21,187 research outputs found
The inference of gene trees with species trees
Molecular phylogeny has focused mainly on improving models for the
reconstruction of gene trees based on sequence alignments. Yet, most
phylogeneticists seek to reveal the history of species. Although the histories
of genes and species are tightly linked, they are seldom identical, because
genes duplicate, are lost or horizontally transferred, and because alleles can
co-exist in populations for periods that may span several speciation events.
Building models describing the relationship between gene and species trees can
thus improve the reconstruction of gene trees when a species tree is known, and
vice-versa. Several approaches have been proposed to solve the problem in one
direction or the other, but in general neither gene trees nor species trees are
known. Only a few studies have attempted to jointly infer gene trees and
species trees. In this article we review the various models that have been used
to describe the relationship between gene trees and species trees. These models
account for gene duplication and loss, transfer or incomplete lineage sorting.
Some of them consider several types of events together, but none exists
currently that considers the full repertoire of processes that generate gene
trees along the species tree. Simulations as well as empirical studies on
genomic data show that combining gene tree-species tree models with models of
sequence evolution improves gene tree reconstruction. In turn, these better
gene trees provide a better basis for studying genome evolution or
reconstructing ancestral chromosomes and ancestral gene sequences. We predict
that gene tree-species tree methods that can deal with genomic data sets will
be instrumental to advancing our understanding of genomic evolution.Comment: Review article in relation to the "Mathematical and Computational
Evolutionary Biology" conference, Montpellier, 201
Distribution System Outage Detection using Consumer Load and Line Flow Measurements
An outage detection framework for power distribution networks is proposed.
Given the tree structure of the distribution system, a method is developed
combining the use of real-time power flow measurements on edges of the tree
with load forecasts at the nodes of the tree. A maximum a posteriori detector
{\color{black} (MAP)} is formulated for arbitrary number and location of
outages on trees which is shown to have an efficient detector. A framework
relying on the maximum missed detection probability is used for optimal sensor
placement and is solved for tree networks. Finally, a set of case studies is
considered using feeder data from the Pacific Northwest National Laboratories.
We show that a 10\% loss in mean detection reliability network wide reduces the
required sensor density by 60 \% for a typical feeder if efficient use of
measurements is performed.Comment: Complete rework of result
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