Deakin University Library : an active partner in the implementation of the new generation e-learning platform Desire2Learn

Deakin University has a long association with e-learning platforms, utilising the functionality of various Learning Management Systems (LMS) over a period of years. Transforming learning and teaching is a key priority of the University and moving to a new generation e-learning platform that supports engaging learning experiences through quality course design is a strategic imperative.

In 2010 Deakin University selected Desire2Learn as its replacement LMS, an innovative platform that offers next generation functionality. The University is investing significant resources in 2011 to implement the new system. The Library is harnessing the opportunity to embed search and discovery and information access throughout the LMS, including presence at the highest level of navigation. A Library widget providing students with clear pathways and immediate access to key library collections, services and features is being developed by the Library in conjunction with the Faculties‟ academic champions and educational developers. Liaison Librarians are negotiating with academic staff to create context-specific pathways, to utilise Desire2Learn Web2.0 capabilities and to imbed more personalised resources and LibGuides aligned with units of study. This is happening at a time when libraries are introducing new approaches to information discovery.

This paper describes Deakin University Library‟s journey in partnering with academic staff and others across the University to implement Desire2Learn as a vital new e-learning platform. It reports on many outcomes including: value created by embedding quality information in learner-centred course delivery; increased awareness of library subscription resources when accessible within students‟ workspace; strong and continuing relationships built with academic staff; enhanced Library staff engagement with flexible learning principles and new technologies. The question of where embedding information access in online courses and units fits with the Library‟s exploration of web scale solutions is also touched upon. And finally, an insight into how recent research undertaken by Deakin University Library has influenced our approach to information discovery solutions suggests an opportunity for many more questions to be explored.

Comparison of food industry policies and commitments on marketing to children and product (re)formulation in Australia, New Zealand and Fiji

Unhealthy food environments are known to be major drivers of diet-related non-communicable diseases globally, and there is an imperative for major food companies to be publicly accountable for their actions to improve the healthiness of food environments. This paper examines the prevalence of publicly available policies and commitments of major packaged food and soft drink manufacturers, and fast-food restaurants in Australia, New Zealand and Fiji with respect to reducing food marketing to children and product (re)formulation. In each country, the most prominent companies in each sector were selected. Company policies, commitments and relevant industry initiatives were gleaned from company and industry association websites. In Australia and New Zealand, there are a higher proportion of companies with publicly available marketing and formulation policies than in Fiji. However, even in Australia, a large proportion of the most prominent food companies do not have publicly available policies. Where they exist, policies on food marketing to children generally focus on those aged less than 12, do not apply to all types of media, marketing channels and techniques, and do not provide transparency with respect to the products to which the policies apply. Product formulation policies, where they exist, focus mostly on salt reduction and changes to the make-up of overall product portfolios, and do not generally address saturated fat, added sugar and energy reduction. In the absence of strong policies and corresponding actions by the private sector, it is likely that government action (e.g. through co-regulation or legislation) will be needed to drive improved company performance.

A new social-family model for eating disorders: A European multicentre project using a case-control design

OBJECTIVE: To examine a new socio-family risk model of Eating Disorders (EDs) using path-analyses. METHOD: The sample comprised 1264 (ED patients = 653; Healthy Controls = 611) participants, recruited into a multicentre European project. Socio-family factors assessed included: perceived maternal and parental parenting styles, family, peer and media influences, and body dissatisfaction. Two types of path-analyses were run to assess the socio-family model: 1.) a multinomial logistic path-model including ED sub-types [Anorexia Nervosa-Restrictive (AN-R), AN-Binge-Purging (AN-BP), Bulimia Nervosa (BN) and EDNOS)] as the key polychotomous categorical outcome and 2.) a path-model assessing whether the socio-family model differed across ED sub-types and healthy controls using body dissatisfaction as the outcome variable. RESULTS: The first path-analyses suggested that family and media (but not peers) were directly and indirectly associated (through body dissatisfaction) with all ED sub-types. There was a weak effect of perceived parenting directly on ED sub-types and indirectly through family influences and body dissatisfaction. For the second path-analyses, the socio-family model varied substantially across ED sub-types. Family and media influences were related to body dissatisfaction in the EDNOS and control sample, whereas perceived abusive parenting was related to AN-BP and BN. DISCUSSION: This is the first study providing support for this new socio-family model, which differed across ED sub-types. This suggests that prevention and early intervention might need to be tailored to diagnosis-specific ED profiles.

