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Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a-nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic-eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.
Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a-nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic-eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.
Mussel-inspired multifunctional coating for bacterial infection prevention and osteogenic induction
(2021)
Bacterial infection and osteogenic integration are the two main problems that cause severe complications after surgeries. In this study, the antibacterial and osteogenic properties were simultaneously introduced in biomaterials, where copper nanoparticles (CuNPs) were generated by in situ reductions of Cu ions into a mussel-inspired hyperbranched polyglycerol (MI-hPG) coating via a simple dip-coating method. This hyperbranched polyglycerol with 10 % catechol groups' modification presents excellent antifouling property, which could effectively reduce bacteria adhesion on the surface. In this work, polycaprolactone (PCL) electrospun fiber membrane was selected as the substrate, which is commonly used in biomedical implants in bone regeneration and cardiovascular stents because of its good biocompatibility and easy post-modification. The as-fabricated CuNPs-incorporated PCL membrane [PCL-(MI-hPG)-CuNPs] was confirmed with effective antibacterial performance via in vitro antibacterial tests against Staphylococcus aureus (S. aureus), Escherichia coli (E. coli), and multi-resistant E. coli. In addition, the in vitro results demonstrated that osteogenic property of PCL-(MI-hPG)-CuNPs was realized by upregulating the osteoblast-related gene expressions and protein activity. This study shows that antibacterial and osteogenic properties can be balanced in a surface coating by introducing CuNPs.
Am 20. November 2010 fand an der Universität Potsdam das 4. Herbsttreffen Patholinguistik statt. Die Konferenzreihe wird regelmäßig seit 2007 vom Verband für Patholinguistik e.V. (vpl) durchgeführt. Der vorliegende Tagungsband veröffentlicht die Hauptvorträge des Herbsttreffens zum Thema "Lesen lernen: Diagnostik und Therapie bei Störungen des Leseerwerbs". Des Weiteren sind die Beiträge promovierender bzw. promovierter PatholinguistInnen sowie der Posterpräsentationen enthalten.