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Estimating the rate of nucleotide and amino acid substitutions is fundamental to our understanding of how fast species evolve. It also enables us to reconstruct their natural history. The development of sophisticated statistical methods and software in molecular sequencing analysis has made it easier for biologists to test and compare various evolutionary hypotheses and infer evolutionary parameters. However, the use of these software packages as a 'blackbox' without paying close attention to the underlying assumptions can lead to spurious rate estimations across different timescales which often result in erroneous inferences of virus evolutionary histories. In this thesis, I develop quantitative methods to understand biological mechanisms that can alter the molecular evolutionary rate of viruses over time and investigate the implications of having a time-dependent rate of evolution for reconstructing the phylogenetic history of viruses. I also explore alternative methods to estimating the time of origin and reconstructing the evolutionary trajectory of viruses using mobility data, national vital statistics, and serology surveys. I show how three evolutionary processes of rapid within-host evolution during chronic infections, purifying selection, and site saturation can determine the changes in evolutionary rate of viruses over different timescales, from a few weeks to millions of years. I also leverage excess mortality and seroprevalence data to correct for under-reporting of COVID-19-related deaths, reconstruct early transmission dynamics of SARS-CoV-2, and estimate the start of the Iranian epidemic as well its growth rate in the absence of high-quality genome sequence and province-level epidemiological data. This work will build towards the goal of making more accurate estimations of the timescale of phylogenetic histories and the rate at which species evolve. It also further strengthens the conceptual link between phylogenetics, population genetics, and molecular evolution.

Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumour. Despite advances in GBM treatment, there is a high frequency of local relapse due to the acquisition of drug resistance. Investigation of glioma cell lines will help us to understand the molecular basis of this hard to treat tumour. In this study, the rat C6 glioma cell line was used as a model alongside two drug selected derivatives (C6-etoposide and C6-irinotecan) to investigate the mechanisms of chemo-resistance in glioma by identifying candidate proteins, genes, and key signalling pathways. Proteomic (2D gel electrophoresis) and genomic (gene array) analyses were performed to determine protein and gene expression changes. Integration of this data with cellular pathway analysis resulted in the prediction that cellular migration and the response to oxidative stress would be distinct in the drug selected C6 cell lines. Cell migration was subsequently assessed using wound scratch repair and transwell migration assays, whilst the response to oxidative stress produced by reactive oxygen species was determined fluorimetrically. The C6 cell line exposed to irinotecan (DNA topoisomerase I inhibitor) showed reduced migration, even under the influence of chemoattractant, compared to other cell lines, consistent with alterations in the expression of collagen genes. The C6 cell line exposed to etoposide (DNA topoisomerase II inhibitor) showed greater resistance to oxidative stress which was proposed to be due to alterations in the signalling pathways downstream of the PTEN/PI3Kinase. Future studies, investigating the effect of PI3Kinase pathway inhibitors are considered and it is proposed that further research into this signalling pathway will be able to uncover the molecular basis of distinct chemo-resistance in this important model cell system for aggressive glioma.

Advance Composite Materials have found various applications due to their unique properties over the past years. Properties such as high specific strength and stiffness, corrosion resistance and fatigue resistance that can be adjusted to specific requirements have made these materials as one of the main candidate considered during design process. The high energy absorbing capabilities of FRP composite materials is one of the main factors in their application in automotive and aerospace structures. They also provide other functional and economic benefits such as enhanced strength, durability, weight reduction and hence lower fuel consumption. The brittle nature of most composites accompanying other forms of energy absorption mechanisms such as fibre breakage, matrix cracking, deboning at the fibre-matrix interface and especially plies delaminations which play important roles on progressive failure mode and energy absorption capability of composite structures. Delamination which is known as principle mode of failure of layered composites due to separation along the interfaces of layers is one of the main concerns in designing of composite structures. This factor plays an important role on progressive failure mode and energy absorption capability of composite structures. Delamination can occur due to various reasons such as impact, imperfection in manufacturing, high stress concentration, environmental effects and post-processing. In this project an advanced manufacturing methods will be applied to stitch carbon-fibre composite testing specimens using sustainable natural flax fibres. The natural fibres running through the thickness of laminated composite structures will increase the resistance to crack propagation and consequently the energy absorption capability of composite absorbers. Natural flax fibres have the main advantage over the synthetic fibres (e.g. carbon and glass) of providing both resistance and progressive failure (effective crack growth resistance) in the wall of composite box absorbers. Progressive failure can provide high energy absorption in a controllable behaviour which reduces the main injuries and death during a crash event.

