No-switching quantum key distribution using broadband modulated coherent light

We realize an end-to-end no-switching quantum key distribution protocol using continuous-wave coherent light. We encode weak broadband Gaussian modulations onto the amplitude and phase quadratures of light beams. Our no-switching protocol achieves high secret key rate via a post-selection protocol that utilizes both quadrature information simultaneously. We establish a secret key rate of 25 Mbits/s for a lossless channel and 1 kbit/s for 90% channel loss, per 17 MHz of detected bandwidth, assuming individual Gaussian eavesdropping attacks. Since our scheme is truly broadband, it can potentially deliver orders of magnitude higher key rates by extending the encoding bandwidth with higher-end telecommunication technology.

TRAP220 is modulated by the antineoplastic agent 6-Mercaptopurine, and mediates the activation of the NR4A subgroup of nuclear receptors

The NR4A1-3 (Nur77, NURR1 and NOR-1) subfamily of nuclear hormone receptors (NRs) has been implicated in Parkinson's disease, schizophrenia, manic depression, atherogenesis, Alzheimer's disease, rheumatoid arthritis, cancer and apoptosis. This has driven investigations into the mechanism of action, and the identification of small molecule regulators, that may provide the platform for pharmaceutical and therapeutic exploitation. Recently, we found that the purine antimetabolite 6-Mercaptopurine (6-MP), which is widely used as an anti-neoplastic and anti-inflammatory drug, modulated the NR4A1-3 subfamily. Interestingly, the agonist-mediated activation did not involve modulation of primary coactivators' (e.g. p300 and SRC-2/GRIP-1) activity and/or recruitment. However, the role of the subsequently recruited coactivators, for example CARM-1 and TRAP220, in 6-MP-mediated activation of the NR4A1-3 subfamily remains obscure. In this study we demonstrate that 6-MP modulates the activity of the coactivator TRAP220 in a dose-dependent manner. Moreover, we demonstrate that TRAP220 potentiates NOR-1-mediated transactivation, and interacts with the NR4A1-3 subgroup in an AF-1-dependent manner in a cellular context. The region of TRAP220 that mediated 6-MP activation and NR4A interaction was delimited to amino acids 1-800, and operates independently of the critical PKC and PKA phosphorylation sites. Interestingly, TRAP220 expression does not increase the relative induction by 6-MP, however the absolute level of NOR-1-mediated trans-activation is increased. This study demonstrates that 6-MP modulates the activity of the NR4A subgroup, and the coactivator TRAP220.

Subgeometric rates of convergence for a class of continuous-time Markov process

Let (Phi(t))(t is an element of R+) be a Harris ergodic continuous-time Markov process on a general state space, with invariant probability measure pi. We investigate the rates of convergence of the transition function P-t(x, (.)) to pi; specifically, we find conditions under which r(t) vertical bar vertical bar P-t (x, (.)) - pi vertical bar vertical bar -> 0 as t -> infinity, for suitable subgeometric rate functions r(t), where vertical bar vertical bar - vertical bar vertical bar denotes the usual total variation norm for a signed measure. We derive sufficient conditions for the convergence to hold, in terms of the existence of suitable points on which the first hitting time moments are bounded. In particular, for stochastically ordered Markov processes, explicit bounds on subgeometric rates of convergence are obtained. These results are illustrated in several examples.

Uniqueness criteria for continuous-time Markov chains with general transition structures

We derive necessary and sufficient conditions for the existence of bounded or summable solutions to systems of linear equations associated with Markov chains. This substantially extends a famous result of G. E. H. Reuter, which provides a convenient means of checking various uniqueness criteria for birth-death processes. Our result allows chains with much more general transition structures to be accommodated. One application is to give a new proof of an important result of M. F. Chen concerning upwardly skip-free processes. We then use our generalization of Reuter's lemma to prove new results for downwardly skip-free chains, such as the Markov branching process and several of its many generalizations. This permits us to establish uniqueness criteria for several models, including the general birth, death, and catastrophe process, extended branching processes, and asymptotic birth-death processes, the latter being neither upwardly skip-free nor downwardly skip-free.

