Search

Abstract The Nord Stream pipeline leaks on 26 September 2022 released 465 ± 20 kt of methane into the atmosphere, which is the largest recorded transient anthropogenic methane emission event. While most of the gas escaped directly to the atmosphere, a fraction dissolved in the water. So far, studies on the fate of this dissolved methane rely on pipeline volumetric estimates or spatially sparse concentration measurements and ocean models. Here, we use atmospheric measurements with broad spatial coverage obtained from an airborne platform to estimate outgassing of 19-48 t h−1 on 5 October 2022. Our results broadly agree with ocean models but reveal uncertainties such as inaccuracies in their spatial emission distribution. Thus, we provide a data-driven constraint on the fate of the methane from the Nord Stream pipelines in the Baltic Sea. These results demonstrate the benefit of a fast-response airborne mission to track a dynamic methane emission event.

Abstract This study assessed the water quality for drinking and irrigation purposes on the southern slopes of Mt. Kilimanjaro during the dry season under low flow conditions. Fifty-one samples covering eight different water sources (i.e., stream water from natural and anthropogenic impacted streams, domestic water, spring water, rainfall, groundwater, lake water and water from irrigation canals) were collected in a snapshot sampling campaign over 10 days in February 2023. First, physical, chemical and biological parameters were analysed and compared with Tanzanian and international drinking and irrigation water quality requirements. The samples were then ranked according to their suitability for drinking and/or irrigation using water quality indices (WQI). All drinking water quality parameters except for E. coli and turbidity were within the permissible limits. A generalised problem of faecal contamination was found in the study area, including in domestic water, highlighting the need to identify sources of contamination and remediate them before distribution. The drinking water quality index (DWQI) classified 89% of the samples as unsuitable and 11% as excellent for drinking. Irrigation water quality parameters were within the guidelines of restriction of use except for pH in 5 samples. In contrast to the DWQI, the vast majority of the water samples (88%) can be used for irrigation without restrictions according to the irrigation water quality index (IWQI). The suitability of water for irrigation was also assessed using three indices, i.e., Kelley’s Index, Soluble Sodium Percentage and Magnesium Ratio, which indicated potential problems with excess of sodium (about 30% of the samples) and magnesium (about 20%). Further studies combining suitability indices, soil characteristics and crop types are recommended to assess water quality for irrigation use.

Study region: Santa River catchment, Cordillera Blanca, Peruvian Andes. Study focus: The effects of global change on the hydrological cycle are amplified in the highly-dynamic tropics. In the high Andes (identified as a hotspot of climate change), a warming climate impacts the cryosphere, and, therefore, alters the composition of downstream discharge. Quantifying these compositions, and evaluating the contributions of meltwater components, is vital for sustainable water resources management under changing hydrological regimes. For such analyses, we used the spatially-distributed cryosphere-hydrological model ‘Spatial Processes in Hydrology,’ forced with PISCO and RAIN4PE distributed meteorological data to simulate daily discharge components (rainfall-runoff, snowmelt, glacier melt, baseflow). We then computed a compensation metric to assess how meltwater compensated for the lack of rainfall-runoff during drought events, and further evaluated if the compensating flows were sufficient to maintain environmental flow requirements. New hydrological insights for the region: During 35 drought events, snowmelt contributions tended to decrease, while glacier melt contributions tended to increase. In total, meltwater contributions generally increased, and were able to compensate for reductions in rainfall-runoff. Meltwater proved critical during 85 % of identified drought events to ensure the total discharge reached an environmentally safe threshold.This research advances the understanding of the compensating effect of meltwater during droughts, which can inform water management in glacier-influenced catchments and shed light on climate change impacts in the tropical Andes.

The political, economic, social, technological, environmental, and legal (PESTEL) dimensions in a local community shape the adoption of specific nature-based solutions (NbS). This study provides crucial insights on NbS tailored to smallholder indigenous and peasant communities heavily reliant on water ecosystem services from headwater streams, lakes, ponds, and reservoirs in the páramo ecosystems of the central Ecuadorian Andes. Combining a multi-stakeholder workshop with bibliometric analysis, we developed a framework that integrates NbS with local communities’ PESTEL dimensions to sustain water ecosystem services. As a result, the lack of political will to sustain healthy aquatic ecosystems, urban-centered environmental investment, and agricultural expansion mainly influence the sustainability of water ecosystem services in the political, economic and environmental dimensions. Social, legal, and technological dimensions encompass community dissatisfaction, resistance to conservation, neglect of clean water and land use regulations, and limited innovation investment. Artificial floating islands and passive river restoration were the NbS adaptable to these PESTEL dimensions in our local communities. Artificial floating islands, a macrophyte-based technology that integrates community plant knowledge, are feasible even with limited financial resources. Passive river restoration complements them to promote headwater vegetation recovery and governance of water ecosystem services. Their integration supports drinking water supply, irrigation, fisheries, water purification, habitat conservation, soil formation carbon sequestration, and the achievement of several Sustainable Development Goals (SDGs). We provide decision-makers with a rigorous assessment of NbS for local communities, with the potential to scale to countries with similar contexts and highlight the need for future research to explore NbS in regional or national frameworks.

