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Estimating the respiratory syncytial virus-associated hospitalisation burden in older adults in European countries: a systematic analysis
Get PDFBackground With respiratory syncytial virus vaccines recently approved for use among older adults, country-level RSV disease burden estimates are needed to inform local RSV immunisation strategy. We aimed to estimate country-level RSV hospitalisation burden in older adults in Europe.MethodsWe compiled data on RSV hospitalisation burden in adults aged ≥60 years in Europe from published studies (systematic review: PROSPERO CRD42024516945), surveillance data, and unpublished data from international collaborators. We adjusted for diagnostic testing, clinical specimens and case definitions through statistical modelling techniques and generated country-level hospitalisation rate estimates; for countries with no available data, we developed an ensemble model to predict RSV hospitalisation rates. We also estimated RSV in-hospital case fatality ratio (hCFR) for countries with available data.ResultsWe included 14 studies (3 unpublished studies). The adjusted RSV-associated hospitalisation rates were overall 2.2 to 6.4 times higher than unadjusted estimates. Among 5 countries with available data, adjusted annual RSV hospitalisation rates ranged from 193/100,000 person-years in the Netherlands (95% confidence interval [CI]: 125–304) and Finland (141–274) to 414/100,000 in Denmark (322–514). The RSV-hospitalisation rates predicted by the ensemble model in 23 additional countries ranging from 223/100,000 to 317/100,000 person-years. RSV hCFR ranged from 6.73% (4.63–9.69) in Spain to 10.14% (4.91–19.79) in Switzerland.ConclusionsThis study addresses knowledge gaps in RSV hospitalisation burden among older adults in Europe while highlighting the importance of adjusting for RSV case under-ascertainment. These findings might be relevant for country’s considerations of RSV immunisation strategies for older adults
AI platforms as cooperation enablers favoring the development of strategic situating capabilities within solution delivery ecosystems
Get PDFAcademic summaryBy integrating artificial intelligence (AI) platforms into their processes, firms aim to enhance their strategic capabilities and gain competitive advantage. This study investigates the impact of such platforms on value-generation within solution-based strategies, proposing two connected mechanisms. First, AI platforms foster collaborative value systems between firms and value-chain agents across the stages of the solution delivery process (i.e., problem identification, solution development, and solution implementation). Second, such cooperation could foster the development of situating capabilities (i.e., grounding, bounding, and recasting), which are conceptually linked to the mitigation of situated agency constraints that stifle value-creation within productive systems. These relationships underscore the value-generation potential of AI platforms for solution providers, extending the premise of situated AI capabilities to the organizational and inter-organizational level. Data collected from 570 Spanish manufacturing firms in 2023 reveals that firms utilizing AI platforms exhibit greater cooperative and situating capability-building behavior during the problem-identification and solution-implementation stages. However, no significant association is found between AI platforms and the more creative stage of solution development. The study provides novel insights into the interplay between AI platforms, user cooperation, situated agency, and strategic capabilities as drivers of value-generation and advancement of the AI-dominated paradigm. Theoretical and practical implications are discussed.Managerial summaryThis study highlights the strategic role of AI platforms in enhancing collaboration between manufacturers and solution seekers throughout the solution delivery process. AI technologies facilitate collective learning, adaptation and knowledge sharing, particularly during the diagnostic and implementation stages, where real-time data processing and predictive analytics help tailor solutions to user-specific challenges. This more effective coordination is essential for mitigating agency problems that arise due to asymmetric information or misaligned objectives within complex solution systems. However, the findings reveal that AI’s influence is limited in the co-creation of solution design and development, which relies heavily on human insight, creativity, and contextual judgment. Managers should therefore not view AI as a substitute for human input, but rather as a complementary tool that enhances efficacy and integration. For firms seeking to strengthen their solution-oriented strategies, the key takeaway is to maintain a balanced approach—combining AI-enabled collaboration with human ingenuity—will improve solution outcomes and sustain competitive advantage in markets increasingly shaped by personalization and customer-specific problem solving.<br/
