HapMap tag-SNP analysis confirms a role for COMT in schizophrenia risk and reveals a novel association

Catechol-O-methyl transferase (COMT) encodes an enzyme involved in the metabolism of dopamine and maps to a commonly deleted region that increases schizophrenia risk. A non-synonymous polymorphism (rs4680) in COMT has been previously found to be associated with schizophrenia and results in altered activity levels of COMT. Using a haplotype block-based gene-tagging approach we conducted an association study of seven COMT single nucleotide polymorphisms (SNPs) in 160 patients with a DSM-IV diagnosis of schizophrenia and 250 controls in an Australian population. Two polymorphisms including rs4680 and rs165774 were found to be significantly associated with schizophrenia. The rs4680 results in a Val/Met substitution but the strongest association was shown by the novel SNP, rs165774, which may still be functional even though it is located in intron five. Individuals with schizophrenia were more than twice as likely to carry the GG genotype compared to the AA genotype for both the rs165774 and rs4680 SNPs. This association was slightly improved when males were analysed separately possibly indicating a degree of sexual dimorphism. Our results confirm that COMT is a good candidate for schizophrenia risk, by replicating the association with rs4680 and identifying a novel SNP association.

Analysis of HapMap tag-SNPs in dysbindin (DTNBP1) reveals evidence of consistent association with schizophrenia

Dystrobrevin binding protein 1 (DTNBP1), or dysbindin, is thought to be critical in regulating the glutamatergic system. While the dopamine pathway is known to be important in the aetiology of schizophrenia, it seems likely that glutamatergic dysfunction can lead to the development of schizophrenia. DTNBP1 is widely expressed in brain, levels are reduced in brains of schizophrenia patients and a DTNBP1 polymorphism has been associated with reduced brain expression. Despite numerous genetic studies no DTNBP1 polymorphism has been strongly implicated in schizophrenia aetiology. Using a haplotype block-based gene-tagging approach we genotyped 13 SNPs in DTNBP1 to investigate possible associations with DTNBP1 and schizophrenia. Four polymorphisms were found to be significantly associated with schizophrenia. The strongest association was found with an A/C SNP in intron 7 (rs9370822). Homozygotes for the C allele of rs9370822 were more than two and a half times as likely to have schizophrenia compared to controls. The other polymorphisms showed much weaker association and are less likely to be biologically significant. These results suggest that DTNBP1 is a good candidate for schizophrenia risk and rs9370822 is either functionally important or in disequilibrium with a functional SNP, although our observations should be viewed with caution until they are independently replicated.

Bayes Factor based speaker segmentation for speaker diarization

This paper proposes the use of the Bayes Factor as a distance metric for speaker segmentation within a speaker diarization system. The proposed approach uses a pair of constant sized, sliding windows to compute the value of the Bayes Factor between the adjacent windows over the entire audio. Results obtained on the 2002 Rich Transcription Evaluation dataset show an improved segmentation performance compared to previous approaches reported in literature using the Generalized Likelihood Ratio. When applied in a speaker diarization system, this approach results in a 5.1% relative improvement in the overall Diarization Error Rate compared to the baseline.

Individual differences in the visual control of intercepting a penalty kick in association football

Recent perceptual-motor studies have revealed variations in learning trajectories of novices. Despite such observation, relatively little attention has been paid to studying individual differences in experienced performers’ perceptual-motor behaviors. The present study examined individual differences for a visual anticipation task. Experienced association football goalkeepers attempted to intercept penalty kicks taken with deceptive and non-deceptive kicking actions. Data revealed that differences in the action capabilities of goalkeepers affected the timing and accuracy of movement response behaviors. Faster goalkeepers tended to wait until later before initiating movement in comparison with slower goalkeepers. The study of affordances in sport environments offers a theoretical framework with which to overcome some of the reported methodological limitations in the visual anticipation literature.

The ecological dynamics of 1v1 sub-phases in association football

This paper analyzes effects of different practice task constraints on heart rate (HR) variability during 4v4 smallsided football games. Participants were sixteen football players divided into two age groups (U13, Mean age: 12.4±0.5 yrs; U15: 14.6±0.5). The task consisted of a 4v4 sub-phase without goalkeepers, on a 25x15 m field, of 15 minutes duration with an active recovery period of 6 minutes between each condition. We recorded players’ heart rates using heart rate monitors (Polar Team System, Polar Electro, Kempele, Finland) as scoring mode was manipulated (line goal: scoring by dribbling past an extended line; double goal: scoring in either of two lateral goals; and central goal: scoring only in one goal). Subsequently, %HR reserve was calculated with the Karvonen formula. We performed a time-series analysis of HR for each individual in each condition. Mean data for intra-participant variability showed that autocorrelation function was associated with more short-range dependence processes in the “line goal” condition, compared to other conditions, demonstrating that the “line goal” constraint induced more randomness in HR response. Relative to inter-individual variability, line goal constraints demonstrated lower %CV and %RMSD (U13: 9% and 19%; U15: 10% and 19%) compared with double goal (U13: 12% and 21%; U15: 12% and 21%) and central goal (U13: 14% and 24%; U15: 13% and 24%) task constraints, respectively. Results suggested that line goal constraints imposed more randomness on cardiovascular stimulation of each individual and lower inter-individual variability than double goal and central goal constraints.

