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Objective Rituximab (RTX)‐treated patients exhibit suboptimal responses to COVID‐19 vaccines. However, existing research primarily involves patients already receiving RTX when vaccines were introduced, failing to account for the current landscape where patients are vaccinated before initiating RTX. Our objective was to compare the serological response to COVID‐19 vaccines in patients vaccinated before or after RTX initiation. Methods We included 254 RTX‐treated patients with autoimmune inflammatory rheumatic diseases (AIIRDs) and 113 blood donors (BDs) in a retrospective, observational cohort study. Patients were categorized based on the timing of RTX treatment relative to primary COVID‐19 vaccination. Serological vaccine responses were assessed using three immunoassays, and logistic regression analysis was used to identify predictors of serological response. Results Patients vaccinated before initiating RTX treatment had significantly higher seroconversion rates of SARS‐CoV‐2 immunoglobulin G (87%) and neutralizing antibodies (91%) compared with those receiving RTX before and after vaccination (n = 132) (61% and 65%, respectively). In the logistic regression analysis, a positive serological response was associated with the number of vaccines administered >9 months after the last RTX treatment. Patients receiving the highest number of vaccines with >9 months after RTX showed a response comparable to that of the BDs. Conclusion Vaccinating before RTX initiation yields a robust serological response in patients with AIIRDs. Furthermore, we highlight the reversibility of antibody impairment after RTX treatment cessation, provided that adequate vaccinations occur within a minimum of 9 months after RTX. Our findings offer essential insights for clinical decision‐making regarding COVID‐19 vaccination and RTX treatment, alleviating concerns about future RTX use.

Accurate streamflow estimation is essential for effective water resource management and adapting to extreme events in the face of changing climate conditions. Hydrological models have been the conventional approach for streamflow interpolation and extrapolation in time and space for the past few decades. However, their large-scale applications have encountered challenges, including issues related to efficiency, complex parameterization, and constrained performance. Deep learning methods, such as long short-term memory (LSTM) networks, have emerged as a promising and efficient approach for large-scale streamflow estimation. In this study, we have conducted a series of experiments to identify optimal hybrid modeling schemes to consolidate physically based models with LSTM aimed at enhancing streamflow estimation in Denmark. The results show that the hybrid modeling schemes outperformed the Danish National Water Resources Model (DKM) in both gauged and ungauged basins. While the standalone LSTM rainfall–runoff model outperformed DKM in many basins, it faced challenges when predicting the streamflow in groundwater-dependent catchments. A serial hybrid modeling scheme (LSTM-q), which used DKM outputs and climate forcings as dynamic inputs for LSTM training, demonstrated higher performance. LSTM-q improved the mean Nash–Sutcliffe efficiency (NSE) by 0.22 in gauged basins and 0.12 in ungauged basins compared to DKM. Similar accuracy improvements were achieved with alternative hybrid schemes, i.e., by predicting the residuals between DKM-simulated streamflow and observations using LSTM. Moreover, the developed hybrid models enhanced the accuracy of extreme events, which encourages the integration of hybrid models within an operational forecasting framework. This study highlights the advantages of synergizing existing physically based hydrological models (PBMs) with LSTM models, and the proposed hybrid schemes hold the potential to achieve high-quality large-scale streamflow estimations.

Denmark's complete reliance on groundwater for water supply presents a unique case study in management of natural resources, urban planning, and water resilience in the face of climate change. This paper examines the groundwater management strategies in Denmark in general, focusing on Denmark's four largest cities—Copenhagen, Aarhus, Odense, and Aalborg— each facing distinct challenges due to their demographic, geographical, hydrogeological, and economic characteristics. Through analysis of these cities' approaches to groundwater management, this research contributes to the global discourse on sustainable urban water supply systems. As coastal groundwater cities (CGC), these urban areas must navigate the complexities of sustaining growing populations, mitigating climate change impacts, and coastal processes while ensuring the long-term viability of their groundwater resources. Copenhagen and Aalborg, built atop semi-confined fractured and locally karstic carbonate rocks, highlights the specific challenges associated with karstic groundwater systems, while, Aarhus, and Odense built on glaciofluvial aquifers faces different issues. The different groundwater challenges in these cities underscores the importance of integrating urban development with water resource management and environmental sustainability, offering valuable insights and lessons learned for other regions facing similar challenges. This study, thus not only sheds light on Denmark's groundwater management practices, but also emphasizes the need for innovative solutions to ensure the resilience of urban water supply systems in a changing climate and increasing pressures of emerging organic contaminants and elevated concentrations of geogenic elements induced by water abstraction and fluctuating water tables. Advanced Danish monitoring and modelling tools applied to support decision-making and innovation within the water sector are continuously developed and improved to support resilient and sustainable management of the available water resources.

