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Differential conservation analysis identifies residues defining constitutive internalization in beta-adrenergic receptors
No full textG protein-coupled receptors (GPCRs) are major drug targets and key regulators of cell signaling. The basis of functional diversification between individual GPCRs and families of GPCRs can be revealed by investigating evolutionary conservation patterns. In this study, we investigated the functional role of specifically conserved residues in the TM1/TM7/H8 dimerization interface of beta-adrenergic receptors (BARs). Residues specifically conserved for B2AR compared to B1AR and B3AR subtypes were identified via phylogenetic analysis. The significance of residues differentially conserved between receptor subtypes at the TM1/TM1 interface was investigated using molecular dynamics (MD) simulations in combination with biophysical and functional studies. Our findings suggest that differentially conserved residues within TM1 of BARs modulate receptor conformation without disrupting dimerization to impact cell surface expression, basal activity, and endocytosis. This highlights the importance of TM1 in modulating receptor function and provides new insights into the evolutionary and functional differences among beta-adrenergic receptor subtypes
Non-invasive measurement of intestinal barrier function in environmental enteropathy using transcutaneous fluorescence sensing
No full textBackground
Undernutrition represents a critical global health concern and is associated with a
multifaceted breakdown in gut function – termed environmental enteropathy (EE) –
which leads to increased intestinal permeability, inflammation and nutrient
malabsorption. Current clinical approaches to assess intestinal permeability are
costly, invasive, unreliable and/or difficult to perform in certain populations.
Objectives
We used transcutaneous fluorescence spectroscopy (TFS) – a novel method for
non-invasive assessment of gut function – to investigate intestinal barrier function
in EE.
Design
Volunteers were recruited in a Zambian community where EE is prevalent, and in
the UK to undergo TFS measurements in a cross-sectional study. Data were
compared between groups and were correlated with the lactulose:rhamnose (LR)
test.
Results
TFS demonstrated significant differences in intestinal barrier function between UK
and Zambian volunteers. Both peak fluorescence intensity (p=0.003) and area
under fluorescence curves (p=0.02) were higher in Zambian than UK participants,
suggesting increased permeation of TFS contrast agent. No differences were
observed in time taken to reach peak, indicating no differences in factors affecting
uptake rate (e.g. gastric emptying). Finally, fluorescence kinetics and regression
analysis revealed strong correlations between TFS data and urinary recoveries of
lactulose and rhamnose (Spearman’s r ≥ 0.78; p < 0.002).
Conclusions
TFS reveals population differences in permeability. It also allows simultaneous
assessment of multiple elements of gut function (intestinal barrier integrity and
gastric emptying) using a rapid, sample-free methodology. Combined with
correlation to the LR test, this implies potential to advance studies of gut health and
to improve clinical monitoring
Sentinel lymph node status and oncological outcomes of high-risk and low-risk cutaneous primary melanoma
No full textIntroduction:
The role of sentinel lymph node biopsy (SLNB) in clinically node-negative melanoma has been questioned in the era of effective adjuvant immunotherapy. We evaluated the staging and prognostic value of SLNB in a single-centre cohort, with emphasis on patients with “high-risk” primary tumours.
Methods:
We retrospectively analysed 300 consecutive patients with cutaneous melanoma who underwent wide local excision with SLNB at a tertiary melanoma centre (April 2018–April 2023). Patients were stratified by AJCC 8th edition T stage into low-risk (T1–T3a) and high-risk (T3b–T4b) groups. Outcomes included SLN positivity, recurrence, and disease-free survival (DFS). DFS was assessed using Kaplan–Meier methods with log-rank testing; restricted mean survival time (RMST) was calculated to 3 years. In high-risk patients with available data, the Melanoma Institute of Australia (MIA) sentinel node risk tool was evaluated for discrimination and calibration.
Results:
Median age was 60.7 years and 51.3% were female. Overall SLN positivity was 22.0% (66/300) and was higher in high-risk than low-risk melanoma (31.5% [28/89] vs 18.0% [38/211], P=0.009). Recurrence occurred in 16.0% (48/300), more frequently in high-risk patients (31.5% vs 9.5%). Estimated DFS at 1 and 3 years was 96.6% and 81.8% for low-risk melanoma versus 87.9% and 68.6% for high-risk melanoma (log-rank P<0.001); RMST to 3 years favoured the low-risk group by 115.9 days (95% CI 20.8–213.3). SLNB resulted in substantial stage migration in high-risk clinically node-negative patients, identifying pathological stage IIIC disease in 31.5% (28/89) who would otherwise be classified as stage IIB/IIC.
