Reaching older people with PA delivered in football clubs: the reach, adoption and implementation characteristics of the Extra Time Programme.

Background Older adults (OA) represent a core priority group for physical activity and Public Health policy. As a result, significant interest is placed on how to optimise adherence to interventions promoting these approaches. Extra Time (ET) is an example of a national programme of physical activity interventions delivered in professional football clubs for OA aged 55+ years. This paper aims to examine the outcomes from ET, and unpick the processes by which these outcomes were achieved. Methods This paper represents a secondary analysis of data collected during the evaluation of ET. From the 985 OA reached by ET, n=486 adopted the programme and completed post-intervention surveys (typically 12 weeks). We also draw on interview data with 18 ET participants, and 7 staff who delivered the programme. Data were subject to thematic analysis to generate overarching and sub themes. Results Of the 486 participants, the majority 95%, (n= 462) were White British and 59.7% (n=290) were female. Most adopters (65.4%/n=318) had not participated in previous interventions in the host clubs. Social interaction was the most frequently reported benefit of participation (77.2%, n=375). While the reach of the club badge was important in letting people know about the programme, further work enhanced adoption and satisfaction. These factors included (i) listening to participants, (ii) delivering a flexible age-appropriate programme of diverse physical and social activities, (iii) offering activities which satisfy energy drives and needs for learning and (iv) extensive opportunities for social engagement. Conclusions Findings emerging from this study indicate that physical activity and health interventions delivered through professional football clubs can be effective for engaging OA

Limitations of Quantum Simulation Examined by Simulating a Pairing Hamiltonian using Nuclear Magnetic Resonance

Quantum simulation uses a well-known quantum system to predict the behavior of another quantum system. Certain limitations in this technique arise, however, when applied to specific problems, as we demonstrate with a theoretical and experimental study of an algorithm to find the low-lying spectrum of a Hamiltonian. While the number of elementary quantum gates does scale polynomially with the size of the system, it increases inversely to the desired error bound $\epsilon$. Making such simulations robust to decoherence using fault-tolerance constructs requires an additional factor of $1/ \epsilon$ gates. These constraints are illustrated by using a three qubit nuclear magnetic resonance system to simulate a pairing Hamiltonian, following the algorithm proposed by Wu, Byrd, and Lidar.Comment: 6 pages, 2 eps figure

Robust Chauvenet Outlier Rejection

Sigma clipping is commonly used in astronomy for outlier rejection, but the number of standard deviations beyond which one should clip data from a sample ultimately depends on the size of the sample. Chauvenet rejection is one of the oldest, and simplest, ways to account for this, but, like sigma clipping, depends on the sample's mean and standard deviation, neither of which are robust quantities: Both are easily contaminated by the very outliers they are being used to reject. Many, more robust measures of central tendency, and of sample deviation, exist, but each has a tradeoff with precision. Here, we demonstrate that outlier rejection can be both very robust and very precise if decreasingly robust but increasingly precise techniques are applied in sequence. To this end, we present a variation on Chauvenet rejection that we call "robust" Chauvenet rejection (RCR), which uses three decreasingly robust/increasingly precise measures of central tendency, and four decreasingly robust/increasingly precise measures of sample deviation. We show this sequential approach to be very effective for a wide variety of contaminant types, even when a significant -- even dominant -- fraction of the sample is contaminated, and especially when the contaminants are strong. Furthermore, we have developed a bulk-rejection variant, to significantly decrease computing times, and RCR can be applied both to weighted data, and when fitting parameterized models to data. We present aperture photometry in a contaminated, crowded field as an example. RCR may be used by anyone at https://skynet.unc.edu/rcr, and source code is available there as well.Comment: 62 pages, 48 figures, 7 tables, accepted for publication in ApJ

Chaos, containment and change: responding to persistent offending by young people

This article reviews policy developments in Scotland concerning 'persistent young offenders' and then describes the design of a study intended to assist a local planning group in developing its response. The key findings of a review of casefiles of young people involved in persistent offending are reported. It emerges that youth crime and young people involved in offending are more complex and heterogeneous than is sometimes assumed. This, along with a review of some literature about desistance from offending, reaffirms the need for properly individualised interventions. Studies of 'desisters' suggest the centrality of effective and engaging working relationships in this process. However, these studies also re-assert the significance of the social contexts of workers’ efforts to bring 'change' out of 'chaos'. We conclude therefore that the 'new correctionalism' must be tempered with appreciation of the social exclusion of young people who offend

Monte Carlo Study of the Separation of Energy Scales in Quantum Spin 1/2 Chains with Bond Disorder

