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Authors = Jörg Müller

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Open AccessArticle Identification of FactorsInfluencing the Puumala Virus Seroprevalence within Its Reservoir in aMontane Forest Environment
Viruses 2014, 6(10), 3944-3967; doi:10.3390/v6103944
Received: 20 May 2014 / Revised: 3 September 2014 / Accepted: 29 September 2014 / Published: 23 October 2014
Cited by 4 | Viewed by 1262 | PDF Full-text (871 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Puumala virus (PUUV) is a major cause of mild to moderate haemorrhagic fever with renal syndrome and is transmitted by the bank vole (Myodes glareolus). There has been a high cumulative incidence of recorded human cases in South-eastern Germany since 2004
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Puumala virus (PUUV) is a major cause of mild to moderate haemorrhagic fever with renal syndrome and is transmitted by the bank vole (Myodes glareolus). There has been a high cumulative incidence of recorded human cases in South-eastern Germany since 2004 when the region was first recognized as being endemic for PUUV. As the area is well known for outdoor recreation and the Bavarian Forest National Park (BFNP) is located in the region, the increasing numbers of recorded cases are of concern. To understand the population and environmental effects on the seroprevalence of PUUV in bank voles we trapped small mammals at 23 sites along an elevation gradient from 317 to 1420m above sea level. Generalized linear mixed effects models(GLMEM) were used to explore associations between the seroprevalence of PUUV in bank voles and climate and biotic factors. We found that the seroprevalence of PUUV was low (6%–7%) in 2008 and 2009, and reached 29% in 2010. PUUV seroprevalence was positively associated with the local species diversity and deadwood layer, and negatively associated with mean annual temperature, mean annual solar radiation, and herb layer. Based on these findings, an illustrative risk map for PUUV seroprevalence prediction in bank voles was created for an area of the national park. The map will help when planning infrastructure in the national park (e.g., huts, shelters, and trails). Full article
(This article belongs to the Special Issue Hantaviruses)
Open AccessArticle LiDAR Remote Sensing of Forest Structure and GPS Telemetry Data Provide Insights on Winter Habitat Selection of European Roe Deer
Forests 2014, 5(6), 1374-1390; doi:10.3390/f5061374
Received: 10 March 2014 / Revised: 10 May 2014 / Accepted: 10 June 2014 / Published: 16 June 2014
Cited by 16 | Viewed by 2538 | PDF Full-text (1166 KB) | HTML Full-text | XML Full-text
Abstract
The combination of GPS-Telemetry and resource selection functions is widely used to analyze animal habitat selection. Rapid large-scale assessment of vegetation structure allows bridging the requirements of habitat selection studies on grain size and extent, particularly in forest habitats. For roe deer, the
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The combination of GPS-Telemetry and resource selection functions is widely used to analyze animal habitat selection. Rapid large-scale assessment of vegetation structure allows bridging the requirements of habitat selection studies on grain size and extent, particularly in forest habitats. For roe deer, the cold period in winter forces individuals to optimize their trade off in searching for food and shelter. We analyzed the winter habitat selection of roe deer (Capreolus capreolus) in a montane forest landscape combining estimates of vegetation cover in three different height strata, derived from high resolution airborne Laser-scanning (LiDAR, Light detection and ranging), and activity data from GPS telemetry. Specifically, we tested the influence of temperature, snow height, and wind speed on site selection, differentiating between active and resting animals using mixed-effects conditional logistic regression models in a case-control design. Site selection was best explained by temperature deviations from hourly means, snow height, and activity status of the animals. Roe deer tended to use forests of high canopy cover more frequently with decreasing temperature, and when snow height exceeded 0.6 m. Active animals preferred lower canopy cover, but higher understory cover. Our approach demonstrates the potential of LiDAR measures for studying fine scale habitat selection in complex three-dimensional habitats, such as forests. Full article
Open AccessArticle A New Integrated Lab-on-a-Chip System for Fast Dynamic Study of Mammalian Cells under Physiological Conditions in Bioreactor
Cells 2013, 2(2), 349-360; doi:10.3390/cells2020349
