55 research outputs found
Galaxy Cluster Pressure Profiles as Determined by Sunyaev Zel'dovich Effect Observations with MUSTANG and Bolocam I: Joint Analysis Technique
We present a technique to constrain galaxy cluster pressure profiles by
jointly fitting Sunyaev-Zel'dovich effect (SZE) data obtained with MUSTANG and
Bolocam for the clusters Abell 1835 and MACS0647. Bolocam and MUSTANG probe
different angular scales and are thus highly complementary. We find that the
addition of the high resolution MUSTANG data can improve constraints on
pressure profile parameters relative to those derived solely from Bolocam. In
Abell 1835 and MACS0647, we find gNFW inner slopes of and , respectively when
and are constrained to 0.86 and 4.67 respectively. The fitted
SZE pressure profiles are in good agreement with X-ray derived pressure
profiles.Comment: 12 pages, 12 figures. Submitted to Ap
A Multi-wavelength Study of the Sunyaev-Zel'dovich Effect in the Triple-Merger Cluster MACS J0717.5+3745 with MUSTANG and Bolocam
We present 90, 140, and 268GHz sub-arcminute resolution imaging of the
Sunyaev-Zel'dovich effect (SZE) in MACSJ0717.5+3745. Our 90GHz SZE data result
in a sensitive, 34uJy/bm map at 13" resolution using MUSTANG. Our 140 and
268GHz SZE imaging, with resolutions of 58" and 31" and sensitivities of 1.8
and 3.3mJy/beam respectively, was obtained using Bolocam. We compare these maps
to a 2-dimensional pressure map derived from Chandra X-ray observations. Our
MUSTANG data confirm previous indications from Chandra of a pressure
enhancement due to shock-heated, >20keV gas immediately adjacent to extended
radio emission seen in low-frequency radio maps. The MUSTANG data also detect
pressure substructure that is not well-constrained by the X-ray data in the
remnant core of a merging subcluster. We find that the small-scale pressure
enhancements in the MUSTANG data amount to ~2% of the total pressure measured
in the 140GHz Bolocam observations. The X-ray template also fails on larger
scales to accurately describe the Bolocam data, particularly at the location of
a subcluster known to have a high line of sight optical velocity (~3200km/s).
Our Bolocam data are adequately described when we add an additional component -
not described by a thermal SZE spectrum - coincident with this subcluster.
Using flux densities extracted from our model fits, and marginalizing over the
temperature constraints for the region, we fit a thermal+kinetic SZE spectrum
to our data and find the subcluster has a best-fit line of sight proper
velocity of 3600+3440/-2160km/s. This agrees with the optical velocity
estimates for the subcluster. The probability of velocity<0 given our
measurements is 2.1%. Repeating this analysis using flux densities measured
non-parametrically results in a 3.4% probability of a velocity<=0. We note that
this tantalizing result for the kinetic SZE is on resolved, subcluster scales.Comment: 10 Figures, 18 pages. this version corrects issues with the previous
arXiv versio
Galaxy Cluster Pressure Profiles as Determined by Sunyaev Zel’dovich Effect Observations with MUSTANG and Bolocam. II. Joint Analysis of 14 Clusters
We present pressure profiles of galaxy clusters determined from high
resolution Sunyaev-Zel'dovich (SZ) effect observations of fourteen clusters,
which span the redshift range . The procedure simultaneously
fits spherical cluster models to MUSTANG and Bolocam data. In this analysis, we
adopt the generalized NFW parameterization of pressure profiles to produce our
models. Our constraints on ensemble-average pressure profile parameters, in
this study , , and , are consistent with those in
previous studies, but for individual clusters we find discrepancies with the
X-ray derived pressure profiles from the ACCEPT2 database. We investigate
potential sources of these discrepancies, especially cluster geometry, electron
temperature of the intracluster medium, and substructure. We find that the
ensemble mean profile for all clusters in our sample is described by the
parameters: , for cool core clusters: , and for disturbed
clusters: . Four of the fourteen clusters have
