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High expression of Cullin1 indicates poor prognosis for NSCLC patients

Pathology –Research and Practice 210(2014)397–401Contents lists available at ScienceDirectPathology –Research andPracticej o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /p rpOriginalArticleHigh expression of Cullin1indicates poor prognosis for NSCLC patientsMingming Xu a ,1,Xiaoming Yang b ,1,Jinli Zhao c ,Jianguo Zhang d ,Shu Zhang d ,Hua Huang d ,Yifei Liu d ,∗,Junhua Liu a ,∗∗aDepartment of Cardiothoracic Surgery,Afﬁliated Hospital of Nantong University,Nantong 226001,Jiangsu,China bDepartment of Neural Biology,Nantong University,Nantong 226001,Jiangsu,China cDepartment of Radiology,Afﬁliated Hospital of Nantong University,Nantong 226001,Jiangsu,China dDepartment of Pathology,Afﬁliated Hospital of Nantong University,Nantong 226001,Jiangsu,Chinaa r t i c l ei n f oArticle history:Received 18October 2013Received in revised form 5January 2014Accepted 30January 2014Keywords:Non-small-cell lung cancer Cullin1Proliferation Prognosisa b s t r a c tBackground:Cullin1is a scaffold protein of the ubiquitin E3ligase Skp1/Cullin1/Rbx1/F-box protein com-plex which ubiquitinates a broad range of proteins participating in biochemical events like cell-cycle progression,signal transduction,and transcription.Cullin1is involved in the progression of several cancers,such as melanoma,breast cancer,and gastric cancer.Methods:To investigate the role of Cullin1in the development of non-small-cell lung cancer (NSCLC),we examined the expression of Cullin1in 8-paired fresh NSCLC tissues.We then constructed immuno-histochemistry (IHC)on 114parafﬁn-embedded slices and evaluated the correlation between Cullin1expression and clinicopathologic variables,as well as patients’overall survival.Results:We found that Cullin1was highly expressed in NSCLC tissues and signiﬁcantly associated with NSCLC’s histological differentiation (P =0.002),clinical stage (P =0.010)and Ki-67(P =0.021).Further-more,we showed a strong correlation between high Cullin1expression and worse overall survival rates in NSCLC patients (P <0.001).Cox regression analysis revealed that Cullin1expression was an independent prognostic factor to predict 5-year patient outcome in NSCLC cancer (P =0.033).Conclusion:These data suggested that Cullin1might promote the progression of NSCLC and be a biotarget for NSCLC’s therapy.©2014Published by Elsevier GmbH.IntroductionLung cancer remains the leading cause of cancer-related deaths in the world,mainly non-small-cell lung cancer (NSCLC)and small-cell lung cancer (SCLC)[1].NSCLC consists of lung squamous cell carcinoma,lung adenocarcinoma and large-cell carcinoma,which is the hotspot of lung cancer research.The incidence of NSCLC is increasing,owing to environmental pollution and smoking cigarette.The 5-year survival rate is merely 15%,although vari-ous advancing therapies come into use [2].Lung carcinogenesis is involved in numerous genetic and epigenetic events,such as vari-ous oncogenes,tumor suppressor genes,cell cycle regulators,and∗Corresponding author at:Afﬁliated Hospital of Nantong University,19Qixiu Road,Nantong,Jiangsu Province 226001,China.Tel.:+8651385052118;fax:+8651385052118.∗∗Corresponding author at:Afﬁliated Hospital of Nantong University,19Qixiu Road,Nantong,Jiangsu Province 226001,China.Tel.:+8651385050901;fax:+8651385050901.E-mail addresses:blueﬁime@ (Y.Liu),ﬁime@ (J.Liu).1Mingming Xu and Xiaoming Yang contributed equally to this work.DNA repair genes.The high proliferative capacity of lung tumor-ous cells appears to be strongly related to abnormal regulation through critical checkpoints involved in cell cycle progression [3].So it is especially important to identify novel and selective cell-cycle-related targets for therapeutic intervention of NSCLC.Cancer development is the process by which normal cells are transformed into carcinoma cells upon aberrant cellular stimuli.This process is regulated by transcription,translation,post-translational modiﬁcations and degradation of key regulatory proteins which has a crucial role in maintaining and regulating cel-lular homeostasis [4,5].Speciﬁc proteolysis of cell-cycle regulators can regulate the cell-cycle progression [6],while two major E3lig-ase complexes,Anaphase-promoting complex/cyclosome (APC/C)and Skp1/Cullin1/F-box protein (SCF)complex,ubiquitinate the target genes [7].As the major category of E3ubiquitin ligase,the SCF complex is involved in the proteolysis of main components of the cell-cycle-related proteins [8–10],regulating the expression of proteins in cell cycle progression,such as p53,p27,CDK,and cyclins [6,11–13].Ubiquitin-mediated degradation of regulatory proteins plays important roles in G1-S transition,signal transduction and transcriptional regulation [14,15].An aberrant E3ubiquitin/10.1016/j.prp.2014.01.0150344-0338/©2014Published by Elsevier GmbH.398M.Xu et al./Pathology–Research and Practice210(2014)397–401ligase has been implicated in the pathogenesis of numerous human diseases and contributes to dysregulated cell-cycle control and differentiation,which leads to carcinogenesis[16–18].Thus it is inviting to identify regulators in these events in order to research cancer.Cullin1,a rigid scaffold component of SCF complexes,is involved in the proteasomal degradation of numerous pro-teins related to cell-cycle progression.Abnormal expression of Cullin1results in the dysfunction of SCF E3ligases[17].Var-ious proteins were reported to be involved in the expression of Cullin1.c-Myc promoted the expression of Cullin1;in turn, Cullin1accelerated ubiquitin-dependent proteolysis,as well as cell cycle progression[19].This enzyme could also play a crit-ical role in tumor progression,and its presence was associated with poor clinical outcome for several cancers[20].Another study showed that Cullin1might function as a tumor sup-pressor by regulating PLK4protein levels[20,21].Cullin1was also a matrix degrading enzyme known to be involved in the remodeling of extracellular matrix proteins.In high-grade neu-roendocrine lung tumors,neddylated forms of Cullin1were speciﬁcally expressed and associated with a high level of cyclin E [10,22].GUANGDI CHEN and GANG LI found that Cullin1expres-sion was increased in early stages of melanoma and regulated melanoma cell growth through degradation of p27by functional SCF complex[17].Loss of Cullin1resulted in early embryonic lethality,and deregulation of cyclin E.Cullin1over-expression was signiﬁcantly associated with high-grade tumors and pre-dicted poor prognosis in invasive ductal carcinoma of the breast [20].However,less was known about differential Cullin1expression and its function in NSCLC.In this study,we detected the expression of Cullin1in8-paired fresh lung tissues,and evaluated its staining in114NSCLC parafﬁn-embedded slices using immunohistochem-istry and analyzed the correlation between Cullin1expression and clinicopathologic parameters and patients’survival.Materials and methodsPatients and tissue samplesThe8-paired fresh samples were frozen in liquid nitrogen immediately after surgical removal and maintained at−80◦C until use for Western blot analysis.NSCLC tissues were obtained from 114patients during2005and2007,who underwent lung resec-tion without preoperative systemic chemotherapy at the Surgery Department of the Afﬁliated Hospital of Nantong University.For histologic examination,all tissue portions wereﬁxed in formalin and embedded in parafﬁn.The main clinical and pathologic vari-ables of the patients are summarized in Table1.The follow-up time was5years,with a range of1–rmed con-sent for tissue use was obtained from all patients.All human tissue was collected using protocols approved by the Ethics Committee of Nantong University Cancer Hospital.Western blot analysis and antibodiesTissue and cell proteins were immediately homogenized in a homogenization buffer(Roche Diagnostics).Protein concen-trations were measured with a Bio-Rad protein assay(BioRad, Hercules,CA,USA).Proteins were separated with SDS-PAGE and then transferred to PVDF membranes(Millipore,Bedford,MA).The membranes were blocked with5%no-fat milk in TBST(150mM NaCl,20mM Tris,0.05%Tween-20)for2h at room temperature later.Then the membranes were washed with TBST for three times.The membranes were incubated overnight with rabbit Table1Cullin1and Ki-67expression and clinicopathologic parameters in114NSCLC specimens.Parameters Total Cullin expression P-valueLow HighAge(year)<604718290.089≥60673730GenderMale6530350.706 Female492524Tumor size(cm)<37841370.227≥3361422Smoking statusYes4720270.345 No673532Histological typeAdenocarcinoma4827210.328 Squamous cell carcinoma532330Adenosquamous carcinoma1358Clinical stageI4027130.010* II421626III321220Histological differentiationWell4028120.002* Mod401723Poor341024Lymph node status04926230.450 >0652936Ki-67expressionLow4528170.021* High692742Note:Statistical analyses were performed by the Pearson 2test.