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An evaluation of the therapeutic potential of human amniotic epithelial cells during ex-vivo donor lung perfusion
Get PDFPh. D. Thesis.Introduction: Ex Vivo Lung Perfusion (EVLP) provides a normothermic isolated
environment for the evaluation and reconditioning of donor lungs deemed unsuitable
for immediate transplantation and offers a unique opportunity to administer advanced
therapeutics, such as cell-based therapies. Human Amniotic Epithelial Cells (hAECs)
have been shown to have immunomodulatory properties that could reduce injury in
donor lungs. Our aim was to assess the anti-inflammatory actions of hAECs when
administered during EVLP to lungs declined for transplant due to poor organ function.
Methods: hAECs were isolated from term placenta through enzymatic digestion. In in
vitro studies, THP-1 derived macrophage phagocytosis and activation was
determined after treatment with hAECs for 6 hours. Neutrophils were migrated
through an IL-1b activated Human Microvascular Endothelial Cells (HMEC)-1
monolayer, after treatment of hAECs. In ex vivo perfusion studies; human lungs
declined for transplant were split, with 150 x 106 hAECs or the HTR-8/SVneo cell line
administered to each single lung and perfused concurrently for up to 4 hours (n=3).
Serial samples of perfusate and tissue biopsies were collected for ELISA, qPCR and
immunofluorescence (IF).
Results: hAECs were isolated with 94 ± 4% purity, with an average isolation yielding
134.2 x 106 with viability >90%. hAECs reduced neutrophil transendothelial migration
(p=0.0128). hAEC treatment of macrophages led to an increase in phagocytosis
observed in vitro and a decrease in CXCL8 (p=0.0465) and TNFa (p=0.0158)
expression. hAEC-treated lungs had significantly reduced TNFa expression in the
tissue (p=0.0415). IF staining demonstrated reduced expression of CXCL8 and 3-
nitrotyrosine in the hAEC-treated lungs compared to the HTR cell treated lungs.
Conclusion: In vitro assays demonstrated the potential of hAECs to minimise proinflammatory macrophage activation and neutrophil migration. hAEC-treated lungs
led to a reduction in pro-inflammatory cytokine production and oxidative stress.
hAECs may offer a therapeutic approach to reduce inflammation in donor lungs
during EVLP.NIHR Blood and Transplant Research Unit in Organ
Donation and Transplantation (BTRU-ODT
Environmental Fate Assessments to Understand the Legacy of Metaldehyde in Agricultural Fields and Surface Water
Get PDFPhD ThesisThere are increasing concerns about the hazards posed to drinking water resources by persistent,
mobile and toxic (PMT) substances in the environment. For example, the extensive use of
metaldehyde-based molluscicide to control slug populations in agricultural fields has frequently led
to pollution of surface waters and contamination of drinking water at levels exceeding the statutory
limit. Regulatory environmental fate assessments and studies in the literature did not predict that
metaldehyde would be persistent in the environment, contrary to observations from monitoring
schemes. To understand the reasons for this disparity, this study conducted a suite of degradation
experiments, covering different soil types and environmentally realistic conditions, and generated a
distribution of DT50 values for metaldehyde to examine whether degradation rates are
underestimated by current risk assessments. The results were found to vary, showing a range of DT50
values (3.6-4150 d), which indicated that metaldehyde had the potential to become persistent,
subject to high soil moisture conditions. Additionally, leaching and dissipation assessments were
conducted in lysimeters, using representative soils to understand the legacy of metaldehyde in the
field. Metaldehyde concentrations were detected in leachate and soil after a period of 120 days,
suggesting that its persistence in the environment could be greater than the predictions made
during the regulatory environmental fate assessments. Lastly, molecular microbiology was employed
to elucidate how the soil microbial population characteristics relate to the degradation potential for
metaldehyde. Molecular techniques, such as qPCR and MinION sequencing revealed that the known
metaldehyde degraders were very rare members of the soil communities. Hence, the trends in
metaldehyde degradation may not be wholly attributed to these species and other organisms might
be utilising metaldehyde that have yet to be characterised. While this research has identified
environmental conditions that may lead to metaldehyde persistence in the field, further research
needs to be done to understand the microbiology of metaldehyde degradation in soil. . Improved
management strategies can then be developed to prevent the pollution of drinking water with
metaldehyde
Understanding and controlling CO2 permeation across dual-phase membranes with tailored, multi- or single-pore microstructures
