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全球首份冰下湖泊编目发布 快报文章
资源环境快报，2022年第02期
作者:  吴秀平
Microsoft Word(14Kb)  |  收藏  |  浏览/下载：643/0  |  提交时间：2022/01/31
Subglacial lakes  first time  cataloged  
Nearest neighbours reveal fast and slow components of motor learning 期刊论文
NATURE, 2020, 577 (7791) : 526-+
作者:  Kollmorgen, Sepp;  Hahnloser, Richard H. R.;  Mante, Valerio
收藏  |  浏览/下载：19/0  |  提交时间：2020/07/03

A new method for analysing change in high-dimensional data is based on nearest-neighbour statistics and is applied here to song dynamics during vocal learning in zebra finches, but could potentially be applied to other biological and artificial behaviours.


Changes in behaviour resulting from environmental influences, development and learning(1-5) are commonly quantified on the basis of a few hand-picked features(2-4,6,7) (for example, the average pitch of acoustic vocalizations(3)), assuming discrete classes of behaviours (such as distinct vocal syllables)(2,3,8-10). However, such methods generalize poorly across different behaviours and model systems and may miss important components of change. Here we present a more-general account of behavioural change that is based on nearest-neighbour statistics(11-13), and apply it to song development in a songbird, the zebra finch(3). First, we introduce the concept of '  repertoire dating'  , whereby each rendition of a behaviour (for example, each vocalization) is assigned a repertoire time, reflecting when similar renditions were typical in the behavioural repertoire. Repertoire time isolates the components of vocal variability that are congruent with long-term changes due to vocal learning and development, and stratifies the behavioural repertoire into '  regressions'  , '  anticipations'  and '  typical renditions'  . Second, we obtain a holistic, yet low-dimensional, description of vocal change in terms of a stratified '  behavioural trajectory'  , revealing numerous previously unrecognized components of behavioural change on fast and slow timescales, as well as distinct patterns of overnight consolidation(1,2,4,14,15) across the behavioral repertoire. We find that diurnal changes in regressions undergo only weak consolidation, whereas anticipations and typical renditions consolidate fully. Because of its generality, our nonparametric description of how behaviour evolves relative to itself-rather than to a potentially arbitrary, experimenter-defined goal(2,3,14,16)-appears well suited for comparing learning and change across behaviours and species(17,18), as well as biological and artificial systems(5).


  
Population flow drives spatio-temporal distribution of COVID-19 in China 期刊论文
NATURE, 2020
作者:  Fernandez, Diego Carlos;  Komal, Ruchi;  Langel, Jennifer;  Ma, Jun;  Duy, Phan Q.;  Penzo, Mario A.;  Zhao, Haiqing;  Hattar, Samer
收藏  |  浏览/下载：86/0  |  提交时间：2020/07/03

Sudden, large-scale and diffuse human migration can amplify localized outbreaks of disease into widespread epidemics(1-4). Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. Here we use 11,478,484 counts of mobile phone data from individuals leaving or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout mainland China. First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographical distribution of infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) until 19 February 2020, across mainland China. Third, we develop a spatio-temporal '  risk source'  model that leverages population flow data (which operationalize the risk that emanates from epidemic epicentres) not only to forecast the distribution of confirmed cases, but also to identify regions that have a high risk of transmission at an early stage. Fourth, we use this risk source model to statistically derive the geographical spread of COVID-19 and the growth pattern based on the population outflow from Wuhan  the model yields a benchmark trend and an index for assessing the risk of community transmission of COVID-19 over time for different locations. This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan the allocation of limited resources ahead of ongoing outbreaks.


Modelling of population flows in China enables the forecasting of the distribution of confirmed cases of COVID-19 and the identification of areas at high risk of SARS-CoV-2 transmission at an early stage.


  
An early start for galactic disks 期刊论文
NATURE, 2020, 581 (7808) : 267-268
作者:  DeWeerdt, Sarah;  Zastrow, Mark;  Conroy, Gemma
收藏  |  浏览/下载：5/0  |  提交时间：2020/07/03

A powerful radio telescope has peered back through time to observe a galaxy that contained a cold, rotating disk of gas not long after the Big Bang - fuelling the debate about when and how disks first formed in galaxies.


A cold rotating gas disk from 12.5 billion years ago.


  
Exotic helium atom lit up 期刊论文
NATURE, 2020, 581 (7806) : 32-33
作者:  Rabkin, Eugen
收藏  |  浏览/下载：15/0  |  提交时间：2020/07/03

Laser excitation of pionic helium.


An elusive type of atom known as pionic helium has been directly excited by laser light for the first time. The work establishes a promising experimental platform for probing fundamental physics.


  
In situ NMR metrology reveals reaction mechanisms in redox flow batteries 期刊论文
NATURE, 2020, 579 (7798) : 224-+
作者:  Ma, Jianfei;  You, Xin;  Sun, Shan;  Wang, Xiaoxiao;  Qin, Song;  Sui, Sen-Fang
收藏  |  浏览/下载：40/0  |  提交时间：2020/07/03

Large-scale energy storage is becoming increasingly critical to balancing renewable energy production and consumption(1). Organic redox flow batteries, made from inexpensive and sustainable redox-active materials, are promising storage technologies that are cheaper and less environmentally hazardous than vanadium-based batteries, but they have shorter lifetimes and lower energy density(2,3). Thus, fundamental insight at the molecular level is required to improve performance(4,5). Here we report two in situ nuclear magnetic resonance (NMR) methods of studying redox flow batteries, which are applied to two redox-active electrolytes: 2,6-dihydroxyanthraquinone (DHAQ) and 4,4 '  -((9,10-anthraquinone-2,6-diyl)dioxy) dibutyrate (DBEAQ). In the first method, we monitor the changes in the H-1 NMR shift of the liquid electrolyte as it flows out of the electrochemical cell. In the second method, we observe the changes that occur simultaneously in the positive and negative electrodes in the full electrochemical cell. Using the bulk magnetization changes (observed via the H-1 NMR shift of the water resonance) and the line broadening of the H-1 shifts of the quinone resonances as a function of the state of charge, we measure the potential differences of the two single-electron couples, identify and quantify the rate of electron transfer between the reduced and oxidized species, and determine the extent of electron delocalization of the unpaired spins over the radical anions. These NMR techniques enable electrolyte decomposition and battery self-discharge to be explored in real time, and show that DHAQ is decomposed electrochemically via a reaction that can be minimized by limiting the voltage used on charging. We foresee applications of these NMR methods in understanding a wide range of redox processes in flow and other electrochemical systems.


  
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：32/0  |  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).


  
Optimizing surveillance strategies for early detection of invasive alien species 期刊论文
ECOLOGICAL ECONOMICS, 2019, 162: 87-99
作者:  Yemshanov, Denys;  Haight, Robert G.;  Koch, Frank H.;  Venette, Robert C.;  Swystun, Tom;  Fournier, Ronald E.;  Marcotte, Mireille;  Chen, Yongguang;  Turgeon, Jean J.
收藏  |  浏览/下载：18/0  |  提交时间：2019/11/27
Uncertainty  Early detection survey  Time to first detection  Scenario-based model  Conditional value-at-risk  Asian longhomed beetle  Ambiguity aversion