143174 research outputs found
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Implanted microelectrode arrays in reinnervated muscles allow separation of neural drives from transferred polyfunctional nerves
No full textTargeted muscle reinnervation surgery reroutes residual nerve signals into spare muscles, enabling the recovery of neural information through electromyography (EMG). However, EMG signals are often overlapping, making the interpretation of limb functions complicated. Regenerative peripheral nerve interfaces surgically partition the nerve into individual fascicles that reinnervate specific muscle grafts, isolating distinct neural sources for precise control and interpretation of EMG signals. Here we combine targeted muscle reinnervation surgery of polyvalent nerves with a high-density microelectrode array implanted at a single site within a reinnervated muscle, and via mathematical source separation methods, we separate all neural signals that are redirected into a single muscle. In participants with upper-limb amputation, the deconvolution of EMG signals from four reinnervated muscles into motor unit spike trains revealed distinct clusters of motor neurons associated with diverse functional tasks. Our method enabled the extraction of multiple neural commands within a single reinnervated muscle, eliminating the need for surgical nerve division. This approach holds promises for enhancing control over prosthetic limbs and for understanding how the central nervous system encodes movement after reinnervation
Mapping hydro-ecological citizen science activities to inform research infrastructure design (Chess Catchment, UK)
No full textThe UK's Floods and Droughts Research Infrastructure (FDRI) is a £38M government investment, aiming to catalyse hydrological research and innovation by improving hydrological datasets and providing a suite of supporting services. Citizen Science (CS) activities are now abundant in the UK and are a significant opportunity for integration if existing projects, their objectives, operational barriers and priorities for support can be identified. From user-design principles, we develop a multiple-methods approach mapping CS activities in the River Chess catchment, Buckinghamshire, using a literature review, 12 key informant interviews and thematic analysis. We identify six ecological and five hydrological CS projects. A shared priority among CS projects was that their data collection creates tangible impacts in science, policy or management. As such, the modal recommendation is for research support on practical questions. Delivery of quality research from CS activities can attract greater contributions of time, research and resources by satisfying objectives shared by citizen scientists and their supporting partners. We recommend replication of this scalable method as infrastructures expand, so that they capture opportunities to leverage existing CS projects with mutual benefits
Data-driven optimisation and discovery for engineering systems
No full textThis thesis explores the integration of machine learning, optimisation, and human expertise for scientific discovery and the improvement of engineering systems. Each chapter applies deductive reasoning through physics-based models, inductive reasoning through data-driven approaches, or a combination of the two, demonstrating how together they drive scientific progress. First, a methodology is established for designing novel chemical flow reactors by combining multi-fidelity Bayesian optimisation with computational fluid dynamics. This approach enables automated discovery of reactor geometries and validation of induced flow characteristics, improving performance by 60\% over conventional designs. Key features of optimal designs are identified and validated experimentally through 3D-printed reactors, demonstrating how mixing-enhancing vortical structures can be induced at previously unattainable conditions. Second, a human-algorithm collaborative Bayesian optimisation framework is presented that integrates domain expertise into data-driven decision-making. By exploiting the hypothesis that humans are more efficient at discrete rather than continuous choices, the framework enables experts to influence solution selection via Bayesian optimisation with minimal effort. Case studies across benchmark functions, reactor design, and bioprocess optimisation demonstrate that even partial expertise can accelerate convergence compared to standard methods, particularly for noisy, high-dimensional problems. Finally, in-silico improvements to Bayesian optimisation are explored using large language models (LLMs) as computational decision-makers. This approach maintains automation while demonstrating convergence improvements similar to those from expert intervention. By using LLMs to select between discrete candidate solutions, the methodology identifies patterns in optimisation trajectories and makes informed selections. This thesis establishes data-driven optimisation as a powerful framework for scientific discovery in engineering systems beyond the paradigms of modelling and optimisation. The practical improvements ranging from more efficient physical reactor designs to accelerated optimisation processes demonstrate how the methodologies presented in this thesis can advance scientific and engineering discovery.Open Acces
