微生物检验技术的研究进展

随着科学技术的不断进步,微生物的检验技术也得到了快速的发展,已经由传统单一的培养方法逐渐发展成为多种检验技术并存的局面,并且朝着检验精度更大、速度更快的方向不断发展。本文介绍了几种近年来涌现出的比较典型的微生物检验技术方法,并分析了每种方法的特点,对未来微生物检验技术的发展趋势进行了展望。     

均衡独立样本设计的研究

均衡独立样本设计是一种把独立样本设计与差异显著性检验相结合的试验设计．它基本上可以保证参试的优劣个体在所分样本（或组）间的分布相对均匀一致,使所分样本既具有独立性又具有均衡性,以增强对处理效果反应的灵敏度,提高试验的准确度．     

病虫发生程度预报质量评估方法

介绍了对病虫发生程度预报质量进行评估的统计检验方法———McNemar检验和Kappa检验,以及这2种检验方法的原理、假设检验和统计学意义。结合2个实例来说明2种统计检验方法在病虫发生程度预报质量评估中的应用,分析结果表明McNemar检验、Kappa检验能对病虫发生程度预测质量给出更加有效、准确的评估。     

一种随机化检验算法及其Matlab实现

在数据满足特定假设的前提下,可用有关统计技术检验样品间的差异．然而,在自然情况下,这些假设往往不成立或未知成立．本研究建立了一种随机化检验算法,可对实数域上的两个样品进行差异显著性分析,给出了Matlab标准源程序．算法具有广适性．设定随机化模拟次数和差异显著性水平,则可计算出检验值P,及差异显著与否．该算法可使用各种差异函数,如欧氏距离等等．应用本算法,对数种实测样品数据,以不同的差异函数进行了差异性分析．     

多变点模型的检验和估计及其性质

对于可能含有多个变点的模型,本文讨论了关于变点的检验和估计问题,并给出了具体的检验和估计方法．我们还推导了估计的相合性．文中指出即使在总体分布未知的情况下,本文所给的检验和估计仍显示出较好的性质．     

妊娠高血压疾病对妊娠结局的影响及防治对策探讨

目的：研究妊娠高血压疾病对妊娠结局的影响及防治对策讨论。方法：选择回顾性研究方法,选择2010年9月到2012年8月到我院的妊娠高血压患者60名作为观察组,并且同期选择60名正常妊娠妇女作为对照组,比较妊娠高血压患者的母体并发症（早产、胎盘早剥、产后出血）等及胎儿妊娠结局等情况与对照组的比较。结果：妊娠高血压患者不同程度对妊娠结局的影响不同P〈0．05,并且妊娠高血压患者的母体并发症与对新生儿的影响与对照组比较有差异P〈0．05,异常情况均较正常妊娠妇女要高。结论：妊娠高血压患者对妊娠结局的影响较为严重,对母婴危害严重,需积极预防。     

亚高寒草甸不同生境植物群落物种多度分布格局的拟合

物种多度分布是群落生态学研究的核心内容。通过对青藏高原东部亚高寒草甸3种不同生境草本植物群落的抽样调查,结合16个物种多度分布模型的两种曲线拟合优度检验得出如下结果：多种不同模型可以拟合同一生境的物种多度分布。相比于其他可拟合模型,几何级数模型在3种生境中两种拟合优度检验方法下的平均拟合效果是最好的,拟合优度值均在最优拟合优度值10左右波动。次优模型鉴于不同生境不同的检验方法表现不一。除了几何级数模型外,Sugihara分数模型在最小二乘法的拟合方法下,也可以拟合3种生境的物种多度分布。研究结果表明,仅用拟合优度检验区分产生不同物种分布格局的模型和机制是不可靠的,需要做进一步的检验性实验研究。     

小麦品种联合试验效应及理论依据──Ⅱ.联合方差分析的一些理论考虑

本文以一个国家级的小麦品种试验结果为材料,检验了方差分析的基本假定相符情况．结果表明,在总体上14个试点间方差不同质．对14个试点分别进行3项检验,其中有7个试点的结果违背了假定．对违背假定的试点数据在统计分析前应先作数据转换后再作方差分析．     

