Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

Improved fabrication of melt electrospun tissue engineering scaffolds using direct writing and advanced electric field control

Direct writing melt electrospinning is an additive manufacturing technique capable of the layer-by-layer fabrication of highly ordered 3d tissue engineering scaffolds from micron-diameter fibres. The utility of these scaffolds, however, is limited by the maximum achievable height of controlled fibre deposition, beyond which the structure becomes increasingly disordered. A source of this disorder is charge build-up on the deposited polymer producing unwanted coulombic forces. In this study we introduce a novel melt electrospinning platform with dual voltage power supplies to reduce undesirable charge effects and improve fibre deposition control. We produced and characterised several 90° cross-hatched fibre scaffolds using a range of needle/collector plate voltages. Fibre thickness was found to be sensitive only to overall potential and invariant to specific tip/collector voltage. We also produced ordered scaffolds up to 200 layers thick (fibre spacing 1 mm, diameter 40 μm) and characterised structure in terms of three distinct zones; ordered, semi-ordered and disordered. Our in vitro analysis indicates successful cell attachment and distribution throughout the scaffolds, with little evidence of cell death after seven days. This study demonstrates the importance of electrostatic control for reducing destabilising polymer charge effects and enabling the fabrication of morphologically suitable scaffolds for tissue engineering.

Development of health risk-based metrics for defining a heatwave : a time series study in Brisbane, Australia

Background: This study attempted to develop health risk-based metrics for defining a heatwave in Brisbane, Australia. Methods: Poisson generalised additive model was performed to assess the impact of heatwaves on mortality and emergency hospital admissions (EHAs) in Brisbane. Results: In general, the higher the intensity and the longer the duration of a heatwave, the greater the health impacts. There was no apparent difference in EHAs risk during different periods of a warm season. However, there was a greater risk of mortality in the second half of a warm season than that in the first half. While elderly (>75 years)were particularly vulnerable to both the EHA and mortality effects of a heatwave, the risk for EHAs also significantly increased for two other age groups (0-64 years and 65-74 years) during severe heatwaves. Different patterns between cardiorespiratory mortality and EHAs were observed. Based on these findings, we propose the use of a teiered heat warning system based on the health risk of heatwave. Conclusions: Health risk-based metrics are a useful tool for the development of local heatwave definitions. thsi tool may have significant implications for the assessment of heatwave-related health consequences and development of heatwave response plans and implementation strategies.

Melt electrospinning writing

This thesis has developed an innovative additive manufacturing technology platform, which combines melt electrospinning with direct writing, allowing the fabrication of a new class of highly-ordered ultrafine fibrous materials. Bioresorbable polymer fibres were printed using a variety of designs, with filament resolutions not demonstrated by established melt-extrusion based direct writing processes, to form novel medical devices. This platform was applied to tissue engineering scaffold design, where structures were prepared in a variety of shapes and forms, characterised and then seeded with cells to investigate their biocompatibility, cell-seeding and proliferation behaviour as well as the ability to guide cell growth and differentiation.

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods which have been recently employed to analyse PNSD data, however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K-means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K-means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K-means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectra to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

Investigation into chemistry of new particle formation and growth in subtropical urban environment

The role of different chemical compounds, particularly organics, involved in the new particle formation (NPF) and its consequent growth are not fully understood. Therefore, this study was conducted to investigate the chemistry of aerosol particles during NPF events in an urban subtropical environment. Aerosol chemical composition was measured along with particle number size distribution (PNSD) and several other air quality parameters at five sites across an urban subtropical environment. An Aerodyne compact Time-of-Flight Aerosol Mass Spectrometer (c-TOF-AMS) and a TSI Scanning Mobility Particle Sizer (SMPS) measured aerosol chemical composition and PNSD, respectively. Five NPF events, with growth rates in the range 3.3-4.6 nm, were detected at two sites. The NPF events happened on relatively warmer days with lower humidity and higher solar radiation. Temporal percent fractions of nitrate, sulphate, ammonium and organics were modelled using the Generalised Additive Model (GAM), with a basis of penalised spline. Percent fractions of organics increased after the NPF events, while the mass fraction of ammonium and sulphate decreased. This uncovered the important role of organics in the growth of newly formed particles. Three organic markers, factors f43, f44 and f57, were calculated and the f44 vs f43 trends were compared between nucleation and non-nucleation days. f44 vs f43 followed a different pattern on nucleation days compared to non-nucleation days, whereby f43 decreased for vehicle emission generated particles, while both f44 and f43 decreased for NPF generated particles. It was found for the first time that vehicle generated and newly formed particles cluster in different locations on f44 vs f43 plot and this finding can be used as a tool for source apportionment of measured particles.

