Tesofensine : a novel potent weight loss medicine

Background: The incidence of obesity is increasing; this is of major concern, as obesity is associated with cardiovascular disease, stroke, type 2 diabetes, respiratory tract disease, and cancer. Objectives/methods: This evaluation is of a Phase II clinical trial with tesofensine in obese subjects. Results: After 26 weeks, tesofensine caused a significant weight loss, and may have a higher maximal ability to reduce weight than the presently available anti-obesity agents. However, tesofensine also increased blood pressure and heart rate, and may increase psychiatric disorders. Conclusions: It is encouraging that tesofensine 0.5 mg may cause almost double the weight loss observed with sibutramine or rimonabant. As tesofensine and sibutramine have similar pharmacological profiles, it would be of interest to compare the weight loss with tesofensine in a head-to-head clinical trial with sibutramine, to properly assess their comparative potency. Also, as teso fensine 0.5 mg increases heart rate, as well as increasing the incidence of adverse effects such as nausea, drug mouth, flatulence, insomnia, and depressed mode, its tolerability needs to be further evaluated in large Phase III clinical trials.

Temperature variation and emergency hospital admissions for stroke in Brisbane, Australia, 1996-2005

Stroke is a leading cause of disability and death. This study evaluated the association between temperature variation and emergency admissions for stroke in Brisbane, Australia. Daily emergency admissions for stroke, meteorologic and air pollution data were obtained for the period of January 1996 to December 2005. The relative risk of emergency admissions for stroke was estimated with a generalized estimating equations (GEE) model. For primary intracerebral hemorrhage (PIH) emergency admissions, the average daily PIH for the group aged < 65 increased by 15% (95% Confidence Interval (CI): 5, 26%) and 12% (95% CI: 2, 22%) for a 1°C increase in daily maximum temperature and minimum temperature in summer, respectively, after controlling for potential confounding effects of humidity and air pollutants. For ischemic stroke (IS) emergency admissions, the average daily IS for the group aged ≥ 65 decreased by 3% (95% CI: -6, 0%) for a 1°C increase in daily maximum temperature in winter after adjustment for confounding factors. Temperature variation was significantly associated with emergency admissions for stroke, and its impact varied with different type of stroke. Health authorities should pay greater attention to possible increasing emergency care for strokes when temperature changes, in both summer and winter.

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

The rate-limiting mechanism for the heterogeneous burning of cylindrical iron rods

This paper presents the findings of an investigation into the rate-limiting mechanism for the heterogeneous burning in oxygen under normal gravity and microgravity of cylindrical iron rods. The original objective of the work was to determine why the observed melting rate for burning 3.2-mm diameter iron rods is significantly higher in microgravity than in normal gravity. This work, however, also provided fundamental insight into the rate-limiting mechanism for heterogeneous burning. The paper includes a summary of normal-gravity and microgravity experimental results, heat transfer analysis and post-test microanalysis of quenched samples. These results are then used to show that heat transfer across the solid/liquid interface is the rate-limiting mechanism for melting and burning, limited by the interfacial surface area between the molten drop and solid rod. In normal gravity, the work improves the understanding of trends reported during standard flammability testing for metallic materials, such as variations in melting rates between test specimens with the same cross-sectional area but different crosssectional shape. The work also provides insight into the effects of configuration and orientation, leading to an improved application of standard test results in the design of oxygen system components. For microgravity applications, the work enables the development of improved methods for lower cost metallic material flammability testing programs. In these ways, the work provides fundamental insight into the heterogeneous burning process and contributes to improved fire safety for oxygen systems in applications involving both normal-gravity and microgravity environments.

A proposed qualitative framework for heterogeneous burning of metallic materials : the 'melting rate triangle'

This paper presents a proposed qualitative framework to discuss the heterogeneous burning of metallic materials, through parameters and factors that influence the melting rate of the solid metallic fuel (either in a standard test or in service). During burning, the melting rate is related to the burning rate and is therefore an important parameter for describing and understanding the burning process, especially since the melting rate is commonly recorded during standard flammability testing for metallic materials and is incorporated into many relative flammability ranking schemes. However, whilst the factors that influence melting rate (such as oxygen pressure or specimen diameter) have been well characterized, there is a need for an improved understanding of how these parameters interact as part of the overall melting and burning of the system. Proposed here is the ‘Melting Rate Triangle’, which aims to provide this focus through a conceptual framework for understanding how the melting rate (of solid fuel) is determined and regulated during heterogeneous burning. In the paper, the proposed conceptual model is shown to be both (a) consistent with known trends and previously observed results, and (b)capable of being expanded to incorporate new data. Also shown are examples of how the Melting Rate Triangle can improve the interpretation of flammability test results. Slusser and Miller previously published an ‘Extended Fire Triangle’ as a useful conceptual model of ignition and the factors affecting ignition, providing industry with a framework for discussion. In this paper it is shown that a ‘Melting Rate Triangle’ provides a similar qualitative framework for burning, leading to an improved understanding of the factors affecting fire propagation and extinguishment.

