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The association between mental health and cognitive ability: Evidence from the Understanding Society survey
No full textYesBackground:
The relationship between poor mental health and cognitive impairments in older age is well-established. Social engagement also influences cognitive ability. However, current research has not simultaneously accounted for the interplay between mental health, social interaction, and socio-demographic factors. This study addressed this gap by using a large, nationally representative dataset to examine the associations between mental health, cognitive ability, social interaction, and key socio-demographic factors.
Method:
In total, 7, 685 individuals aged 65 or older were drawn from the UK Household Longitudinal Study Understanding Society. Cognitive abilities were assessed using self-reports and performance on five tasks (immediate and delayed word recall, subtraction, number series, and numerical ability). Mental health scores were derived from the General Health Questionnaire (GHQ-12). We controlled for social interaction, gender, ethnicity, educational background, marital status, number of children, and geographic location.
Results:
We found positive relationships between mental health and all measures of cognitive ability except performance on subtraction, and number series tasks. These relationships remained after controlling for social interaction. Socio-demographic factors that contributed to the relationship between mental health and cognitive ability included being White and having higher education. Being male predicted better performance on numerical tasks, while being female, married or divorced predicted better performance on the verbal memory tasks.
Conclusion:
We examined a wide range of cognitive domains using a large, nationally representative dataset. Overall, our findings provide novel insight into the relationship between mental health and cognitive abilities. This relationship persists when controlling for social interaction alongside socio-demographic factors in older adults, underscoring the importance of addressing these factors in policies and interventions for healthy ageing. Interventions should promote access to education, cognitive stimulation, and inclusive mental health services tailored to older adults
Multicomponent crystal forms of albendazole for bioavailability improvement
No full textYesIn this study, a large-scale screening of multicomponent crystal forms of albendazole (ABZ) was carried out, aiming to improve its solubility and dissolution rate for bioavailability improvement. A computational prescreening tool based on the hydrogen bond energy was employed for initial selection of the coformer candidates to shorten the period and number of the experimental investigations, leading to the discovery of eight ABZ multicomponent solids, e.g., 1:1 albendazole-alpha-ketoglutaric acid (ABZ-AKA), 1:1 albendazole - etidronic acid (ABZ-ETA), 1:1 albendazole - (R)-mandelic acid (ABZ-RMA), albendazole - (S)-mandelic acid (1:1 ABZ-SMA), albendazole - citraconic acid (1:1 ABZ-CTA), 1:1 albendazole - mandelic acid (ABZ-MDA), 1:1 albendazole - cyclamic acid (ABZ-CYA), and albendazole - malonic acid (1:1 ABZ-MLA). Although these solids are salt cocrystal adducts based on the ΔpK a rule, they are all salt forms according to the structure analyses and FTIR results. The dissolution study has shown that the cocrystals of both ABZ-ETA and ABZ-CYA significantly improved the ABZ dissolution performance in comparison with the parent drug of ABZ.Financial support from Daiwa Foundation Award (ref: 14179/15016) and Shandong Provincial Grant (ref: WSR2023087) to foster the UK, Japan and China collaborations to carry out the part of the research work
Two-stage ensemble learning framework for automated classification of keratoconus severity
No full textYesBackground and objective
Accurate staging of keratoconus (KC) is crucial for timely intervention and improving patient quality of life. Unlike prior studies that relied on traditional base machine learning (ML) models, this paper proposes a more advanced two-stage ensemble learning model, designed to automate KC severity staging and track disease progression with improved performance.
Method
A clinical dataset collected from Pentacam corneal tomography devices serves as a comprehensive source of corneal data. Following extensive pre-processing, key Pentacam indices strongly correlated with KC severity staging are identified and clinically validated through a rigorous feature selection process. These selected indices are used to train, validate and optimize a two-stage ensemble learner that combines the strengths of four top-performing base ML models—Random Forest (RF), Gradient Boost (GB), Decision Tree (DT), and Support Vector Machine (SVM)—for KC severity staging. Three of these base learners are stacked to leverage their complementary strengths, with their predictions aggregated into a new feature matrix. This matrix is then passed as input to the fourth model, a meta-classifier, which generates the final KC staging results.
