Search

To ensure the safe and efficient application of ocean alkalinity enhancement (OAE), it is crucial to investigate its impacts on the carbonate system. While modeling studies reported a sequestration potential of 3–30 Gt carbon dioxide (CO2) per year (Oschlies et al., 2023), there has been a lack of empirical data to support the applicability of this technology in natural environments. Recent studies have described the effect of runaway carbonate precipitation in the context of OAE, showing that calcium carbonate (CaCO3) formation was triggered if certain Ωaragonite saturation thresholds were exceeded. This effect could potentially lead to a net loss of the initially added alkalinity, counteracting the whole concept of OAE. The related precipitation can adversely affect the carbon storage capacity and may in some cases result in CO2 emissions. Experiments at the Espeland marine biological station (Bergen, Norway) were conducted to systematically study the chemical consequences of OAE deployment. The experiments lasted for 20–25 d to monitor the temporal development of carbonate chemistry parameters after alkalinity addition and the subsequent triggered carbonate precipitation process. Identified uniform patterns before and during the triggered runaway process can be described by empirical functional relationships. For approaches equilibrated to the CO2 concentration of the atmosphere, total alkalinity (TA) levels of up to 6500 µmol kg−1 remained stable without loss of total alkalinity (TA) for up to 20 d. Higher implemented TA levels, up to 11 200 µmol kg−1, triggered runaway carbonate formation. Once triggered, the loss of alkalinity continued until the Ωaragonite values leveled out at 5.8–6.0, still resulting in a net gain of 3600–4850 µmol kg−1 in TA. The non-CO2-equilibrated approaches, however, only remained stable for TA additions of up to 1000 µmol kg−1. The systematic behavior of treatments exceeding this level allows us to predict the duration of transient stability and the quantity of TA loss after this period. Once triggered, the TA loss continued in the non-CO2-equilibrated approaches until Ωaragonite values of 2.5–5.0 were reached, in this case resulting in a net loss of TA. To prevent a net loss of TA, treated water must be diluted below the time-dependent critical levels of TA and Ωaragonite within the identified transient stability duration. Identified stability and loss patterns of added TA depend on local environmental conditions impacting the carbonate system, such as salinity, temperature, biological activity, and particle abundance. Incorporating such stability and loss patterns into ocean biogeochemical models, which are capable of resolving dilution processes of treated and untreated water parcels, would, from a geochemical perspective, facilitate the prediction of safe local application levels of OAE. This approach would also allow an accurate determination of the fate of added alkalinity and a more realistic carbon storage potential estimation compared to the assessments that neglect carbonate system responses to OAE.

Abstract Periodically driven quantum systems exhibit a diverse set of phenomena but are more challenging to simulate than their equilibrium counterparts. Here, we introduce the Quantum High-Frequency Floquet Simulation (QHiFFS) algorithm as a method to simulate fast-driven quantum systems on quantum hardware. Central to QHiFFS is the concept of a kick operator which transforms the system into a basis where the dynamics is governed by a time-independent effective Hamiltonian. This allows prior methods for time-independent simulation to be lifted to simulate Floquet systems. We use the periodically driven biaxial next-nearest neighbor Ising (BNNNI) model, a natural test bed for quantum frustrated magnetism and criticality, as a case study to illustrate our algorithm. We implemented a 20-qubit simulation of the driven two-dimensional BNNNI model on Quantinuum’s trapped ion quantum computer. Our error analysis shows that QHiFFS exhibits not only a cubic advantage in driving frequency ω but also a linear advantage in simulation time t compared to Trotterization.

Abstract Aberrant information processing in the basal ganglia and connected cortical areas are key to many neurological movement disorders such as Parkinson’s disease. Investigating the electrophysiology of this system is difficult in humans because non-invasive methods, such as electroencephalography or magnetoencephalography, have limited sensitivity to deep brain areas. Recordings from electrodes implanted for therapeutic deep brain stimulation, in contrast, provide clear deep brain signals but are not suited for studying cortical activity. Therefore, we combine magnetoencephalography and local field potential recordings from deep brain stimulation electrodes in individuals with Parkinson’s disease. Here, we make these data available, inviting a broader scientific community to explore the dynamics of neural activity in the subthalamic nucleus and its functional connectivity to cortex. The dataset encompasses resting-state recordings, plus two motor tasks: static forearm extension and self-paced repetitive fist clenching. Most patients were recorded both in the medicated and the unmedicated state. Along with the raw data, we provide metadata on channels, events and scripts for pre-processing to help interested researchers get started.

Global warming is amplified in the Arctic. However, numerical models struggle to represent key processes that determine Arctic weather and climate. To collect data that help to constrain the models, the HALO–(𝒜𝒞)3 aircraft campaign was conducted over the Norwegian and Greenland seas, the Fram Strait, and the central Arctic Ocean in March and April 2022. The campaign focused on one specific challenge posed by the models, namely the reasonable representation of transformations of air masses during their meridional transport into and out of the Arctic via northward moist- and warm-air intrusions (WAIs) and southward marine cold-air outbreaks (CAOs). Observations were made over areas of open ocean, the marginal sea ice zone, and the central Arctic sea ice. Two low-flying and one long-range, high-altitude research aircraft were flown in colocated formation whenever possible. To follow the air mass transformations, a quasi-Lagrangian flight strategy using trajectory calculations was realized, enabling us to sample the same moving-air parcels twice along their trajectories. Seven distinct WAI and 12 CAO cases were probed. From the quasi-Lagrangian measurements, we have quantified the diabatic heating/cooling and moistening/drying of the transported air masses. During CAOs, maximum values of 3 K h−1 warming and 0.3 g kg−1 h−1 moistening were obtained below 1 km altitude. From the observations of WAIs, diabatic cooling rates of up to 0.4 K h−1 and a moisture loss of up to 0.1 g kg−1 h−1 from the ground to about 5.5 km altitude were derived. Furthermore, the development of cloud macrophysical (cloud-top height and horizontal cloud cover) and microphysical (liquid water path, precipitation, and ice index) properties along the southward pathways of the air masses were documented during CAOs, and the moisture budget during a specific WAI event was estimated. In addition, we discuss the statistical frequency of occurrence of the different thermodynamic phases of Arctic low-level clouds, the interaction of Arctic cirrus clouds with sea ice and water vapor, and the characteristics of microphysical and chemical properties of Arctic aerosol particles. Finally, we provide a proof of concept to measure mesoscale divergence and subsidence in the Arctic using data from dropsondes released during the flights.

