Showing papers by "Deakin University published in 2019"

Australian vegetated coastal ecosystems as global hotspots for climate change mitigation

Abstract: Policies aiming to preserve vegetated coastal ecosystems (VCE; tidal marshes, mangroves and seagrasses) to mitigate greenhouse gas emissions require national assessments of blue carbon resources. Here, we present organic carbon (C) storage in VCE across Australian climate regions and estimate potential annual CO2 emission benefits of VCE conservation and restoration. Australia contributes 5–11% of the C stored in VCE globally (70–185 Tg C in aboveground biomass, and 1,055–1,540 Tg C in the upper 1 m of soils). Potential CO2 emissions from current VCE losses are estimated at 2.1–3.1 Tg CO2-e yr-1, increasing annual CO2 emissions from land use change in Australia by 12–21%. This assessment, the most comprehensive for any nation to-date, demonstrates the potential of conservation and restoration of VCE to underpin national policy development for reducing greenhouse gas emissions. Policies aiming to preserve vegetated coastal ecosystems (VCE) to mitigate greenhouse gas emissions require national assessments of blue carbon resources. Here the authors assessed organic carbon storage in VCE across Australian and the potential annual CO2 emission benefits of VCE conservation and find that Australia contributes substantially the carbon stored in VCE globally.

Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

Abstract: Deep autoencoder has been extensively used for anomaly detection. Training on the normal data, the autoencoder is expected to produce higher reconstruction error for the abnormal inputs than the normal ones, which is adopted as a criterion for identifying anomalies. However, this assumption does not always hold in practice. It has been observed that sometimes the autoencoder "generalizes" so well that it can also reconstruct anomalies well, leading to the miss detection of anomalies. To mitigate this drawback for autoencoder based anomaly detector, we propose to augment the autoencoder with a memory module and develop an improved autoencoder called memory-augmented autoencoder, i.e. MemAE. Given an input, MemAE firstly obtains the encoding from the encoder and then uses it as a query to retrieve the most relevant memory items for reconstruction. At the training stage, the memory contents are updated and are encouraged to represent the prototypical elements of the normal data. At the test stage, the learned memory will be fixed, and the reconstruction is obtained from a few selected memory records of the normal data. The reconstruction will thus tend to be close to a normal sample. Thus the reconstructed errors on anomalies will be strengthened for anomaly detection. MemAE is free of assumptions on the data type and thus general to be applied to different tasks. Experiments on various datasets prove the excellent generalization and high effectiveness of the proposed MemAE.

Global, regional, and national burden of suicide mortality 1990 to 2016: systematic analysis for the Global Burden of Disease Study 2016

Abstract: Objectives To use the estimates from the Global Burden of Disease Study 2016 to describe patterns of suicide mortality globally, regionally, and for 195 countries and territories by age, sex, and Socio-demographic index, and to describe temporal trends between 1990 and 2016. Design Systematic analysis. Main outcome measures Crude and age standardised rates from suicide mortality and years of life lost were compared across regions and countries, and by age, sex, and Socio-demographic index (a composite measure of fertility, income, and education). Results The total number of deaths from suicide increased by 6.7% (95% uncertainty interval 0.4% to 15.6%) globally over the 27 year study period to 817 000 (762 000 to 884 000) deaths in 2016. However, the age standardised mortality rate for suicide decreased by 32.7% (27.2% to 36.6%) worldwide between 1990 and 2016, similar to the decline in the global age standardised mortality rate of 30.6%. Suicide was the leading cause of age standardised years of life lost in the Global Burden of Disease region of high income Asia Pacific and was among the top 10 leading causes in eastern Europe, central Europe, western Europe, central Asia, Australasia, southern Latin America, and high income North America. Rates for men were higher than for women across regions, countries, and age groups, except for the 15 to 19 age group. There was variation in the female to male ratio, with higher ratios at lower levels of Socio-demographic index. Women experienced greater decreases in mortality rates (49.0%, 95% uncertainty interval 42.6% to 54.6%) than men (23.8%, 15.6% to 32.7%). Conclusions Age standardised mortality rates for suicide have greatly reduced since 1990, but suicide remains an important contributor to mortality worldwide. Suicide mortality was variable across locations, between sexes, and between age groups. Suicide prevention strategies can be targeted towards vulnerable populations if they are informed by variations in mortality rates.

