Gene polymorphism of transforming growth factor-β1 in Egyptian patients with type 2 diabetes and diabetic nephropathy Sherif M. El-Sherbini, Samar M. Shahen, Youssef M. Mosaad, Mohamed S. Abdelgawadand Roba M. Talaat Acta Biochim Biophys Sin (Shanghai). 2013 Apr;45(4):330-8. doi: 10.1093/abbs/gmt003 Molecular Immunology, Molecular Biology Department, Genetic Engineering and Biotechnology Research Institute (GEBRI), Menofia University, Menofia 22857, Egypt Role of the transforming growth factor-β1 (TGF-β1) gene polymorphisms located at codons 10 and 25 in the genetic predisposition to type 2 diabetes (T2D) and in diabetic nephropathy (DN) in Egyptian patients was investigated. A case control study was done for 99 unrelated Egyptian patients with T2D (50 DN− and 49 DN+) and 98 age- and sex-matched healthy controls. TGF-β1 T869C (codon 10) and G915C (codon 25) polymorphism detection was done by amplification refractory mutation system method. DN+ patients were younger, with higher body mass index, serum triglycerides, serum creatinine, and lower serum albumin than those in DN− patients. Moderate and bad grades of diabetic control were associated with DN (P 0.001). The TGF-β1 (T869C) C allele, TC and TC + CC genotypes were significantly higher in patients; the T allele and TT genotype were significantly higher in controls (Pc 0.001). The TGF-β1 TC genotype was associated with DN (Pc 0.05). Non-significant differences were detected between T2D patients and controls in the frequencies of TGF-β1 (G915C) alleles and genotypes. In conclusion, these preliminary data showed that the TGF-β1 codon 10 C allele, and C allele-containing genotypes may be susceptible, and T allele/TT genotype may be protective factors for T2D and DN+ complications. 全文: http://abbs.oxfordjournals.org/content/45/4/330.full
text-indent:2em; text-align:left; # # Copyright (c) 2010-~ siqin.hou All rights reserved. # # This source code is released for free distribution under the terms of the # GNU General Public License # # ---Author: siqinsiqin.hou@gmail.com # Date-Time: 2014-04-26 16:38:07 # File-Name: Introduction_to_R.r # --Version: 1.0 # -Function: Statistics One课程中提供的Introduction to R, Andrew Conway # -Source From： # -菜鸟学R语言：用R做数据分析（零基础）之前言 | Yang Liu fr # -http://yangliufr.com/2013/09/24/r-introduction/ # -菜鸟学R语言：用R做数据分析（零基础）之数据类型 | Yang Liu fr # - http://yangliufr.com/2013/09/29/r-introduction-i/ # -Andrew Conway @ coursera.org: Statistics One # -https://accounts.coursera.org/signin?course_id=970574r=https%3A%2F%2Fclass.coursera.org%2Fstats1-002%2Flectureuser_action=classtopic_name=Statistics%20One #首先，下载与安装R软件，网站：http://www.r-project.org/。 #当然，如果你一定是找不到这个网站中R语言的程序位置，那么请看这里：http://ftp.ctex.org/mirrors/CRAN/，在这里，请点击install R for the first time， #安装时请注意一点：安装路径不要有中文字符，以避免一些不必要的麻烦。 #推荐大家再安装另外一个软件，叫做Rstudio。http://www.rstudio.com/ide/download/ # -------------------------------------------------------------------------------- # Basic mathematical operations# 四则运算 # -------------------------------------------------------------------------------- 3 + 4 5 * 5 12 / 3 5^5 # 这几行代码就充分展现出了R的优势之一，即时互动。大家只要在console中输入以上一行代码，就可以直接得到结果，省去了传统语言编译的过程（并不是说R中不存在编译）。 # 3 + 4 # 7 # 5 * 5 # 25 # 12 / 3 # 4 # 5^5 # 3125 #以上就是运行结果。 # -------------------------------------------------------------------------------- # R objects R的对象 # -------------------------------------------------------------------------------- #R的数据对象有向量，列表，矩阵，数据框，依据不同需求使用不同数据对象类型。 #vector class/vector type查看数据对象的类型 class(v) # ------------------------------------------------------------ # Vector 向量 ## Most basic object in R ## Contains elements of the same class ## Can be: character, numeric, integer, complex, logical(True/False)) #向量（vector）是R中最基本的对象。 #R的对象中大部分能且只能包含同一类型的若干个元素，但是list除外，可以包含若干类的若干个元素，这也是为什么要有list的原因。 #常见的向量有：字符向量,数值向量,整数向量,复数向量, # Create a vector，使用Combine首字母，同类型的对象集合 #下面来创建一个含有数值1,3,5,7的向量 v=c(1,3,5,7) #c() Combine是集合的意思 v class(v) # Vector Type 向量中的数据类型 x - c(0.5, 0.6) ## numeric 数值向量 x - c(TRUE, FALSE) ## logical 逻辑向量 x - c(T, F) ## logical 逻辑向量 x - c(a, b, c) ## character 字符向量 x - 9:29 ## integer 整数向量 x - c(1+0i, 2+4i) ## complex 复数向量 class(x) # complex #但，如果把两种向量c()到一起，会得到什么样的向量集合呢？ y - c(1.7, a) ## numeric+character-character y - c(TRUE, 2) ## logical+numeric—numeric y - c(a, TRUE) ## character+logical-character #你会发现，这时向量集会被强制（coercion）转成同一类，为了符合向量的定义。 #这时，如果你需要提取其中的假向量用于运算，那就需要as.**functions， #其中**可以是numeric，logical，character，complex等。如下， #methods(as)或者methods(is)查看 x - 0:6 class(x) #class()用于查看向量的类 # integer as.numeric(x) 0 1 2 3 4 5 6 as.logical(x) # FALSE TRUE TRUE TRUE TRUE TRUE TRUE as.character(x) # 0 1 2 3 4 5 6 as.complex(x) # 0+0i 1+0i 2+0i 3+0i 4+0i 5+0i 6+0i #如果使用as.**function把某类向量转化为另一类，则会得到NAs。如， x - c(a, b, c) as.numeric(x) # NA NA NA #Warning message: #NAs introduced by coercion as.logical(x) # NA NA NA #有时你可能还需要空白向量，可以用vector()来创建，例如创建一个长度为10的数值向量x， x - vector(numeric, length = 10) x # 0 0 0 0 0 0 0 0 0 0 # ------------------------------------------------------------ # List 列表 ## (Vector with different class of objects) ## 包含不同类型的对象 l=list(Blue, 2, 5, Red) l #-------------------------------------------------------------------------------- # Create a matrix # matrix （矩阵） #矩阵（pl. matrices）具有维度（dimension）属性（attribute）的向量。该维度属性本身是一个长度为2的整数向量（nrow,ncol），行数和列数。 #-------------------------------------------------------------------------------- m - matrix(nrow = 2, ncol = 3) m # # NA NA NA # NA NA NA #function dim()：Retrieve or set the dimension of an object #Useage #(x)，Retrieve the dimension of an object获取矩阵的维度属性 #dim(x) - value，set the dimension of an object设置矩阵的维度属性 #Examples #x - 1:12 ; dim(x) - c(3,4) #x # simple versions of nrow and ncol could be defined as follows #nrow0 - function(x) dim(x) #nrow0(m) #ncol0 - function(x) dim(x) #ncol0(m) dim(m) #Retrieve the dimension of matrix x #获取矩阵的维度属性 # 2 3 #rows: 2, colluns: 3， 2x3的矩阵 # Check the attributes attributes(m) #获取矩阵的维度属性 #$dim # 2 3 # Call a particular cell in a matrix # 调取矩阵中特定位置的元素 m m ## Matrix creation is column-wise #在R中建矩阵是遵从列优先（column-wise）的原则，即，优先按照顺序从左上角开始向右一列一列地填充。如， m - matrix(1:6, nrow = 2, ncol = 3) #m=matrix(1:6,2,3) m # # 1 3 5 # 2 4 6 #在了解了这个特性之后，我们就可使用dim()来为向量集创建矩阵了，大家只要注意列优先原则即可，如， # Create a matrix from a vector #从向量开始创建啊一个矩阵 #第一步，创建一个同类型对象集合--向量 m=matrix(1:10) #m - 1:10 m # 1 2 3 4 5 6 7 8 9 10 # Then add dimensionality #第二步，设置矩阵m的维度属性，决定了对象的排列情况 dim(m)=c(2,5) #dim(m) - c(2, 5) m # # 1 3 5 7 9 # 2 4 6 8 10 # Create a matrix by binding columns or rows #此外，在R中，你也可以使用cbind()（column-binding）和rbind()（row-binding）functions来创建矩阵。如， x - 1:3 y - 10:12 # Create a matrix by binding column cbind(x, y) # x y # 1 10 # 2 11 # 3 12 # Create a matrix by binding rows rbind(x, y) # #x 1 2 3 #y 10 11 12 #-------------------------------------------------------------------------------- # Dataframes 数据框 ## Different than matrices = can store different classes of objects # 能存储不同类型的对象 ## Usually called with read.table() #-------------------------------------------------------------------------------- # Create a dataframe创建数据框对象 d=data.frame(subjectID=1:5,gender=c(M,F,F,M,F),score=c(8,3,6,5,5)) d # Number of rows 获取数据框的行数 nrow(d) # Number of columns 获取数据框的列数 ncol(d) # Check the attributes 获取数据框的维度属性 attributes(d) # Call a particular cell in a dataframe # 调取数据框中某一个特定的元素 d d # Display dataframe 显示数据框，注意大小写，是首字母大写的V View(d) # Edit dataframe 编辑数据框的数据 edit(d) #-------------------------------------------------------------------------------- # 附录 #-------------------------------------------------------------------------------- # Getting help on a function # 获得某一个函数的使用帮助 ?functionname #get all packages #获得当前安装的所有R包，看看需不需要安装相应的包 .packages(all.available = TRUE) # Download and install packages # 下载与安装R包 ## Need to specify CRAN the 1st time # 第一次使用需要设置 CRAN，选择使用哪个国家的服务器下载，就近原则 install.packages(psych) # Load package # 加载R包 # library(package_name) library(psych) #RODBC read xls file data example #使用RODBC包读取Excel文件.xls里的数据 library(RODBC) z - odbcConnectExcel(rexceltest.xls) data - sqlFetch(z,Sheet1) close(z) #R中if else 的写法-else不能单独一行 if(){ }else{ } 格式化不了，科学网的编辑器怎么了？
