Hypothesis Testing (HS Yam - Rev A)
体外哺乳动物细胞染色体畸变试验
九、体外哺乳动物细胞染色体畸变试验In Vitro Mammalian Cells Chromosome Aberration Test1 范围本规范规定了体外哺乳动物细胞染色体畸变试验的基本原则、要求和方法。
本规范适用于检测化妆品原料及其产品的致突变性。
2 规范性引用文件OECD Guidelines for Testing of Chemicals ( No.473, July 1997)3试验目的本试验是用于检测培养的哺乳动物细胞染色体畸变，以评价受试物致突变的可能性。
4 定义染色体型畸变(Chromosome-type aberration)：染色体结构损伤，表现为在两个染色单体相同位点均出现断裂或断裂重组的改变。
染色单体型畸变(Chromatid-type aberration)：染色体结构损伤，表现为染色单体断裂或染色单体断裂重组的损伤。
染色体数目改变(Numerical aberration)：所用细胞株的正常染色体数目的变化。
结构畸变(Structural aberration)：在细胞分裂的中期相阶段，用显微镜检出的染色体结构改变，表现为缺失、断片、互换等。
有丝分裂指数(Mitotic index)：中期相细胞数与所观察的细胞总数之比值；是一项反映细胞增殖程度的指标。
5 试验基本原则在加入和不加入代谢活化系统的条件下，使培养的哺乳动物细胞暴露于受试物中。
用中期分裂相阻断剂（如秋水仙素或秋水仙胺）处理，使细胞停止在中期分裂相，随后收获细胞，制片，染色，分析染色体畸变。
6 试验方法6.1 试剂和受试物制备6.1.1 阳性对照物：可根据受试物的性质和结构选择适宜的阳性对照物，阳性对照物应是已知的断裂剂，能引起可检出的、并可重复的阳性结果。
当外源性活化系统不存在时，可使用甲磺酸甲酯（methyl methanesulphonate (MMS)）、甲磺酸乙酯（ethyl methanesulphonate(EMS)）、乙基亚硝基脲(ethyl nitrosourea)、丝裂霉素C(mitomycin C)、4-硝基喹啉-N-氧化物（4-nitroquinoline-N-oxide）。
抗S.paratyphi+A+O2抗原单克隆抗体制备及ELISA检测方法的建立
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实验室常规检测、诊断甲型副伤寒的主要方法为细菌培养、肥达氏凝集反应和 伤寒血球快速诊断。这些方法费时费力，不利于早期诊断，给流行病学专家在追踪 传染源、早期控制与临床医生早期诊断工作带来了一定的难度。以酶联免疫吸附试 验为原理建立起来的各种免疫学检测方法，特异性好、灵敏度高、操作简便、样品 处理量大，已经广泛应用于临床和食品中病原菌的检测。但是目前国内外还很少有 用ＥＬＩＳＡ方法检测甲型副伤寒沙门氏菌的报道，本研究中，我们先制备抗甲型副伤 寒沙门氏菌０２抗原的单克隆抗体和多克隆抗体，然后以此为基础建立夹心ＥＬＩＳＡ 方法，快速检测该菌。
hypothesis testing 好理解
hypothesis testing 好理解：
假设检验（hypothesis testing）是统计学中常用的方法，用于评估关于总体参数的假设。
通常情况下，我们会提出两种相互对立的假设：零假设（null hypothesis）和备择假设（alternative hypothesis）。
通过收集样本数据并进行统计推断，我们可以判断是否有足够的证据来拒绝零假设。
在假设检验中，我们首先设定一个显著水平（significance level），通常用α表示，它代表了我们愿意接受犯第一类错误（拒绝了其实是正确的零假设）的风险大小。
然后，我们使用样本数据计算出一个统计量，并基于此统计量对比设定的显著水平，来判断是否拒绝零假设。
假设检验可以帮助我们从样本数据中得出关于总体的重要结论，例如判断药物是否有效、广告营销策略是否有效等。
通过对比样本数据与假设进行检验，我们可以做出科学合理的推断，并支持决策和结论的形成。
Power of a hypothesis test
Power of a hypothesis testPower = P(reject H 0 | H 1 is true) = 1 – P(type II error) = 1 - βThat is, the power of a hypothesis test is the probability that it will reject when it’s supposed to.Power⎪⎪⎭⎫⎝⎛-+-<≈-n Z N P σμμα||)1,0(012/1nσ01Distribution under H 01Example: Post-hoc power calc for Gum studyFor the group-A gum data our test did not reject the null hypothesis of no mean change in DMFS.• WE KNOW: our test does not furnish us goodevidence of a change in DMFS.• WE DON’T KNOW: Whether or not the DMFS trulychanges because the lack of evidence could be the result of either:a. the mean DMFS truly doesn’t change, orb. the test wasn’t powerful enough to provide evidence of change.We can assess b. by estimating the power of our test.WE KNOW: n=25, α=0.05ASSUME:• true average change in the population is 1 DMFS • true population standard deviation is σ=5.37.152.)03.1()03.1(2537.5|01|96.1)1,0(=>=-<=⎪⎪⎭⎫⎝⎛-+-<Z P Z P N POnly 15% power. This implies the truth could be a. or b.Example: Post-hoc power calc (continued)Suppose now that we had seen the same result (did not reject null hypothesis of no mean change), but the sample size had been n=250.The power from this hypothesis test would have been:836.)98.0(25037.5|01|96.1)1,0(=<=⎪⎪⎭⎫⎝⎛-+-<Z P N PThis says that if there truly were a change in dmfs of 1 or greater, our test probably (83.6% chance) would have rejected the null hypothesis.This yields more evidence for us to “accept” the null hypothesis.Important Point:Ensuring reasonably high power of your test will not only increase your chance of rejecting null hypothesis, it will also facilitate interpretation of your result should your test fail to reject.Factors that affect the power of a test for μPower⎪⎪⎭⎫⎝⎛-+-<≈-n Z N P σμμα||)1,0(012/1• Power ↑ as |μ0- μ1| ↑ • Power ↑ as n ↑ • Power ↑ a s σ ↓ • Power ↑ as α ↑nσμμ01-Distribution under H 01Sample Size CalculationWe can invert the power formula to find the minimum n that will give a specified power.To have power 1- β to reject for a test withsignificance level α to reject the null hypothesis H 0: μ = μ0, in favor of H1: μ ≠ μ0 then the sample size should be at least()()201212/12μμσβα-+=--Z Z nExample:In our previous example, to have 80% power to detect a difference of 1 DMFS, the sample size should be at least()()4.22601842.0960.137.5222=-+=n ,so should enroll 227 kids.To have 80% power to find a difference of 2 DMFS, the sample size should be()()6.5602842.0960.137.5222=-+=n .Components in to a sample-size calculation1. the desired power1- β2.the significance level α3.the population standard deviation σ4. the differenc e in the means |μ0- μ1|1. The desired power1- β:Common “industry standard” is minimum 80%. Tests attempting to demonstrate evidence of equality (instead of differences) will sometimes specify higher powers (95%)2.Significance level α:Usual ch oices are α = 0.05 or α = 0.01. Sometimes adjustments for multiple testing will lead to specifying other levels for α.3. Population