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Examining well-being and cognitive function in people with long COVID and ME/CFS, and age-matched healthy controls:A case-case-control study
Get PDFBackgroundWell-being and cognitive function had not previously been compared between people with long COVID and people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Therefore, this study examined well-being and cognitive function in people with long COVID (∼16 months illness duration; n = 17) and ME/CFS (∼16 years illness duration; n = 24), versus age-matched healthy controls (n = 16).MethodsWell-being was examined using several questionnaires, namely the Health Visual Analogue Scale (VAS), Fatigue Severity Scale (FSS), post-exertional malaise (PEM), Pittsburgh Sleep Quality Index (PSQI), European Quality of Life-5 Domains (EQ-5D), MRC Dyspnoea, Self-Efficacy (SELTC), The Edinburgh Neurosymptoms Questionnaire (ENS), General Anxiety Disorder 7 (GAD-7) and Patient Health Questionnaire 9 (PHQ-9). Cognitive function was examined using Single Digit Modalities Test (SDMT), Stroop test and Trails A and B. These were delivered via a mobile application (app) built specifically for this remote data collection.ResultsThe main findings of the present investigation were that people with ME/CFS and people with long COVID were generally comparable on all well-being and cognitive function measures, but self-reported worse values for pain, fatigue, post-exertional malaise, sleep quality, general well-being in relation to mobility, usual activities, self-care, breathlessness, neurological symptoms, self-efficacy and other well-being such as anxiety and depression, compared to controls. There was no effect of group for cognitive function measures.ConclusionsThese data suggest that both people with long COVID and people with ME/CFS have similar impairment on well-being measures examined herein. Therefore, interventions that target well-being of people with ME/CFS and long COVID are required
Extrapolation Performance of Convolutional Neural Network-Based Combustion Models for Large-Eddy Simulation:Influence of Reynolds Number, Filter Kernel and Filter Size
Get PDFThe extrapolation performance of Convolutional Neural Network (CNN)-based models for Large-Eddy Simulations (LES) has been investigated in the context of turbulent premixed combustion. The study utilises a series of Direct Numerical Simulation (DNS) datasets of turbulent premixed methane/air and hydrogen/air jet flames to train the CNN models. The methane/air flames, which are characterised by increasing Reynolds numbers, are used to model the subgrid-scale flame wrinkling. The hydrogen/air flame, exhibiting complex thermodiffusive instability, is employed to test the ability of the CNN-based combustion models to predict the filtered progress variable source term. This study focuses on the influence of varying training Reynolds numbers, filter sizes, and filter kernels to evaluate the performance of the CNN models to out-of-sample conditions, i.e., not seen during training. The objectives of this study are threefold: (i) analyse the performance of CNN models at different Reynolds numbers compared to the one trained with; (ii) analyse the performance of CNN models at different filter sizes compared to the one trained with; (iii) assess the influence of using different filter kernels (i.e., Gaussian and box filter kernels) between training and testing, to emulate a posteriori applications. The results demonstrate that the CNN models show good extrapolation performance when the training Reynolds number is sufficiently high. Vice versa, when CNN models are trained on low-Reynolds-number flame data, their performance degrades as they are applied to flames with progressively higher Reynolds numbers. When these CNN models are tested on datasets with filter sizes not included in the training process, they exhibit sufficient interpolation capabilities, the extrapolation performance is less precise but still satisfactory overall. This indicates that CNN models can be effectively trained using data filtered with a limited range of filter sizes and then successfully applied across a broader spectrum of filter sizes. Furthermore, when CNNs trained on box-filtered data are applied to Gaussian-filtered data, or vice versa, the models perform well for smaller filter sizes. However, as the filter size increases, the accuracy of the predictions diminishes. Interestingly, increasing the quantity of training data does not significantly enhance model performance. Yet, when training data are distributed with greater weighting towards larger filter sizes, the model’s overall performance improves. This suggests that the strategic selection and weighting of training data can lead to more robust generalization across different filter conditions.</p
The generic Markov CoHA is not spherically generated
Get PDFAbstract: Let be the Markov quiver, and let be an infinitely mutable potential for . We calculate some low degree refined BPS invariants for the resulting Jacobi algebra, and use them to show that the critical cohomological Hall algebra is not necessarily spherically generated, and is not independent of the choice of infinitely mutable potential . This leads to a counterexample to a conjecture of Gaiotto, Grygoryev and Li \cite[§2.1]{GGL}, but also suggestions for how to modify it. In the case of generic cubic , we discuss a way to modify the conjecture, by excluding the non-spherical part via the decomposition of according to the characters of a discrete symmetry group
Adsorption Column Performance Analysis for Volatile Organic Compound (VOC) Emissions Abatement in the Pharma Industry
