A cluster-DEE-based strategy to empower protein design

The Medical and Pharmaceutical industries have shown high interest in the precise engineering of protein hormones and enzymes that perform existing functions under a wide range of conditions. Proteins are responsible for the execution of different functions in the cell: catalysis in chemical reactions, transport and storage, regulation and recognition control. Computational Protein Design (CPD) investigates the relationship between 3-D structures of proteins and amino acid sequences and looks for all sequences that will fold into such 3-D structure. Many computational methods and algorithms have been proposed over the last years, but the problem still remains a challenge for Mathematicians, Computer Scientists, Bioinformaticians and Structural Biologists. In this article we present a new method for the protein design problem. Clustering techniques and a Dead-End-Elimination algorithm are combined with a SAT problem representation of the CPD problem in order to design the amino acid sequences. The obtained results illustrate the accuracy of the proposed method, suggesting that integrated Artificial Intelligence techniques are useful tools to solve such an intricate problem.     

A semantic role labelling-based framework for learning ontologies from Spanish documents

Currently, most of the information available in the Web is adapted primarily for human consumption, but there is so much information that can no longer be processed by a person in a reasonable time, either in digital or physical formats. To solve this problem, the idea of the Semantic Web arose. The Semantic Web deals with adding machine-readable information to Web pages. Ontologies represent a very important element of this web, as they provide a valid and robust structure to represent knowledge based on concepts, relations, axioms, etc. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semiautomatic methods to learn ontologies. In this sense, this paper proposes a new ontology learning methodology based on semantic role labeling from digital Spanish documents. The method makes it possible to represent multiple semantic relations specially taxonomic and partonomic ones in the standardized OWL 2.0. A set of experiments has been performed with the approach implemented in educational domain that show promising results.     

Mining: The Challenge Knocks on our Door

Mining will continue to be an important economic component due to the incessant demand for raw materials, but the industry needs to balance the extraction of mineral resources with its obligations to abide by all laws, benefit the local community, and contribute to regional development. Proper management of mine water through reuse, reduced consumption, and desalination is key to attaining a higher level of public acceptance, as is making sure that the public learns of the advantages that our industry brings.     

Efficient implementation of the implicit error correction nVoD schema

Implicit Error Correction is a near Video-on-Demand (nVoD) schema that trades bandwidth utilization for initial playback delay while potentially allowing an infinite number of users. Additionally, it provides error protection without further bandwidth increase by exploiting the implicit redundancy of nVoD protocols, using linear combinations of the segments transmitted in a given time slot. This paper introduces a new implementation that outperforms the original schema by adding two new mechanisms: redundancy channels and feedback error correction.     

Charge Migration in Organic Materials: Can Propagating Charges Affect the Key Physical Quantities Controlling Their Motion?

Charge migration is a ubiquitous phenomenon with profound implications throughout many areas of chemistry, physics, biology, and materials science. The long-term vision of designing functional materials with tailored molecular-scale properties has triggered an increasing quest to identify prototypical systems where truly molecular conduction pathways play a fundamental role. Such pathways can be formed due to the molecular organization of various organic materials and are widely used to discuss electronic properties at the nanometer scale. Here, we present a computational methodology to study charge propagation in organic molecular stacks at nano and sub-nanoscales and exploit this methodology to demonstrate that moving charge carriers strongly affect the values of the physical quantities controlling their motion. The approach is also expected to find broad application in the field of charge migration in soft matter systems.     

Preliminary Developments of Miniplate-Type Fuel of U-2.5Zr-7.5Nb Alloy Dispersed and Cladded in Zircaloy

Nuclear fuel based on uranium metal alloys is utilized in research and test reactors. For the purpose of the reduction of fuel enrichment, high densities of uranium-235 in this kind of fuel are needed. This can be achieved when uranium alloys are used containing elements such as Zr, Mo and Nb. The construction of fuel element with high-uranium density requires materials with low cross sections for neutron absorption, stability under irradiation and absence of the chemical interactions between the fuel and cladding elements. In case of U-Zr-Nb alloys, Zry （zircaloy） cladding is a better option due to the fact that they have a higher chemical compatibility when compared with the use of aluminum alloys. This study aims to develop plate type nuclear fuel using the U-2.5Zr-7.5Nb alloy dispersed in Zry. Powders of this uranium based alloy and Zry were obtained by hydriding-dehydriding process. These powders were homogenized, compacted in pellet that was sandwiched in plates and frame of Zry. This assembly was hot rolled forming the dispersion fuel miniplate.     

Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density (MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case–control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case–control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available.     

Heat transfer in a fluid through a porous medium over a permeable stretching surface with thermal radiation and variable thermal conductivity

This paper treats about the flow and heat transfer of a viscous incompressible fluid in a porous medium over a permeable stretching surface taking into account thermal radiation and the variation of the thermal conductivity with temperature. Analytical solutions for the stream function are obtained and two cases are studied, namely, (i) prescribed surface temperature (PST case) and (ii) prescribed heat flux (PHF case). The effects of Prandtl number, permeability, suction/blowing, variable thermal conductivity, thermal radiation and surface temperature parameters on heat‐transfer characteristics are shown through tables and graphs and discussed. Ce document concerne l´écoulement et le transfer de chaleur dans le cas d´un fluide visqueux incompressible dans un milieu poreux sur une poreux surface s´étendant en tenant compte de la radiation thermique et de la conductivité thermodépendante. Les solutions analytiques pour la fonction de courant sont obtenus et deux conditions à la frontière pour la tempèrature et le flux de chaleur sont apliquées: (i) predicted surface tempèrature (PST) et (ii) le flux de chaleur predicted (PHF). Les effects du nombre de Prandtl, perméabilité, succion/soufflage, conductivité thermodépendante, radiation thermique et de la surface tempèrature sur le transfer de chaleur sont indiqués dans des tableaux et des graphiques, puis sont discutés. © 2011 Canadian Society for Chemical Engineering