A distance education case study: A comparison of instructional delivery methods practiced by higher education faculty and emerging standards

The purpose of this research was to compare the delivery methods as practiced by higher education faculty teaching distance courses with recommended or emerging standard instructional delivery methods for distance education. Previous research shows that traditional-type instructional strategies have been used in distance education and that there has been no training to distance teach. Secondary data, however, appear to suggest emerging practices which could be pooled toward the development of standards. This is a qualitative study based on the constant comparative analysis approach of grounded theory.^ Participants (N = 5) of this study were full-time faculty teaching distance education courses. The observation method used was unobtrusive content analysis of videotaped instruction. Triangulation of data was accomplished through one-on-one in-depth interviews and from literature review. Due to the addition of non-media content being analyzed, a special time-sampling technique was designed by the researcher--influenced by content analyst theories of media-related data--to sample portions of the videotape instruction that were observed and counted. A standardized interview guide was used to collect data from in-depth interviews. Coding was done based on categories drawn from review of literature, and from Cranton and Weston's (1989) typology of instructional strategies. The data were observed, counted, tabulated, analyzed, and interpreted solely by the researcher. It should be noted however, that systematic and rigorous data collection and analysis led to credible data.^ The findings of this study supported the proposition that there are no standard instructional practices for distance teaching. Further, the findings revealed that of the emerging practices suggested by proponents and by faculty who teach distance education courses, few were practiced even minimally. A noted example was the use of lecture and questioning. Questioning, as a teaching tool was used a great deal, with students at the originating site but not with distance students. Lectures were given, but were mostly conducted in traditional fashion--long in duration and with no interactive component.^ It can be concluded from the findings that while there are no standard practices for instructional delivery for distance education, there appears to be sufficient information from secondary and empirical data to initiate some standard instructional practices. Therefore, grounded in this research data is the theory that the way to arrive at some instructional delivery standards for televised distance education is a pooling of the tacitly agreed-upon emerging practices by proponents and practicing instructors. Implicit in this theory is a need for experimental research so that these emerging practices can be tested, tried, and proven, ultimately resulting in formal standards for instructional delivery in television education. ^

Headspace analysis of smokeless powders: Development of mass calibration methods using microdrop printing for chromatographic and ion mobility spectrometric detection

Smokeless powder additives are usually detected by their extraction from post-blast residues or unburned powder particles followed by analysis using chromatographic techniques. This work presents the first comprehensive study of the detection of the volatile and semi-volatile additives of smokeless powders using solid phase microextraction (SPME) as a sampling and pre-concentration technique. Seventy smokeless powders were studied using laboratory based chromatography techniques and a field deployable ion mobility spectrometer (IMS). The detection of diphenylamine, ethyl and methyl centralite, 2,4-dinitrotoluene, diethyl and dibutyl phthalate by IMS to associate the presence of these compounds to smokeless powders is also reported for the first time. A previously reported SPME-IMS analytical approach facilitates rapid sub-nanogram detection of the vapor phase components of smokeless powders. A mass calibration procedure for the analytical techniques used in this study was developed. Precise and accurate mass delivery of analytes in picoliter volumes was achieved using a drop-on-demand inkjet printing method. Absolute mass detection limits determined using this method for the various analytes of interest ranged between 0.03–0.8 ng for the GC-MS and between 0.03–2 ng for the IMS. Mass response graphs generated for different detection techniques help in the determination of mass extracted from the headspace of each smokeless powder. The analyte mass present in the vapor phase was sufficient for a SPME fiber to extract most analytes at amounts above the detection limits of both chromatographic techniques and the ion mobility spectrometer. Analysis of the large number of smokeless powders revealed that diphenylamine was present in the headspace of 96% of the powders. Ethyl centralite was detected in 47% of the powders and 8% of the powders had methyl centralite available for detection from the headspace sampling of the powders by SPME. Nitroglycerin was the dominant peak present in the headspace of the double-based powders. 2,4-dinitrotoluene which is another important headspace component was detected in 44% of the powders. The powders therefore have more than one headspace component and the detection of a combination of these compounds is achievable by SPME-IMS leading to an association to the presence of smokeless powders.

