An analytical method for assessing stage-specific drug activity in Plasmodium vivax malaria : implications for ex vivo drug susceptibility testing

The emergence of highly chloroquine (CQ) resistant P. vivax in Southeast Asia has created an urgent need for an improved understanding of the mechanisms of drug resistance in these parasites, the development of robust tools for defining the spread of resistance, and the discovery of new antimalarial agents. The ex vivo Schizont Maturation Test (SMT), originally developed for the study of P. falciparum, has been modified for P. vivax. We retrospectively analysed the results from 760 parasite isolates assessed by the modified SMT to investigate the relationship between parasite growth dynamics and parasite susceptibility to antimalarial drugs. Previous observations of the stage-specific activity of CQ against P. vivax were confirmed, and shown to have profound consequences for interpretation of the assay. Using a nonlinear model we show increased duration of the assay and a higher proportion of ring stages in the initial blood sample were associated with decreased effective concentration (EC50) values of CQ, and identify a threshold where these associations no longer hold. Thus, starting composition of parasites in the SMT and duration of the assay can have a profound effect on the calculated EC50 for CQ. Our findings indicate that EC50 values from assays with a duration less than 34 hours do not truly reflect the sensitivity of the parasite to CQ, nor an assay where the proportion of ring stage parasites at the start of the assay does not exceed 66%. Application of this threshold modelling approach suggests that similar issues may occur for susceptibility testing of amodiaquine and mefloquine. The statistical methodology which has been developed also provides a novel means of detecting stage-specific drug activity for new antimalarials.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

Shear design of cold-formed steel beams using direct strength method

This paper presents the direct strength method (DSM) equations for cold-formed steel beams subject to shear. Light gauge cold-formed steel sections have been developed as more economical building solutions to the alternative heavier hot-rolled sections in the commercial and residential markets. Cold-formed lipped channel beams (LCB), LiteSteel beams (LSB) and hollow flange beams (HFB) are commonly used as flexural members such as floor joists and bearers. However, their shear capacities are determined based on conservative design rules. For the shear design of cold-formed web panels, their elastic shear buckling strength must be determined accurately including the potential post-buckling strength. Currently the elastic shear buckling coefficients of web panels are determined by assuming conservatively that the web panels are simply supported at the junction between the flange and web elements and ignore the post-buckling strength. Hence experimental and numerical studies were conducted to investigate the shear behaviour and strength of LSBs, LCBs and HFBs. New direct strength method (DSM) based design equations were proposed to determine the ultimate shear capacities of cold-formed steel beams. An improved equation for the higher elastic shear buckling coefficient of cold-formed steel beams was proposed based on finite element analysis results and included in the DSM design equations. A new post-buckling coefficient was also introduced in the DSM equation to include the available post-buckling strength of cold-formed steel beams.

Developing a method for assessing product inclusivity

In order to develop more inclusive products and services, designers need a means of assessing the inclusivity of existing products and new concepts. Following previous research on the development of scales for inclusive design at University of Cambridge, Engineering Design Centre (EDC) [1], this paper presents the latest version of the exclusion audit method. For a specific product interaction, this estimates the proportion of the Great British population who would be excluded from using a product or service, due to the demands the product places on key user capabilities. A critical part of the method involves rating of the level of demand placed by a task on a range of key user capabilities, so the procedure to perform this assessment was operationalised and then its reliability was tested with 31 participants. There was no evidence that participants rated the same demands consistently. The qualitative results from the experiment suggest that the consistency of participants’ demand level ratings could be significantly improved if the audit materials and their instructions better guided the participant through the judgement process.

An ontology-based method for sparsity problem in tag recommendation

Tags or personal metadata for annotating web resources have been widely adopted in Web 2.0 sites. However, as tags are freely chosen by users, the vocabularies are diverse, ambiguous and sometimes only meaningful to individuals. Tag recommenders may assist users during tagging process. Its objective is to suggest relevant tags to use as well as to help consolidating vocabulary in the systems. In this paper we discuss our approach for providing personalized tag recommendation by making use of existing domain ontology generated from folksonomy. Specifically we evaluated the approach in sparse situation. The evaluation shows that the proposed ontology-based method has improved the accuracy of tag recommendation in this situation.

