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Metabolic plasticity:an evolutionary perspective on metabolic and circadian dysregulation in bipolar disorder
Get PDFThe emerging field of metabolic psychiatry has brought mechanisms of metabolic dysfunction into focus in bipolar disorder research. In this manuscript, we propose that the metabolic features of bipolar disorder provide a new vector from which to understand the role of circadian dysfunction in this condition. A notable feature of bipolar disorder is the photoperiod driven, seasonal occurrence of symptoms and episodes mediated by circadian systems, with mania occurring more frequently in the spring and autumn at times of rapid rate of change in photoperiod, and depression being more prevalent in the winter when photoperiod is attenuated. In this manuscript we note that seasonal adaptations in metabolism are highly conserved evolutionary traits across diverse taxa. Several of the underlying mechanisms mediating seasonal changes in metabolism are conserved in human biology and are implicated in bipolar disorder pathophysiology. Such mechanisms encompass targets of lithium involved in insulin signaling (the phosphatidylinositol cycle, GSK3β and Akt), clock genes (CLOCK and BMAL1), targets of psychiatric and metabolic medications (mTOR and AMPK) and hormonal signaling (melatonin and cortisol). We propose that bipolar disorder may represent a dysregulation of conserved mechanisms of chronometabolic regulation and provide a discussion of the evolutionary context of such mechanisms. Genetic predisposition coupled to novel environmental inputs to human biology including artificial light at night and sustained refined sugar and carbohydrate intake may contribute to states of metabolic and circadian dysregulation in bipolar disorder underlying episodes of mania and depression.</p
Open Prototyping – A Toolkit for Open Engineering?: The Case of The New Real Observatory, an Environmentally-Conscious Generative AI Platform
Get PDFThe framework of Open Engineering (OE) describes the practical realization of collaborative interdisciplinary and cross-organisational innovation projects, combining the Research and Development (R&D) processes and knowledge management, with stakeholder engagement and hands-on (systems) engineering activities. However, these concepts have so far been used analytically and practical tools are needed to turn them into integrated applied practices. In this paper we describe the development and use of one such methodology and process model called Open Prototyping (OP). We are examining its six stages as used in developing The New Real Observatory, a generative Artificial Intelligence (AI) platform for environmentally-conscious exploration of data processing. The results demonstrate the utility of this toolkit and its alignment with OE framework, whilst also exposing the critical role of facilitators in managing stakeholders' expectations as well as negotiating values and possibilities to ensure eventual delivery of successful outputs. This reinforces the previous work on practices of OE and the critical role of innovation intermediaries in advancing collaborative high-tech R&D
The New Real Observatory: Art and AI in Conversation with the Environment
Get PDFAn artist stares intently at a computer screen. She's not looking at environmental data in the usual way – charts, graphs or satellite imagery. Instead, she's using an AI system to explore how machines and humans might together make sense of our changing planet. As she adjusts parameters on the screen, the system generates new interpretations of local greenery, revealing something profound about how both humans and machines perceive nature. This is The New Real Observatory, where artists and scientists are working together to create new ways of seeing our changing world
Associations and interactions between premorbid cognitive health, apolipoprotein e4 genotype, and incident Alzheimer’s disease in UK Biobank (N = 252,340)
Get PDFIt is unclear to what extent genetic risk offsets the protective effects of better premorbid cognitive health on the risk of Alzheimer’s disease (AD). We tested for associations between measures of premorbid cognitive health, apolipoprotein (APOE) e4 ‘risk’ genotype, and their interaction, with risk of incident AD and age of diagnosis, in UK Biobank participants aged ≥55 years at baseline, adjusted for potential confounders. During follow-up, 3,505/252,340 (1.39%) participants received an incident diagnosis of AD. There were significant associations between better performance on each cognitive test with lower risk of incident AD, and later age at diagnosis. However, the benefit of better baseline cognitive scores on AD risk was significantly attenuated in APOE e4 carriers. These data demonstrate that the association between premorbid cognitive health and subsequent risk of AD is influenced by APOE e4 genotype. This has implications for risk stratification and targeted intervention
Optimized Lattice-Structured Flexible EIT Sensor for Tactile Reconstruction and Classification
Get PDFFlexible electrical impedance tomography (EIT) offers a promising alternative to traditional tactile sensing approaches, enabling low-cost, scalable, and deformable sensor designs. Here, we propose an optimized lattice-structured flexible EIT tactile sensor incorporating a hydrogel-based conductive layer, systematically designed through 3-D coupling field simulations (3D-CFSs) to optimize structural parameters for enhanced sensitivity and robustness. By tuning the lattice channel width and conductive layer thickness, we achieve significant improvements in tactile reconstruction quality and classification performance. Experimental results demonstrate high-quality tactile reconstruction with correlation coefficients (CCs) up to 0.9275, peak signal-to-noise ratios (PSNRs) reaching 29.0303 dB, and structural similarity indexes up to 0.9660, while maintaining low relative errors down to 0.3798. Furthermore, the optimized sensor accurately classifies 12 distinct tactile stimuli with an accuracy reaching 99.6%. These results highlight the potential of simulation-guided structural optimization for advancing flexible EIT-based tactile sensors toward practical applications in wearable systems, robotics, and human–machine interfaces (HMIs).All data are publicly available in Edinburgh DataShare with the identifier https://doi.org/10.7488/ds/7982</p
