8 resultados para Non-uniform temperature distribution

em Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco


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This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. The model is estimated assuming a Gram-Charlier series expansion of the normal density function for the error term, which is easier to estimate than the non-central t distribution proposed by Harvey and Siddique (1999). Moreover, this approach accounts for time-varying skewness and kurtosis while the approach by Harvey and Siddique (1999) only accounts for nonnormal skewness. We apply this method to daily returns of a variety of stock indices and exchange rates. Our results indicate a significant presence of conditional skewness and kurtosis. It is also found that specifications allowing for time-varying skewness and kurtosis outperform specifications with constant third and fourth moments.

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Enactive approaches foreground the role of interpersonal interaction in explanations of social understanding. This motivates, in combination with a recent interest in neuroscientific studies involving actual interactions, the question of how interactive processes relate to neural mechanisms involved in social understanding. We introduce the Interactive Brain Hypothesis (IBH) in order to help map the spectrum of possible relations between social interaction and neural processes. The hypothesis states that interactive experience and skills play enabling roles in both the development and current function of social brain mechanisms, even in cases where social understanding happens in the absence of immediate interaction. We examine the plausibility of this hypothesis against developmental and neurobiological evidence and contrast it with the widespread assumption that mindreading is crucial to all social cognition. We describe the elements of social interaction that bear most directly on this hypothesis and discuss the empirical possibilities open to social neuroscience. We propose that the link between coordination dynamics and social understanding can be best grasped by studying transitions between states of coordination. These transitions form part of the self-organization of interaction processes that characterize the dynamics of social engagement. The patterns and synergies of this self-organization help explain how individuals understand each other. Various possibilities for role-taking emerge during interaction, determining a spectrum of participation. This view contrasts sharply with the observational stance that has guided research in social neuroscience until recently. We also introduce the concept of readiness to interact to describe the practices and dispositions that are summoned in situations of social significance (even if not interactive). This latter idea links interactive factors to more classical observational scenarios.

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Numerous transcription factors self-assemble into different order oligomeric species in a way that is actively regulated by the cell. Until now, no general functional role has been identified for this widespread process. Here, we capture the effects of modulated self-assembly in gene expression with a novel quantitative framework. We show that this mechanism provides precision and flexibility, two seemingly antagonistic properties, to the sensing of diverse cellular signals by systems that share common elements present in transcription factors like p53, NF-kappa B, STATs, Oct and RXR. Applied to the nuclear hormone receptor RXR, this framework accurately reproduces a broad range of classical, previously unexplained, sets of gene expression data and corroborates the existence of a precise functional regime with flexible properties that can be controlled both at a genome-wide scale and at the individual promoter level.

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The EC (entorhinal cortex) is fundamental for cognitive and mnesic functions. Thus damage to this area appears as a key element in the progression of AD (Alzheimer's disease), resulting in memory deficits arising from neuronal and synaptic alterations as well as glial malfunction. In this paper, we have performed an in-depth analysis of astroglial morphology in the EC by measuring the surface and volume of the GFAP (glial fibrillary acidic protein) profiles in a triple transgenic mouse model of AD [3xTg-AD (triple transgenic mice of AD)]. We found significant reduction in both the surface and volume of GFAP-labelled profiles in 3xTg-AD animals from very early ages (1 month) when compared with non-Tg (non-transgenic) controls (48 and 54%, reduction respectively), which was sustained for up to 12 months (33 and 45% reduction respectively). The appearance of Lambda beta (amyloid beta-peptide) depositions at 12 months of age did not trigger astroglial hypertrophy; nor did it result in the close association of astrocytes with senile plaques. Our results suggest that the AD progressive cognitive deterioration can be associated with an early reduction of astrocytic arborization and shrinkage of the astroglial domain, which may affect synaptic connectivity within the EC and between the EC and other brain regions. In addition, the EC seems to be particularly vulnerable to AD pathology because of the absence of evident astrogliosis in response to A beta accumulation. Thus we can consider that targeting astroglial atrophy may represent a therapeutic strategy which might slow down the progression of AD.

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The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies.