standard deviation σ:The population standard deviation will not be known, and must be estimated from previous studies. These estimates should be conservative (err on the high side).Example: gum dataWe estimated σ using s from a sample of size n=25. The 95% confidence interval for σ in this case would be (4.19, 7.47)*, so we see using σ = 5.37 may be an underestimate of the true population SD.Suppose we assumed σ = 5.37, so used a sample of 227 in hopes to have 80% power to detect adifference of 1 dmfs. BUT, say that σ really was 6.00. Then our true power would be only709.)55.0(22700.6|01|96.1)1,0(=<=⎪⎪⎭⎫⎝⎛-+-<Z P N P .A conservative method is to use the upper 80% confidence limit for σ, as an estimate for σ, whichis given by 220.0,12)1(--n s n χ*.*see Rosner, section 6.7 for details of calculation4. Difference in the means |μ0- μ1|Specifying the alternative hypothesis mean is the most tricky part of the calculation, and the choice can greatly affect the power estimates (and thus sample-size estimates).0.00.5 1.0 1.5 2.0 2.5 3.0power: n=57, sigma=5.37, alpha=0.05true mean differencep o w e r0%20%40%60%80%100%Ideally one should specify μ1 to be the minimal clinically significant difference.Your study will have reasonable power to find a difference of the size you specify in μ1. However, if the true μ is smaller than μ1 your study has a good chance of not rejecting H 0. Thus you’d like μ1 to specify the smallest difference you would consider an interesting finding.。
Kendall's W检验-SPSS教程
Kendall's W检验-SPSS教程一、问题与数据某研究者拟分析5位放射科医生对疾病严重程度诊断的一致性。
现搜集50位研究对象的MRI检查结果，并要求放射科医生分别针对每份MRI检查给予Grade I（最轻）到Grade V（最重）五个等级的临床诊断，Grade I、Grade II、GradeIII、Grade IV和Grade V赋值分别为1、2、3、4和5，部分数据如图1。
图1 部分数据二、对问题分析在本研究中，研究者拟探讨5位放射科医生对疾病严重程度（5分类）诊断的一致性。
对于这种存在3位及以上观察者，观测变量为连续变量或有序分类变量的一致性检验，我们推荐使用Kendall’s W检验。
一般来说，采用Kendall’s W 检验的研究设计需要满足以下3项假设：假设1：观察者不少于3人，判定结果是连续变量或有序分类变量。
如本研究中需要判断5位放射科医生诊断结果的一致性，且观测变量是Grade I到Grade V 五个等级，属于有序分类变量。
假设2：要求判定结果配对，即不同观测者判定的对象相同。
如本研究中，5位放射科医生诊断的是同一组研究对象的MRI，编号统一。
假设3：观察者之间相互独立。
这要求不同观测者独立完成结果判定，相互不干扰。
根据研究设计，我们认为本研究符合Kendall’s W 检验的3项假设，可以采用该方法进行一致性评价。
三、SPSS操作在主界面点击Analyze→NonparametricTests→Related Samples，确认What is your objective?栏中点选了Automatically compare observed data to hypothesized，如图2。
图2 NonparametricTests: Two or More Related Samples点击Fields→Use custom field assignments，并将50个观测变量P1到P50放入Test Fields栏。
细胞自噬检测