The cross-linguistic role of animacy in grammar structures
Get PDFAnimacy is a semantic feature of nominals and follows a hierarchy: personal pronouns > human > animate > inanimate. In several languages, animacy imposes hard constraints on grammar. While it has been argued that these constraints may emerge from universal soft tendencies, it has been difficult to provide empirical evidence for this conjecture due to the lack of data annotated with animacy classes. In this work, we first propose a method to reliably classify animacy classes of nominals in 11 languages from 5 families, leveraging multilingual large language models (LLMs) and word sense disambiguation datasets. Then, through this newly acquired data, we verify that animacy displays consistent cross-linguistic tendencies in terms of preferred morphosyntactic constructions, although not always in line with received wisdom: animacy in nouns correlates with the alignment role of agent, early positions in a clause, and syntactic pivot (e.g., for relativisation), but not necessarily with grammatical subjecthood. Furthermore, the behaviour of personal pronouns in the hierarchy is idiosyncratic as they are rarely plural and relativised, contrary to high-animacy nouns
Devolving digitalisation:Local government, local welfare and the digital welfare state
Get PDFThere is increasing interest in examining the digital welfare state. To date, much ethical and analytical scrutiny of digital welfare has focused on the large, nation-state level initiatives of digital transformation, with less attention given to what is happening in local government, especially in the UK. This is despite local authorities playing an essential role in citizen-state relations, and their increasing (yet uneven) move towards an ‘interface first’ governance for the provision of local services. This article has three key purposes. First, to outline the increasing importance of one field of local government activity that raises demands for digitalisation of processes: local welfare (such as discretionary and local welfare assistance). Where local government has the burden for the design and delivery of policy – as with local welfare – this comes with tied responsibility for the design and delivery of digitalisation of these processes. Second, to outline the variations of local welfare administration and reflect on the role of digital interfaces in this context. To do so, we draw on examples from commissioned funding through the UK Ministry of Housing, Communities and local government. Finally, building on the arguments throughout the article, we set out areas for future research in local digital welfare provision
Conformal prediction for electricity price forecasting in the day-ahead and real-time balancing market
Get PDFThe integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market operations. Accurate and reliable electricity price forecasting is crucial for effective market participation, where price dynamics can be significantly more challenging to predict. Probabilistic forecasting, through prediction intervals, efficiently quantifies the inherent uncertainties in electricity prices, supporting better decision making for market participants. This study explores the enhancement of probabilistic price prediction using Conformal Prediction (CP) techniques, specifically Ensemble Batch Prediction Intervals and Sequential Predictive Conformal Inference. These methods provide precise and reliable prediction intervals, outperforming traditional models in validity metrics. We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques. This ensemble delivers both narrow prediction intervals and high coverage, leading to more reliable and accurate forecasts. We further evaluate the practical implications of CP techniques through a simulated trading algorithm applied to a battery storage system. The ensemble approach demonstrates improved financial returns in energy trading in both the Day-Ahead and Balancing Markets, highlighting its practical benefits for market participants
CoRPA:Adversarial image generation for chest X-rays using concept vector perturbations and generative models
Get PDFDeep learning models for medical image classification tasks are becoming widely implemented in AI-assisted diagnostic tools, aiming to enhance diagnostic accuracy, reduce clinician workloads, and improve patient outcomes. However, their vulnerability to adversarial attacks poses significant risks to patient safety. Current attack methodologies use general techniques such as model querying or pixel value perturbations to generate adversarial examples designed to fool a model. These approaches may not adequately address the unique characteristics of clinical errors stemming from missed or incorrectly identified clinical features. We propose the Concept-Based Report Perturbation Attack (CoRPA), a clinically-focused black-box adversarial attack framework tailored to the medical imaging domain. CoRPA leverages clinical