In this study, we upscale and simplify hydrostratigraphic information from a detailed model for Denmark to a pan-European scale. This is part of a larger project to develop a harmonised overview of the volume and depth of groundwater resources in a quasi-3D European groundwater resource model. A 10 km grid and a maximum of c. 10 hydrostratigraphic layers were chosen as the common scale for the European database. The Danish information is based on the national water resources model (the DK-model), where the information is significantly more detailed (100 m grid and up to 26 layers). Information was transferred from the DK-model to the quasi-3D model by a method involving computations of mean volumes and expert assessment to reduce layers in each cell. In this process, detailed hydrostratigraphic information is lost, which could otherwise be used for local groundwater flow modelling in Denmark. However, the strength of the quasi-3D model is that it still contains the volumes of all hydrostratigraphic units, both the saturated and unsaturated parts. Hence, the upscaled model can contribute to a relatively precise calculation of European groundwater resources for the quantitative assessment of groundwater status across Europe at a 10 × 10 km scale.

Objectives To investigate lectin pathway proteins (LPPs) as biomarkers for axial spondyloarthritis (axSpA) in a cross-sectional cohort with a suspicion of axSpA, comprising newly diagnosed axSpA and chronic low back pain (cLBP) individuals.Methods Serum samples from 515 participants within the OptiRef cohort, including 151 axSpA patients and 364 cLBP patients, were measured using immunoassays for LPPs (mannan-binding lectin (MBL), collectin liver-1 (CL-L1), M-ficolin, H-ficolin and L-ficolin, MBL-associated serine proteases (MASP)−1, –2 and –3, MBL-associated proteins (MAp19 and MAp44) and the complement activation product C3dg).Results Serum levels of L-ficolin, MASP-2 and C3dg were elevated in axSpA patients, whereas levels of MASP-3 and CL-L1 were decreased, and this remained significant for C3dg and MASP-3 after adjustment for C reactive protein (CRP). A univariate regression analysis showed serum levels of CL-L1, MASP-2, MASP-3 and C3dg to predict the diagnosis of axSpA, and MASP-3 and C3dg remained significant in a multivariate logistic regression analysis. Assessment of the diagnostic potential showed that a combination of human leukocyte antigen B27 (HLA-B27) and measurements of L-ficolin, MASP-3 and C3dg increased the diagnostic specificity for axSpA, however, with a concomitant loss of sensitivity.Conclusions Serum levels of complement activation, that is, C3dg, and MASP-3 differed significantly between axSpA and cLBP patients after adjustment for CRP. Although combining HLA-B27 with measurements of L-ficolin, MASP-3 and C3dg increased the diagnostic specificity for axSpA, this seems unjustified due to the concomitant loss of sensitivity. However, both C3dg and MASP-3 were associated with axSpA diagnosis in multivariate logistic regression, suggesting an involvement of complement in the inflammatory processes and possibly pathogenesis in axSpA.

A simple analytical vortex model is presented and used to study an idealized wind turbine rotor in uniform and sheared inflow, respectively. Our model predicts that 1D momentum theory should be applied locally when modelling a non-uniformly loaded rotor in a sheared inflow. Hence the maximum local power coefficient (expressed with respect to the local, upstream velocity) of an ideal rotor is not affected by the presence of shear. We study the interaction between the wake vorticity generated by the rotor and the wind shear vorticity and find that their mutual interaction results in no net generation of axial vorticity: the wake effects and the shear effects exactly cancel each other out. This means that there are no resulting cross-shear-induced velocities and therefore also no cross-shear deflection of the wake in this model.

There is an urgent demand for assessments of climate change impacts on the hydrological cycle at high spatial resolutions. In particular, the impacts on shallow groundwater levels, which can lead to both flooding and drought, have major implications for agriculture, adaptation, and urban planning. Predicting such hydrological impacts is typically performed using physically based hydrological models (HMs). However, such models are computationally expensive, especially at high spatial resolutions. This study is based on the Danish national groundwater model, set up as a distributed, integrated surface–subsurface model at a 500 m horizontal resolution. Recently, a version at a higher resolution of 100 m was created, amongst others, to better represent the uppermost groundwater table and to meet end-user demands for water management and climate adaptation. However, the increase in resolution of the hydrological model also increases computational bottleneck. To evaluate climate change impacts, a large ensemble of climate models was run with the 500 m hydrological model, while performing the same ensemble run with the 100 m resolution nationwide model was deemed infeasible. The desired outputs at the 100 m resolution were produced by developing a novel, hybrid downscaling method based on machine learning (ML). Hydrological models for five subcatchments, covering around 9 % of Denmark and selected to represent a range of hydrogeological settings, were run at 100 m resolutions with forcings from a reduced ensemble of climate models. Random forest (RF) algorithms were established using the simulated climate change impacts (future – present) on water table depth at 100 m resolution from those submodels as training data. The trained downscaling algorithms were then applied to create nationwide maps of climate-change-induced impacts on the shallow groundwater table at 100 m resolutions. These downscaled maps were successfully validated against results from a validation submodel at a 100 m resolution excluded from training the algorithms, and compared to the impact signals from the 500 m HM across Denmark. The suggested downscaling algorithm also opens for the spatial downscaling of other model outputs. It has the potential for further applications where, for example, computational limitations inhibit running distributed HMs at fine resolutions.

Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analysed, and the result of a research experiment is presented using model weighting with the participation of six climate model experts and six hydrological model experts. For the experiment, seven climate models are a priori selected from a larger EURO-CORDEX (Coordinated Regional Downscaling Experiment – European Domain) ensemble of climate models, and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual rounds of elicitation of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological-impact modellers in general are more open for assigning weights to different models in a multi-model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only re-establish a uniform weight between climate models.

We developed ordinary least squares regression models to predict uptake of cadmium and lead, two metals that are of public health significance because of their toxicity, in the edible tissues of lettuce. Models were parameterised using data on soil metal concentration, pH, and organic carbon. To assess the impact of physical contamination in form of aerial deposition and soil-splash on the metal concentration in lettuce, separate linear regression models were parameterised for indoor- and outdoor-grown lettuce, assuming the physical contamination to be negligible for indoor conditions. Both Cd models showed high model fit and strong predictive performance, when tested on an independent dataset, suggesting uptake via roots to be dominant. For Pb, the indoor model performed better than the outdoor model, indicating that physical contamination, contributes significantly to metal concentration in lettuce leaves. Our results highlight the importance of the parameterisation data when developing uptake models for predictions and risk assessment. Regression models for predicting Pb concentration in lettuce based on indoor data should not be used for predicting lettuce concentrations cultivated in outdoor conditions unless the contribution of physical contamination is explicitly accounted for.

The power performance of a wind turbine in complex terrain is studied by means of large eddy simulations (LESs). The simulations show that the turbine performance is significantly different compared to what should be expected from the available wind. The reason for this deviation is that the undisturbed flow field behind the turbine is non-homogeneous and therefore results in a very different wake development and induction than seen for a turbine in flat homogeneous terrain.

Blockage effects due to the interaction of five wind turbines in a row are investigated through both Reynolds-averaged Navier–Stokes simulations and site measurements. Since power performance tests are often carried out at sites consisting of several turbines in a row, the objective of this study is to evaluate whether the power performance of the five turbines differs from that of an isolated turbine. A number of simulations are performed, in which we vary the turbine inter-spacing (1.8, 2 and 3 rotor diameters) and the inflow angle between the incoming wind and the orthogonal line to the row (from 0 to 45∘). Different values of the free-stream velocity are considered to cover a broad wind speed range of the power curve. Numerical results show consistent power deviations for all five turbines when compared to the isolated case. The amplitude of these deviations depends on the location of the turbine within the row, the inflow angle, the inter-spacing and the power curve region of operation. We show that the power variations do not cancel out when averaging over a large inflow sector (from −45 to +45∘) and find an increase in the power output of up to +1 % when compared to the isolated case under idealised conditions (neutral atmospheric conditions, no vertical wind shear or ground effects). We simulate power performance “measurements” with both a virtual mast and nacelle-mounted lidar and find a combination of power output increase and upstream velocity reduction, which causes an increase of +4 % in the power coefficient under idealised conditions. We also use measurements from a real site consisting of a row of five wind turbines to validate the numerical results. From the analysis of the measurements, we also show that the power performance is impacted by the neighbouring turbines. Compared to when the inflow is perpendicular to the row, the power output varies by +1.8 % and −1.8 % when the turbine is the most downwind and upwind of the line, respectively.

Pesticides are contaminants of priority concern that continue to present a significant risk to drinking water quality. While pollution mitigation in catchment systems is considered a cost-effective alternative to costly drinking water treatment, the effectiveness of pollution mitigation measures is uncertain and needs to be able to consider local biophysical, agronomic, and social aspects. We developed a probabilistic decision support tool (DST) based on spatial Bayesian belief networks (BBNs) that simulates inherent pesticide leaching risk to ground- and surface water quality to inform field-level pesticide mitigation strategies in a small (3.1 km2) drinking water catchment with limited observational data. The DST accounts for the spatial heterogeneity in soil properties, topographic connectivity, and agronomic practices; the temporal variability of climatic and hydrological processes; and uncertainties related to pesticide properties and the effectiveness of management interventions. The rate of pesticide loss via overland flow and leaching to groundwater and the resulting risk of exceeding a regulatory threshold for drinking water was simulated for five active ingredients. Risk factors included climate and hydrology (e.g. temperature, rainfall, evapotranspiration, and overland and subsurface flow), soil properties (e.g. texture, organic matter content, and hydrological properties), topography (e.g. slope and distance to surface water/depth to groundwater), land cover and agronomic practices, and pesticide properties and usage. The effectiveness of mitigation measures such as the delayed timing of pesticide application; a 10 %, 25 %, or 50 % reduction in the application rate; field buffers; and the presence/absence of soil pan on risk reduction were evaluated. Sensitivity analysis identified the month of application, the land use, the presence of buffers, the field slope, and the distance as the most important risk factors, alongside several additional influential variables. The pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, whereas the groundwater leaching risk was uniformly low, with the exception of prosulfocarb. Combined interventions of a 50 % reduced pesticide application rate, management of the plough pan, delayed application timing, and field buffer installation notably reduced the probability of a high risk of overland runoff and groundwater leaching, with individual measures having a smaller impact. The graphical nature of BBNs facilitated interactive model development and evaluation with stakeholders to build model credibility, while the ability to integrate diverse data sources allowed a dynamic field-scale assessment of “critical source areas” of pesticide pollution in time and space in a data-scarce catchment, with explicit representation of uncertainties.

