6,384 research outputs found

    The study of cells using scanning force microscopy

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    A Brief DBT Skills Group for Bulimia Nervosa: A Feasibility Study

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    The focus of this thesis is eating disorders, specifically treatment outcomes for individuals with eating disorders. This thesis consists of three parts. The first part of the thesis is a systematic literature review on the treatment outcomes and dropout rates for men with eating disorders. Men with eating disorders are often excluded from research because of the low prevalence rates of eating disorders in men. The consequence of this is that treatment guidelines are developed based on research that has few, if any, male participants. This review aimed to review the currently available evidence on menā€™s treatment outcomes and dropout rates, and consider whether these are similar to womenā€™s treatment outcomes and dropout rates. The clinical and research implications of the findings of the review are discussed. The second part of the thesis is an empirical paper on the feasibility of a 12-week Dialectical Behaviour Therapy (DBT) skills group for women with bulimia nervosa. The results showed significant improvements in the participantā€™s eating disorder symptoms and functional impairment following the intervention. Feedback from participants also suggested that the intervention was acceptable to clients. Limitations, clinical implications, and research implications of the study are discussed. The data collection for this study was conducted jointly with another trainee investigating the change in acceptance and mindfulness following a DBT skills group. The third part of this thesis is a critical appraisal that reflects on some of the issues that arose during the research process. This critical appraisal focuses on three topics, the practical problems that arose in the research, the group processes that were observed in the DBT skills groups, and the relationship between sexuality and eating disorders in men

    Spatial heterogeneity lowers rather than increases host-parasite specialization.

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    This is the final version of the article. Available from the publisher via the DOI in this record.Abiotic environmental heterogeneity can promote the evolution of diverse resource specialists, which in turn may increase the degree of host-parasite specialization. We coevolved Pseudomonas fluorescens and lytic phage Ļ•2 in spatially structured populations, each consisting of two interconnected subpopulations evolving in the same or different nutrient media (homogeneous and heterogeneous environments, respectively). Counter to the normal expectation, host-parasite specialization was significantly lower in heterogeneous compared with homogeneous environments. This result could not be explained by dispersal homogenizing populations, as this would have resulted in the heterogeneous treatments having levels of specialization equal to or greater than that of the homogeneous environments. We argue that selection for costly generalists is greatest when the coevolving species are exposed to diverse environmental conditions and that this can provide an explanation for our results. A simple coevolutionary model of this process suggests that this can be a general mechanism by which environmental heterogeneity can reduce rather than increase host-parasite specialization.This project was funded by NERC (NE/G006938/2), BBSRC and the AXA Research Fund. ABu is supported by a Royal Society Wolfson Research Merit Award. ABe is supported by a Leverhulme Early Career Research Fellowship

    Clinically feasible diffusion MRI in muscle: Time dependence and initial findings in Duchenne muscular dystrophy

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    Purpose: To characterize the diffusion time-dependence in muscle in healthy adult volunteers, boys with Duchenneā€™s muscular dystrophy (DMD), and age-matched controls in a clinically feasible acquisition time for pediatric applications. / Methods: Diffusion data were acquired using a pulsed gradient stimulated echo diffusion preparation at 5 different diffusion times (70, 130, 190, 250, and 330 ms), at 4 different b-values (0, 200, 400, 600, and 800 s/mm2) and 6 directions (orthogonal x, y, and z and diagonal xy, xz, and yz) and processed to obtain standard diffusion indices (mean diffusivity [MD] and fractional anisotropy [FA]) at each diffusion time. / Results Time-dependent diffusion was seen in muscle in healthy adult volunteers, boys with DMD, and age-matched controls. Boys with DMD showed reduced MD and increased FA values in comparison to age matched controls across a range of diffusion times. A diffusion time of Ī” = 190 ms had the largest effect size. / Conclusions: These results could be used to optimize diffusion imaging in this disease further and imply that these diffusion indices may become an important biomarker in monitoring progression in DMD in the future

    Anomalous Hall effect in a two-dimensional electron gas

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    The anomalous Hall effect in a magnetic two-dimensional electron gas with Rashba spin-orbit coupling is studied within the Kubo-Streda formalism in the presence of pointlike potential impurities. We find that all contributions to the anomalous Hall conductivity vanish to leading order in disorder strength when both chiral subbands are occupied. In the situation that only the majority subband is occupied, all terms are finite in the weak scattering limit and the total anomalous Hall conductivity is dominated by skew scattering. We compare our results to previous treatments and resolve some of the discrepancies present in the literature.Comment: 11 pages, 5 figure

    Estimating the Accuracy of Spectral Learning for HMMs

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    Hidden Markov models (HMMs) are usually learned using the expectation maximisation algorithm which is, unfortunately, subject to local optima. Spectral learning for HMMs provides a unique, optimal solution subject to availability of a sufficient amount of data. However, with access to limited data, there is no means of estimating the accuracy of the solution of a given model. In this paper, a new spectral evaluation method has been proposed which can be used to assess whether the algorithm is converging to a stable solution on a given dataset. The proposed method is designed for real-life datasets where the true model is not available. A number of empirical experiments on synthetic as well as real datasets indicate that our criterion is an accurate proxy to measure quality of models learned using spectral learning
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