Results
Experimental evaluation of the proposed ensemble learner achieved superior performance compared to previous studies. This approach achieved an overall validation accuracy of 99.41 %, a precision of 99.43 %, and a sensitivity of 99.41 %. The F1 and F2 scores were 99.42 % and 99.41 %, respectively. The classification quality, measured by Matthew's Correlation Coefficient, also attained a value of 0.993. Additionally, the model was evaluated on 100 previously unseen test samples, which were entirely excluded from training and cross-validation. It achieved an accuracy of 99 %, demonstrating exceptional consistency, robustness, and generalizability in distinguishing among the distinct stages of KC severity (0-4).
Conclusion
The proposed model, developed in collaboration with clinicians, provides a robust foundation for creating a reliable and practical diagnostic tool to detect KC severity stages, track disease progression over time, and evaluate the effectiveness of specific treatments.The full-text of this article will be released for public view at the end of the publisher embargo on 25 Jun 2026
Empathy, Ethics and Efficacy: The 3Es of Implementing Artificial Intelligence for Consumer Encounters
No full textYesDrawing insights from virtue ethics and psychological perspectives on empathy, we propose a framework for integrating empathy, ethics, and efficacy (3E) to guide the responsible development and deployment of artificial intelligence (AI) technologies in consumer service encounters. It encourages developers, vendors, and users to improve efficiencies and productivity but, more importantly, to adopt an empathetic perspective with ethical decision-making during AI development and deployment. By simultaneously adopting descriptive, analytical, and prescriptive approaches to engage academics, practitioners, and public policymakers, we outline a future research agenda for enriching AI-consumer service encounters. Beyond conceptual integration, the framework offers practical insights for AI designers, businesses, and regulators by emphasising empathy as a bridge between efficacy and ethics. This perspective supports the development of AI technologies that not only enhance operational effectiveness but also foster consumer trust and well-being, ensuring AI-driven services remain human-centred and ethically sound. A structured decision-making model demonstrates how AI-driven services can balance automation with ethical considerations and empathetic engagement, offering a pathway for more responsible AI implementation
Predictability of isostructurality within monosubstituted cinnamide crystals
No full textYesThe use of isostructural crystals as a crystal engineering
design tool to identify potential mixed crystals is common for inorganic
materials but has not been extensive in molecular crystal engineering
due to their wider variety. Therefore, it is important to develop a
greater understanding of the features that promote isostructural
chemical families. A series of substituted cinnamides has been
investigated, experimentally and computationally, identifying five
isostructural sets, which are also predicted as low-energy structures.
While o- and p-substituted compounds show groupings with multiple
members, the m-substituted systems have fewer groups and show a
higher level of disorder in the crystal structures. All the structures
comprise consistent hydrogen-bonding motifs with only differences in
the packing of these components; this suggests that further
isostructural polymorphs may be possible by alteration of growth conditions or the use of other phases as hetero-seeds
Inactivation of histone chaperone HIRA unmasks a link between normal embryonic development of melanoblasts and maintenance of adult melanocyte stem cells
No full textYesEvidence indicates that the integrity of in utero development influences late life healthy or unhealthy aging; however, specific links between them are unclear. Histone chaperone HIRA is thought to play a role in both life stages, and here, we explore this role using the murine pigmentary system by investigating and comparing the effects of its lineage-specific knockout, either conditionally during embryogenesis or postnatally. Embryonic knockout of Hira in tyrosinase+ neural crest-derived lineages, including melanoblasts, led to reduced melanoblast numbers during embryogenesis, with single-cell RNA sequencing analysis indicating evidence of lineage-specificity defects. This was supported in an in vitro model using melb-a melanoblasts in which Hira knockdown affected lineage identity and melanoblast differentiation potential, with ATAC-seq data indicating a role of HIRA in orchestrating chromatin accessibility. Interestingly, however, newborn Hira knockout mice had wild type numbers of differentiated melanocytes, albeit functionally defective, as demonstrated by very mild hypopigmentation of the first hair coat, increased melanocyte telomere-associated DNA damage foci, and impaired response to proliferative challenge. Moreover, as they aged, mice with embryonic melanoblast Hira knockout displayed marked defects in melanocyte stem cell maintenance and premature hair graying. Importantly, this phenotype was not observed after postnatal inducible knockout, indicating an essential role for HIRA at embryonic stages that is transmitted to adulthood, rather than a direct postnatal requirement within the pigmentary system. This genetic model shows that HIRA function during early development lays a foundation for maintaining lineage identity and subsequent maintenance of adult tissue-specific stem cells during aging.Cancer Research UK Core Funding to the CRUK Scotland Institute. Grant Number: A17196. CRUK Glasgow Centre. Grant Number: A25142. National Institutes of Health. Grant Numbers: P01 AG031862#x2010;13, R01AR078559, R01AG068048, T15LM011271, U24 CA194107, U24 CA220341, U24 CA248457. National Center for Advancing Translational Sciences (NCATS). Grant Number: UL1 TR0002377. Ted Nash Long Life Foundation. California Institute for Regenerative Medicine. Grant Number: EDUC4-12813. Cancer Research UK Core Funding to Laura Machesky. Grant Numbers: A15673, A24452
