Conserved polycomb repressive complex 2 (PRC2) mediates H3K27me3 to direct transcriptional repression and has a key role in cell fate determination and cell differentiation in both animals and plants. PRC2 subunits have undergone independent multiplication and functional divergence in higher plants. However, relevant information is still absent in gymnosperms.To launch gymnosperm PRC2 research, we identified and cloned the PRC2 core component genes in the conifer model species Picea abies, including one Esc/FIE homolog PaFIE, two p55/MSI homologs PaMSI1a and PaMSI1b, two E(z) homologs PaKMT6A2 and PaKMT6A4, a Su(z)12 homolog PaEMF2 and a PaEMF2-like fragment. Phylogenetic and protein domain analyses were conducted. The Esc/FIE homologs were highly conserved in the land plant, except the monocots. The other gymnospermous PRC2 subunits underwent independent evolution with angiospermous species to different extents. The relative transcript levels of these genes were measured in endosperm and zygotic and somatic embryos at different developmental stages. The obtained results proposed the involvement of PaMSI1b and PaKMT6A4 in embryogenesis and PaKMT6A2 and PaEMF2 in the transition from embryos to seedlings. The PaEMF2-like fragment was predominantly expressed in the endosperm but not in the embryo. In addition, immunohistochemistry assay showed that H3K27me3 deposits were generally enriched at meristem regions during seed development in P. abies.This study reports the first characterization of the PRC2 core component genes in the coniferous species P. abies. Our work may enable a deeper understanding of the cell reprogramming process during seed and embryo development and may guide further research on embryonic potential and development in conifers.
Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.
No abstract is provided for this article.
No abstract is provided for this article.
This paper proposes an adaptation of the Random- Key Cuckoo Search (RKCS) algorithm for solving the famous Quadratic Assignment Problem (QAP). We used a simplified and efficient random-key encoding scheme to convert a continous space (real numbers) into a combinatorial space. We also consid- ered the displacement of a solution in both spaces by using Le´vy flights. The performance of the RKCS for QAP is tested against a set of benchmarks of QAP from the well-known QAPLIB library, and the comparison with a set of other methaheuristics is also carried out.
Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.
The stability of discrete time-varying bilinear systems is studied. The control u(t) in the bilinear systems is considered as output feedback functions with time delay, i.e. u(t)=f(y(t), y(t-1),. . .,y(t-r+1)), which is an important case and is absent in the literature. Also, the authors assumed that the feedback function f is of larger classes than the classes given by current literature. The sufficient conditions derived in theorems in the paper depend only on the coefficient matrices of the bilinear systems, so that these results are convenient to check and to apply in engineering problems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Although K-means clustering algorithm is simple and popular, it has a fundamental drawback of falling into local optima that depend on the randomly generated initial centroid values. Optimization algorithms are well known for their ability to guide iterative computation in searching for global optima. They also speed up the clustering process by achieving early convergence. Contemporary optimization algorithms inspired by biology, including the Wolf, Firefly, Cuckoo, Bat and Ant algorithms, simulate swarm behavior in which peers are attracted while steering towards a global objective. It is found that these bio-inspired algorithms have their own virtues and could be logically integrated into K-means clustering to avoid local optima during iteration to convergence. In this paper, the constructs of the integration of bio-inspired optimization methods into K-means clustering are presented. The extended versions of clustering algorithms integrated with bio-inspired optimization methods produce improved results. Experiments are conducted to validate the benefits of the proposed approach.
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No abstract is provided for this article.
Bat algorithm (BA) is a recent metaheuristic optimization algorithm proposed by Yang. In the present study, we have introduced chaos into BA so as to increase its global search mobility for robust global optimization. Detailed studies have been carried out on benchmark problems with different chaotic maps. Here, four different variants of chaotic BA are introduced and thirteen different chaotic maps are utilized for validating each of these four variants. The results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks.
