Feedback learning particle swarm optimization

This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published and is available at the link below - Copyright @ Elsevier 2011In this paper, a feedback learning particle swarm optimization algorithm with quadratic inertia weight (FLPSO-QIW) is developed to solve optimization problems. The proposed FLPSO-QIW consists of four steps. Firstly, the inertia weight is calculated by a designed quadratic function instead of conventional linearly decreasing function. Secondly, acceleration coefficients are determined not only by the generation number but also by the search environment described by each particle’s history best fitness information. Thirdly, the feedback fitness information of each particle is used to automatically design the learning probabilities. Fourthly, an elite stochastic learning (ELS) method is used to refine the solution. The FLPSO-QIW has been comprehensively evaluated on 18 unimodal, multimodal and composite benchmark functions with or without rotation. Compared with various state-of-the-art PSO algorithms, the performance of FLPSO-QIW is promising and competitive. The effects of parameter adaptation, parameter sensitivity and proposed mechanism are discussed in detail.This research was partially supported by the National Natural Science Foundation of PR China (Grant No 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No 200802550007), the Key Creative Project of Shanghai Education Community (Grant No 09ZZ66), the Key Foundation Project of Shanghai(Grant No 09JC1400700), the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, and the Alexander von Humboldt Foundation of Germany

Crystal structure of the N‐terminal region of human Ash2L shows a winged‐helix motif involved in DNA binding

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102216/1/embr2011101-sup-0001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102216/2/embr2011101.reviewer_comments.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102216/3/embr2011101.pd

Immune profiles and DNA methylation alterations related with non-muscle-invasive bladder cancer outcomes

Background: Non-muscle-invasive bladder cancer (NMIBC) patients receive frequent monitoring because ≥ 70% will have recurrent disease. However, screening is invasive, expensive, and associated with significant morbidity making bladder cancer the most expensive cancer to treat per capita. There is an urgent need to expand the understanding of markers related to recurrence and survival outcomes of NMIBC. Methods and results: We used the Illumina HumanMethylationEPIC array to measure peripheral blood DNA methylation profiles of NMIBC patients (N = 603) enrolled in a population-based cohort study in New Hampshire and applied cell type deconvolution to estimate immune cell-type proportions. Using Cox proportional hazard models, we identified that increasing CD4T and CD8T cell proportions were associated with a statistically significant decreased hazard of tumor recurrence or death (CD4T: HR = 0.98, 95% CI = 0.97–1.00; CD8T: HR = 0.97, 95% CI = 0.95–1.00), whereas increasing monocyte proportion and methylation-derived neutrophil-to-lymphocyte ratio (mdNLR) were associated with the increased hazard of tumor recurrence or death (monocyte: HR = 1.04, 95% CI = 1.00–1.07; mdNLR: HR = 1.12, 95% CI = 1.04–1.20). Then, using an epigenome-wide association study (EWAS) approach adjusting for age, sex, smoking status, BCG treatment status, and immune cell profiles, we identified 2528 CpGs associated with the hazard of tumor recurrence or death (P \u3c 0.005). Among these CpGs, the 1572 were associated with an increased hazard and were significantly enriched in open sea regions; the 956 remaining CpGs were associated with a decreased hazard and were significantly enriched in enhancer regions and DNase hypersensitive sites. Conclusions: Our results expand on the knowledge of immune profiles and methylation alteration associated with NMIBC outcomes and represent a first step toward the development of DNA methylation-based biomarkers of tumor recurrence

