Biconical structures in two-dimensional anisotropic Heisenberg antiferromagnets

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生物化学:名词解释大全

生物化学:名词解释大全

【生物化学:名词说明大全】之马矢奏春创作第一章蛋白质1.两性离子(dipolarion)2.必须氨基酸(essential amino acid)3.等电点(isoelectric point,pI)4.罕有氨基酸(rare amino acid)5.非蛋白质氨基酸(nonprotein amino acid) 6.构型(configuration)7.蛋白质的一级机关(protein primary structure)8.构象(conformation)9.蛋白质的二级机关(protein secondary structure)10.机关域(domain)11.蛋白质的三级机关(protein tertiary structure)12.氢键(hydrogen bond)13.蛋白质的四级机关(protein quaternary structure)14.离子键(ionic bond)15.超二级机关(super-secondary structure) 16.疏水键(hydrophobic bond)17.范德华力( van der Waals force) 18.盐析(salting out)19.盐溶(salting in)20.蛋白质的变性(denaturation)21.蛋白质的复性(renaturation)22.蛋白质的沉淀传染感动(precipitation) 23.凝胶电泳(gel electrophoresis)24.层析(chromatography)第二章核酸1.单核苷酸(mononucleotide)2.磷酸二酯键(phosphodiester bonds)3.不合错误称比率(dissymmetry ratio)4.碱基互补规律(complementary base pairing)5.反暗码子(anticodon)6.顺反子(cistron)7.核酸的变性与复性(denaturation、renaturation)8.退火(annealing)9.增色效应(hyper chromic effect)10.减色效应(hypo chromic effect)11.噬菌体(phage)12.发夹机关(hairpin structure)13.DNA 的熔解温度(melting temperature Tm)14.分子杂交(molecular hybridization)15.环化核苷酸(cyclic nucleotide)第三章酶与辅酶1.米氏常数(Km 值)2.底物专一性(substrate specificity)3.辅基(prosthetic group)4.单体酶(monomeric enzyme)5.寡聚酶(oligomeric enzyme)6.多酶系统(multienzyme system)7.激活剂(activator)8.抑制剂(inhibitor inhibiton)9.变构酶(allosteric enzyme)10.同工酶(isozyme)11.引导酶(induced enzyme)12.酶原(zymogen)13.酶的比活力(enzymatic compare energy)14.活性中心(active center)第四章生物氧化与氧化磷酸化1.生物氧化(biological oxidation)2.呼吸链(respiratory chain)3.氧化磷酸化(oxidative phosphorylation)4.磷氧比P/O(P/O)5.底物程度磷酸化(substrate level phosphorylation)6.能荷(energy charg第五章糖代谢1.糖异生(glycogenolysis)2.Q 酶(Q-enzyme)3.乳酸轮回(lactate cycle)4.发酵(fermentation)5.变构调节(allosteric regulation)6.糖酵解途径(glycolytic pathway)7.糖的有氧氧化(aerobic oxidation)8.肝糖原分化(glycogenolysis)9.磷酸戊糖途径(pentose phosphate pathway)10.D-酶(D-enzyme)11.糖核苷酸(sugar-nucleotide)第六章脂类代谢1.必须脂肪酸(essential fatty acid)2.脂肪酸的α-氧化(α- oxidation)3.脂肪酸的β-氧化(β- oxidation)4.脂肪酸的ω-氧化(ω- oxidation)5.乙醛酸轮回(glyoxylate cycle)6.柠檬酸穿梭(citriate shuttle)7.乙酰CoA 羧化酶系(acetyl-CoA carnoxylase)8.脂肪酸合成酶系统(fatty acid synthase system)第八章含氮化合物代谢1.蛋白酶(Proteinase)2.肽酶(Peptidase)3.氮平衡(Nitrogen balance)4.生物固氮(Biological nitrogen fixation)5.硝酸还原传染感动(Nitrate reduction)6.氨的同化(Incorporation of ammonium ions into organic molecules)7.转氨传染感动(Transamination)8.尿素轮回(Urea cycle)9.生糖氨基酸(Glucogenic amino acid)10.生酮氨基酸(Ketogenic amino acid)11.核酸酶(Nuclease)12.限制性核酸内切酶(Restriction endonuclease)13.氨基蝶呤(Aminopterin)14.一碳单位(One carbon unit)第九章核酸的生物合成1.半保存复制(semiconservative replication)2.不合错误称转录(asymmetric trancription)3.逆转录(reverse transcription)4.冈崎片段(Okazaki fragment)5.复制叉(replication fork)6.领头链(leading strand)7.随后链(lagging strand)8.有意义链(sense strand)9.恢复生(photoreactivation)10.重组修复(recombination repair)11.内含子(intron)12.外显子(exon)13.基因载体(genonic vector)14.质粒(plasmid)第十一章代谢调节1.引导酶(Inducible enzyme)2.标兵酶(Pacemaker enzyme)3.把持子(Operon)4.衰减子(Attenuator)5.隔断物(Repressor)6.辅隔断物(Corepressor)7.降解物基因活化蛋白(Catabolic gene activator protein)8.腺苷酸环化酶(Adenylate cyclase)9.共价修饰(Covalent modification)10.级联系统(Cascade system)11.反应抑制(Feedback inhibition)12.交叉调节(Cross regulation)13.前馈激活(Feedforward activation)14.钙调蛋白(Calmodulin)第十二章蛋白质的生物合成1.暗码子(codon)2.反义暗码子(synonymous codon)3.反暗码子(anticodon)4.变偶假说(wobble hypothesis)5.移码突变(frameshift mutant)6.氨基酸同功受体(isoacceptor)7.反义RNA(antisense RNA)8.旗子灯号肽(signal peptide)9.简并暗码(degenerate code)10.核糖体(ribosome)11.多核糖体(poly some)12.氨酰基部位(aminoacyl site)13.肽酰基部位(peptidy site)14.肽基转移酶(peptidyl transferase) 15.氨酰- tRNA 合成酶(amino acy-tRNA synthetase)16.蛋白质折叠(protein folding)17.核蛋白体轮回(polyribosome)18.锌指(zine finger)19.亮氨酸拉链(leucine zipper)20.顺式传染感动元件(cis-acting element) 21.反式传染感动因子(trans-acting factor) 22.螺旋-环-螺旋(helix-loop-helix)第一章蛋白质1.两性离子:指在同一氨基酸分子上含有等量的正负两种电荷,又称兼性离子或偶极离子.2.必须氨基酸:指人体(和其它哺乳动物)自身不克不及合成,机体又必须,需要从饮食中获得的氨基酸.3. 氨基酸的等电点:指氨基酸的正离子浓度和负离子浓度相等时的pH 值,用符号pI暗示. 4.罕有氨基酸:指消掉于蛋白质中的20 种罕有氨基酸以外的其它罕有氨基酸,它们是正常氨基酸的衍生物.5.非蛋白质氨基酸:指不消掉于蛋白质分子中而以游离状态和结合状态消掉于生物体的各类组织和细胞的氨基酸.6.构型:指在立体异构体中不合错误称碳原子上相连的各原子或代替基团的空间排布.构型的修改陪伴着共价键的断裂和从新形成.7.蛋白质的一级机关:指蛋白质多肽链中氨基酸的排列次序,以及二硫键的地位.8.构象:指有机分子中,不修改共价键机关,仅单键周围的原子扭转所产生的原子的空间排布.一种构象修改成另一种构象时,不涉及共价键的断裂和从新形成.构象修改不会修改分子的光学活性.9.蛋白质的二级机关:指在蛋白质分子中的局部区域内,多肽链沿必定标的目的盘绕和折叠的办法.10.机关域:指蛋白质多肽链在二级机关的根本长进一步卷曲折叠成几个相对自力的近似球形的组装体.11.蛋白质的三级机关:指蛋白质在二级机关的根本上借助各类次级键卷曲折叠成特定的球状分子机关的构象.12.氢键:指蛋白质在二级机关的根本上借助各类次级键卷曲折叠成特定的球状分子机关的构象.13.蛋白质的四级机关:指多亚基蛋白质分子中各个具有三级机关的多肽链以适当办法聚合所呈现的三维机关.14.离子键:带相反电荷的基团之间的静电引力,也称为静电键或盐键.15.超二级机关:指蛋白质分子中相邻的二级机关单位组合在一路所形成的有规则的、在空间上能识此外二级机关组合体.16.疏水键:非极性分子之间的一种弱的、非共价的互相传染感动.如蛋白质分子中的疏水侧链避开水相而互相聚集而形成的传染感动力.17.范德华力:中性原子之间经由过程瞬间静电互相传染感动产生的一种弱的分子间的力.当两个原子之间的距离为它们的范德华半径之和时,范德华力最强.18.盐析:在蛋白质溶液中参加必定量的高浓度中性盐(如硫酸氨),使蛋白质消融度降低并沉淀析出的现象称为盐析.19.盐溶:在蛋白质溶液中参加少量中性盐使蛋白质消融度增加的现象.20.蛋白质的变性传染感动:蛋白质分子的自然构象遭到破坏导致其生物活性损掉落的现象.蛋白质在受到光照、热、有机溶剂以及一些变性剂的传染感动时,次级键遭到破坏导致自然构象的破坏,但其一级机关不产生修改.21.蛋白质的复性:指在必定前提下,变性的蛋白质分子恢复其原有的自然构象并恢复生物活性的现象.22.蛋白质的沉淀传染感动:在外界成分影响下,蛋白质分子掉落去水化膜或被中和其所带电荷,导致消融度降低从而使蛋白质变得不稳定而沉淀的现象称为蛋白质的沉淀传染感动. 23.凝胶电泳:以凝胶为介质,在电场传染感动下别离蛋白质或核酸等分子的别离纯化技能. 24.层析:按照在移动相(可所以气体或液体)和固定相(可所以液体或固体)之间的分拨比例将混淆成分分隔的技能.第二章核酸1. 单核苷酸(mononucleotide):核苷与磷酸缩合生成的磷酸酯称为单核苷酸.2. 磷酸二酯键(phosphodiester bonds):单核苷酸中,核苷的戊糖与磷酸的羟基之间形成的磷酸酯键.3. 不合错误称比率(dissymmetry ratio):不合生物的碱基组成由很大的差别,这可用不合错误称比率(A+T)/(G+C)示.4. 碱基互补规律(complementary base pairing):在形成双螺旋机关的过程中,因为各类碱基的大小与机关的不合,使得碱基之间的互补配对只能在GC(或CG)和AT(或TA)之间进行,这种碱基配对的规律就称为碱基配对规律(互补规律).5. 反暗码子(anticodon):在tRNA 链上有三个特定的碱基,组成一个暗码子,由这些反暗码子按碱基配对原则辨认mRNA 链上的暗码子.反暗码子与暗码子的标的目的相反.6. 顺反子(cistron):基因成效的单位;一段染色体,它是一种多肽链的暗码;一种机关基因.7. 核酸的变性、复性(denaturation、renaturation):当呈双螺旋机关的DNA 溶液迟缓加热时,个中的氢键便断开,双链DNA 便脱解为单链,这叫做核酸的“消融”或变性.在适合的温度下,别分开的两条DNA 链可以完整从新结合成和本来一样的双股螺旋.这个DNA 螺旋的重组过程称为“复性”.8. 退火(annealing):当将双股链呈别离状态的DNA 溶液迟缓冷却时,它们可以产生不合程度的从新结合而形成双链螺旋机关,这现象称为“退火”.9. 增色效应(hyper chromic effect):当DNA 从双螺旋机关变成单链的无规则卷曲状态时,它在260nm 处的吸收便增加,这叫“增色效应”.10. 减色效应(hypo chromic effect):DNA 在260nm 处的光密度比在DNA 分子中的各个碱基在260nm 处吸收的光密度的总和小得多(约少35%~40%), 这现象称为“减色效应”.11. 噬菌体(phage):一种病毒,它可破坏细菌,并在个中繁衍.也叫细菌的病毒.12. 发夹机关(hairpin structure):RNA 是单链线形分子,只有局部区域为双链机关.这些机关是因为RNA 单链分子经由过程自身回折使得互补的碱基对相遇,形成氢键结合而成的,称为发夹机关.13. DNA 的熔解温度(Tm 值):引起DNA 产生“熔解”的温度变更范围只不过几度,这个温度变更范围的中点称为熔解温度(Tm).14. 