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The coil states are loosely gathered while the native states can form a black cluster with extremely high density in the 2-dimensional projection plane. ÕâÀï´ÓµÚÒ»¾äµ½µÚ¶þ¾äÐÅÏ¢ÎÞ·¨Á÷¶¯¡£¡°The coil states¡±²»ÖªµÀÊǴӺζøÀ´µÄ¡£¶ÁÕ߻ᷢÏÖÏÂÃæ¸Ä¶¯ºóµÄ¾ä×Ó¸üÈÝÒ×Ã÷°×¡£ Kinetic trajectories are projected onto xx and yy variables in Figure 7. This figure shows two populated states. One corresponds to loosely gathered coil states while the other is the native state with a higher density. ÔÚÕâ¸öжÎÀвåÈëµÄµÚ¶þ¾äʹÿ¾ä¾ùÄÜ´Ó¾ÉÐÅÏ¢³ö·¢µ½ÐÂÐÅÏ¢½áÊø¡£µÚÒ»¾äÓëµÚ¶þ¾äÖ®¼äÒÔ¡°Figure ¡±ÏàÁ¬¶øµÚ¶þ¾äÓëµÚÈý¾äÖ®¼äÒÔ¡°two states¡±ÏàÁ¬¡£¶øÐÂÐÅÏ¢¡°coil states ¡±Ôò³öÏÖÔÚµÚÈý¾äµÄ×îºó¡£Õû¶Î»·»·ÏàÁ¬£¬³ÉΪһ¸öÕûÌå¡£ÔÙ¿´Ò»¸öÀý×Ó£º The accuracy of the model structures is given by TM-score. In case of a perfect match to the experimental structure, TM-score would be 1. ÔÚµÚ¶þ¸ö¾ä×ÓÀ¾ÉÐÅÏ¢¡°TM-score¡±±»ÂñÔÚÖм䣬±»ÐÂÐÅÏ¢¡°a perfect match to experimental structure ¡±´ò¶ÏÁË¡£ÕâÀィÒéÐÞ¸ÄÈçÏ£º The accuracy of the model structures is measured by TM-score, which is equal to 1 if there is a perfect match to the experimental structure. ¿Æ¼¼Ð´×÷ÖеÄ×î´óÎÊÌâ¾ÍÊÇоÉÐÅϢ˳Ðòµßµ¹¡£ÐÂÐÅÏ¢ºÍ¾ÉÐÅÏ¢¶Ô×÷ÕßÀ´Ëµ¿ÉÄܲ»ÊǺܺÃÇø·Ö£¬ÒòΪËû·Ç³£ÊìϤËùÓеÄÐÅÏ¢¡£ÎªÁ˱ÜÃâÕâÖÖÎÊÌ⣬²»¹Üʲôʱºò£¬Ã¿µ±Ä㿪ʼдо䣬ÄãÓ¦¸ÃÎÊÎÊ×Ô¼º£¬ÕâЩ´ÊÇ°ÃæÓÐûÓб»Ìáµ½¹ý¡£Ò»¶¨Òª°ÑÌáµ½¹ýµÄ·ÅÇ°Ãæ£¬Ã»Ìá¹ýµÄ·ÅºóÃæ¡£ 2. ¶ÁÕßÏëÔÚÖ÷ÓïÖ®ºóÁ¢¿Ì¿´µ½ÐÐΪ¶¯´Ê¡£¶ÔÒ»¸ö˵Ã÷ËÔÚ×öʲôµÄ¾ä×Ó£¬¶ÁÕßÐèÒªÕÒµ½¶¯´Ê²ÅÄÜÀí½â¡£Èç¹û¶¯´ÊºÍÖ÷ÓïÖ®¼äÏà¸ô̫Զ£¬ÔĶÁ¾Í»á±»Ñ°ÕÒ¶¯´Ê´ò¶Ï¡£¶ø´ò¶ÏÔĶÁ¾Í»áʹ¾ä×ÓÄÑÒÔÀí½â¡£ÕâÀïÓиöÀý×Ó£º The smallest URFs (URFA6L), a 207-nucleotide (nt) reading frame overlapping out of phase the NH2-terminal portion of the adenosinetrip hosphatase (ATPase) subinit 6 gene has been identified as the animal equivalent of the recently discovered yeast H+-ATPase subunit 8 gene. ͬÑùµÄ¾ä×Ó£¬½«¶¯´Ê·ÅÔÚÖ÷ÓïÖ®ºó£º The smallest of the URFs is URFA6L, a 207-nucleotide (nt) reading frame overlapping out of phase the NH2-terminal portion of the adenosinetriphosphatase (ATPase) subinit 6 Gene; it has been identified as the animal equivalent of the recently discovered yeast H+-ATPase subunit 8 gene. ÕâÑùеľä×Ӿ͸ü¼ÓƽºâÁË¡£¾¡Á¿±ÜÃâ¹ý³¤µÄÖ÷ÓïºÍ¹ý¶ÌµÄ±öÓï¡£Õâ¾ÍÏñÍ·ÖØ½ÅÇáµÄÈ˺ÜÄÑÕ¾ÎÈ¡£¶ÌµÄÖ÷Óï½ô¸ú×Ŷ¯´Ê¼ÓÉϳ¤µÄ±öÓïЧ¹û»á¸üºÃ¡£ 