Background Patients with systemic lupus erythematosus (SLE) have an increased risk of thrombosis even when they do not have antiphospholipid syndrome (APS). Interactions between complement activation and activated platelets have been suggested in SLE and APS and could play a role in the increased thrombosis risk. Objectives To explore factors potentially related to the prothrombotic pathophysiology in patients with SLE, primary APS, and healthy controls, by investigating lectin pathway proteins (LPPs), complement activation, platelet aggregation, and platelet activation. Methods This cross-sectional cohort study included 20 SLE patients, 17 primary APS, and 39 healthy controls. Flow cytometry and light transmission aggregometry were used to assess platelet activation and aggregation. Using time-resolved immunofluorometric assays, the plasma concentrations of 11 LPPs and C3dg, reflecting complement activation, were measured. Results H-ficolin plasma concentrations were higher in SLE and APS patients than in controls (p = 0.01 and p = 0.03). M-ficolin was lower in SLE than in APS (p = 0.01) and controls (p = 0.03). MAp19 was higher in APS patients than in SLE patients (p = 0.01) and controls (p

Objective With a vaccine effectiveness of 95% for preventing coronavirus disease 2019 (COVID‐19), Pfizer‐BioNTech BNT162b2 (BNT162b2) was the first vaccine against severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) to be approved. However, immunosuppressive therapy was an exclusion criterion in the phase 3 trial that led to approval. Thus, extrapolation of the trial results to patients with rheumatic diseases treated with immunosuppressive drugs warrants caution. Methods Patients with systemic lupus erythematosus (SLE; n = 61) and rheumatoid arthritis (RA; n = 73) were included from the COPANARD (Corona Pandemic Autoimmune Rheumatic Disease) cohort, followed since the beginning of the COVID‐19 pandemic. Patients received the BNT162b2 vaccine between December 2020 and April 2021. All patients had total antibodies against SARS‐CoV‐2 measured before vaccination and 1 week after the second vaccination (VITROS Immunodiagnostic Products). Results Of 134 patients (median age, 70 years), 77% were able to mount a detectable serological response to the vaccine. Among patients treated with rituximab, only 24% had detectable anti–SARS‐CoV‐2 antibodies in their serum after vaccination. The time since the last rituximab treatment did not seem to influence the vaccine response. No significant difference was observed between patients with RA or SLE when adjusting for treatment, and no correlation between antibody levels and age was detected (r = −0.12; P = 0.18). Conclusion Antibody measurements against SARS‐CoV‐2 in patients with RA and SLE after two doses of the BNT162b2 vaccine demonstrated that 23% of patients could not mount a detectable serological response to the vaccine. B cell–depleting therapy (BCDT) is of specific concern, and our findings call for particular attention to the patients receiving BCDT.

Background Vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic (VEXAS) is an autoinflammatory condition with overlapping features of rheumatology and haematology caused by somatic mutations in the UBA1 gene. Patients present with highly variable symptoms and their path towards diagnosis are often complicated and characterised by extensive examinations. It is, therefore, pivotal that clinicians become familiar with the clinical presentation of VEXAS to advance identification of patients with the disease.Objectives We aimed to (1) characterise patients diagnosed with VEXAS in a tertiary rheumatology referral centre, (2) identify common rheumatological biomarkers that may distinguish VEXAS from other rheumatic diseases and (3) suggest which clinical findings should motivate genetic testing for VEXAS.Methods Patients were identified and diagnosed at the department of Rheumatology, Aarhus University Hospital (AUH), Denmark. Blood samples were examined for VEXAS-associated UBA1 variants by Sanger sequencing at the department of Clinical Immunology, AUH. Clinical and biochemical data were retrieved from the hospital electronic patient chart.Results Eleven male patients with clinical suspicion of VEXAS underwent sequencing. Five of these carried known VEXAS-associated variants. Median age at diagnosis was 84 (75–87) years. All patients had significantly elevated inflammatory markers with a median C-reactive protein (CRP) of 297 (196–386) mg/L and macrocytic anaemia. None of the patients presented common biomarkers for autoimmunity.Conclusion Danish patients with VEXAS syndrome are men with persistent inflammation, constitutional symptoms and heterogeneous clinical presentations. Shared features for all patients in this study were highly elevated inflammatory markers, macrocytic anaemia and negative autoimmune biomarkers.

In order to design future large wind turbines, knowledge is needed about the impact of aero-elasticity on the rotor loads and performance and about the physics of the atmospheric flow surrounding the turbines. The objective of the present work is to study both effects by means of high-fidelity rotor-resolved numerical simulations. In particular, unsteady computational fluid dynamics (CFD) simulations of a 2.3 MW wind turbine are conducted, this rotor being the largest design with relevant experimental data available to the authors. Turbulence is modeled with two different approaches. On one hand, a model using the well-established technique of improved delayed detached eddy simulation (IDDES) is employed. An additional set of simulations relies on a novel hybrid turbulence model, developed within the framework of the present work. It consists of a blend of a large-eddy simulation (LES) model by Deardorff for atmospheric flow and an IDDES model for the separated flow near the rotor geometry. In the same way, the assessment of the influence of the blade flexibility is performed by comparing two different sets of computations. The first group accounts for a structural multi-body dynamics (MBD) model of the blades. The MBD solver was coupled to the CFD solver during run time with a staggered fluid–structure interaction (FSI) scheme. The second set of simulations uses the original rotor geometry, without accounting for any structural deflection. The results of the present work show no significant difference between the IDDES and the hybrid turbulence model. In a similar manner, and due to the fact that the considered rotor was relatively stiff, the loading variation introduced by the blade flexibility was found to be negligible when compared to the influence of inflow turbulence. The simulation method validated here is considered highly relevant for future turbine designs, where the impact of blade elasticity will be significant and the detailed structure of the atmospheric inflow will be important.

