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.

Readability formulas are used widely in education, and increasingly in business and government. Over 30 years of research on more than 200 readability formulas has demonstrated moderate to strong predictive correlations with reading comprehension. In this study, five well-known readability formulas correlated highly with each other when applied to selected recent historical articles (N = 22) from two music education research journals. The mean level of difficulty (readability) for all 22 articles was grade 14.04, near the beginning of the second year of college. Since research shows that most people read below their highest completed school grade and also prefer easier materials, this is probably an appropriate level of difficulty for the presumptive readers of these two journals (i.e., holders of undergraduate and graduate degrees). Professors, librarians, and others responsible for guiding students toward reading material at appropriate levels of readability could benefit from these results.

Poster about meeting the academic and cultural needs of international students at the Arizona State University Libraries and the University of Arizona Libraries. The poster presentation focuses on:

1. Strategies to promote information literacy skills of international students in the two university libraries.
2. What the libraries are doing to improve services to meet the needs and encourage library use among international students.
3. Partnerships that have been established with other academic departments or institutions.

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.

This presentation highlights SHARE’s ongoing initiatives as a free, open data set about research and scholarly activities across their life cycle. It includes information about the SHARE open technology and the ongoing community contributions. A variety of data set use cases and their implementation will be described to allow others to apply similar tools and techniques to their home institution or organization. SHARE aggregates free, open metadata about scholarship that includes proposals, registrations, data, publications, and more from more than 125 sources including ASU.