Cell-cell communication in three dimensional microenvironments

Cellular behavior is dependent on a variety of extracellular cues required for normal tissue function, wound healing, and activation of the immune system. Removed from their in vivo microenvironment and cultured in vitro, cells lose many environmental cues and that may result in abberant behavior, making it difficult to study cellular processes. In order to mimic native tissue environments, optical tweezer and microfluidic technologies were used to place cells within defined areas of the culture environment. To provide three dimensional supports found in natural tissues, hydrogel scaffolds of poly (ethylene glycol) diacrylate and the basement membrane matrix Matrigel were used. Optical tweezer technology allowed precision placement and formation of homotypic and heterotypic arrays of human U937, HEK 293, and porcine mesenchymal stem cells. Alternatively, two microfluidic devices were designed to pattern Matrigel scaffolds. The first microfluidic device utilized laminar flow to spatially pattern multiple cell types within the device. Gradients of soluble molecules were then be formed and manipulated across the Matrigel scaffolds. Patterning Matrigel using laminar flow techniques require microfluidic expertise and do not produce consistent patterning conditions, limiting their use difficult in most cell culture laboratories. Thus, a buried Matrigel polydimethylsiloxane (PDMS) device was developed for spatial patterning of biological scaffolds. Matrigel is injected into micron sized channels of PDMS fabricated by soft lithography and allowed to thermally cure. Following curing, a second PDMS device was placed on top of the buried Matrigel channels to support media flow. In order to validate these systems, a cell-cell communication model system was developed utilizing LPS and TNFα signaling with fluorescent reporter systems to monitor communication in real time. We demonstrated the utility of microfluidic devices to support the cell-cell communication model system by co culturing three cell types within Matrigel scaffolds and monitoring signaling activity via fluorescent reporters.

Fully Anisotropic Solution of the Three Dimensional Boltzmann Transport Equation

The development of accurate modeling techniques for nanoscale thermal transport is an active area of research. Modern day nanoscale devices have length scales of tens of nanometers and are prone to overheating, which reduces device performance and lifetime. Therefore, accurate temperature profiles are needed to predict the reliability of nanoscale devices. The majority of models that appear in the literature obtain temperature profiles through the solution of the Boltzmann transport equation (BTE). These models often make simplifying assumptions about the nature of the quantized energy carriers (phonons). Additionally, most previous work has focused on simulation of planar two dimensional structures. This thesis presents a method which captures the full anisotropy of the Brillouin zone within a three dimensional solution to the BTE. The anisotropy of the Brillouin zone is captured by solving the BTE for all vibrational modes allowed by the Born Von-Karman boundary conditions.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.

Three-dimensional kinetic adaptations of gait throughout pregnancy and postpartum

Biomechanical adaptations that occur during pregnancy can lead to changes on gait pattern. Nevertheless, these adaptations of gait are still not fully understood. The purpose was to determine the effect of pregnancy on the biomechanical pattern of walking, regarding the kinetic parameters. A three-dimensional analysis was performed in eleven participants. The kinetic parameters in the joints of the lower limb during gait were compared at the end of the first, second, and third trimesters of pregnancy and in the postpartum period, in healthy pregnant women. The main results showed a reduction in the normalized vertical reaction forces, throughout pregnancy, particularly the third peak. Pregnant women showed, during most of the stance phase, medial reaction forces as a motor response to promote the body stability. Bilateral changes were observed in hip joint, with a decrease in the participation of the hip extensors and in the eccentric contraction of hip flexors. In ankle joint a decrease in the participation of ankle plantar flexors was found. In conclusion, the overall results point to biomechanical adjustments that showed a decrease of the mechanical load of women throughout pregnancy, with exception for few unilateral changes of hip joint moments.

