Detection of transgenic and endogenous plant DNA in rumen fluid, duodenal digesta, milk, blood, and feces of lactating dairy cows

The objective was to determine the presence or absence of transgenic and endogenous plant DNA in ruminal fluid, duodenal digesta, milk, blood, and feces, and if found, to determine fragment size. Six multiparous lactating Holstein cows fitted with ruminal and duodenal cannulas received a total mixed ration. There were two treatments (T). In T1, the concentrate contained genetically modified (GM) soybean meal (cp4epsps gene) and GM corn grain (cry1a[b] gene), whereas T2 contained the near isogenic non-GM counterparts. Polymerase chain reaction analysis was used to determine the presence or absence of DNA sequences. Primers were selected to amplify small fragments from single-copy genes (soy lectin and corn high-mobility protein and cp4epsps and cry1a[b] genes from the GM crops) and multicopy genes (bovine mitochondrial cytochrome b and rubisco). Single-copy genes were only detected in the solid phase of rumen and duodenal digesta. In contrast, fragments of the rubisco gene were detected in the majority of samples analyzed in both the liquid and solid phases of ruminal and duodenal digesta, milk, and feces, but rarely in blood. The size of the rubisco gene fragments detected decreased from 1176 bp in ruminal and duodenal digesta to 351 bp in fecal samples.

A study of the effect of heat source location in a ventilated room using multiple regression analysis

Multiple regression analysis is a statistical technique which allows to predict a dependent variable from m ore than one independent variable and also to determine influential independent variables. Using experimental data, in this study the multiple regression analysis is applied to predict the room mean velocity and determine the most influencing parameters on the velocity. More than 120 experiments for four different heat source locations were carried out in a test chamber with a high level wall mounted air supply terminal at air change rates 3-6 ach. The influence of the environmental parameters such as supply air momentum, room heat load, Archimedes number and local temperature ratio, were examined by two methods: a simple regression analysis incorporated into scatter matrix plots and multiple stepwise regression analysis. It is concluded that, when a heat source is located along the jet centre line, the supply momentum mainly influences the room mean velocity regardless of the plume strength. However, when the heat source is located outside the jet region, the local temperature ratio (the inverse of the local heat removal effectiveness) is a major influencing parameter.

Effects of endogenous flour lipids on the quality of semisweet biscuits

Fractionation and reconstitution techniques were used to study the contribution of enclogenous flour lipids to the quality of semisweet (Rich Tea-type) biscuits. Biscuit flour was defatted with chloroform and baked with bakery fat but without enclogenous lipid addition. Semisweet biscuits baked from defatted flour were flatter, denser, and harder and showed collapse of gas cells during baking when compared with control biscuits. Defatted flour semisweet doughs exhibited a different rheological behavior from the control samples showing higher storage and loss moduli (G' and G" values), that is, high viscoelasticity. Functionality was restored when total nonstarch flour lipids were added back to defatted flour. Both the polar and nonpolar lipid fractions had positive effects in restoring flour quality, but the polar lipid fraction was of greatest benefit. Both fractions were needed for complete restoration of both biscuit quality and dough rheological characteristics.

Effects of endogenous flour lipids on the quality of short-dough biscuits

Fractionation and reconstitution techniques were used to study the contribution of endogenous flour lipids to the quality of short-dough (shortcake type) biscuits. Biscuit flour was defatted with chloroform and baked with bakery fat, but without endogenous lipid. Short-dough biscuits baked from defatted flour had smaller diameters, and were flatter, denser and harder than control biscuits. Defatted flour shortcake doughs exhibited different rheological behaviour from the control samples, showing higher storage and loss moduli (G' and G" values), ie higher viscoelasticity. Functionality was restored when total non-starch flour lipids were added back to defatted flour. The polar lipid fraction had a positive effect in restoring flour quality whereas the non-polar lipid fraction had no effect. Both fractions were needed for complete restoration of both biscuit quality and dough rheological characteristics. A study of the microstructure of defatted biscuits revealed that their gluten protein was more hydrated and developed than the gluten of the control biscuits. This conclusion was supported by the higher water absorption of the defatted gluten. (C) 2004 Society of Chemical Industry.

Regression, developmental trajectory and associated problems in disorders in the autism spectrum: the SNAP study

We report rates of regression and associated findings in a population derived group of 255 children aged 9-14 years, participating in a prevalence study of autism spectrum disorders (ASD); 53 with narrowly defined autism, 105 with broader ASD and 97 with non-ASD neurodevelopmental problems, drawn from those with special educational needs within a population of 56,946 children. Language regression was reported in 30% with narrowly defined autism, 8% with broader ASD and less than 3% with developmental problems without ASD. A smaller group of children were identified who underwent a less clear setback. Regression was associated with higher rates of autistic symptoms and a deviation in developmental trajectory. Regression was not associated with epilepsy or gastrointestinal problems.

A sparse kernel density estimation algorithm using forward constrained regression

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

Fully complex-valued radial basis function networks for orthogonal least squares regression

We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.