Approximation of ill-posed boussinesq equations

In this paper we compare the dynamics on the centre manifold of the solutions of an ill-posed Boussinesq equation with a well-posed version. We show that the dynamics in the centre manifold of the ill-posed equation tracks the dynamics of the well-posed equation. Our results give partial justification to the long-wave perturbation theory.     

Protein modification by a Maillard reaction intermediate methylglyoxal. Immunochemical detection of fluorescent 5-methylimidazolone derivatives in vivo

Methylglyoxal (MG), an endogenous metabolite that increases in diabetes, is a common intermediate in nonenzymatic glycation (Maillard reaction) in vivo. Here we describe the immunochemical approach to the detection of MG adducts in proteins in vitro and in atherosclerotic lesions of human aorta in vivo. The reaction of protein (bovine serum albumin) with MG led to selective loss of arginine and lysine residues, accompanied by the formation of 5-methylimidazolone (N delta-(5-methylimidazolon-2-yl)ornithine) and imidazolysine (1,3-di-lysino-4-methylimidazole) derivatives, respectively. The anti-5-methylimidazolone antibody was prepared by immunizing rabbits with a MG-keyhole limpet hemocyanin conjugate and purifying the serum on an affinity gel prepared by covalent attachment of the 5-methylimidazolone derivative. The antibody cross-reacted with the proteins treated with not only MG but trioses, such as hydroxyacetone, dihydroxyacetone, and glyceraldehyde. The immunohistochemical analysis revealed that atherosclerotic lesions of human aorta contained 5-methylimidazolone derivatives whose distributions were identical to those of advanced glycation end products (AGEs) detected by the anti-AGE antibody.     

Web-Based Semantic Similarity: An Evaluation in the Biomedical Domain

Computation of semantic similarity between concepts is a very common problem in many language related tasks and knowledge domains. In the biomedical field, several approaches have been developed to deal with this issue by exploiting the structured knowledge available in domain ontologies (such as SNOMED-CT or MeSH) and specific, closed and reliable corpora (such as clinical data). However, in recent years, the enormous growth of the Web has motivated researchers to start using it as the corpus to assist semantic analysis of language. This paper proposes and evaluates the use of the Web as background corpus for measuring the similarity of biomedical concepts. Several ontology-based similarity measures have been studied and tested, using a benchmark composed by biomedical terms, comparing the results obtained when applying them to the Web against approaches in which specific clinical data were used. Results show that the similarity values obtained from the Web for ontology-based measures are at least and even more reliable than those obtained from specific clinical data, showing the suitability of the Web as information corpus for the biomedical domain.     

A Secure Elliptic Curve-Based RFID Protocol

Nowadays, the use of Radio Frequency Identification (RFID) systems in industry and stores has increased. Nevertheless, some of these systems present privacy problems that may discourage potential users. Hence, high confidence and effient privacy protocols are urgently needed. Previous studies in the literature proposed schemes that are proven to be secure, but they have scalability problems. A feasible and scalable protocol to guarantee privacy is presented in this paper. The proposed protocol uses elliptic curve cryptography combined with a zero knowledge-based authentication scheme. An analysis to prove the system secure, and even forward secure is also provided. This work is supported by the Generalitat de Catalunya under Grant No. FIC 2007FIC 00880, and the projects of the Spanish MCyT MTM2007-66842-C02-02 and TIN2006-15662-C02-02.     

Ontology-driven web-based semantic similarity

Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language processing, knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit knowledge—such as the structure of a taxonomy—or implicit knowledge—such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some problems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies –like specific domain ontologies- and massive corpus –like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge. The goal is to avoid the measures’ dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.     

Time series prediction evolving Voronoi regions

Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able to find predictable local behaviors, and thus, attaining a better level of total prediction. This new method is based on a division of the input space into Voronoi regions by means of Evolution Strategies. Our method has been tested using different time series domains. One of them that represents the water demand in a water tank, through a long period of time. The other two domains are well known examples of chaotic time series (Mackey-Glass) and natural phenomenon time series (Sunspot). Results prove that, in most of cases, the proposed algorithm obtain better results than other algorithms commonly used.     

Ontology-based semantic similarity: A new feature-based approach

Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach’s results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.     

Superconducting Proximity Effects in Ferromagnet/Superconductor Heterostructures

We consider in this paper superconducting proximity effects in clean Ferromagnet/Ferromagnet/Superconductor (F1/F2/S) heterostructures. We make use of a numerical self-consistent solution of the microscopic Bogoliubov?Cde Gennes equations to investigate the superconducting transition temperatures as a function of the angle ?? between the magnetizations in the ferromagnetic layers. Unlike in F1/S/F2 trilayers, we find that the superconducting transition temperature is a nonmonotonic function of ??.     

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.