Conclusion:
SLNB continues to provide clinically important staging and prognostic information, particularly in high-risk clinically node-negative melanoma where occult nodal disease is common. Omitting SLNB risks systematic understaging and loss of prognostic resolution
Competing energy absorption and shape recovery in 3D-printed composite meta-honeycombs under cyclic compression
No full textThis study investigates the cyclic compressive behavior of 3D-printed composite meta-honeycombs, focusing on the trade-off between energy absorption and shape recovery. Three configurations, hexagonal (HEX), auxetic re-entrant (ARE), and double arrow-head (DAH), were fabricated using Nylon and Onyx and tested under quasi-static cyclic compression in both in-plane and out-of-plane directions. Key metrics, including specific energy absorption (SEA), undulation of load-carrying capacity (ULC), shape recovery ratio (SRR), and energy dissipation ratio (EDR), were used to quantify repeatable energy absorption and recovery performance. Results reveal a pronounced competition between crashworthiness and recoverability. Fiber reinforcement enhances stiffness, strength, and SEA but reduces shape recovery, highlighting a material-level trade-off. SEA is generally higher under out-of-plane loading, while in-plane SRR is 25-35 % greater than the out-of-plane SRR, showing strong sensitivity to loading direction. Cyclic compression at different deformation stages leads to stage-dependent degradation, with SEA reductions of approximately 10 %, 50 %, and 100 % during the elastic, plateau, and densification stages, respectively. Finite element simulations elucidate configuration-dependent deformation mechanisms and support the experimental observations. This study establishes the universality of the trade-off across material systems, topologies, loading directions, cyclic stages, and provides quantitative insights for designing reusable energy-absorbing honeycomb structures
The shape of data: statistical topology across biology and AI
No full textTopological data analysis (TDA) provides multiscale summaries of complex data, capturing topological features such as connected components, loops, and higher-dimensional structures beyond classical geometric or statistical summaries. Persistent homology (PH), a central tool in TDA, encodes these features across scales. Its algebraic invariants, such as persistence diagrams, are powerful but inhabit non-Euclidean spaces that complicate statistical treatment. To make PH compatible with inference, there is a pressing need for representations and models that balance the faithfulness of topological information with statistical tractability and computational efficiency.
This thesis develops statistical methodologies towards this need, spanning theory, methodology and applications. At the theoretical level, this thesis studies an alternative pre-existing invariant, the rank function, establishing new guarantees that bring them to practical use, ensuring interpretability and reliability, and thereby positioning them as functional summaries directly amenable to functional data analysis. Empirical studies show outperformance over common vectorisations while preserving complete topological information. Deriving asymptotic properties of another functional summary, the multiparameter persistence landscapes, provides a principled route to uncertainty quantification. At the methodological level, this thesis proposes a Topological Gaussian Mixture Model (TGMM), a probabilistic framework that treats persistence diagram points as weighted observations to embed topological information within an interpretable model. At the applied level, methods were validated in two domains. In biology, TGMM provided an interpretable probabilistic description of vascular remodelling. In machine learning, PH summaries uncovered consistent multiscale signatures of adversarial perturbations in large language models and enabled a systematic critique of PH-based generalisation measures.
Together, these contributions demonstrate that TDA can be placed on a firm statistical foundation and developed into practical inferential tools. By advancing functional and model-based approaches in real-world settings, this work contributes to broader research in statistical topology: a discipline uniting algebraic invariants, probability theory, and applications in the life sciences and AI.Open Acces
Enhancing laser powder bed fusion processability of copper with nanocoated powder and in-situ monitoring
No full textLaser powder bed fusion (LPBF) offers great potential for producing complex metal components, yet processing pure copper remains challenging due to its high reflectivity and thermal
conductivity, which limit laser energy absorption and cause process instability. This thesis addresses these challenges through an integrated framework of nanocoating surface modification,
dynamic absorptivity characterisation, and intelligent in-situ monitoring in three main research chapters.
A bioinspired polydopamine (PDA) nanocoating was developed to enhance the laser absorptivity of copper powders, optimally increasing it from below 20% to 67% with a 56 nm coating.
