Optimierung des Wasserstandes zur Verbesserung der Klimabilanz in landwirtschaftlich genutzten Hochmooren

Entwässerte Moore sind Hotspots für Treibhausgasemissionen (THG), insbesondere für Kohlenstoffdioxid (CO2). Die Menge an freigesetztem CO2 in die Atmosphäre wird dabei maßgeblich über den Wasserstand im Moorkörper gesteuert. So sind vor allem intensiv genutzte Moorstandorte mit tiefen Wasserständen durch sehr hohe THG-Emissionen gekennzeichnet. Eine adäquate Entwässerung ist für die landwirtschaftliche Moornutzung essentiell, um die Bewirtschaftung (z.B. Düngung, Ernte) sowie eine optimale Pflanzenentwicklung zu gewährleisten. Die hohe flächenmäßige Bedeutung der landwirtschaftlichen Moornutzung in Niedersachsen (ca. 256.000 ha auf Nieder- und Hochmoor, davon ca. 81% Grünland) in Kombination mit hohen THG-Emissionen führen dazu, dass Moore in der Treibhausgasbilanz des Bundeslandes eine wichtige Größe darstellen (ca. 10 % der Gesamtemissionen). Aufgrund dieser landesweiten Bedeutung von landwirtschaftlich genutzten Moorflächen könnte eine Reduktion von THG-Emissionen durch angepasstes Wassermanagement zu einem großen THG-Einsparpotenzial in Niedersachsen führen. Dem Anheben des Wasserstandes durch geeignete Wassermaßnahmen zur Reduktion der THG steht die Notwendigkeit von tragfähigen Grasnarben und guter Befahrbarkeit für die Bewirtschaftung der Flächen gegenüber, welche derzeit noch vorwiegend über sehr tief abgesenkte Wasserstände erreicht wird. Ein Ziel des Modellprojektes im Gnarrenburger Moor ist es daher, die Wasserstände im Jahresverlauf so zu optimieren, dass eine Reduktion der THG-Emissionen unter Beibehaltung der landwirtschaftlichen Nutzung möglich wird. Darüber hinaus soll durch aktive Einbindung der kooperierenden Landwirte in das Projekt die grundsätzliche Akzeptanz für Wassermanagementmaßnahmen auf landwirtschaftlichen Grünlandflächen geschaffen werden. Grundbaustein des Modellprojektes ist das Anlegen von Demonstrationsversuchen. Basierend auf der Nutzungsintensität (Intensivgrünland, Extensivgrünland) werden in den Versuchen verschiedene Wassermanagementoptionen (z.B. Grabenanstau, Unterflurbewässerung) auf ihre Umsetzbarkeit sowie hinsichtlich der Auswirkungen auf die Bewirtschaftung, die Pflanzenerträge und die Wasserstandsdynamik untersucht. Das Poster stellt den grundlegenden Aufbau sowie die Ziele des Projektes dar und liefert darüber hinaus Informationen zu den Demoversuchen sowie den wissenschaftlichen Begleituntersuchungen

NNcon: improved protein contact map prediction using 2D-recursive neural networks

Protein contact map prediction is useful for protein folding rate prediction, model selection and 3D structure prediction. Here we describe NNcon, a fast and reliable contact map prediction server and software. NNcon was ranked among the most accurate residue contact predictors in the Eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8), 2008. Both NNcon server and software are available at http://casp.rnet.missouri.edu/nncon.html

NNcon: improved protein contact map prediction using 2D-recursive neural networks

Protein contact map prediction is useful for protein folding rate prediction, model selection and 3D structure prediction. Here we describe NNcon, a fast and reliable contact map prediction server and software. NNcon was ranked among the most accurate residue contact predictors in the Eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8), 2008. Both NNcon server and software are available at http://casp.rnet.missouri.edu/nncon.html

