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取１ｍＬ稀释样品，分别浇注于ＭＲＳ培养基、Ｅｌｌｉｋｅｒ培养基和Ｍ１７培养基，在适宜条件下培养，用于样品中乳酸菌的分离［１１．２．２分离菌株的分离和保存将１．２．１得到的菌落在相应的平板培养基上划线纯化得到纯培养物，进行革兰氏染色，接触酶实验。
纯化菌株在ＭＲＳ斜面上４℃短期保存，置于３０％（Ｗ／Ｗ）无菌甘油中在一７０℃超低温冰箱中长期保存‘１１．２．３利用茵落拉丝法初步筛选产胞外多糖的乳酸菌将分离纯化菌株在ＭＲｓ筛选培养基上进行划线分离，在２５℃厌氧培养４８ｈ，观察并记录菌落特征。
用无菌牙签接触菌落，轻轻地向外拉，然后在２ｓ内垂直离开以在培养基表面形成连续的拉丝，重复操作５—６个菌落，每个菌落平行做２次，测量菌落拉丝的最大长度（伽ｎ），结果以“平均值士标准方差”表示。
１．２．４乳酸茵胞外多糖的提取将１．２．３得到的菌株接种于筛选ＭＲＳ液体培养基中，３０℃发酵２４ｈ，取１０ｍＬ培养物，沸水浴１０ｍｉｎ，冷却至室温，加质量分数为８０％的三氯乙酸至终浓度为４％，４℃静置过夜，１２ｏｏｏｇ离心２０ｍｉｎ，轻倾上清液于透析袋中，对流水透析２４ｈ，再对双蒸水透析３６ｈ，４次换水，定容，待用。
１．２．５硫酸－苯酚法测定乳酸茵胞外多糖（ＥＰＳ）的含量将１．２．４得到的胞外多糖样品用Ｄｕｂｏｉｓ推荐的硫酸一苯酚法［１５］测定，用葡萄糖做标准曲线（如图１），从曲线上求得ＥＰＳ的含量。
以空白培养基为对照，扣除背景干扰。
’１．２。
６·显微镜观察产胞外多糖乳酸茵细茵形态对分离菌株进行革兰氏染色，用ＭｏｔｉｃＰＭＢ５—２２３２—５摄影显微镜观察细胞形态。
１．２．７产胞外多糖乳酸茵的鉴定产胞外多糖乳酸菌的鉴定采用法国梅里埃公司鲁奎、蠢棺益图１硫酸一苯酚法测定胞外多糖含量的标准曲线的ＡＰＩ细菌鉴定系统进行，将纯菌株在ＭＲＳ琼脂培养基上３７℃微厌氧培养４８ｈ，用无菌棉拭子收集细菌，在ＡＰＩ５０ＣＨ试剂条凹槽中加ＡＰＩ５０ＣＨＬ培养基并接种，用石蜡油封好，３７℃培养２４～２８ｈ，记录菌株对碳水化合物的发酵结果，将其输入梅里埃公司的鉴定软件ＡＰＩＬＡＢＰｌｕｓ进行鉴定。
牛肉样品中产志贺毒素大肠埃希氏菌和肠致病性大肠埃希氏菌的分离鉴定朱应飞，聂 翔，熊丽霞，吴学平，占忠旭，匡佩琳（江西省检验检测认证总院食品检验检测研究院，江西南昌 330000）摘 要：于2018年9月—2019年3月，每个月从超市和农贸市场中抽取20个样本，共抽取140个牛肉及制品样本，在qPCR初筛时stx阳性率为45.8%，eae的阳性率为52.2%。
每月的stx初筛阳性率分别为65%、60%、80%、5%、25%、45%和40%；eae初筛阳性率分别为70%、70%、65%、0%、40%、65%和55%。
检出携带stx基因的产类志贺毒素大肠埃希氏菌样本4份，样本阳性菌株检出率为2.9%；检出携带eae基因的肠致病性大肠埃希氏菌样本12份，样本阳性菌株检出率为8.6%。
关键词：产志贺毒素大肠埃希氏菌；肠致病性大肠埃希氏菌；分离；鉴定Isolation and Identification of Shiga Toxin Producing Escherichia coli and Enteropathogenic Escherichia coli in BeefSamplesZHU Yingfei, NIE Xiang, XIONG Lixia, WU Xueping, ZHAN Zhongxu, KUANG Peilin (Jiangxi General Institute of Testing and Certification Food Testing Institute, Nanchang 330000, China)Abstract: From September 2018 to March 2019, 20 samples were taken from supermarkets and farmers’ markets every month, with a total of 140 beef and product samples taken. the positive rate of stx was 45.8%, and the positive rate of eae was 52.2%. The monthly positive rates of stx initial screening positive rates of 65%, 60%, 80%, 5%, 25%, 45%, and 40%, respectively; The positive rates of eae initial screening were 70%, 70%, 65%, 0%, 40%, 65%, and 55%, respectively. Four Shiga toxin producing Escherichia coli samples carrying the stx gene were detected, with a positive strain detection rate of 2.9%; From September 2018 to March 2019, 12 samples of enteropathogenic Escherichia coli carrying the eae gene were detected, with a positive strain detection rate of 8.6%.Keywords: Shiga toxin producing Escherichia coli; enterogenic Escherichi coli; separation; appraisal产志贺菌毒素的大肠杆菌（Shiga Toxin Producing Escherichia coli，STEC）和肠致病性埃希氏菌（Enterogenic Escherichi coli，EPEC）是世界上最重要的食源性病原体之一。
CFDA SE (细胞增殖示踪荧光探针) 产品编号产品名称包装C1031 CFDA SE (细胞增殖示踪荧光探针) 5mg产品简介：CFDA SE 的全称为Carboxyfluorescein diacetate, succinimidyl ester ，是一种近年来被广泛应用的细胞增殖检测用荧光探针，也可以用于细胞的荧光示踪。
基于CFDA SE 荧光标记的细胞增殖检测和[3H]-thymidine 掺入、BrdU 标记获得的检测结果完全一致，但同时可以提供更多的细胞增殖信息。
使用CFDA SE 检测可以提供整个细胞群中有多少比例的细胞分裂了1次、2次或更多次数，同时如果和其它荧光探针联用，可以获取不同分裂次数细胞的其它相关信息。
CFDA-SE 的分子式为C 29H 19NO 11，分子量为557.47，CAS number 为150347-59-4。
CFDA SE 可以通透细胞膜，进入细胞后可以被细胞内的酯酶(esterase)催化分解成CFSE ，CFSE 可以偶发性地(spontaneously)并不可逆地和细胞内蛋白的Lysine 残基或其它氨基发生结合反应，并标记这些蛋白。
在加入荧光探针CFDA SE 后大约24小时，即可充分标记细胞。
被CFDA SE 标记的非分裂细胞的荧光非常稳定，稳定标记的时间可达数个月。
CFDA SE 标记细胞的荧光非常均一，比以前使用的其它细胞示踪荧光探针例如PKH26的荧光更加均一，并且分裂后的子代细胞的荧光分配也更均匀。
由于CFDA SE 标记细胞的荧光非常均匀和稳定，每分裂一次子代细胞的荧光会减弱一半，这样通过流式细胞仪检测就可以检测出没有分裂的细胞，分裂一次的细胞(1/2的荧光强度)，分离两次的细胞(1/4的荧光强度)，分裂三次的细胞(1/8的荧光强度)以及类似的其它分裂次数的细胞。
采用CFDA SE 通过流式细胞仪检测获得的检测结果参考右图。
每一个峰代表一种分裂次数的细胞，从右至左的峰通常依次为分裂0次、1次、2次、3次等次数的细胞。
腈水解酶催化腈水解的研究进展刘颖，苏昕*1（沈阳药科大学 生命科学与生物制药学院，沈阳 110016）摘要:腈水解酶催化具有毒性、致畸性、致癌性的腈水解合成羧酸因其反应条件温和、成本低、环境污染少及高选择性（立体、化学、区域）而备受学者和企业家的青睐，产物羧酸广泛用于精细化工、医药中间体、维生素前体等，具有高增值价值，因此腈水解酶具有良好的工业应用前景和巨大的经济价值。
目前对腈水解酶的来源、作用机制、筛选途径、酶结构、催化特性以及腈水解酶基因克隆、纯化、固定化、修饰等研究均有报道。
参考20余篇文献，本文综述腈水解酶催化腈类化合物水解的研究进展。
