Degree Sequences with Repeated Values

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一步一步教你做转录组分析(HISAT--StringTie-and-Ballgown)

一步一步教你做转录组分析(HISAT--StringTie-and-Ballgown)

一步一步教你做转录组分析(HISAT, StringTie andBallgown)该分析流程主要根据2016年发表在Nature Prot ocols上的一篇名为Transcript-level expressionanalysis of RNA-seq experiments with HISAT,StringTie and Ballgown的文章撰写的,主要用到以下三个软件:HISAT (http://ccb.jhu.edu/software/hisat/index.shtml)利用大量FM索引,以覆盖整个基因组,能够将RNA-Seq的读取与基因组进行快速比对,相较于STAR、Tophat,该软件比对速度快,占用内存少。

StringTie(http://ccb.jhu.edu/software/stringtie/)能够应用流神经网络算法和可选的de novo组装进行转录本组装并预计表达水平。

与Cufflin ks等程序相比,StringTie实现了更完整、更准确的基因重建,并更好地预测了表达水平。

Ballgown(https://github.com/alyssafrazee/ballgown)是R语言中基因差异表达分析的工具,能利用RNA-Seq实验的数据(StringTie, RSEM,Cufflinks)的结果预测基因、转录本的差异表达。

然而Ballgown并没有不能很好地检测差异外显子,而DEXseq、rMATS和MISO可以很好解决该问题。

一、数据下载Linux系统下常用的下载工具是wget,但该工具是单线程下载,当使用它下载较大数据时比较慢,所以选择axel,终端中输入安装命令:$sudo yum install axel然后提示输入密码获得root权限后即可自动安装,安装完成后,输入命令axel,终端会显示如下内容,表示安装成功。

Axel工具常用参数有:axel[选项][下载目录][下载地址]-s:指定每秒下载最大比特数-n:指定同时打开的线程数-o:指定本地输出文件-S:搜索镜像并从Xservers服务器下载-N:不使用代理服务器-v:打印更多状态信息-a:打印进度信息-h:该版本命令帮助-V:查看版本信息号#Axel安装成功后在终端中输入命令:$axel ftp://ftb.jhu.edu/pub/RNAseq_protocol/chrX_data.tar.gz此时在终端中会显示如下图信息,如果不想该信息刷屏,添加参数q,采用静默模式即可。

cofecha输出文件翻译

cofecha输出文件翻译

cofecha输出⽂件翻译[] Dendrochronology Program Library Run 9 Program COF 11:05 Wed 13 Jul 2011 Page 1 [] P R O G R A M C O F E C H A Version 6.06P 27954------------------------------------------------------------------------------------------------------------------------------------ QUALITY CONTROL AND DATING CHECK OF TREE-RING MEASUREMENTS树⽊年轮测量的质量控制和定年检查File of DATED series: 9.RWLCONTENTS:Part 1: Title page, options selected, summary, absent rings by series第1部分:标题页,已选项,总结,缺轮Part 2: Histogram of time spans第2部分:时间跨度直⽅图Part 3: Master series with sample depth and absent rings by year第3部分:主序列每年的样本和缺轮数量Part 4: Bar plot of Master Dating Series第4部分:主序列柱状图Part 5: Correlation by segment of each series with Master第5部分:每序列各段与主序列的相关性研究Part 6: Potential problems: low correlation, divergent year-to-year changes, absent rings, outliers 第6部分:潜在的问题:关联度低,年间发散变化,缺轮,异常值Part 7: Descriptive statistics第7部分:描述性统计Time span of Master dating series is 1815 to 2009 195 yearsContinuous time span is 1815 to 2009 195 yearsPortion with two or more series is 1816 to 2009 194 years*****************************************C* Number of dated series4 *C* 定年的样芯数量*O* Master series 1815 2009 195 yrs *O* 主序列*F* Total rings in all series 768 *F* 所有轮数*E* Total dated rings checked 767 *E* 被定年的轮数*C* Series intercorrelation .299 *C* 序列相关系数*H* Average mean sensitivity .195 *H* 平均敏感度*A* Segments, possible problems 26 *A* 可能有问题的部分数*** Mean length of series 192.0 *** 序列平均长度****************************************ABSENT RINGS listed by SERIES: (See Master Dating Series for absent rings listed by year) No ring measurements of zero value------------------------------------------------------------------------------------------------------------------------------------PART 6: POTENTIAL PROBLEMS: 第6部分:潜在的问题:关联度低,年间发散变化,缺轮,异常值08:08 Thu 14 Jul 2011 Page 5------------------------------------------------------------------------------------------------------------------------------------For each series with potential problems the following diagnostics may appear:检测出来的每个序列可能存在的潜在问题。

《大学英语》精读 第四册 Test Yourself 1 2选择题及其答案

《大学英语》精读 第四册 Test Yourself 1 2选择题及其答案

Test Yourself 11.Inside,in the warm living room, with a glass of wine to drink and Mozart to listen to on the CD, she was far from the tiredness she had ___C___earlier.A advocatedB conceivedC claimedD accumulated2.If you've worked for one employer for two years or more before leaving to have your baby,you may be __B___maternity pay(产妇薪酬)A drawn onB entitled to (有权益)C settled for (满足于)D rested on (依靠)3.Tons of food was laid out on the big table and crates of beer were ____D__ready for consumption.A reserved(保留)B expended(花费)C generated(产生)D stacked(一堆)4.You'd better keep your leaders___A____of your activities so that they can ensure you are adequately supported.A informed(告知)B amused(有趣的)C outlined(概括)D swallowed(吞咽)5.According to the latest poll,the president's 43 percent support has _B____to 32percent.A minimized(最小化)B shrunk(减少)C cited(引证)D predicted(预言)6.Managers might not borrow as much as they should,if they want to retain a large____C__of borrowing power in case of problems.A delivery(递交)B burden (负担)C reserve (D deposit(沉淀)7.It is said that one____D__a work of art and brings it forth as a child is _____and brought forth into the world.A reproduces, reproduced(繁殖)B safeguards, safeguarded (防护措施)C tolerates,tolerated (忍受)D conceives,conceived (构思、怀孕)8.Our case against piracy was won,but over $75000 had been _B_____in legal costs in the proceedings ,far more than the compensation awarded by the court.A advanced (提高)B expended(花费)C attained(获得)D blasted(爆炸)9.If the environment is to be properly____C___policies must be formulated which will encourage the green industry.A bluffed(吓唬)B promoted(促进)C safeguarded(保障)D varnished (装饰)10.Although we are often successful in securing accommodation for people with AIDS, it is a problem which stretches our _C_____to the full and needs taken care of immediately.A bonuses (奖金)B dilemmas (窘境)C resource (资源)D limitations (限制)11.Mark's latest work ___D___the learning theories of the 1980s as well as his own experience inteaching English to foreigners.A built on(建立。

中国科学英文版模板

中国科学英文版模板

中国科学英文版模板1.Identification of Wiener systems with nonlinearity being piece wise-linear function HUANG YiQing,CHEN HanFu,FANG HaiTao2.A novel algorithm for explicit optimal multi-degree reduction of triangular surfaces HU QianQian,WANG GuoJin3.New approach to the automatic segmentation of coronary arte ry in X-ray angiograms ZHOU ShouJun,YANG Jun,CHEN WuFan,WANG YongTian4.Novel Ω-protocols for NP DENG Yi,LIN DongDai5.Non-coherent space-time code based on full diversity space-ti me block coding GUO YongLiang,ZHU ShiHua6.Recursive algorithm and accurate computation of dyadic Green 's functions for stratified uniaxial anisotropic media WEI BaoJun,ZH ANG GengJi,LIU QingHuo7.A blind separation method of overlapped multi-components b ased on time varying AR model CAI QuanWei,WEI Ping,XIAO Xian Ci8.Joint multiple parameters estimation for coherent chirp signals using vector sensor array WEN Zhong,LI LiPing,CHEN TianQi,ZH ANG XiXiang9.Vision implants: An electrical device will bring light to the blind NIU JinHai,LIU YiFei,REN QiuShi,ZHOU Yang,ZHOU Ye,NIU S huaibining search space partition and search Space partition and ab straction for LTL model checking PU Fei,ZHANG WenHui2.Dynamic replication of Web contents Amjad Mahmood3.On global controllability of affine nonlinear systems with a tria ngular-like structure SUN YiMin,MEI ShengWei,LU Qiang4.A fuzzy model of predicting RNA secondary structure SONG D anDan,DENG ZhiDong5.Randomization of classical inference patterns and its applicatio n WANG GuoJun,HUI XiaoJing6.Pulse shaping method to compensate for antenna distortion in ultra-wideband communications WU XuanLi,SHA XueJun,ZHANG NaiTong7.Study on modulation techniques free of orthogonality restricti on CAO QiSheng,LIANG DeQun8.Joint-state differential detection algorithm and its application in UWB wireless communication systems ZHANG Peng,BI GuangGuo,CAO XiuYing9.Accurate and robust estimation of phase error and its uncertai nty of 50 GHz bandwidth sampling circuit ZHANG Zhe,LIN MaoLiu,XU QingHua,TAN JiuBin10.Solving SAT problem by heuristic polarity decision-making al gorithm JING MingE,ZHOU Dian,TANG PuShan,ZHOU XiaoFang,ZHANG Hua1.A novel formal approach to program slicing ZHANG YingZhou2.On Hamiltonian realization of time-varying nonlinear systems WANG YuZhen,Ge S. S.,CHENG DaiZhan3.Primary exploration of nonlinear information fusion control the ory WANG ZhiSheng,WANG DaoBo,ZHEN ZiYang4.Center-configur ation selection technique for the reconfigurable modular robot LIU J inGuo,WANG YueChao,LI Bin,MA ShuGen,TAN DaLong5.Stabilization of switched linear systems with bounded disturba nces and unobservable switchings LIU Feng6.Solution to the Generalized Champagne Problem on simultane ous stabilization of linear systems GUAN Qiang,WANG Long,XIA B iCan,YANG Lu,YU WenSheng,ZENG ZhenBing7.Supporting service differentiation with enhancements of the IE EE 802.11 MAC protocol: Models and analysis LI Bo,LI JianDong,R oberto Battiti8.Differential space-time block-diagonal codes LUO ZhenDong,L IU YuanAn,GAO JinChun9.Cross-layer optimization in ultra wideband networks WU Qi,BI JingPing,GUO ZiHua,XIONG YongQiang,ZHANG Qian,LI ZhongC heng10.Searching-and-averaging method of underdetermined blind s peech signal separation in time domain XIAO Ming,XIE ShengLi,F U YuLi11.New theoretical framework for OFDM/CDMA systems with pe ak-limited nonlinearities WANG Jian,ZHANG Lin,SHAN XiuMing,R EN Yong1.Fractional Fourier domain analysis of decimation and interpolat ion MENG XiangYi,TAO Ran,WANG Yue2.A reduced state SISO iterative decoding algorithm for serially concatenated continuous phase modulation SUN JinHua,LI JianDong,JIN LiJun3.On the linear span of the p-ary cascaded GMW sequences TA NG XiaoHu4.De-interlacing technique based on total variation with spatial-t emporal smoothness constraint YIN XueMin,YUAN JianHua,LU Xia oPeng,ZOU MouYan5.Constrained total least squares algorithm for passive location based on bearing-only measurements WANG Ding,ZHANG Li,WU Ying6.Phase noise analysis of oscillators with Sylvester representation for periodic time-varying modulus matrix by regular perturbations FAN JianXing,YANG HuaZhong,WANG Hui,YAN XiaoLang,HOU ChaoHuan7.New optimal algorithm of data association for multi-passive-se nsor location system ZHOU Li,HE You,ZHANG WeiHua8.Application research on the chaos synchronization self-mainten ance characteristic to secret communication WU DanHui,ZHAO Che nFei,ZHANG YuJie9.The changes on synchronizing ability of coupled networks fro m ring networks to chain networks HAN XiuPing,LU JunAn10.A new approach to consensus problems in discrete-time mult iagent systems with time-delays WANG Long,XIAO Feng11.Unified stabilizing controller synthesis approach for discrete-ti me intelligent systems with time delays by dynamic output feedbac k LIU MeiQin1.Survey of information security SHEN ChangXiang,ZHANG Hua ngGuo,FENG DengGuo,CAO ZhenFu,HUANG JiWu2.Analysis of affinely equivalent Boolean functions MENG QingSh u,ZHANG HuanGuo,YANG Min,WANG ZhangYi3.Boolean functions of an odd number of variables with maximu m algebraic immunity LI Na,QI WenFeng4.Pirate decoder for the broadcast encryption schemes from Cry pto 2005 WENG Jian,LIU ShengLi,CHEN KeFei5.Symmetric-key cryptosystem with DNA technology LU MingXin,LAI XueJia,XIAO GuoZhen,QIN Lei6.A chaos-based image encryption algorithm using alternate stru cture ZHANG YiWei,WANG YuMin,SHEN XuBang7.Impossible differential cryptanalysis of advanced encryption sta ndard CHEN Jie,HU YuPu,ZHANG YueYu8.Classification and counting on multi-continued fractions and its application to multi-sequences DAI ZongDuo,FENG XiuTao9.A trinomial type of σ-LFSR oriented toward software implemen tation ZENG Guang,HE KaiCheng,HAN WenBao10.Identity-based signature scheme based on quadratic residues CHAI ZhenChuan,CAO ZhenFu,DONG XiaoLei11.Modular approach to the design and analysis of password-ba sed security protocols FENG DengGuo,CHEN WeiDong12.Design of secure operating systems with high security levels QING SiHan,SHEN ChangXiang13.A formal model for access control with supporting spatial co ntext ZHANG Hong,HE YePing,SHI ZhiGuo14.Universally composable anonymous Hash certification model ZHANG Fan,MA JianFeng,SangJae MOON15.Trusted dynamic level scheduling based on Bayes trust model WANG Wei,ZENG GuoSun16.Log-scaling magnitude modulated watermarking scheme LING HeFei,YUAN WuGang,ZOU FuHao,LU ZhengDing17.A digital authentication watermarking scheme for JPEG image s with superior localization and security YU Miao,HE HongJie,ZHA NG JiaShu18.Blind reconnaissance of the pseudo-random sequence in DS/ SS signal with negative SNR HUANG XianGao,HUANG Wei,WANG Chao,L(U) ZeJun,HU YanHua1.Analysis of security protocols based on challenge-response LU O JunZhou,YANG Ming2.Notes on automata theory based on quantum logic QIU Dao Wen3.Optimality analysis of one-step OOSM filtering algorithms in t arget tracking ZHOU WenHui,LI Lin,CHEN GuoHai,YU AnXi4.A general approach to attribute reduction in rough set theory ZHANG WenXiuiu,QIU GuoFang,WU WeiZhi5.Multiscale stochastic hierarchical image segmentation by spectr al clustering LI XiaoBin,TIAN Zheng6.Energy-based adaptive orthogonal FRIT and its application in i mage denoising LIU YunXia,PENG YuHua,QU HuaiJing,YiN Yong7.Remote sensing image fusion based on Bayesian linear estimat ion GE ZhiRong,WANG Bin,ZHANG LiMing8.Fiber soliton-form 3R regenerator and its performance analysis ZHU Bo,YANG XiangLin9.Study on relationships of electromagnetic band structures and left/right handed structures GAO Chu,CHEN ZhiNing,WANG YunY i,YANG Ning10.Study on joint Bayesian model selection and parameter estim ation method of GTD model SHI ZhiGuang,ZHOU JianXiong,ZHAO HongZhong,FU Qiang。

重复序列分析文档

重复序列分析文档

1 重复序列分析重复序列广泛存在于真核生物基因组中,这些重复序列或集中成簇,或分散在基因之间,根据分布把重复序列分为分散重复序列和串联重复序列。

分散重复序列分为四种:LTR、LINE、SINE、和DNA转座子、LTR,长末端重复转座子(long terminal repeat),是由RNA反转录而成的元件,它在两端有长大数百碱基对的LTR。

Length: 1.5-10kbp Encode reverse transcriptase Flanked by 300-1000bps terminal repeatsLINE,长散在重复序列(long interspersed nuclear elements),意为散在分布的长细胞核因子,是散在分布在哺乳动物基因组中的一类重复,这种重复序列比较长,平均长度大于1000bp,平均间隔3500-5000bp,如:rRNA,tRNA基因,形成基因家族。

SINE 为短散在重复序列(short interspersed nuclear elements)。

SINE是非自主转座的反转录转座子,来源于RNA聚合酶III的转录物,它的平均长度约为300bp,平均间隔1000bp,如:Alu家族,Hinf家族序列。

DNA 转座子: single intron-less open reading frame Encode transposase Two short inverted repeat sequences flanking the reading frame。

串联重复序列根据重复序列的重复单位的长度可分为卫星DNA、小卫星DNA 和微卫星DNA。

微卫星DNA又称为串联重复序列(short Tandem Repeat. STR)●Simple Sequence Repeats (SSR)+SatellitesGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG (G)ATATATATATATATATATATATATATATATATATATAT (AT)n●Lower complexity region(低复杂性区段)TTTTTTATTTTTTGTTTTTTTTTT(1)研究表明一些简单的重复序列与许多疾病有关。

