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Modeling Idaho Health (MIH)

Working Group leader: Helen Brown

Group members: Christopher Murphy, Erich Seamon, Chris Williams, Nurbanu Bursa, Jennifer Hinds

Originated: November 2016

Description:

This working group started out as, “Small Area Estimation of Obesity-related Indicators (Modeling Obesity Indicators – MOI).”

MIH has continued to refine and expand work to generate small-area estimates (SAE) of Idaho health indicators at a county level using data from the American Community Survey (ACS) and Behavioral Risk Factor Surveillance System (BRFSS). We have worked to refine the model with the goal of identifying counties with higher prevalence of overweight, obesity and, diabetes. We created an interactive dashboard of the health indicators and are in process of expanding this work to include other health indicators.

Publications:

Initiating a watch list for Ebola virus antibody escape mutations

Miller CR, Johnson EL, Burke AZ, Martin KP, Miura TA, Wichman HA, Brown CJ, Ytreberg FM (2016) Initiating a watch list for Ebola virus antibody escape mutations. PeerJ 4:e1674.

Evolution of Tandemly-Replicated Opsin Genes: Molecular Models That Predict Spectral Shifts

Evolution of Tandemly-Replicated Opsin Genes: Molecular Models That Predict Spectral Shifts

A snapshot from an atomistic molecular simulation of a vertebrate cone visual pigment (green – opsin, red – retinal, magenta – palmitoyl moiety) embedded in an explicit lipid bilayer (yellow & purple – lipid head group, grey – lipid tail) and surrounded by a layer of water molecules (cyan).

 

 

Working Group leader: Deborah Stenkamp

Group members: Jagdish Patel, Marty Ytreberg

Originated: Summer 2016

Description:

This working group on an as-needed basis, to discuss progress and approaches for modeling a set of tandemly-replicated cone opsins from the zebrafish, medaka, cichlid, and guppy. They selected these RH2-type cone opsins based upon the availability of a reasonable (reasonably homologous) model from an X-ray crystallography structure, and reliable information from the literature regarding sequence data and spectral sensitivity data.

Docking models attempted on these opsins did not predict spectral shift.

Standard all-atom molecular dynamics simulations were then performed to find out if these predict spectral shift. Challenges that were overcome included modeling of the covalently attached chromophore, and the influence of the surrounding lipid bilayer. In addition, we included three “ancestral” RH2 opsins with known spectral sensitivities to increase the scope of the study to seven RH2 opsins.

The molecular dynamics simulations were carried out in December 2016. Visualization of the distinct fluctuations and analysis of internal parameters associated with the chromophore’s heavy atoms suggested that some of these features may predict spectral shift, and quantitative/statistical approaches to determine if this is the case are underway.

RH2 cone opsins of medaka, guppy, and cichlid were added to the analysis to increase the robustness of our approach, providing a total of 14 distinct RH2 sequences. Two of the output metrics (RMSF and the C7-C6-C5-C18 Torsion) were identified as most highly predictive of “blue” vs. “green” peak spectral sensitivity. A mathematical model was generated, using these two parameters, which predicts spectral sensitivity with a correlative R2 of 0.94 (!!!!).

The manuscript was submitted in fall 2017 – to Cell, then PNAS, then PLOS Biology, and then eLife. The editorial staff at each journal considered it more appropriate for a specialty journal. In December 2017 we submitted it to PLOS Computational Biology. It was favorably reviewed, and after minor revisions, accepted. It is now published, with an epub date of January 24, 2018.

Publications:

Predicting peak spectral sensitivities of vertebrate cone visual pigments using atomistic molecular simulations

Patel JS, Brown CJ, Ytreberg FM, Stenkamp DL (2018) Predicting peak spectral sensitivities of vertebrate cone visual pigments using atomistic molecular simulations. PLoS Comput Biol 14(1): e1005974. https://doi.org/10.1371/journal.pcbi.1005974

Transmissible Vaccines (Trans Vax)

Working Group leader: Jim Bull

Group members: Scott Nuismer, Chris Remein, Courtney Schreiner, Tanner Varrelman, Nathan Layman, Andrew Basinski, Anna Sjodin, Breanna Sipley, Beth Tuschhoff

Originated: Summer 2016

Description:

We develop theory for transmissible vaccines while generating papers and training students and postdocs.

Publications:

Nuismer SL, Basinski A, Bull JJ. 2019. Evolution and containment of transmissible recombinant vector vaccines.  Evol Appl. 2019 Jun 12;12(8):1595-1609.

Bull JJ, Nuismer SL, Antia R. 2019. Recombinant vector vaccine evolution.  PLoS Comput Biol. 15(7):e1006857.

Varrelman TJ, Basinski AJ, Remien CH, Nuismer SL. 2019. Transmissible vaccines in heterogeneous populations: Implications for vaccine design. One Health. 7:100084.

Basinski AJ, Nuismer SL, Remien CH. 2019.  A little goes a long way: Weak vaccine transmission facilitates oral vaccination campaigns against zoonotic pathogens.  PLoS Negl Trop Dis. 13(3):e0007251.

Smithson MW, Basinki AJ, Nuismer SL, Bull JJ. 2019. Transmissible vaccines whose dissemination rates vary through time, with applications to wildlife. Vaccine 37(9):1153-1159.

Basinski AJ, Varrelman TJ, Smithson MW, May RH, Remien CH, Nuismer SL. 2018.  Evaluating the promise of recombinant transmissible vaccines. Vaccine 36(5):675-682.

Bull JJ, Smithson MW, Nuismer SL. 2018. Transmissible Viral Vaccines.  Trends Microbiol.  26(1):6-15.

Mathematical Modeling of Human Motions Using Recurrent Neural Networks (HuMoNN)

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 & Rehabilitation5(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. Data3(1), 2. http://doi.org/10.3390/data3010002

Reproducibility in Sciences (SciRep)

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.