Joint multiple quantitative trait loci mapping for allometries of body compositions and metabolic traits to body weights in broiler

In order to map quantitative trait loci (QTLs) for allometries of body compositions and metabolic traits in chicken, we phenotypically characterize the allometric growths of multiple body components and metabolic traits relative to BWs using joint allometric scaling models and then establish random regression models (RRMs) to fit genetic effects of markers and minor polygenes derived from the pedigree on the allometric scalings. Prior to statistically inferring the QTLs for the allometric scalings by solving the RRMs, the LASSO technique is adopted to rapidly shrink most of marker genetic effects to zero. Computer simulation analysis confirms the reliability and adaptability of the so-called LASSO-RRM mapping method. In the F₂ population constructed by multiple families, we formulate two joint allometric scaling models of body compositions and metabolic traits, in which six of nine body compositions are tested as significant, while six of eight metabolic traits are as significant. For body compositions, a total of 14 QTLs, of which 9 dominant, were detected to be associated with the allometric scalings of drumstick, fat, heart, shank, liver and spleen to BWs; while for metabolic traits, a total of 19 QTLs also including 9 dominant be responsible for the allometries of T4, IGFI, IGFII, GLC, INS, IGR to BWs. The detectable QTLs or highly linked markers can be used to regulate relative growths of the body components and metabolic traits to BWs in marker-assisted breeding of chickens.     

Spontaneous regression of cancer: New insights

Suppression of oncogene expression and of host- or tumour-expressed growth factors and receptors may precipitate spontaneous regression or dormancy in human cancer. Loss of oncogenes necessary for progressive proliferation can lead to differentiation changes. Both natural factors and chemical agents can trigger such a change, and of the naturally occurring agents, growth factors and immunological factors have been most studied. We may find new clues to biological methods of prolonging arrest of cancer, by looking for cytogenetic abnormalities, alterations in oncogene expression and immunocytological composition, in patients showing prolonged dormancy of cancer.     

Discretizing a Normal Prior for Change Point Estimation in Switching Regressions

Bayes decision procedures are considered for change point estimation in the simple bilinear segmented model. A discretized normal prior density is employed as the prior distribution for the change point index. Posterior probability functions are developed for this index under a vague prior formulation on the regression parameters. The procedure is applied to an example involving mercury toxicity data.     

Distribution of plant functional types along gradients of disturbance intensity and resource supply in an agricultural landscape

Abstract. In this study, plant functional types are understood as groups of plants with similar biological traits displaying significant optima or maxima on a gradient plane of resource supply and disturbance intensity. The biological traits refer to expansion, vegetative regeneration, generative reproduction, dispersal and seed bank longevity. 129 vegetation samples were taken in an agricultural landscape in southwestern Germany, covering a wide range of terrestrial vegetation types – but with the exception of forests and wetlands. For each site, also soil data were recorded. Mean daily soil moisture was estimated with a simple model. Soil moisture, balanced nitrogen supply and available phosphorus were combined into a factor ‘resource supply’. In addition, disturbance intensity was estimated for each site. This factor was based on (1) frequency of disturbance, (2) disturbance depth below or above the soil surface, and (3) proportion of the area affected by a discrete disturbance event. 30 plant groups with similar biological characteristics resulted from a cluster analysis, based on a compilation of 19 biological traits for a regional species pool. Logistic regression on a gradient plane of disturbance intensity and resource supply yielded response curves for 28 groups. The dependent variable was defined as the probability of encountering all members of a group in a sample. 17 groups display a significant response curve on the gradient plane. Plants with a potential for long- range dispersal are concentrated on sites with low or high disturbance intensities (e.g. fallow land, fields, lawns). On sites with medium disturbance intensity (e.g. meadows) and low to medium resource supply, small-range dispersal predominates. There are no distinct trends concerning seed bank longevity. The potential for vertical and lateral expansion increases with decreasing disturbance intensity. Only at medium disturbance intensities does vertical expansion correlate positively with resource supply. Rapid detachment of daughter individuals occurs more often on productive sites than on less productive sites. Diversity of groups with similar biological traits is highest on sites with medium disturbance intensities.     

Maximum Likelihood Neural Network Prediction Models

Neural networks have received much attention in recent years mostly by non-statisticians. The purpose of this paper is to incorporate neural networks in a non-linear regression model and obtain maximum likelihood estimates of the network parameters using a standard Newton-Raphson algorithm. We use maximum likelihood estimators instead of the usual back-propagation technique and compare the neural network predictions with predictions of quadratic regression models and with non-parametric nearest neighbor predictions. These comparisons are made using data generated from a variety of functions. Because of the number of parameters involved, neural network models can easily over-fit the data, hence validation of results is crucial.     

Effect of pea bacterial blight (Pseudomonas syringae pv. pisi) on the yield of spring sown combining peas (Pisum sativum)

Field trials to examine the effect of pea bacterial blight (Pseudomonas syringae pv. pisi) (Psp) on the yield of combining peas were carried out at five sites (HRI Wellesbourne, ADAS Rosemaund, ADAS Terrington, PGRO, SASA East Craigs) in the UK in 1990, 1991 and 1992. Healthy seed, cv. Solara, and seed naturally infected with Psp Race 2 was sown in large plots (c. 200 m²) in the open or under nets to prevent bird damage by pigeons. Despite relatively low disease severity levels (< 15% leaf area) and separation by at least 12 m of cv. Consort (resistant to Race 2) between plots there was considerable spread of disease into plots sown with healthy seed. Regression analysis demonstrated a significant relationship between yield and disease. Of the disease measurements examined, disease severity on the leaves (stipules) at growth stage 208 was found to be the best predictor of yield. A model containing environmental and bird damage terms, in addition to disease, suggested that a yield loss of 0.98 t/ha would be expected for a disease severity score of 1, equivalent to 5% leaf area affected, at growth stage 208.