AMPERE Polar Cap Boundaries

The high-latitude atmosphere is a dynamic region with processes that respond to forcing from the Sun, magnetosphere, neutral atmosphere, and ionosphere. Historically, the dominance of magnetosphere–ionosphere interactions has motivated upper atmospheric studies to use magnetic coordinates when examining magnetosphere–ionosphere–thermosphere coupling processes. However, there are significant differences between the dominant interactions within the polar cap, auroral oval, and equatorward of the auroral oval. Organising data relative to these boundaries has been shown to improve climatological and statistical studies, but the process of doing so is complicated by the shifting nature of the auroral oval and the difficulty in measuring its poleward and equatorward boundaries. This study presents a new set of open–closed magnetic field line boundaries (OCBs) obtained from Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) magnetic perturbation data. AMPERE observations of field-aligned currents (FACs) are used to determine the location of the boundary between the Region 1 (R1) and Region 2 (R2) FAC systems. This current boundary is thought to typically lie a few degrees equatorward of the OCB, making it a good candidate for obtaining OCB locations. The AMPERE R1–R2 boundaries are compared to the Defense Meteorological Satellite Program Special Sensor J (DMSP SSJ) electron energy flux boundaries to test this hypothesis and determine the best estimate of the systematic offset between the R1–R2 boundary and the OCB as a function of magnetic local time. These calibrated boundaries, as well as OCBs obtained from the Imager for Magnetopause-to-Aurora Global Exploration (IMAGE) observations, are validated using simultaneous observations of the convection reversal boundary measured by DMSP. The validation shows that the OCBs from IMAGE and AMPERE may be used together in statistical studies, providing the basis of a long-term data set that can be used to separate observations originating inside and outside of the polar cap

Array-based sequencing of filaggrin gene for comprehensive detection of disease-associated variants

The filaggrin gene (FLG) is essential for skin differentiation and epidermal barrier formation. FLG loss-of-function (LoF) variants are associated with ichthyosis vulgaris and the major genetic risk factor for developing atopic dermatitis (AD).1, 2, 3 Genetic stratification of patients with AD according to FLG LoF risk is a common practice for both research and clinical studies; however, few studies comprehensively sequence the entire FLG coding region. Most studies that include FLG genotyping have screened for common predominant LoF variants to report allele frequencies after full Sanger sequencing of a smaller batch of test patient samples or previously published data. This strategy potentially results in underreporting of the genetic contribution especially in ethnicities where FLG LoF variants are highly diverse.4 Distinct LoF variants have been reported for most ethnicities studied to date. For example, 2 predominant sequence variants (p.R501X and c.2282del4) make up approximately 80% of the mutation burden in northern Europeans,5 whereas in East Asian ethnicities, a larger FLG LoF mutation spectrum is found with fewer predominating variants.6, 7 However, routinely Sanger sequencing the entire FLG coding region for large cohorts is not always feasible, although desirable as it is essential to correctly stratify patients. To address this, we developed a robust and cost-effective high-throughput PCR-based method for analyzing the entire coding region of FLG using Fluidigm microfluidics technology and next-generation sequencing (NGS). We have applied this method to fully resequence cohorts of Chinese, Malay, and Indian patients with AD from the Singaporean population.ASTAR (Agency for Sci., Tech. and Research, S’pore)Published versio

Transcription profiling reveals potential mechanisms of dysbiosis in the oral microbiome of rhesus macaques with chronic untreated SIV infection.

A majority of individuals infected with human immunodeficiency virus (HIV) have inadequate access to antiretroviral therapy and ultimately develop debilitating oral infections that often correlate with disease progression. Due to the impracticalities of conducting host-microbe systems-based studies in HIV infected patients, we have evaluated the potential of simian immunodeficiency virus (SIV) infected rhesus macaques to serve as a non-human primate model for oral manifestations of HIV disease. We present the first description of the rhesus macaque oral microbiota and show that a mixture of human commensal bacteria and "macaque versions" of human commensals colonize the tongue dorsum and dental plaque. Our findings indicate that SIV infection results in chronic activation of antiviral and inflammatory responses in the tongue mucosa that may collectively lead to repression of epithelial development and impact the microbiome. In addition, we show that dysbiosis of the lingual microbiome in SIV infection is characterized by outgrowth of Gemella morbillorum that may result from impaired macrophage function. Finally, we provide evidence that the increased capacity of opportunistic pathogens (e.g. E. coli) to colonize the microbiome is associated with reduced production of antimicrobial peptides

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era