3. ¶ÁÕ߯ÚÍûÿ¾äÖ»ÓÐÒ»¸öÖØµã£¬Õâ¸öÖØµãͨ³£ÔÚ¾äβ¡£±È½ÏÏÂÃæÁ½¸ö¾ä×Ó£¬ÎÒÃÇ¿ÉÒԸоõµ½ËûÃÇ×ÅÖØÇ¿µ÷²»Í¬µÄ¶«Î÷¡£ URFA6L has been identified as the animal equivalent of the recently discovered yeast H+-ATPase subunit 8 gene. Recently discovered yeast H+-ATPase subunit 8 gene has a corresponding animal equivalent gene URFA6L. ºÜÃ÷ÏÔ£¬Ç°ÃæµÄ¾ä×ÓÊǹØÓÚÒ»¸ö×î½ü·¢ÏֵĽÍĸ»ùÒò£¬¶øµÚ¶þ¾äÔò×ÅÖØÇ¿µ÷ÁËËüÓÐÒ»¸öºÍ¶¯ÎïÒ»ÖµĻùÒò¡£ÁíÍâÒ»¸öÀý×Ó£º The enthalpy of hydrogen bond formation between the nucleoside bases 2-deoxyguanosine (dG) and 2-deoxycytidine (dC) has been determined by direct measurement. Õâ¸ö¾ä×Ó¿´ÆðÀ´ºÃÏñÊÇÔÚÇ¿µ÷¡°direct measurement ¡±¡£Õⲻ̫ÏñÊÇÔ×÷ÕßµÄÄ¿µÄ¡£µßµ¹Ò»Ï»áʹ¾ä×Ó¸ü¼Óƽºâ¡£ We have directly measured the enthalpy of hydrogen bond formation between the nucleoside bases 2-deoxyguanosine (dG) and 2-deoxycytidine (dC). еľä×Ó¸ü¼òµ¥¶øÇÒ¸ü¶Ì£¬Í¬Ê±±ÜÃâÁËÍ·ÖØ½ÅÇáµÄÖ¢×´¡£×ÜÖ®£¬¾äβÊǶÁÕ߶Ըþä×îºóµÄÓ¡Ïó¡£°Ñ×îºÃµÄ£¬×îÖØÒªµÄ£¬ºÍÏëÒª¶ÁÕß¼ÇסµÄ¶«Î÷·ÅÔÚ¾äβ¡£ ¶ÁÕß¶Ô¶ÎÂäµÄÆÚÍû ÿһ¸ö¶ÎÂä¶¼Ó¦¸ÃÖ»½²Ò»¸ö¹ÊÊ¡£ÔÚÒ»¶ÎÀï±íÊö¶à¸ö¹Ûµã»áʹ¶ÁÕߺÜÄÑÖªµÀ¸Ã¼Çסʲô¡¢Õâ¶ÎÏë±í´ïʲô¡£Ò»¶ÎµÄµÚÒ»¾äÒª¸æËß¶ÁÕßÕâÒ»¶ÎÊǽ²Ê²Ã´µÄ¡£ÕâÑù¶ÁÕßÏëÌø¹ýÕâ¶Î¾Í¿ÉÒÔÌø¹ý¡£Ò»¶ÎµÄ×îºóÒ»¾äÓ¦¸ÃÊÇÕâ¶ÎµÄ½áÂÛ»òÕ߸æËß¶ÁÕßÏÂÒ»¶ÎÊÇʲô¡£¶ÎÂäÖеľä×ÓÓ¦¸ÃÓÉʼµ½ÖÕͨ¹ýÂß¼¹ØÏµÁ¬½Ó£¬ÊµÏÖÓɾÉÐÅÏ¢µ½ÐÂÐÅÏ¢µÄÁ÷¶¯¡£±ÈÈçÕâÒ»¶Î£º[1] The enthalpy of hydrogen bond formation between the nucleoside bases 2-deoxyguanosine (dG) and 2-deoxycytidine (dC) has been determined by direct measurement. dG and dC were derivatized at the 5 and 3 hydroxylith triisopropylsilyl groups to obtain solubility of the nucleosides in non-aqueous solventsand(sw)to prevent the ribose hydroxyls from forming hydrogen bonds. From isoperibolic titration measurements, the enthalpy of dC:dG base pair formation is -6.650.32 kcal/mol. ºÜÄÑÖªµÀ×÷ÕßÔÚÕâ¶ÎÀïÏë±í´ïʲô¡£´ÓÕâ¶ÎµÄÆðʼºÍ½áÊø¿´À´£¬ìÊ(enthalpy) Ó¦¸ÃÊÇËûÏë±í´ïµÄÖØµã¡£ÏÂÃæÊÇÖØÐÂ×éºÏºóµÄ¶ÎÂä¡£[1] We have directly measured the enthalpy of hydrogen bond formation between the nucleoside bases 2-deoxyguanosine (dG) and 2-deoxycytidine (dC). dG and dC were derivatized at the 5 and 3 hydroxyls with triisopropylsilyl groups; these groups serve both to solubilize the nuclides in non-aqueous solvents and to prevent the ribose hydroxyls from forminghydrogenbond(eos)s. The enthalpy of dC:dG base pair formation is -6.650.32 