Nucleic acid hybridizations as radioactive tool for rapid detection of banana bunchy top virus

Banana bunchy top disease (BBTD) caused by banana bunchy top virus (BBTV) was radioactively detected by nucleic acid hybridization techniques. Results showed that, 32P-labelled insert of pBT338 was hybridized with nucleic acid extracts from BBTV-infected plants from Egypt and Australia but not with those from CMV-infected plants from Egypt. Results revealed that BBTV was greatly detected in midrib, roots, meristem, corm, leaves and pseudostem respectively. BBTV was also detected in symptomless young plants prepared from diseased plant materials grown under tissue culture conditions but was not present in those performed from healthy plant materials. The sensitivity of dot blot and Southern blot hybridizations for the detection of BBTV was also performed for the detection of BBTV.

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.

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.

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.

Three-dimensional analysis of budding sites and released virus suggests a revised model for HIV-1 morphogenesis

Current models of HIV-1 morphogenesis hold that newly synthesized viral Gag polyproteins traffic to and assemble at the cell membrane into spherical protein shells. The resulting late-budding structure is thought to be released by the cellular ESCRT machinery severing the membrane tether connecting it to the producer cell. Using electron tomography and scanning transmission electron microscopy, we find that virions have a morphology and composition distinct from late-budding sites. Gag is arranged as a continuous but incomplete sphere in the released virion. In contrast, late-budding sites lacking functional ESCRT exhibited a nearly closed Gag sphere. The results lead us to propose that budding is initiated by Gag assembly, but is completed in an ESCRT-dependent manner before the Gag sphere is complete. This suggests that ESCRT functions early in HIV-1 release-akin to its role in vesicle formation-and is not restricted to severing the thin membrane tether.

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.

Heat-related morbidity in Brisbane, Australia : spatial variation and area-level predictors

Background: Extreme heat is a leading weather-related cause of illness and death in many locations across the globe, including subtropical Australia. The possibility of increasingly frequent and severe heat waves warrants continued efforts to reduce this health burden, which could be accomplished by targeting intervention measures toward the most vulnerable communities. Objectives: We sought to quantify spatial variability in heat-related morbidity in Brisbane, Australia, to highlight regions of the city with the greatest risk. We also aimed to find area-level social and environmental determinants of high risk within Brisbane. Methods: We used a series of hierarchical Bayesian models to examine city-wide and intracity associations between temperature and morbidity using a 2007–2011 time series of geographically referenced hospital admissions data. The models accounted for long-term time trends, seasonality, and day of week and holiday effects. Results: On average, a 10°C increase in daily maximum temperature during the summer was associated with a 7.2% increase in hospital admissions (95% CI: 4.7, 9.8%) on the following day. Positive statistically significant relationships between admissions and temperature were found for 16 of the city’s 158 areas; negative relationships were found for 5 areas. High-risk areas were associated with a lack of high income earners and higher population density. Conclusions: Geographically targeted public health strategies for extreme heat may be effective in Brisbane, because morbidity risk was found to be spatially variable. Emergency responders, health officials, and city planners could focus on short- and long-term intervention measures that reach communities in the city with lower incomes and higher population densities, including reduction of urban heat island effects.

Perfect start to a river cruise

A travel article about Amsterdam. BY COINCIDENCE, I flew to Amsterdam a week after I'd read Ian McEwan's novel of the same name. Amsterdam is a modern take on the theme of duelling and, in many ways, he couldn't have chosen a more appropriate place for his title. This is a city that duels with itself. I flew in at dawn, traditionally the moment to test your abilities at 10 paces. The countryside below was dark but blocks of orange light pulsed in the fields...