The Ames Salmonella/microsome mutagenicity assay (Salmonella test; Ames test) is a short-term bacterial reverse mutation assay specifically designed to detect a wide range of chemical substances that can produce genetic damage that leads to gene mutations. The test employs several histidine dependent Salmonella strains each carrying different mutations in various genes in the histidine operon. These mutations act as hot spots for mutagens that cause DNA damage via different mechanisms. When the Salmonella tester strains are grown on a minimal media agar plate containing a trace of histidine, only those bacteria that revert to histidine independence (his(+)) are able to form colonies. The number of spontaneously induced revertant colonies per plate is relatively constant. However, when a mutagen is added to the plate, the number of revertant colonies per plate is increased, usually in a dose-related manner. The Ames test is used world-wide as an initial screen to determine the mutagenic potential of new chemicals and drugs. The test is also used for submission of data to regulatory agencies for registration or acceptance of many chemicals, including drugs and biocides. International guidelines have been developed for use by corporations and testing laboratories to ensure uniformity of testing procedures. This review provides historical aspects of how the Ames was developed and detailed procedures for performing the test, including the design and interpretation of results.

http://people.uleth.ca/%7Eselibl/Biol3200/AmesTest/AmesTestreview.pdf

The Ames Salmonella/microsome mutagenicity assay

Though simple and attractive, the role of hydration for the prophylaxis of contrast nephrotoxicity has not been definitively established. We prospectively evaluated the role of deliberate saline hydra

A Randomized Prospective Trial to Assess the Role of Saline Hydration on the Development of Contrast Nephrotoxicity

Drinking Water Microbiology

The problem of identifying the lowest dose level for which the mean response differs from that at the zero dose level is considered. A general framework for stepwise testing procedures that use contrasts among the dose level means is proposed. Using this framework, several new procedures are derived. These and some existing procedures, including that of Williams (1971, Biometrics 27, 103-117; 1972, Biometrics 28, 519-531), are compared analytically and by an extensive simulation study for the normal theory balanced one-way layout case. It is pointed out that the procedures based on the so-called step and basin contrasts proposed by Ruberg (1989, Journal of American Statistical Association 84, 816-822) have excessively high type I familywise error rates (FWEs) and, hence, they should not be used. Some findings of the simulation study are as follows: For monotone dose mean configurations, Williams' procedure and two step-down test procedures based on Helmert and linear contrasts offer the best performance. For nonmonotone dose mean configurations, the performance of Williams' procedure does degrade somewhat, but the other two procedures are still the best. For more complex designs, a simple step-down test procedure that uses any alpha-level tests (not necessarily t-tests) to compare each dose level with the zero dose level controls the FWE and is the only alternative available, but its power is rather low, especially under nonmonotone configurations. Step-up procedures are generally dominated by step-down procedures when the same contrasts are used although the differences are not great.

http://users.iems.northwestern.edu/~ajit/papers/20)%20MTPs%20for%20Dose%20Finding.pdf

Multiple test procedures for dose finding

Tests for detecting negative binomial departures from a Poisson model are studied for the one-way-layout and regression-through-the-origin cases. Approximations to the null and alternative distributions of these test statistics are presented. Locally optimal tests and tests suggested in the literature are compared in terms of asymptotic relative efficiency. Small sample comparisons are included.

https://www.jstor.org/stable/pdfplus/2287906.pdf

Testing Goodness of Fit for the Poisson Assumption When Observations are Not Identically Distributed

SUMMARY The problem of comparing several ordered dose levels with a control when a larger sample size is taken on the control is considered. The distributions of Bartholomew's tests are determined for the limiting case where the control mean is known and an approximation is given for the problem. The existing tables for Bartholomew's tests are extended. It is considered that these tests are superior in all situations where the sample size for the control is greater than the sample sizes for the nonzero dose levels.

On testing for ordered alternatives with increased sample size for a control

SUMMARY The effect of small errors of dilution is shown to be sizeable when serial dilutions are required to estimate particle concentrations. Optimal dilution designs are given. The error effect and optimal design are also investigated for ratios of estimates. We wish to investigate the problem of estimating the concentration of particles such as bacteria or phage in suspension in a fluid. The actual counting process may involve a counter or, as in the problem motivating this study, counting growth colonies on an agar plate. It is often necessary or desirable to dilute the initial concentration before taking the final sample to be counted. An example where dilution is an integral part of an estimation scheme is given by Alling (1971); there dilutions are required to obtain an estimate of an unknown parameter. Dilution of the initial concentration is called for in other situations because the concentration prohibits accurate counting. For example, too many colonies on a plate are difficult to count due to sheer numbers, overlapping of colonies, possible interference, etc. The purpose of this note is to investigate the effect of small errors of dilution on the distribution of the number of particles in the final sample to be counted. The precision of calibration of equipment such as pipettes used in the dilution process is often expressed in terms of the coefficient of variation. A coefficient of variation of less than two percent is often claimed. However, a coefficient of variation as large as four or five percent is quite possible when the equipment is used. For example, incomplete mixing and human error, often compounded by problems such as a time constraint on the process, may contribute to the problem. It will be seen that errors of this magnitude can result in dramatic increases in the variation of the estimate of the initial concentration. In ? 2 we introduce the notation and discuss the distribution of sample counts for dilutions known without error. The errors on the dilutions are introduced in ? 3 and expressions for the mean, variance and mean squared error of the usual estimate are given. The results of ? 3 are compared with those of ? 2 to illustrate the effect of dilution errors on the parameters of interest. Optimal dilution designs are also given. An application using ratios of estimates is discussed briefly in ? 4 and a table is given to illustrate the effect of dilution errors on the variance of the ratio estimate.

Serial dilutions: Error effects and optimal designs

A method for constructing two-stage (double samble) tests is presented which does not require the evaluation of complicated bivariate distribution function. The procedure results from a modification of Fisher's method for combining independent tests of significance and is distribution free in the way it combines the test results from the two sampies. However, the one sample test statistics for the two samples are assumed to have continuous distributions and may be parametric. A rule is also given or the selection of a particular test out of a family of possible two-stage tests which can be generated by this method. Specific examples are given and comparisons are made with two double sample tests which have previously been presented in the literature.

Double samble tests— a distribution free procedure

Analytical and Monte Carlo results are used to investigate the robustness of the exact statistical test suggested by Nelson for testing the hypothesis of equality of two availabilities. A non-parametric test based on a generalized U-statistic is proposed which does not depend on the assumption that the times to failure and times to repair are exponentially distributed. Additional Monte Carlo results are used to point out the strengths and weaknesses of this test.

Gerald R. Chase

Papers

On testing for ordered alternatives with increased sample size for a control

Serial dilutions: Error effects and optimal designs

Double samble tests— a distribution free procedure

On Testing for Equality of Two Availabilities