The future of Blue Carbon science

Abstract: The term Blue Carbon (BC) was first coined a decade ago to describe the disproportionately large contribution of coastal vegetated ecosystems to global carbon sequestration. The role of BC in climate change mitigation and adaptation has now reached international prominence. To help prioritise future research, we assembled leading experts in the field to agree upon the top-ten pending questions in BC science. Understanding how climate change affects carbon accumulation in mature BC ecosystems and during their restoration was a high priority. Controversial questions included the role of carbonate and macroalgae in BC cycling, and the degree to which greenhouse gases are released following disturbance of BC ecosystems. Scientists seek improved precision of the extent of BC ecosystems; techniques to determine BC provenance; understanding of the factors that influence sequestration in BC ecosystems, with the corresponding value of BC; and the management actions that are effective in enhancing this value. Overall this overview provides a comprehensive road map for the coming decades on future research in BC science.

Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data

Abstract: Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation. Several recent 3D object classification methods have reported state-of-the-art performance on CAD model datasets such as ModelNet40 with high accuracy (~92\%). Despite such impressive results, in this paper, we argue that object classification is still a challenging task when objects are framed with real-world settings. To prove this, we introduce ScanObjectNN, a new real-world point cloud object dataset based on scanned indoor scene data. From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions. We identify three key open problems for point cloud object classification, and propose new point cloud classification neural networks that achieve state-of-the-art performance on classifying objects with cluttered background. Our dataset and code are publicly available in our project page https://hkust-vgd.github.io/scanobjectnn/.

Healthy dietary indices and risk of depressive outcomes: a systematic review and meta-analysis of observational studies

Abstract: With depression being the psychiatric disorder incurring the largest societal costs in developed countries, there is a need to gather evidence on the role of nutrition in depression, to help develop recommendations and guide future psychiatric health care. The aim of this systematic review was to synthesize the link between diet quality, measured using a range of predefined indices, and depressive outcomes. Medline, Embase and PsychInfo were searched up to 31st May 2018 for studies that examined adherence to a healthy diet in relation to depressive symptoms or clinical depression. Where possible, estimates were pooled using random effect meta-analysis with stratification by observational study design and dietary score. A total of 20 longitudinal and 21 cross-sectional studies were included. These studies utilized an array of dietary measures, including: different measures of adherence to the Mediterranean diet, the Healthy Eating Index (HEI) and Alternative HEI (AHEI), the Dietary Approaches to Stop Hypertension, and the Dietary Inflammatory Index. The most compelling evidence was found for the Mediterranean diet and incident depression, with a combined relative risk estimate of highest vs. lowest adherence category from four longitudinal studies of 0.67 (95% CI 0.55-0.82). A lower Dietary Inflammatory Index was also associated with lower depression incidence in four longitudinal studies (relative risk 0.76; 95% CI: 0.63-0.92). There were fewer longitudinal studies using other indices, but they and cross-sectional evidence also suggest an inverse association between healthy diet and depression (e.g., relative risk 0.65; 95% CI 0.50-0.84 for HEI/AHEI). To conclude, adhering to a healthy diet, in particular a traditional Mediterranean diet, or avoiding a pro-inflammatory diet appears to confer some protection against depression in observational studies. This provides a reasonable evidence base to assess the role of dietary interventions to prevent depression. This systematic review was registered in the PROSPERO International Prospective Register of Systematic Reviews under the number CRD42017080579.

Machine learning in mental health: a scoping review of methods and applications.

