Dielectrophoretic differentiation of mouse ovarian surface epithelial cells, macrophages, and fibroblasts using contactless dielectrophoresis

Ovarian cancer is the leading cause of death from gynecological malignancies in women. The primary challenge is the detection of the cancer at an early stage, since this drastically increases the survival rate. In this study we investigated the dielectrophoretic responses of progressive stages of mouse ovarian surface epithelial (MOSE) cells, as well as mouse fibroblast and macrophage cell lines, utilizing contactless dielectrophoresis (cDEP). cDEP is a relatively new cell manipulation technique that has addressed some of the challenges of conventional dielectrophoretic methods. To evaluate our microfluidic device performance, we computationally studied the effects of altering various geometrical parameters, such as the size and arrangement of insulating structures, on dielectrophoretic and drag forces. We found that the trapping voltage of MOSE cells increases as the cells progress from a non-tumorigenic, benign cell to a tumorigenic, malignant phenotype. Additionally, all MOSE cells display unique behavior compared to fibroblasts and macrophages, representing normal and inflammatory cells found in the peritoneal fluid. Based on these findings, we predict that cDEP can be utilized for isolation of ovarian cancer cells from peritoneal fluid as an early cancer detection tool.     

Robust block second order sliding mode control for a quadrotor

This paper presents the design of a controller based on the block control technique combined with the super twisting control algorithm for trajectory tracking of a quadrotor helicopter. A first order exact differentiator is used in order to estimate the virtual control inputs, which simplifies the control law design. In addition, the wind parameter resulting from the aerodynamic forces is also estimated in order to ensure robustness against these unmatched perturbations. The stability and finite time convergence of the exact differentiator have been recently proved by means of Lyapunov functions, and therefore the stability analysis of the proposed controller has been carried out along the same lines. The performance and effectiveness of the proposed controller are tested in a simulation study taking into account external disturbances.     

Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization

This paper presents the design and performance analysis of Proportional Integral Derivate (PID) controller for an Automatic Voltage Regulator (AVR) system using recently proposed simplified Particle Swarm Optimization (PSO) also called Many Optimizing Liaisons (MOL) algorithm. MOL simplifies the original PSO by randomly choosing the particle to update, instead of iterating over the entire swarm thus eliminating the particles best known position and making it easier to tune the behavioral parameters. The design problem of the proposed PID controller is formulated as an optimization problem and MOL algorithm is employed to search for the optimal controller parameters. For the performance analysis, different analysis methods such as transient response analysis, root locus analysis and bode analysis are performed. The superiority of the proposed approach is shown by comparing the results with some recently published modern heuristic optimization algorithms such as Artificial Bee Colony (ABC) algorithm, Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) algorithm. Further, robustness analysis of the AVR system tuned by MOL algorithm is performed by varying the time constants of amplifier, exciter, generator and sensor in the range of ?50% to +50% in steps of 25%. The analysis results reveal that the proposed MOL based PID controller for the AVR system performs better than the other similar recently reported population based optimization algorithms.     

Correlation analysis of performance measures for multi-label classification

In many important application domains, such as text categorization, scene classification, biomolecular analysis and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research in multi-label classification. In order to evaluate and compare multi-label classifiers, researchers have adapted evaluation measures from the single-label paradigm, like Precision and Recall; and also have developed many different measures specifically for the multi-label paradigm, like Hamming Loss and Subset Accuracy. However, these evaluation measures have been used arbitrarily in multi-label classification experiments, without an objective analysis of correlation or bias. This can lead to misleading conclusions, as the experimental results may appear to favor a specific behavior depending on the subset of measures chosen. Also, as different papers in the area currently employ distinct subsets of measures, it is difficult to compare results across papers. In this work, we provide a thorough analysis of multi-label evaluation measures, and we give concrete suggestions for researchers to make an informed decision when choosing evaluation measures for multi-label classification.     

Dietary Fats and Oxidative Stress: A Cross-Sectional Study Among Coronary Artery Disease Subjects Consuming Coconut Oil/Sunflower Oil

Coconut oil has been used by the people of Kerala as a cooking medium for several decades. Due to its alleged hypercholesterolemic activity, general population in recent times is shifting to cooking oils rich in polyunsaturated fats, the most popular being sunflower oil. The effect of long-term consumption of sunflower oil on oxidative stress in humans is not well investigated. We studied oxidative stress among coronary artery disease (CAD) patients who were consuming coconut oil or sunflower oil as a part of their routine diet. Men, aged 35–70 years, with established CAD, who presented to the hospital for routine cardiac evaluations, were enrolled in this observational study. Group 1 and 2 consisted of 73 and 80 subjects consuming coconut oil and sunflower oil respectively for over a period of 2 years. Lipid profile and parameters for oxidative stress were evaluated among them. Conventional lipid parameters did not differ significantly between the two groups. Mean vitamin C concentration was significantly reduced for subjects on sunflower oil compared to those consuming coconut oil (P = 0.044). Malondialdehyde was higher for sunflower oil consumers compared to coconut oil consumers (P < 0.0001). Other parameters such as oxidized LDL, GSH, GPx and SOD were not found to be significantly different between the two groups. The results of the present study show that coconut oil did not induce hypercholesterolemia compared to sunflower oil. On the other hand, sunflower oil group had elevated oxidative stress compared to coconut oil group.