Multi-dimensional local binary pattern texture descriptors and their application for medical image analysis

Texture can be broadly stated as spatial variation of image intensities. Texture analysis and classification is a well researched area for its importance to many computer vision applications. Consequently, much research has focussed on deriving powerful and efficient texture descriptors. Local binary patterns (LBP) and its variants are simple yet powerful texture descriptors. LBP features describe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture descriptions. A comprehensive evaluation of different LBP variants on a common benchmark dataset is missing in the literature. This thesis presents the performance for different LBP variants on texture classification and retrieval tasks. The results show that multi-scale local binary pattern variance (LBPV) gives the best performance over eight benchmarked datasets. Furthermore, improvements to the Dominant LBP (D-LBP) by ranking dominant patterns over complete training set and Compound LBP (CM-LBP) by considering 16 bits binary codes are suggested which are shown to outperform their original counterparts. The main contribution of the thesis is the introduction of multi-dimensional LBP features, which preserve the relationships between different scales by building a multi-dimensional histogram. The results on benchmarked classification and retrieval datasets clearly show that the multi-dimensional LBP (MD-LBP) improves the results compared to conventional multi-scale LBP. The same principle is applied to LBPV (MD-LBPV), again leading to improved performance. The proposed variants result in relatively large feature lengths which is addressed using three different feature length reduction techniques. Principle component analysis (PCA) is shown to give the best performance when the feature length is reduced to match that of conventional multi-scale LBP. The proposed multi-dimensional LBP variants are applied for medical image analysis application. The first application is nailfold capillary (NC) image classification. Performance of MD-LBPV on NC images is highest, whereas for second application, HEp-2 cell classification, performance of MD-LBP is highest. It is observed that the proposed texture descriptors gives improved texture classification accuracy

A Framework for Sequential Planning in Multi-Agent Settings

This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state space. Agents maintain beliefs over physical states of the environment and over models of other agents, and they use Bayesian updates to maintain their beliefs over time. The solutions map belief states to actions. Models of other agents may include their belief states and are related to agent types considered in games of incomplete information. We express the agents autonomy by postulating that their models are not directly manipulable or observable by other agents. We show that important properties of POMDPs, such as convergence of value iteration, the rate of convergence, and piece-wise linearity and convexity of the value functions carry over to our framework. Our approach complements a more traditional approach to interactive settings which uses Nash equilibria as a solution paradigm. We seek to avoid some of the drawbacks of equilibria which may be non-unique and do not capture off-equilibrium behaviors. We do so at the cost of having to represent, process and continuously revise models of other agents. Since the agents beliefs may be arbitrarily nested, the optimal solutions to decision making problems are only asymptotically computable. However, approximate belief updates and approximately optimal plans are computable. We illustrate our framework using a simple application domain, and we show examples of belief updates and value functions

Community based trial of home blood pressure monitoring with nurse-led telephone support in patients with stroke or transient ischaemic attack recently discharged from hospital.

BACKGROUND: High blood pressure in patients with stroke increases the risk of recurrence but management in the community is often inadequate. Home blood pressure monitoring may increase patients' involvement in their care, increase compliance, and reduce the need for patients to attend their General Practitioner if blood pressure is adequately controlled. However the value of home monitoring to improve blood pressure control is unclear. In particular its use has not been evaluated in stroke patients in whom neurological and cognitive ability may present unique challenges. DESIGN: Community based randomised trial with follow up after 12 months. PARTICIPANTS: 360 patients admitted to three South London Stroke units with stroke or transient ischaemic attack within the past 9 months will be recruited from the wards or outpatients and randomly allocated into two groups. All patients will be visited by the specialist nurse at home at baseline when she will measure their blood pressure and administer a questionnaire. These procedures will be repeated at 12 months follow up by another researcher blind as to whether the patient is in intervention or control group. INTERVENTION: INTERVENTION patients will be given a validated home blood pressure monitor and support from the specialist nurse. Control patients will continue with usual care (blood pressure monitoring by their practice). Main outcome measures in both groups after 12 months: 1. Change in systolic blood pressure.2. Cost effectiveness: Incremental cost of the intervention to the National Health Service and incremental cost per quality adjusted life year gained

Differential expression of parental alleles of BRCA1 in human preimplantation embryos

Gene expression from both parental genomes is required for completion of embryogenesis. Differential methylation of each parental genome has been observed in mouse and human preimplantation embryos. It is possible that these differences in methylation affect the level of gene transcripts from each parental genome in early developing embryos. The aim of this study was to investigate if there is a parent-specific pattern of BRCA1 expression in human embryos and to examine if this affects embryo development when the embryo carries a BRCA1 or BRCA2 pathogenic mutation. Differential parental expression of ACTB, SNRPN, H19 and BRCA1 was semi-quantitatively analysed by minisequencing in 95 human preimplantation embryos obtained from 15 couples undergoing preimplantation genetic diagnosis. BRCA1 was shown to be differentially expressed favouring the paternal transcript in early developing embryos. Methylation-specific PCR showed a variable methylation profile of BRCA1 promoter region at different stages of embryonic development. Embryos carrying paternally inherited BRCA1 or 2 pathogenic variants were shown to develop more slowly compared with the embryos with maternally inherited BRCA1 or 2 pathogenic mutations. This study suggests that differential demethylation of the parental genomes can influence the early development of preimplantation embryos. Expression of maternal and paternal genes is required for the completion of embryogenesis

Functional characterization and discovery of modulators of SbMATE, the agronomically important aluminium tolerance transporter from Sorghum bicolor.