关键词：腈水解酶，腈化合物，生物催化Advances in nitrilase hydrolyzing nitrilesLIU Ying, SU Xin*(School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, Shenyang 110016, China) Abstract: The nitrilases attract substantial attention from scholars and entrepreneurs because of their mild reaction conditions, low-cost, small environmental pollution and high selectivity (stereo-, chemical-, regional-) when hydrolyzing toxic, mutagenic and carcinogenic nitriles. The production of carboxylic acid with high added value is widely used in fine chemicals, pharmaceutical intermediates and vitamin premises. Therefore, the nitrilase has good application prospect and the great economic value. Articles about the sources of enzyme, mechanism, screening approach, structure, catalytic properties, nitrilase gene cloning, purification of nitrilase, immobilization, modification etc have been reported. Based on more than 20 domestic and foreign literatures, recent progress on nitrilases hydrolyzing nitriles was reviewed.Keywords: Nitrilase, Nitriles, Biocatalysis腈是一类含−CN基团的有机物，是合成酰胺、羧酸、酯等的起始原料。
一株高产蛋白酶菌株的筛选及其产酶条件*林玩庄，林淑娜，陈汶聪，刘荣莲，黄可佳，黄丹敏，谢桂仁，陈宇豹，邓毛程，王瑶，李静广东轻工职业技术学院，广州，510300摘要：为了提高水产行业蛋白质资源的综合利用率，从南海海域大型鱼类的肠道中筛选蛋白酶高产菌株。
采用平板透明圈法和摇瓶发酵法进行筛选，获得一株蛋白酶高产菌株PE11。
通过菌体形态观察、生理生化实验和16S rDNA鉴定，菌株PE11被鉴定为解淀粉芽孢杆菌(Bacillus amyloliquefaciens)。
通过摇瓶发酵试验，优选出可溶性淀粉和牛肉膏分别为最佳的碳源和氮源，并确定菌株PE11产蛋白酶的最佳条件为：温度30 °C、初始pH7.0、转速200 rpm和时间36 h。
在最佳的产酶条件下，发酵液中的蛋白酶活力可达376 U/mL。
关键词：蛋白酶；高产；筛选；产酶条件Study on screening and enzyme-producing conditions of a highprotease producing strainLING Wan-zhuang, LING Shu-Na, CHEN Wen-cong, LIU Rong-lian, HUANG Ke-jia, HUANG Dan-min, XIE Gui-ren, CHEN Yu-bao, DENG Mao-cheng, WANG Yao, LI Jing(Guangdong Industry Technical College, Guangzhou 510300)Abstract：In order to improve the comprehensive utilization rate of protein resources from aquatic industry, strains having the ability to produce protease were isolated and screened from the gastrointestinal tract of large fish of South China Sea. Using flat transparent circle and shake flask fermentation test, a high producing protease strain PE11 was obtained. The strain PE11 was identified as Bacillus amyloliquefaciens through the systematic investigations of morphology, physiological and biochemical characteristics and 16S rDNA sequences analysis. By means of shake flask fermentation tests, the optimal carbon resource and nitrogen resource for strain PE11 were soluble starch and beef extract, respectively. In addition, the best conditions for protease-producing were determined as temperature of 30 °C, initial pH of 7.0 and rotation speed of 200 rpm. At the optimal condition, the highest protease activity of fermentation broth reached 376 U/mL.Key words：protease；high producing；screening；enzyme-producing condition*基金项目：广东高校特色调味品工程技术开发中心建设项目（GCZX-B1103），广东省教育部产学研结合项目（2012B091000040），广东轻工职业技术学院自然科学启动基金项目（KJ201307），广东轻工职业技术学院自然科学启动基金项目（KJ201203）。
terms of reputations.In this co-evolution between communications and authors,distributions of cita-tions function,among other things,as contested boundaries between specialties.Since the indicators are distributed,the boundaries remain to be validated. Functions are expected to change when the research front moves further.By using references,authors position their knowledge claims within one specialty area or another.Some selections are chosen for stabilization,for example,when codification into citation classics occurs.Some stabilizations are selec-ted for globalization at a next-order level,for example, when the knowledge component is integrated into a technology.4.ConclusionThe focus on evolutionary dynamics relates sciento-metrics increasingly with the further development of evolutionary economics(Leydesdorffand Van den Besselaar1994).How can systems of innovation be delineated?How can the complex dynamics of such systems be understood?How is the(potentially ran-dom)variation guided by previously codified expecta-tions?How can explorative variation be increased in otherwise‘locked-in’trajectories of technological regimes or paradigms?From this perspective,the indication of newness may become more important than the indication of codification.The Internet,of course,offers a research tool for what has also now been called‘sitations’(Rousseau1997).