TmCalculator 1.0.3 核苷酸序列融解温度计算器说明书

TmCalculator 1.0.3 核苷酸序列融解温度计算器说明书

Package‘TmCalculator’October12,2022Type PackageTitle Melting Temperature of Nucleic Acid SequencesVersion1.0.3Date2022-02-20Author Junhui LiMaintainer Junhui Li<****************.cn>Description This tool is extended from methods in Bio.SeqUtils.MeltingTemp of python.The melt-ing temperature of nucleic acid sequences can be calculated in three method,the Wal-lace rule(Thein&Wallace(1986)<doi:10.1016/S0140-6736(86)90739-7>),empirical formu-las based on G and C content(Marmur J.(1962)<doi:10.1016/S0022-2836(62)80066-7>,Schildkraut C.(2010)<doi:10.1002/bip.360030207>,Wet-mur J G(1991)<doi:10.3109/10409239109114069>,Unter-gasser,A.(2012)<doi:10.1093/nar/gks596>,von Ah-sen N(2001)<doi:10.1093/clinchem/47.11.1956>)and nearest neighbor thermodynamics(Bres-lauer K J(1986)<doi:10.1073/pnas.83.11.3746>,Sugi-moto N(1996)<doi:10.1093/nar/24.22.4501>,Allawi H(1998)<doi:10.1093/nar/26.11.2694>,San-taLu-cia J(2004)<doi:10.1146/annurev.biophys.32.110601.141800>,Freier S(1986)<doi:10.1073/pnas.83.24.9373>,Xia T(19 mar-ito S(2000)<doi:10.1093/nar/28.9.1929>,Turner D H(2010)<doi:10.1093/nar/gkp892>,Sugi-moto N(1995)<doi:10.1016/S0048-9697(98)00088-6>,Allawi H T(1997)<doi:10.1021/bi962590c>,Santalu-cia N(2005)<doi:10.1093/nar/gki918>),and it can also be corrected with salt ions and chemi-cal compound(SantaLucia J(1996)<doi:10.1021/bi951907q>,SantaLu-cia J(1998)<doi:10.1073/pnas.95.4.1460>,Owczarzy R(2004)<doi:10.1021/bi034621r>,Owczarzy R(2008)<doi:10.102 BugReports https:///JunhuiLi1017/TmCalculator/issuesLicense GPL(>=2)Depends R(>=2.10)NeedsCompilation noRepository CRANRoxygenNote7.1.2Date/Publication2022-02-2104:10:03UTC12c2s R topics documented:c2s (2)check_filter (3)chem_correction (4)complement (5)GC (6)print.TmCalculator (6)s2c (7)salt_correction (8)Tm_GC (9)Tm_NN (12)Tm_Wallace (15)Index17 c2s convert a vector of characters into a stringDescriptionSimply convert a vector of characters such as c("H","e","l","l","o","W","o","r","l","d")into a single string"HelloWorld".Usagec2s(characters)Argumentscharacters A vector of charactersValueRetrun a stringsAuthor(s)Junhui LiReferencescitation("TmCalculator")Examplesc2s(c("H","e","l","l","o","W","o","r","l","d"))check_filter3check_filter Check andfilter invalid base of nucleotide sequencesDescriptionIn general,whitespaces and non-base characters are removed and characters are converted to up-percase in given method.Usagecheck_filter(ntseq,method)Argumentsntseq Sequence(5’to3’)of one strand of the DNA nucleic acid duplex as string orvector of charactersmethod TM_Wallace:check and return"A","B","C","D","G","H","I","K","M","N","R","S","T","V","W"and"Y"TM_GC:check and return"A","B","C","D","G","H","I","K","M","N","R","S","T","V","W","X"and"Y"TM_NN:check and return"A","C","G","I"and"T"ValueReturn a sequence which fullfils the requirements of the given method.Author(s)Junhui LiReferencescitation("TmCalculator")Examplesntseq<-c("ATCGBDHKMNRVYWSqq")check_filter(ntseq,method= Tm_Wallace )check_filter(ntseq,method= Tm_NN )4chem_correction chem_correction Corrections of melting temperature with chemical substancesDescriptionCorrections coefficient of melting temperature with DMSO and formamide and these corrections are rough approximations.Usagechem_correction(DMSO=0,fmd=0,DMSOfactor=0.75,fmdmethod=c("concentration","molar"),fmdfactor=0.65,ptGC)ArgumentsDMSO Percent DMSOfmd Formamide concentration in percentage(fmdmethod="concentration")or molar (fmdmethod="molar").DMSOfactor Coefficient of Tm decreases per percent DMSO.Default=0.75von Ahsen N (2001)<PMID:11673362>.Other published values are0.5,0.6and0.675.fmdmethod"concentration"method for formamide concentration in percentage and"molar"for formamide concentration in molarfmdfactor Coefficient of Tm decrease per percent formamide.Default=0.65.Several pa-pers report factors between0.6and0.72.ptGC Percentage of GC(%).Detailsfmdmethod="concentration"Correction=-factor*percentage_of_formamidefmdmethod="molar"Correction=(0.453*GC/100-2.88)x formamideAuthor(s)Junhui Licomplement5 Referencesvon Ahsen N,Wittwer CT,Schutz E,et al.Oligonucleotide melting temperatures under PCR conditions:deoxynucleotide Triphosphate and Dimethyl sulfoxide concentrations with comparison to alternative empirical formulas.Clin Chem2001,47:1956-C1961.Exampleschem_correction(DMSO=3)chem_correction(fmd=1.25,fmdmethod="molar",ptGC=50)complement complement and reverse complement base of nucleotide sequencesDescriptionget reverse complement and complement base of nucleotide sequencesUsagecomplement(ntseq,reverse=FALSE)Argumentsntseq Sequence(5’to3’)of one strand of the nucleic acid duplex as string or vector of charactersreverse Logical value,TRUE is reverse complement sequence,FALSE is not.Author(s)Junhui LiReferencescitation("TmCalculator")Examplescomplement("ATCGYCGYsWwsaVv")complement("ATCGYCGYsWwsaVv",reverse=TRUE)6print.TmCalculator GC Calculate G and C content of nucleotide sequencesDescriptionCalculate G and C content of nucleotide sequences.The number of G and C in sequence is divided by length of sequence(when totalnt is TRUE)or the number of all A,T,C,G and ambiguous base.UsageGC(ntseq,ambiguous=FALSE,totalnt=TRUE)Argumentsntseq Sequence(5’to3’)of one strand of the nucleic acid duplex as string or vector of characters.ambiguous Ambiguous bases are taken into account to compute the G and C content when ambiguous is TRUE.totalnt Sum of’G’and’C’bases divided by the length of the sequence when totalnt is TRUE.ValueContent of G and C(range from0to100Author(s)Junhui LiExamplesGC(c("a","t","c","t","g","g","g","c","c","a","g","t","a"))#53.84615GC("GCATSWSYK",ambiguous=TRUE)#55.55556print.TmCalculator Prints melting temperature from a TmCalculator objectDescriptionprint.TmCalculator prints to console the melting temperature value from an object of class TmCalculator.s2c7Usage##S3method for class TmCalculatorprint(x,...)Argumentsx An object of class TmCalculator....UnusedValueThe melting temperature value.s2c convert a string into a vector of charactersDescriptionSimply convert a single string such as"HelloWorld"into a vector of characters such as c("H","e","l","l","o","W","o","r","l","d Usages2c(strings)Argumentsstrings A single string such as"HelloWorld"ValueRetrun a vector of charactersAuthor(s)Junhui LiReferencescitation("TmCalculator")Exampless2c(c("HelloWorld"))8salt_correctionsalt_correction Corrections of melting temperature with salt ionsDescriptionCorrections coefficient of melting temperature or entropy with different operationsUsagesalt_correction(Na=0,K=0,Tris=0,Mg=0,dNTPs=0,method=c("Schildkraut2010","Wetmur1991","SantaLucia1996","SantaLucia1998-1", "SantaLucia1998-2","Owczarzy2004","Owczarzy2008"),ntseq,ambiguous=FALSE)ArgumentsNa Millimolar concentration of NaK Millimolar concentration of KTris Millimolar concentration of TrisMg Millimolar concentration of MgdNTPs Millimolar concentration of dNTPsmethod Method to be applied including"Schildkraut2010","Wetmur1991","SantaLucia1996", "SantaLucia1998-1","SantaLucia1998-2","Owczarzy2004","Owczarzy2008".Firstfourth methods correct Tm,fifth method corrects deltaS,sixth and seventh meth-ods correct1/Tm.See details for the method description.ntseq Sequence(5’to3’)of one strand of the nucleic acid duplex as string or vectorof characters.ambiguous Ambiguous bases are taken into account to compute the G and C content whenambiguous is TRUE.DetailsThe methods are:1Schildkraut C(2010)<doi:10.1002/bip.360030207>2Wetmur J G(1991)<doi:10.3109/10409239109114069>3SantaLucia J(1996)<doi:10.1021/bi951907q>4SantaLucia J(1998)<doi:10.1073/pnas.95.4.1460>5SantaLucia J(1998)<doi:10.1073/pnas.95.4.1460>6Owczarzy R(2004)<doi:10.1021/bi034621r>7Owczarzy R(2008)<doi:10.1021/bi702363u>methods1-4:Tm(new)=Tm(old)+correctionmethod5:deltaS(new)=deltaS(old)+correctionmethods6+7:Tm(new)=1/(1/Tm(old)+correction)Author(s)Junhui LiReferencesSchildkraut C.Dependence of the melting temperature of DNA on salt concentration[J].Biopoly-mers,2010,3(2):195-208.Wetmur J G.DNA Probes:Applications of the Principles of Nucleic Acid Hybridization[J].CRC Critical Reviews in Biochemistry,1991,26(3-4):3Santalucia,J,Allawi H T,Seneviratne P A.Improved Nearest-Neighbor Parameters for Predicting DNA Duplex Stability,[J].Biochemistry,1996,35(11):3555-3562.SantaLucia,J.A unified view of polymer,dumbbell,and oligonucleotide DNA nearest-neighbor thermodynamics[J].Proceedings of the National Academy of Sciences,1998,95(4):1460-1465.Owczarzy R,You Y,Moreira B G,et al.Effects of Sodium Ions on DNA Duplex Oligomers: Improved Predictions ofMelting Temperatures[J].Biochemistry,2004,43(12):3537-3554.Owczarzy R,Moreira B G,You Y,et al.Predicting Stability of DNA Duplexes in Solutions Containing Magnesium and Monovalent Cations[J].Biochemistry,2008,47(19):5336-5353. Examplesntseq<-c("acgtTGCAATGCCGTAWSDBSYXX")salt_correction(Na=390,K=20,Tris=0,Mg=10,dNTPs=25,method="Owczarzy2008",ntseq)Tm_GC Calculate the melting temperature using empirical formulas based onGC contentDescriptionCalculate the melting temperature using empirical formulas based on GC content with different optionsUsageTm_GC(ntseq,ambiguous=FALSE,userset=NULL,variant=c("Primer3Plus","Chester1993","QuikChange","Schildkraut1965","Wetmur1991_MELTING","Wetmur1991_RNA","Wetmur1991_RNA/DNA","vonAhsen2001"),Na=0,K=0,Tris=0,Mg=0,dNTPs=0,saltcorr=c("Schildkraut2010","Wetmur1991","SantaLucia1996","SantaLucia1998-1", "Owczarzy2004","Owczarzy2008"),mismatch=TRUE,DMSO=0,fmd=0,DMSOfactor=0.75,fmdfactor=0.65,fmdmethod=c("concentration","molar"),outlist=TRUE)Argumentsntseq Sequence(5’to3’)of one strand of the nucleic acid duplex as string or vectorof characters.ambiguous Ambiguous bases are taken into account to compute the G and C content whenambiguous is TRUE.userset A vector of four coefficient ersets override value sets.variant Empirical constants coefficient with8variant:Chester1993,QuikChange,Schild-kraut1965,Wetmur1991_MELTING,Wetmur1991_RNA,Wetmur1991_RNA/DNA,Primer3Plus and vonAhsen2001Na Millimolar concentration of Na,default is0K Millimolar concentration of K,default is0Tris Millimolar concentration of Tris,default is0Mg Millimolar concentration of Mg,default is0dNTPs Millimolar concentration of dNTPs,default is0saltcorr Salt correction method should be chosen when provide’userset’.Options are"Schildkraut2010","Wetmur1991","SantaLucia1996","SantaLucia1998-1","Owczarzy2004","Owczarzy2Note that"SantaLucia1998-2"is not available for this function.mismatch If’True’(default)every’X’in the sequence is counted as mismatchDMSO Percent DMSOfmd Formamide concentration in percentage(fmdmethod="concentration")or molar(fmdmethod="molar").Tm_GC11 DMSOfactor Coeffecient of Tm decreases per percent DMSO.Default=0.75von Ahsen N (2001)<PMID:11673362>.Other published values are0.5,0.6and0.675.fmdfactor Coeffecient of Tm decrease per percent formamide.Default=0.65.Several pa-pers report factors between0.6and0.72.fmdmethod"concentration"method for formamide concentration in percentage and"molar"for formamide concentration in molaroutlist output a list of Tm and options or only Tm value,default is TRUE.DetailsEmpirical constants coefficient with8variant:Chester1993:Tm=69.3+0.41(Percentage_GC)-650/NQuikChange:Tm=81.5+0.41(Percentage_GC)-675/N-Percentage_mismatchSchildkraut1965:Tm=81.5+0.41(Percentage_GC)-675/N+16.6x log[Na+]Wetmur1991_MELTING:Tm=81.5+0.41(Percentage_GC)-500/N+16.6x log([Na+]/(1.0+0.7 x[Na+]))-Percentage_mismatchWetmur1991_RNA:Tm=78+0.7(Percentage_GC)-500/N+16.6x log([Na+]/(1.0+0.7x[Na+])) -Percentage_mismatchWetmur1991_RNA/DNA:Tm=67+0.8(Percentage_GC)-500/N+16.6x log([Na+]/(1.0+0.7x [Na+]))-Percentage_mismatchPrimer3Plus:Tm=81.5+0.41(Percentage_GC)-600/N+16.6x log[Na+]vonAhsen2001:Tm=77.1+0.41(Percentage_GC)-528/N+11.7x log[Na+]Author(s)Junhui LiReferencesMarmur J,Doty P.Determination of the base composition of deoxyribonucleic acid from its thermal denaturation temperature.[J].Journal of Molecular Biology,1962,5(1):109-118.Schildkraut C.Dependence of the melting temperature of DNA on salt concentration[J].Biopoly-mers,2010,3(2):195-208.Wetmur J G.DNA Probes:Applications of the Principles of Nucleic Acid Hybridization[J].CRC Critical Reviews in Biochemistry,1991,26(3-4):33.Untergasser A,Cutcutache I,Koressaar T,et al.Primer3–new capabilities and interfaces[J].Nucleic Acids Research,2012,40(15):e115-e115.von Ahsen N,Wittwer CT,Schutz E,et al.Oligonucleotide melting temperatures under PCR conditions:deoxynucleotide Triphosphate and Dimethyl sulfoxide concentrations with comparison to alternative empirical formulas.Clin Chem2001,47:1956-1961.Examplesntseq<-c("ATCGTGCGTAGCAGTACGATCAGTAG")out<-Tm_GC(ntseq,ambiguous=TRUE,variant="Primer3Plus",Na=50,mismatch=TRUE)outout$Tmout$OptionsTm_NN Calculate melting temperature using nearest neighbor thermodynam-icsDescriptionCalculate melting temperature using nearest neighbor thermodynamicsUsageTm_NN(ntseq,ambiguous=FALSE,comSeq=NULL,shift=0,nn_table=c("DNA_NN4","DNA_NN1","DNA_NN2","DNA_NN3","RNA_NN1","RNA_NN2","RNA_NN3","R_DNA_NN1"),tmm_table="DNA_TMM1",imm_table="DNA_IMM1",de_table=c("DNA_DE1","RNA_DE1"),dnac1=25,dnac2=25,selfcomp=FALSE,Na=0,K=0,Tris=0,Mg=0,dNTPs=0,saltcorr=c("Schildkraut2010","Wetmur1991","SantaLucia1996","SantaLucia1998-1", "SantaLucia1998-2","Owczarzy2004","Owczarzy2008"),DMSO=0,fmd=0,DMSOfactor=0.75,fmdfactor=0.65,fmdmethod=c("concentration","molar"),outlist=TRUE)Argumentsntseq Sequence(5’to3’)of one strand of the nucleic acid duplex as string or vectorof characters.ambiguous Ambiguous bases are taken into account to compute the G and C content whenambiguous is TRUE.Default is FALSE.comSeq Complementary sequence.The sequence of the template/target in3’->5’direc-tionshift Shift of the primer/probe sequence on the template/target sequence,default=0.for example:when shift=0,thefirst nucleotide base at5‘end of primer align tofirst one at3‘end of template.When shift=-1,the second nucleotide base at5‘end of primer align tofirst one at3‘end of template.When shift=1,thefirst nucleotide base at5‘end of primer align to second oneat3‘end of template.The shift parameter is necessary to align primer/probeand template/target if they have different lengths or if they should have danglingends.nn_table Thermodynamic NN values,eight tables are implemented.For DNA/DNA hybridizations:DNA_NN1,DNA_NN2,DNA_NN3,DNA_NN4For RNA/RNA hybridizations:RNA_NN1,RNA_NN2,RNA_NN3For RNA/DNA hybridizations:R_DNA_NN1tmm_table Thermodynamic values for terminal mismatches.Default:DNA_TMM1imm_table Thermodynamic values for internal mismatches,may include insosine mismatches.Default:DNA_IMM1de_table Thermodynamic values for dangling ends.DNA_DE1(default)and RNA_DE1dnac1Concentration of the higher concentrated strand[nM].Typically this will be theprimer(for PCR)or the probe.Default=25.dnac2Concentration of the lower concentrated strand[nM].selfcomp Sequence self-complementary,default=False.If’True’the primer is thoughtbinding to itself,thus dnac2is not considered.Na Millimolar concentration of Na,default is0K Millimolar concentration of K,default is0Tris Millimolar concentration of Tris,default is0Mg Millimolar concentration of Mg,default is0dNTPs Millimolar concentration of dNTPs,default is0saltcorr Salt correction method should be chosen when provide’userset’Options are"Schildkraut2010","Wetmur1991","SantaLucia1996","SantaLucia1998-1","SantaLucia1998-2","Owczarzy2004","Owczarzy2008".Note that NA means no salt correction.DMSO Percent DMSOfmd Formamide concentration in percentage(fmdmethod="concentration")or molar(fmdmethod="molar").DMSOfactor Coeffecient of Tm decreases per percent DMSO.Default=0.75von Ahsen N(2001)<PMID:11673362>.Other published values are0.5,0.6and0.675.fmdfactor Coeffecient of Tm decrease per percent formamide.Default=0.65.Several pa-pers report factors between0.6and0.72.fmdmethod"concentration"method for formamide concentration in percentage and"molar"for formamide concentration in molar.outlist output a list of Tm and options or only Tm value,default is TRUE.DetailsDNA_NN1:Breslauer K J(1986)<doi:10.1073/pnas.83.11.3746>DNA_NN2:Sugimoto N(1996)<doi:10.1093/nar/24.22.4501>DNA_NN3:Allawi H(1998)<doi:10.1093/nar/26.11.2694>DNA_NN4:SantaLucia J(2004)<doi:10.1146/annurev.biophys.32.110601.141800>RNA_NN1:Freier S(1986)<doi:10.1073/pnas.83.24.9373>RNA_NN2:Xia T(1998)<doi:10.1021/bi9809425>RNA_NN3:Chen JL(2012)<doi:10.1021/bi3002709>R_DNA_NN1:Sugimoto N(1995)<doi:10.1016/S0048-9697(98)00088-6>DNA_TMM1:Bommarito S(2000)<doi:10.1093/nar/28.9.1929>DNA_IMM1:Peyret N(1999)<doi:10.1021/bi9825091>&Allawi H T(1997)<doi:10.1021/bi962590c> &Santalucia N(2005)<doi:10.1093/nar/gki918>DNA_DE1:Bommarito S(2000)<doi:10.1093/nar/28.9.1929>RNA_DE1:Turner D H(2010)<doi:10.1093/nar/gkp892>Author(s)Junhui LiReferencesBreslauer K J,Frank R,Blocker H,et al.Predicting DNA duplex stability from the base se-quence.[J].Proceedings of the National Academy of Sciences,1986,83(11):3746-3750.Sugimoto N,Nakano S,Yoneyama M,et al.Improved Thermodynamic Parameters and Helix Ini-tiation Factor to Predict Stability of DNA Duplexes[J].Nucleic Acids Research,1996,24(22):4501-5.Allawi,H.Thermodynamics of internal C.T mismatches in DNA[J].Nucleic Acids Research,1998, 26(11):2694-2701.Hicks L D,Santalucia J.The thermodynamics of DNA structural motifs.[J].Annual Review of Biophysics&Biomolecular Structure,2004,33(1):415-440.Freier S M,Kierzek R,Jaeger J A,et al.Improved free-energy parameters for predictions of RNA duplex stability.[J].Proceedings of the National Academy of Sciences,1986,83(24):9373-9377.Xia T,Santalucia,J,Burkard M E,et al.Thermodynamic Parameters for an Expanded Nearest-Neighbor Model for Formation of RNA Duplexes with Watson-Crick Base Pairs,[J].Biochemistry, 1998,37(42):14719-14735.Chen J L,Dishler A L,Kennedy S D,et al.Testing the Nearest Neighbor Model for Canonical RNA Base Pairs:Revision of GU Parameters[J].Biochemistry,2012,51(16):3508-3522.Bommarito S,Peyret N,Jr S L.Thermodynamic parameters for DNA sequences with dangling ends[J].Nucleic Acids Research,2000,28(9):1929-1934.Turner D H,Mathews D H.NNDB:the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure[J].Nucleic Acids Research,2010,38(Database issue):D280-D282.Sugimoto N,Nakano S I,Katoh M,et al.Thermodynamic Parameters To Predict Stability of RNA/DNA Hybrid Duplexes[J].Biochemistry,1995,34(35):11211-11216.Allawi H,SantaLucia J:Thermodynamics and NMR of internal G-T mismatches in DNA.Bio-chemistry1997,36:10581-10594.Santalucia N E W J.Nearest-neighbor thermodynamics of deoxyinosine pairs in DNA duplexes[J].Nucleic Acids Research,2005,33(19):6258-67.Peyret N,Seneviratne P A,Allawi H T,et al.Nearest-Neighbor Thermodynamics and NMR of DNA Sequences with Internal A-A,C-C,G-G,and T-T Mismatches,[J].Biochemistry,1999, 38(12):3468-3477.Examplesntseq<-c("AAAATTTTTTTCCCCCCCCCCCCCCGGGGGGGGGGGGTGTGCGCTGC")out<-Tm_NN(ntseq,Na=50)outout$OptionsTm_Wallace Calculate the melting temperature using the’Wallace rule’DescriptionThe Wallace rule is often used as rule of thumb for approximate melting temperature calculations for primers with14to20nt length.UsageTm_Wallace(ntseq,ambiguous=FALSE,outlist=TRUE)Argumentsntseq Sequence(5’to3’)of one strand of the DNA nucleic acid duplex as string or vector of characters(Note:Non-DNA characters are ignored by this method).ambiguous Ambiguous bases are taken into account to compute the G and C content when ambiguous is TRUE.outlist output a list of Tm and options or only Tm value,default is TRUE.Author(s)Junhui LiReferencesThein S L,Lynch J R,Weatherall D J,et al.DIRECT DETECTION OF HAEMOGLOBIN E WITH SYNTHETIC OLIGONUCLEOTIDES[J].The Lancet,1986,327(8472):93.Examplesntseq=c( acgtTGCAATGCCGTAWSDBSY )#for wallace ruleout<-Tm_Wallace(ntseq,ambiguous=TRUE)outout$OptionsIndexc2s,2check_filter,3chem_correction,4complement,5GC,6print.TmCalculator,6s2c,7salt_correction,8Tm_GC,9Tm_NN,12Tm_Wallace,1517。

