946 resultados para giving-up density
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In this letter the core-core-valence Auger transitions of an atomic impurity, both in bulk or adsorbed on a jellium-like surface, are computed within a DFT framework. The Auger rates calculated by the Fermi golden rule are compared with those determined by an approximate and simpler expression. This is based on the local density of states (LDOS) with a core hole present, in a region around the impurity nucleus. Different atoms, Na and Mg, solids, Al and Ag, and several impurity locations are considered. We obtain an excellent agreement between KL1V and KL23V rates worked out with the two approaches. The radius of the sphere in which we calculate the LDOS is the relevant parameter of the simpler approach. Its value only depends on the atomic species regardless of the location of the impurity and the type of substrate. (C) 2003 Elsevier B.V. All rights reserved.
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Introduction: The accurate identification of tissue electron densities is of great importance for Monte Carlo (MC) dose calculations. When converting patient CT data into a voxelised format suitable for MC simulations, however, it is common to simplify the assignment of electron densities so that the complex tissues existing in the human body are categorized into a few basic types. This study examines the effects that the assignment of tissue types and the calculation of densities can have on the results of MC simulations, for the particular case of a Siemen’s Sensation 4 CT scanner located in a radiotherapy centre where QA measurements are routinely made using 11 tissue types (plus air). Methods: DOSXYZnrc phantoms are generated from CT data, using the CTCREATE user code, with the relationship between Hounsfield units (HU) and density determined via linear interpolation between a series of specified points on the ‘CT-density ramp’ (see Figure 1(a)). Tissue types are assigned according to HU ranges. Each voxel in the DOSXYZnrc phantom therefore has an electron density (electrons/cm3) defined by the product of the mass density (from the HU conversion) and the intrinsic electron density (electrons /gram) (from the material assignment), in that voxel. In this study, we consider the problems of density conversion and material identification separately: the CT-density ramp is simplified by decreasing the number of points which define it from 12 down to 8, 3 and 2; and the material-type-assignment is varied by defining the materials which comprise our test phantom (a Supertech head) as two tissues and bone, two plastics and bone, water only and (as an extreme case) lead only. The effect of these parameters on radiological thickness maps derived from simulated portal images is investigated. Results & Discussion: Increasing the degree of simplification of the CT-density ramp results in an increasing effect on the resulting radiological thickness calculated for the Supertech head phantom. For instance, defining the CT-density ramp using 8 points, instead of 12, results in a maximum radiological thickness change of 0.2 cm, whereas defining the CT-density ramp using only 2 points results in a maximum radiological thickness change of 11.2 cm. Changing the definition of the materials comprising the phantom between water and plastic and tissue results in millimetre-scale changes to the resulting radiological thickness. When the entire phantom is defined as lead, this alteration changes the calculated radiological thickness by a maximum of 9.7 cm. Evidently, the simplification of the CT-density ramp has a greater effect on the resulting radiological thickness map than does the alteration of the assignment of tissue types. Conclusions: It is possible to alter the definitions of the tissue types comprising the phantom (or patient) without substantially altering the results of simulated portal images. However, these images are very sensitive to the accurate identification of the HU-density relationship. When converting data from a patient’s CT into a MC simulation phantom, therefore, all possible care should be taken to accurately reproduce the conversion between HU and mass density, for the specific CT scanner used. Acknowledgements: This work is funded by the NHMRC, through a project grant, and supported by the Queensland University of Technology (QUT) and the Royal Brisbane and Women's Hospital (RBWH), Brisbane, Australia. The authors are grateful to the staff of the RBWH, especially Darren Cassidy, for assistance in obtaining the phantom CT data used in this study. The authors also wish to thank Cathy Hargrave, of QUT, for assistance in formatting the CT data, using the Pinnacle TPS. Computational resources and services used in this work were provided by the HPC and Research Support Group, QUT, Brisbane, Australia.