clear substructure in our SZ observations, while an additional two clusters
exhibit potential substructure.Comment: 22 pages, 9 figures, accepted to Ap
MEASUREMENTS of the SUNYAEV-ZEL'DOVICH EFFECT in MACS J0647.7+7015 and MACS J1206.2-0847 at HIGH ANGULAR RESOLUTION with MUSTANG
We present high resolution (9?) imaging of the Sunyaev-Zel'dovich Effect (SZE) toward two massive galaxy clusters, MACS J0647.7+7015 (z = 0.591) and MACS J1206.2-0847 (z = 0.439). We compare these 90 GHz measurements, taken with the Multiplexed Squid/TES Array at Ninety Gigahertz (MUSTANG ) receiver on the Green Bank Telescope, with generalized Navarro-Frenk-White (gNFW) models derived from Bolocam 140 GHz SZE data as well as maps of the thermal gas derived from Chandra X-ray observations. We adopt a serial-fitting approach, in which gNFW models are first fit to the Bolocam data and then compared to the MUSTANG data to determine an overall best-fit model. For MACS J0647.7+7015, we find a gNFW profile with core slope parameter ? = 0.9 fits the MUSTANG image with and probability to exceed (PTE) = 0.34. For MACS J1206.2-0847, we find , , and PTE = 0.70. In addition, we find a significant (>3s) residual SZE feature in MACS J1206.2-0847 coincident with a group of galaxies identified in Very Large Telescope data and filamentary structure found in a weak-lensing mass reconstruction. We suggest the detected sub-structure may be the SZE decrement from a low mass foreground group or an infalling group. Giant Metrewave Radio Telescope measurements at 610 MHz reveal diffuse extended radio emission to the west, which we posit is either an active galactic nucleus-driven radio lobe, a bubble expanding away from disturbed gas associated with the SZE signal, or a bubble detached and perhaps re-accelerated by sloshing within the cluster. Using the spectroscopic redshifts available, we find evidence for a foreground (z = 0.423) or infalling group, coincident with the residual SZE feature
Perceived difficulty and appropriateness of decision making by General Practitioners: a systematic review of scenario studies
Background: Health-care quality in primary care depends largely on the appropriateness of General Practitioners’ (GPs; Primary Care or Family Physicians) decisions, which may be influenced by how difficult they perceive decisions to be. Patient scenarios (clinical or case vignettes) are widely used to investigate GPs’ decision making. This review aimed to identify the extent to which perceived decision difficulty, decision appropriateness, and their relationship have been assessed in scenario studies of GPs’ decision making; identify possible determinants of difficulty and appropriateness; and investigate the relationship between difficulty and appropriateness.
Methods: MEDLINE, EMBASE, PsycINFO, the Cochrane Library and Web of Science were searched for scenario studies of GPs’ decision making. One author completed article screening. Ten percent of titles and abstracts were checked by an independent volunteer, resulting in 91% agreement. Data on decision difficulty and appropriateness were extracted by one author and descriptively synthesised. Chi-squared tests were used to explore associations between decision appropriateness, decision type and decision appropriateness assessment method.
Results: Of 152 included studies, 66 assessed decision appropriateness and five assessed perceived difficulty. While no studies assessed the relationship between perceived difficulty and appropriateness, one study objectively varied the difficulty of the scenarios and assessed the relationship between a measure of objective difficulty and appropriateness. Across 38 studies where calculations were possible, 62% of the decisions were appropriate as defined by the appropriateness standard used. Chi-squared tests identified statistically significant associations between decision appropriateness, decision type and decision appropriateness assessment method. Findings suggested a negative relationship between decision difficulty and appropriateness, while interventions may have the potential to reduce perceived difficulty.
