Wheat seed embryo excision enables the creation of axenic seedlings and Koch’s postulates testing of putative bacterial endophytes

Early establishment of endophytes can play a role in pathogen suppression and improve seedling development. One route for establishment of endophytes in seedlings is transmission of bacteria from the parent plant to the seedling via the seed. In wheat seeds, it is not clear whether this transmission route exists, and the identities and location of bacteria within wheat seeds are unknown. We identified bacteria in the wheat (Triticum aestivum) cv. Hereward seed environment using embryo excision to determine the location of the bacterial load. Axenic wheat seedlings obtained with this method were subsequently used to screen a putative endophyte bacterial isolate library for endophytic competency. This absence of bacteria recovered from seeds indicated low bacterial abundance and/or the presence of inhibitors. Diversity of readily culturable bacteria in seeds was low with 8 genera identified, dominated by Erwinia and Paenibacillus. We propose that anatomical restrictions in wheat limit embryo associated vertical transmission, and that bacterial load is carried in the seed coat, crease tissue and endosperm. This finding facilitates the creation of axenic wheat plants to test competency of putative endophytes and also provides a platform for endophyte competition, plant growth, and gene expression studies without an indigenous bacterial background

Exploring Statistical and Population Aspects of Network Complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples