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 & Population. The organization has 14283 authors who have published 29845 publications receiving 682152 citations.

Capsid Protein-Mediated Recruitment of Host DnaJ-Like Proteins Is Required for Potato Virus Y Infection in Tobacco Plants

Abstract: The capsid protein (CP) of potyviruses is required for various steps during plant infection, such as virion assembly, cell-to-cell movement, and long-distance transport. This suggests a series of compatible interactions with putative host factors which, however, are largely unknown. By using the yeast two-hybrid system the CP from Potato virus Y (PVY) was found to interact with a novel subset of DnaJ-like proteins from tobacco, designated NtCPIPs. Mutational analysis identified the CP core region, previously shown to be essential for virion formation and plasmodesmal trafficking, as the interacting domain. The ability of NtCPIP1 and NtCPIP2a to associate with PVY CP could be confirmed in vitro and was additionally verified in planta by bimolecular fluorescence complementation. The biological significance of the interaction was assayed by PVY infection of agroinfiltrated leaves and transgenic tobacco plants that expressed either full-length or J-domain-deficient variants of NtCPIPs. Transient expression of truncated dominant-interfering NtCPIP2a but not of the functional protein resulted in strongly reduced accumulation of PVY in the inoculated leaf. Consistently, stable overexpression of J-domain-deficient variants of NtCPIP1 and NtCPIP2a dramatically increased the virus resistance of various transgenic lines, indicating a critical role of functional NtCPIPs during PVY infection. The negative effect of impaired NtCPIP function on viral pathogenicity seemed to be the consequence of delayed cell-to-cell movement, as visualized by microprojectile bombardment with green fluorescent protein-tagged PVY. Therefore, we propose that NtCPIPs act as important susceptibility factors during PVY infection, possibly by recruiting heat shock protein 70 chaperones for viral assembly and/or cellular spread.

The HCI Benchmark Suite: Stereo and Flow Ground Truth with Uncertainties for Urban Autonomous Driving

Abstract: Recent advances in autonomous driving require more and more highly realistic reference data, even for difficult situations such as low light and bad weather. We present a new stereo and optical flow dataset to complement existing benchmarks. It was specifically designed to be representative for urban autonomous driving, including realistic, systematically varied radiometric and geometric challenges which were previously unavailable. The accuracy of the ground truth is evaluated based on Monte Carlo simulations yielding full, per-pixel distributions. Interquartile ranges are used as uncertainty measure to create binary masks for arbitrary accuracy thresholds and show that we achieved uncertainties better than those reported for comparable outdoor benchmarks. Binary masks for all dynamically moving regions are supplied with estimated stereo and flow values. An initial public benchmark dataset of 55 manually selected sequences between 19 and 100 frames long are made available in a dedicated website featuring interactive tools for database search, visualization, comparison and benchmarking.

Needle Steering in 3-D Via Rapid Replanning

Abstract: Steerable needles have the potential to improve the effectiveness of needle-based clinical procedures such as biopsy and drug delivery by improving targeting accuracy and reaching previously inaccessible targets that are behind sensitive or impenetrable anatomical regions. We present a new needle steering system capable of automatically reaching targets in 3-D environments while avoiding obstacles and compensating for real-world uncertainties. Given a specification of anatomical obstacles and a clinical target (e.g., from preoperative medical images), our system plans and controls needle motion in a closed-loop fashion under sensory feedback to optimize a clinical metric. We unify planning and control using a new fast algorithm that continuously replans the needle motion. Our rapid replanning approach is enabled by an efficient sampling-based rapidly exploring random tree (RRT) planner that achieves orders-of-magnitude reduction in computation time compared with prior 3-D approaches by incorporating variable curvature kinematics and a novel distance metric for planning. Our system uses an electromagnetic tracking system to sense the state of the needle tip during the procedure. We experimentally evaluate our needle steering system using tissue phantoms and animal tissue ex vivo. We demonstrate that our rapid replanning strategy successfully guides the needle around obstacles to desired 3-D targets with an average error of less than 3 mm.

Extracting Landmarks with Data Mining Methods

Abstract: The navigation task is a very demanding application for mobile users The algorithms of present software solutions are based on the established methods of car navigation systems and thus exhibit some inherent disadvantages: findings in spatial cognition research have shown that human users need landmarks for an easy and successful wayfinding Typically, however, an object is not a landmark per se, but can be one relative to its environment Unfortunately, these objects are not part of route guidance information systems at the moment