Schistosomiasis, a neglected tropical disease caused by parasitic worms, poses a major public health challenge in economically disadvantaged regions, especially in Sub-Saharan Africa. Climate factors, such as temperature and rainfall patterns, play a crucial role in the transmission dynamics of the disease. This study presents a deterministic model that aims to evaluate the temporal and seasonal transmission dynamics of schistosomiasis by examining the influence of temperature and rainfall over time. Equilibrium states are examined to ascertain their existence and stability employing the center manifold theory, while the basic reproduction number is calculated using the next-generation technique. To validate the model's applicability, demographic and climatological data from Uganda, Kenya, and Tanzania, which are endemic East African countries situated in the tropical region, are utilized as a case study region. The findings of this study provide evidence that the transmission of schistosomiasis in human populations is significantly influenced by seasonal and monthly variations, with incidence rates varying across countries depending on the frequency of temperature and rainfall. Consequently, the region is marked by both schistosomiasis emergencies and re-emergences. Specifically, it is observed that monthly mean temperatures within the range of 22–27 °C create favorable conditions for the development of schistosomiasis and have a positive impact on the reproduction numbers. On the other hand, monthly maximum temperatures ranging from 27 to 33 °C have an adverse effect on transmission. Furthermore, through sensitivity analysis, it is projected that by the year 2050, factors such as the recruitment rate of snails, the presence of parasite egg-containing stools, and the rate of miracidia shedding per parasite egg will contribute significantly to the occurrence and control of schistosomiasis infections. This study highlights the significant influence of seasonal and monthly variations, driven by temperature and rainfall patterns, on the transmission dynamics of schistosomiasis. These findings underscore the importance of considering climate factors in the control and prevention strategies of schistosomiasis. Additionally, the projected impact of various factors on schistosomiasis infections by 2050 emphasizes the need for proactive measures to mitigate the disease's impact on vulnerable populations. Overall, this research provides valuable insights to anticipate future challenges and devise adaptive measures to address schistosomiasis transmission patterns.

The quickly developing drone technology can be used efficiently in the field of pipeline leak detection. The aim of this article is to provide drone mission concepts for detecting releases from pipelines. It provides an overview of the current applications of natural gas pipeline surveys, it considers environmental conditions by plume modelling, it discusses suitable commercially available sensors, and develops concepts for routine monitoring of pipelines and short term missions for localising and identifying a known leakage. Suitable platforms depend on the particular mission and requirements concerning sensors and legislation. As an illustration, a feasibility study during a release experiment is introduced. The main challenge of this study was the variability of wind direction on a time scale of minutes, which produces considerable differences to the plume simulations. Nevertheless, the leakage rates derived from the observations are in the same order of magnitude as the emission rates. Finally the results from the modeling, the release experiment and possible drone scenarios are combined and requirements for future application derived.

Abstract Schistosomiasis, a prevalent water-borne disease second only to malaria, significantly impacts impoverished rural communities, primarily in Sub-Saharan Africa where over 90% of the severely affected population resides. The disease, majorly caused by Schistosoma mansoni and S. haematobium parasites, relies on freshwater snails, specifically Biomphalaria and Bulinus species, as crucial intermediate host (IH) snails. Targeted snail control is advisable, however, there is still limited knowledge about the community structure of the two genera especially in East Africa. Utilizing a machine learning approach, we employed random forest to identify key features influencing the distribution of both IH snails in this region. Our results reveal geography and climate as primary factors for Biomphalaria, while Bulinus occurrence is additionally influenced by soil clay content and nitrogen concentration. Favorable climate conditions indicate a high prevalence of IHs in East Africa, while the intricate connection with geography might signify either dispersal limitations or environmental filtering. Predicted probabilities demonstrate non-linear patterns, with Bulinus being more likely to occur than Biomphalaria in the region. This study provides foundational framework insights for targeted schistosomiasis prevention and control strategies in the region, assisting health workers and policymakers in their efforts.

A profound knowledge of pollutant emissions and transport processes is essential to better assess the impact on local air quality, which ultimately affects human health. This is of special importance in the proximity of airports, as flight activities are a major source of ultrafine aerosol particles (UFP) that are associated with adverse health effects. A quantification of the aerosol population in the horizontal and in particular in the vertical distribution has not been sufficiently characterized so far, but is of crucial relevance, as the atmospheric boundary layer (ABL) is strongly interacting with aerosols. For this purpose, the fixed-wing research drone called ALADINA (Application of Light-weight Aircraft for Detecting in-situ Aerosol) was operated at a distance of approximately 4 km downwind of the German airport Berlin Brandenburg (BER) on October 11–19, 2021. During the investigation period, 140 vertical profiles of different meteorological parameters and aerosol particle sizes were obtained on six measurement days between the surface and up to a maximum altitude of 750 m above ground level (a.g.l.). The investigations indicate several features: The stability of the ABL is a key characteristic for the vertical distribution of aerosol population with highest concentrations close to ground. Inversion layers further enhance horizontal transport so that airport pollutants can be moved to a further distance away. The airborne observations of total particle number concentration (TNC) coincide with ground-based data from fix-point sites. They show a high variability depending on the distance to the plume as well as upwind position and highest concentrations of TNC related to rush hours of airport operations.

Abstract Modern civilization relies on a complex, globally interconnected industrial agriculture system to produce food. Its unprecedented yields hinge on external inputs like machinery, fertilizers, and pesticides, rendering it vulnerable to disruptions in production and international trade. Such a disruption could be caused by large‐scale damage to the electrical grid. Solar storms, nuclear detonations in the upper atmosphere, pandemics, or cyber‐attacks, could cause this severe damage to electrical infrastructure. To assess the impact of such a global catastrophic infrastructure loss on major food crops (corn, rice, soybean, wheat), we employ a generalized linear model. The predictions show a crop‐specific yield reduction between 15% and 37% in phase 1, the year after the catastrophe, assuming rationed use of fertilizers, pesticides, and fuel stocks. In phase 2, when all stocks are depleted, yields decrease by 35%–48%. Soybean is less affected in phase 1, while all crops experience strong declines in phase 2. Europe, North and South America, and parts of India, China, and Indonesia face major yield reductions, potentially up to 75%, while most African countries are less affected. These findings underscore the necessity for preparation by highlighting the vulnerability of the food system.

