Säveltäjien puhetta musiikista ja muustakin

Luettua

Detection of metabolite induction in fungal co-cultures on solid media by high-throughput differential ultra-high pressure liquid chromatography-time-of-flight mass spectrometry fingerprinting.

Access to new biological sources is a key element of natural product research. A particularly large number of biologically active molecules have been found to originate from microorganisms. Very recently, the use of fungal co-culture to activate the silent genes involved in metabolite biosynthesis was found to be a successful method for the induction of new compounds. However, the detection and identification of the induced metabolites in the confrontation zone where fungi interact remain very challenging. To tackle this issue, a high-throughput UHPLC-TOF-MS-based metabolomic approach has been developed for the screening of fungal co-cultures in solid media at the petri dish level. The metabolites that were overexpressed because of fungal interactions were highlighted by comparing the LC-MS data obtained from the co-cultures and their corresponding mono-cultures. This comparison was achieved by subjecting automatically generated peak lists to statistical treatments. This strategy has been applied to more than 600 co-culture experiments that mainly involved fungal strains from the Fusarium genera, although experiments were also completed with a selection of several other filamentous fungi. This strategy was found to provide satisfactory repeatability and was used to detect the biomarkers of fungal induction in a large panel of filamentous fungi. This study demonstrates that co-culture results in consistent induction of potentially new metabolites.

Towards a diffusion image processing validation and accuracy prediction framework

Validation is the main bottleneck preventing theadoption of many medical image processing algorithms inthe clinical practice. In the classical approach,a-posteriori analysis is performed based on someobjective metrics. In this work, a different approachbased on Petri Nets (PN) is proposed. The basic ideaconsists in predicting the accuracy that will result froma given processing based on the characterization of thesources of inaccuracy of the system. Here we propose aproof of concept in the scenario of a diffusion imaginganalysis pipeline. A PN is built after the detection ofthe possible sources of inaccuracy. By integrating thefirst qualitative insights based on the PN withquantitative measures, it is possible to optimize the PNitself, to predict the inaccuracy of the system in adifferent setting. Results show that the proposed modelprovides a good prediction performance and suggests theoptimal processing approach.