Abstract: BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Kaempferol: A Key Emphasis to Its Anticancer Potential

Abstract: A marked decrease in human cancers, including breast cancer, bone cancer, and cervical cancer, has been linked to the consumption of vegetable and fruit, and the corresponding chemoprotective effect has been associated with the presence of several active molecules, such as kaempferol. Kaempferol is a major flavonoid aglycone found in many natural products, such as beans, bee pollen, broccoli, cabbage, capers, cauliflower, chia seeds, chives, cumin, moringa leaves, endive, fennel, and garlic. Kaempferol displays several pharmacological properties, among them antimicrobial, anti-inflammatory, antioxidant, antitumor, cardioprotective, neuroprotective, and antidiabetic activities, and is being applied in cancer chemotherapy. Specifically, kaempferol-rich food has been linked to a decrease in the risk of developing some types of cancers, including skin, liver, and colon. The mechanisms of action include apoptosis, cell cycle arrest at the G2/M phase, downregulation of epithelial-mesenchymal transition (EMT)-related markers, and phosphoinositide 3-kinase/protein kinase B signaling pathways. In this sense, this article reviews data from experimental studies that investigated the links between kaempferol and kaempferol-rich food intake and cancer prevention. Even though growing evidence supports the use of kaempferol for cancer prevention, further preclinical and clinical investigations using kaempferol or kaempferol-rich foods are of pivotal importance before any public health recommendation or formulation using kaempferol.

Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems

Abstract: Recent years have witnessed a paradigm shift in the storage of Electronic Health Records (EHRs) on mobile cloud environments, where mobile devices are integrated with cloud computing to facilitate medical data exchanges among patients and healthcare providers. This advanced model enables healthcare services with low operational cost, high flexibility, and EHRs availability. However, this new paradigm also raises concerns about data privacy and network security for e-health systems. How to reliably share EHRs among mobile users while guaranteeing high-security levels in the mobile cloud is a challenging issue. In this paper, we propose a novel EHRs sharing framework that combines blockchain and the decentralized interplanetary file system (IPFS) on a mobile cloud platform. Particularly, we design a trustworthy access control mechanism using smart contracts to achieve secure EHRs sharing among different patients and medical providers. We present a prototype implementation using Ethereum blockchain in a real data sharing scenario on a mobile app with Amazon cloud computing. The empirical results show that our proposal provides an effective solution for reliable data exchanges on mobile clouds while preserving sensitive health information against potential threats. The system evaluation and security analysis also demonstrate the performance improvements in lightweight access control design, minimum network latency with high security and data privacy levels, compared to the existing data sharing models.

Blood Flow Restriction Exercise: Considerations of Methodology, Application, and Safety

Abstract: The current manuscript sets out a position stand for blood flow restriction (BFR) exercise, focusing on the methodology, application and safety of this mode of training. With the emergence of this technique and the wide variety of applications within the literature, the aim of this position stand is to set out a current research informed guide to BFR training to practitioners. This covers the use of BFR to enhance muscular strength and hypertrophy via training with resistance and aerobic exercise and preventing muscle atrophy using the technique passively. The authorship team for this article was selected from the researchers focused in BFR training research with expertise in exercise science, strength and conditioning and sports medicine.

Precipitation strengthening of aluminum alloys by room-temperature cyclic plasticity

Abstract: High-strength aluminum alloys are important for lightweighting vehicles and are extensively used in aircraft and, increasingly, in automobiles. The highest-strength aluminum alloys require a series of high-temperature "bakes" (120° to 200°C) to form a high number density of nanoparticles by solid-state precipitation. We found that a controlled, room-temperature cyclic deformation is sufficient to continuously inject vacancies into the material and to mediate the dynamic precipitation of a very fine (1- to 2-nanometer) distribution of solute clusters. This results in better material strength and elongation properties relative to traditional thermal treatments, despite a much shorter processing time. The microstructures formed are much more uniform than those characteristic of traditional thermal treatments and do not exhibit precipitate-free zones. These alloys are therefore likely to be more resistant to damage.

Cardiovascular disease as a leading cause of death: how are pharmacists getting involved?

