Modelling the fracture of advanced carbon and related materials

This thesis outlines the development of a novel computational model which is used to simulate the mechanical response of nuclear graphites on a microstructural scale. Application of finite element analysis (FEA) to the simulated microstructure models allows for the determination of material properties and demonstrates the effect of porosity on these outputs. Further, a methodology for crack propagation through the model enables the simulation of load-displacement curves and fracture parameters. A comprehensive microstructural characterisation programme was undertaken to ascertain pore data for use in computational models. Composite images were generated through optical microscopy in order to sample large areas (10 x 10 mm) of the graphite surface. Results for this work demonstrated the inherent variability of graphite and successfully quantified the pore size distribution. Extensive mechanical testing was undertaken to determine the failure distribution of graphite and two additional brittle materials (glass and ligament material). Biaxial and three-point flexural experiments were employed in order to test a large number of samples. Data from these test programmes was determined to be consistent with a normal distribution and did not provide conclusive evidence for disparate flaw populations. Additional experimental tests were performed to provide data that could be used in the determination of suitable modelling input parameters. Development and solution of the microstructure model allowed accurate representation of pore distributions in an FEA environment which in turn enabled computationally derived mechanical properties to be determined. These properties were comparable to values expected of graphite. Additionally, some simulated fracture parameters compared favourably with experimental results. However, not all properties were representative due to the significant geometric contrast between computational models and experimental samples

Modelling the fracture of advanced carbon and related materials

This thesis outlines the development of a novel computational model which is used to simulate the mechanical response of nuclear graphites on a microstructural scale. Application of finite element analysis (FEA) to the simulated microstructure models allows for the determination of material properties and demonstrates the effect of porosity on these outputs. Further, a methodology for crack propagation through the model enables the simulation of load-displacement curves and fracture parameters.A comprehensive microstructural characterisation programme was undertaken to ascertain pore data for use in computational models. Composite images were generated through optical microscopy in order to sample large areas (10 x 10 mm) of the graphite surface. Results for this work demonstrated the inherent variability of graphite and successfully quantified the pore size distribution.Extensive mechanical testing was undertaken to determine the failure distribution of graphite and two additional brittle materials (glass and ligament material). Biaxial and three-point flexural experiments were employed in order to test a large number of samples. Data from these test programmes was determined to be consistent with a normal distribution and did not provide conclusive evidence for disparate flaw populations. Additional experimental tests were performed to provide data that could be used in the determination of suitable modelling input parameters.Development and solution of the microstructure model allowed accurate representation of pore distributions in an FEA environment which in turn enabled computationally derived mechanical properties to be determined. These properties were comparable to values expected of graphite. Additionally, some simulated fracture parameters compared favourably with experimental results. However, not all properties were representative due to the significant geometric contrast between computational models and experimental samples

Text-based over-representation analysis of microarray gene lists with annotation bias

A major challenge in microarray data analysis is the functional interpretation of gene lists. A common approach to address this is over-representation analysis (ORA), which uses the hypergeometric test (or its variants) to evaluate whether a particular functionally defined group of genes is represented more than expected by chance within a gene list. Existing applications of ORA have been largely limited to pre-defined terminologies such as GO and KEGG. We report our explorations of whether ORA can be applied to a wider mining of free-text. We found that a hitherto underappreciated feature of experimentally derived gene lists is that the constituents have substantially more annotation associated with them, as they have been researched upon for a longer period of time. This bias, a result of patterns of research activity within the biomedical community, is a major problem for classical hypergeometric test-based ORA approaches, which cannot account for such bias. We have therefore developed three approaches to overcome this bias, and demonstrate their usability in a wide range of published datasets covering different species. A comparison with existing tools that use GO terms suggests that mining PubMed abstracts can reveal additional biological insight that may not be possible by mining pre-defined ontologies alone

Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets

<p>Abstract</p> <p>Background</p> <p>Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test). The second test then verifies whether the theme of the geneset is relevant (usually done with an empirical test that compares the geneset of interest with random genesets). Current methods do not test this second null hypothesis because it has been assumed that the distribution of p-values for random genesets (when tested against the first null hypothesis) is uniform. Here we demonstrate that such an assumption is generally incorrect and consequently, such methods may erroneously associate the biology of a particular geneset with cancer prognosis.</p> <p>Results</p> <p>To assess the impact of non-uniform distributions for random genesets in such studies, an automated theme-driven method was developed. This method empirically approximates the p-value distribution of sets of unrelated genes based on a permutation approach, and tests whether predefined sets of biologically-related genes are associated with survival. The results from a comparison with a published theme-driven approach revealed non-uniform distributions, suggesting a significant problem exists with false positive rates in the original study. When applied to two public cancer datasets our technique revealed novel ontological categories with prognostic power, including significant correlations between "fatty acid metabolism" with overall survival in breast cancer, as well as "receptor mediated endocytosis", "brain development", "apical plasma membrane" and "MAPK signaling pathway" with overall survival in lung cancer.</p> <p>Conclusions</p> <p>Current methods of theme-driven survival studies assume uniformity of p-values for random genesets, which can lead to false conclusions. Our approach provides a method to correct for this pitfall, and provides a novel route to identifying higher-level biological themes and pathways with prognostic power in clinical microarray datasets.</p