About 50% of the world's arable land is strongly acidic (pH ≤ 5). The low pH solubilizes root-toxic ionic aluminium (Al3+) species from clay minerals, driving the evolution of counteractive adaptations in cultivated crops. The food crop Sorghum bicolor upregulates the membrane-embedded transporter protein SbMATE in its roots. SbMATE mediates efflux of the anionic form of the organic acid, citrate, into the soil rhizosphere, chelating Al3+ ions and thereby imparting Al-resistance based on excluding Al+3 from the growing root tip. Here, we use electrophysiological, radiolabeled, and fluorescence-based transport assays in two heterologous expression systems to establish a broad substrate recognition profile of SbMATE, showing the proton and/or sodium-driven transport of 14C-citrate anion, as well as the organic monovalent cation, ethidium, but not its divalent analog, propidium. We further complement our transport assays by measuring substrate binding to detergent-purified SbMATE protein. Finally, we use the purified membrane protein as an antigen to discover native conformation-binding and transport function-altering nanobodies using an animal-free, mRNA/cDNA display technology. Our results demonstrate the utility of using Pichia pastoris as an efficient eukaryotic host to express large quantities of functional plant transporter proteins. The nanobody discovery approach is applicable to other non-immunogenic plant proteins

Cost-effectiveness of a physical exercise programme for residents of care homes: a pilot study

BACKGROUND: Oomph! Wellness organises interactive exercise and activity classes (Oomph! classes) for older people in care homes. We investigated the cost-effectiveness of Oomph! classes. METHODS: Health-related quality of life was measured using the EQ-5D-5 L questionnaire at three time points; 3 months and 1 week prior to the start of the classes and after 3 months of Oomph! classes. Costs included the costs of organising the classes, training instructors and health service use (General Practitioner (GP) and hospital outpatient visits). To determine the cost-effectiveness of Oomph! classes, total costs and quality-adjusted life-years (QALYs) during the 3 months after initiation of the classes were compared to the total costs and QALYs of the 3 months prior to the classes and extrapolated to a 1-year time horizon. Uncertainty was taken into account using one-way and probabilistic sensitivity analysis. RESULTS: Sixteen residents completed all three EQ-5D-5 L questionnaires. There was a decrease in mean health related quality of life per participant in the 3 months before Oomph! classes (0.56 to 0.52, p = 0.26) and an increase in the 3 months after the start of Oomph! classes (0.52 to 0.60, p = 0.06), but the changes were not statistically significant. There were more GP visits after the start of Oomph! classes and fewer hospital outpatient visits, leading to a slight decrease in NHS costs (mean £132 vs £141 per participant), but the differences were not statistically significant (p = 0.79). In the base case scenario, total costs for Oomph! classes were £113 higher per participant than without Oomph! classes (£677 vs £564) and total QALYs were 0.074 higher (0.594 vs 0.520). The incremental costs per QALY gained were therefore £1531. The 95 % confidence intervals around the cost/QALY gained varied from dominant to dominated, meaning there was large uncertainty around the cost-effectiveness results. Given a willingness to pay threshold of £20,000 per QALY gained, Oomph! classes had a 62 %-86 % probability of being cost-effective depending on the scenario used. CONCLUSIONS: Preliminary evidence suggests that Oomph! classes may be cost-effective, but further evidence is needed about its impact on health-related quality of life and health service use

Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs

Acting in domains where an agent must plan several steps ahead to achieve a goal can be a challenging task, especially if the agentʼs sensors provide only noisy or partial information. In this setting, Partially Observable Markov Decision Processes (POMDPs) provide a planning framework that optimally trades between actions that contribute to the agentʼs knowledge and actions that increase the agentʼs immediate reward. However, the task of specifying the POMDPʼs parameters is often onerous. In particular, setting the immediate rewards to achieve a desired balance between information-gathering and acting is often not intuitive. In this work, we propose an approximation based on minimizing the immediate Bayes risk for choosing actions when transition, observation, and reward models are uncertain. The Bayes-risk criterion avoids the computational intractability of solving a POMDP with a multi-dimensional continuous state space; we show it performs well in a variety of problems. We use policy queries—in which we ask an expert for the correct action—to infer the consequences of a potential pitfall without experiencing its effects. More important for human–robot interaction settings, policy queries allow the agent to learn the reward model without the reward values ever being specified

Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs

Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Traditional I-POMDPs model this dependence on the actions of other agents using joint action and model spaces. Therefore, the solution complexity grows exponentially with the number of agents thereby complicating scalability. In this paper, we model and extend anonymity and context-specific independence -- problem structures often present in agent populations -- for computational gain. We empirically demonstrate the efficiency from exploiting these problem structures by solving a new multiagent problem involving more than 1,000 agents.Comment: 8 page article plus two page appendix containing proofs in Proceedings of 25th International Conference on Autonomous Planning and Scheduling, 201

A Bayesian nonparametric approach to modeling battery health

The batteries of many consumer products are both a substantial portion of the product's cost and commonly a first point of failure. Accurately predicting remaining battery life can lower costs by reducing unnecessary battery replacements. Unfortunately, battery dynamics are extremely complex, and we often lack the domain knowledge required to construct a model by hand. In this work, we take a data-driven approach and aim to learn a model of battery time-to-death from training data. Using a Dirichlet process prior over mixture weights, we learn an infinite mixture model for battery health. The Bayesian aspect of our model helps to avoid over-fitting while the nonparametric nature of the model allows the data to control the size of the model, preventing under-fitting. We demonstrate our model's effectiveness by making time-to-death predictions using real data from nickel-metal hydride battery packs.United States. Army Research Office (Nostra Project STTR W911NF-08-C-0066)iRobo