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Abstract Soils, just like all other ecosystem compartments, change over time and, consequently, conditions for soil‐inhabiting organisms are also changing, affecting their composition and diversity. Soil biodiversity is a critical component of ecosystems that supports many essential ecosystem functions and services, such as nutrient cycling, carbon sequestration, water regulation and biomass production for food, fodder, fibre and energy. However, and despite the importance of soil biodiversity for ecosystem health and human well‐being, neither current state, drivers, potential consequences for ecosystem services nor options for sustainable governance of soil biodiversity are well understood. Here, we provide a framework for and argue that conducting a national assessment of soil biodiversity, albeit being a complex endeavour, is fundamental to building a baseline to understand the current state and trends of soil biodiversity, but also to identify the main drivers of change, the impacts of soil biodiversity loss and the potential pathways for conservation and sustainable governance of soil biodiversity.

Stochastic models in hydrology are very useful and widespread tools for making reliable probabilistic predictions. However, such models are only accurate at making predictions if model parameters are first of all calibrated to measured data in a consistent framework such as the Bayesian one, in which knowledge about model parameters is described through probability distributions. Unfortunately, Bayesian parameter calibration, a. k. a. inference, with stochastic models, is often a computationally intractable problem with traditional inference algorithms, such as the Metropolis algorithm, due to the expensive likelihood functions. Therefore, the prohibitive computational cost is often overcome by employing over-simplified error models, which leads to biased parameter estimates and unreliable predictions. However, thanks to recent advancements in algorithms and computing power, fully fledged Bayesian inference with stochastic models is no longer off-limits for hydrological applications. Our goal in this work is to demonstrate that a computationally efficient Hamiltonian Monte Carlo algorithm with a timescale separation makes Bayesian parameter inference with stochastic models feasible. Hydrology can potentially take great advantage of this powerful data-driven inference method as a sound calibration of model parameters is essential for making robust probabilistic predictions, which can certainly be useful in planning and policy-making. We demonstrate the Hamiltonian Monte Carlo approach by detailing a case study from urban hydrology. Discussing specific hydrological models or systems is outside the scope of our present work and will be the focus of further studies.

Targeted nanoparticle delivery to sites of interest is important for targeted therapeutics. Here, the authors improve the targeting efficiency of antibodies on nanoparticles using a monobody adapter to correctly orientate the antibody to preserve targeting function and demonstrate application in targeting the vascular endothelium of human kidneys.

Deep learning methods have frequently outperformed conceptual hydrologic models in rainfall-runoff modelling. Attempts of investigating such deep learning models internally are being made, but the traceability of model states and processes and their interrelations to model input and output is not yet fully understood. Direct interpretability of mechanistic processes has always been considered an asset of conceptual models that helps to gain system understanding aside of predictability. We introduce hydrologic neural ordinary differential equation (ODE) models that perform as well as state-of-the-art deep learning methods in stream flow prediction while maintaining the ease of interpretability of conceptual hydrologic models. In neural ODEs, internal processes that are represented in differential equations, are substituted by neural networks. Therefore, neural ODE models enable the fusion of deep learning with mechanistic modelling. We demonstrate the basin-specific predictive performance for 569 catchments of the continental United States. For exemplary basins, we analyse the dynamics of states and processes learned by the model-internal neural networks. Finally, we discuss the potential of neural ODE models in hydrology.

Within the 9th European Framework programme, since 2021 EUROfusion is operating five tokamaks under the auspices of a single Task Force called ‘Tokamak Exploitation’. The goal is to benefit from the complementary capabilities of each machine in a coordinated way and help in developing a scientific output scalable to future largre machines. The programme of this Task Force ensures that ASDEX Upgrade, MAST-U, TCV, WEST and JET (since 2022) work together to achieve the objectives of Missions 1 and 2 of the EUROfusion Roadmap: i) demonstrate plasma scenarios that increase the success margin of ITER and satisfy the requirements of DEMO and, ii) demonstrate an integrated approach that can handle the large power leaving ITER and DEMO plasmas. The Tokamak Exploitation task force has therefore organized experiments on these two missions with the goal to strengthen the physics and operational basis for the ITER baseline scenario and for exploiting the recent plasma exhaust enhancements in all four devices (PEX: Plasma EXhaust) for exploring the solution for handling heat and particle exhaust in ITER and develop the conceptual solutions for DEMO. The ITER Baseline scenario has been developed in a similar way in ASDEX Upgrade, TCV and JET. Key risks for ITER such as disruptions and run-aways have been also investigated in TCV, ASDEX Upgrade and JET. Experiments have explored successfully different divertor configurations (standard, super-X, snowflakes) in MAST-U and TCV and studied tungsten melting in WEST and ASDEX Upgrade. The input from the smaller devices to JET has also been proven successful to set-up novel control schemes on disruption avoidance and detachment.

