Diagnosis of vascular parkinsonism: A new tool for gait hypokinesia occurring in older persons.

INTRODUCTION: Reliable diagnosis of vascular parkinsonism (VaP) in the presence of a gait hypokinesia is an issue that is encountered in geriatrics. The EVAMAR-AGEX study was focusing on the phenomenon of recurrent falls in older persons (OP) with this parkinsonian gait. The present study is focusing on the diagnosis of VaP-related parkinsonian gait by developing a diagnostic guidance model adapted to OP. METHODS: Data from baseline and the 2-year follow-up visit were used to carry out univariate analysis and calculation of odds ratios, allowing to identify relevant variables to include in the diagnostic guidance model. To evaluate the model, confusion matrices were created, evaluating true positive, false negative, false positive and true negative incidences, sensitivity and specificity, and negative and positive predictive values. RESULTS: 79 patients included 58% male; average age 81.24 years. VaP diagnosis according to Zijlmans criteria occurred in 28%; neurodegenerative parkinsonian syndromes in 72%. A 4-criteria model was established to facilitate diagnostic: lack of prior hallucinations, lack of movement disorders tremor excluded, no cognitive fluctuations, and ≥75 years of age at diagnosis. In combination of 4/4 criteria, all of them were required to disclose a specificity of 91% in the diagnosis of VaP. In combination of 3/4, in case of negative test, a negative predictive value for VaP diagnosis of 0.97 was obtained. CONCLUSION: The challenge of our tool is both to be able to rule out what is probably not a VaP and to argue what makes a VaP diagnosis probable in OP.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Older Persons Displaying Parkinsonian Gait: Prediction and Explanation of the Recurrent Falling Phenomenon and Its Related Poor Prognosis.

INTRODUCTION: Parkinsonian gait in older persons is a major risk factor for recurrent falling. This prospective, longitudinal study (named EVAMAR-AGEX) aimed to validate the threshold value of two or more falls per year for distinguishing non-recurrent (NRF) from recurrent fallers (RF), to explore predictive factors for recurrent falling, and to identify factors which underlie the transition of patients from NRF to RF. The study took place over 2 years, with an intermediate analysis at 1 year of follow-up. Herein, we report results after 2 years of follow-up. METHODS: Participants over the age of 65, diagnosed with parkinsonian gait, were followed over the course of 2 years. Induced parkinsonian syndrome and uncontrolled orthostatic hypotension were excluded. Assessments of motor, visual, and cognitive functions were carried out during visits at baseline. Between visits at 12 and 24 months of follow-up, data were collected by phone call every 2 months (falls, traumatic falls, hospitalizations, cognitive fluctuations, delirium, and mortality). Odds ratios (ORs) for a panel of predictive factors for recurrent falling were established using a Bayesian model. RESULTS: Sixty-six of the 79 initially enrolled participants progressed to the second year of the study, with a mean age of 80.57 (SD 6.3), 56% male, presenting parkinsonian gait (53% Parkinson's disease, 15% atypical neurodegenerative parkinsonism, 21% vascular parkinsonism, and 11% diffuse Lewy body disease). At 2 years of follow-up, 67% were RF. Univariate analysis revealed a previous history of falls to be the most significant predictive factor of recurrent falls (OR 13.16, credibility interval [CrI] [95%] 4.04-53.73), and this was reinforced at 2 years of follow-up compared to the intermediate 1-year analysis (OR 11.73, CrI [95%] 4.33-35.28). Multivariate analysis confirmed a previous history of falls (OR 13.20, CrI [95%] 3.29-72.08) and abnormal posture (OR 3.59, CrI [95%] 1.37-11.26) to be predictive factors for recurrent falling. Cognitive decline and fluctuating cognition were associated with the transition from NRF to RF (-3.5 MMSE points for participants transitioning from NRF to RF). CONCLUSION: Within this population of older persons presenting parkinsonian gait, a previous history of falls and abnormal posture may be used to easily identify individuals at risk of recurrent falls. Cognitive decline and fluctuations may underlie the transition of NRF to RF.

Estimating the carbon storage potential and greenhouse gas emissions of French arable cropland using high-resolution modeling.

Many studies have assessed the potential of agricultural practices to sequester carbon (C). A comprehensive evaluation of impacts of agricultural practices requires not only considering C storage but also direct and indirect emissions of greenhouse gases (GHG) and their side effects (e.g., on the water cycle or agricultural production). We used a high-resolution modeling approach with the Simulateur mulTIdisciplinaire pour les Cultures Standard soil-crop model to quantify soil organic C (SOC) storage potential, GHG balance, biomass production and nitrogen- and water-related impacts for all arable land in France for current cropping systems (baseline scenario) and three mitigation scenarios: (i) spatial and temporal expansion of cover crops, (ii) spatial insertion and temporal extension of temporary grasslands (two sub-scenarios) and (iii) improved recycling of organic resources as fertilizer. In the baseline scenario, SOC decreased slightly over 30 years in crop-only rotations but increased significantly in crop/temporary grassland rotations. Results highlighted a strong trade-off between the storage rate per unit area (kg C ha-1  year-1 ) of mitigation scenarios and the areas to which they could be applied. As a result, while the most promising scenario at the field scale was the insertion of temporary grassland (+466 kg C ha-1  year-1 stored to a depth of 0.3 m compared to the baseline, on 0.68 Mha), at the national scale, it was by far the expansion of cover crops (+131 kg C ha-1  year-1 , on 17.62 Mha). Side effects on crop production, water irrigation and nitrogen emissions varied greatly depending on the scenario and production situation. At the national scale, combining the three mitigation scenarios could mitigate GHG emissions of current cropping systems by 54% (-11.2 from the current 20.5 Mt CO2 e year-1 ), but the remaining emissions would still lie far from the objective of C-neutral agriculture.

The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands.

Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: -64 ±â€¯74 g C m-2 yr-1 (animal density reduction) and -81 ±â€¯74 g C m-2 yr-1 (N and animal density reduction), against the baseline of -30.5 ±â€¯69.5 g C m-2 yr-1 (LSU [livestock units] ≥ 0.76 ha-1 yr-1). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N2O-N emissions decreased from 0.34 ±â€¯0.22 (baseline) to 0.1 ±â€¯0.05 g N m-2 yr-1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ±â€¯8.1 t C LSU-1 yr-1 across sites). The highest N2O-N intensities (N2O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions.

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes.

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

Modeled Changes in Potential Grassland Productivity and in Grass-Fed Ruminant Livestock Density in Europe over 1961-2010.

About 25% of European livestock intake is based on permanent and sown grasslands. To fulfill rising demand for animal products, an intensification of livestock production may lead to an increased consumption of crop and compound feeds. In order to preserve an economically and environmentally sustainable agriculture, a more forage based livestock alimentation may be an advantage. However, besides management, grassland productivity is highly vulnerable to climate (i.e., temperature, precipitation, CO2 concentration), and spatial information about European grassland productivity in response to climate change is scarce. The process-based vegetation model ORCHIDEE-GM, containing an explicit representation of grassland management (i.e., herbage mowing and grazing), is used here to estimate changes in potential productivity and potential grass-fed ruminant livestock density across European grasslands over the period 1961-2010. Here "potential grass-fed ruminant livestock density" denotes the maximum density of livestock that can be supported by grassland productivity in each 25 km × 25 km grid cell. In reality, livestock density could be higher than potential (e.g., if additional feed is supplied to animals) or lower (e.g., in response to economic factors, pedo-climatic and biotic conditions ignored by the model, or policy decisions that can for instance reduce livestock numbers). When compared to agricultural statistics (Eurostat and FAOstat), ORCHIDEE-GM gave a good reproduction of the regional gradients of annual grassland productivity and ruminant livestock density. The model however tends to systematically overestimate the absolute values of productivity in most regions, suggesting that most grid cells remain below their potential grassland productivity due to possible nutrient and biotic limitations on plant growth. When ORCHIDEE-GM was run for the period 1961-2010 with variable climate and rising CO2, an increase of potential annual production (over 3%) per decade was found: 97% of this increase was attributed to the rise in CO2, -3% to climate trends and 15% to trends in nitrogen fertilization and deposition. When compared with statistical data, ORCHIDEE-GM captures well the observed phase of climate-driven interannual variability in grassland production well, whereas the magnitude of the interannual variability in modeled productivity is larger than the statistical data. Regional grass-fed livestock numbers can be reproduced by ORCHIDEE-GM based on its simple assumptions and parameterization about productivity being the only limiting factor to define the sustainable number of animals per unit area. Causes for regional model-data misfits are discussed, including uncertainties in farming practices (e.g., nitrogen fertilizer application, and mowing and grazing intensity) and in ruminant diet composition, as well as uncertainties in the statistical data and in model parameter values.

