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Global, regional, and national estimates of tuberculosis incidence averted by eliminating undernutrition in adults: a modelling study
No full textBackground
Current efforts to reduce global tuberculosis incidence have proved insufficient, highlighting that urgent
action is needed to address underlying modifiable risk factors such as undernutrition. We aimed to
estimate the global impact of eliminating undernutrition on tuberculosis incidence among adults
accounting for varying nutritional status by country, sex, and age, in addition to incorporating the
continuous, non-linear relationship between body mass index (BMI) and tuberculosis risk.
Methods
We used a continuous risk framework to consider the population-level implications of BMI distributions
for tuberculosis incidence for those aged ≥15 years. We generated BMI distributions for each country,
sex, and age group applying a bilinear model for the logarithmic relative risk of tuberculosis incidence at
different BMI values. We assessed the impact of eliminating moderate/severe undernutrition
(BMI<17kg/m2) or all undernutrition (BMI<18.5kg/m2) on tuberculosis incidence by constructing
counterfactual BMI distributions that redistributed those with low BMI to higher BMI, proportional to the
remaining density.
Findings
We estimated that eliminating moderate/severe undernutrition could avert 1.4 million (95%UI, 1.1-1.7)
tuberculosis episodes globally, representing 14.6% (12.6-16.6) of global adult incidence, while
eliminating all undernutrition could avert 2.3 million (1.8-2.7) episodes, a reduction of 23.7% (20.9-26.5).
The largest proportional reductions in tuberculosis incidence could be achieved by eliminating
undernutrition in the African, South-East Asian, and Eastern Mediterranean regions; females; and
adolescent or elderly adults.
Interpretation
Around a quarter of global tuberculosis incidence in adults could be averted by eliminating
undernutrition, approximately two and a half times higher than current estimates. These findings highlight
the urgent need to scale up population-level nutritional interventions, which may have myriad social and
health benefits beyond tuberculosis, alongside research to determine optimal implementation strategies
and impacts.
Funding
No specific fundin
A statistical model for lung function trajectory and mortality in patients with fibrotic interstitial lung disease
No full textBackground. Fibrotic interstitial lung diseases (ILDs) cause loss of forced vital capacity (FVC) and increased risk of death over time. Most clinical trials aim to slow FVC decline and reduce mortality. However, the association of lower FVC with higher mortality will bias simple estimates of differences in FVC progression between groups. Therefore, both the time-dependent decline in FVC and increase in mortality should be jointly modelled.
Methods. We developed a Bayesian, joint mixed effects disease progression model (DPM), using minimally informative prior distributions, for FVC trajectory and the hazard for ILD-related mortality over time. This model minimizes bias due to mortality in estimating differences in the rate of FVC decline and is suitable for use when characterizing populations or in estimating a treatment effect in a clinical trial. The DPM was applied to individual patient data from prospective cohort studies of fibrotic ILD.
Results. The DPM yields a higher estimated rate of FVC decline (6.0 vs 4.7%/year) and a more precise fit than a linear mixed model of FVC alone, and replicates the non-linear pattern in the observed data. By modelling the full FVC trajectory rather than only the change from baseline at a given time point, the DPM increases the information from each patient and reduces both the time to information and the effect of variability in baseline FVC measurements on the estimation of treatment effects.
Conclusions. The joint DPM provides an integrated approach to minimizing bias in the estimation of treatment effects in clinical trials in fibrotic ILDs
Engaging the public with antimicrobial resistance through social media videos – a content analysis study
No full textIntroduction: YouTube, the dominant global video-sharing social media platform, has created new opportunities to provide regulated health content directly with the public, including on broader public health threats such as antimicrobial resistance (AMR). Little is known about the most effective strategy with which to engage the public with online AMR content.
Method: This study comprehensively evaluated the top two-hundred viewed YouTube videos on AMR by extracting data on video characteristics, narratives and quality, and explored factors associated with viewer engagement through proxy analytics (views, likes and comments).
Results: We found that content focused upon the mechanisms of AMR and antibiotics were most viewed, yet engaging videos do not necessarily convey high-quality information. Videos on internet media and non-medical channels were more popular.