Three-dimensional kinetic adaptations of gait throughout pregnancy and postpartum

Biomechanical adaptations that occur during pregnancy can lead to changes on gait pattern. Nevertheless, these adaptations of gait are still not fully understood. The purpose was to determine the effect of pregnancy on the biomechanical pattern of walking, regarding the kinetic parameters. A three-dimensional analysis was performed in eleven participants. The kinetic parameters in the joints of the lower limb during gait were compared at the end of the first, second, and third trimesters of pregnancy and in the postpartum period, in healthy pregnant women. The main results showed a reduction in the normalized vertical reaction forces, throughout pregnancy, particularly the third peak. Pregnant women showed, during most of the stance phase, medial reaction forces as a motor response to promote the body stability. Bilateral changes were observed in hip joint, with a decrease in the participation of the hip extensors and in the eccentric contraction of hip flexors. In ankle joint a decrease in the participation of ankle plantar flexors was found. In conclusion, the overall results point to biomechanical adjustments that showed a decrease of the mechanical load of women throughout pregnancy, with exception for few unilateral changes of hip joint moments.

One-dimensional Model for Fluids of Third-grade in Tubes with Constant Radius

Our goal in this paper is to extend previous results obtained for Newtonian and secondgrade fluids to third-grade fluids in the case of an axisymmetric, straight, rigid and impermeable tube with constant cross-section using a one-dimensional hierarchical model based on the Cosserat theory related to fluid dynamics. In this way we can reduce the full threedimensional system of equations for the axisymmetric unsteady motion of a non-Newtonian incompressible third-grade fluid to a system of equations depending on time and on a single spatial variable. Some numerical simulations for the volume flow rate and the the wall shear stress are presented.

Primary alkaline battery cathodes a three-scale model

A mathematical model for the galvanostatic discharge and recovery of porous, electrolytic manganese dioxide cathodes, similar to those found within primary alkaline batteries is presented. The phenomena associated with discharge are modeled over three distinct size scales, a cathodic (or macroscopic) scale, a porous manganese oxide particle (or microscopic) scale, and a manganese oxide crystal (or submicroscopic) scale. The physical and chemical coupling between these size scales is included in the model. In addition, the model explicitly accounts for the graphite phase within the cathode. The effects that manganese oxide particle size and proton diffusion have on cathodic discharge and the effects of intraparticle voids and microporous electrode structure are predicted using the model.

Exploring non-cancer pain conditions in a community sample : critiquing a current conceptual model of the acute to chronic pain transition and examining predictors of chronicity

This program of research examines the experience of chronic pain in a community sample. While, it is clear that like patient samples, chronic pain in non-patient samples is also associated with psychological distress and physical disability, the experience of pain across the total spectrum of pain conditions (including acute and episodic pain conditions) and during the early course of chronic pain is less clear. Information about these aspects of the pain experience is important because effective early intervention for chronic pain relies on identification of people who are likely to progress to chronicity post-injury. A conceptual model of the transition from acute to chronic pain was proposed by Gatchel (1991a). In brief, Gatchel’s model describes three stages that individuals who have a serious pain experience move through, each with worsening psychological dysfunction and physical disability. The aims of this program of research were to describe the experience of pain in a community sample in order to obtain pain-specific data on the problem of pain in Queensland, and to explore the usefulness of Gatchel’s Model in a non-clinical sample. Additionally, five risk factors and six protective factors were proposed as possible extensions to Gatchel’s Model. To address these aims, a prospective longitudinal mixed-method research design was used. Quantitative data was collected in Phase 1 via a comprehensive postal questionnaire. Phase 2 consisted of a follow-up questionnaire 3 months post-baseline. Phase 3 consisted of semi-structured interviews with a subset of the original sample 12 months post follow-up, which used qualitative data to provide a further in-depth examination of the experience and process of chronic pain from respondents’ point of view. The results indicate chronic pain is associated with high levels of anxiety and depressive symptoms. However, the levels of disability reported by this Queensland sample were generally lower than those reported by clinical samples and consistent with disability data reported in a New South Wales population-based study. With regard to the second aim of this program of research, while some elements of the pain experience of this sample were consistent with that described by Gatchel’s Model, overall the model was not a good fit with the experience of this non-clinical sample. The findings indicate that passive coping strategies (minimising activity), catastrophising, self efficacy, optimism, social support, active strategies (use of distraction) and the belief that emotions affect pain may be important to consider in understanding the processes that underlie the transition to and continuation of chronic pain.

