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Institution

Brown University

EducationProvidence, Rhode Island, United States
About: Brown University is a education organization based out in Providence, Rhode Island, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 35778 authors who have published 90896 publications receiving 4471489 citations. The organization is also known as: brown.edu & Brown.


Papers
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Journal ArticleDOI
30 Apr 2010-Science
TL;DR: A synthesis of grass evolutionary biology with grassland ecosystem science will further knowledge of the evolution of traits that promote dominance in grassland systems and will provide a new context in which to evaluate the relative importance of C4 photosynthesis in transforming ecosystems across large regions of Earth.
Abstract: The evolution of grasses using C4 photosynthesis and their sudden rise to ecological dominance 3 to 8 million years ago is among the most dramatic examples of biome assembly in the geological record. A growing body of work suggests that the patterns and drivers of C4 grassland expansion were considerably more complex than originally assumed. Previous research has benefited substantially from dialog between geologists and ecologists, but current research must now integrate fully with phylogenetics. A synthesis of grass evolutionary biology with grassland ecosystem science will further our knowledge of the evolution of traits that promote dominance in grassland systems and will provide a new context in which to evaluate the relative importance of C4 photosynthesis in transforming ecosystems across large regions of Earth.

878 citations

Journal ArticleDOI
30 Jun 2016
TL;DR: With earlier recognition and more compliance to best practices, sepsis has become less of an immediate life-threatening disorder and more of a long-term chronic critical illness, often associated with prolonged inflammation, immune suppression, organ injury and lean tissue wasting.
Abstract: For more than two decades, sepsis was defined as a microbial infection that produces fever (or hypothermia), tachycardia, tachypnoea and blood leukocyte changes. Sepsis is now increasingly being considered a dysregulated systemic inflammatory and immune response to microbial invasion that produces organ injury for which mortality rates are declining to 15-25%. Septic shock remains defined as sepsis with hyperlactataemia and concurrent hypotension requiring vasopressor therapy, with in-hospital mortality rates approaching 30-50%. With earlier recognition and more compliance to best practices, sepsis has become less of an immediate life-threatening disorder and more of a long-term chronic critical illness, often associated with prolonged inflammation, immune suppression, organ injury and lean tissue wasting. Furthermore, patients who survive sepsis have continuing risk of mortality after discharge, as well as long-term cognitive and functional deficits. Earlier recognition and improved implementation of best practices have reduced in-hospital mortality, but results from the use of immunomodulatory agents to date have been disappointing. Similarly, no biomarker can definitely diagnose sepsis or predict its clinical outcome. Because of its complexity, improvements in sepsis outcomes are likely to continue to be slow and incremental.

876 citations

Journal ArticleDOI
01 Jul 2012
TL;DR: This paper is the first large scale exploration of human sketches, developing a bag-of-features sketch representation and using multi-class support vector machines, trained on the sketch dataset, to classify sketches.
Abstract: Humans have used sketching to depict our visual world since prehistoric times. Even today, sketching is possibly the only rendering technique readily available to all humans. This paper is the first large scale exploration of human sketches. We analyze the distribution of non-expert sketches of everyday objects such as 'teapot' or 'car'. We ask humans to sketch objects of a given category and gather 20,000 unique sketches evenly distributed over 250 object categories. With this dataset we perform a perceptual study and find that humans can correctly identify the object category of a sketch 73% of the time. We compare human performance against computational recognition methods. We develop a bag-of-features sketch representation and use multi-class support vector machines, trained on our sketch dataset, to classify sketches. The resulting recognition method is able to identify unknown sketches with 56% accuracy (chance is 0.4%). Based on the computational model, we demonstrate an interactive sketch recognition system. We release the complete crowd-sourced dataset of sketches to the community.

874 citations

Journal ArticleDOI
Alan Needleman1
TL;DR: In this paper, the role of material rate dependence in setting the character of governing equations is illustrated in the context of a simple one-dimensional problem, and numerical results are presented that illustrate the localization behavior of slightly rate-dependent solids under both quasi-static and dynamic loading conditions.
Abstract: The role of material rate dependence in setting the character of governing equations is illustrated in the context of a simple one-dimensional problem. For rate-dependent solids, the incremental equilibrium equations for quasi-static problems remain elliptic and wave speeds for dynamic problems remain real, even in the presence of strain-softening. The pathological mesh sensitivity associated with numerical solutions of localization problems for rate-independent solids is eliminated. In effect, material rate dependence implicity introduces a length scale into the governing equations, although the constitutive description does not contain a parameter with the dimensions of length. Numerical results are presented that illustrate the localization behavior of slightly rate-dependent solids under both quasi-static and dynamic loading conditions.

874 citations


Authors

Showing all 36143 results

NameH-indexPapersCitations
Walter C. Willett3342399413322
Robert Langer2812324326306
Robert M. Califf1961561167961
Eric J. Topol1931373151025
Joan Massagué189408149951
Joseph Biederman1791012117440
Gonçalo R. Abecasis179595230323
James F. Sallis169825144836
Steven N. Blair165879132929
Charles M. Lieber165521132811
J. S. Lange1602083145919
Christopher J. O'Donnell159869126278
Charles M. Perou156573202951
David J. Mooney15669594172
Richard J. Davidson15660291414
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023126
2022591
20215,549
20205,321
20194,806
20184,462