Get PDFPhD ThesisThe importance of finding new ways of CO2 separation or improving the existing ones, has
increased significantly in recent years, because CO2 emissions have become a serious
environmental concern. CO2 separation from different process streams, such as flue gases, has
been researched extensively over the past few years. One way of separating CO2 is through
molten carbonate dual-phase membranes, which consist of a porous ceramic support
infiltrated with a molten salt. They can operate continuously at elevated temperatures (400-
900 °C) with high selectivity and low energetic penalties as opposed to other separation
methods, such as absorption. One of the key challenges is understanding the contribution of
various factors towards CO2 permeation, such as operating conditions, membrane structure
and gas phase composition. In this thesis, dual-phase membrane systems consisting of a
zirconia or alumina support with various pore geometries and an alkali metal carbonate
eutectic mixture were investigated. It was found that below 600 °C, CO2 permeation is largely
controlled by the geometry of the support material rather than its composition. Therefore,
multi- or single-pore channels were laser drilled in dense polycrystalline and single crystal
materials, and the geometry of the channels was tailored with high precision. By using an
Al2O3 –carbonate multiple-pore system, it was found that at around 700 °C, CO2 permeation
is generally limited by the diffusion in the melt, while at temperatures around 550 °C, the rate
is limited by reactions at the gas-melt interface. In single-pore systems, an effect of
permeation was visualised by equilibrating the internal gas phase (gas phase behind the
meniscus) to the external gas phase and observing the displacement of the molten salt
meniscus. Permeation rates were extracted at low driving forces, necessary for real
applications. To enhance permeation, the use of humidified gas streams was investigated. It
was found that above 550 °C, CO2 permeance was on the order of 10-7 mol m-2
s
-1 Pa-1
compared to 10-9 mol m-2
s
-1 Pa-1 under dry conditions. Furthermore, by coupling the
permeation of CO2 with H2O, CO2 could be permeated against its own chemical potential
difference. This work provides an understanding on membrane performance by unprecedented
control over pore geometry and the effect of water with well-defined chemical potential
gradients across the membrane
A GNSS velocity field for estimating tectonic plate motion and testing global glacial isostatic adjustment models
Get PDFPhD ThesisThe two main causes of the long-term deformation of the Earth on a global scale are
tectonic plate motion and Glacial Isostatic Adjustment (GIA). GIA results in vertical
as well as lateral movements of the Earth’s surface. It is difficult to distinguish from
local and regional effects, such as the deformational response to decadal and longer-term
changes in continental water storage and the mass balance of glaciers and ice sheets. On
a global scale, GIA is also, to some extent, difficult to distinguish from millennial-term
lateral motion due to plate tectonics. The effects of GIA must therefore be modelled.
GIA models use an ice sheet history combined with an estimate for Earth rheology to
produce predictions of present-day GIA velocities. GIA models are typically tuned to
fit evidence for past and present vertical motion, as determined from historical relative
sea-level data, and they may additionally be tuned to fit GNSS-derived present-day uplift
rates. However, GNSS-derived horizontal rates have not traditionally been used to tune
GIA models. Lateral Earth structure can significantly influence horizontal GIA rates, and
most GIA models do not account for lateral structure, these are so-called 1D GIA models.
Recently, GIA models accounting for lateral Earth structure have been developed, known
as 3D GIA models.
Vertical GIA velocities are important for studies of surface mass loading, sea-level change,
mass balance of glaciers and ice sheets, and vertical reference systems. Horizontal GIA
velocities are also important for interpreting surface mass loading, as well as tectonic plate
rigidity, with implications for horizontal components of reference systems.
Consequently, this project aims to create a bespoke 3D GNSS surface velocity field to test
and compare a set of recent 1D and 3D GIA models and investigate tectonic plate motion.
In turn, this velocity field has several applications beyond this project. It may be used to
investigate present-day surface loading due to ice melting as well as other aspects of the
global hydrological cycle, and loading studies in general. The main motivation for creating
a bespoke 3D velocity field (as opposed to using, e.g. the most recent International
Terrestrial Reference Frame ITRF2014) is to include a larger number of GNSS sites in
the GIA-affected areas of investigation, namely North America, Europe, and Antarctica.
