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Hair-bearing human skin generated entirely from pluripotent stem cells 期刊论文
NATURE, 2020
作者:  von Appen, Alexander;  LaJoie, Dollie;  Johnson, Isabel E.;  Trnka, Michael J.;  Pick, Sarah M.;  Burlingame, Alma L.;  Ullman, Katharine S.;  Frost, Adam
收藏  |  浏览/下载:52/0  |  提交时间:2020/07/03

Skin organoids generated in vitro from human pluripotent stem cells form complex, multilayered skin tissue with hair follicles, sebaceous glands and neural circuitry, and integrate with endogenous skin when grafted onto immunocompromised mice.


The skin is a multilayered organ, equipped with appendages (that is, follicles and glands), that is critical for regulating body temperature and the retention of bodily fluids, guarding against external stresses and mediating the sensation of touch and pain(1,2). Reconstructing appendage-bearing skin in cultures and in bioengineered grafts is a biomedical challenge that has yet to be met(3-9). Here we report an organoid culture system that generates complex skin from human pluripotent stem cells. We use stepwise modulation of the transforming growth factor beta (TGF beta) and fibroblast growth factor (FGF) signalling pathways to co-induce cranial epithelial cells and neural crest cells within a spherical cell aggregate. During an incubation period of 4-5 months, we observe the emergence of a cyst-like skin organoid composed of stratified epidermis, fat-rich dermis and pigmented hair follicles that are equipped with sebaceous glands. A network of sensory neurons and Schwann cells form nerve-like bundles that target Merkel cells in organoid hair follicles, mimicking the neural circuitry associated with human touch. Single-cell RNA sequencing and direct comparison to fetal specimens suggest that the skin organoids are equivalent to the facial skin of human fetuses in the second trimester of development. Moreover, we show that skin organoids form planar hair-bearing skin when grafted onto nude mice. Together, our results demonstrate that nearly complete skin can self-assemble in vitro and be used to reconstitute skin in vivo. We anticipate that our skin organoids will provide a foundation for future studies of human skin development, disease modelling and reconstructive surgery.


  
Rossby Waves Detection in the CO2 and Temperature Multilayer Climate Network 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2020, 47 (2)
作者:  Ying, N.;  Zhou, D.;  Han, Z. G.;  Chen, Q. H.;  Ye, Q.;  Xue, Z. G.
收藏  |  浏览/下载:12/0  |  提交时间:2020/07/02
mid-troposphere CO2 concentrations  surface air temperature  multilayer climate network  Rossby waves  
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:24/0  |  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).


  
Simulation of temperature series and small networks from data 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019, 39 (13) : 5104-5123
作者:  Washington, Benjamin;  Seymour, Lynne;  Lund, Robert;  Willett, Kate
收藏  |  浏览/下载:5/0  |  提交时间:2020/02/17
general circulation model  temperature network  vector autoregression  
Simulation of extreme temperatures using a new method: TIN-copula 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019, 39 (13) : 5201-5214
作者:  Lazoglou, Georgia;  Graeler, Benedikt;  Anagnostopoulou, Christina
收藏  |  浏览/下载:6/0  |  提交时间:2020/02/17
copula  extremes  temperature  triangular irregular network (TIN)  
Anticipating global terrestrial ecosystem state change using FLUXNET 期刊论文
GLOBAL CHANGE BIOLOGY, 2019, 25 (7) : 2352-2367
作者:  Yu, Rong;  Ruddell, Benjamin L.;  Kang, Minseok;  Kim, Joon;  Childers, Dan
收藏  |  浏览/下载:10/0  |  提交时间:2019/11/27
eddy covariance  FLUXNET  functional elasticity  information flow  phenology  precipitation  process network  radiation  structural state  temperature  
Detecting global urban expansion over the last three decades using a fully convolutional network 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2019, 14 (3)
作者:  He, Chunyang;  Liu, Zhifeng;  Gou, Siyuan;  Zhang, Qiaofeng;  Zhang, Jinshui;  Xu, Linlin
收藏  |  浏览/下载:5/0  |  提交时间:2019/04/09
fully convolutional network  global urban expansion  deep learning  nighttime light data  vegetation index  land surface temperature  
Seasonal prediction of high-resolution temperature at 2-m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2018, 38 (14) : 5418-5429
作者:  Bayasgalan, Gerelchuluun;  Ahn, Joong-Bae
收藏  |  浏览/下载:16/0  |  提交时间:2019/04/09
artificial neural network  coupled general circulation model  Mongolian temperature  seasonal prediction  
An improved retrieval method of atmospheric parameter profiles based on the BP neural network 期刊论文
ATMOSPHERIC RESEARCH, 2018, 213: 389-397
作者:  Zhao, Yuxin;  Zhou, Di;  Yan, Hualong
收藏  |  浏览/下载:7/0  |  提交时间:2019/04/09
Artificial neural network  Jacobian matrix  Layered retrieval method  Vertical temperature and water vapor profiles  
A method for solar radiation error correction of temperature measured in a reinforced plastic screen for climatic data collection 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2018, 38 (3) : 1328-1336
作者:  Yang, Jie;  Liu, Qingquan;  Dai, Wei
收藏  |  浏览/下载:4/0  |  提交时间:2019/04/09
radiation error  surface air temperature  computational fluid dynamics  neural network  reinforced plastic screen  climate change