Some specific features of the thermotropic gelation of carp actomyosin

This paper is devoted to the study of the heat treatment regime affection on the rheological properties of the actomyosin (AM) thermotropic gels of the muscle tissue of carp. It is shown, that systems containing AM undergo several conformational transitions during heating. These transitions make the essential contributions into the gelation process since they predetermine the dissociation, reassociation and aggregation of AM, i.e. they determine in the end the nature of the physicomechanical properties of the spatial network. The two- or three-staged heat treatment of the AM dispersions at narrow temperature intervals was found to be optimal in order to obtain the more elastic thermotropic gels.     

Beitrag zur Mikrorißbildung im Martensit

Licht- und elektronenmikroskopische Untersuchungen über die Bildung von Mikrorissen im Gefüge von Legierungen des Eisens mit rd. 0,2 bis 0,8%, G und 16,7 bis 27,9% Ni nach Austenitisieren bei unterschiedlichen Temperaturen und anschlieβendem Tiefkühlen (zur Martensitbildung). Einfluβ einer vorangehenden Umformung des Austenits (thermomechanische Behandlung) sowie der Austenitkorngröβe und von Versetzungen.     

Rotation forest: A new classifier ensemble method

We propose a method for generating classifier ensembles based on feature extraction. To create the training data for a base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and Principal Component Analysis (PCA) is applied to each subset. All principal components are retained in order to preserve the variability information in the data. Thus, K axis rotations take place to form the new features for a base classifier. The idea of the rotation approach is to encourage simultaneously individual accuracy and diversity within the ensemble. Diversity is promoted through the feature extraction for each base classifier. Decision trees were chosen here because they are sensitive to rotation of the feature axes, hence the name "forest.” Accuracy is sought by keeping all principal components and also using the whole data set to train each base classifier. Using WEKA, we examined the Rotation Forest ensemble on a random selection of 33 benchmark data sets from the UCI repository and compared it with Bagging, AdaBoost, and Random Forest. The results were favorable to Rotation Forest and prompted an investigation into diversity-accuracy landscape of the ensemble models. Diversity-error diagrams revealed that Rotation Forest ensembles construct individual classifiers which are more accurate than these in AdaBoost and Random Forest, and more diverse than these in Bagging, sometimes more accurate as well.     

On iterated Crank-Nicolson methods for hyperbolic and parabolic equations

Different versions of the iterated Crank-Nicolson method are considered and their relation to the explicit Runge-Kutta methods is studied. Stability and accuracy properties of these methods are compared for both hyperbolic and parabolic equations. It is shown that Runge-Kutta methods offer more accurate and stable options even for a few evaluations of the right-hand sides. When applied to nonlinear equations, the iterated Crank-Nicolson methods have an accuracy barrier, which does not appear for Runge-Kutta methods.     

Self‐Organized Superlattice and Phase Coexistence inside Thin Film Organometal Halide Perovskite

Organometal halide perovskites have attracted widespread attention as the most favorable prospective material for photovoltaic technology because of their high photoinduced charge separation and carrier transport performance. However, the microstructural aspects within the organometal halide perovskite are still unknown, even though it belongs to a crystal system. Here direct observation of the microstructure of the thin film organometal halide perovskite using transmission electron microscopy is reported. Unlike previous reports claiming each phase of the organometal halide perovskite solely exists at a given temperature range, it is identified that the tetragonal and cubic phases coexist at room temperature, and it is confirmed that superlattices composed of a mixture of tetragonal and cubic phases are self‐organized without a compositional change. The organometal halide perovskite self‐adjusts the configuration of phases and automatically organizes a buffer layer at boundaries by introducing a superlattice. This report shows the fundamental crystallographic information for the organometal halide perovskite and demonstrates new possibilities as promising materials for various applications.     

