Mapping nitrate leaching to upper groundwater in the sandy regions of The Netherlands, using conceptual knowledge

The European Community asks its Member States to provide a comprehensive and coherent overview of their groundwater chemical status. It is stated that simple conceptual models are necessary to allow assessments of the risks of failing to meet quality objectives. In The Netherlands two monitoring networks (one for agriculture and one for nature) are operational, providing results which can be used for an overview. Two regression models, based upon simple conceptual models, link measured nitrate concentrations to data from remote sensing images of land use, national forest inventory, national cattle inventory, fertiliser use statistics, atmospheric N deposition, soil maps and weather monitoring. The models are used to draw a nitrate leaching map and to estimate the size of the area exceeding the EU limit value in the early 1990s. The 95% confidence interval for the fraction nature and agricultural areas where the EU limit value for nitrate (50 mg/l) was exceeded amounted to 0.77–0.85 while the lower 97.5% confidence limit for the fraction agricultural area where the EU limit value was exceeded amounted to 0.94. Although the two conceptual models can be regarded as simple, the use of the models to give an overview was experienced as complex

GIS and spatial data analysis: Converging perspectives

We take as our starting point the state of geographic information systems (GIS) and spatial data analysis 50 years ago when regional science emerged as a new field of enquiry. In the late 1950s and 1960s advances in computing technology were making possible forms of automated cartography that in due course would lead to th

Graph Regionalization with Clustering and Partitioning: an Application for Daily Commuting Flows in Albania

The paper presents an original application of the recently proposed spatial data mining method named GraphRECAP on daily commuting flows using 2011 Albanian census data. Its aim is to identify several clusters of Albanian municipalities/communes; propose a classification of the Albanian territory based on daily commuting flows among municipalities/communes. Starting from 373 local units, we first applied a spatial clustering technique without imposing any constraining strategy. Based on the input variables, we obtained 16 clusters. In the second step of our analysis, we impose a set of constraining parameters to identify intermediate areas between the local level (municipality/commune) and the national one. We have defined 12 derived regions (same number as the actual Albanian prefectures but with different geographies). These derived regions are quite different from the traditional ones in terms of both geographical dimensions and boundarie

Spatial Random Slope Multilevel Modeling Using Multivariate Conditional Autoregressive Models: A Case Study of Subjective Travel Satisfaction in Beijing

This article explores how to incorporate a spatial dependence effect into the standard multilevel modeling (MLM). The proposed method is particularly well suited to the analysis of geographically clustered survey data where individuals are nested in geographical areas. Drawing on multivariate conditional autoregressive models, we develop a spatial random slope MLM approach to account for the within-group dependence among individuals in the same area and the spatial dependence between areas simultaneously. Our approach improves on recent methodological advances in the integrated spatial and MLM literature, offering greater flexibility in terms of model specification by allowing regression coefficients to be spatially varied. Bayesian Markov chain Monte Carlo (MCMC) algorithms are derived to implement the proposed model. Using two-level travel satisfaction data in Beijing, we apply the proposed approach as well as the standard nonspatial random slope MLM to investigate subjective travel satisfaction of residents and its determinants. Model comparison results show strong evidence that the proposed method produces a significant improvement against a nonspatial random slope MLM. A fairly large spatial correlation parameter suggests strong spatial dependence in district-level random effects. Moreover, spatial patterns of district-level random effects of locational variables have been identified, with high and low values clustering together

The epigenetic landscape of T cell exhaustion.

Exhausted T cells in cancer and chronic viral infection express distinctive patterns of genes, including sustained expression of programmed cell death protein 1 (PD-1). However, the regulation of gene expression in exhausted T cells is poorly understood. Here, we define the accessible chromatin landscape in exhausted CD8+ T cells and show that it is distinct from functional memory CD8+ T cells. Exhausted CD8+ T cells in humans and a mouse model of chronic viral infection acquire a state-specific epigenetic landscape organized into functional modules of enhancers. Genome editing shows that PD-1 expression is regulated in part by an exhaustion-specific enhancer that contains essential RAR, T-bet, and Sox3 motifs. Functional enhancer maps may offer targets for genome editing that alter gene expression preferentially in exhausted CD8+ T cells

Association of warfarin dose with genes involved in its action and metabolism

We report an extensive study of variability in genes encoding proteins that are believed to be involved in the action and biotransformation of warfarin. Warfarin is a commonly prescribed anticoagulant that is difficult to use because of the wide interindividual variation in dose requirements, the narrow therapeutic range and the risk of serious bleeding. We genotyped 201 patients for polymorphisms in 29 genes in the warfarin interactive pathways and tested them for association with dose requirement. In our study, polymorphisms in or flanking the genes VKORC1, CYP2C9, CYP2C18, CYP2C19, PROC, APOE, EPHX1, CALU, GGCX and ORM1-ORM2 and haplotypes of VKORC1, CYP2C9, CYP2C8, CYP2C19, PROC, F7, GGCX, PROZ, F9, NR1I2 and ORM1-ORM2 were associated with dose (P < 0.05). VKORC1, CYP2C9, CYP2C18 and CYP2C19 were significant after experiment-wise correction for multiple testing (P < 0.000175), however, the association of CYP2C18 and CYP2C19 was fully explained by linkage disequilibrium with CYP2C9*2 and/or *3. PROC and APOE were both significantly associated with dose after correction within each gene. A multiple regression model with VKORC1, CYP2C9, PROC and the non-genetic predictors age, bodyweight, drug interactions and indication for treatment jointly accounted for 62% of variance in warfarin dose. Weaker associations observed for other genes could explain up to ∼10% additional dose variance, but require testing and validation in an independent and larger data set. Translation of this knowledge into clinical guidelines for warfarin prescription will be likely to have a major impact on the safety and efficacy of warfarin. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available in the online version of this article at http://dx.doi.org/10.1007/s00439-006-0260-8 and is accessible for authorized users