Conclusions: Scenario-based research into GPs’ decisions rarely considers the relationship between perceived decision difficulty and decision appropriateness. The links between these decisional components require further investigation
Expression of phosphorylated eIF4E-binding protein 1, but not of eIF4E itself, predicts survival in male breast cancer
Background: Male breast cancer is rare and treatment is based on data from females. High expression/activity of eukaryotic initiation factor 4E (eIF4E) denotes a poor prognosis in female breast cancer, and the eIF4E pathway has been targeted therapeutically. eIF4E activity in female breast cancer is deregulated by eIF4E over-expression and by phosphorylation of its binding protein, 4E-BP1, which relieves inhibitory association between eIF4E and 4E-BP1. The relevance of the eIF4E pathway in male breast cancer is unknown. Methods: We have assessed expression levels of eIF4E, 4E-BP1, 4E-BP2 and phosphorylated 4E-BP1 (p4E-BP1) using immunohistochemistry in a large cohort of male breast cancers (n=337) and have examined correlations with prognostic factors and survival. Results: Neither eIF4E expression or estimated eIF4E activity were associated with prognosis. However, a highly significant correlation was found between p4E-BP1 expression and disease-free survival, linking any detectable p4E-BP1 with poor survival (univariate log rank p=0.001; multivariate HR 8.8, p=0.0001). Conclusions: Our data provide no support for direct therapeutic targeting of eIF4E in male breast cancer, unlike in females. However, as p4E-BP1 gives powerful prognostic insights that are unrelated to eIF4E function, p4E-BP1 may identify male breast cancers potentially suitable for therapies directed at the upstream kinase, mTOR
Genetic Associations and Architecture of Asthma-COPD Overlap
BACKGROUND: Some people have characteristics of both asthma and COPD (asthma-COPD overlap), and evidence suggests they experience worse outcomes than those with either condition alone. RESEARCH QUESTION: What is the genetic architecture of asthma-COPD overlap, and do the determinants of risk for asthma-COPD overlap differ from those for COPD or asthma? STUDY DESIGN AND METHODS: We conducted a genome-wide association study in 8,068 asthma-COPD overlap case subjects and 40,360 control subjects without asthma or COPD of European ancestry in UK Biobank (stage 1). We followed up promising signals (P < 5 x 10(-6)) that remained associated in analyses comparing (1) asthma-COPD overlap vs asthma-only control subjects, and (2) asthma-COPD overlap vs COPD-only control subjects. These variants were analyzed in 12 independent cohorts (stage 2). RESULTS: We selected 31 independent variants for further investigation in stage 2, and discovered eight novel signals (P < 5 x 10(-8)) for asthma-COPD overlap (meta-analysis of stage 1 and 2 studies). These signals suggest a spectrum of shared genetic influences, some predominantly influencing asthma (FAM105A, GLB1, PHB, TSLP), others predominantly influencing fixed airflow obstruction (IL17RD, C5orf56, HLA-DQB1). One intergenic signal on chromosome 5 had not been previously associated with asthma, COPD, or lung function. Subgroup analyses suggested that associations at these eight signals were not driven by smoking or age at asthma diagnosis, and in phenome-wide scans, eosinophil counts, atopy, and asthma traits were prominent. INTERPRETATION: We identified eight signals for asthma-COPD overlap, which may represent loci that predispose to type 2 inflammation, and serious long-term consequences of asthma.Peer reviewe
Author Correction: Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk
Correction to: Nature Geneticshttps://doi.org/10.1038/s41588-023-01314-0, published online 13 March 2023. In the version of the article initially published, the sample sizes in the main text and Supplementary Tables 1 and 2 were incorrect. In the abstract, the last paragraph of the Introduction, the first paragraph of the Results, the top box in Figure 1a and the Supplementary Information, the total sample size has been corrected from 580,869 to 588,452 participants and the size of the European cohort from 468,062 to 475,645. Some of the effect sizes in Supplementary Table 14 (columns W, Z, AC, AF) had the wrong sign. There was also an error in Supplementary Table 3 where the sample size instead of the variant count was shown for EXCEED. The errors do not affect the conclusions of the study. Additionally, two acknowledgments for use of INTERVAL pQTL and Lung eQTL consortium data were omitted from the Supplementary Information. These errors have been corrected in the Supplementary Information and HTML and PDF versions of the article
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