Serum total 8-iso-prostaglandin F2α: A new and independent predictor of peripheral arterial disease

ObjectiveCirculating 8-iso-prostaglandin F2α (8-iso-PGF2α) has been proposed as new indicator of oxidative stress, which is involved in the pathophysiologic changes of atherosclerosis. We proposed to test the hypothesis that 8-iso-PGF2α is an independent predictor of symptomatic peripheral arterial disease (PAD).MethodsA case-control study in 100 patients with symptomatic PAD and 100 control subjects matched for age, sex, and diabetes mellitus was conducted. Smokers and subjects using lipid-lowering drugs were excluded. Serum total 8-iso-PGF2α was quantified with an enzyme immunoassay.ResultsMedian 8-iso-PGF2α was higher in patients with PAD than in control subjects (63 vs 42 pg/mL; P = .001). Logistic regression with hypertension, body mass index, and creatinine, low-density lipoprotein (LDL) cholesterol, triglyceride, high-sensitivity C-reactive protein (hs-CRP), 8-iso-PGF2α, and total homocysteine concentrations as independent variables and case-control status as dependent variable revealed significant odds ratios (OR) for hypertension (OR, 3.74; 95% confidence interval [CI], 1.85-7.53), low-density lipoprotein cholesterol (OR, 1.16, for an increment of 10 mg/dL; 95% CI, 1.07-1.27), high-sensitivity C-reactive protein (OR, 1.02, for an increment of 1 mg/L; 95% CI, 1.00-1.03), and 8-iso-PGF2α (OR, 1.11, for an increment of 10 pg/mL; 95% CI, 1.03-1.20).ConclusionsSerum total 8-iso-PGF2α was an independent predictor of PAD in the population studied. This finding supports the hypothesis that 8-iso-PGF2α is a risk marker for PAD. Our results indicate increased systemic oxidative stress in patients with PAD

Measuring root system traits of wheat in 2D images to parameterize 3D root architecture models

Background and aimsThe main difficulty in the use of 3D root architecture models is correct parameterization. We evaluated distributions of the root traits inter-branch distance, branching angle and axial root trajectories from contrasting experimental systems to improve model parameterization.MethodsWe analyzed 2D root images of different wheat varieties (Triticum aestivum) from three different sources using automatic root tracking. Model input parameters and common parameter patterns were identified from extracted root system coordinates. Simulation studies were used to (1) link observed axial root trajectories with model input parameters (2) evaluate errors due to the 2D (versus 3D) nature of image sources and (3) investigate the effect of model parameter distributions on root foraging performance.ResultsDistributions of inter-branch distances were approximated with lognormal functions. Branching angles showed mean values <90°. Gravitropism and tortuosity parameters were quantified in relation to downwards reorientation and segment angles of root axes. Root system projection in 2D increased the variance of branching angles. Root foraging performance was very sensitive to parameter distribution and variance.Conclusions2D image analysis can systematically and efficiently analyze root system architectures and parameterize 3D root architecture models. Effects of root system projection (2D from 3D) and deflection (at rhizotron face) on size and distribution of particular parameters are potentially significant

A new model for root growth in soil with macropores

Abstract: Background and aimsThe use of standard dynamic root architecture models to simulate root growth in soil containing macropores failed to reproduce experimentally observed root growth patterns. We thus developed a new, more mechanistic model approach for the simulation of root growth in structured soil. Methods: In our alternative modelling approach, we distinguish between, firstly, the driving force for root growth, which is determined by the orientation of the previous root segment and the influence of gravitropism and, secondly, soil mechanical resistance to root growth. The latter is expressed by its inverse, soil mechanical conductance, and treated similarly to hydraulic conductivity in Darcy’s law. At the presence of macropores, soil mechanical conductance is anisotropic, which leads to a difference between the direction of the driving force and the direction of the root tip movement. Results: The model was tested using data from the literature, at pot scale, at macropore scale, and in a series of simulations where sensitivity to gravity and macropore orientation was evaluated. Conclusions: Qualitative and quantitative comparisons between simulated and experimentally observed root systems showed good agreement, suggesting that the drawn analogy between soil water flow and root growth is a useful one