*P<0.05was considered signiﬁcant.anti-human GAPDH polyclonal antibody(diluted1:1000)and mouse anti-human Cullin1monoclonal antibody(diluted1:500) from Santa Cruz Biotechnology,USA,and later horseradish peroxidase-linked IgG as the secondary antibodies.The band density was measured with a computer-assisted image-analysis system(Imaging Technology,Ontario,Canada),and normalized against GAPDH levels.Values were responsible for at least three independent reactions.Immunohistochemistry(IHC)staining and evaluationImmunostaining was performed using the avidin biotin perox-idase complex.The sections were deparafﬁnized using a graded ethanol series,and endogenous peroxidase activity was blocked by soaking in3%hydrogen peroxide for10min.And then,the sections were processed in10mmol/L citrate buffer(PH=6.0)and heated to121◦C in an autoclave for20min to retrieve the antigen.After rinsing in PBST(PH=7.2),the sections were then incubated with anti-Cullin1antibody(diluted1:400;Santa Cruz Biotechnology, USA)and anti-Ki-67antibody(diluted1:400;Santa Cruz Biotech-nology,USA)for2h at room temperature.Negative control slides were processed in parallel using a nonspeciﬁc immunoglobulin IgG (Sigma Chemical Co.,St.Louis,MO)at the same concentration as the primary antibody.All slides were processed using the peroxidase-anti-peroxidase method(DAKO,Hamburg,Germany).After being rinsed in PBST,the peroxidase reaction was visualized by incu-bating the sections with DAB(DAKO).After rinsing in water,the sections were counterstained with hematoxylin,dehydrated,and cover slipped.M.Xu et al./Pathology–Research and Practice210(2014)397–401399Fig.1.Expression proﬁles of Cullin1in NSCLC and non-tumorous adjacent tissues.Immunohistochemical evaluationThe intensity and distribution patterns of the staining reaction were evaluated by3blinded,independent pathologists.For assess-ment of Cullin1and Ki-67,ﬁve high-powerﬁelds in each specimen were selected randomly,cytoplasm and nuclear staining were examined.More than1000cells were counted to determine the mean percentage,which represented the percentage of immunos-tained cells relative to the total number of cells.In more than one half of the samples,staining was repeated three times to avoid technical errors,and a consensus was achieved.To evaluate the Cullin1protein immunoreaction,staining intense was classiﬁed to be0(negatively,poorly or moderately staining),1(strongly stain-ing).When evaluating the Ki-67protein immunoreaction,staining was scored in a semi-quantitative fashion.A cut-off value of50%or more positively stained nuclei was used to deﬁne Ki-67staining: low expression group(<50%)and high expression group(≥50%). Statistical analysisStatistical analysis was performed using the SPSS13.0statistical program.The association between Cullin1and Ki-67expression and the clinicopathologic features was analyzed using the 2 test.For analysis of survival data,Kaplan–Meier curves were constructed,and the log-rank test was performed.Multivariate analysis was performed using Cox proportional hazards model. P<0.05was considered statistically signiﬁcant.ResultsCullin was highly expressed in NSCLC tissuesThe expression of Cullin1was assessed in eight paired NSCLC and adjacent non-tumorous tissues by Western blot assay.We found that Cullin1protein was highly expressed in NSCLC tis-sues compared with the adjacent normal tissue(Fig.1A and B). To conﬁrm the expression of Cullin1in NSCLC,we investigated the expression of Cullin1by using IHC on114samples from patients with NSCLC.Expectedly,Cullin1was highly expressed in poor dif-ferentiated specimens compared with well differentiated ones, which had the same tendency with Ki-67.Representative examples of reactivity for Cullin1and Ki-67are shown in Fig.2.Cullin1was expressed mainly in the cytoplasm whereas Ki-67was expressed mainly in the nuclei(Fig.2).Tumorigenesis was associated with multiple factors,and Cullin1overexpression might be an impor-tant factor in NSCLC.Thus,we hypothesized that overexpression of Cullin1might contribute to the proliferation of NSCLC. Correlation of Cullin1expression with clinicopathologic featuresin NSCLCThe clinicopathologic data of the patients are summarized in Table1.We evaluated the association of Cullin1and Ki-67expres-sion with clinicopathologic variables.For statistical analysis of the expression of Cullin1and Ki-67,the carcinoma specimens were divided into2groups,as previously described.Expression of Cullin1was signiﬁcantly associated with histological differen-tiation(P=0.002)and clinical stage(P=0.010),but there was no relation between Cullin1expression and other prognostic factors, like age,gender,tumor size,histological type,and lymph node sta-tus(Table1).Furthermore,in most specimens,the high expression of Cullin1was similar to the high expression of Ki-67(P=0.021). Cullin1was signiﬁcantly associated with the survival of NSCLC patientsSurvival information was available for all the114patients.When all variables were compared separately with survival status,Cullin1 (P=0.033),Ki-67(P<0.001),histological differentiation(P<0.001), tumor size(P=0.002),and clinical stage(P=0.023)signiﬁcantly inﬂuenced the patients’survival(Table2).In univariate analy-sis,the Kaplan–Meier survival curves showed that high Cullin1 expression correlated with poor survival with statistical signiﬁ-cance(P<0.001;Fig.3).In a word,multivariate analysis using the Cox’s proportional hazards model proved that Cullin1(P=0.033; Table2)was a prognostic factor for patients’overall survival. DiscussionIn this study,we showed that Cullin1expression correlated with several clinicopathological factors such as histological differentia-tion and tumor size in114NSCLC patients.Therefore,Cullin1could be considered to play an important role in promoting NSCLC pro-gression,and the identiﬁcation of this protein might be helpful in predicting outcome and improving prognostic models.400M.Xu et al./Pathology –Research and Practice 210(2014)397–401Fig.2.Representative microphotographs for Cullin1by IHC in NSCLC.Table 2Contribution of various potential prognostic factors to survival by Cox regression analysis in 114NSCLC specimens.Hazard ratio95.0%Conﬁdence intervalP Age 1.2400.802–1.9200.334Gender 0.6910.421–1.1320.142Tumor size2.075 1.305–3.3010.002*Smoking status 1.4160.841–2.3850.190Histological type 0.7650.505–1.1580.205Clinical stage1.364 1.044–1.7840.023*Histological differentiation2.758 2.020–3.765<0.001*Lymph node status 0.6710.447–1.0080.055Cullin1expression 0.5770.347–0.9580.033*Ki-67expression2.4641.495–4.059<0.001*Note:Statistical analyses were performed by the Cox regression analysis.*P <0.05was consideredsigniﬁcant.Fig.3.Correlation between Cullin1expression and patients’survival.As a general rule,normal cell proliferation depends on an orderly and efﬁciently cell cycle process which ensures the duplication and transduction of genetic information during cell generation [23].Dysregulation of cell proliferation always led to an abnor-mal cell cycle,which was the fundamental of carcinogenesis.The ubiquitin–proteasome system played a crucial role in main-taining the balance between normal growth and uncontrolled proliferation,regulating cellular homeostasis and controling the abundance of a variety of cellular proteins,including ␤-catenin,P27,and cyclins [24–27].The SCF complex,a core element of the ubiquitin–proteasome system,played well established roles in cell growth.Cullins,as scaffold proteins,could endow multi-meric complex of E3ligases with substrate speciﬁcity [28].Cullin1,as a scaffold protein of the SCF complex,had a key role in the ubiquitin-dependent degradation pathway regulating the expres-sion of cyclins (cyclin D1and cyclin G1)and CDK inhibitors (p27and p21)[29,30].Cullin1-mediated substrate degradation dictated a wide range of cellular processes such as proliferation,differ-entiation,and apoptosis [23].Previous studies found that Cullin1regulated cyclin E degradation [28].Loss of Cullin1resulted in early embryonic lethality and dysregulation of cyclin E [31].Many recent studies have demonstrated that Cullin1overex-pression is associated with various malignant tumors,like gastric cancer and malignant melanoma [24,32].Cullin1might integrate with Skp2to regulate G1-S transition in gastric cancer via Skp2-dependent p27degradation [24].In particular,Cullin1,the most characterized member of the Cullin family,predicted poor prog-nosis of patients with gastric carcinoma when overexpressed [24].High Cullin1expression was signiﬁcantly correlated with worse overall survival in breast cancer patients [33].In addition,Cullin1was reported to promote trophoblast invasion and migration,based on several lines of evidence [29].Since uncontrolled cell division was a feature of oncogenesis,it was tempting for us to conjec-ture the role of Cullin1in NSCLC.Firstly,we found that Cullin1was highly expressed in NSCLC fresh tissues compared with adja-cent normal tissues (Fig.1).This was conﬁrmed by IHC on 114M.Xu et al./Pathology–Research and Practice210(2014)397–401401parafﬁn-embedded slices,which showed that Cullin1was correla-tively expressed with Ki-67.Both Cullin1and Ki-67were highly expressed in poorly differentiated NSCLC slices(Fig.2).Besides, Cullin1was signiﬁcantly associated with histological differenti-ation(P=0.002),clinical stage(P=0.010),and Ki-67(P=0.021; Table1).Multivariate analysis using the Cox’s proportional hazards model indicated that Cullin1might be an independent indicator of NSCLC patients’prognosis(P=0.033;Table2).Survival curve revealed that high Cullin1expression correlated with poor survival with statistical signiﬁcance(P<0.001;Fig.3).Thus,ourﬁndings supported the notion that Cullin1could be a positive regulator and potentially a therapeutic target of NSCLC.So far,there is barely any study about Cullin1in NSCLC;in this study we are theﬁrst to study the function of Cullin1on NSCLC.In summary,we found that high Cullin1expression was signiﬁ-cantly associated with high histological grade and Ki-67expression as well as poor prognosis in patients with NSCLC.However,a larger group of patients needs to be investigated to conﬁrm this conclu-sion.Consequently,Cullin1expression could affect overall survival with tumor progression and thus represented a potential target for the treatment of NSCLC.Because cell proliferation had essen-tial roles in carcinogenesis,controlling of cancer cell proliferation was important for inhibiting cancer progression.Our present stud-ies supported an important role for Cullin1in promoting NSCLC progression,and suggested a possible pathological mechanism of NSCLC.All these indicated that Cullin1might function as a therapy target for NSCLC.Thereby,in order to clarify the molecular mecha-nisms of Cullin1in NSCLC pathogenesis,further studies are of great necessity.Parafﬁn-embedded tissue sections were stained with antibodies for Cullin1and Ki-67and counterstained with hematoxylin(A–L). Both Cullin1and Ki-67were highly expressed in lung squamous cell carcinoma cells(A,B,E,F,I,J)and lung adenocarcinoma cells(C,D,G, H,K,L).According to the intensity of Cullin1expression,we divided the samples into histologic differentiation grade I(A–D),histologic differentiation grade II(E–H),and histologic differentiation grade III(I–L).Kaplan–Meier survival curves for low versus high Cullin1 expression on114patients with NSCLC showed a highly signiﬁcant separation between curves(P<0.001).Conﬂict of interestAll the authors declare no conﬂict of interest.References[1]J.Okamoto,T.Hirata,Z.Chen,H.M.Zhou,I.Mikami,et al.,EMX2is epigeneticallysilenced and suppresses growth in human lung cancer,Oncogene29(2010) 5969–5975.[2]A.Jemal,R.Siegel,J.Xu,E.Ward,Cancer statistics,CA:Cancer J.Clin.60(2010)277–300.[3]B.B.Chen,J.R.Glasser,T.A.Coon,R.K.Mallampalli,F-box protein FBXL2exertshuman lung tumor suppressor-like activity by ubiquitin-mediated degradation of cyclin D3resulting in cell cycle arrest,Oncogene31(2012)2566–2579. 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survival analysis生存分析