Voluntary National Reviews (VNRs) provide a platform for participating countries to share their experiences, failures, and successes in achieving the United Nations (UN) Sustainable Development Goals (SDGs). The objective of this study is to gain a deeper understanding of the narrative elements, particularly the sentiment, in VNRs in order to more effectively assess and support global SDG progress. A total of 232 VNRs from 166 countries are analyzed using Aspect-Based Sentiment Analysis (ABSA) to extract each country's sentiment toward the 17 SDGs. The sentiment scores are then compared to the corresponding official UN SDG scores, and countries are grouped by their sentiment toward all 17 SDGs to identify potential common development pathways. The analysis uncovers a notable positive correlation between the reported sentiment and official SDG scores for SDG 2 (zero hunger) and SDG 11 (sustainable cities and communities), and a negative correlation for SDG 5 (gender equality). Conversely, this relationship is not significant for the majority of SDGs, suggesting that VNR narratives may not directly reflect actual progress. A t-distributed stochastic neighbor embedding (t-SNE) approach indicates a consistent sentiment score among developed countries. In contrast, there are greater differences in reporting sentiment among Emerging Markets, Frontier Markets, and Least Developed Countries (LDCs), where there is greater dispersion (especially among LDCs) and sentiment in reporting on SDG progress that appears to have changed from one reporting year to another. These findings highlight the need to interpret VNRs in the context of each country's unique situation and challenges specific to each country.

Abstract Demand for sustainably produced biomass is expected to increase with the need to provide renewable commodities, improve resource security and reduce greenhouse gas emissions in line with COP26 commitments. Studies have demonstrated additional environmental benefits of using perennial biomass crops (PBCs), when produced appropriately, as a feedstock for the growing bioeconomy, including utilisation for bioenergy (with or without carbon capture and storage). PBCs can potentially contribute to Common Agricultural Policy (CAP) (2023–27) objectives provided they are carefully integrated into farming systems and landscapes. Despite significant research and development (R&D) investment over decades in herbaceous and coppiced woody PBCs, deployment has largely stagnated due to social, economic and policy uncertainties. This paper identifies the challenges in creating policies that are acceptable to all actors. Development will need to be informed by measurement, reporting and verification (MRV) of greenhouse gas emissions reductions and other environmental, economic and social metrics. It discusses interlinked issues that must be considered in the expansion of PBC production: (i) available land; (ii) yield potential; (iii) integration into farming systems; (iv) R&D requirements; (v) utilisation options; and (vi) market systems and the socio‐economic environment. It makes policy recommendations that would enable greater PBC deployment: (1) incentivise farmers and land managers through specific policy measures, including carbon pricing, to allocate their less productive and less profitable land for uses which deliver demonstrable greenhouse gas reductions; (2) enable greenhouse gas mitigation markets to develop and offer secure contracts for commercial developers of verifiable low‐carbon bioenergy and bioproducts; (3) support innovation in biomass utilisation value chains; and (4) continue long‐term, strategic R&D and education for positive environmental, economic and social sustainability impacts.

Collective cell migration is an important process during biological development and tissue repair but may turn malignant during tumor invasion. Mathematical and computational models are essential to unravel the mechanisms of self-organization that underlie the emergence of collective migration from the interactions among individual cells. Recently, guidance-by-followers was identified as one such underlying mechanism of collective cell migration in the embryo of the zebrafish. This poses the question of how the guidance stimuli are integrated when multiple cells interact simultaneously. In this study, we extend a recent individual-based model by an integration step of the vectorial guidance stimuli and compare model predictions obtained for different variants of the mechanism (arithmetic mean of stimuli, dominance of stimulus with largest transmission interface, and dominance of most head-on stimulus). Simulations are carried out and quantified within the modeling and simulation framework Morpheus. Collective cell migration is found to be robust and qualitatively identical for all considered variants of stimulus integration. Moreover, this study highlights the role of individual-based modeling approaches for understanding collective phenomena at the population scale that emerge from cell-cell interactions.

Lipofuscin labeling and 14 C retrospective birth-dating of neurons, along with mathematical modelling, here suggest continued postnatal neurogenesis in the human amygdala, rather than protracted maturation of developmentally generated neurons.

Abstract Pakistan’s agriculture is characterized by insecure water supply and poor irrigation practices. We investigate the economic and environmental feasibility of alternative improved irrigation technologies (IIT) by estimating the site-specific irrigation costs, groundwater anomalies, and CO2 emissions. IIT consider different energy sources including solar power in combination with changes in the irrigation method. The status quo irrigation costs are estimated to 1301 million US$ year−1, its groundwater depletion to 6.3 mm year−1 and CO2 emissions to 4.12 million t year−1, of which 96% originate from energy consumption and 4% via bicarbonate extraction from groundwater. Irrigation costs of IIT increase with all energy sources compared to the status quo, which is mainly based on diesel engine. This is because of additional variable and fixed costs for system’s operation. Of these, subsidized electricity induces lowest costs for farmers with 63% extra costs followed by solar energy with 77%. However, groundwater depletion can even be reversed with 35% rise in groundwater levels via IIT. Solar powered irrigation can break down CO2 emissions by 81% whilst other energy sources boost emissions by up to 410%. Results suggest that there is an extremely opposing development between economic and ecological preferences, requiring stakeholders to negotiate viable trade-offs.

Abstract Background Antimicrobial stewardship (AMS) strategies worldwide focus on optimising the use of antibiotics. Selective susceptibility reporting is recommended as an effective AMS tool although there is a lack of representative studies investigating the impact of selective susceptibility reporting on antibiotic use. The aim of this study was to investigate the impact of selective susceptibility reporting of Staphylococcus aureus (S. aureus) on antibiotic consumption. Enhancing the use of narrow-spectrum beta-lactam antibiotics such as flucloxacillin/cefazolin/cefalexin is one of the main goals in optimising antibiotic therapy of S. aureus infections. Methods This interventional study with control group was conducted at a tertiary care hospital in Germany. During the one-year interventional period susceptibility reports for all methicillin-sensitive S. aureus (MSSA) were restricted to flucloxacillin/cefazolin/cefalexin, trimethoprim-sulfamethoxazole, clindamycin, gentamicin and rifampin/fosfomycin, instead of reporting all tested antibiotics. The impact of implementing selective reporting was analysed by monitoring total monthly antibiotic consumption in our hospital and in a reference hospital (recommended daily dose/100 occupied bed days: RDD/100 BD), as well as on an individual patient level by analysing days of therapy adjusted for bed days (DOT/ 100 BD) for patients with S. aureus bacteremia (SAB) and respectively skin and soft tissue infections (SSTI). Results MSSA-antibiograms were acquired for 2836 patients. The total use of narrow-spectrum beta-lactams more than doubled after implementing selective reporting (from 1.2 to 2.8 RDD/100 BD, P

