127 resultados para geographically weighted regression


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In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

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Purpose: To present the long-term outcome (LTO) of 10 adolescents and young adults with documented cognitive and behavioral regression as children due to non-lesional focal, mainly frontal epilepsy with continuous spike-waves during slow wave sleep (CSWS). Method: Past medical and EEG data of all patients were reviewed and neuropsychological tests exploring main cognitive functions were administered. Result: After a mean duration of follow-up of 15.6 years (range 8-23 years), none of the 10 patients had recovered fully, but four regained borderline to normal intelligence and were almost independent. Patients with prolonged global intellectual regression had the worst outcome, whereas those with more specific and short-lived deficits recovered best. The marked behavioral disorders that were so disturbing during the active period (AP) resolved in all but one patient. Executive functions were neither severely nor homogenously affected. Three patients with a frontal syndrome during the AP disclosed only mild residual executive and social cognition deficits. The main cognitive gains occurred shortly after the AP, but qualitative improvements continued to occur. LTO correlated best with duration of CSWS. Conclusion: Our findings emphasize that cognitive recovery after cessation of CSWS depends on the severity and duration of the initial regression. None of our patients had major executive and social cognition deficits with preserved intelligence as reported in adults with destructive lesions of the frontal lobes during childhood. Early recognition of epilepsy with CSWS and rapid introduction of effective therapy are crucial for a best possible outcome.

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In a weighted spatial network, as specified by an exchange matrix, the variances of the spatial values are inversely proportional to the size of the regions. Spatial values are no more exchangeable under independence, thus weakening the rationale for ordinary permutation and bootstrap tests of spatial autocorrelation. We propose an alternative permutation test for spatial autocorrelation, based upon exchangeable spatial modes, constructed as linear orthogonal combinations of spatial values. The coefficients obtain as eigenvectors of the standardised exchange matrix appearing in spectral clustering, and generalise to the weighted case the concept of spatial filtering for connectivity matrices. Also, two proposals aimed at transforming an acessibility matrix into a exchange matrix with with a priori fixed margins are presented. Two examples (inter-regional migratory flows and binary adjacency networks) illustrate the formalism, rooted in the theory of spectral decomposition for reversible Markov chains.

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PURPOSE: To evaluate the utility of inversion recovery with on-resonant water suppression (IRON) in combination with injection of the long-circulating monocrystalline iron oxide nanoparticle (MION)-47 for contrast material-enhanced magnetic resonance (MR) angiography. MATERIALS AND METhods: Experiments were approved by the institutional animal care committee. Eleven rabbits were imaged at baseline before injection of a contrast agent and then serially 5-30 minutes, 2 hours, 1 day, and 3 days after a single intravenous bolus injection of 80 micromol of MION-47 per kilogram of body weight (n = 6) or 250 micromol/kg MION-47 (n = 5). Conventional T1-weighted MR angiography and IRON MR angiography were performed on a clinical 3.0-T imager. Signal-to-noise and contrast-to-noise ratios were measured in the aorta of rabbits in vivo. Venous blood was obtained from the rabbits before and after MION-47 injection for use in phantom studies. RESULTS: In vitro blood that contained MION-47 appeared signal attenuated on T1-weighted angiograms, while characteristic signal-enhanced dipolar fields were observed on IRON angiograms. In vivo, the vessel lumen was signal attenuated on T1-weighted MR angiograms after MION-47 injection, while IRON supported high intravascular contrast by simultaneously providing positive signal within the vessels and suppressing background tissue (mean contrast-to-noise ratio, 61.9 +/- 12.4 [standard deviation] after injection vs 1.1 +/- 0.4 at baseline, P < .001). Contrast-to-noise ratio was higher on IRON MR angiograms than on conventional T1-weighted MR angiograms (9.0 +/- 2.5, P < .001 vs IRON MR angiography) and persisted up to 24 hours after MION-47 injection (76.2 +/- 15.9, P < .001 vs baseline). CONCLUSION: IRON MR angiography in conjunction with superparamagnetic nanoparticle administration provides high intravascular contrast over a long time and without the need for image subtraction.

