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We present radio observations of the galaxy cluster Abell S1136 at 888 MHz, using the Australian Square Kilometre Array Pathfinder radio telescope, as part of the Evolutionary Map of the Universe Early Science program. We compare these findings with data from the Murchison Widefield Array, XMM-Newton, the Wide-field Infrared Survey Explorer, the Digitised Sky Survey, and the Australia Telescope Compact Array. Our analysis shows the X-ray and radio emission in Abell S1136 are closely aligned and centered on the Brightest Cluster Galaxy, while the X-ray temperature profile shows a relaxed cluster with no evidence of a cool core. We find that the diffuse radio emission in the centre of the cluster shows more structure than seen in previous low-resolution observations of this source, which appeared formerly as an amorphous radio blob, similar in appearance to a radio halo; our observations show the diffuse emission in the Abell S1136 galaxy cluster contains three narrow filamentary structures visible at 888 MHz, between $\sim$80 and 140 kpc in length; however, the properties of the diffuse emission do not fully match that of a radio (mini-)halo or (fossil) tailed radio source.
The entry into force of the Treaty on the Prohibition of Nuclear Weapons (TPNW) in January 2021 has sparked much discussion of the Treaty's positive obligations under Article 6. But while victim assistance under Article 6(1) has received considerable attention, the environmental remediation obligation within Article 6(2) remains underexplored. Filling this gap, this article examines a specific issue relating to environmental remediation under Article 6(2): the scope of nuclear weapons-related activities captured by the obligation imposed upon TPNW parties. Ultimately, it is revealed that significant ambiguity exists as to the scope of activities covered when applying the rules of treaty interpretation of the 1969 Vienna Convention on the Law of Treaties. After offering some policy arguments both for and against a broad interpretation, this paper recommends that TPNW parties should begin to advance and clarify their positions on this issue in order to clearly identify the scope of Article 6(2).
The National Healthcare Safety Network (NHSN) Antibiotic Resistance (AR) Option is a valuable tool that can be used by acute-care hospitals to track and report antibiotic resistance rate data. Selective and cascading reporting results in suppressed antibiotic susceptibility results and has the potential to adversely affect what data are submitted into the NHSN AR Option. We describe the effects of antibiotic suppression on NHSN AR Option data.
Methods:
NHSN AR Option data were collected from 14 hospitals reporting into an existing NHSN user group from January 1, 2017, to December 31, 2018, and linked to commercial automated antimicrobial susceptibility testing instruments (cASTI) that were submitted as part of unrelated Tennessee Emerging Infections Program surveillance projects. A susceptibility result was defined as suppressed if the result was not found in the NHSN AR Option data but was reported in the cASTI data. Susceptibility results found in both data sets were described as released. Proportions of suppressed and released results were compared using the Pearson χ2 and Fisher exact tests.
Results:
In total, 852 matched isolates with 3,859 unique susceptibilities were available for analysis. At least 1 suppressed antibiotic susceptibility result was available for 726 (85.2%) of the isolates. Of the 3,859 susceptibility results, 1,936 (50.2%) suppressed antibiotic susceptibility results were not reported into the NHSN AR option when compared to the cASTI data.
Conclusion:
The effect of antibiotic suppression described in this article has significant implications for the ability of the NHSN AR Option to accurately reflect antibiotic resistance rates.
We present the data and initial results from the first pilot survey of the Evolutionary Map of the Universe (EMU), observed at 944 MHz with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The survey covers
$270 \,\mathrm{deg}^2$
of an area covered by the Dark Energy Survey, reaching a depth of 25–30
$\mu\mathrm{Jy\ beam}^{-1}$
rms at a spatial resolution of
$\sim$
11–18 arcsec, resulting in a catalogue of
$\sim$
220 000 sources, of which
$\sim$
180 000 are single-component sources. Here we present the catalogue of single-component sources, together with (where available) optical and infrared cross-identifications, classifications, and redshifts. This survey explores a new region of parameter space compared to previous surveys. Specifically, the EMU Pilot Survey has a high density of sources, and also a high sensitivity to low surface brightness emission. These properties result in the detection of types of sources that were rarely seen in or absent from previous surveys. We present some of these new results here.