A Critical Success Factor (CSF) Framework for implementing Net-Positive Housing in the Water, Energy, Food, Environment (WEFE) Nexus: Insights from Jordan and South Africa
No full textYesAddressing the specific challenges faced by low-income communities, such as poor housing conditions, is crucial while simultaneously working toward global sustainability goals, including net-zero emissions and climate change mitigation. This study explores the Critical Success Factors (CSFs) influencing the Water, Energy, Food, and Environment (WEFE) nexus within the Sustainable, Innovative, Affordable Housing (SIAH) framework in Jordan and South Africa. A Systematic Literature Review (SLR) was conducted to identify Net-Positive SIAH strategies, incorporating document searching, filtering, and content analysis to classify CSFs. Additionally, a semi-structured questionnaire campaign collected data from both countries, which was analyzed using unsupervised Machine Learning (ML) techniques to cluster responses and establish the relative importance and interconnections of CSFs through a Gephi network. Findings highlights key priorities within the WEFE nexus, providing insights into region-specific sustainability challenges and solutions. The study contributes a novel, evidence-based approach to affordable and sustainable housing by systematically quantifying and mapping CSF interrelationships. This research advances the understanding of Net-Positive housing by integrating technological, environmental, and social dimensions, offering practical implications for policymakers, urban planners, and stakeholders in the Global South
Role of hydrogen in polymer electrolyte fuel cell systems
No full textNoIn recent years, there has been a growing concern surrounding hydrogen energy and polymer electrolyte membrane (PEM) fuel cells. However, the advancement of fuel cell commercial development has been hindered by the limitations in constructing hydrogen refueling infrastructure, as well as hydrogen storage and transportation. This review provides a comprehensive analysis and comparison of various aspects, ranging from resources to end-using of hydrogen.
The analysis focuses on the examination of the benefits associated with the application of surplus hydrogen of industrial sectors and the hydrogen produced by steam reforming gas in PEM fuel cell systems. The utilization of industrial wasted hydrogen presents a notable potential for a substantial decrease in the overall cost of hydrogen. Additionally, it is suggested that the employment of onboard hydrogen generation through steam methanol reforming can effectively address the challenges associated with the efficient storage and transportation of gaseous hydrogen. The introduction typically encompasses a discussion of the impacts arising from the presence of impurities in the reformate gas. Following the reformate gas becomes suitable for utilization in PEM fuel cells. Ultimately, recommendations are put forth for future investigations
Model-based generation of manufacturing process plans through on-the-fly topology formation
No full textYesIn advanced manufacturing systems, the production of complex and highly customised products requires the preparation of many different product specifications and associated manufacturing process plans. The creation of these plans involves the search for the production resources (e.g. robots, machine tools, sensors, end effectors) that are needed to implement the product specifications, and how to orchestrate them. This paper presents a model-based approach to the automatic generation of manufacturing process plans from the models of the target products and available resources. The modelling language is based on labelled transition systems, which are useful to represent and manipulate efficiently sequences of high-level tasks that can be executed on the production resources. The proposed solution improves on existing approaches by forming the system topology on the fly while generating a process plan, creating only the topology states that are actually needed and, therefore, drastically reducing computational space and time. Experimental results demonstrate the greater computational performance and scalability of the presented techniques with an increasing number of resources, as well as the efficiency of the generated (optimal) plans. In summary, the benefits of this work are the following: minimal human intervention, as process planners only have to create the models; shorter process planning time, generation of optimal plans and, consequently, reduced manufacturing costs and shorter lead time for customised products.SURE Research Projects Fund of the University of Bradford