分子杂交(molecular hybridization):不合的DNA 片段之间,DNA 片段与RNA 片段之间,假如彼此间的核苷酸排列次序互补也可以复性,形成新的双螺旋机关.这种按照互补碱基配对而使不完整互补的两条多核苷酸互相结合的过程称为分子杂交.15. 环化核苷酸(cyclic nucleotide):单核苷酸中的磷酸基辨别与戊糖的3’-OH 及5’-OH形成酯键,这种磷酸内酯的机关称为环化核苷酸.第三章酶与辅酶1.米氏常数(Km 值):用Km值暗示,是酶的一个主要参数.Km 值是酶反应速度(V)达到最大反应速度(Vmax)一半时底物的浓度(单位M 或mM).米氏常数是酶的特色常数,只与酶的性质有关,不受底物浓度和酶浓度的影响.2.底物专一性:酶的专一性是指酶对底物及其催化反应的严格选择性.常日酶只能催化一种化学反应或一类相似的反应,不合的酶具有不合程度的专一性,酶的专一性可分为三种类型:绝对专一性、相对专一性、立体专一性.3.辅基:酶的辅因子或结合蛋白质的非蛋白部分,与酶或蛋白质结合得很是慎密,用透析法不克不及除去.4.单体酶:只有一条多肽链的酶称为单体酶,它们不克不及解离为更小的单位.分子量为13,000——35,000.5.寡聚酶:有几个或多个亚基组成的酶称为寡聚酶.寡聚酶中的亚基可所以相同的,也可所以不合的.亚基间以非共价键结合,随意马虎为酸碱,高浓度的盐或其它的变性剂别离.寡聚酶的分子量从35 000 到几百万.6.多酶系统:由几个酶彼此嵌合形成的复合体称为多酶系统.多酶复合体有利于细胞中一系列反应的中断进行,以提高酶的催化效率,同时便于机体对酶的调控.多酶复合体的分子量都在几百万以上.7.激活剂:但凡能提高酶活性的物质,都称激活剂,个中大部分是离子或简单的有机化合物. 8.抑制剂:能使酶的必须基团或酶活性部位中的基团的化学性质修改而降低酶的催化活性甚至使酶的催化活性完整损掉落的物质.9.变构酶:或称别构酶,是代谢过程中的关头酶,它的催化活性受其三维机关中的构象变更的调节.10.同工酶:是指有机体内能够催化同一种化学反应,但其酶蛋白本身的分子机关组成却有所不合的一组酶.11.引导酶:是指当细胞中参加特定引导物后引导产生的酶,它的含量在引导物存鄙人显著增高,这种引导物往往是该酶底物的相似物或底物本身.12.酶原:酶的无活性前体,常日在有限度的蛋白质水解传染感动后,修改成具有活性的酶. 13.酶的比活力:比活力是指每毫克蛋白质所具有的活力单位数,可以用下式暗示:活力单位数比活力= 蛋白质量(mg)14.活性中心:酶分子中直接与底物结合,并催化底物产生化学反应的部位,称为酶的活性中心.第四章生物氧化与氧化磷酸化1.生物氧化:生物体内有机物质氧化而产生大量能量的过程称为生物氧化.生物氧化在细胞内进行,氧化过程花费氧放出二氧化碳和水,所以有时也称之为“细胞呼吸”或“细胞氧化”.生物氧化包含:有机碳氧化变成CO2;底物氧化脱氢、氢及电子经由过程呼吸链传递、分子氧与传递的氢结成水;在有机物被氧化成CO2 和H2O的同时,释放的能量使ADP 修改成ATP. 2.呼吸链:有机物在生物体内氧化过程中所脱下的氢原子,经由一系列有严格排列次序的传递体组成的传递系统进行传递,最终与氧结合生成水,这样的电子或氢原子的传递系统称为呼吸链或电子传递链.电子在慢慢的传递过程中释放出能量被用于合成ATP,以作为生物体的能量来源.3.氧化磷酸化:在底物脱氢被氧化时,电子或氢原子在呼吸链上的传递过程中陪伴ADP 磷酸化生成ATP 的传染感动,称为氧化磷酸化.氧化磷酸化是生物体内的糖、脂肪、蛋白质氧化分化合成ATP 的主要办法.4、磷氧比:电子经由呼吸链的传递传染感动最终与氧结合生成水,在此过程中所释放的能量用于ADP 磷酸化生成ATP.经此过程花费一个原子的氧所要花费的无机磷酸的分子数(也是生成ATP 的分子数)称为磷氧比值(P/O).如NADH 的磷氧比值是3,FADH2 的磷氧比值是2. 5.底物程度磷酸化:在底物被氧化的过程中,底物分子内部能量从新分布产生高能磷酸键(或高能硫酯键),由此高能键供应能量使ADP(或GDP)磷酸化生成ATP(或GTP)的过程称为底物程度磷酸化.此过程与呼吸链的传染感动无关,以底物程度磷酸化办法只产生少量ATP.如在糖酵解(EMP)的过程中,3-磷酸甘油醛脱氢后产生的1,3-二磷酸甘油酸,在磷酸甘油激酶催化下形成ATP 的反应,以及在2-磷酸甘油酸脱水后产生的磷酸烯醇式丙酮酸,在丙酮酸激酶催化形成ATP 的反应均属底物程度的磷酸化反应.别的,在三羧酸环(TCA)中,也有一步反应属底物程度磷酸化反应,如α-酮戊二酸经氧化脱羧后生成高能化合物琥珀酰~CoA,其高能硫酯键在琥珀酰CoA 合成酶的催化下转移给GDP 生成GTP.然后在核苷二磷酸激酶传染感动下,GTP 又将末尾的高能磷酸根转给ADP 生成ATP.6.能荷:能荷是细胞中高能磷酸状态的一种数量上的衡量,能荷大小可以说明生物体中ATP-ADP-AMP 系统的能量状态.能荷=[ATP]+12 [ADP][ATP]+[ADP]+[AMP]第五章糖代谢1.糖异生:非糖物质(如丙酮酸乳酸甘油生糖氨基酸等)修改成葡萄糖的过程.2.Q 酶:Q 酶是介入支链淀粉合成的酶.成效是在直链淀粉分子上催化合成(α-1,6)糖苷键,形成支链淀粉.3.乳酸轮回乳:酸轮回是指肌肉缺氧时产生大量乳酸,大部分经血液运到肝脏,经由过程糖异生传染感动肝糖原或葡萄糖填补血糖,血糖可再被肌肉运用,这样形成的轮回称乳酸轮回. 4.发酵:厌氧有机体把糖酵解生成NADH 中的氢交给丙酮酸脱羧后的产品乙醛,使之生成乙醇的过程称之为酒精发酵.假如将氢交给病酮酸丙生成乳酸则叫乳酸发酵.5.变构调节:变构调节是指某些调节物能与酶的调节部位结合使酶分子的构象产生修改,从而修改酶的活性,称酶的变构调节.6.糖酵解途径:糖酵解途径指糖原或葡萄糖分子分化至生成丙酮酸的阶段,是体内糖代谢最主要途径.7.糖的有氧氧化:糖的有氧氧化指葡萄糖或糖原在有氧前提下氧化成水和二氧化碳的过程.是糖氧化的主要办法.8.肝糖原分化:肝糖原分化指肝糖原分化为葡萄糖的过程.9.磷酸戊糖途径:磷酸戊糖途径指机体某些组织(如肝、脂肪组织等)以6-磷酸葡萄糖为肇端物在6-磷酸葡萄糖脱氢酶催化下形成6-磷酸葡萄糖酸进而代谢生成磷酸戊糖为中心代谢物的过程,又称为磷酸已糖旁路.10.D-酶:一种糖苷转移酶,传染感动于α-1,4 糖苷键,将一个麦芽多糖的片段转移到葡萄糖、麦芽糖或其它多糖上.11.糖核苷酸:单糖与核苷酸经由过程磷酸酯键结合的化合物,是双糖和多糖合成中单糖的活化形式与供体.第六章脂类代谢1.必须脂肪酸:为人体成长所必须但有不克不及自身合成,必须从事物中摄取的脂肪酸.在脂肪中有三种脂肪酸是人体所必须的,即亚油酸,亚麻酸,花生四烯酸.2.α-氧化:α-氧化传染感动是以具有3-18碳原子的游离脂肪酸作为底物,有分子氧间接介入,经脂肪酸过氧化物酶催化传染感动,由α碳原子开始氧化,氧化产品是D-α-羟脂肪酸或少一个碳原子的脂肪酸.3. 脂肪酸的β-氧化:脂肪酸的β-氧化传染感动是脂肪酸在一系列酶的传染感动下,在α碳原子和β碳原子之间断裂,β碳原子氧化成羧基生成含2个碳原子的乙酰CoA 和比本来少2 个碳原子的脂肪酸.4. 脂肪酸ω-氧化:ω-氧化是C5、C6、C10、C12脂肪酸在远离羧基的烷基末尾碳原子被氧化成羟基,再进一步氧化而成为羧基,生成α,ω-二羧酸的过程.5. 乙醛酸轮回:一种被修改的柠檬酸轮回,在其异柠檬酸和苹果酸之间反应次序有修改,以及乙酸是用作能量和中心物的一个来源.某些植物和微生物体内有此轮回,他需要二分子乙酰辅酶A的介入;并导致一分子琥珀酸的合成.6. 柠檬酸穿梭:就是线粒体内的乙酰CoA 与草酰乙酸缩合成柠檬酸,然后经内膜上的三羧酸载体运至胞液中,在柠檬酸裂解酶催化下,需花费ATP 将柠檬酸裂解回草酰乙酸和,后者就可用于脂肪酸合成,而草酰乙酸经还原后再氧化脱羧成丙酮酸,丙酮酸经内膜载体运回线粒体,在丙酮酸羧化酶传染感动下从新生成草酰乙酸,这样就可又一次介入转运乙酰CoA 的轮回. 7.乙酰CoA 羧化酶系:大肠杆菌乙酰CoA 羧化酶含生物素羧化酶、生物素羧基载体蛋白(BCCP)和转羧基酶三种组份,它们合营传染感动催化乙酰CoA 的羧化反应,生成丙二酸单酰-CoA.8.脂肪酸合酶系统:脂肪酸合酶系统包含酰基载体蛋白(ACP)和6 种酶,它们辨别是:乙酰转酰酶;丙二酸单酰转酰酶;β-酮脂酰ACP 合成酶;β-酮脂酰ACP 还原酶;β-羟;脂酰ACP 脱水酶;烯脂酰ACP 还原酶.第八章含氮化合物代谢1.蛋白酶:以称肽链内切酶(Endopeptidase),传染感动于多肽链内部的肽键,生成较本来含氨基酸数少的肽段,不合来源的蛋白酶水解专一性不合.2.肽酶:只传染感动于多肽链的末尾,按照专一性不合,可在多肽的N-端或C-端水解下氨基酸,如氨肽酶、羧肽酶、二肽酶等.3.氮平衡:正常人摄入的氮与排出氮达到平衡时的状态,反应正常人的蛋白质代谢情况. 4.生物固氮:运用微生物中固氮酶的传染感动,在常温常压前提下将大气中的氮还原为氨的过程(N2 + 3H2→2 NH3).5.硝酸还原传染感动:在硝酸还原酶和亚硝酸还原酶的催化下,将硝态氮修改成氨态氮的过程,植物体内硝酸还原传染感动主要在叶和根进行.6.氨的同化:由生物固氮和硝酸还原传染感动产生的氨,进入生物体后被修改成含氮有机化合物的过程.7.转氨传染感动:在转氨酶的传染感动下,把一种氨基酸上的氨基转移到α-酮酸上,形成另一种氨基酸.8.尿素轮回:尿素轮回也称鸟氨酸轮回,是将含氮化合物分化产生的氨修改成尿素的过程,有解除氨危害的传染感动.9.生糖氨基酸:在分化过程中能修改成丙酮酸、α-酮戊二酸乙、琥珀酰辅酶A、延胡索酸和草酰乙酸的氨基酸称为生糖氨基酸.10.生酮氨基酸:在分化过程中能修改成乙酰辅酶A 和乙酰乙酰辅酶A 的氨基酸称为生酮氨基酸.11.核酸酶:传染感动于核酸分子中的磷酸二酯键的酶,分化产品为寡核苷酸或核苷酸,按照传染感动地位不合可分为核酸外切酶和核酸内切酶.12.限制性核酸内切酶:能传染感动于核酸分子内部,并对某些碱基次序有专一性的核酸内切酶,是基因工程中的主要器械酶.13.氨基蝶呤:对嘌呤核苷酸的生物合成起竞争性抑制作用的化合物,与四氢叶酸机关相似,又称氨基叶酸.14.一碳单位:仅含一个碳原子的基团如甲基(CH3-、亚甲基(CH2=)、次甲基(CH≡)、甲酰基(O=CH-)、亚氨甲基(HN=CH-)等,一碳单位可来源于甘氨酸、苏氨酸、丝氨酸、组氨酸等氨基酸,一碳单位的载体主假如四氢叶酸,成效是介入生物分子的修饰.第九章核酸的生物合成1.半保存复制:双链DNA 的复制办法,个中亲代链别离,每一子代DNA 分子由一条亲代链和一条新合成的链组成.2.不合错误称转录:转录常日只在DNA 的任一条链长进行,这称为不合错误称转录.3.逆转录:Temin 和Baltimore 各自发明在RNA 肿瘤病毒中含有RNA 指导的DNA 聚合酶,才证实产生逆向转录,即以RNA 为模板合成DNA.4.冈崎片段:一组短的DNA 片段,是在DNA 复制的肇端阶段产生的,随后又被连接酶连接形成较长的片段.在大肠杆菌成长时期,将细胞短时间地流露在氚标识表记标帜的胸腺嘧啶中,就可证实冈崎片段的消掉.冈崎片段的创造为DNA 复制的科恩伯格机理供应了按照.5.复制叉:复制DNA 分子的Y 形区域.在此区域产生链的别离及新链的合成.6.领头链:DNA 的双股链是反向平行的,一条链是5/→3/标的目的,另一条是3/→5/标的目的,上述的起点处合成的领头链,沿着亲代DNA 单链的3/→5/标的目的(亦即新合成的DNA沿5/→3/标的目的)不竭延长.所以领头链是中断的.7.随后链:已知的DNA 聚合酶不克不及催化DNA 链朝3/→5/标的目的延长,在两条亲代链起点的3/ 端一侧的DNA 链复制是不中断的,而分为多个片段,每段是朝5/→3/标的目的进行,所以随后链是不中断的.8.有意义链:即华森链,华森 克里格型DNA 中,在体内被转录的那股DNA 链.简写为Wstrand.9.恢复生:将受紫外线照射而引起损伤的细菌用可见光照射,大部分损伤细胞可以恢复,这种可见光引起的修复过程就是恢复生传染感动.10.重组修复:这个过程是前辈行复制,再进行修复,复制时,子代DNA 链损伤的对应部位消掉缺口,这可经由过程分子重组从完整的母链上,将一段响应的多核苷酸片段移至子链的缺口处,然后再合成一段多核昔酸键来填补母链的缺口,这个过程称为重组修复.11.内含子:真核生物的mRNA 前体中,除了贮存遗传序列外,还消掉非编码序列,称为内含子. 12.外显子:真核生物的mRNA 前体中,编码序列称为外显子.13.基因载体:外源DNA 片段(目的基因)要进入受体细胞,必须有一个适当的运载器械将带入细胞内,并载着外源DNA 一路进行复制与表达,这种运载器械称为载体.14.质粒:是一种在细菌染色体以外的遗传单元,一般由环形双链DNA 组成,其大小从1—200Kb.第十一章代谢调节1. 引导酶:因为引导物的消掉,使本来封锁的基因凋零,从而引起某些酶的合成数量显著增加,这样的酶称为引导酶2. 标兵酶:在多酶促系列反应中,受控制的部位常日是系列反应开首的酶,这个酶一般是变构酶,也称标兵酶.3. 把持子:在转录程度上控制基因表达的折衷单位,包含启动子(P)、把持基因(O)和在成效上相关的几个机关基因.4. 衰减子:位于机关基因上游前导区调节基因表达的成效单位,前导区转录的前导RNA经由过程构象变更终止或减弱转录.5. 隔断物:由调节基因产生的一种变构蛋白,当它与把持基因结应时,能够抑制转录的进行.6. 辅隔断物:能够与掉落活的阻碣蛋白结合,并恢复隔断蛋白与把持基因结合才能的物质.辅隔断物一般是酶反应的产品.7. 降解物基因活化蛋白:由调节基因产生的一种cAMP 受体蛋白,当它与cAMP 结应时被激活,并结合到启动子上促进转录进行.是一种正调节传染感动.8. 腺苷酸环化酶:催化ATP 焦磷酸裂解产生环腺苷酸(cAMP)的酶.9. 共价修饰:某种小分子基团可以共价结合到被修饰酶的特定氨基酸残基上,引起酶分子构象变更,从而调节代谢的标的目的和速度.10. 级联系统:在连锁代谢反应中一个酶被激活后,中断地产生其它酶被激活,导致原始调节旗子灯号的逐级缩小,这样的连锁代谢反应系统称为级联系统.11. 反应抑制:在代谢反应中,反应产品对反应过程中起传染感动的酶产生的抑制作用.12. 交叉调节:代谢产品不但对本身的反应过程有反应抑制作用,并且可以控制另一代谢物在不合途径中的合成.13. 前馈激活:在反应序列中,前身物质对后面的酶起激活传染感动,使反应向提高行.14. 钙调蛋白:一种依靠于钙的蛋白激酶,酶蛋白与钙结合引起酶分子构象变更,调解酶的活性.如磷酸化酶激酶是一种依靠于钙的蛋白激酶.第十二章蛋白质的生物合成。

