Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods

We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of including hyper-parameters, which are themselves informed by the data, and show that this method can serve to reduce the effect of errors in assumptions made about the a priori emissions and model-measurement uncertainties. We then apply this method to the estimation of sulfur hexafluoride (SF6) emissions over 2012 for the regions surrounding four Advanced Global Atmospheric Gases Experiment (AGAGE) stations. We find that improper accounting of model representation uncertainties, in particular, can lead to the derivation of emissions and associated uncertainties that are unrealistic and show that those derived using the hierarchical method are likely to be more representative of the true uncertainties in the system. We demonstrate through this SF6 case study that this method is less sensitive to outliers in the data and to subjective assumptions about a priori emissions and model-measurement uncertainties than traditional methods

Fast-ion conduction and flexibility of glassy networks

We observe two thresholds in the variations of electrical conductivity of dry solid electrolyte (AgI)x(AgPO3)1-x glasses, when the AgI additive concentration x increases to 9.5% and to 37.8%. Raman scattering complemented by calorimetric measurements confirm that these thresholds are signatures of the rigidity phase transitions; at x = 9.5% from a stressed rigid to an isostatically (stress free) rigid phase, and at x = 37.8% from isostatically rigid to a flexible phase. In the flexible phase, the electrical conductivity seems to increase as a power of x, this is in good agreement with the theoretical prediction based on 3d percolation.Comment: Accepted by PR

Hemodynamic Correlates of Blood Pressure in Older Adults: The Atherosclerosis Risk in Communities (ARIC) Study

The primary aim of the present study was to identify the hemodynamic correlates of both steady and pulsatile blood pressure in community-dwelling older adults. In 3,762 adults aged 70–89 years, we observed that significant hemodynamic determinants of systolic blood pressure included arterial stiffness as measured by aortic pulse wave velocity, stroke volume (via echocardiography), arterial wave reflection, left ventricular ejection time, and upstroke time. The strongest influence was exerted by arterial stiffness. The steady state component of blood pressure, mean arterial pressure, was associated with both cardiac index and total peripheral resistance (TPR), but was more strongly associated with TPR. Results were similar when participants taking antihypertensive medications were excluded from analyses. The overall findings suggest that mean arterial pressure is associated strongly with TPR and that significant hemodynamic correlates of systolic blood pressure included arterial stiffness, stroke volume, and arterial wave reflection

Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion

Nitrous oxide is a potent greenhouse gas (GHG) and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions, which includes prior emissions with truncated Gaussian distributions and Gaussian model errors, in order to examine the drivers of the atmospheric surface growth rate. We show that both emissions and climatic variability are key drivers of variations in the surface nitrous oxide growth rate between 2011 and 2020. We derive increasing global nitrous oxide emissions, which are mainly driven by emissions between 0 and 30∘ N, with the highest emissions recorded in 2020. Our mean global total emissions for 2011–2020 of 17.2 (16.7–17.7 at the 95 % credible intervals) Tg N yr−1, comprising of 12.0 (11.2–12.8) Tg N yr−1 from land and 5.2 (4.5–5.9) Tg N yr−1 from ocean, agrees well with previous studies, but we find that emissions are poorly constrained for some regions of the world, particularly for the oceans. The prior emissions used in this and other previous work exhibit a seasonal cycle in the extra-tropical Northern Hemisphere that is out of phase with the posterior solution, and there is a substantial zonal redistribution of emissions from the prior to the posterior. Correctly characterizing the uncertainties in the system, for example in the prior emission fields, is crucial for deriving posterior fluxes that are consistent with observations. In this hierarchical inversion, the model-measurement discrepancy and the prior flux uncertainty are informed by the data, rather than solely through “expert judgement”. We show cases where this framework provides different plausible adjustments to the prior fluxes compared to inversions using widely adopted, fixed uncertainty constraints.</p

Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion

Nitrous oxide is a potent greenhouse gas (GHG) and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions, which includes prior emissions with truncated Gaussian distributions and Gaussian model errors, in order to examine the drivers of the atmospheric surface growth rate. We show that both emissions and climatic variability are key drivers of variations in the surface nitrous oxide growth rate between 2011 and 2020. We derive increasing global nitrous oxide emissions, which are mainly driven by emissions between 0 and 30∘ N, with the highest emissions recorded in 2020. Our mean global total emissions for 2011–2020 of 17.2 (16.7–17.7 at the 95 % credible intervals) Tg N yr−1, comprising of 12.0 (11.2–12.8) Tg N yr−1 from land and 5.2 (4.5–5.9) Tg N yr−1 from ocean, agrees well with previous studies, but we find that emissions are poorly constrained for some regions of the world, particularly for the oceans. The prior emissions used in this and other previous work exhibit a seasonal cycle in the extra-tropical Northern Hemisphere that is out of phase with the posterior solution, and there is a substantial zonal redistribution of emissions from the prior to the posterior. Correctly characterizing the uncertainties in the system, for example in the prior emission fields, is crucial for deriving posterior fluxes that are consistent with observations. In this hierarchical inversion, the model-measurement discrepancy and the prior flux uncertainty are informed by the data, rather than solely through “expert judgement”. We show cases where this framework provides different plausible adjustments to the prior fluxes compared to inversions using widely adopted, fixed uncertainty constraints.</p

Correlations and forecast of death tolls in the Syrian conflict

The Syrian armed conflict has been ongoing since 2011 and has already caused thousands of deaths. The analysis of death tolls helps to understand the dynamics of the conflict and to better allocate resources and aid to the affected areas. In this article, we use information on the daily number of deaths to study temporal and spatial correlations in the data, and exploit this information to forecast events of deaths. We found that the number of violent deaths per day in Syria varies more widely than that in England in which non-violent deaths dominate. We have identified strong positive auto-correlations in Syrian cities and non-trivial cross-correlations across some of them. The results indicate synchronization in the number of deaths at different times and locations, suggesting respectively that local attacks are followed by more attacks at subsequent days and that coordinated attacks may also take place across different locations. Thus the analysis of high temporal resolution data across multiple cities makes it possible to infer attack strategies, warn potential occurrence of future events, and hopefully avoid further deaths

The SIPHER consortium : introducing the new UK hub for systems science in public health and health economic research

The conditions in which we are born, grow, live, work and age are key drivers of health and inequalities in life chances. To maximise health and wellbeing across the whole population, we need well-coordinated action across government sectors, in areas including economic, education, welfare, labour market and housing policy. Current research struggles to offer effective decision support on the cross-sector strategic alignment of policies, and to generate evidence that gives budget holders the confidence to change the way major investment decisions are made. This open letter introduces a new research initiative in this space. The SIPHER (Systems Science in Public Health and Health Economics Research) Consortium brings together a multi-disciplinary group of scientists from across six universities, three government partners at local, regional and national level, and ten practice partner organisations. The Consortium’s vision is a shift from health policy to healthy public policy, where the wellbeing impacts of policies are a core consideration across government sectors. Researchers and policy makers will jointly tackle fundamental questions about: a) the complex causal relationships between upstream policies and wellbeing, economic and equality outcomes; b) the multi-sectoral appraisal of costs and benefits of alternative investment options; c) public values and preferences for different outcomes, and how necessary trade-offs can be negotiated; and d) creating the conditions for intelligence-led adaptive policy design that maximises progress against economic, social and health goals. Whilst our methods will be adaptable across policy topics and jurisdictions, we will initially focus on four policy areas: Inclusive Economic Growth, Adverse Childhood Experiences, Mental Wellbeing and Housing

Effect of Care Guided by Cardiovascular Magnetic Resonance, Myocardial Perfusion Scintigraphy, or NICE Guidelines on Subsequent Unnecessary Angiography Rates : The CE-MARC 2 Randomized Clinical Trial

