Greenhouse Gas Policy and Canadian Agriculture

To meet its commitment to the Kyoto Protocol (KP), Canada must reduce its current annual GHG emissions (694 MT CO2 equivalent) by 240 MT CO2. Canada’s agricultural industries have the potential to generate a carbon sink of 10 MT CO2 (Government of Canada, 2003). The purpose of this article is to consider the manner in which Canadian agricultural policy has responded to Canada’s KP commitment and the likelihood that Canadian agriculture will participate significantly in carbon emission trading.Agricultural and Food Policy, Environmental Economics and Policy,

Liver Shape Analysis using Statistical Parametric Maps at Population Scale

Background: Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals. Methods: Here we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes. Results: We found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease. Conclusions: The results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions

Precision MRI Phenotyping Enables Detection of Small Changes in Body Composition for Longitudinal Cohorts

Longitudinal studies provide unique insights into the impact of environmental factors and lifespan issues on health and disease.Here we investigate changes in body composition in 3,088 free-living participants, part of the UK Biobank in-depth imagingstudy. All participants underwent neck-to-knee MRI scans at the first imaging visit and after approximately two years (secondimaging visit). Image-derived phenotypes for each participant were extracted using a fully-automated image processing pipeline,including volumes of several tissues and organs: liver, pancreas, spleen, kidneys, total skeletal muscle, iliopsoas muscle,visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), as well as fat and iron content in liver, pancreasand spleen. Overall, no significant changes were observed in BMI, body weight, or waist circumference over the scanninginterval, despite some large individual changes. A significant decrease in grip strength was observed, coupled to small, butstatistically significant, decrease in all skeletal muscle measurements. Significant increases in VAT and intermuscular fat in thethighs were also detected in the absence of changes in BMI, waist circumference and ectopic-fat deposition. Adjusting fordisease status at the first imaging visit did not have an additional impact on the changes observed. In summary, we showthat even after a relatively short period of time significant changes in body composition can take place, probably reflecting theobesogenic environment currently inhabited by most of the general population in the United Kingdom

Artifact-Free Fat-Water Separation in Dixon MRI using Deep Learning

Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK~Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps

Abdominal imaging associates body composition with COVID-19 severity

The main drivers of COVID-19 disease severity and the impact of COVID-19 on long-term health after recovery are yet to be fully understood. Medical imaging studies investigating COVID-19 to date have mostly been limited to small datasets and post-hoc analyses of severe cases. The UK Biobank recruited recovered SARS-CoV-2 positive individuals (n = 967) and matched controls (n = 913) who were extensively imaged prior to the pandemic and underwent follow-up scanning. In this study, we investigated longitudinal changes in body composition, as well as the associations of pre-pandemic image-derived phenotypes with COVID-19 severity. Our longitudinal analysis, in a population of mostly mild cases, associated a decrease in lung volume with SARS-CoV-2 positivity. We also observed that increased visceral adipose tissue and liver fat, and reduced muscle volume, prior to COVID-19, were associated with COVID-19 disease severity. Finally, we trained a machine classifier with demographic, anthropometric and imaging traits, and showed that visceral fat, liver fat and muscle volume have prognostic value for COVID-19 disease severity beyond the standard demographic and anthropometric measurements. This combination of image-derived phenotypes from abdominal MRI scans and ensemble learning to predict risk may have future clinical utility in identifying populations at-risk for a severe COVID-19 outcome

Mass Univariate Regression Analysis for Three-Dimensional Liver Image-Derived Phenotypes

Image-derived phenotypes of abdominal organs from magnetic resonance imaging reveal variations in volume and shape and may be used to model changes in a normal versus pathological organ and improve diagnosis. Computational atlases of anatomical organs provide many advantages in quantifying and modeling differences in shape and size of organs for population imaging studies. Here we made use of liver segmentations derived from Dixon MRI for 2,730 UK Biobank participants to create 3D liver meshes. We computed the signed distances between a reference and subject-specific meshes to define the surface-to-surface (S2S) phenotype. We employed mass univariate regression analysis to compare the S2S values from the liver meshes to image-derived phenotypes specific to the liver, such as proton density fat fraction and iron concentration while adjusting for age, sex, ethnicity, body mass index and waist-to-hip ratio. Vertex-based associations in the 3D liver mesh were extracted and threshold-free cluster enhancement was applied to improve the sensitivity and stability of the statistical parametric maps. Our findings show that the 3D liver meshes are a robust method for modeling the association between anatomical, anthropometric, and phenotypic variations across the liver. This approach may be readily applied to different clinical conditions as well as extended to other abdominal organs in a larger population

ABC-SysBio-approximate Bayesian computation in Python with GPU support.

Motivation: The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions. Results: Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. It is designed to work with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models can be analyzed in ABC-SysBio

Estimating the Effect of Liver and Pancreas Volume and Fat Content on Risk of Diabetes: A Mendelian Randomization Study

Fat content and volume of liver and pancreas are associated with risk of diabetes in observational studies; whether these associations are causal is unknown. We conducted a Mendelian randomization (MR) study to examine causality of such associations. We used genetic variants associated (P < 5 × 10-8) with the exposures (liver and pancreas volume and fat content) using MRI scans of UK Biobank participants (n = 32,859). We obtained summary-level data for risk of type 1 (9,358 cases) and type 2 (55,005 cases) diabetes from the largest available genome-wide association studies. We performed inverse-variance weighted MR as main analysis and several sensitivity analyses to assess pleiotropy and to exclude variants with potential pleiotropic effects. Observationally, liver fat and volume were associated with type 2 diabetes (odds ratio per 1 SD higher exposure 2.16 [2.02, 2.31] and 2.11 [1.96, 2.27], respectively). Pancreatic fat was associated with type 2 diabetes (1.42 [1.34, 1.51]) but not type 1 diabetes, and pancreas volume was negatively associated with type 1 diabetes (0.42 [0.36, 0.48]) and type 2 diabetes (0.73 [0.68, 0.78]). MR analysis provided evidence only for a causal role of liver fat and pancreas volume in risk of type 2 diabetes (1.27 [1.08, 1.49] or 27% increased risk and 0.76 [0.62, 0.94] or 24% decreased risk per 1SD, respectively) and no causal associations with type 1 diabetes. Our findings assist in understanding the causal role of ectopic fat in the liver and pancreas and of organ volume in the pathophysiology of type 1 and 2 diabetes. [Abstract copyright: © 2022 by the American Diabetes Association.

Crystal surfaces with correlated disorder: Phase transitions between roughening and superroughening

A theory for surface transitions in the presence of a disordered pinning potential is presented. Arbitrary disorder correlations are treated in the framework of a dynamical functional renormalization group. The roughening transition, where surface roughness and mobility behave discontinuously, is shown to turn smoothly into the continuous superroughening transition, when the range of disorder correlations is decreased. Implications for random-field $XY$-models and vortex glasses are discussed.Comment: 13 pages with 2 figures, latex+revte