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PURPOSE: To suppress the noise, by sacrificing some of the signal homogeneity for numerical stability, in uniform T1 weighted (T1w) images obtained with the magnetization prepared 2 rapid gradient echoes sequence (MP2RAGE) and to compare the clinical utility of these robust T1w images against the uniform T1w images. MATERIALS AND METHODS: 8 healthy subjects (29.0±4.1 years; 6 Male), who provided written consent, underwent two scan sessions within a 24 hour period on a 7T head-only scanner. The uniform and robust T1w image volumes were calculated inline on the scanner. Two experienced radiologists qualitatively rated the images for: general image quality; 7T specific artefacts; and, local structure definition. Voxel-based and volume-based morphometry packages were used to compare the segmentation quality between the uniform and robust images. Statistical differences were evaluated by using a positive sided Wilcoxon rank test. RESULTS: The robust image suppresses background noise inside and outside the skull. The inhomogeneity introduced was ranked as mild. The robust image was significantly ranked higher than the uniform image for both observers (observer 1/2, p-value = 0.0006/0.0004). In particular, an improved delineation of the pituitary gland, cerebellar lobes was observed in the robust versus uniform T1w image. The reproducibility of the segmentation results between repeat scans improved (p-value = 0.0004) from an average volumetric difference across structures of ≈6.6% to ≈2.4% for the uniform image and robust T1w image respectively. CONCLUSIONS: The robust T1w image enables MP2RAGE to produce, clinically familiar T1w images, in addition to T1 maps, which can be readily used in uniform morphometry packages.

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The concept of antibody-mediated targeting of antigenic MHC/peptide complexes on tumor cells in order to sensitize them to T-lymphocyte cytotoxicity represents an attractive new immunotherapy strategy. In vitro experiments have shown that an antibody chemically conjugated or fused to monomeric MHC/peptide can be oligomerized on the surface of tumor cells, rendering them susceptible to efficient lysis by MHC-peptide restricted specific T-cell clones. However, this strategy has not yet been tested entirely in vivo in immunocompetent animals. To this aim, we took advantage of OT-1 mice which have a transgenic T-cell receptor specific for the ovalbumin (ova) immunodominant peptide (257-264) expressed in the context of the MHC class I H-2K(b). We prepared and characterized conjugates between the Fab' fragment from a high-affinity monoclonal antibody to carcinoembryonic antigen (CEA) and the H-2K(b) /ova peptide complex. First, we showed in OT-1 mice that the grafting and growth of a syngeneic colon carcinoma line transfected with CEA could be specifically inhibited by systemic injections of the conjugate. Next, using CEA transgenic C57BL/6 mice adoptively transferred with OT-1 spleen cells and immunized with ovalbumin, we demonstrated that systemic injections of the anti-CEA-H-2K(b) /ova conjugate could induce specific growth inhibition and regression of well-established, palpable subcutaneous grafts from the syngeneic CEA-transfected colon carcinoma line. These results, obtained in a well-characterized syngeneic carcinoma model, demonstrate that the antibody-MHC/peptide strategy can function in vivo. Further preclinical experimental studies, using an anti-viral T-cell response, will be performed before this new form of immunotherapy can be considered for clinical use.

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OBJECTIVE: To detect anatomical differences in areas related to motor processing between patients with motor conversion disorder (CD) and controls. METHODS: T1-weighted 3T brain MRI data of 15 patients suffering from motor CD (nine with hemiparesis and six with paraparesis) and 25 age- and gender-matched healthy volunteers were compared using voxel-based morphometry (VBM) and voxel-based cortical thickness (VBCT) analysis. RESULTS: We report significant cortical thickness (VBCT) increases in the bilateral premotor cortex of hemiparetic patients relative to controls and a trend towards increased grey matter volume (VBM) in the same region. Regression analyses showed a non-significant positive correlation between cortical thickness changes and symptom severity as well as illness duration in CD patients. CONCLUSIONS: Cortical thickness increases in premotor cortical areas of patients with hemiparetic CD provide evidence for altered brain structure in a condition with presumed normal brain anatomy. These may either represent premorbid vulnerability or a plasticity phenomenon related to the disease with the trends towards correlations with clinical variables supporting the latter.