高等教学大一英语下PPT课件:Nucleic Acid Structure and Function

高等教学大一英语下PPT课件:Nucleic Acid Structure and Function
4
5
✓ Features of double helix(1): 1.Two antiparallel helical DNA chains wound around
the same axis to form a right handed double helix, diameter is 2nm。 2.The hydrophilic backbones of alternating deoxyribose and phosphate groups are on the outside of the double helix, facing the surrounding water. 3.The purine and pyrimidine bases of both strands are stacked inside the double helix, with their hydrophobic and nearly planar ring structures very close together and perpendicular to the long axis.
gene expression or in genetic recombination.
12
13
✓Triplex DNA
• The triplexes are most stable at low pH. • The triplexes also form most readily within long
① 73~93 nucleotides single strand RNA
② 30 % of bases is invariable→ related to fundamental fun

生物科学专业英语第一课

生物科学专业英语第一课

Organelles are suspended within it, supported by the filamentous network of the cytoskeleton.
Dissolved in the cytoplasmic fluid are nutrients, ions, soluble proteins, and other materials needed for cell functioning.
found in the flagella and microtubules of eukaryotic cells and possessing ATPase activity.
Myosin 肌球蛋白 A protein that, with actin, constitutes the
principal element of the contractile apparatus of muscle.
Nuclear envelope 核膜,核被膜 A double membrane (two lipid bilayers
and associated proteins) that is the outermost portion of a cell nucleous. Nucleoid 核质体 The DNA-containing area of a prokaryote cell, analogous to eukaryote nucleus but not membrane bounded.
Stroma 子座,基质
Region within a chloroplast that has no chlorophyll.
Plastid 质体

Digital watermarking of chemical structure sets

Digital watermarking of chemical structure sets

Digital Watermarking of Chemical Structure SetsJoachim J.Eggers,W.-D.Ihlenfeldt,and Bernd GirodTelecommunications Laboratory,University of Erlangen-NurembergCauerstr.7/NT,91058Erlangen,Germany,eggers@LNT.deComputer Chemistry Center,University of Erlangen-NurembergN¨a gelsbachstr.25,91052Erlangen,Germany,wdi@ccc.chemie.uni-erlangen.de Information Systems Laboratory,Stanford UniversityStanford,CA94305-9510,USA,girod@ 4th Information Hiding Workshop Pittsburgh,PA,USA 25-27April,2001Abstract.The information about3D atomic coordinates of chemical structuresis valuable knowledge in many respect.For large sets of different structures,thecomputation or measurement of these coordinates is an expensive process.There-fore,the originator of such a data set is interested in enforcing his intellectualproperty right.In this paper,a method for copyright protection of chemical struc-ture sets based on digital watermarking is proposed.A complete watermarkingsystem including synchronization of the watermark detector and verification ofthe decoded watermark message is presented.The basic embedding scheme,de-noted SCS(Scalar Costa Scheme)watermarking,is based on considering water-marking as a communications problem with side information at the encoder.1IntroductionChemical structures are inherently three-dimensional,although most structure databases store them only asflat graphs.For many scientific studies,for example the development of drugs,the3-D structure is a major factor determining the application potential of a compound.It is possible to determine3-D atomic coordinates by experimental tech-niques,but this is very expensive.As an alternative,computational methods of various precision levels exist which take a structure graph or very rough3-D structure approx-imation as input and compute3-D atomic coordinates.For large datasets containing hundreds of thousands of molecules,quantum-chemical or fully optimizing force-field methods are not usable because they are too computationally expensive.Expensive opti-mizations can largely be avoided by model builders which employ complex rule-driven heuristics.The development of such programs is difficult,and represents a significant investment.Consequently,these programs are expensive when bought commercially, and coordinate sets,which are needed to isolate functional principles common among compounds with similar biological activity,represent a tangible value,even if the un-derlying structures are in the public domain.Due to the value of computed structure data,the originator is interested in enforcing the copyright of the data.Thus,robust labeling and identification of structure data is desired.Here,digital watermarking of the molecule structure data is investigated as one method for such labeling and identifi-cation.The intellectual property of the data set resides only in the atomic coordinates. Taking into account the limited precision of the model builder,a variation of the co-ordinates is acceptable and can be used for watermarking purposes.Given the smallsize of typical records for one structure,it is certainly not possible to robustly mark every record,but this is not necessary.We are mainly interested in identifying the ori-gin of large data sets,e.g.,including100,000-200,000structures.Resistance against tampering by adding small amounts of random jitter to the coordinates,in addition to resistance against rotations and translations,is desirable.A more comprehensive list of possible attacks is given in Section4.1.Digital Watermarking has been investigated intensively during the last years in the context of multimedia data,e.g.,audio,image or video data.Most blind watermarking techniques,where the watermark detector has no access to the original data,are based on spread-spectrum techniques,but recently much more powerful techniques have been proposed.One such method is called SCS(Scalar Costa Scheme)watermarking.SCS watermarking is appropriate for many different data characteristics,and thus is used here for embedding watermarks into the molecule data.In Section2,the basic principles and design criteria for SCS watermarking are re-viewed.Next,the problem of detecting the existence of a SCS watermark is discussed in Section3.In Section4,the specific system design for SCS watermark embedding into and detection from the chemical structure data is described.The performance of the proposed scheme is investigated experimentally,and simulation results are presented in Section5.2SCS WatermarkingWe consider digital watermarking as a communication problem.The watermark en-coder derives from the watermark message(sometimes also called“payload”)and the host data an appropriate watermark sequence which is added to the host data to produce the watermarked data.must be chosen such that the distortion betweenand is negligible.Next,an attacker might modify the watermarked data into data to impair watermark communication.The attack is only constrained with respect to the distortion between and.Finally,the decoder determines from the received dataan estimate of the embedded watermark message.The encoder and decoder must be designed such that with high probability.In blind watermarking schemes, the host data are not available to the decoder.The codebook used by the watermark encoder and decoder is randomized dependent on a key to achieve secrecy of wa-termark ually,a key sequence is derived from to enable secure watermark embedding for each host data element.Here,,,,and are vectors,and ,,,and refer to their respective th elements.Fig.1depicts a block diagram of blind watermark communication,where an attack by additive white Gaussian noise(AWGN)is assumed.The depicted scenario can be considered communication with side information about the host signal at the encoder. For this scenario,Costa[3]showed theoretically that for a Gaussian host signal of power,a watermark signal of power,and AWGN of power the maximum rate of reliable communication(capacity)is,independent of .The result is surprising since it shows that the host signal need not be considered as interference at the decoder although the decoder does not know.IIcheme)[4].Note that SCS is very similar to Costa’s original scheme,except for the suboptimal scalar quanitzer.Thewatermark message is encoded into a sequence of watermark letters,where in case of binary SCS.Note that this encoding process is usually divided into three steps.First,is represented by a vector with binary elements.Second,is encoded into by a binary error correcting code.Finally,is mapped on by selection or repetition of single coded bits so that each of the watermark letters can be embedded into the corresponding host element.The embedding rule for the th element is given by(1) where denotes scalar uniform quantization with step size,and is the er-ror of subtractive dithered quantization.The key is a pseudo-random sequence with .The upper plot of Fig.2depicts one period of the PDF of the sent elements conditioned on the sent watermark letter and.The described embedding scheme depends on two parameters:the quantizer step size and the scale factor. Both parameters can be jointly optimized to achieve a good trade-off between embed-ding distortion and detection reliability for a given noise variance of an AWGN attack. Optimal values for and are given in[4].In general,if accurate statistical models of the host data are unavailable,and a MSE distortion measure is used,and can be designed for an AWGN attack with a specific watermark-to-noise power ratio(WNR). Note that this heuristic is only useful if a potential attacker does not have an accurate model for the host signal either.At the decoder,the received data is demodulated to obtain the data.The demod-ulation rule for the th element is(2)where.should be close to zero if was sent,and close to for .The lower plot in Fig.2shows the PDF of the demodulated elements afterIIIp (s |d )sp (y |d )yFig.2.One period of the PDFs of the sent and the received signal for binary SCS (=1,WNR dB,,).The filled areas represent the probability of detection errors assuming was sent.The dotted line in the lower plot depicts the PDF when detecting with a wrong key .AWGN attack conditioned on the sent watermark letter.can be computed numerically as described in [4].In case of using an incorrect key at the receiver,the distribution of will be uniform for any possible .This is indicated by the dotted line in the lower plot of Fig.2.The performance of SCS watermarking is discussed in detail in [4,5].It can be shown that for a large range of different WNRs SCS watermarking is superior to com-mon blind spread-spectrum watermarking schemes since spread-spectrum watermark-ing suffers from large host signal interference.Note that the resiliency of SCS against AWGN attacks is independent from the host distribution.This property is particularly important for the application at hand,since the molecule coordinates of chemical struc-tures do not have a smooth distribution,e.g.,Gaussian or Laplacian,which is usually assumed in the design of detectors for spread-spectrum watermarks.It was also shown that at low watermarking rates,Spread Transform (ST)SCS watermarking is superior to SCS watermarking with simple repetition coding [5].ST watermarking was originally proposed by Chen and Wornell [2]to improve binary dither modulation watermarking.In ST watermarking,the watermark is not directly embedded into the host signal ,but into the projection of onto a random sequence of length .Any noise orthogo-nal to the spreading vector does not impair watermark detection.Thus,an attacker,not knowing the exact spreading direction ,has to introduce much larger distortions to im-pair an ST-SCS watermark than a simple SCS watermark.For AWGN attack and MSE distortion measurements,doubling the spreading length gives an additional power ad-vantage of 3dB for the ST-SCS watermark.Of course,this gain in detection reliability comes with a decrease of the watermark rate.In general,a spread transform of lengthIVrequires-times more data elements for watermark embedding.The optimal spreadinglength depends on the strength of attacks to be survived.3Verification of Decoded Watermark InformationSo far,watermarking was considered as a communication problem where at the water-mark decoder a watermark message is received assuming that a watermark is embed-ded with the key.However,in many watermarking applications the detector has todecide whether a watermark with key is embedded in the received data at all.Note that this problem differs somewhat from the communication problem.For SCS watermarking we do not distinguish between the following cases:–receiving non-watermarked data,–receiving data that is watermarked with a different watermarking technique,–receiving data being SCS-watermarked with a different key than the key.This is justified by the host signal independent nature of SCS watermark detection and the use of a key sequence with being uniformly distributed in.Subsequently,we only distinguish between watermark detection from data watermarked with keyand from data not watermarked with key.We assume that SCS watermarking was designed to communicate the message asreliably as possible via the watermarking channel.However,trying to detect an SCS wa-termark using a wrong key,leads to demodulated data that is uniformly distributed within as indicated by the dotted line in Fig.2.Thus,the decoded water-mark message will be a random bit sequence with. The problem of deciding whether is a valid watermark message or just a random bitsequence can be formulated as a hypothesis test between–hypothesis:no watermark message is embedded in with key,and–hypothesis:the watermark message is embedded in.In general,both hypotheses cannot be separated perfectly.Thus,we have to trade offthe probability of accepting when is true(false positive)and the probability of accepting when is true(false negative).Here,we devote a sub-vector of length of the watermark message for ver-ifying the validity of a received watermark message.We compare two methods to decide between and using the verification bit vector.In ourfirst approach, called method A,is equal to thefirst bits of and error correction coding of is such that thefirst bits of the coded watermark message are independent from the remaining watermark message bits.When detecting an SCS watermark letter from a data element where the embedded letter is one of the coded verification bits,the probabilities for receiving a demodulated value depending on hypothesis or are given asLet denote the index set of all data elements with embedded coded verification bits. Due to the independent identically distributed key sequence,the respective probabil-ities for detection from all data elements with index are given by(5)(6) Applying Bayes’solution to the hypothesis test with equal a priori probabilities and equal costs for both hypotheses,is accepted if4.1Attacks on Chemical Structure DataThe type of attacks which can be envisioned for structure data sets is notably different from those applicable to audio-visual data and similar,classical areas of digital wa-termarking.First of all,raw watermarked structures can again be subjected to various different energy minimization procedures,including the algorithm which was initially used to generate the data,essentially re-computing the protected information.Protection against this type of attack is not possible.The initial information about the structural identity needs to be contained in the datafile and can be used as basis for any further computation.However,we assume that no unlicensed copies of the software used to generate the original protected data are in circulation.Further,the computation time for larger datasets is often significant.Depending on the type of algorithm used and the size of the dataset,it can be up to several CPU months.Thus,simple re-generation of the data is often not a feasible approach.Attacks to remove or dilute the watermark are then limited to a small set of general,computationally inexpensive operations.These include:–Removal of data from the original dataset,or injection of additional structures that are not watermarked,but possess coordinates from other,unmarked or differently marked sources.