Genetic algorithm for S-transform optimisation in the analysis and classification of electrical signal perturbations

At present, many analytical methods are used for the analysis, detection and classification of electrical signal perturbations. One of these methods, the S-transform, has proven effective under specific conditions for acquiring information and parameters of interest associated with a signal. However, depending on the nature of the signal and the input parameters, this method offers different results that sometimes negatively impact the quality of information obtained in the time and frequency domains.This paper describes the design of a genetic algorithm that optimises the S-transform for analysis and classification of the perturbations in electrical signals. This algorithm provides the best parameter values for optimising the Gaussian window, maximising the precision obtained with regard to classification and, later, analysis (via other techniques, such as neural networks or rule-based systems).This paper demonstrates the effectiveness of the S-transform (specified herein) with respect to the original S-transform and reports the best values obtained after optimisation via a comparative study that includes both typical cases and perturbations in modern electrical systems.     

gef Gene Expression in MCF-7 Breast Cancer Cells is Associated with a Better Prognosis and Induction of Apoptosis by p53-Mediated Signaling Pathway

Breast cancer research has developed rapidly in the past few decades, leading to longer survival times for patients and opening up the possibility of developing curative treatments for advanced breast cancer. Our increasing knowledge of the biological pathways associated with the progression and development of breast cancer, alongside the failure of conventional treatments, has prompted us to explore gene therapy as an alternative therapeutic strategy. We previously reported that gef gene from E. coli has shown considerable cytotoxic effects in breast cancer cells. However, its action mechanism has not been elucidated. Indirect immunofluorescence technique using flow cytometry and immunocytochemical analysis were used to detect breast cancer markers: estrogen (ER) and progesterone (PR) hormonal receptors, human epidermal growth factor receptor-2 proto-oncogene (c-erbB-2), ki-67 antigen and p53 protein. gef gene induces an increase in ER and PR expressions and a decrease in ki-67 and c-erbB-2 gene expressions, indicating a better prognosis and response to treatment and a longer disease-free interval and survival. It also increased p53 expression, suggesting that gef-induced apoptosis is regulated by a p53-mediated signaling pathway. These findings support the hypothesis that the gef gene offers a new approach to gene therapy in breast cancer.     

A parallel multi-objective algorithm for two-dimensional bin packing with rotations and load balancing

Bin packing problems are NP-hard combinatorial optimization problems of fundamental importance in several fields, including computer science, engineering, economics, management, manufacturing, transportation, and logistics. In particular, the non-guillotine version of the single-objective two-dimensional bin packing problem with rotations is a highly complex scheduling problem that consists in packing a set of items into the minimum number of bins, where items can be rotated 90° and are characterized by having different heights and widths. Recently, some authors have proposed multi-objective formulations that also consider additional objectives, such as the balancing the bin load in order to increase its stability. The load imbalance minimization, which depends on the distribution of the items packed in them, is a critical point in many real applications. This paper analyzes how to solve two-dimensional bin packing problems with rotations and load balancing using parallel and multi-objective memetic algorithms that apply a set of search operators specifically designed to solve this problem. Results obtained using a set of test problems show the good performance of parallel and multi-objective memetic algorithms in comparison with other methods found in the literature.     

Modulation of MDR1 and MRP3 Gene Expression in Lung Cancer Cells after Paclitaxel and Carboplatin Exposure

Carboplatin-paclitaxel is a reference regimen in the treatment of locally advanced or disseminated non-small cell lung cancer (NSCLC). This paper discusses the multidrug resistance developed with this drug combination, which is one of the major obstacles to successful treatment. In order to understand and overcome the drug resistance pattern of NSCLC after carboplatin plus paclitaxel exposure, levels of mRNA expression of multidrug resistance 1 (MDR1) and multidrug resistance-associated protein 3 (MRP3) were investigated in primary NSCLC cell lines (A-549 and A-427) and a metastasis-derived NSCLC cell line (NODO). Our results showed that exposure of the three NSCLC lines to plasma concentrations of paclitaxel (5 μM) produced an increase in MDR1 expression, while MRP3 showed no alteration in expression. By contrast, the same cells exposed to carboplatin plasma concentrations (30 μM) showed overexpression of MRP3. In these cells, MDR1 showed no expression changes. Interestingly, the combination of both paclitaxel and carboplatin caused increased expression of the MDR1 drug resistance gene rather than the individual treatments. These results suggest that carboplatin and paclitaxel may induce drug resistance mediated by MDR1 and MRP3, which may be enhanced by the simultaneous use of both drugs.     

Minimization of voltage deviation and power losses in power networks using Pareto optimization methods

Voltage regulation is an important task in electrical engineering for controlling node voltages in a power network. A widely used solution for the problem of voltage regulation is based on adjusting the taps in under load tap changers (ULTCs) power transformers and, in some cases, turning on Flexible Alternating Current Transmission Systems (FACTS), synchronous machines or capacitor banks in the substations. Most papers found in the literature dealing with this problem aim to avoid voltage drops in radial networks, but few of them consider power losses or meshed networks. The aim of this paper is to present and evaluate the performance of several multi-objective algorithms, including hybrid approaches, in order to minimize both voltage deviation and power losses by operating ULTCs located in high voltage substations. In particular, a well-known multi-objective algorithm, PAES, is used for this purpose. PAES finds a set of solutions according to Pareto-optimization concepts. Furthermore, this algorithm is hybridized with simulated annealing and tabu search to improve the quality of the solutions. The implemented algorithms are evaluated using two test networks, and the numerical results are analyzed with two metrics often used in the multi-objective field. The results obtained demonstrate the good performance of these algorithms.     

