Structural and electronic determinants of lytic polysaccharide monooxygenase reactivity on polysaccharide substrates

Lytic polysaccharide monooxygenases (LPMOs) are industrially important copper-dependent enzymes that oxidatively cleave polysaccharides. Here we present a functional and structural characterization of two closely related AA9-family LPMOs from Lentinus similis (LsAA9A) and Collariella virescens (CvAA9A). LsAA9A and CvAA9A cleave a range of polysaccharides, including cellulose, xyloglucan, mixed-linkage glucan and glucomannan. LsAA9A additionally cleaves isolated xylan substrates. The structures of CvAA9A and of LsAA9A bound to cellulosic and non-cellulosic oligosaccharides provide insight into the molecular determinants of their specificity. Spectroscopic measurements reveal differences in copper co-ordination upon the binding of xylan and glucans. LsAA9A activity is less sensitive to the reducing agent potential when cleaving xylan, suggesting that distinct catalytic mechanisms exist for xylan and glucan cleavage. Overall, these data show that AA9 LPMOs can display different apparent substrate specificities dependent upon both productive protein–carbohydrate interactions across a binding surface and also electronic considerations at the copper active site

QCD and strongly coupled gauge theories : challenges and perspectives

We highlight the progress, current status, and open challenges of QCD-driven physics, in theory and in experiment. We discuss how the strong interaction is intimately connected to a broad sweep of physical problems, in settings ranging from astrophysics and cosmology to strongly coupled, complex systems in particle and condensed-matter physics, as well as to searches for physics beyond the Standard Model. We also discuss how success in describing the strong interaction impacts other fields, and, in turn, how such subjects can impact studies of the strong interaction. In the course of the work we offer a perspective on the many research streams which flow into and out of QCD, as well as a vision for future developments.Peer reviewe

Sub-nanometre resolution imaging of polymer-fullerene photovoltaic blends using energy-filtered scanning electron microscopy

The resolution capability of the scanning electron microscope has increased immensely in recent years, and is now within the sub-nanometre range, at least for inorganic materials. An equivalent advance has not yet been achieved for imaging the morphologies of nanostructured organic materials, such as organic photovoltaic blends. Here we show that energy-selective secondary electron detection can be used to obtain high-contrast, material-specific images of an organic photovoltaic blend. We also find that we can differentiate mixed phases from pure material phases in our data. The lateral resolution demonstrated is twice that previously reported from secondary electron imaging. Our results suggest that our energy-filtered scanning electron microscopy approach will be able to make major inroads into the understanding of complex, nano-structured organic materials

Gender differences in the use of cardiovascular interventions in HIV-positive persons; the D:A:D Study

Peer reviewe

Partial Order Based Approach to Synthesis of Speed-Independent Circuits

This paper introduces a novel technique for synthesis of speed-independent circuits from their Signal Transition Graph specifications. The new method uses partial order in the form of the STG-unfolding segment to derive the logic implementation using approximation techniques. It is based on a new notion of slice, which localises the behaviour of a particular signal instance in a structural fragment of the segment. The experimental results show the power of the approximation approach in comparison with the existing methods. 1. Introduction There exists a variety of approaches to synthesis of speed-independent circuits from their Signal Transition Graph (STG) specifications. These approaches can be divided according to the library of elements used in implementations. For example, [5, 1] use a memory latch for each signal and a network of gates to drive it. Early methods, e.g. [2], assume that each signal is implemented as a single complex gate. Later techniques, e.g. [16, 6], attempt t..

Optogenetics in Silicon: A Neural Processor for Predicting Optically Active Neural Networks

We present a reconfigurable neural processor for real-time simulation and prediction of opto-neural behaviour. We combined a detailed Hodgkin-Huxley CA3 neuron integrated with a four-state Channelrhodopsin-2 (ChR2) model into reconfigurable silicon hardware. Our architecture consists of a Field Programmable Gated Array (FPGA) with a custom-built computing data-path, a separate data management system and a memory approach based router. Advancements over previous work include the incorporation of short and long-term calcium and light-dependent ion channels in reconfigurable hardware. Also, the developed processor is computationally efficient, requiring only 0.03 ms processing time per sub-frame for a single neuron and 9.7 ms for a fully connected network of 500 neurons with a given FPGA frequency of 56.7 MHz. It can therefore be utilized for exploration of closed loop processing and tuning of biologically realistic optogenetic circuitry

eLIAN: Enhanced Algorithm for Angle-Constrained Path Finding

Problem of finding 2D paths of special shape, e.g. paths comprised of line segments having the property that the angle between any two consecutive segments does not exceed the predefined threshold, is considered in the paper. This problem is harder to solve than the one when shortest paths of any shape are sought, since the planer's search space is substantially bigger as multiple search nodes corresponding to the same location need to be considered. One way to reduce the search effort is to fix the length of the path's segment and to prune the nodes that violate the imposed constraint. This leads to incompleteness and to the sensitivity of the 's performance to chosen parameter value. In this work we introduce a novel technique that reduces this sensitivity by automatically adjusting the length of the path's segment on-the-fly, e.g. during the search. Embedding this technique into the known grid-based angle-constrained path finding algorithm - LIAN, leads to notable increase of the planner's effectiveness, e.g. success rate, while keeping efficiency, e.g. runtime, overhead at reasonable level. Experimental evaluation shows that LIAN with the suggested enhancements, dubbed eLIAN, solves up to 20\% of tasks more compared to the predecessor. Meanwhile, the solution quality of eLIAN is nearly the same as the one of LIAN