Strategien zu Umsetzung tierbezogener Kriterien entwickeln - Legehennengesundheit: Status quo und Zielgrößen

In dem workshop wurden erste Ergebnisse von 19 deutschen ökologischen Legehennenbetrieben aus dem europäischen Forschungsprojekt HealthyHens präsentiert. Auf dieser Basis wurden folgende Punkte diskutiert - die Anwendbarkeit der Tieruntersuchungen auf den Betrieben - der Nutzen der Untersuchungsergebnisse für die HennenhalterInnen, Zertifizierungs- und Kontrollstellen, KonsumentInnen und die Wissenschaft - Szenarien zur zukünftigen Anwendung - Vor- und Nachteile von Grenzwerten zu den Tiergesundheits-Parameter

Improved linkage analysis of Quantitative Trait Loci using bulk segregants unveils a novel determinant of high ethanol tolerance in yeast

Background: Bulk segregant analysis (BSA) coupled to high throughput sequencing is a powerful method to map genomic regions related with phenotypes of interest. It relies on crossing two parents, one inferior and one superior for a trait of interest. Segregants displaying the trait of the superior parent are pooled, the DNA extracted and sequenced. Genomic regions linked to the trait of interest are identified by searching the pool for overrepresented alleles that normally originate from the superior parent. BSA data analysis is non-trivial due to sequencing, alignment and screening errors. Results: To increase the power of the BSA technology and obtain a better distinction between spuriously and truly linked regions, we developed EXPLoRA (EXtraction of over-rePresented aLleles in BSA), an algorithm for BSA data analysis that explicitly models the dependency between neighboring marker sites by exploiting the properties of linkage disequilibrium through a Hidden Markov Model (HMM). Reanalyzing a BSA dataset for high ethanol tolerance in yeast allowed reliably identifying QTLs linked to this phenotype that could not be identified with statistical significance in the original study. Experimental validation of one of the least pronounced linked regions, by identifying its causative gene VPS70, confirmed the potential of our method. Conclusions: EXPLoRA has a performance at least as good as the state-of-the-art and it is robust even at low signal to noise ratio's i.e. when the true linkage signal is diluted by sampling, screening errors or when few segregants are available

Key parameters in thermally conductive polymer composites

The thermal conductivity of polymer composites is measured for several tubular carbon nanofillers (nanotubes, fibres, and whiskers). The highest enhancement in the thermal conductivity is observed for functionalized multiwalled carbon nanotubes (90% enhancement for 1 vol. %) and Pyrograf carbon fibres (80%). We model the experimental data using an effective thermal medium theory and determine the thermal interface resistance (RK ) at the filler-matrix interface. Our results show that the geometry of the nanofibres and the interface resistance are two key factors in engineering heat transport in a composite

Enhanced spin-orbit coupling in core/shell nanowires

The spin-orbit coupling (SOC) in semiconductors is strongly influenced by structural asymmetries, as prominently observed in bulk crystal structures that lack inversion symmetry. Here, we study an additional effect on the SOC: the asymmetry induced by the large interface area between a nanowire core and its surrounding shell. Our experiments on purely wurtzite GaAs/AlGaAs core/shell nanowires demonstrate optical spin injection into a single free-standing nanowire and determine the effective electron g-factor of the hexagonal GaAs wurtzite phase. The spin relaxation is highly anisotropic in time-resolved micro-photoluminescence measurements on single nanowires, showing a significant increase of spin relaxation in external magnetic fields. This behavior is counterintuitive compared to bulk wurtzite crystals. We present a model for the observed electron spin dynamics highlighting the dominant role of the interface-induced SOC in these core/shell nanowires. This enhanced SOC may represent an interesting tuning parameter for the implementation of spin-orbitronic concepts in semiconductor-based structures

Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception

This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty

Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception

This thesis presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The uncertainty in the environment arises by the fact that the intentions as well as the future trajectories of the surrounding drivers cannot be measured directly but can only be estimated in a probabilistic fashion. Even the perception of objects is uncertain due to sensor noise or possible occlusions. When driving in such environments, the autonomous car must predict the behavior of the other drivers and plan safe, comfortable and legal trajectories. Planning such trajectories requires robust decision making when several high-level options are available for the autonomous car. Current planning algorithms for automated driving split the problem into different subproblems, ranging from discrete, high-level decision making to prediction and continuous trajectory planning. This separation of one problem into several subproblems, combined with rule-based decision making, leads to sub-optimal behavior. This thesis presents a global, closed-loop formulation for the motion planning problem which intertwines action selection and corresponding prediction of the other agents in one optimization problem. The global formulation allows the planning algorithm to make the decision for certain high-level options implicitly. Furthermore, the closed-loop manner of the algorithm optimizes the solution for various, future scenarios concerning the future behavior of the other agents. Formulating prediction and planning as an intertwined problem allows for modeling interaction, i.e. the future reaction of the other drivers to the behavior of the autonomous car. The problem is modeled as a partially observable Markov decision process (POMDP) with a discrete action and a continuous state and observation space. The solution to the POMDP is a policy over belief states, which contains different reactive plans for possible future scenarios. Surrounding drivers are modeled with interactive, probabilistic agent models to account for their prediction uncertainty. The field of view of the autonomous car is simulated ahead over the whole planning horizon during the optimization of the policy. Simulating the possible, corresponding, future observations allows the algorithm to select actions that actively reduce the uncertainty of the world state. Depending on the scenario, the behavior of the autonomous car is optimized in (combined lateral and) longitudinal direction. The algorithm is formulated in a generic way and solved online, which allows for applying the algorithm on various road layouts and scenarios. While such a generic problem formulation is intractable to solve exactly, this thesis demonstrates how a sufficiently good approximation to the optimal policy can be found online. The problem is solved by combining state of the art Monte Carlo tree search algorithms with near-optimal, domain specific roll-outs. The algorithm is evaluated in scenarios such as the crossing of intersections under unknown intentions of other crossing vehicles, interactive lane changes in narrow gaps and decision making at intersections with large occluded areas. It is shown that the behavior of the closed-loop planner is less conservative than comparable open-loop planners. More precisely, it is even demonstrated that the policy enables the autonomous car to drive in a similar way as an omniscient planner with full knowledge of the scene. It is also demonstrated how the autonomous car executes actions to actively gather more information about the surrounding and to reduce the uncertainty of its belief state