Radio loud AGN and the L_X - \sigma relation of galaxy groups and clusters

We use the ROSAT All-Sky Survey to study the X-ray properties of a sample of 625 groups and clusters of galaxies selected from the Sloan Digital Sky Survey. We stack clusters with similar velocity dispersions and investigate whether their average X-ray luminosities and surface brightness profiles vary with the radio activity level of their central galaxies. We find that at a given value of $\sigma$, clusters with a central radio AGN have more concentrated X-ray surface brightness profiles, larger central galaxy masses, and higher X-ray luminosities than clusters with radio-quiet central galaxies. The enhancement in X-ray luminosity is more than a factor of two, is detected with better than 6$\sigma$ significance, and cannot be explained by X-ray emission from the radio AGN itself. This difference is largely due to a subpopulation of radio-quiet, high velocity dispersion clusters with low mass central galaxies. These clusters are underluminous at X-ray wavelengths when compared to otherwise similar clusters where the central galaxy is radio-loud, more massive, or both.Comment: Section 5.2 is updated, more discussion on the dependence of L_X - \sigma relation on the stellar mass of BCG

Systematic study of X-ray Cavities in the brightest galaxy of the Draco Constellation NGC 6338

We present results based on the systematic analysis of currently available Chandra archive data on the brightest galaxy in the Draco constellation NGC 6338, in order to investigate the properties of the X-ray cavities. In the central ~6 kpc, at least a two and possibly three, X-ray cavities are evident. All these cavities are roughly of ellipsoidal shapes and show a decrement in the surface brightness of several tens of percent. In addition to these cavities, a set of X-ray bright filaments are also noticed which are spatially coincident with the H{\alpha} filaments over an extent of 15 kpc. The H{\alpha} emission line filaments are perpendicular to the X- ray cavities. Spectroscopic analysis of the hot gas in the filaments and cavities reveal that the X-ray filaments are cooler than the gas contained in the cavities. The emission line ratios and the extended, asymmetric nature of the H{\alpha} emission line filaments seen in this system require a harder ionizing source than that produced by star formation and/or young, massive stars. Radio emission maps derived from the analysis of 1.4 GHz VLA FIRST survey data failed to show any association of these X-ray cavities with radio jets, however, the cavities are filled by radio emission. The total power of the cavities is 17\times 1042 erg s-1 and the ratio of the radio luminosity to cavity power is ~ 10-4, implying that most of the jet power is mechanical.Comment: The paper contains 12 figures and 3 tables, Accepted 2011 December 7 for publication in MNRA

The Use of Research Evidence in Public Health Decision Making Processes: Systematic Review

BACKGROUND: The use of research evidence to underpin public health policy is strongly promoted. However, its implementation has not been straightforward. The objectives of this systematic review were to synthesise empirical evidence on the use of research evidence by public health decision makers in settings with universal health care systems. METHODS: To locate eligible studies, 13 bibliographic databases were screened, organisational websites were scanned, key informants were contacted and bibliographies of included studies were scrutinised. Two reviewers independently assessed studies for inclusion, extracted data and assessed methodological quality. Data were synthesised as a narrative review. FINDINGS: 18 studies were included: 15 qualitative studies, and three surveys. Their methodological quality was mixed. They were set in a range of country and decision making settings. Study participants included 1063 public health decision makers, 72 researchers, and 174 with overlapping roles. Decision making processes varied widely between settings, and were viewed differently by key players. A range of research evidence was accessed. However, there was no reliable evidence on the extent of its use. Its impact was often indirect, competing with other influences. Barriers to the use of research evidence included: decision makers' perceptions of research evidence; the gulf between researchers and decision makers; the culture of decision making; competing influences on decision making; and practical constraints. Suggested (but largely untested) ways of overcoming these barriers included: research targeted at the needs of decision makers; research clearly highlighting key messages; and capacity building. There was little evidence on the role of research evidence in decision making to reduce inequalities. CONCLUSIONS: To more effectively implement research informed public health policy, action is required by decision makers and researchers to address the barriers identified in this systematic review. There is an urgent need for evidence to support the use of research evidence to inform public health decision making to reduce inequalities

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease