Case studies in Bayesian microbial risk assessments

<p>Abstract</p> <p>Background</p> <p>The quantification of uncertainty and variability is a key component of quantitative risk analysis. Recent advances in Bayesian statistics make it ideal for integrating multiple sources of information, of different types and quality, and providing a realistic estimate of the combined uncertainty in the final risk estimates.</p> <p>Methods</p> <p>We present two case studies related to foodborne microbial risks. In the first, we combine models to describe the sequence of events resulting in illness from consumption of milk contaminated with VTEC O157. We used Monte Carlo simulation to propagate uncertainty in some of the inputs to computer models describing the farm and pasteurisation process. Resulting simulated contamination levels were then assigned to consumption events from a dietary survey. Finally we accounted for uncertainty in the dose-response relationship and uncertainty due to limited incidence data to derive uncertainty about yearly incidences of illness in young children. Options for altering the risk were considered by running the model with different hypothetical policy-driven exposure scenarios. In the second case study we illustrate an efficient Bayesian sensitivity analysis for identifying the most important parameters of a complex computer code that simulated VTEC O157 prevalence within a managed dairy herd. This was carried out in 2 stages, first to screen out the unimportant inputs, then to perform a more detailed analysis on the remaining inputs. The method works by building a Bayesian statistical approximation to the computer code using a number of known code input/output pairs (training runs).</p> <p>Results</p> <p>We estimated that the expected total number of children aged 1.5-4.5 who become ill due to VTEC O157 in milk is 8.6 per year, with 95% uncertainty interval (0,11.5). The most extreme policy we considered was banning on-farm pasteurisation of milk, which reduced the estimate to 6.4 with 95% interval (0,11). In the second case study the effective number of inputs was reduced from 30 to 7 in the screening stage, and just 2 inputs were found to explain 82.8% of the output variance. A combined total of 500 runs of the computer code were used.</p> <p>Conclusion</p> <p>These case studies illustrate the use of Bayesian statistics to perform detailed uncertainty and sensitivity analyses, integrating multiple information sources in a way that is both rigorous and efficient.</p

Reduced Order Modelling of a Reynolds Number 10⁶ Jet Flow Using Machine Learning Approaches

The extraction of the most dynamically important coherent flow structures using reduced order models (ROM) is a challenging task in various fluid dynamics applications. In particular, for high-speed round jet flows, the axisymmetric pressure mode of interest is known to be responsible for sound radiation at small angles to the jet axis and dominant contribution to the jet noise peak. In this work the axisymmetric pressure mode of the Navier-Stokes solution of a high speed jet flow at low frequency is reconstructed from simulation data using popular Machine Learning (ML) methods, whose output can later be exploited for data-driven design of effective turbulent acoustic source models. The data used as input for the ML techniques are derived from the Large Eddy Simulation database obtained by application of the high-resolution CABARET method accelerated on GPU cards for flow solutions to NASA Small Hot Jet Acoustic Rig (SHJAR) jets. The SHJAR simulation database is fed to Spectral Proper Orthogonal (SPOD), and the resulting time coefficients of the turbulent pressure fluctuations are the targets of the three machine learning methods put to test in this work. The first Machine Learning method used is the Feed-forward Neural Networks technique, which was successfully implemented for a turbulent flow over a plunging aerofoil in the literature. The second method is based on the application of Genetic Programming, which is a symbolic regression method well-known in optimisation research, but it has not been applied for turbulent flow reconstruction before. The third method, commonly known as Echo State Networks (ESNs), is a time series prediction and reconstruction method from the field of Reservoir Computing. A report on the attempts to apply these methods for approximation and extrapolation of the turbulent flow signals are discussed

Provision of non-invasive coronary and carotid vascular imaging results on changes in diet and physical activity in asymptomatic adults: A scoping review

