Redshifts and velocity dispersions of galaxy clusters in the horologium-reticulum supercluster

We present 118 new optical redshifts for galaxies in 12 clusters in the Horologium-Reticulum supercluster (HRS) of galaxies. For 76 galaxies, the data were obtained with the Dual Beam Spectrograph on the 2.3 m telescope of the Australian National University at Siding Spring Observatory. After combining 42 previously unpublished redshifts with our new sample, we determine mean redshifts and velocity dispersions for 13 clusters in which previous observational data were sparse. In 6 of the 13 clusters, the newly determined mean redshifts differ by more than 750 km s(-1) from the published values. In three clusters, A3047, A3109, and A3120, the redshift data indicate the presence of multiple components along the line of sight. The new cluster redshifts, when combined with other reliable mean redshifts for clusters in the HRS, are found to be distinctly bimodal. Furthermore, the two redshift components are consistent with the bimodal redshift distribution found for the intercluster galaxies in the HRS by Fleenor and coworkers.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the A phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. in this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Particle number emissions and source signatures of an industrial facility

The work presented was conducted within the scope of a larger study investigating impacts of the Stuart Oil Shale project, a facility operating to the north of the industrial city of Gladstone, Australia. The aims of the investigations were threefold: (a) the identification of the plant signatures in terms of particle size distributions in the submicrometer range (13-830 nm) through stack measurements, (b) exploring the applicability of these signatures in tracing the source contributions at locations of interest, at a distance from the plant, and (c) assessing the contribution of the plant to the total particle number concentration at locations of interest. The stack measurements conducted for three different conditions of plant operation showed that the particle size distributions were bimodal with average modal count median diameters (CMDs) of 24 (SD 4) and 52 (SD 9) nm. The average of all the particle size distributions recorded within the plant sector at a site located 4.5 km from the plant, over the sampling period when the plant was operating, also showed a bimodal distribution. The modal CMDs in this case were 27 and 50 nm, similar to those at the stack. This bimodal size distribution is distinct from the size distribution of the most common ambient anthropogenic emission source, which is vehicle emissions, and can be considered as a signature of this source. The average contribution of the plant (for plant sector winds) was estimated to be (10.0 +/- 3.8) x 10(2) particles cm(-3) and constituted approximately a 50% increase over the local particle ambient concentration for plant sector winds. This increase in particle number concentration compared to the local background concentration, while high compared to the clean environment concentration, is not significant when compared to concentrations generally encountered in the urban environment of Brisbane.