Determinants of participation restriction among community dwelling stroke survivors : a path analysis

Background: Apart from promoting physical recovery and assisting in activities of daily living, a major challenge in stroke rehabilitation is to minimize psychosocial morbidity and to promote the reintegration of stroke survivors into their family and community. The identification of key factors influencing long-term outcome are essential in developing more effective rehabilitation measures for reducing stroke-related morbidity. The aim of this study was to test a theoretical model of predictors of participation restriction which included the direct and indirect effects between psychosocial outcomes, physical outcome, and socio-demographic variables at 12 months after stroke.--------- Methods: Data were collected from 188 stroke survivors at 12 months following their discharge from one of the two rehabilitation hospitals in Hong Kong. The settings included patients' homes and residential care facilities. Path analysis was used to test a hypothesized model of participation restriction at 12 months.---------- Results: The path coefficients show functional ability having the largest direct effect on participation restriction (β = 0.51). The results also show that more depressive symptoms (β = -0.27), low state self-esteem (β = 0.20), female gender (β = 0.13), older age (β = -0.11) and living in a residential care facility (β = -0.12) have a direct effect on participation restriction. The explanatory variables accounted for 71% of the variance in explaining participation restriction at 12 months.---------- Conclusion: Identification of stroke survivors at risk of high levels of participation restriction, depressive symptoms and low self-esteem will assist health professionals to devise appropriate rehabilitation interventions that target improving both physical and psychosocial functioning.

Stroke education : promising effects on the health beliefs of those at risk

Background: The effect of patient education on reducing stroke has had mixed effects, raising questions about how to achieve optimal benefit. Because past evaluations have typically lacked an appropriate theoretical base, the design of past research may have missed important effects. --------- Method: This study used a social cognitive framework to identify variables that might change in response to education. A mixed design was used to evaluate two approaches to an intervention, both of which included education. Fifty seniors completed a measure of stroke knowledge and beliefs twice: before and after an intervention that was either standard (educational brochure plus activities that were not about stroke) or enhanced (educational brochure plus activities designed to enhance beliefs about stroke). Outcome measures were health beliefs, intention to exercise to reduce stroke, and stroke knowledge. --------- Results: Selected beliefs changed significantly over time but not differentially across conditions. Beliefs that changed were (a) perceived susceptibility to stroke and (b) perceived benefit of exercise to reduce risk. Benefit beliefs, in particular, were strongly and positively associated with intention to exercise. -------- Conclusion: Findings suggest that basic approaches to patient education may influence health beliefs. More effective stroke prevention programs may result from continued consideration of the role of health beliefs in such programs.

Social Participation of Community Dwelling Stroke Survivors [Published Abstract: From the Quality of Care and Outcomes Research in Cardiovascular Disease and Stroke Conference 2009]

Disability following a stroke can impose various restrictions on patients’ attempts at participating in life roles. The measurement of social participation, for instance, is important in estimating recovery and assessing quality of care at the community level. Thus, the identification of factors influencing social participation is essential in developing effective measures for promoting the reintegration of stroke survivors into the community. Data were collected from 188 stroke survivors (mean age 71.7 years) 12 months after discharge from a stroke rehabilitation hospital. Of these survivors, 128 (61 %) had suffered a first ever stroke, and 81 (43 %) had a right hemisphere lesion. Most (n = 156, 83 %) were living in their own home, though 32 (17 %) were living in residential care facilities. Path analysis was used to test a hypothesized model of participation restriction which included the direct and indirect effects between social, psychological and physical outcomes and demographic variables. Participation restriction was the dependent variable. Exogenous independent variables were age, functional ability, living arrangement and gender. Endogenous independent variables were depressive symptoms, state self-esteem and social support satisfaction. The path coefficients showed functional ability having the largest direct effect on participation restriction. The results also showed that more depressive symptoms, low state self-esteem, female gender, older age and living in a residential care facility had a direct effect on participation restriction. The explanatory variables accounted for 71% of the variance in explaining participation restriction. Prediction models have empirical and practical applications such as suggesting important factors to be considered in promoting stroke recovery. The findings suggest that interventions offered over the course of rehabilitation should be aimed at improving functional ability and promoting psychological aspects of recovery. These are likely to enhance stroke survivors resume or maximize their social participation so that they may fulfill productive and positive life roles.

Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species.

Poster abstract : Energy-efficient rate adaptive MAC Protocol (RA-MAC) for long-lived sensor networks

This paper introduces an energy-efficient Rate Adaptive MAC (RA-MAC) protocol for long-lived Wireless Sensor Networks (WSN). Previous research shows that the dynamic and lossy nature of wireless communication is one of the major challenges to reliable data delivery in a WSN. RA-MAC achieves high link reliability in such situations by dynamically trading off radio bit rate for signal processing gain. This extra gain reduces the packet loss rate which results in lower energy expenditure by reducing the number of retransmissions. RA-MAC selects the optimal data rate based on channel conditions with the aim of minimizing energy consumption. We have implemented RA-MAC in TinyOS on an off-the-shelf sensor platform (TinyNode), and evaluated its performance by comparing RA-MAC with state-ofthe- art WSN MAC protocol (SCP-MAC) by experiments.

Use of IP addresses for high rate flooding attack detection

High-rate flooding attacks (aka Distributed Denial of Service or DDoS attacks) continue to constitute a pernicious threat within the Internet domain. In this work we demonstrate how using packet source IP addresses coupled with a change-point analysis of the rate of arrival of new IP addresses may be sufficient to detect the onset of a high-rate flooding attack. Importantly, minimizing the number of features to be examined, directly addresses the issue of scalability of the detection process to higher network speeds. Using a proof of concept implementation we have shown how pre-onset IP addresses can be efficiently represented using a bit vector and used to modify a “white list” filter in a firewall as part of the mitigation strategy.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.