Showing papers by "Deakin University published in 2017"

Molecule-Level g-C3N4 Coordinated Transition Metals as a New Class of Electrocatalysts for Oxygen Electrode Reactions

Abstract: Organometallic complexes with metal–nitrogen/carbon (M–N/C) coordination are the most important alternatives to precious metal catalysts for oxygen reduction and evolution reactions (ORR and OER) in energy conversion devices. Here, we designed and developed a range of molecule-level graphitic carbon nitride (g-C3N4) coordinated transition metals (M–C3N4) as a new generation of M–N/C catalysts for these oxygen electrode reactions. As a proof-of-concept example, we conducted theoretical evaluation and experimental validation on a cobalt–C3N4 catalyst with a desired molecular configuration, which possesses comparable electrocatalytic activity to that of precious metal benchmarks for the ORR and OER in alkaline media. The correlation of experimental and computational results confirms that this high activity originates from the precise M–N2 coordination in the g-C3N4 matrix. Moreover, the reversible ORR/OER activity trend for a wide variety of M−C3N4 complexes has been constructed to provide guidance for the m...

Peripheral cytokine and chemokine alterations in depression: a meta-analysis of 82 studies.

Abstract: Objective To conduct a systematic review and meta-analysis of studies that measured cytokine and chemokine levels in individuals with major depressive disorder (MDD) compared to healthy controls (HCs). Method The PubMed/MEDLINE, EMBASE, and PsycINFO databases were searched up until May 30, 2016. Effect sizes were estimated with random-effects models. Result Eighty-two studies comprising 3212 participants with MDD and 2798 HCs met inclusion criteria. Peripheral levels of interleukin-6 (IL-6), tumor necrosis factor (TNF)-alpha, IL-10, the soluble IL-2 receptor, C-C chemokine ligand 2, IL-13, IL-18, IL-12, the IL-1 receptor antagonist, and the soluble TNF receptor 2 were elevated in patients with MDD compared to HCs, whereas interferon-gamma levels were lower in MDD (Hedge's g = −0.477, P = 0.043). Levels of IL-1β, IL-2, IL-4, IL-8, the soluble IL-6 receptor (sIL-6R), IL-5, CCL-3, IL-17, and transforming growth factor-beta 1 were not significantly altered in individuals with MDD compared to HCs. Heterogeneity was large (I2: 51.6–97.7%), and sources of heterogeneity were explored (e.g., age, smoking status, and body mass index). Conclusion Our results further characterize a cytokine/chemokine profile associated with MDD. Future studies are warranted to further elucidate sources of heterogeneity, as well as biosignature cytokines secreted by other immune cells.

A randomised controlled trial of dietary improvement for adults with major depression (the ‘SMILES’ trial)

Abstract: The possible therapeutic impact of dietary changes on existing mental illness is largely unknown. Using a randomised controlled trial design, we aimed to investigate the efficacy of a dietary improvement program for the treatment of major depressive episodes. ‘SMILES’ was a 12-week, parallel-group, single blind, randomised controlled trial of an adjunctive dietary intervention in the treatment of moderate to severe depression. The intervention consisted of seven individual nutritional consulting sessions delivered by a clinical dietician. The control condition comprised a social support protocol to the same visit schedule and length. Depression symptomatology was the primary endpoint, assessed using the Montgomery–Asberg Depression Rating Scale (MADRS) at 12 weeks. Secondary outcomes included remission and change of symptoms, mood and anxiety. Analyses utilised a likelihood-based mixed-effects model repeated measures (MMRM) approach. The robustness of estimates was investigated through sensitivity analyses. We assessed 166 individuals for eligibility, of whom 67 were enrolled (diet intervention, n = 33; control, n = 34). Of these, 55 were utilising some form of therapy: 21 were using psychotherapy and pharmacotherapy combined; 9 were using exclusively psychotherapy; and 25 were using only pharmacotherapy. There were 31 in the diet support group and 25 in the social support control group who had complete data at 12 weeks. The dietary support group demonstrated significantly greater improvement between baseline and 12 weeks on the MADRS than the social support control group, t(60.7) = 4.38, p < 0.001, Cohen’s d = –1.16. Remission, defined as a MADRS score <10, was achieved for 32.3% (n = 10) and 8.0% (n = 2) of the intervention and control groups, respectively (χ 2 (1) = 4.84, p = 0.028); number needed to treat (NNT) based on remission scores was 4.1 (95% CI of NNT 2.3–27.8). A sensitivity analysis, testing departures from the missing at random (MAR) assumption for dropouts, indicated that the impact of the intervention was robust to violations of MAR assumptions. These results indicate that dietary improvement may provide an efficacious and accessible treatment strategy for the management of this highly prevalent mental disorder, the benefits of which could extend to the management of common co-morbidities. Australia and New Zealand Clinical Trials Register (ANZCTR): ACTRN12612000251820 . Registered on 29 February 2012.

