to compute asAlso, the average of the computed mean squared value of FRE over all the fiducial markers is(43)is chosen As shown in [9], if the orientation of such that , then By using (44) in (43), (43) can be further simplified as (45). From (45), the mean squared value of FRE at the th fiducial marker can be computed as follows:(47) Equation (47) is the extension of Sibson [1] and Fitzpatrick et al. [2] derivations for the case where FLE has an inhomogeneous and isotropic zero-mean Gaussian distribution. The distribution of FRE at the th fiducial marker can be obtained by using the eigenvalues of (45) in (32). C. FLE Has an Identical and Anisotropic Zero-Mean Gaussian Distribution If FLE has an identical and anisotropic zero-mean Gaussian , where distribution, its distribution can be modeled as(46) Equation (46) is the extension of Balachandran and Fitzpatrick formulation [11] that calculates the mean squared value of FRE when FLE has an inhomogeneous and identical zero-mean Gaussian distribution.(48) In this case, every fiducial marker has the same distribution which has distinct property along different directions, i.e., is is centered at the origin, then becomes anisotropic. If(44)MOGHARI AND ABOLMAESUMI: DISTRIBUTION OF FIDUCIAL REGISTRATION ERROR IN RIGID-BODY POINT-BASED REGISTRATION1797zero, and therefore, (30) can be used to estimateasFinally, the distribution of FRE at the th fiducial marker can be obtained by using the eigenvalues of (51) in (32). D. FLE Has an Inhomogeneous and Anisotropic Zero-Mean Gaussian Distribution(49) As shown in the Appendix, if axes, we have is aligned along its principal(50) where and are defined in the Appendix. By using (50) in (49), (49) can be fully written in (51). From (51), the mean squared value of FRE at the th fiducial marker can be computed as follows:When FLE has an inhomogeneous and anisotropic zero-mean Gaussian distribution, it is difficult to derive a closed-form solution for the mean squared value of FRE; however, such solution can be numerically calculated: One can use (21) to calculate the covariance matrix of FRE at the desired fiducial marker. The eigenvalues of the resultant covariance matrix can then be utilized in (32) to derive the distribution of FRE. In addition, the summation of those eigenvalues gives the mean squared value of FRE. In a very general scenario, when FLE has an arbitrary distribution, which may not necessarily be Gaussian, one needs to first calculate the variance of the registration parameters from (7), and use the result in (19) to compute the covariance of the FRE at the desired fiducial location. Then, the estimated covariance of FRE can be used in (32), and (33), as before, to calculate the distribution of FRE and its means squared value. IV. NUMERICAL SIMULATIONS To support our derivations, we use four different simulations to numerically and analytically calculate the characteristics of FRE when FLE has different zero-mean Gaussian distributions. First, we run the simulations on the randomly generated fiducial markers which are perturbed by identical and isotropic zero-mean Gaussian distribution, to derive the distribution of FRE and its mean squared value. We then repeat this experiment in the second simulation, where the generated fiducial markers are perturbed by an inhomogeneous and isotropic zero-mean Gaussian distribution. Finally, in the third and fourth simulations, the FRE characteristics are estimated when FLE perturbing the fiducial markers, has an identical and anisotropic, and inhomogeneous and anisotropic zero-mean Gaussian distribution, respectively. 1) FLE Has an Identical and Isotropic Zero-Mean Gaussian Distribution: We choose , the number of fiducial markers, to be 3. For this value of , we randomly generate the preoperative data set within a cube with sides of mm. The randomly are listed in generated fiducials in the preoperative data set Table I. The same data set is used in the remaining simulations. To generate the intraoperative data set , we perturb each point in by a zero-mean Gaussian random noise with covariance matrix 1 mm . is then registered to by using (9) and (10), and FRE at all the fiducial markers are calculated. One simulation consists of 100 000 repetitions of perturbation, registration and FRE calculation which allows us to numerically(52) Equation (52) is the extension of Balachandran and Fitzpatrick formulation [11] when FLE has an identical and anisotropic zero-mean Gaussian distribution. Also, one can simply calculate the average of the mean squared value of FRE over all the fiducial markers as(53) Equation (53) is the extension of Sibson [1] and Fitzpatrick et al. [2] derivations that estimates the overall mean squared value of FRE for the case where FLE has an identical and anisotropic zero-mean Gaussian distribution.1798IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 28, NO. 11, NOVEMBER 2009TABLE I RANDOMLY GENERATED FIDUCIAL MARKERS IN THE PREOPERATIVE DATA SET THAT ARE USED IN ALL THE SIMULATIONSXFig. 