NEMA, a functional–structural model of nitrogen economy within wheat culms after flowering. II. Evaluation and sensitivity analysis

Background and Aims Simulating nitrogen economy in crop plants requires formalizing the interactions between soil nitrogen availability, root nitrogen acquisition, distribution between vegetative organs and remobilization towards grains. This study evaluates and analyses the functional–structural and mechanistic model of nitrogen economy, NEMA (Nitrogen Economy Model within plant Architecture), developed for winter wheat (Triticum aestivum) after flowering.Methods NEMA was calibrated for field plants under three nitrogen fertilization treatments at flowering. Model behaviour was investigated and sensitivity to parameter values was analysed. Key Results Nitrogen content of all photosynthetic organs and in particular nitrogen vertical distribution along the stem and remobilization patterns in response to fertilization were simulated accurately by the model, from Rubisco turnover modulated by light intercepted by the organ and a mobile nitrogen pool. This pool proved to be a reliable indicator of plant nitrogen status, allowing efficient regulation of nitrogen acquisition by roots, remobilization from vegetative organs and accumulation in grains in response to nitrogen treatments. In our simulations, root capacity to import carbon, rather than carbon availability, limited nitrogen acquisition and ultimately nitrogen accumulation in grains, while Rubisco turnover intensity mostly affected dry matter accumulation in grains. Conclusions NEMA enabled interpretation of several key patterns usually observed in field conditions and the identification of plausible processes limiting for grain yield, protein content and root nitrogen acquisition that could be targets for plant breeding; however, further understanding requires more mechanistic formalization of carbon metabolism. Its strong physiological basis and its realistic behaviour support its use to gain insights into nitrogen economy after flowering

Size effect on magnetism of Fe thin films in Fe/Ir superlattices

In ferromagnetic thin films, the Curie temperature variation with the thickness is always considered as continuous when the thickness is varied from $n$ to $n+1$ atomic planes. We show that it is not the case for Fe in Fe/Ir superlattices. For an integer number of atomic planes, a unique magnetic transition is observed by susceptibility measurements, whereas two magnetic transitions are observed for fractional numbers of planes. This behavior is attributed to successive transitions of areas with $n$ and $n+1$ atomic planes, for which the $T_c$'s are not the same. Indeed, the magnetic correlation length is presumably shorter than the average size of the terraces. Monte carlo simulations are performed to support this explanation.Comment: LaTeX file with Revtex, 5 pages, 5 eps figures, to appear in Phys. Rev. Let

Modelling Kinetics of Plant Canopy Architecture - Concepts and Applications

Most crop models simulate the crop canopy as an homogeneous medium. This approach enables modelling of mass and energy transfer through relatively simple equations, and is useful for understanding crop production. However, schematisation of an homogeneous medium cannot address the heterogeneous nature of canopies and interactions between plants or plant organs, and errors in calculation of light interception may occur. Moreover, conventional crop models do not describe plant organs before they are visible externally e.g young leaves of grasses. The conditions during early growth of individual organs are important determinants of final organ size, causing difficulties in incorporating effects of environmental stresses in such models. Limited accuracy in describing temporal source-sink relationships also contributes to difficulty in modelling dry matter distribution and paramaterisation of harvest indices. Functional-architectural modelling overcomes these limitations by (i) representing crops as populations of individual plants specified in three dimensions and (ii) by modelling whole plant growth and development from the behaviour of individual organs, based on sound models of organs such as leaves and internodes. Since individual plants consist of numerous organs, generic models of organ growth applicable across species are desirable. Consequently, we are studying the development of individual organs, and parameterising it in terms of environmental variables and plant characteristics. Models incorporating plant architecture are currently applied in education, using dynamic visual representation for teaching growth and development. In research, the 3D representation of plants addresses issues presented above and new applications including modelling of pesticide distribution, fungal spore dispersal through splashing and plant to plant heterogeneity

Frailty Index and incident mortality, hospitalization and institutionalization in Alzheimer's disease: data from the ICTUS study.

BACKGROUND: The identification of an objective evaluation of frailty capable of predicting adverse outcomes in Alzheimer's disease is increasingly discussed. The purpose of this study was to investigate whether the Frailty Index (FI) predicts hospitalization, institutionalization, and mortality in Alzheimer's disease patients. METHODS: A prospective multicenter cohort study (follow-up = 2 years) that included 1,191 participants with Alzheimer's disease was carried out. The outcomes of interest were incident hospitalization, institutionalization, and mortality. The FI was calculated as the ratio of actual to thirty potential deficits, that is, deficits presented by the participant divided by 30. Severity of dementia was assessed using the Clinical Dementia Rating score. Cox proportional hazard models were performed. RESULTS: Mean age of the study sample was 76.2 (SD = 7.6) years. A quadratic relationship of the FI with age was reported at baseline (R 2 = .045, p < .001). The FI showed a statistically significant association with mortality (age- and gender-adjusted hazard ratio [HR] = 1.019, 95% confidence interval [CI] = 1.002-1.037, p = .031) and hospitalization (age- and gender-adjusted HR = 1.017, 95% CI = 1.006-1.029, p = .004) and a borderline significance with institutionalization. When the Clinical Dementia Rating score was simultaneously included in the age- and gender-adjusted models, the FI confirmed its predictive capacity for hospitalization (HR = 1.019, 95% CI = 1.006-1.032, p = .004), whereas the Clinical Dementia Rating score was the strongest predictor for mortality (HR = 1.922, 95% CI = 1.256-2.941, p = .003) and institutionalization (HR = 1.955, 95%CI = 1.427-2.679, p < .001). CONCLUSIONS: The FI is a robust predictor of adverse outcomes even after the stage of the underlying dementia is considered. Future work should evaluate the clinical implementation of the FI in the assessment of demented individuals in order to improve the personalization of care

Electron-hadron shower discrimination in a liquid argon time projection chamber

By exploiting structural differences between electromagnetic and hadronic showers in a multivariate analysis we present an efficient Electron-Hadron discrimination algorithm for liquid argon time projection chambers, validated using Geant4 simulated data

Climate-smart solutions for Mali: Findings from implementing the Climate-Smart Agriculture Prioritization Framework

This info note summarizes findings of a pilot project aiming to develop a participatory framework to prioritize CSA practices and interventions to guide CSA investments in Mali. It was undertaken by researchers from the Malian Association of Awareness to Sustainable Development (AMEDD) and the International Center Tropical Agriculture (CIAT) as part of the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). Implementation was led by the Agency of Environment and Sustainable Development (AEDD) on behalf of the National Science-Policy dialogue platforms on Climate Change, Agriculture and Food Security (CCASA). Supported by the West Africa Regional Program, this research is part of a multi-region Prioritization project funded by CCAFS Flagship 1 on Climate-Smart Agricultural Practices