AMS(带图)中英文

AMS(带图)中英文

绝对值编码器 / Absolute Encoder 型号: AMS =多圈/ ASS = 单圈(SSI 接口)绝对值编码器AMS/ASS 是带有串行输出(SSI )的编码器。

这种编码器与我们经过长期考验的增量式编码器一样都是为极其恶劣的问题环境而设计的,坚固耐用,机械和电气性能卓越。

编码器所产生的位置数据被转换成串行SSI 信号传送。

这种装置可提供多圈(AMS)和单圈(ASS )两种制式,并且可依靠RS422接口编程。

The absolute encoder AMS / ASS are encoders with serial data output (SSI). These encoders are mechanically and electrically robust units as our approved incremental encoder, especially designed for use under extreme and problematic ambient conditions.The position data generated by the encoder will be transmitted serially acc. to the SSI processing. The unit can be supplied in multiturn (AMS) and in singleturn version (ASS) and is programmable by means of serial interface RS 422主要特点: ● 适用于轧机系统● 高机械性能抗冲击抗震动能力 ● 高保护等级 IP55/IP56● 机械结构相同于增量式编码器 FG4 ●可编程Key features:● Suitable for rolling mill application ● High mechanical resistance to shock and vibrations● High degree of protection IP55 / IP56 ● Mechanically exchangeable to incremental encoder type FG4 ● Programmable尺寸图HM95M54 174KDimension drawing HM 95 M54 174 K construction B5 AM_SSDO2.DOC6/99电气数据/ Electrical data AMS = 多圈/ ASS = 单圈供电电压:12V ... + 30V DC分辨率AMS:最大12 bit (4096 步/转)最大12 bit (4096 转)分辨率ASS:最大12 bit (4096 步/转)SSI 接口时钟输入光耦信号振幅5V输入电流6mA时钟频率80 kHz-1 MHz 时钟速率/传送率AMS 25时钟速率/传送率ASS 13传送周期无数据重复>30 µs有数据重复<20 µs 数据输出: RS 422SEL –输入光耦信号振幅5V输入电流6mA错误输出:RS 422输出电流25 mA程序输入和输出RS 4228 bit, 1 Stopbit, 9600 Baud可编程功能:零点的设定多圈和单圈的位数设置偏差1+2或读取计数方向输出码为二进制或格雷码识别码编程日期的读取标准设定Supply voltage:+ 12 V ... +30 V DCResolution AMS:max. 12 bit (4096 steps/rev.)max. 12 bit (4096 revolutions)Resolution ASS:max. 12 bit (4096 steps/rev.)SSI-interfaceClock input: opto couplerSignal amplitude: 5 VInput current: 6 mAClock frequency: 80 kHz-1 MHz Clock rate/transmission AMS: 25Clock rate/transmission ASS: 13Time between transmission cycleswithout data repetition > 30µswith data repetition <20 µs Data output: RS 422SEL - Input opto coupler Signal amplitude 5VInput current: 6 mAError output:RS 422Output current: 25mA Programm input and outputRS 4228 bit, Stopbit, 9600 BaudProgrammable functionsSetting of zero pointBits for multiturn and singleturn rangeSetting of Offset 1 + 2 or readingCounting directionOutput code binary/grayIdentificationReading of programming dateStandard setting数据传送:数据传送依据SSI 信号来说的(串行同步接口)。

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

摘 要
在竞争激烈的工业自动化生产过程中,机器视觉对产品质量的把关起着举足 轻重的作用,机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检 测技术相比,自动化的视觉检测系统更加经济、快捷、高效与 安全。纹理物体在 工业生产中广泛存在,像用于半导体装配和封装底板和发光二极管,现代 化电子 系统中的印制电路板,以及纺织行业中的布匹和织物等都可认为是含有纹理特征 的物体。本论文主要致力于纹理物体的缺陷检测技术研究,为纹理物体的自动化 检测提供高效而可靠的检测算法。 纹理是描述图像内容的重要特征,纹理分析也已经被成功的应用与纹理分割 和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检 测算法。这种算法能容忍物体变形引起的图像配准误差,对纹理的影响也具有鲁 棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义,如缺陷区域 的大小、形状、亮度对比度及空间分布等。同时,在参考图像可行的情况下,本 算法可用于同质纹理物体和非同质纹理物体的检测,对非纹理物体 的检测也可取 得不错的效果。 在整个检测过程中,我们采用了可调控金字塔的纹理分析和重构技术。与传 统的小波纹理分析技术不同,我们在小波域中加入处理物体变形和纹理影响的容 忍度控制算法,来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字 塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段,我们检测了一系列 具有实际应用价值的图像。实验结果表明 本文提出的纹理物体缺陷检测算法具有 高效性和易于实现性。 关键字: 缺陷检测;纹理;物体变形;可调控金字塔;重构
Keywords: defect detection, texture, object distortion, steerable pyramid, reconstruction
II

原核生物与真核生物染色体区别(全英文教学)

原核生物与真核生物染色体区别(全英文教学)

A Severe Problem of Packaging
1. Largest human chromosome: ~3 x 108 bp How long is it? 3 x 108 bp x 3.4 Å/bp = 10 x 104um! 2. A typical cell = 10 um
Conclusion: chromosome in length/size of cell
The Escherichia coli chromosome
DNA-binding proteins
蛋白 结构 功能 含量/每细 胞 4万个二聚 体 3万个二聚 体 ? 相当于其核 蛋白 H2B 基因 HU α 和 β 亚 基 , 使DNA压缩、类核 每个9KD 凝聚,刺激复制, 和1HF有关 两个相同 促使双链的互补、 复性 亚基,各 28KD α:10.5KD; 有助于att位点配对 β:9.5KD 重组 15KD亚基 和 DNA 结 合 , 与 DNA拓扑结构有关 ? ? hup A.B ?
Summary of Chromatin Packaging
Section D3: Eukaryotic chromosome structure
1.The mitotic chromosome
Telomere
sister chromotids 姊妹染色单体
Chromatid 染色 单体
染色体沿着纵轴出现着丝粒 区/主缢痕、染色体臂、副缢 痕、随体和端粒等不同结构 域
Linker DNABeads on a string
Nucleosome repeat: Core + linker DNA 200 bp
The 11 nm fiber

Constructions of Mutually Unbiased Bases

Constructions of Mutually Unbiased Bases
tr((κ−λ)(k+α)) ωp . k ∈F q
Indeed, the right hand side equals 0 when κ = λ because the argument k + α ranges through all values of Fq ; and equals 1 when κ = λ. √ Note that all components of the sequence bλ,α have absolute value 1/ q , hence the basis Bα and the standard basis are mutually unbiased, for any α ∈ Fq . By computing the inner product | bκ,α , bλ,β | for α = β , we see that the terms cubic in k cancel out and, moreover, that the exponent is given by the trace of a quadratic polynomial in k . By Lemma 1 the inner product evaluates to q −1/2 , hence Bα and Bβ are mutually unbiased. 2 Remark 1. A remarkable feature of the previous construction is that knowledge of one basis Bα is sufficient because shifting the indices by adding a field element yields the other bases. The construction does not work in characteristic 2 and 3 because in these cases the sets Bα and Bβ , with α = β , are not mutually unbiased. Ivanovi´ c gave a fresh impetus to the field in 1981 with his seminal paper [14]. Among other things, he gave explicit constructions of p + 1 mutually unbiased bases of Cp , for p a prime. His construction was later generalized in the influential paper by Wootters and Fields [22], who gave the first proof of the following theorem. This proof was recently rephrased by Chaturvedi [9], and an alternate proof was given by Bandyopadhyay et al. [3]. We give a particularly short proof by taking advantage of Weil sums. Theorem 2. Let Fq be a finite field with odd characteristic p. Denote by Ba = {va,b | b ∈ Fq } the set of vectors given by

slope——精选推荐

slope——精选推荐

Bounded-degree graphs can have arbitrarily largeslope numbersJ´a nos Pach∗and D¨o m¨o t¨o r P´a lv¨o lgyiR´e nyi Institute,Hungarian Academy of SciencesSubmitted:Oct21,2005;Accepted:Dec22,2005;Published:Jan7,2006Mathematics Subject Classification:05C62AbstractWe construct graphs with n vertices of maximum degree5whose every straight-linedrawing in the plane uses edges of at least n1/6−o(1)distinct slopes.A straight-line drawing of a graph G=(V(G),E(G))is a layout of G in the plane suchthat the vertices are represented by distinct points,the edges are represented by(possiblycrossing)line segments connecting the corresponding point pairs and not passing throughany other point that represents a vertex.If it creates no confusion,the vertex(edge)ofG and the point(segment)representing it will be denoted by the same symbol.Wade and Chu[WC94]defined the slope number sl(G)of G as the smallest number of distinctedge slopes used in a straight-line drawing of G.Dujmovi´c et al.[DSW04]asked whetherthe slope number of a graph of maximum degree d can be arbitrarily large.The followingshort argument shows that the answer is yes for d≥5.Define a“frame”graph F on the vertex set{1,...,n}by connecting vertex1to2by an edge and connecting every i>2to i−1and i−2.Adding a perfect matchingM between these n points,we obtain a graph G M:=F∪M.The number of different matchings is at least(n/3)n/2.Let G denote the huge graph obtained by taking the union of disjoint copies of all G M.Clearly,the maximum degree of the vertices of G isfive. Suppose that G can be drawn using at most S slopes,andfix such a drawing.For every edge ij∈M,label the points in G M corresponding to i and j by the slopeof ij in the drawing.Furthermore,label each frame edge ij(|i−j|≤2)by its slope.Notice that no two components of G receive the same labeling.Indeed,up to translationand scaling,the labeling of the edges uniquely determines the positions of the pointsrepresenting the vertices of G M.Then the labeling of the vertices uniquely determinesthe edges belonging to M.Therefore,the number of different possible labelings,which isS|F|+n<S3n,is an upper bound for the number of components of G.On the other hand, we have seen that the number of components(matchings)is at least(n/3)n/2.Thus,for any S we obtain a contradiction,provided that n is sufficiently large.With some extra care one can refine this argument to obtainTheorem.For any d≥5,there exist graphs G with n vertices of maximum degree d, whose slope numbers satisfy sl(G)≥n1d−2−o(1).Proof.Now instead of a matching,we add to the frame F in every possible way a(d−4)-regular graph R on the vertex set{1,...,n}.Thus,we obtain at least(cn/d)(d−4)n/2 different graphs G R:=F∪R,each having maximum degree at most d(here c>0 is a constant;see e.g.[BC78]).Suppose that each G R can be drawn using S slopes σ1<...<σS.Now we cannot insist that these slopes are the same for all G R,therefore, these numbers will be regarded as variables.Fix a graph G R=F∪R and one of its drawings with the above properties,in which vertex1is mapped into the origin and vertex2is mapped into a point whose x-coordinate bel every edge belonging to F by the symbolσk representing its slope.Furthermore,label each vertex j with a(d−4)-tuple of theσk s:with the symbols corresponding to the slopes of the d−4edges incident to j in R(with possible repetition). Clearly,the total number of possible labelings of the frame edges and vertices is at most S|F|+(d−4)n<S(d−2)n.Now the labeling itself does not necessarily identify the graph G R, because we do not know the actual values of the slopesσk.However,we can show that the number of different G R s that receive the same labeling cannot be too large.To prove this,first notice that for afixed labeling of the edges of the frame,the coordinates of every vertex i can be expressed as the ratio of two polynomials of degree at most n in the variablesσ1,...,σS.Indeed,letσ(ij)denote the label of ij∈F and let x(i)and y(i)denote the coordinates of vertex i.Since,by assumption,we have x(1)=y(1)=0and x(2)=1,we can conclude that y(2)=σ(12).We have the following equations for the coordinates of3:y(3)−y(1)=σ(13)(x(3)−x(1)),y(3)−y(2)=σ(23)(x(3)−x(2)).Solving them,we obtainx(3)=σ(12)−σ(23)σ(13)−σ(23),and so on.In particular,x(i)=Q i(σ1,...,σS)Q i Q j,we can decide whether the image of i is to the left of the image of j,to the right of it,or they have the same x-coordinate,provided that weknow the “sign pattern”of the polynomials P ij :=Q i Q j −Q i Q j and P ij :=Q i Q j ,i.e.,we know which of them are positive,negative,or zero.Thus,altogether we have 3 n 2 polynomials P ij ,P ij ,P ij (1≤i <j ≤n )in S variables,each of degree at most 2n .Forany fixed labeling of the frame edges and vertices,the sign pattern of these polynomials uniquely determines the graph G R .(Observe that if the label of a vertex i is a (d −4)-tuple containing the symbol σk ,then from the sign pattern of the above polynomials we can reconstruct the sequence of all vertices that belong to the line of slope σk passing through i ,from left to right.From this sequence,we can select all elements whose label contains σk ,and determine all edges of R along this line.)To conclude the proof,we need the Thom-Milnor theorem [BPR03]:Given N poly-nomials in S ≤N variables,each of degree at most 2n ,the number of sign patterns determined by them is at most (CNn/S )S ,for a suitable constant C >0.In our case,the number of graphs G R is at most the number of labelings (<S (d −2)n )times the maximum number of sign patterns of the polynomials P ij ,P ij ,P ij (1≤i <j ≤n ).By the Thom-Milnor theorem,this latter quantity is at most C 3 n 2n S,for a suitable constant C .Thus,the number of G R s is at most S (d −2)n (C 3n 3)S .Comparing this to the lower bound (cn/d )(d −4)n/2,stated in the first paragraph of the proof,we obtain that S ≥n 1d −2−o (1),as required.Acknowledgment.Bar´a t et al.[BMW05]independently found some similar,but slightly weaker results for the slope number.In particular,for d =5,they have a more complicated proof for the existence of graphs with maximum degree five and arbitrarily large slope numbers,that does not give any good explicit lower bound for the growth rate of the slope number,as the number of vertices tends to infinity.They have also established similar results for the geometric thickness ,defined as the smallest integer S with the property that the graph G admits a straight-line drawing,in which the edges can be colored by S colors so that no two edges of the same color cross each other [E04].Clearly,this number cannot exceed sl(G ).We are grateful to B.Aronov for his valuable remarks.References[BMW05]J.Bar´a t,J.Matouˇs ek,and D.R.Wood:Bounded-degree graphs have arbitrar-ily large geometric thickness,The Electronic bin.,13(2006),R3.[BPR03]S.Basu,R.Pollack,and M.-F.Roy:Algorithms in Real Algebraic Geometry ,Springer-Verlag,Berlin,2003.[BC78]E.A.Bender and E.R.Canfield:The asymptotic number of labeled graphs withgiven degree sequences,bin.Theory Ser.A 24(1978),296–307.[DSW04]V.Dujmovi´c ,M.Suderman,and D.R.Wood:Really straight graph drawings,in:Graph Drawing (GD ’04)(J.Pach,ed.),Lecture Notes in Computer Science 3383,Springer-Verlag,Berlin,2004,122–132.[E04]D.Eppstein:Separating thickness from geometric thickness,in:Towards a Theory of Geometric Graphs(J.Pach,ed.),Contemporary Mathematics342,Amer.Math. Soc.,2004,75–86.[WC94]G.A.Wade and J.-H.Chu:Drawability of complete graphs using a minimal slope set,The Computer J.37/2(1994),139–142.。

