Visualization tools for high angular resolution diffusion imaging

There is a major effort in medical imaging to develop algorithms to extract information from DTI and HARDI, which provide detailed information on brain integrity and connectivity. As the images have recently advanced to provide extraordinarily high angular resolution and spatial detail, including an entire manifold of information at each point in the 3D images, there has been no readily available means to view the results. This impedes developments in HARDI research, which need some method to check the plausibility and validity of image processing operations on HARDI data or to appreciate data features or invariants that might serve as a basis for new directions in image segmentation, registration, and statistics. We present a set of tools to provide interactive display of HARDI data, including both a local rendering application and an off-screen renderer that works with a web-based viewer. Visualizations are presented after registration and averaging of HARDI data from 90 human subjects, revealing important details for which there would be no direct way to appreciate using conventional display of scalar images.

Investigating brain connectivity heritability in a twin study using diffusion imaging data

Heritability of brain anatomical connectivity has been studied with diffusion-weighted imaging (DWI) mainly by modeling each voxel's diffusion pattern as a tensor (e.g., to compute fractional anisotropy), but this method cannot accurately represent the many crossing connections present in the brain. We hypothesized that different brain networks (i.e., their component fibers) might have different heritability and we investigated brain connectivity using High Angular Resolution Diffusion Imaging (HARDI) in a cohort of twins comprising 328 subjects that included 70 pairs of monozygotic and 91 pairs of dizygotic twins. Water diffusion was modeled in each voxel with a Fiber Orientation Distribution (FOD) function to study heritability for multiple fiber orientations in each voxel. Precision was estimated in a test-retest experiment on a sub-cohort of 39 subjects. This was taken into account when computing heritability of FOD peaks using an ACE model on the monozygotic and dizygotic twins. Our results confirmed the overall heritability of the major white matter tracts but also identified differences in heritability between connectivity networks. Inter-hemispheric connections tended to be more heritable than intra-hemispheric and cortico-spinal connections. The highly heritable tracts were found to connect particular cortical regions, such as medial frontal cortices, postcentral, paracentral gyri, and the right hippocampus.

The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

Estimating the under-reporting of road crash injuries to police using multiple linked data collections

The reliance on police data for the counting of road crash injuries can be problematic, as it is well known that not all road crash injuries are reported to police which under-estimates the overall burden of road crash injuries. The aim of this study was to use multiple linked data sources to estimate the extent of under-reporting of road crash injuries to police in the Australian state of Queensland. Data from the Queensland Road Crash Database (QRCD), the Queensland Hospital Admitted Patients Data Collection (QHAPDC), Emergency Department Information System (EDIS), and the Queensland Injury Surveillance Unit (QISU) for the year 2009 were linked. The completeness of road crash cases reported to police was examined via discordance rates between the police data (QRCD) and the hospital data collections. In addition, the potential bias of this discordance (under-reporting) was assessed based on gender, age, road user group, and regional location. Results showed that the level of under-reporting varied depending on the data set with which the police data was compared. When all hospital data collections are examined together the estimated population of road crash injuries was approximately 28,000, with around two-thirds not linking to any record in the police data. The results also showed that the under-reporting was more likely for motorcyclists, cyclists, males, young people, and injuries occurring in Remote and Inner Regional areas. These results have important implications for road safety research and policy in terms of: prioritising funding and resources; targeting road safety interventions into areas of higher risk; and estimating the burden of road crash injuries.

Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.

Utilising big transit data for transfer coordination

In public transport, seamless coordinated transfer strengthens the quality of service and attracts ridership. The problem of transfer coordination is sophisticated due to (1) the stochasticity of travel time variability, (2) unavailability of passenger transfer plan. However, the proliferation of Big Data technologies provides a tremendous opportunity to solve these problems. This dissertation enhances passenger transfer quality by offline and online transfer coordination. While offline transfer coordination exploits the knowledge of travel time variability to coordinate transfers, online transfer coordination provides simultaneous vehicle arrivals at stops to facilitate transfers by employing the knowledge of passenger behaviours.