Importance Among patients with suspected coronary heart disease (CHD), rates of invasive angiography are considered too high. Objective To test the hypothesis that among patients with suspected CHD, cardiovascular magnetic resonance (CMR)–guided care is superior to National Institute for Health and Care Excellence (NICE) guidelines–directed care and myocardial perfusion scintigraphy (MPS)–guided care in reducing unnecessary angiography. Design, Setting, and Participants Multicenter, 3-parallel group, randomized clinical trial using a pragmatic comparative effectiveness design. From 6 UK hospitals, 1202 symptomatic patients with suspected CHD and a CHD pretest likelihood of 10% to 90% were recruited. First randomization was November 23, 2012; last 12-month follow-up was March 12, 2016. Interventions Patients were randomly assigned (240:481:481) to management according to UK NICE guidelines or to guided care based on the results of CMR or MPS testing. Main Outcomes and Measures The primary end point was protocol-defined unnecessary coronary angiography (normal fractional flow reserve >0.8 or quantitative coronary angiography [QCA] showing no percentage diameter stenosis ≥70% in 1 view or ≥50% in 2 orthogonal views in all coronary vessels ≥2.5 mm diameter) within 12 months. Secondary end points included positive angiography, major adverse cardiovascular events (MACEs), and procedural complications. Results Among 1202 symptomatic patients (mean age, 56.3 years [SD, 9.0]; women, 564 [46.9%] ; mean CHD pretest likelihood, 49.5% [SD, 23.8%]), number of patients with invasive coronary angiography after 12 months was 102 in the NICE guidelines group (42.5% [95% CI, 36.2%-49.0%])], 85 in the CMR group (17.7% [95% CI, 14.4%-21.4%]); and 78 in the MPS group (16.2% [95% CI, 13.0%-19.8%]). Study-defined unnecessary angiography occurred in 69 (28.8%) in the NICE guidelines group, 36 (7.5%) in the CMR group, and 34 (7.1%) in the MPS group; adjusted odds ratio of unnecessary angiography: CMR group vs NICE guidelines group, 0.21 (95% CI, 0.12-0.34, P < .001); CMR group vs the MPS group, 1.27 (95% CI, 0.79-2.03, P = .32). Positive angiography proportions were 12.1% (95% CI, 8.2%-16.9%; 29/240 patients) for the NICE guidelines group, 9.8% (95% CI, 7.3%-12.8%; 47/481 patients) for the CMR group, and 8.7% (95% CI, 6.4%-11.6%; 42/481 patients) for the MPS group. A MACE was reported at a minimum of 12 months in 1.7% of patients in the NICE guidelines group, 2.5% in the CMR group, and 2.5% in the MPS group (adjusted hazard ratios: CMR group vs NICE guidelines group, 1.37 [95% CI, 0.52-3.57]; CMR group vs MPS group, 0.95 [95% CI, 0.46-1.95]). Conclusions and Relevance In patients with suspected angina, investigation by CMR resulted in a lower probability of unnecessary angiography within 12 months than NICE guideline–directed care, with no statistically significant difference between CMR and MPS strategies. There were no statistically significant differences in MACE rates. Trial Registration Clinicaltrials.gov Identifier: NCT01664858

Large-Scale Evidence for the Effect of the COLIA1 Sp1 Polymorphism on Osteoporosis Outcomes: The GENOMOS Study.

Background Osteoporosis and fracture risk are considered to be under genetic control. Extensive work is being performed to identify the exact genetic variants that determine this risk. Previous work has suggested that a G/T polymorphism affecting an Sp1 binding site in the COLIA1 gene is a genetic marker for low bone mineral density (BMD) and osteoporotic fracture, but there have been no very-large-scale studies of COLIA1 alleles in relation to these phenotypes. Methods and Findings Here we evaluated the role of COLIA1 Sp1 alleles as a predictor of BMD and fracture in a multicenter study involving 20,786 individuals from several European countries. At the femoral neck, the average (95% confidence interval [CI]) BMD values were 25 mg/cm2 (CI, 16 to 34 mg/cm2) lower in TT homozygotes than the other genotype groups ( p &lt; 0.001), and a similar difference was observed at the lumbar spine; 21 mg/cm2 (CI, 1 to 42 mg/cm2), ( p = 0.039). These associations were unaltered after adjustment for potential confounding factors. There was no association with fracture overall (odds ratio [OR] = 1.01 [CI, 0.95 to 1.08]) in either unadjusted or adjusted analyses, but there was a non-significant trend for association with vertebral fracture and a nominally significant association with incident vertebral fractures in females (OR = 1.33 [CI, 1.00 to 1.77]) that was independent of BMD, and unaltered in adjusted analyses. Conclusions Allowing for the inevitable heterogeneity between participating teams, this study—which to our knowledge is the largest ever performed in the field of osteoporosis genetics for a single gene—demonstrates that the COLIA1 Sp1 polymorphism is associated with reduced BMD and could predispose to incident vertebral fractures in women, independent of BMD. The associations we observed were modest however, demonstrating the importance of conducting studies that are adequately powered to detect and quantify the effects of common genetic variants on complex diseases