No abstract

Highlights PSGL-1 protein is frequently expressed at the surface of malignant T cells. Enforced expression of PSGL-1 promotes T cell tumorigenesis in mice. PSGL-1 expression accelerates malignant T cell dissemination from tumors to several organs. PSGL-1 expression promotes malignant T cell expansion in kidneys and lungs.

Missing data is a frequent problem in meteorological and hydrological temporal observation data sets. Finding effective solutions to this problem is essential because complete time series are required to conduct reliable analyses. This study used daily rainfall data from 60 rain gauges spatially distributed within Portugal's Guadiana River basin over a 30-year reference period (1976–2005). Gap-filling approaches using kriging-based interpolation methods (i.e. ordinary kriging and simple cokriging) are presented and compared to a deterministic approach proposed by the Food and Agriculture Organization (FAO method). The suggested procedure consists of fitting monthly semi-variogram models using the average daily rainfall from all available meteorological stations for each month in a reference period. This approach makes it possible to use only 12 monthly semi-variograms instead of one for each day of the gap period. Ordinary kriging and simple cokriging are used to estimate the missing daily precipitation using the semi-variograms of the month of interest. The cokriging method is applied considering the elevation data as the secondary variable. One year of data were removed from some stations to assess the efficacy of the proposed approaches, and the missing precipitation data were estimated using the three procedures. The methods were validated through a cross-validation process and compared using different performance metrics. The results showed that the geostatistical methods outperformed the FAO method in daily estimation. In the investigated study area, cokriging did not significantly improve the estimates compared to ordinary kriging, which was deemed the best interpolation method for a large majority of the rainfall stations.

<p>Groundwater resources in Mediterranean coastal aquifers are under threat due to overexploitation and climate change impacts, resulting in saltwater intrusion. This situation is deteriorated by the absence of sustainable groundwater resources management plans. Efficient management and monitoring of groundwater systems requires interpreting all sources of available data. This work aims at the development of a set of plausible 3D geological models combining 2D geophysical profiles, spatial data analytics and geostatistical simulation techniques. The resulting set of models represents possible scenarios of the structure of the coastal aquifer system under investigation. Inverted resistivity profiles, along with borehole data, are explored using spatial data science techniques to identify regions associated with higher uncertainty. Relevant parts of the profiles will be used to generate 3D models after detailed Anisotropy and variogram analysis. Multidimensional statistical techniques are then used to select representative models of the true subsurface while exploring the uncertainty space. The resulting models will help to identify primary gaps in existing knowledge about the groundwater system and to optimize the groundwater monitoring network. A comparison with a numerical groundwater flow model will identify similarities and differences and it will be used to develop a typical hydrogeological model, which will aid the management and monitoring of the area's groundwater resources. This work will help the development of a reliable groundwater flow model to investigate future groundwater level fluctuations at the study area under climate change scenarios.</p><p> </p><p>This work was developed under the scope of the InTheMED project. InTheMED is part of the PRIMA programme supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 1923.</p>