The transformation of banana with potential virus resistance genes

One approach to reducing the yield losses caused by banana viral diseases is the use of genetic engineering and pathogen-derived resistance strategies to generate resistant cultivars. The development of transgenic virus resistance requires an efficient banana transformation method, particularly for commercially important 'Cavendish' type cultivars such as 'Grand Nain'. Prior to this study, only two examples of the stable transformation of banana had been reported, both of which demonstrated the principle of transformation but did not characterise transgenic plants in terms of the efficiency at which individual transgenic lines were generated, relative activities of promoters in stably transformed plants, and the stability of transgene expression. The aim of this study was to develop more efficient transformation methods for banana, assess the activity of some commonly used and also novel promoters in stably transformed plants, and transform banana with genes that could potentially confer resistance to banana bunchy top nanovirus (BBTV) and banana bract mosaic potyvirus (BBrMV). A regeneration system using immature male flowers as the explant was established. The frequency of somatic embryogenesis in male flower explants was influenced by the season in which the inflorescences were harvested. Further, the media requirements of various banana cultivars in respect to the 2,4-D concentration in the initiation media also differed. Following the optimisation of these and other parameters, embryogenic cell suspensions of several banana (Musa spp.) cultivars including 'Grand Nain' (AAA), 'Williams' (AAA), 'SH-3362' (AA), 'Goldfinger' (AAAB) and 'Bluggoe' (ABB) were successfully generated. Highly efficient transformation methods were developed for both 'Bluggoe' and 'Grand Nain'; this is the first report of microprojectile bombardment transformation of the commercially important 'Grand Nain' cultivar. Following bombardment of embryogenic suspension cells, regeneration was monitored from single transfom1ed cells to whole plants using a reporter gene encoding the green fluorescent protein (gfp). Selection with kanamycin enabled the regeneration of a greater number of plants than with geneticin, while still preventing the regeneration of non-transformed plants. Southern hybridisation confirmed the neomycin phosphotransferase gene (npt II) was stably integrated into the banana genome and that multiple transgenic lines were derived from single bombardments. The activity, stability and tissue specificity of the cauliflower mosaic virus 358 (CaMV 35S) and maize polyubiquitin-1 (Ubi-1) promoters were examined. In stably transformed banana, the Ubi-1 promoter provided approximately six-fold higher p-glucuronidase (GUS) activity than the CaMV 35S promoter, and both promoters remained active in glasshouse grown plants for the six months they were observed. The intergenic regions ofBBTV DNA-I to -6 were isolated and fused to either the uidA (GUS) or gfjJ reporter genes to assess their promoter activities. BBTV promoter activity was detected in banana embryogenic cells using the gfp reporter gene. Promoters derived from BBTV DNA-4 and -5 generated the highest levels of transient activity, which were greater than that generated by the maize Ubi-1 promoter. In transgenic banana plants, the activity of the BBTV DNA-6 promoter (BT6.1) was restricted to the phloem of leaves and roots, stomata and root meristems. The activity of the BT6.1 promoter was enhanced by the inclusion of intron-containing fragments derived from the maize Ubi-1, rice Act-1, and sugarcane rbcS 5' untranslated regions in GUS reporter gene constructs. In transient assays in banana, the rice Act-1 and maize Ubi-1 introns provided the most significant enhancement, increasing expression levels 300-fold and 100-fold, respectively. The sugarcane rbcS intron increased expression about 10-fold. In stably transformed banana plants, the maize Ubi-1 intron enhanced BT6.1 promoter activity to levels similar to that of the CaMV 35S promoter, but did not appear to alter the tissue specificity of the promoter. Both 'Grand Nain' and 'Bluggoe' were transformed with constructs that could potentially confer resistance to BBTV and BBrMV, including constructs containing BBTV DNA-1 major and internal genes, BBTV DNA-5 gene, and the BBrMV coat protein-coding region all under the control of the Ubi-1 promoter, while the BT6 promoter was used to drive the npt II selectable marker gene. At least 30 transgenic lines containing each construct were identified and replicates of each line are currently being generated by micropropagation in preparation for virus challenge.