After a long device enhancement phase, scientific operation resumed in 2022. The main new device components are the water cooling of all plasma facing components and the new water-cooled high heat flux divertor units. Water cooling allowed for the first long-pulse operation campaign. A maximum discharge length of 8 min was achieved with a total heating energy of 1.3 GJ. Safe divertor operation was demonstrated in attached and detached mode. Stable detachment is readily achieved in some magnetic configurations but requires impurity seeding in configurations with small magnetic pitch angle within the edge islands. Progress was made in the characterization of transport mechanisms across edge magnetic islands: Measurement of the potential distribution and flow pattern reveals that the islands are associated with a strong poloidal drift, which leads to rapid convection of energy and particles from the last closed flux surface into the scrape-off layer. Using the upgraded plasma heating systems, advanced heating scenarios were developed, which provide improved energy confinement comparable to the scenario, in which the record triple product for stellarators was achieved in the previous operation campaign. However, a magnetic configuration-dependent critical heating power limit of the electron cyclotron resonance heating was observed. Exceeding the respective power limit leads to a degradation of the confinement.

The propagation and absorption of the slow waves (SWs) in the three-ion plasma of the Wendelstein 7-X stellarator have been investigated by ray tracing. The aim of the study is to obtain a qualitative notion of the penetration into the plasma and absorption of the wave excited by the potential difference between the antenna conductors and the antenna box. It has been discovered that the rays propagate along the magnetic field lines over a significant distance, up to 6 m, from the antenna. They weakly, to a depth of 0.1 m, penetrate into the plasma. Absorption of the SWs by electrons does not lead to the generation of the currents capable of affecting the plasma equilibrium. Most of the SWs are absorbed beyond the region of ion–ion hybrid resonance in the zone of cyclotron resonance of ^3 He ions at the periphery of the plasma. The ICRH of the three-ion W 7-X plasma will be used to heat ^3 He ions to high energies and simulate the confinement of alpha particles in an optimized stellarator configuration. The presence of a source of hot ^3 He ions, which are poorly confined, at the periphery of the plasma can affect the experimental results.

Las investigaciones relacionadas con el período de retorno de vientos extremos en Cuba contienen un número limitado de estaciones meteorológicas y considera las rachas máximas anuales del viento. En tal sentido, el objetivo de este artículo es evaluar el peligro producido por vientos máximos en Cuba utilizando valores máximos del viento diarios de los registros de estaciones, el cual es cuantificado usando el periodo de retorno, empleando la técnica de “Picos sobre la referencia” para fijar la curva de valores extremos a través de la Distribución Generalizada de Pareto. En el artículo también se presenta una técnica computacional eficiente para la separación de los tipos de viento y el correspondiente cálculo de la curva de PR para los sinópticos y de tormenta con el Intervalo de Confiabilidad para una probabilidad del 95%. Esto es de suma importancia para las autoridades que deben elaborar planes de gestión de riesgos y la incorporación de las medidas requeridas, así como ayuda a la planeación urbana, tratando de evitar el desarrollo de centros urbanos en regiones de alto peligro. En general se encontró que el peligro producido por vientos no huracanados en Cuba es relativamente bajo. También se comprobó que los registros de las estaciones son adecuados para calcular el peligro producido por viento, aunque en algunas estaciones se encontraron errores en la caracterización de las condiciones de tiempo presente, por lo que como parte de este trabajo se implementó una técnica sencilla que permite corregir estos errores.

Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and relatively efficient methodology (i) to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii) to separate prediction uncertainty into its components. Our approach acknowledges that urban drainage predictions are biased. This is mostly due to input errors and structural deficits of the model. We address this issue by describing model bias in a Bayesian framework. The bias becomes an autoregressive term additional to white measurement noise, the only error type accounted for in traditional uncertainty analysis. To allow for bigger discrepancies during wet weather, we make the variance of bias dependent on the input (rainfall) or/and output (runoff) of the system. Specifically, we present a structured approach to select, among five variants, the optimal bias description for a given urban or natural case study. We tested the methodology in a small monitored stormwater system described with a parsimonious model. Our results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected. Furthermore, our probabilistic predictions can discriminate between three uncertainty contributions: parametric uncertainty, bias, and measurement errors. In our case study, the best performing bias description is the output-dependent bias using a log-sinh transformation of data and model results. The limitations of the framework presented are some ambiguity due to the subjective choice of priors for bias parameters and its inability to address the causes of model discrepancies. Further research should focus on quantifying and reducing the causes of bias by improving the model structure and propagating input uncertainty.

Electron-cyclotron resonance heating (ECRH) is the main heating mechanism in the Wendelstein 7-X (W7-X) stellarator. Although second-harmonic ECRH (X2) has been used routinely for plasma startup, startup at third harmonic (X3) is known to be much more difficult. In this work, we investigate the energy gain of particles during nonlinear wave–particle interaction for conditions relevant to second- and third-harmonic startups in W7-X. We take into account both the beam and the ambient magnetic field inhomogeneities. The latter is shown to significantly increase the mean energy gain resulting from a single wave–particle resonant interaction. In W7-X-like conditions, the improvement in maximum gained energy is up to 4 times the analogous uniform magnetic field case. However, this improvement is not enough to ensure X3 startup. The optimal magnetic field inhomogeneity length scale for average energy gain and start up in W7-X-like conditions is found to be in the range of $1$ to $3\ {\rm km}^{-1}$. A possibility of using multiple beams with neighbouring resonances is also considered. A considerable enhancement of the energy gain is demonstrated. [ABSTRACT FROM AUTHOR]

