Assessing the role of spatial correlations during collective cell spreading

Spreading cell fronts are essential features of development, repair and disease processes. Many mathematical models used to describe the motion of cell fronts, such as Fisher’s equation, invoke a mean–field assumption which implies that there is no spatial structure, such as cell clustering, present. Here, we examine the presence of spatial structure using a combination of in vitro circular barrier assays, discrete random walk simulations and pair correlation functions. In particular, we analyse discrete simulation data using pair correlation functions to show that spatial structure can form in a spreading population of cells either through sufficiently strong cell–to–cell adhesion or sufficiently rapid cell proliferation. We analyse images from a circular barrier assay describing the spreading of a population of MM127 melanoma cells using the same pair correlation functions. Our results indicate that the spreading melanoma cell populations remain very close to spatially uniform, suggesting that the strength of cell–to–cell adhesion and the rate of cell proliferation are both sufficiently small so as not to induce any spatial patterning in the spreading populations.

Arterial short-term travel time prediction using Bluetooth data

This project recognized lack of data analysis and travel time prediction on arterials as the main gap in the current literature. For this purpose it first investigated reliability of data gathered by Bluetooth technology as a new cost effective method for data collection on arterial roads. Then by considering the similarity among varieties of daily travel time on different arterial routes, created a SARIMA model to predict future travel time values. Based on this research outcome, the created model can be applied for online short term travel time prediction in future.

Deterministic control of plasma-assembled self-organized Ge/Si quantum dot arrays

Self-assembly of size-uniform and spatially ordered quantum dot (QD) arrays is one of the major challenges in the development of the new generation of semiconducting nanoelectronic and photonic devices. Assembly of Ge QD (in the ∼5-20 nm size range) arrays from randomly generated position and size-nonuniform nanodot patterns on plasma-exposed Si (100) surfaces is studied using hybrid multiscale numerical simulations. It is shown, by properly manipulating the incoming ion/neutral flux from the plasma and the surface temperature, the uniformity of the nanodot size within the array can be improved by 34%-53%, with the best improvement achieved at low surface temperatures and high external incoming fluxes, which are intrinsic to plasma-aided processes. Using a plasma-based process also leads to an improvement (∼22% at 700 K surface temperature and 0.1 MLs incoming flux from the plasma) of the spatial order of a randomly sampled nanodot ensemble, which self-organizes to position the dots equidistantly to their neighbors within the array. Remarkable improvements in QD ordering and size uniformity can be achieved at high growth rates (a few nms) and a surface temperature as low as 600 K, which broadens the range of suitable substrates to temperature-sensitive ultrathin nanofilms and polymers. The results of this study are generic, can also be applied to nonplasma-based techniques, and as such contributes to the development of deterministic strategies of nanoassembly of self-ordered arrays of size-uniform QDs, in the size range where nanodot ordering cannot be achieved by presently available pattern delineation techniques.

A quantification of the interaction and spatial ordering in nano-arrays

This paper reports on the use of a local order measure to quantify the spatial ordering of a quantum dot array (QDA). By means of electron ground state energy analysis in a quantum dot pair, it is demonstrated that the length scale required for such a measure to characterize the opto-electronic properties of a QDA is of the order of a few QD radii. Therefore, as local order is the primary factor that affects the opto-electronic properties of an array of quantum dots of homogeneous size, this order was quantified through using the standard deviation of the nearest neighbor distances of the quantum dot ensemble. The local order measure is successfully applied to quantify spatial order in a range of experimentally synthesized and numerically generated arrays of nanoparticles. This measure is not limited to QDAs and has wide ranging applications in characterizing order in dense arrays of nanostructures.

