Magnetic hydrophobic affinity nanobeads for lysozyme separation

N-Methacryloyl-l-phenylalanine (MAPA) containing poly(2-hydroxyethylmethacrylate) based magnetic [mag-poly(HEMA–MAPA)] nanobeads was prepared for lysozyme purification form chicken egg white. MAPA was synthesized by reacting methacryloyl chloride with l-phenylalanine methyl ester and provided hydrophobic functionality to the nanobeads. Size of mag-poly(HEMA–MAPA) nanobeads was 386 nm and obtained by surfactant free emulsion polymerization of HEMA and MAPA having a specific surface area of 580 m²/g. Mag-poly(HEMA–MAPA) nanobeads were characterized by FTIR, AFM, TEM, ESR, and elemental analysis. Lysozyme adsorption experiments were investigated under different conditions in batch system (i.e., medium pH, protein concentration, temperature, salt type). Lysozyme adsorption capacity of mag-poly(HEMA) and mag-poly(HEMA–MAPA) nanobeads from aqueous solutions was estimated as 24 and 517 mg/g, respectively. Lysozyme molecules were desorbed with 50% ethylene glycol solution with 98% recovery. It was observed that mag-poly(HEMA–MAPA) nanobeads can be used without significant decrease in lysozyme adsorption capacity after ten adsorption–desorption cycles. Mag-poly(HEMA–MAPA) nanobeads was used for the purification of lysozyme from chicken egg white. Purity of lysozyme was estimated by SDS-PAGE.     

Perturbation-Based Eigenvector Updates for On-Line Principal Components Analysis and Canonical Correlation Analysis

Principal components analysis is an important and well-studied subject in statistics and signal processing. Several algorithms for solving this problem exist, and could be mostly grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second order statistical criterion (like reconstruction error or output variance), and fixed point update rules with deflation. In this study, we propose an alternate approach that avoids deflation and gradient-search techniques. The proposed method is an on-line procedure based on recursively updating the eigenvector and eigenvalue matrices with every new sample such that the estimates approximately track their true values as would be calculated analytically from the current sample estimate of the data covariance matrix. The perturbation technique is theoretically shown to be applicable for recursive canonical correlation analysis, as well. The performance of this algorithm is compared with that of a structurally similar matrix perturbation-based method and also with a few other traditional methods like Sanger’s rule and APEX.

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Minimax mutual information approach for independent component analysis

Minimum output mutual information is regarded as a natural criterion for independent component analysis (ICA) and is used as the performance measure in many ICA algorithms. Two common approaches in information-theoretic ICA algorithms are minimum mutual information and maximum output entropy approaches. In the former approach, we substitute some form of probability density function (pdf) estimate into the mutual information expression, and in the latter we incorporate the source pdf assumption in the algorithm through the use of nonlinearities matched to the corresponding cumulative density functions (cdf). Alternative solutions to ICA use higher-order cumulant-based optimization criteria, which are related to either one of these approaches through truncated series approximations for densities. In this article, we propose a new ICA algorithm motivated by the maximum entropy principle (for estimating signal distributions). The optimality criterion is the minimum output mutual information, where the estimated pdfs are from the exponential family and are approximate solutions to a constrained entropy maximization problem. This approach yields an upper bound for the actual mutual information of the output signals - hence, the name minimax mutual information ICA algorithm. In addition, we demonstrate that for a specific selection of the constraint functions in the maximum entropy density estimation procedure, the algorithm relates strongly to ICA methods using higher-order cumulants.     

Effects of nitrite levels,endpoint temperature and storage on pink color development in turkey rolls

The objective of this research was to determine the relationship between minimum nitrite level, endpoint temperature and storage period on pink discoloration of turkey rolls. Rolls were prepared with 0, 3 or 5 parts per million (ppm) NO₂^–, cooked to an endpoint temperature of 75 °C or 85 °C and stored at 4 °C. During the 13-day storage period, on the 0th, 4th, 8th and 13th days cured pigment, total pigment, residual nitrite and color parameters were measured and sensory color was evaluated. At each endpoint temperature, addition of 3 ppm or 5 ppm NO₂^– significantly increased cured pigment and total pigment levels. Cured pigment levels of turkey rolls cooked to 75 °C were higher than rolls cooked to 85 °C, regardless of the NO₂^– level. There was a significant decrease in cured pigment levels at the end of the storage period (13th day). Analysis of residual NO₂^– in all treatment groups showed no detectable amounts. Endpoint temperature and addition of NO₂^– affected L* values: rolls cooked to the 75 °C endpoint had higher L* values. The additon of nitrite with the 75 °C endpoint temperature resulted in higher a* values. a* values also increased during the storage. The higher endpoint temperature (85 °C) resulted in lower a* values. Yellowness was not affected by final cooking temperature. Both the addition of NO₂^– and storage decreased b* values. The panel found no differences in pinkness intensity between the two levels of added nitrite (3 and 5 ppm). Sensory pinkness intensity in nitrite-added samples increased with increasing storage.     