–Re-ordering of the individual records in the dataset.–Re-ordering of atoms and bonds in the structure records.–Global3-D transforms.Rotating or shifting the structures in3-D space does not change their usability,since the intermolecular distances,angles and torsions define the characteristics of a molecule,not its orientation in3-D space.–Variation of structure notation.In some cases,structural features can be represented by different notational conventions without changing the identity of the structure.For instance,in a common format aromatic systems are represented as Kekul´e sys-tems.The sequence of single and double bonds can be re-arranged without chang-ing the structure.These are comparatively simple operations,and all identification algorithms which use the structure as access key or generate canonic orderings of atoms will have to cope with this variability.–Removal of atoms from structures.This operation is clearly a major modification of the structure,and the only case where the data retains at least a part of its usefulness is the global removal of hydrogen atoms.4.2Initial Considerations for the System DesignIn general,a single structure does not contain enough data for an entire watermark message.Thus,the watermark message is distributed over several molecule structures, and watermark detection is only possible when several,perhaps modified,structures are available.The illegal use of single molecules cannot be proven,however,heavy illegal use of a large amount of the structure data should be detectable.The watermark detector must have some information about the exact location of embedded watermark bits even after data re-ordering attacks as mentioned above.This problem is related to the well-known synchronization problem of watermark detectors.VIIOur system design is such that perfect synchronization of the watermark detector is always ensured.The required algorithms are described below.The watermark message is represented by a vector of binary elements().can include different information,e.g.,an identifier of the copyright holder, a verification bit vector,and/or the date of computation of the molecule data.Fur-ther,the watermark embedding is dependent on a key,which is only known to the copyright holder and perhaps a trusted third party.The detection reliability may be improved by error correction codes.Thus,is en-coded into a binary vector of length.The influence of different error correction codes is investigated experimentally in Section5.Note that only some of the en-coded watermark bits in will be embedded into one molecule.Thus,decoding of must be possible even if some of the encoded bits are not available from the data given at the watermark detector.To solve this problem,as much watermark information as possible is collected from each molecule,and this information has to be combined correctly to decode the watermark message.4.3Structure Normalization and Hash ComputationIn an attempt to embed or detect a watermark,the structure needs to be normalized and identified.Only parts of the encoded watermark message are embedded into each single structure(see Section4.4).The specific message part to be embedded is determined by a hash code generated from the structure at hand.The hash code depends solely on the structure description.Thus,the watermark encoding and decoding process is independent of the order of structures in a large dataset.Also,insertion of unmarked records and deletion of marked records can be accepted to a comparatively high extent, since the additional or missing structures will only reduce the detection reliability,but no synchronization problems will ensue.Hash codes for chemical structures being invariant to the operations mentioned in Section4.1,exhibiting good randomness and negligible correlation in all bits,and do not generate hash code collisions for closely related structures,are not trivial,and have been studied extensively in chemoinformatics.We are using a state-of-the-art64-bit hash code[7]which has some proven advantages over earlier attempts.Since the hash code depends on the hydrogen addition status,we always add a standard hydrogen set to the structure before computing the hash.If the hydrogen atoms are present,this is a null operation,otherwise new atoms are added with undefined or at least unmarked coordinates.These atoms,if added,will reduce detection reliability,but will ensure that the original structure hash code is regenerated and the original canonical atom order can be obtained.Once the encoded message part has been identified with help of the hash code,the next preparation step is to move the structure to an unique3-D orientation and generate a canonical ordering of the atoms.The canonical order of the atoms is determined by a symmetry-breaking sphere-expansion process.We use an adapted version of the Unique SMILES algorithm by Weininger et.al.[8].This method is fast and exact for practically relevant structures.A few errors in re-establishing the precise atom order in highly symmetrical structures can be tolerated.We have enhanced the original algorithm toVIIIinclude hydrogen atoms(whose coordinates are important)and to break some additional symmetric cases in a deterministic fashion.4.4Watermark Embedding into a Single Molecule StructureThe embedding of encoded watermark bits from in the th structure is consid-ered.A block diagram of the embedding scheme is depicted in Fig.3.First,a canonic representation of the structure is obtained as described above.Next,the host data vec-tor is extracted(see also Section4.7).Here,it is assumed that elements are extracted from the structure,and the elements of are scaled such that the water-mark can be embedded with a variance.,a pseudo-random key vector with elements is required to hide the embedded watermark to malicious attackers.The pseudo-random vectors,,and must be perfectly reconstructible at the watermark detector and should not be known to unauthorized parties.Thus,the64bit hash value of the structure is taken as seed for a cryptographic secure random number generator which is used to compute,andfrom this hash value dependent on the key of the copyright holder.In the current implementation a pseudo-random number generator based on DES encryption is used.The watermark letters are embedded into and the watermarked vector is obtained.Finally,the inverse spread transform is applied to obtain which is combined with the unmodified structure information to synthesize the watermarked molecule structure(10)These probabilities are collected in the vector.The required conditional proba-bilities and depend on the used watermarking scheme,but also on possible attacks.We designed our scheme for an AWGN attack of a certain noise variance,e.g.WNR dB.This heuristic is useful since up to now little about possible statistical attacks on the watermarked structure data is known.The vectors and are the result of the detection process for the molecule.4.6Joint Watermark Detection from Several MoleculesAssume that structures are received,with.The vectors and of length are derived as described above from each received structure.Further, we assume that the attack on the embedded watermark is memoryless,that is all demod-ulated watermark letters are statistically independent.Thus,the probabilitythat the th coded watermark bit is1,is given by(11)XThe quality of a3-D structure dataset is measured by the energy(enthalpy of for-mation)of the conformers.Good coordinate generators will display a good balance be-tween execution speed and conformer energy.The quality of a dataset can be checkedby comparing the energy of the dataset structures to the energies obtained by using amore computationally expensive method to optimize the3-D structures.Our primary test dataset was generated by the3-D coordinate generator CORINA[6]which is veryfast and employs only a low level of theory(rule-based initial coordinate generationand pseudo-forcefield energies for optimization).Since the testing of the acceptability of the watermarked structures requires a better level of theory than the original gen-erator,we used the AM1implementation of the V AMP package[1]which has been successfully used to process the same data set in a very expensive computational effort.The acceptable level of distortion of the original coordinates depends on the preci-sion of the original results.For CORINA coordinates,a change of2-3%of the structureenergy is tolerable.For an AM1data set,less than1%would be acceptable.For the CORINA dataset,we measured the compound energy before and after watermarkingby performing a single-point AM1computation which will not change and re-optimize the coordinates but only compute the energy of that coordinate set.In the current im-plementation,the modification of the atomic coordinates does not take into accountthe atomic environment at all.However,not all distortions of the structures lead to the same energy change.Thus,improved allocation of the watermark power to differentcoordinates should be investigated in the future.5Performance EvaluationThe described system for watermarking of chemical structure sets involves many differ-ent parameters,like the error correction code,the spread transform length,the water-mark message length,the parameter and for SCS watermarking,and the choiceof verification bits.A detailed discussion of all parameters is beyond the scope of this paper.Here,we consider a watermark message offixed length bits(equiv-alent to12ASCII characters).The parameter and were designed for an AWGNattack with WNR dB.Thus,the SCS scheme was optimized for an AWGN at-tack where the power of additive noise is twice as large as the watermark power .Most of the experiments discussed below were performed on synthetic data since many simulations are required to measure low error probabilities.Nevertheless,some simulations results for chemical structure sets will be discussed,too.5.1Required Amount of Received Data ElementsThe watermark bit error probability was investigated experimentally for different amounts of received data elements.In practice,reliable detection from as few data elements as possible is desired.We restrict the discussion to an AWGN attack with WNR dB.Rate1/3convolutional codes(CC)with memory andwere used to encode all watermark bits into the coded bit vector with length and,respectively.XIIrandom data elements were chosen as host signal.This data was transformed into the spread transform domain where the projected data has el-ements.Note that for.For each element in,one bit of the en-coded watermark message was randomly selected and embedded.Simulations with 20000random watermark messages were performed so that bit error probabilities about can be measured reliably.Fig.5shows the measured bit error probabilities for CC with and,and spread transform lengths and. Obviously,the scheme with and performed best.Only2000data elements are required to achieve.This corresponds to a watermark rate of aboutbit/element.About1000more data elements need to be received when using the less complex convolutional code with.Another500more data elements are required when leaving out the spread transform().Fig.5.Measured bit error probabilities for receiving96watermark message bits af-ter AWGN attack with WNR=-3.0dB. The watermark message was encoded with a rate1/3convolutional code with differ-ent memory length.Simulation results for spread transform lengths andare shown.Fig.6.False positive and false negative error probabilities for watermark verifica-tion.Two methods using15verification bits are compared.The watermarked data is at-tacked by AWGN with WNR dB.Note that the considered detection case is different from detection after a simple AWGN attack.The detection performance is impaired also by the randomness with which certain data elements are received.Simulation results show that lower error prob-abilities could be achieved when the number of embedding positions would be identical for all coded bits.However,in the application at hand,it is impossible to ensure that the watermark detector receives all watermarked data elements.5.2Verification of Decoded WatermarkTwo methods for verifying the validity of a received watermark message were proposed in Section3.Here,simulation results for both methods are compared.Fig.6shows the measured false positive and false negative probability for a verification bit vector of length.The watermark message was embedded with a rate CC with memory length.XIIIThe detection of200000random watermark messages was simulated and different amounts of received data was considered.The SCS parameter and channel noise werechosen as in the previous subsection.Hypothesis was valid in half of the cases,thus the error probabilities were estimated from100000decisions.For method B,a false positive probability can be expected.This value is verified bythe simulation results shown in Fig.6.The false negative error probability of method B depends on the bit error probability which decreases for an increased number of received data elements.Fig.6shows that of method B also decreases slowly withthe number of received data elements.Contrary,for method A the error probabilities and are almost identical when receiving few data elements.For an increasednumber of received data elements more false negative errors than false positive errors occur.Method B is superior with respect to the false positive rate when detecting from few data elements.However,the overall error probability is lower for method A.Notethat for method A it also possible to achieve lower false positive rates by increasing the decision threshold which was0.5in(7).Of course higher false negative rates have to be accepted in such a case.5.3Perfect Attack on Parts of the DataIt is likely that an attacker has perfect knowledge about the original data for some part of the data set.In this case,the attacker simply replaces the watermarked data by theoriginal data,thus erasing the watermark from the specific data elements.In general we found that reliable watermark detection can be achieved even for a substitution of80%of the watermarked data elements.However,this is only possible when many data elements are available at the decoder.Thus,it is worth to select for the watermarking process only data elements which are unlikely to be known by an attacker.The disturb-ing influence of data replacement can be prevented this way.5.4Simulations with Example Molecule DataPreliminary experiments with example molecule data were conducted.The host vectorswere composed by all atom coordinates of one molecule structure.The coordinate values were scaled by a factor of1000such that a watermark of power can be embedded.For this setting the AM1energies in a200-structure test set were changedby less than0.3%on average,without producing outliers with unacceptable energies (more than1.5%energy increase,corresponding to unusable structures).25%of the structures were actually lower in AM1energy after watermarking,demonstrating the imperfectness of the CORINA optimizer.The watermark was detectable on this comparatively small dataset with near100%confidence even after performing the following set of operations:Delete10random structures,add10similar structures without a watermark,re-compute unmarked coor-dinates for10random molecules,shuffle the sequence by moving50random structures into different slots andfinally randomly rotate and translate all molecules.The algo-rithm proved to be very robust against this set of operations which we consider a typical smokescreen which could be applied by an attacker to conceal the origin of the data.XIV。

