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Genomic characterization of Sabiá virus in Brazil, 2019–2020:Implications for diagnostics, virus evolution, and receptor binding
Get PDFBetween December 2019 and January 2020, two patients suspected of having severe yellow fever were admitted to a tertiary healthcare facility in São Paulo, Brazil, presenting with acute hemorrhagic syndrome and neurological alterations; both cases had fatal outcomes. Upon admission, both tested negative for yellow fever viral RNA, and Sabiá virus (SABV), a New World arenavirus, was identified as the causative pathogen. To date, only four humans naturally acquired SABV infections have been confirmed, all fatal and linked to rural settings. We applied next-generation sequencing to generate complete and near-complete genomes from two patients (SP17 and SP19). Existing molecular diagnostics failed to detect SABV; therefore, new molecular tests were developed. Genetic analyses of SP17 and SP19 genomes along with other arenaviruses, revealed that the new cases were genetically diverse, showing 93-98.2% amino acid identity at the NP level among SP17, SP19, and the 1990 reference strain (SPH114202). Time-scaled phylogenetic analyses confirmed that SP17 and SP19 were not epidemiologically linked and suggested that SABV has been circulating undetected in Brazil for over a century. Additionally, homology modeling and structure-based mapping provided insights into SABV receptor-binding sequence conservation, suggesting that SABV shares similar receptor binding structure to other clade B arenaviruses, despite some amino acid variation around receptor binding site. Our findings underscore the need for retrospective and prospective surveillance of undiagnosed hemorrhagic fever cases to assess the public health impact of SABV in Brazil
Off Grid:The Problem of Early-Eighteenth-Century Caribbean Sinew Populations
Get PDFTo understand the early modern Caribbean, we must understand the societies that inhabited it. The parameters through which historians approach these societies have changed drastically in the last decade. While recent interventions have proven useful for framing our attitude to how populations in the Caribbean formed, they are less effective when applied to societies whose longevity was uncertain that, in some cases, fractured or collapsed. It is in this context that some historians have identified what they term “sinew populations”: communities whose “off-grid” nature necessitates different ways of thinking about how they functioned. Recent works have discussed how sinew populations ensured the long-term viability of their communities, but this approach also requires attention to the factors that could render a sinew population’s existence unviable. This article uses an eighteenth-century Caribbean population of pirates as a case study to illustrate the issue of viability within sinew populations. In particular, the article emphasizes the weak social foundations on which this sinew population was built and the lack of interest among the pirates themselves, after 1718, in maintaining a large pirate population. In thinking about how pirates related to one another and what this meant for the long-term survival of the pirate sinew population, this article demonstrates the importance of social maintenance for understanding how Caribbean societies operated
Mental health advice on TikTok
Get PDFIn this paper, we provide the first, large-scale corpus-pragmatic analysis of mental health advice by social media influencers on TikTok. We identify advice-giving in large datasets focusing on if-conditionals as a specific form that allows us to analyse how the audience is positioned relative to a need and the solution which is then proposed. To identify the different ways in which mental health issues are presented, we use an adapted version of the ‘mental health quotient’ (Newson and Thiagarajan, 2020), as a linguistically informed framework for differentiating between lay discussions of mental health and those that invoke specific disorders. We sample a corpus of over 27,000 TikTok videos from 85 mental health influencers, using corpus-scale identification to extract and analyse if-conditionals produced by mental health professionals and wellness influencers. Our analysis of the protasis shows how these two types of influencers use prompts that share some similarities but also rely on fundamentally different models of healthcare. The relationship between these prompts and the information and recommendations in the apodosis show how health professionals rely on diagnostic information and therapeutic advice, while wellness influencers recommend embodied practice and products to treat mental health issues. These findings set out the distinctive ecosystem of healthcare which is emerging within the algorithmically driven contexts of sites like TikTok
Mnemonic Conviviality in ‘Post-Cold War’ Britain:Entangled Memories in Urban Space
No full textThis article asks how sharing everyday memories in Britain’s superdiverse urban spaces can foster understanding across difference. Drawing on narratives and images produced in 39 arts based photography workshops in West Bromwich and Hyson Green, it develops the concept of mnemonic conviviality: a mode of “living with difference” grounded in the exchange of personal, familial, and collective memories. Through a multi sited, multi group methodology – combining photovoice, visual analysis, and ethnographic dialogue – the article demonstrates how ordinary objects, places, and images spark conversations that entangle memories of socialism, post socialism, colonialism and migration. Through these micro narratives, participants forge temporary commonalities while revealing the enduring impact of the Cold War, colonial hierarchies, and racialisation on contemporary British life. The article demonstrates how memory studies can deepen understandings of conviviality in hyperlocal superdiverse spaces
Metaphor identification using large language models:A comparison of RAG, prompt engineering, and fine-tuning
No full textMetaphor is a pervasive feature of discourse and a powerful lens for examining cognition, emotion, and ideology. Large-scale analysis, however, has been constrained by the need for manual annotation due to the context-sensitive nature of metaphor. This study investigates the potential of large language models (LLMs) to automate metaphor identification in full texts. We compare three methods: (i) retrieval-augmented generation (RAG), where the model is provided with a codebook and instructed to annotate texts based on its rules and examples; (ii) prompt engineering, where we design task-specific verbal instructions; and (iii) fine-tuning, where the model is trained on hand-coded texts to optimize performance. Within prompt engineering, we test zero-shot, few-shot, and chain-of-thought strategies. Our results show that state-of-the-art closed-source LLMs can achieve high accuracy, with fine-tuning yielding a median F1 score of 0.79. A comparison of human and LLM outputs reveals that most discrepancies are systematic, reflecting well-known grey areas and conceptual challenges in metaphor theory. We propose that LLMs can be used to at least partly automate metaphor identification and can serve as a testbed for developing and refining metaphor identification protocols and the theory that underpins them