Background: Although a healthy diet and physical activity have been shown to prevent or delay cardiovascular disease (CVD) hospitalizations and deaths, most adults do not meet current guidelines. Provision of coronary artery calcification (CAC) and carotid ultrasound (CUS) imaging results may motivate beneficial lifestyle changes. We scoped the existing literature for studies providing non-invasive vascular imaging results and reporting diet, physical activity, and/or anthropometric measures to identify knowledge gaps and opportunities for further research. Methods: A systematic search was performed across three electronic databases, in line with PRISMA ScR guidelines and Arksey and O\u27Malley\u27s scoping review framework. Results: Twenty studies (thirteen observational and seven randomized controlled trials) examining the impact of provision of CAC/CUS imaging results on diet and/or physical activity behaviors were included. Nearly half the studies did not clearly state whether participants received dietary and physical activity advice along with vascular imaging results, and these were secondary outcomes in most studies, with data assessment and reporting being inconsistent. Conclusion: Well-designed clinical trials with consistent and clear messaging based on detailed subjective and objective measures of diet and physical activity are needed to determine whether this approach may stimulate long-term dietary and physical activity change

Effects of American ginseng (Panax quinquefolius) on neurocognitive function: an acute, randomised, double-blind, placebo-controlled, crossover study

Over the last decade, Asian ginseng (Panax ginseng) has been shown to improve aspects of human cognitive function. American ginseng (Panax quinquefolius) has a distinct ginsenoside profile from P. ginseng, promising cognitive enhancing properties in preclinical studies and benefits processes linked to human cognition. The availability of a highly standardised extract of P. quinquefolius (Cereboost (TM)) led us to evaluate its neurocognitive properties in humans for the first time. This randomised, double-blind, placebo-controlled, crossover trial (N = 32, healthy young adults) assessed the acute mood, neurocognitive and glycaemic effects of three doses (100, 200 400 mg) of Cereboost (TM) (P. quinquefolius standardised to 10.65% ginsenosides). Participants' mood, cognitive function and blood glucose were measured 1, 3 and 6 h following administration. There was a significant improvement of working memory (WM) performance associated with P. quinquefolius. Corsi block performance was improved by all doses at all testing times. There were differential effects of all doses on other WM tasks which were maintained across the testing day. Choice reaction time accuracy and 'calmness' were significantly improved by 100 mg. There were no changes in blood glucose levels. This preliminary study has identified robust working memory enhancement following administration of American ginseng. These effects are distinct from those of Asian ginseng and suggest that psychopharmacological properties depend critically on ginsenoside profiles. These results have ramifications for the psychopharmacology of herbal extracts and merit further study using different dosing regimens and in populations where cognition is fragile

The impact of poor adult health on labor supply in the Russian Federation

We examine the labor supply consequences of poor health in the Russian Federation, a country with exceptionally adverse adult health outcomes. In both baseline OLS models and in models with individual fixed effects, more serious ill-health events, somewhat surprisingly, generally have only weak effects on hours worked. At the same time, their effect on the extensive margin of labor supply is substantial. Moreover, when combining the effects on both the intensive and extensive margins, the effect of illness on hours worked increases considerably for a range of conditions. In addition, for most part of the age distribution, people with poor self-assessed health living in rural areas are less likely to stop working, compared to people living in cities. While there is no conclusive explanation for this finding, it could be related to the existence of certain barriers that prevent people with poor health from withdrawing from the labor force in order to take care of their health

Two-loop representations of low-energy pion form factors and pi-pi scattering phases in the presence of isospin breaking

Dispersive representations of the pi-pi scattering amplitudes and pion form factors, valid at two-loop accuracy in the low-energy expansion, are constructed in the presence of isospin-breaking effects induced by the difference between the charged and neutral pion masses. Analytical expressions for the corresponding phases of the scalar and vector pion form factors are computed. It is shown that each of these phases consists of the sum of a "universal" part and a form-factor dependent contribution. The first one is entirely determined in terms of the pi-pi scattering amplitudes alone, and reduces to the phase satisfying Watson's theorem in the isospin limit. The second one can be sizeable, although it vanishes in the same limit. The dependence of these isospin corrections with respect to the parameters of the subthreshold expansion of the pi-pi amplitude is studied, and an equivalent representation in terms of the S-wave scattering lengths is also briefly presented and discussed. In addition, partially analytical expressions for the two-loop form factors and pi-pi scattering amplitudes in the presence of isospin breaking are provided.Comment: 57 pages, 12 figure