Summary Contemporary medical reports from Britain and Germany on patients suffering from a pandemic infection between 1889 and 1891, which was historically referred to as the Russian flu, share a number of characteristics with COVID‐19. Most notable are aspects of multisystem affections comprising respiratory, gastrointestinal and neurological symptoms including loss of taste and smell perception; a protracted recovery resembling long covid and pathology observations of thrombosis in multiple organs, inflammation and rheumatic affections. As in COVID‐19 and unlike in influenza, mortality was seen in elderly subjects while children were only weakly affected. Contemporary reports noted trans‐species infection between pet animals or horses and humans, which would concur with a cross‐infection by a broad host range bovine coronavirus dated by molecular clock arguments to an about 1890 cross‐species infection event.

The flow field induced by multirotor drones is of high interest for atmospheric research, as it locally influences the atmosphere and therefore may have an impact on the sensors installed for atmospheric measurements. Further, on-board vibrations can cause significant interference with the measurement equipment. To investigate the near flow field, an approach combining measurements of pressure and temperature distribution in-flight and in a laboratory setup together with numerical simulations was applied. Existing high-frequency measurement equipment was piggybacked during the initial flight tests with a newly developed 25 kg quadcopter system in a low-cost early-stage-error approach to obtain initial data and experience. During the flights, high resolution sensors for measuring pressure, temperature, acceleration, and deformation were applied with different setups at different locations below one of the rotor planes, respectively, at one rotor arm, to determine the multicopter’s influence on pressure and temperature measurements, to investigate rotor arm deformations, and to obtain data to compare with numerical simulations of this rotor setup. An external Schlieren-type measurement technique was tested to visualise the rotor vortices. The applied measurement techniques proved to be suitable for acquiring the state of the rotor-induced flow, but with some limitations. The comparison of measurements and simulations showed basic agreement and allowed for the identification of necessary adaptations for subsequent studies. The interaction of the rotor wakes with the rotor arms could be identified as the main source of the measured structural vibrations. The need for necessary improvements in the measurement setup, flight operation, and simulation setup is presented in detail.

One of the most deadly neglected tropical diseases known to man is schistosomiasis. Understanding how the disease spreads and evaluating the relevant control strategies are key steps in predicting its spread. We propose a mathematical model to evaluate the potential impact of four strategies: chemotherapy, awareness programs, the mechanical removal of snails and molluscicides, and the impact of a change in temperature on different molluscicide performances based on their half-lives and the length of time they persist in contact with target species. The results show that the recruitment rate of humans and the presence of cercaria and miracidia parasites are crucial factors in disease transmission. However, schistosomiasis can be entirely eradicated by combining all of the four strategies. In the face of climate change and molluscicide degradation, the results show that increasing the temperatures and the number of days a molluscicide persists in the environment before it completely degrades decreases the chemically induced mortality rate of snails while increasing the half-life of different molluscicides increases the death rate of snails. Therefore, eradicating schistosomiasis effectively necessitates a comprehensive integration of all preventative measures. Moreover, regions with different weather patterns and seasonal climates need strategies that have been adapted in terms of the appropriate molluscicide and time intervals for reapplication and effective schistosomiasis control.

The understanding of water quality and its underlying processes is important for the protection of aquatic environments. With the rare opportunity of access to a domain expert, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series. The XAI provides explanations that are interpretable by domain experts. In three steps, it combines a data-driven choice of a distance measure with supervised decision trees guided by projection-based clustering. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The framework, called DDS-XAI, does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two state of the art XAIs called eUD3.5 and iterative mistake minimization (IMM) were unable to provide meaningful and relevant explanations from the three multivariate time series data. The DDS-XAI framework can be swiftly applied to new data. Open-source code in R for all steps of the XAI framework is provided and the steps are structured application-oriented.

Abstract We focus on how different global temperature increases represented in IPCC reports have shifted over time. While the first four assessment reports had a roughly equal focus on temperatures above and below 2°C, the more recent fifth and sixth assessment reports have a considerably stronger focus on warming below 2°C. This is concerning as warming above 2°C is more likely given current emissions trajectories and is more influential on climate risk assessments.

Schistosomiasis affects over 700 million people globally. 90% of the infected live in sub-Saharan Africa, where the trematode species Schistosoma mansoni and S. haematobium transmitted by intermediate hosts (IH) of the gastropod genera Biomphalaria and Bulinus are the major cause of the human disease burden. Understanding the factors influencing the distribution of the IH is vital towards the control of human schistosomiasis. We explored the applicability of a machine learning algorithm, random forest, to determine significant predictors of IH distribution and their variation across different geographic scales in crater lakes in western Uganda. We found distinct variation in the potential controls of IH snail distribution among the two snail genera as well as across different geographic scales. On the larger scale, geography, diversity of the associated mollusk fauna and climate are important predictors for the presence of Biomphalaria, whereas mollusk diversity, water chemistry and geography mainly control the occurrence of Bulinus. Mollusk diversity and geography are relevant for the presence of both genera combined. On the scale of an individual crater lake field, Biomphalaria is solely controlled by geography, while mollusk diversity is most relevant for the presence of Bulinus. Our study demonstrates the importance of combining a comprehensive set of predictor variables, a method that allows for variable selection and a differentiated assessment of different host genera and geographic scale to reveal relevant predictors of distribution. The results of our study contribute to making realistic predictions of IH snail distribution and schistosomiasis prevalence and can help in supporting strategies towards controlling the disease.

