The effect of heparin hydrogel embedding on glutaraldehyde fixed bovine pericardial tissues: Mechanical behavior and anticalcification potential

Heart valve diseases remain common in industrialized countries. Bioprosthetic heart valves, introduced as free of anticoagulation therapy alternatives to mechanical substitutes. Still they suffer from long term failure due to calcification. Different treatment methods introduced to inhibit calcification, have so far been limited in success. Glycosaminoglycans (GAGs) possess properties including high negative charge, anticoagulation and anti-inflammatory activity that make them a potential solution for calcification problem. In this study, heparin hydrogel was prepared and characterized both chemically and mechanically. After that, heparin hydrogel embedded bovine pericardial tissues, fixed with glutaraldehyde, were produced and tested for their mechanical behavior and anticalcifcation potential in vitro using the constant composition model. In the calcification experiments, tissues were divided into three groups: a) Controls without treatment, b) Hydrogel treated tissues and c) Tissues with raw heparin dissolved in the calcification solution. The results showed that embedding of tissue with hydrogel had no stiffening effect on its mechanical behavior. Calcification assessment showed a significant efficacy on inhibition of calcium phosphate deposition of hydrogel treated (second group) in comparison to untreated tissues (control, first group). Calcification inhibition potential was very similar in both the second and raw heparin (third group). Histological data confirmed the obtained results, suggesting that heparin treatment is a promising anticalcification agent.

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Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model

Hidden Markov (chain) models using finite Gaussian mixture models as their hidden state distributions have been successfully applied in sequential data modeling and classification applications. Nevertheless, Gaussian mixture models are well known to be highly intolerant to the presence of untypical data within the fitting data sets used for their estimation. Finite Student's t-mixture models have recently emerged as a heavier-tailed, robust alternative to Gaussian mixture models, overcoming these hurdles. To exploit these merits of Student's t-mixture models in the context of a sequential data modeling setting, we introduce, in this paper, a novel hidden Markov model where the hidden state distributions are considered to be finite mixtures of multivariate Student's t-densities. We derive an algorithm for the model parameters estimation under a maximum likelihood framework, assuming full, diagonal, and factor-analyzed covariance matrices. The advantages of the proposed model over conventional approaches are experimentally demonstrated through a series of sequential data modeling applications.     

Wireless Convergence Architecture: A Case Study Using GSM and Wireless LAN

The evolution of wireless networks has motivated the expansion of the static business environment to a mobile and wireless one. However, current and forthcoming wireless technologies are characterized by different attributes, regarding coverage area, offered bandwidth and delay. The transparent conjunction of various wireless technologies into a single mobile terminal can further boost the wireless explosion. This paper presents the Wireless Convergence Architecture (WCA) that incorporates different wireless interfaces under the same mobile terminal. Depending on the location and the availability of the underlying wireless network, automatic and seamless switching is performed to the most appropriate wireless network interface. Special care is taken to maintain continuous connectivity at the transport layer (TCP). WCA introduces software components both at the terminal and network side. A specific implementation is presented, based on two complementary wireless technologies – in terms of coverage area – an IEEE 802.11-compliant in the short local and a GSM in the wide area. To demonstrate the operational correctness of the architecture, experiments were performed using standard and proprietary networking applications.     

On the base stations antenna system design for mobile communications

The design of a base station antenna for mobile communications is presented. The orthogonal method (OM) is applied under constraints on nulls of the radiation pattern. In the synthesis process the mutual coupling between the elements of the antenna array could be taken into account. Beam tilting and direction of arrival are also considered. The whole design is completed by implementing a special architecture of the antenna. Finally, some examples show the applicability of our technique.     

Joint source-channel coding for wireless object-based video communications utilizing data hiding.

In recent years, joint source-channel coding for multimedia communications has gained increased popularity. However, very limited work has been conducted to address the problem of joint source-channel coding for object-based video. In this paper, we propose a data hiding scheme that improves the error resilience of object-based video by adaptively embedding the shape and motion information into the texture data. Within a rate-distortion theoretical framework, the source coding, channel coding, data embedding, and decoder error concealment are jointly optimized based on knowledge of the transmission channel conditions. Our goal is to achieve the best video quality as expressed by the minimum total expected distortion. The optimization problem is solved using Lagrangian relaxation and dynamic programming. The performance of the proposed scheme is tested using simulations of a Rayleigh-fading wireless channel, and the algorithm is implemented based on the MPEG-4 verification model. Experimental results indicate that the proposed hybrid source-channel coding scheme significantly outperforms methods without data hiding or unequal error protection.     