Overview of the INEX 2010 XML mining track : clustering and classification of XML documents

The XML Document Mining track was launched for exploring two main ideas: (1) identifying key problems and new challenges of the emerging field of mining semi-structured documents, and (2) studying and assessing the potential of Machine Learning (ML) techniques for dealing with generic ML tasks in the structured domain, i.e., classification and clustering of semi-structured documents. This track has run for six editions during INEX 2005, 2006, 2007, 2008, 2009 and 2010. The first five editions have been summarized in previous editions and we focus here on the 2010 edition. INEX 2010 included two tasks in the XML Mining track: (1) unsupervised clustering task and (2) semi-supervised classification task where documents are organized in a graph. The clustering task requires the participants to group the documents into clusters without any knowledge of category labels using an unsupervised learning algorithm. On the other hand, the classification task requires the participants to label the documents in the dataset into known categories using a supervised learning algorithm and a training set. This report gives the details of clustering and classification tasks.

Using data mining on road asset management data in analysing road crashes

Road safety is a major concern worldwide. Road safety will improve as road conditions and their effects on crashes are continually investigated. This paper proposes to use the capability of data mining to include the greater set of road variables for all available crashes with skid resistance values across the Queensland state main road network in order to understand the relationships among crash, traffic and road variables. This paper presents a data mining based methodology for the road asset management data to find out the various road properties that contribute unduly to crashes. The models demonstrate high levels of accuracy in predicting crashes in roads when various road properties are included. This paper presents the findings of these models to show the relationships among skid resistance, crashes, crash characteristics and other road characteristics such as seal type, seal age, road type, texture depth, lane count, pavement width, rutting, speed limit, traffic rates intersections, traffic signage and road design and so on.

Identifying differences in wet and dry road crashes using data mining

It is commonly accepted that wet roads have higher risk of crash than dry roads; however, providing evidence to support this assumption presents some difficulty. This paper presents a data mining case study in which predictive data mining is applied to model the skid resistance and crash relationship to search for discernable differences in the probability of wet and dry road segments having crashes based on skid resistance. The models identify an increased probability of wet road segments having crashes for mid-range skid resistance values.

Using data mining to predict road crash count with a focus on skid resistance values

Road crashes cost world and Australian society a significant proportion of GDP, affecting productivity and causing significant suffering for communities and individuals. This paper presents a case study that generates data mining models that contribute to understanding of road crashes by allowing examination of the role of skid resistance (F60) and other road attributes in road crashes. Predictive data mining algorithms, primarily regression trees, were used to produce road segment crash count models from the road and traffic attributes of crash scenarios. The rules derived from the regression trees provide evidence of the significance of road attributes in contributing to crash, with a focus on the evaluation of skid resistance.

Scaffolding large units for small group collaborative learning : a case study.

Undergraduates working in teams can be a problematic endeavour, sometimes exacerbated for the student by poor prior experiences, a predisposition to an individual orientation of assessment, and simply the busyness that now typifies the life of a student. But effort in pedagogical design is worthwhile where team work is often a prerequisite in terms of graduate capabilities, robust learning, increased motivation, and indeed in terms of equipping individuals for emergent knowledge-age work practice, often epitomised by collaborative effort in both blended and virtual contexts. Through an iterative approach, based extensively on the established literature, we have developed a successful scaffold which is workable with a large cohort group (n >800), such that it affords students the lived experience of being a part of a learning network. Individuals within teams work together, to develop individual components that are subsequently aggregated and reified to an overall team knowledge artefact. We describe our case and propose a pedagogical model of scaffolding based on three perspectives: conceptual, rule-based and community-driven. This model provides designers with guidelines for producing and refining assessment tasks for team-based learning.

Perioperative mortality after hemiarthroplasty related to fixation method : a study based on the Australian Orthopaedic Association National Joint Replacement Registry

Background and purpose: The appropriate fixation method for hemiarthroplasty of the hip as it relates to implant survivorship and patient mortality is a matter of ongoing debate. We examined the influence of fixation method on revision rate and mortality.----- ----- Methods: We analyzed approximately 25,000 hemiarthroplasty cases from the AOA National Joint Replacement Registry. Deaths at 1 day, 1 week, 1 month, and 1 year were compared for all patients and among subgroups based on implant type.----- ----- Results: Patients treated with cemented monoblock hemiarthroplasty had a 1.7-times higher day-1 mortality compared to uncemented monoblock components (p < 0.001). This finding was reversed by 1 week, 1 month, and 1 year after surgery (p < 0.001). Modular hemiarthroplasties did not reveal a difference in mortality between fixation methods at any time point.----- ----- Interpretation: This study shows lower (or similar) overall mortality with cemented hemiarthroplasty of the hip.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Social Impact of Mining Survey : Aggregate Results Queensland Communities

This is the final report from a study into the social impact of mining in Queensland.

Policy into practice : adoption of hazard mitigation measures by local government in Queensland : a collaborative research project between Queensland University of Technology and Emergency Management Queensland in association with Local Government of Queensland Disaster Management Alliance

The focus of the present research was to investigate how Local Governments in Queensland were progressing with the adoption of delineated DM policies and supporting guidelines. The study consulted Local Government representatives and hence, the results reflect their views on these issues. Is adoption occurring? To what degree? Are policies and guidelines being effectively implemented so that the objective of a safer, more resilient community is being achieved? If not, what are the current barriers to achieving this, and can recommendations be made to overcome these barriers? These questions defined the basis on which the present study was designed and the survey tools developed. While it was recognised that LGAQ and Emergency Management Queensland (EMQ) may have differing views on some reported issues, it was beyond the scope of the present study to canvass those views. The study resolved to document and analyse these questions under the broad themes of: • Building community capacity (notably via community awareness). • Council operationalisation of DM. • Regional partnerships (in mitigation/adaptation). Data was collected via a survey tool comprising two components: • An online questionnaire survey distributed via the LGAQ Disaster Management Alliance (hereafter referred to as the “Alliance”) to DM sections of all Queensland Local Government Councils; and • a series of focus groups with selected Queensland Councils

A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well.