989 resultados para dose distribution


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Objective The aim of this systematic review and meta-analysis was to determine the overall effect of resistance training (RT) on measures of muscular strength in people with Parkinson’s disease (PD). Methods Controlled trials with parallel-group-design were identified from computerized literature searching and citation tracking performed until August 2014. Two reviewers independently screened for eligibility and assessed the quality of the studies using the Cochrane risk-of-bias-tool. For each study, mean differences (MD) or standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated for continuous outcomes based on between-group comparisons using post-intervention data. Subgroup analysis was conducted based on differences in study design. Results Nine studies met the inclusion criteria; all had a moderate to high risk of bias. Pooled data showed that knee extension, knee flexion and leg press strength were significantly greater in PD patients who undertook RT compared to control groups with or without interventions. Subgroups were: RT vs. control-without-intervention, RT vs. control-with-intervention, RT-with-other-form-of-exercise vs. control-without-intervention, RT-with-other-form-of-exercise vs. control-with-intervention. Pooled subgroup analysis showed that RT combined with aerobic/balance/stretching exercise resulted in significantly greater knee extension, knee flexion and leg press strength compared with no-intervention. Compared to treadmill or balance exercise it resulted in greater knee flexion, but not knee extension or leg press strength. RT alone resulted in greater knee extension and flexion strength compared to stretching, but not in greater leg press strength compared to no-intervention. Discussion Overall, the current evidence suggests that exercise interventions that contain RT may be effective in improving muscular strength in people with PD compared with no exercise. However, depending on muscle group and/or training dose, RT may not be superior to other exercise types. Interventions which combine RT with other exercise may be most effective. Findings should be interpreted with caution due to the relatively high risk of bias of most studies.

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Species distribution models (SDMs) are considered to exemplify Pattern rather than Process based models of a species' response to its environment. Hence when used to map species distribution, the purpose of SDMs can be viewed as interpolation, since species response is measured at a few sites in the study region, and the aim is to interpolate species response at intermediate sites. Increasingly, however, SDMs are also being used to also extrapolate species-environment relationships beyond the limits of the study region as represented by the training data. Regardless of whether SDMs are to be used for interpolation or extrapolation, the debate over how to implement SDMs focusses on evaluating the quality of the SDM, both ecologically and mathematically. This paper proposes a framework that includes useful tools previously employed to address uncertainty in habitat modelling. Together with existing frameworks for addressing uncertainty more generally when modelling, we then outline how these existing tools help inform development of a broader framework for addressing uncertainty, specifically when building habitat models. As discussed earlier we focus on extrapolation rather than interpolation, where the emphasis on predictive performance is diluted by the concerns for robustness and ecological relevance. We are cognisant of the dangers of excessively propagating uncertainty. Thus, although the framework provides a smorgasbord of approaches, it is intended that the exact menu selected for a particular application, is small in size and targets the most important sources of uncertainty. We conclude with some guidance on a strategic approach to identifying these important sources of uncertainty. Whilst various aspects of uncertainty in SDMs have previously been addressed, either as the main aim of a study or as a necessary element of constructing SDMs, this is the first paper to provide a more holistic view.

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We propose a new information-theoretic metric, the symmetric Kullback-Leibler divergence (sKL-divergence), to measure the difference between two water diffusivity profiles in high angular resolution diffusion imaging (HARDI). Water diffusivity profiles are modeled as probability density functions on the unit sphere, and the sKL-divergence is computed from a spherical harmonic series, which greatly reduces computational complexity. Adjustment of the orientation of diffusivity functions is essential when the image is being warped, so we propose a fast algorithm to determine the principal direction of diffusivity functions using principal component analysis (PCA). We compare sKL-divergence with other inner-product based cost functions using synthetic samples and real HARDI data, and show that the sKL-divergence is highly sensitive in detecting small differences between two diffusivity profiles and therefore shows promise for applications in the nonlinear registration and multisubject statistical analysis of HARDI data.

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Diffusion weighted magnetic resonance (MR) imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of 6 directions, second-order tensors can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve crossing fiber tracts. Recently, a number of high-angular resolution schemes with greater than 6 gradient directions have been employed to address this issue. In this paper, we introduce the Tensor Distribution Function (TDF), a probability function defined on the space of symmetric positive definite matrices. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the diffusion orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function.

