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Global hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change). Global hydrological reanalyses are powerful datasets that can fill the observational gap in river discharge observation. They make wide ranging downstream applications possible worldwide, from water resources to ocean monitoring and early warning systems, through earth system model diagnostic, hydrological extreme understanding and model calibration and training. The GloFAS hydrological reanalysis dataset is a product of the Copernicus Emergency Management Service freely available from the Copernicus Climate Data store, offering daily time series from early 1980 until recent, updated daily with a 3‐ to 5‐day delay.

A major challenge for managing impacts and implementing effective mitigation and adaptation strategies for coastal zones affected by future sea level (SL) rise is our very limited capacity to predict SL change on coastal scales, over various timescales. Predicting coastal SL requires the ability to monitor and simulate a multitude of physical processes affecting SL, from local effects of wind waves and river runoff to remote influences of the large-scale ocean circulation on the coast. Here we assess our current understanding of the causes of coastal SL variability on seasonal to multi-decadal timescales, including geodetic, oceanographic and atmospheric aspects of the problem, and review available observing systems informing on coastal SL. We also review the ability of current models and data assimilation systems to estimate coastal SL variations and of atmosphere-ocean global coupled models and related regional downscaling efforts to project future SL changes. We discuss (1) key observational gaps and uncertainties, and priorities for the development of an optimal and integrated coastal SL observing system, (2) strategies for advancing model capabilities in forecasting short-term processes and projecting long-term changes affecting coastal SL, and (3) possible future developments of sea level services enabling better connection of scientists and user communities and facilitating assessment and decision making for adaptation to future coastal SL change.

SummaryThis study in 8 countries across Europe found that about 75% of elderly women seen in primary care who were at high risk of osteoporosis-related fractures were not receiving appropriate medication. Lack of osteoporosis diagnosis appeared to be an important contributing factor.IntroductionTreatment rates in osteoporosis are documented to be low. We wished to assess the osteoporosis treatment gap in women ≥ 70 years in routine primary care across Europe.MethodsThis cross-sectional observational study in 8 European countries collected data from women 70 years or older visiting their general practitioner. The primary outcome was treatment gap: the proportion who were not receiving any osteoporosis medication among those at increased risk of fragility fracture (using history of fracture, 10-year probability of fracture above country-specific Fracture Risk Assessment Tool [FRAX] thresholds, T-score ≤ − 2.5).ResultsMedian 10-year probability of fracture (without bone mineral density [BMD]) for the 3798 enrolled patients was 7.2% (hip) and 16.6% (major osteoporotic). Overall, 2077 women (55%) met one or more definitions for increased risk of fragility fracture: 1200 had a prior fracture, 1814 exceeded the FRAX threshold, and 318 had a T-score ≤ − 2.5 (only 944 received a dual-energy x-ray absorptiometry [DXA] scan). In those at increased fracture risk, the median 10-year probability of hip and major osteoporotic fracture was 11.2% and 22.8%, vs 4.1% and 11.5% in those deemed not at risk. An osteoporosis diagnosis was recorded in 804 patients (21.2%); most (79.7%) of these were at increased fracture risk. The treatment gap was 74.6%, varying from 53% in Ireland to 91% in Germany. Patients with an osteoporosis diagnosis were found to have a lower treatment gap than those without a diagnosis, with an absolute reduction of 63%.ConclusionsThere is a large treatment gap in women aged ≥ 70 years at increased risk of fragility fracture in routine primary care across Europe. The gap appears to be related to a low rate of osteoporosis diagnosis.

Despite their ethical intentions, ethically minded consumers rarely purchase ethical products (Auger and Devinney: 2007, Journal of Business Ethics 76, 361-383). This intentions-behaviour gap is important to researchers and industry, yet poorly understood (Belk et al.: 2005, Consumption, Markets and Culture 8(3), 275-289). In order to push the understanding of ethical consumption forward, we draw on what is known about the intention— behaviour gap from the social psychology and consumer behaviour literatures and apply these insights to ethical consumerism. We bring together three separate insights — implementation intentions (Gollwitzer: 1999, American Psychologist 54(7), 493-503), actual behavioural control (ABC) (Ajzen and Madden: 1986, Journal of Experimental Social Psychology 22, 453-474; Sheeran et al.: 2003, Journal of Social Psychology, 42, 393-410) and situational context (SC) (Belk: 1975, Journal of Consumer Research 2, 157— 164) — to construct an integrated, holistic conceptual model of the intention— behaviour gap of ethically minded consumers. This holistic conceptual model addresses significant limitations within the ethical consumerism literature, and moves the understanding of ethical consumer behaviour forward. Further, the operationalisation of this model offers insight and strategic direction for marketing managers attempting to bridge the intention-behaviour gap of the ethically minded consumer.

