Classifying patients by their characteristics and clinical presentations; the use of latent class analysis

In this article, we introduce the general statistical analysis approach known as latent class analysis and discuss some of the issues associated with this type of analysis in practice. Two recent examples from the respiratory health literature are used to highlight the types of research questions that have been addressed using this approach.

Class-specific sparse codes for representing activities

In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state-of-the art performance with several feature descriptors it still suffers from large quantization errors and reduces the overall performance. Recently proposed sparse representation methods have been shown to effectively represent features as a linear combination of an over complete dictionary by minimizing the reconstruction error. In contrast to most of the sparse representation methods which focus on Sparse-Reconstruction based Classification (SRC), this paper focuses on a discriminative classification using a SVM by constructing class-specific sparse codes for motion and appearance separately. Experimental results demonstrates that separate motion and appearance specific sparse coefficients provide the most effective and discriminative representation for each class compared to a single class-specific sparse coefficients.

See you in class: Promoting a quality program for pre-service educators in regional locations through a virtual classroom

The quality of an online university degree is paramount to the student, the reputation of the university and most importantly, the profession that will be entered. At the School of Education within Curtin University, we aim to ensure that students within rural and remote areas are provided with high quality degrees equal to their city counterparts who access face-to-face classes on campus.In 2010, the School of Education moved to flexible delivery of a fully online Bachelor of Education degree for their rural students. In previous years, the degree had been delivered in physical locations around the state. Although this served the purpose for the time, it restricted the degree to only those rural students who were able to access the physical campus. The new model in 2010 allows access for students in any rural area who have a computer and an internet connection, regardless of their geographical location. As a result enrolments have seen a positive increase in new students. Academic staff had previously used an asynchronous environment to deliver learning modules housed within a learning management system (LMS). To enhance the learning environment and to provide high quality learning experiences to students learning at a distance, the adoption of synchronous software was introduced. This software is a real-time virtual classroom environment that allows for communication through Voice over Internet Protocol (VoIP) and videoconferencing, along with a large number of collaboration tools to engage learners. This research paper reports on the professional development of academic staff to integrate a live e-learning solution into their current LMS environment. It involved professional development, including technical orientation for teaching staff and course participants simultaneously. Further, pedagogical innovations were offered to engage the students in a collaborative learning environment. Data were collected from academic staff through semi-structured interviews and participant observation. The findings discuss the perceived value of the technology, problems encountered and solutions sought.

3D QSAR studies of N-4-arylacryloylpiperazin-1-yl-phenyl-oxazolidinones: A novel class of antibacterial agents

Three-dimensional QSAR studies for N-4-arylacryloylpiperazin-1-yl-phenyl-oxazolidinones were conducted using TSAR 3.3. The in vitro activities (MICs) of the compounds against Staphylococcus aureus ATCC 25923 exhibited a strong correlation with the prediction made by the model developed in the present study.

A potent nonporphyrin class of photodynamic therapeutic agent: Cellular localisation, cytotoxic potential and influence of hypoxia

We have developed a totally new class of nonporphyrin photodynamic therapeutic agents with a specific focus on two lead candidates azadipyrromethene (ADPM)01 and ADPM06. Confocal laser scanning microscopy imaging showed that these compounds are exclusively localised to the cytosolic compartment, with specific accumulation in the endoplasmic reticulum and to a lesser extent in the mitochondria. Light-induced toxicity assays, carried out over a broad range of human tumour cell lines, displayed EC50 values in the micro-molar range for ADPM01 and nano-molar range for ADPM06, with no discernable activity bias for a specific cell type. Strikingly, the more active agent, ADPM06, even retained significant activity under hypoxic conditions. Both photosensitisers showed low to nondeterminable dark toxicity. Flow cytometric analysis revealed that ADPM01 and ADPM06 were highly effective at inducing apoptosis as a mode of cell death. The photophysical and biological characteristics of these PDT agents suggest that they have potential for the development of new anticancer therapeutics. © 2005 Cancer Research UK.

Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs

State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar´ f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Exploring the post-genomic world: Differing explanatory and manipulatory functions of post-genomic sciences

Richard Lewontin proposed that the ability of a scientific field to create a narrative for public understanding garners it social relevance. This article applies Lewontin's conceptual framework of the functions of science (manipulatory and explanatory) to compare and explain the current differences in perceived societal relevance of genetics/genomics and proteomics. We provide three examples to illustrate the social relevance and strong cultural narrative of genetics/genomics for which no counterpart exists for proteomics. We argue that the major difference between genetics/genomics and proteomics is that genomics has a strong explanatory function, due to the strong cultural narrative of heredity. Based on qualitative interviews and observations of proteomics conferences, we suggest that the nature of proteins, lack of public understanding, and theoretical complexity exacerbates this difference for proteomics. Lewontin's framework suggests that social scientists may find that omics sciences affect social relations in different ways than past analyses of genetics.

The relationship between class participation and law students' learning, engagement and stress: Do demographics matter?

Student participation in the classroom has long been regarded as an important means of increasing student engagement and enhancing learning outcomes by promoting active learning. However, the approach to class participation common in U.S. law schools, commonly referred to as the Socratic method, has been criticised for its negative impacts on student wellbeing. A multiplicity of American studies have identified that participating in law class discussions can be alienating, intimidating and stressful for some law students, and may be especially so for women, and students from minority backgrounds. Using data from the Law School Student Assessment Survey (LSSAS), conducted at UNSW Law School in 2012, this Chapter provides preliminary insights into whether assessable class participation (ACP) at an Australian law school is similarly alienating and stressful for students, including the groups identified in the American literature. In addition, we compare the responses of undergraduate Bachelor of Laws (LLB) and graduate Juris Doctor (JD) students. The LSSAS findings indicate that most respondents recognise the potential learning and social benefits associated with class participation in legal education, but remain divided over their willingness to participate. Further, in alignment with general trends identified in American studies, LLB students, women, international students, and non-native English speakers perceive they contribute less frequently to class discussions than JD students, males, domestic students, and native English speakers, respectively. Importantly, the LSSAS indicates students are more likely to be anxious about contributing to class discussions if they are LLB students (compared to their JD counterparts), and if English is not their first language (compared to native English speakers). There were no significant differences in students’ self-reported anxiety levels based on gender, which diverges from the findings of American research.

'More like the kids than the other teachers': One working-class pre-service teacher's experiences in a middle-class profession

While teaching is largely a White, middle-class profession, some teachers, including White teachers, come from low socio-economic backgrounds. This paper examines how one working-class pe-service teacher in Australia experiences studying in a predominantly middle-class teacher education program. Drawing on Bourdieu, this paper seeks to explore what we can learn from the pre-service teaching reflections of one woman who is a member of this smaller group of teachers and who brings to her teaching the habitus and life history that aligns with many of her students and the low socio-economic communities in which she teaches.