Grounded theory : a methodological spiral from positivism to postmodernism

Aim. Our aim in this paper is to explain a methodological/methods package devised to incorporate situational and social world mapping with frame analysis, based on a grounded theory study of Australian rural nurses' experiences of mentoring. Background. Situational analysis, as conceived by Adele Clarke, shifts the research methodology of grounded theory from being located within a postpositivist paradigm to a postmodern paradigm. Clarke uses three types of maps during this process: situational, social world and positional, in combination with discourse analysis. Method. During our grounded theory study, the process of concurrent interview data generation and analysis incorporated situational and social world mapping techniques. An outcome of this was our increased awareness of how outside actors influenced participants in their constructions of mentoring. In our attempts to use Clarke's methodological package, however, it became apparent that our constructivist beliefs about human agency could not be reconciled with the postmodern project of discourse analysis. We then turned to the literature on symbolic interactionism and adopted frame analysis as a method to examine the literature on rural nursing and mentoring as secondary form of data. Findings. While we found situational and social world mapping very useful, we were less successful in using positional maps. In retrospect, we would argue that collective action framing provides an alternative to analysing such positions in the literature. This is particularly so for researchers who locate themselves within a constructivist paradigm, and who are therefore unwilling to reject the notion of human agency and the ability of individuals to shape their world in some way. Conclusion. Our example of using this package of situational and social worlds mapping with frame analysis is intended to assist other researchers to locate participants more transparently in the social worlds that they negotiate in their everyday practice. © 2007 Blackwell Publishing Ltd.

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.

Matrix regularization techniques for online multitask learning

In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.