Combining heterogeneous sources of data for the reverse-engineering of gene regulatory networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Gene Regulatory Networks (GRNs) represent how genes interact in various cellular processes by describing how the expression level, or activity, of genes can affect the expression of the other genes. Reverse-engineering GRN models can help biologists understand and gain insight into genetic conditions and diseases. Recently, the increasingly widespread use of DNA microarrays, a high-throughput technology that allows the expression of thousands of genes to be measured simultaneously in biological experiments, has led to many datasets of gene expression measurements becoming publicly available and a subsequent explosion of research in the reverse-engineering of GRN models. However, microarray technology has a number of limitations as a data source for the modelling of GRNs, due to concerns over its reliability and the reproducibility of experimental results. The underlying theme of the research presented in this thesis is the incorporation of multiple sources and different types of data into techniques for reverse-engineering or learning GRNs from data. By drawing on many data sources, the resulting network models should be more robust, accurate and reliable than models that have been learnt using a single data source. This is achieved by focusing on two main strands of research. First, the thesis presents some of the earliest work in the incorporation of prior knowledge that has been generated from a large body of scientific papers, for Bayesian network based GRN models. Second, novel methods for the use of multiple microarray datasets to produce Bayesian network based GRN models are introduced. Empirical evaluations are used to show that the incorporation of literature-based prior knowledge and combining multiple microarray datasets can provide an improvement, when compared to the use of a single microarray dataset, for the reverse-engineering of Bayesian network based GRN models

LEGAL EDUCATION—OPEN YOUR CASEBOOKS PLEASE: IDENTIFYING OPEN ACCESS ALTERNATIVES TO LANGDELL’S LEGACY

Nonprofits, academic institutions, and educators have collaborated, at all academic levels, to create quality Open Educational Resources (OER) since that term was defined by UNESCO in 2002. These opensource educational materials are in the public domain and published under an open license, meaning that they can be freely copied, used, adapted, and re-shared with the public. They include not only textbooks but supplemental educational materials in various media formats. Their value is such that even federal and state legislatures are taking note and passing laws to incentivize the creation and use of OER in both secondary and higher education. Despite the momentum in academics toward the adoption of open textbooks and supplemental materials, legal academia has been slower to embrace open casebooks. By design, OER offers a great deal of flexibility for educators and the promise of cost savings for academic institutions and students. This paper examines the modern history of casebooks and the OER movement, as well as the various OER platforms ideally suited to create open content for law courses. The authors posit that a greater understanding of OER will give law professors and students a wider range of choice and ownership in course materials

Combining heterogeneous sources of data for the reverse-engineering of gene regulatory networks

Gene Regulatory Networks (GRNs) represent how genes interact in various cellular processes by describing how the expression level, or activity, of genes can affect the expression of the other genes. Reverse-engineering GRN models can help biologists understand and gain insight into genetic conditions and diseases. Recently, the increasingly widespread use of DNA microarrays, a high-throughput technology that allows the expression of thousands of genes to be measured simultaneously in biological experiments, has led to many datasets of gene expression measurements becoming publicly available and a subsequent explosion of research in the reverse-engineering of GRN models. However, microarray technology has a number of limitations as a data source for the modelling of GRNs, due to concerns over its reliability and the reproducibility of experimental results. The underlying theme of the research presented in this thesis is the incorporation of multiple sources and different types of data into techniques for reverse-engineering or learning GRNs from data. By drawing on many data sources, the resulting network models should be more robust, accurate and reliable than models that have been learnt using a single data source. This is achieved by focusing on two main strands of research. First, the thesis presents some of the earliest work in the incorporation of prior knowledge that has been generated from a large body of scientific papers, for Bayesian network based GRN models. Second, novel methods for the use of multiple microarray datasets to produce Bayesian network based GRN models are introduced. Empirical evaluations are used to show that the incorporation of literature-based prior knowledge and combining multiple microarray datasets can provide an improvement, when compared to the use of a single microarray dataset, for the reverse-engineering of Bayesian network based GRN models.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

First Documentation of the European Gut Fungus, \u3cem\u3eEphemerellomyces\u3c/em\u3e, and Other Insect Associated Endosymbionts in the Dry Creek Drainage, Boise, Idaho

