2-2 ELT_Method_CLT_TBLT

Package‘calmate’October12,2022Version0.13.0Depends R(>=3.2.1),R.utils(>=2.11.0),aroma.core(>=3.2.2)Imports utils,MASS,R.methodsS3(>=1.8.1),R.oo(>=1.24.0),matrixStats(>=0.61.0),R.filesets(>=2.14.0)Suggests DNAcopyTitle Improved Allele-Specific Copy Number of SNP Microarrays forDownstream SegmentationDescription The CalMaTe method calibrates preprocessed allele-specific copy number estimates(AS-CNs)from DNA microarrays by controlling for single-nucleotide polymorphism-specific al-lelic crosstalk.The resulting ASCNs are on average more accurate,which in-creases the power of segmentation methods for detecting changes between copy num-ber states in tumor studies including copy neutral loss of heterozygosity.CalMaTe ap-plies to any ASCNs regardless of preprocessing method and microarray technol-ogy,e.g.Affymetrix and Illumina.License LGPL(>=2.1)URL https:///HenrikBengtsson/calmate/BugReports https:///HenrikBengtsson/calmate/issuesLazyLoad TRUEbiocViews aCGH,CopyNumberVariants,SNP,Microarray,OneChannel,TwoChannel,GeneticsNeedsCompilation noAuthor Maria Ortiz[aut,ctb],Ander Aramburu[ctb],Henrik Bengtsson[aut,cre,cph],Pierre Neuvial[aut,ctb],Angel Rubio[aut,ctb]Maintainer Henrik Bengtsson<*****************>Repository CRANDate/Publication2022-03-0823:00:03UTC12calmate-package R topics documented:calmate-package (2)calmateByThetaAB.array (3)calmateByTotalAndFracB.array (4)CalMaTeCalibration (6)Index10 calmate-package Package calmateDescriptionThe CalMaTe method calibrates preprocessed allele-specific copy number estimates(ASCNs)from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk.The resulting ASCNs are on average more accurate,which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity.CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology,e.g.Affymetrix and Illumina.RequirementsThis package depends on a set of packages that are all available via CRAN.It has been tested and verified to run on all common operating systems on which R runs,including Linux,Windows and OSX.Installation and updatesTo install this package,do install.packages("calmate").To get started1.To process SNP and non-polymorphic signals,see calmateByTotalAndFracB().If you areworking solely with SNP signals,calmateByThetaAB()is also available,but we recommend the former.2.For processing data in the aroma framework,see CalMaTeCalibration.How to citePlease cite[1]when using CalMaTe.LicenseLGPL(>=2.1).Author(s)Maria Ortiz[aut,ctb],Ander Aramburu[ctb],Henrik Bengtsson[aut,cre,cph],Pierre Neuvial[aut, ctb],Angel Rubio[aut,ctb].calmateByThetaAB.array3 References[1]M.Ortiz-Estevez,A.Aramburu,H.Bengtsson,P.Neuvial and A.Rubio,CalMaTe:A methodand software to improve allele-specific copy number of SNP arrays for downstream segmentation, Bioinformatics,2012[PMC3381965].calmateByThetaAB.arrayNormalize allele-specific copy numbers(CA,CB)DescriptionNormalize allele-specific copy numbers(CA,CB).Usage##S3method for class arraycalmateByThetaAB(data,references=NULL,...,truncate=FALSE,refAvgFcn=NULL,flavor=c("v2","v1"),verbose=FALSE)Argumentsdata An Jx2xI numeric array,where J is the number of SNPs,2is the number of alleles,and I is the number of samples.references An index vector in[1,I]or a logical vector of length I specifying which samples are used when calculating the reference signals.If NULL,all samplesare used.At least3samples....Additional arguments passed to the internalfit function fitCalMaTeInternal.truncate If TRUE,final ASCNs are forced to be non-negative while preserving the total CNs.refAvgFcn(optional)A function that takes a JxI numeric matrix an argument na.rm and returns a numeric vector of length J.It should calculate some type of averagefor each of the J rows,e.g.rowMedians.If specified,then the total copy numbersof the calibrated ASCNs are standardized toward(twice)the average of the totalcopy numbers of the calibrated reference ASCNs.flavor A character string specifying whichflavor of the CalMaTe algorithm to use forfitting the model.verbose See Verbose.ValueReturns an Jx2xI numeric array with the same dimension names as argument data.FlavorsFor backward compatibility,we try to keep all major versions of the CalMaTe algorithm available.Older versions can be used by specifying argument flavor.The defaultflavor is v2.For more information about the differentflavors,see fitCalMaTeInternal.References[1]M.Ortiz-Estevez,A.Aramburu,H.Bengtsson,P.Neuvial and A.Rubio,CalMaTe:A