Phylogenetic Networks Do not Need to Be Complex: Using Fewer Reticulations to Represent Conflicting Clusters

Phylogenetic trees are widely used to display estimates of how groups of species evolved. Each phylogenetic tree can be seen as a collection of clusters, subgroups of the species that evolved from a common ancestor. When phylogenetic trees are obtained for several data sets (e.g. for different genes), then their clusters are often contradicting. Consequently, the set of all clusters of such a data set cannot be combined into a single phylogenetic tree. Phylogenetic networks are a generalization of phylogenetic trees that can be used to display more complex evolutionary histories, including reticulate events such as hybridizations, recombinations and horizontal gene transfers. Here we present the new CASS algorithm that can combine any set of clusters into a phylogenetic network. We show that the networks constructed by CASS are usually simpler than networks constructed by other available methods. Moreover, we show that CASS is guaranteed to produce a network with at most two reticulations per biconnected component, whenever such a network exists. We have implemented CASS and integrated it in the freely available Dendroscope software

Algorithms for Visualizing Phylogenetic Networks

We study the problem of visualizing phylogenetic networks, which are extensions of the Tree of Life in biology. We use a space filling visualization method, called DAGmaps, in order to obtain clear visualizations using limited space. In this paper, we restrict our attention to galled trees and galled networks and present linear time algorithms for visualizing them as DAGmaps.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

Corporate governance structure and firm performance : empirical evidence from Brusa Malaysia, Kuala Lumper

The issue of corporate governance has been emerging as important phenomena that has been searched extensively both in developed countries due to its strategic impact on the monitoring of management activities and firms&rsquo; performance. Yet little attempt has been made in developing countries like Malaysia to ascertain what constitute corporate governance and its impact on firm\u27s performance. Therefore, this study aims at examining the structure of the corporate governance and its impact on firm&rsquo;s performance. This study is based on 100 firms, which are the component of the Composite Index (CI) serve as market barometer. This study employs cross-sectional annual multiple regression model to examine, what constitutes the corporate governance structure and its impact on performance of the firm. The analysis was based on annual regression over 5 years period from 1997 through 2001. Three different blend of surrogate for corporate governance were developed for good corporate governance structure. These are the independent non-executive (outside) directors, audit committee and remuneration committee. To isolate the size effect from the impact of corporate governance structure on firm&rsquo;s performance, firm&rsquo;s size was also included are variable in the model. The ratio of net income before tax to total asset is used as a surrogate for firm&rsquo;s performance. Evidence from the study indicates that there is partial relation between corporate governance structure and corporate performance. The presence of both audit and remuneration committee serves as an important monitoring device to control management activities that lead to increase firm\u27s performance. While on average, the presence of independent nonexecutive directors does not provide any significant explanation for the firm\u27s performance. However, the firm size appears to have significant impact on corporate performance.<br /

Statistical approaches to sensitivity analysis of mathematical models : applications in ecology

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Circular Networks from Distorted Metrics

Trees have long been used as a graphical representation of species relationships. However complex evolutionary events, such as genetic reassortments or hybrid speciations which occur commonly in viruses, bacteria and plants, do not fit into this elementary framework. Alternatively, various network representations have been developed. Circular networks are a natural generalization of leaf-labeled trees interpreted as split systems, that is, collections of bipartitions over leaf labels corresponding to current species. Although such networks do not explicitly model specific evolutionary events of interest, their straightforward visualization and fast reconstruction have made them a popular exploratory tool to detect network-like evolution in genetic datasets. Standard reconstruction methods for circular networks, such as Neighbor-Net, rely on an associated metric on the species set. Such a metric is first estimated from DNA sequences, which leads to a key difficulty: distantly related sequences produce statistically unreliable estimates. This is problematic for Neighbor-Net as it is based on the popular tree reconstruction method Neighbor-Joining, whose sensitivity to distance estimation errors is well established theoretically. In the tree case, more robust reconstruction methods have been developed using the notion of a distorted metric, which captures the dependence of the error in the distance through a radius of accuracy. Here we design the first circular network reconstruction method based on distorted metrics. Our method is computationally efficient. Moreover, the analysis of its radius of accuracy highlights the important role played by the maximum incompatibility, a measure of the extent to which the network differs from a tree.Comment: Submitte

Muddied Roots

Keara Huson is currently a junior pursuing a BSA in Animal Science with a Pre-Professional concentration and a minor in AFLS Communications. They moved from the rainy state of Washington to attend the University of Arkansas and now call Fayetteville home. They enjoy writing prose and poetry in their free time and hope to continue doing so as they continue to pursue further education in veterinary medicine