In simplest terms, agroforestry involves growing trees on farms for a range of socio-economic and ecological benefits. Agroforestry as a land management technique has been practiced for thousands of years. As climate change and environmental impacts of agricultural intensification have become increasingly evident in recent years, agroforestry has garnered renewed scientific and public interest. Indeed, due to trees´ high carbon sequestration potential, their integration into cropland has been highlighted as a promising natural solution for climate change mitigation. In this context, we review research gaps and data needs that constitute barriers to increased agroforestry implementation and improved carbon accounting in temperate regions, with a focus on Europe. A lack of clear agroforestry classification systems, as well as methodological and logistical constraints, emerge as key challenges. We provide recommendations to address these issues and identify future research areas which should be prioritized to support climate change mitigation and agricultural system resilience through agroforestry expansion.

Acute neurologic complications from Varicella-Zoster-Virus reactivation occur in both immunocompromised and immunocompetent patients. In this report, we describe a case of a previously healthy immunocompetent boy who had received two doses of varicella vaccine at 1 and 4 years. At the age of 12 he developed acute aseptic meningitis caused by vaccine-type varicella-zoster-virus without concomitant skin eruptions. VZV-vaccine strain DNA was detected in the cerebrospinal fluid. The patient made a full recovery after receiving intravenous acyclovir therapy. This disease course documents another case of a VZV vaccine-associated meningitis without development of a rash, i.e., a form of VZV infection manifesting as “zoster sine herpete”.

We introduce a novel lattice-gas cellular automaton (LGCA) for compressible vectorial active matter with polar and nematic velocity alignment. Interactions are, by construction, zero-range. For polar alignment, we show the system undergoes a phase transition that promotes aggregation with strong resemblance to the classic zero-range process. We find that above a critical point, the states of a macroscopic fraction of the particles in the system coalesce into the same state, sharing the same position and momentum (polar condensate). For nematic alignment, the system also exhibits condensation, but there exist fundamental differences: a macroscopic fraction of the particles in the system collapses into a filament, where particles possess only two possible momenta. Furthermore, we derive hydrodynamic equations for the active LGCA model to understand the phase transitions and condensation that undergoes the system. We also show that generically the discrete lattice symmetries—e.g. of a square or hexagonal lattice—affect drastically the emergent large-scale properties of on-lattice active systems. The study puts in evidence that aligning active matter on the lattice displays new behavior, including phase transitions to states that share similarities to condensation models.

Currently, the main in situ upper air database for numerical weather prediction relies on radiosonde and aircraft-based information. Typically, radiosondes are launched at specific sites daily, up to four times per day, and data are distributed worldwide via the GTS net. Aircraft observations are limited to frequent flight routes, and vertical profiles are provided in the vicinity of large cities. However, there are large areas with few radiosonde launches, in particular above the oceans and in the polar areas. In this article, the development and technical details of the unmanned aerial system LUCA (Lightweight Unmanned high Ceiling Aerial system) are described. LUCA has the potential to complement radiosonde and aircraft-based observations up to 10 km in altitude. The system ascends and descends (by electrical power) in spiral trajectories and returns to the launching site. This article discusses the requirements for obtaining high data availability under mid-European and Antarctic conditions, with highly automated take-offs and landings under high surface winds, the capacity to deal with icing, and the ability to operate under high wind speeds. The article presents technical solutions for the design and construction of the system and demonstrates its potential.

Recent advances in laser spectroscopy has made it feasible to measure stable isotopes of water in high temporal resolution (i.e., sub-daily). High-resolution data allow the identification of fine-scale, short-term transport and mixing processes that are not detectable at coarser resolutions. Despite such advantages, operational routine and long-term sampling of stream and groundwater sources in high temporal resolution is still far from being common. Methods that can be used to interpolate infrequently measured data at multiple sampling sites would be an important step forward. This study investigates the application of a Long Short-Term Memory (LSTM) deep learning model to predict complex and non-linear high-resolution (3 h) isotope concentrations of multiple stream and groundwater sources under different landuse and hillslope positions in the Schwingbach Environmental Observatory (SEO), Germany. The main objective of this study is to explore the prediction performance of an LSTM that is trained on multiple sites, with a set of explanatory data that are more straightforward and less expensive to measure compared to the stable isotopes of water. The explanatory data consist of meteorological data, catchment wetness conditions, and natural tracers (i.e., water temperature, pH and electrical conductivity). We analyse the model's sensitivity to different input data and sequence lengths. To ensure an efficient model performance, a Bayesian optimization approach is employed to optimize the hyperparameters of the LSTM. Our main finding is that the LSTM allows for predicting stable isotopes of stream and groundwater by using only short-term sequence (6 h) of measured water temperature, pH and electrical conductivity. The best performing LSTM achieved, on average of all sampling sites, an RMSE of 0.7‰, MAE of 0.4‰, R2 of 0.9 and NSE of 0.7. The LSTM can be utilized to predict and interpolate the continuous isotope concentration time series either for data gap filling or in case where no continuous data acquisition is feasible. This is very valuable in practice because measurements of these tracers are still much cheaper than stable isotopes of water and can be continuously conducted with relatively minor maintenance.

Abstract The mechanisms of organ size control remain poorly understood. A key question is how cells collectively sense the overall status of a tissue. We addressed this problem focusing on mouse liver regeneration. Using digital tissue reconstruction and quantitative image analysis, we found that the apical surface of hepatocytes forming the bile canalicular network expands concomitant with an increase in F‐actin and phospho‐myosin, to compensate an overload of bile acids. These changes are sensed by the Hippo transcriptional co‐activator YAP, which localizes to apical F‐actin‐rich regions and translocates to the nucleus in dependence of the integrity of the actin cytoskeleton. This mechanism tolerates moderate bile acid fluctuations under tissue homeostasis, but activates YAP in response to sustained bile acid overload. Using an integrated biophysical–biochemical model of bile pressure and Hippo signaling, we explained this behavior by the existence of a mechano‐sensory mechanism that activates YAP in a switch‐like manner. We propose that the apical surface of hepatocytes acts as a self‐regulatory mechano‐sensory system that responds to critical levels of bile acids as readout of tissue status.