GIA and plate motion velocities are at the mm level so the choice of a stable and accurate
reference frame plays a crucial role. Here I create the GNSS surface velocity field using
the IGS repro2 data and other similarly processed GNSS datasets. The networks are
deconstrained, combined and aligned to ITRF2014 on a daily level. For this, I use the
Newcastle University-developed reference frame combination software Tanya. Within this
project, the software has been updated to be compatible with ITRF2014, including the
discontinuity information and post-seismic deformation models. This resulted in 57%
reduction of the WRMS of the alignment post-fit residuals compared to the alignment to
ITRF2008. The time series of daily GNSS solutions were used to create the GNSS velocity
field. After additional data screening and quality control, the final GNSS velocity field
has horizontal uncertainties mostly within 0.5 mm/yr, and vertical uncertainties mostly
within 1 mm/yr, which make it suitable for testing GIA models.
I use a suite of GIA models that have been produced by combining three different ice
models (ICE-5G, ICE-6G and W12) with a range of 1D and 3D Earth models. By
subtracting this ensemble from the velocity field, I identify and compare a range of plate
motion models (PMMs), which are then expected to be unaffected by GIA. The impact
of GIA on the PMM estimates is investigated and the resulting PMMs are compared with
previously published ones. The results show that there can be significant GIA-related
horizontal motion which may be modelled into the plate motion if left uncorrected. Using
an extensive set of 1D and 3D GIA models allows to include more GNSS sites in the PMM
estimate. These sites are in GIA-affected areas which have typically been excluded from
PMM estimates. A joint estimation of PMM with GIA is beneficial when investigating
GIA with GNSS observations because it reduces dependency on a pre-existing PMM which
can be contaminated by GIA.
Next, the predicted horizontal and vertical velocities of each GIA model are subtracted
from the GNSS surface velocity field after removing the respective PMM. Median Absolute
Deviations (MADs) are computed for the suite of residual fields including the null-GIA
case, where GIA predictions were not taken into account. For the 3D GIA models,
applying GIA corrections reduces the MAD in all regions. For 1D GIA models, applying
GIA corrections reduces the MAD in the majority of regions. Exceptions are found for
the vertical component of the velocity field in Antarctica, and the horizontal component
in the global case. The latter result indicates that it is not possible to replicate the global
horizontal GIA velocity field by combining a 1D Earth model with the global ice models
being tested here. Based on the results of this project, it is not possible to conclude that
3D GIA models consistently outperform 1D GIA models or vice-versa. However, it is
possible to identify common GIA model features that correspond to better MADs.
Furthermore, a group of best-performing GIA models is selected for each region of interest
based on their MADs, and the range of GIA predictions from this group is assumed to
represent the uncertainty of GIA models. For Antarctica, a range of equivalent water
height values is computed from the group of best-performing GIA models, which in turn
can be used as an uncertainty measure when applying GIA corrections in GRACE studies
of ice mass change. The total GIA contribution to annual mass change in Antarctica
ranges from 5 Gt/yr to 45 Gt/yr depending on which of the best-performing GIA models
is used.jointly funded by NERC Iapetus Doctoral Training Partnership
and School of Engineering at Newcastle Universit
Variational inference for stochastic processes
Get PDFPhD ThesisStochastic process models such as stochastic differential equations (SDEs), state-space models
(SSMs), Gaussian processes (GPs) and latent force models (LFMs), provide a powerful
collection of modelling techniques to better our understanding of many physical systems. In
treating these models within the Bayesian paradigm, we further yield a rich expression of
our uncertainty, and gain the ability to incorporate our prior beliefs. However, performing
Bayesian posterior inference is not without significant challenge. Exact likelihood calculations
can often be intractable, take an infeasibly long time to compute, or be challenging to
approximate in the presence of missing data. Therefore, designing new approaches to perform
Bayesian inference for this family of stochastic process models is of great scientific interest.
Variational inference (VI) has had great success is scaling Bayesian inference across a range of
problem domains. Historically, however, its successful application to stochastic process models
has been limited. The reason is two-fold. Firstly, mini-batch likelihood estimation techniques
often employed by VI have only previously been applicable to models of independent data.