‘Webometrics’may develop as a further extension of scientometrics relating thisfield with other subspecialities of science and technology studies,such as the public understanding of science or the appropriation of technology and innovation using patent statistics.See also:Communication:Electronic Networks and Publications;History of Science;Libraries;Science and Technology,Social Study of:Computers and Information Technology;Science and Technology Studies:Experts and ExpertiseBibliographyBraun T(ed.)1998Topical discussion issue on theories of citation.Scientometrics43:3–148Burt R S1982Toward a Structural Theory of Action.Academic Press,New YorkCallon M,Courtial J-P,Turner W A,Bauin S1983From translations to problematic networks:an introduction to co-word analysis.Social Science Information22:191–235 Collins H M1985The possibilities of science policy.Social Studies of Science15:554–8Crane D1969Social structure in a group of scientists.American Sociological Re iew36:335–352Elkana Y,Lederberg J,Merton R K,Thackray A,Zuckerman H1978Toward a Metric of Science:The Ad ent of Science Indicators.Wiley,New YorkGarfield E1979Citation Indexing.Wiley,New York Gibbons M,Limoges C,Nowotny H,Schwartzman S,Scott P, Trow M1994The New Production of Knowledge:The Dynamics of Science and Research in Contemporary Societies. 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Scientometrics20:333–57Martin B R,Irvine J1983Assessing basic research:Some partial indicators of scientific progress in radio astronomy.Research Policy12:61–90Narin F1976E aluati e puter Horizons Inc., Cherry Hill,NJPrice D,de Solla1963Little Science,Big Science.Columbia University Press,New YorkRaan A F J Van(ed.)1988Handbook of Quantitati e Studies of Science and Technology.Elsevier,North-Holland, AmsterdamRousseau R1997Sitations:An exploratory study.Cybermetrics 1:1http:\\www.cindoc.csic.es\cybermetrics\articles\v1i1p1. htmlSmall H1973Co-citation in the scientific literature:A new measure of the relationship between two documents.Journal of the American Society for Information Science24:265–9 Wouters P1999The citation culture.PhD Thesis,University of AmsterdamL.LeydesdorffScreening and Selection1.IntroductionIn order to assist in selecting individuals possessing either desirable traits such as an aptitude for higher education or skills needed for a job or undesirable ones such as having an infection,illness or a propensity to lie,screening tests are used as afirst step.A more rigorous selection device,e.g.,diagnostic test or de-tailed interview is then used for thefinal classification.13755Screening and SelectionFor some purposes,such as screening blood donations for a rare infection,the units classified as positive are not donated but further tests on donors may not be given as most will not be infected.Similarly,for estimating the prevalence of a trait in a population,the screening data may suffice provided an appropriate estimator that incorporates the error rates of the test is used(Hilden1979,Gastwirth1987,Rahme and Joseph 1998).This article describes the measures of accuracy used to evaluate and compare screening tests and issues arising in the interpretation of the results.The importance of the prevalence of the trait on the population screened and the relative costs of the two types of misclassification are discussed.Methods for estimating the accuracy rates of screening tests are briefly described and the need to incorporate them in estimates of prevalence is illustrated.2.Basic ConceptsThe purpose of a screening test is to determine whether a person or object is a member of a particular class,C or its complement,C k.The test result indicating that the person is in C will be denoted by S and a result indicating non-membership by S k.The accuracy of a test is described by two probabilities:ηl P[S Q C]being the probability that someone in C is correctly classified,or the sensitivity of the test;andθl P[S k Q C k]being the probability that someone not in C is correctly classified,or the specificity of the test.Given the prevalence,πl P(C),of the trait in the population screened,from Bayes’theorem it follows that the predictive value of a positive test(PVP)isP[C Q S]lπη\[πηj(1kπ)(1kθ)]. Similarly,the predictive value of a negative test is P[C k Q S k]l(1kπ)θ\[(1kπ)θjπ(1kη)].In thefirst two sections,we will assume that the accuracy rates and the prevalence are known.When they are estimated from data,appropriate sampling errors for them and the PVP are given in Gastwirth (1987).For illustration,consider an early test for HIV, having a sensitivity of0.98and a specificity of0.93, applied in two populations.Thefirst has a very low prevalence,1.0i10−$,of the infection while the second has a prevalence of0.25.From Eqn.