liquid–liquid phase separation

liquid–liquid phase separation

Biophysical Chemistry 109(2004)105–1120301-4622/04/$-see front matter ᮊ2003Elsevier B.V .All rights reserved.doi:10.1016/j.bpc.2003.10.021Cloud-point temperature and liquid–liquid phase separation ofsupersaturated lysozyme solutionJie Lu *,Keith Carpenter ,Rui-Jiang Li ,Xiu-Juan Wang ,Chi-Bun Ching a ,a a b bInstitute of Chemical and Engineering Sciences,Ayer Rajah Crescent 28,࠻02-08,Singapore 139959,Singapore aChemical and Process Engineering Center,National University of Singapore,Singapore 117576,SingaporebReceived 31July 2003;received in revised form 8October 2003;accepted 16October 2003AbstractThe detailed understanding of the structure of biological macromolecules reveals their functions,and is thus important in the design of new medicines and for engineering molecules with improved properties for industrial applications.Although techniques used for protein crystallization have been progressing greatly,protein crystallization may still be considered an art rather than a science,and successful crystallization remains largely empirical and operator-dependent.In this work,a microcalorimetric technique has been utilized to investigate liquid–liquid phase separation through measuring cloud-point temperature T for supersaturated lysozyme solution.The effects of cloud ionic strength and glycerol on the cloud-point temperature are studied in detail.Over the entire range of salt concentrations studied,the cloud-point temperature increases monotonically with the concentration of sodium chloride.When glycerol is added as additive,the solubility of lysozyme is increased,whereas the cloud-point temperature is decreased.ᮊ2003Elsevier B.V .All rights reserved.Keywords:Biocrystallization;Microcalorimetry;Cloud-point temperature;Liquid–liquid phase separation1.IntroductionKnowledge of detailed protein structure is essen-tial for protein engineering and the design of pharmaceuticals.Production of high-quality pro-tein crystals is required for molecular structure determination by X-ray crystallography.Although considerable effort has been made in recent years,obtaining such crystals is still difficult in general,and predicting the solution conditions where pro-*Corresponding author.Tel.:q 65-6874-4218;fax:q 65-6873-4805.E-mail address:lujie@.sg (J.Lu ).teins successfully crystallize remains a significant obstacle in the advancement of structural molecu-lar biology w 1x .The parameters affecting protein crystallization are typically reagent concentration,pH,tempera-ture,additive,etc.A phase diagram can provide the method for quantifying the influence of solu-tion parameters on the production of crystals w 2,3x .To characterize protein crystallization,it is neces-sary to first obtain detailed information on protein solution phase behavior and phase diagram.Recently physics shows that there is a direct relationship between colloidal interaction energy106J.Lu et al./Biophysical Chemistry109(2004)105–112and phase diagram.Gast and Lekkerkerker w4,5x have indicated that the range of attraction between colloid particles has a significant effect on the qualitative features of phase diagram.A similar relationship should hold for biomacromolecules, i.e.the corresponding interaction potentials govern the macromolecular distribution in solution,the shape of the phase diagram and the crystallization process w6x.Many macromolecular crystallizations appear to be driven by the strength of the attractive interactions,and occur in,or close to,attractive regimes w7,8x.Recent intensive investigation has revealed that protein or colloidal solution possesses a peculiar phase diagram,i.e.liquid–liquid phase separation and sol–gel transition exists in general in addition to crystallization w9,10x.The potential responsible for the liquid–liquid phase separation is a rather short range,possibly van der Waals,attractive potential w11,12x.The measurement of cloud-point temperature T can provide useful informationcloudon the net attractive interaction between protein molecules,namely,the higher the cloud-point tem-perature,the greater the net attractive interaction. Herein Taratuta et al.w13x studied the effects of salts and pH on the cloud-point temperature of lysozyme.Broide et al.w14x subsequently meas-ured the cloud-point temperature and crystalliza-tion temperature for lysozyme as a function of salt type and concentration.From these works the cloud-point temperature was found to be typically 15–458C below the crystallization temperature. Furthermore,Muschol and Rosenberger w15x deter-mined the metastable coexistence curves for lyso-zyme through cloud-point measurements,and suggested a systematic approach to promote pro-tein crystallization.In general,an effective way to determine the strength of protein interactions is to study temperature-induced phase transitions that occur in concentrated protein solutions.Liquid–liquid phase separation can be divided into two stages w11x:(1)the local separation stage at which the separation proceeds in small regions and local equilibrium is achieved rapidly;and(2) the coarsening stage at which condensation of these small domains proceeds slowly to reduce the loss of interface free energy w16x.The coexisting liquid phases both remain supersaturated but differ widely in protein concentration.The effect of a metastable liquid–liquid phase separation on crystallization remains ambiguous w17x.Molecular dynamics simulations and analyt-ical theory predict that the phase separation will affect the kinetics and the mechanisms of protein crystal nucleation w18x.tenWolde and Frenkel w19x have demonstrated that the free energy barrier for crystal nucleation is remarkably reduced at the critical point of liquid–liquid phase separation, thus in general,after liquid–liquid phase separa-tion,crystallization occurs much more rapidly than in the initial solution,which is typically too rapid for the growth of single crystal with low defect densities w15x.The determination of the location of liquid–liquid phase separation curve is thus crucial for efficiently identifying the optimum solution conditions for growing protein crystals. Microcalorimetry has the potential to be a useful tool for determining:(1)the metastable-labile zone boundary;(2)the temperature-dependence of pro-tein solubility in a given solvent;and(3)the crystal-growth rates as a function of supersatura-tion w20x.Microcalorimeters can detect a power signal as low as a few microwatts whereas standard calorimeters detect signals in the milliwatt range. Because of this greater sensitivity,samples with small heat effects can be analyzed.In addition, microcalorimetry has the advantage of being fast, non-destructive to the protein and requiring a relatively small amount of material.The present work is concerned with the analysis of the transient heat signal from microcalorimeter to yield liquid–liquid phase separation information for lysozyme solutions at pH4.8.To further examine the role of salt and additive on interprotein interactions, cloud-point temperature T has been determinedcloudexperimentally as a function of the concentrations of salt,protein and glycerol.2.Materials and methods2.1.MaterialsSix times crystallized lysozyme was purchased from Seikagaku Kogyo,and used without further107J.Lu et al./Biophysical Chemistry 109(2004)105–112purification.All other chemicals used were of reagent grade,from Sigma Chemical Co.2.2.Preparation of solutionsSodium acetate buffer (0.1M )at pH 4.8was prepared with ultrafiltered,deionized water.Sodi-um azide,at a concentration of 0.05%(w y v ),was added to the buffer solution as an antimicrobial agent.Protein stock solution was prepared by dissolving protein powder into buffer.To remove undissolved particles,the solution was centrifuged in a Sigma centrifuge at 12000rev.y min for 5–10min,then filtered through 0.22-m m filters (Mil-lex-VV )into a clean sample vial and stored at 48C for further experiments.The concentration of protein solution was determined by measuring the absorbance at 280nm of UV spectroscopy (Shi-madzu UV-2550),with an extinction coefficient of 2.64ml y (mg cm )w 21x .Precipitant stock solution was prepared by dissolving the required amount of sodium chloride together with additive glycerol into buffer.The pH of solutions was measured by a digital pH meter (Mettler Toledo 320)and adjusted by the addition of small volumes of NaOH or HAc solution.2.3.Measurement of solubilitySolubility of lysozyme at various temperatures and precipitant y additive concentrations was meas-ured at pH 4.8in 0.1M acetate buffer.Solid–liquid equilibrium was approached through both crystallization and dissolution.Dissolving lasted 3days,while the period of crystallization was over 2weeks.The supernatant in equilibrium with a macroscopically observable solid was then filtered through 0.1-m m filters (Millex-VV ).The concen-tration of diluted supernatant was determined spec-troscopically and verified by refractive meter(Kruss)until refractive index remained unchanged ¨at equilibrium state.Solubility of each sample was measured in duplicate.2.4.Differential scanning microcalorimetry Calorimetric experiments were performed with a micro-differential scanning calorimeter with anultra sensitivity,micro-DSC III,from Setaram SA,France.The micro-DSC recorded heat flow in microwatts vs.temperature,thus can detect the heat associated with phase transition during a temperature scan.The sample made up of equal volumes of protein solution and precipitant solu-tion was filtered through 0.1-m m filters to remove dust particles further.To remove the dissolved air,the sample was placed under vacuum for 3min while stirring at 500rev.y min by a magnetic stirrer.The degassed sample was placed into the sample cell of 1.0ml,and a same concentration NaCl solution was placed into the reference cell.The solutions in the micro-DSC were then cooled at the rate of 0.28C y min.After every run,the cells were cleaned by sonicating for 10–15min in several solutions in the following order:deionized water,methanol,ethanol,acetone,1M KOH and finally copious amounts of deionized water.This protocol ensured that lysozyme was completely removed from the cells.The cells were then placed in a drying oven for several hours.The rubber gaskets were cleaned in a similar manner except acetone and 1M KOH were omitted and they were allowed to dry at low temperature.3.Results and discussionA typical micro-DSC scanning experiment is shown in Fig.1.The onset of the clouding phe-nomenon is very dramatic and easily detected.The sharp increase in the heat flow is indicative of a liquid–liquid phase separation process producing a latent heat.This is much consistent with many recent investigations of the liquid–liquid phase separation of lysozyme from solution w 22,23x .In fact,such a liquid–liquid phase separation is a phase transition with an associated latent heat of demixing.In this work,the cloud-point tempera-tures at a variety of lysozyme,NaCl and glycerol concentrations are determined by the micro-DSC at the scan rate of 128C y h.3.1.Effect of protein concentrationIn semilogarithmic Fig.2we plot the solid–liquid and liquid–liquid phase boundaries for lyso-108J.Lu et al./Biophysical Chemistry 109(2004)105–112Fig.1.Heat flow of a typical micro-DSC scan of lysozyme solution,50mg y ml,0.1M acetate buffer,pH 4.8,3%NaCl.The scan rate 128C y h is chosen referenced to the experimental results of Darcy and Wiencek w 23x .Note the large deflection in the curve at approximately 4.38C indicating a latent heat resulting from demixing (i.e.liquid–liquid phase separation )process.Fig.2.Cloud-point temperature and solubility determination for lysozyme in 0.1M acetate buffer,pH 4.8:solubility (5%NaCl )(s );T (5%NaCl,this work )(d );T (5%cloud cloud NaCl,the work of Darcy and Wiencek w 23x )(*);solubility (3%NaCl )(h );T (3%NaCl )(j ).cloud Fig.3.Cloud-point temperature determination for lysozyme as a function of the concentration of sodium chloride,50mg y ml,0.1M acetate buffer,pH 4.8.zyme in 0.1M acetate buffer,pH 4.8,for a range of protein concentrations.It is worth noting that,at 5%NaCl,our experimental data of T from cloud micro-DSC are quite consistent with those from laser light scattering and DSC by Darcy and Wiencek w 23x ,with difference averaging at approx-imately 0.88C.This figure demonstrates that liquid–liquid phase boundary is far below solid–liquid phase boundary,which implies that the liquid–liquid phase separation normally takes place in a highly metastable solution.In addition,cloud-point temperature T increases with the cloud concentration of protein.3.2.Effect of salt concentrationFig.3shows how cloud-point temperature changes as the concentration of NaCl is varied from 2.5to 7%(w y v ).The buffer is 0.1M acetate (pH 4.8);the protein concentration is fixed at 50mg y ml.Over the entire range of salt concentrations studied,the cloud-point temperature strongly depends on the ionic strength and increases monotonically with the concentration of NaCl.Crystallization is driven by the difference in chemical potential of the solute in solution and in the crystal.The driving force can be simplified as w 24xf sy Dm s kT ln C y C (1)Ž.eq109J.Lu et al./Biophysical Chemistry 109(2004)105–112Fig.4.The driving force required by liquid–liquid phase sep-aration as a function of the concentration of sodium chloride,50mg y ml lysozyme solution,0.1M acetate buffer,pH 4.8.In the same way,we plot the driving force,f ,required by liquid–liquid phase separation as a function of the concentration of sodium chloride in Fig.4.At the moderate concentration of sodium chloride,the driving force required by liquid–liquid phase separation is higher than that at low or high salt concentration.As shown in Fig.3,with NaCl concentration increasing,the cloud-point temperature increases,which is in accord with the results of Broide et al.w 14x and Grigsby et al.w 25x .It is known that protein interaction is the sum of different potentials like electrostatic,van der Waals,hydrophobic,hydration,etc.The liquid–liquid phase separation is driven by a net attraction between protein molecules,and the stronger the attraction,the higher the cloud-point temperature.Ionic strength is found to have an effect on the intermolecular forces:attractions increase with ionic strength,solubility decreases with ionic strength,resulting in the cloud-point temperature increases with ionic strength.It is worth noting that,the effect of ionic strength on cloud-point temperature depends strongly on the specific nature of the ions w 13x .Kosmotropic ions bind adjacent water molecules more strongly than water binds itself.When akosmotropic ion is introduced into water,the entro-py of the system decreases due to increased water structuring around the ion.In contrast,chaotropes bind adjacent water molecules less strongly than water binds itself.When a chaotrope is introduced into water,the entropy of the system increases because the water structuring around the ion is less than that in salt-free water.This classification is related to the size and charge of the ion.At high salt concentration ()0.3M ),the specific nature of the ions is much more important w 25x .The charges on a protein are due to discrete positively and negatively charged surface groups.In lysozyme,the average distance between thesecharges is approximately 10Aw 26x .As to the salt ˚NaCl used as precipitant,Na is weakly kosmo-q tropic and Cl is weakly chaotropic w 27x .At low y NaCl concentrations,as the concentration of NaCl increases,the repulsive electrostatic charge–charge interactions between protein molecules decrease because of screening,resulting in the increase of cloud-point temperature.While at high NaCl con-centrations,protein molecules experience an attrac-tion,in which differences can be attributed to repulsive hydration forces w 14,25x .That is,as the ionic strength increases,repulsive electrostatic or hydration forces decrease,protein molecules appear more and more attractive,leading to higher cloud-point temperature.At various salt concentra-tions,the predominant potentials reflecting the driving force for liquid–liquid phase separation are different.Fig.4shows that the driving force,f ,is parabolic with ionic strength,while Grigsby et al.w 25x have reported that f y kT is linear with ionic strength for monovalent salts.The possible reasons for that difference include,their model is based on a fixed protein concentration of 87mg y ml,which is higher than that used in our study,yet f y kT is probably dependent on protein concentration,besides the solutions at high protein and salt concentrations are far from ideal solutions.3.3.Effect of glycerolFig.5compares cloud-point temperature data for 50mg y ml lysozyme solutions in absence of glycerol and in presence of 5%glycerol,respec-110J.Lu et al./Biophysical Chemistry109(2004)105–112parison of cloud-point temperatures for lysozyme at different glycerol concentrations as a function of the con-centration of sodium chloride,50mg y ml,0.1M acetate buffer, pH4.8:0%glycerol(s);5%glycerol(j).Fig.6.Cloud-point temperatures for lysozyme at different glycerol concentrations,50mg y ml lysozyme,5%NaCl,0.1M acetate buffer,pH4.8.Fig.7.Cloud-point temperature and solubility determination for lysozyme at different concentrations of glycerol in0.1M acetate buffer,5%NaCl,pH4.8:solubility(0%glycerol)(s); T(0%glycerol)(d);solubility(5%glycerol)(h);cloudT(5%glycerol)(j).cloudtively.Fig.6shows the cloud-point temperature as a function of the concentration of glycerol.The cloud-point temperature is decreased as the addi-tion of glycerol.In semilogarithmic Fig.7we plot the solid–liquid and liquid–liquid phase boundaries at dif-ferent glycerol concentrations for lysozyme in0.1 M acetate buffer,5%NaCl,pH4.8,for a range of protein concentration.This figure demonstrates that liquid–liquid and solid–liquid phase bounda-ries in the presence of glycerol are below those in absence of glycerol,and the region for growing crystals is narrowed when glycerol is added. Glycerol has the property of stabilizing protein structure.As a result,if crystallization occurs over a long period of time,glycerol is a useful candidate to be part of the crystallization solvent and is often included for this purpose w28x.In addition,glycerol is found to have an effect on the intermolecular forces:repulsions increase with glycerol concentra-tion w29x.Our experiment results of solubility and cloud-point temperature can also confirm the finding.The increased repulsions induced by glycerol can be explained by a number of possible mecha-nisms,all of which require small changes in the protein or the solvent in its immediate vicinity.The addition of glycerol decreases the volume of protein core w30x,increases hydration and the size of hydration layer at the particle surface w31,32x. In this work,we confirm that glycerol shifts the solid–liquid and liquid–liquid phase boundaries. The effect of glycerol on the phase diagram strong-111 J.Lu et al./Biophysical Chemistry109(2004)105–112ly depends on its concentration and this canprovide opportunities for further tuning of nuclea-tion rates.4.ConclusionsGrowing evidence suggests protein crystalliza-tion can be understood in terms of an order ydisorder phase transition between weakly attractiveparticles.Control of these attractions is thus keyto growing crystals.The study of phase transitionsin concentrated protein solutions provides one witha simple means of assessing the effect of solutionconditions on the strength of protein interactions.The cloud-point temperature and solubility datapresented in this paper demonstrate that salt andglycerol have remarkable effects on phase transi-tions.The solid–liquid and liquid–liquid bounda-ries can be shifted to higher or lower temperaturesby varying ionic strength or adding additives.Ourinvestigation provides further information upon therole of glycerol used in protein crystallization.Glycerol can increase the solubility,and decreasethe cloud-point temperature,which is of benefit totuning nucleation and crystal growth.In continuingstudies,we will explore the effects of other kindsof additives like nonionic polymers on phasetransitions and nucleation rates.Much more theo-retical work will be done to fully interpret ourexperimental results.AcknowledgmentsThis work is supported by the grant from theNational Natural Science Foundation of China(No.20106010).The authors also thank Professor J.M.Wiencek(The University of Iowa)for kinddiscussion with us about the thermal phenomenaof liquid–liquid phase separation.Referencesw1x A.McPherson,Current approaches to macromolecular crystallization,Eur.J.Biochem.189(1990)1–23.w2x A.M.Kulkarni, C.F.Zukoski,Nanoparticle crystal nucleation:influence of solution conditions,Langmuir18(2002)3090–3099.w3x E.E.G.Saridakis,P.D.S.Stewart,L.F.Lloyd,et al., Phase diagram and dilution experiments in the crystal-lization of 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力学中二维曲线曲率半径的表达式研究