kcal/mol according to isoperibolic titration measurements, Ê×¾äÃèÊöÁËÕû¶ÎµÄÖ÷Ìâ¡£Ô¶ÎÀïµÄµÚÒ»¾äµßµ¹ÊÇΪÁË1£© ʹÐÂÐÅÏ¢¡°dG ¡±ºÍ¡°dC¡± ÔÚ¾ä×Ó×îºó²¢Ç¿µ÷ËüÃÇ¡£2£© ¸üºÃµØ¸úÏÂÃæÒ»¾äÏνӡ£Ô¶ÎÀïµÄµÚ¶þ¾ä±»·Ö³ÉÁ½²¿·Ö£¬ÕâÑùÿһ²¿·ÖÖ»±í´ïÁËÒ»¸ö¹Ûµã¡£×îºóÒ»¾äʱ×ܽáÕû¶Î¡£ÔÙ¿´ÁíÒ»¸öÀý×Ó£º Large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates at which tectonic plates move and accumulate strain at their boundaries are approximately uniform. Therefore, in first approximation, one may expect that large ruptures of the same fault segment will occur at approximately constant time intervals. If subsequent main shocks have different amounts of slipacross the fault, then the recurrence time may vary, and the basic idea of periodic main shocks must be modified. ÔÚÕâ¸öÀý×ÓÀǰÁ½¾ä¹²Í¬²ûÃ÷ÁË»ýÀÛÕÅÁ¦µÄËÙ¶È£¨Rate Of Strain Accumulation)¡£È»¶ø£¬µÚÒ»¾äÀïµÄ¾ÉÐÅÏ¢²¢Ã»ÓзÅÔÚµÚ¶þ¾äµÄ¿ªÊ¼¡£¶ÁÕß¶Áµ½µÚÈý¾äµÄʱºòͨ³£¾Í²»Ã÷°×Õâ¶Îµ½µ×Òª½²Ê²Ã´ÁË¡£¸üÇåÎúµÄÃèÊöÓ¦¸ÃÈçÏ£º Large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates of strain accumulation at the boundaries of tectonic plates are approximately uniform. Therefore, nearly constant time interval(at first approximation) would be expected between large ruptures of the same fault segment.(s)[However?], the recurrence time may vary; the basic idea of periodic main shocks may need to be modified if subsequent main shocks have different amounts of slip across the fault. жÎÏÖÔÚ×ÅÖØ²ûÃ÷Á˵ØÕðµÄ·¢ÉúƵÂÊ¡£Ï»®Ïß±êÃ÷ÁËÒÔǰÃèÊö¹ýµÄ¾ÉÐÅÏ¢¡£ºÜÃ÷ÏÔ£¬Ð¾ÉÐÅÏ¢µÄÁ¬½ÓÊÇÀí½âÕâ¶ÎµÄ¹Ø¼ü¡£´Ó¾ÉÐÅÏ¢µ½ÐÂÐÅÏ¢µÄÁ÷¶¯ÊÇʹ¶ÁÕßÇáËÉÔĶÁµÄ×î¼Ñ·½Ê½¡£Ð´ÎÄÕµÄÄ¿µÄ²»ÊÇÈ¥²âÊÔ¶ÁÕßµÄÔĶÁÄÜÁ¦£¬¶øÊÇ¿¼Ñé×÷Õߵıí´ïÄÜÁ¦¡£²»ÄܹÖÈËû¿´¶®£¬Ö»ÄܹÖ×Ô¼ºÃ»Ð´Çå³þ¡£³£³£Ìýµ½ÕâÑùµÄ±§Ô¹£ºÄÇÉó¸åÈËÁ¬Õâ¶¼²»¶®! 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Knowing secondary structures of proteins iessential for their structure classification, understanding folding dynamics and mechanis(s)ms, and discovering conserved structural/functional motifs. Secondary structure informatiis also useful for sequence and multiple sequence alignment, structure alignment,andsequ(on)ence to structure alignment (or threading). As a result, predicting secondary structures from protein sequences continues to be an active field of research fifty six years after Pauling and Corey first