Proxy and self-report agreement on the Stroke and Aphasia Quality of Life Scale-39

Background and purpose: Health related quality of life outcomes are increasingly used to measure the effectiveness of stroke interventions. People with severe aphasia after stroke may be unable to self-report on such measures, necessitating the use of proxy respondents. We explored the level of agreement between people with aphasia (PWA) and their proxies on the Stroke and Aphasia Quality of Life Scale (SAQOL-39) and whether this agreement is influenced by demographic variables and proxy levels of depression and carer strain. Methods: People with chronic aphasia (≥6 months post stroke) were recruited through the UK national charity for PWA. They were interviewed on the SAQOL-39 and their nominated proxies were interviewed on the SAQOL-39, the General Health Questionnaire and the Caregiver Strain Index. Proxy respondents had to be ≥18 years of age, see the person with aphasia at least twice a week and have no known severe mental health problems or cognitive decline. Results: 50 of 55 eligible pairs (91%) took part in the study. Proxies rated PWA as more severely affected than PWA rated themselves. The SDs of the difference scores were large and the difference was significant for three of the four SAQOL-39 domains and the overall mean (p≤0.01). However, the bias as indicated by effect sizes was small to moderate (0.2–0.5). The strength of the agreement was excellent for the overall SAQOL-39 and the physical domain (intra-class correlation coefficient ICC 0.8), good for the psychosocial and communication domains (0.7) and fair for the energy domain (0.5). Demographic variables and proxy’s mood and carer strain did not affect the level of agreement. Conclusions: For group comparisons, proxy respondents who are in frequent contact with people with chronic aphasia can reliably report on their health related quality of life, using the SAQOL-39. Although there are significant differences between PWA and proxy responses, the magnitude of this difference is small to moderate

Frameworks, Symmetry and Rigidity

Symmetry equations are obtained for the rigidity matrix of a bar-joint framework in R^d. These form the basis for a short proof of the Fowler-Guest symmetry group generalisation of the Calladine-Maxwell counting rules. Similar symmetry equations are obtained for the Jacobian of diverse framework systems, including constrained point-line systems that appear in CAD, body-pin frameworks, hybrid systems of distance constrained objects and infinite bar-joint frameworks. This leads to generalised forms of the Fowler-Guest character formula together with counting rules in terms of counts of symmetry-fixed elements. Necessary conditions for isostaticity are obtained for asymmetric frameworks, both when symmetries are present in subframeworks and when symmetries occur in partition-derived frameworks.Comment: 5 Figures. Replaces Dec. 2008 version. To appear in IJCG

Robust gradient-based discrete-time iterative learning control algorithms

This paper considers the use of matrix models and the robustness of a gradient-based Iterative Learning Control (ILC) algorithm using both fixed learning gains and gains derived from parameter optimization. The philosophy of the paper is to ensure monotonic convergence with respect to the mean square value of the error time series. The paper provides a complete and rigorous analysis for the systematic use of matrix models in ILC. Matrix models make analysis clearer and provide necessary and sufficient conditions for robust monotonic convergence. They also permit the construction of sufficient frequency domain conditions for robust monotonic convergence on finite time intervals for both causal and non-causal controller dynamics. The results are compared with recent results for robust inverse-model based ILC algorithms and it is seen that the algorithm has the potential to improve robustness to high frequency modelling errors provided that resonances within the plant bandwidth have been suppressed by feedback or series compensation

Consistency of Markov chain quasi-Monte Carlo on continuous state spaces

The random numbers driving Markov chain Monte Carlo (MCMC) simulation are usually modeled as independent U(0,1) random variables. Tribble [Markov chain Monte Carlo algorithms using completely uniformly distributed driving sequences (2007) Stanford Univ.] reports substantial improvements when those random numbers are replaced by carefully balanced inputs from completely uniformly distributed sequences. The previous theoretical justification for using anything other than i.i.d. U(0,1) points shows consistency for estimated means, but only applies for discrete stationary distributions. We extend those results to some MCMC algorithms for continuous stationary distributions. The main motivation is the search for quasi-Monte Carlo versions of MCMC. As a side benefit, the results also establish consistency for the usual method of using pseudo-random numbers in place of random ones.Comment: Published in at http://dx.doi.org/10.1214/10-AOS831 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Scalable Inference for Markov Processes with Intractable Likelihoods

Bayesian inference for Markov processes has become increasingly relevant in recent years. Problems of this type often have intractable likelihoods and prior knowledge about model rate parameters is often poor. Markov Chain Monte Carlo (MCMC) techniques can lead to exact inference in such models but in practice can suffer performance issues including long burn-in periods and poor mixing. On the other hand approximate Bayesian computation techniques can allow rapid exploration of a large parameter space but yield only approximate posterior distributions. Here we consider the combined use of approximate Bayesian computation (ABC) and MCMC techniques for improved computational efficiency while retaining exact inference on parallel hardware

Rigidity of Frameworks Supported on Surfaces

A theorem of Laman gives a combinatorial characterisation of the graphs that admit a realisation as a minimally rigid generic bar-joint framework in \bR^2. A more general theory is developed for frameworks in \bR^3 whose vertices are constrained to move on a two-dimensional smooth submanifold \M. Furthermore, when \M is a union of concentric spheres, or a union of parallel planes or a union of concentric cylinders, necessary and sufficient combinatorial conditions are obtained for the minimal rigidity of generic frameworks.Comment: Final version, 28 pages, with new figure

Uranium distribution as a proxy for basin-scale fluid flow in distributive fluvial systems

This work was supported by the Fluvial Systems Research Group sponsors BG Group, BP, Chevron, ConocoPhilips, and Total. We thank reviews from Martin Stokes, an anonymous reviewer and Editor Stuart Jones.Peer reviewedPostprin

How much improvement in mental health can be expected when people stop smoking? Findings from a national survey

Background and aims: There is evidence that mental health improves when smokers stop. This study aimed to assess in a nationally representative sample how far anxiety and depression in long-term ex-smokers can be expected eventually to reach levels found in those who have never smoked. Methods: Data from the Smoking Toolkit Study (STS) were used. The STS involves monthly household surveys of representative samples of the adult population of England. Anxiety and depression were compared using an item from the EQ5-D in respondents aged 40+ years where were either current smokers, never smokers, or had stopped for at least a year, adjusting statistically for age, gender and social grade. Results: The prevalence of anxiety or depression was 10.0% (95% CI 9.1-10.9) in never smokers, 18.3% (95% CI 16.0-20.6) in current smokers, and 11.3% (95% CI 9.6-13.0) in long-term ex-smokers. After adjusting for age, sex and social grade, long-term ex-smokers were similar to never smokers (OR=1.15, 95% CI=0.94-1.41). Current smokers had higher prevalence than never smokers (OR=1.69, 95% CI=1.39-2.04) and ex-smokers (OR=1.47, 95% CI=1.15-1.86). Conclusions: Prevalence of anxiety and depression in long-term ex-smokers appears to be similar to what is found in never smokers