力学中二维曲线曲率半径的表达式研究

2021年 5月 Journal of Science of Teachers′College and University May 2021文章编号:1007-9831(2021)05-0039-05力学中二维曲线曲率半径的表达式研究邵云(南京晓庄学院 电子工程学院,江苏 南京 211171)摘要:介绍了力学中质点二维运动轨迹曲率半径的几种常见的计算公式,证明了它们之间的等价性.利用其中的平面自然坐标系下的公式d d sr j=较简便地推导出平面极坐标系中曲率半径的一般计算公式,据此推导出在通常的极坐标系(以焦点为极点)中圆锥曲线曲率半径的统一表达式,进而推导出在通常的直角坐标系(以中心或顶点为原点)中各圆锥曲线曲率半径的表达式. 关键词:极坐标系;直角坐标系;圆锥曲线;曲率半径中图分类号:O311.1 文献标识码:A doi:10.3969/j.issn.1007-9831.2021.05.008Research on the expression of curvature radius oftwo dimensional curve in mechanicsSHAO Yun(School of Electronic Engineering,Nanjing Xiaozhuang College,Nanjing 211171,China)Abstract:Introduces several common formulas for calculating the radius of curvature of two-dimensional trajectory of particle in mechanics,and proves their equivalence.By using the formula of d d sr j=in the plane natural coordinate system,the general formula for calculating the radius of curvature in plane polar coordinate system is derived simply.Based on this formula,the unified expression of curvature radius of conic curves in the usual polar coordinate system(with the focus as the pole)is derived,and then the expression of the radius of curvature of each conic curve in the usual rectangular coordinate system(with the center or vertex as the origin)is derived. Key words:polar coordinate system;rectangular coordinate system;conic curve;radius of curvature1 几种常见的曲率半径计算公式及相互等价关系在直角坐标系中,二维曲线()y y x =的曲率半径通常表示为()3/221y y r ¢+=¢¢(1)其中:d d y y x ¢=;22d d yy x¢¢=.而在自然坐标系下(见图1),质点二维运动轨迹L 的方程可以写成()s s j =,其曲率半径则可表示成[1]12d d sr j=(2)收稿日期:2021-01-12基金项目:江苏省教育科学“十三五”规划课题(D/2020/01/55)作者简介:邵云(1973-),男,江苏镇江人,讲师,从事理论物理研究.E-mail:*******************其中自然坐标s 定义为:沿自然坐标轴O s ¢正方向从原点O ¢到质点P 的路程(有正负之分);自然坐标j 则定义为:过P 点的切线与x 轴正方向的夹角(要求随质点运动连续变化).在微分几何中,三维曲线的曲率半径常常又被表示成[2]17112t 2d d d d s s r --==e r(3)其中:r 是质点的位置矢量;s 即上述自然坐标,t d d s=re 为切向单位矢量.而在力学中,二维曲线的曲率半径既可表示为d /d tr w j ==v v(4) 其中:v 为做二维曲线运动质点的速率;d /d t w j =为质点绕曲率圆心做瞬间小圆弧运动的角速率;j 同上,又可表达成[3]673r =´v v a(5)其中:v 和a 分别为质点的瞬时速度矢量和瞬时加速度矢量.式(1)~(5)这5个表达式其实是彼此等价的.下文将从直角坐标系下曲率半径的计算公式(1)导出极坐标系下的计算公式(2)[4],进而导出式(3)~(5). 由图1可见d tan d y y xj ¢== (6)将式(6)两边对x 求导得2d sec d y xjj ¢¢= (7) 再将式(6)(7)代入式(1),可得d sec d xr jj= (8) 于是根据几何关系:d sec d s x j =,即得d d sr j=. 只需将式(2)中右边导数的分子和分母同除以d t ,即得式(4);而将图1中右下角所显示的微分几何关系:t d d j =e 代入式(2),即得式(3). 在平面自然坐标系下,二维曲线上质点的运动速度可表示为t =v v e (9)则质点的加速度为y第5期 邵云:力学中二维曲线曲率半径的表达式研究 41t t d d d d d d t t t==+e a e vv v (10) 从图1中右下角的微分分析可知t nd d j =e e(11)于是,将d d s r j =、d d st=v 、式(11)一起代入式(10),即得 2t n d d t r=+a e e v v (12)此即通常的力学教材中质点在自然坐标系下的加速度公式.由式(9)(12)可得333t n r r ==´´a e e v v vv , 这样就从式(2)即d d sr j=证明了式(5).经验表明,式(5)极为实用. 从以上简单的推证可知,式(1)~(5)确实是等价的.2 极坐标系下曲率半径的一种简便的推理方法由于质点P 的元位移d r 的方向就是质点速度的方向(见图2),因此图2中的j 角就相当于图1中的j 角.在图2的极坐标系中,元位移d r 可表示为d d d r r r q q =+re e (13) 于是可见,j 角可以表示为d arctan arctan d r r rr qj q q æöæö=+=+ç÷ç÷¢èøèø(14) 其中:d d r r q ¢=.将式(14)两边对q 求导并整理,得2222d 2d r rr r r rj q ¢¢¢-+=¢+ (15) 其中:22d d rr q¢¢=.此外,根据式(13)有d d s ==r(16)d d sq= (17) 于是,将式(15)(17)代入式(2),即得()3/22222d d /d d d /d 2r r s s r rr rqr j j q ¢+===¢¢¢-+ (18) 此即在极坐标系下二维曲线曲率半径的一般计算公式[5-6].与其它的推理方法[6]14-15相比较,这里利用式(2)即d d sr j=来推理式(18)的方法要简便许多. 3 在通常的极坐标系和直角坐标系中圆锥曲线的曲率半径在通常的极坐标系(以焦点为极点)中,圆锥曲线方程可以统一表示成[1]521cos pr e q=+ (19) 其中:p 为半正焦弦长;e 为偏心率.于是有42 高 师 理 科 学 刊 第41卷()22d sin sin d 1cos r pe r e r pe q q q q ¢===+ (20)2222d 2sin cos d r er re r p p qq q æö¢¢==+ç÷èø(21)将式(19)~(21)一起代入式(18),经计算整理后,得 ()3/222212r e pr pr éù-+ëû= (22)此即在通常的极坐标系下圆锥曲线曲率半径的统一表达式.若将圆锥曲线准线的性质:r e x x =-准代入式(22),并利用圆锥曲线诸参量(如a ,b ,c ,e ,p )之间的关系,即可推得在通常的直角坐标系(以中心或顶点为原点)中诸正圆锥曲线的曲率半径[7]194-196.具体的推理过程如下:(1)在直角坐标系中(见图3),对于椭圆:22221x y a b+=,根据准线知识有2a r e x a ex c æö=-=-ç÷èø(23)其中:椭圆的偏心率为/e c a =.需要说明:r ¹,下同.将式(23)及椭圆的半正焦弦长:()221b p a e a=-=代入式(22),即得()()()()3/223/23/222222223/21/22p a ex p a ex a e x a e x a abpa pr éù--+-êú--ëû===×椭 (24)(2)对于双曲线(见图4):22221x y a b-=(左支),根据准线知识有()2a r e x a ex c æö=--=-+ç÷èø(25)其中:双曲线的偏心率/1e c a =>,左支的x a £-.将式(25)及双曲线的半正焦弦长()221b p a e a=-=代入式(22),即得()()()()3/223/23/222222223/21/22pa ex p a ex e x a e xa a abp a p r éù+-+êú--ëû===双 (26)易见,该结论同样适用于图4中双曲线的右支(x a ³).(3)对于抛物线(见图5):22y px =,根据准线知识有22p pr e x x éùæö=--=+ç÷êúèøëû(27)其中:抛物线的偏心率1e =.将式(27)及1e =代入式(22)(注:该式与抛物线的开口方向无关),则得()3/23/221/2222p p x x p p pr éùæö+ç÷êú+èøëû==抛(28)图3 正椭圆及其准线第5期 邵云:力学中二维曲线曲率半径的表达式研究 434 结语本文介绍了5种常见的曲率半径计算公式(1)~(5),并从式(1)逐步推导出式(2)~(5),显示出它们之间的等价性;利用式(2),即d d sr j=推导出极坐标系下二维曲线曲率半径的一般计算公式(18),即()3/222222r r r rr r r ¢+=¢¢¢-+,该推理方法十分简便,值得推荐;最后,利用式(18)推导出在通常的极坐标系(以焦点为极点)中圆锥曲线曲率半径的统一表达式(22),即()3/222212r e pr pr éù-+ëû=,进而利用它及圆锥曲线的准线性质:r e x x =-准,推导出在通常的直角坐标系(以中心或顶点为原点)中3种正圆锥曲线的曲率半径表达式(24)(26)(28).虽然式(24)(26)(28)可以从传统的直角坐标计算公式(1)直接推得[8],但是本文却是从式(22)推得.这在提供了一种新的推理思路的同时,也揭示出这些公式之间内在的联系,或更便于相关记忆.另外,从本质上说,本文中出现的质点运动学知识实际上也是微分几何知识[2,6,9],也可以说属于数学范畴.需要说明的是,本文主要阐述的是曲率半径在3个不同坐标系中的一般计算式(1)(2)(18),和在运动学中的3个计算式(3)~(5),以及圆锥曲线曲率半径的几个具体的表达式(22)(24)(26)(28).当质点做二维运动的轨迹方程或运动方程已知时,利用这些计算式或表达式便可求出相应的曲率半径,但是方法各异,不一而足[3,7,10].本文在此不再赘述. 参考文献:[1] 周衍柏.理论力学教程[M].3版.北京:高等教育出版社,2009:12,52. [2] 彭家贵,陈卿.微分几何[M].北京:高等教育出版社,2002:15-20.[3] 王化银.一般方法求解曲率半径举隅[J].物理教师,2014,35(5):67,69.[4] 同济大学数学系.高等数学:上册[M].7版.北京:高等教育出版社,2014:169-173. [5] 中国矿业学院数学教研室.数学手册[M].2版.北京:科学出版社,1980:85.[6] 邵云.简析极坐标系下曲线曲率半径的数学与力学推理方法[J].大学物理,2020,39(8):14-17.[7] 李崇虎.用动力学方法求圆锥截线上各点的曲率半径[J].西南师范大学学报(自然科学版),2006(4):193-196. [8] 杨胜,梁双凤.圆锥曲线的渐屈线和曲率圆[J].楚雄师范学院学报,2009,24(6):29-34. [9] 蔡肖兵.对物理学之几何化发展的哲学思考[J].哲学研究,2011(3):86-92.[10]宋辉武,陈钢.用质点匀速率曲线运动的方法求解曲线任意点处的曲率半径[J].物理教师,2018,39(06):56-58.。

(英汉对照)分子生物学-- 名词解释

(英汉对照)分子生物学-- 名词解释

α helix α螺旋A helical secondary structure in proteins.Pl. α helices. 蛋白质中一种螺旋形的二级结构。

复数:α helices。

α-amanitin α鹅膏蕈碱A toxin that inhibits the three eukaryotic RNA polymerases to different extents. Name derives from mushroom of genus Amanita in which toxin is found. 一种能不同程度地抑制三种真核生物RNA聚合酶的毒素。

名称来自于产生此毒素的Amanita属蘑菇。

β-galactosidase β-半乳糖苷酶Enzyme that cleaves lactose into galactose and glucose. Name origin: the bond cut by this enzyme is called a β-galactosidic bond. 将乳糖分解为半乳糖和葡萄糖的酶。

名称来源:该酶切割的键称为β-半乳糖苷键。

β sheet β折叠A secondary structure in proteins, relatively flat and formed hydrogen bonding between two parallel or anti-parallel stretches of polypeptide. 蛋白质的一种二级结构,相对平坦,在两条平行的或反向平行的肽段之间形成氢键。

σ subunit σ亚基Component of prokaryotic RNA polymerase holoenzyme. Required for recognition of promoters. 原核生物RNA聚合酶全酶的组成成分。

翻译沃森和克里克于1953年发表在《科学杂志》关于DNA双螺旋模型的论文

翻译沃森和克里克于1953年发表在《科学杂志》关于DNA双螺旋模型的论文

分子生物学作业:翻译沃森和克里克于1953年发表在《科学杂志》上的关于DNA双螺旋模型的论文Nature科学杂志Equipment,and to Dr. G. E. R. Deacon and the captain and officers of R.R.S.Discovery II for their part,in making the observations.Molecular structure of nucleic acids核酸分子结构A structure for Deoxyribose nucleic acid脱氧核糖核酸的结构We wish to suggest a structure for the salt of deoxyribose nucleic acid (D.N.A). This structure has novel features which are of considerable biological interest.我们希望去提出一种结构是刺激性的脱氧核糖核酸即DNA。

这个结构有一些新的特征对于生物学有很多重要的意义。

A structure for nucleic acid has already been proposed by Pauling and Corey2.鲍林和科瑞提出了核酸的结构。

they kindly made their manuscript available to us in advance of publication.在他们出版前,他们爽快的将对他们有用的手稿给我们。

Their model consists of three intertwined chains,with the phosphates near the fibre axis,and the bases on the outside.他们提出的模型由三个缠绕的链组成,以磷酸盐靠近纤维轴线并且盘绕在外部。

全反式维甲酸增强肺炎链球菌对小鼠的致病性

全反式维甲酸增强肺炎链球菌对小鼠的致病性

Chinese Journal of VeNrinam Medicine中国兽医杂志2020年(第56卷)第11期93全菌对小鼠的致病牛小飞1>2,王宏艳2,连朋敬1,玉1,李静云1,张子卉1,杨俊琦2,赵立红1,健1(1.中国农业大学动物医学院,北京海淀100193;2.河北工程大学生学与食品工程学院,河北邯郸056038)摘要:肺炎链球菌(8.P")是导致人类感染和死亡的重要病原,全反式维甲酸(ATRA)在临床上应用非常广泛。

为了探究ATRA的应用是否影响S.pn对小鼠的致病性,分别给小鼠腹腔1、5、10mg(kg-bw)ATRA后,鼻腔接种S.pp,然从体重、采食量、、肺系数和肺组织方面将ATRA感染组与感染组进行比较$结果显示,1mg(kg-bw)ATRA 感染组的体重、采食量、存活率和肺系数与感染组相比均无显著差异(P>0.05);5、10mg(kg-bw)ATRA感染组的体重、采食量与感染组相比均显著降低(P<0.05",且10mg(kg-bw)ATRA感染组的 与感染组相比显著降低(P< 0.05);随着剂量的增加,1、5、10mg(kg-bw)ATRA感染组的肺组织病变程度与感染组相比逐渐增加。

表明随着ATRA剂量的增加,S.pn对小鼠的性逐渐增强。

关键词:全反;肺炎链球菌;致病性;小鼠中图分类号:R378.1+2文献标志码:A文章编号:0529—6005(2020)11—0093—04All-hans Retinoic Acin Enhances the Pathogenicity of Streptococcus pneumoniae to Mice NIU Xiao-fel1,2,WANG Hony-ywi2,LINN Peny-jiny1,BAI Yu1,LI Jiny-yun1,ZHANG Zi-hul1,YANG Jun-qi2,ZHAO Li-hony1,QINO Jan1(1.Co i egeoeVeeeainaayMedicine,ChinaAgaicuieuaaiUniveasiey,Beiuing100193,China;2.Co i egeoeLieeSciencesand Food Engineeaing,HebeiUniveasieyoeEngineeaing,Handan056038,China)Abstract:Streptococcus pneumoniae(S.pn)is an irnpoiant pathogen leading to human infection and death,and W1-trans retino­ic acid(ATRA)is widely used in clinic.To investigate whether the application of ATRA Wfects the pathogenicity of S.pn in mice,mice were given intraperitoneal injections of1,5and10mg(kg•bw)of ATRA,respectively,and then nasal inoculation with S.pn. TheATRAineeceion gaoup wascompaaed wieh eheineeceion gaoup in eeamsoebodyweighe,eood ineake,suaviva,aaee,,ungcoe e i ciene,and lung histopatholog.The results showed that there were no significant diRemnces in body weight,food intake,survival rate and lung coefficient betreen the1mg(kg•bw)ATRA infection group and the infection group(P>0.05).The body weight and food intake of the5and10mg(kg•bw)ATRA infection group were significantly lowev than those of the infection group(P<0.05),and the survival rate of the10mg(kg•bw)ATRA infection group was significan2u lowev than those of the iiRection group(P< 0.05).With the increase of dose,the lung histopathological changes in the1,5and10mg(kg•bw)ATRA infection group were gaadua i yincaea&ed compaaed wiih iheineeciion gaoup.Theae&uiiindicaieihaiihepaihogeniciiyoe S.pn iomiceincaea&e&wiih ihe increase of ATRA dose.Key words:W1-trans retinoic acid;Streptococcus pneumoniae;pathogenicity;miceCorresponding authors:QIAO Jian,E-mail:qiaojian@;ZHAO Li-hong,E-mail:zhlUong@收稿日期:2020—06—05基金项目:国家重点研发计划项目(2016YFD0501200);河北省第二期现代化产业体系蛋肉鸡创新团队专项资助项目(HBCT2018150101,HBCT2018150207):牛小飞(1979-),男,讲师,博士,从事中兽医学和兽医病理学工作,E-mail:hdniuxiaofei@163-com艳(1981-),女,讲师,博士,从事兽医病理学工作,R mai: *********************.cn注:艳与牛小飞对本文具有同等贡献通讯作者:乔健,E-mail:qiaojian@;赵立红,E-mail: zh,************.cn肺炎链球菌(Streptococcus pneumoniae,S-pn)是一种定植于部的革兰阳性球菌,属于条件性菌,可引起肺炎、脑膜炎、菌血种疾[1]$肺链球菌性球严重的卫生问一,也导儿童、老年人及免疫缺陷群体发病和死亡的重要原因,全球年约200死于S-pn感染⑵$反(AA-tans retinoic acid,ATRA)生A的代谢产物,为营充剂和免疫调节剂在临床上应分,是孕妇和儿童补充维生素A,以及治疗儿童麻疹、座94中国兽医杂志2020年(第56卷)第11期Chinese Journal of Veterina—Medicine疮、夜盲症、急性早幼粒细胞白血病等的一线用药3]。

