Bioeconomic modelling of male Holstein-Friesian dairy calf-to-beef production systems on Irish farms

peer-reviewedWith the abolition of milk quota in 2015 and increase in the use of Holstein-Friesian sires in recent years there is predicted to be an increase in the number of male Holstein-Friesian animals available for beef production. In broad terms, farmers have two options for finishing these animals; as bulls or steers. In either case, Irish beef cattle systems are based on maximising lifetime live-weight gain from grass-based diets. Managing the relationship between the supply and demand for grazed grass is complicated in these pasture-based systems due to the seasonal variability in grass growth. The Grange Dairy Beef Systems Model (GDBSM) was used to simulate the relationship between grazed grass supply and demand and then determine the profitability of Holstein-Friesian male animals finished as bulls at 16 (B16), 19 (B19) and 22 (B22) months of age and steers at 24 (S24) months of age. Combinations of these cattle finishing options were also evaluated. The most profitable system was S24. All systems were very sensitive to variations in beef and concentrate prices and less sensitive to calf price changes with fertiliser price changes having very little effect. Bull systems were more sensitive than the steer system to variation in beef, calf and concentrate prices. There was no advantage of combination systems in terms of utilisation of grass grown or net margin

A high-fat diet containing whole walnuts (Juglans regia) reduces tumour size and growth along with plasma insulin-like growth factor 1 in the transgenic adenocarcinoma of the mouse prostate model.

Prostate cancer (PCa) has been linked to fat intake, but the effects of both different dietary fat levels and types remain inconsistent and incompletely characterised. The effects on PCa in the transgenic adenocarcinoma of the mouse prostate (TRAMP) cancer model of an elevated fat (20 % of energy as fat) diet containing 155 g of whole walnuts were compared to those of an elevated fat (20 % of energy as soyabean oil) diet with matched macronutrients, tocopherols as well as a low-fat (8 % of energy as soyabean oil) diet. Mice, starting at 8 weeks of age, consumed one of the three different diets ad libitum; and prostates, livers and blood were obtained after 9, 18 or 24 weeks of feeding. No differences were observed in whole animal growth rates in either high-fat (HF) diet group, but prostate tumour weight and growth rate were reduced in the walnut diet group. Walnut diet group prostate weight, plasma insulin-like growth factor 1, resistin and LDL were lower at 18 weeks, while no statistically significant prostate weight differences by diet were seen at 9 or 24 weeks. Multiple metabolites in the livers differed by diet at 9 and 18 weeks. The walnut diet's beneficial effects probably represent the effects of whole walnuts' multiple constituents and not via a specific fatty acid or tocopherols. Moreover, as the two HF diets had dissimilar effects on prostate tumour growth rate and size, and yet had the same total fat and tocopherol composition and content, this suggests that these are not strongly linked to PCa growth

Assessment of the learning curve in health technologies: a systematic review

Objective: We reviewed and appraised the methods by which the issue of the learning curve has been addressed during health technology assessment in the past. Method: We performed a systematic review of papers in clinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE, HealthSTAR, MEDLINE, Science Citation Index, and Social Science Citation Index) using the search term "learning curve:" Results: The clinical search retrieved 4,571 abstracts for assessment, of which 559 (12%) published articles were eligible for review. Of these, 272 were judged to have formally assessed a learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). The majority of the studies were case series (95%). Some 47% of studies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the method was unclear for 31%. The statistical methods used were simple graphs (44%), splitting the data chronologically and performing a t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%). Conclusions: Learning curves are rarely considered formally in health technology assessment. Where they are, the reporting of the studies and the statistical methods used are weak. As a minimum, reporting of learning should include the number and experience of the operators and a detailed description of data collection. Improved statistical methods would enhance the assessment of health technologies that require learning

Assessing the learning curve effect in health technologies: Lessons from the non-clinical literature