Using the nationally representative High School Longitudinal Study of 2009 (HSLS:09), this study documents that rural and small-town students were significantly less likely to enroll in postsecondary STEM (science, technology, engineering, and mathematics) degree programs, compared with their suburban peers. This study also shows that schools attended by rural and small-town students offered limited access to advanced coursework and extracurricular programs in STEM and had lower STEM teaching capacity. Those opportunities to learn in STEM were linked to the widening geographic gaps in STEM academic preparation. Overall, our findings suggest that during high school rural and small-town students shifted away from STEM fields and that geographic disparities in postsecondary STEM participation were largely explained by students' demographics and precollege STEM career aspirations and academic preparation.

Accurate daily streamflow estimates are crucial for water resources management. Yet, many regions lack high‐temporal‐resolution data due to limited monitoring infrastructure, often relying on monthly aggregates or intermittent observations. Predicting streamflow in these sparsely sampled watersheds remains challenging. This study proposes a deep learning‐based approach using Long Short‐Term Memory, leveraging its inherent advantages in learning long‐term dependencies within hydrological variables and processes to enhance streamflow predictions in sparsely sampled watersheds. The approach was evaluated for simulating daily flow patterns from monthly aggregated and monthly or weekly intermittent observations in two contrasting hydrological settings: near‐natural and human‐influenced watersheds. Results showed that the proposed approach reliably predicts daily flows from monthly aggregates with a median Nash‐Sutcliffe efficiency (NSE) of 0.61 for near‐natural and 0.48 for human‐influenced watersheds. The proposed approach performed even better for daily flow predictions from monthly or weekly intermittent observation, achieving a median NSE of 0.70 and 0.55 for near‐natural and human‐influenced watersheds, respectively. The proposed approach remained robust across different seasons and hydrological regimes, with a median percentage bias of ±5%, except in arid regions. Moreover, data sensitivity analysis indicated that data from wet seasons were crucial for improving model predictions and that weekly data could yield results comparable to daily observations. Overall, this study demonstrates that the deep learning‐based approach offers a robust and accurate representation of daily streamflow patterns from aggregated or intermittent observations, providing valuable hydrological insights and promising solutions for improving water resource management in regions with limited monitoring infrastructures. Plain Language Summary In regions where streamflow observations are available only at regular or irregular weekly to monthly intervals, converting these sparse observations into daily data is crucial for water resources management applications. This study presents a new deep learning‐based approach for estimating daily streamflow from monthly aggregated or intermittent monthly and weekly observations. We applied this method to two types of watersheds: those minimally affected by human activities and those with human influences. The results showed that this approach reliably predicts daily streamflow patterns from both monthly aggregated and intermittent monthly or weekly observations in both watershed types. Notably, we found that weekly observations provide predictions almost as accurate as daily ones, indicating that more frequent data collection may not always be necessary. Additionally, observations collected during the wet season were essential for improving model accuracy. This deep learning‐based method effectively captures key streamflow patterns across different seasons and conditions, making it a valuable tool for managing water resources in regions with sparse or irregular data. Key Points We proposed a deep learning‐based approach for streamflow simulation using aggregated and intermittent observations across varied hydrological settings The proposed approach predicted well in diverse flow regimes for near‐natural and human‐influenced watersheds, except in arid zones The proposed approach demonstrated adaptability to watershed heterogeneity and anthropogenic influences without additional data

Observational data of the Earth’s weather and climate at the level of ground-based weather stations are prone to gaps due to a variety of causes. These gaps can inhibit scientific research as they impede the use of numerical models for agricultural, meteorological and climatological applications as well as introducing analytic biases. In this research, different machine learning techniques are evaluated together with traditional approaches to gap filling automated weather station data. When filling gaps for a specific data stream, data from neighbouring weather stations are used in addition to reanalysis data from the European Centre for Medium-Range Weather Forecasts atmospheric reanalyses of the global climate, ERA-5 Land. A novel gap creation method is introduced that provides 100% coverage in sampling the dataset while ensuring that the sampled data are randomly distributed. Gap filling across a range of different gap lengths and target variables are compared using a range of error functions. The variables selected for modelling are mean air temperature, dew point, mean relative humidity and leaf wetness. Our results show that models perform best on gap-filling temperature and dew point with worst performance on leaf wetness. As expected, model performance decreases with increasing gap length. Comparison between machine learning and reanalysis approaches show very promising results from a number of the machine learning models.

