Materials Used
Random copolymers of styrene (S) and methylmethacrylate (MMA) with the 2% reactive benzocyclobutane (BCB) functionality randomly incorporated into the backbone were used having molecular weights of between 20,000 and 100,000 and a PDI of 1.18 which corresponds to an average of 7 BCB units per chain. The polystyrene-poly(methylmethacrylate) block copolymer (PS-b-PMMA) used in these studies was prepared by anionic polymerization and had a weight average molecular weight of 88,000, a polydispersity of 1.03, and a 0.723 volume fraction of PS. In the bulk, the morphology consists of hexagonally-packed cylindrical microdomains of PMMA in a PS matrix with a lattice spacing L o =34.1 nm. PS (Polymer Laboratories, M n=30,000, PDI=1.03) and PMMA (Polymer Laboratories, M n=25,000 PDI=1.03) homopolymers were used for contact angle and wetting studies.
Adhesion Tests
Shown in S1 A and B are droplets of water on P(S-r-BCB-r-MMA) coated silicon/silicon oxide substrates. On the left, the P(S-r-BCB-r-MMA) film ~7 nm was not crosslinked and, on the right, the P(S-r-BCB-r-MMA) films was crosslinked at 250 o C for 10 minutes. On the left hand side of each image, an adhesive layer was placed on the surface and peeled off at 180o at a 300mm/min. The water contact angle remains the same in all cases indicating that the random copolymer has remained on the surface.
Figure S1. Water droplets on the silicon substrates coated with a 7~8 nm thick uncrosslinked film (A) of P(S-r-BCB-r-MMA) and crosslinked film (B) at 250 o C for 10 min. Each left part was tested by water droplets after 180o peeling off of adhesive layer. Water contact angle for each case is 76±0.3o regardless of crosslinking of thin films.
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Chapter4 Introduction to Analysis-of-Variance Procedures Chapter T able of Contents 52Chapter4.Introduction to Analysis-of-Variance Procedures SAS OnlineDoc?:Version8 Chapter4 Introduction to Analysis-of-Variance Procedures 54Chapter4.Introduction to Analysis-of-Variance Procedures The following section presents an overview of some of the fundamental features of analysis of variance.Subsequent sections describe how this analysis is performed with procedures in SAS/STAT software.For more detail,see the chapters for the individual procedures.Additional sources are described in the“References”section on page61. De?nitions Analysis of variance(ANOV Ais a technique for analyzing experimental data in which one or more response(or dependent or simply Yvariables are measured un-der various conditions identi?ed by one or more classi?cation variables.The com-binations of levels for the classi?cation variables form the cells of the experimental design for the data.For example,an experiment may measure weight change(the dependent variablefor men and women who participated in three different weight-loss programs.The six cells of the design are formed by the six combinations of sex (men,womenand program(A,B,C.
Generalized Hough Transform (GHT) (Ballard and Brown, section 4.3.4, Sonka et al., section 5.2.6) -The Hough transform was initially developed to detect analytically de?ned shapes (e.g., lines, circles, ellipses etc.). -The generalized Hough transform can be used to detect arbitrary shapes (i.e., shapes having no simple analytical form). -It requires the complete speci?cation of the exact shape of the target object.? Special case: ?xed orientation and size y x =x c +x ′or x c =x ?x ′y =y c +y ′or y c =y ?y ′ cos (π?α)=y ′r or y ′=rcos (π?α)=?rsin (α)sin (π?α)=x ′r or x ′=rsin (π?α)=?rcos (α)
-Combining the above equations we have: x c=x+rcos(α) y c=y+rsin(α) Preprocessing step (1) Pick a reference point (e.g.,(x c,y c)) (2) Draw a line from the reference point to the boundary. (3) Computeφ(i.e., perpendicular to gradient’s direction). (4) Store the reference point(x c,y c)as a function ofφ(i.e., build the R-table) φ1:(r11,α11),(r12,α12),... φ2:(r21,α21),(r22,α22),... φn:(r n1,αn1),(r n2,αn2),... -The R-table allows us to use the contour edge points and gradient angle to recompute the location of the reference point. Note:we need to build a separate R-table for each different object.
SPSS详细操作：广义估计方程 SPSS详细操作：广义估计方程 2017-03-18 17:40一、问题与数据 在临床研究中，经常会比较两种治疗方式对患者结局的影响，并且多次测量结局。例如，为了研究两种降压药物对血压的控制效果是否存在差异，研究者会对两个人群服药后在不同时间点记录血压值，然后评价降压效果。或者对两组动物分别施加两种干预，连续记录多个时间点的结局，然后比较两种干预的效果。 这种设计可以用如下示意图表示： 另外，有时研究只需要收集一个时间点的数据，但是一个研究对象会提供多个部位的数据点。例如，研究者想评价冠心病患者在冠脉搭桥术后应用阿司匹林是否可以有效降低患 者血管的再堵塞，评价的方法是术后1年做冠脉造影观察血管是否堵塞，但是每个患者可能会在同一次手术中对多条冠状动脉血管进行搭桥，因此有的患者可能会贡献多组数据。这种设计可以用如下示意图表示：
以上两种设计，不管是临床试验还是动物试验都非常常见，它的特点在于数据间非独立，同一个体间数据具有相关性。对于这样的设计类型，该如何分析呢？ 今天我们来介绍另外一种非常好的方法——广义估计方程（GEE）。GEE既可以处理连续型结局变量也可以处理分类型结局变量，它实际上代表了一种模型类别，即在传统模型的基础上对相关性数据进行了校正，可以拟合Logistic回归、泊松回归、Probit回归、一般线性回归等广义线性模型。 本文将以阿司匹林预防冠脉搭桥后血管再堵塞为例介绍运 用SPSS进行GEE的操作方法。以下为数据格式： 表1. 数据格式 每名患者贡献数据量不等。如编号为1的患者只对一根血管进行了搭桥手术，编号为2的患者则有两根血管进行搭桥手术。 表2. 变量赋值 （注：本例中数据纯属虚构，分析结果不能产生任何结论。性别为待调整变量。） 二、SPSS分析方法 1. 数据录入SPSS 首先在SPSS变量视图（Variable View）中新建上述表2中
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