4,319 research outputs found
Ruthenium metallotherapeutics: a targeted approach to combatting multidrug resistant pathogens
The discovery of antibiotics revolutionised healthcare practice. However due to overuse, inappropriate use, widespread prophylaxis therapy and the lack of new developments, the threat of antimicrobial resistance is now a major global threat to health. By 2050, it is estimated that mortality due to antimicrobial resistant infections will exceed 10 million people per annum, superseding cancer as the leading cause of global mortality. The use of drug repurposing to identify potential therapies which combat antimicrobial resistance is one potential solution. Metals have been used as antimicrobial agents throughout the history of medicine for a broad range of applications, including the use of Silver as an antimicrobial agent which dates back to antiquity. More recently, Ruthenium metallotherapeutic complexes have been shown to exhibit highly active antimicrobial properties by targeting a range of bacterial species, and in contrast to traditional antibiotics, these compounds are thought to elicit antibacterial activity at multiple sites within the bacterial cell, which may reduce the possibility of resistance evolution. This study aimed to evaluate the antimicrobial activity of a series of Ruthenium metallotherapeutic complexes against multidrug-resistant bacterial pathogens, with a focus on use within wound care applications.
Antimicrobial susceptibility assays identified two lead candidates, Hexaammineruthenium (III) chloride and [Chlorido(η6-p-cymene)(N-(4-chlorophenyl)pyridine-2-carbothioamide) ruthenium (II)] chloride which demonstrated activity against Pseudomonas aeruginosa and Staphylococcus aureus respectively with MIC values ranging between 4 μg mL-1 and 16 μg mL-1. Furthermore, Hexaammineruthenium (III) chloride demonstrated antibiofilm activity in both a time and concentration-dependent manner. Synergy studies combining lead complexes with antibiotics demonstrated the potential for use as resistance breakers. Subsequent in vitro infection modelling using scratch assays with skin cell lines, coupled with a 3D full thickness skin wound infection model was used to determine potential applied applications of Hexaammineruthenium (III) chloride for use as topical antimicrobial agent against P. aeruginosa infections.
Antimicrobial mechanistic studies demonstrated that Hexaammineruthenium (III) chloride targeted the bacterial cell ultrastructure of P. aeruginosa strain PAO1 as cell perturbations were observed when treated cells were analysed by scanning electron microscopy. Furthermore, exposure of P. aeruginosa PAO1 to Hexaammineruthenium (III) chloride also resulted in a concentration dependent membrane depolarisation, which further supported the antimicrobial mechanistic role.
Finally, global changes in gene expression following exposure of P. aeruginosa strain PAO1 to Hexaammineruthenium (III) chloride were explored by RNA sequencing. Genes involved in ribosome function, cofactor biosynthesis and membrane fusion were downregulated, which provided a further insight into the wider mechanisms of antibacterial activity.
The research conducted in the present study indicated the potential use of Hexaammineruthenium (III) chloride (and derivatives) as a potential treatment option for chronic wounds infected with P. aeruginosa, which could be applied as either a direct treatment or used within antimicrobial wound care applications
Multi-epoch machine learning for galaxy formation
In this thesis I utilise a range of machine learning techniques in conjunction with hydrodynamical cosmological simulations. In Chapter 2 I present a novel machine learning method for predicting the baryonic properties of dark matter only subhalos taken from N-body simulations. The model is built using a tree-based algorithm and incorporates subhalo properties over a wide range of redshifts as its input features. I train the model using a hydrodynamical simulation which enables it to predict black hole mass, gas mass, magnitudes, star formation rate, stellar mass, and metallicity. This new model surpasses the performance of previous models. Furthermore, I explore the predictive power of each input property by looking at feature importance scores from the tree-based model. By applying the method to the LEGACY N-body simulation I generate a large volume mock catalog of the quasar population at z=3. By comparing this mock catalog with observations, I demonstrate that the IllustrisTNG subgrid model for black holes is not accurately capturing the growth of the most massive objects. In Chapter 3 I apply my method to investigate the evolution of galaxy properties in different simulations, and in various environments within a single simulation. By comparing the Illustris, EAGLE, and TNG simulations I show that subgrid model physics plays a more significant role than the choice of hydrodynamics method. Using the CAMELS simulation suite I consider the impact of cosmological and astrophysical parameters on the buildup of stellar mass within the TNG and SIMBA models.