(1),the PVP in the first population equals0.0138,i.e.,only about one-and-one-half percent of individuals classified as infected would actually be so.Nearly99percent would be false positives.Notice that if the fraction of positives, expected to be0.0797,in the screened data were used to estimate prevalence,a severe overestimate would result.Adjusting for the error rates yields an accurate estimate.In the higher prevalence group,the PVP is 0.8235,indicating that the test could be useful in identifying individuals.Currently-used tests have accuracy rates greater than0.99,but even these still have a PVP less than0.5when applied to a low prevalence population.A comprehensive discussion is given in Brookmeyer and Gail(1994).The role of the prevalence,also called the base rate, of the trait in the screened population and how well people understand its effect has been the subject of substantial research,recently reviewed by Koehler (1996).An interesting consequence is that when steps are taken to reduce the prevalence of the trait prior to screening,the PVP declines and the fraction of false-positives increases.Thus,when high-risk donors are encouraged to defer or when background checks eliminate a sizeable fraction of unsuitable applicants prior to their being subjected to polygraph testing the fraction of classified positives who are truly positive is small.In many applications screening tests yield data that are ordinal or essentially continuous,e.g.,scores on psychological tests or the concentration of HIV antibodies.Any value,t,can be used as a cut-offpoint to delineate between individuals with the trait and ‘normal.’Each t generates a corresponding sensitivity and specificity for the test and the user must then incorporate the relative costs of the two different errors and the likely prevalence of the trait in the population being screened to select the cut off.The receiver operating characteristic(ROC)curve displays the trade-offbetween the sensitivity and specificity defined by various choices of t and also yields a method for comparing two or more screening tests. To define the ROC curve,assume that the dis-tribution of the measured variable(test score or physical quantity)is F(x)for the‘normal’members of the population but is G(x)for those with the trait.The corresponding density functions are f(x)and g(x) respectively and g(x)will be shifted(to the right(left) if large(small)scores indicate the trait)of f(x)for a good screening test.Often onefixes the probability (αl1k yθor one minus the specificity)of classifying a person without the characteristic as having it at a small value.Then t is determined from the equation F(t)l1kθ.The sensitivity,η,of the test is1k G(t). The ROC curve plotsηagainst1kθ.A perfect test would haveηl1so the closer the ROC is to the upper left corner in Fig.1,the better the screening test.In Fig.1we assume f is a normal density with mean0and variance1while g"is normal with mean1and the same variance.For comparison we also graphed the ROC for a second test,which has a density g#with mean2 and variance1.Notice that the ROC curve for the13756Screening and SelectionFigure1The ROC curves for screening tests1and2.The solid line is the curve when the diseased group has mean1 and the dashed curve is for the second group with mean2second test is closer to the left corner(0,1)than that of thefirst test.A summary measure(Campbell1994),which is useful in comparing two screening tests is the area,A, under the ROC.The closer A is to its maximum value of1.0,the better the test is.In Fig.1,the areas under the ROC curves for the two tests are0.761and0.922, respectively.Thus,the areas reflect the fact that the ROC curve for the second test is closer to what a ‘perfect’test would be.This area equals the probability a randomly chosen individual will have a higher score on the screening test than a normal one.This prob-ability is the expected value of the Mann–Whitney form of the Wilcoxon test for comparing two distribu-tions and methods for estimating it are in standard non-parametric statistics texts.Non-parametric methods for estimating the entire ROC curve are given by Wieand et al.