Enterotoxigenic Profiling of Emetic Toxin‐ and Enterotoxin‐Producing Bacillus cereus,Isolated from Food,Environmental, and Clinical Samples by Multiplex PCR

Bacillus cereus comprises the largest group of endospore‐forming bacteria and can cause emetic and diarrheal food poisoning. A total of 496 B. cereus strains isolated from various sources (food, environmental, clinical) were assessed by a multiplex PCR for the presence of enterotoxin genes. The detection rate of nheA, entFM, hblC, and cytK enterotoxin genes among all B. cereus strains was 92.33%, 77.21%, 59.47%, and 47.58%, respectively. Enterotoxigenic profiles were determined in emetic toxin‐ (8 patterns) and enterotoxin‐producing strains (12 patterns). The results provide important information on toxin prevalence and toxigenic profiles of B. cereus from various sources. Our findings revealed that B. cereus must be considered a serious health hazard and Bacillus thuringiensis should be considered of a greater potential concern to food safety among all B. cereus group members. Also, there is need for intensive and continuous monitoring of products embracing both emetic toxin and enterotoxin genes.     

Wavelet PSO‐Based LQR Algorithm for Optimal Structural Control Using Active Tuned Mass Dampers

This study presents a new method to find the optimal control forces for active tuned mass damper. The method uses three algorithms: discrete wavelet transform (DWT), particle swarm optimization (PSO), and linear quadratic regulator (LQR). DWT is used to obtain the local energy distribution of the motivation over the frequency bands. PSO is used to determine the gain matrices through the online update of the weighting matrices used in the LQR controller while eliminating the trial and error. The method is tested on a 10‐story structure subject to several historical pulse‐like near‐fault ground motions. The results indicate that the proposed method is more effective at reducing the displacement response of the structure in real time than conventional LQR controllers.     

Optimal control of steel structures by improved particle swarm

Active control is one of the modern approaches in seismic design of steel structures. Recently, induced by economic considerations, especially high expenses of control systems, optimality has become an important issue. In this paper an active system is used to control a steel structure’s displacements by a simplified pole assignment method. To optimize the number, the locations, and the total driving force of the required actuators, an improved particle swarm algorithm is presented focusing on the parameters of the velocity equation. A Geographical neighborhood topology and an adaptive inertia weight are used to improve the standard PSO algorithm. In addition to the local and global best solutions, the positions of the best particles in the geographical neighborhood are mathematically represented in an additional term. The performance of the proposed algorithm is compared with the traditional Genetic Algorithm (GA) and the standard particle swarm considering the optimal control of a 12-story steel structure as a numerical example. High capabilities of the proposed method in terms of the control target, convergence rate, and accuracy are simultaneously clarified by the results.     

Genetic Polymorphisms in the APOA1 Gene and their Relationship with Serum HDL Cholesterol Levels

Low high density lipoprotein cholesterol (HDL-C) is a known risk factor of coronary artery disease. Apolipoprotein A1 (APOA1) is the most abundant component of HDL-C. This study aimed at identifying sequence variations (rare and common) in the APOA1 gene and its association with serum HDL-C levels. This study was conducted from April 2012 to February 2013 on 79 Tehranians (participants of Tehran Lipid and Glucose Study) with extremely low HDL-C (within the 5th percentile) and 63 individuals with extremely high HDL-C (within the 95th percentile) levels. After DNA amplification by PCR, DNA sequencing of all three exons and 700 bps of promoter region of the APOA1 gene was performed. Sequence results were analyzed and interpreted using the appropriate software and variants were identified. After sequencing 42 common and rare variants were identified, 11 of which were known variants and the others had been unreported so far. Of the exonic variants, 11 were missense, 6 were synonymous and 1 was nonsense. There was a significant association between serum HDL-C and variant of rs2070665 as well as variants Chr.11:116707788, Chr.11:116708059, Chr.11:116708036, Chr.11:116707729, rs201148448, Chr.11:116707018, Chr.11:116707801, Chr.11:116708530, Chr.11:116708088, rs121912724 and Chr.11:116706966 (p < 0.001). Variants Chr.11:116707018, rs121912724 and 2070665 were independent predictors of the HDL-C level (p < 0.001). SNP Chr.11:116707018 was the strongest predictor of the HDL-C level (OR 7.527, p < 0.001). This study identified 42 variants in APOA1 gene, 31 of which were new variants. Three variants of rs2070665, rs121912724 and Chr.11:116707018 could predict the HDL-C level independently. Variant rs2070665 was protective against low-HDL-C levels while variants rs121912724 and Chr.11:116707018 were risk factors for that in our population.     

