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JournalISSN: 0028-0836

Nature

About: Nature is an academic journal. The journal publishes majorly in the area(s): Population & Antigen. It has an ISSN identifier of 0028-0836. Over the lifetime, 206031 publication(s) have been published receiving 21147916 citation(s).

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Topics: Population, Antigen, Gene ... show more
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206,031 results found


Journal ArticleDOI: 10.1038/227680A0
Ulrich K. Laemmli1Institutions (1)
15 Aug 1970-Nature
Abstract: Using an improved method of gel electrophoresis, many hitherto unknown proteins have been found in bacteriophage T4 and some of these have been identified with specific gene products. Four major components of the head are cleaved during the process of assembly, apparently after the precursor proteins have assembled into some large intermediate structure.

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Topics: Bacteriophage T5 (61%), Head morphogenesis (59%), Gap junction assembly (57%) ... show more

229,303 Citations


Open accessJournal Article
01 Jan 1970-Nature
Abstract: Using an improved method of gel electrophoresis, many hitherto unknown proteins have been found in bacteriophage T4 and some of these have been identified with specific gene products. Four major components of the head are cleaved during the process of assembly, apparently after the precursor proteins have assembled into some large intermediate structure.

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Topics: Cleavage (embryo) (57%)

203,017 Citations


Journal ArticleDOI: 10.1038/354056A0
Sumio Iijima1Institutions (1)
01 Nov 1991-Nature
Abstract: THE synthesis of molecular carbon structures in the form of C60 and other fullerenes1 has stimulated intense interest in the structures accessible to graphitic carbon sheets. Here I report the preparation of a new type of finite carbon structure consisting of needle-like tubes. Produced using an arc-discharge evaporation method similar to that used for fullerene synthesis, the needles grow at the negative end of the electrode used for the arc discharge. Electron microscopy reveals that each needle comprises coaxial tubes of graphitic sheets, ranging in number from 2 up to about 50. On each tube the carbon-atom hexagons are arranged in a helical fashion about the needle axis. The helical pitch varies from needle to needle and from tube to tube within a single needle. It appears that this helical structure may aid the growth process. The formation of these needles, ranging from a few to a few tens of nanometres in diameter, suggests that engineering of carbon structures should be possible on scales considerably greater than those relevant to the fullerenes. On 7 November 1991, Sumio Iijima announced in Nature the preparation of nanometre-size, needle-like tubes of carbon — now familiar as 'nanotubes'. Used in microelectronic circuitry and microscopy, and as a tool to test quantum mechanics and model biological systems, nanotubes seem to have unlimited potential.

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Topics: Single-Walled Nanotube (57%), Colossal carbon tube (56%), Carbon nanotube (56%) ... show more

36,871 Citations


Journal ArticleDOI: 10.1038/30918
Duncan J. Watts1, Steven H. Strogatz1Institutions (1)
04 Jun 1998-Nature
Abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

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Topics: Complex network (67%), Evolving networks (65%), Network motif (62%) ... show more

35,972 Citations


Journal ArticleDOI: 10.1038/NATURE14539
Yann LeCun1, Yann LeCun2, Yoshua Bengio3, Geoffrey E. Hinton4  +1 moreInstitutions (5)
28 May 2015-Nature
Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

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33,931 Citations


Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
20211,941
20202,297
20192,390
20182,496
20172,499
20162,791

Top Attributes

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Journal's top 5 most impactful authors

Philip Ball

1K papers, 5.8K citations

Declan Butler

790 papers, 7.5K citations

Alison Abbott

774 papers, 7.1K citations

Heidi Ledford

692 papers, 5.6K citations

Quirin Schiermeier

535 papers, 3.8K citations

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