12 resultados para Regressão linear local

em Université de Lausanne, Switzerland


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PURPOSE: To determine the local control and complication rates for children with papillary and/or macular retinoblastoma progressing after chemotherapy and undergoing stereotactic radiotherapy (SRT) with a micromultileaf collimator. METHODS AND MATERIALS: Between 2004 and 2008, 11 children (15 eyes) with macular and/or papillary retinoblastoma were treated with SRT. The mean age was 19 months (range, 2-111). Of the 15 eyes, 7, 6, and 2 were classified as International Classification of Intraocular Retinoblastoma Group B, C, and E, respectively. The delivered dose of SRT was 50.4 Gy in 28 fractions using a dedicated micromultileaf collimator linear accelerator. RESULTS: The median follow-up was 20 months (range, 13-39). Local control was achieved in 13 eyes (87%). The actuarial 1- and 2-year local control rates were both 82%. SRT was well tolerated. Late adverse events were reported in 4 patients. Of the 4 patients, 2 had developed focal microangiopathy 20 months after SRT; 1 had developed a transient recurrence of retinal detachment; and 1 had developed bilateral cataracts. No optic neuropathy was observed. CONCLUSIONS: Linear accelerator-based SRT for papillary and/or macular retinoblastoma in children resulted in excellent tumor control rates with acceptable toxicity. Additional research regarding SRT and its intrinsic organ-at-risk sparing capability is justified in the framework of prospective trials.

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Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed an upscaling procedure based on a Bayesian sequential simulation approach. This method is then applied to the stochastic integration of low-resolution, regional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this upscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

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We present a novel spatiotemporal-adaptive Multiscale Finite Volume (MsFV) method, which is based on the natural idea that the global coarse-scale problem has longer characteristic time than the local fine-scale problems. As a consequence, the global problem can be solved with larger time steps than the local problems. In contrast to the pressure-transport splitting usually employed in the standard MsFV approach, we propose to start directly with a local-global splitting that allows to locally retain the original degree of coupling. This is crucial for highly non-linear systems or in the presence of physical instabilities. To obtain an accurate and efficient algorithm, we devise new adaptive criteria for global update that are based on changes of coarse-scale quantities rather than on fine-scale quantities, as it is routinely done before in the adaptive MsFV method. By means of a complexity analysis we show that the adaptive approach gives a noticeable speed-up with respect to the standard MsFV algorithm. In particular, it is efficient in case of large upscaling factors, which is important for multiphysics problems. Based on the observation that local time stepping acts as a smoother, we devise a self-correcting algorithm which incorporates the information from previous times to improve the quality of the multiscale approximation. We present results of multiphase flow simulations both for Darcy-scale and multiphysics (hybrid) problems, in which a local pore-scale description is combined with a global Darcy-like description. The novel spatiotemporal-adaptive multiscale method based on the local-global splitting is not limited to porous media flow problems, but it can be extended to any system described by a set of conservation equations.

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BACKGROUND: Activation of coagulation and fibrinolysis play a role in the pathophysiology of experimental arthritis. Objective: To determine the extent of activation of the coagulation and fibrinolytic pathways in different joint diseases in humans and to ascertain the factors that may influence fibrin deposition within the joint. METHODS: Plasma from normal subjects (controls, n= 21) and plasma and synovial fluid samples from patients with rheumatoid arthritis (RA; n = 64), osteoarthritis (OA; n = 29), spondyloarthropathy (SpA; n = 22) and crystal arthritis (CA; n = 25) were analyzed for the levels of TF (tissue factor) and tissue factor pathway inhibitor (TFPI) activities, thrombin-antithrombin III (TAT) complexes, and F1 + 2 (thrombin fragment), fibrin d-dimer and thrombin-activated fibrinolysis inhibitor (TAFI) antigenic levels. The measurements were analyzed by pairwise correlation with each other as well as with standard parameters of inflammation [C-reactive protein (CRP), joint leukocyte count]. Inter-group comparisons were performed to look for disease-specific differences. RESULTS: Compared with healthy controls, patients with joint diseases had higher levels of TAT, F1 + 2 and d-dimers in their plasma. In the synovial fluid, TF activity, TAT, d-dimers, and TAFI were significantly higher in inflammatory arthritides than in OA. The levels were highest in RA patients. In the plasma, TF activity was correlated with TAT and d-dimer levels with CRP, TFPI, and TAT. In the synovial fluid, TF activity correlated with plasma CRP levels, synovial fluid leukocyte count, and synovial TAT and TAFI levels. In addition, synovial d-dimers correlated with CRP, and synovial TAFI levels were correlated with synovial F1 + 2 and TAT. CONCLUSIONS: Activation of the coagulation and fibrinolytic cascades in the joint and in the circulation is evident in both inflammatory and degenerative joint diseases. Within the joint, inflammatory mechanisms leading to TF-mediated activation of the coagulation pathway and subsequent fibrin deposition is the most likely explanation for the observed findings. In the plasma, the link between inflammation (CRP increase) and TF activation is weak, and a non-TF-mediated mechanism of coagulation activation could explain these findings. RA is characterized by significantly higher levels of TAT in the synovial fluid and plasma than other arthritides. Although fibrinolytic activity is linked to inflammation, the increased amounts of TAFI in the joint, particularly in RA, may explain why fibrin formation is so prominent in this condition compared with other joint diseases.

