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Evaluating the effectiveness of age simulation gloves
No full textObjective: This study evaluated the effectiveness of age simulation gloves in replicating age 16 related declines in hand function.
Background: Commercially available age simulation gloves are increasingly used. However, their ability to replicate age-related physical decline remains largely unverified.
Method: Twenty healthy adults (mean age: 26.8 years) completed assessments under three conditions: with no glove, using a Cambridge Simulation Glove (CG), and using the CG combined with tremor simulation (TS). Grip and pinch strength (Biometrics electronic dynamometer and pinch meter), gross (Box and Block Test) and fine (Grooved Pegboard Test) motor dexterity, hand function (Southampton Hand Assessment Procedure), postural tremor (MetaMotionC accelerometers), tactile sensitivity (Semmes-Weinstein Monofilaments), and usability (System Usability Scale) were evaluated. Performance with glove conditions was compared against normative data of older adults if available.
Results: Grip strength and gross and fine motor dexterity declined in both glove conditions, aligning with normative ageing values. However, pinch strength and functional performance did not show consistent replication of normative ageing. Usability scores were below acceptable thresholds for both gloves. While the addition of tremor simulation increased peak frequency consistent with ageing, it did not replicate the rise in amplitude.
Conclusion: Overall, the gloves partially replicated age-related hand function decline. Improvements in pinch force, tremor fidelity, and ergonomic design are needed to enhance realism and usability in educational, clinical, and design contexts.Application: Findings can guide in selecting or improving age simulation tolls to better support age inclusive product development and assessment
Genome-wide analysis exploring mechanisms used by Shigella sonnei to survive long-term nutrient starvation
No full textShigella is a major cause of severe diarrhea, with Shigella flexneri and Shigella sonnei accounting for over 90% of infections. Progressive economic growth
worldwide correlates with the replacement of S. flexneri by S. sonnei as the dominant cause of shigellosis. The basis of the epidemiological shift remains unclear, but it
highlights the urgent need for further studies on the increasingly prevalent, but less well-studied, S. sonnei. Here, we investigated whether S. sonnei is better equipped to survive nutrient starvation, a crucial condition for persistence both outside the host and
within the colonic lumen. S. sonnei exhibited greater survival under long-term nutrient starvation (LTNS) than S. flexneri, rapidly activating survival mechanisms. We interrogated the genome of S. sonnei using transposon-directed insertion-site sequencing (TraDIS), revealing that metabolic pathways (ATP, nucleotide, and amino acid synthesis) and envelope homeostasis systems (e.g., Tol-Pal complex, Bam complex, peptidoglycan recycling, and RpoE stress response) are conditionally essential for LTNS. TraDIS findings were validated by non-competitive and competitive survival of wild-type and deletion
mutant strains. We compared the homology of conditionally essential genes between S. sonnei and S. flexneri to identify genes potentially involved in differential LTNS survival between the species. However, S. sonnei strains in which a single gene was replaced
with the S. !exneri allele showed wild-type survival phenotypes. This suggests that the divergent survival of these two species may be more complex than a monogenic difference. Together, these data define the molecular adaptations of starvation resistance
in S. sonnei and provide insights into its epidemiological dominance in high-income countries
Statistical methods for predicting the presence of Salmonella Typhi in wastewater samples at Asante Akyem Agogo, Ghana
No full textBackground
Monitoring wastewater is vital for tracking typhoid fever in endemic areas. This study evaluated the performance of both spatial and non-spatial models in predicting Salmonella Typhi detection in wastewater from the Asante Akim North district in Ghana and identified key environmental risk factors.
Methods
We collected wastewater samples of Moore swabs at 40 sites across Agogo, Juansa, Hwidiem, and Domeabra over a period of 27 months. Multiplex PCR was used to detect Salmonella Typhi, focusing on the ttr, tviB, and staG genes. An Aquaprobe AP-2000 was also used to measure different physicochemical factors, such as pH, temperature, dissolved oxygen, and salinity. Three non-spatial models, namely Generalized Estimating Equations (Logistic), Mixed-Effects Models, and Random Forest, as well as four spatial models, including Bayesian Generalized Additive Models (GAM) and Spatial Generalized Linear Mixed Models (GLMM), were fitted to the wastewater dataset. Model fitting was done using 5-fold cross-validation, stratified by site. Model performance was evaluated using accuracy, sensitivity, and specificity. We also used SHapley Additive exPlanations (SHAP) analysis to find the most important predictors.
