53 resultados para Möller, Kaspar Henrik


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The analysis of short segments of noise-contaminated, multivariate real world data constitutes a challenge. In this paper we compare several techniques of analysis, which are supposed to correctly extract the amount of genuine cross-correlations from a multivariate data set. In order to test for the quality of their performance we derive time series from a linear test model, which allows the analytical derivation of genuine correlations. We compare the numerical estimates of the four measures with the analytical results for different correlation pattern. In the bivariate case all but one measure performs similarly well. However, in the multivariate case measures based on the eigenvalues of the equal-time cross-correlation matrix do not extract exclusively information about the amount of genuine correlations, but they rather reflect the spatial organization of the correlation pattern. This may lead to failures when interpreting the numerical results as illustrated by an application to three electroencephalographic recordings of three patients suffering from pharmacoresistent epilepsy.

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OBJECTIVES: We sought to compare the diagnostic performance of screen-film radiography, storage-phosphor radiography, and a flat-panel detector system in detecting forearm fractures and to classify distal radius fractures according to the Müller-AO and Frykman classifications compared with the true extent, depicted by anatomic preparation. MATERIALS AND METHODS: A total of 71 cadaver arms were fractured in a material testing machine creating different fractures of the radius and ulna as well as of the carpal bones. Radiographs of the complete forearm were evaluated by 3 radiologists, and anatomic preparation was used as standard of reference in a receiver operating curve analysis. RESULTS: The highest diagnostic performance was obtained for the detection of distal radius fractures with area under the receiver operating curve (AUC) values of 0.959 for screen-film radiography, 0.966 for storage-phosphor radiography, and 0.971 for the flat-panel detector system (P > 0.05). Exact classification was slightly better for the Frykman (kappa values of 0.457-0.478) compared with the Müller-AO classification (kappa values of 0.404-0.447), but agreement can be considered as moderate for both classifications. CONCLUSIONS: The 3 imaging systems showed a comparable diagnostic performance in detecting forearm fractures. A high diagnostic performance was demonstrated for distal radius fractures and conventional radiography can be routinely performed for fracture detection. However, compared with anatomic preparation, depiction of the true extent of distal radius fractures was limited and the severity of distal radius fractures tends to be underestimated.

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Smoking is known to be linked to skin ageing and there is evidence for premature senescence of parenchymal lung fibroblasts in emphysema. To reveal whether the emphysema-related changes in cellular phenotype extend beyond the lung, we compared the proliferation characteristics of lung and skin fibroblasts between patients with and without emphysema. Parenchymal lung fibroblasts and skin fibroblasts from the upper torso (thus limiting sun exposure bias) were obtained from patients without, or with mild, or with moderate to severe emphysema undergoing lung surgery. We analysed proliferation rate, population doublings (PD), staining for senescence-associated beta-galactosidase (beta-gal) and gene expression of IGFBP-3 and IGFBP-rP1. Population doubling time of lung fibroblasts differed between control, mild, and moderate to severe emphysema (median (IQR) 29.7(10.0), 33.4(6.1), 44.4(21.2) h; p=0.012) and staining for beta-gal was elevated in moderate to severe emphysema. Compared to control subjects, skin fibroblasts from patients with emphysema did not differ with respect to proliferation rate, PD and beta-gal staining, and showed a lower abundance of mRNA for IGFBP-3 and -rP1 (p<0.05, each). These results suggest that the induction of a senescent fibroblast phenotype by cigarette smoke, as observed in emphysema, primarily occurs in the lung.

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An important problem in unsupervised data clustering is how to determine the number of clusters. Here we investigate how this can be achieved in an automated way by using interrelation matrices of multivariate time series. Two nonparametric and purely data driven algorithms are expounded and compared. The first exploits the eigenvalue spectra of surrogate data, while the second employs the eigenvector components of the interrelation matrix. Compared to the first algorithm, the second approach is computationally faster and not limited to linear interrelation measures.