Mechanical properties of atomically thin boron nitride and the role of interlayer interactions

Abstract: Atomically thin boron nitride (BN) nanosheets are important two-dimensional nanomaterials with many unique properties distinct from those of graphene, but investigation into their mechanical properties remains incomplete. Here we report that high-quality single-crystalline mono- and few-layer BN nanosheets are one of the strongest electrically insulating materials. More intriguingly, few-layer BN shows mechanical behaviours quite different from those of few-layer graphene under indentation. In striking contrast to graphene, whose strength decreases by more than 30% when the number of layers increases from 1 to 8, the mechanical strength of BN nanosheets is not sensitive to increasing thickness. We attribute this difference to the distinct interlayer interactions and hence sliding tendencies in these two materials under indentation. The significantly better interlayer integrity of BN nanosheets makes them a more attractive candidate than graphene for several applications, for example, as mechanical reinforcements.

Shedding the cobra effect: problematising thematic emergence, triangulation, saturation and member checking.

Abstract: Context Qualitative research is widely accepted as a legitimate approach to inquiry in health professions education (HPE). To secure this status, qualitative researchers have developed a variety of strategies (e.g. reliance on post-positivist qualitative methodologies, use of different rhetorical techniques, etc.) to facilitate the acceptance of their research methodologies and methods by the HPE community. Although these strategies have supported the acceptance of qualitative research in HPE, they have also brought about some unintended consequences. One of these consequences is that some HPE scholars have begun to use terms in qualitative publications without critically reflecting on: (i) their ontological and epistemological roots; (ii) their definitions, or (iii) their implications. Objectives In this paper, we share our critical reflections on four qualitative terms popularly used in the HPE literature: thematic emergence; triangulation; saturation, and member checking. Methods We discuss the methodological origins of these terms and the applications supported by these origins. We reflect critically on how these four terms became expected of qualitative research in HPE, and we reconsider their meanings and use by drawing on the broader qualitative methodology literature. Conclusions Through this examination, we hope to encourage qualitative scholars in HPE to avoid using qualitative terms uncritically and non-reflexively.

The Power of Bias in Economics Research

Abstract: We investigate two critical dimensions of the credibility of empirical economics research: statistical power and bias. We survey 159 empirical economics literatures that draw upon 64,076 estimates of economic parameters reported in more than 6,700 empirical studies. Half of the research areas have nearly 90% of their results under-powered. The median statistical power is 18%, or less. A simple weighted average of those reported results that are adequately powered (power ≥ 80%) reveals that nearly 80% of the reported effects in these empirical economics literatures are exaggerated; typically, by a factor of two and with one-third inflated by a factor of four or more.

Carbon fiber reinforced metal matrix composites: Fabrication processes and properties

Abstract: This paper reviews the research and development works conducted over the past few decades on carbon fiber reinforced metal matrix composites (CFR-MMC). The structure and composition of carbon fiber and its bonding to metal matrix have an impact on the properties of the resulting CFR-MMC remarkably. The research efforts on process optimization and utilizing of carbon fibers are discussed in this review. The effect of carbon fiber on structural, physical and mechanical properties of metal matrix composite are studied as well. This review also provide an overview of the research to date on various fabrication methods that is used for production of CFR-MMC.

Global patterns in mangrove soil carbon stocks and losses

Abstract: This research presents global baseline estimates of mangrove soil C stocks enabling countries to begin to assess their mangrove soil C stocks and the emissions that might arise from mangrove deforestation.

