Simultaneous Multiscale Polyaffine Registration by Incorporating Deformation Statistics

Locally affine (polyaffine) image registration methods capture intersubject non-linear deformations with a low number of parameters, while providing an intuitive interpretation for clinicians. Considering the mandible bone, anatomical shape differences can be found at different scales, e.g. left or right side, teeth, etc. Classically, sequential coarse to fine registration are used to handle multiscale deformations, instead we propose a simultaneous optimization of all scales. To avoid local minima we incorporate a prior on the polyaffine transformations. This kind of groupwise registration approach is natural in a polyaffine context, if we assume one configuration of regions that describes an entire group of images, with varying transformations for each region. In this paper, we reformulate polyaffine deformations in a generative statistical model, which enables us to incorporate deformation statistics as a prior in a Bayesian setting. We find optimal transformations by optimizing the maximum a posteriori probability. We assume that the polyaffine transformations follow a normal distribution with mean and concentration matrix. Parameters of the prior are estimated from an initial coarse to fine registration. Knowing the region structure, we develop a blockwise pseudoinverse to obtain the concentration matrix. To our knowledge, we are the first to introduce simultaneous multiscale optimization through groupwise polyaffine registration. We show results on 42 mandible CT images.

Multiscale Characterization of Acrylic Bone Cement Modified with Functionalized Mesoporous Silica Nanoparticles

Acrylic bone cement is widely used to anchor orthopedic implants to bone and mechanical failure of the cement mantle surrounding an implant can contribute to aseptic loosening. In an effort to enhance the mechanical properties of bone cement, a variety of nanoparticles and fibers can be incorporated into the cement matrix. Mesoporous silica nanoparticles (MSNs) are a class of particles that display high potential for use as reinforcement within bone cement. Therefore, the purpose of this study was to quantify the impact of modifying an acrylic cement with various low-loadings of mesoporous silica. Three types of MSNs (one plain variety and two modified with functional groups) at two loading ratios (0.1 and 0.2 wt/wt) were incorporated into a commercially available bone cement. The mechanical properties were characterized using four-point bending, microindentation and nanoindentation (static, stress relaxation, and creep) while material properties were assessed through dynamic mechanical analysis, differential scanning calorimetry, thermogravimetric analysis, FTIR spectroscopy, and scanning electron microscopy. Four-point flexural testing and nanoindentation revealed minimal impact on the properties of the cements, except for several changes in the nano-level static mechanical properties. Conversely, microindentation testing demonstrated that the addition of MSNs significantly increased the microhardness. The stress relaxation and creep properties of the cements measured with nanoindentation displayed no effect resulting from the addition of MSNs. The measured material properties were consistent among all cements. Analysis of scanning electron micrographs images revealed that surface functionalization enhanced particle dispersion within the cement matrix and resulted in fewer particle agglomerates. These results suggest that the loading ratios of mesoporous silica used in this study were not an effective reinforcement material. Future work should be conducted to determine the impact of higher MSN loading ratios and alternative functional groups. (C) 2014 Elsevier Ltd. All rights reserved.

The Redistribution of Property Rights, Corporate Governance and Company Restructuring after Privatisation"

The project covered the main issues of privatisation, corporate governance and company restructuring after privatisation in Hungary and in the Russian Republic, together with a summary of the broader picture of company-level changes in Central and Eastern Europe, discussing the issues of micro-financial restructuring in the Czech Republic, Hungary, Poland and Slovakia. The two countries selected as the focus of research can be regarded as the two most widely differing cases of the economic transformation in Central and Eastern Europe. Hungary began its transition very early in 1989, while Russia was very late in doing so. Hungary first implemented a series of institutional and systemic reforms before stabilising its public finances, while Russia has struggled with financial stabilisation for years without great success. Company restructuring and the introduction of new forms of governance only began in Russia in the mid-1990s. Hungary opted for "traditional" western methods of privatisation and invited a large amount of foreign direct investment (FDI) while in Russia the bulk of state-owned property was privatised either by free distribution or by a strange blend of ESOP-MBO schemes. FDI in Russia remained modest because of the high risk and uncertainty surrounding economic transactions there. Hungary was a forerunner in privatising public utilities, while Russia has moved cautiously in this area. The group's studies show that the Hungarian economy is now over the "transformation recession" and its economic success is largely due to its successful privatisation and to the dominant participation of foreign investors in company take-overs and in the restructuring process. The study of Russia provides a comprehensive account of the main factors in the so-far modest results in Russian privatisation and economic transformation.

A novel computer simulation method for simulating the multiscale transduction dynamics of signal proteins

Signal proteins are able to adapt their response to a change in the environment, governing in this way a broad variety of important cellular processes in living systems. While conventional molecular-dynamics (MD) techniques can be used to explore the early signaling pathway of these protein systems at atomistic resolution, the high computational costs limit their usefulness for the elucidation of the multiscale transduction dynamics of most signaling processes, occurring on experimental timescales. To cope with the problem, we present in this paper a novel multiscale-modeling method, based on a combination of the kinetic Monte-Carlo- and MD-technique, and demonstrate its suitability for investigating the signaling behavior of the photoswitch light-oxygen-voltage-2-Jα domain from Avena Sativa (AsLOV2-Jα) and an AsLOV2-Jα-regulated photoactivable Rac1-GTPase (PA-Rac1), recently employed to control the motility of cancer cells through light stimulus. More specifically, we show that their signaling pathways begin with a residual re-arrangement and subsequent H-bond formation of amino acids near to the flavin-mononucleotide chromophore, causing a coupling between β-strands and subsequent detachment of a peripheral α-helix from the AsLOV2-domain. In the case of the PA-Rac1 system we find that this latter process induces the release of the AsLOV2-inhibitor from the switchII-activation site of the GTPase, enabling signal activation through effector-protein binding. These applications demonstrate that our approach reliably reproduces the signaling pathways of complex signal proteins, ranging from nanoseconds up to seconds at affordable computational costs.