Findings
In general, 44.13% of the samples tested positive for S. Typhi. Detection was much higher during wet seasons (50.17% vs. 35.11%; p < 0.001), with fast flows (64.45%), and in channels that were 1–2 meters wide (58.70%). Positive samples had relatively higher pH (7.46 vs. 7.40; p < 0.001), dissolved oxygen (46.97% vs. 36.77%; p < 0.001), and rainfall (3.92mm vs. 3.30mm; p = 0.022). In comparing both non-spatial and spatial models, the non-spatial Random Forest model demonstrated the highest performance with an accuracy of 0.993, sensitivity of 0.997, and specificity of 0.989. In the SHAP analysis of the preferred non-spatial random forest model, it was found that pH, season, dissolved oxygen, positivity from the previous month, and channel width were identified as the best predictors.
Conclusion
S. Typhi detection is influenced by wastewater physicochemical properties, with pH, seasonal rainfall, and hydraulic conditions being the most significant. The non-spatial random forest model significantly outperforms both spatial and other non-spatial statistical methods
The effect of Biktarvy (B/F/TAF) on whole‐body insulin sensitivity, lipid and endocrine profile in healthy volunteers
No full textBackground
Biktarvy is a single-tablet anti-retroviral regimen composed of a second-generation integrase-inhibitor (Bictegravir), in combination with tenofovir alafenamide and emtricitabine. Biktarvy has been shown to be highly effective in achieving and sustaining viral suppression. However, several studies have highlighted altered glycaemic control in individuals switching to Biktarvy from other regimens. The aim of this study was to quantify glucose disposal rates (GDR) in HIV-seronegative healthy volunteers following the administration of Biktarvy.
Method
A 72 day, open-label, two-arm, crossover, single-centre study. Participants were randomized 1:1 to either start 28 days of Biktarvy followed by 44 days without treatment (Group 1), or no treatment for 43 days followed by Biktarvy treatment for 28 days (Group 2). A hyper-insulinaemic-euglycaemic clamp was carried out at days 1, 28 and 72 with a 14-day washout period following the second clamp. Statistical assessments of change in estimated GDR were carried out using Wilcoxon signed-rank test (within-group) and Two-sample Wilcoxon rank-sum (Mann–Whitney) test (between-group). The primary study outcome was change from baseline in total body glucose disposal by euglycaemic clamp method following 28 days treatment.
Results
A total of 18 volunteers completed the study, with 11 in group 1 and seven in group 2. Within Group 1 the mean GDR was 7.52 mg/kg/min (SD 3.67) at baseline versus 8.50 mg/kg/min (SD 3.72) at day 28 (p = 0.32) and a mean percentage change of −13% (0.98). Within Group 2, the mean GDR was 6.54 mg/kg/min (SD 1.86) on day 28 versus 5.85 mg/kg/min (2.67) on day 72 (p-value = 0.38) mean percentage change −11% (−0.69). There was no statistically significant change between the groups at baseline (Mean 7.52 [SD 3.67] vs Mean 6.11 [SD 2.94], p-value = 0.31), at Day 28 (Mean 8.50 [SD 3.72] vs Mean 6.54 [SD 1.86], p-value = 0.27), or at Day 72 (Mean 9.65 [SD 5.07] vs Mean 5.85 [SD 2.67], p-value = 0.13). GDR on the final day of administration of bictegravir/emtricitabine/tenofovir alafenamide (B/F/TAF) was not significantly different (p = 0.104) between Group 1 Day 28 (6.64 [interquartile range (IQR) 5.83, 10.59] mg/kg/min) and Group 2 day 72 (0.28 [IQR 3.99, 8.79] mg/kg/min).