Pseudomonas aeruginosa fimL regulates multiple virulence functions by intersecting with Vfr-modulated pathways

Virulence of Pseudomonas aeruginosa involves the co-ordinate expression of a range of factors including type IV pili (tfp), the type III secretion system (TTSS) and quorum sensing. Tfp are required for twitching motility, efficient biofilm formation, and for adhesion and type III secretion (TTS)-mediated damage to mammalian cells. We describe a novel gene (fimL) that is required for tfp biogenesis and function, for TTS and for normal biofilm development in P. aeruginosa. The predicted product of fimL is homologous to the N-terminal domain of ChpA, except that its putative histidine and threonine phosphotransfer sites have been replaced with glutamine. fimL mutants resemble vfr mutants in many aspects including increased autolysis, reduced levels of surface-assembled tfp and diminished production of type III secreted effectors. Expression of vfr in trans can complement fimL mutants. vfr transcription and production is reduced in fimL mutants whereas cAMP levels are unaffected. Deletion and insertion mutants of fimL frequently revert to wild-type phenotypes suggesting that an extragenic suppressor mutation is able to overcome the loss of fimL. vfr transcription and production, as well as cAMP levels, are elevated in these revertants, while Pseudomonas quinolone signal (PQS) production is reduced. These results suggest that the site(s) of spontaneous mutation is in a gene(s) which lies upstream of vfr transcription, cAMP, production, and PQS synthesis. Our studies indicate that Vfr and FimL are components of intersecting pathways that control twitching motility, TTSS and autolysis in P. aeruginosa.

Searching for convergence in phylogenetic Markov chain Monte Carlo

Markov chain Monte Carlo (MCMC) is a methodology that is gaining widespread use in the phylogenetics community and is central to phylogenetic software packages such as MrBayes. An important issue for users of MCMC methods is how to select appropriate values for adjustable parameters such as the length of the Markov chain or chains, the sampling density, the proposal mechanism, and, if Metropolis-coupled MCMC is being used, the number of heated chains and their temperatures. Although some parameter settings have been examined in detail in the literature, others are frequently chosen with more regard to computational time or personal experience with other data sets. Such choices may lead to inadequate sampling of tree space or an inefficient use of computational resources. We performed a detailed study of convergence and mixing for 70 randomly selected, putatively orthologous protein sets with different sizes and taxonomic compositions. Replicated runs from multiple random starting points permit a more rigorous assessment of convergence, and we developed two novel statistics, delta and epsilon, for this purpose. Although likelihood values invariably stabilized quickly, adequate sampling of the posterior distribution of tree topologies took considerably longer. Our results suggest that multimodality is common for data sets with 30 or more taxa and that this results in slow convergence and mixing. However, we also found that the pragmatic approach of combining data from several short, replicated runs into a metachain to estimate bipartition posterior probabilities provided good approximations, and that such estimates were no worse in approximating a reference posterior distribution than those obtained using a single long run of the same length as the metachain. Precision appears to be best when heated Markov chains have low temperatures, whereas chains with high temperatures appear to sample trees with high posterior probabilities only rarely. [Bayesian phylogenetic inference; heating parameter; Markov chain Monte Carlo; replicated chains.]

Sensor scheduling in electronic support using Markov chains

In electronic support, receivers must maintain surveillance over the very wide portion of the electromagnetic spectrum in which threat emitters operate. A common approach is to use a receiver with a relatively narrow bandwidth that sweeps its centre frequency over the threat bandwidth to search for emitters. The sequence and timing of changes in the centre frequency constitute a search strategy. The search can be expedited, if there is intelligence about the operational parameters of the emitters that are likely to be found. However, it can happen that the intelligence is deficient, untrustworthy or absent. In this case, what is the best search strategy to use? A random search strategy based on a continuous-time Markov chain (CTMC) is proposed. When the search is conducted for emitters with a periodic scan, it is shown that there is an optimal configuration for the CTMC. It is optimal in the sense that the expected time to intercept an emitter approaches linearity most quickly with respect to the emitter's scan period. A fast and smooth approach to linearity is important, as other strategies can exhibit considerable and abrupt variations in the intercept time as a function of scan period. In theory and numerical examples, the optimum CTMC strategy is compared with other strategies to demonstrate its superior properties.

Analysing time series structure with hidden Markov models

This paper consides the problem of extracting the relationships between two time series in a non-linear non-stationary environment with Hidden Markov Models (HMMs). We describe an algorithm which is capable of identifying associations between variables. The method is applied both to synthetic data and real data. We show that HMMs are capable of modelling the oil drilling process and that they outperform existing methods.

Time delay estimation with hidden Markov models

Most traditional methods for extracting the relationships between two time series are based on cross-correlation. In a non-linear non-stationary environment, these techniques are not sufficient. We show in this paper how to use hidden Markov models to identify the lag (or delay) between different variables for such data. Adopting an information-theoretic approach, we develop a procedure for training HMMs to maximise the mutual information (MMI) between delayed time series. The method is used to model the oil drilling process. We show that cross-correlation gives no information and that the MMI approach outperforms maximum likelihood.