Comparing online and blended learner's self-regulated learning strategies and academic performance

Abstract: The existing literature suggests that self-regulated learning (SRL) strategies are relevant to student grade performance in both online and blended contexts, although few, if any, studies have compared them. However, due to challenges unique to each group, the variety of SRL strategies that are implicated, and their effect size for predicting performance may differ across contexts. One hundred and forty online students and 466 blended learning students completed the Motivated Strategies for Learning Questionnaire. The results show that online students utilised SRL strategies more often than blended learning students, with the exception of peer learning and help seeking. Despite some differences in individual predictive value across enrolment status, the key SRL predictors of academic performance were largely equivalent between online and blended learning students. Findings highlight the relative importance of using time management and elaboration strategies, while avoiding rehearsal strategies, in relation to academic subject grade for both study modes.

Effect of classroom-based physical activity interventions on academic and physical activity outcomes: a systematic review and meta-analysis

Abstract: Physical activity is associated with many physical and mental health benefits, however many children do not meet the national physical activity guidelines. While schools provide an ideal setting to promote children’s physical activity, adding physical activity to the school day can be difficult given time constraints often imposed by competing key learning areas. Classroom-based physical activity may provide an opportunity to increase school-based physical activity while concurrently improving academic-related outcomes. The primary aim of this systematic review and meta-analysis was to evaluate the impact of classroom-based physical activity interventions on academic-related outcomes. A secondary aim was to evaluate the impact of these lessons on physical activity levels over the study duration. A systematic search of electronic databases (PubMed, ERIC, SPORTDiscus, PsycINFO) was performed in January 2016 and updated in January 2017. Studies that investigated the association between classroom-based physical activity interventions and academic-related outcomes in primary (elementary) school-aged children were included. Meta-analyses were conducted in Review Manager, with effect sizes calculated separately for each outcome assessed. Thirty-nine articles met the inclusion criteria for the review, and 16 provided sufficient data and appropriate design for inclusion in the meta-analyses. Studies investigated a range of academic-related outcomes including classroom behaviour (e.g. on-task behaviour), cognitive functions (e.g. executive function), and academic achievement (e.g. standardised test scores). Results of the meta-analyses showed classroom-based physical activity had a positive effect on improving on-task and reducing off-task classroom behaviour (standardised mean difference = 0.60 (95% CI: 0.20,1.00)), and led to improvements in academic achievement when a progress monitoring tool was used (standardised mean difference = 1.03 (95% CI: 0.22,1.84)). However, no effect was found for cognitive functions (standardised mean difference = 0.33 (95% CI: -0.11,0.77)) or physical activity (standardised mean difference = 0.40 (95% CI: -1.15,0.95)). Results suggest classroom-based physical activity may have a positive impact on academic-related outcomes. However, it is not possible to draw definitive conclusions due to the level of heterogeneity in intervention components and academic-related outcomes assessed. Future studies should consider the intervention period when selecting academic-related outcome measures, and use an objective measure of physical activity to determine intervention fidelity and effects on overall physical activity levels.

Muscle damage and inflammation during recovery from exercise.

Abstract: Unaccustomed exercise consisting of eccentric (i.e., lengthening) muscle contractions often results in muscle damage characterized by ultrastructural alterations in muscle tissue, clinical signs and symptoms (e.g., reduced muscle strength and range of motion, increased muscle soreness and swelling, efflux of myocellular proteins). The time course of recovery following exercise-induced muscle damage depends on the extent of initial muscle damage, which in turn is influenced by the intensity and duration of exercise, joint angle/muscle length and muscle groups used during exercise. The effects of these factors on muscle strength, soreness and swelling are well characterized. By contrast, much less is known about how they affect intramuscular inflammation and molecular aspects of muscle adaptation/remodeling. Although inflammation has historically been viewed as detrimental for recovery from exercise, it is now generally accepted that inflammatory responses-if tightly regulated-are integral to muscle repair and regeneration. Animal studies have revealed that other cell types including mast cells, eosinophils, CD8 and T regulatory lymphocytes, fibro-adipogenic progenitors and pericytes also help to facilitate muscle tissue regeneration. However, more research is required to determine whether these cells respond to exercise-induced muscle damage. A large body of research has investigated the efficacy of physicotherapeutic, pharmacological and nutritional interventions for reducing the signs and symptoms of exercise-induced muscle damage, with mixed results. More research is needed to examine if/how these treatments influence inflammation and muscle remodeling during recovery from exercise.

