Institution
Stanford University
Education•Stanford, California, United States•
About: Stanford University is a education organization based out in Stanford, California, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 125751 authors who have published 320347 publications receiving 21892059 citations. The organization is also known as: Leland Stanford Junior University & University of Stanford.
Topics: Population, Transplantation, Medicine, Cancer, Gene
Papers published on a yearly basis
Papers
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TL;DR: A mediator is a software module that exploits encoded knowledge about certain sets or subsets of data to create information for a higher layer of applications as discussed by the authors, which simplifies, abstracts, reduces, merges, and explains data.
Abstract: For single databases, primary hindrances for end-user access are the volume of data that is becoming available, the lack of abstraction, and the need to understand the representation of the data. When information is combined from multiple databases, the major concern is the mismatch encountered in information representation and structure. Intelligent and active use of information requires a class of software modules that mediate between the workstation applications and the databases. It is shown that mediation simplifies, abstracts, reduces, merges, and explains data. A mediator is a software module that exploits encoded knowledge about certain sets or subsets of data to create information for a higher layer of applications. A model of information processing and information system components is described. The mediator architecture, including mediator interfaces, sharing of mediator modules, distribution of mediators, and triggers for knowledge maintenance, are discussed. >
2,441 citations
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TL;DR: Alternatives to the usual maximum likelihood estimates for the covariance matrices are proposed, characterized by two parameters, the values of which are customized to individual situations by jointly minimizing a sample-based estimate of future misclassification risk.
Abstract: Linear and quadratic discriminant analysis are considered in the small-sample, high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covariance matrices are proposed. These alternatives are characterized by two parameters, the values of which are customized to individual situations by jointly minimizing a sample-based estimate of future misclassification risk. Computationally fast implementations are presented, and the efficacy of the approach is examined through simulation studies and application to data. These studies indicate that in many circumstances dramatic gains in classification accuracy can be achieved.
2,440 citations
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Stanford University1, Harvard University2, University of Florida3, University of Washington4, University of Texas Medical Branch5, University of Colorado Denver6, University of Texas Southwestern Medical Center7, University of Rochester8, University of Pittsburgh9, University of Toronto10, University of California, San Francisco11, Loyola University Chicago12, Washington University in St. Louis13, Rutgers University14
TL;DR: This study shows that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another.
Abstract: A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R2 between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.
2,438 citations
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TL;DR: FMRI results indicate that the rFIC is likely to play a major role in switching between distinct brain networks across task paradigms and stimulus modalities, and have important implications for a unified view of network mechanisms underlying both exogenous and endogenous cognitive control.
Abstract: Cognitively demanding tasks that evoke activation in the brain's central-executive network (CEN) have been consistently shown to evoke decreased activation (deactivation) in the default-mode network (DMN). The neural mechanisms underlying this switch between activation and deactivation of large-scale brain networks remain completely unknown. Here, we use functional magnetic resonance imaging (fMRI) to investigate the mechanisms underlying switching of brain networks in three different experiments. We first examined this switching process in an auditory event segmentation task. We observed significant activation of the CEN and deactivation of the DMN, along with activation of a third network comprising the right fronto-insular cortex (rFIC) and anterior cingulate cortex (ACC), when participants perceived salient auditory event boundaries. Using chronometric techniques and Granger causality analysis, we show that the rFIC-ACC network, and the rFIC, in particular, plays a critical and causal role in switching between the CEN and the DMN. We replicated this causal connectivity pattern in two additional experiments: (i) a visual attention "oddball" task and (ii) a task-free resting state. These results indicate that the rFIC is likely to play a major role in switching between distinct brain networks across task paradigms and stimulus modalities. Our findings have important implications for a unified view of network mechanisms underlying both exogenous and endogenous cognitive control.
2,436 citations
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TL;DR: In contrast to heterogeneous Ziegler-Natta catalysts, homogeneous metallocene-based catalysts as discussed by the authors allow efficient control of regio-and stereoregularities, molecular weights and molecular weight distributions, and comonomer incorporation.
Abstract: Current studies on novel, metallocenebased catalysts for the polymerization of α-olefins have far-reaching implications for the development of new materials as well as for the understanding of basic reaction mechanisms responsible for the growth of a polymer chain at a catalyst center and the control of its stereoregularity. In contrast to heterogeneous Ziegler–Natta catalysts, polymerization by a homogeneous, metallocene-based catalyst occurs principally at a single type of metal center with a defined coordination environment. This makes it possible to correlate metallocene structures with polymer properties such as molecular weight, stereochemical microstructure, crystallization behavior, and mechanical properties. Homogeneous catalyst systems now afford efficient control of regio- and stereoregularities, molecular weights and molecular weight distributions, and comonomer incorporation. By providing a means for the homo- and copolymerization of cyclic olefins, the cyclopolymerization of dienes, and access even to functionalized polyolefins, these catalysts greatly expand the range and versatility of technically feasible types of polyolefin materials.
For corrigendum see DOI:10.1002/anie.199513681
2,436 citations
Authors
Showing all 127468 results
Name | H-index | Papers | Citations |
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Eric S. Lander | 301 | 826 | 525976 |
George M. Whitesides | 240 | 1739 | 269833 |
Yi Cui | 220 | 1015 | 199725 |
Yi Chen | 217 | 4342 | 293080 |
David Miller | 203 | 2573 | 204840 |
David Baltimore | 203 | 876 | 162955 |
Edward Witten | 202 | 602 | 204199 |
Irving L. Weissman | 201 | 1141 | 172504 |
Hongjie Dai | 197 | 570 | 182579 |
Robert M. Califf | 196 | 1561 | 167961 |
Frank E. Speizer | 193 | 636 | 135891 |
Thomas C. Südhof | 191 | 653 | 118007 |
Gad Getz | 189 | 520 | 247560 |
Mark Hallett | 186 | 1170 | 123741 |
John P. A. Ioannidis | 185 | 1311 | 193612 |