13 research outputs found

    Resistance and reconfiguration of natural flexible submerged vegetation in hydrodynamic river modelling

    Get PDF
    In-stream submerged macrophytes have a complex morphology and several species are not rigid, but are flexible and reconfigure along with the major flow direction to avoid potential damage at high stream velocities. However, in numerical hydrodynamic models, they are often simplified to rigid sticks. In this study hydraulic resistance of vegetation is represented by an adapted bottom friction coefficient and is calculated using an existing two layer formulation for which the input parameters were adjusted to account for (i) the temporary reconfiguration based on an empirical relationship between deflected vegetation height and upstream depth-averaged velocity, and (ii) the complex morphology of natural, flexible, submerged macrophytes. The main advantage of this approach is that it removes the need for calibration of the vegetation resistance coefficient. The calculated hydraulic roughness is an input of the hydrodynamic model Telemac 2D, this model simulates depth-averaged stream velocities in and around individual vegetation patches. Firstly, the model was successfully validated against observed data of a laboratory flume experiment with three macrophyte species at three discharges. Secondly, the effect of reconfiguration was tested by modelling an in situ field flume experiment with, and without, the inclusion of macrophyte reconfiguration. The inclusion of reconfiguration decreased the calculated hydraulic roughness which resulted in smaller spatial variations of simulated stream velocities, as compared to the model scenario without macrophyte reconfiguration. We discuss that including macrophyte reconfiguration in numerical models input, can have significant and extensive effects on the model results of hydrodynamic variables and associated ecological and geomorphological parameters

    Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

    Get PDF
    A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

    A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration

    Get PDF
    Background High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ −6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER. Methods The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression. Findings In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17–21%), 2% (1–3%), 8% (7–10%) and 6% (3–9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75–0.81), 0.58 (0.53–0.64), 0.71 (0.69–0.74) and 0.67 (0.62–0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92–1.24). Interpretation Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted fo
    corecore