methodand software to improve allele-specific copy number of SNP arrays for downstream segmentation, Bioinformatics,2012[PMC3381965].See AlsoTo calibrate(total,fracB)data,see*calmateByTotalAndFracB().We strongly recommend to al-ways work with(total,fracB)data instead of(CA,CB)data,because it is much more general.For further information on the internalfit functions,see fitCalMaTeInternal.Examples#Load example(thetaA,thetaB)signalspath<-system.file("exData",package="calmate");theta<-loadObject("thetaAB,100x2x40.Rbin",path=path);#Calculate(CA,CB)thetaR<-matrixStats::rowMedians(theta[,"A",]+theta[,"B",],na.rm=TRUE);C<-2*theta/thetaR;#Calibrate(CA,CB)by CalMaTeCC<-calmateByThetaAB(theta);#Plot two"random"arraysClim<-c(0,4);subplots(4,ncol=2,byrow=FALSE);for(ii in c(1,5)){sampleName<-dimnames(C)[[3]][ii];sampleLabel<-sprintf("Sample#%d( %s )",ii,sampleName);plot(C[,,ii],xlim=Clim,ylim=Clim);title(main=sampleLabel);plot(CC[,,ii],xlim=Clim,ylim=Clim);title(main=sprintf("%s\ncalibrated",sampleLabel));}calmateByTotalAndFracB.arrayNormalize allele-specific copy numbers(total,fracB)DescriptionNormalize allele-specific copy numbers(total,fracB),where total is the total(non-polymorphic) signal and fracB is the allele B fraction.It is only loci with a non-missing(NA)fracB value that are considered to be SNPs and normalized by CalMaTe.The other loci are left untouched.Usage##S3method for class arraycalmateByTotalAndFracB(data,references=NULL,...,refAvgFcn=NULL,verbose=FALSE) Argumentsdata An Jx2xI numeric array,where J is the number of loci,2is total and fracB(in that order,if unnamed),and I is the number of samples.references A logical or numeric vector specifying which samples should be used as the reference set.By default,all samples are considered.If not NULL at least3samples....Additional arguments passed to*calmateByThetaAB().refAvgFcn(optional)A function that takes a JxI numeric matrix an argument na.rm and returns a numeric vector of length J.It should calculate some type of averagefor each of the J rows,e.g.rowMedians.If specified,then the total copy numbersof the calibrated ASCNs are standardized toward(twice)the average of the totalcopy numbers of the calibrated reference ASCNs.verbose See Verbose.ValueReturns an Jx2xI numeric array with the same dimension names as argument data. References[1]M.Ortiz-Estevez,A.Aramburu,H.Bengtsson,P.Neuvial and A.Rubio,CalMaTe:A methodand software to improve allele-specific copy number of SNP arrays for downstream segmentation, Bioinformatics,2012[PMC3381965].See AlsoTo calibrate(thetaA,thetaB)or(CA,CB)signals,see*calmateByThetaAB().Examples#Load example(thetaA,thetaB)signalspath<-system.file("exData",package="calmate");theta<-loadObject("thetaAB,100x2x40.Rbin",path=path);#Transform to(total,fracB)signalsdata<-thetaAB2TotalAndFracB(theta);#Calibrate(total,fracB)by CalMaTedataC<-calmateByTotalAndFracB(data);#Calculate copy-number ratiostheta<-data[,"total",];thetaR<-matrixStats::rowMedians(theta,na.rm=TRUE);data[,"total",]<-2*theta/thetaR;#Plot two"random"arraysClim<-c(0,4);Blim<-c(0,1);subplots(4,ncol=2,byrow=FALSE);for(ii in c(1,5)){sampleName<-dimnames(data)[[3]][ii];sampleLabel<-sprintf("Sample#%d( %s )",ii,sampleName);plot(data[,,ii],xlim=Clim,ylim=Blim);title(main=sampleLabel);plot(dataC[,,ii],xlim=Clim,ylim=Blim);title(main=sprintf("%s\ncalibrated",sampleLabel));}#Assert that it also works with a single unitdummy<-calmateByTotalAndFracB(data[1,,,drop=FALSE]);stopifnot(length(dim(dummy))==3);CalMaTeCalibration The CalMaTeCalibration classDescriptionPackage:calmateClass CalMaTeCalibrationObject~~|~~+--ParametersInterface~~~~~~~|~~~~~~~+--CalMaTeCalibrationDirectly known subclasses:public static class CalMaTeCalibrationextends ParametersInterfaceThis class represents the CalMaTe method[1],which corrects for SNP effects in allele-specific copy-number estimates(ASCNs).UsageCalMaTeCalibration(data=NULL,tags="*",references=NULL,flavor=c("v2","v1"),...)Argumentsdata A named list with data set named"total"and"fracB"where the formershould be of class AromaUnitTotalCnBinarySet and the latter of class AromaUnitFracBCnBinarySet.The two data sets must be for the same chip type,have the same number of sam-ples and the same sample names.tags Tags added to the output data sets.references An optional numeric vector specifying which samples should be as referencesamples for estimating the model parameters.If NULL,all samples are used.flavor A character