Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery

Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm) remote sensing can be used to discriminate between grazed vegetation belonging to different grassland successional stages. Vascular plant species were recorded in 104.1 m2 plots on the island of Öland (Sweden) and the functional properties of the plant species recorded in the plots were characterized in terms of the ground-cover of grasses, specific leaf area and Ellenberg indicator values. Plots were assigned to three different grassland age-classes, representing 5–15, 16–50 and >50 years of grazing management. Partial least squares discriminant analysis models were used to compare classifications based on aerial hyperspectral data with the age-class classification. The remote sensing data successfully classified the plots into age-classes: the overall classification accuracy was higher for a model based on a pre-selected set of wavebands (85%, Kappa statistic value = 0.77) than one using the full set of wavebands (77%, Kappa statistic value = 0.65). Our results show that nutrient availability and grass cover differences between grassland age-classes are detectable by spectral imaging. These techniques may potentially be used for mapping the spatial distribution of grassland habitats at different successional stages

Modeling the Space Debris Environment with MASTER-2009 and ORDEM2010

Spacecraft analysis using ORDEM2010 uses a high-fidelity population model to compute risk to on-orbit assets. The ORDEM2010 GUI allows visualization of spacecraft flux in 2-D and 1-D. The population was produced using a Bayesian statistical approach with measured and modeled environment data. Validation of sizes 1mm is on-going

Predictive Markers of Honey Bee Colony Collapse

Across the Northern hemisphere, managed honey bee colonies, Apis mellifera, are currently affected by abrupt depopulation during winter and many factors are suspected to be involved, either alone or in combination. Parasites and pathogens are considered as principal actors, in particular the ectoparasitic mite Varroa destructor, associated viruses and the microsporidian Nosema ceranae. Here we used long term monitoring of colonies and screening for eleven disease agents and genes involved in bee immunity and physiology to identify predictive markers of honeybee colony losses during winter. The data show that DWV, Nosema ceranae, Varroa destructor and Vitellogenin can be predictive markers for winter colony losses, but their predictive power strongly depends on the season. In particular, the data support that V. destructor is a key player for losses, arguably in line with its specific impact on the health of individual bees and colonies

Instrumented Activity Dice for Assessing Throwing Performance: A Pilot Study

Assessment tests are crucial but often viewed as tedious work by the participants and the experimentalists. Consequently, research has been done to integrate assessments into serious games and to automate observations using computer vision or wearable sensors. However, the use of camera generates privacy concerns. Moreover, the behavior of a person can change when the person feels being recorded or when the person has to wear sensors. Hence, we investigate a different methodology to assess the physical status of a person by integrating sensors into a tangible toy commonly used by the participants - an activity dice that can be embedded in serious games. We demonstrate that with this instrumented activity dice, limitations of throwing performance of a person can be assessed purely based on the recording taken from the activity dice when the person throws the activity dice. We further demonstrate that variables extracted from the dice are similar to the variables extracted from a video

Pyruvate carboxylase is a major bottleneck for glutamate and lysine production by Corynebacterium glutamicum

Peters-Wendisch P, Schiel B, Wendisch VF, et al. Pyruvate carboxylase is a major bottleneck for glutamate and lysine production by Corynebacterium glutamicum. Journal of Molecular Microbiology and Biotechnology. 2001;3(2):295-300.Corynebacterium glutamicum possesses both phosphoenolpyruvate carboxylase (PEPCx) and pyruvate carboxylase (PCx) as anaplerotic enzymes for growth on carbohydrates. To analyze the significance of PCx for the amino acid production by this organism, the wild-type pyc gene, encoding PCx, was used for the construction of defined pyc-inactive and pyc-overexpressing strains and the glutamate, lysine and threonine production capabilities of these recombinant strains of C. glutamicum were tested in comparison to the respective host strains. No PCx activity was observed in the pyc-inactive mutants whereas the pyc-overexpressing strains showed eight-to elevenfold higher specific PCx activity when compared to the host strains. In a detergent-dependent glutamate production assay, the pyc-overexpressing strain showed more than sevenfold higher, the PCx-deficient strain about twofold lower glutamate production than the wild-type, Overexpression of the pyc gene and thus increasing the PCx activity in a lysine-producing strain of C. glutamicum resulted in approximately 50% higher lysine accumulation in the culture supernatant whereas inactivation of the pyc gene led to a decrease by 60%, In a threonine-producing strain of C. glutamicum, the overexpression of the pyc gene led to an only 10 to 20% increase in threonine production, however, to a more than 150% increase in the production of the threonine precursor homoserine. These results identify the anaplerotic PCx reaction as a major bottleneck for amino acid production by C. glutamicum and show that the enzyme is an important target for the molecular breeding of hyperproducing strains