Understanding body malodour in a measurable manner is essential for developing personal care products. Body malodour is the result of bodily secretion of a highly complex mixture of volatile organic compounds. Current body malodour measurement methods are manual, time consuming and costly, requiring an expert panel of assessors to assign a malodour score to each human test subject. This article proposes a technology-based solution to automate this task by developing a custom-designed malodour score classification system comprising an electronic nose sensor array, a sensor readout interface and a machine learning hardware fabricated on low-cost flexible substrates. The proposed flexible integrated smart system is to augment the expert panel by acting like a panel assessor but could ultimately replace the panel to reduce the test and measurement costs. We demonstrate that it can classify malodour scores as good as or even better than half of the assessors on the expert panel.

This work presents the results of a controlled study with the aim to quantify the effect of emotional stress on physiologic tremor. A paced auditory addition test is utilized to induce emotional stress. The tremor is measured by means of a wearable activity sensor (GENEA), were empirical mode decomposition is used to extract the tremor signal. An autoregressive model and the fractal dimension of the signal are used to construct tremor features. The result of an ANOVA test provides evidence that the stress condition increases the tremor strength compared to the control. The observed changes of the spectral properties indicate that emotional stress affects intentional tremor. These findings support the usage of wearable activity sensors for the investigation of stress-related tremor changes and the evaluation of emotional context.

SummaryThe paper proposes the modelling and analysis of image texture by using an extension of a locally stationary wavelet process model into two dimensions for lattice processes. Such a model permits construction of estimates of a spatially localized spectrum and localized autocovariance which can be used to characterize texture in a multiscale and spatially adaptive way. We provide the necessary theoretical support to show that our two-dimensional extension is properly defined and has the proper statistical convergence properties. Our use of a statistical model permits us to identify, and correct for, a bias in established texture measures based on non-decimated wavelet techniques. The method proposed performs nearly as well as optimal Fourier techniques on stationary textures and outperforms them in non-stationary situations. We illustrate our techniques by using pilled fabric data from a fabric care experiment and simulated tile data.

Parallel coordinates are widely used in many applications for visualization of multivariate data. Because of the nature of parallel coordinates, the visualization technique is often used for data overview. However, when the number of tuples to be visualized becomes very large, this technique makes it difficult to distinguish the overall structure. In This work we present a novel technique which uses a classification approach, the self-organizing map (an unsupervised learning algorithm), to solve this problem by creating an initial clustering of the data. By initially only visualizing the resulting representational clusters, the inherited global structure can be shown. Using linked views and allowing the user to perform drill-down and filtering on these representations reveals the single data items without loss of context.

Extracting actionable insight from large high-dimensional data sets, and its use for more effective decision-making, has become a pervasive problem across many application fields in both research and industry. The objective of our presentation is to report on some investigations of this problem covering both these areas. Taking as the problem domain the area of "unsupervised learning", we show that by tightly coupling statistical analysis technique with combinations of visualization components and techniques for interactivity, real-time analysis of multidimensional data can be efficiently made. We give particular attention to the ways in which dynamic visual representations can be used in these contexts to facilitate shared understanding. Our system is implemented and validated in the context of 3D medical imaging knowledge construction, knowledge management and geovisualisation.

Extracting actionable insight from large high dimensional data sets, and its use for more effective decision-making, has become a pervasive problem across many fields in research and industry. This paper describes an investigation of the application of tightly coupled statistical and visual analysis techniques to this task. The approach we choose in this study is "unsupervised learning" where we investigate the advantages offered by close coupling of the self-organizing map algorithm with new combinations of visualization components and techniques for interactivity.

The need for visualisation applications developed for small handheld devices such as PDAs and intelligent mobiles are growing. A visual user interface VUI model based on zooming user interface techniques (ZUI), to adapt two complete different visualisation application areas; on-line brand shopping and flood warning system for PDAs, is presented. The on-line brand shopping was evaluated in a benchmark usability study comparing it to traditional PC based eNet shopping. This paper is also a blueprint to inform researchers and software engineers about our experience in developing visualisation applications for PDAs with existing development platforms.

Aims: To validate an in vitro model for the analysis of physiological and ecological responses to sugar challenge in bacterial populations, and subsequent changes in enamel mineralization.Methods and Results: A seven-organism bacterial consortium was grown in a biofilm mode on enamel and hydroxyapatite (HA) surfaces in a continuous culture system and exposed to repeated sucrose challenges. This produced 'pH-cycling' conditions within the system. Populations on HA surfaces were enumerated. Changes in relative proportions of the different populations, and in the total viable count, were observed, between different treatments. Microradiography of the enamel sections showed increasing demineralization with increasing sucrose concentration. The lesions formed were similar to 'white-spot' lesions found in vivo. Differences in the quality of biofilms formed were also observed using Confocal Laser Scanning Microscopy.Conclusions: An in vitro model has been validated for the analysis of both physiological and ecological responses to sucrose challenges in bacterial populations, and subsequent changes in enamel mineralization.Significance and Impact of the Study: This model should facilitate the study of changes in bacterial populations in response to application of putative anticaries agents and concomitant changes in enamel mineralization.

Assessment of the role of biofilm microstructure in biofilm-specific activities requires non-destructive measurement techniques for parameterization of structural characteristics in parallel with relevant biochemical and physiological data. This paper briefly reviews some current methods for biofilm structural analysis, with emphasis on new developments in optical imaging and mathematical modeling methods. Fluorescence imaging studies of bacterial colonization events occurring on exposed model tooth surfaces indicated that bacterial adhesion to sessile organisms was of central importance to the early colonization process and that this occurred in a non-random manner. Structural studies of mature biofilms by confocal microscopy demonstrated the spatial distribution of individual species using fluorescent antibodies. Biofilms grown under different physiological conditions exhibited differences in structure, and methods were developed for parameterizing the spatial orientations of the bacteria. Diffusive processes within biofilm microstructures were studied using a random walk model in both 2-D and 3-D. Modeling of convective flow within biofilm microstructures was achieved by application of lattice Boltzmann methodology.

Numerous studies have postulated that bacteria which reside in a biofilm differ from planktonic bacteria. These differences are thought to affect biofilm permeability and, indirectly, the susceptibility of biofilm bacteria to antibacterial agents. In this study fluorescence recovery after photobleaching (FRAP) was used to monitor the diffusion and binding characteristics of a set of size fractionated fluorescein isothiocyanate (FTTC)-conjugated dextrans over small areas (ca. 10 micron) in bacterial biofilms. From these measurements it was straightforward to calculate apparent diffusion rates. Initial studies on the concentration dependence of dextran interaction with planktonic bacteria showed that no irreversible interaction was occurring, however, anomalous faster than free solution diffusion rates were obtained. This phenomenon was modelled using novel analytical and numerical methods which incorporate reversible binding with associated fluorescence changes. Apparent diffusion rates measured in biofilms were highly dependent on biofilm preparation. Sucrose starved biofilms produced an apparent slow-down of two- to fivefold depending on dextran molecular mass and location within the biofilm, indicating that diffusion within the biofilm is hindered. Sucrose supplemented biofilms produced apparent diffusion rates close to those in free solution, suggesting less hindered diffusion. Ex vivo plaque showed diffusion and binding similar to the sucrose supplemented biofilms. The FRAP approach provides a fast and convenient method for determining diffusion rates over small areas within bacterial biofilms. This study reinforces the importance of considering the influence of reversible binding and associated fluorescence changes, as these may have a marked effect on the measured apparent diffusion rate.

Summary. This report is an appendix to the paper Locally stationary wavelet fields with application to the modelling and analysis of image texture, providing proofs for all the major results.