生存分析的方法分类

非参数法：乘积极限法、寿命表法等；
参数法：指数分布法、威布尔分布（Weibull Distribution）法、对数正态回归分析法以及对数logistic回归分析法等；

半参数法：Cox模型分析方法。
生存分析方法的SAS过程
非参数法：lifetest过程；

参数法：lifereg过程；
phreg过程

phreg过程针对生存数据执行基于Cox比例风险模型（Cox proportional hazards model）的回归分析；可以检验有关回归参数的线性假设；针对配对病例-对照研究执行条件logistic回归分析过程；创建包含有关统计量的输出数据集等。
phreg过程可包含的语句
proc phreg语句
proc phreg语句为调用phreg过程的开始，语句中可设置的语句选项较少，其设置方法及功能详见下表。
生存分析的SAS编程操作
薛富波 2006/05/21
生存分析的有关概念

事件时间（time-to-event，TTE）：又称为生存时间（survival time）或失效时间（failure time），是指从研究开始到相应事件发生的时间。删失值（censored value）：是指TTE在某些观测上其确切的取值是未知的，所能知道的仅仅是其测量值肯定大于或小于某个特定值（即其取值的下限或上限），或者位于某个特定的取值范围内（取值区间）。右侧删失：反映真实值下限的数据。左侧删失：反映真实值上限的数据。区间删失：反映真实值所在区间的数据。
log(time绘图 )

高中语法学案--特殊名词形式变化背记清单(附练习答案)