Secondly, approximating distributions have often imposed unrealistic assumptions over the
posterior. Fortunately, however, recent advances in generative modelling have provided
the framework with which to solve these problems. Here, artificial neural networks can be
used to flexibly construct powerful density approximations, which are then amenable to fast
computation using modern GPUs. This is otherwise known as black-box-variational inference.
This thesis presents a collection of black-box variational methods for the purposes of approximate inference in SDEs, SSMs, GPs and LFMs. Here we leverage artificial neural networks
to parametrise our approximate posterior distributions, permitting accurate inference in a
short time. We begin by presenting two methods for SDE inference. The first, inspired by
the Euler-Maruyama discretisation, approximates the discrete-time solution to a conditioned
diffusion process using recurrent neural networks. The second, which extends the first, eschews a discretisation scheme and approximates the continuous-time process directly. Finally
we consider the use of normalising flows for inference using SSMs (including discrete-time
SDEs), GPs and LFMs. Here we design a generative architecture that permits mini-batch
optimization, allowing approximate inference for big dat
Synthetic bacterial communities for plant growth promotion
Get PDFPhD ThesisIncreasing food demands have driven the adoption of new global strategies to intensify
productivity without relying on heavy chemical treatments. In the last decades, plant-growth
promoting rhizobacteria (PGPR) have emerged as potential biofertilisers and biopesticides in
agriculture. The overall aim of this study was to research and develop approaches to
genetically engineer PGPR to improve their beneficial activities toward the plant partner.
A simplified PGPR community, a Bacillus consortium of three strains, was adopted to
study the complexity of the interactions occurring within the consortium and the plant
microbiome. Firstly, the comparative genomic analysis of the consortium highlighted the
unique and shared features responsible for plant promotion, microbial interaction and
cooperation among the strains (niche partitioning, organisation in biofilms with cooperative
mechanisms of quorum sensing, cell density control and antibiotic detoxification). Flux
balance analysis identified cross-feeding interactions among the strains and the metabolic
capability of the consortium to provide nitrogen to the plant, transforming it into forms
available for plant utilisation.
The consortium PGP potential was then investigated in vitro (LEAP mesocosm assay) and
in vivo (pot experiment) on the vegetable crop Brassica rapa. These tests show increased plant
growth when the strains were inoculated together rather than individually and when the
consortium was used as a supplement of the natural bulk soil microbiome. The in silico study
and the plant experiments highlighted areas for genetic improvement of the consortium
genomes.
Lastly, this work describes the development of a conjugation system that could be used
to efficiently engineer non-domesticated bacteria and bacterial communities, such as
rhizobacteria and plant microbiomes. The system, based on the plasmid pLS20, was developed
in Bacillus subtilis 168 and successfully tested on twenty-three wild type Bacillus strains and
three rhizobacillus communities.
The research presented here provides tools and approaches for the genetic
manipulation of rhizobacterial communities, with the ultimate aim of generating sustainable
agricultural bioformulations and sheds light on the complex interactions that can occur in a
model microbial PGPR consortia
The value of multi-functional urban agriculture in creating sustainable cities
Get PDFPhD ThesisChina's cities continue to expand rapidly and under severe challenge of sustainable
urban development. The Chinese Government has decided to bring agriculture back
into the city in a state-controlled way and to re-educate urban residents to enjoy
agriculture activities in urban areas. This research explores the Chinese
Government’s approach to new urban agriculture in China. It seeks to better
understand and evaluate the impacts of multifunctional urban agriculture on
sustainable urban development.
The work is set within the context of China’s extremely rapid urbanization and
concerns about pollution, poor lifestyles and an over-emphasis on manufacturing as
the economic driver of growth. This thesis has presented a first attempt to redefine
the term ‘urban’ in relation to urban agriculture, extending it to the urban core areas,
desakota areas and exurban areas. In this way it suggests a new typology of urban
agriculture in China, with a potentially broader range of objectives and possibilities
that might normally be associated with the subject or practice.
Taking Beijing as the case study city, this study selects 3 of its 16 districts:
Chaoyang, Changping and Miyun representing core, desakota and exurban areas.
