Gute Chancen für den Öko-Sojaanbau in Deutschland

From 2011 to 2013 a total of 35 different soybean cultivars were tested in organically managed replicated trials under a wide range of pedo-climatic conditions in Germany. The resulting yields were affected by the climatic region, soybean maturity group, water availability during grain filling and soybean cultivar. From our results we can conclude that with current cultivars a successful soybean cropping in most parts of Germany is possible. Only in the cooler regions of northern Germany and in the uplands yields are usually not competitive with other crops

Hepatic excretory function in sepsis: implications from biophotonic analysis of transcellular xenobiotic transport in a rodent model

INTRODUCTION: Hepatobiliary elimination of endo- and xenobiotics is affected by different variables including hepatic perfusion, hepatocellular energy state and functional integrity of transporter proteins, all of which are altered during sepsis. A particular impairment of hepatocellular transport at the canalicular pole resulting in an accumulation of potentially hepatotoxic compounds would have major implications for critical care pharmacology and diagnostics. METHODS: Hepatic transcellular transport, that is, uptake and hepatobiliary excretion, was studied in a rodent model of severe polymicrobial sepsis by two different biophotonic techniques to obtain insights into the handling of potentially toxic endo- and xenobiotics in sepsis. Direct and indirect in vivo imaging of the liver was performed by intravital multifluorescence microscopy and non-invasive whole-body near-infrared (NIRF) imaging after administration of two different, primarily hepatobiliary excreted xenobiotics, the organic anionic dyes indocyanine green (ICG) and DY635. Subsequent quantitative data analysis enabled assessment of hepatic uptake and fate of these model substrates under conditions of sepsis. RESULTS: Fifteen hours after sepsis induction, animals displayed clinical and laboratory signs of multiple organ dysfunction, including moderate liver injury, cholestasis and an impairment of sinusoidal perfusion. With respect to hepatocellular transport of both dyes, excretion into bile was significantly delayed for both dyes and resulted in net accumulation of potentially cytotoxic xenobiotics in the liver parenchyma (for example, specific dye fluorescence in liver at 30 minutes in sham versus sepsis: ICG: 75% versus 89%; DY635 20% versus 40% of maximum fluorescence; P < 0.05). Transcutaneous assessment of ICG fluorescence by whole body NIRF imaging revealed a significant increase of ICG fluorescence from the 30th minute on in the bowel region of the abdomen in sham but not in septic animals, confirming a sepsis-associated failure of canalicular excretion. CONCLUSIONS: Hepatocytes accumulate organic anions under conditions of sepsis-associated organ dysfunction. These results have potential implications for monitoring liver function, critical care pharmacology and the understanding of drug-induced liver injury in the critically ill

The GL(1|1)-symplectic fermion correspondence

In this note we prove a correspondence between the Wess-Zumino-Novikov-Witten model of the Lie supergroup GL(1|1) and a free model consisting of two scalars and a pair of symplectic fermions. This model was discussed earlier by LeClair. Vertex operators for the symplectic fermions include twist fields, and correlation functions of GL(1|1) agree with the known results for the scalars and symplectic fermions. We perform a detailed study of boundary states for symplectic fermions and apply them to branes in GL(1|1). This allows us to compute new amplitudes of strings stretching between branes of different types and confirming Cardy's condition.Comment: 34 page

From cave dragons to genomics

Throughout most of the kingdom Animalia, evolutionary transitions from surface life to a life permanently bound to caves and other subterranean habitats have occurred innumerous times. Not so in tetrapods, where a mere 14 cave-obligate species—all plethodontid and proteid salamanders—are known. We discuss why cave tetrapods are so exceptional and why only salamanders have made the transition. Their evolution follows predictable and convergent, albeit independent pathways. Among the many known changes associated with transitions to subterranean life, eye degeneration, starvation resistance, and longevity are especially relevant to human biomedical research. Recently, sequences of salamander genomes have become available opening up genomic research for cave tetrapods. We discuss new genomic methods that can spur our understanding of the evolutionary mechanisms behind convergent phenotypic change, the relative roles of selective and neutral evolution, cryptic species diversity, and data relevant for conservation such as effective population size and demography

Predictive modelling of plankton dynamics in freshwater lakes using genetic programming