Impact models projections are used in integrated assessments of climate change. Here the authors test systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions.

[This corrects the article DOI: 10.1371/journal.pcbi.1008412.].

Catchments are complex systems, which require regular monitoring of hydro-chemical parameters in space and time to provide comprehensive datasets. These are needed to characterize catchment behavior on a local level, make future projections based on models, implement mitigation measures and meet policy targets. However, many developing countries lack a good infrastructure for hydrological monitoring since its establishment is costly and the required resources are often not available. To overcome such challenges in data scarce regions like Kenya, a participatory citizen science approach can be a promising strategy for monitoring water resources. This study evaluates the potential of using a contributory citizen science approach to explore spatiotemporal turbidity and suspended sediment dynamics in the Sondu-Miriu river basin, western Kenya. A group of 19 citizen scientists was trained to monitor turbidity using turbidity tubes and water levels with water level gauges in six nested subcatchments of the Sondu-Miriu river basin. Over the course of the project, a total of 37 citizen scientists participated and contributed to the overall dataset of turbidity. The sampling effort and data contribution varied from year to year and among participants with the majority of the data (72%) originating from 8 (22%) citizen scientists. Comparison between citizen-scientist collected suspended sediment data and measurements from automated stations showed high correlation (R2 > 0.9) which demonstrates that data collected by citizen scientists can be comparable to data collected using expensive monitoring equipment. However, there was reduced precision of the measurements of suspended sediment concentrations at low and high levels attributed largely to the detection limitations of the turbidity tubes and citizen scientists not capturing major sediment export events. Suspended sediment concentrations were significantly higher downstream (109 ± 94 mg L−1), a subcatchment dominated by agriculture and rangeland with low forest vegetation cover, as compared to a subcatchment with high forest cover (50 ± 24.7 mg L−1). This finding indicates that forest cover is a key landscape feature to control suspended sediment concentrations in the region. Future citizen science projects should focus on motivation and engagement strategies and the application of robust methods with improved detection limits and resolution to advance hydrological monitoring.

Estimating the maximum event water fraction, at which the event water contribution to streamflow reaches its peak value during a precipitation event, gives insight into runoff generation mechanisms and hydrological response characteristics of a catchment. Stable isotopes of water are ideal tracers for accurate estimation of maximum event water fractions using isotopic hydrograph separation techniques. However, sampling and measuring of stable isotopes of water is laborious, cost intensive, and often not conceivable under difficult spatiotemporal conditions. Therefore, there is a need for a proper predictive model to predict maximum event water fractions even at times when no direct sampling and measurements of stable isotopes of water are available. The behavior of maximum event water fraction at the event scale is highly dynamic and its relationships with the catchment drivers are complex and non-linear. In last two decades, machine learning algorithms have become increasingly popular in the various branches of hydrology due to their ability to represent complex and non-linear systems without any a priori assumption about the structure of the data and knowledge about the underlying physical processes. Despite advantages of machine learning, its potential in the field of isotope hydrology has rarely been investigated. Present study investigates the applicability of Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms to predict maximum event water fractions in streamflow using precipitation, soil moisture, and air temperature as a set of explanatory input features that are more straightforward and less expensive to measure compared to stable isotopes of water, in the Schwingbach Environmental Observatory (SEO), Germany. The influence of hyperparameter configurations on the model performance and the comparison of prediction performance between optimized ANN and optimized SVM are further investigated in this study. The performances of the models are evaluated using mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe Efficiency (NSE). For the ANN, the results showed that an appropriate number of hidden nodes and a proper activation function enhanced the model performance, whereas changes of the learning rate did not have a major impact on the model performance. For the SVM, Polynomial kernel achieved the best performance, whereas Linear yielded the weakest performance among the kernel functions. The result showed that maximum event water fraction could be successfully predicted using only precipitation, soil moisture, and air temperature. The optimized ANN showed a satisfactory prediction performance with MAE of 10.27%, RMSE of 12.91%, R2 of 0.70, and NSE of 0.63. The optimized SVM was superior to that of ANN with MAE of 7.89%, RMSE of 9.43%, R2 of 0.83, and NSE of 0.78. SVM could better capture the dynamics of maximum event water fractions across the events and the predictions were generally closer to the corresponding observed values. ANN tended to underestimate the events with high maximum event water fractions and to overestimate the events with low maximum event water fractions. Machine learning can prove to be a promising approach to predict variables that are not always possible to be estimated due to the lack of routine measurements.

Hydrological theory often assumes an exponential relationship between storage and discharge, but how often do we find such a behavior in real catchments? We analyze catchment attributes, discharge and weather conditions from 1992 to 2018 for 88 catchments in Hesse, Germany. All catchments have similar weather conditions, but a wide range of catchment characteristics. We find that only a fraction of catchments follow a roughly exponential behavior in their storage-discharge relationship. Far more catchments are complex and depict irregular patterns with even extreme variation from 1 year to another. This large set of catchments with similar weather conditions reveals that physical attributes that drive catchment complexity are diverse and include soil permeability, hydraulic conductivity, geology, and soil type. We link the examined simple and complex behavior to the fill and spill hypotheses and the interconnectedness of the catchments. Simpler catchments have more preferential flow and more connected hillslopes and thus lower runoff generation thresholds. This creates a more straightforward relationship between storage and discharge.

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin.

