The Upper Paleolithic archaeological record of the Tolbor Valley (northern Mongolia) documents a shift in emphasis from large-blade production to smaller-format bladelet production, characteristic of the Initial Upper Paleolithic (IUP) to Early Upper Paleolithic (EUP) transition in Central and Northeast Asia. This periodization is often associated with, respectively, early dispersal and permanent instalment of Homo sapiens populations in eastern Eurasia. In some regions of West Asia and Europe, an increase in lithic sharp-edge productivity has been described in the EUP, raising the hypothesis that for some groups intensified bladelet production represented a potential solution to constraints on sharp-edge availability. We test this using an allometric approach that controls for blank size, drawing on data excavated from three Upper Paleolithic sites in the Tolbor Valley dated between ca. 45 ka and < 28 ka. A diachronic increase in edge length ( 10 mm) is detected among small flakes (< 5 g) between the IUP and EUP samples, while laminar blanks (blades/bladelets) show no consistent increase. This suggests that the increased emphasis on bladelet production during the EUP at Tolbor was not primarily a response to constraints on sharp-edge availability. Instead, it coincided with more productive small flakes, whether as byproducts of bladelet manufacture or as a parallel/integrated blank production strategy. These results highlight the importance of accounting for blank size when evaluating diachronic trends in sharp-edge productivity.
The increase in shipping in the Canadian Arctic has significant impacts on Inuit coastal communities and their traditional way of life. Examples include the risk of chemical spills, underwater noise and ships’ hulls acting as vectors for non-indigenous species, all of which impact ecosystems and wildlife which Inuit rely on for health, food security and cultural sustainability. However, the number and types of ships travelling near communities and the associated risks remain poorly quantified, limiting effective management strategies. We use ship tracklines generated from Automatic Identification System (AIS) ship positions between 2013 and 2022 to calculate voyages within 20 km of 43 communities distributed throughout Northern Canada (north of 60° N and Hudson Bay). Over 10 years, voyages increased significantly by a factor of 1.7 (from 116 in 2013 to 317 in 2022), with the largest increases due to dry bulk, cargo and government/research vessels. This varies between communities, with 15 (35
Human pharmaceuticals are increasingly detected in environments around the world, with growing international calls to mitigate the ecological and human health risks posed by these novel entities. Exposure to pharmaceutical pollutants can negatively affect the behaviour, reproduction, and health of wildlife, contributing towards declining ecological health and global biodiversity loss. Pharmaceuticals in the environment are also driving rising levels of antimicrobial resistance, a major public health threat. Developing strategies to mitigate these public and environmental health risks has been greatly limited by diverse and often conflicting stakeholder interests and the need to retain the major human health and socioeconomic benefits that pharmaceuticals provide. In this Personal View, we propose a multistakeholder, systems-based approach for high-income countries to develop transformational national mitigation strategies. Applying this approach to a UK case study highlighted the growing risks caused by the unsustainability of the current UK health-care pharmaceutical system and enabled us to identify 37 synergistic intervention points that target both the tangible easy wins and the deep-rooted social drivers of the issue. We believe our approach will support high-income countries in minimising the public and environmental health risks associated with pharmaceutical pollution, by driving long-term sustainability across the pharmaceutical lifecycle, for a positive pharmaceutical future.
Background/Objectives: Artificial intelligence (AI) is increasingly being evaluated for ophthalmic diagnosis, screening, and triage, yet its role in paediatric eye care remains less established than in adult ophthalmology. This systematic review aimed to synthesise evidence on AI-enabled tools for paediatric ophthalmic diagnosis, screening, triage, surveillance, and referral, with an emphasis on diagnostic performance, safety, workflow integration, equity, and implementation readiness in primary, community, and primary care-relevant settings. Methods: A PRISMA-guided systematic review was conducted using MEDLINE, Embase, Web of Science, Scopus, and IEEE Xplore from inception to 30 March 2026. Eligible studies evaluated AI or machine-learning tools for children and adolescents aged 0-18 years in relation to paediatric eye conditions. Study selection and data extraction were undertaken independently by reviewers, with disagreements resolved by consensus or third-reviewer adjudication. Methodological and reporting quality was evaluated using an author-adapted six-domain rubric informed by APPRAISE-AI. Diagnostic-accuracy studies were assessed using an author-adapted QUADAS-2 framework incorporating QUADAS-AI-informed AI-specific considerations, the prediction-model study was assessed using PROBAST+AI, and the non-randomised treatment-effect study was assessed using ROBINS-I. The public dataset descriptor was evaluated separately using an author-developed dataset-quality, representativeness, and applicability framework. Because of clinical and methodological heterogeneity, findings were synthesised thematically. Results: Twelve empirical studies and one public dataset descriptor were included, covering retinopathy of prematurity, retinoblastoma, amblyopia risk, myopia, congenital cataract, and visual-acuity assessment. AI systems frequently demonstrated promising diagnostic or screening performance, including sensitivity-first detection of treatment-requiring retinopathy of prematurity, high discrimination for retinoblastoma activity, and strong myopia prediction using fundus images. Several studies supported feasibility in neonatal, school, and community workflows using smartphone-based imaging, task-shifted operators, tele-referral, and human-in-the-loop review. However, external and temporal validation, calibration, patient-level reporting, subgroup and fairness assessment, and economic evaluation were limited. Conclusions: AI-enabled tools show promise for supporting selected paediatric ophthalmic screening, triage, and surveillance pathways, particularly when combined with image-quality control, explicit escalation, and human oversight. However, confidence in the reported performance is limited by single-centre studies and enriched samples, small numbers of clinically important cases, heterogeneous analytical units, potentially optimistic aggregation procedures, limited external or temporal validation, incomplete calibration, and absent fairness analyses. Routine autonomous implementation remains premature.
Many industries rely on wave data to understand the potential for wave energy extraction, or to understand the wave environment for the design of marine structures and to plan operations and maintenance. Three ocean reanalysis datasets, ERA5, WAVEWATCH III and Copernicus Global Ocean Waves Analysis and Forecast, are compared to in-situ wave buoy data collected along the north of Scotland. All reanalysis datasets correlated well with the wave buoy data, with the Copernicus Global Ocean Waves Analysis and Forecast dataset being statistically the closest to the buoy data. However, all three reanalysis datasets underpredict significant wave height during extreme wave events. From comparisons of the wave buoy data at one site, it was found that although extreme events are underpredicted, the WAVEWATCH III reanalysis data performed the best, although still under predicted extreme wave heights. Of the reanalysis models compared against wave buoy data here, it is suggested that for extreme wave analysis the WAVEWATCH III model is recommended, whilst for long term statistics and weather windowing the Copernicus Global Ocean Waves Analysis is a good option. Whilst reanalysis data sets are a valuable resource for marine renewable energy, developers should be aware of the limitations of these datasets, in particular for extreme wave conditions.
