Feature Selection via Chaotic Antlion Optimization

Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used.This work was partially supported by the IPROCOM Marie Curie initial training network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grants agreement No. 316555, and by the Romanian National Authority for Scientific Research, CNDIUEFISCDI, project number PN-II-PT-PCCA-2011-3.2- 0917. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Chemotherapeutic Sensitization of Leptomycin B Resistant Lung Cancer Cells by Pretreatment with Doxorubicin

The development of novel targeted therapies has become an important research focus for lung cancer treatment. Our previous study has shown leptomycin B (LMB) significantly inhibited proliferation of lung cancer cells; however, p53 wild type lung cancer cells were resistant to LMB. Therefore, the objective of this study was to develop and evaluate a novel therapeutic strategy to sensitize LMB-resistant lung cancer cells by combining LMB and doxorubicin (DOX). Among the different treatment regimens, pretreatment with DOX (pre-DOX) and subsequent treatment with LMB to A549 cells significantly decreased the 50% inhibitory concentration (IC50) as compared to that of LMB alone (4.4 nM vs. 10.6 nM, P<0.05). Analysis of cell cycle and apoptosis by flow cytometry further confirmed the cytotoxic data. To investigate molecular mechanisms for this drug combination effects, p53 pathways were analyzed by Western blot, and nuclear proteome was evaluated by two dimensional-difference gel electrophoresis (2D-DIGE) and mass spectrometry. In comparison with control groups, the levels of p53, phospho-p53 (ser15), and p21 proteins were significantly increased while phospho-p53 (Thr55) and survivin were significantly decreased after treatments of pre-DOX and LMB (P<0.05). The 2D-DIGE/MS analysis identified that sequestosome 1 (SQSTM1/p62) had a significant increase in pre-DOX and LMB-treated cells (P<0.05). In conclusion, our results suggest that drug-resistant lung cancer cells with p53 wild type could be sensitized to cell death by scheduled combination treatment of DOX and LMB through activating and restoring p53 as well as potentially other signaling pathway(s) involving sequestosome 1

Meta-analysis of the detection of plant pigment concentrations using hyperspectral remotely sensed data

Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550–560nm) and red edge (680–750nm) regions; chlorophyll b on the red, (630–660nm), red edge (670–710nm) and the near-infrared (800–810nm); carotenoids on the 500–580nm region; and anthocyanins on the green (550–560nm), red edge (700–710nm) and near-infrared (780–790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a

A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment

This paper proposes a short term hydroelectric plant dispatch model based on the rule of maximizing the benefit. For the optimal dispatch model, which is a large scale nonlinear planning problem with multi-constraints and multi-variables, this paper proposes a novel self-adaptive chaotic particle swarm optimization algorithm to solve the short term generation scheduling of a hydro-system better in a deregulated environment. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed approach introduces chaos mapping and an adaptive scaling term into the particle swarm optimization algorithm, which increases its convergence rate and resulting precision. The new method has been examined and tested on a practical hydro-system. The results are promising and show the effectiveness and robustness of the proposed approach in comparison with the traditional particle swarm optimization algorithm

Performance evaluation of ZVS/ZCS high efficiency AC/DC converter for high power applications

The increased power density, reduced switching losses with minimum electromagnetic interference (EMI), and high efficiency are essential requirements of power converters. To achieve these characteristics, soft power converters employing soft switching techniques are indispensable. In this paper, a ZCS/ZVS PWM AC/DC converter topology has been emphasized, which finds applications in high power systems such as automobile battery charging and renewable energy systems. This converter scheme maintains zero current and zero voltage switching conditions at turn on and turn off moments of semiconductor switches, respectively and soft operation of rectifier diodes that lead to negligible switching and diode reverse recovery losses. Moreover, it improves power quality and presents high input power factor, low total harmonic distortion of the input current (THDI ) and improved efficiency. The validity of theoretical analysis of the proposed converter has been carried out experimentally on a 10 kW laboratory prototype. Experimental results prove that the soft switching operation of the semiconductor switches and diodes is maintained at 98.6% rated load efficiency. In addition, the performance evaluation has been performed by comparative analysis of the proposed converter with some prior art high power AC/DC converters. Efficiencies of the proposed and prior art high power topologies have been determined for different load conditions. The highest efficiency, power factor and lower THDI of the proposed converter topology complies with international standards

Capturing CO2 in flue gas from fossil fuel-fired power plants using dry regenerable alkali metal-based sorbent

CO2 capture and storage (CCS) has received significant attention recently and is recognized as an important option for reducing CO2 emissions from fossil fuel combustion. A particularly promising option involves the use of dry alkali metal-based sorbents to capture CO2 from flue gas. Here, alkali metal carbonates are used to capture CO2 in the presence of H2O to form either sodium or potassium bicarbonate at temperatures below 100 °C. A moderate temperature swing of 120–200 °C then causes the bicarbonate to decompose and release a mixture of CO2/H2O that can be converted into a “sequestration-ready” CO2 stream by condensing the steam. This process can be readily used for retrofitting existing facilities and easily integrated with new power generation facilities. It is ideally suited for coal-fired power plants incorporating wet flue gas desulfurization, due to the associated cooling and saturation of the flue gas. It is expected to be both cost effective and energy efficient. This paper provides the first comprehensive review of the major research progress on this technology. To date such research has focused on two main areas: sorbent development and process development. In the case of sorbent development, pure sodium carbonate and potassium carbonate were tested directly. More recent research has concentrated on using supported sorbents which provide the necessary attrition resistance for use with fluidized-bed or transport reactors. Research on sorbent development has included an examination of the physical properties, carbonation and regeneration reaction behavior, reaction kinetic behavior, and multi-cycle behavior of these alkali metal-based sorbents. By contrast, process development activities have focused on solving the many unique challenges associated with post-combustion CO2 capture using alkali metal-based sorbents. The research on process development included exploration of the effects of operation conditions such as reaction temperature, gas composition, operation pressure, and gas impurities on CO2 capture behavior, continuous operation of the CO2 capture process, and economic evaluation of this process. Finally, this paper discusses the research challenges and opportunities that exist with this technology