(1989)and Hilgers(1991)obtained distribution-free confidence bounds for it.Campbell(1994)uses the confidence bounds on F and G to construct a joint confidence interval for the sensitivity and one minus the specificity in addition to proposing alternative confidence bounds for the ROC itself.Hseih and Turnbull(1996)de-termine the value of t that maximizes the sum of sensitivity and specificity.Their approach can be extended to maximizing weighted average of the two accuracy rates,suggested by Gail and Green(1976). Wieand et al.(1989)also developed related statistics focusing on the portion of the ROC lying above a region,α"andα#so the analysis can be confined to values of specificity that are practically useful.Green-house and Mantel(1950)determine the sample sizes needed to test whether both the specificity and sen-sitivity of a test exceed pre-specified values.The area A under the ROC can also be estimated using parametric distributions for the densities f and g. References to this literature and an alternative ap-proach using smoothed histograms to estimate the densities is developed in Zou et al.(1997).They also consider estimating the partial area over the important region determined by two appropriate small values ofα.The tests used to select employees need to be reliable and valid.Reliability means that replicate values are consistent while validity means that the test measures what it should,e.g.,successful academic performance. Validity is often assessed by the correlation between the test score(X)and subsequent performance(Y). Often X and Y can be regarded as jointly normal random variables,especially as monotone transforma-tions of the raw scores can be used in place of them.If a passing score on the screening or pre-employment test is defined as X t and successful performance is defined as Y d,then the sensitivity of the test is P[X t Q Y d],the specificity is P[X t Q Y d]and the prevalence of the trait is P[Y d]in the population of potential applicants.Hence,the aptitude and related tests can be viewed from the general screening test paradigm.When the test and performance scores are scaled to have a standard bivariate normal distribution,both the sensitivity and specificity increase with the cor-relation,ρ.For example,suppose one desired to obtain employees in the upper half of the performance distribution and used a cut-offscore,t,of one-standard deviation above the mean on the test(X).Whenρl 0.3,the sensitivity is0.217while the specificity is0.899. Ifρl0.5,the sensitivity is0.255and the specificity is 0.937.The use of a high cut-offscore eliminates the less able applicants but also disqualifies a majority of applicants who are in the upper half of the per-formance distribution.Reducing the cut-offscore to one-half a standard deviation above the average raises the sensitivities to0.394and0.454for the two values of ρbut lowers the corresponding specificities to0.777 and0.837.This trade-offis a general phenomenon as seen in the ROC curves.3.The Importance of the Context in Interpreting the Results of Screening TestsIn medical and psychological applications,an in-dividual who tests positive for a disease or condition on a screening test will be given a more accurate confirmatory test or intensive interview.The cost of a ‘false positive’screening result on a medical exam is13757Screening and Selectionoften considered very small relative to a ‘false nega-tive,’which could lead to the failure of suitable treatment to be given in a timely fashion.A false positive result presumably would be identified in a subsequent more detailed exam.Similarly,when gov-ernment agencies give employees in safety or security sensitive jobs a polygraph test,the loss of potentially productive employee due to a false positive was deemed much less than the risk of hiring an employee who would might be a risk to the public or a security risk.One can formalize the issue by including the costs of various errors and the prevalence,π,of the trait in the population being screened in determining the cut-offvalue of the screening test.Then the expected cost,which weights the probability of each type of error by its cost is given byw α(1k π)j (1k w )πG (t ).Here the relative costs of a false positive (negative)are w and 1k w ,respectively and as before t l F −"(1k α).The choice of cut-offvalue,t o ,minimizing the expected cost satisfies:g (t )f (t )lw (1k π)(1k w )π(1)Whenever the ratio,g :f ,of the density functions is a monotone function the previous equation yields an optimum cut-offpoint,t o,which depends on the costs and prevalence of the trait.Note that for any value of π,the greater the cost of a false positive,the larger will be the optimum value,t o .This reflects the fact that the specificity needs to be high in order to keep the false positive rate low.Although the relative costs of the two types of error are not always easy to obtain and the prevalence may only be approximately known,Eqn.