In the final chapter I apply a combination of neural networks and symbolic regression methods to construct a semi-analytic model which reproduces the galaxy population from a cosmological simulation. The neural network based approach is capable of producing a more accurate population than a previous method of binning based on halo mass. The equations resulting from symbolic regression are found to be a good approximation of the neural network
Quantum algorithm for smoothed particle hydrodynamics
We present a quantum computing algorithm for the smoothed particle hydrodynamics (SPH) method. We use a normalization procedure to encode the SPH operators and domain discretization in a quantum register. We then perform the SPH summation via an inner product of quantum registers. Using a one-dimensional function, we test the approach in a classical sense for the kernel sum and first and second derivatives of a one-dimensional function, using both the Gaussian and Wendland kernel functions, and compare various register sizes against analytical results. Error convergence is exponentially fast in the number of qubits. We extend the method to solve the one-dimensional advection and diffusion partial differential equations, which are commonly encountered in fluids simulations. This work provides a foundation for a more general SPH algorithm, eventually leading to highly efficient simulations of complex engineering problems on gate-based quantum computers
Spatiotemporal Control of Chemical Reaction Networks using Droplet Microfluidics
A number of cellular organisms, such as yeast, bacteria and slime moulds, exhibit dynamic behaviour, in particular switching and rhythms that are controlled by feedback mechanisms in enzyme-catalysed reactions. The mechanisms of these processes are well understood, and recently there has been a focus on generating similar reactions in synthetic biocatalytic systems to establish bioinspired analogues for applications in materials and medicine. In this context, compartmentalisation of biochemical reactions within synthetic cell models such as micelles, vesicles, and W/O/W-based double emulsions is attracting growing attention for applications in the field of therapeutics. In this respect, it is necessary to adopt easier-to-use stimuli-responsive (react to pH, temperature or light) biochemical reactions, to apply artificial cell models to the biomedical context, and regulate artificial cell communication in a spatiotemporal controlled way. As a first step, it is crucial to control the output of a chemical reaction that maybe exploited for applications in the field of programmable materials and biomedicine. Droplet emulsion and synthetic vesicle systems have been widely employed as bioinspired micro- or nanoreactors for production of materials such as hydrogels and ceramic particles. They also provide test platform for biomimetic cell like behaviour.
To address this, we have developed and fine-tuned a platform with synthetic bottom-up chemistry that has enabled us to systematically and thoroughly investigate the effects of entrapment on a feedback-driven enzymatic reaction. As a result of this process, we have revealed a system that is more intricate than originally thought. Firstly, taking advantage from pressure driven droplet microfluidics, we developed a system of enzyme-encapsulated (urea-urease) double emulsion (W/O/W) droplets to obtain a localised pH pulse, with a controllable induction time to program material properties. The urease-catalysed hydrolysis of urea (urea-urea reaction), has a feedback through the production of the base (NH3). This leads to a change from an acidic to a basic pH after an induction time (Tind), resulting in an environment with auto-changing pH conditions. Reaction was initiated by addition of urea and a pulse in base (ammonia) was observed in the droplets after a time lag of the order of minutes. The pH-time profile can be manipulated by the diffusion timescale of urea and ammonia through the oil layer, resulting in localised pH changes not accessible in bulk solutions.
Secondly, we performed a computational investigation of the nonlinear reaction chemistry (urea-urease) within the designed platform of the W/O/W-based reactor. A radially distributed reaction diffusion model is presented for a layered sphere mimicking a double emulsion. Here we have combined the experiments with simulations (shell-core model) to demonstrate the influence of urea transport triggered by the shell, the core and the external solution surrounding the cell model (µ-reactor) on the induction time/period (Tind) of urea-urease reaction.
Third, inspired from natural cellular systems (e.g. bacterial quorum sensing), we focus on the use of urea-urease reaction confined to double emulsions to investigate chemical communications. We observed a system that resulted in a system of microreactors acting as individual units with distinct induction periods (Tind) for the first time. We show that in contrast to other systems, the release of ammonia can accelerate the reaction in all the droplets but there is no evident synchronisation of activity characterised by a wide distribution of induction times across the population of micro-reactors. However, the investigation of behaviour of population/group of µ-reactors as a function of substrate urea concentration and the density of µ-reactors highlights the possibility of transitions to collective behaviours.
Finally, we aimed to use the double emulsion template for potential biomedical and therapeutic applications using the autocatalytic urea-urease reaction. We used the platform to produce thiol-acrylate gels in the form of double emulsion loaded gel films and spherical microcapsules for potential drug delivery applications. In addition, we employed the encapsulated double emulsion platform of the enzyme urease to study the inhibition of the enzyme itself; which is important in the development of anti-microbials for ureolytic bacteria.
By building this platform, we have not only learned how to control the kinetic output of the reaction (urea-urease), but have also demonstrated its potential in future applications
Temperature Reduction Technologies Meet Asphalt Pavement: Green and Sustainability
This Special Issue, "Temperature Reduction Technologies Meet Asphalt Pavement: Green and Sustainability", covers various subjects related to advanced temperature reduction technologies in bituminous materials. It can help civil engineers and material scientists better identify underlying views for sustainable pavement constructions
Flow characterization of compressible particulate biomass materials
Biomass materials like trees and crops can be converted to biochemical products and have been considered as one of the most promising alternatives for energy and fuels due to their abundance and easy access. However, the commercialization of bioenergy has been significantly constrained by severe issues during the handling of particulate biomass materials, manifested as unstable flow and jamming in handling equipment such as hoppers, feeders, or conveyors. Solving these issues centers on the mechanistic understanding of the flowability of milled biomass materials and their rheological and constitutive behaviors in various industrial equipment.
