University of Illinois at Chicago

About: University of Illinois at Chicago is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 57071 authors who have published 110536 publications receiving 4264936 citations.

Myc-mediated apoptosis requires wild-type p53 in a manner independent of cell cycle arrest and the ability of p53 to induce p21waf1/cip1.

Abstract: Deregulated expression of the c-myc proto-oncogene can lead to apoptosis under certain physiological conditions. By introducing a conditionally active Myc allele into primary embryo fibroblasts null for p53, and into fibroblasts without endogenous p53 expression but ectopically expressing a temperature-sensitive p53 allele, we show that expression of wild-type p53 is required for susceptibility to Myc-mediated apoptosis. Although ectopic expression of wild-type p53 blocked cells in the G1 phase of the cell cycle, G1 arrest by isoleucine starvation, in a manner independent of p53, did not confer susceptibility to apoptosis. Thus, growth arrest per se is not sufficient to induce Myc-mediated apoptosis; instead, a property intrinsic to p53 is specifically required. Moreover, apoptosis did not require induction of p53 target proteins, including the cyclin-dependent kinase inhibitor p21waf1/cip1. Therefore, the role of p53 in apoptosis may be distinct from its role in cell cycle arrest.

Full-information item bi-factor analysis

Abstract: A plausibles-factor solution for many types of psychological and educational tests is one that exhibits a general factor ands − 1 group or method related factors. The bi-factor solution results from the constraint that each item has a nonzero loading on the primary dimension and at most one of thes − 1 group factors. This paper derives a bi-factor item-response model for binary response data. In marginal maximum likelihood estimation of item parameters, the bi-factor restriction leads to a major simplification of likelihood equations and (a) permits analysis of models with large numbers of group factors; (b) permits conditional dependence within identified subsets of items; and (c) provides more parsimonious factor solutions than an unrestricted full-information item factor analysis in some cases.

Principles of artificial neural networks

Abstract: The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strengths and weaknesses of the different networks.

Nonnegative Matrices and Applications

Abstract: This book provides an integrated treatment of the theory of nonnegative matrices (matrices with only positive numbers or zero as entries) and some related classes of positive matrices, concentrating on connections with game theory, combinatorics, inequalities, optimisation and mathematical economics. The wide variety of applications, which include price fixing, scheduling and the fair division problem, have been carefully chosen both for their elegant mathematical content and for their accessibility to students with minimal preparation. Many results in matrix theory are also presented. The treatment is rigorous and almost all results are proved completely. These results and applications will be of great interest to researchers in linear programming, statistics and operations research. The minimal prerequisites also make the book accessible to first-year graduate students.