辅助信息-FASTQ格式文件解析与Phred质量分数比较及转换方法说明书

辅助信息-FASTQ格式文件解析与Phred质量分数比较及转换方法说明书

Learning the Comparing and Converting Method of Sequence PhredQuality ScoreHenghua Shi1, a, Weiyu Li2, b and Xin Xu3, c1School of Computer and Information Engineering, Beijing University of Agriculture, China 2College of Plant Science and Technology, Beijing University of Agriculture, China3Communication Technology Bureau, Xinhua News Agency, Chinaa b cKeywords: Phred quality scores; Sequence; Bioinformatics; FASTQ formatAbstract. The Phred quality score can measure the sequence quality, and quality scores are normally stored together with the nucleotide sequence in the widely accepted FASTQ format. For sequence raw reads with various FASTQ formats, the range of scores will depend on the technology and the base caller used. With the different technology, the range of scores is different. For learning the comparing and converting method of sequence Phred quality score, we do an experiment, and analyze the different between various FASTQ quality formats.IntroductionA Phred quality score is a measure of the quality of the identification of the nucleobases generated by automated DNA sequencing [1]. It was originally developed for Phred base calling to help in the automation of DNA sequencing in the Human Genome Project. Phred quality scores are assigned to each nucleotide base call in automated sequencer traces [2].FASTQ format is a text-based format for storing both a biological sequence and its corresponding quality scores. Both the sequence letter and quality score are each encoded with a single ASCII character for brevity. For sequence raw reads with various FASTQ formats, the range of scores will depend on the technology and the base caller used.In this paper, we study Phred quality score and FASTQ format. Then, we make a RNA sequence as the experiment resource and do an experiment to analyze the different between various FASTQ quality formats for learning the comparing and converting method of sequence Phred quality score.Phred Quality ScorePhred quality scores have become widely accepted to characterize the quality of DNA sequences, and can be used to compare the efficacy of different sequencing methods. Perhaps the most important use of Phred quality scores is the automatic determination of accurate, quality-based consensus sequences.Phred quality scores are used for assessment of sequence quality, recognition and removal of low-quality sequence (end clipping), and determination of accurate consensus sequences.Originally, Phred quality scores were primarily used by the sequence assembly program Phrap. Phrap was routinely used in some of the largest sequencing projects in the Human Genome Sequencing Project and is currently one of the most widely used DNA sequence assembly programs in the biotech industry. Phrap uses Phred quality scores to determine highly accurate consensus sequences and to estimate the quality of the consensus sequences. Phrap also uses Phred quality scores to estimate whether discrepancies between two overlapping sequences are more likely to arise from random errors, or from different copies of a repeated sequence.Within the Human Genome Project, the most important use of Phred quality scores was for automatic determination of consensus sequences. Before Phred and Phrap, scientists had to carefully look at discrepancies between overlapping DNA fragments; often, this involved manual determinationof the highest-quality sequence, and manual editing of any errors. Phrap's use of Phred quality scores effectively automated finding the highest-quality consensus sequence; in most cases, this completely circumvents the need for any manual editing. As a result, the estimated error rate in assemblies that were created automatically with Phred and Phrap is typically substantially lower than the error rate of manually edited sequence.In 2009, many commonly used software packages make use of Phred quality scores, albeit to a different extent. Programs like Sequencher use quality scores for display, end clipping, and consensus determination; other programs like CodonCode Aligner also implement quality-based consensus methods.Phred quality scores Q are defined as a property which is logarithmically related to the base-calling error probabilities P [2].-QP = 10 (1)10Phred quality scores are logarithmically linked to error probabilities as Table 1.Table 1 Phred quality scores, probability of incorrect base call and base call accuracyPhred Quality Score Probability of incorrect base call Base call accuracy10 1 in 10 90%20 1 in 100 99%30 1 in 1000 99.9%40 1 in 10,000 99.99%50 1 in 100,000 99.999%60 1 in 1,000,000 99.9999%Quality scores are normally stored together with the nucleotide sequence in the widely accepted FASTQ format. They account for about half of the required disk space in the FASTQ format (before compression), and therefore the compression of the quality values can significantly reduce storage requirements and speed up analysis and transmission of sequencing data. Both lossless and lossy compression are recently being considered in the literature. For example, the algorithm QualComp [3] performs lossy compression with a rate (number of bits per quality value) specified by the user. Based on rate-distortion theory results, it allocates the number of bits so as to minimize the MSE (mean squared error) between the original (uncompressed) and the reconstructed (after compression) quality values. Other algorithms for compression of quality values include SCALCE [4] and Fastqz.[5] Both are lossless compression algorithms that provide an optional controlled lossy transformation approach. For example, SCALCE reduces the alphabet size based on the observation that “neighboring” quality values are similar in general.FASTQ FormatFASTQ format is a text-based format for storing both a biological sequence and its corresponding quality scores. Both the sequence letter and quality score are each encoded with a single ASCII character for brevity. It was originally developed at the Wellcome Trust Sanger Institute to bundle a FASTA sequence and its quality data, but has recently become the de facto standard for storing the output of high-throughput sequencing instruments such as the Illumina Genome Analyzer [6].A FASTQ file normally uses four lines per sequence.∙Line 1 begins with a '@' character and is followed by a sequence identifier and an optional description (like a FASTA title line).∙Line 2 is the raw sequence letters.∙Line 3 begins with a '+' character and is optionally followed by the same sequence identifier (and any description) again.Line 4 encodes the quality values for the sequence in Line 2, and must contain the same number of symbols as letters in the sequence. Here are the quality value characters in left-to-right increasing order of quality (ASCII):!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghij klmnopqrstuvwxyz{|}~The range of scores of various FASTQ formats will depend on the technology and the base caller used. Fig. 1 is the comparing of different range of scores for Sanger, Illumina and Solexa.Figure 1. The different range of scores for Sanger, Illumina and SolexaExperiment ResultsFASTQ read simulation has been approached by several tools [7] [8]. A comparison of those tools can be seen in [9]. In this paper, we do an experiment with the above tools to analyze the different between various FASTQ quality formats for learning the comparing and converting method of sequence Phred quality score. The following is an Illumina 1.8+-assigned [10] identifier example with foure lines per sequence.@HWI-ST1268:95:D1MY0ACXX:4:1101:2314:2086 1:N:0:AGTTCCNCGATTGAATGGTCCGGTTGGAATTCTCGGGTGCCAAGGAACTCCAGTCA+#1=DDF?EHHGHFGIGIAFHIHEGIIEIHGI8@?BFG;BFHGGIF<BFHFWe respectively convert the above example to a Sanger-assigned and a Solexa-assigned. The experiment results of converting from Illumina 1.8+-assigned to Sanger-assigned is as following, and we can see that Sanger-assigned is the same with Illumina 1.8+-assigned. It is because the range of scores is similar as in Fig. 1.@HWI-ST1268:95:D1MY0ACXX:4:1101:2314:2086 1:N:0:AGTTCCNCGATTGAATGGTCCGGTTGGAATTCTCGGGTGCCAAGGAACTCCAGTCA+#1=DDF?EHHGHFGIGIAFHIHEGIIEIHGI8@?BFG;BFHGGIF<BFHFThe experiment results of converting from Illumina 1.8+-assigned to Solexa-assigned is as following, and we can see that Solexa-assigned is different with Illumina 1.8+-assigned. It is because the range of scores is different as in Fig. 1.@HWI-ST1268:95:D1MY0ACXX:4:1101:2314:2086 1:N:0:AGTTCCNCGATTGAATGGTCCGGTTGGAATTCTCGGGTGCCAAGGAACTCCAGTCA+;;;BBE;CGGFGEFHFH;EGHGCFHHCHGFH;;;>EF;>EGFFHE;>EGESummaryPhred quality scores are assigned to each nucleotide base call in automated sequencer traces. The range of scores of sequence raw reads with various FASTQ formats is different, and will depend on the technology and the base caller used. For learning the comparing and converting method of sequence Phred quality score, we do the converting from Illumina 1.8+-assigned to Sanger-assigned and Solexa-assigned experiments in this paper, and compare and analyze the different between various FASTQ quality formats.With this paper for the comparing and converting method of sequence Phred quality score, we can covert and compare sequence Phred quality score between FASTQ quality formats better, and make a basic for learning other bioinformatics analysis method more easy.AcknowledgementCorresponding author is Shi Henghua. The authors would like to acknowledge the supports provided by 2016 General Scientific Research Project of Beijing Municipal Education Commission (PXM2016_014207_000008).References[1] B. Ewing, L. Hillier, M. C. Wendl, P. Green: Base-calling of automated sequencer traces usingphred. I. Accuracy assessment. Genome Res. 8(3): p.175-185.[2] B. Ewing, P. Green: Base-calling of automated sequencer traces using phred. II. Errorprobabilities. Genome Res. 8(3):p.186-194.[3]I. Ochoa, et al. QualComp: a new lossy compressor for quality scores based on rate distortiontheory. BMC Bioinformatics 14.1 (2013): p.187.[4] F. Hach, I. Numanagi ́c, C. Alkan, S. C. Sahinalp: SCALCE: boosting sequence compressionalgorithms using locally consistent encoding. Bioinformatics2012, 28(23): p. 3051-3057.[5]Information on /dc/fastqz[6]P. J. A. Cock, C. J. Fields, N. Goto, M. L. Heuer, P. M. Rice: The Sanger FASTQ file format forsequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Research.38 (6): p. 1767-1771.[7]W. Huang, L. Li, J. R. Myers, G. T. Marth: ART: a next-generation sequencing read simulator.Bioinformatics 28, p.593-594.[8] D. Pratas, A. J. Pinho, O. S. Rodrigues, J. M. XS: a FASTQ read simulator. BMC Res. Notes 7, p.40.[9]M. Escalona, S. Roch, D. Posada: A comparison of tools for the simulation of genomicnext-generation sequencing data. Nature Reviews Genetics, 17, p.459-469.[10]I nformation on /。

高性能线性AC电源AMX系列说明书

高性能线性AC电源AMX系列说明书

1981AMX SeriesS INGLE AND T HREEP HASE AC P OWERS OURCES500 V ATO12,000 V ATAKE CONTROL OF YOURAC TEST POWERThe AMX Series is a family of High Performance Linear AC power sources covering the power range from 500 V A to 12 kV A. The product line offers both single and three phase models. Units are conservatively designed and output power ratings are based on the most severe combination of input line, output voltage, power factor, and temperature. This approach to product design allows the AMX Series to excel when delivering the high peak load currents demanded in the AC test environment. Great emphasis has been placed on low acoustic noise, ease of installation, and maximum power per cubic inch of rack space. Control and operating features provide a high degree of application versatility and ease of use for the test engineer. Applications range from simple, manually controlled frequency conversion to harmonic testing and sophisticated bus programmable transient simulation.An exceptionally broad bandwidth (50 kHzsmall signal) combined with peak/RMScurrent of 4-6:1 give the AMX Series theability to produce high quality, lowdistortion output power into the mostdynamic loads.Pacific Model 308AMX with UPC ControllerKEY FEATURES PROVIDE APPLICATION VERSATILITY•IEEE-488.2 or RS-232C with SCPI compatibility •LabVIEW for Windows®/LabWindows® drivers •Waveform Creation by Harmonic Synthesis •Graphical Analysis (Voltage and Current)•Harmonic Analysis (Voltage and Current)•Metering of RMS and Peak Values •Continuous Self Calibration (CSC)•Line Sync Option•6:1 Peak Current Capability •Low Impedance for IEC555 Testing •Programmable Output Impedance•Up to 0-300 V AC Direct Coupled Out • 1 Phase / 3 Phase Switch Selectable •20-5000 Hz Full Power Bandwidth •Power Levels from 500 V A to 12 kV A •Externally Referenced Meter Calibration •CE and ETL Mark availableDESIGN PROVIDES TOTAL CONTROL OF AC POWER•All AMX Series power source models may be equipped with either a digitally programmable Oscillator/Controller (UPC type) or a manually controlled Oscillator (UMC type).•Single phase power source models may be controlled to operate on either a 0-135 V AC range or a0-270 V AC range. Three phase models are additionally switchable to 3ø/1ø output power form.•Total control of the output power form and the selection of either the direct output or the optional transformer output is available from the front panel or the bus.•All operating functions may be controlled from either the front panel or from a remote RS-232 or IEEE 488.2/ SCPIBUS. LabVIEW for Windows® and LabWindows® Instrument Drivers are available.2Oscillograph of voltage and current waveform at load due to distribution losses. THD=6.6%Same conditions with programmable Zo engaged. THD=0.25%TIME BASED TRANSIENTSCYCLE BASED TRANSIENTSWA VEFORM EDITTHD=8.7%THD=22.2%THD=18.1%WaA VEFORM SYNTHESISHARMONIC CONTENT OFSPECIAL AMX SERIES OPERATING FEATURESCONTINUOUS SELF CALIBRATIONProvides for exceptional accuracy of the AC output V oltage. When enabled, accuracy improves to ±0.03% referenced to the power source internal voltmeter.PROGRAMMABLE DYNAMIC OUTPUT IMPEDANCE (OPTIONAL)Provides positive or negative output impedance. The output voltage waveform at the right is flattened as a result of a high peak load current drawn by an electronic load at the peak of the sinewave.Engaging the dynamic output impedance (Zº) feature dynamically compensates,as shown at the right, for the distribution or transformer losses up to ±10% of the output voltage.W A VEFORM LIBRARYUp to 99 different waveforms may be stored in the waveform library for execution as part of a steady state test program or for substitution in any output phase as part of a transient test program. Memory location #1 is a non-editable high resolution sine wave. Locations 2-16 are editable and may be substituted in any output phase. Locations 19-99 are factory stored, non editable waveforms that may be copied to 2-16 for edit and execute.W A VEFORM LIBRARYUp to 99 different waveforms may be stored in the waveform library for execution as part of a steady state test program or for substitution in any output phase as part of a transient test program. Memory location #1 is a non-editable high resolution sine wave. Locations 2-16 are editable and may be substituted in any output phase. Locations 19-99 are factory stored, non editable waveforms that may be copied to 2-16 for edit and execute.W A VEFORM EDITProvides the ability to modify a stored waveform by specifying the waveform amplitude desired at each specific phase angle. This method can be used toquickly create spikes, dropouts, notches and other sub-cycle wave conditions. The resulting modified waveform can be stored for execution.W A VEFORM ANALYSIS (OPTIONAL)Provides both a graphic and numeric display of the harmonic structure of a voltage or current waveform. The waveform is sampled at 512 samples per cycle using a 12 bit A/D converter. The resulting high fidelity waveform is analyzed for its harmonic structure up through the 51st harmonic. Data presented includes the magnitude of each harmonic in %, the total harmonic distortion, and the odd and even harmonic distortion in %.W A VEFORM SYNTHESIS (OPTIONAL)Provides the ability to quickly create virtually any AC Test Waveform that may be required by building it out of harmonics. The process is as simple as keying in the harmonic multiple, the amplitude, and the phase angle for each desired harmonic up through the 51st. If desired, waveforms may also be created in the time domain by making entries from the front panel or by downloading from a host PC.TIME BASED TRANSIENTSProvide the ability to create and execute on command, transients thatoccur linearly over a specified time segment to modify output voltage or frequency.CYCLE BASED TRANSIENTSProvide the ability to create and execute, on command, transients that substitute a selected waveform in the output for 1 to 100 cycles. The waveform being substituted can be selected and/or modified from the waveform library. Substitution is for an integer number of cycles,regardless of frequency.METERINGW A VEFORM CONTROL/ANALYSISFUNCTION KEYSPECIAL FUNCTIONS ACCESSED THROUGH UPC SETUP MENU•SENSE Establishes either local or external sense for metering and CSC.•CSCContinuous Self Calibration –provides for exceptional voltage accuracy.•PROGRAM Programmable output impedance Z °dynamically compensates for output transformer or linedistribution losses. Can simulate a soft power grid.• TRANSITION Permits control of the transition TIME time when changing the outputvoltage and frequency.• FREQUENCY Sets min and max programmable LIMITS frequency limits.• VOLTAGE Sets min and max programmable LIMITSvoltage limits.4TOTAL CONTROL, METERING,AND ANALYSIS OF AC POWER.SIMPLE INTUITIVE OPERATION.INFORMATIVE 160 CHARACTERLCD DISPLAY• Soft green backlight• AdjustablePARAMETER SELECT KEYSSelect phase voltages and operating frequency when manual controlis desired. The selected parameter is indicated by the LCD display.The clear key erases entries and keeps erasing with repeated pressinguntil the basic VI screen is displayed.EXECUTE KEYInstantly executes a stored program that has beenselected with the program key.SLEW KEYSSmoothly change the designated voltage orfrequency parameters. Rates are separatelyprogrammable.TRANSIENT (TRANS) KEYTurns time based or cycle based transients On orOff. Indicator is On when transient is executed.OUTPUT ENABLE KEYTurns the output contactor of the power source Onor Off. Indicator is On when the contactor is closed.ENTER KEYStores new parameter data that has been keyed in.PROGRAM KEYSelects 1 of 99 programs for edit or execution.EDIT KEYSelects the program edit mode and prompts for newentry.STORE KEYStores a program upon completion of editing.DISPLAY KEYSequences through each metering screen:• VI Meter• Power Meter• AMPS Meter• Waveform Analysis (option)5AMX POWER SOURCE MODELSNotes :1. All single phase units are operable with dual voltage ranges as listed. All three phase units are operable as single phase with dual voltage range capability or as three phase.Output voltage ranges and 1f / 3f conversions are selected by front panel or bus command.2. Output voltage ranges listed are for standard units. VMAX is achievable with nominal input voltage at full load. Other voltage ranges are available with the output magnetics option.3. Current ratings at 125 VRMS output.4. Input power frequency is 47-63 Hz. Single Phase: 100, 110, 120, 200, 208, 220, 230, 240, V AC ± 10%. Three phase: 208, 220, 240, 380, 416 V AC ± 10%.POWER SOURCE SPECIFICATIONSOutput Frequency:20 to 5000 Hz. Full Power Line Regulation:0.1% max for a 10% line change Load Regulation:0.25% 20 to 2000 Hz.0.5% 2000 to 5000 Hz.Can be improved to less than 0.03%with CSC engaged.Output Distortion:0.1% THD from 20 to 1000 Hz0.25% THD from 1000 to 5000 HzRipple and Noise:-72 dB Response Time:5µsec. typical to a step load change. Small signal band-width is 5 Hz. to 50 kHz, typical.MECHANICAL SPECIFICATIONSAll models are designed for operation in 19 inch equipment racks.Models above 1800 V A have side handles for ease of handling.Mounting:Standard 19 inch rack. Slide rails are available as an option for all models.Height:See model table above for panel height.Depth:Will not exceed 24 inches from the front panel to the rear of the chassis, including connectors, handles and cabling.Cooling:Forced air, front or side intakes, rear exhaust with auto fan speed control for low acoustic noise operation.POWER SOURCE SPECIFICATIONSAMX Series Power sources can be equipped with output transformers to provide an alternate output voltage range. Selec-tion of direct or transformer coupled range is performed by the controller via front panel or bus command. The standard frequency range for transformer coupled outputs is 45 to 5000 Hz. Standard output ratios are 1.5:1, 2.0:1, and 2.5:1.Transformer outputs are supplied internally or externally via a Magnetics Module. Consult the factory for additional information regarding special output ranges not listed.MODEL105AMX 108AMX 112AMX 125AMX 140AMX 305AMX 308AMX 312AMX 1200750500400025001200750500OUTPUT FORM (Note 1)RATED P0WER (VA)OUTPUT VOLTS MAX(V RMS )(Note 2)OUTPUT AMPS (A RMS )(Note 3)OUTPUT AMPS (A PK )INPUT POWER FORM (Note 4)PANEL HEIGHT (IN.)WEIGHT (LBS.)1Ø135/2704/240/206/340/2040/2010/590/45 5 1/46565110170656570655 1/410 1/2145 1/45 1/45 1/45 1/4140/7045/1515/Ø45/1515/Ø45/1515/Ø20/1032/164/21.5/Ø6/22/Ø10/3.33.3/Ø135/270150/300150/300135/270135/270135 L-N 135/270135 L-N 150/300150 L-N 1Ø1Ø1Ø1Ø3Ø3Ø3Ø318AMX 320AMX 330AMX 345AMX 360AMX 390AMX 3120AMX120009000600045003000200018003Ø135/270135 L-N 15/55/Ø60/2020/Ø18/66/Ø60/2020/Ø150/5050/Ø24/128/Ø165/5555/Ø8 3/4100100177185175 x 2185 x 21628 3/4141414282 each x 14282 each x 14210/7070/Ø330/110110/Ø420/140140/Ø36/1212/Ø48/1616/Ø72/2424/Ø96/3232/Ø135/270135 L-N 135/270135 L-N 135/270135 L-N 135/270135 L-N 135/270135 L-N 135/270135 L-N3Ø3Ø3Ø3Ø3Ø3Ø1Ø1Ø1Ø3Ø3Ø1Ø1Ø1Ø3Ø3Ø3Ø3Ø3Ø3Ø3Ø6UPC CONTROLLER SPECIFICATIONSThe UPC controller is essentially a 3f AC arbitrary waveform generator and Precision AC metering system. Each waveform stored in the UPC is encoded with 12 bit amplitude and 10 bit time resolution for each cycle. The waveform for each phase may be independently selected and may be independentlyvaried in amplitude and phase angle with respect to phase A.The UPC output metering samples the output volts and amps at 512 samples per measurement using a 12 bit A/D converter. This technique provides exceptional metering accuracy and resolution (20 bits), and delivers a high-fidelity waveform back to a host computer for analysis.The UPC includes a remote GPIB interface compatible with IEEE 488.2 and SCPI. An available option is an RS-232 serial port that operates up to 38.4 kBaud.Frequency:20.00 to 5000 Hz ±0.01%V oltage:Programmable, 0-VMAX, in 0.1 volt steps Direct (see table on page 6)V oltage:Multi-range units are equipped with Transformer output transformers. When alternate range isselected, voltage at transformer output isprogrammable in steps of 0.5 volts. Accuracy:Executive voltage is within ±50 mv (0.03%) Command of command voltage, referenced to theV oltage internal voltmeter with CSC engaged. Accuracy:± 0.01%, 20-5000 HzOutput Zo:Dynamic output impedance (Zo) is (Optional)programmable, 0 to ± Zo max. in 0.1%steps. Zo value in milliohms varies withdifferent models but usually results in a ±10% change in output voltage at maximumload amps.Phase:Phase Angle (f) of Phases B and CAngle relative to Phase A is programmable from 0-359° in 1° increments ± 0.5°.Current:Current limit is programmable from 0Limit to Ipeak maximum of the power source.Accuracy is ± 1%, resolution ± 0.05%. Library:Stores up to 99 steady state parameter Steady sets consisting of waveform, voltage,State frequency, f angle and current limit. Can be Programs executed by program number from the frontpanel or the bus.Library:Stores up to 99 transient programs - one Transient associated with each steady state program. Programs Allows for changes in volts and frequencyvs. time, or waveform changes by cycle count. Library:Stores up to 99 waveforms that can be edited Waveform and executed in any manner and in anyoutput phase.V oltmeter:Range -0-354 volts L-N0-708 volts L-LResolution -0.10V AC to front panel0.001V AC to remote interfaceAccuracy -± 0.25% of reading ± 0.1% ofrange (50-500Hz) Ammeter:Range -300% of system current ratingResolution -0.01AAC to front panel0.001AAC to remote interfaceAccuracy -± 0.25% of reading ± 0.1% ofrange (50-500Hz) Power Meter:Range -Based on ammeter rangeResolution - 1.0 watts or V A to front panel0.001 Watts or V A to remoteinterfaceAccuracy -± 1% of Full Scale Power Factor: Calculated and displayed to three significant& Crest Factor digits.Ext. Input:Each phase is algebraically summed withUPC waveform and amplified 25X to thedirect output.Amplitude:±10 volt input for each phaseMod. Input modulates the output ± 100%Sync Outputs:1) Zero crossing, Phase A2) Transient start-stop3) True when Transient is enabled4) Clock - 1024 times the output freq.Command:Average time to start of parameter changeResponse from bus command (end of string character) Time is 50 ms. Ramp transition time to final valueis settable from 250 µs to 300 sec.Waveform :Permits waveform creation by entering %Synthesis amplitude and phase angle for the 2ndthrough the 51st harmonics.Waveform:Reports voltage and current waveformAnalysis harmonic content in % and phase anglefor the 2nd through the 51st harmonics.Displays THD, OHD, EHD in %.7MANUAL CONTROL OF AC POWERProvide easy manual control with Pacific’s UMC-31 Manual AC Power Controller.UMC-31 Manual ControllerThe UMC-31 provides operational control and high quality oscillator signals for both single and three phase Power Sources.•Obtain precision frequency and phase conversion for manufacturing and test.•Provide high quality, general purpose lab power where test versatility is required.•Achieve low cost and power form flexibility for power supply tests.SPECIFICATIONS UMC-31 CONTROLLERPhase:Select single, split, or three phase operation by internal jumper. Phase angles are fixed at 120° and 240° for three phase operation.Frequency:Select 50, 60, or 400 Hz fixed or a variable frequency mode of 45 to 500 Hz.V oltage:0-VMAX via 10 turn potentiometer on the front panel.Metering:Autoranging V olts, Amps, and Frequency.For additional data sheets or technical application assistance, please call or fax Pacific Power Source, attn.: Sales Department.Pacific Power Source 15122 Bolsa Chica StreetHuntington Beach, CA 92649 USA Tel:+1 714 898 2691+1 800 854 2433Fax:+1 714 898 8076A Division of Thermo Voltek, a Thermo Electron Company Thermo Voltek©Thermo Voltek Corporation. Specifications subject to change without notice.Authorized Representative8。

