Jerboas (Jaculus jaculus) are bipedal hopping rodents that frequently transition between gaits (running, hopping, and skipping) throughout their entire speed range. It has been hypothesized that these non-cursorial bipedal gait transitions are likely to enhance their maneuverability and predator evasion ability. However, it is difficult to use the underlying dynamics of these locomotion patterns to predict gait transitions due to the large number of degrees of freedom expressed by the animals. To this end, we used empirical jerboa kinematics and dynamics to develop a unified Spring Loaded Inverted Pendulum model with defined passive swing leg motions. The simulated trajectories from the model precisely matched the experimental data. Jerboas were observed to apply different neutral swing leg angles during locomotion. By investigating the gait structure of the model with coupled and uncoupled neutral swing leg, we found two set of mechanism may explain the frequent gait transitions of jerboas.
Ding, Moore, Gan (submitted) A template model explains jerboa gait transitions across a broad range of speeds. Frontiers in Bioengineering And Biotechnology
In this work, we trained gradient boosted trees using XGBoost to predict the SYM-H forecasting using different combinations of solar wind and interplanetary magnetic field (IMF) parameters. Data are in csv and Python pickle formats.
Iong, D., Y. Chen, G. Toth, S. Zou, T. I. Pulkkinen, J. Ren, E. Camporeale, and T. I. Gombosi, New Findings from Explainable SYM-H Forecasting using Gradient Boosting Machines, Space Weather,11, accepted, 2022. https://doi.org/10.1002/essoar.10508063.3
Understanding how phenotypes evolve requires disentangling the effects of mutation generating new variation from the effects of selection filtering it. Tests for selection frequently assume that mutation introduces phenotypic variation symmetrically around the population mean, yet few studies have tested this assumption by deeply sampling the distributions of mutational effects for particular traits. Here, we examine distributions of mutational effects for gene expression in the budding yeast Saccharomyces cerevisiae by measuring the effects of thousands of point mutations introduced randomly throughout the genome. We find that the distributions of mutational effects differ for the ten genes surveyed and are inconsistent with normality. For example, all ten distributions of mutational effects included more mutations with large effects than expected for normally distributed phenotypes. In addition, some genes also showed asymmetries in their distribution of mutational effects, with new mutations more likely to increase than decrease the gene’s expression or vice versa. Neutral models of regulatory evolution that take these empirically determined distributions into account suggest that neutral processes may explain more expression variation within natural populations than currently appreciated.
Hodgins-Davis, A., Duveau, F., Walker, E. A., & Wittkopp, P. J. (2019). Empirical measures of mutational effects define neutral models of regulatory evolution in Saccharomyces cerevisiae. BioRxiv, 551804. https://doi.org/10.1101/551804
The dataset includes all citations considered for inclusion in the systematic review. The citations are accessible in Endnote (Clarivate), as well as through the primary citation export files from each database. The literature search strategies are included for reproducibility and transparency purposes. See the published methods for more information.
Gordon H. Sun, Stephanie W. Chen, Mark P. MacEachern & Jing Wang (2020) Successful decannulation of patients with traumatic spinal cord injury: A scoping review, The Journal of Spinal Cord Medicine, DOI: 10.1080/10790268.2020.1832397
These codes were produced as part of the Army Research Office Multi-University Research Initiative ARO MURI W911NF-17-1-0306 "From Data-Driven Operator Theoretic Schemes to Prediction, Inference, and Control of Systems"
The code can be run using the runAll.sh shell script (in Linux and OS-X); code should work similarly under windows.
This interview protocol was designed to investigate the research question: How do self-identified refugees in the receiving societies of Greece and Germany engage with information spaces to navigate identity during liminal and post-liminal portions of their refugee experiences?
Schöpke-Gonzalez, A., Thomer, A., & Conway, P. (2020). Identity Navigation During Refugee Experiences: The International Journal of Information, Diversity, & Inclusion (IJIDI), 4(2), 36–67. https://doi.org/10.33137/ijidi.v4i2.33151
Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/
, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf, Large Lake Statistical Water Balance Model (L2SWBM). https://www.glerl.noaa.gov/data/WaterBalanceModel/, and Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
This dataset includes three MATLAB data files for each subject: raw motion capture and force plate data, processed motion capture and force plate data, and sagittal-plane data segmented into individual trials labeled “nominal” or “tripped.” We include two example scripts for using the segmented trial data to tabulate trip recovery strategies across subjects and plot the sorted recovery strategies.
S. M. Danforth, X. Liu, M. J. Ward, P.D. Holmes, and R. Vasudevan, "Predicting sagittal-plane swing hip kinematics in response to trips," IEEE Robotics and Automation Letters, 2022.
This publication contains anonymized planar whole body images of two patients. Patient scans were taken at 4 different time points in the week following a therapeutic dose of Lu-177 DOTATATE. Both anterior and posterior views are provided. All images are in DICOM format.