Speaker: Gideon Zamba (U. Iowa)
Title: Recurrence of Subsequent Malignancies following Diagnosis of and Treatment for Hodgkin Lymphoma Diagnosis
Abstract: Hodgkin’s Lymphoma (HL) is a type of cancer that affects the lymphatic system and compromises the body’s ability to fight infection. HL typically starts in white blood cells. HL occurs when a specific type of cell, the Reed-Stenberg cell, is present in the host’s system, causing the body’s infection fighting cells to develop a mutation in their DNA. Each year, there are several thousand people in the United States and worldwide who develop HL. Although there are many prognostic factors for HL and post treatment malignancies, it has also been hypothesized that initial treatment after diagnosis may be associated with subsequent new malignancies or death. We explored the association between prognostic factors and subsequent malignancies using the Oncology Registry at the University of Iowa Hospitals and Clinics. In this exploration we account for subject random effect through a gamma frailty model for recurrent events, which acts multiplicatively and jointly on both the hazard of new malignancies and the hazard of death. The parameters of the model were iteratively estimated using a penalized marginal likelihood approach. The findings suggest a significant within subject correlation, and a significant treatment effect on both the hazard of recurrence and the hazard of death.
Speaker: Elizabeth Gross (San Jose State U.)
Title: Goodness of fit of statistical network models
Abstract: Exponential random graph models (ERGMs) are families of distributions defined by a set of network statistics and, thus, give rise to interesting graph theoretic questions. Indeed, goodness-of-fit testing for these models can be achieved if we know how to sample uniformly from the space of all graphs with the same network statistics as the observed network. Examples of commonly used network statistics include edge count, degree sequences, k-star counts, and triangle counts. In this talk, we will introduce exponential random graph models, discuss the geometry of these models, and show the role toric ideals play in determining the quality of model fit.
Speaker:
Leslie Hogben
Iowa State University and American Institute of Mathematics
Title:
Power domination and zero forcing: Using graphs to model real-world problems
Abstract:
A graph $G = (V, E)$ is a set of vertices $V = {1, dots , n}$ and set of edges $E$ of
two element sets of vertices. A graph can be used to model connections between
vertices, such as airline routes between cities, internet connections, a quantum
system, or an electric power network.
Power domination and zero forcing are related coloring processes on graphs.
We start with a set of vertices colored blue and the rest colored white. We apply
a color change rule to color the white vertices blue. A set of blue vertices that
can color all vertices blue by using the power domination color change rule (or
zero forcing color change rule) is called a power dominating set (or a zero forcing
set). Finding a such set allows us to solve various problems, and a minimum
such set can provide an optimal solution.
In an electric power network, a power dominating set (blue vertices) gives
a set of locations from which monitoring units can observe the entire network.
In a quantum system, a zero forcing set (blue vertices) gives a set of locations
from which the entire system can be controlled.
This talk will describe power domination and zero forcing processes on
graphs and some of their applications.