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

Simulation of quantum computers

We describe a simulation approach to study the functioning of Quantum Computer hardware. The latter is modeled by a collection of interacting spin-1/2 objects. The time evolution of this spin system maps one-to-one to a quantum program carried out by the Quantum Computer. Our simulation software consists of code that solves the time-dependent Schrodinger equation for a sequence of time-dependent Hamiltonians and a graphical user interface to program and control the solver. We present results of simulations on a 4-qubit Quantum Computer executing Grover's database search algorithm

Tracking competent host defence to chronic inflammation: An In Vivo model of peritonitis

Anti-microbial host defence is dependent on the rapid recruitment of inflammatory cells to the site of infection, the elimination of invading pathogens, and the efficient resolution of inflammation so as to minimise damage to the host. The peritoneal cavity provides an easily accessible and physiologically relevant system where the delicate balance of these processes may be studied. Here, we describe murine models of peritoneal inflammation that enable studies of both competent anti-microbial immunity and inflammation associated tissue damage as a consequence of recurrent bacterial challenge. The inflammatory hallmarks of these models reflect the clinical and molecular features of peritonitis episodes seen in renal failure patients on peritoneal dialysis. Development of these models relies on the preparation of a cell-free supernatant derived from an isolate of Staphylococcus epidermidis (termed SES). Intraperitoneal administration of SES induces a TLR2-driven acute inflammatory response that is characterised by an initial transient influx of neutrophils that are replaced by a more sustained recruitment of mononuclear cells and lymphocytes. Adaptation of this model using a repeated administration of SES allows investigations into the development of adaptive immunity and memory responses, and the hallmarks associated with tissue remodelling and fibrosis. These models are therefore clinically relevant and provide exciting opportunities to study both innate and adaptive immune responses in the control of bacterial infection and pathogenesis