Empirically Derived Suitability Maps to Downscale Aggregated Land Use Data

Understanding mechanisms that drive present land use patterns is essential in order to derive appropriate models of land use change. When static analyses of land use drivers are performed, they rarely explicitly deal with spatial autocorrelation. Most studies are undertaken on autocorrelation-free data samples. By doing this, a great deal of information that is present in the dataset is lost. This paper presents a spatially explicit, cross-sectional, logistic analysis of land use drivers in Belgium. It is shown that purely regressive logistic models can only identify trends or global relationships between socio-economic or physico-climatic drivers and the precise location of each land use type. However, when the goal of a study is to obtain the best model of land use distribution, a purely autoregressive (or neighbourhood-based) model is appropriate. Moreover, it is also concluded that a neighbourhood based only on the 8 surrounding cells leads to the best logistic regression models at this scale of observation. This statement is valid for each land use type studied – i.e. built-up, forests, cropland and grassland.

The nature of the GRE influences the screening for GR-activity enhancing modulators

Glucocorticoid resistance (GCR), i.e. unresponsiveness to the beneficial anti-inflammatory activities of the glucocorticoid receptor (GR), poses a serious problem in the treatment of inflammatory diseases. One possible solution to try and overcome GCR, is to identify molecules that prevent or revert GCR by hyper-stimulating the biological activity of the GR. To this purpose, we screened for compounds that potentiate the dexamethasone (Dex)induced transcriptional activity of GR. To monitor GR transcriptional activity, the screen was performed using the lung epithelial cell line A549 in which a glucocorticoid responsive element (GRE) coupled to a luciferase reporter gene construct was stably integrated. Histone deacetylase inhibitors (HDACi) such as Vorinostat and Belinostat are two broad-spectrum HDACi that strongly increased the Dex-induced luciferase expression in our screening system. In sharp contrast herewith, results from a genome-wide transcriptome analysis of Dexinduced transcripts using RNAseq, revealed that Belinostat impairs the ability of GR to transactivate target genes. The stimulatory effect of Belinostat in the luciferase screen further depends on the nature of the reporter construct. In conclusion, a profound discrepancy was observed between HDACi effects on two different synthetic promoter-luciferase reporter systems. The favorable effect of HDACi on gene expression should be evaluated with care, when considering them as potential therapeutic agents. GEO accession number GSE96649