Working Group leader: Alex Vakanski

Group members: David Paul, Russell Baker, Min Xian, Joshua Bailey

Originated: May 2016, supported by a CMCI MAG; currently this working group coincides with a Pilot Grant

Description:

This group focuses on the development of mathematical models for representing human motions, to potentially benefit patients undertaking a physical rehabilitation therapy (e.g., following a stroke, or due to other medical conditions). The research employs deep neural networks for modeling human motions at multiple hierarchical levels of abstraction. The aim is to encode the movements of a patient in performing physical exercises into a set of trajectory features and use the features for automated evaluation of the patient’s performance.

The group developed a deep learning framework for assessment of rehabilitation exercises, as well as the team created a dataset of physical rehabilitations exercises. The current focus of the improving and evaluating the proposed deep learning models.

We submitted an NIH R01 grant application in July 2022, but our application was not funded. Our plan is to revise the application and re-apply in the second half of 2023.

Publications:

Mathematical Modeling and Evaluation of Human Motions in Physical Therapy Using Mixture Density Neural Networks

Vakanski A, Ferguson JM, Lee S, (2016) Mathematical modeling and evaluation of human motions in physical therapy using mixture density neural networks. Journal of Physiotherapy & Physical Rehabilitation, 1(118). PMC5242735

Metrics for Performance Evaluation of Patient Exercises during Physical Therapy

Vakanski, A., Ferguson, J. M., & Lee, S. (2017). Metrics for Performance Evaluation of Patient Exercises during Physical Therapy. International Journal of Physical Medicine & Rehabilitation, 5(3), 403.

A Data Set of Human Body Movements for Physical Rehabilitation Exercises

Vakanski, A., Jun, H., Paul, D., & Baker, R. (2018). A Data Set of Human Body Movements for Physical Rehabilitation Exercises. Data, 3(1), 2. http://doi.org/10.3390/data3010002

Working Group leader: Berna Devezer

Group members: Erkan Buzbas, Gustavo Nardin, Bert Baumgaertner

Originated: Fall 2015

Description:

Our team aims to study the current problem of nonreproducibility of scientific results across multiple disciplines by advancing theory. We use statistical theory and stochastic modeling to identify theoretical meaning and determinants of irreproducibility.

Berna Devezer and the work she is doing with this group was highlighted in the Lewiston Tribune on October 21, 2018.

Working Group leader: Craig Miller

Group members: Holly Wichman, JT Van Leuven, Yesol Sapozhnikov, Keera Paull, Jacob Schow, Tessa Wedmyer

Originated: June 2015

Description:

This working group focuses on phage and phage-host interactions. We have several research directions. One project involves attenuating the bacteriophage phiX174 using synonymous recoding and asking questions about how mutational effects combine and how fitness recovery transpires. This work is funded by an NIH R01 grant. A second project is focused on how altering the stability of phage capsid proteins alters fitness. Here we are asking asking how well can molecular and statistical models predict viral fitness. This work is funded by an NSF Track II grant. A third (and recently begun) branch of work involves testing how bacterial host cells evolve resistance to different phage using whole genome sequencing and bioinformatics tools.

Publications:

Selecting among three basic fitness landscape models: additive, multiplicative and stickbreaking

Miller, C.R., Leuven, J.T., Wichman, H.A., & Joyce, P. (2017). Selecting among three basic fitness landscape models: Additive, multiplicative and stickbreaking. Theoretical population biology.

Working Group leader: Christopher Remien

Group members: Ben Ridenhour, Tuan Phan

Originated: June 2015

Description:

We are developing new methods to study and analyze microbial community dynamics and applying the methods to publicly available data and data collected by members of the group. If a grant is funded, the working group will last as long as the funding exists.  If we are unable to obtain funding within the next year and a half, we will reassess our focus or dissolve.