Stanford University

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.

American college of rheumatology preliminary definition of improvement in rheumatoid arthritis

Abstract: Objective. Trials of rheumatoid arthritis (RA) treatments report the average response in multiple outcome measures for treated patients. It is more clinically relevant to test whether individual patients improve with treatment, and this identifies a single primary efficacy measure. Multiple definitions of improvement are currently in use in different trials. The goal of this study was to promulgate a single definition for use in RA trials. Methods. Using the American College of Rheumatology (ACR) core set of outcome measures for RA trials, we tested 40 different definitions of improvement, using a 3-step process. First, we performed a survey of rheumatologists, using actual patient cases from trials, to evaluate which definitions corresponded best to rheumatologists' impressions of improvement, eliminating most candidate definitions of improvement. Second, we tested 20 remaining definitions to determine which maximally discriminated effective treatment from placebo treatment and also minimized placebo response rates. With 8 candidate definitions of improvement remaining, we tested to see which were easiest to use and were best in accord with rheumatologists' impressions of improvement. Results. The following definition of improvement was selected: 20% improvement in tender and swollen joint counts and 20% improvement in 3 of the 5 remaining ACR core set measures: patient and physician global assessments, pain, disability, and an acutephase reactant. Additional validation of this definition was carried out in a comparative trial, and the results suggest that the definition is statistically powerful and does not identify a large percentage of placebo-treated patients as being improved. Conclusion. We present a definition of improvement which we hope will be used widely in RA trials.

First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole

Abstract: When surrounded by a transparent emission region, black holes are expected to reveal a dark shadow caused by gravitational light bending and photon capture at the event horizon. To image and study this phenomenon, we have assembled the Event Horizon Telescope, a global very long baseline interferometry array observing at a wavelength of 1.3 mm. This allows us to reconstruct event-horizon-scale images of the supermassive black hole candidate in the center of the giant elliptical galaxy M87. We have resolved the central compact radio source as an asymmetric bright emission ring with a diameter of 42 +/- 3 mu as, which is circular and encompasses a central depression in brightness with a flux ratio greater than or similar to 10: 1. The emission ring is recovered using different calibration and imaging schemes, with its diameter and width remaining stable over four different observations carried out in different days. Overall, the observed image is consistent with expectations for the shadow of a Kerr black hole as predicted by general relativity. The asymmetry in brightness in the ring can be explained in terms of relativistic beaming of the emission from a plasma rotating close to the speed of light around a black hole. We compare our images to an extensive library of ray-traced general-relativistic magnetohydrodynamic simulations of black holes and derive a central mass of M = (6.5 +/- 0.7) x 10(9) M-circle dot. Our radio-wave observations thus provide powerful evidence for the presence of supermassive black holes in centers of galaxies and as the central engines of active galactic nuclei. They also present a new tool to explore gravity in its most extreme limit and on a mass scale that was so far not accessible.

Modeling Term Structures of Defaultable Bonds

Abstract: This article presents convenient reduced-form models of the valuation of contingent claims subject to default risk, focusing on applications to the term structure of interest rates for corporate or sovereign bonds. Examples include the valuation of a credit-spread option. This article presents a new approach to modeling term structures of bonds and other contingent claims that are subject to default risk. As in previous “reduced-form” models, we treat default as an unpredictable event governed by a hazard-rate process. 1 Our approach is distinguished by the parameterization of losses at default in terms of the fractional reduction in market value that occurs at default. Specifically, we fix some contingent claim that, in the event of no default, pays X at time T . We take as given an arbitrage-free setting in which all securities are priced in terms of some short-rate process r and equivalent martingale measure Q [see Harrison and Kreps (1979) and Harrison and Pliska (1981)]. Under this “risk-neutral” probability measure, we letht denote the hazard rate for default at time t and let Lt denote the expected fractional loss in market value if default were to occur at time t , conditional

AMPA Receptor Trafficking and Synaptic Plasticity

Abstract: Activity-dependent changes in synaptic function are believed to underlie the formation of memories. Two prominent examples are long-term potentiation (LTP) and long-term depression (LTD), whose mechanisms have been the subject of considerable scrutiny over the past few decades. Here we review the growing literature that supports a critical role for AMPA receptor trafficking in LTP and LTD, focusing on the roles proposed for specific AMPA receptor subunits and their interacting proteins. While much work remains to understand the molecular basis for synaptic plasticity, recent results on AMPA receptor trafficking provide a clear conceptual framework for future studies.

Social LSTM: Human Trajectory Prediction in Crowded Spaces

Abstract: Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting the future trajectory of people based on their past positions. Following the recent success of Recurrent Neural Network (RNN) models for sequence prediction tasks, we propose an LSTM model which can learn general human movement and predict their future trajectories. This is in contrast to traditional approaches which use hand-crafted functions such as Social forces. We demonstrate the performance of our method on several public datasets. Our model outperforms state-of-the-art methods on some of these datasets. We also analyze the trajectories predicted by our model to demonstrate the motion behaviour learned by our model.