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Effective COVID-19 prevention and control in areas of ultra-dense population: Lessons from Macau SAR

Published on: 3rd June, 2020

OCLC Number/Unique Identifier: 8628651927

In this paper we summarise, in chronological order, all COVID-19 preventive measures undertaken by the Macau Special Administrative Region (SAR) government during the first quarter of 2020. The information and narrative contained herein may be of useful to other parts of the world in COVID-19 control and prevention, especially cities with ultra-high population densities. The four main lessons from Macau SAR are: (1) Proactive leadership and early prevention. (2) Strict adherence to community endemic control. (3) Clear prioritising of public health. (4) Planed relief for financial hardships amidst the post-pandemic recession.
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Comparison of resting-state functional and effective connectivity between default mode network and memory encoding related areas

Published on: 24th April, 2020

OCLC Number/Unique Identifier: 8589567444

Currently brain connectivity modelling, constructed from data acquired by non-invasive technique such as functional magnetic resonance imaging (fMRI), is a well-received approach to illustrate brain function. However, not all connectivity models contains equal amount of information. There are two types of connectivity model that could be constructed from fMRI data, functional and effective connectivity. Effective connectivity includes information about the direction of the connection, while functional connectivity does not. This makes interpretation of effective connectivity more meaningful than functional connectivity. The objective of this study is to show the improvement in interpretability of effective connectivity model in comparison to functional connectivity model. In this study, we show how the difference in the information contained within these two model impacts the interpretation of the resulting connectivity model by analyzing resting-state fMRI data on episodic memory-related cognitive function using CONN Toolbox bivariate correlation measurement for functional connectivity analysis and Tigramite causal discovery framework for effective connectivity analysis on an episodic memory related resting-state fMRI dataset. The comparison between functional and effective connectivity results show that effective connectivity contains more information than the functional connectivity, and the difference in the information contained within these two types of model could significantly impact the intepretation of true brain function. In conclusion, we show that for the connectivity between specific pair of brain regions, effective connectivity analysis reveals more informative characteristic of the connectivity in comparison to functional connectivity where the depicted connectivity lack any additional characteristic information such as the direction of the connection or whether it is a unidirectional or bidirectional. These additional information improve interpretability of brain connectivity study. Thus, we would like to emphasis the important of brain function study using effective connectivity modelling to obtain valid interpretation of true brain function as currently a large body of research in this field focuses only on functional connectivity model.
Cite this ArticleCrossMarkPublonsHarvard Library HOLLISGrowKudosResearchGateBase SearchOAI PMHAcademic MicrosoftScilitSemantic ScholarUniversite de ParisUW LibrariesSJSU King LibrarySJSU King LibraryNUS LibraryMcGillDET KGL BIBLiOTEKJCU DiscoveryUniversidad De LimaWorldCatVU on WorldCat