COVID-19 vaccine hesitancy and barriers to vaccination in South Africa
Get PDFThe COVID-19 pandemic created significant social, economic and health-related challenges. The rapid development of vaccines that could help curb transmission and mortality from the virus was vital. The University of Johannesburg/Human Sciences Research Council (UJ/HSRC) COVID-19 Democracy survey was established as a cross-sectional online and telephone survey designed to provide rapid response data to inform the pandemic response. Between December 2020 and November 2021, the survey fielded questions about vaccine acceptance, hesitancy and structural barriers to vaccination. Drawing from the Strategic Advisory Group of Experts on Immunization (SAGE) Vaccine Hesitancy Determinants Matrix, this article explores the intersection of contextual and individual reasons for COVID-19 vaccine hesitancy. We find that young people, particularly those aged 18–24 years, were the most vaccine-hesitant. Also, White adults exhibited more than double the odds of hesitancy of Black African adults, yet were also much more likely to be vaccinated. The article explores this seeming paradox by analysing explanations for vaccine hesitancy as well as considering structural barriers to vaccination. Explanations for vaccine hesitancy mostly related to concerns about side effects, the effectiveness of the vaccine and distrust in the vaccine and/or government. In contrast, structural barriers, such as a lack of information about where to receive a vaccination and vaccination sites being inaccessible, may have deterred those who were broadly favourable about vaccination from accessing one. Overall, our analysis illustrates the importance of understanding health crises as more than medical problems but as fundamentally social problems. It is therefore vital that social science research informs future responses to public health emergencies.</p
Design Space Visualization and Technoeconomic Evaluation of a Batch Manufacturing Process for the Green Production of an Anti-cancer Drug (Adavosertib) Precursor
Get PDFThe development of cost-effective and sustainable manufacturing processes is a growing priority in the pharmaceutical industry, with many companies turning to computational modeling to reduce reliance on experimental campaigns. Established simulation frameworks must however support in silico pharma R&D, especially for detailed process design and optimization. This paper introduces a robust but simple and flebible modeling framework for simulating and optimizing batch manufacturing processes, applying it to the production of AZD1775 HMS (a small-molecule intermediate required for the synthesis of the experimental anti-cancer drug Adavosertib). The entire design space is mapped using high-fidelity submodels for batch reactors and liquid–liquid extraction (LLE) units. The impact of solvent selection, reagent concentration, separation solvent ratio, operating temperature, and equipment size on process viability has also been evaluated, towards identification of a cost-optimal and environmentally friendly LLE solvent system (water–acetonitrile–toluene) and process conditions. Drug development can thus be streamlined by this systematic pathway for sustainable manufacturing, achieving economic and environmental goals simultaneously
Infusing clinical knowledge into language models by subword optimisation and embedding initialisation
No full textOBJECTIVE: This study introduces a novel tokenisation methodology, K-Tokeniser, to infuse clinical knowledge into language models for clinical text processing.METHODS: Technically, at initialisation stage, K-Tokeniser populates global representations of tokens based on semantic types of domain concepts (such as drugs or diseases) from either a domain ontology like Unified Medical Language System or the training data of the task related corpus. At training or inference stage, sentence level localised context will be utilised for choosing the optimal global token representation to realise the semantic-based tokenisation. To avoid pretraining using the new tokeniser, an embedding initialisation approach is proposed to generate representations for new tokens.RESULTS: Using three transformer-based language models, a comprehensive set of experiments are conducted on four real-world datasets for evaluating K-Tokeniser in a wide range of clinical text analytics tasks including clinical concept and relation extraction, automated clinical coding, clinical phenotype identification, and clinical research article classification. Overall, our models demonstrate consistent improvements over their counterparts in all tasks. In particular, substantial improvements are observed in the automated clinical coding task with 13% increase on Micro F1 score. Furthermore, K-Tokeniser also shows significant capacities in facilitating quicker convergence of language models.CONCLUSION: Models built using K-Tokeniser have shown faster convergence. Specifically,the language models would only require 50% of the training data to achieve the best performance of the baseline tokeniser using all training data in the concept extraction task and less than 20% of the data for the automated coding task. It is worth mentioning that all these improvements require no pre-training process, making the approach generalisable. Code availability: Our full implementation is openly available at https://github.com/abulhasanbbk/K-Tokenizer.</p
AIpology:When saying sorry is the hardest string to compute
No full textApologies play an important role in trust recovery in post-conflict scenarios. As we increasingly interact with autonomous systems, HCI researchers too have discovered the power of apologies for situations where AIs or robots violated justified expectations of the humans they interact with. But are AIs the type of entity that can meaningfully apologise? Drawing on conceptions of apologies across a range of legal field, the chapter identifies requirements for robot-generated apologies that ensure not only their ethically sound deployment, but also, potentially, their recognition in law
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