The actuator line method is a widely used technique to model wind turbines in computational fluid dynamics, as it significantly reduces the required computational expense in comparison to simulations using geometrically resolved blades. Actuator line coupled to an aeroelastic solver enables not only the study of detailed wake dynamics but also aeroelastic loads, flexible blade deformation and how this interacts with the flow. Validating aeroelastic actuator line predictions of blade loading, deflection and turbine wakes in complex inflow scenarios is particularly relevant for modern turbine designs and wind farm studies involving realistic inflows, wind shear or yaw misalignment. This work first implements a vortex-based smearing correction in an aeroelastic coupled actuator line, and performs a grid resolution and smearing parameter study which demonstrates significant improvement in the blade loading and in the numerical dependencies of predicted thrust and power output. A validation is then performed using a 2.3 MW turbine with R = 40 m radius, comparing against blade resolved fluid-structure interaction simulations and full-scale measurement data, in both laminar and turbulent inflows including both high shear and high yaw misalignment. For an axisymmetric laminar inflow case, the agreement between blade resolved and actuator line simulations is excellent, with prediction of integrated quantities within 0.2%. In more complex flow cases, good agreement is seen in overall trends but the actuator line predicts lower blade loading and flapwise deflection, leading to underpredictions of thrust by between 5.3% and 8.4%. The discrepancies seen can be attributed to differences in wake flow, induction, the reliance of the actuator line on the provided airfoil data and the force application into the computational domain. Comparing the wake between coupled actuator line and blade resolved simulations for turbulent flow cases also shows good agreement in wake deficit and redirection, even under high yaw conditions. Overall, this work validates the implementation of the vortex-based smearing correction and demonstrates the ability of the actuator line to closely match blade loading and deflection predictions of blade resolved simulations in complex flows, at a significantly lower computational cost.

Study region: Denmark. Study focus: Groundwater-surface water interaction (GSI) occurs when groundwater discharges into surface water, or surface water seeps into the groundwater. Understanding GSI aids in understanding the quality and quantity of water and the ecosystem's health, assisting water resource management and the protection of land and aquatic environment. This paper presents a classification mapping routine to classify the Danish stream network into an established GSI typology framework. We mapped three classes for GSI contact types: connected clayey, connected sandy, and disconnected, and three classes for riparian flow paths: diffuse and/or overland, direct, and artificial drainage. New hydrological insights for the region: Results revealed that about 85% of Denmark's streams are connected to groundwater with approximately equal portions in sandy and clayey subsoil areas. Also, about 87% of these streams exchange water with groundwater, either as the only flow path or combined with other flow paths. Furthermore, 41% and 19% of stream sections receive riparian flow water through artificial drainage and diffuse/overland flow paths as the dominant flow path or combined. These numbers correspond to Denmark's land use, which is approximately 60% agricultural, restricting runoff potential, and half of which is artificially drained. We anticipate that the maps will assist policymakers in controlling water and pollutant transfer from the uplands to streams to attain good environmental status for surface and groundwater bodies.

Detailed knowledge of the uppermost water table representing the shallow groundwater system is critical in order to address societal challenges that relate to the mitigation and adaptation to climate change and enhancing climate resilience in general. Machine learning (ML) allows for high resolution modeling of the water table depth beyond the capabilities of conventional numerical physically-based hydrological models with respect to spatial resolution and overall accuracy. For this, in-situ well and proxy observations are used as training data in combination with high resolution covariates. The objective of this study is to model the depth of the uppermost water table for a typical summer and winter condition at 10 m spatial resolution over entire Denmark (43,000 km2). CatBoost, a state of the art implementation of gradient boosting decision trees, is employed in this study to model the water table depth and the associated uncertainties. The groundwater domain has not been the most prominent field of applications of recent hydrological ML advances due to the lack of big data. This study brings forward a novel knowledge-guided ML framework to overcome this limitation by integrating simulation results from a physically-based groundwater flow model. The simulation data are utilized to (1) identify wells that represent the uppermost water table, (2) augment missing training data by accounting for simulated water level seasonality, and (3) expand the list of covariates. The curated training dataset contains around 13,000 wells, 19,000 groundwater proxy observations at lakes, streams and coastline as well as 15 covariates. Cross validation attests that the ML model generalizes well with a mean absolute error of around 115 cm considering solely well observations and a MAE of

We perform large eddy simulation of flow in a complex terrain under neutral atmospheric stratification. We study the self-similar behavior of a turbine wake as a function of varying terrain complexity and perform comparisons with a flat terrain. By plotting normalized velocity deficit profiles in different complex terrain cases, we verify that self-similarity is preserved as we move downstream from the turbine. We find that this preservation is valid for a shorter distance downstream compared to what is observed in a flat terrain. A larger spread of the profiles toward the tails due to varying levels of shear is also observed.

