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DNA-PKcs has KU-dependent function in rRNA processing and haematopoiesis 期刊论文
NATURE, 2020, 579 (7798) : 291-+
作者:  Avellaneda, Mario J.;  Franke, Kamila B.;  Sunderlikova, Vanda;  Bukau, Bernd;  Mogk, Axel;  Tans, Sander J.
收藏  |  浏览/下载:11/0  |  提交时间:2020/07/03

The DNA-dependent protein kinase (DNA-PK), which comprises the KU heterodimer and a catalytic subunit (DNA-PKcs), is a classical non-homologous end-joining (cNHEJ) factor(1). KU binds to DNA ends, initiates cNHEJ, and recruits and activates DNA-PKcs. KU also binds to RNA, but the relevance of this interaction in mammals is unclear. Here we use mouse models to show that DNA-PK has an unexpected role in the biogenesis of ribosomal RNA (rRNA) and in haematopoiesis. The expression of kinase-dead DNA-PKcs abrogates cNHEJ(2). However, most mice that both expressed kinase-dead DNA-PKcs and lacked the tumour suppressor TP53 developed myeloid disease, whereas all other previously characterized mice deficient in both cNHEJ and TP53 expression succumbed to pro-B cell lymphoma(3). DNA-PK autophosphorylates DNA-PKcs, which is its best characterized substrate. Blocking the phosphorylation of DNA-PKcs at the T2609 cluster, but not the S2056 cluster, led to KU-dependent defects in 18S rRNA processing, compromised global protein synthesis in haematopoietic cells and caused bone marrow failure in mice. KU drives the assembly of DNA-PKcs on a wide range of cellular RNAs, including the U3 small nucleolar RNA, which is essential for processing of 18S rRNA(4). U3 activates purified DNA-PK and triggers phosphorylation of DNA-PKcs at T2609. DNA-PK, but not other cNHEJ factors, resides in nucleoli in an rRNA-dependent manner and is co-purified with the small subunit processome. Together our data show that DNA-PK has RNA-dependent, cNHEJ-independent functions during ribosome biogenesis that require the kinase activity of DNA-PKcs and its phosphorylation at the T2609 cluster.


  
Classification with a disordered dopantatom network in silicon 期刊论文
NATURE, 2020, 577 (7790) : 341-+
作者:  Vagnozzi, Ronald J.;  Maillet, Marjorie;  Sargent, Michelle A.;  Khalil, Hadi;  Johansen, Anne Katrine Z.;  Schwanekamp, Jennifer A.;  York, Allen J.;  Huang, Vincent;  Nahrendorf, Matthias;  Sadayappan, Sakthivel;  Molkentin, Jeffery D.
收藏  |  浏览/下载:23/0  |  提交时间:2020/07/03

Classification is an important task at which both biological and artificial neural networks excel(1,2). In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable(3,4), simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density(5), inherent parallelism and energy efficiency(6,7). However, existing approaches either rely on the systems'  time dynamics, which requires sequential data processing and therefore hinders parallel computation(5,6,8), or employ large materials systems that are difficult to scale up(7). Here we use a parallel, nanoscale approach inspired by filters in the brain(1) and artificial neural networks(2) to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction(9-11) through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates(12) up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters(2) that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data(13). Our results establish a paradigm of silicon-based electronics for smallfootprint and energy-efficient computation(14).


  
Gap Filling of Monthly Temperature Data and Its Effect on Climatic Variability and Trends 期刊论文
JOURNAL OF CLIMATE, 2019, 32 (22) : 7797-7821
作者:  Begueria, Santiago;  Tomas-Burguera, Miquel;  Serrano-Notivoli, Roberto;  Pena-Angulo, Dhais;  Vicente-Serrano, Sergio M.;  Gonzalez-Hidalgo, Jose-Carlos
收藏  |  浏览/下载:5/0  |  提交时间:2020/02/17
Data processing  Databases  Bias  Interpolation schemes  
GSDR: A Global Sub-Daily Rainfall Dataset 期刊论文
JOURNAL OF CLIMATE, 2019, 32 (15) : 4715-4729
作者:  Lewis, Elizabeth;  Fowler, Hayley;  Alexander, Lisa;  Dunn, Robert;  McClean, Fergus;  Barbero, Renaud;  Guerreiro, Selma;  Li, Xiao-Feng;  Blenkinsop, Stephen
收藏  |  浏览/下载:20/0  |  提交时间:2019/11/27
Rainfall  Climate records  Data processing  
Estimating Turbulence Kinetic Energy Dissipation Rates in the Numerically Simulated Stratocumulus Cloud-Top Mixing Layer: Evaluation of Different Methods 期刊论文
JOURNAL OF THE ATMOSPHERIC SCIENCES, 2019, 76 (5) : 1471-1488
作者:  Akinlabi, Emmanuel O.;  Waclawczyk, Marta;  Mellado, Juan Pedro;  Malinowski, Szymon P.
收藏  |  浏览/下载:7/0  |  提交时间:2019/11/26
Clouds  Data processing  Measurements  Statistics  Time series  Clouds  
First Effort at Constructing a High-Density Photosynthetically Active Radiation Dataset during 1961-2014 in China 期刊论文
JOURNAL OF CLIMATE, 2019, 32 (10) : 2761-2780
作者:  Qin, Wenmin;  Wang, Lunche;  Zhang, Ming;  Niu, Zigeng;  Luo, Ming;  Lin, Aiwen;  Hu, Bo
收藏  |  浏览/下载:7/0  |  提交时间:2019/11/26
Atmosphere  Asia  Radiative forcing  Shortwave radiation  Data processing  Databases  
Separating Geophysical Signals Using GRACE and High-Resolution Data: A Case Study in Antarctica 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2018, 45 (22) : 12340-12349
作者:  Engels, Olga;  Gunter, Brian;  Riva, Riccardo;  Klees, Roland
收藏  |  浏览/下载:5/0  |  提交时间:2019/04/09
GRACE post-processing  high spatial resolution  data-driven approach  GIA  ice mass changes  Antarctica  
Fuzzy-logic detection and probability of hail exploiting short-range X-band weather radar 期刊论文
ATMOSPHERIC RESEARCH, 2018, 201: 17-33
作者:  Capozzi, Vincenzo;  Picciotti, Errico;  Mazzarella, Vincenzo;  Marzano, Frank Silvio;  Budillon, Giorgio
收藏  |  浏览/下载:4/0  |  提交时间:2019/04/09
Weather radar  X-band  Hailstorm  Detection methods  Data processing  Urban hydrometeorology  
Coupled Chemistry-Meteorology/ Climate Modelling (CCMM): status and relevance for numerical weather prediction, atmospheric pollution and climate research 科技报告
来源:World Meteorological Organization (WMO). 出版年: 2016
作者:  World Meteorological Organization
收藏  |  浏览/下载:7/0  |  提交时间:2019/04/05
Data processing  Numerical weather prediction  Air pollution  Climate model  - International  World Weather Research Programme (WWRP)  Global Atmosphere Watch Programme (GAW)  World Climate Research Programme (WCRP)  
WMO/UNEP Dobson Data Quality Workshop 科技报告
来源:World Meteorological Organization (WMO). 出版年: 2016
作者:  World Meteorological Organization
收藏  |  浏览/下载:5/0  |  提交时间:2019/04/05
Global Atmosphere Watch Programme (GAW)  Data processing  - International