Flippase (FLP) recombinase-mediated marker recycling in the dermatophyte Arthroderma vanbreuseghemii.

Biological processes can be elucidated by investigating complex networks of relevant factors and genes. However, this is not possible in species for which dominant selectable markers for genetic studies are unavailable. To overcome the limitation in selectable markers for the dermatophyte Arthroderma vanbreuseghemii (anamorph: Trichophyton mentagrophytes), we adapted the flippase (FLP) recombinase-recombination target (FRT) site-specific recombination system from the yeast Saccharomyces cerevisiae as a selectable marker recycling system for this fungus. Taking into account practical applicability, we designed FLP/FRT modules carrying two FRT sequences as well as the flp gene adapted to the pathogenic yeast Candida albicans (caflp) or a synthetic codon-optimized flp (avflp) gene with neomycin resistance (nptII) cassette for one-step marker excision. Both flp genes were under control of the Trichophyton rubrum copper-repressible promoter (PCTR4). Molecular analyses of resultant transformants showed that only the avflp-harbouring module was functional in A. vanbreuseghemii. Applying this system, we successfully produced the Ku80 recessive mutant strain devoid of any selectable markers. This strain was subsequently used as the recipient for sequential multiple disruptions of secreted metalloprotease (fungalysin) (MEP) or serine protease (SUB) genes, producing mutant strains with double MEP or triple SUB gene deletions. These results confirmed the feasibility of this system for broad-scale genetic manipulation of dermatophytes, advancing our understanding of functions and networks of individual genes in these fungi.

Using active learning for monitoring networks design: The example of wind power plants sites evaluation

Locating new wind farms is of crucial importance for energy policies of the next decade. To select the new location, an accurate picture of the wind fields is necessary. However, characterizing wind fields is a difficult task, since the phenomenon is highly nonlinear and related to complex topographical features. In this paper, we propose both a nonparametric model to estimate wind speed at different time instants and a procedure to discover underrepresented topographic conditions, where new measuring stations could be added. Compared to space filling techniques, this last approach privileges optimization of the output space, thus locating new potential measuring sites through the uncertainty of the model itself.