Super Yang-Mills Theory as a Random Matrix Model

We generalize the Gervais-Neveu gauge to four-dimensional N=1 superspace. The model describes an N=2 super Yang-Mills theory. All chiral superfields (N=2 matter and ghost multiplets) exactly cancel to all loops. The remaining hermitian scalar superfield (matrix) has a renormalizable massive propagator and simplified vertices. These properties are associated with N=1 supergraphs describing a superstring theory on a random lattice world-sheet. We also consider all possible finite matrix models, and find they have a universal large-color limit. These could describe gravitational strings if the matrix-model coupling is fixed to unity, for exact electric-magnetic self-duality.Comment: 15 pg., uuencoded compressed postscript file (.ps.Z.uu), other formats (.dvi, .ps, .ps.Z, 8-bit .tex) available at http://insti.physics.sunysb.edu/~siegel/preprints/ or at ftp://max.physics.sunysb.edu/preprints/siege

MSACompro: protein multiple sequence alignment using predicted secondary structure, solvent accessibility, and residue-residue contacts

<p>Abstract</p> <p>Background</p> <p>Multiple Sequence Alignment (MSA) is a basic tool for bioinformatics research and analysis. It has been used essentially in almost all bioinformatics tasks such as protein structure modeling, gene and protein function prediction, DNA motif recognition, and phylogenetic analysis. Therefore, improving the accuracy of multiple sequence alignment is important for advancing many bioinformatics fields.</p> <p>Results</p> <p>We designed and developed a new method, MSACompro, to synergistically incorporate predicted secondary structure, relative solvent accessibility, and residue-residue contact information into the currently most accurate posterior probability-based MSA methods to improve the accuracy of multiple sequence alignments. The method is different from the multiple sequence alignment methods (e.g. 3D-Coffee) that use the tertiary structure information of some sequences since the structural information of our method is fully predicted from sequences. To the best of our knowledge, applying predicted relative solvent accessibility and contact map to multiple sequence alignment is novel. The rigorous benchmarking of our method to the standard benchmarks (i.e. BAliBASE, SABmark and OXBENCH) clearly demonstrated that incorporating predicted protein structural information improves the multiple sequence alignment accuracy over the leading multiple protein sequence alignment tools without using this information, such as MSAProbs, ProbCons, Probalign, T-coffee, MAFFT and MUSCLE. And the performance of the method is comparable to the state-of-the-art method PROMALS of using structural features and additional homologous sequences by slightly lower scores.</p> <p>Conclusion</p> <p>MSACompro is an efficient and reliable multiple protein sequence alignment tool that can effectively incorporate predicted protein structural information into multiple sequence alignment. The software is available at <url>http://sysbio.rnet.missouri.edu/multicom_toolbox/</url>.</p

Identification of a PA-Binding Peptide with Inhibitory Activity against Influenza A and B Virus Replication

There is an urgent need for new drugs against influenza type A and B viruses due to incomplete protection by vaccines and the emergence of resistance to current antivirals. The influenza virus polymerase complex, consisting of the PB1, PB2 and PA subunits, represents a promising target for the development of new drugs. We have previously demonstrated the feasibility of targeting the protein-protein interaction domain between the PB1 and PA subunits of the polymerase complex of influenza A virus using a small peptide derived from the PA-binding domain of PB1. However, this influenza A virus-derived peptide did not affect influenza B virus polymerase activity. Here we report that the PA-binding domain of the polymerase subunit PB1 of influenza A and B viruses is highly conserved and that mutual amino acid exchange shows that they cannot be functionally exchanged with each other. Based on phylogenetic analysis and a novel biochemical ELISA-based screening approach, we were able to identify an influenza A-derived peptide with a single influenza B-specific amino acid substitution which efficiently binds to PA of both virus types. This dual-binding peptide blocked the viral polymerase activity and growth of both virus types. Our findings provide proof of principle that protein-protein interaction inhibitors can be generated against influenza A and B viruses. Furthermore, this dual-binding peptide, combined with our novel screening method, is a promising platform to identify new antiviral lead compounds