Conclusion
Treatment with Biktarvy for 28 days was not associated with a statistically significant impact on total body insulin sensitivity as measured using a hyper-insulinaemic-euglycaemic clamp method. However, long-term data on the metabolic effects of Biktarvy are needed
Photonic-chemostat engineering for efficient continuous cultivation of cyanobacteria
No full textOptimising continuous phototrophic cultivation remains a major challenge for scalable, energy-efficient cyanobacterial bioprocesses. Here, we combine controlled photophysiology, long-term continuous experimentation, multi-parameter analysis, and batch-derived Monod kinetic modelling to define a precise operational window for Synechocystis sp. PCC
6803 under flat-plate photobioreactor (FP-PBR) illumination. Using a fully calibrated FP-PBR platform, we first quantified intrinsic growth limits (µmax
= 0.081–0.118 day−1) across low, moderate, and high irradiance regimes, establishing the illumination-driven growth ceilings that constrain downstream continuous operation. Guided by these kinetic boundaries, continuous cultivation demonstrated that productive steady-state growth emerges only within a narrow regime governed by light intensity (500–700 µmol photons m−2 s−1 ), temperature (32–34 °C), and dilution rate (0.12–0.14 day−1). Single-parameter and 3D interaction analyses revealed strong coupling between photonic supply, thermal sensitivity, and hydraulic residence time, while multi-factor modelling captured these nonlinear constraints and accurately predicted washout boundaries. Translating these insights into sustainability metrics, the optimised regime supports 0.07–0.125 g L−1 day−1 of biomass productivity, equivalent to 8.4–15.0 g biomass day−1 and 176–315 kJ
day−1 of chemical energy in a 120 L mini-pilot system. Stoichiometric analysis indicates this corresponds to 15.6–27.6 g CO2 day−1 sequestered, demonstrating measurable environmental benefit even at a small scale. Together, these results provide a mechanistically grounded, kinetically constrained framework for designing inherently efficient, low waste, and model-predictive cyanobacterial photobioprocesses aligned with green chemistry and future carbon-neutral
manufacturing
Emulating non-LTE ultraviolet spectra in late type stars with neural networks
No full textMagnetic activity in late-type stars drives variability in their ultraviolet output, with significant implications for stellar physics and exoplanetary atmospheres. Accurate modelling of this variability requires non-local thermodynamic equilibrium (NLTE) radiative transfer, but direct calculations are computationally intensive, particularly when applied to three-dimensional magnetohydrodynamic (MHD) simulations. In this thesis, I develop and apply neural network emulators to reproduce NLTE ultraviolet spectra of cool stars, trained on MURaM simulations spanning quiet and magnetically active stellar conditions. The emulators achieve high accuracy across both G- and K-type stars, capturing centre-to-limb variation, facular contrasts, and the spectra of both quiet and active atmospheres, while reducing computational costs by orders of magnitude. Analysis with integrated gradients identifies the temperature stratification as a key contributor to the network predictions, linking the emulation process to the underlying physics. Overall, this work shows that neural networks provide a practical and efficient method for modelling stellar NLTE spectra, enabling systematic exploration of stellar variability and its observational consequences.Open Acces
Short-term ensemble prediction of convective cells using data-driven methods
No full textVery short-term forecasting (nowcasting) of convective rainfall remains a challenge due to the highly localised and intense nature of convective storms. Capturing swift changes in convective cells continues to stretch operational systems. This thesis introduces an ensemble nowcasting framework for predicting key cell properties, namely size, major axis length and reflectivity, using radar data processed by an enhanced TITAN algorithm.
An initial assessment exposes the limitations of existing field-based methods for isolated, fast growing storms and motivates the development of an analogue-based approach. A basic analogue model is first constructed and then refined by applying adaptive spatial thresholds to the process of selecting analogues. Further analysis demonstrates that accurate prediction of a cell’s track type is essential for modelling its temporal evolution.
To forecast track type, a range of machine learning classifiers are evaluated, with random forests being the most effective. Track type predictions from these classifiers are then incorpo- rated into the analogue framework so that only historical cells sharing the predicted category are retained as analogues. This combined method improves the reliability of categorical forecasts, enhances the accuracy of deterministic forecasts and yields ensembles with better calibration than those based on simple persistence or unfiltered analogue methods.