The new field of ‘precision psychiatry’

Abstract: Precision medicine is a new and important topic in psychiatry. Psychiatry has not yet benefited from the advanced diagnostic and therapeutic technologies that form an integral part of other clinical specialties. Thus, the vision of precision medicine as applied to psychiatry – ‘precision psychiatry’ – promises to be even more transformative than in other fields of medicine, which have already lessened the translational gap. Herein, we describe ‘precision psychiatry’ and how its several implications promise to transform the psychiatric landscape. We pay particular attention to biomarkers and to how the development of new technologies now makes their discovery possible and timely. The adoption of the term ‘precision psychiatry’ will help propel the field, since the current term ‘precision medicine’, as applied to psychiatry, is impractical and does not appropriately distinguish the field. Naming the field ‘precision psychiatry’ will help establish a stronger, unique identity to what promises to be the most important area in psychiatry in years to come. In summary, we provide a wide-angle lens overview of what this new field is, suggest how to propel the field forward, and provide a vision of the near future, with ‘precision psychiatry’ representing a paradigm shift that promises to change the landscape of how psychiatry is currently conceived.

Peripheral Alterations in Cytokine and Chemokine Levels After Antidepressant Drug Treatment for Major Depressive Disorder: Systematic Review and Meta-Analysis.

Abstract: Mounting evidence suggests that aberrations in immune-inflammatory pathways contribute to the pathophysiology of major depressive disorder (MDD), and individuals with MDD may have elevated levels of predominantly pro-inflammatory cytokines and C-reactive protein. In addition, previous meta-analyses suggest that antidepressant drug treatment may decrease peripheral levels of interleukin-1 beta (IL-1β) and IL-6. Recently, several new studies examining the effect of antidepressants on these cytokines have been published, and so we performed an updated meta-analysis of studies that measured peripheral levels of cytokines and chemokines during antidepressant treatment in patients with MDD. The PubMed/MEDLINE, EMBASE, and PsycInfo databases were searched from inception through March 9, 2017. Forty-five studies met inclusion criteria (N = 1517). Peripheral levels of IL-6, tumor necrosis factor-alpha (TNF-α), IL-1β, IL-10, IL-2, IL-4, interferon-γ, IL-8, the C-C motif ligand 2 chemokine (CCL-2), CCL-3, IL-1 receptor antagonist, IL-13, IL-17, IL-5, IL-7, and the soluble IL-2 receptor were measured in at least three datasets and thus were meta-analyzed. Antidepressant treatment significantly decreased peripheral levels of IL-6 (Hedges g = −0.454, P <0.001), TNF-α (g = −0.202, P = 0.015), IL-10 (g = −0.566, P = 0.012), and CCL-2 (g = −1.502, P = 0.006). These findings indicate that antidepressants decrease several markers of peripheral inflammation. However, this meta-analysis did not provide evidence that reductions in peripheral inflammation are associated with antidepressant treatment response although few studies provided separate data for treatment responders and non-responders.

Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System

Abstract: With the recent development in information and communication technology, more and more smart devices penetrate into people’s daily life to promote the life quality. As a growing healthcare trend, medical cyber-physical systems (MCPSs) enable seamless and intelligent interaction between the computational elements and the medical devices. To support MCPSs, cloud resources are usually explored to process the sensing data from medical devices. However, the high quality-of-service of MCPS challenges the unstable and long-delay links between cloud data center and medical devices. To combat this issue, mobile edge cloud computing, or fog computing, which pushes the computation resources onto the network edge (e.g., cellular base stations), emerges as a promising solution. We are thus motivated to integrate fog computation and MCPS to build fog computing supported MCPS (FC-MCPS). In particular, we jointly investigate base station association, task distribution, and virtual machine placement toward cost-efficient FC-MCPS. We first formulate the problem into a mixed-integer non-linear linear program and then linearize it into a mixed integer linear programming (LP). To address the computation complexity, we further propose an LP-based two-phase heuristic algorithm. Extensive experiment results validate the high-cost efficiency of our algorithm by the fact that it produces near optimal solution and significantly outperforms a greedy algorithm.

A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment

Abstract: With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability. With the expansion of the scale of the random forest model and the Spark cluster, the advantage of the PRF algorithm is more obvious.