Headspace Analysis of Smokeless Powders: Development of Mass Calibration Methods using Microdrop Printing for Chromatographic and Ion Mobility Spectrometric Detection

Smokeless powder additives are usually detected by their extraction from post-blast residues or unburned powder particles followed by analysis using chromatographic techniques. This work presents the first comprehensive study of the detection of the volatile and semi-volatile additives of smokeless powders using solid phase microextraction (SPME) as a sampling and pre-concentration technique. Seventy smokeless powders were studied using laboratory based chromatography techniques and a field deployable ion mobility spectrometer (IMS). The detection of diphenylamine, ethyl and methyl centralite, 2,4-dinitrotoluene, diethyl and dibutyl phthalate by IMS to associate the presence of these compounds to smokeless powders is also reported for the first time. A previously reported SPME-IMS analytical approach facilitates rapid sub-nanogram detection of the vapor phase components of smokeless powders. A mass calibration procedure for the analytical techniques used in this study was developed. Precise and accurate mass delivery of analytes in picoliter volumes was achieved using a drop-on-demand inkjet printing method. Absolute mass detection limits determined using this method for the various analytes of interest ranged between 0.03 - 0.8 ng for the GC-MS and between 0.03 - 2 ng for the IMS. Mass response graphs generated for different detection techniques help in the determination of mass extracted from the headspace of each smokeless powder. The analyte mass present in the vapor phase was sufficient for a SPME fiber to extract most analytes at amounts above the detection limits of both chromatographic techniques and the ion mobility spectrometer. Analysis of the large number of smokeless powders revealed that diphenylamine was present in the headspace of 96% of the powders. Ethyl centralite was detected in 47% of the powders and 8% of the powders had methyl centralite available for detection from the headspace sampling of the powders by SPME. Nitroglycerin was the dominant peak present in the headspace of the double-based powders. 2,4-dinitrotoluene which is another important headspace component was detected in 44% of the powders. The powders therefore have more than one headspace component and the detection of a combination of these compounds is achievable by SPME-IMS leading to an association to the presence of smokeless powders.

Properties of seawater and particulate matter from a WETLabs AC-S spectrophotometer and a WETLabs chlorophyll fluorometer mounted on the continuous surface water sampling system during the Tara Oceans expedition 2009-2013

The Tara Oceans Expedition (2009-2013) was a global survey of ocean ecosystems aboard the Sailing Vessel Tara. It carried out extensive measurements of evironmental conditions and collected plankton (viruses, bacteria, protists and metazoans) for later analysis using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data publication provides permanent links to original and updated versions of validated data files containing measurements from the Continuous Surface Sampling System [CSSS]. Water was pumped at the front of the vessel from ~2m depth, then de-bubbled and circulated to a WETLabs AC-S spectrophotometer and a WETLabs chlorophyll fluorometer. Systems maintenance (instrument cleaning, flushing) was done approximately once a week and in port between successive legs. All data were stamped with a GPS.

Integrated geoarchaeological methods for the determination of site activity areas : A study of a Viking Age house in Reykjavik, Iceland

Acknowledgements The excavation was funded by the City of Reykjavík, and the geoarchaeological research was funded by a SSHRCC Doctoral Fellowship from the government of Canada, an Overseas Research Studentship, the Cambridge Commonwealth Trust, Pelham Roberts and Muriel Onslow Research Studentships from Newnham College, Cambridge, and Canadian Centennial Scholarships from the Canadian High Commission in London. Garðar Guðmundsson took the micromorphology samples, and supervised sampling on site. The bones were counted by Clayton Tinsley, the thin sections were made by Julie Boreham, and Steve Boreham and his team in the Department of Geography, University of Cambridge, provided technical support for all of the bulk geochemical analyses that were conducted by K. Milek, except for ICP–AES, which was conducted by ALS Chemex. Our gratitude is extended to Charles French, Catherine Hills, Peter Jordan and two anonymous reviewers for their support and helpful comments on earlier drafts of this paper, and to Óskar Gísli Sveinbjarnarson for his assistance with the figures.