Effect of gamma irradiation on the properties of tyre cords

Gamma irradiation of high tenacity Nylon 6.6 (Ny 66) and polyester (PET) tyre cords was investigated. The untreated and treated tyre cords with different twist levels were irradiated at different dose rates in air. The effects of irradiation on both Ny 66 and PET cords were not found to be depending on the twist levels of the cords. The changes in the mechanical and thermal properties with absorbed dose at two different dose rates were measured. The mechanical properties were observed to deteriorate with increasing dose for Ny 66 cords, whereas remained almost unchanged for PET cords both in greige and dipped forms. Hot shrinkage value for the greige Ny 66 cords was found to be improved, i.e. decreased. This decrease was much lower for greige PET than Ny 66 cords. It is concluded that PET cord has higher radiation resistance than Ny 66 cord and the effects of high energy irradiation on tyre cords have to be taken into consideration during tyre design if pre-vulcanization with high energy radiation is to be applied.     

Cognitive ability predicts objectively measured counterproductive work behaviors.

Over the past 2 decades, increasing attention has been directed at the relationship between individual differences and counterproductive work behaviors (CWB). However, most of this research has focused on personality variables as potential predictors of CWB; surprisingly little research has investigated the link between counterproductivity and cognitive ability. This study presents the first focal investigation of the cognitive ability-CWB relationship. The authors measured organizational and interpersonal CWB using organizational records of formally recorded incidents (e.g., destruction of property, physical violence). In a predictive study, for a large sample of law enforcement job applicants, a standardized psychometric test of cognitive ability predicted CWB, whereas educational attainment did not. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Maximally Informative Feature and Sensor Selection in Pattern Recognition Using Local and Global Independent Component Analysis

In pattern recognition, a suitable criterion for feature selection is the mutual information (MI) between feature vectors and class labels. Estimating MI in high dimensional feature spaces is problematic in terms of computation load and accuracy. We propose an independent component analysis based MI estimation (ICA-MI) methodology for feature selection. This simplifies the high dimensional MI estimation problem into multiple one-dimensional MI estimation problems. Nonlinear ICA transformation is achieved using piecewise local linear approximation on partitions in the feature space, which allows the exploitation of the additivity property of entropy and the simplicity of linear ICA algorithms. Number of partitions controls the tradeoff between more accurate approximation of the nonlinear data topology and small-sample statistical variations in estimation. We test the ICA-MI feature selection framework on synthetic, UCI repository, and EEG activity classification problems. Experiments demonstrate, as expected, that the selection of the number of partitions for local linear ICA is highly problem dependent and must be carried out properly through cross validation. When this is done properly, the proposed ICA-MI feature selection framework yields feature ranking results that are comparable to the optimal probability of error based feature ranking and selection strategy at a much lower computational load.     

Two approaches for solving the buffer allocation problem in unreliable production lines

This paper presents an integrated approach to solve the buffer allocation problem in unreliable production lines so as to maximize the throughput rate of the line with minimum total buffer size. The proposed integrated approach has two control loops; the inner loop and the outer loop. While the inner loop control includes an adaptive tabu search algorithm proposed by Demir et al. [8], binary search and tabu search are proposed for the outer loop. These nested loops aim at minimizing the total buffer size to achieve the desired throughput level. To improve the efficiency of the proposed tabu search, alternative neighborhood generation mechanisms are developed. The performances of the proposed algorithms are evaluated by extensive computational experimentation, and the results are reported.     

Improvement of Mechanical Properties of Unsaturated Polyesters by Acrylonitrile

Propylene glycol- and diethylene glycol-based unsaturated polyesters were prepared and hardened by using styrene and acrylonitrile monomer mixtures. The addition of 12% acrylonitrile to a propylene glycol-based polyester containing 40% styrene increased the hardness from 12 BHN to 26 BHN. The addition of 20% acrylonitrile increased the impact strength of the same polyester from 14 J/m width to 39 J/m width. The diethylene glycol-based polyester containing 40% styrene and 40% acrylonitrile achieved a hardness of 23 BHN and an impact strength of 59 J/m width.     

Leaching of blended copper slag in microwave oven

Leaching of blended slag (BS) was investigated in a microwave oven using hydrogen peroxide and acetic acid. The BS was a mixture of converter and flash furnace slag containing 51% Fe₂O₃, 3.8% CuO, and 3.2% ZnO. The important variables that influence the metal extraction yield were leaching time, liquid-solid ratio, H₂O₂ and CH₃COOH concentrations. The preferred leaching conditions were as follows: CH₃COOH concentration 4 mol/L; H₂O₂ concentration 4 mol/L; microwave power 900 W; leaching time 30 min; liquid-solid ratio 25 mL/g BS; leaching temperature 100 °C. Under these conditions, the metal extractions of 95% Cu, 1.6% Fe, and 30% Zn were obtained. The results were compared with the traditional leaching results. It is evident that microwave heating causes a reduction in the leaching time. Also, the extraction yield results indicate that selective leaching of BS can be achieved under the preferred conditions. The dissolution kinetic of BS in hydrogen peroxide with acetic acid is controlled by a shrinking unreacted core model equation. The apparent activation energy and reaction order were found to be 16.64 kJ/mol and 1.09, respectively.