Priming effect and microbial diversity in ecosystem functioning and response to global change: a modeling approach using the SYMPHONY model.

Integration of the priming effect (PE) in ecosystem models is crucial to better predict the consequences of global change on ecosystem carbon (C) dynamics and its feedbacks on climate. Over the last decade, many attempts have been made to model PE in soil. However, PE has not yet been incorporated into any ecosystem models. Here, we build plant/soil models to explore how PE and microbial diversity influence soil/plant interactions and ecosystem C and nitrogen (N) dynamics in response to global change (elevated CO2 and atmospheric N depositions). Our results show that plant persistence, soil organic matter (SOM) accumulation, and low N leaching in undisturbed ecosystems relies on a fine adjustment of microbial N mineralization to plant N uptake. This adjustment can be modeled in the SYMPHONY model by considering the destruction of SOM through PE, and the interactions between two microbial functional groups: SOM decomposers and SOM builders. After estimation of parameters, SYMPHONY provided realistic predictions on forage production, soil C storage and N leaching for a permanent grassland. Consistent with recent observations, SYMPHONY predicted a CO2 -induced modification of soil microbial communities leading to an intensification of SOM mineralization and a decrease in the soil C stock. SYMPHONY also indicated that atmospheric N deposition may promote SOM accumulation via changes in the structure and metabolic activities of microbial communities. Collectively, these results suggest that the PE and functional role of microbial diversity may be incorporated in ecosystem models with a few additional parameters, improving accuracy of predictions.

Disentangling coordination among functional traits using an individual-centred model: impact on plant performance at intra- and inter-specific levels.

BACKGROUND: Plant functional traits co-vary along strategy spectra, thereby defining trade-offs for resource acquisition and utilization amongst other processes. A main objective of plant ecology is to quantify the correlations among traits and ask why some of them are sufficiently closely coordinated to form a single axis of functional specialization. However, due to trait co-variations in nature, it is difficult to propose a mechanistic and causal explanation for the origin of trade-offs among traits observed at both intra- and inter-specific level. METHODOLOGY/PRINCIPAL FINDINGS: Using the G(EMINI) individual-centered model which coordinates physiological and morphological processes, we investigated with 12 grass species the consequences of deliberately decoupling variation of leaf traits (specific leaf area, leaf lifespan) and plant stature (height and tiller number) on plant growth and phenotypic variability. For all species under both high and low N supplies, simulated trait values maximizing plant growth in monocultures matched observed trait values. Moreover, at the intraspecific level, plastic trait responses to N addition predicted by the model were in close agreement with observed trait responses. In a 4D trait space, our modeling approach highlighted that the unique trait combination maximizing plant growth under a given environmental condition was determined by a coordination of leaf, root and whole plant processes that tended to co-limit the acquisition and use of carbon and of nitrogen. CONCLUSION/SIGNIFICANCE: Our study provides a mechanistic explanation for the origin of trade-offs between plant functional traits and further predicts plasticity in plant traits in response to environmental changes. In a multidimensional trait space, regions occupied by current plant species can therefore be viewed as adaptive corridors where trait combinations minimize allometric and physiological constraints from the organ to the whole plant levels. The regions outside this corridor are empty because of inferior plant performance.

The TOCATTA-χ model for assessing 14C transfers to grass: an evaluation for atmospheric operational releases from nuclear facilities.

Radioactive (14)C is formed as a by-product of nuclear power generation and from the operation of nuclear fuel reprocessing plants like AREVA-NC La Hague (North France), which releases about 15 TBq per year of (14)C into the atmosphere. This article evaluates a recently improved radioecology model (TOCATTA-χ) to assess (14)C transfers to grassland ecosystems under normal operating conditions. The new version of the TOCATTA model (TOCATTA-χ) includes developments that were derived from PaSiM, a pasture model for simulating grassland carbon and radiocarbon cycling. The TOCATTA-χ model has been tested against observations of (14)C activity concentrations in grass samples collected monthly from six plots which are located around the periphery of the reprocessing plant. Simulated (14)C activities are consistent with observations on both intensively managed and poorly managed grasslands, but an adaptation of the mean turn-over time for (14)C within the plant is necessary in the model to account for different management practices. When atmospheric (14)C activity concentrations are directly inferred from observations, TOCATTA-χ performs better than TOCATTA (the root mean square error is decreased by 45%), but when atmospheric (14)C activity concentrations are not known and must be calculated, the uncertainty associated with the TOCATTA-χ model outcomes is estimated to be larger than the standard deviation of the observations.