The resulting system strikes a practical balance between forecast skill, calibration and computational efficiency, making it well suited for urban surface-water flooding applications. Future work will develop automated parameter-tuning routines, integrate higher-resolution environmental predictors and reconstruct full gridded rainfall fields for hydrological modelling.Open Acces
A structural model of interbank network formation and contagion
No full textWe study the equilibrium relationship between interbank exposures and bank default risk: how exposures affect risk, and how banks account for this when forming the
exposures network. We leverage novel data on aggregate interbank exposures across multiple types of financial instrument. We find that contagion is material (risk would be 0.2% higher if exposures increased by 1%) but that banks account for this in equilibrium (risk would be 10% higher if they did not). We also find systematic heterogeneity in contagion based on the characteristics of the banks involved, with implications for the identification of systemically important banks and regulation
Human-robot cooperation for teleoperation in robotic surgery
No full textThis thesis investigates data-driven methods for implementing human-robot interaction in the da Vinci Research Kit (dVRK) platform, with a particular focus on skill modeling, integration of autonomy in the surgical workflow, and real-time performance assessment. As robotic surgery becomes increasingly prevalent, there is a growing need for intelligent systems that can both assist human operators and also understand and adapt to their actions in real-time. This work addresses this challenge by developing a high performing control framework through a sequence of studies that span through the different types of controllers in the spectrum of teleoperative human-robot cooperation and skill assessment: shared control, supervisory control, and assistive control.
Early chapters focus on shared control and supervisory control frameworks, introducing novel methods for learning bimanual surgical trajectories from demonstrations, enabling robots to generalise motion patterns from expert behavior.
The subsequent work explores control paradigms that modulate robotic assistance in a data-driven approach, balancing autonomy and human input to achieve both higher performances and lower perceived workload. Objective evaluations demonstrate that adaptive assistance can benefit novice users without significantly impeding expert performance.
Building upon these foundations, the last chapter shifts focus to real-time surgical skill assessment using deep learning models trained on multimodal inputs. These systems are designed to provide frame-level predictions of technical skill, enabling continuous feedback during task execution.
Across all studies, this thesis emphasises the integration of expert data, task structure, and real-time capabilities to build responsive surgical systems that can fit into the surgical workflow. The contributions are validated with user studies and supported by extensive experimentation on datasets.
User studies with participants show improved performance and perceived workload throughout all experiments when using the proposed control systems. Together, these contributions advance the state-of-the-art in human-robot cooperation and provide a foundation for intelligent systems in surgical assistance.Open Acces
Physical activity and the risk of cataract and age-related macular degeneration: a systematic review and meta-analysis of cohort studies
No full textBackground: Physical activity has been associated with a lower risk of cataract and age-related macular degeneration in some studies; however, the available evidence has not been fully consistent. We conducted a meta-analysis of cohort studies to clarify the association between physical activity and cataract and age-related macular degeneration.
Methods: The PubMed and Embase databases were searched for relevant prospective studies up to September 18, 2025. Random effects models were used to calculate summary relative risks (RRs) and 95% confidence intervals (CIs) for the association between physical activity and the risk of cataract and age-related macular degeneration. World Cancer Research Fund (WCRF) criteria was used to evaluate the strength of evidence.
Results: A total of 10 cohort studies (8 publications) with 163,065 cases and 1,914,137 participants were included in the analysis of physical activity and cataract and 14 cohort studies (9 publications) with 17,653 cases and 566,895 participants were included in the analysis of age-related macular degeneration. The summary RR for high vs. low physical activity and cataract was 0.90 (95% CI: 0.86-0.94, I2=74%, n=10) and for age-related macular degeneration was 0.92 (95% CI: 0.84-1.01, I2=60%, n=14). The summary RR per 20 MET-hours/week increment in leisure-time physical activity was 0.91 (0.84-0.99, I2=66%, n=3) for cataract and 0.92 (0.74-1.13, I2=48%, n=3) for age-related macular degeneration, and there was no indication of nonlinear dose-response relationships (pnonlinearity=0.32 and pnonlinearity=0.34, respectively). There was no indication of publication bias. The evidence (judging the likelihood of causality) using WCRF criteria was judged as probable for cataract and limited-no conclusion for age-related macular degeneration.
Conclusion: This meta-analysis provides further support for an inverse association between physical activity and risk of cataract, but an association with age-related macular degeneration was less evident. Any further studies should clarify the dose-response relationship and associations between different domains of physical activity in relation to these outcomes. These findings support public health recommendations to the general population to be physically active