An effective model of using negative relevance feedback for information filtering

Over the years, people have often held the hypothesis that negative feedback should be very useful for largely improving the performance of information filtering systems; however, we have not obtained very effective models to support this hypothesis. This paper, proposes an effective model that use negative relevance feedback based on a pattern mining approach to improve extracted features. This study focuses on two main issues of using negative relevance feedback: the selection of constructive negative examples to reduce the space of negative examples; and the revision of existing features based on the selected negative examples. The former selects some offender documents, where offender documents are negative documents that are most likely to be classified in the positive group. The later groups the extracted features into three groups: the positive specific category, general category and negative specific category to easily update the weight. An iterative algorithm is also proposed to implement this approach on RCV1 data collections, and substantial experiments show that the proposed approach achieves encouraging performance.

The understanding of avatar design : various types and styles of avatars and the design considerations

People have adopted various formats of media such as graphics, photo and text (nickname) in order to represent themselves when communicate with others online. Avatar is known as a visual form representing a user oneself and one's identity wished. Its form can vary from a two-dimensional model to a three-dimensional model, and can be visualised with various visual forms and styles. In general, two-dimensional images including an animated image are used in online forum communities and live chat software; while three-dimensional models are often used in computer games. Avatar design is often regarded as a graphic designer's visual image creation or a user's output based on one's personal preference, yet it often causes the avatar design having no consideration of its practical visual design and users' interactive communication experience aspects. This paper will review various types and styles of avatar and discuss about avatar design from visual design and online user experience perspectives. It aims to raise a design discourse in avatar design and build up a well-articulated set of design principles for effective avatar design.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

A model of teacher professional development based on the principles of lesson study

The researcher’s professional role as an Education Officer was the impetus for this study. Designing and implementing professional development activities is a significant component of the researcher’s position description and as a result of reflection and feedback from participants and colleagues, the creation of a more effective model of professional development became the focus for this study. Few studies have examined all three links between the purposes of professional development that is, increasing teacher knowledge, improving teacher practice, and improving student outcomes. This study is significant in that it investigates the nature of the growth of teachers who participated in a model of professional development which was based upon the principles of Lesson Study. The research provides qualitative and empirical data to establish some links between teacher knowledge, teacher practice, and student learning outcomes. Teacher knowledge in this study refers to mathematics content knowledge as well as pedagogical-content knowledge. The outcomes for students include achievement outcomes, attitudinal outcomes, and behavioural outcomes. As the study was conducted at one school-site, existence proof research was the focus of the methodology and data collection. Developing over the 2007 school year, with five teacher-participants and approximately 160 students from Year Levels 6 to 9, the Lesson Study-principled model of professional development provided the teacher-participants with on-site, on-going, and reflective learning based on their classroom environment. The focus area for the professional development was strategising the engagement with and solution of worded mathematics problems. A design experiment was used to develop the professional development as an intervention of prevailing teacher practice for which data were collected prior to and after the period of intervention. A model of teacher change was developed as an underpinning framework for the development of the study, and was useful in making decisions about data collection and analyses. Data sources consisted of questionnaires, pre-tests and post-tests, interviews, and researcher observations and field notes. The data clearly showed that: content knowledge and pedagogical-content knowledge were increased among the teacher-participants; teacher practice changed in a positive manner; and that a majority of students demonstrated improved learning outcomes. The positive changes to teacher practice are described in this study as the demonstrated use of mixed pedagogical practices rather than a polarisation to either traditional pedagogical practices or contemporary pedagogical practices. The improvement in student learning outcomes was most significant as improved achievement outcomes as indicated by the comparison of pre-test and post-test scores. The effectiveness of the Lesson Study-principled model of professional development used in this study was evaluated using Guskey’s (2005) Five Levels of Professional Development Evaluation.

Developing a model of technology appropriation: A Marxian perspective

This paper explores the philosophical origins of appropriation of Information Systems (IS) using Marxian and other socio-cultural theory. It provides an in-depth examination of appropriation and its application in extant IS theory. We develop a three-tier model using Marx’s foundational concepts and from this generate four propositions that we test in an empirical example of IS in anesthesia. Using Marxian theory, this paper seeks common ground among existing theories of technology appropriation in IS research. This work contributes to IS research by (1) opening philosophical discussions on appropriation and the human ↔ technology nexus, (2) drawing on these varying perspectives to propose a general conceptualization of technology appropriation and (3) providing a starting point towards a general causal model of technology appropriation.