http://mcfromnz.wordpress.com/2011/03/02/anova-type-iiiiii-ss-explained/ Not my post, just bookmarking this. It’s from http://goanna.cs.rmit.edu.au/~fscholer/anova.php ANOVA (and R) The ANOVA Controversy ANOVA is a statistical process for analysing the amount of variance that is contributed to a sample by different factors. It was initially derived by R. A. Fisher in 1925, for the case of balanced data (equal numbers of observations for each level of a factor). When data is unbalanced, there are different ways to calculate the sums of squares for ANOVA. There are at least 3 approaches, commonly called Type I, II and III sums of squares (this notation seems to have been introduced into the statistics world from the SAS package but is now widespread). Which type to use has led to an ongoing controversy in the field of statistics (for an overview, see Heer ). However, it essentially comes down to testing different hypotheses about the data. Type I, II and III Sums of Squares Consider a model that includes two factors A and B; there are therefore two main effects, and an interaction, AB. The full model is represented by SS(A, B, AB). Other models are represented similarly: SS(A, B) indicates the model with no interaction, SS(B, AB) indicates the model that does not account for effects from factor A, and so on. The influence of particular factors (including interactions) can be tested by examining the differences between models. For example, to determine the presence of an interaction effect, an F-test of the models SS(A, B, AB) and the no-interaction model SS(A, B) would be carried out. It is convenient to define incremental sums of squares to represent these differences. Let SS(AB | A, B) = SS(A, B, AB) – SS(A, B) SS(A | B, AB) = SS(A, B, AB) – SS(B, AB) SS(B | A, AB) = SS(A, B, AB) – SS(A, AB) SS(A | B) = SS(A, B) – SS(B) SS(B | A) = SS(A, B) – SS(A) The notation shows the incremental differences in sums of squares, for example SS(AB | A, B) represents “the sum of squares for interaction after the main effects”, and SS(A | B) is “the sum of squares for the A main effect after the B main effect and ignoring interactions” . The different types of sums of squares then arise depending on the stage of model reduction at which they are carried out. In particular: Type I, also called “sequential” sum of squares: SS(A) for factor A. SS(B | A) for factor B. SS(AB | B, A) for interaction AB. This tests the main effect of factor A, followed by the main effect of factor B after the main effect of A, followed by the interaction effect AB after the main effects. Because of the sequential nature and the fact that the two main factors are tested in a particular order, this type of sums of squares will give different results for unbalanced data depending on which main effect is considered first. For unbalanced data, this approach tests for a difference in the weighted marginal means. In practical terms, this means that the results are dependent on the realized sample sizes, namely the proportions in the particular data set. In other words, it is testing the first factor without controlling for the other factor (for further discussion and a worked example, see Zahn ). Note that this is often not the hypothesis that is of interest when dealing with unbalanced data. Type II: SS(A | B) for factor A. SS(B | A) for factor B. This type tests for each main effect after the other main effect. Note that no significant interaction is assumed (in other words, you should test for interaction first (SS(AB | A, B)) and only if AB is not significant, continue with the analysis for main effects). If there is indeed no interaction, then type II is statistically more powerful than type III (see Langsrud for further details). Computationally, this