Institution

# Yale University

Education•New Haven, Connecticut, United States•

About: Yale University is a(n) education organization based out in New Haven, Connecticut, United States. It is known for research contribution in the topic(s): Population & Poison control. The organization has 89824 authors who have published 220665 publication(s) receiving 12834776 citation(s). The organization is also known as: Yale & Collegiate School.

Topics: Population, Poison control, Health care, Galaxy, Cancer

##### Papers published on a yearly basis

##### Papers

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TL;DR: This paper examines eight published reviews each reporting results from several related trials in order to evaluate the efficacy of a certain treatment for a specified medical condition and suggests a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.

Abstract: This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment of homogeneity of treatment effect before pooling. We discuss a random effects approach to combining evidence from a series of experiments comparing two treatments. This approach incorporates the heterogeneity of effects in the analysis of the overall treatment efficacy. The model can be extended to include relevant covariates which would reduce the heterogeneity and allow for more specific therapeutic recommendations. We suggest a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.

29,821 citations

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University of California, Berkeley

^{1}, Lawrence Berkeley National Laboratory^{2}, Instituto Superior Técnico^{3}, Pierre-and-Marie-Curie University^{4}, Stockholm University^{5}, European Southern Observatory^{6}, Collège de France^{7}, University of Cambridge^{8}, University of Barcelona^{9}, Yale University^{10}, Space Telescope Science Institute^{11}, European Space Agency^{12}, University of New South Wales^{13}Abstract: We report measurements of the mass density, Omega_M, and
cosmological-constant energy density, Omega_Lambda, of the universe based on
the analysis of 42 Type Ia supernovae discovered by the Supernova Cosmology
Project. The magnitude-redshift data for these SNe, at redshifts between 0.18
and 0.83, are fit jointly with a set of SNe from the Calan/Tololo Supernova
Survey, at redshifts below 0.1, to yield values for the cosmological
parameters. All SN peak magnitudes are standardized using a SN Ia lightcurve
width-luminosity relation. The measurement yields a joint probability
distribution of the cosmological parameters that is approximated by the
relation 0.8 Omega_M - 0.6 Omega_Lambda ~= -0.2 +/- 0.1 in the region of
interest (Omega_M <~ 1.5). For a flat (Omega_M + Omega_Lambda = 1) cosmology we
find Omega_M = 0.28{+0.09,-0.08} (1 sigma statistical) {+0.05,-0.04}
(identified systematics). The data are strongly inconsistent with a Lambda = 0
flat cosmology, the simplest inflationary universe model. An open, Lambda = 0
cosmology also does not fit the data well: the data indicate that the
cosmological constant is non-zero and positive, with a confidence of P(Lambda >
0) = 99%, including the identified systematic uncertainties. The best-fit age
of the universe relative to the Hubble time is t_0 = 14.9{+1.4,-1.1} (0.63/h)
Gyr for a flat cosmology. The size of our sample allows us to perform a variety
of statistical tests to check for possible systematic errors and biases. We
find no significant differences in either the host reddening distribution or
Malmquist bias between the low-redshift Calan/Tololo sample and our
high-redshift sample. The conclusions are robust whether or not a
width-luminosity relation is used to standardize the SN peak magnitudes.

15,392 citations

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Abstract: A new software suite, called Crystallography & NMR System (CNS), has been developed for macromolecular structure determination by X-ray crystallography or solution nuclear magnetic resonance (NMR) spectroscopy. In contrast to existing structure-determination programs the architecture of CNS is highly flexible, allowing for extension to other structure-determination methods, such as electron microscopy and solid-state NMR spectroscopy. CNS has a hierarchical structure: a high-level hypertext markup language (HTML) user interface, task-oriented user input files, module files, a symbolic structure-determination language (CNS language), and low-level source code. Each layer is accessible to the user. The novice user may just use the HTML interface, while the more advanced user may use any of the other layers. The source code will be distributed, thus source-code modification is possible. The CNS language is sufficiently powerful and flexible that many new algorithms can be easily implemented in the CNS language without changes to the source code. The CNS language allows the user to perform operations on data structures, such as structure factors, electron-density maps, and atomic properties. The power of the CNS language has been demonstrated by the implementation of a comprehensive set of crystallographic procedures for phasing, density modification and refinement. User-friendly task-oriented input files are available for nearly all aspects of macromolecular structure determination by X-ray crystallography and solution NMR.

15,032 citations

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TL;DR: The Mesenchymal and Tissue Stem Cell Committee of the International Society for Cellular Therapy proposes minimal criteria to define human MSC, believing this minimal set of standard criteria will foster a more uniform characterization of MSC and facilitate the exchange of data among investigators.

Abstract: The considerable therapeutic potential of human multipotent mesenchymal stromal cells (MSC) has generated markedly increasing interest in a wide variety of biomedical disciplines. However, investig...

12,805 citations

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Yale University

^{1}TL;DR: The analysis described shows K I does not equal I 50 when competitive inhibition kinetics apply; however, K I is equal to I 50 under conditions of either noncompetitive or uncompetitive kinetics.

Abstract: A theoretical analysis has been made of the relationship between the inhibition constant ( K I ) of a substance and the ( I 50 ) value which expresses the concentration of inhibitor required to produce 50 per cent inhibition of an enzymic reaction at a specific substrate concentration. A comparison has been made of the relationships between K I and I 50 for monosubstrate reactions when noncompetitive or uncompetitive inhibition kinetics apply, as well as for bisubstrate reactions under conditions of competitive, noncompetitive and uncompetitive inhibition kinetics. Precautions have been indicated against the indiscriminate use of I 50 values in agreement with the admonitions previously described in the literature. The analysis described shows K I does not equal I 50 when competitive inhibition kinetics apply; however, K I is equal to I 50 under conditions of either noncompetitive or uncompetitive kinetics.

12,173 citations

##### Authors

Showing all 89824 results

Name | H-index | Papers | Citations |
---|---|---|---|

Richard A. Flavell | 231 | 1328 | 205119 |

Eugene Braunwald | 230 | 1711 | 264576 |

Matthias Mann | 221 | 887 | 230213 |

Bruce S. McEwen | 215 | 1163 | 200638 |

Robert J. Lefkowitz | 214 | 860 | 147995 |

Edward Giovannucci | 206 | 1671 | 179875 |

Rakesh K. Jain | 200 | 1467 | 177727 |

Francis S. Collins | 196 | 743 | 250787 |

Lewis C. Cantley | 196 | 748 | 169037 |

Martin White | 196 | 2038 | 232387 |

Ronald Klein | 194 | 1305 | 149140 |

Thomas C. Südhof | 191 | 653 | 118007 |

Michael Rutter | 188 | 676 | 151592 |

David H. Weinberg | 183 | 700 | 171424 |

Douglas R. Green | 182 | 661 | 145944 |