A quantum chemical study of the mechanism of action of Vitamin K epoxide reductase (VKOR) II. Transition states

A reaction path including transition states is generated for the Silverman mechanism [R.B. Silverman, Chemical model studies for the mechanism of Vitamin K epoxide reductase, J. Am. Chem. Soc. 103 (1981) 5939-5941] of action for Vitamin K epoxide reductase (VKOR) using quantum mechanical methods (B3LYP/6-311G**). VKOR, an essential enzyme in mammalian systems, acts to convert Vitamin K epoxide, formed by Vitamin K carboxylase, to its (initial) quinone form for cellular reuse. This study elaborates on a prior work that focused on the thermodynamics of VKOR [D.W. Deerfield II, C.H. Davis, T. Wymore, D.W. Stafford, L.G. Pedersen, Int. J. Quant. Chem. 106 (2006) 2944-2952]. The geometries of proposed model intermediates and transition states in the mechanism are energy optimized. We find that once a key disulfide bond is broken, the reaction proceeds largely downhill. An important step in the conversion of the epoxide back to the quinone form involves initial protonation of the epoxide oxygen. We find that the source of this proton is likely a free mercapto group rather than a water molecule. The results are consistent with the current view that the widely used drug Warfarin likely acts by blocking binding of Vitamin K at the VKOR active site and thereby effectively blocking the initiating step. These results will be useful for designing more complete QM/MM studies of the enzymatic pathway once three-dimensional structural data is determined and available for VKOR.     

Efficient angular spectrum decomposition of acoustic sources. I. Theory

The angular spectrum decomposition is evaluated in terms of plane wave angular range, angular resolution, and spatial aliasing error using two-dimensional FFT (2-D FFT). The algorithm makes possible the source plane decomposition of normal velocity and pressure fields radiated by transducers of arbitrary shape, with significantly faster results achievable for planar sources. Although the angular spectrum is equally applicable to fields far from the transducer, the efficient calculation is derived specifically for fields in or very close to the source plane. An antialiasing algorithm is proposed that allows the source to be discretized with fewer sample points for a given accuracy than required with simple discretization techniques. Guidelines for the selection of sampling interval, discretization size, etc. are developed on an application-specific basis and indicate the best ratio of numerical accuracy to computational cost.     

Casting traceability with direct part marking using reconfigurable pin-type tooling based on paraffin–graphite actuators

Green sand moulding machines for cast iron foundries are presently unable to uniquely identify individual castings. An insert tool concept is developed and tested via incremental mock-up development. The tool is part of the pattern plate and changes shape between each moulding, thus giving each mould a unique ID by embossing a Data Matrix symbol into the sand. In the process of producing the mould, each casting can be given a unique (DPM), enabling part tracking throughout the casting's life cycle. Sand embossing is achieved with paraffin-actuated reconfigurable pin-type tooling under simulated processing conditions. The marker geometry limitations have been tested using static symbol patterns, both for sand embossing and actual casting marking. The marked castings have successfully been identified with decoding software. The study shows that the function of each element of this technology can be successfully applied within the foundry industry.     

Interesting Interest Points

Not all interest points are equally interesting. The most valuable interest points lead to optimal performance of the computer vision method in which they are employed. But a measure of this kind will be dependent on the chosen vision application. We propose a more general performance measure based on spatial invariance of interest points under changing acquisition parameters by measuring the spatial recall rate. The scope of this paper is to investigate the performance of a number of existing well-established interest point detection methods. Automatic performance evaluation of interest points is hard because the true correspondence is generally unknown. We overcome this by providing an extensive data set with known spatial correspondence. The data is acquired with a camera mounted on a 6-axis industrial robot providing very accurate camera positioning. Furthermore the scene is scanned with a structured light scanner resulting in precise 3D surface information. In total 60 scenes are depicted ranging from model houses, building material, fruit and vegetables, fabric, printed media and more. Each scene is depicted from 119 camera positions and 19 individual LED illuminations are used for each position. The LED illumination provides the option for artificially relighting the scene from a range of light directions. This data set has given us the ability to systematically evaluate the performance of a number of interest point detectors. The highlights of the conclusions are that the fixed scale Harris corner detector performs overall best followed by the Hessian based detectors and the difference of Gaussian (DoG). The methods based on scale space features have an overall better performance than other methods especially when varying the distance to the scene, where especially FAST corner detector, Edge Based Regions (EBR) and Intensity Based Regions (IBR) have a poor performance. The performance of Maximally Stable Extremal Regions (MSER) is moderate. We observe a relatively large decline in performance with both changes in viewpoint and light direction. Some of our observations support previous findings while others contradict these findings.     

Optimization of practical trusses with constraints on eigenfrequencies, displacements, stresses, and buckling

In this paper we consider the optimization of general 3D truss structures. The design variables are the cross-sections of the truss bars together with the joint coordinates, and are considered to be continuous variables. Using these design variables we simultaneously carry out size optimization (areas) and shape optimization (joint positions). Topology optimization (removal and introduction of bars) is only considered in the sense that bars of minimum cross-sectional area will have a negligible influence on the performance of the structure. The structures are subjected to multiple load cases and the objective of the optimizations is minimum mass with constraints on (possibly multiple) eigenfrequencies, displacements, and stresses. For the case of stress constraints, we deal differently with tensile and compressive stresses, for which we control buckling on the element level. The stress constraints are imposed in correlation with industrial standards, to make the optimized designs valuable from a practical point of view. The optimization problem is solved using SLP (Sequential Linear Programming).     

Diffractive optical coupling element for surface plasmon resonance sensors

Design, technological features, and performance of polymeric diffractive optical coupling elements (DOCEs), developed for surface plasmon resonance (SPR) sensors are reported. The concept is based on input and output coupling of collimated and perpendicularly incident light beams to exchangeable SPR sensor chips. In the SPR sensor chips, one DOCE couples the input light beam to a (bio-)chemical sensor surface and another DOCE images the output light beam onto a detector array. The manufacturing technique is based on an injection moulding process similarly to the manufacturing process of compact discs allowing precision manufacturing and reproduction of the grating topography of the DOCEs. The DOCE-based SPR sensor chip is specifically suitable for miniaturisation and large scale production, while maintaining high optical quality and performance.     

A longitudinal analysis of anxiety and depressive symptoms.

The authors modeled depressive and anxiety symptom data from 1,391 participants in a longitudinal study of middle-aged and older Swedish twins (M age?=?60.9 years, SD?=?13.3). Although anxiety and depression were highly correlated, a model with distinct Anxiety and Depression factors fit the data better than models with Positive and Negative Affect factors or a single Mental Health factor. Lack of well-being was associated with anxiety rather than depression. Over two 3-year intervals, anxiety symptoms led to depressive symptoms, but the relationship was not reciprocal. Anxiety symptoms were more stable than depression. These findings provide additional support for the idea that anxiety symptoms may reflect a personality trait such as neuroticism more than do depressive symptoms and suggest that low positive affect may not be as specific to depression among older adults as in younger people. (PsycINFO Database Record (c) 2010 APA, all rights reserved)