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Correction: A social prescribing model for tackling the health and social inequalities of people living with severe mental illness: a protocol paper
No full textCorrection: BMC Public Health 25, 3211 (2025)https://doi.org/10.1186/s12889-025-24075-3Following publication of the original article [1], the authors identified an error in Pamela Whitaker’s name and an error in the affiliation listed for both Pamela Whitaker and Saul Golden.The incorrect author name is: Pamela Whittaker.The correct author name is: Pamela Whitaker. Pamela Whitaker is affiliated to Belfast School of Art, Ulster University, Belfast, UK.Saul Golden is affiliated to Belfast School of Architecture and the Built Environment, Ulster University, Belfast, UK.The author group has been updated above and the original article [1] has been correcte
Ritter reactions in continuous flow catalysed by a solid-supported sulfonic acid catalyst
No full textThe Ritter reaction allows the 100% atom economical synthesis of amides via acid-catalysed coupling between nitriles and alcohol substrates. However, this reaction has traditionally required harsh acid catalysts which must be separated from the product stream. Here, we demonstrate that commercial polymer-supported Brønsted acids catalyse the Ritter reaction under continuous flow conditions. The products are generated in high yield and free from acidic catalyst impurities. Continuous flow conditions deliver high yields in significantly shorter reaction times compared with batch reactions (1 hour vs. 24 hours) and the catalyst remains effective after 43 hours of continuous operation
Secondary analysis of the Game of Stones trial for men with obesity: examining moderator effects and exploratory outcomes
No full textObjectiveThe objective was to explore whether socioeconomic, health, and behavioral characteristics moderate the effectiveness of a text message intervention with or without financial incentives versus a control group and to examine differences in exploratory outcomes.MethodsThis three‐group randomized trial including 585 men with obesity compared daily automated behavioral text messages alongside financial incentives, text messages alone, and a waiting list control for 12 months. Moderator analyses examined percentage weight change after 12 months for 9 socioeconomic and 11 health factors. Exploratory outcomes included the following: self‐reported physical activity, sedentary behavior, smoking and alcohol behaviors, engagement in 15 weight‐management strategies, and weight‐management–related confidence.ResultsNo moderator effects were found by any factors for either comparison versus control. There were no differences across groups for health behaviors. The texts with incentives group had higher levels of engagement in six strategies including weight goals, food changes, and self‐weighing and higher levels of confidence compared with the control.ConclusionsThe Game of Stones interventions were equally effective across various subgroups based on socioeconomic, health, or well‐being status. Texts with financial incentives group participants showed better engagement for some intervention elements. The implementation of Game of Stones is unlikely to increase health inequalities. Future studies should focus on increasing engagement
Sorcha: optimized solar system ephemeris generation
No full textSorcha is a solar system survey simulator built for the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and future large-scale wide-field surveys. Over the 10 yr survey, the LSST is expected to collect roughly a billion observations of minor planets. The task of a solar system survey simulator is to take a set of input objects (described by orbits and physical properties) and determine what a real or hypothetical survey would have discovered. Existing survey simulators have a computational bottleneck in determining which input objects lie in each survey field, making them infeasible for LSST data scales. Sorcha can swiftly, efficiently, and accurately calculate the on-sky positions for sets of millions of input orbits and surveys with millions of visits, identifying which exposures these objects cross, in order for later stages of the software to make detailed estimates of the apparent magnitude and detectability of those input small bodies. In this paper, we provide the full details of the algorithm and software behind Sorcha’s ephemeris generator. Like many of Sorcha’s components, its ephemeris generator can be easily used for other surveys.</p
“Now you’re talking my language” - Improving health literacy and patient-directed knowledge of scientific abstracts through provision of plain language summaries created by artificial intelligence: A cross sectional infodemiology study
No full textBackgroundDue to free and digital availability of scientific abstracts in medical journals, as well as search engines including PubMed, many patients are increasingly looking to these as reliable and trusted sources of information, amidst an information ecosystem of potential mis- and disinformation. However, such scientific abstracts are difficult-to-read by the lay community, as they are not written purposefully for a lay audience. The Plain Language Summary now offers such readers a new medium to engage with, thereby helping with their health literacy and understanding of the research findings being described. The aims and objectives of the present study were to: calculate the readability of all scientific abstracts published in the Ulster Medical Journal over the five year period 2020 - 2024 (n=48), (ii) using artificial intelligence, prepare a plain language summary of each scientific abstract (n=48) with (a) minimal prompts and (b) with extensive prompts and (iii) calculate the readability of AI-generated plain language summaries.MethodsReadability was calculated using Readable software, defined by the (i) Flesch Reading Ease (FRE), (ii) Flesch-Kincaid Grade Level (FKGL), (iii) Gunning Fog Index and (iv) SMOG Index and four text metrics [word count, sentence count, words/sentence, syllables/word] on abstracts from all original clinical papers (n=48) published in the Ulster Medical Journal in the last five years (2020-2024). Plain language summaries were created from the existing scientific abstract using artificial intelligence with (a) minimal prompts and (b) extensive prompts. The readability of all AI-created plain language summaries was further determined.ResultsScientific abstracts had a mean FRE and FKGL score of 24.2±14.1 (standard deviation) and 14.4±2.8, respectively (Reference target values of ≥60 and ≤8, respectively). AI created plain language summaries with improved readability scores of 59.8±7.4 and 8.9±1.6, respectively for summaries with minimal prompts, thereby almost meeting reference readability targets. AI-created summaries with extensive prompts had mean readability scores of 71.3±6.1 and 