Enhancement of in vitro antitumour activity of epirubicin in HER2+ breast cancer cells using immunoliposome formulation

Epirubicin (EPI) is one of the potent breast cancer (BC) chemotherapeutic agents, but its adverse effects limit its efficacy. Herein, EPI was selected to be loaded in liposomal carrier, which has been targeted by a monoclonal antibody, Herceptin. The preparation process of liposomes was a modified ethanol injection method followed by Herceptin conjugation. The in vitro cell toxicity and cellular uptake of optimum formulation against HER2+ and HER2− cancer cell lines were evaluated. The results showed that the drug loading (DL%) and encapsulation efficiency (EE%) of liposome preparation method yielded 30.62% ± 0.49% and 62.39% ± 8.75%, respectively. The average size of naked liposomes (EPI‐Lipo) and immunoliposomes (EPI‐Lipo‐mAb) was 234 ± 9.86 and 257.26 ± 6.25 nm, with a relatively monodisperse distribution, which was confirmed by SEM micrographs. The release kinetic followed Higuchi model for both naked and immunoliposomes. In vitro cytotoxicity study on three different BC cell lines including BT‐20, MDA‐MB‐453 and MCF‐7 demonstrated higher toxicity of EPI in the Herceptin conjugated form (EPI‐Lipo‐mAb) in comparison with the free EPI and EPI‐Lipo in HER2 overexpressing cell line. In addition, the cellular uptake study showed a higher uptake of immunoliposomes by MCF‐7 cells in comparison with naked liposomes. In conclusion, these data show that the targeted delivery of EPI to breast cancer cells can be achieved by EPI‐Lipo‐mAb in vitro, and this strategy could be used for breast cancer therapy with further studies.     

A reputation system for e-marketplaces based on pairwise comparison

Implementing a reputation system is an effective strategy to facilitate trust and security in an online environment. In addition to that, reputation systems can help online customers through decision-making process. However, in real-world situations, these systems have to deal with plenty of problems and challenges. This paper aims to solve four problems that are common to reputation systems in e-marketplaces, namely the subjectivity of ratings, inequality of transactions, multi-context reputation and dynamic behavior of users. The proposed model starts with the pairwise comparison, which is a powerful tool for removing bias from ratings. Then, we extend the concept of pairwise comparison to contests between users. A pairwise comparison has only a winner and a loser, but we can associate a score differential with a pairwise comparison when we consider it as a match. This score differential is adjusted in a way that three other problems can be solved. We implemented our model in a multi-agent simulation in which real-world data were also incorporated. We compared our model with some of previous reputation systems. Experiments show that our model outperforms previous ones when faced with real-world challenges.     

Dynamic modeling and adaptive control of voltage in proton exchange membrane fuel cell using water management

Maintaining a constant voltage in polymer electrolyte membrane fuel cells (PEMFCs) has always attracted the attention of many researchers, and many articles have been published on this issue. Furthermore, water management in PEMFC has become an important challenge because it can improve cell efficiency and lifetime. This paper will develop a one‐dimensional dynamic model for a single PEMFC, which correlates changes in the cell voltage to changes in the cell current density and humidification rate. Subsequently, a recurrent neural network controller based on the approximation of nonlinear autoregressive moving average model is proposed. The controller manipulates the anode and the cathode water mole fractions in order to fix cell voltage and preserve cell water content within a satisfactory interval regardless of the varying cell current. The model and the controller are simulated in matlab /Simulink (Mathworks Inc., Natick, MA) software, and the results are compared with a PID controller from different viewpoints such as current disturbance and plant parameter variation. Copyright © 2011 John Wiley & Sons, Ltd.     

A New Method for Clustering Based on Development of Imperialist Competitive Algorithm

Clustering is one of the most widely used data mining techniques that can be used to create homogeneous clusters.K-means is one of the popular clustering algorithms that,despite its inherent simplicity,has also some major problems.One way to resolve these problems and improve the k-means algorithm is the use of evolutionary algorithms in clustering.In this study,the Imperialist Competitive Algorithm（ICA） is developed and then used in the clustering process.Clustering of IRIS,Wine and CMC datasets using developed ICA and comparing them with the results of clustering by the original ICA,GA and PSO algorithms,demonstrate the improvement of Imperialist competitive algorithm.     

