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欧盟资助两项新一代地球系统模式开发项目 快报文章
地球科学快报,2021年第12期
作者:  刘燕飞
Microsoft Word(15Kb)  |  收藏  |  浏览/下载:434/0  |  提交时间:2021/06/24
Earth System Models  Storm-Resolving Earth System Models  climate projection  
Seasonal and regional changes in temperature projections over the Arabian Peninsula based on the CMIP5 multi-model ensemble dataset 期刊论文
ATMOSPHERIC RESEARCH, 2020, 239
作者:  Almazroui, Mansour;  Khalid, M. Salman;  Islam, M. Nazrul;  Saeed, Sajjad
收藏  |  浏览/下载:10/0  |  提交时间:2020/08/18
Temperature projection  Seasons  Regions  CMIP5 multi-models  Arabian Peninsula  
Unprecedented Europe Heat in June-July 2019: Risk in the Historical and Future Context 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2020, 47 (11)
作者:  Ma, Feng;  Yuan, Xing;  Jiao, Yang;  Ji, Peng
收藏  |  浏览/下载:6/0  |  提交时间:2020/05/13
heat  anthropogenic climate change  attribution  future projection  CMIP6  
A Control of ENSO Transition Complexity by Tropical Pacific Mean SSTs Through Tropical-Subtropical Interaction 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2020, 47 (12)
作者:  Fang, Shih-Wei;  Yu, Jin-Yi
收藏  |  浏览/下载:5/0  |  提交时间:2020/05/13
ENSO  ENSO complexity  ENSO transition  ENSO asymmetry  subtropical ENSO onset  ENSO projection  
Future changes in precipitation over Central Asia based on CMIP6 projections 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2020, 15 (5)
作者:  Jiang Jie;  Zhou Tianjun;  Chen Xiaolong;  Zhang Lixia
收藏  |  浏览/下载:6/0  |  提交时间:2020/05/13
Central Asia  precipitation  projection  moisture budget  evaporation  
Olfactory receptor and circuit evolution promote host specialization 期刊论文
NATURE, 2020
作者:  Chen, Tse-An;  Chuu, Chih-Piao;  Tseng, Chien-Chih;  Wen, Chao-Kai;  Wong, H. -S. Philip;  Pan, Shuangyuan;  Li, Rongtan;  Chao, Tzu-Ang;  Chueh, Wei-Chen;  Zhang, Yanfeng;  Fu, Qiang;  Yakobson, Boris I.;  Chang, Wen-Hao;  Li, Lain-Jong
收藏  |  浏览/下载:8/0  |  提交时间:2020/07/03

The evolution of animal behaviour is poorly understood(1,2). Despite numerous correlations between interspecific divergence in behaviour and nervous system structure and function, demonstrations of the genetic basis of these behavioural differences remain rare(3-5). Here we develop a neurogenetic model, Drosophila sechellia, a species that displays marked differences in behaviour compared to its close cousin Drosophila melanogaster(6,7), which are linked to its extreme specialization on noni fruit (Morinda citrifolia)(8-16). Using calcium imaging, we identify olfactory pathways in D. sechellia that detect volatiles emitted by the noni host. Our mutational analysis indicates roles for different olfactory receptors in long- and short-range attraction to noni, and our cross-species allele-transfer experiments demonstrate that the tuning of one of these receptors is important for species-specific host-seeking. We identify the molecular determinants of this functional change, and characterize their evolutionary origin and behavioural importance. We perform circuit tracing in the D. sechellia brain, and find that receptor adaptations are accompanied by increased sensory pooling onto interneurons as well as species-specific central projection patterns. This work reveals an accumulation of molecular, physiological and anatomical traits that are linked to behavioural divergence between species, and defines a model for investigating speciation and the evolution of the nervous system.


A neurogenetic model, Drosophila sechellia-a relative of Drosophila melanogaster that has developed an extreme specialization for a single host plant-sheds light on the evolution of interspecific differences in behaviour.


  
Projected near-term changes of temperature extremes in Europe and China under different aerosol emissions 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2020, 15 (3)
作者:  Luo, Feifei;  Wilcox, Laura;  Dong, Buwen;  Su, Qin;  Chen, Wei;  Dunstone, Nick;  Li, Shuanglin;  Gao, Yongqi
收藏  |  浏览/下载:8/0  |  提交时间:2020/07/02
temperature extremes  anthropogenic aerosols  projection  HadGEM3-GC2  
Global warming to increase violent crime in the United States 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2020, 15 (3)
作者:  Harp, Ryan D.;  Karnauskas, Kristopher B.
收藏  |  浏览/下载:7/0  |  提交时间:2020/07/02
climate  impacts  health  violent  crime  projection  
Dopamine D2 receptors in discrimination learning and spine enlargement 期刊论文
NATURE, 2020, 579 (7800) : 555-+
作者:  Luo, Zhaochu;  Hrabec, Ales;  Dao, Trong Phuong;  Sala, Giacomo;  Finizio, Simone;  Feng, Junxiao;  Mayr, Sina;  Raabe, Joerg;  Gambardella, Pietro;  Heyderman, Laura J.
收藏  |  浏览/下载:24/0  |  提交时间:2020/07/03

Detection of dopamine dips by neurons that express dopamine D2 receptors in the striatum is used to refine generalized reward conditioning mediated by dopamine D1 receptors.


Dopamine D2 receptors (D2Rs) are densely expressed in the striatum and have been linked to neuropsychiatric disorders such as schizophrenia(1,2). High-affinity binding of dopamine suggests that D2Rs detect transient reductions in dopamine concentration (the dopamine dip) during punishment learning(3-5). However, the nature and cellular basis of D2R-dependent behaviour are unclear. Here we show that tone reward conditioning induces marked stimulus generalization in a manner that depends on dopamine D1 receptors (D1Rs) in the nucleus accumbens (NAc) of mice, and that discrimination learning refines the conditioning using a dopamine dip. In NAc slices, a narrow dopamine dip (as short as 0.4 s) was detected by D2Rs to disinhibit adenosine A(2A) receptor (A(2A)R)-mediated enlargement of dendritic spines in D2R-expressing spiny projection neurons (D2-SPNs). Plasticity-related signalling by Ca2+/calmodulin-dependent protein kinase II and A(2A)Rs in the NAc was required for discrimination learning. By contrast, extinction learning did not involve dopamine dips or D2-SPNs. Treatment with methamphetamine, which dysregulates dopamine signalling, impaired discrimination learning and spine enlargement, and these impairments were reversed by a D2R antagonist. Our data show that D2Rs refine the generalized reward learning mediated by D1Rs.


  
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).