Integrated geoarchaeological methods for the determination of site activity areas : A study of a Viking Age house in Reykjavik, Iceland

Acknowledgements The excavation was funded by the City of Reykjavík, and the geoarchaeological research was funded by a SSHRCC Doctoral Fellowship from the government of Canada, an Overseas Research Studentship, the Cambridge Commonwealth Trust, Pelham Roberts and Muriel Onslow Research Studentships from Newnham College, Cambridge, and Canadian Centennial Scholarships from the Canadian High Commission in London. Garðar Guðmundsson took the micromorphology samples, and supervised sampling on site. The bones were counted by Clayton Tinsley, the thin sections were made by Julie Boreham, and Steve Boreham and his team in the Department of Geography, University of Cambridge, provided technical support for all of the bulk geochemical analyses that were conducted by K. Milek, except for ICP–AES, which was conducted by ALS Chemex. Our gratitude is extended to Charles French, Catherine Hills, Peter Jordan and two anonymous reviewers for their support and helpful comments on earlier drafts of this paper, and to Óskar Gísli Sveinbjarnarson for his assistance with the figures.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Interspecific competition between the potato cyst nematode species Globodera pallida and G. rostochiensis

The two potato cyst nematode species, Globodera pallida and G. rostochiensis, are among the most important pests of potato. PCN are difficult to manage, while the two species respond differently to the main control methods. An increase in the incidence of G. pallida had been reported and is generally attributed to greater effectiveness of control measures against G. rostochiensis. The status of PCN in Ireland was studied using PCR. The results demonstrated qPCR to be an efficient means of high-throughput PCN sampling, being able to accurately identify both species in mixed-species populations. Species discrimination using qPCR revealed an increase in the incidence of G. pallida in Ireland in the absence of G. pallida-selective control measures. The population dynamics of G. pallida and G. rostochiensis in Ireland were studied in mixed- and single-species competition assays in vivo. G. pallida proved to be the more successful species, with greater multiplication in mixed- than single-species populations, with G. rostochiensis showing the opposite. This effect was similarly observed in staggered inoculation trials and population proportion trials. It was hypothesised that the greater G. pallida competitiveness could be attributed to its later hatch. G. pallida exhibited a later peak in hatching activity and more prolonged hatch, relative to G. rostochiensis. G. rostochiensis hatch was significantly reduced in mixedspecies hatching assays. G. pallida hatch was significantly higher when hatch was induced in potato root leachates containing G. rostochiensis-specific compounds, indicating that G. pallida hatch is stimulated upon perception of G. rostochiensis–derived compounds. Rhizotron studies revealed that root damage, caused by feeding of the early-hatching G. rostochiensis, resulted in increased lateral root proliferation and significantly increased G. pallida multiplication. Split-root trials indicated a significant G. pallida-induced ISR effect. G. rostochiensis multiplication was significantly reduced in split-root rhizotrons when G. pallida colonised roots before or after G. rostochiensis infection.

Assessment of exposure to the Penicillium glabrum complex in cork industry using complementing methods

Cork oak is the second most dominant forest species in Portugal and makes this country the world leader in cork export. Occupational exposure to Chrysonilia sitophila and the Penicillium glabrum complex in cork industry is common, and the latter fungus is associated with suberosis. However, as conventional methods seem to underestimate its presence in occupational environments, the aim of our study was to see whether information obtained by polymerase chain reaction (PCR), a molecular-based method, can complement conventional findings and give a better insight into occupational exposure of cork industry workers. We assessed fungal contamination with the P. glabrum complex in three cork manufacturing plants in the outskirts of Lisbon using both conventional and molecular methods. Conventional culturing failed to detect the fungus at six sampling sites in which PCR did detect it. This confirms our assumption that the use of complementing methods can provide information for a more accurate assessment of occupational exposure to the P. glabrum complex in cork industry.