生物信息学主要英文术语及释义(续完)

生物信息学主要英文术语及释义(续完)

⽣物信息学主要英⽂术语及释义(续完)These substitutions may be found in an amino acid substitution matrix such as the Dayhoff PAM and Henikoff BLOSUM matrices. Columns in the alignment that include gaps are not scored in the calculation. Perceptron(感知器,模拟⼈类视神经控制系统的图形识别机) A neural network in which input and output states are directly connected without intervening hidden layers. PHRED (⼀种⼴泛应⽤的原始序列分析程序,可以对序列的各个碱基进⾏识别和质量评价) A widely used computer program that analyses raw sequence to produce a 'base call' with an associated 'quality score' for each position in the sequence. A PHRED quality score of X corresponds to an error probability of approximately 10-X/10. Thus, a PHRED quality score of30 corresponds to 99.9% accuracy for the base call in the raw read. PHRAP (⼀种⼴泛应⽤的原始序列组装程序) A widely used computer program that assembles raw sequence into sequence contigs and assigns to each position in the sequence an associated 'quality score', on the basis of the PHRED scores of the raw sequence reads. A PHRAP quality score of X corresponds to an error probability of approximately 10-X/10. Thus, a PHRAP quality score of 30 corresponds to 99.9% accuracy for a base in the assembled sequence. Phylogenetic studies(系统发育研究) PIR (主要蛋⽩质序列数据库之⼀,翻译⾃GenBank) A database of translated GenBank nucleotide sequences. PIR is a redundant (see Redundancy) protein sequence database. The database is divided into four categories: PIR1 - Classified and annotated. PIR2 - Annotated. PIR3 -Unverified. PIR4 - Unencoded or untranslated. Poisson distribution(帕松分布) Used to predict the occurrence of infrequent events over a long period of time 143or when there are a large number of trials. In sequence analysis, it is used to calculate the chance that one pair of a large number of pairs of unrelated sequences may give a high local alignment score. Position-specific scoring matrix (PSSM)(特定位点记分矩阵,PSI-BLAST等搜索程序使⽤) The PSSM gives the log-odds score for finding a particular matching amino acid in a target sequence. Represents the variation found in the columns of an alignment of a set of related sequences. Each subsequent matrix column corresponds to the next column in the alignment and each row corresponds to a particular sequence character (one of four bases in DNA sequences or 20 amino acids in protein sequences). Matrix values are log odds scores obtained by dividing the counts of the residue in the alignment, dividing by the expected number of counts based on sequence composition, and converting the ratio to a log score. The matrix is moved along sequences to find similar regions by adding the matching log odds scores and looking for high values. There is no allowance for gaps. Also called a weight matrix or scoring matrix. Posterior (Bayesian analysis) A conditional probability based on prior knowledge and newly uated relationships among variables using Bayes rule. See also Bayes rule. Prior (Bayesian analysis) The expected distribution of a variable based on previous data. Profile(分布型) A matrix representation of a conserved region in a multiple sequence alignment that allows for gaps in the alignment. The rows include scores for matching sequential columns of the alignment to a test sequence. The columns include substitution scores for amino acids and gap penalties. See also PSSM. Profile hidden Markov model(分布型隐马尔可夫模型) A hidden Markov model of a conserved region in a multiple sequence alignment that includes gaps and may be used to search new sequences for similarity to the aligned sequences. Proteome(蛋⽩质组) The entire collection of proteins that are encoded by the genome of an organism. Initially the proteome is estimated by gene prediction and annotation methods but eventually will be revised as more information on the sequence of the expressed genes is obtained. Proteomics (蛋⽩质组学) Systematic analysis of protein expression_r of normal and diseased tissues that involves the separation, identification and characterization of all of the proteins in an organism. Pseudocounts Small number of counts that is added to the columns of a scoring matrix to increase the variability either to avoid zero counts or to add more variation than was found in the sequences used to produce the matrix. 144PSI-BLAST (BLAST系列程序之⼀) Position-Specific Iterative BLAST. An iterative search using the BLAST algorithm. A profile is built after the initial search, which is then used in subsequent searches. The process may be repeated, if desired with new sequences found in each cycle used to refine the profile. Details can be found in this discussion of PSI-BLAST. (Altschul et al.) PSSM (特定位点记分矩阵) See position-specific scoring matrix and profile. Public sequence databases (公共序列数据库,指GenBank、EMBL和DDBJ) The three coordinated international sequence databases: GenBank, the EMBL data library and DDBJ. Q20 (Quality score 20) A quality score of > or = 20 indicates that there is less than a 1 in 100 chance that the base call is incorrect. These are consequently high-quality bases. Specifically, the quality value "q" assigned to a basecall is defined as: q = -10 x log10(p) where p is the estimated error probability for that basecall. Note that high quality values correspond to low error probabilities, and conversely. Quality trimming This is an algorithm which uses a sliding window of 50 bases and trims from the 5' end of the read followed by the 3' end. With each window, the number of low quality (10 or less) bases is determined. If more than 5 bases are below the threshold quality, the window is incremented by one base and the process is repeated. When the low quality test fails, the position where it stopped is recorded. The parameters for window length low quality threshold and number of low quality bases tolerated are fixed. The positions of the 5' and 3' boundaries of the quality region are noted in the plot of quality values presented in the" Chromatogram Details" report. Query (待查序列/搜索序列) The input sequence (or other type of search term) with which all of the entries in a database are to be compared. Radiation hybrid (RH) map (辐射杂交图谱) A genome map in which STSs are positioned relative to one another on the basis of the frequency with which they are separated by radiation-induced breaks. The frequency is assayed by analysing a panel of human–hamster hybrid cell lines, each produced by lethally irradiating human cells and fusing them with recipient hamster cells such that each carries a collection of human chromosomal fragments. The unit of distance is centirays (cR), denoting a 1% chanceof a break occuring between two loci Raw Score (初值,指最初得到的联配值S) The score of an alignment, S, calculated as the sum of substitution and gap scores. Substitution scores are given by a look-up table (see PAM, BLOSUM). Gap scores are typically calculated as the sum of G, the gap opening penalty 145and L, the gap extension penalty. For a gap of length n, the gap cost would be G+Ln. The choice of gap costs, G and L is empirical, but it is customary to choose a high value for G (10-15)and a low value for L (1-2). Raw sequence (原始序列/读胶序列) Individual unassembled sequence reads, produced by sequencing of clones containing DNA inserts. Receiver operator characteristic The receiver operator characteristic (ROC) curve describes the probability that a test will correctly declare the condition present against the probability that the test will declare the condition present when actually absent. This is shown through a graph of the tesls sensitivity against one minus the test specificity for different possible threshold values. Redundancy (冗余) The presence of more than one identical item represents redundancy. In bioinformatics, the term is used with reference to the sequences in a sequence database. If a database is described as being redundant, more than one identical (redundant) sequence may be found. If the database is said to be non-redundant (nr), the database managers have attempted to reduce the redundancy. The term is ambiguous with reference to genetics, and as such, the degree of non-redundancy varies according to the database manager's interpretation of the term. One can argue whether or not two alleles of a locus defines the limit of redundancy, or whether the same locus in different, closely related organisms constitutes redundency. Non-redundant databases are, in some ways, superior, but are less complete. These factors should be taken into consideration when selecting a database to search. Regular expression_rs This computational tool provides a method for expressing the variations found in a set of related sequences including a range of choices at one position, insertions, repeats, and so on. For example, these expression_rs are used to characterize variations found in protein domains in the PROSITE catalog. Regularization A set of techniques for reducing data overfitting when training a model. See also Overfitting. Relational database(关系数据库)Organizes information into tables where each column represents the fields of informa-tion that can be stored in a single record. Each row in the table corresponds to a single record. A single database can have many tables and a query language is used to access the data. See also Object-oriented database. Scaffold (⽀架,由序列重叠群拼接⽽成) The result of connecting contigs by linking information from paired-end reads from plasmids, paired-end reads from BACs, known messenger RNAs or other sources. The contigs in a scaffold are ordered and oriented with respect to one another. 146 Scoring matrix(记分矩阵) See Position-specific scoring matrix. SEG (⼀种蛋⽩质程序低复杂性区段过滤程序) A program for filtering low complexity regions in amino acid sequences. Residues that have been masked are represented as "X" in an alignment. SEG filtering is performed by default in the blastp subroutine of BLAST 2.0. (Wootton and Federhen) Selectivity (in database similarity searches)(数据库相似性搜索的选择准确性) The ability of a search method to locate members of a protein family without making a false-positive classification of members of other families. Sensitivity (in database similarity searches)(数据库相似性搜索的灵敏性) The ability of a search method to locate as many members of a protein family as possi-ble, including distant members of limited sequence similarity. Sequence Tagged Site (序列标签位点) Short cDNA sequences of regions that have been physically mapped. STSs provide unique landmarks, or identifiers, throughout the genome. Useful as a framework for further sequencing. Significance(显著⽔平) A significant result is one that has not simply occurred by chance, and therefore is prob-ably true. Significance levels show how likely a result is due to chance, expressed as a probability. In sequence analysis, the significance of an alignment score may be calcu-lated as the chance that such a score would be found between random or unrelated sequences. See Expect value. Similarity score (sequence alignment) (相似性值) Similarity means the extent to which nucleotide or protein sequences are related. The extent of similarity between two sequences can be based on percent sequence identity and/or conservation. In BLAST similarity refers to a positive matrix score. The sum of the number of identical matches and conservative (high scoring) substitu-tions in a sequence alignment divided by the total number of aligned sequence charac-ters. Gaps are usually ignored. Simulated annealing A search algorithm that attempts to solve the problem of finding global extrema. The algorithm was inspired by the physical cooling process of metals and the freezing process in liquids where atoms slow down in movement and line up to form a crystal. The algorithm traverses the energy levels of a function, always accepting energy levels that are smaller than previous ones, but sometimes accepting energy levels that are greater, according to the Boltzmann probability distribution. Single-linkage cluster analysis An analysis of a group of related objects, e.g., similar proteins in different genomes to identify both close and more distant relationships, represented on a tree or dendogram. The method joins the most closely related pairs by the neighbor-joining algorithm by representing these pairs as outer branches on 147the tree. More distant objects are then pro-gressively added to lower tree branches. The method is also used to predict phylogenet-ic relationships by distance methods. See also Hierarchical clustering, Neighbor-joining method. Smith-Waterman algorithm(Smith-Waterman算法) Uses dynamic programming to find local alignments between sequences. The key fea-ture is that all negative scores calculated in the dynamic programming matrix are changed to zero in order to avoid extending poorly scoring alignments and to assist in identifying local alignments starting and stopping anywhere with the matrix. SNP (单核苷酸多态性) Single nucleotide polymorphism, or a single nucleotide position in the genome sequence for which two or more alternative alleles are present at appreciable frequency (traditionally, at least 1%) in the human population. Space or time complexity(时间或空间复杂性) An algorithms complexity is the maximum amount of computer memory or time required for the number of algorithmic steps to solve a problem. Specificity (in database similarity searches)(数据库相似性搜索的特异性) The ability of a search method to locate members of one protein family, including dis-tantly related members. SSR (简单序列重复) Simple sequence repeat, a sequence consisting largely of a tandem repeat of a specific k-mer (such as (CA)15). Many SSRs are polymorphic and have been widely used in genetic mapping. Stochastic context-free grammar A formal representation of groups of symbols in different parts of a sequence; i.e., not in the same context. An example is complementary regions in RNA that will form sec-ondary structures. The stochastic feature introduces variability into such regions. Stringency Refers to the minimum number of matches required within a window. See also Filtering. STS (序列标签位点的缩写) See Sequence Tagged Site Substitution (替换) The presence of a non-identical amino acid at a given position in an alignment. If the aligned residues have similar physico-chemical properties the substitution is said to be "conservative". Substitution Matrix (替换矩阵) A substitution matrix containing values proportional to the probability that amino acid i mutates into amino acid j for all pairs of amino acids. such matrices are constructed by assembling a large and diverse sample of verified pairwise alignments of amino acids. If the sample is large enough to be statistically significant, the resulting matrices should reflect the true probabilities of mutations occuring through a period of evolution. 148Sum of pairs method Sums the substitution scores of all possible pair-wise combinations of sequence charac-ters in one column of a multiple sequence alignment. SWISS-PROT (主要蛋⽩质序列数据库之⼀) A non-redundant (See Redundancy) protein sequence database. Thoroughly annotated and cross referenced. A subdivision is TrEMBL. Synteny The presence of a set of homologous genes in the same order on two genomes. Threading In protein structure prediction, the aligning of the sequence of a protein of unknown structure with a known three-dimensional structure to determine whether the amino acid sequence is spatially and chemically compatible with that structure. TrEMBL (蛋⽩质数据库之⼀,翻译⾃EMBL) A protein sequence database of Translated EMBL nucleotide sequences. Uncertainty(不确定性) From information theory, a logarithmic measure of the average number of choices that must be made for identification purposes. See also Information content. Unified Modeling Language (UML) A standard sanctioned by the Object Management Group that provides a formal nota-tion for describing object-oriented design. UniGene (⼈类基因数据库之⼀) Database of unique human genes, at NCBI. Entries are selected by near identical presence in GenBank and dbEST databases. The clusters of sequences produced are considered to represent a single gene. Unitary Matrix (⼀元矩阵) Also known as Identity Matrix.A scoring system in which only identical characters receive a positive score. URL(统⼀资源定位符) Uniform resource locator. Viterbi algorithm Calculates the optimal path of a sequence through a hidden Markov model of sequences using a dynamic programming algorithm. Weight matrix See Position-specifc scoring matrix.。