We do not understand why non-white ethnicity and chronic kidney disease increase susceptibility to COVID-19. The lectin pathway of complement activation is a key contributor to innate immunity and inflammation. Concentrations of plasma lectin pathway proteins influence pathway activity and vary with ethnicity. We measured circulating lectin proteins in a multi-ethnic cohort of chronic kidney disease patients with and without COVID19 infection to determine if lectin pathway activation was contributing to COVID19 severity. We measured 11 lectin proteins in serial samples from a cohort of 33 patients with chronic kidney impairment and COVID19. Controls were single plasma samples from 32 patients on dialysis and 32 healthy individuals. We demonstrated multiple associations between recognition molecules and associated proteases of the lectin pathway and COVID-19, including COVID-19 severity. Some of these associations were unique to patients of Asian and White ethnicity. Our novel findings demonstrate that COVID19 infection alters the concentration of plasma lectin proteins and some of these changes were linked to ethnicity. This suggests a role for the lectin pathway in the host response to COVID-19 and suggest that variability within this pathway may contribute to ethnicity-associated differences in susceptibility to severe COVID-19.

Objectives SLE displays large clinical heterogeneity that beyond genetic factors may be determined by environmental exposures. In this Danish nationwide study, we aimed to determine if clinical subsets of SLE were associated with smoking history.Methods At each of six participating centres, incident or prevalent inpatients and outpatients with SLE were consecutively included. Manifestations forming the basis of SLE classification were registered in an electronic chart system. Patients also provided questionnaire-based data on environmental exposures, including smoking history. Hierarchical cluster analysis was conducted to determine and characterise subsets of patients with similar traits of disease manifestations. Levels of smoking exposure by pack-years were correlated to the identified SLE subsets, as well as discrete SLE manifestations.Results The cohort consisted of 485 patients (88% women and 92% Caucasian) with SLE of which 51% were ever smokers. Common disease manifestations comprised non-erosive arthritis (81%), malar rash (57%), lymphopenia (55%), photosensitivity (50%) and persistent proteinuria (41%). We identified three distinct phenotypic clusters characterised by their preponderance of (A) neurological, serosal and mucosal involvement; (B) renal, haematological and immunological disorders; and (C) acute and chronic skin manifestations. Cluster B was the youngest and had the lowest level of smoking exposure. Age-adjusted regression analyses showed that compared with never smokers a smoking history of >20 pack-years was associated with neurological disorder (OR=3.16), discoid rash (OR=2.22), photosensitivity (OR=2.19) and inversely with haematological disorder (OR=0.40), renal disorder (OR=0.40) and non-erosive arthritis (OR=0.45), p

Background: Nucleic acids are potent stimulators of type I interferon (IFN-I) and antiviral defense, but may also promote pathological inflammation. A range of diseases are characterized by elevated IFN-I, including systemic lupus erythematosus (lupus). The DNA-activated cGAS-STING pathway is a major IFN-I-inducing pathway, and activation of signaling is dependent on trafficking of STING from the ER to the Golgi. Methods: Here we used cell culture systems, a mouse lupus model, and material from lupus patients, to explore the mode of action of a STING antagonistic peptide, and its ability to modulate disease processes. Findings: We report that the peptide ISD017 selectively inhibits all known down-stream activities of STING, including IFN-I, inflammatory cytokines, autophagy, and apoptosis. ISD017 blocks the essential trafficking of STING from the ER to Golgi through a mechanism dependent on the STING ER retention factor STIM1. Importantly, ISD017 blocks STING activity in vivo and ameliorates disease development in a mouse model for lupus. Finally, ISD017 treatment blocks pathological cytokine responses in cells from lupus patients with elevated IFN-I levels. Interpretation: These data hold promise for beneficial use of STING-targeting therapy in lupus. Funding: The Novo Nordisk Foundation, The European Research Council, The Lundbeck Foundation, European Union under the Horizon 2020 Research, Deutsche Forschungsgemeinschaft, Chulalongkorn University.

Study region: Two large-scale catchments in Denmark: Ringkøbing fiord and Mid-Zealand catchments. Study focus: Future changes in hydrological regime, extremes, groundwater levels and ecological quality ratio (EQR) for fish were assessed. Based on an ensemble of 17 bias-corrected regional climate models forced by the emission scenario RCP8.5, a dry, a medium and a wet model were identified for the future period 2071–2100. Changes in water balances for catchments, impacts on groundwater level and fish ecological quality ration (fish EQR) were evaluated. The impact changes were based on an integrated, transient, coupled groundwater and surface-water flow model, set up with a horizontal resolution of 200m × 200m for the two catchments. New hydrological insights for the region: Strengths and weaknesses of EQR assessments based on symbolic regression methods compared to expert assessments based on flow calendar and max flow changes in percent exceedance thresholds (Q95) were identified. While the modelled impact of climate change on monthly flow and flow calendar follows the same pattern at all monitoring stations, the impact on groundwater level and fish EQRs are more complex, and somewhat ambiguous across realisations and catchments.