is equivalent to running a type I analysis with different orders of the factors, and taking the appropriate output (the second, where one main effect is run after the other, in the example above). Type III: SS(A | B, AB) for factor A. SS(B | A, AB) for factor B. This type tests for the presence of a main effect after the other main effect and interaction. This approach is therefore valid in the presence of significant interactions. However, it is often not interesting to interpret a main effect if interactions are present (generally speaking, if a significant interaction is present, the main effects should not be further analysed). If the interactions are not significant, type II gives a more powerful test. NOTE: when data is balanced, the factors are orthogonal, and types I, II and III all give the same results. Summary: Usually the hypothesis of interest is about the significance of one factor while controlling for the level of the other factors. This equates to using type II or III SS. In general, if there is no significant interaction effect, then type II is more powerful, and follows the principle of marginality. If interaction is present, then type II is inappropriate while type III can still be used, but results need to be interpreted with caution (in the presence of interactions, main effects are rarely interpretable). The anova and aov Functions in R The anova and aov functions in R implement a sequential sum of squares (type I). As indicated above, for unbalanced data, this rarely tests a hypothesis of interest, since essentially the effect of one factor is calculated based on the varying levels of the other factor. In a practical sense, this means that the results are interpretable only in relation to the particular levels of observations that occur in the (unbalanced) data set. Fortunately, based on the above discussion, it should be clear that it is relatively straightforward to obtain type II SS in R. Type II SS in R Since type II SS tests each main effect after the other main effects, and assumes no interactions, the correct SS can be obtained using anova() and varying the order of the factors. For example, consider a data frame (search) for which the response variable is the time that it takes users to find a relevant answer with an information retreival system (time). The user is assigned to one of two experimental search systems on which they run the test (sys). They are also assigned a number of different search queries (topic). To obtain type I SS: anova(lm(time ~ sys * topic, data=search)) If the data is unbalanced, you will obtain slightly different results if you instead use: anova(lm(time ~ topic * sys, data=search)) The type II SS is obtained by using the second line of output from each of the above commands (since in type I SS, the second component will be the second factor, after the first factor). That is, you obtain the type II SS results for topic from the first command, and the results for sys from the second. Type III SS in R This is slightly more involved than the type II results. First, it is necessary to set the contrasts option in R. Because the multi-way ANOVA model is over-parameterised, it is necessary to choose a contrasts setting that sums to zero, otherwise the ANOVA analysis will give incorrect results with respect to the expected hypothesis. (The default contrasts type does not satisfy this requirement.) options(contrasts = c(“contr.sum”,”contr.poly”)) Next, store the model: model - lm(time ~ topic * sys, data=search) Finally, call the drop1 function on each model component: drop1(model, .