Get PDFVolatile Organic Compounds (VOCs) are essential for primary pharmaceutical manufacturing. Their permissible emission levels are strictly regulated due to their toxic effects both on human health and the environment. Activated carbon adsorption columns are used in industry to treat VOC gaseous waste streams from industrial plants, but their process efficiency suffers from quick and unpredictable saturation of the adsorbent material. This study presents the application of a validated, non-isothermal, multicomponent adsorption model using the Langmuir Isotherm and the Linear Driving Force model to examine multicomponent VOC mixture breakthrough. Specifically, three binary mixtures (hexane–acetone, hexane–dichloromethane, hexane–toluene) are simulated for four different bed lengths (0.25, 0.50, 0.75, 1 m) and six different superficial velocities (0.1, 0.2, 0.3, 0.5, 0.7, 0.9 m s−1). Key breakthrough metrics reveal preferential adsorption of acetone and toluene over hexane, and hexane over dichloromethane, as well as breakthrough onset patterns. Temperature peaks are moderate while pressure drops increase for longer column lengths and higher flow rates. A new breakthrough onset metric is introduced, paving the way to improved operating regimes for more efficient industrial VOC capture bed utilisation via altering multicomponent mixture composition, feed flowrate, and column length
Aortic valve replacement in patients with asymptomatic severe aortic stenosis: the devil is in the detail
Get PDFFor most patients with severe but asymptomatic aortic stenosis, current guidelines favor a conservative approach, with close surveillance for the development of symptoms, an unexplained decline in left ventricular systolic function or rapid hemodynamic progression. However, recent randomized data has reignited debate, including calls for these guidelines to be revised and aortic valve replacement (AVR) recommended for all patients with severe aortic stenosis, irrespective of symptoms. Before implementing any change, it is important that we carefully review the evidence, particularly that provided by the 4 recent randomized clinical trials. The Randomized Comparison of Early Surgery versus Conventional Treatment in Very Severe Aortic Stenosis (RECOVERY) and Aortic Valve Replacement Versus Conservative Treatment in Asymptomatic Severe Aortic Stenosis (AVATAR) trials both compared a conventional strategy of clinical surveillance with surgical AVR. In the RECOVERY trial, patients randomized to surgical AVR had a lower incidence of peri-operative or cardiovascular death after a median of 6 years. Likewise, in the AVATAR trial, surgical AVR reduced the risk of death, myocardial infarction, stroke, or heart failure hospitalization at both 3 and 5 years. Indeed, at 5 years, there was a lower risk of all-cause, but not cardiovascular, death. While these data are at face value compelling, it is important to acknowledge that both the RECOVERY and AVATAR trials were small and recruited highly selected populations, who were relatively young (mean age 64 and 67 years, respectively), at low-risk and with high transvalvular gradients.<br/
Tailoring cardiovascular risk prediction to females
Get PDFAtherosclerotic cardiovascular disease (ASCVD) is one of the leading causes of morbidity and mortality in females worldwide. In this review, we provide insights into how sex differences may affect traditional risk factors associated with ASCVD, and give an overview of non-traditional risk factors that have the potential to enhance cardiovascular risk prediction in females. We review clinically applied cardiovascular risk estimation systems discussing the integration of promising risk factors within these systems. We also explore the role of novel approaches and future directions to refine primary prevention of ASCVD in females. The development of ASCVD varies by sex and age, therefore cardiovascular risk estimation systems should incorporate both sex and age interactions with risk factors to improve ASCVD risk estimates. As the incidence of non-ASCVD (such as heart failure and arrhythmias) in females continue to rise, it is crucial to adopt a more holistic approach to risk assessment that extends beyond ASCVD outcomes. This review highlights the need for further studies on female-prevalent diseases and female-specific factors that may refine cardiovascular risk estimation in young females. Raising awareness is crucial to ensure studies include individuals from deprived areas and ethnic minorities, as more insights on the intersection between sex and social determinants of health will enhance understanding of the underlying mechanisms of ASCVD risk prevention in females. And finally, taking steps to improve and standardise data of female-specific risk factors throughout a female's life course could improve preventive cardiovascular care for females
Investigating age-related differences in semantic control mechanisms involved in creative cognition