特殊名词变化背记清单一、名词的特殊复数形式：(一) 名词的复数形式不规则变化：1. child ---children小孩2. foot --- feet脚3.toot--- teeth牙齿4. goose --- geese鹅5. mouse ---mice老鼠6. ox ---oxen 公牛7. analysis --- analyses分析8. crisis --- crises危机9. thesis --- theses论文10. man --- men男人11. woman --- women女人12. medium --- media 媒体13.basis --- bases基础14. datum --- data数据15. bacterium --- bacteria细菌16. criterion --- criteria标准17. phenomenon --- phenomena现象18. German --- Germans 德国人19. Frenchman--- Frenchmen 法国人20. Englishman ---Englishmen 英国人(二) 有些名词单复数同型:1. Chinese --- Chinese中国人2. Japanese --- Japanese日本人3. Swiss--- Swiss瑞士人4. spacecraft --- spacecraft 航天器5. aircraft --- aircraft 飞机6.headquarters---headquarters 总部7. means ---means 方式8. species --- species 物种9. news --- news 消息10. deer --- deer 鹿11. fish --- fish鱼12. sheep --- sheep 绵羊(三) 有些以f结尾的名词变复数直接加s:1.belief --- beliefs 信念2.roof --- roofs 屋顶3.cliff --- cliffs 悬崖4.staff --- staffs 员工5.proof --- proofs 证据6.chief --- chiefs 首领7.gulf --- gulfs 海湾8.grief --- griefs 伤心事9.serf --- serfs 农奴10.safe --- safes 保险箱(四) “单位”名词的单复数：1. dollar--- dollars美元2. franc--- francs法郎3. yuan --- yuan 元4. meter/metre --- meters 米5. jin --- jin 斤6. mu --- mu 母(五) 复合名词的复数形式：1. looker-on --- lookers-on旁观者2. passer-by --- passers-by过路人3. man doctor --- men doctors男医生4. woman teacher -- women teachers 女老师5 brother-in-law -- brothers-in -law 姐夫（妹夫）6. sister-in-law --- sisters-in-law 嫂子（弟媳）7. mother-in-law --- mothers-in-law 岳母（婆婆）8. father-in-law --- fathers-in-law 岳父（公公二、下列名词没有单数形式, 是复数概念，在句中作主语时谓语动词用复数形式：1. clothes2. police3. cattle4. people三、下列名词的单复数含义不同：1. paper纸--- papers试卷，文件，论文2. good 好的--- goods 货物3. ash 灰--- ashes 骨灰，遗骸4. glass 玻璃--- glasses 眼镜5. sand 沙子--- sands 沙滩6. wood 木材--- woods 树林7. green绿色的---greens 青菜8. time 时间--- times 时代9. drink酒--- drinks 饮料10. arm 手臂--- arms 武器11. look看---looks 外表，面容12. line 线--- lines 台词13. manner方式,样式---manners 礼貌,规矩14. work 工作--- works 工厂,作品,工事15. damage 损害---damages 损失赔偿费,费用16. cloth 布--- clothes 衣服17. pain 疼痛--- pains 辛劳，痛苦18. ruin 毁坏--- ruins 废墟19. fruit 水果--- fruits 各种水果20. noise 噪音--- noises 各种噪音21. color 颜色--- colors 各种颜色22. custom 风俗;习惯--- customs 海关23. fish 鱼肉--- a fish鱼24. chicken 鸡肉--- a chicken 小鸡25. tin 锡--- a tin 罐头26. relation 关系--- a relation 亲戚,亲属27. iron 铁--- an iron 熨斗28. beauty 美丽--- a beauty 美人,美的东西29. power 威力;力量;电力--- a power 大国30. room空间--- a room 房间31. glass 玻璃--- a glass 玻璃杯32. success成功--- a success 一个成功的人(事)33. communication 通讯;交流--- communications 通讯系统,通讯工具34. convenience 方便，便利--- conveniences便利设备35. necessity 需要,必要性--- necessities 必需品四、构词法----名词和动词的相互转化：1. 很多动词可以转化为名词：1) have a look ( chat, talk, wash, swim, rest, try, quarrel, interview, taste, slip, ect.)2) make a study ( guess, call, survey, change, answer, slip, visit, appointment ect.)3) come to a stop (a pause, an end, ect.)2. 有些名词也可当动词用：1) Have you booked your ticket? (预订)2) The hall can seat two thousand people. (容纳)3) We’ll back you up. (支持)4) We’ll head for Shanghai tomorrow. (朝….前进)5) If so, you will be badly fooled. (上当,愚弄)6) They were hosted by the members of the embassy. (招待)7) This helped to bridge over our difficulties. (度过)8) Over three hundred students stormed into the building. (涌入；冲进)五、注意下列动词形容词变化的名词：1. grow growth2. warm warmth3. true truth4. repeat repetition5. describe description6. argue argumentpete competition8.necessary necessity9.recognize recognition10.decide decision11.confuse confusion12.divide division13.relax relaxation14.pronounce pronunciation15.propose proposal16.arrive arrival 17.refuse refusal18.survive survival19.remove removal20.approve approval21.proud pride22.dry drought23.permit permission24.discover discovery25.speak speech26.fortunate fortune27.differ difference28.strong strength29.marry marriagerm information31.popular popularity32.succeed success六、用名词的正确形式填空:1.There are two ____________ (box) of ____________ (match), three ____________ (knife), four ____________(brush), five ____________ (key), six ____________ (photo), seven ____________ (dictionary), eight____________ (tomato), nine ____________ (glass), ten ____________ (radio) and eleven ____________ (toy bus) on that table.2.Look at those ____________ (wolf), ____________ (sheep), ____________ (deer), ____________ (fox),____________(ox) ____________ (monkey), and ____________ (goose).3.Who are they? They are all ____________ (hero). And they are all ________________ (woman doctor)4.There are so many public____________ (convenience) in cities .5.Three ____________ (Swiss), two ____________(German) and five _____________(Frenchman) are present at themeeting.6.Those little _____________(child) are playing with those little____________ (mouse).7.The rainbow is one of the most beautiful ____________ (phenomenon) in nature.8.Two____________(aircraft) and two__________(spacecraft) are being designed in the ___________(headquarters).9.We have tried all the ________(means), but not every_________(means) has worked.10.It tastes like________(chicken). Do you raise ________ (chicken)?11.He regarded music as one of life's ____________ (necessary).12.They took great __________ (proud) in their excellent children.13.English __________________ (pronounce) is terribly difficult.14.The job involved the constant _____________ (repeat) of the same movements15.The police have issued a detailed ______________ (describe) of the missing woman.16.The ____________ (consume) of alcohol on the premises（经营场所）is forbidden.17.Sometimes there's a lot of ____________ (compete) between children for their mother's attention.18.There is general_________ (recognize) that the study techniques of many students are weak.19.The news aroused a lot of _____________ (curious) among local people.20.The fear of unemployment can be a source of deep ___________ (anxious) to people.21.I think the government has lost touch with___________ (real).22.The lake has more than 20 _______________ (vary) of fish.23.We need an effective strategy to fight ____________ (poor).24.This latest interview was further __________ (prove) of how good at her job Cara was.25.Several members hold very right-wing _________(believe) .26.This year (a) severe ___________ (dry) has ruined the crops.27.With seven people squashed（塞入）in one house, you don't get much ___________ (private).28.In most people’s eyes, she was nothing more tha n a common ___________ (crime).29.It was his ___________ (brave) that saved the child.30.Meditation allows you to enter a state of deep_____________(relax) .31.The thought occurred to me that he might not be telling the _________(true).32.All at once they were aware of the evening of light and___________ (warm).33.We finally came to a firm _________ (decide) on the matter.34.The French government has approved ___________ (propose) for a new waste law.35.The party hopes to win the _____________ (argue) about how to reform the health systemkey名词key: 1. boxes, matches, knives, brushes, keys, photos, dictionaries, tomatoes, glasses, radios, buses2. wolves, sheep, deer, foxes, oxen, monkeys, geese3. heroes, women doctors4. conveniences5. Swiss, Germans, Frenchmen6. children, mice7. phenomena 8. aircraft, spacecraft, headquarters9. means, means 10. chicken, chickens11. necessities 12. pride 13. pronunciation 14. repetition 15. description 16. consumption 17. competition 18. recognition 19. curiosity 20. anxiety 21. reality 22. varieties23. poverty 24. proof 25. beliefs 26. drought 27. privacy 28. criminal 29. bravery 30. relaxation 31. truth 32. warmth 33. decision 34. proposals 35. argument。

win 8.1出现蓝屏wificlass.sys后的临时解决方案

Win8.1出现蓝屏（wificlass.sys ）后的临时解决方案我的美版's ɜːfɪs pr əʊ θriː （这是音标，俗称“苏菲婆3代”，但不能写明，否则百度文库说我在为's ɜːfɪs 做宣传，这文章就无法公开，只有我自己能看见），刚买来就有任务栏闪烁的问题，后来又频繁蓝屏，错误内容为IRQL_NOT_LESS_OR_EQUAL 或SYSTEM_THREAD_EXCEPTION_NOT_HANDLED ，没办法，到网上找了64bit 的PE ，用UltraISO 制作了启动U 盘，重装了64bit 的中文企业版。

这样一来，不再出现以上两种错误的蓝屏，任务栏也不再闪烁。

但是音量键无法调节音量，只能在开机时按住进入高级启动或进UEFI 固件设置（说明不是音量键硬件故障），电源键只能开关机，不能睡眠（也不是硬件故障），Home 键无效，仅有震感（估计又不是硬件故障）。

这些键失效都应该是缺少驱动（我格式化硬盘之前忘记备份驱动了，失误失误），用驱动精灵、驱动人生、鲁大师都解决不了。

更麻烦的是，一个软件说某个设备的驱动是最新的，另一个软件说这个设备的驱动可以更新；一个说所有设备的驱动都已正常安装，另一个说有几个未知设备无法安装驱动。

平静的日子没过多久，又起波澜，一大波蓝屏再度袭来,这次内容为SYSTEM_THREAD_EXCEPTION_NOT_HANDLED(wificlass.sys),之前那个……NOT_HANDLED 后面没有wificlass.sys. 在无数次wificlass.sys 蓝屏之中还出现过一次CRITICAL_PROCESS_DIED我的蓝屏提示本是中文，由于我没拍下来，就用的别人的图片。

国外的一些网站也提到wificlass.sys 这个问题，参见/index.php/2014/08/01/blue-screen-crash-on-surface-pro-3-with-wificlass-sys/但没给出解决方案。

阅读理解题型及常规答题方法

阅读理解题型及常规答题方法大学英语四六级考试阅读理解部分是众多考生最为担心的部分。

此部分得分高低，对整个考试的成功与否起着决定性作用。

阅读理解不仅考查学生的词汇量、语法知识、阅读速度等基本功，而且还考查学生判断、推理、归纳、总结等综合能力。

阅读理解题虽说对考生要求较高，但我们在深入研究历届四六级阅读理解真题后发现，阅读理解的命题考点和测试题型均有一定的内在规律。

考生只要基本功尚可，然后掌握了这些规律，其应试技巧必将大大提高，从而在众多强手中脱颖而出。

命题考点规律及其对应题型分析研究英语四六级考试阅读理解历届考题，可以发现命题者命制的考点是有一定规律的，且考点规律常与某种题型（主旨题、细节题、逻辑题、观点态度题、词义题）相对应。

如果考生掌握了这些规律，就能在第一遍快速阅读短文时，敏锐地捕捉到考点并能预测可能会出的题型。

考生此时应用笔在这些可能会出题的考点轻轻划上记号，等看完短文开始做题时，针对题干的提问，迅速找到做记号的考点，再仔细分析、答题。

这样，考生就能节省不少时间，从而避免开始做题时又要通阅全文盲目找考点。

下面，我们结合历届四六级真题和大学英语四六级考试90分突破《阅读与简答》分册（王长喜主编，学苑出版社，以下简称《分册》），将这些考点规律及对应题型归纳如下：1、列举处常考列举处指的是first,…，second, …，third, …等逐步列出，然后要求考生从列举出的内容中，选出符合题干要求的答案项。

该考点常出题型是"细节事实题"。

例1. at third big difference between the drama detective and the real one is the unpleasant pressures: firstly, as members of a police force they always have to be have absolutely in accordance with the law. secondly,as expensive public servants they have to get results. they can hardly do both. most of the time some of them have to break the rules in small ways.q: what's the policeman's biggest headache(a) he has to get the most desirable results without breaking the law in any way.(b) he has to justify his arrests while unable to provide sufficient evidence in most cases.(c) he can hardly find enough time to learn criminal law while burdened with numerous criminal cases.(d) he has to provide the best possible public service at the least possible expense.(分析：选a。

08 SAS生存分析

S(0)=1；
S(2) ：2年生存率，个体生存时间超过 2年的概率
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二、生存率的基本估计方法
乘积极限法估计生存率

Kaplan-meier法：乘积极限法（ProductLimit Method，PL法）适用于小样本资料，对删失数据无校正不需要对被估计的资料分布作任何假设利用tk时刻之前各时点上生存概率的乘积来估计在时刻tk的生存率

16
Lifereg 参数回归
17
指数分布

Lamda是指数分布的危险度
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指数回归模型
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Weibull分布
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Weibull回归模型
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proc lifereg; model days*censor(0)=group renal; run;

proc lifereg; model days*censor(0)=group renal / dist=exponential;run;
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Strata—by—group-test
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Strata—by—group-test
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例15.2