Sampling strategies for characterization of neuropeptides using mass spectrometry and functional implications into cell-cell signaling

In this dissertation, there are developed different analytical strategies to discover and characterize mammalian brain peptides using small amount of tissues. The magnocellular neurons of rat supraoptic nucleus in tissue and cell culture served as the main model to study neuropeptides, in addition to hippocampal neurons and mouse embryonic pituitaries. The neuropeptidomcis studies described here use different extraction methods on tissue or cell culture combined with mass spectrometry (MS) techniques, matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI). These strategies lead to the identification of multiple peptides from the rat/mouse brain in tissue and cell cultures, including novel compounds One of the goals in this dissertation was to optimize sample preparations on samples isolated from well-defined brain regions for mass spectrometric analysis. Here, the neuropeptidomics study of the SON resulted in the identification of 85 peptides, including 20 unique peptides from known prohormones. This study includes mass spectrometric analysis even from individually isolated magnocellular neuroendocrine cells, where vasopressin and several other peptides are detected. At the same time, it was shown that the same approach could be applied to analyze peptides isolated from a similar hypothalamic region, the suprachiasmatic nucleus (SCN). Although there were some overlaps regarding the detection of the peptides in the two brain nuclei, different peptides were detected specific to each nucleus. Among other peptides, provasopressin fragments were specifically detected in the SON while angiotensin I, somatostatin-14, neurokinin B, galanin, and vasoactive-intestinal peptide (VIP) were detected in the SCN only. Lists of peptides were generated from both brain regions for comparison of the peptidome of SON and SCN nuclei. Moving from analysis of magnocellular neurons in tissue to cell culture, the direct peptidomics of the magnocellular and hippocampal neurons led to the detection of 10 peaks that were assigned to previously characterized peptides and 17 peaks that remain unassigned. Peptides from the vasopressin prohormone and secretogranin-2 are attributed to magnocellular neurons, whereas neurokinin A, peptide J, and neurokinin B are attributed to cultured hippocampal neurons. This approach enabled the elucidation of cell-specific prohormone processing and the discovery of cell-cell signaling peptides. The peptides with roles in the development of the pituitary were analyzed using transgenic mice. Hes1 KO is a genetically modified mouse that lives only e18.5 (embryonic days). Anterior pituitaries of Hes1 null mice exhibit hypoplasia due to increased cell death and reduced proliferation and in the intermediate lobe, the cells differentiate abnormally into somatotropes instead of melanotropes. These previous findings demonstrate that Hes1 has multiple roles in pituitary development, cell differentiation, and cell fate. AVP was detected in all samples. Interestingly, somatostatin [92-100] and provasopressin [151-168] were detected in the mutant but not in the wild type or heterozygous pituitaries while somatostatin-14 was detected only in the heterozygous pituitary. In addition, the putative peptide corresponding to m/z 1330.2 and POMC [205-222] are detected in the mutant and heterozygous pituitaries, but not in the wild type. These results indicate that Hes1 influences the processing of different prohormones having possible roles during development and opens new directions for further developmental studies. This research demonstrates the robust capabilities of MS, which ensures the unbiased direct analysis of peptides extracted from complex biological systems and allows addressing important questions to understand cell-cell signaling in the brain.

