691 research outputs found
Description and validation of a novel real-time RT-PCR enterovirus assay
Journal ArticleEnteroviruses are a leading cause of aseptic meningitis in adult and pediatric populations. We describe the development of a real-time RT-PCR assay that amplifies a small target in the 5' nontranslated region upstream of the classical Rotbart enterovirus amplicon. The assay includes an RNA internal control and incorporates modified nucleotide chemistry
Scale-Invariant Random Spatial Networks
Real-world road networks have an approximate scale-invariance property; can
one devise mathematical models of random networks whose distributions are {\em
exactly} invariant under Euclidean scaling? This requires working in the
continuum plane. We introduce an axiomatization of a class of processes we call
{\em scale-invariant random spatial networks}, whose primitives are routes
between each pair of points in the plane. We prove that one concrete model,
based on minimum-time routes in a binary hierarchy of roads with different
speed limits, satisfies the axioms, and note informally that two other
constructions (based on Poisson line processes and on dynamic proximity graphs)
are expected also to satisfy the axioms. We initiate study of structure theory
and summary statistics for general processes in this class.Comment: 56 page
Autophagy gene expression profiling identifies a defective microtubule-associated protein light chain 3A mutant in cancer.
The cellular stress response autophagy has been implicated in various diseases including neuro-degeneration and cancer. The role of autophagy in cancer is not clearly understood and both tumour promoting and tumour suppressive effects of autophagy have been reported, which complicates the design of therapeutic strategies based on targeting the autophagy pathway. Here, we have systematically analyzed gene expression data for 47 autophagy genes for deletions, amplifications and mutations in various cancers. We found that several cancer types have frequent autophagy gene amplifications, whereas deletions are more frequent in prostate adenocarcinomas. Other cancer types such as glioblastoma and thyroid carcinoma show very few alterations in any of the 47 autophagy genes. Overall, individual autophagy core genes are altered at low frequency in cancer, suggesting that cancer cells require functional autophagy. Some autophagy genes show frequent single base mutations, such as members of the ULK family of protein kinases. Furthermore, we found hotspot mutations in the arginine-rich stretch in MAP1LC3A resulting in reduced cleavage of MAP1LC3A by ATG4B both in vitro and in vivo, suggesting a functional implication of this gene mutation in cancer development
Dynamic critical exponents of Swendsen-Wang and Wolff algorithms by nonequilibrium relaxation
With a nonequilibrium relaxation method, we calculate the dynamic critical
exponent z of the two-dimensional Ising model for the Swendsen-Wang and Wolff
algorithms. We examine dynamic relaxation processes following a quench from a
disordered or an ordered initial state to the critical temperature T_c, and
measure the exponential relaxation time of the system energy. For the
Swendsen-Wang algorithm with an ordered or a disordered initial state, and for
the Wolff algorithm with an ordered initial state, the exponential relaxation
time fits well to a logarithmic size dependence up to a lattice size L=8192.
For the Wolff algorithm with a disordered initial state, we obtain an effective
dynamic exponent z_exp=1.19(2) up to L=2048. For comparison, we also compute
the effective dynamic exponents through the integrated correlation times. In
addition, an exact result of the Swendsen-Wang dynamic spectrum of a
one-dimension Ising chain is derived.Comment: 13 pages, 6 figure
Quasi-fully dynamic algorithms for two-connectivity, cycle equivalence and related problems
Duplication-divergence model of protein interaction network
We show that the protein-protein interaction networks can be surprisingly
well described by a very simple evolution model of duplication and divergence.
The model exhibits a remarkably rich behavior depending on a single parameter,
the probability to retain a duplicated link during divergence. When this
parameter is large, the network growth is not self-averaging and an average
vertex degree increases algebraically. The lack of self-averaging results in a
great diversity of networks grown out of the same initial condition. For small
values of the link retention probability, the growth is self-averaging, the
average degree increases very slowly or tends to a constant, and a degree
distribution has a power-law tail.Comment: 8 pages, 13 figure
Universal Distributions for Growth Processes in 1+1 Dimensions and Random Matrices
We develop a scaling theory for KPZ growth in one dimension by a detailed
study of the polynuclear growth (PNG) model. In particular, we identify three
universal distributions for shape fluctuations and their dependence on the
macroscopic shape. These distribution functions are computed using the
partition function of Gaussian random matrices in a cosine potential.Comment: 4 pages, 3 figures, 1 table, RevTeX, revised version, accepted for
publication in PR
Condensation of the roots of real random polynomials on the real axis
We introduce a family of real random polynomials of degree n whose
coefficients a_k are symmetric independent Gaussian variables with variance
= e^{-k^\alpha}, indexed by a real \alpha \geq 0. We compute exactly
the mean number of real roots for large n. As \alpha is varied, one finds
three different phases. First, for 0 \leq \alpha \sim
(\frac{2}{\pi}) \log{n}. For 1 < \alpha < 2, there is an intermediate phase
where grows algebraically with a continuously varying exponent,
\sim \frac{2}{\pi} \sqrt{\frac{\alpha-1}{\alpha}} n^{\alpha/2}. And finally for
\alpha > 2, one finds a third phase where \sim n. This family of real
random polynomials thus exhibits a condensation of their roots on the real line
in the sense that, for large n, a finite fraction of their roots /n are
real. This condensation occurs via a localization of the real roots around the
values \pm \exp{[\frac{\alpha}{2}(k+{1/2})^{\alpha-1} ]}, 1 \ll k \leq n.Comment: 13 pages, 2 figure
Random tree growth by vertex splitting
We study a model of growing planar tree graphs where in each time step we
separate the tree into two components by splitting a vertex and then connect
the two pieces by inserting a new link between the daughter vertices. This
model generalises the preferential attachment model and Ford's -model
for phylogenetic trees. We develop a mean field theory for the vertex degree
distribution, prove that the mean field theory is exact in some special cases
and check that it agrees with numerical simulations in general. We calculate
various correlation functions and show that the intrinsic Hausdorff dimension
can vary from one to infinity, depending on the parameters of the model.Comment: 47 page
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