Abstract: Cardiovascular diseases (CVDs) are a leading cause of death globally. This article explores the evidence surrounding community pharmacist interventions to reduce cardiovascular events and related mortality and to improve the management of CVD risk factors. We summarize a range of systematic reviews and leading randomized controlled trials and provide critical appraisal. Major observations are that very few trials directly measure clinical outcomes, potentially owing to a range of challenges in this regard. By contrast, there is an extensive, high-quality evidence to suggest that improvements can be achieved for key CVD risk factors such as hypertension, dyslipidemia, tobacco use, and elevated hemoglobin A1c. The heterogeneity of interventions tested and considerable variation of the context under which implementation occurred suggest that caution is warranted in the interpretation of meta-analyses. It is highly important to generate evidence for pharmacist interventions in developing countries where a majority of the global CVD burden will be experienced in the near future. A growing capacity for clinical registry trials and data linkage might allow future research to collect clinical outcomes data more often.

The Effects of Dietary Improvement on Symptoms of Depression and Anxiety: A Meta-Analysis of Randomized Controlled Trials

Abstract: Objective Poor diet can be detrimental to mental health. However, the overall evidence for the effects of dietary interventions on mood and mental well-being has yet to be assessed. We conducted a systematic review and meta-analysis examining effects of dietary interventions on symptoms of depression and anxiety.

pavo 2: New tools for the spectral and spatial analysis of colour in r

Abstract: Author(s): Maia, Rafael; Gruson, Hugo; Endler, John; White, Thomas | Abstract: Abstract Biological colouration presents a canvas for the study of ecological and evolutionary processes. Enduring interest in colour-based phenotypes has driven, and been driven by, improved techniques for quantifying colour patterns in ever-more relevant ways, yet the need for flexible, open frameworks for data processing and analysis persists. Here we introduce pavo 2 , the latest iteration of the R package pavo . This release represents the extensive refinement and expansion of existing methods, as well as a suite of new tools for the cohesive analysis of the spectral and (now) spatial structure of colour patterns and perception. At its core, the package retains a broad focus on (a) the organisation and processing of spectral and spatial data, and tools for the alternating (b) visualisation, and (c) analysis of data. Significantly, pavo 2 introduces image-analysis capabilities, providing a cohesive workflow for the comprehensive analysis of colour patterns. We demonstrate the utility of pavo with a brief example centred on mimicry in Heliconius butterflies. Drawing on visual modelling, adjacency, and boundary strength analyses, we show that the combined spectral (colour and luminance) and spatial (pattern element distribution and boundary salience) features of putative models and mimics are closely aligned. pavo 2 offers a flexible and reproducible environment for the analysis of colour, with renewed potential to assist researchers in answering fundamental questions in sensory ecology and evolution.

High thermal conductivity of high-quality monolayer boron nitride and its thermal expansion.

Abstract: Heat management has become more and more critical, especially in miniaturized modern devices, so the exploration of highly thermally conductive materials with electrical insulation is of great importance. Here, we report that high-quality one-atom-thin hexagonal boron nitride (BN) has a thermal conductivity (κ) of 751 W/mK at room temperature, the second largest κ per unit weight among all semiconductors and insulators. The κ of atomically thin BN decreases with increased thickness. Our molecular dynamic simulations accurately reproduce this trend, and the density functional theory (DFT) calculations reveal the main scattering mechanism. The thermal expansion coefficients of monolayer to trilayer BN at 300 to 400 K are also experimentally measured for the first time. Owing to its wide bandgap, high thermal conductivity, outstanding strength, good flexibility, and excellent thermal and chemical stability, atomically thin BN is a strong candidate for heat dissipation applications, especially in the next generation of flexible electronic devices.

Textile Strain Sensors: A Review of the Fabrication Technologies, Performance Evaluation and Applications

Abstract: The recent surge in using wearable personalized devices has made it increasingly important to have flexible textile-based sensor alternatives that can be comfortably worn and can sense a wide range of body strains. Typically fabricated from rigid materials such as metals or semiconductors, conventional strain sensors can only withstand small strains and result in bulky, inflexible, and hard-to-wear devices. Textile strain sensors offer a new generation of devices that combine strain sensing functionality with wearability and high stretchability. In this review, we discuss recent exciting advances in the fabrication, performance enhancement, and applications of wearable textile strain sensors. We describe conventional and novel approaches to achieve textile strain sensors such as coating, conducting elastomeric fiber spinning, wrapping, coiling, coaxial fiber processing, and knitting. We also discuss how important performance parameters such as electrical conductivity, mechanical properties, sensitivity, sensing range, and stability are influenced by fabrication strategies to illustrate their effects on the sensing mechanism of textile sensors. We summarize the potential applications of textile sensors in structural health monitoring, wearable body movement measurements, data gloves, and entertainment. Finally, we present the challenges and opportunities that exist to date in order to provide meaningful guidelines and directions for future research.