Seed and vegetative propagation methods for the rare Florida native species Chionanthus pygmaeus (pygmy fringetree)

Chionanthus pygmaeus Small (pygmy fringetree) (Oleaceae) is an endemic and rare Florida species, which has an attractive, small habit giving it great potential for use in managed landscapes. Members of the genus Chionanthus are difficult to propagate via cuttings and possess complex seed dormancies that are not well understood. Conservation of pygmy fringetree and its potential for commercial propagation for use in managed landscapes is contingent on a better understanding of its complex seed dormancy and enhancement of its propagation. I conducted two experiments to assess sexual and asexual propagation methods for pygmy fringetree. The first experiment was conducted to determine what factors are involved in overcoming seed dormancy. Various scarification treatments, which mimicked conditions seeds are exposed to in the wild, were investigated to determine their effects on germination of 20-year-old seeds originally collected from the species’ native range. Treatments included endocarp removal, sulfuric acid, boiling-water, and smoke-water treatments. Prior to treatment initiation, seed viability was estimated to be 12%. Treated seeds went through two cold- and two warm-stratification periods of 4°C and 25°C, respectively, in a dark growth chamber. After 180 days, none of the treatments induced early germination. Seeds were then tested for viability, which was 11%. Seed dormancy of the species is apparently complex, allowing some of the seeds to retain some degree of viability, but without dormancy requirements satisfied. The second experiment was conducted to assess if pygmy fringetree could be successfully propagated via hardwood or root cuttings if the appropriate combination of environmental conditions and hormones were applied. Hardwood and root cuttings were treated with either 1000 ppm IBA talc, 8000 ppm IBA talc, or inert talc. All cuttings were placed on a mist bench in a greenhouse for 9 weeks. Hardwood cuttings were supplemented with bottom heat at 24 °C. No treatments were successful in inducing adventitious root formation. I conclude that pygmy fringetree seeds possess complex dormancy that was not able to be overcome by the treatments utilized. However, this result is confounded by the age of the seeds used in the experiment. I also conclude that vegetative propagation of pygmy fringetree is highly dependent on the time of year cuttings are harvested. Further research of both seed and asexual propagation methods need to be explored before pygmy fringetree can be propagated on a commercial scale.

Variation in susceptibility among macadamia genotypes and species to Phytophthora root decay caused by Phytophthora cinnamomi

Phytophthora cinnamomi is a major pathogen of cultivated macadamia (Macadamia integrifolia, Macadamia tetraphylla and their hybrids) worldwide. The susceptibility of the two non-edible Macadamia species (Macadamia ternifolia and Macadamia jansenii) to P. cinnamomi is not well-understood. Commercial macadamia trees are established on grafted seedling (seed propagation) or own-rooted cutting (vegetative propagation) rootstocks of hybrids of the cultivated species. There is little information to support the preferential use of rootstock propagated by either seedling or own-rooted cutting methods in macadamia. In this study we assessed roots of macadamia plants of the four species and their hybrids, derived from the two methods of propagation, for their susceptibility to P. cinnamomi infection. The roots of inoculated plant from which P. cinnamomi was recovered showed blackening symptoms. The non-cultivated species, M. ternifolia and M. jansenii and their hybrids were the most susceptible germplasm compared with M. tetraphylla and M. integrifolia. Of these two species, M. tetraphylla was less susceptible than M. integrifolia. Significant differences were observed among the accessions of their hybrids. A strong association (R2 > 0.75) was recorded between symptomatic roots and disease severity. Root density reduced with increasing disease severity rating in both own-rooted cuttings (R2 = 0.65) and germinated seedlings (R2 = 0.55). P. cinnamomi severity data were not significantly (P > 0.05) different between the two methods of plant propagation. The significance of this study to macadamia breeding and selection of disease resistant rootstocks is discussed.