Carbon saturation of arrays of Ni catalyst nanoparticles of different size and pattern uniformity on a silicon substrate

The kinetics of saturation of Ni catalyst nanoparticle patterns of the three different degrees of order, used as a model for the growth of carbon nanotips on Si, is investigated numerically using a complex model that involves surface diffusion and ion motion equations. It is revealed that Ni catalyst patterns of different degrees of order, with Ni nanoparticle sizes up to 12.5 nm, exhibit different kinetics of saturation with carbon on the Si surface. It is shown that in the cases examined (surface coverage in the range of 1-50%, highly disordered Ni patterns) the relative pattern saturation factor calculated as the ratio of average incubation times for the processes conducted in the neutral and ionized gas environments reaches 14 and 3.4 for Ni nanoparticles of 2.5 and 12.5 nm, respectively. In the highly ordered Ni patterns, the relative pattern saturation factor reaches 3 for nanoparticles of 2.5 nm and 2.1 for nanoparticles of 12.5 nm. Thus, more simultaneous saturation of Ni catalyst nanoparticles of sizes in the range up to 12.5 nm, deposited on the Si substrate, can be achieved in the low-temperature plasma environment than with the neutral gas-based process.

Taking travel journalism seriously : suggestions for scientific inquiry into a neglected genre

The practice of travel journalism is still largely neglected as a field of inquiry for communication and journalism scholars, despite the fact that news media are increasingly focussing on softer news. Lifestyle sections of newspapers, for example, have been growing in size over the past few decades, and given corresponding cutbacks in international news reporting, particularly travel journalism is now playing a growing role in the representation of ‘the Other’. While this need for research into the field has been identified before, very little actual investigation of travel journalism has been forthcoming. This paper reviews the current state of research by reviewing what studies have been conducted into the production, content and reception of travel journalism. It argues that while there does now exist a very small number of studies, these have often been conducted in isolation and with significant limitations, and much remains to be done to sufficiently explore this sub-field of journalism. By analysing what we do know about travel journalism, the paper suggests a number of possibilities in each area on how we can advance this knowledge. Above all, it contends that dated prejudices against the field have to be put to the side, and the practice of travel journalism needs to be taken seriously in order to do its growing importance justice.

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

Innovation in the Australian Spatial Information Industry

Business literature reveals the importance of generating innovative products and services, but much of the innovation research has been conducted in large firms and not replicated in small firms. These firms are likely to have different perspectives on innovation, which means that they will probably behave differently to large firms. Our study aims to unpack how firms in Spatial Information perceive and engage in innovation as a part of their business operation. To investigate these questions we conduct 20 in depth interviews of top management team members in Spatial Information firms in Australia. We find that small firms define innovation very broadly and measure innovation by its effect on productivity or market success. Innovation is seen as crucial to survival and success in a competitive environment. Most firms engage in product and/or service innovations, while some also mentioned marketing, process and organisational innovations. Most innovations were more exploitative rather than exploratory with only a few being radical innovations. Innovation barriers include time and money constraints, corporate culture and Government tendering practices. Our study sheds a light on our understanding of innovation in an under-researched sector; that is spatial information industry.

A hierarchical model of goal directed navigation selects trajectories in a visual environment

We have developed a Hierarchical Look-Ahead Trajectory Model (HiLAM) that incorporates the firing pattern of medial entorhinal grid cells in a planning circuit that includes interactions with hippocampus and prefrontal cortex. We show the model’s flexibility in representing large real world environments using odometry information obtained from challenging video sequences. We acquire the visual data from a camera mounted on a small tele-operated vehicle. The camera has a panoramic field of view with its focal point approximately 5 cm above the ground level, similar to what would be expected from a rat’s point of view. Using established algorithms for calculating perceptual speed from the apparent rate of visual change over time, we generate raw dead reckoning information which loses spatial fidelity over time due to error accumulation. We rectify the loss of fidelity by exploiting the loop-closure detection ability of a biologically inspired, robot navigation model termed RatSLAM. The rectified motion information serves as a velocity input to the HiLAM to encode the environment in the form of grid cell and place cell maps. Finally, we show goal directed path planning results of HiLAM in two different environments, an indoor square maze used in rodent experiments and an outdoor arena more than two orders of magnitude larger than the indoor maze. Together these results bridge for the first time the gap between higher fidelity bio-inspired navigation models (HiLAM) and more abstracted but highly functional bio-inspired robotic mapping systems (RatSLAM), and move from simulated environments into real-world studies in rodent-sized arenas and beyond.

