A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

School-community engagement : a critical approach to involving young people in an urban planning project

This study examines the participation of a group of high school students in designing a Heritage Journey as part of an urban redevelopment project in their community. School-community engagement offers young people an opportunity to engage in community life and influence decisions that affect them. Forging links between community and school is becoming more important for teachers as they attempt to create new authentic learning opportunities for young people within a changing world. Increasingly, researchers and urban planners are including children and young people as active decision makers and participants in community engagement projects. However, models of participation tend to be adult-focussed, conceive participation in terms of low to high graduated levels and lack a clearly articulated theoretical basis. The research problem in this study focuses on investigating whether the inclusion of young people in school-community engagement results in value adding to urban planning and is an example of genuine participation. The aim of the study is to provide a theoretically informed, empirically rich understanding of the inclusion of young people in a community engagement strategy for an urban planning project. Theories of space developed by Henri Lefebvre and Edward Soja are drawn upon for understanding how space is understood, used, and redeveloped by the students and other stakeholders. The study also draws on David Harvey’s notion of utopia and space to consider the imaginative possibilities of the students’ designs and ideas. The study uses a participatory research approach and documents the opportunities and challenges of this methodology. The thesis argues that school-community engagement within a "Thirdspace" offers many new opportunities for the emergence of authentic learning situations. Key findings from the study show young people’s participation in an urban planning project can achieve successful results when young people are given opportunities for full participation in decision-making processes; multiple pathways for active engagement are incorporated into the research design; opportunities for mentoring are provided; realistic timelines are communicated to all stakeholders and the needs and social practices of the local community are acknowledged. A new spatial model of community engagement is proposed as an outcome of the study. Unlike previous models of participation, this model demonstrates how exclusion and inclusion can be conceived visually, and may prove effective for conceptualising future community engagement projects that involve young people.

Urban pre-service teachers’ conceptions of teaching in rural communities

Encouraging quality teaching staff to apply for and accept teaching placements in rural and remote locations is an ongoing concern internationally. The value of different support mechanisms provided for pre-service teachers attending a rural and remote practicum[1] are investigated through theories of place and the school-community nexus. Qualitative data regarding the experiences of the pre-service teachers were collected through interviews and case study notes. This project adds to our understanding of practicum in rural areas by employing a conceptual understanding of place to propose how the experiences of a four-week practicum may contribute to urban pre-service teachers’ conceptions of work and life in a rural community

Taxonomy for rainfall events based on pollutant wash-off potential in urban areas

Conventional rainfall classification for modelling and prediction is quantity based. This approach can lead to inaccuracies in stormwater quality modelling due to the assignment of stochastic pollutant parameters to a rainfall event. A taxonomy for natural rainfall events in the context of stormwater quality is presented based on an in-depth investigation of the influence of rainfall characteristics on stormwater quality. In the research study, the natural rainfall events were classified into three types based on average rainfall intensity and rainfall duration and the classification was found to be independent of the catchment characteristics. The proposed taxonomy provides an innovative concept in stormwater quality modelling and prediction and will contribute to enhancing treatment design for stormwater quality mitigation.

Experimental comparison of odometry approaches

Odometry is an important input to robot navigation systems, and we are interested in the performance of vision-only techniques. In this paper we experimentally evaluate and compare the performance of wheel odometry, monocular feature-based visual odometry, monocular patch-based visual odometry, and a technique that fuses wheel odometry and visual odometry, on a mobile robot operating in a typical indoor environment.