Hierarchical models for 2D presence/absence data having ambiguous zeroes: With a biogeographical case study on dingo behaviour

This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions. The essence of the thesis can be described as follows. Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose that the researcher wishes to estimate the effects of covariates on the binary responses, whilst taking into account underlying spatial variation, which is itself of some interest. This situation arises in many contexts and the dingo, cypress and toad case studies described in the motivation chapter are examples of this. Two main approaches to modelling and inference are investigated in this thesis. The first is frequentist and based on generalized linear models, with spatial variation modelled by using a block structure or by smoothing the residuals spatially. The EM algorithm can be used to obtain point estimates, coupled with bootstrapping or asymptotic MLE estimates for standard errors. The second approach is Bayesian and based on a three- or four-tier hierarchical model, comprising a logistic regression with covariates for the data layer, a binary Markov Random field (MRF) for the underlying spatial process, and suitable priors for parameters in these main models. The three-parameter autologistic model is a particular MRF of interest. Markov chain Monte Carlo (MCMC) methods comprising hybrid Metropolis/Gibbs samplers is suitable for computation in this situation. Model performance can be gauged by MCMC diagnostics. Model choice can be assessed by incorporating another tier in the modelling hierarchy. This requires evaluation of a normalization constant, a notoriously difficult problem. Difficulty with estimating the normalization constant for the MRF can be overcome by using a path integral approach, although this is a highly computationally intensive method. Different methods of estimating ratios of normalization constants (N Cs) are investigated, including importance sampling Monte Carlo (ISMC), dependent Monte Carlo based on MCMC simulations (MCMC), and reverse logistic regression (RLR). I develop an idea present though not fully developed in the literature, and propose the Integrated mean canonical statistic (IMCS) method for estimating log NC ratios for binary MRFs. The IMCS method falls within the framework of the newly identified path sampling methods of Gelman & Meng (1998) and outperforms ISMC, MCMC and RLR. It also does not rely on simplifying assumptions, such as ignoring spatio-temporal dependence in the process. A thorough investigation is made of the application of IMCS to the three-parameter Autologistic model. This work introduces background computations required for the full implementation of the four-tier model in Chapter 7. Two different extensions of the three-tier model to a four-tier version are investigated. The first extension incorporates temporal dependence in the underlying spatio-temporal process. The second extensions allows the successes and failures in the data layer to depend on time. The MCMC computational method is extended to incorporate the extra layer. A major contribution of the thesis is the development of a fully Bayesian approach to inference for these hierarchical models for the first time. Note: The author of this thesis has agreed to make it open access but invites people downloading the thesis to send her an email via the 'Contact Author' function.

On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty, and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exists where the more mind changes the learner is willing to accept, the lesser the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

Acoustic keyword spotting in speech with applications to data mining

Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spotting to data mining tasks. The thesis makes a number of major contributions to the ¯eld of keyword spotting. The ¯rst major contribution is the development of a novel keyword veri¯cation method named Cohort Word Veri¯cation. This method combines high level lin- guistic information with cohort-based veri¯cation techniques to obtain dramatic improvements in veri¯cation performance, in particular for the problematic short duration target word class. The second major contribution is the development of a novel audio document indexing technique named Dynamic Match Lattice Spotting. This technique aug- ments lattice-based audio indexing principles with dynamic sequence matching techniques to provide robustness to erroneous lattice realisations. The resulting algorithm obtains signi¯cant improvement in detection rate over lattice-based audio document indexing while still maintaining extremely fast search speeds. The third major contribution is the study of multiple veri¯er fusion for the task of keyword veri¯cation. The reported experiments demonstrate that substantial improvements in veri¯cation performance can be obtained through the fusion of multiple keyword veri¯ers. The research focuses on combinations of speech background model based veri¯ers and cohort word veri¯ers. The ¯nal major contribution is a comprehensive study of the e®ects of limited training data for keyword spotting. This study is performed with consideration as to how these e®ects impact the immediate development and deployment of speech technologies for non-English languages.