Discussion: The study calls for more strategic production of engaging videos on AMR. Global platforms should strive to facilitate audience in accessing reliable health information
Bottom-up emissions and energy assessment for decommissioning offshore platform structures in the North Sea: Brent and Tern case studies
No full textDecommissioning of offshore energy infrastructure in the North Sea presents a significant environmental challenge for operators, the supply chain, government agencies, and society. Considering the UK's 2050 net zero target, it is essential to understand the offshore oil and gas (O&G) decommissioning sector's contribution to overall emissions. Such insight is crucial for evaluating decommissioning projects and informing policy development aimed at reducing emissions and achieving net zero goals. This study proposes a bottom-up emissions and energy assessment (EEA) approach for decommissioning offshore O&G platform topside and jacket structures. The approach quantifies the greenhouse gas (GHG) emissions and energy demand associated with offshore and onshore activities during the decommissioning phase. It leverages detailed, site-specific operational data to improve the precision and reliability of these assessments and is underpinned by the latest available data from the North Sea O&G decommissioning industry. The approach is validated through application to decommissioning of platform topsides and jackets in the Brent and Tern fields. Numerical comparisons reveal acceptable differences between the energy demand and CO2 emission estimates from this study and those reported in North Sea industry reports. The study also presents insights into the reliable EEA of decommissioning projects
Scientific machine learning for modelling and optimisation of nonlinear partial differential equations in fluids
No full textThe prediction and control of flow and acoustic systems present a fundamental challenge due to the high-dimensional, nonlinear and chaotic dynamics with multi-physics interactions. This thesis proposes scientific machine learning methods for modelling these systems, reconstruction of their full state from partial observations, and their optimisation. First, we model thermoacoustic dynamics from synthetic sensor data. We develop Galerkin neural networks, which learn from data, whilst being constrained with prior knowledge of the acoustics by (i) employing periodic activations; (ii) a physics-informed loss in the training; and (iii) a hard-constrained architecture in a physically-motivated solution space. We test the network on a prototypical nonlinear time-delayed model, i.e., the Rijke tube, and a higher-fidelity model. We accurately reconstruct the velocity from only pressure measurements. Second, we infer gradients (sensitivities) from data with the adjoint method. The parameter-aware echo state network learns the dynamics of nonlinear regimes with varying parameters. We derive its adjoint, and infer the climate sensitivity of the chaotic Lorenz system to the system's parameters. The time-delayed thermoacoustic echo state network improves generalisability on the Rijke tube, and accurately infers the adjoint sensitivities of the acoustic energy with respect to the flame parameters and initial conditions, whilst identifying local bifurcations. We suppress a nonlinear oscillation via gradient-based optimisation. Third, we enable the active control of chaotic systems with partial observability. Data-assimilated model-informed reinforcement learning integrates (i) low-order models to approximate high-dimensional dynamics; (ii) sequential data assimilation to correct the model prediction when observations become available; and (iii) an off-policy actor-critic reinforcement learning algorithm to discover an optimal control strategy. We test the framework on the chaotic solutions of the Kuramoto-Sivashinsky equation. We estimate its full state with (i) a physics-based coarse-grained model; and (ii) the control-aware echo state network to stabilise its chaotic dynamics.Open Acces
On compatible finite elements for atmosphere modelling
No full textSimulations of atmospheric dynamics are the foundation of numerical weather prediction and climate projections. It is crucial for numerical models of large-scale geophysical flows to capture the relevant balances exactly. Compatible finite elements have been applied successfully for geophysical flow simulations, allowing for a variety of underlying mesh structures and higher-order approximations, while maintaining desirable structure-preserving properties.
The presence of orography has long been a challenge in numerical weather prediction. Pressure gradient errors, for instance, that occur in finite-difference models as a result of using sigma-coordinates, appear also in compatible finite element discretisations on terrain-following meshes. Here, they are a consequence of the Piola transform that introduces a vertical component into the horizontal part of the velocity space. While the natural velocity space on flat meshes decomposes into horizontal and vertical parts, this is no longer the case in the presence of orography. We propose a finite element space for the fluid velocity that retains the split into horizontal and vertical components. By reformulating the discrete finite element problem, we show that this space is suitable for approximations of the governing compressible equations.