Spatial-temporal epidemiological analyses of two sympatric, co-endemic alphaviral diseases in Queensland, Australia

Background: The two most reported mosquito-borne diseases in Queensland, a northern state of Australia, are Ross River virus (RRV) disease and Barmah Forest virus (BFV) disease. Both diseases are endemic in Queensland and have similar clinical symptoms and comparable transmission cycles involving a complex inter-relationship between human hosts, various mosquito vectors, and a range of nonhuman vertebrate hosts, including marsupial mammals that are unique to the Australasian region. Although these viruses are thought to share similar vectors and vertebrate hosts, RRV is four times more prevalent than BFV in Queensland. Methods: We performed a retrospective analysis of BFV and RRV human disease notification data collected from 1995 to 2007 in Queensland to ascertain whether there were differences in the incidence patterns of RRV and BFV disease. In particular, we compared the temporal incidence and spatial distribution of both diseases and considered the relationship between their disease dynamics. We also investigated whether a peak in BFV incidence during spring was indicative of the following RRV and BFV transmission season incidence levels. Results: Although there were large differences in the notification rates of the two diseases, they had similar annual temporal patterns, but there were regional variations between the length and magnitude of the transmission seasons. During periods of increased disease activity, however, there was no association between the dynamics of the two diseases. Conclusions: The results from this study suggest that while RRV and BFV share similar mosquito vectors, there are significant differences in the ecology of these viruses that result in different epidemic patterns of disease incidence. Further investigation is required into the ecology of each virus to determine which factors are important in promoting RRV and BFV disease outbreaks.

Refining a teaching pattern: Reflection around artefacts

This book is about understanding the nature and application of reflection in higher education. It provides a theoretical model to guide the implementation of reflective learning and reflective practice across multiple disciplines and international contexts in higher education. The book presents research into the ways in which reflection is both considered and implemented in different ways across different professional disciplines, while maintaining a common purpose to transform and improve learning and/or practice. Chapter 13 'Refining a Teaching Pattern: Reflection Around Artefacts' explores reflective practices of an artefact, in this case fashion design garment samples.

An investigation of the impact of various geographical scales for the specification of spatial dependence

Ecological studies are based on characteristics of groups of individuals, which are common in various disciplines including epidemiology. It is of great interest for epidemiologists to study the geographical variation of a disease by accounting for the positive spatial dependence between neighbouring areas. However, the choice of scale of the spatial correlation requires much attention. In view of a lack of studies in this area, this study aims to investigate the impact of differing definitions of geographical scales using a multilevel model. We propose a new approach -- the grid-based partitions and compare it with the popular census region approach. Unexplained geographical variation is accounted for via area-specific unstructured random effects and spatially structured random effects specified as an intrinsic conditional autoregressive process. Using grid-based modelling of random effects in contrast to the census region approach, we illustrate conditions where improvements are observed in the estimation of the linear predictor, random effects, parameters, and the identification of the distribution of residual risk and the aggregate risk in a study region. The study has found that grid-based modelling is a valuable approach for spatially sparse data while the SLA-based and grid-based approaches perform equally well for spatially dense data.

The choice of spatial scales and spatial smoothness priors for various spatial patterns

Given the drawbacks for using geo-political areas in mapping outcomes unrelated to geo-politics, a compromise is to aggregate and analyse data at the grid level. This has the advantage of allowing spatial smoothing and modelling at a biologically or physically relevant scale. This article addresses two consequent issues: the choice of the spatial smoothness prior and the scale of the grid. Firstly, we describe several spatial smoothness priors applicable for grid data and discuss the contexts in which these priors can be employed based on different aims. Two such aims are considered, i.e., to identify regions with clustering and to model spatial dependence in the data. Secondly, the choice of the grid size is shown to depend largely on the spatial patterns. We present a guide on the selection of spatial scales and smoothness priors for various point patterns based on the two aims for spatial smoothing.

Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.