Palaeomagnetic investigation of Palaeoproterozoic dykes from Botswana
No full textThe geomagnetic field including its intensity is poorly constrained during the Proterozoic era (2500 to 541 Ma). Understanding geomagnetic field behaviour during this long-time interval is key to understand the evolution of the Earth including dating the age of the Earth’s inner core nucleation. Palaeointensity experiments suggest low magnetic field intensities during the Palaeoproterozoic era between (2500 to 1500 Ma). To address this issue, we present palaeodirectional and palaeointensity results conducted on three previously dated Palaeoproterozoic localities in Botswana: 1) the Moshaneng Complex gabbros (2054 ± 2 Ma), 2) the Moshaneng dolerite dykes (1927 ± 1.5 Ma), and 3) the Pilanesberg dolerite dykes (1347 ± 97 Ma). Virtual geomagnetic poles were obtained from all three localities. Only the Moshaneng dolerite dyke yielded a virtual dipole moment (VDM) of 2.4 ± 0.4 × 1022Am2 from the three studied sites, which is consistent with a dipole low during the Palaeoproterozoic era. The results show that during the Palaeoproterozoic era, there was low VDM recorded which is similar to other values obtained in similarly aged Palaeoproterozoic studies
Scalable and sustainable synthesis of chiral amines by biocatalysis
No full textIn recent years, industrial biocatalysis has significantly advanced, largely due to innovations in DNA sequencing, bioinformatics, and protein engineering. However, the challenge of implementing biocatalysis at an industrial scale while ensuring sustainability and cost-effectiveness remains a critical barrier. This study presents the development of a flash thermal racemization protocol for chemoenzymatic dynamic kinetic resolution (FTR-CE-DKR) of chiral amines, encompassing an investigation of substrate scope, catalyst screening, and optimization studies. The outcomes of this research facilitated the successful scale-up of an industrially relevant amide within a recycle-batch platform, achieving unprecedented scales of up to 100 grams and space-time yield (STY) values of up to 73.2 g L⁻¹ h⁻¹. Furthermore, the process exhibited very favorable sustainability metrics when benchmarked against previous reports, including atom economy, reaction mass efficiency, and process mass intensity. These findings represent a significant milestone in the biocatalytic production of optically active amines
Apxs derived geochemistry of shallow water lens bodies within the Mirador formation, Gale Crater, Mars - evidence for intermittent wet periods and implications for the water record
No full textThe Mount Sharp group in Gale crater is typically interpreted to record a progressive change (with increasing elevation) from wetter fluvio-lacustrine environments to dry, eolian environments. This shift has been linked to orbital evidence for a global change in environmental and depositional conditions leading to an overall drying out of Mars. The Mars Science Laboratory Curiosity rover has documented evidence at Gale for the periodic continuation of aqueous depositional processes within the Mount Sharp group within the Clay Sulfate Transition, suggesting that, in Gale crater, it was not a simple, unidirectional drying out pattern. We present data on a series of interdune lenses at Gale crater, providing a window into locally changing environmental conditions. They represent wet (fluvial to lacustrine) deposits in the otherwise dry eolian Contigo dune field. Alpha Particle X-Ray Spectrometer geochemical data reveal compositional similarities between the interdune lenses and both the host Contigo member and unaltered basaltic sediments. Modeling indicates that the lenses may contain up to 52% basaltic sands/soils mixed with local sediment. A high degree of homogeneity in composition and sedimentary structures are identified across the lenses. This suggests a common source of influx material and a repeated process (wet episodes in a dry environment over several cycles of seasonal variations). Furthermore, differences in composition between the interdune lenses and the host Contigo member help to elucidate the timing of alteration within the Mount Sharp group
New methods for computer-assisted proofs: (hypo)-elliptic problems, numerical analysis and stochastic dynamics
No full textThis thesis is concerned with the treatment of problems, arising from various areas of application of Mathematics --- Biology, Fluid Mechanics, Mathematical Physics, Stochastic Dynamics, … --- via computer-assisted proofs. In particular, we are interested in the application of fundamental ideas in the study of stochastic differential equations and their associated partial differential equations to develop tools in Numerical Analysis and solve new problems with the aid of the computer.
