Unlabelled: Nitrogen (N) deposition is globally considered as a major threat to ecosystem functioning with important consequences for biodiversity, carbon sequestration and N retention. Lowered N retention as manifested by elevated concentrations of inorganic N in surface waters indicates ecosystem N saturation. Nitrate (NO3) concentrations in runoff from semi-natural catchments typically show an annual cycle, with low concentrations during the summer and high concentrations during the winter. Process-oriented catchment-scale biogeochemical models provide tools for simulation and testing changes in surface water and soil chemistry in response to changes in sulphur (S) and N deposition and climate. Here we examine the ability of MAGIC to simulate the observed monthly as well as the long-term trends over 10-35 years of inorganic N concentrations in streamwaters from four monitored headwater catchments in Europe: Čertovo Lake in the Czech Republic, Afon Gwy at Plynlimon, UK, Storgama, Norway and G2 NITREX at Gårdsjön, Sweden. The balance between N inputs (mineralization+deposition) and microbial immobilization and plant uptake defined the seasonal pattern of NO3 leaching. N mineralization and N uptake were assumed to be governed by temperature, described by Q10 functions. Seasonality in NO3 concentration and fluxes were satisfactorily reproduced at three sites (R2 of predicted vs. modelled concentrations varied between 0.32 and 0.47 and for fluxes between 0.36 and 0.88). The model was less successful in reproducing the observed NO3 concentrations and fluxes at the experimental N addition site G2 NITREX (R2=0.01 and R2=0.19, respectively). In contrast to the three monitored sites, Gårdsjön is in a state of change from a N-limited to N-rich ecosystem due to 20 years of experimental N addition. At Gårdsjön the measured NO3 seasonal pattern did not follow typical annual cycle for reasons which are not well understood, and thus not simulated by the model.
Capsule: The MAGIC model is able to simulate NO3 leaching on a monthly as well as an annual basis, and thus to reproduce the seasonal and short-term variations in N dynamics.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.scitotenv.2015.05.047 | DOI Listing |
Front Endocrinol (Lausanne)
January 2025
Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Background: Insulin resistance is tightly related to cognition; however, the causal association between them remains a matter of debate. Our investigation aims to establish the causal relationship and direction between insulin resistance and cognition, while also quantifying the mediating role of brain cortical structure in this association.
Methods: The publicly available data sources for insulin resistance (fasting insulin, homeostasis model assessment beta-cell function and homeostasis model assessment insulin resistance, proinsulin), brain cortical structure, and cognitive phenotypes (visual memory, reaction time) were obtained from the MAGIC, ENIGMA, and UK Biobank datasets, respectively.
J Pharm Sci
January 2025
Drug Delivery and Disposition, KU Leuven, Department of Pharmaceutical and Pharmacological Sciences, Campus Gasthuisberg ON2, Herestraat 49 b921, 3000 Leuven, Belgium. Electronic address:
In order to evaluate the stability of an amorphous solid dispersion (ASD) it is crucial to be able to accurately determine whether the ASD components are homogeneously mixed or not. Several solid-state analysis techniques are at the disposal of the formulation scientist, such as for example modulated differential scanning calorimetry (mDSC) and solid-state nuclear magnetic resonance spectroscopy (ssNMR). ssNMR is a robust, versatile, and accurate analysis technique with a large number of application possibilities.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
This paper proposes a hierarchical framework-based solution to address the challenges of vehicle state estimation and lateral stability control in four-wheel independent drive electric vehicles. First, based on a three-degrees-of-freedom four-wheel vehicle model combined with the Magic Formula Tire model (MF-T), a hierarchical estimation method is designed. The upper layer employs the Kalman Filter (KF) and Extended Kalman Filter (EKF) to estimate the vertical load of the wheels, while the lower layer utilizes EKF in conjunction with the upper-layer results to further estimate the lateral forces, longitudinal velocity, and lateral velocity, achieving accurate vehicle state estimation.
View Article and Find Full Text PDFHealth Res Policy Syst
January 2025
Center for Clinical Research and Prevention, Health Promotion and Prevention, Frederiksberg Hospital, Frederiksberg, Denmark.
Background: Childhood obesity is a preventable global public health challenge, increasingly recognized as a complex problem, stemming from complex drivers. Obesity is characterized by multiple interdependencies and diverse influences at different societal levels. Tackling childhood obesity calls for a holistic approach that engages with complexity and recognizes that there is no single "magic bullet" intervention to prevent obesity.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Physics, Indian Institute of Science, Bangalore 560012, India.
The low-frequency resistance fluctuations, or noise, in electrical resistance not only set a performance benchmark in devices but also form a sensitive tool to probe nontrivial electronic phases and band structures in solids. Here, we report the measurement of such noise in the electrical resistance in twisted bilayer graphene (tBLG), where the layers are misoriented close to the magic angle (θ ∼ 1°). At high temperatures ( ≳ 60-70 K), the power spectral density (PSD) of the fluctuation inside the low-energy moiré bands is predominantly ∝1/, where is the frequency, being generally lowest close to the magic angle, and can be well-explained within the conventional McWhorter model of the '1/ noise' with trap-assisted density-mobility fluctuations.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!