Childhood‐onset cardiomyopathy is a genetically heterogeneous group of conditions with several genes implicated. Recently, biallelic loss‐of‐function variants in PPP1R13L have been reported in association with a syndromic form of dilated cardiomyopathy (DCM). In addition, affected children manifest skin and hair abnormalities, cleft lip and palate (CLP), and eye findings. Here, we delineate the condition further by describing the phenotype associated with a homozygous frameshift variant (p.Arg330 ProfsTer76) in PPP1R13L detected in two sibships in a consanguineous family with six affected children. The index case had DCM and wooly hair, two of his siblings had DCM and CLP while three cousins had, in addition, glaucoma. Global developmental delay was observed in one child. All the children, except one, died during early childhood. Whole exome sequencing and whole genome sequencing did not reveal any other plausible variant. We provide further evidence that implicates PPP1R13L in a variable syndromic form of severe childhood‐onset DCM and suggests expanding the spectrum of this condition to include glaucoma. Given the variability of the phenotype associated with PPP1R13‐related DCM, a thorough evaluation of each case is highly recommended even in the presence of an apparently isolated DCM. [ABSTRACT FROM AUTHOR]

The transport network is the backbone of relief delivery operations during natural disasters such as typhoons. In this study, we constructed a transport network model connecting the cities and municipalities of the Visayas island group in the Philippines with their regional relief or food hubs. Using this model, we simulated relief delivery under various damage scenarios arising from the impact of a chosen typhoon. Using Typhoon Haiyan ("Yolanda") as an example, we measured the relief delivery efficiency of the transport network over a range of damage scenarios relative to the baseline undamaged case. We find that the Visayas transport network is sensitive to small damages, with 30 out of 251 town centers (12%) getting disconnected from the network and relief delivery efficiency dropping to 75%, resulting in time delays of up to 12 h, when just 1% of the nodes are damaged. Our results explain the difficulty of achieving timely relief delivery efforts in the aftermath of typhoons, especially in archipelagic geographies. We also present a prototype decision support tool that can efficiently generate simulations of multiple projected scenarios, including different typhoon paths and additional food hub locations, to help decision-makers forecast expected improvements in relief delivery efficiency. This tool underlines the importance of strategic prepositioning of relief goods and data-driven disaster risk reduction and management plans. [ABSTRACT FROM AUTHOR]

Accurate estimates of greenhouse gas emissions and sinks are critical for understanding the carbon cycle and identifying key drivers of anthropogenic climate change. In this study, we investigate the variability in CO and CO2 concentrations and their ratio over the Mexico City metropolitan area (MCMA) using long-term, time-resolved columnar measurements at three stations, employing solar-absorption Fourier transform infrared spectroscopy (FTIR). Using a simple model and the mixed-layer height derived from a ceilometer, we determined the CO and CO2 concentrations in the mixed layer from the total column measurements and found good agreement with surface cavity ring-down spectroscopy measurements. In addition, we used the diurnal pattern of CO columnar measurements at specific time intervals to estimate an average growth rate that, when combined with the space-based Tropospheric Monitoring Instrument (TROPOMI) CO measurements, allowed for the derivation of annual CO and CO2 MCMA emissions from 2016 to 2021. A CO emission decrease of more than 50 % was found during the COVID-19 lockdown period with respect to the year 2018. These results demonstrate the feasibility of using long-term EM27/SUN column measurements to monitor the annual variability in the anthropogenic CO2 and CO emissions in Mexico City without recourse to complex transport models. This simple methodology could be adapted to other urban areas if the orography of the regions favours low ventilation for several hours per day and the column growth rate is dominated by the emission flux.

Abstract Although abdominal aortic aneurysms (AAA) are more common in men, women are at greater risk for AAA growth/rupture. Vascular deformation mapping (VDM) utilizes deformable image registration to qualify and quantify 3D-AAA growth using computed tomography angiograms (CTA). In this study we leveraged VDM to investigate sex differences in AAA growth patterns. Patients with infra-renal AAA and ≥ 2 CTA were identified. Males and females were matched for age, hypertension, and smoking history. Patient characteristics, maximum diameter (Dmax), and AAA volume were obtained. CTA images were segmented, and VDM was conducted to quantify 3D AAA growth rate per year (GR, cm/year). Statistical shape modeling was utilized to compute mean aneurysm shapes and 3D GR. Average GR was evaluated at specific regions of the aortic surface for males and females. Seventeen males and 17 females were matched. At the individual level, there were no sex differences in changes in Dmax or AAA volume. However, females had larger GR across the anterior and right lateral AAA (1.33 vs 0.89 and 1.56 vs 0.74 cm/year, respectively), despite no difference in posterior or left lateral AAA GR. Despite comparable changes in Dmax, AAA volume, and GR magnitude, women demonstrated a more eccentric, anterior-predominant, AAA growth pattern.