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Institution

University of Queensland

EducationBrisbane, Queensland, Australia
About: University of Queensland is a education organization based out in Brisbane, Queensland, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 51138 authors who have published 155721 publications receiving 5717659 citations. The organization is also known as: UQ & The University of Queensland.


Papers
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Journal ArticleDOI
TL;DR: A unique sandwich structure with pure sulfur between two graphene membranes is designed as a very simple but effective approach for the fabrication of Li–S batteries with ultrafast charge/discharge rates and long-life.
Abstract: Lithium-sulfur (Li–S) batteries have high specific capacities and are considered as next-generation batteries for large-scale energy storage and electric vehicles. However, rapid capacity fade and low sulfur utilisation inhibit their use. We designed a unique sandwich structure with pure sulfur between two graphene membranes, which are continuously produced over a large area, as a very simple but effective approach for the fabrication of Li–S batteries with ultrafast charge/discharge rates and long-life. One membrane was used as a graphene current collector (GCC) to replace the conventional aluminium foil current collector, and sulfur was coated onto this membrane as the active material. The other membrane was coated onto a conventional polymer separator (G-separator). This electrode showed a high specific capacity of 1340 mA h g−1 at 300 mA g−1, a Coulombic efficiency approaching 100%, excellent high-rate performance and long cyclic stability. The GCC and G-separator not only effectively reduce the internal resistance of the sulfur cathode but also function as buffer layers to trap/immobilise and reuse the dissolved lithium polysulfides. Furthermore, for the first time, three-dimensional X-ray microtomography was used to investigate sulfur diffusion during electrochemical charge/discharge.

923 citations

Proceedings ArticleDOI
07 Jun 2015
TL;DR: This work proposes a new supervised hashing framework, where the learning objective is to generate the optimal binary hash codes for linear classification, and introduces an auxiliary variable to reformulate the objective such that it can be solved substantially efficiently by employing a regularization algorithm.
Abstract: Recently, learning based hashing techniques have attracted broad research interests because they can support efficient storage and retrieval for high-dimensional data such as images, videos, documents, etc. However, a major difficulty of learning to hash lies in handling the discrete constraints imposed on the pursued hash codes, which typically makes hash optimizations very challenging (NP-hard in general). In this work, we propose a new supervised hashing framework, where the learning objective is to generate the optimal binary hash codes for linear classification. By introducing an auxiliary variable, we reformulate the objective such that it can be solved substantially efficiently by employing a regularization algorithm. One of the key steps in this algorithm is to solve a regularization sub-problem associated with the NP-hard binary optimization. We show that the sub-problem admits an analytical solution via cyclic coordinate descent. As such, a high-quality discrete solution can eventually be obtained in an efficient computing manner, therefore enabling to tackle massive datasets. We evaluate the proposed approach, dubbed Supervised Discrete Hashing (SDH), on four large image datasets and demonstrate its superiority to the state-of-the-art hashing methods in large-scale image retrieval.

923 citations

Journal ArticleDOI
TL;DR: In this article, a study combining an experimental approach for monitoring the dynamics of strongly correlated cold atoms with theoretical analysis provides quantitative insights into the problem of quantum many-body systems relax from an initial non-equilibrium state.
Abstract: How quantum many-body systems relax from an initial non-equilibrium state is one of the outstanding problems in quantum statistical physics. A study combining an experimental approach for monitoring the dynamics of strongly correlated cold atoms with theoretical analysis now provides quantitative insights into the problem.

922 citations

Journal ArticleDOI
TL;DR: It is found that MYC2 negatively regulates Trp and Trp-derived secondary metabolism such as indole glucosinolate biosynthesis during JA signaling and positively regulates JA-mediated resistance to insect pests, and tolerance to oxidative stress, possibly via enhanced ascorbate redox cycling and flavonoid biosynthesis.
Abstract: The Arabidopsis thaliana basic helix-loop-helix Leu zipper transcription factor (TF) MYC2/JIN1 differentially regulates jasmonate (JA)-responsive pathogen defense (e.g., PDF1.2) and wound response (e.g., VSP) genes. In this study, genome-wide transcriptional profiling of wild type and mutant myc2/jin1 plants followed by functional analyses has revealed new roles for MYC2 in the modulation of diverse JA functions. We found that MYC2 negatively regulates Trp and Trp-derived secondary metabolism such as indole glucosinolate biosynthesis during JA signaling. Furthermore, MYC2 positively regulates JA-mediated resistance to insect pests, such as Helicoverpa armigera, and tolerance to oxidative stress, possibly via enhanced ascorbate redox cycling and flavonoid biosynthesis. Analyses of MYC2 cis binding elements and expression of MYC2-regulated genes in T-DNA insertion lines of a subset of MYC2–regulated TFs suggested that MYC2 might modulate JA responses via differential regulation of an intermediate spectrum of TFs with activating or repressing roles in JA signaling. MYC2 also negatively regulates its own expression, and this may be one of the mechanisms used in fine-tuning JA signaling. Overall, these results provide new insights into the function of MYC2 and the transcriptional coordination of the JA signaling pathway.

921 citations

Journal ArticleDOI
TL;DR: How a deeper understanding of the mechanisms underlying the cancer immunoediting process can provide insight into the development of resistance to immunotherapies and the strategies that can be used to overcome such resistance is discussed.
Abstract: Anticancer immunotherapies involving the use of immune-checkpoint inhibitors or adoptive cellular transfer have emerged as new therapeutic pillars within oncology. These treatments function by overcoming or relieving tumour-induced immunosuppression, thereby enabling immune-mediated tumour clearance. While often more effective and better tolerated than traditional and targeted therapies, many patients have innate or acquired resistance to immunotherapies. Cancer immunoediting is the process whereby the immune system can both constrain and promote tumour development, which proceeds through three phases termed elimination, equilibrium and escape. Throughout these phases, tumour immunogenicity is edited, and immunosuppressive mechanisms that enable disease progression are acquired. The mechanisms of resistance to immunotherapy seem to broadly overlap with those used by cancers as they undergo immunoediting to evade detection by the immune system. In this Review, we discuss how a deeper understanding of the mechanisms underlying the cancer immunoediting process can provide insight into the development of resistance to immunotherapies and the strategies that can be used to overcome such resistance.

920 citations


Authors

Showing all 52145 results

NameH-indexPapersCitations
Graham A. Colditz2611542256034
George Davey Smith2242540248373
David J. Hunter2131836207050
Daniel Levy212933194778
Christopher J L Murray209754310329
Matthew Meyerson194553243726
Luigi Ferrucci1931601181199
Nicholas G. Martin1921770161952
Paul M. Thompson1832271146736
Jie Zhang1784857221720
Alan D. Lopez172863259291
Ian J. Deary1661795114161
Steven N. Blair165879132929
Carlos Bustamante161770106053
David W. Johnson1602714140778
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023507
20221,728
202111,678
202010,832
20199,671
20189,015