Cdc53 is a scaffold protein for multiple Cdc34/Skp1/F-box proteincomplexes that regulate cell division and methionine biosynthesis in yeast

In budding yeast, ubiquitination of the cyclin-dependent kinase (Cdk) inhibitor Sic1 is catalyzed by the E2 ubiquitin conjugating enzyme Cdc34 in conjunction with an E3 ubiquitin ligase complex composed of Skp1, Cdc53 and the F-box protein, Cdc4 (the SCFCdc4 complex). Skp1 binds a motif called the F-box and in turn F-box proteins appear to recruit specific substrates for ubiquitination. We find that Skp1 interacts with Cdc53 in vivo, and that Skp1 bridges Cdc53 to three different F-box proteins, Cdc4, Met30, and Grr1. Cdc53 contains independent binding sites for Cdc34 and Skp1 suggesting it functions as a scaffold protein within an E2/E3 core complex. F-box proteins show remarkable functional specificity in vivo: Cdc4 is specific for degradation of Sic1, Grr1 is specific for degradation of the G1 cyclin Cln2, and Met30 is specific for repression of methionine biosynthesis genes. In contrast, the Cdc34-Cdc53-Skp1 E2/E3 core complex is required for all three functions. Combinatorial control of SCF complexes may provide a basis for the regulation of diverse cellular processes.     

The Impact of Plasma‐modified Films with Sulfur Dioxide,Sodium Oxide on Food Pathogenic Microorganisms

In this study, plasma modification was carried out in barrier discharge under atmospheric pressure. Sulfur dioxide and/or sodium oxide was used as the coating precursor. The results show that the films treated with a mixture of argon and sulfur dioxide inhibited microbial growth considerably better than those with coatings containing argon and sodium oxide. The highest levels of growth inhibition, of 86% for Staphylococcus aureus and 82% for Escherichia coli, were achieved with films carrying a sulfur‐containing coating (1.4% SO₂). Copyright © 2014 John Wiley & Sons, Ltd.     

Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models

Water Resources Management - Various methods are used in the literature for calibration of conceptual rainfall-runoff models. However, very rarely the question on the relation between the number of...     

Evaluation of plant products from the Legnicko–Głogowski region for their contamination with arsenic

Contents of arsenic were determined in plant products originating from the region of two copperworks, G?ogów and Legnica. Analyses were carried out by means of atomic absorption spectrometry, using an MHS-10 unit for hydride generation (acetylene/argon), after wet mineralisation of samples.     

Dataless Transitions Between Concise Representations of Frequent Patterns

For many data mining problems in order to solve them it is required to discover frequent patterns. Frequent itemsets are useful e.g. in the discovery of association and episode rules, sequential patterns and clusters. Nevertheless, the number of frequent itemsets is usually huge. Therefore, a number of lossless representations of frequent itemsets have recently been proposed. Two of such representations, namely the closed itemsets and the generators representation, are of particular interest as they can efficiently be applied for the discovery of most interesting non-redundant association and episode rules. On the other hand, it has been proved experimentally that other representations of frequent patterns happen to be more concise and more quickly extractable than these two representations even by several orders of magnitude. Hence, such concise representations seem to be an interesting alternative for materializing and reusing the knowledge of frequent patterns. The problem however arises, how to transform the intermediate representations into the desired ones efficiently and preferably without accessing the database. This article tackles this problem. As a result of investigating the properties of representations of frequent patterns, we offer a set of efficient algorithms for dataless transitioning between them.     

An Integrated Approach to Factors Affecting the Shelf Life of Products in Modified Atmosphere Packaging (MAP)

The aim of this work was to analyze the main factors influencing the shelf life of peanut products in modified atmosphere packaging (MAP). Roasted and salted peanuts produced by three well-known commercial companies were presented as examples. Different types of packaging (multilayer laminated materials, cans) with different gas mixture composition (0-1% O₂, 1-3% O_2, 3-6% O₂) were compared. The main aim of shelf-life investigation is to ensure consumer acceptance of a product. Factors affecting the shelf life of products in MAP are presented as an integrated pyramid. All elements of this shelf-life pyramid should be analyzed and included during shelf-life investigations. This study indicates that estimation of the shelf life of a food is integrally related to its packaging (e.g., peanuts in MAP); both product conditions and the package as well as gas mixture composition should be considered.     

Turkey breast roasting – Process optimization

The objective of the present study was to perform experimental and theoretical analyses of the process of roasting turkey breast slices in a steam-convection oven with a view to determining the optimal values of temperature and steam saturation during roasting, under the assumption of a constant slice thickness. The roasting process was conducted at air temperature of 160 °C, 180 °C, 200 °C and 220 °C and steam saturation of 0%, 20%, 50%, 70% and 90%. Two qualitative criteria based on eating quality and nutritive value have been applied in process optimization. Those criteria proved to be mutually opposing. As regards eating quality, the best product was that roasted at a temperature of 160 °C without the application of steam in the oven. The same product was characterized by the lowest nutritive value. Nevertheless, it should be noted that the protein content of turkey meat, largely responsible for its nutritive value, is related to changes in protein concentrations in samples resulting from weight loss during cooking. The highest nutritive value was noted in respect of the product roasted at a temperature of 200 °C with 20% steam saturation. Both optimal products were microbiologically safe.     

Are Evolutionary Algorithms Effective in Calibrating Different Artificial Neural Network Types for Streamwater Temperature Prediction?

Streamwater temperature may be severely affected by the global warming. Different types of models could be used to evaluate the regime of water temperatures in future climatic conditions, including artificial neural networks. As neural networks have no physical background, they require calibration of large number of parameters. This is typically done by gradient-based algorithms, however there is an ongoing debate on usefulness of metaheuristics for this task. In this paper more than ten Swarm Intelligence and Evolutionary Algorithms, including one developed especially for this study, are tested to train four kinds of artificial neural networks (multi-layer perceptron, product-units, adaptive-network-based fuzzy inference systems and wavelet neural networks) for daily water temperature prediction in a natural river located in temperate climate zone. The results are compared with the ones obtained when the classical Levenberg-Marquardt algorithm is used. Finally, the ensemble aggregating approach is tested. Although the research confirms that most metaheuristics do not suite well for training any kind of neural networks, there are exceptions that include the newly proposed heuristic. However, the gain achieved when using even the best metaheuristics is low, comparing to the effort (computational time and complexity of such algorithms). Using ensemble aggregation approach seems to have higher impact on the model performance than seeking for new training methods.