CLEC中国英语学习者语料库

CLEC中国英语学习者语料库

CLEC中国英语学习者语料库CLEC收集了包括中学生、大学英语4级和6级、专业英语低年级和高年级在内的5种学生的语料一百多万词,并对言语失误进行标注。

其目的就是观察各类学生的英语特征和言语失误的情况,希望通过定量和定性的方法对中国学习者英语作出较为精确的描写,为我国学生的英语教学提供有用的反馈信息。

表1 CLEC语料分布类型词次ST2 208088ST3 209043ST4 212855ST5 214510ST6 226106总计 1070602言语失误标注原则1. 简单合理,易于系统操作。

参与标注的人比较多,分类表过于繁复,就难于掌握。

我们采取两级分类,第一级有11类:词形(fm)、动词短语(vp)、名词短语(np)、代词(pr)、形容词短语(aj)、副词(ad)、介词短语(pp)、连词(cj)、词汇(wd)、搭配(cc)、句子(sn)。

每一类里再用数目字细分。

如[cc]为词语搭配不当,[cc1]表示名词和名词的搭配,[cc2]表示名词和动词的搭配,[cc3]表示动词和名词的搭配,等等。

2. 分类表的类别要适中。

过粗容易统一,但信息太少,不利于分析学习者的失误/过细难以统一,容易把同一种失误归到不同类别。

目前我们采取的办法是对常见的失误从细(如vp和np都有9小类),对少见的失误从粗(如cj只有两小类)。

现在的分类表有61个失误码,是属于中等规模的分类表。

提供足够的失误信息(失误本身、失误类型和失误发生范围)。

例如In the past,[vp6, 4-] kind to each other…, 失误用方括号表示,放在失误people are 之后。

[vp6]为vp(动词)第6种(时态)失误,4-为失误发生的范围,-表示失误的位置,4表示失误前有4个词。

要联系这4个词,才能判断are这个词用错了。

开放性。

容许研究者根据需要对失误类型进行补充或进一步再分出细类。

例如[sn8]为句子结构有缺陷,研究者可以对这种失误再分为若干细类来研究。

经管实证英文文献常用的缺失值处理方法

经管实证英文文献常用的缺失值处理方法

经管实证英文文献常用的缺失值处理方法全文共10篇示例,供读者参考篇1Handling missing values in empirical studies is super important, guys! Like, imagine you're doing this really cool project for your economics class, and then you realize some of your data is missing. Oh no! What do you do now?Well, don't worry, because there are some common methods you can use to deal with missing values. One way is to just delete the observations with missing data. But be careful with this one, because deleting too many observations can mess up your results.Another way is to replace the missing values with something else, like the mean or median of the other observations. This can help keep your data set complete, but it might not always give you the most accurate results.You could also use regression imputation, where you use a regression model to predict the missing values based on the other variables in your data set. This method is more complex, but it can be really useful if you have a lot of missing data.And finally, there's multiple imputation, where you create multiple versions of your data set with different imputed values for the missing data. This can give you a more accurate estimate of the uncertainty in your results.So there you have it, guys! Don't let missing values get you down. With these methods, you can keep your data set on track and make sure your results are as accurate as possible. Happy data crunching!篇2Title: Fun Ways to Deal with Missing Values in Empirical StudiesHey guys! Today I'm gonna talk about a super duper important topic in economics and management studies - dealing with missing values in your data. I know it sounds boring, but trust me, it's gonna be fun!So, what are missing values? Well, missing values are basically when some of the data you collected is, well, missing. It could be because someone forgot to fill out a survey question, or maybe there was a problem with the data collection process. Whatever the reason, missing values are a big no-no in research studies.But don't worry, there are lots of cool ways to deal with missing values. One way is called mean imputation. This means that you take the average of the values you do have and fill in the missing values with that average. It's like guessing what the missing values would be based on the data you do have.Another fun way to deal with missing values is called multiple imputation. This is where you create multiple possible values for the missing data based on the data you do have. It's kind of like playing a game of pretend and coming up with different scenarios for what the missing values could be.There are also other ways to deal with missing values, like using regression analysis or machine learning algorithms. These methods are a bit more complicated, but they can be really powerful in helping you fill in the missing pieces of your data puzzle.So there you have it, guys! Dealing with missing values in your research studies doesn't have to be boring. With a little creativity and some fun techniques, you can make sure your data is complete and accurate. Happy researching!篇3Hey guys! Today I'm gonna talk about how we deal with missing values in empirical studies in economics and management. It's super important 'cause we wanna make sure our data is accurate and reliable. So, let's dive in!1. Complete Case Analysis: This method is like when you throw out any observations that have missing values. It's easy but you might lose some important info.2. Mean/Median/Mode Imputation: This is when you replace missing values with the average, median, or mode of the variable. It's simple but it can mess up the distribution of your data.3. Multiple Imputation: This is a fancier method where you create several different datasets with different imputed values and then combine them for analysis. It's more accurate but also more complicated.4. Regression Imputation: This is when you use regression models to predict missing values based on other variables. It's pretty cool but you gotta be careful with overfitting.5. Using Advanced Algorithms: These are like machine learning methods that can handle missing values automatically. They're super fancy but also require a lot of expertise.So there you have it, some common methods for dealing with missing values in empirical studies. Remember, it's important to choose the right method based on your data and research question. Happy analyzing, everyone!篇4Hello everyone, today I'm going to talk about the common methods for handling missing values in empirical studies in the field of economics and management. When we collect data for our research, sometimes we may find that some values are missing. It's important to handle these missing values properly so that our results are accurate and reliable.One common method for handling missing values is to delete the observations with missing values. This method is easy to implement, but it may lead to a loss of information and reduce the sample size. Another method is to replace the missing values with the mean or median of the variable. This method can help retain the sample size, but it may introduce bias into the results.Another popular method is to use multiple imputation, where missing values are replaced with estimated values based on the relationship between variables. This method is morecomplex, but it can provide more accurate results when dealing with missing values.It's also important to consider the reasons for missing values and whether they are missing completely at random, missing at random, or missing not at random. Understanding the reasons for missing values can help us choose the most appropriate method for handling them.In conclusion, handling missing values is an important step in empirical studies in economics and management. By using appropriate methods, we can ensure that our results are reliable and accurate.篇5Hey guys, today I'm gonna talk about some common methods for handling missing values in empirical research in management and economics. Missing values are a common problem in data analysis, so it's important to know how to deal with them properly.One common method for handling missing values is to simply remove the observations with missing data. This can be a quick and easy way to deal with missing values, but it can alsolead to biased results if the missing data is not random. So be careful when using this method.Another method is to impute the missing values with the mean or median of the available data for that variable. This can help to preserve the overall distribution of the data and reduce bias in the analysis. However, this method may not be appropriate for variables with a skewed distribution.You can also use regression or machine learning algorithms to predict missing values based on the other variables in the dataset. This can be a more sophisticated approach and may provide more accurate results, but it can also be computationally intensive and may require a large amount of data.There are other methods for handling missing values, such as using multiple imputation techniques or weighting the observations based on the probability of being missing. It's important to choose the method that is most appropriate for your data and research question.In conclusion, handling missing values in empirical research is crucial for obtaining accurate and reliable results. By using appropriate methods for dealing with missing data, you can ensure that your analysis is sound and your conclusions are valid. So don't forget to check for missing values in your data andchoose the best method for handling them. Thanks for listening, guys!篇6Hey guys, have you ever wondered how researchers deal with missing values in their economic and management studies? Well, today I'm gonna tell you all about it!When we're doing research, sometimes we come across data that is missing or incomplete. This can happen for lots of reasons, like errors in data collection or just plain old bad luck. But don't worry, there are several common ways that researchers handle missing values.One popular method is called mean imputation. This is when we calculate the average value of a variable and use that average to fill in the missing data. It's a simple and easy way to deal with missing values, but it can sometimes skew our results if there are a lot of missing values.Another method is called hot-deck imputation. This is when we look at similar cases in our data set and use those values to fill in the missing data. It's a bit more complex than mean imputation, but it can give us more accurate results.There's also regression imputation, where we use regression analysis to predict missing values based on other variables in our data set. It's a more sophisticated method, but it can be really helpful when we have a lot of missing data.And lastly, there's multiple imputation, where we create multiple complete data sets with different imputed values and then analyze them all together. This method can give us more accurate and reliable results, but it's also more time-consuming.So there you have it, guys! Those are some of the common ways that researchers deal with missing values in their economic and management studies. Remember, dealing with missing data is an important part of doing good research, so make sure to choose the right method for your study. Thanks for listening, and happy researching!篇7Title: Fun Ways to Handle Missing Values in Empirical Management StudiesHey there pals! Today, I want to talk about something super important when we're doing research in management studies. Yep, you guessed it – handling missing values in our data! Sometimes, our data is not complete and we need to figure outwhat to do with those missing pieces. But don't worry, I've got some cool methods that we can use to deal with missing values.First off, let's talk about mean substitution. This is when we replace missing values with the mean of the variable. It's a simple and easy way to fill in those gaps, but be careful because it can skew our results if we have a lot of missing values.Another fun method is using regression imputation. This is where we use other variables to predict the missing values and fill them in using regression analysis. It's like solving a puzzle with our data!A popular technique is multiple imputation. This method creates multiple copies of our dataset, fills in missing values in each copy, and then combines the results. It's like having a bunch of backup plans to make sure our data is complete.And finally, we have the cool-sounding hot-deck imputation. This method matches observations with missing values to similar observations with complete data. It's like making new friends for our missing values to hang out with!So there you have it, some fun and practical ways to handle missing values in our management studies. Just remember tochoose the method that works best for your data and research questions. Happy researching, little scholars!篇8Title: Missing Value Handling Methods in Empirical ResearchHey guys, have you ever wondered what to do when you have missing data in your research? Don't worry, I'm here to tell you all about the common methods used to handle missing values in empirical studies.1. Complete Case Analysis:This is the simplest method where you just remove any observations with missing values. However, this can lead to a loss of important information and may bias your results.2. Mean/Mode Imputation:This method involves replacing missing values with the mean (for continuous variables) or mode (for categorical variables) of the observed values. While this is easy to do, it may distort the original data distribution.3. Multiple Imputation:This method creates multiple imputed datasets by filling in missing values with randomly generated values. This helps account for the uncertainty of the missing data and produces more accurate results.4. Regression Imputation:In this method, missing values are estimated based on other variables in the dataset using regression analysis. This can be a more accurate way to impute missing values, but it relies on the assumption that the relationship between variables is linear.5. K-Nearest Neighbors Imputation:This method estimates missing values based on the values of similar observations in the dataset. It takes into account the characteristics of the nearest neighbors to impute missing values.Remember, the method you choose to handle missing values should depend on the nature of your data and research question. Always be transparent about the methods you use in your research and consider the implications of missing data on your results.So next time you encounter missing values in your dataset, don't panic! Just remember these common methods and choose the one that best fits your research needs. Happy analyzing!篇9Title: Fun Ways to Handle Missing Values in Empirical Management StudiesHey guys! Today, I want to talk about something super important in our cool big kid world of empirical management studies – dealing with missing values in our data. Yep, it can be a bit tricky sometimes, but don't worry, I've got some fun ways to help you out!So, what are missing values? Well, sometimes when we collect data for our awesome research projects, some of the information might be, well, missing. It could be because someone forgot to fill in a question on the survey, or maybe there was a computer error – whoopsie daisies!But fear not, my fellow researchers! There are some nifty ways to handle those missing values and still get great results. Let me tell you about a few of them:1. Mean/Median/Mode Imputation: This fancy term basically means filling in the missing values with the average number, the middle number, or the most common number in the data set. It's like guessing what the missing value might be based on what we already know – pretty cool, right?2. Last Observation Carried Forward: This method is like playing a fun game of "telephone." We take the last known value before the missing one and just carry it forward until we reach the next known value. It's a bit like connecting the dots!3. Multiple Imputation: Now, this one is a bit more advanced, but super neat. We create multiple "guesses" for the missing values based on the patterns in our data and then combine all those guesses to get a more accurate picture. It's like having a whole bunch of smart friends helping us out!4. Deleting Rows/Columns: Sometimes, if there are just too many missing values or they're in a really crucial part of the data, we might have to say goodbye to that row or column. It's like cleaning up our room – out with the old, in with the new!So there you have it, my friends – some fun ways to deal with missing values in our super cool empirical management studies. Just remember, it's all part of the fun and excitement of being aresearcher. Keep on asking questions, exploring data, and having a blast with your research projects. You're all superstars!Well, that's all from me for now. Until next time, happy researching! Keep on shining bright and never stop exploring the amazing world of data and management studies. See you later, alligators!篇10Handling Missing Values in Empirical Management and Economics ResearchHey guys! Today we're going to talk about a super important topic in research - how to deal with missing values in empirical studies in management and economics. Missing values are a common problem in research, and if we don't handle them correctly, it can mess up our results and conclusions.So, what are missing values? Missing values occur when data points are not recorded or are incomplete in a dataset. This can happen for a variety of reasons, such as errors in data collection, survey non-response, or data entry mistakes.There are several methods we can use to deal with missing values in our research. One common method is to simply ignoreobservations with missing values. This is known as listwise deletion, where we just delete any observations with missing values from our dataset. While this method is simple, it can lead to biased results if the missing values are not random.Another method is mean imputation, where we replace missing values with the mean of the observed values for that variable. This method is easy to implement but can also lead to biased results if the missing values are not missing at random.A more sophisticated method is multiple imputation, where we create multiple imputed datasets by filling in missing values with plausible values based on the observed data. We then analyze each imputed dataset separately and combine the results to produce a final estimate. Multiple imputation is a more robust method for handling missing values, but it can be computationally intensive.Lastly, we can also use regression imputation, where we predict missing values based on other variables in our dataset using regression analysis. This method can be effective if there is a strong relationship between the variables, but it can also introduce bias if the relationship is misspecified.In conclusion, handling missing values in empirical management and economics research is crucial for obtainingaccurate and reliable results. It's important to carefully consider the reasons for missing values and choose the appropriate method for dealing with them. By using methods such as listwise deletion, mean imputation, multiple imputation, and regression imputation, we can ensure that our research is sound and valid. Remember, missing values don't have to be a problem as long as we handle them correctly!。

