Microarray analysis of autoimmune diseases by machine learning procedures

—Microarray-based global gene expression profiling, with the use of sophisticated statistical algorithms is providing new insights into the pathogenesis of autoimmune diseases. We have applied a novel statistical technique for gene selection based on machine learning approaches to analyze microarray expression data gathered from patients with systemic lupus erythematosus (SLE) and primary antiphospholipid syndrome (PAPS), two autoimmune diseases of unknown genetic origin that share many common features. The methodology included a combination of three data discretization policies, a consensus gene selection method, and a multivariate correlation measurement. A set of 150 genes was found to discriminate SLE and PAPS patients from healthy individuals. Statistical validations demonstrate the relevance of this gene set from an univariate and multivariate perspective. Moreover, functional characterization of these genes identified an interferon-regulated gene signature, consistent with previous reports. It also revealed the existence of other regulatory pathways, including those regulated by PTEN, TNF, and BCL-2, which are altered in SLE and PAPS. Remarkably, a significant number of these genes carry E2F binding motifs in their promoters, projecting a role for E2F in the regulation of autoimmunity.

Estimate of the total ice volume of Svalbard glaciers and their potential contribution to sea-level rise = Estimación del volumen de hielo de Svalbard y su contribución potencial a la subida del nivel del mar

El objetivo final de las investigaciones recogidas en esta tesis doctoral es la estimación del volumen de hielo total de los ms de 1600 glaciares de Svalbard, en el Ártico, y, con ello, su contribución potencial a la subida del nivel medio del mar en un escenario de calentamiento global. Los cálculos más exactos del volumen de un glaciar se efectúan a partir de medidas del espesor de hielo obtenidas con georradar. Sin embargo, estas medidas no son viables para conjuntos grandes de glaciares, debido al coste, dificultades logísticas y tiempo requerido por ellas, especialmente en las regiones polares o de montaña. Frente a ello, la determinación de áreas de glaciares a partir de imágenes de satélite sí es viable a escalas global y regional, por lo que las relaciones de escala volumen-área constituyen el mecanismo más adecuado para las estimaciones de volúmenes globales y regionales, como las realizadas para Svalbard en esta tesis. Como parte del trabajo de tesis, hemos elaborado un inventario de los glaciares de Svalbard en los que se han efectuado radioecosondeos, y hemos realizado los cálculos del volumen de hielo de más de 80 cuencas glaciares de Svalbard a partir de datos de georradar. Estos volúmenes han sido utilizados para calibrar las relaciones volumen-área desarrolladas en la tesis. Los datos de georradar han sido obtenidos en diversas campañas llevadas a cabo por grupos de investigación internacionales, gran parte de ellas lideradas por el Grupo de Simulación Numérica en Ciencias e Ingeniería de la Universidad Politécnica de Madrid, del que forman parte la doctoranda y los directores de tesis. Además, se ha desarrollado una metodología para la estimación del error en el cálculo de volumen, que aporta una novedosa técnica de cálculo del error de interpolación para conjuntos de datos del tipo de los obtenidos con perfiles de georradar, que presentan distribuciones espaciales con unos patrones muy característicos pero con una densidad de datos muy irregular. Hemos obtenido en este trabajo de tesis relaciones de escala específicas para los glaciares de Svalbard, explorando la sensibilidad de los parámetros a diferentes morfologías glaciares, e incorporando nuevas variables. En particular, hemos efectuado experimentos orientados a verificar si las relaciones de escala obtenidas caracterizando los glaciares individuales por su tamaño, pendiente o forma implican diferencias significativas en el volumen total estimado para los glaciares de Svalbard, y si esta partición implica algún patrón significativo en los parámetros de las relaciones de escala. Nuestros resultados indican que, para un valor constante del factor multiplicativo de la relacin de escala, el exponente que afecta al área en la relación volumen-área decrece según aumentan la pendiente y el factor de forma, mientras que las clasificaciones basadas en tamaño no muestran un patrón significativo. Esto significa que los glaciares con mayores pendientes y de tipo circo son menos sensibles a los cambios de área. Además, los volúmenes de la población total de los glaciares de Svalbard calculados con fraccionamiento en grupos por tamaño y pendiente son un 1-4% menores que los obtenidas usando la totalidad de glaciares sin fraccionamiento en grupos, mientras que los volúmenes calculados fraccionando por forma son un 3-5% mayores. También realizamos experimentos multivariable para obtener estimaciones óptimas del volumen total mediante una combinación de distintos predictores. Nuestros resultados muestran que un modelo potencial simple volumen-área explica el 98.6% de la varianza. Sólo el predictor longitud del glaciar proporciona significación estadística cuando se usa además del área del glaciar, aunque el coeficiente de determinación disminuye en comparación con el modelo más simple V-A. El predictor intervalo de altitud no proporciona información adicional cuando se usa además del área del glaciar. Nuestras estimaciones del volumen de la totalidad de glaciares de Svalbard usando las diferentes relaciones de escala obtenidas en esta tesis oscilan entre 6890 y 8106 km3, con errores relativos del orden de 6.6-8.1%. El valor medio de nuestras estimaciones, que puede ser considerado como nuestra mejor estimación del volumen, es de 7.504 km3. En términos de equivalente en nivel del mar (SLE), nuestras estimaciones corresponden a una subida