Faculty of Science
http://hdl.handle.net/10294/147
Sun, 01 Feb 2015 21:15:41 GMT2015-02-01T21:15:41ZIdentification of a Pantoea Biosynthetic Cluster That Directs the Synthesis of an Antimicrobial Natural Product
http://hdl.handle.net/10294/5380
Identification of a Pantoea Biosynthetic Cluster That Directs the Synthesis of an Antimicrobial Natural Product
Stavrinides, John; Smith, Derek D. N.; Walterson, Alyssa M.
Fire Blight is a destructive disease of apple and pear caused by the enteric bacterial pathogen, Erwinia amylovora. E. amylovora initiates infection by colonizing the stigmata of apple and pear trees, and entering the plants through natural openings. Epiphytic populations of the related enteric bacterium, Pantoea, reduce the incidence of disease through competition and antibiotic production. In this study, we identify an antibiotic from Pantoea ananatis BRT175, which is effective against E. amylovora and select species of Pantoea. We used transposon mutagenesis to create a mutant library, screened approximately 5,000 mutants for loss of antibiotic production, and recovered 29 mutants. Sequencing of the transposon insertion sites of these mutants revealed multiple independent disruptions of an 8.2 kb cluster consisting of seven genes, which appear to be coregulated. An analysis of the distribution of this cluster revealed that it was not present in any other of our 115 Pantoea isolates, or in any of the fully sequenced Pantoea genomes, and is most closely related to antibiotic biosynthetic clusters found in three different species of Pseudomonas. This identification of this biosynthetic cluster highlights the diversity of natural products produced by Pantoea.
Thu, 15 May 2014 00:00:00 GMThttp://hdl.handle.net/10294/53802014-05-15T00:00:00ZA note on the fixed-point iteration for the matrix equations $X\pm A^*X^{-1}A=I$
http://hdl.handle.net/10294/5271
A note on the fixed-point iteration for the matrix equations $X\pm A^*X^{-1}A=I$
Fital, Sandra; Guo, Chun-Hua
The fixed-point iteration is a simple method for finding the maximal
Hermitian positive
definite solutions of the matrix equations $X\pm A^*X^{-1}A=I$
(the plus/minus equations).
The convergence
of this method
may be very slow if the initial matrix is not chosen carefully. A strategy
for choosing
better initial matrices has been recently proposed by Ivanov, Hasanov and
Uhlig. They
proved that this strategy can improve the convergence in general and observed
from numerical
experiments that dramatic improvement happens for the plus equation
with some matrices $A$. It turns
out that
the matrices $A$ are normal for those examples. In this note
we prove a result
that explains the dramatic improvement in convergence for normal
(and thus nearly
normal) matrices for the plus equation. A similar result is also proved
for the minus
equation.
Tue, 01 Jan 2008 00:00:00 GMThttp://hdl.handle.net/10294/52712008-01-01T00:00:00ZDetecting and solving hyperbolic quadratic eigenvalue problems
http://hdl.handle.net/10294/5270
Detecting and solving hyperbolic quadratic eigenvalue problems
Guo, Chun-Hua; Higham, Nicholas J.; Tisseur, Francoise
Hyperbolic quadratic matrix polynomials $Q(\lambda) = \lambda^2 A + \lambda B + C$ are an important
class of Hermitian matrix polynomials with real eigenvalues, among which the overdamped quadratics
are those with nonpositive eigenvalues. Neither the definition of overdamped nor any of the standard
characterizations provides an efficient way to test if a given $Q$ has this property. We show that
a quadratically convergent matrix iteration based on cyclic reduction, previously studied by Guo
and Lancaster, provides necessary and sufficient conditions for $Q$ to be overdamped. For weakly
overdamped $Q$ the iteration is shown to be generically linearly convergent with constant at worst
1/2, which implies that the convergence of the iteration is reasonably fast in almost all cases of
practical interest. We show that the matrix iteration can be implemented in such a way that when
overdamping is detected a scalar $\mu < 0$ is provided that lies in the gap between the $n$ largest and $n$ smallest eigenvalues of the $n \times n$ quadratic eigenvalue problem (QEP) $Q(\lambda)x = 0$. Once such a
$\mu$ is known, the QEP can be solved by linearizing to a definite pencil that can be reduced, using
already available Cholesky factorizations, to a standard Hermitian eigenproblem. By incorporating
an initial preprocessing stage that shifts a hyperbolic $Q$ so that it is overdamped, we obtain an
efficient algorithm that identifies and solves a hyperbolic or overdamped QEP maintaining symmetry
throughout and guaranteeing real computed eigenvalues.
Thu, 01 Jan 2009 00:00:00 GMThttp://hdl.handle.net/10294/52702009-01-01T00:00:00ZConvergence Analysis of the Doubling Algorithm for Several Nonlinear Matrix Equations in the Critical Case
http://hdl.handle.net/10294/5269
Convergence Analysis of the Doubling Algorithm for Several Nonlinear Matrix Equations in the Critical Case
Chiang, Chun-Yueh; Chu, Eric King-Wah; Guo, Chun-Hua; Huang, Tsung-Ming; Lin, Wen-Wei; Xu, Shu-Fang
In this paper, we review two types of doubling algorithm and some
techniques for analyzing them.
We then use the techniques to study the doubling algorithm for three
different nonlinear matrix equations in the critical case.
We show that the convergence of the doubling algorithm is at least linear
with rate 1/2. As compared to earlier work on this topic, the results we present here
are more general, and the analysis here is much simpler.
Thu, 01 Jan 2009 00:00:00 GMThttp://hdl.handle.net/10294/52692009-01-01T00:00:00Z