Search

Genetic maps order genetic markers along chromosomes. They are, for instance, extensively used in marker-assisted selection to accelerate breeding programs. Even for the same species, people often have to deal with several alternative maps obtained using different ordering methods or different datasets, e.g. resulting from different segregating populations. Having efficient tools to identify the consistency and discrepancy of alternative maps is thus essential to facilitate genetic map comparisons. We propose to encode genetic maps by bucket order, a kind of order, which takes into account the blurred parts of the marker order while being an efficient data structure to achieve low complexity algorithms. The main result of this paper is an O(n log(n)) procedure to identify the largest agreements between two bucket orders of n elements, their Longest Common Subsequence (LCS), providing an efficient solution to highlight discrepancies between two genetic maps. The LCS of two maps, being the largest set of their collinear markers, is used as a building block to compute pairwise map congruence, to visually emphasize maker collinearity and in some scaffolding methods relying on genetic maps to improve genome assembly. As the LCS computation is a key subroutine of all these genetic map related tools, replacing the current LCS subroutine of those methods by ours –to do the exact same work but faster– could significantly speed up those methods without changing their accuracy. To ease such transition we provide all required algorithmic details in this self contained paper as well as an R package implementing them, named LCSLCIS, which is freely available at: . [ABSTRACT FROM AUTHOR]

The increasingly serious problem of traffic congestion has become a critical issue that urban managers need to focus on. However, as urban scale and structure have already taken shape, the use of existing road resources to achieve effective route selection for vehicles is the key to solving this traffic congestion problem. Existing research has mainly focused on the following three points: (1) algorithms for controlling traffic signal lamp period at single intersections; (2) route recommendation algorithms for a single vehicle; and (3) route recommendation algorithms based on the traffic history experienced by a vehicle. These studies, however, have the following limitations: (1) the evaluation factor is singular, and therefore, cannot fully express the advantages and disadvantages of the route selection method; (2) real-time route selection is absent; (3) route selection for a single vehicle is ineffective in avoiding local congestion. In view of these problems, this paper proposes an improved quantum genetic algorithm (IQGA) to solve the problem of traffic congestion in route selection. The algorithm includes the following: (1) proposing a quantum chromosome initialization strategy (QCIS) to convert and code real traffic conditions and to construct quantum chromosomes based on the quantum coding for vehicles and roads; (2) proposing a quantum chromosome mapping algorithm (QCMA) to transform the calculation bits of quantum chromosomes into the results of route selection for different vehicles; (3) proposing a contemporary optimal solution decision strategy (COSDS) to judge the current route selection results; (4) proposing a quantum update algorithm (QUA) to update and iterate the quantum coding of the population. Two types of experiments were conducted in this study: (1) Artificial traffic networks with different scales were designed to carry out comparative experiments between IQGA and other algorithms. The experimental results show that IGQA has better robustness and adaptive ability. (2) Comparative experiments on an actual urban traffic network verified the high-performance and real-time performance capabilities of IQGA. [ABSTRACT FROM AUTHOR]

Our understanding of how chromosomes structurally organize and dynamically interact has been revolutionized through the lens of long-chain polymer physics. Major protein contributors to chromosome structure and dynamics are condensin and cohesin that stochastically generate loops within and between chains, and entrap proximal strands of sister chromatids. In this paper, we explore the ability of transient, protein-mediated, gene-gene crosslinks to induce clusters of genes, thereby dynamic architecture, within the highly repeated ribosomal DNA that comprises the nucleolus of budding yeast. We implement three approaches: live cell microscopy; computational modeling of the full genome during G1 in budding yeast, exploring four decades of timescales for transient crosslinks between 5kbp domains (genes) in the nucleolus on Chromosome XII; and, temporal network models with automated community (cluster) detection algorithms applied to the full range of 4D modeling datasets. The data analysis tools detect and track gene clusters, their size, number, persistence time, and their plasticity (deformation). Of biological significance, our analysis reveals an optimal mean crosslink lifetime that promotes pairwise and cluster gene interactions through “flexible” clustering. In this state, large gene clusters self-assemble yet frequently interact (merge and separate), marked by gene exchanges between clusters, which in turn maximizes global gene interactions in the nucleolus. This regime stands between two limiting cases each with far less global gene interactions: with shorter crosslink lifetimes, “rigid” clustering emerges with clusters that interact infrequently; with longer crosslink lifetimes, there is a dissolution of clusters. These observations are compared with imaging experiments on a normal yeast strain and two condensin-modified mutant cell strains. We apply the same image analysis pipeline to the experimental and simulated datasets, providing support for the modeling predictions. [ABSTRACT FROM AUTHOR]