库什曼螺旋体英文介绍

库什曼螺旋体英文介绍

库什曼螺旋体英文介绍《库什曼螺旋体:一种奇特的微生物》The "Kushman Spirillum: An Extraordinary Microorganism"Introduction:Microorganisms constitute a diverse range of life forms on Earth, including bacteria, viruses, fungi, and protozoa. One intriguing microbe is the Kushman spirillum, also known as the spirochete bacterium. This microorganism exhibits distinctive physical characteristics and a fascinating mode of life that has captivated the attention of scientists worldwide.Physical Description:The Kushman spirillum is a helical-shaped bacterium, resembling a tightly coiled spring or a corkscrew. Its spiral shape is the result of their unique cell structure and motility machinery, which includes periplasmic flagella. These flagella allow it to twist and rotate its body, propelling itself through the surrounding environment. With an average length of 10 to 20 micrometers, the Kushman spirillum appears as a long, thin filament under a microscope.Habitat and Distribution:These microorganisms inhabit a variety of environments, including freshwater bodies, marine ecosystems, and even gastrointestinal tracts of animals. They are often found in environments with high organic content, such as sewage and decaying matter. Kushman spirilla are known to form biofilms – communities of microorganisms attached to surfaces – in order to protect themselves and efficiently access nutrients.Metabolism:Kushman spirilla are chemoorganotrophic bacteria, meaning they obtain energy by breaking down organic compounds through respiration. They are capable of using a wide range of organic substances, including sugars, amino acids, and fatty acids, as energy sources. These microorganisms also play critical roles in various biochemical cycles, such as nitrogen and sulfur cycles, contributing to the recycling of essential elements in the ecosystem.Role in Disease:While the majority of Kushman spirilla are harmless, certain strains have been associated with diseases in humans and other animals. For example, some species of spirochetes are responsible for causing Lyme disease and syphilis. Understanding the pathogenic characteristics and mechanisms of these bacteria is essential for the development of effective treatments and preventive measures. Scientific Research:Research on Kushman spirilla is multidisciplinary, involving microbiology, genetics, molecular biology, and ecology. Scientists are keen to decipher the mechanisms of its unique helical structure, motility, and its ability to adapt to various environments. Furthermore, investigations into the genetics and metabolism of this microorganism have contributed to advancements in biotechnology, such as the production of useful enzymes and biofuels.Conclusion:The Kushman spirillum is an extraordinary microorganism that continues to fascinate scientists due to its distinct helical shape and unique biological characteristics. Its ability to adapt to diverse environments and perform various essential functions highlights its ecological importance. Furthermore, research on this microorganism has paved the way for numerous applications in biotechnology and medical science. As we delve deeper into the world of microorganisms, the Kushman spirillum remains a remarkable subject of study, unveiling the mysteries of the microbial world.。

新疆汉代羊毛织物染料的飞行时间二次离子质谱表征

新疆汉代羊毛织物染料的飞行时间二次离子质谱表征

ZHANG Hongying. Comparative studies on secretory glandular trichome morphology and leaf surface chemi-stry between tobacco varieties[J]. Tobacco Science & Technology, 2021, 54(1): 10-16(in Chinese).SEVERSON R F, ARRENDALE R F, CHORTYK O T, JOHNSON A W, JACKSON D M, GWYNN G R, CHAPLIN J F, STEPHENSON M G. Quantitation of the major cuticular components from green leaf of different tobacco types[J]. Journal of Agricultural and Food Chemistry, 1984, 32(3): 566-570.[5]XIA Y, JOHNSON A W, CHORTYK O T. Enhanced toxicity of sugar esters to the tobacco aphid (homoptera: Aphididae) using humectants[J]. Journal of Economic Entomology, 1997, 90(4): 1 015-1 021.[6]ASHRAF-KHORASSANI M, NAZEM N, TAYLOR L T, COLEMAN W M III. Separation and identification of sucrose esters from Turkish tobacco using liquid chro-matography-mass spectrometry[J]. Beiträ ge Zur Tabak-forschung International, 2005, 21(7): 381-389.[7]ASHRAF-KHORASSANI M, NAZEM N, TAYLOR L T, COLEMAN W M III. Isolation, fractionation, and identification of sucrose esters from various oriental tobaccos employing supercritical fluids[J]. Beiträ ge Zur Tabakforschung International, 2008, 23(1): 32-45.[8]范若静, 陈秀萍, 张芳, 张菁, 郭寅龙. 液相色谱-离子淌度-四极杆/飞行时间串联质谱法快速检测烟叶中蔗糖酯[J]. 质谱学报, 2016, 37(4): 301-309.FAN Ruojing, CHEN Xiuping, ZHANG Fang, ZHANG Jing, GUO Yinlong. Fast detection of sucrose esters in tobacco leaf using liquid chromatography coupled with ion mobility-quadrupole/time of flight mass spectrome-try[J]. Journal of Chinese Mass Spectrometry Society, 2016, 37(4): 301-309(in Chinese).[9]EINOLF W N, CHAN W G. Estimation of sucrose esters in tobacco by direct chemical ionization mass spectrome-try[J]. Journal of Agricultural and Food Chemistry, 1984, 32(4): 785-789.[10]DING L, XIE F, ZHAO M, WANG S, XIE J, XU G.Rapid quantification of sucrose esters in oriental tobacco by liquid chromatography-ion trap mass spectrometry[J].Journal of Separation Science, 2007, 30(1): 35-41. [11]DING L, XIE F, ZHAO M, XIE J, XU G. Rapid charac-[12]terization of the sucrose esters from oriental tobacco using liquid chromatography/ion trap mass spectrometry[J]. Rapid Communications in Mass Spec-trometry, 2006, 20(19): 2 816-2 822.DING L, XIE F, XU G, LIU K, WANG S, XIE J. Sepa-ration and detection of polar cuticular components from Oriental tobacco leaf by integration of normal-phase liq-uid chromatography fractionation with reversed-phase liquid chromatography-mass spectrometry[J]. Journal of Separation Science, 2010, 33(21): 3 429-3 436.[13]JIA C, WANG Y, ZHU Y, XU C, MAO D. Preparative isolation and structural characterization of sucrose ester isomers from oriental tobacco[J]. Carbohydrate Research, 2013, 372: 73-77.[14]ZHU H, FENG Y, YANG J, PAN W, LI Z, TU Y, ZHU X, HUANG G. Separation and characterization of sucrose esters from Oriental tobacco leaves using accel-erated solvent extraction followed by SPE coupled to HPLC with ion-trap MS detection[J]. Journal of Separa-tion Science, 2013, 36(15): 2 486-2 495.[15]SHINOZAKI Y, MATSUZAKI T, SUHARA S, TOBITA T, SHIGEMATSU H, KOIWAI A. New types of glycolipids from the surface lipids of nicotiana umbratica[J]. Agricultural and Biological Chemistry, 1991, 55(3): 751-756.[16]MATSUZAKI T, SHINOZAKI Y, SUHARA S, SHIGE-MATSU H, KOIWAI A. Isolation and characterization of tetra- and triacylglucose from the surface lipids of nico-tiana miersii[J]. Agricultural and Biological Chemistry, 1989, 53(12): 3 343-3 345.[17]SCHUMACHER J N. The isolation of 6-O-acetyl-2, 3, 4-tri-O-[(+)-3-methylvaleryl]-β-d-glucopyranose from toba-cco[J]. Carbohydrate Research, 1970, 13(1): 1-8.[18]贾春晓, 王瑞玲, 王莹莹, 毛多斌. 超声萃取-气相色谱-质谱法测定烟叶中的葡萄糖四酯[J]. 分析试验室, 2013, 32(12): 55-60.JIA Chunxiao, WANG Ruiling, WANG Yingying, MAO Duobin. Determination of glucose tetra-esters in tobac-cos by ultrasonic extraction coupled with gas chromatog-raphymass spectrometry[J]. Chinese Journal of Analysis Laboratory, 2013, 32(12): 55-60(in Chinese).[19](收稿日期:2023-07-25;修回日期:2023-09-07)第 3 期袁凯龙等:白肋烟中糖酯类化合物的分离与鉴定385第 45 卷 第 3 期质 谱 学 报Vol. 45 No. 3 2024 年 5 月Journal of Chinese Mass Spectrometry Society May 2024新疆汉代羊毛织物染料的飞行时间二次离子质谱表征刘 婕1,2,陈相龙3,梁汉东1,2,铁 偲1,李展平4(1. 煤炭精细勘探与智能开发全国重点实验室,北京 100083;2. 中国矿业大学(北京)地球科学与测绘工程学院,北京 100083;3. 中国社会科学院考古研究所,北京 100101;4. 清华大学化学系,有机光电子与分子工程教育部重点实验室,北京 100084)摘要:汉代女性干尸着鲜艳的红-蓝-浅黄三色羊毛纤维编制的华丽服饰,本研究以织物片段作为研究样本,主要采用飞行时间二次离子质谱(TOF-SIMS)法对其染料进行表征。

properties of DNA(07-2)改

properties of DNA(07-2)改

Other forms: A-DNA: 1) 11 bases/turn, Right-handed helical 2) The helix formed by RNA-RNA, by DNA-RNA hybrids Z-DNA:
1)Zig- Zag appearance, 12 bases/turn, left-handed helical
Base pairing via hydrogen bonds
A:T
G:C
Helical turn:
•Double helix
•B form:
Right-handed helical 10 base pairs/turn 3.4nm /turn(螺距)
Diameter: 2.0nm
Major groove: 大沟 Minor groove: 小沟 Idealized form of structure adopted by virtually all DNA in vivo.
4. Purity of DNA A260/A280: (the ratio of absorbance at 260 and 280nm) dsDNA--1.8 pure RNA--2.0 protein--0.5 dsDNA>1.8 RNA contamination dsDNA<1.8 protein contamination
Chapter 1 (Section C) The Structure and Properties of Nucleic Acid
Three conceptions need to be differentiated
Bases (碱基)
Nucleosides (核苷) Nucleotides (核苷酸)

生物物理学课件讲义——第01讲+蛋白质结构基础

生物物理学课件讲义——第01讲+蛋白质结构基础
目前已经测出三级结构的生物大分 子都储存在蛋白质数据库中 (Protein data bank,PDB),借助 软件可查阅显示其空间结构,还可 以在不同方向旋转以获得空间结构 的细节。
40
嗜热菌蛋白酶与人碳酸酐酶的结构图
41
研究蛋白质三级结构的方法
X射线晶体衍射(X-ray crystallography) 多维核磁共振( multi-dimensional NMR) 三维电子显微镜技术(3-dimensiional EM) 扫描探针显微术( Scanning Probe Microscopy ,SPM)
Tertiary Structure (三级结构) Quaternary Structure (四级结构)
2
3
Primary Structure (一级结构)
4
Side chain
Backbone
Peptide bond
5
Peptide torsion angles (扭转角)
phi (F), psi (Y), and omega (W)
26
Prediction of secondary structure (预测)
(a). Homology. If sequence >25-30%, structure similarity (b). Statistical. Chou & Fasman (1978). (c). Stereochemical Schiffer and Edmundson (1967)
37
Tuberculosis (肺结核) RNA Polymerase active site Hinge region (铰链区)
38
Protein Tertiary Structure (蛋白的三级结构)

沃森克里克提出的dna双螺旋结构模型

沃森克里克提出的dna双螺旋结构模型

沃森克里克提出的dna双螺旋结构模型English VersionIn 1953, James Watson and Francis Crick proposed the double helix model of DNA, which revolutionized our understanding of genetics and molecular biology. This groundbreaking discovery was based on the work of scientists such as Rosalind Franklin, Maurice Wilkins, and Linus Pauling.The double helix structure of DNA consists of two intertwined strands that form a twisted ladder. The strands are made up of nucleotides, which are the building blocks of DNA. Each nucleotide contains a sugar molecule, a phosphate group, and one of four nitrogenous bases: adenine, thymine, guanine, or cytosine.The key to the stability of the DNA double helix is the complementary base pairing between the nitrogenous bases. Adenine always pairs with thymine, and guanine always pairs with cytosine. This pairing allows for the accurate replication of DNA during cell division.The discovery of the DNA double helix structure laid the foundation for the field of molecular biology and has had a profound impact on fields such as medicine, agriculture, and forensics. It has provided insights into genetic diseases, evolutionary relationships, and the development of new technologies for gene editing and genetic engineering.In conclusion, the DNA double helix model proposed by Watson and Crick has had a lasting impact on the scientific community and continues to shape our understanding of genetics and biology.中文翻译:1953年,詹姆斯·沃森和弗朗西斯·克里克提出了DNA的双螺旋模型,这一突破性的发现彻底改变了我们对遗传学和分子生物学的理解。

显微结构 英语

显微结构 英语

显微结构英语Microscopic StructureThe world around us is a complex and intricate tapestry, woven with the intricate patterns and structures that make up the very fabric of our existence. At the heart of this tapestry lies the microscopic structure, a realm that often eludes the naked eye yet holds the key to understanding the fundamental building blocks of our universe.The microscopic structure, also known as the microstructure, refers to the arrangement and organization of the smallest components that make up a material or a living organism. This includes the arrangement of atoms, molecules, and the various cellular structures that make up the building blocks of life. From the delicate and intricate patterns of a snowflake to the intricate network of neurons in the human brain, the microscopic structure is the foundation upon which the world we inhabit is built.One of the most fascinating aspects of the microscopic structure is its ability to reveal the hidden complexities of the world around us. Through the use of advanced microscopy techniques, such as scanning electron microscopy (SEM) and transmission electronmicroscopy (TEM), scientists are able to delve deep into the microscopic realm and uncover the intricate details that are otherwise invisible to the naked eye.These techniques allow researchers to study the arrangement and organization of atoms, molecules, and cellular structures, providing invaluable insights into the fundamental properties of materials and living organisms. For example, the study of the microscopic structure of a material can reveal its strength, durability, and even its potential for use in various applications, such as the development of new materials for construction or the design of more efficient electronic devices.Similarly, the study of the microscopic structure of living organisms can provide crucial insights into the mechanisms that underlie the various processes that sustain life, from the intricate dance of proteins in a cell to the complex networks of neurons that give rise to consciousness.One of the most remarkable aspects of the microscopic structure is its ability to reveal the hidden patterns and symmetries that are often obscured by the complexity of the macroscopic world. For instance, the study of the microscopic structure of snowflakes has revealed the intricate and mathematically precise patterns that emerge from the simple process of water freezing. These patterns are a testament tothe inherent beauty and order that exists at the microscopic scale, a beauty that is often overlooked in the chaos of the everyday world.Similarly, the study of the microscopic structure of living organisms has revealed the incredible complexity and interconnectedness of the various cellular components that make up the building blocks of life. From the delicate and intricate structures of proteins to the complex networks of neurons that give rise to the human brain, the microscopic world is a realm of awe-inspiring complexity and beauty.As we continue to explore the microscopic structure of the world around us, we are constantly reminded of the incredible power and potential of this hidden realm. Through the lens of advanced microscopy techniques, we are able to unlock the secrets of the smallest and most fundamental building blocks of our universe, and in doing so, we gain a deeper understanding of the world in which we live.Whether we are studying the microscopic structure of materials or the intricate cellular structures that make up living organisms, the insights we gain from this exploration can have profound implications for our understanding of the world and our ability to harness its potential for the betterment of humanity.In conclusion, the microscopic structure is a realm of incrediblecomplexity and beauty, a realm that holds the key to unlocking the secrets of our universe. Through the continued study and exploration of this hidden world, we can gain invaluable insights into the fundamental building blocks of our existence, and ultimately, use this knowledge to create a better and more sustainable future for all.。