Abstract Previous attempts to classify flow units in Iranian carbonate reservoirs, based on porosity and permeability, have faced challenges in correlating the rock's pore size distribution with the capillary pressure profile. The innovation of this study highlights the role of clustering techniques, such as Discrete Rock Type, Probability, Global Hydraulic Element, and Winland's Standard Chart in enhancing the reservoir's rock categorization. These techniques are integrated with established flow unit classification methods. They include Lucia, FZI, FZI*, Winland R35, and the improved stratigraphic modified Lorenz plot. The research accurately links diverse pore geometries to characteristic capillary pressure profiles, addressing heterogeneity in intricate reservoirs. The findings indicate that clustering methods can identify specific flow units, but do not significantly improve their classification. The effectiveness of these techniques varies depending on the flow unit classification method employed. For instance, probability-based methods yield surpassing results for low-porosity rocks when utilizing the FZI* approach. The discrete technique generates the highest number of flow unit classes but provides the worst result. Not all clustering techniques reveal discernible advantages when integrated with the FZI method. In the second part, the study creatively suggests that rock classification can be achieved by concurrently clustering irreducible water saturation (SWIR) and porosity in unsuccessful flow unit delineation cases. The SWIR log was estimated by establishing a smart correlation between porosity and SWIR in the pay zone, where water saturation and SWIR match. Then, the estimated saturation was dispersed throughout the reservoir. Subsequently, the neural network technique was employed to cluster and propagate the three finalized flow units. This methodology is an effective recommendation when conventional flow unit methods fail. The study also investigates influential factors causing the failure of flow unit classification methods, including pore geometry, oil wettability, and saturation in heterogeneous reservoirs.

Groundwater level (GWL) dynamics result from a complex interplay between groundwater systems and the Earth system. This study aims to identify common hydrogeological patterns and to gain a deeper understanding of the underlying similarities and their link to physiographic, climatic, and anthropogenic controls of groundwater in coastal regions. The most striking aspects of GWL dynamics and their controls were identified through a combination of statistical metrics, calculated from about 8000 groundwater hydrographs, pattern recognition using clustering algorithms, classification using random forest, and SHapley Additive exPlanations (SHAPs). Hydrogeological similarity was defined by four clusters representing distinct patterns of GWL dynamics. These clusters can be observed globally across different continents and climate zones but simultaneously vary regionally and locally, suggesting a complicated interplay of controlling factors. The main controls differentiating GWL dynamics were identified, but we also provide evidence for the currently limited ability to explain GWL dynamics on large spatial scales, which we attribute mainly to uncertainties in the explanatory data. Finally, this study provides guidance for systematic and holistic groundwater monitoring and modeling and motivates a consideration of the different aspects of GWL dynamics, for example, when predicting climate-induced GWL changes, and the use of explainable machine learning techniques to deal with GWL complexity – especially when information on potential controls is limited or needs to be verified.

Abstract Haplo-insufficiency of the gene encoding the myelin protein PMP22 leads to focal myelin overgrowth in the peripheral nervous system and hereditary neuropathy with liability to pressure palsies (HNPP). Conversely, duplication of PMP22 causes Charcot-Marie-Tooth disease type 1A (CMT1A), characterized by hypomyelination of medium to large caliber axons. The molecular mechanisms of abnormal myelin growth regulation by PMP22 have remained obscure. Here, we show in rodent models of HNPP and CMT1A that the PI3K/Akt/mTOR-pathway inhibiting phosphatase PTEN is correlated in abundance with PMP22 in peripheral nerves, without evidence for direct protein interactions. Indeed, treating DRG neuron/Schwann cell co-cultures from HNPP mice with PI3K/Akt/mTOR pathway inhibitors reduced focal hypermyelination. When we treated HNPP mice in vivo with the mTOR inhibitor Rapamycin, motor functions were improved, compound muscle amplitudes were increased and pathological tomacula in sciatic nerves were reduced. In contrast, we found Schwann cell dedifferentiation in CMT1A uncoupled from PI3K/Akt/mTOR, leaving partial PTEN ablation insufficient for disease amelioration. For HNPP, the development of PI3K/Akt/mTOR pathway inhibitors may be considered as the first treatment option for pressure palsies.

Abstract Neurological manifestations of coronavirus disease 2019 (COVID-19) have been frequently described. In this prospective study of hospitalized COVID-19 patients without a history of neurological conditions, we aimed to analyze their prevalence and prognostic value based on established, standardized and objective methods. Patients were investigated using a multimodal electrophysiological approach, accompanied by neuropsychological and neurological examinations. Prevalence rates of central (CNS) and peripheral (PNS) nervous system affections were calculated and the relationship between neurological affections and mortality was analyzed using Firth logistic regression models. 184 patients without a history of neurological diseases could be enrolled. High rates of PNS affections were observed (66% of 138 patients receiving electrophysiological PNS examination). CNS affections were less common but still highly prevalent (33% of 139 examined patients). 63% of patients who underwent neuropsychological testing (n = 155) presented cognitive impairment. Logistic regression models revealed pathology in somatosensory evoked potentials as an independent risk factor of mortality (Odds Ratio: 6.10 [1.01–65.13], p = 0.049). We conclude that hospitalized patients with moderate to severe COVID-19 display high rates of PNS and CNS affection, which can be objectively assessed by electrophysiological examination. Electrophysiological assessment may have a prognostic value and could thus be helpful to identify patients at risk for deterioration.

Abstract Efficacy of cancer vaccines remains low and mechanistic understanding of antigen presenting cell function in cancer may improve vaccine design and outcomes. Here, we analyze the transcriptomic and immune-metabolic profiles of Dendritic Cells (DCs) from 35 subjects enrolled in a trial of DC vaccines in late-stage melanoma (NCT01622933). Multiple platforms identify metabolism as an important biomarker of DC function and patient overall survival (OS). We demonstrate multiple immune and metabolic gene expression pathway alterations, a functional decrease in OCR/OXPHOS and increase in ECAR/glycolysis in patient vaccines. To dissect molecular mechanisms, we utilize single cell SCENITH functional profiling and show patient clinical outcomes (OS) correlate with DC metabolic profile, and that metabolism is linked to immune phenotype. With single cell metabolic regulome profiling, we show that MCT1 (monocarboxylate transporter-1), a lactate transporter, is increased in patient DCs, as is glucose uptake and lactate secretion. Importantly, pre-vaccination circulating myeloid cells in patients used as precursors for DC vaccine generation are significantly skewed metabolically as are several DC subsets. Together, we demonstrate that the metabolic profile of DC is tightly associated with the immunostimulatory potential of DC vaccines from cancer patients. We link phenotypic and functional metabolic changes to immune signatures that correspond to suppressed DC differentiation.

Abstract The development of high intensity petawatt lasers has created new possibilities for ion acceleration and nuclear fusion using solid targets. In such laser-matter interaction, multiple ion species are accelerated with broad spectra up to hundreds of MeV. To measure ion yields and for species identification, CR-39 solid-state nuclear track detectors are frequently used. However, these detectors are limited in their applicability for multi-ion spectra differentiation as standard image recognition algorithms can lead to a misinterpretation of data, there is no unique relation between track diameter and particle energy, and there are overlapping pit diameter relationships for multiple particle species. In this report, we address these issues by first developing an algorithm to overcome user bias during image processing. Second, we use calibration of the detector response for protons, carbon and helium ions (alpha particles) from 0.1 to above 10 MeV and measurements of statistical energy loss fluctuations in a forward-fitting procedure utilizing multiple, differently filtered CR-39, altogether enabling high-sensitivity, multi-species particle spectroscopy. To validate this capability, we show that inferred CR-39 spectra match Thomson parabola ion spectrometer data from the same experiment. Filtered CR-39 spectrometers were used to detect, within a background of ~ 2 × 1011 sr−1 J−1 protons and carbons, (1.3 ± 0.7) × 108 sr−1 J−1 alpha particles from laser-driven proton-boron fusion reactions.

