Targeting Mitochondria in Diabetes

Type 2 diabetes (T2D), one of the most prevalent noncommunicable diseases, is often preceded by insulin resistance (IR), which underlies the inability of tissues to respond to insulin and leads to disturbed metabolic homeostasis. Mitochondria, as a central player in the cellular energy metabolism, are involved in the mechanisms of IR and T2D. Mitochondrial function is affected by insulin resistance in different tissues, among which skeletal muscle and liver have the highest impact on whole-body glucose homeostasis. This review focuses on human studies that assess mitochondrial function in liver, muscle and blood cells in the context of T2D. Furthermore, different interventions targeting mitochondria in IR and T2D are listed, with a selection of studies using respirometry as a measure of mitochondrial function, for better data comparison. Altogether, mitochondrial respiratory capacity appears to be a metabolic indicator since it decreases as the disease progresses but increases after lifestyle (exercise) and pharmacological interventions, together with the improvement in metabolic health. Finally, novel therapeutics developed to target mitochondria have potential for a more integrative therapeutic approach, treating both causative and secondary defects of diabetes.     

Dye–fibre bond stabilities of some reactive dyes on cotton

Cotton fabric dyed with commercial reactive dyes of the monochloro-s-triazine, bis(monochloro-s-triazine), and bis(monofluoro-s-triazine) type was treated in buffer solutions pH 10 and 12 at 60, 85 and 98°C. Hydrolysis has been investigated using spectrophotometrical measurements of hydrolysed dye at λ_max. The percent of hydrolysed dye was calculated.     

Rheological properties of hydroxyl‐terminated and end‐capped aliphatic hyperbranched polyesters

The rheological behavior of two series of aliphatic hyperbranched (HB) polyesters, based on 2,2‐bis(hydroxymethyl)propionic acid (bis‐MPA) and di‐trimethylol propane (Di‐TMP) as a tetrafunctional core, was studied. The effect of the size (pseudo‐generation number, from second to eight) and structure on the melt rheological properties was investigated for a series of hydroxyl‐terminated HB polyesters. In addition, the influence of the nature and degree of modification of the terminal OH groups in a series of fourth‐generation polyesters end‐capped with short and long alkyl chains and some aryl groups on the rheological properties was analyzed. The time–temperature superposition procedure was applied for the construction of master curves and for the analysis of the rheological properties of HB polyesters. The data obtained from WLF analysis of the HB polyesters showed that the values of the thermal coefficient of expansion of free volume α_f and the fractional free volume at the glass transition temperature, f_g, increase with increasing size of the HB polyesters. It was shown that the modified HB polyesters exhibited lower T_g and T_G′=G″ temperatures, above which viscous became dominant over elastic behavior. From an analysis of the master curves of the modified HB polyesters, it was observed that with increasing degree of modification, both storage and loss modules and complex dynamic viscosity and apparent energy for viscoelastic relaxation decrease, because of reduced intermolecular hydrogen interactions. They do not exhibit a plateau of rubbery behavior, which confirms that no entanglements are present and that the molar masses are below the critical molar mass. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41479.     

Heterologous Expression and Partial Characterization of a Putative Opine Dehydrogenase from a Metagenomic Sequence of Desulfohalobium retbaense

The aim of this research was to prove the function of the putative opine dehydrogenase from Desulfohalobium retbaense and to characterize the enzyme in terms of functional and kinetic parameters. A putative opine dehydrogenase was identified from a metagenomic library by a sequence-based technique search of the metagenomic library, and afterward was successfully heterologously produced in Escherichia coli. In order to examine its potential for applications in the synthesis of secondary amines, first the substrate specificity of the enzyme towards different amino donors and amino acceptors was determined. The highest affinity was observed towards small amino acids, preferentially L-alanine, and when it comes to α-keto acids, pyruvate proved to be a preferential amino acceptor. The highest activity was observed at pH 6.5 in the absence of salts. The enzyme showed remarkable stability in a wide range of experimental conditions, such as broad pH stability (from 6.0–11.0 after 30 min incubation in buffers at a certain pH), stability in the presence of NaCl up to 3.0 M for 24 h, it retained 80 % of the initial activity after 1 h incubation at 45 °C, and 65 % of the initial activity after 24 h incubation in 30 % dimethyl sulfoxide.     

Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey

This paper presents a comprehensive review of hybrid and ensemble-based soft computing techniques applied to bankruptcy prediction. A variety of soft computing techniques are being applied to bankruptcy prediction. Our focus is on techniques, namely how different techniques are combined, but not on obtained results. Almost all authors demonstrate that the technique they propose outperforms some other methods chosen for the comparison. However, due to different data sets used by different authors and bearing in mind the fact that confidence intervals for the prediction accuracies are seldom provided, fair comparison of results obtained by different authors is hardly possible. Simulations covering a large variety of techniques and data sets are needed for a fair comparison. We call a technique hybrid if several soft computing approaches are applied in the analysis and only one predictor is used to make the final prediction. In contrast, outputs of several predictors are combined, to obtain an ensemble-based prediction.     

Algorithm for blinds control based on the optimization of blind tilt angle using a genetic algorithm and fuzzy logic

This paper presents the calculation of the power of solar rays that pass through the window of an observed room and their impact on warming up and lighting of the room. The calculations were performed using a mathematical model that takes into account the geographical position of the object, time zone, orientation of windows, day of the year, and current time. This paper also includes the calculation of geometry of the solar radiation and its intensity, artificial light and cooling/heating demands. Based on data from above, the optimization of blind tilt angle was performed to achieve the best possible brightness of the room and energy savings when heating or cooling, depending on ambient temperature. Optimization was performed using a genetic algorithm and fuzzy logic. After an analysis of the results obtained from optimization of the blind tilt angle, an algorithm for blinds control was developed in order to achieve energy savings and comfort in the observed room. Based on the derived conclusions, an UML diagram was made that describes the algorithm for determining optimal blind tilt angle.     

Mechanochemical reaction in the K2CO3–Nb2O5 system

We studied the mechanochemical synthesis of KNbO₃, starting from a powder mixture of K₂CO₃ and Nb₂O₅. The milling experiments were designed with different ball-impact energies in order to investigate the mechanochemical reactions. X-ray diffraction, thermogravimetric analysis, infrared spectroscopy, Raman spectroscopy and transmission electron microscopy were used to characterize the samples. Based on the results, we propose a mechanism for the mechanochemical reaction between K₂CO₃ and Nb₂O₅. The first stage of the reaction is characterized by the formation of an amorphous carbonato complex, which decomposes after prolonged milling at higher ball-impact energy giving rise to the crystallization of KNbO₃ and other niobate phases with a molar ratio K/Nb < 1. The reaction course is discussed and compared with the Na₂CO₃–Nb₂O₅ system.     

Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava

Water temperature directly affects the physical, biological and chemical characteristics of the river and determines the fitness and life of all aquatic organisms. It has direct and indirect effects on nearly all aspects of stream ecology. Accurately estimating water temperature is a complex problem. The purpose of this article is to analyze the relationship between the air and water temperature of the River Drava by constructing an artificial neural network (ANN) model and choosing appropriate network architectures for the River Drava’s daily river water temperature as well as demonstrating its application in improving the interpretation of the results. A linear regression model, as well as a stochastic model are also constructed and compared to ANN models consisting of a multilayer perceptron neural network and a radial basis function network. The results indicate that the ANN models are much better models and that ANNs are powerful tools that can be used for the estimation of daily mean river temperature.     

Building energy simulation and optimization: A case study of industrial halls with varying process loads and occupancy patterns

Industrial halls are mainly low-rise rectangular-shaped structures of simple construction. The relatively loose requirements in space conditioning and the comparatively high internal heat gain make the approach in industrial hall design quite different from that of office building design. The simplicity in building geometry and construction method allows the investigation of energy consumption for building services to be limited to a few demand-side parameters, namely, resistance of the roof and wall insulation, airtightness, and amount of daylighting. This paper investigates the impact of varying these demand-side parameters on the energy consumption for space conditioning and lighting for a typical industrial hall. Through building energy simulation, such impacts can be investigated, and by applying optimization, the configurations of the most optimal combinations of demand-side parameters with the lowest energy consumption can be identified. The result suggests that there is a significant energy-saving potential. For industrial halls, energy consumption for building services can be very sensitive to changes in the process load and occupancy pattern, which in reality, fluctuate widely due to economic cycles, and other factors. Optimized design solutions for industrial halls intended for a particular process load and occupancy pattern might not perform as predicted due to potential changes. To account for potential changes, uncertainty analysis can be performed to determine if the optimized design solutions are in fact robust enough to such changes and to identify solutions that are less susceptible to uncertainty.