Rayleigh mixture model for plaque characterization in intravascular ultrasound

Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology.     

Natural rubber filled SiC and B4C ceramic composites as a new NTC thermistors and piezoresistive sensor materials

A novel electrically conductive composite for NTC thermistor and piezoresistive sensor was successfully fabricated by homogeneously dispersing conductive SiC and B₄C in an insulating natural rubber (NR) matrix. The morphology of the composites was investigated by means of scanning electron microscopy, cross linking density (n), volume fraction of rubber (V_r), and interparticle distance among conductive phases (r_p). The influence of the filler concentrations on the mechanical properties such as modulus of elasticity (E); hardness shore A (H), and elongation at break (EB) were studied in details. The dependences of volume resistivity of NR based composites filled with B₄C and SiC as a function of fillers concentration was investigated. Temperature dependencies of volume resistivity were also measured to examine the possible application of the composites to polymer linear negative temperature (NTC) thermistors. Furthermore, the temperature dependencies of dielectric constant of the composites were studied. For practical application, the thermal stability of the composites was examined by means of resistivity temperature and pressure hysteresis cycle. In parallel, the conduction mechanism of conductivity of the composites was interpreted in terms of the computed the activation and hopping energy. The applicability of the composites to piezoresistive sensor was examined too. The good mechanical properties and thermal stability of NR composites behavior can be utilized for fabricating various electronic devices as NTC thermistors and piezoresistive sensor (i.e. transducers in pressure sensors). POLYM. COMPOS., 29:109–118, 2008. © 2007 Society of Plastics Engineers     

Anti-Idiotype scFv Localizes an Autoepitope in the Globular Domain of C1q

We addressed the issue of C1q autoantigenicity by studying the structural features of the autoepitopes recognized by the polyclonal anti-C1q antibodies present in Lupus Nephritis (LN) sera. We used six fractions of anti-C1q as antigens and selected anti-idiotypic scFv antibodies from the phage library “Griffin.1”. The monoclonal scFv A1 was the most potent inhibitor of the recognition of C1q and its fragments ghA, ghB and ghC, comprising the globular domain gC1q, by the lupus autoantibodies. It was sequenced and in silico folded by molecular dynamics into a 3D structure. The generated 3D model of A1 elucidated CDR similarity to the apical region of gC1q, thus mapping indirectly for the first time a globular autoepitope of C1q. The V_H CDR2 of A1 mimicked the ghA sequence GSEAD suggested as a cross-epitope between anti-DNA and anti-C1q antibodies. Other potential inhibitors of the recognition of C1q by the LN autoantibodies among the selected recombinant antibodies were the monoclonal scFv F6, F9 and A12.     

Molecular Mechanism of the Anti-Inflammatory Action of Heparin

Our objective is to reveal the molecular mechanism of the anti-inflammatory action of low-molecular-weight heparin (LMWH) based on its influence on the activity of two key cytokines, IFNγ and IL-6. The mechanism of heparin binding to IFNγ and IL-6 and the resulting inhibition of their activity were studied by means of extensive molecular-dynamics simulations. The effect of LMWH on IFNγ signalling inside stimulated WISH cells was investigated by measuring its antiproliferative activity and the translocation of phosphorylated STAT1 in the nucleus. We found that LMWH binds with high affinity to IFNγ and is able to fully inhibit the interaction with its cellular receptor. It also influences the biological activity of IL-6 by binding to either IL-6 or IL-6/IL-6Rα, thus preventing the formation of the IL-6/IL-6Rα/gp130 signalling complex. These findings shed light on the molecular mechanism of the anti-inflammatory action of LMWH and underpin its ability to influence favourably conditions characterised by overexpression of these two cytokines. Such conditions are not only associated with autoimmune diseases, but also with inflammatory processes, in particular with COVID-19. Our results put forward heparin as a promising means for the prevention and suppression of severe CRS and encourage further investigations on its applicability as an anti-inflammatory agent.     

Contamination and Ecological Risk Assessment of Long-Term Polluted Sediments with Heavy Metals in Small Hydropower Cascade

The identification of contamination level and ecological risks, associated with heavy metal pollution of sediments in small hydropower cascade was done on the base of index analyses approach. The concentration of As, Cd, Cu, Hg, Pb, Zn, total organic carbon and percentage of fines were determined in sediments of two habitats in cascade sequence – river and dam. Correlation and multivariate analyses suggest that As, Cu, Pb and Zn are associated with similar anthropogenic source. Cadmium and mercury originate from different source and have specific moving. Based on the contamination and background indices the sediments in Middle Iskar cascade are moderate contaminated at least and are subject of intensive hydrological and technological mixing. The potential ecological risk index (PERI) classifies the sediments in dam site with the higher risk level. Suitable indicators for express assessment of metal pollution in “river” sediments are contamination/enrichment indices which are more sensitive for local concentration increase of less toxic metals. In “dam” sites the process of sedimentation affects strongly the degree of metals accumulation and differences in toxicity are clearly presented – PER/PERI in combination with content of fine sediment fractions and TOC have a potential for rapid identification of sediment-associated risks.     

Resource management in LEO satellite networks

To provide truly global coverage needed by increased Personal Communication Services (PCS), a new generation of mobile satellite networks has been proposed. These low Earth orbit (LEO) mobile satellite networks handle multimedia traffic and can be used for non-real-time as well as real-time service to remote areas. Due to the many handoffs, resource management and connection admission control are important tasks for fair bandwidth sharing and QoS guarantees. Because the total link capacity has to be divided among several carriers and given the limited buffer capacity of the ATM switch, resource management is vital. It ensures the ability of the network to provide users with their negotiated QoS while protecting the network and the end-systems from congestion. We introduced a simple connection admission control (CAC) priority policy based on the delay and cell loss requirements for the investigated types of traffic. We took into account the handoff status of the satellite beams involved. Thus, we propose an onboard buffer architecture with separated buffers for new calls and intra-satellite handoff calls. The priority scheme applied is as follows: highest priority is given to CBR, followed by rt-VBR, nrtVBR and ABR.     

ECOC-DRF: Discriminative random fields based on error correcting output codes

We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.     

Distance learning for similarity estimation

In this paper, we present a general guideline to find a better distance measure for similarity estimation based on statistical analysis of distribution models and distance functions. A new set of distance measures are derived from the harmonic distance, the geometric distance, and their generalized variants according to the Maximum Likelihood theory. These measures can provide a more accurate feature model than the classical Euclidean and Manhattan distances. We also find that the feature elements are often from heterogeneous sources that may have different influence on similarity estimation. Therefore, the assumption of single isotropic distribution model is often inappropriate. To alleviate this problem, we use a boosted distance measure framework that finds multiple distance measures which fit the distribution of selected feature elements best for accurate similarity estimation. The new distance measures for similarity estimation are tested on two applications: stereo matching and motion tracking in video sequences. The performance of boosted distance measure is further evaluated on several benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods.     

Traffic sign recognition system with β -correction

Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.