Linking rhizosphere processes across scales: Opinion

Purpose Simultaneously interacting rhizosphere processes determine emergent plant behaviour, including growth, transpiration, nutrient uptake, soil carbon storage and transformation by microorganisms. However, these processes occur on multiple scales, challenging modelling of rhizosphere and plant behaviour. Current advances in modelling and experimental methods open the path to unravel the importance and interconnectedness of those processes across scales. Methods We present a series of case studies of state-of-the art simulations addressing this multi-scale, multi-process problem from a modelling point of view, as well as from the point of view of integrating newly available rhizosphere data and images. Results Each case study includes a model that links scales and experimental data to explain and predict spatial and temporal distribution of rhizosphere components. We exemplify the state-of-the-art modelling tools in this field: image-based modelling, pore-scale modelling, continuum scale modelling, and functional-structural plant modelling. We show how to link the pore scale to the continuum scale by homogenisation or by deriving effective physical parameters like viscosity from nano-scale chemical properties. Furthermore, we demonstrate ways of modelling the links between rhizodeposition and plant nutrient uptake or soil microbial activity. Conclusion Modelling allows to integrate new experimental data across different rhizosphere processes and scales and to explore more variables than is possible with experiments. Described models are tools to test hypotheses and consequently improve our mechanistic understanding of how rhizosphere processes impact plant-scale behaviour. Linking multiple scales and processes including the dynamics of root growth is the logical next step for future research.Open Access funding enabled and organized by Projekt DEAL.deutsche forschungsgemeinschaftdeutsche forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659natural environment research council (uk)Forschungszentrum Jülich GmbH (4205

Linking rhizosphere processes across scales: opinion

Purpose: Simultaneously interacting rhizosphere processes determine emergent plant behaviour, including growth, transpiration, nutrient uptake, soil carbon storage and transformation by microorganisms. However, these processes occur on multiple scales, challenging modelling of rhizosphere and plant behaviour. Current advances in modelling and experimental methods open the path to unravel the importance and interconnectedness of those processes across scales. Methods: We present a series of case studies of state-of-the art simulations addressing this multi-scale, multi-process problem from a modelling point of view, as well as from the point of view of integrating newly available rhizosphere data and images. Results: Each case study includes a model that links scales and experimental data to explain and predict spatial and temporal distribution of rhizosphere components. We exemplify the state-of-the-art modelling tools in this field: image-based modelling, pore-scale modelling, continuum scale modelling, and functional-structural plant modelling. We show how to link the pore scale to the continuum scale by homogenisation or by deriving effective physical parameters like viscosity from nano-scale chemical properties. Furthermore, we demonstrate ways of modelling the links between rhizodeposition and plant nutrient uptake or soil microbial activity. Conclusion: Modelling allows to integrate new experimental data across different rhizosphere processes and scales and to explore more variables than is possible with experiments. Described models are tools to test hypotheses and consequently improve our mechanistic understanding of how rhizosphere processes impact plant-scale behaviour. Linking multiple scales and processes including the dynamics of root growth is the logical next step for future research.Natural Environment Research Council (NERC): NE/S004920/

Mechanistic framework to link root growth models with weather and soil physical properties, including example applications to soybean growth in Brazil

Background and aimsRoot elongation is generally limited by a combination of mechanical impedance and water stress in most arable soils. However, dynamic changes of soil penetration resistance with soil water content are rarely included in models for predicting root growth. Better modelling frameworks are needed to understand root growth interactions between plant genotype, soil management, and climate. Aim of paper is to describe a new model of root elongation in relation to soil physical characteristics like penetration resistance, matric potential, and hypoxia.MethodsA new diagrammatic framework is proposed to illustrate the interaction between root elongation, soil management, and climatic conditions. The new model was written in Matlab®, using the root architecture model RootBox and a model that solves the 1D Richards equations for water flux in soil. Inputs: root architectural parameters for Soybean; soil hydraulic properties; root water uptake function in relation to matric flux potential; root elongation rate as a function of soil physical characteristics. Simulation scenarios: (a) compact soil layer at 16 to 20 cm; (b) test against a field experiment in Brazil during contrasting drought and normal rainfall seasons.Results(a) Soil compaction substantially slowed root growth into and below the compact layer. (b) Simulated root length density was very similar to field measurements, which was influenced greatly by drought. The main factor slowing root elongation in the simulations was evaluated using a stress reduction function.ConclusionThe proposed framework offers a way to explore the interaction between soil physical properties, weather and root growth. It may be applied to most root elongation models, and offers the potential to evaluate likely factors limiting root growth in different soils and tillage regimes

Statistical Characterization of the Root System Architecture Model CRootBox

The connection between the parametrization of three-dimensional (3D) root architecture models and characteristic measures of the simulated root systems is often not obvious. We used statistical methods to analyze the simulation outcome of the root architecture model CRootBox and built meta-models that determine the dependency of root system measures on model input parameters. Starting with a reference parameter set, we varied selected input parameters one at a time and used CRootBox to compute 1000 root system realizations as well as their root system measures. The obtained data sets were then statistically analyzed with regard to dependencies between input parameters, as well as distributions and correlations between different root system measures. While absolute root system measures (e.g., total root length) were approximately normally distributed, distributions of ratios of root system measures (e.g., root tip density) were highly asymmetric and could be approximated with inverse gamma distributions. We derived regression models (meta-models) that link significant model parameters to 18 widely used root system measures and determined correlations between different root system measures. Statistical analysis of 3D root architecture models helps to understand the impact of input parametrization on specific root architectural measures. Our developed meta-models can be used to determine the effect of parameter variations on the distribution of root system measures without running a full simulation. Model intercomparison and benchmarking of root architecture models is still missing. Our approach provides a means to compare different models with each other and with experimental data