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Global conservation of species' niches 期刊论文
NATURE, 2020, 580 (7802) : 232-+
作者:  Guo, Xiaoyan;  Aviles, Giovanni;  Liu, Yi;  Tian, Ruilin;  Unger, Bret A.;  Lin, Yu-Hsiu T.;  Wiita, Arun P.;  Xu, Ke;  Correia, M. Almira;  Kampmann, Martin
收藏  |  浏览/下载:29/0  |  提交时间:2020/07/03

Environmental change is rapidly accelerating, and many species will need to adapt to survive(1). Ensuring that protected areas cover populations across a broad range of environmental conditions could safeguard the processes that lead to such adaptations(1-3). However, international conservation policies have largely neglected these considerations when setting targets for the expansion of protected areas(4). Here we show that-of 19,937 vertebrate species globally(5-8)-the representation of environmental conditions across their habitats in protected areas (hereafter, niche representation) is inadequate for 4,836 (93.1%) amphibian, 8,653 (89.5%) bird and 4,608 (90.9%) terrestrial mammal species. Expanding existing protected areas to cover these gaps would encompass 33.8% of the total land surface-exceeding the current target of 17% that has been adopted by governments. Priority locations for expanding the system of protected areas to improve niche representation occur in global biodiversity hotspots(9), including Colombia, Papua New Guinea, South Africa and southwest China, as well as across most of the major land masses of the Earth. Conversely, we also show that planning for the expansion of protected areas without explicitly considering environmental conditions would marginally reduce the land area required to 30.7%, but that this would lead to inadequate niche representation for 7,798 (39.1%) species. As the governments of the world prepare to renegotiate global conservation targets, policymakers have the opportunity to help to maintain the adaptive potential of species by considering niche representation within protected areas(1,2).


Protected areas would need to expand to 33.8% of the total land surface to adequately represent environmental conditions across the habitats of amphibians, birds and terrestrial mammals, far exceeding the current 17% target.


  
Centrosome anchoring regulates progenitor properties and cortical formation 期刊论文
NATURE, 2020
作者:  Guo, Xiaoyan;  Aviles, Giovanni;  Liu, Yi;  Tian, Ruilin;  Unger, Bret A.;  Lin, Yu-Hsiu T.;  Wiita, Arun P.;  Xu, Ke;  Correia, M. Almira;  Kampmann, Martin
收藏  |  浏览/下载:14/0  |  提交时间:2020/07/03

CEP83-mediated anchoring of the centrosome to the apical membrane in radial glial progenitor cells regulates their mechanical properties and thereby influences the size and configuration of the mammalian cortex.


Radial glial progenitor cells (RGPs) are the major neural progenitor cells that generate neurons and glia in the developing mammalian cerebral cortex(1-4). In RGPs, the centrosome is positioned away from the nucleus at the apical surface of the ventricular zone of the cerebral cortex(5-8). However, the molecular basis and precise function of this distinctive subcellular organization of the centrosome are largely unknown. Here we show in mice that anchoring of the centrosome to the apical membrane controls the mechanical properties of cortical RGPs, and consequently their mitotic behaviour and the size and formation of the cortex. The mother centriole in RGPs develops distal appendages that anchor it to the apical membrane. Selective removal of centrosomal protein 83 (CEP83) eliminates these distal appendages and disrupts the anchorage of the centrosome to the apical membrane, resulting in the disorganization of microtubules and stretching and stiffening of the apical membrane. The elimination of CEP83 also activates the mechanically sensitive yes-associated protein (YAP) and promotes the excessive proliferation of RGPs, together with a subsequent overproduction of intermediate progenitor cells, which leads to the formation of an enlarged cortex with abnormal folding. Simultaneous elimination of YAP suppresses the cortical enlargement and folding that is induced by the removal of CEP83. Together, these results indicate a previously unknown role of the centrosome in regulating the mechanical features of neural progenitor cells and the size and configuration of the mammalian cerebral cortex.


  
Improved protein structure prediction using potentials from deep learning 期刊论文
NATURE, 2020, 577 (7792) : 706-+
作者:  Ma, Runze;  Cao, Duanyun;  Zhu, Chongqin;  Tian, Ye;  Peng, Jinbo;  Guo, Jing;  Chen, Ji;  Li, Xin-Zheng;  Francisco, Joseph S.;  Zeng, Xiao Cheng;  Xu, Li-Mei;  Wang, En-Ge;  Jiang, Ying
收藏  |  浏览/下载:142/0  |  提交时间:2020/07/03

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence(1). This problem is of fundamental importance as the structure of a protein largely determines its function(2)  however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures(3). Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force(4) that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction(5) (CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores(6) of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined(7).


  
Ground-to-satellite quantum teleportation 期刊论文
NATURE, 2017, 549 (7670) : 70-+
作者:  Ren, Ji-Gang;  Xu, Ping;  Yong, Hai-Lin;  Zhang, Liang;  Liao, Sheng-Kai;  Yin, Juan;  Liu, Wei-Yue;  Cai, Wen-Qi;  Yang, Meng;  Li, Li;  Yang, Kui-Xing;  Han, Xuan;  Yao, Yong-Qiang;  Li, Ji;  Wu, Hai-Yan;  Wan, Song;  Liu, Lei;  Liu, Ding-Quan;  Kuang, Yao-Wu;  He, Zhi-Ping;  Shang, Peng;  Guo, Cheng;  Zheng, Ru-Hua;  Tian, Kai;  Zhu, Zhen-Cai;  Liu, Nai-Le;  Lu, Chao-Yang;  Shu, Rong;  Chen, Yu-Ao;  Peng, Cheng-Zhi;  Wang, Jian-Yu;  Pan, Jian-Wei
收藏  |  浏览/下载:13/0  |  提交时间:2019/11/27