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a b c d e f g h i j k l m n o p q r s t u v w x y z是什么意思!收藏
这不是单词但是可以告诉你26个英文字母的由来
人们都知道，英文有26个字母。但这26个字母的来历，知道的人恐怕就不多了。原来，英文字母渊源于拉丁字母，拉丁字母渊源于希腊字母，而希腊字母则是由腓尼基字母演变而来的。
腓尼基是地中海东岸的文明古国，其地理位置大约相当于今天黎巴嫩和叙利亚的沿海一带。“腓尼基”是希腊人对这一地区的称谓，意思是“紫色之国”，因该地盛产紫色染料而得名。罗马人则称之为“布匿”。
公元前20世纪初，在腓尼基产生一些小的奴隶制城邦，但从未形成统一的国家。在古代，腓尼基以工商业和航海业闻名于世。至公元前10世纪前后，其活动范围已达今塞浦路斯、西西里岛、撒丁岛、法国、西班牙和北部非洲，并建立了许多殖民地。公元前8世纪以后，亚述、新巴比伦等国相继侵入腓尼基。公元前6世纪，腓尼基终于被波斯帝国兼并。
大约公元前13世纪，腓尼基人创造了人类历史上第一批字母文字，共22个字母（无元音）。这是腓尼基人对人类文化的伟大贡献。腓尼基字母是世界字母文字的开端。在西方，它派生出古希腊字母，后者又发展为拉丁字母和斯拉夫字母。而希腊字母和拉丁字母是所有西方国家字母的基础。在东方，它派生出阿拉美亚字母，由此又演化出印度、阿拉伯、希伯莱、波斯等民族字母。中国的维吾尔、蒙古、满文字母也是由此演化而来。
据考证，腓尼基字母主要是依据古埃及的图画文字制定的。在古埃及，“A”是表示“牛头”的图画；“B”是表示“家”或“院子”的图画；“C”和“G”是表示“曲尺”的图画；“D”是表示“门扇”的图画；“E”是表示一个“举起双手叫喊的人”的图画；“F”、“V”、“Y”是表示“棍棒”或“支棒”的图画；“H”是表示“一节麻丝卷”的图画；“I”是表示“展开的手”的图画；“K”是表示“手掌”的图画；“M”是表示“水”的图画；“N”是表示“蛇”的图画；“O”是表示“眼睛”的图画；“P”是表示“嘴巴”的图画；“Q”是表示“绳圈”的图画；“R”是表示“人头”的图画；“S”和“X”是表示“丘陵地”或“鱼”的图画；“T”是表示“竖十字型”的图画；“Z”是表示“撬”或“箭”的图画。公元前2世纪时，拉丁字母已包括了这23个字母。后来，为了雕刻和手写的方便，并为了使元音的“V”和辅音的“V”相区别，便把原来的“V”的下方改成圆形而定为元音“U”；又把两个“V”连起来变出了一个做辅音用的“W”，这个“W”的出现已是11世纪的事了。后来人们又把“I”稍稍变化而另创出一个辅音字母“J”。这样，原来的23个字母再加上“U”、“W”、“J”三个字母，就构成了26个字母的字母表了。中世纪时，拉丁字母基本定型，后世西方文字（当然也包括英文）都是由它演变而来。
原载1993年《历史大观园》
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为兴趣而生，贴吧更懂你。或Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., Gocayne, J.D., Amanatides, P., Ballew, R.M., Huson, D.H., Wortman, J.R., Zhang, Q., Kodira, C.D., Zheng, X.H., Chen, L., Skupski, M., Subramanian, G., Thomas, P.D., Zhang, J., Gabor Miklos, G.L., Nelson, C., Broder, S., Clark, A.G., Nadeau, J., McKusick, V.A., Zinder, N., Levine, A.J., Roberts, R.J., Simon, M., Slayman, C., Hunkapiller, M., Bolanos, R., Delcher, A., Dew, I., Fasulo, D., Flanigan, M., Florea, L., Halpern, A., Hannenhalli, S., Kravitz, S., Levy, S., Mobarry, C., Reinert, K., Remington, K., Abu-Threideh, J., Beasley, E., Biddick, K., Bonazzi, V., Brandon, R., Cargill, M., Chandramouliswaran, I., Charlab, R., Chaturvedi, K., Deng, Z., Di Francesco, V., Dunn, P., Eilbeck, K., Evangelista, C., Gabrielian, A.E., Gan, W., Ge, W., Gong, F., Gu, Z., Guan, P., Heiman, T.J., Higgins, M.E., Ji, R.R., Ke, Z., Ketchum, K.A., Lai, Z., Lei, Y., Li, Z., Li, J., Liang, Y., Lin, X., Lu, F., Merkulov, G.V., Milshina, N., Moore, H.M., Naik, A.K., Narayan, V.A., Neelam, B., Nusskern, D., Rusch, D.B., Salzberg, S., Shao, W., Shue, B., Sun, J., Wang, Z., Wang, A., Wang, X., Wang, J., Wei, M., Wides, R., Xiao, C., Yan, C., Yao, A., Ye, J., Zhan, M., Zhang, W., Zhang, H., Zhao, Q., Zheng, L., Zhong, F., Zhong, W., Zhu, S., Zhao, S., Gilbert, D., Baumhueter, S., Spier, G., Carter, C., Cravchik, A., Woodage, T., Ali, F., An, H., Awe, A., Baldwin, D., Baden, H., Barnstead, M., Barrow, I., Beeson, K., Busam, D., Carver, A., Center, A., Cheng, M.L., Curry, L., Danaher, S., Davenport, L., Desilets, R., Dietz, S., Dodson, K., Doup, L., Ferriera, S., Garg, N., Gluecksmann, A., Hart, B., Haynes, J., Haynes, C., Heiner, C., Hladun, S., Hostin, D., Houck, J., Howland, T., Ibegwam, C., Johnson, J., Kalush, F., Kline, L., Koduru, S., Love, A., Mann, F., May, D., McCawley, S., McIntosh, T., McMullen, I., Moy, M., Moy, L., Murphy, B., Nelson, K., Pfannkoch, C., Pratts, E., Puri, V., Qureshi, H., Reardon, M., Rodriguez, R., Rogers, Y.H., Romblad, D., Ruhfel, B., Scott, R., Sitter, C., Smallwood, M., Stewart, E., Strong, R., Suh, E., Thomas, R., Tint, N.N., Tse, S., Vech, C., Wang, G., Wetter, J., Williams, S