concepts to generate adversarial radiological reports and images that closely mirror realistic clinical misdiagnosis scenarios. We demonstrate the utility of CoRPA using the MIMIC-CXR-JPG dataset of chest X-rays and radiological reports. Our evaluation reveals that deep learning models exhibiting strong resilience to conventional adversarial attacks are significantly less robust when subjected to CoRPA’s clinically-focused perturbations. This underscores the importance of addressing domain-specific vulnerabilities in medical AI systems. By introducing a specialized adversarial attack framework, this study provides a foundation for developing robust, real-world-ready AI models in healthcare, ensuring their safe and reliable deployment in high-stakes clinical environments
Is the time right for transplanting immature testicular tissue or cells to restore male fertility? Expert perspectives on clinical implementation of autotransplantation of cryopreserved testicular tissue or cells for fertility restoration
Get PDFImplementability of contraceptive implant insertions during midwife postnatal home visits: an exploratory qualitative study
No full textProblemMany women lack access to contraception information and services during pregnancy and postnatally and are at risk of unintentionally falling pregnant again soon after birth.BackgroundContraception enables women to attain their desired number and spacing of births. Better access to postpartum contraception is critical for informed decision-making, higher uptake and improved health outcomes. Home-based provision of the contraceptive implant may contribute to increasing access.AimTo explore the views of midwives on the implementability of midwives providing contraceptive implants during postnatal home visitsMethodsWe conducted an exploratory qualitative interview study with 21 midwives. Reflexive thematic analysis was used to construct themes.FindingsHome implant insertions were seen as generally acceptable and potentially feasible. Midwives’ primary concerns related to workload and scheduling, although most felt this could be manageable, particularly in continuity models. Two factors to promote implementability included i) enhanced ‘contraception conversations’ in maternity settings, and ii) strong leadership and support, including a policy and training framework, opportunities for practice, and consideration of workload.DiscussionMidwives felt home insertions would be well-suited to continuity care models. Although this may be a beneficial starting point, it means key groups of women who would also benefit from contraception may be missed.ConclusionMidwives viewed provision of home implant insertions as generally acceptable and potentially feasible, in the context of early and ongoing contraception conversations and proper planning and support. Further research trialling implant insertions during midwife postnatal home visits is warranted to determine feasibility and acceptability in real settings
Reducing redundancy and enhancing accuracy through a phylogenetically-informed microbial community metabolic modeling approach
No full textMotivation: Metabolic modeling has emerged as a powerful tool for predicting community functions. However, current modeling approaches face significant challenges in balancing the metabolic trade-offs between individual and community-level growth. In this study, we investigated the effect of metabolic relatedness among taxa on growth rate calculations by merging related taxa based on their metabolic similarity, introducing this approach as PhyloCOBRA. Results: This approach enhanced the accuracy and efficiency of microbial community simulations by combining genome-scale metabolic models (GEMs) of closely related organisms, aligning with the concepts of niche differentiation and nestedness theory. To validate our approach, we implemented PhyloCOBRA within the MICOM and OptCom package (creating PhyloMICOM and PhyloOptCom, respectively), and applied it to metagenomic data from 186 individuals and four-species synthetic community (SynCom). Our results demonstrated significant improvement in the accuracy and reliability of growth rate predictions compared to the standard methods. Sensitivity analysis revealed that PhyloMICOM models were more robust to random noise, while Jaccard index calculations showed a reduction in redundancy, highlighting the enhanced specificity of the generated community models. Furthermore, PhyloMICOM reduced the computational complexity, addressing a key concern in microbial community simulations. This approach marks a significant advancement in community-scale metabolic modeling, offering a more stable, efficient, and ecologically relevant tool for simulating and understanding the intricate dynamics of microbial ecosystems. Availability and implementation: PhyloCOBRA implementations are available as extensions to the MICOM packages and can be accessed at https://github.com/sepideh-mofidifar/PhyloCOBRA
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