predicted that the most common regular patterns of proteibackbones are the ¦Á-helix and the ¦Â-sheet. Prediction and application of proteinsecon(n)dary structures rely on prior assignment of the secondary-structure elements from a given protein structure by human or computational methods. Many computational methods have been developed to automate the assignment of secondary structures. Examplare DSSP,STRIDE, DEFINE, P-SEA, KAKSI,P-CURVE, XTLSSTR,SECSTR,SEGNO,(es)and VoTAP. These methods are based on either the hydrogen-bond pattern, geometric features, expert knowledge or their combinations. However, they often disagree on their assignments. For example, disagreement among DSSP, P-CURVE, and DEFINE can be as large as 25%. More beta sheet is assigned by XTLSSTR and more pi-helix by SECSTR than by DSSP. The discrepancy among different methods is caused by non-ideal configurations of helices and sheets. As a result, defining the boundaries between helix, sheet, and coil is problematical and a significant source of discrepancies between different methods. Inconsistent assignment of secondary structures by different methods highlights the need for a criterion or a benchmark of ¡°standard¡± assignments that could be used to assess and compare assignment methods. One possibility is to use the secondary structures assigned by the authors who solved the protein structures. STRIDE, in fact, has been optimized to achieve the highest agreement with the authors¡¯ annotations. However, it is not clear what is the criterion used for manual or automatic assignment of secondary structures by different authors. Another possibility is to treat the consensus prediction by several methods as the gold standard. However, there is no obvious reason why each method should weight equally in assigning secondary structures and which method should be used in consensus. Other used criteriinclude helix-capping propensity, the deviation from ideal helical and sheet configurations,and(a)structural accuracy produced by sequence-to-structure alignment guided by secondary structure assignment. In this paper, we propose to use sequence-alignment benchmarks for assessing secondary structure assignments. These benchmarks are produced by 3D-structure