(1)may aid in choosing the critical value.In practice,one should also assess the effect slight changes in the costs and assumed prevalence have on the choice of the cut-offvalue.The choice of t that satisfies condition (1)may not be optimal if one desires to estimate the prevalence of the trait in a population rather than classifying individuals.Yanagawa and Tokudome (1990)deter-mine t when the objective is to minimize the relative absolute error of the estimator of prevalence on the basis of the screening test results.The HIV \AIDS epidemic raised questions about the standard assumptions about the relative costs of the two types of error.A ‘false positive’classification would not only mean that a well individual would worry until the results of the confirmatory test were completed,they also might have social and economic consequences if friends or their employer learned of the result.Similar problems arise in screening blood donors and in studies concerning the association of genetic markers and serious diseases.Recall that the vast majority of donors or volunteers for genetic studies are doing a public service and are being screened to protect others or advance knowledge.If a donation tests positive,clearly it should not be used for transfusion.Should a screened-positive donor be informed of their status?Because the prevalence of infected donors is very small,the PVP is quite low so that most of the donors screened positive are ‘false.’Thus,blood banks typically do not inform them and rely on approaches to encourage donors from high-risk groups to exclude themselves from the donor pool (Nusbacher et al.1986).Similarly,in a study (Hartge et al.1998)of the prevalence of mutations in two genes that have been linked to cancer the study participants were not notified of their results.The screening test paradigm is useful in evaluating tests used to select employees.The utility of a test depends on the costs associated with administering the test and the costs associated with the two types of error.Traditionally,employers focused on the costs of a false positive,hiring an employee who does not perform well,such as termination costs,and the possible loss of customers.The costs of a false negative are more difficult to estimate.The civil-rights law,which was designed to open job opportunities to minorities,emphasized the import-ance of using appropriate tests,i.e.,tests that selected better workers.Employers need to check whether the tests or job requirements (e.g.,possession of a high school diploma)have a disparate impact upon a legally protected group.When they exclude a significantly greater fraction of minority members than majority ones,the employer needs to validate it,i.e.,show it is predictive of on the job performance.Arvey (1979)and Paetzold and Willborn (1994)discuss these issues.4.Estimating the Accuracy of the Screening TestsSo far,we have assumed that we can estimate the accuracy of the screening tests on samples from two populations where the true status of the individuals is known with certainty.In practice,this is often not the case and can lead to biased estimates of the sensitivity and specificity of a screening test,as some of the individuals believed to be normal have the trait,and vice versa.If one has samples from only one population to which to apply both the screening and confirmatory test,then one cannot estimate the accuracy rates.The data would be organized into a 2i 2table,with four cells,only three of which are independent.There are five parameters,however,the two accuracy rates of the two tests plus the prevalence of the trait in the13758Screening and Selectionpopulation.In some situations,the prevalence of the trait may vary amongst sub-populations.If one can find two such sub-populations and if the