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Introduction: The use of amorphous-silicon electronic portal imaging devices (a-Si EPIDs) for dosimetry is complicated by the effects of scattered radiation. In photon radiotherapy, primary signal at the detector can be accompanied by photons scattered from linear accelerator components, detector materials, intervening air, treatment room surfaces (floor, walls, etc) and from the patient/phantom being irradiated. Consequently, EPID measurements which presume to take scatter into account are highly sensitive to the identification of these contributions. One example of this susceptibility is the process of calibrating an EPID for use as a gauge of (radiological) thickness, where specific allowance must be made for the effect of phantom-scatter on the intensity of radiation measured through different thicknesses of phantom. This is usually done via a theoretical calculation which assumes that phantom scatter is linearly related to thickness and field-size. We have, however, undertaken a more detailed study of the scattering effects of fields of different dimensions when applied to phantoms of various thicknesses in order to derive scattered-primary ratios (SPRs) directly from simulation results. This allows us to make a more-accurate calibration of the EPID, and to qualify the appositeness of the theoretical SPR calculations. Methods: This study uses a full MC model of the entire linac-phantom-detector system simulated using EGSnrc/BEAMnrc codes. The Elekta linac and EPID are modelled according to specifications from the manufacturer and the intervening phantoms are modelled as rectilinear blocks of water or plastic, with their densities set to a range of physically realistic and unrealistic values. Transmissions through these various phantoms are calculated using the dose detected in the model EPID and used in an evaluation of the field-size-dependence of SPR, in different media, applying a method suggested for experimental systems by Swindell and Evans [1]. These results are compared firstly with SPRs calculated using the theoretical, linear relationship between SPR and irradiated volume, and secondly with SPRs evaluated from our own experimental data. An alternate evaluation of the SPR in each simulated system is also made by modifying the BEAMnrc user code READPHSP, to identify and count those particles in a given plane of the system that have undergone a scattering event. In addition to these simulations, which are designed to closely replicate the experimental setup, we also used MC models to examine the effects of varying the setup in experimentally challenging ways (changing the size of the air gap between the phantom and the EPID, changing the longitudinal position of the EPID itself). Experimental measurements used in this study were made using an Elekta Precise linear accelerator, operating at 6MV, with an Elekta iView GT a-Si EPID. Results and Discussion: 1. Comparison with theory: With the Elekta iView EPID fixed at 160 cm from the photon source, the phantoms, when positioned isocentrically, are located 41 to 55 cm from the surface of the panel. At this geometry, a close but imperfect agreement (differing by up to 5%) can be identified between the results of the simulations and the theoretical calculations. However, this agreement can be totally disrupted by shifting the phantom out of the isocentric position. Evidently, the allowance made for source-phantom-detector geometry by the theoretical expression for SPR is inadequate to describe the effect that phantom proximity can have on measurements made using an (infamously low-energy sensitive) a-Si EPID. 2. Comparison with experiment: For various square field sizes and across the range of phantom thicknesses, there is good agreement between simulation data and experimental measurements of the transmissions and the derived values of the primary intensities. However, the values of SPR obtained through these simulations and measurements seem to be much more sensitive to slight differences between the simulated and real systems, leading to difficulties in producing a simulated system which adequately replicates the experimental data. (For instance, small changes to simulated phantom density make large differences to resulting SPR.) 3. Comparison with direct calculation: By developing a method for directly counting the number scattered particles reaching the detector after passing through the various isocentric phantom thicknesses, we show that the experimental method discussed above is providing a good measure of the actual degree of scattering produced by the phantom. This calculation also permits the analysis of the scattering sources/sinks within the linac and EPID, as well as the phantom and intervening air. Conclusions: This work challenges the assumption that scatter to and within an EPID can be accounted for using a simple, linear model. Simulations discussed here are intended to contribute to a fuller understanding of the contribution of scattered radiation to the EPID images that are used in dosimetry calculations. Acknowledgements: This work is funded by the NHMRC, through a project grant, and supported by the Queensland University of Technology (QUT) and the Royal Brisbane and Women's Hospital, Brisbane, Australia. The authors are also grateful to Elekta for the provision of manufacturing specifications which permitted the detailed simulation of their linear accelerators and amorphous-silicon electronic portal imaging devices. Computational resources and services used in this work were provided by the HPC and Research Support Group, QUT, Brisbane, Australia.
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Does participation in a tournament influence prosocial behaviour in subsequent interactions? We designed an experiment to collect data on charitable donations made by participants out of their earnings from a real-effort tournament. We varied the earnings associated with ranks across our treatments thereby allowing us to observe donations by participants who end up at different ranks but have the same earnings. Prior to finding out how well they performed, participants were also asked to report their expected rank. Controlling for differences in effort and earnings, participants who were ranked first donated significantly more than others, supporting the view that positive affect from winning may increase generosity. However, we find that this effect diminishes when the difference between realised and expected ranks are controlled for, lending support to the idea that positive surprise from winning also increases generosity.
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Advances in mobile telephone technology and available dermoscopic attachments for mobile telephones have created a unique opportunity for consumer-initiated mobile teledermoscopy. At least 2 companies market a dermoscope attachment for an iPhone (Apple), forming a mobile teledermoscope. These devices and the corresponding software applications (apps) enable (1) lesion magnification (at least ×20) and visualization with polarized light; (2) photographic documentation using the telephone camera; (3) lesion measurement (ruler); (4) adding of image and lesion details; and (5) e-mail data to a teledermatologist for review. For lesion assessment, the asymmetry-color (AC) rule has 94% sensitivity and 62 specificity for melanoma identification by consumers [1]. Thus, consumers can be educated to recognize asymmetry and color patterns in suspect lesions. However, we know little about consumers' use of mobile teledermoscopy for lesion assessment.
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The ‘Giving Australia’ project is an initiative of the Prime Minister’s Community Business Partnership, coordinated by the Australian Council of Social Service (ACOSS) in collaboration with the Centre for Australian Community Organisations and Management (CACOM) at the University of Technology, Sydney, the Australian Centre of Philanthropy and Nonprofit Studies (ACPNS)at the Queensland University of Technology, Roy Morgan Research (RMR),McNair Ingenuity Research and the Fundraising Institute - Australia (FIA).