Received: 1 April 2013 / Revised: 29 April 2013 / Accepted: 16 May 2013 / Published: 27 May 2013
Cited by 6 | Viewed by 2200 | PDF Full-text (1004 KB) | HTML Full-text | XML Full-text
Abstract
For the quantitative analysis of cellular metabolism and its dynamics it is essential to achieve rapid sampling, fast quenching of metabolism and the removal of extracellular metabolites. Common manual sample preparation methods and protocols for cells are time-consuming and often lead to the
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For the quantitative analysis of cellular metabolism and its dynamics it is essential to achieve rapid sampling, fast quenching of metabolism and the removal of extracellular metabolites. Common manual sample preparation methods and protocols for cells are time-consuming and often lead to the loss of physiological conditions. In this work, we present a microchip-bioreactor setup which provides an integrated and rapid sample preparation of mammalian cells. The lab-on-a-chip system consists of five connected units that allow sample treatment, mixing and incubation of the cells, followed by cell separation and simultaneous exchange of media within seconds. This microsystem is directly integrated into a bioreactor for mammalian cell cultivation. By applying overpressure (2 bar) onto the bioreactor, this setup allows pulsation free, defined, fast, and continuous sampling. Experiments evince that Chinese Hamster Ovary cells (CHO-K1) can be separated from the culture broth and transferred into a new medium efficiently. Furthermore, this setup permits the treatment of cells for a defined time (9 s or 18 s) which can be utilized for pulse experiments, quenching of cell metabolism, and/or another defined chemical treatment. Proof of concept experiments were performed using glutamine containing medium for pulse experiments. Continuous sampling of cells showed a high reproducibility over a period of 18 h. Full article
(This article belongs to the Special Issue Feature Papers 2013)
Open AccessArticle Modelling Forest α-Diversity and Floristic Composition — On the Added Value of LiDAR plus Hyperspectral Remote Sensing
Remote Sens. 2012, 4(9), 2818-2845; doi:10.3390/rs4092818
Received: 5 August 2012 / Revised: 14 September 2012 / Accepted: 17 September 2012 / Published: 21 September 2012
Cited by 27 | Viewed by 3542 | PDF Full-text (9284 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The decline of biodiversity is one of the major current global issues. Still, there is a widespread lack of information about the spatial distribution of individual species and biodiversity as a whole. Remote sensing techniques are increasingly used for biodiversity monitoring and especially
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The decline of biodiversity is one of the major current global issues. Still, there is a widespread lack of information about the spatial distribution of individual species and biodiversity as a whole. Remote sensing techniques are increasingly used for biodiversity monitoring and especially the combination of LiDAR and hyperspectral data is expected to deliver valuable information. In this study spatial patterns of vascular plant community composition and α-diversity of a temperate montane forest in Germany were analysed for different forest strata. The predictive power of LiDAR (LiD) and hyperspectral (MNF) datasets alone and combined (MNF+LiD) was compared using random forest regression in a ten-fold cross-validation scheme that included feature selection and model tuning. The final models were used for spatial predictions. Species richness could be predicted with varying accuracy (R2 = 0.26 to 0.55) depending on the forest layer. In contrast, community composition of the different layers, obtained by multivariate ordination, could in part be modelled with high accuracies for the first ordination axis (R2 = 0.39 to 0.78), but poor accuracies for the second axis (R2 ≤ 0.3). LiDAR variables were the best predictors for total species richness across all forest layers (R2 LiD = 0.3, R2 MNF = 0.08, R2 MNF+LiD = 0.2), while for community composition across all forest layers both hyperspectral and LiDAR predictors achieved similar performances (R2 LiD = 0.75, R2 MNF = 0.76, R2 MNF+LiD = 0.78). The improvement in R2 was small (≤0.07)—if any—when using both LiDAR and hyperspectral data as compared to using only the best single predictor set. This study shows the high potential of LiDAR and hyperspectral data for plant biodiversity modelling, but also calls for a critical evaluation of the added value of combining both with respect to acquisition costs. Full article
(This article belongs to the Special Issue Remote Sensing of Biological Diversity)
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