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- ItemResponse of the wood-decay fungus Schizophyllum commune to co-occurring microorganisms(San Francisco, California, US : PLOS, 2020) Krause, Katrin; Jung, Elke-Martina; Lindner, Julia; Hardiman, Imam; Petschner, Jessica; Madhavan, Soumya; Matthäus, Christian; Kai, Marco; Menezes, Riya Christina; Popp, Jürgen; Svatoš, Aleš; Kothe, ErikaMicroorganisms are constantly interacting in a given environment by a constant exchange of signaling molecules. In timber, wood-decay fungi will come into contact with other fungi and bacteria. In naturally bleached wood, dark, pigmented lines arising from confrontation of two fungi often hint at such interactions. The metabolites (and pigment) exchange was investigated using the lignicolous basidiomycete Schizophyllum commune, and co-occurring fungi and bacteria inoculated directly on sterilized wood, or on media. In interactions with competitive wood degrading fungi, yeasts or bacteria, different competition strategies and communication types were observed, and stress reactions, as well as competitor-induced enzymes or pigments were analyzed. Melanin, indole, flavonoids and carotenoids were shown to be induced in S. commune interactions. The induced genes included multi-copper oxidases lcc1, lcc2, mco1, mco2, mco3 and mco4, possibly involved in both pigment production and lignin degradation typical for wood bleaching by wood-decay fungi.
- ItemCorrecting systematic errors by hybrid 2D correlation loss functions in nonlinear inverse modelling(San Francisco, California, US : PLOS, 2023) Mayerhöfer, Thomas G.; Noda, Isao; Pahlow, Susanne; Heintzmann, Rainer; Popp, JürgenRecently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results.