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.     

Application of full-factorial design for optimization of the moisture determination in wheat flour using infrared scales

We studied the parameters that may affect the analysis of the moisture content in wheat flour using infrared scales. The parameters were studied by one-factor at a time methodology and then, by full-factorial design (2³). The best condition using infrared scales occurred at 120 °C, with a sample weight of 1.5 g and an exposure time to infrared radiation of 6 min. We observed the interaction effect between the three factors studied. The analysis time was 60 times lower than classical method. The optimized method for determination of the moisture content in wheat flour using infrared scales was validated to range between 9 and 17 %.     

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.     

Dual‐Layer Hollow Fibers for Sulfur Removal from Fuels

Emission of sulfur compounds to the atmosphere is universally recognized as one key target to be reduced. For membrane pervaporation which is considered as a potential purification process of fuels, dual‐layer polyurethane (PU)/polyethersulfone hollow‐fiber membranes were prepared. A novel fabrication technique is proposed using a quadruple spinneret to produce the fiber with such morphology by simultaneous spinning of two polymer solutions in the presence of two corresponding precipitation media. Activated carbon was added into the PU solution to improve the transport properties of the selective layer. Resulting hollow‐fiber membranes showed very good adhesion between the selective layer and its support, in addition to an effective removal of a sulfur compound such as 2‐methyl thiophene from a typical model fuel, an indication of good prospects for both the fabrication technique and for sulfur removal by pervaporation of fuels.     

Bimodal polypropylene through binary metallocene catalytic systems: comparison between hybrid and mixed heterogeneous catalysts

Polypropylene with wider or bimodal molecular weight distribution is required for numerous applications since low molecular weight chains improve processability and high molecular weight fraction is required to get good mechanical properties. There are several routes to achieve a bimodal resin but the use of a binary catalytic system seems to be the most attractive, particularly with metallocenes combination. From a previous work two metallocenes were selected because they lead to polypropylenes with average molecular weights that differ in one order of magnitude. Two types of binary systems have been investigated, hybrid catalysts (two metallocenes loaded on the same support) and physical mixtures (two independent supported metallocenes that are introduced to the reactor and start the polymerization together), using different ratios, i.e., 25–75, 50–50, 75–25, at three reaction temperatures, i.e., 30, 50 and 70 °C. Most of the binary catalytic systems lead to bimodal molecular weight distributions. Polypropylenes produced by mixed catalysts are greatly influenced by the most active catalyst, while in PP coming from hybrid catalysts, as there is a strong interaction between both metallocenes, each one contributes according to its presence in the hybrid catalyst. Therefore, properties of obtained bimodal polypropylenes are quite influenced by the ratio between both metallocenes.