Biomedical subjects

K J Scurrah

Publications and source records attributed to K J Scurrah.

5 recordsLinked to original sources

PubMed2006

Inactivation of Bacillus spores in reconstituted skim milk by combined high pressure and heat treatment.

AIMS: To determine the resistance of a variety of Bacillus species spores to a combined high pressure and heat treatment; and to determine the affect of varying sporulation and treatment conditions on the level of inactivation achieved. METHODS AND RESULTS: Spores from eight Bacillus species (40 isolates) were high pressure-heat treated at 600 MPa, 1 min, initial temperature 72 degrees C. The level of inactivation was broad (no inactivation to 6 log10 spores ml(-1) reduction) and it varied within species. Different sporulation agar, high pressure equipment and pressure-transmitting fluid significantly affected the response of some isolates. Varying the initial treatment temperature (75, 85 or 95 degrees C) shifted the relative order of isolate high pressure-heat resistance. CONCLUSIONS: The response of Bacillus spores to combined high pressure-heat treatment is variable and can be attributed to both intrinsic and extrinsic factors. The combined process resulted in a high level of spore inactivation for several Bacillus species and is a potential alternative treatment to traditional heat-only processes. SIGNIFICANCE AND IMPACT OF THE STUDY: Sporulation conditions, processing conditions and treatment temperature all affect the response of Bacillus spores to the combined treatment of high pressure and heat. High levels of spore inactivation can be achieved but the response is variable both within and between species.

Animals↗

PubMed2001

Genome-wide linkage analysis in a general population sample using sigma 2A random effects (SSARs) fitted by Gibbs sampling.

We used variance components analysis to investigate the underlying determinants of the quantitative phenotypes (Q1-Q5) and their interrelationships in replicate 42 of the Genetic Analysis Workshop 12 simulated general population. Variance components models were fitted using Gibbs sampling in WinBUGS v1.3. Sigma-squared-A-random-effects (SSARs) were estimated for each phenotype, and were used as derived phenotypes in subsequent linkage analyses. Whole-genome, multipoint linkage analyses were based upon a new Haseman-Elston identity-by descent sib-pair method that takes a weighted combination of the trait-sum and trait-difference. The five quantitative traits simulated were closely correlated with each other and with affection status. The whole-genome screen of quantitative traits associated with the simulated complex disease suggested that one or more major loci regulating Q1 localizes to chromosome 2p and that one or more major loci regulating Q5 may localize to chromosome 1p.

Algorithms↗

PubMed2001

Association and linkage for age at onset of a common oligogenic disease using genetic variance component models.

The aims of our analysis were: (1) to investigate association of single nucleotide polymorphisms (SNPs) and other covariates with age at onset in the simulated Genetic Analysis Workshop (GAW) 12 general population data, and (2) to use the polygenic random effects estimated during model fitting (sigma squared A random effects) as input to a Haseman-Elston linkage analysis. The association analyses used genetic variance component models in a generalized linear mixed models framework and were fitted using Gibbs sampling. This method successfully detected the only three sequenced genes that were also major genes. The single-point linkage analysis used all markers provided. Regions of linkage were found close to all four of the sites of major genes that explained a non-trivial component of the variance of age at onset. In all four cases the linkage peak fell within 5 cM of the true location. In three cases the peak significance was p < 0.01.

Adolescent↗

PubMed2000

Variance components analysis for pedigree-based censored survival data using generalized linear mixed models (GLMMs) and Gibbs sampling in BUGS.

Complex human diseases are an increasingly important focus of genetic research. Many of the determinants of these diseases are unknown and there is often a strong residual covariance between relatives even when all known genetic and environmental factors have been taken into account. This must be modeled correctly whether scientific interest is focused on fixed effects, as in an association analysis, or on the covariance structure itself. Analysis is straightforward for multivariate normally distributed traits, but difficulties arise with other types of trait. Generalized linear mixed models (GLMMs) offer a potentially unifying approach to analysis for many classes of phenotype including right censored survival times. This includes age-at-onset and age-at-death data and a variety of other censored traits. Markov chain Monte Carlo (MCMC) methods, including Gibbs sampling, provide a convenient framework within which such GLMMs may be fitted. In this paper, we use BUGS ("Bayesian inference using Gibbs sampling": a readily available, generic Gibbs sampler) to fit GLMMs for right-censored survival times in nuclear and extended families. We discuss parameter interpretation and statistical inference, and show how to circumvent a number of important theoretical and practical problems. Using simulated data, we show that model parameters are consistent. We further illustrate our methods using data from an ongoing cohort study. Finally, we propose that the random effects associated with a genetic component of variance (e.g., sigma(2)(A)) in a GLMM may be regarded as an adjusted "phenotype" and used as input to a conventional model-based or model-free linkage analysis. This provides a simple way to conduct a linkage analysis for a trait reflected in a right-censored survival time while comprehensively adjusting for observed confounders at the level of the individual and latent environmental effects shared across families.

Bayes Theorem↗

PubMed2000

Survey for psychrotrophic bacterial pathogens in minimally processed lettuce.

A total of 120 minimally processed, cut and packaged lettuce samples were purchased from retail supermarkets or provided by a salad production facility over an 8-month period. The samples were tested for total aerobic plate counts and for the presence of potentially pathogenic species belonging to the genera of Listeria, Aeromonas and Yersinia. The aerobic plate counts ranged from 103 to 109 colony forming units (cfu) g-1. Most samples (76%) contained between 105 and 107 cfu g-1 total aerobic bacteria. Listeria monocytogenes was isolated from three samples, Aeromonas hydrophila or Aeromonas caviae from 66 samples, and Yersinia enterocolitica from 71 samples. The pathogenic potential of Y. enterocolitica isolates was determined by screening for an array of biochemical, serological and genetic traits (heat-stable enterotoxin gene, the attachment and invasion gene locus, the invasin gene locus and the virulence plasmid). The Y. enterocolitica isolates lacked many of the phenotypic and genetic markers associated with virulence in primary pathogenic strains. As the roles of the reputed virulence factors of Aeromonas spp. in human infection are uncertain, the pathogenic potential of the Aeromonas isolates in lettuce remains unclear.

Aeromonas hydrophila↗