Airway epithelial IL-15 transforms monocytes into dendritic cells

IL-15 has recently been shown to induce the differentiation of functional dendritic cells (DCs) from human peripheral blood monocytes. Since DCs lay in close proximity to epithelial cells in the airway mucosa, we investigated whether airway epithelial cells release IL-15 in response to inflammatory stimuli and thereby induce differentiation and maturation of DCs. Alveolar (A549) and bronchial (BEAS-2B) epithelial cells produced IL-15 spontaneously and in a time- and dose-dependent manner after stimulation with IL-1beta, IFN-gamma, or TNF-alpha. Airway epithelial cell supernatants induced an increase of IL-15Ralpha gene expression in ex vivo monocytes, and stimulated DCs enhanced their IL-15Ralpha gene expression up to 300-fold. Airway epithelial cell-conditioned media induced the differentiation of ex vivo monocytes into partially mature DCs (HLA-DR+, DC-SIGN+, CD14+, CD80-, CD83+, CD86+, CCR3+, CCR6(+), CCR7-). Based on their phenotypic (CD123+, BDCA2+, BDCA4+, BDCA1(-), CD1a-) and functional properties (limited maturation upon stimulation with LPS and limited capacity to induce T cell proliferation), these DCs resembled plasmacytoid DCs. The effects of airway epithelial cell supernatants were largely blocked by a neutralizing monoclonal antibody to IL-15. Thus, our results demonstrate that airway epithelial cell-conditioned media have the capacity to differentiate monocytes into functional DCs, a process substantially mediated by epithelial-derived IL-15.

An integral conservative gridding--algorithm using Hermitian curve interpolation

The problem of re-sampling spatially distributed data organized into regular or irregular grids to finer or coarser resolution is a common task in data processing. This procedure is known as 'gridding' or 're-binning'. Depending on the quantity the data represents, the gridding-algorithm has to meet different requirements. For example, histogrammed physical quantities such as mass or energy have to be re-binned in order to conserve the overall integral. Moreover, if the quantity is positive definite, negative sampling values should be avoided. The gridding process requires a re-distribution of the original data set to a user-requested grid according to a distribution function. The distribution function can be determined on the basis of the given data by interpolation methods. In general, accurate interpolation with respect to multiple boundary conditions of heavily fluctuating data requires polynomial interpolation functions of second or even higher order. However, this may result in unrealistic deviations (overshoots or undershoots) of the interpolation function from the data. Accordingly, the re-sampled data may overestimate or underestimate the given data by a significant amount. The gridding-algorithm presented in this work was developed in order to overcome these problems. Instead of a straightforward interpolation of the given data using high-order polynomials, a parametrized Hermitian interpolation curve was used to approximate the integrated data set. A single parameter is determined by which the user can control the behavior of the interpolation function, i.e. the amount of overshoot and undershoot. Furthermore, it is shown how the algorithm can be extended to multidimensional grids. The algorithm was compared to commonly used gridding-algorithms using linear and cubic interpolation functions. It is shown that such interpolation functions may overestimate or underestimate the source data by about 10-20%, while the new algorithm can be tuned to significantly reduce these interpolation errors. The accuracy of the new algorithm was tested on a series of x-ray CT-images (head and neck, lung, pelvis). The new algorithm significantly improves the accuracy of the sampled images in terms of the mean square error and a quality index introduced by Wang and Bovik (2002 IEEE Signal Process. Lett. 9 81-4).