Can smoking initiation contexts predict how adult Aboriginal smokers assess their smoking risks? A cross-sectional study using the 'Smoking Risk Assessment Target'

Objectives: Smoking prevalence is slow to reduce among Indigenous Australians of reproductive age. We analysed the relationships between age of smoking initiation, recalled initiation influences and self-assessment of smoking risks in Aboriginal smokers. Design, setting and participants: A community-based cross-sectional survey of Aboriginal smokers aged 18–45 years (N=121; 58 men) was undertaken, using single-item measures. The Smoking Risk Assessment Target (SRAT) as the primary outcome measure enabled self-assessment of smoking risks from 12 options, recategorised into 3 groups. Participants recalled influences on their smoking initiation. Multinomial logistic regression modelling included age, gender, strength of urges to smoke, age at initiation (regular uptake) and statistically significant initiation influences on χ2 tests (‘to be cool’, alcohol and cannabis). Results: Frequent initiation influences included friends (74%; SD 0.44), family (57%; SD 0.5) and alcohol (40%; SD 0.49). 54% (n=65) of smokers had the highest risk perception on the SRAT, selected by those who cared about the smoking risks and intended to quit soon. On multivariate analyses, compared with the highest level of SRAT, male gender, lower age of uptake and strong urges to smoke were significantly associated with the lowest level of SRAT, selected by those who refuted risks or thought they could not quit. Lower age of uptake and alcohol were associated with mid-level of SRAT, selected by those who cared about smoking risks, but did not consider quitting as a priority. Conclusions: Characteristics of smoking initiation in youth may have far-reaching associations with how smoking risks are assessed by adults of reproductive age, and their intentions to quit smoking. Becoming a regular smoker at under the age of 16 years, and influences of alcohol on smoking uptake, were inversely associated with high-level assessment of smoking risks and intention to quit in regional Aboriginal smokers. The SRAT may help tailor approaches to Indigenous smoking cessation

Confirmation of co-denitrification in grazed grassland

peer-reviewedPasture-based livestock systems are often associated with losses of reactive forms of nitrogen (N) to the environment. Research has focused on losses to air and water due to the health, economic and environmental impacts of reactive N. Di-nitrogen (N2) emissions are still poorly characterized, both in terms of the processes involved and their magnitude, due to financial and methodological constraints. Relatively few studies have focused on quantifying N2 losses in vivo and fewer still have examined the relative contribution of the different N2 emission processes, particularly in grazed pastures. We used a combination of a high 15N isotopic enrichment of applied N with a high precision of determination of 15N isotopic enrichment by isotope-ratio mass spectrometry to measure N2 emissions in the field. We report that 55.8 g N m−2 (95%, CI 38 to 77 g m−2) was emitted as N2 by the process of co-denitrification in pastoral soils over 123 days following urine deposition (100 g N m−2), compared to only 1.1 g N m−2 (0.4 to 2.8 g m−2) from denitrification. This study provides strong evidence for co-denitrification as a major N2 production pathway, which has significant implications for understanding the N budgets of pastoral ecosystems.The authors are grateful for the funding that was provided through the Research Stimulus Fund Program administered by the Department of Agriculture & Food under the National Development Plan 2007–2013 RSF 07536. The first author is grateful for the funding provided by Teagasc through the Walsh Fellowship Scheme

dAUTOMAP:decomposing AUTOMAP to achieve scalability and enhance performance

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the domain transformation of AUTOMAP, making the model scale linearly. We show dAUTOMAP outperforms AUTOMAP with significantly fewer parameters.Comment: Presented at ISMRM 27th Annual Meeting & Exhibition (Abstract #658

Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders

Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health. The aim of this study is to analyze the impact of systolic blood pressure (SBP) on cardiac function while preserving the interpretability of the model using known clinical biomarkers in a large cohort of the UK Biobank population. We propose a novel framework that combines deep learning based estimation of interpretable clinical biomarkers from cardiac cine MR data with a variational autoencoder (VAE). The VAE architecture integrates a regression loss in the latent space, which enables the progression of cardiac health with SBP to be learnt. Results on 3,600 subjects from the UK Biobank show that the proposed model allows us to gain important insight into the deterioration of cardiac function with increasing SBP, identify key interpretable factors involved in this process, and lastly exploit the model to understand patterns of positive and adverse adaptation of cardiac function