Modelling galaxy stellar mass evolution from z~0.8 to today

We apply the empirical method built for z=0 in the previous work of Wang et al. to a higher redshift, to link galaxy stellar mass directly with its hosting dark matter halo mass at z~0.8. The relation of the galaxy stellar mass and the host halo mass M_infall is constrained by fitting both the stellar mass function and the correlation functions at different stellar mass intervals of the VVDS observation, where M_infall is the mass of the hosting halo at the time when the galaxy was last the central galaxy. We find that for low mass haloes, their residing central galaxies are less massive at high redshift than those at low redshift. For high mass haloes, central galaxies in these haloes at high redshift are a bit more massive than the galaxies at low redshift. Satellite galaxies are less massive at earlier times, for any given mass of hosting haloes. Fitting both the SDSS and VVDS observations simultaneously, we also propose a unified model of the M_stars-M_infall relation, which describes the evolution of central galaxy mass as a function of time. The stellar mass of a satellite galaxy is determined by the same M_stars-M_infall relation of central galaxies at the time when the galaxy is accreted. With these models, we study the amount of galaxy stellar mass increased from z~0.8 to the present day through galaxy mergers and star formation. Low mass galaxies gain their stellar masses from z~0.8 to z=0 mainly through star formation. For galaxies of higher mass, the increase of stellar mass solely through mergers from z=0.8 can make the massive galaxies a factor ~2 larger than observed at z=0. We can also predict stellar mass functions of redshifts up to z~3, and the results are consistent with the latest observations.Comment: 12 pages, 10 figures, accepted for publication in MNRA

The Reliability Function of Lossy Source-Channel Coding of Variable-Length Codes with Feedback

We consider transmission of discrete memoryless sources (DMSes) across discrete memoryless channels (DMCs) using variable-length lossy source-channel codes with feedback. The reliability function (optimum error exponent) is shown to be equal to $\max\{0, B(1-R(D)/C)\},$ where $R(D)$ is the rate-distortion function of the source, $B$ is the maximum relative entropy between output distributions of the DMC, and $C$ is the Shannon capacity of the channel. We show that, in this setting and in this asymptotic regime, separate source-channel coding is, in fact, optimal.Comment: Accepted to IEEE Transactions on Information Theory in Apr. 201

On the farm : an online virtual farmyard for pre-school and primary school children

University of Technology, Sydney. Faculty of Arts and Social Sciences.This thesis presents a detailed description of the research project that provided the basis for the development of a children website and the conceptual framework that was put into place as a basis for the project development. The website is targeted at young children aged four to seven and is intended to facilitate children's learning about the ecology of farm animals, provide an understanding of farm animals' life cycles and to develop in young children an awareness of the role of farm animals in the production of our daily food. The data based on surveys and interviews with children and teachers in two countries, Australia and Taiwan, are analysed on the first chapter and use to inform the project development. This section also includes a critical survey of the relevant literature relating to appropriate pedagogical frameworks, children's computer literacy and a review of current online resources about farm animals targeting young children. The examination of the relevant educational theory is presented as a foundation for the development process and a critical review of currently available children's learning resources about farm animals is also presented in order to establish best practice design principles for the website which is final called ON THE FARM. This thesis traces the development of the project from these theoretical bases through to its implementation and initial evaluation. As well as a detailed description of the development process and the provision of an initial evaluation of the project, this thesis also suggests that the learning model of ON THE FARM can form the basis for the development of high quality applications in the future. The application of this data could be applied to new technologies such as Augmented Reality (AR) and could include survey results showing the entertainment preferences of young people and a social-constructivist educational framework that was developed for the project. The final product provides a benchmark reference for the creation of online learning materials for young children, and has a rich potential for future development

Integrated signaling pathway and gene expression regulatory model to dissect dynamics of <em>Escherichia coli </em>challenged mammary epithelial cells

AbstractCells transform external stimuli, through the activation of signaling pathways, which in turn activate gene regulatory networks, in gene expression. As more omics data are generated from experiments, eliciting the integrated relationship between the external stimuli, the signaling process in the cell and the subsequent gene expression is a major challenge in systems biology. The complex system of non-linear dynamic protein interactions in signaling pathways and gene networks regulates gene expression.The complexity and non-linear aspects have resulted in the study of the signaling pathway or the gene network regulation in isolation. However, this limits the analysis of the interaction between the two components and the identification of the source of the mechanism differentiating the gene expression profiles. Here, we present a study of a model of the combined signaling pathway and gene network to highlight the importance of integrated modeling.Based on the experimental findings we developed a compartmental model and conducted several simulation experiments. The model simulates the mRNA expression of three different cytokines (RANTES, IL8 and TNFα) regulated by the transcription factor NFκB in mammary epithelial cells challenged with E. coli. The analysis of the gene network regulation identifies a lack of robustness and therefore sensitivity for the transcription factor regulation. However, analysis of the integrated signaling and gene network regulation model reveals distinctly different underlying mechanisms in the signaling pathway responsible for the variation between the three cytokine's mRNA expression levels. Our key findings reveal the importance of integrating the signaling pathway and gene expression dynamics in modeling. Modeling infers valid research questions which need to be verified experimentally and can assist in the design of future biological experiments