potencial del nivel del mar de 17-20 mm SLE, promediando 19_2 mm SLE, donde el error corresponde al error en volumen antes indicado. En comparación, las estimaciones usando las relaciones V-A de otros autores son de 13-26 mm SLE, promediando 20 _ 2 mm SLE, donde el error representa la desviación estándar de las distintas estimaciones. ABSTRACT The final aim of the research involved in this doctoral thesis is the estimation of the total ice volume of the more than 1600 glaciers of Svalbard, in the Arctic region, and thus their potential contribution to sea-level rise under a global warming scenario. The most accurate calculations of glacier volumes are those based on ice-thicknesses measured by groundpenetrating radar (GPR). However, such measurements are not viable for very large sets of glaciers, due to their cost, logistic difficulties and time requirements, especially in polar or mountain regions. On the contrary, the calculation of glacier areas from satellite images is perfectly viable at global and regional scales, so the volume-area scaling relationships are the most useful tool to determine glacier volumes at global and regional scales, as done for Svalbard in this PhD thesis. As part of the PhD work, we have compiled an inventory of the radio-echo sounded glaciers in Svalbard, and we have performed the volume calculations for more than 80 glacier basins in Svalbard from GPR data. These volumes have been used to calibrate the volume-area relationships derived in this dissertation. Such GPR data have been obtained during fieldwork campaigns carried out by international teams, often lead by the Group of Numerical Simulation in Science and Engineering of the Technical University of Madrid, to which the PhD candidate and her supervisors belong. Furthermore, we have developed a methodology to estimate the error in the volume calculation, which includes a novel technique to calculate the interpolation error for data sets of the type produced by GPR profiling, which show very characteristic data distribution patterns but with very irregular data density. We have derived in this dissertation scaling relationships specific for Svalbard glaciers, exploring the sensitivity of the scaling parameters to different glacier morphologies and adding new variables. In particular, we did experiments aimed to verify whether scaling relationships obtained through characterization of individual glacier shape, slope and size imply significant differences in the estimated volume of the total population of Svalbard glaciers, and whether this partitioning implies any noticeable pattern in the scaling relationship parameters. Our results indicate that, for a fixed value of the factor in the scaling relationship, the exponent of the area in the volume-area relationship decreases as slope and shape increase, whereas size-based classifications do not reveal any clear trend. This means that steep slopes and cirque-type glaciers are less sensitive to changes in glacier area. Moreover, the volumes of the total population of Svalbard glaciers calculated according to partitioning in subgroups by size and slope are smaller (by 1-4%) than that obtained considering all glaciers without partitioning into subgroups, whereas the volumes calculated according to partitioning in subgroups by shape are 3-5% larger. We also did multivariate experiments attempting to optimally predict the volume of Svalbard glaciers from a combination of different predictors. Our results show that a simple power-type V-A model explains 98.6% of the variance. Only the predictor glacier length provides statistical significance when used in addition to the predictor glacier area, though the coefficient of determination decreases as compared with the simpler V-A model. The predictor elevation range did not provide any additional information when used in addition to glacier area. Our estimates of the volume of the entire population of Svalbard glaciers using the different scaling relationships that we have derived along this thesis range within 6890-8106 km3, with estimated relative errors in total volume of the order of 6.6-8.1% The average value of all of our estimates, which could be used as a best estimate for the volume, is 7,504 km3. In terms of sea-level equivalent (SLE), our volume estimates correspond to a potential contribution to sea-level rise within 17-20 mm SLE, averaging 19 _ 2 mm SLE, where the quoted error corresponds to our estimated relative error in volume. For comparison, the estimates using the V-A scaling relations found in the literature range within 13-26 mm SLE, averaging 20 _ 2 mm SLE, where the quoted error represents the standard deviation of the different estimates.

Estimate of the total volume of Svalbard glaciers, and their potential contribution to sea-level rise, using new regionally based scaling relationships

We present a set of new volume scaling relationships specific to Svalbard glaciers, derived from a sample of 60 volume–area pairs. Glacier volumes are computed from ground-penetrating radar (GPR)-retrieved ice thickness measurements, which have been compiled from different sources for this study. The most precise scaling models, in terms of lowest cross-validation errors, are obtained using a multivariate approach where, in addition to glacier area, glacier length and elevation range are also used as predictors. Using this multivariate scaling approach, together with the Randolph Glacier Inventory V3.2 for Svalbard and Jan Mayen, we obtain a regional volume estimate of 6700 ± 835 km3, or 17 ± 2 mm of sea-level equivalent (SLE). This result lies in the mid- to low range of recently published estimates, which show values as varied as 13 and 24 mm SLE. We assess the sensitivity of the scaling exponents to glacier characteristics such as size, aspect ratio and average slope, and find that the volume of steep-slope and cirque-type glaciers is not very sensitive to changes in glacier area.