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
The two R codes are related to the feasible balance region calculations for Figures 2, 3, and 4 in the paper.
The MATLAB codes are related to the simulations of the recoverable initial quasi-static states, the results of which are shown in Figure 5 of the paper.
Shahshahani, P. M., & Ashton-Miller, J. A. (2020). On the importance of the hip abductors during a clinical one legged balance test: A theoretical study. PLOS ONE, 15(11), e0242454. https://doi.org/10.1371/journal.pone.0242454
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
The sensitive measurement of specific protein biomarkers is important for medical diagnostics and research. However, existing methods for quantifying proteins use antibody probes that cannot distinguish between specific and nonspecific binding, limiting their sensitivity and specificity. This work establishes a method for distinguishing between specific binding to the target protein and nonspecific binding to assay surfaces using single-molecule kinetic measurements with dynamically binding probes. This is significant because it permits extremely sensitive protein measurements without requiring a high-affinity detection antibody or any washing steps, enabling streamlined and sensitive quantification of proteins even when no pair of high-quality, tightly binding antibodies is available.
A high-resolution study of bulk properties in a peat sequence from the Xinjiang Altai Mountains of northwestern China, has allowed reconstruction of local variations in peat properties and peat C and N accumulation rates (CAR and NAR) during the Holocene. Analyses of peat bulk density, loss on ignition, and concentrations of TOC and TN and their elemental ratios and stable isotopic values suggest that changes in peat-forming vegetation types during different parts of this epoch are the major factors responsible for the variations of peat properties in this sequence.
This data is a subset of that originally produced as part of an effort to characterize GnRH neuron activity during prepubertal development in control and PNA mice and investigate the potential influences of sex and PNA treatment on this process (1). It was later used in (2) to further investigate the firing patterns of GnRH neurons in these categories of mice and determine how these patterns might differ based on age and treatment condition.
The data files can be opened and examined using Wavemetric's Igor Pro software. Code used to further examine and visualize the data can be found at https://gitlab.com/um-mip/mc-project-code.
This research was supported by National Institute of Health/Eunice Kennedy Shriver National Institute of Child Health and Human Development R01 HD34860 and P50 HD28934.
(1) Dulka EA, Moenter SM. Prepubertal development of gonadotropin-releasing hormone (GnRH) neuron activity is altered by sex, age and prenatal androgen exposure. Endocrinology 2017; 158:3941-3953
(2) Penix JJ, DeFazio RA, Dulka EA, Schnell S, Moenter SM. Firing patterns of gonadotropin-releasing hormone (GnRH) neurons are sculpted by their biology. Pending.
Dulka EA, Moenter SM. Prepubertal development of gonadotropin-releasing hormone neuron activity is altered by sex, age and prenatal androgen exposure. Endocrinology 2017; 158:3943-3953. https://dx.doi.org/10.1210%2Fen.2017-00768 and Penix JJ, DeFazio RA, Dulka EA, Schnell S, Moenter SM. Firing patterns of gonadotropin-releasing hormone (GnRH) neurons are sculpted by their biology. Pending.
The datafiles and Matlab code files in this repository contain the information needed to produce the figures in the paper. We also include the code used to process the raw model output files into spectra.
Citation to related publication:
B.K. Arbic, S. Elipot, J.M. Brasch, D. Menemenlis, A.L. Ponte, J.F. Shriver, X. Yu, E.D. Zaron, M.H. Alford, M.C. Buijsman, R. Abernathey, D. Garcia, L. Guan, P.E. Martin, and A.D. Nelson (2022), Near-surface oceanic kinetic energy distributions from drifter observations and numerical models. In review.
The precipitation data itself is the output of the models/datasets that we analyze in our paper. Most of it is in .nc or .nc4 format, although we provide code to extract the data into time series .mat files.
We used MATLAB to perform our analysis.
We conducted a search through BioMed Central's 54 medicine and public health journals that provide OPR documentation in order to identify systematic review papers published in 2017. For each article we determined if OPR data, reviewer and author comments, were accessible. If so, we assessed the search methodology and reporting quality of the search process with a grading rubric based on PRISMA and PRESS standards, and then mined peer reviewer comments for references to the search methodology.
The following files include supplementary materials for our CHI 2020 paper "Crowdsourced Detection of Emotionally Manipulative Language". Namely, these materials include the dataset that was used in the evaluation. See the paper for more details.
J.S. Huffaker, J.K. Kummerfeld, W.S. Lasecki, M.S. Ackerman. Crowdsourced Detection of Emotionally Manipulative Language. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2020). Honolulu, HI. 2020.