1. Probability density functions of the estimated distribution of the fiducial using numerical simulations and the proposed algorithm, localization error at when FLE has an identical and isotropic zero-mean Gaussian distribution.xestimate the distribution of FRE and its mean squared value at each fiducial marker. Also, we use (32) and (33) to analytically estimate the distribution of FRE and its mean squared value, respectively. Fig. 1 shows the estimated distribution of FRE using the proposed formulations at the first fiducial marker and numerical simulations. Furthermore, Table II displays the estimated average of the mean squared value of FRE over all the fiducial markers and the using numerical simulations mean squared value of FRE at and the proposed algorithm, respectively. 2) FLE Has an Inhomogeneous and Isotropic Zero-Mean Gaussian Distribution: In this simulation, the same data set as in the previous simulation is used. But, to generate the fixed data set , each point in is perturbed by an inhomogeneous and isotropic zero-mean Gaussian noise with covariance . is uniformly drawn between 0.001 of to by using (9) and (10), and 2 mm . Then, we register and calculate FRE at each fiducial marker. As before, this simulation is repeated 100 000 times to numerically estimate the distribution of FRE and its mean squared value. Furthermore, we use (32) and (33) to analytically estimate the distribution of FRE and its mean squared value. Fig. 2 shows the estimated distribution of FRE at using numerical simulations and the proposed algorithm for one simulation run. Table II displays the average of the mean squared values of FRE over all fiducial markers and the mean squared value of FRE at . 3) FLE Has an Identical and Anisotropic Zero-Mean is produced as Gaussian Distribution: In this simulation, before. To generate , each individual point in is perturbed by an identical and anisotropic, zero-mean Gaussian noisewith covariance matrix diag (0.5 mm , 1.5 mm , 1 mm ). As the first simulation, the two data sets are registered by using (9) and (10), and FRE at each fiducial marker is numerically calculated. Equations (32) and (33) are also used to analytically calculate the characteristics of FRE. Fig. 3 shows the estimated using numerical simulations and the distribution of FRE at proposed algorithm. The mean squared value of FRE over all the fiducial markers and the mean squared value of FRE at using the proposed derivations and numerical simulations are also listed in Table II. 4) FLE Has an Inhomogeneous and Anisotropic Zero-Mean Gaussian Distribution: In this simulation, to generate , which is produced as before, is perturbed by each point in inhomogeneous and anisotropic zero-mean Gaussian noise . The matrix is assumed to be with covariance matrix , where and are independently and randomly drawn between 0.001 and 2 mm . is then registered to by using (9) and (10), and FRE at each fiducial marker is numerically calculated. Equations (32) and (33) are utilized to estimate the distribution of FRE and its mean squared value at . Fig. 4 shows the estimated using the proposed algorithm and distribution of FRE at numerical simulations. Furthermore, the computed average of the mean squared value of FRE over all the fiducial markers and are reported in Table II. the mean squared value of FRE at 5) Effect of the Number of Fiducial Markers and Fiducial Configurations: In the last simulation, we repeated Simulations 1, 2, 3, and 4 for 50 trials (each trial containing 100 000 simulations for a given fiducial configuration) with random configuration of fiducial markers where , the number of fiducial markers, is 3, 5, and 10, respectively, and the fiducials are drawn mm. Data sets and , and within a cube with sides of the noise modeling FLE are generated as explained in the above simulations, and the mean squared value of FRE is numerically and analytically calculated. Table III displays the mean and variance of error difference between numerically and analytically computed overall mean squared values of FRE; and the mean and variance of error difference between the numerically and analytically computed mean squared values of FRE at . The mean of the normalized estimation error of FRE for the first and all the fiducials is also reported in Table III. Finally, we repeated the previous simulations with these modimm to fications: The size of data sets was increased from mm; and the mean of overall variance of the noise, rather than 1 mm , was assumed to be 3 mm . Table IV displays the results. V. SUMMARY AND DISCUSSION We derived the first closed-form solutions for estimating the distribution of FRE and its mean squared value when FLE has an arbitrary zero-mean Gaussian distribution. We analytically illustrated that when FLE has an identical and isotropic zeromean Gaussian distribution, the proposed derivations match the ones derived by Sibson [1] and Balachandran and Fitzpatrick [11]. In addition, we used numerical simulations to verify our derivations. As shown in Fig. 1 and Table II, the proposed formulations’ results accurately match the ones numerically calculated.MOGHARI AND ABOLMAESUMI: DISTRIBUTION OF