Building predictive time series models for freshwater systems is important both for understanding the dynamics of these natural systems and in the development of decision support and management software. This work describes the application of a machine learning technique, namely genetic programming (GP), to the prediction of chlorophyll-a. The system endeavoured to evolve several mathematical time series equations, based on limnological and climate variables, which could predict the dynamics of chlorophyll-a on unseen data. The predictive accuracy of the genetic programming approach was compared with an artificial neural network and a deterministic algal growth model. The GP system evolved some solutions which were improvements over the neural network and showed that the transparent nature of the solutions may allow inferences about underlying processes to be made. This work demonstrates that non-linear processes in natural systems may be successfully modelled through the use of machine learning techniques. Further, it shows that genetic programming may be used as a tool for exploring the driving processes underlying freshwater system dynamics.UnpublishedBobbin, J. and F. Recknagel, 1999. Mining water quality time series for predictive rules for algal blooms by genetic algorithms. Proc. of the Int. Conference MODSIM 99 (in press). Gruau, F. 1996. On using Syntactic Constraints with Genetic Programming. In:P. a. K. Angeline, Jr., K.E., (Editor) Advances in Genetic Programming 2. 402-417. Holland, J. H. 1992. Adaptation in Natural and Artificial Systems. Cambridge, Mass.: MIT Press Koza, J. R. 1990. Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm. In:H. P. a. M. Schwefel, R., (Editor) Parallel Problem Solving from Nature. 124-129. Koza, J. R. 1992. Genetic Programming:on the programming of computers by means of natural selection. Cambridge, Mass.:MIT Press McKay, R. I., Pearson, R.A. and Whigham, P.A. 1997. Learning Spatial Relationships: Some Approaches. In GeoComputation ’97. R. T. Pascoe, (Editor), University of Otago, Dunedin, New Zealand. 69-79. Recknagel, F. 1997. ANNA - Artificial Neural Network model for predicting species abundance and succession of blue-green algae. Hydrobiologia. 394:47-57. Recknagel, F., and J. Benndorf. 1982. Validation of the ecological simulation model SALMO. Int. Revue ges .Hydrobiol. 67:113-125. Recknagel, F., T. Fukushima, T. Hanazato, N. Takamura, and H. Wilson. 1998. Modelling and Prediction of Phyto- and Zooplankton Dynamics in Lake Kasumigaura by Artificial Neural Networks. Lakes and Reservoirs: Research and Management. 3:123-133. Recknagel, F., and H. Wilson. 1999. Elucidation and prediction of aquatic ecosystems by artificial neural networks. Ecological Modelling. (in press). Reynolds, C. S. 1984. The ecology of freshwater phytoplankton. Press Syndicate of the University of Cambridge, New York Roston, G., and R. Sturges. 1995. A Genetic Design Methodology for Stucture Configuration. ASME Advances in Design Automation. DE 82:73-90. Whigham, P. A., Crapper, P.F. 1999. Time series modelling using genetic programming: An application to rainfall-runoff models. In:L. Spector, Langdon, W.B., O’Reilly, U. and Angeline, P.J., (Editor) Advances in Genetic Programming 3. . MIT Press, Cambridge, MA, USA. 89-104

Predictive modelling of plankton dynamics in freshwater lakes using genetic programming

Building predictive time series models for freshwater systems is important both for understanding the dynamics of these natural systems and in the development of decision support and management software. This work describes the application of a machine learning technique, namely genetic programming (GP), to the prediction of chlorophyll-a. The system endeavoured to evolve several mathematical time series equations, based on limnological and climate variables, which could predict the dynamics of chlorophyll-a on unseen data. The predictive accuracy of the genetic programming approach was compared with an artificial neural network and a deterministic algal growth model. The GP system evolved some solutions which were improvements over the neural network and showed that the transparent nature of the solutions may allow inferences about underlying processes to be made. This work demonstrates that non-linear processes in natural systems may be successfully modelled through the use of machine learning techniques. Further, it shows that genetic programming may be used as a tool for exploring the driving processes underlying freshwater system dynamics.UnpublishedBobbin, J. and F. Recknagel, 1999. Mining water quality time series for predictive rules for algal blooms by genetic algorithms. Proc. of the Int. Conference MODSIM 99 (in press). Gruau, F. 1996. On using Syntactic Constraints with Genetic Programming. In:P. a. K. Angeline, Jr., K.E., (Editor) Advances in Genetic Programming 2. 402-417. Holland, J. H. 1992. Adaptation in Natural and Artificial Systems. Cambridge, Mass.: MIT Press Koza, J. R. 1990. Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm. In:H. P. a. M. Schwefel, R., (Editor) Parallel Problem Solving from Nature. 124-129. Koza, J. R. 1992. Genetic Programming:on the programming of computers by means of natural selection. Cambridge, Mass.:MIT Press McKay, R. I., Pearson, R.A. and Whigham, P.A. 1997. Learning Spatial Relationships: Some Approaches. In GeoComputation ’97. R. T. Pascoe, (Editor), University of Otago, Dunedin, New Zealand. 69-79. Recknagel, F. 1997. ANNA - Artificial Neural Network model for predicting species abundance and succession of blue-green algae. Hydrobiologia. 394:47-57. Recknagel, F., and J. Benndorf. 1982. Validation of the ecological simulation model SALMO. Int. Revue ges .Hydrobiol. 67:113-125. Recknagel, F., T. Fukushima, T. Hanazato, N. Takamura, and H. Wilson. 1998. Modelling and Prediction of Phyto- and Zooplankton Dynamics in Lake Kasumigaura by Artificial Neural Networks. Lakes and Reservoirs: Research and Management. 3:123-133. Recknagel, F., and H. Wilson. 1999. Elucidation and prediction of aquatic ecosystems by artificial neural networks. Ecological Modelling. (in press). Reynolds, C. S. 1984. The ecology of freshwater phytoplankton. Press Syndicate of the University of Cambridge, New York Roston, G., and R. Sturges. 1995. A Genetic Design Methodology for Stucture Configuration. ASME Advances in Design Automation. DE 82:73-90. Whigham, P. A., Crapper, P.F. 1999. Time series modelling using genetic programming: An application to rainfall-runoff models. In:L. Spector, Langdon, W.B., O’Reilly, U. and Angeline, P.J., (Editor) Advances in Genetic Programming 3. . MIT Press, Cambridge, MA, USA. 89-104

Dunedin New Zealand

Building predictive time series models for freshwater systems is important both for understanding the dynamics of these natural systems and in the development of decision support and management software. This work describes the application of a machine learning technique, namely genetic programming (GP), to the prediction of chlorophyll-a. The system endeavoured to evolve several mathematical time series equations, based on limnological and climate variables, which could predict the dynamics of chlorophyll-a on unseen data. The predictive accuracy of the genetic programming approach was compared with an artificial neural network and a deterministic algal growth model. The GP system evolved some solutions which were improvements over the neural network and showed that the transparent nature of the solutions may allow inferences about underlying processes to be made. This work demonstrates that non-linear processes in natural systems may be successfully modelled through the use of machine learning techniques. Further, it shows that genetic programming may be used as a tool for exploring the driving processes underlying freshwater system dynamics