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Variability of the aridity index and related drought parameters in Greece using climatological data over the last century (1900-1997) 期刊论文
ATMOSPHERIC RESEARCH, 2020, 240
作者:  Tsiros, Ioannis X.;  Nastos, Panagiotis;  Proutsos, Nikolaos D.;  Tsaousidis, Alexandros
收藏  |  浏览/下载:16/0  |  提交时间:2020/08/18
Drought  Aridity index  Mediterranean basin  Climate variability  Thornthwaite water balance model  
Increased Dust Aerosols in the High Troposphere Over the Tibetan Plateau From 1990s to 2000s 期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2020, 125 (13)
作者:  Feng, Xingya;  Mao, Rui;  Gong, Dao-Yi;  Zhao, Chun;  Wu, Chenglai;  Zhao, Chuanfeng;  Wu, Guangjian;  Lin, Zhaohui;  Liu, Xiaohong;  Wang, Kaicun;  Sun, Yijie
收藏  |  浏览/下载:12/0  |  提交时间:2020/08/18
the Tibetan Plateau  dust aerosols  middle east  ice core  CMIP 6  
Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2020, 47 (13)
作者:  Jiang, Shijie;  Zheng, Yi;  Solomatine, Dimitri
收藏  |  浏览/下载:12/0  |  提交时间:2020/06/16
artificial intelligence  deep learning  Earth science  geosystem dynamics  hydrology  predictions in ungauged basins  
Development of advanced artificial intelligence models for daily rainfall prediction 期刊论文
ATMOSPHERIC RESEARCH, 2020, 237
作者:  Binh Thai Pham;  Lu Minh Le;  Tien-Thinh Le;  Kien-Trinh Thi Bui;  Vuong Minh Le;  Hai-Bang Ly;  Prakash, Indra
收藏  |  浏览/下载:11/0  |  提交时间:2020/07/02
Rainfall  Artificial Neural Networks  Robustness analysis  Support Vector Machines  Adaptive Network based Fuzzy Inference System  Particle Swarm Optimization  
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.


  
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.


  
Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol-cloud interactions 期刊论文
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2020, 20 (3) : 1607-1626
作者:  Saponaro, Giulia;  Sporre, Moa K.;  Neubauer, David;  Kokkola, Harri;  Kolmonen, Pekka;  Sogacheva, Larisa;  Arola, Antti;  de Leeuw, Gerrit;  Karset, Inger H. H.;  Laaksonen, Ari;  Lohmann, Ulrike
收藏  |  浏览/下载:13/0  |  提交时间:2020/07/02
The aridity Index under global warming 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2019, 14 (12)
作者:  Greve, P.;  Roderick, M. L.;  Ukkola, A. M.;  Wada, Y.
收藏  |  浏览/下载:8/0  |  提交时间:2020/02/17
aridity  climate change  water availability  vegetation  
Effects of Eucalyptus plantations on streamflow in Brazil: Moving beyond the water use debate 期刊论文
FOREST ECOLOGY AND MANAGEMENT, 2019, 453
作者:  de Barros Ferraz, Silvio Frosini;  Rodrigues, Carolina Bozetti;  Garcia, Lara Gabrielle;  Alvares, Clayton Alcarde;  Lima, Walter de Paula
收藏  |  浏览/下载:5/0  |  提交时间:2020/02/17
Experimental catchment  Fast-wood plantation  Water availability  Sustainability  Hydrosolidarity  
Machine learning and artificial intelligence to aid climate change research and preparedness 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2019, 14 (12)
作者:  Huntingford, Chris;  Jeffers, Elizabeth S.;  Bonsall, Michael B.;  Christensen, Hannah M.;  Lees, Thomas;  Yang, Hui
收藏  |  浏览/下载:15/0  |  提交时间:2020/02/17
climate change  global warming  extreme weather  drought  artificial intelligence  machine learning  climate simulations