University of Adelaide

About: University of Adelaide is a education organization based out in Adelaide, South Australia, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 27251 authors who have published 79167 publications receiving 2671128 citations. The organization is also known as: The University of Adelaide & Adelaide University.

Fluctuating asymmetry: a biological monitor of environmental and genomic stress.

Abstract: Increased fluctuating asymmetry (FA) of morphological traits occurs under environmental and genomic stress Such conditions will therefore lead to a reduction in developmental homeostasis Based upon temperature extreme experiments, relatively severe stress is needed to increase FA under field conditions Increasing asymmetry tends, therefore, to occur in stressed marginal habitats Genetic perturbations implying genomic stress include certain specific genes, directional selection, inbreeding, and chromosome balance alterations It is for these reasons that transgenic organisms may show increased FA As there is evidence that the effects of genomic and environmental stress are cumulative, organisms in a state of genomic stress may provide sensitive biological monitors of environmental stress

Fixed or random effects meta-analysis? Common methodological issues in systematic reviews of effectiveness.

Abstract: Systematic review aims to systematically identify, critically appraise, and summarize all relevant studies that match predefined criteria and answer predefined questions. The most common type of systematic review is that assessing the effectiveness of an intervention or therapy. In this article, we discuss some of the common methodological issues that arise when conducting systematic reviews and meta-analyses of effectiveness data, including issues related to study designs, meta-analysis, and the use and interpretation of effect sizes.

Engineering surface atomic structure of single-crystal cobalt (II) oxide nanorods for superior electrocatalysis.

Abstract: Engineering the surface structure at the atomic level can be used to precisely and effectively manipulate the reactivity and durability of catalysts. Here we report tuning of the atomic structure of one-dimensional single-crystal cobalt (II) oxide (CoO) nanorods by creating oxygen vacancies on pyramidal nanofacets. These CoO nanorods exhibit superior catalytic activity and durability towards oxygen reduction/evolution reactions. The combined experimental studies, microscopic and spectroscopic characterization, and density functional theory calculations reveal that the origins of the electrochemical activity of single-crystal CoO nanorods are in the oxygen vacancies that can be readily created on the oxygen-terminated {111} nanofacets, which favourably affect the electronic structure of CoO, assuring a rapid charge transfer and optimal adsorption energies for intermediates of oxygen reduction/evolution reactions. These results show that the surface atomic structure engineering is important for the fabrication of efficient and durable electrocatalysts.

Multi-Label Image Recognition With Graph Convolutional Networks

Abstract: The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognition performance. To capture and explore such important dependencies, we propose a multi-label classification model based on Graph Convolutional Network (GCN). The model builds a directed graph over the object labels, where each node (label) is represented by word embeddings of a label, and GCN is learned to map this label graph into a set of inter-dependent object classifiers. These classifiers are applied to the image descriptors extracted by another sub-net, enabling the whole network to be end-to-end trainable. Furthermore, we propose a novel re-weighted scheme to create an effective label correlation matrix to guide information propagation among the nodes in GCN. Experiments on two multi-label image recognition datasets show that our approach obviously outperforms other existing state-of-the-art methods. In addition, visualization analyses reveal that the classifiers learned by our model maintain meaningful semantic topology.