Hitchhiker’s指南说明书

Hitchhiker’s指南说明书

The Hitchhiker’s Guide to UEBLiteraryUEB Curricula Support Writing GroupFirst published 2008Revised 2013Contents - LiteraryQuick Reference (1)Definitions used in guide (4)Explanatory Rules (4)Punctuation (5)Capitalisation (6)Grade 1 Indicators (7)Typeforms (8)Simple Upper Wordsigns and Groupsigns (9)Lower-Cell Wordsigns and Groupsigns (12)Final Letter Group Signs (14)Initial Wordsigns/Groupsigns (15)Shortform Words (16)Māori and Foreign Words (17)Numbers (18)Typical and Problem Words (19)AcknowledgementsThanks go to the Ministry of Education for their support in the development of this resource which is licensed under the Creative Commons Attribution-Noncommercial-Sharealike licence. To view a copy of this licence, visit /licenses/by-nc-sa/3.0/nz/The members of the 2008 UEB Curricula Support Writing Group were Elaine Gilmour, Jenny McFadden, Catherine West, Diane Glynan, Janet Reynolds, Isobel Dinning and Steve Bellamy.UEB Literary Quick ReferenceUEB ContractionsA a con 3 him hm ou \their _! about ab conceive (con)cv himself hmf ought "\themselves (the)mvs above abv conceiving (con)cvg his 8 ound .D there "! according ac could cd I i ount .T these ~! across acr D D immediate imm ourselves (ou)rvs this ?after af day "D in 9 out \those ^? afternoon afn deceive dcv ing + ow [through "? afterward afw deceiving dcvg it x P p thyself (th)yf again ag declare dcl its xs paid pd time "T against ag(st) declaring dclg itself xf part "P tion ;N almost alm dis 4 ity ;y people p today td already alr do d J j perceive p(er)cv together tgralso al E E just j perceiving p(er)cvg tomorrow tm although al(th) ea 1 K k perhaps p(er)h tonight tn altogether alt ed $ know "k Q q U u always alw either ei knowledge k question "Q under "U ance .e en 5 L l quick qk upon ^Uand & ence ;e less .S quite q us uar > enough 5 letter lr R r V vas z er ] like l rather r very vB b ever "E little ll receive rcv W wbb 2 every e lord "L receiving rcvg was 0be 2 F F M m rejoice rjc were 7 because (be)c father "F many _M rejoicing rjcg wh : before (be)f ff 6 ment ;T right "R where ": behind (be)h first f(st) more m S s which : below (be)l for = mother "M said sd whose ^: beneath (be)n friend fr much m(ch) sh %will w beside (be)s from f must m(st) shall %with ) between (be)t ful ;L myself myf should (sh)d word ^W beyond (be)y G G N n sion .N work "W blind bl gg 7 Name "N so s world _W braille brl gh < necessary nec some "S would wdbut b go g neither nei spirit _S X xC c good gd ness ;S st /Y ycan c great grt oot n still /you y cannot _c H H O o such s(ch) young "Ycc 3 had _H Of ( T t your yrch * have H one "O th ?yourself yrf character "* here "H oneself (one)f that t yourselves yrvs child * herself h(er)f ong ;g the !Z z children (ch)nPunctuation and Special Symbolsampersand & `& colon : 3italic symbol .2at sign @ `A semicolon; 2 italic word .1apostrophe ' comma , 1italic passage .7asterisk "9 dash –,- italic passage terminator .'backslash \ _* long dash —",- numeric indicator #forward slash / _/ degree sign ~J percent % .0bold symbol ^2dollar sign `S question mark ? 8bold word ^1ellipsis 444 outer quotes 8 0bold passage ^7exclamation ! 6 inner quotes (single) ,8 ,0bold terminator ^' full stop or decimal point 4 inner quotes (double) ^8 ^0round bracket ( ) "< ">grade 1 symbol indicator ; open transcriber’s note @.<square bracket [ ] .< .>grade 1 word indicator ;;close transcriber’s note@.>bullet _4 grade 1 passage indicator ;;; underline symbol _2capital sign , grade 1 terminator ;' underline word _1capital word ,,hyphen - - underline passage _7capital passage ,,,underline terminator _'capital terminator ,'underscore _ .-IntroductionThe Hitchhiker’s Guide to UEB first edition was written in 2008 by a grou p of dedicated Resource Teachers Vision from the Blind and Low Vision Network New Zealand together with support from staff at the Royal New Zealand Foundation of the Blind, Accessible Format Production.The team members gifted their time, knowledge and passion for braille, to produce a resource to support staff, learners and producers with Unified English Braille (UEB) production, as New Zealand’s adoption of UEB became a reality in the education and braille worlds.The Hitchhiker’s Guide to UEB was dev eloped to serve as a quick memory jogger. It is not a comprehensive braille instruction guide. This edition, updated in 2013, reinforces the use of the guide as a reference tool to be used by Resource Teachers Vision (RTVs), teachers, teacher aides, Whānau and parents, who find themselves needing to braille texts quickly for student use.For more in-depth braille rules please refer to the Braille Authority of New Zealand Aotearoa Trust BANZAT website at from which the current editions of the manuals listed below can be downloaded.∙Unified English Braille Manual: New Zealand Edition∙Unified English Braille Guidelines for Technical Material∙The Rules of Unified English BrailleDefinitions used in guide1.Simple sign – a sign occupying one cell onlyposite sign – a sign occupying two or more cells3.Upper sign – a sign containing dot 1, or dot 4, or both.4.Lower sign – a sign containing neither dot 1 nor dot 4.5.Contraction – a sign which represents a word or group of letters.6.Groupsign – a contraction which represents a group of letters.7.Wordsign – a contraction which represents a whole word.8.Shortform – a contraction consisting of a word specially abbreviated inBraille.Explanatory RulesGeneral Rules for the use of ContractionsBridging Rule: In general, use a groupsign which bridges a prefix and the remainder of a word unless its use would hinder the recognition or pronunciation of the word. In particular, use the groupsigns for "ed", "en", "er", "of" and "st".Similarly, use a groupsign which bridges a word and its suffix unless its use would hinder the recognition or pronunciation of the word.Do not use a groupsign which would bridge the words which make up an unhyphenated compound word.professor pr(essor mistake mi/akeedition $i;n twofold twofoldLaw of Preference Rule: In order to save space, certain Braille contractions take priority over others. This means that upper group signs usually take preference over lower group signs.e.g. bear- “ar” takes preference to “ea”No-Two-Lower-Signs-Touching-Without-a-Chaperone Law: No two lower signs May stand together without an empty space between them unless they touch a symbol that contains an upper dot (their chaperone)Most punctuation marks are unspaced from the preceding or following word. The ellipsis is an exception.Colours: red; green; blue, (etc).,Col\rs3 r$2 gre52 blue1 "<etc">4Spacing of the hyphen, dash and ellipsis can follow print but can also be standardised for readability. In print the hyphen is a shorter line and joins words together. The dash is a longer line that breaks the text. The long dash can be used to indicate blanks to be filled in.Take a curtain-call.,Take a curta9-call4Drop the curtain – call the police!,Drop ! curta9,-call ! police6Another word for curtain is ––––,Ano!r ^w = curta9 is ",-Help, I'm . . . oh no –,Help1 ,I'm 444 oh no ,-Use the standard braille quotation marks for the main quotes used in the print text.If the main quotes are double in print then use single braille quotes for any inner quotes.If the main quotes are single in print then use double braille quotes for any inner quotes.“Don’t say ‘No Way’ to me,” she said.8,Don't say ,8,No ,Way,0 to me10 %e sd4Use the dot 6 prefix for a single capital letter, either standing alone, at the start of a word or at the start of a contraction.Mr J Smith ,mr ;,j ,smi?Mrs P. O' Toole ,mrs ;,p4 ,o',tooleAnglo-Saxon ,anglo-,saxonNot Again! ,n ,ag6Use the capitalised word indicator when all the letters of a word are in capitals. It should be repeated after punctuation such as the hyphen or the apostrophe.PETER JONES ,,PET] ,,J"OSNOT AGAIN! ,,N ,,AG6ANGLO-SAXON ,,ANGLO-,,SAXONDON'T ,,DON',TIf three or more consecutive words are in capitals, put the capitalised passage indicator before the first word and the capitals terminator after the last word.SONS AND LOVERS ,,,SONS & LOV]S,'I CAN'T BEAR IT! ,,,I C'T BE> X6,' The capitals terminator is also used to change back to lowercase within a word.unSELFish un,,self,'i%MPs ,,mp,'sIIIrd ,,iii,'rdGrade 1 IndicatorsA braille symbol may have several meanings. For example:;d the letter d (its grade 1 meaning)d the word do (the contraction or grade 2 meaning)#d the number 4 (the numeric meaning)Use the grade 1 symbol indicator (previously known as the letter sign) whenever a print letter (or letters) could be misread as a contraction or number in braille.Questions h and i are in Part A not Part B.,"Qs ;h & i >e 9 ,"P ,A n ,"P ;,B4 (note that the letters a, i and o cannot be misread as contractions)300cm equals 3m #cjj;cm equals #cm (note that only the letters a to j can be misread as numbers)Question (d) in part B-1 ,"Q "<;d"> 9 "P ;,b-#aMr J. Smith ,Mr ;,J4 ,Smi?(note that letters can still be misread as contractions when touching punctuation)The line AB ,! l9e ;,,ab(note that a group of letters can sometimes be misread as a shortform contraction) Use the grade 1 word indicator when several letters are in an unspaced sequence c-h-e-e-s-e ;;c-h-e-e-s-eEmail and web addresses are allowed to contain contractions. They can also be***************************************************.nzsup]4t1*]@abl5nz4s*ool4nz orsuper4teacher@ablennz4school4nzUse the grade 1 passage indicator and terminator when a passage contains letters,print symbols and spaces but no contractions, for example a set of algebra exercises.TypeformsIf a single word is emphasised, put the corresponding word indicator before the word.That is my chair. ,t is .1my *air4.I am not happy. ,I am ^1n happy4Do not repeat the word indicator after the hyphen in a compound word.I want lace-ups. ,I want ^1lace-ups4If two consecutive words are emphasised, put the word indicator before each word.This is the Cost Price.,? is ! _1,co/ _1,price4If three or more consecutive words are emphasised, put the passage indicator before the first word and the terminator after the last word.On the Beach is overdue..7,On ! ,B1*.' is ov]due4If the last word is followed by punctuation, the terminator should generally be placed after the punctuation.We saw Out of Africa.,We saw _7,\ ( ,Africa4_'Use the symbol indicator if a single character is emphasised.The o in h o t ,! ^2o 9 h^2otThe l in co l d ,! ^2;l 9 co^2ldIf several consecutive letters are emphasised in the middle of a word, use the word indicator before them and the terminator after them.The ie in f ie ld ,! .1ie 9 f.1ie.'ldSimple Upper Wordsigns and GroupsignsUse a Braille letter standing alone to represent a whole word.∙May be used as a possessiveExample: Will's ,w's∙May be used as proper nounsExample: Mr. More ,mr4 ,m∙May be used as a hyphenated compound word.Example: so-so s-s∙May be used when immediately followed by an apostrophe and the word represented is kept distinctExample: Can’t,c't∙Cannot be used as a syllable.Word SignsGroup signs (and, for, of, the, with)∙Must be used in preference to other contractions.Example: o(the)r o!rRefer to Bridging Rule (Page 4)Wordsigns∙These contractions must stand alone to represent the whole word. It is correct to use a wordsign after a hyphen.Example: step-child /ep-*∙ A wordsign may be used when immediately followed by an apostrophe Example: The child’s doll,! *'s dollUpper Groupsigns (ch, sh, st, th, wh)These contractions may be used in any part of a word for the letters they represent.∙Do not use in abbreviationsExample: St ,st∙Do not use in numbers.Example: 4th#dthGroup Signs Contractions∙These contractions may represent their own sounds.Examples:ar! (pirate's laugh) >6er (stuttering sound) }Ed (name) ,$ow! (sound of pain) [6∙They may bridge prefix and root word or root word and suffix Group Sign - Middle and End∙Used in the middle or end of words only.Examples: s(ing)le s+le ors(ing) s+Lower-Cell Wordsigns and GroupsignsLower-Cell Word Contractions:∙Must stand alone.Example: Chocolate milk is enough for him.,*ocolate milk is 5 = hm4∙May not adjoin any marks of punctuation.Example: Is this his?,is ? his8∙May adjoin a capital sign (which is a composition sign) unless the capital sign is preceded by a mark of punctuation.Example: His face is cute.,8 face is cute4∙May have any number of lower-cell word contractions together as along they are spaced.Example: She was in enough classes.,%e 0 9 5 classes4Lower-cell Group Signs:∙May be used in any part of a word; in particular they are the only lower groupsigns that may be used at the end of a word.Example: d(en)D5Lower-Cell Group Signs - Leader or Beginning-of-Word Contractions:∙Must always be used at the beginning of a word in which they form the first syllable.∙No-Two-Lower-Signs-Touching-Without-a-Chaperone Law. No two lower signs may stand together without an empty space between them unless they touch asymbol that contains an upper dot (their chaperone).∙con, dis - may only be used as parts of words, they have no whole-word meaning. ∙May not be used in the middle or the end of a word.∙May not be used before punctuation.Lower-cell Group Signs - Sandwich Contractions:∙May only appear in the middle of word, must appear between letters or contractions.Example: be(gg)(ed) be7$∙Has the lowest priority - Law of Preference applies.∙May not adjoin punctuation.Final Letter Group Signs∙Use as parts of words.Example: d(ance) d.e∙Final letter group signs always take first priority.Example: (th)(ence) ;e∙May not begin a wordExample: fulfill fulfillInitial Wordsigns/GroupsignsDot 5 Words:Dots 4,5 Words: Dots 4,5,6 Words:Initial Wordsigns:∙Are used as whole words and as parts of words.Examples: (here) "H(part)n(er) "pn]∙Are used as parts of words when it retains its original meaning.Examples: bir(th)(day) bir?"dgr(and)(father) gr&"f∙Are used as parts of words when it retains its original sound or pronunciation.Examples: ad(here) ad"hs(mother) s"mShortform WordsShortforms may be used whenever they are standing alone, regardless of meaning and regardless of whether the word is used as an ordinary word or as a proper name.Shortforms may generally be used within a longer word (including proper names) provided that there is no interference in spelling and an original meaning of the basic shortform word is retained.∙However a shortform may not be used within a longer word if this turns it into a different word. (eg about cannot be used in abouts).∙In particular the shortforms after, blind and friend cannot be used before a vowel or a y. (eg friend cannot be used in befriended)∙ A shortform beginning with be or con can only be part of a longer word if it occurs at the start of that word (eg between cannot be used in inbetween)∙ A grade one indicator should be used if an unusual word could be read as a shortform (eg hm could be read as him)Mr Much,mr ,m* walkabout walkabFirstbank,f/bank unfriendly unfrlyHm …;,hm 444 not,hm 444If in doubt refer to the “UEB Shortforms List” which is in Appe ndix 1 of The Rules of Unified English Braille.Māori and Foreign Words∙All Māori words should be written uncontracted except for the wh contraction. ∙Single letters do not need the grade 1 symbol indicator.∙Macrons are represented by preceding the vowel with dots 456.Tirohia te āhua o tēnei whare!,Tirohia te _ahua o t_enei :are6 Aroha lives in Whenuapai.,Aroha lives 9 ,:enuapai4Occasional foreign words within English text should have their accents represented according to the table above. Unaccented letters can be contracted.We’ll have pâté and vin rosé.,we'll h p~%at~/e & v9 ros~/e4 Longer foreign phrases that are set apart within English text are better uncontracted but should still use the accents in the table above.“Où est le Café?” he asked.8,o~*u est le ,caf~/e80 he ask$4 Texts for the study of a foreign language should use the one cell braille accents defined in the braille code for that language, and not the two cell accents above.Numbers1 #a 6 #f2 #b 7 #g3 #c 8 #h4 #d 9 #i5 #e 0 #jcomma 1decimal point or full stop 4simple fraction line /123 3,408 4.3 .5 ½#abc#c1djh #d4c#4e #a/bRepeat the numeric indicator after a hyphen1914 - 18 #aiad-#ahRepeat the numeric indicator after all punctuation except the decimal point or full stop, the comma, and the fraction line.3:45 #c3#DE 3.45 #c4DE7(2) #g"<#b"> 7(b) #g"<b">1/2/07 #a_/#b_/#jg 1½#a#a/bThe numeric indicator initiates Grade 1 mode so ordinal endings are uncontracted.1st and 5th #ast & #ethRoman numbers follow the same rules as other letter sequencesi II v (iv) (V) v thi ,,ii ;v "<iv"> "<;,V"> v?Typical and Problem WordsThis list is a New Zealand draft. The braille form of each word is in accordance with the Duxbury translation table for UEB as at December 2012.。

Visiting address

Visiting address
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the ring R are closed, to show that a (non-homogeneous) ideal I in R , whose associated homogeneous ideal grT (I ) is locally nitely generated, is closed. Furthermore, we prove analogous results to some of the \approximation results" of 4], for non-homogeneous ideals.
1
c 1997 Department of Mathematics, Stockholm University
2
JAN SNELLMAN
ideal of a non-homogeneous ideal determined by the associated homogeneous ideals of the truncations of the ideal? In a brief section, we collect some observations that allow us to formulate results from 3, 5] in a more compact fashion. 2. Notations Let K be any eld. For any set S , we denote by S ] the free commutative semigroup with unit on S , and by K S ] the monoid ring on S ] with values in K , that is, the set of all nitely supported maps S ] ! K with componentwise addition and multiplication with scalars, and with the natural convolution product. Of course K S ] is nothing but the polynomial ring on S . Similarly, we denote by K S ]] the generalized semigroup ring in the sense of Ribenboim 1], that is, the set of all maps S ] ! K , with the same operations as before. Then K S ]] is the ring of formal power series on S . Finally, we denote by K S ]]0 the subring of K S ]] consisting of all maps for which there is a common bound for the total degrees of the elements in the support of the map. The total degree of an element in S ] is de ned by giving each element in S ] degree 1, and then demanding that j j : S ] ! N is a monoid homomorphism from S ] to the additive monoid of the non-negative integers. Taking X = fx1 ; x2; x3 ; : : : g we put R = K X ]] and R0 = K X ]]0 , and we set M = X ]. We may view X ] as the set of nitely supported maps N + ! N ; with this convention, we de ne Supp(m) N + for a monomial in X ] n f1g as the support of m, and maxsupp(m) as the maximal (w.r.t the natural ordering on N + ) element of the support of m. We de ne Mn, for any n, as the (free commutative) subsemigroup generated by x1 ; : : : ; xn, that is, as the set of monomials in M with maximal support n, and M n] as the set of monomials with maximal support n (we adjoin 1 to both semigroups, of course). 0 The following fact is implicit in 2]: for any positive integer n, the ring R is isomorphic to Un x1 ; : : : ; xn], where Un = K fxn+1 ; xn+2; xn+3; : : : g]]0. The isomorphism is obtained by \regarding the variables xn+1 ; xn+2xn+3 ; : : : as coe cients" and is a variation of the well-know fact that for two disjoint sets A and B , K A B ] ' K A] B ], and K A B ]] ' K A]] B ]]. Recall from 5] that An = ker n : R0 ! K x1 ; : : : ; xn], where we abuse the notation R and denote by n both the quotient epimorphism R RK xn+1;xn+2;::: ]] 1 ' K x1 ; x2 ; : : : ]] and its restriction to R0 (the image of the restriction is contained in K x1 ; : : : ; xn]). These homomorphisms we call the truncation maps. The An's form a decreasing, exhaustive (if we de ne A0 = R0 n K and A?1 = R0 ) and separated ltration on R0 . With respect to the topology de ned by this ltration, the closure on any ideal (or indeed subset) I is given by ~ I = \1=0 (I + An), and the completion is the ring R = f 2 R n (f ) 2 n (I ) for all n . n The completion is of course the inverse limit of the inverse system

SPSS术语中英文对照

SPSS术语中英文对照

【常用软件】SPSS术语中英文对照SPSS的统计分析过程均包含在Analysis菜单中。

我们只学以下两大分析过程:Descriptive Statistics(描述性统计)和Multiple Response(多选项分析)。

Descriptive Statistics(描述性统计)包含的分析功能:1.Frequencies 过程:主要用于统计指定变量各变量值的频次(Frequency)、百分比(Percent).2.Descriptives过程:主要用于计算指定变量的均值(Mean)、标准差(Std。