ห้องสมุดไป่ตู้
data life2; input days renal@@; censor=(days<0); group=(_n_>12)+1; days=abs(days); cards; 8 1 52 0 58 1 63 1 63 1 220 0 365 0 452 0 496 0 -528 0 -560 0 -676 0 13 1 18 1 23 1 70 0 76 0 180 0 195 0 210 0 232 0 300 0 396 0 -490 0 -540 0 ; proc lifetest plots=(s); time days*censor(1); strata group; test renal; run;

lr常见报错及解决的办法

LR常见报错及解决的办法1、LoadRunner 26377、26388错误码的成因脚本如下：脚本的是请求下载，如果是三个cot请求，就不会有错，我现在是有10个cot请求，从"objectURI4"就提示以下错误信息，请大有帮忙解决一下。

谢谢错误码如下：Error -26377: No match found for the requested parameter "objectURI10". Check whether the requested boundaries exist in the response data. Also, if the data you want to save exceeds 1516000 bytes, use web_set_max_html_param_len to increase the parameter size [MsgId: MERR-26377]web_url("entry") highest severity level was "ERROR", 1631 body bytes, 199 header bytes [MsgId: MMSG-26388]1.首先看下脚本中有没有使用了自动关联(web_reg_save_param)2.在Virtual的脚本里查询下web_reg_save_param的参数使用位置,然后把这个参数化给还原回来,比如web_reg_save_param("Siebel_Analytic_ViewState2",............然后就在全文查询Siebel_Analytic_ViewState23,至于修改成什么东西要看几个地方,如果是启动了自动关联,一般在脚本上面会有一段被自动注释掉的:关联变量名="值"比如上面的Siebel_Analytic_ViewState2大概就是// {Siebel_Analytic_ViewState2}= "/wEPDwUKMTI5Nzk1OTc3NmRkikSkNLllgC5BL8dbmU5bHIwtt4I="那么这里的/wEPDwUKMTI5Nzk1OTc3NmRkikSkNLllgC5BL8dbmU5bHIwtt4I=就是要找的值了,这个也可以在"View Tree"里找到4.把"View script"里的被关联的那部分参数改成/wEPDwUKMTI5Nzk1OTc3NmRkikSkNLllgC5BL8dbmU5bHIwtt4I=就好了(不是修改web_reg_save_param里的参数,要把它注释掉,从下面正文里查询另一个带Siebel_Analytic_ViewState2的东东,把它改掉)把web_set_max_html_param_len（50000）值加大这个函数要放在所有参数化前面。

生存分析 Survival analysis 英文

Survival Analysis in SPSSSurvival analysis is found under its own sub-menu in the “Analyze” menu of SPSS. This example shows survival rates for cancer treatment. Look under “Analyze,” then “Survival.” You will see four choices in a sub-menu:We’re only going to use two of these four. Start with the “Life Tables” command.In this dialog box, select two variables:--The predictor variable in survival analysis is always time, and goes in the “Time” box. You also have to use this area under the variable name to select “Intervals,” used to break up the time into different amounts. Look at the data to see how high the numbers go: put that highest number in the first box. Then divide the first number by 10 and put the result in the second box (rounding is fine—we’re just trying to get about 10 different intervals for the data; not too many and not too few).--The criterion variable in survival analysis is the “Status” variable. In this case, the two possible outcomes are alive (1) vs. dead (0). Death is the “event” that we’re interested in. Once we have “Alive” entered into the correct box as the status variable, we also need to tell SPSS what code to look for to indicate that the negative event has occurred. To do this, click the “Define Event” button, which will show you the sub-dialog pictured above. In this case, the event is indicated by a single value (0, which equals “died”). Type this into the appropriate box, and click “Continue.”Then you can click “OK” in the main dialog box to see the results.Here are the results:SurvivalThis subfile contains: 214 observationsLife TableSurvival Variable MONTHS months after start of treatmentNumber Number Number Number CumulIntrvl Entrng Wdrawn Exposd of Propn Propn Propn Proba-Start this During to Termnl Termi- Sur- Surv bility Hazard Time Intrvl Intrvl Risk Events nating viving at End Densty Rate ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ .0 214.0 .0 214.0 .0 .0000 1.0000 1.0000 .0000 .0000 5.0 214.0 2.0 213.0 .0 .0000 1.0000 1.0000 .0000 .0000 10.0 212.0 76.0 174.0 24.0 .1379 .8621 .8621 .0276 .0296 15.0 112.0 10.0 107.0 4.0 .0374 .9626 .8298 .0064 .0076 20.0 98.0 12.0 92.0 16.0 .1739 .8261 .6855 .0289 .0381 25.0 70.0 19.0 60.5 22.0 .3636 .6364 .4362 .0499 .0889 30.0 29.0 7.0 25.5 11.0 .4314 .5686 .2481 .0376 .1100 35.0 11.0 1.0 10.5 6.0 .5714 .4286 .1063 .0283 .1600 40.0 4.0 1.0 3.5 .0 .0000 1.0000 .1063 .0000 .0000 45.0 3.0 .0 3.0 .0 .0000 1.0000 .1063 .0000 .0000 50.0+ 3.0 .0 3.0 3.0 1.0000 .0000 .0000 ** ** ** These calculations for the last interval are meaningless.The median survival time for these data is 28.72This table is the main output for this procedure. The left-hand column (“interval start time”) shows you the beginning of each step, as the procedure counts forward through time. For instance, the first row shows the results for months 0-5, the next row shows the results for months 5-10, and so forth. Here are the other important columns:--“Number exposed to risk” is the number of people counted as starting that time interval for purposes of the survival analysis. As you can see, it’s a different number from the “number entering this interval”—it has been adjusted for censored data (the cases where we have data, but we don’t yet know what the outcome will be).--“Cumulative Proportion Surviving at End” is the percentage of people (out of 100% at the beginning) who have survived up to the end of the time interval. So, for instance in row 3 (the one marked 10), 86% of those who originally started have made it as far as month 15 (the end of that time interval).--“Hazard Rate” is the % chance of having a terminal event, for the group of people who were still alive at the start of that particular time interval. Again in row 3, there’s a 2.96% chance of having a terminal event, for people who already made it as far as month 10. If we look for the largest hazard rate, we can see that the time of greatest risk is between months 35 and 40 (when the hazard rate goes up to 16%).Finally, at the bottom of this table, we can see the median survival time. Recall from the lecture notes that this is the time at which 50% of those who originally started out have had the terminal event happen. In this case, the median survival time is 28;72 months, but those people who make it past month 35 are actually more at risk later on.SE of SE ofIntrvl Cumul Proba- SE ofStart Sur- bility HazardTime viving Densty Rate------- ------ ------ ------.0 .0000 .0000 .00005.0 .0000 .0000 .000010.0 .0261 .0052 .006015.0 .0297 .0032 .003820.0 .0410 .0066 .009525.0 .0498 .0090 .018530.0 .0513 .0096 .031935.0 .0438 .0096 .059940.0 .0438 .0000 .000045.0 .0438 .0000 .000050.0+ .0000 ** **These are just a few more columns that didn’t fit on the page—nothing that we need to see here.Let’s go back to the “life table” command. There’s one more thing to try.Leave the setup exactly the same as before, except this time, add a grouping variable to the procedure. You can use this method to get different survival times for different subgroups of people. In this instance, we’re interested in whether people who get different drug treatments have different average survival times.Put “druglvl” into the “by factor” box, and then click “define range” to get the other window shown. We need to tell SPSS what possible values of “druglvl” we want to compare—tell it everthing from druglvl = 0 (the minimum) to druglvl = 5 (the maximum). Then hit “continue” in the small box, and “OK” in the large one to go on.Here’s the revised output. As you can see, you get a different life table for each different valueof the factor you entered. Each table has its own pattern of hazard rates and median survival time. If we compare the median survival times (circled below), we can see an effect of the different levels of the drug, in terms of the average number of months that participants in each drug condition survived. When druglvl = 3, we see the longest survival times.This subfile contains: 214 observationsLife TableSurvival Variable MONTHS months after start of treatmentfor DRUGLVL level of medication given= 0Number Number Number Number CumulIntrvl Entrng Wdrawn Exposd of Propn Propn Propn Proba-Start this During to Termnl Termi- Sur- Surv bility Hazard Time Intrvl Intrvl Risk Events nating viving at End Densty Rate ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ .0 48.0 .0 48.0 .0 .0000 1.0000 1.0000 .0000 .0000 5.0 48.0 1.0 47.5 .0 .0000 1.0000 1.0000 .0000 .0000 10.0 47.0 41.0 26.5 4.0 .1509 .8491 .8491 .0302 .0327 15.0 2.0 .0 2.0 1.0 .5000 .5000 .4245 .0849 .1333 20.0 1.0 .0 1.0 .0 .0000 1.0000 .4245 .0000 .0000 25.0 1.0 .0 1.0 .0 .0000 1.0000 .4245 .0000 .0000 30.0 1.0 .0 1.0 .0 .0000 1.0000 .4245 .0000 .0000 35.0 1.0 .0 1.0 .0 .0000 1.0000 .4245 .0000 .0000 40.0 1.0 .0 1.0 .0 .0000 1.0000 .4245 .0000 .0000 45.0 1.0 .0 1.0 .0 .0000 1.0000 .4245 .0000 .0000 50.0+ 1.0 .0 1.0 1.0 1.0000 .0000 .0000 ** ** ** These calculations for the last interval are meaningless.The median survival time for these data is 19.11Life TableSurvival Variable MONTHS months after start of treatmentfor DRUGLVL level of medication given= 1Number Number Number Number CumulIntrvl Entrng Wdrawn Exposd of Propn Propn Propn Proba-Start this During to Termnl Termi- Sur- Surv bility Hazard Time Intrvl Intrvl Risk Events nating viving at End Densty Rate ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ .0 39.0 .0 39.0 .0 .0000 1.0000 1.0000 .0000 .0000 5.0 39.0 .0 39.0 .0 .0000 1.0000 1.0000 .0000 .0000 10.0 39.0 11.0 33.5 5.0 .1493 .8507 .8507 .0299 .0323 15.0 23.0 4.0 21.0 1.0 .0476 .9524 .8102 .0081 .0098 20.0 18.0 5.0 15.5 4.0 .2581 .7419 .6011 .0418 .0593 25.0 9.0 4.0 7.0 4.0 .5714 .4286 .2576 .0687 .1600 30.0 1.0 .0 1.0 1.0 1.0000 .0000 .0000 .0515 .4000 The median survival time for these data is 26.47Survival Variable MONTHS months after start of treatmentfor DRUGLVL level of medication given= 2Number Number Number Number CumulIntrvl Entrng Wdrawn Exposd of Propn Propn Propn Proba-Start this During to Termnl Termi- Sur- Surv bility Hazard Time Intrvl Intrvl Risk Events nating viving at End Densty Rate ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ .0 40.0 .0 40.0 .0 .0000 1.0000 1.0000 .0000 .0000 5.0 40.0 .0 40.0 .0 .0000 1.0000 1.0000 .0000 .0000 10.0 40.0 9.0 35.5 5.0 .1408 .8592 .8592 .0282 .0303 15.0 26.0 2.0 25.0 1.0 .0400 .9600 .8248 .0069 .0082 20.0 23.0 3.0 21.5 4.0 .1860 .8140 .6713 .0307 .0410 25.0 16.0 7.0 12.5 5.0 .4000 .6000 .4028 .0537 .1000 30.0 4.0 2.0 3.0 1.0 .3333 .6667 .2685 .0269 .0800 35.0 1.0 .0 1.0 1.0 1.0000 .0000 .0000 .0537 .4000 The median survival time for these data is 28.19Life TableSurvival Variable MONTHS months after start of treatmentfor DRUGLVL level of medication given= 3Number Number Number Number CumulIntrvl Entrng Wdrawn Exposd of Propn Propn Propn Proba-Start this During to Termnl Termi- Sur- Surv bility Hazard Time Intrvl Intrvl Risk Events nating viving at End Densty Rate ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ .0 40.0 .0 40.0 .0 .0000 1.0000 1.0000 .0000 .0000 5.0 40.0 1.0 39.5 .0 .0000 1.0000 1.0000 .0000 .0000 10.0 39.0 10.0 34.0 3.0 .0882 .9118 .9118 .0176 .0185 15.0 26.0 .0 26.0 1.0 .0385 .9615 .8767 .0070 .0078 20.0 25.0 2.0 24.0 4.0 .1667 .8333 .7306 .0292 .0364 25.0 19.0 4.0 17.0 4.0 .2353 .7647 .5587 .0344 .0533 30.0 11.0 2.0 10.0 4.0 .4000 .6000 .3352 .0447 .1000 35.0 5.0 1.0 4.5 3.0 .6667 .3333 .1117 .0447 .2000 40.0 1.0 1.0 .5 .0 .0000 1.0000 .1117 .0000 .0000 The median survival time for these data is 31.31Survival Variable MONTHS months after start of treatmentfor DRUGLVL level of medication given= 4Number Number Number Number CumulIntrvl Entrng Wdrawn Exposd of Propn Propn Propn Proba-Start this During to Termnl Termi- Sur- Surv bility Hazard Time Intrvl Intrvl Risk Events nating viving at End Densty Rate ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ .0 39.0 .0 39.0 .0 .0000 1.0000 1.0000 .0000 .0000 5.0 39.0 .0 39.0 .0 .0000 1.0000 1.0000 .0000 .0000 10.0 39.0 5.0 36.5 6.0 .1644 .8356 .8356 .0329 .0358 15.0 28.0 4.0 26.0 .0 .0000 1.0000 .8356 .0000 .0000 20.0 24.0 1.0 23.5 3.0 .1277 .8723 .7289 .0213 .0273 25.0 20.0 3.0 18.5 6.0 .3243 .6757 .4925 .0473 .0774 30.0 11.0 3.0 9.5 5.0 .5263 .4737 .2333 .0518 .1429 35.0 3.0 .0 3.0 1.0 .3333 .6667 .1555 .0156 .0800 40.0 2.0 .0 2.0 .0 .0000 1.0000 .1555 .0000 .0000 45.0 2.0 .0 2.0 .0 .0000 1.0000 .1555 .0000 .0000 50.0+ 2.0 .0 2.0 2.0 1.0000 .0000 .0000 ** ** ** These calculations for the last interval are meaningless.The median survival time for these data is 29.84Life TableSurvival Variable MONTHS months after start of treatmentfor DRUGLVL level of medication given= 5Number Number Number Number CumulIntrvl Entrng Wdrawn Exposd of Propn Propn Propn Proba-Start this During to Termnl Termi- Sur- Surv bility Hazard Time Intrvl Intrvl Risk Events nating viving at End Densty Rate ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ .0 8.0 .0 8.0 .0 .0000 1.0000 1.0000 .0000 .0000 5.0 8.0 .0 8.0 .0 .0000 1.0000 1.0000 .0000 .0000 10.0 8.0 .0 8.0 1.0 .1250 .8750 .8750 .0250 .0267 15.0 7.0 .0 7.0 .0 .0000 1.0000 .8750 .0000 .0000 20.0 7.0 1.0 6.5 1.0 .1538 .8462 .7404 .0269 .0333 25.0 5.0 1.0 4.5 3.0 .6667 .3333 .2468 .0987 .2000 30.0 1.0 .0 1.0 .0 .0000 1.0000 .2468 .0000 .0000 35.0 1.0 .0 1.0 1.0 1.0000 .0000 .0000 .0494 .4000 The median survival time for these data is 27.44Finally, let’s use a different command—Cox Regression—to get a graph of the survival curve.In this dialog box, you don’t have to put in the time intervals—just put the predictor variable into the “time” box, and SPSS will figure out for itself how to break it out.You do still have to select the “status” variable, and use the “define event” button to tell SPSS what number indicates that the terminal event has occurred.Click “Continue” to close the small dialog box. Then click on the “Plots” button to see the nextdialog window.In the “plots” window, just click on the check-box to say that you want a “survival” graph.Hit “Continue,” and then “OK” in the main dialog box to see the graph.Cox RegressionCase Processing SummaryNPercent Event(a) 86 40.2% Censored 126 58.9% Cases available in analysisTotal212 99.1% Cases with missing values0 .0% Cases with negative time0 .0% Censored cases before the earliest event in a stratum 2 .9% Cases droppedTotal2 .9% Total214100.0%a Dependent Variable: months after start of treatmentOmnibus Tests of Model Coefficients-2 Log Likelihood 698.308In this output we have a –2 log L statistic, and then the graph.It’s possible to show a different graph for each of several different sub-groups, the same way we got different life tables for different sub-groups. Again, we’ll use “druglvl” as the grouping variable.This time, druglvl is called a “covariate” in the dialog box (another one of those inexplicable wording changes in SPSS). It’s a grouping variable, but SPSS doesn’t immediately recognize that (there would be a “(Cat)” after the variable name if it did). So we need to use the “Categorical” button to tell SPSS that this is a categorical variable.the left to the box on the right.in the main dialog box, to see the graph.Here’s the final output, showing different-colored lines for the survival pattern seen with each different level of the drug treatment.。