accuracy rates of both tests are the same in both of those sub-populations,then one has two2i2tables with six independent cells,with which to estimate six par-ameters.Then estimation can then be carried out (Hui and Walter1980).This approach assumes that the two tests are conditionally independent given the true status of the individual.When this assumption is not satisfied, Vacek(1985)showed that the estimates of sensitivity and specificity of the tests are biased.This topic is an active area of research,recently reviewed by Hui and Zhou(1998).A variety of latent-class models have been developed that relax the assumption of con-ditional independence(see Faraone and Tsuang1994 and Yang and Becker1997and the literature they cite).5.Applications and Future ConcernsHistorically screening tests were used to identify individuals with a disease or trait,e.g.,as afirst stage in diagnosing medical or psychological conditions or select students or employees.They are being increas-ingly used,often in conjunction with a second, confirmatory test,in prevalence surveys for public health planning.The techniques developed are often applicable,with suitable modifications,to social sci-ence surveys.Some examples of prevalence surveys illustrate their utility.Katz et al.(1995)compared two instruments for determining the presence of psychiatric disorders in part to assess the needs for psychiatric care in the community and the available services.They found that increasing the original cut-offscore yielded higher specificity without a substantial loss of sensitivity. Similar studies were carried out in Holland by Hodia-mont et al.(1987)who found a lower prevalence(7.5 percent)than the16percent estimate in York.The two studies,however,used different classification systems illustrating that one needs to carefully examine the methodology underlying various surveys before mak-ing international comparisons.Gupta et al.(1997) used several criteria based on the results of an EKG and an individual’s medical history to estimate the prevalence of heart disease in India.Often one has prior knowledge of the prevalence of a trait in a population,especially if one is screening similar populations on a regular basis,as would be employers,medical plans,or blood centers.Bayesian methods incorporate this background information and can yield more accurate estimates(see Geisser 1993,and Johnson,Gastwirth and Pearson2001).A cost-effective approach is to use an inexpensive screen at afirst stage and retest the positives with a more definitive test.Bayesian methodology for such studies was developed by Erkanli et al.(1997).The problem of misclassification arises often in questionnaire urikka et al.(1995)estimated the sensitivity and specificity of self-reporting of varicose veins.While both measures were greater than 0.90,the specificity was lower(0.83)for individuals with a family history than those with negative his-tories.Sorenson(1998)observed that often self-reports are accepted as true and found that the potential misclassification could lead to noticeable(10 percent)errors in estimated mortality rates.The distortion misclassification errors can have on esti-mates of low prevalence traits because of the high fraction of false positive classifications,was illustrated in the earlier discussion of screening tests for HIV\ AIDS.Hemenway(1997)applies these concepts to demonstrate that surveys typically overestimate rare events;in particular the self-defense uses of guns.Thus it is essential to incorporate the accuracy rates into the prevalence estimate(Hilden1979,Gastwirth1987, Rahme and Joseph1998).Sinclair and Gastwirth(1994)utilized the Hui-Walter paradigm to assess the accuracy of both the original and re-interview(by supervisors)classifica-tions in labor force surveys.In its evaluation,the Census Bureau assumes that the re-interview data are correct;however,those authors found that both interviews had similar accuracy rates.In situations where one can obtain three or more classifications,all the parameters are identifiable(Walter and Irwig1988) Gastwirth and Sinclair(1998)utilized this feature of the screening test approach to suggest an alternative design for judge–jury agreement studies that had another expert,e.g.,law professor or retired judge, assess the evidence.6.ConclusionMany selection or classification problems can be viewed from the screening test paradigm.The context of the application determines the relative costs of a misclassification or erroneous identification.In crimi-nal trials,society has decided that the cost of an erroneous conviction far outweighs the cost of an erroneous acquittal.While,in testing job applicants, the cost of not hiring a competent worker is not as serious.The two types of error vary with the threshold or cut-offvalue and the accuracy rates corresponding to these choices is summarized by the ROC curve. There is a burgeoning literature in this area as researchers are incorporating relevant covariates,e.g., prior health status or educational background into the classification procedures.Recent issues of Biometrics and Multi ariate Beha ioral Research,Psychometrika and Applied Psychological Measurement as well as the medical journals cited in the article contain a variety of13759Screening and Selectionarticles presenting new techniques and applications of them to the problems discussed.See also:Selection Bias,Statistics ofBibliographyArvey R D1979Fairness in Selecting Employees.Addison-Wesley,Reading,MABrookmeyer R,Gail M H1994AIDS Epidemiology:A Quan-titati e Approach.Oxford University Press,New York Campbell G1994Advances in statistical methodology for the evaluation of diagnostic and laboratory tests.Statistics in Medicine13:499–508Erkanli A,Soyer R,Stangl D1997Bayesian inference in two-phase prevalence studies.Statistics in Medicine16:1121–33 Faraone S V,Tsuang M T1994Measuring diagnostic accuracy in the absence of a gold standard.American Journal of Psychiatry151:650–7Gail M H,Green S B1976A generalization of the one-sided two-sample Kolmogorov–Smirnov statistic for evaluating diagnostic tests.Biometrics32:561–70Gastwirth J L1987The statistical precision of medical screening procedures:Application to polygraph and AIDS antibodies test data(with discussion).Statistical Science2:213–38 Gastwirth J L,Sinclair M D1998Diagnostic test methodology in the design and analysis of judge–jury agreement studies. 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Shepard’s\McGraw Hill,Colorado Springs,CORahme E,Joseph L1998Estimating the prevalence of a rare disease:adjusted maximum likelihood.The Statistician47: 149–58Sinclair M D,Gastwirth J L1994On procedures for evaluating the effectiveness of reinterview survey methods:Application to labor force data.Journal of American Statistical Assn91: 961–9Sorenson S B1998Identifying Hispanics in existing databases.E aluation Re iew22:520–34Vacek P M1985The effect of conditional dependence on the evaluation of diagnostic tests.Biometrics41:959–68Walter S D,Irwig L M1988Estimation of test error rates, disease prevalence and relative risk from misclassified data:A review.Journal of Clinical Epidemiology41:923–37 Wieand S,Gail M H,James B R,James K1989A family of non-parametric statistics for comparing diagnostic markers with paired or unpaired data.Biometrika76:585–92 Yanagawa T,Tokudome S1990Use of screening tests to assess cancer risk and to estimate the risk of adult T-cell leukemia\ lymphoma.En ironmental Health Perspecti es87:77–82 Yang I,Becker M P1997Latent variable modeling of diagnostic accuracy.Biometrics52:948–58Zou K H,Hall W J,Shapiro D E1997Smooth non-parametric receiver operating characteristic(ROC)curves for continuous diagnostic tests.Statistics in Medicine16:214–56J.L.GastwirthSearch,Economics ofThe economics of search study the implications of market frictions for economic behavior and market performance.‘Frictions’in this context include any-thing that interferes with the smooth and instan-taneous exchange of goods and services.The most commonly-studied problems arise from imperfect information about the location of buyers and sellers, their prices,and the quality of the goods and services that they trade.The key implication of these frictions is that individuals are prepared to spend time and other resources on exchange;they search before buying or selling.The labor market has attracted most13760Screening and SelectionCopyright#2001Elsevier Science Ltd.All rights reserved.International Encyclopedia of the Social&Behavioral Sciences ISBN:0-08-043076-7。