FIDUCIAL REGISTRATION ERROR IN RIGID-BODY POINT-BASED REGISTRATION1799TABLE II ESTIMATED AVERAGE (MM) OF THE ROOT MEAN SQUARED VALUES OF FRE OVER ALL FIDUCIAL MARKERS AND THE ROOT MEAN SQUARED VALUES OF FRE AT THE FIRST FIDUCIAL MARKER USING THE NUMERICAL SIMULATIONS AND THE PROPOSED ALGORITHM, WHEN FLE HAS A NONIDENTICAL ZERO-MEAN GAUSSIAN DISTRIBUTION. PLEASE NOTE THAT THE RESULTS FOR NUMERICAL SIMULATIONS AND PROPOSED DERIVATIONS ARE LABELED WITH AND , RESPECTIVELY00Fig. 2. Probability density functions of the estimated distribution of the fiducial localization error at x using numerical simulations and the proposed algorithm, when FLE has an inhomogeneous and isotropic zero-mean Gaussian distribution.Fig. 4. Probability density functions of the estimated distribution of the fiducial localization error at x using numerical simulations and the proposed algorithm, when FLE has an inhomogeneous and anisotropic zero-mean Gaussian distribution.distribution. In this scenario, as Fig. 4 and Table II display, the proposed formulations’ results accurately match the distribution of FRE and its mean squared value which are numerically calculated. Finally, Tables III and IV demonstrate the accuracy of the derived formulations for different number of fiducials, different fiducial configurations, and different variances of the noise. VI. CONCLUSION We have presented a general solution that estimates the distribution of FRE for the cases where FLE has an arbitrary zeromean Gaussian distribution. With (7), (30), (32), and (33), we have derived approximate expressions for the distribution of FRE and its mean squared value in the presence of different types of FLE distribution at each fiducial marker. That our proposed algorithm closely agrees with the performed numerical simulations demonstrates its high level of accuracy in estimating the distribution of FRE and its mean squared value in the presence of identical or inhomogeneous and isotropic or anisotropic zero-mean Gaussian noise. We should emphasize that the proposed derivations remain accurate as long as the assumptions under which they were derived are valid. First, all noise modeling FLE at each point of the data sets should have an independent zero-mean Gaussian distribution. If the distribution of the noise should transpire to be nonGaussian, then that distribution, instead of Gaussian distribution, should be considered in the likelihood function . Second, the proposed algorithm is optimum as long as the registered dataFig. 3. Probability density functions of the estimated distribution of the fiducial localization error at x using numerical simulations and the proposed algorithm, when FLE has an identical and anisotropic zero-mean Gaussian distribution.We then extended Sibson [1] and Balachandran and Fitzpatrick [11] derivations for the cases where FLE has either inhomogeneous and isotropic or identical and anisotropic zero-mean Gaussian noise distribution. As it is demonstrated in Figs. 2 and 3 and Table II, the proposed derivations as accurately as numerical simulations estimate the distribution of FRE and its mean squared value, when FLE has inhomogeneous and isotropic, or identical and anisotropic zero-mean Gaussian distribution. Finally, we examined the proposed derivations for the case where FLE has inhomogeneous and anisotropic zero-mean Gaussian。

英国文学简史完全笔记Part one：early and medieval english literatureChapter 1: the making of england1 the Briton2 the Roman Consequent3 the English Consequent4 the social condition of the Anglo-SaxonsChapter 2: Beowulf<Beowulf>贝奥武夫:the national epic of the Anglo-SaxonsEpic: long narrative poems that record the adventures or heroic deeds of a hero enacted in vast landscapes. The style of epic is grand and elevated.e.g. Homer’s Iliad and OdysseyArtistic features:1 Using alliteration2 Using metaphor and understatementDefinition of alliteration: a rhetorical device, meaning some words in a sentence begin with the same consonant sound（头韵）Some examples on P5Definition of understatement: expressing something in a controlled way Understatement is a typical way for Englishmen to express their ideasChapter 3 : Feudal England1 the Norman Conquest:①the Danish invasionKing Alfred: the Anglo-Saxon Chronicle②the Norman Conquest:Marks the establishment of feudalism in England\2 Feuda EnglandSocial features of the Feuda England：Two classes（landlord and peasant）The miseries of the peasant：Black DeathThe raising of 13813 the Romance: knightFamous three:King ArthurSir Gawain and the Green KnightBeowulfChapter 4 William LanglandPiers The Plowman耕者皮尔斯:a picture of feudal England①the exposure of the ruling classes②the story of the Cat and Rats③the marriage of lady Meed④the condition of the peasants⑤the search for truth⑥a representative of the most oppressed section of the peasantryArtistic features:It is written in the form of a dream visionUsing symbolismChapter 5 the English Bllads民谣Oral literatureBallad: is a story told in song, usually in 4-line stanzas, with the second and fourth lines rhymed.The Robin Hood BalladChapter 6 Geoffery Chaucer英国文学史上首先用伦敦方言写作。