The management of heart failure cardiogenic shock:an international RAND appropriateness panel

Background: Observational data suggest that the subset of patients with heart failure related CS (HF-CS) now predominate critical care admissions for CS. There are no dedicated HF-CS randomised control trials completed to date which reliably inform clinical practice or clinical guidelines. We sought to identify aspects of HF-CS care where both consensus and uncertainty may exist to guide clinical practice and future clinical trial design, with a specific focus on HF-CS due to acute decompensated chronic HF. Methods: A 16-person multi-disciplinary panel comprising of international experts was assembled. A modified RAND/University of California, Los Angeles, appropriateness methodology was used. A survey comprising of 34 statements was completed. Participants anonymously rated the appropriateness of each statement on a scale of 1 to 9 (1–3 as inappropriate, 4–6 as uncertain and as 7–9 appropriate). Results: Of the 34 statements, 20 were rated as appropriate and 14 were rated as inappropriate. Uncertainty existed across all three domains: the initial assessment and management of HF-CS; escalation to temporary Mechanical Circulatory Support (tMCS); and weaning from tMCS in HF-CS. Significant disagreement between experts (deemed present when the disagreement index exceeded 1) was only identified when deliberating the utility of thoracic ultrasound in the immediate management of HF-CS. Conclusion: This study has highlighted several areas of practice where large-scale prospective registries and clinical trials in the HF-CS population are urgently needed to reliably inform clinical practice and the synthesis of future societal HF-CS guidelines

Risks of breast or ovarian cancer in BRCA1 or BRCA2 predictive test negatives: findings from the EMBRACE study.

Purpose BRCA1/BRCA2 predictive test negatives are proven noncarriers of a BRCA1/BRCA2 mutation that is carried by their relatives. The risk of developing breast cancer (BC) or epithelial ovarian cancer (EOC) in these women is uncertain. The study aimed to estimate risks of invasive BC and EOC in a large cohort of BRCA1/BRCA2 predictive test negatives. Methods We used cohort analysis to estimate incidences, cumulative risks, and standardized incidence ratios (SIRs). Results A total of 1,895 unaffected women were eligible for inclusion in the BC risk analysis and 1,736 in the EOC risk analysis. There were 23 incident invasive BCs and 2 EOCs. The cumulative risk of invasive BC was 9.4% (95% confidence interval (CI) 5.9-15%) by age 85 years and the corresponding risk of EOC was 0.6% (95% CI 0.2-2.6%). The SIR for invasive BC was 0.93 (95% CI 0.62-1.40) in the overall cohort, 0.85 (95% CI 0.48-1.50) in noncarriers from BRCA1 families, and 1.03 (95% CI 0.57-1.87) in noncarriers from BRCA2 families. The SIR for EOC was 0.79 (95% CI 0.20-3.17) in the overall cohort. Conclusion Our results did not provide evidence for elevated risks of invasive BC or EOC in BRCA1/BRCA2 predictive test negatives. Genetics in Medicine advance online publication, 22 March 2018; doi:10.1038/gim.2018.44

The MCRA toolbox of models and data to support chemical mixture risk assessment

A model and data toolbox is presented to assess risks from combined exposure to multiple chemicals using probabilistic methods. The Monte Carlo Risk Assessment (MCRA) toolbox, also known as the EuroMix toolbox, has more than 40 modules addressing all areas of risk assessment, and includes a data repository with data collected in the EuroMix project. This paper gives an introduction to the toolbox and illustrates its use with examples from the EuroMix project. The toolbox can be used for hazard identification, hazard characterisation, exposure assessment and risk characterisation. Examples for hazard identification are selection of substances relevant for a specific adverse outcome based on adverse outcome pathways and QSAR models. Examples for hazard characterisation are calculation of benchmark doses and relative potency factors with uncertainty from dose response data, and use of kinetic models to perform in vitro to in vivo extrapolation. Examples for exposure assessment are assessing cumulative exposure at external or internal level, where the latter option is needed when dietary and non-dietary routes have to be aggregated. Finally, risk characterisation is illustrated by calculation and display of the margin of exposure for single substances and for the cumulation, including uncertainties derived from exposure and hazard characterisation estimates.</p