The structural and mechanical integrity of historic wood

Little is known about historic wood as it ages naturally. Instead, most studies focus on biological decay, as it is often assumed that wood remains otherwise stable with age. This PhD project was organised by Historic Scotland and the University of Glasgow to investigate the natural chemical and physical aging of wood. The natural aging of wood was a concern for Historic Scotland as traditional timber replacement is the standard form of repair used in wooden cultural heritage; replacing rotten timber with new timber of the same species. The project was set up to look at what differences could exist both chemically and physically between old and new wood, which could put unforeseen stress on the joint between them. Through Historic Scotland it was possible to work with genuine historic wood from two species, Oak and Scots pine, both from the 1500’s, rather than relying on artificial aging. Artificial aging of wood is still a debated topic, with consideration given to whether it is truly mimicking the aging process or just damaging the wood cells. The chemical stability of wood was investigated using Fourier-transform infrared (FTIR) microscopy, as well as wet chemistry methods including a test for soluble sugars from the possible breakdown of the wood polymers. The physical properties assessed included using a tensile testing machine to uncover possible differences in mechanical properties. An environmental chamber was used to test the reaction to moisture of wood of different ages, as moisture is the most damaging aspect of the environment to wooden cultural objects. The project uncovered several differences, both physical and chemical, between the modern and historic wood which could affect the success of traditional ‘like for like’ repairs. Both oak and pine lost acetyl groups, over historic time, from their hemicellulose polymers. This chemical reaction releases acetic acid, which had no effect on the historic oak but was associated with reduced stiffness in historic pine, probably due to degradation of the hemicellulose polymers by acid hydrolysis. The stiffness of historic oak and pine was also reduced by decay. Visible pest decay led to loss of wood density but there was evidence that fungal decay, extending beyond what was visible, degraded the S2 layer of the pine cell walls, reducing the stiffness of the wood by depleting the cellulose microfibrils most aligned with the grain. Fungal decay of polysaccharides in pine wood left behind sugars that attracted increased levels of moisture. The degradation of essential polymers in the wood structure due to age had different impacts on the two species of wood, and raised questions concerning both the mechanism of aging of wood and the ways in which traditional repairs are implemented, especially in Scots pine. These repairs need to be done with more care and precision, especially in choosing new timber to match the old. Within this project a quantitative method of measuring the microfibril angle (MFA) of wood using polarised Fourier transform infrared (FTIR) microscopy has been developed, allowing the MFA of both new and historic pine to be measured. This provides some of the information needed for a more specific match when selecting replacement timbers for historic buildings.

The optical properties of metamaterials

This thesis studies the parametric investigation, polarisation dependence and characterization of fishnet structure at near infrared wavelengths. Detailed simulations are performed to understand the behaviour of the structure at near infrared and optical wavelengths. Simulations are performed to obtain negative refractive index of the fishnet structure formed from nanoimprint lithography (NIL) by taking into account the effect of substrate and polymethyl methacrylate (PMMA) beneath it. Two different structures have been designed and fabricated of varying dimensions using NIL and their resonant wavelength measured in the near infrared at 1.45 µm and 1.88 µm. Simulations suggest that a negative refractive index real part with the magnitude -0.24 is found at 1.53 µm and this decrease to a maximum magnitude of -0.57 at 1.9 µm. The PMMA and suppressed pillars are here responsible for the increasing material losses and limiting the value of negative refractive index. An analytical approach has been suggested to characterise fishnet structures at oblique incidence. The expressions for an absorbing medium are rewritten for an alternative definition of refractive index. The expressions are initially validated for a dielectric slab and a metal film. These results provide the possibility that this proposal may yield a general algorithm for obtaining the complex reflection and transmission coefficients for artificial structures. FDTD simulations have been extensively used in this thesis to understand the optical metamaterials and their characterization.

Sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution

Understanding how virus strains offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses, including Foot-and-Mouth Disease Virus (FMDV) and the Influenza virus where multiple serotypes often co-circulate, in vitro testing of large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Vaccines will offer cross-protection against closely related strains, but not against those that are antigenically distinct. To be able to predict cross-protection we must understand the antigenic variability within a virus serotype, distinct lineages of a virus, and identify the antigenic residues and evolutionary changes that cause the variability. In this thesis we present a family of sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution (SABRE), as well as an extended version of the method, the extended SABRE (eSABRE) method, which better takes into account the data collection process. The SABRE methods are a family of sparse Bayesian hierarchical models that use spike and slab priors to identify sites in the viral protein which are important for the neutralisation of the virus. In this thesis we demonstrate how the SABRE methods can be used to identify antigenic residues within different serotypes and show how the SABRE method outperforms established methods, mixed-effects models based on forward variable selection or l1 regularisation, on both synthetic and viral datasets. In addition we also test a number of different versions of the SABRE method, compare conjugate and semi-conjugate prior specifications and an alternative to the spike and slab prior; the binary mask model. We also propose novel proposal mechanisms for the Markov chain Monte Carlo (MCMC) simulations, which improve mixing and convergence over that of the established component-wise Gibbs sampler. The SABRE method is then applied to datasets from FMDV and the Influenza virus in order to identify a number of known antigenic residue and to provide hypotheses of other potentially antigenic residues. We also demonstrate how the SABRE methods can be used to create accurate predictions of the important evolutionary changes of the FMDV serotypes. In this thesis we provide an extended version of the SABRE method, the eSABRE method, based on a latent variable model. The eSABRE method takes further into account the structure of the datasets for FMDV and the Influenza virus through the latent variable model and gives an improvement in the modelling of the error. We show how the eSABRE method outperforms the SABRE methods in simulation studies and propose a new information criterion for selecting the random effects factors that should be included in the eSABRE method; block integrated Widely Applicable Information Criterion (biWAIC). We demonstrate how biWAIC performs equally to two other methods for selecting the random effects factors and combine it with the eSABRE method to apply it to two large Influenza datasets. Inference in these large datasets is computationally infeasible with the SABRE methods, but as a result of the improved structure of the likelihood, we are able to show how the eSABRE method offers a computational improvement, leading it to be used on these datasets. The results of the eSABRE method show that we can use the method in a fully automatic manner to identify a large number of antigenic residues on a variety of the antigenic sites of two Influenza serotypes, as well as making predictions of a number of nearby sites that may also be antigenic and are worthy of further experiment investigation.