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Each year The Australian Centre for Philanthropy and Nonprofit Studies (ACPNS) at QUT analyses statistics on tax-deductible donations made by Australians in their individual income tax returns to Deductible Gift Recipients (DGRs). The information presented below is based on the amount and type of tax-deductible donations made by Australian taxpayers to DGRs for the period 1 July 2010 to 30 June 2011 extracted from the Australian Taxation Office's publication Taxation Statistics 2010-2011.1
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This research aims to develop a reliable density estimation method for signalised arterials based on cumulative counts from upstream and downstream detectors. In order to overcome counting errors associated with urban arterials with mid-link sinks and sources, CUmulative plots and Probe Integration for Travel timE estimation (CUPRITE) is employed for density estimation. The method, by utilizing probe vehicles’ samples, reduces or cancels the counting inconsistencies when vehicles’ conservation is not satisfied within a section. The method is tested in a controlled environment, and the authors demonstrate the effectiveness of CUPRITE for density estimation in a signalised section, and discuss issues associated with the method.
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The Low-Density Lipoprotein Receptor (LDLR) gene is a cell surface receptor that plays an important role in cholesterol homeostasis. We investigated the (TA)n polymorphism in exon 18 of the LDLR gene on chromosome 19p13.2 performing an association analysis in 244 typical migraine-affected patients, 151 suffering from migraine with aura (MA), 96 with migraine without aura (MO) and 244 unaffected controls. The populations consisted of Caucasians only, and controls were age- and sex-matched. The results showed no significant difference between groups for allele frequency distributions of the (TA)n polymorphism even after separation of the migraine-affected individuals into subgroups of MA and MO affected patients. This is in contradiction to Mochi et al. who found a positive association of this variant with MO. Our study discusses possible differences between the two studies and extends this research by investigating circulating cholesterol levels in a migraine-affected population.
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RFLPs at the low density lipoprotein receptor locus (LDLR) display marked linkage disequilibrium between each other. Cross-sectional analysis of a bi-alleleic ApaLI RFLP of LDLR showed that the 9.4- and 6.6-kb alleles were present in similar frequency between a group of 84 Caucasian essential hypertensive (HT) and a group of 96 normotensive subjects whose parents each had a similar blood pressure status at age > or = 50. After subdividing HTs into lean and obese, however, the frequency of the 6.6-kb allele in the 27 HTs with BMI > or = 26 kg/m2 was 0.63, compared with 0.39 for HTs with BMI < 26 (chi 2 = 8.8; P = 0.004). The difference in genotype frequencies was even more striking (chi 2 = 23; P = 0.00008), with a virtual absence of 9.4-kb homozygotes in the obese HT group (1 vs 22). Genetic variation at LDLR (19p13.2) is thus associated with obesity in HT.
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OBJECTIVE To determine whether a microsatellite polymorphism located towards the 3' end of the low density lipoprotein receptor gene (LDLR) is associated with obesity. DESIGN A cross-sectional case-control study. SUBJECTS One hundred and seven obese individuals, defined as a body mass index (BMI) ≤ 26 kg/m2, and 163 lean individuals, defined as a BMI < 26 kg/m2. MEASUREMENTS BMI, blood pressure, serum lipids, alleles of LDLR microsatellite (106 bp, 108 bp and 112 bp). RESULTS There was a significant association between variants of the LDLR microsatellite and obesity, in the overall tested population, due to a contributing effect in females (χ2 = 12.3, P = 0.002), but not in males (χ2 = 0.3, P = 0.87). In females, individuals with the 106 bp allele were more likely to be lean, while individuals with the 112 bp and/or 108 bp alleles tended to be obese. CONCLUSIONS These results suggest that in females, LDLR may play a role in the development of obesity.
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Obese (BMI ≥ 26 kg/m 2; n = 51) and lean (BMI <26 kg/m 2; n = 61) Caucasian patients with severe, familial essential hypertension, were compared with respect to genotype and allele frequencies of a HincII RFLP of the low density lipoprotein receptor gene (LDLR). A similar analysis was performed in obese (n = 28) and lean (n = 68) normotensives. A significant association of the C allele of the T→C variant responsible for this RFLP was seen with obesity (χ 2 = 4.6, P = 0.029) in the hypertensive, but not in the normotensive, group (odds ratio = 3.0 for the CC genotype and 2.7 for CT). Furthermore, BMI tracked with genotypes of this allele in the hypertensives (P = 0.046). No significant genotypic relationship was apparent for plasma lipids. Significant linkage disequilibrium was, moreover, noted between the HincII RFLP and an ApaLI RFLP (χ 2 = 33, P<0.0005) that has previously shown even stronger association with obesity (odds ratio 19.6 for cases homozygous for the susceptibility allele and 15.2 for het-erozygotes). The present study therefore adds to our previous evidence implicating LDLR as a locus for obesity in patients with essential hypertension.
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In the recent manuscript published by Egodawatta et al. (2013), the authors investigated the build-up process of heavy metals (HMs) associated with road-deposited sediment (RDS) on residential road surfaces, and presented empirical models for the prediction of both the surface loads and build-up rates of HMs on these surfaces...