Il·lusionisme. Nova Construcció i Rehabilitació Integral de la Vil·la Urània com a complex d'equipaments

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Gene transfer of the Ly-3 chain gene of the mouse CD8 molecular complex: co-transfer with the Ly-2 polypeptide gene results in detectable cell surface expression of the Ly-3 antigenic determinants.

The CD8 molecule is a glycoprotein expressed on a subset of mature T lymphocytes. It has been postulated to be a receptor for class I major histocompatibility complex molecules. In the mouse, CD8 is a heterodimer composed of Ly-2 and Ly-3 chains. We have isolated and analyzed cDNA and cosmid clones corresponding to the Ly-3 subunit. One of the isolated, cosmid clones was subsequently transfected, alone or in combination with the Ly-2 gene, into mouse Ltk- cells. Analysis of the Ly-2,3 molecules expressed at the surface of the double transfectants indicated that they are serologically and biochemically indistinguishable from their normal counterparts expressed on lymphoid cells. Ltk- cells transfected with the Ly-2 gene alone were shown to react with a subset of anti-CD8 monoclonal antibodies whereas Ly-3 transfectants did not stain with any of the anti-Ly-3 antibodies employed in this study. Since at least one of these antibodies (53-5.8) has been previously shown to recognize an epitope which is retained on the Ly-3 subunit after dissociation of the heterodimeric Ly-2,3 complex, these observations suggest that the expression of the Ly-2 polypeptide is required to permit the detectable cell surface expression of the antigenic determinants carried by the Ly-3 subunit.

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.