998 resultados para Algorithms genetics
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As an endangered animal group, musk deer (genus Moschus) are not only a great concern of wildlife conservation, but also of special interest to evolutionary studies due to long-standing arguments on the taxonomic and phylogenetic associations in this group. Using museum samples, we sequenced complete mitochondrial cytochrome b genes (1140 bp) of all suggested species of musk deer in order to reconstruct their phylogenetic history through molecular information. Our results showed that the cytochrome b gene tree is rather robust and concurred for all the algorithms employed (parsimony, maximum likelihood, and distance methods). Further, the relative rate test indicated a constant sequence substitution rate among all the species, permitting the dating of divergence events by molecular clock. According to the molecular topology, M. moschiferus branched off the earliest from a common ancestor of musk deer (about 700,000 years ago); then followed the bifurcation forming the M. berezouskii lineage and the lineage clustering M. fuscus, M. chrysogaster, and M. leucogaster (around 370,000 years before present), interestingly the most recent speciation event in musk deer happened rather recently (140,000 years ago), which might have resulted from the diversified habitats and geographic barriers in southwest China caused by gigantic movements of the Qinghai-Tibetan Plateau in history. Combining the data of current distributions, fossil records, and molecular data of this study, we suggest that the historical dispersion of musk deer might be from north to south in China. Additionally, in our further analyses involving other pecora species, musk deer was strongly supported as a monophyletic group and a valid family in Artiodactyla, closely related to Cervidae. (C) 1999 Academic Press.
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Algorithms are presented for detection and tracking of multiple clusters of co-ordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithms maintain a discrete approximation of the filtering density of the clusters' state. The filters' tracking efficiency is enhanced by incorporating various sampling improvement strategies into the basic Metropolis-Hastings scheme. Thus, an evolutionary stage consisting of two primary steps is introduced: 1) producing a population of different chain realizations, and 2) exchanging genetic material between samples in this population. The performance of the resulting evolutionary filtering algorithms is demonstrated in two different settings. In the first, both group and target properties are estimated whereas in the second, which consists of a very large number of targets, only the clustering structure is maintained. © 2009 IFAC.
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This workshop was held at the National Bureau of Fish Genetic Resources and followed on from the Indian mackerel Working group meeting in Colombo (28-29 May, 2012). Activities included; DNA extraction; PCR (Polymerase Chain Reaction) for microsatellites; genotyping microsatellites; data analysis; emerging technologies; and an action plan
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Detailed investigations of the effectiveness of three widely adopted optical orthogonal frequency division multiplexing (OOFDM) adaptive loading algorithms, including power loading (PL), bit loading (BL), and bit-and-power loading (BPL), are undertaken, over < 100km single-mode fibre (SMF) system without incorporating inline optical amplification and chromatic dispersion (CD) compensation. It is shown that the BPL (PL) algorithm always offers the best (worst) transmission performance. The absolute transmission capacity differences between these algorithms are independent of transmission distance and launched optical power. Moreover, it is shown that in comparison with the most sophisticated BPL algorithm, the simplest PL algorithm is effective in escalating the OOFDM SMF links performance to its maximum potential. On the other hand, when employing a large number of subcarriers and a high digital-to-analogue DAC)/analogue-to-digital (ADC) sampling rate, the sophisticated BPL algorithm has to be adopted. © 2011 IEEE.
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This paper describes a derivation of the adjoint low Mach number equations and their implementation and validation within a global mode solver. The advantage of using the low Mach number equations and their adjoints is that they are appropriate for flows with variable density, such as flames, but do not require resolution of acoustic waves. Two versions of the adjoint are implemented and assessed: a discrete-adjoint and a continuous-adjoint. The most unstable global mode calculated with the discrete-adjoint has exactly the same eigenvalue as the corresponding direct global mode but contains numerical artifacts near the inlet. The most unstable global mode calculated with the continuous-adjoint has no numerical artifacts but a slightly different eigenvalue. The eigenvalues converge, however, as the timestep reduces. Apart from the numerical artifacts, the mode shapes are very similar, which supports the expectation that they are otherwise equivalent. The continuous-adjoint requires less resolution and usually converges more quickly than the discrete-adjoint but is more challenging to implement. Finally, the direct and adjoint global modes are combined in order to calculate the wavemaker region of a low density jet. © 2011 Elsevier Inc.
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We present a new haplotype-based approach for inferring local genetic ancestry of individuals in an admixed population. Most existing approaches for local ancestry estimation ignore the latent genetic relatedness between ancestral populations and treat them as independent. In this article, we exploit such information by building an inheritance model that describes both the ancestral populations and the admixed population jointly in a unified framework. Based on an assumption that the common hypothetical founder haplotypes give rise to both the ancestral and the admixed population haplotypes, we employ an infinite hidden Markov model to characterize each ancestral population and further extend it to generate the admixed population. Through an effective utilization of the population structural information under a principled nonparametric Bayesian framework, the resulting model is significantly less sensitive to the choice and the amount of training data for ancestral populations than state-of-the-art algorithms. We also improve the robustness under deviation from common modeling assumptions by incorporating population-specific scale parameters that allow variable recombination rates in different populations. Our method is applicable to an admixed population from an arbitrary number of ancestral populations and also performs competitively in terms of spurious ancestry proportions under a general multiway admixture assumption. We validate the proposed method by simulation under various admixing scenarios and present empirical analysis results from a worldwide-distributed dataset from the Human Genome Diversity Project.
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Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
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While a large amount of research over the past two decades has focused on discrete abstractions of infinite-state dynamical systems, many structural and algorithmic details of these abstractions remain unknown. To clarify the computational resources needed to perform discrete abstractions, this paper examines the algorithmic properties of an existing method for deriving finite-state systems that are bisimilar to linear discrete-time control systems. We explicitly find the structure of the finite-state system, show that it can be enormous compared to the original linear system, and give conditions to guarantee that the finite-state system is reasonably sized and efficiently computable. Though constructing the finite-state system is generally impractical, we see that special cases could be amenable to satisfiability based verification techniques. ©2009 IEEE.