A honeybee-mating approach for cluster analysis

Cluster analysis, which is the subject of active research in several fields, such as statistics, pattern recognition, machine learning, and data mining, is to partition a given set of data or objects into clusters. K-means is used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, K-means has two shortcomings. First, dependency on the initial state and convergence to local optima. The second is that global solutions of large problems cannot be found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. Over the last decade, modeling the behavior of social insects, such as ants and bees, for the purpose of search and problem solving has been the context of the emerging area of swarm intelligence. Honeybees are among the most closely studied social insects. Honeybee mating may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of marriage in real honeybee. Neural networks algorithms are useful for clustering analysis in data mining. This study proposes a two-stage method, which first uses self-organizing feature maps (SOM) neural network to determine the number of clusters and then uses honeybee mating optimization algorithm based on K-means algorithm to find the final solution. We compared proposed algorithm with other heuristic algorithms in clustering, such as GA, SA, TS, and ACO, by implementing them on several well-known datasets. Our finding shows that the proposed algorithm works better than others. In order to further demonstration of the proposed approach’s capability, a real-world problem of an Internet bookstore market segmentation based on customer loyalty is employed.     

Multiobjective approach for detecting communities in heterogeneous networks

Online social networks have a strong potential to be divided into a number of dense substructures, called communities. In such heterogeneous networks, the communities refer not only to dense parts of links but also to clusters present among other dimensions such as users' profiles, comments, and information flows. To find communities in these networks, researchers have developed a number of methods; however, to the best of the authors' knowledge, these methods are limited in taking only 2 dimensions into account, and they are also not able to give a sense of how users behave in their communities. To deal with these issues, this paper proposes a multiobjective optimization model in which a specific objective function has been used for each considered dimension in a given network. Because of the NP‐hardness of the studied problem, an efficient and effective multiobjective metaheuristic algorithm has been developed. By juxtaposing the nondominated solutions obtained, the proposed algorithm can demonstrate how users behave in their communities. To illustrate the effectiveness of the algorithm, a set of experiments with a comprehensive evaluation method is provided. The results show the superiority and the stability of the proposed algorithm.     

Multi-Objective Optimization of Integrated Surface and Groundwater Resources Under the Clean Development Mechanism

Acquiring sustainable water resources for water-based development of countries is the experts? concern in this field, who seek to follow the clean development mechanism (CDM) regulations and overcome water crisis through integrated water resources management (IWRM). The Great Karun River basin is one of the major basins in the Middle East. This basin, containing six of the largest reservoir dams with a cumulative power plant capacity of more than 10,500 MW generates about 93% of hydropower of Iran. The water balance of the aquifer in the study area was simulated using MODFLOW model while water resources and surface water reserves were simulated by the water evaluation and planning (WEAP) model. A separate simulation was performed with each of two models and the results of two models were coupled using a link file. The multi-objective function optimization process including the maximized supply of demands and hydropower and the minimized aquifer drawdown was completed using non-dominated sorting genetic algorithm (NSGA-II). All effective system components, such as inter-basin water transfer, integrated use of water resources, variation of irrigation network efficiencies, and the effect of water shortage were studied and analyzed under the targeted scenarios. Finally, the best scenario, which was capable to supply the future needs until time horizon of 2040 was planned for the basin considering minimization of aquifer drawdown and optimal generation of hydropower resulting in a maximum decrease in emission of greenhouse gases.

     

Predicting a model for agile NPD process with fuzzy cognitive map: the case of Iranian manufacturing enterprises

Successful development of the new product (NPD) is one of the most critical tasks in manufacturing process and challenging issues that managers usually face. In response to unanticipated changes in volatile markets, agile NPD is a focus to manufacturing companies. Identifying the causal relationships between the main factors of agile NPD process as a complex system, can influence the effectiveness of managers’ decision making, and the productivity of actions in this kind of process. This study reports the results of an empirical research done on the main factors affecting the agile NPD process and the relationships between them in Iranian manufacturing enterprises. These factors are identified and categorized in six main groups by explanatory factor analysis method, and the relationships between them are introduced with the simple fuzzy cognitive map method.     

A novel algorithm based on hybridization of artificial immune system and simulated annealing for clustering problem

A hybrid clustering method is proposed in this paper based on artificial immune system and simulated annealing. An integration of simulated annealing and immunity-based algorithm, combining the merits of both these approaches, is used for developing an efficient clustering method. Tuning the parameters of method is investigated using Taguchi method in order to select the optimum levels of parameters. Proposed method is implemented and tested on three real datasets. In addition, its performance is compared with other well-known meta-heuristics methods, such as ant colony optimization, genetic algorithm, simulated annealing, Tabu search, honey-bee mating optimization, and artificial immune system. Computational simulations show very encouraging results in terms of the quality of solution found, the average number of function evaluations and the processing time required, comparing with mentioned methods.