Forschungsdatensätze der BAM
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- 2019 (4) (entfernen)
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- Englisch (4)
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- Artificial neural networks (1)
- Data processing (1)
- EBSD (1)
- Electron energy (1)
- Energy distribution (1)
- Fungus (1)
- Kikuchi pattern (1)
- Metabolomics (1)
- Olivine (1)
- R package (1)
- Simulation (1)
- Software (1)
- Weathering (1)
- X-ray reflectivity (1)
Organisationseinheit der BAM
Metabolomics, the analysis of potentially all small molecules within a biological system, has become a valuable tool for biomarker identification and the elucidation of biological processes. While metabolites are often present in complex mixtures at extremely different concentrations, the dynamic range of available analytical methods to capture this variance is generally limited. Here, we show that gas chromatography coupled to atmospheric pressure chemical ionization mass spectrometry (GC-APCI-MS), a state of the art analytical technology applied in metabolomics analyses, shows an average linear range (LR) of 2.39 orders of magnitude for a set of 62 metabolites from a representative compound mixture. We further developed a computational tool to extend this dynamic range on average by more than 1 order of magnitude, demonstrated with a dilution series of the compound mixture, using robust and automatic reconstruction of intensity values exceeding the detection limit. The tool is freely available as an R package (CorrectOverloadedPeaks) from CRAN (https://cran.r-project.org/) and can be incorporated in a metabolomics data processing pipeline facilitating large screening assays.
AI-reflectivity is a code based on artificial neural networks trained with simulated reflectivity data that quickly predicts film parameters from experimental X-ray reflectivity curves. This project has a common root with (ML-reflectivity)[https://github.com/schreiber-lab/ML-reflectivity] and evolved in parallel. Both are linked to the following publication:
Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks A. Greco, V. Starostin, C. Karapanagiotis, A. Hinderhofer, A. Gerlach, L. Pithan, S. Liehr, F. Schreiber, S. Kowarik (2019). J. Appl. Cryst.
For an online live demonstration using a pre-trained network have a look at github.
This file contains all the data used for the figures shown in the Dissertation of Ruben Gerrits with the title "An experimental study of fungal olivine weathering".
In this study, the weathering-affecting, rock-inhabiting fungus, Knufia petricola A95 and the Fe-bearing olivine (Fe0.2Mg1.8SiO4) were selected to investigate fungi-induced effects on mineral dissolution. The availability of a melanin-deficient mutant (ΔKppks) of K. petricola A95, that produced more extracellular polymeric substances (EPS) than the wild type (WT), enabled comparative studies of the role of melanin and EPS in weathering processes.