Practical applications of interactive voice technologies: Some accomplishments and prospects

A technology assessment of the application of computers and electronics to complex systems is presented. Three existing systems which utilize voice technology (speech recognition and speech generation) are described. Future directions in voice technology are also described

β-adrenergic receptor activation in immortalized human urothelial cells stimulates inflammatory responses by PKA-independent mechanisms

BACKGROUND: Interstitial cystitis (IC) is a debilitating disease characterized by chronic inflammation of the urinary bladder, yet specific cellular mechanisms of inflammation in IC are largely unknown. Multiple lines of evidence suggest that β-adrenergic receptor (AR) signaling is increased in the inflamed urothelium, however the precise effects of these urothelial cell signals have not been studied. In order to better elucidate the AR signaling mechanisms of inflammation associated with IC, we have examined the effects of β-AR stimulation in an immortalized human urothelial cell line (UROtsa). For these studies, UROtsa cells were treated with effective concentrations of the selective β-AR agonist isoproterenol, in the absence or presence of selective inhibitors of protein kinase A (PKA). Cell lysates were analyzed by radioimmunoassay for generation of cAMP or by Western blotting for induction of protein products associated with inflammatory responses. RESULTS: Radioligand binding demonstrated the presence of β-ARs on human urothelial UROtsa cell membranes. Stimulating UROtsa cells with isoproterenol led to concentration-dependent increases of cAMP production that could be inhibited by pretreatment with a blocking concentration of the selective β-AR antagonist propranolol. In addition, isoproterenol activation of these same cells led to significant increases in the amount of phosphorylated extracellular signal-regulated kinase (pERK), inducible nitric oxide synthase (iNOS) and the induced form of cyclooxygenase (COX-2) when compared to control. Moreover, preincubation of UROtsa cells with the selective PKA inhibitors H-89 or Rp-cAMPs did not diminish this isoproterenol mediated phosphorylation of ERK or production of iNOS and COX-2. CONCLUSION: Functional β-ARs expressed on human urothelial UROtsa cell membranes increase the generation of cAMP and production of protein products associated with inflammation when activated by the selective β-AR agonist isoproterenol. However, the increased production of iNOS and COX-2 by isoproterenol is not blocked when UROtsa cells are preincubated with inhibitors of PKA. Therefore, UROtsa cell β-AR activation significantly increases the amount of iNOS and COX-2 produced by a PKA-independent mechanism. Consequently, this immortalized human urothelial cell line can be useful in characterizing potential AR signaling mechanisms associated with chronic inflammatory diseases of the bladder

The Cauchy convergence of T and P-approximant templates for test-mass Kerr binary systems

In this work we examine the Cauchy convergence of both post-Newtonian (T-approximant) and re-summed post-Newtonian (P-approximant) templates for the case of a test-mass orbiting a Kerr black hole along a circular equatorial orbit. The Cauchy criterion demands that the inner product between the $n$ and $n+1$ order approximation approaches unity, as we increase the order of approximation. In previous works, it has been shown that we achieve greater fitting factors and better parameter estimation using the P-approximant templates for both Schwarzschild and Kerr black holes. In this work, we show that the P-approximant templates also display a faster Cauchy convergence making them a superior template to the standard post-Newtonian templates.Comment: 5 pages, Replaced with shortened published versio

Experiments with a machine-centric approach to realise distributed emergent software systems

Modern distributed systems are exposed to constant changes in their operating environment, leading to high uncertainty. Self-adaptive and self-organising approaches have become a popular solution for runtime reactivity to this uncertainty. However, these approaches use predefined, expertly-crafted policies or models, constructed at design-time, to guide system (re)configuration. They are human-centric, making modelling or policy-writing difficult to scale to increasingly complex systems; and are inflexible in their ability to deal with the unexpected at runtime (e.g. conditions not captured in a policy). We argue for a machine-centric approach to this problem, in which the desired behaviour is autonomously learned and emerges at runtime from a large pool of small alternative components, as a continuous reaction to the observed behaviour of the software and the characteristics of its operating environment. We demonstrate our principles in the context of data-centre software, showing that our approach is able to autonomously coordinate a distributed infrastructure composed of emergent web servers and a load balancer. Our initial results validate our approach, showing autonomous convergence on an optimal configuration, and also highlight the open challenges in providing fully machine-led distributed emergent software systems

Restrictions and Stability of Time-Delayed Dynamical Networks

This paper deals with the global stability of time-delayed dynamical networks. We show that for a time-delayed dynamical network with non-distributed delays the network and the corresponding non-delayed network are both either globally stable or unstable. We demonstrate that this may not be the case if the network's delays are distributed. The main tool in our analysis is a new procedure of dynamical network restrictions. This procedure is useful in that it allows for improved estimates of a dynamical network's global stability. Moreover, it is a computationally simpler and much more effective means of analyzing the stability of dynamical networks than the procedure of isospectral network expansions introduced in [Isospectral graph transformations, spectral equivalence, and global stability of dynamical networks. Nonlinearity, 25 (2012) 211-254]. The effectiveness of our approach is illustrated by applications to various classes of Cohen-Grossberg neural networks.Comment: 32 pages, 9 figure