Mitochondrial phosphoenolpyruvate carboxykinase (PEPCK-M) and serine biosynthetic pathway genes are co-ordinately increased during anabolic agent-induced skeletal muscle growth.

We aimed to identify novel molecular mechanisms for muscle growth during administration of anabolic agents. Growing pigs (Duroc/(Landrace/Large-White)) were administered Ractopamine (a beta-adrenergic agonist; BA; 20 ppm in feed) or Reporcin (recombinant growth hormone; GH; 10 mg/48 hours injected) and compared to a control cohort (feed only; no injections) over a 27-day time course (1, 3, 7, 13 or 27-days). Longissimus Dorsi muscle gene expression was analyzed using Agilent porcine transcriptome microarrays and clusters of genes displaying similar expression profiles were identified using a modified maSigPro clustering algorithm. Anabolic agents increased carcass (p = 0.002) and muscle weights (Vastus Lateralis: p < 0.001; Semitendinosus: p = 0.075). Skeletal muscle mRNA expression of serine/one-carbon/glycine biosynthesis pathway genes (Phgdh, Psat1 and Psph) and the gluconeogenic enzyme, phosphoenolpyruvate carboxykinase-M (Pck2/PEPCK-M), increased during treatment with BA, and to a lesser extent GH (p < 0.001, treatment x time interaction). Treatment with BA, but not GH, caused a 2-fold increase in phosphoglycerate dehydrogenase (PHGDH) protein expression at days 3 (p < 0.05) and 7 (p < 0.01), and a 2-fold increase in PEPCK-M protein expression at day 7 (p < 0.01). BA treated pigs exhibit a profound increase in expression of PHGDH and PEPCK-M in skeletal muscle, implicating a role for biosynthetic metabolic pathways in muscle growth.

A Bayesian elicitation of veterinary beliefs regarding systemic dry cow therapy: variation and importance for clinical trial design.

The two key aims of this research were: (i) to conduct a probabilistic elicitation to quantify the variation in veterinarians' beliefs regarding the efficacy of systemic antibiotics when used as an adjunct to intra-mammary dry cow therapy and (ii) to investigate (in a Bayesian statistical framework) the strength of future research evidence required (in theory) to change the beliefs of practising veterinary surgeons regarding the efficacy of systemic antibiotics, given their current clinical beliefs. The beliefs of 24 veterinarians in 5 practices in England were quantified as probability density functions. Classic multidimensional scaling revealed major variations in beliefs both within and between veterinary practices which included: confident optimism, confident pessimism and considerable uncertainty. Of the 9 veterinarians interviewed holding further cattle qualifications, 6 shared a confidently pessimistic belief in the efficacy of systemic therapy and whilst 2 were more optimistic, they were also more uncertain. A Bayesian model based on a synthetic dataset from a randomised clinical trial (showing no benefit with systemic therapy) predicted how each of the 24 veterinarians' prior beliefs would alter as the size of the clinical trial increased, assuming that practitioners would update their beliefs rationally in accordance with Bayes' theorem. The study demonstrated the usefulness of probabilistic elicitation for evaluating the diversity and strength of practitioners' beliefs. The major variation in beliefs observed raises interest in the veterinary profession's approach to prescribing essential medicines. Results illustrate the importance of eliciting prior beliefs when designing clinical trials in order to increase the chance that trial data are of sufficient strength to alter the clinical beliefs of practitioners and do not merely serve to satisfy researchers.

Use of individual cow milk recording data at the start of lactation to predict the calving to conception interval.

Milk recording data collected in 2,128 dairy herds in England and Wales between 2004 and 2006 were used to predict the calving to conception intervals. The average cumulative milk production was 8,200 kg. Conception (or not) within 5 intervals measured in days (20 to 60 d, 61 to 81 d, 82 to 102 d, 103 to 123 d, 124 to 144 d) was modeled as a function of milk yields and milk constituents at the start of lactation using multilevel discrete-time survival models. Milk yield, weight and percentage of fat, protein, and lactose, and somatic cell counts on the first 2 test-days of lactation were corrected for either stage of lactation alone or stage of lactation and time of year. Five hundred and 1,628 herds, respectively, were used for parameter estimation and cross-validation. Covariates were retained in the final model if their coefficient was at least twice its standard error and their inclusion resulted in a decrease in the deviance. Overall, 73% of cows recalved. The percentage of cows that had conceived by d 20, 61, 82, 103, 124, and 145 were 0.5, 7.3, 17.9, 29.3, 38.7, and 46, respectively. The probability of conception before 145 d in milk increased with lower milk production on the second test-day, higher percentage of protein on the second test-day, and higher percentage of lactose on the first test-day. Positive associations were of a limited magnitude but nonetheless significant with the percentage of protein on the first test-day, the percentage of butterfat on the first test-day, and somatic cell count on both test-days. The model predicted the probability of conception in the cross validation data set very well. Despite the common use of fat to protein ratio as a measure of energy balance, this parameter exhibited wide variation with stage of lactation and time of the year and had a much-reduced ability to predict an early conception compared with other combinations of milk quantity and constituents.

Landmark-based morphometrics of the normal adult brain using MRI.

We describe the application of statistical shape analysis to homologous landmarks on the cortical surface of the adult human brain. Statistical shape analysis has a sound theoretical basis. Landmarks are identified on the surface of a 3-D reconstruction of the segmented cortical surface from magnetic resonance image (MRI) data. Using publicly available software (morphologika) the location and size dependence of the landmarks are removed and the differences in landmark distribution across subjects are analysed using principal component analysis. These differences, representing shape differences between subjects, can be visually assessed using wireframe models and transformation grids. The MRI data of 58 adult brains (27 female and 15 left handed) were examined. Shape differences in the whole brain are described which concern the relative orientation of frontal lobe sulci. Analysis of all 116 hemispheres revealed a statistically significant difference (P < 0.001) between left and right hemispheres. This finding was significant for right- but not left-handed subjects alone. No other significant age, gender, handedness, or brain-size correlations with shape differences were found.

The K-function for nearly regular point processes.

We propose modeling a nearly regular point pattern by a generalized Neyman-Scott process in which the offspring are Gaussian perturbations from a regular mean configuration. The mean configuration of interest is an equilateral grid, but our results can be used for any stationary regular grid. The case of uniformly distributed points is first studied as a benchmark. By considering the square of the interpoint distances, we can evaluate the first two moments of the K-function. These results can be used for parameter estimation, and simulations are used to both verify the theory and to assess the accuracy of the estimators. The methodology is applied to an investigation of regularity in plumes observed from swimming microorganisms.

Highly resistant regression and object matching.

In many disciplines, it is of great importance to match objects. Procrustes analysis is a popular method for comparing labeled point configurations based on a least squares criterion. We consider alternative procedures that are highly resistant to outlier points, and we describe an application in electrophoretic gel matching. We consider procedures based on S estimators, least median of squares, and least quartile difference estimators. Practical implementation issues are discussed, including random subset selection and intelligent subset selection (where subsets with small size or near collinear subsets are ignored). The relative performances of the resistant and Procrustes methods are examined in a simulation study.