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A consensus statement on child and family health during the COVID-19 pandemic and recommendations for post-pandemic recovery and re-build
No full textIntroduction: As health systems struggled to respond to the catastrophic effects of SARS-CoV-2, infection prevention and control measures significantly impacted on the delivery of non-COVID children's and family health services. The prioritisation of public health measures significantly impacted supportive relationships, revealed their importance for both mental and physical health and well-being. Drawing on findings from an expansive national collaboration, and with the well-being of children and young people in mind, we make recommendations here for post-pandemic recovery and re-build.
Methods: This consensus statement is derived from a cross-disciplinary collaboration of experts. Working together discursively, we have synthesised evidence from collaborative research in child and family health during the COVID-19 pandemic. We have identified and agreed priorities areas for both action and learning, which we present as recommendations for research, healthcare practice, and policy.
Results: The synthesis led to immediate recommendations grouped around what to retain and what to remove from “pandemic” provision and what to reinstate from pre-pandemic, healthcare provision in these services. Longer-term recommendations for action were also made. Those relevant to children's well-being concern equity and relational healthcare.
Discussion: The documented evidence-base of the effects of the pandemic on children's and family services is growing, providing foundations for the post-pandemic recovery and re-setting of child and family health services and care provision. Recommendations contribute to services better aligning with the values of equity and relational healthcare, whilst providing wider consideration of care and support for children and families in usual vs. extra-ordinary health system shock circumstances
Label-free classification of breast cancer using Raman spectroscopy and machine learning
No full textWe applied Raman spectroscopy to an ex vivo study of breast cancer, and demonstrated accurate classification of healthy and cancerous samples using Machine Learning
PROM1/CD133 marks a proliferative stem cell-like population of blasts in KMT2A rearranged infant ALL
No full textRobotics‐assisted acoustic surveys could deliver reliable, landscape‐level biodiversity insights
No full textTerrestrial remote sensing approaches, such as acoustic monitoring, deliver finely resolved and reliable biodiversity data. However, the scalability of surveys is often limited by the effort, time and cost needed to deploy, maintain and retrieve sensors. Autonomous unmanned aerial vehicles (UAVs, or drones) are emerging as a promising tool for fully autonomous data collection, but there is considerable scope for their further use in ecology. In this study, we explored whether a novel approach to UAV-based acoustic monitoring could detect biodiversity patterns across a varied tropical landscape in Costa Rica. We simulated surveys of UAVs employing intermittent locomotion-based sampling strategies on an existing dataset of 26,411 h of audio recorded from 341 static sites, with automated detections of 19 bird species (n = 1819) and spider monkey (n = 2977) vocalizations. We varied the number of UAVs deployed in a single survey (sampling intensity) and whether the UAVs move between sites randomly, in a pre-determined route to minimize travel time, or by adaptively responding to real-time detections (sampling strategy), and measured the impact on downstream ecological analyses. We found that avian species detections and spider monkey occupancy were not impacted by sampling strategy, but that sampling intensity had a strong influence on downstream metrics. Whilst our simulated UAV surveys were effective in capturing broad biodiversity trends, such as spider monkey occupancy and avian habitat associations, they were less suited for exhaustive species inventories, with rare species often missed at low sampling intensities. As autonomous UAV systems and acoustic AI analyses become more reliable and accessible, our study shows that combining these technologies could deliver valuable biodiversity data at scale
Effects of auditory distance cues and reverberation on spatial perception and listening strategies
No full textSpatial hearing—the brain’s ability to identify sound origins using auditory cues—is inherently multisensory, integrating vision, hearing, and proprioception to reduce uncertainty and support adaptive interaction with the environment. While simplified experimental paradigms have advanced our understanding, their limited ecological validity limits real-world applicability. This study investigates how listener movement, reverberation, and distance affect localisation accuracy in more naturalistic settings. Participants performed an active localisation task without prescribed listening strategies, in either anechoic or reverberant conditions. Sound sources were positioned around them in both horizontal and vertical planes, at varying distances. Results show increased head movement in reverberant environments, suggesting an adaptive response to degraded binaural cues. While distance did not influence listening strategies, it significantly affected localisation accuracy. These findings highlight the importance of considering ecological factors when studying spatial hearing and suggest that natural listener behaviour plays a key role in maintaining spatial accuracy under different reverberant conditions
Understanding the high-order network plasticity mechanisms of ultrasound neuromodulation
No full textTranscranial ultrasound stimulation (TUS) is an emerging non-invasive neuromodulation technique, offering a potential alternative to pharmacological treatments for psychiatric and neurological disorders. While functional analysis has been instrumental in characterizing the TUS effects, understanding its indirect influence across the network remains challenging. Here, we developed a whole-brain model to represent functional changes as measured by fMRI, enabling us to investigate how TUS-induced effects propagate throughout the brain with increasing stimulus intensity. We implemented two mechanisms: one based on anatomical distance and another on broadcasting dynamics, to explore plasticity-driven changes in specific brain regions. Finally, we highlighted the role of higher-order functional interactions in localizing spatial effects of off-line TUS at two target areas—the right thalamus and inferior frontal cortex—revealing distinct patterns of functional reorganization. This work lays the foundation for mechanistic insights and predictive models of TUS, advancing its potential clinical applications