Introduction: Many health technologies exhibit some form of learning effect, and this represents a barrier to rigorous assessment. It has been shown that the statistical methods used are relatively crude. Methods to describe learning curves in fields outside medicine, for example, psychology and engineering, may be better. Methods: To systematically search non–health technology assessment literature (for example, PsycLit and Econlit databases) to identify novel statistical techniques applied to learning curves. Results: The search retrieved 9,431 abstracts for assessment, of which 18 used a statistical technique for analyzing learning effects that had not previously been identified in the clinical literature. The newly identified methods were combined with those previously used in health technology assessment, and categorized into four groups of increasing complexity: a) exploratory data analysis; b) simple data analysis; c) complex data analysis; and d) generic methods. All the complex structured data techniques for analyzing learning effects were identified in the nonclinical literature, and these emphasized the importance of estimating intra- and interindividual learning effects. Conclusion: A good dividend of more sophisticated methods was obtained by searching in nonclinical fields. These methods now require formal testing on health technology data sets

Automated assessment of disease progression in Acute Myeloid Leukemia by probabilistic analysis of flow cytometry data

Objective: Flow cytometry (FC) is a widely acknowledged technology in diagnosis of acute myeloid leukemia (AML) and has been indispensable in determining progression of the disease. Although FC plays a key role as a post-therapy prognosticator and evaluator of therapeutic efficacy, the manual analysis of cytometry data is a barrier to optimization of reproducibility and objectivity. This study investigates the utility of our recently introduced non-parametric Bayesian framework in accurately predicting the direction of change in disease progression in AML patients using FC data. Methods: The highly flexible non-parametric Bayesian model based on the infinite mixture of infinite Gaussian mixtures is used for jointly modeling data from multiple FC samples to automatically identify functionally distinct cell populations and their local realizations. Phenotype vectors are obtained by characterizing each sample by the proportions of recovered cell populations, which are in turn used to predict the direction of change in disease progression for each patient. Results: We used 200 diseased and non-diseased immunophenotypic panels for training and tested the system with 36 additional AML cases collected at multiple time points. The proposed framework identified the change in direction of disease progression with accuracies of 90% (9 out of 10) for relapsing cases and 100% (26 out of 26) for the remaining cases. Conclusions: We believe that these promising results are an important first step towards the development of automated predictive systems for disease monitoring and continuous response evaluation. Significance: Automated measurement and monitoring of therapeutic response is critical not only for objective evaluation of disease status prognosis but also for timely assessment of treatment strategies

Coarse-graining microscopic strains in a harmonic, two-dimensional solid and its implications for elasticity: non-local susceptibilities and non-affine noise

In soft matter systems the local displacement field can be accessed directly by video microscopy enabling one to compute local strain fields and hence the elastic moduli using a coarse-graining procedure. We study this process for a simple triangular lattice of particles connected by harmonic springs in two-dimensions. Coarse-graining local strains obtained from particle configurations in a Monte Carlo simulation generates non-trivial, non-local strain correlations (susceptibilities), which may be understood within a generalized, Landau type elastic Hamiltonian containing up to quartic terms in strain gradients (K. Franzrahe et al., Phys. Rev. E 78, 026106 (2008)). In order to demonstrate the versatility of the analysis of these correlations and to make our calculations directly relevant for experiments on colloidal solids, we systematically study various parameters such as the choice of statistical ensemble, presence of external pressure and boundary conditions. We show that special care needs to be taken for an accurate application of our results to actual experiments, where the analyzed area is embedded within a larger system, to which it is mechanically coupled. Apart from the smooth, affine strain fields, the coarse-graining procedure also gives rise to a noise field made up of non-affine displacements. Several properties of this noise field may be rationalized for the harmonic solid using a simple "cell model" calculation. Furthermore the scaling behavior of the probability distribution of the noise field is studied and a master curve is obtained.Comment: 16 pages, 12 figure

Field Tests of Some Liming Treatments for Growing Burley Tobacco on Acid Soils

A major problem in the production of tobacco on acid soils in Kentucky is manganese toxicity, despite the fact that it can be easily prevented. Since manganese toxicity is caused by high levels of soil acidity, prevention of soil acidity by liming is the best method of control. And soil testing is the only way to determine how acid a field has become