Evaluating the Risk of Air Pollution to Forests in Central and Eastern Europe

Foliar damage to trees by air pollution in Central and Eastern Europe has been a major scientific and political issue. Emissions of toxic gases such as sulfur dioxide and nitrogen oxides can have wide-ranging effects on local and regional vegetation that can be compounded by other environmental stresses to plant growth. Since uptake and physiological effects of these gases on tree leaves are largely mediated by stomata, surrogate methods for estimating pollutant conductances into leaves and forest canopies may lead to risk assessments for major vegetation types that can then be used in regional planning. Management options to ameliorate or mitigate air pollutant damage to forests and losses in productivity are likely to be more difficult to widely implement than on-the-stack emissions abatement, Informed management and policy decisions regarding Central and Eastern European forests are dependent on the development of quantitative tools and models for risk assessment of the effects of atmospheric pollutants on ecosystem health and productivity

Glycoinositolphospholipids from Leishmania braziliensis and L. infantum: Modulation of Innate Immune System and Variations in Carbohydrate Structure

The essential role of the lipophosphoglycan (LPG) of Leishmania in innate immune response has been extensively reported. However, information about the role of the LPG-related glycoinositolphospholipids (GIPLs) is limited, especially with respect to the New World species of Leishmania. GIPLs are low molecular weight molecules covering the parasite surface and are similar to LPG in sharing a common lipid backbone and a glycan motif containing up to 7 sugars. Critical aspects of their structure and functions are still obscure in the interaction with the vertebrate host. In this study, we evaluated the role of those molecules in two medically important South American species Leishmania infantum and L. braziliensis, causative agents of visceral (VL) and cutaneous Leishmaniasis (CL), respectively. GIPLs derived from both species did not induce NO or TNF-α production by non-primed murine macrophages. Additionally, primed macrophages from mice (BALB/c, C57BL/6, TLR2−/− and TLR4−/−) exposed to GIPLs from both species, with exception to TNF-α, did not produce any of the cytokines analyzed (IL1-β, IL-2, IL-4, IL-5, IL-10, IL-12p40, IFN-γ) or p38 activation. GIPLs induced the production of TNF-α and NO by C57BL/6 mice, primarily via TLR4. Pre incubation of macrophages with GIPLs reduced significantly the amount of NO and IL-12 in the presence of IFN-γ or lipopolysaccharide (LPS), which was more pronounced with L. braziliensis GIPLs. This inhibition was reversed after PI-specific phospholipase C treatment. A structural analysis of the GIPLs showed that L. infantum has manose rich GIPLs, suggestive of type I and Hybrid GIPLs while L. braziliensis has galactose rich GIPLs, suggestive of Type II GIPLs. In conclusion, there are major differences in the structure and composition of GIPLs from L. braziliensis and L. infantum. Also, GIPLs are important inhibitory molecules during the interaction with macrophages

Posture tracking using a machine learning algorithm for a home AAL environment

The number of home office workers sitting for many hours is increasing. The sensor chair is tracking users’ sitting behavior which the help of pressure sensors and tries to avoid wrong postures which may cause diseases. The system provides live monitoring of the pressure distribution via web interface, as well as sitting posture prediction in real time. Posture analysis is realized through machine learning algorithm using a decision tree classifier that is compared to a random forest. Data acquisition and aggregation for the learning process happens with a mobile app adding users biometrical data and the taken sitting posture as label. The sensor chair is able to differentiate between an arched back, a neutral posture or a laid back position taken on the chair. The classifier achieves an accuracy of 97.4% on our test set and is comparable to the performance of the random forest with 98.9%