2,577 research outputs found

    Trapped lipopolysaccharide and LptD intermediates reveal lipopolysaccharide translocation steps across the Escherichia coli outer membrane

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    Lipopolysaccharide (LPS) is a main component of the outer membrane of Gram-negative bacteria, which is essential for the vitality of most Gram-negative bacteria and plays a critical role for drug resistance. LptD/E complex forms a N-terminal LPS transport slide, a hydrophobic intramembrane hole and the hydrophilic channel of the barrel, for LPS transport, lipid A insertion and core oligosaccharide and O-antigen polysaccharide translocation, respectively. However, there is no direct evidence to confirm that LptD/E transports LPS from the periplasm to the external leaflet of the outer membrane. By replacing LptD residues with an unnatural amino acid p-benzoyl-L-phenyalanine (pBPA) and UV-photo-cross-linking in E.coli, the translocon and LPS intermediates were obtained at the N-terminal domain, the intramembrane hole, the lumenal gate, the lumen of LptD channel, and the extracellular loop 1 and 4, providing the first direct evidence and “snapshots” to reveal LPS translocation steps across the outer membrane

    Fuzzy Fibers: Uncertainty in dMRI Tractography

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    Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research

    Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison

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    A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties. Two main classes will be discussed: temporal difference (TD) rules and correlation based (differential hebbian) rules and some transition cases. In general we will focus on neuronal implementations with changeable synaptic weights and a time-continuous representation of activity. In a machine learning (non-neuronal) context, for TD-learning a solid mathematical theory has existed since several years. This can partly be transfered to a neuronal framework, too. On the other hand, only now a more complete theory has also emerged for differential Hebb rules. In general rules differ by their convergence conditions and their numerical stability, which can lead to very undesirable behavior, when wanting to apply them. For TD, convergence can be enforced with a certain output condition assuring that the δ-error drops on average to zero (output control). Correlation based rules, on the other hand, converge when one input drops to zero (input control). Temporally asymmetric learning rules treat situations where incoming stimuli follow each other in time. Thus, it is necessary to remember the first stimulus to be able to relate it to the later occurring second one. To this end different types of so-called eligibility traces are being used by these two different types of rules. This aspect leads again to different properties of TD and differential Hebbian learning as discussed here. Thus, this paper, while also presenting several novel mathematical results, is mainly meant to provide a road map through the different neuronally emulated temporal asymmetrical learning rules and their behavior to provide some guidance for possible applications

    A cone on Mercury: analysis of a residual central peak encircled by an explosive volcanic vent

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    We analyse a seemingly-unique landform on Mercury: a conical structure, encircled by a trough, and surrounded by a 23,000 km2 relatively bright and red anomaly of a type interpreted elsewhere on the planet as a pyroclastic deposit. At first glance, this could be interpreted as a volcanically-constructed cone, but if so, it would be the only example of such a landform on Mercury. We make and test the alternative hypothesis that the cone is the intrinsic central peak of an impact crater, the rim crest of which is visible beyond the cone-encircling trough, and that the trough is a vent formed through explosive volcanism that also produced the surrounding bright, red spectral anomaly. We test this hypothesis by comparing the morphology of the cone and the associated landform assemblage with morphologically-fresh impact craters of the same diameter as the putative host crater, and additionally, by modelling the original morphology of such a crater using a hydrocode model. We show that the present topography can be explained by formation of a vent completely encircling the crater’s central peak and also make the observation that explosive volcanic vents frequently occur circumferential to the central peaks of impact craters on Mercury. This indicates that, although this cone initially appears unique, it is in fact an unusually well-developed example of a common process by which impact-related faults localize magma ascent near the centre of impact craters on Mercury, and represents an extreme end-member of the resulting landforms
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