scispace - formally typeset
Search or ask a question
Institution

Monash University

EducationMelbourne, Victoria, Australia
About: Monash University is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 35920 authors who have published 100681 publications receiving 3027002 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, the authors called for organizations to be more flexible, adaptive, entrepreneurial, and innovative in meeting the changing demands of today's environment, and appropriate leadership to effect such ch...
Abstract: Research has called for organizations to be more flexible, adaptive, entrepreneurial, and innovative in meeting the changing demands of today's environment. Appropriate leadership to effect such ch...

581 citations

Journal ArticleDOI
TL;DR: The mechanisms through which OMVs induce host pathology or immune tolerance are described, and the development of OMVs as innovative nanotechnologies are discussed.
Abstract: Gram-negative bacteria shed extracellular outer membrane vesicles (OMVs) during their normal growth both in vitro and in vivo. OMVs are spherical, bilayered membrane nanostructures that contain many components found within the parent bacterium. Until recently, OMVs were dismissed as a by-product of bacterial growth; however, findings within the past decade have revealed that both pathogenic and commensal bacteria can use OMVs to manipulate the host immune response. In this Review, we describe the mechanisms through which OMVs induce host pathology or immune tolerance, and we discuss the development of OMVs as innovative nanotechnologies.

580 citations

Journal ArticleDOI
01 Jan 1984-Nature
TL;DR: In one of these women a donated oocyte, fertilized by her husband's spermatozoa7 and cultured to the two-cell stage in vitro, was transferred in utero, resulting in a normal pregnancy and the delivery of a healthy child.
Abstract: Ovarian steroid replacement therapy in the ovariectomized ewe, given in the correct sequence to mimic endogenous steroid changes in the normal ovulatory cycle, allows the development of embryos transferred in utero. A similar type of sequential therapy was designed for steroid replacement in women with primary ovarian failure. This produces the histological changes in uterine endometrial morphology and plasma oestradiol and progesterone similar to those observed in the normal ovulatory cycle. We now report that in one of these women a donated oocyte, fertilized by her husband's spermatozoa and cultured to the two-cell stage in vitro, was transferred in utero, resulting in a normal pregnancy and the delivery of a healthy child. Oestrogen therapy was withdrawn at 12 weeks and progesterone at 19 weeks gestation. This technique allows the treatment of human infertility due to primary ovarian failure.

579 citations

Journal ArticleDOI
TL;DR: A large number of patients with or at risk of diabetes and metabolic complications of preexisting diabetes, including diabetic ketoacidosis and h...
Abstract: Diabetes and Covid-19 Diabetes is associated with an increased risk of severe Covid-19. New-onset diabetes and metabolic complications of preexisting diabetes, including diabetic ketoacidosis and h...

578 citations

Proceedings ArticleDOI
23 Aug 2020
TL;DR: This paper proposes a general graph neural network framework designed specifically for multivariate time series data that outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.
Abstract: Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. In recent years, meanwhile, graph neural networks (GNNs) have shown high capability in handling relational dependencies. GNNs require well-defined graph structures for information propagation which means they cannot be applied directly for multivariate time series where the dependencies are not known in advance. In this paper, we propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module, into which external knowledge like variable attributes can be easily integrated. A novel mix-hop propagation layer and a dilated inception layer are further proposed to capture the spatial and temporal dependencies within the time series. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework. Experimental results show that our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.

576 citations


Authors

Showing all 36568 results

NameH-indexPapersCitations
Bert Vogelstein247757332094
Kenneth W. Kinzler215640243944
David J. Hunter2131836207050
David R. Williams1782034138789
Yang Yang1712644153049
Lei Jiang1702244135205
Dongyuan Zhao160872106451
Christopher J. O'Donnell159869126278
Leif Groop158919136056
Mark E. Cooper1581463124887
Theo Vos156502186409
Mark J. Smyth15371388783
Rinaldo Bellomo1471714120052
Detlef Weigel14251684670
Geoffrey Burnstock141148899525
Network Information
Related Institutions (5)
University of New South Wales
153.6K papers, 4.8M citations

97% related

University of Sydney
187.3K papers, 6.1M citations

97% related

University of Queensland
155.7K papers, 5.7M citations

97% related

University of Melbourne
174.8K papers, 6.3M citations

96% related

National University of Singapore
165.4K papers, 5.4M citations

92% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023250
20221,020
20219,402
20208,419
20197,409
20186,437