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Background and aims: Plant seeds are emerging micro–habitats, whose importance as reservoir and vector of beneficial microbes just begins to be recognized. Here we aimed to characterize the bacterial microbiota of the Anadenanthera colubrina seed endosphere, with special focus to beneficial traits and to the colonization pattern. Methods: Cultivation–dependent (isolation from surface–sterilized seeds) and cultivation–independent (pyrosequencing of 16S rRNA gene from metagenomic seed DNA) analyses, functional tests and microscopical investigations (fluorescence in situ hybridization coupled with confocal laser scanning microscopy (FISH-CLSM) were performed. Results: We isolated several Methylobacterium and Staphylococcus spp., exhibiting both plant growth promotion and antimicrobial activities. The two taxonomic groups showed complementary traits, which supports a functional selection. Both genera were detected also by pyrosequencing, together with further taxa. The genera Friedmaniella, Bifidobacterium, Delftia, Anaerococcus and Actinomyces appeared here for the first time as seed endophytes. We detected bacterial cells and micro–colonies in seed cryosections by FISH-CLSM. Alphaproteobacteria, Firmicutes and other bacteria colonized intercellular spaces of the parenchyma and associated to transport vessels. Conclusions: This work sheds light onto the diversity, functions and colonization pattern of the Anadenanthera colubrina seed endophytes, and strongly suggest a role as beneficial partners for seed-associated microbiota.

Photoelectron diffraction (PED) is an experimental technique widely used to perform structural determinations of solid surfaces. Similarly to low-energy electron diffraction (LEED), structural determination by PED requires a fitting procedure between the experimental intensities and theoretical results obtained through simulations. Multiple scattering has been shown to be an effective approach for making such simulations. The quality of the fit can be quantified through the so-called R-factor. Therefore, the fitting procedure is, indeed, an R-factor minimization problem. However, the topography of the R-factor as a function of the structural and non-structural surface parameters to be determined is complex, and the task of finding the global minimum becomes tough, particularly for complex structures in which many parameters have to be adjusted. In this work we investigate the applicability of the genetic algorithm (GA) global optimization method to this problem. The GA is based on the evolution of species, and makes use of concepts such as crossover, elitism and mutation to perform the search. We show results of its application in the structural determination of three different systems: the Cu(111) surface through the use of energy-scanned experimental curves; the Ag(110)-c(2 × 2)-Sb system, in which a theory-theory fit was performed; and the Ag(111) surface for which angle-scanned experimental curves were used. We conclude that the GA is a highly efficient method to search for global minima in the optimization of the parameters that best fit the experimental photoelectron diffraction intensities to the theoretical ones. © IOP Publishing Ltd.