Follistatin was originally described as an ovarian polypeptide hormone able to suppress pituitary follicle-stimulating hormone (FSH) secretion in in vitro systems. We have isolated rat ovarian follistatin cDNA clones and used these to investigate the possibility that decidual tissue expresses the follistatin gene. We found that the follistatin gene is highly expressed during early pregnancy in tissue derived from the site of blastocyst implantation. Follistatin mRNA was observed in tissue obtained from the implantation site at days 6, 8 and 10 of gestation, but was not found in the placenta at later stages of gestation. This same pattern of follistatin expression was observed in pseudopregnant animals in which decidualization had been artificially induced, indicating that decidual tissue is the primary source of follistatin mRNA. In situ hybridization was used to examine follistatin gene expression in decidua in greater detail. The mRNA is preferentially found in antimesometrial tissue, and is strictly confined to the endometrial-derived decidual cells. These results suggest that follistatin might function as an important endocrine hormone during implantation of the fetus, perhaps by acting to appropriately modulate maternal FSH secretion during early pregnancy.

An accurate budget of substance emissions is fundamental for protecting freshwater resources. In this context, the European Union asks all member states to report an emission inventory of substances for river basins. The river basin management system MoRE (Modeling of Regionalized Emissions) was developed as a flexible open-source instrument which is able to model pathway-specific emissions and river loads on a catchment scale. As the reporting tool for the Federal Republic of Germany, MoRE is used to model annual emissions of nutrients, heavy metals, micropollutants like polycyclic aromatic hydrocarbons (PAH), Bis(2-ethylhexyl)phthalate (DEHP), and certain pharmaceuticals. Observed loads at gauging stations are used to validate the calculated emissions. In addition to its balancing capabilities, MoRE can consider different variants of input data and quantification approaches, in order to improve the robustness of different modeling approaches and to evaluate the quality of different input data. No programming skills are required to set up and run the model. Due to its flexible modeling base, the effect of reduction measures can be assessed. Within strategic planning processes, this is relevant for the allocation of investments or the implementation of specific measures to reduce the overall pollutant emissions into surface water bodies and therefore to meet the requirements of water policy.

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