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Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. With High-angular resolution diffusion imaging (HARDI) and the tensor distribution function (TDF), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. We found that FA, when compared with TDF-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. By contrast, mean diffusivity (MD) as defined in standard DTI appears to be more accurate. Overall, we argue that novel measures derived from the TDF approach may yield more sensitive and accurate information than DTI-derived measures.

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High-angular resolution diffusion imaging (HARDI) can reconstruct fiber pathways in the brain with extraordinary detail, identifying anatomical features and connections not seen with conventional MRI. HARDI overcomes several limitations of standard diffusion tensor imaging, which fails to model diffusion correctly in regions where fibers cross or mix. As HARDI can accurately resolve sharp signal peaks in angular space where fibers cross, we studied how many gradients are required in practice to compute accurate orientation density functions, to better understand the tradeoff between longer scanning times and more angular precision. We computed orientation density functions analytically from tensor distribution functions (TDFs) which model the HARDI signal at each point as a unit-mass probability density on the 6D manifold of symmetric positive definite tensors. In simulated two-fiber systems with varying Rician noise, we assessed how many diffusionsensitized gradients were sufficient to (1) accurately resolve the diffusion profile, and (2) measure the exponential isotropy (EI), a TDF-derived measure of fiber integrity that exploits the full multidirectional HARDI signal. At lower SNR, the reconstruction accuracy, measured using the Kullback-Leibler divergence, rapidly increased with additional gradients, and EI estimation accuracy plateaued at around 70 gradients.

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This paper describes part of an engineering study that was undertaken to demonstrate that a multi-megawatt Photovoltaic (PV) generation system could be connected to a rural 11 kV feeder without creating power quality issues for other consumers. The paper concentrates solely on the voltage regulation aspect of the study as this was the most innovative part of the study. The study was carried out using the time-domain software package, PSCAD/EMTDC. The software model included real time data input of actual measured load and scaled PV generation data, along with real-time substation voltage regulator and PV inverter reactive power control. The outputs from the model plot real-time voltage, current and power variations throughout the daily load and PV generation variations. Other aspects of the study not described in the paper include the analysis of harmonics, voltage flicker, power factor, voltage unbalance and system losses.

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This study investigated a potential source of inaccuracy for diode measurements in modulated beams; the effect of diode housing asymmetry on measurement results. The possible effects of diode housing asymmetry on the measurement of steep dose gradients were evaluated by measuring 5x5 cm2 beam profiles, with three cylindrical diodes and two commonly used ionization chambers, with each dosimeter positioned in a 3D scanning water tank with its stem perpendicular to the beam axis (horizontal) and parallel to the direction of scanning. The resulting profiles were used to compare the penumbrae measured with the diode stem pointing into (equivalent to a “stem-first” setup) and out of the field (equivalent to a “stem-last” setup) in order to evaluate the effects of dosimeter alignment and thereby identify the effects of dosimeter asymmetry. The stem-first and stem-last orientations resulted in differences of up to 0.2 mm in the measured 20-80% penumbra widths and differences of up to 0.4 mm in the off axis position of the 90% isodose. These differences, which are smaller than previously reported for older model dosimeters, were apparent in the profile results for both diodes and small volume ionization chambers. As an extension to this study, the practical use of all five dosimeters was exemplified by measuring point doses in IMRT test beams. These measurements showed good agreement (within 2%) between the diodes and the small volume ionization chamber, with all of these dosimeters being able to identify a region 3% under-dosage which was not identified by a larger volume (6 mm diameter) ionization chamber. The results of this work should help to remove some of the barriers to the use of diodes for modulated radiotherapy dosimetry in the future.

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The development of Electric Energy Storage (EES) integrated with Renewable Energy Resources (RER) has increased use of optimum scheduling strategy in distribution systems. Optimum scheduling of EES can reduce cost of purchased energy by retailers while improve the reliability of customers in distribution system. This paper proposes an optimum scheduling strategy for EES and the evaluation of its impact on reliability of distribution system. Case study shows the impact of the proposed strategy on reliability indices of a distribution system.