细胞自噬检测引言细胞自噬（Autophagy）是一种维持细胞内稳态的重要细胞生理过程。
它通过将细胞内的有害垃圾物质、受损蛋白质和细胞器通过包裹成双层膜体而将其降解和回收。
细胞自噬不仅对于维持细胞内环境的平衡，也在细胞营养不足、应激等情况下发挥至关重要的作用。
为了研究细胞自噬的机制、功能以及其在疾病中的作用，科研人员常常需要进行细胞自噬的检测。
本文将介绍一些常用的细胞自噬检测方法，旨在帮助科研人员更好地开展相关研究。
方法一：免疫组化检测免疫组化检测是常用的细胞自噬检测方法之一，它利用特异性抗体识别和定位自噬相关的蛋白质标记。
以下是免疫组化检测的步骤：1.固定和透化：将细胞样品固定在载玻片上，并通过透化剂处理，以便抗体能够穿透细胞膜进入细胞内。
2.抗体反应：加入特异性的抗体，使其与目标自噬蛋白结合。
3.洗涤：进行多次洗涤，以除去未结合的抗体。
4.二抗结合：加入与第一抗体来源不同种类的第二抗体，它能连接到第一抗体上，使得目标蛋白能够被可见光或荧光染料标记。
5.洗涤：进行多次洗涤，以除去未结合的第二抗体。
6.显色或荧光检测：加入适当的显色剂或荧光标记物，观察细胞自噬标记的结果。
免疫组化检测方法适用于观察细胞内特定蛋白质的表达和定位情况，从而间接地了解细胞自噬的活性。
方法二：转染自噬标记基因为了直接观察细胞内自噬相关的蛋白质的表达情况，科研人员可以利用转染自噬标记基因的方法。
以下是一种常用的自噬标记基因：mRFP-GFP-LC3。
mRFP-GFP-LC3是一种融合蛋白，其中GFP（绿色荧光蛋白）和mRFP（红色荧光蛋白）与自噬相关的蛋白LC3融合在一起。
GFP能够在细胞自噬过程中被降解，而mRFP则能够在自噬过程中保持稳定。
转染mRFP-GFP-LC3基因后，科研人员可以通过观察细胞中的荧光信号来确定细胞的自噬活性。
当细胞自噬活性较低时，绿色和红色荧光都很强；而当细胞自噬活性增加时，绿色荧光减弱而红色荧光增强。
HSP27和HSP70mRNA在大肠癌中表达的临床意义
HSP27和HSP70mRNA在大肠癌中表达的临床意义杨淼;张晓丽;薄爱华;马利红【期刊名称】《神经药理学报》【年(卷),期】2008(025)005【摘要】目的:探讨HSP27和HSP70mRNA在大肠癌中表达的临床意义.方法:取大肠癌标本77例,采用免疫组织化学方法检测HSP27的表达状况,从中选取36例手术标本,采用核酸原位杂交技术(ISH)检测HSP70mRNA的表达.结果:HSP27和HSP70mRNA在大肠癌中的阳性率分别为40.3%和66.9%,HSP27和HSP70mRNA均与大肠癌的分化程度相关,与发生部位、浸润深度及淋巴结转移无明显相关性.结论:检测HSP27和HSP70mRNA可作为大肠癌患者预后的一个重要指标.【总页数】3页(P15-17)【作者】杨淼;张晓丽;薄爱华;马利红【作者单位】河北北方学院附属第一医院急诊科,河北,张家口,075000;河北北方学院实验中心电镜室,河北,张家口,075000;河北北方学院实验中心电镜室,河北,张家口,075000;河北北方学院附属第一医院急诊科,河北,张家口,075000【正文语种】中文【中图分类】R735.34【相关文献】1.膀胱移行细胞癌患者癌组织HSP27和HSP70mRNA的表达及临床意义 [J], 张江兰;张晓丽;侯勇;吕洋;张耕2.大肠癌中hsp27和p14ARF的表达及意义 [J], 左国进;朱润庆;刘卫容;夏东3.HSP27在青老年大肠癌中的表达差异 [J], 彭晓峰;余志金;黄文峰;邱欢余;许岸高;杨清绪4.大肠癌组织中HSP27和bcl-2及p53蛋白的表达及其临床意义 [J], 郭建波;蔡永清;赵鲁笳;张跃伟;曲东霞;冯燕5.大肠癌中HSP27、p14^(ARF)和caspase-3蛋白的表达及意义 [J], 刘卫容;雷元卫;郑鹏才;苏波因版权原因，仅展示原文概要，查看原文内容请购买。
化学品安全性快速评价方法_尹志刚
知识介绍化学品安全性快速评价方法尹志刚1,2宋学谦2 赵德丰1*(1大连理工大学精细化工国家重点实验室 大连 116012;2郑州轻工业学院化工系 郑州 450002)尹志刚 男,37岁,硕士,副教授,主要从事医药中间体合成及非诱变染料的研究。
*联系人国家自然科学基金资助项目(29972006)2002-06-24收稿,2002-10-23修回摘 要 简要评述了化学品安全性的传统评价方法。
详细介绍了一种快速评价化学品安全性的重要方法)))诱变性测试法。
该方法选用几种标准(或特定)的鼠伤寒沙门氏菌(Salmonella typ himurium )作为观察对象来研究待测化学品对菌株的诱变情况,以菌株回复突变的数目表征待测化学品的诱变性。
当回复突变数目为零剂量(化学品)的2倍以上时,所测定化学品具有诱变性。
具有诱变性的化学品为不安全化学品。