Microwave-assisted synthesis of a MK2 inhibitor by Suzuki-Miyaura coupling for study in Werner syndrome cells

Microwave-assisted Suzuki-Miyaura cross-coupling reactions have been employed towards the synthesis of three different MAPKAPK2 (MK2) inhibitors to study accelerated aging in Werner syndrome (WS) cells, including the cross-coupling of a 2-chloroquinoline with a 3-pyridinylboronic acid, the coupling of an aryl bromide with an indolylboronic acid and the reaction of a 3-amino-4-bromopyrazole with 4-carbamoylphenylboronic acid. In all of these processes, the Suzuki-Miyaura reaction was fast and relatively efficient using a palladium catalyst under microwave irradiation. The process was incorporated into a rapid 3-step microwave-assisted method for the synthesis of a MK2 inhibitor involving 3-aminopyrazole formation, pyrazole C-4 bromination using N-bromosuccinimide (NBS), and Suzuki-Miyaura cross-coupling of the pyrazolyl bromide with 4-carbamoylphenylboronic acid to give the target 4-arylpyrazole in 35% overall yield, suitable for study in WS cells

Microwave-assisted synthesis of a MK2 inhibitor by Suzuki-Miyaura coupling for study in Werner syndrome cells

Microwave-assisted Suzuki-Miyaura cross-coupling reactions have been employed towards the synthesis of three different MAPKAPK2 (MK2) inhibitors to study accelerated aging in Werner syndrome (WS) cells, including the cross-coupling of a 2-chloroquinoline with a 3-pyridinylboronic acid, the coupling of an aryl bromide with an indolylboronic acid and the reaction of a 3-amino-4-bromopyrazole with 4-carbamoylphenylboronic acid. In all of these processes, the Suzuki-Miyaura reaction was fast and relatively efficient using a palladium catalyst under microwave irradiation. The process was incorporated into a rapid 3-step microwave-assisted method for the synthesis of a MK2 inhibitor involving 3-aminopyrazole formation, pyrazole C-4 bromination using N-bromosuccinimide (NBS), and Suzuki-Miyaura cross-coupling of the pyrazolyl bromide with 4-carbamoylphenylboronic acid to give the target 4-arylpyrazole in 35% overall yield, suitable for study in WS cells

The Relationship between CD27 Negative and Positive B Cell Populations in Human Peripheral Blood

CD27 expression has been used to distinguish between memory and naive B cells in humans. However, low levels of mutated and isotype-switched CD27−IgD− cells are seen in healthy adults, and these are increased in some autoimmune diseases and in the elderly. Thus CD27 is not a universal marker of memory B cells in humans. Various hypotheses have been put forward as to the function of the CD27− memory population. Since we have previously found high-throughput IGHV repertoire analysis useful to distinguish “innate-like” memory B cells (CD27+IgD+), we have employed similar analyses to elucidate the relationship between CD27− and CD27+ memory B cells. IgM+IgD− memory cells in both the CD27+ and CD27− compartments share the unique characteristics of the “innate-like” IgM+IgD+CD27+ cells. The switched CD27+ and CD27− memory cells share a similar IGHV repertoire, having more in common with each other than with “innate-like” memory cells, although it is interesting that IgG2 and IgA2 subclasses of antibody in both switched memory populations have a more “innate-like” repertoire. Clonality analysis shows evidence of a close clonal relationship between the two populations in that both CD27− and CD27+ switched memory cells can be found in the same genealogical tree. The expression of CD27 does not appear to occur in a linear developmental fashion, since we see CD27− cells as precursors of CD27+ cells and vice versa. Despite the similarities, the CDR-H3 repertoire of the CD27− cells is significantly different from both the CD27+IgD+ and CD27+IgD− populations, indicating that perhaps the lack of CD27 might be related to binding properties of the Ig CDR-H3 region

The effect of RO3201195 and a pyrazolyl ketone P38 MAPK inhibitor library on the proliferation of Werner syndrome cells

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