Get PDFCreative thinking is a complex higher-order ability that draws on multiple cognitive systems. However, the contribution of specific semantic control processes to creativity remains unclear. The current study had two goals: First, we investigated how individual differences in semantic knowledge and control contribute to divergent and convergent styles of creative thinking, beyond the involvement of domain-general executive functions. Second, we explored whether there were age-related differences in semantic and executive abilities, and if these differences influenced the ability to think creatively. Specifically, we examined the role of the two components of semantic control: controlled retrieval and semantic selection. In our study, 63 younger adults and 64 older adults completed semantic, executive, and creative thinking measures. Younger adults demonstrated better executive functioning, while older adults exhibited superior semantic knowledge, controlled retrieval, and convergent thinking abilities. Crucially, there were no age differences across several divergent thinking metrics: automated originality scoring, human ratings or uniqueness. Regression analyses indicated that semantic knowledge and updating executive ability influenced convergent thinking abilities across both age groups. In contrast, semantic control abilities were predictive of divergent thinking skills, but only in the younger group. Our results emphasize the key role of the semantic system in creative thought, and, critically, indicate that divergent and convergent thinking may rely on different aspects of semantic cognition. Moreover, the recruitment of these abilities varies across the lifespan, in line with increased knowledge reserves and declines in executive control seen in older adults
The role of regional demand in pathways of agro-industrialization:Evidence from small maize milling firms in Tanzania
No full textThe growth of regional demand in food chains is often assumed to offer particular opportunities to small scale agro-processing firms in Africa, promising a route to a more inclusive pathway of industrialization for the continent. The aim of the article is to interrogate this assumption by providing a critical assessment of the impacts of Kenya’s growing demand for maize flour on small maize milling firms in Tanzania. It uses an in-depth survey with small millers, interviews with actors in the value chain, combined with secondary manufacturing and trade data, to make a theoretical and empirical contribution to these debates. It offers an expanded conceptual framework to examine the direct and indirect temporal, spatial and political impacts of regional demand on market structure and competition. To conclude it draws out the implications of focussing on regional demand for industrial policy, resilience and economic inclusion
Statistical learning to identify and characterise neurodevelopmental outcomes at 2 years in babies born preterm: model development and validation using population-level data from England and Wales
No full textBACKGROUND: Children born preterm face elevated risks of neurodevelopmental impairments across domains. Prior studies have relied on expert-imposed typologies within single domains. This study applies statistical learning to a national database to identify transdomain clusters and their maternal and neonatal predictors.METHODS: Latent class analysis (LCA) was used to derive transdomain clusters from parent-reported visual, auditory, neuromotor, and communication impairments in preterm-born children at two years corrected age using the UK National Neonatal Research Database data (N = 27,261). Replication was conducted in an independent sample from Wales (N = 975). Clusters were clinically validated using cerebral palsy diagnosis, Bayley Scales of Infant and Toddler Development (3rd edition), and global neurodevelopmental delay. Random forest identified cluster-specific and shared predictors.FINDINGS: Four homogeneous clusters were derived (silhouette score = 0.71) and replicated in Wales with high balanced accuracy (93%): (1) typically developing (84.8%), (2) communication impairments (8.4%), (3) neuro-motor impairments (4.1%), and (4) multiple neuro-morbidity (2.7%). Clusters had high clinical validity and were distinguishable by shared and cluster-specific predictors. Neonatal brain injuries were most predictive of neuro-motor and multiple neuro-morbidity clusters. Birthweight, gestational age, socio-economic deprivation, and sex were stronger predictors of the communication cluster than preterm co-morbidities.INTERPRETATION: This study provides first evidence of the transdomain nature of neurodevelopmental impairments after preterm birth using LCA. The finding that socio-demographic and perinatal factors rather than co-morbidities increase the risk of communication impairment highlights the importance of environmental modification alongside clinical interventions. Applying data-driven approaches to routinely collected data may offer a cost-effective way to stratify at-risk children and inform targeted support strategies.FUNDING: UKRI Medical Research Council.</p
In-situ tailored repair of thermoplastic composites by overprinting of continuous carbon fibre reinforced polymer filaments
No full textThis study proposes an efficient and convenient in-situ repair approach for damaged thermoplastic composites by 3D overprinting. Continuous carbon fibre reinforced polyphenylene sulphide (CCF/PPS) filament was employed to overprint repair patches onto conventionally manufactured woven polyamide 6 (PA6) laminates. The process window was optimised through thermal analysis of the target polymers, with a temperature range spanning from the melting point of PA6 (216.8°C) to the crystallisation point of PPS (227.6°C). A nominated interface temperature of 220 °C was evidenced as the most effective, raising the tensile strength of repaired specimens to 205.9 MPa, an improvement of 35 % compared to open-hole laminates and a recovery of nearly 50 % of the strength lost relative to undamaged specimens. Additionally, bio-inspired spider web printing paths were tailored for static indention loading, restoring 94 % of the original laminate strength while reducing material usage by 67 % compared to conventional unidirectional patches. This novel overprinting approach offers a highly efficient and flexible solution for repairing damaged thermoplastic structures
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