This thesis investigates the flow behavior of milled woody biomass across multiple scales and flow regimes. The study experimentally quantifies the mechanical and rheological properties of particulate biomass at particle, meso, and industrial scales, complemented by FEM simulations of biomass flow through hoppers and inclined planes at meso and industrial scales. The jamming physics of woody particles in wedge-shaped hoppers is analyzed in consideration of hopper geometry, particle density, packing, and surcharge. With these results, parameters governing the arching, mass flow, and funnel flow of milled biomass through industry hoppers are identified. These findings enable the design and optimization of industry hoppers for the efficient handling of milled woody biomass. In addition, the constitutive model characterizing the flow of milled woody biomass at both quasi-static and dynamic flow regimes is formulated and validated against laboratory data. In the end, the impacts of moisture content on the mechanical and flow behavior of milled woody biomass are evaluated. This study promotes the fundamental understanding of the flow physics of milled biomass materials across various scales, fosters high-fidelity numerical prediction models of the constitutive responses of compressible particulate biomass, and enables the development of the next-generation high-efficiency biomass handling equipment to reduce the cost and increase the safety of feedstock processing.Ph.D
Smoothed particle hydrodynamics method for free surface flow based on MPI parallel computing
In the field of computational fluid dynamics (CFD), smoothed particle hydrodynamics (SPH) is very suitable for simulating problems with large deformation, free surface flow and other types of flow scenarios. However, traditional smoothed particle hydrodynamics methods suffer from the problem of high computation complexity, which constrains their application in scenarios with accuracy requirements. DualSPHysics is an excellent smoothed particle hydrodynamics software proposed in academia. Based on this tool, this paper presents a largescale parallel smoothed particle hydrodynamics framework: parallelDualSPHysics, which can solve the simulation of large-scale free surface flow. First, an efficient domain decomposition algorithm is proposed. And the data structure of DualSPHysics in a parallel framework is reshaped. Secondly, we proposed a strategy of overlapping computation and communication to the parallel particle interaction and particle update module, which greatly improves the parallel efficiency of the smoothed particle hydrodynamics method. Finally, we also added the pre-processing and post-processing modules to enable parallelDualSPHysics to run in modern high performance computers. In addition, a thorough evaluation shows that the 3 to 120 million particles tested can still maintain more than 90% computing efficiency, which demonstrates that the parallel strategy can achieve superior parallel efficiency
Peering into the Dark: Investigating dark matter and neutrinos with cosmology and astrophysics
The LCDM model of modern cosmology provides a highly accurate description of our universe.
However, it relies on two mysterious components, dark matter and dark energy. The cold dark matter
paradigm does not provide a satisfying description of its particle nature, nor any link to the Standard
Model of particle physics.
I investigate the consequences for cosmological structure formation in models with a coupling
between dark matter and Standard Model neutrinos, as well as probes of primordial black holes as
dark matter.
I examine the impact that such an interaction would have through both linear perturbation theory and
nonlinear N-body simulations. I present limits on the possible interaction strength from cosmic
microwave background, large scale structure, and galaxy population data, as well as forecasts on the
future sensitivity. I provide an analysis of what is necessary to distinguish the cosmological impact of
interacting dark matter from similar effects. Intensity mapping of the 21 cm line of neutral hydrogen at
high redshift using next generation observatories, such as the SKA, would provide the strongest
constraints yet on such interactions, and may be able to distinguish between different scenarios
causing suppressed small scale structure. I also present a novel type of probe of structure formation,
using the cosmological gravitational wave signal of high redshift compact binary mergers to provide
information about structure formation, and thus the behaviour of dark matter. Such observations
would also provide competitive constraints.
Finally, I investigate primordial black holes as an alternative dark matter candidate, presenting an
analysis and framework for the evolution of extended mass populations over cosmological time and
computing the present day gamma ray signal, as well as the allowed local evaporation rate. This is
used to set constraints on the allowed population of low mass primordial black holes, and the
likelihood of witnessing an evaporation
Sloshing reduced-order model trained with Smoothed Particle Hydrodynamics simulations
The main goal of this paper is to provide a Reduced Order Model (ROM) able to predict the liquid induced dissipation of the violent and vertical sloshing problem for a wide range of liquid viscosities, surface tensions and tank filling levels. For that purpose, the Delta Smoothed Particle Hydrodynamics (δ-SPH) formulation is used to build a database of simulation cases where the physical parameters of the liquid are varied. For each simulation case, a bouncing ball-based equivalent mechanical model is identified to emulate sloshing dynamics. Then, an interpolating hypersurface-based ROM is defined to establish a mapping between the considered physical parameters of the liquid and the identified ball models. The resulting hypersurface effectively estimates the bouncing ball design parameters while considering various types of liquids, producing results consistent with SPH test simulations. Additionally, it is observed that the estimated bouncing ball model not only matches the liquid induced dissipation but also follows the liquid center of mass and presents the same sloshing force and phase-shift trends when varying the tank filling level. These findings provide compelling evidence that the identified ROM is a practical tool for accurately predicting critical aspects of the vertical sloshing problem while requiring minimal computational resources
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