No abstract

Biological control of soilborne plant pathogens in the field has given variable results. By combining specific strains of microorganisms, multiple traits antagonizing the pathogen can be combined and this may result in a higher level of protection. Pseudomonas putida WCS358 suppresses Fusarium wilt of radish by effectively competing for iron through the production of its pseudobactin siderophore. However, in some bioassays pseudobactin-negative mutants of WCS358 also suppressed disease to the same extent as WCS358, suggesting that an, as yet unknown, additional mechanism may be operative in this strain. P. putida strain RE8 induced systemic resistance against fusarium wilt. When WCS358 and RE8 were mixed through soil together, disease suppression was significantly enhanced to approximately 50% as compared to the 30% reduction for the single strain treatments. Moreover, when one strain failed to suppress disease in the single application, the combination still resulted in disease control. The enhanced disease suppression by the combination of P. putida strains WCS358 and RE8 is most likely the result of the combination of their different disease-suppressive mechanisms. These results demonstrate that combining biocontrol strains can lead to more effective, or at least, more reliable biocontrol of fusarium wilt of radish.

The dose of ZD1694 recommended for phase II trials is 3.0 mg/m2.

No abstract

High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.