The improved energy coupling enabled stable melt pools, denser structures, and no detectable contamination. Synergistically, combining nanocoating with laser defocusing ensured sufficient
melting while mitigating spatter and overheating, significantly improving process stability. Dynamic absorptivity studies further revealed that focus offset strongly influenced energy coupling,
raising absorptivity from 12% to 51%. These results highlighted dynamic absorptivity as a superior predictor of LPBF performance compared to static measurement.
High-speed imaging was employed to capture the plume and spatter dynamics, and the results were analysed using advanced image processing. PDA-coated powders processed under
optimised parameters exhibited markedly reduced thermal and particle instabilities, as evidenced by a flatter plume shape and narrower spatter statistical distributions. Furthermore, convolutional neural networks were successfully implemented to predict plume orientation features with an accuracy of up to 96%. Collectively, these results demonstrate the feasibility and
promise of data-driven, closed-loop control strategies for future LPBF systems.
This work establishes a novel methodology that integrates surface engineering, process optimisation, and real-time monitoring, advancing copper LPBF and offering transferable insights
for other reflective, high-conductivity materials in laser-based additive manufacturing. Future work on topics such as bulk parts printing or conductivity properties is also discussed.Open Acces
Machine learning for process monitoring and adaptive control in continuous chromatography
No full textProcess efficiency and adaptability are becoming growing priorities in biopharmaceutical manufacturing. In recent years, the industry has moved towards intensified and continuous operations to meet rising demand for monoclonal antibodies and other therapeutic proteins. While such approaches promise higher productivity and reduced variability, they also introduce cyclic dynamics and non-linear behaviour that complicate downstream purification. Within this context, chromatographic separations remain a critical challenge. Mechanistic models offer detailed predictions for these systems, but their high computational cost can restrict their use in online applications. This highlights an opportunity for machine learning approaches that combine mechanistic fidelity with the computational efficiency required for real-time decision-making.
This thesis presents modelling and control frameworks that address these challenges. Firstly, the potential of data-driven models is examined, using neural networks to approximate full elution profiles and enable rapid evaluation. Building on this, a hybrid modelling approach is introduced, embedding mechanistic structure within a neural network to improve robustness while avoiding spatial discretisation. Both data-driven and hybrid models are then integrated into Bayesian optimisation workflows, demonstrating how surrogate-based methods can sustain reliable performance under online horizons where mechanistic models fail to converge. Finally, reinforcement learning is explored as a complementary paradigm for adaptive control, trained to regulate flowrate and switching times under variability, disturbances, and measurement noise.
To conclude, the presented findings establish a toolbox of machine learning methods that extend the scope of mechanistic models into online applications. The results point towards future integration of optimisation and adaptive control within digital biomanufacturing, supporting the transition to more efficient and resilient chromatographic processes.Open Acces
A map of high-altitude wetlands in the world’s major mountain regions
No full textWe present a first global high-resolution map (30 m x 30 m) of high-altitudinal wetlands in the world’s major mountain regions, i.e. the Andes, Rocky Mountains, Alps and High Mountain Asia. To map these wetlands, we employed a supervised classification approach using a random forest machine learning model and a selected set of predictors including vegetation, topographic, and surface moisture features. The predictors were derived from freely available
radar and optical satellite imagery (Sentinel-1 and Sentinel-2), SRTM elevation data, and the global ecoregion map RESOLVE. We identify a total area of >30,500 km2 of high-mountain wetlands. With this map we aim to enhance the understanding of wetland distribution in remote and often inaccessible mountain regions and enable a more reliable understanding of their role in the ecosystem functioning and water cycles of high mountain areas
Impact of using artificial intelligence as a second reader in breast screening including arbitration
No full textThe impact of incorporating artificial intelligence (AI) into a double-read breast-screening workflow, including arbitration, is unclear. This retrospective study included 50,000 representative women from two NHS breast-screening centers. All the women had long-term follow-up, allowing us to determine whether use of AI leads to earlier cancer detection. Cases requiring arbitration (8,732 cases) were read by 22 readers in a reader study, following their normal arbitration workflow. Overall, after arbitration, replacing the second reader with AI was noninferior (5% margin) to two human readers in terms of sensitivity and specificity (P < 0.001) while offering a workload benefit. Arbitration improved the specificity of the AI arm by overruling cases incorrectly recalled by the AI tool; however, it also overruled the AI tool recall decision for some interval and next-round cancers. Further development of the AI tool alongside improvement in its explainability could lead to the earlier detection of cancers