________________________________________________ Session S3C MINORITY ENGINEERING PROGRAM C

________________________________________________ 1Joseph E. Urban, Arizona State University, Department of Computer Science and Engineering, P.O. Box 875406, Tempe, Arizona, 85287-5406, joseph.urban@ 2Maria A. Reyes, Arizona State University, College of Engineering and Applied Sciences, Po Box 874521, Tempe, Arizona 852189-955, maria@ 3Mary R. Anderson-Rowland, Arizona State University, College of Engineering and Applied Sciences, P.O. Box 875506, Tempe, Arizona 85287-5506, mary.Anderson@MINORITY ENGINEERING PROGRAM COMPUTER BASICS WITH AVISIONJoseph E. Urban 1, Maria A. Reyes 2, and Mary R. Anderson-Rowland 3Abstract - Basic computer skills are necessary for success in an undergraduate engineering degree program. Students who lack basic computer skills are immediately at risk when entering the university campus. This paper describes a one semester, one unit course that provided basic computer skills to minority engineering students during the Fall semester of 2001. Computer applications and software development were the primary topics covered in the course that are discussed in this paper. In addition, there is a description of the manner in which the course was conducted. The paper concludes with an evaluation of the effort and future directions.Index Terms - Minority, Freshmen, Computer SkillsI NTRODUCTIONEntering engineering freshmen are assumed to have basic computer skills. These skills include, at a minimum, word processing, sending and receiving emails, using spreadsheets, and accessing and searching the Internet. Some entering freshmen, however, have had little or no experience with computers. Their home did not have a computer and access to a computer at their school may have been very limited. Many of these students are underrepresented minority students. This situation provided the basis for the development of a unique course for minority engineering students. The pilot course described here represents a work in progress that helped enough of the students that there is a basis to continue to improve the course.It is well known that, in general, enrollment, retention, and graduation rates for underrepresented minority engineering students are lower than for others in engineering, computer science, and construction management. For this reason the Office of Minority Engineering Programs (OMEP, which includes the Minority Engineering Program (MEP) and the outreach program Mathematics, Engineering, Science Achievement (MESA)) in the College of Engineering and Applied Sciences (CEAS) at Arizona State University (ASU) was reestablished in 1993to increase the enrollment, retention, and graduation of these underrepresented minority students. Undergraduate underrepresented minority enrollment has increased from 400 students in Fall 1992 to 752 students in Fall 2001 [1]. Retention has also increased during this time, largely due to a highly successful Minority Engineering Bridge Program conducted for two weeks during the summer before matriculation to the college [2] - [4]. These Bridge students were further supported with a two-unit Academic Success class during their first semester. This class included study skills, time management, and concept building for their mathematics class [5]. The underrepresented minority students in the CEAS were also supported through student chapters of the American Indian Science and Engineering Society (AISES), the National Society of Black Engineers (NSBE), and the Society of Hispanic Professional Engineers (SHPE). The students received additional support from a model collaboration within the minority engineering student societies (CEMS) and later expanded to CEMS/SWE with the addition of the student chapter of the Society of Women Engineers (SWE) [6]. However, one problem still persisted: many of these same students found that they were lacking in the basic computer skills expected of them in the Introduction to Engineering course, as well as introductory computer science courses.Therefore, during the Fall 2001 Semester an MEP Computer Basics pilot course was offered. Nineteen underrepresented students took this one-unit course conducted weekly. Most of the students were also in the two-unit Academic Success class. The students, taught by a Computer Science professor, learned computer basics, including the sending and receiving of email, word processing, spreadsheets, sending files, algorithm development, design reviews, group communication, and web page development. The students were also given a vision of advanced computer science courses and engineering and of computing careers.An evaluation of the course was conducted through a short evaluation done by each of five teams at the end of each class, as well as the end of semester student evaluations of the course and the instructor. This paper describes theclass, the students, the course activities, and an assessment of the short-term overall success of the effort.M INORITY E NGINEERING P ROGRAMSThe OMEP works actively to recruit, to retain, and to graduate historically underrepresented students in the college. This is done through targeted programs in the K-12 system and at the university level [7], [8]. The retention aspects of the program are delivered through the Minority Engineering Program (MEP), which has a dedicated program coordinator. Although the focus of the retention initiatives is centered on the disciplines in engineering, the MEP works with retention initiatives and programs campus wide.The student’s efforts to work across disciplines and collaborate with other culturally based organizations give them the opportunity to work with their peers. At ASU the result was the creation of culturally based coalitions. Some of these coalitions include the American Indian Council, El Concilio – a coalition of Hispanic student organizations, and the Black & African Coalition. The students’ efforts are significant because they are mirrored at the program/staff level. As a result, significant collaboration of programs that serve minority students occurs bringing continuity to the students.It is through a collaboration effort that the MEP works closely with other campus programs that serve minority students such as: Math/Science Honors Program, Hispanic Mother/Daughter Program, Native American Achievement Program, Phoenix Union High School District Partnership Program, and the American Indian Institute. In particular, the MEP office had a focus on the retention and success of the Native American students in the College. This was due in large part to the outreach efforts of the OMEP, which are channeled through the MESA Program. The ASU MESA Program works very closely with constituents on the Navajo Nation and the San Carlos Apache Indian Reservation. It was through the MESA Program and working with the other campus support programs that the CEAS began investigating the success of the Native American students in the College. It was a discovery process that was not very positive. Through a cohort investigation that was initiated by the Associate Dean of Student Affairs, it was found that the retention rate of the Native American students in the CEAS was significantly lower than the rate of other minority populations within the College.In the spring of 2000, the OMEP and the CEAS Associate Dean of Student Affairs called a meeting with other Native American support programs from across the campus. In attendance were representatives from the American Indian Institute, the Native American Achievement Program, the Math/Science Honors Program, the Assistant Dean of Student Life, who works with the student coalitions, and the Counselor to the ASU President on American Indian Affairs, Peterson Zah. It was throughthis dialogue that many issues surrounding student success and retention were discussed. Although the issues andconcerns of each participant were very serious, the positiveeffect of the collaboration should be mentioned and noted. One of the many issues discussed was a general reality that ahigh number of Native American students were c oming to the university with minimal exposure to technology. Even through the efforts in the MESA program to expose studentsto technology and related careers, in most cases the schoolsin their local areas either lacked connectivity or basic hardware. In other cases, where students had availability to technology, they lacked teachers with the skills to help them in their endeavors to learn about it. Some students were entering the university with the intention to purse degrees in the Science, Technology, Engineering, and Mathematics (STEM) areas, but were ill prepared in the skills to utilize technology as a tool. This was particularly disturbing in the areas of Computer Science and Computer Systems Engineering where the basic entry-level course expected students to have a general knowledge of computers and applications. The result was evident in the cohort study. Students were failing the entry-level courses of CSE 100 (Principals of Programming with C++) or CSE 110 (Principals of Programming with Java) and CSE 200 (Concepts of Computer Science) that has the equivalent of CSE 100 or CSE 110 as a prerequisite. The students were also reporting difficulty with ECE 100, (Introduction to Engineering Design) due to a lack of assumed computer skills. During the discussion, it became evident that assistance in the area of technology skill development would be of significance to some students in CEAS.The MEP had been offering a seminar course inAcademic Success – ASE 194. This two-credit coursecovered topics in study skills, personal development, academic culture issues and professional development. The course was targeted to historically underrepresented minority students who were in the CEAS [3]. It was proposed by the MEP and the Associate Dean of Student Affairs to add a one-credit option to the ASE 194 course that would focus entirely on preparing students in the use of technology.A C OMPUTERB ASICSC OURSEThe course, ASE 194 – MEP Computer Basics, was offered during the Fall 2001 semester as a one-unit class that met on Friday afternoons from 3:40 pm to 4:30 pm. The course was originally intended for entering computer science students who had little or no background using computer applications or developing computer programs. However, enrollment was open to non-computer science students who subsequently took advantage of the opportunity. The course was offered in a computer-mediated classroom, which meantthat lectures, in- class activities, and examinations could all be administered on comp uters.During course development prior to the start of the semester, the faculty member did some analysis of existing courses at other universities that are used by students to assimilate computing technology. In addition, he did a review of the comp uter applications that were expected of the students in the courses found in most freshman engineering programs.The weekly class meetings consisted of lectures, group quizzes, accessing computer applications, and group activities. The lectures covered hardware, software, and system topics with an emphasis on software development [9]. The primary goals of the course were twofold. Firstly, the students needed to achieve a familiarity with using the computer applications that would be expected in the freshman engineering courses. Secondly, the students were to get a vision of the type of activities that would be expected during the upper division courses in computer science and computer systems engineering and later in the computer industry.Initially, there were twenty-two students in the course, which consisted of sixteen freshmen, five sophomores, and one junior. One student, a nursing freshman, withdrew early on and never attended the course. Of the remaining twenty-one students, there were seven students who had no degree program preference; of which six students now are declared in engineering degree programs and the seventh student remains undecided. The degree programs of the twenty-one students after completion of the course are ten in the computing degree programs with four in computer science and six in computer systems engineering. The remaining nine students includes one student in social work, one student is not decided, and the rest are widely distributed over the College with two students in the civil engineering program and one student each in bioengineering, electrical engineering, industrial engineering, material science & engineering, and mechanical engineering.These student degree program demographics presented a challenge to maintain interest for the non-computing degree program students when covering the software development topics. Conversely, the computer science and computer systems engineering students needed motivation when covering applications. This balance was maintained for the most part by developing an understanding that each could help the other in the long run by working together.The computer applications covered during the semester included e-mail, word processing, web searching, and spreadsheets. The original plan included the use of databases, but that was not possible due to the time limitation of one hour per week. The software development aspects included discussion of software requirements through specification, design, coding, and testing. The emphasis was on algorithm development and design review. The course grade was composed of twenty-five percent each for homework, class participation, midterm examination, and final examination. An example of a homework assignment involved searching the web in a manner that was more complex than a simple search. In order to submit the assignment, each student just had to send an email message to the faculty member with the information requested below. The email message must be sent from a student email address so that a reply can be sent by email. Included in the body of the email message was to be an answer for each item below and the URLs that were used for determining each answer: expected high temperature in Centigrade on September 6, 2001 for Lafayette, LA; conversion of one US Dollar to Peruvian Nuevo Sols and then those converted Peruvian Nuevo Sols to Polish Zlotys and then those converted Polish Zlotys to US Dollars; birth date and birth place of the current US Secretary of State; between now and Thursday, September 6, 2001 at 5:00 pm the expected and actual arrival times for any US domestic flight that is not departing or arriving to Phoenix, AZ; and your favorite web site and why the web site is your favorite. With the exception of the favorite web site, each item required either multiple sites or multiple levels to search. The identification of the favorite web site was introduced for comparison purposes later in the semester.The midterm and final examinations were composed of problems that built on the in-class and homework activities. Both examinations required the use of computers in the classroom. The submission of a completed examination was much like the homework assignments as an e-mail message with attachments. This approach of electronic submission worked well for reinforcing the use of computers for course deliverables, date / time stamping of completed activities, and a means for delivering graded results. The current technology leaves much to be desired for marking up a document in the traditional sense of hand grading an assignment or examination. However, the students and faculty member worked well with this form of response. More importantly, a major problem occurred after the completion of the final examination. One of the students, through an accident, submitted the executable part of a browser as an attachment, which brought the e-mail system to such a degraded state that grading was impossible until the problem was corrected. An ftp drop box would be simple solution in order to avoid this type of accident in the future until another solution is found for the e-mail system.In order to get students to work together on various aspects of the course, there was a group quiz and assignment component that was added about midway through the course. The group activities did not count towards the final grade, however the students were promised an award for the group that scored the highest number of points.There were two group quizzes on algorithm development and one out-of-class group assignment. The assignment was a group effort in website development. This assignment involved the development of a website that instructs. The conceptual functionality the group selected for theassignment was to be described in a one-page typed double spaced written report by November 9, 2001. During the November 30, 2001 class, each group presented to the rest of the class a prototype of what the website would look like to the end user. The reports and prototypes were subject to approval and/or refinement. Group members were expected to perform at approximately an equal amount of effort. There were five groups with four members in four groups and three members in one group that were randomly determined in class. Each group had one or more students in the computer science or computer systems engineering degree programs.The three group activities were graded on a basis of one million points. This amount of points was interesting from the standpoint of understanding relative value. There was one group elated over earning 600,000 points on the first quiz until the group found out that was the lowest score. In searching for the group award, the faculty member sought a computer circuit board in order to retrieve chips for each member of the best group. During the search, a staff member pointed out another staff member who salvages computers for the College. This second staff member obtained defective parts for each student in the class. The result was that each m ember of the highest scoring group received a motherboard, in other words, most of the internals that form a complete PC. All the other students received central processing units. Although these “awards” were defective parts, the students viewed these items as display artifacts that could be kept throughout their careers.C OURSE E VALUATIONOn a weekly basis, there were small assessments that were made about the progress of the course. One student was selected from each team to answer three questions about the activities of the day: “What was the most important topic covered today?”, “What topic covered was the ‘muddiest’?”, and “About what topic would you like to know more?”, as well as the opportunity to provide “Other comments.” Typically, the muddiest topic was the one introduced at the end of a class period and to be later elaborated on in the next class. By collecting these evaluation each class period, the instructor was able to keep a pulse on the class, to answer questions, to elaborate on areas considered “muddy” by the students, and to discuss, as time allowed, topics about which the students wished to know more.The overall course evaluation was quite good. Nineteen of the 21 students completed a course evaluation. A five-point scale w as used to evaluate aspects of the course and the instructor. An A was “very good,” a B was “good,” a C was “fair,” a D was “poor,” and an E was “not applicable.” The mean ranking was 4.35 on the course. An average ranking of 4.57, the highest for the s even criteria on the course in general, was for “Testbook/ supplementary material in support of the course.” The “Definition and application of criteria for grading” received the next highest marks in the course category with an average of 4.44. The lowest evaluation of the seven criteria for the course was a 4.17 for “Value of assigned homework in support of the course topics.”The mean student ranking of the instructor was 4.47. Of the nine criteria for the instructor, the highest ranking of 4.89 was “The instructor exhibited enthusiasm for and interest in the subject.” Given the nature and purpose of this course, this is a very meaningful measure of the success of the course. “The instructor was well prepared” was also judged high with a mean rank of 4.67. Two other important aspects of this course, “The instructor’s approach stimulated student thinking” and “The instructor related course material to its application” were ranked at 4.56 and 4.50, respectively. The lowest average rank of 4.11 was for “The instructor or assistants were available for outside assistance.” The instructor keep posted office hours, but there was not an assistant for the course.The “Overall quality of the course and instruction” received an average rank of 4.39 and “How do you rate yourself as a student in this course?” received an average rank of 4.35. Only a few of the students responded to the number of hours per week that they studies for the course. All of the students reported attending at least 70% of the time and 75% of the students said that they attended over 90% of the time. The students’ estimate seemed to be accurate.A common comment from the student evaluations was that “the professor was a fun teacher, made class fun, and explained everything well.” A common complaint was that the class was taught late (3:40 to 4:30) on a Friday. Some students judged the class to be an easy class that taught some basics about computers; other students did not think that there was enough time to cover all o f the topics. These opposite reactions make sense when we recall that the students were a broad mix of degree programs and of basic computer abilities. Similarly, some students liked that the class projects “were not overwhelming,” while other students thought that there was too little time to learn too much and too much work was required for a one credit class. Several students expressed that they wished the course could have been longer because they wanted to learn more about the general topics in the course. The instructor was judged to be a good role model by the students. This matched the pleasure that the instructor had with this class. He thoroughly enjoyed working with the students.A SSESSMENTS A ND C ONCLUSIONSNear the end of the Spring 2002 semester, a follow-up survey that consisted of three questions was sent to the students from the Fall 2001 semester computer basics course. These questions were: “Which CSE course(s) wereyou enrolled in this semester?; How did ASE 194 - Computer Basi cs help you in your coursework this semester?; and What else should be covered that we did not cover in the course?”. There were eight students who responded to the follow-up survey. Only one of these eight students had enrolled in a CSE course. There was consistency that the computer basics course helped in terms of being able to use computer applications in courses, as well as understanding concepts of computing. Many of the students asked for shortcuts in using the word processing and spreadsheet applications. A more detailed analysis of the survey results will be used for enhancements to the next offering of the computer basics course. During the Spring 2002 semester, there was another set of eight students from the Fall 2001 semester computer basi cs course who enrolled in one on the next possible computer science courses mentioned earlier, CSE 110 or CSE 200. The grade distribution among these students was one grade of A, four grades of B, two withdrawals, and one grade of D. The two withdrawals appear to be consistent with concerns in the other courses. The one grade of D was unique in that the student was enrolled in a CSE course concurrently with the computer basics course, contrary to the advice of the MEP program. Those students who were not enrolled in a computer science course during the Spring 2002 semester will be tracked through the future semesters. The results of the follow-up survey and computer science course grade analysis will provide a foundation for enhancements to the computer basics course that is planned to be offered again during the Fall 2002 semester.S UMMARY A ND F UTURE D IRECTIONSThis paper described a computer basics course. In general, the course was considered to be a success. The true evaluation of this course will be measured as we do follow-up studies of these students to determine how they fare in subsequent courses that require basic computer skills. Future offerings of the course are expected to address non-standard computing devices, such as robots as a means to inspire the students to excel in the computing field.R EFERENCES[1] Office of Institutional Analysis, Arizona State UniversityEnro llment Summary, Fall Semester , 1992-2001, Tempe,Arizona.[2] Reyes, Maria A., Gotes, Maria Amparo, McNeill, Barry,Anderson-Rowland, Mary R., “MEP Summer Bridge Program: A Model Curriculum Project,” 1999 Proceedings, American Society for Engineering Education, Charlotte, North Carolina, June 1999, CD-ROM, 8 pages.[3] Reyes, Maria A., Anderson-Rowland, Mary R., andMcCartney, Mary Ann, “Learning from our MinorityEngineering Students: Improving Retention,” 2000Proceedings, American Society for Engineering Education,St. Louis, Missouri, June 2000, Session 2470, CD-ROM, 10pages.[4] Adair, Jennifer K,, Reyes, Maria A., Anderson-Rowland,Mary R., McNeill, Barry W., “An Education/BusinessPartnership: ASU’s Minority Engineering Program and theTempe Chamber of Commerce,” 2001 Proceeding, AmericanSociety for Engineering Education, Albuquerque, NewMexico, June 2001, CD-ROM, 9 pages.[5] Adair, Jennifer K., Reyes, Maria A., Anderson-Rowland,Mary R., Kouris, Demitris A., “Workshops vs. Tutoring:How ASU’s Minority Engineering Program is Changing theWay Engineering Students Learn, “ Frontiers in Education’01 Conference Proceedings, Reno, Nevada, October 2001,CD-ROM, pp. T4G-7 – T4G-11.[6] Reyes, Maria A., Anderson-Rowland, Mary R., Fletcher,Shawna L., and McCartney, Mary Ann, “ModelCollaboration within Minority Engineering StudentSocieties,” 2000 Proceedings, American Society forEngineering Education, St. Louis, Missouri, June 2000, CD-ROM, 8 pages.[7] Anderson-Rowland, Mary R., Blaisdell, Stephanie L.,Fletcher, Shawna, Fussell, Peggy A., Jordan, Cathryne,McCartney, Mary Ann, Reyes, Maria A., and White, Mary,“A Comprehensive Programmatic Approach to Recruitmentand Retention in the College of Engineering and AppliedSciences,” Frontiers in Education ’99 ConferenceProceedings, San Juan, Puerto Rico, November 1999, CD-ROM, pp. 12a7-6 – 12a7-13.[8] Anderson-Rowland, Mary R., Blaisdell, Stephanie L.,Fletcher, Shawna L., Fussell, Peggy A., McCartney, MaryAnn, Reyes, Maria A., and White, Mary Aleta, “ACollaborative Effort to Recruit and Retain UnderrepresentedEngineering Students,” Journal of Women and Minorities inScience and Engineering, vol.5, pp. 323-349, 1999.[9] Pfleeger, S. L., Software Engineering: Theory and Practice,Prentice-Hall, Inc., Upper Saddle River, NJ, 1998.。