., Williams, M., Windsor, S., Winn-Deen, E., Wolfe, K., Zaveri, J., Zaveri, K., Abril, J.F., Guigo, R., Campbell, M.J., Sjolander, K.V., Karlak, B., Kejariwal, A., Mi, H., Lazareva, B., Hatton, T., Narechania, A., Diemer, K., Muruganujan, A., Guo, N., Sato, S., Bafna, V., Istrail, S., Lippert, R., Schwartz, R., Walenz, B., Yooseph, S., Allen, D., Basu, A., Baxendale, J., Blick, L., Caminha, M., Carnes-Stine, J., Caulk, P., Chiang, Y.H., Coyne, M., Dahlke, C., Mays, A., Dombroski, M., Donnelly, M., Ely, D., Esparham, S., Fosler, C., Gire, H., Glanowski, S., Glasser, K., Glodek, A., Gorokhov, M., Graham, K., Gropman, B., Harris, M., Heil, J., Henderson, S., Hoover, J., Jennings, D., Jordan, C., Jordan, J., Kasha, J., Kagan, L., Kraft, C., Levitsky, A., Lewis, M., Liu, X., Lopez, J., Ma, D., Majoros, W., McDaniel, J., Murphy, S., Newman, M., Nguyen, T., Nguyen, N., Nodell, M., Pan, S., Peck, J., Peterson, M., Rowe, W., Sanders, R., Scott, J., Simpson, M., Smith, T., Sprague, A., Stockwell, T., Turner, R., Venter, E., Wang, M., Wen, M., Wu, D., Wu, M., Xia, A., Zandieh, A. and Zhu, X. (2001) The sequence of the human genome. Science, .
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被如下文章引用：
AUTHORS: ,,,,
KEYWORDS: Pharmaceutical Targets for Drug D G-Protein Coupled R Scoring M Hit Rates
JOURNAL NAME:
Sep 05, 2014
Target identification is a
critical step following the discovery of small molecules that elicit a
biological phenotype. G-protein coupled recaptors (GPCRs) are among the most
important drug targets for the pharmaceutical industry. The present work seeks to
provide an in silico model of known GPCR protein fishing technologies in
order to rapidly fish out potential drug targets on the basis of amino acid sequences
and seven transmembrane regions (TMs) of GPCRs. Some scoring matrices were trained
on 22 groups of GPCRs in the GPCRDB database. These models were employed to predict
the GPCR proteins in two groups of test sets. On average, the mean correct rate
of each TM of 38 GPCRs from two test sets (ST23 and ST24)
was found 62% and 57.5%, respectively, using training set 18 (SLD18);
the mean hit rate of each TM of 38 GPCRs from ST23 and ST24 was found 68.1% and 64.7%, respectively. Based on the scoring matrices of
PreMod, the mean correct rate of each TM of GPCRs from ST23 and ST24 was found 62% and 62.04%, the mean
hit rate of each TM of GPCRs from ST23 and ST24 was found 67.7% and 68.0%, respecttively. The means of GPCRs in ST23 based on SLD18 is close to those based on PreM whereas
the means of GPCRs in ST24 based on&SLD18 is less than those based on PreMod. Moreover, the accuracy (“2”) and validity
(“2 + 1”) rates of prediction all seven TMs of 38 GPCRs by the scoring matrices
of PreMod are more than those by SLD18, SLA14 and SLA3; whereas the hit rates (94.74% and 97.37%) by
PreMod are less than those of&SLA3 but bigger than
those of&SLD18 and SLA14,
respectively. This is the reason that we choose PreMod to predict some
potential drug targets. 22 GPCR proteins in the sense chain of chromosome 19
constructing validation set were predicted and validated by PreMod whose hit
rate is up to 90.91%. Further evaluation is under investigation.数学的q.w.e.r.e.t.y.u.i.i.o.p.l.m.k.n.j.b.h.v.g.c.f.x.d.z.s.a.什么意思, 数学的q.w.e.r.e.t.y.u.i.i.o.p
数学的q.w.e.r.e.t.y.u.i.i.o.p.l.m.k.n.j.b.h.v.g.c.f.x.d.z.s.a.什么意思
静子°-24 数学的q.w.e.r.e.t.y.u.i.i.o.p.l.m.k.n.j.b.h.v.g.c.f.x.d.z.s.a.什么意思
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