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.

While PhD dissertations are typically accessible many other terminal degree projects remain invisible and inaccessible to a greater audience. Over the past year and a half, librarians at Arizona State University collaborated with faculty and departmental administrators across a variety of fields to develop and create institutional repository collections that highlight and authoritatively share this type of student scholarship with schools, researchers, and future employers. This poster will present the benefits, challenges, and considerations required to successfully implement and manage these collections of applied final projects or capstone projects. Specifically, issues/challenges related to metadata consistency, faculty buy-in, and developing an ingest process, as well as benefits related to increased visibility and improved educational and employment opportunities will be discussed. This interactive presentation will also discuss lessons learned from the presenter’s experiences in context of how they can easily apply to benefit their respective institutions.

Digital technology has enabled us to record and share our memories and histories faster and in greater numbers than previously imagined. However digital files rely on hardware, software, and descriptive information to be used. As formats change and equipment to read them goes out of use we are all challenged to connect our present to our future. How long do you want your digital files to last? Decades or even a few years from now will you still be able to access and enjoy those pictures, documents and other digital items you create today?

Libraries, museums and archives spend countless hours and resources preserving physical items from the past and present, but may be forfeiting the longevity of our digital work and connecting to future generations through unintended neglect. Using practical examples and employing best practices of research institutions, participants will learn important first steps to digital preservation including the importance of metadata to personal history, recommended file formats, and approaches they can immediately use to ensure the work they create today will still be enjoyed tomorrow. Help yourself, your organization, and your patrons continue to connect their digital heritage to the generations yet to come.