alignment of structurally homologous proteins. Instead of assessing the accuracy of secondary-structure assignment directly, which is not yet feasible, we compare the two assignments of secondary structures in structurally aligned positions. We assume that the best method should assign the same secondary-structure element to the highest fraction of structurally aligned positions. Certainly, structurally aligned positions do not always have the same secondary structures. Moreover, different structure-alignment methods do not always produce the same lt. Nevertheless, this criterion provides a means to locate a secondary-structure assig(resu)nment method that is most consistent with tertiary structure alignment. We suggest that this approach provides an objective evaluation of secondary structure assignment methods. 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We cannot give a direct answer because the DMD silation is required to obtain well-converged results for the thermodynamics. However, the critical ph(mu)enomena predicted for a fluid composed of partiles interacting with a square-well potentil are as realistic as those predicted for a fluidco(c)mposed of particles interacting withaLJp(a)otential. Also an analogous complex phase diagram is found in simulations of LJ clusters. The present results for square-well homopolymers may well be found in more realistic homopolymer models and even in real polymers. ÕâÒ»¶Î̽¾¿Á˿ɹ©Ñ¡ÔñµÄ½âÊÍ¡£ ÕªÒª²¿·Ö ÕûƪÎÄÕÂдÍêÁË¡£ÄãÐèҪдÎÄÕµÄÕªÒªÁË¡£µäÐ͵ÄÕªÒª°üÀ¨¿ÎÌâÁìÓòµÄÖØÒªÐÔ£¨»Øµ½±êÌâ)£¬ÒªÑо¿µÄÎÊÌ⣬Äã·½·¨µÄ¶ÀÌØÐÔ£¬½á¹ûµÄÒâÒåºÍÓ°Ïì¡£ÕâÀïÓиöÀý×Ó¡£ How to make an objective assignment of secondary structures based on a protein structure is an unsolved problem. Defining the boundaries between helix, sheet, and coil structures is arbitrary, and commonly accepted standard assignments do not exist. Here, we propose a criterion that assesses secondary-structure assignment based on the similarity of the secondary structures assigned to structurally aligned residues in sequence-alignment benchmarks. Thiiteriiused to rank six secondary-structure assignmentmethods: STRIDE, DSSP, SECSTR,(scr) KAKSI (on),(s) P-SEA, and SEGNO with three established sequence-alignment benchmarks (PREFAB, SABmark