56 research outputs found
Extremely red radio galaxies
At least half the radio galaxies at z>1 in the 7C Redshift Survey have
extremely red colours (R-K>5), consistent with stellar populations which formed
at high redshift (z>5). We discuss the implications of this for the evolution
of massive galaxies in general and for the fraction of near-IR-selected EROs
which host AGN, a result which is now being tested by deep, hard X-ray surveys.
The conclusion is that many massive galaxies undergo at least two active
phases: one at z~5 when the black hole and stellar bulge formed and another at
z~1-2 when activity is triggered by an event such as an interaction or merger.Comment: 6 pages, 2 figures, to appear in the proceedings of the workshop on
"QSO hosts and their environments", IAA, Granada, 10-12 Jan 2001, Ed. I.
Marque
Gipc3 mutations associated with audiogenic seizures and sensorineural hearing loss in mouse and human
Sensorineural hearing loss affects the quality of life and communication of millions of people, but the underlying molecular mechanisms remain elusive. Here, we identify mutations in Gipc3 underlying progressive sensorineural hearing loss (age-related hearing loss 5, ahl5) and audiogenic seizures (juvenile audiogenic monogenic seizure 1, jams1) in mice and autosomal recessive deafness DFNB15 and DFNB95 in humans. Gipc3 localizes to inner ear sensory hair cells and spiral ganglion. A missense mutation in the PDZ domain has an attenuating effect on mechanotransduction and the acquisition of mature inner hair cell potassium currents. Magnitude and temporal progression of wave I amplitude of afferent neurons correlate with susceptibility and resistance to audiogenic seizures. The Gipc3343A allele disrupts the structure of the stereocilia bundle and affects long-term function of auditory hair cells and spiral ganglion neurons. Our study suggests a pivotal role of Gipc3 in acoustic signal acquisition and propagation in cochlear hair cells
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
Euclid preparation: V. Predicted yield of redshift 7<z<9 quasars from the wide survey
We provide predictions of the yield of 7 < z < 9 quasars from the Euclid wide survey, updating the calculation presented in the
Euclid Red Book in several ways. We account for revisions to the Euclid near-infrared filter wavelengths; we adopt steeper rates
of decline of the quasar luminosity function (QLF; Φ) with redshift, Φ ∝ 10k(z−6)
, k = −0.72, and a further steeper rate of decline,
k = −0.92; we use better models of the contaminating populations (MLT dwarfs and compact early-type galaxies); and we make use
of an improved Bayesian selection method, compared to the colour cuts used for the Red Book calculation, allowing the identification
of fainter quasars, down to JAB ∼ 23. Quasars at z > 8 may be selected from Euclid OY JH photometry alone, but selection over
the redshift interval 7 < z < 8 is greatly improved by the addition of z-band data from, e.g., Pan-STARRS and LSST. We calculate
predicted quasar yields for the assumed values of the rate of decline of the QLF beyond z = 6. If the decline of the QLF accelerates
beyond z = 6, with k = −0.92, Euclid should nevertheless find over 100 quasars with 7.0 < z < 7.5, and ∼ 25 quasars beyond the
current record of z = 7.5, including ∼ 8 beyond z = 8.0. The first Euclid quasars at z > 7.5 should be found in the DR1 data release,
expected in 2024. It will be possible to determine the bright-end slope of the QLF, 7 < z < 8, M1450 < −25, using 8 m class telescopes
to confirm candidates, but follow-up with JWST or E-ELT will be required to measure the faint-end slope. Contamination of the
candidate lists is predicted to be modest even at JAB ∼ 23. The precision with which k can be determined over 7 < z < 8 depends on
the value of k, but assuming k = −0.72 it can be measured to a 1σ uncertainty of 0.07
Euclid preparation: V. Predicted yield of redshift 7 < z < 9 quasars from the wide survey
We provide predictions of the yield of 7 8 may be selected from Euclid OY JH photometry alone, but selection over the redshift interval 7 7.5 should be found in the DR1 data release, expected in 2024. It will be possible to determine the bright-end slope of the QLF, 7 < z < 8, M1450 < −25, using 8 m class telescopes to confirm candidates, but follow-up with JWST or E-ELT will be required to measure the faint-end slope. Contamination of the candidate lists is predicted to be modest even at JAB ∼ 23. The precision with which k can be determined over 7 < z < 8 depends on the value of k, but assuming k = −0.72 it can be measured to a 1σ uncertainty of 0.07
Increasing frailty is associated with higher prevalence and reduced recognition of delirium in older hospitalised inpatients: results of a multi-centre study
Purpose Delirium is a neuropsychiatric disorder delineated by an acute change in cognition, attention, and consciousness. It is common, particularly in older adults, but poorly recognised. Frailty is the accumulation of deficits conferring an increased risk of adverse outcomes. We set out to determine how severity of frailty, as measured using the CFS, affected delirium rates, and recognition in hospitalised older people in the United Kingdom. Methods Adults over 65 years were included in an observational multi-centre audit across UK hospitals, two prospective rounds, and one retrospective note review. Clinical Frailty Scale (CFS), delirium status, and 30-day outcomes were recorded. Results The overall prevalence of delirium was 16.3% (483). Patients with delirium were more frail than patients without delirium (median CFS 6 vs 4). The risk of delirium was greater with increasing frailty [OR 2.9 (1.8–4.6) in CFS 4 vs 1–3; OR 12.4 (6.2–24.5) in CFS 8 vs 1–3]. Higher CFS was associated with reduced recognition of delirium (OR of 0.7 (0.3–1.9) in CFS 4 compared to 0.2 (0.1–0.7) in CFS 8). These risks were both independent of age and dementia. Conclusion We have demonstrated an incremental increase in risk of delirium with increasing frailty. This has important clinical implications, suggesting that frailty may provide a more nuanced measure of vulnerability to delirium and poor outcomes. However, the most frail patients are least likely to have their delirium diagnosed and there is a significant lack of research into the underlying pathophysiology of both of these common geriatric syndromes
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