聚合酶链技术检验淋球菌,沙眼衣原休,解脲支原体感染的方法与分析

由淋球菌和沙眼衣原体、解脲支原体感染引起的特异性尿道炎,临床上缺乏快速准确的诊断方法。近年来发展起来的聚合酶链反应技术,通过对每种病原体特有的ＤＮＡ序列进行体外特异扩增,可以快速准确鉴定之。本文用该技术对引起泌尿系统感染的三种病原体进行检测研究,它具有快速、特异、准确等优点,并检测１２３例感染者,结果,淋球菌感染占７４％,沙眼衣原体感染占３９％,解脲支原体感染占３０．９％。     

一种新型中药制剂--肤康的生产

介绍了治疗皮肤病的新型纯中药制剂的配药比例．生产方法及成品检验标准。     

羟基磷灰石生物安全性试验研究

为了评价羟基磷灰石的生物安全性,依据相关的医疗器械行业标准和GB/T16886系列标准,对该材料进行了迟发型超敏反应试验、热原试验、急性全身毒性试验、细胞毒性试验等一系列生物安全性试验,并对试验数据进行了分析评价,结果表明,羟基磷灰石具有良好的安全性。     

A Multivariate Goodness-of-Fit Test for Stochastically Ordered Distributions

There are a number of nonparametric procedures known for testing goodness-of-fit in the univariate case. Similar procedures can be derived for testing goodness-of-fit in the multivariate case through an application of the theory of statistically equivalent blocks (SEB). The SEB transforms the data into coverages which are distributed as spacings from a uniform distribution on [0,1], under the null hypothesis. In this paper, we present a multivariate nonparametric test of goodness-of-fit based on the SEB when the multivariate distributions under the null hypothesis and the alternative hypothesis are “weakly” ordered. Empirical results are given on the performance of the proposed test in an application to the problem of assessing the reliability of a p-component system.     

Testing for Poisson Zero Inflation in Disease Mapping

When analyzing mortality data due to rare diseases in small areas, it is common to find several health zones with no mortality cases. In these circumstances, the classical homogeneous model based on the Poisson distribution used to estimate the relative risks within each area may encounter lack of fit due to a disproportionately large frequency of zeros. To cope with these zeros, the zero inflated Poisson model can be used. In this paper, we propose a test for detecting zero inflation in the context of disease mapping which is based on bootstrap techniques. The test is illustrated using male mortality data due to brain cancer in Navarra, Spain. In addition, comparisons with other tests for Poisson zero inflation such as the score test and the likelihood ratio test are carried out in terms of empirical power and size using the brain cancer scenario. The proposed bootstrap test has good power and size and works well when detecting the excess of zeros in small area data sets. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Trend Test for Overdispersed Proportions

The Cochran-Armitage test has commonly been used for a trend test in binomial proportions. The quasi-likelihood method provides a simple approach to model extra-binomial proportions. Two versions of the score and Wald tests using different parameterizations for the extra-binomial variance were investigated: one in terms of intercluster correlation, and another in terms of variance. The Monte Carlo simulation was used to evaluate the performance of the each version of the score test and the Wald test, and the Cochran-Armitage test. The simulation shows that the Cochran-Armitage test has the proper size only for the binomial sample data, and the test is no longer valid when applied to the extra-binomial data. The Wald test is more likely to exceed the nominal level than the score test under either intercluster correlation model or variance model. Both score tests performed very well even with the binomial data; the tests control the type I error and in the meantime maintain the power of detecting the dose effects. Based on the design considered in this paper, the two scores test are comparable. The score test based on the intercluster correlations model seems better controlling the Type I error but appears less powerful than that based on the variance model. An example from a developmental toxicity experiment is given.     

Adjusting O'Brien's test to control type I error for the generalized nonparametric Behrens-Fisher problem

O'Brien (1984, Biometrics 40, 1079-1087) introduced a simple nonparametric test procedure for testing whether multiple outcomes in one treatment group have consistently larger values than outcomes in the other treatment group. We first explore the theoretical properties of O'Brien's test. We then extend it to the general nonparametric Behrens-Fisher hypothesis problem when no assumption is made regarding the shape of the distributions. We provide conditions when O'Brien's test controls its error probability asymptotically and when it fails. We also provide adjusted tests when the conditions do not hold. Throughout this article, we do not assume that all outcomes are continuous. Simulations are performed to compare the adjusted tests to O'Brien's test. The difference is also illustrated using data from a Parkinson's disease clinical trial.     

Drawbacks to noninteger scoring for ordered categorical data

A proposal to improve trend tests by using noninteger scores is examined. It is concluded that despite improved power such tests are usually inferior to the simpler integer scored approach.