University of Texas at Austin

About: University of Texas at Austin is a education organization based out in Austin, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 94352 authors who have published 206297 publications receiving 9070052 citations. The organization is also known as: UT-Austin & UT Austin.

Reconceptualizing the Determinants of Risk Behavior

Abstract: Past research has resulted in contradictory findings concerning the effect of risk on decision-making behavior in organizations. This article proposes a model that reconciles these unresolved contradictions by examining the usefulness of placing risk propensity and risk perception in a more central role than has been previously recognized. Based on this analysis, it is posited that risk propensity dominates both the actual and perceived characteristics of the situation as a determinant of risk behavior. Propositions derived from the conceptual model provide an agenda for future research on individual risk behavior in organizational settings.

Primary structure and expression of a functional human glucocorticoid receptor cDNA

Abstract: Identification of complementary DNAs encoding the human glucocorticoid receptor predicts two protein forms, of 777 (alpha) and 742 (beta) amino acids, which differ at their carboxy termini. The proteins contain a cysteine/lysine/arginine-rich region which may define the DNA-binding domain. Pure radiolabelled glucocorticoid receptor, synthesized in vitro, is immunoreactive and possesses intrinsic steroid-binding activity characteristic of the native glucocorticoid receptor.

First-Line Nivolumab in Stage IV or Recurrent Non–Small-Cell Lung Cancer

Abstract: BackgroundNivolumab has been associated with longer overall survival than docetaxel among patients with previously treated non–small-cell lung cancer (NSCLC). In an open-label phase 3 trial, we compared first-line nivolumab with chemotherapy in patients with programmed death ligand 1 (PD-L1)–positive NSCLC. MethodsWe randomly assigned, in a 1:1 ratio, patients with untreated stage IV or recurrent NSCLC and a PD-L1 tumor-expression level of 1% or more to receive nivolumab (administered intravenously at a dose of 3 mg per kilogram of body weight once every 2 weeks) or platinum-based chemotherapy (administered once every 3 weeks for up to six cycles). Patients receiving chemotherapy could cross over to receive nivolumab at the time of disease progression. The primary end point was progression-free survival, as assessed by means of blinded independent central review, among patients with a PD-L1 expression level of 5% or more. ResultsAmong the 423 patients with a PD-L1 expression level of 5% or more, the media...

A Brief Version of the Fear of Negative Evaluation Scale

Abstract: Although the Fear of Negative Evaluation (FNE) Scale has widespread applicability to many areas of research in personality and social psychology, its utility is sometimes limited by its length. This article presents a brief, 12-item version of the FNE that correlates very highly (f96) with the original scale and that demonstrates psychometric properties that are nearly identical to those of the full-length scale.

Co-clustering documents and words using bipartite spectral graph partitioning

Abstract: Both document clustering and word clustering are well studied problems. Most existing algorithms cluster documents and words separately but not simultaneously. In this paper we present the novel idea of modeling the document collection as a bipartite graph between documents and words, using which the simultaneous clustering problem can be posed as a bipartite graph partitioning problem. To solve the partitioning problem, we use a new spectral co-clustering algorithm that uses the second left and right singular vectors of an appropriately scaled word-document matrix to yield good bipartitionings. The spectral algorithm enjoys some optimality properties; it can be shown that the singular vectors solve a real relaxation to the NP-complete graph bipartitioning problem. We present experimental results to verify that the resulting co-clustering algorithm works well in practice.