First, taking inspiration from the well-established Functional Analysis of weighted Sobolev spaces associated with gradient diffusion processes, we propose a novel but elementary Numerical Analysis of some differential equations which are naturally well-posed on these topologies. In doing so, we propose an elegant solution to the problem of the construction of Sobolev orthogonal polynomials and their use in the resolution of differential equations. Furthermore, we establish a direct link between nonlinear Painlevé recurrence equations and the compactness of embeddings of semi-classically weighted Sobolev spaces. These notions can be applied to both the numerical and rigorous resolution of differential equations via computer-assisted methods: these include Schrödinger-type equations, problems arising from stochastic dynamics and the rigorous computation of (forward) self-similar profiles of semilinear parabolic equations.
Second, we propose a powerful method to give rigorous, tight and explicit bounds on ergodic averages of stochastic flows based on their associated Poisson equation. Our approach is general in the sense that it applies under weak hypoellipticity conditions and outside of perturbative regimes. This allows us to provide a new technique for attacking the fundamental problem of determining the dynamic qualities of a stochastic system via the sign of its Lyapunov exponent. Our method is applied to prove the chaotic nature of both new and classical models, well beyond the prior state-of-the-art.Open Acces
Neural information storage and processing in the presence of noise and nonlinearity
No full textArtificial intelligence has grown exponentially in popularity in recent years, thanks to impressive applications, especially in the field of generative modelling in visual and linguistic domains. A multitude of advancements in understanding of the theory of intelligence and of artificial neural networks have been made, which have drawn modern neural network architectures and algorithms further and further from their origins as models of networks of biological neurons in the human brain. This thesis argues that since the human brain is the source of our definition of intelligence, furthering our understanding of its computational methods will be the key to unbridling the field of AI from the reins of its huge energy costs, and bridging the gap between algorithms and the physical world. To this end, we explore a number of computational primitives present in biological neurons and neural networks, looking at the benefits, challenges and limitations of implementing them artificially, and arguing the case for an approach to AI research with awareness and exploitation of noise and non-ideality as fundamental components. To this end, we take a particular focus on devices called memristors, which have been proposed as passive electronic components that could be used for neuro-inspired computing. We investigate the ability of memristors to implement neuronal computational primitives, and address the problem of noisy information storage on the devices in the presence of resistive drift noise, using joint source-channel coding. We further explore the notion of intelligent processing in the presence of noise through identifying a use case for adversarial noise, generated as part of a competitive minimax communication game, exploring how it can be used to enable distribution matching in a generative modelling problem
A Bayesian multisource fusion model for spatiotemporal PM₂.₅ in an urban setting
No full textAirborne particulate matter (PM2.5) is a major public health concern in urban environments, where population density and emission sources exacerbate exposure risks. We present a novel Bayesian spatiotemporal fusion model to estimate monthly PM2.5 concentrations over Greater London (2014–2019) at 1 km resolution. The model integrates multiple PM2.5 data sources, including outputs from two atmospheric air quality dispersion models, and predictive variables, such as vegetation and satellite aerosol optical depth, while explicitly modeling a latent spatiotemporal field. Spatial misalignment of the data is addressed through a hierarchical fusion and spatial interpolation approach to predict across the entire area. Building on stochastic partial differential equations (SPDE) within the integrated nested Laplace approximations (INLA) framework, our method introduces spatially- and temporally-varying coefficients to flexibly calibrate datasets and capture fine-scale variability. Model performance and complexity
are balanced using predictive metrics such as the predictive model choice criterion and thorough cross-validation. The best performing model shows excellent fit and robust predictive performance, enabling reliable high-resolution spatiotemporal mapping of PM2.5 concentrations with the associated uncertainty. Furthermore, the model outputs, including full posterior predictive distributions, can be used to map exceedance probabilities of regulatory thresholds, supporting air quality management and targeted
interventions in vulnerable urban areas, as well as providing refined exposure estimates of PM2.5 for epidemiological applications