Abstract Background A subarachnoid hemorrhage (SAH) is a debilitating stroke. Activation of the lectin pathway (LP) of the complement system in SAH patients could worsen the prognosis; however, conflicting results have been reported. This potentially reflects that pathological changes at the site of injury are not reflected in peripheral blood. Aims of the study To measure the concentration of LP proteins in blood from the site of brain injury compared with peripheral blood in SAH patients, and to determine the concentration of LP proteins in cerebrospinal fluid (CSF). Methods We included 11 SAH patients undergoing aneurysm clipping or external ventricular drainage. Blood was collected from the site of injury and from a peripheral artery and/or CSF simultaneously. LP proteins were measured using time‐resolved immunofluorometric assays. Results In all patients, the cerebral blood concentration of mannan‐binding lectin, collectin liver‐1 and collectin kidney‐1, and mannan‐associated serine proteases 1 and 2 were lower than in peripheral blood. The LP proteins were almost undetectable in CSF. Conclusion Lectin pathway protein concentrations measured in peripheral blood do not always reflect changes at the site of injury. For some proteins, more information could be obtained in blood from the site of injury when investigating pathogenic mechanisms.

We aimed at establishing a sensitive and robust assay for estimation of systemic complement activation at complement component C3 level in mouse and human plasma samples. In order to capture the activation products iC3b and C3dg in a specific and physiological relevant manner we utilized a construct consisting of the iC3b/C3dg-binding site of human complement receptor 2 (CR2) attached to an Fc-part of mouse IgG. This construct binds C3dg and iC3b from both mice and humans. We purified the CR2-IgG construct from mouse B myeloma cell line supernatants, J558L-CR2-IgG, by protein G affinity chromatography. The CR2-IgG construct was used for capturing C3 fragments in microtiter wells and an anti-mouse or an anti-human-C3 antibody was used for detection of bound C3 fragments. Initially we tested the specificity of the assays with the use of purified C3 fragments. Further, with the use of the CR2-based assay, we measured an up to three-fold higher signal in activated mouse serum as compared to non-activated mouse serum, whereas activated serum from a C3 knock-out mouse gave no signal. We tested in vivo generated samples from a mouse experiment; complement activation was induced by injecting cobra venom factor or heat aggregated IgG into C57bl6 mice, followed by withdrawal of EDTA blood samples at different time points and measurement of iC3b/C3dg. We observed a clear time-dependent distinction in signals between samples with expected high and low complement activation. Furthermore, with the use of the assay for human C3 fragments, we observed that patients with systemic lupus erythematosus (SLE) (n = 144) had significantly higher iC3b/C3dg levels as compared to healthy individuals (n = 144) (p < 0.0001). We present two functional immunoassays, that are able to measure systemic levels of the C3-activation products iC3b and C3dg in mice and humans. To our knowledge, these are the first assays for complement activation that use a physiological relevant capture construct such as CR2. These assays will be a relevant tool when investigating mouse models and human diseases involving the complement system.

For load calculations on wind turbines it is usually assumed that the turbulence approaching the rotor does not change its statistics as it goes through the induction zone. We investigate this assumption using a nacelle-mounted forward-looking pulsed lidar that measures low-frequency wind fluctuations simultaneously at distances between 0.5 and 3 rotor diameters upstream. The measurements show that below rated wind speed the low-frequency wind variance is reduced by up to 10 % at 0.5 rotor diameters upstream and above rated enhanced by up to 20 %. A quasi-steady model that takes into account the change in thrust coefficient with wind speed explains these variations partly. Large eddy simulations of turbulence approaching an actuator disk model of a rotor support the finding that the slope of the thrust curve influences the low-frequency fluctuations.

IgA nephropathy (IgAN) is characterized by glomerular deposition of galactose-deficient IgA1 and complement proteins and leads to renal impairment. Complement deposition through the alternative and lectin activation pathways is associated with renal injury. Methods: To elucidate the contribution of the lectin pathway to IgAN, we measured the 11 plasma lectin pathway components in a well-characterized cohort of patients with IgAN. Results: M-ficolin, L-ficolin, mannan-binding lectin (MBL)–associated serine protease (MASP)-1 and MBL-associated protein (MAp) 19 were increased, whereas plasma MASP-3 levels were decreased in patients with IgAN compared with healthy controls. Progressive disease was associated with low plasma MASP-3 levels and increased glomerular staining for C3b/iC3b/C3c, C3d, C4d, C5b-9, and factor H–related protein 5 (FHR5). Glomerular FHR5 deposition positively correlated with glomerular C3b/iC3b/C3c, C3d, and C5b-9 deposition, but not with glomerular C4d. These observations, together with the finding that glomerular factor H (fH) deposition was reduced in progressive disease, are consistent with a role for fH deregulation by FHR5 in renal injury in IgAN. Conclusion: Our data indicate that circulating MASP-3 levels could be used as a biomarker of disease severity in IgAN and that glomerular staining for FHR5 could both indicate alternative complement pathway activation and be a tissue marker of disease severity.