A simple model for the barrier properties of brushes with an arbitrary chain length distribution
No full textPolymer brushes can form protective barriers on surfaces, reducing fouling and adsorption of foreign entities. Predicting how the properties of such surfaces depend on physical brush parameters has technological implications for the applications of these coatings. However, most theoretical models require in-depth knowledge or advanced mathematical and computational skills, which prevents their broad use. Here, we present a simple model extending the Alexander–De Gennes ansatz for arbitrary chain length distributions, allowing us to easily rationalize the effect of polydispersity, which is a feature of all realistic brushes. This model can predict the interaction of a brush with nanoparticles or an opposing wall, as demonstrated by good qualitative agreement with molecular dynamics simulations. An open-source Python implementation of our model is freely available on GitHub, and the model allows for efficient screening of brush designs to reach technological applications
A data-driven macroelement model for suction buckets in sand
No full textMacroelement models characterise the macroscopic behaviour of foundations in terms of resultant forces and displacements at a reference point within the foundation. Conventional macroelement models are governed by constitutive equations whose parameters are calibrated against experimental or numerical results; however, the models’ response is not necessarily accurate for varying conditions when adopting a unique set of parameters. Data-driven approaches can be more flexible as they are not bounded by any mathematical formulation and so can fit the underlying foundation behaviour with greater accuracy. A new data-driven macroelement (DdM) model for suction buckets is developed in this paper. Firstly, a database of 3D finite element (FE) analyses was created to train the DdM model. The 3D FE analyses considered suction bucket foundations installed in sand. A state-parameter dependent constitutive model incorporating a nonlinear elastic overlay model was adopted with parameters calibrated for the Dunkirk PISA site. The database includes variations in the foundations embedment-to-diameter ratio, initial relative density of the sand and the applied loading direction. An artificial neural network (ANN) was then employed as the underlying DdM model. The ANN was trained to predict the foundations force components (vertical, horizontal and bending) given the current deformation state of the foundation. The performance of the ANN was extensively validated, demonstrating an excellent generalisation ability to unseen data. Overall, the proposed DdM model demonstrates the feasibility and potential of this class of models for future design of suction buckets
Impact of cigarette prices and age-of-sale policies on smoking prevalence among youth in 26 European Member States (2012–2023): a longitudinal ecological study using repeated cross-sectional data
No full textBackground
Reducing tobacco and nicotine use and preventing smoking initiation among youth are key public health priorities. We evaluated the impact of cigarette prices and age-of-sale laws on youth smoking prevalence in the European Union (EU).
Methods
In this ecological study with 26 EU Member States as the unit of analysis, we estimated smoking prevalence among individuals aged 15–24, using five Eurobarometer waves (2012–2023, n = 12,087). We used fixed-effects panel regression models to assess the association between cigarette prices, the introduction of 18+ age-of-sale laws for tobacco products and changes in youth smoking prevalence, controlling for time and tobacco control policy implementation.
Findings
Weighted youth smoking prevalence decreased from 28.4% (841/2818) in 2012 to 22.2% (490/2222) in 2023, although the trend was not consistently downward. A €1 increase in inflation-adjusted cigarette prices per pack was associated with a 3.4 percentage point reduction in male youth prevalence (95% CI: −6.40 to −0.45), while there was no significant association for females or at the EU level. Regional variation was observed, with price increases associated with substantial reductions in youth smoking among both sexes in Southern Europe and among males in Northern Europe. In contrast, no such associations were found in Western or Eastern Europe. Age-of-sale laws were not significantly associated with youth smoking prevalence at the EU level.
Interpretation
Current taxation and age-of-sale policies remain insufficient, with impacts varying by sex and region. Achieving the tobacco endgame requires harmonised EU-level measures and stronger enforcement, particularly of these two policies, to prevent the ongoing influx of new youth smoking initiates. This study suggests that their potential impact has been constrained by inadequate enforcement to date rather than by policy ineffectiveness.
Funding
None
Recycled carbon fibre/cement-based triboelectric nanogenerators toward energy-efficient and smart civil infrastructure
No full textThis study investigated the development of recycled carbon fibre (rCF)-reinforced cementitious composites for cement-based triboelectric nanogenerators (CBTENGs), marking a novel integration of rCF into cementitious systems for energy-harvesting in buildings and civil infrastructure. By incorporating rCF into cement matrices, the electrical conductivity and mechanical properties of the composites were significantly improved, addressing the limitations of traditional cementitious materials. A comprehensive series of tests evaluated the electrical, mechanical, and triboelectric performance of CBTENGs with rCF contents ranging from 0 to 5 % by weight of the binder. The results revealed that an optimal rCF content of 0.5 % yielded the highest triboelectric output, with a peak power density of 281 mW/m2, a short-circuit current of 7 μA, and an open-circuit voltage of 250 V. However, higher rCF concentrations led to fibre agglomerations, reducing both mechanical strength and electrical performance. The results demonstrated practical applications, including a laboratory-scale simulation in which a CBTENG interacted with a polytetrafluoroethylene (PTFE)-covered wheel, generating measurable electrical outputs. In a field-scale simulation, the CBTENGs successfully charged a 10 μF capacitor to nearly 4.0 V over 1200 wheel passes, powering 26 LEDs. These findings highlight the potential of rCF-reinforced CBTENGs as sustainable, renewable and, cost-effective solutions for energy-harvesting in buildings and civil infrastructure, paving the way for smart and energy-efficient construction materials