string specifying whichflavor of the CalMaTe algorithm to useforfitting the model.See fitCalMaTeInternal for details....Additional arguments passed to calmateByTotalAndFracB().Fields and MethodsMethods:findUnitsTodo-getDataSets-getFullName-getName-getOutputDataSets-getPath-getReferences-getRootPath-getTags-nbrOfFiles-process-setTags-Methods inherited from ParametersInterface:getParameterSets,getParameters,getParametersAsStringMethods inherited from Object:$,$<-,[[,[[<-,as.character,attach,attachLocally,clearCache,clearLookupCache,clone,detach,equals,extend,finalize,getEnvironment,getFieldModifier,getFieldModifiers,getFields,getInstan-tiationTime,getStaticInstance,hasField,hashCode,ll,load,names,objectSize,print,save,asThisReference samplesIn order to estimate the calibration parameters,the model assumes that,for any given SNP,thereare a majority of samples that are diploid at that SNP.Note that it does not have to be the same setof samples for all SNPs.By using argument references,it is possible so specify which samples should be used whenestimating the calibration parameters.This is useful when for instance there are several tumorsamples with unknown properties as well as a set of normal samples that can be assumed to bediploid.Theoretical,a minimum of three reference samples are needed in order for the model to be identifi-able.If less,an error is thrown.However,in practice more reference samples should be used,thatis,in the order of at least6-10reference samples with a diverse set of genotypes.FlavorsFor backward compatibility,we try to keep all major versions of the CalMaTe algorithm avail-able.Older versions can be used by specifying argument flavor.For more information about thedifferentflavors,see fitCalMaTeInternal.References[1]M.Ortiz-Estevez,A.Aramburu,H.Bengtsson,P.Neuvial and A.Rubio,CalMaTe:A methodand software to improve allele-specific copy number of SNP arrays for downstream segmentation,Bioinformatics,2012[PMC3381965].See AlsoLow-level versions of the CalMaTe method is available via the calmateByThetaAB()and calmateByTotalAndFracB() methods.For further information on the internalfit functions,see fitCalMaTeInternal.Examples##Not run:#------------------------------------#CRMAv2-Preprocess raw Affymetrix data#------------------------------------library("aroma.affymetrix");#Needed for CRMAv2dataSet<-"Affymetrix_2006-TumorNormal";chipType<-"Mapping250K_Nsp";dsList<-doCRMAv2(dataSet,chipType=chipType,combineAlleles=FALSE,plm="RmaCnPlm",verbose=-10);print(dsList);#------------------------------------#CalMaTe-Post-calibration of ASCNs estimates#------------------------------------asn<-CalMaTeCalibration(dsList);print(asn);#For speed issues,we will here only process loci on Chromosome17.chr<-17;ugp<-getAromaUgpFile(dsList$total);units<-getUnitsOnChromosome(ugp,chr);dsNList<-process(asn,units=units,verbose=verbose);print(dsNList);#------------------------------------#Plot allele B fractions(before and after)#------------------------------------#Sample#1and Chromosome17ii<-1;#Extract raw(TCN,BAF)df<-getFile(dsList$total,ii);dfR<-getAverageFile(dsList$total,verbose=verbose);gamma<-extractRawCopyNumbers(df,logBase=NULL,chromosome=chr); gammaR<-extractRawCopyNumbers(dfR,logBase=NULL,chromosome=chr); gamma<-2*divideBy(gamma,gammaR);df<-getFile(dsList$fracB,ii);beta<-extractRawAlleleBFractions(df,chromosome=chr);#Extract calibrated(TCN,BAF)dfN<-getFile(dsNList$fracB,ii);betaN<-extractRawAlleleBFractions(dfN,chromosome=chr);dfN<-getFile(dsNList$total,ii);gammaN<-extractRawCopyNumbers(dfN,logBase=NULL,chromosome=chr);#Plotsubplots(4,ncol=2,byrow=FALSE);plot(beta);title(sprintf("%s",getName(beta)));plot(gamma);plot(betaN);title(sprintf("%s(CalMaTe)",getName(betaN)));plot(gammaN);##End(Not run)Index∗classesCalMaTeCalibration,6∗methodscalmateByThetaAB.array,3calmateByTotalAndFracB.array,4∗packagecalmate-package,2*calmateByThetaAB,5*calmateByTotalAndFracB,4 AromaUnitFracBCnBinarySet,7 AromaUnitTotalCnBinarySet,7array,3,5calmate(calmate-package),2calmate-package,2 calmateByThetaAB,2,8 calmateByThetaAB(calmateByThetaAB.array),3 calmateByThetaAB.array,3 calmateByTotalAndFracB,2,7,8 calmateByTotalAndFracB(calmateByTotalAndFracB.array),4 calmateByTotalAndFracB.array,4 CalMaTeCalibration,2,6character,3,7 fitCalMaTeInternal,3,4,7,8 function,3,5list,7logical,3,5matrix,3,5NA,5NULL,3,7numeric,3,5,7Object,6ParametersInterface,6rowMedians,3,5TRUE,3vector,3,5,7Verbose,3,510。

