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.

In spring 2013, the presenters developed a survey on academic library streaming video and distributed it broadly through various discussion and mailing lists.

This is the first large-scale and most comprehensive effort to date to collect data on streaming video funding, licensing, acquisition, and hosting in academic libraries. Its results will provide benchmark data for future explorations of this rapidly expanding approach to video in academic libraries.

Streaming video is becoming a common occurrence on many campuses today. Its fast growth is due in part to the steady growth of online classes and programs. Technology has also played a role in this growth as alternatives for ingesting and accessing content have expanded. Multiple options are now available including in-house approaches, cloud storage, and third party vendors.

This survey collected data on how academic institutions address the day-to-day operations related to streaming video as well as perceived directions for future action.

Survey questions addressed selection and acquisition of video in both hard copy and streaming formats, funding for acquisitions, current and planned hosting interfaces, cataloging and access, and current practice and policy on digitization of hard copy titles for streaming. This session reviews the instrument used, and provides a preliminary look at some of the key data collected.