The analysis of noisy quantum states prepared on current quantum computers is getting beyond the capabilities of classical computing. Quantum neural networks based on parametrized quantum circuits, measurements and feed-forward can process large amounts of quantum data to reduce measurement and computational costs of detecting nonlocal quantum correlations. The tolerance of errors due to decoherence and gate infidelities is a key requirement for the application of quantum neural networks on near-term quantum computers. Here we construct quantum convolutional neural networks (QCNNs) that can, in the presence of incoherent errors, recognize different symmetry-protected topological phases of generalized cluster-Ising Hamiltonians from one another as well as from topologically trivial phases. Using matrix product state simulations, we show that the QCNN output is robust against symmetry-breaking errors below a threshold error probability and against symmetry-preserving errors provided the error channel is invertible. This is in contrast to string order parameters and the output of previously designed QCNNs, which vanish in the presence of any symmetry-breaking errors. To facilitate the implementation of the QCNNs on near-term quantum computers, the QCNN circuits can be shortened from logarithmic to constant depth in system size by performing a large part of the computation in classical postprocessing. These constant-depth QCNNs reduce sample complexity exponentially with system size in comparison to the direct sampling using local Pauli measurements.

Machine learning has shown significant breakthroughs in quantum science, where in particular deep neural networks exhibited remarkable power in modeling quantum many-body systems. Here, we explore how the capacity of data-driven deep neural networks in learning the dynamics of physical observables is correlated with the scrambling of quantum information. We train a neural network to find a mapping from the parameters of a model to the evolution of observables in random quantum circuits for various regimes of quantum scrambling and test its $generalization$ and $extrapolation$ capabilities in applying it to unseen circuits. Our results show that a particular type of recurrent neural network is extremely powerful in generalizing its predictions within the system size and time window that it has been trained on for both, localized and scrambled regimes. These include regimes where classical learning approaches are known to fail in sampling from a representation of the full wave function. Moreover, the considered neural network succeeds in $extrapolating$ its predictions beyond the time window and system size that it has been trained on for models that show localization, but not in scrambled regimes.

The Schmidt decomposition is the go-to tool for measuring bipartite entanglement of pure quantum states. Similarly, it is possible to study the entangling features of a quantum operation using its operator-Schmidt or tensor-product decomposition. While quantum technological implementations of the former are thoroughly studied, entangling properties on the operator level are harder to extract in the quantum computational framework because of the exponential nature of sample complexity. Here, we present an algorithm for unbalanced partitions into a small subsystem and a large one (the environment) to compute the tensor-product decomposition of a unitary the effect of which on the small subsystem is captured in classical memory, while the effect on the environment is accessible as a quantum resource. This quantum algorithm may be used to make predictions about operator nonlocality and effective open quantum dynamics on a subsystem, as well as for finding low-rank approximations and low-depth compilations of quantum circuit unitaries. We demonstrate the method and its applications on a time-evolution unitary of an isotropic Heisenberg model in two dimensions.

The weathering rate of carbonate minerals is several orders of magnitude higher than for silicate minerals. Therefore, small amounts of carbonate minerals have the potential to control the dissolved weathering loads in silicate-dominated catchments. Both weathering processes produce alkalinity under the consumption of CO2. Given that only alkalinity generation from silicate weathering is thought to be a long-term sink for CO2, a misattributed weathering source could lead to incorrect conclusions about long- and short-term CO2 fixation. In this study, we aimed to identify the weathering sources responsible for alkalinity generation and CO2 fixation across watershed scales in a degrading permafrost landscape in northern Norway, 68.7–70.5∘ N, and on a temporal scale, in a subarctic headwater catchment on the mountainside of Iskorasfjellet, characterized by sporadic permafrost and underlain mainly by silicates as the alkalinity-bearing lithology. By analyzing total alkalinity (AT) and dissolved inorganic carbon (DIC) concentrations, as well as the stable isotope signature of the latter (δ13C-DIC), in conjunction with dissolved cation and anion loads, we found that AT was almost entirely derived from weathering of the sparse carbonate minerals. We propose that in the headwater catchment the riparian zone is a hotspot area of AT generation and release due to its enhanced hydrological connectivity and that the weathering load contribution from the uphill catchment is limited by insufficient contact time of weathering agents and weatherable materials. By using stable water isotopes, it was possible to explain temporal variations in AT concentrations following a precipitation event due to surface runoff. In addition to carbonic acid, sulfuric acid, probably originating from oxidation of pyrite or reduced sulfur in wetlands or from acid deposition, is shown to be a potential corrosive reactant. An increased proportion of sulfuric acid as a potential weathering agent may have resulted in a decrease in AT. Therefore, carbonate weathering in the studied area should be considered not only as a short-term CO2 sink but also as a potential CO2 source. Finally, we found that AT increased with decreasing permafrost probability, and attributed this relation to an increased water storage capacity associated with increasing contact of weathering agent and rock surfaces and enhanced microbial activity. As both soil respiration and permafrost thaw are expected to increase with climate change, increasing the availability of weathering agents in the form of CO2 and water storage capacity, respectively, we suggest that future weathering rates and alkalinity generation will increase concomitantly in the study area.

The carbon cycle component of the newly developed Earth system model of intermediate complexity CLIMBER-X is presented. The model represents the cycling of carbon through the atmosphere, vegetation, soils, seawater and marine sediments. Exchanges of carbon with geological reservoirs occur through sediment burial, rock weathering and volcanic degassing. The state-of-the-art HAMOCC6 model is employed to simulate ocean biogeochemistry and marine sediment processes. The land model PALADYN simulates the processes related to vegetation and soil carbon dynamics, including permafrost and peatlands. The dust cycle in the model allows for an interactive determination of the input of the micro-nutrient iron into the ocean. A rock weathering scheme is implemented in the model, with the weathering rate depending on lithology, runoff and soil temperature. CLIMBER-X includes a simple representation of the methane cycle, with explicitly modelled natural emissions from land and the assumption of a constant residence time of CH4 in the atmosphere. Carbon isotopes 13C and 14C are tracked through all model compartments and provide a useful diagnostic for model–data comparison. A comprehensive evaluation of the model performance for the present day and the historical period shows that CLIMBER-X is capable of realistically reproducing the historical evolution of atmospheric CO2 and CH4 but also the spatial distribution of carbon on land and the 3D structure of biogeochemical ocean tracers. The analysis of model performance is complemented by an assessment of carbon cycle feedbacks and model sensitivities compared to state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Enabling an interactive carbon cycle in CLIMBER-X results in a relatively minor slow-down of model computational performance by ∼ 20 % compared to a throughput of ∼ 10 000 simulation years per day on a single node with 16 CPUs on a high-performance computer in a climate-only model set-up. CLIMBER-X is therefore well suited to investigating the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to >100 000 years.

