Polarized light imaging of white matter architecture

Polarized light imaging (PLI) is a method to image fiber orientation in gross histological brain sections based on the birefringent properties of the myelin sheaths. The method uses the transmission of polarized light to quantitatively estimate the fiber orientation and inclination angles at every point of the imaged section. Multiple sections can be assembled into a 3D volume, from which the 3D extent of fiber tracts can be extracted. This article describes the physical principles of PLI and describes two major applications of the method: the imaging of white matter orientation of the rat brain and the generation of fiber orientation maps of the human brain in white and gray matter. The strengths and weaknesses of the method are set out.     

Interface with angels: the future of VIR and AR interfaces

Although real guardian angels aren't easy to get hold of, some of the computer technology needed for such a personal assistant is already available. Other parts exist in the form of research prototypes, but some technological breakthroughs are necessary before we can realize their potential, let alone integrate into our daily routines. Future VR and AR interfaces won't necessarily try to provide a perfect imitation of reality but instead will adapt their display mechanisms to their users' individual requirements. The emergence of these interfaces won't rely on a single technology but will depend on the advances in many areas, including computer graphics, display technology, tracking and recognition devices, natural and intuitive interactions, 3D interaction techniques, mobile and ubiquitous computing, intelligent agents, and conversational user interfaces, to name a few. The guardian angel scenario exemplifies how future developments in AR and VR user interfaces might change the way we interact with computers. Although this example is just one of several plausible scenarios, it demonstrates that AR and VP, in combination with user-centered design of their post-WIMP interfaces, can provide increased access, convenience, usability, and efficiency     

Semiautomated finite element mesh generation methods for a long bone

The objective of this work was to develop and test a semi-automated finite element mesh generation method using computed tomography (CT) image data of a canine radius. The present study employs a direct conversion from CT Hounsfield units to elastic moduli. Our method attempts to minimize user interaction and eliminate the need for mesh smoothing to produce a model suitable for finite element analysis. Validation of the computational model was conducted by loading the CT-imaged canine radius in four-point bending and using strain gages to record resultant strains that were then compared to strains calculated with the computational model. Geometry-based and uniform modulus voxel-based models were also constructed from the same imaging data set and compared. The nonuniform voxel-based model most accurately predicted the axial strain response of the sample bone (R(2)=0.9764).     

Learning with "relevance": using a third factor to stabilize Hebbian learning

It is a well-known fact that Hebbian learning is inherently unstable because of its self-amplifying terms: the more a synapse grows, the stronger the postsynaptic activity, and therefore the faster the synaptic growth. This unwanted weight growth is driven by the autocorrelation term of Hebbian learning where the same synapse drives its own growth. On the other hand, the cross-correlation term performs actual learning where different inputs are correlated with each other. Consequently, we would like to minimize the autocorrelation and maximize the cross-correlation. Here we show that we can achieve this with a third factor that switches on learning when the autocorrelation is minimal or zero and the cross-correlation is maximal. The biological counterpart of such a third factor is a neuromodulator that switches on learning at a certain moment in time. We show in a behavioral experiment that our three-factor learning clearly outperforms classical Hebbian learning.     

Generalization in the XCSF classifier system: analysis, improvement, and extension

We analyze generalization in XCSF and introduce three improvements. We begin by showing that the types of generalizations evolved by XCSF can be influenced by the input range. To explain these results we present a theoretical analysis of the convergence of classifier weights in XCSF which highlights a broader issue. In XCSF, because of the mathematical properties of the Widrow-Hoff update, the convergence of classifier weights in a given subspace can be slow when the spread of the eigenvalues of the autocorrelation matrix associated with each classifier is large. As a major consequence, the system's accuracy pressure may act before classifier weights are adequately updated, so that XCSF may evolve piecewise constant approximations, instead of the intended, and more efficient, piecewise linear ones. We propose three different ways to update classifier weights in XCSF so as to increase the generalization capabilities of XCSF: one based on a condition-based normalization of the inputs, one based on linear least squares, and one based on the recursive version of linear least squares. Through a series of experiments we show that while all three approaches significantly improve XCSF, least squares approaches appear to be best performing and most robust. Finally we show how XCSF can be extended to include polynomial approximations.     

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.     

The clinical repair of teeth using direct filling materials: engineering considerations

This paper reviews the way in which teeth damaged by caries may be repaired clinically. The mechanical effects of caries are described, as are the materials available to repair the damage caused by this disease. Studies are reported which have shown that caries reduces the compressive strength of the tooth to less than 50 per cent of its original value and that, by use of appropriate materials and placement techniques, this can be restored to some 80 per cent of this value. However, very few studies have been carried out which view tooth repair from an engineering perspective. Instead, emphasis is placed on determining clinical durability of repairs. This is related to repair strength but brings in other factors, such as the oral hygiene of the patient. Despite this complication, durability studies show that modern restorative materials perform well under clinical conditions, from which it may be concluded that the repair process allows a structure to be fabricated that is essentially sound from an engineering viewpoint, even if inferior to the original tooth structure provided by nature.     

On the Efficient Evaluation of Coalescence Integrals in Population Balance Models

The solution of population balance equations is a function f(t,r,x) describing a population density of particles of the property x at time t and space r. For instance, the additional independent variable x may denote the particle size. The describing partial differential equation contains additional sink and source terms involving integral operators. Since the coordinate x adds at least one further dimension to the spatial directions and time coordinate, an efficient numerical treatment of the integral terms is crucial. One of the more involved integral terms appearing in population balance models is the coalescence integral, which is of the form ∫ ₀ ^x κ(x–y, y) f(y) f(x–y)dy. In this paper, we describe an evaluation method of this integral which needs only operations, where n is the number of degrees of freedom with respect to the variable x. This cost can also be obtained in the case of a grid geometrically refined towards x=0.     

Optimal neuronal tuning for finite stimulus spaces

The efficiency of neuronal encoding in sensory and motor systems has been proposed as a first principle governing response properties within the central nervous system. We present a continuation of a theoretical study presented by Zhang and Sejnowski, where the influence of neuronal tuning properties on encoding accuracy is analyzed using information theory. When a finite stimulus space is considered, we show that the encoding accuracy improves with narrow tuning for one- and two-dimensional stimuli. For three dimensions and higher, there is an optimal tuning width.