Resilience of Complex Networks to Random Breakdown

Using Monte Carlo simulations we calculate $f_c$, 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 $f_c$ 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

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The document attached has been archived with permission from the editor of the Medical Journal of Australia.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.&nbsp; 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.&nbsp; 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.&nbsp; A strategy to extend the holding period without excess risk would be to use the protective put option strategy, sometimes referred to as &ldquo;investment insurance&rdquo;.&nbsp; 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.&nbsp; 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.&nbsp; 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.&nbsp; 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 $T_{uv}$ (the probability that infection of $u$ causes infection of $v$) depends on the infectivity of $u$ and the susceptibility of $v$. 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 $k$-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