Clouds are assumed to play an important role in the Arctic amplification process. This motivated a detailed investigation of cloud processes, including radiative and turbulent fluxes. Data from the aircraft campaign ACLOUD were analyzed with a focus on the mean and turbulent structure of the cloudy boundary layer over the Fram Strait marginal sea ice zone in late spring and early summer 2017. Vertical profiles of turbulence moments are presented from contrasting atmospheric boundary layers (ABLs) from 4 d. They differ by the magnitude of wind speed, boundary-layer height, stability, the strength of the cloud-top radiative cooling and the number of cloud layers. Turbulence statistics up to third-order moments are presented, which were obtained from horizontal-level flights and from slanted profiles. It is shown that both of these flight patterns complement each other and form a data set that resolves the vertical structure of the ABL turbulence well. The comparison of the 4 d shows that especially during weak wind, even in shallow Arctic ABLs with mixing ratios below 3 g kg−1, cloud-top cooling can serve as a main source of turbulent kinetic energy (TKE). Well-mixed ABLs are generated where TKE is increased and vertical velocity variance shows pronounced maxima in the cloud layer. Negative vertical velocity skewness points then to upside-down convection. Turbulent heat fluxes are directed upward in the cloud layer as a result of cold downdrafts. In two cases with single-layer stratocumulus, turbulent transport of heat flux and of temperature variance are both negative in the cloud layer, suggesting an important role of large eddies. In contrast, in a case with weak cloud-top cooling, these quantities are positive in the ABL due to the heating from the surface. Based on observations and results of a mixed-layer model it is shown that the maxima of turbulent fluxes are, however, smaller than the jump of the net terrestrial radiation flux across the upper part of a cloud due to the (i) shallowness of the mixed layer and (ii) the presence of a downward entrainment heat flux. The mixed-layer model also shows that the buoyancy production of TKE is substantially smaller in stratocumulus over the Arctic sea ice compared to subtropics due to a smaller surface moisture flux and smaller decrease in specific humidity (or even humidity inversions) right above the cloud top. In a case of strong wind, wind shear shapes the ABL turbulent structure, especially over rough sea ice, despite the presence of a strong cloud-top cooling. In the presence of mid-level clouds, cloud-top radiative cooling and thus also TKE in the lowermost cloud layer are strongly reduced, and the ABL turbulent structure becomes governed by stability, i.e., by the surface–air temperature difference and wind speed. A comparison of slightly unstable and weakly stable cases shows a strong reduction of TKE due to increased stability even though the absolute value of wind speed was similar. In summary, the presented study documents vertical profiles of the ABL turbulence with a high resolution in a wide range of conditions. It can serve as a basis for turbulence closure evaluation and process studies in Arctic clouds.

According to modelling studies, ocean alkalinity enhancement (OAE) is one of the proposed carbon dioxide removal (CDR) approaches with large potential, with the beneficial side effect of counteracting ocean acidification. The real-world application of OAE, however, remains unclear as most basic assumptions are untested. Before large-scale deployment can be considered, safe and sustainable procedures for the addition of alkalinity to seawater must be identified and governance established. One of the concerns is the stability of alkalinity when added to seawater. The surface ocean is already supersaturated with respect to calcite and aragonite, and an increase in total alkalinity (TA) together with a corresponding shift in carbonate chemistry towards higher carbonate ion concentrations would result in a further increase in supersaturation, and potentially to solid carbonate precipitation. Precipitation of carbonate minerals consumes alkalinity and increases dissolved CO2 in seawater, thereby reducing the efficiency of OAE for CO2 removal. In order to address the application of alkaline solution as well as fine particulate alkaline solids, a set of six experiments was performed using natural seawater with alkalinity of around 2400 µmol kgsw−1. The application of CO2-equilibrated alkaline solution bears the lowest risk of losing alkalinity due to carbonate phase formation if added total alkalinity (ΔTA) is less than 2400 µmol kgsw−1. The addition of reactive alkaline solids can cause a net loss of alkalinity if added ΔTA > 600 µmol kgsw−1 (e.g. for Mg(OH)2). Commercially available (ultrafine) Ca(OH)2 causes, in general, a net loss in TA for the tested amounts of TA addition, which has consequences for suggested use of slurries with alkaline solids supplied from ships. The rapid application of excessive amounts of Ca(OH)2, exceeding a threshold for alkalinity loss, resulted in a massive increase in TA (> 20 000 µmol kgsw−1) at the cost of lower efficiency and resultant high pH values > 9.5. Analysis of precipitates indicates formation of aragonite. However, unstable carbonate phases formed can partially redissolve, indicating that net loss of a fraction of alkalinity may not be permanent, which has important implications for real-world OAE application. Our results indicate that using an alkaline solution instead of reactive alkaline particles can avoid carbonate formation, unless alkalinity addition via solutions shifts the system beyond critical supersaturation levels. To avoid the loss of alkalinity and dissolved inorganic carbon (DIC) from seawater, the application of reactor techniques can be considered. These techniques produce an equilibrated solution from alkaline solids and CO2 prior to application. Differing behaviours of tested materials suggest that standardized engineered materials for OAE need to be developed to achieve safe and sustainable OAE with solids, if reactors technologies should be avoided.

Riverine transport of nutrients and carbon from inland waters to the coastal and finally the open ocean alters marine primary production (PP) and carbon (C) uptake regionally and globally. So far, this process has not been fully represented and evaluated in the state-of-the-art Earth system models. Here we assess changes in marine PP and C uptake projected under the Representative Concentration Pathway 4.5 climate scenario using the Norwegian Earth system model, with four riverine transport configurations for nutrients (nitrogen, phosphorus, silicon, and iron), carbon, and total alkalinity: deactivated, fixed at a recent-past level, coupled to simulated freshwater runoff, and following four plausible future scenarios. The inclusion of riverine nutrients and carbon at the 1970 level improves the simulated contemporary spatial distribution of annual mean PP and air–sea CO2 fluxes relative to observations, especially on the continental margins (5.4 % reduction in root mean square error (RMSE) for PP) and in the North Atlantic region (7.4 % reduction in RMSE for C uptake). While the riverine nutrients and C input is kept constant, its impact on projected PP and C uptake is expressed differently in the future period from the historical period. Riverine nutrient inputs lessen nutrient limitation under future warmer conditions as stratification increases and thus lessen the projected decline in PP by up to 0.66 ± 0.02 Pg C yr−1 (29.5 %) globally, when comparing the 1950–1999 with the 2050–2099 period. The riverine impact on projected C uptake depends on the balance between the net effect of riverine-nutrient-induced C uptake and riverine-C-induced CO2 outgassing. In the two idealized riverine configurations the riverine inputs result in a weak net C sink of 0.03–0.04 ± 0.01 Pg C yr−1, while in the more plausible riverine configurations the riverine inputs cause a net C source of 0.11 ± 0.03 Pg C yr−1. It implies that the effect of increased riverine C may be larger than the effect of nutrient inputs in the future on the projections of ocean C uptake, while in the historical period increased nutrient inputs are considered the largest driver. The results are subject to model limitations related to resolution and process representations that potentially cause underestimation of impacts. High-resolution global or regional models with an adequate representation of physical and biogeochemical shelf processes should be used to assess the impact of future riverine scenarios more accurately.

Assessing metabolic activity within single cells rather than at a population level has a number of advantages. Here, the authors use a flow and mass cytometry based approach that assess the metabolic differences between populations of human immune stimulatory and tolerogenic dendritic cells.

Quantum neural networks could help analysing the output of quantum computers and quantum simulators of growing complexity. Here, the authors use a 7-qubit superconducting quantum processor to show how a quantum convolutional neural network can correctly recognise the phase of a quantum many-body state.