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Linker histone H1 plays an important role in chromatin folding. Phosphorylation by cyclin-dependent kinases is the main post-translational modification of histone H1. We studied the effects of phosphorylation on the secondary structure of the DNA-bound H1 carboxy-terminal domain (CTD), which contains most of the phosphorylation sites of the molecule. The effects of phosphorylation on the secondary structure of the DNA-bound CTD were site-specific and depended on the number of phosphate groups. Full phosphorylation significantly increased the proportion of -structure and decreased that of -helix. Partial phosphorylation increased the amount of undefined structure and decreased that of -helix without a significant increase in -structure. Phosphorylation had a moderate effect on the affinity of the CTD for the DNA, which was proportional to the number of phosphate groups. Partial phosphorylation drastically reduced the aggregation of DNA fragments by the CTD, but full phosphorylation restored to a large extent the aggregation capacity of the unphosphorylated domain. These results support the involvement of H1 hyperphosphorylation in metaphase chromatin condensation and of H1 partial phosphorylation in interphase chromatin relaxation. More generally, our results suggest that the effects of phosphorylation are mediated by specific structural changes and are not simply a consequence of the net charge.

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[ES]El objetivo principal de este proyecto se centra en conseguir las características mecánicas requeridas por los componentes del chasis de los vehículos con una reducción de peso y mejora de productividad, para ellos es necesario simular el proceso de templado que se da durante la estampación en caliente de una pieza de chapa mediante utillajes con conductos de refrigeración. Para ello se ha utilizado el programa de elementos finitos ANSYS hasta obtener un proceso de embutición simulado que represente con una desviación dentro de los límites aceptables el comportamiento real de la chapa en la etapa de refrigeración. Como objetivo secundario se encuentra el afianzamiento de las bases teóricas de ciencia de materiales y la adquisición de más conocimientos relacionados con la transmisión de calor entre cuerpos sólidos, centrándose sobre todo en la distribución de temperaturas sobre la superficie de éstos. En una primera parte se tratarán los conceptos generales de la estampación en caliente y sus posibles variantes. También se explicará la necesidad del uso de nuevos materiales para la industria automovilística, así como la razón por la cual se utilizan conductos de refrigeración. A continuación, se definirá la geometría de la chapa a analizar, tanto las consecuentes geometrías de los utillajes, que tendrán diferentes distribuciones de conductos de refrigeración. Además se establecerán los criterios para realizar el análisis térmico transitorio del conjunto troquel – chapa – matriz. Una vez seleccionado el tipo de análisis se profundizará en su estudio, aplicándolo a los diferentes utillajes ya citados. Se analizarán los resultados obtenidos y los errores y se buscarán posibles alternativas. Finalmente, se procederá a sacar las conclusiones de la simulación realizada y se procederá a comparar los resultados obtenidos con las diferentes distribuciones de conductos de refrigeración en los utillajes.

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We review the appropriateness of using SNIa observations to detect potential signatures of anisotropic expansion in the Universe. We focus on Union2 and SNLS3 SNIa datasets and use the hemispherical comparison method to detect possible anisotropic features. Unlike some previous works where nondiagonal elements of the covariance matrix were neglected, we use the full covariance matrix of the SNIa data, thus obtaining more realistic and not underestimated errors. As a matter of fact, the significance of previously claimed detections of a preferred direction in the Union2 dataset completely disappears once we include the effects of using the full covariance matrix. Moreover, we also find that such apreferred direction is aligned with the orthogonal direction of the SDSS observational plane and this suggests a clear indication that the SDSS subsample of the Union2 dataset introduces a significant bias, making the detected preferred direction unphysical. We thus find that current SNIa surveys are inappropriate to test anisotropic features due to their highly non-homogeneous angular distribution in the sky. In addition, after removal of the highest in homogeneous sub-samples, the number of SNIa is too low. Finally, we take advantage of the particular distribution of SNLS SNIa sub- sample in the SNLS3 data set, in which the observations were taken along four different directions. We fit each direction independently and find consistent results at the 1 sigma level. Although the likelihoods peak at relatively different values of Omega(m), the low number of data along each direction gives rise to large errors so that the likelihoods are sufficiently broad as to overlap within 1 sigma. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons. org/licenses/by/4.0/).