Innovative Electrolytes Based on Ionic Liquids and Polymers for Next-Generation Solid-State Batteries

Abstract: ConspectusElectrolytes based on organic solvents used in current Li-ion batteries are not compatible with the next-generation energy storage technologies including those based on Li metal. Thus, there has been an increase in research activities investigating solid-state electrolytes, ionic liquids (ILs), polymers, and combinations of these. This Account will discuss some of the work from our teams in these areas. Similarly, other metal-based technologies including Na, Mg, Zn, and Al, for example, are being considered as alternatives to Li-based energy storage. However, the materials research required to effectively enable these alkali metal based energy storage applications is still in its relative infancy. Once again, electrolytes play a significant role in enabling these devices, and research has for the most part progressed along similar lines to that in advanced lithium technologies. Some of our recent contributions in these areas will also be discussed, along with our perspective on future directions...

Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples

Abstract: OBJECTIVE: Neuroimaging studies show structural alterations of various brain regions in children and adults with attention deficit hyperactivity disorder (ADHD), although nonreplications are frequent. The authors sought to identify cortical characteristics related to ADHD using large-scale studies. METHODS: Cortical thickness and surface area (based on the Desikan-Killiany atlas) were compared between case subjects with ADHD (N=2,246) and control subjects (N=1,934) for children, adolescents, and adults separately in ENIGMA-ADHD, a consortium of 36 centers. To assess familial effects on cortical measures, case subjects, unaffected siblings, and control subjects in the NeuroIMAGE study (N=506) were compared. Associations of the attention scale from the Child Behavior Checklist with cortical measures were determined in a pediatric population sample (Generation-R, N=2,707). RESULTS: In the ENIGMA-ADHD sample, lower surface area values were found in children with ADHD, mainly in frontal, cingulate, and temporal regions; the largest significant effect was for total surface area (Cohen's d=-0.21). Fusiform gyrus and temporal pole cortical thickness was also lower in children with ADHD. Neither surface area nor thickness differences were found in the adolescent or adult groups. Familial effects were seen for surface area in several regions. In an overlapping set of regions, surface area, but not thickness, was associated with attention problems in the Generation-R sample. CONCLUSIONS: Subtle differences in cortical surface area are widespread in children but not adolescents and adults with ADHD, confirming involvement of the frontal cortex and highlighting regions deserving further attention. Notably, the alterations behave like endophenotypes in families and are linked to ADHD symptoms in the population, extending evidence that ADHD behaves as a continuous trait in the population. Future longitudinal studies should clarify individual lifespan trajectories that lead to nonsignificant findings in adolescent and adult groups despite the presence of an ADHD diagnosis.

Parkinson's Disease and the Environment.

Abstract: Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder that affects an estimated 10 million sufferers worldwide. The two forms of PD include familial and sporadic, and while the etiology of PD is still largely unknown, the condition is likely to be multifactorial with genetic and environmental factors contributing to disease genesis. Diagnosis of the condition is attained through the observation of cardinal clinical manifestations including resting tremor, muscle rigidity, slowness or loss of movement, and postural instability. Unfortunately, by the time these features become apparent extensive neurological damage has already occurred. A cure for PD has not been identified and the current therapy options are pharmaceutical- and/or surgical-based interventions to treat condition symptoms. There is no specific test for PD and most diagnoses are confirmed by a combination of clinical symptoms and positive responses to dopaminergic drug therapies. The prevalence and incidence of PD vary worldwide influenced by several factors such as age, gender, ethnicity, genetic susceptibilities, and environmental exposures. Here, we will present environmental factors implicated in sporadic PD onset. By understanding the mechanisms in which environmental factors interact with, and affect the brain we can stride toward finding the underlying cause(s) of PD.