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Summary points: - The bias introduced by random measurement error will be different depending on whether the error is in an exposure variable (risk factor) or outcome variable (disease) - Random measurement error in an exposure variable will bias the estimates of regression slope coefficients towards the null - Random measurement error in an outcome variable will instead increase the standard error of the estimates and widen the corresponding confidence intervals, making results less likely to be statistically significant - Increasing sample size will help minimise the impact of measurement error in an outcome variable but will only make estimates more precisely wrong when the error is in an exposure variable

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Understanding factors that shape ranges of species is central in evolutionary biology. Species distribution models have become important tools to test biogeographical, ecological and evolutionary hypotheses. Moreover, from an ecological and evolutionary perspective, these models help to elucidate the spatial strategies of species at a regional scale. We modelled species distributions of two phylogenetically, geographically and ecologically close Tupinambis species (Teiidae) that occupy the southernmost area of the genus distribution in South America. We hypothesized that similarities between these species might have induced spatial strategies at the species level, such as niche differentiation and divergence of distribution patterns at a regional scale. Using logistic regression and MaxEnt we obtained species distribution models that revealed interspecific differences in habitat requirements, such as environmental temperature, precipitation and altitude. Moreover, the models obtained suggest that although the ecological niches of Tupinambis merianae and T. rufescens are different, these species might co-occur in a large contact zone. We propose that niche plasticity could be the mechanism enabling their co-occurrence. Therefore, the approach used here allowed us to understand the spatial strategies of two Tupinambis lizards at a regional scale.

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BACKGROUND: Three-dimensional (3D) navigator-gated and prospectively corrected free-breathing coronary magnetic resonance angiography (MRA) allows for submillimeter image resolution but suffers from poor contrast between coronary blood and myocardium. Data collected over >100 ms/heart beat are also susceptible to bulk cardiac and respiratory motion. To address these problems, we examined the effect of a T2 preparation prepulse (T2prep) for myocardial suppression and a shortened acquisition window on coronary definition. METHODS AND RESULTS: Eight healthy adult subjects and 5 patients with confirmed coronary artery disease (CAD) underwent free-breathing 3D MRA with and without T2prep and with 120- and 60-ms data-acquisition windows. The T2prep resulted in a 123% (P<0. 001) increase in contrast-to-noise ratio (CNR). Coronary edge definition was improved by 33% (P<0.001). Acquisition window shortening from 120 to 60 ms resulted in better vessel definition (11%; P<0.001). Among patients with CAD, there was a good correspondence with disease. CONCLUSIONS: Free-breathing, T2prep, 3D coronary MRA with a shorter acquisition window resulted in improved CNR and better coronary artery definition, allowing the assessment of coronary disease. This approach offers the potential for free-breathing, noninvasive assessment of the major coronary arteries.

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The predictive potential of six selected factors was assessed in 72 patients with primary myelodysplastic syndrome using univariate and multivariate logistic regression analysis of survival at 18 months. Factors were age (above median of 69 years), dysplastic features in the three myeloid bone marrow cell lineages, presence of chromosome defects, all metaphases abnormal, double or complex chromosome defects (C23), and a Bournemouth score of 2, 3, or 4 (B234). In the multivariate approach, B234 and C23 proved to be significantly associated with a reduction in the survival probability. The similarity of the regression coefficients associated with these two factors means that they have about the same weight. Consequently, the model was simplified by counting the number of factors (0, 1, or 2) present in each patient, thus generating a scoring system called the Lausanne-Bournemouth score (LB score). The LB score combines the well-recognized and easy-to-use Bournemouth score (B score) with the chromosome defect complexity, C23 constituting an additional indicator of patient outcome. The predicted risk of death within 18 months calculated from the model is as follows: 7.1% (confidence interval: 1.7-24.8) for patients with an LB score of 0, 60.1% (44.7-73.8) for an LB score of 1, and 96.8% (84.5-99.4) for an LB score of 2. The scoring system presented here has several interesting features. The LB score may improve the predictive value of the B score, as it is able to recognize two prognostic groups in the intermediate risk category of patients with B scores of 2 or 3. It has also the ability to identify two distinct prognostic subclasses among RAEB and possibly CMML patients. In addition to its above-described usefulness in the prognostic evaluation, the LB score may bring new insights into the understanding of evolution patterns in MDS. We used the combination of the B score and chromosome complexity to define four classes which may be considered four possible states of myelodysplasia and which describe two distinct evolutional pathways.

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Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.