This study is the first to consider, and estimate, the influence of gardening routines on exposures to both health benefits and health risks. This holistic approach helped to contrast the healthy lifestyle of gardening with health risks from exposures to potentially toxic elements such as Cd and Pb in urban environments. A total of 120 participants who grew their own produce in an urban setting were recruited to the study. A detailed questionnaire was developed that included sections on gardening activity, cultivation and consumption of produce, consumption of commercially grown produce, and other lifestyle factors. Administered alongside the questionnaire was the Short Form 36 (v2) as a standardised tool for measuring physical and mental health. Fruit and vegetable consumption was found to be correlated with the amount of gardening individuals did in autumn/winter and was greater than fruit and vegetable consumption, on average, in the UK general population. Levels of physical activity were also found to be higher in our study than regional averages, whilst BMI was lower than average. This is the first study to find a relationship between gardening more regularly (in autumn/winter) and the physical component of the Short Form 36, and this relationship was elevated compared to non-gardening populations. The physical component scores from this study were also significantly higher for older participants, compared to means from a Western population. This finding supports studies suggesting that gardening may be more beneficial for the elderly generation. These benefits were assessed in the context of potential exposures estimated from the type and frequency of produce being consumed. The benefit of maintaining a healthy lifestyle is likely to outweigh the health risks of gardening on soils mildly contaminated with Cd and Pb but requires formal consideration within a risk management framework.

Introduction/objectivesIn 2012, hypocomplementemia was included in the classification criteria of systemic lupus erythematosus (SLE). The suggested measurement of C3 or C4 often reflect disease activity poorly. Our objective was to establish an assay measuring C3dg, which is generated following complement activation, and to evaluate the assay in a cross-sectional SLE cohort.MethodWe included SLE patients (n = 169) and controls (n = 170) and developed a modified C3dg assay where C3dg fragments were separated from the large plasma proteins by polyethylene glycol (PEG), and the supernatant containing the C3dg fragment was used for analysis in an antibody-based sandwich-type assay. Gel permeation chromatography and western blotting were used to establish the optimal conditions for PEG precipitation.Results16% PEG was optimal for separating C3dg from C3 and the larger protein fragments. The assay showed a high degree of stability when using EDTA plasma, and measurements correlated well with commercially available complement activation assays. SLE patients had higher concentrations in plasma of C3dg than controls (p

This paper presents a first attempt to estimate future groundwater levels by applying extreme value statistics on predictions from a hydrological model. Climate scenarios for the future period, 2081–2100, are represented by projections from nine combinations of three global climate models and six regional climate models, and downscaled (including bias correction) with two different methods. An integrated surface water/groundwater model is forced with precipitation, temperature, and potential evapotranspiration from the 18 models and downscaling combinations. Extreme value analyses are performed on the hydraulic head changes from a control period (1991–2010) to the future period for the 18 combinations. Hydraulic heads for return periods of 21, 50 and 100 yr (T21–100) are estimated. Three uncertainty sources are evaluated: climate models, downscaling and extreme value statistics. Of these sources, extreme value statistics dominates for return periods higher than 50 yr, whereas uncertainty from climate models and extreme value statistics are similar for lower return periods. Uncertainty from downscaling only contributes to around 10% of the uncertainty from the three sources.

Precipitation gauge catch correction is often given very little attention in hydrological modelling compared to model parameter calibration. This is critical because significant precipitation biases often make the calibration exercise pointless, especially when supposedly physically-based models are in play. This study addresses the general importance of appropriate precipitation catch correction through a detailed modelling exercise. An existing precipitation gauge catch correction method addressing solid and liquid precipitation is applied, both as national mean monthly correction factors based on a historic 30 yr record and as gridded daily correction factors based on local daily observations of wind speed and temperature. The two methods, named the historic mean monthly (HMM) and the time–space variable (TSV) correction, resulted in different winter precipitation rates for the period 1990–2010. The resulting precipitation datasets were evaluated through the comprehensive Danish National Water Resources model (DK-Model), revealing major differences in both model performance and optimised model parameter sets. Simulated stream discharge is improved significantly when introducing the TSV correction, whereas the simulated hydraulic heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimised model parameters are much more physically plausible for the model based on the TSV correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better performance for parameter values based on the TSV correction. Similarly, the performances of the TSV correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests). We conclude that TSV precipitation correction should be carried out for studies requiring a sound dynamic description of hydrological processes, and it is of particular importance when using hydrological models to make predictions for future climates when the snow/rain composition will differ from the past climate. This conclusion is expected to be applicable for mid to high latitudes, especially in coastal climates where winter precipitation types (solid/liquid) fluctuate significantly, causing climatological mean correction factors to be inadequate.

Uncertainty in conceptual model structure and in environmental data is of essential interest when dealing with uncertainty in water resources management. To make quantification of uncertainty possible is it necessary to identify and characterise the uncertainty in geological and hydrogeological data. This paper discusses a range of available techniques to describe the uncertainty related to geological model structure and scale of support. Literature examples on uncertainty in hydrogeological variables such as saturated hydraulic conductivity, specific yield, specific storage, effective porosity and dispersivity are given. Field data usually have a spatial and temporal scale of support that is different from the one on which numerical models for water resources management operate. Uncertainty in hydrogeological data variables is characterised and assessed within the methodological framework of the HarmoniRiB classification.