Citizen science is gaining popularity as a way to engage people to participate in environmental projects. In addition to potential challenges regarding data quality and the choice of variables, a key factor in the success of participatory monitoring projects is the active participation of volunteers, the “citizen scientists.” To learn more about the motivation of citizen scientists to participate and their socio-economic background, a telephone survey was implemented with participants of a citizen science water level monitoring program in the Sondu-Miriu basin, western Kenya. We analyzed the data using descriptive statistics and random forest models to elucidate the characteristics of the participant population, underlying motivations, and the challenges and opportunities to develop recommendations for sustainable community-based water monitoring programs. As citizen scientists who engaged long-term were 30–49 years old, had primary or secondary school education and passed by the monitoring station frequently, targeting sensitization activities at people with such background could increase participation in community-based water monitoring. Sensitization meetings were key in engaging long-term volunteers, but participants indicated that continued feedback through education and communication of the project findings is required to keep volunteers motivated. The use of cellphone credit to submit data was identified as challenge for participation, highlighting the need for data submission methods that do not incur costs for the participants. Greater volunteer support could also be achieved by active involvement of the members of Water Resource Users Associations, since membership increased the likelihood of continuous engagement in water monitoring under the participants. Furthermore, many participants indicated that their motivation was to help water management and conservation, as most people rely directly on rivers for their water supply. Providing a platform to contribute to better water resources management could therefore result in direct benefits (e.g., improved water supply) for the participants, and thus an incentive to participate actively.

How epithelial cells coordinate their polarity to form functional tissues is an open question in cell biology. Here, we characterize a unique type of polarity found in liver tissue, nematic cell polarity, which is different from vectorial cell polarity in simple, sheet-like epithelia. We propose a conceptual and algorithmic framework to characterize complex patterns of polarity proteins on the surface of a cell in terms of a multipole expansion. To rigorously quantify previously observed tissue-level patterns of nematic cell polarity (Morales-Navarrete et al., eLife 2019), we introduce the concept of co-orientational order parameters, which generalize the known biaxial order parameters of the theory of liquid crystals. Applying these concepts to three-dimensional reconstructions of single cells from high-resolution imaging data of mouse liver tissue, we show that the axes of nematic cell polarity of hepatocytes exhibit local coordination and are aligned with the biaxially anisotropic sinusoidal network for blood transport. Our study characterizes liver tissue as a biological example of a biaxial liquid crystal. The general methodology developed here could be applied to other tissues and in-vitro organoids.

Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.

Air quality measurements usually consist of ground-based instrumentation at fixed locations. However, vertical profiles of pollutants are of interest for understanding processes, distribution, dilution and concentration. Therefore, a multicopter system has been developed to investigate the vertical distribution of the concentration of aerosol particles, black carbon, ozone, nitrogen oxides (NOx) and carbon monoxide and the meteorological parameters of temperature and humidity. This article presents the requirements by different users, the setup of the quadrocopter system, the instrumentation and the results of first applications. The vertical distribution of particulate matter next to a highway was strongly related to atmospheric stratification, with different concentrations below and above the temperature inversion present in the morning. After the qualification phase described in this article, two identically equipped multicopters will be used upwind and downwind of line or diffuse sources such as highways or urban areas to quantify the influence of their emissions on the local air quality.

Work-time constraints during surgical residency along with managing a private life usually take up the majority of the time of young surgeons. For many, work with a surgical society seems like something neither generally promising nor personally worthwhile, thus raising the question, why bother? This article sets out to show examples of the effects that surgical societies and young surgeon committees can have on surgery and residency training. Additionally, we highlight the personal side of being active on a committee. Our aim is to raise interest in participating in societal work by showing the rewarding general effects as well as personal benefits. While this article is based primarily on experiences made in Germany, we believe that aspects can be transferred to other medical systems.

Wind shear at low altitudes represents a potential hazard to landing aircraft. Based on two wind lidar data sets of one year, the occurrence of low-level jets (LLJs), the vertical wind shear and the rotation of the wind direction were analysed. The lidar system was located at the sites of Braunschweig in the North German Plain, Germany, and Clausthal-Zellerfeld in the low mountain range Harz, Germany. The observed wind shear gradients between the altitude of 40 m and the altitude of the maximum wind speed was in the range of −0.23 s−1 to +0.20 s−1. The rotation of the wind direction with altitude occurred both in clockwise and anticlockwise direction. The ratio of clockwise versus anticlockwise occurrence of directional shear was 4:1 for Braunschweig and 3:1 for Clausthal-Zellerfeld. The observed wind shear gradients were compared to values for hazard potential of different levels for a typical aircraft. Although the LLJ was not hazardous for manned aircraft in any observed case, the awareness of LLJ helps to reduce the pilot’s workload and possible pilot-introduced oscillations caused as a result of the wind shear and aircraft characteristics. In contrast to manned aviation, the value of changes in wind speed and direction during LLJ conditions can cause significant risks for unmanned aerial system operations with less than 25 kg of take-off weight. This is a result of the lower airspeed-wind-speed ratio and the flight control and flight planning.

Climate change, increasing environmental pollution, continuous loss of biodiversity, and a growing human population with increasing food demand, threaten the functioning of agro-ecosystems and their contribution to people and society. Agroforestry systems promise a number of benefits to enhance nature’s contributions to people. There are a wide range of agroforestry systems implemented representing different levels of establishment across the globe. This range and the long time periods for the establishment of these systems make empirical assessments of impacts on ecosystem functions difficult. In this study we investigate how simulation models can help to assess and predict the role of agroforestry in nature’s contributions. The review of existing models to simulate agroforestry systems reveals that most models predict mainly biomass production and yield. Regulating ecosystem services are mostly considered as a means for the assessment of yield only. Generic agroecosystem models with agroforestry extensions provide a broader scope, but the interaction between trees and crops is often addressed in a simplistic way. The application of existing models for agroforestry systems is particularly hindered by issues related to code structure, licences or availability. Therefore, we call for a community effort to connect existing agroforestry models with ecosystem effect models towards an open-source, multi-effect agroforestry modelling framework.