Trichomycetes, a former class of obligate endosymbiotic fungi, are now recognized as an ecological group that inhabits the gut(s) of immature insects. Though the biodiversity and geographical distribution of trichomycetes are worldwide, our knowledge of the group in the Pacific Northwest is limited due to the few researchers conducting studies on them. Dry Creek drainage in Boise, Idaho was selected in the winter of 2009-10 as a potential site to find gut fungi. This initial survey provides the first account of Ephemerellomyces aquilonius, a species previously documented and studied only in Norway. Ephemerellomyces aquilonius (a monotypic genus) is a member of the Harpellales, an order of trichomycetes, and was dissected from the digestive tracts of mayflies (Ephemeroptera) collected from the creek. Amongst the various aquatic habitats surveyed and hosts recovered, gut fungi were also documented in stoneflies (Plecoptera) and black flies (Diptera: Simuliidae). Our investigation provides a significant new record of the very rare and unusual taxon, E. aquilonius, previously only known in Western Europe. The success of this brief survey demonstrates promise for further discoveries of gut fungi at this site, in Idaho and the Pacific Northwest. This disjunct distribution bridges an enormous geographical divide, although the species actually may be more widespread than earlier anticipated. Future collections and research on specimens from Dry Creek, with sequence data to be generated in our laboratory will be eagerly awaited as we place this unusual species in our expanding molecular-based phylogenies. We highlight here the morphology of E. aquilonius and some of the other endosymbionts found there

Consensus and meta-analysis regulatory networks for combining multiple microarray gene expression datasets

Microarray data is a key source of experimental data for modelling gene regulatory interactions from expression levels. With the rapid increase of publicly available microarray data comes the opportunity to produce regulatory network models based on multiple datasets. Such models are potentially more robust with greater confidence, and place less reliance on a single dataset. However, combining datasets directly can be difficult as experiments are often conducted on different microarray platforms, and in different laboratories leading to inherent biases in the data that are not always removed through pre-processing such as normalisation. In this paper we compare two frameworks for combining microarray datasets to model regulatory networks: pre- and post-learning aggregation. In pre-learning approaches, such as using simple scale-normalisation prior to the concatenation of datasets, a model is learnt from a combined dataset, whilst in post-learning aggregation individual models are learnt from each dataset and the models are combined. We present two novel approaches for post-learning aggregation, each based on aggregating high-level features of Bayesian network models that have been generated from different microarray expression datasets. Meta-analysis Bayesian networks are based on combining statistical confidences attached to network edges whilst Consensus Bayesian networks identify consistent network features across all datasets. We apply both approaches to multiple datasets from synthetic and real (Escherichia coli and yeast) networks and demonstrate that both methods can improve on networks learnt from a single dataset or an aggregated dataset formed using a standard scale-normalisation

Using Student Achievement Data to Support Instructional Decision Making

As educators face increasing pressure from federal, state, and local accountability policies to improve student achievement, the use of data has become more central to how many educators evaluate their practices and monitor students’ academic progress (Knapp et al., 2006). Despite this trend, questions about how educators should use data to make instructional decisions remain mostly unanswered. In response, this guide provides a framework for using student achievement data to support instructional decision making. These decisions include, but are not limited to, how to adapt lessons or assignments in response to students’ needs, alter classroom goals or objectives, or modify student-grouping arrangements. The guide also provides recommendations for creating the organizational and technological conditions that foster effective data use. Each recommendation describes action steps for implementation, as well as suggestions for addressing obstacles that may impedeprogress. In adopting this framework, educators will be best served by implementing the recommendations in this guide together rather than individually

Specialist breast cancer nurses’ views on implementing a fear of cancer recurrence intervention in practice: a mixed methods study

Introduction Fear of cancer recurrence (FCR) in people with breast cancer affects treatment recovery, quality of life, service utilisation and relationships. Our aim was to investigate how specialist breast cancer nurses (SBCN) respond to their patients’ fears of cancer recurrence and analyse SBCN’s views about embedding a new psychological intervention, the Mini-AFTERc, into their consultations. Method A mixed methods sequential design was used, informed by normalisation process theory. Phase 1: UK SBCNs were emailed a web-based survey to investigate how breast cancer survivors’ FCR is currently identified and managed, and their willingness to utilise the Mini-AFTERc. Phase 2: a purposive sample of respondents (n = 20) were interviewed to augment phase 1 responses, and explore views on the importance of addressing FCR, interest in the Mini-AFTERc intervention, its content, skills required and challenges to delivering the intervention. Results Ninety nurses responded to the survey. When SBCN’s were asked to identify the proportion of patients experiencing FCR in their caseload, there was no consensus on the size of the problem or unmet need. They estimated that 20–100% people experience moderate FCR and 10–70% severe FCR. The interviews identified that clinical conversations are focused primarily on giving information about signs and symptoms of recurrence rather than addressing the psychological aspects of fear. Conclusion Findings indicate wide variability in how FCR was identified, assessed and supported by a sample of UK SBCNs. The introduction of a structured intervention into practice was viewed favourably and has implications for nursing and health professional ways of working in all cancer services