关键词 毒性 致癌性 诱变性测试 鼠伤寒沙门氏菌Fast Method for Evaluating the Security of C hemicalsYin Zhigang 1,2,Song Xueqian 2,Zhao Defeng1*(1State Key Laboratory of Fine Chemicals,Dalian Universi ty of T echnology,Dalian 116012;2Chemical Engineering Department,Zhengzhou Insti tute of Light Industry,Zhengzhou 450002,China)Abstract The present paper covers the comment on the traditional methods for evaluating the security of chem-i cals and the modern evaluating method referred to as Ames Test or Ames P Salmonella Assay which is based on the use of several s trains of the bacteria Salmonella typhimurium .The procedure detects reverse mutations that occur in these strains in the p resence of a test chemical.The mutagenic activity of the test chemical will be given in a quanti tative measure on the revertants.A test chemical is considered to be mu tagenic and insecure when the number of the rever -tants per plate is at leas t two ti mes the base count (zero dose of the test chemical).Key words Toxicity ,Carcinogenesis,Ames Test or Ames P Salmonella assay,Sa lmone lla typhimurium随着科学技术的高速发展,人们越来越重视日常生活与工作中所生产或使用化学品的安全性。
一种磺酰胺类衍生物及其制备方法和应用[发明专利]
专利名称：一种磺酰胺类衍生物及其制备方法和应用专利类型：发明专利
发明人：孙华,吴岩,张欣颖,张一楠
申请号：CN202010688883.5
申请日：20200717
公开号：CN111646941A
公开日：
20200911
专利内容由知识产权出版社提供
摘要：本发明涉及一种磺酰胺类衍生物及其制备方法和应用。
本发明首次合成并发现该类化合物具有一定的抑制人结肠癌细胞(SW480和HCT116)的活性，在抗肿瘤药物开发和应用方面具有潜在价值；同时本发明对所合成的化合物进行了α‑葡萄糖苷酶抑制活性评价，结果表明该类化合物也具有一定的α‑葡萄糖苷酶抑制活性，说明该类化合物在治疗糖尿病药物的开发与应用方面也具有广阔前景。
申请人：天津科技大学
地址：300457 天津市滨海新区经济技术开发区第13大街29号
国籍：CN
更多信息请下载全文后查看。
Hypothesis_Testing(统计学假设检验)
Chapter 9
INTRODUCTION TO HYPOTHESIS TESTING
1
Hypothesis Testing
9.1
9.2 9.3
Null and Alternative Hypotheses and Errors in Testing z Tests about a Population with known s t Tests about a Population with unknown s
quo (a statement of “no effect” or “no difference”, or a statement of equality) and is not rejected unless there is convincing sample evidence that it is false.
3. Finally, we compare the sample data with the hypothesis. If the data are consistent with the hypothesis, we will conclude that the hypothesis is reasonable. But if there is a big discrepancy between the data and the hypothesis, we will decide that the hypothesis is wrong.
• These two hypotheses are mutually exclusive and exhaustive.
7
Determined by the level of significance or the alpha level
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p-Value less than
No
Accept H0
Yes Reject H0
is the risk of concluding that H0 is false, when it is true.
Also called Type I Error or Producer’s Risk.
1- is the Confidence Level for H0.
H0
observed value
P-Value
_ is observed, This is the probability that a value as extreme as _ x (i.e. x) given that H0 is true. We reject H0 if the obtained P-Value is less than .
