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A developmental landscape of 3D-cultured human pre-gastrulation embryos 期刊论文
NATURE, 2020, 577 (7791) : 537-+
作者:  Xiang, Lifeng;  Yin, Yu;  Zheng, Yun;  Ma, Yanping;  Li, Yonggang;  Zhao, Zhigang;  Guo, Junqiang;  Ai, Zongyong;  Niu, Yuyu;  Duan, Kui;  He, Jingjing;  Ren, Shuchao;  Wu, Dan;  Bai, Yun;  Shang, Zhouchun;  Dai, Xi;  Ji, Weizhi;  Li, Tianqing
收藏  |  浏览/下载:12/0  |  提交时间:2020/07/03

Our understanding of how human embryos develop before gastrulation, including spatial self-organization and cell type ontogeny, remains limited by available two-dimensional technological platforms(1,2) that do not recapitulate the in vivo conditions(3-5). Here we report a three-dimensional (3D) blastocyst-culture system that enables human blastocyst development up to the primitive streak anlage stage. These 3D embryos mimic developmental landmarks and 3D architectures in vivo, including the embryonic disc, amnion, basement membrane, primary and primate unique secondary yolk sac, formation of anterior-posterior polarity and primitive streak anlage. Using single-cell transcriptome profiling, we delineate ontology and regulatory networks that underlie the segregation of epiblast, primitive endoderm and trophoblast. Compared with epiblasts, the amniotic epithelium shows unique and characteristic phenotypes. After implantation, specific pathways and transcription factors trigger the differentiation of cytotrophoblasts, extravillous cytotrophoblasts and syncytiotrophoblasts. Epiblasts undergo a transition to pluripotency upon implantation, and the transcriptome of these cells is maintained until the generation of the primitive streak anlage. These developmental processes are driven by different pluripotency factors. Together, findings from our 3D-culture approach help to determine the molecular and morphogenetic developmental landscape that occurs during human embryogenesis.


A 3D culture system to model human embryonic development, together with single-cell transcriptome profiling, provides insights into the molecular developmental landscape during human post-implantation embryogenesis.


  
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:15/0  |  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.


  
EXPRESSION OF DOUBT 期刊论文
NATURE, 2020, 578 (7796) : 502-504
作者:  Benton, Donald J.;  Gamblin, Steven J.;  Rosenthal, Peter B.;  Skehel, John J.
收藏  |  浏览/下载:4/0  |  提交时间:2020/07/03

Although AI companies market software for recognizing emotions in faces, psychologists debate whether expressions can be read so easily.


Although AI companies market software for recognizing emotions in faces, psychologists debate whether expressions can be read so easily.


  
Artificial Intelligence Accidentally Learned Ecology through Video Games 期刊论文
TRENDS IN ECOLOGY & EVOLUTION, 2020, 35 (7) : 557-560
作者:  Barbe, Lou;  Mony, Cendrine;  Abbott, Benjamin W.
收藏  |  浏览/下载:8/0  |  提交时间:2020/05/13
Somatic inflammatory gene mutations in human ulcerative colitis epithelium 期刊论文
NATURE, 2020, 577 (7789) : 254-+
作者:  Nanki, Kosaku;  Fujii, Masayuki;  Shimokawa, Mariko;  Matano, Mami;  Nishikori, Shingo;  Date, Shoichi;  Takano, Ai;  Toshimitsu, Kohta;  Ohta, Yuki;  Takahashi, Sirirat;  Sugimoto, Shinya;  Ishimaru, Kazuhiro;  Kawasaki, Kenta;  Nagai, Yoko;  Ishii, Ryota;  Yoshida, Kosuke;  Sasaki, Nobuo;  Hibi, Toshifumi;  Ishihara, Soichiro;  Kanai, Takanori;  Sato, Toshiro
收藏  |  浏览/下载:14/0  |  提交时间:2020/07/03

