Applying interprofessional education to the practice setting.

Interprofessional education is a key requirement identified in various professional and regulatory body education standards in the UK. However, recent high-profile investigatory reports into adverse incidents in NHS organisations have demonstrated failures of translating interprofessional education into practice. This paper explores how a university in the south of England uses service improvement projects to address this. Working with key senior clinicians, small groups of students from a variety of professional backgrounds collaborate to address an identified problem in practice to bring about better, safer practice to benefit patients. This style of learning enables students to acquire essential attributes in preparation for employment, such as critical thinking, teamworking, ethical practice and leadership

A mechanistic-empirical based overlay design method for reflective cracking

This paper describes a new and innovative mechanistically based pavement overlay design method that considers the most predominant type of overlay distress observed in the field: Reflective cracking above old cracks in the underlying pavement surface. Both dense-graded hot mix asphalt and gap-graded asphalt rubber (wet process) mixes were studied, in the laboratory and in the field, to derive the necessary mechanistic relationships and statistically based equations. The models proposed are based on a finite element model that closely approximates actual field phenomena. Many field test sections, in Arizona, California and Portugal, were studied during the course of the research. Other HMA mixes used for overlays may also be calibrated and used through the proposed method. However, the relevant mix properties of any additional materials or environmental zones must first be determined. The two mix types studied are mainly used in the desert southwest region of Arizona and California. The overlay design program is available from the Rubber Pavements Association or Arizona Department of Transportation in the form of an Excel spreadsheet with an easy-to-use visual basic computer program (macro)

Note and Comment

The Ownership of Sunken Logs; Combination Among Physicians to Fix Prices for Professional Services; The Issuance of Receivers\u27 Certificates to pay Interest, Etc.; The Federal Constitution is Not Violated by a State Law Compelling one Accused of Crime to Testify Against Himself; Transfer of Negotiable Instrument Without Endorsemen

The effect of regular exercise on insulin sensitivity in type 2 diabetes mellitus: A systematic review and meta-analysis

The purpose of this study was to examine the effect of regular exercise training on insulin sensitivity in adults with type 2 diabetes mellitus ( T2DM ) using the pooled data available from randomised controlled trials. In addition, we sought to determine whether short-term periods of physical inactivity diminish the exercise-induced improvement in insulin sensitivity. Eligible trials included exercise interventions that involved ≥3 exercise sessions, and reported a dynamic measurement of insulin sensitivity. There was a significant pooled effect size ( ES ) for the effect of exercise on insulin sensitivity ( ES, –0.588; 95% confidence interval [CI], –0.816 to –0.359; P < 0.001 ). Of the 14 studies included for meta-analyses, nine studies reported the time of data collection from the last exercise bout. There was a significant improvement in insulin sensitivity in favour of exercise versus control between 48 and 72 hours after exercise ( ES, –0.702; 95% CI, –1.392 to –0.012; P=0.046 ); and this persisted when insulin sensitivity was measured more than 72 hours after the last exercise session ( ES, –0.890; 95% CI, –1.675 to –0.105; P=0.026 ). Regular exercise has a significant benefit on insulin sensitivity in adults with T2DM and this may persist beyond 72 hours after the last exercise session

Large Scale Structure in the SDSS Galaxy Survey

The Large Scale Structure (LSS) in the galaxy distribution is investigated using the Sloan Digital Sky Survey Early Data Release (SDSS EDR). Using the Minimal Spanning Tree technique we have extracted sets of filaments, of wall-like structures, of galaxy groups, and of rich clusters from this unique sample. The physical properties of these structures were then measured and compared with the expectations from Zel'dovich' theory. The measured characteristics of galaxy walls were found to be consistent with those for a spatially flat $\Lambda$CDM cosmological model with $\Omega_m\approx$ 0.3 and $\Omega_\Lambda \approx$ 0.7, and for Gaussian initial perturbations with a Harrison -- Zel'dovich power spectrum. Furthermore, we found that the mass functions of groups and of unrelaxed structure elements generally fit well with the expectations from Zel'dovich' theory, although there was some discrepancy for lower mass groups which may be due to incompleteness in the selected sample of groups. We also note that both groups and rich clusters tend to prefer the environments of walls, which tend to be of higher density, rather than the environments of filaments, which tend to be of lower density. Finally, we note evidence of systematic differences in the properties of the LSS between the Northern Galactic Cap stripe and the Southern Galactic Cap stripe -- in particular, in the physical properties of the walls, their spatial distribution, and the relative numbers of clusters embedded in walls. Because the mean separation of walls is $\approx$ 60 -- 70$h^{-1}$ Mpc, each stripe only intersects a few tens of walls. Thus, small number statistics and cosmic variance are the likely drivers of these systematic differences.Comment: 13 pages, 11 figures, MNRAS submitte

New Approaches To Photometric Redshift Prediction Via Gaussian Process Regression In The Sloan Digital Sky Survey

Expanding upon the work of Way and Srivastava 2006 we demonstrate how the use of training sets of comparable size continue to make Gaussian process regression (GPR) a competitive approach to that of neural networks and other least-squares fitting methods. This is possible via new large size matrix inversion techniques developed for Gaussian processes (GPs) that do not require that the kernel matrix be sparse. This development, combined with a neural-network kernel function appears to give superior results for this problem. Our best fit results for the Sloan Digital Sky Survey (SDSS) Main Galaxy Sample using u,g,r,i,z filters gives an rms error of 0.0201 while our results for the same filters in the luminous red galaxy sample yield 0.0220. We also demonstrate that there appears to be a minimum number of training-set galaxies needed to obtain the optimal fit when using our GPR rank-reduction methods. We find that morphological information included with many photometric surveys appears, for the most part, to make the photometric redshift evaluation slightly worse rather than better. This would indicate that most morphological information simply adds noise from the GP point of view in the data used herein. In addition, we show that cross-match catalog results involving combinations of the Two Micron All Sky Survey, SDSS, and Galaxy Evolution Explorer have to be evaluated in the context of the resulting cross-match magnitude and redshift distribution. Otherwise one may be misled into overly optimistic conclusions.Comment: 32 pages, ApJ in Press, 2 new figures, 1 new table of comparison methods, updated discussion, references and typos to reflect version in Pres

Novel Methods for Predicting Photometric Redshifts from Broad Band Photometry using Virtual Sensors

We calculate photometric redshifts from the Sloan Digital Sky Survey Main Galaxy Sample, The Galaxy Evolution Explorer All Sky Survey, and The Two Micron All Sky Survey using two new training-set methods. We utilize the broad-band photometry from the three surveys alongside Sloan Digital Sky Survey measures of photometric quality and galaxy morphology. Our first training-set method draws from the theory of ensemble learning while the second employs Gaussian process regression both of which allow for the estimation of redshift along with a measure of uncertainty in the estimation. The Gaussian process models the data very effectively with small training samples of approximately 1000 points or less. These two methods are compared to a well known Artificial Neural Network training-set method and to simple linear and quadratic regression. Our results show that robust photometric redshift errors as low as 0.02 RMS can regularly be obtained. We also demonstrate the need to provide confidence bands on the error estimation made by both classes of models. Our results indicate that variations due to the optimization procedure used for almost all neural networks, combined with the variations due to the data sample, can produce models with variations in accuracy that span an order of magnitude. A key contribution of this paper is to quantify the variability in the quality of results as a function of model and training sample. We show how simply choosing the "best" model given a data set and model class can produce misleading results.Comment: 36 pages, 12 figures, ApJ in Press, modified to reflect published version and color figure