A NEW TUMOR SUPPRESSOR GENE CANDIDATE REGULATED BY THE NON-CODING RNA PCA3 IN HUMAN PROSTATE CANCER

Prostate cancer is the second leading cause of cancer-related death and the most common non-skin cancer in men in the USA. Considerable advancements in the practice of medicine have allowed a significant improvement in the diagnosis and treatment of this disease and, in recent years, both incidence and mortality rates have been slightly declining. However, it is still estimated that 1 man in 6 will be diagnosed with prostate cancer during his lifetime, and 1 man in 35 will die of the disease. In order to identify novel strategies and effective therapeutic approaches in the fight against prostate cancer, it is imperative to improve our understanding of its complex biology since many aspects of prostate cancer initiation and progression still remain elusive. The study of tumor biomarkers, due to their specific altered expression in tumor versus normal tissue, is a valid tool for elucidating key aspects of cancer biology, and may provide important insights into the molecular mechanisms underlining the tumorigenesis process of prostate cancer. PCA3, is considered the most specific prostate cancer biomarker, however its biological role, until now, remained unknown. PCA3 is a long non-coding RNA (ncRNA) expressed from chromosome 9q21 and its study led us to the discovery of a novel human gene, PC-TSGC, transcribed from the opposite strand and in an antisense orientation to PCA3. With the work presented in this thesis, we demonstrate that PCA3 exerts a negative regulatory role over PC-TSGC, and we propose PC-TSGC to be a new tumor suppressor gene that contrasts the transformation of prostate cells by inhibiting Rho-GTPases signaling pathways. Our findings provide a biological role for PCA3 in prostate cancer and suggest a new mechanism of tumor suppressor gene inactivation mediated by non-coding RNA. Also, the characterization of PCA3 and PC-TSGC led us to propose a new molecular pathway involving both genes in the transformation process of the prostate, thus providing a new piece of the jigsaw puzzle representing the complex biology of prostate cancer.

Towards a new model of care: integrating mental health, substance use, and somatic care

Although research and clinical interventions for patients with dual disorders have been described since as early as the 1980s, the day-to-day treatment of these patients remains problematic and challenging in many countries. Throughout this book, many approaches and possible pathways have been outlined. Based upon these experiences, some key points can be extracted in order to guide to future developments. (1) New diagnostic approaches are warranted when dealing with patients who have multiple problems, given the limitations of the current categorical systems. (2) Greater emphasis should be placed on secondary prevention and early intervention for children and adolescents at an increased risk of later-life dual disorders. (3) Mental, addiction, and somatic care systems can be integrated, adopting a patient-focused approach to care delivery. (4) Recovery should be taken into consideration when defining treatment intervention and outcome goals. (5) It is important to reduce societal risk factors, such as poverty and early childhood adversity. (6) More resources are needed to provide adequate mental health care in the various countries. The development of European guidance initiatives would provide benefits in many of these areas, making it possible to ensure a more harmonized standard of care for patients with dual disorders.

BEST SUBSET SELECTION FOR CATEGORICAL DATA

When choosing among models to describe categorical data, the necessity to consider interactions makes selection more difficult. With just four variables, considering all interactions, there are 166 different hierarchical models and many more non-hierarchical models. Two procedures have been developed for categorical data which will produce the "best" subset or subsets of each model size where size refers to the number of effects in the model. Both procedures are patterned after the Leaps and Bounds approach used by Furnival and Wilson for continuous data and do not generally require fitting all models. For hierarchical models, likelihood ratio statistics (G('2)) are computed using iterative proportional fitting and "best" is determined by comparing, among models with the same number of effects, the Pr((chi)(,k)('2) (GREATERTHEQ) G(,ij)('2)) where k is the degrees of freedom for ith model of size j. To fit non-hierarchical as well as hierarchical models, a weighted least squares procedure has been developed.^ The procedures are applied to published occupational data relating to the occurrence of byssinosis. These results are compared to previously published analyses of the same data. Also, the procedures are applied to published data on symptoms in psychiatric patients and again compared to previously published analyses.^ These procedures will make categorical data analysis more accessible to researchers who are not statisticians. The procedures should also encourage more complex exploratory analyses of epidemiologic data and contribute to the development of new hypotheses for study. ^

Fuzzy min-max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes.

Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs

There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson’s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.

An imperative duty. A novel,

Mode of access: Internet.

Managing performance of organisational units with PIMsoft:a new DEA software

DEA literature continues apace but software has lagged behind. This session uses suitably selected data to present newly developed software which includes many of the most recent DEA models. The software enables the user to address a variety of issues not frequently found in existing DEA software such as: -Assessments under a variety of possible assumptions of returns to scale including NIRS and NDRS; -Scale elasticity computations; -Numerous Input/Output variables and truly unlimited number of assessment units (DMUs) -Panel data analysis -Analysis of categorical data (multiple categories) -Malmquist Index and its decompositions -Computations of Supper efficiency -Automated removal of super-efficient outliers under user-specified criteria; -Graphical presentation of results -Integrated statistical tests

The New Software Package for Dynamic Hierarchical Clustering for Circles Types of Shapes

In data mining, efforts have focused on finding methods for efficient and effective cluster analysis in large databases. Active themes of research focus on the scalability of clustering methods, the effectiveness of methods for clustering complex shapes and types of data, high-dimensional clustering techniques, and methods for clustering mixed numerical and categorical data in large databases. One of the most accuracy approach based on dynamic modeling of cluster similarity is called Chameleon. In this paper we present a modified hierarchical clustering algorithm that used the main idea of Chameleon and the effectiveness of suggested approach will be demonstrated by the experimental results.

Appraising Work Group Performance: New Productivity Opportunities in Hospitality Management

In - Appraising Work Group Performance: New Productivity Opportunities in Hospitality Management – a discussion by Mark R. Edwards, Associate Professor, College of Engineering, Arizona State University and Leslie Edwards Cummings, Assistant Professor, College of Hotel Administration University of Nevada, Las Vegas; the authors initially provide: “Employee group performance variation accounts for a significant portion of the degree of productivity in the hotel, motel, and food service sectors of the hospitality industry. The authors discuss TEAMSG, a microcomputer based approach to appraising and interpreting group performance. TEAMSG appraisal allows an organization to profile and to evaluate groups, facilitating the targeting of training and development decisions and interventions, as well as the more equitable distribution of organizational rewards.” “The caliber of employee group performance is a major determinant in an organization's productivity and success within the hotel and food service industries,” Edwards and Cummings say. “Gaining accurate information about the quality of performance of such groups as organizational divisions, individual functional departments, or work groups can be as enlightening...” the authors further reveal. This perspective is especially important not only for strategic human resources planning purposes, but also for diagnosing development needs and for differentially distributing organizational rewards.” The authors will have you know, employee requirements in an unpredictable environment, which is what the hospitality industry largely is, are difficult to quantify. In an effort to measure elements of performance Edwards and Cummings look to TEAMSG, which is an acronym for Team Evaluation and Management System for Groups. They develop the concept. In discussing background for employees, Edwards and Cummings point-out that employees - at the individual level - must often possess and exercise varied skills. In group circumstances employees often work at locations outside of, or move from corporate unit-to-unit, as in the case of a project team. Being able to transcend individual-to-group mentality is imperative. “A solution which addresses the frustration and lack of motivation on the part of the employee is to coach, develop, appraise, and reward employees on the basis of group achievement,” say the authors. “An appraisal, effectively developed and interpreted, has at least three functions,” Edwards and Cummings suggest, and go on to define them. The authors do place a great emphasis on rewards and interventions to bolster the assertion set forth in their thesis statement. Edwards and Cummings warn that individual agendas can threaten, erode, and undermine group performance; there is no - I - in TEAM.

New Advancements of Scalable Statistical Methods for Learning Latent Structures in Big Data

Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.

Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.

Why is it Important to Involve New Fathers in the Care of Their Child

Research into fathers’ unique contributions to the physical, emotional, social and cognitive wellbeing of their offspring has been ongoing for several decades. Health and family care policy has focused increasingly on the imperative to include fathers in services and to see them as a vital resource for mothers and children. The author identified papers from 2000 onwards that illuminate health visitors’ level of engagement with fathers of young families. The review covers policy relating to health and family services for fathers, the nature of fathering in the 21st century, the influence of involved fathers on their partners and babies, what fathers say they want from family services, and future directions for research into fathering.