Institution
Cornell University
Education•Ithaca, New York, United States•
About: Cornell University is a education organization based out in Ithaca, New York, United States. It is known for research contribution in the topics: Population & Gene. The organization has 102246 authors who have published 235546 publications receiving 12283673 citations. The organization is also known as: Cornell & CUI.
Topics: Population, Gene, Cancer, Poison control, Transplantation
Papers published on a yearly basis
Papers
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TL;DR: A simplified, scored form of the cognitive mental status examination, the “Mini-Mental State” (MMS) which includes eleven questions, requires only 5-10 min to administer, and is therefore practical to use serially and routinely.
76,181 citations
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TL;DR: The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death fromComorbid disease for use in longitudinal studies and further work in larger populations is still required to refine the approach.
39,961 citations
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TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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.
39,297 citations
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06 Sep 2014
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Abstract: We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.
30,462 citations
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TL;DR: An overview of the analysis pipeline and links to raw data and processed output from the runs with and without denoising are provided.
Abstract: Supplementary Figure 1 Overview of the analysis pipeline. Supplementary Table 1 Details of conventionally raised and conventionalized mouse samples. Supplementary Discussion Expanded discussion of QIIME analyses presented in the main text; Sequencing of 16S rRNA gene amplicons; QIIME analysis notes; Expanded Figure 1 legend; Links to raw data and processed output from the runs with and without denoising.
28,911 citations
Authors
Showing all 103081 results
Name | H-index | Papers | Citations |
---|---|---|---|
Eric S. Lander | 301 | 826 | 525976 |
David Miller | 203 | 2573 | 204840 |
Lewis C. Cantley | 196 | 748 | 169037 |
Charles A. Dinarello | 190 | 1058 | 139668 |
Scott M. Grundy | 187 | 841 | 231821 |
Paul G. Richardson | 183 | 1533 | 155912 |
Chris Sander | 178 | 713 | 233287 |
David R. Williams | 178 | 2034 | 138789 |
David L. Kaplan | 177 | 1944 | 146082 |
Kari Alitalo | 174 | 817 | 114231 |
Richard K. Wilson | 173 | 463 | 260000 |
George F. Koob | 171 | 935 | 112521 |
Avshalom Caspi | 170 | 524 | 113583 |
Derek R. Lovley | 168 | 582 | 95315 |
Stephen B. Baylin | 168 | 548 | 188934 |