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.
This publication contains the anonymized SPECT/CT scans of two patients. Patient scans were taken at 4 different time points in the week following a therapeutic dose of Lu-177 DOTATATE. Each of the scans contains 5 subfolders, 3 of which contain SPECT projection data used for reconstructing SPECT images, and 2 contain the linear attenuation coefficient maps for the CT scans that correspond to each patients SPECT projections. All images are in DICOM format.
To study the effect of whistler mode waves on the superthermal electron velocity space at Mars, a numerical model was built to solve the bounce-averaged quasi-linear diffusion equation on a crustal field. This dataset includes the input and output variables to this model for the simulations performed in Shane and Liemohn, 2022. The studies using this dataset were conducted by Alex Shane in the Climate and Space Sciences and Engineering Department at the University of Michigan. This research was supported by the National Aeronautics and Space Administration (NASA) Grant NNX16AQ04G to the University of Michigan and the Rackham Predoctoral Fellowship.
Shane, A. D., & Liemohn, M. W. (2022). Modeling wave-particle interactions with photoelectrons on the dayside crustal fields of Mars. Geophysical Research Letters, 49, e2021GL096941. https://doi.org/10.1029/2021GL096941
This study was conducted to detect and analyze modules, or clusters of genes, associated with sepsis, using RNAseq data obtained from 12 participants who died of sepsis and 12 participants who died of non-infectious critical illness while hospitalized.
This deposit contains the input data and parameters needed to reproduce the weighted gene co-expression network analysis (WGCNA) and gene enrichment analysis performed on this data. This analysis requires the R packages "WGCNA" version 1.68 and "DESeq2" version 1.22.2 available for download from bioconductor ( http://bioconductor.org). The external bioinformatics tool DAVID version 6.8 ( https://david.ncifcrf.gov/) was used as an additional gene enrichment analysis. Please see the supplemental methods document within this deposit and published research letter for more detailed information.
Bustamante, A.C., Opron, K., Ehlenbach, W.J., Larson, E.B., Crane, P.K., Keene, C.D., Standiford, T.J., Singer, B.H., 2020. Transcriptomic Profiles of Sepsis in the Human Brain. Am J Respir Crit Care Med. https://doi.org/10.1164/rccm.201909-1713LE