A Web-based system enabling the integration, analysis, and 3D sub-surface visualization of groundwater monitoring data and geological models

The 3D Water Chemistry Atlas is an intuitive, open source, Web-based system that enables the three-dimensional (3D) sub-surface visualization of ground water monitoring data, overlaid on the local geological model (formation and aquifer strata). This paper firstly describes the results of evaluating existing virtual globe technologies, which led to the decision to use the Cesium open source WebGL Virtual Globe and Map Engine as the underlying platform. Next it describes the backend database and search, filtering, browse and analysis tools that were developed to enable users to interactively explore the groundwater monitoring data and interpret it spatially and temporally relative to the local geological formations and aquifers via the Cesium interface. The result is an integrated 3D visualization system that enables environmental managers and regulators to assess groundwater conditions, identify inconsistencies in the data, manage impacts and risks and make more informed decisions about coal seam gas extraction, waste water extraction, and water reuse.

The life of data: Evolving national testing

To this point, the collection has provided research-based, empirical accounts of the various and multiple effects of the National Assessment Program – Literacy and Numeracy (NAPLAN) in Australian schooling as a specific example of the global phenomenon of national testing. In this chapter, we want to develop a more theoretical analysis of national testing systems, globalising education policy and the promise of national testing as adaptive, online tests. These future moves claim to provide faster feedback and more useful diagnostic help for teachers. There is a utopian testing dream that one day adaptive, online tests will be responsive in real time providing an integrated personalised testing, pedagogy and intervention for each student. The moves towards these next generation assessments are well advanced, including the work of Pearson’s NextGen Learning and Assessment research group, the Organization for Economic Co-operation and Development’s (OECD) move into assessing affective skills and the Australian Curriculum, Assessment and Reporting Authority’s (ACARA) decision to phase in NAPLAN as an online, adaptive test from 2017...

Manipulating the data: Teaching and NAPLAN in the control society

High-stakes testing is changing what it means to be a ‘good teacher’ in the contemporary school. This paper uses Deleuze and Guattari's ideas on the control society and dividuation in the context of National Assessment Program Literacy and Numeracy (NAPLAN) testing in Australia to suggest that the database generates new understandings of the ‘good teacher’. Media reports are used to look at how teachers are responding to the high-stakes database through manipulating the data. This article argues that manipulating the data is a regrettable, but logical, response to manifestations of teaching where only the data counts.

Non-parametric consistency test for multiple-sensing-modality data fusion

Fusing data from multiple sensing modalities, e.g. laser and radar, is a promising approach to achieve resilient perception in challenging environmental conditions. However, this may lead to \emph{catastrophic fusion} in the presence of inconsistent data, i.e. when the sensors do not detect the same target due to distinct attenuation properties. It is often difficult to discriminate consistent from inconsistent data across sensing modalities using local spatial information alone. In this paper we present a novel consistency test based on the log marginal likelihood of a Gaussian process model that evaluates data from range sensors in a relative manner. A new data point is deemed to be consistent if the model statistically improves as a result of its fusion. This approach avoids the need for absolute spatial distance threshold parameters as required by previous work. We report results from object reconstruction with both synthetic and experimental data that demonstrate an improvement in reconstruction quality, particularly in cases where data points are inconsistent yet spatially proximal.

Molecular data extend Australian Cricotopus midge (Chironomidae) species diversity, and provide a phylogenetic hypothesis for biogeography and freshwater monitoring