Shortening rye stems to improve lodging resistance is among the major tasks awaiting breeders of this cereal. The most straightforward way to achieve this goal is the implementation of a dominant dwarfing gene into high yielding cultivars. The choice of dominant dwarfing genes in rye is limited to Ddw1 and Ddw3 loci, which are well characterized with respect to map position and tightly linked molecular markers on the long arms of chromosomes 5RL and 1RL, respectively. This paper reports on the identification and preliminary characterization of a novel dominant dwarfing gene, Ddw4, from line S44. This was mapped within the centromeric region of chromosome 3R. The Ddw4 gene is sensitive to exogenous gibberellin. Its introduction into the rye populational cultivar Dańkowskie Amber decreased plant height by c. 54% without any negative effects on spike length and number of kernels per spike. Further genetic studies are needed to determine the perspectives for application of the newly detected dwarfing gene into breeding programs for short-stem rye. [ABSTRACT FROM AUTHOR]

With a more Internet-savvy and sophisticated user base, there are more demands for interactive applications and services. However, it is a challenge for existing radio access networks (e.g. 3G and 4G) to cope with the increasingly demanding requirements such as higher data rates and wider coverage area. One potential solution is the inter-collaborative deployment of multiple radio devices in a 5G setting designed to meet exacting user demands, and facilitate the high data rate requirements in the underlying networks. These heterogeneous 5G networks can readily resolve the data rate and coverage challenges. Networks established using the hybridization of existing networks have diverse military and civilian applications. However, there are inherent limitations in such networks such as irregular breakdown, node failures, and halts during speed transmissions. In recent years, there have been attempts to integrate heterogeneous 5G networks with existing ad hoc networks to provide a robust solution for delay-tolerant transmissions in the form of packet switched networks. However, continuous connectivity is still required in these networks, in order to efficiently regulate the flow to allow the formation of a robust network. Therefore, in this paper, we present a novel network formation consisting of nodes from different network maneuvered by Unmanned Aircraft (UA). The proposed model utilizes the features of a biological aspect of genomes and forms a delay tolerant network with existing network models. This allows us to provide continuous and robust connectivity. We then demonstrate that the proposed network model has an efficient data delivery, lower overheads and lesser delays with high convergence rate in comparison to existing approaches, based on evaluations in both real-time testbed and simulation environment. [ABSTRACT FROM AUTHOR]

Increasing the number of mice used to calculate recombinant inbred (RI) strain means increases the accuracy of determining the phenotype associated with each genotype (strain), which in turn enhances quantitative trait locus (QTL) detection and mapping. The purpose of this paper is to examine quantitatively the effect of within-strain sample size (n ) on additive QTL mapping efficiency and to make comparisons with F2 and backcross (BC) populations, where each genotype is represented by only a single mouse. When 25 RI strains are used, the estimated equivalent number of F2 mice yielding the same power to detect QTLs varies inversely as a function of the heritability of the trait in the RI population $(h^2\_\{\backslash rm\; RI\})$. For example, testing 25 strains with n = 10 per strain is approximately equivalent to 160 F2 mice when $h^2\_\{\backslash rm\; RI\}\; =\; 0.2,$ but only 55 when $h^2\_\{\backslash rm\; RI\}\; =\; 0.6.$ While increasing n is always beneficial, the gain in power as n increases is greatest when $h^2\_\{\backslash rm\; RI\}$ is low and is much diminished at high $h^2\_\{\backslash rm\; RI\}$ values. Thus, when $h^2\_\{\backslash rm\; RI\}$ is high, there is little advantage of large n , even when n approaches infinity. A cost analysis suggested that RI populations are more cost-effective than conventional selectively genotyped F2 populations at $h^2\_\{\backslash rm\; RI\}$ values likely to be seen in behavioral studies. However, with DNA pooling, this advantage is greatly reduced and may be reversed depending on the values of $h^2\_\{\backslash rm\; RI\}$ and n . [ABSTRACT FROM AUTHOR]