| 1. |
- Ewert, Frank, et al.
(författare)
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A methodology for enhanced flexibility of integrated assessment in agriculture
- 2009
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Ingår i: Environmental Science and Policy. - : Elsevier BV. - 1462-9011. ; 12:5, s. 546-561
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Tidskriftsartikel (refereegranskat)abstract
- Agriculture is interrelated with the socio-economic and natural environment and faces increasingly the problem of managing its multiple functions in a sustainable way. Growing emphasis is on adequate policies that can support both agriculture and sustainable development. Integrated Assessment and Modelling (IAM) can provide insight into the potential impacts of policy changes. An increasing number of Integrated Assessment (IA) models are being developed, but these are mainly monolithic and are targeted to answer specific problems. Approaches that allow flexible IA for a range of issues and functions are scarce. Recently, a methodology for policy support in agriculture has been developed that attempts to overcome some of the limitations of earlier IA models. The proposed framework (SEAMLESS-IF) integrates relationships and processes across disciplines and scales and combines quantitative analysis with qualitative judgments and experiences. It builds on the concept of systems analysis and attempts to enable flexible coupling of models and tools. The present paper aims to describe progress in improving flexibility of IAM achieved with the methodology developed for SEAMLESS-IF. A brief literature review identifying limitations in the flexibility of IAM is followed by a description of the progress achieved with SEAMLESS-IF. Two example applications are used to illustrate relevant capabilities of SEAMLESS-IF. The examples refer to (i) the impacts on European agriculture of changes in world trade regulations and (ii) regional impacts of the EU Nitrates Directive in combination with agro-management changes. We show that improving the flexibility of IAM requires flexibility in model linking but also a generic set up of all IA steps. This includes problem and scenario definition, the selection and specification of indicators and the indicator framework, the structuring of the database, and the visualization of results. Very important is the flexibility to integrate, select and link models, data and indicators depending on the application. Technical coupling and reusability of model components is greatly improved through adequate software architecture (SEAMLESS-IF uses OpenMI). The use of ontology strongly supports conceptual consistency of model linkages. However, the scientific basis for linking models across disciplines and scales is still weak and requires specific attention in future research. We conclude that the proposed framework significantly advances flexibility in IAM and that it is a good basis to further improve integrated modelling for policy impact assessment in agriculture. (C) 2009 Elsevier Ltd. All rights reserved.
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| 2. |
- Magnuson, Martin, 1965-, et al.
(författare)
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Large magnetic circular dichroism in resonant inelastic x-ray scattering at the Mn L-edge of Mn-Zn ferrite
- 2006
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Ingår i: Physical Review B. Condensed Matter and Materials Physics. - : American Physical Society. - 1098-0121 .- 1550-235X. ; 74
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Tidskriftsartikel (refereegranskat)abstract
- Wereport resonant inelastic x-ray scattering (RIXS) excited by circularly polarizedx rays on Mn-Zn ferrite at the Mn L2,3 resonances.We demonstrate that crystal-field excitations, as expected for localized systems,dominate the RIXS spectra and thus their dichroic asymmetry cannotbe interpreted in terms of spin-resolved partial density of states,which has been the standard approach for RIXS dichroism. Weobserve large dichroic RIXS at the L2 resonance which weattribute to the absence of metallic core hole screening inthe insulating Mn ferrite. On the other hand, reduced L3-RIXSdichroism is interpreted as an effect of longer scattering timethat enables spin-lattice core hole relaxation via magnons and phononsoccurring on a femtosecond time scale.
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| 3. |
- Wabnik, Krzysztof, et al.
(författare)
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Gene expression trends and protein features effectively complement each other in gene function prediction
- 2009
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 25:3, s. 322-330
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Tidskriftsartikel (refereegranskat)abstract
- MOTIVATION: Genome-scale 'omics' data constitute a potentially rich source of information about biological systems and their function. There is a plethora of tools and methods available to mine omics data. However, the diversity and complexity of different omics data types is a stumbling block for multi-data integration, hence there is a dire need for additional methods to exploit potential synergy from integrated orthogonal data. Rough Sets provide an efficient means to use complex information in classification approaches. Here, we set out to explore the possibilities of Rough Sets to incorporate diverse information sources in a functional classification of unknown genes. RESULTS: We explored the use of Rough Sets for a novel data integration strategy where gene expression data, protein features and Gene Ontology (GO) annotations were combined to describe general and biologically relevant patterns represented by If-Then rules. The descriptive rules were used to predict the function of unknown genes in Arabidopsis thaliana and Schizosaccharomyces pombe. The If-Then rule models showed success rates of up to 0.89 (discriminative and predictive power for both modeled organisms); whereas, models built solely of one data type (protein features or gene expression data) yielded success rates varying from 0.68 to 0.78. Our models were applied to generate classifications for many unknown genes, of which a sizeable number were confirmed either by PubMed literature reports or electronically interfered annotations. Finally, we studied cell cycle protein-protein interactions derived from both tandem affinity purification experiments and in silico experiments in the BioGRID interactome database and found strong experimental evidence for the predictions generated by our models. The results show that our approach can be used to build very robust models that create synergy from integrating gene expression data and protein features. AVAILABILITY: The Rough Set-based method is implemented in the Rosetta toolkit kernel version 1.0.1 available at: http://rosetta.lcb.uu.se/
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