Resolving species relationships and confirming diagnostic morphological characters for insect clades that are highly plastic, and/or include morphologically cryptic species, is crucial for both academic and applied reasons. Within the true fly (Diptera) family Chironomidae, a most ubiquitous freshwater insect group, the genera CricotopusWulp, 1874 and ParatrichocladiusSantos-Abreu, 1918 have long been taxonomically confusing. Indeed, until recently the Australian fauna had been examined in just two unpublished theses: most species were known by informal manuscript names only, with no concept of relationships. Understanding species limits, and the associated ecology and evolution, is essential to address taxonomic sufficiency in biomonitoring surveys. Immature stages are collected routinely, but tolerance is generalized at the genus level, despite marked variation among species. Here, we explored this issue using a multilocus molecular phylogenetic approach, including the standard mitochondrial barcode region, and tested explicitly for phylogenetic signal in ecological tolerance of species. Additionally, we addressed biogeographical patterns by conducting Bayesian divergence time estimation. We sampled all but one of the now recognized Australian Cricotopus species and tested monophyly using representatives from other austral and Asian locations. Cricotopus is revealed as paraphyletic by the inclusion of a nested monophyletic Paratrichocladius, with in-group diversification beginning in the Eocene. Previous morphological species concepts are largely corroborated, but some additional cryptic diversity is revealed. No significant relationship was observed between the phylogenetic position of a species and its ecology, implying either that tolerance to deleterious environmental impacts is a convergent trait among many Cricotopus species or that sensitive and restricted taxa have diversified into more narrow niches from a widely tolerant ancestor.

Comparative analysis of genome maintenance genes in naked mole rat, mouse, and human

Genome maintenance (GM) is an essential defense system against aging and cancer, as both are characterized by increased genome instability. Here, we compared the copy number variation and mutation rate of 518 GM-associated genes in the naked mole rat (NMR), mouse, and human genomes. GM genes appeared to be strongly conserved, with copy number variation in only four genes. Interestingly, we found NMR to have a higher copy number of CEBPG, a regulator of DNA repair, and TINF2, a protector of telomere integrity. NMR, as well as human, was also found to have a lower rate of germline nucleotide substitution than the mouse. Together, the data suggest that the long-lived NMR, as well as human, has more robust GM than mouse and identifies new targets for the analysis of the exceptional longevity of the NMR.

An empirical examination of the consequences of national pride: Analyses of survey and experimental data

National pride is both an important and understudied topic with respect to economic behaviour, hence this thesis investigates whether: 1) there is a "light" side of national pride through increased compliance, and a "dark" side linked to exclusion; 2) successful priming of national pride is linked to increased tax compliance; and 3) East German post-reunification outmigration is related to loyalty. The project comprises three related empirical studies, analysing evidence from a large, aggregated, international survey dataset; a tax compliance laboratory experiment combining psychological priming with measurement of heart rate variability; and data collected after the fall of the Berlin Wall (a situation approximating a natural experiment).

Urban informatics beyond data: Media architecture, placemaking, and citizen action

Since 2006, we have been conducting urban informatics research that we define as “the study, design, and practice of urban experiences across different urban contexts that are created by new opportunities of real-time, ubiquitous technology and the augmentation that mediates the physical and digital layers of people networks and urban infrastructures” [1]. Various new research initiatives under the label “urban informatics” have been started since then by universities (e.g., NYU’s Center for Urban Science and Progress) and industry (e.g., Arup, McKinsey) worldwide. Yet, many of these new initiatives are limited to what Townsend calls, “data-driven approaches to urban improvement” [2]. One of the key challenges is that any quantity of aggregated data does not easily translate directly into quality insights to better understand cities. In this talk, I will raise questions about the purpose of urban informatics research beyond data, and show examples of media architecture, participatory city making, and citizen activism. I argue for (1) broadening the disciplinary foundations that urban science approaches draw on; (2) maintaining a hybrid perspective that considers both the bird’s eye view as well as the citizen’s view, and; (3) employing design research to not be limited to just understanding, but to bring about actionable knowledge that will drive change for good.

Quality of information for quality of life: Healthcare big data analytics

Big data analysis in healthcare sector is still in its early stages when comparing with that of other business sectors due to numerous reasons. Accommodating the volume, velocity and variety of healthcare data Identifying platforms that examine data from multiple sources, such as clinical records, genomic data, financial systems, and administrative systems Electronic Health Record (EHR) is a key information resource for big data analysis and is also composed of varied co-created values. Successful integration and crossing of different subfields of healthcare data such as biomedical informatics and health informatics could lead to huge improvement for the end users of the health care system, i.e. the patients.