2021新译林版高中英语选择性必修二单元词汇详解(unit4)

McCarthy was asked to analyse the data from the first phase of trials of the vaccine.麦卡西被要求分析从这种疫苗试验的第一阶段取得的数据。
The patient suffered a massive heart attack.病人严重心脏病发作了。
8. breakthrough /ˈbreɪkθruː/ n.重大进展，突破adj.突破性的(45)
【原句背诵】
However, for a long time, AI technology developed very slowly. A major breakthrough in AI came in 1997, when Deep Blue, a chess- playing computer, beat the world chess champion Garry Kasparov.
（2014江西卷）Nevertheless, we must not commit the mistake of analyzing progress only from one point of view.然而，我们决不能犯错误，只从一个角度分析进展的情况。
（2012）陕西卷）For instance, a report that analysed nearly two decades of research on major GM food crops shows that GM engineering has failed to significantly increase US crop production.例如，一份分析了近20年主要转基因食品作物研究的报告显示，转基因工程未能显著提高美国作物产量。

生存分析（survivalanalysis）

⽣存分析（survivalanalysis）⼀、⽣存分析(survival analysis)的定义⽣存分析：对⼀个或多个⾮负随机变量进⾏统计推断，研究⽣存现象和响应时间数据及其统计规律的⼀门学科。

⽣存分析：既考虑结果⼜考虑⽣存时间的⼀种统计⽅法，并可充分利⽤截尾数据所提供的不完全信息，对⽣存时间的分布特征进⾏描述，对影响⽣存时间的主要因素进⾏分析。

⽣存分析不同于其它多因素分析的主要区别点：⽣存分析考虑了每个观测出现某⼀结局的时间长短。

应⽤场景什么是⽣存？⽣存的意义很⼴泛，它可以指⼈或动物的存活（相对于死亡),可以是患者的病情正处于缓解状态（相对于再次复发或恶化），还可以是某个系统或产品正常⼯作（相对于失效或故障），甚⾄可是是客户的流失与否等。

在⽣存分析中，研究的主要对象是寿命超过某⼀时间的概率。

还可以描述其他⼀些事情发⽣的概率，例如产品的失效、出狱犯⼈第⼀次犯罪、失业⼈员第⼀次找到⼯作等等。

在某些领域的分析中，常常⽤追踪的⽅式来研究事物的发展规律，⽐如研究某种药物的疗效，⼿术后的存活时间，某件机器的使⽤寿命等。

在医学研究中，常常⽤追踪的⽅式来研究事物发展的规律。

如，了解某药物的疗效，了解⼿术的存活时间，了解某医疗仪器设备使⽤寿命等等。

对⽣存资料的分析称为⽣存分析。

所谓⽣存资料就是描述寿命或者⼀个发⽣时间的数据。

更详细的说⼀个⼈的⽣存时间的长短与许多因素有联系的，研究因素与⽣存时间的联系有⽆及程度⼤⼩，称为⽣存分析。

例如研究病⼈感染了病毒后，多长时间会死亡；⼯作的机器多长时间会发⽣崩溃等。

这⾥“个体的存活”可以推⼴抽象成某些关注的事件。

所以SA就成了研究某⼀事件与它的发⽣时间的联系的⽅法。

这个⽅法⼴泛的⽤在医学、⽣物学等学科上，近年来也越来越多⼈⽤在互联⽹数据挖掘中，例如⽤survival analysis去预测信息在社交⽹络的传播程度，或者去预测⽤户流失的概率。

⽣存分析研究的内容 1.描述⽣存过程研究⽣存时间的分布特点，估计⽣存率及平均存活时间，绘制⽣存曲线等，根据⽣存时间的长短，可以估算出各个时点的⽣存率，并根据⽣存率来估计中位⽣存时间，也可以根据⽣存曲线分析其⽣存特点，⼀般使⽤Kaplan-Meier法和寿命表法。

cultivate survival analysis -回复

cultivate survival analysis -回复题目：Cultivating Survival Analysis: A Step-by-Step GuideIntroduction:Survival analysis is a statistical method that helps us analyze and interpret time-to-event data in a comprehensive manner. It is extensively used in medical research, engineering, economics, and social sciences. The objective of survival analysis is to understand the likelihood of an event occurring over a specific period or the time until that event occurs, considering the presence of censoring. In this article, we will walk you through the steps of cultivating survival analysis, enabling you to understand and implement this powerful technique effectively.Step 1: Define the Research Question and Data Collection PlanThe first step involves identifying the specific research question or objective of the study and understanding the nature of the data. Survival analysis can be applied to various scenarios, such as estimating disease-free survival, analyzing customer churn, or investigating time to failure of mechanical parts. Once theresearch question is defined, create a plan for data collection, making sure to consider the quality and completeness of the data needed to analyze survival times accurately.Step 2: Data Preparation and ExplorationNext, gather the data and clean it to ensure it is ready for analysis. This may involve removing missing values, transforming variables, and addressing outliers. Once the data is prepared, explore it by generating summary statistics and visualizations to gain a comprehensive understanding of the dataset's characteristics and identify potential patterns or relationships.Step 3: Create the Survival Analysis DatasetTo perform survival analysis, we need to create a dataset that includes the time-to-event variable (e.g., survival time or time until an event occurs), a binary variable denoting whether the event of interest has occurred, and additional covariates that might affect survival. If there are multiple event types, a multistate dataset may be necessary. This step involves merging and transforming the data to meet the requirements of the chosen survival analysismodel.Step 4: Determine the Survival DistributionThe choice of survival distribution determines the appropriate analysis and modeling techniques. The most commonly used distributions include the exponential, Weibull, and log-normal distributions. Plotting the empirical survival function, the Kaplan-Meier estimator, can help assess the form of the data and determine which distribution is most suitable.Step 5: Conduct Survival Analysis ModelingOnce the survival distribution is determined, various modeling techniques can be employed. Popular approaches include the Cox proportional hazards model, accelerated failure time models, and parametric survival models. These models allow for the inclusion of covariates to investigate their impact on the survival outcome. The selection of the appropriate model should be based on the nature of the research question and assumptions of the chosen model.Step 6: Interpret and Validate the ResultsAfter fitting the survival model, interpret the estimated coefficients and hazard ratios to understand the effects of covariates on survival times. These interpretations can provide valuable insights into the relationship between variables and the event of interest. Additionally, validate the model by assessing its assumptions, such as proportional hazards assumption or goodness-of-fit, using appropriate techniques like log-rank tests or deviance residuals.Step 7: Communicate and Report the FindingsFinally, present the results of your survival analysis in a clear and concise manner. Use appropriate visualization techniques, such as survival curves, forest plots, or heatmaps, to effectively communicate the findings. Include important details such as the estimated hazard ratios, confidence intervals, and p-values. Ensure the results are accurately interpreted and emphasize the implications and practical significance of the study's outcomes.Conclusion:Survival analysis is a valuable tool for analyzing time-to-event data, providing insights into various fields of research. By following the step-by-step guide outlined in this article, you can cultivate an understanding of survival analysis and apply it effectively to your own research. This methodological approach allows for a deeper investigation into the dynamics of events, enabling better decision-making and improved understanding of the underlying processes that impact survival times.。

we use scissors文件

we use scissors文件简介Scissor R包提出的Scissor算法( function Scissor )，这是一种新颖的单细胞数据分析方法。

利用批量（bulk）表型从单细胞测序(scRNA-seq)数据中识别与表型高度相关的细胞亚群。

其优点如下:首先，通过Scissor识别的表型相关的细胞亚群具有独特的分子特性，其中可能涉及到特定表型的关键标记基因和生物学过程。

其次，Scissor不需要对单细胞数据进行任何无监督聚类，这避免了对细胞聚类数或聚类分辨率的主观决策。

最后，Scissor提供了一个灵活的框架，将各种外部表型整合到批量数据（bulk）中，以指导单细胞数据(scRNA-seq)分析，实现在无假设前提下去识别临床和生物学相关细胞的亚群。

Scissor 使用实例Scissor的输入包括三类数据：单细胞表达矩阵、bulk表达矩阵和感兴趣的表型。

每个bulk的表型注释可以是连续因变量、二元组指标向量或临床生存数据。

在本教程中，我们使用几个在肺腺癌(LUAD) 癌细胞scRNA-seq上的应用作为示例，展示如何在实际应用中执行Scissor。

实例一在第一个例子中，我们使用带有Scissor生存息的LUAD bulk样本来识别LUAD癌细胞中的侵袭性癌细胞亚群。

1.1G，查看一部分，确认一下是什么数据sc_dataset[1:2,1:4]#在LUAD scRNA-seq数据中，每一行代表一个基因，每一列代表一个癌细胞数据展示dim(sc_dataset)dim()结果这表明共有个基因和4102个癌细胞。