Mussel-inspired bioceramics with self-assembled Ca-P/polydopamine composite nanolayer : preparation, formation mechanism, improved cellular bioactivity and osteogenic differentiation of bone marrow stromal cells

The nanostructured surface of biomaterials plays an important role in improving their in vitro cellular bioactivity as well as stimulating in vivo tissue regeneration. Inspired by the mussel’s adhesive versatility, which is thought to be due to the plaque–substrate interface being rich in 3,4-dihydroxy-L-phenylalamine (DOPA) and lysine amino acids, in this study we developed a self-assembly method to prepare a uniform calcium phosphate (Ca-P)/polydopamine composite nanolayer on the surface of b-tricalcium phosphate (b-TCP) bioceramics by soaking b-TCP bioceramics in Tris–dopamine solution. It was found that the addition of dopamine, reaction temperature and reaction time are three key factors inducing the formation of a uniform Ca-P/polydopamine composite nanolayer. The formation mechanism of a Ca-P/polydopamine composite nanolayer involved two important steps: (i) the addition of dopamine to Tris–HCl solution decreases the pH value and accelerates Ca and P ionic dissolution from the crystal boundaries of b-TCP ceramics; (ii) dopamine is polymerized to form self-assembled polydopamine film and, at the same time, nanosized Ca-P particles are mineralized with the assistance of polydopamine, in which the formation of polydopamine occurs simultaneously with Ca-P mineralization (formation of nanosized microparticles composed of calcium phosphate-based materials), and finally a self-assembled Ca-P/polydopamine composite nanolayer forms on the surface of the b-TCP ceramics. Furthermore, the formed self-assembled Ca-P/polydopamine composite nanolayer significantly enhances the surface roughness and hydrophilicity of b-TCP ceramics, and stimulates the attachment, proliferation, alkaline phosphate (ALP) activity and bone-related gene expression (ALP, OCN, COL1 and Runx2) of human bone marrow stromal cells. Our results suggest that the preparation of self-assembled Ca-P/polydopamine composite nanolayers is a viable method to modify the surface of biomaterials by significantly improving their surface physicochemical properties and cellular bioactivity for bone regeneration application.

A novel method for purification of low grade diatomite powders in centrifugal fields

This study presented a novel method for purification of three different grades of diatomite from China by scrubbing technique using sodiumhexametaphosphate (SHMP) as dispersant combinedwith centrifugation. Effects of pH value and dispersant amount on the grade of purified diatomitewere studied and the optimumexperimental conditions were obtained. The characterizations of original diatomite and derived products after purification were determined by scanning electron microscopy (SEM), X-ray diffraction (XRD), infrared spectroscopy (IR) and specific surface area analyzer (BET). The results indicated that the pore size distribution, impurity content and bulk density of purified diatomite were improved significantly. The dispersive effect of pH and SHMP on the separation of diatomite from clay minerals was discussed systematically through zeta potential test. Additionally, a possible purification mechanism was proposed in the light of the obtained experimental results.

Survival tucker : improved diet and health indicators in an Aboriginal community

The poor nutritional status of Aboriginal Australians is a serious and complex public health concern. We describe an unusually successful health and nutrition project initiated by the people of Minjilang, which was developed, implemented and evaluated with the community. Apparent community dietary intake, assessed by the ‘store-turnover’ method, and biochemical, anthropometric and haematological indicators of health and nutritional status were measured before intervention and at three-monthly intervals during the intervention year. Following intervention, there was a significant decrease in dietary intake of sugar and saturated fat, an increase in micronutrient density, corresponding improvements in biochemical indices (for example, a 12 per cent decrease in mean serum cholesterol, increases in serum and red cell folate, serum vitamin B6 and plasma ascorbic acid), decrease in mean systolic and diastolic blood pressures, a normalisation of body mass index, and a normalisation of haematologic indices. The success of this project demonstrates that Aboriginal communities can bring about improvements in their generally poor nutritional status, and that the store-turnover method provides a valid, inexpensive and noninvasive method for evaluating the resultant changes in community diet. Although the project was undoubtedly effective in the short term, further work is in progress to assess individual strategies with respect to sustainability, cost-effectiveness and generalisability.