《英语教学理论与实践》教学大纲Theory and Practice for English language Teaching课程代码：RRX036316 学时：36 学分：2理论学时：36 实验或讨论学时：0适用专业：英语课程性质：选修执笔人：李银玲审定人：康光明一、说明1．课程的性质、地位和任务“英语教学理论与实践”是面向英语专业高年级学生开设的一门个性发展课程。

是一门多边缘的、发展中的、理论与实践相结合的理论学科，课程在回顾外语教学历史、研究语言和学习规律、介绍先进教学理论的同时，要求学生通过语音、语法、词汇、听力、口语、阅读、写作、等课堂教学的实践，掌握教学理论，灵活运用教学理论。

因此，本课程具有知识介绍和理论实践两大特点，既注重知识的传授，又注意教学能力的培养。

2．课程教学的基本要求该课程要求学生有很强的自主学习能力和参与意识。

根据人才培养模式的要求和本课程设置的特点，学生在学习时应做到以下几点：1）做好充分的预习工作该课程要求学生必须在上课前做好充分的预习工作。

在课前预习时应该做到以下几点：预习教材，完成所有的tasks；阅读相关的辅助材料，如上海外语教育出版社和外研社都出版了成套的英语教学和研究类图书，另外很多国内的刊物，如“外语教学与研究”“外语界””国外外语教学”“中小学外语教学”等，国外的如TESOL Quarterly, ELT Journal, Forum等上面都有很多理论与理论应用性的研究，阅读这些书籍和刊物不仅可以扩充自己的知识，也可以学习一些研究方法；自己应该能够根据教材中的教学理论，设计各种微型课堂活动。

