3,589 research outputs found
Resilience of Complex Networks to Random Breakdown
Using Monte Carlo simulations we calculate , the fraction of nodes which
are randomly removed before global connectivity is lost, for networks with
scale-free and bimodal degree distributions. Our results differ with the
results predicted by an equation for proposed by Cohen, et al. We discuss
the reasons for this disagreement and clarify the domain for which the proposed
equation is valid
Percolation Critical Exponents in Scale-Free Networks
We study the behavior of scale-free networks, having connectivity
distribution P(k) k^-a, close to the percolation threshold. We show that for
networks with 3<a<4, known to undergo a transition at a finite threshold of
dilution, the critical exponents are different than the expected mean-field
values of regular percolation in infinite dimensions. Networks with 2<a<3
possess only a percolative phase. Nevertheless, we show that in this case
percolation critical exponents are well defined, near the limit of extreme
dilution (where all sites are removed), and that also then the exponents bear a
strong a-dependence. The regular mean-field values are recovered only for a>4.Comment: Latex, 4 page
Gwydir Shire Council background report: Deliberative panel and local government services 2015
Local Government Growing Regional Australia
This report identifies the factors contributing to building strong sustainable regional capitals and regions and local government's role in regional development
Oocyte cryopreservation as an adjunct to the assisted reproductive technologies
The document attached has been archived with permission from the editor of the Medical Journal of Australia. An external link to the publisher’s copy is included. See page 2 of PDF for this item.Keith L Harrison, Michelle T Lane, Jeremy C Osborn, Christine A Kirby, Regan Jeffrey, John H Esler and David Mollo
Long-Term Capital Gains Tax Strategies: Correlated Protective Put Strategy
A reduction in the long-term capital gains tax rate provides investors with new strategies to minimize taxes and protect investment gains. One such opportunity exists when an investor decides to sell a profitable stock with a holding period of less than one-year, resulting in short-term ordinary taxes. The investor would find it more beneficial to sell the stock after one-year lapses, resulting in lower long-term capital gain taxes, although the longer holding period exposes the investor to the uncertainty of stock price movement. A strategy to extend the holding period without excess risk would be to use the protective put option strategy, sometimes referred to as “investment insurance”. The strategy involves the purchase of a put option to protect against the possible decline in the stock price, to take advantage of the lower long-term capital gains tax rate, and to preserve the upside potential of the stock. Pursuant to IRS Publication 550, the IRS does not allow the use of a protective put to extend the holding period on the same security considered for sale. Since the IRS does not allow a direct protective put hedge, this study will explore an alternative strategy involving the purchase of a put on a highly correlated investment to extend the holding period to recognize lower capital gains tax rates. The paper presents example situations when an investor benefits from utilizing the correlated protective put option strategy
Generalized percolation in random directed networks
We develop a general theory for percolation in directed random networks with
arbitrary two point correlations and bidirectional edges, that is, edges
pointing in both directions simultaneously. These two ingredients alter the
previously known scenario and open new views and perspectives on percolation
phenomena. Equations for the percolation threshold and the sizes of the giant
components are derived in the most general case. We also present simulation
results for a particular example of uncorrelated network with bidirectional
edges confirming the theoretical predictions
Predicting the size and probability of epidemics in a population with heterogeneous infectiousness and susceptibility
We analytically address disease outbreaks in large, random networks with
heterogeneous infectivity and susceptibility. The transmissibility
(the probability that infection of causes infection of ) depends on the
infectivity of and the susceptibility of . Initially a single node is
infected, following which a large-scale epidemic may or may not occur. We use a
generating function approach to study how heterogeneity affects the probability
that an epidemic occurs and, if one occurs, its attack rate (the fraction
infected). For fixed average transmissibility, we find upper and lower bounds
on these. An epidemic is most likely if infectivity is homogeneous and least
likely if the variance of infectivity is maximized. Similarly, the attack rate
is largest if susceptibility is homogeneous and smallest if the variance is
maximized. We further show that heterogeneity in infectious period is
important, contrary to assumptions of previous studies. We confirm our
theoretical predictions by simulation. Our results have implications for
control strategy design and identification of populations at higher risk from
an epidemic.Comment: 5 pages, 3 figures. Submitted to Physical Review Letter
Clustering in complex networks. II. Percolation properties
The percolation properties of clustered networks are analyzed in detail. In
the case of weak clustering, we present an analytical approach that allows to
find the critical threshold and the size of the giant component. Numerical
simulations confirm the accuracy of our results. In more general terms, we show
that weak clustering hinders the onset of the giant component whereas strong
clustering favors its appearance. This is a direct consequence of the
differences in the -core structure of the networks, which are found to be
totally different depending on the level of clustering. An empirical analysis
of a real social network confirms our predictions.Comment: Updated reference lis
Saturation Mutagenesis of Lysine 12 Leads to the Identification of Derivatives of Nisin A with Enhanced Antimicrobial Activity
peer-reviewedIt is becoming increasingly apparent that innovations from the “golden age” of antibiotics are becoming ineffective, resulting in a pressing need for novel therapeutics. The bacteriocin family of antimicrobial peptides has attracted much attention in recent years as a source of potential alternatives. The most intensively studied bacteriocin is nisin, a broad spectrum lantibiotic that inhibits Gram-positive bacteria including important food pathogens and clinically relevant antibiotic resistant bacteria. Nisin is gene-encoded and, as such, is amenable to peptide bioengineering, facilitating the generation of novel derivatives that can be screened for desirable properties. It was to this end that we used a site-saturation mutagenesis approach to create a bank of producers of nisin A derivatives that differ with respect to the identity of residue 12 (normally lysine; K12). A number of these producers exhibited enhanced bioactivity and the nisin A K12A producer was deemed of greatest interest. Subsequent investigations with the purified antimicrobial highlighted the enhanced specific activity of this modified nisin against representative target strains from the genera Streptococcus, Bacillus, Lactococcus, Enterococcus and Staphylococcus.This work was supported by the Irish Government under the National Development Plan; by the Irish Research Council for Science Engineering and Technology (IRCSET); by Enterprise Ireland; and by Science Foundation Ireland (SFI), through the Alimentary Pharmabiotic Centre (APC) at University College Cork, Ireland, which is supported by the SFI-funded Centre for Science, Engineering and Technology (SFI-CSET) and provided P.D.C., C.H. and R.P.R. with SFI Principal Investigator funding
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