Shedding Light on the Galaxy Luminosity Function

From as early as the 1930s, astronomers have tried to quantify the statistical nature of the evolution and large-scale structure of galaxies by studying their luminosity distribution as a function of redshift - known as the galaxy luminosity function (LF). Accurately constructing the LF remains a popular and yet tricky pursuit in modern observational cosmology where the presence of observational selection effects due to e.g. detection thresholds in apparent magnitude, colour, surface brightness or some combination thereof can render any given galaxy survey incomplete and thus introduce bias into the LF. Over the last seventy years there have been numerous sophisticated statistical approaches devised to tackle these issues; all have advantages -- but not one is perfect. This review takes a broad historical look at the key statistical tools that have been developed over this period, discussing their relative merits and highlighting any significant extensions and modifications. In addition, the more generalised methods that have emerged within the last few years are examined. These methods propose a more rigorous statistical framework within which to determine the LF compared to some of the more traditional methods. I also look at how photometric redshift estimations are being incorporated into the LF methodology as well as considering the construction of bivariate LFs. Finally, I review the ongoing development of completeness estimators which test some of the fundamental assumptions going into LF estimators and can be powerful probes of any residual systematic effects inherent magnitude-redshift data.Comment: 95 pages, 23 figures, 3 tables. Now published in The Astronomy & Astrophysics Review. This version: bring in line with A&AR format requirements, also minor typo corrections made, additional citations and higher rez images adde

The importance of influenza prevention for public health

Annual epidemics of seasonal (inter-pandemic) influenza represent a significant burden on society in terms of morbidity, mortality, hospitalizations and lost working time. The impact of influenza depends on a mix of direct and indirect effects and is not easy to assess. Nevertheless there is a consensus in considering influenza prevention and mitigation high priorities for public health. We review the available evidence to assess the impact of influenza prevention focusing especially on vaccines and immunization strategies

Renal and vascular glutathione S-transferase mu is not affected by pharmacological intervention to reduce systolic blood pressure

Background Our previous studies demonstrated reduced rat glutathione S-transferase m type 1 (Gstm1) expression in stroke-prone spontaneously hypertensive rats (SHRSPs), when compared with the normotensive Wistar-Kyoto rat. Methods This study investigated the effects of angiotensin II type 1 receptor blocker (ARB) and a diuretic/vasodilator combination on the expression levels of rat Gstm1 and other Gstm isoforms. Results Antihypertensive treatments of young and mature SHRSPs with an ARB and a diuretic/vasodilator combination improved SBP but did not affect the expression levels of Gstm1. Although Gstm1 is a member of a family of highly homologous genes, with the exception of Gstm2, there was no evidence for compensatory increase in expression of other Gstm isoforms. In contrast, we observed reduced expression of several other Gstm isoforms in untreated SHRSPs. Untreated SHRSPs demonstrated increased renal and vascular oxidative stress, both of which were not significantly affected by the antihypertensive treatments. Untreated SHRSPs scored significantly higher when assessed for renal histopathological damage, and this was improved by antihypertensive treatments. Conclusion These results suggest that reduced Gstm1 expression in SHRSPs is due to strain-dependent genetic abnormalities, playing a causative role in the development of hypertension, probably through oxidative stress pathway. Renal changes occur as a consequence of increased blood pressure and can be improved when treated with antihypertensive drugs. In silico comparative genome analysis combined with expression studies in rat and human vascular tissue revealed that there are possible four human homologues (GSTM1, GSTM2, GSTM4 and GSTM5) for rat Gstm1