Targeting the LOX/hypoxia axis reverses many of the features that make pancreatic cancer deadly: inhibition of LOX abrogates metastasis and enhances drug efficacy

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer‐related mortality. Despite significant advances made in the treatment of other cancers, current chemotherapies offer little survival benefit in this disease. Pancreaticoduodenectomy offers patients the possibility of a cure, but most will die of recurrent or metastatic disease. Hence, preventing metastatic disease in these patients would be of significant benefit. Using principal component analysis (PCA), we identified a LOX/hypoxia signature associated with poor patient survival in resectable patients. We found that LOX expression is upregulated in metastatic tumors from Pdx1‐Cre KrasG12D/+ Trp53R172H/+ (KPC) mice and that inhibition of LOX in these mice suppressed metastasis. Mechanistically, LOX inhibition suppressed both migration and invasion of KPC cells. LOX inhibition also synergized with gemcitabine to kill tumors and significantly prolonged tumor‐free survival in KPC mice with early‐stage tumors. This was associated with stromal alterations, including increased vasculature and decreased fibrillar collagen, and increased infiltration of macrophages and neutrophils into tumors. Therefore, LOX inhibition is able to reverse many of the features that make PDAC inherently refractory to conventional therapies and targeting LOX could improve outcome in surgically resectable disease

Supporting families managing childhood eczema:Developing and optimising Eczema Care Online using qualitative research

Background: childhood eczema is often poorly controlled due to under-use of emollients and topical corticosteroids. Parents/carers report practical and psychosocial barriers to managing their child’s eczema, including child resistance. Online interventions could potentially support parents/carers; however, rigorous research developing such interventions has been limited. Aim: to develop an online behavioural intervention to help parents/carers manage and co-manage their child’s eczema. Design and setting: Intervention development using a theory-, evidence- and Person-Based Approach with qualitative research. Methods: a systematic review and qualitative synthesis (32 studies) and interviews with parents/carers (N=30) were used to identify barriers and facilitators to effective eczema management, and a prototype intervention was developed. Think-aloud interviews with parents/carers (N=25) were then used to optimise the intervention to increase its acceptability and feasibility. Results: qualitative research identified that parents/carers had concerns about using emollients and topical corticosteroids; incomplete knowledge and skills around managing eczema; and reluctance to transitioning to co-managing eczema with their child. Think-aloud interviews highlighted that while experienced parents/carers felt they knew how to manage eczema, some information about how to use treatments was still new. Techniques for addressing barriers included: providing a rationale explaining how emollients and topical corticosteroids work; demonstrating how to use treatments; and highlighting that the intervention provided new, up-to-date information. Conclusions: parents/carers need support in effectively managing and co-managing their child’s eczema. The key output of this research is Eczema Care Online (ECO) for Families; an online intervention for parents/carers of children with eczema, which is being evaluated in a randomised trial

Assessing the Potential Effects of Fungicides on Nontarget Gut Fungi (Trichomycetes) and Their Associated Larval Black Fly Hosts

Fungicides are moderately hydrophobic and have been detected in water and sediment, particularly in agricultural watersheds, but typically are not included in routine water quality monitoring efforts. This is despite their widespread use and frequent application to combat fungal pathogens. Although the efficacy of these compounds on fungal pathogens is well documented, little is known about their effects on nontarget fungi. This pilot study, a field survey in southwestern Idaho from April to December 2010 on four streams with varying pesticide inputs (two agricultural and two reference sites), was conducted to assess nontarget impact of fungicides on gut fungi, or trichomycetes. Tissues of larval black flies (Diptera: Simuliidae), hosts of gut fungi, were analyzed for pesticide accumulation. Fungicides were detected in hosts from streams within agricultural watersheds but were not detected in hosts from reference streams. Gut fungi from agricultural sites exhibited decreased percent infestation, density and sporulation within the gut, and black fly tissues had elevated pesticide concentrations. Differences observed between the sites demonstrate a potential effect on this symbiotic system. Future research is needed to parse out the details of the complex biotic and abiotic relationships; however, these preliminary results indicate that impacts to nontarget organisms could have far-reaching consequences within aquatic ecosystems