At present, analysis of diet and bladder cancer (BC) is mostly based on the intake of individual foods. The examination of food combinations provides a scope to deal with the complexity and unpredictability of the diet and aims to overcome the limitations of the study of nutrients and foods in isolation. This article aims to demonstrate the usability of supervised data mining methods to extract the food groups related to BC. In order to derive key food groups associated with BC risk, we applied the data mining technique C5.0 with 10-fold cross-validation in the BLadder cancer Epidemiology and Nutritional Determinants study, including data from eighteen case–control and one nested case–cohort study, compromising 8320 BC cases out of 31 551 participants. Dietary data, on the eleven main food groups of the Eurocode 2 Core classification codebook, and relevant non-diet data (i.e. sex, age and smoking status) were available. Primarily, five key food groups were extracted; in order of importance, beverages (non-milk); grains and grain products; vegetables and vegetable products; fats, oils and their products; meats and meat products were associated with BC risk. Since these food groups are corresponded with previously proposed BC-related dietary factors, data mining seems to be a promising technique in the field of nutritional epidemiology and deserves further examination.
Prescribers who wrote at least 1 antibiotic prescription filled at a retail pharmacy in Tennessee in 2016.
Methods:
Multivariable logistic regression, including prescriber gender, birth decade, specialty, and practice location, and patient gender and age group, to determine the association with high prescribing.
Results:
In 2016, 7,949,816 outpatient oral antibiotic prescriptions were filled in Tennessee: 1,195 prescriptions per 1,000 total population. Moreover, 50% of Tennessee’s outpatient oral antibiotic prescriptions were written by 9.3% of prescribers. Specific specialties and prescriber types were associated with high prescribing: urology (odds ratio [OR], 3.249; 95% confidence interval [CI], 3.208–3.289), nurse practitioners (OR, 2.675; 95% CI, 2.658–2.692), dermatologists (OR, 2.396; 95% CI, 2.365–2.428), physician assistants (OR, 2.382; 95% CI, 2.364–2.400), and pediatric physicians (OR, 2.340; 95% CI, 2.320–2.361). Prescribers born in the 1960s were most likely to be high prescribers (OR, 2.574; 95% CI, 2.532–2.618). Prescribers in rural areas were more likely than prescribers in all other practice locations to be high prescribers. High prescribers were more likely to prescribe broader-spectrum antibiotics (P < .001).
Conclusions:
Targeting high prescribers, independent of specialty, degree, practice location, age, or gender, may be the best strategy for implementing cost-conscious, effective outpatient antimicrobial stewardship interventions. More information about high prescribers, such as patient volumes, clinical scope, and specific barriers to intervention, is needed.
A survey of hospital antimicrobial stewardship programs was performed to validate core element achievement data from the National Healthcare Safety Network’s (NHSN) Patient Safety Component Annual Survey. In total, 89% of hospitals met all 7 core elements, compared to only 68% according to the NHSN survey.
Archaeological fieldwork preceding housing development revealed a Mesolithic site in a primary context. A central hearth was evident from a cluster of calcined flint and bone, the latter producing a modelled date for the start of occupation at 8220–7840 cal bc and ending at 7960–7530 cal bc (95% probability). The principal activity was the knapping of bladelets, the blanks for microlith production. Impact-damaged microliths indicated the re-tooling of hunting weaponry, while microwear analysis of other tools demonstrated hide working and butchery activity at the site. The lithics can be classified as a Honey Hill assemblage type on the basis of distinctive leaf-shaped microlithic points with inverse basal retouch.
Such assemblages have a known concentration in central England and are thought to be temporally intermediate between the conventional British Early and Late Mesolithic periods. The lithic assemblage is compared to other Honey Hill type and related Horsham type assemblages from south-eastern England. Both assemblage types are termed Middle Mesolithic and may be seen as part of wider developments in the late Preboreal and Boreal periods of north-west Europe. Rapid climatic warming at this time saw the northward expansion of deciduous woodland into north-west Europe. Emerging new ecosystems presented changes in resource patterns and the Middle Mesolithic lithic typo-technological developments reflect novel foraging strategies as adaptations to the new opportunities of Boreal forest conditions. While Honey Hill-type assemblages are seen as part of such wider processes their distinctive typological signature attests to autochthonous, regional developments of human groups infilling the landscape. Such cultural insularity may reflect changing social boundaries with reduction in mobility range and physical isolation caused by rising sea level and the creation of the British archipelago.
Edited by
Alex S. Evers, Washington University School of Medicine, St Louis,Mervyn Maze, University of California, San Francisco,Evan D. Kharasch, Washington University School of Medicine, St Louis