Leibniz University of Hanover

About: Leibniz University of Hanover is a education organization based out in Hanover, Niedersachsen, Germany. It is known for research contribution in the topics: Finite element method & Computer science. The organization has 14283 authors who have published 29845 publications receiving 682152 citations.

Mycotoxins: Producing Fungi and Mechanisms of Phytotoxicity

Abstract: Mycotoxins are secondary fungal metabolites, toxic to humans, animals and plants. Among the hundreds of known mycotoxins, aflatoxins, citrinin, patulin, penicillic acid, tenuazonic acid, ochratoxin A, cytochalasins, deoxynivalenol, fumonisins, fusarin C, fusaric acid, and zearalenone are considered the types that most contaminate cereal grain. The majority of the mycotoxins in these groups are produced by three fungal genera: Aspergillus, Penicillium and Fusarium. These metabolites primarily affect the seed quality, germination, viability, seedling vigour, growth of root and cleoptile. Additionally, since the fungi responsible for the production of these mycotoxins are often endophytes that infect and colonize living plant tissues, accumulation of mycotoxins in the plant tissues may at times be associated with development of plant disease symptoms. The presence of mycotoxins, even in the absence of disease symptoms, may still have subtle biological effects on the physiology of plants. Several studies highlight the toxic effects of mycotoxins on animals and cell lines but little is known about the mode of action of most of these metabolites on plant cells. The most important mycotoxins with phytotoxic effects and their producers in addition to their discovery are briefly outlined below and will be addressed in this article.

Self-organized adaptation of a simple neural circuit enables complex robot behaviour

Abstract: Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Present robotic solutions operate with limited autonomy and are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors by means of a simple neural control circuit, thereby generating 11 basic behavioural patterns (for example, orienting, taxis, self-protection and various gaits) and their combinations. The control signal quickly and reversibly adapts to new situations and also enables learning and synaptic long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to self-organize versatile behaviours in autonomous agents with many degrees of freedom. Mimicking even the simplest of animal behaviour, such as walking along uneven terrain, is a challenging task. A study finds that incorporating a simple but inherently chaotic pattern generator into the control system of an autonomous robot allows it to show adaptive behaviour, enabling it to successfully navigate through a complex environment.

Antiferromagnetic heisenberg spin chain of a few cold atoms in a one-dimensional trap

Abstract: We report on the deterministic preparation of antiferromagnetic Heisenberg spin chains consisting of up to four fermionic atoms in a one-dimensional trap. These chains are stabilized by strong repulsive interactions between the two spin components without the need for an external periodic potential. We independently characterize the spin configuration of the chains by measuring the spin orientation of the outermost particle in the trap and by projecting the spatial wave function of one spin component on single-particle trap levels. Our results are in good agreement with a spin-chain model for fermionized particles and with numerically exact diagonalizations of the full few-fermion system.