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Distribution Revolution is a collection of interviews with leading film and TV professionals concerning the many ways that digital delivery systems are transforming the entertainment business. These interviews provide lively insider accounts from studio executives, distribution professionals, and creative talent of the tumultuous transformation of film and TV in the digital era. The first section features interviews with top executives at major Hollywood studios, providing a window into the big-picture concerns of media conglomerates with respect to changing business models, revenue streams, and audience behaviors. The second focuses on innovative enterprises that are providing path-breaking models for new modes of content creation, curation, and distribution—creatively meshing the strategies and practices of Hollywood and Silicon Valley. And the final section offers insights from creative talent whose professional practices, compensation, and everyday working conditions have been transformed over the past ten years. Taken together, these interviews demonstrate that virtually every aspect of the film and television businesses is being affected by the digital distribution revolution, a revolution that has likely just begun. Interviewees include: • Gary Newman, Chairman, 20th Century Fox Television • Kelly Summers, Former Vice President, Global Business Development and New Media Strategy, Walt Disney Studios • Thomas Gewecke, Chief Digital Officer and Executive Vice President, Strategy and Business Development, Warner Bros. Entertainment • Ted Sarandos, Chief Content Officer, Netflix • Felicia D. Henderson, Writer-Producer, Soul Food, Gossip Girl • Dick Wolf, Executive Producer and Creator, Law & Order

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This thesis examined the level of food safety compliance with government regulations and investigated routes of microbiological contaminations in raw finfish within Vietnamese domestic seafood distribution chains. Findings from direct observation, microbiological analysis and employee surveys were synthesized to identify the main factors affecting food safety and hygiene practices of fish distributors. The studies produced clear recommendations for food safety management in the domestic distribution chains. The findings may contribute to national efforts to decrease the risks of fish-borne illness for consumers in Vietnam.

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Pharmacology is the science underpinning dosing, mechanisms of action and effectiveness of drugs. Central to pharmacology, are the studies of pharmacokinetics (PK) and pharmacodynamics (PD). On one hand, PK defines the time-course of drug concentrations in the body and incorporates the broad concepts of drug absorption, distribution, metabolism and elimination. On the other hand, PD describes the relationship between drug concentrations and pharmacological effects. In practice, PK is often referred as “what the body does to the drug” whilst PD as “what the drug does to the body”. Thus, PK/PD describes the relationship between drug dose and pharmacological effects with changes in drug concentrations leading to different pharmacological effects.

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Critical illness, acute renal failure and continuous renal replacement therapy (CRRT) are associated with changes in pharmacokinetics. Initial antibiotic dose should be based on published volume of distribution and generally be at least the standard dose, as volume of distribution is usually unchanged or increased. Subsequent doses should be based on total clearance. Total clearance varies with the CRRT clearance which mainly depends on effluent flow rate, sieving coefficient/saturation coefficient. As antibiotic clearance by healthy kidneys is usually higher than clearance by CRRT, except for colistin, subsequent doses should generally be lower than given to patients without renal dysfunction. In the future therapeutic drug monitoring, together with sophisticated pharmacokinetic models taking into account the pharmacokinetic variability, may enable more appropriate individualized dosing.

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Diffusion weighted magnetic resonance imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of six directions, second-order tensors (represented by three-by-three positive definite matrices) can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve more complicated white matter configurations, e.g., crossing fiber tracts. Recently, a number of high-angular resolution schemes with more than six gradient directions have been employed to address this issue. In this article, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Using the calculus of variations, we solve the TDF that optimally describes the observed data. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function. Moreover, a tensor orientation distribution function (TOD) may also be derived from the TDF, allowing for the estimation of principal fiber directions and their corresponding eigenvalues.

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Cord cutting refers to the act of cable and satellite consumers cancelling their subscriptions and opting instead for non-traditional distribution outlets, like streaming media platforms. The trend has been the subject of much debate in the trade press and a source of much concern for the industry. Yet many questions remain unanswered: Is it really a major trend? Does it save consumers money? Can viewers still find the content they love? How do we even “cut the cord” anyway?