The specific projects in these three districts are totally different, and together they
represent the three levels in the model of Chinese new urban agriculture. Each level
of model is informed and supported by case study of practical projects. These are:
Government fully-owned large projects, Government-supported privately run projects
and Folk Custom Villages. Data was collected from direct observation,
documentation, archive, physical survey, interviews and questionnaires.
This thesis found that the “Chinese” urban agriculture model, through three different
types of projects, aims to make people rethink the role of agriculture and see it not
simply as something undertaken by others in a rural area, nor as something simply to
provide food. Rather, it can be something which enhances the urban experience,
improves the urban environment, offers leisure facilities, engages people in
traditional culture and provides a diverse range of employment and livelihood
activities.
A well planned modern agricultural production is required to create an agricultural
environment with reasonable spatial layout to reduce pollution and to create
aesthetically pleasing and sustainable landscapes. It can help urban agriculture
ii
integrate into the city system in a more sustainable way by reconnecting urban life
and rural culture. This model, therefore, sets urban agriculture in a central role within
planned urbanization. In summary, this thesis suggests that this model could become
an important strategy for land use planning, urbanization and the sustainable
development of Chinese cities, indeed, all cities, in the future. This study will be of
interest to those scholars who are seeking to explore the Chinese urban agriculture
as an effective method for land use in sustainable urban development
Computational approaches for analysing and engineering micropollutant degradation in microbial communities
Get PDFPhD ThesisThe presence of micropollutants in wastewater is problematic, as many micropollutants
exert negative ecological and toxicological effects in their environment. A well-known
effect of micropollutants is the feminisation of aquatic wildlife by environmental estrogens, a proportion of which enter water courses from municipal sources via wastewater
treatment plants (WWTPs). While WWTPs remove some micropollutants, they are
not designed to do so. Given that WWTPs already have high operating costs (both
financially and energetically), there is a need for novel approaches to micropollutant
removal that are both cost-effective and environmentally sustainable. One proposed
approach is to use enzymes to degrade micropollutants, which requires an understanding of metabolic pathways for the desired micropollutant, and a strategy for deploying
the enzymes in the environment.
Although tools exist to assist with metabolic pathway prediction and enzyme discovery,
there are currently no computational approaches that are able to identify enzymes from
a user’s collection of proteins (given a query compound and expected change to that
query compound). To address this research gap, we developed EnSeP, a data-driven,
transformation-specific approach to enzyme discovery. Using EnSeP, we then identified
candidate enzymes involved in estradiol degradation.
Recent advances in synthetic biology mean that deploying a single synthetic construct
in multiple microorganisms is feasible. In the context of micropollutant metabolism,
this means that a biodegradative pathway could be introduced into multiple organisms
in a community simultaneously, providing more opportunities for the construct (and
its functionality) to persist in the population long-term. However, current design
tools have not yet been adapted for multiple organism applications. To address this
research gap, we developed an evolutionary algorithm (EA) that optimises a single
coding sequence (CDS) for multiple hosts. Finally, based on insights from developing
the EA, we developed an improved version of the single-organism CDS optimisation
algorithm that the EA is based on
Simplifying Internet of Things (IoT) Data Processing Work ow Composition and Orchestration in Edge and Cloud Datacenters
Get PDFPh. D. Thesis.Internet of Things (IoT) allows the creation of virtually in nite connections into a
global array of distributed intelligence. Identifying a suitable con guration of devices,
software and infrastructures in the context of user requirements are fundamental to
the success of delivering IoT applications. However, the design, development, and
deployment of IoT applications are complex and complicated due to various unwarranted
challenges. For instance, addressing the IoT application users' subjective and
objective opinions with IoT work
ow instances remains a challenge for the design of
a more holistic approach. Moreover, the complexity of IoT applications increased exponentially
due to the heterogeneous nature of the Edge/Cloud services, utilised to
lower latency in data transformation and increase reusability.