Design of optimum gain pyramidal horn using self‐adaptive differential evolution algorithms

In this work, we apply classical and new self‐adaptive differential evolution (DE) algorithms to the design of optimum gain pyramidal horns. The application of DE algorithms to pyramidal horn design allows the accurate calculation of their physical dimensions in a short amount of time. Moreover, self‐adaptive DE does not require the prespecified choice of control parameters reducing significantly the users' effort. Representative examples demonstrate the applicability of our proposal. Our study shows that self‐adaptive DE algorithms outperform or produce similar results with other methods in the literature in terms of solution accuracy and convergence speed. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Theta-burst stimulation of the right neocerebellar vermis selectively disrupts the practice-induced acceleration of lexical decisions.

The present study reports an experiment of cerebellar transcranial magnetic stimulation in a lexical decision task. In contrast to the study by Argyropoulos (2011), no effect of cerebellar stimulation was observed on priming sizes. However, when subjects confronted the same stimuli in the second session of participation, lexical decision latencies did not become any shorter after stimulation of the right neocerebellar vermis, in contrast to all other conditions. This finding is discussed in the light of current research in cerebellar cognitive and linguistic functions, and provides some first evidence for the recently entertained hypothesis that neocerebellar loci are significant in acquiring, storing, and retrieving associative memory traces of repeatedly co-occurring neural events in the language domain. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

A variational Bayesian methodology for hidden Markov models utilizing Student's-t mixtures

The Student's-t hidden Markov model (SHMM) has been recently proposed as a robust to outliers form of conventional continuous density hidden Markov models, trained by means of the expectation-maximization algorithm. In this paper, we derive a tractable variational Bayesian inference algorithm for this model. Our innovative approach provides an efficient and more robust alternative to EM-based methods, tackling their singularity and overfitting proneness, while allowing for the automatic determination of the optimal model size without cross-validation. We highlight the superiority of the proposed model over the competition using synthetic and real data. We also demonstrate the merits of our methodology in applications from diverse research fields, such as human computer interaction, robotics and semantic audio analysis.     

3D Tracking and Classification System Using a Monocular Camera

This paper details a 3D tracking and recognition system using a single camera. The system is able to track and classify targets in outdoors and indoors scenarios, as long as they move (at least approximately) on a plane. The system first detects and validates targets and then tracks them in a state-space employing cylindrical models (horizontal and vertical position on the ground, their radius and height) utilising Particle Filters. The tracker fuses visual measurements that utilise the targets’ foreground and colour models. Finally the system classifies the tracked objects based on the visual metrics extracted by our algorithm. We have tested our model in an outdoor setting using humans and automobiles passing through the field of view of the camera at various speeds and distances. The results presented in this paper show the validity our approach.     

Interaction of calcium carbonates with lead in aqueous solutions

Pure calcium carbonate (calcite and aragonite) solid materials of different particle size (100-200 microm fragments and millimeter-sized single crystals) were interacted with Pb in aqueous solutions at room temperature under atmospheric PCO2. In the case of the micrometer-sized samples, the macroscopic investigation using a batch-type treatment procedure (solutions between 10 and 1000 mg/L Pb) and ICP-AES, SEM-EDS, and powder-XRD showed that the metal is readily removed from the aqueous media by both materials and indicated the sorption processes (mainly surface precipitation leading to overgrowth of cerussite and hydrocerussite crystals) taking place in parallel with surface dissolution processes. The various processes occurring at the calcium carbonate solid-water interface were clearly distinguished and defined in the case of the millimeter-sized samples interacted with 1000 mg/L Pb using a combination of wet-chemical, in-situ (AFM) and ex-situ (AFM, SEM) microscopic, and surface spectroscopic (XPS, 12C-RBS) techniques. The in-situ AFM data revealed the dissolution processes on the surface of the calcium carbonates and the simultaneous heterogeneous nucleation of lead carbonate phases and confirmed the secondary dissolution of lead carbonate crystals grown epitaxially from the initial nuclei. The XPS spectra confirmed that adsorption of Pb occurs simultaneously to dissolution at short interaction times (less than approximately 10 min, prior to precipitation-nucleation/crystal growth) in the case of both CaCO3 polymorphs and that the calcite surface with adsorbed Pb may have an aragonite-type character. The 12C-RBS spectra indicated that absorption (incorporation of Pb2+ ions) also takes place in parallel at the surface layers of the calcium carbonates, resulting in formation of solid solutions.