Improved line tracker using IMU and vision for visual servoing

This paper introduces an improved line tracker using IMU and vision data for visual servoing tasks. We utilize an Image Jacobian which describes motion of a line feature to corresponding camera movements. These camera motions are estimated using an IMU. We demonstrate impacts of the proposed method in challenging environments: maximum angular rate ~160 0/s, acceleration ~6m /s2 and in cluttered outdoor scenes. Simulation and quantitative tracking performance comparison with the Visual Servoing Platform (ViSP) are also presented.

A feasibility study of multislice x-ray CT imaging of gel dosimeters using the “zero scan” method

This study extends the ‘zero scan’ method for CT imaging of polymer gel dosimeters to include multi-slice acquisitions. Multi slice CT images consisting of 24 slices of 1.2 mm thickness were acquired of an irradiated polymer gel dosimeter, and processed with the zero scan technique. The results demonstrate that zero scan based gel readout can be successfully applied to generate a three dimensional image of the irradiated gel field. Compared to the raw CT images the processed figures and cross gel profiles demonstrated reduced noise and clear visibility of the penumbral region. Moreover these improved results further highlight the suitability of this method in volumetric reconstruction with reduced CT data acquisition per slice. This work shows that 3D volumes of irradiated polymer gel dosimeters can be acquired and processed with x-ray CT.

A rule-based hybrid method for anomaly detection in online-social-network graphs

Detecting anomalies in the online social network is a significant task as it assists in revealing the useful and interesting information about the user behavior on the network. This paper proposes a rule-based hybrid method using graph theory, Fuzzy clustering and Fuzzy rules for modeling user relationships inherent in online-social-network and for identifying anomalies. Fuzzy C-Means clustering is used to cluster the data and Fuzzy inference engine is used to generate rules based on the cluster behavior. The proposed method is able to achieve improved accuracy for identifying anomalies in comparison to existing methods.

An improved distinguisher for dragon

Dragon stream cipher is one of the focus ciphers which have reached Phase 2 of the eSTREAMproject. In this paper, we present a new method of building a linear distinguisher for Dragon. The distinguisher is constructed by exploiting the biases of two S-boxes and the modular addition which are basic components of the nonlinear function F. The bias of the distinguisher is estimated to be around 2−75.32 which is better than the bias of the distinguisher presented by Englund and Maximov. We have shown that Dragon is distinguishable from a random cipher by using around 2150.6 keystream words and 259 memory. In addition, we present a very efficient algorithm for computing the bias of linear approximation of modular addition.

A method for risk analysis across governance systems : a great barrier reef case study

Healthy governance systems are key to delivering sound environmental management outcomes from global to local scales. There are, however, surprisingly few risk assessment methods that can pinpoint those domains and sub-domains within governance systems that are most likely to influence good environmental outcomes at any particular scale, or those if absent or dysfunctional, most likely to prevent effective environmental management. This paper proposes a new risk assessment method for analysing governance systems. This method is then tested through its preliminary application to a significant real-world context: governance as it relates to the health of Australia's Great Barrier Reef (GBR). The GBR exists at a supra-regional scale along most of the north eastern coast of Australia. Brodie et al (2012 Mar. Pollut. Bull. 65 81-100) have recently reviewed the state and trend of the health of the GBR, finding that overall trends remain of significant concern. At the same time, official international concern over the governance of the reef has recently been signalled globally by the International Union for the Conservation of Nature (IUCN). These environmental and political contexts make the GBR an ideal candidate for use in testing and reviewing the application of improved tools for governance risk assessment. © 2013 IOP Publishing Ltd.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Improved image set classification via joint sparse approximated nearest subspaces

Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. However, this may result in the two closest clusters to represent different characteristics of an object, due to different undesirable environmental conditions (such as variations in illumination and pose). To address this problem, we propose to constrain the clustering of each query image set by forcing the clusters to have resemblance to the clusters in the gallery image sets. We first define a Frobenius norm distance between subspaces over Grassmann manifolds based on reconstruction error. We then extract local linear subspaces from a gallery image set via sparse representation. For each local linear subspace, we adaptively construct the corresponding closest subspace from the samples of a probe image set by joint sparse representation. We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold. Experiments on Honda, ETH-80 and Cambridge-Gesture datasets show that the proposed method consistently outperforms several other recent techniques, such as Affine Hull based Image Set Distance (AHISD), Sparse Approximated Nearest Points (SANP) and Manifold Discriminant Analysis (MDA).