In March 2013, the Springtime Atmospheric Boundary Layer Experiment (STABLE) was carried out in the Fram Strait region and over Svalbard to investigate atmospheric convection and boundary layer modifications due to interactions between sea ice, the atmosphere, and open water. A major goal was the observation of marine cold-air outbreaks (MCAOs), which are typically characterised by the transport of very cold air masses from the ice-covered ocean over a relatively warm water surface and which often affect local and regional weather conditions. During STABLE, MCAOs were observed on 4 d within a period displaying a strongly northward-shifted sea ice edge north of Svalbard and, thus, with an unusually large Whaler's Bay polynya. The observations mainly consisted of in situ measurements from airborne instruments and of measurements by dropsondes. Here, we present the corresponding data set from a total of 15 aircraft vertical profiles and 22 dropsonde releases. Besides an overview of the flight patterns and instrumentation, we provide a detailed presentation of the individual quality-processing mechanisms, which ensure that the data can be used, for example, for model validation. Moreover, we discuss the effects of the individual quality-processing mechanisms, and we briefly present the main characteristics of the MCAOs based on the quality-controlled data. All 37 data series are published on the World Data Center PANGAEA (Lüpkes et al., 2021a, https://doi.org/10.1594/PANGAEA.936635).

Nonequilibrium time evolution of large quantum systems is a strong candidate for quantum advantage. Variational quantum algorithms have been put forward for this task, but their quantum optimization routines suffer from trainability and sampling problems. Here, we present a classical preprocessing routine for variational Hamiltonian simulation that circumvents the need for a quantum optimization by expanding rigorous error bounds in a perturbative regime for suitable time steps. The resulting cost function is efficiently computable on a classical computer. We show that there always exists potential for optimization with respect to a Trotter sequence of the same order and that the cost value has the same scaling as Trotter in simulation time and system size. Unlike previous work on classical preprocessing, the method is applicable to any Hamiltonian system independent of locality and interaction lengths. Via numerical experiments for spin-lattice models, we find that our approach significantly improves digital quantum simulation capabilities with respect to Trotter sequences for the same resources. For short times, we find accuracy improvements of more than three orders of magnitude for our method as compared to Trotter sequences of the same gate number. Moreover, for a given gate number and accuracy target, we find that the preoptimization we introduce enables simulation times that are consistently more than ten times longer for a target accuracy of 0.1%.

This article presents a comprehensive analysis of the foehn episode which occurred over Svalbard on 30–31 May 2017. This episode is well documented by multiplatform measurements carried out during the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) and Physical feedbacks of Arctic PBL, Sea ice, Cloud And AerosoL (PASCAL) campaigns. Both orographic wind modification and foehn warming are considered here. The latter is found to be primarily produced by the isentropic drawdown, which is evident from observations and mesoscale numerical modeling. The structure of the observed foehn warming was in many aspects very similar to that for foehns over the Antarctic Peninsula. In particular, it is found that the warming was proportional to the height of the mountain ridges and propagated far downstream. Also, a strong spatial heterogeneity of the foehn warming was observed with a clear cold footprint associated with gap flows along the mountain valleys and fjords. On the downstream side, a shallow stably stratified boundary layer below a well-mixed layer formed over the snow-covered land and cold open water. The foehn warming downwind of Svalbard strengthened the north–south horizontal temperature gradient across the ice edge near the northern tip of Svalbard. This suggests that the associated baroclinicity might have strengthened the observed northern tip jet. A positive daytime radiative budget on the surface, increased by the foehn clearance, along with the downward sensible heat flux provoked accelerated snowmelt in the mountain valleys in Ny-Ålesund and Adventdalen, which suggests a potentially large effect of the frequently observed Svalbard foehns on the snow cover and the glacier heat and mass balance.

Background: Classification systems for orthopaedic infection include patient health status, but there is no consensus about which comorbidities affect prognosis. Modifiable factors including substance use, glycaemic control, malnutrition and obesity may predict post-operative recovery from infection. Aim: This systematic review aimed (1) to critically appraise clinical prediction models for individual prognosis following surgical treatment for orthopaedic infection where an implant is not retained; (2) to understand the usefulness of modifiable prognostic factors for predicting treatment success. Methods: EMBASE and MEDLINE databases were searched for clinical prediction and prognostic studies in adults with orthopaedic infections. Infection recurrence or re-infection after at least 6 months was the primary outcome. The estimated odds ratios for the primary outcome in participants with modifiable prognostic factors were extracted and the direction of the effect reported. Results: Thirty-five retrospective prognostic cohort studies of 92 693 patients were included, of which two reported clinical prediction models. No studies were at low risk of bias, and no externally validated prediction models were identified. Most focused on prosthetic joint infection. A positive association was reported between body mass index and infection recurrence in 19 of 22 studies, similarly in 8 of 14 studies reporting smoking history and 3 of 4 studies reporting alcohol intake. Glycaemic control and malnutrition were rarely considered. Conclusion: Modifiable aspects of patient health appear to predict outcomes after surgery for orthopaedic infection. There is a need to understand which factors may have a causal effect. Development and validation of clinical prediction models that include participant health status will facilitate treatment decisions for orthopaedic infections.

Abstract The KArlsruhe TRItium Neutrino experiment (KATRIN) aims to determine the effective electron (anti)-neutrino mass with a sensitivity of 0.2eV/c $$^2$$ 2 by precisely measuring the endpoint region of the tritium $$\beta $$ β -decay spectrum. It uses a tandem of electrostatic spectrometers working as magnetic adiabatic collimation combined with an electrostatic (MAC-E) filters. In the space between the pre-spectrometer and the main spectrometer, creating a Penning trap is unavoidable when the superconducting magnet between the two spectrometers, biased at their respective nominal potentials, is energized. The electrons accumulated in this trap can lead to discharges, which create additional background electrons and endanger the spectrometer and detector section downstream. To counteract this problem, “electron catchers” were installed in the beamline inside the magnet bore between the two spectrometers. These catchers can be moved across the magnetic-flux tube and intercept on a sub-ms time scale the stored electrons along their magnetron motion paths. In this paper, we report on the design and the successful commissioning of the electron catchers and present results on their efficiency in reducing the experimental background.