The intensification of food production systems has resulted in landscape simplification, with trees and hedges disappearing from agricultural land, principally in industrialized countries. However, more recently, the potential of agroforestry systems and small woody landscape features (SWFs), e.g., hedgerows, woodlots, and scattered groups of trees, to sequester carbon was highlighted as one of the strategies to combat global climate change. Our study was aimed to assess the extent of SWFs embedded within agricultural landscapes in Germany, estimate their carbon stocks, and investigate the potential for increasing agroforestry cover to offset agricultural greenhouse gas (GHG) emissions. We analyzed open-source geospatial datasets and identified over 900,000 hectares of SWFs on agricultural land, equivalent to 4.6% of the total farmland. The carbon storage of SWFs was estimated at 111 ± 52 SD teragrams of carbon (Tg C), which was previously unaccounted for in GHG inventories and could play a role in mitigating the emissions. Furthermore, we found cropland to have the lowest SWF density and thus the highest potential to benefit from the implementation of agroforestry, which could sequester between 0.2 and 2 Tg of carbon per year. Our study highlights that country-specific data are urgently needed to refine C stock estimates, improve GHG inventories and inform the large-scale implementation of agroforestry in Germany.

Summary: The Hedgehog (Hh) and Wnt/β-Catenin (Wnt) cascades are morphogen pathways whose pronounced influence on adult liver metabolism has been identified in recent years. How both pathways communicate and control liver metabolic functions are largely unknown. Detecting core components of Wnt and Hh signaling and mathematical modeling showed that both pathways in healthy liver act largely complementary to each other in the pericentral (Wnt) and the periportal zone (Hh) and communicate mainly by mutual repression. The Wnt/Hh module inversely controls the spatiotemporal operation of various liver metabolic pathways, as revealed by transcriptome, proteome, and metabolome analyses. Shifting the balance to Wnt (activation) or Hh (inhibition) causes pericentralization and periportalization of liver functions, respectively. Thus, homeostasis of the Wnt/Hh module is essential for maintaining proper liver metabolism and to avoid the development of certain metabolic diseases. With caution due to minor species-specific differences, these conclusions may hold for human liver as well. : Wnt/β-catenin and Hh signaling contribute to embryogenesis as well as to the maintenance of organ homeostasis through intensive crosstalk. Here, Kolbe et al. describe that both pathways act largely complementary to each other in the healthy liver and that this crosstalk is responsible for the maintenance of metabolic zonation.

Functional tissue architecture originates by self-assembly of distinct cell types, following tissue-specific rules of cell-cell interactions. In the liver, a structural model of the lobule was pioneered by Elias in 1949. This model, however, is in contrast with the apparent random 3D arrangement of hepatocytes. Since then, no significant progress has been made to derive the organizing principles of liver tissue. To solve this outstanding problem, we computationally reconstructed 3D tissue geometry from microscopy images of mouse liver tissue and analyzed it applying soft-condensed-matter-physics concepts. Surprisingly, analysis of the spatial organization of cell polarity revealed that hepatocytes are not randomly oriented but follow a long-range liquid-crystal order. This does not depend exclusively on hepatocytes receiving instructive signals by endothelial cells, since silencing Integrin-β1 disrupted both liquid-crystal order and organization of the sinusoidal network. Our results suggest that bi-directional communication between hepatocytes and sinusoids underlies the self-organization of liver tissue.

Land use becomes more diverse when it considers uncertain interactions of multiple ecosystem services. Here, Knoke and colleagues show that uncertainty plays a larger role if ecosystem services are optimized only for a single service, or if services correlate.

Reactive nitrogen (N _r ) that is released to the environment has several negative implications for the atmosphere, hydrosphere, biodiversity and human health. A nitrogen (N) footprint is a measure that can help to assess and communicate the impact of personal lifestyle and consumption choices regarding their influences on N _r losses. The N-Calculator tool was developed to estimate this footprint. However, underlying loss factors for the food sector in the N-Calculator rely on data from the US, for which the calculator was originally established. Since the conditions in agriculture and the food industry differ significantly between the US and other countries, and the fact that the food sector is considered the main source of N _r losses in the N-Calculator, a revision of the N-Calculator is required if applied to other countries. Here we present a revised N-Calculator for Germany that is based on German food production data. In this study, virtual nitrogen factors describe the losses of nitrogen in a supply chain. Losses were calculated for 20 plant-based products, 17 feed materials, 18 compound feeds and 14 animal-based products. The N footprint varies considerably between products. While plant-based products amount to a weighted average of 3.4 g N loss per kg product, animal-based products cause significantly higher losses with 40.5 g N loss per kg. Overall, the average N footprint for the German consumer is calculated to be at 9.94 kg per capita and year. To validate the results, the individual categories were scaled up to the national level and then compared with statistical data on N flows in Germany. In general, the results showed good agreement with key production figures and the overall N budget for Germany. Furthermore, some improvements are proposed to increase the informative value and user acceptance of an N-Calculator.

We compare the probability of different warming rates to their mentions in IPCC reports through text mining. We find that there is a substantial mismatch between likely warming rates and research coverage. 1.5 °C and 2 °C scenarios are substantially overrepresented. More likely higher end warming scenarios of 3 °C and above, despite potential catastrophic impacts, are severely neglected.

A recent commentary in Carbon Management by Fidel et al. (http://www.tandfonline.com/doi/full/10.1080/17583004.2017.1306408) question the data selection and the analysis techniques used by Bach et al. (Carbon Management, https://doi.org/10.1080/17583004.2016.1213608) to assess two key aspects of biochar application in crop production, the effect on crop yield and the long-term carbon sequestration in soil. However, the arguments by Fidel et al. are not convincing. For farmer's decision whether biochar use in crop production might be economically profitable the low median values and large variability of results on yield effect in field trials form a sufficient base to abstain from biochar amendment to soil, therefore a detailed meta-analysis of data is not necessary. Future research may demonstrate under which conditions a pronounced positive and long-lasting effect of biochar application can be expected.