and SALIGN). STRIDE and KAKSI achieve comparable success rates in assigning the same secondary structure elements to structurally aligned residues in the three benchmarks. Their success rates are between 1-4% higher than those of the other four methods. The consensus of STRIDE, KAKSI, SECSTR, and P-SEA, called SKSP, improves assignments over the best single method in each benchmark by an additional 1%. These results support the usefulness of the sequence alignment benchmarks as the benchmarks for secondary structure assignment. ǰÁ½¾ä³ÂÊöÁËÎÊÌâ¡£µÚÈý¾äÌá³öÁ˽â¾ö°ì·¨¡£ÕâЩ¾ä×ÓºóÃæ¸ú׎á¹û¡£Õû¸öÕªÒªÒÔ×ܽáÊÕβ¡£×¢ÒâÕªÒªÀïµÄÖ÷Ì岿·ÖÊǽá¹û¼°ÆäÒâÒåºÍÓ°Ïì¡£ ×Ü ½á 1. ÈÏÕæ¶Ô´ýд×÷¡£¾¡Äã×î´óŬÁ¦»¨Ê±¼äд×÷¡£ËüÊÇ¿ÆÑ§Ñо¿µÄÖØÒªÒ»»·¡£ÎÄÕÂûдºÃ£¬Ã»ÈË¿´£¬Ã»ÈËÓ㬵ÈÓÚû·¢±í¡£ 2. ³ý·ÇÕâ¸öÑо¿ÊÇÈ«Ãæ³¹µ×µÄ£¬¶øÇÒÄãÊÔÁËËùÓпÉÒÔÖ§³ÖÄã½áÂ۵ķ½·¨£¬·ñÔò²»ÒªÈ¥·¢±í¡£ 3. ÖØÐÂ˼¿¼£¬²¢ºÏÀí½âÊÍΪʲô×öÕâÏ×÷£¬×öÁËʲô£¬Ê²Ã´ÊÇ×îÖØÒªµÄ·¢ÏÖ£¿ÎªÊ²Ã´ÓÃÕâ¸ö·½·¨£¿ÎªÊ²Ã´ÓÃÕâЩ²ÎÊý£¿Ê²Ã´ÊÇÒÔǰ×ö¹ýµÄ£¨¸üÐÂÎÄÏ×ËÑË÷)£¿²»Í¬ÔÚʲôµØ·½£¿ 4. Òª´ÓÅúÅеĽǶÈÀ´¿´ÄãµÄ¹¤×÷£¬ÏëÒ»Ïë±ðÈË»áÔõÑùÀ´Ìô벡¡£Ö»ÓÐÕâÑù£¬²ÅÄÜÕÒµ½Èõµã£¬½øÒ»²½·¢Õ¹¡£ÎÒµÄÐí¶àÂÛÎÄÊÇÔÚ·´¸´ÌÖÂÛÖдó·ù¶ÈÐ޸ģ¬Ðí¶à¼ÆËã¾³£ÒªÖØ×ö¡£Ö»ÓÐÀí˳ºÍÀí½â½á¹û£¬ÎÄÕ²Żá¸üÓÐÒâÒå¡£ 5. ÒªÄܻشðËùÓкÏÀíµÄÖÊÒÉ¡£Èç¹ûÄã×Ô¼ºÓÐÒÉÎÊ£¬Ò»¶¨Òª¸ãÇå³þ£¬·ñÔò±ðÈËÓÖÔõ»áÏàÐÅ¡£²»ÒªÇáÒ×ÏàÐŵõ½µÄ¸ïÃüÐÔ·¢ÏÖ¡£ 6. 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ÎÒÔڶſ˴óѧGopen ½ÌÊÚ1995 ÄêÄê¶È¶Ìѵ°àÊÜÒæ·Çdz¡£ÎÒÒªÌØ±ð¸ÐлÎҵĵ¼Ê¦MartiKarplus( ¹þ·ð´óѧ)£¬George Stell (ŦԼÖÝÁ¢´óѧ£Ê¯ÏªÐ£Çø), Harold L. Friedman ( ŦԼÖÝÁ¢´óѧ£Ê¯ÏªÐ£Çø) ºÍ(n)Carol Hall (±±¿¨ÂÞÀ´ÄÉÖÝÁ¢´óѧ)µÄ¹ÄÀøºÍÖ¸µ¼¡£Ã»ÓÐËûÃÇ£¬ÎÒ²»»áÓÐÄÇô¶à»ú»áÁ·Ï°Ó¢ÎÄд×÷¡£×îºó£¬ÎÒÒª¸ÐлÎÒµÄѧÉúºÍ²©Ê¿ºó¡£ËûÃǶԿÆÑ§µÄ¹±Ï×ʹÎÒ¿ÉÒÔ¼ÌÐøÐ´ÂÛÎÄ£¬»ù½ðÉêÇ룬»òÆÀÂÛ¡£´ËÎÄÖеÄÒ»²¿·ÖÀý×ÓÀ´×ÔÓëËûÃǺÏ×÷µÄÎÄÕ¡£´ËÎijõ¸åÊÇÓÃÓ¢ÎÄдµÄ¡£ÓÉÓÚÎÒµÄÖÐÎÄ´ò×ÖËÙ¶ÈÌ«Âý£¬Ìرð¸ÐлÐ챴˼°ïÎÒ·Òë³ÉÖÐÎijõ¸å¡£Èç¹ûÓв»Í׵ĵط½ÊÇÎÒµÄÎÊÌ⣬Çë¶àÖ¸½Ì¡£´ËÎÄÔÚÍøÉϳöÏÖÒԺ󣬵õ½²»ÉÙ¹Ø×¢¡£Ìرð¸ÐлÕÔÁ¢Æ½½ÌÊڵĽ¨Òé¼°¸ÐлÐí¶àУÓѺÍÍøÓѵÄÖ¸ÕýºÍ¹ÄÀø¡£ Òý Óà [1] G. D. Gopen and J. A. Swan, American Scientist, 78, 550-558 (1990) [2] H. Zhou and Y. Zhou, Bioinformatics 21, 3615--3621 (2005). [3] W. Zhang, K. Dunker, and Y. Zhou, Proteins, in press (2007). [4] Y. Zhou, C. K. Hall, and M. Karplus, Phys. Rev. Lett. 77 , 2822 (1996). [ Last edited by ½ÄϺÀÇé on 2008-5-14 at 19:50 ] |
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