With ageing, normal human tissues experience an expansion of somatic clones that carry cancer mutations(1-7). However, whether such clonal expansion exists in the non-neoplastic intestine remains unknown. Here, using whole-exome sequencing data from 76 clonal human colon organoids, we identify a unique pattern of somatic mutagenesis in the inflamed epithelium of patients with ulcerative colitis. The affected epithelium accumulates somatic mutations in multiple genes that are related to IL-17 signalling-including NFKBIZ, ZC3H12A and PIGR, which are genes that are rarely affected in colon cancer. Targeted sequencing validates the pervasive spread of mutations that are related to IL-17 signalling. Unbiased CRISPR-based knockout screening in colon organoids reveals that the mutations confer resistance to the proapoptotic response that is induced by IL-17A. Some of these genetic mutations are known to exacerbate experimental colitis in mice(8-11), and somatic mutagenesis in human colon epithelium may be causally linked to the inflammatory process. Our findings highlight a genetic landscape that adapts to a hostile microenvironment, and demonstrate its potential contribution to the pathogenesis of ulcerative colitis.


  
AI tracks a beating heart's function over time 期刊论文
NATURE, 2020, 580 (7802)
作者:  Ball, Philip
收藏  |  浏览/下载:8/0  |  提交时间:2020/07/03

Clinicians use ultrasound videos of heartbeats to assess subtle changes in the heart'  s pumping function. A method that uses artificial intelligence might simplify these complex assessments when heartbeats are out of rhythm.


  
AI shows promise for breast cancer screening 期刊论文
NATURE, 2020, 577 (7788) : 35-36
作者:  Pisano, Etta D.
收藏  |  浏览/下载:1/0  |  提交时间:2020/07/03
Direct structural evidence of Indian continental subduction beneath Myanmar 期刊论文
NATURE COMMUNICATIONS, 2020, 11 (1)
作者:  Zheng, Tianyu;  He, Yumei;  Ding, Lin;  Jiang, Mingming;  Ai, Yinshuang;  Mon, Chit Thet;  Hou, Guangbing;  Sein, Kyaing;  Thant, Myo
收藏  |  浏览/下载:7/0  |  提交时间:2020/05/13
Evidence of metasomatism in the interior of Vesta 期刊论文
NATURE COMMUNICATIONS, 2020, 11 (1)
作者:  Zhang, Ai-Cheng;  Kawasaki, Noriyuki;  Bao, Huiming;  Liu, Jia;  Qin, Liping;  Kuroda, Minami;  Gao, Jian-Feng;  Chen, Li-Hui;  He, Ye;  Sakamoto, Naoya;  Yurimoto, Hisayoshi
收藏  |  浏览/下载:8/0  |  提交时间:2020/05/13
Video-based AI for beat-to-beat assessment of cardiac function 期刊论文
NATURE, 2020, 580 (7802) : 252-+
作者:  Pleguezuelos-Manzano, Cayetano;  Puschhof, Jens;  Huber, Axel Rosendahl;  van Hoeck, Arne;  Wood, Henry M.;  Nomburg, Jason;  Gurjao, Carino;  Manders, Freek;  Dalmasso, Guillaume;  Stege, Paul B.;  Paganelli, Fernanda L.;  Geurts, Maarten H.;  Beumer, Joep;  Mizutani, Tomohiro;  Miao, Yi;  van der Linden, Reinier;  van der Elst, Stefan;  Garcia, K. Christopher;  Top, Janetta;  Willems, Rob J. L.;  Giannakis, Marios;  Bonnet, Richard;  Quirke, Phil;  Meyerson, Matthew;  Cuppen, Edwin;  van Boxtel, Ruben;  Clevers, Hans
收藏  |  浏览/下载:116/0  |  提交时间:2020/07/03

A video-based deep learning algorithm-EchoNet-Dynamic-accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.


Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease(1), screening for cardiotoxicity(2) and decisions regarding the clinical management of patients with a critical illness(3). However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training(4,5). Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.