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Institution

New York University

EducationNew York, New York, United States
About: New York University is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 72380 authors who have published 165545 publications receiving 8334030 citations. The organization is also known as: NYU & University of the City of New York.


Papers
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Journal ArticleDOI
TL;DR: This work considers the latent TGFβ complex as an extracellular sensor in which the TGF β propeptide functions as the detector, latent-TGFβ-binding protein (LTBP) functions as a localizer, and TGF-β functions as an effector.
Abstract: TGFβ is secreted as part of a latent complex that is targeted to the extracellular matrix A variety of molecules, `TGFβ activators,' release TGFβ from its latent state The unusual temporal discontinuity of TGFβ synthesis and action and the panoply of TGFβ effects contribute to the interest in TGF-β However, the logical connections between TGFβ synthesis, storage and action are obscure We consider the latent TGFβ complex as an extracellular sensor in which the TGFβ propeptide functions as the detector, latent-TGFβ-binding protein (LTBP) functions as the localizer, and TGF-β functions as the effector Such a view provides a logical continuity for various aspects of TGFβ biology and allows us to appreciate TGFβ biology from a new perspective

1,537 citations

Journal ArticleDOI

1,537 citations

Journal Article
TL;DR: The study strongly suggests that methylprednisolone has significant beneficial effects in human spinal cord injury, that these effects occur only when the drug is given within 8 hr, and that it helps even in patients with severe spinal cord injuries.
Abstract: In 1990, the Second National Acute Spinal Cord Injury Study reported that high-dosage methylprednisolone improves neurologic recovery in spinal-injured humans. The study showed that patients who received the drug within 8 hr after injury improved, whereas those who received the drug later did not. The drug significantly increased recovery even in severely injured patients who were admitted with no motor or sensory function below the lesion, contradicting a long-held dogma that such patients would not recover. Some researchers, however, have questioned the stratification of the patient population, the use of summed neurologic change scores, and the absence of functional assessments. The stratification by injury severity and treatment time was planned a priori and based on objective criteria. Detailed analyses revealed no differences between groups attributable to stratification or randomization. While multivariate analyses of the summed neurologic scores were used, the conclusions were corroborated by other analytical approaches that did not rely on summed scores. For example, treatment with methylprednisolone more than doubled the probability that patients would convert from quadriplegia or paraplegia to quadriparesis or paraparesis, analgesia to hypalgesia, and anesthesia to hypesthesia. The treatment also significantly improved neurologic scores in lumbosacral segments, indicating that beneficial effects were not limited to segments close to the lesion site. The treatment did not significantly affect mortality or morbidity. The study strongly suggests that methylprednisolone has significant beneficial effects in human spinal cord injury, that these effects occur only when the drug is given within 8 hr, and that it helps even in patients with severe spinal cord injuries. These conclusions have important implications for spinal cord injury care and research.

1,535 citations

Journal ArticleDOI
Nicholas J Kassebaum1, Megha Arora1, Ryan M Barber1, Zulfiqar A Bhutta2  +679 moreInstitutions (268)
TL;DR: In this paper, the authors used the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2015.

1,533 citations

Proceedings Article
21 Jun 2010
TL;DR: Two versions of a very fast algorithm that produces approximate estimates of the sparse code that can be used to compute good visual features, or to initialize exact iterative algorithms are proposed.
Abstract: In Sparse Coding (SC), input vectors are reconstructed using a sparse linear combination of basis vectors. SC has become a popular method for extracting features from data. For a given input, SC minimizes a quadratic reconstruction error with an L1 penalty term on the code. The process is often too slow for applications such as real-time pattern recognition. We proposed two versions of a very fast algorithm that produces approximate estimates of the sparse code that can be used to compute good visual features, or to initialize exact iterative algorithms. The main idea is to train a non-linear, feed-forward predictor with a specific architecture and a fixed depth to produce the best possible approximation of the sparse code. A version of the method, which can be seen as a trainable version of Li and Osher's coordinate descent method, is shown to produce approximate solutions with 10 times less computation than Li and Os-her's for the same approximation error. Unlike previous proposals for sparse code predictors, the system allows a kind of approximate "explaining away" to take place during inference. The resulting predictor is differentiable and can be included into globally-trained recognition systems.

1,533 citations


Authors

Showing all 73237 results

NameH-indexPapersCitations
Rob Knight2011061253207
Virginia M.-Y. Lee194993148820
Frank E. Speizer193636135891
Stephen V. Faraone1881427140298
Eric R. Kandel184603113560
Andrei Shleifer171514271880
Eliezer Masliah170982127818
Roderick T. Bronson169679107702
Timothy A. Springer167669122421
Alvaro Pascual-Leone16596998251
Nora D. Volkow165958107463
Dennis R. Burton16468390959
Charles N. Serhan15872884810
Giacomo Bruno1581687124368
Tomas Hökfelt158103395979
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023245
20221,205
20218,761
20209,108
20198,417
20187,680