A Wavelet‐Based Adaptive Pole Assignment Method for Structural Control

This article presents a time varying wavelet‐based pole assignment (WPA) method to control seismic vibrations in multi‐degree of freedom (MDOF) structural systems. The discrete wavelet transform is used to determine the energy content over the frequency band of the response in real time. The frequency content was implemented in the Big Bang–Big Crunch algorithm to update the optimum values of the closed‐loop poles of the structural system adaptively. To calculate optimum gain matrix, a robust pole placement algorithm was used. The gain matrix is calculated online based on response characteristic in real time and must not be calculated a priori (offline) choice. The WPA is tested on a 10‐story structural system subject to several historical ground motions. It is observed that the WPA has advantages in some design problems. Numerical examples illustrate that the proposed approach reduces the displacement response of the structure in real time more than conventional linear quadratic regulator (LQR) controller.     

Optimal Porosity in an Air Heater Conduit Filled with a Porous Matrix

In the present study, flow and forced convective heat transfer in an air heater conduit filled with a porous matrix with a uniform constant solar heat flux has been investigated analytically, based on minimal entropy generation principle. While trying to decrease entropy generation due to heat transfer, pressure loss entropy generation increases, which indicates that an optimal porosity value exists. The influence of Reynolds number, fluid properties, constant uniform heat flux, flow, and geometry of the system on the optimum matrix porosity has been investigated. It was revealed that optimum matrix porosity values increase as Reynolds number increases. In the range of the present study, a correlation predicting optimal matrix porosity was proposed using least squares analysis.     

Development of Predictive Models for the Growth of Escherichia coli O157:H7 on Cabbage in Korea

Abstract: Cabbage is the main material of coleslaw, a popular side dish in Korea as well as many other countries. In the present study, the combined effect of temperature (15, 25, and 35 °C) and relative humidity (60%, 70%, and 80%) on the growth of Escherichia coli O157:H7 on cabbage was investigated. The polynomial models for growth rate (GR), lag time (LT), and maximum population density (MPD) estimated from the Baranyi model were conducted with high coefficients of determination (R²> 0.98). Subsequently, performance and reliability of the models were assessed through external validation, employing three indices as bias factor (B_f), accuracy factor (A_f), and the standard error of prediction expressed in percentage (%SEP). The B_f, A_f, and %SEP values of the predictive models for GR were 1.008, 1.127 and 18.70%, while 1.033, 1.187 and 20.79% for LT and 0.960, 1.044 and 5.22% for MPD, respectively. The results demonstrated that the developed secondary models showed a good agreement between the observed and predicted values. Therefore, the established models can be suitable to estimate and control E. coli O157:H7 growth risk on cabbage at some steps from farm to table in Korea as a valuable tool. Practical Application: The combined effect of temperature and relative humidity on the growth or survival of Escherichia coli O157:H7 on cabbage was investigated. The validated predictive models are qualified to provide good predictions for E. coli O157:H7 growth, which can help to conduct the quantitative microbiological risk assessment (QMRA) of E. coli O157:H7 on cabbage from farm to table in Korea.     

Gradient‐based back‐propagation dynamical iterative learning scheme for the neuro‐fuzzy inference system

In this paper, a gradient‐based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro‐fuzzy system. Premise and consequent parameters of the neuro‐fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output with respect to the structure parameters. The first order derivative of the model output with respect to the structure parameters determines the sensitivity of the model to structure parameters. The sensitivity values are then used to set the tuning factors and parameters updating step sizes. Therefore, an adaptive dynamical iterative scheme is achieved which adapts the learning procedure to the current state of the performance during the optimization process. Larger tuning step sizes make the convergence speed higher and vice versa. In this regard, this parameter is treated according to the calculated sensitivity of the model to the parameter. The proposed learning algorithm is compared with the least square back propagation method, genetic algorithm and chaotic genetic algorithm in the neuro‐fuzzy model structure optimization. Smaller mean square error and shorter learning time are sought in this paper, and the performance of the proposed learning algorithm is versified regarding these criteria.     

Dynabeads protein G antibody conjugates combined with modified brain heart infusion broth for the enrichment and separation of Bacillus cereus in artificially contaminated vegetables

Food Science and Biotechnology - A modified brain heart infusion (MBHI) broth and a protocol of immunomagnetic separation (IMS) using antibody-coated Dynabeads® protein G were developed for...