The aim of this project was to demonstrate and develop a confocal readout method for fluorescent immunosorbent assays and investigate its potential advantages in comparison to traditional immunoassays. The key point of a confocal immunosorbent assay is the ability to detect the thin layer of immunoassay in the presence of unbound fluorescent reagents without washing the overlayer. Heterogeneous and homogeneous sandwich immunoassays of human IgG model were demonstrated successfully followed by the use of an empirical decomposition method for quantitative separation of the signals of the thin fluorescent assay layer from the overlayer. The detection limits for the homogeneous and heterogeneous formats of the model were 2.2 and 5.5 ng/ml, respectively. The application of confocal microscopy in kinetic analysis of the antigen-antibody reaction of the human IgG model was studied for homogeneous and heterogeneous formats and two fluorescent labels antibodies (FITC and QDs). The association rates of binding of FITC and QD605 conjugated antibodies to human IgG on prepared surfaces were 5.7×104 and 2.6×104 (M-1s-1) respectively. Confocal detection immunosorbent assay enables the detection of more than one assay along the z-axis. By replacing standard substrates with multiple 30 :m layers of substrates, a high density array of immunosorbent assays was created within a stratified medium. Stacks of up to five modified thin mica substrates of model immunoassays were detected by confocal microscopy. When applied to model assays consisting of human and mouse IgGs on different layers, the z-axis multiplexing of immunosorbant assays was demonstrated. The arrays of multiplexed immunosorbent assays were extended to 3D format by using microcontact printing and the assay density was increased twice by detecting the stack of two substrates which each contained two IgGs assays.

High-throughput microfluidics-based assays can potentially increase the speed and quality of yeast replicative lifespan measurements that are related to aging. One major challenge is to efficiently convert large volumes of time-lapse images into quantitative measurements of yeast cell lifespan measurements. To address these issues, we developed several deep learning methods to analyze a large number of images collected from microfluidic experiments. First, we compared three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. Second, we evaluated convolutional neural networks for detecting cells from microfluidic images. The YOLO and Mask R-CNN are trained with yeast microfluidic images and tested for object detection, and features extraction. The results indicate that YOLO had better performance in terms of object detection and accuracy. In contrast, the Mask R-CNN had better performance in terms of cell area and better detection when the number of cells inside the trap is less than 3 cells. Third, prototyping an algorithm that can evaluate cell division events through family trees of cells. We generated a null distribution using single cells inside microfluidic traps. Based on this null distribution, we prototyped a likelihood algorithm for cell tracking between images at different time-points. We inferred cell family trees through a trace-back method. The replicative lifespan of a mother cell can be counted as the number of bifurcating branches of its family tree. Linear regression showed that predictions of our prototype correlated with experimental observations. Forth, since it is challenging to visualize and interpret the time-series data gathered through time-lapse microscopy images, we have developed a circular plotting software tool, mPolar, to visualize the trends and patterns of the cell movements, and cell division events in a time-series. Overall, our methods have the potential to accelerate the efficiency and expand the range of quantitative measurement of yeast replicative aging experiments. This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging.