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Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the scale of a field site represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed downscaling procedure based on a non-linear Bayesian sequential simulation approach. The main objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity logged at collocated wells and surface resistivity measurements, which are available throughout the studied site. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariatekernel density function. Then a stochastic integration of low-resolution, large-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities is applied. The overall viability of this downscaling approach is tested and validated by comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure allows obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

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The distribution limits of Crocidura russula (Hermann, 1780) and C. leucodon (Hermann, 1780) were investigated during an interval of 25 years in the bottom of the Rhone valley above Lake Geneva, Switzerland (total data set: 105 spatio-temporal occurrences, 1137 shrews). In 1975, the contact zone between the two species was situated in the region of Martigny. In 1999/2000, new sampling revealed three results: (1) The contact zone showed an upward shift of about 25 km. (2) In the expanded range of C. russula, the resident species has totally disappeared (confirmed by owl pellets analysis). (3) This demonstrates a dominance of C, russula over C. leucodon. Three hypotheses which may explain the range expansion of C. russula were evaluated: (1) habitat modification favouring linear dispersal due to the construction of a highway; (2) temporal event favoured by climate fluctuations, or (3) ongoing postglacial colonisation of Europe. Hypothesis 1 was rejected, because the progression of the shrews anticipated the construction. Hypothesis 3 received only weak support because range limits of C. russula in the region of Nice have been stable for thousands of years. Therefore hypothesis 2, admitting that ongoing climate change has facilitated range expansion, is the most probable.

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Introduction: The beneficial effect of physical exercise on bone mineral density (BMD) is at least partly explained by the forces exerted directly on the bones. Male runners present generally higher BMD than sedentary individuals. We postulated that the proximal tibia BMD is related to the running distance as well as to the magnitude of the shocks (while running) in male runners. Methods: A prospective study (three yearly measurements) included 81 healthy male subjects: 16 sedentary lean subjects and three groups of runners (5-30 km/week, n=19; 30-50 km/week, n=29; 50-100 km/week, n=17). Several measurements were performed at the proximal tibia level: volumetric BMD (vBMD), cortical index (CI) i.e. an index of cortical bone thickness and peak accelerations (an index of shocks during heel strike) while running (measured by a 3-D accelerometer). A general linear model assessed the prediction of vBMD or CI by a) simple effects (running distance, peak accelerations, time) and b) interactions (for instance if vBMD prediction by peak acceleration depends on running distance). Results: CI and vBMD a) increase with running distance to reach a plateau over 30 km/wk, b) are positively associated with peak accelerations over 30 km/week. Discussion: Running may be associated with high peak accelerations in order to have beneficial effects on BMD. More important strains are needed to be associated with the same increase in BMD during running sessions of short duration than those of long duration. Conclusion: CI and vBMD are associated with the magnitude of the shocks during heel strike in runners. Key words: Bone mineral density, strains, physical exercise, running distance.