光合作用中细胞各部分结构的联系

光合作用中细胞各部分结构的联系

英文回答:The process of photosynthesis involves the intricate coordination of various cellular structures within plant cells. These structures epass the chloroplasts, thylakoid membranes, stroma, and grana. The chloroplasts, serving as the primary organelles for photosynthesis, consist of thylakoid membranes that amodate the photosystems responsible for capturing light energy, while the stroma hosts the enzymes essential for the Calvin cycle, the secondary phase of photosynthesis. Moreover, the grana,prised of stacked thylakoid membranes, play a pivotal role in facilitating the light-dependent reactions of photosynthesis.光合作用的过程涉及植物细胞内各种细胞结构的复杂协调。

这些结构通过氯仿、Thylakoid膜、石膏和颗粒。

作为光合作用的主要管子的氯聚变器由热液膜组成,该膜对负责捕捉光能的光系统产生摩擦作用,而斯特罗玛则拥有对卡尔文循环至关重要的酶,即光合作用二级。

由叠叠的Thylakoid膜制成的颗粒在促进光合作用依赖光的反应方面发挥着关键作用。

The chloroplasts have this double layer of membranes that kind of act like bodyguards for all the photosynthesis stuff inside.Inside these membranes, there are these stacks called grana where the light-dependent reactions happen. These stacks are packed with these green pigments called chlorophyll that suck up all the light energy and kick off the whole photosynthesis process. Then there's this fluid-filled space outside the stacks called the stroma, where the Calvin cycle happens. This is where the carbon dioxide gets turned into glucose with the help of enzymes and ATP that's made during the light-dependent reactions.氯仿机有一层双层膜里面所有光合作用的东西都像保镖一样在这些膜内部,有这些被称为grana的堆积物,在那里发生依赖光的反应。

肌组织与神经组织(英文版)

肌组织与神经组织(英文版)


atoms Neutron中子 Proton质子
atomic nucleus electron
组织学
研究正常机体的细微结构 及其相关功能的科学。
上皮组织 结缔组织 肌组织 神经组织
基本组织
Constitution of human body from macroscopy to microscopy
Mesenchyme
*Connective Tissues Proper •Loose Connective Tissue
•Collagen Fibers •Fibers •Reticular Fibers •Elastic Fibers
*Function •Adipose Cells •未分化的间充质细胞 •白细胞
human body
subcellular structures
molecules
systems
cells and intercellular materials
atoms
organs
tissues
atomic nucleus electron
neutron proton
EPITHELIAL TISSUE and CONNECTIVE TISSUE
Constitution of human body from macroscopy to microscopy
human body
subcellular structures
EM
systems
cells and intercellular Materials
间质
organs
tissues
Molecules
MUSCLE TISSUE
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a r X i v :0710.1251v 2 [c o n d -m a t .s t a t -m e c h ] 19 D e c 2007Biconical structures in two–dimensional anisotropic Heisenberg antiferromagnetsM.Holtschneider and W.SelkeInstitut f¨u r Theoretische Physik B,RWTH Aachen,52056Aachen,GermanySquare lattice Heisenberg and XY antiferromagnets with uniaxial anisotropy in a field along theeasy axis are studied.Based on ground state considerations and Monte Carlo simulations,the role of biconical structures in the transition region between the antiferromagnetic and spin–flop phases is analyzed.In particular,adding a single–ion anisotropy to the XXZ antiferromagnet,one observes,depending on the sign of that anisotropy,either an intervening biconical phase or a direct transition of first order separating the two phases.In case of the anisotropic XY model,the degeneracy of the ground state,at a critical field,in antiferromagnetic,spin–flop,and bidirectional structures seems to result,as in the case of the XXZ model,in a narrow disordered phase between the antiferromagnetic and spin–flop phases,dominated by bidirectional fluctuations.PACS numbers:68.35.Rh,75.10.Hk,05.10.LnRecently,two–dimensional uniaxially anisotropic Heisenberg antiferromagnets in a magnetic field along the easy axis have been studied theoretically rather intensively 1,2,3,4,5,6,7,8,9,motivated by exper-iments on intriguing magnetic properties of layered cuprates 1,10,11,12,13and by experimental findings on complex phase diagrams for other quasi two–dimensional antiferromagnets 14,15,16,17,18exhibiting,typically,multicritical behavior.A generic model describing such systems is the XXZ Heisenberg antiferromagnet on a square lattice,with the Hamiltonian H =J(i,j )∆(S x i S x j+S y i S yj )+S z i S zj−HiS z i (1)where we consider the classical variant,with the spin atsite i , S i =[S x i ,S y i,S z i ],being a vector of length one. S i is coupled to its four neighboring spins Sj at sites j .The exchange integral J is antiferromagnetic,J >0,and the anisotropy parameter ∆may vary from zero (Ising limit)to one (isotropic Heisenberg model).The magnetic field H acts along the easy axis,the z –axis.As known for many years 19,the phase diagram of the XXZ model includes the long–range ordered antiferromagnetic (AF),the algebraically ordered spin–flop (SF),and the paramagnetic phases.Only very recently,attention has been drawn to the role of biconical (BC)structures and fluctuations,in the ground state and in the transition region between the AF and SF phases 6.In a BC ground state configuration the spins on the two sublattices (i.e.on neighboring sites),A and B ,form different cones around their two different tilt angles,θA and θB ,with respect to the easy axis,see Fig.1.In the XXZ model,these structures occur at the critical field,H c 1,which separates the AF and SF structures at T =0.The two tilt angles of the BC ground states are interrelated by 6.θB =arccos√1−√20.00.20.40.60.8k B T/J0.00.51.01.52.02.53.03.54.04.5H /Jantiferromagneticspin-floppara-magneticbiconicalH c1b H c1aFIG.2:Simulated phase diagram of the XXZ model,∆=0.8,with an additional positive single–ion anisotropy,D =0.2.The squares denote the SF,the circles the AF phase boundary.Error bars are omitted unless they are larger than the symbol sizes.anisotropy.The sign of D will have drastic consequences for the phase diagram.In both cases,the ground state properties can be determined exactly 23.In case of a positive single–ion anisotropy,D >0,BC structures are ground states in a non–zero range of fields,H c 1a <H <H c 1b ,in between the AF (H ≤H c 1a )and SF (H ≥H c 1b )structures 23.Note that at fixed field in that range,only BC structures with a unique pair of tilt angles θA and θB ,are stable,with a modified relation between the angles,compared to Eq.(2),depending now also on D .At T >0,there is a BC phase,ordered simultane-ously in the spin components parallel and perpendicular to the easy axis 21,intervening between the AF and SF phases.This is found in extensive Monte Carlo simula-tions studying square lattices with up to L ×L =240×240sites,and performing runs with up to 108Monte Carlo steps per site (MCS),applying the standard Metropolis algorithm.A typical phase diagram is shown in Fig.2,where we set ∆=0.8,as usual 3,4,19,and D/J =0.2.The phase boundaries are determined by finite–size ex-trapolations of the staggered susceptibilities,the specific heat,and the Binder cumulant 23.Obviously,see Fig.2,the extent of the BC phase shrinks with increasing tem-perature.Eventually,the BC phase may terminate at a tetracritical point 21,22,24,25,26,27,where the AF,SF,BC and paramagnetic phases meet.It may be estimated to be roughly at k B T/J =0.35±0.05.The critical properties of the boundary lines of the BC phase to the AF and SF phases have been simulated and studied in much detail at k B T/J =0.2,see Fig.2.Theboundary line between the BC and SF phases,where the staggered magnetization in the direction of the field (or ’longitudinal staggered magnetization’)vanishes,seems to belong to the Ising universality class.For example,the effective critical exponent of that susceptibility is found to approach 7/4,when analyzing the size dependence of the peak height 23.Note that,in this case,large systems,L ≥120,are needed for getting close to the supposed asymptotics.In turn,at the boundary line between the BC and AF phases the algebraic order in the transverse staggered magnetization,which we observe in the BC phase,gets lost.The finite-size behavior of that magne-tization,for L ≥40,agrees with the transition belong-ing to the Kosterlitz–Thouless universality class.Note that renormalization group arguments on the universal-ity classes of the boundary lines of biconical phases 22sug-gest transitions in the Ising and XY–universality classes as well.In case of a negative single-ion anisotropy,D <0,at all fields,no BC structures are ground states.At low tem-peratures,the Monte Carlo simulations (with computa-tional efforts as for D >0)provide evidence for a direct transition of first order between the AF and SF phases.Such evidence is exemplified in Fig.3for ∆=0.8and D/J =−0.2,where the peak height of the longitudi-nal staggered susceptibility,χmax ,is shown to grow with size L proportionally to L 2,as expected for a transition of first order.The coexistence of the AF and SF phases at a first–order transition is also seen in the behavior of the probability P (θ)to find the tilt angle θin a configu-ration.P (θ)shows more and more pronounced maxima at the values of θcharacterizing the AF and SF phases,when increasing the system size.Note that for small system sizes,biconical fluctuations are observed in the transition region between the AF and SF phases 23.Let us turn to another variant of the XXZ model,the anisotropic XY antiferromagnet.According to renormal-ization group calculations 7,27,28,applied to the case of uniaxiality,the number of spin components n is expected to determine the nature of the multicritical point,at which the AF,SF,and,possibly,BC phases meet with the paramagnetic phase.For n being not too large,the multicritical point has been supposed to be a bicritical,tetracritical or critical end–point.Now,in two dimen-sions,a bicritical point of O(n)–symmetry with n =3,as it is the case in the XXZ model,is ruled out at a non-zero temperature in two dimensions by the well–known Mermin–Wagner theorem.However,it may occur when reducing the number of spin components from n =3to n =2.Then the bicritical point would be of O(2)–symmetry,and thus would be allowed at T >0,belong-ing to the Kosterlitz–Thouless universality class.Thence,it looks interesting to consider the n =2variant of the XXZ antiferromagnet,namely the anisotropic XY model,described by the HamiltonianH =J(i,j )(S x i S x j+∆S y i S y j )−HiS xi(4)3H/Jχs tzFIG.3:Selected raw data for the longitudinal staggered sus-ceptibility χz st versus field at fixed temperature,k B T /J =0.3near the transition,H AF /J =2.798±0.0005,between the AF and SF phases for the XXZ model,∆=0.8,D =−0.2.Data for Systems of sizes 20(circles),40(squares),and 80(diamonds)are shown.In the inset,the finite–size,L ,depen-dence of the peak height,χmax ,is depicted,with the straight,solid line showing χmax ∝L 2.Error bars are included when they are larger than the symbol sizes.with the classical spins having now only two components.The ground state analysis can be done in complete analogy to the one for the XXZ model.Of course,the spins are now restricted to the XY–plane,and the tilt angle θis defined for the spin orientation with respect to the easy x –axis.Especially,the biconical structures are replaced by bidirectional (BD)structures.At the critical field,H c 1,the AF and SF structures are degenerate with the set of BD configurations,for which θA and θB are interrelated as in the XXZ case,Eq.(2).The phase diagram of the anisotropic XY antiferro-magnet,with ∆=0.8,is depicted in Fig.4.It has been obtained from extensive Monte Carlo simulations study-ing lattices sizes up to L ×L =120×120,and perform-ing runs with up to 108MCS.The phase boundaries are determined by finite–size extrapolations,similar to the analysis for the XXZ model with a single–ion anisotropy.The topology of the phase diagram looks like in the XXZ case 3,4,19.The AF and SF boundary lines approach each other very closely near the maximum of the SF phase boundary in the (T,H )–plane,see Fig. 4.Ac-cordingly,at low temperatures,there seems to be either a direct transition between the AF and SF phases,or two separate transitions with an extremely narrow interven-ing phase may occur.Away from that intriguing transition region,one ex-0.00.20.40.60.81.01.21.4k B T/J012345678H /Jpara-magnetic0.60.7k B T/J2.52.6H /Jantiferromagneticspin-flopAFSFPM H c1FIG.4:Simulated phase diagram of the anisotropic XY model,with ∆=0.8.The squares denote the phase boundary of the SF,the circles the one of the AF phase.The inset mag-nifies the part,where AF and SF phase boundaries approach each other.pects the transition not only from the AF but also the one from the SF phase to the paramagnetic phase to be in the Ising universality class.In the SF phase of the XY antiferromagnet,there is just one ordering compo-nent,the y –component.The expectation is confirmed by the Monte Carlo data for the specific heat (where the peak at the AF phase boundary gets rather weak on ap-proach to the transition region)and for the staggered susceptibilities.The quantities exhibit critical behavior of Ising–type,as follows from the corresponding effec-tive exponents describing size dependences of the various peak heights.In the transition region of the AF and SF phases,BD fluctuations dominate,as one may conveniently infer from the joint probability distribution p (θA ,θB )for find-ing the tilt angles θA and θB at neighboring sites,i.e.for the two different sublattices.A typical result is depicted in Fig.5,showing the behavior of p in a grayscale rep-resentation.Two features are of interest:first,the two tilt angles are strongly correlated like in the degenerate ground state,with a line of local maxima in p closely following Eq.(2).Second,all those bidirectional struc-tures occur simultaneously with (almost)equal proba-bility,i.e.along the line of maxima p is (almost)con-stant.Note that this behavior is only weakly affected by finite–size effects for the system sizes we simulated.Moving away from the transition region,for instance,by fixing the temperature and varying the field,the line of local maxima initially does not change significantly,but,along that line,pronounced peaks start to show up at4FIG.5:Joint probability p(θA,θB)for the anisotropic XY antiferromagnet with∆=0.8for a system with100×100 lattice sites in the transition region between the AF and SF phases at k B T/J=0.558and H/J=2.44.p(θA,θB)is proportional to the grayscale.The superimposed solid line depicts the relation between the two tilt angles in the ground state,see Eq.(2).positions corresponding to the AF,at lowerfields,or to the SF phases,at higherfields.Similar observations for the joint probability distribution hold for the XXZ model without single–ion anisotropy6.Additional evidence on critical phenomena in the tran-sition region may be obtained by analyzing effective ex-ponents.We did that for the staggered susceptibilities and the specific heat.Results(especially,at H/J=2.44 and0.54<k B T/J<0.57)are compatible with Ising–type criticality,but rather large corrections to scaling had to be presumed.For example,the effective criti-cal exponents for describing the size–dependences of the peak height for the staggered susceptibilities are about 1.8to1.85,largely independent of system size.The sup-posedly strong corrections to scaling may be due to very large correlation lengths in that region,and the asymp-totics may be reached only for very large systems.To conclude,the simulational data seem to suggest for the anisotropic XY antiferromagnet the existence of an extremely narrow,presumably,disordered phase,inter-vening between the AF and SF phases,like in the XXZ case3,4.The transition region between the two phases is clearly dominated by,in the ground state completely degenerate,bidirectionalfluctuations.AcknowledgmentsWe thank Amnon Aharony for useful correspondence. 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