对于Scissor中使用的scRNA-seq数据，我们使用Seurat包中的函数对这些数据进行预处理，并构建一个细胞-细胞相似网络。

（Seurat R包包含预处理数据和构造网络的函数）。

在Scissor包中，我们将Seurat分析管道封装到Seurat_preprocessing() 函数中Seurat_preprocessing( counts,#一种matrix的对象，具有非标准化的数据，列细胞，行为特征（如基因） project = "Scissor_Single_Cell", min.cells =400,#一个特征至少得在多少个细胞中检测到，若要将被排除掉的特征重新引入，就将cutoff的阈值调小一点min.features =0,#一个细胞至少包含多少个特征 normalization.method = "LogNormalize",#'LogNormalize','CLR','RC' scale.factor =,#为细胞水平标准化设置比例因子 selection.method = "vst",#'vst','mvp','disp' resolution =0.6,#如果要获得更多（更少）的聚类，使其高于（低于）1.0。

coxregressionkaplanmeier分析

信访、电话等得到。
（三）整理资料
认真检查、核对原始数据，包括影响因素、生存时间和生存结局。
尽量避免缺失值。建立数据库
FoxBase、Foxpro、Virual Foxpro等专业数据库
统计软件数据库（SAS、SPSS等） Office办公软件中的Excel、Access
大肠癌生存资料
活满一年例数 p 年初观察例数
生存率：
（survival rate, survival function ）
指观察对象经历t个单位时段后仍存活的
可能性。
3年生存率＝
活满3年例数期初观察例数
5年生存率＝
活满5年例数期初观察例数
条件生存概率和生存率的计算
例：手术治疗100例食管癌患者，术后1、 2、3年的死亡数分别为10、20、30，若无截尾数据，试求各年条件生存概率及逐年生存率。生存率计算方法：
多因素分析方法不考虑生存时间分布利用截尾数据
一、Cox模型的基本形式
h(t, X ) h0 (t) exp( 1 X1 2 X 2 p X p )
h(t,X)—t 时刻风险函数、风险率或瞬时死亡率（hazard function）。
h0(t)— 基准风险函数，即所有变量都取 0 时 t 时刻风险函数。
单一表：因素较多时。
调查表中应包括可能的影响因素
三联体数据
观察起点和终点（年、月、日）
生存时间
生存结局
样本含量：非截尾例数至少是可能影响因素的10倍。
（二）搜集资料可能的影响因素：
从病历获得。生存时间及结局：
短期可观察到的结局可从病历获得；长期结局一般不能从病历直接获得，通过

《2024年Meta分析系列之五_贝叶斯Meta分析与WinBUGS软件》范文

《Meta分析系列之五_贝叶斯Meta分析与WinBUGS软件》篇一Meta分析系列之五_贝叶斯Meta分析与WinBUGS软件Meta 分析系列之五：贝叶斯Meta分析与WinBUGS软件一、引言Meta分析作为一种综合分析多个独立研究结果的方法，在许多领域得到了广泛的应用。

其中，贝叶斯Meta分析以其独特的统计方法和灵活的模型设定，在处理复杂数据时具有显著的优势。

本文将详细介绍贝叶斯Meta分析的原理、方法和应用，并重点介绍WinBUGS软件在贝叶斯Meta分析中的应用。

二、贝叶斯Meta分析的原理与方法1. 贝叶斯Meta分析的原理贝叶斯Meta分析基于贝叶斯统计理论，通过结合先验信息和样本信息，对总体参数进行推断。

在Meta分析中，它通过整合多个独立研究的效应量及其置信区间，得到一个综合的效应量和置信区间，从而更准确地估计研究结果的真实性。

2. 贝叶斯Meta分析的方法贝叶斯Meta分析主要包括两个步骤：一是建立合适的模型，包括固定效应模型和随机效应模型等；二是根据模型的设定和样本数据，通过MCMC方法（Markov Chain Monte Carlo）估计参数的后验分布。

在这个过程中，WinBUGS软件等统计软件被广泛用于实现这一过程。

三、WinBUGS软件在贝叶斯Meta分析中的应用WinBUGS是一款强大的贝叶斯统计软件，被广泛应用于各种复杂数据分析。

在贝叶斯Meta分析中，WinBUGS具有以下优势：1. 灵活的模型设定：WinBUGS提供了丰富的模型设定选项，可以根据不同的研究需求和数据进行定制化的模型设定。

2. 强大的计算能力：WinBUGS采用MCMC方法进行参数估计，具有强大的计算能力，可以处理大规模的数据和复杂的模型。

3. 直观的用户界面：WinBUGS提供了直观的用户界面，使得用户可以轻松地完成数据输入、模型设定和结果查看等操作。

4. 良好的兼容性：WinBUGS可以与其他统计软件和编程语言进行数据交换，使得用户可以更方便地进行数据处理和分析。

8 死因数据清洗分析工具及专题分析-死因培训班用

采用统一的模型寿命表Coale and Demeny regional (East, North, West South) model life tables
Coale and Demeny level west 26 用于女性SEYLL 计算 e0=82.5 Coale and Demeny level west 25 用于男性SEYLL 计算 e0=80
死因数据专题分析-寿命表
中国疾病预防控制中心
死因数据专题分析-寿命表基础数据：
人口数(nPx) 死亡数(nDx)：
• 计算去死因期望寿命时，去掉相应死因的死亡数
指标计算：
年龄别死亡率(nMx)： nMx= nDx/nPx
x 岁到 x n 岁之间死亡人数年龄别死亡概率(nqx):表示一批人在年龄x到年龄x+n 活满 x 岁的人口数岁之间的死亡概率 nMx=
中国疾病预防控制中心
死因数据专题分析-寿命表
2009年
分地区期望寿命
76.82
76.34 73.84 74.66 73.64
期望寿命
75
74.79
65 全国城市农村地区
中国疾病预防控制中心
东部
中部
西部
死因数据专题分析-寿命表
2009年
80
去死因期望寿命
4.19 2.51
75
1.44
1.13
2 .4 4
* 3 .8 9 7 7 .9 5 8 0 .2 8
中国疾病预防控制中心
死因资料分析-YLL
YLL(0,0)：
YL L N * L
L=早死造成的标准期望寿命损失 N=死亡人数
YLL(增加贴现率)：

生存分析R的示例

生存分析R的示例生存分析是生物医学领域中常用的一种统计分析方法，用于研究人群或个体在其中一种特定事件（如死亡、复发、治愈等）发生前的时间间隔或风险。

生存分析经常被应用于生存率分析、风险评估和时间对因素的建模。

在R语言中，有许多用于生存分析的包，如survival、survminer和KMsurv等。

在下面的示例中，我们将使用survival包来进行生存分析。

首先，我们需要加载所需的R包，并创建一个示例数据集。

这里我们使用的是survival包内置的lung数据集，该数据集包含了来自NCCTG实验组的228个肺癌患者的信息。

```R#加载所需的包library(survival)#创建示例数据集data(lung)head(lung)```上述代码加载了survival包，并创建了一个名为lung的数据集。

我们可以使用head(函数来查看数据集的前几行，以了解其结构和内容。

在进行生存分析之前，我们首先需要定义两个重要的变量：生存时间（Survival Time）和事件状态（Event Status）。

在这个示例中，生存时间是指患者从入组开始到死亡或失访（censoring）之间的时间间隔，事件状态是指患者是否已死亡（1表示死亡，0表示失访）。

接下来，我们可以画出生存曲线（Survival Curve），展示患者在不同时间点的生存概率。

```R#创建生存对象#画生存曲线```另外，我们可以根据不同的因素对生存曲线进行分组比较。

```R#根据性别分组比较生存曲线plot(survfit(lung_surv ~ sex, data = lung), xlab = "Time", ylab = "Survival Probability", main = "Survival Curve by Gender") #根据治疗方法分组比较生存曲线plot(survfit(lung_surv ~ rx, data = lung), xlab = "Time", ylab = "Survival Probability", main = "Survival Curve by Treatment")```上述代码中，我们使用~符号将生存对象lung_surv与不同的因素（如性别和治疗方法）进行分组比较。

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4
Analysis of Survival Data
• Common analyses include:
– Compare survival function between groups defined by a single categorical variable. – Regression model relating hazard to one or multiple variables.
Class 8 Survival Analysis
BS845
1
Survival Analyses
• A binary outcome of event • Time to event (lifetime/survival time),T, is the interest • T is continuous 0-∞, with cumulative distribution F(t), density f(t) – Survival function
• If there is no censoring, estimation of survival function is just 1- empirical cumulative distribution function (ecdf) of a continuous variable: time to event (T)
H (t )
– Cumulative hazard function

t
0
h( s )ds
– Relationships between three functions f (t ) h(t ) , H (t ) -log S (t ) S (t )
2
Types of Censoring and Truncation
10
Function for Create Survival Object
Surv(time, time2, event, type=c('right', 'left', 'interval', 'counting', 'interval2'), origin=0) Arguments time : Follow up time for right censored data, or starting time for the interval censored. event :Survival status indicator, normally 0=alive, 1=dead. Other choices are TRUE/FALSE (TRUE = death) or 1/2 (2=death). Or 0=right censored, 1=event at time, 2=left censored, 3=interval censored for other types of censoring time2 ending time of the interval for interval censored. Interval is (start, end] type Type of censoring.
Interval <1 [1,3) [3,5) [5,6) [6,8) [8,9) ≥9
r(ti) 10 10 8 6 5 4 3
di 0 1 0 1 1 1 1
S(ti) 10/10=1 (10/10)*(9/10)=0.9 (10/10)*(9/10)*(8/8)=0.9 (10/10)*(9/10)*(8/8)*(5/6) =0.75 (10/10)*(9/10)*(8/8)*(5/6) *(4/5)=0.6 (10/10)*(9/10)*(8/8)*(5/6) *(4/5)*(3/4)=0.45 (10/10)*(9/10)*(8/8)*(5/6) *(4/5)*(3/4)*(2/3)=0.3
Censoring means a value is partially known
• • • Right censoring: T not observed, but we know T >C Left censoring: T not observed, but we know T <C Interval censoring: T not observed, but we know C1<T <C2
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An hypothetical example no censoring
T<-c(3,5,1,1,9,8,3,9,9,6)#10 surival times par(mfrow=c(1,2)) hist(T) plot(ecdf(T)) sort(T) [1] 1 1 3 3 5 6 8 9 9 9 The ecdf of T are estimated as: Interval #Events F(t)=P(T<t)= S(t)= cumulative #Events/N 1-F(t) <1 [1,3) 0 2 0/10=0 2/10=0.2 1 0.8
• This assumption is violated if a subject is removed from the study because of a worsen condition, this constitutes informative censoring
– Encompass random censoring(or noninformative censoring), i.e. T and C are independent. E.g. the censoring at the end of study—type I censoring – Include type II censoring, study ends when # of events reach certain number.
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KM estimate with censoring
It is customary to use KM to estimate survival function with censored times.
T<-c(3,5,1,1,9,8,3,9,9,6) status<-c(1,2,1,2,1,2,1,2,1,2) #censored=1; event=2 data <-data.frame(T, status) data[order(T),] T status 3 1 1 4 1 2 1 3 1 7 3 1 2 5 2 10 6 2 6 8 2 5 9 1 8 9 2 9 9 1 1. Those censored before time t are not included as risk set for time t. 2. Number highlighted in red indicates changes from previous example of no censoring
[3,5) [5,6)
[6,8) [8,9) ≥9
2 1
1 பைடு நூலகம் 3
4/10=0.4 5/10=0.5
6/10=0.6 7/10=0.7 10/10=1
0.6 0.5
0.4 0.3 0
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Kaplan-Meier (KM) estimator of S(t)
• S(t) can also be estimated equivalent with Kaplan-Meier estimator
Consider a set of intervals I i [ti , ti 1 ) with ti being an event time r(t i ) is number of subjects at risk before t i , di is number of events in I i 1 di is probability of surviving interval I i , given surviving interval I i 1 r (ti ) P (T ti | T ti 1 )P (T ti 1 | T ti 2 )P (T ti 2 )
C C C1
Right censoring Left censoring Interval censoring
T0
C2
Solid line indicates time that a person is event free Dashed line indicates time that a person may have a event, but exact time of event is unclear Cross indicates the censoring time
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Independent Censoring
• Most analysis assumes independent censoring : – Hazard at time t conditional on the whole history only depends the survival of that individual to time t
– ecdf is area under a histogram up to time t – For histogram, the x-axis (T) is evenly divided into bins, and frequency of observations falling into each bin is the height of the bar – ecdf are step function with intervals being distinct survival times