Deviation).3.Crosstabs 过程:主要用于两个或两个以上变量的交叉分类。

Multiple Response(多选项分析)的分析功能:1.Define Set过程:该过程定义一个由多选项组成的多响应变量。

2.Frequencies过程:该过程对定义的多响应变量提供一个频数表。

3.Crosstabs过程:该过程提供所定义的多响应变量与其他变量的交叉分类表.Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals,绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction,任意方向上的加速度Acceleration normal,法向加速度Acceleration space dimension,加速度空间的维数Acceleration tangential,切向加速度Acceleration vector,加速度向量Acceptable hypothesis,可接受假设Accumulation,累积Accuracy,准确度Actual frequency, 实际频数Adaptive estimator,自适应估计量Addition, 相加Addition theorem,加法定理Additivity,可加性Adjusted rate, 调整率Adjusted value,校正值Admissible error,容许误差Aggregation,聚集性Alternative hypothesis, 备择假设Among groups,组间Amounts,总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance,方差分析Angular transformation, 角转换ANOVA (analysis of variance), 方差分析ANOVA Models,方差分析模型Arcing,弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG ,评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper,算术格纸Arithmetic mean,算术平均数Arrhenius relation,艾恩尼斯关系Assessing fit,拟合的评估Associative laws,结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency,渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data,属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals,残差的自相关Average,平均数Average confidence interval length,平均置信区间长度Average growth rate,平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes‘ theorem ,Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best—trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare,双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population,双变量正态总体Biweight interval,双权区间Biweight M—estimator, 双权M估计量Block,区组/配伍组BMDP(Biomedical computer programs),BMDP统计软件包Boxplots,箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case—control study, 病例对照研究Categorical variable, 分类变量Catenary,悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship,因果关系Cell,单元Censoring,终检Center of symmetry, 对称中心Centering and scaling,中心化和定标Central tendency, 集中趋势Central value,中心值CHAID —χ2 Automatic Interaction Detector,卡方自动交互检测Chance,机遇Chance error,随机误差Chance variable,随机变量Characteristic equation, 特征方程Characteristic root,特征根Characteristic vector, 特征向量Chebshev criterion of fit,拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test,卡方检验/χ2检验Choleskey decomposition,乔洛斯基分解Circle chart,圆图Class interval, 组距Class mid—value, 组中值Class upper limit,组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code,代码Coded data,编码数据Coding, 编码Coefficient of contingency,列联系数Coefficient of determination,决定系数Coefficient of multiple correlation,多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production—moment correlation,积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness,偏度系数Coefficient of variation, 变异系数Cohort study,队列研究Column,列Column effect,列效应Column factor,列因素Combination pool, 合并Combinative table,组合表Common factor,共性因子Common regression coefficient, 公共回归系数Common value,共同值Common variance,公共方差Common variation, 公共变异Communality variance,共性方差Comparability,可比性Comparison of bathes, 批比较Comparison value,比较值Compartment model, 分部模型Compassion,伸缩Complement of an event,补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event,联合事件Composite events,复合事件Concavity,凹性Conditional expectation,条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear,依条件线性Confidence interval,置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit,置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research,证实性实验研究Confounding factor, 混杂因素Conjoint,联合分析Consistency,相合性Consistency check,一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint,约束Contaminated distribution,污染分布Contaminated Gausssian,污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour,边界线Contribution rate, 贡献率Control, 对照Controlled experiments,对照实验Conventional depth,常规深度Convolution, 卷积Corrected factor,校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness,正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance,协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares,最小二乘准则Critical ratio,临界比Critical region, 拒绝域Critical value,临界值Cross-over design, 交叉设计Cross—section analysis, 横断面分析Cross—section survey,横断面调查Crosstabs ,交叉表Cross—tabulation table,复合表Cube root, 立方根Cumulative distribution function,分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature,曲率Curve fit ,曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method,尝试法Cycle, 周期Cyclist,周期性D test, D检验Data acquisition,资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies,数据缺乏Data handling,数据处理Data manipulation,数据处理Data processing,数据处理Data reduction,数据缩减Data set, 数据集Data sources,数据来源Data transformation, 数据变换Data validity, 数据有效性Data—in,数据输入Data—out, 数据输出Dead time,停滞期Degree of freedom,自由度Degree of precision,精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points,数据点的密度Dependent variable,应变量/依变量/因变量Dependent variable,因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods,无导数方法Design, 设计Determinacy, 确定性Determinant,行列式Determinant,决定因素Deviation,离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation,微分方程Direct standardization, 直接标准化法Discrete variable,离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function,判别值Dispersion,散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape,分布形状Distribution—free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve,剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution,双指数分布Double logarithmic, 双对数Downward rank,降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan‘s new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector,特征向量Ellipse, 椭圆Empirical distribution,经验分布Empirical probability,经验概率单位Enumeration data,计数资料Equal sun-class number,相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II,第二类错误Estimand, 被估量Estimated error mean squares,估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event,事件Event,事件Exceptional data point, 异常数据点Expectation plane,期望平面Expectation surface, 期望曲面Expected values,期望值Experiment, 实验Experimental sampling,试验抽样Experimental unit,试验单位Explanatory variable,说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索—摘要Exponential curve,指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter,附加参数Extrapolation,外推法Extreme observation,末端观测值Extremes,极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score,因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative,假阴性False negative error,假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate,病死率Field investigation, 现场调查Field survey, 现场调查Finite population,有限总体Finite-sample,有限样本First derivative, 一阶导数First principal component,第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value,拟合值Fitting a curve,曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast,预测Four fold table,四格表Fourth,四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency,频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution,伽玛分布Gauss increment,高斯增量Gaussian distribution, 高斯分布/正态分布Gauss—Newton increment,高斯—牛顿增量General census,全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean,几何平均数Gini‘s mean difference, 基尼均差GLM (General liner models),一般线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant,行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity,大错敏感度Group averages,分组平均Grouped data,分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators,汉佩尔M估计量Happenstance,偶然事件Harmonic mean,调和均数Hazard function, 风险均数Hazard rate, 风险率Heading,标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity,不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method,系统聚类法High—leverage point,高杠杆率点HILOGLINEAR,多维列联表的层次对数线性模型Hinge,折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS,多重响应分析Homogeneity of variance,方差齐性Homogeneity test,齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event,不可能事件Independence,独立性Independent variable, 自变量Index, 指标/指数Indirect standardization,间接标准化法Individual, 个体Inference band, 推断带Infinite population,无限总体Infinitely great,无穷大Infinitely small,无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation,内插法Interquartile range, 四分位距Interval estimation,区间估计Intervals of equal probability, 等概率区间Intrinsic curvature,固有曲率Invariance,不变性Inverse matrix,逆矩阵Inverse probability, 逆概率Inverse sine transformation,反正弦变换Iteration,迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability,联合概率Joint probability distribution,联合概率分布K means method, 逐步聚类法Kaplan-Meier,评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall‘s rank correlation, Kendall等级相关Kinetic,动力学Kolmogorov—Smirnove test,柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test,Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis,峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test,大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage,泄漏Least favorable configuration,最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method,最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute—residuals fit,最小绝对残差拟合Least-absolute-residuals line,最小绝对残差线Legend,图例L-estimator,L估计量L-estimator of location,位置L估计量L—estimator of scale, 尺度L估计量Level,水平Life expectance,预期期望寿命Life table,寿命表Life table method, 生命表法Light—tailed distribution,轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming,线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance,位置尺度同变性Location equivariance, 位置同变性Location invariance,位置不变性Location scale family,位置尺度族Log rank test,时序检验Logarithmic curve,对数曲线Logarithmic normal distribution,对数正态分布Logarithmic scale,对数尺度Logarithmic transformation,对数变换Logic check,逻辑检查Logistic distribution,逻辑斯特分布Logit transformation,Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function,损失函数Low correlation,低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading,主辞标目Marginal density function, 边缘密度函数Marginal probability,边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution,分布的匹配Matching of transformation, 变换的匹配Mathematical expectation,数学期望Mathematical model,数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean,均数Mean squares between groups, 组间均方Mean squares within group,组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish,中位数平滑Median test, 中位数检验Minimal sufficient statistic,最小充分统计量Minimum distance estimation,最小距离估计Minimum effective dose,最小有效量Minimum lethal dose,最小致死量Minimum variance estimator, 最小方差估计量MINITAB,统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification,模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity,连续性模Morbidity,发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL),多维尺度/多维标度Multinomial Logistic Regression ,多项逻辑斯蒂回归Multiple comparison,多重比较Multiple correlation ,复相关Multiple covariance,多元协方差Multiple linear regression, 多元线性回归Multiple response ,多重选项Multiple solutions, 多解Multiplication theorem,乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution,多元T分布Mutual exclusive,互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead,自然死亡Natural zero, 自然零Negative correlation,负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method,q检验NK method,q检验No statistical significance, 无统计意义Nominal variable,名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression,非线性相关Nonparametric statistics,非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate,正态离差Normal distribution,正态分布Normal equation, 正规方程组Normal ranges, 正常范围Normal value,正常值Nuisance parameter,多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable,数值变量Objective function, 目标函数Observation unit,观察单位Observed value,观察值One sided test,单侧检验One—way analysis of variance, 单因素方差分析Oneway ANOVA ,单因素方差分析Open sequential trial,开放型序贯设计Optrim,优切尾Optrim efficiency, 优切尾效率Order statistics,顺序统计量Ordered categories,有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable,有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions,正交条件ORTHOPLAN,正交设计Outlier cutoffs, 离群值截断点Outliers,极端值OVERALS , 多组变量的非线性正规相关Overshoot,迭代过度Paired design,配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola,抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation,偏相关Partial regression,偏回归Partial sorting,偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling,退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P—estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Pivot,枢轴量Planar, 平坦Planar assumption,平面的假设PLANCARDS,生成试验的计划卡Point estimation,点估计Poisson distribution,泊松分布Polishing,平滑Polled standard deviation,合并标准差Polled variance,合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population,总体Population attributable risk, 人群归因危险度Positive correlation,正相关Positively skewed, 正偏Posterior distribution,后验分布Power of a test,检验效能Precision, 精密度Predicted value,预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability,先验概率Probabilistic model, 概率模型probability,概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers,成比例次级组含量Prospective study,前瞻性调查Proximities, 亲近性Pseudo F test,近似F检验Pseudo model,近似模型Pseudosigma,伪标准差Purposive sampling, 有目的抽样QR decomposition,QR分解Quadratic approximation,二次近似Qualitative classification,属性分类Qualitative method, 定性方法Quantile-quantile plot,分位数-分位数图/Q-Q图Quantitative analysis,定量分析Quartile,四分位数Quick Cluster,快速聚类Radix sort,基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event,随机事件Randomization,随机化Range,极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate,比率Ratio,比例Raw data,原始资料Raw residual,原始残差Rayleigh‘s test, 雷氏检验Rayleigh‘s Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators,回降估计量Reducing dimensions,降维Re—expression,重新表达Reference set, 标准组Region of acceptance,接受域Regression coefficient,回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion,相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization,重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance,耐抗性Resistant line,耐抗线Resistant technique,耐抗技术R—estimator of location, 位置R估计量R-estimator of scale,尺度R估计量Retrospective study, 回顾性调查Ridge trace,岭迹Ridit analysis, Ridit分析Rotation,旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor,行因素RXC table, RXC表Sample,样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error,抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale,尺度/量表Scatter diagram,散点图Schematic plot,示意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative,二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi—logarithmic graph, 半对数图Semi—logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set,顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test,贯序检验法Serial tests,系列试验Short—cut method, 简捷法Sigmoid curve, S形曲线Sign function,正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling,简单整群抽样Simple correlation,简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table,简单表Sine estimator,正弦估计量Single—valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope,斜率Smirnov test,斯米尔诺夫检验Source of variation,变异来源Spearman rank correlation,斯皮尔曼等级相关Specific factor,特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation,假性相关Square root transformation, 平方根变换Stabilizing variance,稳定方差Standard deviation,标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate,标准估计误差Standard error of rate,率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value,起始值Statistic, 统计量Statistical control,统计控制Statistical graph,统计图Statistical inference,统计推断Statistical table,统计表Steepest descent, 最速下降法Stem and leaf display,茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage,存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency,严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic,充分统计量Sum of products, 积和Sum of squares,离差平方和Sum of squares about regression,回归平方和Sum of squares between groups,组间平方和Sum of squares of partial regression, 偏回归平方和Sure event,必然事件Survey,调查Survival, 生存分析Survival rate,生存率Suspended root gram, 悬吊根图Symmetry,对称Systematic error, 系统误差Systematic sampling,系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line,切线Target distribution, 目标分布Taylor series,泰勒级数Tendency of dispersion,离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series,时间序列Tolerance interval,容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit,容忍上限Torsion, 扰率Total sum of square,总平方和Total variation, 总变异Transformation, 转换Treatment,处理Trend, 趋势Trend of percentage,百分比趋势Trial, 试验Trial and error method,试错法Tuning constant, 细调常数Two sided test,双向检验Two-stage least squares,二阶最小平方Two-stage sampling,二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance,双因素方差分析Two—way table, 双向表Type I error,一类错误/α错误Type II error,二类错误/β错误UMVU,方差一致最小无偏估计简称Unbiased estimate,无偏估计Unconstrained nonlinear regression ,无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution,均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories,无序分类Upper limit,上限Upward rank,升秩Vague concept,模糊概念Validity, 有效性VARCOMP (Variance component estimation),方差元素估计Variability,变异性Variable,变量Variance, 方差Variation, 变异Varimax orthogonal rotation,方差最大正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi—square test,加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean,加权平均数Weighted mean square,加权平均方差Weighted sum of square,加权平方和Weighting coefficient,权重系数Weighting method,加权法W—estimation,W估计量W—estimation of location, 位置W估计量Width,宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value,野值/狂值Winsorized mean,缩尾均值Withdraw,失访Youden‘s index, 尤登指数Z test,Z检验Zero correlation,零相关Z-transformation,Z变换。

KaKs_Calculator

KaKs_Calculator
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substitutions. On the other hand, the maximum likelihood method integrates evolutionary features (reflected in nucleotide models) into codon-based models and uses the probability theory to finish all the three steps in one go (4 ). However, these methods adopt different substitution or mutation models based on different assumptions that take account of various sequence features, giving rise to varied estimates of evolutionary distance (5 ). In other words, Ka and Ks estimation is sensitive to underlying assumptions or mutation models (3 ). In addition, since the amount and the degree of sequence substitutions vary among datasets from diverse taxa, a single model or method may not be adequate for accurate Ka and Ks calculations. Therefore, a model selection step, that is, to choose a best-fit model when estimating Ka and Ks, becomes critical for capturing appropriate evolutionary information (6 , 7 ). Toward this end, we have applied model selection and model averaging techniques for Ka and Ks estimations. We use a maximum likelihood method based on a set of candidate substitution models and adopt the Akaike information criterion (AIC) to measure fitness between models and data. After choosing the Vol. 4 No. 4 2006 259
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Abstract A given nonincreasing sequence D = (d1 , d2 , · · · , dn ) is said to contain a (nonincreasing) repetition sequence D∗ = (di1 , di2 , · · · , dik ) for some k ≤ n − 2 if all values of D − D∗ are distinct and for any dil ∈ D∗ there exists some dt ∈ D − D∗ such that dil = dt . For any pair of integers n and k with n ≥ k + 2, we investigate the existence of a graphic sequence which contains a given repetition sequence. Our main theorem contains the known results for the special case di1 = dik if k = 1 or k = 2 (see [1, 5, 2]).
l≤z
d il
1 Without loss of generality we will assume that dik ≤ n− 2 . (If necessary, take the complementary graph and let dil = n − 1 − dik+1−l for 1 ≤ l ≤ k . Then one may use D = {di1 , di1 , · · · , dik }.) Set
1 supported
in part by a foundation of Academia Sinica.
1
1
Introduction
A nonincreasing sequence (d1 , ..., dn ) with 0 ≤ di ≤ n − 1 for each i is called graphic if it gives the vertex degrees of some simple graph of order n. These sequences are well characterized by the classical result of Erd˝ os and Gallai (see Theorem 5), but some questions cannot be answered directly from that result. An easier result is that a graphic sequence must contain at least one repetition. In this paper we study repetition patterns in degree sequences, that is, possible patterns of repeated degrees. For example, it is known that if a graphic sequence contains precisely n one repetition, then that repetition must lie between n 2 − 1 and 2 (see Theorem 1). Is there, for example, a graphic sequence with k repeated values of the integer n 2 , for 3 ≤ k ≤ n − 1, with all other values distinct? n Is it possible to have a graph such that two degrees are 34 , two are n 2 , two n are 4 , and all others are distinct? The first question has been answered previously in Theorems 2 and 3 (in the affirmative). A consequence of the present work is that the answer to the second question is also yes. Definition: Let D : d1 ≥ d2 ≥ · · · ≥ dn be a finite sequence of nonnegative integers. A subsequence D∗ : di1 ≥ di2 ≥ · · · ≥ dik is called the repetition subsequence, denoted by RS, if (1) all values of D − D∗ are distinct, and (2) for any dil ∈ D∗ , there exists some dt ∈ D − D∗ such that dil = dt , and t < il . Given two integers n and k with k ≤ n − 1 and a sequence D∗ : n > di1 ≥ · · · ≥ dik ≥ 0, we investigate the existence of a graphic sequence D with single valued repetitions having D∗ as its RS. When k = n − 1, this question asks about the existence of a d1 -regular graph on n vertices, and it is an elementary exercise to show that such a graph exists if and only if not both d1 and n are odd; hence we assume k ≤ n − 2. For the special case di1 = dik , the following three results (see [1, 2, 5]) concern graphic sequences with single repetitions. Theorem 1 (Behzad, Chartrand) If n ≥ 2, there exists a graphic sequence (d1 , . . . , dn ) with a repetition sequence D∗ : di1 = j if and only if n n −1≤j ≤ . 2 2
2
2 Theorem 2 (Hutchinson) If n ≥ 4 then there exists a graphic sequence (d1 , . . . , dn ) with a repetition sequence D∗ : di1 = di2 = j if and only if n−3 3n − 1 ≤j≤ . 4 4 2 Theorem 3 (Chen, Piotrowski, Shreve) If n ≥ k +2, then there exists a graphic sequence having D∗ : di1 = di2 = · · · = dik = j as its RS if and only if n−k−1 n−k−1 ≤j ≤n−1− . 2k 2k 2 Note that, with j = n 2 and 3 ≤ k ≤ n − 1, this last inequality holds, thus giving an affirmative answer to one question posed above. Let n and k be positive integers with n ≥ k + 2, and for two nonincreasing sequences of nonnegative values D = (d1 , d2 , · · · , dn ) and D∗ = (di1 , di2 , · · · , dik ), we define a = a(D) = min{x : dx+1 ≤ x}, and for 0 ≤ z ≤ k ≤ n − 1, we define 1 1−k S (z ) = (z − 1)(z − k ) + n(z − ) + , 2 2 and f (z ) =
m = m(D∗ ) = min{x : dix+1 ≤ Thus, 0 ≤ m ≤ k − 1.
n − k + 2x }. 2
n n with the Example 1 Consider the complete bipartite graph K 23 ,3 n n smaller part completed to K n . Thus, D = ( n − 1 , . . . , n − 1 , 3 , . . . , 3 ) and 3
3
D∗ = (d2 , . . . , d n , dn , . . . , dn ). That is, (n − 1) appears in the sequence 3 3 +2 n 2n ∗ D with multiplicity n 3 − 1 and 3 appears with multiplicity 3 − 1 Thus, n n k = n − 2, a = 3 , and m = 3 − 1. In this paper, we will prove the following result. Theorem 4 (1) If there exists a graphic sequence D = (d1 , d2 , · · · , dn ) having D∗ as its RS, then there exists some q with iq ≤ a(D) < iq+1 ( il > a(D) for all 1 ≤ l ≤ k when q = 0) such that f (q ) ≤ S (q ). (2) Given n and D∗ = di1 , di2 , . . . , dik , a nonincreasing sequence with 0 ≤ dil ≤ n − 1 for each entry, then if di1 ≤ n − k + m − 2, dik ≥ m and f (m) ≤ S (m), then there is a graphic sequence having D∗ as its RS. 2
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