Dynamical systems for discovering protein complexes and functional modules from biological networks

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k-Subgraph Problem (k-HSP), which itself is NPhard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a “spurious” heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in “spurious” heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.

Variable step-size LMS algorithm with a quotient form

An improved robust variable step-size least mean square (LMS) algorithm is developed in this paper. Unlike many existing approaches, we adjust the variable step-size using a quotient form of filtered versions of the quadratic error. The filtered estimates of the error are based on exponential windows, applying different decaying factors for the estimations in the numerator and denominator. The new algorithm, called more robust variable step-size (MRVSS), is able to reduce the sensitivity to the power of the measurement noise, and improve the steady-state performance for comparable transient behavior, with negligible increase in the computational cost. The mean convergence, the steady-state performance and the mean step-size behavior of the MRVSS algorithm are studied under a slow time-varying system model, which can be served as guidelines for the design of MRVSS algorithm in practical applications. Simulation results are demonstrated to corroborate the analytic results, and to compare MRVSS with the existing representative approaches. Superior properties of the MRVSS algorithm are indicated.

Diverse approaches : a study of Australian journalism educators' use of web-based curriculum modules for reporting cultural difference

The Reporting Diversity website provides four curriculum modules to assist journalism educators to teach students about the issues and practice of reporting on cultural difference. The researchers co-ordinated the second round of trials of these resources in 2009, which involved the participation of academics and students from five Australian universities. A further 30 academics were surveyed to gauge their level of awareness of the materials. This paper reports on the educators’ evaluation of the resources and reveals the innovative ways in which the modules are being used and adapted in different classroom settings. The researchers argue that sharing different teaching approaches to the materials through the Reporting Diversity website would assist other academics to adapt the resources for their own use.

Validation of autism spectrum quotient ddult version in an Australian sample

The Autism Spectrum Quotient is used to assess autistic spectrum traits in intellectually competent adults in both the general population and the autism spectrum community. While the autism spectrum Quotient has been validated in several different cultures, to date no study has assessed the psychometrics of the Autism Spectrum Quotient on an Australian population. The purpose of this study was to assess the psychometrics of the autism spectrum Quotient in an Australian sample of both typically developing individuals (n = 128) and individuals with autism spectrum disorder (n = 104). The results revealed that the internal consistency and the test-retest reliability were satisfactory; individuals with autism spectrum disorder scored higher on total Autism Spectrum Quotient score and its subscales than typically developing individuals; however, gender differences were not apparent on total score. Possible cultural differences may explain some of the psychometric variations found. The results of this analysis revealed that the Autism Spectrum Quotient was a reliable instrument for investigating variation in autistic symptomology in both typically developing and Autism Spectrum Disorders populations within an Australian population.

Making the most of the self-quotient image in face recognition

The self-quotient image is a biologically inspired representation which has been proposed as an illumination invariant feature for automatic face recognition. Owing to the lack of strong domain specific assumptions underlying this representation, it can be readily extracted from raw images irrespective of the persons's pose, facial expression etc. What makes the self-quotient image additionally attractive is that it can be computed quickly and in a closed form using simple low-level image operations. However, it is generally accepted that the self-quotient is insufficiently robust to large illumination changes which is why it is mainly used in applications in which low precision is an acceptable compromise for high recall (e.g. retrieval systems). Yet, in this paper we demonstrate that the performance of this representation in challenging illuminations has been greatly underestimated. We show that its error rate can be reduced by over an order of magnitude, without any changes to the representation itself. Rather, we focus on the manner in which the dissimilarity between two self-quotient images is computed. By modelling the dominant sources of noise affecting the representation, we propose and evaluate a series of different dissimilarity measures, the best of which reduces the initial error rate of 63.0% down to only 5.7% on the notoriously challenging YaleB data set.

A new fuzzy ratio and its application to the single input rule modules connected fuzzy inference system

The principle of ratios has been applied to many real world problems, e.g. the part-to-part and part-to-whole ratio formulations. As it is difficult for humans to provide an exact ratio in many real situations, we introduce a fuzzy ratio in this paper. We use some notions from fuzzy arithmetic to analyze fuzzy ratios captured from humans. An application of the formulated fuzzy ratio to a Single Input Rule Modules connected Fuzzy Inference System (SIRMs-FIS) is demonstrated. Instead of using a precise weight, fuzzy sets are employed to represent the relative importance of each rule module. The resulting fuzzy weights are explained as a fuzzy ratio on a weight domain. In addition, a new SIRMs-FIS model with fuzzy weights and part-to-whole fuzzy ratio is devised. A simulated example is presented to clarify the proposed SIRM-FIS model.

A simulated annealing global maximum power point tracking approach for PV modules under partial shading conditions

This paper proposes a simulated annealing (SA)-based global maximum power point tracking (GMPPT) technique designed for photovoltaic (PV) systems which experience partial shading conditions (PSC). The proposed technique is compared with the common perturb and observe MPPT technique and the particle swarm optimization method for GMPPT. The performance is assessed by considering the time taken to converge and the number of sample cases where the technique converges to the GMPP. Simulation results indicate the improved performance of the SA-based GMPPT algorithm, with arbitrarily selected parameters, in tracking to the global maxima in a multiple module PV system which experiences PSC. Experimental validation of the technique is presented based on PV modules that experience nonuniform environmental conditions. Additionally, studies regarding the influence of the key parameters of the SA-based algorithm are described. Simulation and experimental results verify the effectiveness of the proposed GMPPT method.

Evidence-based learning: Interactive, online EBP modules for first year nursing. A case study

Deakin University Library designed a series of six modules to provide interactive, online learning for a first year nursing unit, Understanding Research Evidence. The modules were developed in response to the changing learning requirements of students in the digital age. Delivered using Smart Sparrow software, the modules were designed to assist students in the development and consolidation of their understandings of evidence-based practice (EBP).The development of the modules represents a shift from unilateral communication to interactive content. Previously, online support had largely consisted of static material that was not presented in the context of curriculum. The Library has now developed integrated content that allows for interactivity, but which may also be customised for other purposes or units across all health disciplines.Feedback and data collected from the modules indicate an encouraging degree of engagement with the content. Data also allows the Library to ensure the continuous improvement of the modules. Library staff have also reported on their improved capacity and confidence in creating learning experiences that integrate core information and digital literacy competencies with students' curriculum. Staff also report improvements in their ability to use technologies to create online learning objects.