Specific Magnetic Isolation of E6 HPV16 Modified Magnetizable Particles Coupled with PCR and Electrochemical Detection

The majority of carcinomas that were developed due to the infection with human papillomavirus (HPV) are caused by high-risk HPV types, HPV16 and HPV18. These HPV types contain the E6 and E7 oncogenes, so the fast detection of these oncogenes is an important point to avoid the development of cancer. Many different HPV tests are available to detect the presence of HPV in biological samples. The aim of this study was to design a fast and low cost method for HPV identification employing magnetic isolation, polymerase chain reaction (PCR) and electrochemical detection. These assays were developed to detect the interactions between E6-HPV16 oncogene and magnetizable particles (MPs) using commercial Dynabeads M-280 Streptavidin particles and laboratory-synthesized “homemade” particles called MANs (MAN-37, MAN-127 and MAN-164). The yields of PCR amplification of E6-HPV16 oncogene bound on the particles and after the elution from the particles were compared. A highest yield of E6-HPV16 DNA isolation was obtained with both MPs particles commercial M-280 Streptavidin and MAN-37 due to reducing of the interferents compared with the standard PCR method. A biosensor employing the isolation of E6-HPV16 oncogene with MPs particles followed by its electrochemical detection can be a very effective technique for HPV identification, providing simple, sensitive and cost-effective analysis.     

Effect of Graphene Oxide on Structure and Properties of Impact-Modified Polyamide 6

Modification of polymer blends with nanofillers is an efficient way to improve material parameters. This work deals with application of neat and stearylamine-modified graphene oxide in the polyamide 6/elastomer system using different mixing protocols. Combination of ethene–propene elastomer and graphene oxide leads to a polyamide material with increased strength, stiffness, and toughness. The reason is synergistic effect of the core–shell structure (nanofiller localized at interface) on mechanical properties. The structure-directing effect of graphene oxide is comparable with that of nanoclay. The mechanism of affecting dynamic phase behavior is different as a consequence of graphene oxide nature and interactions with polymer components.     

Concentration threshold effect on properties of zinc‐doped lithium niobate crystals

Results of LiNbO₃:Zn researches often diverge because sharp changes in physicochemical characteristics of the system melt‐crystal that occur at doping are usually not taken into consideration. When a series of doped crystals is grown Zn is usually added to the melt with step 1.5‐2 mol%. This obstructs detection of exact threshold concentration. We have grown LiNbO₃:Zn crystals doped by 4.0÷9.0 mol% ZnO with step ~1 mol%. Around the dopant, threshold concentration step was narrowed to ~0.1 mol%. We have researched physicochemical properties of the system melt‐crystal and structure evolution by Raman spectroscopy and full‐profile analysis of XRD. We have detected exact threshold concentration for LiNbO₃:Zn and concluded that crystals doped by prethreshold concentrations of ZnO are the best for most applications because of the higher optical homogeneity.     

Fuzzy Logic and Chaos Theory in Time Series Forecasting

This paper presents a method of time series forecasting based on the integration of fuzzy logic and chaos theory. The proposed method has two stages. On the first stage, we consider the time series as a dynamic system and using the methods of mutual information and false nearest neighbors, as a part of applied chaos theory, we reconstruct the phase portrait corresponding to the original time series. On the second stage, we are learning the neuro fuzzy network as a model of time series forecasting using the vectors points of reconstructed phase portrait. We consider all the formalisms necessary for understanding the method and present the results of two computer experiments proving the ability of fuzzy inference accuracy increasing using the selection of optimal parameters of time delay and phase portrait dimension.     

Mechanical characteristics of mesenchymal stem cells under impact of silica-based nanoparticles

Silica-based nanoparticles (NPs) pose great potential for medical and biological applications; however, their interactions with living cells have not been investigated in full. The objective of this study was to analyze the mechanical characteristics of mesenchymal stem cells when cultured in the presence of silica (Si) and silica-boron (SiB) nanoparticles. Cell stiffness was measured using atomic force microscopy; F-actin structure was evaluated using TRITC-phalloidin by confocal microscopy. The obtained data suggested that the cell stiffness increased within the following line: ‘Control’ - ‘Si’ - ‘SiB’ (either after 1-h cultivation or 24-h incubation). Moreover, the cell stiffness was found to be higher after 1-h cultivation as compared to 24-h cultivation. This result shows that there is a two-phase process of particle diffusion into cells and that the particles interact directly with the membrane and, further, with the submembranous cytoskeleton. Conversely, the intensity of phalloidin fluorescence dropped within the same line: Control - Si - SiB. It could be suggested that the effects of silica-based particles may result in structural reorganization of cortical cytoskeleton with subsequent stiffness increase and concomitant F-actin content decrease (for example, in recruitment of additional actin-binding proteins within membrane and regrouping of actin filaments).