~., test=”F”) The results give the type III SS, including the p-values from an F-test. Type II and III SS Using the car Package A somewhat easier way to obtain type II and III SS is through the car package. This defines a new function, Anova(), which can calculate type II and III SS directly. Type II, using the same data set defined above: Anova(lm(time ~ topic * sys, data=search, type=2)) Type III: Anova(lm(time ~ topic * sys, data=search, contrasts=list(topic=contr.sum, sys=contr.sum)), type=3)) NOTE: Again, due to the way in which the SS are calculated when incorporating the interaction effect, for type III you must specify the contrasts option to obtain sensible results (an explanation is given here). References John Fox. “Applied Regression Analysis and Generlized Linear Models”, 2nd ed., Sage, 2008. David G. Herr. “On the History of ANOVA in Unbalanced, Factorial Designs: The First 30 Years”, The American Statistician, Vol. 40, No. 4, pp. 265-270, 1986. Oyvind Langsrud. “ANOVA for unbalanced data: Use Type II instead of Type III sums of squares”, Statistics and Computing, Volume 13, Number 2, pp. 163-167, 2003. Ista Zahn. “Working with unbalanced cell sizes in multiple regression with categorical predictors”, 2009. prometheus.scp.rochester.edu/zlab/sites/default/files/InteractionsAndTypesOfSS.pdf
v d e Variable star Pulsating Cepheids and cepheid-like Cepheid W Virginis RR Lyrae Delta Scuti SX Phoenicis Blue-white with earlyspectra Beta Cephei PV Telescopii Long Period and Semiregular Mira Semiregular Slow irregular Other RV Tauri Alpha Cygni Pulsating white dwarf Eruptive Protostar Herbig Ae/Be Orion FU Orionis Main Sequence Wolf-Rayet Flare Giants and supergiants Luminous blue Gamma Cassiopeiae R Coronae Borealis Eruptive binary RS Canum Venaticorum Cataclysmic or explosive Cataclysmic variable star Dwarf nova Nova Supernova Z Andromedae Rotating Non-spherical Ellipsoidal Stellar spots FK Comae Berenices BY Draconis Magnetic fields Alpha-2 Canum Venaticorum SX Arietis Pulsar Eclipsing Algol Beta Lyrae W Ursae Majoris 参考文献： http://en.wikipedia.org/wiki/Beta_Lyrae_variable 2010-05-17于京
Type I secretion system The type I secretion system (T1SS) is one of the mechanisms used by Gram-negative bacteria to secrete proteins to the external medium. Secretion occurs in a single, energy-coupled step via an exporter complex that spans the inner and outer membranes (Fig. 1). The relatively simple exporter complex consists of only three protein subunits: an inner membrane-bound ATP-binding cassette (ABC) protein that forms a complex with a membrane fusion protein (MFP) in the periplasmic space, and an outer membrane protein (OMP) that resides in the periplasmic space and is embedded in the outer membrane. Figure 1. A model of bacterial T1SS. ABC protein (white), membrane fusion protein (MFP; light gray), and outer membrane protein (OMP; dark gray) form a transporter complex protruding through the inner and outer membranes of Gram-negative bacteria. The secretion signal is shown by a gray box near the C-terminal end of the passenger protein . Protein secretion occurs in a single step, bypassing the periplasmic space directly to the extracellular medium (the direction of protein secretion is shown by a large shaded arrow). ATP hydrolysis by the ATPase domain of ABC protein provides energy for protein transport. The C-terminal secretion signal is recognized by the ABC protein, stimulating conformational change of the transporter complex and ATP hydrolysis, which leads to secretion of the passenger protein . A -roll structure, formed by the repetitive sequences in the presence of Ca2+, is also shown. The ABC protein subunit of the T1SS consists of two transmembrane domains (TMDs) that are embedded in the inner membrane, and two nucleotide-binding domains (NBDs) that are exposed to the cytoplasm. The NBD has ATPase activity and provides the energy needed for protein secretion. The ABC protein of the T1SS belongs to the well-characterized ABC protein superfamily, the largest protein superfamily found in all kingdoms of life, which is related to the import or export of various molecules, from small ions to polysaccharides and proteins. cited from :Cell. Mol. Life Sci. 63 (2006) 28042817
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