Finding a time-stepping scheme for compatible finite element atmosphere models that allows for large stable time-steps has remained an open challenge. The choice of a time-stepping scheme is critical to balancing computational efficiency, numerical stability, and accuracy in atmosphere models. The multi-scale nature of atmospheric flows poses significant challenges, with the fast wave dynamics often restricting maximum stable time-steps. We propose a scalable semi-implicit projection time-stepping scheme based on a splitting of advection and wave dynamics with time-steps constrained by the advection step only. Starting with the shallow-water equations, we show the numerical robustness of this scheme. In a second step, we provide a formulation for the full compressible equations.Open Acces
Berry curvature of low-energy excitons in rhombohedral graphene
No full textWe investigate low-energy excitons in rhombohedral pentalayer graphene encapsulated by hexagonal boron
nitride (hBN/R5G/hBN), focusing on the regime at the experimental twist angle θ = 0.77◦ and with an applied
electric field. We introduce a new low-energy two-band model of rhombohedral graphene that captures the band structure more accurately than previous models while keeping the number of parameters low. Using this model, we show that the centres of the exciton Wannier functions are displaced from the moiré unit cell origin by a quantized amount—they are instead localized at C3-symmetric points on the boundary. We also find that the exciton shift is electrically tunable: by varying the electric field strength, the exciton Wannier center can be exchanged between inequivalent corners of the moiré unit cell. Our results suggest the possibility of detecting excitonic corner or edge modes, as well as novel excitonic crystal defect responses in hBN/R5G/hBN. Lastly, we find that the excitons in hBN/R5G/hBN inherit excitonic Berry curvature from the underlying electronic bands, enriching their semiclassical transport properties. Our results position rhombohedral graphene as a compelling tunable platform for probing exciton topology in moiré materials
Cooperation after the algorithm: designing human-AI coexistence beyond the illusion of collaboration
No full textPreprint versio
Optimal water quality control in dynamically adaptive distribution networks
No full textWater utilities face significant challenges in maintaining water quality across distribution networks, with current practices being largely manual and reactive. This thesis investigates control strategies to proactively manage water quality within the operational framework of dynamically adaptive networks. Specifically, it focuses on applying optimization methods to advance the modelling and control of discolouration risk and disinfectant residuals.
To control discolouration risk, this thesis formulates an optimization model to maximize network self-cleaning flow velocities. Flow velocities are controlled by jointly optimizing the placement and settings of pressure control and automatic flushing valves. A heuristic algorithm based on convex optimization is developed to solve the resulting mixed-integer nonlinear program. Simulation results using a large-scale operational network in the UK indicate potential self-cleaning improvements of up to 20%. The optimization model is then extended to coordinate self-cleaning with existing pressure management objectives over a daily control horizon. Distributed optimization techniques are leveraged to enable near real-time valve scheduling in complex, large-scale networks.
To better manage disinfectant residuals, this thesis advances water quality modelling through a computationally efficient Bayesian parameter estimation framework. High-resolution water quality data from a real-world distribution network is leveraged to formulate and solve a Bayesian inverse problem, quantifying disinfectant decay uncertainty under varying sensor noise levels. The resulting posterior distributions of decay parameters enable probabilistic water quality predictions and support the formulation of robust optimization problems for control.
This thesis proposes a holistic water quality control strategy that combines the new self-cleaning objective and probabilistic disinfectant decay modelling with existing pressure control schemes. The resulting joint quantity-quality optimization model demonstrates the potential for proactive water quality control in dynamically adaptive network operations. The thesis concludes by outlining a solution framework for the joint optimization model, methods to incorporate uncertainty into the model, and practical considerations for implementation in real-world settings.Open Acces
Platelet-inspired microparticles for targeted drug delivery to the atherosclerotic plaque
No full textAtherosclerosis is a leading cause of coronary heart disease, characterised by chronic arterial inflammation and the development of occlusive lipid plaques. Current treatments rely primarily on systemic administration of lipid-lowering agents and antiplatelet drugs, but these are often associated with significant side effects, highlighting the need for more specific and localised therapeutic approaches. Platelets naturally attach to vessel injuries. This thesis presents the development of a gelatin microparticle (GMP) platform designed to mimic platelet adhesion and target atherosclerotic plaques. To efficiently assess the adhesion of particles in an atherosclerotic environment in vitro, a microfluidic model recapitulating the physical and biochemical environment of atherosclerosis was developed. The device recapitulated a central pathophysiological event, the elongation of von Willebrand factor (vWF) under high shear stress and successfully demonstrated platelet rolling and capture. A gelatin based microparticle was formulated on a water-in-oil emulsion based protocol, which demonstrated deformability, compared to rigid polystyrene particles. The gelatin microparticles could adhere specifically to vWF within the atherosclerosis chip, under high shear flow.
Furthermore, proof-of-concept drug encapsulation and release experiments showed that GMPs achieved high loading efficiency and exhibited enhanced release under shear stimulation, consistent with their deformable nature. A bio-fabricated lymphatic construct was developed in parallel, demonstrating how similar systems could be applied to probe the role of mechanical cues, such as matrix stiffness and cyclic stretching, in regulating vascular drug delivery. Together, these results demonstrate the promise of gelatin microparticles as a drug delivery system tailored for the haemodynamic and biochemical environment of atherosclerosis. Beyond establishing a platform for plaque-targeted therapy, this work also establishes a foundation for exploring how mechanical cues such as shear stress, stiffness, and cyclic strain could influence vascular drug delivery strategies.Open Acces