Limiting global mean temperature changes to well below 2 ∘C likely requires a rapid and large-scale deployment of negative emission technologies (NETs). Assessments so far have shown a high potential of biomass-based terrestrial NETs, but only a few assessments have included effects of the commonly found nutrient-deficient soils on biomass production. Here, we investigate the deployment of enhanced weathering (EW) to supply nutrients to areas of afforestation–reforestation and naturally growing forests (AR) and bioenergy grasses (BG) that are deficient in phosphorus (P), besides the impacts on soil hydrology. Using stoichiometric ratios and biomass estimates from two established vegetation models, we calculated the nutrient demand of AR and BG. Insufficient geogenic P supply limits C storage in biomass. For a mean P demand by AR and a low-geogenic-P-supply scenario, AR would sequester 119 Gt C in biomass; for a high-geogenic-P-supply and low-AR-P-demand scenario, 187 Gt C would be sequestered in biomass; and for a low geogenic P supply and high AR P demand, only 92 Gt C would be accumulated by biomass. An average amount of ∼150 Gt basalt powder applied for EW would be needed to close global P gaps and completely sequester projected amounts of 190 Gt C during the years 2006–2099 for the mean AR P demand scenario (2–362 Gt basalt powder for the low-AR-P-demand and for the high-AR-P-demand scenarios would be necessary, respectively). The average potential of carbon sequestration by EW until 2099 is ∼12 Gt C (∼0.2–∼27 Gt C) for the specified scenarios (excluding additional carbon sequestration via alkalinity production). For BG, 8 kg basalt m−2 a−1 might, on average, replenish the exported potassium (K) and P by harvest. Using pedotransfer functions, we show that the impacts of basalt powder application on soil hydraulic conductivity and plant-available water, to close predicted P gaps, would depend on basalt and soil texture, but in general the impacts are marginal. We show that EW could potentially close the projected P gaps of an AR scenario and nutrients exported by BG harvest, which would decrease or replace the use of industrial fertilizers. Besides that, EW ameliorates the soil's capacity to retain nutrients and soil pH and replenish soil nutrient pools. Lastly, EW application could improve plant-available-water capacity depending on deployed amounts of rock powder – adding a new dimension to the coupling of land-based biomass NETs with EW.

The weathering of silicates is a major control on atmospheric CO2 at geologic timescales. It was proposed to enhance this process to actively remove CO2 from the atmosphere. While there are some studies that propose and theoretically analyze the application of rock powder to agricultural land, results from field experiments are still scarce. In order to evaluate the efficiency and side effects of Enhanced Weathering (EW), a mesocosm experiment was set up and agricultural soil from Belgium was amended with olivine-bearing dunite ground to two different grain sizes, while distinguishing setups with and without crops. Based on measurements of Mg, Si, pH, and DIC, the additional weathering effect of olivine could be confirmed. Calculated weathering rates are up to 3 orders of magnitude lower than found in other studies. The calculated CO2 consumption by weathering based on the outlet water of the mesocosm systems was low with 2.3–4.9 tCO2km-2a-1 if compared with previous theoretical estimates. Suspected causes were the removal or dilution of Mg as a weathering product by processes like adsorption, mineralization, plant uptake, evapotranspiration, and/or preferential flow, not specifically addressed in previous EW experiments for CO2 consumption. The observation that Mg concentrations in the upper soil layers were about 1 order of magnitude higher than in the outlet water indicates that a careful tracking of weathering indicators like Mg in the field is essential for a precise estimate of the CO2 consumption potential of EW, specifically under global deployment scenarios with a high diversity of ecosystem settings. Porewater Mg∕Si molar ratios suggest that dissolved Si is reprecipitating, forming a cation-depleted Si layer on the reactive mineral surface of freshly ground rocks. The release of potentially harmful trace elements is an acknowledged side effect of EW. Primarily Ni and Cr are elevated in the soil solution, while Ni concentrations exceed the limits of drinking water quality. The use of olivine, rich in Ni and Cr, is not recommended, and alternative rock sources are suggested for the application.

Rivers are a major source of nutrients, carbon and alkalinity to the global ocean. In this study, we firstly estimate pre-industrial riverine loads of nutrients, carbon and alkalinity based on a hierarchy of weathering and terrestrial organic matter export models, while identifying regional hotspots of the riverine exports. Secondly, we implement the riverine loads into a global ocean biogeochemical model to describe their implications for oceanic nutrient concentrations, net primary production (NPP) and air–sea CO2 fluxes globally, as well as in an analysis of coastal regions. Thirdly, we quantitatively assess the terrestrial origins and the long-term fate of riverine carbon in the ocean. We quantify annual bioavailable pre-industrial riverine loads of 3.7 Tg P, 27 Tg N, 158 Tg Si and 603 Tg C delivered to the ocean globally. We thereby identify the tropical Atlantic catchments (20 % of global C), Arctic rivers (9 % of global C) and Southeast Asian rivers (15 % of global C) as dominant suppliers of carbon for the ocean. The riverine exports lead to a simulated net global oceanic CO2 source of 231 Tg C yr−1 to the atmosphere, which is mainly caused by inorganic carbon (source of 183 Tg C yr−1) and by organic carbon (source of 128 Tg C yr−1) riverine loads. Additionally, a sink of 80 Tg C yr−1 is caused by the enhancement of the biological carbon uptake from dissolved inorganic nutrient inputs from rivers and the resulting alkalinity production. While large outgassing fluxes are simulated mostly in proximity to major river mouths, substantial outgassing fluxes can be found further offshore, most prominently in the tropical Atlantic. Furthermore, we find evidence for the interhemispheric transfer of carbon in the model; we detect a larger relative outgassing flux (49 % of global riverine-induced outgassing) in the Southern Hemisphere in comparison to the hemisphere's relative riverine inputs (33 % of global C inputs), as well as an outgassing flux of 17 Tg C yr−1 in the Southern Ocean. The addition of riverine loads in the model leads to a strong NPP increase in the tropical west Atlantic, Bay of Bengal and the East China Sea (+166 %, +377 % and +71 %, respectively). On the light-limited Arctic shelves, the NPP is not strongly sensitive to riverine loads, but the CO2 flux is strongly altered regionally due to substantial dissolved inorganic and organic carbon supplies to the region. While our study confirms that the ocean circulation remains the main driver for biogeochemical distributions in the open ocean, it reveals the necessity to consider riverine inputs for the representation of heterogeneous features in the coastal ocean and to represent riverine-induced pre-industrial carbon outgassing in the ocean. It also underlines the need to consider long-term CO2 sources from volcanic and shale oxidation fluxes in order to close the framework's atmospheric carbon budget.

The aim of this study was to demonstrate that pseudocholinesterase (CHE) serum level is a useful diagnostic biomarker for untreated Wilson’s disease (WD). Between 2013 and 2019, about 75 patients were referred to the outpatient department of the University of Düsseldorf with suspected Wilson’s disease. In 31 patients with suspected Wilson’s disease (WD-SUS-group), WD was excluded by means of investigations other than analysis of blood and urine. A total of 27 parameters of blood and urine in these 31 patients were compared to those of 20 de novo patients with manifest WD (WD-DEF-group), which parameter showed the highest significance level of difference between the WD-DEF-group and the WD-SUS-group. Thereafter, receiver operating characteristics (ROC-curves) were analyzed to evaluate which parameter showed the largest area under the curve (AUC) to detect WD. Finally, a logistic regression analysis was performed to analyze which combination of parameters allowed the best classification of the 51 patients either into the WD-DEF-group or into the WD-SUS-group. CHE showed the highest significance level for a difference between the WD-DEF- and WD-SUS-group, had the highest AUC, and, in combination with ceruloplasmin, allowed 100% correct classification. Without CHE, no other combination of parameters reached this level of correct classification. After the initiation of treatment, which regularly results in an improvement in CHE, the high diagnostic accuracy of this biomarker was lost. Cholinesterase turns out to be an excellent biomarker for differentiation between untreated de novo patients with manifest WD and heterozygotic gene carriers.

