The Association of Human Herpesviruses with Malignant Brain Tumor Pathology and Therapy: Two Sides of a Coin

The role of certain viruses in malignant brain tumor development remains controversial. Experimental data demonstrate that human herpesviruses (HHVs), particularly cytomegalovirus (CMV), Epstein–Barr virus (EBV) and human herpes virus 6 (HHV-6), are implicated in brain tumor pathology, although their direct role has not yet been proven. CMV is present in most gliomas and medulloblastomas and is known to facilitate oncomodulation and/or immunomodulation, thus promoting cancer cell proliferation, invasion, apoptosis, angiogenesis, and immunosuppression. EBV and HHV-6 have also been detected in brain tumors and high-grade gliomas, showing high rates of expression and an inflammatory potential. On the other hand, due to the neurotropic nature of HHVs, novel studies have highlighted the engagement of such viruses in the development of new immunotherapeutic approaches in the context of oncolytic viral treatment and vaccine-based strategies against brain tumors. This review provides a comprehensive evaluation of recent scientific data concerning the emerging dual role of HHVs in malignant brain pathology, either as potential causative agents or as immunotherapeutic tools in the fight against these devastating diseases.     

Switching networked attitude control of an unmanned quadrotor

In this article, a switching networked attitude controller for an unmanned quadrotor over a wireless sensor network is presented. To deal with the network induced time varying delays, the quadrotor is being modeled as a switching time varying linear system, while the applied switching output feedback control scheme, is calculated based on Linear Matrix Inequalities, and is able to guarantee the stability of the quadrotor under arbitrary changes in the time delays.     

Spectral preconditioning of Krylov spaces: Combining PLS and PC regression

In regularized regression the vectors that lie in Krylov and eigen subspaces, formed in PLS and PC regressions respectively, provide useful low dimensional approximations for the LS regression coefficient vector. By preconditioning the LS normal equations we provide a framework in which to combine these methods, and so exploit both of their respective advantages. The link between the proposed method to orthogonal signal correction and to cyclic subspace regression is made. The performance of the proposed solution in reducing the dimension of the regression problem, and the shrinkage properties of the resulting coefficient vector, are both examined.     

Dependability and performance stochastic modelling of a two-unit repairable production system with preventive maintenance

In this paper, a two-unit multistate repairable production system is considered in which preventive maintenance (PM) is implemented in order to improve its dependability and performance. A general model is provided for the production system using a semi-Markov process, for examining system’s limiting behaviour. Apart from combining redundancy with PM, we introduce scenarios like imperfect and failed maintenance which are usually met in real life production systems. For the proposed model, we calculate the availability, the mean time to failure and the total operational cost and we formulate optimisation problems settled with respect to the system’s inspection times. The main aim of our work is to determine the optimal inspection times and consequently the optimal PM policies to be adopted in order to optimise system’s dependability and performance.     

Switching model predictive attitude control for a quadrotor helicopter subject to atmospheric disturbances

In this article a switching model predictive attitude controller for an unmanned quadrotor helicopter subject to atmospheric disturbances is presented. The proposed control scheme is computed based on a piecewise affine (PWA) model of the quadrotor's attitude dynamics, where the effects of the atmospheric turbulence are taken into consideration as additive disturbances. The switchings among the PWA models are ruled by the rate of the rotation angles and for each PWA system a corresponding model predictive controller is computed. The suggested algorithm is verified in experimental studies in the execution of sudden maneuvers subject to forcible wind disturbances. The quadrotor rejects the induced wind disturbances while performing accurate attitude tracking.     

Low bit depth representation motion estimation algorithms: a comparative study

A comparative study of low complexity motion estimation algorithms is presented. The algorithms included in the study are the 1-bit transform, the 2-bit transform, the constrained 1-bit transform and the multiplication free 1-bit transform which are using different motion estimation strategies compared to standard exhaustive search algorithm-mean absolute difference or similar combinations. These techniques provide better performance in terms of computational load compared to traditional algorithms. Although the accuracy of motion compensation is only slightly lower comparing to the other techniques, results in terms of objective quality (peak signal-to-noise ratio) and entropy are comparable. This fact, nominates them as suitable candidates for inclusion in embedded devices applications where lower complexity translates to lower power consumption and consequently improved device autonomy.     

Wear identification in rotor-bearing systems by measurements of dynamic bearing characteristics

During operation of rotating machines, journal and bearing mechanisms are progressively worn down. To prevent catastrophic failure of a rotating system, it is necessary both to detect wear precisely, without shutting down and dismantling the machinery and to predict future replacement needs. In this work, Computational Fluid Dynamics (CFD) analysis is used to solve the Navier–Stokes equations. Diagrams of bearing characteristics such as relative eccentricity, attitude angle, lubricant side flow and friction coefficient versus Sommerfeld number are presented for various wear depths and used for online wear identification. A graphical detection method is analytically presented to identify the wear depth associated with the measured dynamic bearing characteristics.     

Data hiding based on image texture classification

In this paper, a new pattern-based fragile, semi-blind, spatial domain data hiding scheme is proposed. The Local Binary Pattern texture classification approach is used, in order to transparently and securely embed secret data into an image. Pixel values are modified in such a way that the texture satisfies the message requirements. The method is thoroughly studied and compared to other techniques in spatial domain in terms of capacity and image quality. The scheme performs well in images with smooth areas and can be used for authentication, tamper proofing, and secret communications.     

Synthesis and biological investigation of novel tricyclic benzodiazepinedione-based RGD analogues

Integrins, a widely expressed family of heterodimeric cell surface adhesion proteins, are expressed in a variety of cell types. They play a decisive role in cell-cell adhesion or cell to extracellular matrix adhesion events. Antagonists of alpha(v)beta(3) or alpha(IIb)beta(3) integrin may have a potential use in suppression of pathological processes. We present the synthesis of novel tricyclic benzodiazepinedione-based RGD analogues, which were subsequently tested in a solid-phase receptor assay in order to investigate their binding affinities towards alpha(v)beta(3) and alpha(IIb)beta(3) integrin.