6.3±0.9, respectively, with 46/48 (96%) of scientific abstracts now reaching reference readability target values. Scientific abstracts and Plain Language Summaries were statistically different (p<0.0001) in terms of both FRE and FKGL scores.Inputting the necessary and appropriate prompts to the AItool is critical to attaining the desired readability values.ConclusionsMedical journals may reach out to lay readers, including service users, patients, family and friends, through new innovation with the inclusion of a Plain Language Summary. Scientific abstracts are written at a level which is beyond the average reading age of 11 years old in the UK. Computational creativity through the employment of AI platforms can successfully generate narrative text for specific reading ages, with optimal readability. Effective communication of medical research findings from medical and scientific papers is vital for service users to enhance their health literacy, thereby helping promote better clinical outcomes, as well as promoting inclusivity for lay readers. With thorough checks and controls by the authors of clinical papers, AI-created plain language summaries may provide a new medium for medical journals to communicate with patients and service users, the results of clinical and original studies. The ability to create fit-for-purpose and easy-to-read Plain Language Summaries allows the lay public and service users to now become included in the family of readers of the journal and further supports the health literacy of patients and service users
Corrigendum to "Repeat application of microneedles does not alter skin appearance or barrier function and causes no measurable disturbance of serum biomarkers of infection, inflammation or immunity in mice in vivo" [Eur. J. Pharm. Biopharm. 117 (2017) 400-407]
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Partnering with GenAI to drive student-centred pedagogical innovation in computing education
No full textThis work-in-progress reports on the use of Generative AI (GenAI) as a reflective partner in the rapid redesign of a second-year web development module for a small computing cohort. Over several weeks, the lecturer co-developed learning materials through structured dialogue with ChatGPT, producing coherent templates, guided solutions, and narrative framing that strengthened pedagogical continuity. The findings show that conversational iteration with GenAI encouraged the educator to make tacit design choices explicit, transforming the process into a reflective dialogue that sharpened coherence and creativity in curriculum design. Sustaining that reflective quality over long, multi-session interactions remains an open challenge
A FoodSafeR perspective on emerging food safety hazards and associated risks
No full textThe recently launched FoodSafeR initiative is a cooperative and coordinated approach to the identification, assessment, and management of emerging food security challenges and associated risks—both chemical and microbial. The FoodSafeR consortium includes global stakeholders across governmental, inter-governmental, academic and industrial institutions involved in food safety, research, and production. Consortium members have led in-depth discussions on identifying, assessing and managing chemical and microbial food safety issues resulting from climate change, emerging microbial and chemical contaminants, and evolving dietary preferences. Food safety research often is episodic in nature, increasing after a crisis and then decreasing when there are no major problems. Timely communications about and a central source containing data on previous outbreaks were identified as crucial issues to reduce the harm that could result from a food safety issue. In the course of the discussions, both new and old microbial and chemical hazards were identified for inclusion in a central database. The database could be used to develop artificial intelligence (AI) models to explain existing and predict emerging food safety risks. The FoodSafeR hub continuously collects and merges government, academic and private sector data to enable all stakeholders to better understand emerging risks, both chemical and microbial, and where they are found. As the database expands, climate change impacts on food safety can be documented and then integrated with public health data to rigorously assess the contributions of food safety to public health risks. The overall goal is to enhance global data sharing, improve food safety standards, and ensure the production of safe, accessible food for all populations thereby reducing the economic burden of foodborne illnesses, enhancing food security, and promoting sustainable food systems. The goal of this paper is to alert the global food safety community of the availability of this new resource and to provide information on the types of data it contains while encouraging others to contribute data that would broaden the information available and enable more timely and accurate identification of potential food safety issues throughout the world.</p
Optimizing task offloading in dynamic satellite–terrestrial integrated computing power networks: a time–space-aware DRL approach
No full textDriven by the increasing demand from emerging applications for wide coverage, low latency, and powerful computing capabilities in networks, the Satellite–Terrestrial Integrated Computing Power Network (ST-CPN) has emerged as a promising solution. However, the dynamic nature of the ST-CPN and the limited resources of individual nodes present significant challenges to the efficient execution of complex applications with multiple interdependent subtasks. This paper investigates the dependent task offloading problem in dynamic ST-CPNs. To tackle the challenges associated with satellite mobility, uneven service distribution, and spatial uncertainty, we propose a Time–Space-Varying Resource Model (TSVRM) to capture the dynamic variations of communication, computation, and storage resources. Spatially, TSVRM predicts link establishment and switching based on satellite trajectories, visibility constraints, and polar region recognition, thereby modeling topology evolution driven by orbital motion. Temporally, a Markov process is used to represent the stochastic evolution of resource states. Building on TSVRM, we develop a Time–Space-Aware Deep Reinforcement Learning (TS-DRL) offloading scheme to determine subtask execution placement. It employs an upward-ranking mechanism for subtask prioritization and a Long Short-Term Memory (LSTM)-based Sequence-to-Sequence (S2S) network to encode structured task features. The network is trained via Proximal Policy Optimization (PPO) to approximate the policy and value functions of the Markov Decision Process, enabling optimized offloading. Simulation results show that our scheme converges effectively and achieves superior QoS compared to baselines, reaching between 94.10% and 98.27% of the optimal performance