One-dimensional Heisenberg spin 1/2 chains with random ferro- and antiferromagnetic bonds are realized in systems such as $Sr_3 CuPt_{1-x} Ir_x O_6$. We have investigated numerically the thermodynamic properties of a generic random bond model and of a realistic model of $Sr_3 CuPt_{1-x} Ir_x O_6$ by the quantum Monte Carlo loop algorithm. For the first time we demonstrate the separation into three different temperature regimes for the original Hamiltonian based on an exact treatment, especially we show that the intermediate temperature regime is well-defined and observable in both the specific heat and the magnetic susceptibility. The crossover between the regimes is indicated by peaks in the specific heat. The uniform magnetic susceptibility shows Curie-like behavior in the high-, intermediate- and low-temperature regime, with different values of the Curie constant in each regime. We show that these regimes are overlapping in the realistic model and give numerical data for the analysis of experimental tests.Comment: 7 pages, 5 eps-figures included, typeset using JPSJ.sty, accepted for publication in J. Phys. Soc. Jpn. 68, Vol. 3. (1999

A 23 GHz Survey of GRB Error Boxes

The Haystack 37-meter telescope was used in a pilot project in May 1995 to observe GRB error boxes at 23~GHz. Seven BATSE error boxes and two IPN arcs were scanned by driving the beam of the telescope rapidly across their area. For the BATSE error boxes, the radio observations took place two to eighteen days after the BATSE detection, and several boxes were observed more than once. Total power data were recorded continuously as the telescope was driven at a rate of 0.2~degrees/second, yielding Nyquist sampling of the beam with an integration time of 50~milliseconds, corresponding to a theoretical rms sensitivity of 0.5~Jy. Under conditions of good weather, this sensitivity was achieved. In a preliminary analysis of the data we detect only two sources, 3C273 and 0552+398, both catalogued sources that are known to be variable at 23~GHz. Neither had a flux density that was unusally high or low at the time of our observations.Comment: 5 pages, 1 postscript figure. To appear in Proceedings of the Third Huntsville Symposium on Gamma-Ray Bursts (eds. C. Kouveliotou, M. S. Briggs, and G. J. Fishman

The effect of organelle discovery upon sub-cellular protein localisation.

Prediction of protein sub-cellular localisation by employing quantitative mass spectrometry experiments is an expanding field. Several methods have led to the assignment of proteins to specific subcellular localisations by partial separation of organelles across a fractionation scheme coupled with computational analysis. Methods developed to analyse organelle data have largely employed supervised machine learning algorithms to map unannotated abundance profiles to known protein–organelle associations. Such approaches are likely to make association errors if organelle-related groupings present in experimental output are not included in data used to create a protein–organelle classifier. Currently, there is no automated way to detect organelle-specific clusters within such datasets. In order to address the above issues we adapted a phenotype discovery algorithm, originally created to filter image-based output for RNAi screens, to identify putative subcellular groupings in organelle proteomics experiments. We were able to mine datasets to a deeper level and extract interesting phenotype clusters for more comprehensive evaluation in an unbiased fashion upon application of this approach. Organelle-related protein clusters were identified beyond those sufficiently annotated for use as training data. Furthermore, we propose avenues for the incorporation of observations made into general practice for the classification of protein–organelle membership from quantitative MS experiments. Biological significance Protein sub-cellular localisation plays an important role in molecular interactions, signalling and transport mechanisms. The prediction of protein localisation by quantitative mass-spectrometry (MS) proteomics is a growing field and an important endeavour in improving protein annotation. Several such approaches use gradient-based separation of cellular organelle content to measure relative protein abundance across distinct gradient fractions. The distribution profiles are commonly mapped in silico to known protein–organelle associations via supervised machine learning algorithms, to create classifiers that associate unannotated proteins to specific organelles. These strategies are prone to error, however, if organelle-related groupings present in experimental output are not represented, for example owing to the lack of existing annotation, when creating the protein–organelle mapping. Here, the application of a phenotype discovery approach to LOPIT gradient-based MS data identifies candidate organelle phenotypes for further evaluation in an unbiased fashion. Software implementation and usage guidelines are provided for application to wider protein–organelle association experiments. In the wider context, semi-supervised organelle discovery is discussed as a paradigm with which to generate new protein annotations from MS-based organelle proteomics experiments. This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012]

Phase-Space Metric for Non-Hamiltonian Systems

We consider an invariant skew-symmetric phase-space metric for non-Hamiltonian systems. We say that the metric is an invariant if the metric tensor field is an integral of motion. We derive the time-dependent skew-symmetric phase-space metric that satisfies the Jacobi identity. The example of non-Hamiltonian systems with linear friction term is considered.Comment: 12 page

Efficiency of symmetric targeting for finite-T DMRG

Two targeting schemes have been known for the density matrix renormalization group (DMRG) applied to non-Hermitian problems; one uses an asymmetric density matrix and the other uses symmetric density matrix. We compare the numerical efficiency of these two targeting schemes when they are used for the finite temperature DMRG.Comment: 4 pages, 3 Postscript figures, REVTe