Bacterial dry matter content and biomass estimations.

Approximately 20% dry-matter content appears to be an accepted standard value for bacterial cells. We have found that the dry-matter content of bacteria may be more than twice as high as generally assumed. The main reason for the low estimates seems to be that proper corrections for intercellular water have not been made when estimating the wet weight of the cells. Using three different bacterial strains, we determined a dry-matter content of cells ranging from 31 to 57%, suggesting not only that the accepted standard value is much too low but also that it is far from standard. To convert bacterial biovolume into biomass (carbon content), we suggest that 0.22 g of C cm-3 should be used as a conversion factor.     

Effect of ambient temperature storage on potable water coliform population estimations.

The effect of the length of time between sampling potable water and performing coliform analyses has been a long-standing controversial issue in environmental microbiology. The issue is of practical importance since reducing the sample-to-analysis time may substantially increase costs for water analysis programs. Randomly selected samples (from those routinely collected throughout the State of Wisconsin) were analyzed for total coliforms after being held at room temperature (20 +/- 2 degrees C) for 24 and 48 h. Differences in results for the two holding times were compared with differences predicted by probability calculations. The study showed that storage of the potable water for up to 48 h had little effect on the public health significance of most samples containing more than two coliforms per 100 ml.     

Finding noncommunicating sets for Markov chain Monte Carlo estimations on pedigrees.

Markov chain Monte Carlo (MCMC) has recently gained use as a method of estimating required probability and likelihood functions in pedigree analysis, when exact computation is impractical. However, when a multiallelic locus is involved, irreducibility of the constructed Markov chain, an essential requirement of the MCMC method, may fail. Solutions proposed by several researchers, which do not identify all the noncommunicating sets of genotypic configurations, are inefficient with highly polymorphic loci. This is a particularly serious problem in linkage analysis, because highly polymorphic markers are much more informative and thus are preferred. In the present paper, we describe an algorithm that finds all the noncommunicating classes of genotypic configurations on any pedigree. This leads to a more efficient method of defining an irreducible Markov chain. Examples, including a pedigree from a genetic study of familial Alzheimer disease, are used to illustrate how the algorithm works and how penetrances are modified for specific individuals to ensure irreducibility.