Learn-to-Distance:Distance Learning for Detecting LLM-Generated Text
No full textModern large language models (LLMs) such as GPT, Claude, and Gemini have transformed the way we learn, work, and communicate. Yet, their ability to produce highly human-like text raises serious concerns about misinformation and academic integrity, making it an urgent need for reliable algorithms to detect LLMgenerated content. In this paper, we start by presenting a geometric approach to demystify rewrite-based detection algorithms, revealing their underlying rationale and demonstrating their generalization ability. Building on this insight, we introduce a novel rewrite-based detection algorithm that adaptively learns the distance between the original and rewritten text. Theoretically, we demonstrate that employing an adaptively learned distance function is more effective for detection than using a fixed distance. Empirically, we conduct extensive experiments with over 100 settings, and find that our approach demonstrates superior performance over baseline algorithms in the majority of scenarios. In particular, it achieves relative improvements from 54.3% to 75.4% over the strongest baseline across different target LLMs (e.g., GPT, Claude, and Gemini). A python implementation of our proposal is publicly available at https://github.com/Mamba413/L2D
Adopting the New Academic Assistant:Student Use and Perceptions of Generative AI in a Multicultural UAE University
No full textThis study investigates how undergraduate students at a multicultural UAE university use Generative AI as an academic assistant and how they perceive its value, risks, and role in learning. Using a qualitative design grounded in constructivist and phenomenological approaches, the study draws on a focus group to examine emotional, cognitive, and behavioural dimensions of AI engagement. Findings show that students view Generative AI as an embedded study companion supporting idea generation, drafting, summarising, and problem solving, with accessibility and efficiency driving regular use. Participants also expressed concern about uneven institutional guidance, dependency risks, and uncertainty around acceptable and ethical use. Clear patterns emerged around developing prompt literacy, navigating inconsistent advice from instructors, and demand for transparent policies and practical instruction. Student readiness was shaped not only by technical familiarity but also by institutional clarity, pedagogical support, and confidence in responsible use within academic tasks and assessment use
Correction:Timeline for establishing a circular economy for lithium-ion batteries
Get PDFCorrection for ‘Timeline for establishing a circular economy for lithium-ion batteries’ by Jennifer M. Hartley et al., EES Batteries, 2025, 1, 1502–1514, https://doi.org/10.1039/D5EB00144G.In the original article, the information in Table 1 was misaligned due to a production error. The corrected version of Table 1 is shown below.[Table presented]The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers
What role do negative self-conscious emotions play in UK medicine? A systematic review and qualitative synthesis of the evidence
No full textNegative self-conscious emotions have long been theorised to play a role in medicine and this paper outlines a systematic review of empirical research that identifies qualitative data for shame, guilt, humiliation or embarrassment in doctors, patients, and students in the UK between 1979 and 2023. PubMed, PsycInfo, CINAHL plus, Web of Sciences and Medline were searched, and a total of 160 papers were identified. Only six papers set out to identify these emotions, while 154 papers had found such experiences while investigating other topics. A Framework Approach was used to create analytical themes from the information. This review provides the most comprehensive analysis of the evidence for negative self-conscious emotions in medicine to date, showing not just how it is experienced, but also how it contributes to adverse health outcomes, and compromises the quality of patient care. We demonstrate how patients experience negative self-conscious emotions as a result of feeling flawed, which can be exacerbated by insensitive treatment or a perception of judgment. Similarly, doctors can experience negative self-conscious emotions due to perceived failures in patient care or a sense of inadequacy in their role. Rather than seeing negative self-conscious emotions as products of personal circumstance or poor practice, however, our critical analysis argues that they need to be seen as inevitable experiences of the system and practice of medicine, which changes how we should understand and address these feelings in policy and practice
Dynamic interplay of sports, social, and economic factors in the English Premier League:A network DEA approach
Get PDFThis study evaluates the operational and revenue generation efficiency of English Premier League (EPL) clubs from 2014/15 to 2023/24 using a novel Dynamic Network Data Envelopment Analysis (DNDEA) model under the Variable Returns to Scale (VRS) assumption. By integrating dynamic and network structures, the model decomposes club performance into sequential stages, specifically operational conversion and revenue generation, and traces the intertemporal transmission of economic, sporting, and social factors. Unlike traditional static DEA models that treat efficiency as time-invariant and “black-boxed”, the DNDEA model provides a temporal-diagnostic lens that detect shifts in efficiency trajectories and inter-stage feedback across seasons. Results reveal two enduring archetypes: high-performing clubs that achieve competitive outcomes with limited financial inputs through effective resource management, and financial giants that excel in revenue generation but struggle to translate investments into consistent sporting success. Dynamic analysis shows system efficiency peaked in 2014/15 before declining sharply in 2015/16 and 2018/19, respectively, driven by imbalances between escalating financial investments and stagnating on-field performance. Operational inefficiencies in Stage 1 (resource conversion) were more critical than those in Stage 2 (revenue generation), underscoring challenges in aligning short-term investments with long-term sustainability. The study advances sports analytics by providing a holistic framework for evaluating football club efficiency, emphasizing actionable strategies such as optimizing talent acquisition, prioritizing dual-return investments, and leveraging fan engagement
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