Effects of silicon treatment and inoculation with Fusarium oxysporum f. sp. vasinfectum on cellular defences in root tissues of two cotton cultivars

BACKGROUND AND AIMS: Silicon has been shown to enhance the resistance of plants to fungal and bacterial pathogens. Here, the effect of potassium silicate was assessed on two cotton (Gossypium hirsutum) cultivars subsequently inoculated with Fusarium oxysporum f. sp. vasinfectum (Fov). Sicot 189 is moderately resistant whilst Sicot F-1 is the second most resistant commercial cultivar presently available in Australia. METHODS: Transmission and light microscopy were used to compare cellular modifications in root cells after these different treatments. The accumulation of phenolic compounds and lignin was measured. KEY RESULTS: Cellular alterations including the deposition of electron-dense material, degradation of fungal hyphae and occlusion of endodermal cells were more rapidly induced and more intense in endodermal and vascular regions of Sicot F-1 plants supplied with potassium silicate followed by inoculation with Fov than in similarly treated Sicot 189 plants or in silicate-treated plants of either cultivar not inoculated with Fov. Significantly more phenolic compounds were present at 7 d post-infection (dpi) in root extracts of Sicot F-1 plants treated with potassium silicate followed by inoculation with Fov compared with plants from all other treatments. The lignin concentration at 3 dpi in root material from Sicot F-1 treated with potassium silicate and inoculated with Fov was significantly higher than that from water-treated and inoculated plants. CONCLUSIONS: This study demonstrates that silicon treatment can affect cellular defence responses in cotton roots subsequently inoculated with Fov, particularly in Sicot F-1, a cultivar with greater inherent resistance to this pathogen. This suggests that silicon may interact with or initiate defence pathways faster in this cultivar than in the less resistant cultivar.

Optimised wheat root architecture for increased yield and yield stability in the face of a changing climate.

This project provided information, selection techniques and strategies to facilitate the development of high-yielding, stay-green wheat varieties for Australian growers through: a) Improved understanding of the relationships between seminal root traits and other root- and shoot-related traits in determining high-yielding, stay-green phenotypes. b). Molecular markers and rapid phenotypic screening methods that allow selection in breeding programs and identification of genetic regions controlling favourable traits. c). Identification of traits leading to high-yielding, stay-green phenotypes for particular target populations of environments using computer simulation studies.

A non-contrast self-navigated 3-dimensional MR technique for aortic root and vascular access route assessment in the context of transcatheter aortic valve replacement: proof of concept.

OBJECTIVES: Due to the high prevalence of renal failure in transcatheter aortic valve replacement (TAVR) candidates, a non-contrast MR technique is desirable for pre-procedural planning. We sought to evaluate the feasibility of a novel, non-contrast, free-breathing, self-navigated three-dimensional (SN3D) MR sequence for imaging the aorta from its root to the iliofemoral run-off in comparison to non-contrast two-dimensional-balanced steady-state free-precession (2D-bSSFP) imaging. METHODS: SN3D [field of view (FOV), 220-370 mm(3); slice thickness, 1.15 mm; repetition/echo time (TR/TE), 3.1/1.5 ms; and flip angle, 115°] and 2D-bSSFP acquisitions (FOV, 340 mm; slice thickness, 6 mm; TR/TE, 2.3/1.1 ms; flip angle, 77°) were performed in 10 healthy subjects (all male; mean age, 30.3 ± 4.3 yrs) using a 1.5-T MRI system. Aortic root measurements and qualitative image ratings (four-point Likert-scale) were compared. RESULTS: The mean effective aortic annulus diameter was similar for 2D-bSSFP and SN3D (26.7 ± 0.7 vs. 26.1 ± 0.9 mm, p = 0.23). The mean image quality of 2D-bSSFP (4; IQR 3-4) was rated slightly higher (p = 0.03) than SN3D (3; IQR 2-4). The mean total acquisition time for SN3D imaging was 12.8 ± 2.4 min. CONCLUSIONS: Our results suggest that a novel SN3D sequence allows rapid, free-breathing assessment of the aortic root and the aortoiliofemoral system without administration of contrast medium. KEY POINTS: • The prevalence of renal failure is high among TAVR candidates. • Non-contrast 3D MR angiography allows for TAVR procedure planning. • The self-navigated sequence provides a significantly reduced scanning time.