The psychological impact of industrial strikes: Does involvement in union activity during strikes make a difference?

The current study investigated the psychological impact of a United Steelworkers of America strike on the steelworkers involved, and the relationship between psychological well-being and individuals' levels of involvement in union activity during the strike. Three hundred and fifty-one steelworkers (302 `strikers' and 49 `non-strikers') completed surveys measuring a range of demographic and psychological well-being variables. Strikers, compared to non-strikers, reported higher levels of depression, anxiety, and irritation, and lower levels of mental health. For strikers, engaging in higher levels of union activity during the strike was associated with better psychological well-being. Jahoda's theory of deprivation during unemployment is used as the lens through which to explain some of the results, supporting the view that latent benefits associated with work are important for psychological well-being. A range of practical implications are offered for unions and their members.

Cholesterol enhances phospholipid-dependent activated protein C anticoagulant activity

The influence of cholesterol on activated protein C (APC) anticoagulant activity in plasma and on factor Va inactivation was investigated. Anticoagulant and procoagulant activities of phosphatidylcholine/phosphatidylserine (PC/PS) vesicles containing cholesterol were assessed in the presence and absence of APC using factor Xa-1-stage clotting and factor Va inactivation assays. Cholesterol at approximate physiological membrane levels (30%) in PC/PS (60%/10% w/w) vesicles prolonged the factor Xa-1-stage clotting time dose-dependently in the presence of APC but not in the absence of APC. APC-mediated cleavage of purified recombinant factor Va variants that were modified at specific APC cleavage sites (Q306/Q679-factor Va; Q506/Q679-factor Va) was studied to define the effects of cholesterol on APC cleavage at R506 and R306. When compared to control PC/PS vesicles, cholesterol in PC/PS vesicles enhanced factor Va inactivation and the rate of APC cleavage at both R506 and R306. Cholesterol also enhanced APC cleavage rates at R306 in the presence of the APC cofactor, protein S. In summary, APC anticoagulant activity in plasma and factor Va inactivation as a result of cleavages at R506 and R306 by APC is markedly enhanced by cholesterol in phospholipid vesicles. These results suggest that cholesterol in a membrane surface may selectively enhance APC activities. © 2005 International Society on Thrombosis and Haemostasis.

Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Fitness and fatness in childhood obesity : implications of physical activity

Overweight and obesity are a significant cause of poor health worldwide, particularly in conjunction with low levels of physical activity (PA). PA is health-protective and essential for the physical growth and development of children, promoting physical and psychological health while simultaneously increasing the probability of remaining active as an adult. However, many obese children and adolescents have a unique set of physiological, biomechanical, and neuromuscular barriers to PA that they must overcome. It is essential to understand the influence of these barriers on an obese child's motivation in order to exercise and tailor exercise programs to the special needs of this population. Chapter Outline • Introduction • Defining Physical Activity, Exercise, and Physical Fitness • Physical Activity, Physical Fitness, And Motor Competence In Obese Children • Physical Activity and Obesity in Children • Physical Fitness in Obese Children • Balance and Gait in Obese Children • Motor Competence in Obese Children • Physical Activity Guidelines for Obese Children • Clinical Assessment of the Obese Child • Physical Activity Characteristics: Mode • Physical Activity Characteristics: Intensity • Physical Activity Characteristics: Frequency • Physical Activity Characteristics: Duration • Conclusion

Facial expression recognition using facial movement features

Facial expression is an important channel for human communication and can be applied in many real applications. One critical step for facial expression recognition (FER) is to accurately extract emotional features. Current approaches on FER in static images have not fully considered and utilized the features of facial element and muscle movements, which represent static and dynamic, as well as geometric and appearance characteristics of facial expressions. This paper proposes an approach to solve this limitation using ‘salient’ distance features, which are obtained by extracting patch-based 3D Gabor features, selecting the ‘salient’ patches, and performing patch matching operations. The experimental results demonstrate high correct recognition rate (CRR), significant performance improvements due to the consideration of facial element and muscle movements, promising results under face registration errors, and fast processing time. The comparison with the state-of-the-art performance confirms that the proposed approach achieves the highest CRR on the JAFFE database and is among the top performers on the Cohn-Kanade (CK) database.

Improving the performance of facial expression recognition using dynamic, subtle and regional features

Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based ‘salient’ Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to the use of the dynamic features. Performance comparison with pervious work also confirms that the proposed approach achieves the highest CRR reported to date on the JAFFE database and a top-level performance on the Cohn-Kanade (CK) database.