To address the composition and orchestration of IoT applications in the cloud and
edge environments, this thesis presents IoT-CANE (Context Aware Recommendation
System) as a high-level uni ed IoT resource con guration recommendation system
which embodies a uni ed conceptual model capturing con guration, constraint and
infrastructure features of Edge/Cloud together with IoT devices. Second, I present
an IoT work
ow composition system (IoTWC) to allow IoT users to pipeline their
work
ows with proposed IoT work
ow activity abstract patterns. IoTWC leverages
the analytic hierarchy process (AHP) to compose the multi-level IoT work
ow that
satis es the requirements of any IoT application. Besides, the users are be tted with
recommended IoT work
ow con gurations using an AHP based multi-level composition
framework. The proposed IoTWC is validated on a user case study to evaluate
the coverage of IoT work
ow activity abstract patterns and a real-world scenario for
smart buildings. Last, I propose a fault-tolerant automation deployment IoT framework
which captures the IoT work
ow plan from IoTWC to deploy in multi-cloud
edge environment with a fault-tolerance mechanism. The e ciency and e ectiveness
of the proposed fault-tolerant system are evaluated in a real-time water
ooding data
monitoring and management applicatio
Algorithm-Hardware Co-Design for Performance-driven Embedded Genomics
Get PDFPhD ThesisGenomics includes development of techniques for diagnosis, prognosis and therapy of
over 6000 known genetic disorders. It is a major driver in the transformation of medicine
from the reactive form to the personalized, predictive, preventive and participatory (P4)
form. The availability of genome is an essential prerequisite to genomics and is obtained
from the sequencing and analysis pipelines of the whole genome sequencing (WGS).
The advent of second generation sequencing (SGS), significantly, reduced the sequencing
costs leading to voluminous research in genomics. SGS technologies, however, generate
massive volumes of data in the form of reads, which are fragmentations of the real
genome. The performance requirements associated with mapping reads to the reference
genome (RG), in order to reassemble the original genome, now, stands disproportionate
to the available computational capabilities. Conventionally, the hardware resources used
are made of homogeneous many-core architecture employing complex general-purpose
CPU cores. Although these cores provide high-performance, a data-centric approach
is required to identify alternate hardware systems more suitable for affordable and
sustainable genome analysis.
Most state-of-the-art genomic tools are performance oriented and do not address
the crucial aspect of energy consumption. Although algorithmic innovations have
reduced runtime on conventional hardware, the energy consumption has scaled poorly.
The associated monetary and environmental costs have made it a major bottleneck to
translational genomics. This thesis is concerned with the development and validation
of read mappers for embedded genomics paradigm, aiming to provide a portable and
energy-efficient hardware solution to the reassembly pipeline. It applies the algorithmhardware co-design approach to bridge the saturation point arrived in algorithmic
innovations with emerging low-power/energy heterogeneous embedded platforms.
Essential to embedded paradigm is the ability to use heterogeneous hardware
resources. Graphical processing units (GPU) are, often, available in most modern devices
alongside CPU but, conventionally, state-of-the-art read mappers are not tuned to use
both together. The first part of the thesis develops a Cross-platfOrm Read mApper
using opencL (CORAL) that can distribute workload on all available devices for high
performance. OpenCL framework mitigates the need for designing separate kernels for
CPU and GPU. It implements a verification-aware filtration algorithm for rapid pruning
and identification of candidate locations for mapping reads to the RG.
Mapping reads on embedded platforms decreases performance due to architectural
differences such as limited on-chip/off-chip memory, smaller bandwidths and simpler
cores. To mitigate performance degradation, in second part of the thesis, we propose a
REad maPper for heterogeneoUs sysTEms (REPUTE) which uses an efficient dynamic
programming (DP) based filtration methodology. Using algorithm-hardware co-design
and kernel level optimizations to reduce its memory footprint, REPUTE demonstrated
significant energy savings on HiKey970 embedded platform with acceptable performance.
The third part of the thesis concentrates on mapping the whole genome on an
embedded platform. We propose a Pyopencl based tooL for gEnomic workloaDs
tarGeting Embedded platfoRms (PLEDGER) which includes two novel contributions.
The first one proposes a novel preprocessing strategy to generate low-memory footprint
(LMF) data structure to fit all human chromosomes at the cost of performance. Second
contribution is LMF DP-based filtration method to work in conjunction with the
proposed data structures. To mitigate performance degradation, the kernel employs
several optimisations including extensive usage of bit-vector operations. Extensive
experiments using real human reads were carried out with state-of-the-art read mappers
on 5 different platforms for CORAL, REPUTE and PLEDGER. The results show that
embedded genomics provides significant energy savings with similar performance
compared to conventional CPU-based platforms