The exclusion of pregnant women and infants from many randomized controlled trials (RCTs) has left critical gaps in medication safety, complicating clinical decision-making during these sensitive life stages. This commentary explores target trial emulation using real-world data as a robust alternative for advancing medication safety research when RCTs are not feasible. Target trial emulation replicates the design principles of RCTs within observational data, accounting for the dynamic nature of medication exposure across gestational stages and adjusting for time-varying confounders. While challenges such as unmeasured confounding, selection bias, and violations of positivity assumptions remain, this method provides crucial insights to address current evidence gaps. Information on medication exposure effects will be obtained, which will inform safer medication guidelines in pregnancy and infancy. Future research integrating artificial intelligence–driven tools, open science practices, and robust data governance frameworks will further strengthen the reliability and impact of target trial emulation. Multinational collaboration and data sharing across diverse sources will accelerate the generation of evidence, ultimately advancing medication safety. Target trial emulation, leveraging real-world data, is a promising alternative when traditional clinical trials are not feasible. This approach promotes safer medication use and improves health outcomes for mothers and infants. Many clinical trials exclude pregnant women and infants, leaving critical gaps in understanding medication safety during pregnancy and early life. Target trial emulation, which applies clinical trial principles to real-world data, offers a promising alternative when traditional trials are not feasible. This method allows researchers to study how medications affect pregnant women and babies at different stages of pregnancy while also considering factors that change over time. While there are still challenges, like unmeasured factors and bias remain, target trial emulation helps fill these knowledge gaps. Future advancements, including AI, Open Science, enhanced data sharing, and international collaboration, can further enhance this method's ability to improve the safety of medications for mothers and infants worldwide. [Display omitted]

Countries routinely offering acellular pertussis vaccine, where long-term protection is not sustained, have the challenge of selecting an optimal schedule to minimise disease among young infants. We conducted a narrative systematic review and synthesis of information to evaluate different pertussis immunisation strategies at controlling pertussis disease, hospitalisation, deaths, and vaccine effectiveness among young infants. We conducted a review of the literature on studies about the primary, booster, and/or maternal vaccination series and synthesised findings narratively. Countries offering the first three doses of vaccine within six-months of life and a booster on or before the second year or life were defined as accelerated primary and booster schedules, respectively. Countries offering primary and booster doses later were defined as extended primary and booster schedules. All search results were screened, and articles reviewed and reconciled, by two authors. The Risk of Bias in Non-randomised Studies of Intervention tool was used to evaluate the risk of bias. A total of 98 studies were included in the analyses and the following recurring themes were described: timing of vaccination, vaccine coverage, waning immunity/vaccine effectiveness, direct and indirect effectiveness, switching from an accelerated to extended schedule, impact of changes in testing. The risk of bias was generally low to moderate for most studies. Comparing schedules is challenging and there was insufficient evidence to that one schedule was superior to another. Countries must select a schedule that maintains high vaccine coverage and reduced the risk of delaying the delivery vaccines to protect infants.

Objective This study aimed to synthesize the existing evidence on biomarkers related to coronavirus disease 2019 (COVID-19) patients who presented neurological events. Methods A systematic review of observational studies (any design) following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the Cochrane Collaboration recommendations was performed (PROSPERO: CRD42021266995). Searches were conducted in PubMed and Scopus (updated April 2023). The methodological quality of nonrandomized studies was assessed using the Newcastle‒Ottawa Scale (NOS). An evidence gap map was built considering the reported biomarkers and NOS results. Results Nine specific markers of glial activation and neuronal injury were mapped from 35 studies published between 2020 and 2023. A total of 2,237 adult patients were evaluated in the included studies, especially during the acute phase of COVID-19. Neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) biomarkers were the most frequently assessed ( n  = 27 studies, 77%, and n  = 14 studies, 40%, respectively). Although these biomarkers were found to be correlated with disease severity and worse outcomes in the acute phase in several studies ( p  < 0.05), they were not necessarily associated with neurological events. Overall, 12 studies (34%) were judged as having low methodological quality, 9 (26%) had moderate quality, and 9 (26%) had high quality. Conclusions Different neurological biomarkers in neurosymptomatic COVID-19 patients were identified in observational studies. Although the evidence is still scarce and conflicting for some biomarkers, well-designed longitudinal studies should further explore the pathophysiological role of NfL, GFAP, and tau protein and their potential use for COVID-19 diagnosis and management.