Cumulative Distribution F(x)
Expected CDF
EmpiriБайду номын сангаасal CDF
X
Example 1
(Anderson-Darling Test)
The diameter of 9 transmission shafts were measured: 4.9 5.1 4.6 5.0 5.1 4.7 4.4 4.7 Verify if the measurements are normally distributed.
.999 .99 .95
Probability
.80 .50 .20 .05 .01 .001 4.4 4.5 4.6 4.7 4.8 4.9 5.0 5.1
Diameter
Average: 4.78889 StDev: 0.247207 N: 9 Anderson-Darling Normality Test A-Squared: 0.327 P-Value: 0.442
Testing of A Population
• H0: process is normally distributed • Anderson-Darling Test • H0: process mean = target mean • 1-Sample Z-Test • 1-Sample t-Test • H0: process variance = target variance • ² (Chi-Square) Test • H0: process proportion = target proportion • 1-Proportion Test
• Testing of more than 2 populations
• H0: mean of all processes are equal • H0: variance of all processes are equal • H0: proportion of all processes are equal Analysis Of Variance Bartlett’s Test Contingency Table Analysis
z
2
0
Accept H0 if
z z z
2
, reject H0 if otherwise.
• Testing of 2 populations
• • • • H0 : H0 : H0 : H0 : mean of process 1 = mean of process 2 variance of process 1 = variance of process 2 proportion of process 1 = proportion of process 2 the 2 populations are correlated 2-Sample Z or 2-Sample t Test F-Test 2-Proportions Test Coefficient of Correlation
1-Sample Z-Test
(H0:
= 0 )
The distance between the sample mean and the target mean is compared against the standard error of the population mean, i.e. x
On the Problem of the Most Efficient Tests of Statistical Hypothesis Philosophical Transactions of the Royal Society (1933)
Sampling Risks or Errors
Sampling Risks
Range Tables Standard Deviation Frequency Distribution Variance Shape
Skewness Kurtosis
Hypothesis
A hypothesis is a statement or claim about a property of a population. The hypotheses to be tested consists of two complementary statements: 1) The null hypothesis (denoted by H0) is a statement about the value of a population parameter; it must contain the condition of equality. 2) The alternative hypothesis (denoted by H1) is the statement that must be true if the null hypothesis is false.
H0
observed value H1
Sampling Risks
H0 : = some value H1 : some value
Decision Accept H0
Null Hypothesis True False
Correct Decision 1– Type I Error Type II Error Correct Decision 1–
p-Value
under H0
x
Accepting/Rejecting of Null Hypothesis
Criteria Accept H0 Reject H0
Confidence Interval
P-Value
Includes H0
> <
Excludes H0
Testing of Hypothesis
Statistics — An Overview
Statistics
Descriptive Statistics
Charts & Tables
Charts
Inferential Statistics
Parameter Estimation
Point Estimate Interval Estimate
4.6
Example 1
(Anderson-Darling Test)
Stat Basic Statistics Normality Test
Example 1
(Anderson-Darling Test)
Normality Test for Transmission Shaft Diameter
Numerical Measures
Location
Hypothesis Testing
Parametric M ethods Nonparametric M ethods
Dot Plot Box Plot Histogram Bar Chart Trend Chart
Mean Median Mode Dispersion
Two-Tailed, Left-Tailed, Right-Tailed Tests
The tails in a distribution are the extreme regions bounded by critical values. We reject H0 if our test statistic (e.g. sample mean) is in the critical region.
Reject H0
Controlling Sampling Risks
1. For any fixed , an increase in the sample size will cause a decrease in .
2. For any fixed sample size, a decrease in will cause an increase in . Conversely, an increase in will cause a decrease in . 3. To decrease both and , increase the sample size.
Applications
• Testing of a population
• • • • H0 : H0 : H0 : H0 : process is normally distributed process mean = target mean process variance = target variance process proportion = target proportion Anderson-Darling Test 1-Sample Z or 1-Sample t Test ² (Chi-Square) Test 1-Proportion Test