2）注意互惠性学习所谓互惠性学习即在学习中与其他同学合作，互惠互利，进行信息共享，共同分析问题、解决问题。

如有可能可以结合成固定的学习小组。

3）加强反思性学习在学习过程中应注意将所学教学理论与自己的教学实践相联系，从理论的角度评估自己的教学。

如在学习“阅读教学”时，根据阅读教学的理论对照自己的阅读教学实践，寻找教学中的差距，这样不仅可以加深对教学理论的理解，也可通过对自己教学的反思，提高自己的业务素质。

p=0.002
80% 67%
60% 47%
40%
20%
0% Initial TFG 3 at angiography
Dual therapy
Triple
• The secondary endpoint of initial TIMI grade 3 flow at angiography was higher in the triple therapy group (47% vs 67%)
Hours
Median time to angiography
40
30
30
26
20
10
0 Median time to angiography
Dual therapy
Triple therapy
www. C2l0in2ic1a/0l t1r/
• In the dual therapy group, median time to angiography was 26 hours. In the triple therapy group, median time to angiography was 30 hrs.
Secondary Endpoint: Initial TIMI flow grade of the culprit artery
www. C2l0in2ic1a/0l t1r/
2 Presented at ESC 2005
ELISA II Trial: Baseline Characteristics
ELISA II Trial
ELISA II Trial
Presented at The European Society of Cardiology

量子点的分子修饰及高效液相色谱法检测马慧莲1王春雷2刘含智3王丽萍3*徐淑坤1*李惟3（1东北大学理学院，沈阳 110004，2 吉林大学超分子结构与材料重点实验室，长春130023， 3 吉林大学生命科学学院，长春 130023）关键词：量子点；分子修饰；高效液相色谱法近年来量子点（quantum dots, QDs）作为一种很有发展潜力的新型荧光生物探针，已受到人们的广泛关注，它吸收光谱宽，发射光谱窄而对称，通过调节组成和大小可以使其发射出不同颜色的光, 并且具有较高的荧光强度和光稳定性等特点，克服了传统有机荧光染料的诸多不足之处[1-4]，有望成为其替代物。

人们已经在细胞成像，免疫分析，DNA杂交，生物传感器等方面得到了较为成功的探索与尝试[5-9]，在多数研究中都涉及了QDs与生物分子的偶联问题，这一步往往是进行深入研究的基础，因此只有对生物分子修饰QDs进行较为全面的研究，才能更好地利用这一新型荧光物质，本文正是着重从这一角度入手，以牛血清白蛋白（bovine serum albumin，BSA）为代表，研究了生物分子修饰QDs的最佳反应条件，发现QDs经BSA修饰后荧光明显增强，并对荧光增强的原因做了比较系统的探讨。

目前所报道的QDs偶联产物的检测主要是采用紫外分光光度计，荧光分光光度计，电泳等[2，10，11]，但这些方法的缺陷在于无法得到偶联产物的单纯组分，其检测结果往往会受到其它组分的干扰，很难应用于生物分析。

为了克服这一不足之处，我们采用了高效液相色谱法（high performance liquid chromatography,HPLC）这一高效、快速、可靠的分析分离手段，利用凝胶色谱柱，根据分子量大小对偶联产物进行分离，采用磷酸盐缓冲溶液作为洗脱液，保持了生物分子的活性和分子结构；用荧光和紫外检测器同时进行检测，提高了分析的选择性和准确性。

1．实验部分1．1 仪器与试剂美国Waters公司515型高效液相色谱仪（包括双泵，2487型紫外检测器，2475型荧光检测器，Millemium3.2工作站），日本岛津FR-5301PC型荧光光谱仪，日本岛津UV-2501PC紫外-可见分光光度计，NHS(N-Hydroxysuccinimide)和EDC・HCl为吉尔生化（上海）有限公司产品,BSA为Roche公司产品，Gly为鼎国生物技术有限公司产品，胰蛋白酶为AMRESCO公司产品。