The combination of plant-expressed cellobiohydrolase and low dosages of cellulases for the hydrolysis of sugar cane bagasse

Background The expression of biomass-degrading enzymes (such as cellobiohydrolases) in transgenic plants has the potential to reduce the costs of biomass saccharification by providing a source of enzymes to supplement commercial cellulase mixtures. Cellobiohydrolases are the main enzymes in commercial cellulase mixtures. In the present study, a cellobiohydrolase was expressed in transgenic corn stover leaf and assessed as an additive for two commercial cellulase mixtures for the saccharification of pretreated sugar cane bagasse obtained by different processes. Results Recombinant cellobiohydrolase in the senescent leaves of transgenic corn was extracted using a simple buffer with no concentration step. The extract significantly enhanced the performance of Celluclast 1.5 L (a commercial cellulase mixture) by up to fourfold on sugar cane bagasse pretreated at the pilot scale using a dilute sulfuric acid steam explosion process compared to the commercial cellulase mixture on its own. Also, the extracts were able to enhance the performance of Cellic CTec2 (a commercial cellulase mixture) up to fourfold on a range of residues from sugar cane bagasse pretreated at the laboratory (using acidified ethylene carbonate/ethylene glycol, 1-butyl-3-methylimidazolium chloride, and ball-milling) and pilot (dilute sodium hydroxide and glycerol/hydrochloric acid steam explosion) scales. We have demonstrated using tap water as a solvent (under conditions that mimic an industrial process) extraction of about 90% recombinant cellobiohydrolase from senescent, transgenic corn stover leaf that had minimal tissue disruption. Conclusions The accumulation of recombinant cellobiohydrolase in senescent, transgenic corn stover leaf is a viable strategy to reduce the saccharification cost associated with the production of fermentable sugars from pretreated biomass. We envisage an industrial-scale process in which transgenic plants provide both fibre and biomass-degrading enzymes for pretreatment and enzymatic hydrolysis, respectively.

Genetic and environmental influences on neuroimaging phenotypes: A meta-analytical perspective on twin imaging studies

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all metaanalyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

Mapping the regional influence of genetics on brain structure variability - A Tensor-Based Morphometry study

Genetic and environmental factors influence brain structure and function profoundly. The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8 ± 1.8 SD years). All 92 twins' 3D brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject's anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions that have a more protracted maturational time-course.

Discovery of genes that affect human brain connectivity: A genome-wide analysis of the connectome

Human brain connectivity is disrupted in a wide range of disorders from Alzheimer's disease to autism but little is known about which specific genes affect it. Here we conducted a genome-wide association for connectivity matrices that capture information on the density of fiber connections between 70 brain regions. We scanned a large twin cohort (N=366) with 4-Tesla high angular resolution diffusion imaging (105-gradient HARDI). Using whole brain HARDI tractography, we extracted a relatively sparse 70×70 matrix representing fiber density between all pairs of cortical regions automatically labeled in co-registered anatomical scans. Additive genetic factors accounted for 1-58% of the variance in connectivity between 90 (of 122) tested nodes. We discovered genome-wide significant associations between variants and connectivity. GWAS permutations at various levels of heritability, and split-sample replication, validated our genetic findings. The resulting genes may offer new leads for mechanisms influencing aberrant connectivity and neurodegeneration. © 2012 IEEE.

