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Climate change and excess deposition of airborne nitrogen (N) are among the main stressors to floristic biodiversity. One particular concern is the deterioration of valuable habitats such as those protected under the European Habitat Directive. In future, climate-driven shifts (and losses) in the species potential distribution, but also N driven nutrient enrichment may threaten these habitats. We applied a dynamic geochemical soil model (VSD+) together with a novel niche-based plant response model (PROPS) to 5 forest habitat types (18 forest sites) protected under the EU Directive in Austria. We assessed how future climate change and N deposition might affect habitat suitability, defined as the capacity of a site to host its typical plant species. Our evaluation indicates that climate change will be the main driver of a decrease in habitat suitability in the future in Austria. The expected climate change will increase the occurrence of thermophilic plant species while decreasing cold-tolerant species. In addition to these direct impacts, climate change scenarios caused an increase of the occurrence probability of oligotrophic species due to a higher N immobilisation in woody biomass leading to soil N depletion. As a consequence, climate change did offset eutrophication from N deposition, even when no further reduction in N emissions was assumed. Our results show that climate change may have positive side-effects in forest habitats when multiple drivers of change are considered.

The integrated assessment models that drive atmospheric pollutant emission reduction policies within Europe, under the Convention on Long-range Transboundary Air Pollution, work at the scale of the EMEP grid (50 × 50 km). Critical loads are used to link anthropogenic deposition to adverse effects for individual ecosystems within each grid cell. The critical loads have two aspects, ecosystem area and the ecosystem specific critical load. The presence of highly acid-sensitive ecosystems within these grid cells can lead to very low critical loads of acidity which provide emission targets that are difficult to achieve: these cells are known as \"binding grid squares\". Since mountain lakes are highly sensitive to environmental change, their inclusion in critical loads assessments might be expected to result in lower EMEP scale critical loads. The combination of mountain lakes data with those from other ecosystems already included in the integrated modelling shows unexpected results. The average accumulated exceedance of critical loads of acidity may increase with either the presence of significant mountain lake catchment areas within grid cells or the presence of highly acidified clusters of sites. These circumstances occur in only a small number of grid cells. Furthermore, the relatively low sensitivity of mountain lakes to acidification in some regions means that their inclusion in EMEP scale models would lead to a relaxation of emission abatement requirements because of an increase in the area of ecosystems not exceeding critical loads and a decrease in AAE. Hence the potential effects of the inclusion of mountain lake critical loads of acidity in EMEP scale models vary greatly on a regional basis. The assumption that mountain lakes data could increase the incidence of \"binding grid squares\" is found to be unlikely.[PUBLICATION ABSTRACT]

High-density lipoprotein (HDL) plays an essential role in the immune system and shows effective antioxidative properties. We investigated correlations of lipid parameters with the sequential organ failure assessment (SOFA) score and the prognostic association with mortality in sepsis patients admitted to intensive care unit (ICU). We prospectively recruited consecutive adult patients with sepsis and septic shock, according to sepsis-3 criteria as well as non-sepsis ICU controls. Fifty-three patients with sepsis (49% with septic shock) and 25 ICU controls without sepsis were enrolled. Dyslipidemia (HDL-C < 40 mg/l) was more common in sepsis compared to non-sepsis patients (85 vs. 52%, = 0.002). Septic patients compared to controls had reduced HDL-C (14 vs. 39 mg/l, < 0.0001), lower arylesterase activity of the antioxidative paraoxonase of HDL (AEA) (67 vs. 111 mM/min/ml serum, < 0.0001), and a non-significant trend toward reduced cholesterol efflux capacity (9 vs. 10%, = 0.091). We observed a strong association between higher AEA and lower risk of 28-day [per 10 mM/min/ml serum increase in AEA: odds ratio (OR) = 0.76; 95% CI, 0.61-0.94; = 0.01) and ICU mortality (per 10 mM/min/ml serum increase in AEA: OR = 0.71, 95% CI, 0.56-0.90, = 0.004) in the sepsis cohort in univariable logistic regression analysis. AEA was confirmed as an independent predictor of 28-day and ICU mortality in multivariable analyses. AEA discriminated well-regarding 28-day/ICU mortality in area under the receiver operating characteristic curve (AUROC) analyses. In survival analysis, 28-day mortality estimates were 40 and 69% with AEA ≥/< the 25th percentile of AEA's distribution, respectively (log-rank = 0.0035). Both compositional and functional HDL parameters are profoundly altered during sepsis. In particular, the functionality parameter AEA shows promising prognostic potential in sepsis patients.

In this paper, we revisit the fiscal theory of the price level (FTPL) within the New Keynesian (NK) model. We show in which cases the average maturity of government debt matters for the transmission of policy shocks. The central task of this paper is to shed light on the theoretical predictions of the maturity structure on macro dynamics with an emphasis on model-implied expectations. In particular, we address the transmission channels of monetary and fiscal policy shocks on the interest rate and inflation dynamics. Our results illustrate the role of the maturity of existing debt in the wake of skyrocketing debt-to-GDP ratios and increasing government expenditures. We highlight our results by quantifying the effects of the large-scale US fiscal packages (CARES) and predict a surge in inflation if the deficits are not sufficiently backed by future surpluses.

In this paper we show how high-frequency financial data can be used in a combined macro-finance framework to estimate the underlying structural parameters. Our formulation of the model allows for substituting macro variables by asset prices in a way that enables casting the relevant estimation equations partly (or completely) in terms of financial data. We show that using only financial data allows for identification of the majority of the relevant parameters. Adding macro data allows for identification of all parameters. In our simulation study, we find that it also improves the accuracy of the parameter estimates. In the empirical application we use interest rate, macro, and S&P500 stock index data, and compare the results using different combinations of macro and financial variables.