Profiling of composition and metabolic activities of the colonic microflora of growing pigs fed diets supplemented with prebiotic oligosaccharides

It is evident that quantitative information on different microbial groups and their contribution in terms of activity in the gastrointestinal (GI) tract of humans and animals is required in order to formulate functional diets targeting improved gut function and host health. In this work, quantitative information on levels and spatial distributions of Bacteroides spp, Eubacterium spp, Clostridium spp, Escherichia coli, Bifidobacterium spp and Lactobacillus/Enterococcus spp. along the porcine large intestine was investigated using 16S rRNA targeted probes and fluorescent in situ hybridisation (FISH). Caecum, ascending colon (AC) and rectum luminal digesta from three groups of individually housed growing pigs fed either a corn-soybean basal diet (CON diet) or a prebiotic diet containing 10 g/kg oligofructose (FOS diet) or trans-galactooligosaccharides (TOS diet) at the expense of cornstarch were analysed. DAPI staining was used to enumerate total number of cells in the samples. Populations of total cells, Bacteroides, Eubacterium, Clostridium and Bifidobacterium, declined significantly (P < 0.05) from caecum to rectum, and were not affected by dietary treatments. Populations of Lactobacillus/ Enterococcus and E coli did not differ throughout the large intestine. The relative percent (%) contribution of each bacterial group to the total cell count did not differ between caecum and rectum, with the exception of Eubacterium that was higher in the AC digesta. FISH analysis showed that the sum of all bacterial groups made up a small percentage of the total cells, which was 12.4%, 21.8% and 10.3% in caecum, AC and rectum, respectively. This supports the view that in swine, the diversity of GI microflora might be higher compared to other species. In terms of microflora metabolic activity, the substantially higher numerical trends seen in FOS and TOS treatments regarding total volatile fatty acid, acetate concentrations and glycolytic activities, it could be postulated that FOS and TOS promoted saccharolytic activities in the porcine colon. (c) 2006 Elsevier Ltd. All rights reserved.

Estimating the hidden number of scrapie affected holdings in great Britain using a simple, truncated count model allowing for heterogeneity.

None of the current surveillance streams monitoring the presence of scrapie in Great Britain provide a comprehensive and unbiased estimate of the prevalence of the disease at the holding level. Previous work to estimate the under-ascertainment adjusted prevalence of scrapie in Great Britain applied multiple-list capture–recapture methods. The enforcement of new control measures on scrapie-affected holdings in 2004 has stopped the overlapping between surveillance sources and, hence, the application of multiple-list capture–recapture models. Alternative methods, still under the capture–recapture methodology, relying on repeated entries in one single list have been suggested in these situations. In this article, we apply one-list capture–recapture approaches to data held on the Scrapie Notifications Database to estimate the undetected population of scrapie-affected holdings with clinical disease in Great Britain for the years 2002, 2003, and 2004. For doing so, we develop a new diagnostic tool for indication of heterogeneity as well as a new understanding of the Zelterman and Chao’s lower bound estimators to account for potential unobserved heterogeneity. We demonstrate that the Zelterman estimator can be viewed as a maximum likelihood estimator for a special, locally truncated Poisson likelihood equivalent to a binomial likelihood. This understanding allows the extension of the Zelterman approach by means of logistic regression to include observed heterogeneity in the form of covariates—in case studied here, the holding size and country of origin. Our results confirm the presence of substantial unobserved heterogeneity supporting the application of our two estimators. The total scrapie-affected holding population in Great Britain is around 300 holdings per year. None of the covariates appear to inform the model significantly.

Estimating the hidden number of scrapie affected holdings in great Britain using a simple, truncated count model allowing for heterogeneity

None of the current surveillance streams monitoring the presence of scrapie in Great Britain provide a comprehensive and unbiased estimate of the prevalence of the disease at the holding level. Previous work to estimate the under-ascertainment adjusted prevalence of scrapie in Great Britain applied multiple-list capture-recapture methods. The enforcement of new control measures on scrapie-affected holdings in 2004 has stopped the overlapping between surveillance sources and, hence, the application of multiple-list capture-recapture models. Alternative methods, still under the capture-recapture methodology, relying on repeated entries in one single list have been suggested in these situations. In this article, we apply one-list capture-recapture approaches to data held on the Scrapie Notifications Database to estimate the undetected population of scrapie-affected holdings with clinical disease in Great Britain for the years 2002, 2003, and 2004. For doing so, we develop a new diagnostic tool for indication of heterogeneity as well as a new understanding of the Zelterman and Chao's lower bound estimators to account for potential unobserved heterogeneity. We demonstrate that the Zelterman estimator can be viewed as a maximum likelihood estimator for a special, locally truncated Poisson likelihood equivalent to a binomial likelihood. This understanding allows the extension of the Zelterman approach by means of logistic regression to include observed heterogeneity in the form of covariates-in case studied here, the holding size and country of origin. Our results confirm the presence of substantial unobserved heterogeneity supporting the application of our two estimators. The total scrapie-affected holding population in Great Britain is around 300 holdings per year. None of the covariates appear to inform the model significantly.