Faculty and Staff

Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm³ to 62 mm³, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.

Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.

Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.

Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.

Materials produced for the workshop hosted by the Humane Cities Initiative, Institute for Humanities Research, Arizona State University. Participants were sent this package of archival materials and links to brief online sources for advance readings. Also enclosed are PDF renderings of introductory powerpoint files by Spindler and Pagan, and an audio recording of the discussion joined in progress.

Presentation slides regarding the history of Victory Village, the trailer park built in 1945-46 to provide housing for WWII Veterans and their families at Arizona State University's Tempe campus. A presentation of research from University Archives records conducted in the summer of 2018. The presentation was videotaped as a lecture for Professor Volker Benkert's online World War II history class.

This research study will discover and evaluate information about existing crowdsourcing or participatory archives projects devoted to archival description, indexing or transcription. Many related projects that use crowdsourcing for collecting archival materials from the public are not specifically addressed here. In this research, the author has specifically sought evaluative information about exemplary projects that can lead to useful specifications for a participatory archives system at Arizona State University Libraries.

The Task Force of thirty faculty members and academic professionals was charged by the University Libraries and the Graduate College to "examine the potential for implementing a university program for Electronic Theses and Dissertations (ETDs). The Task Force was specifically asked to collect information about existing programs elsewhere, identify ASU graduate programs that could host pilot programs and prepare a comprehensive white paper..." with a recommended plan for implementation.