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).

Brain structure in healthy adults is related to serum transferrin and the H63D polymorphism in the HFE gene

Control of iron homeostasis is essential for healthy central nervous system function: iron deficiency is associated with cognitive impairment, yet iron overload is thought to promote neurodegenerative diseases. Specific genetic markers have been previously identified that influence levels of transferrin, the protein that transports iron throughout the body, in the blood and brain. Here, we discovered that transferrin levels are related to detectable differences in the macro- and microstructure of the living brain. We collected brain MRI scans from 615 healthy young adult twins and siblings, of whom 574 were also scanned with diffusion tensor imaging at 4 Tesla. Fiber integrity was assessed by using the diffusion tensor imaging-based measure of fractional anisotropy. In bivariate genetic models based on monozygotic and dizygotic twins, we discovered that partially overlapping additive genetic factors influenced transferrin levels and brain microstructure. We also examined common variants in genes associated with transferrin levels, TF and HFE, and found that a commonly carried polymorphism (H63D at rs1799945) in the hemochromatotic HFE gene was associated with white matter fiber integrity. This gene has a well documented association with iron overload. Our statistical maps reveal previously unknown influences of the same gene on brain microstructure and transferrin levels. This discovery may shed light on the neural mechanisms by which iron affects cognition, neurodevelopment, and neurodegeneration.

A genetic analysis of cortical thickness in 372 twins

Imaging genetics is a new field of neuroscience that blends methods from computational anatomy and quantitative genetics to identify genetic influences on brain structure and function. Here we analyzed brain MRI data from 372 young adult twins to identify cortical regions in which gray matter volume is influenced by genetic differences across subjects. Thickness maps, reconstructed from surface models of the cortical gray/white and gray/CSF interfaces, were smoothed with a 25 mm FWHM kernel and automatically parcellated into 34 regions of interest per hemisphere. In structural equation models fitted to volume values at each surface vertex, we computed components of variance due to additive genetic (A), shared (C) and unique (E) environmental factors, and tested their significance. Cortical regions in the vicinity of the perisylvian language cortex, and at the frontal and temporal poles, showed significant additive genetic variance, suggesting that volume measures from these regions may provide quantitative phenotypes to narrow the search for quantitative trait loci that influence brain structure.

The contribution of genes to cortical thickness and volume

We analyzed brain MRI data from 372 young adult twins toidentify cortical regions in which gray matter thickness and volume are influenced by genetics. This was achieved using an A/C/E structural equation model that divides the variance of these traits, at each point on the cortex, into additive genetic (A), shared (C), and unique environmental (E) components. A strong genetic influencewas found in frontal and parietal regions. Inaddition, we correlated cortical thickness with full-scale intelligence quotient for comparison with the A/C/E maps, and several regions where cortical structure was correlated with intelligence quotient are under genetic control. These cortical measures may be useful phenotypes to narrow the searchfor quantitative trait lociinfluencing brain structure.

Multivariate variance-components analysis in DTI

Twin studies are a major research direction in imaging genetics, a new field, which combines algorithms from quantitative genetics and neuroimaging to assess genetic effects on the brain. In twin imaging studies, it is common to estimate the intraclass correlation (ICC), which measures the resemblance between twin pairs for a given phenotype. In this paper, we extend the commonly used Pearson correlation to a more appropriate definition, which uses restricted maximum likelihood methods (REML). We computed proportion of phenotypic variance due to additive (A) genetic factors, common (C) and unique (E) environmental factors using a new definition of the variance components in the diffusion tensor-valued signals. We applied our analysis to a dataset of Diffusion Tensor Images (DTI) from 25 identical and 25 fraternal twin pairs. Differences between the REML and Pearson estimators were plotted for different sample sizes, showing that the REML approach avoids severe biases when samples are smaller. Measures of genetic effects were computed for scalar and multivariate diffusion tensor derived measures including the geodesic anisotropy (tGA) and the full diffusion tensors (DT), revealing voxel-wise genetic contributions to brain fiber microstructure.