Institution
Brown University
Education•Providence, 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 published on a yearly basis
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01 Dec 1989TL;DR: A model of causal reasoning that accounts for knowledge concerning cause‐and‐effect relationships and knowledge concerning the tendency for propositions to persist or not as a function of time passing is described.
Abstract: In this paper, we describe a model of causal reasoning that accounts for knowledge concerning cause-and-effect relationships and knowledge concerning the tendency for propositions to persist or not as a function of time passing. Our model has a natural encoding in the form of a network representation for probabilistic models. We explore the computational properties of our model by considering recent advances in computing the consequences of models encoded in this network representation.
1,137 citations
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Case Western Reserve University1, Emory University2, National Institutes of Health3, University of Alabama at Birmingham4, Yale University5, Harvard University6, University of Miami7, University of Cincinnati8, University of Tennessee Health Science Center9, University of Texas Southwestern Medical Center10, Indiana University – Purdue University Indianapolis11, Brown University12, University of New Mexico13, Wayne State University14, Stanford University15, University of Texas Health Science Center at Houston16, Research Triangle Park17
TL;DR: There have been no significant increases in survival without neonatal and long-term morbidity among VLBW infants between 1997 and 2002, and it is speculated that to improve survival without morbidity requires determining, disseminating, and applying best practices using therapies currently available, and also identifying new strategies and interventions.
1,135 citations
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1,133 citations
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TL;DR: A baseline algorithm for 3D articulated tracking that uses a relatively standard Bayesian framework with optimization in the form of Sequential Importance Resampling and Annealed Particle Filtering is described, and a variety of likelihood functions, prior models of human motion and the effects of algorithm parameters are explored.
Abstract: While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing methods to establish the current state of the art. We present data obtained using a hardware system that is able to capture synchronized video and ground-truth 3D motion. The resulting HumanEva datasets contain multiple subjects performing a set of predefined actions with a number of repetitions. On the order of 40,000 frames of synchronized motion capture and multi-view video (resulting in over one quarter million image frames in total) were collected at 60 Hz with an additional 37,000 time instants of pure motion capture data. A standard set of error measures is defined for evaluating both 2D and 3D pose estimation and tracking algorithms. We also describe a baseline algorithm for 3D articulated tracking that uses a relatively standard Bayesian framework with optimization in the form of Sequential Importance Resampling and Annealed Particle Filtering. In the context of this baseline algorithm we explore a variety of likelihood functions, prior models of human motion and the effects of algorithm parameters. Our experiments suggest that image observation models and motion priors play important roles in performance, and that in a multi-view laboratory environment, where initialization is available, Bayesian filtering tends to perform well. The datasets and the software are made available to the research community. This infrastructure will support the development of new articulated motion and pose estimation algorithms, will provide a baseline for the evaluation and comparison of new methods, and will help establish the current state of the art in human pose estimation and tracking.
1,130 citations
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Daniel J. Klionsky1, Amal Kamal Abdel-Aziz2, Sara Abdelfatah3, Mahmoud Abdellatif4 +2980 more•Institutions (777)
TL;DR: In this article, the authors present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes.
Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.
1,129 citations
Authors
Showing all 36143 results
Name | H-index | Papers | Citations |
---|---|---|---|
Walter C. Willett | 334 | 2399 | 413322 |
Robert Langer | 281 | 2324 | 326306 |
Robert M. Califf | 196 | 1561 | 167961 |
Eric J. Topol | 193 | 1373 | 151025 |
Joan Massagué | 189 | 408 | 149951 |
Joseph Biederman | 179 | 1012 | 117440 |
Gonçalo R. Abecasis | 179 | 595 | 230323 |
James F. Sallis | 169 | 825 | 144836 |
Steven N. Blair | 165 | 879 | 132929 |
Charles M. Lieber | 165 | 521 | 132811 |
J. S. Lange | 160 | 2083 | 145919 |
Christopher J. O'Donnell | 159 | 869 | 126278 |
Charles M. Perou | 156 | 573 | 202951 |
David J. Mooney | 156 | 695 | 94172 |
Richard J. Davidson | 156 | 602 | 91414 |