Estimating animal abundance in ground beef batches assayed with molecular markers

Estimating animal abundance in industrial scale batches of ground meat is important for mapping meat products through the manufacturing process and for effectively tracing the finished product during a food safety recall. The processing of ground beef involves a potentially large number of animals from diverse sources in a single product batch, which produces a high heterogeneity in capture probability. In order to estimate animal abundance through DNA profiling of ground beef constituents, two parameter-based statistical models were developed for incidence data. Simulations were applied to evaluate the maximum likelihood estimate (MLE) of a joint likelihood function from multiple surveys, showing superiority in the presence of high capture heterogeneity with small sample sizes, or comparable estimation in the presence of low capture heterogeneity with a large sample size when compared to other existing models. Our model employs the full information on the pattern of the capture-recapture frequencies from multiple samples. We applied the proposed models to estimate animal abundance in six manufacturing beef batches, genotyped using 30 single nucleotide polymorphism (SNP) markers, from a large scale beef grinding facility. Results show that between 411～1367 animals were present in six manufacturing beef batches. These estimates are informative as a reference for improving recall processes and tracing finished meat products back to source.     

Production of rats in synchronised batches.

Successful synchronisation of copulations and births for the 1st parity permit continued synchrony of parturitions in subsequent parities. Foetal implantation after post-partum copulation was delayed by multiples of the oestrous cycle; both season and number of sucking neonates influenced the time of implantation. Post-weaning matings gave a high conception rate with a parturition spread of 36 hours for 94% of the original group.     

Estimating the number of motor units using random sums with independently thinned terms

The problem of estimating the numbers of motor units N in a muscle is embedded in a general stochastic model using the notion of thinning from point process theory. In the paper a new moment type estimator for the numbers of motor units in a muscle is denned, which is derived using random sums with independently thinned terms. Asymptotic normality of the estimator is shown and its practical value is demonstrated with bootstrap and approximative confidence intervals for a data set from a 31-year-old healthy right-handed, female volunteer. Moreover simulation results are presented and Monte-Carlo based quantiles, means, and variances are calculated for N in{300,600,1000}.     

Estimating renal function for drug dosing decisions

In order to adjust the dose of renally excreted drugs in response to reduced renal function, it is necessary to make a quantitative estimate of the glomerular filtration rate (GFR) of the patient. Traditionally this has been done with the use of the Cockcroft and Gault equation or a measured creatinine clearance. More recently the MDRD (Modification of Diet in Renal Disease) formula has become available, providing an estimate of GFR readily available on routine pathology reports. The presence of these different methods of assessing renal function has created some confusion for healthcare workers as to the best approach. In this paper the two methods are compared, together with a newer formula CKD-EPI (named after the Chronic Kidney Disease Epidemiology Collaborative), and a proposal is made for future practice.     

Vegetative segregation of mitochondria in yeast: Estimating parameters using a random model

Summary Yeast zygotes which are heteroplasmic for mitochondrial genes reproduce vegetatively to form clones of diploid progeny which are homoplasmic. This vegetative segregation of mitochondrial genes has been interpreted in terms of a random distribution of mitochondria or mitochondrial genomes between mother and bud at cell division. We have developed equations which permit calculation of the number of segregating units in the zygote and the number of those units which enter the bud, assuming that segregation of the units is genetically random and numerically variable or equal. Use of the equations requires data from partial pedigree analyses: we isolate zygotes, separate the first bud, then determine the frequency of mitochondrial alleles among the progeny of mother cells whose first buds were homoplasmic. Application of this method to data from five crosses suggests that most zygotes have a small number of segregating units (usually less than a dozen) and only one or two enter the first bud. Analysis of the frequency of buds which are nearly but not quite homoplasmic indicates that the segregating units may be mitochondria or portions thereof which include many mitochondrial genomes, all the genomes in a unit being genetically identical in most but not all cases. These results are compatible with, but do not prove, the hypothesis of random vegetative segregation of mitochondria.     

Estimating genetic variance contributed by a quantitative trait locus: A random model approach

Detecting quantitative trait loci (QTL) and estimating QTL variances (represented by the squared QTL effects) are two main goals of QTL mapping and genome-wide association studies (GWAS). However, there are issues associated with estimated QTL variances and such issues have not attracted much attention from the QTL mapping community. Estimated QTL variances are usually biased upwards due to estimation being associated with significance tests. The phenomenon is called the Beavis effect. However, estimated variances of QTL without significance tests can also be biased upwards, which cannot be explained by the Beavis effect; rather, this bias is due to the fact that QTL variances are often estimated as the squares of the estimated QTL effects. The parameters are the QTL effects and the estimated QTL variances are obtained by squaring the estimated QTL effects. This square transformation failed to incorporate the errors of estimated QTL effects into the transformation. The consequence is biases in estimated QTL variances. To correct the biases, we can either reformulate the QTL model by treating the QTL effect as random and directly estimate the QTL variance (as a variance component) or adjust the bias by taking into account the error of the estimated QTL effect. A moment method of estimation has been proposed to correct the bias. The method has been validated via Monte Carlo simulation studies. The method has been applied to QTL mapping for the 10-week-body-weight trait from an F₂ mouse population.     

Estimating nucleotide diversity from random amplified polymorphic DNA and amplified fragment length polymorphism data

A way to estimate the index of nucleotide diversity (pi) from band match frequencies in random amplified polymorphic DNA and amplified fragment length polymorphism data is described. pi is shown to be a simple function of the proportion of mismatched bands between two individuals drawn at random from a population (phi) and the number of discriminating sites in the amplification system. The method is computationally and conceptually simple and avoids some of the assumptions inherent in other approaches: the relationship is independent of the base composition of the target DNA and avoids the bias inherent in estimations of allelic frequencies in dominant systems. Only two individuals from a population are needed to estimate pi. This economy of material suggests utility of this approach in conservation genetics or other fields where obtaining large samples is impractical or undesirable.