A Simulated Annealing-based parallel multi-objective approach to vehicle routing problems with time windows

The Capacitated Vehicle Routing Problem with Time Windows (VRPTW) consists in determining the routes of a given number of vehicles with identical capacity stationed at a central depot which are used to supply the demands of a set of customers within certain time windows. This is a complex multi-constrained problem with industrial, economic, and environmental implications that has been widely analyzed in the past. This paper deals with a multi-objective variant of the VRPTW that simultaneously minimizes the travelled distance and the imbalance of the routes. This imbalance is analyzed from two perspectives: the imbalance in the distances travelled by the vehicles, and the imbalance in the loads delivered by them. A multi-objective procedure based on Simulated Annealing, the Multiple Temperature Pareto Simulated Annealing (MT-PSA), is proposed in this paper to cope with these multi-objective formulations of the VRPTW. The procedure MT-PSA and an island-based parallel version of MT-PSA have been evaluated and compared with, respectively, sequential and island-based parallel implementations of SPEA2. Computational results obtained on Solomon’s benchmark problems show that the island-based parallelization produces Pareto-fronts of higher quality that those obtained by the sequential versions without increasing the computational cost, while also producing significant reduction in the runtimes while maintaining solution quality. More specifically, for the most part, our procedure MT-PSA outperforms SPEA2 in the benchmarks here considered, with respect to the solution quality and execution time.     

Evolutionary algorithms for the design of grid-connected PV-systems

The sale of electric energy generated by photovoltaic (PV) plants has attracted much attention in recent years. The installation of PV plants aims to obtain the maximum benefit of captured solar energy. The current methodologies for planning the design of the different components of a PV plant are not completely efficient. This paper addresses the optimization of the design of PV plants with solar tracking, which consists of the optimization of the variables that make up the PV plant to obtain the minimum electric (Joule) losses possible. These variables are the size and distribution of solar modules in the solar tracker, the distribution of the solar trackers in the field and the choice of inverter. Evolutionary algorithms (EAs) are adaptive methods based on natural evolution that may be used for searching and optimization. Four different EAs have been used for optimizing the design of PV plants: steady-state genetic algorithm, generational genetic algorithm, CHC algorithm and DE algorithm. In order to test the performance of these algorithms we have used different proposed fields to mount PV plants. The results obtained show that EAs, and specifically DE with rand mutation schemes, are promising techniques to optimize design of PV plants. Furthermore, the results are contrasted with nonparametric statistical tests to support our conclusions.     

Nanomedicine: application areas and development prospects

Nanotechnology, along with related concepts such as nanomaterials, nanostructures and nanoparticles, has become a priority area for scientific research and technological development. Nanotechnology, i.e., the creation and utilization of materials and devices at nanometer scale, already has multiple applications in electronics and other fields. However, the greatest expectations are for its application in biotechnology and health, with the direct impact these could have on the quality of health in future societies. The emerging discipline of nanomedicine brings nanotechnology and medicine together in order to develop novel therapies and improve existing treatments. In nanomedicine, atoms and molecules are manipulated to produce nanostructures of the same size as biomolecules for interaction with human cells. This procedure offers a range of new solutions for diagnoses and "smart" treatments by stimulating the body's own repair mechanisms. It will enhance the early diagnosis and treatment of diseases such as cancer, diabetes, Alzheimer's, Parkinson's and cardiovascular diseases. Preventive medicine may then become a reality.     

Specific Colon Cancer Cell Cytotoxicity Induced by Bacteriophage E Gene Expression under Transcriptional Control of Carcinoembryonic Antigen Promoter

Colorectal cancer is one of the most prevalent cancers in the world. Patients in advanced stages often develop metastases that require chemotherapy and usually show a poor response, have a low survival rate and develop considerable toxicity with adverse symptoms. Gene therapy may act as an adjuvant therapy in attempts to destroy the tumor without affecting normal host tissue. The bacteriophage E gene has demonstrated significant antitumor activity in several cancers, but without any tumor-specific activity. The use of tumor-specific promoters may help to direct the expression of therapeutic genes so they act against specific cancer cells. We used the carcinoembryonic antigen promoter (CEA) to direct E gene expression (pCEA-E) towards colon cancer cells. pCEA-E induced a high cell growth inhibition of human HTC-116 colon adenocarcinoma and mouse MC-38 colon cancer cells in comparison to normal human CCD18co colon cells, which have practically undetectable levels of CEA. In addition, in vivo analyses of mice bearing tumors induced using MC-38 cells showed a significant decrease in tumor volume after pCEA-E treatment and a low level of Ki-67 in relation to untreated tumors. These results suggest that the CEA promoter is an excellent candidate for directing E gene expression specifically toward colon cancer cells.