Abstract Background Aseptic meningitis epidemics may pose various health care challenges. Methods We describe the German enterovirus meningitis epidemics in the university hospital centers of Düsseldorf, Cologne and Berlin between January 1st and December 31st, 2013 in order to scrutinize clinical differences from other aseptic meningitis cases. Results A total of 72 enterovirus (EV-positive) meningitis cases were detected in our multicenter cohort, corresponding to 5.8% of all EV-positive cases which were voluntarily reported within the National Enterovirus surveillance (EVSurv, based on investigation of patients with suspected aseptic meningitis/encephalitis and/or acute flaccid paralysis) by physicians within this period of time. Among these 72 patients, 38 (52.8%) were enterovirus positive and typed as echovirus (18 pediatric and 20 adult cases, median age 18.5 years; echovirus 18 (1), echovirus 2 (1), echovirus 30 (31), echovirus 33 (1), echovirus 9 (4)). At the same time, 45 aseptic meningitis cases in our cohort were excluded to be due to enteroviral infection (EV-negative). Three EV-negative patients were tested positive for varicella zoster virus (VZV) and 1 EV-negative patient for herpes simplex virus 2. Hospitalization was significantly longer in EV-negative cases. Cerebrospinal fluid analysis did not reveal significant differences between the two groups. After discharge, EV-meningitis resulted in significant burden of sick leave in our pediatric cohort as parents had to care for the children at home. Conclusions Voluntary syndromic surveillance, such as provided by the EVSurv in our study may be a valuable tool for epidemiological research. Our analyses suggest that EV-positive meningitis predominantly affects younger patients and may be associated with a rather benign clinical course, compared to EV-negative cases.

The Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign was carried out north-west of Svalbard (Norway) between 23 May and 6 June 2017. The objective of ACLOUD was to study Arctic boundary layer and mid-level clouds and their role in Arctic amplification. Two research aircraft (Polar 5 and 6) jointly performed 22 research flights over the transition zone between open ocean and closed sea ice. Both aircraft were equipped with identical instrumentation for measurements of basic meteorological parameters, as well as for turbulent and radiative energy fluxes. In addition, on Polar 5 active and passive remote sensing instruments were installed, while Polar 6 operated in situ instruments to characterize cloud and aerosol particles as well as trace gases. A detailed overview of the specifications, data processing, and data quality is provided here. It is shown that the scientific analysis of the ACLOUD data benefits from the coordinated operation of both aircraft. By combining the cloud remote sensing techniques operated on Polar 5, the synergy of multi-instrument cloud retrieval is illustrated. The remote sensing methods were validated using truly collocated in situ and remote sensing observations. The data of identical instruments operated on both aircraft were merged to extend the spatial coverage of mean atmospheric quantities and turbulent and radiative flux measurement. Therefore, the data set of the ACLOUD campaign provides comprehensive in situ and remote sensing observations characterizing the cloudy Arctic atmosphere. All processed, calibrated, and validated data are published in the World Data Center PANGAEA as instrument-separated data subsets (Ehrlich et al., 2019b, https://doi.org/10.1594/PANGAEA.902603).

Numerous publications propose the deployment of negative emission technologies, which intend to actively remove CO2 from the atmosphere with the goal to reach the 1.5∘ target as discussed by the IPCC. The increasing number of scientific studies on the individual potential of different envisaged technologies and methods indicates that no single method has enough capacities to mitigate the issue by itself. It is thus expected that technology portfolios are deployed. As some of them utilize the same environmental compartment, co-deployment effects are expected. Those effects are particularly important to evaluate with respect to additional CO2 uptake. Considering soils as one of the main affected compartments, we see a plethora of processes which can positively benefit from each other, canceling out negative side effects or increasing overall CO2 sequestration potentials. To derive more reliable estimates of negative emission potentials and to evaluate common effects on global carbon pools, it is now necessary to intensively study interrelated effects of negative emission technology deployment while minimizing negative side effects.

In recent years, the requirements in the industrial production of elongated objects, e.g., aircraft, have been increased. An essential aspect of the production process is the 3D object detection as well as the qualitative assessment of the captured data. On the one hand high accuracy requirements with a 3D standard deviation of σ3D = 1 mm have to be fulfilled, on the other hand an efficient 3D object capturing is needed. In terms of efficiency, kinematic terrestrial laser scanning (k-TLS) has proven its strength in the recent years. It can be seen as an alternative and is even more powerful than to the well established static terrestrial laser scanning (s-TLS). In order to perform a high accurate 3D object capturing with k-TLS, the 3D object capturing of the initial sensor, the (geo-)referencing of the mobile platform, the synchronisation of all sensors and the system calibration, which means the determination of six extrinsic parameters have to be performed with suitable accuracy. Within this contribution we focus on the system calibration. Therefore an approach based on known reference geometries, here planes, is used (Strübing and Neumann, 2013). As a result, the lever arm and boresight angles are determined. Hereby the number as well as the position and orientation of the reference geometries is of importance. Therefore, an optimal arrangement has to be found. Here a sensitive analysis based on uncertainty propagation is used. A selective search of an optimised arrangement is carried out by a genetic algorithm. Within some examples we demonstrate some theoretical aspects and how an optimisation of the reference geometry arrangement can be achieved.

Basalt weathering is one of many relevant processes balancing the global carbon cycle via land–ocean alkalinity fluxes. The CO2 consumption by weathering can be calculated using alkalinity and is often scaled with runoff and/or temperature. Here, it is tested if the surface age distribution of a volcanic system derived by geological maps is a useful proxy for changes in alkalinity production with time. A linear relationship between temperature normalized alkalinity fluxes and the Holocene area fraction of a volcanic field was identified using information from 33 basalt volcanic fields, with an r2=0.93. This relationship is interpreted as an aging function and suggests that fluxes from Holocene areas are ∼10 times higher than those from old inactive volcanic fields. However, the cause for the decrease with time is probably a combination of effects, including a decrease in alkalinity production from material in the shallow critical zone as well as a decline in hydrothermal activity and magmatic CO2 contribution. The addition of fresh reactive material on top of the critical zone has an effect in young active volcanic settings which should be accounted for, too. A comparison with global models suggests that global alkalinity fluxes considering Holocene basalt areas are ∼60 % higher than the average from these models imply. The contribution of Holocene areas to the global basalt alkalinity fluxes is today however only ∼5 %, because identified, mapped Holocene basalt areas cover only ∼1 % of the existing basalt areas. The large trap basalt proportion on the global basalt areas today reduces the relevance of the aging effect. However, the aging effect might be a relevant process during periods of globally intensive volcanic activity, which remains to be tested.

The utility of classical neural networks as universal approximators suggests that their quantum analogues could play an important role in quantum generalizations of machine-learning methods. Inspired by the proposal in Torrontegui and García-Ripoll [Europhys. Lett. 125, 30004 (2019)1286-485410.1209/0295-5075/125/30004], we demonstrate a superconducting qubit implementation of a controlled gate, which generalizes the action of a classical perceptron as the basic building block of a quantum neural network. In a two-qubit setup we show full control over the steepness of the perceptron activation function, the input weight and the bias by tuning the gate length, the coupling between the qubits, and the frequency of the applied drive, respectively. In its general form, the gate realizes a multiqubit entangling operation in a single step, whose decomposition into single- and two-qubit gates would require a number of gates that is exponential in the number of qubits. Its demonstrated direct implementation as perceptron in quantum hardware may therefore lead to more powerful quantum neural networks when combined with suitable additional standard gates.

