886 research outputs found

    The Beta Generalized Exponential Distribution

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    We introduce the beta generalized exponential distribution that includes the beta exponential and generalized exponential distributions as special cases. We provide a comprehensive mathematical treatment of this distribution. We derive the moment generating function and the rrth moment thus generalizing some results in the literature. Expressions for the density, moment generating function and rrth moment of the order statistics also are obtained. We discuss estimation of the parameters by maximum likelihood and provide the information matrix. We observe in one application to real data set that this model is quite flexible and can be used quite effectively in analyzing positive data in place of the beta exponential and generalized exponential distributions

    Can quantitative analysis of multi-parametric MRI independently predict failure of focal salvage HIFU therapy in men with radio-recurrent prostate cancer?

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    OBJECTIVES: Focal salvage HIFU is a feasible therapeutic option in some men who have recurrence after primary radiotherapy for prostate cancer. We aimed to determine if multi-parametric quantitative parameters, in addition to clinical factors, might have a role in independently predicting focal salvage HIFU outcomes. METHODS: A retrospective registry analysis included 150 consecutive men who underwent focal salvage HIFU (Sonablate500) (2006-2015); 89 had mpMRI available. Metastatic disease was excluded by nodal assessment on pelvic MRI, a radioisotope bone-scan and/or choline or FDG PET/CT scan. All men had mpMRI and either transperineal template prostate mapping biopsy or targeted and systematic TRUS-biopsy. mpMRI included T2-weighted, diffusion-weighted and dynamic contrast-enhancement. Pre-HIFU quantitative mpMRI data was obtained using Horos DICOM Viewer v3.3.5 for general MRI parameters and IB DCE v2.0 plug-in. Progression-free survival (PFS) was defined by biochemical failure and/or positive localized or distant imaging results and/or positive biopsy and/or systemic therapy and/or metastases/prostate cancer-specific death. Potential predictors of PFS were analyzed by univariable and multivariable Cox-regression. RESULTS: Median age at focal salvage HIFU was 71 years (interquartile range [IQR] 65-74.5) and median PSA pre-focal salvage treatment was 5.8ng/ml (3.8-8). Median follow-up was 35 months (23-47) and median time to failure was 15 months (7.8-24.3). D-Amico low, intermediate and high-risk disease was present in 1% (1/89), 40% (36/89) and 43% (38/89) prior to focal salvage HIFU (16% missing data). 56% (50/89) failed by the composite outcome. A total of 22 factors were evaluated on univariable and 8 factors on multivariable analysis. The following quantitative parameters were included: Ktrans, Kep, Ve, Vp, IS, rTTP and TTP. On univariable analysis, PSA, prostate volume at time of radiotherapy failure and Ve (median) value were predictors for failure. Ve represents extracellular fraction of the whole tissue volume. On multivariable analysis, only Ve (median) value remained as an independent predictor. CONCLUSIONS: One pharmacokinetic quantitative parameter based on DCE sequences seems to independently predict failure following focal salvage HIFU for radio-recurrent prostate cancer. This likely relates to the tumor microenvironment producing heat-sinks which counter the heating effect of HIFU. Further validation in larger datasets and evaluating mechanisms to reduce heat-sinks are required

    An Assessment of Recent and Future Temperature Change over the Sichuan Basin, China, using CMIP5 Climate Models

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    The Sichuan basin is one of the most densely populated regions of China, making the area particularly vulnerable to the adverse impacts associated with future climate change. As such, climate models are important for understanding regional and local impacts of climate change and variability, like heat stress and drought. In this study, climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are validated over the Sichuan basin by evaluating how well each model can capture the phase, amplitude, and variability of the regionally observed mean, maximum, and minimum temperature between 1979 and 2005. The results reveal that the majority of the models do not capture the basic spatial pattern and observed means, trends, and probability distribution functions. In particular, mean and minimum temperatures are underestimated, especially during the winter, resulting in biases exceeding −3°C. Models that reasonably represent the complex basin topography are found to generally have lower biases overall. The five most skillful climate models with respect to the regional climate of the Sichuan basin are selected to explore twenty-first-century temperature projections for the region. Under the CMIP5 high-emission future climate change scenario, representative concentration pathway 8.5 (RCP8.5), the temperatures are projected to increase by approximately 4°C (with an average warming rate of +0.72°C decade−1), with the greatest warming located over the central plains of the Sichuan basin, by 2100. Moreover, the frequency of extreme months (where mean temperature exceeds 28°C) is shown to increase in the twenty-first century at a faster rate compared to the twentieth century.Funding for this research was provided by the Engineering and Physical Sciences Research Council (EPSRC) as part of the Low Carbon Climate-Responsive Heating and Cooling of Cities (LoHCool) project (EP/N009797/1)

    Ogre and Pythia: An Invariance Proof Method for Weak Consistency Models

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    We design an invariance proof method for concurrent programs parameterised by a weak consistency model. The calculational design of the invariance proof method is by abstract interpretation of a truly parallel analytic semantics. This generalises the methods by Lamport and Owicki-Gries for sequential consistency. We use cat as an example of language to write consistency specifications of both concurrent programs and machine architectures

    Contributions of team climate in the study of interprofessional collaboration: a conceptual analysis

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    The concept of team climate is widely used to understand and evaluate working environments. It shares some important features with Interprofessional Collaboration (IPC). The four-factor theory of climate for work group innovation, which underpins team climate, could provide a better basis for understanding both teamwork and IPC. This article examines in detail the common ground between team climate and IPC, and assesses the relevance of team climate as a theoretical approach to understanding IPC. There are important potential areas of overlap between team climate and IPC that we have grouped under four headings: (1) interaction and communication between team members; (2) common objectives around which collective work is organised; (3) responsibility for performing work to a high standard; and (4) promoting innovation in working practices. These overlapping areas suggest common characteristics that could provide elements of a framework for considering the contribution of team climate to collaborative working, both from a conceptual perspective and, potentially, in operational terms as, for example, a diagnostic tool
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