In order to continue the progress of nanotechnology in producing effective and efficient mechanical, electrical, and optical nanostructure devices, the physics of the devices in micro and nanoscale need to be understood and accurately modeled. For instance, the demand for high areal density data storage devices such as hard disk drives (HDDs) has led to heat-assisted magnetic recording (HAMR) and microwave-assisted magnetic recording (MAMR) technologies. These technologies require layered nanostructures with thicknesses of a few nanometers and a high temperature difference across the layers. These technologies introduce thermo-mechanical design challenges since the nanoscale heat transfer mechanics do not follow the classical heat transfer theories. In this work, the mechanisms of heat transfer in the nanoscale are studied.A typical nanostructure is composed of several conductive and thin insulating layers (a few microns to a few nanometers) with a temperature difference between the layers where some insulating layers maintain the temperature gradient in such small spacings. In these structures, the dominant heat transfer mechanisms are radiation due to high temperature gradient and phonon conduction due to thin conductive layers. First, we review the fundamentals of electromagnetism and lattice vibrations and derive the classical heat transfer theories, and discuss the assumptions that lead to them. We show that the classical theories do not consider the wave nature of the heat transfer mechanisms (heat carriers) and are derived based on equilibrium thermodynamics. These assumptions make the obtained results inapplicable to our problems. Next, we study the recent theories and methods used to solve steady-state nanoscale heat transfer problems, and we further develop more accurate models of calculations.In this work, we study the mechanisms of heat transfer in nanostructures and present simulations for the thermal analysis of various multilayered structures. We show how the radiative and phonon conduction heat fluxes across a nanoscale vacuum gap vary as a function of the gap spacing, and focus on the problems with large heat flux across nanostructures. Next, we consider the effect of the interference of the heat carriers, due to their wave nature, on radiation and phonon conduction. The heat transfer coefficient (HTC) for various structures used in the current HDD technology is characterized by different temperature differences and conditions. The presented results are also used in collaboration with other researchers in simulations of HDI of HAMR HDDs.Next, the presented algorithms are used to simulate radiation in multilayered structures with absorbing materials, where the speed of propagation for absorbing materials is based on a recent model in literature that predicts the depth-dependent speed of electromagnet waves inside materials. We also present a novel way to describe the distribution of the thermal emission inside a multilayered structure using one distribution function instead of using multiple distributions for layers with different temperatures, which we call the unified field. This model is more accurate than the models in the preceding chapters at predicting the HTC for the phonon conduction in materials under some conditions.

Since its inception, quantum information processing (QIP) has been branched into many new directions. As well as well-known applications such as teleportation and metrology, researchers are beginning to investigate interdisciplinary areas such as quantum machine learning. One of the interesting areas is where quantum information science meets stochastic modelling, from the field of complexity science. Recently, it has been shown theoretically that, for simulating classical systems, quantumassisted models and simulators are more efficient in terms of memory storage they require to do simulations, compared with classical computers. That is, for most systems, classical simulators demand an excessive amount of memory storage, while quantum simulators can do the same simulation with less memory. The main focus of this thesis is on experimental realisation of quantum simulators that are capable of simulating stochastic processes with a reduced amount of memory. To implement the (nearly) exact simulation of stochastic processes using quantum simulators, it is essential to have low-noise state preparation, robust unitary operations, and high-precision read-out. These requirements, and the flexibility and precision of photonic quantum optics, make photonics the ideal system for developing the science of this new quantum advantage and for making strides towards its technological realisation. In the context of stochastic simulation, I have experimentally studied three key problems that serve as stepping stones in advancing this new field. In the first experiment, an error-tolerant quantum simulator was designed, and realised to simulate a 1D Ising spin chain, using internal states that store less information than the corresponding classical approach. Furthermore, an interesting and fundamental phenomenon named the ambiguity of simplicity is witnessed. This is the inconsistency that we observe in the order of relative complexity of two systems when we change the simulators from classical to quantum. In the second experiment, a quantum simulator was built that can simulate classical processes for more than one step of the simulation at a time, storing the information from multiple steps coherently. In the third experiment, a new type of quantum memory advantage, which is based on the dimensionality of the memory register, rather than on information entropy measure, is demonstrated. This advantage is realisable in a single simulator, in contrast to previous works. Realising the dimensionality memory advantage in practical applications does not rely on running multiple simulators in parallel.In the field of optical QIP, realising an ideal single-photon source has been a longstanding challenge. In another part of my PhD, I have worked on a source that aims to tackle some of the existing issues in this context. We built a source of high-quality entangled photons with a high heralding efficiency, which has the potential to be used in multi-photon experiments. This source was also essential to demonstrate a fundamental task in quantum non-locality, one-way steering, which was performed conclusively for the first time.