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In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

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Aims: To assess the potential distribution of an obligate seeder and active pyrophyte, Cistus salviifolius, a vulnerable species in the Swiss Red List; to derive scenarios by changing the fire return interval; and to discuss the results from a conservation perspective. A more general aim is to assess the impact of fire as a natural factor influencing the vegetation of the southern slopes of the Alps. Locations: Alps, southern Switzerland. Methods: Presence-absence data to fit the model were obtained from the most recent field mapping of C. salviifolius. The quantitative environmental predictors used in this study include topographic, climatic and disturbance (fire) predictors. Models were fitted by logistic regression and evaluated by jackknife and bootstrap approaches. Changes in fire regime were simulated by increasing the time-return interval of fire (simulating longer periods without fire). Two scenarios were considered: no fire in the past 15 years; or in the past 35 years. Results: Rock cover, slope, topographic position, potential evapotranspiration and time elapsed since the last fire were selected in the final model. The Nagelkerke R-2 of the model for C. salviifolius was 0.57 and the Jackknife area under the curve evaluation was 0.89. The bootstrap evaluation revealed model robustness. By increasing the return interval of fire by either up to 15 years, or 35 years, the modelled C. salviifolius population declined by 30-40%, respectively. Main conclusions: Although fire plays a significant role, topography and rock cover appear to be the most important predictors, suggesting that the distribution of C. salviifolius in the southern Swiss Alps is closely related to the availability of supposedly competition-free sites, such as emerging bedrock, ridge locations or steep slopes. Fire is more likely to play a secondary role in allowing C. salviifolius to extend its occurrence temporarily, by increasing germination rates and reducing the competition from surrounding vegetation. To maintain a viable dormant seed bank for C. salviifolius, conservation managers should consider carrying out vegetation clearing and managing wild fire propagation to reduce competition and ensure sufficient recruitment for this species.

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Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed a downscaling procedure based on a non-linear Bayesian sequential simulation approach. The basic objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity, which is available throughout the model space. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariate kernel density function. This method is then applied to the stochastic integration of low-resolution, re- gional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this downscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the downscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

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PURPOSE: The longitudinal relaxation rate (R1 ) measured in vivo depends on the local microstructural properties of the tissue, such as macromolecular, iron, and water content. Here, we use whole brain multiparametric in vivo data and a general linear relaxometry model to describe the dependence of R1 on these components. We explore a) the validity of having a single fixed set of model coefficients for the whole brain and b) the stability of the model coefficients in a large cohort. METHODS: Maps of magnetization transfer (MT) and effective transverse relaxation rate (R2 *) were used as surrogates for macromolecular and iron content, respectively. Spatial variations in these parameters reflected variations in underlying tissue microstructure. A linear model was applied to the whole brain, including gray/white matter and deep brain structures, to determine the global model coefficients. Synthetic R1 values were then calculated using these coefficients and compared with the measured R1 maps. RESULTS: The model's validity was demonstrated by correspondence between the synthetic and measured R1 values and by high stability of the model coefficients across a large cohort. CONCLUSION: A single set of global coefficients can be used to relate R1 , MT, and R2 * across the whole brain. Our population study demonstrates the robustness and stability of the model. Magn Reson Med, 2014. © 2014 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. Magn Reson Med 73:1309-1314, 2015. © 2014 Wiley Periodicals, Inc.

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INTRODUCTION: Osteoset(®) T is a calcium sulphate void filler containing 4% tobramycin sulphate, used to treat bone and soft tissue infections. Despite systemic exposure to the antibiotic, there are no pharmacokinetic studies in humans published so far. Based on the observations made in our patients, a model predicting tobramycin serum levels and evaluating their toxicity potential is presented. METHODS: Following implantation of Osteoset(®) T, tobramycin serum concentrations were monitored systematically. A pharmacokinetic analysis was performed using a non-linear mixed effects model based on a one compartment model with first-degree absorption. RESULTS: Data from 12 patients treated between October 2006 and March 2008 were analysed. Concentration profiles were consistent with the first-order slow release and single-compartment kinetics, whilst showing important variability. Predicted tobramycin serum concentrations depended clearly on both implanted drug amount and renal function. DISCUSSION AND CONCLUSION: Despite the popularity of aminoglycosides for local antibiotic therapy, pharmacokinetic data for this indication are scarce, and not available for calcium sulphate as carrier material. Systemic exposure to tobramycin after implantation of Osteoset(®) T appears reassuring regarding toxicity potential, except in case of markedly impaired renal function. We recommend in adapting the dosage to the estimated creatinine clearance rather than solely to the patient's weight.