Metal organic framework based catalysts for CO2 conversion

Abstract: Metal organic frameworks (MOFs) are hybrid crystalline materials, exhibiting high specific surface areas, controllable pore sizes and surface chemistry. These properties have made MOFs attractive for a wide range of applications including gas separation, gas storage, sensing, drug delivery and catalysis. This review focuses on recent progress in the application of MOF materials as catalysts for CO2 conversion through chemical fixation, photocatalysis and electrocatalysis. In particular, this review discusses the co-relationship between the physicochemical properties of MOF materials including their catalytic performance as well as their stability and recyclability under different reaction conditions, relevant to CO2 conversion. Current modification techniques for improving MOF performance are highlighted along with the recent understanding of their electronic properties. The limitations of MOF based catalysts are also discussed and potential routes for improvement are suggested.

A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition

Abstract: This paper proposes a facial expression recognition system using evolutionary particle swarm optimization (PSO)-based feature optimization. The system first employs modified local binary patterns, which conduct horizontal and vertical neighborhood pixel comparison, to generate a discriminative initial facial representation. Then, a PSO variant embedded with the concept of a micro genetic algorithm (mGA), called mGA-embedded PSO, is proposed to perform feature optimization. It incorporates a nonreplaceable memory, a small-population secondary swarm, a new velocity updating strategy, a subdimension-based in-depth local facial feature search, and a cooperation of local exploitation and global exploration search mechanism to mitigate the premature convergence problem of conventional PSO. Multiple classifiers are used for recognizing seven facial expressions. Based on a comprehensive study using within- and cross-domain images from the extended Cohn Kanade and MMI benchmark databases, respectively, the empirical results indicate that our proposed system outperforms other state-of-the-art PSO variants, conventional PSO, classical GA, and other related facial expression recognition models reported in the literature by a significant margin.

Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study

Abstract: Fog computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, to date, have been considered standing-alone. However, because of their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the future Internet. Motivated by this consideration, the goal of this position paper is fivefold. First, we review the technological attributes and platforms proposed in the current literature for the standing-alone FC and IoE paradigms. Second, by leveraging some use cases as illustrative examples, we point out that the integration of the FC and IoE paradigms may give rise to opportunities for new applications in the realms of the IoE, Smart City, Industry 4.0, and Big Data Streaming, while introducing new open issues. Third, we propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, that integrates FC and IoE and then we detail the main building blocks and services of the corresponding technological platform and protocol stack. Fourth, as a proof-of-concept, we present the simulated energy-delay performance of a small-scale FoE prototype, namely, the V-FoE prototype. Afterward, we compare the obtained performance with the corresponding one of a benchmark technological platform, e.g., the V-D2D one. It exploits only device-to-device links to establish inter-thing “ad hoc” communication. Last, we point out the position of the proposed FoE paradigm over a spectrum of seemingly related recent research projects.

Differentially Private Data Publishing and Analysis: A Survey

Abstract: Differential privacy is an essential and prevalent privacy model that has been widely explored in recent decades. This survey provides a comprehensive and structured overview of two research directions: differentially private data publishing and differentially private data analysis. We compare the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy. We identify two basic frameworks for differentially private data analysis and list the typical algorithms used within each framework. The results are compared and discussed based on output accuracy and efficiency. Further, we propose several possible directions for future research and possible applications.

$\mathtt {Deepr}$: A Convolutional Net for Medical Records.

Abstract: Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space.

Mechanical basis of bone strength: influence of bone material, bone structure and muscle action

Abstract: This review summarises current understanding of how bone is sculpted through adaptive processes, designed to meet the mechanical challenges it faces in everyday life and athletic pursuits, serving as an update for clinicians, researchers and physical therapists. Bone’s ability to resist fracture under the large muscle and locomotory forces it experiences during movement and in falls or collisions is dependent on its established mechanical properties, determined by bone’s complex and multidimensional material and structural organisation. At all levels, bone is highly adaptive to habitual loading, regulating its structure according to components of its loading regime and mechanical environment, inclusive of strain magnitude, rate, frequency, distribution and deformation mode. Indeed, the greatest forces habitually applied to bone arise from muscular contractions, and the past two decades have seen substantial advances in our understanding of how these forces shape bone throughout life. Herein, we also highlight the limitations of in vivo methods to assess and understand bone collagen, and bone mineral at the material or tissue level. The inability to easily measure or closely regulate applied strain in humans is identified, limiting the translation of animal studies to human populations, and our exploration of how components of mechanical loading regimes influence mechanoadaptation.