A family is described as having two recessively inherited metabolic diseases and three differently affected children. During the explantation of a drain tube grommet under general anesthesia, a prolonged resuscitation and wake-up period occurred in the key case when he was 8 years old. This led to a family screening for butyrylcholinesterase deficiency, which was confirmed not only in the key case but also in his 5-year-old sister; it was not confirmed in his 10-year-old brother. However, the key case not only had reduced serum levels of BCHE, but also elevated liver enzyme levels, which are atypical for BCHE deficiency. After the exclusion of viral and autoimmune hepatitis, Wilson’s disease (WD) was eventually diagnosed and also confirmed in his elder brother, but not in his sister. This family is presented to highlight an extremely rare WD-patient in whom a low serum level of BCHE did not occur because of WD but because of BCHE deficiency.

This study investigated the clinical long-term effect of incobotulinumtoxinA (incoBoNT/A) in 33 cervical dystonia (CD) patients who had developed partial secondary therapy failure (PSTF) under previous long-term botulinum toxin (BoNT) treatment. Patients were treated four times every 12 weeks with incoBoNT/A injections. Physicians assessed treatment efficacy using the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) at the baseline visit, week 12 and 48. Patients rated quality of life of CD with the Craniocervical Dystonia Questionnaire (CDQ-24). Titres of neutralizing antibodies(NAB) were determined at start of the study and after 48 weeks. All patients had experienced significant and progressive worsening of symptoms in the last 6 months of previous BoNT treatment. Repeated incoBoNT/A injections resulted in a significant reduction in mean TWSTRS at week 12 and 48. Patients' rating of quality of life was highly correlated with TWSTRS but did not change significantly over 48 weeks. During the 48 weeks -period of incoBoNT/A treatment NAB titres decreased in 32.2%, did not change in 45.2%, and only increased in 22.6% of the patients. Thus, repeated treatment with the low dose of 200 MU incoBoNT/A over 48 weeks provided a beneficial clinical long-term effect in PSTF and did not booster titres of NAB.

Non-technical summary There has been a long history of conflicts, studies, and debate over how to both protect rivers and develop them sustainably. With a pause in new developments caused by the global pandemic, anticipated further implementation of the Paris Agreement and high-level global climate and biodiversity meetings in 2021, now is an opportune moment to consider the current trajectory of development and policy options for reconciling dams with freshwater system health. Technical summary We calculate potential loss of free-flowing rivers (FFRs) if proposed hydropower projects are built globally. Over 260,000 km of rivers, including Amazon, Congo, Irrawaddy, and Salween mainstem rivers, would lose free-flowing status if all dams were built. We propose a set of tested and proven solutions to navigate trade-offs associated with river conservation and dam development. These solution pathways are framed within the mitigation hierarchy and include (1) avoidance through either formal river protection or through exploration of alternative development options; (2) minimization of impacts through strategic or system-scale planning or re-regulation of downstream flows; (3) restoration of rivers through dam removal; and (4) mitigation of dam impacts through biodiversity offsets that include restoration and protection of FFRs. A series of examples illustrate how avoiding or reducing impacts on rivers is possible – particularly when implemented at a system scale – and can be achieved while maintaining or expanding benefits for climate resilience, water, food, and energy security. Social media summary Policy solutions and development pathways exist to navigate trade-offs to meet climate resilience, water, food, and energy security goals while safeguarding FFRs.

The polarization observables T,E,P,H, and G in photoproduction of η mesons off protons are measured for photon energies from threshold to W=2400 MeV (T), 2280 MeV (E), 1620 MeV (P,H), or 1820 MeV (G), covering nearly the full solid angle. The data are compared to predictions from the SAID, MAID, JüBo, and BnGa partial-wave analyses. A refit within the BnGa approach including further data yields precise branching ratios for the Nη decay of nucleon resonances. A Nη-branching ratio of 0.33±0.04 for N(1650)1/2− is found, which reduces the large and controversially discussed Nη-branching ratio difference of the two lowest mass JP=1/2−-resonances significantly. Keywords: Baryon spectroscopy, Meson photoproduction, Polarization observables

Background: The Global Anticoagulant Registry in the FIELD–Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. Methods and results: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012–2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P

In recent years, the requirements in the industrial production, e.g., ships or planes, have been increased. In addition to high accuracy requirements with a standard deviation of 1 mm, an efficient 3D object capturing is required. In terms of efficiency, kinematic laser scanning (k-TLS) has been proven its worth in recent years. It can be seen as an alternative to the well established static terrestrial laser scanning (s-TLS). However, current k-TLS based multi-sensor-systems (MSS) are not able to fulfil the high accuracy requirements. Thus, a new k-TLS based MSS and suitable processing algorithms have to be developed. In this contribution a new k-TLS based MSS will be presented. The main focus will lie on the (geo-)referencing process. Due to the high accuracy requirements, a novel procedure of external (geo-)referencing is used here. Hereby, a mobile platform, which is equipped with a profile laser scanner, will be tracked by a laser tracker. Due to the fact that the measurement frequency of the laser scanner is significantly higher than the measurement frequency of the laser tracker a direct point wise (geo-)referencing is not possible. To enable this a Kalman filter model is set up and implemented. In the prediction step each point is shifted according to the determined velocity of the platform. Because of the nonlinear motion of the platform an iterative extended Kalman filter (iEKF) is used here. Furthermore, test measurements of a panel with the k-TLS based MSS and with s-TLS were carried out. To compare the results, the 3D distances with the M3C2-algorithm between the s-TLS 3D point cloud and the k-TLS 3D point cloud are estimated. It can be noted, that the usage of a system model for the (geo-)referencing is essential. The results show that the mentioned high accuracy requirements have been achieved.

Global terrestrial nitrogen (N) and phosphorus (P) cycles are coupled to the global carbon (C) cycle for net primary production (NPP), plant C allocation, and decomposition of soil organic matter, but N and P have distinct pathways of inputs and losses. Current C-nutrient models exhibit large uncertainties in their estimates of pool sizes, fluxes, and turnover rates of nutrients, due to a lack of consistent global data for evaluating the models. In this study, we present a new model–data fusion framework called the Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines the CARbon DAta MOdel fraMework (CARDAMOM) data-constrained C-cycle analysis with spatially explicit data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. We calculated the steady-state N- and P-pool sizes and fluxes globally for large biomes. Our study showed that new N inputs from biological fixation and deposition supplied > 20 % of total plant uptake in most forest ecosystems but accounted for smaller fractions in boreal forests and grasslands. New P inputs from atmospheric deposition and rock weathering supplied a much smaller fraction of total plant uptake than new N inputs, indicating the importance of internal P recycling within ecosystems to support plant growth. Nutrient-use efficiency, defined as the ratio of gross primary production (GPP) to plant nutrient uptake, were diagnosed from our model results and compared between biomes. Tropical forests had the lowest N-use efficiency and the highest P-use efficiency of the forest biomes. An analysis of sensitivity and uncertainty indicated that the NPP-allocation fractions to leaves, roots, and wood contributed the most to the uncertainties in the estimates of nutrient-use efficiencies. Correcting for biases in NPP-allocation fractions produced more plausible gradients of N- and P-use efficiencies from tropical to boreal ecosystems and highlighted the critical role of accurate measurements of C allocation for understanding the N and P cycles.