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澳大利亚AIMS发布《2023-2024年度珊瑚礁状况年度总结报告》 快报文章
资源环境快报,2024年第16期
作者:  魏艳红
Microsoft Word(24Kb)  |  收藏  |  浏览/下载:691/3  |  提交时间:2024/09/01
AIMS  Coral Reef Condition  GBR  Coral Bleaching  
AIMS发布《澳大利亚海洋科学研究所2030战略》 快报文章
资源环境快报,2023年第16期
作者:  魏艳红
Microsoft Word(34Kb)  |  收藏  |  浏览/下载:531/0  |  提交时间:2023/09/01
AIMS  Marine Environment  Strategy  
International evaluation of an AI system for breast cancer screening 期刊论文
NATURE, 2020, 577 (7788) : 89-+
作者:  McKinney, Scott Mayer;  Sieniek, Marcin;  Godbole, Varun;  Godwin, Jonathan;  Antropova, Natasha;  Ashrafian, Hutan;  Back, Trevor;  Chesus, Mary;  Corrado, Greg C.;  Darzi, Ara;  Etemadi, Mozziyar;  Garcia-Vicente, Florencia;  Gilbert, Fiona J.;  Halling-Brown, Mark;  Hassabis, Demis;  Jansen, Sunny;  Karthikesalingam, Alan;  Kelly, Christopher J.;  King, Dominic;  Ledsam, Joseph R.;  Melnick, David;  Mostofi, Hormuz;  Peng, Lily;  Reicher, Joshua Jay;  Romera-Paredes, Bernardino;  Sidebottom, Richard;  Suleyman, Mustafa;  Tse, Daniel;  Young, Kenneth C.;  De Fauw, Jeffrey;  Shetty, Shravya
收藏  |  浏览/下载:67/0  |  提交时间:2020/07/03

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful(1). Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives(2). Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.


  
Rebuilding marine life 期刊论文
NATURE, 2020, 580 (7801) : 39-51
作者:  Carlos M. Duarte;  Susana Agusti;  Edward Barbier;  Gregory L. Britten;  Juan Carlos Castilla;  Jean-Pierre Gattuso;  Robinson W. Fulweiler;  Terry P. Hughes;  Nancy Knowlton;  Catherine E. Lovelock;  Heike K. Lotze;  Milica Predragovic;  Elvira Poloczanska;  Callum Roberts;  Boris Worm
收藏  |  浏览/下载:35/0  |  提交时间:2020/05/13

Sustainable Development Goal 14 of the United Nations aims to "conserve and sustainably use the oceans, seas and marine resources for sustainable development". Achieving this goal will require rebuilding the marine life-support systems that deliver the many benefits that society receives from a healthy ocean. Here we document the recovery of marine populations, habitats and ecosystems following past conservation interventions. Recovery rates across studies suggest that substantial recovery of the abundance, structure and function of marine life could be achieved by 2050, if major pressures-including climate change-are mitigated. Rebuilding marine life represents a doable Grand Challenge for humanity, an ethical obligation and a smart economic objective to achieve a sustainable future.